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Long-term changes of wildfire regimes in eastern Siberia
An evaluation based on lake sediment indicators
and individual-based modeling
Ramesh Glückler
Universitätsdissertation
zur Erlangung des akademischen Grades
„doctor rerum naturalium“
(Dr. rer. nat.)
in der Wissenschaftsdisziplin
„Geoökologie“
eingereicht in Form einer kumulativen Arbeit an der
Mathematisch-Naturwissenschaftlichen Fakultät
der Universität Potsdam
angefertigt am
Alfred-Wegener-Institut
Helmholtz-Zentrum für Polar- und Meeresforschung
Potsdam, 10. Juli 2024 (Tag der Einreichung)
Disputation: 8. November 2024
This thesis was written without the use of text generated by artificial intelligence tools.
The Republic of Sakha (Yakutia) uses both Sakha and Russian as official languages. This thesis
uses common English transcriptions of Sakha place names and other terms, which are often
derived from the Russian version. In some instances, the Sakha version is additionally included
in brackets behind the English transcription.
Hauptbetreuer/in: Prof. Dr. Ulrike Herzschuh
Main supervisor: University of Potsdam, Germany
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany
Betreuer/innen: Prof. Dr. Elisabeth Dietze
Supervisors: University of Göttingen, Germany
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany
Dr. Stefan Kruse
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany
Mentor/in: Dr. Kai Mangelsdorf
Mentor: GFZ German Research Center for Geosciences, Germany
Gutachter/innen: Prof. Dr. Ulrike Herzschuh
Referees: University of Potsdam, Germany
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany
Dr. Lyudmila Shumilovskikh
University of Göttingen, Germany
Prof. Dr. Richard Vachula
Auburn University, USA
Unless otherwise indicated, this work is licensed under a Creative Commons License
Attribution 4.0 International.
This does not apply to quoted content and works based on other permissions.
To view a copy of this licence visit:
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Published online on the Publication Server of the University of Potsdam:
https://doi.org/10.25932/publishup-66643
https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-666434
“One involuntarily longs to be able to give up one’s
time to the study of these forests […].”
Fridtjof Nansen in “Through Siberia: The Land of the Future”, 1914 (p. 391)
I
SUMMARY
Wildfires constitute a key ecological disturbance in the world’s boreal forests. Driven by
conditions of the atmosphere and vegetation, wildfires are also inherently connected to recent
global change. Unusually intense fire seasons in Siberia, Canada, or Alaska in recent years are
making headlines around the world. With their location in the high latitudes, boreal forests
experience above-average climatic warming, and continued climate change is expected to
further intensify boreal fire regimes. Remote sensing data and paleo-ecological methods are
commonly used to evaluate relationships between fire regimes, climate, vegetation, and human
activity, on various temporal and spatial scales. However, satellite data remains limited to only
few decades of observations, preventing a direct assessment of long-term wildfire dynamics.
Studies utilizing paleo-ecological approaches, on the other hand, including the well-established
analysis of charcoal particles in lake sediments as an indicator of past wildfires, remain scarce
in Siberia. Compared to other regions of the boreal zone, wildfire activity in boreal Siberia and
its drivers and impacts remain poorly understood, especially on long timescales. Eastern Siberia
is particularly under-represented in the global distribution of paleo-ecological reconstructions
of long-term wildfire activity. Despite the high ecological significance of eastern Siberia’s
unique deciduous larch forests, growing on deep permafrost in one of the coldest regions on
Earth, this pronounced lack of data means that little is known about past trends of wildfire
activity or long-term relationships of fire to its environment and human livelihoods.
This thesis uncovers long-term fire regime changes in the Republic of Sakha (Yakutia), eastern
Siberia, throughout the past c. 20,000 years by applying a combination of paleo-ecological and
modeling approaches. Eleven new records of wildfire activity throughout the Holocene are
obtained, based on macroscopic charcoal particles in lake sediments from south-west Yakutia,
Central Yakutia, the southern Verkhoyansk Mountains, and the Oymyakon Highlands. The new
data, covering periods of the last c. 700 to 10,800 years, enable the creation of the first
composite of Holocene charcoal accumulation for the region, representing trends of biomass
burning. A high-resolution record of wildfire activity for the first time allows for a
determination of fire return intervals throughout the past two millennia. Reconstructed wildfire
activity is compared to reconstructions of past vegetation cover and human land use from
palynological analyses and sedimentary ancient DNA, as well as climate data. The paleo-
ecological approach is complemented by simulations in the individual-based, spatially explicit
forest model LAVESI (Larix Vegetation Simulator). The model is expanded by a new fire
II
module and applied to simulate long-term impacts of climate-driven fire regime changes on
fine-scale forest dynamics since the Last Glacial Maximum. Findings show that open
woodlands and a warm climate coincided with severe wildfires in the Early Holocene, c. 10,000
years ago, from which a potential positive feedback between thinning forests and intensifying
wildfires is inferred. Simulations suggest medium-intensity wildfires at return intervals of 50
years or more are benefitting the dominance of fire-resisting larches, whereas stand-replacing
fires facilitate the establishment of evergreen conifers. Over the last two millennia, the role of
climatic trends was increasingly overruled by human interference as key driver of fire regime
changes. A combination of both paleo-ecological and modeling approaches enables a
preliminary identification of indigenous land use 800 years ago and its ability to decrease
wildfire severity around settlements. Considering that many indigenous land use practices today
are less often conducted, or, in the case of the traditional, controlled use of fire in the landscape,
were prohibited, these findings have implications for present-day policies in a region where fire
regimes are expected to continue intensifying. This thesis for the first time uncovers regional
wildfire activity in Yakutia throughout the Holocene by applying a novel combination of paleo-
ecological and modeling approaches, unravelling natural and human drivers, and discussing
findings and their implications for present and future wildfire activity in a unique region already
faced with rapid environmental changes.
III
ZUSAMMENFASSUNG
Waldbrände stellen einen entscheidenden ökologischen Störungsprozess in den borealen
Wäldern der Welt dar. Angetrieben von Verhältnissen der Atmosphäre und Vegetation sind
Waldbrände inhärent verknüpft mit dem aktuellen globalen Wandel. Ungewöhnlich intensive
Feuerperioden in Sibirien, Kanada, oder Alaska in den vergangenen Jahren sorgen für
Schlagzeilen um die Welt. Durch ihre Lage in den hohen Breitengraden erfahren boreale
Nadelwälder eine überdurchschnittliche Erwärmung der Temperatur, und es wird erwartet, dass
der voranschreitende Klimawandel boreale Feuerregimes weiter intensiviert.
Fernerkundungsdaten und paläo-ökologische Methoden werden weitverbreitet genutzt um
Zusammenhänge zwischen Feuerregimes, Klima, Vegetation, und menschlicher Aktivität auf
verschiedenen zeitlichen und räumlichen Skalen auszuwerten. Allerdings bleiben
Satellitendaten auf den Zeitraum weniger Jahrzehnte beschränkt, was eine direkte Einschätzung
langfristiger Waldbranddynamiken verhindert. Studien, die dagegen paläo-ökologische
Ansätze wie die gut etablierte Analyse von Holzkohlepartikeln in Seesedimenten nutzen, sind
rar in Sibirien. Verglichen mit anderen Regionen der borealen Zone ist die Waldbrandaktivität
in Sibirien, einschließlich ihrer Einflussgrößen und Auswirkungen, schlecht verstanden,
besonders auf langen Zeitskalen. Vor allem Ostsibirien ist unterrepräsentiert bezüglich der
globalen Verteilung von paläo-ökologischen Rekonstruktionen langfristiger
Waldbrandaktivität. Trotz der großen ökologischen Signifikanz der einzigartigen,
sommergrünen Lärchenwälder Ostsibiriens, die auf tiefem Permafrost in einer der kältesten
Regionen der Welt wachsen, bedeutet der Mangel an Daten, dass wenig bekannt ist über frühere
Trends der Waldbrandaktivität oder langfristige Zusammenhänge zu deren Umgebung und
menschlichen Aktivitäten.
Diese Dissertation deckt langfristige Veränderungen von Feuerregimes in der Republik Sacha
(Jakutien), Ostsibirien, über die vergangenen ca. 20.000 Jahre auf, indem eine Kombination aus
paläo-ökologischen und Modellierungsverfahren angewendet wird. Elf neue Aufzeichnungen
zur Waldbrandaktivität im Holozän werden erzeugt, basierend auf makroskopischen
Holzkohlepartikeln in Seesedimenten aus Süd-West Jakutien, Zentraljakutien, dem südlichen
Werchojansker Gebirge, und dem Hochland von Oimjakon. Die neuen Daten, die
Zeitabschnitte der letzten ca. 700 bis 10.800 Jahre abdecken, ermöglichen zum ersten Mal das
Erstellen einer Zusammensetzung holozäner Holzkohleakkumulation für die Region,
repräsentativ für Trends der Verbrennung von Biomasse. Eine hochaufgelöste Aufzeichnung
der Waldbrandaktivität erlaubt eine erste Ermittlung von Waldbrand-Intervallen über die
IV
letzten zwei Jahrtausende. Die rekonstruierte Waldbrandaktivität wird mit Rekonstruktionen
der früheren Vegetationsbedeckung und menschlicher Landnutzung aus palynologischen
Analysen und sedimentärer alter DNA verglichen, wie auch mit Klimadaten. Der paläo-
ökologische Ansatz wird ergänzt mit Simulationen im Individuen-basierten, räumlich
expliziten Waldmodell LAVESI (Larix Vegetation Simulator). Das Modell wird um ein
Feuermodul erweitert und angewendet um langfristige Auswirkungen klimagesteuerter
Feuerregimes auf feinskalige Walddynamik seit dem letzten glazialen Maximum zu simulieren.
Die Ergebnisse zeigen, dass sich eine offene Waldlandschaft und ein warmes Klima im frühen
Holozän, ca. 10.000 Jahre vor heute, mit einer Phase schwerer Waldbrände überschnitten,
woraus eine mögliche positive Rückkopplung zwischen lichteren Wäldern und intensiveren
Feuerregimes abgeleitet wird. Simulationen legen nahe, dass Waldbrände mittlerer Intensität
bei Wiederkehrintervallen von 50 Jahren oder mehr die Dominanz der feuer-resistenten
Lärchen unterstützen, während dagegen hoch-intensive Waldbrände die Ansiedlung
immergrüner Koniferen ermöglichen. In den letzten zwei Jahrtausenden wurde die Rolle
klimatischer Trends als entscheidende Einflussgröße hinter Veränderungen von Feuerregimes
zunehmend von menschlichen Einflüssen abgelöst. Eine Kombination aus paläo-ökologischen
und Modellierungsverfahren ermöglicht eine vorläufige Identifizierung indigener Landnutzung
vor 800 Jahren und deren Fähigkeit die Schwere von Waldbränden im Umfeld von Siedlungen
zu verringern. In Anbetracht der Tatsache, dass viele indigene Landnutzungspraktiken heute
selten betrieben werden oder, im Fall der traditionellen, kontrollierten Nutzung von Feuer in
der Landschaft, verboten wurden, haben diese Erkenntnisse Implikationen für gegenwärtige
Strategien in einer Region, in der Feuerregimes sich weiter intensivieren werden. Diese
Dissertation enthüllt zum ersten Mal die regionale Waldbrandaktivität in Jakutien im gesamten
Holozän indem eine neuartige Kombination aus paläo-ökologischen und
Modellierungsverfahren angewendet wird, natürliche und menschliche Einflussgrößen
entflechtet werden, und Erkenntnisse und deren Implikationen für gegenwärtige und zukünftige
Waldbrandaktivität diskutiert werden in einer einzigartigen Region, die bereits heute mit
rasanten Umweltveränderungen konfrontiert ist.
V
ACKNOWLEDGEMENTS
This thesis was facilitated by the warmest support of many people. A few lines of gratitude will
not do justice, but I will give it a try.
First of all, I am thankful to Ulrike Herzschuh, Elisabeth Dietze, and Stefan Kruse, whose
guidance throughout the last years helped me navigate this undertaking. Thank you for your
continuous supervision. I am deeply grateful to you for introducing me to the rich realms of
larches and fire. Thank you, Kai Mangelsdorf, for the many helpful talks and exchanges.
All research presented in thesis is, directly or indirectly, based on the cooperation with our
partners and friends in the Republic of Sakha (Yakutia). I am sincerely grateful to Luidmila A.
Pestryakova for her part in enabling this research. Thank you, Evgenii S. Zakharov, Lena A.
Ushnitskaya, Izabella A. Baisheva, Aital V. Egorov, and all others who were part of our joint
“Yakutia 2021” expedition. It is thanks to all of you that I caught a glimpse of Yakutia, and I
deeply hope that the future holds the opportunity to cooperate again.
I remain much obliged to Shiro Tsuyuzaki and Youhei Yamashita, as well as Natsuko Yukino
and the Japan Society for the Promotion of Science, for hosting and supporting me during my
stay at Hokkaido University. Japan’s stunning northern island, with its remarkable Ainu
heritage, now occupies a special place in my heart.
Thank you, Philip Meister and Barbara von Hippel, for sharing your office with me in the early
days, and for being the best colleagues one could possibly wish for. Thank you, Weihan Jia, for
sharing an office in even earlier days. Thanks to all the fantastic AWI colleagues and guests
along the way, many more than I can list here: Sigrun Gräning, Kristina Brenner, Sarah
Olischläger, Janine Klimke, Mikaela Weiner, Justin Lindemann, Birgit Heim, Andrei Andreev,
Simeon Lisovski, Kathleen Stoof-Leichsenring, Boris Biskaborn, Hanno Meyer, Jens Strauss,
Ximena Tabares, Nadine Bernhard, Mareike Wieczoreck, Thomas Böhmer, Stuart Vyse,
Gregor Pfalz, Jérémy Courtin, Luise Schulte, Rongwei Geng, Amelie Stieg, Josias Gloy, Timon
Miesner, Laura Schild, Iris Eder, Sarah Haupt, Léa Enguehard, Uğur Çabuk, Chenzhi Li, Sisi
Liu, Moein Mellat, Chiranjeevi Nalapalu, Hanna Dyck, Alexander Postl, Anna-Lena Geis,
Lennart Grimm, Paul Adam, Karen Vogt, Jan Kahl, Volkmar Aßmann. Thanks to Rahel Paasch,
Jonas Büchner, and Hannes Petersen, for accompanying us for fieldwork in the Alps.
Furthermore, I want to thank all those who supported this thesis during bachelor or master
theses or internships, especially all who spent their sunny summer days in a cold underground
climate chamber with me, subsampling sediment cores.
VI
I much appreciate the support by the fantastic POLMAR graduate school. Dear Claudia
Hanfland, Claudia Sprengel, and Christine Hieber, thank you for enabling so many highly
valuable workshops, seminars, and conference participations. Thanks also to the Potsdam
Graduate School and the German Society for Geomorphology for their travel support. I thank
Achim Brauer for teaching me how to obtain my very first lake sediment core, and Andreas
Vött, whose competent guidance motivated me to step further into science. Thanks to all the
outreach-oriented scientists who initially sparked my appreciation of the scientific method and
the joy of letting the evidence guide my perception of the world.
I also want to acknowledge here the many lessons learnt at the fire department of Potsdam
(Freiwillige Feuerwehr Potsdam Zentrum). Thanks to the whole team for providing a different,
yet complementary perspective on the subject of fire and beyond, and for taking such great
interest in my research. Support your local fire department, everyone!
I am grateful to my supportive family and my friends, with my most heartfelt gratitude
belonging to Izabella Baisheva. You are a truly fantastic partner, whether it’s showing me
around a balaghan in Yakutia, paper writing in Poland, volcano hiking in Italy, late-night ferry
rides to Sweden, or exploring tiny islands in Japan. It may be the biggest achievement of all to
now refer to you as my loving wife. Thank you for your warmest support at all times – may
there be many more adventures waiting ahead.
VII
TABLE OF CONTENTS
SUMMARY .................................................................................................................................... I
ZUSAMMENFASSUNG ................................................................................................................. III
ACKNOWLEDGEMENTS ............................................................................................................... V
LIST OF FIGURES ........................................................................................................................ XI
LIST OF TABLES ...................................................................................................................... XIII
LIST OF ABBREVIATIONS ......................................................................................................... XIV
1. INTRODUCTION ................................................................................................................. 3
1.1. Motivation: Wildfires in boreal eastern Siberia .............................................................. 3
1.2. Long-term wildfire regime changes ................................................................................ 5
1.3. Relationships of fires and larch forests............................................................................ 7
1.4. Human component of fire regime changes ...................................................................... 8
1.5. Study region ................................................................................................................... 10
1.6. Analyzing long-term wildfire regime changes .............................................................. 12
1.7. Thesis objectives............................................................................................................ 14
1.7.1. Past fire regime variability ...................................................................................... 15
1.7.2. Larch forests shaped by fire .................................................................................... 15
1.7.3. Human drivers of fire regimes ................................................................................ 16
1.8. Methods ......................................................................................................................... 16
1.8.1. Paleo-ecological approach ...................................................................................... 17
1.8.2. Modeling approach ................................................................................................. 18
1.9. Manuscripts and author contributions ........................................................................... 19
1.9.1. Manuscript I (Glückler et al., 2021) ........................................................................ 19
1.9.2. Manuscript II (Glückler et al., 2022) ...................................................................... 20
1.9.3. Manuscript III (Glückler et al., 2024) ..................................................................... 20
1.9.4. Manuscript IV (Glückler et al., in prep.) ................................................................ 20
1.9.5. Complementary research ........................................................................................ 21
2. MANUSCRIPT I ................................................................................................................. 25
2.1. Abstract .......................................................................................................................... 26
2.2. Introduction ................................................................................................................... 27
2.3. Study site and methods .................................................................................................. 29
VIII
2.3.1. Location .................................................................................................................. 29
2.3.2. Fieldwork and subsampling .................................................................................... 31
2.3.3. Laboratory analyses ................................................................................................ 32
2.4. Results ........................................................................................................................... 36
2.4.1. Lithological sediment properties and chronology ................................................... 36
2.4.2. Reconstructed fire regime ....................................................................................... 39
2.4.3. Vegetation history ................................................................................................... 42
2.5. Discussion ...................................................................................................................... 43
2.5.1. Fire regime history of the last two millennia at Lake Khamra ............................... 43
2.5.2. Drivers of fire regime variations ............................................................................. 47
2.6. Conclusions ................................................................................................................... 52
Data availability .................................................................................................................... 53
Financial support .................................................................................................................. 53
Acknowledgements .............................................................................................................. 53
References ............................................................................................................................ 53
3. MANUSCRIPT II ............................................................................................... 73
3.1. Abstract .......................................................................................................................... 74
3.2. Introduction ................................................................................................................... 75
3.3. Materials and methods ................................................................................................... 77
3.3.1. Location .................................................................................................................. 77
3.3.2. Fieldwork and subsampling scheme ....................................................................... 80
3.3.3. Core dating .............................................................................................................. 80
3.3.4. Charcoal and pollen analysis .................................................................................. 81
3.3.5. Sedimentary ancient DNA approach ...................................................................... 81
3.3.6. Statistical methods .................................................................................................. 82
3.4. Results ........................................................................................................................... 83
3.4.1. Chronology ............................................................................................................. 83
3.4.2. Charcoal .................................................................................................................. 84
3.4.3. Pollen ...................................................................................................................... 86
3.4.4. Sedimentary ancient DNA ...................................................................................... 88
3.5. Discussion ...................................................................................................................... 89
3.5.1. Reconstructed wildfire activity ............................................................................... 89
3.5.2. Reconstructed vegetation composition ................................................................... 93
IX
3.5.3. Fire-vegetation feedbacks on millennial timescales ............................................... 95
3.6. Conclusion ..................................................................................................................... 99
Data availability .................................................................................................................... 99
Financial support .................................................................................................................. 99
Acknowledgements ............................................................................................................ 100
References .......................................................................................................................... 100
4. MANUSCRIPT III ............................................................................................ 115
4.1. Abstract ........................................................................................................................ 116
4.2. Background .................................................................................................................. 117
4.3. Methods ....................................................................................................................... 119
4.3.1. Study location ....................................................................................................... 119
4.3.2. Model description ................................................................................................. 121
4.3.3. Wildfire module in LAVESI-FIRE ....................................................................... 122
4.3.4. Model inputs and simulation scenarios ................................................................. 124
4.3.5. Statistical analyses of simulation output ............................................................... 126
4.4. Results ......................................................................................................................... 127
4.4.1. Sensitivity analysis ................................................................................................ 127
4.4.2. Fixed FRI and FI scenarios ................................................................................... 127
4.4.3. Simulated fire activity and forest structure since the Last Glacial Maximum ...... 129
4.5. Discussion .................................................................................................................... 132
4.5.1. Wildfire impacts since the LGM ........................................................................... 132
4.5.2. Capability of LAVESI-FIRE ................................................................................ 135
4.6. Conclusions ................................................................................................................. 138
Data availability .................................................................................................................. 139
Financial support ................................................................................................................ 139
Acknowledgements ............................................................................................................ 139
References .......................................................................................................................... 139
5. MANUSCRIPT IV ............................................................................................ 151
5.1. Abstract ........................................................................................................................ 152
5.2. Introduction ................................................................................................................. 153
5.3. Results ......................................................................................................................... 156
5.3.1. Trends of Holocene wildfire activity .................................................................... 156
X
5.3.2. Environmental development of the last 1200 years .............................................. 157
5.3.3. Simulated climate-driven wildfire activity ........................................................... 159
5.4. Discussion .................................................................................................................... 160
5.4.1. Reconstructed fire dynamics ................................................................................. 160
5.4.2. Natural drivers behind fire regime changes .......................................................... 162
5.4.3. Human impacts on wildfire regimes ..................................................................... 165
5.5. Conclusion ................................................................................................................... 168
5.6. Methods ....................................................................................................................... 169
5.6.1. Location ................................................................................................................ 169
5.6.2. Fieldwork and sediment core subsampling ........................................................... 169
5.6.3. 14C dating ............................................................................................................... 170
5.6.4. Macroscopic charcoal analysis ............................................................................. 170
5.6.5. Palynological analysis and mercury measurement for Lake 449 .......................... 172
5.6.6. Simulations in LAVESI-FIRE at Lake 449 .......................................................... 173
Data availability .................................................................................................................. 174
Funding ............................................................................................................................... 174
Acknowledgements ............................................................................................................ 174
References .......................................................................................................................... 174
6. SYNTHESIS ..................................................................................................... 189
6.1. Past fire regime variability .......................................................................................... 189
6.2. Larch forests shaped by fire......................................................................................... 194
6.3. Human drivers of fire regimes ..................................................................................... 198
6.4. Outlook ........................................................................................................................ 201
REFERENCES ........................................................................................................................... 203
Full references for works quoted before each main chapter .................................................. 216
APPENDIX 1 (MANUSCRIPT I) .................................................................................................. 217
APPENDIX 2 (MANUSCRIPT II) ................................................................................................. 225
APPENDIX 3 (MANUSCRIPT III) ............................................................................................... 228
APPENDIX 4 (MANUSCRIPT IV) ............................................................................................... 233
EIDESSTATTLICHE ERKLÄRUNG .............................................................................................. 239
XI
LIST OF FIGURES
Figure 1.1: Photos taken during the joint German-Russian expedition “Yakutia 2021” […] .. 4
Figure 1.2: Physical geography of the Republic of Sakha (Yakutia) […] .............................. 12
Figure 1.3: Photos showing parts of the sediment coring process […] .................................. 13
Figure 1.4: Photos showing the different steps of the paleo-ecological macroscopic charcoal
method for the reconstruction of past wildfire activity […] .................................................... 18
Figure 1.5: Schematic overview of the research methodology applied in this thesis […] ...... 19
Figure 2.1: (a) Position of Lake Khamra […] ......................................................................... 31
Figure 2.2: (a) Bayesian age-depth model […] ...................................................................... 39
Figure 2.3: Overview of the charcoal record […] ................................................................... 41
Figure 2.4: Principal component analysis […] ....................................................................... 42
Figure 2.5: Pollen and non-pollen palynomorph percentage diagram […] ............................ 43
Figure 2.6: Comparison of the charcoal record with climate, vegetation, and general human
settlement phases […] .............................................................................................................. 50
Figure 3.1: (A) Location of Lake Satagay […] ....................................................................... 78
Figure 3.2: (A) ERA5 reanalysis data at Lake Satagay […] ................................................... 80
Figure 3.3: (A) Bulk-sediment 14C-based chronology […] .................................................... 84
Figure 3.4: (A) TraCE 21ka climate model data […] ............................................................. 85
Figure 3.5: REVEALS-transformed pollen record […] .......................................................... 87
Figure 3.6: (A) Principal component analysis […] ................................................................. 88
Figure 3.7: Sedimentary ancient DNA (sedaDNA) record for terrestrial plants […] ............. 89
Figure 3.8: (A) Photos of a low-severity surface fire plume and its impact […] ................... 91
Figure 4.1: Map of the study area […] .................................................................................. 120
Figure 4.2: Conceptual diagram describing the new fire module […] ................................. 123
Figure 4.3: Sensitivity analysis […] ...................................................................................... 127
Figure 4.4: Superposed epoch analysis for compiled fire intensity (FI) scenarios […] ....... 128
Figure 4.5: Timeseries of main simulation and reference run without fires […] ................. 130
Figure 4.6: Forest structure as simulated with and without fire occurrence […] ................. 132
Figure 5.1: Map indicating the location of all charcoal records […] .................................... 155
Figure 5.2: Trends of charcoal accumulation rate throughout the Holocene […] ................ 157
Figure 5.3: Quantitative reconstruction of land cover at Lake 449 […] ............................... 158
Figure 5.4: Indicators of human activity at Lake 449 […] ................................................... 159
XII
Figure 5.5: LAVESI-FIRE simulated burned area with and without artificial fuel reduction
after 1200 CE […] .................................................................................................................. 160
Figure 5.6: Schematic compilation of the different ways Sakha people may have affected their
landscapes, and with it the occurrence of wildfires […] ........................................................ 167
Figure 6.1: Schematic overview of inferred Holocene wildfire activity in relation to vegetation,
climate (summer temperature), landscape development, and human activity […] ................ 201
XIII
LIST OF TABLES
Table 2.1: (a) 14C age results of bulk and macrofossil samples from core EN18232-3 […] .. 38
Table 2.2: Reconstructed fire episodes, fire return interval (FRI) in years, and distributions of
signal-to-noise index (SNI) […] .............................................................................................. 42
Table 3.1: 14C dating results for sediment core EN18224-4. .................................................. 84
XIV
LIST OF ABBREVIATIONS
14C Radiocarbon
AMS Accelerator mass spectrometry
AP Arboreal pollen
a.s.l. Above sea level
AWI Alfred Wegener Institute
BA Burned area
BCE Before the Common Era
BP Before Present (i.e., before 1950 CE)
CCI Climate Change Initiative
CE Common Era
CFFDRS Canadian Forest Fire Danger Rating System
CHAR Charcoal accumulation rate
CMIP Coupled Model Intercomparison Project
CRU TS Climate Research Unit Timeseries
DEM Digital elevation model
DGVM Dynamic global vegetation model
DNA Deoxyribonucleic acid
ECMWF European Centre for Medium-Range Weather Forecasts
ed. Editor
eds. Editors
EDX Energy-dispersive X-ray analysis
e.g. For example (from Latin: exempli gratia)
EPSG European Petroleum Survey Group
ERA ECMWF Reanalysis
ESA European Space Agency
ESM Earth system model
et al. And others (from Latin: et alii)
F14C Fraction of radiocarbon
FI Fire intensity
Fig. Figure
FireMIP Fire Modeling Intercomparison Project
FPR Fire probability rating
FRI Fire return interval
FRP Fire radiative power
FTIR Fourier-transform infrared spectroscopy
FWI Fire weather index
GIS Geographic information system
HR Historical run
i.e. That is (from Latin: id est)
in prep. In preparation
LAVESI Larix Vegetation Simulator
LGM Last Glacial Maximum
XV
LIA Little Ice Age
LiDAR Laser imaging, detection, and ranging
LOESS Locally estimated scatterplot smoothing
LR Long run
MA Monosaccharide anhydride
MC Monte Carlo
MCA Medieval Climate Anomaly
MICADAS Mini Carbon Dating System
MODIS Moderate-resolution Imaging Spectroradiometer
MPI Max Planck Institute
NAP Non-arboreal pollen
NEFU North-Eastern Federal University
n.d. No date
NPP Non-pollen palynomorph
P Precipitation
p. Page
PAGES Past Global Changes
Pb/Cs Lead-210 and cesium-137
PC Principal component
PCA Principal component analysis
pp. Pages
REVEALS Regional estimates of vegetation abundance from large sites
sedaDNA Sedimentary ancient DNA
SEM Scanning electron microscope
SNI Signal-to-noise index
sp. Species (singular)
spp. Species (plural)
SSP Shared Socioeconomic Pathway
T Temperature
TraCE Transient Climate Evolution
TWI Topographic wetness index
UNEP United Nations Environment Programme
UVAFME University of Virginia Forest Model Enhanced
WGS World Geodetic System
“Fire is changing because we are changing the
conditions in which it occurs. Not all fires are harmful,
and not all fires need to be extinguished as they serve
important ecological purpose. However, wildfires that
burn for weeks and that may affect millions of people
over thousands of square kilometres present a challenge
that, right now, we are not prepared for.”
UNEP Rapid Response Assessment “Spreading Like Wildfire:
The Rising Threat of Extraordinary Landscape Fires”, 2022 (p. 6)
3
1. INTRODUCTION
Wildfires, the main subject of this thesis, are a complex yet important ecological disturbance,
an understanding of which is crucial in current times of global changes. This first chapter
outlines recent wildfire activity in eastern Siberia, introducing the need for long-term
perspectives for a thorough understanding of relationships to climate, vegetation, and human
activity. The study region in the Republic of Sakha (Yakutia) is introduced and an overview of
paleo-ecological and modeling approaches is given. Then, this chapter presents the objectives
and hypotheses that this thesis examines in detail, and briefly details the methods applied. An
overview of four individual manuscripts is provided, which constitute the following chapters
of this cumulative thesis.
1.1. Motivation: Wildfires in boreal eastern Siberia
Boreal forests, the largest terrestrial biome on Earth, stretch around the Arctic, covering large
parts of Russia, Canada, Alaska, and northern Europe. Located broadly between 50–70° North,
boreal forests account for one third of all forested area on the planet, with large areas that remain
unmanaged (GAUTHIER ET AL., 2015). They are characterized by short growing seasons, long
and severe winters, and a low diversity of tree species compared to other forest biomes
(GAUTHIER ET AL., 2015). Boreal forest distribution also overlaps with regions of permafrost,
where frozen organic matter in the ground resembles a planetary-scale carbon storage (SCHUUR
ET AL., 2015). The Republic of Sakha (Yakutia), the largest administrative division within the
state of Russia, covers the majority of eastern Siberia. Despite being widely known for its
extremely cold winters and its record as the coldest permanently inhabited region on Earth (LI,
2016), what brought Yakutia to news headlines around the world more recently was not ice, but
fire (VINOKUROVA ET AL., 2022).
In 2021 Yakutia experienced an intense fire season, recording the largest annually burned area
since the beginning of systematic satellite observations (HAYASAKA, 2021). Numerous
settlements were threatened by the fires, or, in the case of Byas Kyuyol (Бэс Күөл) in Gorny
District, partially destroyed (VINOKUROVA ET AL., 2022). Elsewhere, destroyed power lines
resulted in power outages. Villages and towns away from regional centers depend on roads,
ferries, and airports to guarantee the delivery of supplies (T. N. Gavrilyeva and V. D. Parilova,
personal communication, 2024) but the wildfires and their smoke plumes forced closures,
interrupting traffic and restricting mobility. This may especially affect those regions negatively
CHAPTER 1: INTRODUCTION
4
which are already comparably poor (GAVRILYEVA ET AL., 2021). Wildfire smoke is a health
hazard, found to increase respiratory morbidity and all-cause mortality (REID ET AL., 2016). In
2021 the exposure to air pollutants in smoke remained elevated far beyond safe levels for weeks,
including the capital city of Yakutsk with a population of more than 300,000 people (ROMANOV
ET AL., 2022; TOMSHIN AND SOLOVYEV, 2022). Intense fire seasons in Yakutia are able to trigger
air pollution warnings as far as Japan and may be connected to excess mortality and economic
losses across East Asia (YASUNARI ET AL., 2018, 2024). It was in this setting that the joint
German-Russian expedition “Yakutia 2021” took place (Fig. 1.1), jointly organized by the
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) and the
North-Eastern Federal University of Yakutsk (NEFU). Complicated by the SARS-CoV-2
pandemic and followed by a geopolitical termination of all scientific cooperation between
Russia and Germany due to the war in Ukraine, this last joint expedition provided many
valuable samples and data, enabling research presented within this thesis (for full expedition
report, see MORGENSTERN ET AL., 2023).
Figure 1.1: Photos taken during the joint German-Russian expedition “Yakutia 2021” (August 2021; R. Glückler).
Left: Driving through a smoke plume on the R504 Kolyma Highway near Churapcha (Чурапчы). Right: A power
line damaged by wildfire near Ytyk-Kyuyol (Ытык-Күөл).
Climate change will further intensify fire regimes in many regions, including eastern Siberia
(JONES ET AL., 2022; LI ET AL., 2024). The Arctic Amplification effect causes temperatures in
high latitudes to increase up to c. four times faster compared to the global average (RANTANEN
ET AL., 2022), and this amplified warming is expected to persist over the coming century
(ENGLAND ET AL., 2021). Higher air temperatures affect wildfires in multiple ways. A prolonged
snow-free period results in the fire season extending further into the shoulder seasons (JAIN ET
AL., 2017), enabling an earlier drying of vegetation in spring, and an increased maximum in
annual fuel buildup and fire danger (GERGEL ET AL., 2017; SCHOLTEN ET AL., 2022). At the same
CHAPTER 1: INTRODUCTION
5
time, an increase in temperature can decrease relative humidity, increasing the vapor pressure
deficit, which facilitates the drying of fuel and increases the number of days with extreme fire
risk (CLARKE ET AL., 2022). Warmer nights additionally decrease the mediating effect of
nighttime air conditions on wildfire spread and intensity (BALCH ET AL., 2022), resulting in
strategic and tactical challenges for fire management agencies (PEACE AND MCCAW, 2024).
More energy in the atmosphere is projected to lead to a higher frequency and ignition
probability of lightning strikes, which constitute the main source of ignition in extratropical
forests (VERAVERBEKE ET AL., 2017; CHEN ET AL., 2021; HESSILT ET AL., 2022; JANSSEN ET AL.,
2023). However, increased fire danger also affects the probability of anthropogenic ignitions,
including accidental ignitions, arson, or runaway planned fires (FUSCO ET AL., 2016). With more
pronounced fire weather wildfires are also increasingly able to reburn areas that burned just
recently (WHITMAN ET AL., 2024). Severe fire weather conditions, resulting in higher fuel
availability and fire intensity, facilitate wildfire spread laterally (WANG ET AL., 2023), but also
vertically – both upwards, with higher flames reaching from the forest ground into the tree
crowns, and downwards, smoldering deeper into organic-rich soils. Underground smoldering
fires can sustain themselves even throughout the extremely cold winters of Yakutia, reappearing
as above-ground wildfires and adding substantially to the burned area in the following fire
season (XU ET AL., 2022).
Despite these clear impacts of climate change and the danger wildfires can pose to society, we
lack an understanding of changes in long-term wildfire activity in the Siberian Far East.
Additionally, wildfires nonetheless also constitute an essential ecological process (BOWMAN ET
AL., 2009). This becomes most obvious when longer timescales than individual recent fire
seasons are considered.
1.2. Long-term wildfire regime changes
Wildfires shaped terrestrial life ever since the first plants established on land in the Silurian
period, approximately 430 million years ago (GLASSPOOL AND GASTALDO, 2022). Fire thus co-
evolved with Earth’s changing terrestrial environments over almost half a billion years – it is a
key ecological disturbance and evolutionary force, located at the interface of atmosphere,
biosphere, hydrosphere, and lithosphere (CONEDERA ET AL., 2009; SANTOS ET AL., 2023). Most
terrestrial ecosystems evolved with a dependence on a certain kind of wildfire occurence
(PAUSAS AND KEELEY, 2019). Much of the scientific understanding of fire ecology and temporal
trends is derived either from historical notes and statistics of the past century, or, more recently,
from satellite-based observations of the last few decades. Despite the very high value of
CHAPTER 1: INTRODUCTION
6
systematic satellite-based observations of burned area and fire intensity, being confined to
timeseries of few decades makes it difficult to evaluate long-term relationships between a
changing climate, vegetation structure and composition, or human activity (CONEDERA ET AL.,
2009). However, it is those relationships that now need to be understood in order to anticipate
future wildfire activity and its implications for society (BOWMAN ET AL., 2009, 2011). A solid
understanding of past wildfire dynamics could also improve the process of informing or testing
fire-enabled earth system models (MARLON ET AL., 2016). In addition, temporally limited
records of past wildfire activity result in a knowledge gap regarding a long-term reference to
recent observations of fire regime variability. General global climatic trends of the past
millennia are are increasingly well understood (e.g., LISIECKI AND RAYMO, 2007; KINDLER ET
AL., 2014; MAYEWSKI ET AL., 2004). Parallel data about equally long-term changes in wildfire
activity could elucidate climate-fire relationships, and, potentially, their impact on the
environments of the past. A reconstruction of past wildfire activity on centennial, millennial,
and longer timescales is possible with the help of natural archives and fire indicators therein
(CONEDERA ET AL., 2009). However, such paleo-ecological studies targeting wildfires, also
named paleofire studies, are rare in eastern Siberia, which, despite its environmental
significance, remains under-represented in the global distribution of such data (MARLON ET AL.,
2016).
The discussion of wildfires and long-term fire activity requires a basic terminology that is here
defined for this thesis. A “fire regime” is a key concept for the analysis of long-term wildfire
activity, along with various descriptors of drivers, attributes, and impacts of wildfires.
Originating in the late 19th and early 20th century among researchers in French colonies in
Africa, the fire regime concept underwent multiple changes in its definition, and is today widely
used in the scientific literature (KREBS ET AL., 2010). Today, it usually describes generalized
attributes of wildfires that were found to be common for a specific location and its
environmental and climatic conditions. In a way, the relationship between a wildfire and a fire
regime is therefore comparable to the relationship between weather and climate. This thesis
follows the definition by KREBS ET AL. (2010), where “fire regime” today aims to summarize
the place (location, extent, ignition points, etc.), timing (chronology, seasonality, etc.), and type
(behavior, intensity, etc.) of fires. Additionally, the term can include attributes of fire conditions
(fuel characteristics, fire weather, ignition sources, etc.) and fire impacts (severity, societal cost,
etc.). “Fire weather” refers to weather conditions in relation to their ability to facilitate wildfires,
and is often presented as a fire weather index (FWI; e.g., VAN WAGNER, 1974). Descriptors of
both individual wildfires and fire regimes used in this thesis include “fire intensity” and “fire
CHAPTER 1: INTRODUCTION
7
severity”, which, following KEELEY (2009), refer to the energy output from a fire in the form
of heat, and the degree of organic matter (“fuel”) consumption by a fire, respectively. A “fire
return interval” (FRI) here describes the time between two consecutive wildfires within the
same region of observation, while “fire frequency” counts the number of wildfire events per
unit of time (HIGUERA ET AL., 2010; KHARUK ET AL., 2016). A short FRI thus corresponds to a
high fire frequency.
1.3. Relationships of fires and larch forests
Although the boreal zone can be referred to as a single biome, there are considerable differences
among regions, resulting in different fire regimes. Most notably, forest composition by most
abundant tree species in North America differs from Siberia, consisting of mainly fire-
embracing black spruce (Picea mariana) and jack pine (Pinus banksiana), fire-avoiding white
spruce (Picea glauca), and few fire resisters, enabling a high-intensity fire regime (WIRTH,
2005; ROGERS ET AL., 2015). Within Siberia, on the other hand, there is a pronounced difference
between evergreen forests in the West, composed of fire-avoiding Siberian cedar (Pinus
sibirica), Scotch pine (Pinus sylvestris), Siberian spruce (Picea obovata), and Siberian fir
(Abies sibirica), as opposed to the deciduous coniferous forests in the East, dominated by fire-
resisting larches (Larix gmelinii, and also L. cajanderi, L. sibirica) and related to common, low-
intensity surface fires (WIRTH, 2005; ROGERS ET AL., 2015; KHARUK ET AL., 2021). The larch-
dominated forests of eastern Siberia are a unique relict of past glacial periods (HERZSCHUH ET
AL., 2016; HERZSCHUH, 2020), and they share a considerable area with permafrost (FEDOROV
ET AL., 2018). Due to a consistent extremely contintental climate throughout much of the
Quaternary period, a lack of large-scale glaciation, and thin snow cover, cold temperature was
able to penetrate deep into the ground, resulting in permanently frozen grounds up to 1.5 km in
depth (STRAUSS ET AL., 2017; BURN, 2023). These individual factors already distinguish eastern
Siberia from other regions of the boreal zone. However, it is the special relationships between
the larch forests, permafrost, and wildfires, that make this region truly unique.
Permafrost, restricting the maximum rooting space in the seasonally thawed active layer, is a
limiting factor for the establishment of deeper rooting tree species (HERZSCHUH ET AL., 2016).
It can also act as a source of water for larches in dry summers (SUGIMOTO ET AL., 2002). In
return, needle litter from the deciduous larch trees adds to an organic layer of moss, lichen, and
duff, insulating the permafrost and protecting it from accelerated degradation in summer
(KHARUK ET AL., 2021). Yakutia’s extremely cold and long winters, slowing down microbial
cycling and biomass decomposition, additionally facilitate fuel accumulation (JONASSON ET AL.,
CHAPTER 1: INTRODUCTION
8
2001). The accumulating organic layer, with its dry needle litter, enables occasional low-
intensity surface fires. By reducing or removing the thick organic layer, these fires open patches
of soil, enabling the germination of light-weight larch seeds (KHARUK ET AL., 2021). Field
studies show a highly increased post-fire abundance of larch saplings (SOFRONOV AND
VOLOKITINA, 2010). Mature larches, on the other hand, possess pyrophytic attributes and are
adapted to resist these surface fires (TSVETKOV, 2004; KHARUK ET AL., 2021). Their thick bark
insulates the heat-sensitive cambium, preventing it from overheating, and a self-pruning of low-
hanging branches limits the abundance of ladder fuels, preventing surface fires from reaching
the crowns (WIRTH, 2005; KHARUK ET AL., 2021).
Larch forests, permafrost, and wildfires can thus sustain each other, and, when in balance, they
increase the resilience of the eastern Siberian boreal ecosystem (HERZSCHUH ET AL., 2016).
However, this balance is increasingly challenged by a climate-driven intensification of fire
regimes, the degradation of permafrost, and increasing tree mortality due to more frequent
weather extremes or insect invasions (TCHEBAKOVA ET AL., 2009; KHARUK ET AL., 2021). Due
to the lack of long-term data on past wildfire activity in Yakutia, the impact of changing fire
regimes on the structure, composition, and extent of the boreal larch forest remains difficult to
predict, especially considering the time-lagged adaptation of forest ecosystems in
disequilibrium with a changing climate (SVENNING AND SANDEL, 2013).
1.4. Human component of fire regime changes
In the more recent history of the c. 430 million years as an essential ecological disturbance in
terrestrial ecosystems, wildfires started to interact with a new sphere – the anthroposphere,
spaces tended, lived in, and affected by humans (CONEDERA ET AL., 2009; BOWMAN ET AL.,
2011). Hominins likely learnt to control fire multiple hundreds of thousands, if not more than a
million years ago (ROEBROEKS AND VILLA, 2011; GOWLETT, 2016), a development that may
have enabled the path towards the world as we know it today (BROWN ET AL., 2009). Taming
fire was regarded by Charles Darwin as the most important human discovery besides language
(WRANGHAM AND CARMODY, 2010). By controlling fire and applying it to their needs, humans
increasingly interfered with natural fire activity (PAUSAS AND KEELEY, 2009). Humans have
started to act as a key driver of fire regimes thousands of years ago in various regions (PINTER
ET AL., 2011; VANNIÈRE ET AL., 2016; BIRD ET AL., 2024). However, the human relationship to
fire was, and remains to be, subject to change. With the spread of sedentary lifestyles, the
systematic reliance on agricultural ressources, rising urbanization, and depending on the
cultural background, wildfires more recently tended to be seen as a threat that had to be
CHAPTER 1: INTRODUCTION
9
eliminated (BOWMAN ET AL., 2011). The modern European perspective on the active use of fire
in the landscape was generally adverse. For example, the term “fire regime” initially referred
to what French colonialists perceived as the destructive suppression of nature by the imposition
of anthropogenic fires in parts of Africa (KREBS ET AL., 2010). Although the use of fire played
a big role across much of human history, fire suppression became the sole dimension of
wildfire-related land management in many modern societies (STEEL ET AL., 2015).
Today many fire regimes around the world are strongly shaped by human activity, which can
happen in a variety of ways from direct to indirect impacts, purposeful to accidental ignitions,
or fire suppression (BOWMAN ET AL., 2011). The latter was optimized over the years, to the
point of applying automated wildfire sensors to detect the earliest signs of fire, and deploy
technologically advanced firefighting units which will make sure no single hotspot remains
visible with their thermal cameras (CZERNIAK-WILMES AND JETZKE, 2023). On the one hand,
such efforts may be necessary to protect human lives and livelihoods. On the other hand, despite
the increased effort to categorically fight fires, catastrophic wildfires keep occurring (MOORE,
2019). This “fire paradox” could be explained once fire ecology, appearing as a scientific
discipline only in the 1960s (KREBS ET AL., 2010), provided a holistic perspective on the
ecological consequences of suppressing fire in landscapes it was once common in (ARNO AND
BROWN, 1991; INGALSBEE, 2017).
Despite a comparably low overall population density in Yakutia, humans do regionally
represent an important driver of fire regimes. Anthropogenic ignitions were found to be more
common than natural ignitions by lightning strike in the more densely populated Central
Yakutia, although natural ignitions dominate in the vast, surrounding stretches of boreal forest
(JANSSEN ET AL., 2023). The role of humans in recent intense fire seasons, such as 2021, is
subject of heated public debates. Laws criminalizing traditional fire use were recently further
tightended, prohibiting cultural burning techniques, although supporters argue that this
prohibition only further aggravates the occurrence of potentially harmful wildfires
(SOLOVYEVA ET AL., 2022).
There is a key knowledge gap about past relationships between humans and fire, and how
former use of fire or fire management in different cultures and environments may have affected
ecosystems (BOWMAN ET AL., 2011). Where past traditional and/or indigenous fire use and
management practices are increasingly well understood, or still applied, such knowledge may
contribute to a more diverse and improved managing of wildfires under climate change (e.g.,
KIMMERER AND LAKE, 2001; YIBARBUK ET AL., 2001). However, the scarcity of data on long-
CHAPTER 1: INTRODUCTION
10
term wildfire activity in Yakutia severely limits any assessment of historical human impacts on
fire regimes. New insights in that regard may constructively contribute to the debate about
wildfire resilience in times of climate change.
1.5. Study region
The Republic of Sakha (Yakutia) comprises much of Russia’s Far Eastern Federal District in
Siberia. From South to North it stretches from dense boreal forest at c. 55° North all the way
through the Arctic tundra and the Lena (Өлүөнэ) Delta, and on to the New Siberian Islands in
the Arctic Ocean at c. 77° North. The most populous city and population center of the republic
is the capital Yakutsk (Дьокуускай; population of c. 300,000), followed by Neryungri
(Нүөрүҥгүрү; population of c. 60,000) and Mirny (Мирнэй; population of c. 35,000; THE
NORTHERN FORUM, N.D.). The population consists mainly of indigenous Sakha (also referred to
as Yakuts), followed by ethnic Russians, indigenous Evenks and Evens, and many smaller
ethnic groups, nationalities, and indigenous communities (FEDERAL STATE STATISTICS SERVICE
OF RUSSIA, 2021)
Study sites presented in this thesis (Fig. 1.2A) are located in south-western Yakutia, where
deciduous larch forests are transitioning to mixed evergreen forests (Lake Kamra,
MANUSCRIPT I), and in the western region of larch-dominated Central Yakutia, near the Vilyuy
(Бүлүү) River (Lake Satagay, MANUSCRIPT II and III). On the right side of the middle Lena
River, study sites are located in the Lena-Amga interfluve of the Central Yakutian Lowlands,
and in the Suntar-Khayata range of the southern Verkhoyansk (Үөһээ Дьааҥы) Mountains
towards Oymyakon (Өймөкөөн; Lakes 402 to 455, MANUSCRIPT IV). A detailed description
of the individual study sites is included within each of the following chapters.
As described before, Yakutia is known for its continental climate with long and severe winters.
Record low minimum temperatures of c. –70°C in Oymyakon and Verkhoyansk rendered the
region the name “Siberian pole of cold” (GERASIMOV, 1961). With a high temperature
amplitude between highest and lowest temperature extremes of a year, able to reach 100°C, the
mean warmest (July) and coldest (November) monthly temperatures recorded in Yakutsk
between 1980 and 2019 were c. 19.7 and –38.0°C, respectively, while annual precipitation
remained low at c. 234 mm (CZERNIAWSKA AND CHLACHULA, 2020). Between 1966 and 2016,
an average increase in mean annual air temperature of 0.68°C per decade was recorded in
Central Yaktutia (Yakutsk), one of the highest rates of warming across the republic (GOROKHOV
AND FEDOROV, 2018).
CHAPTER 1: INTRODUCTION
11
According to the Köppen-Geiger climate classification scheme (KÖPPEN, 1900; GEIGER, 1954),
updated for the period 1990–2020 (BECK ET AL., 2023), almost all of Yakutia is categorized
within either the cold climate classes or the polar tundra type (Fig. 1.2B). Prevalent climate
sub-types, defined solely by cold summers or very cold winters, are Dfc (55.4%), mostly on the
western side, followed by Dwc (11.1%), Dsc (9.7%), and Dwd (7.5%), all of which are located
mostly on the more mountainous eastern side (Fig. 1.2C). Simulations with CMIP6 climate
models predict significant changes to Yakutia’s climate classification until the period 2071–
2099 (BECK ET AL., 2023). Under a medium forcing pathway (SSP4-6.0), climate sub-types
characterized by a very cold winter disappear (e.g., Dwd declines from 7.5% to <0.1%), and
the polar tundra climate decreases from 12.2% to 5.0%. Instead, cold climate sub-types
characterized by warm or hot summers begin to appear (e.g., Dfa and Dfb, 1.1%), especially in
southern Yakutia and the vicinity of Yakutsk (own calculation based on BECK ET AL., 2023).
Besides detailing the present climatic heterogeneity, the simulated future Köppen-Geiger
classification hints at impacts the warming climate may have on both wildfires in the population
center of Central Yakutia, as well as cultural practices rooted in the occurrence of very cold
winter seasons.
Deep permafrost grounds, widespread in Yakutia (Fig. 1.2D), result in unique cultural histories,
adaptations, and challenges, apart from their ecological significance (e.g., TAKAKURA ET AL.,
2021; CRATE, 2021). There is a wide variety of periglacial landforms in Yakutia, including
baidzharakhs (Бадьараах; conical relic mounds; TSUYUZAKI ET AL., 2010), bulgunnyakhs
(Булгунньах; perennial frost mounds, also named pingos; GROSSE AND JONES, 2011), ice-
wedge polygons (SCHIRRMEISTER ET AL., 2018), tukulans (Тукулаан; periglacial dune massifs
of aeolian origin; PAVLOVA ET AL., 2017; DESYATKIN ET AL., 2021), or retrogressive thaw
slumps (COURTIN ET AL., 2022). The distribution of permafrost landforms partially depends on
the permafrost formation process and its resulting composition. Ice-rich permafrost with
syngenetic ice wedges, named yedoma, is especially prone to degradation processes involving
ground subsidence and basin formation (STRAUSS ET AL., 2017). Resulting permafrost-thaw or
thermokarst lakes are a key geomorphological feature of Central Yakutia (CRATE ET AL., 2017).
With the exception of Lake Khamra and the easternmost “Yakutia 2021” sites (Lake 402, 408,
and 410), classified as intermontane basin or glacial lakes, all other lakes studied in this thesis
are of thermokarst origin (Fig. 1.2A). Thermokarst lake formation starts with degrading
permafrost, which can be triggered by the climate (MORGENSTERN ET AL., 2013) or local
disturbances, including wildfires (HOLLOWAY ET AL., 2020), and is commonly followed by
several development stages (CRATE ET AL., 2017). The final thermokarst development stage is
CHAPTER 1: INTRODUCTION
12
named alaas (Алаас; also transcribed to English as alas), representing a wide, shallow, non-
forested permafrost basin, often featuring the remaining lake at its center (SOLOVIEV, 1959;
CRATE ET AL., 2017). Alaases are of great cultural and spiritual importance for the Sakha,
providing the foundation for traditional livelihoods centered on animal husbandry (CRATE ET
AL., 2017).
Figure 1.2: Physical geography of the Republic of Sakha (Yakutia) in Russia (grey background) and the study
sites of this thesis. A: Boreal forest extent (© ESA CCI Land Cover project), burned area in 2021 (coverage only
<70°N; MODIS-derived product MCD64A1 Collection 6.1 via University of Maryland; GIGLIO ET AL., 2018),
major rivers and lakes (Natural Earth), and study sites (Lake Khamra: Manuscript I; Lake Satagay: Manuscript II
& III; “Yakutia 2021” sites comprising Lake 402, 408, 410, 419, 421, 433, 437, 449, 455: Manuscript IV). B:
Köppen-Geiger climate classification from BECK ET AL. (2023). BSk: Arid, steppe, cold; Dsc: Cold, dry summer,
cold summer; Dsd: Cold, dry summer, very cold winter; Dwc: Cold, dry winter, cold summer; Dwd: Cold, dry
winter, very cold winter; Dfc: Cold, no dry season, cold summer; Dfd: Cold, no dry season, very cold winter; ET:
Polar, tundra; EF: Polar, frost. C: Elevation, stretching from sea level to a maximum of c. 3000 m a.s.l. (Natural
Earth). D: Permafrost distribution (ESA Permafrost CCI v4; WESTERMANN ET AL., 2024). Projection: WGS 1984
EPSG Russian Polar Stereographic. Map: R. Glückler, with support by I. Baisheva.
1.6. Analyzing long-term wildfire regime changes
Natural archives can provide records of environmental and climatic changes on timescales far
exceeding modern observational data or historical records. They include, for example, tree rings
(dendrochronology; e.g., ESPER ET AL., 2002; DROBYSHEV ET AL., 2010), ice cores and samples
CHAPTER 1: INTRODUCTION
13
from ice sheets, or permafrost ice wedges (e.g., KINDLER ET AL., 2014; MEYER ET AL., 2015),
marine sediments (e.g., HALIUC ET AL., 2023; ZIMMERMANN ET AL., 2023), growth patterns of
hard tissue of organisms (sclerochronology; e.g., SCHÖNE AND GILLIKIN, 2013; WATANABE ET
AL., 2019), or speleothems in caves (e.g., LACHNIET, 2009; HOMANN ET AL., 2023). There is a
wide variety of archives beyond these examples, all with their own strengths and applications.
Natural archives tend to share a habit of incremental growth, dependent on the environmental
conditions, while incorporating physical or chemical signals, which can be analyzed as indirect
indicators, or proxies, of past environmental changes.
Past wildfire activity can be reconstructed with the help of multiple archives. However, lake
sediments have become a wide-spread choice when targeting fire signals integrated over local
to regional scales and covering many millennia (WHITLOCK AND LARSEN, 2001; CONEDERA ET
AL., 2009). Depending on their invididual setting, lakes can serve as archives for a variety of
different proxies, including combustion products such as charcoal particles to reconstruct
wildfire activity (MILLS ET AL., 2017). During a wildfire, charred plant biomass is lifted into the
air by the fire’s convection and spread across the environment. Charcoal particles falling on a
lake surface, or being washed into a lake by surface runoff within the lake’s catchment, settle
down and become part of the accumulating sediment matrix (CONEDERA ET AL., 2009). In ideal
cases, the lake sediment shows non-disturbed, annual layering (varves; BRAUER, 2004),
enabling a high-resolution analysis and precise age estimation. A sediment core, pushed
vertically through the accumulated sediment, can be subsampled to quantify charcoal particles
(or other proxies) stored in each layer or sampling increment (GLEW ET AL., 2001; Fig. 1.3).
This process enables a tracking of environmental changes throughout the past.
Figure 1.3: Photos showing parts of the sediment coring process (R. Glückler). Left: Fieldwork in Germany
(2021). Hanging from the catamaran frame is a gravity corer, used to push a PVC pipe into the sediment and
recover a short sediment core. Right: A short sediment core from Yakutia, after splitting it length-wise in two
CHAPTER 1: INTRODUCTION
14
halves (2022). Sediment layers are visible. One half is used for subsampling and proxy analyses, whereas the other
half is archived. The archived half can optionally be used for any non-destructive analytical methods.
Another approach for the analysis of wildfire regimes and their impacts on the environment is
the use of ecological models. Simulations of fire-vegetation interactions can not only improve
the understanding of past and present-day processes within the complex Earth system, they also
enable a prediction of future environmental conditions under climate change. There is a wide
variety of models for different purposes, temporal and spatial scales. Due to the large variations
between models, comparison projects were introduced to improve the models and run model
ensembles, yielding more robust simulation results (e.g., MEEHL ET AL., 2000). With the recent
increase in wildfire activity in many regions of the world, there was a specific need and effort
to improve fire-enabled dynamic global vegetation models (DGVMs), for example, within the
Fire Modeling Intercomparison Project (FireMIP; HANTSON ET AL., 2016; RABIN ET AL., 2017).
However, simulations of wildfire activity can be challenging due to the complex nature of fire
(HANTSON ET AL., 2016), and modeling efforts may benefit from diverse perspectives, apart
from DGVMs.
LAVESI (Larix Vegetation Simulator) is an individual-based, spatially explicit forest model,
simulating annual cycles of developmental dynamics between individual trees with explicit
spatial positions within a simulation area (KRUSE ET AL., 2016). It was initially conceived to
simulate the climate-driven northern treeline advance into tundra regions in Siberia (KRUSE ET
AL., 2016, 2019; WIECZOREK ET AL., 2017; KRUSE AND HERZSCHUH, 2022), resulting in a strong
localization for larch-dominated forests of Yakutia. In contrast to DGVMs, which generally
assume homogeneous conditions within each cell of their global-scale, grid-based architecture,
LAVESI is thus able to simulate fine-scale interactions among individual trees and their
environment, forced by long-term climate data. Additionally, its inclusion of trees of various
species and different life cycle stages, and its native representation of permafrost active layer
depth, are beneficial for capturing the unique ecological relationships of the eastern Siberia
larch forests. For these reasons, it provides a suitable foundation for an analysis of long-term
wildfire impacts, which would first need to be implemented into the model.
1.7. Thesis objectives
This section presents the main aims of this thesis in the form of three hypotheses, each
accompanied by a set of research objectives. The manuscripts of the following chapters evaluate
these research objectives. Based on the findings of the individual manuscripts, conclusions for
each hypothesis are then discussed in the final synthesis chapter.
CHAPTER 1: INTRODUCTION
15
1.7.1. Past fire regime variability
Considering the timely need for an improved understanding of long-term wildfire activity in
boreal eastern Siberia, a key aim of this thesis is to contribute new data from natural archives.
Macroscopic charcoal particles in lake sediments are a well-established indicator of past
wildfires, and many studies have been conducted around the world. However, very few of them
were set in eastern Siberia. The few existing studies suggested that no changes occurred to fire
regimes in Central Yakutia throughout the Holocene, a finding that seems unlikely given the
pronounced climatic and biogeographic shifts that occurred during this time. The scarcity of
data and limited previous findings lead me to my first hypothesis:
• Hypothesis 1: Past fire regimes in Yakutia were variable throughout the Holocene.
The research objectives related to this hypothesis include the initial establishment and
application of the charcoal method at AWI, using sediment cores from Yakutia. The resulting
data should then be used to characterize changing wildfire regimes throughout past millennia,
and highlight trends and variability of past biomass burning. An increased number of available
charcoal records should also enable a regional composition, distilling regional trends from
individual records influenced also by local factors.
• Objective 1.1: Characterize changes of wildfire activity throughout past millennia using
sedimentary charcoal as a fire proxy.
• Objective 1.2: Synthesize regional trends of past wildfire activity by compositing local
data.
1.7.2. Larch forests shaped by fire
Present-day larch forests of Yakutia co-exist with a low-intensity surface fire regime. Wildfires
constitute a key disturbance and are predicted to intensify with a warming climate. It is not well
understood how a climate-driven intensification of wildfires may affect ecosystem resilience
by changing forest structure and composition, especially on long timescales. The second
hypothesis of this thesis is thus guided by the assumption that changes of wildfire activity are
fundamentally related to the functioning of larch forests:
• Hypothesis 2: Structure and composition of larch forests in Central Yakutia are shaped by
changing wildfire regimes.
The research objectives for this hypothesis aim at a combination of the new long-term
reconstructions of wildfire activity (Hypothesis 1) with parallel data on past vegetation
CHAPTER 1: INTRODUCTION
16
composition from palynological analyses and an application of sedimentary ancient DNA
(sedaDNA). The individual-based, spatially explicit forest model LAVESI should be expanded
by implementing wildfire occurrence, and simulate long-term impacts of climate-driven fire
regime changes on forest dynamics.
• Objective 2.1: Characterize past interactions of changing fire regimes and larch forests by
comparing reconstructed wildfire activity to pollen and sedaDNA-based vegetation
reconstructions.
• Objective 2.2: Evaluate impacts of climate-driven wildfire regime changes on larch forest
structure and composition by simulating fire-vegetation dynamics in LAVESI.
1.7.3. Human drivers of fire regimes
It is unknown when and how humans may have first started to affect fire regimes in Yakutia.
However, multiple major cultural shifts occurred in the last millennium, including the
settlement of the pastoralist Sakha, the colonization of Yakutia by the Russians, and an
intensification of land management during collectivization in the Soviet period. Despite some
historical records mentioning the use of fire as a land management tool, my third hypothesis is
guided by the current lack of evidence for human impacts on fire regimes before the rapid
changes of the most recent centuries:
• Hypothesis 3: Human activity became a driver of fire regimes with the beginning of
intensified land use in the 19th century.
To evaluate this hypothesis, research objectives aim at a comparison of the new charcoal-based
reconstructions of past wildfires (Hypothesis 1) with the history of Yakutia, and a separation of
human from natural drivers behind past fire regime changes.
• Objective 3.1: Compare the land use history of Yakutia to reconstructed wildfire activity.
• Objective 3.2: Separate natural and human drivers behind past fire regime changes.
1.8. Methods
This section introduces the methods specifically used within this thesis. They can be separated
into a paleo-ecological approach, reconstructing past environmental changes using indicators
in lake sediments, and a modeling approach, simulating fine-scale forest development. The
methods used within each of the following chapters will be presented in more detail in each
manuscript. They fall within one of the two approaches outlined here, or strive to combine both.
CHAPTER 1: INTRODUCTION
17
1.8.1. Paleo-ecological approach
In this thesis, the accumulation of macroscopic charcoal particles (here defined as >150 µm) in
a total of eleven lake sediment cores is used to infer trends of biomass burning (WHITLOCK AND
LARSEN, 2001). In a contiguous sampling scheme across the sediment cores, charcoal particles
are extracted from the sediment matrix by applying a well-established wet sieving method,
followed by a bleaching step and quantification under a stereomicroscope (Fig. 1.4; CONEDERA
ET AL., 2009). In addition, charcoal particle size classes, morphotypes, or length-to-width ratios
are occasionally used to derive information about the type of biomass burning (e.g., CRAWFORD
AND BELCHER, 2014; VACHULA ET AL., 2021), fire regime attributes (FEURDEAN ET AL., 2023),
and/or the charcoal source area (WHITLOCK AND LARSEN, 2001).
Complementary to the analysis of macroscopic charcoal as a fire proxy, methods applied to all
or some of the sediment cores include the age dating of bulk sediment samples and plant
microfossils using radiocarbon (14C; HAJDAS ET AL., 2021) and lead-210 and cesium-137
approaches (Pb/Cs; APPLEBY, 2001). Pollen and non-pollen palynomorphs (NPPs) are used to
reconstruct past changes of vegetation composition (SEPPÄ AND BENNETT, 2003) and
environmental changes (SHUMILOVSKIKH AND VAN GEEL, 2020). Microscopic charcoal (here
defined as <150 µm) is quantified on pollen slides for general trends of regional biomass
burning (PATTERSON ET AL., 1987). An additional REVEALS-transformation of pollen data,
correcting pollen counts for variable pollen productivity and dispersal range of individual taxa,
is used to infer past vegetation coverage (SUGITA, 2007). A novel method applied is the analysis
of sedimentary ancient DNA (sedaDNA) of terrestrial plants by metabarcoding, which enables
a higher taxonomic resolution than the pollen method while highlighting a more local source
area (PARDUCCI ET AL., 2017; LIU ET AL., 2020). Total organic carbon (TOC) measurements are
conducted to facilitate the 14C bulk sediment age dating process of included sediment cores.
The mercury concentration in a sediment core is measured as an indicator of potentially human-
caused erosion in a lake’s catchment (CARON ET AL., 2008; CHEN ET AL., 2016; Fig. 1.5).
CHAPTER 1: INTRODUCTION
18
Figure 1.4: Photos showing the different steps of the paleo-ecological macroscopic charcoal method for the
reconstruction of past wildfire activity (R. Glückler). Left side (from left to right): Sediment sample in the sieve,
separating the macroscopic charcoal fraction from the smaller pollen fraction. Falcon tubes with the separated
macroscopic charcoal and pollen samples, respectively. Petri dish containing the macroscopic charcoal sample
under a stereomicrosope. Right side: “Large” macroscopic charcoal particle (>500 µm) as seen through the
microscope. Below the particle are bleached organic sediment remains.
1.8.2. Modeling approach
Because of its strong localization for eastern Siberian larch-dominated forests, LAVESI (KRUSE
ET AL., 2016) is used in this thesis as a foundation to simulate long-term fire-vegetation
interactions (Fig. 1.5). The model is informed by long-term climate data, localized to the study
sites of this thesis with recent observational data. The simulation area size and main grid-cell
resolution are defined by a digital elevation model, including derived gridded values for slope
and a topographic wetness index (KRUSE ET AL., 2022). Besides its forest dynamics from
establishment to tree mortality, the climatic forcing, and environmental information for the
simulation area, LAVESI already features multiple tree species, a representation of seeds in
cones and on the ground, a litter layer, and permafrost active layer depth. With this existing
configuration, climate-driven wildfire occurrence is implemented, informed by modern
relationships of temperature and precipitation to satellite-based burned area estimations. The
new wildfire module is partially informed by field observations and findings from the parallel
paleo-ecological approach. The newly expanded LAVESI-FIRE version of the model is used
to simulate impacts of climate-driven wildfires on forest structure since the Last Glacial
Maximum (c. 20,000 years BP). Simulation results are compared to findings from the paleo-
ecological approach to improve conclusions of long-term wildfire regime changes in Yakutia.
CHAPTER 1: INTRODUCTION
19
Figure 1.5: Schematic overview of the research methodology applied in this thesis for an evaluation of past
wildfire regime changes in Yakutia, Siberia.
1.9. Manuscripts and author contributions
This section provides an overview of the author contributions to published manuscripts,
manuscripts in preparation, and complementary research with relevance to this thesis.
Contribution statements are adapted from the corresponding manuscripts. Manuscripts I to IV
are incorporated in the following chapters, taking care not to change their original content.
However, formatting and numbering of figures, tables, and appendices had to be adapted to the
style of this thesis. References for each manuscript are featured at the end of the respective
chapter, whereas references for the introduction and synthesis chapters are included at the end
of the thesis.
1.9.1. MANUSCRIPT I (GLÜCKLER ET AL., 2021)
Status: Published July 2021 in Biogeosciences
Full citation: Glückler, R., Herzschuh, U., Kruse, S., Andreev, A., Vyse, S.A.,
Winkler, B., Biskaborn, B.K., Pestryakova, L., & Dietze, E. (2021).
Wildfire history of the boreal forest of south-western Yakutia (Siberia)
over the last two millennia documented by a lake-sediment charcoal
record. Biogeosciences, 18(13), 4185–4209. https://doi.org/10.5194/bg-
18-4185-2021
Contribution: R.G. and S.A.V. sampled the sediment core and performed the lab
analysis. R.G. conducted all the charcoal proxy analysis, supported by
CHAPTER 1: INTRODUCTION
20
E.D. and S.K. R.G. wrote the paper with inputs from E.D. All the authors
reviewed the final manuscript.
1.9.2. MANUSCRIPT II (GLÜCKLER ET AL., 2022)
Status: Published August 2022 in Frontiers in Ecology and Evolution
Full citation: Glückler, R., Geng, R., Grimm, L., Baisheva, I., Herzschuh, U., Stoof-
Leichsenring, K.R., Kruse, S., Andreev, A., Pestryakova, L., & Dietze,
E. (2022). Holocene wildfire and vegetation dynamics in Central
Yakutia, Siberia, reconstructed from lake-sediment proxies. Frontiers in
Ecology and Evolution, 10. https://doi.org/10.3389/fevo.2022.962906
Contribution: R.Gl. designed the sediment study supervised by E.D. and U.H. R.Gl.
and K.S.-L. subsampled the sediment core. R.Gl. prepared the age dating
process and created the chronology. L.G. conducted the charcoal-related
laboratory work and analysis, supported by R.Gl. and E.D. R.Gl. wrote
the initial version of the manuscript, supervised by E.D. All authors
commented on the initial manuscript.
1.9.3. MANUSCRIPT III (GLÜCKLER ET AL., 2024)
Status: Published January 2024 in Fire Ecology
Full citation: Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024).
Simulating long-term wildfire impacts on boreal forest structure in
Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology,
20(1), 1. https://doi.org/10.1186/s42408-023-00238-8
Contribution: R.G. and S.K. wrote the new model code, with support from J.G. R.G.
and S.K. analyzed the simulation data, with help from U.H. and E.D.
R.G. wrote the initial version of the manuscript, supervised by S.K. All
authors reviewed and approved the final manuscript.
1.9.4. MANUSCRIPT IV (GLÜCKLER ET AL., IN PREP.)
Status: Draft, in preparation
Full citation: Glückler, R., Andreev, A., Dietze, E., Kruse, S., Zakharov, E.S.,
Baisheva, I., Stieg, A., Tsuyuzaki, S., Strauss, J., Schild, L., Pestryakova,
L.A., & Herzschuh, U. (in prep.). Wildfire activity may have been
mediated by indigenous land use practices since 800 years in the boreal
forest of Central Yakutia, Siberia.
Contribution: U.H., S.K., and L.P. designed and led the fieldwork, supported by R.G.
R.G., U.H., E.Z., I.B., and A.S. conducted fieldwork at the lake sites.
R.G. designed the charcoal analysis study, supervised by U.H. R.G.
subsampled the sediment cores. R.G. prepared the age dating process and
created the chronologies. R.G. conducted the charcoal-related laboratory
CHAPTER 1: INTRODUCTION
21
work and analysis, supported by S.T. and E.D. R.G. wrote the initial
version of the manuscript, supervised by U.H. All authors reviewed the
initial manuscript.
1.9.5. Complementary research
BAISHEVA ET AL. (2023) Baisheva, I., Pestryakova, L., Glückler, R., Biskaborn, B., Vyse,
S., Heim, B., Herzschuh, U., & Stoof-Leichsenring, K. (2023).
Permafrost-thaw lake development in Central Yakutia:
Sedimentary ancient DNA and element analyses from a Holocene
sediment record. Journal of Paleolimnology.
https://doi.org/10.1007/s10933-023-00285-w
In BAISHEVA ET AL. (2023), R.G. subsampled the sediment core,
prepared the age dating process and TOC and TIC measurements,
created the chronology, and reviewed the initial and final versions
of the manuscript.
SAYEDI ET AL. (2024) Sayedi, S.S., Abbott, B.W., Vannière, B., Leys, B., Colombaroli,
D., Romera, G.G., Słowiński, M., Aleman, J.C., Blarquez, O.,
Feurdean, A., Brown, K., Aakala, T., Alenius, T., Allen, K.,
Andric, M., Bergeron, Y., Biagioni, S., Bradshaw, R., …
Glückler, R., …, Daniau, A.-L. (2024). Assessing changes in
global fire regimes. Fire Ecology, 20(1), 18.
https://doi.org/10.1186/s42408-023-00237-9
In SAYEDI ET AL. (2024), R.G. responded to the questionnaire and
provided input to the writing of the manuscript.
BAISHEVA ET AL. (2024) Baisheva, I., Biskaborn, B.K., Stoof-Leichsenring, K.R.,
Andreev, A., Heim, B., Meucci, S., Ushnitskaya, L.A., Zakharov,
E.S., Dietze, E., Glückler, R., Pestryakova, L.A., & Herzschuh,
U. (2024). Late Glacial and Holocene vegetation and lake
changes in SW Yakutia, Siberia, inferred from sedaDNA, pollen,
and XRF data. Frontiers in Earth Science, 12.
https://doi.org/10.3389/feart.2024.1354284
In BAISHEVA ET AL. (2024), R.G. contributed to visualization,
writing-review, and editing.
“In short, fire history parallels the general geologic
history of Earth. […] Charcoal, which is virtually
immune to further decomposition, holds the archives.
Fire was the means to preserve the record of its own
history. It was both actor and archivist.”
Stephen J. Pyne in “The Pyrocene: How we created an age of fire, and what happens next”, 2021 (pp. 25–26)
25
2. MANUSCRIPT I
Wildfire history of the boreal forest of south-western Yakutia (Siberia) over
the last two millennia documented by a lake-sediment charcoal record
Ramesh Glückler1,2*, Ulrike Herzschuh1,2,3, Stefan Kruse1, Andrei Andreev1, Stuart Andrew
Vyse1, Bettina Winkler1,4, Boris K. Biskaborn1, Luidmila Pestryakova5, and Elisabeth Dietze1*
1: Section of Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar
and Marine Research, 14473 Potsdam, Germany
2: Institute for Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany
3: Institute for Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
4: Istitute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
5: Istitute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, 677007, Russia
*Correspondence: Ramesh Glückler (ramesh.glueckler@awi.de) and Elisabeth Dietze
(elisabeth.dietze@awi.de)
Status: Published July 2021 in Biogeosciences. DOI: 10.5194/bg-18-4185-2021
Appendix: This manuscript is related to Appendix 1.
CHAPTER 2: MANUSCRIPT I
26
2.1. Abstract
Wildfires, as a key disturbance in forest ecosystems, are shaping the world’s boreal landscapes.
Changes in fire regimes are closely linked to a wide array of environmental factors, such as
vegetation composition, climate change, and human activity. Arctic and boreal regions and, in
particular, Siberian boreal forests are experiencing rising air and ground temperatures with the
subsequent degradation of permafrost soils leading to shifts in tree cover and species
composition. Compared to the boreal zones of North America or Europe, little is known about
how such environmental changes might influence long-term fire regimes in Russia. The larch-
dominated eastern Siberian deciduous boreal forests differ markedly from the composition of
other boreal forests, yet data about past fire regimes remain sparse. Here, we present a high-
resolution macroscopic charcoal record from lacustrine sediments of Lake Khamra (southwest
Yakutia, Siberia) spanning the last ca. 2200 years, including information about charcoal particle
sizes and morphotypes. Our results reveal a phase of increased charcoal accumulation between
600 and 900 CE, indicative of relatively high amounts of burnt biomass and high fire
frequencies. This is followed by an almost 900-year-long period of low charcoal accumulation
without significant peaks likely corresponding to cooler climate conditions. After 1750 CE fire
frequencies and the relative amount of biomass burnt start to increase again, coinciding with a
warming climate and increased anthropogenic land development after Russian colonization. In
the 20th century, total charcoal accumulation decreases again to very low levels despite higher
fire frequency, potentially reflecting a change in fire management strategies and/or a shift of
the fire regime towards more frequent but smaller fires. A similar pattern for different charcoal
morphotypes and comparison to a pollen and non-pollen palynomorph (NPP) record from the
same sediment core indicate that broad-scale changes in vegetation composition were probably
not a major driver of recorded fire regime changes. Instead, the fire regime of the last two
millennia at Lake Khamra seems to be controlled mainly by a combination of short-term climate
variability and anthropogenic fire ignition and suppression.
CHAPTER 2: MANUSCRIPT I
27
2.2. Introduction
Wildfires in Siberia have become larger and more frequent in recent years (WALKER ET AL.,
2019), drawing attention from both scientists and the wider public. The occurrence of wildfires
in high latitudes is closely linked to a dry climate and to rising temperatures, which have been
increasing at more than twice the rate of elsewhere (JANSEN ET AL., 2020; LENTON, 2012). With
ongoing warming, the established fire regimes will likely be subject to future change. Fire has
long been the most important ecological disturbance in boreal forests, shaping the appearance
and composition of the world’s largest terrestrial biome (GOLDAMMER AND FURYAEV, 1996). It
acts as a main driver behind the boreal forest’s carbon pool, which comprises a third of all
globally stored terrestrial carbon (KUULUVAINEN AND GAUTHIER, 2018), and strikes a fine
balance between emitting carbon during combustion and sequestering it during the following
regrowth (ALEXANDER ET AL., 2012; ITO, 2005; KÖSTER ET AL., 2018). Wildfires are thought to
turn this balance towards acting as a net source of emissions (KELLY ET AL., 2016; WALKER ET
AL., 2019) while posing increasing risks to human livelihoods and infrastructure (FLANNIGAN
ET AL., 2009). Additionally, fire in boreal forests may trigger tipping points in tree mortality
and tree density, as well as shifts in vegetation composition with continued global warming
(HERZSCHUH, 2020; LENTON ET AL., 2008; SCHEFFER ET AL., 2012; WANG AND HAUSFATHER,
2020). For these reasons, a prediction of potential future changes in boreal fire regimes is
imperative to inform and prepare adapted fire management strategies in a warming arctic and
subarctic boreal environment. However, data about long-term changes in fire regimes, a
prerequisite for model validation, are still very sparse for large parts of the Russian boreal forest
despite it comprising more than half of the world’s coniferous tree stocks (NILSSON AND
SHVIDENKO, 1998).
Macroscopic charcoal particles in sediment archives, derived from biomass burning, are
commonly used as a proxy for fire activity (e.g. CLARK, 1988; CONEDERA ET AL., 2009; REMY
ET AL., 2018; WHITLOCK AND LARSEN, 2001). Charcoal records are an important tool for
tracking past changes in fire regimes and searching for underlying causes and effects, from
local to global scales (MARLON ET AL., 2008, 2013; POWER ET AL., 2008). In recent years the
evaluation of charcoal particle distributions was expanded to infer past fire frequencies
(HIGUERA ET AL., 2007) and estimate source areas (DUFFIN ET AL., 2008; LEYS ET AL., 2015),
the type of vegetation burning with charcoal morphotypes (ENACHE AND CUMMING, 2007;
FEURDEAN ET AL., 2020; MUSTAPHI AND PISARIC, 2014), and fire intensity with charcoal
reflectance (HUDSPITH ET AL., 2015). Yet, as the distribution of lake sediment studies in Russia
CHAPTER 2: MANUSCRIPT I
28
is generally sparse (SUBETTO ET AL., 2017), the Global Paleofire Database
(https://www.paleofire.org; POWER ET AL., 2010) lists only a few continuously sampled
macroscopic charcoal records across the Siberian boreal forest. Only recently have charcoal
records of sufficient temporal resolution allowed for the assessment of fire return intervals
(FRIs) in boreal European Russian and western Siberian evergreen forests, revealing close fire–
vegetation relationships (BARHOUMI ET AL., 2019; FEURDEAN ET AL., 2020). More studies have
been conducted in North America (e.g. FRÉGEAU ET AL., 2015; HÉLY ET AL., 2010; HOECKER ET
AL., 2020; WAITO ET AL., 2018) and boreal Europe (e.g. AAKALA ET AL., 2018; FEURDEAN ET
AL., 2017; MOLINARI ET AL., 2020; WALLENIUS, 2011). However, comparisons across boreal
study sites are complicated by the differing predominant fire regimes in North America (high-
intensity crown fires) and Eurasia (lower-intensity surface fires) (DE GROOT ET AL., 2013;
ROGERS ET AL., 2015). The main fire regimes in the European and western Siberian evergreen
boreal forest also differ markedly from those of its larch-dominated, deciduous counterpart in
eastern Siberia. Many prevalent evergreen conifers (Pinus sibirica, Picea obovata, Abies
sibirica) are commonly seen as fire avoiders and are more susceptible to crown fires (DIETZE
ET AL., 2020; ISAEV ET AL., 2010; ROGERS ET AL., 2015).
The predominant eastern Siberian larches (Larix gmelinii, L. cajanderi, L. sibirica), on the other
hand, can resist fires with an insulating bark protecting the cambium from heat, while their
deciduous and self-pruning nature restricts fires from reaching the crown (WIRTH, 2005).
Moreover, larches are thought to benefit from occasional surface fires, leading to more saplings
by clearing the lower vegetation layers of plant litter and mosses, although this might become
a risk for young trees if fire frequencies increase above a certain threshold (SOFRONOV AND
VOLOKITINA, 2010). Surface fires in larch forests might also play a role in the long-term
preservation of permafrost (DIETZE ET AL., 2020; HERZSCHUH, 2020; HERZSCHUH ET AL., 2016).
These different fire strategies within the Siberian boreal forest reinforce the need for fire
reconstructions towards the eastern part to evaluate changes in fire regimes depending on the
prevalent tree species and to obtain a biome-specific overview of fire regimes throughout time.
The main goal of this study is to start filling a pronounced gap in the global distribution of
macroscopic charcoal records by providing the first continuously sampled, high-resolution
macroscopic charcoal record from eastern Siberia, using charcoal size classes and morphotypes.
We specifically aim to answer the following research questions. (i) How did the fire regime in
south-west Yakutia change throughout the last two millennia? (ii) How might a reconstructed
fire history relate to common drivers behind changes in fire regimes (climate, vegetation,
humans)?
CHAPTER 2: MANUSCRIPT I
29
2.3. Study site and methods
2.3.1. Location
Lake Khamra (59.97° N, 112.96° E) is located ca. 30 km north-west of the Lena River in south-
west Yakutia (Republic of Sakha, Lensky District), at an elevation of 340 m a.s.l. (above sea
level; Fig. 2.1a). Covering an area of 4.6 km2 the lake has a maximum water depth of 22.3 m.
The catchment area of 107.4 km2 stretches around the gentle slopes surrounding the lake and
extends in a south-western direction along the main inflow stream. It is embedded in a region
of Cambrian bedrock made up of dolomite and limestone with transitions to silty Ordovician
sandstone and locally restricted areas of clayey Silurian limestone (CHELNOKOVA ET AL., 1988).
The surroundings of the lake were unglaciated during the Last Glacial Maximum (LGM;
EHLERS AND GIBBARD, 2007), and discontinuous or sporadic permafrost can be present within
the study area (FEDOROV ET AL., 2018). Morphological features suggest that Lake Khamra is an
intermontane basin lake that is not of thermokarst origin because of a lack of steep slopes
(KATAMURA ET AL., 2009A), a maximum lake depth of 22.3 m compared to thermokarst lakes
reaching mostly <10 m (BOUCHARD ET AL., 2016; WEST AND PLUG, 2008), and the absence of
cryogenic features such as ice wedges indicating thermokarst processes (KATAMURA ET AL.,
2009B; SÉJOURNÉ ET AL., 2015). Accordingly, the regional soil volumetric ice content as
broadly defined by FEDOROV ET AL. (2018) is probably below the 30 % required for the
development of thermokarst lakes (GROSSE ET AL., 2013).
The region lies within the transition zone of evergreen to deciduous boreal forest. The lake
catchment is covered by dense mixed-coniferous forest consisting predominantly of Larix
gmelinii, together with Pinus sylvestris, P. sibirica, Picea obovata, Abies sibirica, Betula
pendula, and Salix sp. (KRUSE ET AL., 2019).
The continental climate of southern Yakutia is characterized by a short, mild growing season
and extremely cold winters. The mean temperature of July at ca. 18 °C is contrasted by the low
mean temperature in January of about −40 °C (FEDOROV ET AL., 2018). Mean annual
precipitation from 2000 to 2016 at the closest weather station in Vitim (ca. 60 km south-west
of Lake Khamra) was ca. 480 mm, with the highest values in July to September (RUSSIAN
INSTITUTE OF HYDROMETEOROLOGICAL INFORMATION, 2020). Wildfires most frequently occur
in the central to southern regions of Yakutia, with varying stand-specific mean fire return
intervals not exceeding 20–50 years at around 60° N, 120° E (IVANOVA, 1996) and 15 years
near Yakutsk (TAKAHASHI, 2006). Longer estimates, increasing with higher latitude, were
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found by PONOMAREV ET AL. (2016) for central Siberia of 80 years at 62° N, 200 years at 66°
N, and 300 years at 71° N, with similar ranges reported by KHARUK ET AL. (2016).
The regional climate is mainly controlled by the largescale Arctic Oscillation (AO) pattern
emerging between cold high-latitude and mild mid-latitude air masses (WU AND WANG, 2002).
It has been suggested that positive phases of AO, with low atmospheric pressure over the Arctic
directing warm air from the south, potentially increase Siberian fire activity (BALZTER ET AL.,
2005; KIM ET AL., 2020).
Overall population density of the Lensky District is rather low at 0.5 inhabitants km−2
(ADMINISTRATIVE CENTER LENSK, 2015). Lake Khamra is ca. 40 km north of one of the larger
district settlements, Peleduy, with a population of ca. 5000 inhabitants. Traces of logging
activity are visible in satellite imagery ca. 10 km from the lake, while in its direct vicinity only
winter forest tracks are kept open. We discovered traces of recent wildfire disturbance at
vegetation plots around the lake, i.e. trees with fire scars on their trunk or burnt bark. These
traces likely correspond to recent fires within the catchment in 2006 and 2014 as captured by
the remotely sensed forest loss data of HANSEN ET AL. (2013) (Fig. 2.1b, c) and MODIS fire
products (GIGLIO ET AL., 2018).
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Figure 2.1: (a) Position of Lake Khamra and existing Global Paleofire Database entries (last access: 6 October
2020) within the deciduous and evergreen boreal forests of Russia (land cover classification based on © ESA
Climate Change Initiative Land cover project, provided via the Centre for Environmental Data Analysis (CEDA).
(b) Infrastructure and burned areas 2001–2019 CE (GIGLIO ET AL., 2018; HANSEN ET AL., 2013) in the vicinity of
the lake (centre). (c) Lake Khamra with bathymetry and catchment area (black line) (service layer credits: Esri,
DigitalGlobe, GeoEye, i-cubed, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS
User Community).
2.3.2. Fieldwork and subsampling
Fieldwork at Lake Khamra was conducted in August 2018. A 242 cm long sediment core,
EN18232-3, was retrieved using a hammer-modified UWITEC gravity corer at the deepest part
of the lake (22.3 m), based on water depth measurements using a surveying rope and a handheld
HONDEX PS-7 LCD digital sounder. From the same location, a parallel short sediment core
(EN18232-2, 39 cm length) was retrieved and subsampled in the field at increments of 0.5 cm
(upper 20 cm) and 1 cm (lower 19 cm) for lead-210 and caesium-137 (Pb/Cs) age dating. The
sediment core, stored in a plastic tube, and all sediment samples, kept in WhirlPak® bags, were
shipped to the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
(AWI), in Potsdam and placed in storage at 4 °C. In October 2018, sediment core EN18232-3
was opened and cut in half in a cooled room under sterile conditions. While one half was
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archived, the other half was subsampled for proxy analysis and radiocarbon (14C) dating. The
sampling scheme included one 2 mL sample from the midpoint of every 2 cm increment (n =
120), used for the combined palynological analysis and charcoal extraction, and two ca. 1 mL
samples between each pair of larger samples (n = 234), used specifically for charcoal extraction
to ensure a continuous record of charcoal concentration. Bulk sediment samples for 14C age
dating were extracted every 20 cm (n = 12). At 85.5 cm core depth, a ca. 2 cm long piece of
wood was removed from the sediment core for 14C dating. Due to a lack of any other larger
organic structures, more macrofossil samples were picked while wetsieving bulk sediment
samples (n = 15). Except for one case, a clear determination of their origin was not possible
because of the small size of these samples.
2.3.3. Laboratory analyses
Lithology and age dating
Sediment core EN18232-3 was visually described before sampling. Water content and bulk
density were determined in subsamples from 120 sampling increments every 2 cm.
To establish a chronology, all bulk and macrofossil samples were sent to AWI Bremerhaven
for accelerator mass spectrometry 14C age dating at the MICADAS (Mini Carbon Dating
System) laboratory. Subsamples of the parallel short core EN18232-2 (n = 19) were sent to the
University of Liverpool Environmental Radioactivity Laboratory for Pb/Cs age dating,
analysing 210Pb, 226Ra, 137Cs, and 241Am by direct gamma assay with ORTEC HPGe GWL
series well-type coaxial low-background intrinsic germanium detectors (APPLEBY ET AL.,
1986). After careful evaluation of age dating results, an age-depth model was computed using
Bacon v.2.4.3 (BLAAUW AND CHRISTEN, 2011; package “rbacon”; BLAAUW ET AL., 2020),
combining Pb/Cs and adjusted 14C ages of all bulk samples calibrated with the IntCal20 14C
calibration curve (REIMER ET AL., 2020).
Macroscopic charcoal
We developed a sample preparation protocol that allows for the extraction of both macroscopic
charcoal and the smaller pollen fraction including non-pollen palynomorphs (NPPs) from the
same sediment sample. Lycopodium tablets (Department of Geology, Lund University), used
as marker grains in the palynological analysis, were dissolved in 10 % HCl and added to the
sediment samples. These were subsequently wet-sieved at 150 μm mesh width for separation
of the macroscopic charcoal from smaller fractions (e.g. CONEDERA ET AL., 2009; DIETZE ET
AL., 2019; HAWTHORNE ET AL., 2018). The suspension with the <150 μm fraction was collected
in a bowl below the sieve. This “pollen subsample” was then iteratively added to a falcon tube,
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centrifuged, and decanted prior to further preparation for palynological analysis. The >150 μm
fraction in the sieve was rinsed together under a gentle stream of tap water before being
transferred into another falcon tube. This macroscopic “charcoal subsample” was then left to
soak overnight in ca. 15 mL of bleach (<5 % NaClO) to minimize the potential counting error
from darker, non-charcoal organic particles (HALSALL ET AL., 2018; HAWTHORNE ET AL., 2018).
The counting of 304 charcoal samples was done under a reflected-light stereomicroscope at 10–
40× magnification. All particles that appeared opaque and mostly jet-black with charred
structures were counted in every given sample (see BRUNELLE AND ANDERSON, 2003;
HAWTHORNE ET AL., 2018). In addition, counted particles were grouped into three size classes
(150–300, 300–500, and >500 μm measured along a particle’s longest axis; DIETZE ET AL.,
2019) and based on similarities in shape (charcoal morphotypes; Enache and Cumming, 2007).
For size reference, preparatory needles with known diameters of 300 and 500 μm were used
that could be placed next to a charcoal particle. These needles also allowed for the careful and
non-destructive evaluation of the flexibility of particles of unknown origin since charcoal
fragments are described as fragile and non-bendable (WHITLOCK AND LARSEN, 2001).
The grouping of particles according to their shape was based on the morphotype classification
scheme by ENACHE AND CUMMING (2007) and extended by three additional types to represent
the variety of charcoal particles found at the study site. The original scheme differentiates
between irregular (types M, P), angular (types S, B, C), and elongated (types D, F) shapes and
further divides those depending on whether they show a visible structure or ramifications. The
three additional types appear as highly irregular particles (type X), elongated, fibrous particles
(type E), and slightly charred, partially transparent particles (type Z), the latter of which are not
included in the total charcoal sum. For correlations and visualizations, the morphotypes were
grouped into their respective main categories (irregular, angular, or elongated). Within the
topmost ca. 50 cm of the sediment core, relative morphotype distributions were retrospectively
derived from counts of 67 subsamples. At that time, 11 samples distributed within the top 40
cm of the sediment core had already been used for other purposes and thus lack information on
morphotype classification (total charcoal concentrations and size classes are available for all
samples).
Eight randomly selected samples were counted a second time to obtain an estimate of counting
uncertainty and ambiguity in charcoal identification.
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Palynological samples
Established protocols following ANDREEV ET AL. (2012) were applied to the pollen subsample
(n = 35). Of these samples, 11 were chosen specifically from intervals with high charcoal
concentrations, whereas the others were spread across the sediment core. Samples were treated
with boiling potassium hydroxide for 10 min, sieved, and left to soak in 18 mL hydrofluoric
acid (40 % HF) overnight. After two additional treatments with hot HF (1.5 h each), acetolysis
was performed using acetic acid and in a second step a mixture of acetic anhydrite and sulfuric
acid. After being fine-sieved in an ultrasonic bath, the samples were suspended in glycerol.
Pollen and NPPs were counted together with the added Lycopodium spores on pollen slides
under a transmitting light microscope with a minimum count sum of 300 particles. For
subsequent statistical analyses, relative frequencies of individual pollen taxa were calculated
from the sum of terrestrial pollen. Spore, algal, and non-pollen palynomorph percentages are
based on the sum of pollen plus either spores, algae, or non-pollen palynomorphs, respectively
(ANDREEV ET AL., 2020).
Statistical methods
Statistical analysis was carried out in R v.4.0.2 (R CORE TEAM, 2020). To assess fire history,
two different approaches were applied that decompose the charcoal record into a background
component, representing long-term variations in charcoal accumulation and particle
taphonomy, and a peak component, representing predominantly local charcoal accumulation
during fire episodes (HIGUERA ET AL., 2007, 2009; KELLY ET AL., 2011; WHITLOCK AND
ANDERSON, 2003).
First, we ran a set of analyses referred to as “classic CHAR” (R script by DIETZE ET AL., 2019),
similar to the charcoal record decomposition in the well-established “CharAnalysis” (HIGUERA
ET AL., 2009). We interpolated the charcoal record to equally spaced time intervals according
to its median resolution and calculated the charcoal accumulation rate (CHAR, particles cm−2
yr−1; package “paleofire” v.1.2.4, function “pretreatment”; BLARQUEZ ET AL., 2014). A
background component was determined by computing a locally estimated scatterplot smoothing
(LOESS) at a window width of 25 % of the total record length (package “locfit” v.1.5-9.4,
function “locfit”; LOADER ET AL., 2020). This window width was found to result in an efficient
distribution of a signal-to-noise index (SNI) >3 based on KELLY ET AL. (2011), which indicates
a high degree of separation between signal and noise (BARHOUMI ET AL., 2019; KELLY ET AL.,
2011). A peak component was created by subtracting the background component from the time
series. By fitting two Gaussian distributions into the histogram of the peak component (package
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“mixtools” v.1.2.0, function “normalmixEM”; BENAGLIA ET AL., 2009), a global threshold was
defined at the 99th percentile of the noise distribution (GAVIN ET AL., 2006; HIGUERA ET AL.,
2011). All peak component values exceeding the threshold were subsequently identified as
signal (representing fire episodes) and marked when they overlapped with periods of SNI >3,
indicating a clear distinction from surrounding noise. Usually in instances of multiple
consecutive samples, only the highest peak above threshold is recognized, but we included all
of them as fire episodes in this case. Recent fires within the lake’s catchment that were just 8
years apart (Fig. 2.1b), as well as a predominantly atmospheric input of charcoal and a quick
recovery of the filtering wetland vegetation around the large lake, led to the suspicion that the
ability of a single fire to create high charcoal counts in multiple samples is limited at this site.
However, an absolute minimum estimate of the number of fire episodes was obtained by
considering only one fire episode from a given peak in CHAR and only those that are clearly
separated from noise (i.e. overlapping with phases of SNI >3). A global threshold was chosen
to fit the steady sedimentation rate and vegetation composition around the lake. We also tested
a local threshold to see how robust our approach is when compared to alternatives. For this,
Gaussian distributions were fitted to samples of the peak component within a moving window
of the same width as used for background component determination (package “zoo”; function
“rollapply”; ZEILEIS AND GROTHENDIECK, 2005). Subsequent threshold values were obtained
the same way as before but additionally smoothed with a default LOESS to minimize the impact
of outliers.
Fire return intervals (FRIs) were determined as the temporal difference between subsequent fire
episodes. An illustration of fire frequency, as the rate of identified fire episodes, was derived
by counting all fire episodes within a moving window spanning 200 years before applying a
LOESS of the same window width to provide a clearer visualization.
To account for accumulated uncertainties from both the chronology and the counting procedure,
we used a Monte-Carlo-based (MC) approach (for detailed description and R script, see DIETZE
ET AL., 2019), referred to as “robust CHAR”. In short, it describes both the age and proxy values
of each sample as Gaussian distributions, creating a pool from which random values are
sampled. As inputs we used the 2σ range of Pb/Cs ages and 1σ range of calibrated and adjusted
14C ages to scale the general magnitudes of uncertainty between the two dating methods to
comparable dimensions. For proxy uncertainty, the average deviation between the repeatedly
counted samples was used (ca. 20 %). Following DIETZE ET AL. (2019), we aggregated three
consecutive samples to scale the high resolution of the charcoal record to the relatively high
age and counting uncertainties. A total of 5000 MC runs were performed, and output data
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resolution was set at 3 times the record’s median temporal resolution (18 years). Robust CHAR
was then divided into background and peak components, similar to classic CHAR but by
computing 1000 randomly sampled LOESS fits at varying window widths ranging from 5 % to
25 % of the record length, thereby not relying on an individual user-input value (BLARQUEZ ET
AL., 2013).
The statistical approach outlined above (classic and robust CHAR including the determination
of SNI, FRIs, and fire frequency) was also applied to the charcoal size classes and morphotype
categories to assess their individual contribution to the sum of all particles, whether different
charcoal groups represent different types of fires, the potential relationships between charcoal
particle size and source area, and/or varying fuel types over time (see Appendix 1).
A principal component analysis (PCA; package “stats”; R CORE TEAM, 2020) was used to assess
relationships among the various centred-log-ratio-transformed (clr) (package “compositions”;
VAN DEN BOOGAART ET AL., 2020) relative distributions of charcoal particle classes. Assuming
that the various charcoal morphotypes were formed by different types of vegetation burning,
we would expect an increase in a specific morphotype to coincide with changes in the
distribution of some plant types in the vegetation composition, also represented by the pollen
spectra. To explore this hypothesis, we applied a correlation test using Kendall’s τ (package
“psych”; REVELLE, 2020) to clr-transformed relative distributions of pollen groups that were
expected to be impacted by wildfires (pollen sums of arboreal, non-arboreal, deciduous, and
evergreen taxa) and charcoal classes following DIETZE ET AL. (2020).
2.4. Results
2.4.1. Lithological sediment properties and chronology
Sediment core EN18232-3 shows no lamination or visible changes in its brown colour or the
texture of the sediment matrix. Its homogenous appearance is underlined by both uniform mean
dry bulk density (184 ± 4 mg cm−3, mean ± 1σ) and water content (83 ± 4 %).
Bulk sediment 14C ages indicate a rather linear chronology, with only the deepest two samples
returning similar ages. In contrast to this, the 14C ages of the macrofossils do not form a clear
chronological pattern and are not in good agreement with the bulk sediment samples. Only 9 of
the 15 macrofossil samples were large enough to be used for 14C dating and most were only just
above the minimum amount of carbon (Table 2.1a). As Pb/Cs dating from the parallel short
core EN18232-2 reveals no disturbances in its uniform sedimentation rate, the ages are expected
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to be applicable to the main core EN18232-3 from the same location within the lake and thus
can be compared to the respective 14C ages. Noticeably, the topmost 14C bulk sample dates to
1415 ± 27 14C years BP (before present, i.e. before 1950 CE), whereas the Pb/Cs method from
the parallel core confirms the expected recent surface age (Table 2.1b). This 14C age offset can
have a variety of causes, such as sediment mixing processes (BISKABORN ET AL., 2012), the
presence of old organic carbon (COLMAN ET AL., 1996; VYSE ET AL., 2020), or dissolved
carbonate rock (hard-water effect; KEAVENEY AND REIMER, 2012; PHILIPPSEN, 2013).
In the case of Lake Khamra, located in a zone of discontinuous permafrost and bedrock
containing early Palaeozoic carbonates, the observed 14C age offset is a likely consequence of
input of both old organic and inorganic carbon through the south-western inflow stream. The
magnitude of this offset is documented by the difference between the 14C bulk surface sample
and the corresponding Pb/Cs age. There are indicators to support the assumption that
accumulation of old carbon not only happened recently but rather constitutes an ongoing
process in this lake system. First, treating the surface 14C age as an outlier without also adjusting
the other 14C dates would necessarily lead to a strong shift in sedimentation rates, which is
reflected neither by the homogenous appearance and density nor by the uniform sedimentation
rate as implied by the Pb/Cs method. This is underlined by reports of stable lake conditions and
thermokarst processes in central Yakutia during the late Holocene (e.g. PESTRYAKOVA ET AL.,
2012; ULRICH ET AL., 2019). Second, macrofossil 14C ages older than the surrounding sediment
matrix provide direct evidence for the potential influence of old carbon on bulk sediment
samples at various depths (e.g. macrofossil age of 9902 ± 97 14C years BP in sediment that dates
back to only ca. 100 years BP according to the parallel core’s Pb/Cs age). For these reasons,
the documented age offset (mean ± 1σ) in the topmost 14C sample was used to adjust all other
14C bulk samples (COLMAN ET AL., 1996; VYSE ET AL., 2020). Although macrofossil samples
are usually thought to be superior in precision to bulk sediment for age-dating purposes
(HAJDAS ET AL., 1995; WOHLFARTH ET AL., 1998) and have also been used to quantify a 14C age
offset throughout a sediment core (GAGLIOTI ET AL., 2014), within the present environment we
cannot exclude a potential permafrost origin, as well as measurement uncertainties due to their
small size. For this reason, they were not used for constructing the chronology.
The age-depth model created according to these observations (Fig. 2.2) shows a smooth
transition from Pb/Cs dates towards adjusted bulk 14C ages. Its uniform sedimentation rate
mirrors the sediment core’s homogenous composition and supports the underlying assumption
of a rather constant magnitude of old carbon influence on bulk 14C ages. Based on this
chronology, the sediment core spans ca. 2350 years across its 242 cm length. The continuously
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sampled charcoal record spans ca. 2160 years and thus reaches back from 2018 CE until ca.
140 BCE. Mean (± 1σ) sampling resolution of the whole record is 7.1 ± 4.1 years (max: 27;
min: 3), including the pollen samples, which cover on average 8.9 ± 3.8 years (max: 25; min:
4).
Table 2.1: (a) 14C age results of bulk and macrofossil samples from core EN18232-3. All samples used for the
chronology are marked with a * in the first column. Lab-IDs 3732.1.1, 3740.1.1, 3743.1.1, 3743.2.1, 3745.1.1, and
3753.1.1 (not shown) belong to the six macrofossil samples that are too small to be successfully dated. (b) Pb/Cs
age results from parallel short core EN18232-2 (analysed by Peter Appleby, personal communication, 2019).
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Figure 2.2: (a) Bayesian age-depth model applying Bacon (BLAAUW AND CHRISTEN, 2011) to several types of
determined sediment ages (red = Pb/Cs; blue = 14C bulk sediment; green = 14C macrofossils; grey lines = 2σ range;
red line = median). (b) Model iteration log. (c, d) Prior (green line) and posterior (grey area) distributions for
accumulation rate and memory, respectively. (e) Dry bulk density of sediment core EN18232-3 (grey line = mean).
2.4.2. Reconstructed fire regime
Samples of the charcoal record contain on average 8.1 ± 5.1 charcoal particles per cubic
centimetre of sediment (min: 0; max: 38.8). Interpreting the classic peak component as
temporally restricted increases in fire activity, 50 such fire episodes within the continuously
sampled core segment spanning the last ca. 2200 years were identified. This results in a record-
wide mean FRI of 43 years (min: 6; max: 594). A maximum estimate of this FRI, based on the
reduced number of fire episodes by limiting one episode per peak with SNI >3, is 95 years (min:
12; max: 876). Results from the alternative local threshold variant slightly differ from the global
threshold in expected ways (fewer fire episodes identified during periods of high CHAR and
more during periods of low CHAR) but do not produce majorly contrasting results (see
Appendix 1.1).
Classic and robust CHAR analysis distinguished four phases, representing different states of
the fire regime (see Fig. 2.3). Phase 1 (ca. 140 BCE to 600 CE) is characterized by relatively
high CHAR of 0.83 ± 0.43 particles cm−2 yr−1 and numerous (n = 23) peaks (i.e. fire episodes,
Fig. 2.3c), with an SNI that is slightly above 3 for the most part but slowly decreasing towards
the following phase 2 (Fig. 2.3b). The mean FRI is 31 years (min: 6; max: 144). Robust CHAR
shows a steadily increasing background component (Fig. 2.3d), whereas its peak component
(Fig. 2.3e) remains at low levels. More fire episodes (n = 16) occur in the following, shorter
phase 2 (600–900 CE), with a lower mean FRI of ca. 14 years. It incorporates some of the
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highest peaks of the whole record using either the classic or robust approach, resulting in a high
CHAR of 1.3 ± 0.54 particles cm−2 yr−1 (including the maximum of 2.8 particles cm−2 yr−1),
while SNI falls below 3. In the transition to phase 3 at around 900 CE a pronounced decrease
in charcoal input leads up to a long period of few to no fire episodes (n = 6) and longer mean
FRI of >60 years (min: 6; max: 594). Hence, CHAR of phase 3 (900–1750 CE) is low with 0.53
± 0.29 particles cm−2 yr−1, while robust CHAR background remains below average. The SNI
decreases to a record-wide minimum due to the lack of any CHAR peaks above the global
threshold in the second half of phase 3. Finally, phase 4 (1750–2018 CE) has higher CHAR
(0.61 ± 0.47 particles cm−2 yr−1) and more frequent occurrences of fire episodes (n = 5), with
mean FRI sharply decreasing to 40 years (min: 6; max: 78). An outstanding peak around 1880
CE leads to a maximum SNI >6 during this phase. Although phase 4 sees increasing CHAR
and fire frequency after the low CHAR of phase 3, charcoal input decreases to a minimum
within the last century (mean CHAR of the last 100 years before core extraction = 0.56 ± 0.29
particles cm−2 yr−1). Similarly, the robust CHAR sum and its components show increases within
phase 4 (Fig. 2.3d, e), with two maxima in the robust peak component around the early 1800s
and 1950 CE and a following decrease in CHAR (Fig. 2.3d–e). In general, the older half of the
record (ca. 140 BCE to 1000 CE) has higher mean CHAR and a higher variability (0.97 ± 0.5
particles cm−2 yr−1) compared to the younger half (1000–2018 CE; CHAR = 0.51 ± 0.32
particles cm−2 yr−1). Even with added uncertainties from counting and the chronology, maxima
of robust CHAR mostly replicate periods of increased classic CHAR.
Over the entire record, 43.7 % of charcoal particles belong to the smallest size class (150–300
μm), while 28.8 % and 27.5 % are part of the medium (300–500 μm) and large size classes
(>500 μm), respectively. When assessed individually, more fire episodes are identified for
smaller particles than for larger particles (Table 2.2). This is expected, as smaller particles tend
to have a larger source area, potentially incorporating more fire events into the signal
(CONEDERA ET AL., 2009). The most prevalent morphotypes present in the sediment are types F
(elongated, 31.7 %), M (irregular, 28.4 %), S (angular and black, 20.6 %), and B (angular and
brownish-black, 7.2 %), with all others (X, C, D, E, P) ranking at or below 3 % each. Noticeably,
two of the highest peaks of the record at ca. 650 and 1880 CE are composed primarily of large
(>500 μm), elongated (type F) particles. The total relative amount of type F particles seems to
correlate with the largest particle size class, whereas type M is more closely associated with
smaller particles (see Fig. 2.4 and Appendix 1.2). Furthermore, the PCA indicates that there are
rather weak grouping patterns of morphotype or size-class distributions in samples of increasing
core depth and age, potentially reflecting a stable vegetation composition around the lake. The
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three charcoal morphotype groups show a similar temporal pattern for their background and
peak component distributions (Fig. 2.6c, d), mostly mirroring the decreased variability in the
second half of the record as described for the sum of all particles. However, when assessed
individually, large particles have a generally lower variability than the other size classes,
whereas the variability in irregular morphotypes is higher than that in elongated or angular
particles (see Appendix 1).
Figure 2.3: Overview of the charcoal record using classic and robust analysis approaches. Vertical dashed lines
mark the different phases of the fire regime. (a) Classic CHAR peak component (dark-grey bars = signal, light-
grey bars = noise, dashed horizontal line = threshold). (b) Signal-to-noise index (SNI) of the classic CHAR peak
component based on KELLY ET AL. (2011) (red horizontal line = SNI cut-off value of 3). (c) Classic CHAR sum
(black line = interpolated CHAR; blue line = LOESS representing the CHAR background component; red vertical
lines = fire episodes with SNI >3; grey vertical lines = fire episodes with SNI <3). (d) Robust CHAR background
component. (e) Robust CHAR peak component (red areas = above-average values). (f) Robust CHAR sum. For
(d)–(f), black line = median and grey area = interquartile range.
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Table 2.2: Reconstructed fire episodes, fire return interval (FRI) in years, and distributions of signal-to-noise index
(SNI) >3 for charcoal morphotype and size classes, as well as the sum of charcoal particles over the last two
millennia.
Figure 2.4: Principal component analysis (PCA) of charcoal particles (size classes and individual morphotypes
with a share of >5 % of the charcoal record), with coloured dots representing potential grouping patterns of
charcoal assemblages with increasing age.
2.4.3. Vegetation history
The pollen and NPP record, covering the whole sediment core and reaching back ca. 2350 years,
generally indicates a relatively stable vegetation composition (Fig. 2.5). The dominant arboreal
pollen (AP) types comprise most of the pollen spectra (average ratio of AP : NAP = 8.3:1) and
include the trees and shrubs recorded around the lake. In descending order regarding their share
of the pollen sum these are Pinus, Betula, Picea, Abies, Alnus, and Larix, with smaller amounts
of Salix, Juniperus, and Populus. Non-arboreal pollen (NAP) types are predominantly
represented by Cyperaceae, followed by less abundant Poaceae, Ericales, and Artemisia.
Despite similar general palynomorph distributions, pollen assemblages can be divided into two
subzones, with the upper subzone (Ib) seeing intervals of increased variability in the shares of
some tree pollen and Cyperaceae (around 10 and 120 cm depth, corresponding to ca. 1950 and
700 CE, respectively). The lower subzone (Ia) demonstrates generally lower shares of Abies
and Cyperaceae pollen. Charcoal particles and pollen types have mostly weak correlations
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without statistical significance at p>0.05, although they hint at weak associations between
irregular morphotypes with AP being contrasted by those of angular morphotypes and NAP
(see Appendix 1.2).
Figure 2.5: Pollen and non-pollen palynomorph percentage diagram from sediment core EN18232-3 at Lake
Khamra (dots represent pollen taxa <1 %; A = algal remains; Z = invertebrate remains; horizontal dashed
line = separation of subzones Ia and Ib).
2.5. Discussion
2.5.1. Fire regime history of the last two millennia at Lake Khamra
We use the term “fire episode” instead of “fire event” when referring to identified peaks in the
record. This is to highlight that multiple fires could have contributed to any peak in the charcoal
record. Consequentially, the FRIs of this study should be regarded rather as “fire episode return
intervals”, marking the time span between periods of increased fire occurrence within the
charcoal source area. This is because the relatively large water-surface area of Lake Khamra
likely captures charcoal from a larger source area than smaller lakes. A larger source area of
charcoal is directly related to an increased number of fires that were able to contribute charcoal
to the present record. The gentle and densely vegetated slopes framing the catchment limit
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secondary charcoal input (WHITLOCK AND LARSEN, 2001), thus emphasizing a direct fire signal
with predominantly primary input through the air. However, a higher number of captured fires
from a larger source area also means that the comparability of FRIs reconstructed in this study
to those obtained from smaller lakes or tree-ring chronologies may be limited since those
usually convey direct fire impact and are more locally constrained (REMY ET AL., 2018).
Although it has been shown that larger charcoal particles originate generally within a few
hundred metres of a lake archive (CLARK ET AL., 1998; HIGUERA ET AL., 2007; OHLSON AND
TRYTERUD, 2000), they have also been observed to travel further depending on vegetation and
fire conditions (PETERS AND HIGUERA, 2007; PISARIC, 2002; TINNER ET AL., 2006; WOODWARD
AND HAINES, 2020). As wildfires in the Siberian boreal forest are predominantly considered
low-intensity surface fires (DE GROOT ET AL., 2013; ROGERS ET AL., 2015), the potential of the
resulting convection to transport large charcoal particles is probably limited compared to high-
intensity crown fires. We therefore assume a charcoal source area between a few hundred
metres directly around the lake for low-intensity fires (CONEDERA ET AL., 2009) and increasing
distance of up to several kilometres for more intense fires producing stronger convection,
resulting in a total source area estimate of up to ca. 100 km2. Even though some extreme fires
may well surpass this estimate and, occasionally, small fires within might fail to contribute
sufficient amounts of charcoal, identified fire episodes in the charcoal record should still be
biased towards fires closer to the lake, especially when they consist of predominantly large
charcoal particles (CONEDERA ET AL., 2009; WHITLOCK AND LARSEN, 2001). The uncertainty
regarding any source area estimate highlights the need for further spatial calibration studies.
Also, it remains unknown whether the charcoal record is dominated more by close-proximity
low-intensity fires or by high-intensity fires from a greater distance. An estimation of fire
intensity from charcoal particles (e.g. measuring charcoal reflectance, HUDSPITH ET AL., 2015,
or the charcoal’s oxygen to carbon ratio, REZA ET AL., 2020) could potentially clarify the
respective contributions and thus help with better constraining the source area.
The macroscopic charcoal record at Lake Khamra (Fig. 2.3) reveals gradually increasing but
relatively stable fire activity from 140 BCE to 600 CE (phase 1). A period of high fire activity
takes place between 600 and 900 CE, expressed as higher CHAR and shorter FRIs (phase 2). It
then transitions into an almost 600-year period without any identified fire episodes and low
CHAR (phase 3). From around 1750 CE the modern fire regime begins to take shape (phase 4),
with regularly identified fire episodes marking increasing fire frequency. However, the most
recent levels of CHAR are still lower than those of the maximum in phase 2 and reach a
minimum in the 20th century, meaning that the amount of modern charcoal accumulation is not
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unprecedented within the last ca. 2200 years. The mostly rather low SNI, which is below 3 for
ca. one third of the record, might result from the lower intensity surface fire regime found
around Lake Khamra. Such fires probably create less distinct peaks than the high intensity
crown fires of other regions, especially considering the large lake size.
Robust CHAR (Fig. 2.3d–f), incorporating uncertainties from the age-depth model and charcoal
counting, necessarily loses the original charcoal record’s short-term variability. It needs to be
noted that any uncertainty potentially arising from the assumed constant rate of old carbon input
to the lake, underlying the sediment core’s chronology, is not included here, as any changes in
magnitude of this reservoirlike effect are impossible to quantify within the scope of this study.
This issue is common in studies in permafrost regions that use 14C age dating (BISKABORN ET
AL., 2012; COLMAN ET AL., 1996; NAZAROVA ET AL., 2013; VYSE ET AL., 2020). In such
instances, applying Pb/Cs age dating adds valuable non-carbon-related estimates of sediment
accumulation rates for the upper part of a sediment core (WHITLOCK AND LARSEN, 2001). One
way of potentially quantifying old carbon age offsets throughout a sediment core might be to
use cleaned charcoal particles, if available in sufficient number, and evaluate their age
difference to bulk 14C samples (similar to GAGLIOTI ET AL., 2014). Since charcoal is assumed
to be delivered to the lake directly via the air, it might provide better estimates of the real
sediment age, even when other plant macrofossils fail to do so.
The general trend of fire regime changes in south-west Yakutia over the last ca. 2200 years
captured by the CHAR background component (Fig. 2.6c) and described above shares many
similarities with a charcoal record from the evergreen forest in the Tomsk region (ca. 1500 km
west of Lake Khamra; FEURDEAN ET AL., 2020). There, one period of exceptionally high CHAR
was observed around 700 CE and then again starting around 1700 CE towards the present, in
parallel with Lake Khamra’s fire history. The onset of increased biomass burning around 1700
CE was also reconstructed using aromatic acids from an ice core on the Severnaya Zemlya
archipelago (ca. 2200 km north-west of Lake Khamra), and it indicates a sudden decrease at the
beginning of the 20th century (GRIEMAN ET AL., 2017). However, the same study finds another
maximum of biomass burning around 1500 CE, in contrast to a clear period without fire
episodes from ca. 1250 to 1750 CE at Lake Khamra. Although comparisons across such long
distances and between different climate zones, archives, and fire proxies are likely to show
differing results, some of the described trends seem to be recorded at several sites worldwide.
A global charcoal record synthesis for the last two millennia by MARLON ET AL. (2008) indicates
decreasing biomass burning from ca. 1 CE towards the industrial era, when, after a maximum
around 1850 CE, it decreases with the onset of the 20th century. A potential explanation for this
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46
similar trend during the most recent centuries, observed across many study sites from different
regions of the world (e.g. DIETZE ET AL., 2019), could be the onset of industrialization with an
accompanying change in land use and subsequent fire management. However, charcoal records
from Siberia are underrepresented in such synthesis studies (MARLON ET AL., 2016), which,
together with a lack of comparable records closer to south-west Yakutia, underline the issue of
sparse data in the region.
Even with the constraints imposed by the large source area at this study site, the reconstructed
record-wide mean FRI of 43 years, incorporating both the exceptionally short mean FRI of
phase 2 (14 years) and the long mean FRI of phase 3 (60 years, excluding the ca. 600-year-long
period without identified fires), lies within the range of the few comparable studies in boreal
European Russia or western Siberia. Even if the record-wide mean FRI would strongly
overestimate the number of included fire episodes, we would not expect the true value to exceed
the maximum estimate of 95 years. BARHOUMI ET AL. (2019) found the shortest FRIs of the
Holocene ranging from 40 to 100 years between 1500 CE and the present day in macroscopic
charcoal records from the northern Ural region. A mean FRI of 45 years during recent centuries
was inferred by FEURDEAN ET AL. (2020). Other studies using tree-stand ages and fire scars in
tree-ring chronologies suggest mean FRIs of 80–90 years for mixed larch forests between the
Yenisei and Tunguska rivers ca. 1000 km north-west of Lake Khamra since ca. 1800 CE,
although the FRI of individual study sites could be as short as ca. 50 years (KHARUK ET AL.,
2008; SOFRONOV ET AL., 1998; VAGANOV AND ARBATSKAYA, 1996). A mean FRI of 52 years
was reported for the northern Irkutsk region ca. 300 km west of Lake Khamra in the 18th
century (WALLENIUS ET AL., 2011) and 50–80 years for some sites in the north-eastern larch-
dominated forests (KHARUK ET AL., 2011; SCHEPASCHENKO ET AL., 2008). In general, fire
frequency tends to increase with decreasing latitude due to higher solar radiation, longer fire
seasons, and higher flammability of dry biomass (IVANOVA, 1996; KHARUK ET AL., 2016, 2011),
which, in addition to the large source area, likely contributes to a relatively short mean FRI at
Lake Khamra when compared to studies set further north. Furthermore, studies based on tree
ring chronologies or fire scars are only able to detect fires where trees survived or for which
deadwood within the range of existing chronologies remains, whereas sedimentary charcoal can
potentially capture all types of fires.
ENACHE AND CUMMING (2007) explain how a large catchment to lake-area ratio might favour
secondary deposition of compact and stable morphotypes, whereas fragile morphotypes are
more prone to fragmentation during surface runoff and thus rather represent primary input
through the air. However, the catchment to lake-area ratio at Lake Khamra (23:1) and the share
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47
of fragile charcoal particles (types F, M, and S alone make up >80 %) are both comparably
large. This might indicate that morphotype distribution within the record is not controlled by
potential filtering effects of secondary charcoal transport but rather by the type of biomass
burning. This is also implied by the predominantly primary charcoal input through the air due
to the densely vegetated surrounding slopes and mirrors the stable vegetation composition seen
in the pollen record.
Experimental charring studies have shown how different types of vegetation produce varied
charcoal appearances (JENSEN ET AL., 2007; MUSTAPHI AND PISARIC, 2014; PEREBOOM ET AL.,
2020). PEREBOOM ET AL. (2020) found elongated charcoal particles after experimentally
burning tundra graminoids, potentially hinting at the origin of the many elongated type F
particles at Lake Khamra. However, these type F particles quite closely match the appearance
of charred Picea needles reported by MUSTAPHI AND PISARIC (2014). Together with the potential
of more intense fires producing larger charcoal particles (WARD AND HARDY, 1991), this could
mean the two previously noted peaks of CHAR (ca. 1880 CE at 19.5–20.5 cm depth and 650
CE at 124.5–125 cm; both dominated by high shares of type F particles >500 μm) are evidence
of higher-intensity fires burning conifer trees more severely and within a few kilometres of the
lake shore. Charring experiments with local vegetation and a regionally adapted morphotype
classification scheme would potentially benefit future studies by providing clear ground-
truthing for links between morphotypes and vegetation.
2.5.2. Drivers of fire regime variations
Vegetation
The overall stable vegetation composition during the time covered by the charcoal record, as
implied by the pollen and NPP record (Fig. 2.5), indicates that vegetation changes were unlikely
to be the main driver behind changes in the fire regime and/or that changes in fire regime did
not lead to large-scale shifts in vegetation composition. Similarly, no prominent shift in
charcoal morphotype composition, and hence in the type of biomass burned over time, can be
inferred (Figs. 2.4 and 2.6c, d). Although some studies draw a similar conclusion (e.g.
CARCAILLET ET AL., 2001, in eastern Canada), this result contrasts with many other studies from
the Eurasian and North American boreal zones, where vegetation changes were found to be
closely connected to changes in fire regimes (BARHOUMI ET AL., 2019, 2020; FEURDEAN ET AL.,
2020; GAVIN ET AL., 2007; KELLY ET AL., 2013). A reason for this might be that studies on
longer timescales capture long-term concurrent trends in both fire and vegetation that are not
observable in a record that only captures the last ca. 2200 years (e.g. glacial to interglacial
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48
changes in vegetation distribution and temperature; see MARLON ET AL., 2013). However, on a
shorter, multi-decadal timescale, phases with more Cyperaceae pollen (sedges) in the Lake
Khamra record and a higher ratio of evergreen to deciduous arboreal pollen types coincide with
periods of high fire activity in phases 2 and 4 (see Fig. 2.6b). This could be due to either the
ability of sedges to quickly settle on freshly disturbed and cleared-out forest areas, and/or
sedges growing in wetter areas, possibly right at the lake shore, which are spared from fires
(ANGELSTAM AND KUULUVAINEN, 2004; ISAEV ET AL., 2010; IVANOVA ET AL., 2014). An
increased proportion of evergreen trees might enable more intense crown fires. Indirectly, dry
periods could lead to receding lake levels and thus an increase in both shoreland sedges and fire
ignitions. However, such clear links are difficult to infer without hydrological data. In addition
to differences in proxy source area and taphonomy between macroscopic charcoal and pollen
grains, a variety of factors likely obscure traces of potential fire impacts: surface fires in the
deciduous forests in central Yakutia mostly result in the elimination of only a share of the
affected tree population depending on fire intensity (MATVEEV AND USOLTZEV, 1996). This
might not be enough to leave behind a clear mark in the pollen record, which also represents a
source area that is probably a lot larger than the area affected by fire. Herbs or shrubs, on the
other hand, may recover too quickly for changes to be detected in our record with a median
temporal sampling resolution of 6 years. Any potential mixing processes and the residence time
of pollen grains before settling in the lake sediment may further diminish visibility of a fire
impact (CAMPBELL, 1999; FÆGRI ET AL., 1989). Furthermore, reconstructions of Larix dynamics
based on fossil pollen are affected by the limited pollen dispersal distance of larch trees, as well
as poor preservation of their pollen grains (MÜLLER ET AL., 2010). This can lead to an
underestimation of Larix in fossil pollen records (EDWARDS ET AL., 2000) and thus complicate
the evaluation of larch tree dynamics. The relatively low proportion of fossil Larix pollen at
Lake Khamra, despite Larix gmelinii being a predominant tree taxon within the study area, may
well reflect that issue. This indicates how future studies aiming at specifically comparing past
fire regime changes with Larix population dynamics may benefit from including plant
macrofossil analysis, if possible (e.g. BIRKS, 2003; STÄHLI ET AL., 2006). Due to a lack of
macrofossils in the Lake Khamra sediment core and their suggested prolonged terrestrial
residence time, as implied by the chronology, this was not an option in the present study. These
factors, together with a remaining ambiguity in morphotype classification, likely explain the
rather weak correlation of the pollen and charcoal records.
CHAPTER 2: MANUSCRIPT I
49
Climate
Although it has been demonstrated that the timing and extent of supposedly ubiquitous warmer
or cooler climatic phases are in fact heterogeneous (GUIOT ET AL., 2010), evidence for their
occurrence in Siberia is seen in several proxy studies (CHURAKOVA SIDOROVA ET AL., 2020;
FEURDEAN ET AL., 2019; KHARUK ET AL., 2010; OSBORN AND BRIFFA, 2006), albeit less
pronounced when it comes to vegetation response in the West Siberian Lowland (PHILBEN ET
AL., 2014). NEUKOM ET AL. (2019) show how such climatic periods arising from averaged
reconstructions at many individual study sites are not spatially or temporally coherent on the
global scale and conclude that environmental reconstructions “should not be forced to fit into
global narratives or epochs”. This might be especially true for studies of a single site, using
chronologies that have 14C reservoir effects. However, the low fire activity in the latter half of
phase 3 (900–1750 CE) strikingly coincides with the Little Ice Age (LIA), when in many
regions of the Northern Hemisphere a cooler climate prevailed from ca. 1400 to 1700 CE. In
contrast to this, high fire activity during phase 2 not matching the proposed timing of the warmer
Medieval Climate Anomaly (MCA; ca. 950 to 1250 CE) demonstrates the limitations of such
comparisons based solely upon the 14C-dated segment of the charcoal record (estimates of LIA
and MCA durations from MANN ET AL., 2009). Due to a lack of regional studies, the PAGES
Arctic 2k temperature reconstruction (MCKAY AND KAUFMAN, 2014) was used to provide a
comparison between the reconstructed fire activity and large-scale changes in Arctic climate
north of 60° N (Fig. 2.6a). This synthesis of circumpolar temperature reconstructions
incorporates many records, although datasets from Siberia are sparse, and thus it is
underrepresented compared to Greenland, North America, and Europe. However, climate at
Lake Khamra is likely to be strongly influenced by the conditions further north as Arctic
temperatures affect the strength of the AO, which has been indirectly linked to fire activity
further south (BALZTER ET AL., 2005; KIM ET AL., 2020). The reconstructed PAGES Arctic 2k
temperature provides evidence for a colder climate around ca. 1600 CE, coinciding with the
LIA and low fire activity at Lake Khamra. The following onset of increased fire frequency in
phase 4 (ca. 1750 CE onwards) is concurrent with a gradual increase in Arctic temperatures
during the last two centuries (Fig. 2.6a, c), although it does not exceed the maximum in fire
frequency of phase 2. The older half of the charcoal record (before ca. 1000 CE) does not match
the temperature reconstruction as clearly. This might be due to a more regionally constrained
climate or a consequence of the lack of comparable data about humidity, which also affects fire
activity (BROWN AND GIESECKE, 2014; POWER ET AL., 2008). A larch tree-ring-based, ca. 1500-
year-long reconstruction of summer vapour pressure deficit in north-eastern Yakutia (ca. 2000
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50
km from Lake Khamra) indicates that the modern, increasing level of drought stress is not yet
unprecedented, being surpassed by a high vapour pressure deficit during the MCA
(CHURAKOVA SIDOROVA ET AL., 2020). This is similar to trends observed in the fire
reconstruction at Lake Khamra. Another possibility for a less clear relationship between Arctic
temperature and fire regime changes in the older half of the record is that the assumed constant
impact of old carbon on the 14C age dating might be less pronounced in this older millennium.
Yet, an impact of colder Arctic temperatures on the reconstructed low fire activity in phase 2 at
Lake Khamra seems probable. Since we can currently rely only on this one record, more
palaeoclimatic reconstructions and high-resolution charcoal records from the region could
greatly improve the validity of such inferred links between climate and the fire regime.
Figure 2.6: Comparison of the charcoal record with climate, vegetation, and general human settlement phases.
Vertical dashed lines mark the different phases of the fire regime. (a) PAGES Arctic 2k temperature (grey
curve = original annual data; yellow line = LOESS). (b) Selected vegetation proxies. (c) Fire frequency: compiled,
smoothed frequencies of fire episodes for the sum of particles and individual size classes and morphotypes. (d)
Compiled classic CHAR background components for the sum of particles and individual size classes and
morphotypes, scaled to equal extents for comparison. (e) General human settlement phases, referenced in the text.
For (c)–(d), lines represent size and morphotype classes, as well as the sum of charcoal particles.
Human activity
The discrepancy of last century’s low CHAR just after a phase of increasing fire frequency and
rising Arctic temperature could potentially be a sign of direct and indirect consequences of
human activity around Lake Khamra. The anthropogenic influence on fire regimes may be the
most difficult to quantify due to missing information about the kind and extent of human fire
use and management throughout time and the complex disentanglement of other drivers such
as climate and vegetation (MARLON ET AL., 2013). Although Lake Khamra is located in a
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51
sparsely populated region, humans have historically been shaping the surrounding landscapes
by building winter forest tracks and roads (ca. 0–30 km distance), logging (ca. 10 km distance),
and building villages and towns (ca. 30 and 40 km distance, respectively). It has also been
shown that even very remote lake systems in Yakutia have been affected by human activity
from industrialization (BISKABORN ET AL., 2021). Yakutia has been populated by humans since
at least ca. 28 000 BCE (PITULKO ET AL., 2004), although the population first noticeably
increased at the end of the LGM around 17 000 BCE (FIEDEL AND KUZMIN, 2007), eventually
forming the indigenous hunting and reindeer herding tribes of Evens, Evenks, and Yukaghirs
(KEYSER ET AL., 2015; PAKENDORF ET AL., 2006). Between 1100 and 1300 CE (in the first half
of phase 3 in the charcoal record) the Sakha people moved in from the south, pressured by an
expanding Mongol empire (FEDOROVA ET AL., 2013). They established a new and distinct form
of semi-nomadic livelihood based on horse and cattle breeding (PAKENDORF ET AL., 1999). Fire
was mainly used at hearths to provide warmth and light but also to sustain grasslands for grazing
(KISILYAKOV, 2009; PYNE, 1996). Population density likely started to increase rapidly after
Russian colonization in the early 17th century (CRUBÉZY ET AL., 2010) and even more
drastically with the onset of new industries such as wide-scale logging and mining in the 20th
century (PYNE, 1996). When compared to the pastoralist societies that existed up to that point,
anthropogenic influence on the fire regime likely increased at the end of phase 3 (ca. 1700 CE
onwards) and throughout phase 4 after colonization and industrialization (DROBYSHEV ET AL.,
2004). It has been shown that human livelihoods and the mentality towards fire use can often
better explain shifts in fire regimes than population density alone (BOWMAN ET AL., 2011;
DIETZE ET AL., 2018). For example, a formerly smaller population could have relied on practices
like slash-and-burn agriculture until there was a transition towards more industrialized, urban
livelihoods and a new focus on active fire suppression to protect forestry resources despite an
increasing population (DIETZE ET AL., 2019; MARLON ET AL., 2013). This is also thought to
explain a pronounced decrease in boreal fire activity within the last century in tree-ring studies
from central Siberia (300 km west of Lake Khamra) and Fennoscandia (WALLENIUS, 2011;
WALLENIUS ET AL., 2011). Forest roads and clearings could have acted as fire breaks, while the
emergence of fire suppression in Russia was conceived as early as 1893 CE and later led to the
founding of the first aerial firefighting unit in 1931 CE (PYNE, 1996). Adding to this, slash-and-
burn agriculture was officially banned at the end of the 18th century but was likely still practised
frequently up until the early 20th century (DROBYSHEV ET AL., 2004; KONAKOV, 1999;
KOZUBOV AND TASKAEV, 1999). Higher fire activity after ca. 1750 CE, marking the onset of
phase 4, therefore coincides with both a rapid increase in anthropogenic activity and land
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development, as well as a warming Arctic climate. Low CHAR within the last century on the
other hand might be a consequence of a cultural shift towards seeing fire as a hazard to ban,
control, and suppress. Whereas high fire frequency in the past corresponded with high amounts
of biomass burned, the recent century has also seen increasing fire frequency with decreasing
total biomass burned (Fig. 2.6c, d). This might indicate that the current fire regime differs from
that experienced by indigenous and Sakha people a few hundred years ago, now potentially
consisting of more frequent but smaller fires. A better understanding of fire use and
management of the various Yakutian societies throughout history is needed to judge the extent
of the human influence on fire regimes in relatively remote regions especially since it has the
potential to obscure the effects of recent global warming in fire reconstructions.
2.6. Conclusions
With its continuous sampling scheme and high median temporal resolution, the macroscopic
charcoal record at Lake Khamra provides first insights into changes in the boreal fire regime of
the last ca. 2200 years in eastern Siberia where comparable data are still lacking. Recent levels
of charcoal accumulation at Lake Khamra are not unprecedented within the last two millennia.
The reconstructed fire regime changes do not coincide with large-scale shifts in vegetation
composition, although short-term increases in evergreen trees and sedges broadly coincide with
periods of increased biomass burning around 700 and 1850 CE (phases 2 and 4). Also, low fire
activity from ca. 900 to 1750 CE (phase 3), expressed as long FRIs and low charcoal
accumulation, corresponds to a colder Arctic climate during the LIA. Despite the generally low
population density, increased anthropogenic forcing after the colonization of Yakutia by the
Russians in the early 17th century might have contributed to an increase in fire frequency,
together with rising temperatures. Although northern regions have been warming rapidly in
recent decades, charcoal input to the lake has been minimal during the last century, coinciding
with new fire management strategies and a ban on fire-related agricultural practices. The mean
FRI of 43 years, with a maximum estimate of 95 years, is within the range of published literature
for the wider region and incorporates a range of individual values of up to almost 600 years.
The large lake size may be an important factor behind these generally shorter FRIs since it is
associated with a large charcoal source area of several kilometres from the lake and thus
captures more fires compared to more locally constrained studies. Overall charcoal
accumulation (classic CHAR background component) and the frequency of identified fire
episodes seem to be directly related to each other for the majority of the record.
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Although this new charcoal record improves data availability from eastern Siberia, more
reconstructions, especially from distinctly deciduous regions, are needed to form a detailed
analysis of past fire regimes in the Siberian boreal forest. An improved understanding of both
fire activity and its drivers throughout history will eventually enable a meaningful assessment
of the presence and future of Siberian wildfires and their consequences.
Data availability
The R script used to analyse the charcoal record presented in this study can be accessed in the
Zenodo database: https://doi.org/10.5281/zenodo.4943274 (GLÜCKLER AND DIETZE, 2021). The
data presented in this study are available in the PANGAEA database
(https://doi.org/10.1594/PANGAEA.923773; GLÜCKLER ET AL., 2020) and will be uploaded to
the Global Paleofire Database (https://ipn.paleofire.org, INTERNATIONAL PALEOFIRE NETWORK,
2021).
Financial support
This research has been supported by the European Research Council (grant no. Glacial Legacy:
772852) and the Deutsche Forschungsgemeinschaft (grant no. DI 2544/1-1: #419058007).
Ramesh Glückler is funded by AWI INSPIRES (INternational Science Program for Integrative
Research). Stuart Andrew Vyse is financially supported by the Earth Systems Knowledge
Platform (ESKP) of the Helmholtz Foundation.
Acknowledgements
We thank Nadine Bernhardt for her help working with the sediment core and Cathy Jenks for
providing English proofreading, as well as the participants of the joint German–Russian
expedition Yakutia 2018 for their support. We would further like to thank Philip Higuera,
Angelica Feurdean, and one anonymous referee for providing valuable remarks for
improvements to this paper. This study is a contribution to the PAGES-endorsed International
Paleofire Network.
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“One feels the unbroken stillness
within that great forest-world, on the floor of which
the trees cast deep shadows in the faint moonlight.”
Fridtjof Nansen in “Through Siberia: The Land of the Future”, 1914 (p. 237)
73
3. MANUSCRIPT II
Holocene wildfire and vegetation dynamics in Central Yakutia, Siberia,
reconstructed from lake-sediment proxies
Ramesh Glückler1,2*, Rongwei Geng1,3,4, Lennart Grimm1, Izabella Baisheva1,2,5, Ulrike
Herzschuh1,2,6, Kathleen R. Stoof-Leichsenring1, Stefan Kruse1, Andrei Andreev1, Luidmila
Pestryakova5 and Elisabeth Dietze1,7,8*
1: Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and
Marine Research, Potsdam, Germany
2: Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany
3: Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural
Resources Research, Chinese Academy of Sciences, Beijing, China
4: College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
5: Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia
6: Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
7: Organic Geochemistry, German Research Centre for Geoscience (GFZ), Potsdam, Germany
8: Institute of Geography, University of Göttingen, Göttingen, Germany
*Correspondence: Ramesh Glückler (ramesh.glueckler@awi.de) and Elisabeth Dietze
(elisabeth.dietze@uni-goettingen.de)
Status: Published August 2022 in Frontiers in Ecology and Evolution. DOI:
10.3389/fevo.2022.962906
Appendix: This manuscript is related to Appendix 2.
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3.1. Abstract
Wildfires play an essential role in the ecology of boreal forests. In eastern Siberia, fire activity
has been increasing in recent years, challenging the livelihoods of local communities.
Intensifying fire regimes also increase disturbance pressure on the boreal forests, which
currently protect the permafrost beneath from accelerated degradation. However, long-term
relationships between changes in fire regime and forest structure remain largely unknown. We
assess past fire-vegetation feedbacks using sedimentary proxy records from Lake Satagay,
Central Yakutia, Siberia, covering the past c. 10,800 years. Results from macroscopic and
microscopic charcoal analyses indicate high amounts of burnt biomass during the Early
Holocene, and that the present-day, low-severity surface fire regime has been in place since c.
4,500 years before present. A pollen-based quantitative reconstruction of vegetation cover and
a terrestrial plant record based on sedimentary ancient DNA metabarcoding suggest a
pronounced shift in forest structure toward the Late Holocene. Whereas the Early Holocene was
characterized by postglacial open larch-birch woodlands, forest structure changed toward the
modern, mixed larch-dominated closed-canopy forest during the Mid-Holocene. We propose a
potential relationship between open woodlands and high amounts of burnt biomass, as well as
a mediating effect of dense larch forest on the climate-driven intensification of fire regimes.
Considering the anticipated increase in forest disturbances (droughts, insect invasions, and
wildfires), higher tree mortality may force the modern state of the forest to shift toward an open
woodland state comparable to the Early Holocene. Such a shift in forest structure may result in
a positive feedback on currently intensifying wildfires. These new long-term data improve our
understanding of millennial-scale fire regime changes and their relationships to changes of
vegetation in Central Yakutia, where the local population is already being confronted with
intensifying wildfire seasons.
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3.2. Introduction
Boreal ecosystems are facing increasingly severe wildfire seasons in recent years (WALKER ET
AL., 2019; KÖSTER ET AL., 2021). In Yakutia (eastern Siberia) wildfire-related carbon emissions
in the year 2021 surpassed those of 2020, which had already set a new record since the
beginning of systematic satellite observations (PONOMAREV ET AL., 2021; VOILAND, 2021).
Large areas were affected by the fires, leading to a partial breakdown of critical infrastructure
and covering cities with harmful smoke for weeks. Furthermore, overwintering fires,
smoldering in peatlands even during Yakutia’s extreme winters, can contribute substantially to
the region’s burnt area in subsequent fire seasons (XU ET AL., 2022). Central Yakutia is now
among the most fireprone regions of eastern Siberia and the whole boreal zone (KIRILLINA ET
AL., 2020).
Eastern Siberia, and the Republic of Sakha (Yakutia) as its largest administrative unit, is unique
among the boreal biomes, with deep permafrost and larch-dominated, deciduous forests
(DELUCA AND BOISVENUE, 2012; ROGERS ET AL., 2015). These forests provide valuable
ecosystem services both for the local communities and on continental to global scales, for
example, by protecting carbon-rich permafrost from accelerated degradation (KUKAVSKAYA ET
AL., 2013; HERZSCHUH ET AL., 2016; HERZSCHUH, 2020; HOLLOWAY ET AL., 2020; STUENZI ET
AL., 2021). Next to weather extremes or insect invasions, wildfires are the most important
ecological disturbance in this region (TEI ET AL., 2019; KHARUK ET AL., 2021). The current fire
regime of Siberia – generally described as consisting of mostly low-intensity surface fires when
compared to the boreal zone of North America (ROGERS ET AL., 2015) – has already been
observed to intensify with increasing temperatures (PONOMAREV ET AL., 2018). Simultaneously,
a prolonged snow-free period (BULYGINA ET AL., 2009) can increase fire probability in months
that were previously not associated with the annual fire season. It has been suggested that
continued global warming and its direct and indirect consequences will lead to increased tree
mortality and changes in species composition (KUKAVSKAYA ET AL., 2013; SHUMAN ET AL.,
2017; TEI ET AL., 2019). Due to a complex network of environmental feedbacks, impacts of
these changing fire regimes on the structure of the vast eastern Siberian larch forests are yet to
be well understood, especially on longer timescales. Considering the slow growth rates of trees
in the extreme continental conditions of Central Yakutia and the time-lagged adaptation of
forests to climatic changes (KRUSE ET AL., 2016), long-term studies are especially important to
obtain a full picture of fire-vegetation interactions.
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Central Yakutia is covered with a great number and variety of lake systems. Although their
sediment bodies are promising long-term archives of past environmental processes and
conditions, few studies have systematically analyzed fire proxies such as macroscopic charcoal
in the lakes of this region. In a study of sediment cores from two lakes close to the republic’s
capital Yakutsk, high charcoal accumulation in the Early Holocene (10,000 years before
present; years BP) coincided with an open larch forest (KATAMURA ET AL., 2009A). During the
Mid-Holocene, after 6,000 years BP, the pollen record indicates a rapid spread of Pinus, while
charcoal accumulation decreases to very low levels. However, the authors suggest that the Early
Holocene part of their sediment core may have been influenced by erosional input, and thus
come to conclude that there is no significant relationship between fire and vegetation. A similar
conclusion has been drawn by KATAMURA ET AL. (2009B), suggesting that a Holocene charcoal
record from a thermokarst lake on the Lena-Aldan fluvial terrace is indicative of redeposition
during lake development rather than a direct result of wildfire activity. GLÜCKLER ET AL. (2021)
have recently contributed a high-resolution charcoal record from an intermontane basin lake in
southwestern Yakutia, covering the last two millennia, and compared it to reconstructed
vegetation, climate, and phases of human expansion. However, since the modern regional
vegetation composition was already established and remained similar during much of the Late
Holocene, no clear impacts of vegetation changes on wildfire activity or vice-versa were found.
Of these few studies containing long-term fire reconstructions in Yakutia, none report clear
relationships between changes in fire regime and vegetation composition. Considering the
strong dependence of fire regimes on their fuel source (ROGERS ET AL., 2015; ARCHIBALD ET
AL., 2018) and the major changes of vegetation structure that occurred during the Holocene and
earlier warm periods (ANDREEV ET AL., 1997; VELICHKO ET AL., 1997; DIETZE ET AL., 2020;
COURTIN ET AL., 2021), this seems surprising. Studies from other regions of the Russian boreal,
however, point toward various potential feedbacks between fire and vegetation. During past
interglacials in Chukotka, boreal forest dominated by larch has been generally related with low-
intensity biomass burning (DIETZE ET AL., 2020). At the southern edge of Lake Baikal,
BARHOUMI ET AL. (2021) find a severe fire regime from c. 11,000 to 6,500 years BP, related to
dark taiga vegetation (Pinus sibirica, Abies sibirica, and Picea obovata), before the onset of the
present-day surface fire regime, coinciding with a shift toward light taiga (Pinus sylvestris and
Betula sp.). FEURDEAN ET AL. (2022) report how frequent high-severity fires could lead to
changes in forest structure and composition in the Tomsk region (western Siberia). High fire
activity coincided with a low peatland water level and intermediate forest density, and it
occurred in both light and dark taiga. In the northern Ural region, BARHOUMI ET AL. (2020)
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report predominantly climate-driven vegetation dynamics in the Early Holocene due to lower
reconstructed fire activity, whereas in the Late Holocene increasing fire activity might have
driven a vegetation shift from dark to light taiga. However, the limited number of long-term
fire-vegetation studies set in Central Yakutia prevents an evaluation of fire-vegetation
feedbacks in this important region.
The main objective of this study is to identify long-term relationships between changing fire
regimes and boreal forest structure in Central Yakutia, and to discuss potential future
trajectories in a warming climate. We do this by reconstructing (I) long-term wildfire history,
derived from a new, continuous record of sedimentary macroscopic charcoal from a
thermokarst lake, and comparing it to (II) the history of vegetation composition with a
REVEALS-transformed pollen record and data on sedimentary ancient DNA (sedaDNA) of
terrestrial plants.
3.3. Materials and methods
3.3.1. Location
Lake Satagay (N 63.078, E 117.998; 114 m a.s.l.) is situated in the Nyurbinsky District of the
western part of the Central Yakutian Lowlands, 600 km west of the republic’s capital Yakutsk
(Figure 3.1). It lies in the vicinity of the Vilyuy River (20 km), a western tributary to the Lena
River. Surrounded by flat topography and other thermokarst lakes, Lake Satagay covers an area
of 1.7 km2 with a maximum water depth of 1.8 m. The region around the lake is underlain by
Jurassic sandstone, covered by alluvial and lacustrine Quaternary sediments (MINISTRY FOR
NATURAL RESOURCES AND ECOLOGY OF THE RUSSIAN FEDERATION, 2012, 2014). The area is
directly accessible via the Vilyuy Highway (5 km) through forest trails. Apart from smaller
settlements, the closest larger town is Nyurba with a population of c. 10,000 people (30 km).
The lakes in this region and their surrounding grasslands (“alaas” landforms) have traditionally
been used for agriculture (e.g., hay-making; CRATE ET AL., 2017).
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Figure 3.1: (A) Location of Lake Satagay [evergreen/deciduous forest classification based on © ESA Climate
Change Initiative land cover project, provided via the Centre for Environmental Data Analysis (CEDA)]. Red dots
mark available sedimentary charcoal data from previous studies (extracted from the Global Paleofire Database and
including only sites where data were provided, last access: 15 January 2022; POWER ET AL., 2010). (B) Lake
Satagay and its surrounding vegetation, based on land cover classification from Sentinel 2 acquisitions with ground
truthing from expedition observations [KRUSE ET AL., 2021; VAN GEFFEN ET AL., 2021 (Preprint); GENG ET AL.,
2022].
Drowning meadows near the shoreline, a flat bathymetry, and shallow water depth make Lake
Satagay a typical late-stage thermokarst lake: it is mostly groundwater dominated and was most
likely initially created by local ice-rich permafrost degradation. The resulting water-collecting
depression subsequently expanded over time while partially filling up with organic-rich
deposits to eventually form the present-day thermokarst lake (BOIKE ET AL., 2016; CRATE ET
AL., 2017). Longterm development of the lake is analyzed in more detail by BAISHEVA ET AL.
(2022A).
Present-day vegetation around the lake, as recorded during fieldwork in 2018 (KRUSE ET AL.,
2019) and observed by remote sensing vegetation classification (GENG ET AL., 2022; see Figure
3.1), is dominated by dense, closed-canopy Larix gmelinii (Gmelin larch) tree stands. In
between, there are occasional mixed, open-canopy stands of Larix together with Picea obovata
(Siberian spruce), Pinus sylvestris (Scots pine), and less often Betula pendula (silver birch).
Ground vegetation close to the lake consists primarily of mosses in addition to some lichens
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and grasses. The presence of deadwood indicates recent disturbances to the forest, such as
windthrow or wildfires (KRUSE ET AL., 2019).
Central Yakutia is known for its extremely continental climate and the resulting large annual
temperature range, having reached record values of around −70°C in winter and almost 40°C
in summer. Based on CRU TS v.4.03 interpolated observational climate data (HARRIS ET AL.,
2020) for the reference period of 1961 to 1990 at Lake Satagay, mean January temperature is
−35°C, whereas mean July temperature is 17°C. Precipitation is very low during winter, most
of the mean annual sum of 280 mm occurs during the warmer summer months (June–August).
The larger region around Lake Satagay and the Central Yakutian Lowlands in general,
especially west of the Lena River, has been struck by record wildfires in recent years. Data from
the MODIS Terra and Aqua satellites combined burned area product (MCD64A1 Version 6;
GIGLIO ET AL., 2016), obtained for a 100 km buffer around Lake Satagay, shows that 4 of the 5
years with the largest burned area occurred only recently in the 20+ years timespan of available
data (in decreasing order: 2021, 2014, 2019, 2002, 2018; Figure 3.2B). An explorative
application of the well-established Canadian Forest Fire Danger Rating System (STOCKS ET AL.,
1989), a fire weather index (FWI) derived from daily ERA5 climate reanalysis data (HERSBACH
ET AL., 2020) at Lake Satagay for the last 70 years, independently displays an increasing trend
of climate-induced wildfire probability in the summer months starting around 2010 (Figure
3.2A). It is likely that the current fire regime will continue to intensify with warming
temperatures, as suggested also by a high positive correlation between observed burnt area and
calculated summer FWI (Figure 3.2C).
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Figure 3.2: (A) ERA5 reanalysis data at Lake Satagay (HERSBACH ET AL., 2020) for temperature, precipitation,
and a fire-weather-index (FWI; calculated from daily values with the R package “cffdrs”; WANG ET AL., 2017)
from 1950 to 2020 CE. (B) Burnt area in a 200 km buffer around Lake Satagay from 2001 to 2020 CE (NASA
EOSDIS Land Processes DAAC product MCD64A1.006; GIGLIO ET AL., 2016). (C) Correlation of mean summer
months FWI and annual burnt area.
3.3.2. Fieldwork and subsampling scheme
Fieldwork at Lake Satagay was conducted in August 2018 (KRUSE ET AL., 2019). Sediment core
EN18224-4 was obtained with a hammer-modified UWITEC gravity corer from the deepest
region of the lake, at a water depth of 1.8 m based on point measurements with a Hondex PS-7
ultrasonic depth sounder. The 121-cm-long sediment core was sealed in its PVC tube, cut into
two segments (0–100 cm and 100–121 cm), and subsequently transported to Germany in a
cooled thermobox to be stored at the Alfred Wegener Institute (AWI) Potsdam at 4°C. After
opening the sediment core, subsampling was done in October 2020 to obtain a continuous
sequence of 111 sediment samples for charcoal analysis (1 cm3), 48 of which were also used
for the extraction of the pollen fraction. Additionally, 61 samples (2 cm3) were extracted for the
analysis of sedaDNA, while ten bulk sediment samples for dating were taken, spread equally
across the core at every c. 10 cm.
3.3.3. Core dating
Bulk samples were freeze-dried and homogenized in a planetary mill before being sent to AWI
Bremerhaven for AMS radiocarbon dating at the MICADAS laboratory, following standard
protocols (MOLLENHAUER ET AL., 2021). Resulting 14C ages were calibrated using the IntCal20
calibration curve (REIMER ET AL., 2020) in R (v.4.0.2; R CORE TEAM, 2020) during age-depth
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modeling with Bacon (v.2.5.7, R package “rbacon”; BLAAUW AND CHRISTEN, 2011; BLAAUW
ET AL., 2021).
3.3.4. Charcoal and pollen analysis
To assess direct relationships between fire and vegetation composition, the same sediment
sample was separated into a macroscopic charcoal and a pollen fraction following GLÜCKLER
ET AL. (2021). In short, sediment samples were disaggregated by soaking in sodium
pyrophosphate (Na4P2O7) overnight. They were then infused with marker spores from
Lycopodium clavatum tablets (Department of Geology, Lund University) that were dissolved
in 10% hydrochloric acid (HCl). All samples were wet-sieved at 150 μm mesh width. The larger
fraction, containing the macroscopic charcoal particles, was bleached with <5% sodium
hypochlorite (NaClO) to improve distinction between charcoal and other dark organic particles.
The smaller fraction, containing the pollen grains and non-pollen palynomorphs, was
subsequently reassembled by multiple rounds of centrifuging, decanting, and adding of the
remaining suspension. After this step, preparation of 48 pollen samples followed the standard
protocols of ANDREEV ET AL. (2012).
Macroscopic charcoal samples were counted under a Zeiss Stemi SV 11 Apo stereomicroscope.
Black, opaque charred particles were quantified and categorized according to size classes, from
150 to 300 μm (small), 300 to 500 μm (medium) to >500 μm (large), and their morphology,
following the charcoal morphotype identification scheme established by ENACHE AND
CUMMING (2007). Charcoal morphotypes were grouped into three higher level categories of
angular, elongated, or irregular shapes. Ten samples were counted a second time to derive a
mean counting error. Pollen grains and non-pollen palynomorphs were identified on glass slides
under a Zeiss Axioskop 2 microscope at 400× magnification, aided by pollen keys and atlases
compiled by ANDREEV ET AL. (2012). Lycopodium marker spores and pollen were counted until
a total of at least 300 terrestrial pollen grains was reached. In 12 pollen samples, at every c. 10
cm of the sediment core, microscopic charcoal particles were counted to a total of at least 300
(sum of microscopic charcoal and Lycopodium spores; FINSINGER AND TINNER, 2005).
3.3.5. Sedimentary ancient DNA approach
The preparation of 61 samples for evaluation of sedaDNA is outlined in detail in BAISHEVA ET
AL. (2022A). In short, DNA was extracted from sediment samples using a Qiagen Power Soil
isolation kit before being concentrated with a GeneJET PCR purification kit. PCR amplification
of plant DNA was done by using the well-established universal plant primers targeting a short
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fragment of the trnL P6 loop on the chloroplast genome (TABERLET ET AL., 2007) modified with
a unique NNN8bp tag on each primer to establish sample separation. Pooled PCR products
were then sequenced in paired-end mode using an Illumina NextSeq 500 sequencing device at
Gensupport. Sequencing raw data was analyzed with Obitools (BOYER ET AL., 2016) and
taxonomic classifications were based on matches against the Arctic and boreal vascular plant
database (SØNSTEBØ ET AL., 2010; WILLERSLEV ET AL., 2014; SOININEN ET AL., 2015). After
removal of aquatic plants from the dataset, all identified terrestrial plant types with a total
abundance of >0.1% at their highest taxonomical level were used for visualization, indicating
the presence or absence of individual plant types within the sedaDNA.
3.3.6. Statistical methods
From the charcoal concentration per sample (particles cm−3), a charcoal accumulation rate
(CHAR, particles cm−2 year−1), interpolated to median temporal resolution, was calculated
using the R script presented in GLÜCKLER ET AL. (2021). The background component of
charcoal accumulation, indicative of the overall trends in the amount of biomass burned
(HIGUERA ET AL., 2007), was determined by locally estimated scatterplot smoothing (LOESS)
in a moving window of 25% of the record. Because of the low temporal resolution of the record
of 100 ± 43 years (mean ± 1σ), the complementary peak component, often used to identify
individual fire episodes, was not determined. Apart from this “classic” charcoal decomposition
approach, we also included the alternative “robust” method, where additional uncertainties from
charcoal counting, dating, as well as specific user input choices (i.e., smoothing window width)
are integrated through Monte-Carlo-based random sampling (DIETZE ET AL., 2019).
The pollen percentage data were transformed via the REVEALS method (SUGITA, 2007) to
estimate the past relative vegetation cover (R package “DISQOVER”; THEUERKAUF ET AL.,
2016), using relative pollen productivity and dispersal estimates of a harmonized dataset for the
Northern Hemisphere extratropic zone (WIECZOREK AND HERZSCHUH, 2020). All pollen types
with a total cumulative coverage across all samples exceeding 0.1% were used for visualization.
To evaluate potential relationships between vegetation cover and charcoal counts, both datasets
were centered log-ratio transformed (R package “compositions”; VAN DEN BOOGAART ET AL.,
2021) and used for principal component analysis (PCA; R package “vegan”, OKSANEN ET AL.,
2020). For correlations, a Pearson correlation coefficient was calculated.
Unique zones within the charcoal, pollen, and sedaDNA distributions were identified with
stratigraphically constrained cluster analysis (GRIMM, 1987; using the R packages “vegan” and
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“rioja”; JUGGINS, 2020). Only a significant number of zones, according to comparison with a
broken-stick model, is visualized (BENNETT, 1996).
3.4. Results
3.4.1. Chronology
Sediment core EN18224-4 displays a mostly homogenous texture with dark-brown colors from
organic-rich deposits, lacking any lamination. A detailed description of the core sediment facies
can be found in BAISHEVA ET AL. (2022A). Bulk sediment 14C dating shows a well-ordered
sequence of dating points without age reversals (Table 3.1). The surface sample, dating back to
1345.5 ± 36.5 cal. years BP (mean ± 2σ), shows a clear age offset. Such surface age offsets
often occur in lakes of eastern Siberia (e.g., COLMAN ET AL., 1996; VYSE ET AL., 2020;
GLÜCKLER ET AL., 2021) and can have a variety of causes. In the present shallow lake, the flat
surrounding topography and the lack of a major inflow limit the ability of surface runoff,
thermokarst slumping, or fluvial input to deposit old organic carbon from eroded permafrost
compared to other lakes (GLÜCKLER ET AL., 2021). Similarly, the lack of nearby carbonaceous
rock formations excludes the presence of a hard water effect (PHILIPPSEN, 2013). Therefore, the
age-offset is most likely a consequence of older sediment mixing with recent deposits in the
uppermost part (BISKABORN ET AL., 2012), which may have been further amplified by
bioturbation. The surface 14C age was therefore not included in the age-depth modeling. Instead,
a recent age was assumed (i.e., year of core extraction, 2018 CE). The resulting chronology
suggests a basal age of the sediment core of c. 10,800 cal. years BP, covering most of the
Holocene (Figure 3.3A; for the original Bacon chronology see Appendix 2.1). The dating
uncertainty, i.e., the 2σ range of all age-depth models included in the chronology, is on average
601 ± 279 years (mean ± 1σ). For all following analyses and figures the median age values are
used. According to those, mean sedimentation rate is 0.14 ± 0.09 mm year−1, with higher rates
of up to 0.42 mm year−1 between c. 37 and 56 cm (Figure 3.3B). This chronology is further
reinforced by a pronounced expansion of pine trees, clearly visible in the pollen record at c.
5,400 cal. years BP. The timing of this expansion fits within the range reported by previous
studies in the region (e.g., MÜLLER ET AL., 2009; ANDREEV AND TARASOV, 2013).
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Table 3.1: 14C dating results for sediment core EN18224-4.
Figure 3.3: (A) Bulk-sediment 14C-based chronology for sediment core EN18224-4. (B) Sedimentation rate, as
derived from the chronology. (C) Concentrations of macroscopic charcoal, divided by size classes (bars), and
microscopic charcoal found in pollen samples (line).
3.4.2. Charcoal
In 111 contiguous samples along the sediment core, a total of 4,206 macroscopic charcoal
particles was identified, resulting in a mean of c. 38 particles per cm3 (Figure 3.3C) or a mean
CHAR of 0.38 ± 0.5 particles cm−2 year−1 (mean ± 1σ, Figure 3.4B). The median temporal
resolution of all charcoal samples is 100 ± 43 years. Charcoal accumulation varies in three
major phases throughout the Holocene with highest CHAR in the Early Holocene, intermediate
CHAR in the Mid-Holocene, and low CHAR during the Late Holocene until present day. Most
prominently, CHAR shows a distinct peak around 9,600 years BP, reaching a recordwide
maximum of 2.58 particles cm−2 year−1. The mean CHAR of the Early Holocene (c. 10,800 to
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8,500 years BP) is 1.0 ± 0.73 particles cm−2 year−1. After that, it decreases to lower intermediate
levels until c. 4.500 years BP (0.29 ± 0.17 cm−2 year−1). From there until present day, CHAR
remains at low levels (0.09 ± 0.06 particles cm−2 year−1). Robust CHAR, with its added
uncertainties from a random sampling approach, mirrors the described trends and closely
overlaps with the smoothed background component of classic CHAR. The low-resolution
microscopic charcoal shows a very similar trend to the macroscopic charcoal concentration
(Figure 3.3C), with a distinct maximum at 9,800 years BP (100 × 103 particles cm−3), followed
by intermediate levels until 5,500 years BP (40 × 103 particles cm−3), and low levels until
present day (10 × 103 particles cm−3).
Figure 3.4: (A) TraCE 21ka climate model data (HE, 2011) for Lake Satagay, displayed as annual June–August
(JJA) temperature anomaly. (B) Classic and robust charcoal accumulation rates (CHAR), with zone separations
from cluster analysis. (C) Relative abundance of charcoal morphotype classes, with zone separations from cluster
analysis. (D) Cover of the most prominent vegetation types from the REVEALS-transformed pollen data,
interpolated to match the median temporal resolution of the displayed charcoal data and with zone separations
from cluster analysis (applied to interpolated data).
Most macroscopic charcoal particles are rather small, with 50.2% belonging to the small size
class (150–300 μm), whereas 27.1 and 22.7% are in the medium (300–500 μm) and large (>500
μm) size classes, respectively. By far the most particles demonstrate angular shapes (85%), with
few contributing to either irregular (10.4%) or elongated (4.6%) morphotype classes. Cluster
analysis of the charcoal sum and its three morphotype timeseries suggests three zones separated
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at 5,300 and 9,000 years BP, marking the three general phases of CHAR described above
(Figures 3.4B,C). The oldest zone, characterized by the record’s highest CHAR values, is
dominated by higher shares of large particles and a high abundance of angular morphotypes at
80–90%. The most recent zone shows low CHAR coinciding with decreasing shares of large
and angular types, and an increase of irregular types to 30–40%.
3.4.3. Pollen
REVEALS-transformed pollen data indicates that the vegetation around Lake Satagay was
dominated by Larix throughout most of the past c. 10,800 years, with maximum cover of up to
70% around 5,500 years BP and within the recent centuries (Figure 3.5). In addition, Betula,
Picea, and Pinus appear with high cover of up to 30%, while Salix (willow), Abies (fir), Alnus
(alder), and Cupressaceae (cypress) mostly stay below 10%. Non-arboreal pollen (NAP) are
dominated by Poaceae and Cyperaceae. Other NAP found are Asteraceae, Thalictrum,
Rosaceae, and small numbers of Artemisia, Ericales, Fabaceae, Onagraceae, and
Caryophyllaceae. The original pollen counts also feature a high number of algae in the lowest
segment of the core (10,800–7,000 years BP), and some Polypodiaceae spores in the upper part
of the core (5,400–2,000 years BP; Appendix 2.2).
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Figure 3.5: REVEALS-transformed pollen record, with zone separations from cluster analysis. AP/NAP ratio
values without unit. Shaded area represents a visual exaggeration, added to pollen types of lower abundance.
Compared to the original, non-transformed pollen record, the REVEALS-transformed,
quantitative vegetation reconstruction greatly increases relative shares of Larix. The reason for
this difference is an underrepresentation of Larix in pollen assemblages compared with other
arboreal taxa such as Pinus, caused by the relatively low pollen production and pollen dispersal
range of larch trees (EDWARDS ET AL., 2000; CAO ET AL., 2019B). Trends observed in the
original pollen record are well captured by the REVEALS-transformed data. Because of that
and the correction of species-specific taphonomy, only the REVEALS data will be discussed in
more detail.
Cluster analysis separates the quantitative vegetation reconstruction into two major zones: the
lower zone (10,800–7,000 years BP) is characterized by a high cover of Poaceae and arboreal
pollen dominated by Larix and Betula. The upper zone (7,000 years BP–present), on the
contrary, displays a mixed forest that is clearly dominated by Larix. Betula decreases and
Poaceae have been giving way to a higher cover of Cyperaceae. When interpolated to the same
sample age intervals as the macroscopic CHAR, cluster analysis identifies additional zones,
including a separation at 5,400 years BP (Figure 3.4D). At this time, pine trees rapidly extend
their cover, while Salix is only seen on a few occasions from there on. The original pollen
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counts (Appendix 2.2) demonstrate similar trends. There, cluster analysis also identifies a zone
at 5,400 years BP, when Betula pollen give way to quickly increasing numbers of Pinus pollen.
The PCA for transformed pollen types clearly distinguishes Early from Late Holocene samples
along principal component 1 (PC1), explaining 23.7% of the dataset variability (Figure 3.6A).
As indicated by the opposing vectors, PC1 describes two major states of forest structure present
in the reconstruction, with open larch-birch woodlands in the Early Holocene (negative values
of PC1), opposed to a denser, larch-dominated forest in the Late Holocene (positive values of
PC1). PC1 is negatively correlated with charcoal concentration (r = −0.71, r2 = 0.51, p < 0.001),
i.e., the dense forest state of PC1 coincides with low charcoal concentration, whereas the open
woodland state coincides with high charcoal concentration (Figure 3.6B).
Figure 3.6: (A) Principal component analysis of REVEALS-transformed vegetation types. (B) Scatterplot of
macroscopic charcoal concentration and principal component 1 (PC1) of the PCA, with LOESS-smoothing.
3.4.4. Sedimentary ancient DNA
Analysis of sedaDNA revealed 79 DNA sequence types of terrestrial plants identified to
different taxonomic levels, which were then collapsed to family level, resulting in 28 different
plant families (Figure 3.7). With cluster analysis, four zones are identified within the sedaDNA
proxy, in agreement with pollen and charcoal zones. The first zone (10,800−9,300 years BP)
comprises Betula, Saliceae, and Populus, together with a great variety of flowering plants and
grasses (Asteraceae, Chenopodiaceae, Onagraceae, Rosaceae, Poaceae, and Urticaceae). The
Onagraceae here include Chamaenerion angustifolium (fireweed). The second zone (9,300–
5,400 years BP) continues with similar identified trees, including now more Larix, while for
non-arboreal plants most samples now have reads from Poaceae and Asteraceae. In the next
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zone (5,400–3,800 years BP), Betula is identified in fewer samples than before, giving way to
Larix, Saliceae, and some Picea. Compared to previous zones, fewer non-arboreal plants have
been identified here. In the most recent zone (3,800 years BP–present) samples show reads from
various tree tribes and genera (Betula, Alnus, Larix, and Saliceae), while no more Picea or
Populus are identified. Fewer samples than in previous zones show reads from Poaceae,
whereas Cyperaceae are more common. Asteraceae are present in most samples.
Figure 3.7: Sedimentary ancient DNA (sedaDNA) record for terrestrial plants, with zone separations from cluster
analysis and displaying the presence or absence of plant types. Typical arboreal families are resolved to show the
highest taxonomic level identified, whereas non-arboreal plants are shown on family level.
3.5. Discussion
3.5.1. Reconstructed wildfire activity
Charcoal analysis suggests a pronounced shift in the fire regime around Lake Satagay in the
Early Holocene (c. 9,000 years BP) and until the Mid-Holocene (c. 5,300 years BP), before the
modern level of CHAR is established c. 4,500 years BP. We suggest that the high CHAR during
the Early Holocene represents a high-severity fire regime (in the sense that all aggregated
wildfires were able to burn large amounts of biomass, not necessarily that individual fires were
of high severity), whereas low CHAR since the Mid-Holocene indicates the establishment of
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the present-day low-severity surface fire regime around Lake Satagay (ROGERS ET AL., 2015).
Following definitions in KEELEY (2009), “fire severity” here describes the amount of above-
and belowground biomass consumed by a fire, which is represented by the overall amounts of
charcoal deposited in a lake, whereas “fire intensity” is a measure of energy output. High fire
intensities generally lead to a higher fire severity with different impacts on forest vegetation
and recovery compared to low intensity fires (ROGERS ET AL., 2015).
A high abundance of charcoal in lake sediments suggests increased amounts of biomass burned,
due to larger burnt areas per fire, more intense fires, more frequent fires, or a mixture of all
three aspects. Although the temporal resolution of the charcoal record is too low to apply the
common peak detection method to screen for individual fire events (WHITLOCK AND LARSEN,
2001), the CHAR background component still suggests a change from high to low amounts of
biomass burned during the Mid-Holocene (Figure 3.4B; WHITLOCK AND ANDERSON, 2003). The
increased share of large particles in the pronounced peak of CHAR around 9,600 years BP
points toward a higher fire intensity in the Early Holocene. High-intensity fires generally
produce larger flames (HOOD ET AL., 2018), enabling them to better scorch trees and thus
produce larger and robust, block-like charcoal particles (ENACHE AND CUMMING, 2006, 2007).
In addition, high fire intensity enables larger charcoal particles to be injected higher into the
atmosphere in stronger plumes (WARD AND HARDY, 1991), which are then better conserved
during deposition.
During low-severity fires, the canopy remains intact, and lower accompanying fire intensities
usually do not lead to strong convection (Figure 3.8). Both factors limit the plume injection
height and subsequent spread of charcoal particles (CLARK, 1988; VACHULA AND RICHTER,
2018), leading to lower CHAR in lake sediments. However, classical methods of using charcoal
as a fire proxy do not currently allow for a quantitative evaluation of fire intensity, specifically.
Additional methods would need to be applied to quantify thermal energy output during charcoal
production, such as using intensity-specific fire biomarkers (DING ET AL., 2015; NAKANE ET
AL., 2017; DIETZE ET AL., 2019, 2020; KARP ET AL., 2020), analyzing the particles’ degree of
aromaticity with reflectance microscopy (HUDSPITH ET AL., 2015) or a scanning electron
microscope enabled for energy dispersive X-ray analysis (SEM-EDX; REZA ET AL., 2020), or
Fourier transform infrared spectroscopy (FTIR; MAEZUMI ET AL., 2021).
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Figure 3.8: (A) Photos of a low-severity surface fire plume and its impact near Nyurba and Lake Satagay in August
2019 (S. Stünzi and E. Dietze, AWI). (B) Photos of a high-severity fire plume and its impact near Ytyk-Kyuyol
(c. 200 km east of Yakutsk) in August 2021 (R. Jackisch and R. Glückler, AWI).
During the fire regime shift around Lake Satagay, we also find a shift from more angular to
more irregular charcoal morphotypes, indicative of changing fuel types. Connecting charcoal
morphotypes with fuel types is an ongoing challenge, with different approaches and
interpretations among the various experimental and applied studies. ENACHE AND CUMMING
(2006) describe angular morphotypes as most likely originating from woody biomass.
However, both angular and irregularly shaped morphotypes have also been shown to resemble
charred grass biomass, resulting in flat sheets with a visible epidermal cell structure (JENSEN ET
AL., 2007; MUSTAPHI AND PISARIC, 2014). Elongated particles are suggested to result from burnt
grasses (ENACHE AND CUMMING, 2006; FEURDEAN, 2021), conifer needles (MUSTAPHI AND
PISARIC, 2014), or from breakage of other particles (ENACHE AND CUMMING, 2007). Further
potential main fuel sources could be fern and shrub leaves, which are categorized as either
angular or irregular, and resins, which form irregular, glassy shapes without visible structure
(JENSEN ET AL., 2007). Based on comparisons with those previous studies, we suggest a
predominantly woody origin for the block-like, angular morphotypes at our study site. Grasses
are more likely represented by irregular or elongated particles because of their fragility and
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clearly visible, often frayed, cellular structure. Therefore, higher shares of woody morphotypes
at Lake Satagay might indicate that, in the Early Holocene, a potentially more severe and intense
fire regime enabled the combustion of tree biomass, which by that time consisted of a large
share of low-growing and quickly re-establishing Betula (ANDREEV ET AL., 1997). In contrast,
low-severity surface fires can explain the increasing shares of predominantly grassy and more
fragile morphotypes in the Late Holocene, only charring the bark of fire-adapted Larix.
Charring experiments with local vegetation samples can help to assign charcoal morphologies
to fuel types more precisely (VACHULA ET AL., 2021).
We assume that charcoal in the Early Holocene has been deposited directly by fires after
atmospheric transport, in contrast to the suggestion of resulting from erosion by KATAMURA ET
AL. (2009A,B) for similar lake systems in Central Yakutia. There, the authors found trends in
CHAR and pollen distributions that very closely match the results of this study. However, they
argue that high Early Holocene charcoal accumulation was caused by early internal lake
sedimentation processes during thermokarst lake formation instead of direct deposition
following fires. We consider secondary charcoal transport as unlikely because the flat
topography around Lake Satagay limits surface runoff and secondary deposition. Also, during
the fieldwork, no signs of thaw slumping along the reed-covered lake shore were found. The
stratigraphic order of 14C ages and mostly homogeneous texture of the sediment core may
further suggest the exclusion of any large-scale erosional events. In addition, the pollen record
suggests that the lake already existed at 10,800 years BP (high counts of algal palynomorphs
even in its deepest samples; see Appendix 2.2). This is reinforced by the presence of diatoms
and aquatic, submerged plants (Potamogetonaceae), revealed by sedaDNA within the lowest
samples of the sediment core (BAISHEVA ET AL., 2022A). Therefore, initial lake development of
Lake Satagay probably occurred a few millennia before the earliest temporal coverage of our
sediment core (i.e., before 10,800 years BP; BAISHEVA ET AL., 2022A). For these reasons, we
suggest that charcoal particles were mainly transported through the air and resemble trends of
biomass burning around the lake. Microscopic charcoal counted in pollen slides (Figure 3.3C)
independently support the trends observed for the larger particles. Additionally, they suggest
that the Holocene change in wildfire regime may not only have occurred locally at this lake,
but also on a broader scale. This is because microscopic particles can stay airborne longer and
thus be transported further, effectively incorporating signals from a larger source area compared
to macroscopic particles (WHITLOCK AND LARSEN, 2001).
The recent fire regime intensification, as seen in the last decade of observational data (Figure
3.1), is not reproduced in the charcoal record. Most likely, the present thermokarst lake system
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records changes in fire regimes with a certain time-lag, i.e., the time from the first visible
changes in wildfire appearance (c. 2010 CE) to the actual change in sedimentary charcoal
deposition may be longer than what is covered by our sediment core obtained in 2018 CE.
Furthermore, the topmost millimeters of a sediment core, corresponding to the most recent
environmental history, are often difficult to interpret due to high water content, active sediment
mixing processes, and/or compaction during sediment core retrieval (GLEW ET AL., 2001), all
of which may apply to the surface sample of this study’s sediment core. In addition, CHAR
heavily depends on the sedimentation rate derived from age-depth modeling. In this study, there
is a sediment-mixing-based radiocarbon age offset in the surface sediment. Although the
chronology is thought to be robust and is independently supported by the results from pollen
analysis, this age offset may introduce some uncertainty into the sedimentation rate of the
topmost core centimeters. For these reasons, the topmost sample of the charcoal record is not
expected to show the most recent changes in fire regime.
Recently, CONSTANTINE AND MOONEY (2021) found that the use of sodium hypochlorite for
bleaching organic excess material in macroscopic charcoal samples can lead to a loss of
charcoal particles from low-intensity fires (<400°C). In a region like eastern Siberia, where a
comparably low-intensity surface fire regime is expected, this may lead to a variable degree of
underestimation of low-intensity fire activity depending on changes in fire regime attributes
through time. However, at Lake Satagay the non-bleached microscopic charcoal independently
mirrors trends of macroscopic charcoal, suggesting that the general trends discussed in this
study are well captured. During the preparation of pollen slides, microscopic charcoal is instead
exposed to other chemicals such as potassium hydroxide. Comparative studies with bleached
and non-bleached sedimentary samples, as well as potential alternative methods of bleaching
or particle quantification, may be useful to safely exclude any bias against low-intensity fires
and further develop charcoal as a paleoenvironmental proxy.
3.5.2. Reconstructed vegetation composition
Both original pollen abundance and the REVEALS-transformed pollen record display a
pronounced Mid-Holocene vegetation change from open larch-birch woodlands toward the
modern, denser mixed larch forest. This is demonstrated by high proportions of Betula,
Poaceae, and Asteraceae pollen in the Early Holocene (around 10,000 years BP), when few tree
taxa, apart from those typical for the postglacial open woodlands in Yakutia, were present
(ANDREEV ET AL., 1997; KATAMURA ET AL., 2006; MÜLLER ET AL., 2009). Birch trees of this
postglacial landscape are thought to have grown in a sparse, open forest at limited height (so-
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called “yornik”; ANDREEV ET AL., 1997), which also constitutes a common post-fire succession
pattern found in the present-day near-tundra forests (ABAIMOV AND SOFRONOV, 1996). The ratio
of deciduous to evergreen trees is exceptionally high during the Early Holocene compared to
the rest of the record. The sudden expansion of Pinus after 5,400 years BP, especially on patches
of sandy substrate (see Figure 3.1), is also a typical feature of Holocene vegetation dynamics
in Yakutia and has been witnessed in many other paleoenvironmental studies (depending on
study site and chronology it has been reported to occur between c. 7,000 and 4,000 years BP;
MÜLLER ET AL., 2009; ANDREEV AND TARASOV, 2013; TIAN ET AL., 2018; CAO ET AL., 2019A,B).
Since the timing of the pine tree expansion in this study falls right within the range of reported
ages, it independently reinforces the suitability of the applied chronology.
After 5,400 years BP a higher cover of Cyperaceae (e.g., Carex), graminoids, or sedges which
are often found in marshes and wetlands, may indicate increased availability of wetland area
around the lake, possibly due to decreasing water levels during late-stage thermokarst lake
development (BAISHEVA ET AL., 2022A). The non-transformed pollen record also shows fern
spores (Polypodiaceae) after 5,400 years BP. Ferns are typical understory vegetation, preferring
shaded locations as found in a denser forest. Salix, prominent during the Early to MidHolocene,
accompanies the earlier open woodland state of the forest, likely growing as an understory shrub
in direct sunlight between scattered higher trees (KATAMURA ET AL., 2006; MÜLLER ET AL.,
2009).
The negative correlation of the REVEALS pollen PC1, indicative of forest density, with
charcoal concentrations suggests that open woodlands may promote increased amounts of
biomass burned, whereas a denser forest coincides with a lower severity fire regime. Other
relationships of REVEALS pollen percentages with charcoal concentration suggest that a more
severe fire regime occurs only once the ratio of AP/NAP falls below c. 4/1, corresponding to a
tree cover of <80% (Appendix 2.3). Similarly, high charcoal concentrations occur when Larix
cover is <50%, Pinus cover is <2.5% and/or Betula cover is >15%. Charcoal concentration and
Poaceae cover display a high positive correlation, whereas charcoal and Cyperaceae are
negatively correlated. High charcoal concentrations only occur when Cypeaceae, mainly
growing on wetter sites, show a cover of <5%.
The sedaDNA data is in good agreement with the pollen-based vegetation reconstruction, but
since DNA taphonomy pathways are more locally restricted, it sets a unique focus on vegetation
close to the lake (LIU ET AL., 2020). In addition, it provides more detailed information with its
higher taxonomic resolution. Betula and non-arboreal plant families are more prevalent in Early
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Holocene samples, including typical disturbance indicators such as C. angustifolium (fireweed),
which is known as a pioneer of freshly disturbed soils (TSUYUZAKI ET AL., 2018). At the same
time, Populus was identified. These light-demanding, fast-growing aspen trees are also
considered typical pioneering plants (CHYTRÝ ET AL., 2008), linked to early stages of post-fire
succession and pointing toward more frequent fire disturbances during the Early Holocene.
Even though present in the pollen record, Pinus is noticeably absent in the sedaDNA data.
There, the Pinaceae include only Larix and Picea. This is likely because Pinus tends to grow in
isolated patches on dry, sandy soils at some distance from Lake Satagay (see Figure 3.1), thus
not contributing sufficiently to the locally derived sedaDNA record.
3.5.3. Fire-vegetation feedbacks on millennial timescales
Wildfires, vegetation, climate, and human activity are closely linked and can influence each
other in many ways (BOWMAN ET AL., 2020). This makes it difficult to distinguish clear causal
from purely statistical relationships in paleoenvironmental studies. At Lake Satagay, we find
links between open larch-birch woodlands and a more severe fire regime (in the Early
Holocene), as well as a denser, mixed larch-dominated forest coinciding with a less severe fire
regime (in the Late Holocene). Furthermore, a high ratio of deciduous to evergreen trees
coincides with more biomass burned (Figure 3.4). Here, we discuss potential fire-vegetation
feedbacks under varying climate and human activity throughout the Holocene that might
explain these links.
Fire regimes in grasslands generally burn high amounts of biomass, as fires tend to be more
frequent and move rapidly through a landscape of sufficient fuel conditions (COFFMAN ET AL.,
2010; ARCHIBALD ET AL., 2013; LEYS ET AL., 2017; WRAGG ET AL., 2018). Reasons for this can
be a higher susceptibility of open grasslands to drying in direct sunlight, together with well-
combustible, fragile herb and shrub vegetation creating optimal, fine fuel conditions. An open
forest structure also leads to increased wind speeds, accelerating fire spread and contributing to
drying of ground vegetation. This directly applies to the often-thick ground vegetation of
Central Yakutia, consisting of mosses, lichens, herbs, and shrubs (e.g., Salix, Betula, and
Vaccinium; ISAEV ET AL., 2010; KRUSE ET AL., 2019). Larch trees themselves are adapted to
occasional surface fires and protected from extensive damage with a thick insulating bark
(WIRTH, 2005).
At Lake Satagay, the anthropogenic influence on the reconstructed fire and vegetation history
is assumed to be low on millennial timescales and might be mostly restricted to the last centuries
only. Assessing the human impact would require more in-depth analyses of archeological and
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historical sources, which was beyond the scope of this study. Despite a continuous human
presence, nomadic cultures living in Central Yakutia before c. 900 years BP are not assumed to
have used fire on a wide scale. In recent centuries, the alaas landscape was used for agricultural
purposes by the semi-nomadic Sakha people (CRATE ET AL., 2017). Direct human impact on the
fire regime likely increased only more recently, after the colonization of Yakutia by the
Russians in the 16th and following industrialization in the 19th century (PYNE, 1996; GLÜCKLER
ET AL., 2021).
The climate, on the other hand, is an important environmental driver throughout the whole
Holocene. Rising temperatures during the Late Glacial likely initiated widespread thermokarst
processes after c. 14,000 years BP (WALTER ET AL., 2007). The Holocene Climate Optimum in
the Early to Mid-Holocene (c. 7,000–5,000 years BP; ULRICH ET AL., 2019) and subsequent
cooling triggered vegetation change across Yakutia, including the rapid expansion of pine trees
(MÜLLER ET AL., 2009; ANDREEV AND TARASOV, 2013), which is also evident in this study
around 5,400 years BP. Because of their establishment on dry and sandy, isolated patches of
soil and the preference for low-intensity surface fires (similar to Larix; KHARUK ET AL., 2021),
this expansion of pine trees likely did not result in a strong net impact on the fire regime, but
further stabilized the less severe fire regime of the Late Holocene. The Holocene Climate
Optimum additionally triggered the formation of new thermokarst lakes and wetlands, as well
as the expansion of existing ones (ULRICH ET AL., 2019). This, together with a potential decrease
of the lake water level, may have contributed to the increased sedimentation rate at Lake
Satagay between c. 6,000 and 5,000 years BP (Figure 3.3). Climate may be most relevant to
wildfires by controlling the overall frequency of fire weather, i.e., the combination of dry,
warm, and windy conditions and the length of the fire season.
This leads to the question of whether rapid (i.e., within a few hundred years) climate-driven
changes of vegetation composition resulted in adapted fire regimes, or if climatic change first
drove fire regime changes that in turn supported a change in vegetation composition. As we
lack vegetation-independent Holocene temperature and precipitation reconstructions for
Central Yakutia, we can infer climate-driven fire regime changes only indirectly. Based on our
cluster analyses, the separation between the Early Holocene high-severity fire and open
woodland state and the intermediate fire and forest state of the Mid-Holocene occurs first in the
transformed pollen record (9,600 years BP), c. 600 years before the zone separation in CHAR
(9,000 years BP; Figure 3.4). The separation of the Mid-Holocene intermediate and the modern
conditions of the Late Holocene occurs almost at the same time in the transformed pollen record
(5,400 years BP) and CHAR (5,300 years BP). However, although the charcoal morphotypes
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immediately mirror shifting vegetation composition at 5,300 years BP, the total sum of CHAR
decreases only c. 800 years later at c. 4,500 years BP. In addition, trends of CHAR mirror the
ratio of deciduous to evergreen trees, but with a time-lag of c. 500 years. Even though this time-
lag is quite long, it might indicate that the fire regime shifts on long timescales occurred in
response to the establishment of a new vegetative state. Even though interpolated to the same
temporal resolution, the lower number of samples for pollen than for charcoal analysis might
bias the zonation, as additional data points within the pollen record may result in a different
number of zones. Based on these considerations, we suggest that Early to Mid-Holocene fire
regime changes were driven by long-term vegetation changes, modified by short-term fire
weather variations, until c. 4,500 years BP. Once the modern, dense larch forest state was
established, only climate remained as the main driver behind less pronounced fire regime
changes on shorter, centennial timescales, eventually accompanied by anthropogenic fire use
and management (GLÜCKLER ET AL., 2021).
With a fire regime burning high amounts of biomass in relation to open woodlands dominated
by deciduous trees, this study adds a new case to the discussion of long-term fire-vegetation
feedbacks in Siberia. It resembles some fire-vegetation dynamics found by DIETZE ET AL.
(2020) at Lake El’gygytgyn in Chukotka, where influxes of fire biomarkers (monosaccharide
anhydrides, MAs) show a significant positive correlation with the presence of deciduous tree
pollen (Larix, Populus, and Alnus) in multiple past interglacials. In wetter periods, as indicated
by abundant Sphagnum spores, fire activity is reduced. Evergreen Picea pollen, on the other
hand, are not found to be related to the fire biomarker influx. However, increased tundra
vegetation coincides with decreased MA influxes at Lake El’gygytgyn. MAs are a burn product
of low-intensity fires (<350°C), whereas the macroscopic charcoal of this study is regarded
mostly as a product of higher intensity fires (depending on fuel source c. 200–600°C;
CONEDERA ET AL., 2009; DIETZE ET AL., 2020). An application of both proxies would be needed
for a better comparison, with the beneficial side effect of enabling the reconstruction of general
fire intensity changes throughout time, using ratios of the two.
Fire seasons at Lake Satagay are becoming more severe since c. 2010 CE, concurrent with a
trend of rising summer temperatures and decreasing precipitation (Figure 3.2). Meanwhile,
vegetation composition and cover are not suspected to have systematically changed during the
last decade. Similarly, it is unlikely that a sudden shift in the share of human-caused fires
occurred around 2010 CE. Even though the forest management system of Yakutia has
undergone several adjustments in recent years (e.g., abstaining from extinguishing fires far from
populated regions), a persistent shortage of funding exacerbated broad-scale suppression even
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before (NARITA ET AL., 2021). With its close proximity to the Vilyuy highway, multiple
settlements, and agriculturally used lands, these management changes are thus not assumed to
have caused the most recent increase in burnt area. Together with the positive correlation of
recently burnt area with the climate-derived fire weather index, this suggests that the recent fire
regime intensification is driven mainly by the rapid change of climate.
Continued climate change is anticipated to lead to an increase of tree mortality as a consequence
of more frequent droughts and insect invasions (KUKAVSKAYA ET AL., 2013; TEI ET AL., 2019).
While this may lead to younger tree populations or a shift in predominant species, it could also
lead to an increased area of open woodlands within the coming decades to centuries, further
supported by logging activities (ISAEV ET AL., 2010; KUKAVSKAYA ET AL., 2013). Considering
such a potential “thinning trend” and a shift toward a more severe fire regime in open
woodlands, comparable to the reconstructed Early Holocene state around Lake Satagay, this
points toward a potential positive feedback on the currently intensifying fire regimes of Central
Yakutia within the next decades to centuries.
Since an existing forest structure can adapt only slowly and is time-lagged to rapidly changing
climatic conditions, the present-day dense larch forest might still be mediating the observed fire
regime intensification. The ability of vegetation to mediate or amplify climate-driven fire
regime changes and associated permafrost degradation has been discussed before (HIGUERA ET
AL., 2009; HERZSCHUH, 2020). However, once the forest structure is pushed out of its current
state (either directly, by slowly adapting to new climatic conditions, or indirectly, by increased
mortality from disturbances) this mediating effect might eventually come to an end. If the fire
regime regains a severity comparable to the Early Holocene, it may be able to sustain the newly
developed open woodlands, stabilizing them as a new and markedly different state of boreal
forest structure. Previous modeling studies support the hypothesis of two stable states of forest
structure and the impact of changing fire regimes (LASSLOP ET AL., 2016). Whether this “open
woodland-fire feedback” is a likely scenario, and whether it could be mediated by a
simultaneous degradation of ice-rich permafrost due to forest thinning that may lead to an
increase in soil moisture and wetland areas (FEDOROV ET AL., 2019; ULRICH ET AL., 2019;
STUENZI ET AL., 2021), presents an important research question for coupled fire-vegetation-
permafrost modeling.
Future modeling studies that consider the open woodland-fire feedback are therefore needed to
better constrain the probability of such a state change in Central Yakutia. Additionally,
modeling could test whether a shift from the modern state toward the open woodland state
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would happen either gradually, as observed during the Early to Mid-Holocene, or display
tipping-like state change behavior once the anthropogenically forced, intensifying fire regime
surpasses a threshold in fire frequency, even on shorter timescales than what we can observe
with paleoenvironmental studies (LENTON, 2012; SCHEFFER ET AL., 2012; REYER ET AL., 2015).
3.6. Conclusion
Through the analysis of sedimentary charcoal, pollen, and ancient plant DNA, fire and
vegetation dynamics were reconstructed for the last c. 10,800 years at Lake Satagay. Results
indicate that Early Holocene, open larch-birch woodlands were accompanied by high amounts
of burnt biomass. From there, forest structure shifted toward the denser larch-dominated forest
mixed with pine and spruce as observed today, which co-developed with a low-severity surface
fire regime since c. 4,500 years BP. Considering an anticipated increase in tree mortality,
potentially leading to sparser tree populations, these results point toward a possible positive
feedback on currently intensifying fire regimes in Central Yakutia. The presence of a dense
larch forest might yet be mediating the true extent of the climate-induced fire regime
intensification observed during the last decade. Ecological modeling should be able to test and
better constrain this hypothesis, whereas spatially extended paleoenvironmental information
could inform whether this suggested fire-vegetation feedback at Lake Satagay is only a local
one, or whether it applies to regional or ecosystem-wide scales, also considering a diverse
degree of human intervention in forest management. As local communities are already
confronted with these changing environments and intensifying fire seasons, the urgency of
understanding long-term and future pathways of fire-vegetation feedbacks only keeps growing.
Data availability
The data presented in this study are deposited in the PANGAEA database (doi:
10.1594/PANGAEA.946648; GLÜCKLER ET AL., 2022) and the Dryad Digital Repository (doi:
10.5061/dryad.vq83bk3w5; BAISHEVA ET AL., 2022B). The charcoal data is also deposited in
the Global Paleofire Database (https://ipn.paleofire.org, site name: Lake Satagay 2.0, site ID:
1266; INTERNATIONAL PALEOFIRE NETWORK, 2022).
Financial support
This research has been supported by the European Research Council (grant no. Glacial Legacy:
772852), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, grant
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nos. DI 2544/1-1: 419058007 and 448651799), and the Russian Ministry of Education and
Science (FSRG-2020-0019).
Acknowledgements
Thanks to all members of the joint German-Russian expedition “Chukotka-Yakutia 2018.”
We thank Stuart Vyse and Paul Adam for supporting work on this study’s sediment core.
Philip Meister, Ingeborg Frommel, Rebecca Morawietz, and Amelie Naderi helped with
subsampling the sediment core. We thank Thomas Böhmer and Peter Ewald for the
transformation of the pollen data. Thanks to Cathy Jenks for help with English editing, and to
Melissa Chipman and Gabriel Servera-Vives for their helpful comments on the initial version
of this manuscript. We acknowledge support by the Open Access Publication Funds of the
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.
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"It was strange, by the way, here, as everywhere in
Siberia, how seldom one saw really big trees;
the forest seemed often to consist of nothing but young
trees; this is not because they are felled, but rather
because they are wantonly burnt; and there is no end to
these fires, one sees signs of them everywhere."
Fridtjof Nansen in “Through Siberia: The Land of the Future”, 1914 (p. 392)
115
4. MANUSCRIPT III
Simulating long-term wildfire impacts on boreal forest structure in
Central Yakutia, Siberia, since the Last Glacial Maximum
Ramesh Glückler1,2,3, Josias Gloy1, Elisabeth Dietze4, Ulrike Herzschuh1,2,5 and Stefan Kruse1*
1: Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and
Marine Research, Telegrafenberg A45, Potsdam 14473, Germany
2: Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Strasse 24-
25, Potsdam 14476, Germany
3: Faculty of Environmental Earth Science, Hokkaido University, N10W5, Sapporo 060-0810, Japan
4: Institute of Geography, Georg-August-University Göttingen, Goldschmidtstrasse 5, Göttingen 37077,
Germany
5: Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Strasse 24-25, Potsdam
14476, Germany.
*Correspondence: Stefan Kruse (stefan.kruse@awi.de)
Status: Published January 2024 in Fire Ecology. DOI: 10.1186/s42408-023-
00238-8
Appendix: This manuscript is related to Appendix 3.
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4.1. Abstract
Background: Wildfires are recognized as an important ecological component of larch-
dominated boreal forests in eastern Siberia. However, long-term fire-vegetation dynamics in
this unique environment are poorly understood. Recent paleoecological research suggests that
intensifying fire regimes may induce millennial-scale shifts in forest structure and composition.
This may, in turn, result in positive feedback on intensifying wildfires and permafrost
degradation, apart from threatening human livelihoods. Most common fire-vegetation models
do not explicitly include detailed individual-based tree population dynamics, but a focus on
patterns of forest structure emerging from interactions among individual trees may provide a
beneficial perspective on the impacts of changing fire regimes in eastern Siberia. To simulate
these impacts on forest structure at millennial timescales, we apply the individual-based,
spatially explicit vegetation model LAVESI-FIRE, expanded with a new fire module. Satellite-
based fire observations along with fieldwork data were used to inform the implementation of
wildfire occurrence and adjust model parameters.
Results: Simulations of annual forest development and wildfire activity at a study site in the
Republic of Sakha (Yakutia) since the Last Glacial Maximum (c. 20,000 years BP) highlight
the variable impacts of fire regimes on forest structure throughout time. Modeled annual fire
probability and subsequent burned area in the Holocene compare well with a local
reconstruction of charcoal influx in lake sediments. Wildfires can be followed by different
forest regeneration pathways, depending on fire frequency and intensity and the pre-fire forest
conditions. We find that medium-intensity wildfires at fire return intervals of 50 years or more
benefit the dominance of fire-resisting Dahurian larch (Larix gmelinii (Rupr.) Rupr.), while
stand-replacing fires tend to enable the establishment of evergreen conifers. Apart from post-
fire mortality, wildfires modulate forest development mainly through competition effects and a
reduction of the model’s litter layer.
Conclusion: With its fine-scale population dynamics, LAVESI-FIRE can serve as a highly
localized, spatially explicit tool to understand the long-term impacts of boreal wildfires on
forest structure and to better constrain interpretations of paleoecological reconstructions of fire
activity.
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4.2. Background
Eastern Siberia has experienced extreme wildfire seasons in recent years (HAYASAKA 2021;
PONOMAREV ET AL. 2023). Despite wildfires being an essential ecological process of the larch-
dominated boreal forest (KHARUK ET AL. 2021), there is growing concern that a continued
increase in fire activity may compromise the resilience of the forests, while at the same time
threatening human health and safety (REISEN ET AL. 2015; EFIMOVA ET AL. 2018). Due to the
complexity of fire ecology, dependent on many interrelated variables, there is high uncertainty
in any simulations of future local fire regime changes (HANTSON ET AL. 2016). This is
exacerbated by a lack of long-term information on fire regime changes and their impacts,
especially in the eastern Siberian part of the boreal zone (GLÜCKLER ET AL. 2022).
Deciduous larch (Larix spp.)—Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Cajander larch
(Larix cajanderi Mayr), and Siberian larch (Larix sibirica Ledeb.)—account for the largest
share of trees in eastern Siberia, their dominance being a remnant of the last glacial period
(HERZSCHUH ET AL. 2016; HERZSCHUH 2020). Within the larch forests, other conifers can
occasionally be found, for example, Siberian spruce (Picea obovata Ledeb.), Scots pine (Pinus
sylvestris L.), or Siberian pine (Pinus sibirica Du Tour). By shedding their needles, larches
growing in relatively dense stands add litter to an insulating organic layer, protecting deep
permafrost grounds from accelerated degradation (ZHANG ET AL. 2011; HERZSCHUH 2020). This
deep permafrost explains why boreal eastern Siberia acts as an important carbon sink within
the ecosystem-wide carbon budget of high latitudes (WATTS ET AL. 2023). Dahurian larches are
thought to possess pyrophytic properties (TSVETKOV 2004) and their accumulated litter of shed
needles benefits occasional low-intensity surface fires. These fires help renew larch populations
by opening the ground for seed germination while limiting invasion by evergreen conifers
(KHARUK ET AL. 2021). However, increased fire intensity and/or frequency may interfere with
this ecological balance and substantially change the structure of the forests, mitigating or even
reversing their function as a carbon sink (FAN ET AL. 2023; WATTS ET AL. 2023). It is expected
that fire regimes in eastern Siberia will continue to intensify (PONOMAREV ET AL. 2018;
TALUCCI ET AL. 2022), but long-term impacts of fire regime changes on forest structure are
poorly understood and depend not only on the immediate fire impacts, but also on the post-fire
regeneration pathways of the forest.
In light of the unique interplay of larch-dominated forest, permafrost, and wildfires, the general
understanding of long-term fire-vegetation interactions in eastern Siberia may benefit from
including a highly localized and long-term perspective of individual trees, their life cycles, and
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competition for growth. Emergent patterns of individual-based tree population structure under
wildfire stress may offer new insights into the consequences of currently intensifying fire
regimes and also benefit any interpretations of reconstructed paleoecological fire records.
Efforts have been made in recent decades to include fire in dynamic global vegetation models
(DGVMs; HANTSON ET AL. 2016). However, due to their coarse grid-based globalized
architecture, DGVMs generally cannot consider fine-scale interactions between different plant
species, life cycles, population dynamics, and fire occurrence, nor consider the position, and
thus related effects, of individual plants in the environment (SHUMAN ET AL. 2011; MCKENZIE
ET AL. 2014). This may limit their analytical power in a region like eastern Siberia, where larch
tree life cycles, successional patterns, and their interaction with the immediate physical
environment are key ecosystem components, making the individual responses of the few
dominant tree species to wildfires especially important to consider (SHUMAN ET AL. 2011).
Apart from DGVMs covering northern Eurasia, multiple fire-vegetation modeling studies have
focused specifically on Siberia while following a variety of research questions and subsequent
model setups. ITO (2005) simulated the carbon budget of wildfire occurrence in the boreal forest
near Yakutsk throughout 1200 years with the model Sim-CYCLE, finding a mean fire return
interval of 64 years and a predominantly surface fire regime, affecting 1.6% of the forested area
per year. Applying SiBCLiM, TCHEBAKOVA ET AL. (2009) simulated the response of dedicated
vegetation classes, permafrost, and fire occurrence to climate-change scenarios. In northern
Siberia, they predict a tundra-to-forest change, whereas in southern Siberia and Central Yakutia
an increase in wildfire activity may be followed by widespread steppe formation with higher
tree mortality. Their results also suggest that, due to the resilience of permafrost, larches will
remain dominant. To include the response of larches, SATO ET AL. (2010) applied an adapted
version of the individual-based SEIB-DGVM to simulate post-fire forest recovery near
Yakutsk. Using fixed scenarios of stand-replacing fire occurrence, their model considered
explicit larch population dynamics. ZHANG ET AL. (2011) used the dynamic vegetation model
FAREAST, coupled with a permafrost model and expanded by the ability of stand-replacing
wildfires to occur, to show that climate warming above c. 2 °C would impact species
composition of the eastern Siberian larch forest, decoupling it from permafrost and possibly
resulting in a forest state change towards dark taiga. An adapted version of FAREAST, the
individual tree-based forest gap model UVAFME, was expanded by a more complex fire
module by SHUMAN ET AL. (2017). Simulating scenarios with and without fire occurrence, they
were able to show how wildfires generally benefit the more fire-adapted larch in its competition
against other conifers. Finally, STUENZI ET AL. (2022) simulated scenario-based disturbance
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effects, including fire scenarios, on tree populations and permafrost hydrology in the coupled
LAVESI-CryoGrid model. Only in the surface fire scenario was the larch forest able to recover
to pre-fire density, although the post-fire recovery was found to be linked to the moisture
conditions of following years.
Apart from model-based studies, any paleoecological evidence for the long-term impacts of
changing fire regimes on boreal vegetation remains sparse in eastern Siberia. Recent evidence
from lake sediment analyses suggests potential positive feedback mechanisms between
intensifying wildfire regimes and more open forests (GLÜCKLER ET AL. 2022), reinforcing the
results of the modeling study by TCHEBAKOVA ET AL. (2009), while emphasizing the need for
an improved understanding of fire regime changes on long timescales. Where paleoecological
studies are lacking, long-term simulations in fire-vegetation models may contribute insights
into forest responses to changing fire regimes.
We aim to contribute to a characterization of long-term impacts and regeneration patterns by
introducing climate-driven fire disturbance to simulated tree population dynamics in the eastern
Siberian boreal forest. To do so, we expand the individual-based, spatially explicit vegetation
model LAVESI (KRUSE ET AL. 2016, 2018, 2022A) with the ability to simulate variable wildfire
regimes. The newly expanded model (LAVESI-FIRE) is used to simulate fine-scale, spatially
explicit fire-vegetation dynamics over the last 20,000 years at a key study site in Central
Yakutia, Siberia. This is complemented by investigating the effects of different fixed fire return
intervals and fire intensities on tree density, stand ages, and species composition.
4.3. Methods
4.3.1. Study location
Central Yakutia, within the Republic of Sakha (Yakutia), the largest administrative unit in
eastern Siberia, is characterized by its vast larch forests, underlain by continuous permafrost,
and its common and culturally important thermokarst basin landforms (alaas; CRATE ET AL.
2017, FEDOROV 2022). The forest is dominated by the deciduous Dahurian larch. In between,
there are patches of mixed forest with Siberian spruce and Scots pine. Within an alaas, the
vegetation consists of grasses and sedges, whereas in the forest, ground vegetation is made up
of mosses and lichens in larch needle litter and duff (comprising the organic layer; SOFRONOV
AND VOLOKITINA 2010; KRUSE ET AL. 2019B).
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The region is also known for its highly continental climate, experiencing short, warm summers
and extremely cold winters. This results in a short vegetation period of 136 ± 6 days
(mean ± 1σ), based on climate data from the Max Planck Institute Earth System Model 1.2
(MPI-ESM1.2) between 2000 to 2020 CE (DALLMEYER ET AL. 2022; KLEINEN ET AL. 2023).
The maximum amplitude between the warmest and coldest temperatures recorded within a
single year can reach 100 °C. Based on CRU TS v4.06 data (HARRIS ET AL. 2020) at the study
site near the town of Nyurba (Fig. 4.1), the mean annual temperature between 2012 and 2020
CE was − 7.1 °C, with mean monthly temperatures in January and July of − 32.3 and 17.8 °C,
respectively. The mean annual precipitation sum was 303 mm. Mean monthly precipitation
sums for January and July were 15 and 56 mm, respectively. The summer months of June, July,
and August accounted for 47% of all annual precipitation.
Figure 4.1: Map of the study area, including the location of the simulation area next to Lake Satagay. A Republic
of Sakha (dark gray) within Russia (light gray) and the location of the study site (star symbol). B Satellite image
of the study site, including the simulation area and the location of the sediment core used for a Holocene
reconstruction of fire-vegetation interactions in GLÜCKLER ET AL. (2022). C Digital elevation model (DEM) and
derived topographic wetness index (TWI) and slope for the simulation area. Hatched raster cells mark water bodies.
Service layer credits: Esri, DigitalGlobe, GeoEye, i-cubed, USDA, USGS, AEX, Getmapping, Aerogrid, IGN,
IGP, swisstopo, and the GIS User Community
Central Yakutia, especially west of the Lena River, experienced severe wildfire seasons in
recent years (relative to years with available satellite observations). An evaluation of MODIS-
derived burned area for 2001 to 2021 is provided by GLÜCKLER ET AL. (2022), showing that
2021 was the year with the largest burned area since 2001 within a 100 km2 buffer around the
study site. Although fire regimes in eastern Siberia are generally described as low-intensity
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surface fires (ROGERS ET AL. 2015), fires in 2021 were observed to engulf whole tree stands and
threaten settlements, including Nyurbachan, c. 30 km north of the study site. It is expected that,
similar to many other regions, Central Yakutia will continue to experience severe wildfire
seasons, among other disturbances, with continued climate change (DE GROOT ET AL. 2013,
KUKAVSYKAYA ET AL. 2013, SAYEDI ET AL. 2023[PREPRINT]).
In this western part of Central Yakutia, Lake Satagay (63.078°, 117.998°; 114 m a.s.l.) recently
served as a study site for sedimentary paleoecological studies. The thermokarst lake, formed
during the Late Glacial (between the Last Glacial Maximum and the Holocene, c. 20,000 to
11,700 ka BP), was analyzed to obtain records of both lake development stages and lake
ecology (BAISHEVA ET AL. 2023), as well as surrounding vegetation and wildfire activity
(GLÜCKLER ET AL. 2022) throughout the past c. 10,800 years. Both studies describe the
thermokarst lake’s surroundings in more detail. The region is representative of the typical
landscape in Central Yakutia, which is why a simulation area close to the western shore of Lake
Satagay was determined to serve as a location for long-term simulations in this study (Fig. 4.1).
4.3.2. Model description
The model LAVESI-FIRE developed in this study is a modified version of the individual-based,
spatially explicit vegetation model LAVESI (Larix Vegetation Simulator), written in C++.
LAVESI was first conceived to model fine-scale population dynamics of larches at the northern
tundra-taiga interface and to simulate the advance of the northern treeline in Siberia under a
warming climate (KRUSE ET AL. 2016, 2019A, 2022B; WIECZOREK ET AL. 2017). A detailed
description of the initial model, parameterization, and validation, as well as localization for the
eastern Siberian boreal forest, was done by KRUSE ET AL. (2016). A simulation with this model
consists of a custom simulation area, including environmental information about, for example,
a litter layer (where needle litter accumulation is simulated as a partial representation of the
organic layer) or the active layer depth, and entities of individual trees and seeds, both in cones
and on the ground, on a 0.2 × 0.2 m sub-grid. In this simulation area, individual trees can grow
in explicit locations. LAVESI computes annual cycles of weather-, environment-, and
competition-dependent tree growth; seed production and dispersal; establishment; aging; and
mortality. Later additions to the original model code include the ability to simulate wind-driven
seed and pollen dispersal (LAVESI-WIND; KRUSE ET AL. 2018). In the process of coupling
LAVESI with the multilayer permafrost model CryoGrid (WESTERMANN ET AL. 2016), the
catalog of tree species simulated within LAVESI was expanded to feature besides Dahurian
larch also Cajander larch, Siberian larch, Siberian spruce, Siberian pine, and Scots pine
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(LAVESI-CryoGrid; KRUSE ET AL. 2022A, STUENZI ET AL. 2022). Additionally, an explicit
representation of landscape was implemented, allowing the model to use information on
elevation, slope, and surface moisture from a topographic wetness index (TWI), derived
beforehand from a digital elevation model (DEM) of the simulation area (KRUSE ET AL. 2022A).
More recently, GLOY ET AL. (2023) implemented and tested the effects of variation and
inheritance of traits such as the weight of seeds or the resistance to drought, whereas KRUSE ET
AL. (2023) applied the model to investigate the upslope advance of the mountainous treeline
under different climate pathways. STUENZI ET AL. (2022) applied the coupled LAVESI-
CryoGrid model to simulate the impacts of scenario-based disturbances, among them wildfire
scenarios, on the forest and underlying permafrost. Because wildfires were introduced in a
specific surface and canopy fire scenario, we aim to build on these findings by implementing
climate-driven, variable fire regimes within the simulated environment of LAVESI. LAVESI-
FIRE is based on the version of LAVESI used for the coupled LAVESI-CryoGrid model, that
is, it includes multiple tree species and an explicit environment, but in its current version is not
coupled to CryoGrid and does not include trait inheritance.
4.3.3. Wildfire module in LAVESI-FIRE
LAVESI-FIRE simulates climate-driven fire occurrence within the simulation area, including
fire impacts on trees and the environment. A wildfire can stochastically occur within each
annual simulation step, with a probability dependent on monthly fire weather conditions (Fig.
4.2). Fire probability for each month (fire probability rating; FPRmon) is empirically estimated
with a linear model of temperature (T) and precipitation (P; Appendix 3.1). This linear model
was derived from a principal component analysis of the number of monthly MODIS-detected
fire pixels in a 100-km radius around the study site (MCD64A1 product; GIGLIO ET AL. 2016),
and T and P from weather station observation-based CRU TS v4.06 climate data for months
above 0 °C (HARRIS ET AL. 2020), showing the best fit among several tested alternatives
(R2 = 0.24; Appendix 3.2).
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Figure 4.2: Conceptual diagram describing the new fire module within LAVESI-FIRE. Red/non-framed elements
represent new components in the model, whereas blue/dash-framed elements were already present in previously
published versions of LAVESI
Values for FPRmon are categorized and counted as either mild (nmild), severe (nsevere), or extreme
(nextreme) fire weather conditions. The threshold for an FPRmon value to be categorized as nmild
was set as the highest FPRmon value predicted for months in which no actual fires were detected
by MODIS (Appendix 3.2). Thresholds for nsevere and nextreme were set as third and fourth
quantiles of the distribution of all possible FPRmon values for the climate input at the study site,
respectively (Appendix 3.3). For each year, FPRmon categories are then summarized as an
annual fire probability rating (FPRann) between zero and one, directly representing ignition
probability (Appendix 3.4). The calculation of FPRann was tuned to result in a mean
FPRann = 0.03 for the climate input at the study site, or an average of one fire occurrence per c.
33 years, as a realistic value based on fire return intervals reconstructed in paleoecological
studies in Yakutia (GLÜCKLER ET AL. 2021).
If an ignition takes place (i.e., if a randomly drawn uniform number between zero and one is
below FPRann), a random coordinate of the simulation area is chosen as the center. Around that
center, a fire occurs, with an affected area (diameter) determined by FPRann relative to the width
of the square-shaped simulation area. Fire intensity is estimated for each cell in the 0.2 × 0.2 m
sub-grid of the fire-affected area, as the FPRann mediated by the local TWI (representing surface
moisture availability; Appendix 3.5). Initially, both fire extent and intensity are thus linked to
the fire weather conditions, which is supported by empirical evidence (JONES ET AL. 2022).
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Additionally, GLÜCKLER ET AL. (2022) demonstrated that around the study site extreme fire
weather is well correlated with burned area. Fire impacts to vegetation in the model are,
however, based on multiple local conditions and thus heterogeneous within a single fire-
affected area.
Within this fire-affected area, trees, cones, seeds, and the litter layer can be affected in a variety
of ways, based on important general fire impacts on vegetation (WIRTH 2005; HOOD ET AL.
2018; BÄR ET AL. 2019; KHARUK ET AL. 2021). Tree mortality is directly affected by a
combination of heat impacts to the stem (simulating cambium necrosis and xylem hydraulic
failure) and/or damage to the canopy (simulating loss of foliage and buds, resulting in carbon
starvation). The magnitude of both effects is decided by local fire intensity, which is directly
linked to flame height (ROTHERMEL AND DEEMING 1980; HESKESTAD 2016). These impacts on
tree mortality can be mediated (or exacerbated) by species-specific and height-dependent traits
such as insulating bark thickness and the ability to re-sprout (WIRTH 2005; SCHULZE ET AL.
2012). These traits were introduced for different tree species by KRUSE ET AL. (2022A). A tree
growing within a sub-grid cell will be affected by that cell’s fire intensity alone, whereas for a
tree growing on the border of multiple cells the mean fire intensity of all included cells is
applied. Seeds in tree cones are removed depending on the local fire intensity, whereas seeds
on the ground are always removed in cells with a fire intensity above zero (KHARUK ET AL.
2021). Finally, fire occurrence will also lead to a partial loss or complete removal of the litter
layer (DELCOURT ET AL. 2021). Previously introduced stochastic, small-scale disturbances of
the litter layer and its regeneration at 0.5 cm year−1 (KRUSE ET AL. 2022A) lead to an average
height of c. 13 cm, corresponding with field observations of organic layer height (KRUSE ET AL.
2019B).
Relevant post-fire processes, for example, a deepening of the active layer due to the removal of
insulating litter and subsequent regeneration (GORBACHEV AND POPOVA 1996; TCHEBAKOVA
ET AL. 2009; KNORRE ET AL. 2019), succession of young trees on freshly cleared soil, or growth
benefits for surviving trees due to a decrease in competition (KHARUK ET AL. 2021), are already
part of vegetation dynamics within LAVESI (KRUSE ET AL. 2016).
4.3.4. Model inputs and simulation scenarios
The simulations were forced by monthly mean temperature (Tmon) and monthly precipitation
sum (Pmon), extracted from a long-term global climate simulation with MPI-ESM1.2
(DALLMEYER ET AL. 2022; KLEINEN ET AL. 2023). The simulation covers the years 24,900 to 0
years BP and ran with the spatial resolution T31 (c. 3.75° × 3.75° on a Gaussian grid) for the
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atmosphere and land component. We used the output for the grid cell that corresponds to the
location of Lake Satagay. The climate timeseries was extended from 1950 to 2021 CE by
appending the MPI-ESM1.2-HR and -LR (SSP126) climate data as featured in CMIP6
(O’NEILL ET AL. 2016).
To test the sensitivity of simulated long-term trends of forest structure towards the climatic
forcing input, we ran additional simulations with both climate data from a slightly different
MPI-ESM model setup covering the time since 25,000 years BP (MPI-ESM-CR; KAPSCH ET
AL. 2022), as well as TraCE-21ka (“Transient Climate Evolution”) modeled climate data
(22,000 years BP to 1990 CE; HE 2011), both at the same spatial resolution of c. 3.75° and
appended to 2021 CE in the previously described way. All individual climate timeseries were
localized by fitting to means of Tmon and Pmon of the CRU TS v4.06 product (HARRIS ET AL.
2020) for the period of 1901 to 1949 (MPI-ESM1.2 and MPI-ESM-CR), 1901 to 2021
(combined MPI-ESM1.2-HR and -LR), and 1901 to 1990 CE (TraCE-21ka), respectively. For
the randomly sampled wind forcing data of LAVESI-FIRE, six-hourly wind speed and direction
data were obtained for Lake Satagay coordinates from the ERA 5 product (HERSBACH ET AL.
2020) for the period from 2000 to 2020 CE.
For a one-at-a-time sensitivity analysis following KRUSE ET AL. (2018), simulations were run
with the main climate forcing data with and without the inclusion of the new wildfire module,
and with Tmon, Pmon, and fire-induced tree mortality set to ± 5%, respectively.
Landscape input for simulations in this study was derived from the TanDEM-X 90 m digital
elevation model product (DEM; RIZZOLI ET AL. 2017). A simulation area of 990 × 990 m was
used, set at the western shore of Lake Satagay (Fig. 4.1), with slope and a topographic wetness
index (TWI) derived from the DEM input in SAGA GIS (CONRAD ET AL. 2015), following
KRUSE ET AL. (2022A). Water-covered grid cells were identified and masked in Google Earth
Engine, using Sentinel 2’s band 8 near-infrared (Copernicus Sentinel data, ESA). Ignoring the
grid cells containing water, elevation of the simulation area ranges between 106.1 and 122.7 m
a.s.l. (mean = 113.8 m a.s.l.), slope values range between 0.1 and 4.5° (mean = 1.7°), and TWI
values range between 7.1 and 15.5 (mean = 9.4).
In total, 25 different simulation scenarios were computed (for a structured overview, see
Appendix 3.6). These include the simulations with and without the new wildfire module and
those evaluating model sensitivity to input parameters, as described before. For the evaluation
of fire return interval (FRI) and fire intensity (FI) impacts on forest structure, we simulated
combinations of low (0.1), medium (0.5), and high fire intensity (1.0) at various fire return
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intervals (10, 50, 100, 200, and 300 years), resulting in 15 scenario-based simulation runs with
fixed FRI and FI. For these scenarios, wildfires were set to affect the whole simulation area
(i.e., in a scenario of low-intensity wildfires at an FRI of 50 years, the whole simulation area
will be affected by a low-intensity fire every 50 years). Finally, two reference simulations using
alternative climate forcing from MPI-ESM-CR (25,000 years BP to 2021 CE) and TraCE-21ka
(22,000 years BP to 2021 CE) were computed.
4.3.5. Statistical analyses of simulation output
LAVESI-FIRE was set to create a list of temporal output data (including, e.g., total stem count,
mean tree height for trees > 200 cm, FPRann, and number of burned cells) at annual resolution
and write spatial output (including, e.g., the mean litter layer height, mean active layer depth,
and tree abundance per species) for each individual 90 × 90 m grid cell of the simulation area
every 100 years to restrict the size of total data output and computation time. Only grid cells
that experienced a fire intensity larger than zero were included in the number of burned cells,
so depending on the environmental conditions (i.e., high TWI), a grid cell within a burned area
may be assigned a fire intensity of zero, thus excluding it from any fire impacts or from the
number of burned cells.
To assess the number of mature trees, the stem count variable includes all trees ≥ 130 cm in
height.
We applied a superposed epoch analysis in R (v.4.0.2; R CORE TEAM 2020) to evaluate common
responses of the forest to the various FRI and FI as applied in the 15 scenario-based simulation
runs. Simulation data input was sorted using the “gtools” package (function
“mixedsort()”; BOLKER ET AL. 2022). Around each fire occurrence in a given simulation
run, from the stem count timeseries 10 years pre- and 30 years post-fire were cut out. These
snippets were superimposed to obtain a median response of each tree species for each scenario.
Quantiles were determined using the “matrixStats” package (function
“rowQuantiles()”; BENGTSSON 2021). Line colors were derived from the
“colorspace” package (function “qualitative_hcl()”; ZEILEIS ET AL. 2009, ZEILEIS
ET AL. 2020). Spatial plots of the simulation area were done using the “lattice” package
(function “levelplot”; SARKAR 2008).
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4.4. Results
4.4.1. Sensitivity analysis
Simulations for the sensitivity analysis, including an unchanged reference run, modified MPI-
ESM1.2 climate input, and modified tree mortality, all followed a similar trend in the simulated
forest development (Fig. 4.3). Runs with increased or decreased precipitation closely followed
the unchanged reference run, indicating a minor influence on stem count when compared to the
other changed variables. Runs with lower or higher tree mortality led to the expected outcome
of a generally increased or decreased stem count, respectively. Runs with changed temperature
deviated furthest from the reference run, indicating that temperature has the strongest impact
on simulated stem count. Simulations with the alternative climate input from MPI-ESM-CR
and TraCE-21ka depicted similar long-term trends to the main forcing data (Appendix 3.7).
Figure 4.3: Sensitivity analysis, showing simulated total stem count of individual simulations (smoothed using a
LOESS with a window width of 0.05)
4.4.2. Fixed FRI and FI scenarios
At low fire intensity (0.1), there was no visible response of the total stem count after simulation
area-wide fire occurrence of any for the tested FRI (Appendix 3.8). At a medium fire intensity
(0.5) and at FRI = 50 years or higher, the number of Dahurian larches increased up to or above
pre-fire numbers 10 to 20 years post-fire. Other tree species were non-existent or occurred only
in very low numbers. At high fire intensity (1.0), effectively resetting the whole forest, tree
abundance was greatly reduced. Dahurian larches ≥ 130 cm height started recovering from 5
years post-fire and, in case of frequent high-intensity fires at an FRI = 50 years, all tree species
reached pre-fire numbers within 30 years. However, at FRI = 100 years or higher, the post-fire
recovery of other species benefited more than that of the Dahurian larch. Other tree species
such as Siberian larch, Siberian spruce, Scots pine, and Siberian pine could establish and were
able to surpass their pre-fire numbers (Appendix 3.8). In general, fires of high intensity,
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resetting the population, tended to benefit trees besides Dahurian larch, whereas low-intensity
fires were an advantage only for the relative share of the Dahurian larch.
Summarizing all trees within the simulated forest at an intermediate FRI = 100 years showed a
similar outcome regarding different fire intensities (Fig. 4.4). Here, low-intensity wildfires
(FI = 0.1) resulted in only a minor increase in tree mortality and thus did not leave an imprint
on the stem count of the simulated forest (which does not include the more likely affected
trees < 130 cm), regardless of the FRI. However, medium-intensity fires (FI = 0.5) led to an
increase in trees 10 to 20 years later. High-intensity fires (FI = 1.0), on the other hand, resetting
the population, resulted in greatly reduced tree abundance and were followed by a slow post-
fire tree stand recovery phase, reaching pre-fire stem counts within 30 years or later (albeit with
a different composition as Dahurian larch will be reduced and other species included in this
stem count sum).
Figure 4.4: Superposed epoch analysis for compiled fire intensity (FI) scenarios. Black vertical line = year with
fire occurrence; red line = median; blue lines = lower and upper quantiles. Gray lines represent the superimposed
individual stem count timeseries that were cut out around each fire occurrence
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4.4.3. Simulated fire activity and forest structure since the
Last Glacial Maximum
In the simulation with climate-driven fire activity, modeled annual fire probability (FPRann) was
low during the Last Glacial Maximum (LGM; c. 20,000 years BP; CLARK ET AL. 2009),
increasing only after c. 17,000 years BP (Fig. 4.5). In the Late Glacial and Early to Mid-
Holocene (c. 15,000 to 8000 years BP), extreme fire probability occurred more frequently than
at any other time. From the Mid- to Late Holocene, FPRann remained at an intermediate level,
before showing slightly increased values again towards the present. The number of burned cells
within the simulation area follows this trend of FPRann, with large fires taking place especially
during the Early Holocene. The simulation-long mean FRI is 27 ± 50 years (mean ± 1σ),
although no fire occurs before c. 17,000 years BP due to low FPRann after the LGM. When
constrained to the Holocene, the mean FRI is 31 ± 52 years. The first half of the Holocene
(11,700 to 6000 years BP) had a considerably shorter mean FRI (19 ± 91 years) when compared
to the recent half (6000 years BP to present; 80 ± 91 years), although the mean FRI became
shorter again in the most recent 150 years (11.5 ± 10 years).
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Figure 4.5: Timeseries of main simulation and reference run without fires. A, B Mean annual temperature and
annual sum of precipitation, from MPI-ESM1.2. C Derived annual fire probability rating (FPRann). D Annually
burned grid cells within the simulation area. E–G Stem count, mean litter layer height, and mean active layer depth
for the main simulation run with fire and the reference without fire, respectively. Note separate y-axes in plot (E)
In the simulation with fire, the total stem count for all tree species remained low during most
of the Late Glacial, increasing sharply around the time of the Bølling-Allerød interstadial after
c. 14,200 years BP. Around 11,600 years BP, a cooler and drier Younger Dryas period resulted
in a c. 400 years long decrease in stem count. After returning to a high level, reaching its
maximum around 11,000 years BP, stem count then gradually declined to low numbers in the
Late Holocene.
The forest is clearly dominated by Dahurian larch; other species only occurred in low numbers.
However, at the onset of favorable growing conditions in the Late Glacial (c. 16,000 years BP),
Siberian larch managed to establish in elevated numbers during a c. 2000-year period, but were
gradually outcompeted by the Dahurian larch population, which increased sharply after c.
14,000 years BP. Siberian spruce, Jack pine, and Siberian pine mostly managed to grow as
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seedlings only, with Cajander larch being the least abundant. Establishment is higher during
periods of high fire activity for all species.
The mean litter layer height across the simulation area was c. 13 cm during the Late Glacial but
started decreasing with more frequent fire activity and could completely disappear after
simulation area-wide high-intensity wildfires. Due to a reduced insulation capacity of the
burned litter layer, the mean active layer depth simultaneously increased from c. 60 cm during
the Late Glacial to c. 100 cm during the Early Holocene. With less frequent fires in the Late
Holocene, the mean litter layer could recover to c. 12 cm, with the active layer depth remaining
at c. 80 cm. Both values correspond to field observations at Lake Satagay in 2018 CE (KRUSE
ET AL. 2019B).
Compared to a simulation without the fire module, the inclusion of wildfire disturbance strongly
impacted simulated forest development. The added fire disturbance resulted in the forest fully
establishing c. 1400 years later and with increased variability in tree abundance during the Late
Glacial and Early Holocene (c. 14,200 to 8000 years BP). Fire occurrence prevented the forest
from full establishment before c. 14,200 years BP, whereas without wildfires, rapid
establishment preceded the Bølling-Allerød interstadial, occurring already at c. 15,600 years
BP. Total stem count was reduced when wildfires could occur, whereas the variability within
the number of trees was increased (Fig. 4.5). Only when wildfires were included did the ratio
of evergreen to deciduous trees increase, especially in the Early and Late Holocene (Fig. 4.6A).
The mean tree height was mostly constant at c. 900 cm in the run without fire, whereas fire
inclusion resulted in increased decadal to centennial variability, more pronounced millennial-
scale trends and generally increased tree height to about 1300 cm during the Late Holocene
(Fig. 4.6B). Even though the stem count was lower in the simulation with fire, small seedlings
(0 to 40 cm height) were more abundant in the Late Glacial and Early Holocene until c. 8000
years BP when compared to the simulation without fires (Fig. 4.6C).
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Figure 4.6: Forest structure as simulated with and without fire occurrence throughout the Holocene. A Ratio of
evergreen to deciduous trees. B Mean tree height for mature trees > 200 cm. C Number of seedlings (trees between
0 and 40 cm)
4.5. Discussion
4.5.1. Wildfire impacts since the LGM
LAVESI-FIRE allows us to evaluate over a long timescale the annual impacts of introducing
climate-driven fire disturbance to simulated tree population dynamics at a study site in the
boreal forest of Central Yakutia, Siberia.
We find that frequent medium- to high-intensity fires, as seen in the Early Holocene, allow
plenty of seedlings to establish, many of which would be consumed by a subsequent fire
occurrence. Fires here act as a catalyst of tree establishment, which has been reported from
previous studies and field observations (TSVETKOV 2004; ZYRYANOVA ET AL. 2007; KHARUK
ET AL. 2021; MIESNER ET AL. 2022; ZHU ET AL. 2023). The main reason for this increase in
establishment in the model is the reduction/removal of the litter layer, exposing soil and thus
enabling seeds to germinate more frequently. The same effect of fires on germination rate has
been experimentally demonstrated in Cajander larch forest of northeastern Yakutia
(ALEXANDER ET AL. 2018). Additionally, any fire damage on top of other mortality factors may
lead to a faster removal of some older trees, creating a forest gap for new establishment. In
reality, this effect is further supported by increased nutrient abundance after wildfires (e.g.,
phosphorus, potassium, and nitrogen; KHARUK ET AL. 2021). In addition, both in the field and
in LAVESI-FIRE, tree mortality after wildfires reduces competition (ZYRYANOVA ET AL. 2007).
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Since smaller trees and seedlings are more likely to sustain severe fire damage, taller trees (here
mostly fire-adapted Dahurian larch) have an advantage, resulting in a generally increased mean
tree height. In the field, decreased crown cover and competition for root space are additional
reasons for an increase in post-fire growth (ZYRYANOVA ET AL. 2007). In LAVESI-FIRE, the
post-fire growth in height of individual trees is increased with a fire regime of infrequent, low-
to medium-intensity fires (Mid to Late Holocene) and decreased with frequent high-intensity
fires (Late Glacial and Early Holocene). Therefore, future fire regime intensification may result
in generally decreased tree height and age in the Central Yakutian larch forests. Considering a
correlation of tree height and the heat-insulating bark thickness, decreased stand ages may
impact general wildfire resistance. Fire-related reductions of tree population ages have been
confirmed in previous studies (ZYRYANOVA ET AL. 2007; KHARUK ET AL. 2021; ZHU ET AL.
2023).
Our results indicate that post-fire regeneration of trees varies depending on the pre-fire forest
structure. It may appear counter-intuitive that medium-intensity wildfires are often followed by
increased tree establishment, whereas the initial establishment of a forest in the Late Glacial is
slowed down by 1400 years when fires are simulated (Figs. 4.4E and 4.5). Fires in a mature
forest can benefit growth of high trees and increase post-fire establishment, stabilizing the
forested landscape, whereas frequent fires in open woodlands with few tall trees may instead
prevent a forest from fully establishing in the first place. This relates to studies on fire-caused
stable states and their potential tipping points (LENTON 2012; SCHEFFER ET AL. 2012), but may
also point towards the existence of different post-fire regeneration pathways that depend on
other factors. In our simulation, the discerning factor between a fire regime preventing full
forest establishment and forests stabilized by the same fire regime may be the underlying
climate-related growth conditions for the trees, meaning that in cooler and/or drier climate, fire
may be more likely to act to prevent forest establishment and vice-versa. These contrasting
post-fire regeneration pathways could potentially occur not only at different points in time, but
also at different locations at the same time. For example, fire impacts of the same fire regime
may be variable on a gradient from southern to northern Siberia.
The mean active layer depth is controlled by both long-term climate trends and local
disturbances to the insulating organic layer, regulating heat fluxes. In LAVESI-FIRE, frequent
high-intensity fires during the Late Glacial and Early Holocene result in a partial decoupling of
the active layer depth from climatic trends. Instead, thawing depth during that time is mainly
controlled by a ten-millennia-long general reduction of the insulating litter layer, due to the FRI
being lower than the time needed for complete litter layer regeneration. The less severe fire
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regime of the Mid- to Late Holocene, in contrast, does not have this effect, as seen by a similar
active layer depth in both simulations with and without fires (Fig. 4.5G). Although LAVESI-
FIRE does not feature a detailed representation of permafrost processes, previous studies have
emphasized the effect of changing fire regimes on the active layer depth (KNORRE ET AL. 2019;
HOLLOWAY ET AL. 2020; PETROV ET AL. 2022).
As observed in the field, Dahurian larch also clearly dominates the simulated forest at Lake
Satagay (KRUSE ET AL. 2019B; MIESNER ET AL. 2022). Larches are adapted to frequent low-
intensity fires with thick insulating bark, preventing cambium necrosis from flames heating the
stem (WIRTH 2005). The simulations suggest that a fire regime consisting of medium-intensity
fires at an FRI ≥ 50 years not only increases the general stem count of the population, but
benefits Dahurian larch dominance through increased post-fire establishment (Fig. 4.4B,
Appendix 3.8). The dependence of Dahurian larch on frequent, low- to medium-intensity fires
has been shown in previous studies (TSVETKOV 2004; ZYRYANOVA ET AL. 2007) and is well
reproduced within LAVESI-FIRE. In contrast, a fire regime of stand-replacing high-intensity
fires results in vastly reduced stem counts and slow regeneration to pre-fire tree numbers (Fig.
4.4C, Appendix 3.8). Other tree species can establish under these cleared conditions. However,
they remain at very low numbers. Currently near Lake Satagay there exist small populations of
Scots pine on slightly elevated, dry and sandy soil patches, as well as small areas where larch
grows mixed with Siberian spruce and Scots pine (GLÜCKLER ET AL. 2022). Due to a relatively
coarse DEM at 90-m resolution and LAVESI-FIRE currently not representing such specific soil
conditions, the establishment of pine or mixed forest patches in the model instead depends on
a species’ climatic preferences, competitiveness, and tolerated minimum active layer depth. It
seems that the last two factors prevent species other than Dahurian larch from establishing in
higher numbers. For example, whereas Dahurian larch can grow at an active layer depth of ≥ 0.2
m, evergreen conifers require deeper thawing (1.0 m for Scots pine, 2.0 m for Siberian spruce
and Siberian pine; KRUSE ET AL. 2022A). This results in Dahurian larch generally establishing
before other evergreen species, building a competitive advantage. In this case, other species can
establish only when that competitive advantage is compromised. This is evident throughout the
simulated stem count of the Holocene, where the inclusion of fire disturbance results in an
increased ratio of evergreen to deciduous trees (Fig. 4.6A).
The trend of simulated FPRann and subsequent annually burned area throughout the Holocene
is in good agreement with the sedimentary charcoal-based reconstruction of local wildfire
activity at Lake Satagay for the past c. 10,800 years. Based on this reconstruction, it was
hypothesized that open woodlands act as a positive feedback on wildfire activity, through
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increased fine fuel loads of grassy vegetation, faster drying of fuel from direct exposure to the
sun, and higher wind speeds (GLÜCKLER ET AL. 2022). In both simulation and reconstruction,
fire activity is highest in the Early Holocene before decreasing until c. 5000 years BP and
remaining at comparatively low levels until the present day. However, in contradiction to the
simulations, the pollen-based quantitative reconstruction of vegetation cover from the sediment
core indicates an open forest landscape in the Early Holocene, growing gradually denser with
decreasing fire activity (GLÜCKLER ET AL. 2022). Strongly decreased stem counts in the high
fire intensity scenarios (Fig. 4.5) partially support the proposed relationship of open woodlands
and intensified fire regimes.
Our results reinforce the findings by STUENZI ET AL. (2022), where the coupled LAVESI-
CryoGrid simulated different impacts on larch forest depending on the fire scenario (surface or
canopy fires). Whereas surface fires increased the density of larch trees, the forest was not able
to recover within 29 years after canopy fires. A similar conclusion was drawn by SHUMAN ET
AL. (2017), simulating scenarios with fire disturbance across Russia in UVAFME. Since this
relationship between Dahurian larch and wildfires is reproduced in various modeling studies
and reinforced by empirical evidence, it appears likely that a continued intensification of fire
regimes may reduce the species’ prevalence in eastern Siberia. In combination with rapidly
warming temperatures and degrading permafrost that favors evergreen conifers (HERZSCHUH
2020), intensifying fire regimes may be an essential factor in determining the future forest
structure and potential shifts from forest to steppe environments (TCHEBAKOVA ET AL. 2009;
SCHEFFER ET AL. 2012). Therefore, an immediate goal within the fire-vegetation modeling
community may be to narrow down a potential measurable threshold between stabilizing and
destabilizing fire impacts in larch-dominated forests.
4.5.2. Capability of LAVESI-FIRE
We find that the inclusion of wildfire disturbance has clear and variable impacts on long-term
forest development and structure, mainly related to the total number of trees per species present
and their height distribution. In this way, LAVESI-FIRE is able to demonstrate the impacts of
introducing climate-driven wildfire disturbance on its simulated forest environment. As an
individual-based model, it is setting a unique focus to evaluate fire impacts on tree populations
undergoing full life cycles within a spatially explicit environment. This enables us to observe
small-scale changes in forest structure and composition throughout the past 20,000 years and
provides a perspective not present in larger-scale, more abstract modeling efforts.
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The sensitivity analysis (Fig. 4.3) indicates the robustness of the simulated and discussed trends
to changes in the climate input and fire-related mortality parameters. Furthermore, changes in
temperature having the largest impact on simulated stem count when compared to the other
variables are a result of the strong localization of LAVESI-FIRE. As a modification of the
vegetation period, this outcome would be expected of a largely temperature-limited
environment in Central Yakutia.
Simulation results should be viewed considering that FPRann is based on a linear model from
climate data to satellite-derived burned area between 2001 and 2021 CE. The relationship
between temperature, precipitation, and fire probability is thus constant through time and based
on values of the present year only. However, weather conditions of the previous year may also
influence fire probability (WANG ET AL. 2021). The importance of non-static disturbance
modules within models simulating long-term vegetation development has recently been
highlighted by DALLMEYER ET AL. (2023), who report a mismatch between reconstructed and
simulated tree cover in Europe throughout the Holocene. Furthermore, it is debatable to what
degree humans interfered in the burned area observed during those past 20 years, and what
impact such interference may have on the long-term climate-fire relationship. For example,
landscape fragmentation may today artificially limit fire extent, and fires close to settlements
or infrastructure are actively suppressed. SOLOVYEVA ET AL. (2020) further mention a centuries-
old tradition of agricultural burning and the collection of deadwood and litter to reduce fuel
loads in Central Yakutia. Such impacts on fire activity are difficult to quantify in retrospect.
However, since the purpose of this study is not to achieve a factual quantification of fire regime
impacts since the LGM, but rather to analyze systematic relationships between changes in fire
regimes and forest structure, we argue that the human component, in both parameter tuning as
well as simulation outcome, does not meaningfully change the reported findings.
In its current state, LAVESI-FIRE simulates only one fire per annual simulation timestep,
depending on FPRann. For local simulation areas a few kilometers in diameter this is sufficient,
but if the simulation area were to be increased to a regional scale, implementing multiple
ignitions per timestep may be appropriate. Local-scale simulations, as presented in this study,
may also benefit from a higher-resolution DEM input, possibly even using a fine-scale LiDAR-
derived DEM to capture microtopography and related effects on fuel moisture.
For approximating long-term fire impacts over multiple millennia, we omitted the inclusion of
active fire spread. While we suggest that the relationship between annual fire probability and
burned area is a valid simplification to evaluate the research objectives of this study, LAVESI-
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FIRE does not currently simulate overwintering fires, although they may add significantly to
the annual burned area with sustained fire regime intensification (XU ET AL. 2022). This is in
part a consequence of omitting fire spread and a related differentiation between running and
sustained surface fires (KHARUK ET AL. 2021). Running surface fires, which can be considered
the standard in LAVESI-FIRE under low FPRann, will generally result in lower mortality of
mature trees than sustained surface fires. The latter tend to burn deeper into the organic layer
and soil and thus result in increased tree mortality by heating the permafrost-limited rooting
space (SOFRONOV AND VOLOKITINA 2010; ALEXANDER ET AL. 2018; BÄR ET AL. 2019). The
present version of LAVESI-FIRE provides a foundation to implement and refine these
contrasting surface fire regimes, and thus enable new simulation scenarios and research
objectives in the future.
While the actual fire impacts are mediated by tree attributes (total height, relative crown height,
bark thickness, etc.) and ground conditions such as estimated moisture availability, other
processes such as fuel quantification have been omitted here. We suggest that for long-term
simulations at annual resolution, our applied relationships between climate-driven fire
probability and total burned area are sufficient. However, in order to test for complete fire-
vegetation feedback, an additional implementation of fuel impacts on fire intensity and size
may be needed. Recent evidence from the North American boreal forest points towards a
significant contribution of fuel availability to fire severity (WALKER ET AL. 2020), even though
it remains to be evaluated whether this applies to eastern Siberia. Dynamic fuel-fire interactions
may enable the model to examine more closely questions related to stable states and tipping
points in forest structure instead of strictly unidirectional impacts of changing fire regimes on
the forest.
Apart from the aforementioned LAVESI-Cryogrid (STUENZI ET AL. 2022), the only comparable
individual-based fire-vegetation model other than LAVESI-FIRE recently applied in Siberia is
the forest gap model UVAFME (SHUMAN ET AL. 2017). Although some basic aspects of fire
implementation are similar between the two models, they work very differently and thus fulfill
different purposes, each with their own advantages. For example, LAVESI-FIRE includes a
spatially explicit environment derived from a custom DEM input at the study site, whereas
UVAFME, as a forest gap model, simulates small 500 m2 plots that are described as being
spatially homogeneous. While LAVESI-FIRE is therefore able to simulate the actual
environment for wildfires to act upon, the individual plots of UVAFME are less
computationally demanding and are set up to be applied on a broader scale (i.e., to cover all of
Russia; SHUMAN ET AL. 2017). Furthermore, fire frequency in UVAFME is determined from
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remote sensing data and translated into a temporally fixed fire probability, only mediated by
aridity. In LAVESI-FIRE the fire probability is directly linked to monthly estimated, variable
fire weather conditions, enabling fire regime changes. Scenario-based simulations limited to
the inclusion or exclusion of fires may be unable to capture non-linear responses of larch trees
to changing fire regimes (KHARUK ET AL. 2021). Another difference is the inclusion of the litter
layer in LAVESI-FIRE, which we find here is an important factor in how wildfires affect tree
establishment. Despite these differences, both models demonstrate how individual-based
modeling provides a valuable, fine-scale, and highly localized perspective on fire-vegetation
interactions.
Going forward, updating the model with pyrogenic carbon production and spread, which could
be readily linked with the already implemented pollen dispersal module, would enable it to
simulate sedimentary charcoal records to be compared to paleoecological reconstructions. Fire
regime attributes and drivers necessary to produce a given record of sedimentary fire proxies
could be better understood by tuning the model outputs to the reconstructed data. Finally,
LAVESI-FIRE may be well suited to analyze and estimate quantitative anthropogenic impacts
on wildfire regimes, for example, effects of historical agricultural burning or the modern degree
of landscape fragmentation by implementing artificial fire breaks. Since human impacts are
among the most challenging to quantify and disentangle from climate and vegetation in long-
term paleoecological fire studies (MARLON ET AL. 2013), this could provide valuable insights
to benefit reconstructions of the past and thus improve simulations of the future.
4.6. Conclusions
In this study, we aimed to evaluate the long-term impacts of climate-driven fire disturbance on
forest structure in Central Yakutia, eastern Siberia. For this, the individual-based vegetation
model LAVESI was extended with a new wildfire module and run to simulate forest
development with fire disturbances at a local study site since the LGM. Simulation results in
LAVESI-FIRE show that the inclusion of wildfires has variable impacts on forest structure
throughout the last c. 20,000 years and differs from a reference simulation without fire in many
ways. While total tree abundance decreases, mean tree height increases, likely due to reduced
competition. The forest fully establishes c. 1400 years later, and in the Late Glacial and Early
Holocene, high numbers of seedlings can temporarily establish between fires due to a decreased
litter layer. Under a similar fire regime, post-fire forest regeneration may follow different
pathways, depending on environmental and climatic conditions. Whereas medium-intensity
fires at a frequency of 50 or more years improve Dahurian larch growing conditions, stand-
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replacing high-intensity fires are followed by a slower and only partial regeneration of the
Dahurian larch population, enabling other evergreen species to establish in low numbers. These
results highlight both the value of long-term simulations, as well as the importance of including
wildfire disturbance when simulating long-term forest development. As they are not merely
destructive events, but result in a non-constant modification of landscapes, wildfire disturbance
is required to fully understand past and future environmental changes.
Data availability
The datasets generated and analyzed in this study are available in the Zenodo repository
(GLÜCKLER AND KRUSE 2023; https://doi.org/10.5281/zenodo.10183691). The LAVESI-FIRE
model code, including the input data used in this study, can be accessed via GitHub
(https://github.com/StefanKruse/LAVESI/tree/fire).
Financial support
Open Access funding enabled and organized by Projekt DEAL. This research has been
supported by the European Research Council (grant no. Glacial Legacy: 772852). Ramesh
Glückler was funded by AWI INSPIRES (International Science Program for Integrative
Research) and is a JSPS (Japan Society for the Promotion of Science) International Research
Fellow.
Acknowledgements
We would like to thank A. Dallmeyer for help with the climate input data from MPI-ESM1.2.
Thanks to S. Lisovski for support with Google Earth Engine. S. Tsuyuzaki and Y. Yamashita
kindly provided working space and time at Hokkaido University for the writing of the initial
manuscript. Thanks to C. Jenks for English proofreading and to M. Loranty and one anonymous
reviewer for their valuable feedback on the initial manuscript. We acknowledge support by the
Open Access Publication Funds of the Alfred Wegener Institute Helmholtz Centre for Polar
and Marine Research.
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“Sakha lived on the land here for a long time and did
well – if they don’t let us do what we know works in our
environment, there will be trash in the fields and there
will be bad pastures and there will be fires…
and the animals will be few.”
Viktor Y. Vasiliyev in Susan A. Crate’s “Once Upon the Permafrost:
Knowledge Culture and Climate Change in Siberia”, 2021 (p. 133)
151
5. MANUSCRIPT IV
Wildfire activity may have been mediated by indigenous land use practices
since 800 years in the boreal forest of Central Yakutia, Siberia
Ramesh Glückler1,2,3, Elisabeth Dietze1,4, Andrei Andreev1, Stefan Kruse1, Evgenii S.
Zakharov5, Izabella Baisheva1,2,5, Amelie Stieg1, Shiro Tsuyuzaki3, Jens Strauss6, Laura
Schild1, Luidmila A. Pestryakova5, Ulrike Herzschuh1,2,7*
1: Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and
Marine Research, Potsdam, Germany
2: Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany
3: Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan
4: Institute of Geography, Georg-August-University Göttingen, Göttingen, Germany
5: Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia
6: Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research,
Potsdam, Germany
7: Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
*Correspondence: Ulrike Herzschuh (ulrike.herzschuh@awi.de) and Ramesh Glückler
(ramesh.glueckler@awi.de)
Status: Draft, in preparation.
Appendix: This manuscript is related to Appendix 4.
Note: This manuscript is a draft that uses data from a sediment core currently subject of
further chronological analysis (EN21449-1, “Lake 449”). Conclusions derived from data
from this key site may therefore be subject to change. A discussion of uncertainty arising
from the current chronology is included in the draft manuscript.
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5.1. Abstract
Known as the coldest permanently inhabited region on Earth, the Republic of Sakha (Yakutia)
is characterized by unique cultural, as well as ecological relationships between permafrost, larch
forests, and wildfires. The boreal biome of eastern Siberia is stabilized by a balanced interplay
of these factors. However, recent intense fire seasons threaten communities and infrastructure,
and raise questions regarding ecological global climatic impacts of fire regime changes. Data
on long-term fire history, extending beyond the few decades of satellite-based observations,
remains scarce in eastern Siberia. We present the first composite of reconstructed wildfire
activity in Yakutia throughout the Holocene, based on nine new records of macroscopic
charcoal in lake sediments in combination with previously published data. High amounts of
biomass burning occurred in the Early Holocene (c. 10,000 years BP) before a decrease around
6000 years BP. After 1200 CE, where most data are available, another pronounced decrease of
biomass burning occurs. Our simulations of climate-driven wildfires in a localized, individual-
based forest model fail to reproduce this trend, despite comparing well to earlier reconstructed
Early and Mid-Holocene trends. Pollen-based quantitative reconstructions of vegetation cover
and environmental indicators suggest the onset of persistent human land management around
this time. We suggest that a cultural shift from nomadic hunter-gatherer and reindeer herding
cultures to mostly sedentary pastoralism 800 years ago, when the Sakha people settled in the
region, may have introduced fire management practices that resulted in a net decrease of fuel
availability. This suggestion is reinforced by an improved fit of simulated to reconstructed fire
activity when implementing an artificial fuel reduction in the forest model, representing the
combined effect of land use practices, after 1200 CE. Considering rising fire activity after the
colonization of Yakutia by the Russians in 1630 CE, which later entailed a prohibition of
cultural burning and severely affected traditional Sakha land use practices, our study highlights
the potential of indigenous knowledge to contribute to the resilience of communities in boreal
eastern Siberia in the face of a rapidly changing environment.
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5.2. Introduction
Despite being referred to as the coldest permanently inhabited region on Earth (LI, 2016),
annual burned area and fire intensity in the Republic of Sakha (Yakutia) in eastern Siberia saw
pronounced increases over the last decades (HAYASAKA, 2021; LI ET AL., 2024). Fire brigades
and voluntary firefighters, challenged by a lack of funding and restrictive policies (NARITA ET
AL., 2021; CANOSA ET AL., 2023), were confronted with unusually intense crown fires engulfing
whole tree stands. Several settlements across Yakutia were threatened by the fires,
infrastructure blocked or destroyed, and the republic’s capital blanketed by hazardous smoke
for weeks (ROMANOV ET AL., 2022; TOMSHIN AND SOLOVYEV, 2022; VINOKUROVA ET AL., 2022;
SHAPAREV ET AL., 2023). It is predicted that fire regimes will continue to intensify under a
steadily warming climate (PONOMAREV ET AL., 2016; XU ET AL., 2022; JONES ET AL., 2022; LI
ET AL., 2024), with complex implications for ecosystem stability (FURYAEV ET AL., 2001;
KHARUK ET AL., 2021).
Faced with long and extremely cold winters, people of Yakutia traditionally value fire for
cultural and spiritual reasons (OKLADNIKOV, 1970; PYNE, 1996; CRATE, 2021). In traditional
Sakha animistic belief, uot ichchite (уот иччитэ) is the important and powerful spirit of fire
(VINOKUROV, 2017; AFANASIEVA AND IVANOVA, 2015; CRATE, 2021). Fire provided warmth
and light, it gave its energy to the hearth, it enabled blacksmithing and improved leather
products by smoking hides, and its use in the open allowed re-shaping the landscape to suit the
needs of pastoralist communities (OKLADNIKOV, 1970; SOLOVYEVA ET AL., 2022).
In Yakutia, this strong human relationship with fire is complemented by a unique ecological
relationship of wildfires, deciduous boreal forest, and deep permafrost. Widespread deciduous
larch (Larix) forests protect the permafrost from degradation with their accumulating, insulating
needle litter, which in turn limits the establishment of other, deep rooting tree species
(HERZSCHUH, 2020; KHARUK ET AL., 2021). Common low-intensity surface fires periodically
clear the forest ground and thus enable larch seedlings to establish, whereas mature larches are
adapted to resist such a fire regime (TSVETKOV, 2004; WIRTH, 2005; KHARUK ET AL., 2021).
Fire is essential for both human livelihoods and ecosystem stability in the larch-dominated
boreal zone. However, these established human and ecological relationships involving fire are
in stark contrast to the extreme wildfire seasons of recent years. Due to the temporally limited
availability of historical and satellite-based observations, long-term trends, drivers, or impacts
of fire regime changes remain difficult to determine (MARLON ET AL., 2016; GLÜCKLER ET AL.,
2021).
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Only few studies aimed at improving the understanding of decadal- to millennial-scale fire
regime changes in boreal Yakutia based on natural archives, i.e., proxies of past wildfire activity
accumulated over time in lake sediments. Macroscopic charcoal particles (100–200 µm and
larger) are commonly used to reconstruct trends of biomass burning and, depending on the
temporal resolution of a sediment core, individual fire events in the vicinity of a lake
(WHITLOCK AND LARSEN, 2001; WHITLOCK AND BARTLEIN, 2003; HIGUERA ET AL., 2009;
CONEDERA ET AL., 2009). Although obtaining data from underrepresented boreal Eurasia was
deemed essential (MARLON ET AL., 2016), few such reconstructions currently exist in Yakutia
(PUPYSHEVA AND BLYAKHARCHUK, 2023). KATAMURA ET AL. (2009AB) contributed charcoal
records near the republic’s capital Yakutsk (Lake Sugun, Lake Chai-ku; Fig. 5.1A) and the
Lena-Aldan interfluve (Maralay Alaas; Fig. 5.1A), spanning the Holocene (c. 11,700 years BP
to present; mentions of Early, Mid-, and Late Holocene refer to subdivisions at c. 8200 and
4200 years BP, respectively, following WALKER ET AL., 2012). They interpret high charcoal
accumulation in the Early Holocene as indicative of thermokarst lake formation rather than
actual fire activity, and conclude that there were no changes in the surface fire regime
throughout the Holocene. From pollen-based vegetation reconstructions, the authors derive that
open woodlands corresponded to low-intensity surface fires and reduced charcoal production.
In contrast, GLÜCKLER ET AL. (2022) suggested the establishment of the modern surface fire
regime around c. 4500 years BP from a Holocene record of biomass burning in western Central
Yakutia (Lake Satagay; Fig. 5.1A). They further suggest that high fire activity in the Early
Holocene is indicative of potential positive feedback between open woodlands and intensifying
fire regimes. High fire activity during the Early Holocene was reproduced by simulations in a
fire-vegetation model (GLÜCKLER ET AL., 2024). In another, high-resolution record of biomass
burning over the last two millennia in southwest Yakutia (Lake Khamra; Fig. 5.1A), GLÜCKLER
ET AL. (2021) found high wildfire activity around 900 CE and low activity until c. 1850 CE.
Instead of the stable vegetation composition, changes in fire regime were assumed to be driven
by climatic trends and later human land management.
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Figure 5.1: Map indicating the location of all charcoal records included in the composite curve. A: Location of
charcoal records within the Republic of Sakha (Yakutia). Blue lines mark major river centerlines from Natural
Earth. Boreal forest extent from ESA Land Cover CCI. Coordinate systems used: WGS 1984 EPSG Russia Polar
Stereographic (left), UTM 54N (upper right), UTM 52N (lower right). B: Satellite view of the focus lake, Lake
449, in an alaas landscape. Coordinate system used: UTM 52N. C: Photograph of Lake 449, taken during fieldwork
from the northwestern shoreline (R. Glückler). “World imagery” basemap sources: Esri, DigitalGlobe, GeoEye, i-
cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.
Contrasting interpretations of fire reconstructions may be a result of the local to regional
character of sedimentary macroscopic charcoal taphonomy, prone to reflect differences in fire,
vegetation, and land use histories in the vicinity of a lake (LEYS ET AL., 2015; HENNEBELLE ET
AL., 2020; VACHULA, 2021), and further exacerbated by the small number of available
reconstructions. Despite recent progress, the prevailing scarcity of data on long-term fire regime
changes in Yakutia results in a knowledge gap of past wildfire activity and its relationships to
climate, vegetation, and human livelihoods. Regardless of the local influences of each
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individual record, a higher number of available reconstructions would enable a compositing of
records to derive common regional trends (POWER ET AL., 2008; MARLON ET AL., 2013;
FLORESCU ET AL., 2018; GIRARDIN ET AL., 2024). In combination with proxies for past human
activity and land use, regional trends of biomass burning could improve the understanding of
the human component in past fire regime changes.
In this study, we want to elucidate Holocene wildfire activity and related potential human
influences in Yakutia, using a combination of paleoecological and modeling approaches.
Specifically, we analyze (I) Holocene wildfire dynamics, based on a composite of nine newly
contributed records of sedimentary charcoal alongside existing data. We evaluate (II) natural
drivers behind reconstructed fire dynamics, using a pollen-based quantitative reconstruction
of vegetation cover and simulations of climate-driven wildfires in a fire-enabled individual-
based forest model. Furthermore, we assess (III) the timing and impacts of past human land
use, using non-pollen palynomorphs and wildfire simulations incorporating the suggested net
effects of inferred land use practices.
5.3. Results
5.3.1. Trends of Holocene wildfire activity
We obtained regional trends of the accumulation of macroscopic charcoal particles in lake
sediments throughout the Holocene by compositing nine new records from the Central Yakutian
lowland, the southern Verkhoyansk Mountains, and the Oymyakon highland (Fig. 5.1A),
combined with four existing records from the region in the Global Paleofire Database (POWER
ET AL., 2010; Fig. 5.2). Our newly contributed records highly increase data availability and
enabled this first regional composite curve of charcoal accumulation for Yakutia.
Charcoal accumulation during the Early Holocene and early Mid-Holocene was high, with two
maxima around 10,000 and 8000 years BP, before decreasing to a lower level at 6000 years BP.
In the Late Holocene (after 4000 years BP), charcoal accumulation slowly increased, before an
abrupt decrease at 800 years BP (1200 CE). The most recent centuries see an increase in
charcoal accumulation again, although remaining below levels recorded both in the Early
Holocene and before 1200 CE. Confidence intervals of the composite curve suggest highest
agreements between the different contributing records for the Early Holocene maximum
(10,000 years BP) and the subsequent Mid-Holocene decrease (6000 years BP). Notably, high
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agreement is also suggested for the most recent decrease of charcoal accumulation (800 years
BP or 1200 CE), where 12 out of 13 records are represented.
Figure 5.2: Trends of charcoal accumulation rate throughout the Holocene. A: Composite curve of Yakutian
charcoal records, standardized. Shaded area represents the 95% confidence interval. B: Temporal coverage of
individual records included in the composite curve.
5.3.2. Environmental development of the last 1200 years
To obtain an insight into the environmental development during the abrupt decrease of charcoal
accumulation after 1200 CE (Fig. 5.2), we evaluated a record of pollen and non-pollen-
palynomorphs from a sediment core at a key site covering the last c. 1200 years (Lake 449; Fig.
5.1B, C), which locally also well mirrors the trends observed in the regional charcoal composite
curve (Appendix 4.1). Quantitatively reconstructed land cover using REVEALS-transformed
pollen data (Fig. 5.3) indicates an initial larch-dominated forest (coverage up to 80%) with an
established occurrence of pine (Pinus sp.; up to 10%), and limited Poaceae grasslands (20–
40%). This changed clearly after 1200 CE, when tree coverage decreased in favor of Poaceae
(up to 60%), Cyperaceae, and herbs such as Rosaceae and Artemisia (Fig. 5.4B). Tree
composition in this open woodland is marked by a decreased coverage of larch and pine, and
increases in birch (Betula sp.), willow (Salix sp.), and alder (Alnus sp.).
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Figure 5.3: Quantitative reconstruction of land cover at Lake 449, based on REVEALS-transformed pollen data.
Individual plot widths are scaled to the share of each pollen type across the dataset. Background shades represent
a tenfold visual exaggeration. Dashed horizontal line represents zone separation according to cluster analysis, as
shown on the right side.
After 1200 CE, common coprophilous fungi (Sordaria, Podospora, Sporormiella, Cercophora)
indicate the presence of large herbivores (Fig. 5.4C). Numerous mycorrhizal fungi of the
Glomus genus, as well as Diporotheca, indicate soil erosion in the surroundings of the lake
system (Fig. 5.4D). Mercury concentrations in samples of the same sediment core (7.7–17.1 µg
kg-1) show a pronounced increase after 1200 CE (Fig. 5.4E).
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Figure 5.4: Indicators of human activity at Lake 449. A: Charcoal concentration. B: Ratio of arboreal (AP) to non-
arboreal pollen (NAP) from REVEALS-transformed pollen data. C: Sum of coprophilous fungal remains as
herbivore indicators (Sordaria, Podospora, Sporormiella, Cercophora). D: Sum of mycorrhizal fungal remains as
erosion and potential agriculture indicators (Glomus, Diporotheca). E: Mercury concentration. F: Simulated
population count from the HYDE v3.3 database (KLEIN GLODEWIJK ET AL., 2017) for the gridcell at Lake 449.
5.3.3. Simulated climate-driven wildfire activity
We used the individual-based, spatially explicit forest model LAVESI-FIRE (GLÜCKLER ET
AL., 2024) to simulate climate-driven wildfire activity in the surroundings of the key site
throughout the Holocene. Simulated burned area (BA) trends compare well to the charcoal-
based, reconstructed composite curve (Fig. 5.5A). Simulations of BA manage to recreate the
high-agreement maximum of reconstructed charcoal accumulation around 10,000 years BP and
the subsequent decrease before 6000 years BP. However, reconstructed trends in the most
recent millennium are not well captured by simulated BA, potentially indicating other dominant
drivers than just climate. Artificially reducing the model’s fuel availability after 1200 CE, as a
suggested consequence of human pastoralist activity, leads to an improved fit of simulated and
reconstructed trends of charcoal accumulation (Fig. 5.5B).
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Figure 5.5: LAVESI-FIRE simulated burned area with and without artificial fuel reduction after 1200 CE, in
comparison to the charcoal composite curve. Colored lines represent the mean of ten simulation repeats per
scenario. Shaded area represents the 95% confidence interval. A: Comparison over the Holocene timeframe. B:
Comparison of trends for the last 1200 years.
5.4. Discussion
5.4.1. Reconstructed fire dynamics
Charcoal accumulation, a well-established indicator of biomass burning (WHITLOCK AND
LARSEN, 2001; CONEDERA ET AL., 2009), was at a maximum in the Early Holocene, around
10,000 years BP, and consequently decreased in the Mid-Holocene until c. 6000 years BP (Fig.
5.2A). This decreasing regional wildfire activity in Yakutia contrasts findings of previous
global-scale charcoal record composites, where a long-term increase of biomass burning was
reported since the beginning of the Holocene (MARLON ET AL., 2013), starting already during
the Last Glacial Maximum (LGM; 21,000 years BP; MARLON ET AL., 2016). A composite of
charcoal records in interior Alaska also showed gradually increasing biomass burning
throughout the past 10,000 years (KELLY ET AL., 2013). However, POWER ET AL. (2008) found
an Early Holocene maximum of biomass burning around 10,000 years BP for northern
extratropical records, especially in eastern North America. MARLON ET AL. (2013) also highlight
regional deviations from their continuously increasing global trend, with high wildfire activity
in the Early Holocene noted for boreal regions (Alaska, Canada, northeast Europe). Our new
composite, filling a previous data gap in boreal Eurasia, indicates that this deviation from the
global trend also occurred in eastern Siberia. That may suggest a unique sensitivity of wildfire
across the boreal biome to the pronounced climatic and environmental changes during the
Pleistocene-Holocene transition.
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Similar to global trends, wildfire activity of the Late Holocene is increasing in Yakutia from c.
4000 to 1000 years BP. MARLON ET AL. (2013) identified a global increase of wildfire activity
from 3000 to 2000 years BP in all regions except Australasia, an explanation of which was not
evident. With the new Yakutian records, it becomes evident that this widespread trend also
occurred in eastern Siberia, albeit on a shorter timescale.
In the last millennium (1000 years BP to present), where most charcoal records are contributing
to the composite curve, there is a pronounced decrease of biomass burning around 1200 CE.
Not only do the new records agree relatively clearly, this pattern of decreasing fire activity is
also captured in previous global composites. MARLON ET AL. (2009) reported a decrease in
burning from 1400 to 1750 CE. This trend was captured in subsequent studies and was found
to be more pronounced in the Northern Hemisphere (MARLON ET AL., 2016). Since the
beginning of industrialization, global biomass burning recorded regionally different trends of
sharp increases or decreases, depending largely on human fire use or suppression (MARLON ET
AL., 2009, 2016). In Yakutia, our composite curve indicates increasing amounts of biomass
burning in the last centuries (Fig. 5.2A).
However, despite this increasing trend of the last centuries, most recent levels of biomass
burning remain below those recorded around 1200 CE or throughout the Holocene maximum
around 10,000 years BP. On the one hand, this may partially be a result of some of the included
sediment cores partially being obtained before the intense fire seasons of recent years (e.g.,
before 2010 CE for Lake Sugun and Maralay Alas; KATAMURA ET AL., 2009AB). On the other
hand, statements about fire activity observed during the satellite era in relation to reconstructed
Holocene trends remain hampered by uncertainties specific to paleoenvironmental data. For
sedimentary charcoal, sources of uncertainty include age dating and charcoal taphonomy.
Highly resolved lake sediment proxies and other archives, such as fire scars in tree ring
chronologies (BARHOUMI ET AL., 2019; WANG ET AL., 2021) together with an improved
understanding of fire proxy taphonomy and solid age estimation, would allow a detailed
analysis of fire regime changes of the last century, while possibly enabling a link to modern
observational data from satellites (KEHRWALD ET AL., 2013). Synthesizing a complete Holocene
record of wildfire activity that quantitatively links reconstructed and modern observational data
could, if possible, provide the means to analyze a potential biome-specific “safe operating
space” of both human societies and ecosystems in regards to fire regime changes (ROCKSTRÖM
ET AL., 2009; JOHNSTONE ET AL., 2016; RICHARDSON ET AL., 2023).
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Our composite curve of charcoal accumulation is used to infer trends of biomass burning,
according to WHITLOCK AND LARSEN (2001). Given a high temporal resolution, macroscopic
charcoal could also enable the identification of short-term fire events and a calculation of fire
return intervals (HIGUERA ET AL., 2009; GLÜCKLER ET AL., 2021). However, the temporal
resolution of most records contributing to the composite curve rather highlights centennial to
millennial trends (average of 86 ± 63 years per sample across all sediment cores and samples),
so interannual up to decadal variability is not represented. Biomass burning, as derived from
charcoal accumulation and without an additional calibration study, thus refers to general
wildfire activity, integrating individual fire regime attributes of extent, intensity, or severity
(ADOLF ET AL., 2018; HENNEBELLE ET AL., 2020).
The individual new charcoal records contributing to the composite curve for Yakutia can be
separated into lowland (<300 m a.s.l, n = 6) and highland sites (>700 m a.s.l., n = 3; Appendix
4.2, 4.3). On average, higher charcoal concentrations (29.3 ± 26.9 particles cm-3) were recorded
at lowland sites compared to highland lakes (9.4 ± 14.1 particles cm-3), indicating higher fire
activity in the vicinity of lowland lakes. While the distribution of size classes is similar,
highland sites tend to capture higher shares of irregular and elongated charcoal morphotypes,
potentially related to grassy fuels (FEURDEAN ET AL., 2021), whereas angular particles,
potentially related to woody fuels (FEURDEAN ET AL., 2021; GLÜCKLER ET AL., 2022), have
higher shares in samples of the lowland sites. Charcoal particle length to width (L:W) ratios are
higher in the highlands (4.43) than in the lowlands (3.64), although both remain above a
threshold of 3.5, which was found to generally characterize fire regimes burning grassy fuels
as opposed to woody fuels (VACHULA ET AL., 2021). L:W ratios >3.5 fit to the predominant
surface fire regimes in Siberia (ROGERS ET AL., 2015), while ratios closer to 3.5 in the lowlands
may reflect an influence of the denser forest compared to the open woodland of the highlands
(GLÜCKLER ET AL., 2023). However, it needs to be noted that interpretations of charcoal
morphologies are hampered by potentially large uncertainties without an accompanying
calibration study (FEURDEAN ET AL., 2023).
5.4.2. Natural drivers behind fire regime changes
Natural drivers of long-term fire regimes can be broken down to climatic conditions (fire
probability, ignitions) and vegetation (fuel type and availability). Drivers of fire activity vary
depending on spatial and temporal scale under examination, from an individual flame to an
active fire and to long-term fire regimes (WHITLOCK ET AL., 2010). Centennial- to millennial-
scale changes in fire activity are controlled by long-term climate variations, regional to
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subcontinental landscape controls, and dominant vegetation types or biome persistence
(WHITLOCK ET AL., 2010). Central Yakutia experienced changes in climate and dominant
vegetation throughout the Holocene, both expected to have an impact on fire regimes.
Trends of reconstructed Early to Mid-Holocene wildfire activity in Yakutia correspond well
with climate changes, further reinforced by the ability of climate-driven burned area (BA)
simulations to match reconstructed trends of biomass burning (Fig. 5.5A). Long-term wildfire
activity is strongly driven by climatic changes (MARLON ET AL., 2009). This climatic forcing
likely results in compilations of macroscopic charcoal records showing coherent trends at
subcontinental to regional scales (POWER ET AL., 2008). Following the transition from the
Pleistocene to the Holocene, a strongly warming climate resulted in the Holocene Climatic
Optimum and facilitated high biomass burning (BEZRUKOVA ET AL., 2011; GLÜCKLER ET AL.,
2022, 2024). Decreasing and low fire activity until 6000 years BP and in the Mid-Holocene
coincide with a gradual cooling that followed the Holocene Climatic Optimum (GLÜCKLER ET
AL., 2022).
However, reconstructed fire activity in the last millennium may not be solely explained by
climatic forcing. Trends of biomass burning since c. 1000 CE (Fig. 5.2A) do broadly coincide
with the timing of the warm Medieval Climate Anomaly (MCA; 950–1250 CE) and the
following cold Little Ice Age (LIA; 1400–1700 CE; MANN ET AL., 2009). The identification of
these periods as a warm and dry MCA and a cold and wet LIA in eastern Siberia (CHURAKOVA
ET AL., 2020; RAZJIGAEVA ET AL., 2023) does suggest a potential climatic control of the
reconstructed fire activity. Nonetheless, even though climate-driven simulations of BA matched
reconstructed biomass burning trends before, simulated BA cannot capture the reconstructed
pattern during this last millennium (Fig. 5.5B). Considering the limitations of the modelled
climate data behind the simulated BA, this could indicate that climate is not solely responsible
for the reconstructed pattern of biomass burning.
Changes in vegetation, also dependent on climatic trends, may have shaped fire regimes of the
Early to Mid-Holocene, but less so during the Late Holocene. Increasing temperatures after the
LGM meant improving conditions for tree establishment in previous tundra landscapes,
allowing larch forests to spread from their glacial refugia (SCHULTE ET AL., 2022). Pollen- and
ancient DNA-based reconstructions show how Early Holocene open woodlands of Yakutia,
populated mainly by larch and birch, became gradually denser and mixed with other trees, such
as pine during the Holocene (MÜLLER ET AL., 2009; COURTIN ET AL., 2021; ANDREEV ET AL.,
2022; GLÜCKLER ET AL., 2022; BAISHEVA ET AL., 2024). MARLON ET AL. (2006) found co-
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varying charcoal accumulation with the share of arboreal pollen in a composite of Holocene
sedimentary records from the northeastern USA, suggesting that fire regimes were controlled
by the availability of woody fuels. Their findings contrast fire-vegetation dynamics as
reconstructed in Central Yakutia, where Early Holocene open woodlands coincided with higher
fire activity compared to Late Holocene dense forests (GLÜCKLER ET AL., 2022).
Chronological uncertainties potentially impact the interpretation of individual environmental
reconstructions for the last millennium, including data from our key site (Lake 449, Fig. 5.1B,
C). In permafrost regions old organic carbon presents a challenge for reliable bulk sediment
radiocarbon (14C) age dating (STRUNK ET AL., 2020). Dating bulk surface sediment with the 14C
and the Lead-210/Cesium-137 (Pb/Cs) methods, a 14C age offset of c. 1000–1400 years was
identified at Lake Khamra (GLÜCKLER ET AL. 2021; BAISHEVA ET AL., 2024). Other lake systems
in eastern Siberia were also found to be affected by such reservoir effect (VYSE ET AL., 2020,
2021). Although it is possible to constrain the resulting age offset for surface sediments, it
remains a challenge to reliably do so down core (STRUNK ET AL., 2020). Especially close to the
sediment surface, the possibility of a reservoir effect needs to be considered when the sediment
surface age (i.e., year of sampling in the field) is used as an age constraint in age-depth
modeling, but no 14C data is available for it. That is currently the case at Lake 449 (Appendix
4.4, 4.5), which is why all following discussion of our reconstruction of the environmental
development for the past c. 1200 years needs to be viewed considering this source of
uncertainty. Going forward, additional age dating using both 14C and Pb/Cs methods is
recommended and should be able to better constrain the chronology at Lake 449 and reduce
uncertainties in our discussion and its implications.
A shift from dense larch forest to an open woodland around 1200 CE (Fig. 5.3, 5.4B), coinciding
with a decrease of biomass burning (Fig. 5.4A, Appendix 4.6), may point towards non-climatic
drivers of environmental and fire regime changes at that time. Such a pronounced change in
vegetation coverage contrasts previous findings, where vegetation composition of the most
recent millennia was found to be rather stable (GLÜCKLER ET AL., 2021, 2022). The decrease of
charcoal concentration with an increased coverage of herbs and shrubs (Fig. 5.4A, B) also
contrasts previous studies. Open woodlands of the Early and Mid-Holocene were found to
instead facilitate more intense fire regimes compared to a dense forest cover (GLÜCKLER ET AL.,
2022; GIRARDIN ET AL., 2024). These opposing relationships between forest structure and
biomass burning point towards a difference of drivers of Early versus Late Holocene fire
activity. Combined with the unusually pronounced shift in forest structure in the Late Holocene,
we suggest that the opening of the forest and the decreasing of biomass burning after 1200 CE
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may not mainly be driven by climatic changes as in the Early Holocene, but may instead be the
result of human activity in the region.
5.4.3. Human impacts on wildfire regimes
People from nomadic hunter-gatherer cultures roamed eastern Siberia since more than 40,000
years BP (PITULKO ET AL., 2016; SIKORA ET AL., 2019), with many archaeologically
distinguished cultures such as those behind the Neolithic Ymyyakhtakh complex
(OKLADNIKOV, 1970; ALEKSEYEV AND DYAKONOV, 2009). However, while considering
uncertainties arising from limited knowledge about their way of life, we suggest that the impact
of their livelihoods on long-term fire regimes remained low. Although low population density
does not exclude the possibility of human impacts on fire regimes by itself (DIETZE ET AL.,
2018), it usually occurred in combination with a nomadic way of living (e.g., in Even, Evenk,
and Yukaghir cultures based on reindeer herding; PAKENDORF ET AL., 2006). We assume that
potential local impacts were limited in spatial scale and non-persistent through time, thus
restricting an imprint in broad, millennial-scale trends of wildfire activity as reconstructed here
(RYABOGINA ET AL., 2024).
A major cultural shift occurred around 1200 CE (1100–1300 CE) when the pastoralist Sakha
people settled in Central Yakutia (OKLADNIKOV, 1970; ZLOJUTRO ET AL., 2009; FEDOROVA ET
AL., 2013; KEYSER ET AL., 2015), which is reflected by a variety of proxies in the sedimentary
record. Coming from southern steppe environments, the Sakha likely initially settled between
the Lena, Aldan, and Amga rivers, where the highest population was recorded in the 17th
century and many of this study’s lakes are located (DOLGIKH, 1960; PAKENDORF ET AL., 2006;
Fig. 5.1A). They introduced a semi-nomadic to sedentary culture focused on horse and cattle
breeding (OKLADNIKOV, 1970; PAKENDORF ET AL., 2006). Haymaking was an important
practice for the Sakha to feed their animals during Yakutia’s long winters. Surrounded by dense
forest, they used meadows in non-forested permafrost drained lake basins (i.e., alaas;
SOLOVIEV, 1959; CRATE ET AL., 2017; BAISHEVA ET AL., 2023; Fig. 5.1B, C) and kept them
suitable for haymaking by removing shrubs or encroaching trees (CRATE, 2021). They also
cleared space in the forest to create open areas for haymaking, and later for growing wheat
(NAUMOV ET AL., 2020), which included the managed use of fire (CRATE, 2021; SOLOVYEVA ET
AL., 2022; VINOKUROVA ET AL., 2022). Horses were grazing freely in forests and pastures. Pine
wood served as preferred building material for houses, sheds, and other structures
(OKLADNIKOV, 1970).
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These developments are reflected in the reconstructed environmental development after 1200
CE, e.g., the shift from dominant tree to grass and herb coverage and a decrease in the
abundance of pine (Fig. 5.3, Fig. 5.4B), the increase in coprophilous fungal spores specific to
large herbivores (Fig. 5.4C), mycorrhizal fungi and mercury as indicators of soil erosion
(CARON ET AL., 2008; Fig. 5.4D, E), all during a phase of increasing population (Fig. 5.4F). The
identification of helminth parasite eggs (Appendix 4.1) is another indicator of lasting human
presence, since these parasites predominantly spread once sedentary cultures with animal
husbandry formed (REINHARD, 1988).
Sakha practices introduced cultural fire and a dispersed, persistent land management to alaas
landscapes, which may have succeeded in keeping severe wildfires at distance by reducing fuel
loads. The combination of introducing grazing animals, collecting wood, removing excess
vegetation, and managing land for the production of hay, ultimately comes down to a systematic
reduction of fuel in the surroundings of settlement and alaases (Fig. 5.6; VINOKUROVA ET AL.,
2022; GIRARDIN ET AL., 2024). Testing this proposed human impact by including an artificial
reduction of fuel availability after 1200 CE manages to improve the fit of simulated BA to
reconstructed biomass burning (Fig. 5.5B). Solely climate-driven wildfires not being able to
capture the decreasing biomass burning trend of the last millennium, and a mostly stable
vegetation composition at other sites (GLÜCKLER ET AL., 2021), reinforces the involvement of
human activity in shaping fire regimes already 800 years BP. Macroscopic charcoal as a proxy
of biomass burning captures local to regional wildfire activity (WHITLOCK AND LARSEN, 2001),
emphasizing impacts of human activity in the surrounding lake systems. In addition, instead of
living in towns, Sakha families used to inhabit separate alaases, especially during the summer
months overlapping with the fire season (CRATE, 2021). This dispersed kind of settlement may
have further amplified the impacts emerging from individual land management across a wider
region.
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Figure 5.6: Schematic compilation of the different ways Sakha people may have affected their landscapes, and
with it the occurrence of wildfires (interpretation by the authors). The text below each marker refers to suggested
traces visible in the proxy record at Lake 449.
Indigenous communities around the world were found to use fire for land management, with
impacts on both fire regimes and vegetation structure and composition (KIMMERER AND LAKE,
2001, MISTRY ET AL., 2005; SHAFFER, 2010; TRAUERNICHT ET AL., 2015). Studies set in the
boreal zone often seem to agree that climate was the main driver of Early and Mid-Holocene
fire activity, whereas humans were starting to impact fire regimes in the Late Holocene
(OLSSON ET AL., 2010; RYABOGINA ET AL., 2024). However, the impact on fire regimes ascribed
to different cultures varies. For example, a shift from hunter-gatherer to sedentary pastoralist
communities in western Siberia around 4000 years BP was found to coincide with an increase
in fire activity (RYABOGINA ET AL., 2024), similar to the expansion of agriculture in northern
Norway around 2000 years BP (TOPNESS ET AL., 2023). Although these cases contrast our
results, many instances of cultural burning and traditional land management were found to
mediate intense wildfires instead. For example, GIRARDIN ET AL. (2024) discuss the potential
relation of indigenous cultural burning practices to a decrease of Late Holocene fire activity in
hemiboreal Canada. Although set in very different environmental conditions compared to
Yakutia, the long history of indigenous burning practices in Australia and their mediating
impact on intense wildfires demonstrate the ability of human societies to change fire regimes
on millennial scales (BIRD ET AL., 2024).
The increase of biomass burning over the most recent centuries (Fig. 5.2A, 5.5B) may reflect
another cultural shift after the colonization of Yakutia by the Russians in the 1630s
(OKLADNIKOV, 1970; KEYSER ET AL., 2015). Increasing land development across Yakutia may
have increased the rate of unintentional anthropogenic ignitions (ACHARD ET AL., 2007;
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SHVETSOV ET AL., 2021). The population increased more rapidly than ever before (Fig. 5.4F),
and with growing networks of infrastructure, urbanization, and industrialization a new
paradigm for wildfires emerged. They were regarded as a threat for modern society and natural
resources. By the 20th century professional fire suppression was established, while slash-and-
burn agriculture was restrained (PYNE, 1996). Despite being recommended from a modern fire
ecology perspective (KIMMERER AND LAKE, 2001) and constituting a fundamental part of
traditional landscape management, remaining cultural burning practices were prohibited in
Yakutia in 2015 CE, and remain prohibited today despite opposition from Sakha locals
(SOLOVYEVA ET AL., 2022; VINOKUROVA ET AL., 2022). However, akin to the “fire paradox”,
attempting to exclude wildfire from a landscape that was used to frequent, controlled
disturbance may eventually lead to fuel buildup and even more intense wildfires once an
ignition occurs (ARNO AND BROWN, 1991; INGALSBEE, 2017). Importantly, indigenous fire
management was found to weaken fire-climate relationships (ROOS ET AL., 2022). Our
reconstruction of Holocene fire activity based on sedimentary charcoal suggests that indigenous
landscape management may have reduced fire activity in a fire-prone environment 800 years
ago. Especially in light of intensifying fire regimes, it might therefore be reasonable to question
attitudes towards modern wildfire management based solely on suppression, and instead
consider traditional ecological knowledge of cultural burning practices in Yakutia.
5.5. Conclusion
We were able to dissect imprints of climate, vegetation, and humans on reconstructed fire
regime changes by applying a mix of paleo-ecological and modeling methods. Our results
strengthen the understanding of Holocene wildfire activity in the unique ecosystem of eastern
Siberia, which was previously underrepresented in the global data distribution.
While considering chronological uncertainty from 14C dating, various independent sedimentary
proxies suggest that human activities may have mediated wildfires in Central Yakutia already
800 years ago. The pastoralist Sakha people likely used cultural burning to shape the
surrounding forest and permafrost landscapes to their needs. We suggest that these traditional
practices reduced the probability of severe wildfires by limiting fuel availability. However,
cultural burning practices currently remain prohibited. The recent paradigm shift away from
attempting to exclude wildfires and towards rather managing them, together with the threat
intensifying fire regimes pose to people living in the boreal zone, presents a chance to
reconsider the prohibition of cultural burning in Yakutia. It is time to recognize indigenous
knowledge and facilitate projects that involve traditional Sakha land use practices, including
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the managed use of fire in the landscape, to evaluate their potential to increase the resilience of
boreal communities in times of global warming.
5.6. Methods
5.6.1. Location
The Republic of Sakha (Yakutia), located in eastern Siberia, is Russia’s largest administrative
subdivision. The region is characterized by vast boreal forest dominated by Larix on deep,
continuous permafrost (FEDOROV ET AL., 2018). Degradation of ice-rich permafrost can lead to
the development of thermokarst lakes and, over longer timescales, non-forested basins with
residual lakes (alaas). The extremely continental climate can reach an annual temperature
amplitude of more than 100°C between the warmest and coldest day (GLÜCKLER ET AL., 2022).
However, despite the extremely cold winters, warm summers and relatively low annual
precipitation of c. 200–400 mm create optimal conditions for frequent, but generally low-
intensity surface fires (ROGERS ET AL., 2015), although recent years saw increases in both fire
extent and severity (KÖSTER ET AL., 2021).
The studied lakes are roughly located on a c. 700 km long transect between the republic’s capital
Yakutsk in the West and Oymyakon in the East, with the westernmost site being Lake 437 (N
62.34470; E 130.37543) near the Lena river, and the easternmost site being Lake 410 (N
63.23035; E 142.95733) near Tomtor in the Oymyakon Highlands. Since it was not possible to
find out the given names of all visited lakes, they will be here referred to instead by their
fieldwork ID (i.e., Lake 402 to Lake 455). Known lake names, as well as a compilation of
general attributes of each site, are included in Appendix 4.2.
5.6.2. Fieldwork and sediment core subsampling
Fieldwork took place in August and September 2021 (GLÜCKLER ET AL., 2023). A total of 66
lakes of various settings were visited, at 58 of which sediment cores were obtained. With the
help of point measurements using Hondex PS-7 ultrasonic depth sounders the deepest parts of
the lakes were estimated. An UWITEC gravity corer (UWITEC GmbH, Austria) was used for
sediment coring from rubber boats, occasionally equipped with a hammer module. Most
sediment cores were obtained in PVC tubes of 9 cm in diameter, although for a few lakes an
alternative setup with 6 cm diameter was used. Sediment cores were tightly sealed at the
campsites. Of all sediment cores presented in this study, only the one from Lake 408 (sediment
core EN21408-2) was instead already subsampled in the field in consecutive 1 cm intervals,
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stored in Whirl-Pak® bags. After the expedition, all sediment cores were collected at the North-
Eastern Federal University in Yakutsk before being shipped to Potsdam, Germany, in cooled
thermoboxes, where they were stored at 4°C.
In April 2022 one sediment core from each lake was selected based on length and visual quality,
and opened lengthwise with an electric saw in a clean climate chamber. The sediment core
inside the opened tube was split in two halves using long metal sheets. One half of each
sediment core was designated for subsampling, with the other half being archived. From all 58
available sediment cores, 14 were selected for subsampling based on lake location, ensuring
coverage along the whole expedition route, as well as length, diameter, and a visual
confirmation of undisturbed sedimentation. Between July and November 2022, these 14
sediment cores were subsampled in contiguous 1 cm segments in a clean climate chamber under
sterile conditions. To avoid contamination, the topmost sediment surface and all sides touching
the PVC tube were carefully removed using sterile scalpels. From each segment we obtained
samples for charcoal and palynological analysis (1 cm³) and for total organic carbon
measurement (1–2 cm³), with excess sediment material being stored for potential other
analyses. Every 10 cm we additionally obtained a bulk sediment sample for 14C dating (2 cm³).
For the sediment core from Lake 408, subsampled in the field, the same amounts of sediment
were taken out of the individual Whirl-Pak® bags for charcoal and palynological analysis, and
14C dating, respectively.
5.6.3. 14C dating
Bulk sediment 14C samples were freeze-dried and homogenized in a planetary mill. TOC was
measured with a soliTOC cube analyzer. AMS 14C dates were obtained at the MICADAS (Mini
Carbon Dating System) laboratory at AWI Bremerhaven, Germany, following standard
protocols (MOLLENHAUER ET AL., 2021). 14C ages were calibrated using the IntCal20 calibration
curve (REIMER ET AL., 2020) during age-depth-modeling with the package “rbacon” (BLAAUW
AND CHRISTEN, 2011) in R (v. 4.3.2; R CORE TEAM, 2023). Based on the resulting sediment core
chronologies, another selection was made to include only those with valid age-depth
relationships, resulting in the nine cores included in this study.
5.6.4. Macroscopic charcoal analysis
Preparation of macroscopic charcoal samples was done by the well-established wet sieving
approach, following previously published protocols (GLÜCKLER ET AL., 2021, 2022). Sediment
samples were soaked in a solution of sodium pyrophosphate (Na4P2O7) for 1–3 days to ease
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disaggregation of the sediment matrix. To enable a palynological analysis one tablet of
Lycopodium clavatum marker spores (Lund University, Department of Geology) per sample
was dissolved in 10% hydrochloric acid (HCl) and added. Then, the samples were poured into
a sieve at 150 µm mesh width, a standard size to separate macroscopic charcoal from the smaller
sediment and pollen fractions (e.g., DIETZE ET AL., 2019). After thorough sieving, the
macroscopic fraction in the sieve was transferred into 50 ml falcon tubes. After letting the
samples rest, they were carefully decanted. Next, c. 20 ml of sodium hypochlorite (NaClO)
bleach were added and samples left to soak overnight. This bleaching step improves charcoal
identification by increasing the contrast between the black, non-reactive charcoal particles
against other bleached organic matter (HAWTHORNE ET AL., 2018). In a final step, samples were
briefly rinsed in a sieve at 63 µm mesh width to improve clarity after bleaching.
Charcoal quantification was done in a gridded petri dish under a Zeiss Stemi SV 11
stereomicroscope. All charcoal particles per sample were counted and grouped according to
size classes and morphology. Size classes (“small”: 150 – ≤300 µm, “medium”: >300 – ≤500
µm, “large”: >500 µm) were estimated by measuring a particle’s longest axis with needles of a
known tip diameter (GLÜCKLER ET AL., 2022). For charcoal morphology we applied the
classification of morphotypes by ENACHE AND CUMMING (2007), and further grouped these into
”irregular”, “angular”, and “elongated” morphologies (GLÜCKLER ET AL., 2022). For four of the
sediment cores the length to width ratios (L:W) of all counted particles were recorded,
determined with the help of an ocular micrometer (Appendix 4.2).
Charcoal concentrations with age information were interpolated to the median temporal
resolution of each sediment core, before calculating charcoal accumulation rates (CHAR), using
the “pretreatment” function of the R package “paleofire” (v1.2.4; BLARQUEZ ET AL., 2014).
Using the same R package a charcoal composite curve was created, following BLARQUEZ ET
AL. (2014). A total of 13 charcoal records was used, including the nine new charcoal records
presented in this study, as well as four records previously published and available in the Global
Paleofire Database (POWER ET AL., 2010; available at www.paleofire.org): Lake Sugun
(KATAMURA ET AL., 2009A), Maralay Alas (KATAMURA ET AL., 2009B), Lake Khamra
(GLÜCKLER ET AL., 2021), and Lake Satagay (GLÜCKLER ET AL., 2022). Other charcoal records
in Yakutia registered in the Global Charcoal Database were excluded, either because they only
recorded microscopic charcoal on pollen slides, and/or because they lacked a contiguous
sampling scheme for macroscopic charcoal. All charcoal records were manually added to the
“paleofire” R package routine in the standard “CharAnalysis” file format (HIGUERA ET AL.,
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2009; function “pfAddData”). Following common recommendations (BLARQUEZ ET AL., 2014;
POWER ET AL., 2008), charcoal records were transformed using a MinMax re-scaling, a Box-
Cox transformation for homogenizing variance across records (BOX AND COX, 1964), and a Z-
score standardization, all for the base period of –70 to 11,000 years BP (function
“pfTransform”). The composite curve was created following a, established method (MARLON
ET AL., 2009; DANIAU ET AL., 2012), binning individual records into non-overlapping 100-year
bins and smoothing each binned record using a locally weighted scatter plot smoothing
(LOWESS) at a window width of 1000 years (function “pfCompositeLF”). Confidence
intervals are generated using the distribution of 1000 bootstrapped replicates from the binned
records (BLARQUEZ ET AL., 2014).
5.6.5. Palynological analysis and mercury measurement for Lake 449
All samples (n = 29) of the focus lake’s sediment core were included in the analysis of pollen
and NPPs, as well as measurement of mercury concentrations.
Preparation of pollen samples followed established protocols (ANDREEV ET AL., 2012;
GLÜCKLER ET AL., 2021). Treatments included the use of boiling potassium hydroxide (KOH),
hydrofluoric acid (40% HF), and acetolysis with acetic acid (CH3COOH), acetic anhydride
(C4H6O3) and sulfuric acid (H2SO4). After a final sieving step, samples were suspended in
glycerol. Pollen and NPPs were counted under a microscope, aiming at a sum of at least 300
pollen and Lycopodium marker spores. For visualization we summed the shares of coprophilous
fungal spores known to indicate the presence of herbivores (Sordaria, Podospora,
Sporormiella, Cercophora; ROZAS-DAVILA ET AL., 2021; GAUTHIER ET AL., 2010), and those of
mycorrhizal fungi indicating soil erosion (Glomus, Diporotheca; HILLBRAND ET AL., 2012;
GAUTHIER ET AL., 2010). Pollen counts were transformed to estimates of quantitative vegetation
cover by applying the REVEALS model (SUGITA, 2007), using the R package “REVEALSinR”
(THEUERKAUF ET AL., 2016). Model parameters and relative pollen productivity and fall speed
values were set following SCHILD ET AL. (2024[PREPRINT]).
Total mercury concentrations were measured in freeze-dried and homogenized sediment
samples using a DMA-80 evo direct mercury analysis system (MLS-MWS; see RUTKOWSKI ET
AL., 2021). Mercury in lake sediments is indicative of erosion in the lake catchment
(STRAKHOVENKO ET AL., 2012), e.g., as a product of agriculture, and records terrestrial rather
than atmospheric signals (CHEN ET AL., 2016).
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5.6.6. Simulations in LAVESI-FIRE at Lake 449
We applied the individual-based, spatially explicit forest model LAVESI (Larix Vegetation
Simulator), which has been described in detail in previous publications (KRUSE ET AL., 2016,
2018, 2022). Specifically, we used the fire-enabled version LAVESI-FIRE as presented by
GLÜCKLER ET AL. (2024). In short, a simulation in LAVESI-FIRE consists of individual trees
and seeds and a litter layer on top of a permafrost active layer, all within a gridded simulation
area. The model simulates annual cycles of individual tree establishment, growth, competition,
and mortality, driven by climatic forcing data of monthly temperature and precipitation, as well
as underlying environmental data (elevation, slope, topographic wetness index). It is
additionally capable of representing climate-driven wildfire activity, where, based on a monthly
determined fire probability, fires can stochastically occur within the simulation area and impact
tree and seed mortality and the litter layer height, depending on local fire intensity. To represent
fuel-fire relationships, we here additionally implemented the possibility of fuel availability
(represented by local tree density and litter layer height) to mediate each affected gridcell’s fire
intensity (Appendix 4.7).
We localized the model’s environmental inputs for a simulation area of 1980 x 1980 m, centered
on the focus lake, using the TanDEM-X 30 m digital elevation model product (RIZZOLI ET AL.,
2017). For climatic forcing we used data from MPI-ESM-CR spanning 25,000 years BP
(KAPSCH ET AL., 2022), localized at the focus lake by fitting to the corresponding overlapping
data from CRU-TS v4.07 (HARRIS ET AL., 2020). The fire module was localized following
GLÜCKLER ET AL. (2024), establishing a linear model between monthly burned area, represented
by burned pixels from satellite observations between 2001 to 2022 CE in a 200 km buffer
around Yakutsk (MCD64A1 product; GIGLIO ET AL., 2018), and temperature and precipitation
from CRU-TS v4.07. Thresholds for mild, severe, and extreme monthly fire probability were
set as third and fourth quartiles of the distribution of all fire probability values generated with
the MPI-ESM-CR climate input.
We ran ten simulations for two scenarios, respectively. To test the impact of climatic forcing
on fire regime changes, in the “normal fuel”-scenario no changes were made regarding the
model’s fuel availability. In the “reduced fuel”-scenario, however, fuel availability was reduced
to 20% once the simulations reached the year 1200 CE. This was done to test whether such an
artificial fuel reduction improves the fit between the simulated and the reconstructed trends of
biomass burning.
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LAVESI-FIRE simulation output of annual burned area, corresponding to the number of burned
grid cells of the simulation area, was smoothed with locally estimated scatterplot smoothing
(LOESS; span = 0.05). Then, mean and standard deviation of all smoothed time series of the
ten simulation repetitions per scenario were used for visualization.
Data availability
Proxy data and simulation output presented in this study will be made publicly available once
this manuscript draft is finalized. The LAVESI-FIRE model code can be accessed via GitHub
(https://github.com/StefanKruse/LAVESI/tree/fire).
Funding
Ramesh Glückler was funded by AWI INSPIRES (International Science Program for
Integrative Research) and JSPS (Japan Society for the Promotion of Science) as an International
Research Fellow.
Acknowledgements
We would like to thank the team of the Russian-German “Yakutia 2021” expedition. We
appreciate the help of David Ibel with conducting the mercury measurement. Walter Finsinger
kindly provided support with R. Thanks to Pavel Uchanov for help in the laboratory and to
everyone supporting sediment core subsampling: Jérémy Courtin, Veronika Döpper, Antonia
Eichner, Léa Enguehard, Lennart Grimm, Paula Hauter, Sarah Haupt, Anna Korup, Hadia Shuja
Malik, Philip Meister, Claudia Messner, Rahel Paasch, Aparna Prasannakumar, Katarina
Schildt, Emilia Topp-Johnson, and Jule Wagner.
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“Fire history delivers surprises that could not
otherwise have been gleaned.”
Cathy Whitlock and Willy Tinner in “PAGES news Vol. 18/2”, 2010 (p. 56)
189
6. SYNTHESIS
The previous manuscripts examined past wildfire regimes from different perspectives,
locations, and using a range of methods. This final chapter serves to compile the new research
findings and summarize their contribution to an improved understanding of long-term wildfire
regimes in Yakutia. The hypotheses of this thesis are discussed in light of the new findings.
This chapter includes recommendations for future research efforts and ends with a concluding
outlook.
6.1. Past fire regime variability
With a combination of paleo-ecological and modeling approaches, this thesis brings to light
long-term changes of past wildfire regimes in Siberia. It contributes a total of eleven new
records of sedimentary charcoal accumulation, distributed across a previously under-
represented region that is today a hotspot of boreal wildfire activity. The new contributions
include the first high-resolution record of macroscopic charcoal in eastern Siberia for the last
two millennia (MANUSCRIPT I), an initial combination of Holocene trends of biomass burning
with novel vegetation proxies (MANUSCRIPT II), simulations of changing climate-driven
wildfire activity in the newly fire-enabled model LAVESI-FIRE (MANUSCRIPT III), and, for
the first time, the creation of a regional composite curve of Holocene biomass burning trends
in Yakutia (MANUSCRIPT IV).
In contrast to the few comparable previous studies (KATAMURA ET AL., 2009AB), research
findings indicate that wildfire activity in Yakutia was variable throughout the Holocene
(MANUSCRIPT I, II, IV). That applies to both the flexible trajectories of long-term trends of
reconstructed biomass burning, as well as variable return intervals of short-term fire episodes
(MANUSCRIPT I). At Lake Khamra, a signal-to-noise index further suggests a significant
deviation of identified peaks in charcoal accummulation from background noise (KELLY ET AL.,
2011), reinforcing the suggestion that the charcoal record does not merely consist of statistical
noise (MANUSCRIPT I). Additionally, despite high variability between sites and differing local
conditions, the new charcoal records share some common patterns in long-term trends
(MANUSCRIPT IV). Considering these factors in evaluating Hypothesis 1, I propose that
past fire regimes in Yakutia were indeed variable throughout the Holocene. Although this
finding confirms initial expectations, the previous studies were not able to come to a clear
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conclusion regarding wildfire regime changes during the Holocene (KATAMURA ET AL.,
2009AB).
High wildfire activity in the Early Holocene (c. 10,000 years BP) is likely related to the warm
climate of the Holocene Climatic Optimum (MANUSCRIPT II, IV), although the number of
available macroscopic charcoal records in that period still remains quite low (n = 3, counting
records included in MANUSCRIPT IV). High charcoal accumulation rates in that period may
suggest that Yakutia experienced climatic conditions facilitating high amounts of biomass
burning. Warm temperatures during the Holocene Climatic Optimum are furthermore suggested
by lake development and thermokarst processes at that time (complementary research
manuscripts BAISHEVA ET AL., 2023, 2024), and charcoal-based studies covering the Early
Holocene elsewhere also often find a relationship to this prominent climatic phase (e.g., EL
GUELLAB ET AL., 2015; HUDSPITH ET AL., 2015; REMY ET AL., 2023). Furthermore, independent
simulations of climate-driven wildfires also show an Early Holocene maximum in annually
burned area (MANUSCRIPT III). Additional charcoal records covering the Early Holocene
should be able to further clarify the prominence of high wildfire activity and potential
differences in its timing across the region.
Fire regimes closer to the modern range of long-term biomass burning were broadly established
between 6000 to 4500 years BP (MANUSCRIPT II, IV). However, that does not exclude
continued local variability throughout the most recent millennia (MANUSCRIPT I), and also does
not necessarily apply to the intense wildfire activity of the most recent decade (MANUSCRIPT
IV). The timing of this establishment of the modern range of variability coincides with gradual
climatic cooling that followed the Holocene Climatic Optimum (complementary research
manuscripts BAISHEVA ET AL., 2023, 2024), as well as the gradual establishment of modern,
mixed larch forests (MANUSCRIPT II). Furthermore, an assessment of wildfire researchers’
perspectives identified a commonly reported shift from mainly climatic to anthropogenic
drivers behind wildfire activity around 5000 years BP (complementary research manuscript
SAYEDI ET AL., 2024). It may have thus been some combination of climatic trends, forest
establishment, and, potentially, human influence that resulted in decreased levels of long-term
regional biomass burning variability in the late Mid- and Late Holocene.
During recent millennia and centuries, trends of biomass burning are characterized by local
variability and some underlying, shared signals. All charcoal records can contribute data in this
recent period (n = 13, counting records included in MANUSCRIPT IV). Biomass burning
generally decreased to low levels around 1200 CE, and increased again towards the most recent
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centuries (MANUSCRIPT IV). Despite the intense wildfire activity of the recent years, unusual
when comparing it to the last two decades of satellite observations of annually burned area
(HAYASAKA, 2021; MANUSCRIPT II), none of the newly contributed charcoal records shows its
highest values of charcoal accumulation in the surface samples. Difficulties of comparing lake
sediment archives, especially those with low temporal resolution and chronological
uncertainties, to the well-constrained satellite data of recent decades are outlined in
MANUSCRIPT IV. Charcoal records covering the most recent decades and centuries can provide
insights into wildfire regime changes in a period of rapid and far-reaching environmental,
cultural, and political developments. However, their use as a reference for modern observational
data remains challenging. While working on this thesis, the possibility of interlinking a fairly
high-resolved charcoal record, enabling the identification of individual fire episodes (such as at
Lake Khamra; MANUSCRIPT I), with a long dendrochronological record of fire scars, and then
an extended observational record of wildfires from remote sensing data was discussed.
However, this idea did not come to fruition yet, due to the comparably low temporal resolution
of charcoal records located nearby dendrochronological study sites (e.g., Lake 437 covering c.
200 years per sample; MANUSCRIPT IV). Especially when combined with targeted modeling
efforts, such an interlinking approach of sedimentary, dendrochronological, and remote sensing
data on wildfire activity may prove very beneficial. For Yakutia, I suggest that statements
comparing recent observed wildfire activity to the new paleo-ecological reconstructions should
be done carefully and under acknowledgement of the remaining uncertainty.
The average charcoal accumulation rate (CHAR) recorded in lake sediments appears to be
decreasing with higher lake elevation. Previously published charcoal records in easter Siberia
resembled solely low-elevation sites (<140 m a.s.l.; KATAMURA ET AL., 2009AB), preventing
any insights into charcoal accumulation dynamics in relation to elevation. The newly
contributed charcoal records (MANUSCRIPT I, II, IV) enable a brief first analysis of charcoal
accumulation by elevation, with study sites for the first time ranging all the way from 114 up
to 1254 m a.s.l. (Lake Satagay and Lake 408, respectively). The average CHAR of all eleven
studied lake sediment cores displays a negative correlation with lake elevation (R = –0.55; p =
0.08). Only lakes below 400 m a.s.l. recorded an average CHAR above 0.2 particles cm-2 yr-1.
Lake size or depth, on the other hand, are not correlated to CHAR (lake size: R = 0.21; lake
depth: R = –0.17; both non-significant). Lower levels of biomass burning at higher elevations
may be the result of shifting fuel and climatic conditions, becoming gradually less favorable for
the occurrence of high fire probability. However, environments along elevation gradients may
show varying sensitivity to climatic changes (CARTER ET AL., 2018; RESCO DE DIOS ET AL.,
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2021), and intermontane basin lakes have the potential to provide more stable sedimentary
archives compared to the widespread, highly dynamic thermokarst lakes in the lowlands
(complementary research manuscripts BAISHEVA ET AL., 2023, 2024). For these reasons and
despite a potentially lower abundance of charcoal particles, lakes at high elevations should be
included in research efforts.
Compositional changes of charcoal size classes, morphotypes and morphometrics over time
were analyzed for the first time in eastern Siberia, hinting at changing fuel types or charcoal
source areas (MANUSCRIPT I, II, IV). However, interpretations remain challenging and
calibration experiments would be necessary to use such metrics at their full potential. In this
thesis, I analyzed different size classes of macroscopic charcoal particles, as well as
microscopic charcoal in pollen slides, with the aim of distinguishing changing source areas
(MANUSCRIPT I, II, IV). For example, an agreement between trends of macroscopic and
microscopic charcoal particles at Lake Satagay was suggested to indicate that trends observed
throughout the Holocene locally at this site may have also occurred on a regional scale
(MANUSCRIPT II). This suggestion was based on the prominent assumption of an increased
charcoal source area for the smaller microscopic particles (c. 10–150 µm). However, micron-
scale differences between particles may not be sufficient for clear interpretations regarding the
source area, considering the presence of other factors deciding charcoal particle sizes
(VACHULA, 2021). Relatively stable distributions of macroscopic charcoal size classes among
records with low temporal resolution (MANUSCRIPT II, IV) may further suggest the importance
of other factors (e.g., charcoal production or taphonomy). Besides a size class analysis, I also
applied the charcoal morphotype classification scheme by ENACHE AND CUMMING (2007),
separating charcoal particles into angular, elongated, or irregular shapes (MANUSCRIPT I, II,
IV), and tested the application of length to width (L:W) ratios (MANUSCRIPT IV). However,
inferring fuel type information from either charcoal morphotypes or L:W ratios proved
challenging due to conflicting assignments in previous studies (MANUSCRIPT II and references
therein). Some suggestions, although uncertain, could be made, based on comparisons to
published morphologies of experimentally produced charcoal (e.g., the occurrence of high-
intensity fires close to Lake Khamra, as inferred from a high share of large, elongated particles
similar in appearance to burned conifer needles; MANUSCRIPT I; MUSTAPHI AND PISARIC,
2014). Recently, charcoal morphotypes and morphometrics have seen increased efforts of
evaluation, standardization, and calibration by the paleofire community (e.g., PEREBOOM ET
AL., 2020; FEURDEAN, 2021; VACHULA ET AL., 2021; FEURDEAN ET AL., 2023), with a main
recommendation being the application of targeted calibration experiments instead of assuming
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universally valid assignments of charcoal morphologies to fuel types. I suggest that such
calibration studies in the eastern Siberian larch forests have the potential to highly improve
interpretations of charcoal morphotypes and morphometrics compared to what was possible
within the scope of this thesis.
As a prerequisite for the research of this thesis, I first adapted, tested, and applied an established
method using sedimentary charcoal particles as a fire proxy (MANUSCRIPT I). Such a method
was previously not carried out at the AWI laboratories. I derived a protocol from published
literature, implementing the extraction of macroscopic charcoal from lake sediment in the
laboratory by disaggregation, wet-sieving, and bleaching, followed by the examination in a petri
dish under a stereomicroscope. Because sediment core material from remote locations is both
valuable and limited, we adapted the protocol to be applied to the same sediment sample that is
used for palynological analysis. By adding the palynological marker spores before the wet-
sieving step and collecting the smaller pollen fraction under the sieve, re-assembled by repeated
centrifuging and decanting, both charcoal and palynological analyses can be conducted from
the same sediment sample and without the need for increased sample volume. Before its
application to a sediment core, some doubt remained whether the charcoal method would be
well applicable in the eastern Siberian taiga setting. Common low-intensity surface fires were
thought to mobilize only small amounts of charcoal, and among the few previous studies, one
indicated that charcoal counts over a Holocene sediment core were very low and lacking clear
variability (KATAMURA ET AL., 2009A). However, the intial application at Lake Khamra
(MANUSCRIPT I), as well as following studies at Lake Satagay (MANUSCRIPT II) and more
lakes in Central Yakutia (MANUSCRIPT IV), all demonstrate that this method is indeed suitable
for reconstructing past wildfire activity in Yakutia.
Fire intensity, a key attribute of fire regimes, is not commonly inferred in paleo-ecological
studies utilizing macroscopic charcoal particles, although new methods are developed that
should be implemented in future research efforts (MANUSCRIPT II and references therein). The
molecular composition of charcoal particles partially depends on the amount of heat energy
present during their formation. Charcoal produced at higher temperatures tends to have a higher
degree of carbonization, enabling the use of the oxygen to carbon (O/C) ratio as indicator of
production temperature and thus fire intensity (WOLF ET AL., 2013; BUDAI ET AL., 2014). Based
on a similar rationale, charcoal reflectance measurements were successfully shown to reflect
production temperature before (e.g., HUDSPITH ET AL., 2015). Although not featured in the
manuscripts of this thesis, I want to briefly highlight an experiment to derive fire intensity from
O/C ratios in charcoal particles. We artificially produced charcoal from various Larix decidua
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biomass samples in an oven at multiple temperatures ranging from 200–600°C, grinded the
resulting charred remains, and analyzed them, sputter-coated with gold, in a scanning electron
microscope (SEM) equipped with an EDX (energy-dispersive X-ray analysis) sensor. Although
in our case the experiment did not yield the expected results, most likely due to an unsuitable
protocol for the experimental charring process, this method may be able to provide a relatively
fast way of conducting numerous, semi-automated point or area measurements of O/C ratios on
charcoal particles, which could then be translated back into charring temperatures. Charcoal
particles for this analysis could be picked out of the petri dish directly after finishing the
examination of a sample under the microscope. An additional benefit would be the parallel
generation of high-quality SEM images, potentially enabling the identification of different
types of biomass from visible cell structure remains in sedimentary charcoal particles.
The macroscopic charcoal method is able to provide a range of information about past wildfire
activity. Recommendations for its application, derived from the research in this thesis, include
the development of a complementary understanding of internal lake system dynamics, a
transparent and well-recorded approach to age-depth modeling and chronology building, the
use of calibration experiments for in-depth morphotype and morphometric interpretations, the
combination of macroscopic charcoal with other fire proxies, enabling creative approaches for
expanded data generation (e.g., regarding measures of fire intensity), and the depositing of all
generated data according to the F.A.I.R. principles (findability, accessability, interoperability,
reusability; WILKINSON ET AL., 2016).
6.2. Larch forests shaped by fire
This thesis highlights and describes the importance of wildfires as a key disturbance in
Yakutia’s larch forests, contributing new findings to an improved understanding of their long-
term relationships. Three new records of past vegetation changes, based on palynological
analysis of samples also used for the charcoal-based fire reconstruction, enable an evaluation
of potential fire-vegetation interactions throughout the Holocene (MANUSCRIPT I, II, IV). For
the first time in this region, the novel sedimentary ancient DNA (sedaDNA) approach is applied
in direct combination with data on reconstructed wildfire activity (MANUSCRIPT II).
Furthermore, wildfire occurrence was implemented into the established forest model LAVESI
to analyze long-term impacts of climate-driven wildfires on fine-scale forest dynamics
(MANUSCRIPT III).
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195
Evaluations of long-term vegetation dynamics in relation to fire activity confirm the importance
of wildfires as a key disturbance in the evolution of eastern Siberia’s larch forest ecosystem
(MANUSCRIPT II, III), although the nature of observed and inferred relationships is also
connected to other factors and variable over time (MANUSCRIPT I, IV). Both paleo-ecological
and modeling approaches independently suggest a pronounced impact of changing fire regimes
on the structure of larch forests since the Last Glacial Maximum (MANUSCRIPT III) and
throughout the Holocene (MANUSCRIPT II). Simulated wildfires are shown to possess the
ability of delaying forest establishment at the onset of post-glacial warming and controling the
abundance of mature trees, mean stand ages, the probability of successful seed germination and
sapling growth, as well as the establishment of other coniferous species besides Larix gmelinii
(MANUSCRIPT III). Paleo-ecological findings at Lake Satagay show changing wildfire regimes
coinciding with major shifts in forest structure, especially in the Early to Mid-Holocene
(MANUSCRIPT II). Despite its location in the mixed evergreen-deciduous forests of
southwestern Yakutia and the reconstructed vegetation composition appearing comparably
stable during the Late Holocene, periods of high fire activity at Lake Khamra also coincide with
an increased ratio of evergreen to deciduous arboreal pollen types and higher shares of sedges
(MANUSCRIPT I). Considering these combined findings in regards to Hypothesis 2, I
propose that changing wildfire regimes have been demonstrated to shape both the
structure and composition of larch forests in Central Yakutia. The combination of various
approaches in this thesis was furthermore able to characterize fire-vegetation relationships in
more detail and highlight some promising future research opportunities.
From paleo-ecological evidence, past fire-vegetation relationships at Lake Satagay and a
potential positive feedback between thinning forests and intensifying fire regimes are inferred,
calling to attention a closer examination of thresholds of larch forest resilience against changing
wildfire regimes (MANUSCRIPT II). This “open woodland-fire feedback”, inferred from Early
Holocene environmental conditions at Lake Satagay, may be caused by a climate-driven
wildfire regime out of balance with the ecological needs of the larch forests, turning from a
stabilizing to a destabilizing agent. Simulations in LAVESI-FIRE indicate the benefit of low to
medium-intensity surface fires, at fire return intervals of 50 years or more, to the abundance
and stabilization of larch forests, while predicting markedly decreased tree abundance under
high-intensity, stand-replacing fires (MANUSCRIPT III). This simulation result mirrors
independently reconstructed FRIs (c. 43–95 years) at Lake Khamra during the relatively stable
vegetation composition of the past two millennia (MANUSCRIPT I). Although, despite the strong
localization and detailed parameterization of the individual-based model, these simulations
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remain abstract compared to the complexity of real fire-vegetation relationships, they highlight
the potential ability of identifying, or even quantifying, fire regime thresholds in larch forest
resilience. Hypothetically, frequent fires at return intervals shorter than the time needed by larch
saplings to develop an initial fire resistance may be able to instead facilitate and stabilize an
open woodland state of the forest, comparable to the Early Holocene.
A relatively stable forest configuration was reached in Central Yakutia in the Mid-Holocene,
around 5500 years BP (MANUSCRIPT II), coinciding with the establishment of the modern
range of fire regime variability (MANUSCRIPT II, IV). For the past millennia, reconstructed fire
regimes and larch forest structure appear to be in balance. However, due to the previously
discussed challenge of connecting paleo-ecologically reconstructed wildfire activity to
observed trends of the last decades, it is currently difficult to assess the present state of such a
“forest-fire equilibrium”. To evaluate more specifically the potential impacts of today’s
climate-driven fire regime variability exceeding beyond its range recorded throughout the Late
Holocene presents a research opportunity brought forward by the findings in this thesis.
Sedimentary ancient DNA and quantitative reconstructions of vegetation cover, derived from
pollen counts, are valuable approaches to infer additional information about past vegetation
changes. While pollen types usually allow an identification of plants on family or genus level,
sedaDNA can contribute information about local vegetation on highest taxonomic levels
(ZIMMERMANN ET AL., 2017). Using a sedaDNA metabarcoding approach targeting terrestrial
plants, the presence of Chamaenerion angustifolium (fireweed) was identified specifically
during high wildfire activity in the Early Holocene at Lake Satagay (MANUSCRIPT II).
Although the corresponding Onagraceae family is present in the accompanying pollen record,
it displays counts troughout the Holocene and does not specifically refer to fireweed, a prime
disturbance indicator. The same holds true for sedaDNA of Populus (aspen) in the Early
Holocene as typical early post-fire succession. Quantitative reconstructions of vegetation cover
using the REVEALS method (SUGITA, 2007), on the other hand, enable a clearer picture of past
vegetation changes from pollen data by correcting some known plant-specific biases such as
pollen production and dispersal distances (MANUSCRIPT II and references therein).
Considering the under-representation of larches in pollen records (NIEMEYER ET AL., 2015), this
transformation is especially important in eastern Siberia and recommended for interpretations
of vegetation coverage and composition in paleo-ecological studies.
In order to assess fire impacts on interactions of vegetation on long timescales in higher detail,
I implemented wildfires into the individual-based, spatially explicit forest model LAVESI
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(Larix Vegetation Simulator; MANUSCRIPT III). The existing structure of LAVESI, simulating
fine-scale, climate-driven forest dynamics in annual timesteps, facilitated the inclusion of a new
wildfire module. The conceptualization of this wildfire module was a crucial step that
demanded careful consideration of fire ecology in relation to our research objectives and the
millennial-long timescales under examination. One outcome of such considerations was the
decision to align the wildfire component to the annual timesteps of the model, by omitting active
fire spread and simulating only one annually burned area, dependent on a stochastic ignition,
the annual, climate-derived fire probability rating, and local fuel conditions. This
implementation captures local fire activity trends and may approximate regional trends,
especially over long timescales. However, if larger areas should be simulated and/or shorter,
sub-centennial timescales evaluated, the wildfire module may be best adapted to feature the
possibility of multiple ignitions per annual timestep. LAVESI-FIRE is thus currently rather
optimized for individual, long-term simulations in a small, square-shaped simulation area (i.e.,
few kilometers in length). It represents a foundation that can be adapted to suit the needs of
various research objectives. For example, a promising connection to the paleo-ecological
approach could be achieved by implementing charcoal production and taphonomy along the
already existing seed and pollen dispersal modules. Tuning simulated charcoal accumulation to
reconstructed data from sediment cores may provide a unique opportunity to improve the
understanding of forest configuration and wildfire regime attributes necessary to create a given
macroscopic charcoal record.
Vegetation proxies such as pollen or sedaDNA constitute a natural fit to paleo-ecological
studies of past wildfire activity, depending on the research objectives. In many cases it will be
a logical step to consider shifting fuel sources behind reconstructed fire regime changes. In this
thesis, I show that different vegetation proxies can fruitfully complement each other and
improve conclusions on past fire-vegetation relationships, and are well complemented by a
modeling approach. Where pollen slides were already prepared, an evaluation of microscopic
charcoal particles can be conducted without much additional effort. Apart from that, vegetation
proxies such as pollen records also have the potential to support the age-depth modeling process
where clear and well-known vegetation shifts are recorded. In Central Yakutia, examples
include a pronounced Pleistocene-Holocene transition with the initial establishment of forests
(complementary research manuscript BAISHEVA ET AL., 2024), or the initial expansion of pine
between c. 7000 to 4000 years BP (MANUSCRIPT II). This thesis also highly emphasizes the
need to consider wildfires when assessing or simulating trends of forest structure and
composition in eastern Siberia.
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6.3. Human drivers of fire regimes
The role of human activity as drivers behind past fire regime changes is complex and among
the more challenging factors to unravel (MARLON ET AL., 2013). The lack of knowledge about
the human factor in Siberia may be visible in the wide range of estimates fire researchers
attributed to the timing of initial human influence on fire regimes, ranging from the Early
Holocene all the way to recent centuries (complementary research manuscript SAYEDI ET AL.,
2024). This thesis discusses both natural and human drivers behind newly reconstructed long-
term wildfire activity in Yakutia (MANUSCRIPT I). For the first time, this thesis reports paleo-
ecological evidence for indigenous land use practices and their impacts on wildfires in Central
Yakutia (MANUSCRIPT IV). Simulations with the forest model LAVESI-FIRE (MANUSCRIPT
III), informed by the new paleo-ecological findings, are used to further distinguish climatic
from potential human drivers of fire regime changes (MANUSCRIPT IV).
Humans may have impacted regional fire regimes in Central Yakutia as early as c. 1200 CE,
coinciding with a major cultural shift from the settlement of the pastoralist Sakha people
(MANUSCRIPT IV). Although this finding currently relies on a preliminary sediment core
chronology, multiple proxies independently suggest the onset of persistent land use together
with a distinct decrease of biomass burning. Simulations of burned area only manage to recreate
this trend of reconstructed biomass burning when an artificial fuel reduction is implemented,
suggested to be a net effect of Sakha land use practices (MANUSCRIPT IV). The increased
number of charcoal records, in combination with palynological and modeling methods, enabled
an identification of human impacts already 800 years ago, contrasting the initial assumption of
human activity influencing fire regimes only after c. 1750 CE at Lake Khamra (MANUSCRIPT
I) and the lack of immediate evidence for human activity at Lake Satagay (Manuscript II).
This leads me to propose that Hypothesis 3 should be rejected: Although human activity
likely influenced fire regimes in the 19th century, paleo-ecological evidence supports
humans as key drivers behind reduced wildfire acivity already around 1200 CE. This
finding has several implications, and may yet not be entirely unexpected when considering
indigenous and historical voices.
The cultural shift from nomadic hunter-gatherer and reindeer herding communities to persistent
sedentary pastoralist activities around 1200 CE is indicated by several proxies independently
(MANUSCRIPT IV). However, as discussed before, the timing needs to be considered as
preliminary due to ongoing refinements of the sediment core chronology. Non-pollen
palynomorphs of coprophilous and mycorrhizal fungi, together with increased mercury levels,
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suggest the presence of large herbivores and soil erosion in the lake’s catchment. This coincides
with a more open forest and decreasing biomass burning (MANUSCRIPT IV). At Lake Satagay,
however, open woodlands of the Early Holocene were suggested to correspond to a more severe
wildfire regime (MANUSCRIPT II). These contrasting relationships between forest structure and
wildfire severity may further point towards a difference in drivers behind the two situations. In
the Early Holocene, open larch-birch woodlands were establishing from tundra vegetation,
whereas in the Late Holocene, established, dense, and mixed larch-dominated forests were
likely opened by humans while also applying other practices that reduced the overall fuel load
(e.g., collection of deadwood, animal husbandry, usage of pine wood, haymaking, burning of
excess fuel material; MANUSCRIPT IV and references therein). These new findings could
therefore imply that the probability for an “open woodland-fire feedback”, inferred from a
reconstruction of the Early Holocene conditions (MANUSCRIPT II), may depend on the structure
and fuel availability within open woodlands and thinning forests.
Combined modeling approaches may facilitate a more in-depth evaluation of paleo-ecological
data on human-fire relationships and its implications. Simulations in LAVESI-FIRE, although
only indirectly informed by and compared to paleo-ecological findings, independently
reinforced the interpretation of past human impacts on wildfires (MANUSCRIPT IV). However,
such combined approaches may have even higher potential if the model were to be more closely
tailored to the paleo-ecological proxy under examination, as suggested before. In addition, I
suggest that the possibilities arising from the fine-scale, individual-based nature of LAVESI-
FIRE have not yet been fully leveraged. Apart from previously discussed charcoal production
and the simulation of virtual sediment cores, the model could well accommodate more detailed
implementations of human activity, such as the impact of grazing animals, forest clearances, or
even approximated firefighting efforts around defined settlement areas. With the foundation
presented in this thesis, LAVESI-FIRE could be adapted and applied to various research
questions related to the human component of fire regime changes.
Indigenous land use and fire management practices in Yakutia appear to be poorly
acknowledged and reported in modern, international scientific literature, despite clear accounts
by indigenous and historical sources throughout the past centuries. A brief search among all
collections of Web of Science for the query “indigenous fire siberia” for all searchable fields in
July 2024 yielded only five results, all published after 2020. When the same search query is
used with “canada”, “usa”, or “australia” as substitution for “siberia”, hundreds of results were
found, respectively, published since the 1980s. Despite the obvious bias introduced by a search
only among research published in English, the scarcity of scientific publications reinforces a
CHAPTER 6: SYNTHESIS
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proposed knowledge gap about Siberian and Yakutian indigenous fire practices among the
international research community. This knowledge gap appears unexpected, considering
abundant references to indigenous fire practices among Sakha locals (e.g., SOLOVYEVA ET AL.,
2022; VINOKUROVA ET AL., 2022; CRATE, 2021) and various historical sources. For example,
already before 1746 Georg Wilhelm Steller described during his expedition in Siberia a
traditional habit of cultural burning in the forests near the Lena River (STELLER, 1774). Fridtjof
Nansen, throughout his expedition in Siberia in 1913, repeatedly commented about wildfires
and the common use of fires in the landscape. In south-eastern Siberia, he noted: “It seemed
strange that a “primeval forest” could look like this, with the ground so clean among the trees;
it was more like a well-kept English deer park than an uninhabited country near the Amur. But
the reason must be that the natives have long been in their habit of burning off the long, dry
grass, to provide better pasture for the game […]” (NANSEN, 1914). It is likely that traditional
land use practices, including the controlled use of fire, were known and applied among the
Sakha already before their initial settlement in Central Yakutia, adapted to the larch forests
from their knowledge of their former southern steppe environments (OKLADNIKOV, 1970).
Maybe a closer inspection of indigenous land use practices in Siberia was impeded by the severe
societal upheavals before, during, and after the Soviet period, which involved state-enacted
“forced forgetting” of Sakha ancestral legacy and traditions (CRATE, 2021). In any case, all
those accounts support the importance of considering humans as potential drivers behind past
fire regime changes in Siberia throughout the past centuries or millennia.
Based on the findings of this thesis (compiled in Fig. 6.1), a closer investigation of indigenous
land use practices with respect to wildfire activity is highly recommended. Efforts to compile
existing information from English, Russian, and Sakha sources, and make them available to the
international scientific community (e.g., in form of a review study) would be highly valuable.
Paleo-ecological studies may benefit from being more specifically tailored towards
examinations of past human activity by implementing suitable proxies and adopting an
interdisciplinary study framework with the involvement of related disciplinces, such as
archaeologists, historians, or anthropologists. Such an approach may also be able to further
clarify any impacts nomadic reindeer herding communities may have had on wildfire activity
before 1200 CE. Finally, the findings of this thesis suggest that research projects evaluating
indigenous fire management practices and their potential implementation in today’s fire
management should be enabled.
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Figure 6.1: Schematic overview of inferred Holocene wildfire activity in relation to vegetation, climate (summer
temperature), landscape development, and human activity, as proposed in this thesis. Drawings for the Early and
Mid-Holocene are interpretations by the author, adapted from the corresponding drawing for the Late Holocene
(Fig. 5.6 in MANUSCRIPT IV). States of wildfire activity, vegetation, and human activity are based on the findings
and suggestions of this thesis (MANUSCRIPTS I–IV). Relative summer temperatures are inferred from the climate
data used alongside the research of this thesis (MANUSCRIPT II, III). Interpreted landscape development guided by
complementary research manuscript BAISHEVA ET AL. (2023).
6.4. Outlook
Until now, long-term wildfire activity in Yakutia, eastern Siberia remained poorly understood.
In this thesis, I presented paleo-ecological and modeling approaches that shed light on long-
term wildfire regime changes, their drivers, and impacts. Novel research findings include the
identification of fire return intervals throughout the past two millennia, a characterization of
Holocene fire-vegetation relationships, and the potential of indigenous fire management to
mediate wildfire activity. Some findings have a special relevance for considerations of future
wildfire resilience of the larch-dominated boreal forest and the communities living within. On
the one side, the view into the past offered some concerning perspectives, for example that a
climate-driven intensification of wildfires may result in positive feedbacks with a thinning
forest structure, with the power to fundamentally alter the appearance of the larch forests we
know today. On the other side, evaluating past wildfire regime changes and their drivers also
revealed that communities may have traditional knowledge with the potential to increase their
resilience against catastrophic wildfires. In that sense, the research I presented in this thesis
highlights potential issues, but also suggests solutions. Based on my findings, I also
recommended research directions that may, from my perspective, benefit future research
efforts. The successful linking of paleo-ecological reconstructions with recent satellite-based
observations of wildfire activity appears as a major potential milestone for truly relating the
present to the past, identifying potential thresholds of wildfire regimes, and evaluating the
presence of ecological tipping points. Paleo-ecological analyses of wildfire activity during the
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Pleistocene-Holocene transition period based on macroscopic charcoal currently seem non-
existent in Yakutia, but may be highly valuable to understand fire dynamics in a rapidly
changing environment. Combined paleo-ecological and modeling approaches, multi-proxy
studies, the application of novel methods such as sedimentary ancient DNA, and creative ways
of proxy development all have the potential to highly increase the fidelity of information gained
from studies analyzing the past. Interdisciplinary research and the incorporation of traditional
and indigenous knowledge become increasingly appreciated, but need to be applied also in
regions where such approaches so far saw limited realization. Finally, in light of recent
concerning developments that threaten the international cooperation among the scientific
community, I want to highlight the role of scientists as public voices for the adherence to reason.
The loss of biodiversity and continued climate change may present existential threats that we
can only solve together and across borders, and only if evidence-based perspectives continue to
rise above attempts of disinformation. Long-term perspectives of environmental changes
throughout past millennia remind us about both the fragility and delight of our existence, but
also highlight the need for fostering resilience. Like wildfire regimes, we have the ability to
either sustain a liveable world, or destabilize it and change it forever. Let’s decide for the first.
203
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217
APPENDIX 1 (MANUSCRIPT I)
Appendix 1.1: Classic CHAR comparing the alternative local threshold (red) to the global
threshold (orange). Vertical dashed lines mark the different phases of the fire regime. (a)
Classic CHAR peak component (dark-grey bars = signal; light-grey bars = noise; coloured
lines = threshold versions). (b) SNI based on Kelly et al. (2011) (black horizontal line = SNI
cut-off value of 3). (c) Classic CHAR sum (black line = interpolated CHAR; blue
line = LOESS representing the CHAR background component; red and orange marks = fire
episodes for local and global threshold versions, respectively; with colour = fire episodes with
SNI >3; in grey = fire episodes with SNI <3).
Appendix 1.2: Correlations (Kendall's τ) of charcoal morphotype classes with selected pollen
groups.
APPENDIX 1 (MANUSCRIPT I)
218
Overview of the different charcoal size classes (S1–S3) and morphotype classes (S4–S6) in
both the classic and robust analysis approach, separated by vertical dashed lines representing
the different phases of the fire regime.
For each figure: (a) Classic CHAR peak component (dark-grey bars = signal, light-grey bars =
noise, dashed horizontal line = threshold). (b) SNI of the classic CHAR peak component after
Kelly et al. (2011) (red horizontal line = SNI cutoff value of 3). (c) Classic CHAR sum (black
line = interpolated CHAR, blue line = LOESS representing the CHAR background component,
red vertical lines = fire episodes with SNI >3, grey vertical lines = fire episodes with SNI <3).
(d) Robust CHAR background component. (e) Robust CHAR peak component (red areas =
above-average values). (f) Robust CHAR sum. For (d)–(f): black line = median, grey area =
interquartile range.
Appendix 1.3: Size class 150–300 µm
Appendix 1.4: Size class 300–500 µm
Appendix 1.5: Size class >500 µm
Appendix 1.6: Angular morphotypes (S, B, C)
Appendix 1.7: Elongated morphotypes (F, D, E)
Appendix 1.8: Irregular morphotypes (M, P, X)
APPENDIX 1 (MANUSCRIPT I)
219
Appendix 1.3: Size class 150–300 µm.
APPENDIX 1 (MANUSCRIPT I)
220
Appendix 1.4: Size class 300–500 µm.
APPENDIX 1 (MANUSCRIPT I)
221
Appendix 1.5: Size class >500 µm.
APPENDIX 1 (MANUSCRIPT I)
222
Appendix 1.6: Angular morphotypes (S, B, C).
APPENDIX 1 (MANUSCRIPT I)
223
Appendix 1.7: Elongated morphotypes (F, D, E).
APPENDIX 1 (MANUSCRIPT I)
224
Appendix 1.8: Irregular morphotypes (M, P, X).
225
APPENDIX 2 (MANUSCRIPT II)
Appendix 2.1: Original age-depth model output for sediment core EN18224-4, applying
Bacon (Blaauw and Christen, 2011) to 14C dated bulk sediment samples. (A) Model iteration
log. (B, C) Prior (green line) and posterior (grey area) distributions for accumulation rate and
memory. (D) Age-depth model (blue distributions = calibrated ages of 14C dated samples; red
distribution = outlier among dated samples; grey lines = 2σ range; red line = median).
APPENDIX 2 (MANUSCRIPT II)
226
Appendix 2.2: Pollen and non-pollen palynomorph percentage diagram for sediment core
EN18224-2, with zone separations from cluster analysis. AP/NAP ratio values without unit.
Shaded area represents a visual exaggeration, added to pollen types of lower abundance.
APPENDIX 2 (MANUSCRIPT II)
227
Appendix 2.3: Scatterplots of macroscopic charcoal concentration and different REVEALS-
transformed pollen types from sediment core EN18224-4, with LOESS-smoothing (grey area
= 2σ range, black line = mean). Colors of the dots represent their age (dark = modern; white =
Early Holocene at c. 10,800 yrs BP).
228
APPENDIX 3 (MANUSCRIPT III)
Additional formulas and figures related to the implementation of fire occurrence in LAVESI-
FIRE are shown here. For the full code or simulation data, please refer to “Availability of data
and materials” in the main research paper.
Appendix 3.1: Estimation of monthly fire probability rating (FPRmon) based on monthly mean
temperature (Tmon) and precipitation (Pmon).
Appendix 3.2: (Left) Linear model for predicting fire probability from T and P compared to
observed fires from MODIS. (Right): Modelled FPRmon values for months without observed
fires. To limit false-positive fire probability in the model, Q4 = 6.6 was used as minimum
threshold for assigning fire probability to a given month.
APPENDIX 3 (MANUSCRIPT III)
229
Appendix 3.3: (Left) of FPRmon above the minimum threshold (6.6), showing in red the
separations of mild, severe and extreme fire probability thresholds. (Right): Boxplot of the
same FPRmon values, indicating how thresholds for the monthly categorization of fire weather
were chosen (for severe fire: Q3 = 7.0, for extreme fire: Q4 = 7.46).
Appendix 3.4: Estimation of annual fire probability rating (FPRann).
Appendix 3.5: Estimation of topographic wetness index (TWI) mediating impact.
APPENDIX 3 (MANUSCRIPT III)
230
Appendix 3.6: Overview of the different simulation scenarios. Climate forcing #1 is the main
MPI-ESM1.2 forcing data, #2 and #3 are the alternative forcing datasets from MPI-ESM-CR
and TraCE-21ka, respectively. For references, please refer to the main research paper.
Simulation ID
Climate forcing
FRI changes
FI changes
Parameter changes
sim_1
1
Climate-driven
Climate-driven
-
sim_2
1
No fire occurrence
No fire occurrence
-
sim_3
1
-
-
T - 5%
sim_4
1
-
-
T + 5%
sim_5
1
-
-
P - 5%
sim_6
1
-
-
P + 5%
sim_7
1
-
-
fire mortality - 5%
sim_8
1
-
-
fire mortality + 5%
sim_9
1
10
Low (0.1)
-
sim_10
1
50
Low (0.1)
-
sim_11
1
100
Low (0.1)
-
sim_12
1
200
Low (0.1)
-
sim_13
1
300
Low (0.1)
-
sim_14
1
10
Medium (0.5)
-
sim_15
1
50
Medium (0.5)
-
sim_16
1
100
Medium (0.5)
-
sim_17
1
200
Medium (0.5)
-
sim_18
1
300
Medium (0.5)
-
sim_19
1
10
High (1.0)
-
sim_20
1
50
High (1.0)
-
sim_21
1
100
High (1.0)
-
sim_22
1
200
High (1.0)
-
sim_23
1
300
High (1.0)
-
sim_24
2
Climate-driven
Climate-driven
-
sim_25
3
Climate-driven
Climate-driven
-
APPENDIX 3 (MANUSCRIPT III)
231
Appendix 3.7: Alternative climate forcing model data and corresponding simulated stem
count.
APPENDIX 3 (MANUSCRIPT III)
232
Appendix 3.8: Superposed epoch analysis for selected FRI/FI scenarios, showing the stem
count median per species for fire occurrences after 14,000 yrs BP.
233
APPENDIX 4 (MANUSCRIPT IV)
Appendix 4.1: Relative shares of pollen and non-pollen-palynomorphs (NPP) for Lake 449.
APPENDIX 4 (MANUSCRIPT IV)
234
Appendix 4.2: Table showing metadata of lakes and corresponding sediment cores newly
analyzed for this study. Last row shows for which sediment cores the charcoal particle length-
to-width ratio (L:W ratio) was determined.
Appendix 4.3: Table showing metrics for the newly analyzed charcoal records of this study,
including distributions of charcoal particle size classes and morphotypes, as well as the
length-to-width ratios. Colors mark the largest (red), intermediate (orange), and smallest
(green) shares, respectively.
APPENDIX 4 (MANUSCRIPT IV)
235
Appendix 4.4: Table showing the 14C age dating results for the newly presented sediment
cores of this study. Additional age dating is being conducted at the time of writing this thesis.
Lake ID Sediment core Lab ID Depth top (cm) Depth bot (cm)
F14C± 1σ 14C years BP ± 1σ cal. years BP ± 2σ Comment
402 EN21402-4 9764.1.1 10 11 0.79 0.0026 1858 26 1769 56
402 EN21402-4 9765.1.1 20 21 0.71 0.0024 2772 27 2810 27
402 EN21402-4 9766.1.1 35 36 0.59 0.0022 4293 30 4863 36
408 EN21408-2 10214.1.3 10 11 0.82 0.0022 1590 22 1467 58
408 EN21408-2 10215.1.3 20 21 0.77 0.0021 2124 22 2021 19
408 EN21408-2 10216.1.3 27 28 0.76 0.0021 2234 22 2225.5 71.5
410 EN21410-1 9767.1.1 10 11 0.88 0.0027 1046 25 976 58
410 EN21410-1 9768.1.1 28 29 0.59 0.0018 4174 24 4602.5 14.5
419 EN21419-2 9769.1.1 10 11 1.12 0.0032
Excluded fro m age-
depth mo deling
419 EN21419-2 9770.1.1 20 21 0.95 0.0029 374 24 353.5 33.5
419 EN21419-2 9771.1.1 37 38 0.93 0.0028 606 24 565.5 17.5
421 EN21421-2 10220.1.2 10 11 0.97 0.0026 252 21 77 77
421 EN21421-2 10221.1.2 20 21 0.88 0.0024 1023 22 935.5 22.5
421 EN21421-2 10222.1.2 31 32 0.87 0.0023 1163 22 1013 37
433 EN21433-1 9774.1.1 10 11 0.43 0.0019 6790 36 7628.5 49.5
Considered outlier by
age-depth mod eling
in "rbacon "
433 EN21433-1 9775.1.1 30 31 0.86 0.0024 1212 22 1120 54
433 EN21433-1 9776.1.1 50 51 0.69 0.0020 3031 23 3225.5 61.5
433 EN21433-1 9777.1.1 65 66 0.61 0.0026 3946 35 4271.5 18.5
437 EN21437-1 10223.1.2 10 11 0.77 0.0021 2106 22 2068 70
437 EN21437-1 10224.1.2 20 21 0.60 0.0017 4071 23 4478.5 35.5
437 EN21437-1 10225.1.2 30 31 0.52 0.0015 5197 24 5953 41
437 EN21437-1 10226.1.2 39 40 0.44 0.0013 6549 24 7485.5 62.5
449 EN21449-1 10359.1.1 10 11 0.90 0.0027 862 24 706 14
449 EN21449-1 10360.1.1 27 28 0.87 0.0026 1140 24 1031 71
455 EN21455-2 10231.1.2 10 11 0.90 0.0024 863 22 710 13
455 EN21455-2 10232.1.2 20 21 0.90 0.0024 870 22 801.5 73.5
455 EN21455-2 10233.1.1 33 34 0.88 0.0024 1054 22 954 32
APPENDIX 4 (MANUSCRIPT IV)
236
EN21402-4 (Lake 402)
EN21408-2 (Lake 408)
EN21410-1 (Lake 410)
EN21419-2 (Lake 419)
EN21421-2 (Lake 421)
EN21433-1 (Lake 433)
APPENDIX 4 (MANUSCRIPT IV)
237
EN21437-1 (Lake 437)
EN21449-1 (Lake 449)
EN21455-2 (Lake 455)
Appendix 4.5: Age-depth models of the newly obtained sediment cores in this study. Original
output from “rbacon” (Blaauw and Christen, 2011). Blue marks in the main plot mark
calibrated radiocarbon age ranges. Shaded area represents density of individual age-depth
models, with a median (red line) and the 95% confidence interval (grey lines).
APPENDIX 4 (MANUSCRIPT IV)
238
Appendix 4.6: Overview of the newly presented charcoal records of this study. For each plot,
charcoal concentration and charcoal accumulation rate (CHAR, in particles cm-2 yr-1),
interpolated to median temporal resolution, are depicted.
Appendix 4.7: Fuel availability implementation in LAVESI-FIRE.
FF: Fuel factor
LH: Litter layer height (0-3000; 3000 equals 30 cm)
FA: Fuel availability (0-1; custom parameter that defaults to 1)
TD: Tree density (0-65535)
239
EIDESSTATTLICHE ERKLÄRUNG
Hiermit erkläre ich, dass ich die vorliegende Arbeit mit dem Titel „Long-term changes of
wildfire regimes in eastern Siberia: An evaluation based on lake sediment indicators and
individual-based modeling” selbstständig und unter Verwendung der angegebenen Literatur
und Hilfsmittel angefertigt habe. Wörtlich oder sinngemäß übernommenes Gedankengut habe
ich als solches kenntlich gemacht. Diese Dissertation wird erstmalig an der Universität Potsdam
eingereicht. Die dem Verfahren zu Grunde liegende Promotionsordnung ist mir bekannt.
____________________ ____________________
Ort, Datum Ramesh Glückler
