Holocene wildfire and vegetation dynamics in Central Yakutia, Siberia, reconstructed from lake-sediment proxies

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. 4500 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 towards the Late Holocene. Whereas the Early Holocene was characterized by postglacial open larch-birch woodlands, forest structure changed towards 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, wildfires), higher tree mortality may force the modern state of the forest to shift towards 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.

Full text

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Holocene wildfire and vegetation dynamics in Central Yakutia,
Siberia, reconstructed from lake-sediment proxies
Ramesh Glückler1,4, Rongwei Geng1,2,3, Lennart Grimm1, Izabella Baisheva1,4,6, Ulrike Herzschuh1,4,5,
Kathleen R. Stoof-Leichsenring1, Stefan Kruse1, Andrei Andreev1, Luidmila Pestryakova6, Elisabeth 5
Dietze1,7
1Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research,
Potsdam, Germany
2Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, 10
Chinese Academy of Sciences, Beijing, China
3University of Chinese Academy of Sciences, Beijing, China
4Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany
5Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
6Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia 15
7Organic Geochemistry, German Research Centre for Geoscience (GFZ), Potsdam, Germany
Correspondence to: Ramesh Glückler (ramesh.glueckler@awi.de) and Elisabeth Dietze (elisabeth.dietze@awi.de)
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 20
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. 4500 years before present. A
pollen-based quantitative reconstruction of vegetation cover and a terrestrial plant record based on sedimentary ancient DNA 25
metabarcoding suggest a pronounced shift in forest structure towards the Late Holocene. Whereas the Early Holocene was
characterized by postglacial open larch-birch woodlands, forest structure changed towards 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, wildfires), higher tree mortality 30
may force the modern state of the forest to shift towards 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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1. Introduction 35
Boreal ecosystems are facing increasingly severe wildfire seasons in recent years (Köster et al., 2021; Walker et al., 2019). 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 40
to the region’s burnt area in subsequent fire seasons (Xu et al., 2022). Central Yakutia is now among the most fire-prone
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 45
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). 50
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. 55
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.
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 60
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; yrs BP) coincided with an open larch
forest (Katamura et al., 2009a). During the Mid-Holocene, after 6000 yrs 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 65
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
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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 70
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 75
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 towards 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.
11,000–6500 yrs BP, related to dark taiga vegetation (Pinus sibirica, Abies sibirica, Picea obovata), before the onset of the 80
present-day surface fire regime, coinciding with a shift towards light taiga (Pinus sylvestris, Betula sp.). Feurdean et al.
(2021[Accepted]) 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) report predominantly climate-
driven vegetation dynamics in the Early Holocene due to lower reconstructed fire activity, whereas in the Late Holocene 85
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, 90
and comparing it to (II) the history of vegetation composition with a REVEALS-transformed pollen record and data on
sedimentary ancient DNA of terrestrial plants.
2. Methods
2.1 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 95
Yakutian Lowlands, 600 km west of the republic’s capital Yakutsk (Fig. 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,
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2014). The area is directly accessible via the Vilyuy Highway (5 km) through forest trails. Apart from smaller settlements, the 100
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).
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-105
rich deposits to eventually form the present-day thermokarst lake (Boike et al., 2016; Crate et al., 2017). Long-term
development of the lake is analyzed in more detail by Baisheva et al. (2022[In prep.]).
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[Accepted]; see Fig. 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 110
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 and grasses. Plenty of deadwood can be found, indicating
disturbances to the forest (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 115
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 120
(MCD64A1 Version 6; Giglio et al., 2016), obtained for a 100 km buffer around Lake Satagay, shows that four of the five
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; Fig. 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 125
in the summer months starting around 2010 (Fig. 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 (Fig 2c).
2.2 Fieldwork and subsampling scheme
Fieldwork at Lake Satagay was conducted in August 2018 (Kruse et al., 2019). Sediment core EN18224-4 was obtained with 130
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
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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 135
the extraction of the pollen fraction. Additionally, 61 samples (2 cm³) were extracted for the analysis of sedimentary ancient
DNA (sedaDNA), while ten bulk sediment samples for dating were taken, spread equally across the core at every c. 10 cm.
2.3 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 140
were calibrated using the IntCal20 calibration curve (Reimer et al., 2020) in R (v.4.0.2; R Core Team, 2020) during age-depth
modeling with Bacon (v.2.5.7, R package “rbacon”; Blaauw et al., 2021; Blaauw and Christen, 2011).
2.4 Charcoal and pollen extraction
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 145
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, 150
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 150–300 µm (small), 300–500 µm (medium) to >500 µm
(large), and their morphology, following the charcoal morphotype identification scheme established by Enache and Cumming 155
(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 400x magnification. 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 160
Lycopodium spores; Finsinger and Tinner, 2005).
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2.5 Sedimentary ancient DNA (sedaDNA) approach
The preparation of 61 samples for evaluation of sedaDNA is outlined in detail in Baisheva et al. (2022[In prep.]). 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 165
a short fragment of the trnL P6 loop on the chloroplast genome (Taberlet et al., 2007) modified with a unique NNN-8bp 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. 2017) 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 170
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.
2.6 Statistical methods
From the charcoal concentration per sample (particles cm-3), a charcoal accumulation rate (CHAR, particles cm-2 yr-1),
interpolated to median temporal resolution, was calculated using the R script presented in Glückler et al. (2021). The 175
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 180
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 185
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 190
analysis (Grimm, 1987; using the R packages “vegan” and “rioja”; Juggins, 2020). Only a significant number of zones,
according to comparison with a broken-stick model, is visualized (Bennett, 1996).
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3. Results
3.1 Chronology
Sediment core EN18224-4 displays a mostly homogenous texture with dark-brown colors from organic-rich deposits, lacking 195
any lamination. A detailed description of the core sediment facies can be found in Baisheva et al. (2022[In prep.]). Bulk
sediment 14C dating shows a well-ordered sequence of dating points without age reversals (Table 1). The surface sample,
dating back to 1345.5 ± 36.5 cal. yrs 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, 200
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 hardwater 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). 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 205
core of c. 10,800 cal. yrs BP, covering most of the Holocene (Fig. 3a; for the original Bacon chronology see Supplementary
Figure 1). The dating uncertainty, i.e. the 2σ range of all age-depth models included in the chronology, is on average 601 ±
279 yrs (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 yr-1, with higher rates of up to 0.42 mm yr-1 between c. 37 and 56 cm (Fig. 3b). This
chronology is further reinforced by a pronounced expansion of pine trees, clearly visible in the pollen record at c. 5400 cal. 210
yrs 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).
3.2 Charcoal
In 111 samples continuously covering the sediment core, a total of 4206 macroscopic charcoal particles was identified,
resulting in a mean of c. 38 particles per cm³ (Fig. 3c) or a mean charcoal accumulation rate (CHAR) of 0.38 ± 0.5 particles 215
cm-2 yr-1 (mean ± 1σ, Fig. 7b). 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 9600 yrs BP, reaching a record-wide maximum of 2.58 particles cm-2 yr-1. The mean CHAR of the Early Holocene (c.
10,800 to 8500 yrs BP) is 1.0 ± 0.73 particles cm-2 yr-1. After that, it decreases to lower intermediate levels until c. 4500 yrs 220
BP (0.29 ± 0.17 particles cm-2 yr-1). From there until present day, CHAR remains at low levels (0.09 ± 0.06 particles cm-2 yr-
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 (Fig. 3c), with a distinct maximum at 9800 yrs BP (100 × 103 particles
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cm-3), followed by intermediate levels until 5500 yrs BP (40 × 103 particles cm-3), and low levels until present day (10 × 103 225
particles cm-3).
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 at 5300 and 9000 yrs 230
BP, marking the three general phases of CHAR described above (Fig. 7b, 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%.
3.3 Pollen 235
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 5500 yrs BP and within the recent centuries (Fig.
4). 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, 240
and Caryophyllaceae. The original pollen counts also feature a high number of algae in the lowest segment of the core (10,800–
7000 yrs BP), and some Polypodiaceae spores in the upper part of the core (5400–2000 yrs BP; Supplementary Figure 2).
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 245
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 (10,800–7000 yrs
BP) is characterized by a high cover of Poaceae and arboreal pollen dominated by Larix and Betula. The upper zone (7000 yrs 250
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
charcoal accumulation rate, cluster analysis identifies additional zones, including a separation at 5400 yrs BP (Fig. 7d). At this
time, pine trees rapidly extend their cover, while Salix is only seen on a few occasions from there on. The original pollen
counts (Supplementary Figure 2) demonstrate similar trends. There, cluster analysis also identifies a zone at 5400 yrs BP, 255
when Betula pollen give way to quickly increasing numbers of Pinus pollen.
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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 (Fig. 8a). 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 260
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 (Fig 8b).
3.4 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 (Fig. 5). With cluster analysis, four zones are 265
identified within the sedaDNA proxy, in agreement with pollen and charcoal zones. The first zone (10,800–9300 yrs BP)
comprises Betula, Saliceae, and Populus, together with a great variety of flowering plants and grasses (Asteraceae,
Chenopodiaceae, Onagraceae, Rosaceae, Poaceae, Urticaceae). The Onagraceae here include Chamaenerion angustifolium
(fireweed). The second zone (9300–5400 yrs 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 zone (5400–3800 yrs BP), Betula 270
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 (3800 yrs BP–present) samples show reads from various
tree tribes and genera (Betula, Alnus, Larix, 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.
4. Discussion 275
4.1 Reconstructed wildfire activity
Charcoal analysis suggests a pronounced shift in the fire regime around Lake Satagay in the Early Holocene (c. 9000 yrs BP)
and until the Mid-Holocene (c. 5300 yrs 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 the present-280
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). 285
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
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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 (Fig. 7b; Whitlock and Anderson, 2003). The increased share of large particles in the pronounced peak of CHAR 290
around 9600 yrs BP points towards 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 295
convection (Fig. 6). 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; Dietze et al., 2020; Karp et al., 2020), analyzing the particles’ degree of 300
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).
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, 305
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 310
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 clearly visible, often frayed, cellular structure. Therefore, higher shares of woody morphotypes at Lake Satagay 315
might indicate that, in the Early Holocene, a potentially more severe and intense fire regime enabled the thorough combustion
of tree biomass. In contrast, low-severity surface fires can explain the increasing shares of predominantly grassy and more
fragile morphotypes in the Late Holocene. 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 320
the suggestion of resulting from erosion by Katamura et al. (2009ab) for similar lake systems in Central Yakutia. There, the
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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 325
thaw slumping along the reed-covered lake shore were found. In addition, the pollen record suggests that the lake already
existed at 10,800 yrs BP (high counts of algal palynomorphs even in its deepest samples; see Supplementary Figure 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., 2022[In prep.]). 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 yrs BP; 330
Baisheva et al., 2022[In prep.]). 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 counts (Fig. 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 335
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 (Fig. 1), is not reproduced in the charcoal
record. Most likely, the present thermokarst lake system 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/centimeters 340
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). 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 345
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 350
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
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well as potential alternative methods of bleaching or particle quantification, may be useful to safely exclude any bias against 355
low-intensity fires and further develop charcoal as a paleoenvironmental proxy.
4.2 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 towards the modern, denser mixed larch forest. This is demonstrated by high
proportions of Betula and Poaceae pollen in the Early Holocene (around 10,000 yrs BP), when few tree taxa, apart from those 360
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-called
“yornik”; Andreev et al., 1997). 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 5400 yrs BP, especially on patches of sandy substrate
(see Fig. 1), is also a typical feature of Holocene vegetation dynamics in Yakutia and has been witnessed in many other 365
paleoenvironmental studies (depending on study site and chronology it has been reported to occur between c. 7000 and 4000
yrs BP; Müller et al., 2009; Andreev and Tarasov, 2013; Tian et al., 2018; Cao et al., 2019ab). 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 5400 yrs BP a higher cover of Cyperaceae (e.g. Carex), graminoids, or sedges which are often found in marshes and 370
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., 2022[In prep.]). The non-transformed pollen record also shows fern
spores (Polypodiaceae) after 5400 yrs BP. Ferns are typical understory vegetation, preferring shaded locations as found in a
denser forest. Salix, prominent during the Early to Mid-Holocene, 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., 375
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% (Supplementary Figure 380
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 385
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 Chamaenerion 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 390
of post-fire succession and pointing towards 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 Fig. 1),
thus not contributing sufficiently to the locally derived sedaDNA record.
4.3 Fire-vegetation feedbacks on millennial timescales 395
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 (Fig. 7). Here, we discuss potential fire-400
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 405
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, 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 et al., 2005).
At Lake Satagay, the anthropogenic influence on the reconstructed fire and vegetation history is assumed to be low. Despite a 410
continuous human presence throughout the Holocene, nomadic cultures living in Central Yakutia before c. 900 yrs 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 (Glückler et al.,
2021). 415
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 yrs BP (Walter et al., 2007). The
Holocene Climate Optimum in the Early to Mid-Holocene (c. 7000–5000 yrs 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 5400 yrs BP. Additionally, the Holocene Climate Optimum triggered 420
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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. 6000 and 5000 yrs BP (Fig. 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 425
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 (9600 yrs BP), c. 600 years before the zone separation in CHAR (9000 430
yrs BP; Fig. 7). 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 (5400 yrs BP) and CHAR (5300 yrs BP). However, although the
charcoal morphotypes immediately mirror shifting vegetation composition at 5300 yrs BP, the total sum of CHAR decreases
only c. 800 years later at c. 4500 yrs 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 435
occurred in response to the establishment of a new vegetative state. 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. 4500 yrs 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). 440
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, Alnus) in multiple
past interglacials. In wetter periods, as indicated by abundant Sphagnum spores, fire activity is reduced. Evergreen Picea 445
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 450
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 (Fig. 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
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occurred around 2010 CE. Even though the forest management system of Yakutia has undergone several adjustments in recent 455
years (e.g. abstaining from extinguishing fires far from populated regions), a persistent shortage of funding exacerbated broad-
scale suppression even 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. 460
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 towards a more severe fire regime in open woodlands, comparable to the reconstructed Early Holocene state around 465
Lake Satagay, this points towards 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., 470
2009; Herzschuh et al., 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 475
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 480
of such a state change in Central Yakutia. Additionally, modeling could test whether a shift from the modern state towards the
open woodland state 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). 485
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5. Conclusions
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 towards 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. 4500 yrs 490
BP. Considering an anticipated increase in tree mortality, potentially leading to sparser tree populations, these results point
towards 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 495
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.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be 500
construed as a potential conflict of interest.
Author contributions
UH, SK and LP designed and lead the field works. RGl designed the sediment study supervised by ED and UH. RGl and KS
subsampled the sediment core, RGl prepared the age dating process and created the chronology. RGe conducted the pollen
analysis, supervised by AA and UH. LG conducted the charcoal-related laboratory work and analysis, supported by RGl and 505
ED. IB and KS performed sedaDNA analyses. RGl wrote the initial version of the manuscript, supervised by ED. All authors
commented on the initial manuscript.
Funding
This research has been supported by the European Research Council (grant no. Glacial Legacy: 772852), the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation, grants no. DI 2544/1-1: #419058007 and 448651799), and the 510
Russian Ministry of Education and Science (FSRG-2020-0019). Ramesh Glückler is funded by AWI INSPIRES (International
Science Program for Integrative Research). Izabella Baisheva is funded by the German Academic Exchange Service e.V.
(DAAD).
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Acknowledgements
Thanks to all members of the joint German-Russian expedition “Chukotka-Yakutia 2018”. We thank Stuart Vyse and Paul 515
Adam for supporting work on this study’s sediment core. Philip Meister, Ingeborg Frommel, Rebecca Morawietz and Amelie
Naderi kindly 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.
Data availability
The data presented in this study will be made publicly available via the PANGAEA database (https://pangaea.de; PANGAEA, 520
2022). The charcoal data will additionally be uploaded to the Global Paleofire Database (https://ipn.paleofire.org; International
Paleofire Network, 2022). The sedaDNA metabarcoding data will be uploaded to the Dryad database (https://datadryad.org/;
Dryad Digital Repository, 2022).
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Tables and figures
Table 1: 14C dating results for sediment core EN18224-4. Samples marked with an * are considered outliers and excluded from the
age-depth model. 815
Lab-ID
Depth (cm)
F14C ± 1σ
14C yrs BP ± 1σ
cal. yrs BP ± 2σ
6172.1.1*
0-1
0.8326 ± 0.0021
1472 ± 21
1345.5 ± 36.5
6173.1.1
13-14
0.8121 ± 0.0021
1672 ± 21
1601.5 ± 73.5
6174.1.1
23-24
0.6782 ± 0.0018
3120 ± 21
3289 ± 36
6175.1.1
33-34
0.5998 ± 0.0016
4107 ± 22
4598 ± 73
6176.1.1
43-44
0.5789 ± 0.0016
4391 ± 22
4935 ± 67
6177.1.1
53-54
0.5749 ± 0.0016
4447 ± 22
5032 ± 69
6178.1.1
68-69
0.4758 ± 0.0013
5966 ± 23
6799 ± 63
6179.1.1
83-84
0.3862 ± 0.0011
7642 ± 24
8438 ± 58
6181.1.1
94-95
0.3504 ± 0.0011
8424 ± 24
9376.5 ± 43.5
6180.1.1
102-103
0.3274 ± 0.0010
8970 ± 25
9946 ± 15
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Figure 1 (A): Location of Lake Satagay (evergreen/deciduous forest classification based on © ESA Climate Change Initiative land
820
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[Accepted]).
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Figure 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 1950-2020 CE. (B): Burnt area in a 200 km
buffer around Lake Satagay from 2001-2020 CE (NASA EOSDIS Land Processes DAAC product MCD64A1.006; Giglio et al., 2016).
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(C): Correlation of mean summer months FWI and annual burnt area.
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Figure 3 (A): Bulk-sediment 14C-based chronology for sediment core EN18224-4. (B): Sedimentation rate, as derived from the 835
chronology. (C): Concentrations of macroscopic charcoal, divided by size classes (bars), and microscopic charcoal found in pollen
samples (line).
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Figure 4: REVEALS-transformed pollen record, with zone separations from cluster analysis. Shaded area represents a visual 840
exaggeration, added to pollen types of lower abundance.
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Figure 5: 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
845
identified, whereas non-arboreal plants are shown on family level.
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Figure 6: (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
850
2021 (R. Jackisch and R. Glückler, AWI).
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Figure 7: (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
855
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).
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Figure 8: (A): PCA of REVEALS-transformed vegetation types. (B): Scatterplot of macroscopic charcoal concentration and
principal component 1 (PC1) of the PCA, with LOESS-smoothing.
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