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Abstract and Figures

Fire is one of the main disturbances in the world's forested ecosystems and its impacts are projected to increase in many regions due to global change. Fire impacts have been studied for decades, but integrative assessments of its effects on multiple ecosystem services (ES) across scales are rare. We thus conducted a global analysis of persistent (>1yr.) fire effects on eight ES reported over the last 30 years, evaluating qualitative and quantitative information from 207 peer-reviewed studies. Significant effects were predominantly positive for 'water provision' and negative for 'water quality', 'climate regulation' and 'erosion control'. For 'food provision' and 'soil fertility' no overall significant effects emerged. For 'recreation' or 'pollination', data was insufficient. Significant effects were generally short-lived (1-2yr.) and were more common after wildfires than after prescribed burns. These overall findings, however, are dominated by data from a few countries/biomes and short timescales, highlighting the need for future studies focusing on underrepresented regions, biomes, timescales and ES. In a nutshell • Fire is a major disturbance in many regions, but its wider effects on ecosystem services remain poorly evaluated. • We conducted a systematic review of 30 years of literature on environmental effects of forest fires, examining 207 studies that allowed assessing eight major ecosystems services. • Effects were significantly positive for 'water provision' and negative for 'water quality', 'climate regulation' and 'erosion control' and non-significant for 'food provision' and 'soil fertility'. • Negative effects were more dominant after wildfires than after prescribed burns, and, generally, effects were short-lived (1-2yr.). • Key gaps are the lack of data for some regions and biomes (e.g. tropical forests) and for long-term impacts (>10yr.).
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Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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A global synthesis of fire effects on ecosystem services of forests
and woodlands
Jose V Roces-Díaz1*
Cristina Santín2,3
Jordi Martínez-Vilalta4,5
Stefan H Doerr1
1: Department of Geography, Swansea University, Swansea SA2 8PP, United Kingdom.
* Corresponding author. Wallace Building, Department of Geography, Swansea University, Swansea SA2 8PP,
United Kingdom. J.V.Roces@swansea.ac.uk
2: Department of Biosciences, Swansea University, Swansea SA2 8PP, United Kingdom
3: Research Unit of Biodiversity, CSIC/UO, Mieres 33600, Spain
4: CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
5: Universitat Autònoma de Barcelona, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
This manuscript was accepted for publication in Frontiers in Ecology and the Environment on 18th February 2021
(DOI: 10.1002/fee.2349). It has a CC-BY license for posting these contents openly online
Abstract
Fire is one of the main disturbances in the world’s forested ecosystems and its impacts are projected to
increase in many regions due to global change. Fire impacts have been studied for decades, but
integrative assessments of its effects on multiple ecosystem services (ES) across scales are rare. We
thus conducted a global analysis of persistent (>1yr.) fire effects on eight ES reported over the last 30
years, evaluating qualitative and quantitative information from 207 peer-reviewed studies. Significant
effects were predominantly positive for water provision and negative for ‘water quality’, ‘climate
regulation’ and ‘erosion control’. For ‘food provision’ and soil fertility no overall significant effects
emerged. For ‘recreation or pollination, data was insufficient. Significant effects were generally
short-lived (1-2yr.) and were more common after wildfires than after prescribed burns. These overall
findings, however, are dominated by data from a few countries/biomes and short timescales,
highlighting the need for future studies focusing on underrepresented regions, biomes, timescales and
ES.
In a nutshell
Fire is a major disturbance in many regions, but its wider effects on ecosystem services
remain poorly evaluated.
We conducted a systematic review of 30 years of literature on environmental effects of forest
fires, examining 207 studies that allowed assessing eight major ecosystems services.
Effects were significantly positive for ‘water provision’ and negative for ‘water quality’,
‘climate regulation’ and ‘erosion control’ and non-significant for ‘food provision’ and ‘soil
fertility’.
Negative effects were more dominant after wildfires than after prescribed burns, and,
generally, effects were short-lived (1-2yr.).
Key gaps are the lack of data for some regions and biomes (e.g. tropical forests) and for long-
term impacts (>10yr.).
1. Introduction
Fire is a key ecological disturbance affecting a large fraction of world’s terrestrial ecosystems, spanning
a broad range of regions and biomes (Bowman et al. 2009; Krawchuk et al. 2009). Fire can consume
large amounts of biomass, alter soil properties, and have very substantial effects on key ecosystem
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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processes, affecting hydrological (Shakesby and Doerr 2006) and biochemical cycles (Santin et al.
2015). From a biogeographical perspective, fire has also played a key role in plant evolution (Bond et
al. 2005), for example promoting specific functional traits such as re-sprouting (Keeley et al. 2011).
Thus, its relevance for the global patterns of biodiversity and vegetation distribution is also widely
acknowledged (Pausas and Ribeiro 2017; Kelly et al. 2020). The specific effects of a given fire depend
on both ecosystem properties (e.g. fire-adapted vs. fire-sensitive ecosystems) and fire and
characteristics (e.g. fire intensity, size or recurrence; Lavorel et al. 1998; Archibald et al. 2013); and,
even during a single fire, impacts can greatly differ spatially and also among the different ecosystem
components (i.e. soil, vegetation, etc.; Keeley 2009; Mataix-Solera et al. 2011). Importantly, current
fire regimes are being modified by global change drivers (cf. Lavorel et al. 1998; Doblas-Miranda et
al. 2017 for Mediterranean systems).
Given its often substantial effects on the environment, fire is widely recognized as a key force affecting
multiple Ecosystem Services [ES; Harper et al. (2018); Pausas and Keeley (2019); Sil et al. (2019)],
defined by MA (2005) as “conditions and processes through which natural ecosystems, and the species
that make them up, sustain and fulfil human life”. Indeed, wildfires are often highlighted as one of the
major disturbances that negatively impact ES in a range of terrestrial ecosystems, including forests and
woodlands (Thom and Seidl 2015). These impacts can affect soil erosion (Shakesby 2011), runoff
(Vieria et al. 2015), water quality (Harper et al. 2018) or soil fertility (Caon et al. 2014). However, fire
can also enhance some ES (directly and indirectly), such as food provision or biological control of
disturbances, due to the key role of natural disturbance regimes in ecological processes (Pausas and
Keeley 2019). To support effective and desirable fire- and land management outcomes, a complete
picture of fire impacts on ES is essential, including the effects of both wildfires and prescribed burns
(i.e. controlled fires with management purposes) (Davies et al. 2016; Pausas and Keeley 2019).
The effects of fire have been examined in some specific types of ecosystems and their functioning [i.e.,
savannas: Bond et al. (2005); montane areas in the Western USA: Vukomanovic and Steelman (2019);
boreal regions in North America: Robinne et al. (2019)], but the role of fire on ES has not yet been
addressed at the global scale. Integrative studies including a wide temporal scale, quantifying fire effects
on a range of ES, with a broad geographical scope (including the most relevant ecoregions and
ecosystem types worldwide) and accounting for specific characteristics of these events (e.g. wildfires
vs. prescribed fires) are still lacking, despite the fact that they are fundamental to gain basic
understanding on the environmental effects of this global phenomenon and improve our capacity to
forecast ecosystem change.
To address this gap, we carried out a quantitative global synthesis of published effects of fire
disturbances on multiple ES in forested and woody-dominated ecosystems. By doing so, we also
describe main trends and gaps of three decades of fire-ES research and identify the main needs for
future research. We hypothesize that (i) fire effects will vary in their impact across different ES and that
these impacts will be partially driven by the type of disturbance (e.g. greater impacts after wildfires than
after prescribed burns) or by the temporal framework after disturbance (stronger impacts on the short
term). As ecosystems from different geographical areas exhibit different natural fire regimes and
different levels of adaptation to fire (Archibald et al. 2013), we also hypothesize (ii) unequal effects on
different pyro-geographical and biogeographical areas (e.g. lower impacts in areas with higher natural
fire frequencies than in areas where natural fires are rare).
2. Material and methods
2.1. Studied ecosystems and data compilation
Information was extracted from the English-language, peer-reviewed literature reporting on field-based
studies published within the last three decades (January 1989- July 2020). No geographical restrictions
were applied. The search parameters used are given in WebTable 1. First, to select studies analysing
fire events, we included papers containing “fire” or other similar terms in the title (e.g. “wildfire”,
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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“burn”, etc.). Second, to focus the search on forest-type ecosystems, we included related terms in the
title, keywords or abstract, such as “forest”, “woodland” or “tree”. In addition, we included terms
describing ecosystems where trees and other continuous woody-vegetation (e.g., “shrublands” or
“scrublands”) are dominant, due to their relevance in some biomes (e.g., Mediterranean). As fire can
alter ecosystems properties at different temporal scales (Wardle et al. 2003), we separated effects into
short (1-2yr.), intermediate (2.5-10yr.) and long-term (>10yr.) after fire. As we were focusing on
persistent impacts, immediate effects (<1yr. post-fire) were not assessed. To cover as many ES as
possible, we used terms related to ES included in the most recent version of the Common International
Classification of Ecosystem Services (CICES) V5.1 (Haines-Young and Potschin 2018) as additional
search parameters to structure our search, covering a wide variety of provisioning, regulating and
cultural ES (WebTable 1).
By applying these search parameters, 2614 articles were identified. The titles and abstracts and, where
necessary, the research design, were examined to select those that fulfilled our criteria (e.g. studies
focused on effects >1yr. after fire). This resulted in a final selection of 207 studies (WebPanel 1), from
which all relevant data were extracted to a database. Information was grouped into 32 variables,
including 19 descriptive variables to characterise the context of the studies, seven explanatory variables
to test the different hypothesis, and six response variables quantifying fire effects on each ES
(WebTable 2). Our seven explanatory variables classified fire events in terms of four different aspects:
i) Time frame after disturbance [short (1-2yr.), intermediate (2.5-10yr.) and long (>10yr.)]; ii) Type of
event (prescribed burns vs. wildfires); iii) Biome as assigned to one of the 14 World Biomes defined by
Olson and Dinerstein (1998) (Fig.1), and then clustered into the following four biome groups: a) Boreal,
b) Temperate, c) Mediterranean, and d) Tropical (these four groups were used in all further statistical
analyses, except in the graphical representation of Fig.1 where the original 14 types are used); and four
variables for Fire regime: iv) regions with high-intensity vs. low-intensity fires, v) regions affected by
frequent fires vs. regions affected by rare fires, vi) ‘fire dependent‘ vs. ‘fire sensitive’, and vii) areas
where fire is considered a ‘natural force’ vs. ‘areas where it is not’. All variables extracted are described
in WebTable 2.
2.2. Data analysis
The data extracted from the 207 studies included eight indicators assessing ES according to the CICES
V5.1 classification. Two indicators for provisioning ES [i) food provision, ii) water provision]; five for
regulating and maintenance ES [iii) mitigation of soil erosion, iv) pollination, v) water quality
regulation; vi) soil fertility regulation, vii) climate regulation]; and the last indicator for cultural ES
[viii) recreation]. Each database entry represented the value of one ES indicator before and at a specific
time after a fire event. We then assigned a positive or negative sign to each of these values according
to the effect on the ES indicator. For example, we considered the increase of runoff water as positive
for water provision, although this increase can bring lower levels of other water fluxes (e.g.
evapotranspiration) and other negative impacts such as water contamination from eroded ash (Bodí et
al. 2014). The total number of entries including all ES was 2474, which were used as the basic units for
further analyses.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Figure 1. Geographical distribution of the entries compiled (A by countries and B by Biomes sensu Olson and
Dinerstein 1998). Terrestrial biomes included were: Boreal forests (Bor), Tundra (Tund), Montane grasslands and
shrublands (Mont), Flooded ecosystems (Flo), Temperate broadleaf and mixed forests (Tem BM), Temperate
conifer forests (Temp C), Temperate grasslands, savannas and shrublands (Temp G), Mediterranean forests,
woodlands and scrubs (Med), Desert and xeric shrublands (Des), Tropical and subtropical moist broadleaf forests
(Trop MF), Tropical and Subtropical Coniferous Forests (Trop CF), Tropical and Subtropical Dry Forests (Trop
DF) and Tropical and subtropical grasslands and savannas (Trop G). The other existing terrestrial biome
(Mangroves) was not covered in the literature examined here. Note for biomes with a wide distribution, most
entries may be located in a specific area (e.g. studies in the boreal forests are mostly located in North America,
not in Eurasia), therefore figures 1A and 1B complement each other.
For each ES, we conducted two analyses based on the comparison of the value of each ES indicator
before and after the fire and using the explanatory variables described above. Firstly, we developed a
semi-quantitative (frequency) analysis for each ES comparing the pre- and post-fire values. Secondly,
we fitted different General Linear Models (GLM) to each target ES. These models included Linear
Regressions (LR) and Mixed-effects Models (ME), the latter using one variable (the study) or two
variables (the study and the ES indicator, crossed) as random factors on the intercept. In all cases, our
dependent variable was the logarithm of the ratio between the ES indicator values after (post-) and
before (pre-) disturbance (log-ratio), which is a common measurement of effect size in meta-analyses
(Hedges et al. 1999). Because it is a unitless ratio, this metric allows combining entries derived from
different indicators evaluating the same ES, even if their units differ. These models could only be fitted
to the cases in which the target ES was assessed before and after the disturbance (76% of all entries).
This modelling approach was not performed for two ES, recreation and pollination, due to the low
number of entries available for them (only 58 and 29 from 5 and 3 different studies, respectively; see
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Fig.2B). The two pyrome variables were used to test the effects of fire regimes on the different ES. The
Akaike Information Criterion (AIC) was used to compare the fit and select among the different models
for each ES, including LR and ME with one or two random factors. We used R software V3.5.3 (R
Development Core Team 2019) for fitting, for assessing the significance of explanatory factors, and for
estimating marginal and conditional R2 values [packages: lme4 (Bates et al. 2014), lmerTest
(Kuznetsova et al. 2017) and MunMIn (Calcagno and de-Mazancourt 2010), respectively]. Significant
differences between levels of a given factor were tested using pairwise contrasts and estimated marginal
means, i.e. the mean response for each factor, adjusted for any other variables, using the emmeans
package (Lenth et al. 2019).
3. Results
3.1. Current status of global fire research on forest and woodland ecosystem services
A marked increase of published studies is evident over the last three decades in this research field
(WebFig.1). The studies included here originated from 27 countries (Fig.1A), with the highest number
of entries being from USA (29%), Spain (19%), Canada (12%) and Australia (12%). Most countries in
the Middle East, Central Europe, Central America and Africa did not present any entries. Regarding the
original classification of World Biomes, our database included entries from 13 of all 14 terrestrial
biomes. However, more than 85% of entries originated from four of them, mainly reflecting the northern
hemisphere (Fig.1B): Temperate conifer forests (29%), Temperate broadleaf and mixed forests (28%),
Mediterranean (20%) and Boreal forests (10%).
The studies covered a wide range of forests, woodlands and woody-dominant ecosystems with over 30
dominant plant genera represented (Fig.2A). Almost half of the entries were from ecosystems where
the genus Pinus was dominant, with the main species studied being P. ponderosa and P. contorta in
North America, P. sylvestris in Eurasia, P. halepensis in the Mediterranean Basin and P. pinaster in
Atlantic Europe (WebFig.2). Quercus species were frequent in studies in Mediterranean Europe, central
Europe and North America. Finally, Eucalyptus species also had many entries, mostly from studies in
their natural distribution area (Australia), but also in the Atlantic coast of SW Europe (mainly Portugal
and Spain).
Figure 2. Panel A shows frequencies of entries (and studies) by plant genus. Panel B shows frequencies of entries
(and studies) by ecosystem service, as described in Online Material (WebFig.3).
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Although most studies investigated wildfires (77%), an important proportion (23%) assessed prescribed
burns. Our database included events with a wide range of sizes (WebFig.1), from events smaller than 2
ha, to very large fires (>25,000 ha). Most studies did not provide quantitative/detailed information for
an accurate characterization of the disturbance regime (such as fire frequency, intensity or severity).
Regulating ES (particularly soil fertility, water quality, erosion control and climate regulation) were the
ones most frequently represented, whereas other ES (such as food or water provision, pollination or
cultural services) were rarely assessed (Fig.2B).
3.2. Impacts of fire on forest and woodland ecosystem services
3.2.1. Frequency analysis
Overall, the number of negative effects reported on seven of the eight ES studied was higher than the
neutral and positive ones (Fig.3 and WebFig.4). Negative impacts were particularly frequent (>50%)
for food provision, recreation, climate regulation and water quality. Water provision showed
predominantly positive effects derived from the increase of post-fire runoff, and other ES, such as soil
fertility and pollination, had 30-35% of entries showing positive effects. Regarding the type of event,
three ES (food provision, climate regulation and water quality regulation) showed higher frequencies
of negative impacts after wildfires than prescribed fires. However, other ES (e.g. soil fertility, erosion
control), showed similar or even higher frequencies of negative effects after prescribed burns.
Figure 3. Frequencies of positive, neutral and negative fire effects on the eight studied ES at overall level, and for
wildfires and prescribed burns. The number on the right of each bar is the total number of entries (n) for each ES
and category; ND: no data.
3.2.2. Model-based analysis
The fitted GLMs identified significant effects of different explanatory variables. WebTable 3 shows all
the 18 models fitted, WebTable 4 shows the results using the two alternative variables for fire regimes,
and Fig.4 shows estimated marginal means, i.e. the mean response for each factor, adjusted for any
other variables, for selected models. A substantial percentage of entries for erosion control (45%) and
water provision (41%) did not include a pre-disturbance reference value (i.e. No Data in Fig.3, these
ND entries were not included in the models). Water provision showed predominant positive effects of
fire events for almost all the factors, whereas water quality regulation showed the opposite pattern.
Wildfires had significantly negative effects on climate regulation, erosion control and water quality
(Fig.4). Prescribed fires also had a negative effect, albeit lower, on water quality. The magnitude of the
effects tended to decline for longer temporal scales, except for climate regulation. The positive effect
of fire on water provision was not significant in pyromes with low fire frequency. Similarly, negative
impacts of fire on climate regulation were not observed for these areas.
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Finally, regarding biomes, significantly positive effects (Fig.4) were found in Temperate and
Mediterranean systems on water provision (the only ones with data) and in Temperate ones also on soil
fertility. Significantly negative effects were found in Temperate and Boreal biomes on climate
regulation and in the Temperate one on erosion control.
Figure 4. Direction and size of effect of fire on ES based on estimated marginal means (emmeans) obtained from
the models for each level of the explanatory variables examined. For each ES studied, estimations show the effects
of each level of the explanatory variables (means and confidence intervals at 95%). Equal letters show non-
significant differences among factor levels; different letters for each variable show significant differences between
ES among levels (a and b for fire type; c and d for temporal scale; e and f for pyromes by intensity, g and h for
pyromes by frequency; i, j and k for biomes). Note that two of the eight studied ES (food provision and recreation)
were not included in the model analysis due to the low number of data entries available.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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4. Discussion
This study provides, to our knowledge, the first global synthesis of reported fire effects on a wide range
of ES by analysing the information extracted from 207 studies published over the last 30 years. It is
clear that the distribution of the obtained information is biased in terms of location and type of
ecosystems analysed. Most studies focus on Northern America, South-western Europe and Australia
(Fig.1A). Biomes located in temperate and Mediterranean areas are overrepresented compared to global
patterns of area burnt (Andela et al. 2017). This is partially the result of our study focussing on
ecosystems dominated by tree and woody species, excluding other fire-prone systems such as savannas
(Zimmermann et al. 2008). Notwithstanding this, the distribution of the data also reveals a scarcity of
published research (at least in English language) in other relevant ecosystems such as tropical and
subtropical forests in Africa, South America or South-eastern Asia (Fig.1B), despite fire being an
important disturbance in these regions (Krawchuk et al. 2009). Our results also show an unbalanced
distribution of the eight ES assessed. More than 90% of the entries of our database were focused on ES
related to soils and the hydrological cycle (e.g. soil fertility, erosion control or water quality). This likely
reflects the growing interest of the scientific community on the interrelationships among fire, water and
soil (e.g. Shakesby et al. 2011; Caon et al. 2014). In contrast, other ES are understudied, especially the
cultural type, which is congruent with their low representation in environmental assessments (Satz et
al. 2013). Similarly, other important aspects, such as fire effects on pollination or water provision, were
examined only in a few studies (Bladon et al. 2014; Carbone et al. 2019).
Of the recorded effects of fires on ES, 28.5% were positive but, overall, the analysis shows a
predominance of negative effects (46.6%). Negative effects were also dominant (>50%) in an ES
analysis for Colorado montane ecosystems by Vukomanovic and Steelman (2019). It is worth
considering, however, that negative environmental impacts of fire have been studied for decades by
researchers and acknowledged by managers (e.g. DeBano and Conrad 1978; Hauer and Spencer 1998),
and that fire is mainly perceived by society as a major environmental and socio-economic hazard (Doerr
and Santin 2016). Therefore, it is conceivable that most studies to date have been designed for detecting
and highlighting negative fire effects rather than focusing on the positive roles of fire in, for example,
ecological functioning (Boisrame et al. 2017; Jones et al. 2019). Furthermore, there may be a
publication bias towards studies that show substantial, rather than limited or no effects (Csada et al.
1996).
Despite the dominance of negative effects, the percentage of positive effects we found is substantial
(WebFig.4). Previous literature shows a range of positive effects, including those on water yield (but
not quality) or food provision, for example, increasing runoff (Shakesby and Doerr 2006), grazing
resources or stimulating germination (Pausas and Keeley, 2019), or positive effects on pollinators
(Carbone et al. 2019). In addition, it is important to understand that indicators considered here as
reflecting a negative impact can also have secondary positive effects on other different ES. For instance,
soil erosion has been classified as negative; however, erosion is a natural process leading to the
redistribution of material. It can interact with other ecological processes at landscape level and affects
other ES in positive ways, such as the burial of soil carbon, a positive ES from a climate regulation
perspective (Van Oost et al. 2007). Therefore, positive effects of fire on ES and the identification of ES
directly derived from fire occurrence (e.g. reducing the risk of more severe future wildfires, maintaining
open spaces for grazing, etc.; Pausas and Keeley 2019) may currently be partially masked by the type
and availability of studies conducted to date. Due to the aforementioned potential bias and data scarcity
for some ES, types of fire events, or pyromes/biomes, the predominantly overall negative effect of fire
on ES reported here needs to be interpreted with caution.
Fitted models showed that effects of wildfires were overall negative for erosion control, climate
regulation and water quality, and positive for water provision (see Fig.4). Regarding the type of event,
prescribed burns only had significantly negative effects on water quality (and this excludes their role in
reducing future fire risk and their potential impacts on ES). Prescribed fire is a widely applied land
management tool for, among other uses, fuel reduction and ecosystem rejuvenation (Fernandes et al.
2013; van Wilgen 2013). Prescribed fires are generally of lower intensity and of smaller size than
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wildfires (Fernandes and Botelho 2003); hence, lower impacts on ES are generally expected (Harper et
al. 2018). However, previous reviews did not find substantial differences between these two fire types
for ES such as erosion control (Shakesby et al. 2015) or pollination (Carbone et al. 2019), and the role
of prescribed burns for multi-purpose management at multiple scales and temporal frameworks remains
under discussion (e.g. Davies et al. 2016). Our database shows a lower number of studies assessing
prescribed burns than wildfire and further research comparing multiple impacts on ES from prescribed-
vs. wildfire events is clearly warranted.
Regarding our first hypothesis, that fire effects will vary in their impact across different ES in space
and time, we found a remarkable relevance of timescale on effects on erosion control and water quality
(Fig.4). These ES showed more negative effects in the short term than for longer periods after fire,
likely linked to the temporal patterns of post-fire vegetation recovery and the associated duration of the
window of disturbance (Shakesby and Doerr 2006; Wittenberg et al. 2007). Fire had a significant
(negative) impact on climate regulation in long term studies, which can be partially related to the relative
decline of the rate of ecosystems carbon accumulation post-fire (Volkova et al. 2018). In contrast, the
effects on food provision or soil fertility did not differ much over time. In general, however, the number
of entries is insufficient for most ES to draw robust conclusions for longer-term impacts (>10yr.).
Our results do not support our second hypothesis of finding differences among pyromes/biomes based
on expected adaptation of their ecosystems to specific fire regimes (Archibald et al. 2013). Fitted
models showed that for climate regulation, fires in regions affected by infrequent fires regimes show
fewer negative impacts than fires in regions with more frequent fires. However, we did not find lower
levels of impact in regions with high-intensity fire regimes when compared with low-intensity regimes
(WebFig.4 and Fig.4), where according to our second hypothesis ecosystems should be more adapted
to fire (Keeley et al. 2011). Similarly, comparisons among biomes revealed very few significant
differences (e.g. on water provision). It should be noted, however, that only a selection of frequently
studied effects of fire are covered here. In addition, interactions among explanatory variables can be
potentially important, but we could not explore them because several combinations of factor levels were
not represented (or had very low sample size) in our database. There are also examples of relevant
ecological processes not included in our search, because we focused on the most common categories of
ES indicators. For example, we did not compile information about snowpack dynamics in the case of
water provision (Robinne et al. 2019) or pyrogenic carbon in the case of climate regulation (Santin et
al. 2016). Finally, the information available in the compiled studies did not allow to explore in detail
the relationships between the individual fire characteristics (e.g. fire intensity, severity, etc.), or current
alterations in their natural/historical regimes, and their specific effects on studied ES. The resulting
main knowledge gaps and future research directions identified in this global synthesis are described in
Panel 1.
Panel 1. Main gaps and suggested future research directions
i. Assessment of fire effects in tropical and subtropical biomes.
ii. Assessment of fire effects on provisioning and cultural services.
iii. Integrated analyses including quantitative information on fire behaviour and characteristics,
such as fire intensity (i.e. energy released), burn severity (i.e. organic matter/biomass
destruction) and fire return interval.
iv. Monitoring long term (>10 yr.) effects of fire.
v. Comprehensive comparison of the effects of prescribed burns and wildfires for a wider range
of ES.
vi. Comprehensive analysis of the role of fire as ES provider, as well its integration in
management strategies to maximize specific services.
vii. Explicit assessment of the impact of future fire regimes, particularly those falling outside
their natural/historical range of variability for different biogeographical/ecological regions.
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5. Conclusions and implications for forest and land management
Fire plays a key role in the world’s forest and woodlands ecosystems, affecting fundamental aspects of
their ecological functioning. Using a multi-ES approach, we analysed and evaluated fire effects on these
ecosystems emerging from the last 30 years of research. The resulting global synthesis demonstrates
that the interest of the scientific community has been focused largely on regulating-type services, and
predominantly those related to the soil component (e.g. soil fertility, erosion mitigation) and water and
carbon cycles, with a strong geographical bias towards some countries (e.g. USA, Spain) and biomes
(such as Temperate forests).
Fire is often perceived by society as a natural hazard with predominantly negative effects (Doerr and
Santin 2016). However, our results showed that only some of the effects on ES were statistically
significant. Also, effects of prescribed burns on ES were generally less negative compared to wildfires.
These findings, however, need to be viewed with caution as most studies may have been focused
specifically on detecting negative impacts. Indeed, most studies focus on shorter-term impacts and are
rarely designed to detect positive effects of fire on ES, although it is well established that fire is essential
in maintaining fire-adapted ecosystems (Pausas and Keeley 2019).
To provide a more complete picture of fire effects on ES and to enable well-informed ecosystem
management decisions, more research from underrepresented regions and biomes is needed, as well as
a stronger focus on identifying and assessing potential positive or neutral fire effects on ES. These
limitations, and the wide geographical scope of our analysis, imply that general outcomes reported here
can provide useful guidance at regional or biome scales, but they may not always be applicable at
specific local scales. The recommendations given for policy makers and forest managers in Panel 2
should, therefore, be viewed largely in a regional or biome context to inform, for instance, coarse-scale
fire management strategies.
Panel 2. General recommendations for policy makers and forest managers
i. Integrated multi-ES approaches are recommended for developing land management and
wildfire mitigation strategies at different (from local to supra-national) spatial scales. Those
ES that are not often assessed (such as the cultural type) should be taken into account before
defining policies, incorporating also ES that were not included on this synthesis (such as the
role of fire mitigating more severe future wildfires).
ii. This global assessment supports the use of prescribed burns as a management tool given its
limited negative effects compared with wildfires for ES such as erosion control or water
quality regulation.
iii. Given the identified lack of knowledge on fire effects on ES for some specific pyromes and
biomes (i.e. tropical and subtropical), caution is advised when extrapolating findings from
other regions to understudied areas.
iv. Most significant effects identified here are not long-lasting, being attenuated a few years
(>10yr.) after the fire disturbance.
Acknowledgments and funding
JVR-D is supported by the Government of Asturias and the FP7-Marie Curie-COFUND program of the
European Commission (Grant ‘Clarín’ ACA17-02). CS is supported by a Sêr Cymru Fellowship co-
funded by European Union's Horizon 2020 research and innovation programme (Marie Skłodowska-
Curie grant agreement No 663830) and SD through insights gained from EU-H2020 COST Action
FIRELinks (CA18135). We thank the four anonymous reviewers and an associate editor for their
feedback, which greatly helped improving the quality of the manuscript.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
11
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Supplementary material
Web Figure 1.
Web Figure 1. Number of entries (A) and studies of fire impacts (B) per year from 1989 to 2020. Frequencies of entries of
fire impacts according to their extension (C), and temporal scopes studied (D). Please note that some most studies included
different temporal measurements and in consequence, the total number of studies of panel D is larger than 207.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Web Figure 2.
Web Figure 2. Frequencies of entries and studies (in parenthesis) of fire impacts by ecosystem services compiled
in the database, and the different types of indicators used for each ES.
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Web Figure 3.
Web Figure 3. Frequencies of entries and studies (in parenthesis) of fire impacts by ecosystem services compiled
in the database, and the different types of indicators used for each ES.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Web Figure 3.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
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Web Figure 3. Frequencies of positive, neutral and negative fire effects on the eight studied ES. Temporal categories: short-term (1-2 years after fire); intermediate-term (2.5-10
years after fire) and long-term (more than 10 years after fire). Pyrome variables (sensu Archibald et al. 2013) were classified by i) intensity: High intensity vs Low intensity; and
ii) frequency: Frequent vs. Rare. The number on the right of each bar is the total number of entries (n) for each ES and category; ND: no data. See Web Table 1 for further details.
Soil fertility and climate regulation had higher frequencies of positive entries 1-2 years after fire than after a longer period. Regarding differences between fire regimes,
disturbances in regions with intense fires showed higher frequency of positive effects for soil fertility than disturbances from areas with lower intensities. In addition, areas with
more frequent wildfires showed higher frequencies of negative impacts for ES such as food provision or water quality. Moreover, ecosystems in temperate zones showed lower
frequencies of negative impacts for ES such as food provision, climate regulation or water quality than ecosystems in other biomes. In the Mediterranean biome, ES such as
pollination, water quality and food provision showed higher frequencies of negative impacts than in temperate biomes. The effect of some of the explanatory variables is difficult
to compare for some ES due to few or absent entries (e.g. tropical or boreal biome groups).
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Web Table 1.
Table 1. Description of the search parameters used for the literature search using the Web of Science (© Clarivate
Analytics) webpage.
Parameters1
Values
Publication time range
January 1989- July 2020
Language
English
Type of document
Article OR Book OR Book Chapter OR Data Paper OR Database
Review OR Discussion OR Letter OR Review
Title
“wildfire*” OR “fire*” OR “*fire*” OR “burn*” OR “bushfire*” OR
“prescrib*"
Search parameters 1.
Topic (title OR keywords OR
abstract)
“forest*” OR “woodland*” OR “tree*” OR “shrubland*” OR “shrub*”
OR “scrubland*” OR “scrub*” OR “heathland*” OR “heath*”
Search parameters 2.
Topic (title OR keywords OR
abstract)
“temporal” OR “time” OR “year*” OR “mid* term” OR “mid-term”
OR “medium-term” OR “long* term” OR “long-term”
Search parameters 3.
Topic (title OR keywords OR
abstract)
"ecosystem service*" OR "ecologic* process*" OR "ecologic*
function*" OR "provision*" OR "regulat*" OR "cultural"
OR "food" OR "mushroom*" OR "fruit*" OR "berry" OR "berries"
OR "fresh water" OR "water supply" OR "drink* water"
OR "climat* regulat*" OR "carbon sequest*" OR "carbon stock*"
OR "carbon stor*"
OR "soil fertilit*" OR "soil nutri*" OR "nutri* cycle*" OR "soil
carbon*" OR "organic carbon"
OR "water regulat*" OR "soil water" OR "water cycle" OR "water
stor*" OR "water qualit*" OR "water depurat*" OR "water filtrat*"
OR "water clean*"
OR "soil erosion" OR "soil protection" OR "erosion protection" OR
"erosion control" OR "soil loss" OR "water erosion"
OR "landscape qualit*" OR "aesthetic*" OR "landscape value*" OR
"recreation*" OR "social percept*"
1: The search parameters were discussed and selected by all authors (this multidisciplinary team of researchers included a
landscape ecologist and expert in forest ES assessments, an environmental scientist and expert in fire impacts on water and
carbon cycles, a forest ecologist and expert in ecosystem functioning and a physical geographer and expert in multiple types of
fire impacts). Tasks such as the compilation and revision of studies, and extraction of data, were always done by the same
researcher (JVR-D).
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
28
Web Table 2.
Web Table 2. Description of the variables included in the database. Variables in italic are those used for estimating the response of the indicators to the fire events. Underlined
variables are those used as explanatory variables for statistical analysis. Our seven explanatory variables classified fire events in terms of four different aspects: i) Time frame
after disturbance [short (1-2yr.), intermediate (2.5-10yr.) and long (>10yr.)]; ii) Type of event (prescribed burns vs. wildfires); iii) Biome as assigned to one of the 14 World Biomes
defined by Olson and Dinerstein (1998) (see Fig.1), and then clustered into the following four biome groups: a) Boreal, b) Temperate (temperate forests, grasslands, savannas
and shrublands), c) Mediterranean, and d) Tropical (tropical and subtropical forests, grasslands and savannas). These four groups were used in all further statistical analyses,
except in the graphical representation of Fig.1 where the original 14 types are used; and iv) Fire regime. We included four variables assessing fire regime. Two were based on
the ‘pyromes’ approach widely-used in fire ecology and defined as combinations of fire characteristics reflecting fundamental types of world fire regimes (Archibald et al. 2013;
Rogers et al. 2020). The first ‘pyrome variable’ distinguished between regions with high-intensity vs. low-intensity fires (‘pyrome by intensity’), the second differentiated regions
affected by frequent fires vs. regions affected by rare fires (‘pyrome by frequency’). The third and fourth variables are less widely used alternatives and therefore only applied in
the model-based analysis (see section 2.2). They were respectively ‘fire dependent‘ vs. ‘fire sensitive’ areas, following the classification from Shlisky et al. (2007); and areas
where fire is considered a ‘natural force’ vs. ‘areas where it is not’, based on ecoregions geographical classification (Dinerstein et al. 2017).
Number
Variable
Description
Values
1
ID
Record number
Number
2
Study code
Code
Text + Number (e.g. FES_001). Used as Random factor for Mixed Models.
3
Authors
Authors description
First and Second author; or First author et al. (e.g. Smith and Garcia; or Smith et al)
4
Year
Year of publication
Year
5
Main species
Main tree species analysed
Species name of the dominant/most characteristic species of the studied ecosystem
6
Type of species
Type of main species
Native or exotic
7
Main Land use
Main type of land use
Forest, grazing, agricultural or others
8
Use level
Intensity of use
Harvested or farmed, conservation (protected area), abandoned or others
9
Country
Country
Country name
10
Disturbance size
Area burnt
Area (ha) (if available)
11
Frequency
Numerical characterization
of the frequency
Nº fires/nº years (if available)
12
Fire intensity
Intensity
Sensu Keeley (2009). Int J Wild Fire 18: 116-26. Energy output from fire. Strictly speaking, it is the time-averaged energy flux in W m−2, but, more broadly, it
can be measured as fire-line intensity, temperature, residence time, radiant energy and other.
This variable was not used finally on our analysis regarding the high proportion of studies without quantitative information about fire intensity.
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
29
Number
Variable
Description
Values
13
Burn severity
Burn severity
Sensu Keeley (2009). Int J Wild Fire 18: 116-26. Loss (or decomposition) of organic matter (both above and belowground) or biomass after fire. Burn severity
in vegetation and/or soil: Scorched, Light, Moderate or Deep burning.
This variable was not used finally on our analysis regarding the high proportion of studies without quantitative information about fire severity.
14
Biome
Terrestrial biome
14 types of Terrestrial Biomes, sensu Olson and Dinerstein (1998). Conservation Biol 12:502-15.
15
Group of Biome
Biome
Reclassified of terrestrial biomes into four main groups (temperate, tropical, mediterranean and boreal) for statistical analysis conducted.
16
Type of
disturbance
General type of fire
Wildfire vs. prescribed burn
17
Specific type of
fire
Specific type of fire
Crown, surface or ground.
This variable was not used finally on our analysis regarding the high proportion of studies without quantitative information about fire severity.
18
Pyrome type
Pyrome
According with Archibald et al. (2013). PNAS 110:6442-47. FIL (Frequent-Intense-Large), RIL (Rare-Intense-Large), ICS (Intermediate-Cool-Small), RCS
(Rare-Cool-Small) or FCS (Frequent-Cool-Small).
19
Pyrome by
intensity
Pyrome by intensity
We reclassified the pryomes in: High intensity-Large [included FIL (Frequent-Intense-Large) and RIL (Rare-Intense-Large)] vs. Low intensity-Small [included
ICS (Intermediate-Cool-Small), RCS (Rare-Cool-Small) or FCS (Frequent-Cool-Small)]. It should be noted that Archibald et al. (2013) classified the fire
intensity of these five pyrome types by using a specific and quantitive metric: MODIS derived Fire Radiative Power (FRP).
20
Pyrome by
frequency
Pyrome by frequency
We reclassified the pryomes in: Frequent (Frequent-Intense-Large, Frequent-Cool-Small and Intermediate-Cool-Small) vs. Rare (Rare-Cool-Small and Rare-
Intense-Large). It should be noted that in Archibald et al. (2013), the Intermediate-Cool-Small type showed a Fire Return Interval similar to the Frequent-
Intense-Large, Frequent-Cool-Small types.
21
Ecoregion
Ecoregion
According with Dinerstein et al. (2017). BioScience 67: 534-45 https://ecoregions2017.appspot.com/
22
Fire as a natural
force
Force
Expert classification based on the ecoregions information, and also using information from each study, we classified that study areas in those where fire is
often a natural driver (sensu fire prone zones) vs. those areas where fire rarely occurs by natural causes.
23
Fire dependency
Fire dependency
Expert classification based on the extended and widely used classification from The Natural Conservance (Shliky et al. 2007), we reclassified each study case
on Fire dependent vs. Fire sensitive. Note that there is a third category: fire independent, but it was not assigned to any study case.
24
Other
disturbances
Presence of other
disturbances
Other disturbances with potential substantial effects on the ecosystems (logging, grazing, etc.)
25
ES analysed
ES analysed
ES name according to used categories (i.e. CICES: https://cices.eu/)
26
ES indicator
Name of the indicator used
Indicator name. A complete list of indicators is in Web Figure 3. Used as Random factor for Mixed Models.
27
Indicator
description
Indicator description
Indicator description
28
Indicator units
Measurement units
Indicator units used in the study
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
30
Number
Variable
Description
Values
29
Indicator value
pre-disturbance
Value pre-disturbance (or
control)
Value of the indicator pre-disturbance (or control)
30
Indicator value
post-disturbance
Value post-disturbance
Value of the indicator post-disturbance
31
Time
Time between
measurements
Time between indicator measurements pre and post disturbance
32
Temporal range
Temporal range analysed
Based in previous variable: Short term (1-2 years), intermediate term (2.5-10 years) or long term (>10 years)
References
Archibald S, Caroline ER, Lehmann JL, et al. 2013. Defining pyromes and global syndromes of fire regimes. PNAS 110: 6442-47.
Bates D, Mächler M, Bolker B, et al. 2014. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823
Calcagno V and de Mazancourt C. 2010. glmulti: an R package for easy automated model selection with (generalized) linear models. J Stat Softwr, 34: 1-29.
Dinerstein E, Olson D, Joshi A, et al. 2017. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67: 534-45.
Keeley JE. 2009. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildland Fire 18: 116-26.
Kuznetsova A, Brockhoff PB and Christensen RHB. 2017. lmerTest package: tests in linear mixed effects models. J Stat Softwr 82: 13.
Olson DM and Dinerstein E. 1998. The Global 200: A representation approach to conserving the Earth’s most biologically valuable ecoregions. Conservation Biol 12: 502-1
Rogers BM, Balch JK, Goetz SJ, et al. 2020. Focus on changing fire regimes: interactions with climate, ecosystems, and society. Environ Res Lett 15: 030201
Russell VL. 2016. Least-Squares Means: The R Package lsmeans. J Stat Softwr 69: 1-33.
Shlisky A, Waugh J, Gonzalez P, et al. 2007. Fire, ecosystems and people: threats and strategies for global biodiversity conservation. Arlington: The Nature Conservancy.
https://www.conservationgateway.org/Files/Documents/fire_ecosystems_and_people.pdf
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
31
Web Table 3.
Web Table 2. Predicted effects for significant factors for each ecosystem service (ES) and for each of the three model types that were fitted: Lineal Regression (LR) and Mixed-Effect Models (ME)
with one (study) or two (Study & Indicator) random factors. The marginal R squared (R2m); conditional R squared (R2c) and AIC of each model is also shown. The intercept of each GLM corresponded
to the effect estimated for the reference factor level selected for each variable: ‘Type = Wildfire, Temporal scope = Short, Pyrome by intensity = High Intensity, Pyrome by frequency = Frequent, Biome
= Temperate’. The effects of the other factor levels were estimated separately and always corresponded to the differential effect relative to these reference factor levels. Remaining columns show the
effects estimated for the other levels of each factor included in the models (relative to the reference conditions), together with their standard error and statistical significance (****: p<0.001; ***p<0.01;
**p<0.05; *p<0.1).
ES
Model
(* means
model
selected)
R2
AIC
Intercept
(Type = Wildfire, Temporal frame = Short, Pyrome
(by intensity) = High Int.-Large, Pyrome (by
frequency) = Frequent, Biome = Temperate
Type
Temporal frame
Pyrome (by
int.)
Pyrome (by
freq.)
Biome
R2m
R2c
Prescribed
Intermediate
Long
Low Int.-
small
Rare
Boreal
Medit.
Trop.
Food
prov.
(n=138)
LR
0.14
-
271.51
-0.67±0.55
0.97±0.30
***
-0.38±0.27
-0.30±0.72
0.01±0.37
0.38±0.50
0.84±0.56
0.33±0.53
0.78±0.53
ME (Study)
0.11
0.80
232.46
-1.32±1.34
0.91±0.90
0.45±0.26
*
-0.07±1.88
-0.13±1.02
0.48±1.17
0.69±1.51
0.79±1.33
1.51±1.25
ME (Study &
Ind)
0.12
0.81
234.29
-1.43±1.29
0.72±0.88
0.47±0.26
0.39±1.84
-0.02±0.95
0.55±1.13
0.90±1.46
0.56±1.30
1.73±1.24
Water
prov.
(n=58)
LR
0.51
-
64.34
0.59±0.27
**
-0.38±0.15
**
-0.12±0.11
-
0.24±0.18
-0.46±0.22
**
-
0.39±0.14
***
-
ME (Study)
0.48
0.77
68.87
0.31±0.48
-0.34±0.23
-0.02±0.10
-
0.39±0.34
-0.22±0.39
-
0.47±0.24
*
-
ME (Study &
Ind)
-
-
-
-
-
-
-
-
-
-
-
-
Climate
reg.
(n=283)
LR
0.09
-
657.18
-0.49 ±0.20
**
0.25±0.13
**
-0.18±0.12
-0.20±0.15
*
0.28±0.12
*
0.17±0.15
-0.45±0.29
0.18±0.21
0.28±0.22
ME (Study)
0.06
0.17
667.12
-0.37±0.25
0.24±0.16
-0.13±0.13
-0.12±0.16
0.17±0.15
0.07±0.19
*
-0.56±0.40
0.17±0.24
0.16±0.28
ME (Study &
Ind)
0.05
0.22
663.48
-0.33±0.27
0.26±0.15
*
-0.15±0.13
-0.12±0.16
0.18±0.15
0.07±0.18
-0.40±0.39
0.12±0.23
0.14±0.27
Eros.
Control
(n=149)
LR
0.17
-
430.10
-1.99±0.38
****
0.91±0.35
***
0.40±0.19
**
-
0.56±0.25
**
0.93±0.34
**
-
-0.23±0.34
-
ME (Study)
0.14
0.52
404.38
-1.70±0.69
**
0.86 ±0.55
0.61±0.17
****
-
0.03±0.05
0.75±0.61
-
0.01±0.56
-
ME (Study &
Ind)
0.14
0.52
406.38
-1.70±0.69
**
0.86 ±0.55
0.61±0.17
****
-
0.03±0.49
0.75±0.61
-
0.01±0.56
-
Soil
fert.
(n=922)
LR
0.11
-
1057.10
0.28±0.05
****
-0.17±0.04
****
-0.21±0.04
****
-0.29±0.05
****
-0.06±0.04
0.06±0.04
-0.28±0.16
*
-0.06±0.05
-0.11±0.07
ME (Study)
0.04
0.65
602.81
0.10±0.15
-0.08±0.11
0.01±0.05
-0.05±0.06
-0.06±0.12
0.11±0.12
-0.33±0.31
-0.29±0.17
-0.18±0.18
ME (Study &
Ind)
0.05
0.65
596.16
0.12±0.15
-0.07±0.11
0.01±0.05
-0.05±0.05
-0.07±0.12
0.11±0.12
-0.31±0.31
-0.04±0.12
-0.19±0.18
Water
qual.
(n=331)
LR
0.15
-
321.08
-0.33±0.15
**
0.04±0.15
0.10±0.05
**
0.14±0.16
**
-0.02±0.05
0.06±0.15
-0.03±0.06
-0.66±0.12
****
-
ME (Study)
0.11
0.44
275.17
-0.37±0.26
0.03±0.24
0.10±0.05
*
0.18±0.14
0.07±0.13
0.07±0.27
-0.01±0.16
-0.51±0.22
**
-
ME (Study &
Ind)
0.08
0.55
246.61
-0.37±0.25
0.06±0.22
0.11±0.05
**
0.16±0.13
0.03±0.12
0.13±0.22
-0.09±0.15
-0.49±0.21
**
-
Frontiers in Ecology and the Environment (in press). DOI: 10.1002/fee.2349
32
Web Table 4.
Web Table 3. Predicted effects for significant factors using the two alternative variables to characterise fire regime areas. For each ESa we show: the best-fitting model (selected by AIC) among
Linear Regression (LR) and Mixed-Effect Models (ME) with one (study) or two (study & indicator) random factors; marginal R squared (R2m); conditional R squared (R2c). The intercepts of
each model estimate fire effects (log-ratio) and its standard error and statistical significance when all factors are set at their reference level (reference conditionsb). Remaining columns show the
effects estimated for the other levels of each factor included in the models (relative to the reference conditions), together with their standard error and statistical significancec. Results using these
two alternative variables were very similar to those using the pyrome variables (see Web Table 2).
ES
Model
selected
R2
Intercept
Type = Wildfire, Temporal
frame = Short, Fire
dependence = Yes, Fire as
natural force = Yes, Biome =
Temperate
Type
Temporal frame
Fire
dep.
Fire
natural
force
Biome
R2m
R2c
Prescribed
Intermediate
Long
No
No
Boreal
Med.
Trop.
Food
prov.
ME (Study)
0.15
0.54
-0.60±
0.48
0.82±
0.58
-0.03±
0.26
0.08±
0.43
1.01±
0.79
-0.63±
0.73
-0.05±
0.66
0.53±
0.68
0.84±
0.75
Water
prov.
LR
0.39
-
0.02±
0.18
-0.29±
0.16
*
-0.18±
0.13
-
-0.33±
0.24
0.63±
0.23
***
-
0.91±
0.20
****
-
Climate
reg.
LR
0.07
-
-0.06±
0.38
0.30±
0.13
**
-0.24±
0.14
*
-0.26±
0.16*
0.21±
0.16
-0.22±
0.40
-0.53±
0.30*
0.25±
0.21
0.17±
0.24
Eros.
Control
ME (Study)
0.19
0.54
-0.84±
0.26
***
0.53±
0.46
0.59±
0.17
****
-
1.07±
0.83
0.70±
0.48
-
-0.67±
0.38
*
-
Soil
ferti.
ME (Study &
Ind)
0.07
0.66
-0.10±
0.28
-0.05±
0.10
0.02±
0.05
-0.04±
0.06
-0.16±
0.13
0.44±
0.29
-0.08±
0.24
-0.01±
0.11
-0.23±
0.18
Water
qual.
ME (Study &
Ind)
0.09
0.55
-0.18±
0.23
0.26±
0.28
0.11±
0.05
**
0.15±
0.13
-0.43±
0.49
-0.04±
0.52
-0.13±
0.18
-0.49±
0.20
**
-
a: Recreation and pollination ES were not included on these analyses due to the low number of entries available for them.
b: Reference conditions correspond to: Type = Wildfire, Temporal frame = Short, Fire dependence = Yes, Fire as natural force = Yes, Biome = Temperate.
c: Statistical significance (both negative and positive): ****: p<0.001; ***p<0.01; **p<0.05; *p<0.1
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Forest fires affect water quality in disrupted watersheds, which can impact aquatic ecosystems including sensitive macroinvertebrates and fish. The West Fork Complex (WFC) fire consumed 44,360 ha of forest in the state of Colorado during the summer of 2013. Damage to the soils was of moderate to high severity in the majority (60%) of the area. The fire surrounded the headwaters of the Rio Grande, affecting water quality and habitat critical to aquatic insects and fish. The current research investigates whether there was a measurable effect on the water quality, insect diversity and fish populations within and downstream of the burn area. Parameters important to the survival of aquatic life, such as discharge, temperature, dissolved oxygen, pH, conductivity, total dissolved solids, total suspended solids, and concentrations of metals and nutrients were measured regularly in the Rio Grande and some of its tributaries for three years after the fire. Macroinvertebrate and fish populations were sampled annually. Precipitation, flow and turbidity data revealed monsoon rain events delivered sediments into the Rio Grande and its tributaries from steep, severely burned hillslopes. The monsoon events caused acute and dramatic fish kills, where hundreds of trout were reported killed in one tributary in a single day event. Turbidity was observed as high as 505 NTU in the impacted stream during the fish kill event and turbidity regularly reached 3000 NTU during subsequent precipitation events. Despite elevated turbidity levels that persisted for three years downstream of severely burned areas, the aquatic ecosystem appears to have recovered. Insect diversity and fish populations recovered to pre-fire levels and were similar to control sites within three years. Results indicate aquatic ecosystems can be resilient to largescale disturbances, such as wildfire.
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Forest recovery may be influenced by several factors, of which fire is the most critical. However, the moderate- and long-term effects of fire on forest recovery are less researched in Northwest China. Thus, the effects of different forest recovery time after fire (1917 (served as the control), 1974, 1983 and 1995) and fire severities (low, moderate and high) on larch (Larix sibirica Ledeb.) forest were investigated in the Kanas National Nature Reserve (KNNR), Northwest China in 2017. This paper analyzed post-fire changes in stand density, total basal area (TBA), litter mass, soil organic carbon (SOC) and soil nutrients (total nitrogen, total phosphorus and total potassium) with one-way analyses of variance. Results indicate that litter mass, TBA, SOC and soil nutrients increased with increasing recovery time after fire and decreasing fire severity, while the stand density showed an opposite response. The effects of fire disturbance on SOC and soil nutrients decreased with increasing soil depth. Moreover, we found that more than 43 a is needed to recover the litter mass, TBA, SOC and soil nutrients to the pre-fire level. In conclusion, high-severity fire caused the greatest variations in stand structure and soil of larch forest, and low-severity fire was more advantageous for post-fire forest stand structure and soil recovery in the KNNR. Therefore, low-severity fire can be an efficient management mean through reducing the accumulation of forest floor fuel of post-fire forests in the KNNR, Northwest China.
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Fire is an important disturbance agent in Chinese boreal forests but the long-term effects of wildfires on soil nitrogen (N)net mineralisation rates (R min )in natural versus human-assisted restorations are not well understood. In this study, we analysed upper (0–10 cm)and lower layer (10–20 cm)soil samples from natural restoration and afforestation plots in a Dahurian larch (Larix gmelinii)forest in north-eastern China 29 years after a mega fire disturbance. Our results showed that the soil inorganic N (NH 4⁺ -N and NO 3⁻ -N)pool of the upper and lower layers of the regenerated plots remained significantly lower than in unburned control plots. This suggests that the effects of a high burn severity fire on soil N availability were still significant almost 30 years after the event. Restoration type (natural restoration versus afforestation)also had significant effects on upper layer soil N availability; compared with afforestation, natural restoration was more beneficial for the accumulation of soil inorganic N and the recovery of R min after fire disturbance. Specifically, the concentration of inorganic N and the mean R min in upper layer soils in the natural restoration plots were approximately 41% greater and 3.6 times greater, respectively, than in the afforestation plots. The differences in soil N availability between the two restoration types were attributed to differences in soil water content (SWC), soil microbial biomass nitrogen (MBN), and the recovery of vegetation after the fire disturbance. Our study demonstrates that natural restoration has proved more successful than afforestation in countering soil N losses in boreal forests in China resulting from a high burn severity fire disturbance.