Abstract
Disturbances such as wildfires are an integral part of forest ecosystems, but climate change is increasing their extent, frequency, intensity and severity, compromising forest ecosystem services (ES) that are fundamental to human well-being. Thus, evaluating the risk of losing ecosystem services due to wildfires is essential for anticipating and adapting to future conditions. Here, we analyze the spatial patterns of the risk of losing key forest ES and biodiversity (that is, carbon sink, bird richness, hydrological control and erosion control) due to wildfires in Catalonia (NE Spain), taking into account exposed values, hazard magnitude, susceptibility and lack of adaptive capacity. We also determine the effect of climate and different forest functional types on the risk of losing ES under average and extreme hazard conditions (defined as median and 90th percentile values of the Fire Weather Index, respectively), as well as on the increase in risk. Our results show that hazard magnitude is the most important component defining risk under average conditions. Under extreme conditions, exposed values (carbon sink capacity and erosion control) emerged as the most important components of risk. Climate was the main driver of ES at risk under average conditions, but the high vulnerability of non-Mediterranean conifer forests with a low adaptive capacity gained importance under extreme conditions. The increase in risk between average and extreme conditions was driven by precipitation, as the highest increases in risk were found in relatively wet forests with low average risk at present. These results have direct implications on the future risk of losing ES to wildfires in Mediterranean forests but also in other regions, and they could contribute to future policies by anticipating conditions associated with particularly high risk that can be used to guide efficient forest management.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Highlights
-
We analyze the risk of losing forest ecosystem services due to wildfires.
-
Non-Mediterranean conifers show the highest risk under extreme hazard conditions.
-
Wet forests with low risk at present will have the highest risk in the future.
Introduction
Forests provide multiple functions and ecosystem services (ES) fundamental to society, such as mitigating greenhouse gas emissions, regulating water flow (Canadell and Raupach 2008; Miura and others 2015), and supporting plant and animal habitats that hold terrestrial biodiversity (Pan and others 2013). Disturbances are an integral part of forest ecosystem dynamics (Turner 2010; Ding and others 2012), but climate change is altering the extent, frequency and intensity of these disturbances (Seidl and others 2017; Abatzoglou and others 2018), resulting in changes in the ES provided by forests (Thom and Seidl 2016; Leverkus and others 2018). Identifying where and to what extent different forest types and ES will be at risk from these disturbances is still a challenge, but it could be critical for guiding effective management and policy interventions.
Wildfires are one of the most common disturbances affecting forests around the world (Van Lierop and others 2015; Bowman and others 2017; Abatzoglou and others 2018). Nevertheless, more extreme fire weather has been leading to unprecedented fire events around the world such as the 2019–2020 Australian bushfires or the 2017 Portuguese fires (Bowman and others 2020, 2017). A huge impact on ES could therefore be expected (Moritz and others 2014), but the effects of such events vary according to the environmental context and the ES in question. Previous studies have reported negative effects of wildfires on ES. Decreases in infiltration and increases in runoff have been reported (Vukomanovic and Steelman 2019), particularly in water-limited environments (Vieira and others 2016), as well as changes in the quality of water for human consumption (Vukomanovic and Steelman 2019). Erosion control – understood as the capacity of vegetation to control soil erosion due to water – diminished after wildfires, especially during the first post-fire rainstorms (Shakesby 2011). Previous studies have shown a reduction in the carbon sink capacity of forests after wildfires (Seidl and others 2014). In contrast, fires can also be beneficial for ES since they can generate open habitats that offer a variety of services for humans (for example, food and pollination) (Pausas and Keeley 2019). Given these conflicting results, the identification of both the forest types that can either lose or gain ES due to wildfires and the main causes of these changes constitutes a research priority.
The risk of losing ES is not easily quantifiable. Here, we follow the IPCC to define the components of risk, which is thus defined as “the potential for consequences where something of value is at stake and where the outcome is uncertain” (IPCC 2018). Risk results from the interaction between exposure, the climate-related hazard and vulnerability, with the latter understood as “the propensity or predisposition to be adversely affected by the disturbance (for example, wildfires), including sensitivity or susceptibility to harm and lack of capacity to cope and adapt” (IPCC 2018). Most of the previous studies assessing forest vulnerability and risk of wildfires have not been based on all the IPCC components, or they have used only specific indicators or variables (Duguy and others 2012; Román and others 2013; Oliveira and others 2018; Buotte and others 2019; Ghorbanzadeh and others 2019; Fremout and others 2020). Previous research indicates that forests subject to a high hazard magnitude (that is, high danger of wildfire or high Fire Weather Index) usually show major impacts on forest ES such as carbon storage, biodiversity, water quality and soil erosion (Shakesby 2011; Thom and Seidl 2016; Harper and others 2018).
More recently, a general framework that includes all the IPCC components and is readily applicable to the main climate-change-related hazards to forests has been proposed (Lecina-Diaz and others 2020). This framework includes the main components of forest vulnerability and risk: exposed values, hazard magnitude, susceptibility and lack of adaptive capacity. Exposed values refer to the ecosystem services that could be affected by the hazard (or disturbance), whereas hazard magnitude quantifies the likelihood of the fire-related hazard. Susceptibility is the predisposition to be affected by the fire hazard, which is defined by forest characteristics that modulate the immediate effects of the hazard. Finally, lack of adaptive capacity refers to the ability of forests to respond to fire hazards within a predefined timeframe (that is, lack of resilience) (Lecina-Diaz and others 2020). These components are defined by intrinsic and extrinsic factors, as well as by explicit indicators that depend on the hazard considered. Although a methodology combining the indicators and components of risk has been also proposed by Lecina-Diaz and others (2020), it has not yet been applied.
The current climate has a large influence on wildfires, especially in extreme conditions (Crockett and Westerling 2018; Holden and others 2018). Nevertheless, the impact of wildfires also depends on forest type and specific functional traits, as plant species have evolved different strategies to trigger, resist and recover from fires. For instance, some species accumulate seed banks that germinate by fire, whereas for other species seed germination is induced by the opening of serotinous fruits or cones (Keeley and Fotheringham 2000). These strategies can influence adaptive capacity (for example, the recovery rate), as some forest functional types have traits that make them able to survive or reestablish after fire (for example, seeding or resprouting capacity), whereas others have limited post-fire regeneration capacity (for example, non-Mediterranean conifers, including several Pinus species) (Rodrigo and others 2004). Moreover, forest types could differ in fire-prone areas such as the Mediterranean Basin as regards the amount of ES exposed, and also the danger of fire (for example, Mediterranean-conifers such as Pinus halepensis have greater wildfire danger) (Mitsopoulos and Dimitrakopoulos 2007). Thus, the individual components that determine risk due to wildfires is influenced by current climatic conditions and forest functional type, but the repercussions of these factors on the ES at risk are not completely understood. Furthermore, climate change is increasing the frequency of extreme climate events (Seidl and others 2017). As regards wildfires, future increases in extreme climate events are expected to increase the probability of ignition, fire size, and severity and the length of the fire season (Flannigan and others 2005; Lozano and others 2016; Parks and others 2016; Ruffault and others 2018). Climate change projections in the Mediterranean Basin suggest increases of more than 50% in days resulting in extreme wildfire events, owing to increasing temperatures and decreasing humidity, especially during the summer season (Bowman and others 2017). The shift to a situation in which current extremes will become the new normal will certainly bring consequences for forest ES. Increases in the geographic extent, duration, intensity and severity of wildfires may change the distribution of the forest ES at risk, and so “new” high risk areas may emerge (Alvarez and others 2012). In fact, previous studies suggest that wildfires could increasingly affect northern latitudes and higher elevations in mountain ranges in the Mediterranean (Vilà-Cabrera and others 2012; Duguy and others 2013), as well as in other areas of the world (Keyser and others 2020). As these areas have not burned historically, the increasing frequency of fires due to climate change will compromise their adaptive capacity (for example, replacement of forest by shrubs) and the provision of key ES (Young and others 2019; Keyser and others 2020).
The general objective of this study is to assess the spatial patterns and drivers of the risk of losing ES due to wildfires, focusing on a region (Catalonia, NE Spain) in the temperate-Mediterranean ecotone that is diverse in terms of both climate and forest type. We take advantage of a general framework recently defined by Lecina-Diaz and others (2020), and we have developed it still further and applied it to the specific case of wildfires. Specifically, we address three questions: (1) which component (exposed values, hazard magnitude, susceptibility, lack of adaptive capacity) is the risk of losing ES most sensitive to?; (2) is the risk of losing forest ES due to wildfires under average and extreme hazard conditions (that is, median and 90th percentile values of the Fire Weather Index, respectively) primarily determined by climate or by forest functional type?; and (3) which climatic factors and forest functional types are associated with higher increases in risk between average and extreme conditions? Given that fire danger is one of the most relevant factors in burned areas (Palheiro and others 2006; Amatulli and others 2013; Pérez-Sánchez and others 2017), and that regeneration strategies have proved critical for forest reestablishment after fire (Keeley and Fotheringham 2000; Rodrigo and others 2004), we hypothesize that hazard magnitude has the greatest influence on ES at risk, followed by lack of adaptive capacity (hypothesis 1). We also hypothesize that the increases in risk between average and extreme conditions are largely driven by climate, but as wildfires are increasingly affecting areas that are not frequently burned by large wildfires (Young and others 2019; Keyser and others 2020), we expect the greatest increases in risk to be found in the most humid forests, which are dominated by species with lower post-fire adaptive capacity (hypothesis 2).
Material and Methods
Study Area
The study area is Catalonia (NE Spain), a region of 32,000 km2 located between 40°50′ and 42°90′ latitude North and 0°20′ and 3°32′ longitude East. It has a heterogeneous geomorphology and high climatic diversity, encompassing mountainous areas such as the Pyrenees (up to 3,143 m.a.s.l), inland plains and coastal zones along the Mediterranean Sea. The climate is Mediterranean, with mean annual temperature ranging from 1 to 17.1 °C and mean annual precipitation ranging from 350 to 1,460 mm (Ninyerola and others 2000). The region’s northern areas are the most humid and the coldest (that is, mean annual precipitation = 1300 mm; mean annual temperature = 5 °C), whereas its southern areas are the hottest and driest (mean annual precipitation = 350 mm with mean annual temperature = 15 °C) (Ninyerola and others 2000). Around 40% of the area is covered by forests (MCSC 2005), mainly dominated by tree species from the Pinaceae and Fagaceae families (pines and oaks).
Definition of Risk and Its Components
We applied the conceptual framework defined in Lecina-Diaz and others (2020) to assess forests’ risk from wildfires. This framework is based on the main concepts of vulnerability and risk defined in the latest IPCC report (IPCC 2018), modified and adapted to the case of forests. These components are structured in a timeline considering the critical processes and variables before, during and after the wildfire event. Before the wildfire occurs, all forests are exposed but they differ in their ‘value’, quantified here in terms of the ES that can be lost, or Exposed Values. However, these ES could only be lost if the wildfire occurs. Thus, in a given location the magnitude of the disturbance/hazard and its probability distribution can be quantified using integrative hazard indexes, which define the Hazard Magnitude. When the wildfire occurs, some characteristics of the forest modulate the immediate effects of the wildfire (for example, forest structure, bark thickness), thus affecting Susceptibility. Finally, after the wildfire, forests may recover using a variety of regeneration strategies, which define their Adaptive Capacity. Hence, risk is defined as follows:
where E refers to Exposed values, HM is the Hazard Magnitude, S is susceptibility and LAC is lack of adaptive capacity. We define E as the presence of ES that could be adversely affected by the wildfire, in this case, carbon sink, bird richness, hydrological control and erosion control (Table 1). HM is the probability distribution of the hazard (or disturbance), in this case assessed using the Fire Weather Index (FWI) (Van Wagner 1987), modified by some additional variables (see below). S is the predisposition to be affected by a wildfire depending on characteristics that modulate the immediate effects of the hazard. Finally, LAC corresponds to the lack of capacity of a forest to recover after a wildfire in the mid-term. Note that E and HM refer to the situation before the wildfire, whereas S refers to the situation during the wildfire and LAC corresponds to the forest’s condition after the wildfire. Furthermore, each component is defined by different indicators that are (1) intrinsic, referring to internal characteristics of the forest (for example, species characteristics); or (2) extrinsic, referring to external factors typically operating at broad spatial scales (for example, topography) (Lecina-Diaz and others 2020) (Figure 1 and Table A1).
Data Sources and Indicators Used
We used different data sources to define the indicators of the different components of risk, that is, exposed values, hazard magnitude, susceptibility and lack of adaptive capacity (Figure 1). Our reference scale is the forest stand (plot), based on the Third Spanish National Forest Inventory (NFI-3), which was conducted in Catalonia between 2000 and 2001 (Ministerio de Medio Ambiente 2007). This inventory consisted of a systematic sampling of permanent plots with a sampling density of one plot per km2 of forest area, where woody species were identified and measured within variable circular size (5 m radius for trees with dbh ≥ 7.5 cm, 10 m radius for trees with dbh ≥ 12.5 cm, 15 m radius for trees with dbh ≥ 22.5 cm, and 25 m radius for trees with dbh ≥ 42.5 cm). As NFI-3 sets the reference scale for the study, all the indicators were computed at the plot scale (Figure 1 and Table A1). We used data from 7,147 to 9,732 plots, depending on the ES considered.
A complete list and additional details of the indicators (definition, scale, references, etc.) is given in Table 1. Further details of each indicator are provided in the Supplementary Material (Section 1).
-
Exposed values (E). Here, we have considered carbon sink, bird richness, hydrological control and erosion control. Carbon sink is the rate of carbon uptake by forests, measured as the difference in carbon stocks (computed from the above-ground and below-ground tree biomass of living trees using species-specific allometric equations; Gracia and others 2004; Montero and others 2005; Vayreda and others 2012b) between the Second and Third National Forest Inventory (NFI), in tons/ha·year. Bird richness has been used as a proxy of biodiversity, and was assessed by using the Second Catalan Breeding Bird Atlas (Estrada and others 2004), counting the total number of species associated with forest habitats (that is, forest specialist and forest generalist species) present in 1 × 1 km pixels centered around each NFI-3 plot. Hydrological control is the capacity of forests to control flooding (that is, the amount of water that is intercepted by forest canopy or retained by soil), assessed as (1—water exported/precipitation) as predicted by the model of De Cáceres and others (2015) for each NFI-3 plot. Erosion control has been defined as the percentage of erosion avoided by the presence of forests, that is, the difference of the Revisited Universal Soil Loss Equation (RUSLE) considering soil without vegetation (that is, Fcover (in c-factor) = 0) and the actual forest cover on the plot (Supplementary material Appendix 1 Section 1.1).
-
Hazard Magnitude (HM). We have used the distribution of daily Fire Weather Index (FWI) values from June–September obtained from the Joint Research Centre at a spatial resolution of 0.28 degrees (Joint Research Centre 2017). The FWI combines temperature, wind speed, relative humidity, and precipitation on a daily basis (including the cumulative effect of the weather in the previous days) to estimate the fire danger (Van Wagner 1987). We have used the Monte Carlo method to obtain repeated random samples of the distribution of daily FWI values. We have incorporated forest continuity at the landscape scale and human visitation (defined by a combination of population, distance to buildings and distance to roads) as modifiers of the hazard magnitude (Supplementary material Appendix 1 Section 1.2). We thus obtained a range of hazard magnitude values (that is, a distribution) for each plot.
-
Susceptibility (S) is defined by intrinsic and extrinsic factors that modulate the immediate effects of the wildfire. Intrinsic factors include structural and functional characteristics. The structural characteristics considered are the forest’s vertical and horizontal continuity and its fuel load (total shrub biomass and fine biomass from trees). As regards functional traits, we have considered bark thickness and flammability, obtained from bibliographic sources at the species level. The extrinsic factors correspond to firefighters' extinction capacity (distances to water bodies, fire stations and fire lookout towers) (Supplementary material Appendix 1 Section 1.3).
-
Lack of Adaptive Capacity (LAC) is calculated as 1—adaptive capacity. Adaptive capacity is defined by intrinsic and extrinsic factors. Intrinsic factors are mainly species regeneration characteristics (that is, resprouting and seeding capacity). Extrinsic factors are the external characteristics that promote species recovery, which are defined by the site index estimated from linear models with tree basal area increment (in cm2/year) as the response variable and radiation, aridity, stoniness and topographic index as explanatory variables (Supplementary material Appendix 1 Section 1.4).
Weighting and Aggregating the Indicators
Except for the exposed values and the FWI, the indicators listed in Figure 1 were standardized (that is, divided by their maximum value) to obtain a range from 0 to 1. The standardized indicators need to be combined, but uncertainties arise in the weighting and aggregating of individual indicators (Gan and others 2017). We applied three of the most widely used weighting methods: (i) equal weights, assigning the same weight to all indicators in a component; (ii) statistical weights, using the statistical importance of the indicators based on their variance, as explained in a Principal Component Analysis; and (iii) expert weights, corresponding to the average value of the weights assigned independently by each of the co-authors of this article (Supplementary material Appendix 1 Table A4). We conducted Pearson’s correlation tests between the estimates of HM, S and LAC resulting from the three different weighting methods. Correlation coefficients were always higher than 0.83, showing that in our study the effect of the weighting method was relatively minor (Supplementary material Appendix 1 Tables A5-A7). Thus, we selected the statistical weights for assessing risk in all further analyses.
Aggregating the Components and Associating them with Values at Risk
We combined the components using Eq. 1. As the relationship between the hazard magnitude and immediate loss of exposed values that define susceptibility is non-linear, mediated by the exponent S, we used FWI data from the literature that corresponded to complete forest loss (immediate losses of values) to adjust the susceptibility coefficient, S (Supplementary material Appendix 1 Section 3). Following Eq. 1, we raised the distribution of hazard magnitude obtained in each plot to its susceptibility and truncated the results so that the maximum immediate loss was 1 (that is, 100% of values were lost). By multiplying the result by the lack of adaptive capacity and the exposed values we obtained one distribution of values at risk in each plot for each ecosystem service at risk. From the distribution of ES at risk, we defined two conditions: average and extreme. Average risk conditions were defined by extracting the median value of each distribution. Extreme risk conditions corresponded to the 90th percentile of each distribution. We mapped the ES at risk under average and extreme conditions, generating one map for each ecosystem service and condition. We also mapped the relative changes in risk associated with extreme vs. average hazard conditions using the log-ratio of extreme to average conditions (that is, log((percentile 90th of the risk)/(median risk))).
Data Analysis
To analyze the influence of the components of risk (E, HM, S and LAC) on the spatial variability of the risk of losing forest ecosystems services, we conducted Pearson’s correlation tests between them to assess whether the different components of risk were spatially associated with each other.
To analyze the effect of the components of risk (E, HM, S and LAC) under average and extreme risk conditions, we conducted a sensitivity analysis using the ‘tgp’ R package on a random sample of 500 plots. This analysis is based on a fully Bayesian Monte Carlo sensitivity analysis, drawing Random Latin hypercube samples at each Markov Chain Monte Carlo iteration to estimate the main effects and first order and total sensitivity indexes (Gramacy 2016).
To determine the effect of climate and forest functional type on the risk of losing forest ES under average and extreme hazard conditions, we conducted regression trees for the four ES at risk (that is, carbon sink, bird richness, hydrological control and erosion control) and the two situations (that is, average and extreme). The explanatory variables were forest functional type (that is, broadleaf evergreen, broadleaf deciduous, Mediterranean conifer and non-Mediterranean conifers (Table A10 and Figure A13)) and climate (Mean Annual Temperature (Temp) and Mean Annual Precipitation (Prec) from the Catalan Digital Climatic Atlas (period 1951–1999 at a resolution of approximately 180 m)) (Figure A13) (Ninyerola and others 2000). Regression trees were conducted using the ‘caret’ R package, based on recursive partitioning techniques that repeatedly split the predictor variables into multiple sub-spaces, so that the outcomes in each final sub-space are as homogeneous as possible (Kuhn and others 2015). To increase the robustness of the regression tree models, we used a random subset of 80% of the data to produce the model – or train it (using repeated cross-validation for control) and the other 20% of the data for testing (cross-validation).
To determine the influence of climate and forest functional type on the increase in risk associated with extreme vs. average hazard conditions, we conducted regression trees for the log-ratio of the risk under extreme and average conditions (log((percentile 90th of the risk)/(median risk))), with forest functional type and climate (Temp and Prec) as explanatory variables (Figure A13).
Results
Influence of Exposed Values, Hazard Magnitude, Susceptibility and Lack of Adaptive Capacity on the Risk of Losing Ecosystem Services
The spatial distribution of the risk components (exposed values, hazard magnitude, susceptibility and lack of adaptive capacity) showed some common patterns depending on the risk condition (that is, average or extreme) and the exposed value considered (Figure 2). Under average conditions, the highest hazard magnitude was in southern areas, corresponding with areas at the highest average risk for all ES, whereas the lowest hazard magnitude and risk were observed in northern areas (Figures 2 and 3). Under extreme conditions, the highest hazard magnitude was in central and southern areas, corresponding with the highest risk for bird richness and hydrological control (Figures 2 and 3).
The sensitivity analysis showed that under average conditions, hazard magnitude was the risk component having the greatest influence for all ES, whereas susceptibility and lack of adaptive capacity were the least influential (Table 1). Under extreme conditions, hazard magnitude remained a very influential factor but its importance was lower than under average conditions and, with respect to carbon sink and erosion control, exposed values became more important than hazard magnitude (Table 1).
Effect of Climate and Forest Functional Type on the Risk of Losing Forest Ecosystem Services
The regression trees showed that climate and forest functional types (Figure A13) were meaningful factors in defining groups of ES at risk from wildfires under average and extreme conditions. Under average conditions, annual precipitation (hereafter precipitation) was the main factor defining risk groups for all ES except for erosion control. In particular, humid forests (that is, with precipitation > 697, 733 or 768 mm/year depending on the ES, see Figure 4) had the lowest risk of losing carbon sink capacity, bird richness and hydrological control capacity in the event of a wildfire. For these three ES, high risk was also associated with warm conditions (temperature > 10 °C). Functional type was also important, with all forest types except Mediterranean conifers being at higher risk of losing carbon sink, and non-Mediterranean conifers being at higher risk of losing bird richness. As regards erosion control, high risk was determined primarily by forest type (higher for non-Mediterranean conifers) and relatively warm temperatures (> 7.8 °C, Figure 4).
Under extreme conditions, the factors defining risk groups were similar to those under average conditions in terms of importance and direction. Nevertheless, some differences were observed in the relative importance of the variables and specific thresholds (Figure 4). In general, the importance of forest type increased and, for all four ES, non-Mediterranean conifers were associated with the highest risks. Precipitation remained the main factor determining the risk of losing bird richness and hydrological control capacity, but with slightly higher thresholds than under average conditions. Warm temperatures also remained associated with high risks for carbon sink and hydrological control, albeit with slightly lower thresholds (around 9 °C). In contrast, high temperatures (> 13 °C) were associated with the lowest risk of losing erosion control capacity.
Influence of Climate and Forest Functional Type on Potential Increases in Risk
Great changes in risk (that is, high log-ratios of extreme vs average conditions) are determined by the distribution of hazard magnitudes, and hence largely by the FWI distribution at each location. The highest increases in risk were observed in forests with low average risk (that is, northern areas, Figure 5A), whereas the lowest increase in risk was observed in areas where average risk was the highest (that is, in the southern areas, Figure 5A) (Figure A14). Precipitation was the main factor determining a change in risk, with two different thresholds, depending on the group considered. The lowest increase in risk was observed in forests with less than 606 mm/year of precipitation, whereas the highest increase in risk was observed in forests with more than 815 mm/year of precipitation (Figure 5B). High increases in risk between average and extreme conditions were associated with forests with high carbon sink capacity, high erosion control and low hydrological control, whereas the correlation with bird richness was weak (correlations of 0.27, 0.46, -0.30 and 0.03, respectively – Supplementary material Appendix 1 Table A12).
Discussion
Overall, hazard magnitude was the most important component defining risk under average conditions and, interestingly, exposed values emerged as the most important component of risk when conditions were extreme. As initially hypothesized (hypothesis 2), climate was the main driving factor of ES at risk under average conditions, but forest functional type – in particular dominance by non-Mediterranean conifers – gained importance under extreme conditions. Nonetheless, the increase in risk (change from extreme to average ES at risk) was driven by precipitation, with the highest increases in risk found in relatively wet forests with low average risk.
Influence of Exposed Values, Hazard Magnitude, Susceptibility and Lack of Adaptive Capacity on the Risk of Losing Ecosystem Services
As initially hypothesized (hypothesis 1), hazard magnitude was the most important component of risk, especially under average conditions (Table 1). The FWI is the main indicator of hazard magnitude as defined here, and it is one of the most widely used indexes to predict fire danger. The FWI has been related to wildfire occurrence and burnt area in Mediterranean regions (Palheiro and others 2006; Amatulli and others 2013; Pérez-Sánchez and others 2017). Our results are consistent with previous studies which found that areas with high fire occurrence and large burnt areas suffered strong impacts on their forest ES (Thom and Seidl 2016; Harper and others 2018; Pausas and Keeley 2019). Although previous studies have shown that adaptive capacity was a relevant component of vulnerability and risk (Román and others 2013; Thorne and others 2018), we found it to be among the least influential factors of ES at risk according to the sensitivity analysis (Table 1). However, adaptive capacity is highly dependent on the forest functional characteristics (for example, Mediterranean conifers have post-fire regeneration strategies whereas non-Mediterranean conifers do not (Rodrigo and others 2004)) and it varies strongly in terms of space (see below).
When conditions were extreme, hazard magnitude lost importance and exposed values became the most important factor for carbon sink and erosion control (Table 1). Under extreme conditions, the extent of high hazard magnitude increased towards central and northern areas (Figure 2), consistent with previous studies suggesting that extreme wildfires could move towards higher latitudes and elevations in the Mediterranean (Vilà-Cabrera and others 2012; Duguy and others 2013). These areas are characterized by broadleaf and non-Mediterranean conifer forests that store more carbon than Mediterranean conifers located in southern areas (Table A9) (Vayreda and others 2012a). Moreover, broadleaf and non-Mediterranean forests tend to be located in areas that are humid but also steep, and thus have higher rain erodibility, vegetation cover and slope-length steepness factors, resulting in high erosion control (Table A9). Therefore, the increase in the extent of risk under extreme conditions would lead to more exposed carbon sink and erosion control being at risk and, consequently, more ES could be lost if a wildfire occurred.
Effect of Climate and Forest Functional Type on the Risk of Losing Forest Ecosystem Services
The risks to ES were primarily driven by climate. Under average conditions, humid forests had the lowest risk of losing all ES except erosion control. This is an expected result because low precipitation has a strong effect on burnt area by decreasing fuel moisture and increasing flammability (Littell and others 2009; Holden and others 2018). Less humid and warm conditions put carbon sink at the highest risk for all forest functional types except Mediterranean conifers (that is, Pinus halepensis, Pinus pinea, Pinus pinaster) (Figure 4). Low precipitation and warm temperatures increased hazard magnitude which, together with the high levels of carbon sink found in all forests except Mediterranean conifers (Table A9) (Vayreda and others 2012a), resulted in the highest risk of losing carbon sink. As regards bird richness, non-Mediterranean conifers (for example, Pinus sylvestris, Pinus nigra, Pinus uncinata, Abies alba) and forests growing under warm conditions were at the highest risk (Figure 4). Temperature was found to negatively affect bird richness in the study area (Lecina-Diaz and others 2018), as most of the forest birds are cold-dwelling species located at the southern limit of their distribution in Europe (Regos and others 2017). Although changes in bird communities are common shortly after fire due to changes in habitat and resource availability, bird richness returns to pre-fire levels after a few years (Saracco and others 2018; Zlonis and others 2019). However, post-fire habitat changes in non-Mediterranean conifer forests could be exacerbated by these species’ lack of post-fire adaptive capacity (Table A9) (for example, replacement by other tree species, such as broadleaves (De Cáceres and others 2013)), with consequences for forest bird communities. As regards erosion control, the highest risk was observed in non-Mediterranean conifers at relatively warm temperatures (> 7.8 °C, Figure 4). Non-Mediterranean conifers’ lack of post-fire adaptive capacity compared to the rest of forest functional types (Rodrigo and others 2004; Tapias and others 2004) could also result in higher erosion risk, due to growth limitations after fire (Maringer and others 2012; Reyes and others 2015), but temperature limited the risk due to hazard magnitude in these forests, at least under average hazard conditions (that is, low FWI in areas with low temperature) (Figure 4).
Under extreme conditions, climate was still a relevant factor for all ES at risk, albeit with different precipitation and temperature thresholds (Figure 4). More humid and colder areas were at higher risk than under average conditions, concurring with previous studies relating less arid climates with more biomass to extreme fire severity (Lecina-Diaz and others 2014). In contrast, the importance of forest type increased, with non-Mediterranean conifers having the highest risk to ES (Figure 4). Under these conditions, the factor that differentiates forest functional types is the highest lack of adaptive capacity presented by non-Mediterranean conifers, which resulted in this forest type being the one with the highest risk to ES. However, previous studies have shown that warming climate is furthering unfavorable post-fire growing conditions, regardless of the forest functional type (for example, lower seedling and resprouting capacity due to unsuitable climate) (Enright and others 2015; Stevens-Rumann and others 2018). Nevertheless, non-Mediterranean conifer forests in humid regions have previously been affected by extreme wildfires and shown limited regeneration compared with the other forest types (Retana and others 2002; Rodrigo and others 2004; Pausas and others 2008). This is generally consistent with previous studies that have described them as vulnerable due to their lack of regenerative capacity, which is closely linked to seed dispersal from surviving trees (Vilà-Cabrera and others 2012; Christopoulou and others 2014). Consequently, these forests often transitioned into other forest types (mainly dominated by resprouter species), or even into other types of vegetation, such as shrublands (Retana and others 2002; Pérez-Cabello and others 2010), resulting in very high impacts on their ES.
Influence of Climate and Forest Functional Type on Potential Increases in Risk
Although wildfires are an integral part of forest ecosystems, ongoing climate change is likely to exacerbate wildfire risk in many areas, making the extreme hazard conditions now being found much more common. Thus, characterizing forests based on a change from (currently) average to extreme conditions could provide new insights into ES at risk from future wildfires. As hypothesized (hypothesis 2), the highest increases in ES at risk occurred in the most humid forests in the study area, which are currently at low risk. These relatively wet forests grow without any water limitations, so they are associated with high carbon sink capacity and erosion control (Table A12). Although these forests are not frequently affected by wildfires (Díaz-Delgado and others 2004; Brotons and others 2013), previous studies have suggested an increase in wildfires in northern latitudes and higher elevations in Mediterranean regions (Duguy and others 2013). Climate change will increase the severity and intensity of drought events in the Mediterranean, resulting in increases of more than 50% in days favorable to extreme wildfire events (Vilà-Cabrera and others 2012; Bowman and others 2017). By the 2080s, future scenarios of temperature increases of 2–4 °C in southern Europe also entail reduction in precipitation of up to 30% (Vautard and others 2014), and increases in wildfire activity are expected in other regions of the world in future (Moritz and others 2012; Liu and others 2013; Coogan and others 2019). Therefore, new areas with a high to forest ES that may appear due to climate change should be considered a priority in management policies aimed at reducing susceptibility or improving adaptive capacity.
Concluding Remarks
Our research assessed the current and future risk of losing ES (carbon sink, hydrological control and erosion control) and biodiversity (bird richness) in the event of a wildfire, highlighting the large differences between forest functional types. In this respect, management approaches favoring broadleaf species over non-Mediterranean conifers can be promoted to increase adaptive capacity and consequently decrease the risk of losing ES. However, it is not clear how future climate conditions may change species distributions and fire regimes, and how these changes will affect future hazard magnitude, susceptibility and adaptive capacity, which collectively define risk. We have approximated future hazard conditions using extreme values of current hazard magnitudes, but a better understanding of the future distribution of hazard magnitude remains a key challenge. This study constitutes an important advance in the quantification of forest vulnerability and risk that could be generalized to other systems. Given the increases in the intensity of forest disturbance regimes in Europe (Seidl and others 2011), and the increasing vulnerability to fire in other regions of the world (Buotte and others 2019), our findings could contribute to future policies by anticipating conditions associated with particularly high risks that could be used to guide efficient forest management.
Data available
Data are available at: https://doi.org/10.6084/m9.figshare.13636748.v1.
References
Abatzoglou JT, Williams AP, Boschetti L, Zubkova M, Kolden CA. 2018. Global patterns of interannual climate–fire relationships. Glob Chang Biol 24:5164–75.
Alvarez A, Gracia M, Vayreda J, Retana J. 2012. Patterns of fuel types and crown fire potential in Pinus halepensis forests in the Western Mediterranean Basin. For Ecol Manage 270:282–90. http://linkinghub.elsevier.com/retrieve/pii/S0378112711000715. Last accessed 20/02/2012
Amatulli G, Camia A, San-Miguel-Ayanz J. 2013. Estimating future burned areas under changing climate in the EU-Mediterranean countries. Sci Total Environ 450–451:209–22. https://doi.org/10.1016/j.scitotenv.2013.02.014.
Bowman DMJS, Williamson GJ, Abatzoglou JT, Kolden CA, Cochrane MA, Smith AMS. 2017. Human exposure and sensitivity to globally extreme wildfire events. Nat Ecol Evol 1:0058. https://doi.org/10.1038/s41559-016-0058.
Bowman DMJS, Kolden CA, Abatzoglou JT, Johnston FH, van der Werf GR, Flannigan M. 2020. Vegetation fires in the Anthropocene. Nat Rev Earth Environ. https://doi.org/10.1038/s43017-020-0085-3.
Brotons L, Aquilué N, de Cáceres M, Fortin M-J, Fall A. 2013. How Fire History, Fire Suppression Practices and Climate Change Affect Wildfire Regimes in Mediterranean Landscapes. Bohrer G, editor. PLoS One 8:e62392. https://doi.org/10.1371/journal.pone.0062392.
Buotte PC, Levis S, Law BE, Hudiburg TW, Rupp DE, Kent JJ. 2019. Near-future forest vulnerability to drought and fire varies across the western United States. Glob Chang Biol 25:290–303. https://doi.org/10.1111/gcb.14490.
Canadell JG, Raupach MR. 2008. Managing Forests for Climate Change Mitigation. Science (80- ) 320:1456–7.
Christopoulou A, Fyllas NM, Andriopoulos P, Koutsias N, Dimitrakopoulos PG, Arianoutsou M. 2014. Post-fire regeneration patterns of Pinus nigra in a recently burned area in Mount Taygetos, Southern Greece: The role of unburned forest patches. For Ecol Manage 327:148–56. https://doi.org/10.1016/j.foreco.2014.05.006.
Coogan SCP, Robinne FN, Jain P, Flannigan MD. 2019. Scientists’ warning on wildfire — a canadian perspective. Can J For Res 49:1015–23.
Crockett JL, Westerling AL. 2018. Greater temperature and precipitation extremes intensify Western U.S. droughts, wildfire severity, and sierra Nevada tree mortality. J Clim 31:341–54.
De Cáceres M, Brotons L, Aquilué N, Fortin M-JJ. 2013. The combined effects of land-use legacies and novel fire regimes on bird distributions in the Mediterranean. Pearman P, editor. J Biogeogr 40:1535–47. https://doi.org/10.1111/jbi.12111.
De Cáceres M, Martínez-Vilalta J, Coll L, Llorens P, Casals P, Poyatos R, Pausas JG, Brotons L. 2015. Coupling a water balance model with forest inventory data to predict drought stress: The role of forest structural changes vs. climate changes. Agric For Meteorol 213:77–90. https://doi.org/10.1016/j.agrformet.2015.06.012.
Díaz-Delgado R, Lloret F, Pons X. 2004. Statistical analysis of fire frequency models for Catalonia (NE Spain, 1975–1998) based on fire scar maps from Landsat MSS data. Int J Wildl Fire 13:89–99.
Ding Y, Zang R, Letcher S, Liu S, He F. 2012. Disturbance regime changes the trait distribution, phylogenetic structure and community assembly of tropical rain forests. Oikos 121:1263–1270.
Duguy B, Alloza JA, Baeza MJ, De la Riva J, Echeverría M, Ibarra P, Llovet J, Cabello FP, Rovira P, Vallejo RV. 2012. Modelling the Ecological Vulnerability to Forest Fires in Mediterranean Ecosystems Using Geographic Information Technologies. Environ Manage 50:1012–26. https://doi.org/10.1007/s00267-012-9933-3.
Duguy B, Paula S, Pausas JG, Alloza JA, Gimeno T, Vallejo R V. 2013. Effects of Climate and Extreme Events on Wildfire Regime and Their Ecological Impacts. In: Navarra A, Tubiana L, editors. Regional Assessment of Climate Change in the Mediterranean: Volume 2: Agriculture, Forests and Ecosystem Services and People. Vol. 52. Advances in Global Change Research. Dordrecht: Springer Netherlands. pp 23–37. https://doi.org/10.1007/978-94-007-5769-1
Enright NJ, Fontaine JB, Bowman DMJS, Bradstock RA, Williams RJ. 2015. Interval squeeze: Altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes. Front Ecol Environ 13:265–72.
Estrada J, Pedrocchi V, Brotons L, Herrando S. 2004. Atles dels ocells nidificants de Catalunya 1999–2002. Barcelona: Lynx Edicions.
Flannigan MD, Amiro BD, Logan KA, Stocks BJ, Wotton BM. 2005. Forest fires and climate change in the 21st century. Mitig Adapt Strateg Glob Chang 11:847–59.
Fremout T, Thomas E, Gaisberger H, Van Meerbeek K, Muenchow J, Briers S, Gutierrez‐Miranda CE, Marcelo‐Peña JL, Kindt R, Atkinson R, Espinosa CI, Aguirre‐Mendoza Z, Muys B. 2020. Mapping tree species vulnerability to multiple threats as a guide to restoration and conservation of tropical dry forests. Glob Chang Biol:1–17.
Gan X, Fernandez IC, Guo J, Wilson M, Zhao Y, Zhou B, Wu J. 2017. When to use what: Methods for weighting and aggregating sustainability indicators. Ecol Indic 81:491–502. https://doi.org/10.1016/j.ecolind.2017.05.068.
Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J. 2019. Forest Fire Susceptibility and Risk Mapping Using Social / Infrastructural Vulnerability and Environmental Variables. Fire.
Gracia C, Burriel JA, Ibàñez JJ, Mata T, Vayreda J. 2004. Inventari Ecològic i Forestal de Catalunya. Bellaterra
Gramacy RB. 2016. ‘tgp’ R package: Bayesian Treed Gaussian Process Models.
Harper AR, Doerr SH, Santin C, Froyd CA, Sinnadurai P. 2018. Prescribed fire and its impacts on ecosystem services in the UK. Sci Total Environ 624:691–703. http://linkinghub.elsevier.com/retrieve/pii/S0048969717335878
Holden ZA, Swanson A, Luce CH, Jolly WM, Maneta M, Oyler JW, Warren DA, Parsons R, Affleck D. 2018. Decreasing fire season precipitation increased recent western US forest wildfire activity. Proc Natl Acad Sci U S A 115:E8349–57.
IPCC. 2018. Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change,. (Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T, editors.).
Joint Research Centre. Fire Weather Index - ERA-Interim (Version 2.0). Zenodo. 2017.
Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, Team TRC, Benesty M, Lescarbeau R, Ziem A, Scrucca L. 2015. Caret: Classification and Regression Training.
Keeley JE, Fotheringham CJ. 2000. Role of fire in regeneration from seed. In: Seeds: the ecology of regeneration in plant communities. Wallingford: CABI. pp 311–30. http://www.cabi.org/cabebooks/ebook/20003037675
Keyser AR, Krofcheck DJ, Remy CC, Allen CD, Hurteau MD. 2020. Simulated Increases in Fire Activity Reinforce Shrub Conversion in a Southwestern US Forest. Ecosystems. https://doi.org/10.1007/s10021-020-00498-4.
Lecina-Diaz J, Alvarez A, Regos A, Drapeau P, Paquette A, Messier C, Retana J. 2018. The positive carbon stocks–biodiversity relationship in forests: co-occurrence and drivers across five subclimates. Ecol Appl 28:1481–93. https://doi.org/10.1002/eap.1749.
Lecina-Diaz J, Alvarez A, Retana J. 2014. Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of mediterranean pine forests. PLoS One. https://doi.org/10.1371/journal.pone.0085127.
Lecina-Diaz J, Martínez-Vilalta M, Alvarez A, Banqué M, Birkmann J, Feldmeyer D, Vayreda J, Retana J. 2020. Characterizing forest vulnerability and risk to climate change hazards. Front Ecol Environ. https://doi.org/10.1002/fee.2278
Leverkus AB, Rey Benayas JM, Castro J, Boucher D, Brewer S, Collins BM, Donato D, Fraver S, Kishchuk BE, Lee E-J, Lindenmayer DB, Lingua E, Macdonald E, Marzano R, Rhoades CC, Royo A, Thorn S, Wagenbrenner JW, Waldron K, Wohlgemuth T, Gustafsson L. 2018. Salvage logging effects on regulating and supporting ecosystem services - a systematic map. Can J For Res 48:983–1000. https://doi.org/10.1139/cjfr-2018-0114.
Littell JS, Mckenzie D, Peterson DL, Westerling AL. 2009. Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecol Appl 19:1003–21. https://doi.org/10.1890/07-1183.1.
Liu Y, Goodrick SL, Stanturf JA. 2013. Future U.S. wildfire potential trends projected using a dynamically downscaled climate change scenario. For Ecol Manage 294:120–35. https://doi.org/10.1016/j.foreco.2012.06.049.
Lozano OM, Salis M, Ager AA, Arca B, Alcasena FJ, Monteiro AT, Finney MA, Del Giudice L, Scoccimarro E, Spano D. 2016. Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas. Risk Anal 37:1898–916. https://doi.org/10.1111/risa.12739.
Maringer J, Wohlgemuth T, Neff C, Pezzatti GB, Conedera M. 2012. Post-fire spread of alien plant species in a mixed broad-leaved forest of the Insubric region. Flora - Morphol Distrib Funct Ecol Plants 207:19–29. https://doi.org/10.1016/j.flora.2011.07.016.
MCSC. 2005. Mapa de Cobertes del Sòl de Catalunya (CREAF, Barcelona). Available at http://www.creaf.uab.es/mcsc.
Ministerio de Medio Ambiente. 2007. Tercer Inventario Forestal Nacional. Madrid, Spain: Direccion General de Conservacion de la Naturaleza.
Mitsopoulos ID, Dimitrakopoulos AP. 2007. Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests. Ann For Sci 64:287–99. https://doi.org/10.1051/forest:2007006.
Miura S, Amacher M, Hofer T, San-Miguel-Ayanz J, Ernawati Thackway R. 2015. Protective functions and ecosystem services of global forests in the past quarter-century. For Ecol Manage 352:35–46. https://doi.org/10.1016/j.foreco.2015.03.039.
Montero G, Ruiz-Peinado R, Muñoz M. 2005. Producción de biomasa y fijación de CO2 por los bosques españoles. Madrid: INIA-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria.
Moritz MA, Batllori E, Bradstock RA, Gill AM, Handmer J, Hessburg PF, Leonard J, McCaffrey S, Odion DC, Schoennagel T, Syphard AD. 2014. Learning to coexist with wildfire. Nature 515:58–66.
Moritz MA, Parisien M-A, Batllori E, Krawchuk MA, Van Dorn J, Ganz DJ, Katharine H, Hayhoe K. 2012. Climate change and disruptions to global fire activity. Ecosp 3(6):1–22.
Ninyerola M, Pons X, Roure JM. 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. Int J Climatol 20:1823–41.
Oliveira S, Félix F, Nunes A, Lourenço L, Laneve G, Sebastián-López A. 2018. Mapping wildfire vulnerability in Mediterranean Europe. Testing a stepwise approach for operational purposes. J Environ Manage 206:158–69.
Palheiro PM, Fernandes P, Cruz MG. 2006. A fire behaviour-based fire danger classification for maritime pine stands: Comparison of two approaches. For Ecol Manage 234:S54.
Pan Y, Birdsey RA, Phillips OL, Jackson RB. 2013. The Structure, Distribution, and Biomass of the World’s Forests. Annu Rev Ecol Evol Syst 44:593–622. https://doi.org/10.1146/annurev-ecolsys-110512-135914
Parks SA, Miller C, Abatzoglou JT, Holsinger LM, Parisien MA, Dobrowski SZ. 2016. How will climate change affect wildland fire severity in the western US? Environ Res Lett 11.
Pausas JG, Keeley JE. 2019. Wildfires as an ecosystem service. Front Ecol Environ:1–7.
Pausas JG, Llovet J, Rodrigo A, Vallejo R. 2008. Are wildfires a disaster in the Mediterranean basin? – A review. Int J Wildl Fire 17:713–23.
Pérez-Cabello F, Ibarra P, Echeverría MT, de la Riva J. 2010. Post-fire land degradation of Pinus sylvestris L. woodlands after 14 years. L Degrad Dev 21:145–60.
Pérez-Sánchez J, Senent-Aparicio J, Díaz-Palmero JM, de Cabezas-Cerezo J, D. 2017. A comparative study of fire weather indices in a semiarid south-eastern Europe region. Case of study: Murcia (Spain). Sci Total Environ 590–591:761–74. https://doi.org/10.1016/j.scitotenv.2017.03.040.
Regos A, Clavero M, D’Amen M, Guisan A, Brotons L. 2017. Wildfire-vegetation dynamics affect predictions of climate change impact on bird communities. Ecography (Cop):1–13. https://doi.org/10.1111/ecog.02990
Retana J, Espelta J, Habrouk A, Ordóñez J, Solà-Morales F. 2002. Regeneration patterns of three Mediterranean pines and forest changes after a large wildfire in northeastern Spain. Ecoscience 9:89–97.
Reyes O, García-Duro J, Salgado J. 2015. Fire affects soil organic matter and the emergence of Pinus radiata seedlings. Ann For Sci 72:267–75. https://doi.org/10.1007/s13595-014-0427-8
Rodrigo A, Retana J, Picó FX. 2004. Direct regeneration is not the only response of Mediterranean forests to large fires. Ecology 85:716–29. https://doi.org/10.1890/02-0492
Román MV, Azqueta D, Rodrígues M. 2013. Methodological approach to assess the socio-economic vulnerability to wildfires in Spain. For Ecol Manage 294:158–65.
Ruffault J, Curt T, Martin-Stpaul NK, Moron V, Trigo RM. 2018. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat Hazards Earth Syst Sci 18:847–56.
Saracco JF, Fettig SM, San Miguel GL, Mehlman DW, Thompson BE, Albert SK. 2018. Avian demographic responses to drought and fire: a community-level perspective. Ecol Appl 28:1773–81. https://doi.org/10.1002/eap.1751
Seidl R, Schelhaas M-J, Rammer W, Verkerk PJ. 2014. Increasing forest disturbances in Europe and their impact on carbon storage. Nat Clim Chang 4:806–10. https://doi.org/10.1038/nclimate2318
Seidl R, Schelhaas MJ, Lexer MJ. 2011. Unraveling the drivers of intensifying forest disturbance regimes in Europe. Glob Chang Biol 17:2842–52.
Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer MJ, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, Nagel TA, Reyer CPO. 2017. Forest disturbances under climate change. Nat Clim Chang 7:395–402.
Shakesby RA. 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Rev 105:71–100. https://doi.org/10.1016/j.earscirev.2011.01.001.
Stevens-Rumann CS, Kemp KB, Higuera PE, Harvey BJ, Rother MT, Donato DC, Morgan P, Veblen TT. 2018. Evidence for declining forest resilience to wildfires under climate change. Ecol Lett 21:243–52.
Tapias R, Climent J, Pardos JA, Gil L. 2004. Life Histories of Mediterranean Pines. Plant Ecol 171:53–68.
Thom D, Seidl R. 2016. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol Rev 91:760–81. https://doi.org/10.1111/brv.12193.
Thorne JH, Choe H, Stine PA, Chambers JC, Holguin A, Kerr AC, Schwartz MW. 2018. Climate change vulnerability assessment of forests in the Southwest USA. Clim Change 148:387–402. https://doi.org/10.1007/s10584-017-2010-4.
Turner M. 2010. Disturbance and landscape dynamics in a changing world. Ecology 91:2833–49.
Van Lierop P, Lindquist E, Sathyapala S, Franceschini G. 2015. Global forest area disturbance from fire, insect pests, diseases and severe weather events. For Ecol Manage 352:78–88. https://doi.org/10.1016/j.foreco.2015.06.010.
Vautard R, Gobiet A, Sobolowski S, Kjellström E, Stegehuis A, Watkiss P, Mendlik T, Landgren O, Nikulin G, Teichmann C, Jacob D. 2014. The European climate under a 2 °c global warming. Environ Res Lett 9.
Vayreda J, Gracia M, Canadell JG, Retana J. 2012a. Spatial Patterns and Predictors of Forest Carbon Stocks in Western Mediterranean. Ecosystems 15:1258–70.
Vayreda J, Martinez-Vilalta J, Gracia M, Retana J. 2012b. Recent climate changes interact with stand structure and management to determine changes in tree carbon stocks in Spanish forests. Glob Chang Biol 18:1028–41.
Vieira DCS, Malvar MC, Fernández C, Serpa D, Keizer JJ. 2016. Annual runoff and erosion in a recently burn Mediterranean forest – The effects of plowing and time-since-fire. Geomorphology 270:172–83. https://doi.org/10.1016/j.geomorph.2016.06.042.
Vilà-Cabrera A, Rodrigo A, Martínez-Vilalta J, Retana J. 2012. Lack of regeneration and climatic vulnerability to fire of Scots pine may induce vegetation shifts at the southern edge of its distribution. J Biogeogr 39:488–96.
Vukomanovic J, Steelman T. 2019. A Systematic Review of Relationships Between Mountain Wildfire and Ecosystem Services. Landsc Ecol 34:1179–94. https://doi.org/10.1007/s10980-019-00832-9.
Van Wagner C. 1987. Development and structure of the Canadian forest fire weather index system. Technical Report 35. Canadian Forestry Service. Ottawa, Ontario
Young DJN, Werner CM, Welch KR, Young TP, Safford HD, Latimer AM. 2019. Post-fire forest regeneration shows limited climate tracking and potential for drought-induced type conversion. Ecology 100:e02571. https://doi.org/10.1002/ecy.2571.
Zlonis EJ, Walton NG, Sturtevant BR, Wolter PT, Niemi GJ. 2019. Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. For Ecol Manage 439:70–80. https://doi.org/10.1016/j.foreco.2019.02.043.
Acknowledgements
We are grateful to Miquel de Cáceres for sharing data of the model De Cáceres and others (2015). We thank the Generalitat de Catalunya fire-fighters corps and Francesc Xavier Castro (Forest Fires Prevention Services, Generalitat de Catalunya) for sharing data, and the Joint Research Centre for providing historical FWI data. We are grateful to the Spanish National Forest Inventory and the Catalan Ornithological Institute for providing data, as well as to the thousands of participants that collected these data. Two anonymous reviewers provided valuable feedback on the manuscript. This study was funded by the Spanish Ministry of Economy and Competitiveness Project ‘FORESTCAST’ (CGL2014-59742- C2-1-R) and ‘INMODES’ (CGL2017-89999-C2-1-R). JLD received a pre-doctoral fellowship funded by Spanish Ministry of Economy and Competitiveness (BES-2015-073854) and is currently supported by national funds through the FCT – Foundation for Science and Technology, I.P., under the FirESmart Project (PCIF/MOG/0083/2017).
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Lecina-Diaz, J., Martínez-Vilalta, J., Alvarez, A. et al. Assessing the Risk of Losing Forest Ecosystem Services Due to Wildfires. Ecosystems 24, 1687–1701 (2021). https://doi.org/10.1007/s10021-021-00611-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10021-021-00611-1