Seasonal changes in the human alteration of fire regimes beyond the climate forcing

For millennia, human activities have altered fire regimes, which are defined as the frequency, seasonality, intensity and size of wildfires (Gill 1975). Addressing this human dimension of fire is critical when attempting to predict how changing fire regimes will impact the Earth's systems (Bowman et al 2009, 2011, Archibald et al 2013). However, whether human-induced changes in fire activity were significant or negligible as compared to climate forcing is still widely debated. Indeed, at broad scales, climate is generally recognized as the main fire driver (Krawchuk et al 2009, Daniau et al 2012), by determining the variability in the resource to burn (fuel biomass) and the occurrence, intensity and duration of dry conditions (fuel moisture) (Meyn et al 2007, Lehmann et al 2014). Beyond climate forcing, temporal shifts in global biomass burning over recent centuries have been related to increasing anthropogenic pressures on ecosystems (Marlon et al 2008). At local scales, the predominant effects of human factors have been highlighted by asynchronous shifts in fire regime (Carcaillet et al 2009, Fulé et al 2012). Therefore, it appears that regional changes in human practices such as land-use, fire ignitions (intentional or accidental) or suppression may override the effects of climate on fire. Addressing how anthropogenic factors drove fire regime changes in the past is thus critical if we are to anticipate the extent to which future fire activity will deviate from expectations based on climatic factors (Liebmann et al 2016, Taylor et al 2016). It is particularly important at regional scales to separate potential contrasts in fire drivers among biogeographical and socioeconomic contexts (Boulanger et al 2012, Trigo et al 2016, Bedia et al 2014b). From these perspectives, we aimed to test the recent changes in the human effect on seasonal fire activity in the pyroclimates of southern France, i.e. among regions which differ in climate, fire regime and their recent temporal trends (Fréjaville and Curt 2015).

Fire–climate relationships can be summarized by considering two antagonistic effects. The rarity of wet conditions limit fire activity in dry ecosystems due to a lack of fuel availability (fuel-limited fire regime), whereas in moist ecosystems the rarity of dry conditions keep fuel from being sufficiently dry (drought-driven fire regime) to sustain fire spread (Krawchuk and Moritz 2011, Pausas and Ribeiro 2013). This antagonistic effect also operates on a multiannual scale through the alternation of wet and dry conditions favouring fires (Meyn et al 2007, Pausas 2004, Zumbrunnen et al 2009), by increasing the amount of fine fuel in litter, grass and shrub layers during wet periods, which burn more intensely in subsequent dry periods (Fréjaville et al 2016). Therefore, drier conditions under a warmer climate may have antagonist effects on fire depending on whether fuel-limited or drought-driven fire regimes dominate (Boer et al 2016), as for instance in Mediterranean ecosystems where both are present (Batllori et al 2013).

In the Mediterranean Basin, human activities directly modify the fire regime by setting or suppressing fires and by changing patterns of vegetation in the landscape (Moreira et al 2011). Moreover, the fuel build-up following agricultural land abandonment as a result of the rural exodus has created an increasing fire hazard in Mediterranean Europe (Moreira et al 2011, Pausas and Fernández-Muñoz 2012). On the other hand, changes in fire suppression policy over the last few decades have probably induced sharp decreases in fires (Pezzatti et al 2013, Moreno et al 2014). Hence the functional relationships linking fire to climate have been partially modified by human activities (Higuera et al 2015), decreasing or increasing the fire activity independently of climate change. Elsewhere, analysing changes in fire–climate relationships has commonly been addressed by comparing (multi-)decadal periods (Higuera et al 2015, Keeley and Syphard 2015, Ruffault and Mouillot 2015). However, we lack an analytical framework to identify the functional mechanisms linking climate to fire variability properly, i.e. by first removing the potential effects induced by human activities, to ensure that causal relationships are not biased from common (or opposite) trends in the data (Lobell et al 2011). For instance, in Euro-Mediterranean ecosystems, the interannual variation in fire activity is reported to be strongly and positively related to extreme fire weather (Fréjaville and Curt 2015, Urbieta et al 2015). However, building statistical models based on trending data with, on one hand, a decreasing fire trend (probably induced by fire suppression), and on the other hand, increasing extreme fire weather (with global warming) might incorrectly indicate a decreasing effect of a warmer climate on fire in these ecosystems.

We developed an analytical framework for modelling the functional relationships between fire and climate and testing seasonal human alteration of regional fire regimes (figure 1). Our method is based on separating fire and climate temporal variation into trend (multi-decadal) and seasonal (interannual variability) components, and on the assumption that effects of changes in human activities predominantly operate at the former level (Turco et al 2014, Urbieta et al 2015). We hypothesize that human activities have recently altered fire regimes beyond the effect of climate forcing in Euro-Mediterranean ecosystems, either: i) by promoting fires (positive alteration) through increasing anthropogenic ignitions along with more extensive wildland–urban interface and tourism pressure (Ganteaume and Jappiot 2013), and increasing landscape flammability associated with fuel recovery dynamics following land-use changes (Moreira et al 2011); or ii) by suppressing fires (negative alteration) with increasing efficiency over time, especially since the recent major changes in fire policy (mid 90s; Moreno et al 2014). In southern France, we expected that the nature and strength of fire regime alteration would vary both seasonally and along a fire-proneness gradient, i.e. by stronger negative alteration in summer and in Mediterranean fire-prone ecosystems than in winter and in Temperate mountains.

Zoom In Zoom Out Reset image size

Analyses of fire–climate relationships (1976–2009) were based on disentangling the effects of contemporary and previous weather conditions (wet or dry) on fire, separately for each of the three pyroclimates of southern France (table 1, figure 2). We focused on these regional contexts, because the potential of human activities to alter fire in recent decades is thought to vary greatly with spatial or socioeconomic context (Pausas and Keeley 2009). The Mediterranean mountains (MM) are characterized by a high seasonality in precipitation and fires (dry summers and wet winters) with the highest burned area fraction, fire season length and the strongest increase in fire danger over the last four decades. In contrast, the wettest/coldest climates, the shortest fire seasons and the lowest fire activities are found in the Temperate mountains (TM). The Mediterranean lowlands (ML) have the driest and warmest climates with strong and dry winds (the Mistral and Tramontane) that favour fire spread in summer. This region has experienced the strongest decrease in fires over the last four decades (Fréjaville and Curt 2015). Human presence and activities decrease from the Mediterranean coast to the rural hinterland, with anthropogenic ignitions accounting for 90% of the number of fires and 96% of the total burned area (Curt et al 2016).

Zoom In Zoom Out Reset image size

Data

Models

Fire–climate models

In cases where the fire trend is partially shaped by anthropogenic factors (other than climate change), correlating fire and climate trends may yield correlations that are not causal. Our modelling approach is based on the assumption that changes in the human alterations of fire regimes occur on a decadal time scale, not inter-annual, with slowly changing preferences, technologies and demographics (Guyette et al 2002). Hence, our model estimates for climatic effects rely on inter-annual fire–climate relationships. For each pyroclimatic region, fire season and fire variable, we applied the following procedure. First, fire (Y_t) and climate (X_t) time series (1976–2009) were detrended by Loess regression models (degree of smoothing = 0.75; degree of polynomials = 2) to decompose the data into a trend component (T_t, multi-decadal trend), a seasonal component (S_t, interannual variability) and a remainder component (residuals) (Cleveland et al 1990):

$$\begin{equation} Y_t = TY_t + SY_t + \varepsilon ;\;X_t = TX_t + SX_t + \varepsilon \end{equation} \tag{ 1 }$$

where the trend component (T_t) is dependent on the predicted trend (T_t^*) and the intercept value for the first year of the studied period (T_t = 1, here 1976), T_t = T_t^* + T_t = 1. According to this notation, TY_t = 1 is the pyroclimate-specific fixed effect for the studied fire variable and season (i.e. the fire trend intercept for 1976). Then, modelling of the seasonal component of fire was performed using multi-linear regression (MLR) models, calibrated on n detrended climatic predictors:

$$\begin{equation} SY_t = \sum\nolimits_i^n {(a_i SX_{i,t} ) + b + \varepsilon } \end{equation} \tag{ 2 }$$

Climatic predictors (X_i,..., X_n) were selected in two steps. First, we ranked all available climatic variables from their cross-correlation with the studied fire variable, season and pyroclimate, using Spearman's correlation coefficients computed on the seasonal components (St, detrended series). Multi-collinearity was removed by selecting the best predictor among collinear variables (r > 0.7). Selected predictors were then used to build an initial balanced pool of two sets of predictors, one expressing contemporary (n_t0 = 7) and the other lagged (n_lag = 7) effects of climate on fire. Second, the best model among an ensemble of models with a final set of n = 6–8 predictors was selected using stepwise regressions with the Akaike's Information Criterion (R package MuMIn). By carrying out this selection procedure, we were able to test the contribution of each climatic predictor in explaining the seasonal component of fires, i.e. the temporal variation in fire activity after removing the potential influence of changes in human activities. In order to estimate a confidence interval of the seasonal component of fires for each pyroclimatic region, fire season and fire variable, an ensemble of N MLR models (N = 50) was built following this procedure, by randomly removing five years over the study period (1976–2009). The confidence interval is defined by the 10th and the 90th percentiles of the ensemble of the N MLR models, i.e. the ensemble of regression parameters calibrated on different sets of years and climatic predictors.

Partitioning climatic effects

To quantify the relative effects of 'contemporary' versus 'previous' and 'dry' versus 'wet' conditions on fire, the explained variance of SY_t was partitioned at each iteration (for each MLR model) into the unique contribution of each of these four climatic sets of drivers. The distinction between the 'contemporary' and 'previous' sets was based on the time period during which parameters were computed, i.e. during the fire season or over preceding seasons and years, respectively. Then, the sets of climatic predictors describing increasing effects of 'dry' versus 'wet' conditions were assembled relative to the predictors' t-value in the MLR model. Specifically, temperature and drought variables were pooled in the 'dry' (or 'wet') set of drivers if their t-values were positive (or negative), indicating an increasing effect of dry (or wet) conditions on year-to-year fire activity; the opposite was applied for precipitation variables. Partitioning the variance was then performed with the R package vegan, following Peres-Neto et al (2006). The relative contribution of each climatic fraction was computed on the basis of the ratio of the variance exclusively explained by one fraction (unique contribution, adjusted R-squared) to the sum of the unique contributions. In this way, we were able to determine the combinations of preceding, contemporary, dry or wet climatic conditions that best explained the fire seasonal component (year-to-year variation).

Testing fire alteration

Following a trend attribution method (e.g. Lobell et al 2011, Turco et al 2014), we applied the trend component to the predicted seasonal component to simulate the fire variation as a function of both climatic and anthropogenic effects (F*t):

$$\begin{equation} F*t = TY_{t = 1} + TY*t + \sum\nolimits_i^n {(a_i SX_{i,t} ) + b} \end{equation} \tag{ 3 }$$

And the fire variation as a function of climate only (C*t):

$$\begin{equation} C*t = TY_{t = 1} + \sum\nolimits_i^n {(a_i (SX_{i,t} + TX_{i,t}^* )) + b} \end{equation} \tag{ 4 }$$

This type of model assumes that fire–climate relationships are functionally the same for the seasonal and trend components, i.e. fires respond similarly to year-to-year climate fluctuations and to longer-term trends in climate (Turco et al 2014). Yearly values of fire alteration (A_t, %) were finally computed as the difference between F*t and C*t, which expresses the deviation at year t of the fire trend compared to the trend expected on the basis of climate:

$$\begin{equation} A_t = 100(F*t - C*t)/F*t \end{equation} \tag{ 5 }$$

The null hypothesis of no fire regime alteration (H0) is given by F*t = C*t. At a given year t, the significance of fire alteration (deviation from H0) was tested by the Student's t-test, whereby the A_t distribution of sample size N (N = 50), mean µ and variance S was compared to a zero-centred normal distribution of equal sample size N and variance S. Deviation from H0 is assumed to be exclusively induced by changes in human activities (i.e., independent of climate change), such as increases or decreases in anthropogenic ignitions, increasing efficiency of suppression strategies as well as human-mediated changes in land cover and species composition. For instance, A_t is independent of the climate-dependent inter-annual variation in fuel productivity, because the latter was accounted for in C*t by the inclusion of the 'previous' climatic set of drivers in MLR models.

To address uncertainties related to our approach, and in particular to ensure that H₀ (A_t = 0) is conserved when removing the potential influence of changing human activities on a decadal time scale, we applied MLR (climate-only) models to randomized time series of fire and climate. Specifically, for each region, fire season and fire metric, we tested whether the observed fire trend deviates from trends expected on the basis of climate only, when time series are random. This procedure consists of removing the anthropogenic component of the fire trend and adding it to the year-to-year variability. Times series were randomized on a annual basis, but each permuted fire year was still related to actual previous climatic conditions (up to the three preceding years) to retain their influence on fires. Results of these sensitivity analyses show convergent trends between fire observations and predictions (conservation of H₀) when removing the long-term influence of human activities (figure S2).

Fire–climate models indicate a strong dependency of the year-to-year seasonal variation of both fire density (FD) and burned area (BA) on climate fluctuation (figure 3). Variance explained by the MLR models was slightly lower for BA than FD, indicating a lower climatic explanation of the cumulative size of fires compared to the occurrence of fires reaching at least 1 ha. Variance partitioning of the fire seasonal component indicated that a mixture of drought-driven and fuel-limited fire regimes operated in southern France (figure 4). In winter, the number and size of fires increased with drier contemporary conditions, but also with the antecedent climate. Precisely, FD and BA increased with wetter antecedent conditions (up to three years) in the Mediterranean mountains or with wetter and warmer previous summer-autumns in the Mediterranean lowlands and Temperate mountains (figure 4, table S1 available at stacks.iop.org/ERL/12/035006/mmedia). In summer, wet years also increased FD and BA in the following dry summer, while in the Mediterranean mountains warm previous winter-springs chiefly increased the extent of fires (relative explained variance, mean ± SD, 49 ± 21%). Surprisingly, the relative contribution of preceding wet conditions was higher for winter fires (from 14 ± 7% to 43 ± 6%) than for summer fires (from 8 ± 6% to 21 ± 6%). In general, selection rates for climatic predictors in MLR models (table S1) indicated that the drought code and, to a lesser extent, the fine fuel moisture code, better described the effects of contemporary dry conditions on fire than did precipitation, temperature or the fire weather index.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

Accounting for these differential climatic effects during the fire season and previous years, observed fire trends weakly to strongly diverged from climatic expectations (figure 5). In addition, these divergences showed contrasting patterns between seasons and regions. Winter and summer fire activity have both decreased in all regions (figure S1). Climate-only models predict increasing fire trends in the Mediterranean mountains and to a lesser extent in the Mediterranean lowlands. In the Mediterranean mountains, climatic predictions of winter fires shifted from increasing to decreasing trends at the end of the 90s. In the Temperate mountains, there was a greater expectation of no change according to the climate-only models.

Zoom In Zoom Out Reset image size

Divergences between fire and climate trends, or fire regime alterations, occurred in all seasons and pyroclimates with specific patterns (table 2, figure 6). In the Mediterranean lowlands, winter and summer fires have been increasingly smaller and less frequent than expected on the basis of climate (−31 to −48% at the end of the study period), like in the Mediterranean mountains for summer fires (−77 and −84% at the end of the study period, respectively). In contrast, the alteration in winter fires was positive in the Mediterranean and Temperate mountains (mean values: +28% and +16%, respectively) in the 70s and 80s, before dropping to being zero or weakly negative since the mid-late 90s. In the Temperate mountains, however, summer fire alteration has been less negative (tends towards a positive change) since the early 90s.

Zoom In Zoom Out Reset image size

Fire–climate relationships during the past four decades vary substantially between regions and seasons in southern France, as do the direction and strength of fire regime alteration, corresponding to the results of Fréjaville and Curt (2015). Our findings revealed that in Euro-Mediterranean ecosystems, the climatic influence on fires was not restricted to the occurrence and duration of drought during a particular year, but that a mixture of drought-driven and fuel-limited fire regimes operated, emphasizing the lagged effects of warm or moist periods on fire (Keeley 2004, Pausas 2004, Meyn et al 2007, Zumbrunnen et al 2009, O'Donnell et al 2011). Furthermore, our modelling approach allowed us to quantify the climate-independent alteration of fire regimes, specifically the part induced by human activity. As expected, we found both positive and negative changes, supporting our hypotheses of contrasting short-term impacts of changes in land-use and fire suppression policy on fire regime, respectively. Because the direction and strength of fire regime changes were also different between regions and seasons, our findings highlight that predicting regional changes in fire activity in response to climate change is highly challenging, at least in areas where fire regimes are strongly shaped by human activities, such as in Euro-Mediterranean areas and beyond (Taylor et al 2016, Bird et al 2012, 2016).

Heterogeneity in climatic effects on fire

Fire regime alteration

Based on the distinction between the year-to-year and the decadal variation in fires, we developed statistical climate models for the three pyroclimates of southern France in order to extract the human component of the temporal trend in regional fire regimes—i.e. the influence of slowly changing patterns in anthropogenic ignitions, fire suppression policy and land-use (vegetation types) with demography, preferences and technologies. The climatic drivers of fires differed between seasons and regions, with important effects of preceding wet conditions on the frequency and size of fires. In the Mediterranean mountains, however, summer BA increased with positive temperature anomalies for the previous spring and the three previous winters. The past four decades have marked a transition from sometimes positive to always negative changes to the fire activity in southern France, with a predominant influence of fire suppression in recent years. We believe that this analytical framework is well suited to test changes in the human effect on fire regimes across a range of ecosystems and time periods, notably to test when, how and the extent to which fire regimes have been reshaped during the Anthropocene (Guyette et al 2002, McWethy et al 2013, Valese et al 2014). For instance, while fire suppression has probably driven recent decreases in fire in southern Europe (Moreno et al 2014, Turco et al 2016), another hierarchy of anthropogenic drivers may have been responsible for increasing fire trends in the western United States (Dennison et al 2014), including the introduction of flammable invasive species (Balch et al 2013). In addition, our findings highlight that predicting changes in fire regime associated with climate changes requires accounting for lagged climatic effects as well as rapid changes in socio-economic drivers, such as anthropogenic ignitions, land-use change and fire suppression.

We thank Peter Z Fulé and three anonymous referees for their helpful comments. Funding for this work was provided by the FUME Project under the European Union's Seventh Framework Programme (FP7/2007–2013) and by grants from the National Research Institute of Science and Technology for Environment and Agriculture (Irstea) to T.F.