offs and synergies between bird conservation and ...

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Journal of Applied Ecology DR ADRIAN REGOS (Orcid ID : 0000-0003-1983-936X) MR VIRGILIO HERMOSO (Orcid ID : 0000-0003-3205-5033)

Article type

: Research Article

Handling Editor: Jeroen Minderman

Trade-offs and synergies between bird conservation and wildfire suppression in the face of global change

RUNNING HEAD: BIRD CONSERVATION AND WILDFIRE SUPPRESSION

Adrián Regos1,2*, Virgilio Hermoso3, Manuela D’Amen4, Antoine Guisan4,5 & Lluís Brotons3,6,7

* Corresponding author: [email protected]

1

Departamento de Zooloxía, Xenética e Antropoloxía Fisica. Universidade de

Santiago de Compostela, Campus Sur, 15702 Santiago de Compostela, Spain. 2

CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources,

ECOCHANGE Group, Campus Agrario de Vairão, R. Padre Armando Quintas, Nº 7, 4485-661 Vairão, Portugal.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/1365-2664.13182 This article is protected by copyright. All rights reserved.

3

CTFC-CREAF, InForest Joint Research Unit, CSIC-CTFC-CREAF, Solsona, 25280.

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Spain. 4

Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne,

Switzerland. 5

Institute of Earth Surface Dynamics, Geopolis, University of Lausanne, 1015

Lausanne, Switzerland. 6

CREAF, Cerdanyola del Vallés, 08193, Spain.

7

CSIC, Cerdanyola del Vallés, 08193, Spain.

Abstract 1. The combined effects of climate change and other factors, such as land use change or fire disturbance, pose daunting challenges for biodiversity conservation worldwide. 2. We predicted the future effectiveness of the Natura 2000 (N2000), the current network of protected areas (PA) in Europe, at maintaining and representing suitable environmental conditions for a set of 79 bird species between 2000 and 2050 in a fire-prone area, strongly affected by land abandonment processes in North East Spain. We then compared PA performance with a set of alternative priority areas for conservation, which consider fire-vegetation dynamics, selected by using a conservation planning tool (MARXAN). Fire-vegetation dynamics were modelled using a process-based model (MEDFIRE MODEL) under alternative fire management and climate change scenarios. Bird assemblage distributions were predicted using the spatially-explicit species assemblage modelling framework (SESAM) using distribution models from individual species that hierarchically integrate climate change and wildfire-vegetation dynamics.

This article is protected by copyright. All rights reserved.

3. The amount of suitable environmental conditions within the N2000 network was

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predicted to fall by around 15%, on average, over the next decades in relation to the initial conditions but could be partially modulated by fire management policies in future. The efficiency of the current PA system was predicted to decrease from 17.4 to 15% over the next decades. However, a more efficient PA system could be achieved with a conservation planning approach that explicitly considers firevegetation dynamics and their management. 4. Synthesis and applications. Our findings show: (1) how the current Natura 2000 could still hold an important bird conservation value by 2050; (2) how the relocation of some protected areas should be considered in order to substantially increase bird conservation effectiveness; and (3) how the integration of fire-vegetation dynamics, fire management policies and their objectives within conservation planning provide ‘win-win’ solutions for bird conservation and fire prevention in fire-prone abandoned landscapes. RESUMEN 1. Los efectos combinados del cambio climático y otros factores, como el cambio en los usos del suelo o los incendios forestales, plantean enormes desafíos para la conservación de la biodiversidad en todo el mundo. 2. Este estudio predijo la futura efectividad de la red Natura 2000 (N2000), la red actual de áreas protegidas (AP) en Europa, para mantener y representar las condiciones ambientales adecuadas para un conjunto de 79 especies de aves entre 2000 y 2050 en un área del noreste de España propensa a incendios y fuertemente afectada por los procesos de abandono rural. Luego comparamos el desempeño de las AP con un conjunto de áreas alternativas de prioridad para la conservación, que consideran las dinámicas de fuego y vegetación, seleccionadas mediante una


herramienta de planificación de la conservación (MARXAN). Las dinámicas de fuego

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y vegetación fueron simuladas utilizando un modelo basado en procesos (MODELO MEDFIRE) bajo escenarios alternativos de gestión de incendios y cambio climático. Las distribuciones de ensamblaje de aves se predijeron utilizando el marco de modelos espacialmente explícitos de ensamblaje de especies (SESAM) utilizando modelos de distribución de especies individuales que integran jerárquicamente el cambio climático y las dinámicas del fuego y la vegetación. 3. Se pronosticó que las condiciones ambientales adecuadas para la conservación de estas especies dentro de la red N2000 caería alrededor del 15%, en promedio, en las próximas décadas con relación a las condiciones iniciales, aunque estas estimas podrían verse parcialmente influenciadas por las futuras políticas de gestión de incendios. Se pronosticó que la eficiencia del sistema de áreas protegidas actual disminuirá del 17,4 al 15% en las próximas décadas. Sin embargo, se podría lograr un sistema más eficiente con un enfoque de planificación que considere explícitamente las dinámicas de fuego y vegetación, así como su manejo. 4. Síntesis y aplicaciones. Nuestros resultados muestran: (1) cómo la actual red Natura 2000 podría mantener un importante valor en términos de conservación de la avifauna en 2050; (2) cómo debería considerarse la reubicación de algunas áreas protegidas para aumentar sustancialmente su efectividad; y (3) cómo la integración de las dinámicas de fuego y vegetación, las políticas de manejo de incendios y sus objetivos dentro de la planificación de la conservación brindan soluciones "win-win" para la conservación de aves y la prevención de incendios en paisajes abandonados propensos a incendios forestales.


Keywords: SESAM; fire management; land abandonment; climate warming;

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process-based models, protected areas, Natura 2000, conservation planning

Introduction Protected areas (hereafter PAs) play a key role in safeguarding biodiversity worldwide. However, their role can be seriously compromised in highly dynamic socioecological systems due to the difficulties of the traditional conservation approaches, generally based on selecting static PAs, to cope with global change and its multiple effects on biodiversity (Bush et al. 2014). For instance, wildfires are predicted to become larger and more severe as a consequence of an increase in fuel accumulation (induced mainly by land abandonment) coupled with drier and warmer climatic conditions (Krawchuk et al. 2009; Moreira et al. 2011). Thus, fire management policies that aim to reduce the impact of greater fire severity will likely play a critical conservation role in fire-prone abandoned landscapes (Moritz et al. 2014; Regos et al. 2016a; Kelly & Brotons 2017). Moreover, socioeconomic issues related to renewable energy policies are also expected to promote large-scale changes in landscape through forest management and their potential effects on fire regime and vegetation dynamics (Regos et al. 2016b). Despite all this knowledge, most studies do not explicitly incorporate landscape dynamics into conservation planning (but see Rayfield et al. 2008) and largely neglect changes in land use and fire management policies (Titeux et al. 2016).

Natura 2000 (N2000) is a network of PAs firmly established to halt biodiversity

loss in Europe. It was designed under the 1979 Directive on the conservation of wild birds (Birds Directive; 79/409/EEC, amended in 2009: 2009/147/EC) and the 1992


Directive on the conservation of other taxa and habitats (Habitat Directive;

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92/43/EEC, consolidated in 2007). In December 2016, the European Commission published its ‘Fitness Check’ evaluation of the EU Nature Directives and concluded that, within the framework of broader EU biodiversity policy, they remain highly relevant and are fit for purpose. However, “[…] fully achieving their objectives and realising their full potential will depend on substantial improvement in their implementation in relation to both effectiveness and efficiency […]” (Milieu, IEEP & ICF 2016). These conclusions are in line with previous research highlighting that while PAs are performing better than random, the system is not optimal, and better outcomes can be obtained applying systematic planning approaches (Kujala et al. 2013; Kukkala & Moilanen 2013; Hermoso et al. 2015). This is particularly interesting in the context of the N2000 network, since it has been widely evaluated (Dimitrakopoulos et al., 2004; Maiorano et al., 2007; Pellissier et al., 2013, among others) and was recently compared to a theoretical optimal spatial design (Jantke, Schleupner & Schneider 2011; Mikkonen & Moilanen 2013; Kukkala et al. 2016), but never under a set of alternative fire management policy scenarios.

This study predicted trade-offs and synergies between bird conservation and

wildfire suppression in the face of global change in a fire-prone abandoned landscape. The purpose was to find ‘win-win’ solutions to increase the resilience of the current N2000 network (i.e., ability of the PAs to maintain their initial conservation values after being affected by environmental changes) while simultaneously reducing wildfire impacts. In particular, we first evaluated the future effectiveness of the N2000 network at representing (henceforth ‘representativeness’) and maintaining (henceforth ‘effectiveness’) suitable


environmental conditions between 2000 and 2050 under a wide range of fire

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suppression scenarios for a set of 79 bird species breeding in a highly dynamic, fireprone landscape in a Mediterranean region (Catalonia). We also predicted the costeffectiveness of the N2000 network (henceforth ‘efficiency’), using total area under protection as proxy for conservation costs (Naidoo et al. 2006).

Overall, we would expect the N2000 network to show good future

performance in terms of effectiveness and representativeness given its large coverage in our study region (around 33% of Catalonia), as already predicted for the study area (Regos et al. 2016a). However, in terms of efficiency, better outcomes could be achieved by applying a conservation approach based on spatial prioritization planning that takes into account fire–vegetation dynamics (henceforth ‘adaptive conservation planning’). Moreover, given that our study area is highly prone to fire, more realistic conservation objectives could be achieved by implementing alternative fire management strategies in the current N2000 network. A combination of adaptive conservation planning with fire management strategies could increase the resilience of the current N2000 network to future environmental changes, especially for those species most at threat from global change in the next decades.

Materials and Methods Study area and the Natura 2000 network The study region is Catalonia, a typical Mediterranean-climate area in north-eastern Spain. The vegetation mainly includes forest and shrubland (Ibañez & Burriel 2010), the two land cover types most affected by fire (Díaz-Delgado, Lloret & Pons 2004).


Farmland abandonment over the last decades has been followed by a conversion

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from abandoned open land (i.e. shrubland) to forest habitats associated with reductions in livestock grazing and wood harvesting (Herrando et al. 2014). Fire is another major landscape driver in the study region, with about 25% of the wildland area affected by fires during the 1975–2010 period (Díaz-Delgado, Lloret & Pons 2004). The interactions between such fire–vegetation dynamics, fire suppression and climate change are expected to induce significant shifts in fire disturbance regime in this Mediterranean region (Brotons et al., 2013).

In Catalonia, the current PA system designed under the EU Nature Directives

is the N2000 network. Despite the recent designation of new PAs, N2000 in Catalonia can be defined as a stable and static system with 115 Special Areas of Conservation (SACs) designed for the protection of habitats and species listed in the Annexes of the Habitats Directive, and 73 Special Protection Areas (SPAs) designed for the protection of birds included in the Birds Directive (Fig. 1). SACs and SPAs cover 32% and 28% of the region, respectively, for a total combined extent of 10,624 km², 87% of which is covered by both SACs and SPAs (GENCAT 2014).

Bird and environmental data We used occurrence (presence/absence) data for breeding bird species at two different spatial extents and resolutions, sourced from: 1) the Atlas of European Breeding Birds (EBCC data), which compiles the occurrence of breeding bird species in Europe between the late 1980s and early 1990s in 3165 grid cells of 50km resolution (Hagemeijer & Blair 1997) to effectively include the climate niche of the target species by encompassing the widest possible distributional range; and, 2) the


Catalan Breeding Birds Atlas (CBBA data), which reports information on breeding

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bird distribution in Catalonia between 1999 and 2002 based on intensive surveys of 3077 grid cells at 1-km resolution (Brotons et al 2008). Among the 214 bird species that breed in Catalonia, we focused on those that are expected to be affected by fire–vegetation dynamics and climate change to illustrate how the combined effect of these drivers can affect the performance of the PAs (De Cáceres et al. 2013; Regos et al. 2018). From this initial dataset, species counting less than 30 presences were removed to ensure sufficient information for modelling. Hence, our bird assemblage comprises a set of 79 species exhibiting different degrees of habitat preference along a gradient from open habitats (i.e., early-successional stages and sparselyvegetated areas) to forested areas. All 79 species were included in the analysis (species listed and unlisted in Annex I of the Birds Directive) to gain a more holistic perspective of the PAs, without missing species that, although not currently included in the Birds Directive, could be negatively affected by global change in the coming decades (thus making them priority species for conservation).

Climate data were obtained from the WorldClim database

(www.worldclim.org/current) for current-day conditions (period 1960–1990; hereafter: ‘current’) and from an average ensemble model of four GCMs (CCCMA-CGCM2, CSIRO-MK2.0, HCCPR-HadCM3 and NIESS99; see details in Appendix S1) available via the International Center for Tropical Agriculture (CIAT) website (http://www.ccafs-climate.org) for future conditions (A2 IPCC-SRES for the period 2040–2069; hereafter: 2050s). This information was computed using the WorldClim database as baseline and is available at 30 arc-second (~1 km) resolution.


Land-cover variables in present-day conditions were compiled by combining

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two raster layers at 100-m resolution: land-cover type and time since last fire. Information on landscape composition was obtained from each land-cover type: (1) coniferous and (2) oak tree species and (3) shrubland as dynamic variables, and (4) cropland as a static variable. Detailed knowledge of the fire-mediated properties of landscapes was obtained based on proportional extent of three different fire ageclasses: (5) older vegetation (> 30 years since last fire) (6) mid-age vegetation (10– 30 years since last fire), and (7) recently-burnt vegetation (< 10 years since fire). All these land-cover layers were then simulated for 2050 using the MEDFIRE model  a spatially explicit process-based model (Brotons et al. 2013)  under six fire management policies and A2 IPCC-SRES climate change scenario (see Table 1, and Appendix S2 for detailed information about the scenario storylines and MEDFIRE simulations). These simulations were run 10 times (i.e., replicates) to deal with the stochastic nature of wildfires (a sensitivity analyses between 3 and 100 replicates showed no significant effect of number of simulations beyond the threshold we used in the analyses; see Appendix S3).

Spatially-Explicit Species Assemblage Modelling (SESAM) We applied a community-level modelling approach based on the Spatially-Explicit Species Assemblage Modelling (SESAM) framework (Guisan & Rahbek 2011) to predict potential change in the bird community composition between 2000 and 2050 under each fire management scenario. This modelling framework can be described as follows (see Fig. 2 for a squematic workflow and Appendix S4 for full description):


Step 1: Stacked-Species Distribution Models (S-SDMs).

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We followed the hierarchical approach proposed by Pearson, Dawson & Liu (2004) to modelling individual species’ distributions. This approach assumes that climate is the dominant factor shaping species’ distributions at the continental scale, while factors such as land-cover type and fire disturbance become increasingly important at regional scale, as they can progressively refine the climate envelope of the species. Thus, bioclimatic envelopes for each target species were first modelled using EBCC bird data and climate variables at European scale under both current and 2050 climate conditions (‘Climate models’ in Fig. 2). These models were then directly projected onto the 1-km-resolution grid cells in Catalonia. In parallel, habitat models were built using the CBBA bird data and land-cover variables derived from the simulations of the MEDFIRE model at Catalonia level (‘Habitat models’ in Fig. 2). Finally, the environmental suitability predicted by both climate and habitat models were included as separate predictors in a final model (as described in Regos et al., 2016a) to balance the contribution of each type of driver in shaping the predicted distributions of species (‘Combined models’ in Fig. 2).

All single-species distribution models (hereafter ‘SDMs’) were built using five

widely-used algorithms (GLM, GAM, CTA, GBM and RF) assuming a binomial distribution and logit link (BIOMOD2; Thuiller et al. 2009). We used a repeated (10 times) split-sample approach to produce predictions independent of the training data. Each model was fitted using 70% of the data and evaluated using the area-underthe-curve (AUC) of a receiver-operating characteristic (ROC) (Fielding & Bell 1997) calculated on the remaining 30%. We applied an ensemble forecasting framework by computing a consensus of single-model projections (from models with AUC > 0.7


using AUC values as model weights) using a weighted average approach (Araújo &

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New 2007; Marmion et al. 2009) (see Table S5.1 and S5.2 in Appendix S5 for SDMs evaluation).

Step 2: Macroecological Models of Species Richness (MEMs). To account for environmental carrying capacity, we modelled observed species richness to determine the maximum number of species that can co-occur in a given site. To do so, we applied the same modelling algorithms used to build the SDMs (i.e., GLM, GAM, CTA, GBM and RF) following the same methodology as described in Step 1, but with a Poisson error distribution and log link function. Observed species richness was calculated as the count of all species belonging to each of the 3165 50-km grid cells at European level and 3077 1-km grid cells at Catalan level. Each model was run 10 times with the 70/30 repeated split-sample procedure described above. An ensemble species richness estimate for each future scenario and time-period was obtained by averaging the predictions from full models run with the 5 different techniques. The ensemble species richness prediction was subsequently projected onto future climate conditions at the Catalan extent and resolution (see Fig. S6.1 in Appendix S6 for model evaluation for species richness).

Step 3: Ecological Assembly Rules (EARs). We applied the ‘probability ranking rule’ (D’Amen et al. 2015) to identify the species that can actually assemble to form the final community on the basis of decreasing environmental suitability calculated by the SDMs in Step 1 until reaching the predicted richness (proxy of the system’s environmental carrying capacity) estimated


from the macroecological models in Step 2 (see Table S6.1 in Appendix S6 for

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comparison with other ecological assembly rules and a null model).

Validation of the whole assemblage modelling framework We calculated ten evaluation metrics for each grid cell, which reflect different aspects of assemblage predictions (Pottier et al. 2013): (1) species richness deviation (i.e., the deviation of the predicted species richness to the observed), (2) overprediction (i.e., the proportion of species predicted as present but not observed among the species predicted as present), (3) underprediction (i.e., the proportion of species predicted as absent but observed among the species observed as present), (4) assemblage prediction success (i.e., the proportion of correct predictions), (5) assemblage specificity (i.e., the proportion of absences that were correctly predicted), (6) assemblage sensitivity (i.e., the proportion of presences that were correctly predicted), (7) assemblage kappa (i.e., the proportion of specific agreement) (8) TSS (i.e., sensitivity + specificity - 1), (9) the Sorensen index (i.e., the similarity of community composition between the observation and the prediction) and (10) the Jaccard index (Pottier et al. 2013; Di Cola et al. 2017). All these evaluation metrics were computed using the function ‘ecospat.CommunityEval’, available in the R package ‘ecospat’ (Di Cola et al. 2017) (see accuracy results in Table S6.1 and details in Appendix S6).

Step 4: Priority areas for bird conservation. The bird community composition predicted by the S-SDMs (Step 1) after applying the richness constraints (Step 2) and the ecological assembly rule (Step 3) was used as input in systematic conservation planning methods (Fig. 2).


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Bird priority conservation areas for 2000 and 2050 under each fire management scenario (Table 1) were identified by running the MARXAN software (Ball, Possingham & Watts 2009) (Fig. 2). MARXAN uses the simulated annealing optimization algorithm to solve the minimum set problem (Cabeza & Moilanen 2001), which is to select a group of spatial units that meets a set of biodiversity targets (see Appendix S7) while minimizing the cost and boundary length of the system (Ball, Possingham & Watts 2009). Given that simulated annealing performs a stochastic search, the process is normally repeated multiple times. We performed 100 MARXAN runs for each analysis, with one million iterations and the species penalty factor (that controls the level of penalty applied when a conservation feature target is not met) set to 10. The boundary length modifier (BLM) was optimized to 3, which offers an efficient trade-off between priority-area system boundary length and size (i.e., compactness) of priority areas, following Stewart & Possingham (2005) (see calibration in Appendix S8). Total area selected (i.e., number of 1-km grid cells) was used as proxy for conservation costs (Naidoo et al. 2006).

Step 5: Protected area indices: effectiveness, efficiency and representativeness We applied three indices based on core concepts of conservation planning (Kukkala & Moilanen 2013) to estimate the effectiveness, efficiency and representativeness of the N2000 network, and the priority areas identified by MARXAN simulations for each target species between 2000 and 2050 (Fig. 2). (1) Effectiveness index, measured as the change in environmental suitability (ENVS) of each species (i) within a particular PA system (s) between 2000 and 2050:


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–

] x 100

(2) Efficiency index, defined as the proportion of ENV-S of species (i) within a particular PA system (s) relative to the total extent of this PA system (s):

(3) Representativeness index, defined as the proportion of ENV-S of each species (i) included within a PA system (s) relative to the ENV-S in the entire study area (total ENV-S).

The overall performance index values of each PA system (i.e., the Natura

2000 network, hereafter called ‘N2000’, or the priority areas proposed by MARXAN simulations, hereafter called ‘MARXAN’) were computed by averaging the performance index values obtained for each species considering the whole set of species. We also calculated performance index values considering only the 25% of species with the lowest indices values to focus on those species predicted to be negatively affected by environmental changes. We represented the variability of overall performance indices across the 6 fire management scenarios by using boxplots, showing the overall performance index values obtained for each of the 10 scenario simulations (i.e., replicates).


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Step 6: Multimodel inference approach. The effects of fire management policies (see Table 1) and type of PA system (i.e., N2000 or MARXAN) on the overall value of each performance index estimated in Step 5 for each scenario simulation (i.e., replicates) were estimated using GLMs with a Gaussian error distribution and the ‘identity’ link function (McCullagh & Nelder 1989) (Fig. 2). In particular, we applied a multimodel inference approach (Burnham & Anderson 2002) to run the GLMs for all (valid) combinations of explanatory variables; i.e., fire management, type of PA system and their interactions (with the ‘dredge’ function available in the MuMIn R package; R Core Team 2015). For each model, we calculated the Akaike Information Criterion (AIC) and ∆I, where ∆I = AICi−AICminimum. All the models with ∆I < 7 were considered to have support (Burnham & Anderson 2002). The importance of each predictor was obtained by adding the Akaike weights (Wi) to the models in which that variable is present (Burnham & Anderson 2002).

Results The overall performance of the PA systems in Catalonia was predicted to be higher in terms of representativeness for 2050 (representativeness index values ranging from 49 to 52% across management scenarios) than for present-day conditions (47.9%) for both the static (i.e., current N2000) and adaptive (i.e., MARXAN solutions) systems (see boxplots for representativeness in relation to the year 2000, black line in Fig. 2). Fire management was found to significantly affect representativeness (WFM = 1; Table 2), being synergistically positive for the target species under a dynamic perspective and high levels of fire suppression (business-as-usual scenario) (WFM*PA = 1; Table 2). However, this strategy should be avoided if the aim is to


increase the performance of the N2000 network for those species with less

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representativeness (compare the whole set of species with the threatened ones in Fig. 3).

The effectiveness of the PAs in protecting all bird species under 2050

conditions was only predicted to be higher than currently when applying an adaptive system (compare ‘MARXAN’ vs ‘N2000’ for effectiveness in Fig. 3; WPA = 0.996; Table 2). However, fire management was also predicted to significantly affect their performance, by itself or in interaction with type of PA system (WFM = 0.996, WFM*PA = 0.939; Table 2). In fact, the amount of suitable environmental conditions within the N2000 network was predicted to decrease by around 15% over the next decades in relation to the initial conditions (i.e. year 2000). This is especially relevant for those species for which N2000 shows the lowest effectiveness, with decreases of around 40–45% of their initial suitability within the PA system, mainly under the business-as-usual scenario (threatened species in Fig. 3).

The efficiency of the current PA system was predicted to decrease from 17.4

to 15% over the next decades; i.e. 1 hectare will be required to protect 0.15 hectares in 2050. However, a more efficient PA system (values ranging from 17.5–19.5%) can only be achieved with a more adaptive approach (WPA = 0.997; Table 2; compare ‘MARXAN’ vs ‘N2000’ for efficiency in Fig. 3). For those species for which N2000 shows the lowest efficiency, fire management was predicted to only slightly affect the efficiency performance of N2000.


Discussion

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To our knowledge, this is the first study predicting the potential trade-offs and synergies between bird conservation and wildfire suppression under global change scenarios, in a fire-prone landscape. It is also one of the first conservation planning studies that integrates climate change, fire–vegetation dynamics (i.e., fire disturbance, natural succession and post-fire regeneration) and fire management while simultaneously accounting for the limited environmental carrying capacity and local assembly processes. Our results show a wide range of ecological responses of bird species to the complex and often interacting effects of climate and fire– vegetation dynamics (Appendix S9), and an increasing role of fire management (Fig. 3). This finding emphasizes the need for a holistic (i.e., multi-species) perspective on bird conservation in fire-prone landscapes. Overall, a conservation planning perspective that takes into account wildfire and vegetation dynamics could increase the resilience of the current N2000 network (i.e., ability of the network to maintain their initial conservation values after being affected by environmental changes) (Fig. 3). More importantly, the integration of fire management policies and their objectives within conservation planning is expected to provide ‘win-win’ solutions for bird conservation and wildfire suppression.

According to our simulations, the environmental suitability for bird species

under the N2000 system would decrease over the next decades due to their overall decline in Catalonia (Fig. 3). However, priority areas for bird conservation were predicted to be better represented by the current Natura 2000 in year 2050 than nowadays (Figs. 3 and S11.1), which suggests that its current design is adequate to prevent future impacts of climate and land cover changes on bird communities. Also,


the number of additional priority areas that would be required to complement the

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current PA system will likely decrease along the next decades, which will reinforce the role of the Natura 2000 network for bird conservation (Fig. S11.1). Despite the large influence of the fire management policies (Fig. 3 and Table 2), the relocation of some protected areas could be therefore considered along the next decades in order to further increase the conservation effectiveness of the current Nature 2000 network (Figs. 3 and S11.1). Our simulations predicted a strong increase in the effectiveness of the current N2000 for open-habitat species such as Dartford warbler (from -50% up to 5%, see Fig. S10.1) by exclusively changing fire management from the business-as-usual scenario (characterized by high levels of fire suppression which involves a large amount of resources invested annually in firefighting) to a strategy based on letting unplanned fire burns under controlled fire-weather conditions (much more cost-effective option; see Houtman et al. 2013; Regos et al. 2014). However, the conservation of suitable conditions for other early-successional species such as Ortolan bunting within the PAs was predicted to be only possible under fire management policies that aim to create new open spaces, and more adaptive planning wherein new priority conservation areas would substantially complement the current N2000 network (Fig. S10.1). Therefore, our simulations highlight the need for a more adaptive perspective on the conservation planning process to ensure that mobile species (such as birds) will be adequately protected in the future (Regos et al. 2016a; Runge et al. 2016).

Land abandonment has been proposed as an opportunity for conservation in

Europe (see the ‘ecological rewilding’ concept in Navarro & Pereira 2012; Helmer et al. 2015), mainly for forest biodiversity (Gil-Tena, Brotons & Saura 2009; Merckx


2015; Regos et al. 2016c). Our study also shows that the availability of suitable

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environmental conditions for some forest species within PAs will likely increase in the future (Fig. S10.1). However, other forest-related species may suffer a strong decline in environmental suitability due to climate change according to our simulations (see e.g. Black woodpecker, in Fig. S10.1; and Regos et al., 2016a). Forest biomass extraction for bioenergy uses could play an important role on the conservation of these forest-specialist species in the near future (Fig. S10.1), while reducing fuel accumulation and, therefore, fire risk (Regos et al. 2016b). The decision to be made will therefore depend on the specific conservation and fire prevention objectives that managers could have within Natura 2000, i.e. a trade-off between reducing fire impacts with side effects on early successional species or ensuring the protection of forest species at the cost of a higher fire risk. Given the context of long-standing land abandonment, early successional species would strongly benefited from fire management strategies alternative to those implemented nowadays (Fig. 3 and S10.1; De Cáceres et al. 2013; Regos et al. 2015), with co-benefits for fire prevention and low side effects on forest species, i.e. a win-win situation.

Our results showed that a more proactive conservation perspective that

explicitly takes into account fire–vegetation dynamics, would increase the resilience of the current N2000 network for birds to future environmental changes (Fig. 3). This is especially important for those species expected to be more affected by the combined effect of climate and land abandonment, as they were predicted to lose more than 40% of their initial environmental conditions under the current N2000 system (see ‘threatened species’ in Fig. 3). A revision of the species listed in the annexes of the EU Directives as well as strategic conservation plans for the most


endangered species will be essential to ensure the PAs system meets its biodiversity

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objectives (Hochkirch et al. 2013). We also identified new areas, based on fire– vegetation dynamics, that would increase the efficiency of the current N2000 system in terms of hectares required to protect a hectare of suitable environmental conditions for birds (Fig. 3). However, these results should be interpreted with caution, as we used area as proxy for conservation costs, thus assuming an ideal scenario wherein all grid cells (i.e., planning units) had a constant cost. Although this approach is useful in the context of our research questions, it is important to note that ignoring the heterogeneity of conservation costs can lead to more expensive PA systems (Naidoo et al. 2006). Moreover, recent studies have also highlighted that the benefits obtainable from reducing the impact of wildfires by applying large-scale fire management strategies and the costs associated with the impacts of large fires are usually underestimated (Mason et al. 2006) and can play a decisive role in their future implementation.

Our results also showed an increasing role of PAs in terms of

representativeness of bird species in the future (Fig. 3). Fire management is also expected to play a key role in this regard, as higher representativeness values will likely be achieved under the BAU scenario (i.e., high levels of fire suppression) in an adaptive PA system or under an alternative fire management strategy based on forest biomass extraction under the current N2000 system (Fig. 3). Again, the decision will depend on the specific conservation and fire prevention objectives, i.e. a balance between maintaining the current fire suppression policy and increasing the PA system or promoting renewable energy-based forest management within the current N2000 network.


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From a methodological viewpoint, some limitations of our approach should be considered to avoid misleading conclusions. Our biodiversity projections can be affected by three main sources of uncertainty (as described in the Supporting Information for Regos et al. 2016a): (1) the general circulation models (GCM) selected, (2) the inherent stochasticity of fire dynamics, and (3) the modelling algorithm applied. Here, future climate change projections were computed by averaging the outcomes of four GCMs to account for the uncertainty arising from the inter-model variability. Applying consensus methods (e.g., by deriving the central tendency of forecasts) among climate models has been recently adopted by ecologists in biodiversity assessments under potential climate changes for reducing variability across all models (Buisson et al. 2010; Naujokaitis-Lewis et al. 2013). However, it is important to bear in mind that, using a central tendency of forecasts, the effects of extreme climate regime scenarios can be hidden (Beaumont, Hughes & Pitman 2008). Moreover, fire is a stochastic process driven by a complex interplay of ignition occurrence, climatic variability, local weather, topographic conditions and vegetation structure, as well as fire management policies (Keane et al. 2004). Our fire simulation scenarios were computed several times in order to account for such stochasticity (see intra-scenario variability in Fig 3). Thus, although computing a central tendency of forecasts helps drawing conclusions, we should also take into account that extreme fire events can also be masked in our simulations. Finally, the combination of different modelling algorithms has been adopted to adjust inherent uncertainty of individual models for each target species (Araújo & New 2007). Our ensemble models, built on a series of competing models, each with a different combination of environmental predictors, may provide more informative and


ecologically correct predictions (Thuiller 2003). However, these predictions should be

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interpreted with caution as ensemble modelling approaches do not reduce uncertainties, but the likelihood of making conservation decisions based on inaccurate forecasts (Araújo & New 2007). It is also important to note that our biodiversity models, based on European and Catalan bird atlases dated from early 90’s and 2000, could be clearly benefited from the upcoming update of the current distribution of species (Herrando, Vorisek & Keller 2013; Herrando et al. 2017).

Conclusions This study sheds light on: (1) how priority areas for bird conservation could be better represented by the current Natura 2000 in the future than nowadays, which suggests that the current network of protected areas might still hold an important bird conservation value by 2050; (3) how the relocation of some protected areas could be considered along the next decades in order to further increase the bird conservation effectiveness of the current network; and finally, and crucially, (3) how the integration of fire-vegetation dynamics, fire management policies and their objectives within conservation planning can provide ‘win-win’ solutions in terms of bird conservation and wildfire prevention in fire-prone abandoned landscapes.

This kind of integrative approach can help the European Commission and the

EU Member States to fully achieve their international commitments and targets by realising the full potential of the EU Directives by making substantial improvements to the implementation of a cornerstone tool for biodiversity conservation in Europethe Natura 2000 network.


Authors’ contributions

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AR, VH and LB conceived the ideas and designed methodology. AR and VH analysed the data. MD and AG designed the modelling framework. AR and VH led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgements This work was partly funded by the EU projects BON (308454; FP7-ENV-2012, European Commission), FORESTCAST (CGL2014-59742), INFORMED (FORESTERRA ERA-NET 29183) and NEWFORESTS (EU Seventh Framework Programme, PIRSES-GA-2013-612645). AR is funded by the Xunta de Galicia (postdoctoral fellowship ED481B2016/084-0). VH also received funding support provided by the Spanish Government through a Ramon y Cajal contract (RYC-2013-13979).

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Tables Table 1. List of scenarios simulating future environmental changes in the study area. Each scenario is a combination of a climate trend (A2 IPCC-SRES) and a fire management policy. For more details, see Appendix S1.

Scenario

Scenario description

Storyline

Incentives/Constraints

Forest harvesting in optimal areas from

Forest

Forest biomass extraction

an environmental and economic

biomass

is prohibited in protected

viewpoint (~ 39,000 hectares annually

extraction

areas

acronym Biomass

extracted) + climate trend according to the A2 IPCC-SRES climate scenario

Biomass

Forest harvesting in optimal areas from a

Forest

Forest biomass extraction

plus

logistic and economic viewpoint (~

biomass

is allowed in protected

62,000 hectares annually extracted) +

extraction

areas

Let-burn

6,500 hectares annually

Let-burn

climate trend according to the A2 IPCCSRES climate scenario An opportunistic fire suppression strategy based on lowly decreasing active

burnt in climatically mild

firefighting efforts in controlled “mild” fire

years

weather conditions to provide further firefighting opportunities in adverse years + climate trend according to the A2 IPCC-SRES climate scenario

Let-burn

An opportunistic fire suppression strategy

plus

based on highly decreasing active

burnt in climatically mild

firefighting efforts in controlled “mild” fire

years

BAU

Let-burn

52,000 hectares annually

weather conditions to provide further firefighting opportunities in adverse years + climate trend according to the A2 IPCC-SRES climate scenario Strong active fire suppression

Stop all fires

management corresponding to the

(business-as-


-

business-as-usual scenario + climate

usual)

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trend according to the A2 IPCC-SRES climate scenario

Non-

No fire suppression strategy + climate

No

-

Supp

trend according to the A2 IPCC-SRES

suppression

climate scenario

Table 2. Model ranking according to ∆AIC (delta Akaike Information Criterion; only models ∆AIC < 7 are shown) for each performance index. Abbreviations: FM (6 fire management policies; see Table 1); PA (type of protected-area system: the current Natura 2000 network and the adaptive PA system, i.e. MARXAN solutions); FM*PA (interaction between fire management and type of PA system).

Index Effectiveness

Efficiency

Representativeness

Models

AIC

Delta (∆AIC)

Weight

FM + PA + FM*PA

597.00

0

0.939

FM + PA

602.61

5.605

0.056

PA

-837.76

0

0.961

FM + PA

-831.26

6.499

0.037

FM + PA + FM*PA

284.19

0

0.999


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Figures

Fig. 1. Location of the study area in Europe. Polygons show the Natura 2000 network in Catalonia. Probability of being selected as priority areas according to the MARXAN simulations (increasing from blue to red colours) under each fire management policy (A: ‘Let-burn’, B: ‘Let-burn plus’, C: ‘Biomass’, D: ‘Biomass Plus’, E: ‘Non-Supp’, and F: BAU).


Spatially-Explicit Species Assemblage Modelling (SESAM) framework Step 2: Macroecological models of species richness

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Step 1: Stacked species distribution models

• 6 scenarios • 10 replicates • 60 projections

• 1 scenario • 1 projection


• 1 scenario • 1 projection

Combined model Catalan level • 6 scenarios • 10 replicates • 60 projections

Habitat model Catalan level

Climate model EU level

Habitat model Catalan level

Climate model EU level

Combined model Catalan level • 6 scenarios • 10 replicates • 60 projections

Step 3. Ecological Assembly Rules (EARs) Probability Ranking Rule (PRR)

Step 4. Priority conservation areas

MARXAN Current conditions

Step 4. Current protected-area system

MARXAN Scenario ‘BAU’

x 10 replicates

MARXAN Scenario ‘Non-Supp’

x 10 replicates

MARXAN Scenario ’Let-burn’

x 10 replicates

MARXAN Scenario ‘Let-burn plus’

x 10 replicates

MARXAN Scenario ‘Biomass’

x 10 replicates

NATURA 2000

MARXAN Scenario ‘Biomass plus’ x 10 replicates

Step 5. Protected areas (PA) indices 1. Representativeness

2. Effectiveness

3. Efficiency


Step 6. Multimodel Inference approach GLMs: PA index ~ Fire management scenario (Table 1) + Type of PA system (N2000/MARXAN) + Interaction

Fig. 2. Schematic diagram of the workflow pipelines and steps.


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Fig. 3. Overall performance index values (‘Effectiveness’, ‘Efficiency’ and ‘Representativeness’) of the current Natura 2000 (labelled as ‘N2000’) and the adaptive PA system (labelled as ‘MARXAN’) predicted for year 2050 under the 6 fire management scenarios (see Table 1). These indices were computed by averaging the performance index values obtained for each species considering the whole set of species (labelled as ‘All Species’) and considering only the 25% of species with the lowest index values (labelled as ‘Threatened Species’). Horizontal black lines indicate the index values for year 2000.
