Factors determining the abundance and occurrence of Hermann’s tortoise Testudo hermanni in France and Spain: Fire regime and landscape changes as the main drivers

Biological Conservation 170 (2014) 177–187

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Factors determining the abundance and occurrence of Hermann’s tortoise Testudo hermanni in France and Spain: Fire regime and landscape changes as the main drivers Thibaut Couturier a,⇑, Aurélien Besnard a, Albert Bertolero b,c, Valérie Bosc d, Guillelme Astruc a, Marc Cheylan a a Laboratoire de Biogéographie et Ecologie des Vertébrés, Ecole Pratique des Hautes Etudes, Centre d’Ecologie Evolutive et Fonctionnelle, UMR 5175, 1919 Route de Mende, F34293 Montpellier Cedex 5, France b Associació Ornitològica Picampall de les Terres de l’Ebre, C/La Galera, 53, 43870 Amposta, Spain c Ecosistemes Aquàtics-IRTA, Ctra Poble Nou, km 5.5, 43540 Sant Carles de la Ràpita, Spain d Conservatoire d’espace naturel de Corse, Association des amis du PNRC, Maison ANDREANI – RN193 Lieu dit Revinco, 20290 Borgo, France

a r t i c l e

i n f o

Article history: Received 4 July 2013 Received in revised form 13 December 2013 Accepted 16 December 2013

Keywords: Wildfire Landscape complexity Maxent Zero-inflated modeling Long-lived species Mediterranean Testudo hermanni

a b s t r a c t Major landscape transformations have occurred in the northern Mediterranean over the last decades, including urbanization, agricultural intensification and land abandonment, which, in turn, increase the risk of the propagation of fire. We used repeated-count surveys conducted at 369 sites in France and Spain to jointly model the effects of environmental covariates on the abundance, occupancy and detection of Hermann’s tortoise, a long-lived and endangered species, using a novel zero-inflated approach. We also employed a large dataset of presence-only data collected in Provence to model environmental influences on occurrence probability using maximum entropy models. In both France and Spain, sites that experienced wildfires over the last 50 years hosted 31% fewer individuals than unburned sites. In Provence, higher wildfire frequency decreased this species’ occurrence probability, from 50% when 0–1 fire had occurred over the last 50 years, to 7% in areas that had burned at least 3 times. We also showed that abundance required a long recovery time (more than 25 years) after wildfires. In Provence, the highest occurrence probability for this species was found in patchy landscapes and scrub and/or herbaceous vegetation. The lowest species occurrence was found in extensive artificial areas, vineyards and arable lands. These results suggest a high risk of population extinction in the future if these types of habitats continue to expand in plains and coastal areas to the detriment of scrublands. Higher wildfire frequency predicted by climate change scenarios in the Mediterranean is also likely to increase the risk of extinction for some populations. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Recent decades have been marked by various landscape transformations in the northern Mediterranean basin (Underwood et al., 2009; Sokos et al., 2012). In lowland and coastal areas, urban development and agricultural intensification have led to profound alteration of natural habitats, which threatens this biodiversity hotspot (Brotons et al., 2004; Sokos et al., 2012). Conversely, areas at higher elevations that were previously used mainly for traditional farming have been abandoned, leading to less humancaused disturbance and favoring natural vegetation dynamics that eventually give rise to landscape homogenization (Moreira and Russo, 2007) and to habitat closure (Mazzoleni et al., 2004; Sokos et al., 2012). The resulting rapid forest progression (Tatoni et al., ⇑ Corresponding author. Tel.: +33 467613294. E-mail address: [email protected] (T. Couturier). 0006-3207/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biocon.2013.12.028

2005; Moreira and Russo, 2007) has been demonstrated to be a major cause of species decline, notably for open-habitat specialists (Sirami et al., 2007; Ribeiro et al., 2009; Pike et al., 2011). The abandonment of agricultural land also leads to an increase in plant biomass (fuel load), which in turn increases the fire hazard in rural areas of the Mediterranean (Tatoni et al., 2005; Moreira and Russo, 2007; Moreira et al., 2011). Moreover, the context of climate change is likely to increase the risk of fire in the future (Moriondo et al., 2006), leading to higher frequency and severity of wildfires, notably in France and Spain (e.g., Mouillot et al., 2002; Pausas, 2004). As Mediterranean species may be adapted to a particular fire regime (Pausas and Keeley, 2009; Sanz-Aguilar et al., 2011), any modification in this regime may increase the extinction probability of these species (Underwood et al., 2009). Both landscape alteration and fire-regime modification profoundly impact vertebrate populations in the Mediterranean area (Moreira and Russo, 2007; Underwood et al., 2009), leading to a

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reduction in distribution and/or abundance. However, the respective influence of these alterations may vary across the geographic range of a species (Fielding and Haworth, 1995; McAlpine et al., 2008), and it is thus important to consider large areas to get an overview of the most determining environmental factors. Most of the regional studies in the northern Mediterranean basin have been conducted on birds (e.g., Herrando and Brotons, 2002; Brotons et al., 2004, 2008; Sirami et al., 2008; Jacquet and Prodon, 2009), which are species with a high dispersal capacity, and have been mainly restricted to passerines, which are short-lived species. These life-history traits give them a higher population recovery capacity because of intrinsic population dynamics and a higher emigration/colonization capability following perturbations. Conversely, species with a long lifespan and low dispersal capacity are likely to be more susceptible to habitat change and wildfires, and ultimately more vulnerable to extinction (Congdon et al., 1993; Santos et al., 2006; Santos and Cheylan, 2013); however, studies conducted on species with such life-history traits are much rarer (but see Anadón et al., 2006; Anadón, 2007; Santos et al., 2006 on Testudo graeca and Vipera latastei). Hermann’s tortoise Testudo hermanni is one of the vertebrates endemic to the northern Mediterranean area with the longest lifespans. It is characterized by a high adult survival rate (0.85–0.97), a longevity reaching more than 50 years old, delayed sexual maturation (8–12 years) and a small home range (generally 1–2 ha) (Cheylan, 2001; Bertolero et al., 2011). These life-history traits are at the opposite extreme of those of passerine birds, which are the focus of most of the case studies regarding the impact of alterations on the Mediterranean area. Archaeozoological data indicates that during antiquity the species inhabited a large part of the Mediterranean coast (Cheylan, 2001; Bertolero et al., 2011). Today, the majority of the remaining populations in France and Spain are highly scattered, and most have been in decline since the last century, a priori as a result of both landscape transformation and wildfires (Bertolero et al., 2011). However, no study has quantified the respective impacts of those recent perturbations. Studying the overall impact of recent landscape changes on Hermann’s tortoise populations involves the careful study of its preferences in terms of habitats. Bertolero et al. (2011) report that the species usually prefers semi-open landscapes with sparse low vegetation and grass. Rozylowicz and Popescu (2013) show that the Eastern Hermann’s tortoise (Testudo hermanni boettgeri) has a preference for grasslands in association with other habitats in a natural park in Romania where traditional management practices are maintained. Yet these studies are mainly descriptive, or were conducted on a small scale, using different methods and different variables. Thus they do not provide enough precision to understand the overall impact of alteration of Mediterranean landscapes; notably, species’ tolerance for artificial, agricultural and forested areas. The impact of wildfires has been investigated using demographic approaches on different Testudo populations (Hailey, 2000; Popgeorgiev, 2008; Couturier et al., 2011; Sanz-Aguilar et al., 2011). However, these studies were conducted over only small areas (ranging from 33 to 88 ha) and showed highly variable mortality rates. Analysis on such a local scale precludes generalizing the results to a larger scale to attain an overview of the consequences of fire on distribution and density. Moreover, these studies did not deal with an important parameter: the accumulation of wildfires on the same site, which may profoundly impact the population-recovery capacity (but see Sanz-Aguilar et al., 2011; Santos and Cheylan, 2013). As wildfires promote scrubland and herbaceous expansion to the detriment of forests, this may result in a corresponding shift in its animal groups. For instance, a recent study showed that a frequent fire regime seemed to favor species that live in open areas, are specialists in their ecological niche,

and have a short lifespan. Conversely, species with a long lifespan such as Hermann’s tortoise tended to decrease with the accumulation of fires (Santos and Cheylan, 2013). The aim of our study was to investigate the respective impacts of fire regime and landscape changes on Hermann’s tortoise on a regional scale using the environmental variables available in the three regions (topography, fire history and vegetation cover/habitat) and two complementary modeling approaches. We first explored the factors determining abundance and occupancy variation across three regions in the species’ western distribution (Corsica, southern France and northeastern Spain) using spatially replicated counts, while taking into account potential imperfect detection of individuals (Wenger and Freeman, 2008) such as visit-related features. In a second step, we used a large presenceonly dataset (available for the Provence area only) to model the distribution of the species and its relationship to environmental features using a maximum entropy approach (Phillips et al., 2006). 2. Materials and methods 2.1. Study areas This study was conducted on the last three native populations of Hermann’s tortoise living in France (in the region of Provence and the island of Corsica) and Spain (Albera) (Fig. 1). These populations all occupy areas with a Mediterranean climate, characterized by hot, dry summers and wet, warm winters. 2.1.1. Provence, southern France The Hermann’s tortoise population in Provence is distributed through the Massif des Maures and the Massif de l’Estérel, over a territory of about 337 km2 that lies between the cities of Toulon and Fréjus. The majority of the population is located in the Massif des Maures and the Plaine des Maures. The Massif des Maures is composed of small hills (altitude less than 780 m), alternating between forested areas (mainly Quercus ilex and Quercus suber) and small clearings consisting of open areas (maquis of Erica sp., Cistus sp., Filaria sp., Pistacia sp.) and vineyards. The Plaine des Maures is an alluvial plain that covers more than 12,000 ha, with a sandstone substrate and alluvial deposits (Permian clay). Habitats consist of mixed forests (mainly Pinus halepensis, Pinus pinea, Q. suber and Q. ilex) and semi-open areas of maquis with sandstone outcrops. The rest of the plain consists of urbanized and agricultural areas (essentially vineyards). The species’ distribution in Provence is subjected to high wildfire frequency. About 38% of this area has burned at least once between 1958 and 2006 (Couturier et al., unpublished data). 2.1.2. Corsica, France In Corsica, the Hermann’s tortoise population is distributed through low-altitude coastal areas (0–400 m) and some inland areas. Its distribution over a territory of about 1550 km2 is fragmented by mountainous areas (up to 2706 m). Its habitats are composed of maquis and forests of Q. suber, Q. ilex and Pinus sp. Livestock farming (mainly ovine and bovine) is still prevalent in Corsica, offering many open areas such as meadows and fallow land that are rarely subjected to wildfires. Anthropic pressures such as intensive agriculture and urbanization remain moderate in this region. About 24% of the distribution area of Hermann’s tortoise in Corsica has burned at least once between 1955 and 2009 (Couturier et al., unpublished data). 2.1.3. Albera, Spain In Spain, the Hermann’s tortoise population is distributed over 134 km2 in the Albera hills of northern Catalonia (Fig. 1). The soil

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Fig. 1. The western distribution of Hermann’s tortoise and the location of the Plaine des Maures.

is schistose-granitic and the vegetation is composed of a patchwork of cork oak forests (Quercetum ilicis-galloprovinciale suberetosum), moorland, gorse, vineyards and olive groves. Nearly all of the Albera has been affected by forest fires of various magnitudes in the last 50 years (Fèlix et al., 1989). At least seven wildfires have affected this area since 1973, with an average of one fire every 4.5 years (Pla de Prevenció d’Incendis Forestals (PPIF) del massís de l’Albera. Departament d’Agricultura, Ramaderia, Pesca, Alimentació i Medi Natural. Generalitat de Catalunya). About 94% of the Hermann’s tortoise’s range burned during large fires in 1986 and 2000 (PPIF). 2.2. Abundance and occupancy in the three regions 2.2.1. Field survey 369 Sampling sites were randomly established within the known distribution range of the species. However, due to logistical constraints (mainly due to the difficulty of obtaining permission to access private property), we did not sample developed or agricultural areas. The 109 sites in the Plaine des Maures and the 142 sites in Corsica (all 5 ha each) were monitored between 2006 and 2009, and the 118 sites (of 4 ha each) in the Albera were monitored in 2008. Each site consisted of homogenous vegetation structure and topography, and, if burned, the fire impact was over more than 80% of the site. Each site was surveyed by different observers three times during a single season, between 13 April and 30 June, the period when this species is the most active (Bertolero et al., 2011). Each site was surveyed on foot, attempting to cover its entire surface and counting the number of individuals encountered. Prospecting took place in the morning, only when weather conditions were favorable for tortoise activity (no rain, warm temperatures and no or low wind). Each sampling site was searched for tortoises by one observer scanning open ground during 60 min in the Plaine des Maures and Corsica, and during 50 min in the Albera. 2.2.2. Environmental features Ten out of thirteen environmental features (used as covariates for the modeling procedure) attributed to the sampling sites were consistent to all three regions (Table 1), but some (n = 3) were not available for the Albera region. The three different regions (Corsica, Plaine des Maures and Albera) were used as site covariates in the models. Topography, i.e., mean slope and dominant aspect (north,

south, east or west) were obtained with digital elevation models extracted from the SRTM digital elevation database (resolution = 90 m) furnished by the CGIAR Consortium for Spatial Information (CGIAR-CSI). Values were obtained by using Arcgis 9.3 (ESRI, 2006) and the Spatial Analyst extension (McCoy and Johnston, 2001). The wildfire occurrence covariate was obtained through maps furnished by the National Forest Agency (Office National des Fôrets: ONF) in the Var (Provence) and by the Corsican Environmental Agency (Office de l’Environnement de la Corse: OEC) and was confirmed in the field in the three regions, notably from evidence such as the presence of carbon marks on tree bark. Wildfire maps of Corsica and the Plaine des Maures provided additional information on the time elapsed since the last fire and the frequency of wildfires over the last 30 years, but such maps were not available for the Albera region. For the vegetation structure covariate, we overlaid a 30-m-spaced point grid over aerial photographs from Google Earth. For each point, we allocated one of the following three features: trees, shrubs or bare soil (also including grass and very small shrubs such as Lavandula sp.). We converted the number of points for each of these three features to percentages per site. We excluded points when it was not possible to distinguish between these features. In the regions of Provence and Corsica, we also assessed the influence of the distance of a site from a permanent stream: information obtained from maps provided by the French National Geographic Institute (IGN). This information was not available for the Albera region. 2.2.3. Visit-related features We considered two factors related to survey visits that could have potentially impacted individual detection probability of the tortoises (Table 1): the observer’s level of experience and the date of the visit. As regards observer experience, we differentiated ‘experienced’ observers (i.e., with previous experience in tortoise prospecting) to ‘novice’ observers. Once ‘novice’ observers had been involved in the previous annual field season, they passed to the category of ‘experienced’ observers in subsequent field seasons. As regards the date of the visit, we defined two different season divisions: we used either the month as the unit (April, May or June) or a period of 2 weeks (totaling 5 periods during the season). 2.2.4. Data analysis Measures of species abundance and site occupancy may be difficult to distinguish when species detection is not perfect

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Table 1 Environmental and visit-related features used as covariates for abundance k, occupancy w and detection probability p of Hermann’s tortoise. Variable name

Variable type

Description

Available for Albera region

Environmental feature Region Slope Aspect Wildfire Time since last wildfire Wildfire frequency Tree Shrub Bare soil Stream distance

Categorical Continuous Categorical Categorical Continuous Ordinal Continuous Continuous Continuous Continuous

Corsica–Plaine des Maures–Albera Mean slope (°) Dominant aspect (North–South–East–West) Wildfire occurrence at least once in the last 30 years (burned–unburned) Number of years elapsed since the last wildfire (log-transformed) No wildfire – 1 wildfire – 2 or more wildfires Percentage of trees Percentage of shrubs Percentage of bare soil Linear distance from the nearest permanent stream

Yes Yes Yes Yes No No Yes Yes Yes No

Visit-related feature Observer experience Monthly periods 2-Week periods

Categorical Categorical Categorical

‘‘Experienced’’–‘‘novice’’ April–May–June 5 Periods of 14 days

Yes Yes Yes

(MacKenzie et al., 2002; Royle, 2004; Royle et al., 2005). Specific models, based on repeated collection of presence–absence data (MacKenzie et al., 2002) or counts (Royle, 2004; Royle et al., 2005) made both spatially (at several sites) and temporally (over several visits), have been developed to address this problem in occupancy probability estimation. Royle (2004) and Royle et al. (2005) have developed models using Poisson and negative binomial distributions for abundance (so-called ‘N-mixture’ models), but the approach can also be employed with other distributions. Wenger and Freeman (2008) noted that a zero-inflated distribution is especially interesting and useful because the resulting model is effectively a combination of Royle et al.’s N-mixture abundance model and the zero-inflated binomial occupancy model of MacKenzie et al. (2002); the first accounts for abundance and the incomplete detection of individuals, and the second accounts for occupancy and the incomplete detection of the target species. Wenger and Freeman (2008) thus proposed a formula for simultaneously estimating all three parameters, i.e., site-occupancy probability (hereafter noted w), abundance (k) and detection probability (p), and to model these parameters as functions of available covariates. The models described above are based on the assumed closure of a site, i.e., the assumption that abundance did not change during the period in which the repeated counts were conducted. As our counts were repeated three times within a period of less than three months, we can rule out mortality occurring during a single season because of the high annual survival probability of the species (ranging from 0.85 to 0.97; Bertolero et al., 2011), and we can rule out population growth through reproduction because hatchlings occur later (August–October; Bertolero et al., 2011). We have no reason to suspect that emigration/immigration rates are asymmetric, and can confidently assume that although completely random temporary emigration might impact detection probability, it would not affect occurrence or abundance estimations (Chandler et al., 2011). Closure can consequently be assumed. Model selection was based on AICc scores (Burnham and Anderson, 2002) of models combining different covariates corresponding to the environmental and survey features (Table 1) and potentially influencing p, k and/or w parameters. Models were fitted using R 2.12 (Ikaha and Gentleman, 1996) and a slightly modified code from Wenger and Freeman (2008). We set the upper bound of integration K at 40, i.e., around twice the mean local abundance (Couturier et al., 2013). We compared zero-inflated Poisson (ZIP) and zero-inflated negative-binomial (ZINB) distribution family adjustments (Joseph et al., 2009) by comparing their AIC scores obtained for the constant model for p, and then kept the ZINB distribution that best fit the data. We evaluated the relative

importance of the covariates by calculating the sum of the AICc weights over all the models including the covariates of interest (Burnham and Anderson, 2002). All the correlation values between features of vegetation structure (i.e., trees, shrubs or bare soil), slope and fire were weak (r < 0.31), consequently we could include all these covariates together in the modeling procedure. As the covariates for the percentage of trees, shrubs and bare soil were correlated (i.e., the sum is always 100% for each site), we never tested these covariates together in the same model. Instead, we only used the one with the best fit, i.e., bare soil (see Section 3) for testing more complex models. We modeled detection probability p, occupancy w and abundance k parameters in four steps. We first determined which survey or environmental features explained variation in p while maintaining a general model for k and for w (region * slope + fire + bare soil). This general model included the most relevant covariates for the species, and as slope range values strongly differed between regions, we maintained an interaction between these covariates. We fitted and tested on p all the visit-related covariates (with monthly and two-week period covariates tested in different models) and the vegetation structure covariate as a linear effect. Overall, we fitted 11 models on p with covariates alone, from which we retained the covariates of the best models (i.e., DAICc < 2). Secondly, we determined which environmental features explained variation in w while keeping the covariates identified during the first step (best models) on p, and maintaining a general model for k (region * slope + fire + bare soil). We fitted 62 models (multiplied by the number of best models retained in the previous step) with combinations of covariates corresponding to the environmental features available for the three regions. Covariates were tested alone, in addition and in interaction (with a maximum of two covariates in interaction), and we retained those corresponding to the best models (i.e., DAICc < 2). In the third step, we modeled k, while retaining the covariates identified during the previous step on p and w. We fitted the same 62 models (multiplied by the number of best models retained in the previous step) used to model w. We also conducted a separate modeling procedure for Corsica and the Plaine des Maures (238 sites) with the environmental features that were only available for these two regions, i.e., time elapsed since the last wildfire, wildfire frequency and the distance of the site from a permanent stream (Table 1). This last feature was tested as a linear effect and as a three-level category (distances less than 100 m, 300 m and 500 m). As we did not have the timeelapsed-since-last-fire data when the fire occurred more than 50 years ago, all the unburned sites were fixed to a value of

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50 years since this was the minimum value. We then log-transformed all the values. For this modeling procedure, we used the best model identified in the third step of the previous analysis (defined as a reference model), in which we added the new covariates one by one and compared their AICc scores. 2.3. Distribution modeling using maximum entropy for Provence 2.3.1. Data collection We used 7377 location records for Hermann’s tortoise collected between 1995 and 2009 by several observers (e.g., environmental managers, volunteers and scientists) using standardized protocols (linear transects, site visits for occupancy estimations) and nonstandardized observations. We only used location records collected after the occurrence of a wildfire. We extracted slope (continuous variable) and aspect (four categories) information from digital elevation models furnished by the IGN (original resolution = 30 m). We generated a distance raster with permanent streams from the maps provided by the IGN and previously used for the regional analysis. We defined four fire-repetition categories using wildfire maps from 1958 to 2008 furnished by the ONF: unburned, burned once, burned twice and burned three times or more since 1958. We defined habitat categories from the OCSOL-PACA database (2006), which uses the same typology as the European CORINE Biotope project, but with higher accuracy (they were established from 30-m resolution, 60-m accuracy satellite pictures). As the number of habitat categories was excessively high for the studied area (N = 31), we excluded the habitat categories that were rare in the studied area (<1%), and grouped the others into eight categories: artificial surfaces (code 1), arable lands (code 2.1), vineyards (code 2.2.1), heterogeneous agricultural areas (code 2.4), broad-leaved forests (code 3.1.1), coniferous forests (code 3.1.2), mixed forests (code 3.1.3), and scrub and/or herbaceous vegetation (code 3.2). We generated a 100-m distance point grid to which we attributed the areas covered by each of the eight habitat variables in three distance buffers, i.e., circles with a 200-m, 500-m or 1000-m radius around each point. We built two landscape-complexity variables: patchiness and evenness. The patchiness metric was defined as the number of individual-habitat polygons included in the three distance buffers. We used the Simpson index of diversity 1-D (Simpson, 1949) as the evenness metric for the three distance buffers. All these 34 environmental variables were extracted with a raster resolution of 100 m for the overall distribution area of Hermann’s tortoise in Provence (Fig. 1). 2.3.2. Analysis Species distribution models were run using the presence-only modeling program Maxent 3.3.3 (Phillips et al., 2006). Maxent uses presence records to statistically relate species occurrence probability to environmental variables using the principle of maximum entropy. We used the default ‘auto features’ option, the default values for the convergence threshold (10 5), the default maximum number of iterations (500) and the regularization multiplier value set to 1. Duplicate presence records in each cell grid were removed. Model performance (defined as the model’s consistency and ability to identify the species’ actual presence and actual absence) was evaluated using AUC, the area under the receiving operating characteristic (ROC) curve. AUC ranges from 0.5, for a model that performs no better than random, to 1.0 for perfect ability to predict presence (Phillips and Dudík, 2008). We ran 10 replications of each model with a crossvalidate run type (samples divided into replicate folds; each fold in turn used for test data). A jackknifing procedure was used to examine the importance of each environmental variable, by comparing the performance of the model without the variable against models including the variable.

181

First we built a model that included all the environmental variables (n = 34) together. We used R 2.15.2 (Ikaha and Gentleman, 1996) and the ‘raster’ package 2.1–49 to estimate the correlations between the habitat variables of the three buffer values. For highly correlated variables (rspearman > 0.75), in the final model we included only the variable that contributed the most to the model to reduce multicollinearity. We excluded from the final model variables that contributed <1% to the model to minimize over-parameterization. The final model retained 12 environmental variables (see Section 3) out of the 34 variables used for the initial model. 3. Results 3.1. Covariates affecting abundance and occupancy over the distribution area 3.1.1. Plaine des Maures, Corsica and Albera The species was detected on 94%, 91% and 52% (naïve occupancy) of the sites in the Plaine des Maures, Corsica and the Albera respectively. The number of tortoises encountered per site ranged from 0 to 20 individuals. The mean number of tortoises detected per visit was 2.04 (SD = 2.30) in the Plaine des Maures, 2.00 (SD = 2.48) in Corsica, and 0.44 (SD = 0.89) in the Albera (score corrected for survey duration). Concerning the detection probability p modeling procedure (step 1), the three best models (DAICc < 2) included the observer, the bare soil and the two-week covariates (Table 2). These covariates were thus kept on p for the subsequent steps of the analysis. Concerning the occupancy w modeling procedure (step 2), the three best models (DAICc < 2) included the observer and the bare soil covariates for the p parameter, and the slope covariate and the absence of a covariate (i.e., constant model) for the w parameter (Table 2). All these covariates were thus retained for the abundance k modeling procedure (step 3). For this step, seven models had AICc < 2. However, the estimations obtained for w for the models with the slope covariate were subject to convergence problems resulting in unrealistic estimates. We consequently excluded those models and only retained the constant models on w (Table 2). The best two models (DAICc < 2) included the additive effects of the region and fire covariates on k. These two models differed for p, the best one being the additive model including the observer and bare soil covariates, while the second best included the observer covariate alone (Table 2). Parameter estimates obtained from the best model show that detection probability p was slightly lower for ‘novice’ observers compared to ‘experienced’ ones (difference ranging from 0.02 to 0.03, Fig. 2) and p increased linearly with the proportion of bare soil (values ranging from 0.14 to 0.21 for experienced observers, Fig. 2). Site occupancy probability w was estimated to 0.99 (IC 95% [0.80–1.00]). In the regions of the Plaine des Maures and Corsica, tortoise abundance k did not differ significantly (respectively 2.71 individuals/ha, IC 95% [2.16–3.39] and 2.54 individuals/ha, IC 95% [2.12–3.05] for unburned sites), but were significantly higher than those in the Albera region (0.67 individuals/ha, IC 95% [0.49–0.91] for unburned sites) (Fig. 3). Wildfires were responsible for a reduction in abundance of 31% compared to unburned sites within the three regions (Fig. 3). 3.1.2. Corsica and the Plaine des Maures only We retained the additive model including the observer experience and bare soil covariates (the best model identified in step 3) for p. But as abundance differences between Corsica and the Plaine des Maures were not significant (Fig. 3), we excluded the regional covariate from our reference model (the most parsimonious model identified in step 3). Moreover, as the mean naïve occupancy value

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Table 2 Results of the ZINB model selection procedure examining the effects of covariates on abundance k, occupancy probability w and detection probability p parameters for the Hermann’s tortoise. We show the models selected during the first 2 steps, and the models with DAICc < 5 for step 3. AICc weights were calculated for the step 3 only. The best model is in bold. Model

AICc

DAICc

AICc weight

Step 1 k(region  slope + fire + bare soil), w(region  slope + fire + bare soil), p(observer) k(region  slope + fire + bare soil), w(region  slope + fire + bare soil), p(observer + bare soil) k(region  slope + fire + bare soil), w region  slope + fire + bare soil), p(observer + two-week)

3450.92 3452.28 3452.53

12.83 14.19 14.44

– – –

Step 2 k(region  slope + fire + bare soil), w(slope), p(observer) k(region  slope + fire + bare soil), w(
), p(observer) k(region  slope + fire + bare soil), w(
), p(observer + bare soil)

3442.11 3442.97 3443.02

4.02 4.88 4.93

– – –

Step 3 k(region + fire), w(
), p(observer + bare soil) k(region + fire), w(
), p(observer) k(slope  fire + region), w(
), p(observer + bare soil) k(slope  fire + region), w(
), p(observer) k(region + fire + bare soil), w(
), p(observer + bare soil) k(region + fire + bare soil), w(
), p(observer) k(bare soil  fire + region), w(
), p(observer + bare soil) k(slope  region + fire), w(
), p(observer) k(shrubs + region + fire), w(
), p(observer) k(trees + region + fire), w(
), p(observer + bare soil) k(trees + region + fire), w(
), p(observer) k(bare soil  fire + region), w(
), p(observer) k(slope  region + fire), w(
), p(observer + bare soil) k(shrubs + region + fire), w(
), p(observer + bare soil) k(bare soil  slope + region + fire), w(
), p(observer) k(bare soil  slope + region + fire), w(
), p(observer + bare soil) k(fire  slope + region + bare soil), w(
), p(observer + bare soil) k(fire  slope + region + bare soil), w(
), p(observer) k(region + fire + bare soil + slope), w(
), p(observer + bare soil) k(bare soil  fire + region + slope), w(
), p(observer + bare soil) k(slope  region + bare soil + fire), w(
), p(observer) k(slope  region + bare soil + fire), w(
), p(observer + bare soil)

3438.09 3439.46 3440.68 3440.69 3440.95 3441.00 3441.02 3441.14 3441.26 3441.32 3441.36 3441.42 3441.57 3441.62 3441.70 3442.05 3442.27 3442.32 3442.94 3442.94 3442.97 3443.02

0.00 1.37 2.59 2.60 2.86 2.91 2.92 3.05 3.16 3.23 3.27 3.33 3.48 3.53 3.61 3.96 4.18 4.23 4.85 4.85 4.88 4.93

0.17 0.08 0.05 0.05 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01

Fig. 2. Hermann’s tortoise detection probability p estimates for the three western regions obtained from the best model k(region + fire), w(
), p(observer + bare soil) of the ZINB modeling procedure.

was very high (mean > 90%) in these two regions, we did not test any covariate on w and maintained this parameter constant. The models including the wildfire frequency covariate and the time elapsed since the last wildfire covariate on k were the most parsimonious (those two covariates being highly correlated: corspearman = 0.98). Note, however, that these models were not statistically different from the subsequent model, i.e., the reference model (DAICc = 0.90 and DAICc = 0.40 respectively). The model including the wildfire frequency covariate provided density estimations of 2.52 individuals/ha (IC 95% [2.13–2.97]) in unburned sites, 1.99 individuals/ha (IC 95% [1.62–2.43]) when burned once, and 1.57 individuals/ha (IC 95% [1.28–1.92]) when burned twice or more. The model including the time elapsed since the last wildfire covariate effect indicated that the densities increased with the number of years elapsed since the last wildfire. More than 50 years (25 years if we consider the lowest IC 95% value) were required to reach the mean abundances obtained at the unburned sites in Corsica and the Plaine des Maures. 3.2. Distribution modeling using maximum entropy for Provence

Fig. 3. Hermann’s tortoise density estimates for the three western regions obtained from the best model k(region + fire), w(
), p(observer + bare soil) of the ZINB modeling procedure.

Except for one habitat (code 3.1.3), all the landscape variables at the 200-m and 500-m buffer scales were excluded from the final model because their contribution was low (<1%) and/or they were correlated with the 1000-m buffer with a lesser contribution (Appendix). The area under the curve (AUC) for the replicate runs of the final model was 0.776 (SD = 0.01), indicating that the model prediction was higher than chance (AUC = 0.5), and was useful for describing species occurrence (AUC > 0.75; Phillips and Dudík, 2008). The variables that contributed most to the final model (Appendix) were slope (contribution = 18.7%), artificial surfaces (code 1, contribution = 16.4%), the patchiness (number of

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individual-habitat polygons, contribution = 16.2%), scrub and/or herbaceous vegetation (code 3.2, contribution = 14.1%), vineyards (code 2.2.1, contribution = 12.7%), arable lands (code 2.1, contribution = 6.6%) and number of fires (contribution = 5.4%). Note that the evenness metric (Simpson diversity index) had a very low contribution (1.9%). A jackknife test showed that the features that produced the greatest gain in the model when considered alone were slope (training gain = 0.13), the patchiness (training gain = 0.13), vineyards (training gain = 0.11), scrub and/or herbaceous vegetation (training gain = 0.08), artificial surfaces (training gain = 0.08), which therefore appeared to be the most determining factors for the distribution of Hermann’s tortoise. The response curves obtained from the final Maxent model (Fig. 4) indicated that the predicted occurrence of T. hermanni decreased when slope, artificial areas, vineyards, arable lands and distance to permanent streams increased (but with a high standard deviation associated with this last variable). The occurrence sharply increased with the number of individual-habitat polygons, but decreased when the number of polygons was high (>33 polygons). In the same way, predicted occurrence increased with scrub and/or herbaceous vegetation, but diminished for the highest value (>250 ha). Occurrence probability sharply decreased with coniferous forests, but a slight increase was registered for the highest values (>180 ha), with an associated high standard deviation (Fig. 4). Occurrence probability was high for heterogeneous agricultural areas, but sharply decreased for the highest values (>80 ha). Concerning the number of fires, occurrence probability

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was high for 0–1 fires. It was divided by about 50% for areas where two fires had occurred over the last 50 years and was nearly null for areas where three or more fires had occurred over the same period. Finally, areas with high Hermann’s tortoise occurrence probability (i.e., > 0.50) are reduced and scattered, with the Plaine des Maures area appearing as the most extended one (Fig. 5). 4. Discussion In western Mediterranean landscapes, large-scale studies conducted on vertebrates rarely concern species with a low-dispersion capacity and a long lifespan, such as reptiles. However, these lifehistory traits increase these species’ susceptibility to both natural and human perturbations (Congdon et al., 1993; Santos et al., 2006). To investigate these factors in a reptile species, our study used large datasets and recent and robust methods to highlight the main factors that influence the abundance and occurrence of the endangered Hermann’s tortoise in its relict distribution. 4.1. The effectiveness and limitations of the zero-inflated method To our knowledge, this study is the first to use replicated counts on a reptile population to jointly estimate a species’ occupancy, abundance and detection probability (Wenger and Freeman, 2008). This approach revealed the effects of two covariates on individual detection probability. This parameter was slightly

Fig. 4. Response curves from the Maxent models created using only the corresponding variable from the set of the final model. Response curves are ordered according to their respective contributions to the final model. The curves show the mean response of the 10 replicate Maxent runs (black) and the mean ± one standard deviation (grey).

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Fig. 5. Occurrence probability of Hermann’s tortoise in Provence according to the prediction of the final Maxent model.

higher for experienced observers, a result in accordance with previous reptile studies (Shirley et al., 2012). But contrary to other reptile studies showing no effect of vegetation structure on capture probability (e.g., Schlesinger, 2007; Craig et al., 2009), our study revealed a linear increase of detection probability with the degree of habitat openness. As vegetation structure is also recognized to be an important factor for ectothermic organisms such as reptiles (Shine, 2005), this result reinforces the importance of using such methods and controlling for this detection bias to avoid misleading interpretations (Mazerolle et al., 2007). A benefit of using zero-inflated distribution is that the distinction between occurrence and abundance terms in the model can have heuristic value in representing the different mechanisms that give rise to observed patterns of species abundance (Wenger and Freeman, 2008). The high occupancy probability value we obtained was expected since we excluded from the sampling design the sites where occupancy was definitely null (e.g., artificial areas). Despite that, the very high value (99%) obtained with the zero-inflated method is surprising because many sites are unoccupied in the Albera region (naïve occupancy = 0.52). We would thus have expected to obtain a mean occupancy probability of less than 1 overall for the three regions, or an effect of the region covariate on this occupancy parameter. We can explain such a discrepancy by the very low abundances observed in the Albera region (mean = 2.26 individuals/site), which is likely to produce null abundance on some sites only by chance (for instance, random trials in a Poisson distribution with a mean of 2.26 result in about 10% null values). Although the method developed by Wenger and Freeman (2008) that we employed indicated that none of the covariates had an effect on occupancy, some, such as wildfires, greatly influenced local abundance. 4.2. Wildfire regime Our study clearly demonstrated for the first time on a large scale that wildfire occurrence sharply impacts the abundance of Hermann’s tortoise, indicating a reduction of 31% in burned areas compared to unburned areas in the three regions studied. Previous

studies on a local scale have shown high variations in mortality rates (Cheylan, 1984; Fèlix et al., 1989; Hailey, 2000; Popgeorgiev, 2008; Couturier et al., 2011), ranging from 5% (Hailey, 2000) to 85% (Cheylan, 1984). These differences regarding the impact of fires are mainly explained by extrinsic factors such as fire severity, the season or the type of habitat pre-fire (Hailey, 2000) and may sometimes result from imperfect detection bias, a problem underlined in other reptile studies (Mazerolle et al., 2007; Smith et al., 2012). Here the overall effect we detected is not subject to such a bias and since it was estimated based on several sites, it is likely to reflect the mean effect of all fire conditions. We can thus conclude that the impact of wildfires is overall high, although their effects largely depend on the fire context on a local scale. In Corsica and the Plaine des Maures, we also detected a long time period (25 years if we considered the lowest IC 95% value) after a wildfire to reach the densities obtained in the unburned sites, and a linear abundance reduction of 21% per fire occurrence over the last 50 years. The maximum entropy analysis conducted in Provence confirmed this tendency of the cumulative effect of fires, with occurrence probability falling from 50% when 0–1 fires occurred, to only 7% in areas where at least three fires occurred over the last 50 years. The long recovery time of Hermann’s tortoise populations, as already suggested by Hailey (2000) in Greece, thus implies that frequent successive fires have a cumulative effect because the interval between fires is too short to allow a population to recover to its original density. This confirms the results obtained by Santos and Cheylan (2013) in the Massif des Maures, and theoretical results obtained by the T. graeca stochastic population models of Sanz-Aguilar et al. (2011). They showed that when the fire-frequency threshold value of one fire every 20–30 years was surpassed, the probability of quasi-extinction of the population increased for all populations, except those with the largest densities (i.e., more than 10 individuals/ha). The two large fires occurring in the Albera region in 1986 and 2000 (an interval of 14 years) can thus explain the mean lower tortoise density there in comparison with that obtained in the burned areas of Corsica and Plaine des Maures. Some major

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wildfire events may have also occurred previous to the last 50 years in the region (data not available), explaining the lower densities encountered in recently (<50 years) unburned areas. We can also hypothesize that large wildfires have favored large areas of homogeneous scrublands and forests (Moreira and Russo, 2007), which are unsuitable for this species of tortoise (see below).

4.3. Landscape changes in Provence Many landscape transformations have occurred in the Provence area over the last century (Tatoni et al., 2005), although no quantitative data of landscape change is available at such a large spatiotemporal scale. Using maximum entropy models (Phillips et al., 2006) to study the occurrence of Hermann’s tortoise allowed us to understand its habitat preferences within this region and to assess the relative impacts of landscape changes, which generally correspond to changes observed across the northern Mediterranean basin. No previous data sets of tortoise presence in Provence are available, impeding any diachronic sampling design (Prodon and Pons, 1992). Contrary to a site-sampling approach, the Maxent analysis used in this study is based on presence-only data collected without sampling design, leading to potential sampling biases (Barry and Elith, 2006), so results should be interpreted cautiously. For instance, hilly areas may have been surveyed less by observers due to inaccessibility or prospection difficulties, which could be partially responsible for the very high occurrence probability encountered in flat areas. However, there is unlikely to be such a sampling bias concerning the habitat variables (defined using the OCSOL-PACA typology), so we consequently have more confidence in the results obtained. They showed that several single habitat variables, as well as the patchiness metric, contributed to the occurrence of Hermann’s tortoise. Conversely, the landscape diversity metric (evenness, defined with the Simpson index) was not a contributing factor. This difference in the impact of the two landscape complexity metrics may appear surprising. However, both low and high diversity areas may be composed of unsuitable habitat types, explaining the low prediction of this landscape complexity metric. As regards the level of patchiness, this affected the species occurrence through several processes. Landscapes with intermediate patchiness values (i.e., between 15 and 34 OCSOL-PACA polygons per 1-km buffer, thus corresponding to parcels of 20–30 ha on average) had a higher occurrence probability for Hermann’s tortoise. Small habitat patches facilitate access to different habitat resources for these tortoises; this result is in line with other studies conducted on Testudo sp. (Anadón et al., 2006; Rozylowicz and Popescu, 2013). However, our results also suggest that the occurrence of Hermann’s tortoise decreases as the patchiness continues to increase (i.e., OCSOL-PACA parcels < 20 ha). When examining the landscape maps, these highly fragmented landscapes correspond to areas that include many urbanized parcels, arable lands and vineyards, which our results show have strongly negative effects on the occurrence of Hermann’s tortoise. Note that these highly patchy areas do not necessarily correspond to areas with high landscape diversity, which also explains the lower ability of the evenness metric in predicting species occurrence than the patchiness metric. Urbanized parcels, arable lands and vineyards do not provide resources for Hermann’s tortoise, a result in accordance with those obtained by Anadón et al. (2006) and Rozylowicz and Popescu (2013) on Testudo species. Given the high occurrence probability of Hermann’s tortoise in flat areas (occurrence probability >50% in areas with slopes <10°), a high risk of population extinction may be predicted if urbanization and the plantation of vineyards continue to expand in the future in plains and coastal areas as has been the case over the last decades (Tatoni et al., 2005).

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A low occurrence probability for Hermann’s tortoise was found in landscapes with lower patchiness values (i.e., OCSOL-PACA parcels > 30 ha on average). These mainly correspond to homogeneous scrublands and forested areas. Scrub encroachment and forest regrowth induced by land abandonment has become generalized in Mediterranean Europe over the last century (Moreira and Russo, 2007), and this phenomenon is particularly marked in Provence, with deciduous oak forests reaching a level not attained since the Middle Ages (Tatoni et al., 2005). The absence of a marked effect of forested areas (contribution 6 3.4% of the forest variables) on the occurrence of Hermann’s tortoise could be explained by the fact that OCSOL-PACA typology does not reflect vegetation structure, notably the presence of small forest clearings where some populations could be maintained in the short to medium term (e.g., Couturier et al., in press). Conversely, the strong positive effect of scrubland and/or herbaceous vegetation, corresponding to high occurrence probability (>50%), was expected since these habitats allow tortoises feeding opportunities and the selection of different microclimates, favoring behavioral thermoregulation (Shine, 2005; Moreira and Russo, 2007). However, the zones where scrubland exceeded 80% of the buffers (i.e., corresponding to a homogeneous landscape) mainly correspond to areas where repeated wildfires have occurred, explaining the lower occurrence there of Hermann’s tortoise. Finally, nearly all the landscape variables had a higher effect at the 1000-m buffer value than at the 500-m and 200-m buffer values, suggesting that the distribution of Hermann’s tortoise is driven by large-scale environmental factors rather than the immediate environment of an individual. The low-dispersion capacity of the species and its long lifespan induce spatial and temporal inertia in its distribution. Some individuals can thus be marginally maintained in unsuitable habitats at a local scale (e.g., the edges of vineyards) in the short to medium term, but the species is likely to disappear from larger unsuitable areas (e.g., large extents of vineyards) in the medium to long term. Consequently, conservation measures should be planned at a large spatio-temporal scale. 4.4. Conclusions The results of our regional study underline the importance of landscape complexity for Hermann’s tortoise, and the strong negative effects of urbanization and the spread of vineyards, a phenomenon that is particularly prevalent in the plains and coastal areas where species occurrence is high. High occurrence of Hermann’s tortoise was also found in scrublands. Those habitats are the most prone to fire in the northern Mediterranean basin (Moreira et al., 2011), and our study demonstrated the strong negative impact of wildfire on the species. Mouillot et al. (2002) have shown that climate changes are tending to decrease the interval between two successive fires from 20 to 16 years in scrubland. Considering the long recovery time our study has suggested for this long-lived species, exceeding at least two decades after a wildfire, such an increase in wildfire frequency could jeopardize Hermann’s tortoise populations. Conservation planning should consequently focus on measures based on wildfire control, maintenance of a high level of landscape complexity, and prevention of the conversion of scrublands to artificial and agricultural areas to improve the conservation status of the species in the near future. Acknowledgements We are grateful to the numerous biologists and volunteers who helped us to collect the data. We would like to thank the Centre for the Observation and Protection of Tortoises and their Habitats (Station d’Observation et de Protection des Tortues et de leurs Milieux:

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SOPTOM) and the Society for the Conservation and Study of the Ecosystems of Provence (Conservatoire-Études et Écosystèmes de Provence: CEEP), who provided us with a large number of the tortoise locations for the Provence distribution. We would also like to thank Lucile Tillion-Lacazale for her photo-interpretation work on vegetation structure. We also thank the Albera Tortoise Recovery Centre (Centre de Recuperació de Tortugues de l’Albera: CRT) and Paratge Natural d’Interès Nacional de l’Albera for its kind collaboration. This study benefited from the financial support of the European Regional Development Fund (FEDER) program ‘Des Tortues et des Hommes’, the DYNABIO program (École Pratique des Hautes Études) and the Departament de Medi Ambient i Habitatge and Forestal Catalana (Generalitat de Catalunya). We thank the anonymous reviewers who made several constructive suggestions to improve this manuscript.

Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biocon.2013.12. 028.

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