Landscape Ecol DOI 10.1007/s10980-015-0237-x
RESEARCH ARTICLE
Different bat guilds perceive their habitat in different ways: a multiscale landscape approach for variable selection in species distribution modelling Laura Ducci . Paolo Agnelli . Mirko Di Febbraro . Ludovico Frate . Danilo Russo . Anna Loy . Maria Laura Carranza . Giacomo Santini . Federica Roscioni
Received: 28 January 2015 / Accepted: 24 June 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract Context Unveiling the scale at which organisms respond to habitat features is crucial to understand how they are influenced by anthropogenic environmental changes. We implemented species distribution models (SDMs) based on multiple-scale landscape pattern analysis for four bat species representative of different foraging guilds: Nyctalus leisleri, Rhinolophus hipposideros, Myotis emarginatus and Pipistrellus pipistrellus. Objectives (a) to assess the environmental factors and the influence of scale on the habitat suitability of
Electronic supplementary material The online version of this article (doi:10.1007/s10980-015-0237-x) contains supplementary material, which is available to authorized users. L. Ducci G. Santini Dipartimento di Biologia, Universita` degli Studi di Firenze, Via Madonna del Piano 6, 50019 Sesto Fiorentino, FI, Italy L. Ducci P. Agnelli Museo di Storia Naturale, Universita` degli Studi di Firenze, Via Romana 17, 50125 Florence, Italy M. Di Febbraro L. Frate A. Loy M. L. Carranza F. Roscioni EnvixLab, Dipartimento Bioscienze e Territorio, Universita` del Molise, 86090 Pesche, IS, Italy
bats; (b) to develop an objective methodology to select the best performing variables from a large variable dataset. Methods We performed the study in central Italy (Tuscany): 381 variables were derived from topographical and habitat maps using a moving windows analysis set at three spatial scales (1, 5 and 10 km) that are ecologically meaningful for bats. For each species, we ran 381 univariate models to select the variables for multivariate SDMs. Results All the variables retained in the SDMs described spatial pattern indices underlining the importance of landscape structure for species distribution. Species reacted differently in terms of both scale and landscape pattern. P. pipistrellus only responded to variables at 10 km; N. leisleri and M. D. Russo (&) Wildlife Research Unit, Laboratorio di Ecologia Applicata, Dipartimento di Agraria, Universita` degli Studi di Napoli Federico II, Portici, NA, Italy e-mail:
[email protected] D. Russo School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, BS8 1TQ Bristol, UK
L. Frate Istituto di Biologia Agro-Ambientale e Forestale, CNR/ IBAF, Monterotondo, RM, Italy
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emarginatus did so at two scales (5 and 10 km); whereas R. hipposideros also responded to variables at 1 km. Conclusions Our findings make it possible to tailor SDMs according to species-specific landscape pattern requirements at appropriate scales. Our approach, which can be easily extended to other taxa and different spatial scales, represents a significant step towards more effective land management planning. Keywords Chiroptera Foraging Landscape pattern Multiscale approach Moving windows, Spatial scale
Introduction Unveiling the spatial scale at which organisms respond to the environmental features that characterize their habitat is crucial to better understand how they are influenced by anthropogenic environmental changes. The interaction between an organism and its environment occurs at many scales (Wiens 1989; Bellamy et al. 2013). Thus, scaling analysis is particularly important for unravelling species–habitat relationships (Moudry´ and Sˇ´ımova´ 2012; Shirk 2012; Wasserman et al. 2012; Sa´nchez et al. 2013). Species respond to habitats for particular life-history functions across a range of spatial scales (Johnson 1980; Schaefer and Messier 1995; Rettie and Messier 2000). Organisms interact with all structures of the landscape, and these are characterized by an upper and lower limit, i.e. the largest and the smallest spatial scales to which they are sensitive (Wiens 1990). Within this scale range, a hierarchy of decisions is adopted by the subject, which influence the selection of one landscape portion with respect to another (Holling 1992; Hostetler and Holling 2000). Multiscale assessments are necessary to adequately characterize habitat features and their spatial configurations that affect the abundance of populations or their assembly into local communities (Thompson and McGarigal 2002; Gorresen et al. 2005). Mapping of the spatial distribution of species is an important tool in conservation biology, and is used in many different fields such as the management of endangered species, species reintroductions, ecosystem restoration, population viability analysis, and
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estimation of the impact of renewable energy sources (Hirzel et al. 2001; Rebelo and Jones 2010; Bosso et al. 2013; Di Febbraro et al. 2013; Roscioni et al. 2013, 2014). However, despite the importance of scale for the ecological requirements of a targeted species, only recently have multi-scale approaches been applied to species distribution modelling (Razgour et al. 2011; Sa´nchez et al. 2013; Bellamy et al. 2013). Clearly, detecting the scale effects of landscape features is crucial to fully understand the spatial ecology of the species (Thompson and McGarigal 2002), and to detect the landscape units that should be targeted for effective management (Pearce and Boyce 2006; Mander and Uuemaa 2010). The aims of this paper are: (1) to investigate the relationship existing between environmental variables, along with their related spatial pattern metrics, and species’ potential distributions; and (2) to explicitly include species spatial scale perception in species distribution models (SDMs). We also present a novel objective procedure of variable selection able to extract from a large variable dataset the array of the best performing ones. For our analysis we selected bats because of their mobility and their ability to perceive their environment at different spatial scales, while different foraging strategies might reveal specific requirements in terms of scale and the spatial pattern of environmental factors occurring in the landscape (Gorresen et al. 2005). Specifically, we considered four bat species representative of separate guilds, each characterized by different feeding strategies whose habitat preferences may ideally be placed along a gradient of increasing habitat clutter (vegetation density and presence of obstacles), as follows: (a)
(b)
Nyctalus leisleri a medium-sized vespertilionid (forearm length 38–47 mm; Schober and Grimmberger 1993) whose relatively high wing-loading and aspect ratio (Norberg and Rayner 1987) make it a fast-flying, unmanoeuvrable species that hunts prey on the wing in open spaces (Waters et al. 1999). Pipistrellus pipistrellus a small vespertilionid (forearm length 29–33 mm; Dietz and Helversen 2004) whose flight style and echolocation characteristics make it adapted to exploit edges (forest edges or trails, treelines, hedgerows, etc.) in a broad range of habitats, from farmland to forest or urban settlements (e.g.
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(c)
(d)
Russo and Jones 2003) while it tends to avoid densely cluttered habitat such as forest interiors (Nicholls and Racey 2006a, b). Myotis emarginatus a small myotid bat (forearm length 36–41 mm) characterized by a fairly manoeuvred flight which allows it to hunt in moderately to heavily cluttered habitats—from scrubland to olive groves and pine plantations (e.g. Flaquer et al. 2008; Goiti et al. 2011)— where prey is mostly seized by gleaning (Krull et al. 1991). Rhinolophus hipposideros, the smallest European rhinolophid (forearm length 37–42 mm, Schober and Grimmberger 1993) whose low wing loading, aspect ratio (Norberg and Rayner 1987) and high duty cycle echolocation (Jones and Rayner 1989; Russo and Jones 2002) make it well-adapted to hunt in cluttered habitat such as forest (Bontadina et al. 2002).
The study was carried out in Tuscany (central Italy), which is broadly representative of the different climates and landscapes present throughout the whole country. Our specific objectives were: (a) to identify the environmental factors and landscape pattern metrics that may influence the habitat suitability of bats with different foraging strategies; (b) to assess the influence of the scale of environmental factors and landscape pattern metrics on species-specific variable selection; (c) to identify the scale at which these bat species are ultimately most sensitive.
Methods Study area and presence records The study area included the whole territory of Tuscany (central Italy), covering an area of 2,299,018 ha (Fig. 1), and representative of most Mediterranean climates and landscapes. The altitude of the region ranges from 0 up to [2000 m a.s.l., while annual precipitation amounts vary from 600 to 1600 mm. The region is mostly hilly (66.5 % of the surface) or mountainous (2 %), with lowland and urban areas encompassing 8.4 and 4 % only of the total area, respectively. Forests are widespread (44 % of the region), with inland stands mainly
comprising hardwoods but conifers dominating along the coast (maritime pine) and in the high mountain areas (Agnelli et al. 2014). The analyses were implemented at a regional scale not only because this represents an appropriate scale to evaluate the suitability of the landscape for highly mobile species like bats, but also because conservation measures in Italy are planned and carried on a regional basis (Roscioni et al. 2013, 2014). Moreover, a fine-scale analysis allows for an accurate description of the local conditions to be made, and is highly effective for regional conservation planning (Grantham et al. 2009; Mills et al. 2010; Roscioni et al. 2014). To implement the analyses, we used 89 presence records for M. emarginatus, 56 for N. leisleri, 189 for P. pipistrellus, and 169 for R. hipposideros. The records encompassed the period between 1990 and 2013 and were derived from roost inspections, museum collections, temporary captures at foraging sites, and acoustic surveys carried out with timeexpansion bat detectors (D980, Pettersson Elektronik AB, Uppsala, Sweden) or direct ultrasound sampling bat detectors (EM3?, Wildlife Acoustics, USA; D1000X, Pettersson Elektronik AB, Uppsala, Sweden). For species recognition in the data obtained by the acoustic surveys, we used the BatSound 4.1. software (Pettersson Elektronik AB, Uppsala, Sweden) to generate oscillograms, spectrograms, and power spectra, selecting one to three echolocation calls per sequence. Echolocation calls were identified following the methods of Russo and Jones (2002), while social calls were identified following the methods of Russo and Jones (2000) and Russ (1999). All records were double-checked for correctness and were geo-referenced. Because the environmental requirements of bats differ depending on the time of the year [hibernation vs activity season (Russ et al. 2003; Jones et al. 2009)], we only considered presence data collected during the active period (from spring to autumn). We corrected for spatial autocorrelation in the occurrence spatial patterns to avoid over-estimated predictive performance (Phillips et al. 2009; Veloz 2009; Merckx et al. 2011; Bellamy et al. 2013; Roscioni et al. 2014). Following Vicente et al. (2013), aggregations in species occurrences were removed by selecting a subset of the original occurrences with a minimal distance optimizing the Clark and Evans
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Fig. 1 Study area and presence records for Myotis emarginatus, Nyctalus leisleri, Pipistrellus pipistrellus, and Rhinolophus hipposideros used to implement the multiscale approach in variable selection for species distribution modelling
(1954) aggregation index (R). This index measures the clustering or ordering of a point pattern as the ratio of the observed mean distance between nearest neighbours in the data to that expected for a Poisson point process of the same intensity. A value of R = 1 indicates that the points are distributed randomly; R [ 1 suggests ordering; while R \ 1 suggests clustering. For each species, we removed from the initial dataset all records closer to their closest neighbour. We proceeded iteratively until the aggregation index reached R = 1, thus ensuring that the pattern of occurrences was no longer clustered. The computation of R was performed using the SPATSTAT package (Baddeley and Turner 2012). After correcting for spatial autocorrelation, we obtained 51 presence data for M. emarginatus, 30 for N. leisleri, 84 for P. pipistrellus, and 80 for R. hipposideros.
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Environmental variables and landscape indices Our approach to individually examine at which geographical scale bats respond to the environmental and landscape variables is schematically illustrated in Fig. 2. For each target species, we implemented a multi-scale landscape approach on topographical, hydrological and land cover maps using a moving windows analysis. Three spatial scales were selected (circular windows with radii of 1, 5 and 10 km) as ecologically meaningful for bat movement and foraging behaviour (Rodrigues et al. 2008; Battersby 2010; Roscioni et al. 2013). Variables were derived from topographical, hydrological and land cover maps at a 100 m resolution: a digital elevation model (DEM), hydrographic map, and the 2006 Corine Land Cover (CLC) map (scale: 1:100,000) (ESM1).
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Fig. 2 Flowchart summarizing the procedure used to implement the multiscale approach in order to detect at which geographical scale environmental and landscape variables were perceived by each of the considered bat species. In the dashed rectangles are described the elaboration phases. The grey rectangles represent the outputs obtained by each of the different procedures
DEM and hydrographic variables for the three selected scales were produced through a moving window device. Specifically, for each pixel, we calculated the mean of surrounding elevation values, and the density of streams within circular windows at the three spatial scales by using the focal statistics tool implemented in Arcmap10 (ESRI, Redlands, CA, USA). Following Roscioni et al. (2014), we reclassified the CLC map into 15 ecologically meaningful categories for bats (see the legend for the CLC map in ESM2). From the reclassified CLC map, we computed a set of landscape indices with the moving window analysis
carried out using FRAGSTATS 4.1 (McGarigal et al. 2012) at both class and landscape levels, considering the three scales mentioned above. Specifically, the moving window operates by moving a fixed-area window over the map one pixel at a time, calculating selected indices within the window and returning that value to the centre pixel. The result is a continuous surface that reflects the context of each land cover pixel in the neighbouring areas (Riitters et al. 2002). Eight indices were calculated at class level (for each reclassified CLC category: 100-urban; 131-mineral extraction sites, 210-cultivation, 220-orchards, 230-pastures, 240-heterogeneous agricultural areas, 310-broad leaved forests, 312-coniferous, 313-mixed forests, 321-steppe, 323-scrubs, 330-bare ground, 410-water, 510-salt water, 3116-riparian forests) to characterize the amount and the spatial distribution of selected land cover types (McGarigal et al. 2005). Specifically, the selected indices were adequate for describing: habitat extent or the total area of the target land cover in the landscape (MPS = mean patch size, PLAND = percentage of landscape); habitat subdivision i.e. the degree of fragmentation (IJI = interspersion and juxtaposition index); patch geometry (AWMSI = area weighted mean shape index; ED = edge density); habitat isolation, i.e. the distance between patches (ENN_MN = mean euclidean nearest-neighbour); and habitat connectivity or the physical continuity of land cover classes across the landscape (COHESION = cohesion index; AI = aggregation index). Five landscape-level indices were chosen based on their ability to capture landscape heterogeneity. Specifically: landscape diversity (PR = patch richness; SHDI = shannon’s diversity index); landscape subdivision (NP = number of patches; PD = patch density); and dispersion (CONTAG = contagion). For a detailed description of class and landscape indices see McGarigal et al. (2012). To eliminate the edge effect in the calculation of all variables, we used maps whose boundaries were extended to 10 km beyond the administrative limits. The sea areas were set to neutral and consequently did not interfere in the computations, being aware that edge effects on variables estimations are limited only to coastal boundaries (Wickham et al. 2008). The output maps from the moving window analysis were then clipped at the administrative boundaries of the study area. By the end of these computational procedures, we had obtained 381 variables (ESM3).
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Variable selection using the univariate approach We present an objective novel procedure of variable selection capable of extracting from a large dataset the array of the best performing variables and scales. A set of statistically significant and uncorrelated variables for each species was selected by the means of univariate SDMs developed through an ensemble forecasting approach, as implemented in the R package ‘‘biomod2’’ (Thuiller et al. 2009). Biomod2 is a modelling platform making it possible to train SDMs using different modelling techniques, to evaluate them and to perform different averaged outputs of the single-model predictions (see Thuiller et al. 2009). Using different statistical methods to model species distribution is highly recommended as prediction discrepancies between different techniques can be very large (Arau´jo et al. 2005; Thuiller et al. 2009). We considered the following seven modelling techniques (Thuiller et al. 2009; Jiguet et al. 2010): (1) generalized linear models (GLM); (2) generalized additive models (GAM); (3) classification tree analysis (CTA); (4) generalized boosted models (GBM); (5) random forests (RF); (6) multivariate adaptive regression spline (MARS); and (7) maximum entropy models (MAXENT). Following Pio et al. (2014), the modelling settings were tuned as follows. GLMs and GAMs were calibrated using a binomial distribution and a logistic link function. A bidirectional stepwise procedure was used for explanatory variable selection, based on the Akaike information criterion (Akaike, 1974). Up to second order polynomials (linear and quadratic terms) were allowed for each explanatory variable in GLMs, and up to third order splines in GAMs. GBMs were calibrated with a maximum number of trees set to 5000, fivefold cross-validation procedures to select the optimal numbers of trees to be kept and a value of five as maximum depth of variable interactions. Random forest models were fitted by growing 750 trees with half the numbers of available predictors sampled for splitting at each node. MARS models were fitted with a maximum interaction degree equal to 2. All these techniques are considered good statistical methods to fit presenceabsence SDMs (see Elith et al. 2006 for model comparisons). Each occurrence dataset was randomly split into an 80 % sample, used for the calibration of the model, and the remaining 20 %, used to evaluate model performance. A set of 10,000 background points was randomly placed in the study area to characterize the
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environment of the area and represent pseudo-absences. The predictive performances of the models were assessed by measuring the area under the receiver operating characteristic curve (AUC) (Hanley and McNeil 1982) and the true skill statistic (TSS) (Allouche et al. 2006). This data splitting procedure was repeated 20 times and the evaluation values averaged. For each species, we ran a total of 381 univariate models. To detect the most important variable that could be statistically meaningful in the species distribution at different spatial scales, and useful for developing the species-specific multivariate model, for each species we first selected only those variables whose area under the receiver operating characteristic curve (AUC: Swets 1988; Bellamy et al. 2013) was C0.85. The variables that passed this threshold were further sub-selected according to multicollinearity. Groups of intercorrelated variables (i.e. those whose correlation was expressed with a Pearson value|r| C 0.5) were excluded from analysis. We adopted this particularly restrictive threshold (Booth et al. 1994; Dormann et al. 2013) because landscape metrics have been shown to be highly redundant and intercorrelated (Cushman et al. 2008). In addition the variables of each group in turn underwent a run of BIOMOD, and only the predictor with the highest AUC, averaged among all the modelling techniques, was retained from each group. Multivariate models The models computed a probability distribution based on environmental variables spread over the entire study area and assigned a probability of suitability to each cell in the study area (Jiguet et al. 2010). To predict the species distributions, the variables selected through the univariate modelling procedure were then used as input data for the multivariate SDMs, which were implemented using the same modelling process as the univariate models. To avoid using poorly calibrated models, only projections from models with AUC C 0.8 and TSS C 0.6 were considered in all subsequent analyses. Model averaging was performed by weighting the individual model projections by their AUC or TSS scores and averaging the result—a method previously shown to be particularly robust (Marmion et al. 2009). Variable contributions to the models were obtained using the BIOMOD computer platform (Jiguet et al. 2010).
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Results
topography and hydrography did not exceed an AUC of 0.85 for any species. Differences among species were found at the level of scale. M. emarginatus and N. leisleri suitability responded to two scales (5 and 10 km); the distribution of P. pipistrellus was influenced by variables only at the 10 km scale; while R. hipposideros distribution responded to three scales (1, 5, and 10 km) (Table 1). Moreover, we also obtained
Variable selection using the univariate approach The univariate SDMs revealed that all the variables determining the suitability of the four bat species refer to landscape indices related to the spatial pattern of CLC categories at different scales (Table 1), while
Table 1 Variables selected after the univariate procedure for the four target species (AUC C 0.85): Myotis emarginatus, Nyctalus leisleri, Pipistrellus pipistrellus, and Rhinolophus hipposideros Species
Index
Myotis emarginatus
AWMSI
Class
Broad leaved forests (311)
5
IJI
Class
Steppe (321)
5
0.86
IJI
Class
Broad leaved forests (311)
5
0.87
MPS
Class
Steppe (321)
5
0.85
AI
Class
Cultivation (210)
10
0.85
AWMSI
Class
Heterogeneous agricultural areas (240)
10
0.85
ENN_MN
Class
Cultivation (210)
10
0.86
ENN_MN
Class
Steppe (321)
10
0.86
AI COHESION
Class Class
Broad leaved forests (311) Cultivation (210)
5 10
0.89 0.85
ENN_MN
Class
Coniferous (312)
10
0.86
IJI
Class
Heterogeneous agricultural areas (240)
10
0.86
IJI
Class
Coniferous (312)
10
0.86
PLAND
Class
Heterogeneous agricultural areas (240)
10
0.89
AWMSI
Class
Broad leaved forests (311)
10
0.86
ED
Class
Broad leaved forests (311)
10
0.86
IJI
Class
Broad leaved forests (311)
10
0.85
PR
Landscape
All
10
0.86
AWMSI
Class
Heterogeneous agricultural areas (240)
10
0.85
CONTAG
Landscape
All
1
0.85
Nyctalus leisleri
Pipistrellus pipistrellus
Rhinolophus hipposideros
Level
Habitat
Scale (km)
AUC 0.87
COHESION
Class
Heterogeneous agricultural areas (240)
5
0.87
ENN_MN
Class
Broad leaved forests (311)
5
0.86
IJI
Class
Broad leaved forests (311)
5
0.86
PLAND AI
Class Class
Heterogeneous agricultural areas (240) Mixed forests (313)
5 10
0.85 0.86
AWMSI
Class
Broad leaved forests (311)
10
0.86
IJI
Class
Cultivation (210)
10
0.85
IJI
Class
Mixed forests (313)
10
0.86
PLAND
Class
Broad leaved forests (311)
10
0.88
PR
Landscape
All
10
0.86
Numbers in parentheses indicate the Corine Land Cover codes AI aggregation index, AWMSI area weighted mean shape index, COHESION patch cohesion index, CONTAG contagion, ED edge density, ENN_MN mean euclidean nearest neighbour, IJI interspersion and juxtaposition index, MPS mean patch size, PLAND percentage of landscape, PR patch richness
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differences in the metrics which determined the suitability of the different species both in terms of the level of selection (class or landscape) and categories of land use. M. emarginatus distribution was influenced only by class level metrics. Specifically, at the broader scale (10 km), mostly metrics representative of the aggregation process of cultivation (AI,ENN_MN) and, at the medium scale (5 km), variables related to size (MPS), shape (AWMSI) and the heterogeneity of patches of broad leaved forests and steppe (IJI) (Table 1). We found a different situation for N. leisleri whose distribution was mostly determined by metrics at the broader scale (10 km), e.g. those associated with a high level of interspersion of heterogeneous agricultural areas (IJI) and with the cohesion among cultivation patches (see response curves in ESM4). N. leisleri distribution too was only influenced by class level metrics (Table 1). P. pipistrellus suitability was mostly related to broadscale landscape characteristics (10 km) and, in particular, to broad leaved edge and heterogeneity IJI, as well as to the shape of heterogeneous agricultural areas (AWMSI). In addition, patch richness, at landscape level, showed good performance in terms of explaining habitat suitability for P. pipistrellus (Table 1). R. hipposideros distribution was mainly described by metrics linked with woodland and, in particular, the aggregation (AI, IJI) and amount (PLAND) of wooded patches at the broader scale (10 km) at the class level; the distances among wooded patches (ENN_MN) appeared crucial at the medium scale (5 km). At the landscape level and on the finer scale (1 km), R. hipposideros suitability is associated with the low dispersion of patches (CONTAG) (Table 1, ESM4). Multivariate models The multivariate SDMs developed for the four bat species using the variables listed in Table 1 had AUC values ranging from 0.998 for P. pipistrellus to 0.922 for R. hipposideros, and TSS values ranging from 0.967 for N. leisleri to 0.766 for M. emarginatus. Evaluation metrics for each algorithm are reported in ESM5. Most suitable areas for M. emarginatus were widespread in the region and mostly concentrated in open areas (Fig. 3a). More specifically, the distribution of this moderate clutter forager was mainly driven by the distances among steppes and cultivations
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(ENN_MN_321, ENN_MN_210) at the broad scale (10 km) and by the interspersion of steppes (IJI_321) at the medium scale (5 km) (Fig. 4, ESM4). The potential distribution of the open space forager N. leisleri was predominantly concentrated in wooded coastal and mountain areas (Fig. 3b) and mainly influenced by the connectivity of cultivation (COHESION_210) and the amount of heterogeneous agricultural areas (PLAND_ 240) measured at the broadest scale (10 km) (Fig. 4, ESM4). The edge forager P. pipistrellus was uniformly distributed (Fig. 3d) and the most significant variable was the amount of edge of broad leaved forests (ED_311) measured at the 10 km scale (Fig. 4, ESM4). The clutter forager R. hipposideros was mostly concentrated in internal hilly areas (Fig. 3c). Four variables at three different scales mostly influenced the suitability for this species (Fig. 4, ESM4): the amount of broad leaved forests (PLAND_311) at the 10 km scale; the aggregation of mixed forests (AI_313) at the 10 km scale; the distance between broad leaved forests (ENN_MN_311) at the 5 km scale; and the dispersion of patches at the landscape level (CONTAG) at the 1 km scale.
Discussion The results confirmed that not only is bat habitat suitability influenced by vegetation types, but especially by their spatial organization in the landscape at different scales, as well as by the general structure of the landscape (Gorresen et al. 2005; Boughey et al. 2011; Hanspach et al. 2012; Li and Wilkins 2014). It is well known that the landscape characteristics associated with population and community attributes are scale-dependent (Gorresen et al. 2005), yet most landscape studies have long evaluated species responses to the spatial structure of habitat measured at a single scale (McGarigal and McComb 1995; Hargis et al. 1999; Villard et al. 1999; Gehrt and Chelsvig 2003; Numa et al. 2005). However, more recently multi-scale approaches have been applied to species distribution modelling and habitat composition (Razgour et al. 2011; Hale et al. 2012; Sa´nchez et al. 2013; Bellamy et al. 2013). Indeed, the size of the landscape to which an organism responds may be determined by the identities of landscape variables. Consequently, there is no single focal scale of the landscape to which species or communities react
Landscape Ecol Fig. 3 Habitat suitability maps for (a) M. emarginatus, (b) N. leisleri, (c) R. hipposideros, and (d) P. pipistrellus in the Tuscany region (central Italy)
(Thompson and McGarigal 2002; Gorresen et al. 2005). Bats may respond to the presence of a resource that is concentrated on a small surface relative to their broad range of movement (e.g., a small body of water), and yet simultaneously be sensitive to features perceived at larger scales, such as woodland cover (Gehrt and Chelsvig 2003). One of the main characteristics of our approach is that, besides considering different spatial scales, we implemented a novel variable selection procedure for species distribution modelling to explore how landscape indices at class and landscape levels influence species distribution. Furthermore, we selected the indices in such a way to represent the five main components of the composition (habitat extent) and configuration (habitat subdivision, patch geometry, habitat isolation and connectivity) of the landscape, and to capture the landscape heterogeneity (McGarigal et al. 2005). The few variables that were entered into the multivariate species-specific models after the univariate-model-based selection were able to return highly reliable distributions of the four species and provided
clear indications of the main factors acting at different scales on the habitat requirements for each species. It is interesting to note that, despite suitable areas for the four species overlapping in the study area, each species nevertheless differed in its response to vegetation classes, to the spatial patterns, and to the scale at which these factors were evaluated, in accordance with our hypothesis that the four species are representative of different bat guilds. We found that all four species responded to the importance of class indices, whereas only two—P. pipistrellus and R. hipposideros—responded to the landscape level, in particular to landscape diversity and dispersion, respectively. This could be explained by the two species being representative of two opposite habitatuse guilds, the former being generalist, the latter specialist (Russo and Jones 2003). Furthermore, we highlighted interspecific differences at the scaleselection level: because different processes (such as commuting, foraging, migratory movements, etc.) may operate at different scales, it is not surprising that the relative importance of variables often changed
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Fig. 4 Box plots of variables importance according to the results of the species-specific habitat suitability maps obtained using the BIOMOD computer platform. The variables are listed in decreasing order of importance from top to bottom. Central solid bars refer to the median of the distribution of the variable importance values. Rectangles delimit the interquartile range and whiskers refer to maximum and minimum values. AI aggregation index, AWMSI area weighted mean shape index, COHESION patch cohesion index, CONTAG contagion, ED
edge density, ENN_MN mean euclidean nearest neighbour, IJI interspersion and juxtaposition index, MPS mean patch size, PLAND percentage of landscape, PR patch richness. See text for definitions. Codes for each class variable are as follows: 210 cultivations, 240 heterogeneous agricultural areas, 311 broad leaved forests, 312 coniferous forests, 313 mixed forests, 321 steppe. Variables not associated to Corine Land Cover codes are at the landscape level
with scale among species (Bellamy et al. 2013). The distribution of M. emarginatus in the study area was associated at the medium scale (5 km) to the interspersion and extension of natural habitats like steppes and forests; while at the broader scale (10 km), the most important feature were the distances among steppes and cultivations. This evidences are in accordance with the ecology of M. emarginatus, as it is well known that habitat heterogeneity is an important characteristic of feeding grounds of this moderateclutter forager (Flaquer et al. 2008). N. leisleri was especially sensitive to the cohesion among cultivated patches on a large scale. These features are likely related to the feeding behaviour of the species, i.e. foraging in open areas (Waters et al. 1999; Russo and Jones 2003; Ehrenbold et al. 2013). In addition, the importance of the cohesion of these landscape structures underline the significance of the structural connectivity for this species, in accordance with its migratory behaviour (Battersby 2010; Voigt et al. 2012; Roscioni et al. 2013, 2014).
A generalist species such as P. pipistrellus shows less strict selection towards habitat components and, according to our results, prefers a high level of heterogeneity at a broader scale, acting as a ‘‘multiplehabitat’’ specialist (Russo 2007). We also found that a more selective bat such as R. hipposideros, especially sensitive to small-scale habitat corridors such as hedgerows (Bontadina et al. 2002), requires a high level of connectivity between habitat patches at a finer scale. One limitation to our approach is the coarse-grained resolution (100 m) used for the regional-scale analysis, as well as the restrictions posed by the availability of detailed maps to investigate small-scale effects. Even so, we were able to detect effects operating on a relatively small scale (1 km). Yet, to replicate our approach on a finer spatial scale, a higher resolution should be applied. Refining the map resolution would make it possible to appreciate the effects of landscape features such as hedgerows, which are otherwise overlooked (Le Coeur et al. 2002). Besides, patch
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identification too depends on the resolution, as a patch is defined as a relatively homogeneous area that differs from its surroundings (Forman 1995); but this difference may or not be noticed depending on how fine the selected resolution is. The discrimination ability (finer vs. coarser scale) varies among species, and it could be particularly important for species perceiving their environment at a more detailed scale. Whereas, for generalist species, the dimension of patches beyond a certain threshold becomes less important because these species are insensitive to the fine structure of the landscape (Sua´rez-Seoane and Baudry 2002).
Conclusions and applications The present study confirmed how different species perceive the various landscape elements of their habitat at different spatial scales. Thus, a non-arbitrary choice of a fixed scale and of the landscape predictors is highly advisable to generate SDMs more capable to capture effectively species-habitat relationships. Selection based on univariate models makes it possible to tailor SDMs according to species-specific requirements in terms of habitat features, spatial patterns, and landscape characteristics, rendering our approach a significant step towards a more effective land management planning for species conservation. The results of the study closely matched the known ecology of the species we investigated, but also provided new important information on relevant landscape features acting at different scales. Considering that the four species were representative of different guilds, we believe our approach to be a powerful tool to detect the spatial scales at which a given species is more sensitive and on such basis steer management actions. Although we used bats as model species, our approach can easily be extended to other taxa and different spatial scales. Acknowledgments We thank NEMO s.r.l. for providing the CLC map. We also acknowledge Filippo Frizzi for his support in providing the topographic maps. Thanks also go to Stefano Vanni for his support in georeferencing the presence data.
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