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Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?
International Journal of Applied Earth Observation and Geoinformation 55 (2017) 1–8
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Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data? ˜ a, Aitor Gastón a , Carlos Ciudad a,∗ , María C. Mateo-Sánchez a , Juan I. García-Vinas a b c César López-Leiva , Alfredo Fernández-Landa , Miguel Marchamalo , Jorge Cuevas a , ˜ de la Fuente a,d , Marie-Josée Fortin e , Santiago Saura a Begona a
ETSI Montes, Forestal y del Medio Natural, Technical University of Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain Agresta Cooperative Society, C/Duque Fernán Nú˜ nez 2, 28012 Madrid, Spain c ETSI Caminos, Canales y Puertos, Technical University of Madrid, C/Profesor Aranguren s/n, 28040 Madrid, Spain d Junta de Castilla y León, Servicio Territorial de Medio Ambiente, Plaza Reina Do˜ na Juana 5, 40001 Segovia, Spain e Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario M5S 3G5, Canada b
a r t i c l e
i n f o
Article history: Received 29 April 2016 Received in revised form 11 August 2016 Accepted 18 October 2016 Keywords: Habitat selection Multi-scale habitat modelling High-resolution data LiDAR Remote sensing Brown bear
a b s t r a c t Spatial resolution of environmental data may influence the results of habitat selection models. As highresolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2 ) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Assessing current and potential distribution of species is crucial to make effective decisions for biodiversity conservation and management (Guisan et al., 2013). Habitat selection models allow the understanding and prediction of species’ occurrence by analyzing the species-habitat relationships through a set of explanatory environmental variables related to land cover, vegetation, terrain, climate, human pressure or other factors (Guisan and Zimmerman, 2000; Klar et al., 2008). A key step in habitat modelling is deciding the radius around a given location at which to quantify the effect of the environmental variables on species presence or absence at
∗ Corresponding author. E-mail address: [email protected] (C. Ciudad). http://dx.doi.org/10.1016/j.jag.2016.10.007 0303-2434/© 2016 Elsevier B.V. All rights reserved.
that location (Bird-Jackson and Fahrig, 2015), typically through a mean value of the environmental variables over the considered radius. Most habitat models consider a single radius (hereafter spatial scale), which is commonly defined a priori based on expert knowledge. However, recent studies have shown that species perceive and use different environmental variables at different spatial scales, ranging from local factors within habitat patches to broad scale determinants of home range selection and population distribution at landscape or regional extents (e.g. Graf et al., 2005; Wasserman et al., 2012; Mateo-Sánchez et al., 2014). Therefore, it is recommended that habitat modelling follows a multi-scale approach in which each environmental variable is quantified at the spatial scale (radius) at which it shows the highest predictive ability (hereafter referred to as operational scale), as this has been shown to improve the performance of the produced models (Cushman and McGarigal, 2004).
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The reliability of habitat models is also tightly related to the spatial resolution (i.e. minimum mapping unit or cell size) of the data used to generate the predictor variables (Keller and Smith, 2014). Implementation of finer spatial resolution data may improve the accuracy of habitat models, but the cost of acquisition and processing can be high (Tattoni et al., 2012; Keller and Smith, 2014). Furthermore, the improvement in habitat models provided by highresolution data may depend on the operational scale (radius) at which each environmental factor is quantified; for example, such improvement may eventually be only minor for a factor that is perceived by the species at a broad spatial scale, with little species sensitivity to the fine-scale variability that is captured by highresolution sensors. Hence, evaluating how much it may be gained from using high-resolution data to estimate species’ distribution (compared to more easily available coarser-resolution maps) is a fundamental question to guide data acquisition and biodiversity conservation efforts (Zellweger et al., 2014; Mateo-Sánchez et al., 2016). Land cover maps are one of the most commonly used data sources to generate environmental predictors for habitat models. These maps usually characterize the composition of the landscape from the interpretation or automatic classification of remote sensing products, such as aerial photographs or satellite images. Nevertheless, land cover maps miss the internal structure of landscape elements that may be relevant to explain habitat use by the focal species (Tattoni et al., 2012). The development of vegetation surveys can provide more detailed information about forest composition and structure, but usually they are not available across broad regions. Light Detection and Ranging (LiDAR) is an active remote sensing technology that allows measuring three-dimensional distribution of vegetation over large areas with high detail (Lefsky et al., 2002). This technology is being widely used to quantify vegetation structure, especially in forest landscapes (Lefsky et al., 2002; Wilsey et al., 2012). Some previous studies have detected an improvement in the performance of habitat models using LiDAR data at a single scale (e.g. Tattoni et al., 2012; Wilsey et al., 2012; Zellweger et al., 2014). However, to our knowledge, no study has compared the accuracy of habitat selection models using different types of resolution data with a multi-scale habitat modelling approach. For this purpose, we analyzed the multi-scale habitat selection of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain) considering different spatial resolution data for vegetation cover. We focused in this species for three reasons. First, the brown bear is an emblematic species in Spain that has suffered an intense historical decline due to human persecution and habitat loss, with the Cantabrian Range holding the last native populations of this species in the Iberian Peninsula. In the last decade the population has started to grow (Ballesteros and Palomero, 2012), but the potentially low availability of suitable habitat may be limiting the likelihood of population expansion, threatening its viability in the long term (Naves et al., 2003; Mateo-Sánchez et al., 2016). Second, brown bears have broad spatial requirements and occupy large, heterogeneous and human-modified landscapes in the Cantabrian Range (Mateo-Sánchez et al., 2016); therefore, our study area (>35,000 km2 ) comprises a large variety of land covers and environmental conditions that may benefit a more general evaluation of our research questions. Third, a considerable amount of high-quality data are available for this species and region, allowing the fulfilment of the general aims of the study: well-studied population habitat requirements (Mateo-Sánchez et al., 2014), large sets of bear location records, and different sources of data with an increasing spatial resolution (including LiDAR data all over the study area). Our objective was to evaluate the effect of the spatial resolution of vegetation data on the operational scales, influence of the predictor variables, habitat suitability spatial patterns and predic-
tive performance of multi-scale habitat selection models for the Cantabrian brown bear. We used three different spatial resolution data commonly utilized in Europe and Spain, which may be also similar to other data sources available in other countries or continents: (i) coarse resolution data from CORINE Land Cover map (minimum mapped unit of 25 ha), available all over Europe, (ii) medium resolution data from the Forest Map of Spain (FMS), with a minimum mapped unit of 2.25 ha, and (iii) high resolution data from LiDAR (0.5 points/m2 ). We first built three habitat suitability models incorporating all environmental variables that have been previously shown to be relevant for brown bears. These variables included (i) information on vegetation cover obtained from each of the datasets with different spatial resolution (one model for each of the three datasets) and (ii) other non-vegetation variables (terrain, human pressure) that were derived from the same datasets and included with the same spatial resolution in all the three models. Secondly, we developed three suitability models with only the variables related to vegetation cover as predictors (one model for each dataset with different resolution), in order to address the pure effect of the spatial resolution of the vegetation data in the habitat selection models (not confounded with the effect of other non-vegetation predictors). Note that in all cases multi-scale habitat models were conducted, i.e. the environmental variables were assessed at different spatial scales as an average, over the considered radius, of the data with different spatial resolutions. 2. Material and methods 2.1. Study area The study area covers 35,700 km2 in the Cantabrian Range (NW Spain) (Fig. 1). This area contains the whole range of the native populations of the brown bear in the Iberian Peninsula and a buffer zone (∼25 km) where future population expansion may be likely. The landscape is mainly composed by a mosaic of patches of forest (dominated by oaks [Quercus sp.], beeches [Fagus sylvatica] and chestnuts [Castanea sativa]), shrublands (Erica sp., Calluna vulgaris, Cytisus sp., etc.), natural grasslands, agricultural lands and other artificial areas (see more details in Mateo-Sánchez et al., 2016). Human activities have led to a substantial alteration of the original forest cover in many parts of the study area. 2.2. Brown bear data We used available brown bear presence records collected from 2000 to 2010 in the Cantabrian Range by trained observers and rangers (details in Mateo-Sánchez et al., 2016). Locations were resampled with 1 ha cell size to summarize potential locations of the same individual in consecutive days and to increase computational efficiency. We obtained a total number of 6207 locations that were used in habitat selection analyses. 2.3. Environmental variables and spatial resolution of vegetation data For each spatial resolution type we attempted to generate the same type of environmental variables related to foraging resources, shelter (vegetation shelter and terrain ruggedness) and human pressure (Table 1). These variables have shown to be suitable predictors of brown bear occurrence in the Cantabrian Range (Naves et al., 2003; García et al., 2007; Mateo-Sánchez et al., 2014, 2016). Variables related to human pressure and terrain ruggedness were quantified using the same data sources and spatial resolution in all the models, while variables related to vegetation foraging resources
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Fig. 1. Study area and brown bear distribution in the Cantabrian Range. Dashed rectangle indicates a focused area shown in Figs. 2 and 3.
Table 1 Environmental variables for each resolution type considered in the analysis of brown bear habitat suitability. Acronyms: FMS (Forest Map of Spain), DEM (Digital Elevation Model), CNIG (Spanish Geographic National Institute), OSM (OpenStreetMap). Metric Foraging resources FR
Shelter ForA
ForH ForCI
ShrubCI
Rugg Human pressure BuildDens HwDens RoadDens
Resolution
Description
CORINE FMS LiDAR
Estimated from CORINE Estimated from FMS and specific models (see methods) Estimated from LiDAR, FMS and specific models
CORINE FMS LiDAR LiDAR CORINE FMS LiDAR CORINE FMS LiDAR CORINE, FMS and LiDAR
Forest area from CORINE Forest area from FMS Forest area from LiDAR Forest height from LiDAR (variable not available for models based on CORINE or FMS) Cohesion index of forests from CORINE Cohesion index of forests from FMS Cohesion index of forests from LiDAR Cohesion index of shrubland from CORINE Cohesion index of shrubland from FMS Cohesion index of shrubland from FMS (LiDAR not reliable for this shrubland variable) Terrain ruggedness from a DEM (CNIG)
CORINE, FMS and LiDAR
and shelter were quantified from the following three data sources with different spatial resolution (Table 1):
– CORINE Land Cover 2006, which is distributed by the European Environment Agency (EEA, 2014). It has 44 land cover classes that were differentiated based on computer-assisted visual interpretation of satellite images at a 1:100,000 scale and with a minimum mapping unit of 25 ha (Büttner et al., 2012). – Forest Map of Spain (FMS) provided by the Spanish Ministry of Agriculture, Food and Environment (MAGRAMA, 2006). It was developed at 1:50,000 scale from the interpretation of aerial photographs, combined with pre-existing maps and field inventory data. The minimum mapping unit is 2.25 ha. FMS contains more detailed information on forest composition and structure than CORINE, including the identification of up to three different forest tree species in each polygon, the abundance of each of these species, and the total forest canopy cover in each polygon.
Building density calculated from CNIG Highway density calculated from OSM Conventional road density calculated from OSM
– LiDAR data obtained from the Spanish National Plan for Aerial Orthophotography (PNOA; Ministerio de Fomento, 2015), with a mean density of 0.5 points/m2 , and processed with FUSION software (McGaughey and Carson, 2003). These data were aggregated using a square lattice of 625 m2 (25 m resolution) for computing forest height and canopy cover. Forest height was estimated as the 95th percentile of vegetation height from the aboveground vegetation returns (>3.5 m, to separate trees from understory vegetation). Forest canopy cover was calculated as the ratio between the number of first returns above 3.5 m and the total number of first returns (details in Mateo-Sánchez et al., 2016).
We aimed to evaluate the influence of having available additional input data with an increased resolution on habitat models, and therefore we assumed that lower resolution datasets were also available for the models to be constructed at a given resolution
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level. Consequently, in some cases we calculated environmental variables for a higher resolution model using data from a lower resolution dataset when such database gave information of potential predictive value (e.g. tree species identity from FMS in the model including LiDAR data), but not the other way around (e.g. no LiDAR data were used in the lower-resolution model using exclusively FMS data). Additionally, in order to disentangle the potential effect of the spatial and thematic resolution of vegetation data (both of which increased when using FMS instead of CORINE), we generated an additional dataset with the spatial resolution of FMS but the coarser thematic detail of CORINE (hereafter referred to as combined FMS-CORINE dataset). To generate this dataset we recoded, based on expert knowledge, the different vegetation types of FMS to one of the classes defined in CORINE Land Cover. All environmental variables were referenced to UTM projection (ETRS89, zone 30) and resampled with 1 ha cell size as for the brown bear locations. To generate multi-scale (multi-radii) raster layers for each environmental variable, we used circular moving windows with radii 0.25, 0.5, 1, 2, 4, 8, 16, 32 and 64 km around each 1-ha cell; the mean of the variable, or the density of elements (for buildings and transport infrastructure only), was calculated in each of these windows (see Mateo-Sánchez et al., 2016 for details). For CORINE, the scale of 0.25 km was excluded from the analysis, because the area covered with this radius is below the minimum mapping unit of this map (i.e. 25 ha). We used ArcGIS 10.1 (ESRI) for calculations. 2.3.1. Foraging resources Foraging resources were assessed following the methodology described in Mateo-Sánchez et al. (2016). According to that study, we measured the importance and abundance of the foraging resources provided by each plant species (trees, shrubs and herbs). The importance of each foraging resource was determined by previous scat analysis for brown bears in the study area (more details in Mateo-Sánchez et al., 2016). The abundance was estimated differently depending on the available data for each spatial resolution. For CORINE, as well as for the combined FMS-CORINE dataset generated at the spatial resolution of FMS but the thematic detail of CORINE, the abundance of foraging resources for each land cover class was estimated using expert knowledge. For Forest Map of Spain, the abundance of tree species was obtained from the information on tree species identity and abundance provided by this map. The abundance of non-tree species was estimated through the information in available floristic inventories, ecological niche modelling based on climatic and lithological factors, and expert knowledge on the compatibility of the presence of particular plant species within plant communities mapped all throughout the study area by FMS (Mateo-Sánchez et al., 2016). Finally, for the models including LiDAR, we improved the previous foraging resources estimation by using the finer-scale canopy cover from LiDAR data to assess the abundance of tree species (with species identity taken from FMS and with the same estimation of the abundance of nontree species as described above for FMS). 2.3.2. Shelter We used variables of forest (canopy cover ≥30%) and shrubland cover as potential surrogates for brown bears shelter (Table 1) (Mateo-Sánchez et al., 2016). For CORINE, forest cover was obtained as the combination of classes Broad-leaved forest, Coniferous forest and Mixed forest; while shrubland included Moors and heathland, Transitional woodland/shrub and Sclerophylous vegetation. In order to evaluate the effect of connectedness of forest and shrubland cover, we also calculated the cohesion index in FRAGSTATS 4.2 (McGarigal et al., 2012) for each resolution type. Shrubland cohesion for the model using LiDAR was assessed based on FMS data, because the LiDAR data used in this study (with a point density of 0.5 points/m2 ) were not able to accurately describe shrubland cover
in many extremely dense shrub areas of the Cantabrian Range. Cohesion of forest and cohesion of shrubland were evaluated only for 7 scales (excluding 32 and 64 km) to reduce “boundary effects” in the computation of the cohesion index (see Mateo-Sánchez et al., 2016). Last, we included forest height as an additional forest structure descriptor only available for LiDAR data. We attempted to improve previous models for brown bear developed in our study area (Mateo-Sánchez et al., 2014, 2016) by including terrain ruggedness. This variable has shown to be a good estimator of shelter associated with topography for large mammals in human dominated landscapes (e.g. Nellemann et al., 2007; Bouyer et al., 2015). We therefore generated a raster layer of terrain ruggedness from a 25-m Digital Elevation Model (DEM; Spanish Geographic National Institute, CNIG), according to Riley et al. (1999). 2.3.3. Human pressure Human pressure was assessed as the density of buildings and transport infrastructures, the latter separated in highways and conventional roads (Table 1). Buildings were obtained from a topographic map (CNIG) and transport infrastructures from Open Street Map (OSM, www.openstreetmap.org). All variables were standardized subtracting the mean and dividing by the standard deviation. 2.4. Habitat selection models using different spatial resolution data In a first step, univariate models were developed for each of the datasets with different spatial resolution (as well as for the combined FMS-CORINE dataset) to identify the spatial scale at which each environmental variable was most strongly related to brown bear occurrence (operational scale) (Grand et al., 2004; MateoSánchez et al., 2014, 2016). We used lrm and pentrace functions from the rms package (Harrell, 2014) in R environment (R Core Team, 2014) to fit penalized logistic regression models with each predictor as a single linear term. For each dataset we chose the operational scale of each predictor using Akaike’s Information Criterion (AIC; Johnson and Omland, 2004). We used twenty thousand random backgrounds (cell size of 1 ha) as pseudo-absences, which were the same for every model. In a second step, we performed multi-scale models to assess the effect of each environmental variable in brown bear distribution. We developed for each spatial resolution dataset (as well as for the combined FMS-CORINE dataset) a model combining environmental variables at their specific operational scales as identified from the univariate models in the previous step. We developed penalized logistic regression models with linear terms only and without interactions among predictors. We used lrm and pentrace functions to fit the logistic regressions models. We assessed the predictive performance of multi-scale models using the area under the receiver operating characteristic curve (AUC; Fielding and Bell, 1997) estimated with ten-fold cross-validation. Finally, we developed multi-scale models excluding human pressure variables and terrain ruggedness in order to assess the sole effect of the variables related to vegetation cover, which were those that differed in spatial resolution for each of the datasets. 3. Results 3.1. Operational scales The univariate models showed remarkable disparity in operational scales of predictors between resolution types (Table 2). The selected operational scale for foraging resources (FR) was broader based on LiDAR and FMS than on CORINE. On the contrary, forest
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Table 2 Fitted habitat suitability models for the three datasets with different spatial resolution on vegetation (including human pressure and terrain ruggedness variables with a constant spatial resolution in the three models). Scale refers to the operational scale (km) for each environmental variable and resolution type. See Table 1 for variable description and acronyms. Significance levels: **<0.001, *<0.05. Predictor
CORINE Scale
FR ForA ForH ForCI ShrubCI Rugg BuildDens HwDens RoadDens AUC
2 8 8 16 1 16 16 16 0.883
FMS Coef.
LiDAR
Scale
0.586** −0.172**
32 1
0.570** 0.338** 0.615** −1.646** −1.104** −1.038**
0.5 16 1 16 16 16 0.906
Coef.
Scale
0.787** 0.429**
16 1 0.5 16 16 1 16 16 16 0.912
0.404** 1.835** 0.384** −0.609** −1.161** −1.088**
Coef. 1.222** 0.262** 0.128** 0.154** 1.856** 0.371** −1.350** −0.365** −0.072
area (ForA) had a much finer operational scale for FMS and LiDAR than for CORINE. Forest height (ForH; only available from LiDAR) had the strongest effect at an even more local scale (0.5 km) than forest area (Table 2). Variables related to human pressure (BuildDens, HwDens and RoadDens) affected habitat suitability at broad scales (16 km), while terrain ruggedness (Rugg) had the strongest effect at a more local scale of 1 km (Table 2). 3.2. Predictors influence Variables related to foraging resources and shelter had a general positive effect on bear habitat suitability, while those related to human pressure had a strong negative effect (Table 2). FR had an important positive effect on habitat suitability for all the resolutions. On the contrary, ShrubCI had the highest weight (high positive coefficients) for LiDAR and FMS resolution, but it had a limited effect (low coefficients) for CORINE resolution. Forest cohesion (ForCI) had a relevant influence at CORINE scale (relatively high positive coefficient), but its importance decreased gradually with resolution (Table 2). ForA showed more discrepancy between resolution types. Adding ForH at LiDAR resolution did not have a great contribution (relatively low coefficient) to brown bear habitat selection models. Rugg showed a positive effect on habitat suitability for all the resolution types, especially for CORINE (Table 2). Results were broadly similar in the multivariate models using all the variables (Table 2) and including only vegetation cover variables (Table 3), but in the latter the results mentioned previously for vegetation variables were more evident. 3.3. Model performance and habitat suitability spatial pattern The models showed a good but variable performance, with AUC ranging from 0.798 to 0.911 depending on spatial resolution and on whether non-vegetation variables were included in the models or not (Tables 2 and 3). Performance of multi-scale models including all the set of variables increased according to spatial resolution; AUC was highest for LiDAR and lowest for CORINE (Table 2). The differences in AUC among spatial resolutions were significant (p < 0.05) in all cases. Likewise, spatial pattern of habitat suitability was more concentrated in bear core population areas for LiDAR (Fig. 2c), and distributed in a comparatively larger set of more dispersed areas for CORINE (Fig. 2a). Multi-scale models showed a similar pattern when using only vegetation cover predictors (i.e. excluding human pressure variables and Rugg), but performance decreased in relation to the complete models (Tables 2 and 3). The reduction in predictive ability was inversely related to spatial resolution, i.e. much more pronounced for CORINE (AUC reduced from 0.883 to 0.798). AUC
Fig. 2. Habitat suitability maps provided by the optimized multi-scale model for each dataset with different spatial resolution on vegetation (including also all the non-vegetation variables as predictors): (a) CORINE resolution, (b) Forest Map of Spain resolution, and (c) LiDAR resolution. See Fig. 1 for the location of the area here shown. Fitted models are given in Table 2.
values were still high for LiDAR even if non-vegetation variables were not included in the models (Table 3). In the same way, spatial pattern of habitat suitability showed more discrepancy between the vegetation-only and the complete models when using coarser
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Table 3 Habitat use according to different types of resolution, excluding human pressure and terrain ruggedness variables. See Tables 1 and 2 for variable description and acronyms. Significance levels: **<0.001, *<0.05. Predictor
CORINE Scale
FR ForA ForH ForCI ShrubCI AUC
2 8 8 16 0.798
FMS Coef.
LiDAR
Scale
Coef.
Scale
Coef.
16 1 0.5 16 16 0.906
1.591** 0.284** 0.087* 0.036 2.908**
0.482** 0.431**
32 1
1.011** 0.560**
0.743** 0.567**
0.5 16 0.884
0.329** 3.246**
less (considering the actual distribution of brown bears in the area) when human pressure and Rugg were not considered (Fig. 3a). The results for the combined FMS-CORINE dataset showed a model performance closer to FMS than to CORINE, for both of the models including and excluding non-vegetation variables (AUC = 0.897 and 0.866, respectively). Besides, the coefficients of the variables (data not shown) were much more similar to those found for the FMS models. These results suggest that the improvement for FMS models compared to CORINE are more associated with the increased spatial resolution than with the more thematic detail of the FSM dataset.
4. Discussion
Fig. 3. Habitat suitability maps provided by the optimized multi-scale model for each dataset with different spatial resolution (using only vegetation cover variables): (a) CORINE resolution, (b) Forest Map of Spain resolution, and (c) LiDAR resolution. See Fig. 1 for the location of the area here shown. Fitted models are given in Table 3.
resolutions, particularly for CORINE (Figs. 2 and 3). For LiDAR, high habitat suitability values still matched well with bear core areas, with the exception of some new small high quality areas in the center of the study area appearing when human pressure was not considered (Fig. 3c). For FMS, areas with high habitat suitability increased and expanded towards the periphery of the study area (Fig. 3b). Finally, for CORINE, the spatial distribution of predicted habitat suitability changed drastically and appeared to be meaning-
Habitat selection by species is a behavioral process involving decisions at multiple scales (Johnson, 1980; Hutto, 1985). In our study area, Mateo-Sánchez et al. (2014) found that the reliability of brown bear habitat selection models improves when the scale is optimized (i.e. the operational scale is detected), supporting the idea that bears select different environmental variables at different spatial scales. Similar results were obtained for other species and study areas (Graf et al., 2005; Wasserman et al., 2012). In the present study, we have observed, using the same mathematical models, that the spatial resolution of source data affected the determination of operational scales, which could be either finer or broader when higher-resolution data were used. This result suggests that the operational scales that are identified through multi-scale habitat modelling should not be necessarily considered as the true biological scales at which the focal species actually perceives a given environmental factor. On the contrary, the identified operational scales seem to be contingent on the spatial resolution at which the environmental variables are mapped, rather than just on species traits and perception of the landscape. We therefore recommend that the operational scales identified in one study should only be adopted for subsequent habitat suitability modelling in a different study area or time period when in both cases the input datasets (and not only the focal species) are at the same resolution. We here considered a species with broad spatial requirements; home range size for brown bears varies from 58 to 1600 km2 in Europe (Swenson et al., 2000). Such a species may be thought to be little dependent on small-scale variations in habitat characteristics. However, we found that the variables related to forest area and height were selected at fine spatial scales (0.5–1 km) when using LiDAR data. This finding suggests that localized highsuitability areas that are used more frequently within broad and heterogeneous home ranges are better identified from the detailed information on forest structure captured by LiDAR. This type of information is missed by coarser land cover or forest maps, which somehow forces the habitat models based on these maps to select the forest-related variables at a broader scale than that better matching bear habitat use (as happened in the CORINE model for forest area).
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Some of our results suggest that the coarse-scale CORINE land cover map may not be accurately classifying forest and shrublands in the Cantabrian Range, at least not in a way that matches well with brown bear requirements and perception of these habitats. First, forest area had a negative effect in the CORINE model that also included all non-vegetation predictors; such result is against knowledge of the species ecology and is also contrary to what was found in many previous studies on brown bear habitat selection in the Cantabrian Range and in other study areas (Naves et al., 2003; Apps et al., 2004; Mateo-Sánchez et al., 2014, 2016), including the results from the other two higher-resolution vegetation datasets in this study. Second, landscape configuration is considered to be a relevant driver of brown bear habitat use. Especially, shrubland cohesion has a major influence in landscape use, providing shelter for bears (Mateo-Sánchez et al., 2016). However, the importance of this predictor declined strongly in the CORINE-based model. Since the cohesion of forest had a higher importance for CORINE than for the other two resolution datasets, it seems plausible to suggest that there might be a considerable misclassification of forests and shrublands by CORINE in the study area, considering that these vegetation types can be hard to differentiate from passive remote sensing images, particularly in topographically complex areas such as the Cantabrian Range. Third, the variable related to foraging resources was selected at finer scale in the CORINE model than in the other two higher-resolution datasets. This difference may be however more related to the lower thematic resolution in CORINE than necessarily indicative of a misclassification of forests per se. This is particularly true because each tree species can largely differ in terms of the foraging resources it provides to bears (e.g. oaks provide six times more food than beeches), while CORINE only differentiates conifer, broadleaved and mixed forest categories. Foraging conditions have been shown to be one of the most important factors driving species habitat selection (e.g. Hutto, 1985; Mateo-Sánchez et al., 2016). Our results support this conclusion, since foraging resources had a remarkable influence in all models, even when estimated from the coarser CORINE land cover data (despite the differences in the operational scale for this variable as discussed above). The addition of forest canopy cover from LiDAR data to quantify the abundance of tree species greatly improved the estimates of foraging resources according to the increased importance of this predictor in the LiDAR habitat models. All considered non-vegetation variables, which were introduced with the same spatial resolution in all models, were found to be significant predictors of bear habitat use and had effects in accordance with previous studies on the species. The inclusion of terrain ruggedness improved the accuracy of previous habitat selection models (i.e. Mateo-Sánchez et al., 2014, 2016) by capturing a positive influence on bear occurrence as rugged areas may allow bears to avoid human disturbances while moving through highly modified landscapes such as those in the Cantabrian Range. Terrain ruggedness had a particularly high importance in the CORINEbased model, suggesting that this variable may be in part operating as a surrogate of vegetation shelter in high-mountain areas with poor soils where misclassification of forest and shrublands may be more likely. Human pressure variables had a remarkable negative effect on bear landscape use in all models. Our results using LiDAR resolution support the idea that building density has a stronger influence on brown bear landscape use than transport infrastructures (Naves et al., 2003; Apps et al., 2004; Mateo-Sánchez et al., 2014, 2016). In contrast, using coarser resolutions, the negative effect of highways and roads increased significantly. This finding could suggest an overestimation of the influence of transport infrastructures on habitat selection due to the relatively poor estimation of other landscape factors, such as vegetation structure and composition, when using coarser spatial resolutions.
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The spatial resolution of the data utilized to produce the environmental predictors can affect the performance of habitat selection models (Tattoni et al., 2012; Keller and Smith, 2014; Zellweger et al., 2014). In this study on multi-scale models we found that model performance was positively correlated with spatial resolution of vegetation data. Despite the increase in AUC values with spatial resolution, the differences between datasets were not very pronounced (e.g. AUC for FMS was only 0.006 lower than for LiDAR) and even CORINE resulted in a good performance (AUC = 0.881) when all the set of variables (including non-vegetation ones) were included in the habitat model. Therefore, for some applications the three types of resolution data may be considered suitable to characterize species-habitat associations. Nevertheless, the considerable decrease in the CORINE model performance when excluding human pressure variables and terrain ruggedness as predictors, together with the erratic pattern of habitat suitability resulting in that case, suggest that coarse land cover maps are not always able to directly discriminate those vegetation characteristics and landscape elements that are relevant for species occurrence and distribution patterns. Only with the help of other indirect variables such limited performance in vegetation characterization may be masked and compensated in the final predictive value of the habitat models. Such dependency of the coarser resolution models on indirect variables raises concerns on their generality and applicability to other areas with different combinations of environmental factors. 5. Conclusion This study has shown that using high resolution LiDAR data in multi-scale habitat selection models provides more accurate understanding and prediction of species-habitat relationships. We found this result for brown bears; the improvement provided by LiDAR to habitat models may be even more pronounced for forest species with smaller body mass and home ranges, which are likely to be more dependent on local fine-scale habitat variability and vegetation structure (see Keller and Smith, 2014). Suitability maps resulting from habitat selection models are broadly applied in numerous management actions, such as defining areas to protect, identifying potential reintroduction areas and creating resistances surfaces (e.g. as the inverse of habitat suitability) for connectivity analysis. Therefore, the use of LiDAR data, by supporting the development of more reliable habitat models, can largely benefit the effectiveness of conservation measures for threatened species and of landscape-scale planning in general. Acknowledgements Funding was provided by the Spanish Ministry of Science and Innovation research grant GEFOUR (AGL2012-31099). We are also grateful to the National Plan for Aerial Orthophotography (PNOA-IGN), to the Fundación Oso Pardo and to the Regional Administration involved in brown bear management for providing data (Junta de Castilla y León, Gobierno de Cantabria, Principado de Asturias and Xunta de Galicia). References Apps, C.D., McLellan, B.N., Woods, J.G., Proctor, M.F., 2004. Estimating grizzly bear distribution and abundance relative to habitat and human influence. J. Wildl. Manage. 68 (1), 138–152. Büttner, G., Kosztra, B., Maucha, G., Pataki, R., 2012. Implementation and Achievements of CLC2006. ETCS IA Report, EEA Project Manager. Markus Erhard, Revised Version. European Environment Agency, Copenhagen. Ballesteros, F., Palomero, G., 2012. Conectividad, demografía y conservación del oso pardo Cantábrico. In: San Miguel, A., Ballesteros, F., Blanco, J.C., Palomero, G. (Eds.), Manual de buenas prácticas para la gestión de corredores oseros en la Cordillera Cantábrica. Fundación Oso Pardo. Ministerio de Agricultura,
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Alimentación y Medio Ambiente. Serie Especies Amenazadas, Madrid, pp. 21–33. Bird-Jackson, H., Fahrig, L., 2015. Are ecologists conducting research at the right scale? Global Ecol. Biogeogr. 24, 52–63. Bouyer, Y., San Martin, G., Poncin, P., Beudels-Jamar, R.C., Odden, J., Linnell, J.D.C., 2015. Eurasian lynx habitat selection in human-modified landscape in Norway: effects of different human habitat modifications and behavioral states. Biol. Conserv. 191, 291–299. Cushman, S.A., McGarigal, K., 2004. Patterns in the species-environment relationship depend on both scale and choice of response variables. Oikos 105, 117–124. EEA, 2014. CORINE Land Cover 2006 Seamless Vector Data – Version 12/2013, http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-dataversion-3. Accessed 03.03.15. Fielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24 (01), 8–49. García, P., Lastra, J., Marquínez, J., Nores, C., 2007. Detailed model of shelter areas for the Cantabrian brown bear. Ecol. Inform. 2 (4), 297–307. Graf, R.F., Bollmann, K., Suter, W., Bugmann, H., 2005. The importance of spatial scale in habitat models: capercaillie in the Swiss Alps. Landsc. Ecol. 20, 703–717. Grand, J., Buonaccorsi, J., Cushman, S.A., Griffin, C.R., Neel, M.C., 2004. A multiscale landscape approach to predicting bird and moth rarity hotspots in a threatened pitch pine–scrub oak community. Conserv. Biol. 18 (4), 1063–1077. Guisan, A., Zimmerman, N.E., 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135 (2), 147–186. Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., et al., 2013. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435. Harrell, F.E., 2014. rms: Regression Modeling Strategies. R Package Version 4. 2-1. http://cran.r-project.org/packages=rms. Hutto, R., 1985. Habitat selection by nonbreeding, migratory land birds. In: Cody, M. (Ed.), Habitat Selection in Birds. Academic Press, London, pp. 455–476. Johnson, J.B., Omland, K.S., 2004. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108. Johnson, D.H., 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71. Keller, J.K., Smith, C.R., 2014. Improving GIS-based Wildlife-Habitat Analysis. Springer Briefs in Ecology. Springer, New York. Klar, N., Fernández, N., Kramer-Schadt, S., Herrmann, M., Trinzen, M., Büttner, I., Niemitz, C., 2008. Habitat selection models for European wildcat conservation. Biol. Conserv. 141, 308–319. Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J., 2002. Lidar remote sensing for ecosystem studies. Bioscience 52 (1), 19–30. ˜ 1:50.000 (MFE50), http://www. MAGRAMA, 2006. Mapa Forestal De Espana magrama.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/ informacion-disponible/mfe50. aspx. Accessed 02.02.15.
Mateo-Sánchez, M.C., Cushman, S.A., Saura, S., 2014. Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). Int. J. Geogr. Inf. Sci. 28 (8), 1531–1546. ˜ Mateo-Sánchez, M.C., Gastón, A., Ciudad, C., García-Vinas, J.I., Cuevas, J., López-Leiva, C., Fernández-Landa, A., Algeet-Abarquero, N., Marchamalo, M., Fortin, M.-J., Saura, S., 2016. Seasonal and temporal changes in species use of the landscape: how do they impact the inferences from multi-scale habitat modeling? Landsc. Ecol. 31 (6), 1261–1276. McGarigal, K., Cushman, S.A., Ene, E., 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at: http://www.umass.edu/landeco/research/fragstats/fragstats.html. McGaughey, R.J., Carson, W.W., 2003. Fusing LIDAR data, photographs, and other data using 2D and 3D visualization techniques. In: Proceedings of Terrain Data: Applications and Visualization–Making the Connection, October 28–30, 2003, pp. 16–24. Ministerio de Fomento, 2015. Plan Nacional de Ortofotografía Aérea. Instituto Geográfico Nacional http://pnoa.ign.es/. Naves, J., Wiegand, T., Revilla, E., Delibes, M., 2003. Endangered species constrained by natural and human factors: the case of brown bears in northern Spain. Conserv. Biol. 17 (5), 1276–1289. Nellemann, C., Støen, O.G., Kindberg, J., Swenson, J.E., Vistnes, I., Ericsson, G., Katajisto, J., Kaltenborn, B.P., Martin, J., Ordiz, A., 2007. Terrain use by an expanding brown bear population in relation to age, recreational resorts and human settlements. Biol. Conserv. 138, 157–165. R Core Team, 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project. org/. Version 3.1.2. Riley, S.J., De Gloria, S.D., Elliot, R., 1999. A terrain ruggedness that quantifies topographic heterogeneity. Intermt. J. Sci. 5 (1–4), 23–27. Swenson, J.E., Gerstl, N., Dahle, B., Zedrosser, A., 2000. Action Plan for the Conservation of the Brown Bear in Europe. Nature and Environment, 114. Council of Europe, Strasbourg, France. Tattoni, C., Rizzolli, F., Pedrini, P., 2012. Can LiDAR data improve bird habitat suitability models? Ecol. Modell. 245, 103–110. Wasserman, T.N., Cushman, S.A., Wallin, D.O., Hayden, J., 2012. Multi scale habitat relationships of Martes americana in northern Idaho, USA. USDA Forest Service RMRS Research Paper RMRS-RP-94. Wilsey, C.B., Lawler, J.J., Cimprich, D.A., 2012. Performance of habitat suitability models for the endangered black-capped vireo built with remotely-sensed data. Remote Sens. Environ. 119, 35–42. Zellweger, F., Morsdorf, F., Purves, R.S., Braunisch, V., Bollmann, K., 2014. Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment. Biodivers. Conserv. 23 (2), 289–307.
Contents lists available at ScienceDirect
International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag
Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data? ˜ a, Aitor Gastón a , Carlos Ciudad a,∗ , María C. Mateo-Sánchez a , Juan I. García-Vinas a b c César López-Leiva , Alfredo Fernández-Landa , Miguel Marchamalo , Jorge Cuevas a , ˜ de la Fuente a,d , Marie-Josée Fortin e , Santiago Saura a Begona a
ETSI Montes, Forestal y del Medio Natural, Technical University of Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain Agresta Cooperative Society, C/Duque Fernán Nú˜ nez 2, 28012 Madrid, Spain c ETSI Caminos, Canales y Puertos, Technical University of Madrid, C/Profesor Aranguren s/n, 28040 Madrid, Spain d Junta de Castilla y León, Servicio Territorial de Medio Ambiente, Plaza Reina Do˜ na Juana 5, 40001 Segovia, Spain e Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario M5S 3G5, Canada b
a r t i c l e
i n f o
Article history: Received 29 April 2016 Received in revised form 11 August 2016 Accepted 18 October 2016 Keywords: Habitat selection Multi-scale habitat modelling High-resolution data LiDAR Remote sensing Brown bear
a b s t r a c t Spatial resolution of environmental data may influence the results of habitat selection models. As highresolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2 ) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Assessing current and potential distribution of species is crucial to make effective decisions for biodiversity conservation and management (Guisan et al., 2013). Habitat selection models allow the understanding and prediction of species’ occurrence by analyzing the species-habitat relationships through a set of explanatory environmental variables related to land cover, vegetation, terrain, climate, human pressure or other factors (Guisan and Zimmerman, 2000; Klar et al., 2008). A key step in habitat modelling is deciding the radius around a given location at which to quantify the effect of the environmental variables on species presence or absence at
∗ Corresponding author. E-mail address: [email protected] (C. Ciudad). http://dx.doi.org/10.1016/j.jag.2016.10.007 0303-2434/© 2016 Elsevier B.V. All rights reserved.
that location (Bird-Jackson and Fahrig, 2015), typically through a mean value of the environmental variables over the considered radius. Most habitat models consider a single radius (hereafter spatial scale), which is commonly defined a priori based on expert knowledge. However, recent studies have shown that species perceive and use different environmental variables at different spatial scales, ranging from local factors within habitat patches to broad scale determinants of home range selection and population distribution at landscape or regional extents (e.g. Graf et al., 2005; Wasserman et al., 2012; Mateo-Sánchez et al., 2014). Therefore, it is recommended that habitat modelling follows a multi-scale approach in which each environmental variable is quantified at the spatial scale (radius) at which it shows the highest predictive ability (hereafter referred to as operational scale), as this has been shown to improve the performance of the produced models (Cushman and McGarigal, 2004).
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The reliability of habitat models is also tightly related to the spatial resolution (i.e. minimum mapping unit or cell size) of the data used to generate the predictor variables (Keller and Smith, 2014). Implementation of finer spatial resolution data may improve the accuracy of habitat models, but the cost of acquisition and processing can be high (Tattoni et al., 2012; Keller and Smith, 2014). Furthermore, the improvement in habitat models provided by highresolution data may depend on the operational scale (radius) at which each environmental factor is quantified; for example, such improvement may eventually be only minor for a factor that is perceived by the species at a broad spatial scale, with little species sensitivity to the fine-scale variability that is captured by highresolution sensors. Hence, evaluating how much it may be gained from using high-resolution data to estimate species’ distribution (compared to more easily available coarser-resolution maps) is a fundamental question to guide data acquisition and biodiversity conservation efforts (Zellweger et al., 2014; Mateo-Sánchez et al., 2016). Land cover maps are one of the most commonly used data sources to generate environmental predictors for habitat models. These maps usually characterize the composition of the landscape from the interpretation or automatic classification of remote sensing products, such as aerial photographs or satellite images. Nevertheless, land cover maps miss the internal structure of landscape elements that may be relevant to explain habitat use by the focal species (Tattoni et al., 2012). The development of vegetation surveys can provide more detailed information about forest composition and structure, but usually they are not available across broad regions. Light Detection and Ranging (LiDAR) is an active remote sensing technology that allows measuring three-dimensional distribution of vegetation over large areas with high detail (Lefsky et al., 2002). This technology is being widely used to quantify vegetation structure, especially in forest landscapes (Lefsky et al., 2002; Wilsey et al., 2012). Some previous studies have detected an improvement in the performance of habitat models using LiDAR data at a single scale (e.g. Tattoni et al., 2012; Wilsey et al., 2012; Zellweger et al., 2014). However, to our knowledge, no study has compared the accuracy of habitat selection models using different types of resolution data with a multi-scale habitat modelling approach. For this purpose, we analyzed the multi-scale habitat selection of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain) considering different spatial resolution data for vegetation cover. We focused in this species for three reasons. First, the brown bear is an emblematic species in Spain that has suffered an intense historical decline due to human persecution and habitat loss, with the Cantabrian Range holding the last native populations of this species in the Iberian Peninsula. In the last decade the population has started to grow (Ballesteros and Palomero, 2012), but the potentially low availability of suitable habitat may be limiting the likelihood of population expansion, threatening its viability in the long term (Naves et al., 2003; Mateo-Sánchez et al., 2016). Second, brown bears have broad spatial requirements and occupy large, heterogeneous and human-modified landscapes in the Cantabrian Range (Mateo-Sánchez et al., 2016); therefore, our study area (>35,000 km2 ) comprises a large variety of land covers and environmental conditions that may benefit a more general evaluation of our research questions. Third, a considerable amount of high-quality data are available for this species and region, allowing the fulfilment of the general aims of the study: well-studied population habitat requirements (Mateo-Sánchez et al., 2014), large sets of bear location records, and different sources of data with an increasing spatial resolution (including LiDAR data all over the study area). Our objective was to evaluate the effect of the spatial resolution of vegetation data on the operational scales, influence of the predictor variables, habitat suitability spatial patterns and predic-
tive performance of multi-scale habitat selection models for the Cantabrian brown bear. We used three different spatial resolution data commonly utilized in Europe and Spain, which may be also similar to other data sources available in other countries or continents: (i) coarse resolution data from CORINE Land Cover map (minimum mapped unit of 25 ha), available all over Europe, (ii) medium resolution data from the Forest Map of Spain (FMS), with a minimum mapped unit of 2.25 ha, and (iii) high resolution data from LiDAR (0.5 points/m2 ). We first built three habitat suitability models incorporating all environmental variables that have been previously shown to be relevant for brown bears. These variables included (i) information on vegetation cover obtained from each of the datasets with different spatial resolution (one model for each of the three datasets) and (ii) other non-vegetation variables (terrain, human pressure) that were derived from the same datasets and included with the same spatial resolution in all the three models. Secondly, we developed three suitability models with only the variables related to vegetation cover as predictors (one model for each dataset with different resolution), in order to address the pure effect of the spatial resolution of the vegetation data in the habitat selection models (not confounded with the effect of other non-vegetation predictors). Note that in all cases multi-scale habitat models were conducted, i.e. the environmental variables were assessed at different spatial scales as an average, over the considered radius, of the data with different spatial resolutions. 2. Material and methods 2.1. Study area The study area covers 35,700 km2 in the Cantabrian Range (NW Spain) (Fig. 1). This area contains the whole range of the native populations of the brown bear in the Iberian Peninsula and a buffer zone (∼25 km) where future population expansion may be likely. The landscape is mainly composed by a mosaic of patches of forest (dominated by oaks [Quercus sp.], beeches [Fagus sylvatica] and chestnuts [Castanea sativa]), shrublands (Erica sp., Calluna vulgaris, Cytisus sp., etc.), natural grasslands, agricultural lands and other artificial areas (see more details in Mateo-Sánchez et al., 2016). Human activities have led to a substantial alteration of the original forest cover in many parts of the study area. 2.2. Brown bear data We used available brown bear presence records collected from 2000 to 2010 in the Cantabrian Range by trained observers and rangers (details in Mateo-Sánchez et al., 2016). Locations were resampled with 1 ha cell size to summarize potential locations of the same individual in consecutive days and to increase computational efficiency. We obtained a total number of 6207 locations that were used in habitat selection analyses. 2.3. Environmental variables and spatial resolution of vegetation data For each spatial resolution type we attempted to generate the same type of environmental variables related to foraging resources, shelter (vegetation shelter and terrain ruggedness) and human pressure (Table 1). These variables have shown to be suitable predictors of brown bear occurrence in the Cantabrian Range (Naves et al., 2003; García et al., 2007; Mateo-Sánchez et al., 2014, 2016). Variables related to human pressure and terrain ruggedness were quantified using the same data sources and spatial resolution in all the models, while variables related to vegetation foraging resources
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3
Fig. 1. Study area and brown bear distribution in the Cantabrian Range. Dashed rectangle indicates a focused area shown in Figs. 2 and 3.
Table 1 Environmental variables for each resolution type considered in the analysis of brown bear habitat suitability. Acronyms: FMS (Forest Map of Spain), DEM (Digital Elevation Model), CNIG (Spanish Geographic National Institute), OSM (OpenStreetMap). Metric Foraging resources FR
Shelter ForA
ForH ForCI
ShrubCI
Rugg Human pressure BuildDens HwDens RoadDens
Resolution
Description
CORINE FMS LiDAR
Estimated from CORINE Estimated from FMS and specific models (see methods) Estimated from LiDAR, FMS and specific models
CORINE FMS LiDAR LiDAR CORINE FMS LiDAR CORINE FMS LiDAR CORINE, FMS and LiDAR
Forest area from CORINE Forest area from FMS Forest area from LiDAR Forest height from LiDAR (variable not available for models based on CORINE or FMS) Cohesion index of forests from CORINE Cohesion index of forests from FMS Cohesion index of forests from LiDAR Cohesion index of shrubland from CORINE Cohesion index of shrubland from FMS Cohesion index of shrubland from FMS (LiDAR not reliable for this shrubland variable) Terrain ruggedness from a DEM (CNIG)
CORINE, FMS and LiDAR
and shelter were quantified from the following three data sources with different spatial resolution (Table 1):
– CORINE Land Cover 2006, which is distributed by the European Environment Agency (EEA, 2014). It has 44 land cover classes that were differentiated based on computer-assisted visual interpretation of satellite images at a 1:100,000 scale and with a minimum mapping unit of 25 ha (Büttner et al., 2012). – Forest Map of Spain (FMS) provided by the Spanish Ministry of Agriculture, Food and Environment (MAGRAMA, 2006). It was developed at 1:50,000 scale from the interpretation of aerial photographs, combined with pre-existing maps and field inventory data. The minimum mapping unit is 2.25 ha. FMS contains more detailed information on forest composition and structure than CORINE, including the identification of up to three different forest tree species in each polygon, the abundance of each of these species, and the total forest canopy cover in each polygon.
Building density calculated from CNIG Highway density calculated from OSM Conventional road density calculated from OSM
– LiDAR data obtained from the Spanish National Plan for Aerial Orthophotography (PNOA; Ministerio de Fomento, 2015), with a mean density of 0.5 points/m2 , and processed with FUSION software (McGaughey and Carson, 2003). These data were aggregated using a square lattice of 625 m2 (25 m resolution) for computing forest height and canopy cover. Forest height was estimated as the 95th percentile of vegetation height from the aboveground vegetation returns (>3.5 m, to separate trees from understory vegetation). Forest canopy cover was calculated as the ratio between the number of first returns above 3.5 m and the total number of first returns (details in Mateo-Sánchez et al., 2016).
We aimed to evaluate the influence of having available additional input data with an increased resolution on habitat models, and therefore we assumed that lower resolution datasets were also available for the models to be constructed at a given resolution
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level. Consequently, in some cases we calculated environmental variables for a higher resolution model using data from a lower resolution dataset when such database gave information of potential predictive value (e.g. tree species identity from FMS in the model including LiDAR data), but not the other way around (e.g. no LiDAR data were used in the lower-resolution model using exclusively FMS data). Additionally, in order to disentangle the potential effect of the spatial and thematic resolution of vegetation data (both of which increased when using FMS instead of CORINE), we generated an additional dataset with the spatial resolution of FMS but the coarser thematic detail of CORINE (hereafter referred to as combined FMS-CORINE dataset). To generate this dataset we recoded, based on expert knowledge, the different vegetation types of FMS to one of the classes defined in CORINE Land Cover. All environmental variables were referenced to UTM projection (ETRS89, zone 30) and resampled with 1 ha cell size as for the brown bear locations. To generate multi-scale (multi-radii) raster layers for each environmental variable, we used circular moving windows with radii 0.25, 0.5, 1, 2, 4, 8, 16, 32 and 64 km around each 1-ha cell; the mean of the variable, or the density of elements (for buildings and transport infrastructure only), was calculated in each of these windows (see Mateo-Sánchez et al., 2016 for details). For CORINE, the scale of 0.25 km was excluded from the analysis, because the area covered with this radius is below the minimum mapping unit of this map (i.e. 25 ha). We used ArcGIS 10.1 (ESRI) for calculations. 2.3.1. Foraging resources Foraging resources were assessed following the methodology described in Mateo-Sánchez et al. (2016). According to that study, we measured the importance and abundance of the foraging resources provided by each plant species (trees, shrubs and herbs). The importance of each foraging resource was determined by previous scat analysis for brown bears in the study area (more details in Mateo-Sánchez et al., 2016). The abundance was estimated differently depending on the available data for each spatial resolution. For CORINE, as well as for the combined FMS-CORINE dataset generated at the spatial resolution of FMS but the thematic detail of CORINE, the abundance of foraging resources for each land cover class was estimated using expert knowledge. For Forest Map of Spain, the abundance of tree species was obtained from the information on tree species identity and abundance provided by this map. The abundance of non-tree species was estimated through the information in available floristic inventories, ecological niche modelling based on climatic and lithological factors, and expert knowledge on the compatibility of the presence of particular plant species within plant communities mapped all throughout the study area by FMS (Mateo-Sánchez et al., 2016). Finally, for the models including LiDAR, we improved the previous foraging resources estimation by using the finer-scale canopy cover from LiDAR data to assess the abundance of tree species (with species identity taken from FMS and with the same estimation of the abundance of nontree species as described above for FMS). 2.3.2. Shelter We used variables of forest (canopy cover ≥30%) and shrubland cover as potential surrogates for brown bears shelter (Table 1) (Mateo-Sánchez et al., 2016). For CORINE, forest cover was obtained as the combination of classes Broad-leaved forest, Coniferous forest and Mixed forest; while shrubland included Moors and heathland, Transitional woodland/shrub and Sclerophylous vegetation. In order to evaluate the effect of connectedness of forest and shrubland cover, we also calculated the cohesion index in FRAGSTATS 4.2 (McGarigal et al., 2012) for each resolution type. Shrubland cohesion for the model using LiDAR was assessed based on FMS data, because the LiDAR data used in this study (with a point density of 0.5 points/m2 ) were not able to accurately describe shrubland cover
in many extremely dense shrub areas of the Cantabrian Range. Cohesion of forest and cohesion of shrubland were evaluated only for 7 scales (excluding 32 and 64 km) to reduce “boundary effects” in the computation of the cohesion index (see Mateo-Sánchez et al., 2016). Last, we included forest height as an additional forest structure descriptor only available for LiDAR data. We attempted to improve previous models for brown bear developed in our study area (Mateo-Sánchez et al., 2014, 2016) by including terrain ruggedness. This variable has shown to be a good estimator of shelter associated with topography for large mammals in human dominated landscapes (e.g. Nellemann et al., 2007; Bouyer et al., 2015). We therefore generated a raster layer of terrain ruggedness from a 25-m Digital Elevation Model (DEM; Spanish Geographic National Institute, CNIG), according to Riley et al. (1999). 2.3.3. Human pressure Human pressure was assessed as the density of buildings and transport infrastructures, the latter separated in highways and conventional roads (Table 1). Buildings were obtained from a topographic map (CNIG) and transport infrastructures from Open Street Map (OSM, www.openstreetmap.org). All variables were standardized subtracting the mean and dividing by the standard deviation. 2.4. Habitat selection models using different spatial resolution data In a first step, univariate models were developed for each of the datasets with different spatial resolution (as well as for the combined FMS-CORINE dataset) to identify the spatial scale at which each environmental variable was most strongly related to brown bear occurrence (operational scale) (Grand et al., 2004; MateoSánchez et al., 2014, 2016). We used lrm and pentrace functions from the rms package (Harrell, 2014) in R environment (R Core Team, 2014) to fit penalized logistic regression models with each predictor as a single linear term. For each dataset we chose the operational scale of each predictor using Akaike’s Information Criterion (AIC; Johnson and Omland, 2004). We used twenty thousand random backgrounds (cell size of 1 ha) as pseudo-absences, which were the same for every model. In a second step, we performed multi-scale models to assess the effect of each environmental variable in brown bear distribution. We developed for each spatial resolution dataset (as well as for the combined FMS-CORINE dataset) a model combining environmental variables at their specific operational scales as identified from the univariate models in the previous step. We developed penalized logistic regression models with linear terms only and without interactions among predictors. We used lrm and pentrace functions to fit the logistic regressions models. We assessed the predictive performance of multi-scale models using the area under the receiver operating characteristic curve (AUC; Fielding and Bell, 1997) estimated with ten-fold cross-validation. Finally, we developed multi-scale models excluding human pressure variables and terrain ruggedness in order to assess the sole effect of the variables related to vegetation cover, which were those that differed in spatial resolution for each of the datasets. 3. Results 3.1. Operational scales The univariate models showed remarkable disparity in operational scales of predictors between resolution types (Table 2). The selected operational scale for foraging resources (FR) was broader based on LiDAR and FMS than on CORINE. On the contrary, forest
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Table 2 Fitted habitat suitability models for the three datasets with different spatial resolution on vegetation (including human pressure and terrain ruggedness variables with a constant spatial resolution in the three models). Scale refers to the operational scale (km) for each environmental variable and resolution type. See Table 1 for variable description and acronyms. Significance levels: **<0.001, *<0.05. Predictor
CORINE Scale
FR ForA ForH ForCI ShrubCI Rugg BuildDens HwDens RoadDens AUC
2 8 8 16 1 16 16 16 0.883
FMS Coef.
LiDAR
Scale
0.586** −0.172**
32 1
0.570** 0.338** 0.615** −1.646** −1.104** −1.038**
0.5 16 1 16 16 16 0.906
Coef.
Scale
0.787** 0.429**
16 1 0.5 16 16 1 16 16 16 0.912
0.404** 1.835** 0.384** −0.609** −1.161** −1.088**
Coef. 1.222** 0.262** 0.128** 0.154** 1.856** 0.371** −1.350** −0.365** −0.072
area (ForA) had a much finer operational scale for FMS and LiDAR than for CORINE. Forest height (ForH; only available from LiDAR) had the strongest effect at an even more local scale (0.5 km) than forest area (Table 2). Variables related to human pressure (BuildDens, HwDens and RoadDens) affected habitat suitability at broad scales (16 km), while terrain ruggedness (Rugg) had the strongest effect at a more local scale of 1 km (Table 2). 3.2. Predictors influence Variables related to foraging resources and shelter had a general positive effect on bear habitat suitability, while those related to human pressure had a strong negative effect (Table 2). FR had an important positive effect on habitat suitability for all the resolutions. On the contrary, ShrubCI had the highest weight (high positive coefficients) for LiDAR and FMS resolution, but it had a limited effect (low coefficients) for CORINE resolution. Forest cohesion (ForCI) had a relevant influence at CORINE scale (relatively high positive coefficient), but its importance decreased gradually with resolution (Table 2). ForA showed more discrepancy between resolution types. Adding ForH at LiDAR resolution did not have a great contribution (relatively low coefficient) to brown bear habitat selection models. Rugg showed a positive effect on habitat suitability for all the resolution types, especially for CORINE (Table 2). Results were broadly similar in the multivariate models using all the variables (Table 2) and including only vegetation cover variables (Table 3), but in the latter the results mentioned previously for vegetation variables were more evident. 3.3. Model performance and habitat suitability spatial pattern The models showed a good but variable performance, with AUC ranging from 0.798 to 0.911 depending on spatial resolution and on whether non-vegetation variables were included in the models or not (Tables 2 and 3). Performance of multi-scale models including all the set of variables increased according to spatial resolution; AUC was highest for LiDAR and lowest for CORINE (Table 2). The differences in AUC among spatial resolutions were significant (p < 0.05) in all cases. Likewise, spatial pattern of habitat suitability was more concentrated in bear core population areas for LiDAR (Fig. 2c), and distributed in a comparatively larger set of more dispersed areas for CORINE (Fig. 2a). Multi-scale models showed a similar pattern when using only vegetation cover predictors (i.e. excluding human pressure variables and Rugg), but performance decreased in relation to the complete models (Tables 2 and 3). The reduction in predictive ability was inversely related to spatial resolution, i.e. much more pronounced for CORINE (AUC reduced from 0.883 to 0.798). AUC
Fig. 2. Habitat suitability maps provided by the optimized multi-scale model for each dataset with different spatial resolution on vegetation (including also all the non-vegetation variables as predictors): (a) CORINE resolution, (b) Forest Map of Spain resolution, and (c) LiDAR resolution. See Fig. 1 for the location of the area here shown. Fitted models are given in Table 2.
values were still high for LiDAR even if non-vegetation variables were not included in the models (Table 3). In the same way, spatial pattern of habitat suitability showed more discrepancy between the vegetation-only and the complete models when using coarser
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Table 3 Habitat use according to different types of resolution, excluding human pressure and terrain ruggedness variables. See Tables 1 and 2 for variable description and acronyms. Significance levels: **<0.001, *<0.05. Predictor
CORINE Scale
FR ForA ForH ForCI ShrubCI AUC
2 8 8 16 0.798
FMS Coef.
LiDAR
Scale
Coef.
Scale
Coef.
16 1 0.5 16 16 0.906
1.591** 0.284** 0.087* 0.036 2.908**
0.482** 0.431**
32 1
1.011** 0.560**
0.743** 0.567**
0.5 16 0.884
0.329** 3.246**
less (considering the actual distribution of brown bears in the area) when human pressure and Rugg were not considered (Fig. 3a). The results for the combined FMS-CORINE dataset showed a model performance closer to FMS than to CORINE, for both of the models including and excluding non-vegetation variables (AUC = 0.897 and 0.866, respectively). Besides, the coefficients of the variables (data not shown) were much more similar to those found for the FMS models. These results suggest that the improvement for FMS models compared to CORINE are more associated with the increased spatial resolution than with the more thematic detail of the FSM dataset.
4. Discussion
Fig. 3. Habitat suitability maps provided by the optimized multi-scale model for each dataset with different spatial resolution (using only vegetation cover variables): (a) CORINE resolution, (b) Forest Map of Spain resolution, and (c) LiDAR resolution. See Fig. 1 for the location of the area here shown. Fitted models are given in Table 3.
resolutions, particularly for CORINE (Figs. 2 and 3). For LiDAR, high habitat suitability values still matched well with bear core areas, with the exception of some new small high quality areas in the center of the study area appearing when human pressure was not considered (Fig. 3c). For FMS, areas with high habitat suitability increased and expanded towards the periphery of the study area (Fig. 3b). Finally, for CORINE, the spatial distribution of predicted habitat suitability changed drastically and appeared to be meaning-
Habitat selection by species is a behavioral process involving decisions at multiple scales (Johnson, 1980; Hutto, 1985). In our study area, Mateo-Sánchez et al. (2014) found that the reliability of brown bear habitat selection models improves when the scale is optimized (i.e. the operational scale is detected), supporting the idea that bears select different environmental variables at different spatial scales. Similar results were obtained for other species and study areas (Graf et al., 2005; Wasserman et al., 2012). In the present study, we have observed, using the same mathematical models, that the spatial resolution of source data affected the determination of operational scales, which could be either finer or broader when higher-resolution data were used. This result suggests that the operational scales that are identified through multi-scale habitat modelling should not be necessarily considered as the true biological scales at which the focal species actually perceives a given environmental factor. On the contrary, the identified operational scales seem to be contingent on the spatial resolution at which the environmental variables are mapped, rather than just on species traits and perception of the landscape. We therefore recommend that the operational scales identified in one study should only be adopted for subsequent habitat suitability modelling in a different study area or time period when in both cases the input datasets (and not only the focal species) are at the same resolution. We here considered a species with broad spatial requirements; home range size for brown bears varies from 58 to 1600 km2 in Europe (Swenson et al., 2000). Such a species may be thought to be little dependent on small-scale variations in habitat characteristics. However, we found that the variables related to forest area and height were selected at fine spatial scales (0.5–1 km) when using LiDAR data. This finding suggests that localized highsuitability areas that are used more frequently within broad and heterogeneous home ranges are better identified from the detailed information on forest structure captured by LiDAR. This type of information is missed by coarser land cover or forest maps, which somehow forces the habitat models based on these maps to select the forest-related variables at a broader scale than that better matching bear habitat use (as happened in the CORINE model for forest area).
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Some of our results suggest that the coarse-scale CORINE land cover map may not be accurately classifying forest and shrublands in the Cantabrian Range, at least not in a way that matches well with brown bear requirements and perception of these habitats. First, forest area had a negative effect in the CORINE model that also included all non-vegetation predictors; such result is against knowledge of the species ecology and is also contrary to what was found in many previous studies on brown bear habitat selection in the Cantabrian Range and in other study areas (Naves et al., 2003; Apps et al., 2004; Mateo-Sánchez et al., 2014, 2016), including the results from the other two higher-resolution vegetation datasets in this study. Second, landscape configuration is considered to be a relevant driver of brown bear habitat use. Especially, shrubland cohesion has a major influence in landscape use, providing shelter for bears (Mateo-Sánchez et al., 2016). However, the importance of this predictor declined strongly in the CORINE-based model. Since the cohesion of forest had a higher importance for CORINE than for the other two resolution datasets, it seems plausible to suggest that there might be a considerable misclassification of forests and shrublands by CORINE in the study area, considering that these vegetation types can be hard to differentiate from passive remote sensing images, particularly in topographically complex areas such as the Cantabrian Range. Third, the variable related to foraging resources was selected at finer scale in the CORINE model than in the other two higher-resolution datasets. This difference may be however more related to the lower thematic resolution in CORINE than necessarily indicative of a misclassification of forests per se. This is particularly true because each tree species can largely differ in terms of the foraging resources it provides to bears (e.g. oaks provide six times more food than beeches), while CORINE only differentiates conifer, broadleaved and mixed forest categories. Foraging conditions have been shown to be one of the most important factors driving species habitat selection (e.g. Hutto, 1985; Mateo-Sánchez et al., 2016). Our results support this conclusion, since foraging resources had a remarkable influence in all models, even when estimated from the coarser CORINE land cover data (despite the differences in the operational scale for this variable as discussed above). The addition of forest canopy cover from LiDAR data to quantify the abundance of tree species greatly improved the estimates of foraging resources according to the increased importance of this predictor in the LiDAR habitat models. All considered non-vegetation variables, which were introduced with the same spatial resolution in all models, were found to be significant predictors of bear habitat use and had effects in accordance with previous studies on the species. The inclusion of terrain ruggedness improved the accuracy of previous habitat selection models (i.e. Mateo-Sánchez et al., 2014, 2016) by capturing a positive influence on bear occurrence as rugged areas may allow bears to avoid human disturbances while moving through highly modified landscapes such as those in the Cantabrian Range. Terrain ruggedness had a particularly high importance in the CORINEbased model, suggesting that this variable may be in part operating as a surrogate of vegetation shelter in high-mountain areas with poor soils where misclassification of forest and shrublands may be more likely. Human pressure variables had a remarkable negative effect on bear landscape use in all models. Our results using LiDAR resolution support the idea that building density has a stronger influence on brown bear landscape use than transport infrastructures (Naves et al., 2003; Apps et al., 2004; Mateo-Sánchez et al., 2014, 2016). In contrast, using coarser resolutions, the negative effect of highways and roads increased significantly. This finding could suggest an overestimation of the influence of transport infrastructures on habitat selection due to the relatively poor estimation of other landscape factors, such as vegetation structure and composition, when using coarser spatial resolutions.
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The spatial resolution of the data utilized to produce the environmental predictors can affect the performance of habitat selection models (Tattoni et al., 2012; Keller and Smith, 2014; Zellweger et al., 2014). In this study on multi-scale models we found that model performance was positively correlated with spatial resolution of vegetation data. Despite the increase in AUC values with spatial resolution, the differences between datasets were not very pronounced (e.g. AUC for FMS was only 0.006 lower than for LiDAR) and even CORINE resulted in a good performance (AUC = 0.881) when all the set of variables (including non-vegetation ones) were included in the habitat model. Therefore, for some applications the three types of resolution data may be considered suitable to characterize species-habitat associations. Nevertheless, the considerable decrease in the CORINE model performance when excluding human pressure variables and terrain ruggedness as predictors, together with the erratic pattern of habitat suitability resulting in that case, suggest that coarse land cover maps are not always able to directly discriminate those vegetation characteristics and landscape elements that are relevant for species occurrence and distribution patterns. Only with the help of other indirect variables such limited performance in vegetation characterization may be masked and compensated in the final predictive value of the habitat models. Such dependency of the coarser resolution models on indirect variables raises concerns on their generality and applicability to other areas with different combinations of environmental factors. 5. Conclusion This study has shown that using high resolution LiDAR data in multi-scale habitat selection models provides more accurate understanding and prediction of species-habitat relationships. We found this result for brown bears; the improvement provided by LiDAR to habitat models may be even more pronounced for forest species with smaller body mass and home ranges, which are likely to be more dependent on local fine-scale habitat variability and vegetation structure (see Keller and Smith, 2014). Suitability maps resulting from habitat selection models are broadly applied in numerous management actions, such as defining areas to protect, identifying potential reintroduction areas and creating resistances surfaces (e.g. as the inverse of habitat suitability) for connectivity analysis. Therefore, the use of LiDAR data, by supporting the development of more reliable habitat models, can largely benefit the effectiveness of conservation measures for threatened species and of landscape-scale planning in general. Acknowledgements Funding was provided by the Spanish Ministry of Science and Innovation research grant GEFOUR (AGL2012-31099). We are also grateful to the National Plan for Aerial Orthophotography (PNOA-IGN), to the Fundación Oso Pardo and to the Regional Administration involved in brown bear management for providing data (Junta de Castilla y León, Gobierno de Cantabria, Principado de Asturias and Xunta de Galicia). References Apps, C.D., McLellan, B.N., Woods, J.G., Proctor, M.F., 2004. Estimating grizzly bear distribution and abundance relative to habitat and human influence. J. Wildl. Manage. 68 (1), 138–152. Büttner, G., Kosztra, B., Maucha, G., Pataki, R., 2012. Implementation and Achievements of CLC2006. ETCS IA Report, EEA Project Manager. Markus Erhard, Revised Version. European Environment Agency, Copenhagen. Ballesteros, F., Palomero, G., 2012. Conectividad, demografía y conservación del oso pardo Cantábrico. In: San Miguel, A., Ballesteros, F., Blanco, J.C., Palomero, G. (Eds.), Manual de buenas prácticas para la gestión de corredores oseros en la Cordillera Cantábrica. Fundación Oso Pardo. Ministerio de Agricultura,
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