Abstract
Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.
Highlights
Biodiversity loss is among the biggest challenges that nature conservation is currently facing
We developed habitat suitability and connectivity models for the brown bear (Ursus arctos arctos) in the Cantabrian Range (Spain) using three spatial datasets that differ in their level of spatial coverage and accessibility, i.e., global, continental and national
The anthropogenic activity created a heterogeneous landscape with a mosaic of land covers where artificial and agricultural surfaces coexist with a diversity of grasslands, shrublands and forests
Summary
Biodiversity loss is among the biggest challenges that nature conservation is currently facing. Many conservation efforts are being continuously made by scientists, government administrations and conservationists. Computer-based approaches are acknowledged as one of the most useful and unbiased instruments to guide the design of nature management plans and policies, with habitat suitability and connectivity models being helpful to produce spatially explicit information for conservation [1,2,3,4,5,6]. Considerable effort has been taken to increase spatial resolution. The use of remote sensing imagery is gaining relevance because of its potential high spatial resolution and its broad temporal and spatial coverage and availability [8,9,10]
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