Abstract
Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.
Highlights
Anthropogenic activities, concurrently with human population growth, are responsible for wiping out wildlife species at rates never experienced before [1]
Wildlife conservation priorities have been focused on megafauna, since species with a large body size have been associated with high extinction risks [4]
The results derived by the 10-fold cross-validation indicated that the random forest (RF) model developed was robust given the low estimated error rate percentage, (19.15%), determining a high explanatory power of included predictors on the occurrence of the endangered Cabrera vole in our study area
Summary
Anthropogenic activities, concurrently with human population growth, are responsible for wiping out wildlife species at rates never experienced before [1]. The Cabrera vole often presents a metapopulation-like spatial structure, which together with the regular destruction and turnover of suitable habitat patches, makes the designation of special areas of conservation for this species a challenging task The designation of these conservation areas is demanded by the European Union, as the species is listed in both Bern Convention (Appendix II; 82/72/CEE) and Habitats Directive (Annexes II and IV; Council Directive 92/43/EEC). The selection of those key areas should be supported by detailed and up-to-date species’ distribution at multiple scales, and the use of efficient tools and frameworks able to appropriately identify them [17]. Correlative species distribution models (SDMs), or habitat suitability/niche models [18], which provide probabilistic estimation of occurrence patterns over broad areas by relating species occurrences with environmental characteristics [18], have become a popular tool to develop potential species range maps
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