A step towards SDMs: A “couple‐and‐weigh” framework based on accessible data for biodiversity conservation and landscape planning

AIM The ongoing global change makes landscape planning and management of ecological corridors crucial to preserve biodiversity. We propose a workflow optimizing the use of different data sources to convert ecological niche models (ENMs) into landscape-focused species distribution models (SDMs), using these latter to compute ecological corridors. We infer corridors connecting present occurrence localities to future climatic refugia as well as to localities where extinct populations occurred. Also, a continuous connectivity change index is proposed to assess current–future differences. Finally, we discuss possible applications of our workflow to conservation, assessing the capability of established protected areas to preserve ecological corridors. LOCATION Europe. METHODS As case study to illustrate our framework, we use a database comprising occurrence localities of Vipera ursinii, one of the most endangered European reptiles. We obtain weighted SDMs for each of the four V. ursinii subspecies by coupling climate-based ENMs with standardized occurrence frequencies along land use and altitude gradients through weighted averaging in GIS. We calculate current and future landscape connectivity for each subspecies based on the corresponding weighted SDM. We compare predictive performance of “traditional” ENMs, including climate, land use and topography as predictors and weighted SDMs. RESULTS Weighted SDMs outperform ENMs, according to Boyce index. SDMs are used to infer connectivity, predicted to decrease in all future scenarios for V. ursinii, and assess where connections may favour movements of individuals to, for example, future suitable areas. Generally, protected areas are predicted to cover low-connectivity territories. MAIN CONCLUSIONS The proposed “couple-and-weigh” approach could represent a helpful tool to investigate biogeography, conservation and landscape planning topics, as it permits to capitalize on occurrence records and accessible environmental predictors by narrowing the target species’ potential distribution, estimated within “traditional” ENMs, to the realized one through post-modelling GIS analyses, which in turn improves estimation of friction maps used to infer connectivity.