Modelling amphibian road crossing points in a dynamic environment

Abstract Roads are ubiquitous infrastructures that have detrimental effects on wildlife and contribute to increased mortality and fragmentation of animal populations. Although several mitigation measures are available to reduce road impacts, their planning rarely considers the dynamic nature of the environment, which may reflect into temporal variation in habitat suitability and connectivity for animal species. Consequently, the effectiveness of such measures may fall short of expectations. By combining high‐resolution satellite imagery and connectivity modelling, we propose a generalizable approach to identify the most probable crossing points across a barrier at different time snapshots. This information may be pivotal in planning mitigation measures that can take into account dynamic components. We collected occurrence data of three farmland‐adapted anurans and high spatiotemporal definition Sentinel‐2 multispectral images to build habitat suitability models that capture the relationship between the environment and the species within the study area (an agricultural area crossed by a highway). We then projected the models onto six snapshots from two subsequent amphibian breeding seasons. Finally, we used circuit theory‐based connectivity models to identify the areas with the highest probability of highway crossing for each snapshot/species. We found remarkable differences over time for each species, both in suitability and connectivity. Furthermore, the distribution and relative importance of the crossing points changed greatly between the 2 years as well as within the same. Some crossing points were stable over time, while others were important for a specific snapshot. Synthesis and applications . Not considering the spatiotemporal variability of the environment can lead to a loss of crucial information when modelling the localities at which the predicted flows of animals intersect the highway. The dynamics of road‐affected ecosystems can be taken into account using freely available remote sensing data. This can be an important element in achieving the goal of maintaining connectivity and minimizing wildlife mortality.