Data from: Modelling amphibian road crossing points in a dynamic environment

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 for each snapshot/species to
identify the areas with the highest probability of highway crossing in
each snapshot. 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 two
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 roads-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.