Climate-Smart Wildlife Corridor Design Using Dispersal and Social Learning Models for Enhanced Species Movement

Abstract

Wildlife corridors serve critical roles in species connectivity in fragmented landscapes by hindering
the ecosystem, and their conventional forms fail to account for behavioral processes and climate
effects. This paper combines both the dispersal and social learning models and climate data in
developing better wildlife corridor designs. The movement of species across both landscapes with and
without social learning in both favorable and unfavorable climate conditions was simulated using an
agent-based model. The design of the corridors was also based on the dispersal, social learning, and
climate projections to evaluate their effects on connectivity and patch occupancy. The analysis
incorporated empirical evidence on the GPS telemetry, field observations, and climate forecasts. It
was found that there was a significant increase in connectivity and patch occupancy when behavioral
and climate factors were combined. Dispersal + Social Learning + Climate model had a 91%
connectivity index and 85% patch occupancy, which was in contrast to 65% and 58% of the Dispersal
only model. Designs that were climate-informed were able to preserve connectivity of up to 90 % in
future climate conditions. The inclusion of dispersal, social learning, and climate data in the wildlife
corridor design plays a crucial role in improving the ecological connectivity, resilience, and efficiency
of the species movement. The paper shows the significance of adopting behavioral ecology and
climate-smart plans in conservation planning. The research in the future should aim at perfecting these
models in various species and landscapes.