AI-Powered Habitat Suitability Models to Inform Migratory Species Protection and Ecological Restoration Efforts

Abstract

Moving animals also play a critical role in the ecology and the preservation of biodiversity through
their interdependence within ecosystems. Nevertheless, patterns of habitat loss, global warming, and
human disturbances have disrupted migration pathways and diminished habitat quality, posing severe
threats to species survival. This work presents a habitat suitability modeling framework powered by
AI, designed to enhance the conservation of migratory species and to aid ecological restoration
projects. The given strategy combines data on animal presence with crucial environmental factors,
including climatic, land-use, vegetation cover, topographical, and human disturbance indicators.
Using machine learning algorithms, intricate associations between species distributions and ecological
conditions are captured, enabling more precise, spatially explicit predictions than traditional
approaches. The produced suitability maps identify key habitats, including breeding sites, stopover
areas, and migration routes, which are essential to the maintenance of migratory populations. The
outcomes of model validation indicate strong predictive ability and reliability across a wide range of
environmental conditions. The results reveal the practical use of AI in animal and environmental
studies, providing important information for conservation planning, ranking habitat restoration
priorities, and informing management decision-making. Altogether, the paper reveals the
opportunities AI-based applications offer to enhance the security of migratory species and support
ecologically sustainable shifts in the evolving environment.