Migration AI: A Predictive Framework for Modeling Seasonal Wildlife Movement and Migration Behavior

Abstract

Migration during different seasons is one of the most noticeable and significant behaviors exhibited by animal species in terms of their ability to survive, reproduce, and affect the structure of the ecosystem. Prediction of the timing, destination, and cause of migration among animals between their seasonal ranges has become one of the main goals for the field of wildlife biology and conservation. In this paper, a framework called Migration AI is introduced that relies on the concepts of behavioral and physiological ecology and incorporates information about movements based on telemetry, phenological data, and individual condition into one comprehensive model of decision-making related to seasonal migration behavior. Instead of considering migration as an innate and constant characteristic of the particular species, this framework views migration as a variable behavior influenced by both physiological state and environmental conditions. The theoretical model incorporates well-known concepts in movement ecology such as the full annual cycle approach and the use of state-dependent behavioral models to explore connections between resource availability, heat resistance, and body condition in relation to migratory tendencies. Biological constraints, such as individual differences and phenotypic flexibility, which limit the predictability of the model are considered, along with applications in population management, conservation corridors, and adaptive wildlife management. Migration AI is conceived as a biologically informed model for arranging animal science knowledge into a forward-looking narrative of migration.