A machine learning approach to managing game bird introductions

Effective management of introduced species requires a clear understanding of their habitat requirements. Species distribution models (SDMs) offer a powerful tool for addressing this challenge. We applied seven modeling techniques to predict a suitable habitat for the introduced Chukar Partridge (Alectoris chukar), including artificial neural networks, generalized additive models, k-nearest neighbor, random forests, support vector machines, extreme gradient boosting, and a weighted ensemble approach. Using site-level data on physiography, climate, land cover, and habitat range, we modeled Chukar distributions by simulating historical introduction efforts and extrapolating predictions into surrounding areas to assess cross-regional transferability. Model performance was evaluated using independent, geographically distinct validation datasets. Our results demonstrate that machine learning-based SDMs provide accurate and transferable predictions of Chukar habitat suitability. This study highlights the value of machine learning for predicting establishment success while emphasizing the importance of incorporating species movement behavior and site fidelity into SDM frameworks. Overall, our findings contribute to advancing conservation planning, species reintroductions, and adaptive management strategies.