A machine learning approach to managing game bird introductions

TL;DR

This study uses machine learning to predict where introduced Chukar Partridges can thrive, helping improve wildlife management and conservation strategies.

Contribution

The study introduces a machine learning approach to enhance the accuracy and transferability of species distribution models for introduced game birds.

Findings

Machine learning models accurately predicted Chukar Partridge habitat suitability.

The models showed good cross-regional transferability when validated with independent data.

Incorporating movement behavior and site fidelity improved model effectiveness.

Abstract

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.

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