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A machine learning approach to managing game bird introductions.

Austin M Smith^1,2, Wendell P Cropper³, Michael P Moulton²

¹School of Natural Resource and Environment, University of Florida, Gainesville, FL, United States of America.

|November 10, 2025

Summary

Predicting suitable habitat for introduced species like the Chukar Partridge (Alectoris chukar) is crucial. Machine learning-based species distribution models (SDMs) accurately forecast habitat suitability and aid conservation efforts.

Keywords:

Alectoris chukar Ensemble modeling Habitat suitability Species distribution modeling Species introductions Wildlife management

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Area of Science:

Ecology
Conservation Biology
Computational Biology

Background:

Effective management of introduced species necessitates understanding their habitat needs.
Species distribution models (SDMs) are valuable tools for predicting species' suitable habitats.
The Chukar Partridge (Alectoris chukar) is an introduced species requiring habitat assessment.

Purpose of the Study:

To predict suitable habitat for the introduced Chukar Partridge using various modeling techniques.
To evaluate the accuracy and transferability of machine learning-based SDMs.
To inform conservation planning and species reintroduction strategies.

Main Methods:

Applied seven modeling techniques: artificial neural networks, generalized additive models, k-nearest neighbor, random forests, support vector machines, extreme gradient boosting, and a weighted ensemble approach.
Utilized site-level data on physiography, climate, land cover, and habitat range.
Simulated historical introductions and extrapolated predictions for cross-regional transferability assessment.

Main Results:

Machine learning-based SDMs demonstrated accurate and transferable predictions of Chukar habitat suitability.
Model performance was validated using independent, geographically distinct datasets.
The study confirmed the efficacy of SDMs in predicting establishment success for introduced species.

Conclusions:

Machine learning significantly enhances the accuracy of species distribution models.
Incorporating species movement behavior and site fidelity is vital for robust SDM frameworks.
Findings support improved conservation planning, species reintroductions, and adaptive management.