New paper in PeerJ Life & Environment

New paper, ‘A machine learning approach to managing game bird introductions’, has been accepted and published in PeerJ Life & Environment. This study examines the challenge of introducing game‐bird species into new environments (e.g., for hunting or conservation), and ask: Do site-level factors predict whether an introduction will succeed or fail?

The study applied machine learning models to a dataset of historical game bird introductions to identify which factors best predict establishment success. By incorporating species traits, release effort, and environmental variables, we found that site-level factors such as habitat suitability and climate were far more important than propagule pressure (number of individuals released) in determining outcomes. These results suggest that successful introductions depend more on choosing ecologically compatible locations than on the scale of release efforts. The findings have practical implications for wildlife managers, emphasizing that predictive modeling and habitat assessments can guide more effective and resource-efficient introduction and conservation strategies.

License

Copyright 2018-present Austin M. Smith .

Released under the MIT license.