A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation

Emma Chollet Ramampiandra, Andreas Scheidegger, Jonas Wydler and Nele Schuwirth

Ecological Modelling, 2023, vol. 481, issue C

Abstract: Species distribution models are commonly applied to predict species responses to environmental conditions. A wide variety of models with different properties exist that vary in complexity, which affects their predictive performance and interpretability. Machine learning algorithms are increasingly used because they are capable to capture complex relationships and are often better in prediction. However, to inform environmental management, it is important that a model predicts well for the right reasons. It remains a challenge to select a model with a reasonable level of complexity that captures the true relationship between the response and explanatory variables as good as possible rather than fitting to the noise in the data.

Keywords: Species distribution model; Statistical models; Interpretable machine learning; Model complexity; Freshwater macroinvertebrates (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:481:y:2023:i:c:s0304380023000819

DOI: 10.1016/j.ecolmodel.2023.110353

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