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

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

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.

Suggested Citation

  • Chollet Ramampiandra, Emma & Scheidegger, Andreas & Wydler, Jonas & Schuwirth, Nele, 2023. "A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation," Ecological Modelling, Elsevier, vol. 481(C).
    • Handle: RePEc:eee:ecomod:v:481:y:2023:i:c:s0304380023000819
    DOI: 10.1016/j.ecolmodel.2023.110353
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    References listed on IDEAS

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    1. Vermeiren, Peter & Reichert, Peter & Schuwirth, Nele, 2020. "Integrating uncertain prior knowledge regarding ecological preferences into multi-species distribution models: Effects of model complexity on predictive performance," Ecological Modelling, Elsevier, vol. 420(C).
    2. Schuwirth, Nele & Borgwardt, Florian & Domisch, Sami & Friedrichs, Martin & Kattwinkel, Mira & Kneis, David & Kuemmerlen, Mathias & Langhans, Simone D. & Martínez-López, Javier & Vermeiren, Peter, 2019. "How to make ecological models useful for environmental management," Ecological Modelling, Elsevier, vol. 411(C).
    3. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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