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A review of supervised learning methods for classifying animal behavioural states from environmental features

November 16, 2022
  1. Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations.
  2. We review SL methods (weighted k-nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles.
  3. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise-averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package (xgboost) facilitate investigation of variable relationships and importance.
  4. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method.

Citation Information

Publication Year 2023
Title A review of supervised learning methods for classifying animal behavioural states from environmental features
DOI 10.1111/2041-210X.14019
Authors Silas Bergen, Manuela Huso, Adam E. Duerr, Missy A Braham, Sara Schmuecker, Tricia A. Miller, Todd E. Katzner
Publication Type Article
Publication Subtype Journal Article
Series Title Methods in Ecology and Evolution
Index ID 70238803
Record Source USGS Publications Warehouse
USGS Organization Forest and Rangeland Ecosystem Science Center

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Manuela M Huso

Scientist Emerita
Forest and Rangeland Ecosystem Science Center
Phone
541-750-0948

Todd E Katzner

Supervisory Research Wildlife Biologist
Forest and Rangeland Ecosystem Science Center

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