ArticleInsects2025
Do Random Forest-Driven Climate Envelope Models Require Variable Selection? A Case Study on
Article in Insects, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Climate Envelope Models (CEMs) commonly employ 19 bioclimatic variables to predict species distributions, yet selecting which variables to include remains a critical challenge. Although it seems logical to select ecologically relevant variables, the biological responses of many target species are poorly understood. Random Forest (RF), a popular method in CEMs, can effectively handle correlated and nonlinear variables. In light of these strengths, this study explores the full model hypothesis, which involves using all 19 bioclimatic variables in an RF model, using
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