Multiple statistical algorithms have been used for species distribution modelling (SDM). Due to shortcomings in species occurrence datasets, presence-only methods (such as MaxEnt) have become increasingly widely used. However, sampling bias remains a challenging issue, particularly for density-based approaches. The Isolation Forest (iForest) algorithm is a presence-only method less sensitive to sampling patterns and over-fitting because it fits the model by describing the unsuitable instead of suitable conditions. Here, we present the itsdm package for species distribution modelling with iForest, which provides a workflow wrapper for the algorithms in iForest family and convenient tools for model diagnostic and post-modelling analysis. itsdm allows users to fit and evaluate an iForest SDM using presence-only occurrence data. It also helps the users to understand relationships between species and the living environment using Shapley values, a suggested technique in explainable artificial intelligence (xAI). Additionally, itsdm can make spatial response maps that indicate how species respond to environmental variables across space and detect areas potentially affected by a changing environment. We demonstrated the usage of the itsdm package and compared iForest with other mainstream SDMs using virtual species. The results enlightened that iForest is an advantageous presence-only SDM when the actual distribution range is unclear. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
Methods in Ecology and Evolution
Song, Lei and Estes, Lyndon, "itsdm: Isolation forest-based presence-only species distribution modelling and explanation in r" (2023). Geography. 563.
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