Bioclimating Modelling: A Machine Learning Perspective

TL;DR

This paper reviews machine learning and statistical methods for bioclimatic modeling, emphasizing factors influencing their success in predicting organism ranges and understanding climate change impacts.

Contribution

It provides a comprehensive analysis of ML techniques in bioclimatic modeling and discusses factors affecting their effectiveness, aiding informed method selection.

Findings

ML success depends on application and problem type

No single ML technique is universally effective

Statistical techniques are also important in bioclimatic models

Abstract

Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approaches depends on a number of factors. While it can be safely said that no particular ML technique can be effective in all applications and success of a technique is predominantly dependent on the application or the type of the problem, it is useful to understand their behaviour to ensure informed choice of techniques. This paper presents a comprehensive review of machine learning-based bioclimatic model generation and analyses the factors influencing success of such models. Considering the wide use of statistical techniques, in our discussion we also include conventional statistical techniques used in bioclimatic modelling.