Machine Learning for Bioclimatic Modelling

TL;DR

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

Contribution

It provides a comprehensive analysis of ML and statistical techniques in bioclimatic modeling, guiding informed method selection based on application-specific factors.

Findings

Success depends on application-specific factors

No single ML technique is universally effective

Understanding model behavior is crucial for reliable predictions

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 behavior 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.