Species Distribution Modeling for Machine Learning Practitioners: A Review

TL;DR

This review introduces machine learning-based Species Distribution Modeling (SDM) to computer scientists, covering key concepts, models, data issues, and challenges to facilitate ecological applications of ML.

Contribution

It provides a comprehensive overview of SDM tailored for computer scientists, highlighting technical challenges and standard models in the field.

Findings

Survey of SDM concepts and terminology

Discussion of data availability and challenges

Review of standard SDM models

Abstract

Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.