Nine tips for ecologists using machine learning

TL;DR

This paper offers nine practical tips to help ecologists effectively implement machine learning models, especially for classification tasks, by addressing common challenges and errors.

Contribution

It provides a structured set of guidelines specifically tailored for ecologists to improve their machine learning applications, filling a gap in practical ecological modeling resources.

Findings

Identifies nine common errors in ecological machine learning applications.

Provides actionable recommendations to avoid pitfalls.

Enhances the usability of machine learning for ecological research.

Abstract

Due to their high predictive performance and flexibility, machine learning models are an appropriate and efficient tool for ecologists. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. Here we provide a series of tips to help ecologists in implementing machine learning models. We focus on classification problems as many ecological studies aim to assign data into predefined classes such as ecological states or biological entities. Each of the nine tips identifies a common error, trap or challenge in developing machine learning models and provides recommendations to facilitate their use in ecological studies.