[Final version available] Explainable Artificial Intelligence enhances the ecological interpretability of black-box species distribution models

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1111/ecog.05360. This is version 4 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Supplementary Files
Authors

Masahiro Ryo, Boyan Angelov, Stefano Mammola , Jamie M. Kass, Blas M. Benito, Florian Hartig

Abstract

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.

DOI

https://doi.org/10.32942/osf.io/w96pk

Subjects

Artificial Intelligence and Robotics, Biodiversity, Computer Sciences, Ecology and Evolutionary Biology, Life Sciences, Physical Sciences and Mathematics, Research Methods in Life Sciences, Terrestrial and Aquatic Ecology

Keywords

Ecological niche modeling, Explainable artificial intelligence, habitat suitability modeling, interpretable machine learning, Local Interpretable Model-agnostic Explanation (LIME), model-agnostic, species distribution model, xAI

Dates

Published: 2020-04-17 05:21

Last Updated: 2020-11-22 08:25

Older Versions
License

CC-By Attribution-ShareAlike 4.0 International