Species Distribution Modeling for Machine Learning Practitioners: A Review

• URL: http://arxiv.org/abs/2107.10400v1
• Date: Sat, 3 Jul 2021 17:50:34 GMT
• Title: Species Distribution Modeling for Machine Learning Practitioners: A Review
• Authors: Sara Beery, Elijah Cole, Joseph Parker, Pietro Perona, Kevin Winner
• Abstract summary: Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence. Despite its considerable importance, SDM has received relatively little attention from the computer science community. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.
• Score: 23.45438144166006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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