Work

Species Distribution Modeling for Machine Learning Practitioners: A Review

Year: 2021

Type: conference-paper

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 [2, 5, 8]. 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.

Source: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS)

DOI: 10.1145/3460112.3471966

Authors: Sara Beery, Elijah Cole, Joseph Parker, Pietro Perona, Kevin Winner

Institutions: California Institute of Technology, Yale University

Language: English

Topic: Species Distribution and Climate Change

Subfield: Ecological Modeling

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: hybrid

Funders: National Science Foundation, California Institute of Technology, Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology