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Species Distribution Modeling for Machine Learning Practitioners: A Review

Beery, Sara and Cole, Elijah and Parker, Joseph and Perona, Pietro and Winner, Kevin (2021) Species Distribution Modeling for Machine Learning Practitioners: A Review. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220224-200801611

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2107.10400arXivDiscussion Paper
ORCID:
AuthorORCID
Beery, Sara0000-0002-2544-1844
Parker, Joseph0000-0001-9598-2454
Perona, Pietro0000-0002-7583-5809
Additional Information:Our research for this paper included informational interviews with Meredith Palmer, Michael Tabak, Corrie Moreau, and Carrie Seltzer. Their insights into the unique challenges of species distribution modeling was invaluable. This work was supported in part by the Caltech Resnick Sustainability Institute and NSFGRFP Grant No. 1745301. The views expressed in this work are those of the authors and do not necessarily reflect the views of the NSF.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
Resnick Sustainability InstituteUNSPECIFIED
NSF Graduate Research FellowshipDGE-1745301
Subject Keywords:species distribution modeling, ecological niche modeling, machine learning
Classification Code:CCS Concepts: Computing methodologies → Machine learning.
Record Number:CaltechAUTHORS:20220224-200801611
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220224-200801611
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113577
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Mar 2022 19:04
Last Modified:01 Mar 2022 19:04

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