Species distribution models rarely predict the biology of real populations

Species distribution models rarely predict the biology of real populations

Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence popula...

Full description

Saved in:
Bibliographic Details
Published in:Ecography (Copenhagen) 2022-06, Vol.2022 (6), p.n/a
Main Authors: A. Lee‐Yaw, Julie, L. McCune, Jenny, Pironon, Samuel, N. Sheth, Seema
Format: Article
Language:eng
Subjects:
abundance
Biology
Decision making
Demography
ecological niche
Ecological niches
Genetic diversity
Geographical distribution
habitat suitability
Habitats
independent data
Mathematical models
occurrence
Parameters
Performance evaluation
Performance prediction
Population
Population biology
Population genetics
population growth
Species
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best‐case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand‐ins for the population parameters we seek to know.
ISSN:0906-7590
1600-0587