Journal
ECOGRAPHY
Volume 2022, Issue 6, Pages -Publisher
WILEY
DOI: 10.1111/ecog.05877
Keywords
abundance; ecological niche; genetic diversity; habitat suitability; independent data; occurrence; performance; population growth
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada Discovery Grants
- Future Leader Fellowship from Royal Botanic Gardens, Kew
- USDA National Inst. of Food and Agriculture Hatch [1016272]
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Species distribution models (SDMs) are widely used in ecology to infer habitat suitability for species of interest; however, studies show a decline in predictive performance from occurrence to genetic diversity, with higher success rates in single species evaluations. The limited accuracy of SDMs reported may reflect the best-case scenario, emphasizing the need for independent data validation when using these models in conservation decisions.
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 (similar to 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.
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