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Wiley, Journal of Ecology, 2(102), p. 544-554, 2014

DOI: 10.1111/1365-2745.12215

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Probabilistic and spatially variable niches inferred from demography

Journal article published in 2014 by Jeffrey M. Diez ORCID, Itamar Giladi, Robert Warren, H. Ronald Pulliam
This paper is available in a repository.
This paper is available in a repository.

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Abstract

1.Mismatches between species distributions and habitat suitability are predicted by niche theory and have important implications for forecasting how species may respond to environmental changes. Quantifying these mismatches is challenging, however, due to the high dimensionality of species niches and the large spatial and temporal variability in population dynamics. 2.Here, we explore how probabilistic assessments of habitat suitability based on demographic models may be used to better bridge niche theory and population dynamics. We use integral projection models (IPMs) to predict population growth rates for a terrestrial orchid in response to environmental variables. By parameterizing these IPMs with hierarchical models, we develop a spatially variable measure of a species’ demographic niche, which can then be compared against its distribution to test ideas about what factors control a species’ distribution. 3.We found that demographic suitability of sites was not well correlated with the orchid's distribution at local scales, with many absences from microsites of high predicted suitability and occurrences in sites with low predicted suitability. However at the population scale, abundance was positively correlated with demographic suitability of the sites. These results are consistent with dispersal limitation and source-sink dynamics at small scales but stronger distribution-suitability matching at larger landscape scales. 4.Synthesis. The relationships between species distributions and demographic performance underlie basic niche theory and have important implications for predicting responses to a changing environment. The complexities of these relationships will require approaches that can encapsulate what we know in probabilistic terms, and allow for spatially varying niche relationships. This article is protected by copyright. All rights reserved.