Document Type
Article
Abstract
Species have been commonly hypothesized to have high population densities in geographic areas which correspond to either the centre of the species geographic range or climatic niche (abundant–centre hypothesis). However, there is mixed empirical support for this relationship, and little theoretical underpinning. We simulate a species spreading across a set of replicated artificial landscapes to examine the expected level of support for abundant–centre relationships in geographic and niche space. Species niche constraints were modeled as a single axis which was related directly to population growth rates. We found strong evidence for abundant–centre relationships when populations follow deterministic growth, dispersal is high, environmental noise is absent and intraspecific competition is low. However, the incorporation of ecological realism reduced the detectability of abundant–centre relationships considerably. Our results suggest that even in carefully constructed artificial landscapes designed to demonstrate abundant–centre dynamics, the incorporation of small amounts of demographic stochasticity, environmental heterogeneity or landscape structure can strongly influence the relationship between species population density and distance to species geographic range or niche centre. While some simulated relationships were of comparable strength to common empirical support for abundant–centre relationships, our results suggest that these relationships are expected to be fairly variable and weak.
Digital Object Identifier (DOI)
Publication Info
Published in Ecography, Volume 43, Issue 9, 2020, pages 1341-1351.
APA Citation
Dallas, T. A., & Santini, L. (2020). The influence of stochasticity, landscape structure and species traits on abundant–centre relationships. Ecography, 43(9), 1341–1351. https://doi.org/10.1111/ecog.05164
Rights
© 2020 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.