Using empirical and simulated data to study the influence of environmental heterogeneity on fish species richness in two biogeographic provinces
- Published
- Accepted
- Subject Areas
- Biodiversity, Biogeography, Conservation Biology, Ecology, Environmental Sciences
- Keywords
- aquatic community assembly, conservation biology, random placement model, diversity, heterogeneity hypothesis, simulation
- Copyright
- © 2014 Massicotte et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Using empirical and simulated data to study the influence of environmental heterogeneity on fish species richness in two biogeographic provinces. PeerJ PrePrints 2:e567v1 https://doi.org/10.7287/peerj.preprints.567v1
Abstract
Loss of species richness in aquatic ecosystems is occurring rapidly and many factors, including habitat heterogeneity, have been suggested to affect the diversity of aquatic communities. We used fish community data (> 200 species) from extensive surveys conducted in two biogeographic provinces (extent > 1000 km) in North America to test the hypothesis that fish species richness is greater in more heterogeneous habitats (grain < 10 km2). Our tests are based on samples collected at nearly 800 stations over a period of five years. Using a set of environmental variables routinely measured by monitoring programs and a random placement model of community assembly, we demonstrate that fish species richness in coastal ecosystems is associated locally with the spatial heterogeneity of environmental variables but not with their magnitude. The observed effect of heterogeneity on species richness was substantially greater than that generated by simulations. Our modeling framework opens avenues for targeted conservation of habitat heterogeneity at broader temporal and spatial scales.