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PLoS One. 2012;7(6):e39609. doi: 10.1371/journal.pone.0039609. Epub 2012 Jun 25.

Exploring macroinvertebrate species distributions at regional and local scales across a sandy beach geographic continuum.

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1
Departamento de Ecología y Biología Animal, Universidad de Vigo, Vigo, Spain. irodil@uvigo.es

Abstract

Exposed sandy beaches are highly dynamic ecosystems where macroinvertebrate species cope with extremely variable environmental conditions. The majority of the beach ecology studies present exposed beaches as physically dominated ecosystems where abiotic factors largely determine the structure and distribution of macrobenthic communities. However, beach species patterns at different scales can be modified by the interaction between different environmental variables, including biotic interactions. In this study, we examined the role of different environmental variables for describing the regional and local scale distributions of common macrobenthic species across 39 beaches along the North coast of Spain. The analyses were carried out using boosted regression trees, a relatively new technique from the field of machine learning. Our study showed that the macroinvertebrate community on exposed beaches is not structured by a single physical factor, but instead by a complex set of drivers including the biotic compound. Thus, at a regional scale the macrobenthic community, in terms of number of species and abundance, was mainly explained by surrogates of food availability, such as chlorophyll a. The results also revealed that the local scale is a feasible way to construct general predictive species-environmental models, since relationships derived from different beaches showed similar responses for most of the species. However, additional information on aspects of beach species distribution can be obtained with large scale models. This study showed that species-environmental models should be validated against changes in spatial extent, and also illustrates the utility of BRTs as a powerful analysis tool for ecology data insight.

PMID:
22761841
PMCID:
PMC3382464
DOI:
10.1371/journal.pone.0039609
[Indexed for MEDLINE]
Free PMC Article
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