Title
Informing conservation units: Barriers to dispersal for the yellow anaconda
Author(s)
Kershaw, F.;Waller, T.;Micucci, P.;Draque, J.;Barros, M.;Buongermini, E.;Pearson, R. G.;Mendez, M.
Published
2013
Publisher
Diversity and Distributions
Published Version DOI
https://doi.org/10.1111/ddi.12101
Abstract
Aim Fine-scale population structure is often unaccounted for in the delineation of conservation units, potentially compromising long-term species persistence. Identifying biogeographic and environmental drivers of population boundaries is therefore of key conservation concern. We aimed to explore barriers to dispersal for the harvested yellow anaconda (Eunectes notaeus) using an ecological niche model. Our secondary aim was to test the relative geographic and environmental contributions of a multisource occurrence data set in species range predictions. Location Paraguay River drainage, central South America. Methods We developed an ecological niche model for the yellow anaconda using Maxent and a multisource species occurrence data set. Following nine iterations of model development, nine environmental variables were selected for model inclusion. We used the models to identify potential barriers to dispersal and employed jackknifing to identify the primary environmental variables that best explain barrier presence. We assessed the geographic and environmental overlap of models built with each data subset. Results Characterization of suitable habitat was found to be most powerful in northern Argentina and southern Paraguay. A persistent barrier to dispersal was identified in northern Argentina and corresponded to the presence of dry Cambisol soils. Data subsets were found to contribute different information to the final model in terms of geographic and environmental space. Main Conclusions Ecologically meaningful barriers to dispersal support recent genetic hypotheses of population subdivision. These barriers should be considered when delineating species management units to ensure sustainable harvest levels. Multisource data sets may produce more powerful niche predictions and represent a useful resource for data-poor species. Further, model results should be interpreted alongside complementary analyses for more effective conservation strategies.

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