Data from: A quantitative framework to estimate the relative importance of environment, spatial variation and patch connectivity in driving community composition
Monteiro, Viviane F., Federal University of Rio de Janeiro
Paiva, Paulo C., Federal University of Rio de Janeiro
Peres-Neto, Pedro R., University of Quebec at Montreal
Published Nov 02, 2017 on Dryad.
Cite this dataset
Monteiro, Viviane F.; Paiva, Paulo C.; Peres-Neto, Pedro R. (2017). Data from: A quantitative framework to estimate the relative importance of environment, spatial variation and patch connectivity in driving community composition [Dataset]. Dryad. https://doi.org/10.5061/dryad.st6tn
Perhaps the most widely used quantitative approach in metacommunity ecology is the estimation of the importance of local environment vs. spatial structuring using the variation partitioning framework. Contrary to metapopulation models, however, current empirical studies of metacommunity structure using variation partitioning assume a space-for-dispersal substitution due to the lack of analytical frameworks that incorporate patch connectivity predictors of dispersal dynamics.
Here, a method is presented that allows estimating the relative importance of environment, spatial variation and patch connectivity in driving community composition variation within metacommunities. The proposed approach is illustrated by a study designed to understand the factors driving the structure of a soft-bottom marine polychaete metacommunity.
Using a standard variation partitioning scheme (i.e. where only environmental and spatial predictors are used), only about 13% of the variation in metacommunity structure was explained. With the connectivity set of predictors, the total amount of explained variation increased up to 51% of the variation.
These results highlight the importance of considering predictors of patch connectivity rather than just spatial predictors. Given that information on connectivity can be estimated by commonly available data on species distributions for a number of taxa, the framework presented here can be readily applied to past studies as well, facilitating a more robust evaluation of the factors contributing to metacommunity structure.