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Data from: Predators regulate prey species sorting and spatial distribution in microbial landscapes


Livingston, George et al. (2018), Data from: Predators regulate prey species sorting and spatial distribution in microbial landscapes, Dryad, Dataset,


1. The role of predation in determining the metacommunity assembly model of prey communities is understudied relative to that of interspecific competition among prey. Previous work on metacommunity dynamics of competing species has shown that sorting by habitat patch type and spatial patterning can be affected by disturbances. 2. Microcosms offer a useful model system to test the effect of multi-trophic interactions and disturbance on metacommunity dynamics. Here, we investigated the potential role of predators in enhancing or disrupting sorting and spatial pattern among prey in experimental landscapes. 3. We exposed multi-trophic protist microcosm landscapes with one predator, two competing prey, two patch resource types, and localized dispersal to three disturbance regimes (none, low, and high). Then, we used variation partitioning and spatial clustering analysis to analyze the results. 4. In contrast with previous experiments that did not manipulate predators, we found that patch type did not structure prey communities very well. Instead, we found that it was the distribution of the predator that most strongly predicted the composition of the prey community. 5. The predator impacted species sorting by 1) preferentially consuming one prey, thereby acting as a strong local environmental driver, and by 2) indirectly magnifying the impact of patch food resources on the less preferred prey. The predator also enhanced spatial signal in the prey community because of its limited dispersal. Our results indicate that predators can strongly influence prey species sorting and spatial patterning in metacommunities in ways that would otherwise be attributed to stochastic effects, such as dispersal limitation or demographic drift. Therefore, whenever possible, predators should be explicitly included as separate explanatory factors in variation partitioning analyses.

Usage Notes


National Science Foundation, Award: DEB-1311032