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The influence of landscape-scale spatial-environmental structure on the predictability of local community composition

University of British Columbia

A primary goal in ecology is to understand why localities with similar abiotic environmental conditions often exhibit differences in species composition. Previous work points to the potential importance of historical or regional processes, such as priority effects and dispersal limitation, but the ways in which landscape structure moderates the impacts of such processes remain unclear. Using spatially explicit simulations of competitive metacommunities, I investigated if spatial autocorrelation of the environment (SAE), the degree of clustering of similar environments, in the broader landscape interacted with dispersal capacity to affect the predictability of local community composition. My study employed a fully crossed factorial design of SAE (random configuration to high positive SAE), dispersal capacity (local versus global [control]), and fundamental niche scenario (niche-differentiated versus neutral [control]). I quantified community composition predictability by measuring Bray-Curtis similarity among the same localities of replicate metacommunities. The results showed that in the absence of either niche differentiation or spatially restricted dispersal, variation in SAE had no impact on the predictability of local community composition. In contrast, in the presence of both niche differentiation and spatially restricted dispersal (characteristics of many metacommunities in nature), increasingly positive SAE increased community composition predictability. This was attributed to the enhancement of landscape connectivity facilitating deterministic species-environmental sorting of differentially adapted species, which reduced the influence of stochastic community assembly processes. Thus, the results suggest that the variation in local species composition often observed among sites with similar environmental conditions could be attributed in part to differences in the spatial configuration of environmental conditions within the broader landscape. My work has potentially profound implications for basic and applied ecology. For example, the results suggest that practitioners should expect the reliability of composition-environment correlations to vary depending upon the spatial attributes of the ecosystem in question; they should be most useful as predictive tools within ecosystems characterized by strongly positive SAE. The results also yield an apparently novel and testable prediction: ecosystems characterized by high positive SAE will exhibit more repeatable community composition-environment relationships than ecosystems characterized by less clustering of similar environmental conditions in the landscape.

Text

2011-07-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/35921

Biology

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/