Habitat isolation diminishes potential of self-organised pattern formation to promote local diversity in metacommunities

Abstract

AbstractProgressive destruction and isolation of natural habitat is a major threat to biodiversity worldwide. In this study we use a trophic metacommunity model with complex, spatially explicit structure to address how the interaction of local and regional processes affects the functional diversity of autotroph (producer) communities within and between individual habitat patches. One important driver of biodiversity in metacommunities is spatial heterogeneity of the environment, as it enables source-sink dynamics between patches. Besides a-priori differences in the environmental conditions, heterogeneous distributions of resources and species biomasses can also emerge through self-organised pattern formation caused by scale-dependent feedback between local trophic and regional dispersal dynamics. We show that this emergent heterogeneity can enhance the functional diversity of local autotroph communities by jointly strengthening source-sink dynamics and reducing stabilising selection pressure. Our results indicate that this effect is particularly strong in highly connected metacommunities, while metacommunity size (number of patches) alone plays a lesser role. We demonstrate that the positive effect on local diversity is driven by an eco-evo-spatial feedback loop that is fueled by the asynchronous biomass- and trait dynamics between the patches created by self-organised pattern formation. In highly connected metacommunities, oscillatory biomass patterns with particularly large amplitude strengthen this feedback loop. Our findings are highly relevant in the light of anthropogenic habitat changes that often destroy dispersal pathways, thereby increasing habitat isolation, lowering overall connectance of metacommunities and ultimately threatening the biodiversity in local habitats. Only a joint investigation of the contributing ecological, evolutionary, and spatial mechanisms in complex model systems can yield comprehensive understanding of these processes, allowing for the development of strategies to mitigate adverse anthropogenic influence.