Harnessing multilayer networks to predict metacommunity responses to
global environmental change
Abstract
Species interact both within and across communities, forming complex
networks of biotic interactions and spatial links that underpin
ecosystem functioning. However, while recent technological advances
permit the analysis of increasingly complex and realistic ecological
networks, data availability remains a major challenge. Here, we present
a novel approach that uses readily available ecological data to build
spatially-structured species interaction networks and predict
metacommunity responses to environmental change. Predictive Multilayer
Networks (PMNs) model the distributions, interspecific interactions, and
spatial connectivity of multiple species across a landscape and quantify
network structure and stability. We provide a proof-of-concept using a
simulated plant-pollinator community, measure network centrality to
identify areas of high functional connectivity, and compare land cover
scenarios to predict effects of forest loss and restoration on PMN
connectance and robustness. PMNs synthesize network approaches from
community and landscape ecology and offer a flexible, predictive
approach for examining the spatial dynamics of species interactions.