Novel Insights to Be Gained From Applying Metacommunity Theory to Long-Term, Spatially Replicated Biodiversity Data

Global loss of biodiversity and associated ecosystem services are occurring at an alarming rate. Metacommunity theory provides a framework to investigate multi-scale processes that drive change in biodiversity across space and time. Short-term ecological studies across space have progressed our understanding of biodiversity through a metacommunity lens, however, have been limited in their ability to explain which processes, at which scales, generate observed spatial patterns. Large gaps in theory and empirical data in temporal dynamics of metacommunities have hindered progress in our understanding of underlying metacommunity processes that give rise to biodiversity patterns. Fortunately, long-term studies with cross-scale spatial replication can provide a means to gain a deeper understanding of the multiscale processes driving biodiversity patterns in time and space to inform metacommunity theory. The maturation of coordinated research and observation networks, such as the U.S. Long-Term Ecological Research program, provides an opportunity to advance explanation and prediction of biodiversity change with observational and experimental data at spatial and temporal scales greater than any single research group could accomplish. Synthesis of Long Term Ecological Research network community datasets illustrates that many long-term studies with spatial replication present an unutilized empirical resource for advancing spatiotemporal metacommunity research. We identify challenges to synthesizing these data and present recommendations for addressing them with insights about how future monitoring efforts by coordinated research and observation networks might better promote future integration of data across space and time to further the development of metacommunity theory and its applications aimed at improving conservation efforts.