Most ecological communities can be observed to undergo turnover through time. Identifying the ‘baseline’ expectation of this turnover is central to interpreting and identifying signals of anthropogenically driven change. Against this dynamic background of community turnover, attribution remains a challenge, particularly without trait data or knowledge of the relevant environmental variables.
Here we use simulated metacommunity models to explore the possible contribution of intrinsically driven autonomous turnover to observed ecosystem dynamics. We build these systems using a spatially explicit set of nodes with randomly competing species and assemble communities to an end state where local communities are ‘saturated’ (such that a newly arrived species causes on average one extinction). To demonstrate the plausibility of the model we examined its capacity to reproduce key macroecological patterns. We then use our dynamic metacommunity model to explore the possibility of using joint-species distribution models fit to presence/absence data from ‘before’ and ‘during’ an imposed environmental change to identify generic signals of disruption. Across a set of simulated metacommunities, we partition the variance explained into spatial, environmental and between-species association (codistribution) components, and examine how these changes over the period of disruption. We also apply the method to two datasets (UK butterflies and birds).
Results/Conclusions
We show that our minimally simple models of simulated metacommunities with random interactions between species (but without abiotic temporal variation) are able to reproduce many fundamental and widely observed spatiotemporal macroecological patterns. These include temporal occupancy distributions, site occupancy distributions and species-area relationships, suggesting that observations of temporal turnover are not in themselves diagnostic of environmental fluctuations.
Variance partitioning from the joint-species distribution models was able to show consistent changes in the explanatory power of the partitions, attributable to coherent responses of certain species to the (assumed to be unobserved) changing environmental variable. In particular, a fall in the codistribution component through time appears to be able to indicate the breakdown of environmental communities and heterogenous responses to environmental change. While in need of further development and ground-truthing, this approach has potential to identify signals of anthropogenic change in community data. Importantly, it does not rely on knowledge of environmental-performance traits or previous measures of the putative environmental driver.
