Neutral metacommunity model of biodiversity in dryland aquatic ecosystems with strong seasonality

Background/Question/Methods

Efforts to sufficiently understand and quantify biodiversity patterns must consider both spatial and temporal dynamics of the study system. Unlike freshwater habitats in wet climates, dryland aquatic ecosystems are highly variable in both space and time. This spatiotemporal variability contributes to the high species diversity observed, in part because it creates a large degree of niche partitioning within a relatively small amount of space. However, it also presents challenges for biodiversity estimation because our ability to detect species also changes through space and time and with the scale of sampling. Neutral metacommunity models has been shown to be useful in capturing biodiversity patterns. It is especially effective in systems with high biodiversity at large spatial scales. However, its application to systems with strong seasonality has not been adequately explored. In this project, we incorporate temporal variability into the neutral model and study how much it can capture biodiversity patterns in a system characterized by strong seasonality.  Theoretical results will be compared with empirical data collected from three dryland aquatic systems in the U.S. 

Results/Conclusions

The theoretical results of the neutral metacommunity model with temporal variability show that the differences in rank-abundance curves corresponding to the dry and wet seasons become apparent only when the dry period is short and intense, i.e., the ratio of habitat capacity for dry and wet periods is low, and the recolonization rate is sufficiently fast. If the dry period is short and intense, but the recolonization is not fast enough, the rank-abundance curves will effectively remain in the pattern similar to that of the dry season.  Preliminary analysis of between-community diversity, or spatial turnover of the species, suggests that Euclidean distance may be an appropriate platform for the dispersal of those aquatic invertebrates in our study systems. A number of model-generated patterns are being compared with empirical datasets, and we are continuing the model development to investigate the extent to which neutral model can capture the biodiversity patterns in a system with strong seasonality.