Local and regional processes in community assembly.
Controversy on whether local (deterministic) or regional (stochastic) factors control the structure of communities persists after decades of research. The main reason for why it has not been resolved may lie in the nature of evidence which largely comes from realized natural communities. In such communities assembly history leaves a mark that may support either set of factors. To avoid the confounding effects of assembly history we controlled for these effects experimentally. We created a null community by mixing 17 rock pool communities. We then divided the null community into replicates and distributed among treatments representing a gradient of factors from local to regional. We hypothesized that if deterministic factors dominate the assembly of communities, community structures should show a corresponding gradient from being very similar and convergent to dissimilar and divergent. In contrast, if local processes are predominantly stochastic in nature, such a gradient of community configurations should emerge even in the homogeneous setting. Our results appear to partially support both hypotheses and thus suggest that both deterministic and stochastic processes contribute to the assembly of communities. Furthermore, we found that to satisfactorily explain patterns observed in natural communities environmental heterogeneity and regional processes must also be considered. In conclusion, although deterministic mechanisms seem to be important in the assembly of communities, in natural systems their signal may be diluted and masked whenever other factors exert meaningful influence. Such factors increase the number of possible paths to the point that the number of paths equals the number of communities in a metacommunity.
Department of Biology, McMaster University, Hamilton, Ontario, Canada. juancmarquezh@gmail.com
Juan C
JC
Márquez
Jurek
J
Kolasa
D000818:Animals; D059001:Aquatic Organisms / Q000502:physiology*; D017753:Ecosystem; D007448:Invertebrates / Q000502:physiology*; D008954:Models, Biological; D013269:Stochastic Processes