2018 ESA Annual Meeting (August 5 -- 10)

COS 41-7 - The dynamic equilibrium theory of community assembly: Testing predictions with observed richness and composition time series

Tuesday, August 7, 2018: 3:40 PM
333-334, New Orleans Ernest N. Morial Convention Center
Michael Kalyuzhny, Department of Ecology, Evolution, and Behavior, Hebrew University of Jerusalem, Jerusalem, Israel, Curtis H. Flather, Rocky Mountain Research Station, USDA, Forest Service, Fort Collins, CO, Micha Mandel, Department of Statistics, Hebrew University of Jerusalem, Jerusalem, Israel, Nadav Shnerb, Department of Physics, Bar-Ilan University, Ramat Gan, Israel and Ronen Kadmon, Department of Ecology, Evolution, and Behavior, The Hebrew University of Jerusalem, Jerusalem, Israel
Background/Question/Methods

Understanding the processes that shape the diversity and composition of ecological communities is among the most important questions in Ecology. It is self-evident that the species present in a community at a given time are those that arrived and haven't yet gone extinct. It is also clear that both the colonization and extinction processes have a stochastic component. The question is – can a model where colonization and extinction are completely random explain the dynamics of richness and composition in nature? And if not – what is missing?

The classical dynamic equilibrium model developed by MacArthur, Wilson and Simberloff is exactly this kind of simple null model. Under this model, each species in the pool has a fixed immigration probability and each extant species has a fixed extinction probability. Despite being the most basic model of community dynamics, rigorous tests of whether time series of natural communities are consistent with the model have rarely been done. In fact, we are not aware of any existing general methods to explicitly and quantitatively test the assumptions and predictions of the model.

Results/Conclusions

We developed a methodological framework for testing the assumptions and predictions of the dynamic equilibrium model given data on species composition over time. In particular, the method allows testing the assumptions of species independence and constancy of the rates. Moreover, comparison of observed community dynamics to model predictions can be used to test: i) If changes in richness are greater than expected, And ii) if species composition turnover is faster than expected. This is done by comparing observed dynamics to calibrated dynamic null models. We tested the methodology using synthetic data and found it to have good statistical properties: Type I error rates generally did not exceed the predetermined alpha, and there was reasonable power to detect deviations when they occurred.

The methodology was applied to the North American Breeding Bird Survey (BBS) and to other datasets. We found that changes in species richness and composition are, in many communities, greater than expected, and this was attributed to the predominantly positive correlation between species. This indicated that competition was less important than shared response to environmental fluctuations in community assembly, and this shared response can generate large changes in richness and composition.