OOS 90-2
Patterns of convergence and divergence: A meta-analysis of the variability of community responses to global change drivers

Friday, August 14, 2015: 8:20 AM
336, Baltimore Convention Center
Kimberly La Pierre, Department of Integrative Biology, University of California, Berkeley, CA
Meghan L. Avolio, Department of Biology, University of Utah, Salt Lake City, UT
Forest I. Isbell, Ecology, Evolution & Behavior, University of Minnesota, Saint Paul, MN
Emily Grman, Biology Department, Eastern Michigan University, Ypsilanti, MI
Gregory R. Houseman, Biological Sciences, Wichita State University, Wichita, KS
David S Johnson, Marine Biological Laboratory
Sally E. Koerner, Nicolas School for the Environment, Duke University, Durham, NC
Kevin R. Wilcox, Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO

Global change drivers can have drastic effects on plant community composition, with consequences for ecosystem function. Ecologists have been tasked with predicting the trajectory and magnitude of community responses to these altered environmental conditions. The methods typically employed in ecological studies focus on mean differences in richness and composition in response to resource manipulations, thus masking the inherent complexity of many ecological systems. Yet variability among replicates within a treatment can be informative for the trajectory and predictability of community responses to global change drivers. For example, when replicates converge, there is high predictability for both community and ecosystem responses. Alternatively, replicate communities that diverge may more difficult to predict, even though divergence could result from either stochastic or deterministic processes. Here, we present a meta-analysis of 81 studies in herbaceous systems examining ecological responses to experimental manipulations of global change drivers. We separate our analysis into two components, the first examining mean changes in community composition with experimental global change manipulation treatments, and the second examining in variance among replicate communities within treatments. Finally, we link these changes to consequences for aboveground net primary productivity (ANPP), an important ecosystem function.


Overall global change treatments significantly altered plant communities relative to control plots. This shift in community composition treatments increased with the duration of the experiment and was stronger when multiple global change drivers were simultaneously manipulated (e.g., combined N and CO2 additions) than when one driver was manipulated alone. In addition to shifts in community composition between treatments, replicate plots within treatments were also observed to vary in community composition. The dispersion among replicates within treatments varied widely relative to the control plots, with some treatments resulting in plant community convergence, others resulting in divergence, and some showing no change in dispersion among replicates. Divergence among treatment replicates relative to the control replicates was more common in ecosystems with high gamma diversity, while convergence was more likely in sites with low gamma diversity. Additionally, divergence increased with the number of global change drivers simultaneously manipulated. Finally, experiments where the plant community shifted with experimental treatments exhibited corresponding shifts in ANPP; further, the predictability of ANPP increased when replicate plant communities converged in response to treatments and decreased when they diverged. Overall, our results illustrate the importance of examining both changes in mean and variance in community composition when examining ecosystem responses to global change.