OOS 4-1 - Climatic controls on ecosystem resilience: Post-fire regeneration in the Cape Floristic Region of South Africa

Monday, August 6, 2012: 1:30 PM
C124, Oregon Convention Center
Adam M. Wilson, Ecology & Evolutionary Biology, Yale University, New Haven, CT, John A. Silander, Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT and Andrew M. Latimer, Plant Sciences, University of California Davis, Davis, CA
Background/Question/Methods

Conservation of natural resources in a changing climate requires understanding of ecosystem resilience to change. Resilience is especially important in disturbance-prone ecosystems such as the Cape Floristic Region (CFR) of South Africa. Recent work has shown that the fire regime in the CFR is sensitive to climate and suggests that climate change may lead to more frequent fires. However, the sensitivity of post-fire recovery and biomass/fuel load accumulation to climate is not well understood despite its importance in the fire regime. In this study, a Hierarchical Bayesian approach is used to model post-fire ecosystem recovery using MODIS-derived NDVI observations as a function of stand age, topography, and climate. The model has three levels, 1) an exponential function fit to the observed NDVI data, 2) multivariate regressions that associate the terms of the exponential function to a suite of topographical, soil, and climate variables, and 3) a set of vague priors. Two models were fit, with and without climate data, and compared using the Deviance Information Criterion (DIC).

Results/Conclusions

The full model had a lower DIC than the topography-only model (ΔDIC=31,356) indicating strong support for the hypothesis that climate has an important influence on post-fire recovery. The coefficient of determination (R2) was used to assess the predictive performance of the model using a hold-out validation data set. The predicted vs. observed R2 values ranged from 0.34 to 0.51 depending on the time-since-fire of the validation data. The rate of recovery was positively associated with soil fertility, minimum July (winter) temperature, and maximum January (summer) precipitation. Precipitation seasonality, maximum January temperature, solar radiation, and elevation all were negatively correlated with the recovery rate. Estimated recovery times ranged from < 5 years to >25 years, depending on the environmental conditions. The important role of climate in driving the recovery process suggests that this critical ecosystem property will be sensitive to climate change.