Climate change and weather disturbances can dramatically alter the structure and carbon dynamics of ecosystems, including tidal wetlands. These ecosystems play disproportionately large roles in biogeochemical cycling relative to their small areas, yet we know little about how these processes are perturbed by disturbances at regional to continental scales. This is in part because potential macroecological patterns are difficult to detect by studying site-specific datasets or processes, and also because it is difficult to draw generalizations about ecosystem stability and response when landscapes and disturbances are so spatially and temporally heterogeneous. Consequently, no firm rules have been found that govern ecosystem resistance and resilience to disturbances. Gross primary production (GPP) is a particularly well-suited metric for summarizing the heterogeneous dynamics that underlie ecosystem-level responses, and recently, a remote sensing-derived GPP dataset for all tidal wetlands in the conterminous United States was made available for the years 2000-2020, with data available at 16-day return intervals. To identify ecosystem-level patterns in tidal wetland resistance and resilience, we first established proxy metrics for resistance and resilience, effect size Eand the return time R of GPP perturbation events, then cataloged the E and R of each GPP perturbation in the time series dataset.
Results/Conclusions
By leveraging the statistical power of this large tidal wetland GPP dataset, we are beginning to discover patterns in perturbation effect sizes and return times that may reflect the operation of natural laws. We found that the metrics E and R are correlated, and that there is an inverse relationship between perturbation effect size and frequency of occurrence, i.e., smaller magnitude GPP perturbations occur more frequently than larger magnitude perturbations. The relative frequency distribution of perturbation effect sizes appears asymmetrical, suggesting different mechanisms and constraints that govern negative and positive E perturbation events. We are currently exploring consistent scaling relations between these events using power laws and other statistical distributions. Evidence that there is a consistent, measurable relationship between our resistance and resilience proxy variables is theoretically intriguing, given the scarcity of generalizations that can be made about whole ecosystem disturbance responses. We contend that our metric approach can be used in a wide range of applications across disciplines to quantify the resistance and resilience of perturbations in any time series dataset, which may yield new and important discoveries about universal tradeoffs between these two strategies.
