2017 ESA Annual Meeting (August 6 -- 11)

COS 158-6 - Trophic interactions influence resilience of aquatic ecosystems to perturbations

Thursday, August 10, 2017: 3:20 PM
E145, Oregon Convention Center
Tamara K. Harms1, James Hood2, Mark D. Scheuerell3, Thomas R. Barnum4, Irena F. Creed5, Shawn Devlin6, Michelle A. Evans-White7, Claire Ruffing1, Albert Ruhí8, Adrianne Smits9, Tanner Williamson10 and Jeremy B. Jones11, (1)Institute of Arctic Biology, University of Alaska, Fairbanks, AK, (2)Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, (3)School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, (4)Smithsonian Environmental Research Center, MD, (5)Biology, Western University, London, ON, Canada, (6)Flathead Lake Biological Station, University of Montana, MT, (7)Department of Biological Sciences, University of Arkansas, Fayetteville, AR, (8)National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD, (9)School of Aquatic and Fisheries Sciences, University of Washington, Seattle, WA, (10)Miami University, Miami, OH, (11)University of Alaska Fairbanks
Background/Question/Methods

Resilience, the capacity to absorb perturbations while maintaining structure and essential functions, is a fundamental characteristic of ecosystems. Resilience is often examined based on a single level of ecological organization (e.g., population size) or an emergent ecosystem property (e.g., productivity), but these assessments do not reveal how perturbations might propagate through ecosystems due to interactions among constituent parts. Theory suggests that interactions among the components of ecosystems, particularly negative feedbacks and interactions between components characterized by “fast” and “slow” dynamics, confer resilience by constraining variance in individual components. We compared resilience, measured as the return time to the mean and the magnitude of displacement following perturbation for aquatic ecosystems, described by time series of solute concentrations, and biomass or abundance of primary producers, consumers, and predators. Metrics of resilience were estimated using multivariate autoregressive state-space (MARSS) models in which we contrasted models of top-down, bottom-up, and distributed influences of perturbations on ecosystems. Perturbations included invasive species, nutrient and sediment inputs, rising temperature, and anomalous river discharge.

Results/Conclusions

Across all sites and types of perturbations examined, variation in solute concentration and population sizes of aquatic biota were best explained by models that included descriptors of perturbations, compared to models that excluded these covariates, indicating the strong influence of perturbations on ecosystem processes. MARSS models indicated that stronger interactions among compartments within ecosystems resulted in greater stability at the ecosystem level, and this pattern applied across diverse perturbation types. A link between interaction strength and stability supports the long-standing hypothesis that interactions contribute to resilience of ecosystems. The pathway by which perturbation was introduced to and propagated through ecosystems had inconsistent effects on stability across perturbation types and among sites influenced by the same types of perturbations, with varying relative estimates of ecosystem resilience observed for top-down, bottom-up, and distributed effects. Thus, whereas strong interactions stabilize ecosystems, the relative importance of the direction of those interactions for resilience appears to depend on additional factors that may include species traits, antecedent conditions, and magnitude of the perturbation.