Functional traits reveal the dominant drivers of long-term community change across a North American Great Lake.

Ecosystems worldwide have been impacted by multiple anthropogenic stressors, yet efforts to understand and manage these impacts have been hindered by difficulties in disentangling relative stressor effects. Theoretically, the actions of individual stressors can be delineated based on associated changes in functional traits and these relationships should be generalizable across communities comprised of different species. Thus, combining trait perspectives with community composition data could help to identify the relative influence of different stressors. We evaluated the utility of this combined approach by quantifying shifts in fish species and trait composition in Lake Erie during the past 50 years (1969-2018) in relation to human-driven changes in nutrient inputs, climate warming, and biological invasions. Species and trait shifts were also compared between two Lake Erie basins, which differ in their environmental and biological characteristics, to identify trait responses that were generalizable across different ecosystems versus those that were context dependent. Our analyses revealed consistent species changes across basins, and shifts in feeding and thermal traits, that were primarily associated with altered nutrient inputs (oligotrophication followed by eutrophication). We found no or inconsistent trait-based evidence for the effects of warming and two invasive fishes. Context-dependent trait responses were also evident; nutrient inputs were related to shifts in species tolerant of turbidity in the shallow, eutrophic western basin, which contrasted to shifts between benthopelagic and benthic species in the deeper central basin. Our results reveal the dominant effects of specific stressors on a large freshwater lake and offer a framework for combining species-based and trait-based approaches to delineate the impacts of simultaneous stressors on communities of perturbed natural ecosystems.

Quantifying and Reducing Uncertainty in Estimated Microcystin Concentrations from the ELISA Method.

We discuss the uncertainty associated with a commonly used method for measuring the concentration of microcystin, a group of toxins associated with cyanobacterial blooms. Such uncertainty is rarely reported and accounted for in important drinking water management decisions. Using monitoring data from Ohio Environmental Protection Agency and from City of Toledo, we document the sources of measurement uncertainty and recommend a Bayesian hierarchical modeling approach for reducing the measurement uncertainty. Our analysis suggests that (1) much of the uncertainty is a result of the highly uncertain "standard curve" developed during each test and (2) the uncertainty can be reduced by pooling raw test data from multiple tests. Based on these results, we suggest that estimation uncertainty can be effectively reduced through the effort of either (1) regional regulatory agencies by sharing and combining raw test data from regularly scheduled microcystin monitoring program or (2) the manufacturer of the testing kit by conducting additional tests as part of an effort to improve the testing kit.