Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models

Abstract

Stream ecosystems face continuous pressures from multiple anthropogenic stressors that reshape biological communities and impact ecosystem health and services. Managers can encounter challenges in stewarding ecosystems threatened by multiple stressors, in part because most multiple stressor studies are experimental and, while valuable, offer limited management relevance in targeting these stressors on the landscape. Recent advances in causal inference coupled with large biomonitoring data sets could further understanding of observational stressor-response relationships, aiding management. In this study, we use bioassessment data in the Chesapeake Bay watershed in the mid-Atlantic region of the United States to identify how water quality and physical habitat stressors influence key benthic macroinvertebrate response metrics, considering hierarchical relationships using Bayesian networks. Results suggest water temperature and specific conductivity were prevalent stressors in a mountainous region (northern Appalachians), whereas in an agriculturally dominated region (southern Appalachians) physical habitat alterations were the predominant stressor. In mixed-land use regions (Piedmont & Coastal Plains), specific conductivity was a key stressor, but habitat heterogeneity was important for macroinvertebrate metrics. To illustrate how these stressor-response relationships can be used to guide management decisions, we applied the resist-accept-direct (RAD) framework to develop a portfolio of management options based on predicted changes in macroinvertebrate metrics in response to physical habitat and water quality stressors. For example, accepting changes in areas with co-occurring stressors may be the most feasible option, whereas directing changes through stream restoration or water quality improvements may be effective in areas with single stressor groups. By leveraging observational bioassessment data and causal inference to identify key stressor-response relationships, this research supports decision making by building a simple, strategic management portfolio.