Who Benefits from National Estuaries? Applying the FEGS Classification System to Identify Ecosystem Services and their Beneficiaries.

In spite of their perceived value, the widespread implementation of ecosystem services assessments has been limited because of perceptions of being too technical, too expensive, or requiring special expertise. For example, federal estuary management programs have widely used ecosystem services concepts to frame management issues and communicate with stakeholders. Yet, indicators assessed, monitored, and reported in estuarine management still have traditionally focused on ecological conditions, with weak connections, if any, to social or economic outcomes. Approaches are needed which expand the range of ecosystem services that can be considered, link ecosystem services explicitly to different stakeholder groups, facilitate effective communication with economists and other social scientists, and expand the array of available valuation techniques. We applied the concept of final ecosystem goods and services to review the broad suite of ecosystem services and their beneficiaries relevant to the management of two federal programs for estuary management, the National Estuary Program (NEP) and the National Estuarine Research Reserve System (NERRS). The Final Ecosystem Goods and Services Classification System provided a structured framework for connecting ecosystem services to their beneficiaries and the environments providing them. Document analysis of management plans assessed the degree to which these programs consider ecosystem services, their beneficiaries, and habitats within the estuarine watershed. The hierarchical list of final ecosystem goods and services generated from document analysis serves as a tool for defining management goals, identifying stakeholders, developing meaningful indicators, and conducting valuation studies in estuarine management planning efforts. Though developed here for estuarine management, the keyword hierarchy and final ecosystem goods and services approach have broad applicability and transferability to other environmental management scenarios.

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.