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Abstract

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate the benefits of proposed regulations. However, the existing WQI focuses mainly on metrics related to human use values, such as recreation, and fails to capture aspects important to nonuse values of aquatic ecosystems, such as existence values. Here, we identify an appropriate index of biological health for use in stated preference (SP) surveys that seek to quantify the nonuse value of streams and lakes anywhere within the conterminous US (CONUS). We used a literature review and focus groups to evaluate two aquatic indices that are regularly reported by the EPA’s National Aquatic Resources Surveys: (1) multimetric indices (MMIs) and (2) the observed-to-expected ratio of taxonomic composition (O/E). Focus group participants had difficulty interpreting the meaning of a hypothetical 5-point change in MMI values on a 100-point scale in response to changes in water or habitat quality. This difficulty arose because a 5-point change can occur due to many unique combinations of the individual metrics that compose an MMI. In contrast, participants found it easier to interpret loss in native taxa (O/E) as an index of biological condition. We chose the O/E index because of this superior interpretability when assessed against MMIs. In addition to index selection, we modeled and interpolated the values of O/E to 1.1 million stream segments and 297,071 lakes across the CONUS to provide data for SP studies at any scope or scale, from local watersheds to the entire lower 48 states. As part of this effort, we also modeled and interpolated the areas of streams (m2) to place them in the same unit as lakes to describe the quantity of resources affected by policy scenarios. Focus groups found comparisons of management scenarios easier to interpret when aquatic resources were placed into the same units and especially when presented as percentages of area. Finally, we discuss future work to link O/E with water quality and habitat models that will allow us to forecast changes in the metric resulting from regulatory action.

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