Assessing impacts of sulfur deposition on aquatic ecosystems: A decision support system for the Southern Appalachians

Abstract With climate change and ongoing impacts from human development and resource extraction, US federal land management agencies are acutely concerned with managing for healthy aquatic ecosystems in the Southern Appalachian Mountain (SAM) Region. Here, we describe development of a spatial decision support application to assess the biological and ecological impacts of atmospheric S and N deposition on aquatic ecosystems of the region. We first summarize foundational published work to predict continuous maps of surface water acid neutralizing capacity (ANC) and soil base cation weathering (BC w ). We use the predicted ANC and BC w maps to estimate steady‐state critical loads (CLs) of atmospheric S and N deposition. We included estimated CLs of atmospheric N to get a complete picture of CLs and potential exceedances. We then present a logic‐based decision support model for assessing effects of S and N deposition based on statistically modeled stream ANC and CL exceedance. The model is easily modified for continuous monitoring of CL exceedance patterns as new S and N deposition and ANC data become available. We present mapped model results for the SAM study area and an important subset of the region, the Great Smoky Mountains National Park. ANC modeling results revealed that predicted acid sensitivity was spatially variable, with areas of relatively low stream ANC (<50 μeq · L −1 ) and soil BC w (<50 meq · m −2 · year −1 ) predominantly found in certain critical areas. Within the Great Smoky Mountains National Park, evidence for S CL exceedance based on an ANC criterion of 50 μeq · L −1 was strong at locations where ambient S deposition was at least two times the CL. We also predicted likely impacts of CL exceedances on aquatic insect species richness and native fish abundance. Responses for insect species richness and fish impact showed variability similar to CL exceedance, with increasing impact positively correlated with elevation. Finally, we discuss ways that the decision support system can be used to prioritize management across the region.