Understanding Landscape Influences on Aquatic Fauna across the Central and Southern Appalachians

Abstract

For the success of aquatic conservation efforts, it is imperative for there to be an understanding of the influences multiple stressors across the landscape have on aquatic biota, as it provides an understanding of spatial patterns and informs regional stakeholders. The central and southern Appalachians contain biodiversity hotspots for aquatic fauna. Therefore, we sought to create a comprehensive multimetric model that is based on the influence of abiotic factors on fish and aquatic macroinvertebrates that could predict watershed quality. Good spatial coverage exists for land use/land cover (LULC) and other physicochemical components throughout the region, yet biological data is unevenly distributed, which creates difficulties in making informed management and conservation decisions across large landscapes. We used boosted regression trees (BRT) to model a variety of biological responses (fish and aquatic macroinvertebrate variables) to abiotic predictors and by combining model outputs created a single score for both abiotic and biotic values throughout the region. The mean variance that was explained by BRT models for fish was 73% (range = 48−85%) and for aquatic macroinvertebrates was 81% (range = 76−89%). We categorized both predictor and response variables into themes and targets, respectively, to better understand large scale patterns on the landscape that influence biological condition of streams. The most important themes in our models were geomorphic condition for fish and water quality for aquatic macroinvertebrates. Regional models were developed for fish, but not for aquatic macroinvertebrates due to the low number of sample sites. There was strong correlation between regional and global watershed scores for fish models but not between fish and aquatic macroinvertebrate models. We propose that the use of such multimetric scores can inform managers, NGOs, and private land owners regarding land use practices, thereby contributing to large landscape scale conservation efforts.

Keywords

• aquatics
• modeling
• boosted regression trees
• appalachians