Biogeographic classification of streams using fish community– and trait–environment relationships. Issue 1 (6th November 2019)

• Record Type: Journal Article
• Authors: Troia, Matthew J.; McManamay, Ryan A.
• Editors: Alice, Hughes
• Is Part Of: Diversity & distributions. Volume 26:Issue 1(2020)
• Page Start: 108
• Page End: 125
• Publication Date: 2019-11-06
• Subjects: conservation prioritization -- fish communities -- GIS mapping -- multivariate regression trees -- stream classification -- trait‐based ecology; Biodiversity -- Periodicals; Biodiversity conservation -- Periodicals; 577
• DOI: 10.1111/ddi.13001
• Languages: English
• ISSNs: 1366-9516

Abstract

Abstract: Aim: Present a hybrid biogeographic and environmentally constrained clustering approach to classify ~853, 000 stream reaches in the eastern United States. Examine the frequency of stream typologies in the landscape relative to anthropogenic stressors to identify potential stream conservation needs. Location: Eastern United States. Methods: Fish communities at 956 least‐disturbed sampling reaches were characterized using taxonomic and functional composition of native species. Environmental variables summarized within stream reaches included stream discharge, channel gradient, hydrologic regime, summer water temperature and boundaries of freshwater ecoregions. Multivariate regression trees were used to relate environmental variables to fish communities while simultaneously developing taxa‐ and trait‐specific biogeographic classifications. We then overlaid stream classifications with indices of anthropogenic disturbances to evaluate rarity and risk of complete loss of natural representation of stream types. Results: Taxonomically based classes represented a combination of ecoregional boundaries and environmental gradients, whereas temperature gradients were most important in differentiating functional composition. An optimal taxonomy‐based classification contained 13 classes and explained 26.4% of community variation, and the optimal trait‐based classification contained 6 classes, explaining 25.2% of community variation. Overlaying class maps with an anthropogenic disturbance index revealed substantial variation in the severity of degradation among classes relative to their abundance in the landscape, especially rare classes (i.e., large rivers and cold headwater systems). Main conclusions: We demonstrate the value of integrating local, regional and historical biogeographic factors with taxonomic and functional characteristics of freshwater biota to classify ecosystems based on mechanistic biota–environment relationships. This approach is important to addressing the challenge of developing stream classifications that reconcile the environmental drivers of community trait composition with the constraints of biogeographic regionality at large spatial scales. Furthermore, the spatially explicit and high‐resolution maps disseminated here can uniquely inform management decisions regarding anthropogenic threats to the biodiverse freshwaters of the eastern United States.