“…It is unclear whether our fish‐based classifications would accurately represent spatial variation in other stream‐dwelling fauna and flora. Some previous studies suggest that fishes and invertebrate taxa respond similarly to natural environmental conditions and/or anthropogenic disturbances (Johnson & Hering, ), whereas others indicate differing responses (Pilière et al, ; Troia, Williams, Williams, & Ford, ). Presently, compilation of occurrence data for non‐fish taxa lags behind that of fishes in the eastern United States (Troia & McManamay, ); however, the growth of open‐access biodiversity data will soon allow the completion of classification schemes for the eastern United States that incorporate these other important taxa.…”
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.
“…It is unclear whether our fish‐based classifications would accurately represent spatial variation in other stream‐dwelling fauna and flora. Some previous studies suggest that fishes and invertebrate taxa respond similarly to natural environmental conditions and/or anthropogenic disturbances (Johnson & Hering, ), whereas others indicate differing responses (Pilière et al, ; Troia, Williams, Williams, & Ford, ). Presently, compilation of occurrence data for non‐fish taxa lags behind that of fishes in the eastern United States (Troia & McManamay, ); however, the growth of open‐access biodiversity data will soon allow the completion of classification schemes for the eastern United States that incorporate these other important taxa.…”
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.
“…The vegetation of this region helps reduce the influence of impervious overland flow that would cause increased velocities. Recent studies have also shown that the Neches River has sections that are adequately connected to its floodplain (Troia, Williams, Williams, & Ford, ). The lack of human alteration to the Neches catchment allows the mussels to remain in the substrate during seasonal flooding and inundation of the floodplain.…”
Unionid freshwater mussels are one of the most imperilled groups in North America. They play an important role in freshwater ecosystems, both as a food source and as filter feeders. Their priority conservation status has generated interest in unionid research.
Here, data from the US Geological Survey was used to produce predictive models of mussel habitat affinities at a resolution of 100 m2 across an area of thousands of square kilometres.
This approach correctly identifies areas that are more suitable for threatened mussel species beds as compared with less suitable areas (>97% of the time) Stream segments were identified that are forecast to have high suitability for threatened mussels.
Potamilus amphichaenus differed from other threatened mussel species by being associated with a wider range of volumetric flow rates and by not being restricted by the clay content of the soils. Of the species examined, it was the most large‐river oriented in habitat use and distribution.
These methods can help conservation planners and land‐use managers make rational decisions about where to focus their efforts in lotic habitats without the need for intensive environmental measurements while still providing high‐resolution information.
“…Process domains (Montgomery, 1999) work well to better articulate geomorphology and ecosystem dynamics in the Pacific Northwest (Brardinoni and Hassan, 2006;Collins and Montgomery, 2011), in southeastern North America (Troia et al, 2015), in Michigan (Neeson et al, 2012), and in different settings within the Rocky Mountains (Wohl, 2010(Wohl, , 2011Polvi et al, 2011;Bellmore and Baxter, 2014;Livers and Wohl, 2015). As originally presented, Montgomery (1999, p. 402) considers process domains 'predictable areas of a landscape within which distinct suites of geomorphic processes govern physical habitat type, structure and dynamics; the disturbance regimes associated with process domains dictate the template upon which ecosystems develop.'…”
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