Evaluation of current approaches to stream classification and a heuristic guide to developing classifications of integrated aquatic networks

Evaluating the biological validity of European river typology systems with least disturbed benthic macroinvertebrate communities

Humans have severely altered freshwater ecosystems globally, causing a loss of biodiversity. Regulatory frameworks, like the Water Framework Directive, have been developed to support actions that halt and reverse this loss. These frameworks use typology systems that summarize freshwater ecosystems into environmentally delineated types. Within types, ecosystems that are minimally impacted by human activities, i.e., in reference conditions, are expected to be similar concerning physical, chemical, and biological characteristics. This assumption is critical when water quality assessments rely on comparisons to type-specific reference conditions. Lyche Solheim et al. (2019) developed a pan-European river typology system, the Broad River Types, that unifies the national Water Framework Directive typology systems and is gaining traction within the research community. However, it is unknown how similar biological communities are within these individual Broad River Types. We used analysis of similarities and classification strength analysis to examine if the Broad River Types delineate distinct macroinvertebrate communities across Europe and whether they outperform two ecoregional approaches: the European Biogeographical Regions and Illies' Freshwater Ecoregions. We determined indicator and typical taxa for the types of all three typology systems and evaluated their distinctiveness. All three typology systems captured more variation in macroinvertebrate communities than random combinations of sites. The results were similar among typology systems, but the Broad River Types always performed worse than either the Biogeographic Regions or Illies' Freshwater Ecoregions. Despite reaching statistical significance, the statistics of analysis of similarity and classification strength were low in all tests indicating substantial overlap among the macroinvertebrate communities of different types. We conclude that the Broad River Types do not represent an improvement upon existing freshwater typologies when used to delineate macroinvertebrate communities and we propose future avenues for advancement: regionally constrained types, better recognition of intermittent rivers, and consideration of biotic communities.

Integrating Regional Frameworks and Local Variability for Riverine Bioassessment

Regional frameworks enable bioassessment methods to detect anthropogenic effects on ecosystems amid natural variability. Conventional approaches to regionalization have used coarse geographical frameworks to separate sites similar in their ecological (ecoregion) or faunal (basin) characteristics. Expectations for individual streams are then adjusted for within-region variability in local environmental characteristics. Integrating regional frameworks and local variability may improve the sensitivity and performance of bioassessments. In this study, we used a biologically-informed stream classification to develop an integrated regional framework for bioassessment considering the effects of ecoregion, basin, and local environmental variables on wadeable stream fish communities of South Carolina, USA. Our integrated framework was compared against conventional regional frameworks indexing ecoregions or basins alone. Frameworks were evaluated by their ability to (1) efficiently partition community variation and (2) allow for the detection of anthropogenic effects on fish communities. We found an integrated framework better described natural variability in stream fish communities. In addition, we found highly regional relationships between fish metrics and anthropogenic disturbance among frameworks, suggesting appropriate bioassessment metrics will differ across regions in our study area. Differences in community response to disturbance among frameworks emphasize the importance of testing metrics for their hypothesized sensitivity before using them in bioassessment. This study ultimately supports the integration of regional frameworks across spatial scales to classify streams for bioassessment, and provides an analytical framework from which to evaluate biotic variation and metric utility in the context of bioassessment.

Incorporating Network Connectivity into Stream Classification Frameworks

Stream classification frameworks are important tools for conserving aquatic resources. Yet despite their utility, most classification frameworks have not incorporated network connectivity. We developed and compared three biologically informed stream classification frameworks considering the effects of variables indexing local habitat and/or connectivity on stream fish communities. The first framework classified streams according to local environmental variables largely following the precedent set by previous stream classifications. The second framework classified streams according solely to network connectivity variables, while the third framework considered both local and connectivity variables. Using fish community data from 291 wadeable streams in South Carolina, USA, we used conditional inference tree analyses to identify either seven or eight discrete types of wadeable streams within each framework. Classifications were evaluated on their ability to describe community composition at a subset of sites not used in model training, and canonical correspondence analysis suggested that each framework performed similarly in describing overall community variation, with about 19% of variation explained. After accounting for the effects of biogeography and land use in our analytical approach, each classification explained a substantially higher amount of community variation with 46% of variation explained by our connectivity-informed classification and 42% explained by our locally informed classification. Classifications differed in their ability to describe elements of community structure; a classification incorporating connectivity predicted species richness better than the one that did not. This study ultimately addresses an important knowledge gap in the classification literature while providing broader implications for the conservation of aquatic organisms and their habitats.