Use of generalised dissimilarity modelling to improve the biological discrimination of river and stream classifications

Abstract

Summary1. Classifications that group rivers and streams with similar ecological characteristics are used increasingly to underpin conservation and resource management planning. Uses include identifying systems that may respond similarly to human activities or management actions, setting guidelines and standards to manage human impacts, interpreting data from inventory (survey) and monitoring, and identifying priority sites for conservation management.2. Traditional approaches to river classification have been based mostly on delineating landscape units (ecoregions), often by grouping adjacent catchments having similar ecological character. However, use of this approach can be complicated by marked local heterogeneity of river systems. Instead, classifications may be more ecologically relevant if individual river or stream segments having similar environmental conditions are grouped together, independent of their geographic locations. The latter approach also allows the use of more rigorous classification procedures, including newly emerging techniques that optimise the ability of a classification to discriminate patterns in parallel sets of biological data.3. Here, we explore the use of one of these newer techniques, generalised dissimilarity modelling (GDM), an extension of generalised regression techniques, that defines an optimal set of transformations of candidate environmental predictors to maximise explanation of species turnover in site‐based biological data.4. Using two biological data sets describing the distributions of freshwater fish and macroinvertebrates and a candidate set of functionally relevant environmental variables, we used GDM to identify the variables, weightings and transformations that best explain biological dissimilarities across sites. We then used these as input to a multivariate classification of 567 000 river and stream segments throughout New Zealand. Weightings and transformations of these variables were also specified from the GDM analysis. The matrix of transformed environmental predictors was classified in a two‐stage process, using non‐hierarchical mediod clustering to define an initial set of 400 groups, with relationships between these groups then defined using hierarchical clustering.5. The resulting classification better discriminates sites with similar biological character than previous classifications, particularly at higher levels of classification detail. Key factors contributing to this success include the use of detailed, segment‐specific environmental variables, coherently accounting for the longitudinal connectivity inherent in rivers including its implications for the construction of biologically relevant predictors, and the use of a modelling technique (GDM) designed to specifically analyse biological turnover and its relationships with environment.