A Community-Level, Mesoscale Analysis of Fish Assemblage Structure in Shoreline Habitats of a Large River Using Multivariate Regression Trees.

The mesoscale (100–102 m) of river habitats has been identified as the scale that simultaneously offers insights into ecological structure and falls within the practical bounds of river management. Mesoscale habitat (mesohabitat) classifications for relatively large rivers, however, are underdeveloped compared with those produced for smaller streams. Approaches to habitat modelling have traditionally focused on individual species or proceeded on a species-by-species basis. This is particularly problematic in larger rivers where the effects of biological interactions are more complex and intense. Community-level approaches can rapidly model many species simultaneously, thereby integrating the effects of biological interactions while providing information on the relative importance of environmental variables in structuring the community. One such community-level approach, multivariate regression trees, was applied in order to determine the relative influences of abiotic factors on fish assemblages within shoreline mesohabitats of San Pedro River, Chile, and to define reference communities prior to the planned construction of a hydroelectric power plant.

Flow depth, bank materials and the availability of riparian and instream cover, including woody debris, were the main variables driving differences between the assemblages. Species strongly indicative of distinctive mesohabitat types included the endemic Galaxias platei. Among other outcomes, the results provide information on the impact of non-native salmonids on river-dwelling Galaxias platei, suggesting a degree of habitat segregation between these taxa based on flow depth. The results support the use of the mesohabitat concept in large, relatively pristine river systems, and they represent a basis for assessing the impact of any future hydroelectric power plant construction and operation. By combing community classifications with simple sets of environmental rules, the multivariate regression trees produced can be used to predict the community structure of any mesohabitat along the reach.