Multilevel Assessment of Fish Species Traits to Evaluate Habitat Degradation in Streams of the Upper Midwest

Publisher Website
Google Scholar
Abstract
We used species traits to examine the variation in fish assemblages for 21 streams in the Northern Lakes and Forests Ecoregion along a gradient of habitat disturbance. Fish species were classified based on five species trait-classes (trophic ecology, substrate preference, geomorphic preference, locomotion morphology, and reproductive strategy) and 29 categories within those classes. We used a habitat quality index to define a reference stream and then calculated Euclidean distances between the reference and each of the other sites for the five traits. Three levels of species trait analyses were conducted: (1) a composite measure (the sum of Euclidean distances across all five species traits), (2) Euclidean distances for the five individual species trait-classes, and (3) frequencies of occurrence of individual trait categories. The composite Euclidean distance was significantly correlated to the habitat index (r = −0.81; P = 0.001), as were the Euclidean distances for four of the five individual species traits (substrate preference: r = −0.70, P = 0.001; geomorphic preference: r = −0.69, P = 0.001; trophic ecology: r = −0.73, P = 0.001; and reproductive strategy: r = −0.64, P = 0.002). Although Euclidean distances for locomotion morphology were not significantly correlated to habitat index scores (r = −0.21; P = 0.368), analysis of variance and principal components analysis indicated that Euclidean distances for locomotion morphology contributed to significant variation in the fish assemblages among sites. Examination of trait categories indicated that low habitat index scores (degraded streams) were associated with changes in frequency of occurrence within the categories of all five of the species traits. Though the objectives and spatial scale of a study will dictate the level of species trait information required, our results suggest that species traits can provide critical information at multiple levels of data analysis.