Predicting Brook Trout Occurrence in Stream Reaches throughout their Native Range in the Eastern United States
Abstract
The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We
developed a model to predict Brook Trout population status within individual stream reaches throughout the
species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to
predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological
drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples
based on the area under the receiver operating curve (»0.78) and Cohen’s kappa statistic (0.44). Predicted water
temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was
also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific
intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased.
Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an
interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature.
Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the
region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing
conservation and management efforts. These decision support tools can be used to identify the extent of potentially
suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream
reaches for a given action. Future work could extend the model to account for additional landscape or habitat
characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use
changes.
Publication Date: 2015
Fair Use OK
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