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Model-Based Clustering in a Brook Trout Classification Study within the Eastern United States

Abstract Data collected on the population status (extirpated or present) of brook trout Salvelinus fontinalis at the landscape level across the eastern United States is useful for identifying important stressors and predicting brook trout status at the watershed level. However, when dealing with data compiled over a large region, a single model may not adequately describe relationships between variables. To find models with better classification performance, we used a Monte Carlo model-based clustering method with logistic regression models to obtain subregions with good predictive performance. To subdivide the eastern United States into subregions, we used Voronoi tessellations with randomly selected centers. The average fraction correctly classified for fit was the criterion used when searching for optimal models within clusters. Logistic regression models were chosen by stepwise selection based on five explanatory variables: Elevation, percentage of forested land, percentage of agricultural land, road ...