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Predictive modeling techniques with application to the Cerulean Warbler (Dendroica cerulea) in the Appalachian Mountains Bird Conservation Region Matthew Buhrl Shumar Many statistical approaches have been used for developing predictive models for wildlife presence/absence and abundance, each with varying levels of accuracy and complexity. As concerns for declining species intensify and anthropogenic impacts on habitats increase, the ability to quickly quantify and map species distributions and abundances over large regions will become increasingly important. To date, there is no set of best practices for modeling specific wildlife groups. My primary objectives with this thesis were to 1) compare model techniques for ease of use and accuracy, and 2) compare resolution of species occurrence data and its effect on model accuracy. For the first objective, I compared two modeling techniques that range from moderately quick and simplistic (decision trees) to conceptually and computationally complex (hierarchical spatial models). I used North American Breeding Bird Survey counts with a suite of explanatory variables to predict presence and abundance of cerulean warblers (Dendroica cerulea) in the Appalachian Mountains Bird Conservation Region. Of the decision tree methods, cerulean warbler occurrence was most accurately described by presence/absence models. Regression tree abundance models under-predicted counts and had low accuracy. Hierarchical spatial models predicted abundance of cerulean warblers similar to actual counts, and with better overall accuracy than regression trees. All techniques produced models using similar variables; interior forest and percent forest were most important for identifying areas with cerulean warblers. For the second objective, I compared two model types, differing in the resolution of the species distribution data. I used North American Breeding Bird Survey (NABBS) counts with a suite of explanatory variables to predict presence and abundance of cerulean warblers (Dendroica cerulea) in the Appalachian Mountains Bird Conservation Region (BCR28). Decision trees were created for route-level and stop-level analyses of presence and abundance. Additionally, output maps have typically been resolved to the resolution of the environmental spatial datasets with little attention given to the scale at which the predictions represent. Using the modeling results, predictive distribution maps were created for cerulean warblers with appropriate resolutions for each model group. Route-level decision trees performed better than stop-level models for predicting both presence and abundance of cerulean warblers. Similar to raw NABBS distribution data, cerulean warblers were predicted to occur in highest concentrations in the central portions of the BCR. Poor performance of stop-level models may result from a mismatch of resolution of environmental data to species survey data, or lack of important environmental covariates at the stop-level scale. The results of this study highlight the importance of correctly matching the resolution of the species distribution data to the resolution of environmental covariates and the extent of analysis. The results and relationships highlighted in this thesis may serve to direct management and monitoring for the cerulean warbler, and other migratory passerines.