Predicting Landscape Quality for Northern Bobwhite from Classified Landsat Imagery

A detailed understanding of the spatial arrangement of northern bobwhite (Colinus virginianus) habitats would allow more focused efforts by wildlife managers. We used a 4-year average of northern bobwhite call-count data in conjunction with remotely sensed habitat maps to study landscape-level habitat associations. Landscape metrics were calculated for the landscape surrounding each stop and were used in 2 modeling exercises to differentiate between high and low northern bobwhite populations. Both pattern recognition (PATREC) and logistic regression models predicted levels of northern bobwhite abundance well for the modeled (73.5% and 73.9%, respectively) and independent (74.6% and 76.6%, respectively) data sets. The revised models were applied to the remotely sensed habitat maps of the eastern 2/3 of Virginia to develop maps expressing the quality of a landscape for supporting a high population of bobwhite based on existing land cover. Both models predicted similar percentages in each of the quality classes.

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