Modeling the effects of land cover and use on landscape capability for urban ungulate populations

Abstract

Expanding ungulate populations are causing concerns for wildlife professionals and residents in many urban areas worldwide. Nowhere is the phenomenon more apparent than in the eastern US, where urban white-tailed deer (Odocoileus virginianus) populations are increasing. Most habitat suitability models for deer have been developed in rural areas and across large (>1000 km2) spatial extents. Only recently have we begun to understand the factors that contribute to space use by deer over much smaller spatial extents. In this study, we explore the concepts, terminology, methodology and state-of-the-science in wildlife abundance modeling as applied to overabundant deer populations across heterogeneous urban landscapes. We used classified, high-resolution digital orthoimagery to extract landscape characteristics in several urban areas of upstate New York. In addition, we assessed deer abundance and distribution in 1-km2 blocks across each study area from either aerial surveys or ground-based distance sampling. We recorded the number of detections in each block and used binomial mixture models to explore important relationships between abundance and key landscape features. Finally, we cross-validated statistical models of abundance and compared covariate relationships across study sites. Study areas were characterized along a gradient of urbanization based on the proportions of impervious surfaces and natural vegetation which, based on the best-supported models, also distinguished blocks potentially occupied by deer. Models performed better at identifying occurrence of deer and worse at predicting abundance in cross-validation comparisons. We attribute poor predictive performance to differences in deer population trajectories over time. The proportion of impervious surfaces often yielded better predictions of abundance and occurrence than did the proportion of natural vegetation, which we attribute to a lack of certain land cover classes during cold and snowy winters. Merits and limitations of our approach to habitat suitability modeling are discussed in detail.