Overabundant deer: Better management through research

Overabundance of white-tailed deer (Odocoileus virginianus) continues to challenge wildlife professionals nationwide, especially in urban settings. Moreover, wildlife managers often lack general site-specific information on deer movements, survival, and reproduction that are critical for management planning. We conducted radio-telemetry research concurrent with deer culling in forest preserves in northeastern Illinois and used empirical data to construct predictive population models. We culled 2,826 deer from 16 forest preserves in DuPage County (1992-1999) including 1,736 from the 10 km2 Waterfall Glen Forest Preserve. We also radio-marked 129 deer from 8 preserves in DuPage and adjacent Cook County (1994-1998). Recruitment was inversely associated with deer density suggesting a classic density-dependent response. Female deer were philopatric and 20% of adult males dispersed. Survival was high for all sex and age classes, and deer-vehicle collisions accounted for >55% of known mortalities. Based upon data from other areas, early attempts to apply population models to deer at Waterfall Glen Forest Preserve were not useful. The subsequent quantification of the density-dependent recruitment response and use of other empirical data strengthened the predictive capability of models. Our experience illustrates the importance of understanding demographics of overabundant deer in order to set realistic objectives and make sound management decisions.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.