An Empirical Model for Predicting Suburban Deer Populations

Management of burgeoning suburban white-tailed deer populations continues to be one of the most immediate and frustrating problems facing wildlife biologists nationwide. In the suburban environment wildlife managers are highly scrutinized and held accountable every day for their management decisions. In such an environment the question is not whether to model, but rather how to model effectively given the available information (Starfield 1997). Modeling insures that managers work through a documentable, problem-oriented solution to managing overabundant deer.

Many suburban deer populations exist at high-densities, yet most management programs to reduce deer numbers mimic catastrophic population crashes. Such drastic shifts in deer density can greatly alter deer population parameters, because both physical and biosocial factors influence reproductive rates, fetal sex ratios, recruitment, dispersal, and survival in deer. Therefore, managers must include these factors when modeling suburban deer populations. During 1992-1998, 2,599 deer were culled from Forest Preserves in DuPage County Illinois in an attempt to reduce and then maintain deer populations at goal density of 6 deer/km² (post-fawning). Additionally, 181 deer were live-captured and marked (147 were radio-marked) from these Forest Preserves and preserves in adjacent Cook County during 1994-1998 to determine population dynamics for suburban deer. These data provided the foundation for the development of an empirical suburban deer population model using Stella 5.0 software. The model treats male and female populations as discrete, because of their different survival, emigration, and reproductive potential. Density-dependent recruitment rates were incorporated to account for changes associated with fluctuating deer-densities. Sensitivity analysis was used to test the ability of different male and female removal strategies to achieve desired deer densities on an annual culling schedule.

Our hope is that this model will form a conceptual framework based on empirical data for managers attempting to predict deer population trends in the Chicago region and nationwide