Individual-based simulation models of Arkansas game species.

摘要

Historically, many ecological models have focused on large-scale attributes of communities such as population density or species interaction coefficients. However, the importance of individual variation and small-scale events to the understanding of large-scale ecological processes is becoming more widely recognized. In particular, population growth models that incorporate individual-level processes should significantly improve the ability of game and resource managers to predict future growth and yield of managed species.; Northern bobwhite (Colinus virginianus), Eastern wild turkey (Meleagris gallopavo silvestris), and White-tailed deer (Odocoileus virginianus) were selected as representative game species to be simulated. Model growth parameters of populations within the State of Arkansas were estimated from the literature and from field studies conducted by state wildlife biologists. All the simulation models focus on the individual life-cycle events that affect population-level demography for each species. Because the models are fundamentally stochastic, they are capable of providing variance estimates for population-level quantities.; These simulations also were used to explore several specific research questions. The conditions under which habitat fragmentation benefitted simulated bobwhite quail populations were investigated. A partial sensitivity analysis revealed that this quail model was most sensitive to changes in the largest mortality factor. Simulations of White-tailed deer harvests were used to predict the harvest effort producing the maximum yield. A generalization of the white-tailed deer model provided the basis for a model that can be parameterized for many species. Wild turkey was chosen as an example.; Not only can game managers use these models to predict the future population trends for selected game species, but estimates of model variance can help quantify the level of confidence in these predictions. Sensitivity analysis can be used to suggest the relative research effort to spend collecting field estimates of models parameters, to increase model accuracy. Finally, the relative effects of selected hunting scenarios can help clarify the criteria used by state officials to set hunting regulations. Stochastic simulations of game population dynamics should therefore provide game managers with credible tools for managing game species.