As the human population grows and its footprint on the landscape increases, land-use decisions play an increasing role in the extinction of plant and animal species. In order to reverse the current tendency of biodiversity loss, planners and land managers must consider the long-term impacts of their decisions on the persistence of living organisms. This paper describes the development and application of a spatial dynamic model designed to help planners and managers assess the long-term repercussions of land-use development alternatives on the population dynamics and movement patterns of animal species. This model, based on a cellular automata approach, is designed to incorporate life history traits of various species. It uses a spatial dynamic model (created under STELLA 7.0.1) distributed across the cells of GIS grids to simulate population demographics and spatial distribution within a landscape. The incorporation of the STELLA model equations within each grid cell and the calculation of simultaneous local interaction between cells, for all model variables is done through the use of the Spatial Modeling Environment (SME). Applied to hypothetical species, over potential land-use change scenarios, our model showed development resulting in limited habitat losses (10%) could nonetheless lead to significant reductions in species population within a landscape (-49%). We therefore believe this model offers planners and managers the ability to anticipate possible repercussions of changes in local and regional land-use policies on sensitive animal species. By simulating animal populations and their movements through the landscape, our model may help environmental managers develop landscape policies maximizing species survival, for example by placing corridors or roads over-paths at location allowing the maximum number of individuals to cross. Animal species are highly adaptable and behavioral changes might be triggered by the increasing presence in their environment of human influenced landscapes. Consequently models that assume fixed behaviors may be of limited use. Our model, through the STELLA - SME interface, can be easily be adapted to account for different behaviors, so that it could be used to test the consequences of behavioral changes in species population dynamics and distribution. Finally the outputs provided by the model, both in terms of quantitative values and display maps can represent a powerful tool in advocating policies for both specialists and non-specialist audiences. This work also underscores the present lack of species-specific life history and habitat preference data and calls for more applied research on the processes governing species response and adaptability to environmental changes.