Potential Effects of a Forest Management Plan on Bachman's Sparrows (Aimophila aestivalis): Linking a Spatially Explicit Model with GIS

By combining a spatially explicit, individual-based population simulation model with a geographic information system, we have simulated the potential effects of a U.S. Forest Service management plan on the population dynamics of Bachman's Sparrow ( Aimophila aestivalis ) at the Savannah River Site, a U.S. Department of Energy facility in South Carolina. Although the Forest Service's management plan explicitly sets management goals for many species, most of the prescribed management strategy deals with the endangered Red-cockaded Woodpecker ( Picoides borealis ) because of legal requirements. We explored how a species (the sparrow) that is not the target of specific management strategies but that shares some habitat requirements with the woodpecker, would fare under the management plan. We found that the major components of the proposed management plan may allow the sparrow population to reach and exceed the minimum management goal set for this species, but only after a substantial initial decline in sparrow numbers and a prolonged transition period. In the model, the sparrow population dynamics were most sensitive to demographic variables such as adult and juvenile survivorship and to landscape variables such as the suitability of young clearcuts and mature pine stands. Using various assumptions about habitat suitability, we estimated that the 50-year probability of population extinction is at least 5% or may be much higher if juvenile survivorship is low. We believe, however, that modest changes in the management plan might greatly increase the sparrow population and presumably decrease the probability of extinction. Our results suggest that management plans focusing on one or a few endangered species may potentially threaten other species of management concern. Spatially explicit population models are a useful tool in designing modifications of management plans that can reduce the impact on nontarget species of management concern.     

Hierarchical analysis of species distributions and abundance across environmental gradients

Abiotic and biotic processes operate at multiple spatial and temporal scales to shape many ecological processes, including species distributions and demography. Current debate about the relative roles of niche-based and stochastic processes in shaping species distributions and community composition reflects, in part, the challenge of understanding how these processes interact across scales. Traditional statistical models that ignore autocorrelation and spatial hierarchies can result in misidentification of important ecological covariates. Here, we demonstrate the utility of a hierarchical modeling framework for testing hypotheses about the importance of abiotic factors at different spatial scales and local spatial autocorrelation for shaping species distributions and abundances. For the two orchid species studied, understory light availability and soil moisture helped to explain patterns of presence and abundance at a microsite scale (<4 m2), while soil organic content was important at a population scale (<400 m2). The inclusion of spatial autocorrelation is shown to alter the magnitude and certainty of estimated relationships between abundance and abiotic variables, and we suggest that such analysis be used more often to explore the relationships between species life histories and distributions. The hierarchical modeling framework is shown to have great potential for elucidating ecological relationships involving abiotic and biotic processes simultaneously at multiple scales.