Modeling the Effect of Human Population on Land Use and Species Viability
H. Resit Akçakaya, W. Troy Tucker
Applied
Biomathematics, Setauket, NY
This project is funded by the National Science Foundation.
This project aims to develop methods and software to evaluate or explore the impact of human population and land-use changes on species viability. Changes in human population and land use affect the viability of native species through habitat loss to agriculture, urban sprawl, and industrial development; habitat fragmentation; decreased habitat quality; and increased direct harvest of species. Applied Biomathematics has developed internationally known RAMAS software for modeling the effect of changes in the quality and amount of habitat on the viability of species. The methods being developed in this project will allow the incorporation of the human element into this methodology. It will lead to software that will be used to forecast the changes in the human population, and the effect of these changes on the land-use and resource-use patterns. These results will be used to predict the changes in the habitat of native species, and to assess species viability and persistence. Projecting landscape change is prerequisite to conservation planning.
Summary of the presentation at the 2004 Annual Meeting of the Society for Conservation Biology (Sunday, August 1, 8:30 am):
Both natural and anthropogenic processes drive landscape change. Relevant human ecological impacts include habitat loss due to direct human use for agriculture and housing, landscape fragmentation due to roads and development, and habitat quality decline due to altered drainage, soil loss, nutrient leaching, pollution, selective harvest, wildfire suppression, the introduction of exotic species, and grazing. Simulating human-induced landscape change remains difficult and problematic and no one best solution has emerged. We have developed an eclectic modeling framework that integrates spatially explicit landscape and metapopulation models with models of human social, economic, and demographic change. This framework is applicable to cases where sophisticated and data intensive models of human population and landscape interaction exist, as well as to cases where data is sparse and anthropogenic impacts are not well understood. In the latter case, while precision may be difficult to achieve, accurate predictions of landscape change relevant to population viability over useful time horizons are possible.