Research Areas - Environmental Policy

HIERARCHICAL MODELING FOR INTEGRATED ENVIRONMENTAL ASSESSMENTS

Enormous gaps still exist in the scientific understanding of precisely how anthropogenic stressors affect resources and the environment. The distribution of biotic resources over large spatial extents is often a function of climate, of land-use, and of the demographics of the human population, but these different classes of independent variables have different spatial scales for their action. One approach to the integration of these effects across scales is to use hierarchical models that incorporate contingencies and constraints in effects. This project seeks to develop such a modeling paradigm by use of classification and regression trees (CART). The hexagonal grid of the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program is used as the spatial grid, with about 12,000 grid points within the conterminous U.S. Mapped data of landscape and habitat types are available from an analysis of Advanced Very High Resolution Radiometry (AVHRR) meteorological satellite images. These data form the basis of the study's landscape metrics. Socio-demographic data are derived from the decennial census using variables deemed most likely to capture key demographic facets and to be related to variation in land use/land cover patterns. Data on temperature, precipitation and elevation, as well as the abundance of roads are also used.

The effects of climate, land-use and human demographics on environmental resources are particularly difficult to estimate in an integrated manner because some effects are broad-scale while others are very localized. If one could do so, a national environmental risk assessment would be feasible in a cost-effective manner. Here a new approach to the problem is tested, using a hierarchical model that first adjusts for broad-scale effects and then assesses regional and local effects in turn. The approach can readily be extended to other environmental response variables and to smaller or larger scales. This project systematically investigates technical uncertainties that might limit the usefulness of the approach.

Funding Agency:  National Science Foundation

Principal Investigator:  Rayrnond O'Connor, Dept. of Wildlife Ecology, University of Maine; and Deirdre M. Mageean, Margaret Chase Smith Center, University of Maine

Collaborator:   Maine State Planning Office