Spatial Demographic Models for the Study of Stress Effects on Wildlife Populations

EPA Grant Number: R829089
Title: Spatial Demographic Models for the Study of Stress Effects on Wildlife Populations
Investigators: Caswell, Hal , Neubert, Michael
Institution: Woods Hole Oceanographic Institution
EPA Project Officer: Carleton, James N
Project Period: December 17, 2001 through December 16, 2005 (Extended to June 16, 2006)
Project Amount: $500,000
RFA: Wildlife Risk Assessment (2001) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Biology/Life Sciences , Ecosystems

Description:

We propose to develop new approaches to assessing risks to wildlife populations, particularly risks characterized by a multiple stressors and by spatial distribution. Our approach will weave together two threads already spun into the fabric of demographic analysis: models for spatially distributed populations and analyses of the effects of stressors. The methods we propose to develop will be capable of assimilating a variety of kinds of demographic and dispersal data. They will also permit quantification of the uncertainty in the conclusions that derives from incomplete or uncertain data.

Approach:

Stressors affect the vital rates (survival, reproduction, growth, development) of individual organisms, and the effects are often stage specific. That is, stress effects may be more pronounced in old individuals than in young individuals, or in smaller than in larger individuals, etc.). Measuring the population consequences of a stressor therefore requires a demographic model to integrate these diverse effects and to calculate from them indices of population performance. Matrix population models are uniquely suited to this purpose; they will form the foundation of our modeling approach.

Because individuals move, and because the effects of a stressor depend on its spatial distribution, our models will include spatial structure. We will use two approaches to the modeling space: the metapopulation paradigm and the continuous landscape paradigm. Each approach lends itself to the analysis of a particular set of stressors and endpoints.

Expected Results:

The use of matrix models in wildlife risk assessment has already proven valuable to ecologists and resource managers. The methods that we will develop build upon matrix models by adding a spatial dimension. As such, they will greatly extend the situations in which population analysis can address stress effects.

Publications and Presentations:

Publications have been submitted on this project: View all 80 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 20 journal articles for this project

Supplemental Keywords:

conservation, ecology, ecological effects, habitat, mathematics, modeling, population, risk assessment., RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Ecosystem Protection/Environmental Exposure & Risk, Ecosystem/Assessment/Indicators, exploratory research environmental biology, wildlife, Mathematics, Ecological Effects - Environmental Exposure & Risk, Monitoring/Modeling, Environmental Monitoring, Environmental Statistics, Ecological Risk Assessment, ecological exposure, predicting risk, spatial distribution, risk assessment, demographic, stressors, contaminants, demographic data, stress effects on wildlife populations, wildlife populations, multiple stressors, Wildlife Risk Assessment, spatial demographic model, sensitive population

Progress and Final Reports:

  • 2002 Progress Report
  • 2003 Progress Report
  • 2004
  • 2005 Progress Report
  • Final Report