Individual Variability, Environmental Stressors, and Sampling Uncertainty in Wildlife Risk AssessmentEPA Grant Number: R829088
Title: Individual Variability, Environmental Stressors, and Sampling Uncertainty in Wildlife Risk Assessment
Investigators: Kendall, Bruce E. , Fox, Gordon A.
Institution: University of California - Santa Barbara , University of South Florida
EPA Project Officer: Hahn, Intaek
Project Period: September 1, 2001 through August 31, 2004 (Extended to December 31, 2005)
Project Amount: $426,954
RFA: Wildlife Risk Assessment (2001) RFA Text | Recipients Lists
Research Category: Biology/Life Sciences , Ecological Indicators/Assessment/Restoration , Ecosystems
Assessing risks posed to wildlife populations by stressors requires linking the effects of stressors on individuals with resulting changes in the demography of the entire population.
This project has two broad objectives, each with four specific questions:
- Determine the statistical power of such assessments. Specifically:
a. Under what circumstances do life table response experiments (LTREs) have the power to detect responses to meaningful environmental variation?
b. Under what circumstances do correlation analyses of field data have the power to detect responses to meaningful environmental variation? How are these predictions affected by multiple, unmeasured stressors?
c. How is the power of these approaches changed by measuring organism condition?
- Examine the importance of variability in the expected demographic
performance of individuals. Specifically:
a. What is the net affect of a spatially heterogeneous stressor?
b. How does this heterogeneity affect the power of LTRE and correlation analyses?
c. What is the pattern of demographic variability in fecundity?
d. What is the magnitude of individual variability in demography?
The power analysis problem will be approached by generating simulated data representing a variety of organisms with different life histories and sources of variability (including individual variability, multiple stressors, and density dependence), and then simulating the analysis process (including sampling error and model specification error) to examine the precision and bias in predicted risk. The questions of heterogeneity and individual variability will be approached by acquiring data from the literature (including the unpublished individual-level data underlying many demographic studies) and analyzing patterns within and across taxa.
Expected results include characterizations of the circumstances (ecology, life history, and stressor mode of action) in which it is possible to perform reasonable wildlife risk assessments with existing techniques, and the sample sizes required to obtain reasonable power. The project will characterize patterns of individual variability and spatial heterogeneity of stressors, and their effects on wildlife risk. Results will be published in peer-reviewed journals. Freely-available software will be developed to perform the power analysis on additional species and stressors. Databases (of the assembled data on individual variability and stressor heterogeneity) will be made available on the internet.