Model Choice Stochasticity, and Ecological ComplexityEPA Grant Number: R829402C005
Subproject: this is subproject number 005 , established and managed by the Center Director under grant R829402
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Center for Integrating Statistical and Environmental Science
Center Director: Stein, Michael
Title: Model Choice Stochasticity, and Ecological Complexity
Investigators: Dwyer, Greg , McCullagh, Peter , Pfister, Cathy , Wootton, Timothy
Current Investigators: Dwyer, Greg , Coram, Marc , Elderd, Bret , Forester, James , Pfister, Cathy , Wang, Mei , Wootton, Timothy
Institution: University of Chicago
EPA Project Officer: Packard, Benjamin H
Project Period: March 12, 2002 through March 11, 2007
RFA: Environmental Statistics Center (2001) RFA Text | Recipients Lists
Research Category: Environmental Statistics , Ecological Indicators/Assessment/Restoration , Health , Ecosystems , Air
Understanding human impacts on the environment ultimately requires an understanding of how those impacts will affect other living things. Such an understanding is most useful if it allows quantitative prediction. The goal of this research project is to create models that can predict how human activities will affect population growth and interactions among species other than humans. Although this task has a long history in ecology, most existing models are deterministic, and are of limited usefulness in real-world applications. The specific objectives of this research project are to: (1) develop stochastic models of population growth and species interactions; and (2) develop methods for choosing among models. We focus on particular organisms, but the models that we are developing and our methodology for choosing among models are not specific to any particular organism or group of organisms. Our intent is that the models that we produce will become part of environmental protocols for assessing the impacts of pollutants on ecological processes or for tracking changes in environmental indicators of ecosystem health. Our work falls into two subprojects, a population growth project and a species interaction project. The population growth project includes a nascent collaboration with Diane Nacci at the Atlantic Ecology Division Laboratory of the U.S. Environmental Protection Agency (EPA). To facilitate EPA collaboration with the species interaction project, Tim Wootton visited the Western Ecology Division in June, and several scientists there expressed interest in this project, most notably, Ann Fairbrother. We will continue to build collaborations for the species interaction project over the next year.
Population Growth. The population growth subproject has been examining the impacts of individual variation on predicting population growth rates. Past ecological studies have used matrix algebra to predict population growth rates and population health. These studies divide populations into distinct age or size groups and, in their analysis of population health, generally assume that age-specific vital rates are sufficient to provide accurate predictions. Cathy Pfister's past work, however, has shown that there are additional sources of variability that must be accounted for, notably growth autocorrelation, before such models will be useful for predicting population growth rates.
Species Interactions. Models that consider the growth of a single species can be useful for species that interact only weakly with other species, or for species whose interactions with other species are at equilibrium. Many organisms, however, do not match these assumptions; therefore, a second main area of our research is aimed at understanding interactions among different species, and how such interactions determine population growth rates. As with our work on population growth, our ultimate goal is to predict how anthropogenic change will affect species interactions, and to predict the effects of human activities on ecosystems.
Publications and Presentations:Publications have been submitted on this subproject: View all 14 publications for this subproject | View all 115 publications for this center
Journal Articles:Journal Articles have been submitted on this subproject: View all 12 journal articles for this subproject | View all 47 journal articles for this center
Supplemental Keywords:human impact, human activity, environment, ecosystem heat, ecological process, quantitative prediction, models, stochastic model, population growth, species interactions, environmental protocol., RFA, Health, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, PHYSICAL ASPECTS, Air, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Applied Math & Statistics, Ecosystem/Assessment/Indicators, Health Risk Assessment, Risk Assessments, Monitoring/Modeling, Ecological Effects - Environmental Exposure & Risk, Ecological Effects - Human Health, Environmental Monitoring, Physical Processes, Environmental Statistics, Ecological Risk Assessment, Engineering, Chemistry, & Physics, Environmental Engineering, EPA Region, Ecological Indicators, ecological effects, monitoring, particulates, risk assessment, health risk analysis, ecological health, particulate, watersheds, stratospheric ozone, ozone , emissions monitoring, computer models, exposure, ozone, sediment transport, air pollution, trend monitoring, chemical transport, chemical transport modeling, human exposure, statistical models, ecological risk, ecosystem health, environmental indicators, PM, water, data models, Region 5, chemical transport models, ecological models, air quality, human health risk, statistical methods, stochastic models
Progress and Final Reports:
Main Center Abstract and Reports:R829402 Center for Integrating Statistical and Environmental Science
Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829402C001 Detection of a Recovery in Stratospheric and Total Ozone
R829402C002 Integrating Numerical Models and Monitoring Data
R829402C003 Air Quality and Reported Asthma Incidence in Illinois
R829402C004 Quasi-Experimental Evidence on How Airborne Particulates Affect Human Health
R829402C005 Model Choice Stochasticity, and Ecological Complexity
R829402C006 Statistical Approaches to Detection and Downscaling of Climate Variability and Change