2002 Progress Report: 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
Project Period Covered by this Report: March 12, 2002 through March 11, 2003
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.
Results to Date
Populations. As a part of this work, Drs. Wang and Pfister are developing methodologies for incorporating growth autocorrelation into matrix models. Their initial work has shown that simply dividing the population into individuals that consistently do better (i.e., "good growers") and those that perform badly (i.e., "bad growers") can dramatically improve population predictions. With models of this complexity, it is difficult to assess the relative importance of individuals in different growth classes, and so previous efforts have relied on very crude methods. With the help of a colleague (L. Sun, Beijing Institute of Technology), Dr. Wang has developed an algorithm that is guaranteed to provide an accurate estimation of the importance of different life-history trajectories. The group has been in contact with Dr. Dianne Nacci at EPA's Atlantic Ecology Division about this work, and she has expressed interest in using these methodologies to explore the impacts of pollutants on a common estuarine species, Fundulus heteroclitus.
Species Interactions. The initial analysis for this section has focused on disease dynamics or two-species interactions. Greg Dwyer, Marc Coram, and Bret Elderd have been developing stochastic models to fit data sets on the interaction between insects and their pathogens, using both maximum likelihood and Bayesian approaches. These models have been shown to give more reliable predictions and better fits to known outbreaks. Individuals at EPA's Western Ecology Division have expressed interest in the applicability of these models to tracing outbreaks of leaf defoliating insects as a way of predicting ecosystem stress. Additionally, Wootton has been examining the use of Markovchain matrix models to quantify individual interactions within a community of organisms.
Population Growth. In future work on the population growth project, we will extend the Pfister and Wang model to allow for stochastic variation in matrix elements, including the probabilities of moving between growth classes within a size class. In addition, we plan to further explore the general suitability of matrix models for making predictions of population growth. We will conduct simulations that keep track of each individual in the population, and compare the resulting model predictions to predictions made by the Pfister and Wang model.
Species Interactions. Our initial work on species interactions has focused on developing models to describe epidemics, especially in insect populations. Existing stochastic epidemic models generally assume that the only source of stochasticity in epidemics is due to small population sizes. In many cases, however, stochastic fluctuations in transmission rates occur in populations that are large enough that small-population effects are unimportant. Greg Dwyer, Marc Coram, and Bret Elderd have developed a stochastic epidemic model that allows for random, daily fluctuations in transmission rates. The resulting model provides highly accurate predictions of epidemics in insect populations. Side-by-side with these model development efforts, the same group has worked out a Bayesian approach to combining data from multiple epidemics. As a test case for this work, they began with a deterministic epidemic model, under the assumption that all of the stochasticity is due to variability between epidemics. To calculate the posterior distribution of the transmission rate, they constructed a Gibbs sampler with Metropolis steps that uses Markov-chain Monte Carlo (MCMC) simulations. This routine is being applied to data for human epidemics.
In the future, we hope to combine our stochastic model of disease spread in insects with the MCMC approach that we are using for human epidemics. Our objective will be to combine the experimental data with observations of epidemics to estimate the probability of different model outcomes, as a means of demonstrating the usefulness of this general procedure, and to produce a more accurate predictive model. Additionally, Wootton and Elderd are attempting to use a dataset of species interactions along with path analysis tools and Markov-chain analysis to examine species interactions within a community setting. By quantifying the strength of species interactions, those interactions that impact the community to the greatest extent can be identified. Changes in these interaction strengths may be the first signs of ecosystem stress.
Journal Articles:No journal articles submitted with this report: View all 14 publications for this subproject
Supplemental Keywords:human impact, human activity, environment, ecosystem heat, ecological process, quantitative prediction, models, stochastic model, population growth, species interactions, environmental protocol., RFA, Scientific Discipline, Health, Economic, Social, & Behavioral Science Research Program, PHYSICAL ASPECTS, Air, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, Applied Math & Statistics, particulate matter, Health Risk Assessment, Ecosystem/Assessment/Indicators, Ecological Effects - Environmental Exposure & Risk, Monitoring/Modeling, Risk Assessments, Environmental Monitoring, Ecological Effects - Human Health, Physical Processes, Ecological Risk Assessment, Environmental Statistics, Environmental Engineering, Engineering, Chemistry, & Physics, Ecological Indicators, EPA Region, particulates, risk assessment, ecological effects, monitoring, health risk analysis, watersheds, emissions monitoring, ecological health, ozone , particulate, stratospheric ozone, ozone, sediment transport, computer models, exposure, air pollution, chemical transport modeling, chemical transport, trend monitoring, statistical models, human exposure, ecological risk, water, ecosystem health, environmental indicators, PM, ecological models, chemical transport models, Region 5, data models, air quality, statistical methodology, human health risk, stochastic models
Progress and Final Reports:Original Abstract
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