Grantee Research Project Results
2007 Progress Report: A Statistical Modeling Methodology for the Detection, Quantification, and Prediction of Ecological Thresholds
EPA Grant Number: R832447Title: A Statistical Modeling Methodology for the Detection, Quantification, and Prediction of Ecological Thresholds
Investigators: Richardson, Curtis J. , Qian, Song S.
Institution: Duke University
EPA Project Officer: Packard, Benjamin H
Project Period: February 1, 2005 through June 30, 2007 (Extended to August 31, 2008)
Project Period Covered by this Report: July 1, 2006 through June 30,2007
Project Amount: $278,876
RFA: Exploratory Research: Understanding Ecological Thresholds In Aquatic Systems Through Retrospective Analysis (2004) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Water
Objective:
The primary objective is to develop a statistical modeling approach for the detection and quantification of ecological thresholds that can be used in aquatic ecosystems worldwide. Specifically, we will use a Bayesian hierarchical modeling approach to integrate the single-species changepoint analysis method developed by Qian et al. (2003) to study the interactions among fast and slow responding species or ecological metrics indicating alternative stable states. Under this structural approach, the resulting species/metric thresholds will be integrated to form an ecosystem-level threshold distribution. A risk-based criteria approach will be developed to assess ecological resilience and predict the threshold for alternative state changes.
Progress Summary:
In the past year, we focused our work in two areas:
- Utilized our database inventory to develop 2 manuscripts on key indicator species and thresholds.
- Developed and are now testing a new integrated statistical method for threshold analysis.
We are refining our data files related to the threshold studies that were done in the Everglades and are testing several more approaches. Our analysis concluded that the traditional confidence interval is not a suitable tool for summarizing uncertainty in ecological threshold. Our Bayesian approach resulted in a more relevant way for summarizing uncertainty and linking the estimated ecological threshold to environmental standards. In a separate study, we investigated the possible applications of a multilevel analysis of variance (ANOVA) method. The multilevel ANOVA model is a potentially valuable tool for ecological threshold study. We wrote a manuscript on multilevel analysis, which was published in 2007. A paper on our threshold work was also published in 2007.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
| Other project views: | All 8 publications | 2 publications in selected types | All 2 journal articles |
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Qian SS, Shen Z. Ecological applications of multilevel analysis of variance. Ecology 2007;88(10):2489-2495. |
R832447 (2006) R832447 (2007) R832447 (Final) |
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Richardson CJ, King RS, Qian SS, Vaithiyanathan P, Qualls RG, Stow CA. Estimating ecological thresholds for phosphorus in the Everglades. Environmental Science & Technology 2007;41(23):8084-8091. |
R832447 (2006) R832447 (2007) R832447 (Final) |
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Supplemental Keywords:
Total phosphorus, soluble reactive phosphorus, ecological thresholds, Bayesian changepoint analysis, credible interval, RFA, Scientific Discipline, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Aquatic Ecosystem, Environmental Monitoring, Ecology and Ecosystems, species interaction, ecosystem monitoring, statistical modeling, aquatic ecosystems, ecological classification, aquatic indicatorsRelevant Websites:
http://www.env.duke.edu/wetland Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.