Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical... Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Applications Ecological Society of America

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

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Publisher
Ecological Society of America
Copyright
Copyright © 2009 by the Ecological Society of America
Subject
Forum-Hierarchical Statistical Models in Ecology
ISSN
1051-0761
D.O.I.
10.1890/07-0744.1
Publisher site
See Article on Publisher Site

Abstract

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Journal

Ecological ApplicationsEcological Society of America

Published: Apr 1, 2009

Keywords: Bayesian modeling ; data model ; design ; empirical Bayes ; harbor seals ; MCMC ; prior ; process model ; spatial process ; spatiotemporal process

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