ORCID Profile
0000-0002-4459-1418
Current Organisation
The University of Texas at Austin
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Publisher: Elsevier BV
Date: 11-2007
Publisher: Elsevier BV
Date: 02-2012
Publisher: Institute of Mathematical Statistics
Date: 03-2009
DOI: 10.1214/09-BA401REJ
Publisher: Wiley
Date: 04-2009
DOI: 10.1890/07-0744.1
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 s ling 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 ex les (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.
Publisher: Institute of Mathematical Statistics
Date: 03-2009
DOI: 10.1214/09-BA401
Publisher: Elsevier
Date: 2009
Publisher: Wiley
Date: 18-09-2007
No related grants have been discovered for Catherine Calder.