The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling
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.
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Cites
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Warton, D. I., Blanchet, F. G., O’Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C., & Hui, F. K. C. (2015) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2015.09.007 · Cited by 854
A guide to eliciting and using expert knowledge in Bayesian ecological models
Kuhnert, P. M., Martin, T. G., & Griffiths, S. P. (2010) · Ecology Letters · doi:10.1111/j.1461-0248.2010.01477.x · Cited by 441
Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
Farley, S. S., Dawson, A., Goring, S. J., & Williams, J. W. (2018) · BioScience · doi:10.1093/biosci/biy068 · Cited by 331
A general biodiversity–function relationship is mediated by trophic level
O'Connor, M. I., Gonzalez, A., Byrnes, J. E. K., Cardinale, B. J., Duffy, J. E., Gamfeldt, L., Griffin, J. N., Hooper, D., Hungate, B. A., Paquette, A., et al. (2017) · Oikos · doi:10.1111/oik.03652 · Cited by 173
The use of Bayesian priors in Ecology: The good, the bad and the not great
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Metadata
- Authors
- Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V., & Wikle, C. K.
- Venue
- Ecological Applications
- Publisher
- Wiley
- Published
- 2009-04
- Volume
- 19
- Issue
- 3
- Pages
- 553-570
- ISSN
- 1051-0761 (ISSN-L)
- 1939-5582
- OpenAlex
- W2171010898
- Cited by
- 541
- References
- 9
- FWCI
- 36.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V., & Wikle, C. K. (2009). Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications. https://doi.org/10.1890/07-0744.1
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