Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023)

Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (<i>Ecology and Evolution</i> 13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark-recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate.

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Publication Details

Journal
Ecology and Evolution
Published
2024-09-01
DOI
https://doi.org/10.1002/ece3.70286
Citations
2
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
3.42
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article

Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023)

Thomas V. Riecke, Daniel Gibson, James S. Sedinger
2 citations
Ecology and Evolution
Insurance, Mortality, Demography, Risk Management
3.42
article

Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023)

Thomas V. Riecke, Daniel Gibson, James S. Sedinger
article en
2 citations

Abstract

Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (<i>Ecology and Evolution</i> 13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark-recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate.

Ecology and EvolutionVol. 14(9)
University of Minnesota (US), Minnesota Department of Natural Resources (US), University of Nevada, Reno (US), Louisiana Department of Natural Resources (US), University of Montana (US)
Openalex Percentile: Top 7%
Insurance, Mortality, Demography, Risk Management
3.42
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Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023) — Thomas V. Riecke, Daniel Gibson, et al. · Ecology and Evolution (2024) | TGRS Research Map | TGRS