Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024)
ABSTRACT Correlations between annual recovery and survival probabilities estimated from tag‐recovery data have been used to quantify the demographic response of exploited populations to harvest. Deane et al. (2023) evaluated the bias and certainty of correlation parameters between recovery and survi...
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2025-02-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.71004 |
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| author | Cody E. Deane Lindsay G. Carlson Curry J. Cunningham Pat Doak Knut Kielland Greg A. Breed |
| author_facet | Cody E. Deane Lindsay G. Carlson Curry J. Cunningham Pat Doak Knut Kielland Greg A. Breed |
| author_sort | Cody E. Deane |
| collection | DOAJ |
| description | ABSTRACT Correlations between annual recovery and survival probabilities estimated from tag‐recovery data have been used to quantify the demographic response of exploited populations to harvest. Deane et al. (2023) evaluated the bias and certainty of correlation parameters between recovery and survival probabilities estimated as random effects drawn from bivariate normal distributions relative to different prior distributions and sample size combinations. Riecke et al. (2024) observed that we incorrectly parameterized a precision matrix with Gamma priors and suggested using a Gamma(1,1) prior distribution for the standard deviations as an alternative. Riecke et al. (2024) provided results from tag‐recovery models that estimate mortality hazard rates after fitting these models to tag‐recovery datasets with large sample sizes. Here, we fit tag‐recovery models to the data we previously simulated (Deane et al. 2023) while using Gamma(1,1) as the prior distribution for standard deviations while parameterizing these models to estimate recovery and survival in discrete time or to estimate cause‐specific mortality as hazard rates. We compare our new results to previous results obtained while using Uniform(0,5) prior distribution for the standard deviations. When sample sizes were large, correlation estimates obtained with either prior distribution provided similarly reliable parameter recovery and inference, replicating results of Riecke et al. (2024). With smaller sample sizes similar to those available for most duck populations in North America, correlations estimated with either prior distribution were uncertain and ambiguous. With decreasing sample sizes, annual survival was estimated with increasing uncertainty when compared to annual recovery, likely contributing to the poor ability to estimate correlation. Consistent with the original interpretation of Deane et al. (2023) and previous literature, we found correlations were often estimated with high uncertainty such that the sign (+ or –) may be the only attribute of these parameters that can be reliably interpreted. |
| format | Article |
| id | doaj-art-61d424973c0d41a2b2794cc8e7b211ca |
| institution | DOAJ |
| issn | 2045-7758 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
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| series | Ecology and Evolution |
| spelling | doaj-art-61d424973c0d41a2b2794cc8e7b211ca2025-08-20T03:13:50ZengWileyEcology and Evolution2045-77582025-02-01152n/an/a10.1002/ece3.71004Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024)Cody E. Deane0Lindsay G. Carlson1Curry J. Cunningham2Pat Doak3Knut Kielland4Greg A. Breed5University of Alaska Fairbanks Department of Biology and Wildlife Fairbanks Alaska USAUniversity of Saskatchewan Department of Biology Saskatoon Saskatchewan CanadaUniversity of Alaska Fairbanks College of Fisheries and Ocean Sciences Juneau Alaska USAUniversity of Alaska Fairbanks Institute of Arctic Biology Fairbanks Alaska USAUniversity of Alaska Fairbanks Department of Biology and Wildlife Fairbanks Alaska USAUniversity of Alaska Fairbanks Department of Biology and Wildlife Fairbanks Alaska USAABSTRACT Correlations between annual recovery and survival probabilities estimated from tag‐recovery data have been used to quantify the demographic response of exploited populations to harvest. Deane et al. (2023) evaluated the bias and certainty of correlation parameters between recovery and survival probabilities estimated as random effects drawn from bivariate normal distributions relative to different prior distributions and sample size combinations. Riecke et al. (2024) observed that we incorrectly parameterized a precision matrix with Gamma priors and suggested using a Gamma(1,1) prior distribution for the standard deviations as an alternative. Riecke et al. (2024) provided results from tag‐recovery models that estimate mortality hazard rates after fitting these models to tag‐recovery datasets with large sample sizes. Here, we fit tag‐recovery models to the data we previously simulated (Deane et al. 2023) while using Gamma(1,1) as the prior distribution for standard deviations while parameterizing these models to estimate recovery and survival in discrete time or to estimate cause‐specific mortality as hazard rates. We compare our new results to previous results obtained while using Uniform(0,5) prior distribution for the standard deviations. When sample sizes were large, correlation estimates obtained with either prior distribution provided similarly reliable parameter recovery and inference, replicating results of Riecke et al. (2024). With smaller sample sizes similar to those available for most duck populations in North America, correlations estimated with either prior distribution were uncertain and ambiguous. With decreasing sample sizes, annual survival was estimated with increasing uncertainty when compared to annual recovery, likely contributing to the poor ability to estimate correlation. Consistent with the original interpretation of Deane et al. (2023) and previous literature, we found correlations were often estimated with high uncertainty such that the sign (+ or –) may be the only attribute of these parameters that can be reliably interpreted.https://doi.org/10.1002/ece3.71004Bayesian analysiscapture‐mark‐recapture modelshierarchical modelsmultivariate normal distributionrandom effects |
| spellingShingle | Cody E. Deane Lindsay G. Carlson Curry J. Cunningham Pat Doak Knut Kielland Greg A. Breed Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) Ecology and Evolution Bayesian analysis capture‐mark‐recapture models hierarchical models multivariate normal distribution random effects |
| title | Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) |
| title_full | Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) |
| title_fullStr | Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) |
| title_full_unstemmed | Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) |
| title_short | Accurately Estimating Correlations Between Demographic Parameters: A Response to Riecke Et al. (2024) |
| title_sort | accurately estimating correlations between demographic parameters a response to riecke et al 2024 |
| topic | Bayesian analysis capture‐mark‐recapture models hierarchical models multivariate normal distribution random effects |
| url | https://doi.org/10.1002/ece3.71004 |
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