DOI: 10.5061/dryad.hmgqnk9h6
title: Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses

description: 1. Recent empirical studies have quantified correlation between survival
 and recovery by estimating these parameters as correlated random effects
 with hierarchical Bayesian multivariate models fit to tag-recovery data.
 In these applications, increasingly negative correlation between survival
 and recovery has been interpreted as evidence for increasingly additive
 harvest mortality. The power of these hierarchal models to detect non-zero
 correlations has rarely been evaluated and these few studies have not
 focused on tag-recovery data, which is a common data type. 2. We assessed
 the power of multivariate hierarchical models to detect negative
 correlation between annual survival and recovery. Using three priors for
 multivariate normal distributions, we fit hierarchical effects models to a
 mallard (Anas platyrhychos) tag-recovery dataset and to simulated data
 with sample sizes corresponding to different levels of monitoring
 intensity. We also demonstrate more robust summary statistics for
 tag-recovery datasets than total individuals tagged. 3. Different priors
 lead to substantially different estimates of correlation from the mallard
 data. Our power analysis of simulated data indicated most prior
 distribution and sample size combinations could not estimate strongly
 negative correlation with useful precision or accuracy. Many correlation
 estimates spanned the available parameter space (–1,1) and underestimated
 the magnitude of negative correlation. Only one prior combined with our
 most intensive monitoring scenario provided reliable results.
 Underestimating the magnitude of correlation coincided with overestimating
 the variability of annual survival, but not annual recovery. 4. The
 inadequacy of prior distributions and sample size combinations previously
 assumed adequate for obtaining robust inference from tag-recovery data
 represents a concern in the application of Bayesian hierarchical models to
 tag-recovery data. Our analysis approach provides a means for examining
 prior influence and sample size on hierarchical models fit to
 capture-recapture data while emphasizing transferability of results
 between empirical and simulation studies.
description: Instructions and metadata are provided at the top of each R
 script, including required R packages listed at the top of individual R
 scripts. Software specifications are included in the README file included
 in the data files. This Dryad repository is for two
 manuscripts: Deane, C. E., L. G.
 Carlson, C. J. Cunningham, P. Doak, K. Kielland, and G. A. Breed. 2023.
 Prior choice and data requirements of Bayesian multivariate hierarchical
 models fit to tag-recovery data: The need for power analyses. Ecology and
 Evolution 13:e9847. https://doi.org/10.1002/ece3.9847 <em>Note: R scripts specific to the power analysis in this manuscript begin with "P"</em> Deane, C. E., L. G. Carlson, C. J. Cunningham, P. Doak, K. Kielland, and G. A. Breed. <em>In prep</em>. Accurately estimating correlations between demographic parameters: a response to Riecke et al. (<em>in press</em>). Ecology and Evolution <em>Note: New R scripts specific to our response begin with the letter "R" </em>
description: # Data for the article "Prior choice and data requirements of
 Bayesian multivariate hierarchical models fit to tag-recovery data: the
 need for power analyses" --- # Reference information * File name:
 README_Dataset-PriorsDataRequirementsBayesianHierarchicalModels.md *
 Authors: C.E. Deane ([cdeane2@alaska.edu](mailto:cdeane2@alaska.edu)) *
 Other contributors: L.G. Carlson, C.J. Cunningham, P. Doak, K. Kielland,
 G.A. Breed. * Date of Issue: 2023-02-15 * Suggested Citations: * Dataset
 citation: &gt; Deane, C.E., L.G. Carlon, C.J. Cunningham, P. Doak, K.
 Kielland, and G.A. Breed. 2023. Data for "Prior choice and data
 requirements of Bayesian multivariate hierarchical models fit to
 tag-recovery data: the need for power analyses", Dryad, Dataset,
 [https://doi.org/10.5061/dryad.hmgqnk9h6](https://doi.org/10.5061/dryad.hmgqnk9h6) * Software citation: &gt; Deane, C.E., L.G. Carlon, C.J. Cunningham, P. Doak, K. Kielland, and G.A. Breed. 2023. Software for "Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag-recovery data: the need for power analyses", Zenodo, Software, [https://doi.org/10.5281/zenodo.13275071](https://doi.org/10.5281/zenodo.13275071 "https://doi.org/10.5281/zenodo.13275071") * Corresponding publication: &gt; Deane, C.E., L.G. Carlon, C.J. Cunningham, P. Doak, K. Kielland, and G.A. Breed. 2023. Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag-recovery data: the need for power analyses. Ecology and Evolution. Accepted. DOI: 10.22541/au.166004819.97373134/v1 # Contact information * Primary contact * Name: Cody E. Deane * Affiliation: Department of Biology and Wildlife, P.O. Box 756100, Fairbanks, AK 99775, USA * ORCID ID: [https://orcid.org/0000-0002-4098-9154](https://orcid.org/0000-0002-4098-9154) * Email: [cdeane2@alaska.edu](mailto:cdeane2@alaska.edu) * Alternate email: [codyedeane@gmail.com](mailto:codyedeane@gmail.com) * Alternative Contact * Name: Greg Breed * Affiliations: Department of Biology and Wildlife and Institute of Arctic BiologyP.O. Box 756100, Fairbanks, AK 99775, USA * ORCID ID: [https://orcid.org/0000-0002-7958-1877](https://orcid.org/0000-0002-7958-1877) * Email: [gabreed@alaska.edu](mailto:gabreed@alaska.edu) ## File/Folder Details ## Details for: MALL\_fhy\_mij\_core\_1961\_1996.txt, MALL\_fahy\_mij\_core\_1961\_1996.txt, MALL\_mhy\_mij\_core\_1961\_1996.txt, MALL\_mahy\_mij\_core\_1961\_1996.txt \===================================== * Description: Mallard banding data and harvest recovery data summarized into m-array format for empirical analysis. See manuscript for geographic and temporal filters applied to mallard data and for details about the m-array format. Sex and age class codes are based at status when releases: fhy (female hatch year/juvenile), fahy (female after hatch-year/adult), mhy (female hatch year/juvenile), mahy (female after hatch-year/adult). These files are created by the R scripts MALL1BandingUpload.R, MALL2RecoveryUpload.R, MALL3CompleteMijArray.R from publically available banding and harvest recovery data maintained by the U.S. Geological Survey Bird Banding Lab (please email for additional info). These m-array data are used by the R script MALL4ExecuteModels.R for statistical analysis (available at Zenodo citation). * Format: .txt * Size: 4 KB * Missing data codes: not applicable ## Details for: MR-f6196-wish3.RDS ``` MR-f6196-unif.RDS MR-f6196-gamma.RDS ``` \============================ * Description: Estimates of annual survival and recovery for juvenile and adult female mallards between 1961-1996. These parameters were estimated from the m-arrays (described above) using the R script MALL4ExecuteModels.R; each .RDS file contines the posterior distributions for monitored parameters for our three models implemented with Wishart (-wish3.RDS) priors, Gamma priors (-gamma.RDS), or Uniform priors (-unif.RDS). The file MR-f6196-unif.RDS are the results on which our power analysis is based; these results are uploaded by the file P22SimRealsMVN.R to simulate vital rates for the power analysis included in the manuscript. These data objects are complex lists. * Format: .RDS (R data structure) * Size: about 72,000 KB (each) * Missing data codes: not applicable ## Details for: mij\_modest\_14174.RDS ``` mij_pulsed_14174.RDS mij_intermediate_14174.RDS mij_intensive_14174.RDS ``` \============================ *Description: These are the datasets from which parameters for our power analysis were estimated. These objects are created from simulated data (P22PlotRealsMVN.R and P23SimCaptureHistories.R) and the sampling code included in the R scripts P24ModestConstant.R, P24ModestPulsed.R, P24Intermediate.R, P24Intensive.R, respectively. As provided, these R scripts upload these .RDS files and execute the Bayesian statistical models so the user does not need to run the simulation and sampling code. These data objects are three dimensional arrays, with rows and columns corresponding to a single m-array and each slice being one of 50 m-arrays for a given monitoring scenario. * Format: .RDS (R data structure) * Size: 40-85 KB (each) * Missing data codes: not applicable ## Sharing/Access information \============================ This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. * Links: [https://www.usgs.gov/labs/bird-banding-laboratory](https://www.usgs.gov/labs/bird-banding-laboratory) ## Code/Software for Deane et al. 2023. Notes from February 16, 2023 \============================ We fit Bayesian models using Program JAGS with code implemented in Program R (and RStudio) along with the following R packages: coda, jagsUI, MASS, rjags, coda. We uploaded two series of R scripts to Zenodo. One series is for the empirical/mallard analysis (MALL---.R) and one series is for the power analysis (P---.R) the the prefix is followed by a sequential numbering scheme and short description (eg. The beginning of each script has information on what a script follows, requires, and proceeds. The first simulation step of our power analysis and our folder organization/directories for the power analysis are based on a seed value '14174' that is included at the top of scripts for the power analysis. Describe any scripts, code, or notebooks (e.g., R, Python, Mathematica, MatLab) as well as the software versions (including loaded packages) that you used to run those files. If your repository contains more than one file whose relationship to other scripts is not obvious, provide information about the workflow that you used to run those scripts and notebooks. * Version information: JAGS (Verion 4.3.0) Program R (Version 4.1.2, 2021-11-01) RStudio (Version 2021.09.1 Build 372) coda (Version 0.19.4) jagsUI (Version 1.5.2) MASS (Version 7.3.54) rjags (Version 4.12) ## Code/Software for Deane et al. in prep (response to Riecke et al.). These notes are new August 8, 2024 \============================ We fit Bayesian models using Program JAGS with code implemented in Program R (and RStudio) along with the following R packages: coda, jagsUI, MASS, rjags, coda. We uploaded a new series of R scripts to Zenodo. This series begins with "R" for response (R---.R) and integrates with the "P" series (P---.R) for the power analysis (described above). The R series includes a script to create a new JAGS model using Gamma(1,1) as the prior distributions for the standard deviations. Then the new code executes these models for the realized datasets from the simulation in Deane et al. (2023); mij_modest_14174.RDS, mij_intermediate_14174.RDS, mij_intensive_14174.RDS * Version information: JAGS (Verion 4.3.0) Program R (Version 4.1.2, 2021-11-01) RStudio (Version 2021.09.1 Build 372) coda (Version 0.19.4) jagsUI (Version 1.5.2) MASS (Version 7.3.54) rjags (Version 4.12) --- Revised August 8, 2024 END OF README