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. (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., The data used in this manuscript were simulated, and the simulation and analysis code are stored in this repository., , # Data from: Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023)

[https://doi.org/10.5061/dryad.3bk3j9kv6](https://doi.org/10.5061/dryad.3bk3j9kv6)

## Description of the data and file structure

The manuscript uses simulated data.

### Files and variables

There are two Stan model files (mGamma.STAN and mUniform.STAN) that analyze the data generated in the simulation_marr_Stan.R file. The figures.R file generates the figures published in the manuscript.

Abbreviations used in simulation_marr_Stan.R:
nS: the number of Simulations to run
nR: the number of individuals to Release during each year
nT: the number of years (i.e., Time-steps) of data to simulate
Gres.rho: storing correlation estimates from each simulation from models using gamma priors
Gres.s1 and Gres.s2: storing standard deviations estimates from each simulation from models using gamma priors
Gres.m1 and Gres.m2: storing mean estimates from each simulation from models using ga...