\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Prior choice and data requirements of Bayesian multivariate mixed
effects models fit to tag-recovery data: the need for power analyses}



\author[1]{Cody Deane}%
\author[2]{Lindsay Carlson}%
\author[3]{Curry Cunningham}%
\author[3]{Pat Doak}%
\author[3]{Knut Kielland}%
\author[3]{Greg Breed}%
\affil[1]{University of Alaska System}%
\affil[2]{University of Saskatchewan}%
\affil[3]{University of Alaska Fairbanks - Troth Yeddha' Campus}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
1. Recent empirical studies have quantified correlation between survival
and recovery by estimating these parameters as correlated random effects
with Bayesian multivariate mixed effects models fit to tag-recovery
data. In these applications, increasingly negative correlation between
survival and recovery indicates increasingly additive harvest mortality.
The power of mixed effects models to detect non-zero correlations has
rarely been evaluated and these few studies have not focused on a common
data type in the form of tag recoveries. 2. We assessed the power of
multivariate mixed effects models to estimate negative correlation
between annual survival and recovery. Using three priors for
multivariate normal distributions, we fit mixed 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 a method of calculating effective sample
size for capture-recapture data. 3) Different priors lead to different
inference about additive harvest when we fit our models to the mallard
data. Our power analysis of simulated data indicated most prior
distribution and sample size combinations resulted in correlation
estimates with substantial bias and imprecision. Many correlation
estimates spanned the available parameter space (--1,1) and were biased
towards zero. Only one prior combined with our most intensive monitoring
scenario allowed our models to consistently recover negative correlation
without bias. 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 typically assumed adequate for robust inference represents
a concern in the application of Bayesian mixed effects models for the
purpose of informing harvest management. Our analysis approach provides
a means for examining prior influence and sample size on mixed-effects
models fit to capture-recapture data while emphasizing transferability
of results between empirical and simulation studies.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`Deane_et_al_Manuscript_2022_6.docx`  available at \url{https://authorea.com/users/499955/articles/580888-prior-choice-and-data-requirements-of-bayesian-multivariate-mixed-effects-models-fit-to-tag-recovery-data-the-need-for-power-analyses}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig1-HYReals-12558/Deane-et-al-Fig1-HYReals-12558}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig2-AHYReals-12558/Deane-et-al-Fig2-AHYReals-12558}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig3-RhoMALL/Deane-et-al-Fig3-RhoMALL}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig4-REBoxplots/Deane-et-al-Fig4-REBoxplots}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig5-RhoJitter/Deane-et-al-Fig5-RhoJitter}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Deane-et-al-Fig6-Intense-Priors-1X3/Deane-et-al-Fig6-Intense-Priors-1X3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

