\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{When enough is enough. Optimising monitoring effort for large-scale wolf
population size estimation in the Italian Alps}



\author[1]{Maria Virginia Boiani}%
\author[2]{Pierre Dupont}%
\author[2]{Richard Bischof}%
\author[2]{Cyril Milleret}%
\author[3]{Olivier Friard}%
\author[1]{Matt Geary}%
\author[4]{Elisa Avanzinelli}%
\author[5]{Achaz von Hardenberg}%
\author[3]{Francesca Marucco}%
\affil[1]{University of Chester}%
\affil[2]{Norwegian University of Life Sciences}%
\affil[3]{University of Turin}%
\affil[4]{Maritime Alps Natural Park}%
\affil[5]{University of Pavia}%


\vspace{-1em}



  
  \date{May 28, 2024}


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





\selectlanguage{english}
\begin{abstract}
The ongoing expansion of wolf (Canis lupus) populations has led to a
growing demand for up-to-date abundance estimates. Non-invasive genetic
sampling (NGS) is now widely used to monitor wolves, as it allows
individual identification and abundance estimation without physically
capturing individuals. However, NGS is resource-intensive, partly
because of the wolf elusive behaviour and wide distribution, but also
because of the cost of DNA analyses. Optimization of sampling strategies
is therefore a requirement for the long-term sustainability of wolf
monitoring programs. Using data from the 2020-2021 Italian Alpine wolf
monitoring, we investigate how (i) reducing the number of samples
genotyped, (ii) reducing the number of transects, and (iii) reducing the
number of repetitions of each search transect, impacted spatial
capture-recapture population size estimates. Our study revealed that a
25\% reduction in the number of transects or, alternatively, a 50\%
reduction in the maximum number of repetitions yielded abundance
estimates comparable to those obtained using the entire dataset. These
modifications would result in a 2,046 km reduction in total transect
length and 19,628 km reduction in total distance searched. Further
reducing the number of transects resulted in up to 15\% lower and up to
17\% less precise abundance estimates. Reducing only the number of
genotyped samples led to higher (5\%) and less precise (20\%) abundance
estimates. Randomly subsampling genotyped samples reduced the number of
detections per individual, whereas subsampling search transects resulted
in a less pronounced decrease in both the total number of detections and
individuals detected. Our work shows how it is possible to optimise wolf
monitoring by reducing search effort while maintaining the quality of
abundance estimates, by adopting a modelling framework that uses a first
survey dataset. We further provide general guidelines on how to optimise
sampling effort when using spatial capture-recapture in large-scale
monitoring programmes.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`MS_EffortOptimization_AlpineWolf.docx`  available at \url{https://authorea.com/users/761364/articles/737444-when-enough-is-enough-optimising-monitoring-effort-for-large-scale-wolf-population-size-estimation-in-the-italian-alps}

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

