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\begin{document}

\title{Time-lapse imagery is cheap and timely in the fight against colonial
species' decline}



\author[1]{Tom Hart}%
\author[1]{Fiona Jones}%
\author[2]{Caitlin Black}%
\author[1]{Chris Lintott}%
\author[3]{Casey Youngflesh}%
\author[4]{Heather Lynch}%
\author[5]{Alasdair Davies}%
\author[6]{Eamonn Maguire}%
\author[1]{Andrew Zisserman}%
\author[1]{Carlos Arteta}%
\author[7]{Peter Barham}%
\author[8]{Colin Southwell}%
\author[8]{Louise Emmerson}%
\author[9]{Mark Jessopp}%
\affil[1]{University of Oxford}%
\affil[2]{University of Cambridge}%
\affil[3]{University of Connecticut}%
\affil[4]{Stony Brook University}%
\affil[5]{Institute of Zoology}%
\affil[6]{7.	Pictet Asset Management, Route des Acacias 60, 1211 Geneva 73.}%
\affil[7]{University of Bristol}%
\affil[8]{Australian Antarctic Division}%
\affil[9]{University College Cork}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Many of the species in decline around the world are subject to different
environmental stressors across their range, so replicated large-scale
monitoring programmes, are necessary to disentangle the relative impacts
of these threats. At the same time as funding for long-term monitoring
is being cut, studies are increasingly being criticised for lacking
statistical power. For those taxa or environments where a single vantage
point can observe individuals or ecological processes, time-lapse
cameras can provide a cost-effective way of collecting time series data
replicated at large spatial scales that would otherwise be impossible.
However, networks of time-lapse cameras needed to cover the range of
species or processes create a problem in that the scale of data
collection easily exceeds our ability to process the raw imagery
manually. Citizen science and machine learning provide solutions to
scaling up data extraction (such as locating all animals in an image).
Crucially, citizen science, machine learning-derived classifiers, and
the intersection between them, are key to understanding how to establish
monitoring systems that are sensitive to -- and sufficiently powerful to
detect --changes in the study system. Citizen science works relatively
`out of the box', and we regard it as a first step for many systems
until machine learning algorithms are sufficiently trained to automate
the process. Using Penguin Watch (www.penguinwatch.org) data as a case
study, we discuss a complete workflow from images to parameter
estimation and interpretation: the use of citizen science and computer
vision for image processing, and parameter estimation and individual
recognition for investigating biological questions. We discuss which
techniques are easily generalizable to a range of questions, and where
more work is needed to supplement `out of the box' tools. We conclude
with a horizon scan of the advances in camera technology, such as
on-board computer vision and decision making.%
\end{abstract}%



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