Analysis of photo‐id data allowing for missed matches and individuals identified from opposite sides. Issue 3 (13th December 2012)

• Record Type: Journal Article
• Authors: Hiby, Lex; Paterson, William D.; Redman, Paula; Watkins, John; Twiss, Sean D.; Pomeroy, Patrick; Freckleton, Robert
• Is Part Of: Methods in ecology and evolution. Volume 4:Issue 3(2013:Mar.)
• Page Start: 252
• Page End: 259
• Publication Date: 2012-12-13
• Subjects: Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210x.12008
• Languages: English
• ISSNs: 2041-210X

Abstract

<abstract abstract-type="main" id="mee312008-abs-0001"> <title>Summary</title> <p> <list id="mee312008-list-0001" list-type="order"> <list-item> <p>In many species, photo‐identification could be used as an alternative to artificial marking to provide data on demographic parameters. However, unless the population is very small or fragmented, software may be required to pre‐screen and reject most image pairs as potential matches.</p> </list-item> <list-item> <p>Depending on the species and method used to obtain images, currently available software may falsely reject some matches. We estimate the false rejection rate (FRR) of the ExtractCompare (EC) program when used to pre‐screen images of female grey seals. Filtering images manually to reduce the FRR involves subjective assessment of image quality, reduces the amount of data available and may bias the results in favour of relatively well‐marked individuals.</p> </list-item> <list-item> <p>The data may contain individuals identified only from the left side or the right side, as well as individuals identified from both sides.</p> </list-item> <list-item> <p>Missed matches resulting from false rejections by pre‐screening software and/or inclusion of individuals identified only from opposite sides cause some individuals to generate multiple encounter histories.</p> </list-item> <list-item> <p>We describe an open population model for data of this type which, given a measured risk of missing a match between a randomly selected pair of images of the same individual, provides maximum likelihood (ML) estimates of initial population size, survival/emigration and immigration/recruitment by calculating the expected frequency of any encounter history that could be generated.</p> </list-item> <list-item> <p>As a case study for the method, we used EC to pre‐screen photographs of female grey seals on a breeding colony and generate encounter histories over five successive seasons. Allowing for the measured FRR, we calculated ML estimates for comparison with estimates from previous studies.</p> </list-item> <list-item> <p>We also used the model with encounter histories simulated using the same FRR to give the same mixture of left side, right side and both sides histories and derived ML estimates for comparison with the values used to drive the simulation.</p> </list-item> <list-item> <p>With FRR set at up to 33%, the method gave estimates of the abundance and survival parameters used in the simulation model that were biased by at most 4·7% up and 3% down, respectively. The results of the grey seal case study were consistent with previous estimates of apparent survival and trends in abundance.</p> </list-item> </list> </p> </abstract>