BibTex format

author = {de, Lorm TA and Horswill, C and Rabaiotti, D and Ewers, RM and Groom, RJ and Watermeyer, J and Woodroffe, R},
doi = {10.1002/ece3.10260},
journal = {Ecology and Evolution},
title = {Optimizing the automated recognition of individual animals to support population monitoring},
url = {},
volume = {13},
year = {2023}

RIS format (EndNote, RefMan)

AB - Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, noninvasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogs is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S-Pattern, and WildID. As a case study, we consider the African wild dog, Lycaon pictus, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). Our automated image preprocessing has immediate application for expanding monitoring based on image matching. However, the difference in accuracy between population
AU - de,Lorm TA
AU - Horswill,C
AU - Rabaiotti,D
AU - Ewers,RM
AU - Groom,RJ
AU - Watermeyer,J
AU - Woodroffe,R
DO - 10.1002/ece3.10260
PY - 2023///
SN - 2045-7758
TI - Optimizing the automated recognition of individual animals to support population monitoring
T2 - Ecology and Evolution
UR -
UR -
UR -
UR -
VL - 13
ER -