Optimizing the automated recognition of individual animals to support population monitoring

Author:

de Lorm Tijmen A.1ORCID,Horswill Catharine234ORCID,Rabaiotti Daniella23ORCID,Ewers Robert M.1,Groom Rosemary J.25ORCID,Watermeyer Jessica5ORCID,Woodroffe Rosie23

Affiliation:

1. Department of Life Sciences Imperial College London Silwood Park UK

2. Institute of Zoology Zoological Society of London London UK

3. Division of Biosciences, Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research University College London London UK

4. Department of Zoology University of Cambridge Cambridge UK

5. African Wildlife Conservation Fund Chishakwe Ranch Zimbabwe

Abstract

AbstractReliable 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 populations highlights that population‐specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts.

Funder

Natural Environment Research Council

Research England

Publisher

Wiley

Subject

Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference54 articles.

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