APHIS: A new software for photo-matching in ecological studies

Highlights

• We present a new computer-aided procedure for photo matching in ecological studies.

• User can select between two approaches to suit the characteristic of the study.

• The software reduced the pre-processing time and allows the automatic matching image batches.

• We illustrate model performance using a real data set on a lizard and an amphibian species.

• The software is freely available and open source.

Abstract

Unique body characteristics are increasingly used for individual recognition to avoid the effort and the potential negative effects of capture–mark–recapture technique. As a consequence there is a growing demand for computer procedures to assist users in photo-recognition of an individual. We present a new software for photo-matching developed to minimize the pre-processing time and maximize the speed of the matching procedure. In APHIS photos can be processed in batches of hundreds and users can select between two alternative matching procedures, one interactive, built as an extension of existing and freely available software, and one automatic. We assessed its performance in terms of individual recognition and time efficiency and illustrate its use with real capture-photo–recapture studies on a reptile and an amphibian species, the Balearic Lizard Podiarcis lilfordi and the Northern spectacled salamander Salamandrina perspicillata, with contrasting skin patterns.

Introduction

Detailed data on individual life-history are used in ecological and evolutionary studies for the estimate of demographic parameters such as population size, survival and fertility of wildlife populations (e.g. Fernández-Chacón et al., 2011, Lebreton and North, 1993, Tavecchia et al., 2001, Tavecchia et al., 2005, Williams et al., 2001). A common solution for the individual recognition of the animals is to apply a mark to the animal body in the form of a tag or a ring with a unique alphanumeric code. However, rings, tags, flipper bands or other marks can alter individual fates and behavior (Gauthier-Clerc et al., 2004, McCarthy and Parris, 2004). In addition to ethical issues (e.g. May, 2004), these negative effects lead to bias the estimates of the parameters of interest. As a consequence there is an increasing interest in using non-invasive methods for individual recognition, such as unique natural marks or body characteristics. These methods have been applied with success in a wide range of taxa, in mammals (Karanth and Nichols, 1998, Langtimm et al., 2004, Martínez-Jauregui et al., 2012), amphibians (Gamble et al., 2008), reptiles (Sacchi et al., 2010), fishes (Speed et al., 2007, Van Tienhoven et al., 2007) or cephalopods (Huffard et al., 2008). However, with few exceptions (i.e. Perera et al., 2001), the photo-identification is restricted to those species featuring distinct colors, spots or marks. Photo-identification procedures consist of comparing a sample picture of an unknown individual with a library of candidate images of previously photographed individuals. This search is, in many cases, conducted by experienced observers who compare patterns and scars between photographs with the naked eye and might be extremely time-consuming when library contains hundreds of images (e.g. Martínez-Jauregui et al., 2012, Verborgh et al., 2009). Naked-eye comparisons are typically assisted by a preliminary grouping of the images using a multi-character score, for example by grouping images with a given chromatic pattern (e.g. absence or presence of specific marks, Carafa and Biondi, 2004). Unaided procedures may also become prone to errors when image libraries expand. There is now a growing demand in developing automatic or computer-aided procedures for photo-matching (Gamble et al., 2008). A computer-aided photo-identification system identifies the most probable sample–candidate matches, reducing the number of images to be inspected. Most photo-identification software solutions concatenate three processing steps. The first is a preprocessing step where a region of interest is selected and the image rotated, scaled or spatially corrected if required by comparison algorithms; the second is usually an automated comparison between the sample and the library of images, which arranges candidates by matching probability or likelihood values; a final step is a visual comparison of sample–candidate pairs for a limited number of plausible matches.

We present a new software solution, APHIS (Automated PHoto-Identification Suite), specially designed to deal with sample sets of over a hundred photographs per field campaign and image libraries containing more than a thousand samples. APHIS proposes two approaches for photo-matching, the Spot Pattern Matching (SPM) and the Image Template Matching (ITM). The former has been built on the already existing I³S algorithm (Van Tienhoven et al., 2007) while the latter is a novel approach based on pixel matching that minimizes the user's preprocessing effort. ITM is a fast-running alternative to study species with apparent or easily recognizable spots or colored parts of the skin. The workflow and graphic interface of APHIS have been designed to reduce the time invested by the researcher in analytical tasks and to enhance user experience. We describe below the general features of the APHIS interface and illustrate the SPM and ITM procedures using real data from two capture-photo–recapture studies on the Balearic Lizard, Podarcis lilfordi, and on the Northern spectacled salamander, Salamandrina perspicillata (Fig. 1).