1. Introduction

Reliable estimates of population size and demographic rates are central to monitoring the status of threatened species. However, obtaining individual-based demographic parameters requires long-term data, gathered through intensive monitoring that is often costly and difficult to conduct (Horswill, Humphreys & Robinson, 2018; Caughlan, 2001). Identification of individuals from photographic records could provide an inexpensive alternative, and open up the possibility of using camera traps and citizen scientists to expand the spatial coverage of monitoring (Seber, 1965; Marnewick et al., 2014; Wearn & Glover-Kapfer, 2019). This method can be used for species where individuals can be identified from individual markings, including many threatened species (Durant et al., 2014, Pierce & Norman, 2016).
Photographic records have already been used to estimate demographic parameters in several endangered species. For example, long term photographic data have been used to obtain survival and abundance estimates of tigers Panthera tigris and cheetahs Acinonyx jubatus (Karanth & Nichols, 2011; Kelly et al., 1998), and tourist images have been used to estimate population sizes of whale sharksRhincodon typus (Davies et al., 2013). In addition, photographs can provide data on individual movement, ranging behaviour, and social structure (Randić et al., 2012; Armstrong et al., 2019). Many species are photographed frequently as part of monitoring programs, and by members of the public, including tourists. Such image catalogues therefore represent a large, and potentially under-used, data resource that inform conservation action.
Nevertheless, visually identifying all individuals in large image databases is time-consuming. To partly automate this process, several software packages are available to match images based on an individual’s unique body markings (e.g. APHIS and WildID, Óscar et al. , 2015; Bolger et al. , 2012). These image-matching software packages assist the user by ranking potential image matches using a similarity score. The algorithms underpinning the software packages find these potential matches by comparing images on either a pixel-by-pixel or feature basis. Pixel-based algorithms, such as APHIS, have been successfully applied to numerous species, including horseshoe whip snakes Hemorrhois hippocrepis and Balearic lizardsPodiarcis lilfordi (Óscar et al., 2015; Rotger, 2019). However, they are susceptible to differences in camera angle, scale, and cropping (Matthé et al., 2017), and are therefore unsuitable for animals that cannot be caught and photographed using a standardised methodology. By contrast, feature-based software packages, such as Wild-ID (Bolger, 2012), I3S-Pattern (Reijns, 2014) and Hotspotter (Crall et al., 2013), match images based on unique features including spots, stripes, blotches, or other marks. The algorithms that feature-based packages use vary, but all have a higher tolerance to differences in camera angle, scale, and lighting conditions than pixel-based algorithms. However, researchers are still required to select images that are suitable for identification, in that the distinctive marks must face the camera and must be clearly visible. Furthermore, when these suitable images are selected, the user has to crop the region of interest from the image, which is laborious, potentially preventing the application to large image catalogues (Miguel et al., 2019).
The feature-based packages have been tested on a range of taxa (Table 1), and the reported proportion of true matches that the software detects, i.e., accuracy rate, varies markedly, ranging between 36% and 100%. This variation can be attributed to differences in species markings, image quality, size of database, how many potential matches were inspected per image, and the image-matching software used (Nipko, Holcombe & Kelly, 2020; Matthé et al., 2017; Crall et al., 2013). Studies directly comparing the accuracy of different feature-based packages are considerably more limited, even though the most accurate software differs between species. For example, studies on jaguarsPanthera onca, ocelots Leopardus pardalis , and Saimaa ringed seals Phoca hispida saimensis found that Hotspotter outperformed Wild-ID (Nipko et al. 2020, Chehrsimin et al. 2018), whilst studies on amphibian species found that Wild-ID outperformed I3S-Pattern (Nipko et al ., 2020, Matthéet al. , 2017), and Hospotter (Morrison et al ., 2016). The only study that directly compared all three software packages found that Hotspotter was superior to I3S-pattern and Wild-ID for identifying individual green toads Bufotes viridis (Burgstaller, Gollmann & Landler, 2021). To date, studies comparing image-matching accuracy across all three software packages for a mammal species are lacking.
African wild dogs Lycaon pictus (hereafter ‘wild dogs’) have unique coat markings, which vary between individuals (Figure 1, Maddock & Mills, 1994). Wild dogs are classified as globally endangered, and a lack of cost-effective large-scale monitoring has been highlighted as a major limitation in developing effective conservation strategies (Woodroffe & Sillero-Zubiri, 2020). Consequently, there is a pressing need to devise new approaches for monitoring wild dogs. Demographic processes of African wild dogs are typically studied by monitoring a subset of individuals fitted with tracking collars (Woodroffe et al. , 2019; Rabaiotti et al 2021; Jenkins et al. , 2015). Such collar-based monitoring is labour-intensive and expensive, so upscaling is difficult. However, many wild dog packs have already been systematically photographed as part of monitoring programs, and many are also regularly photographed by tourists. Therefore, photographic identification of wild dogs potentially offers a non-invasive, cheaper approach for monitoring, and could reduce uncertainties in demographic rates and expand the spatial representation of monitoring (Maddock & Mills, 1994; Marnewick et al., 2014).
Wild dog coat patterns contain tan, white, and black patches that can vary considerably between populations. For example, wild dogs in eastern African populations tend to have coats consisting of predominantly black fur, whilst those in southern African populations have relatively more white and tan blotches (Figure 1, McIntosh, Woodroffe & Rabaiotti, 2016). In amphibian species with contrasting colour patterns of similar blotches, Wild-ID, Hotspotter, and I3S-Pattern have been shown to effectively match images of the same individual, reaching accuracy rates of up to 97% (Matthé et al., 2017; Burgstaller, Gollmann & Landler, 2021). Therefore, it is likely that feature-based image matching algorithms will effectively identify individual wild dogs from image catalogues. However, variation in the degree of contrast in the colour patterns amongst populations could affect the image-matching accuracy, and the best performing software package could therefore also vary between populations.
In this study, we develop a method to automatically isolate and crop images from catalogues that are suitable for automated image-matching. We then use these images to compare the efficacy of three feature-based software packages with different underlying image-matching algorithms (I3S-Pattern, Hotspotter, and Wild-ID; Reijns, 2014; Bolger, 2012; Crall et al. , 2013). Finally, we compare whether there is a difference in the accuracy of each software package between two populations with differing coat patterns.

2. Methods