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\begin{document}

\title{Optimising the automated recognition of individual animals to support
population monitoring}



\author[1]{Tijmen de Lorm}%
\author[2]{Catharine Horswill}%
\author[2]{Daniella Rabaiotti}%
\author[1]{Robert Ewers}%
\author[2]{Rosemary Groom}%
\author[3]{Jessica Watermeyer}%
\author[2]{Rosie Woodroffe}%
\affil[1]{Imperial College London - Silwood Park Campus}%
\affil[2]{Zoological Society of London Institute of Zoology}%
\affil[3]{African Wildlife Conservation Fund}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
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, non-invasive 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 catalogues 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 intra-specific
variation in the performance of software packages, we compare
identification accuracy between two populations (in Kenya and Zimbabwe)
that have markedly different coat colouration patterns. The process of
selecting suitable images was automated using Convolutional Neural Nets
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
pre-processing 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.%
\end{abstract}%



\sloppy


\textbf{Optimising the automated recognition of individual animals to
support population monitoring}

\textbf{T.A. de Lorm\textsuperscript{1}, C.
Horswill\textsuperscript{2,3,4}, D. Rabaiotti\textsuperscript{2,3}, R.M.
Ewers\textsuperscript{1}, R. J. Groom\textsuperscript{2,5}, J.
Watermeyer\textsuperscript{5}, R. Woodroffe\textsuperscript{2,3}}

\textbf{Correspondence author: T.A. de Lorm, tad20@ic.ac.uk}

\subsection*{}

{\label{section}}

\subsection*{Affiliations:}

{\label{affiliations}}

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, Downing St, Cambridge
CB2 3EJ, UK

5: African Wildlife Conservation Fund, Chishakwe Ranch, Zimbabwe

Running head: Automating African wild dog recognition

\subsection*{Abstract}

{\label{abstract}}

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, non-invasive 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 catalogues 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,
I\textsuperscript{3}S-Pattern, and WildID. As a case study, we consider
the African wild dog \emph{Lycaon pictus} , a species whose conservation
is limited by a lack of cost-effective large-scale monitoring. To
evaluate intra-specific variation in the performance of software
packages, we compare identification accuracy between two populations (in
Kenya and Zimbabwe) that have markedly different coat colouration
patterns.

The process of selecting suitable images was automated using
Convolutional Neural Nets 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 pre-processing 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.

\textbf{Key words:} automated individual recognition, Hotspotter,
I\textsuperscript{3}S-pattern, \emph{Lycaon pictus,} photographic
identification, Wild-ID

\subsection*{1. Introduction}

{\label{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 \emph{Panthera tigris} and cheetahs \emph{Acinonyx
jubatus} (Karanth \& Nichols, 2011; Kelly et al., 1998), and tourist
images have been used to estimate population sizes of whale
sharks\emph{Rhincodon typus} (Davies et al., 2013). In addition,
photographs can provide data on individual movement, ranging behaviour,
and social structure (Randi\selectlanguage{polish}ć \selectlanguage{english}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, \selectlanguage{ngerman}Óscar \emph{et al.} , 2015;
Bolger \emph{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 \emph{Hemorrhois hippocrepis} and Balearic lizards\emph{Podiarcis
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),
I\textsuperscript{3}S-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
jaguars\emph{Panthera onca,} ocelots \emph{Leopardus pardalis} , and
Saimaa ringed seals \emph{Phoca hispida saimensis} found that Hotspotter
outperformed Wild-ID (Nipko et al. 2020, Chehrsimin \emph{et al.} 2018),
whilst studies on amphibian species found that Wild-ID outperformed
I\textsuperscript{3}S-Pattern (Nipko \emph{et al} ., 2020,
Matthé\emph{et al.} , 2017), and Hospotter (Morrison \emph{et al} .,
2016). The only study that directly compared all three software packages
found that Hotspotter was superior to I\textsuperscript{3}S-pattern and
Wild-ID for identifying individual green toads \emph{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 \emph{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 \emph{et
al.} , 2019; Rabaiotti et al 2021; Jenkins \emph{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 I\textsuperscript{3}S-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
(I\textsuperscript{3}S-Pattern, Hotspotter, and Wild-ID; Reijns, 2014;
Bolger, 2012; Crall \emph{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.

\subsection*{2. Methods}

{\label{methods}}

\textbf{2.1 \emph{Image datasets}}

To examine whether the performance of feature-based image matching
software packages varies for different populations of African wild dogs,
we considered image catalogues from two wild dog populations, one that
spans Laikipia, Samburu, and Isiolo Counties in Kenya (37°2\selectlanguage{english}'E, 0deg6\selectlanguage{english}'N)
and another from the Save Valley Conservancy in Zimbabwe (32deg4'E,
20deg3'S). The Kenyan dataset contained images taken between 2004 and 2017
(n = 9139), and the Zimbabwean dataset contained images taken between
2010 and 2013 (n = 2066). In Kenya, these images were taken with ten
different cameras (Olympus(c) C765UZ, Canon(c) PowerShot A720IS, EOS Digital
Rebel XT, 10D, 40D, and 60D, Fujifilm(c) FinePix S5500, Kodak(c) Easyshare
Z1015IS, Nikon(c) D70s, and Nikon(c) Coolpix90). In Zimbabwe, they were
taken with five different cameras (Canon(c) EOS 450D, 20D, and Digital
Rebel XT, and Panasonic(c) DMC-FZ20, and Zoran(c) Coach). Both datasets were
collected as part of long-term monitoring programmes, and contained
images of both single wild dogs and groups of wild dogs, ranging in
their posture from lying down to walking.

\textbf{2.2 \emph{Pre-processing steps}}

To automate the selection of suitable images for image-matching we
developed a five-step image pre-processing method (Figure 2).

2.2.1 Detecting and cropping individuals from images

The aim of the first step in the image pre-processing method was to
automatically detect and crop wild dog individuals from the images. To
do this, we used the Microsoft AI for Earth MegaDetector (hereafter
`MegaDetector', Beery, Morris \& Yang, 2019) that automatically detects
and crops animals in images. We assessed the efficacy of this method by
visually recording the presence of wild dogs in a subset of 1060 images
from the Kenyan dataset and 246 images from the Zimbabwean dataset, and
comparing the results to the cropped images (hereafter `crops') produced
by the MegaDetector for the same subset of images. In this way, we
obtained the MegaDetector's number of true positives (wild dogs that
were successfully detected), false positives (detections which did not
contain a wild dog), and false negatives (wild dogs which were found by
visual inspection, but not by the MegaDetector). All images contained
wild dogs, so there were no true negatives in the dataset.

2.2.2 Aspect-ratio filtering

The aim of the second step in the image pre-processing method was to
filter out images that were unsuitable for identification due to the
individual's body rotation in the image. We considered crops suitable
for image-matching if approximately \selectlanguage{english}[?]80\% of the individual's flank was
visible, and the angle between the image axis and animal's flank was
less than approximately 30deg, i.e., the flank was facing the camera.
Crops where the angle between the image axis and the animal's flank was
more than 30deg were expected to be narrower than crops suitable for
image-matching and therefore demonstrate a relatively low aspect-ratio.
By contrast, crops where the flank was concealed because the individual
was lying down, or obscured by vegetation, were expected to be
considerably wider and demonstrate a relatively high aspect-ratio. These
criteria were visually assessed for the crops that the MegaDetector
produced in the previous step. We then calculated the range of
aspect-ratios for suitable crops, i.e., where an unobscured flank was
facing the camera, using the ``jpeg'' package (Urbanek, 2021) in Program
R (version 4.0.4, R Core Team, 2020). Images with an aspect-ratio
outside of this range were removed from the dataset.

2.2.3 Selecting standing individuals

Not all sitting or lying individuals could be filtered out solely using
image aspect-ratios. Therefore, the aim of the third step in the image
pre-processing method was to filter out the remaining crops that were
unsuitable for identification because the individual's body position,
i.e. sitting or lying, obscured the full coat pattern. To do this, we
trained a Convolutional Neural Net (CNN) to classify crops as either a
standing wild dog or a sitting wild dog. To obtain data to train this
image classifier, we used the full image catalogues from both sites (n =
11205). The crops produced by steps 1 and 2 of the pre-processing (n =
21745) were then manually classified as either containing a standing
wild dog (n = 13500) or sitting wild dog (n = 6512). We removed all
crops depicting anything other than wild dogs (e.g., birds, rocks or
logs), or wild dogs where it could not be confirmed whether they were
standing or sitting, because only a small part of the animal was visible
(n = 1733). We trained a CNN using the remaining 20012 pre-processed
crops, to classify these as containing either a standing wild dog or
not. The CNN was made using Tensorflow (Abadi et al., 2016) in Python
(Version 3.6.10). The model was trained with 16012 crops, validated with
2000 crops, and tested with 2000 crops.

CNN's consist of convolutional layers (Albawi, Mohammed \& Al-Zawi,
2017): filter layers which digitally `slide' over the image and aim to
recognise specific features. The convolutional layers pass a map of
specific features to the next layer, a Max Pooling layer. The Max
Pooling layer reduces the resolution of this feature map, thus reducing
the importance of the position of features within this map. This step
can help prevent the model from becoming too fine-tuned to the training
data, which causes over-fitting and reduces the generalisability of the
classifier. After this, a dropout layer is applied, which randomly
removes 50\% of connections made between layers. This benefits the model
by teaching it to recognise robust features, again preventing
over-fitting. The data are then passed on to a flattening layer, which
turns the data into a 1-dimensional string, which is passed onto the
final two layers. Firstly, the string goes through a layer which
connects all the data from the previous layer and produces prediction
scores from the inputs. Secondly, another layer turns these scores into
a single prediction: standing, or not standing (for a more detailed
description of CNNs, see O'Shea \& Nash, 2015 and Albawi, Mohammed \&
Al-Zawi, 2017).

The number of convolutional layers and the size of the filters that they
comprise was optimised using KerasTuner (O'Malley et al., 2019).
KerasTuner runs CNNs with a range of values, and automatically selects
the model with the highest validation accuracy, i.e., the proportion of
correct classifications on the validation database. KerasTuner ran CNNs
with between one and three convolutional layers, with 16, 32 and 64
filters per layer, and with a kernel size (the number of pixels in the
filters) of 3x3 pixels. This was done for 20 different random
combinations for the number of convolutional layers and number of
filters per layer. Test runs showed that the maximum accuracy was
reached before the 70\textsuperscript{th} epoch, and therefore each
combination was run for 70 epochs, meaning that the training data were
passed through the CNN 70 times. The learning rate of the model, that
is, the speed at which the model improved itself, was also optimised
with KerasTuner, testing a rate of 10\textsuperscript{-3},
10\textsuperscript{-4}, and 10\textsuperscript{-5}, with the optimal
number of convolutional layers. The model with the highest test-accuracy
was selected as the final model.

2.2.4 Separating left and right flanks

The aim of the fourth step in the image pre-processing method was to
separate crops depicting left- and right-flanks of a wild dog, because
image-matching software packages can only match images for one side of
the animal. To do this, we made another CNN to automate the separation
of left- and right-flanks. To obtain training data for this CNN, we
visually classified all crops of standing dogs used for the CNN in step
three (n = 12357) whose side was facing the camera, as showing the right
(n = 6140) or left flank (n = 6217). We optimised this CNN's parameters
as described in step three of the image pre-processing method, using
KerasTuner to find the optimal number of convolutional layers and
learning rate. Each CNN ran for 100 epochs, because test runs showed
that this model took longer than the previous model to reach its maximum
accuracy. The first layer of this CNN was an average pooling layer, a
layer which reduced the resolution of the input images by a factor of
four, which prevents overfitting. This layer was added to this CNN,
because preliminary runs showed this CNN was more prone to overfitting
than the CNN developed in step three of the image pre-processing method.
We used 9857 crops as training data, 1246 as validation data, and 1246
as testing data. All other layers were equal to the previous CNN. For
the full model conditions, see Table S1 in Supporting Information.

2.2.5 Image background removal

Lastly, we removed the image backgrounds of suitable images using the
``rembg'' package in Python (Gatis, 2020). We removed image backgrounds
to remove the risk of the background confounding image-matching results,
while eliminating the need to manually select an individual's flank.

2.3 Image-matching software packages

We compared the performance of three feature-based image-matching
software packages that differ in the underlying algorithms used to match
individuals: I\textsuperscript{3}S-Pattern (Reijns, 2014), WildID
(Bolger et al., 2012) and Hotspotter (Crall et al., 2013). All three
assist the user by listing potential matches for each image, ranked by a
similarity score. The user then confirms which of these potential
matches are true matches.

2.3.1 I\textsuperscript{3}S-Pattern

I\textsuperscript{3}S-Pattern uses the Speeded-up Robust Features (SURF)
algorithm (Reijns, 2014; Bay et al., 2008) that selects key points and
compares each image-pair in a dataset based on the size and position of
these key points. The software requires the user to select three
reference points per image, as well as the outline of the animal. As
reference points, we used the base of the tail, the withers (i.e., the
ridge between the shoulder blades), and the base of the neck (Figure
S1).

2.3.2 Wild-ID

Wild-ID uses the Scale Invariant Feature Transform (SIFT) algorithm
(Bolger et al., 2012; Lowe, 2004). It requires the user to input crops
of the region of interest: the part of the animal which bears unique
marks. The SIFT algorithm detects salient features regardless of their
scale and viewpoint. For each image pair in a database, these features
are compared, both in how the features look, and how the different
features are positioned relative to each other. Based on these two
characteristics, a goodness-of-fit score is computed per image pair.

2.3.3 Hotspotter

Hotspotter also uses the SIFT algorithm to conduct pairwise comparisons
(Lowe, 2004; Crall et al., 2013). Users enter either entire pictures of
individuals and select the rectangular region of interest, or image
crops containing the region of interest. Hotspotter supplements the
pairwise comparisons used by Wild-ID with a one-vs-many approach that
uses a Local Naive Bayes Nearest Neighbour method (McCann \& Lowe, 2012)
to take all of the images in the database into account when computing
similarity scores.

2.4 Performance of the image-matching software

To test which image-matching software most accurately matched crops of
the same individual, we created two separate datasets for the Kenyan and
Zimbabwean populations. To select suitable crops, we used the four-step
image pre-processing method described above. We also visually inspected
discarded crops to avoid missing suitable crops. We then manually
identified individuals from the dataset of right flank crops, to provide
a standard against which automated identifications could be compared,
and randomly selected two crops per individual. To prevent similar
lighting conditions and posture from creating a bias towards matching
images of the same individual, we ensured selected images were taken on
different days. The two generated datasets consisted of 104 individuals
from the Kenyan population, and 48 individuals from the Zimbabwean
population. To increase the dataset for the Zimbabwean population, we
also included left-flank crops for 41 individuals and horizontally
mirrored the crops to enable comparison with the right-flank crops. This
increased the total number of unique flanks from the Zimbabwean
population to 89. The coat pattern of wild dogs differs between right
and left flanks, and we have no reason to expect that including
left-flank crops would bias our results.

We analysed the Kenyan dataset with each of the three image-matching
software packages: Hotspotter, WildID, and
I\textsuperscript{3}S-Pattern. We then analysed the Zimbabwean dataset
with Hotspotter and WildID. I\textsuperscript{3}S-Pattern was not tested
with the Zimbabwean dataset because tests with the Kenyan dataset
identified it to be considerably less accurate than the other software
packages and considerably more time-consuming to input images and assign
reference points.

We also examined whether image background removal increased the accuracy
of WildID and Hotspotter. I\textsuperscript{3}S-Pattern requires users
to manually select the outline of the animal in the program and
therefore was not included in this analysis, because it does not take
the background into account in its default use. We compared the
image-matching results obtained using images from which we manually
cropped just the individuals' flanks, with those based on crops of
complete individuals from which the background was automatically removed
(see Figure S2). For three of the 178 images from the Zimbabwe site, the
algorithm did not crop out the wild dog, instead cropping out vegetation
in the foreground. For these images, a manually cropped flank of the
wild dog was used.

To compare the image-matching performance of each software package, we
examined the 10 crops identified as most similar to the sample
individual. We used the first 10 ranked images, as the best performing
software's accuracy started levelling off around this rank, indicating
that inspecting the first 10 image matches could maximise recognition
rates, while minimising the time spent visually inspecting and
confirming potential matches. We used a mixed effects logistic
regression to test for differences in the efficacy of the software
packages. Here, the response variable was a binary variable describing
whether or not an individual was successfully matched in the first 10
ranked images, and software package was the explanatory variable.
Individual identity was included as a random effect to avoid
pseudoreplication. Post-hoc pairwise comparisons were carried out using
Tukey contrasts. This analysis was performed separately for the
Zimbabwean and Kenyan datasets. Models were run using the ``lme4'' (v.
1.1-27.1, Bates et al., 2015) package in Program R (R Core Team, 2020,
version 4.0.4).

Previous studies have shown that the image-matching performance of
different software packages is affected by database size (Matthe et al.,
2017). Therefore, to compare software performance on wild dogs from
Kenyan and Zimbabwean populations, we randomly selected a subset of the
Kenyan individuals to equal the number of identified individuals in the
Zimbabwean dataset (n = 89). We then used the best performing software
package identified in the previous step of the analysis to rerun the
image-matching analysis for both datasets. Differences in software
performance between the two populations were then assessed using a mixed
effects logistic regression with a binomial link function. The response
variable in the model was whether or not a match was detected in the
first 10 ranked images, and study site (Kenya or Zimbabwe) was the
explanatory variable. To correct for possible differences in image
quality, two proxies for image quality were included in the model.
Firstly, we included image size (total number of pixels of the crop) as
a continuous predictor. Secondly, all images were visually scored on a
scale of 1 to 3, based on how well their distinct marks could be
recognised. This approach followed Nipko, Holcombe \& Kelly (2020),
where score 1 was given to images that were out of focus, of a moving
animal, or badly lit, score 2 was given to images of intermediate
quality, and score 3 was given to images where all features were clearly
visible (for examples, see Figure S3). Score was included as a fixed
effect, and individual identity was included as a random effect.
Furthermore, a Wilcoxon Rank Sum test was performed to test for
differences between the quality score of crops from Kenya and Zimbabwe.
The model was fit using the ``lme4'' package (v 1.1-27.1, Bates et al.,
2015) in R (version 4.0.4, R Core Team, 2020).

\subsection*{3. Results}

{\label{results}}

3.1 Automatic detection and aspect-ratio filtering

The Microsoft AI for Earth MegaDetector, which is designed to
automatically detect and crop animals from images, produced 2652 crops
from the test dataset of 1306 images, meaning that on average,
approximately two detections were made per image. Of these, 2523 crops
contained a wild dog (true positive rate = 0.951), 129 were false
detections, such as rocks or vegetation (false positive rate = 0.049),
while 531 wild dogs were not successfully detected (false negative rate
= 0.174). However, only five of these false negatives were images
suitable for image-matching. By contrast, the flank of the wild dog was
not visible in the other 526 false negatives. In total, 722 crops were
suitable for identification using image matching software, of which five
were not detected by the automated processing (false negative rate =
0.007). For the 2652 crops that were produced by the MegaDetector, all
crops considered suitable for identification on visual inspection had an
aspect-ratio between 0.65 and 2.25. Applying the MegaDetector to the
entire image dataset (n = 11205), as opposed to the test dataset,
resulted in 21745 crops. Of these, 5788 (21\%) fell outside the suitable
range of aspect ratios and were therefore removed from the dataset.

3.2 Using Convolutional Neural Nets to filter out unsuitable images

The optimal conditions for the Convolutional Neural Net (CNN) trained to
recognise wild dogs standing up were two convolutional layers, with
kernel sizes of 32 and 64, respectively, and a learning rate of
10\textsuperscript{-5}. This model achieved a training accuracy of
100\%, a validation accuracy of 91\% (95\% C.I. 90 -- 92), and a testing
accuracy of 90\% (95\% C.I. 88 -- 91, Table 2). For the CNN designed to
separate images of the left and right flanks, the optimal conditions
were three convolutional layers, one with a kernel size of 64 and two
with kernel sizes of 32, with a learning rate of 10\textsuperscript{-4}.
Its training, validation and testing accuracy were 100\%, 96\% (95\%
C.I. 95 - 97), and 95\% (95\% C.I. 94 - 96), respectively.

3.3 Performance of the image-matching software packages

For both the Kenyan and Zimbabwean datasets, Hotspotter achieved the
highest image-matching accuracy (Figure 3). For the Kenyan dataset,
using Hotspotter with crops of the full individual from which the
background was removed, was most effective. This method detected 62\% of
the matches in the 10 highest ranked crops (Figure 3B). This was
significantly higher than using manually cropped flanks of the
individual in both WildID (z = 5.0, p \textless{} 0.01) and Hotspotter
(z = 2.8, p = 0.046), as well as crops of the full individual in WildID
(z = 4.7, p \textless{} 0.01) and I\textsuperscript{3}S-Pattern (z =
5.0, p \textless{} 0.01).For the Zimbabwean dataset, Hotspotter detected
88\% of matches within the first 10 ranked images when the background
was removed from a crops of the full individual (Figure 3A). The
matching performance was significantly lower when crops of just the
flank were used (z = 2.7, p = 0.03). Hotspotter with background removal
performed significantly better than WildID with background removal, (z =
4.7, p \textless{} 0.01), as well as WildID with crops of the flanks (z
= 5.1, p \textless{} 0.01).

The probability of accurate image-matching occurring within the first 10
ranked images was significantly higher for wild dogs from Zimbabwe than
wild dogs from Kenya (OR = 9.64, 95\% CI 3.65 - 15.63, Figure 4). The
proportion of matched individuals identified in this analysis was not
significantly associated with image size
(X\textsuperscript{2}\textsubscript{1} = 0.16, p = 0.69) or image
quality (OR\textsubscript{Quality Score 2 / Quality Score 1} = 0.89,
95\% C.I. -2.26 -- 4.04, OR\textsubscript{Quality Score 3 / Quality
Score 1} = 1.82, 95\% C.I. -2.20 -- 5.83). In addition, the image
quality score did not differ between the populations (W = 15008, p =
0.33).

\subsection*{4. Discussion}

{\label{discussion}}

This study presents a novel framework for automating the individual
recognition of species with distinct marks. The framework includes an
automated pre-processing method for identifying images suitable for
image-matching, and then using image-matching software for individual
recognition. The automated pre-processing method consists of five steps
that (1) crop all images containing animals from a large database, (2)
filter out a portion of the unsuitable images based on image aspect
ratio, (3) use convolutional neural nets to select images of standing
individuals (accuracy of 90\%), (4) separate images into left and right
flanks (accuracy of 95\%), and (5) remove image backgrounds. As a case
study, we applied the described methods to an image catalogue of African
wild dogs and found that Hotspotter (Crall \emph{et al.,} 2013) was the
most efficient software package for matching images. Image-matching
performance was also significantly improved by using the full image of
an individual from which the background was removed, as opposed to just
the cropped flank. Finally, we found that image-matching performance
differed between populations of wild dogs with different coat coloration
patterns. This work showed that image-matching software could become a
powerful method for monitoring populations of African wild dogs.
However, caution is needed as detection rates are likely to vary between
-- and even within -- populations. This could affect the certainty of
derived population-specific demographic parameters, such that careful
consideration is needed to account for individual heterogeneity in
detection when large variation in coat colouration occurs within a
population.

The automated pre-processing method presented in this study could
eliminate the need to manually select suitable images for image-matching
and crop individuals from original photographs. This method thus enables
processing of large image catalogues where selection using visual
inspection would be extremely time consuming. We found that the method
does discard a small number of suitable images, and therefore in
situations where it is important to include all suitable images, the
pre-processing method outlined here could also be used as a pre-sorting
approach. The user could then visually review images that were
classified as not suitable, to prevent usable images from being
discarded.

The described method of pre-processing is particularly useful for wild
dogs, since an individuals' posture varies substantially between images.
Images taken by tourists provide an opportunity to bolster and spatially
extend image catalogues. However, these images are also likely to
contain many images unsuitable for identification, as they are not taken
for the purpose of identification. Accordingly, filtering unsuitable
images from these datasets using an automated approach could be
especially timesaving. The described pre-processing method is therefore
highly suitable to species targeted by wildlife watching excursions,
that have distinctive marks and where individual posture influences
image suitability, for example cheetahs, leopards \emph{Panthera pardus}
, and tigers.

Hotspotter outperformed I\textsuperscript{3}S-Pattern and Wild-ID at
matching images of individual wild dogs. This finding agrees with
studies on green toads that compared Hotspotter and
I\textsuperscript{3}S-pattern (Burgstaller, Gollmann \& Landler 2021),
as well as studies comparing Hotspotter and WildID (Nipko, Holcombe \&
Kelly, 2020; Burgstaller, Gollmann \& Landler, 2021; Chehrsimin et al.,
2018). Nevertheless, this result is not ubiquitous. Wild-ID was superior
to Hotspotter at matching images for a blotched amphibian species, the
Wyoming toad \emph{Anaxyrus baxteri} (Morrison et al., 2016). This
indicates that the identification performance of different software
packages is dependent on species, even when two species' patterns show
similarities. Consequently, we recommend that all three software
packages are tested on new species before deciding on which one to use.

Using crops of full individuals from which the background was removed
significantly increased the image-matching accuracy of Hotspotter,
compared to using crops of just individuals' flanks. This method also
speeds up image pre-processing by eliminating the need to manually crop
the region of interest. The improved accuracy is likely caused by two
factors. Firstly, removing the background prevents images being matched
based on similar backgrounds, as the flanks are not perfect rectangles,
meaning that crops of the flank also contain some background (see Figure
S2). Secondly, using complete individuals allows images to be matched
based on unique features on the legs, in addition to the flanks. This
result is in line with studies on Saimaa ringed seals \emph{Pusa
hispida} and Thornicroft's giraffes \emph{Giraffa camelopardalis
thornicrofti} , which found evidence that using a full individual from
which the background is removed, could result in a higher accuracy
(Chermin \emph{et al.,} 2018; Halloran, Murdoch \& Becker 2015).
However, neither of these previous studies statistically tested whether
background removal increased identification accuracy. Our study
therefore provides the first statistical evidence that background
removal can increase the performance of image-matching software. This
also indicates that the common usage of Hotspotter, in which a
rectangular region of interest is manually cropped (e.g. Dunbar \emph{et
al.} , 2021; Nipko, Holcombe \& Kelly, 2020), could be improved by
removing the image background.

Hotspotter was significantly better at matching images from Zimbabwean
wild dogs, compared to Kenyan individuals. The higher image-matching
accuracy found for the Zimbabwean population is likely to reflect the
regional difference in wild dog coat colouration patterns. The Kenyan
population has darker, more uniform coats, consisting of large black
patches, often with few white or tan areas (McIntosh, Woodroffe \&
Rabaiotti, 2016, Daniels, Woodroffe \& Rabaiotti, 2022). By contrast,
the proportion of tan fur is \textasciitilde{}1.5 times higher, and the
proportion of white fur is almost 7 times higher for the Zimbabwean
population (Figure 1, Daniels, Woodroffe \& Rabaiotti, 2022). Therefore,
the higher contrast within the patterns of the Zimbabwean wild dogs
could make it easier for the software to match images of these
individuals. The identified relationship between image-matching
performance and software package remained unaltered when image quality
and image size were included in analyses, and there was no significant
difference between the image quality scores between the Zimbabwean and
Kenyan populations. The image quality score approach was modelled after
Nipko, Holcombe \& Kelly (2020), who found that it significantly
affected the probability of matching ocelot and jaguar individuals. As a
result, we are confident that the differences in coat colouration
patterns between wild dogs from Zimbabwe and Kenya reflect variation in
identification performance between populations.

Inter-population variation in image-matching performance indicates that
detection probabilities derived from using this approach will not be
directly comparable between populations. Since the probability of
finding an accurate image-match depends on individual coat pattern, this
finding highlights that individual heterogeneity in detection may also
occur if large variation in coat colouration occurs within a population.
Capture-mark-recapture techniques assume individuals experience equal
detection probability across a population (White and Burnham, 2009).
Therefore, individual coat pattern may also need accounting for when
deriving survival estimates using such analysis. This also applies to
other species whose coat pattern varies regionally, such as Asian golden
cats \emph{Catopuma temminckii} and ocelots (Allen et al., 2011; Khan,
Ali \& Mohammed, 2017). Furthermore, the coat patterns of other wild dog
populations can differ considerably from the two populations included in
this study (McIntosh, Woodroffe \& Rabaiotti, 2016, Daniels, Woodroffe
\& Rabaiotti, 2022). Consequently, we advocate that estimating a
population-specific image-matching accuracy score becomes an essential
pre-requisite step for applying these techniques in different locations.

Automatically pre-processing wild dog image datasets and using
image-matching software facilitates the use of archived and citizen
science image catalogues where visually identifying all individuals
would be extremely time-consuming. Although the best performing
image-matching software did not detect all matches, it could be used to
identify a large proportion of the individuals in a dataset. Afterwards,
individuals that were not matched to any other images could be visually
identified, to prevent missing actual matches. Using image-matching
software in this way still saves time by rapidly identifying a large
portion of the matches, without compromising on accuracy. Furthermore,
it is plausible that the likelihood of correctly detecting matching
images increases if more than two images per individual are included,
for example if multiple viewpoints per individual are present in a
dataset, the probability of matching these is expected to increase
(Crall et al., 2013). Our accuracy values therefore represent a
conservative estimate of Hotspotter's true accuracy.

Our study indicates that image-matching could provide a valuable new
approach for monitoring wild dogs. A combination of citizen science and
image-matching has already been successfully employed to monitor other
species, such as Blanding's turtles \emph{Emydoidea blandingii} and
whale sharks (Araujo \emph{et al.} , 2017; Cross \emph{et al.} , 2021).
Similarly, previous studies have used tourist images to estimate the
population size of wild dogs in Kruger National Park, South Africa
(Marnewick et al., 2014). Combining citizen science, image-matching
software, and capture-recapture methods therefore has the potential to
improve understanding of wild dog demography. However, more research is
needed to investigate whether photographic data could improve our
understanding of wild dog demography beyond population size, by
estimating parameters such as pack structure, dispersal rates, and death
and birth rates. This can be achieved by applying image-matching
software to existing image datasets, to assess whether they generate
enough data to estimate key demographic parameters, or whether more
intensive monitoring - for example using long term camera trap surveys -
would be necessary.

In conclusion, we have developed a new automated method for
pre-processing image datasets, by automatically cropping animals from
images, removing images in which the individuals' posture hinders
identification, separating left and right flanks, and removing the image
background. This framework will enable large image datasets to be
analysed rapidly, thereby expanding monitoring efforts and expediting
conservation action. Furthermore, we have shown how well different
image-matching software packages perform on African wild dogs.
Hotspotter outperformed the other software packages, while its
performance differed between two populations which exhibit
intra-specific variation in their coat patterns. Our pre-processing
method, in combination with Hotspotter, has immediate application in
research and monitoring efforts for wild dogs and other species. Data
obtained in this way could provide cost-effective large-scale monitoring
for endangered species, therefore supporting the implementation of
effective conservation.

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applied to wildlife management. \emph{Herpetological Journal} . 29 (4),
304--307. doi:10.33256/hj29.4.304307.

Sannolo, M., Gatti, F., Mangiacotti, M., Scali, S. \& Sacchi, R. (2016)
Image-identification in amphibian studies: a test of I3S
Pattern.\emph{Acta Herpetologica} . 63-68 Pages.
doi:10.13128/ACTA\_HERPETOL-17198.

Seber, G.A.F. (1965) A Note on the Multiple-Recapture
Census.\emph{Biometrika} . 52 (1/2), 249--259. doi:10.2307/2333827.

Suriyamongkol, T. \& Mali, I. (2018) Feasibility of Using
Computer-Assisted Software for Recognizing Individual Rio Grande Cooter
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Tieleman, T. \& Hinton, E. (2012) \emph{Lecture 6.5 - rmsprop, COURSERA:
Neural networks for machine learning} .

Urbanek, S. (2021) \emph{Package `jpeg'} . http://www.rforge.net/jpeg/.

Wearn, O.R. \& Glover-Kapfer, P. (2019) Snap happy: camera traps are an
effective sampling tool when compared with alternative
methods.\emph{Royal Society Open Science} . 6 (3), 181748.
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White, G. C., \& Burnham, K. P. (2009). Program MARK: survival
estimation from populations

of marked animals. 3657. doi: 10.1080/00063659909477239

Woodroffe, R., Rabaiotti, D., Ngatia, D.K., Smallwood, T.R.C., Strebel,
S. \& O'Neill, H.M.K. (2019) Dispersal behaviour of African wild dogs in
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doi:10.1111/aje.12689.

Woodroffe, R. \& Sillero-Zubiri, C. (2020) Lycaon pictus: The IUCN Red
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assessment). Available from:
doi:10.2305/IUCN.UK.2020-1.RLTS.T12436A166502262.en {[}Accessed: 16
November 2020{]}.

\textbf{Table 1.} A selection of studies that tested the identification
accuracy achieved by different image-matching software packages.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Species\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Species\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Software package\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Software package\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Accuracy (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Accuracy (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Number of inspected ranks per image\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Number of inspected ranks per image\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Reference\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar (\emph{Panthera onca)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar (\emph{Panthera onca)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
77\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
77\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Nipko, Holcombe \& Kelly, 2020\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Nipko, Holcombe \& Kelly, 2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ocelot (\emph{Leopardus pardalis)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ocelot (\emph{Leopardus pardalis)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
76\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
76\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
68\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
68\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ocelot\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ocelot\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
63\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
63\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Thornicroft's giraffe (\emph{Giraffa camelopardalis thornicrofti)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Thornicroft's giraffe (\emph{Giraffa camelopardalis thornicrofti)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
71.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
71.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Halloran \emph{et al.,} 2014\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Halloran \emph{et al.,} 2014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Thornicroft's giraffe\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Thornicroft's giraffe\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Bolger \emph{et al,} 2012\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Bolger \emph{et al,} 2012\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Leopard cat \emph{(Prionailurus bengalensis)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Leopard cat \emph{(Prionailurus bengalensis)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Park \emph{et al.}, 2019\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Park \emph{et al.}, 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Jaguar\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Crall \emph{et al.}, 2013\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Crall \emph{et al.}, 2013\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Giraffe (\emph{Giraffa giraffa)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Giraffe (\emph{Giraffa giraffa)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Ambystoma opachum}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Ambystoma opachum}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
30.8--48.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
30.8--48.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Matth\selectlanguage{ngerman}é \emph{et al.}, 2017\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Matth\selectlanguage{ngerman}é \emph{et al.}, 2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
65.9-82.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
65.9-82.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Yellow-bellied toad (\emph{Bombina variegata})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Yellow-bellied toad (\emph{Bombina variegata})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
88.6-92.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
88.6-92.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
96.4-97.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
96.4-97.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Rio grande cooter \emph{(Pseudemys gorzugi})\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Rio grande cooter \emph{(Pseudemys gorzugi})\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
41.94\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
41.94\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Suriyamongkol \& Mali, 2018\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Suriyamongkol \& Mali, 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Wild-ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
66.13\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
66.13\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Green toad (\emph{Bufotes viridis})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Green toad (\emph{Bufotes viridis})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern Wild-ID Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern Wild-ID Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textasciitilde{}100 \textasciitilde{}60 \textasciitilde{}60\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textasciitilde{}100 \textasciitilde{}60 \textasciitilde{}60\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Burgstaller, Gollmann \& Landler, 2021\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Burgstaller, Gollmann \& Landler, 2021\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Wyoming toad (\emph{Anaxyrus baxteri})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Wyoming toad (\emph{Anaxyrus baxteri})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Wild-ID Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Wild-ID Hotspotter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
53 36\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
53 36\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Morrison \emph{et al.}, 2016\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Morrison \emph{et al.}, 2016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Italian crested newt (\emph{Triturus carnifex})\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
I\textsuperscript{3}S-Pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Sannolo \emph{et al.,} 2016\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Sannolo \emph{et al.,} 2016\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} : The accuracy and 95\% confidence intervals of the
best performing Convolution Neural Nets aiming to classify images of
African wild dog into (1) those depicting an individual standing up and
not standing up and (2) those depicting left or right flanks.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Model} & \textbf{Training Accuracy (\%)} & \textbf{Validation
Accuracy (\%)} & \textbf{Testing Accuracy (\%)}\tabularnewline
\midrule
\endhead
\textbf{Standing/not standing classifier} & 100 & 91 (90 - 92) & 90 (88
- 91)\tabularnewline
\textbf{Left/right flank classifier} & 100 & 96 (94 - 97) & 95 (94 -
96)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/594521/articles/628946-optimising-the-automated-recognition-of-individual-animals-to-support-population-monitoring}

\textbf{Figure 2:} A flowchart describing the image pre-processing steps
applied to the combined Zimbabwean and Kenyan image catalogue,
illustrated using two example images. Images outlined in red are
filtered out of the dataset.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/594521/articles/628946-optimising-the-automated-recognition-of-individual-animals-to-support-population-monitoring}

\textbf{Figure 3} : The proportion of true matches in the dataset that
were detected within the top 20 ranked images using different software
packages for A) 89 Zimbabwean and B) 104 Kenyan African wild dogs, that
were visually matched as a reference.

\textbf{Hosted file}

\verb`image6.emf`  available at \url{https://authorea.com/users/594521/articles/628946-optimising-the-automated-recognition-of-individual-animals-to-support-population-monitoring}

\textbf{Figure 4} : The proportion of image matches detected within the
10 highest ranked images by Hotspotter for the Zimbabwean and Kenyan
populations (n = 89 individuals for both populations). Error bars denote
95\% confidence intervals.

\textbf{Supplementary Information for}

\textbf{Optimising the automated recognition of individual animals to
support population monitoring}

\textbf{T.A. de Lorm, C. Horswill, D. Rabaiotti, R.M. Ewers, R. J.
Groom, J. Watermeyer, R. Woodroffe}

\textbf{Table S1} The structure of a Convolutional Neural Net that
classifies images of African Wild Dogs into ``standing'' or ``not
standing'' (S1.A), and a Convolutional Neural Net that classifies images
of African Wild Dogs into ``left flank'' or ``right flank'' (S1.B). The
layers are given in the order at which they occur in the model. The
models were optimised using RMSprop, an algorithm which guides how the
model improves itself (Tieleman \& Hinton, 2012). The activation column
refers to which activation function was used in each layer, which
determines how the nodes within layers convert its input to an
output-value. ReLu was used as activator function, as this has been
found to improve multi-layer networks (Glorot, Bordes \& Bengio, 2011).
The last layer is activated with a Sigmoid function, which turns the
input into a single, binary prediction (``standing'' or ``not
standing'', ``left'' or ``right'').

\textbf{S1.A}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Layer} & \textbf{Kernel size} &
\textbf{Activation}\tabularnewline
\midrule
\endhead
\textbf{Convolutional} & 32 & Rectified Linear Activator (ReLu) (Agarap,
2019)\tabularnewline
\textbf{Convolutional} & 64 & ReLu\tabularnewline
\textbf{Max Pooling 2D} & NA & NA\tabularnewline
\textbf{Dropout} & NA & NA\tabularnewline
\textbf{Flatten} & NA & NA\tabularnewline
\textbf{Dense} & 128 & ReLu\tabularnewline
\textbf{Dense} & 1 & Sigmoid\tabularnewline
\bottomrule
\end{longtable}

\textbf{S1.B}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Layer} & \textbf{Kernel size} &
\textbf{Activation}\tabularnewline
\midrule
\endhead
\textbf{Average Pooling 2D} & NA & NA\tabularnewline
\textbf{Convolutional} & 32 & ReLu\tabularnewline
\textbf{Convolutional} & 64 & ReLu\tabularnewline
\textbf{Convolutional} & 64 & ReLu\tabularnewline
\textbf{Max Pooling 2D} & NA & NA\tabularnewline
\textbf{Dropout} & NA & NA\tabularnewline
\textbf{Flatten} & NA & NA\tabularnewline
\textbf{Dense} & 128 & ReLu\tabularnewline
\textbf{Dense} & 1 & Sigmoid\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure S1.} An image with the reference points used for
I\textsuperscript{3}S-Pattern: (1) the withers, (2) the base of the
neck, and (3) the base of the tail.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure S2.} The two ways in which WildID and Hotspotter were
tested: with crops of the full individual from which the background was
removed (left), and manually cropped flanks of the individual (right).

\textbf{Hosted file}

\verb`image10.emf`  available at \url{https://authorea.com/users/594521/articles/628946-optimising-the-automated-recognition-of-individual-animals-to-support-population-monitoring}

\textbf{Figure S3.} Three images representative of the image quality
scores that were assigned to images included in a Mixed Effect Logistic
Regression. The regression tested whether there was a difference in the
accuracy of Hotspotter between the Kenyan and Zimbabwean population. The
image quality score was included to correct for any potential influence
of image quality.

\subsection*{References}

{\label{references-1}}

Glorot, X., Bordes, A. \& Bengio, Y. (2011) Deep Sparse Rectifier Neural
Networks. In: Proceedings of the Fourteenth International Conference on
Artificial Intelligence and Statistics. 14 June 2011 JMLR Workshop and
Conference Proceedings. pp. 315--323.
https://proceedings.mlr.press/v15/glorot11a.html.

Tieleman, T. \& Hinton, E. (2012) \emph{Lecture 6.5 - rmsprop, COURSERA:
Neural networks for machine learning} .

\selectlanguage{english}
\FloatBarrier
\end{document}

