Feasibility of Using Computer-Assisted Software for Recognizing Individual
Rio Grande Cooter (Pseudemys gorzugi)
and Ivana Mali
Mark-recapture methods used in population demography studies involve marking of animals, such as tagging,
notching, and tattooing. These techniques are invasive and potentially harmful to the animals. Photo-identification
using natural animal markings is less invasive and has become more widely used for a range of taxa including
invertebrates, fishes, reptiles, amphibians, and mammals. During 2016 and 2017, we studied the demographics of the
Rio Grande Cooter (Pseudemys gorzugi) using traditional mark-recapture techniques (i.e., shell notching and toe
clipping). However, P. gorzugi displays plastral marks that could potentially be used for individual recognition. Because
the photo-identification process ‘by-eye’ is time consuming, we tested the efficiency of three pieces of software, I
, Wild.ID, and APHIS, for individual identification of P. gorzugi using plastron pattern. Matching results of each
program were generated into ranks with the 1
rank being the most likely match. Within the top 20 ranked images,
Wild.ID yielded the highest number of correct matches (83.87%), followed by APHIS (ITM; 69.35%), APHIS (SPM;
67.74%), and I
(61.29%). We found the quality of photos significantly contributed to the software
effectiveness; however, turtle age and plastron wear did not affect the accuracy of the photo-identification software.
We concluded that Wild.ID can be used as a non-invasive photo-recognition technique for P. gorzugi in a short-term
STUDYING the abundance and density of wildlife
populations is the first step to developing practical
wildlife management regimes (Thompson et al., 1998;
Dutton et al., 2005; Anderson et al., 2010). Capture-Mark-
Recapture (CMR) is a technique used to identify individuals
within a target population to estimate population size and
other vital rates (i.e., Sreekar et al., 2013; Sannolo et al.,
2016). Marking can be done using several methods such as
visual implant elastomer (VIE) tag, passive integrative
transponder (PIT) tag, toe-clipping, branding, and tattooing
(i.e., Mellish et al., 2007; Morrison et al., 2016; Mali et al.,
2018; Peterson et al., 2018). However, these techniques
involve physical handling and modifying the bodies of
animals, which is considered invasive and could affect
animal behavior or survival (Bloch and Irschick, 2005;
Schmidt and Schwarzkopf, 2010). In turtles, one of the
oldest and most common marking techniques is notching
marginal scutes, but this technique is not recommended for
hatchlings due to the potential of causing shell deformity
and infections (Cagle, 1939; Cooley et al., 2013). An
alternative, non-invasive approach to individual identifica-
tion is to photograph natural markings (i.e., spot, color
pattern, scars), which can reduce stress and injuries associ-
ated with traditional marking techniques (Bolger et al., 2012;
Sacchi et al., 2016).
Photo-identification of an animal’s natural marks has been
used in a wide range of taxa including fishes (Speed et al.,
2007; Dala-Corte et al., 2016), reptiles (Cooley et al., 2013;
Sreekar et al., 2013; Carpentier et al., 2016), amphibians
(Caorsi et al., 2012; Morrison et al., 2016; Matth´
e et al.,
2017), mammals (Anderson et al., 2010; Bolger et al., 2012),
and invertebrates (Huffard et al., 2008). However, this
technique can only be used if organisms have unique
external body patterns that are distinguishable among
individuals and the pattern remains stable over time (Dala-
Corte et al., 2016). Although photographic matching of
individuals using the naked eye generates high accuracy, the
process is often time consuming (Elgue et al., 2014; Matth ´
al., 2017). To reduce image matching time, many computer-
based programs have been developed, such as AMPHIDENT
e et al., 2008), APHIS (Moya et al., 2015), I
(Van Tienhoven et al., 2007), Manta Matcher (Town et al.,
2013), MYDAS (Carter et al., 2014), and Wild.ID (Bolger et al.,
2012). Nonetheless, during development, software is usually
tested on a particular species and the applicability to other
species must be validated (Sacchi et al., 2016). Recent studies
have used photographs to identify individual turtles based on
carapace and plastron patterns in Painted Turtle (Chrysemys
picta belli; Cooley et al., 2013) and Eastern Box Turtle
(Terrapene carolina carolina; Cross et al., 2014) as well as facial
patterns of Green Sea Turtle (Chelonia mydas; Carpentier et
al., 2016). Here, we sought to evaluate the usefulness of using
plastron patterns for individual identification in Rio Grande
Cooter (Pseudemys gorzugi).
The Rio Grande Cooter is a relatively large freshwater turtle
native to the lower Rio Grande and Pecos River drainages in
New Mexico, Texas, and Mexico (Pierce et al., 2016). Their
plastron consists of a black pattern on a yellow background
that appears to be unique to individuals (Fig. 1). In 2016, a
monitoring program was established for the species on the
Black River, New Mexico (Mali et al., 2018). The surveys
generated more than 600 plastron images over a two-year
period. Using these images, we tested the recognition ability
of three pieces of different photo-recognition software,
, and APHIS, for individual recognition
based on plastron photographs taken under field conditions.
Furthermore, we investigated biases in population size
estimation using mark-recapture data based on photo
recognition ability of each piece of software.
MATERIALS AND METHODS
Data collection.—The study was conducted on the Black
River, New Mexico from May to August of 2016 and 2017.
Turtles were captured using single-opening, wide mouth
hoop-net traps with diameter of 76.2 cm (Memphis Net and
Twine Company, Memphis, TN) baited with canned sardines
and shrimp (Mirabal et al., 2018). We measured, photo-
Department of Biology, Eastern New Mexico University, 1500 S AVE K Station 33, Portales, New Mexico 88130; Email: (TS) Thanchira.
Suriyamongkol@enmu.edu; and (IM) Ivana.Mali@enmu.edu. Send reprint requests to TS.
Submitted: 30 July 2018. Accepted: 22 October 2018. Associate Editor: D. S. Siegel.
Ó2018 by the American Society of Ichthyologists and Herpetologists DOI: 10.1643/CH-18-101 Published online: 29 November 2018
Copeia 106, No. 4, 2018, 646–651
graphed, and marked each turtle using one of the three
marking techniques depending on the turtle size: toe-
clipping (juveniles), PIT tagging (subadults only due to
limited budget), and shell notching (adults). While PIT
tagging and shell notching give turtles unique identification
numbers, toe-clipping was done in cohort. Therefore, toe-
clipped recaptures were later identified by experienced
researchers who were familiar with the species using ‘by-
Over the course of our study, we photographed 635
plastron images from 529 individuals using an Olympus
Tough TG-870 16.0 MP Compact Digital Camera (Olympus
Co. Shinjuku, Tokyo, Japan). The photographs were taken in
situ on the river bank or on a boat, which generated real-time
images under field conditions (i.e., natural lighting, various
angles, and different levels of turtle cleanliness). Due to
variations in the quality of the photographs, we standardized
the orientation and extent of all images to display only the
ventral region of the turtles by cropping and rotating using
Adobe Photoshop Express (Adobe Systems, Inc., San Jose,
CA). We then categorized images into good-quality photos
and poor-quality photos and created two databases based on
the visibility of the plastron pattern to minimize the source
of bias during photo-matching analyses (Fig. 2).
Performance of photo-identification software.—We evaluated
the performance of three pieces of freely available, open-
source photo-identification software: Wild.ID (ver. 0.9.28),
(ver. 4.1), and APHIS (ver. 1.0). Wild.ID uses the
Scale Invariant Feature Transform (SIFT) algorithm for
pattern extraction and key points detection (Lowe, 2004;
Bolger et al., 2012). By contrast, I
uses a Speed Up
Robust Feature (SURF) algorithm, which detects the key
points based on pixels representing pattern and non-pattern
areas selected by users (I
manual, version 4.1,
2016). Finally, APHIS contains two approaches for image
matching, Spot Pattern Matching (SPM) and Image Template
Matching (ITM) (Moya et al., 2015). The SPM approach treats
and matches images using the exhaustive search version of
SURF algorithm implemented in I
S series, but APHIS allows
users to pre-process all images before proceeding to match
calculations of the whole set (Moya et al., 2015). This
approach requires users to pre-process each image by
defining reference points and marking spots within the area
for comparison (Moya et al., 2015), which is nearly as time
consuming as using by-eye technique. The ITM approach
uses the matchTemplate function of the Open Computer
Vision Libraries, which divides the pattern area into patches
and overlaps separate images to find the best matches (Moya
et al., 2015; Matth´
e et al., 2017).
To evaluate the performance of the photo-identification
software, we created a test set of known matching images
collected in 2016 and 2017. We created a good-quality
database (i.e., photos with highly visible plastron pattern) of
368 photos which included 62 recaptures (124 photographs)
and 244 single captures (244 photographs) for the compar-
ison. To test the software on photo quality, we also generated
a poor-quality database (i.e., photos with plastron pattern
covered in mud, algae, hand of photographer, or bad
lighting) of 267 photos that included 44 recaptures (88
photographs) and 179 single captures (179 photographs). We
ran each data set (i.e., good quality and poor quality) using
the three selected software programs. Because we knew the
correct matches of recaptured individuals, the performance
of the software was expressed as the percentage of correct
matches that fell within the top 20 highest ranks. We chose
the first 20 ranks as the maximum number of acceptable
ranks of the correct matches generated by each piece of
Fig. 1. Examples of the Rio Grande Cooter (Pseudemys gorzugi) plastron pattern of different size classes: 32 mm (left), 123 mm (middle), and 246
mm (right) plastron length. Based on our observations, the pattern starts to fade when a turtle’s plastron length reaches 90 mm.
Suriyamongkol and Mali—Photo-identification of Rio Grande Cooter 647
software. Wild.ID only generates matching results up to the
maximum of 20 ranks, while I
and APHIS generate
results up to 50 and 100 ranks, respectively.
Implications for population size estimation.—We developed a
set of capture histories over six sampling events that reflect
our survey efforts in the field. For this test, we used only
individuals captured in 2017, where individuals with poor-
quality photos were excluded. Any individual previously
captured in 2016 was considered a new individual in 2017.
To compare biases in estimating population size based on
ability of the software to identify recaptured individuals, a
dataset was developed for each piece of software based on its
performance in recognizing recaptures of P. gorzugi. For
example, if an individual was a recapture and the software
failed to place a correct match within the first 20 ranks, that
individual was treated as a new capture.
Capture histories were analyzed in program R (R Core
Team, 2016) using package Rcapture developed to provide
estimation of abundance for closed populations, open
populations, and robust design for capture-recapture models
using Poisson regressions (Baillargeon and Rivest, 2007). For
simplicity, and given the short sampling period, we assumed
that births, deaths, immigration, and emigration did not
occur during our study; therefore, the population was
assumed to be closed. We ran models that included different
combinations of factors that can affect capture probabilities:
temporal effect (t), heterogeneity (h), and behavioral effect
(b), and chose the best fit model based on the Akaike
Information Criterion (AIC; Burnham and Anderson, 1998).
Performance of photo-identification software.—We found that
Wild.ID had the best image-matching performance among
the tested software for both the good-quality database and
the poor-quality database (Table 1). For the good-quality
database, Wild.ID generated 66.13% and 83.87% correct
matches as the top ranking (i.e., the first rank) and among
the top-20 ranking, respectively. I
had the poorest
performance, generating 41.94% and 61.29% correct match-
es as the top ranking and among the top-20 ranking,
respectively. APHIS (SPM) generated 43.55% and 67.74%
correct matches as the top ranking and among the top-20
ranking, respectively, while APHIS (ITM) generated 61.29%
Fig. 2. Examples of the (A) good-quality images and (B) poor-quality images taken of the plastron pattern of the Rio Grande Cooter (Pseudemys
gorzugi). Good-quality images have highly visible plastron pattern, and poor-quality images have the plastron pattern covered in mud, algae, or the
Table 1. Percentage of correct matching pairs of Rio Grande Cooter (Pseudemys gorzugi) out of 62 pairs for good-quality database and 44 pairs for
poor-quality database generated by Wild.ID, I
, APHIS (ITM), and APHIS (SPM) that fell within the top rank and the top-twenty ranks.
Top rank Top twenty Top rank Top twenty
Wild.ID 66.13% 83.87% 61.36% 77.27%
41.94% 61.29% 18.18% 38.64%
APHIS (ITM) 61.29% 69.35% 65.91% 75.00%
APHIS (SPM) 43.55% 67.74% 34.09% 69.91%
648 Copeia 106, No. 4, 2018
and 69.35% correct matches as the top ranking and among
the top-20 ranking, respectively. Poor-quality images reduced
the number of correct matches recognized by the software
within the top-20 ranking by 2–23%, with I
the most affected software (Table 1).
Implications for population size estimation.—Based on tradi-
tional marking techniques (i.e., toe-clip, PIT tag, and shell
notch), there were 309 captures of 281 individuals. Similarly,
for Wild.ID, there were 309 captures of 281 individuals
because Wild.ID correctly recognized all recaptures in our
sample dataset (i.e., good-quality photos taken within the
same season). Based on I
, there were 309 captures
of 287 individuals (i.e., the software failed to recognize six
recaptures). Based on APHIS (SPM), there were 309 captures
of 285 individuals, and based on APHIS (ITM), there were 309
captures of 282 individuals. Heterogeneity and behavioral
effect model (M
) was the most appropriate model for this
study. Among the tested software, Wild.ID resulted in the
most accurate individual recognitions for estimating popu-
lation abundances in comparison with the traditional
marking techniques (Fig. 3). For traditional marking tech-
niques and Wild.ID, population size was estimated to be
974.2640.9. For I
, population size was estimated
to be 1,246.3631.7. For APHIS (SPM), population size was
estimated to be 1,297.0631.9, while for APHIS (ITM),
population size was estimated to be 982.2633.4 (Fig. 3).
Photo-identification software appeared to be effective in
recognizing plastron marks of individual P. gorzugi even
though none of the software was free of error. Wild.ID
exhibited the highest success of generating correct matches
among the tested software. Our results showed that the
performance of software varied depending on photo-match-
ing algorithm and quality of images. Wild.ID was the
simplest and the most user-friendly software, because it did
not require users to pre-analyze images (i.e., manually
indicate pattern and background areas for each photo).
Therefore, Wild.ID reduces the error due to inconsistency of
identifying the area for matching especially with inexperi-
enced users. Moreover, we found that Wild.ID always
suggested the correct matching candidates within the top-
five ranks, most often the first rank; however, if Wild.ID
could not recognize an individual within the first five ranks,
the matching candidate would not be listed within the first
As expected, the good-quality database yielded higher
image-matching accuracy by all software tested. For poor-
quality photos, the plastron was covered in mud, algae, or
sometimes photographer fingers, and some photos also
contained glares or shadow (Fig. 2). Due to the pattern being
covered, it was nearly impossible to use photo-identification
software that requires pre-processing of photos (i.e., indicat-
ing pattern and background area) such as I
APHIS (SPM). Surprisingly, Wild.ID still yielded a high
number of correct matches. However, we noticed that
Wild.ID was likely recognizing individuals based on the
mud and algae pattern on the plastron rather than the
plastron pattern itself, which is not appropriate for a long-
term study as these patterns can change with time.
The main strength of computer-assisted software is the
time efficiency and low probability of falsely identifying a
unique turtle as a recapture. Because of the requirement for
users to visually confirm the correct matches, a researcher
familiar with the species can conclusively confirm whether
the match is contained within the first 20 ranks. However,
false negatives (i.e., when a correct matching image is not
within the first 20 ranks) can still be a common source of
error for the software (Morrison et al., 2016). This is
important when estimating population size (Elgue et al.,
2014). In particular, false negatives can result in overestima-
tion of population size and underestimation of survivorship
(Bolger et al., 2012). According to our findings, when using
the sample dataset with only good-quality images within a
single season of surveys, inability to recognize only four
individuals as recaptures resulted in an increase of over 300
individuals in a population, as seen with APHIS (SPM). In
contrast, Wild.ID showed no error in recognizing recaptures
over the short period of time, and therefore, was comparable
to traditional marking techniques.
It is important to note that the plastron pattern of Rio
Grande Cooter changes over time. We have observed that the
pigmentation patterns fade in larger turtles (.90 mm in
plastron length). The change of pattern in P. gorzugi differed
from other turtle species such as Painted Turtle (Cooley et al.,
Fig. 3. A comparison between five
traditional marking (i.e., shell notch-
ing, PIT tag, toe-clip), Wild.ID, APHIS
, and APHIS (SPM)
to estimate population abundances
of Rio Grande Cooter (Pseudemys
gorzugi) using mark-recapture meth-
od in Rcapture package based on the
sample dataset containing only good-
quality photos collected within a
single season of surveys. Bars repre-
sent one standard deviation from the
Suriyamongkol and Mali—Photo-identification of Rio Grande Cooter 649
2013) and Eastern Box Turtle (Cross et al., 2014), where the
pattern will not fully develop until later in life. This finding
indicates that the distinct plastron pattern cannot be used to
recognize the same individual in a long-term study. The
pattern characteristics of different size classes did not affect
the performance of photo-assisted software in a short time
period (i.e., within a season). However, we noticed that the
ability to find correct matches was reduced for individuals
that were captured more than one year apart even though
the pattern similarities were still recognizable by eye. Due to
the fading of plastron pattern over time, it is necessary to
incorporate other marking techniques when conducting a
long-term study of this species. Although Rio Grande Cooter
might not be the most suitable turtle for using computer-
assisted programs because of this issue, this technique is
useful for recognition of hatchlings, which cannot be marked
using traditional marking techniques (i.e., shell notching)
until they reach an appropriate size. Nonetheless, the success
rate obtained in a single-season study by Wild.ID suggested
that this computer-assisted photo-identification software can
be used reliably for the Rio Grande Cooter and probably
other species of turtles with similar plastron markings.
This study was supported by the Share with Wildlife Program
at the New Mexico Department of Game and Fish and State
Wildlife, Grant T-32-4, #11 under NMDGF Authorization
#3621 and ENMU IACUC 03-02/2016. Moreover, we thank
the Bureau of Land Management, private land owners,
undergraduate and graduate students, and researchers for
helping with surveys: A. W. Letter, K. J. Waldon, A.
Villamizar, A. Parandhaman, S. Sirsi, J. Mirabal, J. L. Curtis,
and M. V. Vandewege.
Anderson, C. J., N. Da Lobo Vitoria, J. D. Roth, and J. M.
Waterman. 2010. Computer-aided photo-identification
system with an application to polar bears based on whisker
spot patterns. Journal of Mammalogy 91:1350–1359.
Baillargeon, S., and L. P. Rivest. 2007. Rcapture: loglinear
models for capture-recapture in R. Journal of Statistical
Bloch, N., and D. J. Irschick. 2005. Toe-clipping dramati-
cally reduces clinging performance in a pad-bearing lizard
(Anolis carolinensis). Journal of Herpetology 39:288–293.
Bolger, D. T., T. A. Morrison, B. Vance, D. Lee, and H. Farid.
2012. A computer-assisted system for photographic mark–
recapture analysis. Methods in Ecology and Evolution 3:
Burnham, K. P., and D. R. Anderson. 1998. Model Selection
and Inference: A Practical Information-Theoretic Ap-
proach. Springer Verlag, New York.
Cagle, F. R. 1939. A system of marking turtles for future
identification. Copeia 1939:170–173.
Caorsi, V. Z., R. R. Santos, and T. Grant. 2012. Clip or snap?
An evaluation of toe-clipping and photo-identification
methods for identifying individual Southern Red-Bellied
Toads, Melanophryniscus cambaraensis. South American
Journal of Herpetology 7:79–84.
Carpentier, A. S., C. Jean, M. Barret, A. Chassagneux, and
S. Ciccione. 2016. Stability of facial scale patterns on green
sea turtles Chelonia mydas over time: a validation for the
use of a photo-identification method. Journal of Experi-
mental Marine Biology and Ecology 476:15–21.
Carter, S. J., I. P. Bell, J. J. Miller, and P. P. Gash. 2014.
Automated marine turtle photograph identification using
artificial neural networks, with application to green turtles.
Journal of Experimental Marine Biology and Ecology 452:
Cooley, C., S. Smith, C. Geier, and T. Puentes. 2013. The use
of photo-identification as a means of identifying western
painted turtles (Chrysemys picta bellii) in long-term demo-
graphic studies. Herpetological Review 44:430–432.
Cross, M. D., G. J. Lipps, Jr., J. M. Sapak, E. J. Tobin, and K.
V. Root. 2014. Pattern-recognition software as a supple-
mental method of identifying individual eastern box
turtles (Terrapene c. carolina). Herpetological Review 45:
Dala-Corte, R. B., J. B. Moschetta, and F. G. Becker. 2016.
Photo-identification as a technique for recognition of
individual fish: a test with the freshwater armored catfish
Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluri-
formes: Loricariidae). Neotropical Ichthyology 14:e150074.
Dutton, D. L., P. H. Dutton, M. Chaloupka, and R. H.
Boulon. 2005. Increase of a Caribbean leatherback turtle
Dermochelys coriacea nesting population linked to long-
term nest protection. Biological Conservation 126:186–
Elgue, E., G. Pereira, F. Achaval-Coppes, and R. Maneyro.
2014. Validity of photo-identification technique to analyze
natural markings in Melanophryniscus montevidensis (Anura:
Bufonidae). Journal of Herpetology 13:59–66.
Huffard, C. L., R. L. Caldwell, N. DeLoach, D. W. Gentry, P.
Humann, B. MacDonald, B. Moore, R. Ross, T. Uno, and
S. Wong. 2008. Individually unique body color patterns in
octopus (Wunderpus photogenicus) allow for photoidentifi-
cation. PLoS ONE 3:e3732.
Lowe, D. G. 2004. Distinctive image features from scale-
invariant keypoints. International Journal of Computer
Mali, I., A. Duarte, and M. R. Forstner. 2018. Comparison of
hoop-net trapping and visual surveys to monitor abun-
dance of the Rio Grande cooter (Pseudemys gorzugi). PeerJ 6:
e, M., M. Sannolo, K. Winiarski, A. Spitzen-van der
Sluijs, D. Goedbloed, S. Steinfartz, and U. Stachow.
2017. Comparison of photo-matching algorithms com-
monly used for photographic capture–recapture studies.
Ecology and Evolution 7:5861–5872.
e, M., T. Sch¨
onbrodt, and G. Berger. 2008. Computer-
utzte Bildanalyse von Bauchfleckenmustern des
Kammmolchs (Triturus cristatus). Zeitschrift f¨
Mellish, J. A., D. Hennen, J. Thomton, L. Petrauskas, S.
Atkinson, and D. Calkins. 2007. Permanent marking in
an endangered species: physiological response to hot
branding in Steller sea lions (Eumetopias jubatus). Wildlife
Mirabal, J., A. W. Letter, K. J. Waldon, and I. Mali. 2018.
Pseudemys gorzugi (Rio Grande Cooter). Attractions to trap
baits. Herpetological Review 49:323–324.
Morrison, T. A., D. Keinath, W. Estes-Zumpf, J. P. Crall,
and C. V. Stewart. 2016. Individual identification of the
endangered Wyoming toad Anaxyrus baxteri and implica-
tions for monitoring species recovery. Journal of Herpetol-
650 Copeia 106, No. 4, 2018
O., P. L. Mansilla, S. Madrazo, J. M. Igual, A. Rotger,
A. Romano, and G. Tavecchia. 2015. APHIS: a new
software for photo-matching in ecological studies. Ecolog-
ical Informatics 27:64–70.
Peterson, D., R. B. Trantham, T. G. Trantham, and C. A.
Caldwell. 2018. Tagging effects of passive integrated
transponder and visual implant elastomer on the small-
bodied white sands pupfish (Cyprinodon tularosa). Fisheries
Pierce, L. J. S., J. N. Stuart, J. P. Ward, and C. W. Painter.
2016. Pseudemys gorzugi Ward 1984—Rio Grande Cooter,
Western River Cooter, Tortuga de Oreja Amarilla, Jicot´
ıo Bravo. Chelonian Research Monographs 5:100.1–
R Core Team. 2016. R: a language and environment for
statistical computing. R Foundation for Statistical Com-
puting, Vienna, Austria. https://www.R-project.org/
Sacchi, R., S. Scali, M. Mangiacotti, M. Sannolo, and M. A.
L. Zuffi. 2016. Digital identification and analysis, p. 59–72.
In: Reptile Ecology and Conservation: A Handbook of
Techniques. C. K. Dodd (ed.). Oxford University Press,
Sannolo, M., F. Gatti, M. Mangiacotti, S. Scali, and R.
Sacchi. 2016. Photo-identification in amphibian studies: a
test of I3S Pattern. Acta Herpetologica 11:63–68.
Schmidt, K., and L. Schwarzkopf. 2010. Visible implant
elastomer tagging and toe-clipping: effects of marking on
locomotor performance of frogs and skinks. The Herpeto-
logical Journal 20:99–105.
Speed, C. W., M. G. Meekan, and C. J. Bradshaw. 2007. Spot
the match—wildlife photo-identification using informa-
tion theory. Frontiers in Zoology 4:2.
Sreekar, R., C. B. Purushotham, K. Saini, S. N. Rao, S.
Pelletier, and S. Chaplod. 2013. Photographic capture-
recapture sampling for assessing populations of the Indian
gliding lizard Draco dussumieri. PloS ONE 8:e55935.
Thompson, W. L., G. C. White, and C. Gowan. 1998.
Monitoring Vertebrate Populations. Academic Press, San
Town, C., A. Marshall, and N. Sethasathien. 2013. Manta
Matcher: automated photographic identification of manta
rays using keypoint features. Ecology and Evolution 3:
Van Tienhoven, A. M., J. E. Den Hartog, R. A. Reijns, and V.
M. Peddemors. 2007. A computer-aided program for
pattern-matching of natural marks on the spotted ragged-
tooth shark Carcharias taurus. Journal of Applied Ecology
Suriyamongkol and Mali—Photo-identification of Rio Grande Cooter 651