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Journal of Applied
Ecology
2007
44
, 273–280
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society
Blackwell Publishing Ltd
METHODOLOGICAL INSIGHTS
A computer-aided program for pattern-matching of natural
marks on the spotted raggedtooth shark
Carcharias taurus
A. M. VAN TIENHOVEN,* J. E. DEN HARTOG,† R. A. REIJNS† and
V. M. PEDDEMORS*
*
School of Biological and Conservation Sciences, University of KwaZulu Natal, Private Bag X54001, Durban 4000,
South Africa; and
†
TNO Science and Industry, PO Box 155, 2600 AD, Delft, the Netherlands
Summary
1.
The ability to identify individual animals is a critical aid in wildlife and conservation
studies requiring information on behaviour, distribution, habitat use, population and
life-history parameters. We present a computer-aided photo-identification technique
that relies on natural marks to identify individuals of
Carcharias taurus
, a shark species
that is critically endangered off the eastern Australian coast and considered globally
vulnerable. The technique could potentially be applied to a range of species of similar
form and bearing natural marks.
2.
The use of natural marks for photo-identification is a non-invasive technique for
identifying individual animals. As photo-identification databases grow larger, and their
implementation spans several years, the historically used visual-matching processes lose
accuracy and speed. A computerized pattern-matching system that requires initial user
interaction to select the key features aids researchers by considerably reducing the time
needed for identification of individuals.
3.
Our method uses a two-dimensional affine transformation to compare two individuals
in a commonly defined reference space. The methodology was developed using a database
of 221 individually identifiable sharks that were photographically marked and
rephotographed over 9 years, demonstrating both the efficacy of the technique and that
the natural pigment marks of
C. taurus
are a reliable means of tracking individuals over
several years.
4.
Synthesis and applications.
The identification of individual animals that are naturally
marked with spots or similar patterns is achieved with an interactive pattern-matching
system that uses an affine transformation to compare selected points in a single-user
computer-aided interface. Our technique has been used successfully on
C. taurus
and we
believe the methodology can be applied to other species of a similar form that have
natural marks or patterns. The identification of individuals allows accurate tracking of
their movements and distribution, and contributes to better population estimates for
improved wildlife management and conservation planning.
Key-words
:computer-aided pattern recognition, conservation biology, natural marks,
photographic identification
Journal of Applied Ecology
(2007)
44
, 273–280
doi: 10.1111/j.1365-2664.2006.01273.x
Introduction
The identification of individuals of a particular species
may be used to track animal movements and develop
population estimates (Hammond 1986). Natural body
markings have been used successfully to identify
individual animals in both terrestrial and aquatic
environments for a range of animals, from greylag geese
Anser anser
(Lorenz 1937) to nurse sharks
Ginglymostoma
cirratum
(Castro & Rosa 2005). Natural marks in marine
animals include features such as callosities and fluke
patterns (Whitehead, Christal & Tyack 2000) as well as
Correspondence: A. M. van Tienhoven, School of Biological
and Conservation Sciences, University of KwaZulu Natal,
Private Bag X54001, Durban 4000, South Africa. E-mail
amvantienhoven@yahoo.com
274
A. M. van
Tienhoven
et al.
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology
,
44
,
273–280
tears, marks and notches in fins and tail flukes (Würsig
& Jefferson 1990; Dufalt & Whitehead 1995). Myrberg
& Gruber (1974) used spot patterns, scars and fin tears
on bonnethead sharks
Sphyrna tiburo
to identify 10
individuals held in a shallow, semi-natural pool. Natural
markings have been used to identify individual sharks
in the wild, including great white sharks
Carcharodon
carcharias
(Anderson & Goldman 1996; Klimley &
Anderson 1996), nurse sharks and whale sharks
Rhincodon typus
(Arzoumanian, Holmberg & Norman
2005).
Several other shark species lend themselves to such
individual identification techniques, including the
raggedtooth shark
Carcharias taurus
(Rafinesque 1810),
also known as the grey nurse or sand tiger shark, which
is a large brown shark with distinct spots or pigment
marks on the flanks (Compagno 2001). Ireland (1984)
first documented use of the pigment spots on the flanks
of
C. taurus
in order to distinguish between two large
males. Subsequently, Peddemors & Thurman (1996)
and Allen & Peddemors (2001) successfully demon-
strated that wild individual
C. taurus
could be distin-
guished using natural marks such as tears and notches
in fins, fin spots and flank spots. The use of natural
marks is also preferred over conventional tagging
approaches because it is stressless, inexpensive and
reliable over a much longer period, features that are
particularly suited to species that are of conservation
interest. World-wide,
C. taurus
is currently listed by the
IUCN as vulnerable (Pollard & Smith 2000) and off the
east coast of Australia as critically endangered (Pollard
et al
. 2003), while on the South African coast it is
currently listed as not threatened.
In this paper we present a system of pattern-matching
using the pigment marks or spots on
C. taurus
. Although
a computer-aided identification system has already been
reported for whale sharks (Arzoumanian, Holmberg
& Norman 2005), our system allows simpler, yet more
rapid, data entry and matching by allowing the user
to select the key pattern features at the outset. This
approach improves accuracy considerably, reduces
processing time and allows analyses of larger databases
than those that have been used to date.
Materials and methods
Carcharias taurus
is strongly migratory in parts of its
range (Compagno 2001). Off the South African coast, the
animals move between the nursery areas in the warm,
temperate waters of the Eastern Cape to the more tropical
waters further north, where courtship and mating occur
(Compagno 2001; Smale 2002). During migration, sharks
congregate at certain sites, such as the Aliwal Shoal, a
submerged sandstone reef approximately 40 km south
of the city of Durban on the KwaZulu–Natal coast
of South Africa (Cliff 1989; Ramsay 1998). The reef is
easily accessed by boat and the depth ranges (
c.
6 to
c.
26 m) make it an important and popular scuba-
diving destination. The placid nature of the
C. taurus
sharks at this reef has permitted their close observation
and photography (Peddemors 1995) as well as attract-
ing recreational divers seeking a safe shark encounter.
Carcharias taurus
has consequently proved ideal for
testing whether their natural markings can be used for
photo-identification purposes.
-
Inevitably, pigmentation marks differ on each side of
an individual. For this study, the left side was chosen for
photography. Photographs were taken from distances
of between 2 and 6 m from the shark, preferably at
closer distances because water turbidity may reduce
photographic image clarity. To ensure replicability and
consistent image quality, photographs were taken
perpendicular to the flank of the shark at a time when
the shark exhibited minimal body flexing. The area
of interest for pattern-matching in
C. taurus
is the
origin of the first dorsal fin to the caudal peduncle.
Digital and transparency photographs of sharks taken
between 1995 and 2003 were used in this study. Slide
transparency images were scanned into a digital format
for further processing. The database subsequently used
for this study included 739 images.
-
Defining the reference system
A common reference system is necessary to enable com-
parison of the markings on two animals. Our technique
maps the relevant information, in this case the pigment
markings or spots of each shark image, to a common
space. Once the marks on the two animals have been
mapped, they are compared and a score is calculated
to indicate the quality of the match between the two
sharks. A two-dimensional affine transformation is used
for the mapping into the common space, which requires
that each shark is strictly regarded as two-dimensional.
The markings of the two sharks are mapped onto each
other using a two-dimensional affine transformation
that can incorporate scaling, rotation, translation and
correction of perspective. The affine transformation
matrix
M
of a coordinate (
x
,
y
) is expressed as:
eqn 1
The calculation of the transformation
M
involves six
unknown variables:
m
11
,
m
21
,
m
12
,
m
22
,
t
1
and
t
2
. These
six unknowns are calculated from a set of three corre-
sponding point pairs, where each pair consists of a source
point and a destination point. Each point comprises
an
x
and a
y
image coordinate. The transformation
matrix
M
maps the source points onto the destination
points.
Mx
y
mm
mm
x
y
t
t
mx m y t
mx m y t
=
+
=++
++
11 21
12 22
1
2
11 21 1
12 22 2
275
Computer-aided
photo-
identification of
C. taurus
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology
,
44
,
273–280
In this case the three point pairs consist of three
points in shark image number 1 and three correspond-
ing points in shark image number 2. To enable reliable
comparison between two shark images, it is essential
that the three reference points are the same for each
shark. For
C. taurus
, the three reference points on each
shark are the origins of the two dorsal fins and the
origin of the pelvic fin (the triangles in Fig. 1). Each
point consists of an
x
and a
y
image coordinate.
Figure 1 illustrates how the two shark images are
mapped onto each other. Using the three known
reference points, a set of six linear equations is derived,
as shown below. For illustration purposes, the trans-
formation of point pair (100, 90) to (90, 80), yields:
m
11
×
100
+
m
21
×
90
+
t
1
=
90
m
12
×
100
+
m
22
×
90
+
t
2
=
80
Similarly, the other two point pairs will yield the
remaining four equations as follows:
m
11
×
140
+
m
21
×
70
+
t
1
=
140
m
12
×
140
+
m
22
×
70
+
t
2
=
90
m
11
×
120
+
m
21
×
60
+
t
1
=
130
m
12
×
120
+
m
22
×
60
+
t
2
=
50
Thus three reference point pairs yield six equations
with six unknowns.
M
will result from solving this set of
six linear equations. We used the Gauss–Jordan elimina-
tion algorithm described together with the source code
in Press
et al
. (1992) to derive the six unknown variables.
Solving the above set of equations for the example in
Fig. 1 results in:
eqn 2
As an example, the transformation of the first coordinate
of image number 1 into the corresponding coordinate
of image number 2 is shown below:
eqn 3
Conversion of the other two points of image number 1
will result in the corresponding points of image number
2. Thus, matrix
M
is the transformation matrix used to
map all the markings of shark image number 1 onto
those of shark image number 2, which then allows
automatic comparison of the markings.
Comparison of natural spot marks
The spot marks of two shark images are transformed
onto a common reference space (Fig. 2). The closed
squares denote the spot marks from the first shark
image, while the open circles represent those of the
second shark image. The lines indicate matching spot
pairs. Spot pairs are accepted as a match if the nearest
alternative spot is at least twice the distance of the
current match. From the matching spot pairs, a distance
metric is calculated that is used to rank each shark image
in the database with respect to the candidate image.
In the case of
n
spot pairs, the Euclidean distance
between the two pairing spots is denoted by
dist
(
n
).
The distance metric to calculate the match between two
shark images is then defined by:
eqn 4
Where images share many spot pairs, there is a greater
likelihood of a match between the shark images. Thus
the number of spot pairs in the denominator is squared
to favour high numbers of spot pairs over low numbers.
A low score in the distance metric indicates a better match
than a high score.
Exhaustive search
The three reference points of the dorsal and pelvic
fins only provide a first estimate for the transformation,
termed the ‘quick search’. In an optional second step,
termed the ‘exhaustive search’, a large number of affine
transformations is calculated from the spot pairs to
get the best possible match between two images. Given
the spot pairs resulting from the first step using only
the three reference points, all possible combinations of
three pairs are selected as input to calculate a new affine
transformation. The best score from all these possible
Mx
y
x
y
=⋅−⋅
⋅⋅
+−
0 875 0 75
1125 1 75
70
190
M⋅
=⋅×−⋅×+
⋅× +⋅×−
=
100
90
0 875 100 0 75 90 70
1125 100 1 75 90 190
90
80
Fig. 2. Comparison of the spot marks in two different images
of the same shark. The closed squares denote the spot marks
from one shark image while the open circles represent those of
the second shark image. The lines indicate matching spot pairs.
dist i
n
i
n
=
∑
1
2
()
Fig. 1. Transformation of reference points (shown as black triangles) from one shark
image onto another.
276
A. M. van
Tienhoven et al.
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology, 44,
273–280
transformations is then taken as the final matching
score between two images.
For example, if the first step results in four spot pairs,
p1.. p4, these pairs can also be used to redo the first step
only with other point pairs. In this example, the follow-
ing sets of three pairs are possible: (p1, p2, p3) (p1, p2, p4)
(p2, p3, p4) (p1, p3, p4). If one of the transformations,
derived from these triple point pairs, yields a better
score than the initial transformation, this score is kept
as the final score.
This second transformation step, the exhaustive search,
can be computationally expensive. A number of n spot pairs
resulting from the first step will yield n · (n − 1) · (n − 2)/6
transformations to evaluate. For example, two shark
images with 16 spot pairs in common will require 560
transformations to be evaluated. Although the second
exhaustive step is about 100 –150 times slower than the
first step, the results of the exhaustive search are much
better. On a modern desktop computer, comparing an
image against a database of hundreds of images, using
the exhaustive search option will require a few seconds
at most.
The software interface
The pattern-matching program was developed using
Java 1.4.2 and C++, and requires the Java Run-time
environment to run on personal computers (available
from http://www.java.com, accessed 2 Jan 2007). The
entire code for the pattern-matching system, known as
I3S (Interactive Individual Identification System), is
available at http://www.reijns.com/i3s (accessed 2 Jan
2007).
An image is opened in the application. Using a
computer mouse, the user manually selects the three
reference points to define the common reference area.
The user then points out the most distinct pigment
spots on the left flank of each shark. Between 12 and 40
spots are selected within the reference area bounded
by the two dorsal fins and the pelvic fin. The centre of
each spot is marked and, where spots overlap or join,
the apparent centres of such spots are marked. The size
of the spots is currently not considered important,
whereas their relative position to one another is.
A key feature of the system is that human judgement
is used to distinguish between pigment marks and
artefacts such as reflections, shadows and particles in
the water. Once all comparisons have been made, the
user is presented with a list of possible matches. The
most likely match, based on the scores calculated from
the distance metric, is listed first. The user can then
compare the image of the unknown shark with up to 50
possible matches provided in the list.
The I3S software was subsequently trialled using a data-
base of 739 images taken on the Aliwal Shoal over 9
years. The effectiveness of the pattern-matching system
was quickly demonstrated as it helped to identify four
sharks in the database that had been erroneously
identified as new individuals in previous studies. Two-
hundred and twenty-one C. taurus were identified as
distinct individuals that visited the Aliwal Shoal during
the 9-year study period.
We found that photographs of moderate quality, for
example if slightly out of focus or blurred because of
movement or high levels of suspension particles, could
still be used as long as the arrangement of the spots, and
their relative position to one another, could be discerned.
A subsample of 10 sharks recognized over several years
showed the consistency of characteristic spot patterns
that were recognized by the I3S software. No changes in
the relative positions of the spots were discerned during
the years (Fig. 3).
The performance of the software was tested rigorously
by randomly selecting one, two and three reference
images per shark from the data set and using all the
remaining shark images as a test set. We tested sharks
that were positively identified using a combination of
features besides spot patterns, such as fin notches,
tears and spots and, in some cases, scars that could be
tracked over several months. Both the quick and exhaus-
tive search options were used (Table 1). We measured
the number of times the correct shark was ranked in
the top 1, 3, 5 and 10 best matches as a percentage. To
correct for random effects, the experiment was repeated
100 times and the average calculated over all results.
Results of the photo-identification data were compared
interannually and the 10 most regularly recorded sharks
investigated in more detail.
Results
The rigorous test of the database using the I3S software
highlighted that previously identified individual sharks
could be recognized successfully using both the quick
and exhaustive search options (Table 1).
Table 1. The likelihood of obtaining a correct match between
shark images, using the two search options and varying the
number of reference images per shark
Search option
Percentage of images correctly
ranked in one of the following
ranks
Top 1 Top 3 Top 5 Top 10
Quick search
1 reference image per shark 35·7 43·5 47·3 53·1
2 reference images per shark 52·8 62·1 66·0 71·5
3 reference images per shark 62·8 71·4 75·0 79·7
Exhaustive search
1 reference image per shark 71·8 76·1 78·1 80·7
2 reference images per shark 87·4 90·2 91·0 92·4
3 reference images per shark 91·7 93·9 94·5 95·4
277
Computer-aided
photo-
identification of
C. taurus
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology, 44,
273–280
The quick search provided a greater than 50% like-
lihood of recognizing previously identified individuals
as a match when there was more than one reference image.
With only one reference image to test against and using
the quick search option, 36% of the images in the test
database were correctly ranked as the correct choice
(top 1). This rose to 63% when three reference images
were available. As seen in Table 1, the efficacy of
correctly identifying individuals rapidly increased with
an increasing number of reference images, implying
that researchers should maintain images of the same
individual in their database, particularly if they require
the quick search option of the software.
The exhaustive search algorithm was more accurate
than the quick search option. Even if only one reference
image was available for the comparison, it yielded a
72% chance of correctly identifying the individual in
the catalogue. This recognition ability increased to
an almost 80% chance of it being in the top 10 ranking
using only a single reference image. Again, the efficacy
of correctly identifying individuals increased rapidly
with an increasing number of reference images to work
off, yielding an almost 92% chance that the correct
shark ranked in the top 1 position, if there were three
reference images in the database (Table 1). If the user
was prepared to confirm from a selection of 10 likely
matches, then the chance of a correct match was 95%.
In this study, even though 40% of the sharks in the
database were represented by only one reference image,
the exhaustive search option still provided a 72% like-
lihood of a positive match (Table 1). This substantially
reduced search effort by researchers with large databases.
Inter-annual variation in the number of photo-identified
individuals and their resightings is shown in Table 2
for each year since 1995. Prior to 1999, data had been
collected opportunistically during recreational scuba
dives. The peak in the number of identified individuals
in 1999 corresponded with the initiation of a new photo-
identification research project. The total number of
identified animals was 221 for the period 1995–2003.
It was evident that a high proportion of previously
recorded individuals was resighted every year, for
example almost 63% of animals recorded in 2003 had
been seen at least once in earlier years.
More detailed analysis of 10 individually recognized
sharks that had been regularly photographed at the
Aliwal Shoal indicated that most animals probably
return to the Aliwal Shoal annually. The only exception
was the shark known as UDparachute, which appeared
to visit biennially (Table 3).
Discussion
The natural pigment marks on the flanks and their
relative arrangement forms the basis from which
identifications are made using the I3S software. The
Fig. 3. Photographs of the same male shark showing how
flank markings are retained and can be traced from year to
year. The first dorsal fin is also an unusual shape and serves as
an additional feature for identification.
278
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Tienhoven et al.
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology, 44,
273–280
identifications were tested using 221 individually
recognizable C. taurus. The ability to identify individuals
with less than perfect representative images is of enormous
benefit to field workers studying free-ranging animals
in challenging marine environments, such as those off
the South African east coast.
The identification algorithm is based on a two-
dimensional affine transformation that assumes that a
shark is a linear, rigid, two-dimensional object. Ideally,
photographs should be taken at right angles to the shark’s
side, but even imperfect photographs taken at an oblique
angle provided a correct match, thereby reducing the
potential heterogeneity bias often associated with photo-
identification studies (Hammond 1990). Correct
identification would still be jeopardized if the body
of the shark was flexing or turning. Nevertheless, even
with imperfect images, the system has proved to be a
useful identification aid, provided that the user is
extremely critical of the suggested matches and verifies
matches using other features. In studies with dolphins,
well-marked individuals are often recognized by more
than one feature, which may include a combination
of attributes such as marked fins, shape of fin, shading
patterns, scrapes, scratches and wound marks, as well
as pigment patterns (Würsig & Jefferson 1990; Karcz-
marski & Cockcroft 1998). The investigator can then
use other features, such as fin shape and notches, tears,
scars and spots in the fin and, in adult male sharks,
clasper size and form, to identify individual sharks.
The pigment spots on C. taurus are largely unchanging
over successive years, and can be traced from year to
year, as illustrated by the example shown in Fig. 3. In
this example, the tip of the first dorsal fin is unusually
squared-off and serves as a double mark.
The key feature of the software is that it is not fully
automated. The user must point out the reference
points, which in the case of C. taurus are the fin origins,
and the most distinctive marks. Finally, the user must
select the best match from a ranked list of possible
known shark images. As the user manually points out
the natural marks, image artefacts such as particle
reflection in the water, backscatter from incorrect flash
position and flash overexposure of the flanks, can be
ignored. Only those natural marks that can be clearly
discerned by the human eye are selected, thereby ensur-
ing the best possible choice. Additionally, the use of
this software is beneficial as a clear image focus is not as
stringent a requirement for spot patterns as it would be
for other natural marks, such as notches and tears in
fins. We believe that this is more beneficial to correct
identification of individuals and represents a preferable
option over the system reported by Arzoumanian,
Holmberg & Norman (2005).
We found that pattern-matching performance improved
with a greater number of reference images against which
comparisons could be made. Thus the image catalogue
should include at least three good-quality images of
each individual for efficient identification. In many
historical studies of photo-identification, only the best
quality images are retained as the reference material
because of limited storage capacity for photographs.
The use of I3S therefore may require a larger catalogue
of images to be maintained, but as it is all digital and
the software searches the entire catalogue, irrespective
of the order of image storage, we believe that a typical
modern computer hard-drive would provide ample
storage space.
Ideally, photographs of both sides of the animal
should be obtained (Würsig & Jefferson 1990) but this
is not always possible. We elected to consistently
photograph the left side to avoid any possible confu-
sion. Although the current application was specifically
tailored to use only the left side of the animal, it is a
simple step to include both sides of the animal in future
versions. The recognition performance could be further
enhanced by making a distinction between males,
females and animals of unknown sex. Sex is difficult
to determine in small individuals, when the claspers of
the male may not yet be clearly distinguishable, or if the
angle of the photograph does not allow the claspers to
be seen.
An added benefit of our system is that only one
computer-based package is required and the entire
process, from image download, spot selection and
matching, requires less than 5 min if the shark already
exists in the database. If a new shark is recorded that has
not been previously identified, then a more rigorous
visual inspection of the database is necessary but still
will be completed in a substantially shorter time than
reported for other identification software packages
(Arzoumanian, Holmberg & Norman 2005).
The I3S software therefore would be a useful tool for
long-term studies that inevitably include large databases.
Table 2. The annual sightings and resightings of individual C. taurus sharks on Aliwal Shoal
2003 2002 2001 2000 1999 1998 1997 1996 1995
Total identified sharks per year
72 76 67 53 112 6 25 16 2
Number of sharks previously sighted at least once before (% of total)
45 49 36 27 37 3 8 1
(62·5) (64·5) (53·7) (50·9) (33·0) (50) (32·0) (6·3)
Table 3. A summary of the annual occurrence of 10 sharks photo-identified on Aliwal
Shoal from 1995 to 2003
Name Sex 2003 2002 2001 2000 1999 1998 1997 1996 1995
Tick F + + + +
Nick M +++++ +
Halfpec F + + + + +
Leigh F + + + +
Walter M ++++++ +
Mary F + + + + +
Neville M +++++
VicM++++
Dice 5 M + + + +
UDparachute M + + + +
279
Computer-aided
photo-
identification of
C. taurus
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology, 44,
273–280
The non-invasive nature of photo-identification mark–
recapture studies makes them ideal for assisting in
population estimates of critically endangered species
such as C. taurus on the east coast of Australia (Otway,
Bradshaw & Harcourt 2004). In this study at Aliwal
Shoal, use of the I3S software substantially eased the
process of correctly identifying individuals and allowed
an estimation of the numbers of animals visiting the
reef each year.
Additionally, this software can assist in obtaining
more detailed data on the movements and residency
status of individual animals. This study highlights the
apparent philopatric nature of C. taurus to a particular
reef system, with individuals apparently returning
on a near-annual basis to this reef. Whether the Aliwal
Shoal serves as a critical habitat for individuals of
C. taurus, and thus may influence the entire population,
requires confirmation of the nature and regularity
of visits to the reef with more substantial data capture
effort. Nevertheless, this work highlights the importance
that protection of individual reefs may have for con-
servation of the species. The I3S software may then serve
as an important tool in assuring accurate data analysis
in international efforts to protect particular species
(such as C. taurus) through proclamation of marine
protected areas.
In its present form, the system has only been rigorously
used and tested with C. taurus. The possibility that
natural markings on the sharks may fade or change
over time does not appear to be a concern. Even with
intervals of several years between photographs, the sharks
can generally be readily distinguished. Potentially the
system could be adapted for other similarly shaped
animals with spots (or other consistent features). Indeed,
trials with tiger sharks and the shorttail stingray
Dasyatis brevicaudata are being considered, while current
tests with whale sharks are extremely promising (Speed,
Meekan & Bradshaw 2007).
Acknowledgements
We thank Graham Thurman and Bryce Allen for the
earlier photographs. We greatly appreciate the support
from the CSIR, Natal Sharks Board, Aliwal Shoal
dive charter operators and the scuba divers who
provided photographs or dive assistance. WWF-SA
Nedbank Green Trust and the South African National
Research Foundation (NRF) provided funding for
the project. A. T. Forbes provided insightful comments
on an earlier draft of the manuscript, as did M.
Smale and an anonymous referee. This technique
was developed during the first author’s doctoral
studies.
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Received 27 March 2006; final copy received 26 November 2006
Editor: Chris Frid
