ArticlePDF Available

A computer-aided program for pattern-matching of natural marks on the spotted raggedtooth shark Carcharias taurus

Authors:

Abstract and Figures

Summary • 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. • 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. • 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. • 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. Journal of Applied Ecology (2007) 44, 273–280 doi: 10.1111/j.1365-2664.2006.01273.x
Content may be subject to copyright.
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
A. M. van
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.
References
Allen, B. & Peddemors, V.M. (2001) Photo-identification
as a long-term mark–recapture technique for individual
raggedtooth shark C. taurus recognition. 6th Indo-Pacific
Fish Conference, Durban, South Africa, 20–25 May 2001.
Scientific Programme and Book of Abstracts, p. 13. Oceano-
graphic Research Institute, Durban (abstract only).
Anderson, S.D. & Goldman, K.J. (1996) Photographic
evidence of white shark movements in California waters.
California Fish and Game, 82, 182–186.
Arzoumanian, Z., Holmberg, J. & Norman, B. (2005) An
astronomical pattern-matching algorithm for computer-aided
identification of whale sharks Rhincodon typus. Journal of
Applied Ecology, 42, 999–1011.
Castro, A.L.F. & Rosa, R.S. (2005) Use of natural marks on
population estimates of the nurse shark Ginglymostoma
cirratum, at Atol das Rocas Biological Reserve, Brazil.
Environmental Biology of Fishes, 72, 213–221.
Cliff, G. (1989) Breeding migration of the sand tiger shark
Carcharias taurus. South African waters. Abstracts of the
5th Annual Meeting, American Elasmobranch Society, San
Francisco, 17–23 June 1989, p. 76. San Francisco State Uni-
versity and California Academy of Science.
Compagno, L.J.V. (2001) Sharks of the World. An Annotated
and Illustrated Catalogue of Shark Species Known to Date.
Volume 2. Bullhead, Mackerel and Carpet Sharks (Hetero-
dontiformes, Lamniformes and Orectolobiformes). FAO Species
Catalogue for Fishery Purposes, 1, Vol. 2. FAO, Rome, Italy.
Dufalt, S. & Whitehead, H. (1995) An assessment of changes
with time in the marking patterns used for photo-identification
of individual sperm whales Physeter macrocephalus. Marine
Mammal Science, 11, 335–343.
Hammond, P.S. (1986) Estimating the size of naturally marked
whale populations using capture–recapture techniques.
Behaviour of Whales in Relation to Management (ed. G.P.
Donovan), Special Issue No. 8, pp. 253–282. International
Whaling Commission, Cambridge, UK.
Hammond, P.S. (1990) Heterogeneity in the Gulf of Maine?
Estimating humpback whale population size when capture
probabilities are not equal. Individual Recognition of Ceta-
ceans (eds P.S. Hammond, S.A. Mizroch & G.P. Donovan),
Special Issue No. 12, pp. 135–139. International Whaling
Commission, Cambridge, U.K.
Ireland, D. (1984) Shark. Underwater, 11, 49–53.
Karczmarski, L. & Cockcroft, V.G. (1998) Matrix photo-
identification technique applied in studies of free-ranging
bottlenose and humpback dolphins. Aquatic Mammals,
234, 143–147.
Klimley, A.P. & Anderson, S.D. (1996) Residency patterns of
white sharks at the South Farallon Islands. Great White
Sharks: The Biology of Carcharodon Carcharias (eds
A.P. Klimley & D.G. Ainley), pp. 365–373. Academic
Press, San Diego, CA.
Lorenz, K. (1937) Imprinting. Auk, 54, 245–273.
Myrberg, A.A. & Gruber, S.H. (1974) The behaviour of the
bonnethead shark Sphyrna tiburo. Copeia, 2, 358–374.
Otway, N.M., Bradshaw, C.J.A. & Harcourt, R.G. (2004)
Estimating the rate of quasi-extinction of the Australian
grey nurse shark Carcharias taurus population using
deterministic age- and stage-classified models. Biological
Conservation, 119, 341–350.
Peddemors, V.M. (1995) Diver impact on raggedtooth shark
distribution and behaviour on the Aliwal Shoal, KwaZulu-
Natal. Proceedings of the Aliwal Shoal Workshop Held at the
Amanzimtoti Town Hall on 8th June 1995 (ed. M.H. Schleyer),
pp. 26–27. Unpublished Reports no. 117. Oceanographic
Research Institute, Durban.
Peddemors, V.M. & Thurman, G. (1996) The use of photo-
identification techniques for recognition of raggedtooth
sharks on Aliwal Shoal, KwaZulu-Natal: implications
for sport divers. 9th Southern African Marine Science Sym-
posium 21–23 November 1996, South Africa. University of
Cape Town, Cape Town.
Pollard, D. & Smith, A. (2000) Carcharias taurus. 2006 IUCN
Red List of Threatened Species. IUCN, Gland, Switzerland.
www.iucnredlist.org [accessed 15 August 2006].
280
A. M. van
Tienhoven et al.
© 2007 The Authors.
Journal compilation
© 2007 British
Ecological Society,
Journal of Applied
Ecology, 44,
273–280
Pollard, D., Gordon, I., Williams, S., Flaherty, A. & McAuley, R.
(2003) Carcharias taurus (east coast of Australia). 2006 IUCN
Red List of Threatened Species. IUCN, Gland, Switzerland.
www.iucnredlist.org [accessed 15 August 2006].
Press, W., Teukolsky, S., Vetterling, W. & Flannery, B. (1992)
Numerical Recipes in C: the Art of Scientific Computing,
pp. 22–32. Cambridge University Press, Cambridge, UK.
Ramsay, P. (1998) Marine Geological Survey of Aliwal Shoal,
Kwazulu Natal. Report 1998–0049. Council for Geoscience,
Durban, South Africa.
Smale, M. (2002) Occurrence of Carcharias taurus in nursery
areas of the Eastern and Western Cape, South Africa.
Marine and Freshwater Research, 53, 551–556.
Speed, C.W., Meekan, M.G. & Bradshaw, C.J.A. (2007) Spot
the match: wildlife photo-identification using information
theory. Frontiers in Zoology, 4: 2. DOI: 10.1186/1742-9994-
4-2.
Whitehead, H., Christal, J. & Tyack, P.L. (2000) Studying ceta-
cean social structure in space and time: innovative techniques.
Cetacean Societies: Field Studies of Dolphins and Whales
(eds J. Mann, R.C. Connor, P.L. Tyack & H. Whitehead),
pp. 65–87. University of Chicago Press, Chicago, IL.
Würsig, B. & Jefferson, T.A. (1990) Methods of photo-
identification for small cetaceans. Individual Recognition
of Cetaceans (eds P.S. Hammond, S.A. Mizroch & G.P.
Donovan), Special Issue No. 12, pp. 43–52. International
Whaling Commission, Cambridge, UK.
Received 27 March 2006; final copy received 26 November 2006
Editor: Chris Frid
... In practice, these constraints limit the use of photos from citizen scientists and some marine biologists still do the identification manually using a handcrafted decision tree. A common idea that has been applied to several species for recognizing individual animals is to search for an affine transformation matching patterns present in two distinct images (lizards [7], arthropods [8], sharks [9], turtles [10]). However, this approach requires annotating body landmarks on each individual image in the same order. ...
... Matching natural patterns has been approached by exhaustively generating two-dimensional affine transformations based on user provided key points and comparing each transformation of a candidate example with the examples stored in a repository [7], [8], [9], [10]. The algorithm was implemented in a solution called APHIS (Automated Photo-Identification Suite) and applied for re-identification of lizards [7], arthropods [8], spotted raggedtooth sharks [9] and turtle flippers [10]. ...
... Matching natural patterns has been approached by exhaustively generating two-dimensional affine transformations based on user provided key points and comparing each transformation of a candidate example with the examples stored in a repository [7], [8], [9], [10]. The algorithm was implemented in a solution called APHIS (Automated Photo-Identification Suite) and applied for re-identification of lizards [7], arthropods [8], spotted raggedtooth sharks [9] and turtle flippers [10]. However, the method requires a user to select key points and identify the most distinctive spots for each image. ...
... Therefore, more automated identification processes are required to facilitate landscape-level population studies and provide opportunities for collaborative datasets among researchers, community scientists, law enforcement, and conservation practitioners. For example, photo identification of individuals has been used for a wide range of species such as sharks (Van Tienhoven et al. 2007;Holmberg et al. 2009), rays (González-Ramos et al. 2017, insects (Caci et al. 2013), reptiles (Knox et al. 2012;Moro and MacAulay 2014;Bauwens et al. 2017), amphibians (Hoque et al. 2011) and mammals (Kelly 2001;Hiby et al. 2009;Halloran et al. 2015). Although data from photo identification has been used to estimate population trajectory (Holmberg et al. 2009) and survival estimates (Morrison et al. 2011), accurate identification of individuals is key for monitoring endangered species as misclassifications can result in inflated population estimates (Suriyamongkol and Mali 2018;Johansson et al. 2020). ...
... Key points were represented as circles on the plastron (see Figure 1), which were compared between each pair of individuals in the database. A distance metric was calculated in I 3 S, which is the sum of the distances between each key point pair divided by the square of the number of key point pairs (Van Tienhoven et al. 2007; den Hartog and Reijns 2014). ...
... For example, in Nova Scotia, only a single plastron photo was required to identify an individual Blanding's turtle whose recaptures were 13 years apart. Nevertheless, photographing individuals across multiple years remains important as identification accuracy increases as reference photos are added to the database (Van Tienhoven et al. 2007;Moro and MacAulay 2014). ...
Article
Full-text available
The ability to uniquely identify individuals is critical to estimating and monitoring trends in population sizes, one of the key metrics used to evaluate a species' conservation status and success of mitigation strategies. For freshwater turtles, shell notching and/or passive integrated transponder (PIT) tags are commonly used to mark individuals. However, because notch codes and PIT tags can be lost over time and require more invasive procedures, we explored if photographs offer an effective method to reliably identify individuals. The Blanding's turtle (Emydoidea blandingii) is a globally endangered species with distinct black and yellow markings on its plastron. We used the I 3 S Pattern software with custom parameters to classify patterns on Blanding's turtle plastrons and to identify individuals. We MARKLE et al. 48 analyzed 826 plastron images from 707 individual Blanding's turtles taken between 1998 and 2019 from 12 study areas distributed throughout their Canadian range. When plastron photos were pooled across the sampled range (i.e., all study areas), there was an 84% probability of correctly identifying an individual turtle within the top 3 suggested matches, whereas when identifying Blanding's turtles within a specific study area, identification accuracy was 82% in Central Ontario and 97% in Nova Scotia. Individual identification from plastron markings did not work well in areas where iron staining obscured the plastron pattern or for hatchlings and juveniles whose patterns changed over time. For example, the only misclassification in the Nova Scotia study area was for a turtle with photos through various life stages. In areas without iron staining, plastron photo identification offers a cost-effective, non-invasive method to identify individual adult Blanding's turtles to support population monitoring and community science initiatives, and has the potential to assist with range-wide coordination to counteract illegal wildlife trade.
... Although elasmobranchs are resilient to a range of external wounds, shark-inflicted injuries can negatively affect their fitness, reproductive abilities and survival through the impairment of swimming and organs of the sensory and reproductive systems (e.g., Marshall & Bennett, 2010). However, there is little published data on healing rates, recovery and survival in free-ranging elasmobranchs, mainly due to the opportunistic and rare nature of those sightings (Chin et al., 2015;Marshall & Bennett, 2010;McGregor et al., 2019;Womersley et al., 2021). ...
... Similarly, a bite wound on an adult blacktip reef shark (Carcharhinus melanopterus) was completely healed within 40 days (Chin et al., 2015). As such, baseline information on wound healing in sensory organs could help to understand the regenerative capacities of elasmobranchs (e.g., McGregor et al., 2019). However, to date, there are no published studies of healing rates and recovery from both natural and anthropogenic injuries in organs of the sensory system of elasmobranchs. ...
... Records showed that the whitespotted eagle ray had the capacity to recover from the wounds, although they have led to the blindness of the eye. Based on the high capacity that elasmobranchs have to recover from wounds (e.g., Chin et al., 2015;Marshall & Bennett, 2010;McGregor et al., 2019), it is possible that the complete healing of the observed whitespotted eagle ray occurred earlier than 253 days elapsed after the first record. ...
Article
Here we provided the first photographic records of the eye healing of a free-ranging whitespotted eagle ray (Aetobatus narinari) following shark-inflicted bite injuries on the cephalic region. The whitespotted eagle ray with fresh wounds on the cephalic region close to its right orbit, upper jaw, and the anterior margin of its right pectoral fin was photographed on 19 July 2017 at the Fernando de Noronha Archipelago. Two subsequent photographs of the whitespotted eagle ray with a blind right eye were taken on 29 March 2018 and 18 April 2018. These records showed the whitespotted eagle ray had the capacity to recover from the wound, although they have led to the blindness of the eye. These findings also demonstrate this individual was able to survive for at least nine months with a non-functional eye. This article is protected by copyright. All rights reserved.
... Image processing and pattern matching techniques have been used to automatically identify individuals of whale-sharks [2], spotted raggedtooth sharks [27], and patterned terrestrial animals [6,24]. However, recently Siamese networks and the use of triplet loss have become a popular means for handling re-identification problems within marine vision. ...
Article
Full-text available
We present the first work where re-identification ofthe Giant Sunfish (Mola alexandrini) is automated using computer vision and deep learning. We propose a pipeline that scores an mAP of 60.34% on a full rank of the novel TinyMola dataset which includes 31 IDs and 91 images. The method requires no domain-adaptation or training which makes it especially suited for low-budget or volunteer-based projects, like Match My Mola, as part of a human-in-the-loop model. The pipeline includes segmentation, keypoint detection and description, keypoint matching, and ranking. The choice of feature descriptor has the largest impact on the performance and we show that the deep learning based SuperPoint descriptor greatly outperforms handcrafted descriptors like SIFT and RootSIFT independent of the segmentation level and matching method. Combining SuperPoint and the graph neural network based SuperGlue matching method produces the best results.
... En caso de que un patrón de manchas no fuera visualmente identificable en las aletas pélvicas, se realizó la identificación al comparar completamente el patrón dorsal completo. La corroboración de las muestras recapturadas se analizó con el software Interactive Individual Identification System (I 3 S) [15], siguiendo metodologías descritas anteriormente [6]. ...
Conference Paper
Full-text available
La Raya Águila de manchas blancas del Pacífico, Aetobatus laticeps, se ha separado recientemente de A. narinari en el Atlántico, con base a evidencia tanto morfológica como genética. Esta especie se caracteriza por un cuerpo oscuro con numerosas manchas blancas por todo el dorso. Considerando el tipo, la forma, el número y la distribución de estas marcas naturales como identificadores potenciales a nivel individual, estudiamos la variación en los patrones de manchas.
... When possible, researchers estimated size, determined sex, and collected photos for individual identification. Identification photos were analyzed using both the Groth (Arzoumanian et al., 2005) and the Interactive Individual Identification System (I 3 S) (Van Tienhoven et al., 2007) algorithms to identify individuals and flag potential duplicate samples (Cochran et al., 2016;. Suspected duplicates were retained and eventually sequenced to confirm photo identification and to ensure that the highest quality sample was used for further analysis. ...
Article
Full-text available
The whale shark Rhincodon typus is found throughout the world's tropical and warm-temperate ocean basins. Despite their broad physical distribution, research on the species has been concentrated at a few aggregation sites. Comparing DNA sequences from sharks at different sites can provide a demographically neutral understanding of the whale shark's global ecology. Here, we created genetic profiles for 84 whale sharks from the Saudi Arabian Red Sea and 72 individuals from the coast of Tanzania using a combination of microsatellite and mitochondrial sequences. These two sites, separated by approximately 4500 km (shortest over-water distance), exhibit markedly different population demographics and behavioral ecologies. Eleven microsatellite DNA markers revealed that the two aggregation sites have similar levels of allelic richness and appear to be derived from the same source population. We sequenced the mitochondrial control region to produce multiple global haplotype networks (based on different alignment methodologies) that were broadly similar to each other in terms of population structure but suggested different demographic histories. Data from both microsatellite and mitochondrial markers demonstrated the stability of genetic diversity within the Saudi Arabian aggregation site throughout the sampling period. These results contrast previously measured declines in diversity at Ningaloo Reef, Western Australia. Mapping the geographic distribution of whale shark lineages provides insight into the species' connectivity and can be used to direct management efforts at both local and global scales. Similarly, understanding historical fluctuations in whale shark abundance provides a baseline by which to assess current trends. Continued development of new sequencing methods and the incorporation of genomic data could lead to considerable advances in the scientific understanding of whale shark population ecology and corresponding improvements to conservation policy.
... 1. manual procedures, even state-of-the-art ones, independent of the species being studied, are time consuming and subject to a bias introduced by the human operator that performs photo-identification [15][16][17]; ...
Chapter
Photo-identification is the non-invasive process of uniquely identifying an individual among a set of individuals, based on the analysis of one or more photos. This is a specific task in cetaceans’ abundance and distribution studies, which can be effectively automated using computer vision and deep learning algorithms in large-scale studies. In this chapter, recent advances in the photo-identification of Risso’s dolphins are presented, covering the process from manual approaches to modern deep learning techniques. This manuscript highlights the strong multidisciplinary approach that is mandatory to accelerate and bring innovations working in multiple domains (marine biology and computer science in this case study). Particular attention is also given to the importance of data sharing, especially because it can be seen as a mandatory step that enables the proficient use of modern deep learning approaches to photo-identify a specimen. In the first part of the chapter, we present the state-of-the-art methods currently applied to the photo-identification task; the second part is devoted to describing the Smart Photo-Identification of Risso’s dolphins (SPIR) methods developed by our research team. Finally, future perspectives and directions of this research are discussed.
Article
Color and pattern are often dynamic traits that change throughout an individual's lifetime. Still, long‐term shifts in coloration have received limited attention. Dendrobatid poison frogs are a classical system in the study of color and pattern evolution in which both sexual selection and predation avoidance are thought to drive the evolution of color and pattern at the population and species level. Here, we highlight an overlooked axis of pattern diversity, within individual variation, using three species in the genus Dendrobates. We collected longitudinal photographs of individuals at the National Aquarium to test the hypothesis that patterns shift predictably throughout the lifetimes of individual frogs. In all three species, we found a consistent reduction in the relative area of aposematic color as individuals aged and that the rate of pattern shift did not differ between the sexes. Consequently, within individual variation in coloration may confound inferences from ecological studies that inherently assume individual pattern is static. Finally, we note that using simple and noninvasive photography protocols, animals in zoos and aquaria have the potential to deepen our understanding of how color and pattern change throughout the lifetimes of a wide range of species. Color and pattern often change throughout an individual’s lifetime. However, this intra‐individual variation has received limited attention. We demonstrate consistent shifts in the color patterns of three species of poison frogs through time characterized by increasing melanization.
Article
Full-text available
Natural marks have increasingly been used as a tool for individual identification. One of the most popular techniques used by natural marks as an individual recognition tool is photo-identification. Photo-identification is a non-invasive alternative to traditional marking, which allows individual recognition of species through time and space. In this study, the APHIS (Automatic Photo Identification Suite) software has been evaluated as software capable of identifying individuals of Acherontia atropos (Linnaeus, 1758). The SPM (Spot Pattern Matching) and ITM (Image Template Matching) procedures were tested and found to achieve 100% success of individuals recognition. Thus, for the first time in a Sphingidae, the colour pattern of the dorsal part of the thorax of A. atropos is demonstrated to represent a suitable natural mark for individual recognition.
Article
Full-text available
Many studies have revealed that animal vocalizations, including those from mammals, are individually distinctive. Therefore, acoustic identification of individuals (AIID) has been repeatedly suggested as a non-invasive and labor efficient alternative to mark-recapture identification methods. We present a pipeline of steps for successful AIID in a given species. By conducting such work, we will also improve our understanding of identity signals in general. Strong and stable acoustic signatures are necessary for successful AIID. We reviewed studies of individual variation in mammalian vocalizations as well as pilot studies using acoustic identification to census mammals and birds. We found the greatest potential for AIID (characterized by strong and stable acoustic signatures) was in Cetacea and Primates (including humans). In species with weaker acoustic signatures, AIID could still be a valuable tool once its limitations are fully acknowledged. A major obstacle for widespread utilization of AIID is the absence of tools integrating all AIID subtasks within a single package. Automation of AIID could be achieved with the use of advanced machine learning techniques inspired by those used in human speaker recognition or tailored to specific challenges of animal AIID. Unfortunately, further progress in this area is currently hindered by the lack of appropriate publicly available datasets. However, we believe that after overcoming the issues outlined above, AIID can quickly become a widespread and valuable tool in field research and conservation of mammals and other animals.
Article
Grey nurse sharks off the east coast of Australia are listed nationally as critically endangered under Schedule 1 of the Environmental Protection and Biodiversity Conservation Act (1999) and may number no more than 300 in New South Wales and southern Queensland waters. They are an inshore, coastal dwelling species and were severely depleted by spearfishing in the 1960s. The population has continued to decline despite protection since 1984. Their life history (long-lived to 25+ years), late maturation (6-8 years), low fecundity (maximum 2 live young biennially), specific habitat requirements, limited inshore distribution, and small population size render them particularly vulnerable to extinction. We estimated the time to quasi-extinction (years elapsed for the population to consist of less than or equal to50 females) for the grey nurse shark population off the east coast of Australia based on current estimates of abundance and known anthropogenic rates of mortality. Estimated minimum population size was 300 as of 2002, and minimum anthropogenic mortality assessed from recovered carcasses was 12/year of which 75% were females. We modelled time to quasi-extinction using deterministic age- and stage-classified models for worst-, likely and best-case scenarios. Population size was estimated at 300 (worst), 1000 (likely) and 3000 (best). Anthropogenic mortality was added to the model assuming either all carcasses are being recovered (best), or conservatively, that only 50% are reported (realistic). Depending on model structure, if all carcasses are being reported, quasi-extinction times for worst-, likely and best-case scenarios range from 13 to 16 years, 84-98 years and 289324 years, respectively. If under-reporting is occurring, time to quasi-extinction ranges from 6 to 8 years, 45-53 years and 173-200 years, respectively. In all scenarios modelled the grey nurse shark population will decline if no further steps are taken to remove anthropogenic sources of mortality. Because estimates of quasi-extinction rate depend on initial population size, and sensitivity analysis revealed that population rate of change was most sensitive to changes in the survival probability of the smallest length classes, obtaining precise estimates of abundance and annual survival of young females is critical.
Article
Behavioral activities of a colony of 10 bonnethead sharks, Sphyrna t. tiburo, held under semi-natural conditions, were examined over a period of six months. All sharks had attained, or were approaching, sexual maturity. Objectives of the study were to describe species-typical motor patterns and postures, to analyze the diurnality of patrolling activity and to characterize pattern(s) of organization underlying social interactions noted within the colony. Eighteen postures and patterns of movement were described, almost half of them having apparent social relevance. In specific instances, functional significance of a pattern was cautiously given. Patrolling activity appeared to have a diurnal rhythm, with a peak occurring in the late afternoon; smaller individuals were more erratic in their patrolling. Finally, a clear but subtle social organization, based on a straight-line, size-dependent, dominance hierarchy was found. Though position within the hierarchy was not determined by sex, data indicated that all individuals tended to shy away from larger males. Sexual differences in the performance of certain patterns of movement were also established.
Article
This study was initiated to investigate the occurrence, size and stage of sexual maturity of ragged tooth sharks (Carcharias taurus) in their putative nursery areas. Information on size and seasonality of catches was obtained from the National Marine Linefish System revealed that juvenile and immature individuals occur in the Eastern Cape throughout the year and make up ~80% of the line-fishing catches. Their occurrence west of Storms River along the south coast of the Western Cape is more common in summer. Individuals large enough to be sexually mature are found in the Eastern Cape almost throughout the year. This suggests that the Eastern Cape is both the both primary and secondary nursery area for this shark. The smallest individuals (<9 kg) occur mainly from September to December. Eastern Cape samples comprised 95 males ranging between 84 cm (4.5 kg) and 262 cm total length (120 kg) and 166 females of 985 cm (5.5 kg) to 288 cm (163 kg). Size at maturity conformed to previous studies and one pregnant female was recorded in October. Catches were from the intertidal zone to 100 m. Underwater observations showed the sharks to prefer high-relief reefs with caves and gullies by day.
Article
International Whaling Commission Decisions - Volume 2 Issue 4 - Robert M. White