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Seeing Spots: Photo-identification as a regional tool for whale shark identification

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Abstract
The identification of individual animals over temporal and spatial scales can provide robust estimates of population size and distribution. While marker tagging can provide an option to achieve this, it can be problematic both in terms of tag loss and the associated difficulties and effects of attaching the tags. Photo-identification of distinctive characteristics which remain stable over time has replaced tagging in some species but usage at regional scales has been hampered by a lack of standardisation of matching methods. We describe the use of a semi-automated computer program (I 3 S) for matching the spot patterns of whale sharks, Rhincodon typus, in the Seychelles aggregation and compare this to images captured from other areas in the Western Indian Ocean. Sharks totalling 443 individuals were uniquely identified in the Seychelles from 2001 – 2009, 109 of which were seen in multiple years. Conventional open mark-recapture models for 2004 – 2009 gave an abundance estimate of 469 to 557 sharks (95% C.I.). I 3 S digital fingerprints were shared with researchers in Djibouti, Mozambique, and Tanzania and, while no matches were found between locations, the ease with which regional comparisons were made will help to define whether the shark populations in these areas are distinct, enabling long-term and broad-scale regional comparisons.
Seeing Spots: Photo-identication as a Regional Tool for
Whale Shark Identication
Katie Brooks1, David Rowat1, Simon J. Pierce2, Daniel Jouannet3 and Michel Vely3
1Marine Conservation Society Seychelles, Box 384, Victoria, Seychelles;
2Marine Megafauna Foundation, Tofo Beach, Mozambique;
3Megaptera, 23 Rue Alexandre Dumas, 75011, Paris, France.
Keywords: Whale shark, Rhincodon typus, Indian Ocean, photo-identication,
mark-recapture, population estimation.
AbstractThe identication of individual animals over temporal and spatial scales
can provide robust estimates of population size and distribution. While marker
tagging can provide an option to achieve this, it can be problematic both in terms
of tag loss and the associated difculties and effects of attaching the tags. Photo-
identication of distinctive characteristics which remain stable over time has replaced
tagging in some species but usage at regional scales has been hampered by a lack
of standardisation of matching methods. We describe the use of a semi-automated
computer program (I3S) for matching the spot patterns of whale sharks, Rhincodon
typus, in the Seychelles aggregation and compare this to images captured from other
areas in the Western Indian Ocean. Sharks totalling 443 individuals were uniquely
identied in the Seychelles from 2001 – 2009, 109 of which were seen in multiple
years. Conventional open mark-recapture models for 2004 – 2009 gave an abundance
estimate of 469 to 557 sharks (95% C.I.). I3S digital ngerprints were shared with
researchers in Djibouti, Mozambique, and Tanzania and, while no matches were
found between locations, the ease with which regional comparisons were made will
help to dene whether the shark populations in these areas are distinct, enabling
long-term and broad-scale regional comparisons.
Western Indian Ocean J. Mar. Sci. Vol. 9, No. 2, pp. 185-194, 2010
© 2010 WIOMSA
Corresponding Author: DR
E-mail: david@mcss.sc
INTRODUCTION
Whale sharks (Rhincodon typus) are pan-
oceanic planktivores that were rst described
from a specimen captured in the Western Indian
Ocean in 1828 (Smith, 1828). They are listed by
the IUCN as Vulnerable (VU A1bd+2d ) based
on observed reductions in landings, actual
levels of exploitation and because further
population decline is deemed likely to occur if
directed sheries remain unmanaged (IUCN,
2009). In November 1999, the whale shark
was added to Appendix II of the Convention on
Migratory Species (CMS) as “a species whose
conservation status would benet from the
implementation of international co-operative
Agreements”. The species was also listed in
Appendix II of the Convention on International
Trade in Endangered Species (CITES) in
November 2002.
Despite broad international interest in the
conservation status of whale sharks, relatively
little is known about regional population sizes
or trends in abundance. Some local declines in
the Indian Ocean have been linked to targeted
sheries (Anderson & Ahmed, 1993; Hanfee
2001; Theberge & Dearden, 2006). In Western
Australia, a decline in shark abundance and
average body length based on tourism operator-
collected data was attributed to shing in other
areas of this population’s range (Bradshaw et
al., 2007 & 2008); however, this decline has
been debated (Holmberg et al., 2008, 2009).
The need to quantify the population abundance
of whale sharks at both local and regional
(oceanic) scales thus remains a priority for
conservation management.
In the Western Indian Ocean, whale sharks
are known to aggregate seasonally around
the islands of the Seychelles (Rowat, 1997;
Fowler, 2000; Rowat & Gore, 2007 ). The
temporal and spatial extent of their distribution
has been monitored intensively since 2001 and
it has been found that the population comprises
a mixture of both site-faithful and migratory
individuals (Rowat et al., 2008; Rowat et
al., 2009a). Seasonal aggregations are also
known to occur in various other coastal sites
throughout the Indian Ocean, specically
Djibouti (Rowat et al., 2006), Madagascar
(Jonahson & Harding, 2007), the Maldives
(Anderson & Ahmed, 1993), Mozambique
(Speed et al., 2008), South Africa (Cliff et al.,
2007), Tanzania (Mahingika & Potenski, 2009)
and Western Australia (Meekan et al., 2006).
Whale sharks need to be uniquely
identied to monitor demographics and
estimate population numbers through
Catch-Mark-Recapture (CMR) modelling
techniques. Sharks can be uniquely identied
by spot patterns on their skin, the area
posterior to the fth gill slit being particularly
suited to this purpose (Arzoumanian et al.,
2005; Speed et al., 2007). With the advent of
digital photography, underwater photography
has become cheaper and easier; digital
image les are also readily manipulated
which has promoted the development and
use of photo-identication software. Once
individuals are uniquely identied, their re-
sighting in subsequent years can be used to
develop population abundance estimates. On
a regional scale, comparisons can be made
between different sites to see if individuals
frequent multiple aggregation sites, thereby
providing an indirect examination of regional-
scale migrations and potentially enabling
large-scale population estimates.
METHODS
Study area
Seychelles
The study area has been previously described
(Rowat et al., 2009a, b). Briey, the granitic
islands of Seychelles are situated on a shallow
continental plateau at 4° S and 55° E in the path
of the westward owing Southern Equatorial
Current in the Western Indian Ocean (New
et al., 2005). From June to October, seasonal
winds blow from the southeast, resulting
in localised primary productivity and the
appearance of whale sharks and other
planktivores such as manta and devil rays
(Manta birostris and Mobula spp.). The study
area was the coastal zone around the island
of Mahe extending to a maximum of 4 km
offshore.
Regional Data
Whale shark photo-ID data were exchanged
with other research programmes operating
in Djibouti, Mozambique and Tanzania to
facilitate regional comparisons.
Identication Studies - Seychelles
Aerial surveys were undertaken from a delta-
wing micro-light aircraft (Aquilla II, Solo
Wings, South Africa) by experienced pilots
trained in aerial survey techniques. Survey
teams were directed to individual sharks by
radio communication with the spotter aboard
the aircraft. During the peak season, aerial and
boat surveys were carried out on a daily basis,
conditions permitting (Rowat et al., 2009b).
186 K. BROOKS ET AL
Wherever possible, sharks encountered
were sexed by the presence (in males) or
absence (in females) of pelvic claspers, sizes
were estimated by an experienced observer,
and any prominent scars or features were
noted and photographed for identication
purposes. From 2001 – 2004, sharks were
opportunistically tagged with marker tags
(Rowat et al., 2009a). The focal area for
photo-identication was the area posterior
to the gill slits where the spot patterns of
the sharks have been found to be unique to
each individual (Arzoumanian et al., 2005;
Meekan et al., 2006; Speed et al., 2007). The
patterns on the left and right of each shark,
however, are different (Speed et al., 2007)
and therefore both sides were photographed
to prevent duplicate entries in the database.
From 2001 to 2004, photographs were
taken opportunistically for potential photo-
identication; from 2004, affordable digital
underwater cameras increased the number of
images collected.
Digital images were matched using the
computer program I3S (van Tienhoven et al.,
2007), which is an effective tool for semi-
automated photo-identication of whale
sharks (Speed et al., 2007). I3S allows the user
to ’ngerprint’ the spot patterns on the skin of
a whale shark and compare these to similarly
ngerprinted images in the database to see if
the shark has been previously photographed.
The images of the area behind the gill slit
were opened with the I3S program and three
reference points were plotted at (1) the top of
the fth gill slit, (2) the posterior-most point
where the pectoral reaches on the body and
(3) the bottom of the fth gill slit. Specifying
these reference points allowed the program
to re-scale or rotate images as required to
ensure standardised comparisons regardless
of photographer orientation or distance from
the shark. The spots on the shark’s anks
were then marked, allowing the program to
calculate the position of each marked spot
relative to the reference points and to compare
the marked spot’s position, through linear
transformation, with its potential ‘pair’ on
each image in the database (van Tienhoven
et al., 2007). A ‘score’ was derived from
the sum of the distances between the paired
spots divided by the number of pairs. The
program presented to the user a list of the
top fty highest-ranked matches and the user
visually analysed these matches, considering
differences in spot selection, other patterning
and scarring, to conrm the nal selection.
A database of sightings for each individual
shark was compiled for tagged individuals
and those with I3S identities. This was used
to create a combined inter-annual history for
CMR models to estimate population size.
Photo-identities of both the left and right side
were not available for all individuals with I3S
identities and so left-side identities were used
because these were more common than those
for the right-side (381 cf 330).
Population estimates were made using
conventional CMR modelling software. To
estimate the population, assuming a closed
population with no net immigration or
emigration (demographic closure), we used
the program CAPTURE (Otis et al., 1978).
This provided goodness-of-t tests for each
model and the program selected the most
probable model(s) for the dataset.
For estimation of population size using
open population models that do not assume
demographic closure, we used the Cormack-
Jolly-Seber (CJS) model (Schwarz &
Arnason, 1966) in the POPAN option in the
program MARK (White & Burnham, 1999).
The POPAN option in MARK does not offer
a bootstrap goodness-of-t, so a recaptures-
only (CJS) analysis was run in MARK, using
the same data to allow a bootstrap goodness-
of-t to the model.
Identication Studies - Regional Data
Whale shark photo-ID data obtained from
other organisations within the Indian Ocean
were examined and re-processed where
necessary for comparison using I3S. The
ngerprinted images were then compiled
with those from the Seychelles into a
regional database to see if any of the sharks
were observed at multiple sites.
Seeing Spots: Photo-Identication as a Regional Tool for Whale Shark Identication 187
RESULTS
Identication Studies - Seychelles
Population Demographics
A total of 443 individual sharks were
identied using photo ID from 2001-2009.
Of these, the sex of 337 individuals was
established, 278 (82.5%) being male and 59
(17.5%) female. The mean size of individuals
identied each year was 5.8 m 1.2 SD),
with the highest size class frequency being
the 5 7.5 m range (51%) followed by the
<5 m range (38%) (Fig. 1a). However, there
were very few sharks recorded below 4 m
(N=23) and, while there were several >7.5 m
(N=59), there were very few >10 m (N=2).
There was some variation in size between
years and between sexes (Fig. 1b) but this
was not signicant.
188 K. BROOKS ET AL
The annual number of photo IDs collected
rose from 2004, as did the percentage of re-
sighted sharks (Table 1). Overall, 109 (24.6%)
of the 443 individuals have been sighted in
multiple years. Two sharks have been seen
in ve different years, 11 in four years, 22 in
three years and 74 in two years. The longest
time-span of sightings was nine years: four
sharks rst seen in 2001 were also seen 2009
as well as in intervening years. The spot
patterns did not change during this period.
Population estimation
Previous attempts to estimate abundance with
re-sighting data from marker tags produced
estimates with a very high error, largely
due to tag loss or deterioration (Rowat et
al., 2009a). Only 34 photo identities were
captured in 2001 - 2003, so population
estimates were only made using the photo ID
data from 2004 – 2009.
Fig. 1a) Size frequency distribution and b) mean shark length
(with standard error) in Seychelles whale shark aggregations in
2001-2009.
0
1
2
3
4
5
6
7
8
2001 2002 2003 2004 2005 2006 2007 2008 2009
Length (m)
Male
Female
Unsexed
a)
b)
Closed population
models generated using
the program CAPTURE
on the latter photo-
identication data indicated
that a model allowing for
variation in the records
due to time, behaviour and
heterogeneity {m(tbh)} was
the most appropriate, but no
population estimator was
available for this model;
also, the data violated the
assumption of closure (Z =
-5.395; P<0.001).
In the program MARK,
candidate models are
ranked by the likelihood of
the goodness-of-t of the
data (c) to the individual
models based on Akaike’s
Information Criterion (AIC)
values and weights (Akaike,
1973). Bootstrap goodness-
of-t is not available with the
POPAN model and we thus
rst modelled the data with
Seeing Spots: Photo-Identication as a Regional Tool for Whale Shark Identication 189
the recaptures-only model that has this option.
The model {Φ(.) p(t)} with time-dependent
variability was ranked the highest. However,
the bootstrap goodness-of-t simulation
yielded some evidence of over-dispersion (p =
0.022), indicating that the probability of capture
was not uniform. Using the routine provided
within MARK, the calculated over-dispersion
was ĉ =1.253. Adjusting the AIC accordingly
within the POPAN open population models, an
abundance estimate of 469 to 557 sharks (95%
C.I., S.E. = 22) was ranked the highest. This
was based on the constant model {Φ(.) p(.)β
(.)N(.)} (Table 2), with a high probability of
capture; however, the level of entry into the
population could not be estimated.
Identication Studies - Regional)
Population Demographics
A total of 1069 individual sharks were identied
in the western Indian Ocean, Seychelles,
Djibouti, Mozambique and Tanzania. This
dataset includes individuals identied in 2009
in Djibouti, Mozambique and Seychelles. In
all three of these aggregations, the number
of males was much greater than females, 76-
83% of the individuals being male (Table 3).
In the Djibouti aggregation, it was possible
to generate a frequency distribution of size
classes (Fig. 2) that indicated that 81%
(n=133) of the individuals identied were
between 3 m and 5 m, while a further 15%
(n=25) were <3 m.
Population distribution
There were no photo-identication matches
between the different geographic sites. In
Tanzania, 66 whale sharks had been identied
by researchers with marker tags over a three
year period (Mahingika & Potenski, 2009);
however, only three sharks could be used
for photo-identication using I3S, none of
which matched any of the other sharks in
the combined regional data set. None of the
sharks tagged in Tanzania were sighted during
monitoring activities in the Seychelles.
Table 1. Whale shark photo-identication records for Seychelles for 2001- 2009 with details of new
records and re-sightings from previous years.
2001 2002 2003 2004 2005 2006 2007 2008 2009
Sharks identied (N) 15 0 24 19 114 186 88 68 88
New identications 15 0 23 19 108 146 49 37 46
Old identications 0 0 1 0 6 40 39 31 42
% Re-sightings 0% 0% 4% 0% 5% 21% 44% 46% 48%
Table 2. Seychelles whale shark population estimates and parameters derived from Cormack-Jolly-Seber
open population model (POPAN option) for the combined photo-identication and tag data for 2004 - 2009,
with estimates of apparent survival (Φ),capture probability (p), probability of entry into the population (β)
and population size (N)
Real Function Parameters of {Φ(.) p(.)β (.) N(.)}
Parameter Estimate Standard error 95% Condence interval
Lower Upper
1: Φ 0.382 0.021 0.343 0.423
2: p 0.739 0.024 0.690 0.783
3: β 1.000 <0.001 <0.001 1.000
4: N 506.218 22.11 469.299 556.863
DISCUSSION
Although monitoring techniques and intensities
differed substantially between the locations
considered in this study, the results show that
the use of standardised photo-identication
protocols and software processing enables
implementation of inter-site comparisons on
a regional scale. While this is the rst attempt
at a regional comparison, and there may be
further regional photo-identities that can be
included, to date, photo-matching has yet to
show movement of sharks away from the
aggregation found in the Seychelles.
All of the aggregations at the sites included
in this comparison were dominated by
immature male sharks. In excess of 75% of the
population were males, with an average size
of <8 m in each of the aggregations. Analysis
of size frequency classes of Seychelles sharks
showed that there has been little variation in
the sizes of sharks reported and that there are
few small juvenile or adult-sized sharks of >8
m (Fig. 1a). Thus, it appears that, as the sharks
reach adult size, they leave this aggregation.
In comparison, in the Djibouti aggregation,
the average size of sharks was 3.7 m, with
15% of identied individuals being <3 m,
only 5% between 5-7.5 m and no larger
sharks; as such, this may indicate that there is
a further size segregation between neonatal-
sized individuals (1-2 m) and those found in
the coastal aggregations (4-8 m).The average
size and sex ratio of sharks in Seychelles
were similar to those reported in Australia
(Bradshaw et al., 2007), Belize (Heyman et
al., 2001), Mozambique (Simon Pierce pers.
comm.) and Maldives (Riley et al., 2010).
These sites all have juvenile male-dominated
populations. This poses the question as to the
location of the adult sharks.
The problem of poor retention of
conventional tags on this species has previously
been noted (Graham & Roberts 2007). Photo-
identication has provided an estimate of tag
retention time and shown the effects this has
on the estimation of population abundance
(Rowat et al., 2009a). Within the Seychelles
aggregation, the addition of a further two years
of photo-identication data has conrmed the
order of magnitude of the previous population
estimate, with lower margins of error: in 2004
2007 the estimate was 348-488 (95% C.I.,
S.E. = 34) compared to 472 – 561 (95% C.I.,
S.E.= 22) in 2004 – 2009 (Rowat et al., 2009a).
This indicates that, in absolute terms, a small
population of whale sharks is using Seychelles
waters. As with previous estimates, the tests for
closure of the population were violated and the
rate of entry into the population could not be
estimated. These ndings are corroborated by
the results of satellite tracking studies that have
shown that whale sharks move considerable
distances away from the Seychelles (Rowat &
Gore, 2007). However, the regional comparison
made here indicated that there were no matches
between the Seychelles, Djibouti, Mozambique
190 K. BROOKS ET AL
Table 3. Basic population demographics for whale sharks photo-identied in three regional aggregations.
Sharks (N) % Male % Female Mean size (m)
Seychelles 443 83 17 5.7
Mozambique 366 76 24 6.5
Djibouti 257 83 17 3.7
Fig. 2. Size frequency distribution of whale
sharks in the Djibouti aggregation in 2009.
and Tanzania populations, suggesting that
the major “known” aggregation sites in the
Western Indian Ocean are not sharing whale
sharks.
Recent work on the genetic diversity of
whale sharks based on haplotype frequency of
complete mitochondrial DNA control regions
has shown little evidence of geographical
clustering (Castro et al., 2007). This is
corroborated by microsatellite studies of
specimens from the Caribbean, Pacic and
Indian Oceans (Schmidt et al., 2009). There
was some evidence of separation between
Atlantic and Indian Ocean samples, but not
between Indian Ocean and Pacic samples
(Castro et al., 2007). Although sample sizes in
both these studies were relatively small, these
ndings support those of satellite tracking that
show widespread movements of sharks away
from the Seychelles (Rowat & Gore, 2007).
This would tend to promote interbreeding,
at least on ocean-basin scales, leading to
low levels of genetic differentiation between
regions. However, the high re-sighting rate
does indicate at least seasonal philopatric
behaviour.
Of note, most of the samples tested in both
these genetic studies were taken from known
aggregation sites and, where recorded, the sharks
ranged from 2.5 - 13.5 m in length, the average
being 6.25 m (Schmidt et al., 2009). It has been
suggested that the low genetic diversity and lack
of structure between geographically separated
populations is an indication of high maternal
gene ow caused by movement of breeding
females (Bradshaw, 2007). This situation may,
however, be complicated by a bias in sampling
mainly immature individuals at what are almost
certainly feeding aggregation sites (Heyman et
al., 2001; Nelson & Eckert, 2007; Bradshaw et
al., 2007): immature individuals from different
breeding populations may aggregate at these
feeding sites, thereby masking population
separation.
The aforementioned genetic studies
both attempted to estimate the effective
(i.e. breeding) population size based on
generational mutation rates. These estimates
ranged from 27,401–179,794 (Schmidt et al.,
2009) to 119,000–238,000 females (Castro
et al., 2007), although the authors of both
studies urged caution in using these values
because of the assumptions they made and
the small sample sizes. These estimates of the
global population appear to be at odds with the
population estimate presented here and one for
Ningaloo Reef in Western Australia (Meekan
et al., 2006), and strongly suggest that transient
feeding aggregations do not comprise the
only or even the principle communities of this
species (Castro et al., 2007).
These ndings therefore reinforce the
importance of implementing more formal
population monitoring in other areas, both
within the region and globally. Presently,
there are very few locations where adult
sharks or pregnant females are found and
very little is known of small sharks under
3 m. Until such time that adult (breeding)
groups are identied and persistent questions
regarding their life-history are answered, in-
depth and consistent long-term monitoring of
shallow-water aggregations of these sharks
is one of the only ways to estimate the status
of the species. The results also emphasise
the need for an ocean-wide approach to the
conservation and management of this, the
largest extant shark in the world.
Photo-identication can play a useful
role in answering some of these questions.
However, due to the fact that whale sharks
are slow-growing and are known to frequent
particular aggregation sites for long periods,
photo-identication cannot be used in
isolation as it appears that once they leave
these aggregations, the sharks are seldom
seen again. These methods need to be used
on a long-term basis and in conjunction
with other monitoring methods, such as
aerial surveys and satellite tagging, to obtain
information regarding whale shark migrations
and behaviour away from the aggregation
sites. Similarly, genetic studies may yet
show regional population and even familial
relationships if carried out at sufcient
intensity. Monitoring needs to be expanded
regionally and, in particular, to areas known
to have different population demographics.
Seeing Spots: Photo-Identication as a Regional Tool for Whale Shark Identication 191
192 K. BROOKS ET AL
Data currently being collected in Djibouti
may, in years to come, show if the smaller
sharks there join other aggregations of larger
sharks when they mature or whether there
are as yet unknown aggregations in the area.
Photo-identities can be captured by personnel
with minimal levels of training, which
broadens the opportunities for data capture.
As long as suitable photos are taken, identities
can be established, thus enabling the public
to participate in whale shark identication
programmes and promote broad-scale
regional comparisons.
AcknowledgmentsOur thanks to the interns
and volunteers of the Marine Conservation
Society Seychelles for their hard work, to
the general public and diving community
in Mozambique who assisted with data
collection, and to the staff and volunteers of
Megaptera who assisted in Djibouti. Thanks
also to Corey Bradshaw and Mark Meekan for
assistance with population estimate modelling.
Funding was provided by the Save Our
Seas Foundation, the Global Environmental
Facility (under the Seychelles Marine
Ecosystem Management Project), and through
public donations. This programme is run with
permission from the Ministry of Environment
and Natural Resources, Seychelles, and in
accordance with the “Wild Animals (whale
shark) Protection Regulations, 2003”.
Research work in Mozambique was supported
by Casa Barry Lodge, Project AWARE
Foundation (International), Rufford Small
Grants, Idea Wild and Tofo Scuba. Thanks
to Andrea Marshall, Andrew Currey and
Nobina Morimoto for their assistance with
the collection and processing of this dataset.
Activities in Djibouti were partially supported
by the Fondation Nature et Découvertes.
The manuscript was greatly enhanced by the
comments of an anonymous reviewer.
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