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Mark-recapture analysis of sperm whale (Physeter macrocephalus) photo-id data from the Azores (1987-1995)

  • Whale Watch Azores

Abstract and Figures

Population estimates for female or immature male sperm whales (Physeter macrocephalus) in a region within the Azores archipelago are given, based on photo-identification studies with mark-recapture analysis. The study area encompassed the Central Group of islands and the island of São Miguel. Data indicate that the recapture rate of animals likely to be females differs from other animals, and this heterogeneity is incorporated into the models. Closed population (Petersen) estimates, using data from within summers, suggest a population of between 300-800 female or immature sperm whales in summer in the study area. Estimates of the population that visits the study area in summer were made using a model selected from the Jolly-Seber family. The open population visiting the study area appeared to vary between about 400-700 between the years 1988-1990, increasing by a factor of three to about 1,600-2,200 between the years 1991-1994. The fraction of whales which are not suitably marked for identification is estimated to be about 12%, so these estimates should be increased by a factor of 1.14. These estimates are reliable if the study area covers the range of a wider population which moves into and out of the study area randomly. The increase in abundance in 1991 is probably due to change in the composition of the population visiting the area. It is not yet possible to clearly define the wider population that the Azores samples are from, nor are migration patterns to and from the area understood. Investigations on a larger spatial scale are needed for a better understanding.
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Mark-recapture analysis of sperm whale (Physeter
macrocephalus) photo-id data from the Azores (1987-1995)
J.N. Matthews
, L. Steiner
and J. Gordon
Contact e-mail:
Population estimates for female or immature male sperm whales (Physeter macrocephalus) in a region within the Azores archipelago are
given, based on photo-identification studies with mark-recapture analysis. The study area encompassed the Central Group of islands and
the island of São Miguel. Data indicate that the recapture rate of animals likely to be females differs from other animals, and this
heterogeneity is incorporated into the models. Closed population (Petersen) estimates, using data from within summers, suggest a
population of between 300-800 female or immature sperm whales in summer in the study area. Estimates of the population that visits the
study area in summer were made using a model selected from the Jolly-Seber family. The open population visiting the study area appeared
to vary between about 400-700 between the years 1988-1990, increasing by a factor of three to about 1,600-2,200 between the years
1991-1994. The fraction of whales which are not suitably marked for identification is estimated to be about 12%, so these estimates should
be increased by a factor of 1.14. These estimates are reliable if the study area covers the range of a wider population which moves into and
out of the study area randomly. The increase in abundance in 1991 is probably due to change in the composition of the population visiting
the area. It is not yet possible to clearly define the wider population that the Azores samples are from, nor are migration patterns to and
from the area understood. Investigations on a larger spatial scale are needed for a better understanding.
The Azores have long been known as both a breeding and
feeding area for sperm whales, and as a whaling region
(Clarke, 1956; 1981). Sperm whales were taken at least as far
back as 1765. Whaling continued, using traditional methods,
until recent times: the last whales were captured in 1987. In
the latter part of the 20
century the Azorean catch was one
of the largest in the North Atlantic. Now, with the
development of whalewatching operations, sperm whales
remain an important economic resource to the Islands.
Population trends following the end of whaling are
important, as the impact of whaling on the population can far
outlast the whaling itself. Whitehead et al. (1997) have
shown that, off the Galapagos Islands, the population of
sperm whales decreased by about 20% each year between
1985-1995. They suggest that the continued decline is the
residual impact of the mainland whaling industry, which
ended in 1981. How the Azorean sperm whales fared after
whaling ceased is not known, as long-term knowledge of the
population is quite limited. Catch data are reported by Clarke
(1956) and Avila de Melo and Martin (1985), and density
estimates were made in 1988 and 1989 using acoustic survey
methods by Leaper et al. (1992).
Whitehead et al. (1997) suggest the overexploitation of
mature males as one cause of the population decline off the
Galapagos. If this is so, then the Azorean population might
be expected to show similar trends. Clarke (1956) writes
that, according to Drouet, ‘… in the sperm whale fishery the
Azores were noted for large whales (1861)’, and in the
Azorean records from the mid-20
century onwards, males
formed the major part of catches (Avila de Melo and Martin,
1985). Males were often preferred by whalers due to their
larger size, or in some northern areas were simply more
available. The lengths of whales caught at several North
Atlantic fisheries declined after the 1950s, providing an
indication of overexploitation (e.g. Martin, 1981). The
decline in lengths in the Azores was less clear-cut than in, for
example, Iceland, and there was evidence of an increase in
the proportion of large whales caught in the last decade of the
hunt (Avila de Melo and Martin, 1985).
The primary interests of this paper are the size and
conservation status of the population of sperm whales in and
around the Azores, in the context of relatively recent
large-scale whaling activity and its current economic
importance to the whalewatching industry. The study
estimates abundance, in the nine years following the end of
whaling, using photo-identification and mark-recapture
techniques. This approach has been used to estimate
abundance in various long-term studies of cetaceans (e.g. see
review in Hammond, 1986). Previous mark-recapture
estimates of sperm whale abundance have been made off
Kaikoura, New Zealand (Childerhouse et al., 1995), and off
the Galapagos Islands (Whitehead, 1990; Whitehead et al.,
1992). Hammond (1986) stresses the importance of
population definition in such studies. In this study, the
‘closed’ populations of sperm whales are defined to be those
animals in the study area in each study season. The ‘open’
population of sperm whales is defined to be those animals in
the wider area that visit the study area over several
Fieldwork and field methods
Fig. 1 shows the study area and the locations of all
encounters with sperm whale groups. Data were collected
between May and September inclusive from Song of the
Whale, a 14m auxiliary powered ketch (between 1987 and
1995) and from Colomban an 18m schooner (between 1993
and 1995). No data were collected in 1992. Since these
Song of the Whale Research Team, International Fund for Animal Welfare, 87-90 Albert Embankment, London, SE1 9UD, UK.
Whale Watch Azores, 6b South Street, Banbury, OX16 7LF, UK.
SMRU, Gatty Marine Laboratory, University of St Andrews, St Andrews, Fife, KY16 8LB, UK.
J. CETACEAN RES. MANAGE. 3(3):219–226, 2001 219
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vessels activities were not dedicated solely to
photo-identification, in common with many studies of this
type (e.g. Calambokidis et al., 1990; Katona and Beard,
1990) sampling was neither completely random nor
systematic. Table 1 shows the length of the field seasons and
years of operation of the two vessels.
Individual sperm whales can be reliably identified from
various nicks and marks on the trailing edge of the flukes
(e.g. Arnbom, 1987; Gordon, 1987). Previous studies have
shown that the majority of sperm whales have distinctive
flukes suitable for photo-id (of animals with good quality
photographs: > 91%, Arnbom, 1987; 100%, Childerhouse et
al., 1995; 82%, Dufault and Whitehead, 1995). In most
cases, these marks appear not to change quickly over periods
of years. Childerhouse and Dawson (1996) examined
photographs of 56 males seen between one and 56 months
apart, all of which were re-identified correctly in a blind
experiment. Thirteen showed some change over time but
only one showed a major change. Dufault and Whitehead
(1995) made comparisons of photographs of flukes of 161
sperm whales encountered between one and eight years
apart, 63 of which had changed over time, with only six
major changes.
Typically, sperm whales were located using passive
acoustic techniques (e.g. Whitehead and Gordon, 1986). In
these waters, sperm whales were generally found in scattered
groups, the composition of which may reflect long-term
associations. Once detected, acoustic methods and sightings
were used to locate sperm whales when they came to the
surface to breathe. Sperm whales in deep diving (probably
feeding) groups typically spend around 10 minutes on the
surface between dives of around 40 minutes (Gordon and
Steiner, 1992). Such whales typically surface in small groups
of 1-3 and move very slowly at the surface, on a constant
heading, blowing regularly. To obtain photographs for
identification, the research vessel would be carefully and
quietly manoeuvred directly behind the whale. If time
allowed, the whales dorsal fin was carefully observed for
traces of a callus using 7 350 binoculars and, if possible,
photographs of the dorsal fin were taken. Sequences of
identification photographs were taken (on Ilford XP2 film
using a Canon T90 camera and a 300mm, f4 fixed focal
length lens) when the whale raised its flukes above the water
to initiate a deep dive. Usually, the research vessel would
spend the remainder of a day collecting fluke photographs
from a dispersed group. Because of their long dive times, it
was rarely possible to collect fluke photographs
systematically. On some occasions, a dispersed group might
be followed overnight and identification effort would
continue during the next day.
Photographs of individuals with sufficiently good marks
to allow identification were organised into a catalogue and
matched to new photographs by eye. The catalogue was
maintained and all matching completed by one of us (LS).
The data used here were the time, date, location and identity
of each photograph for which identification was possible,
and auxiliary information used to classify whales.
Fig. 1. Study area around the Central Group of islands (Faial, Graciosa, Pico, São Jorge, Terceira) and the island of São Miguel. Points where
photo-identifications were made are also shown.
Population estimation
Over short time periods the assumption of population closure
may be adequate, even in an open population. In this study,
closed-population estimates were made for each season.
Each years data were divided to form two samples based on
time: before 1 August (sample 1) and on or after 1 August
(sample 2) and Petersen estimates calculated for each
season. In the longer term, processes of birth, death,
immigration and emigration are expected. The present data
spanned nine years, so open models which incorporate
these processes were applied. Using summers as sampling
units and a yearly sampling interval gives larger sample sizes
and numbers of recaptures (Hammond, 1986).
Open models were selected from the Jolly-Seber (JS)
family (Pollock et al., 1990; Schwarz and Arnason, 1996).
The fully-parameterised JS model (Jolly, 1965) gives
sample-by-sample estimates of abundance, survival (i.e.
the proportion of animals that do not emigrate or die) and
birth (i.e. the number of immigrants or births). Members of
the JS family are modifications of this with constraints
placed on the parameter set. For example, survival may be
constrained not to vary from sample to sample but to remain
constant throughout the study. Using fewer parameters can
make the model easier to interpret and may improve
Schwarz and Arnason (1996) provide a formulation of the
JS model which overcomes certain technical difficulties, and
allows the application of very general models, including
modelling by covariates and stratification into multiple
groups. In this formulation, JS family models have
parameters drawn from the set {f
, , f
} where K is the number of samples, f
probability of survival from sample ito i+1, p
probability of capture at sample i, and b
the normalised birth
rate for sample i. Where appropriate, the population may be
further stratified into G sub-groups, giving a total parameter
set of G(3K-1).
Models are described by a vector (f
) (following
Lebreton et al., 1992; Schwarz and Arnason, 1996), where
the subscripts q,r,s are one of :
gcapture constant over time but varies with group (G
tcapture varies with time but not with group (K
g*tcapture varies with time and group (GxK
none capture is a constant across all groups and times (1
An example: the model (f
, p, b
) has survival rate constant
in time but differing between groups, a constant capture
probability and time-varying birth rates that do not differ by
group. These models were applied using the program
POPAN5 (Arnason et al., 1998). Adjustments to survival
rates were also made for the year (1992) with missing
Care must be taken with the specification of these general
models to keep track of non-identifiable parameters
(Arnason et al., 1998). In the full JS model, within each
sub-group the parameter combinations (f
, p
), (b
, p
) and
, p
) are non-identifiable, i.e. cannot be estimated
separately. As a consequence, some parameters of interest
are not estimable in this study (specifically, f
, f
, B
, N
In mark-recapture studies, heterogeneity of capture
probability causes biased estimates of population size.
Stratification of the population into sub-groups with (more)
homogeneous capture probabilities partially addresses this
problem. The population was stratified into those individuals
which were thought to be probably females, because they
had been noted as having dorsal fin calluses or escorting or
observed suckling calves on at least one occasion (probable
females); mature males, based on size or head proportions
(males); and the remainder (other). Observations of
mature males, which probably migrate from the Azores to
more northern waters, such as those off Iceland (Martin,
1982), were rare and excluded from the population analyses.
The population estimates made here are thus for the female
and immature male component of the sperm whale
population only.
The results indicated that probable females are more likely
to be recaptured than others within seasons. Because of these
different recapture rates, Petersen estimates for the stratified
population were made assuming that the probability of
capture is equal between the two sub-groups in the first
sample, but different in the second sample. Let n
be the
number of captures in the first and second samples, and
the number of marks in the second sample. The pooled
abundance estimate is (Seber, 1982):
For a sub-group of abundance X, with number of captures in
sample 1 of n
In the remainder of the paper F and O are used as
subscripts to denote probable females and others
Goodness of fit
Pollock et al. (1985; 1990) describe goodness-of-fit tests for
the Jolly-Seber model, based on c
statistics from pooled
contingency tables. These are now familiar as TEST 2 and
TEST 3 (Lebreton et al., 1992). The statistic c
2 + TEST 3) provides a test of the goodness-of-fit of the full
Jolly-Seber model. In forming the statistic, individual tables
containing cells with expected values less than two are
omitted (Pollock et al., 1990). This value is also used in
variance estimation, as described below.
Variance estimation
With the probable exception of older males, sperm whales
are sociable animals. As a consequence of their
gregariousness, encounters with individual animals tend not
to be independent of one another. However, these social
groups are not wholly stable. Social groups themselves may
associate for periods of days, and there is also a certain
amount of fission and fusion of these groups. Whitehead et
al. (1991) have proposed a simple model where, among
females and immatures, there is a mixture of relationships,
some constant (lasting for years) and some casual (lasting
for days). This dynamic and gregarious sociality causes
difficulties in analysis, as neither individuals nor groups can
be considered as completely independent units.
Anderson et al. (1994) consider the statistical effects of
schooling: Members of such populations can be expected to
have a positive correlation among individuals; such
J. CETACEAN RES. MANAGE. 3(3):219226, 2001 221
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dependence causes overdispersion [T]he estimators of
model parameters often remain unbiased , but the
model-based, theoretical variances are underestimated.
Where there is moderate dispersion (a common occurrence
in capture-recapture data) they recommend the estimation of
a variance inflation factor,c. Each empirical variance
estimate vâr(q
) of parameter q
is increased to c.vâr(q
). In
practice, a single average c = c
/df is often reasonable for
all variance estimates (Anderson et al., 1994).
Model selection
Parsimonious models can be selected using likelihood-based
criteria. With overdispersed data, as in the present study, the
model with minimum (modified) Akaike Information
Criterion (QAIC) is selected, as recommended by Anderson
et al. (1994), defined as:
.log[ ( ˆ)]
max q
where L
is the maximum-likelihood, cis the variance
ˆ{ˆ,ˆ,..., ˆ}qqq q=12 p
inflation factor, , the vector of parameter
estimates, and p is the number of parameters. For ease of
reading, details of the model selection process are given in
the Results section.
A total of 2,355 identifications were made from
photographs, representing 762 individual sperm whales. Of
the identified whales, 45 were mature males, 285 were
probable females and 432 were others. Identifications could
not be made from about 12% of attempted captures with
suitable photographs, because the whales were not clearly
marked. Sampling effort varied considerably from year to
year. The length of field seasons, number of active days
within field seasons, and priority that could be given to
photo-identification varied, and in later years (1993 and
1995) there were two boats at work (Table 1). The
probability of capture of whales is therefore expected to vary
from year to year.
Within each season, the majority of individuals were seen
on only one day, but some individuals were resighted over
the season. There was a tendency to resight probable females
over a longer period within a season. Fig. 2 shows
histograms of the number of days between first and last
sighting by sub-group for animals seen on more than one
day. The proportion of resightings within a season that were
over one day apart was 31% for probable females and 12%
for others. Probable females were also more likely to be
resighted between seasons than others. Fig. 3 shows
histograms of the number of years between first and last
sighting for individuals resighted between seasons by
sub-group. The proportion of individuals sighted over more
than one season was 18% for females and 6% for others.
Among animals resighted between seasons, probable
females tend to be seen over a longer period. These patterns
justify the stratification of the data into two sub-groups in
both closed and open population models.
The number of years seen are shown in Table 2, broken
down by sub-group. There were no between-year resightings
of mature males. The probable females sub-group is further
split in this table into those animals which had been seen
nursing and those which had not (these latter classed as
female on the basis of callus information only). There is no
obvious difference in relative between-year resighting
frequency, so the pooling as probable females was
Within-season (closed) population analysis
Table 3 shows within-season Petersen estimates for different
years. The pooled estimates are fairly consistent from year to
year, and suggest a population of about 300-800 animals in
Fig. 2. Histogram of number of days between first and last sighting
within each season, for each individual seen on more than one day,
by sub-group.
Fig. 3. Histogram of number of years between first and last sighting for
each individual seen in more than one season, by sub-group.
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the summer. The number of recaptures varied by year and
was generally low, leading to less precision and possible bias
in some cases (Seber, 1982). The variation in proportion of
females from year to year may be real or may be due to
changes in the efficiency and experience of field observers in
different seasons, when noting behaviours and calluses.
Between-season (open) population analysis
Goodness-of-fit and variance inflation
Attempts to apply the goodness-of-fit tests separately to the
sub-groups were not successful due to sparse data. This
sparsity arises because the recapture rate between samples is
low, as is evident in the summary of the data shown in Table
2. The tests applied to the pooled data gave c
df= 11 and P<0.01. The model therefore fails the
goodness-of-fit test. There does not appear to be a systematic
pattern of deviation from expected values in the tables.
As discussed by Lebreton et al. (1992) and Anderson et al.
(1994), lack-of-fit may be due to structural deficiencies in
the model, the presence of overdispersion in the data, or
both. One cause of the lack-of-fit is the known heterogeneity
between the sub-groups, which could not be tested
separately for goodness-of-fit. A second is the social
behaviour of sperm whales, which leads us to expect some
overdispersion. Indeed, correlation between some
individuals was evident in the observed distribution of
capture histories.
Based on experience, Anderson et al. (1994) state that,
Once one has found an adequate model structure,
overdispersion cseems often to be just above one to as much
as three. The value of c in this study is:
28 23
11 257
which lies within the range suggested. Therefore, the
assumption is made that this lack-of-fit is attributable to
overdispersion, while emphasising that this is an assumption
and structural problems with the model cannot be
Model selection
Fig. 4 schematically displays the path taken during the
decision-making process from the most general JS model
(A) to the most parsimonious model (G).
Step A to B
A closed model fits very poorly compared to the full, open JS
model. Select A.
Step A to C or D
Assume plausible model has time-varying capture
probability (due to varying effort over the years) and survival
which varies with group (see Table 2). Compare constant
survival within groups versus time-varying survival within
groups. Select D.
Step D to E, F or G
Constraints now placed on the birth rate (constant,
time-varying or group varying). Select G, time-varying
The closed model B, in which there is no loss and no gain
in the population, has a very poor relative fit compared with
all other models examined (A, C-G). This is to be expected
in a large population of mobile animals and as more
members of the population are identified over the seasons.
The final model is somewhat as expected. Survey effort
varied from sample to sample (Table 1), so a time effect is
included for capture probabilities. Survival in females is
higher (Table 2), so a group effect is expected. The
time-varying numbers of births and survival rates are in all
likelihood due to immigration and emigration, as opposed to
mortality or reproduction, which are both low in this
long-lived animal (Lockyer, 1981; Best et al., 1984).
Parameter estimates
Abundance estimates for the selected JS model are plotted in
Fig. 5. No estimates are available for N
or N
because the
underlying parameters are non-identifiable. The standard
errors are correlated with the estimates, a known property of
JS estimates. Abundances appear to be more consistent
within periods 1988-1990 and 1991-1994. The results
suggest an increase in the population between 1990 and 1991
by a factor of about three.
Table 4 shows the estimates of survival and births for the
two groups. As expected from Table 2, the survival rate for
females is usually higher. The increase in abundance in 1991
(Table 4) corresponds to high numbers of births in the 1990
sample. The estimates are variable and sometimes required
constraint to above zero (births) or below one (survival).
Fig. 4. Path of decisions made during model selection using QAIC.
Letters index the model. The QAIC value is shown underneath each
model with the number of parameters (total number parameters
number constraints) in parentheses. Heavy arrows show steps to
preferred models, with model G finally selected.
J. CETACEAN RES. MANAGE. 3(3):219226, 2001 223
Validity of assumptions
Some major relevant assumptions of the models used are
discussed below.
(1) Random sampling
In the present study, regular, relatively uniform sampling
was approximated within a relatively small area around the
islands of Pico, Faial and São Jorge, but was less methodical
over the wider area around the Central Group of islands.
Apart from this, the survey vessels did not concentrate effort
in any particular area within or between seasons.
(2) Population closure (Petersen estimates)
The primary assumption made for these estimates is that the
population is closed (both geographically and
demographically). The photo-identification data in this study
suggest that some whales are resighted over periods of
months around the islands, that is, entire sampling periods,
but the majority of whales were seen over at most a few days.
It is not known whether the failure to resight is due to
emigration or failed capture.
(3) No re-immigration/temporary emigration (JS estimates)
The closed population model applied over the full period
(nine years) of this study fitted significantly less well than
open models, illustrating, as expected, that in the long term
the assumption of closure is not valid.
It seems likely that the open population models in this
study faced the same problem as some other cetacean
mark-recapture studies (e.g. Calambokidis et al., 1990;
Hammond et al., 1990), namely that temporary emigration
occurs (violating an assumption of the JS model) because
sampling is being carried out from only a small part of a
roaming population. The study area is undoubtedly only a
small part of the range of these whales, and does not even
encompass the entire archipelago. The presence of
temporary emigration means that from sample to sample
marked individuals missing from the study area are not at
risk of capture. This is a form of heterogeneity of capture
probabilities (a component of zero values is introduced in
each sample). How this violation affects the estimates
depends on the way in which the population visits the study
area, and is further discussed below.
(4) Homogeneity of capture
Hammond (1990, p.135) writes that in practice, unequal
catchability [is] likely to be a fact of life in all
photo-identification studies of whales. The degree of
individual heterogeneity can be investigated in closed
models with several samples (e.g. Hammond, 1990), but in
this study the numbers of captures within seasons were
generally not sufficiently high for this type of analysis. A
measure taken here to reduce heterogeneity was
stratification into two sub-groups with different catchability.
The higher resighting rate of the probable females
sub-group may be due to greater site-fidelity, but could also
be explained by a tendency for observers to preferentially
photograph these animals, or a correlation with other
attributes affecting their sightability. If for example, callused
whales are more likely to be mature females than
non-callused whales, they might be more approachable and
less easily disturbed, and they are also more likely to have
calves, which could constrain them to make shorter dives
and thus fluke up more often. Also, callused females are
likely to be older and therefore better-marked. It is important
to bear in mind that assignments to sub-groups could not be
made with any certainty (in contrast to some other
mark-recapture studies, e.g. Darocher and Stirling, 1995).
(5) Independence of captures
Photographs taken from groups of sperm whales are clearly
not independent, given the persistence of long-term
associations between many individuals. This is assumed to
decrease the precision of estimates, but not to bias them
greatly. Estimates of variance were inflated here to reflect
this problem but this technique is an area of ongoing
statistical research. The approach of Whitehead et al. (1992;
1997) to the problem of non-independence in analysis of
sperm whale photo-identification data was to calculate
confidence limits by Monte Carlo simulation, based on
assumed permanent units. This method may be preferable
when stable groups are identifiable. However, it has not been
applied here because sperm whale group structure is
dynamic and identification of groups can be somewhat
arbitrary (e.g. relying on cluster analysis) meaning that the
assumption of fixed groups may not be a fair one.
Population definition
The open model estimates in this study refer to the
population that visits the Azores, but current knowledge is
such that it is not clear what that population is. Dufault et al.
(1999) review suggestions that the North Atlantic population
is a single stock but the information is poor and mainly based
on information from three long range movements of tags and
harpoons on male whales (Azores and Nova Scotia to Spain,
Azores to Iceland). Females may have a more discrete
population structure.
Fig. 5. Abundance estimates of the two sub-groups, with inflated upper
standard error shown.
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The migration habits and site-fidelity of the two main
components of the population visiting the Azores (mature or
maturing males and mixed groups) are not well understood.
Although Clarke (1956) suggested that mixed groups might
migrate to the Azores from breeding grounds to the south
and spend the summer around the islands, there is little
evidence for (or against) this suggestion. Some mature or
maturing males may be found in temperate and tropical
waters, including the Azores, in winter. However, recent
findings seem to emphasise our ignorance of population
movements. According to Avila de Melo and Martin (1985),
catches of mature males peaked in the summer
simultaneously with those of Iceland, so that
a substantial part of this segment of the population remains
throughout the year in warm temperate waters. Many, possibly most,
mature males thus do not undertake an annual migration to and from
higher latitudes.
Based on strandings and sightings data from the North
Atlantic, Evans (1997) concludes that since the mid-1970s
an increased proportion of immature [males] [have been]
migrating to high latitudes where some remain
Smith et al. (1999) describe a mark-recapture study of
humpback whales carried out on the scale of the ocean basin.
Such large-scale collaborative studies give a better
understanding of migration patterns and the population
structure, with an associated improvement in models and
estimates. The recent establishment of the North Atlantic
and Mediterranean Sperm Whale Catalogue
(, which includes the images
used in this analysis, is an encouraging step in this
Population size
The Petersen estimates given here are relatively consistent
from year to year, suggesting between 300-800 marked
female and immature whales are found around the Central
Group of the Azores in the summer. The open population
models gave abundances of between about 400-800 between
the years 1988-1990, increasing by a factor of three to
between 1,600-2,200 animals between the years 1991-1994.
The closed population estimates are of the animals in the
study area in each season, while the open population
estimates are of the more widely dispersed population,
which visits the study area over several seasons. The fraction
of unmarked whales is estimated at about 12%, so the
abundance of both marked and unmarked whales may be
estimated by increasing the Petersen and JS estimates by a
factor of 1.14.
The study area forms only a part of the Azores archipelago
and only a very small part of the range of the population, it
is thus likely that there is temporary emigration from the
study area. How temporary emigration affects the estimates
is dependent on how the animals transit the study area. If
there is random movement between the population in the
study area and a population in some wider region, then the
study may be regarded as representatively sampling the
population in that wider region. The JS estimates are then
approximately unbiased estimates of the wider population
associated with the study area (Kendall et al., 1997). On the
other hand, if movements through the study area are in some
way heterogeneous, then the estimates are biased in a way
that cannot be assessed without further information.
Another consequence of the small study area relative to
the population range may be that roaming individuals and
groups come into the study area infrequently. The increase in
abundance in 1991 is clearly not due to population growth, as
sperm whales do not reproduce at such rates (Best et al.,
1984). The best interpretation of this increase may be that the
composition of the population visiting the Azores changed at
this time. There is a decrease in the proportion of recaptures
in 1991 in the data. The results of the model suggest that a
previously unmarked part of the population arrived in the
region in 1991. The presence of new visitors does not seem
to have caused any discernible increase in the summer
density around the Azores, however.
Leaper et al. (1992) calculated density estimates with four
surveys conducted around the central islands of Pico, São
Jorge and Faial using acoustic survey methods in 1988 and
1989. Table 5 shows density estimates by the closed
population mark-recapture and acoustic methods. The closed
population estimates are used because the area of the
population is better defined, whereas the area inhabited by
the population estimated by the open population models is
not. The area of sea within the study area is 28,378 n.miles
The estimates are quite similar, and although this is not a
rigorous comparison (for example, the surveyed areas
overlap but are not identical), the similarity in density
estimates is encouraging.
The Azores sperm whale catch varied between 400-741 per
annum between the years 1937-1954 (Clarke, 1956). Most of
these were caught in summer. The scale of this take could not
have been sustained under the present day estimate of about
2,500 or less, indicating that the numbers visiting the Azores
at that time may have been substantially larger. Movement
patterns in the mid-20
century may also have differed, so
whaling on the local population could have been replenished
by the immigration of whales from the wider area.
The International Fund for Animal Welfare funds the work
of Song of the Whale. We are grateful to the Ministry of
Fisheries in the Azores for granting permits to work in
Azorean waters, and to the Department of Oceanography and
Fisheries of the University of the Azores for much help and
cooperation over the years. The fieldwork would have been
impossible without the hard work and dedication of a large
number of sailors, scientists and student interns, too
numerous to mention individually. However, we would
particularly like to acknowledge Russell Leaper, Richard
McLanaghan and Kit Rogers, who skippered Song of the
Whale at various times. This paper is a much-enlarged and
improved version of unpublished paper SC/51/CAWS27
presented to the IWC Scientific Committee, May 1999. Dr
H. Whitehead (Dalhousie University) kindly provided
J. CETACEAN RES. MANAGE. 3(3):219226, 2001 225
comments and advice on the first submitted manuscript, and
Russell Leaper on the second. We are also grateful to three
anonymous reviewers for providing helpful remarks. One of
these gave encouraging and extremely constructive
comments, which greatly improved the analysis.
Anderson, D.R., Burnham, K.P. and White, G.C. 1994. AIC model
selection in overdispersed capture-recapture data. Ecology
Arnason, A.N., Schwarz, C.J. and Boyer, G. 1998. POPAN-5: A Data
Maintenance and Analysis System for Mark-Recapture Data.
Department of Computer Science, University of Manitoba,
Winnipeg, Canada. viii+265pp.
Arnbom, T. 1987. Individual identification of sperm whales. Rep. int.
Whal. Commn 37:201-4.
Avila de Melo, A.M. and Martin, A.R. 1985. A study of male sperm
whale length data from the Azorean and Madeiran catches, 1947-82.
Rep. int. Whal. Commn 35:209-15.
Best, P.B., Canham, P.A.S. and MacLeod, N. 1984. Patterns of
reproduction in sperm whales, Physeter macrocephalus.Rep. int.
Whal. Commn (special issue) 6:51-79.
Calambokidis, J., Cubbage, J.C., Steiger, G.H., Balcomb, K.C. and
Bloedel, P. 1990. Population estimates of humpback whales in the
Gulf of the Farallones, California. Rep. int. Whal. Commn (special
issue) 12:325-33.
Childerhouse, S.J. and Dawson, S.M. 1996. Stability of fluke marks
used in individual photoidentification of male sperm whales at
Kaikoura, New Zealand. Mar. Mammal Sci. 12(3):447-51.
Childerhouse, S.J., Dawson, S.M. and Slooten, E. 1995. Abundance
and seasonal residence of sperm whales at Kaikoura, New Zealand.
Can. J. Zool. 73(4):723-31.
Clarke, R. 1956. Sperm whales of the Azores. Discovery Rep.
Clarke, R. 1981. Whales and dolphins of the Azores and their
exploitation. Rep. int. Whal. Commn 31:607-15.
Darocher, A.E. and Stirling, I. 1995. Estimation of polar bear
population size and survival in western Hudson Bay. J. Wildl.
Manage. 59:215-21.
Dufault, S. and Whitehead, H. 1995. An assessment of changes with
time in the marking patterns used for photoidentification of
individual sperm whales, Physeter macrocephalus.Mar. Mammal
Sci. 11(3):335-43.
Dufault, S., Whitehead, H. and Dillon, M. 1999. An examination of the
current knowledge on the stock structure of sperm whales (Physeter
macrocephalus) worldwide. J. Cetacean Res. Manage. 1(1):1-10.
Evans, P.G.H. 1997. Ecology of sperm whales (Physeter
macrocephalus) in the eastern North Atlantic, with special reference
to sightings and strandings records from the British Isles. Biol.
Unserer Zeit 67-Suppl.:37-46.
Gordon, J.C.D. 1987. The behaviour and ecology of sperm whales off
Sri Lanka. Ph.D. Thesis, University of Cambridge. 347pp.
Gordon, J. and Steiner, L. 1992. Ventilation and dive patterns in sperm
whales, Physeter macrocephalus, in the Azores. Rep. int. Whal.
Commn 42:561-5.
Hammond, P.S. 1986. Estimating the size of naturally marked whale
populations using capture-recapture techniques. Rep. int. Whal.
Commn (special issue) 8:253-82.
Hammond, P.S. 1990. Heterogeneity in the Gulf of Maine? Estimating
humpback whale population size when capture probabilities are not
equal. Rep. int. Whal. Commn (special issue) 12:135-9.
Hammond, P.S., Sears, R. and Bérubé, M. 1990. A note on problems in
estimating the number of blue whales in the Gulf of St. Lawrence
from photo-identification data. Rep. int. Whal. Commn (special
issue) 12:141-2.
Jolly, G.M. 1965. Explicit estimates from capture-recapture data with
both death and immigration - a stochastic model. Biometrika
Katona, S.K. and Beard, J.A. 1990. Population size, migrations and
feeding aggregations of the humpback whale (Megaptera
novaeangliae) in the western North Atlantic Ocean. Rep. int. Whal.
Commn (special issue) 12:295-305.
Kendall, W.L., Nichols, J.D. and Hines, J.E. 1997. Estimating
temporary emigration using capture-recapture data with Pollock's
robust design. Ecology 78:563-78.
Leaper, R., Chappell, O. and Gordon, J. 1992. The development of
practical techniques for surveying sperm whale populations
acoustically. Rep. int. Whal. Commn 42:549-60.
Lebreton, J.-D., Burnham, K.P., Clobert, J. and Anderson, D.R. 1992.
Modeling survival and testing biological hypotheses using marked
animals: a unified approach with case studies. Ecol. Monogr.
Lockyer, C. 1981. Estimates of growth and energy budget for the sperm
whale, Physeter catodon.FAO Fish. Ser. (5) Mammals in the Seas
Martin, A.R. 1981. Further analysis of the post-war Icelandic sperm
whale catch. Rep. int. Whal. Commn 31:765-8.
Martin, A.R. 1982. A link between the sperm whales occurring off
Iceland and the Azores. Mammalia 46:259-60.
Pollock, K.H., Hines, J. and Nichols, J.D. 1985. Goodness-of-fit tests
for open capture-recapture models. Biometrics 41:399-410.
Pollock, K.P., Nichols, J.D., Brownie, C. and Hines, J.E. 1990.
Statistical inference for capture-recapture experiments. Wildl.
Monogr. 107:1-97.
Schwarz, C.J. and Arnason, A.N. 1996. A general methodology for the
analysis of open-model capture-recapture experiments. Biometrics
Seber, G.A.F. 1982. The Estimation of Animal Abundance and Related
Parameters. 2nd Edn. Charles Griffin and Company Ltd., London.
Smith, T.D., Allen, J., Clapham, P.J., Hammond, P.S., Katona, S.,
Larsen, F., Lien, J., Mattila, D., Palsbøll, P.J., Sigurjónsson, J.,
Stevick, P.T. and Øien, N. 1999. An ocean-basin-wide
mark-recapture study of the North Atlantic humpback whale
(Megaptera novaeangliae). Mar. Mammal Sci. 15(1):1-32.
Whitehead, H. 1990. Mark-recapture estimates with emigration and
re-immigration. Biometrics 46:473-9.
Whitehead, H. and Gordon, J. 1986. Methods of obtaining data for
assessing and modelling sperm whale populations which do not
depend on catches. Rep. int. Whal. Commn (special issue)
Whitehead, H., Waters, S. and Lyrholm, T. 1991. Social organization of
female sperm whales and their offspring: constant companions and
casual acquaintances. Behav. Ecol. Sociobiol. 29:385-9.
Whitehead, H., Waters, S. and Lyrholm, T. 1992. Population structure
of female and immature sperm whales (Physeter macrocephalus) off
the Galapagos islands. Can. J. Fish. Aquat. Sci. 49(1):78-84.
Whitehead, H., Christal, J. and Dufault, S. 1997. Past and distant
whaling and the rapid decline of sperm whales off the Galápagos
Islands. Conserv. Biol. 11(6):1,387-396.
... Mark-recapture data from individually photo-identified sperm whales in the study area have been used in the past to estimate abundance and population size (Matthews et al., 2001) and identify migration patterns (Steiner et al., 2012) but so far, no effort was made to estimate their encounter probabilities using spatially-explicit models. ...
... Past mark-recapture studies in the area confirmed that the sperm whale population is subject to temporary emigration, most individuals are assumed to stay for a few days, and only a minority are seen over a period of several months (Matthews et al., 2001). Silva et al. (2014) found that large female groups spend an average time of two weeks in the area. ...
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There has been a globally growing interest in cetacean-based tourism resulting in increasing cumulative impacts on many wildlife species. In the Azores archipelago, the local whale watching industry has drastically evolved over the last two decades, especially targeting a population of sperm whales that use the habitat as important feeding ground and can be found year-round. Some individuals return annually to the area and stay over several weeks to breed, which makes them especially vulnerable to human-induced disturbance. Hence, this study aimed to contribute to the establishment of a conservation framework to ensure the future sustainability of this industry by including the comprehensive evaluation of cumulative exposure of sperm whales to whale watching boats in the area into the management of human activities. For the first time spatially-explicit capture-recapture (SECR) models have been developed to quantify the cumulative interaction time between photo-identified individual sperm whales and whale watching boats in the area. The study provided baseline estimates of sperm whale encounter probabilities which were integrated together with the whale watching intensity in the area to estimate spatial variations of exposure on individual-level. Model estimates revealed that whale watching activities were mainly concentrated in two distinct areas north of Faial and south of Pico island, consequently exposure levels were found to be significantly higher in respective ‘core’ areas of disturbance. Furthermore, results indicated seasonal variations in daily individual exposure levels with a peak in June, where the most repeated interactions between the same individual and whale watching boats took place. The present findings stress the importance of taking the individual exposure into consideration when it comes to the management of potentially harmful human activities and support a precautionary approach.
... Furthermore, their sexing is impossible from the surface as they do not show any apparent sexual dimorphism (Arnbom and Whitehead 1989;Gero et al. 2013). More, adult females and large immatures have similar sizes and may therefore be difficult to distinguish (Matthews et al. 2001;Gero et al. 2014). ...
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The long-term monitoring of long-lived animals often requires individual identification. For cetaceans, this identification may be based on morphological characters observable from a boat such as shape, spots and cuts of the back, fluke and dorsal fins. However, for some species such as the sperm whales (Physeter macrocephalus), this approach may be challenging as individuals display a rather uniform skin pigmentation. They also do not very often show their fluke, complicating individual identification from a boat. Immature sperm whales that usually have an unharmed fluke may be excluded from photo-identification catalogues. Within the framework of the Maubydick project, focusing on the long-term monitoring of sperm-whales in Mauritius, passive underwater observation and video recording were used to identify long-lasting body markers (e.g. sex, ventral white markings, cut-outs of fins). A catalogue of 38 individuals (six adult males, 18 adult females and 14 immatures) enabled observers to record some nearly-daily, and yearly resightings. Advantages and disadvantages of this method are presented here. Such catalogues represent a robust baseline for conducting behavioural, genetic and acoustic studies in social species. Benefits of such newly acquired knowledge are of primary importance to implement relevant conservation plans in the marine realm.
... Individuals can be reliably and repeatedly identified by the unique nicks and notches along their flukes (Armbom 1987;Whitehead 1990), which can then form the basis of photo-identification-based mark-recapture models to estimate population size and structure (e.g. Matthews et al. 2001;Whitehead and Gero 2015). ...
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Photo-identification of individual sperm whales (Physeter macrocephalus) is the primary technique for mark-recapture-based population analyses for the species The visual appearance of the fluke - with its distinct nicks and notches - often serves as the primary visual differentiator, allowing humans to make recorded sightings of specific individuals. However, the advent of digital photography and the significant increase in volume of images from multiple projects in combination with pre-existing historical catalogs has made applying the method more challenging.with the required human labor for de-duplication (reduction of Type II errors) and reconciliation of sightings between large datasets too cost- and time- prohibitive. To address this, we trained and evaluated the accuracy of PIE v2 (a triplet loss network) along with two existing fluke trailing edge-matching algorithms, CurvRank v2 and Dynamic Time Warping (DTW), as a mean to speed comparison among a high volume of photographs. Analyzed data were collected from a curated catalog of well-known sperm whales sighted across years (2005-2018) off the island of Dominica. The newly-trained PIE model outperformed the older CurvRank and DTW algorithms, and PIE provided the following top-k individual ID matching accuracy on a standard min-3/max-10 sighting training data set: Rank-1: 87.0%, Rank-5: 90.5%, and Rank-12: 92.5%. An essential aspect of PIE is that it can learn new individuals without network retraining, which can be immediately applied in the presence of (and for the resolution of) duplicate individuals in overlapping catalogs. Overall, our results recommend the use of PIE v2 and CurvRank v2 for ID reconciliation in combination due to their complementary performance.
... Each individual can be identified from the trailing edge of its tail (Arnbom, 1987) where nicks and notches are distinctive. Since then, many aspects of sperm whale biology have been studied, such as movements at sea (Whitehead, 2003), population sizes (Matthews et al., 2001;Whitehead 1990;Whitehead et al., 1992Whitehead et al., , 1997, and social structure (Christal et al., 1998;Lettevall et al., 2002;Whitehead & Arnbom, 1987;Whitehead et al., 1992). ...
Sperm whales are present along the Caribbean islands in family groups of mature females, juveniles, and newborns. Their abundance and demographic rates remain poorly known. Using photo‐identification data, we estimated the abundance and annual survival of sperm whales in Guadeloupe from January to April 2001–2013. A total of 1,492 photographs of tail flukes were scored (ranging from 1 to 5) in terms of quality (Q) and marking (M) level. A total of 789 photographs were selected with Q ≥ 3 and M ≥ 3, corresponding to 109 individually identified adult females or immatures of both sexes. Capture histories were built and analyzed using the Pollock's robust design capture‐mark‐recapture model. The study consisted of 13 primary periods sequenced in 5–8 secondary periods. Mean annual survival rate of residents was 0.945, 95% CI [0.864, 0.979]. Abundance varied between years from 75 to 35 individuals. The geometric growth rate over the period 2001–2013 was 0.938, 95% CI [0.878, 0.997], suggesting a population decline of −6.2%/year. Sperm whales are moving between Guadeloupe and Dominica, with a high temporary emigration rate (0.163) and 50% of the individuals of Guadeloupe resighted in Dominica. These results highlight the fragility of the population and the necessity to consider it as one unit.
... In communities or groups of species, it is possible to classify or cluster species according to covariate information through their preferences by using finite mixture modeling (Dunstan et al., 2013;Fernández-Michelli et al., 2016;Frame & Jammalamadaka, 2007;McLachlan & Peel, 2000). One particular application of this approach is to deal with over-dispersed data and to model ecological processes in parallel for different species (Matthews et al., 2001;Tracey et al., 2013;Zhang et al., 2004). ...
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• Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity. • In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity. • We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best‐performing methods to a dataset of three frog species (Mixophyes). • These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.
... Some authors have been reporting these species seasonally (e.g. Matthews et al., 2001;Silva et al., 2003;Pereira, 2006), and G. griseus have been sighted year-round in this area from 1992 to 2005 (João Gonçalves, personal communication). Photo-identification of P. macrocephalus indicates a year-round presence of the species, but only time-limited patterns of individual occurrence have been reported (Magalhães et al., 2005). ...
... southern right whales, Carroll et al., 2011) and patterns of pigmentation, scarring and barnacles (e.g., gray whales, Yakovlev and Tyurneva, 2005). Deep diving cetaceans can be identified using nicks and marks on the trailing edge of flukes (e.g., sperm whales, Matthews et al., 2001) and patterns of scars (e.g., beaked whales, Falcone et al., 2009;Rosso et al., 2011), and pinnipeds can be identified uniquely using their pelage patterns (e.g., harbor seals, Cordes and Thompson, 2015;ringed seals, Zhelezniakov et al., 2015). Other methods of capture have been employed. ...
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Assessing the non-lethal effects of disturbance and their population-level consequences is a significant ecological and conservation challenge, because it requires extensive baseline knowledge of behavioral patterns, life-history and demography. However, for many marine mammal populations, this knowledge is currently lacking and it may take decades to fill the gaps. During this time, undetected population declines may occur. In this study we identify methods that can be used to monitor populations subject to disturbance and provide insights into the processes through which disturbance may affect them. To identify and address the knowledge gaps highlighted above, we reviewed the literature to identify suitable response variables and methods for monitoring these variables. We also used existing models of the population consequences of disturbance (PCoD) to identify demographic characteristics (e.g., the proportion of immature animals in the population, or the ratio of calves/pups to mature females) that may be strongly correlated with population status and therefore provide early warnings of future changes in abundance. These demographic characteristics can be monitored using established methods such as visual surveys combined with photogrammetry, and capture-recapture analysis. Individual health and physiological variables can also inform PCoD assessment and can be monitored using photogrammetry, remote tissue sampling, hands-on assessment and individual tracking. We then conducted a workshop to establish the relative utility and feasibility of all these approaches for different groups of marine mammal species. We describe how future marine mammal monitoring programs can be designed to inform population-level analysis.
... Mark-recapture analysis is an important tool not only as a means for abundance estimation, but also for allowing estimation of a variety of life history parameters (Kraus et al., 2001;Williams et al., 2002;Amstrup et al., 2005). Traditionally, mark-recapture methods involved the actual capture, marking, and recapture of individuals (Rice and Harder, 1977;Hestbeck et al., 1991;Pradel et al., 1997), but their use quickly expanded to species where individuals could be identified based on natural markings, and thus "capturing" in these cases meant taking a picture that could be used for identification (Wilson et al., 1999;Matthews et al., 2001;Langtimm et al., 2004;Bradshaw et al., 2007). More recently, the increasing ease with which individual-specific genetic profiles can be obtained from small sources of DNA has made it possible (and sometimes more efficient) to use genetic data as the primary means of individual identification, and thus the basis for mark-recapture analyses (Mowat and Strobeck, 2000;Miller et al., 2005;Petit and Valiere, 2006). ...
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Estimating abundance is one of the most fundamental and important aspects of population biology, with major implications on how the status of a population is perceived and thus on conservation and management efforts. Although typically based on one of two methods (distance sampling or mark-recapture), there are many individual identification methods that can be used for mark-recapture purposes. In recent years, the use of genetic data for individual identification and abundance estimation through mark-recapture analyses have increased, and in some situations such genetic identifications are more efficient than their field-based counterparts for population monitoring. One issue with mark-recapture analyses, regardless of which method of individual identification is used, is that the study area must provide adequate opportunities for “capturing” all individuals within a population. However, many populations are unevenly and widely distributed, making it unfeasible to adequately sample all necessary areas. Here we develop an analytical technique that accounts for unsampled locations, and provides a means to infer “missing” individuals from unsampled locations, and therefore obtain more accurate abundance estimates when it is not possible to sample all sites. This method is validated using simulations and is used to estimate abundance of the Eastern Canada-West Greenland (EC-WG) bowhead whale population. Based on these analyses, the estimated size of this population is 11,747 individuals during the sampling period, with a 95% highest density interval of 8,169–20,043. Keywords: Abundance estimation, Mark-recapture, Genetic identification, Bowhead whale, Whale, Genetic mark-recapture
... When possible, the age F I G U R E 1 Map of the Azores archipelago, showing the location of São Miguel Island in the Eastern group of islands. class and sex of sperm whales was noted, based on similar descriptions in previous studies (e.g., Gordon, Matthews, Lewis, & Steiner, 1999;Matthews, Steiner, & Gordon, 2001). To be classified as a mature male a whale had to be clearly larger than 13 m and have a prominent head, and this had to be confirmed by an experienced biologist of the crew. ...
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Female and immature sperm whales form stable social units in tropical to subtropical waters. One such area is the Azores archipelago, where details of their year‐round occurrence and social organization are not well known. We used year‐round sightings data collected from whale watching vessels to assess sperm whale occurrence and social structure off São Miguel Island, Azores from 2010 to 2017. Individuals were photo‐identified by their flukes (n = 393) and dorsal pigmentation marks were examined to explore their use in assisting with reidentifications. Of all cataloged whales, 78.8% were sufficiently distinctive to be reidentified from dorsal pigmentation patterns. Associations between individuals were analyzed to determine social structure and delineate social units. We identified 12 units comprising 2–13 members that had stable, and perhaps preferred associations for periods up to the eight years of the study. Preferences between some pairs of units may exist, but more research is required to better understand dynamics within and between units. This local scale study is an important contributor to our knowledge of geographic variation on a global scale. We recommend making use of all available information from the entire archipelago, to further increase our understanding of sperm social organization in the Azores.
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The long-term monitoring of long-lived animal populations often requires individual identification. For cetacean populations, this identification is mostly based on morphological characters observable from a boat such as shape, spots and cuts of the back, caudal and dorsal fins. This is well suited for species easily displaying their caudal fins, such as the humpback whales Megaptera novaeangliae , or those whose skin pigmentation patterns enable individual identification. However, for elusive or shier species such as the sperm whales Physeter macrocephalus , this approach may be more challenging as individuals display a rather uniform skin pigmentation. They also do not show very often their caudal fin that must be photographed perpendicularly to the water surface, vertically and fully emerged, uneasing the individual identification from a boat. Immature sperm whales that usually have a caudal fin without any distinctive marks may sometimes be excluded from photo-identification catalogues. Within the framework of the Maubydick project, focusing on the long-term monitoring of sperm whales in Mauritius, passive underwater observation and video recording were used to identify long-lasting body markers (e.g., sex, ventral white markings, cut outs of fins) to improve individual identification. A catalogue of individual identity cards was developed and 38 individuals were recorded (six adult males, 18 adult females and 14 immatures). This catalogue was used in the field and enabled observers to record some nearly-daily and yearly recaptures. Advantages and disadvantages of this method are presented here. Such catalogues represent a robust baseline for conducting behavioural, genetic and acoustic studies in marine megafauna social species. Benefits of such newly acquired knowledge are of first importance to implement relevant conservation plans in the marine realm.
Two techniques have been used to obtain Physeter macrocephalus bearings, a directional hydrophone and a system of towed hydrophones. Directional hydrophones allow bearings to be measured directly, but necessitate stopping the survey vessel at each listening station. They are susceptible to boat and surface water noise and need to be rigidly mounted to the vessel. Towed hydrophone systems can be monitored while the boat is moving and are simple to deploy from any modest sized vessel. The collection of data does not require an experienced operator, but the determination of bearings relies on fairly complex signal processing. -from Authors
Physeter macrocephalus are captured throughout the year at Madeira, with females most abundant in April, at the peak of the mating period. Very few animals occur around the Azores until spring and catches of both sexes peak in summer. Large bulls of > 14 m in length became very scarce at Madeira after 1960 and, at the close of the fishery in 1981, were still rarely encountered. In Azorean waters, however, there is evidence that these reproductively active males have become more numerous in recent years and this fishery is now probably recovering after heavy exploitation in the 1940s and 50s. -from Authors
Mean observed inter-blow intervals were 12.7s for small whales (females and immature males) and 19.3s for large males. Blows later in a surfacing, which had longer intervals, were more likely to be scored. Blow intervals increased through a surfacing but decreased markedly for the last three blows before fluke-up. The modal duration of a complete dive cycle was 52.5 mins. Data suggest an overall mean blow interval for feeding sperm whales of 70.6s for small whales and 107.1s for large males. The relevance of these findings to sightings surveys is briefly discussed. -from Authors
Over 9000 photographs of humpback whale flukes have been collected. As of 31 December 1986, 3647 individual whales were represented. Over 1100 photographically-verified resightings of individually-known humpbacks demonstrated the existence of at least 4, probably 5, separate feeding aggregations: Iceland-Denmark Strait; W Greenland; Newfoundland (including the Labrador coast); Gulf of St. Lawrence; and Gulf of Maine-Scotian Shelf. Individual whales returned annually to a particular feeding region, but whales from all feeding aggregations migrated to nearshore areas and banks in the Dominican Republic, Puerto Rico and the Virgin Islands for breeding. The variance-weighted mean of annual capture-recapture estimates of the total North Atlantic humpback population for years 1979-1986 was 5502 ± 2617 (95% CI). -from Authors
The peak period of breeding seems to occur between October-December in the Southern Hemisphere and April-June in the Northern Hemisphere. While large males (>13.7 m in length) may be the prime breeding animals, males may be physiologically fertile at an average length of 12.5 m. Gestation may last 15-16 months. Equal numbers of male and female calves are born. Diving ability of newborn animals seems to be relatively poor. Neonates demonstrate a strong 'following response' that wanes rapidly with age. An interdependence of school members is suggested from behavioural observations. Solid food is taken for the first time before the age of 1 yr. Mean calving interval for Donkergat (W coast of South Africa) is estimated as 5.2 yr and 6.0-6.5 years for Durban (E coast of South Africa). -from Authors