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SC/68C/ASI/02
Sub-committees/working group name: ASI
HUMPBACK WHALE BREEDING STOCK G: UPDATED POPULATION ESTIMATE
BASED ON PHOTO-ID MATCHES BETWEEN BREEDING AND FEEDING AREAS
Fernando Félix1, Jorge Acevedo2, Anelio Aguayo-Lobo3, Isabel C. Ávila4, Natalia Botero-
Acosta5, Andrea Calderón6, Benjamín Cáceres7, Juan Capella8, Romina Carnero9,
Cristina Castro10, Ted Cheeseman11, Luciano Dalla Rosa12, Natalia Dellabianca13, Judith
Denkinger14, Ari Friedlaender15, Héctor Guzmán16, Ben Haase17, Daniela Haro18,
Rodrigo Hucke-Gaete19, Martha Llano20, Lenin Oviedo21, Aldo Pacheco22, Juan
Pacheco21, Daniel M. Palacios23, José Palacios-Alfaro24, Logan Pallin15, María José
Pérez25, Kristin Rasmussen26, Cristina Sanchez-Godinez27, Luis Santillán28, Eduardo
Secchi12, Mónica A. Torres13, Edgar Vásquez29
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1
HUMPBACK WHALE BREEDING STOCK G: UPDATED
POPULATION ESTIMATE BASED ON PHOTO-ID MATCHES
BETWEEN BREEDING AND FEEDING AREAS
Fernando Félix1, Jorge Acevedo2, Anelio Aguayo-Lobo3, Isabel C. Ávila4, Natalia Botero-Acosta5,
Andrea Calderón6, Benjamín Cáceres7, Juan Capella8, Romina Carnero9, Cristina Castro10, Ted
Cheeseman11, Luciano Dalla Rosa12, Natalia Dellabianca13, Judith Denkinger14, Ari Friedlaender15,
Héctor Guzmán16, Ben Haase17, Daniela Haro18, Rodrigo Hucke-Gaete19, Martha Llano20, Lenin
Oviedo21, Aldo Pacheco22, Juan Pacheco21, Daniel M. Palacios23, José Palacios-Alfaro24, Logan
Pallin15, María José Pérez25, Kristin Rasmussen26, Cristina Sanchez-Godinez27, Luis Santillán28,
Eduardo Secchi12, Mónica A. Torres13, Edgar Vásquez29
1 Pontificia Universidad Católica del Ecuador (PUCE). Av. 12 de Octubre y Roca, Quito, Ecuador.
2 Centro de Estudios del Cuaternario de Fuego-Patagonia y Antártica (CEQUA). Av. España 184, Punta Arenas, Chile.
3 Instituto Antártico Chileno (INACH). Plaza Muñoz Gamero 1055, Punta Arenas, Chile.
4 Universidad del Valle. Ciudad Universitaria Meléndez, Calle 13 N°100-00, Cali, Colombia.
5 Fundación Macuáticos. Calle 27 N°79-167, Medellín, Colombia.
6 Biology Department, City College of New York, New York, New York 10031, USA.
7 Museo de Historia Natural de Río Seco. Punta Arenas, Chile.
8 Whalesound Ltda. Lautaro Navarro 1163, Punta Arenas, Chile.
9 Pacific Adventures-Manejo Integral del Ambiente Marin S.A.C. Los Órganos, Perú.
10 Pacific Whale Foundation. Malecón Julio Izurieta y Abdón Calderón, Puerto López, Ecuador.
11 Happywhale, Columbia St, Santa Cruz, California, USA and Southern Cross University, Lismore, Australia.
12 Universidade Federal do Rio Grande (FURG). Av Itália km 8 s/n, Campus Carreiros, Río Grande, Brasil.
13 Centro Austral de Investigaciones Científicas (CADIC). Bernardo Houssay 200, Ushuaia, Argentina.
14 Universidad San Francisco de Quito. Calle Pampite e Interoceánica, Quito, Ecuador.
15 Institute of Marine Science, University of California Santa Cruz, Santa Cruz, A, USA.
16 Smithsonian Tropical Research Institute. PO Box 0843-03092, Panamá, Panamá.
17 Museo de Ballenas. Av. Enríquez Gallo entre calles 47 y 50 Salinas, Ecuador.
18 Centro Bahía Lomas, Facultad de Ciencias,Universidad Santo Tomás. Avenida Costanera 01834, Punta Arenas, Chile.
19 Universidad Austral de Chile (UACH). Valdivia, Chile.
20 Proyecto Resiliencias. Tv 46 c #42 a este 480, Vía amor y amista # 645, Medellín, Colombia.
21 Centro de Investigación de Cetáceos de Costa Rica (CEIC). San José, Costa Rica.
22 Universidad de Nacional Mayor de San Marcos, Av. Universitaria s/n, Lima, Perú.
23Marine Mammal Institute, Oregon State University, Newport, Oregon USA.
24 Fundación de Investigación y Conservación Marina-Costera (KETO). Mariposario, Uvita, Osa, Puntarenas, Costa Rica.
25 Centro de Investigación Eutropia. Valparaíso, Chile.
26 Panacetacea. 1554 Delware Ave, St Paul, MN 55118 USA.
27 Fundación MARVIVA. Rohrmoser San José, Costa Rica
28 Centro Peruano de Estudios Cetológicos. Lima, Perú.
29 Fundación Omacha. Calle 84 No. 21-47, Bogotá, Colombia.
ABSTRACT
We report a new mark-recapture-based population estimate for the humpback whale Breeding Stock
G (BSG), defined by breeding grounds on the northwestern coast of South America and southwestern
Central America and feeding grounds around the Antarctic Peninsula and southern Chile.
Photographic fluke catalogs from 23 research groups working in both breeding and feeding areas
were compiled in the largest photo-ID matching effort ever made for this stock. A total of 6,354
unique individuals including 1,698 (26.7%) from feeding areas and 4,656 (73.3%) from breeding
areas covering the period 1991-2018 were used for this purpose. The dataset was fitted to closed
2
population models to estimate population size and Jolly-Seber models to estimate apparent survival,
both implemented in the software Mark. Mixture models with two different data types, full likelihood
and conditional likelihood, produced similar results of 11,784 and 11,786 (SE = 266 for both
estimates) whales, respectively. In both cases, a model with two mixtures {Mth2} provided the best fit.
Two Cormack-Jolly-Seber with Pledger mixtures models produced apparent survival estimates for
the two mixtures (0.924 and 0.959, SE = 0.003 and 0.008; respectively). The new population estimate
is 181% higher than a previously obtained in 2006. The annual rate of increase in the 27-year study
period was 5.07%. Sources of bias were associated with effort heterogeneity, population stratification
and the time scale. These and other sources of bias should be considered in future modeling estimates.
KEYWORDS: humpback whale, breeding grounds, feeding grounds, abundance estimate, apparent
survival, Southeast Pacific, Antarctic Peninsula.
INTRODUCTION
The humpback whale (Megaptera novaeangliae) Breeding Stock G (BSG) also referred to as the
Southeast Pacific Stock, is one of the seven stocks of this species in the Southern Hemisphere
recognized by the International Whaling Commission (IWC, 2006). BSG whales breeding off the
northwestern coast of South America between northern Peru and southern Nicaragua in Central
America (Flórez-González, 1991, Félix et al., 2001a, Rasmussen et al., 2007, Pacheco et al., 2009,
DeWeerdt et al., 2020) are connected to three discrete feeding areas (Acevedo et al., 2013) located
around the Antarctic Peninsula (e.g., Stevick et al., 2004, Acevedo et al., 2017, Rasmussen et al.,
2007) and central and southern Chile (e.g., Acevedo et al., 2007, 2017; Hucke-Gaete et al. 2013). The
BSG is the most genetically differentiated stock in the Southern Hemisphere (Olavarría et al., 2007;
Amaral et al., 2016), despite some connections found through photo-ID and genetic studies with other
stocks breeding off Brazil and Oceania (Stevick et al., 2013; Steel et al., 2017; Félix et al., 2020),
suggesting complex migratory and connectivity dynamics among the southern stocks.
The first attempts to estimate the size of the BSG with mark-recapture models date from studies in
the mid-1990s, based on local studies on the central coast of Colombia (Capella et al., 1998; N
=1,120-2,190), Ecuador (Scheidat et al. 2000; Felix and Haase, 2001b; N = 405, 95% CI 221-531,
and N = 2,683 (95% CI = 397-4,969), respectively), Panamá (Guzmán et al., 2015; N = 221, 95% CI
= 170-290) and around the Antarctic Peninsula (Stevick et al., 2006; N = 3,851, 95% CI 3,666-4,036).
Efforts continued in Ecuador, where most of the research effort of this whale population has been
concentrated for many years. Off Ecuador, based on a 16 years dataset, the population of the BSG
was estimated at 6,504 individuals (95% CI 4,270-9,907) in 2006 with the Petersen model modified
by Chapman (Félix et al., 2011a). During that time, an attempt was also made to estimate the survival
rate using the Jolly-Seber model for open populations, obtaining lower than expected values due to
different sources of heterogeneity in the dataset. Such heterogeneity would be related not only to an
irregular effort between years but also to aspects associated with the whales´ migratory behavior and
repeated monitoring in the same area (Félix et al., 2011a).
Recent satellite tagging studies in Ecuador and Panama showed that the BSG is highly spatially
structured on the breeding grounds (Guzmán and Félix, 2017), supporting previous findings through
genetic studies (Félix et al., 2012) and photo-ID (Acevedo et al., 2007, 2013, 2017, Valdivia et al.,
2017). This structure is also consistent with lower population estimates from central and northern
sites of the breeding grounds, from Colombia north (e.g. Flórez-González, 1991, Guzmán et al., 2015)
compared to those obtained in Ecuador, which is both a breeding ground and migratory corridor. A
3
more reliable population estimate of the BSG should integrate information from multiple sites, both
in the breeding and feeding areas, to deal with spatial structure.
IWC-SC 66 recommended a collaborative photo-ID approach to humpback whales in the Southeast
Pacific, integrating data from multiple research programs across the full range of the BSG (Jackson
et al., 2016). This is now possible through the integration of datasets gathered over the last 25 years
– some based on whale-watching tourism – along the west coast of Central and South America and
around the Antarctic Peninsula. During the biennial meeting of the Latin American Society of Aquatic
Mammals, SOLAMAC, in December 2016, a workshop on a collaborative approach to carrying out
a new abundance estimate on the BSG was convened (IWC, 2017a, b). In this context, we report
preliminary BSG abundance and survival estimates resulting from these efforts.
MATERIALS AND METHODS
Data sources
Humpback whale monitoring programs have been established in all countries throughout the
Southeast Pacific region since 1990, comprising from 11°N to 65°S (Figure 1). Throughout 2017 and
2018, we compiled, reconciled, and compared photo-ID catalogs and capture-recapture histories from
23 research groups (see Appendix 1). A total of 8,451 fluke images taken between 1991 and 2018
were collated, of which 1,961 (23.2%) came from the three feeding areas and southern migratory
corridor off central Chile and 6,490 (76.8%) from breeding areas. The number of images of unique
whales received per year from each research group is shown in Table 1.
Image selection
Images were graded as a high-, medium- or low-quality for the analysis based on five criteria: 1)
exposure/contrast/illumination; 2) angle of the fluke in relation to the surface of the water; 3) lateral
angle of the fluke with respect to the photographer; 4) focus and sharpness; and 5) visible proportion
of the flukes. Both high- and medium-quality images were included in the analysis. After selection,
6,474 images (76.6% of the total compiled photographs) were selected. Each photograph was pre-
treated (lighting and contrast) and trimmed, leaving only the fluke.
Matching process
The process of image matching within and among the photo-identification catalogs started with the
post-treatment images by the use of the HotSpotter recognition software version 1.0 (Crall et al.,
2013), scoring the likelihood of potential matches based on a combination of the SIFT algorithms of
Wild-ID (Lowe 2004) and a "local naïve Bayes nearest-neighbor algorithm". To reduce bias, both the
selection and the matching process were carried out by the same person, who was experienced in
matching humpback whale photographs (JA). First, the photographs within each catalog were
compared internally to eliminate potential duplicate whales (N = 120 individuals). Then, the catalogs
were compared to each other, allowing us to build capture-recapture histories. A second comparison
process was carried out later using the automated image recognition algorithm hosted at the
Happywhale web-based platform (https://happywhale.com/home, Cheeseman et al., in press), which
allowed the detection of additional matches not found in the first comparison process.
4
Figure 1. Area of distribution of the Breeding Stock G and sampling sites. Red circles are breeding
sites and red squares feeding areas.
Recapture rates
Recapture rates for each sampling area in both breeding and feeding grounds were calculated for
those datasets with more than 100 individuals by dividing the number of different recaptured
individuals by the total number of individuals identified. Individuals with low-quality images were
excluded.
Population abundance
Two different modeling approaches to estimate abundance open and closed populations are
commonly used with data from mark-recapture studies. Open population models allow gains from
immigration and births and losses from emigration and mortality, while closed population models
consider the population to be constant during the study period (Seber and Schwarz, 1999). Both
approaches assume conditions of equal and consistent capture probability across all sampling periods,
such as unique, permanent correctly recorded marks that do not affect catchability (Hammond, 2010).
The violation of such assumptions may lead to biased estimates.
For the analyses, capture-recapture histories of the 5,197 individuals were constructed using the
binary sequence "1" and "0", where "1" indicates that the individual was observed during that
sampling period and "0" indicates that the individual was not observed in that sampling period (Cooch
and White, 2009). The complete dataset corresponds to 28 sampling periods (1991-2018). Individuals
5
recorded at breeding and feeding grounds in the same year were included in the same sampling period
to achieve a larger sample. Because of the small effort with few identified individuals in the first five
years (1991-1995), data were pooled in one single period. Thus, 24 annual periods were used for the
analysis. Open and closed population models were fitted to estimate abundance using program Mark
9.0 (White and Burnham, 1999).
The fourteen models for closed populations implemented in Mark are divided into two main data
types "full likelihood" (Otis et al, 1978) and “conditional likelihood” (Huggins, 1989). Full likelihood
models take into account the probability of an individual not being observed or captured, that is, the
scenario "000" is given, while the conditional likelihood models eliminate this scenario from their
calculations. Full likelihood models are based on the parameterization of three types of parameters:
1) p = the probability that an animal in the population is captured and marked for the first time; 2) c
= the probability that an individual has been captured at least once before; and 3) f0 = the number of
individuals in the population that have not been counted. Conditional likelihood models are restricted
to the number of animals detected; therefore, f0 is not taken into consideration and only includes the
parameters p and c. An advantage of the conditional likelihood approach is that covariates can be
used to model the encounter process.
Four of the fourteen models belonged to a group called heterogeneity models, which contain an
additional parameter to p and c called mixture parameter pi (π), which calculates the heterogeneity
that exists between individuals at the time of capture. The following six models incorporated another
parameter that considers the probability of identifying an individual correctly in its first observation
"α". Finally, there were the four Huggins models with parameters "p" and "c" with random effects
that use numerical integrations to add individual differences in the match probabilities.
Closed population models in Mark used the following notations:
M0: probability that an animal is captured and marked for the first time (p) remains constant.
Mt: probability that an animal is captured and marked for the first time (p) varies with time.
Mb: response of the behavior of individuals.
Mh: probability that an animal is captured and marked for the first time (p) is heterogeneous.
Mh2: probability that an animal is captured and marked for the first time (p) is heterogeneous, and the
population comprises a mixture of two types of animals.
Mark chooses the most parsimonious model based on the Akaike information criterion (AIC), where
the model with the lowest value (AICc or AICweight) is the one that best fits the data (Freitas and
Marino 2012). Since AICc values between full likelihood and conditional likelihood models are not
comparable, the analyses were conducted separately.
Apparent survival
Apparent survival Phi (φ) was estimated using Jolly-Seber models for open populations implemented
in Mark. We fitted 105 different models to the data using the different formulations implemented in
Mark: POPAN, Link-Barker, Pradel-recruitment, Burnham JS, and Pradel-λ. The difference among
such formulations is the way they parameterize new entrants to the population.
6
Table 1. Information compiled in this study of the different research groups in the Southeast Pacific and Antarctic Peninsula, number of fluke images
per site and sampling period (year), for the period 1991-2018. Numbers in the table indicate new individuals discovered in the season by each research
group.
Contributor
Site
Total
91
92
93
94
95
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
Feeding ground
INACH
Antarctic Peninsula
276
23
40
46
19
38
55
10
45
PROANTAR
Antarctic Peninsula
766
2
53
60
21
65
106
51
17
22
13
21
1
53
18
26
57
54
51
75
AF
Antarctic Peninsula
256
21
33
10
38
69
85
CEQUA_ANT
Antarctic Peninsula
38
38
MHNRS
Antarctic Peninsula
17
6
11
OTROS_ANT
Antarctic Peninsula
105
2
7
28
11
55
2
MBS_ANT
Antarctic Peninsula
9
1
8
ACMA
Antarctic Peninsula
35
35
CHAC
Chañaral - Chile
10
2
1
5
1
1
EUTROPIA
Chañaral - Chile
15
1
2
3
1
4
2
2
CBA
Corcovado Gulf - Chile
41
2
1
2
15
3
2
4
4
1
7
CEQUA
Magellan St. - Chile
177
11
27
29
16
10
3
8
10
24
2
21
16
EMA
Magellan St. - Chile
178
18
3
6
7
12
10
7
12
17
10
8
10
7
11
9
5
14
12
CADICCB
Beagle Ch.- Argentina
37
3
2
3
8
21
Breeding ground
KETO
Osa Pen.- Costa Rica
130
2
11
13
1
7
19
9
9
37
22
PAN
Costa Rica
41
1
1
3
10
9
1
1
15
CEIC
Dulce G.- Costa Rica
30
8
1
2
13
1
1
4
PAN
Chiriqui G.- Panama
552
5
8
4
4
34
28
34
12
35
79
106
126
77
PMA-LP
Las Perlas Arch.-
Panama
173
12
8
22
51
31
2
4
8
6
2
7
9
11
PAN-CONT
Panama
18
18
ICA
Solano and Malaga Ba.-
Colombia
42
6
11
4
3
8
4
3
3
7
BGT
Tribuga G.- Colombia
543
16
127
98
66
236
SENTIR
Tribuga G.- Colombia
36
7
3
26
EC
North/Central Ecuador
628
24
7
8
14
82
96
65
143
57
4
10
7
3
4
26
4
12
39
23
PWF
Machalilla- Ecuador
1468
20
12
26
23
13
60
88
111
237
258
159
178
283
MBS
Salinas- Ecuador
2157
8
13
1
15
29
79
70
3
69
88
132
183
266
284
252
158
229
37
48
65
56
38
34
MBS
Perú
2
2
MBS
Panama
2
2
PAO
Los Órganos - Perú
650
13
34
16
96
111
84
131
165
CEPEC
Sechura - Perú
18
1
13
4
TOTAL
8451
8
14
2
17
53
163
128
105
150
54
223
387
364
606
589
620
620
646
355
455
174
323
637
540
543
585
57
32
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RESULTS
Recapture rates
Recapture rates were highly variable within the dataset in both feeding and breeding areas (Table 2).
Major datasets from Antarctic Peninsula (n=3) ranged between 0.025 to 0.048 and the number of
recaptures per individual between 1.0 and 1.16, but in the continental feeding area (Magellan Strait),
the recapture rate was six times higher on average (0.645 and 0.765, CEQUA and Whalesound,
respectively). High variability was also found in the breeding grounds datasets where the recapture
rate ranged between 0.026 to 0.194 and the number of recaptures per individual between 1 and 1.38.
The overall comparison process resulted in 5,197 unique individuals. A total of 2,329 recaptures of
1,176 whales across catalogs were found. The overall recapture rate was 0.226 and the number of
recaptures per individual was 1.98 (range 1-16) on average.
Table 2. Calculated recapture rates of individual datasets from feeding and breeding areas containing
more than 100 individuals.
Research group
No.
Individuals
No.
Recaptured
individual
Recapture
rate
No recaptures/
individual
Feeding areas
INACH
227
6
0.026
1.00
Proantar-Furg
608
29
0.048
1.07
Institute of Marine Science UCSC
255
6
0.025
1.16
Fundación CEQUA-Magallanes
169
109
0.645
4.09
Whalesound
161
121
0.765
6.22
Breeding areas
Panacetacea
485
80
0.165
1.33
KETO
112
3
0.026
1.33
MBS
1541
173
0.112
1.21
Macuático
425
15
0.035
1
Pacific Adventure
551
26
0.047
1.07
UFSQ
257
50
0.194
1.38
Héctor Guzmán
135
4
0.029
1.25
Pacific Whale Foundation
1045
86
0.082
1.24
Population abundance estimates
Abundance estimates obtained with open population models did not reach numerical convergence or
produced unrealistic estimates due to the spatial and temporal structure of the data and high
heterogeneity resulting from uneven sampling effort along a large geographic region. Estimates
obtained with closed population models were considered more suitable with the current data structure.
Results obtained with two different closed population data types are shown in Table 3. In both cases,
the model that best fitted the data allowed capture probability to vary by time with heterogeneity in
capture probability with two mixtures (Mth2). The population size obtained with both models was
similar (11,784 and 11,786, respectively; SE= 266 in both cases).
9
Table 3. Population abundance estimates for the BSG obtained with the two data types using
information from breeding and feeding areas of the period 1991-2018, fitted using closed population
models in program Mark 9.0.
Data type
Model
N-hat
SE
Lower
Upper
Full likelihood
{Mth2}
11,784
266
11,282
12,326
Conditional likelihood
{Mth2)}
11,786
266
11,284
12,328
Apparent survival estimates
Two CJS models with heterogeneous capture probabilities (Cormack-Jolly-Seber model with Pledger
mixtures) resulted in the same AICc value and model likelihood best fitted the data: 1) constant
survival and heterogeneity and time-dependent capture probability; and 2) constant survival and time-
dependent heterogeneity and capture probability. These models incorporate a mixture parameter (pi)
to model heterogeneity in both phi and p. Thus, two groups with different variation in both parameters
are reported for each model. Both models produced the same values of survival in the two mixtures
0.924 and 0.959, with marginal differences in the standard errors (Table 4).
Table 4. Survival estimates obtained with the Cormack-Jolly-Seber with Pledger mixtures models.
Model
Mixture
Estimate
Standard
error
Lower
Upper
{pi(.) Phi(.) p(t)}
1
0.924
0.003
0.916
0.978
2
0.959
0.008
0.938
0.973
{pi(t) Phi(.) p(t)}
1
0.924
0.006
0.911
0.936
2
0.959
0.008
0.937
0.973
DISCUSSION
These updated abundance estimates of the BSG resulted from the collaborative effort of research
groups working throughout the entire distribution range (~11°N to 65°S), allowing the integration of
data from breeding and feeding areas for the first time. However, it is recognized that different sources
of bias persist, particularly those associated with the effort heterogeneity and the time scale that
models most probably were unable to depict completely (see Table 1). Likewise, the low rate of
recaptures, despite the enormous research effort, precluded the use of models for open populations.
In such a long-term data series, closed population models could introduce an important downward
bias affecting the estimate, and therefore our estimates should be considered conservative. The
enormous extent of the distribution range, its high level of population structure in the breeding
(Guzmán and Félix, 2017) and feeding areas (Acevedo et al., 2013), and even during migration (Félix
and Guzmán, 2014) are aspects difficult to quantify but should be considered in future population
modeling attempts.
In both abundance and apparent survival estimates, mixture models fitted the data best, which
confirms the heterogeneity within datasets. An example of such heterogeneity can be found in the
two subsets from Magellan Strait (CEQUA and EMA). These two datasets include 15 years of
sampling from small population units with a high level of annual philopatry and therefore with a
different capture probability when compared to the full range of BSG breeding, feeding and transit
10
areas. Recent estimates for Magellan Strait using a robust design Bayesian framework estimated
humpback whale abundance at 204 (95% CI 199-210) for the period 2004-2016 (Monnahan et al.,
2019), which presents the highest confidence estimate for any population unit in the full dataset. By
contrast, some datasets accounted for once or few years of effort with low recapture probabilities.
Furthermore, breeding areas were disproportionately more frequently sampled than feeding areas
(3.3:1; see Figure 1 and Table 1).
These new abundance estimates show a population increase of 181% with respect to a previous
estimate made with information only from the breeding area off Ecuador in 2006 (Felix et al., 2011a),
yielding an annual average growth rate of 5.07% in 12 years (2006-2018). This annual growth rate is
low compared with other southern hemisphere humpback whale populations, ~ 10% in the Western
Australian population (Bannister and Hedley, 2001) and 7.4% in the Southwestern Atlantic
population (Ward et al., 2011). The maximum plausible rate of increase (ROI) for this species is
estimated at 11.8% (Zerbini et al., 2010). Differences between estimates with data from one breeding
area and the estimate obtained in this study including a combined dataset of breeding and feeding
areas could be caused, among other factors, by the following: 1) a previous overestimation; 2) the
dataset from the feeding areas included whales from an area not sampled in the breeding zone; and
3) the datasets from the feeding areas included whales sampled in Antarctica that do not belong to the
BSG. In the first case, the estimate made in 2006 has a wide range of confidence (95% CI 4,270-
9,907) (Félix et al., 2011a), so in a strict sense, the new estimate could be considered consistent with
such calculation. In the second case, unmonitored areas may persist in the Southeast Pacific such as
the Galapagos Islands, where one female was identified to belong to the BSG through molecular
studies (Felix et al., 2011b) but no fluke images were available, as well as in other oceanic islands
such as Malpelo in Colombia (Herrera et al., 2011, Palacios et al. 2012) and perhaps Cocos Island in
Costa Rica (Acevedo-Gutierrez and Smultea, 1995). Lastly, in the third factor above, a certain degree
of mixing exists between humpback whales from different Southern Hemisphere stocks in Antarctic
waters (Dawbin, 1964, Amaral et al., 2016; Steel et al., 2017), so it cannot be ruled out that some
whales photographed in the Antarctic Peninsula and included in the new dataset do not belong to the
BSG.
The current analyses also showed an improvement regarding apparent survival estimates. The former
average survival estimated at 0.919 (Félix et al., 2011a) is lower than the value obtained for the
mixture with the lowest value (0.924). The apparent survival values are also higher than the recent
estimate in the Magellan Strait feeding aggregation (0.892, CI: 0.871–0.910) which also showed an
annual increasing rate of 55% lower than the whole BSG (2.3%: CI 2.1%-3.1%) (Monnahan et al.,
2019). The mixture with the highest apparent survival value (0.959) is within the range reported in
other humpback whale populations (Zerbini et al. 2010).
The population increase rate of the BSG could be influenced by anthropogenic factors such as the
high rate of whale entanglement in fishing gear reported in waters of Ecuador and Colombia (e.g.,
Capella et al. 2001, Félix et al., 2011c) and ecological factors such as the increase in the predation
rate, suggested by an increase of scars from killer whale Orcinus orca in the flukes of BSG individuals
over time (Capella et al., 2018; Testino et al., 2019). Other threats of anthropogenic origin include
vessel collision (Van Waerebeek et al., 2007) and vessel disturbance (Scheidat et al., 2004; Ávila et
al., 2015), as well as emergent issues with a potential effect on cetaceans such as marine litter (Panti
et al., 2019) and climate change (Askin et al., 2017), particularly, changes in the extent of sea
coverage/pack mass influencing food availability in Antarctic (Ávila et al., 2020).
ACKNOWLEDGEMENTS
11
AF research was supported by the National Science Foundation of Polar Programs, WWF and the
Southern Ocean Research Partnership. CC thanks Pacific Whale Foundation and Yaqu Pacha for
financial support. FF and BH thanks tourist operators in Puerto López and Salinas, Ecuador.
CETACEA project in norther and central Ecuador is funded by research grants of the University San
Francisco de Quito, Rufford Grant and support of the local fishermen. DH thank Luis González of
Turismo Orca, Chile. Fundación Keto thanks ASOTU and ASOGUIBA from Uvita de Osa and
financial aid by CMS Small Funds Program (2012-13). ICA is grateful with Pacífico Extremo
(Colombia) and Oscar Rocha for collaboration in collecting some data. NAD and MAT are grateful
to Ushuaia tourist boats and crews to share records and logistic assistance, the Consejo Nacional de
Investigaciones Científicas y Técnica (CONICET), and the Wildlife Conservation Society for their
financial support. Pacífico Adventures thanks their crew; skippers, tour guides, volunteers for
collecting photographs every year. Panacetacea was supported by the Moore Charitable Foundation
and the Islas Secas Foundation. GP and MJPA Thanks to Asociación Turística Chañaral de Aceituno,
Turismo Arca de Noé, Turismos Orca and Aurelio Aguirre. Samples from the Brazilian Antarctic
Program (PROANTAR) were obtained with financial aid from the National Council for Research and
Technological Development (CNPq), the Brazilian Navy and the Secretariat of the Interministerial
Commission for the Resources of the Sea (SECIRM). The Research group Ecologia e Conservao
da Megafauna Marinha – EcoMega/CNPq of the Federal University of Rio Grande-FURG
contributed to this study. LS thanks Rufford Small Grants supported surveys in Sechura By in Perú.
AAL thanks to Carlos Olavarría, Antonio Larrea and Jordi Plana for assisting in the field with photo-
identification and organizing the INACH catalogue. AAL funding research was supported by INACH-
08-93 and INACH-163 projects of the Chilean Antarctic Institute. JA thanks all crew members of
Chonos yacht and M/N Forrest, as well as the field assistants and CEQUA for logistical support in
the Magellan Strait, Chile. JA funding have been supported by the National Commission for Science
and Technology, Regional Government of Magallanes and BIOMAR Foundation project. NBA
would like to thank all funding agencies well as whale watching companies, community councils and
the local communities within the Gulf of Tribugá, northern Colombian Pacific. A significant
acknowledgement to Colciencias in relation to NB’s doctoral fellowship.
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16
Appendix 1.
Research group
Study area
Primary contact
person
Email address
B
R
E
E
D
I
N
G
G
R
O
U
N
D
1
Fundación de Investigación y
Conservación Marina-Costera (Keto)
Osa Peninsula – Costa Rica
José David Palacios
pala1611@gmail.com
2
Centro de Investigación de Cetáceos de
Costa Rica (CEIC)
Dulce Gulf – Costa Rica
Juan Diego Pacheco
dpachecop@gmail.com
3
Panacetacea
Osa Peninsula – Costa Rica (past)
Chiriqui Gulf – Panama (current)
Kristin Rasmussen
panamakristin@gmail.com
4
Smithsonian Tropical Research
Institute (STRI – Panama)
Panama Gulf – Panama
Las Perlas Archipelago – Panama
Héctor Guzmán
guzmanh@si.edu
5
Anonymous contributor
Panama Gulf – Panama
--
--
6
Fundación Macuáticos
Tribuga Gulf – Colombia
Natalia Botero
natalia.botero@eagles.usm.edu
7
SENTIR/ Proyecto Resiliencias
Tribuga Gulf – Colombia (past)
Martha Llano
marthaelenallano@gmail.com
8
Universidad del Valle
Solano Bay – Colombia
Malaga Bay – Colombia
Isabel Cristina Ávila
isabel_c_avila@yahoo.com
9
Universidad San Francisco de Quito
Esmeralda Bay – Ecuador
Judith Denkinger
judenkinger@gmail.com
10
Pacific Whale Foundation
Machalilla – Ecuador
Cristina Castro
cristinacastro@pacificwhale.org
11
Museo de Ballenas Salinas
Salinas –Ecuador
Fernando Félix
fefelix90@hotmail.com
12
Pacific Adventure
Los Órganos – Peru
Aldo Pacheco
babuchapv@yahoo.com
13
Centro de Estudios del Pacífico
Secchura – Peru
Luis Santillán
lsantillancorrales@yahoo.com
F
E
E
D
I
N
G
G
R
O
U
14
Centro de Investigación Eutropia
Chañaral de Aceituno – Chile
María José Pérez
mjose.perez@gmail.com
15
Universidad Santo Tomás
Chañaral de Aceituno – Chile (past)
Daniela Haro
daniela.haro.diaz@gmail.com
16
Universidad Austral de Chile
Corcovado Gulf – Chile
Rodrigo Hucke-Gaete
rhucke@uach.cl
17
Whalesound Ltda
Magellan Strait – Chile
Juan Capella
jjcapella@yahoo.com
18
Fundación CEQUA
Magellan Strait – Chile
Antarctic Peninsula
Jorge Acevedo
jacevedo@cequa.cl
19
Centro Austral de Investigaciones
Científicas (CADIC)
Beagle Channel – Argentina
Natalia Dellabianca
ndellabianc@gmail.com
20
Instituto Antártico Chileno (INACH)
Antarctic Peninsula
Anelio Aguayo-Lobo
aaguayo@inach.cl
21
Universidade Federal do Rio Grande
(FURG)
Antarctic Peninsula
Luciano Dalla Rosa
l.dalla@furg.br
17
N
D
22
Institute of Marine Science UCSC
Antarctic Peninsula
Ari Friedlaender
Ari.friedlaender@ucsc.edu
23
Museo de Historia Natural de Río Seco
Antarctic Peninsula
Benjamín Cáceres
benjamincaceresm@gmail.com
24
Fundación Omacha
Antarctic Peninsula
Edgar Vásquez
ragdeadrian@gmail.com
25
Other contributors
Antarctic Peninsula
--
--
18
