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Climate change may affect the foraging success of bowhead whales Balaena mysti -cetusby altering the diversity and abundance of zooplankton species available as food. However,assessing climate-induced impacts first requires documenting feeding conditions under currentenvironmental conditions. We collected seasonal movement and dive-behaviour data from 25Eastern Canada−West Greenland bowheads instrumented with time-depth telemetry tags andused state-space models to examine whale movements and dive behaviours. Zooplankton sampleswere also collected in Cumberland Sound (CS) to determine species composition and biomass. Wefound that CS was used seasonally by 14 of the 25 tagged whales. Area-restricted movement wasthe dominant behaviour in CS, suggesting that the tagged whales allocated considerable time tofeeding. Prey sampling data suggested that bowheads were exploiting energy-rich Arctic cope-pods such as Calanus glacialisand C. hyperboreusduring summer. Dive behaviour changed sea-sonally in CS. Most notably, probable feeding dives were substantially shallower during springand summer compared to fall and winter. These seasonal changes in dive depths likely reflectchanges in the vertical distribution of calanoid copepods, which are known to suspend develop-ment and overwinter at depth during fall and winter when availability of their phytoplankton preyis presumed to be lower. Overall, CS appears to be an important year-round foraging habitat forbowheads, but is particularly important during the late summer and fall. Whether CS will remaina reliable feeding area for bowhead whales under climate change is not yet known.
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Mar Ecol Prog Ser
Vol. 643: 197–217, 2020 Published June 11
The Arctic is warming at a rate more than double
the global average (Screen et al. 2012) and is experi-
encing unprecedented decreases in the extent and
thickness of sea ice (Stroeve et al. 2007, Kwok et al.
2009). Such environmental changes are likely affect-
ing the community structure, distribution and abun-
dance of Arctic zooplankton, which are sensitive to
changes in water temperature (Hays et al. 2005, Chust
et al. 2014). Continued warming of Arctic waters may
result in the large, lipid-rich Arctic species being re-
placed with smaller temperate/subarctic species that
thrive in warmer conditions and are comparatively
lower in energy content (Beaugrand et al. 2002, Beau-
grand 2009). Such ecosystem changes will likely alter
© Inter-Research and Fisheries and Oceans Canada 2020 ·
*Corresponding author:
Seasonal diving and foraging behaviour of
Eastern Canada−West Greenland bowhead whales
Sarah M. E. Fortune1, 5,*, Steven H. Ferguson2, Andrew W. Trites1, Bernard LeBlanc3,
Valerie LeMay4, Justine M. Hudson2, Mark F. Baumgartner5
1Department of Zoology and Marine Mammal Research Unit, Institute for the Oceans and Fisheries,
University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
2Fisheries and Oceans Canada, Freshwater Institute, Winnipeg, Manitoba R3T 2N2, Canada
3Fisheries and Oceans Canada, Quebec, Quebec G1K 7Y7, Canada
4Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
5Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543-1050, USA
ABSTRACT: Climate change may affect the foraging success of bowhead whales Balaena mysti -
cetus by altering the diversity and abundance of zooplankton species available as food. However,
assessing climate-induced impacts first requires documenting feeding conditions under current
environmental conditions. We collected seasonal movement and dive-behaviour data from 25
Eastern Canada−West Greenland bowheads instrumented with time-depth telemetry tags and
used state-space models to examine whale movements and dive behaviours. Zooplankton samples
were also collected in Cumberland Sound (CS) to determine species composition and biomass. We
found that CS was used seasonally by 14 of the 25 tagged whales. Area-restricted movement was
the dominant behaviour in CS, suggesting that the tagged whales allocated considerable time to
feeding. Prey sampling data suggested that bowheads were exploiting energy-rich Arctic cope-
pods such as Calanus glacialis and C. hyperboreus during summer. Dive behaviour changed sea-
sonally in CS. Most notably, probable feeding dives were substantially shallower during spring
and summer compared to fall and winter. These seasonal changes in dive depths likely reflect
changes in the vertical distribution of calanoid copepods, which are known to suspend develop-
ment and overwinter at depth during fall and winter when availability of their phytoplankton prey
is presumed to be lower. Overall, CS appears to be an important year-round foraging habitat for
bowheads, but is particularly important during the late summer and fall. Whether CS will remain
a reliable feeding area for bowhead whales under climate change is not yet known.
KEY WORDS: Balaena mysticetus · Feeding ecology · Zooplankton · State-space modeling ·
Dive analysis · Satellite telemetry
Resale or republication not permitted without written consent of the publisher
Mar Ecol Prog Ser 643: 197–217, 2020
the stability of current food web structures (McMeans
et al. 2013) and impact the foraging success of zoo-
planktivorous marine predators, such as bowhead
whales Balaena mysticetus (Meyer-Gutbrod & Greene
Bowhead whales are considered to be moderately
vulnerable to future environmental changes that will
likely alter their current prey resource (Moore &
Huntington 2008). The replacement of large-bodied
zooplankton with comparatively smaller species has
already been documented during short-term warming
events in the Arctic (Lalande et al. 2013). Long-term
trends in decreasing zooplankton body size have also
been observed in the North Sea (Beaugrand 2009),
and the abundance, distribution, and diversity of zoo-
plankton species available to bowhead whales are
also likely to change in the future. Such potential
changes to the feeding regime of bowheads in the
Eastern Canadian Arctic makes understanding how
they forage under current environmental conditions
essential to evaluating the sensitivity of the species to
future changes in prey quality and quantity.
Much of what is known about the foraging strategy
of the Eastern Canada−West Greenland (ECWG)
bowhead whale population is the result of archival
tagging (Laidre et al. 2007, Heide-Jørgensen et al.
2010, 2013, Laidre & Heide-Jorgensen 2012) and
prey sampling (Madsen et al. 2001, Laidre et al. 2007,
Swalethorp et al. 2011) studies in Disko Bay on the
western coast of Greenland, in late winter and early
spring. Relatively little is known about the diet and
feeding behaviour of this population in Canadian
waters. Furthermore, the sex ratio in these studies
was heavily skewed towards non-lactating adult
females (85:15 female:male), and is thus not repre-
sentative of the entire population (Laidre et al. 2007,
Heide-Jørgensen et al. 2010). During the spring
(February− May), adult females in Disko Bay con-
sumed predominately temperate/subarctic calanoid
cope pods such as Calanus finmarchicus. These
female bowhead whales made deep, long dives dur-
ing late winter and comparatively shallower dives
during spring (Heide-Jørgensen et al. 2013). Such
temporal differences in diving behaviour were likely
due to seasonal vertical movements of their prey. The
tagged whales were presumed to seasonally adjust
their foraging strategy to maximize prey consump-
tion by feeding on dormant (i.e. diapausing) cope-
pods at depth during winter, and on active copepods
nearer the surface during spring (Heide-Jørgensen
et al. 2013). It is not known whether this is a general
strategy across different habitats, seasons and demo-
graphic groups.
Little is known about the foraging ecology of bow-
head whales in other regions of the ECWG popula-
tion range. For example, despite the long history of
bowhead whale occupancy and commercial exploita-
tion that occurred in Cumberland Sound (Reeves et
al. 1983), few foraging studies have been conducted
to date. The sex ratio of whales in Cumberland
Sound between 2011 and 2013 was 80:125 (female:
male), closer to parity than in Disko Bay, but some-
what biased towards males (Frasier et al. 2020). Both
males and females are found along the east coast of
Baffin Island (Nielsen et al. 2015, Chambault et al.
2018), which suggests that these males and those in
Cumberland Sound may be the counterparts to the
adult females found in Disko Bay (Heide-Jørgensen
et al. 2010). Given the apparent use of Cumberland
Sound by all demographic groups (including juve-
niles and mother−calf pairs), assessing diets, forag-
ing behaviour and habitat use in this region fills an
important gap in knowledge about the foraging ecol-
ogy of the ECWG bowhead population.
Regional differences in zooplankton species com-
position and abundance have been attributed to the
strong influence of the Baffin Island Current, which is
of Arctic origin in Cumberland Sound (Dunbar 1951,
Aitken & Gilbert 1989, McMeans et al. 2012), along
with the West Greenland Current, which is of Atlantic
Origin in Disko Bay (Heide-Jørgensen et al. 2013).
However, both locations are likely to contain some
Arctic and Atlantic fauna because the Baffin Island
Current and West Greenland Current are known to
mix in both regions (McMeans et al. 2012, Heide-
Jørgensen et al. 2013). This may be particularly true
along the Davis Strait sill, which may sometimes facil-
itate the movement of the West Greenland Current to
Cumberland Sound (Bedard et al. 2015). Conse-
quently, the quality and quantity of bowhead prey
and thus their feeding ecology likely differs season-
ally and between habitats due to physical oceano-
graphic processes.
Zooplankton communities are expected to differ be-
tween Disko Bay and Cumberland Sound, which in
turn should influence the feeding behaviour and rela-
tive quality of prey consumed by bowhead whales. For
example, bowheads in Disko Bay feed predominately
on the smaller temperate/subarctic calanoid copepod
Calanus finmarchicus, rather than on the less abun-
dant, but larger-bodied Arctic copepods C. hyper-
boreus and C. glacialis (Laidre et al. 2007). Laidre et al.
(2007) collected zooplankton at 25 stations in Disko
Bay and found that the mean ± SD biomass concentra-
tion (mg C m−3) of C. finmarchicus was 49 ± 39 com-
pared with 12.3 ± 14.9 for C. hyperboreus and 2.8 ± 2.3
Fortune et al.: Bowhead whale diving and foraging behaviour
for C. glacialis. Conversely, zooplankton species in
Cumberland Sound are likely to be dominated by Arc-
tic copepods, but it is not known whether they are pre-
ferred by the bowhead whales that feed there. Fur-
thermore, differences in the life histories of the Arctic
and temperate/subarctic copepods likely require dif-
ferent feeding strategies to capture them.
To examine the seasonal foraging behaviour of
different demographic groups of ECWG bowhead
whales in Cumberland Sound, we used time−depth
tele metry tags that recorded horizontal and vertical
movements. We examined spatiotemporal trends in
movement to determine how bowhead whales used
Cumberland Sound (Fig. 1) throughout the day,
month and year. We characterized how feeding be -
haviour (dive depth, shape and duration) changed
over seasonal and diel time scales. We then analysed
bowhead whale dive shape, depth and duration and
combined information on the species composition
and biomass of zooplankton obtained through net
collections to determine the importance of Cumber-
land Sound as a foraging ground. This research im -
proves our understanding of the diet and seasonal
foraging characteristics of an understudied segment
of the ECWG bowhead whale population.
2.1. Telemetry
Bowhead whales (n = 25) were equipped with long-
term platform transmitter terminal (PTT) satellite
telemetry tags containing time−depth recorders and
Argos radio transmitters (Wildlife Computers SPLASH
MK10) to record horizontal and vertical movements.
The SPLASH tag provided information on date, time,
location and summary dive behaviour (e.g. depth, du-
ration and shape). To increase longe vity of the tag, the
PTTs were programmed to transmit up to 400 times a
day every second hour during summer, and less fre-
quently during winter (i.e. 100 times every second
day). For our study, summer included June to August,
fall was between September and November, winter
ranged from December to February, and spring oc-
curred between March and May.
The whales were tagged in Foxe Basin and Cum-
berland Sound during summer (2012 and 2013;
Table 1). Juvenile and non-lactating adult animals
were selected for tagging, which meant excluding
animals <9 m long that were likely calves and those
in mother−calf pairs. Each tag was attached with a
Fig. 1. Known range of Eastern Canada−West Greenland bowhead whales with key areas identified: FB: Frobisher Bay,
CS: Cumberland Sound, DB: Disko Bay, A: Admiralty Inlet, PRI: Prince Regent Inlet and GB: Gulf of Boothia
Mar Ecol Prog Ser 643: 197–217, 2020
~20 cm stainless steel anchor, and a skin and blubber
sample was simultaneously collected from a 4 cm
biopsy tip attached to the tag deployment device.
The anchor and biopsy tip were both sterilized with
1:10 bleach:water solution prior to use. The tags were
deployed from a wooden canoe freighter using an
8 m fiberglass hand-held tagging pole. The tags
were attached dorsally and behind the blow holes to
improve data transmission by maximizing the time
the transmitter was out of water during a surfacing
event. Biopsy samples were collected to genetically
determine sex by amplification of a zinc finger gene
intron using LGL331 and LGL335 primers (Shaw et
al. 2003). The PCR product was subsequently stained
with GelRed (Biotium) and then visualized on a 1.5%
agarose gel. To infer sex, we then used the banding
pattern of X (~975 bp) and Y (~1040 bp) fragments.
2.2. Horizontal movement analysis
To increase the number of useful positions and
improve the accuracy of low quality Argos locations
(e.g. 0, A and B) that are common for large whale tag-
ging studies (Silva et al. 2014, Lowther et al. 2015),
the data are often filtered. In our study, we re-pro-
cessed the data with the square root unscented Kal -
man filter (SRUKF) by Service Argos. The SRUKF
algorithm uses a correlated random walk model that
predicts the future position of an animal and its esti-
mated error based on the individual’s previous loca-
tion and estimated error (Silva et al. 2014) as a result
of the diving behaviour of the whales (e.g. short
surface intervals).
The SRUKF-filtered data were subsequently run
through a speed filter using the ‘vmask’ function in
the ‘argosfilter’ package in R version 3.2.1 (R Devel-
opment Core Team 2019). This function filters Argos
satellite tracking data and is especially useful for
marine animal telemetry data because of the preva-
lence of poor-quality data and the need to filter un-
likely locations. We used a speed threshold of 2 m s−1,
as this speed approaches the maximum swimming
speed of bowhead whales (2.5 m s−1) and encom-
passes both foraging and migratory behavioural
states for balaenids (Mayo & Marx 1990, Baumgartner
& Mate 2003, Werth 2004, Simon et al. 2009, Nielsen
et al. 2015). Argos locations that resulted from swim-
ming speeds above this threshold were subsequently
We fit a hierarchical switching-state-space model
(HSSSM) (Jonsen et al. 2005, 2013) to our filtered
telemetry data to: (1) estimate the horizontal move-
ment (e.g. swimming speed, tortuosity) of individual
animals; and (2) determine individuals’ behavioural
states (area-restricted movement and travelling). The
‘bsam’ package in R (R Development Core Team
2019) provided in the supplement of Jonsen et al.
(2013) was used to fit a correlated random walk
(CRW) model that switched between 2 CRWs that re-
flected probable area-restricted movement (ARM)
and travelling behavioural states (Jonsen et al. 2005).
The 2 CRWs and the associated behavioural states
differ in mean turn angle and movement persistence
(autocorrelation in speed and direction). ARM re -
flected instances of low swimming speeds and high
turning angles (consistent with foraging behaviour),
PTT HSSSM Gap CS Location Length Sex
(d) (d) (d) (m)
114494 265 26 − FB 12 F
114495 723 678 6 FB 11−12 F
114496 529 306 1 FB 11 F
114497 309 226 − FB 12 M
114498 187 134 1 FB 11 M
114499 338 338 2 FB 13−14 F
114500 593 479 − FB 12−13 M
114501 184 173 − FB
114502 338 243 177 CS 10 M
114503 519 338 25 CS 10 F
114504 345 290 250 CS 10−11 F
114505 319 319 118 CS 11−12 M
114506 19 19 19 CS 13−14 F
114507 400 92 72 CS 10 M
114508 738 249 95 CS 9−10 M
114509 289 227 111 CS 9−10 M
128145 504 498 − FB 11−12 F
128146 694 557 − FB 13−14 F
128148 335 287 − FB 13 F
128149 13 13 − FB 12−13
128150 728 677 − FB 10 F
128151 721 700 − FB 9−10 M
128152 684 677 157 FB 9−10 M
128153 384 348 − FB 12−13 M
128154 320 320 9 FB 11−12 M
Table 1. Summary information for all 25 bowhead whales
tagged in Cumberland Sound (CS) and Foxe Basin (FB) with
SPLASH Tags (Wildlife Computers, MK10) between 2012
and 2013. PTT: platform transmitter terminal used to identify
unique individuals (animals that visited CS are in bold);
HSSSM: total number of days for which hierarchical switch-
ing-state-space model (HSSSM) locations were predicted af-
ter running the ‘vmask’ function; Gap: number of days for
which HSSSM locations were predicted after removing loca-
tions resulting from gaps > 4 consecutive days in raw satellite
telemetry data; CS: total number of days a given animal was
in CS as predicted by the HSSSM with gaps removed; Loca-
tion: habitat where the animal was tagged; Length: body
length estimated from the tagging vessel. –: instances where
data were not available
Fortune et al.: Bowhead whale diving and foraging behaviour
whereas travelling consisted of faster, more linear
movements. The model (HSSSM) was fit to each data
set (n = 25) containing individual specific location
data with 40 000 Markov chain Monte Carlo (MCMC)
iterations, dropping the first 30 000 (i.e. burn-in) and
retaining every 10th sample from the remaining
10 000 iterations, resulting in a total of 1000 samples
per chain (n = 2 chains). The HSSSM predicts 2 loca-
tions per day (i.e. 12 h timestep), per individual.
The HSSSM was chosen because it yields regularly
spaced location estimates and categorizes movement
behaviour by simultaneously fitting a single model to
all individual bowhead whale tracks, which is neces-
sary for evaluating the seasonal foraging behaviour
of bowhead whales. Behavioural states (b) were clas-
sified based on mean estimates from the MCMC
samples, which assumed that b = 1 was traveling and
b = 2 was ARM. The cut-off points we used were the
same as in previous studies and locations, where
mean estimates of b> 1.75 were assumed to indicate
ARM; b< 1.25 reflected transient behaviour; and val-
ues of bbetween 1.25 and 1.75 were unclassified. We
applied the same HSSSM formulated by Silva et al.
(2013), using the transition equation established by
Jonsen et al. (2007), such that each individual whale
(or PTT) is indexed by k, whereby:
dt,k ~ N2[γbt,kT(θbt,k)dt−1,k,Σ] (1)
where dt−1,krepresents the displacement of an indi-
vidual whale (k) between 2 unobserved locations (xt−1
and xt−2) and dt,k is the displacement of the same
whale between 2 unobserved locations (xtand xt−1)
that occurred earlier in time, T(θ) is the transition
matrix providing the mean turning angle (θ, i.e. tor-
tuosity or change in heading) required to transition
from dt−1 to dt. Furthermore, the move persistence co -
efficient (γ)combines autocorrelation in both direc-
tion and speed, and the randomness in whale move-
ment (N2) is represented by a bivariate Gaussian
distribution with a covariance matrix Σ. Most impor-
tantly, the mean turning angle (θ) and move persist-
ence coefficient (γ)are indexed by behavioural state
(bt) such that each individual whale (k) at each dis-
placement (t) corresponds to an estimated behav-
ioural state (b) that yields the best model fit. A set of
previously established priors were placed on the
movement parameters (θand γ) as determined by
Breed et al. (2009) such that during travel, swim
direction will be more linear (e.g. turn angle close to
0°) while ARM behaviour occurs when speed and
turning angle are highly autocorrelated.
Many empirical studies describe the movement of
predators relative to the distribution and abundance
of their prey. Feeding, or expected feeding based on
prior experiences of the animal, has been inferred
from ARM (or area-restricted search) over different
spatial scales in fish (Hill et al. 2000), birds (Paiva et
al. 2010) and terrestrial (Byrne & Chamberlain 2012)
and aquatic mammals (Thums et al. 2011). Predators
exhibiting ARM alter their movement pattern to in -
crease the time spent in productive areas by in -
creasing their turning rate after detecting prey (or
anticipating the detection of food) and reducing
their speed if prey abundance is high (e.g. Kareiva
& Odell 1987, Haskell 1997, Fauchald & Tveraa
2003). Consequently, feeding animals are thought to
spend more time in a given area if they are consum-
ing or searching for food. However, it is also possi-
ble that animals conducting other non-feeding be -
haviours such as mating (Würsig et al. 1993,
Richardson et al. 1995) and rock-rubbing (Fortune
et al. 2017) can produce similar movement patterns
(e.g. low swimming speed and high tortuosity).
Therefore, it is important to consider vertical move-
ments when inferring feeding behaviour from hori-
zontal movement patterns.
2.3. Vertical movement analysis
We analyzed the vertical movement of SPLASH-
tagged animals to determine how foraging effort
changed seasonally in Cumberland Sound using the
time-depth-recorder dive data. Dive duration, shape
and minimum and maximum dive depth were re -
corded with the time-depth recorder (TDR). Dives
were classified as vertical excursions to depths
8 m, because this was the minimum depth thresh-
old used by Wildlife Computers for dive classifica-
tion and thus dives shallower than this threshold
were not recorded. This dive threshold is also bio-
logically relevant, as it approaches the minimum
estimated body length of the tagged whales. How-
ever, it prevented us from quantifying surface
foraging behaviour such as skim-feeding, which is
expected to occur during spring. The dive shape
was classified according to 3 broad categories de -
fined by Wildlife Computers: V-shaped dives repre-
sented those where 20% of dive duration was
spent at maximum depth, U-shaped dives occurred
when >20 and 50% of the dive duration was spent
at maximum depth, and square dives included those
where >50% of the dive duration was spent at max-
imum depth. Previous studies that examined dive
profiles of balaenid whales (North Atlantic right
whales Eubalaena glacialis and bowhead whales) in
Mar Ecol Prog Ser 643: 197–217, 2020
relation to prey availability found that V-shaped
dives reflected search behaviour (i.e. non-feeding
dives), whereas square and U-shaped dives where
whales maximized their bottom time were most
likely representative of foraging dives (Baum gartner
& Mate 2003, Laidre et al. 2007, Heide-Jørgensen et
al. 2013). For example, during springtime in Disko
Bay, bowhead whales conducted deep U-shaped
dives near the sea bottom where high abundances
of pre-ascension Calanus finmarchicus occurred.
Following fine-scale foraging studies on North
Atlantic right whales (e.g. Baumgartner & Mate
2003, Baumgartner et al. 2003, 2017) that showed
animals feeding at the maximum depth of their
square or U-shaped dive where zooplankton abun-
dance was greatest, bowheads were shown to
undergo temporal changes in dive depth, suggest-
ing that individuals adjust their foraging behaviour
according to the vertical distribution of their prey
(Heide-Jørgensen et al. 2013). Consequently, forag-
ing behaviour may be inferred by examining bow-
head whale dive characteristics.
We filtered the predicted locations from the
HSSSM to include those that occurred within Cum-
berland Sound only (i.e. latitude ranged from 64° 00’
to 67° 00’ N and longitude ranged from 67° 00’ to
63° 30’ W) and removed gaps exceeding 4 consecu-
tive days based on the SRUKF data as a way to
exclude uncertain location estimates. We then fil-
tered the bowhead whale dive behaviour data based
on the HSSSM-predicted location data by matching
dates when tagged animals were inside Cumberland
Sound. We assumed that animals did not make short
(<24 h) excursions outside of Cumberland Sound and
that if there was an Argos location within Cumber-
land Sound on a particular day, all dives occurring
during that same day were conducted inside Cum-
berland Sound.
We investigated whether there were diel and sea-
sonal impacts on bowhead diving behaviour (e.g.
dive depth and duration) using linear-mixed effects
models with the ‘lme’ statistical function in R 3.6.1
(R Development Core Team 2019). We separated
dives that occurred during the day from those that
occur red at night during early and late August 2012
and used day/night as a fixed effect and maximum
depth (m) and dive duration (min) for square dives
as the response variables. Similarly, we examined
whether there were seasonal and dive shape effects
(fixed effects) on maximum depth (m) and dive
duration (min) (response variables). We fitted sev-
eral nested linear mixed-effects models and used
likelihood ratio tests to examine how season and
time of day affect bowhead whale dive depth and
duration, along with Akaike’s information criterion
(AIC) to indicate model support. Since there were
multiple dive records per animal (i.e. repeated
measures), we included a hierarchical error struc-
ture of individual, year, month and day, along with
a continuous autoregressive process within day
since measures were irregularly spaced in time (i.e.
‘CAR(1)’ process; Pinheiro & Bates 2000). We found
support for the random effects structure (i.e. lower
AIC and supported by likelihood ratio tests) relative
to other simpler random effect error structures for
all models. We also used graphs of standardized
residuals to further confirm this error structure
along with normality.
2.4. Zooplankton sampling and species
identification and enumeration
We collected prey samples to understand why
bowhead whales may be using Cumberland Sound
as a summertime feeding habitat and how the spe-
cies composition of zooplankton may differ from
Disko Bay (69° 15’ N, 53°33’ W; straight line distance
from mouth of Disko Bay to Cumberland Sound is
~675 km). Zooplankton samples were collected in
the fluke print of diving bowhead whales using ver-
tical hauling methods where we sampled from 10 m
above the sea bottom to the sea surface with a sam-
pling depth (mean ± SD) of 207.6 ± 30.52 m using a
333 µm conical mesh net with a 60 cm diameter
mouth opening. The net was outfitted with a Gen-
eral Oceanics helical flow meter and a Sensus Ultra
time−depth recorder to determine the sampling dis-
tance used to calculate the volume of sampled
water. All samples were obtained between 23 and
26 August 2013 from Kingnait Fiord (65° 55’ N,
65° 25’ W), where bowhead whales are regularly
observed conducting deep and long dives. Once the
vertical tow was completed, the net was sprayed
down immediately with seawater and all collected
organisms were concentrated into the cod-end
bucket at the end of the net. The concentrated
organisms were first transferred to a 333 µm mesh
sieve and then to a 250 ml sample jar and fixed with
5% buffered formalin for preservation.
Zooplankton species composition and abundance
was determined using taxonomic identification and
enumeration methods in the laboratory. Each sam-
ple was filtered through a 333 µm mesh sieve, and
subsequently rinsed with freshwater and trans-
ferred to a beaker and diluted with water. The
Fortune et al.: Bowhead whale diving and foraging behaviour
sample volume was recorded and a Hensen-
Stempel pipette was used to obtain a homogeneous
aliquot (i.e. sub-sample of known volume). A Fol-
som plankton splitter was used to sub-sample
dense samples, and the total number of times each
sample was split depended on the total number of
sample organisms. Each aliquot contained a mini-
mum of 200 calanoid copepods, and each organism
was identified to the lowest possible taxon (e.g.
species and genus for calanoid copepods) and life
stage for Calanus spp. and Pseudocalanus spp.
using a dissecting microscope. We discriminated
be tween morphologically similar Calanus species
(e.g. C. hyperboreus, C. glacialis and C. finmarchi-
cus; Grainger 1961, Jaschnov 1970) by measuring
prosome lengths (e.g. Unstad & Tande 1991, Hirche
et al. 1994) for all Calanus spp. with undamaged
exo skeletons using a dissecting microscope, stage
micro meter and ocular micrometer. To minimize
measurement variability, we measured all organisms
from the same orientation (right lateral side down).
We used species-specific prosome size ranges re -
ported by Madsen et al. (2001) to differentiate spe-
cies. However, Calanus spp. are known to overlap in
prosome length, which is particularly likely to intro-
duce error into the identification of early life-stages
of C. glaci alis, resulting in an over-estimation of
C. finmar chi chus (Parent et al. 2011).
Zooplankton biomass was estimated for all Calanus
organisms with prosome measurements (PL) using
known relationships between prosome length (mm)
and body weight (mg C) using:
Cmg = a× PLmmb(2)
where a= 0.0048 and b= 3.5687 for C. finmarchicus
and C. glacialis (Madsen et al. 2001), and a= 0.0014
and b= 3.3899 for C. hyperboreus (Hirche & Mumm
1992, Thor et al. 2005). We estimated the individual
carbon content of early (CI−CIV) and late (CV−adult)
Pseudocalanus spp. by assuming that early-stage
organisms had mean PL of 0.597 mm and late-stage
organisms measured 1.009 mm (Liu & Hopcroft
Previously collected vertical zooplankton data
(Madsen et al. 2001) were also used to further eluci-
date whether square dives were feeding dives and
whether temporal shifts in dive depth reflected
changes in the vertical distribution of prey. We com-
pared the maximum depth of square dives with the
depth of maximum zooplankton biomass (samples
collected over a 14 mo period between 1996 and 1997
using vertical hauling methods) in Disko Bay (Mad-
sen et al. 2001).
3.1. Telemetry
The sex ratio of the tagged whales occupying Cum-
berland Sound was somewhat biased towards males
(57:43%) with 8 males and 6 females, and estimated
body lengths ranged from 9.5 to 13.5 m (Table 1).
Age class was broadly inferred based on previous
studies (George et al. 1999, 2011, Higdon & Ferguson
2010, Koski et al. 2010) using boat-based estimates of
body lengths that approximated the distance be -
tween the tip of the whale’s snout to the fluke notch.
These estimates revealed that 33% (n = 2) of the 6
females were probable adults (>13 m, >25 yr), and
66% (n = 4) were sub-adults (10 and 12.5 m and
<25 yr). No estimated female body lengths were
within the range of calves (<7.5 m and 0−1 yr) or
young juveniles (7.5 and <10 m and 1−4 yr). How-
ever, we found that 62.5% (n = 5) of the 8 males were
sub-adults, and 37.5% (n = 3) were young juveniles.
Consequently, our body length estimates suggested
that young juveniles, sub-adults and adult animals
use Cumberland Sound. However, our tagged whale
data were dominated by reproductively immature
animals for both sexes particularly males, as no
adults appear to have been tagged. The average
body length (mean ± SD) of tagged animals was rela-
tively small (10.9 ± 1.3 m) in part because of our
somewhat biased sampling design (e.g. not tagging
mothers and calves).
Animals tagged in 2012 transmitted for 397 d on
average (range: 19−737 d), while those tagged in
2013 transmitted for 485 d (13−729 d; Table 1). The
HSSSM predicted 2 daily locations for each animal,
resulting in 16 406 locations throughout the Eastern
Canadian Arctic. We chose this 12 h time-step for the
HSSSM because the majority of tagged animals had
at least 2 Argos locations per day. A portion of these
locations occurred within Cumberland Sound
(12.5%). However, of the 14 animals that visited
Cum ber land Sound (Fig. S1 in the Supplement at
www. int-res. com/ articles/ suppl/ m643 p197 _ supp.
pdf), almost one-quarter (24%; n = 2044) of their 8446
locations were inside the sound. Furthermore, 1 ani-
mal (PTT 114506) stayed nearly an entire year (from
9 August 2012 to 18 July 2013) inside Cumberland
Sound and just outside the mouth of the sound. When
excluding gaps (>4 consecutive days), we found that
the mean number of days that individuals spent in
Cumberland Sound was highly variable, mean ± SD
75 ± 79.6 d. We found 21% (n = 3) of the tagged
whales spent only 1−2 d in Cumberland Sound, while
Mar Ecol Prog Ser 643: 197–217, 2020
29% (n = 4) spent 6 to 25 d, 43% (n = 6) spent 72 to
177 d, and 7% (n = 1) resided in Cumberland Sound
for 250 d.
We found that 10 of the animals that occurred in
Cumberland Sound had gaps in the SRUKF data that
exceeded 4 consecutive days when including all data
(i.e. locations outside of Cumberland Sound). Over-
all, there were 11 482 HSSSM-predicted locations for
all 14 animals that visited Cumberland Sound, of
which 13% (n = 1530) were generated during gaps
>4 d. Furthermore, we found that HSSSM-predicted
locations generated by identified gaps were most
common during fall (31%; n = 480), followed by win-
ter (27%; n = 420), spring (22%; n = 340) and summer
(19%; n = 290).
Seasonal patterns in Cumberland Sound occupancy
were found for SPLASH-tagged whales. When data
for all years were combined, bowhead whales (n = 14)
had the greatest number of locations (2 per day) in
Cumberland Sound during the fall (n = 882, 43%), fol-
lowed by summer (n = 537, 26%), spring (n = 405,
20%) and winter (n = 220, 11%) (Fig. S2). When data
were separated by year, tagged animals had the
highest occupancy in Cumberland Sound during the
fall of 2012 (n = 841 locations), summer of 2012 (n =
295) and spring 2013 (n = 243) (Fig. S2). Tagged bow-
head whales spent the least amount of time in Cum-
berland Sound during the winter (range 21−116 d
between 2012 and 2015). The low occupancy during
winter may be partially an artifact of the tag settings,
as fewer transmissions were scheduled during winter
months to increase tag longevity. However, the great-
est proportion of gaps in HSSSM-predicted locations
occurred during the fall, which provides support for
these seasonal patterns in occupancy.
3.2. Behaviour
The bowhead whales in Cumberland Sound dis-
played pronounced differences in the proportions of
behavioural states (b) as determined by the HSSSM.
The majority of all estimated locations in Cumberland
Sound (n = 2044) were associated with ARM (presum-
ably foraging behaviour) based on the weighted av-
erage (mean ± SD: 91.4 ± 10.82%; Fig. 2), whereby
the percentage of HSSSM locations with ARM for an
individual whale was weighted by the total number
Fig. 2. Argos satellite locations for 4 SPLASH-tagged bowhead whales (PTT 114502, 114503, 114504, 114505) derived from hi-
erarchical switching state-space models (HSSSM) in Cumberland Sound with 2 locations per day for illustrative purposes.
HSSSM-predicted locations resulting from gaps in Kalman-filtered Argos data exceeding 4 consecutive days were considered
less reliable than predictions made from locations closer in time, and were removed from analysis. Three behavioural states
derived from the HSSSM are indicated, with yellow, green and grey circles reflecting area-restricted movement (ARM, i.e.
probable feeding), travelling behaviour and an unknown behavioural state, respectively. HSSSM-predicted locations that oc-
curred between 64.00 and 67.00 latitude and 67.00 and 63.50 longitude (decimal degrees) were considered to occur within
Cumberland Sound. Kingnait Fiord is outlined (----) for identification purposes
Fortune et al.: Bowhead whale diving and foraging behaviour
of HSSSM locations for that whale when calculating
the weighted average. Traveling behaviour rarely oc-
curred and represented only 2.20 ± 3.71% of all loca-
tions. The remaining 6.41 ± 7.93% of locations were
of an unknown behavioural state. Furthermore, we
found seasonal differences in the proportion of loca-
tions assumed to be associated with ARM behaviour.
Most notably, ARM was greatest during the fall
(95.2 ± 8.03% of all locations based on weighted
mean), followed by the spring (91.1 ± 19.77%), sum-
mer (89.2 ± 12.68%) and winter (81.8 ± 26.4%). The
high percentages of ARM behaviour suggest that
bowhead whales consistently allocated time to forag-
ing activities while occupying Cumberland Sound.
3.3. Diving
Of the 14 tagged whales that visited Cumberland
Sound, 1 individual (PTT 114498) had only a single
location within the sound and no associated dives.
The remaining 13 whales dove a total of 20 976 times
over 450 d in Cumberland Sound (Fig. 3, Table S1).
The whales conducted predominately square (68.3%,
n = 14 331) and U-shaped dives (22.1 %, n = 4640),
whereas V-shaped (8.5%, n = 1781) and unclassified
dives (1.1%, n = 226) represented a small portion of
the total (Fig. 3, Table 2). We inspected the summary
dive statistics (e.g. range, mean ± SD) for unusually
high values that would exceed the physiological div-
ing limits of the species. We found 2 V-shaped dives
that were extraordinarily deep and long in duration
(maximum depth: 976 m, minimum dive duration:
114 h). We subsequently removed these biologically
Fig. 3. Maximum depth (m) and minimum dive duration (min) for square, U- and V-shaped dives for 13 bowhead whales
equipped with SPLASH tags while in Cumberland Sound between 2012 and 2015. Data were pooled across months and sea-
sons, and were plotted using a hexagonal heatmap, which divides the area of the graph into hexagons and counts the number
of data points contained within each hexagon. The minimum number of counts per hexagon is 1 for all dive shapes, and the
maximum value is 150 for square dives, 240 for U-shaped, 60 for V-shaped and 450 when all data are combined. Square dives
were considerably deeper and longer in duration compared to V-shaped dives, and are likely indicative of feeding behaviour
Dive Range Mean ± SD Skew Kurtosis
shape (min.–max.)
Maximum dive depth (m)
Square 8−655.5 165.45 ± 128.90 0.099 1.52
U 8−543.5 62.11 ± 88.57 3.352 15.70
V 8−451.5 45.70 ± 58.51 2.206 7.16
Minimum dive duration (min)
Square 0.42−47.4 16.47 ± 8.31 −0.081 2.15
U 0.12−40.3 6.69 ± 6.71 1.632 6.92
V 0.58−50.8 8.06 ± 6.34 1.597 5.44
Table 2. Summary dive statistics for square, V- and U-
shaped dives in Cumberland Sound between 2012 and 2015
for 13 bowhead whales. Data were pooled across months
and years. Two V-shaped dives (> 700 m depth and > 75 min
in duration) were considered erroneous and were removed
from the analysis
improbable outliers from our analysis.
Furthermore, only 2 tagged animals
occupied Cumberland Sound during
February (n = 354 square dives) and
March (n = 410 square dives), which is
fewer than other times of year.
When evaluating whether bowheads
allocated more or less time to probable
feeding dives during different seasons,
we found an interaction between sea-
son and dive shape for dive duration
(Table 3; log-likelihood ratio test [LRT] =
1217.2, p < 0.0001). In particular, square
dives had the longest duration, particu-
larly during winter (Fig. 4). To permit
inferences about the seasonal vertical
movement of zooplankton based on the
assumed connection be tween the zoo-
plankton depth and bowhead whale pro -
bable feeding depth, we examined im -
pacts of dive shape and season on dive
depth. As with dive duration, we found
an interaction be tween dive shape and
Mar Ecol Prog Ser 643: 197–217, 2020
Model Fixed df AIC LRT (p) ΔAIC
Dive duration (min)
Null − 19979 306397.9 − −
1 Shape 19976 302483.3 3920.647 (<0.0001) 3914.6
2 Shape+Season 19976, 31 302457.8 31.43108 (<0.0001) 25.5
3 Shape×Season 19967,31 301258.6 1217.196 (<0.0001) 1199.2
Maximum dive depth (m)
Null − 19979 241789.2 − −
4 Shape 19976 240354.7 1440.476 (<0.0001) 1434.5
5 Shape+Season 19976, 31 240342.1 18.5641 (0.0003) 12.6
6 Shape×Season 19967,31 239081.1 1278.991 (<0.0001) 1261
Table 3. Linear mixed-effects models for the impacts of bowhead whale dive
type (square, U-, and V-shaped, and unknown) and season (summer, fall,
winter and spring) on dive duration and maximum depth. The null model in-
cludes no fixed effects. The random effects are consistent across models and
included the platform transmitter terminal (PTT) used to identify unique indi-
viduals, year, month and day in a hierarchical order. The change in AIC
(ΔAIC) and likelihood ratio tests (LRT) are relative to the model earlier in the
list for dive duration and for maximum depth following stepwise selection.
These indicate that there are interactions between dive shape and season for
both dive duration and maximum depth. Instances where there are no fixed-
effects, LRT or ΔAIC values are noted (−)
Fig. 4. Minimum dive duration (min) and maximum dive depth (m) by season (S: summer, F: fall, W: winter and Sp: spring) and
type of dive (square, U- and V-shaped) for 13 bowhead whales that occupied Cumberland Sound. The number of tagged
whales is indicated above the boxplot whiskers. The width of the boxes is proportional to the square-root of the number of
dives per month (i.e. the wider the box, the greater the sample size). Instances where the notches of 2 boxes do not overlap
provide evidence that the medians differ. The black bar represents the median, the box represents the interquartile range,
the whiskers reflect the non-extreme maximum and minimum values, and the grey dots indicate extreme values
Fortune et al.: Bowhead whale diving and foraging behaviour
season (Table 3; LRT = 1278.9, p < .0001). For ex -
ample, square dive depth was shallow (50 m) during
spring and early to mid-summer and comparatively
deeper (150 m) during fall and winter (Fig. 4). We
also found that the maximum depth of square dives
agreed well with the seasonal depths of maximum
zooplankton biomass in Disko Bay (Madsen et al.
2001) (Fig. 5), providing further evidence that the
depth where bowhead whale feeding occurs changes
seasonally in Cumberland Sound.
Through initial examination of bowhead whale
dive depths during the day and night (Figs. 6 & 7), we
found evidence of diel diving behaviour for 8 animals
during early and late August 2012. During early
August (1−15 August), we found that the maximum
depth (Table 4; LRT = 20.2, p < 0.0001) and minimum
dive duration (Table 4; LRT = 29.7, p < 0.0001) of
square dives differed for periods of daylight (day)
versus darkness (night). However, these results con-
cerning diel effects should be interpreted with cau-
tion, since the changes in AIC compared to the null
model were relatively small. During early August,
the maximum depths (mean ± SD) of square dives
were 122 ± 80 m during the day and 59 ± 46 m during
the night based on the average depth of each individ-
ual’s mean square dive depth. Similarly, we found
that the depth (Table 4; LRT = 36.4, p < 0.0001) and
duration (Table 4; LRT = 69.9, p < 0.0001) of square
dives differed substantially in late August (16−31
August) during the day and night. Although less pro-
nounced than during early August, the depths of
square dives were considerably deeper during the
day (250 ± 32 m) compared with the night (159 ±
59 m) during late August. Bowhead whales similarly
conducted longer dives during the daytime (18.85 ±
1.936 min) than the nighttime (14.61 ± 2.664 min)
during late August.
Overall, we found that bowhead whales conducted
deeper and longer square dives during daylight
hours in August (Figs. 5 & 6). However, there was
considerable variability in dive depth that is likely
due to individual variation in foraging strategies and
variability in the vertical distribution of prey (Fig. 7).
It is important to consider that these diel diving mod-
els were constructed for specific time periods based
on initial examination of the dive data and our pre-
diction that zooplankton would undergo diel vertical
migration during summer when surface phytoplank-
ton concentrations are expected to occur.
We found that bowhead whales spent a portion of
their day (21−22%) conducting square and U-shaped
dives in Cumberland Sound during summer. Overall,
the whales appeared to allocate the most time to
probable feeding dives during summer (5.0 ± 1.52 h)
and the least during spring (2.5 ± 0.76 h; Table 5).
However, the time bowheads allocated to probable
foraging dives on a daily basis was quite variable
during summer. For example, the maximum time
individual animals spent conducting square dives
Fig. 5. Comparison of the maximum depth (m) of square
dives for bowhead whales during the daytime in Cumber-
land Sound (n = 13 animals) and the depth of maximum zoo-
plankton biomass (m) of 3 dominant calanoid copepod spe-
cies (Calanus finmarchicus, C. glacialis and C. hyperboreus)
collected in Disko Bay, a habitat along western Greenland at
a similar latitude to Cumberland Sound. The plotted zoo-
plankton data were collected and reported by Madsen et al.
(2001). Boxplot definitions are the same as in Fig. 4
Fig. 6. Maximum dive depth (m) during square dives for
8 bowhead whales in Cumberland Sound during early
(1−15 August) and late (16−31 August) August 2012.
Boxplot definitions are the same as in Fig. 4
Mar Ecol Prog Ser 643: 197–217, 2020
ranged from 9.7 to 14.2 h in 2012 and
between 5.8 and 11.7 h in 2013. These
results suggest that individuals occa-
sionally allocated considerable time to
feeding activities, but tended to use a
relatively small portion of the day to
feed on average (i.e. 20.8% or 5 h).
3.4. Zooplankton
We collected 7 zooplankton samples
near diving bowhead whales in King-
nait Fiord during August 2013. Species
identification revealed that the full
water-column tows consisted almost
ex clusively of calanoid copepods (mean
± SD: 94 ± 0.03%). Of the copepods,
Pseudo calanus spp. were the most com-
mon (55 ± 0.05%) followed by Calanus
spp. (36 ± 0.57%; Fig. 8). Due to the
prevalence of Pseudocalanus spp. and
Calanus spp., we only calculated abun-
dance (ind. m−3) for these organisms.
Fig. 7. Maximum dive depth (m) of square dives during the day and night for 8 bowhead whales in Cumberland Sound during
August 2012. Boxplot definitions are the same as in Fig. 4
Model Fixed df AIC LRT (p) ΔAIC
Early August maximum square dive depth (m)
Null − 1313 14324.1 − −
7 day.night 1312 14306.1 20.043 (<0.0001) 18.05
Early August minimum square dive duration (min)
Null − 1313 18642.0 − −
8 day.night 1312 18614.0 30.057 (<0.0001) 28.05
Late August maximum dive depth (m)
Null − 1490 18682.0 − −
9 day.night 1489 18644.6 39.474 (<0.0001) 37.5
Late August minimum dive duration (min)
Null − 1490 22651.6 − −
10 day.night 1489 22579.1 74.439 (<0.0001) 72.4
Table 4. Linear mixed-effects models for the impacts of time of day (i.e. cate-
gorical variable ‘day.night’ where each bowhead whale dive is classified as oc-
curring during the day or night based on the time of sunset and sunrise during
early and late August 2012) on square dive duration (August) and maximum
depth (August). The random effects are consistent across models and included
the platform transmitter terminal (PTT) used to identify unique individuals,
year, month and day in a hierarchical order. The null model includes no fixed
effects. The change in AIC (ΔAIC) and likelihood ratio tests (LRT) are relative
to the model earlier in the list for dive duration and for maximum depth and in-
dicate that there are diel effects on both dive duration and maximum depth.
Instances where there are no fixed effects, LRT or ΔAIC values are noted (−)
Fortune et al.: Bowhead whale diving and foraging behaviour
We found that Pseudocalanus spp. represented the
greatest proportion of total abundance (61 ± 5.9 %) on
average, followed by C. glacialis (27% ± 5.8), C. fin -
mar chi cus (8.4 ± 2.0%) and C. hyperboreus (4.2 ±
1.5%; Fig. 9). We measured prosome lengths for 91%
(n = 623) of all staged Calanus spp. (n = 682). Prosome
measurements were variable within and between
taxa due to species-specific and ontogenetic variation
in size. On average, an individual copepod was
estimated to contain 0.015 ± 0.036 mg C ind.−1 for
C. finmarchicus as compared with 0.107 ± 0.159 mg C
ind.−1 for C. glacialis and 0.124 ± 0.122 mg C ind.−1
for C. hyper boreus. We used mean estimates of dry
weight (mg C ind.−1) for early (CI−CIV) and late
(CV−adult) individuals per species and their associ-
ated abundance per tow to calculate mean dry weight
per cubic meter (mg C m−3). We found that estimated
dry weight was dominated by C. glacialis (72 ± 7.9%;
0.87 ± 0.41 mg m−3), followed by Pseudocalanus spp.
(12 ± 4.7%; 0.13 ± 0.04 mg m−3), C. hyper boreus (11 ±
5.6%; 0.13 ± 0.11 mg m−3), and C. finmarchicus (5 ±
2.4%; 0.06 ± 0.02 mg m−3; Fig. 9).
Our analysis of long-term (including attachments
365 d) bowhead whale horizontal and vertical move-
ments provides new insights into habitat-use patterns
and feeding behaviour that previous studies have not
captured using smaller datasets and with bio loggers
Season Square dives Years Animals
(h) (n)
Summer 5.0 ± 1.52 2012, 2013 9
Fall 4.6 ± 1.56 2012, 2013 6
Winter 4.7 ± 2.44 2012−2015 3
Spring 2.9 ± 0.72 2013, 2015 5
Table 5. Average time (mean ± SD) spent by bowhead whales
making square dives per day in Cumberland Sound. Dive du-
rations were pooled across years for each animal that spent a
minimum of 5 d in Cumberland Sound per season. Dive dura-
tions were first averaged to provide a mean daily dive time
per individual, and then averaged across individuals
Fig. 8. Proportion of enumerated calanoid copepods per sam-
ple from Cumberland Sound for Pseudocalanus spp., Calanus
glacialis, C. finmarchicus, C. hyperboreus, Metridia spp., Oi -
tho na spp. and Acartia longiremis with all life-stages grouped
together. Boxplot definitions are the same as in Fig. 4
Fig. 9. Percent contribution by species to (A) total biomass
(mg C m−3) and (B) total abundance (ind. m−3) based on 7 net
tows in Kingnait Fiord during August 2013. Abundance and
biomass calculations were made for the most common spe-
cies identified, i.e. Calanus glacialis, C. hyperboreus, C. fin-
marchicus and Pseudocalanus spp. The boxplot shading is
relative to the total lipid content per individual species, with
the darkest shading representing the species with the high-
est energy content (C. hyperboreus) and the lightest repre-
senting the lowest-energy prey (Pseudocalanus spp.) (De-
Lorenzo Costa et al. 2006, Falk-Petersen et al. 2009); other
boxplot definitions are the same as in Fig. 4
Mar Ecol Prog Ser 643: 197–217, 2020
that provided less detailed dive information. Most no-
tably, we found that whales resided in Cumberland
Sound during all 4 seasons, with 1 animal remaining
all year. Some animals tended to be infrequent visitors
to Cumberland Sound, spending only a day to several
weeks, while others had considerably longer resi-
dency times, spending several consecutive months in
the area that occasionally in cludes overwintering (n =
1). However, peak occupancy occurred during sum-
mer (26%) and fall (43%). Furthermore, almost all of
the satellite tele metry locations (91 %) were associated
with ARM, suggesting that Cumberland Sound is a
year-round feeding area. These findings provide a
new understanding of the feeding behaviour of bow-
head whales, and the biological significance of Cum-
berland Sound to the ECWG population.
4.1. Evidence of feeding behaviour
The feeding behaviours of bowhead whales and
the closely related North Atlantic right whale have
been inferred from horizontal movement data col-
lected from satellite telemetry tags and from vertical
dive data recorded using time−depth recorders. Pre-
vious studies that examined dive profiles of right
whales (Baumgartner & Mate 2003, Baumgartner et
al. 2017) and bowhead whales (Laidre et al. 2007,
Heide-Jørgensen et al. 2013) in relation to prey avail-
ability found that square and U-shaped dives where
whales maximized their bottom time were represen-
tative of foraging dives. In one study, bowhead
whales conducted deep U-shaped dives near the sea
bottom in Disko Bay where high abundances of pre-
ascension Calanus finmarchicus occurred (Laidre et
al. 2007). Other studies of bowhead whales in the
Eastern Canadian Arctic found that changes in
swimming speed, turning radius and diving fre-
quency could be used to evaluate bowhead whale
feeding activity (Pomerleau et al. 2011, Nielsen et al.
2015). Together, these studies demonstrate that at-
surface locations (satellite telemetry) and summa-
rized dive data (time-depth recorder) can provide
useful information regarding the sub-surface forag-
ing behaviour of large whales. Furthermore, because
of the longevity of our biologgers, we were able to
make seasonal inferences about individual foraging
behaviour, which high-resolution biologgers used to
study the kinematics of foraging-related behaviour
using accelerometry and fine-scale time−depth re -
corders are unable to capture because of their limited
deployment durations (e.g. Simon et al. 2009, Wright
et al. 2017, Tennessen et al. 2019).
We observed the tagged whales occupying Cumber-
land Sound during all months. However, peak foraging
likely occurs during late summer and early fall based on
high occupancy during that time inferred from HSSSMs
that were parameterized with satellite telemetry data
(Figs. 3 & 4). The greatest number of HSSSM locations
occurred during August (2012 = 14.3 %; 2013 = 3%), fol-
lowed by September (2012 = 14.9%; 2013 = 1.9%), Oc-
tober (2012 = 15.1%; 2013 = 0%) and November (2012
= 11.1%; 2013 = 0.09%) for 14 animals. The residency
period was long (25 d; 6−31 August 2012) for the 8 ani-
mals tagged in Cumberland Sound during August
2012, suggesting that this is an important area for a por-
tion of the population. Furthermore, results from the
state-space models demonstrated that just over 90% of
all HSSSM locations were associated with behaviours
typically thought to reflect feeding activities (e.g. slow
swimming speed and high turning angles).
Bowhead whales in Cumberland Sound allocated a
small proportion of their daily activities to feeding
based on the vertical dive data (8 m). They made
mostly square (68%) and U-shaped (22%) dives that
were consistent with foraging dives recorded for
North Atlantic right whales (Baumgartner & Mate
2003) in the western Atlantic. However, unlike North
Atlantic right whales that are believed to spend
the majority (50−90%; Goodyear 1996) of their day
foraging while occupying their summer feeding
grounds such as the Bay of Fundy, bowhead whales
spent only a small fraction (21−22%) of their day con-
ducting square and U-shaped dives during the sum-
mer in Cumberland Sound. This finding provides
support that Cumberland Sound is a multi-use habi-
tat that serves functions beyond feeding, such as
rock-rubbing for exfoliation (Fortune et al. 2017).
Overall, the dive durations for right whales (8−
15 min, Goodyear 1996; and 12.2 ± 2.22 min, Baum-
gartner & Mate 2003) and bowhead whales (12.0 ±
3.3 min) were remarkably similar during the summer.
Both species dove to comparable depths on average
(mean dive data averaged across individuals): right
whales dove to 121.2 ± 24.2 m in the Bay of Fundy
and Roseway Basin (Baumgartner & Mate 2003) and
134 m on average in the Bay of Fundy (Goodyear
1996), and bowheads similarly dove to 117.4 ±
52.4 m. This suggests that the increased time that
right whales allocated to foraging is unlikely related
to differences in the vertical distribution of their prey.
Interspecific differences in daily feeding activities
may instead reflect disparities in: (1) the quality and
quantity of available prey; (2) the spatial heterogene-
ity of prey patches; (3) energetic requirements; and
(4) environmental conditions.
Fortune et al.: Bowhead whale diving and foraging behaviour
4.2. Seasonal feeding patterns
Some large whales have highly seasonal feeding pe-
riods marked by intense feeding during summer in
productive high latitude habitats, and fasting over
winter in lower latitude areas (e.g. Corkeron & Connor
1999, Kenney et al. 2001, Christiansen et al. 2013).
However, unlike a proportion of right whales that pre-
sumably fast for a significant portion of the year while
occupying southern calving grounds be tween Decem-
ber and March (Keller et al. 2012), bowheads appear to
feed year-round in Cumberland Sound based on
>50% of their dives each season being probable forag-
ing dives. We found that bowheads allocated the most
time to foraging dives during the summer in Cumber-
land Sound on average (5.0 ± 1.52 h) and the least
amount during spring (2.5 ± 0.76 h; Table 5). However,
there was a lot of variability in the time allocated to
probable foraging dives, suggesting that bowheads al-
ternated between days when they spent over half the
day feeding (e.g. 60%) and others when they spent
only a small fraction of the day engaged in feeding ac-
tivities. Variability in daily feeding times may reflect
differences in energy requirements based on age, sex,
reproductive and nutritive condition (Lockyer 1981,
George 2009, Fortune et al. 2013).
The apparent reduced feeding time during spring
may reflect the presence of zooplankton near the
surface during the phytoplankton bloom, making it
more accessible with less effort by the whales. It is
also possible that feeding time was underestimated
during spring if whales were exploiting prey patches
located between 0 and 7 m in the water column (as
North Atlantic right whales do during spring; Baum-
gartner et al. 2017). Another possible explanation for
why bowhead whales may allocate less time than
right whales to feeding activities during the summer
is that they may have comparatively lower daily food
requirements, in part because they appear to feed
continuously throughout the year. Bowheads also
have comparatively thicker blubber stores (Haldi -
man & Tarpley 1993, Rosa 2006, George et al. 2007)
compared with North Atlantic right whales (Moore et
al. 2004, Miller et al. 2011) and may opt to catabolize
this energy store during lean years, providing a
greater capacity to fast. It is also possible that bow-
head whales have lower basal metabolic rates (i.e.
hypo metabolic condition; George 2009), and hence
lower daily energy requirements, compared to North
Atlantic right whales.
Our conclusion that bowheads feed year-round in
Cumberland Sound is consistent with prior telemetry
and diet studies. Previous satellite-tagging studies
similarly found that bowhead whales occupied Cum-
berland Sound during winter months. One study
reported that predominately adult females, originat-
ing from Disko Bay, occupied Cumberland Sound
between late July and mid-December (Nielsen et al.
2015). Another study recorded one whale in Cumber-
land Sound during late July that then travelled to the
high Arctic and subsequently returned to Cumber-
land Sound in early January and remained within the
area until the start of May (Pomerleau et al. 2011).
Our conclusion that bowhead whales are feeding
year-round is further supported by dietary stable iso-
tope analysis of ECWG bowhead whales that also
reported year-round foraging (Matthews & Ferguson
2015). However, Cumberland Sound may be one of
the only areas where ECWG bowhead whales feed
during all seasons. Year-round feeding may be a con-
sequence of (1) a population below carrying capacity
and (2) favourable physical and biological oceano-
graphic conditions that support calanoid copepod
production throughout their range. Consequently,
due to their apparently flexible feeding strategy,
bow head whales may be able to reduce their sum-
mertime foraging effort compared to right whales.
The plasticity of the bowhead whale feeding strat-
egy is also reflected by seasonal adjustments in dive
behaviour. The depth of probable foraging dives (i.e.
square dives) varied seasonally, suggesting that the
vertical distribution of zooplankton fluctuates season-
ally in Cumberland Sound. This was seen in the maxi-
mum depth of square dives becoming increasingly
deeper during summer (122.7 ± 59.3 m), fall (218.9 ±
22.5 m) and winter (253.1 ± 111.5 m), and becoming
shallower during spring (73.5 ± 50.4 SD; all years
combined for all individuals that spent a minimum of
5 d inside Cumberland Sound per season; Fig. 4).
Inferences may be made about the vertical distribu-
tion of bowhead prey in Cumberland Sound based on
the life-history characteristics of calanoid copepods
and zooplankton sampling research conducted in an
adjacent habitat: Disko Bay, Greenland. The composi-
tion of zooplankton in Disko Bay is similarly dominated
by herbivorous calanoid copepods, such as Calanus
finmarchicus, C. hyperboreus and C. gla ci alis, which
occur in maximum numbers at depths that change sea-
sonally (Madsen et al. 2001). However, we found that
summertime biomass in Cumberland Sound was dom-
inated by larger-bodied Arctic species on average (C.
glacialis 72%, C. hyperboreus 11% and C. finmarchi-
cus 5%), which differs from Disko Bay, where Calanus
spp. biomass was dominated by C. finmarchicus
(76%), a temperate/subarctic species, followed by C.
hyperboreus (20%) and C. glacialis (4%). Interestingly,
Mar Ecol Prog Ser 643: 197–217, 2020
depths of maximum zooplankton biomass from Disko
Bay were similar across taxa and compared well with
seasonal differences in the depths of bowhead whale
foraging dives in Cumberland Sound (Fig. 5). This
agreement between summertime zooplankton depth
distribution and bow head diving behaviour suggests
that Calanus spp. have similar seasonal vertical move-
ments at comparable latitudes on either side of Davis
Strait, and that bowhead whales likely adjust their for-
aging be haviour during summer (e.g. deep vs. shallow
square dives) in response to changes in the vertical
distribution of their prey. However, the seasonal
timing of changes in copepod vertical distribution may
be somewhat different in Cumberland Sound than in
Disko Bay because of oceanographic differences. Con-
sequently, for a rigorous analysis, these data should be
collected concurrently.
Calanoid copepods of the families Calanidae and
Eucalanidae have life cycles that correspond with
seasonal changes in physical and biological oceano-
graphic conditions and feature pronounced vertical
movements. In response to reductions in food avail-
ability following the spring phytoplankton bloom and
increasing predator abundance, lipid-rich life stages
of Calanus spp. descend to deeper waters (below the
winter convective mixed layer for organisms in fiords
and shelf seas; Irigoien 2004). Once organisms have
vertically migrated, they commence diapause (a form
of dormancy). At this time, the cooler water tempera-
tures and reduced activity suppress metabolic rates
to decrease catabolism of lipid reserves (Madsen et
al. 2001, Heide-Jørgensen et al. 2007, Laidre et al.
2007). Some species (e.g. C. finmarchicus and C. gla -
ci alis) may ascend prior to or at the start of the spring
phytoplankton bloom to either refuel their lipid
reserves to permit spawning or replenish their
energy reserves after egg production (Tande 1982,
Niehoff et al. 2002, Madsen et al. 2008). Larger-bod-
ied and longer-lived species such as C. hyperboreus,
however, employ a different reproductive strategy
and spawn during the winter while at depth using
stored energy (Hirche & Niehoff 1996). The lipid-rich
and positively buoyant eggs develop to feeding stage
nauplii as they ascend to the surface waters, ready to
begin grazing at the start of the phytoplankton bloom
(Jung-Madsen et al. 2013).
Zooplankton biomass should be high in the surface
waters during the spring phytoplankton bloom and
de crease over the late summer months as phyto-
plankton is grazed down and copepods begin to
migrate to depth and enter diapause. The timing and
duration of diapause, however, is highly variable and
depends on the life history and reproductive strategy
of different species (Falk-Petersen et al. 2009) and on
seasonally induced changes in environmental condi-
tions such as ice retreat and solar irradiance (Baum-
gartner & Tarrant 2017).
Bowhead whale diving behaviour is likely to reflect
seasonal movements in the vertical distribution of
their prey. Previous studies found that phytoplankton
production was highest in Cumberland Sound during
late June 2007 and August 2008 (McMeans et al.
2012). Consequently, we would anticipate surface
ag gre ga tions of zooplankton to begin forming during
early to mid-summer following the retreat of sea ice
and persisting until late summer, with diapause com-
mencing during early fall. These seasonal patterns in
vertical zooplankton distribution should be reflected
in the depth of probable foraging dives of bowhead
whales (e.g. Figs. 6 & 7). We found that tagged bow-
heads made shallow square dives during May−
August and began making deep dives during the lat-
ter half of August — presumably when a pronounced
reduction in phytoplankton occurred and zooplank-
ton initiated diapause. Consequently, it appears as
though August and September are a transitional
period in Cumberland Sound at which time zoo-
plankton begin their vertical migration to depth.
4.3. Diel patterns in feeding activity
Zooplankton undergo short-term daily vertical
movements in addition to longer-term seasonal shifts
in their distribution. For example, Calanus spp. make
daily excursions below the euphotic zone at dawn to
avoid presumed visual predators such as zooplankti -
vorous fish (Bollens & Frost 1989). Zooplankton will
ascend towards the surface after dusk to graze on
phytoplankton that is concentrated at the mixed layer
(e.g. Bollens & Frost 1989, Durbin et al. 1995, Baum-
gartner et al. 2011, Sainmont et al. 2013, Vestheim et
al. 2013). Such diel vertical migration (DVM) appears
to be a strategy employed to minimize predation risk.
However, predator avoidance means foregoing feed-
ing opportunities for part of the day and incurring en-
ergetic costs to move through the water column.
Not all copepods undergo DVM. One possible rea-
son is that some size classes may be too small to be at
risk of being eaten by visual predators and therefore
can remain feeding (i.e. smaller copepods are less
likely to be visually detected and thus less likely to
undergo vertical migration; Hays 1995). However,
some copepods are also known to undertake reverse
DVM, whereby organisms occupy surface waters dur-
ing the day and descend to depth at night in response
Fortune et al.: Bowhead whale diving and foraging behaviour
to standard DVM by their invertebrate pre dators
(Ohman et al. 1983). Finally, individuals with full or
nearly full oil sacs may conduct DVM to avoid preda-
tors, whereas organisms with less full oil sacs may re-
main in the surface waters to feed during the day and
night because the benefit of accumulating more lipid
outweighs the potential risk of predation (Huntley &
Brooks 1982, Baumgartner et al. 2011). In all likeli-
hood, the avoidance of predators and accumulation of
lipid during most years of high primary productivity
likely drives strong diel rhythms in copepod vertical
distribution in Cumberland Sound during summer.
We observed changes in bowhead whale diving
be haviour that were consistent with DVM of their
prey. Interestingly, the depth of square dives was
consistently deeper during the day compared with
the night during August, when there are pronounced
periods of daylight and darkness (12 h separation).
Between the end of June and the beginning of July
there are over 20 h of daylight, leaving little darkness
to warrant DVM. We found that square dive depth
was consistently deeper during the day compared
with the night for 8 whales occupying Cumberland
Sound during August 2012. During the first 2 wk of
August, the depth of square dives was significantly
deeper during the day (122 ± 80 m; mean dive depth
averaged for each unique individual) and shallower
at night (59 ± 46 m; Fig. 8). Similarly, in late August,
bowhead whale dives were significantly deeper dur-
ing the day (250 ± 32 m) and shallower during the
night (159 ± 60 m). However, these late August dives
were consistently deeper than dives made earlier
that month regardless of time of day. Changes in
photoperiod and light intensity may be a circadian
cue that initiates diel vertical migration, thus affect-
ing the vertical distribution of bowhead whale prey
(Forward 1988). Furthermore, we hypothesize that
the lack of a diel pattern in bowhead diving behav-
iour during September and October (when daylight
lengths were 12 h 58 min and 9 h 48 min, respec-
tively) reflects of the initiation of diapause.
In contrast to our observation of diel patterns in
bowhead diving behaviour in Cumberland Sound, a
prior study in the Eastern Canadian Arctic (Pomer-
leau et al. 2011) found no such behaviour. One possi-
ble explanation for this difference is that bowhead
whales in the Eastern Arctic study were at higher
latitudes (e.g. Gulf of Boothia), where day length is
comparatively longer. Longer daylight (e.g. midnight
sun) may diminish or eliminate the mass diel move-
ment of zooplankton (Blachowiak-Samolyk et al.
2006). Further south in Disko Bay, which is at a simi-
lar latitude to Cumberland Sound, bowhead whale
prey (e.g. C. glacialis and C. hyperboreus) have been
reported to undergo DVM during late April and early
May (Swalethorp et al. 2011).
In the absence of data on the vertical distribution of
zooplankton in Cumberland Sound, we can only spec-
ulate about what influenced bowhead whale foraging
behaviour. It seems unlikely that the shift in dive
depths during early and late August was due to tem-
poral differences in day length, because there was
only about one additional hour of darkness during late
August (~16 h day length) compared with early Au-
gust (~17 h day length). Of the bowhead Argos loca-
tions in Cumberland Sound during early August, only
57.9% of the locations were in Kingnait Fiord. Con-
versely, during late August, the spatial distribution of
tagged whales changed, as they were almost exclu-
sively found in Kingnait Fiord. It is possible that physi-
cal oceanographic processes differed in Kingnait Fiord,
thus altering the vertical structure of prey in the water
column or that the prey bowhead whales were target-
ing in Cumberland Sound during early August (Ro-
gachev et al. 2008) were following classic DVM due to
the co-occurrence of other known zooplanktivorous
predators, such as Arctic char Salvelinus alpinus and
capelin Mallotus villosus (Marcoux et al. 2012), that
were otherwise absent in Kingnait Fiord. However,
during late August, some copepods (e.g. lipid-rich C.
glacialis and C. hyperboreus) may have begun their
vertical descent to depth to commence diapause.
It is possible that copepods with less accumulated
lipid may remain in the surface waters to continue
foraging, and only enter diapause once they have ac-
cumulated sufficient lipids (e.g. Visser & Jónasdóttir
1999, Rey-Rassat et al. 2002, Campbell & Dower
2003, Irigoien 2004, Maps et al. 2010, 2012, Baum-
gartner & Tarrant 2017). Asynchronous diapause has
been observed for C. finmarchicus in the North At -
lan tic Ocean (Tarrant et al. 2008). If diapause were
similarly asynchronous in Cumberland Sound, bow-
heads may exploit deep-water aggregations of dia-
pausing copepods during the day and night while
also exploiting shallowly aggregated active prey after
dusk. Future zooplankton sampling studies will be
required to determine the spatio-temporal variability
in diel vertical migration and the relationship be-
tween zooplankton depth distribution and bowhead
whale dive behaviour (e.g. Baumgartner et al. 2011).
Our findings provide new insight into the flexible
feeding strategy of an understudied segment of the
Mar Ecol Prog Ser 643: 197–217, 2020
ECWG bowhead whale population and the impor-
tance of Cumberland Sound as a year-round foraging
area. Through analysis of the time spent conducting
horizontal (e.g. slow swimming speed and high tortu-
osity) and vertical (e.g. square dives) movements, we
found that both sexes likely fed during all months in
Cumberland Sound, although late summer and early
fall appear to be particularly important feeding times.
Unlike Disko Bay, where zooplankton biomass ap-
pears to be dominated by temperate/subarctic species
(e.g. C. finmarchicus), it appears that bowheads in
Cumberland Sound exploit mostly Arctic species (e.g.
C. glacialis), which similarly comprise the greatest
biomass and are comparatively larger in size and
higher in lipid content than the temperate/ sub-arctic
species (Falk-Petersen et al. 2009). There were also
distinct seasonal and diel patterns in bowhead whale
dive behaviours that appear to correspond to temporal
changes in the vertical distribution of their prey re-
lated to well-studied life-history characteristics. The
apparent flexibility of bowhead whales to exploit sea-
sonally available prey throughout the year in Cum-
berland Sound bodes well for their ability to adapt to
climate-induced changes to their habitat. What is less
certain, however, is how climate change will alter the
species composition and abundance of their primary
prey, and whether bowhead whales can adapt their
foraging strategies to contend effectively with such
changes to their prey base.
Acknowledgements. We are grateful to our community part-
ners, Levi Qaunaq and Natalino Piugattak from Igloolik, and
Noah Ishulutaq and Timeosie Akpalialuk from Pangnirtung,
who were responsible for vessel operations that ensured the
success of this research. We appreciate the invaluable logis-
tical support provided by the Igloolik and the Pang nir tung
Hunters and Trappers Organizations and the Government of
Nunavut and Cory Matthews and Natalie Reinhart for their
assistance in the field. Thank you to the reviewers, who pro-
vided thoughtful edits and comments that improved the
paper. Bowhead whale behavioural data were collected
under Department of Fisheries and Oceans License to Fish
for Scientific Purposes S-12/13-1014-NU and S-13/14-1009-
NU and Animal Use Protocol FWI-ACC-2012-034 and FWI-
ACC-2013-018. Fieldwork was funded by Fisheries and
Oceans Canada (Emerging Fisheries), Nuna vut Wildlife
Research Trust Fund, Nunavut General Monitoring Pro-
gram, Ocean Tracking Network, University of Manitoba,
ArcticNet Centre of Excellence awarded to S.H.F. This study
was supported in part by a Natural Sciences and Engineer-
ing Research Council Canadian Graduate Scholarship,
Northern Scientific Training Program (Canadian Polar Com-
mission), UBC-Affiliated Fellowship and the W. Gar field
Weston Award for Northern Research awarded to S.M.E.F
and the Molson Foundation awarded to S.H.F. This study is
partially based on a PhD thesis by S.M.E.F at the University
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Editorial responsibility: Elliott Hazen,
Pacific Grove, California, USA
Submitted: January 1, 2020; Accepted: May 5, 2020
Proofs received from author(s): June 3, 2020
... We defined seasons as being summer (June, July, August), fall (September, October, November), winter (December, January, February) and spring (March, April, May), and used mean estimates from the Markov Chain Monte Carlo (MCMC) samples to classify behaviour states (b), which assumed that b=1 was transit and b=2 was resident mode. We used the same cut off points for b as used by others (Jonsen et al., 2007;Fortune et al., 2020b;Fortune et al., 2020c) such that locations with mean estimates of b > 1.75 were assumed to reflect resident and b <1.25 reflected transit behaviour. Values of b that fell between 1.25 and 1.75 were assigned an unclassified behavioural state. ...
... We also assumed that if there was a resident associated HSSSM location within a habitat on a particular day, all dives occurring during that same day were similarly conducted inside that habitat. This assumption was based on observed multi-scale bowhead whale tagging and focal follow data that showed individuals resided in a region of interest (e.g., Cumberland Sound) for consecutive weeks to months (Fortune et al., 2020a;Fortune et al., 2020b). Furthermore, observed patterns in HSSSM data demonstrate that transit locations occur when individuals are exiting a region of interest (Fortune et al., 2020a;Fortune et al., 2020b). ...
... This assumption was based on observed multi-scale bowhead whale tagging and focal follow data that showed individuals resided in a region of interest (e.g., Cumberland Sound) for consecutive weeks to months (Fortune et al., 2020a;Fortune et al., 2020b). Furthermore, observed patterns in HSSSM data demonstrate that transit locations occur when individuals are exiting a region of interest (Fortune et al., 2020a;Fortune et al., 2020b). ...
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The ecological impact of environmental changes at high latitudes (e.g., increasing temperature, and decreased sea ice cover) on low-trophic species, such as bowhead whales, are poorly understood. Key to understanding the vulnerability of zooplanktivorous predators to climatic shifts in prey is knowing whether they can make behavioural or distributional adjustments to maintain sufficient prey acquisition rates. However, little is known about how foraging behaviour and associated environmental conditions fluctuate over space and time. We collected long-term movement (average satellite transmission days were 397 (± 204 SD) in 2012 and 484 (± 245 SD) in 2013) and dive behaviour data for 25 bowhead whales (Balaena mysticetus) equipped with time-depth telemetry tags, and used hierarchical switching-state-space models to quantify their movements and behaviours (resident and transit). We examined trends in inferred two-dimensional foraging behaviours based on dive shape of Eastern Canada-West Greenland bowhead whales in relation to season and sea ice, as well as animal sex and age via size. We found no differences with regards to whale sex and size, but we did find evidence that subsurface foraging occurs year-round, with peak foraging occurring in fall (7.3 hrs d-1 ± 5.70 SD; October) and reduced feeding during spring (2.7 hrs d-1 ± 2.55 SD; May). Although sea ice cover is lowest during summer foraging, whales selected areas with 65% (± 36.1 SD) sea ice cover. During winter, bowheads occurred in areas with 90% (± 15.5 SD) ice cover, providing some open water for breathing. The depth of probable foraging varied across seasons with animals conducting epipelagic foraging dives (< 200 m) during spring and summer, and deeper mesopelagic dives (> 400 m) during fall and winter that approached the sea bottom, following the seasonal vertical migration of lipid-rich zooplankton. Our findings suggest that, compared to related species (e.g., right whales), bowheads forage at relatively low rates and over a large geographic area throughout the year. This suggests that bowhead whales have the potential to adjust their behaviours (e.g., increased time allocated to feeding) and shift their distributions (e.g., occupy higher latitude foraging grounds) to adapt to climate-change induced environmental conditions. However, the extent to which energetic consumption may vary seasonally is yet to be determined.
... Bowhead whales dives have been classified into 3 categories based on profile shape that reflect different behaviours: V-, Uand square-shaped dives are characterized by the percentage of time spent at maximum depth, ≤ 20%, > 20 and ≤ 50%, and > 50% respectively (e.g. Fortune et al. 2020b). V-shaped dives are considered search dives allowing the whale to locate dense patches of zooplankton in the water column (Laidre et al. 2007). ...
... Once prey patches are identified, bowhead whales target them and perform U-or square-shaped foraging dives depending on the vertical distribution of prey (Fortune et al. 2020a). Higher proportions of shorter and shallower U-shaped dives are performed when feeding on near-surface prey aggregations compared to higher proportions of longer and deeper square-shaped dives when feeding near the sea bottom (Fortune et al. 2020b). During foraging dives, bowhead whales only open their mouth at maximum depth to reduce drag in the descent and ascent phases (Simon et al. 2009). ...
... (2017) even though they are shorter than blue whales. As bowhead whales also undergo a molt during summer (Fortune et al. 2017), we assume skin samples represent the isotopic niche integrated over a maximum period of one year, but likely reflects the isotopic niche of the previous summer/fall when skin is re-grown and the most intense foraging occurs (Fortune et al. 2020b). ...
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Shifts in zooplankton quantity and quality caused by climate change could challenge the ability of bowhead whales to meet their energetic requirements. When facing such selection pressure, intra-population variation dampens the negative effects and provides population-level resilience. Previous studies observed inter-individual diet variation in bowhead whales, but the mechanism responsible for the variation was undetermined. We investigated foraging variability in Eastern Canada-West Greenland bowhead whales using dietary biomarkers (stable isotopes, fatty acids) and movement data (satellite telemetry with time-depth recorders) from the same individuals. We found that bowhead whale individuals using distinct summer and fall foraging habitats displayed differences in horizontal movements, foraging dive depth, and diet. For individuals using the Canadian Arctic Archipelago habitat (Foxe Basin, Gulf of Boothia, Prince Regent Inlet, Lancaster Sound and Admiralty Inlet, Nunavut), they performed long distance movements across regions, and their foraging dive depth was generally shallow, but increased from July to November. These whales displayed higher δ ¹³ C and δ ¹⁵ N values and ratios of C16:1n7/C16:0. Individuals using the West Baffin Bay habitat (Cumberland Sound, Baffin Bay, Davis Strait) were more localized in their horizontal movements and consistent over time in their foraging dive depth, which was generally deeper. These whales displayed lower δ ¹³ C and δ ¹⁵ N values and ratios of C16:1n7/C16:0. Overall, this inter-individual variation in diet and foraging behaviour could indicate some niche variation which would be beneficial for the population under changing habitats and prey availability.
... S6, S9). Areas where bowhead whales congregate are likely related to foraging activity (Finley, 1990;Fortune et al., 2020a;Harwood et al., 2017b;Olnes et al., 2020), which increases the risk of vessel strike given that bowhead whales forage close to the surface at times (Fortune et al., 2020b) and may continue foraging even when a vessel is nearby (Wartzok et al., 1989). Vessels follow similar paths in these regions for a variety of reasons. ...
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Collisions between vehicles and wildlife is a global conservation concern, and vessel strikes are a leading cause of serious injury and mortality for baleen whales. Yet vessel strikes have rarely been studied in the Arctic. Vessel traffic is increasing throughout the Arctic as sea ice is declining, leading to increased overlap between vessels and whales. We examined hypothetical vessel strike risk for the Bering-Chukchi-Beaufort (BCB) and Eastern Canada-West Greenland (ECWG) populations of bowhead whales during the open-water shipping season. We used satellite telemetry and aerial survey data to calculate monthly relative density of both populations, and satellite vessel tracking data to calculate monthly vessel density and speed. We estimated vessel strike risk by multiplying whale density by vessel density corrected by vessel speed. For the BCB population, the highest relative risk was near Utqiaġvik and Prudhoe Bay, Alaska, USA, and near Tuktoyaktuk, Northwest Territories, Canada. For the ECWG population, the highest risk was in the Gulf of Boothia, Cumberland Sound, and near Isabella Bay, Nunavut, Canada. Strike risk was highest in August and September, corresponding with monthly trends in vessel traffic. This study provides important information for focussed monitoring and to minimize/mitigate the threat of vessel strikes to bowhead whales. Although vessel strike risk is presently lower for these populations than for other temperate large cetacean populations, bowhead whale behaviour and projected increases in traffic elevates their risk in the Arctic. Measures to mitigate vessel strike risk to bowhead whales will likely benefit other Arctic marine mammals like beluga and narwhal.
... presumably due to a lack of light effects on krill concentrations [34], or in narwhal 128 ingestion events in Scoresby Sound [35]. To our knowledge, only [36] Weddell seals at 66 • S also dive deeper in the afternoon, particularly toward the end of 132 the austral summer [37]. Mid-water narwhal prey (such as squid Gonatus sp. and Polar 133 cod Boreogadus saida [6,35]) is known for diel vertical migration to deeper water in the 134 afternoon [38,39], possibly explaining the observed narwhal foraging behavior. ...
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Detecting structures within the continuous diving behavior of marine animals is challenging, and no universal framework is available. We captured such diverse structures using chaos theory. By applying time-delay embedding to exceptionally long dive records (83 d) from the narwhal, we reconstructed the state-space portrait. Using measures of chaos, we detected a diurnal pattern and its seasonal modulation, classified data, and found how sea-ice appearance shifts time budgets. There is more near-surface rest but deeper dives at solar noon, and more intense diving during twilight and at night but to shallower depths (likely following squid); sea-ice appearance reduces rest. The introduced geometrical approach is simple to implement and potentially helpful for mapping and labeling long-term behavioral data, identifying differences between individual animals and species, and detecting perturbations.
... For example, no daily patterns have been found in blue whale calls around Antarctica, presumably due to a lack of light effects on krill concentrations [35], or in narwhal ingestion events in Scoresby Sound [36]. To our knowledge, only [37] found diurnal diving behavior of bowhead whales near Baffin Island (65˚N) at latitudes close to that of our study area (70˚N), with deeper dives in daytime in August, similar to our findings. Weddell seals at 66˚S also dive deeper in the afternoon, particularly toward the end of the austral summer [38]. ...
Full-text available
Detecting structures within the continuous diving behavior of marine animals is challenging, and no universal framework is available. We captured such diverse structures using chaos theory. By applying time-delay embedding to exceptionally long dive records (83 d) from the narwhal, we reconstructed the state-space portrait. Using measures of chaos, we detected a diurnal pattern and its seasonal modulation, classified data, and found how sea-ice appearance shifts time budgets. There is more near-surface rest but deeper dives at solar noon, and more intense diving during twilight and at night but to shallower depths (likely following squid); sea-ice appearance reduces rest. The introduced geometrical approach is simple to implement and potentially helpful for mapping and labeling long-term behavioral data, identifying differences between individual animals and species, and detecting perturbations.
... Eight of nine males in this study had T peaks preceding putative summer δ 15 N peaks by approximately three months, suggesting that breeding in this bowhead whale population occurs during late winter to spring, consistent with observations of increased social activity among bowhead whales from March through May ( Tervo et al. 2009( Tervo et al. , 2011Würsig and Koski 2021 ). Satellite telemetry data show that the majority of this bowhead whale population (East Canada-West Greenland population, EC-WG) is located in Hudson Strait and off southeastern Baffin Island at this time ( Ferguson et al. 2010 ;Fortune et al. 2020 ; Fig. 1 ), while some large mature females congregate in Disko Bay ( Heide-Jørgensen et al. 2010 ). These regions may therefore represent important mating habitats. ...
Full-text available
Synopsis Male mammals of seasonally reproducing species typically have annual testosterone (T) cycles, with T usually peaking during the breeding season, but occurrence of such cycles in male mysticete whales has been difficult to confirm. Baleen, a keratinized filter-feeding apparatus of mysticetes, incorporates hormones as it grows, such that a single baleen plate can record years of endocrine history with sufficient temporal resolution to discern seasonal patterns. We analyzed patterns of T every 2 cm across the full length of baleen plates from nine male bowhead whales (Balaena mysticetus) to investigate occurrence and regularity of T cycles and potential inferences about timing of breeding season, sexual maturation, and reproductive senescence. Baleen specimens ranged from 181–330 cm in length, representing an estimated 11 years (smallest whale) to 22 years (largest whale) of continuous baleen growth, as indicated by annual cycles in stable isotopes. All baleen specimens contained regularly spaced areas of high T content (T peaks) confirmed by time series analysis to be cyclic, with periods matching annual stable isotope cycles of the same individuals. In 8 of the 9 whales, T peaks preceded putative summer isotope peaks by a mean of 2.8 months, suggesting a mating season in late winter / early spring. The only exception to this pattern was the smallest and youngest male, which had T peaks synchronous with isotope peaks. This smallest, youngest whale also did not have T peaks in the first half of the plate, suggesting initiation of T cycling during the period of baleen growth. Linear mixed effect models suggest that whale age influences T concentrations, with the two largest and oldest males exhibiting a dramatic decline in T peak concentration across the period of baleen growth. Overall, these patterns are consistent with onset of sexual maturity in younger males and possible reproductive senescence in older males. We conclude that adult male bowheads undergo annual T cycles, and that analyses of T in baleen may enable investigation of reproductive seasonality, timing of the breeding season, and life history of male whales.
Baleen whale migration emerges as a foundational theme of cetacean behavioral ecology and the relationships that bind humans and whales together. From facilitating the culmination of the great human migration many centuries ago, to their roles as ecosystem service providers, baleen whales have influenced the path of human history. With a focus on modern technologically enabled insights, we provide an overview of what scientists currently know about the spatial and temporal distribution of baleen whales and their migratory behaviors. Although a coarse model of seasonally paced north–south migration generally applies, a deeper analysis reveals the remarkable diversity of baleen whale migrations. Some species, including gray whales (Eschrichtius robustus), migrate relatively close to shore; others, including humpback whales (Megaptera novaeangliae), tend to migrate across ocean basins. Some species, including Bryde’s whales (Balaenoptera edeni), appear to largely reside in middle to low-latitude ecosystems, relatively removed from cold, high-latitude water. In contrast, bowhead whales (Balaena mysticetus) remain within Arctic ecosystems all year, and others, including Omura’s whales (Balaenoptera omurai), may not migrate at all. The scientific focus to date has largely been on population-specific studies of where whales go, what their behaviors are, and when they undertake their migrations. Thus, there remains much to be learned, particularly regarding why baleen whales migrate and how they navigate during their long-distance migrations. Technological innovations such as satellite tags and passive acoustics have revolutionized our understanding of baleen whale behavioral ecology and ethology, and technology will continue to play a critical role in advancing the science of baleen whale migration.KeywordsBaleen whaleMigrationTelemetrySatellite trackingPassive acousticsMigratory behaviorMovement behaviorAcoustic behaviorOrientationNavigation
Stable isotope ratios have proven a valuable tool to investigate marine mammal ecology, including diet, distribution, and migratory movements. While most studies have focused on δ ¹⁵ N and δ ¹³ C values, δ ³⁴ S values have been little used because their pattern of variation and tissue dynamics remain unclear. We examined the sequential variation of δ ¹⁵ N, δ ¹³ C, and δ ³⁴ S values along the baleen plates from fin whales occurring off West Iceland in summer. All baleen plates exhibited fluctuations along their growing axis. A significant synchronic correlation was found between δ ¹⁵ N and δ ³⁴ S values, while the relation of these values to the δ ¹³ C value was highly variable and inconsistent. These results were similar to those obtained in previous studies of Greenland bowhead whales, although the pattern of the oscillations in fin whales showed an increase in values during winter, while those of bowhead whales showed a decrease. Although seasonal variations in food intake and the associated cycles of protein synthesis and catabolism may have played a role in such fluctuations and the observed differences between species, we suggest that the main driver for the δ ³⁴ S fluctuations reflected in baleen plates is the variation of local baselines between winter and summer grounds. This suggests ample potential for using δ ³⁴ S values to study migratory movements and destinations of marine megafauna, provided that the geographic variation in δ ³⁴ S baselines is clarified.
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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
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Studies of odontocete foraging ecology have been limited by the challenges of observing prey capture events and outcomes underwater. We sought to determine whether subsurface movement behavior recorded from archival tags could accurately identify foraging events by fish-eating killer whales. We used multisensor bio-logging tags attached by suction cups to Southern Resident killer whales (Orcinus orca) to: (1) identify a stereotyped movement signature that co-occurred with visually confirmed prey capture dives; (2) construct a prey capture dive detector and validate it against acoustically confirmed prey capture dives; and (3) demonstrate the utility of the detector by testing hypotheses about foraging ecology. Predation events were significantly predicted by peaks in the rate of change of acceleration ('jerk peak'), roll angle and heading variance. Detection of prey capture dives by movement signatures enabled substantially more dives to be included in subsequent analyses compared with previous surface or acoustic detection methods. Males made significantly more prey capture dives than females and more dives to the depth of their preferred prey, Chinook salmon. Additionally, only half of the tag deployments on females (5 out of 10) included a prey capture dive, whereas all tag deployments on males exhibited at least one prey capture dive (12 out of 12). This dual approach of kinematic detection of prey capture coupled with hypothesis testing can be applied across odontocetes and other marine predators to investigate the impacts of social, environmental and anthropogenic factors on foraging ecology.
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The effects of climate change constitute a major concern in Arctic waters due to the rapid decline of sea ice, which may strongly alter the movements and habitat availability of Arctic marine mammals. We tracked 98 bowhead whales by satellite over an 11-year period (2001-2011) in Baffin Bay - West Greenland to investigate the environmental drivers (specifically sea surface temperature and sea ice) involved in bowhead whale's movements. Movement patterns differed according to season, with aggregations of whales found at higher latitudes during spring and summer likely in response to sea-ice retreat and increasing sea temperature (SST) facilitated by the warm West Greenland Current. In contrast, the whales moved further south in response to sea temperature decrease during autumn and winter. Statistical models indicated that the whales targeted a narrow range of SSTs from -0.5 to 2 °C. Sea surface temperatures are predicted to undergo a marked increase in the Arctic, which could expose bowhead whales to both thermal stress and altered stratification and vertical transport of water masses. With such profound changes, bowhead whales may face extensive habitat loss. Our results highlight the need for closer investigation and monitoring in order to predict the extent of future distribution changes.
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Bowhead whales (Balaena mysticetus) have a nearly circumpolar distribution, and occasionally occupy warmer shallow coastal areas during summertime that may facilitate molting. However, relatively little is known about the occurrence of molting and associated behaviors in bowhead whales. We opportunistically observed whales in Cumberland Sound, Nunavut, Canada with skin irregularities consistent with molting during August 2014, and collected a skin sample from a biopsied whale that revealed loose epidermis and sloughing. During August 2016, we flew a small unmanned aerial system (sUAS) over whales to take video and still images to: 1) determine unique individuals; 2) estimate the proportion of the body of unique individuals that exhibited sloughing skin; 3) determine the presence or absence of superficial lines representative of rock-rubbing behavior; and 4) measure body lengths to infer age-class. The still images revealed that all individuals (n = 81 whales) were sloughing skin, and that nearly 40% of them had mottled skin over more than two-thirds of their bodies. The video images captured bowhead whales rubbing on large rocks in shallow, coastal areas—likely to facilitate molting. Molting and rock rubbing appears to be pervasive during late summer for whales in the eastern Canadian Arctic.
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Background We sought to quantitatively describe the fine-scale foraging behavior of northern resident killer whales (Orcinus orca), a population of fish-eating killer whales that feeds almost exclusively on Pacific salmon (Oncorhynchus spp.). To reconstruct the underwater movements of these specialist predators, we deployed 34 biologging Dtags on 32 individuals and collected high-resolution, three-dimensional accelerometry and acoustic data. We used the resulting dive paths to compare killer whale foraging behavior to the distributions of different salmonid prey species. Understanding the foraging movements of these threatened predators is important from a conservation standpoint, since prey availability has been identified as a limiting factor in their population dynamics and recovery. ResultsThree-dimensional dive tracks indicated that foraging (N = 701) and non-foraging dives (N = 10,618) were kinematically distinct (Wilks’ lambda: λ16 = 0.321, P < 0.001). While foraging, killer whales dove deeper, remained submerged longer, swam faster, increased their dive path tortuosity, and rolled their bodies to a greater extent than during other activities. Maximum foraging dive depths reflected the deeper vertical distribution of Chinook (compared to other salmonids) and the tendency of Pacific salmon to evade predators by diving steeply. Kinematic characteristics of prey pursuit by resident killer whales also revealed several other escape strategies employed by salmon attempting to avoid predation, including increased swimming speeds and evasive maneuvering. Conclusions High-resolution dive tracks reconstructed using data collected by multi-sensor accelerometer tags found that movements by resident killer whales relate significantly to the vertical distributions and escape responses of their primary prey, Pacific salmon.
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The proportion of food actually required for growth relative to maintenance after the first year of life is very small, <6%, regardless of growth rate. The proportional extra food energy required to effect faster growth and earlier maturation in minke whales is similar to that required to produce the observed changes in these parameters in fin whales Balaenoptera physalus. The relative total costs of pregnancy and lactation in minke whales are also similar to those in fin whales, implying that the currently proposed annual calving is energetically possible for the minke whale. -from Current Antarctic Literature
Human activities have placed populations of many endangered species at risk and mitigation efforts typically focus on reducing anthropogenic sources of mortality. However, failing to recognize the additional role of environmental factors in regulating birth and mortality rates can lead to erroneous demographic analyses and conclusions. The North Atlantic right whale population is currently the focus of conservation efforts aimed at reducing mortality rates associated with ship strikes and entanglement in fishing gear. Consistent monitoring of the population since 1980 has revealed evidence that climate-associated changes in prey availability have played an important role in the population's recovery. The considerable interdecadal differences observed in population growth coincide with remote Arctic and North Atlantic oceanographic processes that link to the Gulf of Maine ecosystem. Here, we build capture-recapture models to quantify the role of prey availability on right whale demographic transitional probabilities and use a corresponding demographic model to project population growth rates into the next century. Contrary to previous predictions, the right whale population is projected to recover in the future as long as prey availability and mortality rates remain within the ranges observed during 1980–2012. However, recent events indicate a northward range shift in right whale prey, potentially resulting in decreased prey availability and/or an expansion of right whale habitat into unprotected waters. An annual increase in the number of whale deaths comparable to that observed during the summer 2017 mass mortality event may cause a decline to extinction even under conditions of normal prey availability. This study highlights the importance of understanding the oceanographic context for observed population changes when evaluating the efficacy of conservation management plans for endangered marine species.
Cumberland Sound, host to a commercially viable fish population in the deepest depths, is a large embayment on the southeast coast of Baffin Island that opens to Davis Strait. Conductivity, temperature, and depth profiles were collected during three summer field seasons (2011–2013), and two moorings were deployed during 2011–2012. Within the sound, salinity increases with increasing depth while water temperature cools reaching a minimum of −1.49°C at roughly 100 m. Below 100 m, the water becomes both warmer and saltier. Temperature-salinity curves for each year followed a similar pattern, but the entire water column in Cumberland Sound cooled from 2011 to 2012, and then warmed through the summer of 2013. Even though the sound's maximum depth is over a kilometer deeper than its sill, water in the entire sound is well oxygenated. A comparison of water masses within the sound and in Davis Strait shows that, above the sill, the sound is flooded with cold Baffin Island Current water following an intermittent geostrophic flow pattern entering the sound along the north coast and leaving along the south. Below the sill, replenishment is infrequent and includes water from both the Baffin Island Current and the West Greenland Current. Deep water replenishment occurred more frequently on spring tides, especially in the fall of 2011. Although the sound's circulation is controlled by outside currents, internal water modifying processes occur such as estuarine flow and wind-driven mixing.