What are the Metabolic Rates of Marine Mammals and What Factors Impact this Value: A review

Abstract

Over the past several decades, scientists have constructed bioenergetic models for marine mammals to assess potential population-level consequences following exposure to a disturbance, stressor, or environmental change, such as under the Population Consequences of Disturbance (pCOD) framework. The animal's metabolic rate (rate of energy expenditure) is a cornerstone for these models, yet the cryptic lifestyles of marine mammals, particularly cetaceans, have limited our ability to quantify basal (BMR) and field (FMR) metabolic rates using accepted ‘gold standard’ approaches (indirect calorimeter via oxygen consumption and doubly labeled water, respectively). Thus, alternate methods have been used to quantify marine mammal metabolic rates, such as extrapolating from known allometric relationships (e.g. Kleiber's mouse to elephant curve) and developing predictive relationships between energy expenditure and physiological or behavioral variables. To understand our current knowledge of marine mammal metabolic rates, we conducted a literature review (1900–2023) to quantify the magnitude and variation of metabolic rates across marine mammal groups. A compilation of data from studies using ‘gold standard’ methods revealed that BMR and FMR of different marine mammal species ranges from 0.2 to 3.6 and 1.1 to 6.1 x Kleiber, respectively. Mean BMR and FMR varied across taxa; for both measures odontocete levels were intermediate to higher values for otariids and lower values of phocids. Moreover, multiple intrinsic (e.g. age, sex, reproduction, molt, individual) and extrinsic (e.g. food availability, water temperature, season) factors, as well as individual behaviors (e.g. animal at water’s surface or submerged, activity level, dive effort and at-sea behaviors) impact the magnitude of these rates. This review provides scientists and managers with a range of reliable metabolic rates for several marine mammal groups as well as an understanding of the factors that influence metabolism to improve the discernment for inputs into future bioenergetic models.

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Volume 11 • 2023 10.1093/conphys/coad077
Review
What are the Metabolic Rates of Marine Mammals
and What Factors Impact this Value: A review
S.R. Noren1, *and David A.S. Rosen2
1Institute of Marine Science, University of California Santa Cruz, Center for Ocean Health, 115 McAllister Way, Santa Cruz, CA 95060, USA
2Marine Mammal Research Unit, Institute for the Oceans and Fisheries, University of British Columbia, 2202 Main Mall, Vancouver, BC, Canada
V6T 1Z4
*Corresponding author: Institute of Marine Science, University of California Santa Cruz, Center for Ocean Health, 115 McAllister Way, Santa Cruz,
CA 95060, USA. Email: snoren@ucsc.edu
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Over the past several decades, scientists have constructed bioenergetic models for marine mammals to assess potential
population-level consequences following exposure to a disturbance, stressor, or environmental change, such as under the
Population Consequences of Disturbance (pCOD) framework. The animal’s metabolic rate (rate of energy expenditure) is a
cornerstone for these models, yet the cryptic lifestyles of marine mammals, particularly cetaceans, have limited our ability
to quantify basal (BMR) and eld (FMR) metabolic rates using accepted ‘gold standard’ approaches (indirect calorimeter
via oxygen consumption and doubly labeled water, respectively). Thus, alternate methods have been used to quantify
marine mammal metabolic rates, such as extrapolating from known allometric relationships (e.g. Kleiber’s mouse to elephant
curve) and developing predictive relationships between energy expenditure and physiological or behavioral variables. To
understand our current knowledge of marine mammal metabolic rates, we conducted a literature review (1900–2023) to
quantify the magnitude and variation of metabolic rates across marine mammal groups. A compilation of data from studies
using ‘gold standard’methods revealed that BMR and FMR of dierent marine mammal species ranges from 0.2 to 3.6 and 1.1
to 6.1 x Kleiber, respectively. Mean BMR and FMR varied across taxa; for both measures odontocete levels were intermediate
to higher values for otariids and lower values of phocids. Moreover, multiple intrinsic (e.g. age, sex, reproduction, molt,
individual) and extrinsic (e.g. food availability, water temperature, season) factors, as well as individual behaviors (e.g. animal
at water’s surface or submerged, activity level, dive eort and at-sea behaviors) impact the magnitude of these rates. This
review provides scientists and managers with a range of reliable metabolic rates for several marine mammal groups as well
as an understanding of the factors that inuence metabolism to improve the discernment for inputs into future bioenergetic
models.
Key words: Bioenergetics, eld metabolic rate, basal metabolic rate, marine mammal, metabolism, modeling, mysticete, odonto-
cete, pinniped, resting metabolic rate
Editor: Steven Cooke
Received 12 March 2022; Revised 22 August 2023; Editorial Decision 8 September 2023; Accepted 14 September 2023
Cite as: Noren SR, Rosen DA (2023) What are the Metabolic Rates of Marine Mammals and What Factors Impact this Value: A review.Conserv Physiol
11(1): coad077; doi:10.1093/conphys/coad077.
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Review Conservation Physiology • Volume 11 2023
The Need for Reliable Metabolic Rate
Data
Over the past several decades, scientists have constructed
bioenergetic models for marine mammals to assess poten-
tial population-level consequences of environmental changes
and other stressors (reviewed in Pirotta, 2022). The ani-
mal’s metabolic rate (rate of energy expenditure) is a cor-
nerstone for these models. Bioenergetic models range from
simple equations representing average energy expenditure, to
detailed energy budgets for each age-class, sex and season, to
complex dynamic models that incorporate changes in envi-
ronmental conditions (Booth et al., 2023). The reliability of
the predictions that bioenergetic models provide are strongly
dependent on the accuracy of the input variables (Enders and
Scruton, 2006), which can be based upon laboratory and
field experiments. The foundation of these models is the cost
of running the animal—the animal’s metabolic rate—which
represents the total energy expenditure (TEE) of an animal
under a given set of circumstances.
Basal metabolic rate (BMR) and its associated measure
resting metabolic rate (RMR) represent the baseline energy
expenditure of an animal that includes the basic physio-
logical costs associated with survival such as maintaining
blood flow, cellular respiration and thermoregulation. BMR
must be measured on animals under strict behavioral and
physiological criteria: resting (awake but quiescent), postab-
sorptive, mature (non-growing) and non-reproductive (non-
pregnant, non-lactating) and within its thermoneutral zone
(Kleiber, 1975). Criteria for RMR are identical to those for
BMR except that RMR may include measures obtained on
immature or even reproductive animals. In comparison, field
metabolic rate (FMR) is a broader term encompassing mea-
sures of energy expenditure of free-living individuals under
a varied but individually well-defined set of circumstances.
FMR is often considered the more ecologically relevant mea-
sure of energy expenditure because it includes the baseline
energy consumption of the animal plus the additional energy
required as the animal moves and survives in its habitat
(i.e. energetic costs associated with feeding, locomoting, etc.;
Nagy, 1994).
Marine mammals, depending on the group, live most if
not all of their lives at sea, and the offshore lifestyles and
prolonged submergence periods of some species (e.g. many
species of beaked whales, sperm whales, Physeter macro-
cephalus) make it exceptionally difficult to measure their
metabolism. For example, of the nearly 90 cetacean species,
both odontocetes (toothed whales, dolphins and porpoises)
and mysticetes (baleen whales), direct empirical measures of
BMR have only been quantified in five odontocete species
(Table 1) and direct measures of active metabolism have only
been undertaken in three odontocete species (Table 2). More-
over, these studies are mostly restricted to smaller species that
are readily maintained in aquaria. While there are more data
from other groups of marine mammals (pinnipeds, mustelids,
sirenians), measurements of FMR from wild individuals are
often limited to times of the year when they are more readily
accessible and hence may not be reflective of other seasons
(reviewed by Davis, 2019). Yet ongoing human perturbations
(e.g. climate change, disturbance from sound) are impacting
food availability and disrupting foraging behaviors across
seasons, making it increasingly important to have founda-
tional knowledge about marine mammal metabolic rates in
order to construct bioenergetic models that can make accurate
assessments of population impacts (e.g. National Research
Council, 2005;Costa, 2012).
Methods for Estimating Metabolic
Rate
In theory, metabolism is most directly measured as heat
production (direct calorimetry), an essentially impossible task
to perform with marine mammals. Hence, there are several
accepted alternate standards for measuring metabolism in
marine mammals that are considered ‘gold standards’. BMR
(and RMR) is usually measured via respirometry (a form
of indirect calorimetry), using an apparatus that quantifies
the rate of oxygen consumption and carbon dioxide (CO2)
production that is then converted to rates of energy consump-
tion. Meanwhile, FMR is usually measured via the turnover
of water containing two different isotopes of hydrogen and
oxygen injected into the animal, known as the doubly labelled
water (DLW) method; this yields an estimate of CO2pro-
duction that is converted into rates of energy consumption
(reviewed in Iverson et al., 2010). Older studies used changes
in body mass to estimate FMR, but this technique can only
be applied to fasting animals where all energy requirements
are met through tissue catabolism. Another group of studies
have measured the cost of subsurface swimming, usually using
respirometry on animals in aquaria. For the purposes of
our review (detailed below), we have included all of these
measures as estimates of FMR.
Due to the logistical difficulties of measuring energy expen-
diture in marine mammals through the aforementioned meth-
ods, researchers have attempted to quantify metabolic rates
using various different approaches. Alternate methods have
included the development of predictive relationships between
metabolic rate and physiological (e.g. heart rate, respira-
tion rate, caloric intake) or behavioral (e.g. swim speed,
measures of body movement or acceleration) characteristics
(Williams et al., 1993;Boyd et al., 1999;Butler et al., 2004;
Fahlman et al., 2008;Young et al., 2011;Dalton et al., 2014;
Noren et al., 2014;Kastelein et al., 2018;McHuron et al.,
2022). Unfortunately, there are inherent inaccuracies when
using such proxies due to unvalidated or weak relationships
and substantial variation between individuals (McPhee et al.
2003). For example, the accuracy of using respiratory rate as a
proxy for metabolic demand, such as when applied to captive
beluga whales (Delphinapterus leucas;George and Noonan,
2014), is unknown when variation in breathing volume and
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Conservation Physiology • Volume 11 2023 Review
Tab le 1 : Mean basal metabolic rate (BMR) for each species expressed as a multiple of Kleiber’s (1975) prediction for terrestrial mammals.
Common Name Species Mean BMR as Multiple
of Kleiber ±SD
References
Beluga whale Delphinapterus leucas 1.41 ±0.39 (1.1–2.2) Rosen and Trites (2013),John (2020)
Bottlenose dolphin Tursiops truncatus 1.96 ±0.45 (1.5–2.5) Karandeeva et al. (1973),Feldkam p et al. (1987),
Williams et al. (1993) Williams et al. (2001) van
der Hoop et al. (2014),Williams et al. (2017),
John (2020)
Killer whale Orcinus orca 1.69 ±0.78 (1.1–2.8) Dunkin-McClenahan, unpub. Data; Kriete
(1995) Wort hy et al. (2014),Williams et al. (2017)
Pacic white-sided dolphin Lagenorhynchus obliquidens 3.39 (n/a) (3.2–3.5) Rechsteiner et al. (2013)
Harbor porpoise Phocoena phocoena 2.45 (n/a) Karandeeva et al. (1973)
ODONTOCETE GRAND MEAN 2.18 ±0.78
Australian fur seal Arctocephalus pusillus doriferus 2.78 (n/a) (1.9–3.6) Ladds et al. (2017c)
Australian sea lion Neophoca cinerea 2.96 (n/a) (2.0–3.6) Ladds et al. (2017c)
California sea lion Zalophus californianus 2.72 ±0.28 (1.9–3.8) Liao (1990),Hurley and Costa (2001)
New Zealand fur seal Arctocephalus pusillus forsteri 2.93 (n/a) (2.0–3.5) Ladds et al. (2017c)
Steller sea lion Eumetopias jubatus 3.64 ±0.67 (3.0–5.7) Fahlman et al. (2008),Goundie (2015),Fahlman
et al. (2016a)
OTARIID GRAND MEAN 3.01 ±0.37
Bearded seal Erignathus barbatus 1.03 (n/a) Thometz et al. (2023)
Grey seal Halichoerus grypus 1.26 ±0.47 (0.7–2.3) Fedak and Anderson (1982),Innes (1984),
Lavigne et al. (1986),Boily and Lavigne (1995),
Boily (1996),Boily and Lavigne (1997),Sparling
et al. (2006)
Harbor seal Phoca vitulina 1.37 ±0.17 (01.1–1.6) Matsuura and Whittow (1973),Innes (1984),
Davis et al. (1985),Rosen and Renouf (1998)
Harp seal Pagophilus groenlandicus 1.01 ±0.22 (0.6–1.6) Øritsland and Ronald (1975),Gallivan and
Ronald (1979),Gallivan (1981),Innes (1984),
Hedd et al. (1997)
Hawaiian monk seal Neomonachus schauinslandi 1.17 (n/a) John et al. (2021)
Ringed seal Pusa hispida 1.13 ±0.54 (0.6–2.0) Parsons (1977),Innes (1984),Thometz et al.
(2021)
Spotted seal Phoca largha 1.69 (n/a) (1.5–1.9) Thometz et al. (2021)
Weddell seal Leptonychotes weddellii 1.87 ±0.35 (1.6–2.3) Kooyman et al. (1973),Castellini et al. (1992),
Williams et al. (2001)
PHOCID GRAND MEAN 1.32 ±0.31
Walrus Odobenus rosmarus 2.93 (n/a) (2.6–3.1) Borque-Espinosa et al. (2021)
ODOBENID GRAND MEAN 2.93 (N/A)
Sea otter Enhydra lutris 2.54 ±0.30 (2.2–3.0) Morrison et al. (1974),Costa and Kooyman
(1982),Costa and Kooyman (1984),Williams
(1989),Yea tes et al. (2007)
(Continued)
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Review Conservation Physiology • Volume 11 2023
Tab le 1 : Mean basal metabolic rate (BMR) for each species expressed as a multiple of Kleiber’s (1975) prediction for terrestrial mammals.
Common Name Species Mean BMR as Multiple
of Kleiber ±SD
References
MUSTELID GRAND MEAN 2.54 (n/a)
W. Indian manatee Trichechusmanatus 0.35 ±0.19 (0.2–0.7) Gallivan and Best (1980),Irvine (1983),John
et al. (2021)
SIRENIAN GRAND MEAN 0.35 (n/a)
Data are derived from studies of marine mammals that used indirect calorimetry (open-ow or pneumotachometer) to measure energy expenditure under conditions
that satised Kleiber’s original criteria. To calculate species means, a single average value from each source study was calculated, and these contributed equally to a
species mean and standard deviation. The range (in parenthesis) was based on individual animal reported values. The grand mean for each taxonomic groupof marine
mammals is based on a single value for each included species. Individual data values are available in Appendix 1.
duration is not taken into account (Roos et al., 2016;Fahlman
et al., 2016b). Although heart rate was correlated with energy
expenditure in some otariids (Butler et al., 1992;Boyd et al.,
1995), it was not accurate for quantifying energetics of indi-
vidual dives (Young et al., 2011). Measures of animal move-
ment through their environment, such as overall dynamic
body acceleration (ODBA), are theorized to be related to
energy expenditure (Halsey et al., 2009). However, ODBA has
been shown to poorly predict measured energy expenditure
in a number of marine mammal species (Volpov et al., 2016;
Ladds et al., 2017a;Pagano and Williams, 2019) due to
the effects of post-exercise recovery, stress, thermoregulation,
specific dynamic action, reproduction and growth (Wilson
et al., 2020). Overall, each of these proxy methods comes with
its own list of assumptions and inherent errors, and therefore
we do not include them in our review as we attempt to answer
the question, ‘What are the energy requirements of marine
mammals?’
Even when using accepted ‘gold standard’ approaches to
measure metabolism there can be variations in the value
reported due to differences in methodology, as well as testing
regime. This is evident when comparing estimates of FMR
for bottlenose dolphins (Tursiops truncatus) that have used
a variety of methods (Figure 1). Even when using a single
technique, such as respirometry to measure either BMR or
swimming metabolism, it is important to regulate the animal’s
behaviors during and prior to data collection. This is because
animal behavior (including psychological stress level) has a
large influence on metabolism, and daily living activity can
have residual effects on the metabolic measurement. Thus,
it is often recommended that subjects must rest a minimum
of 20 minutes prior to the start of the experiment, while
moderate to vigorous activity must be controlled for hours
before the test as it can have an even longer carryover effect
(Compher et al., 2006). Although arbitrarily extending the
measurement period does not necessarily increase the accu-
racy of BMR measurements (Reeves et al., 2004), it has
been recommended that, to obtain accurate resting metabolic
measurements, the test duration should be 10 minutes in
duration with the first 5 minutes discarded, and the remaining
5 minutes averaged; and these 5 minutes are only averaged
if there is a coefficient of variation <10% (Compher et al.,
2006). While this approach is not possible with a one breath
sample from a pneumotachometer, studies report that results
obtained by this method are similar to those from tradi-
tional respirometry (Allen et al., 2022). In addition to these
methodological constraints, there are a number of technical
and analytical procedures that must be carefully followed in
order to obtain meaningful estimates of energy expenditure
from respirometry (Lighton, 2008).
Although the use of DLW for estimating FMR is well estab-
lished (Lifson and McClintock, 1966), the results are also
impacted by subtle differences in methodology (Speakman
and Krol, 2005). For example, the calculated metabolic rate
reported will depend on which specific mathematical equa-
tion was used to convert chemical turnover to energy expen-
diture (e.g. compare Nagy, 1980,Speakman et al., 1993).
Moreover, despite the ability of DLW to provide estimates
of energy expenditure in free-ranging animals, it is difficult
to plug these values into a bioenergetic model because they
represent mean energy expenditure over a period that includes
numerous types of behaviors (Skinner et al., 2014), and
these behaviors will vary temporarily and spatially within
and across individuals and species. For example, measure-
ments of FMR in lactating female pinnipeds will vary signif-
icantly depending if the time the animal spent on land was
included or if only the time spent at sea is considered (Costa
et al., 1991).
Given the difficulties of measuring marine mammal
metabolism, it is tempting to extract the required values
from previously published allometric equations. For example,
one might use Kleiber’s (1975) equation predicting BMR
of terrestrial mammals from body mass to estimate the
energy expenditure of marine mammals, since it has been
long-established that body size accounts for the majority of
the variation in metabolic demand in mammals (Benedict,
1938;Kleiber, 1975;Nagy, 2005). However, there are
considerable lifestyle, ecological and taxonomic factors
that significantly alter the exact relationship between body
mass and measures of energy expenditure (Glazier, 2005).
Thus, using a curve based on terrestrial mammals could
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Conservation Physiology • Volume 11 2023 Review
Tab le 2 : Mean eld metabolic rate (FMR) for each species expressed as a multiple of Kleiber’s (1975) prediction for terrestrial mammals. Energy
expenditure data are derived from studies of marine mammals that used indirect calorimetry after swimming on captive animals and wild
Weddell seals (open-ow or pneumotachometer), or over a range of activities measured body mass changes of fasted wild animals or used doubly
labeled water in wild animals and captive dolphins and porpoise.
Common Name Species Mean FMR as Multiple
of Kleiber ±SD
References
Beluga whale Delphinapterus leucas 3.01 (n/a) (2.4–4.1) John (2020)
Bottlenose dolphin Tursiops truncatus 4.18 ±1.68 (2.1–7.2) van der Hoop et al. (2014),Fahlman etal. (2016b),
Bejarano et al. (2017),Williams et al. (2017),John
(2020),Rimbach et al. (2021)
Harbor porpoise Phocoena phocoena 3.36 (n/a/) 2.9–3.7 Rojano-Doñate et al. (2018)
ODONTOCETE GRAND MEAN 3.52 ±0.62
Antarctic fur seal Arctocephalus gazella 4.37 ±0.89 (3.3–5.5) Costa and Trillmich (1988),Costa et al. (1989),Boyd
and Duck (1991),Arnould et al. (1996),
Jeanniard-du-Dot et al. (2016)
Australian fur seal Arctocephalus pusillus 5.72 ±0.79 (4.8–6.4) Ladds et al. (2017a),Ladds et al. (2017c)
Australian sea lion Neophoca cinerea 4.85 ±0.98 (2.3–7.6) Costa (1991),Costa and Gales (2003),Ladds et al.
(2017a),Ladds et al. (2017c)
California sea lion Zalophus californianus 5.52 ±0.90 (2.9–6.9) Costa (1986),Costa et al. (1991),McHuron et al. (2018)
Galapagos fur seal Arctocephalus galapagoensis 1.07 (n/a) (0.7–1.8) Costa and Trillmich (1988)
Galapagos sea lion Zalophus wollebaeki 5.06 (n/a) (4.5–5.9) Villegas-Amtmann et al. (2017)
New Zealand fur seal Arctocephalus pusillus forsteri 6.06 ±0.60 (2.2–9.3) Ladds et al. (2017a),Ladds et al. (2017c)
Northern fur seal Callorhinus ursinus 5.29 ±0.81 (3.5–7.3) Costa et al. (1985),Costa and Gentry (1986),
Jeanniard-du-Dot et al. (2016),McHuron et al. (2019)
Steller sea lion Eumetopias jubatus 4.97 ±1.50 (3.7–6.6) Goundie et al. (2015),Ladds et al. (2017a)
OTARIID GRAND MEAN 4.73 ±1.57
Grey seal Halichoerus grypus 3.24 ±1.99 (1.3–4.6) Anderson and Fedak (1985),Sparling et al. (2008)
Harbor seal Phoca vitulina 3.76 ±1.65 (2.3–6.0) Davis et al. (1985),Reilly and Fedak (1991),Bowen et
al. (1992),Coltman et al. (1998)
Hawaiian monk seal Neomonachus schauinslandi 3.17 (n/a) (2.8–3.5) John (2020)
Hooded seal Cystophora cristata 4.22 (n/a) Kovacs et al. (1996)
Northern elephant seal Mirounga angustirostris 1.19 ±0.11 (1.0–1.5) Maresh (2014),Maresh et al. (2014)
Weddell seal Leptonychotes weddellii 2.17 ±1.18 (1.3–4.5) Kooyman et al. (1973),Kooyman et al. (1980),
Kooyman et al. (1983),Bartsh et al. (1992),Castellini et
al. (1992),Ponganis et al. (1993)
PHOCID GRAND MEAN 2.96 ±1.11
Walrus Odobenus rosmarus 5.28 ±1.91 (2.8–7.2) Acquarone et al. (2006),Rosen (2020),
Borque-Espinosa et al. (2021)
ODOBENID GRAND MEAN 5.28 (n/a)
Sea otter Enhydra lutris 4.49 (n/a) Yea te s et al. (2007)
MUSTELID GRAND MEAN 4.49 (n/a)
To calculate species means,a single average value from each source study was calculated, and these contributed equally to a species mean and standard deviation. The
range (in parenthesis) was based on individual animal reported values. The grand mean for each taxonomic group of marine mammals is based on a single value for
each included species. Individual data values are available in Appendix 2.
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Review Conservation Physiology • Volume 11 2023
Figure 1: Empirical measurements of eld metabolic rate (FMR) in
the bottlenose dolphin (Tursiops truncatus) using dierent
methodologies (bars). Data are presented for studies that used
doubly labeled water to measure total energy expenditure (TEE) of
wild dolphins (Bejarano et al., 2017) and captive dolphins (Rimbach
et al., 2021), estimates based on food intake as a proportion of body
mass of captive dolphins (Bejarano et al., 2017) and studies on captive
dolphins where metabolic rate was measured after subsurface
swimming using either open ow respirometry (van der Hoop et al.,
2014;Williams et al., 2017;John, 2020) or a pneumotachometer
(Fahlman et al., 2016b). Bars represent mean FMR ±standard
deviation across reported FMR values and are expressed as a multiple
of Kleiber (data in Appendix 2). Horizontal dashed lines illustrate the
general range of estimates for FMR (between 3-7x Kleiber) that have
been traditionally reported in the literature for various marine
mammals, as illustrated in Costa and Maresh (2018).
result in erroneous conclusions when marine mammals
have numerous adaptations to a life in water that would
complicate such extrapolations. Indeed, as discussed further
below, some studies suggest that marine mammals have
elevated BMR compared with terrestrial mammals of similar
size. Hypothesized reasons for an elevated BMR include the
increased cost of staying warm in water, a medium with much
higher thermal capacity and conductivity than air (South
et al., 1976,Costa and Kooyman, 1984,Costa and Williams,
1999,Williams, 1999), and the consequences of a carnivorous
diet (Williams et al., 2001). Conversely,the amount of blubber
stored by marine mammals to stay warm could complicate
the scaling of metabolism with body size since fat is largely
metabolically inactive (Sparti et al., 1997). For example, lean
mass was found to be a better predictor of BMR than total
body mass in adult harp seals (Pagophilus groenlandicus;
Aarseth et al., 1999) and RMR in northern elephant seal pups
(Mirounga angustirostris;Rea and Costa, 1992). In addition,
the body mass of larger-bodied cetaceans is well beyond the
predictive ‘mouse to elephant curve’ (Kleiber, 1975); it would
be statistically erroneous to extrapolate the curve beyond the
range of body mass data presented.
Are Metabolic Rates of Marine Mam-
mal Elevated Compared to Equally
Sized Terrestrial Mammals?
The question of whether marine mammals have elevated
metabolic rates compared to their counterparts has been
debated for decades. Its importance is heightened by the
potential impact of marine mammal predation on fisheries
resources (Matthiopoulos et al., 2008). Past reviews of the
literature do not provide a consensus about the magnitude of
marine mammal metabolic rates. Some reviews conclude that
BMR of marine mammals are not significantly different from
terrestrial counterparts (Lavigne et al., 1986;Boyd, 2002;
Hunter, 2005), while others conclude the opposite (Williams
et al., 2001;Davis, 2019). Several authors similarly suggest
that most marine mammal groups have elevated FMRs. A
review by Williams et al. (2020) indicated that the FMRs
of most marine mammal groups are about 1.3 times higher
than similarly sized terrestrial carnivores, a finding similarly
reported by Rimbach et al. (2021) who also noted that there
was substantial overlap between the two groups. Meanwhile,
others have suggested that the scaling exponent for the rela-
tionship between body mass and FMR in most marine mam-
mal groups is lower than that for terrestrial mammals (Boyd,
2002;Maresh, 2014;Costa and Maresh, 2018). This would
mean that the field energy expenses of the smallest marine
mammals are higher than predicted compared to equivalent
terrestrial mammals, while the energy expenditures of larger
marine mammals (>250 kg) are lower than expected (Boyd,
2002;Maresh, 2014). This relationship may exist because
once a marine mammal is large enough their low surface-
area to volume ratios minimize heat loss to the environment
and their overall maintenance costs are lower than that of
terrestrial mammals given that they do not use energy to
support their body weight due to the buoyant force of water
(Innes, 1986;Boyd, 2002).
Given the diverse views on whether marine mammals have
elevated metabolic rates compared to equally sized terrestrial
mammals, we should revisit the difficulties surrounding mea-
suring BMR of marine mammals. In their review, Lavigne
et al. (1986) noted that some studies of marine mammal
metabolism did not conform to the standardized measure-
ment criteria as defined by Kleiber (1975), and when such
‘Kleiber conditions’ are not met the measured metabolic rate
(almost by definition) can be higher. In addition to inappropri-
ate measures on immature and reproductive individuals, one
common issue is that food rewards may be used as an incen-
tive for obtaining metabolic measures from trained animals,
which will have a significant effect on metabolism (Costa and
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Conservation Physiology • Volume 11 2023 Review
Kooyman, 1984,Barbour, 1993,Rosen and Trites,1997). For
example, the metabolism of juvenile South American fur seals
(Arctocephalus australis) increased by 63% from the post
absorptive to postprandial condition (Dassis et al., 2014) and
harbor porpoises (Phocoena phocoena) showed a 1.3 increase
in metabolism after food consumption (Reed et al., 2000).
What Are the Metabolic Rates of Marine
Mammals if We Only Consider ‘Gold
Standard’ Measurements
Given the long-standing debate over whether marine mam-
mal metabolism is higher compared to terrestrial mammals,
and with the knowledge that some methods to quantify
metabolism are not as reliable as others, we compiled the
metabolic data published for marine mammals from 1900
to 2023 that used the ‘gold standard’ methods. For BMR,
we only considered measures obtained via indirect calorime-
try; either open-flow respirometry via a dome or pneumo-
tachometer, plus a few early studies using closed breathing
or mask systems. While the usual standard for FMR are
measures obtained via DLW, due to limited number of such
studies from cetaceans we also included empirical studies that
provided FMR based on swimming metabolic rate (obtained
via respirometry), as well as FMR based on mass loss in
fasting animals (predominantly pinnipeds).
Several previous studies have similarly compiled avail-
able data (Lavigne et al., 1986;Boyd, 2002;Hunter, 2005;
Maresh, 2014;Davis, 2019), and our review included careful
re-examination of whether the BMR values met required,
standard conditions (i.e. fasted, thermal neutral zone, resting
but not sleeping, mature but non-reproductive at the time of
the study). We also endeavored to record data at the level
of the individual animal rather than just reporting the mean
from each study or an overall species mean, although this
information was not always provided in the original papers.
In addition, we tried to ensure that identical data points
that have been reported across multiple publications were
not duplicated in our database. In a few cases, there were
data from multiple studies that used the same study animals;
these were included in our database. We also noted the testing
medium (water or air) and the methodology used (e.g. open-
flow respirometry vs. pneumotach). For FMR measures, we
noted the methodology (e.g. DWL, respirometry, body mass
changes) and life history/behavioral stage represented during
the study (e.g. lactating female vs at-sea portion only vs fasting
breeding male, swimming, etc.). Only FMR data from adult
animals were used, to make them partially comparable to
collated BMR values.
To compare across species, we standardized the reported
rates of energy expenditure into kcal per day (kcal d−1) from
various reported units of energy expenditure (e.g. kJ•d−1,
Watts per hour, mL O2per min, etc.) using accepted conver-
sions. Although we report values in kcal, this can easily be
converted to MJ with the conversion of 1 MJ equivalent to
238.85 kcal. These rates of absolute energy expenditure were
then converted to mass-specific measures to allow us to stan-
dardize across marine mammal species that represent a large
range in body mass. Although there is no consensus on how
to account for inter- and intra-specific differences in body
mass (Hochachka et al., 2003), we chose to express metabolic
rates on a mass-specific basis (kcal d−1per kg). This process
was necessary, but unfortunately, it reduced the number of
studies that could be included in our analyses since several
studies did not provide the mass of their subjects. We then
converted these absolute values to multiples of that predicted
from Kleiber’s (1975) ‘mouse-to-elephant’ equation for RMR
of terrestrial mammals (70 ∗Mass0.75 kcal d−1) for those that
wish to revisit the long-standing debate about whether marine
mammals have elevated metabolic rates compared to their
terrestrial counterparts.
These individual converted data appear in Appendix 1
(BMR) and Appendix 2 (FMR). From these data, we cal-
culated the mean for each species within each study. From
those means we calculated an overall mean for the species,
which are provided in Table 1 (BMR) and Table 2 (FMR); in
this way, each study (regardless of number of measures) con-
tributed equally to the species mean. Finally, from the appro-
priate species means, we calculated grand taxa means for
odontocetes, sirenians, phocids, odobenids and mustelids. The
results confirm that marine mammals are not a homogenous
group. The range of BMRs (Appendix 1; Table 1;Figure 2)
and FMRs (Appendix 2; Table 2;Figure 3) varied across
individuals, species and marine mammal taxa.
Our analysis revealed that there are differences in the
baseline energy needs across the three families of pinnipeds:
Phocidae (‘true seals’), Otariidae (sea lions and fur seals)
and Odobenidae (walruses). Within this taxa, phocids had
the lowest grand mean BMR (1.32 ±0.31 x Kleiber), with
species means ranging from 1.0 for the harp seal (Pagophilus
groenlandicus) to 1.9 for the Weddell seal (Leptonychotes
weddellii). The latter may be an over-estimate, as the measures
were done at naturalistic breathing holes and it was unclear in
many of the studies whether foraging had occurred. It should
also be noted that across marine mammals, the phocids had
the largest dataset with data for 7 of 19 extant species.
Interestingly, the closely related otariids had the highest grand
mean BMR (3.01 ±0.37) among all marine mammal taxa.
There was a high level of consistency among four of the five
otariid species that have been studied, where RMR ranged
from 2.7 to 3.0 x Kleiber. The BMR (3.6 x Kleiber) of the
fifth species, the Steller sea lion (Eumetopias jubatus), was
notably higher than the others; this result may, in part, be
due to high BMRs in a few of the studies (Appendix 1).
Meanwhile, there is only a single published study for a mature
odobenid, the walrus (Odobenus rosmarus;Borque-Espinosa
et al., 2021). They reported a relatively high BMR (2.9
x Kleiber) compared to the other marine mammals, and
this value was curiously higher than the RMR (1.9 x
Kleiber) reported for two juvenile walrus (Rosen, 2020).
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Review Conservation Physiology • Volume 11 2023
Figure 2: Basal metabolic rate (BMR) represented as a multiple of
Kleiber’s (1975) equation for various species of marine mammals. Box
plots (including statistical outliers represented as circles) are based
upon individual data provided in Appendix 1, which contains details
on the methods used and the original references. This graph only
includes data that satised all of the physiological conditions for
“Kleiber condition” BMR and used one of the “gold standard”
approaches for measuring BMR (respirometry using a dome, mask, or
pneumotachometer). Within the Odontocetes, dierent shades of
blue dierentiate the families Monodontidae (beluga whale; light
blue), Delphinidae (bottlenose dolphin, killer whale, Pacic
white-sided dolphin; medium blue) and Phocenidae (harbor
porpoise; dark blue). Similarly, pinnipeds are grouped by family
Otariidae (Australian fur seal, Australian sea lion, California sea lion,
New Zealand fur seal, Steller sea lion; red), Phocidae (bearded seal,
gray seal, harbor seal, harp seal, Hawaiian monk seal; orange), or
Odobenidae (walrus; pink). Data for the sea otter (black) and West
Indian manatee (grey) are also provided.
As noted by Borque-Espinosa et al. (2021), the elevated BMR
in the adult walrus may have been related to the animal being
somewhat active during testing.
As mentioned previously in the text, it has been extremely
difficult to measure the BMR of cetaceans. As a result, only
five species of odontocetes have been studied to date. These
data represent species from three families, Monodontidae
(one species), Delphinidae (three species) and Phocoenidae
(one species). The grand mean BMR across these odontocetes
was 2.18 ±0.78 x Kleiber, which is intermediate to the grand
means reported for the pinniped families. Closer inspection
reveals that the Arctic dwelling monodontid (beluga whale)
has the lowest BMR (1.41 ±0.3 x Kleiber) amongst the odon-
tocete species measured to date. Next are two of the three del-
phinids, killer whale (Orcinus orca) at 1.69 ±0.45 x Kleiber
and bottlenose dolphin at 1.96 ±0.45 x Kleiber, followed by
the harbor porpoise at 2.45 x Kleiber and the third delphinid
(Pacific white-sided (PWS) dolphin; Lagenorhynchus obliq-
uidens) at 3.39 x Kleiber. The harbor porpoise and PWS are
Figure 3: Field metabolic rate represented as a multiple of Kleiber’s
(1975) equation for various species of marine mammals. Box plots
(including statistical outliers represented as circles) are based upon
individual data provided in Appendix 2, which contains details on the
methods used and the original references. Within the Odontocetes,
dierent shades of blue dierentiate the families Monodontidae
(beluga whale; light blue), Delphinidae (bottlenose dolphin; medium
blue) and Phocenidae (harbor porpoise; dark blue). Similarly,
pinnipeds are grouped by family Otariidae (Antarctic fur seal,
Australian fur seal, Australian sea lion, California sea lion, Galapagos
fur seal, Galapagos sea lion, New Zealand fur seal, Northern fur seal,
Steller sea lion; red), Phocidae (gray seal, harbor seal, harp seal,
Hawaiian monk seal, hooded seal, Northern elephant seal, Weddell
seal; orange), or Odobenidae (walrus; pink). Data for the sea otter
(black) are also provided.
comparatively small odontocetes that live in cold temperate
waters; thus, an intrinsically high metabolism may be required
for thermal neutrality.
Finally, of all the marine mammals, the lowest BMR (0.35
x Kleiber) is found in the sirenians (represented by the West
Indian manatee, Trichechus manatus). This low BMR is likely
related to their herbivorous and sedentary lifestyle in a warm-
water environment. Surprisingly, the BMR (2.54 x Kleiber)
for Mustelids represented by the sea otter (Enhydra lutris),
is intermediate to that of odontocetes and otariids. This is an
interesting finding since numerous studies have suggested that
sea otters, as the smallest marine mammal and the only marine
mammal without a blubber layer, must rely on intrinsically
high BMR to maintain thermal neutrality (e.g. Yeates et al.,
2007). Alternatively, sea otters may rely on thermal substitu-
tion from digestion (heat increment of feeding) or activity to
maintain thermoneutrality (Costa and Kooyman, 1984).
Unfortunately, it is extremely difficult to obtain direct
measures of BMR from large cetaceans. Indeed, there are no
respirometry estimates of BMR available for mysticetes, as
their large size typically precludes even temporary holding.
Wahrenbrock et al. (1974), however, used a combination of
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Conservation Physiology • Volume 11 2023 Review
captured breath samples and respiration rates to measure the
resting metabolism of two gray whale calves (Eschrichtius
robustus), with estimates ranging from 0.9 to 2.4 x Kleiber.
Estimating metabolic expenditure from respiration character-
istics has been explored by several researchers, particularly for
larger odontocetes (Roos et al., 2016;Fahlman et al., 2016b;
Nazario et al., 2022). Alternate approaches for estimating
resting energy expenditure of odontocetes from physiological
data have included pulmonary mechanics in pilot whales,
Globicephala scammoni (6.4 x Kleiber; Olsen et al., 1969)
and heat exchange thermodynamics in Hawaiian spinner
dolphin, Stenella longirostris (1.5 x Kleiber; Hampton and
Whittow, 1976).
Although there are more estimates of FMR than BMR
among marine mammals, there is less consistency in what
these values represent. As discussed later, the magnitude of
FMR measures is highly dependent upon the type of behavior
captured. Still, there is value in comparing across broad
taxonomic and behavioral categories. Like BMR, we find dif-
ferences in FMR across the 3 families of pinnipeds (Table 2).
Also like BMR, the lowest FMR across the pinnipeds was
found in phocids, where the grand mean of six phocid species
was 2.96 ±1.11 x Kleiber, and ranged from 1.2 to 4.2 x
Kleiber across the species. Meanwhile, the grand mean FMR
of nine species of otariids was 4.73 ±1.57 x Kleiber (range,
1.1–5.7 x Kleiber) and the mean FMR for odobenids (rep-
resented by walrus) was 5.28 ±1.91 x Kleiber (range, 2.8–
7.2 x Kleiber). Interestingly, the FMR for the sea otter (4.49
x Kleiber) is intermediate to the grand means provided for
the three pinniped families, similar to the trend seen in BMR.
However, it should be noted that this value was derived from a
single study that combined captive measurements of behavior-
specific energy expenditure and behavioral budgets of wild
male otters and, therefore, does not represent a direct measure
of FMR (Yeates et al., 2007).
To date, the FMR of cetaceans has only been measured
in three odontocete species. As with BMR, the grand mean
FMR for odontocetes (3.52 ±0.62 x Kleiber) is intermediate
to the values reported for the pinniped families (Table 2).
The FMR means across odontocete species ranged from 3.0
to 4.2 x Kleiber, with the Arctic dwelling beluga whale
having the lowest FMR, followed by the harbor porpoise,
and lastly, the bottlenose dolphin (Table 2). It is curious that
the mean FMR for the bottlenose dolphin is higher than
the mean FMR for the harbor porpoise, considering that
the bottlenose dolphin mean BMR was lower than that of
the porpoise. Looking at the FMR values from each study,
we find that the range of reported FMR values was wider
for the bottlenose dolphin (Table 2), which could be because
different methodologies and types of measurements were used
across the bottlenose dolphin studies (Appendix 2). While
DLW estimates of total energy expenditure in captive dolphins
were similar to the costs of short-term submerged swimming
measured via respirometry in captive dolphins, TEE estimates
using DLW from wild dolphins were approximately 2 times
greater (Fig. 1). Given the lack of empirical FMR data for
mysticetes, some researchers have tried to produce predictive
equations derived from other cetaceans based solely upon
body mass. However, as just discussed, treating all marine
mammal taxa similarly will likely lead to erroneous estimates.
What Is the Relationship between
Dierent Measures of Metabolism in
Marine Mammals?
One of the reasons measures of BMR (and RMR) are con-
sidered valuable for studying the energetics of animals is that
they are often assumed to be related to daily energy expendi-
ture (Carpenter et al., 1995;Johnson et al., 2001;Burton et
al., 2011). Therefore, we were curious to know what propor-
tion of FMR was attributable to maintenance costs (BMR)
for these animals. From our review of the literature, it was
evident that BMR has been measured in many more marine
mammal species than FMR (Appendix 1 and 2). However, if
there is a reliable relationship between RMR and FMR, it may
be possible to use some multiple of BMR to estimate FMR for
species without empirical measures. Admittedly, the paucity
of FMR studies limits this analysis (especially for cetaceans).
We also acknowledge that is it not ideal to combine data
across studies, where the animals varied in age, sex, repro-
ductive status and satiation and exhibited different behaviors,
and experienced different conditions like time of day, season,
air and water temperature. Yet despite these limitations, the
initial findings of this type of analysis are intriguing. Broadly,
there was similarity across taxa, which was surprising since
evolutionarily, cetaceans, pinnipeds and mustelids were not
derived from a common terrestrial ancestor. Across three
odontocete species, BMR represented 56% of FMR, which
was similar in value to that found for the three pinniped
families, phocid seals (50%; four species), otariids (56%; five
species) and walrus (56%), as well as the mustelid the sea otter
(57%; Figure 4). There were of course notable exceptions
within these overall averages, which included BMR account-
ing for a higher proportions of FMR for harbor porpoise
(73%), Weddell seals (86%) and Steller sea lions (73%). These
results were generally due to both higher-than-average BMR
and lower-than-average FMR estimates.
Factors that Impact Non-Active
Metabolic Rate
As insightful as it is to quantify differences in energy expendi-
ture under the tightly defined conditions of BMR, it is equally
important to understand how various intrinsic factors (e.g.
age, sex, reproduction, molt, individual) and extrinsic factors
(e.g. food availability, water temperature, season) can inf lu-
ence metabolism. Under these conditions, one cannot (by defi-
nition) restrict the comparisons to studies that match Kleiber’s
criteria, and the nomenclature becomes problematic. For this
portion of the review we use the term resting metabolic rate
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Review Conservation Physiology • Volume 11 2023
Figure 4: Basal metabolic rate (BMR) in comparison to eld
metabolic rate (FMR) for various species of marine mammals
expressed as a multiple of Kleiber’s (1975) equation. The box plots
representing BMR (colored box plots) and FMR (clear box plots) are
based on individual study means for each species, as shown in Fig. 2
and Fig. 3, respectively. The calculations for BMR as a proportion of
FMR are shown as percentages and are based on species means
detailed in Table 1 and Table 2 (which includes references for the
original data). The grand mean of BMR as a proportion of FMR (%)
across all odontocetes species and across all pinniped species are
shown in bolded font. The value for “Other”solely represents the sea
otter.
(RMR) to designate measures of non-active (resting) energy
expenditure obtained under ‘non-Kleiber conditions’.
By examining RMR outside of ‘Kleiber conditions’, we
can explore the influence of intrinsic factors that can impact
metabolism. As evidenced in numerous species, including
northern elephant seals (Maresh et al., 2014), harbor seals
(Phoca vitulina;Rosen and Renouf, 1998), as well as the Aus-
tralian fur seal (Arctocephalus pusillus), New Zealand fur seal
(Arctocephalus forsteri) and Australian sea lion (Neophoca
cinerea)(Ladds et al., 2017c), we find that mass-specific
RMR decreases with age as physiology matures and growth
rates slow. In most pinnipeds, females have a higher mass-
specific RMR than males (Boyd and Duck, 1991;Rosen
and Renouf, 1995;Boyd and Croxall, 1996;Hurley and
Costa, 2001;Ladds et al., 2017c). Yet surprisingly, female
reproduction does not appear to alter RMR, although it does
alter TEE as evidenced by increased food consumption (e.g.
Williams et al., 2007). For example, RMR of lactating and
non-lactating northern fur seals (Callorhinus ursinus) were
similar (Costa and Gentry, 1986), as was RMR of pregnant,
lactating and non-reproductive California sea lions (Zalophus
californianus)(Williams et al., 2007). Moreover, the RMR
of bottlenose dolphins did not change throughout gestation
(Yazdi et al., 1999). In contrast, RMR of pregnant sea otters
declines during gestation, and then increases significantly over
the first 3 months of lactation (Thometz et al., 2016).
Figure 5: Variation in basal metabolic rate (BMR) within and across
individuals for three phocid seal species. Unique symbols are used for
each individual seal within each of the species, with white and black
symbols denoting the molting BMR and non-molting BMR,
respectively. Data is from Thometz et al., 2021, who stated that
intraindividual variation in BMR for all four spotted and three ringed
seals was associated with the cost of the molt. Yet it is important to
note that BMR variation across individuals of the same species is
larger than the BMR variations observed within individuals. There
was no eect of the molt on BMR of bearded seals as denoted by NS,
non-signicant.
The molt is another intrinsic factor that influences the
basic metabolic expenditure of pinnipeds. The molt appears
to increase BMR in numerous species of phocids, including
the southern elephant (Mirounga leonina)(Boyd et al., 1993),
gray (Halichoerus grypus)(
Sparling et al., 2006), harbor
(Paterson et al., 2021), spotted (Phoca largha) and ringed
(Pusa hispida)(Thometz et al., 2021) seals. A similar effect
is seen among otariids, including Australian sea lion, as
well as the Australian, New Zealand (Ladds et al., 2017b)
and northern (Dalton et al., 2015;McHuron et al., 2019)
fur seals. However, it should be noted that differences
amongst the metabolism of individuals in these species can
be larger than the variation in metabolism associated with
the molt (Figure 5). Interestingly, the molt only increased
the metabolism of non-reproductive California sea lions;
it did not alter the RMR of reproductive females, which
could suggest that reproductive females were constrained by
a metabolic ceiling (Williams et al., 2007). Also of note is
that the BMR of the bearded seal (Erignathus barbatus), the
phocid species with the longest molt (119 days compared
to approximately 30 days for many other species) does
not seem to change, probably because the energetic cost of
the molt is spread out over a long duration (Thometz et
al., 2021). In contrast, BMR decreased during the molt in
harbor (Ashwell-Erickson et al., 1986;Rosen and Renouf,
1998) and northern elephant (Worthy et al., 1992) seals, but
these animals were also fasting, which can contribute to a
phenomenon called metabolic depression.
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Conservation Physiology • Volume 11 2023 Review
Metabolic depression is a common physiological response
to either normal or unexpected periods of nutritional depri-
vation, typically measured as a decrease in RMR (Guppy and
Withers, 1999). Its onset is thought to be associated with the
shift from glycogenolysis to gluconeogenesis (phase 2 fasting;
Cahill, 1978,Castellini and Rea, 1992,Cherel et al., 1992).
Metabolic depression has been noted in numerous phocids
during natural fasting periods associated with the molt and
weaning (Worthy and Lavigne, 1987;Nordøy et al., 1990;
Markussen, 1995), as well as during unexpected decreases
in food intake (Rosen and Trites, 2002;Rosen et al., 2014).
A life history that includes long-term fasting conditions may
translate into low FMRs (Castellini and Rea, 1992), such as
observed in the intrinsically low FMR of northern elephant
seals, which is 68% lower than predicted for other marine
mammals (Williams et al., 2020). Villegas-Amtmann et al.
(2017) also suggested that the intrinsically low FMRs of
Galapagos sea lion (Zalophus wollebaeki) and Galapagos fur
seals (Arctocephalus galapagoensis) is an adaptation to living
in a limited resource habitat (an extrinsic factor).
Other extrinsic factors that could impact BMR are water
temperature and season. Indeed, it has been hypothesized that
the main driver of the high metabolic rate of small marine
mammals is elevated heat loss in water (Kanwisher and
Sundnes, 1966;Yeates et al., 2007), while the comparatively
low expenditure of Galapagos fur seals was attributed to an
adaptative response to reduce thermal stress in warm water
(Costa and Trillmich, 1988). However, the apparent inf luence
of water temperature is not consistent across species, nor can
it be directly attributable to thermoregulatory costs. Three
pinniped species endemic to Australian waters (Australian fur
seal, New Zealand fur seal and Australian sea lion) showed
disparate results in relation to natural changes in water
temperature, where one species increased BMR in response to
lower water temperatures, one decreased BMR, and the third
had no change in BMR (Ladds et al., 2017b). These disparate
results may be due to confounding influences. Indeed, among
pinnipeds seasonal variation in BMR in many species have
been associated with the molt or changes in body mass, overall
energy expenditure or food intake rather than environmental
temperatures per se, as seen in California sea lions (Williams
et al., 2007), harbor seals (Rosen and Renouf, 1998), grey
seals (Sparling et al., 2006) and northern fur seals (Dalton
et al., 2015;McHuron et al., 2019). Likewise, the BMR of
some cetaceans, including harbor porpoise (Rojano-Doñate
et al., 2018) and Pacific white-sided dolphin (Rechsteiner
et al., 2013), appear to be unaltered by seasonal changes in
water temperature.
Factors That Impact Field Metabolic
Rate
Unlike BMR, measures of FMR can encompass a wide
range of behaviors and life history stages. For many marine
mammals, FMR measures are made during the reproductive
season, partly for scientific interest but also due to the
logistical requirements for recapturing individuals. As
reproduction can be an energetically expensive endeavor, care
must be taken when applying these estimates to other times of
the year. Even within a reproductive season for an individual
species, FMR can vary tremendously depending on the target
behavior. For example, FMR of breeding adult Antarctic fur
seals (Arctocephalus gazella) is dependent on whether one
is studying fasting territorial males (3.3 x Kleiber; Boyd and
Duck, 1991), or lactating females during their initial postpar-
tum fast (3.6 x Kleiber; Costa and Trillmich, 1988) or their
at-sea foraging phase (6.7 x Kleiber; Costa et al., 1989). For
studies on lactating females, some studies report the energy
expenditure over an entire at-sea on-land nursing cycle while
others may only report the substantially more expensive at-
sea portion (Costa et al., 1991). Differences in FMR estimates
for the entire nursing cycle versus only the at-sea portion are
associated with the amount of time spent swimming and
diving since locomotion is a major component of TEE.
Given that the cost of locomotion is a key component
to consider for the TEE of animals, many researchers have
investigated the cost of locomotion in marine mammals using
respirometry in laboratory settings. As we mentioned pre-
viously, we included these measures as relevant alternate
measures of FMR on the assumption that locomotion was a
major cost of at-sea behaviour. However, the cost of swim-
ming in some species was revealed to be much lower than
FMR measured by the more traditional means of DLW. As
previously noted, energy expenditure of bottlenose dolphins
while swimming was 4.1 x Kleiber, while at-sea expenditure of
wild dolphins measured via DLW was 7.1 x Kleiber (Figure 1).
A similar difference was seen in the reported cost of swimming
vs. at-sea FMR in harbor seals (2.8 vs 5.0 x Kleiber) and
walrus (4.6 vs 6.0 x Kleiber), but this difference was not
seen in Australian sea lion (4.8 vs 4.5 x Kleiber) (calculated
from values in Appendix 2). Still, estimates of the cost of
locomotion are invaluable for calculating FMR from activity
budgets (e.g. Jeanniard du Dot et al., 2016) and may still
prove to be useful proxies if they represent the only measures
of energy expenditure for many species of marine mammals.
There are also numerous studies that have measured the
cost of diving in marine mammals. To illustrate the influence
of submergence, California sea lions resting at the water’s
surface have a BMR of 2–3 x Kleiber but when they perform
prolonged sedentary submergences their metabolic expen-
diture approached 1 x Kleiber (Hurley and Costa, 2001).
Likewise, the metabolism of Weddell seals resting at the water
surface were 1.6 to 1.8 x Kleiber (Castellini et al., 1992;
Williams et al., 2004) but decreased to 1.1 x Kleiber when
submerged (Williams et al., 2004), which is similar to the
decrease observed in Steller sea lions (Hastie et al., 2007).
Increased submergence time also lowers the metabolic rate
of grey seals (Reed et al., 1994) and three Australian otariid
species (Ladds et al., 2017c).
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Review Conservation Physiology • Volume 11 2023
Care must be taken to not apply estimates of the cost of
diving and swimming interchangeably, as the two are physio-
logically and energetically different. How exercise underwater
impacts the metabolism of marine mammals is complicated
because there is the ‘dive response’ (e.g. Scholander, 1940;
Irving et al., 1941;Scholander, 1963) that lowers rates of
oxygen consumption (as evidenced by the studies discussed
above) overlaid on top of a conflicting ‘exercise’ response that
increases heart rate, which is an indicator of increased rates
of oxygen consumption (Noren et al., 2012). Indeed, looking
at results from two odontocete species we find that oxygen
consumption increased when the animal was active at depth,
but oxygen consumption during submerged swimming was
still lower than oxygen consumption during surface swim-
ming (John, 2020). As the activity level (measured variously
as swim speed, stroke rate, ODBA, etc.) of the diving seal or
odontocete increases so does metabolic rate (Williams et al.,
2004;Fahlman et al., 2013;Maresh et al., 2014;Goundie
et al., 2015;Ladds et al., 2017c).
This effect of activity on metabolism is the basis for using
measures of body movement to predict energy expenditure.
Comparisons of ODBA with energy expenditure derived from
DLW techniques suggests that ODBA may be a good proxy
for energy expenditure when energy costs are accounted
for on a behavior-specific basis (Jeanniard du Dot et al.,
2016). However, ODBA can underestimate energy expendi-
ture if changes in other physiological processes such as basal
metabolism (as may occur during post-exercise recovery peri-
ods), stress, thermoregulation, specific dynamic action, repro-
duction or growth are not accounted for (Dalton et al., 2014;
Volpov et al., 2016;Pagano and Williams, 2019). Mean-
while, the disparate physiological responses to submergence
(decreased metabolism) and activity (increased metabolism)
confounds the link between metabolic rate and any one
specific at-sea behavior, such as dive depth, dive type, dive
rate, % time diving, dive duration, foraging effort and trip
duration. This is because at-sea behaviors vary in submer-
gence time and exercise intensity. For example, although the
FMR of California sea lions varied with dive duration and
bout duration, it was primarily influenced by dive depth
(McHuron et al., 2018) while the FMR of other otariid
species was influenced by dive depth and percent time diving
(Arnould et al., 1996;Costa and Gales, 2000) or time spent at
sea (Villegas-Amtmann et al., 2017;McHuron et al., 2019).
Interestingly, interspecific comparisons of energy expenditure
in free-ranging otariids indicate that benthic-diving species
often have higher at-sea FMRs than pelagic-foraging species,
leading to the hypothesis that benthic diving is an energeti-
cally expensive strategy (Costa et al., 2004).
Summary
Estimates of energy expenditure are central to conservation
efforts involving marine mammals. Metabolic rates determine
both the resources required for healthy animal populations
as well as the potential impacts of those animals on their
ecosystems. Estimates of basal and resting metabolism typi-
cally serve as the foundation for individual-based bioenergetic
models while measures of field metabolic rate are frequently
used in large-scale ecosystem models to estimate total prey
intake. The utility of such modelling is fundamentally tied to
both the accuracy of these estimates and also their appropriate
application.
This review has highlighted the need for discernment when
choosing values for the metabolic rates that form the basis
of bioenergetic models. First, the diverse methodologies and
testing conditions used to estimate metabolism do not pro-
vide the same value. Second, marine mammals cannot be
treated as one homogenous group; there are large taxa and
species-specific variations in BMR, FMR and the relation
between the two (BMR as a proportion of FMR). Third,
there are several factors that impact the metabolic rates of
marine mammals, including intrinsic (growth, size, sex and
life history traits such as reproduction and the molt), extrinsic
(prey availability, water temperature and season) and behav-
ioral (swim effort, overall activity, dive depth, dive duration,
bout duration, percentage of time diving, time spent at sea
and dive type) factors. Moreover, the metabolic response
to these different factors varies across species. Thus, one
estimate of metabolic rate does not fit all, within an individual
across seasons, within a species across age- and sex-class,
and across taxonomic groups. Moreover, one FMR does not
even fit a given species as prey distributions change season-
ally and annually that require marine mammals to respond
by altering at-sea behaviors. Clearly, additional research is
needed to improve our understanding of what determines the
metabolic rates of marine mammals. One recommendation
is to expand controlled experimentation on marine mammal
species housed in aquaria and research facilities. Numerous
studies have demonstrated that the underlying physiology of
wild and aquaria animals is fundamentally similar. However,
their lifestyles and, hence, total energy expenditure, often
differ. For example, estimates of FMR derived from aquaria
animals likely represent minimum daily active costs for wild
animals. However, it is important to note that studies with
aquaria animals are still vital. Each of the factors listed as
impacting metabolism can be explored in the same individ-
uals, experiencing the same conditions and using the same
methodology. We also need to continue validation work to
develop alternate methods that reliably measure metabolic
rate of difficult to study species. Finally, given the complexities
of the interacting factors that influence metabolism, we rec-
ommend that those constructing bioenergetic models consult
with a marine mammal bioenergetics specialist to ensure
appropriate parametrization of the model.
Author Contributions
SRN conceptualized the ideas and wrote the original
manuscript and analyses. DASR revised the analyses and
added additional ideas and concepts to the manuscript.
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Conservation Physiology • Volume 11 2023 Review
Acknowledgements
This review was inspired by D.P. Costa, who invited SN to
present a review on the metabolic rate of marine mammals
to attendees of a Marine Mammal Bioenergetics Workshop,
which was held remotely in 2021. Many thanks to C. Booth
for organizing our presentations into a Special Issue in Conser-
vation Physiology. Thanks to J. Maresh for providing us with
a copy of her dissertation, which included a lengthy appendix
of references on metabolic measurements in marine mammals.
We also thank the other workshop contributors who provided
comments on the earliest draft of this manuscript.
Conicts of Interest Statement
There are no conflicts of interest to report.
Data Availability
This is a review paper; data were drawn from peer-reviewed
papers. The Appendixes in the supplemental data for this
paper provide the data we used from the original papers.
Full citation information for those papers are listed in our
reference section.
Funding
This work was primarily funded under an award from the
Office of Naval Research (N00014202392) and with support
from the Marine Mammal Commission awards MMC19-173
and MMC21-056.
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