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ARTICLE
Naturally stressed? Glucocorticoid profiles
in blubber of blue and gray whales in response
to life history parameters
Valentina Melica
1
| Shannon Atkinson
1
|
John Calambokidis
2
| Diane Gendron
3
| Aimee Lang
4
|
Jonathan Scordino
5
1
Fisheries Department, College of Fisheries
and Ocean Sciences, University of Alaska
Fairbanks, Juneau, Alaska
2
Cascadia Research, Olympia, Washington
3
Instituto Politecnico Nacional, Centro
Interdisciplinario de Ciencias Marina (IPN-
CICIMAR), La Paz, Baja California Sur, Mexico
4
Ocean Associates Inc., on contract to NOAA
Southwest Fisheries Science Center, La Jolla,
California
5
Marine Mammal Program, Makah Fisheries
Management, Neah Bay, Washington
Correspondence
Shannon Atkinson, Fisheries Department,
College of Fisheries and Ocean Sciences,
University of Alaska Fairbanks, 17101 Point
Lena Loop Road, Juneau, AK 99801.
Email: shannon.atkinson@alaska.edu
Present address
Valentina Melica, Marine Mammal
Conservation Physiology, Fisheries and
Oceans Canada, West Vancouver, British
Columbia, Canada
Funding information
Alaska INBRE, Grant/Award Number:
P20GM103395; American Cetacean Society;
Office of Naval Research, Grant/Award
Number: N0014-14-1-0425; University of
Alaska Fairbanks Calvin Lensink Fellowship;
University of Alaska Fairbanks Resilience and
Adaptation Program; BLaST Equipment Fund
Abstract
The goal of the present study was to carry out a thorough
methodological validation and describe baseline profiles for
glucocorticoid hormones (cortisol and corticosterone) in
blubber from blue (n=77) and gray (n=103) whales from
the eastern North Pacific Ocean. For each species, we mod-
elled cortisol and corticosterone concentrations in response
to life history parameters (age, sex, reproductive status) and
season or geographic location. In blue whales, cortisol con-
centrations did not vary significantly by age class, sex, or
reproductive status, whereas corticosterone was signifi-
cantly lower in immature than in adult females (p< .001). In
gray whales, cortisol concentrations were significantly
higher in lactating whales (p< .05), while corticosterone
was significantly different between females and males
(p=.001) and elevated in calves (p=.003). In gray whales,
corticosterone concentrations were significantly lower in
males sampled later in the year (August to November) com-
pared to both sexes sampled between March and August
(p=.05), but no seasonal trend occurred in blue whales.
Our results indicate that glucocorticoid actions vary
between species and sex in large whales. Analysis of multi-
ple hormones improves our understanding of the physiol-
ogy of maintaining metabolic homeostasis or coping with
chronic stressors.
Received: 18 October 2021 Accepted: 28 April 2022
DOI: 10.1111/mms.12954
Mar Mam Sci. 2022;1–25. wileyonlinelibrary.com/journal/mms © 2022 Society for Marine Mammalogy. 1
KEYWORDS
blue whale, corticosterone, cortisol, gray whale, metabolic
biomarkers, stress
1|INTRODUCTION
Increased wildlife-human interactions, as well as rapid changes in environmental conditions, have warranted the
need for the development of biomarkers of animal health in response to potential stressors. Chronic stress, including
prolonged or excessive exposure to one or multiple stressors, is a key concern for endangered wildlife populations,
as its long-term repercussions to an organism's health may include a compromised immune system or reproductive
failure (Atkinson et al., 2015; Tilbrook et al., 2000; Wasser et al., 2017; Webster Marketon & Glaser, 2008). Never-
theless, before we can assess the effects on physiological pathways triggered by perceived or real stressors, it is nec-
essary to have a thorough understanding of such pathways in response to naturally occurring stimuli in the species
of interest.
Blue (Balaenoptera musculus) and gray (Eschrichtius robustus) whale populations from the eastern North Pacific
(ENP) have been studied extensively over the past three decades. However, most postcommercial whaling studies
have focused on photo-identification and population abundance, habitat, migration, and behavior (Busquets-Vass
et al., 2021; Calambokidis & Barlow, 2020; Calambokidis & Perez, 2017; Calambokidis et al., 1990; Gendron
et al., 2015; Lang et al., 2014; Paniagua-Mendoza et al., 2017). Much of the initial research on the physiology of blue
and gray whales was conducted during commercial whaling (Mackintosh & Wheeler, 1929; Rice & Wolman, 1971),
and updated with studies in the past decade (Atkinson et al., 2020; Busquets-Vass et al., 2017; Hayden et al., 2017;
Hunt et al., 2018; Lemos et al., 2020; Melica et al., 2021a,2021b; Trumble et al., 2013; Valenzuela-Molina
et al., 2018). Feeding on lower trophic levels, these species play an important role in the ecosystems: on one hand,
whales exercise top-down effects on prey abundances (Burnham & Duffus, 2016,2018); on the other hand, they are
the ultimate recipient of bottom-up driven systems (Busquets-Vass et al., 2021; Croll et al., 2005; Szesciorka
et al., 2020). Therefore, their population dynamics can be affected by environmental changes and oceanographic per-
turbations (Calambokidis et al., 2009; Moore, 2008; Szesciorka et al., 2020). For instance, migration and abundance
of these two species are affected by the Pacific Decadal Oscillation (PDO), El Niño and La Niña oscillations, and sea
ice (Calambokidis et al., 2009; Moore et al., 2003,2007; Perryman et al., 2002,2020; Urbán et al., 2003). In particu-
lar, long-term studies report evidence of lower numbers of gray whale calves following years of negative PDO
regimes and longer sea ice permanence, indicating the importance of earlier access to feeding grounds for reproduc-
tive success (Perryman et al., 2020; Perryman & Lynn, 2002). In addition, both populations are exposed to a variety
of anthropogenic sources of disturbance along their migration route, including contaminants (Gulland et al., 2005;
Krahn et al., 2001; Metcalfe et al., 2004; Tilbury et al., 2002; Trumble et al., 2013), underwater noise (Goldbogen
et al., 2013; Moore & Clarke, 2002), fishing operations, and collisions with ships (Baird et al., 2002; Berman-
Kowalewski et al., 2010; Carretta et al., 2013; Douglas et al., 2008). These disturbances or potential stressors can be
detrimental to an organism's health, particularly affecting its reproductive capacity (Wasser et al., 2017).
Corticosteroids are a class of steroid hormones produced in the adrenal cortex and involved in metabolism and
in the endocrine response to stressors in terrestrial and aquatic mammals (Atkinson et al., 2015). This hormone class
is further divided into two main groups: glucocorticoids (GC: cortisol and corticosterone) and mineralocorticoids (pri-
marily aldosterone). While they are both regulated by the hypothalamo-pituitary–adrenal (HPA) axis, these two
groups of steroids differ in their physiological actions. GC are involved in metabolic processes, with their primary
action being energy regulation (Busch & Hayward, 2009). For example, GC concentrations increase when the organ-
ism's energy demands exceed the energy availability (McEwen & Wingfield, 2003). One of the main functions of GC
is to stimulate gluconeogenesis, a biochemical pathway that produces glucose from noncarbohydrate substrates, to
2MELICA ET AL.
increase glucose levels. In vertebrates, this process mainly takes place in the liver. When this process is triggered
over a long period of time, GC have a catabolic action on both skeletal muscle and adipose tissue (e.g., blubber) in
order to provide a supply of compounds (e.g., amino acids or glycerol) for gluconeogenesis (Peckett et al., 2011;
Tanaka et al., 2017). Elevated or chronic production of GC can result in poor body condition and can have negative
effects on reproduction. For instance, in domestic ungulates and primates, cortisol can suppress gonadotrophin
secretion (e.g., luteinizing hormone) that drives gonadal development (Tilbrook et al., 2000). Mineralocorticoids, with
aldosterone as a major hormone, are primarily regulated by the renin-angiotensin system (Atkinson et al., 2015);
however, their secretion is secondarily activated by the HPA during a stress response (Kubzansky & Adler, 2010).
During the stress response when the cardiovascular ionic and osmotic balance is altered by the catabolic effect of
glucocorticoids, mineralocorticoids likely function to restore osmotic homeostasis as well as to stabilize blood pres-
sure (Burgess et al., 2017).
Glucocorticoid hormones have been studied in a wide range of tissues and mammal species, as possible indica-
tors of physiological stress. Over the past two decades, pinnipeds and cetaceans under human care, from which sam-
ples could be easily obtained, have been the subjects of pioneer projects aimed at investigating stress responses to
acute stimuli (Champagne et al., 2018; Keogh & Atkinson, 2015; Mashburn & Atkinson, 2004,2007; Robeck
et al., 2017; Steinman et al., 2016). However, information on endocrine patterns and long-term detrimental effects is
still limited for free-ranging populations of these taxa.
Profiles of GC were described for a handful of mysticete species, using a variety of matrices: baleen, feces, and
blubber (Atkinson et al., 2020; Burgess et al., 2017; Cates et al., 2020; Dalle Luche et al., 2020,2021; Hunt
et al., 2006,2014,2017; Kellar et al., 2015; Lemos et al., 2020; Mingramm et al., 2020; Rolland et al., 2012,2019;
Trumble et al., 2018; Valenzuela-Molina et al., 2018). Feces can be collected noninvasively and were used to assess
GC profiles and monitor response to ocean noise and anthropogenic injuries in North Atlantic right whales
(Eubalaena glacialis; Hunt et al., 2006; Rolland et al., 2012,2019). In blue whales, glucocorticoids were detected and
measured in feces, blubber, baleen, and one earplug (Atkinson et al., 2020; Hunt et al., 2017,2018; Trumble
et al., 2013,2018; Valenzuela-Molina et al., 2018). Corticosterone and mainly cortisol in baleen and earplug showed
“peaks”during the life of the individuals, sometimes in synchrony with testosterone (Hunt et al., 2018; Trumble
et al., 2013). In feces from blue whales, corticosterone was found as the primary glucocorticoid and concentrations
were elevated in pregnant females (Valenzuela-Molina et al., 2018). In gray whales, GC were validated in blubber and
feces (Hayden et al., 2017; Lemos et al., 2020), and fecal metabolites were compared among demographic groups,
with adult males showing elevated concentrations (Lemos et al., 2020).
Because the collection of feces, earplugs, or baleen is highly opportunistic and somewhat limited, blubber has
become a prominent tissue for endocrine studies in cetaceans, especially mysticetes, over the past decade (Atkinson
et al., 2020; Carone et al., 2019; Cates et al., 2019; Champagne et al., 2018; Dalle Luche et al., 2020,2021; Kellar
et al., 2015; Melica et al., 2021a,2021b; Mingramm et al., 2020; Trana et al., 2015). Steroid hormones accumulate in
blubber through passive diffusion and likely reflects events which happened somewhat recently (Champagne
et al., 2017; Kellar et al., 2013). Furthermore, blubber can be collected from live whales using remote biopsy tech-
niques, a minimally invasive approach (Noren & Mocklin, 2012). In baleen whales, baseline blubber GC have been
studied in two populations of humpback whales (Megaptera novaeangliae; Cates et al., 2020; Dalle Luche et al., 2021;
Mingramm et al., 2020), and in ENP blue (Atkinson et al., 2020) and gray whales (Hayden et al. 2017). However,
these studies are limited to the detection of a single GC hormone (e.g., cortisol for blue whales (Atkinson
et al., 2020), Australian humpback whales (Dalle Luche et al., 2021; Mingramm et al., 2020), and gray whales
(Hayden et al., 2017), and corticosterone for North Pacific humpback whales (Cates et al., 2020)), thus overlooking
the interaction between or the suitability of other corticoid hormones as possible biomarkers for stress.
The goal of the present study was to construct a thorough validation of two glucocorticoids (cortisol and cortico-
sterone) in blubber of two whale species from ENP, blue and gray whales, and to describe hormone profiles in rela-
tion to life history parameters.
MELICA ET AL.3
The specific research questions were: (1) Can enzyme immunoassays accurately detect and measure glucocorti-
coid hormones in blubber of blue and gray whales? (2) Do baseline glucocorticoid hormones concentrations change
across life history parameters such as age class, sex, and reproductive state, and among areas/seasons of sampling?
To answer these questions, we first established analytical protocols to determine the detectability and measure-
ment accuracy of these hormones using commercially available enzyme immunoassays (EIA); then we tested GC con-
centrations for any relationships with the life history parameters as well as geographic areas where or seasons
during which the animals were sampled.
2|METHODS
2.1 |Biopsy collection and life history data
Blubber samples of 77 blue whales (42 females, 35 males) and 103 gray whales (68 females, 35 males) were collected
from live animals (Table 1). For blue whales, 62 biopsies were collected in the Gulf of California (GoC;
24.33N–30.84N, 114.09W–109.09W) using 7 mm diameter biopsy darts and crossbow (Costa-Urrutia
et al., 2013) by the Centro Interdisciplinario de Ciencias Marinas (CICIMAR) between 2002 and 2016, frozen at sea
in liquid nitrogen, and then in a 80C freezer. An additional 15 biopsies were collected along the US West Coast
(USWC; 32.77N–40.82N, 124.62W–117.4W) by the Cascadia Research Collective (CRC) between 2005 and 2015,
frozen at sea in liquid nitrogen, and then stored at 80C at NOAA Southwest Fisheries Science Center's (SWFSC)
Marine Mammal and Sea Turtle Research Collection. Male and female blue whales were categorized into different age
classes based on length of sighting history (LSH), and females were also divided based on reproductive status (Table 1).
Life history information regarding sex and LSH were obtained from the CICIMAR and the CRC photo ID catalogs for
each biopsy (Table S1); female reproductive state was determined using the criteria and calculated probabilities of a
whale of being pregnant from Melica et al. (2021b)(Tables1and S1). Briefly, the distribution of progesterone concentra-
tions from pregnant and nonpregnant females were used to fit a logistic curve of probability of pregnancy with 95%
confidence intervals at each progesterone concentration. Conservative thresholds of probability of pregnancy were then
used to assign reproductive status to unknown whales: for instance, whales with probability >95% were considered as
presumed pregnant (pres p); whereas whales with probabilities <5% were considered presumed nonpregnant (pres np).
For gray whales, the 103 biopsies were collected using a crossbow and biopsy darts by the Makah Tribe Marine
Mammal Program, CRC, NOAA SWFSC, and University of Alaska Southeast (UAS) and stored in 80C freezers. The
sampling efforts occurred between northern California (38.3N, 122.2W) and Kodiak Island, Alaska, (57.4N,
152.4W) between 2004 and 2016. Biopsies were collected in the months of March (n=1), June (n=4), July
(n=16), August (n=17), September (n=27), October (n=37), and November (n=1). Given the high variability of
the number of samples each month, we grouped the samples in two arbitrarily chosen season bins: Spring–Summer
(n=38), including all biopsies collected between March and August and Fall–Winter (n=65), including all biopsies
collected between September and November. Similar to blue whales, individuals were divided based on age class
and reproductive status, using life history information from the CRC Photo ID catalog (Tables 1and S2). Reproduc-
tive status was assigned as noted in Table 1: 11 whales were considered as pres p, 25 as pres np, and 12 had proba-
bility values within those thresholds. Of the latter, 6 were classified as adult whales (adult unknown) and 6 were of
unknown age class (unknown). The majority of individuals (n=88) were identified as part of the Pacific Coast Feed-
ing Group (PCFG), a subgroup of whales of the ENP population that are sighted between northern California and
British Columbia (41N–52N; excluding whales observed in Puget Sound) in more than one year between June 1
and November 30 (International Whaling Commission, 2012). One whale (ID 531) was identified as part of the North
Puget Sound group (NPS), a small group of approximately a dozen whales known to detour to the North Puget Sound
during their spring migration to feed on ghost shrimp (Callianassa californiensis; Weitkamp et al., 1992). The remaining
14 were considered part of the overall ENP population.
4MELICA ET AL.
TABLE 1 Life history categories applied to individual blue (B. musculus) and gray whales (E. robustus).Parameters considered are age class for both males and females, and
reproductive state for females of both species. Age class was determined based on length of sighting history. Numbers in parenthesis indicate the number of calves, within the
immature group.
Age class Description E. robustus B. musculus
Male
Adult Males with known age or LSH of 8 or more years. 16 18
Immature (calf) Males sighted as a calf the year of sampling or with known year of birth and known age <8 years. 10 (4) 2 (1)
Unknown Males that could not be classified as adult (i.e., no known age and LSH <8 years). 9 15
Total: 35 35
Female
Adult Females with known age or LSH of 8 or more years, or with records of being sighted with calf. 37 27
Immature (calf) Females sighted as calf the year of sampling or with known age <8 years. 10 (4) 6(1)
Total: 47 34
Reproductive state Description E.robustus B.musculus
Lactating Adult females seen with a calf the year of sampling. 6 8
Pregnant Adult females seen with a calf the year after sampling. 4 3
Presumed pregnant
(pres p)
Females with a calculated probability of being pregnant >95% (Melica et al., 2021a,2021b). 11 6
Presumed nonpregnant
(pres np)
Females with a calculated probability of being pregnant <5% (Melica et al., 2021a,2021b). 25 6
Adult unknown Adult females with unknown reproductive status (i.e., not sighted with calf the year of or after
sampling, or calculated probability of being pregnant between 5% and 95%).
64
Unknown Whales with no sufficient LSH to determine age class or enough information to determine
reproductive status (i.e., not sighted with calf the year of or after sampling, or calculated
probability of being pregnant between 5% and 95%).
69
Total: 58 38
MELICA ET AL.5
2.2 |Sample preparation and hormone extraction
Blubber biopsies were subsampled at their storage location (e.g., NOAA SWFSC, CICIMAR, UAS) and transferred to
the University of Alaska Fairbanks facility in Juneau, Alaska, where they were stored at 80C until extraction. Sam-
ple weights ranged between 0.03 and 0.29 g for blue whales and between 0.03 and 0.17 g for gray whales. We per-
formed hormone extraction following the stepwise process described in Atkinson et al. (2020) and Melica et al.
(2021b), adapted from Mansour et al. (2002) and Kellar et al. (2006). Briefly, samples were first macerated manually
using a Teflon tip homogenizer in 1 ml of ethanol, centrifuged for 15 min at 3,000 rpm, and the supernatant was
transferred into a clean glass tube and dried under forced air. Two milliliters of ethanol:acetone solution (4:1) were
added to the extract, which was then vortexed, centrifuged, and the liquid transferred to a clean tube and dried
under forced air. For the next extraction, 1 ml of ethyl ether was added to the residue, vortexed, centrifuged and
transferred to a clean tube. After being dried under forced air, the extracts were reconstituted in 1 ml of acetonitrile
and 1 ml of hexane, vortexed and centrifuged. The acetonitrile layer was removed, transferred to a clean tube, and
purified with an additional 1 ml of hexane. The hexane layer was removed, and acetonitrile supernatant was dried
under forced air and stored frozen at 20C. To prepare sample dilutions for the EIA, first we rehydrated the
extracts with 1 ml of methanol and moved the aliquot required for the appropriate dilution into a new glass tube.
After being dried under forced air, we added the volume of assay buffer based on manufacturer protocols. To vali-
date and measure cortisol and corticosterone, we used two commercially available EIA and followed manufacturer
protocols (Arbor Assay, Ann Arbor, MI). The cortisol assay (Arbor Assay K003) standard curve was made by fitting a
four parameters logistic curve (4PLC) to seven points representing standards dilutions ranging from 50 pg/ml to
3,200 pg/ml. The manufacturer reported the following cross-reactivities for this assay: dexamethasone =18.8%,
prednisolone =7.8%, corticosterone and cortisone =1.2%, progesterone, eEstradiol, and testosterone <0.1%. The
corticosterone assay (Arbor Assay K014) standard curve was made by fitting a 4PLC to nine standard dilutions, rang-
ing from 39.06 pg/ml to 10,000 pg/ml. The manufacturer reported the following cross-reactivities for this assay:
1-dehydrocorticosterone =18.9%, desoxycorticosterone =12.3%, aldosterone =0.62%, cortisol =0.38%, proges-
terone =0.24%, dexamethasone =0.12%, testosterone, cortisone, and estradiol <0.1%.
2.3 |Methodological validation
For the methodological validation of each EIA, we used female and male pools of extracts from blubber of both
stranded (1 female, 2 males) and live animals (9 females, 8 males) for blue whales and of only stranded animals
(10 females and 4 males) for gray whales (Table S4). We obtained blubber samples of the three stranded blue whales
from NOAA SWFSC and Oregon State University and of the 14 stranded gray whales through the Marine Mammal
Health and Stranding Response Program (MMHSRP; Melica, 2020; Melica et al., 2021a). None of these individuals
were live strandings.
We tested the blubber pools of extracts for parallelism and accuracy. Each pool was serially diluted (1:1, 1:2, 1:4,
1:8, and 1:16) and tested for parallel displacement to the standard curve for each hormone. We assessed parallelism
visually (Figure 1) and statistically, as follows: we fitted a 4PLC to the assay standards and the percent binding for at
least four dilutions of each pool using the R package drc (Ritz et al., 2015). We then used a Student's t-test (package
stats; R Core Team, 2021) to determine any significant difference in the slope parameter from the two curves, with
lack of significance supporting parallelism. This step is used to confirm that the assay's antibody can reliably bind the
antigen throughout the range of the standard curve. It also indicated the dilution at which the pool concentration
reached approximately 50% binding.
The accuracy test was used to determine if any other compounds in the extracts were interfering with the bind-
ing between antigen and antibody of the assay, and for testing how well the measured concentrations corresponded
to added concentrations of each hormone. For this step, we spiked an equal volume of each pool with each standard
6MELICA ET AL.
and then graphed the recovered mass against the added mass and tested for linearity. Linearity is met when a linear
slope close to 1.0 (acceptable range: 0.7–1.3) is achieved (Hunt et al., 2017). Due to the weak parallelism of the male
blue whale pool to the corticosterone assay and the limited extract volumes, we chose to not measure corticoste-
rone in male blue whales.
2.4 |Baseline hormone profiles and life history parameters
Extracts from the present study were previously used for the measurement of reproductive steroids (Melica
et al., 2021a,2021b), thus due to volume limitations, we measured cortisol concentrations in 74 blue whales and in
90 gray whales and corticosterone concentrations in 37 female blue whales and in 97 gray whales. All samples were
run in duplicate, at dilutions displacing close to 50% binding based on the parallelism test. Raw data obtained in
pg/ml were corrected for dilution factor and initial mass of the blubber extracted and were expressed as ng/g.
In our exploratory data analysis (EDA) we tested the distribution of hormone concentrations for outliers using
“boxplot”(package: graphics; R Core Team, 2021) and “qqnorm”(package: stats; R Core Team, 2021) functions, for
normality using the Shapiro test, and for homogeneity of variances using the Bartlett's test (package: stats; R Core
Team, 2021). Additionally, we performed a Pearson's correlation test to determine any existing correlation between
cortisol and corticosterone concentrations. Based on the EDA results, cortisol and corticosterone concentrations
were transformed on a logarithmic scale to meet normality requirements, whereas the Bartlett's test indicated homo-
geneity of variances for transformed cortisol and corticosterone concentrations when grouped by sex, age class, and
reproductive status. All statistical analyses were performed on transformed cortisol and corticosterone concentra-
tions, whereas untransformed hormone concentrations were plotted in graphs.
FIGURE 1 Parallelism of standard curves (filled circles and solid lines) in blue and gray whales for cortisol (A) and
corticosterone (B). For serial dilutions of pools of gray whale females (filled squares), gray whale males (filled
triangles), blue whale females (open squares), and blue whale males (open triangles), the y-axis represents the
percent binding relative to each dose, and the x-axis the relative dose of hormone concentration. The curves for
each set of serial dilutions are offset by an arbitrarily chosen constant (+2) on the x-axis for better visual
comparison.
MELICA ET AL.7
We used a generalized linear model (GLM) to investigate how much of the variability of cortisol and corticosterone
concentrations was explained by possible combinations of life history and sampling factors, i.e., sex (female, male) and age
class (calf [gray whales only], immature, adult, unknown), by area for blue whales (USWC and GoC), and season for gray
whales (Spring–Summer and Fall–Winter). The most parsimonious model was identified based on the lowest Akaike's
information criterion with correction for smallsamplesize(AICc).TheAkaikeweights(w
a
) were computed to quantify the
relative weight of evidence for different models. We used the “dredge”function (R package MuMIn; Bartòn, 2019)for
automated model selection. Identified models were then tested for significance using ANOVA or Student's t-test. To
investigate differences in cortisol and corticosterone among females of different reproductive states (pregnant, lactating,
immature, calf) we used ANOVA followed by a Tukey post hoc test and graphical visualization of the residuals.
3|RESULTS
3.1 |Methodological validation
For the cortisol methodological validations of blue and gray whale blubber, serial dilutions from both species and
both sexes showed parallelism to the standard curve (all p> .05; Table 2, Figure 1A); 70% binding was detected at
1:1 dilution for female blue whales and 2:1 for male blue whales. For gray whale pools, 50% binding fell at 1:2 for
females and 40% binding at a 1:4 dilution for males. The accuracy check showed a linear relationship between the
standard mass added and the mass recovered, with slopes ranging from 0.7 to 1.0 (Table 2).
For corticosterone, serial dilutions resulted in parallel displacement to the standard curve of the assay (p> .05
for all species-sex combinations; Table 2). While our analysis did not find differences in slopes to be statistically sig-
nificant (p=.4), dilutions from the blue whale male pool decreased more slowly than the standard curve (Figure 1B).
Because visual and statistical assessment of parallelism did not agree, we proceeded to measure and analyze cortico-
sterone concentrations only in female blue whales. The pooled sample from female blue whales showed binding at
43% at a 1:1 dilution, while the one for male blue whales bound at 85% at a 1:1 dilution. For gray whales, the female
pool exhibited 42% binding at 1:1 dilution, while the male pool showed 66% binding at 1:1 dilution. The slopes of
the linear regressions for the accuracy check ranged between 0.9 and 1.2 (Table 2).
TABLE 2 Methodological validation for each hormone-species combination for blue (B. musculus) and gray whales
(E. robustus). Parallelism was tested using a Student's t-test to compare the distribution of the slope of the standard
curve and serial dilutions for each hormone-pool combination. The lack of significance was evidence of parallelism.
Accuracy was statistically tested fitting a linear regression to the standard mass recovered against standard mass
added.
Female Male
Parallelism Accuracy Parallelism Accuracy
Balaenoptera musculus
Cortisol p=.9 y=0.7x+31.9
R
2
=0.99
p=.8 y=0.9x20.3
R
2
=0.99
Corticosterone p=.1 y=0.9x+22.2
R
2
=0.99
p=.4 y=0.9x21.9
R
2
=0.98
Eschrichtius robustus
Cortisol p=.9 y=1.0x+144.6
R
2
=0.95
p=.9 y=1.0x+16.1
R
2
=0.97
Corticosterone p=.9 y=1.0x15.7
R
2
=0.99
p=.6 y=1.2x144.2
R
2
=0.99
8MELICA ET AL.
The intraassay percent coefficient of variation (CV) was <10%, for all hormones. If any sample had a CV > 10%, it
was rediluted accordingly and reassayed. Interassay validation was determined using two biological controls made
from gray whale pools of extracts for each hormone. The interassay coefficients of variation for two internal controls
were 11.1% and 15.6% for cortisol and 16.9% and 12.2% for corticosterone. The Pearson correlation coefficient indi-
cated that the correlation between cortisol and corticosterone was not significant in blue whales (r=0.1, p=.4) but
was significant in gray whales (r=0.36; p=.001).
3.2 |Baseline hormone profiles and life history parameters
In blue whales, cortisol concentrations were measured in 74 samples and ranged from 0.13 to 2.48 ng/g
(M=0.64 ng/g). Because our EDA indicated two potential outliers (0.13 ng/g and 2.48 ng/g), we carried out any
subsequent analysis with and without these data points. However, since the results did not change, we opted to
include these data in the analysis reported below. We identified as the most parsimonious model the intercept-only
model (AICc =141.5, w
a
=0.3), followed by the second-best model including only sex (AICc =142.0, w
a
=0.3) and
TABLE 3 Generalized linear model selection for log-transformed cortisol and corticosterone concentrations in
response to life history parameters (sex and age class) and sampling location in blue whales. The most parsimonious
model was identified based on the lowest Akaike's information criterion with correction for small sample size (AICc),
and the Akaike weights (w
a
) were computed to assess the relative weight of evidence for different models. Based on
the dredge function (R Package “MuMIn”) for automated model selection, for each model the following parameters
are reported: degrees of freedom (df), log-likelihood (LogLik; which quantifies probabilities for each model on a log
scale), AICc, ΔAICc (the difference between the AICc of each model and the most parsimonious model), and Akaike
weights (w
a
).
Model for cortisol concentrations in blue whales
Full model: Log(CTS) SEX +LOCATION +AGE CLASS df LogLik AICc ΔAICc w
a
1268.68 141.5 0.00 0.32
SEX 3 67.83 142.0 0.47 0.25
LOCATION 3 68.53 143.4 1.88 0.12
SEX +LOCATION 4 67.58 143.8 2.22 0.11
SEX +AGE CLASS 5 66.78 144.4 2.91 0.07
AGE CLASS 4 67.97 144.5 2.99 0.07
SEX +LOCATION +AGE CLASS 6 66.53 146.3 4.77 0.03
LOCATION +AGE CLASS 5 67.87 146.6 5.09 0.03
Model for corticosterone concentrations in female blue whales
Full model:
Log(CTN) AGE.CLASS +REPRO.STATUS +LOCATION
df LogLik AICc ΔAICc w
a
AGE CLASS 4 43.58 96.4 0.00 0.75
AGE CLASS +LOCATION 5 43.53 99.0 2.58 0.21
REPRO.STATUS 8 41.23 103.6 7.20 0.02
AGE CLASS +REPRO.STATUS 8 41.23 103.6 7.20 0.02
REPRO.STATUS +LOCATION 9 41.21 107.1 10.68 0.00
AGE CLASS +REPRO.STATUS +LOCATION 9 41.21 107.1 10.68 0.00
1252.61 109.6 13.16 0.00
LOCATION 3 52.59 111.9 15.50 0.00
MELICA ET AL.9
the third-best model including only geographic location of sampling (AICc =143.4, w
a
=0.1) as explanatory factors
(Table 3). However, our analysis did not find mean cortisol concentration in females (n=40, M[range] =0.58 [0.16–
1.73] ng/g) to be significantly different from that in males (n=34, M=0.71 [0.13–2.48] ng/g; t=1.3, df =72,
p=.2; Table S3). As far as geographic sampling location, mean cortisol concentration from samples collected in the
GoC (n=62, M=0.64 [0.13–1.73] ng/g) was not significantly different from mean cortisol from samples collected
off the USWC (n=12, M=0.65 [0.20–2.48] ng/g; t=0.5, df =72, p=.6; Table S3). No significant difference was
found in cortisol concentrations between immature (n=7, M=0.83 [0.2–1.36] ng/g) and adult whales (n=43,
M=0.63 [0.2–2.48] ng/g; t=1.1 df =48, p=.3; Figure 2A and Table S3). The effects of sex and location and of
sex and age class variables were tested using ANOVA test, but neither of the two combinations resulted in statistical
significance (SEX+LOCATION: F=1.3, df =2, p=.3; SEX+AGE CLASS: F=1.0, df =2, p=.4; Table S3). In female
blue whales, no significant difference was found in cortisol concentrations among reproductive state (ANOVA:
F=0.7, df =5, p=.7; Figure 3A and Table S3).
We measured corticosterone concentrations only in female blue whales (n=37) and values ranged from 0.02 to
2.42 ng/g (M=0.52 ng/g). Our model selection identified age class as the explanatory factor for the most parsimoni-
ous model for corticosterone (AICc =96.4, w
a
=0.7). Models considering the age class and location (AICc =99.0,
FIGURE 2 Cortisol (A) and corticosterone (B) concentrations (ng/g) in biopsies of blue whales of known age
classes. No significant difference was found in cortisol concentrations between females (n=31) and males (n=19)
of known age classes, or among adult (n=43) or immature (n=7) groups. In females of known age class (n=31),
corticosterone (B) concentrations were significantly different between immature (n=6) and adult whales (n=25)
(t=4.5, df =29, p=.0001). Boxplots denote median (thick line), mean (dashed line), upper (75%) and lower (25%)
quartile (boxes), and largest and smallest value within 1.5 times interquartile range below 25% and above 75%
(whiskers). Values outside the whiskers (outliers) are shown as filled circles. Single data points are shown as gray
triangles.
10 MELICA ET AL.
w
a
=0.2) and only reproductive status (AICc =103.6, w
a
=0.02) were classified as the second and third best
(Table 3). We found corticosterone concentrations to differ significantly between immature (n=6, M=0.14 [0.02–
0.32] ng/g) and adult females (n=25, M=0.65 [0.08–2.42] ng/g; t=4.5, df =29, p=.0001; Figure 2B and
Table S3). Nevertheless, mean corticosterone was not found to be significantly different between females sampled
in the GoC (n=30, M=0.50 [0.02–2.42] ng/g) and off the USWC (n=7, M=0.61 [0.06–1.58] ng/g; t=0.2,
df =35, p=.9; Table S3). When we considered the interaction of age class and location, we found no significant dif-
ference in corticosterone concentrations (ANOVA: F=0.2, df =1, p=.6; Table S3). When we tested corticosterone
in response to reproductive status, we found corticosterone in immature whales to be significantly different from
lactating (n=8, M=0.53 [0.26–0.83] ng/g, p=.01), pregnant (n=3, M=0.64 [0.35–0.85] ng/g, p=.04), pres p
(n=6, M=0.70 [0.41–1.19] ng/g, p=.01) and adult unknown (n=3, M=1.37 [0.10–2.42] ng/g, p=.02) but not
pres np (n=6, M=0.40 [0.08–1.04] ng/g, p=.31) (Figure 3B, Table S3). Graphical visualization of these data during
EDA indicated two possible outliers, both in the adult unknown categories. The ANOVA test was then rerun without
these data points and excluding the adult-unknown whale group, and the results did not change (p< .001): immature
females had lower corticosterone concentrations than lactating (p=.001), pregnant (p=.008), and pres p (p< .001)
FIGURE 3 Cortisol (A) and corticosterone (B) concentrations (ng/g) in biopsies of noncalf female blue whales of
different reproductive status. No significant difference was found in cortisol concentrations between whales
categorized as immature (n=5), pregnant (n=3), lactating (n=8), pres p (n=6), pres np (n=5), adult-unknown
(n=4), and unknown (n=9) (F=0.7, df =5, p=.5). Conversely, corticosterone concentrations in immature whales
were significantly different from lactating (n=8, p=.01), pregnant (n=3, p=.04), pres p (n=6, p=.01), and adult
unknown (n =3, p=.02) but not pres np (n=6, p=0.31) individuals. Boxplots denote median (thick line), mean
(dashed line), upper (75%), and lower (25%) quartile (boxes) and largest and smallest value within 1.5 times
interquartile range below 25% and above 75% (whiskers). Values outside the whiskers (outliers) are shown as filled
circles. Single data points are shown as gray triangles.
MELICA ET AL.11
whales. Finally, we tested for difference in corticosterone concentrations in response to both reproductive status
and geographic sampling location but found no significant difference (ANOVA: F=1.5, df =4, p=.7; Table S3).
In gray whales, cortisol concentrations were measured in 90 samples and ranged between 0.07 and 3.95 ng/g
(M=0.76 ng/g). Our model selection function indicated the model considering the factors of season and sex as the
best model (AICc =191.9, w
a
=0.20), the model including only season (AICc =192.1, w
a
=0.17), and only sex
(AICc =192.2, w
a
=0.17), as the second and third best models, respectively (Table 4). A Student's t-test did not find
cortisol concentrations to be significantly different between samples collected in different seasons (Spring–Summer:
n=31, M=0.99 [0.07–3.95] ng/g; Fall–Winter: n=59, M=0.65 (0.09–2.00) ng/g; t=1.6, df =47.2, p=.11).
Weak, but not statistically significant differences, were found in cortisol concentrations between males (n=28,
M=0.57 [0.09–1.09] ng/g) and females (n=62, M=0.85 [0.07–3.95] ng/g; t=1.76, df =88, p=.08; Table S3)
and among calf (n=8, M=0.96 [0.25–2.51] ng/g), immature (n=11, M=0.46 [0.09–0.72] ng/g) and adult (n=47,
M=0.87 (0.07–3.95) ng/g; ANOVA: F=0.99, df =2, p=.08; Figure 4A, Table S3) groups. We did not find the
interaction of sex and season to be significant (ANOVA: F=0.04, df =1, p=.8; Table S3). Reproductive status was
found to be a significant explanatory factor for cortisol in noncalf females (ANOVA: F=4.7, df =6, p=.001;
Table S3), with lactating females (n=5, M=2.13 [0.79–3.95] ng/g) having higher concentrations than pregnant
TABLE 4 Generalized linear model selection for log-transformed cortisol and corticosterone concentrations in
response to life history parameters (sex and age class) and season in gray whales. The most parsimonious model was
identified based on the lowest Akaike's Information Criterion with correction for small sample size (AICc), and the
Akaike weights (w
a
) were computed to assess the relative weight of evidence for different models. Based on the
dredge function (R Package “MuMIn”) for automated model selection, for each model the following parameters are
reported: degrees of freedom (df), log-likelihood (LogLik; which quantifies probabilities for each model on a log
scale), AICc, ΔAICc (the difference between the AICc of each model and the most parsimonious model), and Akaike
weights (w
a
).
Model for cortisol concentrations in gray whales
Full model: Log(CTS) SEX +AGE CLASS +SEASON df LogLik AICc ΔAICc w
a
SEX +SEASON 4 91.70 191.9 0.00 0.20
SEASON 3 92.93 192.1 0.26 0.17
SEX 3 92.98 192.2 0.36 0.17
SEX +AGE CLASS 6 90.02 193.1 1.20 0.11
1294.54 193.2 1.35 0.10
SEASON +AGE CLASS 6 90.21 193.4 1.57 0.09
SEX +AGE CLASS +SEASON 7 89.10 193.6 1.70 0.08
AGE CLASS 5 91.45 193.6 1.76 0.08
Model for corticosterone concentrations in gray whales
Full model:
Log(CTN) SEX +AGE CLASS +SEASON
df LogLik AICc ΔAICc w
a
SEX +AGE CLASS 6 109.76 232.5 0.00 0.61
SEX +AGE CLASS +SEASON 7 109.19 233.6 1.19 0.34
SEX +SEASON 4 115.15 238.7 6.28 0.03
SEX 3 116.39 239.0 6.58 0.02
AGE CLASS 5 115.97 242.6 10.14 0.00
SEASON +AGE CLASS 6 115.56 244.1 11.60 0.00
12121.78 247.7 15.25 0.00
SEASON 3 120.84 247.9 15.48 0.00
12 MELICA ET AL.
(n=4, M=0.44 [0.07–1.01] ng/g; p=.001), pres p (n=10, M=0.61 [0.2–1.40] ng/g; p=.01), pres np (n=22,
M=0.71 [0.16–2.00] ng/g; p=.01), immature (n=6, M=0.56 [0.36–0.72] ng/g; p=.02), and unknown whales
(n=6, M=0.64 [0.2–1.1] ng/g; p=.02; Figure 5A). Additionally, adult whales of unknown reproductive status were
found to have significantly higher cortisol concentrations (n=5, M=1.3 [0.63–2.44] ng/g) than pregnant females
(p=.02; Figure 5A). Graphical visualization of these data during EDA indicated one possible outlier in the lactating
group (3.95 ng/g). The ANOVA test was then rerun without this data point and the results indicated that cortisol
concentrations are significantly higher in lactating females compared only to pregnant (p=.005) and pres p (p=.05)
females.
We measured corticosterone in 97 individual gray whales and concentrations ranged from 0.07 to 2.69 ng/g
(M=0.60 ng/g). EDA and graphical analysis identified one outlier (2.69 ng/g). However, results from ANOVA and
Student's t-test with and without this data point did not change, thus we included this value in our final analysis. Our
most parsimonious model explaining corticosterone variability included sex and age class as explanatory factors
(AICc =232.5, w
a
=0.61), followed by the second-best model considering the factors of sex, age class, and season
(AICc =233.6, w
a
=0.34) and the third best model considering the factors of season and sex (AICc =238.7,
w
a
=0.03) (Table 4). The follow up ANOVA test indicated only the interaction between season and sex to signifi-
cantly explain corticosterone concentrations (F=4.10, df =1, p=.05; Table S3), with males (n=20) sampled in
Fall–Winter showing significantly different corticosterone levels from females sampled in Fall–Winter (n=41,
p=.001) and in Spring–Summer (n=23, p=.003), but not from males sampled in Spring–Summer (n=13, p=.12;
FIGURE 4 Cortisol (A) and corticosterone (B) concentrations (ng/g) in biopsies of gray whales of known age
classes. No significant difference was found in cortisol concentrations between females (n=44) and males (n=23)
of known age classes, or among adult (n=47), immature (n=11), or calf (n=8) groups. Corticosterone (B)
concentrations were significantly different between males (n=33) and females (n=64) (t=3.34, df =95,
p=.001), and calves (n=8) showed significantly elevated concentrations compared to adult (n=49, p=.04) and
immature (n=11, p=0.003) gray whales. Boxplots denote median (thick line), mean (dashed line), upper (75%) and
lower (25%) quartile (boxes), and largest and smallest value within 1.5 times interquartile range below 25% and
above 75% (whiskers). Values outside the whiskers (outliers) are shown as filled circles. Single data points are shown
as gray triangles.
MELICA ET AL.13
Figure 6). Additionally, females (n=64, M=0.69 [0.11–2.69] ng/g) had significantly higher corticosterone concen-
trations than males (n=33, M=0.42 [0.07–2.18] ng/g; t=3.34, df =95 p=.001; Table S3), while the calves
(n=8, M=1.08 [0.51–2.13] ng/g) showed significantly elevated levels compared to adult (n=49, M=0.57 [0.09–
2.69] ng/g; p=.03) and immature (n=11, M=0.37 [0.07–0.86] ng/g; p=.003) gray whales (Figure 4B, Table S3).
In contrast, no significant difference was found in corticosterone concentrations among noncalf females of known
reproductive states (ANOVA: F=1.1, df =6, p=.35; Figure 5B and Table S3).
4|DISCUSSION
The goal of the present study was to provide a thorough methodological validation of glucocorticoid hormones (cor-
tisol and corticosterone) in blubber tissue of blue and gray whales from the ENP and to describe endocrine profiles
in relation to life history parameters such as age class, sex, and reproductive state, and location or season of sam-
pling. Our results suggest that detectability of the two hormones varies between species and sex, highlighting the
importance of carrying out thorough methodological validations. Most interestingly, our study presents baseline pro-
files of GC in relation to life history parameters. We found age class to be a significant factor explaining corticoste-
rone in female blue whales, with immature animals having lower concentrations than adults. In gray whales, our
findings indicate reproductive status to be a prominent factor explaining cortisol variability. Specifically, elevated
FIGURE 5 Cortisol (A) and corticosterone (B) concentrations (ng/g) in biopsies of noncalf female gray whales of
different reproductive statuses. Reproductive status was found to be a significant explanatory factor for cortisol in
females (ANOVA: F=3.9, df =7, p=.002), with lactating females (n=5) having higher concentrations than
pregnant (n=4, p=.001), pres p (n=10, p=.01), pres np (n=22, p=.02), immature (n=6, p=.05), and
unknown whales (n=5, p=.04). In contrast, no significant difference was found in corticosterone concentrations
among females of known reproductive states (ANOVA: F=1.3, df =6, p=.29). Boxplots denote median (thick line),
mean (dashed line), upper (75%) and lower (25%) quartile (boxes), and largest and smallest value within 1.5 times
interquartile range below 25% and above 75% (whiskers). Values outside the whiskers (outliers) are shown as filled
circles. Single data points are shown as gray triangles.
14 MELICA ET AL.
concentrations were found in lactating females suggesting that this reproductive group might be experiencing exces-
sive metabolic demands resulting in nutritional stress.
The first question addressed the methodological validation of cortisol and corticosterone in blubber tissue of
two whale species. While steroid endocrine patterns and actions are generally conserved in vertebrate groups
(Busch & Hayward, 2009), the use of new tissues requires thorough validation, for choosing the most accurate assay
and discerning the most appropriate compound (Atkinson et al., 2015). The methodological validations performed in
the present study tested for parallelism and accuracy using pooled blubber samples from female and male whales for
each species. Serial dilutions of each pool were tested for parallelism to the assay standard curve, both to confirm
that the assay's antibody can reliably bind the antigen throughout the range of the standard curve, and to ascertain
the proper dilutions for sample analysis. Then, for the accuracy test, each pool was spiked with the same aliquot of
each standard and the mass recovered was tested for linearity with the standard mass added, to determine if there
are any compounds in the matrix that may interfere with assay binding. All combinations of hormones and pools in
the current study satisfied the criteria for acceptable accuracy based on the slope of linear regressions ranging
between 0.7 and 1.2 (Table 2). While the accuracy is relatively easy to test statistically, requiring the fitting of a linear
regression, different approaches are used to confirm parallelism (Rodbard, 1974). In some studies, parallelism was
assessed visually (Atkinson et al., 2020; Cates et al., 2020; Valenzuela-Molina et al., 2018), whereas other publica-
tions included additional statistical tests (Burgess et al., 2017; Hunt et al., 2014,2017; Kellar et al., 2015; Lemos
et al., 2020; Melica et al., 2021a,2021b; Mingramm et al., 2020; Rolland et al., 2019). A relatively new method is
comparing the slopes of the linear portions of the standard curve and the serial dilutions from extracts using a F-test
FIGURE 6 Cortisol (A) and corticosterone concentrations (ng/g) in individual gray whales sampled in different
seasons. Fall–Winter (cortisol: n=59; corticosterone: n=61) groups samples collected between September and
November, Spring–Summer (cortisol: n=31, corticosterone: n=36) collected between March and August. Samples
in each season bin are divided by sex. The interaction of sex and season was found as a significant variable in
explaining corticosterone variability (ANOVA F=4.1, df =1, p=.05). Boxplots denote median (thick line), mean
(dashed line), upper (75%) and lower (25%) quartile (boxes), and largest and smallest value within 1.5 times
interquartile range below 25% and above 75% (whiskers). Values outside the whiskers (outliers) are shown as filled
circles. Single data points are shown as gray triangles.
MELICA ET AL.15
(Burgess et al., 2017; Hunt et al., 2014,2017; Lemos et al., 2020; Rolland et al., 2019). In the present study, a
Student's t-test was performed to statistically determine differences between the slope of each standard curve and
serial dilutions, with lack of significance supporting parallelism (Table 2). However, dilutions from one of the pools (i.
e., blue whale males) did not displace along the linear section of the standard curve, rather between 70% and 100%
binding. This likely reflects generally low hormone concentrations at the dilutions tested or in the overall pool. Paral-
lelism was also assessed by visual examination of parallelism of the two curves (serial dilutions and standard curve;
Figure 1). Conclusions from each approach were in agreement for seven out of eight hormone-pool combinations.
The methodological validation in the present study highlighted that concentrations of glucocorticoid hormones
are dependent on both sex and species, indicating that metabolic pathways for this class of hormones in blubber
may vary according to these factors. It also indicates that there may be multiple markers for physiological stress and
that robust comparisons should be carried out between specific hormones and for each sex. Blubber is a dynamic tis-
sue and lipids are mobilized during specific life stages (e.g., lactation) or as an energy source (Iverson &
Koopman, 2019). Knowledge on the extent of such mobilization and, most importantly, the metabolic transforma-
tions that stored compounds such as steroid hormones undergo when accumulated, remains limited
(Mingramm, 2018; Trana et al., 2015). Some of the extracts included in the pools used in this study were made from
stranded animals. Often the methodological validation through parallelism and accuracy test requires larger extract
aliquots (e.g., >1 ml) compared to the aliquots required for measuring sample concentrations (approximately 250 μl
per hormone assay). For most samples, the blubber weight was enough for only one extraction process, making the
final 1 ml extract the only volume available for all analysis. On the other hand, blubber samples from stranded ani-
mals were bigger in size and had plenty of tissue for multiple extractions. However, it is important to take into
account that the decomposition state of the carcasses may affect hormone concentrations. It is possible that due to
lipid leakage or other catabolic processes, the pools were biased towards higher or lower concentrations of hor-
mones (Mello et al., 2017; Mingramm, 2018; Trana et al., 2015). Further validation comparing pools between live
and stranded animals, and between females and males may provide a better understanding of decomposition effects
on these compounds. Similarly, the use of high-performance liquid chromatography (HPLC) may help to identify the
hormone metabolites present in each pool, and to understand how decomposition affects the chemistry of such
compounds (Atkinson et al., 2020).
Methodological validations alone have less meaning without physiological assessment in which endocrine pro-
files are tested in response to known factors such as age class, sex, reproductive state, or other potential stressors.
This assessment may be carried out by an adrenocorticotropic hormone (ACTH) challenge, where adrenocortical
activity and subsequent secretion of corticoid hormones is experimentally stimulated. These studies are feasible for
pinnipeds (Keogh & Atkinson, 2015; Mashburn & Atkinson, 2004) and dolphins (Champagne et al., 2017) under
human care, but not free ranging whales. For North Atlantic right whales, reduction in glucocorticoid concentrations
was measured in response to acute stimuli, such as decreased vessel traffic after the events of September 11, 2001
(Rolland et al. 2012). The present study provides insights on profiles of two corticoid hormones (cortisol and cortico-
sterone) in relation to life history parameters (e.g., age, sex, reproductive state) from two whale species. This is an
important step that provides baseline GC profiles, necessary to detect alteration of homeostasis as part of the
response to chronic stressors.
In blue whales, we found no significant differences in cortisol concentrations in response to age class and sex
(Figure 2A). Our analysis did not indicate reproductive state as an explanatory factor for cortisol concentrations in
females (Figure 3A). These results agree with what was previously found by Atkinson et al. (2020), where cortisol
appeared not to vary significantly between males and females, or among reproductive states in blubber biopsies of
blue whales collected from the GoC. The ENP population of blue whales is known to spend the summer season feed-
ing off the USWC and then migrating towards their wintering grounds in the tropical North Pacific (Calambokidis
et al., 2009; Mate et al., 1999; Reilly & Thayer, 1990). The GoC is the only studied reproductive area for this popula-
tion, with about 300 whales estimated to be present annually between January and April, many of which are females
sighted multiple times with calves (Busquets-Vass et al., 2017; Gendron, 2002; Paniagua-Mendoza et al., 2017;
16 MELICA ET AL.
Valenzuela-Molina et al., 2018). Unlike other seasonal breeders, blue whales are not fasting while in the GoC
(Gendron, 2002), which has been confirmed to be an important feeding area for the ENP population (Busquets-Vass
et al., 2021). Our study complemented Atkinson et al. (2020) by evaluating hormone concentrations in blubber biop-
sies collected from important reproductive and feeding grounds for ENP blue whales, the GoC and the USWC,
respectively. However, we found no significant difference in mean cortisol concentrations in samples from the two
areas. This may be due to the limited power of our analysis driven by uneven sample sizes between the two areas
(n=62 for GoC and n=12 for the USWC) and among the reproductive states within each area. For instance, only
one of the three confirmed pregnant females was sampled in the GoC, while one of the five immature females and
none of the lactating whales were sampled off the USWC. Long-term effort for biopsy collection is needed to further
investigate the possible synergistic effects of reproduction and migratory grounds on this species, specifically com-
paring areas with high and low anthropogenic activities.
Corticosterone may be a suitable metabolic biomarker for female blue whales, as it appears to be more metaboli-
cally active in this species (Valenzuela-Molina et al., 2018). Our model selection indicated both age class and repro-
ductive state to be significant factors in explaining corticosterone variability in this group. When analyzed separately,
both indicated that immature whales had significantly lower corticosterone concentrations than adult females, spe-
cifically from lactating, confirmed pregnant, pres p, and adult whales of unknown reproductive status (Figure 2B).
However, they were not found to be significantly different from pres np whales and whales of unknown age class.
Even if the sample size was not large enough (n=5) to evaluate any relationship with age, it is worth noting that the
oldest whales in the immature group (ID 2095 and 329, Table S1) had the lowest corticosterone concentrations for
their category (0.06 and 0.02 ng/g, respectively), while the three remaining animals ranged from 0.07 to 0.32 ng/g.
Our results did not corroborate previous evidence of reproductive status as the main driver of differences in cor-
ticosterone concentrations in adult females. Pregnant and pres p whales had high corticosterone means (M=0.67
and M=0.70 ng/g, respectively), but not statistically different from nonpregnant ones (e.g., lactating, pres np). Dif-
ferently, Valenzuela-Molina et al. (2018) found significantly elevated corticosterone in fecal samples of pregnant blue
whales sampled in the GoC. This is likely due to the different tissues (e.g., blubber and feces) used for the analysis,
reflecting different accumulation time frames. An alternative hypothesis could be that high GC concentrations in
whales sampled in the GoC were reflective of later pregnancy stages, whereas samples from our study were reflec-
tive of early gestation. GC concentrations have been shown to increase closer to parturition in other cetaceans
(Dalle Luche et al., 2020; Robeck et al., 2017; Steinman et al., 2016). The study from Dalle Luche et al. (2020)on
humpback whales concluded that high GC in blubber of females, deemed as pregnant, were due to approaching par-
turition as whales were sampled while moving closer to their breeding ground. No pregnancy was confirmed in that
study for example through the sighting of a calf. In our study, we did not find any statistical difference in hormone
concentrations in response to reproductive stage and geographic location, however we acknowledge that our sample
size within groups was small.
Similar to blue whales, neither age class, sex, nor season appeared to be significant factors in modulating cortisol
concentrations in gray whales (Figure 4A). Conversely, reproductive state was a significant factor influencing cortisol
concentrations in females, and lactating females showed significantly higher values compared to all noncalf reproduc-
tive groups. (Figure 5A). This was expected as low concentrations of blubber cortisol can be an indication of good
nutritional status and lactating females are commonly observed in poorer conditions than other females on the feed-
ing grounds (Akmajian et al., 2021; Bradford et al., 2012). As body condition studies show, our finding of elevated
mean cortisol concentrations in lactating females may indicate that this group is in a low nutritional state. Lactation
requires the highest metabolic cost for reproduction (Rice & Wolman, 1971; Swartz, 2018), especially since the milk
of gray whales contains the highest fat (53%) among cetaceans (Oftedal, 1997; Zenkovich, 1938) and lactating
females do not feed substantially while in their wintering ground. Our results differed from what has been previously
found in this species. While not significant, we found elevated cortisol concentrations in females compared to males,
opposite to the results of Lemos et al. (2020), who found fecal GC elevated in mature male gray whales, compared
to other demographic groups. It is possible that the difference in our study was driven by lactating females having
MELICA ET AL.17
the highest hormone concentrations. However, when we repeated our Student's t-test excluding this group, the lack
of difference between sexes was corroborated by a higher p-value (p=.2).
The present study found corticosterone concentrations in gray whales to be significantly elevated in female
whales and in calves of either sex (Figure 4B), and conversely, reproductive status did not appear to significantly
affect hormone concentrations (Figure 5B). As for blue whales, corticosterone in gray whales appeared to vary in
response to age, rather than to reproductive state. Similar evidence was found in other species: for instance, in males
from the Hawaii distinct population segment of humpback whales, corticosterone concentrations were found to be
initially high in calves, then decreasing and peaking again when animals were 15–25 years old (Cates et al., 2020).
Our analysis further suggests that corticosterone, but not cortisol, varied between seasons in gray whales. Gray
whales reach their feeding grounds in the ENP by early spring and their wintering grounds along the west coast of
the La Baja California Peninsula in late fall, with each direction of migration taking approximately 2 months (Rice &
Wolman, 1971; Swartz, 2018). We hypothesize that gray whale life history causes the seasonal differences in GC
concentrations. Differently from blue whales which forage also in their winter reproductive grounds (Busquets-Vass
et al., 2021), gray whales do not feed substantially while on their wintering grounds; therefore, when they first reach
their feeding areas, they are likely in a lessened nutritional state. The metabolic costs of fasting and migration may
explain the higher corticosterone concentrations found in gray whales sampled in the first phase of their feeding sea-
son (from March to June) compared to those sampled between September to November (Figure 6). Lower cortico-
sterone concentrations later in the feeding season may reflect improved body conditions observed in this species,
particularly in whales from the Pacific Coast Feeding Ggroup (PCFG; Akmajian et al., 2021). Additional studies
looking at GC concentrations and body conditions could help better understand the relationships between blubber
glucocorticoids and food abundance in blubber tissue. For instance, in male Australian humpback whales, blubber
cortisol was found to significantly decrease in the later months of the migration, possibly as a strategy to conserve
energy (Dalle Luche et al., 2021). In other vertebrates, the expected inverse relationship (i.e., high food abundance,
low GC) is generally met (Jenni-Eiermann et al., 2008; Wasser et al., 2004), or increased GC concentration is associ-
ated with increased energy demands in breeding or territorial behaviors (Landys et al., 2010). Gray whales may be
adapted to seasonal alternation of fasting and feeding, maintaining low and moderate GC levels until a certain
threshold of body condition is met (Busch & Hayward, 2009). Our study only included samples collected from the
feeding grounds, therefore a comparison between feeding vs. fasting individuals was not possible. Future studies
including samples collected on both the wintering and the feeding grounds are necessary to better understand the
variability of GC concentrations among different migratory phases.
Our findings show that the endocrine adrenal response may vary between species, resulting in different metabo-
lites (cortisol vs. corticosterone) accumulating in blubber. In gray whales, both cortisol and corticosterone metabolites
seem to accumulate in measurable amounts in blubber. Conversely, in female blue whales, corticosterone metabolite
appeared to be more active than cortisol. HPLC performed on fecal samples from female blue whales indicated
higher percentage of coelution with the corticosterone tracer, compared to cortisol (Valenzuela-Molina et al., 2018).
While we did not perform HPLC, we found corticosterone to be more immunoreactive in the female pool. The appli-
cation of HPLC techniques could inform on the chemical nature of the metabolites present in blubber, thus improv-
ing our knowledge on steroid metabolism in the blubber tissue. Furthermore, the influence of different life history
parameters on hormone concentrations suggests that it is important to evaluate multiple biomarkers in tandem to
gather a more complete assessment of the metabolic status of the animal. The use of novel approaches such as
LC–MS (Dalle Luche et al., 2020,2021; Hayden et al., 2017) that can simultaneously measure multiple hormones in
the same sample, may enhance the amount of information on potentially interacting hormones that can be gleaned
from a single small sample. A thorough calibration and analysis that combines and compare EIA, LC–MS, and HPLC
results, could further address questions on the consistency and comparability of results on hormone profiles across
laboratories.
Results from our study did not support previous research that measured glucocorticoid concentrations in fecal
sample from either species (Lemos et al., 2020; Valenzuela-Molina et al., 2018). While we have offered possible
18 MELICA ET AL.
biological explanations, it is worth mentioning that such incongruities might be due to the choice of tissue, with blub-
ber and feces reflecting different lag times from the stimulus. Steroids perfusion rates in large whales from blood to
blubber or feces remain unclear. Based on progesterone in serum and blubber of bowhead whales a lag-time of
weeks to months was suggested (Kellar et al., 2013); on the other hand, the time of excretion of steroid hormones
through feces is likely shorter, on the order of days, as it is dependent upon food transit times through the gastroin-
testinal system (Amaral, 2010; Horstmann, 2018). Multi-matrices comparisons of hormone concentrations may be of
aid in further clarifying perfusion rates and thus assessing the suitability of blubber for monitoring GC profiles.
Finally, we acknowledge that qualities of blubber tissue itself may lead to variability in hormone concentrations.
Because blubber is not a homogeneous tissue but instead has different degrees of vascularization and the uneven
presence of connective tissue (Iverson & Koopman, 2019), it is important to record and evaluate where on the
whale's body the biopsy was collected. While most biopsies are generally taken from the dorsal side of the animal in
the region posterior to the blowhole and anterior to the dorsal fin for blue whales or dorsal ridges for gray whales,
detailed information on the location on the whale's body where each sample was collected was not available for the
samples used in our study. Because we were not able to accurately test for hormone concentrations in relation to
the location of sampling on the whale's body, this remained an uncontrolled source of variability in our analysis.
Future studies may benefit from photographic and video documentation of biopsy collection to map the sampling
location more precisely.
The physiological response to stress is complex and endocrine pathways are just one part of it (Atkinson
et al., 2015). Nevertheless, our results shows that GC hormone profiles change in response to naturally occurring
“stressors”such as maturity, pregnancy, and lactation, and they support the validity and feasibility of using hormones
as biomarkers for metabolic status of blue and gray whales. When combined with existing photogrammetry or any
visual assessment of body condition (Akmajian et al., 2021; Christiansen et al., 2021) such data can provide a robust
and comprehensive tool to assess and monitor the metabolic health and well-being of these species.
AUTHOR CONTRIBUTIONS
Valentina Melica: Conceptualization; data curation; formal analysis; investigation; methodology; validation; visualiza-
tion; writing –original draft. Shannon Atkinson: Conceptualization; formal analysis; funding acquisition; investiga-
tion; methodology; resources; supervision; visualization; writing –review and editing. John Calambokidis:
Investigation; methodology; resources. Diane Gendron: Conceptualization; methodology; resources; supervision;
writing –review and editing. Aimee Lang: Investigation; methodology; resources; writing –review and editing.
Jonathan Scordino: Methodology; resources; writing –review and editing.
ACKNOWLEDGMENTS
Gray whale biopsy samples were collected under research permits (MMPA#16111-00 and 14245) issued to Cascadia
Research Collective (CRC) and to the NOAA Collaborative Large Whale Survey (CLaWS). We thank the members of
the research teams who collected the samples and thank the Makah Tribal Council for access to samples collected
by Makah Fisheries Management. Sighting history and genetic information were provided by CRC and SWFSC
Genetics Laboratory. For blue whale samples, sampling and the sighting history database from the Gulf of California
was funded by the Instituto Politecnico Nacional (to Dr. Diane Gendron) and was conducted under annual research
permits issued by the Direcci
on General de Vida Silvestre. We thank Luis Enriquez Paredes (Universidad Autonoma
de Baja California) for sexing all the GoC samples. We thank Kelly Robertson (NOAA SWFSC) for gathering and ship-
ping archived samples, Alie Perez and Annie Douglas (CRC) for providing sighting history information, and NOAA
MMHSRP, SWFSC, Oregon State University and the Marine Mammal Center for providing samples and information
from stranded blue and gray whales. We also thank Dr. Dorian Houser and the anonymous reviewers for their com-
ments and feedback that have greatly improved this manuscript.
Funding for this work was provided from the Office of Naval Research (Grant # N0014-14-1-0425), the
American Cetacean Society, the University of Alaska Fairbanks Resilience and Adaptation Program, Alaska INBRE,
MELICA ET AL.19
the University of Alaska Fairbanks Calvin Lensink Fellowship, University of Alaska Southeast and University of Alaska
Fairbanks. Research reported in this publication was partially supported by an Institutional Development Award
(IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant num-
ber P20GM103395 and by BLaST Equipment fund, supported by National Institutes of Health Common Fund,
through the Office of Strategic Coordination, Office of the NIH Director with the linked awards: RL5GM118990,
TL4 GM118992 and 1UL1GM118991. The content is solely the responsibility of the authors and does not necessar-
ily reflect the official views of the NIH. The authors have no conflicts of interest to declare.
ORCID
Valentina Melica https://orcid.org/0000-0002-1820-3418
Shannon Atkinson https://orcid.org/0000-0003-1536-9209
John Calambokidis https://orcid.org/0000-0002-5028-7172
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SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this
article.
How to cite this article: Melica, V., Atkinson, S., Calambokidis, J., Gendron, D., Lang, A., & Scordino, J. (2022).
Naturally stressed? Glucocorticoid profiles in blubber of blue and gray whales in response to life history
parameters. Marine Mammal Science,1–25. https://doi.org/10.1111/mms.12954
MELICA ET AL.25
