Variation in faecal testosterone levels in male gray whales on a foraging ground relative to maturity and timing

Abstract

Understanding wildlife reproductive seasonality is crucial for effective management and long-term monitoring of species. This study investigates the seasonal variability of testosterone in male Pacific Coast Feeding Group (PCFG) gray whales, using an eight-year dataset (2016–2023) of individual sightings, drone-based photogrammetry and endocrine analysis of faecal samples. We analyzed the relationship between faecal testosterone levels and total body length (TL), body condition (body area index, BAI), sexual maturity and day of the year using generalized additive mixed models. Our findings reveal a significant increase in faecal testosterone levels in mature males (MM) towards the end of the foraging season. This increase was not observed in JM, highlighting age-dependent development of sexual haracteristics. No significant relationship was found between testosterone levels and TL. Additionally, BAI was not significantly associated with testosterone levels. Our results suggest that the increasing testosterone levels in MM gray whales may indicate preparation for mating before the southbound migration. These findings provide valuable insights into the reproductive biology of PCFG gray whales and underscore the importance of non-invasive faecal sampling for studying reproductive seasonality in large whales. Our approach not only provides further insights into the seasonality of male reproduction for the PCFG gray whales but also offers tools to enhance the understanding of male reproduction in baleen whales broadly with non-invasive approaches.

Full text

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Volume 13 • 2025 10.1093/conphys/coae094
Research article
Variation in faecal testosterone levels in male
gray whales on a foraging ground relative to
maturity and timing
A. Fernandez Ajó1,*, C.L. Buck2,K.E. Hunt3,E. Pirotta4,L. New5,D. Dillon2,6,K.C. Bierlich1,
L. Hildebrand1, C.N. Bird1and L.G. Torres1
1Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Department of Fisheries, Wildlife and Conservation Sciences, Oregon
State University, 2030 SE Marine Science Dr, Newport, OR 97365, USA
2Department of Biological Sciences, Northern Arizona University, 617 S. Beaver St., Flagsta, AZ 86011, USA
3Smithsonian-Mason School of Conservation & Department of Biology, George Mason University, 1500 Remount Rd, Front Royal, VA 22630, USA
4Centre for Research into Ecological and Environmental Modelling, University of St Andrews, Buchanan Gardens, St Andrews, KY16 9LZ, UK
5Department of Mathematics, Computer Science and Statistics, Ursinus College, 601 E Main St, Collegeville, PA 19426, USA
6Present address: Wildlife and Ocean Health Program Anderson Cabot Center for Ocean LifeNew England Aquarium, New England Aquarium,
1 Central Wharf, Boston, MA 02110, USA
*Corresponding author. Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Department of Fisheries, Wildlife and
Conservation Sciences, Oregon State University, Newport, OR 97365, USA. Email: fernaale@oregonstate.edu
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Understanding wildlife reproductive seasonality is crucial for eective management and long-term monitoring of species.
This study investigates the seasonal variability of testosterone in male Pacic Coast Feeding Group (PCFG) gray whales, using
an eight-year dataset (2016–2023) of individual sightings, drone-based photogrammetry and endocrine analysis of faecal
samples. We analyzed the relationship between faecal testosterone levels and total body length (TL), body condition (body
area index, BAI), sexual maturity and day of the year using generalized additive mixed models. Our ndings reveal a signicant
increase in faecal testosterone levels in mature males (MM) towards the end of the foraging season. This increase was not
observed in JM, highlighting age-dependent development of sexual characteristics. No signicant relationship was found
between testosterone levels and TL. Additionally, BAI was not signicantly associated with testosterone levels. Our results
suggest that the increasing testosterone levels in MM gray whales mayindicate preparation for mating before the southbound
migration. These ndings provide valuable insights into the reproductive biology of PCFG gray whales and underscore the
importance of non-invasive faecal sampling for studying reproductive seasonality in large whales. Our approach not only
provides further insights into the seasonality of male reproduction for the PCFG gray whales but also oers tools to enhance
the understanding of male reproduction in baleen whales broadly with non-invasive approaches.
Lay Summary
Mature male gray whales show increased faecal testosterone towards the end of the foraging season, indicating preparation
for reproduction before the southbound migration.
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Research article Conservation Physiology • Volume 13 2025
Key words:Enzyme immunoassay, Gray whale, Male reproduction, Pcfg, Testosterone
Editor: Jodie Rummer
Received 16 July 2024; Revised 23 October 2024; Editorial Decision 18 December 2024; Accepted 23 December 2024
Cite as: Fernandez Ajó A, Buck CL, Hunt KE, Pirotta E, New L, Dillon D, Bierlich KC, Hildebrand L, Bird CN, Torres LG (2025) Variation
in faecal testosterone levels in male gray whales on a foraging ground relative to maturity and timing .Conserv Physiol 13(1): coae094;
doi:10.1093/conphys/coae094.
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Introduction
Understanding the spatiotemporal patterns of wildlife repro-
ductive cycles provides critical information for the develop-
ment of effective management strategies to mitigate human
disturbance on reproductive performance. Furthermore, dis-
cerning reproductive trends of a population is an essential
component of long-term monitoring of any species, as signifi-
cant deviations from the reproductive patterns of a healthy,
growing population may indicate broader changes in the
ecosystem (Eberhardt,2002). Such reproductive information
is particularly relevant for small populations with limited
ranges, given their increased vulnerability to environmental
changes and human disturbance (Willi et al., 2006).
Reproduction in seasonally breeding mammals is often
characterized by annual cycles in the reproductive hormones,
which are triggered by changes in photoperiod or other envi-
ronmental cues, as well as by endogenous circannual cycles
(Norris and Lopez, 2011). Testosterone, a steroid hormone
secreted from the testes, is one of the main androgens in mam-
mals. Elevated androgens are necessary to support testicular
maturation and spermatogenesis, and influence expression
of reproductive behaviours such as courtship, mating, male–
male competition (Buck and Barnes, 2003;Atkinson and
Yoshioka, 2007) and circannual timing (Richter et al., 2017).
In seasonally breeding vertebrates, testosterone concentra-
tions measured in plasma and faeces typically begin increasing
approximately 1–3 months before the breeding season, as
testicular recrudescence and spermatogenesis require several
weeks of preparation before functional sperm can be pro-
duced (Bronson, 1989). Testosterone then typically reaches
an annual peak during or just before the breeding season.
The occurrence and amplitude of this testosterone peak can
vary with age, with immature males (juvenile males, JM) often
exhibiting low testosterone concentrations even during the
breeding season. Once sexual maturity is reached, mature
males (MM), typically present dramatic increases in testos-
terone concentrations during the breeding season (Beehner
et al., 2009). In some species, the amplitude of seasonal
testosterone peaks can decline in older males, likely due to
reproductive senescence (Hunt et al., 2022). Testosterone
concentrations can also vary relative to body size and body
condition of an individual, which can impact the physiol-
ogy, behaviour, timing of sexual maturity and reproductive
attempts of mammals (Buck and Barnes, 1999;Hau et al.,
2017;Williams et al., 2017). This variability due to age
and body condition, along with additional factors such as
social cues, exposure to stressors and past experiences, con-
tributes to strong individual differences in testosterone pat-
terns (Sapolsky and Wingfield, 2003;Romero and Wingfield,
2016;Hunt et al., 2018).
Testosterone, like other steroid hormones (e.g. progestins,
estrogens, androgens, glucocorticoids, mineralocorticoids),
plays a key role in reproduction and stress responses in
mammals. As a result, hormone quantification is widely used
as a biomarker to monitor stress and reproductive status in
wildlife, including cetaceans (Goymann, 2012;Madliger et al.,
2018). Steroids are primarily cleared from the bloodstream
by the liver, excreted into the gut via bile ducts and modified
by gut microbiota, producing ’faecal hormone metabolites’
that are excreted in faeces, with some also excreted in urine
(Palme et al., 1996,2005;Goymann, 2012). Faecal hormone
metabolites can be measured using antibodies that bind to the
parent hormone and ideally show high cross-reactivity with
common mammalian faecal metabolites (Schwarzenberger
et al., 1996;Palme et al., 2005). The time between hormone
secretion and its excretion in faeces depends on species-
specific clearance rates and intestinal transit time, typically
ranging from 1 to 2 days in large mammals (Palme et al.,
1996;Wasser et al., 2000), so hormone assessments from
faecal samples provide an integrated measurement of the
endocrine state of the individual during that time (Lemos
et al., 2020). Decades of validation studies in terrestrial and
marine vertebrates confirm that faecal hormone metabolite
analysis is a reliable, non-invasive method for assessing
various steroid hormones (Wasser et al., 2010;Hunt et al.,
2013;Madliger et al., 2018;Palme, 2019).
Seasonal testosterone patterns are well-documented in
many male vertebrates, including terrestrial mammals,
pinnipeds, and odontocetes (Kellar et al., 2009;O’Brien et al.,
2017;Richard et al., 2017;Funasaka et al., 2018;Husak et al.,
2021). Less is known, however, about testosterone patterns in
mysticetes (baleen whales). Baleen whales typically undergo
annual migrations from high-latitude feeding grounds in
summer to subtropical breeding grounds in winter. Calving
generally occurs during specific portions of the winter months
(Lockyer, 1981), which suggests a regular alternation between
reproductively active and inactive states (Bronson, 1985).
A growing body of data indicates that annual cyclicity in
male testosterone occurs in some baleen whale species (Vu
et al., 2015;Hunt et al., 2018,2022;Cates et al., 2019;
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Conservation Physiology • Volume 13 2025 Research article
Melica et al., 2021), including some resident non-migratory
populations (e.g. fin whales, Balaenoptera physalus,Carone
et al., 2019). However, for most mysticete populations such
patterns remain poorly understood (Rolland et al., 2005;
Corkeron et al., 2017;Hunt et al., 2019), which limits man-
agement of spatially and temporally variable anthropogenic
activities that may impact reproductive effort in these pop-
ulations, such as whale watching, vessel traffic and elevated
ocean noise (Stewart et al., 2021,2022;Pirotta et al., 2023;
Hague and McWhinnie, 2024).
Gray whales (Eschrichtius robustus) range along the east-
ern and western coasts of the North Pacific Ocean, with two
recognized populations: the Western North Pacific (WNP)
and Eastern North Pacific (ENP). These whales migrate annu-
ally from high-latitude waters where food is abundant in
the summer, to lower latitude overwintering areas that are
less productive (Rice and Wolman, 1971;Swartz, 2018). The
reproductive cycle of gray whales is closely linked to this
migration. Pregnant females initiate the southward migration
first, followed by females that recently ovulated, then adult
males and finally immature whales (Swartz et al., 2023).
Non-pregnant females are theorized to ovulate in Novem-
ber and December, with mating likely occurring during this
southbound migration (Rice and Wolman, 1971). Some calves
are born during the southbound migration of the following
year, but most births occur on the winter grounds in late
December or early January (Jones, 1984). The northward
migration begins in late January, with newly pregnant females
leading, followed by adult males and juveniles (Rice and
Wolman, 1971), and finally, in April through May, lactating
females with their calves. During summer and fall, most
gray whales in the ENP return to their feeding grounds
in the Chukchi, Beaufort and the northwestern Bering Seas
(Pike, 1962;Swartz et al., 2023). However, a relatively small
subgroup of whales, known as the ’Pacific Coast Feeding
Group’ (PCFG), consisting of approximately 212 individuals
(Harris et al., 2022), shortens their migration and feeds along
the Pacific coast between the southeast Alaska and northern
California from May to November (Swartz et al., 2023).
The population trajectory of this group appears to be stable
(Harris et al., 2022;Barlow et al., 2024).
Information on gray whale reproductive biology is
primarily based on scientific whaling efforts off the central
California coast between 1959 and 1969, when over 116
adult female gray whales and 166 male gray whales of various
demographic units were killed (Rice and Wolman, 1971).
These data indicate that male and female gray whales attain
sexual maturity between 5 and 11 years of age, averaging
8 years for both sexes (Rice and Wolman, 1971;Bradford
et al., 2010). Females were estimated to gestate for ∼13 months,
wean calves 6–7 months postpartum (Rice and Wolman,
1971), and typically produce a single calf every two
years. Recent advancements in non-lethal collection and
analysis of non-plasma biological samples (e.g. blubber,
Melica et al., 2021; and faecal samples, Lemos et al., 2020;
Fernandez Ajó et al., 2023) have enabled a greater under-
standing of reproductive profiles, seasonality and variability
of reproductive hormones in gray whales. However, much of
this research has focused on progesterone (i.e. for pregnancy
diagnosis) and cortisol (i.e. for examination of impacts of
stressors (Lemos et al., 2022a,2022b;Pirotta et al., 2023),
while patterns of testosterone in males remain understudied.
In this study, we investigated the seasonal variability of
testosterone concentration in male PCFG gray whales, using
a consecutive eight-year dataset (2016–2023) of individual
sightings, drone-based photogrammetry and endocrine anal-
ysis of faecal samples collected at the PCFG summer foraging
ground off the central Oregon coast, USA.By integrating these
datasets and employing generalized additive mixed models
(GAMMs), we analyze the variability of faecal testosterone
in males in relation to total body length (TL), body condition
(body area index, BAI) and day of the year (DOY). We tested
the following hypotheses: testosterone concentration varies
in relation to (i) TL, which serves as a proxy for age and
maturity, (ii) body condition, which reflects nutritional needs
to support the energetic demands of reproduction, (iii) DOY,
which corresponds to the phenology of the reproductive cycle
in gray whales, and (iv) year that may reflect population level
changes such as overall prey abundance or broad disturbance.
Materials and Methods
Sample collection, eld methods and study
area
Our study was conducted from 2016 to 2023 during the
PCFG foraging seasons (late May to mid-October) along
the central Oregon coast, USA (off Newport, 44◦3813
N, 124◦0308 W). Using a 5.4 m, rigid-hulled, inflatable
boat, we located whales and photographed individuals for
identification purposes. When weather conditions allowed,
we also performed unoccupied aircraft systems (UAS) flights
for photogrammetry analysis (details below). We collected
faecal samples opportunistically with two dipnets (300-μm
nylon mesh), recording date, time and location of collection.
Faecal material was transferred to 500-ml plastic sterile jars
and stored on ice until returned to the lab (∼3–6 h), followed
by long-term storage at −20◦C, until the sample was freeze-
dried and assayed (see below). Testosterone assays were per-
formed within 12 months of sample collection. Each sample
was collected from an individual when no other whale was
in near proximity, the whale was travelling alone, or we have
confidently identified which individual produced the faecal
sample (i.e. no other whale was observed defecating while
approaching to collect the faecal sample). Each sample was
then linked to a specific whale using photo-identification
(see below). When we obtained multiple samples from the
same whale on a single day, we either combined them into
one jar before analysis to increase sample mass, or when
samples from the same individual were analyzed separately,
the sample with larger mass was used for statistical analyses.
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Research article Conservation Physiology • Volume 13 2025
This research was conducted under the NOAA/NMFS permits
#16011 and #21678 issued to John Calambokidis. Drone
operations were conducted by a Federal Aviation Authority
(FAA) certified private pilots with a Part107 licence or under
a Certificate of Authorization (2016-WSA-101-COA).
Photo-identication, age, sex and
reproductive maturity
We photo-identified individual whales by comparing pho-
tographs taken in the field with identification catalogues of
PCFG gray whales held by the Cascadia Research Collective
(Olympia, WA, USA) and the Marine Mammal Institute at
Oregon State University. Sex of each whale is determined by
sighting history (e.g. as female if previously observed with
a calf), genetic analyses from biopsies (Lang et al., 2014)
or genetic analyses of the faecal samples (detailed methods
in Lemos et al., 2020). Our dataset consisted of 353 faecal
samples including females (n= 200 from 44 unique individ-
uals), males (n= 121 from 34 unique individuals) and sam-
ples from animals of unknown sex (n= 29 from 17 unique
individuals). In this study, we included the observations from
males only. Age in years is calculated from the date of first
sighting, providing either a known age for those whales that
were first sighted as calves or a minimum age estimate for
non-calves. Non-calves were assumed to be at least one year
old at the time they were identified. Based on the mean age
of sexual maturity for the species (Rice and Wolman, 1971),
we classified individuals with a known age or minimum
age ≥8 years as mature, while individuals with a known
age <8 were considered juveniles. For individuals with a
minimum age less than 8 years, we determined sexual matu-
rity using their TL, which was derived from the individual
growth model described in Pirotta et al. (2024) that accounts
for photogrammetric uncertainty. Since PCFG reach shorter
asymptotic lengths than ENP whales (Bierlich et al., 2023), we
estimated the length at maturity of PCFG males using the ratio
between the length at maturity and the asymptotic length for
ENP males (see Supplementary Material). Individuals were
considered mature if at least 50% of the posterior predictive
distribution of their TL from Pirotta et al. (2024) was greater
than the calculated length at maturity for male PCFG gray
whales (i.e. >10.69 m).
Faecal hormone extractions and assays
Faecal samples contain metabolized products of parent testos-
terone hormone, i.e. faecal androgen metabolites (hereafter:
Tm) (Palme, 2005). Tm concentrations from 2016 to 2018 are
from Lemos et al. (2020), while Tm data from 2019 to 2023
samples are reported for the first time here. Identical methods
were used throughout 2016–2023. In brief, faecal samples
were filtered, desalinated and freeze-dried, followed by weigh-
ing of 0.02–0.05 g of dried, homogenized samples to the
nearest 0.001 g (Lemos et al., 2020). Samples <0.02 g were
excluded to avoid spurious inflated values associated with the
"small sample effect" (Hunt et al., 2006;Fernández Ajó et al.,
2022). We extracted hormones from aliquoted faecal samples
using 90% methanol (HPLC grade, Fisher Chemical™) and
quantified Tm using a commercial testosterone immunoassay
kit (Enzo Life Sciences #ADI-900-065) that has been validated
specifically for Tm of gray whale faecal samples (Lemos
et al., 2020), following the manufacturer’s protocols (https://
www.enzolifesciences.com). For quality assurance and control,
all samples and standards were run in duplicate. Samples
were re-analyzed if the optical density between duplicates
exceeded a coefficient of variation (CV) of 15%. If a sample’s
concentration fell outside the 15–98% percent-bound range,
we adjusted the dilution accordingly prior to reanalysis. One
sample was below the limit of detection of the assay (<LOD)
and was assigned a concentration of half the LOD reported
by the manufacturer. The inclusion or exclusion of this sample
did not affect the overall results. Final data are expressed as
ng of immunoreactive hormone per g of dried faeces.
UAS-based photogrammetry
We collected aerial videos from four different types of UAS as
described in Lemos et al. (2022a); the UAS and camera spec-
ifications are detailed in Supplementary Material, Table S1.
Videos were recorded at an altitude between 20 and 60 m
(with one >70 m). No behavioural responses from the whales
to the UAS were observed, i.e. no alterations in behaviour
such as changes in travel direction or interruption of foraging
activities. Individual snapshots of whales were extracted from
videos using VLC Media Player (Version 3.16 VideoLAN) and
then imported into MorphoMetriX (v1, v2; Torres and Bier-
lich, 2020) to measure TL and perpendicular body widths in
5% increments of TL. All measurements were processed using
CollatriX (Bird and Bierlich, 2020). The length and body
widths between 20 and 70% of TL were used to calculate BAI,
a length-standardized metric of body condition (Burnett et al.,
2019) with low uncertainty allowing for precise comparison
of body condition across time and demographic units (Bierlich
et al., 2021a). We accounted for photogrammetric uncertainty
associated with each UAS in TL and BAI measurements
following the methodology outlined by Pirotta et al. (2024)
and Bierlich et al. (2021a,2021b). To explore the relationship
between BAI, TL and variation in faecal testosterone concen-
trations of individual whales, we assessed BAI measurements
alongside faecal Tm data collected from the same whale on
the same day, or when UAS photogrammetry images from a
whale were not available from the same day as faecal sample
collection, we incorporated BAI values measured ±14 days of
faecal sample collection from the same individual, assuming
that gray whales are not expected to substantially change
their body condition size within a two-week period (Soledade
Lemos et al., 2020). TL was estimated at the yearly scale (see
Pirotta et al., 2024).
Data analysis
We included only males with complete observations, i.e. mor-
phometrics (BAI and TL) and hormone quantifications (Tm)
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Tab le 1: Summary statistics for mor phometric variables (TL = total body length, BAI = body area index) and faecal androgen
metabolite (Tm) concentrations of gray whales, grouped by demographic unit (JM =juveniles males or MM = mature males)
JM
Vari able Count Mean Median SD Min Max
TL (m) 21 9.62 9.69 0.27 9.10 10.16
BAI 13 25.36 25.18 2.78 21.80 29.57
Tm (ng/g) 24 11.62 6.85 13.31 0.02 50.89
MM
TL (m) 77 11.33 11.34 0.70 9.65 12.11
BAI 65 26.69 27.06 2.86 19.09 32.39
Tm (ng/g) 86 136.21 19.38 334.51 0.01 2107.77
in our data analyses. Twelve samples were excluded from
analysis for the following reasons: five samples were from
unknown whales; five samples were from whales of unknown
or unconfirmed sex; one sample had insufficient mass for the
extraction (<0.02 g); one sample had an abnormal appear-
ance (mucus consistency with bloody appearance). Faecal Tm
concentrations were log-transformed for analyses due to their
non-normal distribution. We estimated Tm baselines for each
age category (JM and MM) using an iterative process that
excludes all data points greater than the mean ±two standard
deviations (Mean ±2SD) until no observations exceed this
value, following methods from (Brown et al., 1988). To detect
outliers in testosterone levels, we calculated the interquartile
range (IQR) by subtracting the first quartile (Q1) from the
third quartile (Q3) of the testosterone data. We defined the
outlier thresholds as 1.5 times the IQR below Q1 and above
Q3. Any values falling below the lower threshold or above
the upper threshold were considered outliers. To explore the
differences in Tm concentrations and BAI between JM and
MM while also controlling for phenological patterns, we
segmented the data into three periods of equal duration, each
47 days long during our field seasons between the date of
earliest faecal Tm sample collection (May 22) and the latest
(October 11): early-season (May 22—July 08), mid-season
(July 09—August 25) and late-season (August 26—October
11). As part of exploratory data analysis, we investigated the
correlation between TL and age using the Pearson correlation
coefficient to determine whether TL could serve as a proxy
for age in this study. Additionally, to explore and visualize
the relationship between Tm and the potential explanatory
variables (TL, age, BAI and DOY), we plotted Tm against
each variable and faceted the plots by year (Supplementary
Material, Figure S1).
We fitted GAMMs to examine the variability in Tm con-
centrations as a function of TL, BAI and DOY.GAMMs allow
for non-linear relationships between the response and the
explanatory variables. Prior to fitting the model, to ensure
comparability and robustness in our analyses, we standard-
ized all continuous variables. This involved rescaling each
variable to have a mean of 0 and a standard deviation of 1,
preventing differences in measurement units from influencing
the results. GAMMs were fitted to the log-transformed Tm
data, using a Gaussian distribution with an identity link and a
restricted maximum likelihood method (REML), in the ‘mgcv’
package for R (R Core Team, 2023; version 1.8–40). Separate
thin-plate regression splines with shrinkage were fitted for
each demographic unit (JM and MM). Individual whale ID
and year of sample collection were included as random effects
to account for both individual and annual variability. The
tested models were checked using residual diagnostic plots.
Model selection involved comparing models with and without
random effects using the Akaike Information Criterion (AIC).
Once the decision was made regarding random effects, we
refitted the models using Maximum Likelihood (ML) estima-
tion. We conducted model selection for fixed effects based on
parsimony and goodness of fit using AIC, and finally refitted
the selected model using REML for inference.
Results
Exploratory data analyses
Our final data set, including only complete observations
from males, consisted of 78 faecal Tm quantifications paired
with morphometric data (BAI) on the same day or within
±14 days of faecal sample, and a TL measurement for the
same year of the sample collection which included 25 indi-
vidual males, comprising 13 observations from JM and 65
from MM; see Supplementary Material, Table S2. Nine indi-
viduals were sampled only once over the eight-year period,
but several were sampled twice or more in different years or
within a single season (Supplementary Material, Table S3).
Notably, one individual was sampled 12 times across the
study period (Supplementary Material, Table S3). The mean,
median, SD and range (maximum and minimum) for each
variable and by demographic group are reported in Table 1.
Tm concentrations of MM were highly variable, ranging from
a minimum of 0.01 ng/g to a maximum of 2107.77 ng/g
(Table 1,Fig. 1). We identified 12 outliers in testosterone
levels that exceeded 1.5 times the interquartile range above
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Research article Conservation Physiology • Volume 13 2025
Figure 1: Group mean comparisons for faecal androgen metabolite concentrations (Tm, ng of immunoreactive hormone per g dried faeces)
between JM and mature male (MM) gray whales between three periods of the sampling season: early- (May 21 to July 21), mid- (July 23 to
August 29) and late-season (August 31 to October 10th). The black horizontal lines represent the group median; the boxes enclose 50% of the
data; whiskers enclose the smallest and largest values within 1.5 times the interquartile range below and above the 25th and 75th percentiles,
respectively; individual values are shown as circles.
the third quartile. All these whales were MM, and the samples
were collected during the third period of the season (Table 3).
Overall and demographic group baseline levels of Tm are
shown in Table 2. The BAI range (maximum and minimum)
for the three season’s periods for Jm and MM is reported
in Table 4. The exploratory plots showing the relationship
between Tm and the explanatory variables are provided in
Supplementary Material, Figure S1. A strong positive correla-
tion is observed between TL and age (r= 0.90, t(76) = 16.08,
P<0.001; Fig. 2). Based on these results, further analyses
included TL as a proxy of age.
GAMMs
Model selection indicated that individual ID should be
retained as a random effect in the model, but not year. For
the fixed effects selection, all models were within ΔAIC
Tab le 2: Baseline concentration of faecal androgen metabolites (Tm)
of gray whales
Dataset Baseline (ng/g) SD (ng/g)
All 6.48 4.27
JM 5.51 4.06
MM 6.89 4.43
Top line shows the full dataset (all males); the other lines show data restricted to
demographic units (JM = juveniles males or MM = mature males). The Tm baselines
are estimated via an iterative process that excluded all data observations greater
than the mean ±2 SD until no points exceeded this maximum value (following
Brown et al., 1988).
<2 and thus can be considered indistinguishable from one
another. Therefore, we identified the best model based on
the parsimony principle. The most parsimonious model
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Conservation Physiology • Volume 13 2025 Research article
Figure 2: Correlation between age (years) and total length (TL; meters) of gray whales. TL is derived from individual growth curves described in
Pirotta et al. (2024), where uncertainty is represented by vertical dashed lines showing the posterior 95% credible intervals. Age type is
calculated from the date of rst sighting, providing either a minimum age estimate (min age) or a known age for whales rst sighted as calves.
Dark grey closed circles represent observations for whales with a minimum age (n= 65), with the grey line showing the linear regression t.
Light green closed circles represent observations for whales with a known age (n= 13), with the green line showing the linear regression t.
Both lines were generated using linear regression (method =’lm’).
from which to draw inference included only the individual-
level random effect and the interaction between DOY and
demographic unit (Table 5). Tm concentrations increased
with DOY in MM, but not in JM (Table 5,Fig. 1). This model
explained 64% of the deviance, with an adjusted R-squared
value of 0.54 (Fig. 3, and Supplementary Material, “Best
GAMM”).
Discussion
Our study reveals a significant association between the DOY
and increased faecal testosterone levels in MM gray whales
as the foraging season progresses, whereas no such pattern
was observed in juveniles. The mean concentration of faecal
testosterone in male gray whales is low in early season and
mid-season (May 22nd through August 25th) in both JM
and MM but is elevated only in MM in the later season,
although not for every MM individual. The finding that Tm
remains constant in JM but elevates in MM in the late season
confirms that development of male sexual characteristics is
age-dependent (Table 1 and Fig. 1). Previous studies based on
different sample types (i.e. blubber) and histological exami-
nation from whaling data are consistent with our findings.
For example, Melica et al. (2021) analyzed hormones from
blubber biopsies and found that testosterone levels in blubber
were elevated in adult male gray whales during the fall season
compared to the summer months. Rice and Wolman (1971)
examined testes from both immature and adult male gray
whales collected during scientific whaling efforts, observing
spermatogenesis in the seminiferous tubules of adult males.
They also noted wider diameters of the seminiferous tubules
and increased testes weights in adult gray whales during the
southbound migration. These histological and physiological
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Research article Conservation Physiology • Volume 13 2025
Figure 3: Estimated relationships between faecal testosterone and DOY by demographic unit (juvenile males, JM, on the left; mature males,
MM, on the right). Given the inclusion of an individual-level random eect in the nal GAM, testosterone concentrations were predicted using
the random intercept from one of the males in the sample, which was selected because it had an intercept close to the mean across individuals.
The shaded area represents approximate 95% condence intervals. DOY 160= May 27 and DOY 280 =October 11.
changes, regulated by increased testosterone levels, suggest
that the whales are preparing for breeding during the late for-
aging season, which may mark the onset of the mating season.
Earlier in the summer (i.e. farther from onset of the breed-
ing season), MM presented low testosterone levels, which are
comparable to those of JM whales (Fig. 1). Low androgens
in MM in non-breeding seasons have also been documented
from analyses of hormone concentrations in baleen of multi-
ple species (i.e. bowhead whale, Balaena mysticetus, North
Atlantic right whale, Eubalaena glacialis and blue whale,
Balaenoptera musculus;Hunt et al., 2018,2022). Notably,
some males presented extreme high values (Table 3), and all
these extreme outliers corresponded to MM in the late season.
Though the expected variation in whale testosterone levels
remains unclear, we believe that these values could actually
be within the normal ranges for actively reproducing MM.
Based on these results, we concluded that efforts to determine
the age class composition (JM vs. MM) in PCFG gray whales,
and likely other seasonally reproducing mammals, based on
values of testosterone is only advisable in those months of the
year when adult males are expected to have elevated levels
of testosterone (e.g. after August 25 in PCFG gray whales).
Furthermore, additional data, particularly involving repeated
sampling of males of known age as they transition from
juvenile to sexually mature, would likely provide more insight
into these questions.
While we hypothesized that Tm would increase with age,
and therefore with TL, the GAMM analyses did not find TL
to be a significant predictor of Tm. Age and TL do exhibit
a high positive correlation (Fig. 2), indicating that TL can
serve as a reliable proxy for age in male PCFG gray whales.
However, in gray whales the age-TL relationship tends to
plateau at an asymptotic length (approximately 11.88 m for
PCFG males, Bierlich et al., 2023), potentially obscuring the
relationship between testosterone levels and age, particularly
for older whales. Additionally, recent research indicates that
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Conservation Physiology • Volume 13 2025 Research article
Tab le 3: Samples identied with testosterone values exceeding 1.5 times the interquartile range of the third quartile
ID Year DOY DU Age BAI TL Tm
Er-0007 2019 284 MM 25∗26.85 11.25 2107.77
Er-0012 2018 249 MM 14 32.41 10.62 1478.04
Er-0369 2023 252 MM 25 29.12 12.15 1073.63
Er-0049 2022 269 MM 13 29.53 11.30 908.09
Er-0017 2023 280 MM 18 28.07 11.03 713.77
Er-0008 2017 267 MM 17 25.61 11.45 695.37
Er-0056 2023 252 MM 27 29.03 11.03 515.54
Er-0034 2016 242 MM 16 30.56 11.53 417.96
Er-0012 2018 220 MM 14 19.59 10.62 325.75
Er-0256 2019 280 MM 25 25.83 11.99 251.25
Er-0017 2021 247 MM 16 24.98 11.01 204.25
Er-0017 2022 242 MM 17 25.17 11.02 139.57
The table includes ID, which correspondsto each whale’s unique photo-identication catalogue code held by the Marine Mammal Institute at Oregon
State University; Year, representing the year of sample collection; DOY, the DOY of sample collection; DU, the demographic unit according to sexual
maturity (MM = mature males), Age, as length of sighting history in years, with the asterix indicating an individual of known age (rst observed as a
calf); BAI, the body area index; TL, the total body length expressed in meters; and Tm, the apparent concentration of testosterone in faecal samples,
expressed in ng/g of dry sample.
Tab le 4: BAI range for sexual maturity; MM = mature males and
JM = juvenile males by season period; early season- (May 21 to July 21),
mid- (July 23 to August 29) and late-season (August 31 to October
10th)
Period JM MM
Min Max Min Max
Early season 21.83 29.45 22.55 30.41
Mid season 22.38 25.90 19.08 30.73
Late season 23.61 29.57 24.27 32.39
PCFG gray whales are now shorter than they were histori-
cally (Pirotta et al., 2024), which might further complicate
the detection of trends in the relationship between age and
testosterone levels in this study. Future studies might benefit
from exploring the relationship between age and Tm with
additional methods of determining age, e.g. epigenetic age
determination from skin biopsies (Barratclough et al., 2024),
or, for stranded specimens, racemization of the eye lens (Hunt
et al., 2022).
Although body size and nutritional condition (i.e. BAI) are
known to influence testosterone levels (Tm) and reproductive
attempts in male mammals (Buck and Barnes, 1999;Hau
et al., 2017;Williams et al., 2017), our analysis did not indi-
cate any relationship between BAI and Tm concentrations.
However, we cannot rule out the possibility that the lack of
correlation is an artefact of our limited sampling range of BAI
during the late season. The minimum BAI value included in
this analysis was 19.09 for a whale sampled in mid-season,
while most early-season whales have a BAI ranging from 21.8
to 30.4 (Table 4). These early-season whales are expected
to be returning from the wintering grounds, where food is
scarce, and they are expected to be nutritionally limited. In
our study, all the MM whales in the late season had a BAI
greater than 29.57, close to the upper limit of the BAI range
for whales in the early season, which might indicate that no
MM in our study were in poor body condition towards the
end of the sampling season. However, not all MM presented
elevated Tm in the late season, raising questions about which
other factors might influence reproductive attempts in male
PCFG whales.
Identifying the onset of the reproductive season in male
whales through testosterone levels assessment can provide
insights into the timing and location of the conceptive season,
which is relevant for management efforts. Our study found a
positive correlation between DOY and Tm. The observed rise
of Tm in the MM over time likely indicates preparation for
mating while still at the foraging grounds, as the southbound
migration nears. Notably, some MM showed a sharp increase
in Tm levels in the late season, after approximately DOY
217 (∼August 5th; Supplementary Material, Figure S.3 and
Table 3). These findings underscore the importance of the
PCFG range not only as a foraging area but also a significant
site for male reproductive preparation, particularly in the late
season.
Moreover, the findings presented here, along with existing
data on the migration phenology and genetic population
structure of gray whales, suggest that the use of different
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Research article Conservation Physiology • Volume 13 2025
Tab le 5: Performance metrics of GAMMs of faecal androgen metabolites (Tm) of gray whales in relation to demographic unit (DU, i.e. juvenile
males vs. mature males), body condition (BAI), TL and DOY
Model REML Response Random eect Predictor dev.exp AIC δAIC
REML.1 Tm ∼ID +Yea r DU +s(BAI) +s( TL) +s(DOY), by = DU 64.3 275.36 3.46
REML.2 Tm ∼ID DU +s(BAI) +s(TL) +s(DOY), by = DU 64.3 271.90 0.00
Model ML Response Random eect Predictor dev.exp AIC δAIC
ML.2 Tm ∼ID DU +s(BAI) +s(TL) +s(DOY), by = DU 64.3 272.24 0.00
ML.3 Tm ∼ID DU +s(TL) +s(DOY), by = DU 62.6 272.38 0.14
ML.4 Tm ∼ID DU +s(DOY), by = DU 62.6 272.38 0.14
Models tted with REML for the random eects selection are shown on top, and the models tted with ML for the xed eects selection on the bottom. Performance
metrics include Akaike Information Criterion (AIC) and percent deviance explained (dev.exp). The preferred model (simplest model within 2 units of the lowest AIC) is
highlighted (ML.4).
foraging grounds across the North Pacific during the sum-
mer may influence reproductive segregation between pop-
ulations. For instance, satellite tagging data indicates that
whales migrating from the WNP to the wintering lagoons
in Baja California, Mexico, remain far to the west during
the theorized peak conception period in late November to
early December (Rice and Wolman, 1971;Mate et al., 2015),
making it unlikely for them to mate with ENP or PCFG
whales. Genetic studies have revealed significant mitochon-
drial and nuclear genetic differentiation between WNP and
ENP gray whales, implying minimal interbreeding and sug-
gesting assortative mating based on location and migratory
timing (Lang et al., 2022). In contrast, no significant nuclear
DNA differences were found between PCFG and other ENP
whales, indicating at least some degree of reproductive mixing
between these groups (Lang et al., 2014;D’Intino et al., 2023).
However, although the exact migration patterns of PCFG
whales remain unknown, evidence of associations among
individual PCFG whales during their southbound migration
has been observed (Calambokidis and Perez, 2017). Thus, our
findings, which indicate that MM are reproductively prepared
while in PCFG foraging grounds late in the season, and conse-
quently during their southbound migration when they inter-
act with mature females, add to the evidence that the PCFG
may be somewhat reproductively isolated from ENP whales.
While current data cannot conclusively prove this reproduc-
tive segregation, further research is needed to explore this
hypothesis, given its important management and conservation
implications.
In conclusion, our findings reveal a significant association
between the DOY and increased Tm levels in MM, while no
such pattern was observed in JM. These results offer insights
into the timing for the onset of the reproductive season for
PCFG gray whales, highlight the foraging grounds of central
Oregon as potentially important areas for males preparing
for reproduction, particularly towards the end of the summer,
and demonstrate the value of non-invasive faecal sampling to
enhance the understanding of population dynamics within the
PCFG gray whale subgroup.
Acknowledgements
We thank the Cetacean Conservation and Genomics Labo-
ratory for assistance with identifying the sex of individuals,
and the PCFG Consortium, NOAA/SWFSC and Cascadia
Research Collective for their contribution to, and curation
of, the photo-identification catalogue and genetic sex data.
We thank Ines Hildebrand for assistance with whale photo
identification, Kate Colson and Todd Chandler for assistance
with fieldwork, and Leila Lemos, Amy Olsen and Shawn
Larson (Seattle Aquarium) for the initial validations of the
testosterone assays and faecal hormone extraction protocols.
Author Contributions
Fernandez Ajó, A.: concept and design, led and participated
in fieldwork, hormone assays, statistical analyses, helped with
photogrammetry measurements and drafted the manuscript.
Hunt K.E., Buck C.L: hormone assays, interpretation of faecal
hormone data, critical revision of the manuscript and fund-
ing acquisition. Pirotta E. and New L.: statistical analyses,
interpretation of data and critical revision of the manuscript.
Dillon, D: hormone assays, laboratory support, manuscript
review. Hildebrand, L.: participated in fieldwork, manage-
ment of sighting history data, photo-identification and critical
revision of the manuscript. Bierlich, K.C. and Bird, C.N.:
participated in fieldwork, drone photogrammetry analyses
and critical revision of the manuscript. Torres, L.G.: funding
acquisition, program management, led and participated in
fieldwork, concept development, provided critical interpre-
tation of the data and drafted the manuscript. All authors
approved the final version and agreed to be accountable for
all aspects of the work in ensuring that questions related to the
accuracy or integrity of any part of the work are appropriately
investigated and resolved.
Conicts of interest
We have no competing interests.
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Conservation Physiology • Volume 13 2025 Research article
Funding
This project was supported by the NOAA National Marine
Fisheries Service Office of Science and Technology, the Office
of Naval Research Marine Mammals and Biology program
[grant number: N00014-20-1-2760], and the Oregon State
University Marine Mammal Institute, and Oregon Sea Grant
[grant number: RECO-40-PD].
Data Availability
The data underlying this article are available in figshare digi-
tal repository, at https://figshare.com/s/acac25d8ab46cf6b7073.
Supplementary material
Supplementary Material is available at Conservation Physiol-
ogy online.
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