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Estimating blue whale skin isotopic incorporation rates and baleen growth rates: Implications for assessing diet and movement patterns in mysticetes

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Stable isotope analysis in mysticete skin and baleen plates has been repeatedly used to assess diet and movement patterns. Accurate interpretation of isotope data depends on understanding isotopic incorporation rates for metabolically active tissues and growth rates for metabolically inert tissues. The aim of this research was to estimate isotopic incorporation rates in blue whale skin and baleen growth rates by using natural gradients in baseline isotope values between oceanic regions. Nitrogen (δ 15 N) and carbon (δ 13 C) isotope values of blue whale skin and potential prey were analyzed from three foraging zones (Gulf of Cali-fornia, California Current System, and Costa Rica Dome) in the northeast Pacific from 1996–2015. We also measured δ 15 N and δ 13 C values along the lengths of baleen plates collected from six blue whales stranded in the 1980s and 2000s. Skin was separated into three strata: basale, externum, and sloughed skin. A mean (±SD) skin isotopic incorporation rate of 163±91 days was estimated by fitting a generalized additive model of the seasonal trend in δ 15 N values of skin strata collected in the Gulf of California and the California Current System. A mean (±SD) baleen growth rate of 15.5±2.2 cm y-1 was estimated by using seasonal oscillations in δ 15 N values from three whales. These oscillations also showed that individual whales have a high fidelity to distinct foraging zones in the northeast Pacific across years. The absence of oscillations in δ 15 N values of baleen sub-samples from three male whales suggests these individuals remained within a specific zone for several years prior to death. δ 13 C values of both whale tissues (skin and baleen) and potential prey were not distinct among foraging zones. Our results highlight the importance of considering tissue isotopic incorporation and growth rates when studying migratory mysticetes and provide new insights into the individual movement strategies of blue whales.
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RESEARCH ARTICLE
Estimating blue whale skin isotopic
incorporation rates and baleen growth rates:
Implications for assessing diet and movement
patterns in mysticetes
Geraldine Busquets-Vass
1
, Seth D. Newsome
2
, John Calambokidis
3
, Gabriela Serra-
Valente
4
, Jeff K. Jacobsen
5
, Sergio Aguı
´ñiga-Garcı
´a
1
, Diane Gendron
1
*
1Instituto Polite
´cnico Nacional, Centro Interdisciplinario de Ciencias Marinas, La Paz, Baja California Sur,
Mexico, 2Biology Department, University of New Mexico, Albuquerque, New Mexico, United States of
America, 3Cascadia Research Collective, Olympia, Washington, United States of America, 4Marine
Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service,
National Oceanic and Atmospheric Administration, La Jolla, California, United States of America,
5Vertebrate Museum, Department of Biological Sciences, Humboldt State University, Arcata, California,
United States of America
*dianegendroncicimar@gmail.com
Abstract
Stable isotope analysis in mysticete skin and baleen plates has been repeatedly used to
assess diet and movement patterns. Accurate interpretation of isotope data depends on
understanding isotopic incorporation rates for metabolically active tissues and growth rates
for metabolically inert tissues. The aim of this research was to estimate isotopic incorpo-
ration rates in blue whale skin and baleen growth rates by using natural gradients in baseline
isotope values between oceanic regions. Nitrogen (δ
15
N) and carbon (δ
13
C) isotope values
of blue whale skin and potential prey were analyzed from three foraging zones (Gulf of Cali-
fornia, California Current System, and Costa Rica Dome) in the northeast Pacific from
1996–2015. We also measured δ
15
N and δ
13
C values along the lengths of baleen plates col-
lected from six blue whales stranded in the 1980s and 2000s. Skin was separated into three
strata: basale, externum, and sloughed skin. A mean (±SD) skin isotopic incorporation rate
of 163±91 days was estimated by fitting a generalized additive model of the seasonal trend
in δ
15
N values of skin strata collected in the Gulf of California and the California Current Sys-
tem. A mean (±SD) baleen growth rate of 15.5±2.2 cm y
-1
was estimated by using seasonal
oscillations in δ
15
N values from three whales. These oscillations also showed that individual
whales have a high fidelity to distinct foraging zones in the northeast Pacific across years.
The absence of oscillations in δ
15
N values of baleen sub-samples from three male whales
suggests these individuals remained within a specific zone for several years prior to death.
δ
13
C values of both whale tissues (skin and baleen) and potential prey were not distinct
among foraging zones. Our results highlight the importance of considering tissue isotopic
incorporation and growth rates when studying migratory mysticetes and provide new
insights into the individual movement strategies of blue whales.
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 1 / 25
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OPEN ACCESS
Citation: Busquets-Vass G, Newsome SD,
Calambokidis J, Serra-Valente G, Jacobsen JK,
Aguı
´ñiga-Garcı
´a S, et al. (2017) Estimating blue
whale skin isotopic incorporation rates and baleen
growth rates: Implications for assessing diet and
movement patterns in mysticetes. PLoS ONE 12
(5): e0177880. https://doi.org/10.1371/journal.
pone.0177880
Editor: Mark S. Boyce, University of Alberta,
CANADA
Received: January 16, 2017
Accepted: May 4, 2017
Published: May 31, 2017
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The authors received financial support
from Instituto Polite
´cnico Nacional:SIP: 20130223;
20140495; 20150115; 20160496 (http://www.ipn.
mx/Paginas/inicio.aspx) and the University of New
Mexico Center for Stable Isotopes (Albuquerque,
NM; http://csi.unm.edu/). GBV received a PhD
Introduction
The blue whale (Balenoptera musculus) in the northeast Pacific is an endangered migratory
mysticete [1]. In summer and fall, blue whales are distributed as far north as the Gulf of Alaska
[2,3], but the highest aggregations have been observed off southern California [4]. By mid-fall
(~October), they usually migrate south to the west coast of the Baja California Peninsula [2,4
8] and then continue migrating to one of two regions that are recognized as overwintering
zones: a calving ground in the Gulf of California [2,912], or the Costa Rica Dome in the east-
ern tropical Pacific [2,7,8]. Calves have also been observed in the Costa Rica Dome, but little is
known about the population dynamics in this zone [13].
Blue whales forage throughout their annual migratory cycle mainly on aggregations of krill
(Order: Euphausiacea) [1418] and occasionally on other crustaceans (i.e. copepods, Calanus
spp.) [16,19] or small fish (i.e. lanternfish: Family Myctophidae) [20]. The observation that
blue whales forage year-round suggests this species has high energetic demands relative to
other migratory mysticetes like the humpback whale (Megaptera novaeangliae) and the gray
whale (Eschrichtius robustus), that typically fast for months during their breeding season in
low latitudes [21,22]. The general migratory patterns of blue whales in the northeast Pacific
have been described [2,3,5,7,10,11,23], specifically for the California feeding population [3];
however, there are still many gaps in our understanding of their feeding ecology and plasticity
in individual movement patterns across multi-year timescales.
Stable isotope analysis (SIA) is a proven tool for studying the diet and movement patterns
of marine mammals [24]. The isotopic composition of animal tissues are influenced by diet
[2527] and the isotopic composition of the base of the food web, which can vary in time and
space within and among oceanic ecosystems [24,2831]. Physiological processes produce pre-
dictable offsets in isotope values between consumers and their diet, which is often called tro-
phic discrimination [24,32]. In general, consumer tissues have carbon (δ
13
C) and nitrogen
(δ
15
N) isotope values that are 0.5–3.0‰ and 2–5‰ higher than that of their prey respectively,
depending on the species, diet quality, and type of tissue analyzed [2426,33,34].
Tissues assimilate dietary inputs at different temporal scales. Most metabolically active
tissues reflect recent dietary inputs, consumed within days to months (e.g. plasma, muscle),
depending on their isotopic incorporation rates that typically scale with body mass such that
larger animals have slower incorporation rates [35]. In contrast, metabolically inert tissues
(e.g. whiskers, nails) deposit at distinct intervals, and each deposition of tissue retains the isoto-
pic composition of dietary sources incorporated when anabolized, thus reflecting dietary input
over several years depending on tissue growth rate [24,36]. Consequently, to make accurate
inferences on ecological aspects of free ranging animals by using SIA it is essential to have infor-
mation on the isotopic incorporation rate of metabolically active tissues and the growth rates of
metabolically inert tissues; otherwise, the interpretation of the data can be highly misleading.
SIA of mysticete skin and baleen plates has frequently been used to infer diet and seasonal
movements of this difficult to study group of cetaceans [3744]. Cetacean skin (epidermis) is a
metabolically active tissue, subdivided into cellular strata: the stratum basale, the stratum spi-
nosum, and the stratum externum [45,46]. Skin growth begins in the stratum basale a single
row of cells that replicate actively. Newly formed cells constantly displace the older cells up-
ward, first to the stratum spinosum, and subsequently to the stratum externum, the outermost
layer of skin. Finally, the stratum externum is sloughed off to the environment as sloughed
skin [45]. Variation in the isotopic composition among these strata has never been described
for any cetacean species. The isotopic incorporation rates of cetacean skin have only been mea-
sured in controlled “diet switch” feeding experiments on captive odontocetes [47,48]. These
studies used exponential fit models because theoretically, after diet switch, changes in the
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 2 / 25
grant from the Consejo Nacional de Ciencia y
Tecnologı
´a (CONACyT; http://www.conacyt.mx/), a
research grant from Cetacean Society International
(http://www.csiwhalesalive.org/index.php)
specifically for processing the baleen plates, and a
grant from American Cetacean Society - Monterey
Bay (http://acsonline.org/) to process skin
samples. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
isotopic composition of tissues will follow an exponential curve over time [4952]. Estimates
of the isotopic incorporation for carbon (δ
13
C) and nitrogen (δ
15
N) in odontocete skin slightly
differ; incorporation for δ
13
C is 2 to 3 months, while that for δ
15
N is longer and more variable
at 2 to 6 months [47,48]. The increasing use of SIA in mysticetes to characterize diet and move-
ment patterns requires the development of a method to estimate skin isotopic incorporation
rates for free-ranging populations.
Baleen consists of a series of keratin plates inserted in the upper gum of mysticetes that
functions as a filter-feeding apparatus [53]. In contrast to skin, baleen is a metabolically inert
tissue that grows continuously from the gums and abrades at the terminal end [54]. The oscil-
lations in isotope values along the length of baleen plates can be used to estimate growth rates
and generate multi-year records of individual movement strategies, habitat use, and diet
[38,4244,5557]. Baleen growth rates have been estimated in several species of mysticetes
[37,4244,55,57], but currently there are no published estimates for blue whale baleen.
Potential prey of blue whales in their distinct summer–fall (California Current System: west
coast of U.S. and Baja California Peninsula; Fig 1) and winter–spring (Gulf of California and
Costa Rica Dome; Fig 1) foraging zones have contrasting isotope values [5864] due to differ-
ences in oceanographic and biogeochemical processes that influence baseline isotope values in
these zones [31,58,62,65]. Specifically, δ
15
N values of prey (e.g. krill) are higher in the Gulf of
California, intermediate in the California Current System, and lowest in the Costa Rica Dome
[5864]. We assumed that blue whale skin strata (stratum basale, stratum externum, and
sloughed skin) and baleen plates record these isotopic differences. Then, we evaluated if the
seasonal patterns of tissue isotope values could be used to estimate the isotopic incorporation
rates and baleen growth rates of blue whale skin and baleen, respectively. We also assessed if
carbon isotopes were useful for examining blue whale diet and movement patterns in the
northeast Pacific, however, we expected little variation in δ
13
C values of prey among foraging
zones based on previous studies [5864]. Overall, our results highlight the importance of care-
fully considering the temporal window represented by metabolically active and inert tissues
when studying migratory mysticetes.
Materials and methods
Ethic statement
All whale tissues used in this study were collected and processed under special permits issued
by the Secretarı
´a de Medio Ambiente y Recursos Naturales (SEMARNAT) in Me
´xico (codes:
180796-213-03, 071197–213–03, DOO 750-00444/99, DOO.0-0095, DOO 02.-8318, SGPA/
DGVS-7000, 00624, 01641, 00560, 12057, 08021, 00506, 08796, 09760, 10646, 00251, 00807,
05036, 01110; 00987; CITES export permit: MX 71395), and the National Oceanic and Atmo-
spheric Administration–National Marine Fisheries Service (NOAA/NMFS) (NMFS MMPA/
Research permits codes: NMFS-873; 1026; 774–1427; 774–1714; 14097; 16111; CITES import
permit: 14US774223/9) in the United States of America. All tissues were collected using non-
lethal sampling techniques.
Sample collection
Blue whale skin biopsies (n = 255) and sloughed skin (n = 174) were selected from tissue banks
at NOAA Southwest Fisheries Science Center (NOAA-SWFSC), Cascadia Research Collective
(CRC), and Centro Interdisciplinario de Ciencias Marinas-Instituto Politecnico Nacional
(CICIMAR-IPN). These samples were collected from 1996–2015 in the Gulf of California
(GC) (Jan–Apr; n = 115 biopsies, n = 81 sloughed skin; Fig 1), California Current System
(CCS) (Jun–Dec; n = 129 biopsies, n = 93 sloughed skin; Fig 1) and the Costa Rica Dome
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 3 / 25
(CRD) (Oct–Nov; n = 11 biopsies; Fig 1). Skin samples were collected during marine mammal
surveys conducted by NOAA-SWFSC, CRC, and CICIMAR-IPN. Skin biopsies were collected
via dart sampling methods [66], and sloughed skin was directly collected from the water with a
net [67] or from suction cups of satellite-tagged whales.
Krill (n = 34) and lanternfish (n = 7) samples were opportunistically collected during
marine mammal surveys conducted by CICIMAR-IPN within the GC (2005–2015). Krill sam-
ples were collected by towing a conical net (diameter 50 cm., mesh size 200 μm) when blue
Fig 1. Northeast Pacific sampling zones. Dots represent blue whale skin samples collected in the California Current System (CCS), Gulf of California
(GC) and Costa Rica Dome (CRD). Dots with a cross represent blue whale baleen plates collected from dead stranded whales.
https://doi.org/10.1371/journal.pone.0177880.g001
Blue whale skin isotopic incorporation rates and baleen growth rates
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whales were observed feeding near the surface. Lanternfish samples were collected with a fish-
ing net (mesh size 5 mm), when aggregations were found near the surface. Prey samples were
preserved frozen in liquid nitrogen (-195˚C). The assignment of lanternfish to the Family
Myctophidae and classification of krill species was made using identification guides [68,69];
Nyctiphanes simplex was the only krill species present in all samples.
To assess the isotope variability between blue whale skin strata it was necessary to identify
tissue structure. Histological preparations of five skin biopsies were stained with hematoxylin
& eosin following the protocol of Sheehan and Hrapchak [70]. Based on these preparations the
skin biopsy was divided into two strata: (1) stratum basale, closest to the blubber, and (2) stra-
tum externum, the outermost layer that easily separated from the stratum spinosum (Fig 2A).
We did not include stratum spinosum in our analysis because we assumed it would exhibit
intermediate isotope values between the stratum basale and the stratum externum. Some skin
biopsy samples were incomplete as they had been used for previous studies, and only one of
the two strata were available. Sloughed skin samples were also included in the analysis, but
were only available for some years (S1 Dataset).
Baleen plates collected from six dead stranded blue whales were obtained from Humboldt
State University Vertebrate Museum (HSU-VM), CICIMAR-IPN, the California Department
of Parks and Recreation-Prairie Creek Redwoods State Park (CDPR-PCRSP), and the Oregon
Marine Mammal Stranding Network (OMMSN) (S1 Table). Stranding reports including sex
identification were available for all but one individual, which was determined at NOAA-
SWFSC using genetic methods [71,72].
Standardizing blue whale skin sample preparation
Numerous studies show that two factors that are unrelated to ecology can alter isotope values
of metabolically active tissues. The first factor is tissue lipid content. Lipids have lower δ
13
C
values than associated carbohydrates and proteins [24,73,74]. Thus, the potential influence of
lipid content on bulk tissue δ
13
C values must be considered when using SIA to make ecological
inferences [24,75,76]. Chemical lipid-extraction removes the influence of lipids on bulk tissues,
but a side effect of this procedure is that it may affect δ
15
N values of tissues [75,76]. To evaluate
the effect of lipid-extraction on the isotope values of blue whale skin, five skin samples were
divided into two subsamples, one subsample was lipid-extracted with three ~24 hour soaks in
a 2:1 chloroform:methanol solvent solution, rinsed with ionized water and lyophilized. The
second subsample was simply lyophilized, and analyzed as bulk tissue.
The second factor that can alter tissue isotopic composition is how samples are preserved
prior to isotopic analysis. Ideally, all tissues would be stored frozen since freezing does not
alter isotope values [24,7779]. Most of the skin samples selected for this study were stored fro-
zen prior to isotope analysis, but some (n= 100) were stored in a 20% salt saturated solution of
dimethyl sulfoxide (DMSO). Previous studies have shown that the effect of DMSO on the iso-
tope values of tissues can be removed via lipid-extraction [76,80,81]. To determine if this strat-
egy would work for blue whale skin samples preserved in DMSO, we selected 25 sloughed skin
samples from the GC (2005–2007). During field collection, each of these skin samples were
divided into two sections and preserved one of two ways for one year before they were pre-
pared for isotope analysis: the first set was preserved in DMSO and the second (control) set
was frozen in liquid nitrogen (-195˚C).
Stable isotope analysis
All skin and prey samples were lipid-extracted, lyophilized, and homogenized by grinding
them into a fine powder; as noted above the small set of subsamples that were analyzed to test
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 5 / 25
the effects of lipid-extraction were not lipid-extracted (bulk tissue). Baleen plates were cleaned
with a solution of 2:1 chloroform:methanol to remove surface contaminants. Sub-samples of
keratin powder were collected with a Dremel rotatory drill fitted to a flexible engraving shaft at
1 cm intervals along the outer edge of each baleen, starting at the proximal section inserted in
the gum (which represents the newest tissue) (Fig 2B). Baleen grows uniformly on the trans-
verse perspective at a constant (but unknown) rate; thus our sampling strategy would yield
equal time intervals between adjacent sub-samples [37,4244,55,57,82]. Previous studies have
confirmed the consistency of isotope values along the length of two adjacent baleen plates of a
gray whale (Eschrichtius robusutu) [82] and two plates from opposing sides of the mouth of a
Fig 2. Methods for blue whale skin and baleen plate preparation. (A) Biopsy skin separation into strata: Stratum Basale (SB),
Stratum Spinosum (SS) Stratum Externum (SE). The dermal papillae (DP) can be observedembedded in the skin. Dashed lines show
were the cuts were made to separate the skin into stratums. (B) Blue whale baleen plate sampling: baleen powder was sub-sampled in
1 cm intervals along the outer edge of the plate starting from the proximal section of the plate nearest the gum.
https://doi.org/10.1371/journal.pone.0177880.g002
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 6 / 25
bowhead whale (Balaena mysticetus) [43]. Consequently, we assumed that each baleen pro-
vides a consistent record of the past foraging history for each blue whale. Lastly, we compiled
δ
13
C and δ
15
N data from the literature of blue whale prey from foraging zones in the northeast
Pacific (S2 Table).
Approximately 0.5–0.6 mg of each tissue sample (dried skin, baleen, and prey) was weighed
into a tin capsule. Carbon (δ
13
C) and nitrogen (δ
15
N) isotope values were measured with a Cost-
ech 4010 elemental analyzer coupled to Thermo Scientific Delta V isotope ratio mass spectrom-
eter at the Center for Stable Isotopes at the University of New Mexico (Albuquerque, NM).
Isotope data are reported as delta δvalues, δ
13
C or δ
15
N = 1000 [(Rsample / Rstandard)—1],
where R =
13
C/
12
C or
15
N/
14
N ratio of sample and standard [83]. Values are in units of parts per
thousand or per mil (‰) and the internationally accepted standards are atmospheric N
2
for
δ
15
N and Vienna-Pee Dee Belemnite limestone (V-PDB) for δ
13
C [83]. Within-run analytical
precision was estimated via analysis of two proteinaceous internal reference materials, which
was ±0.2‰ for both δ
13
C and δ
15
N values. We also measured the weight percent carbon and
nitrogen concentration of each sample and used the C/N ratio as a proxy of lipid content [84].
Statistical analysis
All statistical analyses were performed using R [85]. The effects of preservation (DMSO-lipid
extracted vs frozen-lipid extracted) and the different treatments (lipid-removal vs bulk tissue)
on skin δ
13
C, δ
15
N and C/N ratios were evaluated with a max-t test for multiple comparisons
of means. This procedure was chosen because it is designed to work in scenarios of unbalanced
group sizes, non-normality and heteroscedasticity [86]. The isotopic variability between skin
strata (basale, externum, sloughed skin) was also evaluated by using the max-t test, which has a
higher power to detect differences between group means compared to other methods [86].
These analyses were performed separately for each zone (GC and CCS) and isotope (δ
13
C or
δ
15
N). The CRD skin isotope values were excluded from this analysis as sloughed skin samples
were not available for this zone.
The prey data were used to establish the reference mean (±SD) baseline isotope values
within each zone, hereafter called the prey zone mean, which was estimated by pooling the
means and variances of all the data. The pooled prey zone mean for the GC included lantern-
fish and the krill species Nyctiphanes simplex, because molecular analysis of fecal samples has
shown that blue whales forage only on combined aggregations of both taxonomic groups in
this zone [14,20]. Lanternfish was the only teleost fish present in blue whale fecal samples [20].
In the CCS, we included isotope values of its main prey, the krill species Thysanoessa spinifera
and Euphausia pacifica [15,18]. In the CRD, diving behavior and the presence of whale fecal
samples confirmed that blue whales forage on patches of krill [17], however, the species of krill
was not identified, so we used previously reported data for krill in this zone [62].
Our approach to estimate the blue whale skin isotopic incorporation rate was to mimic a
diet switch in controlled feeding experiments, but at population level (sampling the same indi-
vidual whale across its annual migratory cycle is logistically impossible). Blue whales in the
northeast Pacific are ideal for this approach because they feed year-round and seasonally
migrate between zones that have distinct baseline isotope values [28,31,58,62,64,87]. To
achieve this, first we evaluated if blue whale skin δ
13
C and δ
15
N values exhibited seasonal
trends in the GC (Jan-Apr) and the CCS (Jun-Dec). Sampling effort within each zone was not
homogeneous for all years, thus blue whale skin samples collected in different years were inte-
grated into a single analysis. We assessed the seasonal trend by fitting a generalized additive
model (GAM) of the skin δ
15
N and δ
13
C values as functions of time (Julian day, which ranges
from 1 to 365). This was done separately for each skin stratum (basale, externum, and sloughed
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 7 / 25
skin) in both foraging zones (GC and CCS). We used GAMs because they are especially useful
when the functional form of the relationship between the response (e.g.δ
15
N and δ
13
C values)
and explanatory variables (e.g. time) is unknown [88]. GAMs were fitted using the “mgcv”
package in R [85,89]. To model the main trend of the data, the smoothing parameters (degrees
of freedom) were set to three. This conservative approach can be applied when sample size is
low [90]. Blue whale skin strata δ
13
C did not show seasonal trends (see Results and S1 Fig),
therefore, the isotopic incorporation rate was only estimated for skin δ
15
N.
To compare the δ
15
N values of the three skin strata to potential prey, we assumed a trophic
discrimination factor (Δ
15
N) of 1.6‰, based on controlled feeding experiments on captive bot-
tlenose dolphins (Tursiops truncatus) [47,48], and calculated the trophic-corrected mean blue
whale skin values for each zone by adding this trophic discrimination factor to the prey zone
mean values. These trophic-corrected skin values would represent the expected mean δ
15
N val-
ues if blue whale skin had fully equilibrated with that of local prey (or reached steady-state iso-
topic equilibrium), and we assumed that this method would allow us to assign any given blue
whale skin isotope value to a specific foraging zone.
Based on the gradient in the prey mean isotope values for each foraging zone (GC>CCS>
CRD; S2 Table), and the trophic-corrected blue whale skin values (see Results), our hypothesis
was that blue whales would arrive to the GC with lower skin δ
15
N values due to consumption
of prey in the CCS and CRD. Skin isotope values would then increase throughout the winter
season as they equilibrate with local prey (see Results). In contrast, most whales would arrive
in the CCS with higher skin isotope values, except for individuals that migrated from the CRD.
Thus, we predicted that skin isotope values would decrease throughout the summer season as
skin isotopically equilibrated with the local prey in the CCS. Therefore, we used the GAMs sea-
sonal predictions to estimate the isotopic incorporation rate for each skin stratum, as the days
that it would take for the skin δ
15
N to increase (GC) or decrease (CCS) by the assumed trophic
discrimination factor (Δ
15
N = 1.6‰) to reach isotopic equilibrium with the local diet. This
period was derived by extrapolating from the distance between the predicted extremes in δ
15
N
for each stratum, from the lowest to the highest in the GC and vice versa for the CCS (S3
Table,S2 Fig). In this case, we assumed that the equivalent to the diet switch stage would be
the lowest initial δ
15
N value within the GC and the highest initial δ
15
N value in the CCS (S2
Fig). We used the same method with the 95% upper and lower confidence intervals to assess
uncertainty (S3 Table,S2 Fig). Unfortunately, the uncertainty associated to individual variabil-
ity in isotopic incorporation rates given the potential variation in individual arrival and depar-
ture times to/from the GC and CCS, could not be considered in the model.
Due to sample size limitations, we had to integrate all the skin data collected in different
years into a single seasonal model to estimate blue whale δ
15
N isotopic incorporation rate.
This assumes that the relative difference in prey δ
15
N values between foraging zones is consis-
tent across years, which has been suggested in previous studies [58,91]. We evaluated this
assumption by fitting a generalized linear model (GLM) of skin δ
15
N values as a function of
time (Julian Date, or date of sample collection). Julian Dates are a continuous count of days
based on a standard starting point, which we chose as January 1, 1970 (Universal Time, Coor-
dinated). This analysis was made separately for each foraging zone (GC, CCS and CRD) by
using all skin strata, which allowed us to evaluate the trends in skin δ
15
N across years in each
zone. The GLMs were fitted by using the “glm” function in R [92].
Oscillations in δ
13
C and δ
15
N values of baleen plates were also evaluated with a GAM
model and smoothing parameters were selected by standard data-driven methods for time
series using Akaike Information Criteria [93,94]. Similar to skin, baleen δ
13
C values were not
distinct among foraging zones (see Results and S3 Fig), consequently growth rates were esti-
mated using δ
15
N values. Blue whale baleen growth rate was determined by assuming that the
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 8 / 25
oscillation in δ
15
N values along the total length of the outer edge of the baleen plates represent
the annual movement between winter/spring and summer/fall foraging grounds. Thus, the
distance between two sequential δ
15
N minimums represents the growth of the baleen plate
during a single year [37,4244,55]. Additionally, to characterize the movement of whales
among isotopically distinct foraging zones, we compared baleen δ
15
N values with the trophic-
corrected δ
15
N values for each foraging zone based on the same Δ
15
N used in the skin analysis
[47,48].
Results
Blue whale skin isotope values are available in S1 Dataset. The max-ttest results comparing the
effect of different treatments (bulk tissue vs lipid-extracted; frozen vs DMSO) on skin δ
15
N,
δ
13
C and C/N ratios are presented in S4 Table. Lipid-extracted skin (-16.5±0.1) had mean δ
13
C
values that were significantly higher (1.9‰) than bulk skin samples (-18.4±0.4; t= -10.4, p=<
0.001), and the weight percent C/N ratios of bulk skin were significantly higher (4.2±0.1)
than lipid extracted samples (3.2±0.0; t= 12.9, p=<0.001). In contrast, skin δ
15
N values did
not differ significantly between lipid-extracted (14.6±0.3) and bulk skin (14.5±0.3; t= -0.4,
p= 0.7). Lastly, δ
15
N, δ
13
C, and C/N ratios of skin samples stored in DMSO (δ
15
N: 13.9±0.9;
δ
13
C: -16.9±0.5; C/N: 3.0±0.2) did no differ significantly from skin stored frozen (δ
15
N: 14.0±
0.9; δ
13
C: -16.9±0.6; C/N: 3.0±0.2); δ
15
N: t= 0.2, p= 0.8; δ
13
C: t= 0.2, p= 0.8; C/N: t= -0.4,
p= 0.7.
The max-ttest results comparing the δ
15
N and δ
13
C values among skin strata (basale, exter-
num and sloughed skin) in each zone (GC and CCS) are shown in S5 Table. Skin δ
15
N and
δ
13
C did not differ significantly between different skin strata within the GC (S5 Table). In the
CCS, mean δ
15
N values of sloughed skin (13.6±0.7‰) and stratum externum (13.4±1.1‰) did
not differ significantly (t= -0.4, p= 0.7), and both of these strata had slightly but significantly
higher δ
15
N (stratum externum: t= 2.6, p=<0.001; sloughed skin: t= -4.9, p=<0.001) than
the stratum basale (13.0±0.8‰). δ
13
C values did not differ significantly among strata in the
CCS (S5 Table).
The GLM model of blue whale skin δ
15
N values as a function of time (Julian Date) was not
significant in the CRD (1999–2003; S6 Table,S4 Fig). Conversely, the relationship between
these variables was significant and positive in the GC and the CCS (S6 Table,S4 Fig). The
GLM model predicts an overall increase of 1.2‰ over 15 years (1996–2011) in the CCS, and
an increase of 0.8‰ over 13 years (2002–2015) in the GC (S6 Table,S4 Fig); overall, these shifts
results in a 0.1‰ increase per year in each zone. Thus, skin δ
15
N values showed a slight and
consistent trend in both zones, therefore the gradient in δ
15
N values between zones would also
remain constant. This result would validate the integration of blue whale skin δ
15
N values in a
single seasonal GAM model to infer skin δ
15
N isotopic incorporation rate for each zone.
Skin isotopic incorporation and baleen growth rates
Prey from the three zones had distinct δ
15
N values (S2 Table), with values decreasing from the
GC to the CCS and CRD. The trophic-corrected blue whale skin δ
15
N values for each foraging
zone are presented in Table 1. The magnitude of differences in prey between these zones ran-
ged from 1.9‰ to 6.1‰ (S2 Table), which allowed us to assign the origin of measured δ
15
N
values of the different blue whale skin strata, independently of the zone where whales were
sampled (Table 1,Fig 3).
The GAM results of the relationship between blue whale skin δ
15
N values and time (sea-
sonal trend) are shown in Table 2. The GAM that used δ
15
N values in blue whale skin stratum
basale and externum in relation to time indicated a weak, but slightly significant positive
Blue whale skin isotopic incorporation rates and baleen growth rates
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relationship in the GC, and a weak, but slightly significant negative relationship for the CCS
(Table 2,Fig 3). These relationships were anticipated based on the observed pattern in prey
δ
15
N values among zones and the trophic-corrected blue whale skin values for each foraging
zone (Table 1,S2 Table). For samples collected in the GC, δ
15
N values increased to ~17‰ by
April (Fig 3), which likely reflected isotopic equilibration with the δ
15
N of local prey (Table 1).
The opposite pattern was observed in the CCS, were the δ
15
N values decreased with time to a
low of ~13‰ by December (Fig 3), which also suggests gradual equilibration of the tissue to
the local prey. In contrast, the relation between sloughed skin δ
15
N values and time was not
significant in the GC or CCS (Table 2). The GAM model for sloughed skin showed a parabolic
relationship with time, with a slight tendency of the δ
15
N values to increase and subsequently
decrease with time in both zones (Fig 3). Therefore, we used the same method than that for the
stratum basale and externum within each zone to estimate the isotopic incorporation rate of
sloughed skin (S2 Fig).
The CRD skin δ
15
N values were used as a reference to determine if the isotopic signal of
this foraging zone was present in the skin sampled in the GC and the CCS. Some of the
observed δ
15
N values in the stratum basale and stratum externum from skin sampled in the
CCS could represent transitional values between the CRD isotopic signal and the CCS signal.
One of the values observed in the stratum externum sampled in August was assigned to the
CRD (Fig 3).
The deviance explained in the relationship between skin δ
15
N values and time for all six
GAM models was low (6.7 to 21.1%; Table 2) due to the high degree of dispersion observed in
skin data. This degree of variation was expected since the duration of time individual whales
had spent in the zone where skin was collected was unknown at the time of sampling. As such,
this variation is likely driven by a combination of recently arrived whales that had isotope val-
ues reflective of other foraging zones, individuals in the equilibration period with intermediate
isotope values that represent a mixture of prey consumed in two foraging zones, or individuals
that had reached skin steady-state isotopic equilibrium with the isotopic composition of local
prey (Fig 3).
Estimates of δ
15
N isotopic incorporation rate of blue whale skin strata in each foraging
zone are shown in Table 3 and S3 Table. In the GC, the stratum basale (81 d), stratum exter-
num (81 d), and sloughed skin (90 d) had similar incorporation rates (Table 3). In the CCS,
the stratum basale had longer incorporation rates (262 d) than the stratum externum (192 d).
Sloughed skin (272 d) had the lowest isotopic incorporation rate in CCS, although the later
estimate had a high degree of uncertainty (Table 3). The average skin strata isotopic incorpo-
ration rate in the CCS (242 d) was 158 days lower than the GC (84 d) (Table 3). The overall
mean of the δ
15
N isotopic incorporation rate of blue whale skin was estimated, integrating all
strata in both zones (163 d, Table 3).
Blue whale baleen isotope values are available in S1 Dataset and S7 Table. Stranding infor-
mation of baleen plates collected from six blue whales (A to F), is presented in S1 Table and
Table 1. Trophic-corrected blue whale skin δ
15
N values for each foraging zone.
Zone Prey zone mean (±SD) δ
15
NΔ
15
N Trophic-corrected blue whale skin δ
15
N
Gulf of California 14.6±1.0 1.6 16.2±1.0
California Current
System
10.4±0.3 1.6 12.0±0.3
Costa Rica Dome 8.5±1.1 1.6 10.1±1.1
Values were estimated by using the prey zone mean±SD (S2 Table) and assuming Δ
15
N of 1.6‰.
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Blue whale skin isotopic incorporation rates and baleen growth rates
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Fig 3. GAM analysis of the seasonal trend of skin strata δ
15
N values in two foraging zones. The points represent the
actual δ
15
N values of skin collected from whales within the Gulf of California (open circles) and the California Current
System (open triangles). The colored lines represent the GAM model fit (predictions) and the fringe around the lines show
the 95% confidence intervals. The gray shaded area represents the mean±SD of the trophic-corrected blue whale skin
values for each foraging zone: Gulf of California (GC), the California Current System (CCS) and the Costa Rica Dome
(CRS).
https://doi.org/10.1371/journal.pone.0177880.g003
Blue whale skin isotopic incorporation rates and baleen growth rates
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Fig 1. The results of the GAM models to assess the fluctuations in δ
15
N values along baleen
plates, and of baleen growth rates estimations are shown in Tables 4and 5, respectively. The
GAM fit showed that the amplitude of the oscillations differed among individuals (Tables 4
and 5,Fig 4). Three baleen plates (A–C, one male and two females; S1 Table) exhibited the
expected fluctuations in δ
15
N ranging from 10.6‰ to 14.9‰ (Fig 4A–4C), and the length of
baleen between these fluctuations ranged between 13 and 19 cm (Table 5). The other three
baleen plates (D–F, all males; S1 Table) maintained relatively constant δ
15
N values, ranging
between 11.7‰ and 13.1‰ along the plate (Fig 4D–4F). Inter-individual differences in the
amplitude of the oscillations are likely related to the individual migratory strategies and resi-
dency time within each foraging zone [37,38,43,55]. By using the trophic-corrected skin δ
15
N
values based on that of prey (Table 1), it was possible to associate these oscillations with the
potential foraging zone that each individual whale visited. From these data, it could be inferred
that whale B moved between all three zones, showing relatively regular cycles (Fig 4B), whereas
whale C did not enter the GC, but moved constantly between the CCS and the CRD, in less
Table 2. GAM results for the seasonal trends of δ
15
N and δ
13
C values in different skin strata sampled in the Gulf of California (GC) and California
Current System CCS).
Isotope Skin stratum Zone n E.df. F Adjusted R
2
PDeviance explained (%)
δ
15
NBasale GC 101 1.9 13.4 0.2 <0.001 21.1
Basale CCS 120 1.0 8.4 0.6 <0.01 6.7
Externum GC 85 1.9 7.4 0.1 <0.01 14.7
Externum CCS 63 1.0 5.5 0.1 <0.1 8.3
Sloughed skin GC 81 1.8 3.3 0.1 0.7 7.7
Sloughed skin CCS 93 1.8 2.6 0.0 0.7 6.7
δ
13
CBasale GC 101 1.0 0.2 -0.0 0.7 0.2
Basale CCS 120 1.9 3.6 0.1 <0.1 6.2
Externum GC 85 1.5 1.3 0.1 0.4 2.8
Externum CCS 63 1.5 0.6 0.0 0.6 2.8
Sloughed skin GC 81 1.0 1.3 0.0 0.3 1.6
Sloughed skin CCS 93 1.0 0.1 -0.0 0.8 0.1
E.df., Estimated degrees of freedom; F, test of whether the smoothed function significantly reduces model deviance; P, p-values in bold were considered
statistically significant (<0.05).
https://doi.org/10.1371/journal.pone.0177880.t002
Table 3. δ
15
N isotopic incorporation rates of blue whale skin strata in the Gulf of California and California Current System. The number of days
were estimated by extrapolating from the GAM predictions (model fit and the upper and lower 95% confidence limits) for skin δ
15
N values to change by 1.6‰
to isotopically equilibrate with local prey in each zone.
Zone Skin Stratum δ
15
N isotopic incorporation rate of blue whale skin
Model fit Lower limit Upper limit
Gulf of California Basale 81 90 69
Externum 81 112 69
Sloughed Skin 90 60 149
Mean±SD 84±5
California Current System Basale 262 222 360
Externum 192 160 240
Sloughed Skin 272 163 816
Mean±SD 242±44
Overall Mean±SD 163±91
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Blue whale skin isotopic incorporation rates and baleen growth rates
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regular cycles (Fig 4C). Whale A remained mainly within the CCS, potentially only migrating
twice to the CRD (Fig 4A). In the case of whales D, E and F, the data suggests that these indi-
viduals remained within the CCS, throughout several years (Fig 4D–4F). Only whales A, B,
and C were used to estimate the baleen growth rates (Fig 4A–4C). The mean (±SD) growth per
year of baleen plates was estimated for each whale (A = 13.5±2.2; B = 14.8±1.7; C = 17.5±1.5
cm y
-1
;Table 5), and also integrated in an overall mean (±SD) (15.5±2.2 cm y
-1
;Table 5).
δ
13
C values of skin and baleen plates
The mean δ
13
C value of the prey in the GC was 0.7‰ and 2.9‰ higher than the CCS and the
CRD, respectively (S2 Table). However, the standard deviation of the CRD overlapped with all
the zones and it was not possible to accurately assign the origin of measured δ
13
C from skin
nor baleen plates.
Table 4. GAM results to assess the fluctuations of δ
15
N and δ
13
C in baleen plates.
Isotope Baleen code n E.df. F Adjusted R
2
PDeviance explained (%)
δ
15
NA 41 23.8 61.4 1 <0.001 99.0
B 68 27.2 108.0 1 <0.001 98.7
C 67 27.3 95.9 1 <0.001 98.6
D 55 23.3 25.2 0.9 <0.001 95.7
E 71 26.5 27.2 0.9 <0.001 94.8
F 58 23.8 31.3 0.9 <0.001 96.3
δ
13
CA 41 21.9 55.8 1 <0.001 98.8
B 68 20.3 15.6 0.9 <0.001 89.3
C 67 27.7 64.9 1 <0.001 98.0
D 55 24.9 68.4 1 <0.001 98.5
E 71 27.5 65.1 1 <0.001 97.8
F 58 25.0 25.1 0.9 <0.001 95.7
E.df., Estimated degrees of freedom; F, test of whether the smoothed function significantly reduces model deviance; P, p-values in bold were considered
statistically significant (<0.05).
https://doi.org/10.1371/journal.pone.0177880.t004
Table 5. Blue whale baleen growth rate: Estimated by using the distance between sequential δ
15
N
minimums along the baleen plates from whales A to C.
Baleen code Sex Intervals between δ
15
N minimums (cm) Growth rate (cm y
-1
)
AMale 10–24 14.0
24–37 13.0
Mean±SD 13.5±0.7
BFemale 4–17 13.0
17–31 14.0
31–48 17.0
48–63 15.0
Mean±SD 14.8±1.7
CFemale 9–27 18.0
27–46 19.0
46–62 16.0
Mean±SD 17.5±1.5
Overall Mean±SD 15.5±2.2
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Blue whale skin isotopic incorporation rates and baleen growth rates
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The GAM model revealed a very weak though significant positive relationship between the
δ
13
C and time for the stratum basale sampled within the CCS. The GAMs applied to the other
skin strata, from the other two foraging zones, did not show any relationship between the δ
13
C
values and time (Table 2,S1 Fig), and thus the isotopic incorporation rate of δ
13
C in blue
whale skin could not be estimated.
Fig 4. δ
15
N values along the baleen plates from six whales, identified as A–F. Points represent actual values. The continuous line (blue: males; red:
females) represents the GAM model fit and the narrow fringe around the lines represent the 95% confidence intervals. The gray shadedarea represents
the mean±SD of the trophic-corrected blue whale skin values for each foraging zone: Gulf of California (GC), the California Current System(CCS) and the
Costa Rica Dome (CRS).
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Blue whale skin isotopic incorporation rates and baleen growth rates
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Mean (±SD) δ
13
C values of six baleen plates (A–F) are presented in S7 Table. The GAM fits
(Table 4,S3 Fig) showed that all individuals presented small oscillations in the δ
13
C values
along the baleen that ranged between -18.3 to -16.1. These oscillations could not be linked to
the foraging zones because of the overlap in prey δ
13
C among zones (S2 Table). Therefore,
baleen growth rates were inferred only using baleen δ
15
N values.
Discussion
Influence of lipid-extraction and DMSO preservation on skin δ
13
C and
δ
15
N values
Our results suggest that lipid-extraction is necessary to remove biases in skin δ
13
C values asso-
ciated with lipid content (S4 Table), which agrees with previous studies on mysticetes [75,76].
In regard to the effects of lipid-extraction on δ
15
N values of cetacean skin, some authors
[48,75,76] recommend analyzing bulk tissues because lipid-extraction can influence δ
15
N val-
ues, although this effect varied between species [75,76] and tissues [75]. In our study, we only
compared the δ
15
N of five biopsy samples from which we analyzed paired bulk and lipid-
extracted subsamples; however, δ
15
N values between these treatments did not differ signifi-
cantly, which would be in accordance with the results reported for other marine organisms
[95]. With regard to preservation in DMSO (S4 Table), after lipid-extraction, blue whale skin
δ
13
C, δ
15
N and C/N ratios of samples preserved in DMSO were similar to those of samples pre-
served frozen. Our results concur with previous studies that show lipid-extraction via a 2:1
chloroform:methanol solvent solution was a sound method for removing the combined effect
that DMSO and tissue lipid content have on skin δ
13
C values [76,81].
Skin δ
15
N isotopic incorporation rates
Only two studies have estimated isotopic incorporation rates of cetacean skin, and both uti-
lized controlled feeding experiments on captive bottlenose dolphins [47,48]. Our approach
was to use gradients in baseline δ
15
N values between the GC and CCS as a natural diet switch
experiment (Fig 3). Our mean estimate of δ
15
N isotopic incorporation rates (163±91 d;
Table 3) for blue whale skin is similar to that observed in the longest experiment on captive
bottlenose dolphins (180±71 d) [48]. The similarity in incorporation rate estimates for these
two distantly related cetacean species that differ in weight by over two orders of magnitude is
striking, but suggests that these estimates can be applied to other odontocetes and mysticetes.
We found that isotopic incorporation rates varied among skin strata and foraging zones
(Table 3); however, all of these estimates fell within the range of those observed for bottlenose
dolphins in previous studies (106–275 d and ~60–90 d)[47,48]. It is possible that the observed
variation in skin incorporation rates among zones could be influenced by water temperature
[10,96100], with higher rates in the warmer waters of GC in comparison to the CCS (Table 3).
In cold waters, marine mammals reduce peripheral blood flow to maintain a constant internal
body temperature, which results in a decrease of epidermal metabolism [101103]. In contrast,
incursion into warmer waters accelerates the turnover of superficial skin cells and increase the
proliferation rate of cells by intensifying blood flow to the skin stratum basale [104]. Observa-
tions suggest that odontocetes, such as belugas (Delphinapterus leucas) [104] and killer whales
(Orcinus orca) [105], move from colder to warmer waters to molt or promote skin regeneration.
A study on blue whales in the GC and CCS found that at sites with lower water temperatures,
sloughed skin was observed less often in comparison to warmer sites [67].
A novel aproach in this study was to analyze different skin strata: basale, externum, and
sloughed skin (Figs 2A and 3). We hypothesised that the different skin strata could provide
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 15 / 25
information about temporal shifts in diet. The stratum basale, where cells are constantly
produced, would most likely reflect the most recent dietary information, while the isotopic
composition of stratum externum and sloughed skin would record information of the diet
consumed in the past, perhaps when individuals were in a different foraging zone than the one
where skin samples were collected. The isotopic comparison of strata in the CCS supports this
hypothesis since the stratum basale had significantly lower δ
15
N values than the stratum exter-
num and sloughed skin (S5 Table), suggesting that the stratum basale was equilibrating with
local prey, characterized by lower δ
15
N values than those which occur in the GC (Table 1,
S2 Table). In the GC, skin strata did not have significantly different δ
15
N values; however,
sloughed skin had δ
15
N values that were similar to those expected if the skin was grown in the
CCS (Table 1,Fig 3), suggesting that sloughed skin samples have a higher probability of pro-
viding information about past diets. Thus, skin samples collected from migratory mysticetes
can reflect information about past diets independent of where sampling occurs, demonstrating
that skin is a valuable tissue to estimate relative contributions of food consumed in different
foraging zones utilized during the annual life cycle. Since collecting skin from free ranging
cetaceans is cost- and time-intensive, we recommend dividing skin biopsies into strata and col-
lecting sloughed skin when available to increase the amount of information that can be gleaned
from isotope analysis of this tissue.
Baleen growth rates
Our estimate of baleen growth rates for blue whales (~15.5±2.2 cm y
-1
;Table 5) are consistent
with previous estimates for other balaenopterids, such as the fin whale (Balaenoptera physalus,
20±2.6 cm y
-1
) [37,55], and minke whale (Balenoptera acutorostrata, 12.9 cm y
-1
) [57], as well
as for other mysticetes such as bowhead whales (16–25 cm y
-1
in adults) [42,43]. In contrast,
baleen growth rate estimates were lower than those for southern right whales (Eubalaena aus-
tralis, ~27 cm y
-1
) [44]. Variation in baleen growth rates among blue whales sampled in this
study (Table 4) could be influenced by differences in individual movement strategies (Fig 4A–
4C), a hypothesis proposed in previous studies of other mysticete species [37,38,43,55]. For
example, variation in the period of time spent within a specific foraging zone or in migration
between zones would produce wider or narrower oscillations in baleen δ
15
N, which would
influence growth rate estimates (Table 5,Fig 4).
Three of the six baleen plates we analyzed did not show marked oscilations in the δ
15
N val-
ues (Fig 4D–4F). These individuals were males: two adults, and one of unknown age class (S1
Table). A potential explanation for a lack of inter-annual variation in δ
15
N is that these whales
remained close or within the CCS foraging zone for several years prior to their death. By apply-
ing the mean annual growth rate of ~15.5 cm y
-1
to the baleen records of these three males,
they remained within the CCS ecosystems for ~3.5 (Fig 4D), ~4.5 (Fig 4E) and ~3.7 (Fig 4F)
years. In contrast, the other three baleen plates, collected from one male and two females,
exhibited oscillations in the δ
15
N values along their outer edge that indicate cyclical migrations
between foraging zones during ~2.5 (Fig 4A), ~4.3 (Fig 4B) and ~4.2 (Fig 4C) years.
The observed differences in movement strategies of blue whale individuals may be influ-
enced by a combination of the following factors. One general explanation is related to changes
in the availability of prey in different foraging zones because it is known that blue whale distri-
bution is influenced by variations in the abundance of their primary prey [2,3]. A more specific
explanation is that females are more likely to migrate to warmer waters in winter/spring to
nurse their calves, a hypothesis that has been proposed for other mysticetes, although other
mysticetes generally do not feed while on their winter/spring breeding grounds [106]. More-
over, the patterns in the baleen of whale C (Fig 4C) suggest a high fidelity of females to
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 16 / 25
returning to specific winter/spring foraging grounds year after year. This would be in accor-
dance with the high site fidelity observed in GC of some well-identified females obtained via
photo-identification and genetic analysis [9,11,107]. In the case of males, our data indicate
three males remained in the CCS and one migrated twice to the CRD (Fig 4). The female:male
sex ratio (1.4:1) in the GC is biased towards females [9,107], suggesting that only a portion of
the males in the northeast Pacific are using this zone in winter/spring. Photo-identification
data has also shown that some males have a high site fidelity to the GC [9] or possibly other
winter/spring foraging grounds. Baleen isotope data from one male in our study also indicates
that it had a high fidelity to the CRD, since it migrated only to this zone (Fig 4A). Blue whales
are not frequently sighted in the CCS during winter and spring [108,109], although this could
be attributed to low search effort during this season. However, vocalizations specific to male
blue whales have been recorded year round in the CCS [110113]. The baleen data of males D,
E, F (Fig 4D–4F) is in agreement with this observation. Therefore, we hypothesize that there
are two migratory strategies for blue whale males in the northeast Pacific. Some individuals
migrate to winter/breeding grounds in the GC or CRD, while others remain within the CCS.
How these two migratory strategies influence mating success for males is not known.
δ
15
N trophic discrimination factors
δ
15
N trophic discrimination factors have not been estimated for blue whale tissues, therefore
our approach was to assume a 1.6‰ (Table 1) discrimination factor between whales and their
prey based on the controlled feeding experiments on bottlenose dolphins [47,48]. Borrell et al.
[114] suggested using a trophic discrimination factor of 2.8‰ for balaenopterid skin and
baleen plates. However, the mean (±SD) baleen δ
15
N value of the three male blue whales (D:
12.2±0.3; E: 12.4±0.3; F: 12.3±0.4; S7 Table,Fig 4D–4F) that presumably remained within the
CCS for ~2–3 years prior to death, and by extension were isotopically equilibrated with local
food sources, were enriched by only 1.7–1.9‰ relative to local prey sources (10.5±0.2; S2
Table), and is similar to estimates for skin of captive bottlenose dolphins (1.6±0.5‰) [47,48].
Temporal consistency of baseline δ
15
N values among foraging zones
The observed seasonal trend in skin δ
15
N values within each zone and the oscillations along
baleen plates support our hypothesis that these tissues record baseline shifts in nitrogen iso-
tope values across the northeast Pacific. Our approach assumes that such baseline gradients
are temporarily consistent at a decadal scale. To test this assumption, it would be ideal to have
prey δ
15
N data from each foraging zone for each year blue whales were sampled; however,
such sampling resolution is logistically impossible. Our approach was to use a GLM to evaluate
inter-annual trends in skin δ
15
N values, which showed that they slightly increased in the GC
and CCS (see Results); no evident trend was observed in the CRD (S6 Table,S4 Fig).
Published datasets show that isotope values of blue whale prey and zooplankton collected
from the CCS were consistent over decadal timescales (1994, 2000–2001, 2013) and between
sites (Monterey Bay and British Columbia) [60,61,63,64,115]. Moreover, the δ
15
N values in
blue whale baleen plates that were assigned to the CCS show a remarkably consistent pattern
regardless of when the baleen was collected (1980s vs. 2000s; S1 Table,Fig 4). These patterns
suggest that a relatively stable δ
15
N baseline existed in the CCS for nearly three decades. Fur-
thermore, these data suggest that the slight inter-annual increase in skin δ
15
N values of blue
whales in the CCS is likely the result of uneven seasonal sampling rather than a shift in the
baseline.
δ
15
N values of the dominant krill species (Nyctiphanes simplex) in the GC are variable, likely
due to their omnivorous feeding behavior [116], but are consistently higher than krill in the
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 17 / 25
CCS and the CRD (S2 Table) [5862,64,117,118]. Isotope data for potential blue whale prey
from the CRD were only available from one study (S2 Table) [62], but zooplankton data also
suggest that this zone has lower δ
15
N values in comparison to the CCS and GC [65]. Addition-
ally, baleen δ
15
N patterns from whales that likely visited the CRD (Fig 4A–4C) indicate that
baseline δ
15
N values may be consistently lower than those of the other zones. Another factor
that may contribute to the observed differences in δ
15
N values among foraging zones is that
blue whales in the GC forage on combined aggregations of krill and higher trophic level lan-
ternfish [20]. Thus, blue whale tissues synthetized in the GC will have higher δ
15
N values that
result from a combination of baseline and diet factors relative to tissues grown in other forag-
ing zones in the northeast Pacific (Table 1,S3 Table, Figs 3and 4).
δ
13
C values in blue whale skin and baleen plates
δ
13
C incorporation rates for skin could not be estimated because of the similarity in δ
13
C val-
ues among prey from different foraging zones (S2 Table), and by extension δ
13
C values were
not useful to estimate baleen growth rates. Another variable that could contribute to the lack
of spatial signal in δ
13
C is movement of blue whales between coastal
13
C-enriched and
13
C-
depleted oceanic ecosystems [24] within a specific foraging zone [2]. Thus, any latitudinal
variation in blue whale skin and baleen δ
13
C values between the CCS, GC, and CRD may be
obscured by longitudinal movement between coastal and offshore areas within foraging zones.
Conclusions
Blue whale skin isotopic incorporation rates and baleen growth rates are similar to other odonto-
cetes and mysticetes, respectively. We recommend collecting skin samples throughout the sea-
sonal residency of migratory mysticetes within specific foraging zones, and dividing skin biopsies
into strata. This approach allows for an assessment of seasonal variation in isotope values that
could provide insights into movement and/or shifts in seasonal foraging strategies. Furthermore,
analyzing both skin and baleen can provide information on the inter-annual variation in prey iso-
tope values within and among foraging zones, as well as provide information about the migratory
strategies of individual whales over several years of life, that currently cannot be obtained from
satellite telemetry tags that (at best) collect a single year of movement information [2].
Supporting information
S1 Fig. GAM analysis relating skin δ
13
C values to Julian day (presented in months). The points
represent the actual δ
13
C values of skin collected from whales within the Gulf of California (open
circles) and the California Current System (open triangles). Lines represent the fit (projections) of
the GAM model and the fringe around the lines show the 95% confidence intervals.
(TIF)
S2 Fig. Sections used from the GAM model predictions to infer δ
15
N isotopic incorpo-
ration rates of blue whale skin strata in each foraging zone. The lines represent the GAM
model fit (predictions) in the Gulf of California (green) and the California Current System
(blue). The fringe around the lines show the 95% confidence intervals. The black dot repre-
sents the initial point (i.e. diet switch) and the red dot the final point of the sections from the
predictions that were used from the fit and the lower and upper confidence intervals. Per mil
(‰) differences and days passed between points were estimated and then used to extrapolated
to a 1.6‰ increase in the Gulf of California, or decrease in California Current System, for skin
to reach steady-state isotopic equilibrium with the local prey isotopic signal.
(TIF)
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 18 / 25
S3 Fig. δ
13
C values along the baleen plates from six whales, identified as A-F. Points repre-
sent actual values, the continuous line (blue: males; red: females) represents the GAM model
fit and the fringe around the lines show the narrow 95% confidence intervals.
(TIF)
S4 Fig. GLM analysis relating skin δ
15
N values to time (Julian date, presented in years).
Points represent the actual δ
15
N values of blue whale skin collected in different zones of the
northeast Pacific. Lines represent the fit of the GLM model and the fringe around the lines
show the 95% confidence intervals. The gray shaded area represents the meand±SD of the tro-
phic-corrected blue whale skin values for each foraging zone; Gulf of California (GC), Califor-
nia Current System (CCS), and Costa Rica Dome (CRD).
(TIF)
S1 Table. Information of baleen plates collected from six blue whales.
(DOCX)
S2 Table. Mean (±SD) δ
13
C, δ
15
N, and weight percent C/N ratios of potential blue whale
prey from each of the three foraging zones in the northeast Pacific.
(DOCX)
S3 Table. Results from the GAM model sections used to infer δ
15
N isotopic incorporation
rates of blue whale skin strata in Gulf of California (GC) and California Current System
(CCS).
(DOCX)
S4 Table. Max-t test results comparing the effect of different treatments on skin δ
15
N, δ
13
C
and weight percent C/N ratios.
(DOCX)
S5 Table. Max-t test results for the comparison of δ
13
C and δ
15
N values among different
skin strata in the Gulf of California (GC) and California Current System (CCS).
(DOCX)
S6 Table. GLM results relating blue whale skin δ
15
N values to time (Julian date) in the Gulf
of California (GC), California Current System (CCS) and Costa Rica Dome (CRD).
(DOCX)
S7 Table. Mean (±SD) δ
13
C, δ
15
N and weight percent C/N ratios of blue whale baleen plates
collected from stranded whales.
(DOCX)
S1 Dataset. δ
13
C, δ
15
N and weight percent C/N ratios of blue whale skin and baleen plates
used in this study.
(XLSX)
Acknowledgments
We would like to thank the institutions that facilitated the use of tissues samples and issued the
permits to collect and process these samples: NOAA-SWFSC, CRC, CICIMAR-IPN, HSU-
Vertebrate Museum, Museo de la Ballena y Ciencias del Mar (La Paz, BCS), the California
Department of Parks and Recreation-Prairie Creek Redwoods State Park, NOAA/NMFS and
SEMARNAT. We are also very grateful to all the personnel from the former institutions, the
Stranding Network of California, and the Center for Stable Isotopes of the University of New
Mexico who participated in the collection and processing of the tissue samples. We would also
Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 19 / 25
like to thank the John H. Prescott Marine Mammal Rescue Assistance Grant Program that has
provided grants to the stranding networks. J. Rice, J. Loomis, T. Holmes, and F.J. Go
´mez-Dı
´az
collaborated during the process of locating potential baleen plates for this study and we are
very beholden for their support. We would also like to give special recognition to K. Robertson
who helped with the logistics of sample selection and the sex identification of an important
baleen sample at the genetics lab in SWFSC. M.A. Pardo, H. Villalobos-Ortiz, C. Arnold, and
two anonymous reviewers made some valuable comments to improve the manuscript.
Author Contributions
Conceptualization: GBV SDN DG.
Formal analysis: GBV.
Funding acquisition: GBV SDN DG SAG.
Investigation: GBV SDN GSV JKJ.
Methodology: GBV DG.
Project administration: GBV.
Resources: GBV SDN JC GSV JKJ SAG DG.
Visualization: GBV SDN.
Writing – original draft: GBV SDN DG.
Writing – review & editing: GBV SDN JC GSV JKJ SAG DG.
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Blue whale skin isotopic incorporation rates and baleen growth rates
PLOS ONE | https://doi.org/10.1371/journal.pone.0177880 May 31, 2017 25 / 25

Supplementary resources (12)

... Studying the migratory and dietary strategies of baleen whales is challenging due to their wide range distribution and long dive patterns, which limit observation. The development of satellite tagging technology (Mate et al., 1999;Palacios et al., 2019) and analysis of intrinsic biomarkers (Busquets-Vass et al., 2017;Busquets-Vass et al., 2021) have emerged in response to the growing need for data on migratory marine mammal populations (Hobson, 1999;Newsome et al., 2010). ...
... Specifically, the use of stable isotope analysis (SIA) has increased exponentially in the past three decades to study the foraging ecology (Bentaleb et al., 2011;Witteveen et al., 2012;Fleming et al., 2016;Busquets-Vass et al., 2021), habitat use (Gendron et al., 2001;Gauffier et al., 2020), migratory patterns (Lee et al., 2005;Busquets-Vass et al., 2017), and physiology (Schell et al., 1989;Busquets-Vass et al., 2017) of this elusive taxonomic group. Several factors influence variation in carbon (d 13 C) and nitrogen (d 15 N) isotope values of animals, including: (1) spatial and temporal variation in the isotopic composition of the base of the food web can cascade up food chains to top consumers like marine mammals; (2) physiologically-mediated isotopic discrimination that occurs between a consumer and its diet; and (3) physiological controls on isotopic incorporation and growth rates for metabolically active and inactive tissues, respectively. ...
... Specifically, the use of stable isotope analysis (SIA) has increased exponentially in the past three decades to study the foraging ecology (Bentaleb et al., 2011;Witteveen et al., 2012;Fleming et al., 2016;Busquets-Vass et al., 2021), habitat use (Gendron et al., 2001;Gauffier et al., 2020), migratory patterns (Lee et al., 2005;Busquets-Vass et al., 2017), and physiology (Schell et al., 1989;Busquets-Vass et al., 2017) of this elusive taxonomic group. Several factors influence variation in carbon (d 13 C) and nitrogen (d 15 N) isotope values of animals, including: (1) spatial and temporal variation in the isotopic composition of the base of the food web can cascade up food chains to top consumers like marine mammals; (2) physiologically-mediated isotopic discrimination that occurs between a consumer and its diet; and (3) physiological controls on isotopic incorporation and growth rates for metabolically active and inactive tissues, respectively. ...
Article
Full-text available
Migration is a complex behavior that has evolved in multiple taxonomic groups as a means of accessing productive foraging grounds and environmentally stable areas suitable for reproduction. For migratory whales that forage throughout the year because of their high energetic demands, changes in the abundance of prey in different areas along their migratory route(s) can have serious implications for individual fitness and population viability. Thus, identifying the regions these species use to forage and breed while evaluating their migratory plasticity at the individual level can provide key information for their management and conservation. Serial stable isotope analysis of whale baleen, a continuously growing but metabolically inert tissue, has proven useful in generating individual migratory and foraging records over several years prior to death. We measured carbon ( δ ¹³ C) and nitrogen ( δ ¹⁵ N) isotope values along the length of baleen plates collected from thirteen blue whales of different sex and age classes, representing the largest collection analyzed to date in the northeast Pacific Ocean. Adult females exhibited relatively stable seasonal movements between temperate latitude foraging grounds and subtropical breeding grounds, although two skipped migration one year and subsequently moved to the same subtropical breeding ground near the Costa Rica Dome, potentially to give birth. Adult males exhibited two movement strategies with most remaining at temperate latitudes for 3-4 years before death, while two migrated to subtropical breeding grounds. In contrast, movement patterns in juveniles were erratic. These results are potentially driven by the energetic requirements during pregnancy and nursing in adult females, intra-specific competition among adult males, and inexperience in locating prey in juveniles. We also describe baleen δ ¹⁵ N patterns in recently weaned whales (<16.5m) that reflect switching from the consumption of milk to solid food (krill). In addition, baleen δ ¹³ C data suggest that weaned whales continue to use stored nutrients (blubber) acquired during the nursing period long after they are weaned. These results broaden our understanding of habitat selection in this species, highlight the importance of nursing for the critical period after weaning, and indicate that the Costa Rica Dome is an important calving region for this endangered population.
... 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 Busquets-Vass et al., 2017;Hayden et al., 2017;Hunt et al., 2018;Lemos et al., 2020;Melica et al., 2021aMelica et al., , 2021bTrumble 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; 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). ...
... The ENP population of blue whales is known to spend the summer season feeding 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 population, 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;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). ...
Article
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 modelled cortisol and corticosterone concentrations in response to life history parameters (age, sex, reproductive status) and season or geographic location. In blue whales, cortisol concentrations did not vary significantly by age class, sex, or reproductive status, whereas corticosterone was significantly 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) compared 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 multiple hormones improves our understanding of the physiology of maintaining metabolic homeostasis or coping with chronic stressors.
... Blue whale baleen grows pg. 91 approximately 15.5 cm/yr (Busquets-Vass et al. 2017), and humpback baleen at about 12 -20 cm/yr (Eisenmann et al. 2016). ...
Thesis
Over the past two decades, the Gulf of Maine (GoM) has experienced an annual increase in sea surface temperature. Impacts to the marine ecosystem due to this thermal stress are not yet fully understood. One hypothesis suggests that the temperature increase has reduced productivity, thus affecting prey distribution. Tracking changes in prey choice is challenging; however, analytical methods such as stable isotope analysis (SIA) allow researchers to examine trophic dynamics in consumers over various time scales and assess ecological impact of possible oceanographic regime shifts. This thesis reports the results for the first two years of a five-year SIA study examining current trophic dynamics of GoM rorquals, including humpback (Megaptera novaeangliae) and fin (Balaenotera physalus) whales, using samples collected in situ via a crossbow-delivered biopsy dart. The results were compared to those from a previous GoM isotopic study completed prior to the warming period to assess any changes. A multivariate analysis of the current data suggests fin whales (n = 2) have a lower trophic level signature in comparison to humpbacks (n = 35), specifically due to differences in δ15N values. Recent δ13C values for humpbacks have shifted from historic values, becoming more negative in 2018 and becoming less negative in 2019, while δ15N values have remained statistically similar both years. Using isotopic fractionation constants, humpback isotopic values map well to contemporary samples of Atlantic herring (Clupea harengus), whose δ13C signals were more negative in 2018 than in 2019. This correlation supports the hypothesis that humpbacks continue to feed on herring, and that the variation in δ13C is a product of the change in the prey’s isotopic signature.
... The analysis of the isotopic composition of metabolically inert but continuously growing tissues (e.g., baleen plates, tooth dentin, vibrissae) provides temporally ordered ecological information over different timescales (Newsome et al., 2009;Busquets-Vass et al., 2017;Botta et al., 2018). The dentin in odontocetes, for example, comprises most of the tooth and is chronologically deposited in annual layers, known as Growth Layer Groups (GLGs; Perrin and Myrick, 1980), that fill in the pulp cavity. ...
Article
We analyzed δ13C and δ15N values in different tooth portions (Growth Layer Groups, GLGs) of franciscanas, Pontoporia blainvillei, to investigate their effect on whole tooth (WT) isotopic values and the implications for dietary estimates. Tooth portions included the dentin deposited during the prenatal development (PND), the first year of life (GLG1) deposited during the nursing period and the central part of the tooth with no distinction amongst subsequent GLGs (Center). Isotopic mixing models estimating the contribution of PND, GLG1 and Center to WT showed that GLG1 has a strong effect on WT isotope values in juveniles, while Center only starts to affect WT isotopic values from age four. Isotopic mixing models estimating prey contribution to the diet of juveniles using WT vs Center tooth portions significantly differed in dietary outputs, demonstrating that GLG1 influence on WT isotope values affects dietary estimates in young franciscanas. As the small tooth size and narrowness of the last GLGs hinder the analysis of individual layers, we recommend excluding GLG1 in studies based on teeth isotope composition in franciscanas and caution when interpreting isotopic values from the WT of other small cetaceans.
... To date, no consensus exists on the turnover time of cetacean skin, and estimates range from several days [133,134] to several months [135]. Given that the samples analysed here originate from animals that were stranded in New Zealand, it is possible that these individuals had already been feeding outside of their usual polar habitat for a considerable time, which means that their isotopic values may not reflect these species' usual foraging ecology. ...
Article
Full-text available
Species occurring in sympatry and relying on similar and limited resources may partition resource use to avoid overlap and interspecific competition. Aotearoa, New Zealand hosts an extraor- dinarily rich marine megafauna, including 50% of the world’s cetacean species. In this study, we used carbon and nitrogen stable isotopes as ecological tracers to investigate isotopic niche overlap between 21 odontocete (toothed whale) species inhabiting neritic, mesopelagic, and bathypelagic waters. Results showed a clear niche separation for the bathypelagic Gray’s beaked whales (Mesoplodon grayi) and sperm whales (Physeter macrocephalus), but high isotopic niche overlap and potential interspecific competition for neritic and mesopelagic species. For these species, competition could be reduced via temporal or finer-scale spatial segregation or differences in foraging behaviour. This study represents the first insights into the coexistence of odontocetes in a biodiverse hotspot. The data presented here provide a critical baseline to a system already ongoing ecosystem change via ocean warming and subsequent effects on prey abundance and distributions.
... The dark blue line depicts the median estimate, and the margins of the blue area represent the highest 95% posterior density intervals. Busquets-Vass et al., 2017;Marcoux et al., 2007). ...
Article
Historical exploitation, and a combination of current anthropogenic impacts, such as climate change and habitat degradation, impact the population dynamics of marine mammalian megafauna. Right whales (Eubalaena spp.) are large cetaceans recovering from hunting, whose reproductive and population growth rate appear to be impacted by climate change. We apply noninvasive genetic methods to monitor southern right whale (E. australis, SRW) and test the application of noninvasive genetics to minimise the observer effects on the population. Our aim is to describe population structure, and interdecadal and interannual changes to assess species status in the Great Acceleration period of Anthropocene. As a basis for population genetic analyses, we collected samples from sloughed skin during post-migration epidermal moult. Considering the exploration-exploitation dilemma, we collaborated with whale watching companies, as part of a citizen science approach and to reduce ad hoc logistic operations and biopsy equipment. We used mitochondrial and microsatellite data and population genetic tools. We report for the first time the genetic composition and differentiation of the Namibian portion of the range. Population genetic parameters suggest that South Africa hosts the largest population. This corresponds with higher estimates of current gene flow from Africa compared to older samples. We have observed considerable interannual variation in population density at the breeding ground and an interdecadal shift in genetic variability, evidenced by an increase in the point estimate inbreeding. Clustering analyses confirmed differentiation between the Atlantic and Indo-Pacific, presumably originating during the ice ages. We show that population monitoring of large whales, essential for their conservation management, is feasible using noninvasive sampling within non-scientific platforms. Observed patterns are concurrent to changes of movement ecology and decline in reproductive success of the South African population, probably reflecting a large-scale restructuring of pelagic marine food webs.
... First stable isotope mixing models, here defined as a-priori models, were used to evaluated the probability of contribution of different prey to the isotopic mixture of female's SC. Cetaceans in general 28,43 , and gray whales in particular 31 , can integrate in their SC isotopic inputs of up to 70 days, a time that represents northern feeding or southbound migration, based on sampling time. Because of this, it was assumed that model estimates for females' SC would indicate a higher a-priori probability of contribution for Bering Sea amphipods, thought to be the main source of carbon and nitrogen for the eastern gray whale population 8 . ...
Article
Full-text available
Eastern gray whales’ distribution range and plasticity in feeding behavior complicates the understanding of critical life-history such as pregnancy and lactation. Our goal was to determine if females who experienced gestation, gave birth, and lactated their calves, assimilated a high proportion of benthic amphipods from the Bering Sea, which are considered the species’ main prey. We used Bayesian stable isotope mixing models to estimate the probability of contribution of food items sampled along the species’ distributional range, using isotopic data on amphipods from the Bering Sea, mysids from Vancouver Island, and amphipods and polychaetes from Ojo de Liebre Lagoon. We sampled epidermal tissue from lactating females (n = 25) and calves (n = 34) and analyzed their carbon and nitrogen isotopic composition. Model outcome indicated that benthic amphipods from the Bering Sea were not the primary food for the eastern gray whale. Each mother performed a different feeding strategy, and prey from Vancouver Island were generally as important as that from the Bering Sea. Moreover, model results indicate a constant use of Ojo de Liebre Lagoon as a feeding ground. Our results appear to agree with previous studies that report continuous feeding by females to satisfy certain physiological requirements (e.g., fatty acids omega-6) during migration and breeding time. Future investigations of the isotopic composition of all those prey items that could be assimilated by the eastern gray whale emerge as critical. Both historical and recent information, that would provide insights in the species feeding ecology under past and present environmental conditions, should be considered as equally important to establish conservation and management plans.
Article
Marine mammals are a diverse group, including 130 species. These animals have acquired, through evolution, traits that allow them to live at sea. One of the most striking features of marine mammals is that, although they have lungs, they can dive to great depths while holding their breath for extended periods. Diving allows them to meet basic needs such as feeding, mating, and can provide protection from predators. The duration and depth of the dives is associated to physiological adaptations, including increased hemoglobin and myoglobin reserves, and antioxidant defenses, that can be influenced by the size of each species. For example, sea otters can reach depths of less than 20 m, while larger marine mammals such as the sperm whale can descend up to 3000 m. This paper presents the general characteristics of marine mammals and briefly describes the morphological and physiological adaptations that allow them to dive without suffering the consequences of oxidative stress.
Article
Understanding reproductive physiology in mysticetes has been slowed by the lack of repeated samples from individuals. Analysis of humpback whale baleen enables retrospective hormone analysis within individuals dating back 3-5 years before death. Using this method, we investigated differences in four steroid hormones involved in reproduction and mating during confirmed pregnant and non-pregnant periods in two female humpback whales (Megaptera novaeangliae) with known reproductive histories based on sightings and necropsy data. Cortisol, corticosterone, testosterone, and estradiol concentrations were determined via enzyme immunoassay using subsamples of each baleen plate at 2 cm intervals. There were no significant differences in cortisol or corticosterone during pregnancy when compared to non-pregnancy (inter-calving interval), but there were significant differences between the two whales in average glucocorticoid concentrations, with the younger whale showing higher values overall. For testosterone, levels for the younger female peaked at parturition in one pregnancy, but also had spikes during non-pregnancy. The older female had three large spikes in testosterone, one of which was associated with parturition. Estradiol had large fluctuations in both whales but had generally lower concentrations during non-pregnancy than during pregnancy. There were peaks in estradiol before each pregnancy, possibly coinciding with ovulation, and peaks coinciding with the month of parturition. Both estradiol and testosterone could be useful for determining ovulation or impending birth. Using baleen to investigate retrospective steroid hormone profiles can be used for elucidating long-term patterns of physiological change during gestation. Lay summary: Case studies of two pregnant humpback whales whose hormones were analyzed in baleen may illuminate when humpback whales ovulate, gestate, and give birth. These physiological metrics could assist in accurate population growth assessments and conservation of the species. This study shows that baleen hormone analysis can be a useful tool for understanding whale reproductive physiology.
Article
Gathering information on the diet composition of baleen whales at different temporal scales is useful for investigating their feeding habits. Bryde's whales (Balaenoptera edeni) and sei whales (B. borealis) are known to feed on crustaceans and pelagic fish in their western North Pacific feeding grounds; however, their feeding habits before arriving in the feeding area are not known. This study clarified the feeding habits of these two whale species by simultaneously analyzing nitrogen and carbon stable isotope ratios and stomach contents of individuals captured in the western North Pacific in 2015 and 2016. A stable isotope Bayesian mixed-model was used to estimate prey composition. The results showed clear temporal differences in the feeding habits of both whales. Specifically, Bryde's whales fed primarily on krill and pelagic and mesopelagic fishes as they migrated northwards, and then fed exclusively on krill during the summer. Sei whales fed on copepods when they arrived in the subarctic region before shifting to pelagic fish species in the summer. The differences in the feeding habits of these two whale species are attributed to their latitudinal segregation and the availability of prey species, especially in sei whales, which migrate to the subarctic region where copepods are abundant.
Article
Full-text available
Management of highly migratory species is reliant on spatially and temporally explicit information on their distribution and abundance. Satellite telemetry provides time-series data on individual movements. However, these data are underutilized in management applications in part because they provide presence-only information rather than abundance information such as density. Eastern North Pacific blue whales are listed as threatened, and ship strikes have been suggested as a key factor limiting their recovery. Here, we developed a satellite-telemetry-based habitat model in a case–control design for Eastern North Pacific blue whales Balaenoptera musculus that was combined with previously published abundance estimates to predict habitat preference and densities. Further, we operationalize an automated, near-real-time whale density prediction tool based on up-to-date environmental data for use by managers and other stakeholders. A switching state-space movement model was applied to 104 blue whale satellite tracks from 1994 to 2008 to account for errors in the location estimates and provide daily positions (case points). We simulated positions using a correlated random walk model (control points) and sampled the environment at each case and control point. Generalized additive mixed models and boosted regression trees were applied to determine the probability of occurrence based on environmental covariates. Models were used to predict 8-day and monthly resolution, year-round density estimates scaled by population abundance estimates that provide a critical tool for understanding seasonal and interannual changes in habitat use. The telemetry-based habitat model predicted known blue whale hot spots and had seasonal agreement with sightings data, highlighting the skill of the model for predicting blue whale habitat preference and density. We identified high interannual variability in occurrence emphasizing the benefit of dynamic models compared to multiyear averages. Synthesis and applications. This near-real-time tool allows a more accurate examination of the year-round spatio-temporal overlap of blue whales with potentially harmful human activities, such as shipping. This approach should also be applicable to other species for which sufficient telemetry data are available. The dynamic predictive product developed here is an important tool that allows managers to consider finer-scale management areas that are more economically feasible and socially acceptable.
Book
Full-text available
Summary and Analysis of Extension Program Evaluation in R 2016. Salvatore Mangiafico. Rutgers Cooperative Extension. 569 pages. Statistical analyses in R for extension program education. Descriptive statistics, plots, confidence intervals, hypothesis testing, Likert data, nonparametric tests, permutation tests, ordinal regression, tests for nominal data, analysis of variance, count data. Least square means, random effects, mixed models. Plus appearances by my favorite cartoon characters. Web: http://rcompanion.org/handbook. Pdf: http://rcompanion.org/documents/RHandbookProgramEvaluation.pdf.
Book
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.