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The Greenland shark (Somniosus microcephalus), an iconic species of the Arctic Seas, grows slowly and reaches >500 centimeters (cm) in total length, suggesting a life span well beyond those of other vertebrates. Radiocarbon dating of eye lens nuclei from 28 female Greenland sharks (81 to 502 cm in total length) revealed a life span of at least 272 years. Only the smallest sharks (220 cm or less) showed signs of the radiocarbon bomb pulse, a time marker of the early 1960s. The age ranges of prebomb sharks (reported as midpoint and extent of the 95.4% probability range) revealed the age at sexual maturity to be at least 156 ± 22 years, and the largest animal (502 cm) to be 392 ± 120 years old. Our results show that the Greenland shark is the longest-lived vertebrate known, and they raise concerns about species conservation.
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dissociation of this excited state, producing rad-
icals, or by the formation of a diol radical after
reaction of an excited-state fatty acid with an
adjacent molecule.
Because fatty acidcovered surfaces are ubiq-
uitous, the photochemicalproductionofgas-phase
unsaturated and functionalized compounds will
affect the local oxidative capacity of the atmo-
sphere and will lead to secondary aerosol for-
mation. This interfacial photochemistry may exert
a very large impact, especially if in general the
mere presence of a surface layer of a carboxylic
acid can trigger this interfacial photochemistry
at ocean surfaces, cloud droplets, and the sur-
face of evanescent aerosol particles.
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ACKNOWL EDGME NTS
This study was supported by the European Research Council (ERC)
under the European Unions Seventh Framework Program
(FP/2007-2013)/ERC Grant Agreement 290852AIRSEA. D.J.D.
acknowledges ongoing support from the Natural Sciences and
Engineering Research Council of Canada. The authors are grateful
to P. Mascunan and N. Cristin for the ICP-MS analysis and
N. Charbonnel and S. Perrier for the technical support provided by
IRCELYON. All the data presented here can be downloaded from
the supplementary materials.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/353/6300/699/suppl/DC1
Materials and Methods
Figs. S1 to S6
Tables S1 to S3
References (2126)
Database S1
29 January 2016; accepted 23 June 2016
10.1126/science.aaf3617
LIFE HISTORY
Eye lens radiocarbon reveals centuries
of longevity in the Greenland shark
(Somniosus microcephalus)
Julius Nielsen,
1,2,3,4
*Rasmus B. Hedeholm,
2
Jan Heinemeier,
5
Peter G. Bushnell,
6
Jørgen S. Christiansen,
4
Jesper Olsen,
5
Christopher Bronk Ramsey,
7
Richard W. Brill,
8,9
Malene Simon,
10
Kirstine F. Steffensen,
1
John F. Steffensen
1
The Greenland shark (Somniosus microcephalus), an iconic species of the Arctic Seas,
grows slowly and reaches >500 centimeters (cm) in total length, suggesting a life
span well beyond those of other vertebrates. Radiocarbon dating of eye lens nuclei
from 28 female Greenland sharks (81 to 502 cm in total length) revealed a life
span of at least 272 years. Only the smallest sharks (220 cm or less) showed
signs of the radiocarbon bomb pulse, a time marker of the early 1960s. The age
ranges of prebomb sharks (reported as midpoint and extent of the 95.4%
probability range) revealed the age at sexual maturity to be at least 156 ± 22 years, and the
largest animal (502 cm) to be 392 ± 120 years old. Our results show that the Greenland
shark is the longest-lived vertebrate known, and they raise concerns about
species conservation.
The Greenland shark (Squaliformes, Som-
niosus microcephalus) is widely distributed
in the North Atlantic, with a vertical dis-
tribution ranging from the surface to at
least 1816-m depth (1,2). Females outgrow
males, and adults typically measure 400 to 500 cm,
making this shark species the largest fish na-
tive to arctic waters. Because reported annual
growth is 1cm(3), their longevity is likely to
be exceptional. In general, the biology of the
Greenland shark is poorly understood, and lon-
gevity and age at first reproduction are com-
pletely unknown. The species is categorized as
Data Deficientin the Norwegian Red List (4).
Conventional growth zone chronologies can-
not be used to age Greenland sharks because of
their lack of calcified tissues (5). To circumvent
this problem, we estimated the age from a chro-
nology obtained from eye lens nuclei by apply-
ing radiocarbon dating techniques. In vertebrates,
the eye lens nucleus is composed of metabol-
ically inert crystalline proteins, which in the cen-
ter (i.e., the embryonic nucleus) is formed during
prenatal development (6,7). This tissue retains
proteins synthetized at approximately age 0: a
unique feature of the eye lens that has been
exploited for other difficult-to-age vertebrates
(6,8,9).
Our shark chronology was constructed from
measurements of isotopes in the eye lens nu-
clei from 28 female specimens (81 to 502 cm
total length, table S1) collected during scien-
tific surveys in Greenland during 20102013
(fig. S1) (see supplementary materials). We used
radiocarbon (
14
C) levels [reported as percent of
modern carbon (pMC)] to estimate ages and
stable isotopes,
13
Cand
15
N (table S1), to eval-
uate the carbon source (supplementary materials).
Depleted d
13
C and enriched d
15
N levels estab-
lished that the embryonic nucleus radiocarbon
source was of dietary origin and represents a
high trophic level. In other words, isotope sig-
natures are dictated by the diet of the sharks
mother, not the sampled animals. We set the
terminal date for our analyses to 2012, because
samples were collected over a 3-year period.
The chronology presumes that size and age are
positively correlated.
Since the mid-1950s, bombproduced radio-
carbon from atmospheric tests of thermonuclear
weapons has been assimilated in the marine
environment, creating a distinct bomb pulse
in carbon-based chronologies (10). The period of
rapid radiocarbon increase is a well-established
time stamp for age validation of marine animals
(1114). Radiocarbon chronologies of dietary ori-
gin (reflecting the food web) and chronologies
reflecting dissolved inorganic radiocarbon of
surface mixed and deeper waters, have shown
that the timing of the bomb pulse onset (i.e., when
702 12 AUGUST 2016 VOL 353 ISSUE 6300 sciencemag.org SCIENCE
1
Marine Biological Section, University of Copenhagen,
Strandpromenaden 5, 3000 Helsingør, Denmark.
2
Greenland
Institute of Natural Resources, Post Office Box 570, Kivioq 2,
3900 Nuuk, Greenland.
3
Den Blå Planet, National Aquarium
Denmark, Jacob Fortlingsvej 1, 2770 Kastrup, Denmark.
4
Department of Arctic and Marine Biology, UiT The Arctic
University of Norway, 9037 Tromsø, Norway.
5
Aarhus AMS
Centre, Department of Physics and Astronomy, Aarhus
University, Ny Munkegade 120, 8000 Aarhus, Denmark.
6
Department of Biological Sciences, Indiana University South
Bend, 1700 Mishawaka Avenue, South Bend, IN, USA.
7
Oxford Radiocarbon Accelerator Unit, University of Oxford,
Dyson Perrins Building, South Parks Road, Oxford OX1 3QY,
UK.
8
National Oceanic and Atmospheric Administration,
National Marine Fisheries Service, Northeast Fisheries
Science Center, James J. Howard Marine Sciences
Laboratory, 74 Magruder Road, Highlands, NJ 07732, USA.
9
Virginia Institute of Marine Science, Post Office Box 1346,
Gloucester Point, VA 23062, USA.
10
Greenland Climate
Research Centre, Greenland Institute of Natural Resources,
Post Office Box 570, Kivioq 2, 3900 Nuuk, Greenland.
*Corresponding author. Email: julius.nielsen@bio.ku.dk
RESEARCH |REPORTS
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bomb-produced radiocarbon becomes detectable
in a chronology) is synchronous within a few years
and no later than early 1960s across the northern
North Atlantic (Fig. 1).
Sexually mature females >400 cm have been
caught across the Greenland continental shelf
at depths between 132 and ~1200 m [(15, 16)
and table S1]. Their diet (1517) and stable iso-
tope signatures (18) (table S1) are comparable
to those of other marine top predators such
as the porbeagle (Lamna nasus), white shark
(Carcharodon carcharias), spiny dogfish (Squalus
acanthias), and beluga whale (Delphinapterus
leucas)(11,14,1924), for which the bomb pulse
onset has been established (Fig. 1). We therefore
considertheearly1960sasappropriateforthe
timing of the bomb pulse onset for the Greenland
shark chronology as well.
The two smallest animals (nos. 1 and 2) had
the highest radiocarbon levels (>99 pMC), im-
plying that they were indeed affected by the
bomb pulse (Fig. 2). However, given the variabi-
lity of bomb pulse curves (Fig. 1), no exact age
can be assigned to these animals other than
that they were born later than the early 1960s.
The third animal in the chronology (no. 3,
95.06 pMC), on the other hand, had a radio-
carbon level slightly above those of the remain-
ing sharks (nos. 4 to 28, pMC <95), placing its
birth year close to the same time as the bomb
pulse onset (i.e., early 1960s, Fig. 2). We there-
fore assign shark no. 3 (total length 220 cm)
an age of ~50 years in 2012 and consider the
remaining 25 larger animals to be of prebomb
origin.
We estimated the age of prebomb sharks
based on the Marine13 radiocarbon calibration
curve (25), which evaluates carbon-based matter
predatingthebombpulsethatoriginatesfrom
surface mixed waters. The observed synchronicity
of the bomb pulse onset (Fig. 1) supports the
presumption that natural temporal changes of
prebomb radiocarbon are imprinted in the ma-
rine food webs with negligible delay. We contend
that the Marine13 curve can contribute to the
assessment of the age of prebomb sharks de-
spite the difficulties associated with (i) the low
variation in the radiocarbon curve over the past
400 years (25); and (ii) that the degree of radio-
carbon depletion in contemporaneous surface
mixed waters (local reservoir age deviations, DR)
differs between regions (26), meaning that the
carbon source of the eye lens nucleus reflects
food webs of potentially different DRlevels. Con-
sequently, radiocarbon levels of prebomb animals
must be calibrated as a time series under a set
of biological and environmental constraints.
We used OxCal (version 4.2) to do this cali-
bration (27). The program uses Bayesian statis-
tics to combine prior knowledge with calibrated
age probability distributions to provide poste-
rior age information (28,29). We constrained
age ranges with presumptions about von Berta-
lanffy growth, size at birth, the age of animal
no. 3 deduced from the bomb pulse onset (bio-
logical constraints), and plausible DRlevels from
the recent past (environmental constraint). This
makes up a Bayesian model that is detailed in
the supplementary materials.
Calibrations of single pMC measurements with-
out biological constraints are shown as proba-
bility distributions of age with very wide ranges
(light blue distributions, Fig. 3). When imposing
the model, constrained and narrower age esti-
mates are produced for each prebomb individ-
ual, shown as posterior probability distributions
of age (dark blue distributions) in Fig. 3 and
posterior calibrated age ranges at 95.4% (2s)
probability in table S2. OxCal also calculated
agreement indices for each individual shark
(Aindex) and for the calibration model (A
model
).
Thisallowedustoevaluatetheconsistencybe-
tween modeled age ranges and Marine13, as well
as the internal agreement between data points
of the model (table S2) (30). To test the effect
ofthefixedageparameter(sharkno.3),asensiti-
vity analysis was made (supplementary materials
and fig. S2), showing that the overall finding
of extreme Greenland shark longevity is robust
SCIENCE sciencemag.org 12 AUGUST 2016 VOL 353 ISSUE 6300 703
Fig. 1. Radiocarbon chronologies of the North Atlantic Ocean. Radiocarbon levels (pMC) of different
origin (inorganic and dietary) over the past 150 years are shown. Open symbols (connected) reflect
radiocarbon in marine carbonates (inorganic carbon source) of surface mixed and deeper waters
(26,3638). Solid symbols reflect radiocarbon in biogenic archives of dietary origin (11,14,22,24).
The dashed vertical line indicates the bomb pulse onset in the marine food web in the early 1960s.
Fig. 2. Radiocarbon in eye lens nuclei of Greenland sharks. Radiocarbon levels (pMC ± SD, table S1)
from 28 females plotted against total length (TL) are shown. Individual animals are identified by the
numbers next to the symbols. Nos. 1 and 2 are of postbomb origin, and nos. 4 to 28 are of prebomb
origin. We consider shark no. 3 to be from the early 1960s, which is the latest timing of the bomb pulse
onset (dashed vertical line).
RESEARCH |REPORTS
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regardless of the exact timing of the bomb pulse
onset (19581980).
The model estimated asymptotical total length
to be 546 ± 42 cm (mean ± SD), a size matching
the largest records for Greenland sharks (2), and
the age estimates (reported as midpoint and
extent of the 95.4% probability range) of the
two largest Greenland sharks to be 335 ± 75 years
(no. 27, 493 cm) and 392 ± 120 years (no. 28,
502 cm). Moreover, because females are reported
to reach sexual maturity at lengths >400 cm
(15), the corresponding age would be at least
156 ± 22 ye ars (no. 19, 392 cm) (table S2). A
model
was 109.6%, demonstrating that samples are in
good internal agreement, implying that the age
estimates are reliable.
The validity of our Greenland shark age esti-
mates is supported by other lines of evidence.
For instance, we found sharks <300 cm to be
younger than 100 years (table S2). Such age
estimates are indirectly corroborated by their
depleted d
13
C levels (table S1), possibly reflect-
ing the Suess effect, another chemical time
mark triggered by emissions of fossil fuels, im-
printed in marine food webs since the early
20th century (31,32). In addition, high levels of
accumulated anthropogenic contaminants may
suggest that ~300-cm females are older than
50 years (33). Taken together, these findings
seem to corroborate an estimated life span of
at least 272 years for Greenland sharks attain-
ing more than 500 cm in length.
Our results demonstrate that the Greenland
shark is among the longest-lived vertebrate spe-
cies, surpassing even the bowhead whale (Balaena
mysticetus, estimated longevity of 211 years) (9).
The life expectancy of the Greenland shark is
exceeded only by that of the ocean quahog
(Arctica islandica, 507 years) (34). Our estimates
strongly suggest a precautionary approach to
the conservation of the Greenland shark, be-
cause they are common bycatch in arctic and
subarctic groundfish fisheries and have been
subjected to several recent commercial exploi-
tation initiatives (35).
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ACKNO WLE DGME NTS
We are grateful for the contributions from M. B. Backe throughout
the manuscript. We thank the Commission of Scientific
Investigations in Greenland (KVUG), Save Our Seas Foundation,
National Geographic Foundation, Carlsberg Foundation, Danish
Centre for Marine Research, Den Blå PlanetNational Aquarium of
Denmark, Greenland Institute of Natural Resources (GINR), and
the Danish Council for Independent Research for financial support.
We thank GINR, the University of Copenhagen and the TUNU
Programme (UIT, The Arctic University of Norway) for ship time.
We are grateful for the collaboration with K.P. Lange. We thank the
crews of the RV Pâmiut,RVDana,RVHelmer Hanssen,RVSanna,
and RV Porsild. Three anonymous reviewers provided helpful
comments and discussion that improved earlier versions of
the manuscript.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/353/6300/702/suppl/DC1
Material and Methods
Supplementary Text
Figs. S1 and S2
Tables S1 and S2
References (3950)
29 December 2015; accepted 10 June 2016
10.1126/science.aaf1703
704 12 AUGUST 2016 VOL 353 ISSUE 6300 sciencemag.org SCIENCE
Fig. 3. Bayesian age ranges of prebomb sharks.The estimated year of birth against total length (TL)
for prebomb sharks (nos. 4 to 28) is shown. Light blue shows the individual age probability
distributions for each shark, and modeled posterior age probability distributions are shown in dark
blue. Fixed age distributions (model input) of one newborn shark (42 cm, 2012 ± 1) and of shark no. 3
(220 cm, born in 1963 ± 5) are shown in orange. The red line is the model fit connecting the geometric
mean for each posterior age probability distribution. (Inset) The model output; i.e., A
model
,L
max
, and
range of birth year for shark no. 28. Also see the supplementary materials.
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(6300), 702-704. [doi: 10.1126/science.aaf1703]353Science
and John F. Steffensen (August 12, 2016)
Ramsey, Richard W. Brill, Malene Simon, Kirstine F. Steffensen
Bushnell, Jørgen S. Christiansen, Jesper Olsen, Christopher Bronk
Julius Nielsen, Rasmus B. Hedeholm, Jan Heinemeier, Peter G.
)Somniosus microcephalusGreenland shark (
Eye lens radiocarbon reveals centuries of longevity in the
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Supplementary Materials for
Eye lens radiocarbon reveals centuries of longevity in the Greenland shark
(Somniosus microcephalus)
Julius Nielsen,* Rasmus B. Hedeholm, Jan Heinemeier, Peter G. Bushnell, Jørgen S.
Christiansen, Jesper Olsen, Christopher Bronk Ramsey, Richard W. Brill, Malene Simon,
Kirstine F. Steffensen, John F. Steffensen
*Corresponding author. Email: julius.nielsen@bio.ku.dk
Published 12 August 2016, Science 353, 702 (2016)
DOI: 10.1126/science.aaf1703
This PDF file includes:
Materials and Methods
Supplementary Text
Figs. S1 and S2
Tables S1 and S2
References
2
Materials and Methods
Sampling of sharks and eye lens nuclei
Analyzed sharks were caught from 2010-2013 as unintended bycatch during the
Annual Fish Survey of Greenland Institute of Natural Resources, by the commercial
fishing fleet and from scientific long lines. All sampling was carried out in accordance
with laws and regulations and with authorization from the Government of Greenland
(Ministry of Fisheries, Hunting & Agriculture, document number 565466 and 935119).
Samples were taken from specimens with lethal injuries caused by conspecifics or fishing
equipment. Sharks were euthanized immediately after capture by direct spinal cord
transection. Total body length was measured and eye globes were removed and stored at
-20o C. The left eye lens was subsequently prepared at the Aarhus AMS Centre
(Department of Physics and Astronomy, Aarhus University, Denmark) by isolating the
embryonic eye lens nucleus under light microscopy from concentrically arranged layers
of secondary fiber cells. A 4-5 mg subsample of the innermost part of the embryonic
nucleus was used for isotopic analyses with Accelerator Mass Spectrometry (AMS) and
Continuous-Flow Isotope Ratio Mass Spectrometry (CF-EA-IRMS).
Sample preparation and isotope measurements
Embryonic nucleus samples were converted to CO2 by combustion at 950o C in
sealed evacuated quartz ampoules with CuO. A subsample of the resulting CO2 gas was
used for δ13C Dual-Inlet analysis on an IsoPrime stable isotope ratio mass spectrometer to
a precision of 0.02‰, while the rest was converted to graphite for AMS 14C
measurements (AMS Laboratory, Accium Biosciences, Seattle, WA, USA (41). The
3
results are reported according to international conventions (42) and 14C content is given
as percentage modern carbon (pMC) based on the measured 14C/12C ratio corrected for
the natural isotopic fractionation by normalizing the δ13C value to -25‰ VPDB (Vienna
Pee Dee Belemnite; δ13C calibration standard). The pMC unit is calculated as 100 * F14C
(43) and reported as mean pMC ± SD. 14C measurements are also presented as non-age
corrected 14C values where 14C = (pMC/100 1) x 1000 (44). Stable isotopes, δ13C
and δ15N, were measured on eye lens nucleus samples weighed into tin cups at the
Aarhus AMS Centre by continuous-flow isotope ratio mass spectrometry (Vario Cube
elemental analyzer coupled to an IsoPrime stable isotope ratio mass spectrometer). All
isotopic measurements are reported as mean ± SD. The instrument precision is
determined by the standard deviation of ~16 measurements on the in house standard
yielding ~0.2‰ for δ13C and 0.2 0.5‰ for δ15N for each analysis batch. The in house
standard is a commercial gelatin which is calibrated against international IAEA
standards. The statistical correlation between TL and δ13C, δ15N and pre-bomb 14C levels,
were evaluated by Spearman’s Rank Correlation Test.
Supplementary Text
Bayesian model design
The biological and environmental constraints of the Bayesian model are: 1) the
largest shark with a bomb-induced 14C signature is 49 ± 5 years old (which in the model
input is fixed as mean ± SD), 2) length and age are positively correlated, where length
increments decline asymptotically with age as expressed by a Von Bertalanffy growth
4
curve, 3) size at birth (i.e., age 0) is given by L0 = 42 cm and 4) ΔR can vary according to
a normal distribution of 75 ± 75 14C years (mean ± SD, N(75,75)).
By setting the largest shark with bomb-induced radiocarbon (no. 3 of 220 cm) to be 49 ±
5 years old (i.e. birth year 1963 ± 5, N(1963,5)) we introduce a time range that
encompasses the earliest and latest detection of the bomb pulse rise in comparable marine
food webs chronologies (Fig. 1) and also the first detection in metabolically active tissues
of pelagic deep sea fauna (45, 46). This timing defines a sharp boundary for the
successive time sequence of birth dates for the larger sharks which were also presumed to
follow an exponential age-length expression:
L=Lmax · [1-exp(-t/τ )]
equivalent to a traditional Von Bertalanffy growth curve (47). Such growth patterns or
derivate thereof have been demonstrated for multiple shark species (48). The sequence
starts at the birth dates of the largest (presumed oldest) sharks and ends with a fictive
newborn 0 years old shark of 42 cm fixed (i.e. year of birth 2012 ± 1). This size was
chosen based on documented near term fetuses of 37 cm (49) and the smallest recorded
free-swimming Greenland sharks of 41.8 cm TL (~42 cm) (50). The Bayesian statistics of
the model assume a strict sequence of birth dates according to shark length. To
incorporate the ΔR uncertainty, the model includes a ΔR value which is allowed to vary
for individual sharks in the model according to a Gaussian distribution of around 75 14C
years with a 1 sigma of 75 14C years. This ΔR range is representative for the resent past in
5
northern North Atlantic surface mixed waters (27). Results of the model output are
illustrated in Fig. 3 as full posterior probability distributions for each shark. We present
the age range estimates for each pre-bomb shark as 95.4 % (2 sigma) probability (table
S2).
Bayesian model function
The Bayesian model was implemented in OxCal (version 4.2) (28-30, 32). In the
Bayesian analysis we define a uniform prior for the age of the longest shark tl and for the
time constant τ. Given the imposed constraints (see above), tl and τ are the only
independent parameters in the model. Given these two parameters, the length Ll of the
longest shark, and the length at birth L0, we can deduce the age t of any animal from its
length L using the equation:
ݐൌെ߬ͳെܮെܮ
ܮെܮ൰ቆͳ൬െݐ
߬൰ቇ൱
We sample over all possible values of the two independent parameters (tl and τ)
conditioned on the likelihood from the radiocarbon calibration applied to the radiocarbon
measurements on the individual specimens. This gives us a marginal posterior
distribution for τ and for the ages of each pre-bomb shark. We have used OxCal to
implement this Bayesian model because it is already set up to calculate the likelihood
distributions from radiocarbon calibration under such an exponential growth model
(equation A44 in 29). The code for implementing this model in OxCal is given below.
6
The agreement between model and data (Amodel) are measured using the agreement
indices which are a measure of the overlap between the un-modeled and modeled
probability distributions provided by Oxcal (51). Generally Amodel below 60% are
considered as poor agreement.
Model priors and likelihoods
The prior for the birth date of the oldest shark is uniform:
ݐ̱ܷെλǡݐ
From this parameter the date of birth of all the oth
e
r
sharks
can
be
estimated:
ݐൌݐെ߬ή݈݊ቀͳെି௅
ಿି௅ή൬ͳ݁ݔ݌ቀെ
ቁ൰
W
e
define
a uniform prior for τ : ̱ܷ߬Ͳǡλ
The local marine
reservoir
for
each
shark is
indep
enden
t
and given a normal
prior:
݀ൌܰ͹ͷǡ͹ͷ
Given this and the marine calibration curv
e
the likelihood from radiocarbon calibration is:
7
݌ߠȁݐͳ
ݏ൅ݏݐ݁ݔ݌ቌെ൫ݎെ݀െݎݐ
ʹቀݏ൅ݏݐ
where Θ is the set of variable parameters. This applied to all the
sharks
(1
< i
N )
except
for the youngest shark which has been given a likelihood
:
݌ߠȁݐןܰܣܦͳͻ͸͵ǡͷ
The collection date is given a prior of:
ݐןܰܣܦʹͲͳʹǡͳ
The
informative independent variables in the model are
τ and
t
N
. The only other
independent variables are the marine reservoir offsets for the sharks
d
i
.
MCMC is used to
sample over the parameter space defined by
{
τ
,
t
N
,
d
i
}
using the
prior
s
and likelihoods
defined above.
Model sensitivity test
Because we cannot verify the exact timing of the bomb pulse onset in the
Greenland shark chronology, four additional model runs (scenarios) were conducted to
test the model sensitivity of the birth year assigned to the shark with fixed age (no. 3, 220
cm, 49 ± 5 years). The four alternative scenarios are:
x Scenario 1: Shark no. 3 (length of 220 cm) is assumed a birth year of 1975,
N(1975AD,5), corresponding to an age of 37 ± 5 years.
x Scenario 2: Shark no. 4 (length of 258 cm) is assumed a birth year of 1963,
N(1963AD,5), corresponding to an age of 49 ± 5 years. In this scenario shark no.
3 is excluded from the model.
8
x Scenario 3: Shark no. 4 (length of 258 cm) is assumed a birth year of 1975,
N(1975AD,5), corresponding to an age of 37 ± 5 years. In this scenario shark no.
3 is excluded from the model.
x Scenario 4: Shark no. 3 (length of 220 cm) is assumed a uniform prior birth year
distribution between 1963 and 2012, U(1963AD,2012AD).
For the model to run these tests adequately the smallest seven sharks (shark nos. 3-10) are
assumed to have an uniform prior age distribution, U(1700AD,1980AD). Studies from
the Pacific Ocean show that all tissue samples from abyssopelagic and abyssobenthic
fishes contained bomb-induced radiocarbon of dietary origin in the 1970s (45, 46, 52).
Therefore, we contend that these alternative scenarios represent the most conservative
estimates for the timing of the bomb pulse onset in the context of calibrating the
Greenland shark chronology.
Model outputs are shown in Fig. S2. It is evident from all scenarios that the estimated age
of shark no. 28 and asymptotic length (Lmax) are robust to changes in fixed age of the
youngest sharks. In all four scenarios the Amodel-values were below 60% (indicating poor
agreement between data and model assumptions), and well below that of the model
presented in Fig. 3 (Amodel = 109.6%). Interestingly, scenario 4, where the birth age of
shark no. 3 was assigned a weak prior age probability distribution, U(1963AD,2012AD),
produced a model output with the highest Amodel (56 %) and is also most similar to the
model presented in Fig. 3. This supports our contention, that the age of shark no. 3 being
9
~50 years is a valid estimate and hence that the fixed input of birth years between 1958-
1968 for this shark in the model presented in Fig. 3 is appropriate.
Oxcal model code
Plot()
{
Curve("Marine13", "marine13.14c");
U_Sequence("Age_vs_Length")
{
Tau_Boundary("Tau")
{
color="green";
};
Delta_R("GS65DR",75, 75);
R_Date("10 (GS65, 502 cm)",617,30)
{
z=502;
color="blue";
};
Delta_R("GS67DR",75, 75);
R_Date("16 (GS67 B, 493 cm)",736,21)
{
z=493;
color="blue";
};
Delta_R("GS42DR",75, 75);
R_Date("10, GS42 (460 cm)",608,25)
{
z=460;
color="blue";
};
Delta_R("GS64DR",75, 75);
R_Date("19 (GS64 B, 451 cm)",612,27)
{
z=451;
color="blue";
};
Delta_R("GS2DR",75, 75);
R_Date("15, GS2 (447 cm)",611,25)
{
z=447;
color="blue";
};
10
Delta_R("GS53DR",75, 75);
R_Date("06 (GS53, 445 cm)",645,27)
{
z=445;
color="blue";
};
Delta_R("GS5DR",75, 75);
R_Date("09, GS5 (442 cm)",682,25)
{
z=442;
color="blue";
};
Delta_R("GS80DR",75, 75);
R_Date("12 (GS80, 440 cm)",516,25)
{
z=440;
color="blue";
};
Delta_R("GS4DR",75, 75);
R_Date("08, GS4 (420 cm)",627,35)
{
z=420;
color="blue";
};
Delta_R("GS59DR",75, 75);
R_Date("09 (GS59, 392 cm)",537,25)
{
z=392;
color="blue";
};
Delta_R("GS58DR",75, 75);
R_Date("04 (GS58, 390 cm)",510,25)
{
z=390;
color="blue";
};
Delta_R("GS14DR",75, 75);
R_Date("07, GS14 (386 cm)",578,25)
{
z=386;
color="blue";
};
Date("Typical",U(1600,2000,5))
{
z=375;
color="green";
};
11
Delta_R("GS6DR",75, 75);
R_Date("13, GS6 (370 cm)",725,35)
{
z=370;
color="blue";
};
Delta_R("GS10DR",75, 75);
R_Date("06, GS10 (355 cm)",594,22)
{
z=355;
color="blue";
};
Delta_R("GS41DR",75, 75);
R_Date("14 (GS41, 354 cm)",586,25)
{
z=354.5;
color="blue";
};
Delta_R("GS55DR",75, 75);
R_Date("15 (GS55, 354 cm)",496,27)
{
z=354;
color="blue";
};
Delta_R("GS16DR",75, 75);
R_Date("05, GS16 (336 cm)",651,25)
{
z=336;
color="blue";
};
Delta_R("JFS2DR",75, 75);
R_Date("JFS2 (330 cm)",573,22)
{
z=330;
color="blue";
};
Delta_R("GS56",75, 75);
R_Date("08 (GS56, 327 cm)",454,26)
{
z=327;
color="blue";
};
Delta_R("GS81",75, 75);
R_Date("17 (GS81, 318 cm)",492,28)
{
z=318;
color="blue";
12
};
Delta_R("GS7",75, 75);
R_Date("07 (GS7, 312 cm)",463,26)
{
z=312;
color="blue";
};
Delta_R("GS12DR",75, 75);
R_Date("04, GS12 (306 cm)",483,25)
{
z=306;
color="blue";
};
Delta_R("GS19DR",75, 75);
R_Date("11, GS19 (276 cm)",509,25)
{
z=276;
color="blue";
};
Delta_R("GS13DR",75, 75);
R_Date("03, GS13 (264 cm)",489,25)
{
z=264;
color="blue";
};
Delta_R("GS3DR",75, 75);
R_Date("02, GS3 (258 cm)",485,25)
{
z=258;
color="blue";
};
Date("Shortest",N(AD(1963),5))
{
z=220;
color="green";
};
Boundary("Newborn",N(AD(2012),1))
{
z=42;
color="green";
};
};
T=Newborn-Tau;
TT=Newborn-Typical;
};
13
Fig. S1.
Capture positions of Greenland sharks around Greenland. Numbers next to the
points identify the individual animals cf. Table S1.
14
Fig. S2
Sensitivity analysis and Bayesian age ranges. Estimated year of birth against total
length (TL, cm) from four different model scenarios. Scenario 1-3 are made with
different fixed age of shark no. 3 (220 cm) or no. 4 (258 cm) with birth year either 1963 ±
5 years or 1975 ± 5 years, respectively. In scenario 4 the age of shark no. 3 is uniform in
years 1963-2012. Light grey shows individual age probability distributions for each
shark, whereas modelled posterior age probability distributions are shown in blue. Fixed
distribution (model input) of one newborn shark (2012 ± 1) and the shark with the same
age as the bomb pulse onset (37 ± 5 years or 49 ± 5 years) are shown in green. The black
line is the model fit connecting the geometric mean for each posterior age probability
distribution. The red line in each figure represents the similar line for the model presented
in Fig. 3. Inserted, the model output i.e. Amodel, Lmax, and range of birth year for shark no.
28.
15
Table S1.
Overview of individual sharks and associated isotope levels. Total body length (TL)
and capture depth for each shark with corresponding stable isotopes (reported as δ13C and
δ15N) and 14C levels in pMC (14C are reported for conventional reasons). Sharks no. 1-3
had pMC levels >95 while the remaining individuals had pMC levels between 91.25-94.5
with a significant negative correlation between size and pMC (t=-4.18, df=23, P<0.001,
cor=-0.66). δ13C values ranged between -16.7 ‰ and -13.8 ‰ (mean ± SD= -14.9 ‰ ±
0.3, N=27) and δ15N ranged between 12.0 ‰ and 17.6 ‰ (mean ± SD= 14.8 ± 0.2,
N=27). δ13C was positively correlated with TL (t=3.52, df=25, P<0.05, cor = 0.57) but
not when only evaluated for sharks >300 cm (t=1.67, df=19, P=0.11, cor=0.36). There
was no significant correlation between δ15N and TL (t=0.49; df=25, P=0.63, cor =0.10).
AAR-number refers to laboratory identification number at Aarhus AMS Centre, Aarhus
University.
No
AAR-ID
TL (cm)
Depth (m)
δ13C ± SD
δ15N ± SD
14C
pMC ± SD
1
19177
81
540
-15.9 ± 0.3
16.0 ± 0.3
34.4
103.44 ± 0.37
2
18075
158
1100
-15.5 ± 0.1
12.0 ± 0.1
-7.2
99.28 ± 0.32
3
19179
220
325
-16.2 ± 0.3
15.2 ± 0.2
-49.4
95.06 ± 0.30
4
18076,3
258
175
-15.3 ± 0.2
14.1 ± 0.2
-58.6
94.14 ± 0.29
5
18077
264
380
-15.1 ± 0.2
13.8 ± 0.2
-59.1
94.09 ± 0.29
6
18085
276
205
-15.2 ± 0.2
14.6 ± 0.2
-61.4
93.86 ± 0.29
7
18078
306
394
-15.0 ± 0.2
16.0 ± 0.2
-58.4
94.16 ± 0.29
8
19183
312
350
-14.0 ± 0.5
13.9 ± 0.4
-56.0
94.40 ± 0.30
9
19193
318
990
-16.7 ± 0.5
17.6 ± 0.4
-59.4
94.06 ± 0.32
10
19184
327
296
-15.4 ± 0.3
13.2 ± 0.4
-55.0
94.50 ± 0.30
11
14646
330
500
-
-
-68.8
93.12 ± 0.27
12
18079
336
596
-14.5 ± 0.2
13.5 ± 0.2
-77.8
92.22 ± 0.29
13
19190
354
492
-15.3 ± 0.3
13.5 ± 0.3
-70.4
92.96 ± 0.29
14
19191
354
407
-14.9 ± 0.3
14.0 ± 0.4
-59.9
94.01 ± 0.31
15
18080,3
355
454
-14.3 ± 0.2
15.5 ± 0.2
-71.3
92.87 ± 0.26
16
18087
370
555
-14.4 ± 0.2
14.8 ± 0.2
-86.3
91.37 ± 0.40
17
18081
386
567
-14.8 ± 0.2
15.2 ± 0.2
-69.4
93.06 ± 0.29
18
19180
390
507
-15.0 ± 0.3
17.6 ± 0.4
-61.5
93.85 ± 0.29
19
19185
391
500
-15.2 ± 0.5
14.7 ± 0.4
-64.7
93.53 ± 0.29
20
18082
420
178
-14.5 ± 0.2
16.9 ± 0.2
-75.0
92.50 ± 0.40
21
19188
440
602
-14.7 ± 0.3
13.2 ± 0.3
-62.2
93.78 ± 0.29
22
18083
442
132
-14.7 ± 0.2
14.3 ± 0.2
-81.4
91.86 ± 0.29
23
19182
445
210
-14.4 ± 0.3
15.7 ± 0.3
-77.1
92.29 ± 0.31
24
18089
447
308
-14.6 ± 0.2
15.3 ± 0.2
-73.2
92.68 ± 0.29
25
19195
451
900
-15.7 ± 0.3
14.2 ± 0.3
-73.4
92.66 ± 0.31
26
18084,3
460
133
-14.6 ± 0.2
12.7 ± 0.2
-72.9
92.71 ± 0.29
27
19192
493
900
-13.8 ± 0.3
16.0 ± 0.4
-87.5
91.25 ± 0.24
28
19186
502
900
-14.5 ± 0.3
14.7 ± 0.3
-74.0
92.60 ± 0.35
16
Table S2.
Modelled age estimates for pre-bomb sharks. For each shark length (TL), the
associated posterior calibrated biological age ranges at 95.4% (2 sigma) probability
(reported as mid-point value ± 1/2 range) are presented together with the associated A
index as produced by the Bayesian model. A index values > 60% reflect a good level of
consistency between modelled age ranges and Marine13. Three sharks had an A index
value < 60%. Although it is not possible to isolate a single reason for this, it is likely to
be a combination of variation in local reservoir age combined with deviations from the
strict age and length assumption in the model.
No
TL (cm)
Age range (95.4 %)
A index (%)
4
258
71 ± 12
128.6
5
264
73 ± 14
130.2
6
276
80 ± 13
129.6
7
306
96 ± 15
139.4
8
312
99 ± 15
143.0
9
318
102 ± 15
136.4
10
327
108 ± 16
139.4
11
330
110 ± 18
99.6
12
336
113 ± 17
50.0
13
354
126 ± 19
123.5
14
354
126 ± 19
100.0
15
355
126 ± 19
96.0
16
370
137 ± 20
20.1
17
386
150 ± 22
111.8
18
390
155 ± 23
113.2
19
392
156 ± 22
116.9
20
420
185 ± 26
108.2
21
440
212 ± 31
71.9
22
442
215 ± 33
106.7
23
445
220 ± 33
125.7
24
447
223 ± 33
122.1
25
451
229 ± 33
122.7
26
460
245 ± 38
121.5
27
493
335 ± 75
120.0
28
502
392 ± 120
35.9
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... Since aging, defined as an age-dependent increase in mortality, is almost universal (28,29), lifespan is clearly under negative selection. On the other hand, the evolution of a long lifespan is possible, as exemplified by very long-lived species that are closely related to short-lived species (30,31). For example, mice typically live ∼3 y, and naked mole rats live over 30 y (32). ...
... Our hypothesis predicts an evolutionary connection between lifespan setpoints and exposure to zoonotic pathogens. Thus, animals living in relatively abiotic environments [e.g., arctic animals (30,31) or troglobites (58)] are expected to have longer lifespan setpoints. On the other hand, animals exposed to a rich diversity of pathogens from related species should display shorter lifespans. ...
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Species-specific limits to lifespan (lifespan setpoint) determine the life expectancy of any given organism. Whether limiting lifespan provides an evolutionary benefit or is the result of an inevitable decline in fitness remains controversial. The identification of mutations extending lifespan suggests that aging is under genetic control, but the evolutionary driving forces limiting lifespan have not been defined. By examining the impact of lifespan on pathogen spread in a population, we propose that epidemics drive lifespan setpoints’ evolution. Shorter lifespan limits infection spread and accelerates pathogen clearance when compared to populations with longer-lived individuals. Limiting longevity is particularly beneficial in the context of zoonotic transmissions, where pathogens must undergo adaptation to a new host. Strikingly, in populations exposed to pathogens, shorter-living variants outcompete individuals with longer lifespans. We submit that infection outbreaks can contribute to control the evolution of species’ lifespan setpoints.
... Greenland sharks (Somniosus microcephalus) have long experienced pressures from commercial shing within the Canadian Arctic, where range overlaps with halibut or shrimp trawls often result in accidental bycatch [1]. For a shark that is long-lived [2], late to mature [3] and capable of extensive migration [4], these pressures may adversely affect genetic variation beyond the spatial scale of commercial shing operations. Though previous molecular genetic work was able to differentiate Greenland sharks from other species in Somniosus [5] and detect hybridization with Paci c sleeper sharks (Somniosus paci cus) [6,7], knowledge of their spatial variation or population boundaries have yet to be described. ...
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Objective The objectives of this research are to isolate, develop and characterize polymorphic microsatellite markers for use in Greenland sharks ( Somniosus microcephalus ). Despite utility in population analyses, microsatellite markers have not been previously developed for this species. Development of these markers, and successful amplification in closely related Pacific sleeper sharks ( S. pacificus ), will facilitate research in the genetic variation of contemporary and future populations of sleeper shark species. Results Thirteen microsatellite loci were successfully amplified and yielded multi-locus genotypes for 32 S. microcephalus individuals from Grise Fjord (n = 16) and Svalbard (n = 20). Each locus yielded between 2 to 9 alleles and observed heterozygosity ranged from 0.11 to 0.70 when estimated across both sites. One locus and three loci deviated from HWE following Bonferroni correction, for individuals sampled from Grise Fjord and Svalbard, respectively. Cross-amplification was successful at every locus for five of the ten S. pacificus individuals.
... Además, se caracterizan por tener una estructura espacial compleja (Bonfil, 1997;Cartamil et al., 2011). Son especies longevas que en promedio viven ~23 años, aunque algunas especies como Galeorhinus galeus llegan a vivir 60 años (Ebert, 2003) o hasta 400 años como Somniosus microcephalus (Nielsen et al., 2016). Actualmente, los condrictios tienen un riesgo general de extinción sustancialmente superior al de otros vertebrados, y se considera que solo un tercio de estas especies mantienen una población que no está comprometida . ...
... Además, se caracterizan por tener una estructura espacial compleja (Bonfil, 1997;Cartamil et al., 2011). Son especies longevas que en promedio viven ~23 años, aunque algunas especies como Galeorhinus galeus llegan a vivir 60 años (Ebert, 2003) o hasta 400 años como Somniosus microcephalus (Nielsen et al., 2016). Actualmente, los condrictios tienen un riesgo general de extinción sustancialmente superior al de otros vertebrados, y se considera que solo un tercio de estas especies mantienen una población que no está comprometida . ...
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... For example, some species of turtles live for decades and show no signs of senescence (de Magalhães 2006, Jones et al. 2014, Quesada et al. 2019. The Greenland shark is yet another vertebrate of extreme longevity and can live more than 400 years (Nielsen et al. 2016). Even among closer species and with similar habits, the lifespan can vary greatly. ...
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Free energy profiles associated with moving atmospheric gases or radicals across the air/water interface were calculated as potentials of mean force by classical molecular dynamics simulations. With the employed force field, the experimental hydration free energies are satisfactorily reproduced. The main finding is that both hydrophobic gases (nitrogen, oxygen, and ozone) and hydrophilic species (hydroxyl radical, hydroperoxy radical, or hydrogen peroxide) have a free energy minimum at the air/water interface. As a consequence, it is inferred that atmospheric gases, with the exception of water vapor, exhibit enhanced concentrations at surfaces of aqueous aerosols. This has important implications for understanding heterogeneous chemical processes in the troposphere.