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Abstract

Sharks are a diverse group of mobile predators that forage across varied spatial scales and have the potential to influence food web dynamics. The ecological consequences of recent declines in shark biomass may extend across broader geographic ranges if shark taxa display common behavioural traits. By tracking the original site of photosynthetic fixation of carbon atoms that were ultimately assimilated into muscle tissues of 5,394 sharks from 114 species, we identify globally consistent biogeographic traits in trophic interactions between sharks found in different habitats. We show that populations of shelf-dwelling sharks derive a substantial proportion of their carbon from regional pelagic sources, but contain individuals that forage within additional isotopically diverse local food webs, such as those supported by terrestrial plant sources, benthic production and macrophytes. In contrast, oceanic sharks seem to use carbon derived from between 30° and 50° of latitude. Global-scale compilations of stable isotope data combined with biogeochemical modelling generate hypotheses regarding animal behaviours that can be tested with other methodological approaches.
Articles
https://doi.org/10.1038/s41559-017-0432-z
© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Sharks are one of the most speciose groups of predators on the
planet and can be found over a broad range of habitats in every
ocean1. Globally, population declines have been reported in
many species of sharks, largely due to fishing pressures and habi-
tat degradation over the last century24. However, the impacts of
these declines on broader ecosystem structure and function remain
uncertain511. Global-scale ecological consequences from declining
shark numbers are likely and may be apparent if shark taxa per-
form broadly similar functions across different regions and habitat
types, such that local effects scale across wide geographic regions. In
marine systems, the impact of an individual on the wider ecosystem
is strongly influenced by trophic interactions12. Thus, the composi-
tion and spatial origin of diet plays an important part in shaping
the ecological roles of individuals, species and functional groups.
Here, we use the term ‘trophic geography’ to refer to spatial aspects
of feeding and nutrition. Broadly quantifying the trophic geography
of marine consumers is particularly challenging because the spatial
and temporal scales over which individuals forage can extend for
thousands of kilometres and over months to years. Nevertheless,
trophic geography provides critical information on how food webs
are structured and the biological connectivity of ecosystems.
Extensive use of stable isotope analysis in localized studies of
marine food webs has provided a wealth of published information
on trophic ecology across broad geographic regions, and numerous
ecosystems within those regions. Of particular utility, the stable iso-
topic composition of carbon (δ
13C) in marine food webs provides
spatial and trophic information on nutrient and biomass residence
and translocation because of the predictable variation in δ
13C val-
ues with latitude and among different primary production types,
such as phytoplankton ( 24‰ to 18‰), macrophytes ( 27‰ to
8‰) and seagrasses ( 15‰ to 3‰)1315. The stable isotope com-
position of carbon in primary producers is directly assimilated by
A global perspective on the trophic geography of
sharks
Christopher S. Bird 1,71*, Ana Veríssimo2,3, Sarah Magozzi1, Kátya G. Abrantes4, Alex Aguilar5,
Hassan Al-Reasi6, Adam Barnett4, Dana M. Bethea7,72 , Gérard Biais8, Asuncion Borrell 5,
Marc Bouchoucha9, Mariah Boyle10, Edward J. Brooks11, Juerg Brunnschweiler12, Paco Bustamante 13,
Aaron Carlisle14, Diana Catarino 15, Stéphane Caut16, Yves Cherel17, Tiphaine Chouvelon18,
Diana Churchill19, Javier Ciancio20, Julien Claes21, Ana Colaço15, Dean L. Courtney 22,73, Pierre Cresson23,
Ryan Daly24,25, Leigh de Necker26, Tetsuya Endo27, Ivone Figueiredo28, Ashley J. Frisch29,
Joan Holst Hansen30, Michael Heithaus31, Nigel E. Hussey32, Johannes Iitembu33, Francis Juanes34,
Michael J. Kinney 35, Jeremy J. Kiszka 36, Sebastian A. Klarian37, Dorothée Kopp38, Robert Leaf39,
Yunkai Li40, Anne Lorrain41, Daniel J. Madigan42, Aleksandra Maljković43, Luis Malpica-Cruz44,
Philip Matich45,46, Mark G. Meekan47, Frédéric Ménard48, Gui M. Menezes15, Samantha E. M. Munroe49,
Michael C. Newman50, Yannis P. Papastamatiou51,52, Heidi Pethybridge53, Jeffrey D. Plumlee54,55,
Carlos Polo-Silva56, Katie Quaeck-Davies1, Vincent Raoult 57, Jonathan Reum58,
Yassir Eden Torres-Rojas59, David S. Shiffman60, Oliver N. Shipley61, Conrad W. Speed47,
Michelle D. Staudinger62,63, Amy K. Teffer64, Alexander Tilley 65, Maria Valls66, Jeremy J. Vaudo67,
Tak-Cheung Wai68, R. J. David Wells54,55, Alex S. J. Wyatt 69, Andrew Yool70 and Clive N. Trueman 1*
Sharks are a diverse group of mobile predators that forage across varied spatial scales and have the potential to influence food
web dynamics. The ecological consequences of recent declines in shark biomass may extend across broader geographic ranges
if shark taxa display common behavioural traits. By tracking the original site of photosynthetic fixation of carbon atoms that
were ultimately assimilated into muscle tissues of 5,394 sharks from 114 species, we identify globally consistent biogeographic
traits in trophic interactions between sharks found in different habitats. We show that populations of shelf-dwelling sharks
derive a substantial proportion of their carbon from regional pelagic sources, but contain individuals that forage within addi-
tional isotopically diverse local food webs, such as those supported by terrestrial plant sources, benthic production and macro-
phytes. In contrast, oceanic sharks seem to use carbon derived from between 30° and 50° of latitude. Global-scale compilations
of stable isotope data combined with biogeochemical modelling generate hypotheses regarding animal behaviours that can be
tested with other methodological approaches.
A full list of affiliations appears at the end of the paper.
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consumers through feeding, and provides a biochemical tracer
linking a consumer to the basal source of carbon and/or latitudi-
nal origin of the food webs that support tissue growth16. The extent
of fractionation of stable isotopes of carbon during photosynthe-
sis by algal phytoplankton varies strongly with latitude, and to a
lesser extent with dissolved nutrient contents, due to temperature
and latitude-dependent variation in factors such as cell size, growth
rates and the concentration and isotopic composition of dissolved
CO214,17. The stable isotope composition of carbon in algal phyto-
plankton has been simulated using isotope-enabled biogeochemical
models17, providing global-scale predictions of latitude-dependent
variation in δ
13C values. Stable isotope data can thus be used as an
indicator of the latitudinal origin of carbon assimilated by mobile
marine consumers, providing insight into cross-ecosystem forag-
ing without the need to directly track the movements of individual
animals13,16. Sharks assimilating food fuelled by primary production
source(s) in one region but captured in an isotopically distinct sec-
ond region should have isotopic compositions that differ from those
of primary producers in the capture location. Here, we compare lati-
tudinal trends in δ
13C values observed in the muscle tissues of sharks
found on continental shelf, open ocean and deep-sea habitats, with
those predicted for phytoplankton from the known capture loca-
tions to establish global patterns of trophic geography in sharks.
We compile a global-scale database of δ
13C values of white muscle
tissue from 5,394 individual sharks from 114 species associated with
continental shelves (neritic waters < 200 m in depth), oceanic (open-
ocean waters but mainly occurring < 200 m) and deep-sea (conti-
nental slopes and seamounts 200 m) habitats (Supplementary
Table 1, Fig. 1). We compare observed shark δ
13C values (δ
13CS)
with the biomass-weighted annual average δ
13C values predicted for
phytoplankton (δ
13CP) within biogeographically distinct ecologi-
cal regions (Longhurst biogeographic provinces) that correspond
to shark capture locations (Fig. 2). We test the null hypothesis that
sharks feed exclusively within the phytoplankton-derived food webs
of their capture locations by comparing the observed and predicted
latitudinal trends in δ
13C values. Capture location δ
13CP values are
calculated from a carbon-isotope-enabled global ocean ecosystem
model17 (Fig. 1). Global-scale isoscapes are not available for sources
of marine production other than phytoplankton, thus we cannot
discount the possibility that all sources of production show consis-
tent latitudinal gradients in δ
13C values. However, the isotopic offset
between phytoplankton, seagrass, macrophytes and benthic pro-
duction varies substantially between sites16. Furthermore, variables
such as cell size, growth rates and dissolved CO2 concentrations have
less influence on the δ
13C values of alternative marine production
sources14. We therefore expect that the δ
13C values of alternative pri-
mary production sources will vary more at the local level, and differ-
ing contributions from production sources within shark food webs
will predominantly influence the variance seen in shark δ
13C values.
A detailed description of the considerations and rationale behind the
isotopic comparisons are given in the Supplementary Information.
Results
The isotopic compositions of carbon in shark muscle (δ
13CS) co-
vary negatively with latitude for oceanic and shelf sharks, but the
relationship between latitude and δ
13CS values differs among habi-
tats (Fig. 2). In continental shelf waters, latitudinal trends observed
in shark muscle were similar to those estimated from biochemical
models. The observed rate of change in δ
13C values per 1° of latitude
was 0.11 for sharks and 0.13 for plankton, although these rates
were statistically distinguishable (ANCOVA F11.864, P = 0.0006).
The average isotopic offset between plankton and shelf sharks
(the difference in intercept values between the best fit linear regres-
sions) is 4.6‰, close to the expected trophic offset of 4.5‰, given
that the median trophic level for sharks is estimated at 4.118 and the
mean isotopic difference between sharks and their prey (that is, the
trophic discrimination factor for δ
13C) is 1.1‰ (Supplementary
Table 2). Best-fit generalized additive models (GAMs) indicate that
the largest amount of deviance in δ
13CS in shelf sharks is explained
by latitude (42.0%), with shark size having very little effect (3.1%)
and a combined explanatory deviance of 46.7% (Supplementary
Table 3). Across all latitudes, the range of δ
13CS values within a given
single-species population of shelf sharks is higher than that of oce-
anic or deep-sea sharks (Fig. 2).
Although oceanic and shelf sharks were sampled from a simi-
lar latitudinal range, the observed latitudinal trends in δ
13CS values
from oceanic sharks are less steep than those predicted for phyto-
plankton from the corresponding Longhurst biogeographic prov-
ince (ANCOVA: F205.63, P < 0.001; Fig. 2). Irrespective of capture
latitude, the observed range of δ
13CS values in oceanic sharks was
small ( 17.22 ± 0.99‰) across the sampling range. The lack of
covariance of δ
13CS with latitude suggests oceanic sharks assimilate
the majority of their carbon from a relatively restricted latitudinal
range, although temporal differences in habitat use and δ
13C val-
ues of prey coupled with relatively slow isotopic turnover rates of
muscle in elasmobranchs could potentially mask variability driven
by latitude (discussed further in Supplementary Information). Best-
fit GAM models indicate that only 20.2% and 4.8% of the deviance
in oceanic shark muscle isotope values is explained by latitude and
shark size, respectively (Supplementary Table 3).
Despite the concentration of deep-sea samples from the North
Atlantic, latitudinal trends in δ
13CS for deep-sea sharks do not co-
vary with latitude (R2 = < 0.001, P = 0.314) or with δ
13CP (ANCOVA:
F1581.9, P < 0.001; Fig. 2), displaying patterns similar to those seen
in oceanic sharks. Body size explained 25.3% and depth of capture
17.6% of the deviance in carbon isotope compositions of deep-sea
sharks (Supplementary Table 3), which implies that their trophic
ecology is strongly depth and size-structured, consistent with other
fishes from continental slopes19.
Discussion
Stable carbon isotope compositions measured in shelf sharks express
similar latitudinal trends to modelled carbon isotope compositions
in phytoplankton and are consistent with our null hypothesis that
shelf shark populations are supported primarily by phytoplanktonic
production close to their capture location. Shelf sharks display rela-
tively high intraspecific variability in stable carbon isotope compo-
sitions compared with oceanic and deep-sea populations (Fig. 2).
Thus although the median isotopic compositions of populations
imply that the bulk of food assimilated by shelf sharks is supported
by phytoplankton production, it seems that individuals within pop-
ulations assimilate nutrients from a range of isotopically distinct
sources. Shelf, and particularly coastal, ecosystems contain a wider
diversity of ecological and isotopic niches than oceanic ecosystems,
including food webs that are supported by seagrasses, benthic pro-
duction, macroalgae and coral13,20. In most shelf habitats, pelagic
phytoplankton yields more negative δ
13C values than alternative
carbon sources13. Foraging across coastal food webs will tend to
produce more varied and less negative δ
13C values than foraging
solely in food webs supported by local phytoplankton. We infer that
at the population level, shelf sharks act as generalist predators, but
populations of at least some of those species are composed of spe-
cialist individuals that forage within distinct food webs during the
timescale of isotopic turnover (probably 1–2 years21). The range of
δ
13CS values observed within populations of shelf sharks is greater
in latitudes lower than around 40° (Fig. 2), potentially indicating
a greater reliance on food webs that are supported by a range of
non-phytoplankton-based resources such as seagrasses and coral
reefs in less productive tropical settings. These hypotheses related
to the range of primary production sources fuelling shark popu-
lations could be further tested using essential amino acid carbon
isotope fingerprinting22.
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Pairing stable isotope analysis with more traditional habitat-use
methodologies could improve our understanding of shark behav-
iour on continental shelves. Tracking studies demonstrate that
while spatial residency and/or repeated return-migrations (philopa-
try) are common traits among sharks that use continental shelves,
some species are capable of undertaking large oceanic migrations
(for example, white and tiger sharks) and philopatry is still under
investigation23. Some species, identified a priori here as shelf sharks
(such as tiger, white and bull sharks), use multiple habitats and can
undertake offshore migrations in excess of 1,000 km24. The isotopic
compositions of sharks classified as mixed-habitat species diverge in
latitudes lower than 35° (Supplementary Fig. 2). Among studies of
species that are capable of utilizing multiple habitats, the majority of
populations surveyed displayed δ
13C values that are more consistent
with obligate shelf sharks than oceanic sharks (Supplementary Fig. 2).
This suggests that while some shelf shark species may be highly
migratory, the carbon supporting tissue growth is largely assimi-
lated from foraging within shelf areas.
In contrast to shelf sharks, the stable isotope compositions of
carbon in oceanic sharks and local phytoplankton do not co-vary,
and oceanic shark populations sampled within these studies show
similar carbon isotope compositions across all reported capture
–18
–30
–24
–21
–27
Shelf
Oceanic
Deep-sea
δ13CP
50°
N
50°
S
150° W 100° W 50° W 50° E 150° E100° E
N
Fig. 1 | Distribution of compiled shark data overlaid on a spatial model of annual average biomass weighted δ13CP within Longhurst biogeographic
provinces from the median sampling year (2009). The coloured points signify the habitat classification of those samples. Most studies provided one
location for multiple samples.
δ13CP
δ13CS
δ13CE
Distance from Equator (°)
δ13C (‰)
a
20 40 600
b
0
3
6
9
BB
A
δ13C range (‰)
−25
−20
−15
−10
−5
20 40 6002040600
n = 3,231 n = 676 n = 1,478
OceanicShelf Deep-sea
Shelf
Oceanic
Deep-sea
Fig. 2 | Carbon isotope data. a, The relationship between δ
13CP from Longhurst biogeographic provinces associated with shark capture locations (solid
black line) and δ
13CS values (dashed black line and open circles) and latitude (bottom row). The confidence envelopes reflect 500 Monte Carlo iterations
considering the variance in δ
13CP values within each Longhurst biogeographic province (grey lines) and the same latitudinal trends predicted for δ
13CS with
an offset of 4.6 added corresponding to the mean offset between δ
13CP and δ
13CS (red lines) and to the trophic effects on δ
13C values. The maps provide
the individual shark sample locations overlaid with the δ
13CP isoscape from Fig. 1. b, Distribution of the observed δ
13CS ranges of species-specific shark
populations in each habitat. The horizontal line is the mean δ
13CS range across shark populations within that habitat. Boxes contain 50% of the data and
lines correspond to the 95% confidence interval. The letters signify analysis of variance, Tukey HSD results for significant difference, with the same letters
representing mean values that are not significantly different from each other.
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latitudes (Fig. 2). The limited isotopic variability seen in oceanic
sharks could reflect either derivation of the majority of nutrients
from a restricted latitudinal range, or extensive foraging across large
latitudinal gradients to produce a consistent average value. In both
cases the consumption of carbon with relatively low δ
13C values
(that is, from higher latitudes) is needed to explain the relatively
13C-depleted values seen in sharks caught at low latitudes. Oceanic
sharks are not commonly found in latitudes greater than approxi-
mately 50° N or S25, limiting the potential to balance diet sources
with higher δ
13C values. We therefore infer that the majority of the
carbon assimilated was relatively depleted in 13C and is consistent
with phytoplankton-based food webs (including mesopelagic food
webs) from intermediate latitudes between approximately 30–50°
from the Equator. The uncertainty surrounding the predictions of
baseline δ
13CP, capture locations and isotopic turnover rates limit
our ability to identify preferential foraging latitudes. Oceanic sharks
could also potentially be intercepting migratory prey that originated
from a restricted latitudinal range, such as squid26. Regardless of the
mechanism(s), our data imply that intermediate latitude areas may
provide globally important sources of energy and nutrients for the
oceanic shark populations sampled in these studies.
Our inferences of regionally restricted foraging areas are con-
sistent with latitudinal trends in oceanic productivity and satel-
lite telemetry studies of several oceanic shark species27,28. Pelagic
ecosystems at intermediate latitudes are typically characterized by
strong thermal gradients that act to concentrate ocean productivity
in frontal and eddy systems (Supplementary Fig. 3) which subse-
quently attract and support oceanic consumers including cetaceans,
fishes, seabirds and marine turtles27,29,30. Tracking data from some
oceanic shark species show high residency within intermediate
latitudes28,30,31, and our interpretation of the stable isotope data sup-
ports these predictions of centralized foraging locations. Migrations
away from productive foraging grounds may provide optimal habi-
tats for behaviours such as breeding, pupping and avoiding intra-
specific competition and harassment28,32. Oceanic sharks have
distributional ranges spanning ocean basins33, therefore, recogniz-
ing that most of the carbon assimilated into their muscle tissues is
derived from photosynthesis occurring in a relatively limited latitu-
dinal region highlights the global importance of regional food webs.
More observations of oceanic sharks and/or potentially migratory
prey from tropical waters are required to test our hypotheses of
centralized foraging.
Similar latitudinal isotopic gradients are observed between
oceanic and deep-sea sharks, which may imply a shared nutrient
resource supporting sharks in both habitats (Supplementary Fig. 4).
Deep-sea sharks rely on the vertical flux of nutrients derived mainly
from surface phytoplanktonic production19, and may therefore be
expected to closely track the stable isotope composition of surface
production. However, the concentration of deep-sea shark samples
from the North Atlantic Ocean (74%) makes it difficult to determine
the tropho-spatial dynamics of this group, because the ameliorating
effects of the Gulf Stream suppresses latitudinal variation in δ
13CP
(Fig. 1). Latitudinal trends are further complicated by the strong
effect of body size and depth (Supplementary Table 3), whereby
some species of deep-sea shark express bathymetric segregations by
size34. Although movement data for most deep-sea shark species is
limited, some larger species undertake long-distance migrations that
are possibly linked to ontogeny, but may also undertake diel verti-
cal migrations linked with foraging35,36. More research is needed to
fully understand the trophic geography of deep-sea sharks and their
functional roles in deep-sea ecosystems.
Concluding remarks
Nearly a quarter of all chondrichthyan species are evaluated as
threatened on the International Union for Conservation of Nature
Red List of Threatened Species, raising concerns on the future of
many populations and the resulting effects such declines may have
on ecosystem function2,4,7,37. Concurrent declines in species with
shared trophic geographies help identify common risks associ-
ated with fishing or climate change. While it is beyond the scope of
this study, and these data, to predict the effects of further removal
of sharks from the oceans, we suggest areas that warrant further
investigation, specifically: (1) many shark species foraging in shelf
environments are typically classed as generalist consumers, but our
data suggest that populations are commonly composed of indi-
viduals that forage in distinct food webs that are supported by a
range of different carbon sources. Such behavioural specialization
within generalist populations could in theory reduce within-species
competition by partitioning resources and habitats, but the role of
individual specialization in regulating shark population densities
is unclear. (2) Oceanic sharks seem to predominantly forage on
carbon resources from a restricted latitudinal range in sub-trop-
ical regions that are characterized by relatively high productivity.
We hypothesize that sharks migrate away from highly productive
regions into warmer waters to engage in alternative behaviours such
as reproduction, but the mechanisms and drivers underpinning lati-
tude-restricted foraging in oceanic sharks remain unknown. Global
patterns of trophic geography in other large mobile marine preda-
tors are generally unknown, but may reveal the role mobile animals
play in distributing nutrients and connecting ecosystems across the
global ocean, and help to predict population responses to changes
in local productivity. We have provided evidence that suggests that
on a global scale sharks typically forage within spatially restricted,
regional seascapes. Conservation of shelf marine environments is
increasingly being addressed through the creation of marine pro-
tected areas (MPAs)38. MPAs may be effective measures for protect-
ing locally resident shelf shark species, providing they encompass
the range of adjacent habitats and core areas utilized by these shark
populations39,40. Although the distributional ranges for most oce-
anic sharks are expansive, core intermediate latitudes seem to be
important for the provision of nutrients and energy. Productive
intermediate latitudes are also targeted by pelagic fisheries, which
increases the susceptibility of oceanic sharks to exploitation28.
Establishing management and protective strategies that encompass
all critical habitats utilized by a species is complex. However, our
results suggest that oceanic sharks may benefit from global strat-
egies that mitigate negative impacts on intermediate-latitude food
webs and from fishing practices that minimize shark mortality in
these areas27,28.
Electronic tagging has revolutionized shark spat ial ecology, pro-
viding detailed records of the movement of individual animals23,30.
Tracking the movement of nutrients can complement information
Table 1 | Regression coefficients for modelled δ
13CP and observed δ
13CS values
δ
13CPδ
13CS
Intercept Slope R2PIntercept Slope R2P
16.87 0.13 0.61 < 0.001 12.54 0.11 0.37 < 0.001
17. 75 0.11 0.80 < 0.001 16.55 0.03 0.17 < 0.001
16.74 0.12 0.67 < 0.001 17.55 < 0.01 < 0.001 0.314
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on individual animal movements by providing a link between the
presence of an animal in an area and the importance of that area
for provisioning, enhancing our knowledge of the extent and scale
of connectivity between oceanic habitats. Locating ecologically
relevant provisioning areas may also assist in the effective design
and placement of MPAs, particularly in open ocean and deep-
water habitats.
Methods
Raw stable carbon isotope data (bulk tissue δ
13C values) were compiled from
54 publications and 7 unpublished datasets yielding measurements from 5,602
individual sharks of 116 species. Where possible, information such as location,
body size, sample size, lipid extraction method and date were reported. The
majority of studies were only able to provide a general area of capture and the
mapped locational assignment was taken as the median of the latitudinal and
longitudinal ranges of these areas. Likewise, some studies sampled landing docks
so were only able to provide the area of that landing dock. The locations provided
by these studies were of the landing docks and it was assumed that fishers were
catching sharks in waters in the vicinity of the landing port. Species habitat
preferences were categorized using published information from their prospective
papers (Supplementary Table 1) and on the advice of the corresponding authors.
Species that had multiple habitat descriptions were classified as shelf sharks.
Examples of this are Hexanchus spp., which are classified here as shelf sharks
(n = 198). Although typically treated as deep-sea sharks, all species in this study
occur consistently over the shelf so were not considered as obligate deep-sea
shark species.
Samples from two plankivorous species (Rhinocodon typus, n = 2641,42;
Megachasma pelagios, n = 2; A. S. J. Wyatt, unpublished observations), from
ecotourism provisioning sites (Carcharhinus perezii, n = 2343), and from a riverine
study (Carcharhinus leucas, n = 12544) were excluded as the study focuses on
marine predators under natural conditions. Within the studies that comprise the
dataset, five chemical treatments were used (no treatment, n = 2,386; water washed,
n = 1,407; 2:1 chloromethanol, n = 748; cyclohexane, n = 696; and petroleum ether,
n = 157). Tests for lipid extraction effects were not significant and it is assumed that
any effect associated with chemical pre-treatment methods are spatially averaged
across the data. Samples with a C:N ratio greater than 10 were removed as it is
highly unlikely that the δ
13C value of these samples represents muscle protein.
A further 314 samples with C:N ratios ranging between 4–10 were subjected to
mathematical correction for lipid influences on δ
13C values45. All other values
were used under the assumption that published values were representations of
true isotopic composition of muscle protein. The data compiled will form the
Chondrichthyan Stable Isotope Data Project and we invite the utilization of these
data and addition of new data to help build on the global geographic trends
observed here.
For each major ocean, annual mean sea surface temperature (SST) and
chlorophyll a concentrations (Chl a) were derived from the moderate-resolution
imaging spectroradiometer (MODIS) 9 km AQUA night-time SST and 9 km
MODIS AQUA Chl a concentration data (NASA Oceancolor) for the median
sampling year for the shark data, 2009 (Supplementary Fig. 3). Environmental data
extraction was constrained to oceanic waters within areas highlighted on the map
(Supplementary Fig. 3).
δ13C baseline predictions. A mechanistic model predicting the spatio-temporal
distribution of global δ
13C values of particulate organic matter (δ
13CP) was
used to interpret shark isotope data17. Briefly, the model estimates δ
13C values
in phytoplankton from ocean carbon chemistry, phytoplankton composition
and phytoplankton growth rate variables output from the NEMO-MEDUSA
biogeochemical model system at 1° and monthly resolutions. Biomass weighted
annual average phytoplankton δ
13C values together with associated spatial and
temporal standard deviations were averaged across each Longhurst biogeochemical
province (Fig. 1). Model-predicted baseline δ
13C values were then inferred for the
capture location for each individual shark data point.
Mathematical models. The relationship between latitude and stable carbon isotope
composition (both δ
13CP and δ
13CS) was modelled using linear regression (Fig. 2,
Table 1). For phytoplankton, we recovered the median and standard deviation of
annual average δ
13CP values simulated within each Longhurst biogeographic province
with a corresponding shark sample. We then ran 500 repeated (Monte Carlo) linear
regressions to account for the spatial variation in predicted δ
13CP values within each
biogeographic province. We predicted null hypothesis shark isotope compositions
by adding 4.6‰ (reflecting 4.1‰ as the median trophic level of sharks and using
published experimental studies of trophic discrimination factors for δ
13C values in
elasmobranch tissues of 1.1‰ (Supplementary Table 2) to the intercept of each of the
500 simulated regression models. ANCOVA analyses were run to compare the slopes
of regressions within a given habitat and between comparable variables between
habitats (δ
13CS, δ
13CP). ANOVA with post-hoc Tukey HSD were used to test for
significant differences between population carbon ranges among habitats.
GAMs were developed to describe latitudinal trends in δ
13CS. Specific habitat
models were used to determine the amount of deviance that could be explained by
single and multiple explanatory variables, including distance from the Equator and
predicted δ
13CP (Supplementary Table 3). A depth parameter was also added to the
deep-sea shark models. δ
13CP values were modelled separately from corresponding
capture locations as a function of distance from the Equator. By comparing the
amount of deviance explained within both the δ
13CS and δ
13CP models, it was
possible to determine how much of the predicted δ
13CP patterns were captured
within δ
13CS values. All models were limited to two smoothing knots to make
models comparable and interpretable. Model comparisons were drawn using
Akaike’s information criterion to determine the most parsimonious model. Final
models were visually inspected using standard residual Q–Q plots to assess model
suitability. All data analysis was performed in R-cran (https://cran.r-project.org)
and mapping visualizations were performed in QGIS (http://www.qgis.org).
Life Sciences Reporting Summary. Further information on experimental design is
available in the Life Sciences Reporting Summary.
Data availability. All data used in these analyses are archived via Dryad (https://
doi.org/10.5061/dryad.d1f0d). This project is an output of the Chondrichthyan
Stable Isotope Data Project (a collection of stable isotope data on sharks, rays and
chimaeras); further details are provided on the project’s GitHub page (https://
github.com/Shark-Isotopes/CSIDP).
Received: 11 April 2017; Accepted: 28 November 2017;
Published: xx xx xxxx
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Acknowledgements
This research was conducted as part of C.S.B.’s Ph.D dissertation, which was funded
by the University of Southampton and NERC (NE/L50161X/1), and through a NERC
Grant-in-Kind from the Life Sciences Mass Spectrometry Facility (LSMSF; EK267-
03/16). We thank A. Bates, D. Sims, F. Neat, R. McGill and J. Newton for their analytical
contributions and comments on the manuscripts.
Author contributions
C.S.B. and C.N.T. contributed the concept and design. C.S.B., C.N.T. and A.V. led the
project. C.S.B. and C.N.T. wrote the manuscript. C.S.B., C.N.T., S.M. and A.Y analysed
and interpreted the data. C.S.B., C.N.T., A.V., K.G.A., A.A., H.A.-R., A.B., D.M.B., G.B.,
A.B., M. Bouchoucha, M. Boyle, E.J.B., J.B., P.B., A.C., D.C., J. Ciancio, J. Claes, A.C.,
D.C., P.C., R.D., L.d.N., T.E., I.F., A.J.F., J.H.H., M.H., N.E.H., J.I., F.J., M.J.K., J.J.K., D.K.,
R.L., Y.L., S.A.K., A.L., D.M., A.M., L.M.-C., P.M., M.M., F.M., G.M.M., S.M., M.N., Y.P.,
H.P., J.D.P., C.P.-S., K.Q.-D., V.R., J.R., Y.E.T.-R., D.S.S., O.N.S., C.W.S., M.S., A. Teffer,
A. Tilley, M.V., J.J.V., T-C.W., R.J.D.W. and A.S.J.W. provided data and/or samples. All
authors have read, provided comments and approved the final manuscript.
Competing interests
The authors declare no competing financial interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41559-017-0432-z.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to C.S.B. or C.N.T.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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Articles
NaTure ecoloGy & evoluTIoN
1Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK. 2CIBIO—Research Center in Biodiversity and
Genetic Resources, Vairão, Portugal. 3Virginia Institute of Marine Science, Gloucester Point, VA, USA. 4College of Science & Engineering, James Cook
University, Cairns, Queensland, Australia. 5IRBio, Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona,
Barcelona, Spain. 6Department of Biology, College of Science, Sultan Qaboos Univeristy, Muscat, Oman. 7NOAA, National Marine Fisheries Service,
Southeast Fisheries Science Center, 3500 Delwood Beach Road, Panama City, FL, USA. 8Ifremer, Unité Halieutique Gascogne Sud, Laboratoire Ressources
Halieutiques de La Rochelle, L’Houmeau, France. 9Ifremer, Unité Littoral, Laboratoire Environnement Ressources Provence Azur Corse, La Seyne sur Mer,
France. 10FishWise, Santa Cruz, CA, USA. 11Shark Research and Conservation Program, Cape Eleuthera Institute, Eleuthera, Bahamas. 12Gladbachstrasse 60,
Zurich, Switzerland. 13Littoral Environnement et Sociétés (LIENSs), UMR 7266, CNRS-Université de La Rochelle, La Rochelle, France. 14Hopkins Marine
Station of Stanford University, Pacific Grove, CA, USA. 15MARE—Marine and Environmental Sciences Centre, Department of Oceanography and Fisheries,
University of the Azores, Azores, Portugal. 16Estación Biológica de Doñana, Consejo Superior de Investigationes Científicas (CSIC), Sevilla, Spain. 17Centre
d’Etudes Biologiques de Chizé, UMR 7372, CNRS-Université de La Rochelle, Villiers-en-Bois, France. 18Unité Biogé ochimie et É cotoxicologie, Laboratoire de
Biogé ochimie des Contaminants Mé talliques, Nantes, France. 19Marine Sciences Program, School of Environment, Arts and Society, Florida International
University, North Miami, FL, USA. 20CESIMAR Centro Nacional Patagónico, CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas), Puerto
Madryn, Chubut, Argentina. 21Laboratoire de Biologie Marine, Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.
22College of Fisheries and Ocean Sciences, Juneau Center, University of Alaska Fairbanks, Point Lena Loop Road, Juneau, AK, USA. 23Ifremer, Unité
Halieutique Manche Mer du Nord, Laboratoire Ressources Halieutiques de Boulogne, Boulogne-sur-Mer, France. 24Port Elizabeth Museum at Bayworld,
Port Elizabeth, South Africa. 25Save Our Seas Foundation—D’Arros Research Centre (SOSF-DRC), Geneva, Switzerland. 26University of Cape Town,
Department of Biological Sciences, Cape Town, South Africa. 27School of Pharmaceutical Sciences, Health Sciences University of Hokkaido, Hokkaido,
Japan. 28Departamento do Mar IPMA, Lisbon, Portugal. 29Reef HQ, Great Barrier Reef Marine Park Authority, Townsville, Queensland, Australia. 30Aquatic
Biology, Department of Bioscience, Aarhus University, Aarhus C, Denmark. 31School of Environment, Arts, and Society, Florida International University,
North Miami, FL, USA. 32Biological Sciences, University of Windsor, Windsor, Canada. 33Department of Fisheries and Aquatic Sciences, University of
Namibia, Henties Bay, Namibia. 34Department of Biology, University of Victoria, Victoria, British Columbia, Canada. 35Ocean Associates, Inc., Southwest
Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, USA. 36Marine Sciences
Program, Department of Biological Sciences, Florida International University, North Miami, FL, USA. 37Centro de Investigacion para la Sustentabilidad,
Facultad de Ecologia y Recursos Naturales, Universidad Andres Bello, Santiago, Chile. 38Ifremer, Unité Sciences et Techniques Halieutiques, Laboratoire de
Technologie et Biologie Halieutique, Lorient, France. 39Division of Coastal Sciences, University of Southern Mississippi, Ocean Springs, MS, USA. 40College
of Marine Sciences, Shanghai Ocean University, Shanghai, China. 41Institut de Recherche pour le Développement (IRD), R 195 LEMAR, UMR 6539 (UBO,
CNRS, IRD, IFREMER), Nouméa, New Caledonia. 42Harvard University Center for the Environment, Harvard University, Cambridge, MA, USA. 43Department
of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada. 44Earth to Ocean Research Group, Department of Biological Sciences,
Simon Fraser University, Burnaby, British Columbia, Canada. 45Marine Sciences Program, Florida International University, North Miami, FL, USA. 46Texas
Research Institute for Environmental Studies, Sam Houston State University, Huntsville, TX, USA. 47Australian Institute of Marine Science, Indian Ocean
Marine Research Centre, The University of Western Australia, Perth, Western Australia, Australia. 48Mediterranean Istitute of Oceanography (MIO), Aix
Marseille Université, Université de Toulon, CNRS, IRD, 13288 Marseille, France. 49Australian Rivers Institute, Griffith University, Nathan, Queensland,
Australia. 50Department of Environmental and Aquatic Animal Health, Virginia Institute of Marine Science, College of William & Mary, Gloucester Point,
VA, USA. 51Department of Biological Sciences, Florida International University, North Miami, FL, USA. 52Scottish Oceans Institute, School of Biology,
University of St. Andrews, St. Andrews, UK. 53CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia. 54Department of Marine Biology, Texas A&M
University at Galveston, Galveston, TX, USA. 55Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX, USA. 56Facultad
de Ciencias Naturales e Ingeniería, Programa de Biología, Universidad de Bogotá Jorge Tadeo Lozano Marina, Bogotá, Colombia. 57Department of
Environmental and Life Sciences, University of Newcastle, Newcastle, New South Wales, Australia. 58Conservation Biology Division, Northwest Fisheries
Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA. 59Instituto de Ecología,
Pesquerías y Oceanografía del Golfo de México (EPOMEX), Universidad Autónoma de Campeche (UAC), Campeche, Campeche, Mexico. 60Earth to
Oceans Group, Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada. 61School of Marine and Atmospheric
Sciences, Stony Brook University, Stony Brook, NY, USA. 62Department of Environmental Conservation, University of Massachusetts Amherst, Amherst,
MA, USA. 63Department of the Interior Northeast Climate Science Center, Amherst, MA, USA. 64Department of Biology, University of Victoria, Victoria,
British Columbia, Canada. 65WorldFish Timor-Leste, Dili, Timor-Leste. 66Instituto Español de Oceanografía, Centre Oceanogràfic de les Balears, Palma,
Spain. 67The Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, USA. 68State Key Laboratory in Marine Pollution, City
University of Hong Kong, Kowloon, Hong Kong, China. 69Department of Chemical Oceanography, Atmosphere and Ocean Research Institute, The
University of Tokyo, Kashiwa, Japan. 70National Oceanography Centre Southampton, Southampton, UK. Present address: 71Centre for Environment,
Fisheries, & Aquaculture Sciences (CEFAS), Lowestoft, UK. 72NOAA National Marine Fisheries Service, Southeast Regional Office, St. Petersburg, FL, USA.
73National Oceanic and Atmospheric Administration, Southeast Fisheries Science Center, Panama City Laboratory, Delwood Beach Road, Panama City, FL,
USA. *e-mail: chrisbirdshark@gmail.com; trueman@noc.soton.ac.uk
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Corresponding Author: Christopher Bird & Clive Trueman
Date: 07/11/2017
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... The trophic ecologies and resource use of elasmobranchs have been extensively explored and attributed to various intrinsic and extrinsic factors, including body size or ontogenetic stage Heithaus et al., 2013;Sommerville et al., 2011), morphology (Yemişken et al., 2018;Yick et al., 2011), prey availability (Armstrong et al., 2016;Frixione et al., 2020;Stewart et al., 2017), geographic location Bird et al., 2018) and resource partitioning or competition with co-occurring species (Kinney et al., 2011;Papastamatiou et al., 2006;Rangel et al., 2019;Raoult et al., 2015). However, much of this existing research is biased towards sharks, with ray species considerably underrepresented in ecological studies, especially those involving trophic interactions or diet. ...
Article
Full-text available
Australian cownose rays (Rhinoptera neglecta) and whitespotted eagle rays (Aetobatus ocellatus) are large myliobatiform rays that co‐occur off temperate eastern Australia. Here, we performed stable‐isotope analyses (δ13C, δ15N and δ34S) on fin clips of both species to gain insights into their trophic interactions and isotopic niches and assess the effect of preservation (ethanol‐stored versus frozen) on isotopic values of fin clip tissue of R. neglecta. Linear mixed models identified species as the main factor contributing to variation among δ15N and δ34S values, and disc width for δ13C. Bayesian ecological niche modelling indicated a 57.4 to 74.5% overlap of trophic niches, with the niche of R. neglecta being smaller and more constrained. Because values of δ13C were similar between species, variation in isotopic niches were due to differences in δ15N and δ34S values. Linear mixed models failed to detect differences in isotopic values of ethanol‐stored and frozen fin tissue of R. neglecta. This study provides the first examination of the trophic ecology of R. neglecta and the comparison of isotopic niche with A. ocellatus, which will facilitate future research into the trophic interactions of these species and aid better resource management. This article is protected by copyright. All rights reserved.
... Therefore, the past role and trophic position of these lost sharks and skates in the Dutch coastal zone fish food web is unknown. Recent isotope studies showed that the trophic ecology of shark and skate species is potentially very complex (Hussey et al 2015;Bird et al 2018;Flowers et al. 2020). ...
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Over the last century the fish community of the Dutch coastal North Sea zone has lost most its shark and skate species. Whether their disappearance has changed the trophic structure of these shallow waters has not been properly investigated. In this study historical dietary data of sharks and skates, being in the past (near)-residents, juvenile marine migrants and marine seasonal visitors of the Dutch coastal North Sea zone were analyzed for the period 1946-1954. Near-resident and juvenile marine migrant species were demersal while all marine seasonal visitors species were pelagic. Based on stomach content composition, the trophic position of four of the various shark and skate species could be reconstructed. The (near)-resident species, the lesser spotted dogfish, the marine juvenile migrant, the starry smooth hound, and the benthopelagic marine seasonal visitor, the thornback ray, had a benthic/demersal diet (polychaetes, molluscs and crustaceans), while the pelagic marine seasonal visitor, the tope shark, fed dominantly on cephalopods and fishes. Diet overlap occurred for fish (tope shark and lesser spotted dogfish), for hermit crabs (lesser spotted dogfish and starry smooth hound) and for shrimps (thornback ray and starry smooth hound). Trophic position ranged from 3.2 for thornback ray preying exclusively on crustaceans to 4.6 for the tope shark consuming higher trophic prey (crustaceans and fish). The analysis indicates that most of the shark and skate species were generalist predators. The calculated trophic positions of shark and skate species indicate that those species were not necessarily at the top of the marine ecosystem food web, but they might have been the top predators of their particular ecological assemblage.
... Stable isotope analysis (SIA) of animal tissues has become a routine tool used in ocean ecology to reconstruct animal movements (e.g., Best and Schell, 1996;Cherel et al., 2009;Carlisle et al., 2015;Bird et al., 2018) and changes in diet and trophic level (e.g., MacNeill et al., 2005;Estrada et al., 2006;Newsome et al., 2009;Pethybridge et al., 2018;Lorrain et al., 2020) (for reviews see Post, 2002;Graham et al., 2010;Boecklen et al., 2011;McMahon et al., 2013;Trueman and St John Glew, 2019). Because the isotopic composition of primary producers varies across space and time (McMahon et al., 2013;Schmittner and Somes, 2016;Magozzi et al., 2017), animal movements can, in theory, be reconstructed retrospectively by relating variation in tissue isotopic composition to variability in isotopic baselines (McMahon et al., 2013;Trueman and St John Glew, 2019). ...
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Variations in stable carbon and nitrogen isotope compositions in incremental tissues of pelagic sharks can be used to infer aspects of their spatial and trophic ecology across life-histories. Interpretations from bulk tissue isotopic compositions are complicated, however, because multiple processes influence these values, including variations in primary producer isotope ratios and consumer diets and physiological processing of metabolites. Here we challenge inferences about shark tropho-spatial ecology drawn from bulk tissue isotope data using data for amino acids. Stable isotope compositions of individual amino acids can partition the isotopic variance in bulk tissue into components associated with primary production on the one hand, and diet and physiology on the other. The carbon framework of essential amino acids (EAAs) can be synthesised de novo only by plants, fungi and bacteria and must be acquired by consumers through the diet. Consequently, the carbon isotopic composition of EAAs in consumers reflects that of primary producers in the location of feeding, whereas that of non-essential amino acids (non-EAAs) is additionally influenced by trophic fractionation and isotope dynamics of metabolic processing. We determined isotope chronologies from vertebrae of individual blue sharks and porbeagles from the North Atlantic. We measured carbon and nitrogen isotope compositions in bulk collagen and carbon isotope compositions of amino acids. Despite variability among individuals, common ontogenetic patterns in bulk isotope compositions were seen in both species. However, while life-history movement inferences from bulk analyses for blue sharks were supported by carbon isotope data from essential amino acids, inferences for porbeagles were not, implying that the observed trends in bulk protein isotope compositions in porbeagles have a trophic Frontiers in Marine Science | www.frontiersin.org 1 September 2021 | Volume 8 | Article 673016 Magozzi et al. Amino Acid-Isotopes Reveal Shark Movements or physiological explanation, or are suprious effects. We explored variations in carbon isotope compositions of non-essential amino acids, searching for systematic variations that might imply ontogenetic changes in physiological processing, but patterns were highly variable and did not explain variance in bulk protein δ 13 C values. Isotopic effects associated with metabolite processing may overwhelm spatial influences that are weak or inconsistently developed in bulk tissue isotope values, but interpreting mechanisms underpinning isotopic variation in patterns in non-essential amino acids remains challenging.
... In this study, we observed a predictable 13 C-enrichment of δ 13 C Teeth relative to muscle, fin, red blood cells and blood plasma, which is consistent across a diverse array of species with known variations in diet and habitat use (Bird et al., 2018;Cortés, 1999). Relationships were statistically significant at community and species-levels, with positive slopes nearly parallel to, but offset from a 1:1 relationship. ...
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The isotopic composition of tooth‐bound collagen has long been used to reconstruct dietary patterns of animals in extant and paleoecological systems. For sharks that replace teeth rapidly in a conveyor‐like system, stable isotopes of tooth collagen (δ13Ctooth & δ15Ntooth) are poorly understood and lacking in ecological context relative to other non‐lethally sampled tissues. This tissue holds promise, because shark jaws may preserve isotopic chronologies from which to infer individual‐level ecological patterns across a range of temporal resolutions. Carbon and nitrogen stable isotope values were measured and compared between extracted tooth collagen and four other, non‐lethally sampled tissues of varying isotopic turnover rates: blood plasma, red blood cells, fin, and muscle, from eight species of sharks. Individual‐level isotopic variability of shark tooth collagen was evaluated by profiling teeth of different ages across whole jaws for the shortfin mako shark Isurus oxyrinchus and sandbar shark Carcharhinus plumbeus. Measurements of δ13Ctooth and δ15Ntooth were positively correlated with isotopic values from the four other tissues. Collagen δ13C was consistently 13C‐enriched relative to all other tissues. Patterns for δ15N were slightly less uniform; tooth collagen was generally 15N‐enriched relative to muscle and red blood cells, but congruent with fin and blood plasma (values clustered around a 1:1 relationship). Significant within‐individual isotopic variability was observed across whole shortfin mako shark (δ13C range = 3.5‰, δ15N range = 3.8‰) and sandbar shark (δ13C range = 2.4‰ ‐5.4‰, δ15N range = 2.4‰ – 5.8‰) jaws, which trended with tooth age. We conclude that tissue amino acid composition and associated patterns of isotopic fractionation result in predictable isotopic offsets between tissues. Future measurements will refine the use of this tissue as an ecological and paleoecological proxy. Within‐individual variability of tooth stable isotope values suggests teeth of different ages may serve as ecological chronologies, which could be applied to studies on migration and individual‐level diet variation across diverse time scales. Improvements in understanding tooth replacement rates, isotopic turnover, and associated fractionation of tooth collagen will enhance inferences that can be made from the isotopic composition of shark tooth, outlining clear goals for future scientific inquiry.
... In this study, we observed a predictable 13 C-enrichment of δ 13 C Teeth relative to muscle, fin, red blood cells and blood plasma, which is consistent across a diverse array of species with known variations in diet and habitat use (Bird et al., 2018;Cortés, 1999). Relationships were statistically significant at community and species-levels, with positive slopes nearly parallel to, but offset from a 1:1 relationship. ...
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1. The isotopic composition of tooth-bound collagen has long been used to reconstruct dietary patterns of animals in extant and paleoecological systems. For sharks that replace teeth rapidly in a conveyor-like system, stable isotopes of tooth collagen (δ13Ctooth & δ15Ntooth) are poorly understood and lacking in ecological context relative to other non-lethally sampled tissues. This tissue holds promise, because shark jaws may preserve isotopic chronologies from which to infer individual-level ecological patterns across a range of temporal resolutions. 2.Carbon and nitrogen stable isotope values were measured and compared between extracted tooth collagen and four other, non-lethally sampled tissues of varying isotopic turnover rates: blood plasma, red blood cells, fin, and muscle, from eight species of sharks. Individual-level isotopic variability of shark tooth collagen was evaluated by profiling teeth of different ages across whole jaws for the shortfin mako shark Isurus oxyrinchus and sandbar shark Carcharhinus plumbeus. 3. Measurements of δ13Ctooth and δ15Ntooth were positively correlated with isotopic values from the four other tissues. Collagen δ13C was consistently 13C-enriched relative to all other tissues. Patterns for δ15N were slightly less uniform; tooth collagen was generally 15N-enriched relative to muscle and red blood cells, but congruent with fin and blood plasma (values clustered around a 1:1 relationship). 4. Significant within-individual isotopic variability was observed across whole shortfin mako shark (δ13C range = 3.5‰, δ15N range = 3.8‰) and sandbar shark (δ13C range = 2.4‰ -5.4‰, δ15N range = 2.4‰ – 5.8‰) jaws, which trended with tooth age. 5. We conclude that tissue amino acid composition and associated patterns of isotopic fractionation result in predictable isotopic offsets between tissues. Future measurements will refine the use of this tissue as an ecological and paleoecological proxy. Within-individual variability of tooth stable isotope values suggests teeth of different ages may serve as ecological chronologies, which could be applied to studies on migration and individual-level diet variation across diverse time scales. Improvements in understanding tooth replacement rates, isotopic turnover, and associated fractionation of tooth collagen will enhance inferences that can be made from the isotopic composition of shark tooth, outlining clear goals for future scientific inquiry.
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Patterns of mother‐embryo fractionation of 13C and 15N were assessed for their predictability across three species of batoids caught as bycatch in south‐eastern Australia. Stable isotope analysis of 24 mothers and their litters revealed that isotope ratios of embryos were significantly different from their corresponding mothers, and that the scale and direction of the difference varied within and across species. The range of variation across species was 3.5‰ for ẟ13C and 4‰ for ẟ15N, equivalent to a difference in trophic level. In one species (Urolophus paucimaculatus) litters could be significantly enriched or depleted in 13C and 15N relative to their mothers' isotope signatures. These results suggest that patterns of mother‐embryo isotope fractionation vary within and between species and that these patterns may not be explained by developmental mode alone. Contrasting patterns of fractionation between and within species makes it difficult to adjust mother‐embryo fractionation with broad‐scale correction factors. This article is protected by copyright. All rights reserved.
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SUPPORTING INFORMATION – Supporting Figures, Tables, and Appendices for: Rodrı́guez M.A. 2020. Measurement error models reveal the scale of consumer movements along an isoscape gradient.
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