Diet Composition and Nutritional Niche Breadth Variability in Juvenile White Sharks (Carcharodon carcharias)

Abstract

Establishing diets and dietary generalism in marine top predators is critical for understanding their ecological roles and responses to environmental fluctuations. Nutrition plays a key mediatory role in species-environment interactions, yet descriptions of marine predators’ diets are usually limited to the combinations of prey species consumed. Here we combined stomach contents analysis (n = 40), literature prey nutritional data and a multidimensional nutritional niche framework to establish the diet and niche breadths of white sharks (Carcharodon carcharias; mean ± SD precaudal length = 187.9 ± 46.4 cm, range = 123.8–369.0 cm) caught incidentally off New South Wales (NSW), Australia. Our nutritional framework also facilitated the incorporation of existing literature diet information for South African white sharks to further evaluate nutritional niches across populations and sizes. Although teleosts including pelagic eastern Australian salmon (Arripis trutta) were the predominant prey for juvenile white sharks in NSW, the diversity of benthic and reef-associated species and batoids suggests regular benthic foraging. Despite a small sample size (n = 18 and 19 males and females, respectively), there was evidence of increased batoid consumption by males relative to females, and a potential size-based increase in shark and mammal prey consumption, corroborating established ontogenetic increases in trophic level documented elsewhere for white sharks. Estimated nutritional intakes and niche breadths did not differ among sexes. Niche breadths were also similar between juvenile white sharks from Australia and South Africa. An increase in nutritional niche breadth with shark size was detected, associated with lipid consumption, which we suggest may relate to shifting nutritional goals linked with expanding migratory ranges.

Full text

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ORIGINAL RESEARCH
published: 07 June 2020
doi: 10.3389/fmars.2020.00422
Edited by:
Mark Meekan,
Australian Institute of Marine Science
(AIMS), Australia
Reviewed by:
Yannis Peter Papastamatiou,
Florida International University,
United States
Taylor Chapple,
Oregon State University,
United States
Charlie Huveneers,
Flinders University, Australia
*Correspondence:
Richard Grainger
richard.grainger@sydney.edu.au
Specialty section:
This article was submitted to
Marine Megafauna,
a section of the journal
Frontiers in Marine Science
Received: 04 February 2020
Accepted: 14 May 2020
Published: 07 June 2020
Citation:
Grainger R, Peddemors VM,
Raubenheimer D and
Machovsky-Capuska GE (2020) Diet
Composition and Nutritional Niche
Breadth Variability in Juvenile White
Sharks (Carcharodon carcharias).
Front. Mar. Sci. 7:422.
doi: 10.3389/fmars.2020.00422
Diet Composition and Nutritional
Niche Breadth Variability in Juvenile
White Sharks (Carcharodon
carcharias)
Richard Grainger1,2*, Victor M. Peddemors3, David Raubenheimer1,2 and
Gabriel E. Machovsky-Capuska1
1Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia, 2School of Life and Environmental Sciences,
The University of Sydney, Sydney, NSW, Australia, 3New South Wales Department of Primary Industries (Fisheries), Sydney
Institute of Marine Science, Mosman, NSW, Australia
Establishing diets and dietary generalism in marine top predators is critical for
understanding their ecological roles and responses to environmental fluctuations.
Nutrition plays a key mediatory role in species-environment interactions, yet descriptions
of marine predators’ diets are usually limited to the combinations of prey species
consumed. Here we combined stomach contents analysis (n= 40), literature prey
nutritional data and a multidimensional nutritional niche framework to establish the diet
and niche breadths of white sharks (Carcharodon carcharias; mean ±SD precaudal
length = 187.9 ±46.4 cm, range = 123.8–369.0 cm) caught incidentally off New South
Wales (NSW), Australia. Our nutritional framework also facilitated the incorporation of
existing literature diet information for South African white sharks to further evaluate
nutritional niches across populations and sizes. Although teleosts including pelagic
eastern Australian salmon (Arripis trutta) were the predominant prey for juvenile white
sharks in NSW, the diversity of benthic and reef-associated species and batoids
suggests regular benthic foraging. Despite a small sample size (n= 18 and 19 males
and females, respectively), there was evidence of increased batoid consumption by
males relative to females, and a potential size-based increase in shark and mammal
prey consumption, corroborating established ontogenetic increases in trophic level
documented elsewhere for white sharks. Estimated nutritional intakes and niche
breadths did not differ among sexes. Niche breadths were also similar between juvenile
white sharks from Australia and South Africa. An increase in nutritional niche breadth
with shark size was detected, associated with lipid consumption, which we suggest
may relate to shifting nutritional goals linked with expanding migratory ranges.
Keywords: diet, stomach contents, nutritional geometry, multidimensional nutritional niche framework, Bayesian
standard ellipse, marine predators, conservation
INTRODUCTION
Marine top predators shape their ecosystems through diet and nutrition (Machovsky-Capuska
and Raubenheimer, 2020). Despite their ecological importance, population declines and foraging-
associated conflicts with humans are also widespread among marine predators, posing significant
management challenges (Heithaus et al., 2008;Guerra, 2019). Although critical for contextualising
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broader aspects of their ecology (e.g. movements, habitat
use) which are relevant for addressing these management
challenges, the diet of many top predators remains
poorly characterised (Ramos and Gonzalez-Solis, 2012;
Machovsky-Capuska et al., 2016a).
Our understanding of the drivers of foraging in marine
predators will remain limited if diets are described solely,
on a taxonomic basis, as the combination of prey species
that a predator consumes. The literature provides abundant
evidence that prey, themselves, are complex and variable
combinations of macro- (e.g. proteins, lipids) and micro-
nutrients (e.g. vitamins and minerals) with diverse functional
implications for the consumer (Simpson and Raubenheimer,
2012). Yet dominant frameworks in foraging and community
ecology are largely conceptualised around energetic value (e.g.
calorific content of prey) as the sole currency that determines
foraging decisions (Lindeman, 1942;Stephens and Krebs, 1986).
Rather, a nutritionally explicit approach that distinguishes
between multiple functional food components (nutrients) and
links physiology, behaviour and ecology can enable a better
understanding of animals’ responses to their environment (del
Rio and Cork, 1997;Raubenheimer et al., 2009).
To provide such a platform, the nutritional geometry
framework (NGF) was developed (Raubenheimer and Simpson,
1993;Simpson and Raubenheimer, 1995;Simpson et al., 2004;
Raubenheimer, 2011). The NGF models foods and diets as
mixtures of macro- (e.g. proteins, lipids and carbohydrates)
and micro-nutrients (e.g. vitamins, minerals), thereby allowing
the influence of specific nutritional components, as well
as their interactions, on foraging decisions to be tested
(Raubenheimer et al., 2009). Proportions-based NGF models
were developed to overcome the logistical constrains of
field-based research, enabling a graphical representation of
the relationships among nutrients, prey, meals and diets
(Raubenheimer, 2011). Laboratory and field-based studies using
NGF in terrestrial (Hewson-Hughes et al., 2013;Erlenbach et al.,
2014;Felton et al., 2016;Coogan et al., 2017) and marine systems
(Raubenheimer et al., 2005;Ruohonen et al., 2007;Rowe et al.,
2018) have since provided consistent evidence that food selection
is likely driven by specific mixtures of nutrients, rather than
energy per se.
Proportions-based NGF was recently applied in a
novel development of niche theory, establishing a new
multidimensional nutritional niche framework (MNNF) for
assessing dietary generalism (Machovsky-Capuska et al., 2016d).
Under this framework, dietary classification along a generalist-
specialist spectrum might differ at the levels of prey eaten
(‘prey composition niche’) and the nutrient content of the diet
given ecological constraints (‘realised nutritional niche’). Species’
classifications as generalists or specialists can predict their success
across environmental contexts (Slatyer et al., 2013;Machovsky-
Capuska et al., 2016d;Senior et al., 2016). Certainly, knowledge
of predator nutritional niche breadths and requirements could
assist in understanding their responses to variations in prey
availability and composition (Machovsky-Capuska et al., 2016d).
Several studies on marine predators, including seabirds (Tait
et al., 2014;Machovsky-Capuska et al., 2016b,c;Miller et al.,
2017), cetaceans (Denuncio et al., 2017), fish, sharks and
pinnipeds (Machovsky-Capuska and Raubenheimer, 2020) have
now drawn from the MNNF to provide fresh insights into their
nutritional ecology.
Machovsky-Capuska et al. (2018) incorporated a standardised
metric (standard ellipse area, SEA) and statistical framework
utilising Bayesian multivariate ellipses (sensu Jackson et al.,
2011) to quantify and compare nutritional niche breadths. The
authors showed how the nutritional composition of prey and
diets, niche breadths, foraging behaviour and habitat use in
marine predators are shaped by environmental fluctuations. Such
approaches can assist in exploring the movements of species
(Simpson et al., 2006;Nie et al., 2015) and foraging patterns that
lead to human-predator conflict (Coogan et al., 2014;Coogan and
Raubenheimer, 2016).
White sharks (Carcharodon carcharias) are large marine
predators found globally in temperate and sub-tropical coastal
and open ocean regions (Domeier, 2012). Listed as vulnerable
by the IUCN (Rigby et al., 2019), white sharks are protected
across much of their range, including Australian waters (Malcom
et al., 2001). Despite the challenges inherent to studying these
large, mobile and threatened marine predators, a variety of
techniques have been employed to investigate their foraging
ecology including direct observation (Tricas and McCosker,
1984;Klimley, 1994) and biologging (Jorgensen et al., 2015;Jewell
et al., 2019;Watanabe et al., 2019a,b), fatty acid (Pethybridge
et al., 2014;Meyer et al., 2019) and stable isotope signatures
(Estrada et al., 2006;Carlisle et al., 2012;Hussey et al., 2012;Kim
et al., 2012;French et al., 2018;Tamburin et al., 2019), and to a
lesser extent stomach contents analysis (Tricas and McCosker,
1984;Bruce, 1992;Hussey et al., 2012). These studies broadly
characterise white sharks as generalist top predators of teleosts,
elasmobranchs and cephalopods which undergo an ontogenetic
increase in tropic level and dietary inclusion of marine mammals
as they transition from juveniles (<3 m total length; TL) to sub-
adults and adults (>3 m TL) (Estrada et al., 2006;Hussey et al.,
2012;Kim et al., 2012). Evidence for ontogenetic variation in
foraging habitat use has also been identified, with an apparent
predominance of nearshore prey resources in the diet of juveniles
and greater exploitation of offshore food webs among adults
(Carlisle et al., 2012;French et al., 2018;Tamburin et al., 2019).
In eastern Australia, white sharks predominantly inhabit
coastal and neritic regions between southern Queensland
(∼22◦S) and northern Tasmania (∼40◦S), exhibiting
seasonal latitudinal movements that might relate to local
prey distributions (Bruce et al., 2006, 2019). While larger
individuals occasionally inhabit this region, the majority of
those encountered are juveniles (<3 m TL; Reid et al., 2011;
Bruce and Bradford, 2012;Bruce et al., 2013). White sharks are
also the primary shark species responsible for bites on humans
in eastern Australia (West, 2011). However, despite efforts to
conserve and manage this species, their diet this region has not
been characterised.
Here, stomach content analyses (SCA) were combined with
prey composition data, MNNF and Bayesian multivariate ellipses
to provide the first dietary and nutritional assessment for white
sharks in eastern Australia. Given the relative abundance of
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smaller individuals within the study area, as discussed above,
the majority of stomach samples were from sharks <3 m TL
(∼2.35 m precaudal length; PCL) and thus our SCA data should
be considered representative of juvenile white sharks (Bruce
and Bradford, 2012). To extend our exploration of variability
in the nutritional niche of white sharks more broadly across
populations and sizes, we also integrate literature stomach
content data for South African white sharks (Hussey et al., 2012)
using our MNNF approach. Observational evidence for nutrient-
specific foraging in white sharks exists (e.g. selective feeding
on body parts and lipid-rich tissues of whales; Fallows et al.,
2013;Tucker et al., 2019), and sex-specific variation in foraging
behaviour and habitat use have also been observed (Bruce and
Bradford, 2015;Bruce et al., 2019). Building on these previous
studies, our specific objectives were to: (1) establish the diet
composition and key prey species of juvenile white sharks in
eastern Australia, (2) examine whether shark sex or size predicts
the prey consumed, (3) determine if sex or size influences the
nutritional composition of prey, diets and niche breadths of
white sharks from eastern Australia and (4) compare nutritional
niche breadths of juvenile white sharks between eastern Australia
and South Africa, and between juveniles, subadults and adults
within South Africa.
MATERIALS AND METHODS
Ethics Statement
All stomachs were collected from deceased white sharks caught
in the New South Wales Shark Meshing (Bather Protection)
Program (NSW SMP), operated by the NSW Department
of Primary Industries (NSW DPI), or from other incidental
mortalities. No animals were killed specifically for this research.
Collection of samples was conducted under NSW DPI permit #
P01/0059(A)-4.0.
Stomach Sampling
Between 2008 and 2019, a total of 52 white shark stomach
samples were obtained through the NSW SMP (n= 47), which
operates annually over the Austral summer (September to
April), and from incidental mortalities (entanglements, beached
carcasses) near Ballina (northern NSW, n= 4) and Jervis Bay
(southern NSW, n= 1) (Figure 1 and Supplementary Table S1).
Sharks were transported to Sydney, Australia, and stored frozen
(−20◦C) for subsequent necropsy. During necropsy, a range
of biological data and morphometrics were recorded including
sex (based on presence/absence of claspers), precaudal length
(PCL, cm), fork length (FL, cm) and total length (TL, cm),
and stomachs (oesophagus to pyloric sphincter) were removed,
sealed with cable ties and refrozen (−20◦C) in polyethylene
bags until analysis.
Stomach Contents Analysis
Stomachs were thawed, intact prey were removed, and remaining
contents (loose bones, otoliths, beaks) were washed through
nested mesh sieves (2, 1, 0.5 mm) with water. Prey were identified
to the lowest possible taxonomic level using published guides
(Smale et al., 1995;Lu and Ickeringill, 2002;Furlani et al., 2007;
Last and Stephens, 2009;Reid, 2016;Froese and Pauly, 2019)
and reference collections at the Australian Museum (otoliths)
and the University of Sydney/Australian National University
(teleost bones)1.
Prey were counted and weighed (to 0.1 or 0.00001 g for
cephalopod beaks and otoliths), with enumeration of digested
items based on the minimum number of individuals by
otoliths (maximum number of left or right otoliths), jaw bones
(maximum number of left or right dentaries/premaxillae) or
cephalopod beaks (maximum number of upper or lower beaks).
Where possible, prey lengths were measured (nearest mm)
using FL for fish, sharks and small rays (where FL = TL
for species without a forked tail), disc width (DW) for eagle
rays (Last and Stephens, 2009), total body length (TBL; tip
of upper rostrum to tail notch, Norris, 1961) for dolphins
and mantle length (ML) for cephalopods (Reid, 2016). Other
body morphometrics (e.g. eagle ray tooth plate width, dentary
length), otolith diameter (Smale et al., 1995), lower beak
crest length (cuttlefish) and lower beak rostrum length (squid)
(Lu and Ickeringill, 2002) were also measured to the nearest
0.01 mm using either ImageJ software or digital callipers
(iGaging OriginCal©), depending on size. These measurements
were used to calculate reconstructed lengths and masses of
prey using length-weight relationships from the literature
(Supplementary Table S2) to help address potential biases in
the wet mass data associated with an advanced digestive state.
Where relevant below, counts and lengths of prey are expressed
as means ±SD.
Indices of Dietary Importance and
Statistical Analyses
Objective 1: Establish the Diet Composition and Key
Prey Species of White Sharks in Eastern Australia
To quantify the relative dietary importance of prey items to
white sharks, standard indices of percent frequency of occurrence
(%F; the percentage of stomachs containing a particular prey
species), percentage number (%N; the number of individuals of
a particular prey species as a percentage of all prey individuals
identified across all stomachs), percentage mass (%M) and
percentage index of relative importance (%IRI) using non-
reconstructed mass were calculated (Hyslop, 1980;Cortés, 1997).
For comparison, %M and %IRI were also calculated using
reconstructed mass (denoted %MRand %IRIR, respectively).
These indices were calculated separately for each sex, and for all
sharks combined.
Objective 2: Examine Whether Sex or Size Influences
the Prey Consumed by White Sharks in Eastern
Australia
For statistical comparisons of the diet, prey species were grouped
into seven functional groups defined by taxonomy and habitat
using FishBase (Froese and Pauly, 2019): pelagic teleosts, reef-
associated teleosts, demersal teleosts, sharks, batoids, mammals
and cephalopods (Table 1). Teleosts and elasmobranchs that
1https://digital.library.sydney.edu.au/nodes/view/6389
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FIGURE 1 | Sampling locations for white sharks in this study through the New South Wales Shark Meshing (Bather Protection) Program (map on right) and incidental
mortalities (Ballina and Jervis Bay). Blue dots denote the beaches at which sharks were sampled and the marginal histogram shows the latitudinal distribution of
non-empty (grey) and empty stomachs (white). Map shapefile source: Geoscience Australia (http://pid.geoscience.gov.au/dataset/ga/60804).
were unable to be identified were excluded from analyses.
To examine whether sex or size influenced the frequency
of occurrence (presence/absence) or number (count) of prey,
binomial and Poisson/negative binomial generalised linear
models (GLMs), respectively, were fitted separately for each
prey functional group. We used GLMs to test for dietary
differences for the following reasons: (1) the number of
prey groups was relatively small; (2) they allowed individual
sharks to be used as samples (traditional distance-based
multivariate approaches usually necessitate random pooling
of multiple stomachs into ‘dietary samples’, requiring larger
sample sizes; see Sommerville et al., 2011); (3) shark length
could be modelled as a continuous predictor; (4) competing
hypotheses about the effect of sex and size on prey consumption
could be evaluated using small-sample corrected Akaike
information criterion (AICc) which examines the trade-off
between model fit and complexity/overfitting (Symonds and
Moussalli, 2011). One male with missing length data was
excluded so that the same samples were considered across
all candidate models during AICcselection (AIC is only
comparable across models based on the same sample set;
Symonds and Moussalli, 2011) and subsequent corrections
for multiple comparisons (Benjamini and Hochberg, 1995).
One female containing unidentified prey was also excluded.
Therefore, a total of 18 males and 19 females with known
lengths were used in these analyses. Although the samples used
for statistical analyses comprised individuals caught between
2008 and 2019, the majority were caught between 2014
and 2018 (92%), and in the Austral spring (Supplementary
Figure S1). Given generally low sample sizes for any one
year (n≤8), and particularly for seasons other than
spring (Supplementary Figure S1), sampling year and season
were not included in models. The proportion of male and
female stomachs was similar across seasons (Supplementary
Figure S1) and thus season is unlikely to confound sex-
based comparisons.
For binomial GLMs, several prey groups were relatively rare
(more absences than presences), where a clog-log link may be
preferable to logit (Zuur et al., 2009). Therefore, initial models
with predictors sex + PCL and either a logit or clog-log link
were fitted for each prey group and the link giving the lowest
AICcwas favoured. Following Denuncio et al. (2017), several
candidate binomial GLMs were then fitted within each prey
functional group using the predictors sex, PCL, sex + PCL, and
a null model with no predictors (intercept only) to investigate
whether sex and PCL did not influence prey occurrences. The
model with the lowest AICcwas selected for each prey functional
group. After Benjamini and Hochberg (1995), a false discovery
rate (FDR) adjusted significance level of p<0.0375 was used
to account for multiple comparisons in the models where
the null was not favoured (Supplementary Tables S3,S4; 4
p-values/ tests).
Poisson or negative binomial GLMs (log link) were also fitted
to test whether the numerical abundance of each functional
group was influenced by sex or PCL. Over- or under-dispersion
was assessed using the ratio of residual deviance/residual
degrees freedom (>1 = overdispersion, <1 = underdispersion;
Zuur et al., 2009). Where evidence of overdispersion was
present, GLMs fit with Poisson and negative binomial error
distributions (glm.nb function from MASS package; Venables
and Ripley, 2002) were compared using a likelihood ratio
test (lrtest function from lmtest package; Zeileis and Hothorn,
2002) to determine if fit was improved using a negative
binomial error (Zuur et al., 2009). Candidate models including
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TABLE 1 | Dietary importance of prey items, identified to the lowest possible taxonomic level, from analyses of stomach contents of white sharks (Carcharodon carcharias) sampled off Australia’s east coast.
Prey groups Functional
group
All Sharks (n= 40) Male Sharks (n= 19) Female Sharks (n= 20)
%F %N %M (%MR) %IRI (%IRIR) %F %N %M (%MR) %IRI (%IRIR) %F %N %M (%MR) %IRI (%IRIR)
TELEOSTS 70.00 71.90 20.09 (54.51) 67.57 (79.96) 63.16 64.18 30.48 (59.54) 56.45 (67.84) 75.00 81.13 13.39 (48.06) 80.83 (89.44)
Anguilliformes
Ophichthidae Ophisurus serpens (serpent eel) DT 2.50 0.83 0.07 (0.32)0.12 (0.13)5.26 1.49 0.18 (0.58)0.44 (0.50)
Unidentified eel DT 5.00 1.65 0.13 (0.05)0.47 (0.37)10.00 3.77 0.21 (0.12)1.44 (1.13)
Mugiliformes
Mugilidae Mugil cephalus (sea mullet) DT 5.00 1.65 0.25 (0.82)0.50 (0.54)10.53 2.99 0.63 (1.47)1.91 (2.13)
Perciformes
Arripidae Arripis trutta (eastern Australian salmon) PT 25.00 17.36 9.95 (25.70)36.30 (46.95)21.05 14.93 14.89 (23.16)31.56 (36.51)30.00 20.75 6.80 (28.98)29.88 (43.15)
Carangidae Pseudocaranx georgianus (silver trevally) RT 5.00 1.65 0.44 (0.50)0.56 (0.47)5.26 1.49 0.56 (0.53)0.54 (0.48)5.00 1.89 0.36 (0.46)0.41 (0.34)
Trachurus novaezelandiae (yellowtail scad) PT 2.50 14.88 3.61 (1.62)2.46 (1.80)5.26 26.87 9.26 (2.91)9.56 (7.14)
Labridae Achoerodus viridis (eastern blue groper) RT 7.50 2.48 1.33 (16.04)1.52 (6.06)10.53 2.99 3.21 (28.76)3.28 (15.22)5.00 1.89 0.13 (0.08)0.37 (0.28)
Unidentified wrasse RT 2.50 0.83 0.01 (0.01)0.11 (0.09)5.00 1.89 0.02 (0.01)0.35 (0.27)
Sillaginidae Sillago ciliata (sand whiting) DT 2.50 1.65 <0.01 (0.35)0.22 (0.22)5.00 3.77 <0.01 (0.80)0.68 (0.66)
Uranoscopidae Ichthyscopus barbatus (fringe stargazer) DT 2.50 0.83 2.20 (2.38)0.40 (0.35)5.00 1.89 3.61 (5.38)0.99 (1.05)
Unidentified stargazer DT 12.50 7.44 0.21 (5.74)5.08 (7.18)5.26 1.49 0.01 (1.63)0.40 (0.75)20.00 15.09 0.33 (10.93)11.15 (15.05)
Pleuronectiformes
Unidentified flatfish DT 2.50 0.83 0.11 (0.10)0.12 (0.10)5.00 1.89 0.18 (0.23)0.37 (0.31)
Scorpaeniformes
Platycephalidae Unidentified flathead DT 7.50 2.48 0.06 (0.15)1.01 (0.86)10.00 3.77 0.03 (0.08)1.37 (1.11)
Unidentified bony fish – 25.00 17.36 1.73 (0.73)25.38 (19.72)15.79 11.94 1.75 (0.51)10.87 (8.96)35.00 24.53 1.72 (1.00)33.21 (25.84)
ELASMOBRANCHS 45.00 19.83 40.08 (23.51) 28.29 (17.62) 68.42 28.36 31.74 (21.32) 38.82 (29.51) 25.00 9.43 45.44 (26.33) 15.64 (8.25)
Lamniformes
Lamnidae Unidentified mackerel shark S 2.50 0.83 2.79 (1.17)0.48 (0.22)5.26 1.49 7.16 (2.10)2.29 (0.86)
Carcharhiniformes
Sphyrnidae Sphyrna zygaena (smooth hammerhead) S 2.50 0.83 24.73 (10.38)3.40 (1.22)5.00 1.89 40.54 (23.48)7.67 (3.67)
Carcharhinidae Carcharhinus sp. (whaler shark) S 2.50 0.83 2.15 (0.90)0.40 (0.19)5.26 1.49 5.51 (1.62)1.85 (0.75)
(Continued)
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TABLE 1 | Continued
Prey groups Functional
group
All Sharks (n= 40) Male Sharks (n= 19) Female Sharks (n= 20)
%F %N %M (%MR) %IRI (%IRIR) %F %N %M (%MR) %IRI (%IRIR) %F %N %M (%MR) %IRI (%IRIR)
Myliobatiformes
Myliobatidae Aetobatus ocellatus (whitespotted eagle ray) B 2.50 0.83 0.01 (0.92)0.11 (0.19)5.26 1.49 0.02 (1.66)0.40 (0.76)
Myliobatis sp. (eagle ray) B 5.00 1.65 3.41 (3.90)1.35 (1.21)10.53 2.99 8.74 (7.00)6.21 (4.79)
Unidentified eagle ray B 2.50 0.83 <0.01 ( < 0.01)0.11 (0.09)5.26 1.49 <0.01 ( <0.01)0.40 (0.36)
Rhinopteridae Rhinoptera neglecta (cownose ray) B 5.00 1.65 2.18 (0.91)1.02 (0.56)10.53 2.99 5.59 (1.64)4.54 (2.22)
Urolophidae Trygonoptera testacea (common stingaree) B 2.50 0.83 0.03 (0.37)0.11 (0.13)5.26 1.49 0.09 (0.67)0.42 (0.52)
Unidentified stingaree B 7.50 2.48 0.82 (0.70)1.32 (1.04)15.79 4.48 2.11 (1.26)5.23 (4.13)
Unidentified stingray B 12.50 4.96 0.55 (0.23)3.66 (2.83)15.79 5.97 1.16 (0.34)5.67 (4.54)10.00 3.77 0.15 (0.09)1.42 (1.12)
Torpediniformes
Hypnidae Hypnos monopterygius (coffin ray) B 2.50 0.83 2.89 (1.21)0.49 (0.22)5.00 1.89 4.74 (2.75)1.20 (0.67)
Unidentified electric ray B 2.50 0.83 0.35 (2.72)0.16 (0.39)5.26 1.49 0.89 (4.88)0.63 (1.53)
Unidentified elasmobranch – 7.50 2.48 0.19 (0.08)1.06 (0.84)10.53 2.99 0.47 (0.14)1.83 (1.50)5.00 1.89 0.01 (0.01)0.34 (0.27)
MAMMALS 7.50 2.48 39.67 (20.95) 3.32 (1.59) 10.53 2.99 37.55 (18.75) 4.03 (1.99) 5.00 1.89 41.05 (23.78) 2.45 (1.18)
Cetacea
Delphinidae Tursiops aduncus (bottlenose dolphin) M 5.00 1.65 37.80 (20.17)10.49 (4.76)5.26 1.49 32.75 (17.34)9.06 (4.51)5.00 1.89 41.05 (23.78)7.76 (3.71)
Unidentified dolphin M 2.50 0.83 1.87 (0.79)0.36 (0.18)5.26 1.49 4.81 (1.41)1.67 (0.70)
UNIDENTIFIED VERTEBRATES 2.50 0.83 0.05 (0.02) 0.02 (0.02) 0.00 0.00 0.00 (0.00) 0.00 (0.00) 5.00 1.89 0.08 (0.05) 0.11 (0.09)
Unidentified vertebrate – 2.50 0.83 0.05 (0.02)0.12 (0.09)5.00 1.89 0.08 (0.05)0.36 (0.28)
CEPHALOPODS 15.00 4.96 0.11 (1.01) 0.80 (0.81) 15.79 4.48 0.23 (0.39) 0.70 (0.67) 15.00 5.66 0.03 (1.79) 0.97 (1.03)
Sepiida
Sepiidae Sepia rozella (rosecone cuttlefish) C 2.50 0.83 0.09 (0.16)0.12 (0.11)5.26 1.49 0.23 (0.30)0.46 (0.43)
Sepia apama (giant cuttlefish) C 2.50 0.83 0.02 (0.79)0.11 (0.18)5.00 1.89 0.03 (1.79)0.35 (0.53)
Teuthida
Loliginidae Sepioteuthis australis (southern calamari) C 5.00 1.65 <0.01 (0.05)0.44 (0.37)5.26 1.49 <0.01 (0.09)0.40 (0.38)5.00 1.89 <0.01 ( < 0.01)0.34 (0.27)
Unidentified squid C 5.00 1.65 <0.01 ( < 0.01)0.44 (0.36)5.26 1.49 <0.01 ( <0.01)0.40 (0.36)5.00 1.89 <0.01 ( < 0.01)0.34 (0.27)
Total raw mass (kg) 68.43 26.66 41.74
Total reconstructed mass (kg) 162.96 90.71 72.06
Measures of dietary importance of prey items are based on frequency of occurrence (%F), percentage number (%N), percentage mass (%M) and a compound metric, percentage index of relative importance (%IRI).
Indices of percentage mass and relative importance based on mass-reconstructed data (%MRand %IRIR, respectively) are also shown in brackets. Data are presented for all sharks grouped together, and when
separated by sex to explore sex-specific diet differences. Sex was unavailable for one shark (included in ‘All Sharks’ group). Functional groups: PT, pelagic teleost; RT, reef-associated teleost; DT, demersal teleost; S,
shark; B, batoid; M, mammal; C, cephalopod.
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the predictors sex, PCL, sex + PCL and a null model
were then compared using AICcand the model with the
lowest AICcwas selected for each prey functional group.
As above, the FDR was controlled among models where
the null was not selected (Supplementary Tables S5,S6; 5
p-values/tests) using a significance level of p<0.01 (Benjamini
and Hochberg, 1995). Analyses were conducted in R-3.6.0
(R Core Team, 2019).
Objective 3: Determine if Sex or Size Predicts the
Nutritional Composition of Prey, Diets and Niche
Breadths of White Sharks From Eastern Australia
To compare whether dietary nutrient intake and nutritional
niche breadths vary with sex or size, data on the proximate
composition of prey species of white sharks in eastern Australia
were extracted from the literature (Supplementary Table S7).
Compositions of prey were expressed as wet mass percentages
of protein (%P), lipid (%L) and water (%W), given that
carbohydrate content is negligible in most marine prey (Craig
et al., 1978). When possible, we extracted prey compositions
from studies conducted in geographical proximity to the
present study (Supplementary Table S7;Tait et al., 2014).
If nutritional information was unavailable for a prey species,
average values for closely related taxa (same genus or family)
with similar ecological attributes were used (Supplementary
Table S7;Eder and Lewis, 2005). To estimate the composition
of the diet, the nutritional composition of each stomach content
sample (%P, %L, %W) was calculated using the proximate
composition of the prey species present, multiplied by their
proportional contribution to the total reconstructed mass of the
stomach sample (Machovsky-Capuska et al., 2016c). To calculate
the energy density of each stomach sample (Edensity, kJ g−1
reconstructed wet mass stomach content), macronutrients were
converted to metabolisable energy using conversion factors of 17
and 37 kJ g−1for protein and lipid, respectively (NRC, 1989).
An estimate of mass-specific energy consumption (Eshark, kJ kg−1
shark mass) was also calculated using the total energy content
of each stomach sample divided by total shark mass (excluding
stomach content weight). Shark mass was measured directly
(most individuals) or estimated using a PCL-mass relationship
[mass (kg) = 1 ×10−8PCL(mm)3.30394,n= 41, r2= 0.92;
Supplementary Table S1]. Diet compositions are expressed as
mean ±SD, where relevant.
Prey and diet compositions were then visualised using
proportions-based NGF models (Raubenheimer, 2011).
These models facilitate plotting of three proportional dietary
components in two dimensions whereby those three components
sum to 100% such that knowing the values for the components
on the x- and y-axes (here %P and %L) implies the value for the
third component (%W) which increases towards the origin (as
%P and %L decrease).
To explore whether sex or size influenced dietary nutritional
components, separate linear models (LM) were fitted to each
of %P, %L, %W, P:L (protein to lipid ratio), Edensity and
Eshark (Machovsky-Capuska et al., 2016c). Prior to analysis,
%P, %L and %W were logit transformed and P:L was
natural log transformed. As for GLMs above, LMs for each
nutritional component were fitted with the predictors sex,
PCL, sex + PCL and a null, and the model favoured
by AICcwas selected. Stomach samples from 18 males
and 19 females were used from these comparisons with
1 male with missing length data and 1 female containing
unidentified prey excluded, as for GLM analyses on prey
functional groups above.
The influence of sex and size on the niche breadth of prey and
diet compositions consumed was explored following Machovsky-
Capuska et al. (2018). Proportions-based NGF modelling was
combined with Bayesian standard ellipses (Jackson et al., 2011)
to produce estimates of prey composition and realised nutritional
niche breadths as SEA (%2). Small-sample corrected SEA (SEAc;
Syvaranta et al., 2013) were calculated for the prey and diet
compositions consumed by each sex (n= 19 males and females;
1 female containing unidentified prey excluded), and for two
size classes of sharks based on those used in Hussey et al.
(2012); class 1:<185 cm PCL (n= 20 sharks), class 2: 185–
234.9 cm PCL (n= 14 sharks). An additional male for which
length data were unavailable was included here because effects
of sex and size on nutritional niche breadth (SEAc) were
investigated using separate analyses, unlike for GLM and LM
analyses above where model selection and multiple comparison
corrections required the exclusion of this male (Benjamini
and Hochberg, 1995;Symonds and Moussalli, 2011). Larger
size classes from Hussey et al. (2012) were not included in
comparisons for eastern Australian white sharks due to low
sample size. To evaluate if SEAcdiffered among groups (sexes
or sizes), Bayesian inference with Markov chain Monte Carlo
(MCMC) simulations was used to produce a range of possible
posterior estimates for SEA (SEAb). MCMC simulations were
performed using 2 ×104iterations with 2 chains, a burn-
in of 1 ×103and thinning of 10. Differences among groups
(sizes and sexes) were determined by comparing the resulting
posterior distributions for SEAbprobabilistically (Jackson et al.,
2011). Analyses were conducted using the SIBER package in R
(Jackson et al., 2011).
Objective 4: Compare Nutritional Niche Breadths of
Juvenile White Sharks Between Eastern Australia and
South Africa, and Between Juveniles, Subadults and
Adults Within South Africa
To place our results from eastern Australia in a broader context,
we combined data on prey nutritional compositions from the
literature with the diet of white sharks from KwaZulu-Natal
(KZN), South Africa (RSA), based on Hussey et al. (2012)
(Supplementary Table S8), enabling further exploration of
nutritional niche breadths across populations and sizes. Prey
composition niches were modelled for four size classes (classes
1 and 2 as above, class 3 = 235–284.9 cm PCL, class 4 = ≥285 cm
PCL) using proportions-based NGF and SEAc. Comparisons
of nutritional niche breadths were then made using SEAbas
above. Low sample size in classes 3 and 4 for Australian white
sharks limited our inter-population comparisons of SEAbto
juveniles (classes 1 and 2). However, within RSA, SEAbcould
be compared across all four size classes to investigate how
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nutritional generalism (prey composition niche breadth) varies
across a broader ontogenetic range (juveniles to adults).
RESULTS
Objective 1: Establish the Diet
Composition and Key Prey Species of
White Sharks in Eastern Australia
Of the 52 white shark stomachs analysed, 40 (76.9%) contained
identifiable prey remains. The size of sharks with non-empty
stomachs ranged from 123.8 cm PCL (162.0 cm TL) to 369.0 cm
PCL (465.0 cm TL) (Figure 2). However, prey data were mostly
from juveniles <235 cm PCL (∼300 cm TL), with similar size
distributions for male and female sharks (Figure 2).
A total of 121 prey items were found across 40 stomachs,
with the numbers of prey individuals per stomach (3.0 ±3.5
individuals) and prey species per stomach (1.9 ±1.2) being
generally low. Across all white sharks, teleost fishes were the
most important prey group (%IRIR), followed by elasmobranchs,
mammals and then cephalopods (Table 1).
Eastern Australian salmon (Arripis trutta, a pelagic-neritic
teleost) was the most important prey species by all measures
of dietary importance (excluding non-reconstructed mass, %M;
Table 1). Remaining identified teleosts were mostly composed of
demersal (e.g. stargazers) and reef-associated species (e.g. eastern
blue groper), leading to a relatively high diversity of non-pelagic
species compared to pelagic (Table 1). The mean reconstructed
FL for all teleosts was 38.9 ±17.4 cm (range 21.0-97.1 cm, n= 59)
while for the most prevalent individual prey species, eastern
Australian salmon, it was 50.3 ±8.3 cm (range 35.8–66.8 cm,
n= 20).
For elasmobranchs, batoids generally had greater dietary
importance (%F, %N, %IRI, %IRIR) than sharks, with some
variation by sex (Table 1). Batoids consisted mostly of
benthopelagic eagle and cownose rays (DW = 50.2 ±15.5 cm,
range = 39.6–68.0 cm, n= 3) and small benthic rays (stingarees;
TL = 34.9 ±6.1 cm, range = 27.9–40.2 cm, n= 4).
Cephalopods and marine mammals had low dietary
importance (%IRIR) due to low %M, and low %F and %N
contributions, respectively (Table 1). The reconstructed ML of
squid and cuttlefish was 14.1 ±9.2 cm (range 3.8–26.2 cm, n= 4).
The only mammal prey observed in stomachs was dolphin,
including the tail stock and fluke of an unidentified dolphin and
two whole bottlenose dolphin (Tursiops aduncus) calves (101.4
and 111.4 cm TBL). The smallest shark that consumed dolphin
measured 173.4 cm PCL (221.0 cm TL).
Objective 2: Examine Whether Sex or
Size Influences the Prey Consumed by
White Sharks in Eastern Australia
A detailed sex-based breakdown of the dietary importance
of prey functional groups to male and female white sharks
suggested some differences in prey consumption (Table 1) which
was supported by results from binomial and Poisson/negative
binomial GLMs (see Supplementary Tables S3,S5 for AICc
model rankings, and Supplementary Tables S4,S6 for full GLM
outputs). For occurrence data, noting an FDR-adjusted critical
level of p<0.0375, males consumed batoids significantly more
frequently than females (est.SexMale = 1.6740 ±0.7862, z= 2.129,
p= 0.0332, Figure 3A). Significant positive relationships were
also observed between PCL and the occurrence of shark
(est.PCL ±SE = 0.0039 ±0.0018, z= 2.091, p= 0.0365) and
mammal prey (est.PCL ±SE = 0.0022 ±0.0008, z= 2.526,
p= 0.0115) in the diet (Figures 3B,C), with no significant effect
of sex on either group (Supplementary Tables S3,S4). For
all other prey groups (pelagic teleosts, reef-associated teleosts,
demersal teleosts and cephalopods), AICcfavoured a null model
(Supplementary Table S3) suggesting that neither sex nor PCL
influenced occurrences of these prey in the diet of white sharks
over the size range assessed here.
For abundance data, males consumed significantly greater
numbers of batoids than females (Figure 3D and Table 1;
est.SexMale ±SE = 1.6635 ±0.6325, z= 2.630, p= 0.0085).
Model rankings with AICcfavoured GLMs including PCL for
mammals, sex + PCL for sharks, and sex only for demersal
teleosts (Supplementary Table S5). However, these were not
significant at an FDR-adjusted critical level of p<0.01
(Supplementary Table S6). There was no significant effect of sex
or PCL on the numerical abundance of the other prey groups
(Supplementary Tables S5,S6).
Objective 3: Determine if Size or Sex
Predicts the Nutritional Composition of
Prey, Diets and Niche Breadths of White
Sharks From Eastern Australia
Proportions-based NGF models showed that white sharks
consumed prey varying broadly in their nutritional composition
between maximum P:L ratios of 62.9:1.0 and 37.3:1.0 for females
and males, respectively, to a minimum P:L of 0.7:1.0 for both
sexes (Figure 4A). General clustering in the compositions of
prey functional groups was also evident, with elasmobranchs
showing high %P and low %L, cephalopods having high
%W and low %P and %L, and teleosts having moderate
%P and variable %L (Figure 4A). The SEAcfor the prey
composition niche of male and female sharks was 54.0 and 64.2,
respectively, with Bayesian modelling indicating no significant
difference (probability SEAbmale <SEAbfemale = 0.70, <0.95;
Figure 4B).
White sharks (sexes pooled) consumed a diet with an
estimated composition of 75.2 ±5.9% water, 19.1 ±2.0% protein,
5.7 ±5.1% lipid, a P:L of 7.9 ±8.3, Edensity of 5.3 ±2.0 kJ g−1
diet mass and Eshark of 210.1 ±285.1 kJ kg−1shark mass, with
protein content being relatively constant and lipids being more
variable (Figure 4C). For %P, sex only had the lowest AICc, %L,
P:L and Edensity included PCL only, and an null model had the
lowest AICcfor the variables %W and Eshark (Supplementary
Table S9). However, there was no significant effect of sex on
dietary %P (est.SexMale ±SE = 0.0789 ±0.0430, t= 1.836,
p= 0.0749) or PCL on %L (est.PCL ±SE = 0.0006 ±0.0004,
t= 1.574, p= 0.1250), P:L (est.PCL ±SE = −0.0005 ±0.0003,
t=−1.615, p= 0.1153) or Edensity (est.PCL ±SE = 0.0011 ±0.0007,
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FIGURE 2 | Size distribution of male and female white sharks from which stomachs were sampled for this study. Shading shows stomachs that were empty (white)
or had prey (black). Size class bins are based on those used in Hussey et al. (2012) for comparison.
t= 1.595, p= 0.1196) at the level of p<0.05 (see Supplementary
Table S10 for full LM outputs). The SEAcfor the realised
nutritional niche breadth of males and females was 32.6 and 29.9,
respectively, with Bayesian modelling indicating no significant
difference (probability SEAbfemale <SEAbmale = 0.60, <0.95;
Figure 4D).
A comparison of two size classes of white sharks from eastern
Australia showed similar prey composition niches (Figure 5A)
with P:L ranging from 62.9:1.0 to 0.7:1.0 (both classes) and SEAc
of 53.8 and 57.8 for classes 1 and 2 sharks, respectively, which
was not significantly different (probability SEAbclass 1 <SEAb
class 2 = 0.57, <0.95; Figure 5B). However, larger sharks had a
significantly broader realised nutritional niche (probability SEAb
class 2 >SEAbclass 1 = 0.96, >0.95), with SEAcof 24.2 and 46.0
for classes 1 and 2 sharks, respectively (Figures 5C,D).
Objective 4: Compare Nutritional Niche
Breadths of Juvenile White Sharks
Between Eastern Australia and
South Africa, and Between Juveniles,
Subadults and Adults Within South Africa
Classes 1 and 2 white sharks from Australia and RSA had
similar prey composition niches with high degrees of overlap
(Figures 6A,B), and there was no significant difference in niche
breadth between regions for class 1 (probability SEAbAustralian
sharks >SEAbRSA sharks = 0.90, <0.95) or class 2 sharks
(probability SEAbAustralian sharks <SEAbRSA sharks = 0.77,
<0.95) (Figure 7). In RSA sharks, prey composition niche
breadth increased sequentially with size, with SEAcof 32.1, 72.1,
179.1 and 272.5 for classes 1 to 4 sharks, respectively (Figure 7).
Bayesian modelling showed that this increase was significant
from classes 1 to 2 (probability SEAbclass 1 <SEAbclass
2 = 0.98, >0.95) and classes 2 to 3 (probability SEAbclass
2<SEAbclass 3 = 0.99, >0.95), but not between classes 3
and 4 sharks (probability SEAbclass 3 <SEAbclass 4 = 0.76,
<0.95) (Figure 7).
DISCUSSION
We have provided the first detailed description of the diet
of juvenile white sharks in eastern Australia, revealing the
importance of both benthic and pelagic prey resources, with
evidence for possible sex-based dietary variation. Our nutritional
framework, a novel application in elasmobranch diet studies, also
enabled us to establish the macronutrient compositions of prey,
diets and the nutritional niches in which white sharks forage,
and how niche breadth may vary with shark size. These findings
help to further our understanding of the nutritional ecology
of white sharks.
White Shark Diet Variation Among Sexes,
Sizes and Populations
Stomach contents analysis provides a unique and important
opportunity to understand the diets of large, cryptic marine
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FIGURE 3 | Results from significant sex- and size-based analyses of the occurrence and numerical abundance of functional prey groups in the diet of white sharks.
(A) Stacked bar plot comparing presence (black) and absence (white) of batoids in stomachs of each sex. The predicted probabilities of the occurrence of shark (B)
and mammal (C) prey by precaudal length, with points showing the size of individual white sharks included in the analysis (n= 37) and the occurrence of these prey
groups in stomachs (1 = present, 0 = absent). (D) The mean (±SE) number of batoids per stomach for male and female white sharks. For (A,D), sample sizes
included in the analysis are indicated in the x-axis labels.
predators with high taxonomic resolution (Young et al., 2015).
Our first detailed diet assessment for juvenile white sharks
in eastern Australia revealed that teleosts, particularly pelagic
eastern Australian salmon but also an array of demersal and reef-
associated species, were the main prey consumed in this region.
A similar pattern has also been observed in juvenile and sub-
adult white sharks (185–285 cm PCL) from South Africa whereby
teleost prey consisted predominantly of pelagic South African
pilchards (Sardinops sagax) and included a high diversity of reef-
associated and demersal species, suggesting regular feeding on
non-pelagic prey sources (Hussey et al., 2012).
In addition to non-pelagic teleosts, benthic and benthopelagic
batoids such as stingarees and eagle rays also comprised a
significant dietary component for juvenile white sharks in
eastern Australia, albeit with some apparent variation by
sex. A predominance of batoid prey is consistent with prior
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FIGURE 4 | Prey composition and realised nutritional niche comparisons for male and female white sharks in eastern Australia. (A) Proportions-based NGF model
showing the nutritional compositions of prey species consumed by males only (squares), females only (circles) or both sexes (triangles) and small-sample corrected
standard ellipse areas (SEAc) as measures of the prey composition niche breadth for each sex (males = grey ellipse, females = black ellipse). (B) Violin plots (box plot
combined with a mirrored kernel density trace) comparing the full posterior distributions of Bayesian SEA estimates (SEAb) for the prey composition niche of each
sex. White crosses and dots indicate maximum-likelihood estimated SEAcand the mode of SEAb, respectively. (C) The calculated nutritional compositions of
stomach samples (males = grey squares, females = black circles) and SEAcfor the realised nutritional niche of each sex (males = grey ellipse, females = black
ellipse). (D) Violin plots comparing posterior distributions of SEAbfor the realised nutritional niche, with SEAcand mode of SEAbindicated as in (B). Sample sizes
(numbers of male and female sharks) used for these comparisons are shown in the x-axis labels in (B,D).
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FIGURE 5 | Prey composition and realised nutritional niche comparisons for two sizes of white sharks in eastern Australia (class 1 = <185 cm PCL,
class 2 = 185–234.9 cm PCL). (A) Proportions-based NGF model showing the nutritional compositions of prey species consumed by class 1 only (circles), class 2
only (squares) or both size classes (triangles) and small-sample corrected standard ellipse areas (SEAc) as measures of the prey composition niche breadth for each
size (class 1 = grey ellipse, class 2 = black ellipse). (B) Violin plots (box plot combined with a mirrored kernel density trace) comparing the full posterior distributions of
Bayesian SEA estimates (SEAb) for the prey composition niche of each size class. White crosses and dots indicate maximum-likelihood estimated SEAcand the
mode of SEAb, respectively. (C) The calculated nutritional compositions of stomach samples (class 1 = grey squares, class 2 = black circles) and SEAcfor the
realised nutritional niche of each size class (class 1 = grey ellipse, class 2 = black ellipse). (D) Violin plots comparing posterior distributions of SEAbfor the realised
nutritional niche, with SEAcand mode of SEAbindicated as in (B). Sample sizes (numbers of sharks in each size class) used for these comparisons are shown on
x-axis labels in (B,D).
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FIGURE 6 | Proportions-based NGF models comparing the nutritional compositions of prey species consumed by four sizes classes of white sharks (A–D,
respectively) from South Africa (RSA; from Hussey et al., 2012) and small-sample corrected standard ellipse areas (SEAc) as measures of the prey composition niche
of each size class (black ellipses). For comparison of niche width and overlap between populations, the prey composition SEAcfor eastern Australian white sharks
(present study, grey ellipses, from Figure 5A) is also overlayed for size classes 1 (A) and 2 (B). The number of Australian and RSA sharks on which these prey
comparisons are based is indicated below the size class title in each panel.
findings for white sharks (193–511 cm TL) in California
(Tricas and McCosker, 1984), but differs from South Africa
where various other sharks, particularly carcharhinids, were the
main elasmobranch prey across juvenile to adult life stages
(Hussey et al., 2012). The KZN region in South Africa contains
nursery habitat for carcharhinid sharks, possibly accounting for
their dietary importance as suggested by Hussey et al. (2012).
Supporting this regional variation in potential prey abundance,
carcharhinids comprise a significant proportion of the KZN shark
control program’s catch (Cliff and Dudley, 2011), whereas batoids
are relatively more common in the NSW SMP (Krogh and Reid,
1996;DPI, 2019). Furthermore, Pethybridge et al. (2014) found
high levels of fatty acid (FA) markers associated with benthic
coastal food sources in the tissues of white sharks from NSW
and South Australia and identified the possible importance of
benthic elasmobranchs (e.g. Port Jackson sharks, Heterodontus
portusjacksoni, or other ecologically similar species) as regular
prey. This is consistent with the prevalence of benthic and
bentho-pelagic batoids we observed, which have similar feeding
ecologies to Port Jacksons (Marshall et al., 2008;Powter et al.,
2010;Sommerville et al., 2011;Frost et al., 2017). Juvenile white
sharks are known to frequent shallow estuarine environments in
eastern Australia which contain a variety of habitats and substrata
(e.g. reefs, seagrass beds, sand flats), providing access to a range
of potential non-pelagic prey (Harasti et al., 2017). Overall,
the prevalence of batoids and non-pelagic teleosts in stomachs
suggests that benthic foraging is common in white sharks and is
consistent with extended periods of bottom-swimming observed
in tagged sharks (Bruce et al., 2006;Weng et al., 2007).
Ontogenetic variations in diet are well documented for
many shark species (Dicken et al., 2017;Nielsen et al., 2019),
including white sharks (Estrada et al., 2006;Hussey et al.,
2012;Kim et al., 2012). Here, we found evidence for increased
occurrence of dolphin and shark prey with size among white
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FIGURE 7 | Violin plots comparing the posterior distributions of Bayesian
estimates for standard ellipse areas (SEAb) of the prey composition niches of
white sharks from eastern Australia (this study) and South Africa (RSA; Hussey
et al., 2012) across 4 size classes (only 2 for Australia); class 1 = <185 cm
precaudal length (PCL), class 2 = 185–234.9 cm PCL, class
3 = 235–284.9 cm PCL, class 4 = ≥285 cm PCL. White crosses and dots
represent maximum-likelihood estimated small-sample corrected standard
ellipse areas (ellipses shown in Figure 6) and the mode of SEAb, respectively.
The number of Australian and RSA sharks on which these comparisons are
based is indicated below each violin plot.
sharks in eastern Australia. The overall rarity of these prey items
across stomachs and our limited sample size of subadult and
adult sharks (>235 cm PCL, ∼3 m TL; Bruce and Bradford,
2012) necessitates caution in interpreting this pattern. However,
consistencies with prior observations add support to our findings
(Hussey et al., 2012;Kim et al., 2012). For instance, although SCA
in South African white sharks suggests that mammals comprise
a relatively high proportion of the subadult and adult diet
(>235 cm PCL and ∼3 m TL), significant increases in mammal
consumption first occurs during the juvenile stage (>185 cm
PCL; Hussey et al., 2012), over the size range represented by
most of our samples (Figures 3B,C). Stable isotope analyses
(δ15N) have also identified increases in trophic level within the
juvenile stage (e.g. 0–5 years of age), and in larger sharks (Estrada
et al., 2006;Kim et al., 2012). Some variation in isotope-derived
foraging profiles is evident, although this likely arises from factors
including concurrent shifts in foraging habitat use (and thus
baseline δ15N), maternal influences, or possibly inter-individual
diet specialisation (Carlisle et al., 2012;Kim et al., 2012;Tamburin
et al., 2019). Our results suggest that eastern Australian white
sharks may start including mammals and higher trophic prey
as juveniles (around 173 cm PCL, 220 cm TL) which is
consistent with findings from South Africa (177 cm PCL; Hussey
et al., 2012). Nevertheless, juveniles are primarily generalist
piscivores, and occasionally consume mammals (Hussey et al.,
2012;Pethybridge et al., 2014). Furthermore, the presence of
small marine mammal prey (dolphin calves) corroborates prior
findings in juveniles (Hussey et al., 2012) and investigations
suggesting that their jaw mineralisation and muscle architecture
should favour softer tissues over tougher ones (e.g. whale blubber;
Dicken, 2008;Ferrara et al., 2011).
While ontogenetic dietary variation is widespread among
sharks, sex-specific prey consumption is less common (McElroy
et al., 2006;Abrantes and Barnett, 2011;Preti et al., 2012;Dicken
et al., 2017). Nonetheless, some SCA studies have identified sex-
based variation, such as a differing predominance of teleost and
elasmobranch prey between large male and female dusky whalers
(Carcharhinus obscurus;Hussey et al., 2011). Although we found
no significant sex differences for most prey groups, increased
consumption of batoids by males was detected. Sex differences
in various aspects of white sharks’ foraging ecology have been
identified previously, including tooth anatomy (French et al.,
2017), food web exploitation (e.g. nearshore/offshore; French
et al., 2018), habitat use and hunting strategies (Kock et al.,
2013;Bruce and Bradford, 2015;Towner et al., 2016;Bruce
et al., 2019), which may lead to potential differences in prey
consumption. In eastern Australia, female juvenile white sharks
tend to inhabit inshore waters more than males and thus might
consume different prey (Bruce et al., 2019). However, this pattern
warrants further exploration given the limitations of SCA and
sample sizes (detailed below).
Although SCA enabled the establishment of key prey species of
white sharks in NSW with good taxonomic resolution, a valuable
opportunity for threatened marine predators, we acknowledge its
well-established limitations (Chipps and Garvey, 2007). Firstly,
digestion can differentially influence the identification of various
prey groups, and estimates of their relative importance, since
smaller, soft-bodied organisms will degrade faster than harder
prey (Sekiguchi and Best, 1997;Tollit et al., 1997). Further studies
could utilise genetic techniques (e.g. DNA metabarcoding) which
are becoming increasingly popular albeit underutilised in taxa
such as sharks (de Sousa et al., 2019). Such techniques can
enable species-level prey identification, even with heavily digested
samples (e.g. Hardy et al., 2017), although evaluations of relative
prey importance based on DNA analyses alone are generally
limited to %F (Amundsen and Sánchez-Hernández, 2019).
Secondly, individual stomachs provide only spatiotemporal
snapshots of recent prey consumption (e.g. hours to days) which
may not represent the overall diet (Chipps and Garvey, 2007).
While our broad findings (e.g. predominance of piscivory, dietary
inclusion of mammals as juveniles, importance of benthic prey
such as batoids) were corroborated by previous studies (Hussey
et al., 2012;Pethybridge et al., 2014), a larger sample size
combined with spatiotemporally-integrated biochemical tracer
methods (e.g. stable isotopes, FA) will help to further establish
the relative importance of prey groups and investigate potential
sex- and size-based variation identified here. For example,
Bayesian stable isotope mixing models can evaluate the dietary
contributions of prey groups over varying spatiotemporal scales,
defined by the isotopic turnover rate of the shark tissue analysed
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Grainger et al. White Shark Diet and Nutrition
(Stock et al., 2018;Raoult et al., 2019). This approach would
also help to establish the dietary importance of opportunistic
or seasonal prey such as whales (Fallows et al., 2013;Tucker
et al., 2019) which were not observed in our stomachs but are
of significant interest in the management of white shark-human
interactions in eastern Australia (Tucker et al., 2018).
White Shark Macronutrient Foraging and
Its Ecological Consequences
Evaluating the nutritional compositions of prey, diets and
nutritional niche breadths is a key first step towards establishing
the broader influence of nutrition in the lives of marine predators
(Machovsky-Capuska et al., 2016d). Our MNNF provided several
new insights into the nutritional ecology of white sharks.
Firstly, despite an observed higher consumption of batoids by
males, and batoids having low %L and high P:L relative to other
prey species, no difference in dietary nutritional composition,
prey composition or realised nutritional niche breadths was
observed. Thus, each sex consumed nutritionally complementary
prey which could be mixed to achieve similar dietary intakes
(Raubenheimer, 2011). For example, batoids and Australian
salmon would enable similar high %P intake for both males and
females, respectively, albeit with different %L, which could be
moderated by mixing with other complementary prey species
that were lower in %P and %L. Variation in growth rates,
size at maturity and reproduction (Francis, 1996;Pratt, 1996;
Hamady et al., 2014;Natanson and Skomal, 2015) likely impose
different physiological demands for male and female white
sharks, a condition where sex-specific macronutrient foraging
might be expected (Machovsky-Capuska et al., 2016c). Indeed,
such physiological differences are a proposed driver behind
sex-specific occurrences of sub-adult and adult white sharks at
pinniped colonies (Bruce and Bradford, 2015). However, our
data suggest similar nutritional goals among sexes for juvenile
white sharks. Given this, it appears more likely that the observed
difference in batoid consumption would arise from other factors
(e.g. prey availability) related to habitat segregation (Bruce et al.,
2019), rather than prey selection to meet different nutritional
requirements. Nonetheless, despite allowing us to address the
challenges of collating proximate compositions for the wide
variety of prey consumed (Machovsky-Capuska et al., 2016a), the
use of literature prey data and inherent variability of stomach
contents does introduce uncertainties to our estimates. Thus,
further exploration of potential factors influencing the observed
difference in batoid consumption is warranted.
A second insight provided by our nutritional niche framework
was an increase in estimated realised nutritional niche breadths
with size among juvenile eastern Australian white sharks,
although this occurred without a concomitant increase in the
prey composition niche. Therefore, it is likely that similar prey
compositions were mixed differently by each size class, producing
a larger realised niche. In South African white sharks, the prey
composition niche expanded with size, up to a certain length
(∼235–285 cm PCL; Figures 6,7). Broadly, these analyses suggest
increasing macronutritional generalism with size in white sharks.
Niche breadth expansion largely occurred along the lipid axis.
Despite our low sample size, this conclusion is logical given the
expected increase in the consumption of high-lipid prey (e.g.
mammals) with size for white sharks, including among juveniles
(Hussey et al., 2012).
An array of niche breadth measures relate positively with
other ecological attributes such as geographic ranges (Slatyer
et al., 2013). For juvenile white sharks in eastern Australia, the
latitudinal range of migrations expands with age (Bruce et al.,
2019). Thus, increasing macronutritional generalism, especially
relating to the consumption of high-lipid prey, could reflect
the adjustment of nutritional priorities to broader movement
patterns (Machovsky-Capuska et al., 2016d). Indeed, lipids are
physiologically important to white sharks as an energy source
for long-distance migrations and while inhabiting oligotrophic
environments offshore (Del Raye et al., 2013). Gallagher et al.
(2019) identified increases in blood plasma FA associated with
blubber consumption (Waugh et al., 2014;Meyer et al., 2019),
and the utilisation of stored energy with size for juvenile white
sharks in NSW. This further highlights the likely importance of
body lipid stores for powering metabolism and movements in
larger juvenile white sharks, even in coastal (less oligotrophic)
settings (Gallagher et al., 2019). Alternatively, rather than
adjusting nutritional intakes to facilitate broader geographic
ranges, increasing latitudinal range could allow greater access
to different prey (Bruce et al., 2019), or more high-lipid
prey (e.g. seal colonies in Victoria, Tasmania and Bass Strait;
Shaughnessy, 1999;McIntosh et al., 2018) which could in turn
satisfy potential size-based shifts in nutritional goals. Under
such a hypothesis, the observed nutritional niche expansion
might also be expected as it could be predicted that larger
juveniles, across their geographic range, would attempt to
mix the prey available to meet any variation in nutritional
priorities with size.
A third insight provided by our nutritional framework
was that, despite the likely importance of lipids, white sharks
feed regularly on low-lipid prey across a range of sizes, and
the mean dietary composition in eastern Australian sharks
was relatively low in lipid (∼6%). Interestingly, nonetheless,
South African pilchard and eastern Australian salmon (important
prey of white sharks in each region) had similar proximate
compositions, high in lipid and protein. While lipid content is
a key determinant of overall prey energy density, which is often
the metric by which prey ‘quality’ is judged (Spitz et al., 2010a,b),
many recent studies in a range of organisms (herbivores,
omnivores and carnivores) suggest that food preferences are
driven by specific macronutrient content and balance, rather
than simply overall energy content (Hewson-Hughes et al., 2013;
Erlenbach et al., 2014;Felton et al., 2016;Coogan et al., 2017;
Rowe et al., 2018). The specific mixture of macronutrients
required depends on the physiological, functional and life history
attributes of an animal such that optimal foraging may be
achieved by regulating multiple nutrients simultaneously, not just
maximising net energy gain (Simpson et al., 2004). Therefore,
it is important to consider foraging from a macronutritionally
explicit perspective (Raubenheimer et al., 2009). Lipids are also
important to white sharks (and other sharks) for functions
not directly related to energy metabolism, such as buoyancy
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Grainger et al. White Shark Diet and Nutrition
control (Del Raye et al., 2013). Furthermore, large white
sharks often mouth whale carcasses prior to feeding and
repeatedly regurgitate chunks of blubber, which is variable in
both protein and lipid content (Lockyer et al., 1985), before
returning to feed (Fallows et al., 2013). This suggests some
capabilities in sensing and differentiating among prey based
on composition (Fallows et al., 2013). The relative importance
of specific macronutrients and/or energy density in white
sharks’ feeding preferences is an important question to resolve.
While we acknowledge limitations in using literature prey
data (e.g. potential spatiotemporal effects on prey proximate
composition affecting comparisons; Tait et al., 2014), this has
presented a valuable opportunity for integrating nutrition with
existing knowledge of white sharks’ diets and spurs interesting
questions about the influence of prey compositions in broader
white shark ecology.
Improving our understanding of white sharks’ diets to infer
drivers of movements has been identified as a key research
objective for this species (Huveneers et al., 2018). Fluctuations
in prey availability and nutritional quality due to seasonal and
environmental factors (e.g. temperature, nutrient upwelling)
can produce complex nutritional environments which predators
must navigate to achieve nutritional goals (Vollenweider et al.,
2011;Machovsky-Capuska et al., 2018). Juvenile white sharks
exhibit seasonal latitudinal movements throughout eastern
Australia, and periods of temporary residency in coastal nursery
regions such as Port Stephens (central NSW) and Corner Inlet
(eastern Victoria; Bruce et al., 2006, 2019), which can lead
to interactions with humans (West, 2011). Occupancy of each
nursery region has been noted to coincide with a local seasonal
peak in chlorophyll A concentrations resulting from nutrient
upwelling associated with bathymetric and oceanographic
features, which could in turn affect prey abundance and/or
nutritional quality (Bruce and Bradford, 2012;Machovsky-
Capuska et al., 2018). How might white sharks’ nutritional
requirements interact with variability in the abundance and
quality of prey species (arising from abiotic and biotic factors)
to influence their movements? Nutrient availability (the product
of both food abundance and nutritional composition) has been
shown to drive migrations elsewhere (Simpson et al., 2006;
Nie et al., 2015). Coogan and Raubenheimer (2016) linked
experimentally-derived nutritional targets, variations in seasonal
food compositions and nutrient limitations to explain grizzly
bears’ (Ursus arctos) urban habitat use, and potential areas of
overlap with humans. Under this framework, information on the
prey composition and realised nutritional niches of white sharks
provided herein, combined with spatiotemporal characterisation
of nutrient availability and acquisition in the form of nutritional
landscapes (sensu Machovsky-Capuska et al., 2018) will provide
a foundation for our knowledge on how nutrition shapes white
shark movements, and cast new light on potential drivers of
human-shark conflicts.
Through a combination of stomach contents and proportions-
based nutritional geometry framework modelling, we provided
the first diet assessment for juvenile white sharks in eastern
Australian waters, and new insights into this species’ nutritional
ecology. Consistent with previous studies, juvenile white sharks
in eastern Australia were predominantly generalist piscivores
consuming a broad variety of prey, notably eastern Australian
salmon, benthic teleosts and batoids, with dietary inclusion of
dolphins also observed during the juvenile stage (∼170–180 cm
PCL). There was evidence for sex-based dietary variation with
an increase in batoid consumption by males detected. However,
this conclusion requires further validation through biochemical
approaches (stable isotopes and FA). Estimated nutritional niches
and requirements appear similar for male and female juvenile
white sharks in eastern Australia. However, we found evidence
for increasing macronutritional generalism with size in white
sharks, even among juveniles, which might relate to shifts in
nutritional goals, lipid consumption and expanding geographic
ranges. Further exploration of the interactions between white
shark nutritional requirements and the dynamics of prey quality
and availability will likely enhance management and conservation
strategies for this top predator.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
Ethical review and approval was not required for the animal study
because all samples were obtained from deceased white sharks
caught in the NSW Shark Meshing (Bather Protection) Program,
operated by the NSW Department of Primary Industries (NSW
DPI), or from other incidental mortalities. No animals were killed
specifically for this research. Collection of samples was conducted
under NSW DPI permit # P01/0059(A)-4.0.
AUTHOR CONTRIBUTIONS
RG, GM-C, and DR conceptualised the study. VP coordinated
the sampling. VP and RG collected the stomachs. RG analysed
the stomach contents and the data. RG, VP, DR, and GM-C
wrote the manuscript.
FUNDING
Project funding and support was provided by the New South
Wales Department of Primary Industries through the New
South Wales Shark Management Strategy (NSW SMS). RG
is supported by an Australian Government Research Training
Program Stipend and supplementary scholarship from the NSW
SMS/University of Sydney.
ACKNOWLEDGMENTS
We thank New South Wales Department of Primary Industries
staff and Shark Meshing Program observers including Cameron
Frontiers in Marine Science | www.frontiersin.org 16 June 2020 | Volume 7 | Article 422

fmars-07-00422 June 5, 2020 Time: 14:57 # 17
Grainger et al. White Shark Diet and Nutrition
Doak, Stephen Chilcott, Sean Blake, Isabelle Thiebaud, Steve
Kay, Euan Provost, Matt Broadhurst and Paul Butcher, and
Shark Meshing Program contractors for their help coordinating
and collecting sharks and conducting necropsies. Without their
assistance this work would not have been possible. Thanks to
Mark McGrouther and Amanda Hay at the Australian Museum
for access to their otolith reference collection which greatly
assisted in the identification of prey items. We thank the three
anonymous reviewers whose comments also greatly improved
this manuscript. This is contribution number 247 to the Sydney
Institute of Marine Science.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2020.00422/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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potential conflict of interest.
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