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Stable Isotope and Signature Fatty Acid Analyses Suggest Reef Manta Rays Feed on Demersal Zooplankton

  • Lydie I. E. Couturier ,

    Affiliations School of Biomedical Sciences, The University of Queensland, St Lucia, Queensland, Australia, Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Dutton Park, Queensland, Australia

  • Christoph A. Rohner,

    Affiliations Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Dutton Park, Queensland, Australia, Manta Ray and Whale Shark Research Centre, Marine Megafauna Foundation, Praia do Tofo, Inhambane, Mozambique, Biophysical Oceanography Group, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, Queensland, Australia

  • Anthony J. Richardson,

    Affiliations Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Dutton Park, Queensland, Australia, Centre for Applications in Natural Resource Mathematics, The University of Queensland, St Lucia, Queensland, Australia

  • Andrea D. Marshall,

    Affiliations Manta Ray and Whale Shark Research Centre, Marine Megafauna Foundation, Praia do Tofo, Inhambane, Mozambique, Wild Me, Praia do Tofo, Inhambane, Mozambique

  • Fabrice R. A. Jaine,

    Affiliations Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Dutton Park, Queensland, Australia, Biophysical Oceanography Group, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, Queensland, Australia

  • Michael B. Bennett,

    Affiliation School of Biomedical Sciences, The University of Queensland, St Lucia, Queensland, Australia

  • Kathy A. Townsend,

    Affiliation School of Biological Sciences, Moreton Bay Research Station, The University of Queensland, Dunwich, Queensland, Australia

  • Scarla J. Weeks,

    Affiliation Biophysical Oceanography Group, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, Queensland, Australia

  • Peter D. Nichols

    Affiliation Wealth from Oceans Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia


Assessing the trophic role and interaction of an animal is key to understanding its general ecology and dynamics. Conventional techniques used to elucidate diet, such as stomach content analysis, are not suitable for large threatened marine species. Non-lethal sampling combined with biochemical methods provides a practical alternative for investigating the feeding ecology of these species. Stable isotope and signature fatty acid analyses of muscle tissue were used for the first time to examine assimilated diet of the reef manta ray Manta alfredi, and were compared with different zooplankton functional groups (i.e. near-surface zooplankton collected during manta ray feeding events and non-feeding periods, epipelagic zooplankton, demersal zooplankton and several different zooplankton taxa). Stable isotope δ15N values confirmed that the reef manta ray is a secondary consumer. This species had relatively high levels of docosahexaenoic acid (DHA) indicating a flagellate-based food source in the diet, which likely reflects feeding on DHA-rich near-surface and epipelagic zooplankton. However, high levels of ω6 polyunsaturated fatty acids and slightly enriched δ13C values in reef manta ray tissue suggest that they do not feed solely on pelagic zooplankton, but rather obtain part of their diet from another origin. The closest match was with demersal zooplankton, suggesting it is an important component of the reef manta ray diet. The ability to feed on demersal zooplankton is likely linked to the horizontal and vertical movement patterns of this giant planktivore. These new insights into the habitat use and feeding ecology of the reef manta ray will assist in the effective evaluation of its conservation needs.


Information on the diet and trophic position of an animal can improve ecological understanding of the underlying drivers of its movements and its role within the ecosystem. Such knowledge can also support conservation plans for areas where the temporal and spatial abundance and distribution of prey are understood [1][3]. Stomach content analysis is the conventional approach used to assess a species’ diet [4] and has many advantages; however, it also has several shortcomings. First, this technique only provides a ‘snapshot’ of recent feeding and may not accurately reflect the composition of prey items that contribute most significantly to its general diet. This technique my also not necessarily account for ontogenetic or seasonal shifts in diet nor regional variability in the diet of a species. For a comprehensive understanding of a species’ diet, many specimens must be examined with samples from different seasons, locations, size classes and sexes. Sample collection therefore becomes challenging for widely distributed and wide-ranging species that may feed in numerous habitat types over large geographic areas. Second, stomach content analysis is heavily biased towards items resistant to digestion such as bones, exoskeletons, chelae and eyeballs [5]. Last, obtaining stomachs from large and threatened marine species is often difficult and killing animals for this purpose is ethically questionable.

The reef manta ray Manta alfredi (Krefft, 1868) is a large planktivorous elasmobranch with a circumglobal distribution in tropical and subtropical waters [6]. The species is listed as globally Vulnerable to extinction on the IUCN Red List of Threatened Species, mainly due to new or expanding targeted fisheries [7]. Many of these fisheries are considered unsustainable due to the relative small native population sizes, likely limited exchange between subpopulations and conservative life history of the species (i.e. slow growth rates, late age of sexual maturity, few offspring, and long life) [7], [8]. Although manta rays have gained considerable scientific attention over the past two decades and are heavily fished in several parts of the world [8], there is little information on their feeding ecology. The limited availability of stomach content samples for reef manta rays highlights the need for suitable alternative methods to study their diet. Biochemical approaches such as stable isotope (SI) and signature fatty acid(s) (FA) analyses can provide information on dietary preferences and trophodynamics in marine animals [9], [10]. Both techniques have the advantage of only requiring a small amount of tissue for analysis, which can be obtained as a biopsy from living animals with little impact on their welfare (e.g. [11], [12]).

Stable isotope analysis has been successfully used to examine aspects of the biology and ecology of several elasmobranch species [13][18]. Shifts in SI values of nitrogen (15N/14N or δ15N) and carbon (13C/12C or δ13C) in a consumer’s tissues are related to its assimilated food and provide an index of its relative trophic position in the ecosystem. δ15N and to a lesser extent δ13C show a predictable stepwise enrichment with each increasing trophic level [19][22]. The trophic position of a species can only be properly assessed with regional isotopic characterisation of the ecosystem as the baseline stable isotope data of the food chain may vary among regions [21]. The conservative fractionation of carbon between primary producers and consumers means that δ13C values provide information on the origin of the carbon entering the food web. δ13C values/signatures differ between benthic and pelagic habitats [23] and are influenced by marine, freshwater and terrestrial inputs [24]. However, there are caveats associated with the application of SI analysis in elasmobranchs. There is little information in the current literature on critical values such as diet-tissue discrimination factors and rate of isotopic incorporation rates, which are needed to interpret SI data correctly. Although a few studies on captive sharks have been undertaken [18], [25], [26], similar captive studies of many large elasmobranch, including manta rays, are impractical. Diet-discriminatory factors therefore need to be assumed from literature values of related species.

Signature FA analysis has been increasingly used to study the diet of a number of marine species including elasmobranchs [27][30]. In animals, FA are used as an energy supply, are stored in adipose tissue, and can play a structural role or be incorporated into specific metabolic pathways [9], [31], [32]. Selected FA are synthesised by higher consumers while others are generally assimilated intact, including the essential long-chain (≥C20) polyunsaturated fatty acids (LC-PUFA). Long-chain-PUFA in fish tissue are most likely to be derived directly from the diet as higher consumers generally lack the ability to biosynthesise these FA de novo [33], [34] and marine fishes are not likely to biosynthesise FA to a significant level due to their naturally PUFA-rich diet [34], [35]. Therefore, the FA signature profile of prey is likely to influence directly the FA profile of its consumer. Several LC-PUFA can also be used as biomarkers to trace the base of the marine food-web (e.g. diatoms and/or flagellate origin for marine phytoplankton) [33], [36].

Current knowledge of the feeding ecology of the reef manta ray is limited to one early and rudimentary description of one stomach content [37] and several field observations of foraging behaviour close to the surface at most known aggregation sites around the world (e.g. Australia [38]; Hawaii [39], Indonesia (M. alfredi but referred to as M. birostris) [40]; the Maldives [41], Mozambique (M. alfredi but referred to as M. birostris) [42] and the Central Pacific [43]). It has thus been presumed that manta rays feed predominantly on aggregations of near-surface zooplankton in productive coastal areas during the daytime. It is unknown, however, how important the observed surface feeding events are in terms of the total dietary intake of these large planktivores. In a pilot study, we reported the unusual FA profiles of the reef manta ray and the whale shark Rhincodon typus Smith 1828, both being dominated by omega-6 (ω6) PUFA [44]. These results were surprising as FA profiles of marine animals, and crustacean zooplankton in particular, are generally dominated by omega-3 (ω3) PUFA [33], [45]. Origins of such a distinctive profile remain ambiguous but it suggests that the feeding ecology of planktivorous elasmobranchs is more complex than previously thought. The purpose of this study was to couple SI and FA analyses of reef manta ray tissue, their known food, the near-surface zooplankton, and other potential prey items to provide a more comprehensive insight into their dietary ecology.

Materials and Methods

Ethics Statement

This study was conducted with permits from the GBR Marine Park Authority (G09/29853.1) and approval from the University of Queensland Animal Ethics Committee (SBMS/206/11/ARC).

Samples Collection

Biopsy samples.

Muscle and/or skin tissue samples were collected from the ventro-posterior area of the pectoral fins of free swimming reef manta rays, using a biopsy needle mounted on a modified Hawaiian hand-sling. Samples were collected from three aggregation sites for reef manta rays in waters off: Lady Elliot Island (24°06′S 152°32′E) and North Stradbroke Island (27°25′S 153°32′E) in Queensland, Australia and Praia do Tofo (23° 52′S 35° 33′E) in Mozambique.

Muscle tissue and skin tissue for biopsies obtained in Australia were prepared separately for SI and FA analyses. Biopsy samples collected in Mozambique were used for FA analysis only, and comprised muscle tissue, although small remnants of skin may have been present in some samples.

All samples were initially kept on ice and then stored at −20°C until required for analysis. Of the 22 reef manta ray biopsies collected in east Australia, 16 were from females and six from males. Of the 12 reef manta rays biopsies obtained from Mozambique, nine were from females and three from males.

Near-surface zooplankton.

A total of 62 zooplankton samples were collected: 54 samples were from eastern Australia and 9 from southern Mozambique. In Australia, samples were collected from the upper 5 m of the water column by towing a 200 µm mesh size plankton net against the tidal current for 5 min at ∼2–4 knots. In Mozambique, a similar plankton tow was performed with a 200 µm mesh net or with a small 100 µm mesh hand-held net towed by a swimmer. Samples were kept on ice and processed (filtered and divided) on the same day. All samples were then frozen at −20°C. Australia: Two categories of zooplankton hauls were conducted to detect changes in the qualitative properties of the near-surface zooplankton that may influence the feeding activity of manta rays: 1) Feeding, when tows were collected within the feeding manta ray trail (n = 32); and 2) Not feeding, where tows were collected when reef manta rays were present but not feeding (n = 22). Samples were collected at Lady Elliot Island (n = 51) in June 2010, October 2010, February 2011, June 2011, August 2011, September 2011 and February 2012. For a greater diversity of samples, a few samples (‘not feeding’) were also included from North Stradbroke Island, 380 km further south and another reef manta ray aggregation site, from December 2011 (n = 2) and January 2012 (n = 1) when feeding can occur. Each sample was filtered and divided into four subsamples of which two were frozen at −20°C as soon as possible for subsequent FA and SI analyses. When large and obvious zooplankton species (e.g. gelatinous zooplankton, large copepods, chaetognaths, shrimp larvae) were abundant, several individuals of the same group were extracted from the fresh samples and frozen to be analysed separately for FA and SI. Representative specimens of each group were also fixed in formalin for subsequent taxonomic identification. Extracted taxa were classified into one of seven taxonomic groups: the calanoid copepod Undinula vulgaris (Dana 1849) (n = 11 samples), the calanoid copepod Candacia ethiopica (Dana 1849) (n = 3 samples), the calanoid copepod Subeucanlanus spp. (n = 1 sample), decapod crab larvae (n = 3 samples), shrimp-like larvae (n = 7 samples), fish larvae (n = 8 specimens) and eel larva (n = 1 specimen). Each sample comprised several specimens of the same category.

Mozambique: Near-surface zooplankton samples from Mozambique comprised three samples collected during reef manta ray feeding events and six samples collected when reef manta rays were not sighted in November and December 2011.

Epipelagic zooplankton.

Australia: Six zooplankton samples were collected in waters 100 m deep off North Stradbroke Island. Vertically integrated hauls using a 200 µm mesh drop net were performed to collect zooplankton samples from between 75 and 82 m depth (n = 3). In addition, deep-horizontal plankton tows using a 200 µm mesh net were conducted at ∼20 m depth for 5 min within the same area (n = 3).

Mozambique: Three zooplankton samples were collected in ∼300 m deep water off the continental shelf ∼15 km east of Praia do Tofo. Vertically integrated hauls using a 200 µm mesh size net were performed at 50, 100 and 200 m depths.

Demersal zooplankton.

Demersal (also known as emergent) zooplankton live within or close to the sea bottom and undergo daily vertical migration, emerging at night in high density [46], [47]. Although it is unknown whether reef manta rays were feeding at night and within the same area at Lady Elliot Island, it is plausible that these planktivores feed on demersal zooplankton, such as observed in Hawai’i [48]. An emergence trap using a 200 µm mesh net, based on the design of Alldredge & King [47] and Melo et al. [49], was secured to the sea-floor at a depth of 15 m and 8 m in waters adjacent to Lady Elliot Island prior to sunset and left in place overnight. Zooplankton that emerged from the substrate overnight were caught and retrieved the next morning. Five separate samples were collected and all were analysed for FA composition only due to limited amount available.

Regional isotopic characterisation for eastern australia.

Muscle tissue was collected from the lateral fillet of two coastal teleost species: sea mullet Mugil cephalus Linnaeus, 1758 (n = 20) and stout whiting Sillago robusta Stead, 1908 (n = 20) caught by commercial vessels operating in southeast Queensland coastal waters. Isotopic values of muscle tissue from other pelagic fishes and elasmobranchs sampled in southeast Queensland were obtained from Revill et al. [50].

Stable Isotope Analysis

Samples were soaked in distilled water for 15 min, rinsed and then oven-dried at 60°C for 24–48 h. Dried tissue (0.5–1.5 mg) was weighted into 8×5 mm tin capsules (SerCon p/n SC0009). All samples were analysed at the Stable Isotopes Analysis Lab, Australian Rivers Institute at Griffith University, Australia. Samples were combusted in a Sercon Europa EA-GSL elemental analyser (Sercon Ltd, UK). The resulting N2 and CO2 gases were chromatographically separated and analysed using Sercon Hydra 20–22 isotope ratio mass spectrometer (Sercon Ltd, UK).

Isotopic values are expressed using the standard δ notation, as part per thousand (‰) deviation from a standard. Stable isotope abundances were calculated using the equation:where X = 15N or 13C, R = the ratio 13C/12C or 15N/14N, with IAEA N1 and IAEA N2 as reference for nitrogen and IAEA-CH-6 for Carbon. All standards are traceable to atmospheric N2 and Vienna PeeDee Belemnite (VPDB) carbon respectively [51][52].

Lipid effect.

Lipids are known to be depleted in 13C, resulting in lower δ13C values for tissue with greater lipid content. A high C: N ratio is indicative of high lipid content in aquatic organisms and lipid extraction or correction using a lipid normalisation model is recommended for tissue with C: N >3.5 [53]. The following lipid normalisation models were applied whenever appropriate to correct δ13C values for lipid effect:where δ13Cnorm is the value of δ13C after lipid normalisation, δ13Cbulk is the direct measurement of δ13C of the target organism and the ratio C: Nbulk is calculated from direct measurement of C and N.

Enrichment values between reef manta rays and known prey were calculated using the equation:Where δX is δ13C or δ15N.

Trophic position.

The trophic position (TP) of the reef manta ray was estimated using the following equation:where λ is the trophic position of the selected consumer used to estimate δ15Nbase, δ15Nconsumer is the direct δ15N value of the target species, δ15Nbase is the δ15N value of a primary consumer for the local food web and Δδ15N is the trophic fractionation of δ15N per trophic level. The δ15N values of the known herbivorous calanoid copepod Undinula vulgaris (n = 6) collected at Lady Elliot Island were used as δ15Nbase. For accurate estimation of the trophic position of a species within the food web, species-specific knowledge on diet tissue discrimination factors (Δ15N = δ15Nconsumer − δ15Nprey) needs to be determined in a controlled environment. Since Δ15N has not been determined for any planktivorous elasmobranch, the discrimination factor from the leopard shark Triakis semifasciata Girard, 1855 of Δ15N = 3.7‰ for muscle tissue as determined by Kim et al. [18] was applied. This discrimination factor is the first value to be rigorously determined in laboratory controlled environment for elasmobranchs.

Fatty Acid Analysis

Lipid extraction.

Wet weight of each reef manta ray tissue and zooplankton sample was determined prior to analysis. Lipids were extracted overnight using the modified Bligh & Dyer [55] method with a one-phase methanol:chloroform:water (2∶1∶0.8 by volume) extraction. Phases were separated by adding water and chloroform leading to a final ratio of 1∶1∶0.9 methanol:chloroform:water and the lower chloroform phase containing the lipids was retained. Lipids were recovered by rotary evaporation of the chloroform in vacuo at ∼40°C. Total lipid extracts were concentrated in tared glass vials by application of a stream of inert nitrogen gas and weighed. Samples were then stored in chloroform at −20°C prior to further analysis.

Lipid classes.

The total lipid extract from each sample was spotted on chromarods that were developed for 25 min in a polar solvent system (hexane:diethyl-ether:acetic acid, 60∶17∶0.1 by volume). Chromarods were then dried in an oven for 10 min at 100°C and analysed immediately. Lipid class composition was determined for each sample using an Iatroscan Mark V TH10 thin layer chromatograph combined with a flame ionisation detector. For comparison purposes, a standard solution containing known quantities of wax esters, triacylglycerols (TAG), free FA (FFA), sterols (ST) and phospholipids (PL) was run with the samples. Each peak was identified by comparison of Rf with the standard chromatogram. Peak areas were measured using SIC-480II Iatroscan™ Integrating Software v.7.0-E (System Instruments Co., Mitsubishi Chemical Medicine Corp., Japan) and quantified to mass per µl spotted using predetermined linear regressions.

Fatty acids.

An aliquot of the total lipid extract was treated with 3 ml of a solution of methanol:hydrochloric acid:chloroform (10∶1∶1), heated at ∼80°C for 2 h. After cooling and addition of MilliQ water, the resulting FA methyl esters were extracted into hexane:chloroform (4∶1). Samples were dried under a stream of nitrogen gas before adding a C19 internal injection standard solution. Samples were then analysed using an Agilent Technologies 7890B gas chromatography (GC) (Palo Alto, California, USA) equipped with a non-polar Equity™-1 fused silica capillary column (15 m×0.1 mm i.d., 0.1 µm film thickness), a Flame Ionisation Detector, a split/splitless injector and an Agilent Technologies 7683 B Series auto sampler. Helium was the carrier gas. Samples were injected in split-less mode at an oven temperature of 120°C. After injection, oven temperature was raised to 270°C at 10°C.min−1 and finally to 300°C at 5°C.min−1. Peaks were quantified with Agilent Technologies ChemStation software (Palo Alto, California, USA). GC results are typically subject to an error of up to ±5% of individual component area. Peak identities were confirmed with a Finnigan ThermoQuest GCQ GC mass-spectrometer (GC-MS) system (Finnigan, San Jose,CA) [56]. The percentage for each FA was converted from the area of chromatogram peaks. All FA are expressed as percentage of total FA.

Data Analyses

Stable isotopes.

One-way ANOVA was used to test for statistical differences in stable isotopes values (δ13C and δ15N) between Lady Elliot Island and North Stradbroke Island using R v2.12.2 [57].

Fatty acids.

Fatty acids were coded as A: B ωD, where A is the number of carbon atoms, B is the number of double bonds in the carbon chain and ωD is the position of the first double bond from the terminal methyl end of the molecule. Fatty acids were categorised as saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA), and each FA expressed as a percentage of the total FA (%TFA). Data are shown as mean ± standard error %TFA. ANOVA and ANOSIM were used to test for significant difference among samples. Pairwise ANOSIM was performed to identify the level of significant difference among the different groups in terms of FA composition. Due to the small sample size, interpretation of ANOSIM-R value was also used to evaluate the level to which groups differed, with R values >0.75 indicating clear separation among groups, R = 0.75–0.25 indicating separate groups with overlapping values and R <0.25 as barely separated groups [58]. All FA detected above trace levels (>0.2%) were used for within-group comparison, while all FA >1% were used for among-groups comparison [groups = reef manta rays (muscle and skin) and zooplankton: near-surface (feeding and non-feeding), epipelagic, demersal, zooplankton taxa]. Data were not transformed to avoid giving more weight to FA present in small quantities. SIMPER was used to identify the contribution of each FA to similarities within a group and to dissimilarities amongst different groups. Non-metric multi-dimensional scaling (MDS) plots were used to visualise groupings within and among reef manta rays, their known prey and other zooplankton collected. ANOSIM, SIMPER and MDS were generated using PRIMER v6 (Primer-E, UK) [58].


Stable Isotopes

Prey and predators in east australia.

There was no significant difference between reef manta ray muscle tissue from Lady Elliot Island and North Stradbroke Island for both δ13C and δ15N values (ANOVA, p>0.05). The mean δ13C and δ15N values for reef manta ray muscle tissue were −17.4±0.1‰ and 8.9±0.3‰ respectively (n = 12). Skin tissue samples (n = 6) were analysed separately and had mean δ13C value of −14.6±0.1‰ and δ15N value of 8.9±0.5‰ (Table 1). No lipid normalisation model was applied to δ13C values as the C: N ratio of all samples was <3.5. The estimated trophic position of reef manta rays was 3 (secondary consumer).

Table 1. Isotopic values (mean ± standard error) of main species analysed.

All near-surface zooplankton samples collected were analysed for stable isotope composition, along with 21 samples of separate species/taxonomic groups (Table 1). The mean δ13C value for all zooplankton tows (n = 54) was −20.2±0.1‰ from direct measurements and −18.5±0.2‰ after applying the lipid normalisation model (C: N ratio for all samples ranged between 3.6–7.9). The mean δ15N value was 6.4±0.2‰. There was no significant difference between isotopic values of plankton collected during reef manta ray feeding events and non-feeding events (ANOVA p>0.05); however, only values of ‘feeding’ zooplankton samples were used for predator-prey comparison purposes. On average, reef manta ray muscle was enriched in 13C by 1.3‰ based on corrected δ13C values, and in 15N by 2.4‰ relative to the zooplankton sampled from feeding events.

Isotopic characterisation of eastern australia fishes.

Mean δ13C and δ15N values were −17.4±0.1‰ and 11.7±0.1‰ respectively for stout whiting, and −19.6±0.9‰ and 10.2±0.7‰ respectively for sea mullet (Table 1, Figure 1). The lipid normalisation model for fish tissue was applied to δ13C values of sea mullets as the mean C: N ratio was >3.5 and the resulting mean δ13C normalised value was −19.8±0.9‰.

Figure 1. δ15N and δ13C values of zooplankton, pelagic predators and reef manta rays from southeast Queensland waters.

Different symbols and colours indicate the mean isotopic values of different groups and species. All zooplankton values are adjusted to account for lipid normalisation based on Post [53]. Pelagic predator values (indicated by triangle icons) are based from Revill et al. [50]. Error bars represent standard error.

Lipid and Fatty Acid Composition

Lipid class profiles of the reef manta ray were dominated by PL (80.8±1.7% of total lipids), followed by ST (10.3±0.9%). Lipids from all zooplankton samples collected in Australia were dominated by PL (ranging between 56–74%) and TAG (18–47%) (Table S1). Lipid classes in surface zooplankton collected in Mozambique were dominated by FFA (57.2%) followed by PL (30.2%), while epipelagic zooplankton collected within the same region showed a slightly higher contribution of PL (42.7%), followed by FFA (40.4%) (Table S1).

Of the 68 FA identified from all samples, 39 were above trace levels in reef manta rays (33 in both Australia and Mozambique) and 43 in zooplankton samples (Tables 2 & 3).

Table 2. Fatty acid composition (mean ± standard error %of total FA) for tissue biopsies of the reef manta ray Manta alfredi collected off eastern Australia and Mozambique.

Table 3. Fatty acid composition (% of total FA) of zooplankton samples collected off eastern Australia and Mozambique.

Reef manta ray tissue.

Considering all FA above trace levels (n = 33), there was no significant difference among reef manta ray FA profiles from Lady Elliot Island and North Stradbroke Island (ANOSIM, R value = 0.056, p = 0.1). Thus, FA profiles from both locations were grouped as ‘Australia’ for further analysis. In addition, no significant difference was detected between sexes considering muscle tissue only (ANOSIM R value = −0.1, p = 0.7). There was a significant difference between muscle tissue (n = 15) and skin tissue (n = 3) FA profiles (ANOSIM R value = 0.9, p = 0.01). Average dissimilarity between the two tissues was 20% (SIMPER) and the three main contributors to differences were 18∶1ω9 (10.9%), docosahexaenoic acid (DHA, 22∶6ω3) (10.2%) and 22∶5ω3 (7.6%) (Table 2). In both tissue types, FA signatures were dominated by PUFA (36% TFA) and the main FA included 18∶0, 18∶1ω9, 16∶0, DHA and arachidonic acid (AA, 20∶4ω6) (Table 2), each contributing >8% to within-group similarity (Table S2). The PUFA profile of Australian reef manta rays showed similar levels of ω3 and ω6 PUFA, with a mean ω3/ω6 ratio of 1.1±0.1 for muscle tissue and 0.9±0.3 for skin tissue (Table 2). Docosahexaenoic acid was the main ω3 PUFA (13.0% in muscle tissue and 9.8% in skin tissue) while AA was the main ω6 PUFA (8.7% in muscle and 8.8% in skin tissue). Only low levels (<2%) of linoleic acid (LA, 18∶2ω6) were found in either tissue type (Table 2). All further comparisons were made using results from the muscle tissue of reef manta rays unless specified.

There was no significant difference in FA profile between male and female Mozambican reef manta ray tissues (ANOSIM R value = −0.1, p = 0.6). The FA profile of Mozambican reef manta rays was dominated by PUFA (38%TFA). The main FA included 18∶0, 18∶1ω9, AA, 16∶0 and DHA (Table 2), and each contributed at least 10% to within-group similarity (Table S2). Arachidonic acid was the main PUFA (14.2%) and DHA was the second highest PUFA (10.3%). Similar to Australian reef manta rays, only low levels (<1%) of LA were found.

Fatty acid profiles (considering all FA >0.2%, n = 39) of reef manta rays from Mozambique and Australia were significantly different from each other, but with a relatively high degree of overlap (ANOSIM, R value = 0.5, P = 0.001) (Figure 2). Average dissimilarity was 17% between the two regions (SIMPER). The PUFA profile of Mozambican reef manta rays was dominated by ω6 FA, with a mean ω3/ω6 ratio of 0.6 (Table 2), significantly lower than observed for the Australian reef manta rays (ANOVA, p<0.001). Mozambican reef manta rays had on average more ω6 than Australian reef manta rays. Arachidonic acid, DHA and 22∶4ω6 were the main contributors to dissimilarity between the two regions (SIMPER, 17.4%, 10.1% and 10.0% respectively), with higher levels of AA and 22∶4ω6 in Mozambican reef manta rays, and higher DHA levels in Australian reef manta rays (Table 2).

Figure 2. Regional comparison of reef manta ray muscle tissue fatty acid (FA) profiles.

Multi-dimensional scaling ordinations of different sexes considering all FA >0.2% (n = 39).

Near-surface zooplankton.

There was no significant difference between near-surface zooplankton FA profiles (considering the main 41 FA >0.2%) collected during reef manta ray ‘feeding’ and ‘non-feeding’ events at Australian sites (ANOSIM, R value = −0.026, p = 0.7). All near-surface samples from Australia were PUFA-dominated with a mean of 48.6±0.5%, and DHA, 16∶0 and eicosapentaenoic acid (EPA, 20∶5 ω3) as major FA (Tables 3 & S2). Only low levels of the ω6 essential PUFA AA (1.5±0.09%) and LA (1.4±0.04%) were detected. The FA profile of near-surface zooplankton was largely dominated by ω3 PUFA with an overall mean ω3/ω6 ratio of 9.1±0.4 (Table 3). Docosahexaenoic acid was the dominant FA for all samples with a mean EPA/DHA ratio of 0.6±0.03 and a mean 16∶1/16∶0 ratio of 0.4±0.02. Significant differences were found among the sampled months, with DHA being the main contributor to dissimilarities between most months (Figure S1).

There was no significant differences between zooplankton collected during reef manta ray feeding events and those collected when no manta ray were sighted in Mozambican waters (ANOSIM R value = 0.1, p = 0.3). All near-surface zooplankton from Mozambique was PUFA dominated (49.8±2.0%) with DHA, 16∶0 and EPA as the three main FA. They also had low levels of AA (2.2±0.2%) similar to the FA profile of samples from Australian waters (Tables 3). The FA profile was also ω3 PUFA-dominated, with a mean ω3/ω6 ratio of 12.8±2.0 and DHA was also the most abundant FA for all samples (Table 3).

The seven zooplankton groups extracted from feeding event samples collected in Australia were analysed separately for FA composition (Table S3). All groups were dominated by PUFA (42.1–60.9% TFA), DHA was the main FA in all samples and the ω3/ω6 ratio varied from 4–10.4 (Table S3). Arachidonic acid and LA were present at low relative levels, ranging between 1.4–3.0%.

Epipelagic zooplankton.

Due to the low sample size, data from both vertical haul (n = 3) and deep tows (n = 3) conducted off North Stradbroke Island were grouped as epipelagic zooplankton for further analysis. No significant difference was detected between epipelagic zooplankton and near-surface zooplankton in Australia (ANOSIM R value = 0.005, p = 0.4). The FA profile of epipelagic zooplankton was very similar to that of near-surface zooplankton, being dominated by PUFA (50.5±0.6%TFA) with DHA as the main FA and the ω3/ω6 ratio was high with a mean of 6.9±0.5 (Table 3).

Similar to the Australian samples, there was no significant difference between near-surface and epipelagic zooplankton in Mozambican waters (ANOSIM R value = 0.27, p = 0.1). The FA profile of epipelagic zooplankton was comparable to that of near-surface zooplankton in being dominated by PUFA with DHA as the main FA and a high ω3/ω6 ratio of 9.3±0.1 (Table 3).

Demersal zooplankton.

The FA profile of the five demersal zooplankton samples collected were significantly different and well separated from epipelagic zooplankton (ANOSIM, R value = 0.79, p>0.05) and near-surface zooplankton (ANOSIM, R value = 0.79, p = 0.01) of Australian waters (Figure 3). The major contributor to dissimilarities between groups was DHA in both comparisons (SIMPER, 22–30%), the second main contributors were EPA between demersal and epipelagic zooplankton (SIMPER, 7.8%) and 18∶0 between demersal and near-surface zooplankton (SIMPER, 6.9%). Arachidonic acid was the third contributor for both comparisons (SIMPER, 6.4–6.9%). Overall samples were dominated by PUFA (45.4%TFA) with EPA and DHA as the two main PUFA (Table 3). The FA 16∶0, EPA and 18∶0 contributed at least 10% to the within-demersal group similarities (Table S2). The ω3/ω6 ratio was lower than for near-surface zooplankton with a mean of 2.9±0.6. Arachidonic acid levels were also higher with a mean of 5.0±0.4% (Table 3).

Figure 3. Comparison of zooplankton fatty acid (FA) profiles.

Multi-dimensional scaling ordinations of zooplankton groups from different sampling areas collected in eastern Australia, considering all FA >0.2% (n = 50).

Reef manta ray FA profiles in relation to zooplankton.

Fatty acid profiles of Australian reef manta ray muscle tissue and zooplankton collected during feeding events at Lady Elliot Island were significantly different with no overlap between the two groups (considering 20 FA, ANOSIM, R value = 1, p = 0.01). Average percentage of dissimilarities between the two groups was 44.9% (SIMPER). Of the 20 FA compared, five contributed to >60% of the dissimilarities between the two groups: EPA (15.0%), 18∶1ω9c (13.6%), 18∶0 (13.2%), DHA (12.5%) and AA (9.1%). Reef manta rays had higher levels of 18∶1ω9, 18∶0 and AA while ‘feeding’ zooplankton had higher levels of EPA and DHA (Tables 2 & 3). Similar results were observed in Mozambique, where reef manta ray tissue was significantly different to near-surface zooplankton. The main FA contributing to >60% of dissimilarities were DHA (19.4%), EPA (13.2%), AA (12.6%), 18∶1ω9 (11.8%) and 18∶0 (8.9%), with an overall average dissimilarity of 51% (SIMPER). As for the Australian samples, 18∶1ω9, 18∶0 and AA were in higher relative proportion in reef manta rays and EPA and DHA were higher in near-surface zooplankton (Tables 2 & 3).

Reef manta ray FA composition was significantly different to all zooplankton types collected in this study for both Australia and Mozambique (Figure 4A–B), and all groups were well separated (ANOSIM R values≈1) for both the Australian and Mozambican samples. Average dissimilarities among reef manta rays and near-surface zooplankton, epipelagic zooplankton and all separated zooplankton taxa were between 42.2% and 51.0% (SIMPER). Average dissimilarity between Australian reef manta rays and demersal zooplankton was slightly lower with a value of 34.5%. The main contributors to dissimilarities between groups were EPA, DHA (both lower in reef manta rays), 18∶1ω9c, 18∶0 and AA (all higher in reef manta rays) (Tables 2, 3 & S3).

Figure 4. Comparison of reef manta ray tissue and zooplankton fatty acid (FA) profiles.

Multi-dimensional scaling ordinations of (A) Australian reef manta ray muscle tissue and different zooplankton groups FA profiles collected off east Australia considering all FA >1% (n = 20), (B) Mozambican reef manta ray muscle tissue and different zooplankton groups collected off Mozambique considering all FA >1% (n = 20).


Reef manta rays have been presumed to feed predominantly on near-surface zooplankton, an assumption based primarily on field observations that are temporally and spatially limited to daytime and coastal areas [38], [40]. To our knowledge, no assessment of the food source of the reef manta ray is available in the scientific literature apart from the single stomach content rudimentarily described for M. alfredi (referred to as Daemomanta alfredi) by Whitley [37]. Here we present the first validation that reef manta rays feed on zooplankton with a new insight on the origin of their prey. The high site affinity individuals display to their aggregation sites over extended periods of time (>4 years [38], [59]) suggest that reef manta rays exploit food sources found within a same region (e.g. mid-eastern Australia). Reef manta rays are large vertebrates that most likely require a large amount of food to sustain their activities. Although manta rays are regularly observed feeding near the surface at aggregation sites, the food obtained from these temporary productivity blooms might not be enough to sustain these animals considering the large distances they seasonally travel (up to 500 km [38]). The results in this study suggest that reef manta rays do not feed predominantly on near surface zooplankton and that a major part of their diet may come from demersal sources.

Our findings demonstrate that the reef manta ray has a trophic level value of ∼3 indicative of a secondary consumer trophic position. The mean nitrogen enrichment value (Δ15N) between reef manta ray tissue and ‘feeding’ zooplankton of 2.4‰ falls within the range estimated of diet-tissue trophic fractionation in elasmobranchs (2.29–3.7‰) [18], [25]. This finding suggests that the nitrogen assimilated by reef manta rays originated from primary consumers, which is consistent with previous assumptions that the species feeds on zooplankton.

Highly depleted δ13C values are indicative of pelagic feeding (δ13C = −22‰ to ∼ −17‰ for pelagic phytoplanktonic origin [23]) while enriched values reflect more inshore and/or benthic foraging (δ13C >−17‰, for marine benthic algae [23]). The δ13C value of reef manta ray muscle tissue (−17.4‰) falls within the transition range between pelagic and inshore-benthic values, suggesting that their diet is not exclusively based on pelagic zooplankton. The δ13C value of reef manta ray skin tissue was more enriched compared to muscle tissue. Although differences in isotopic values between these two tissue types were found in other studies (e.g. [11]), there has been no experimental investigation on the origin of these variations. Yet, Pinnegar and Polunin [60] showed that teleost white muscle provided the best average of assimilated diet when compared to other tissue types. Interestingly, the stout whiting, that mostly feeds on benthic animals [61], [62], had a similar δ13C signature to the reef manta ray muscle tissue, which suggests that benthic organisms may form a part of the diet of reef manta rays.

The enrichment value of δ13C between reef manta ray muscle and zooplankton collected from feeding events appeared relatively high (1.3‰) when compared to other studies. Post [21] estimated that the carbon discriminator factor for teleost fishes was 0.4±1.3‰, while Hussey et al. [25] found a mean of 0.9±0.33‰ based on results for several elasmobranch species. Kim et al. [18] conducted the first study of elasmobranch stable isotopes under a fully controlled environment and found a discriminating factor of 1.7‰ for the leopard shark. However, this relatively high value is likely to be biased by the protein-rich diet the sharks were fed on, which led to enriched δ13C. Considering these previous studies, δ13C enrichment value for reef manta rays relative to their known prey is within the diet-tissue discrimination factor range for elasmobranchs, but is higher than average. This suggests that an important part of the diet of reef manta rays may be more carbon-enriched than the collected pelagic zooplankton. Pitt et al. [63] showed that emergent zooplankton is more enriched in carbon than pelagic zooplankton. Although these latter findings are limited to a coastal lagoon system, they provide some support for the suggestion that reef manta rays could feed on demersal zooplankton.

The difference in lipid class proportion between zooplankton from Australia and Mozambique was consistent with lower storage lipids in the Mozambican samples, and also showed degradation of the Mozambican samples, causing higher FFA levels. All zooplankton samples had relatively high levels of TAG that are usually associated with energy storage [64], [65]. Previous studies on chondrichthyan species showed that TAG tend to be stored in the liver [27], which may explain the low level of TAG found in reef manta ray muscle. As with other elasmobranch species [27], FA were mostly integrated as PL in reef manta ray muscle tissues which were high in PUFA. Dietary FA are selectively incorporated into different tissues and little is known about which tissue FA profile would best mirror the diet FA profile of elasmobranchs. Beckmann et al. [66] found that the liver of captive Port Jackson sharks Heterodontus portusjacksoni (Meyer, 1793) can reflect dietary FA within a short timescale (10 weeks) under controlled feeding experiments. McMeans et al. [35] showed that muscle FA profile in the Greenland shark Somniosus microcephalus (Bloch & Schneider, 1801) is highly representative of its prey FA profiles, and indicated that most FA undergo direct assimilation into this particular tissue. Although PL are less influenced by changes in diet than TAG [67], PL-rich muscle tissue can still provide an integrated diet signal over a longer period of time and it may be more representative for PUFA-rich prey items [27][29].

Near surface and epipelagic zooplankton in both Australia and Mozambique were largely dominated by ω3 PUFA, which is typical for most pelagic marine animals [33], [68]. All samples were DHA-dominated and low values of EPA/DHA and 16∶1/16∶0 (both <1) indicate a dominant flagellate-based diet in Australian and Mozambican pelagic zooplankton [33]{Dalsgaard, 2003 #24;Dalsgaard, 2003 #24}. Seasonal variation in the relative levels of DHA at Lady Elliot Island likely indicates temporal changes in available phytoplankton. Docosahexaenoic acid was the dominant PUFA in reef manta rays in east Australian waters and the second major PUFA in those from Mozambique, which likely reflect the intake of regional pelagic zooplankton in their diet. However, the high levels of ω6 PUFA for reef manta rays from both regions indicate that their diet is not restricted to ω6 PUFA-poor near-surface and epipelagic zooplankton. Although variations in FA compositions between muscle and skin tissues were detected in Australian reef manta rays, both tissue types provided similar relative proportions of assimilated FA and, had similar ω3/ω6 ratios. Results obtained from Mozambican reef manta rays (muscle and residual skin) are thus considered comparable to muscle tissue obtained in east Australia. The difference in FA profiles of reef manta rays from Australia and Mozambique could be due to more prominent foraging activity on ω6 PUFA-rich zooplankton by reef manta rays in Mozambique. The trophic pathway of high levels of ω6 PUFA in animals is still ambiguous. Whale sharks, which share many common life history traits with reef manta rays, also had unusually high levels of ω6 FA in their tissue, although whale sharks had much lower values of DHA than reef manta rays [30], [44]. Based on a range of comparative analyses of available FA profiles, together with modelled FA profiles and stomach content analysis, whale sharks were suggested to feed on demersal and deep-sea macrozooplankton and small fishes in addition to epipelagic zooplankton [30]. Demersal zooplankton from our study had relatively higher levels of ω6 PUFA compared to near-surface and epipelagic zooplankton. This corroborates previous studies that showed that benthic animals tend to have higher relative levels of ω6 PUFA and especially AA (e.g. [68][71]). Transfer of ω6 PUFA to demersal and benthic zooplankton could be through direct intake of ω6 PUFA-rich macroalgae [65], but also through the consumption of micro-heterotrophs that are present in the sediment and potentially feed on ω6 PUFA-rich phytodetritus [72][75].

The zooplankton density threshold that may trigger foraging activity in reef manta rays is not known, but it is likely that these large planktivores target patches of high zooplankton density and biomass as has been shown for whale sharks and basking sharks Cetorhinus maximus (Gunnerus, 1765) [76][79]. Demersal zooplankton is highly abundant in shallow coastal areas and usually has larger individuals than pelagic zooplankton, leading to greater biomass [46], [47]. It is thus highly plausible that reef manta rays target demersal zooplankton when emerging from the sediment, especially at night. Consumption of this food source could explain the origin of the ω6 PUFA-rich profile of reef manta rays and their enriched δ13C values relative to values for pelagic zooplankton species. To date, studies that have focused on the FA or SI composition of demersal zooplankton in tropical and subtropical systems are scarce [63]. Behavioural observations at foraging sites revealed that reef manta rays can adapt their feeding strategy according to zooplankton distribution and individuals have been photographed feeding near the sea floor during the day (as illustrated in Figure 5). In addition, recent investigations of vertical movements of satellite-tracked reef manta rays in eastern Australia revealed that individuals commonly spend long periods of time at depth in the epipelagic zone, which could be associated with feeding activity in specific layers of the water column (FR Jaine, unpublished data).

Figure 5. Reef manta ray feeding close to the sea bottom.

This is occasionally observed during the day and proposed to be more common at night.

Species-specific studies on a wide range of elasmobranch fishes are required to examine differences in biochemical composition between tissue types and determine how accurately the information provided by FA and SI analyses reflects a species’ diet. Muscle and skin tissues should be a major focus of study, since they can easily be collected as live-animal biopsies from large and mobile marine species, such as manta rays. Data on the vertical habitat use of reef manta rays from different regions, along with information on the local FA composition of different types of zooplankton (i.e. epipelagic, deep-sea, and in particular demersal/emergent) will help further resolve the role demersal zooplankton plays in the feeding ecology of reef manta rays. Our results suggest that future work should investigate potential food sources present in deeper waters, particularly in terms of their FA profiles and potential origins (e.g. benthic, upwelling origin, deep scattering layers).

Our findings challenge the prevailing paradigm on the feeding ecology of reef manta rays, and suggest that these large planktivores also feed on demersal and deeper-water zooplankton, and supplement their diet with opportunistic feeding in near-surface waters. A comprehensive knowledge of the foraging habits of the reef manta ray is necessary to identify the trophic and ecological role of the species and provide a broader understanding of its community dynamics. Identifying critical foraging habitats should help inform conservation management in areas where the species is most vulnerable.

Supporting Information

Figure S1.

Comparison of zooplankton fatty acid (FA) profiles. Multi-dimensional scaling ordinations of near- surface zooplankton FA profiles sampled at Lady Elliot Island from June 2010 to February 2012, considering all FA >0.2% (n = 41). There was a significant difference among samples (ANOSIM, R value = 0.74, p = 0.001) and pairwise comparison revealed that all sampled months were significantly different from each other (pairwise ANOSIM, p<0.05). Most groups were well separated with an R value >0.75. Some degree of overlap (ANOSIM, R value ranged between 0.50 and 0.75) was detected between June 2010 and June 2011, September 2011 and February 2011, and June 2010 and August 2011. A relatively high degree of overlap was found between June 2011 and August 2011 (R value = 0.4) and February 2011 and February 2012 (R value = 0.3). The three main FA contributing to discrimination of particular months were DHA, EPA and 16∶0 (SIMPER). The major contributor to dissimilarities between most months was DHA and it was the second main contributor in three cases, where either EPA (between June 2010 and August 2011) or 18∶1ω9 (between November 2010 and February 2012, October 2010 and February 2012) was the major contributor. All samples were dominated by DHA and 16∶0.


Table S1.

Lipid class composition (% of total lipids) and total lipid content (mg.g−1 of wet weight, ww) of (a) reef manta rays muscle tissue and (b) zooplankton samples.


Table S2.

Results of similarity percentage analysis (SIMPER) of fatty acid data for reef manta rays and zooplankton. Fatty acids with an average contribution >8% are included. Data were not transformed prior to analysis


Table S3.

Fatty acid composition (% of total FA) of zooplankton taxa collected off eastern Australia.



We thank P. Mansour and R. Diocares for their assistance with laboratory techniques and equipment, and D. Holdsworth for management of the CSIRO GC-MS facility. We are grateful to T. Kashiwagi, A. Prosser, P. Gartrell, J. Stead, M. Ooi, C. Garraway, S. McGrellis, R. Cheseldene-Culley, A. Donelly, C. Gillies and the many Earthwatch volunteers for their assistance in sample collection, and to K. Burgess and C. Bustamante for their assistance and comments on the manuscript. We also thank R. Phleger, R. Connolly and Brian Fry for their comments on the manuscript.

Author Contributions

Conceived and designed the experiments: LC CR AR MB PN. Performed the experiments: LC CR AM FJ KT SW PN. Analyzed the data: LC CR AR PN. Contributed reagents/materials/analysis tools: AR AM MB PN. Wrote the paper: LC CR AR FJ AM MB KT SW PN.


  1. 1. Canese S, Cardinali A, Fortuna CM, Giusti M, Lauriano G, et al. (2006) The first identified winter feeding ground of fin whales (Balaenoptera physalus) in the Mediterranean Sea. J Mar Biol Assoc U K 86: 903–907.
  2. 2. Hooker SK, Gerber LR (2004) Marine reserves as a tool for ecosystem-based management: the potential importance of megafauna. Bioscience 54: 27–39.
  3. 3. López-Mendilaharsu M, Gardner SC, Seminoff JA, Riosmena-Rodriguez R (2005) Identifying critical foraging habitats of the green turtle (Chelonia mydas) along the Pacific coast of the Baja California peninsula, Mexico. Aquat Conserv 15: 259–269.
  4. 4. Cortés E (1997) A critical review of methods of studying fish feeding based on analysis of stomach contents: application to elasmobranch fishes. Can J Fish Aquat Sci 54: 726–738.
  5. 5. Richardson A, Lamberts C, Isaacs G, Moloney C, Gibbons M (2000) Length-weight relationships for some important forage crustaceans from South Africa. Naga 23: 29–33.
  6. 6. Marshall AD, Compagno LJV, Bennett MB (2009) Redescription of the genus Manta with resurrection of Manta alfredi (Krefft, 1868) (Chondrichthyes; Myliobatoidei; Mobulidae). Zootaxa 2301: 1–28.
  7. 7. Marshall A, Kashiwagi T, Bennett MB, Deakos MH, Stevens G, et al.. (2011) Manta alfredi. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.1. <>. Downloaded on 23 November 2012.
  8. 8. Couturier L, Marshall A, Jaine F, Kashiwagi T, Pierce S, et al. (2012) Biology, ecology and conservation of the Mobulidae. J Fish Biol 80: 1075–1119.
  9. 9. Iverson SJ, Field C, Don Bowen W, Blanchard W (2004) Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol Monogr 74: 211–235.
  10. 10. Wada E, Mizutani H, Minagawa M (1991) The use of stable isotopes for food web analysis. Crit Rev Food Sci Nutr 30: 361–371.
  11. 11. Carlisle AB, Kim SL, Semmens BX, Madigan DJ, Jorgensen SJ, et al. (2012) Using stable isotope analysis to understand the migration and trophic ecology of northeastern Pacific white sharks (Carcharodon carcharias). PLoS One 7: e30492.
  12. 12. Herman DP, Burrows DG, Wade PR, Durban JW, Matkin CO, et al. (2005) Feeding ecology of eastern North Pacific killer whales Orcinus orca from fatty acids, stable isotope, and organochlorine analyses of blubber biopsies. Mar Ecol Prog Ser 302: 275–291.
  13. 13. Borrell A, Cardona L, Kumarran RP, Aguilar A (2011) Trophic ecology of elasmobranchs caught off Gujarat, India, as inferred from stable isotopes. ICES J Mar Sci 68: 547–554.
  14. 14. Estrada JA, Rice AN, Lutcavage ME, Skomal GB (2003) Predicting trophic position in sharks of the north-west Atlantic Ocean using stable isotope analysis. J Mar Biol Assoc UK 83: 1347–1350.
  15. 15. Hussey NE, MacNeil MA, Fisk AT (2010) The requirement for accurate diet-tissue discrimination factors for interpreting stable isotopes in sharks. Hydrobiologia 654: 1–5.
  16. 16. Logan JM, Lutcavage ME (2010) Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644: 231–244.
  17. 17. MacNeil MA, Skomal GB, Fisk AT (2005) Stable isotopes from multiple tissues reveal diet switching in sharks. Mar Ecol Prog Ser 302: 199–206.
  18. 18. Kim SL, del Rio CM, Casper D, Koch PL (2012) Isotopic incorporation rates for shark tissues from a long-term captive feeding study. J Exp Biol 215: 2495–2500.
  19. 19. Hobson KA (1999) Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120: 314–326.
  20. 20. Kelly JF (1999) Stable isotopes of carbon and nitrogen in the study of avian and mammalian trophic ecology. Can J Zool 78: 1–27.
  21. 21. Post DM (2002) Using stable isotopes to estimate trophic postion: models, methods and assumptions. Ecology 83: 703–718.
  22. 22. Hansson S, Hobbie JE, Elmgren R, Larsson U, Fry B, et al. (1997) The stable nitrogen isotope ratio as a marker of food-web interactions and fish migration. Ecology 78: 2249–2257.
  23. 23. France R (1995) Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications. Mar Ecol Prog Ser 124: 307–312.
  24. 24. Fry B, Sherr EB (1984) δ13C measurements as indicators of carbon flow on marine and freshwater ecosystems. Contrib Mar Sci 27: 13–47.
  25. 25. Hussey NE, Brush J, McCarthy ID, Fisk AT (2010) δ15N and δ13C diet-tissue discrimination factors for large sharks under semi-controlled conditions. Comp Biochem Physiol A Mol Integr 155: 445–453.
  26. 26. Malpica-Cruz L, Herzka S, Z., Sosa-Nishizaki O, Lazo JP (2012) Tissue-specific isotope trophic discrimination factors and turnover rates in a marine elasmobranch: empirical and modeling results. Can J Fish Aquat Sci 69: 551–564.
  27. 27. Pethybridge H, Daley R, Virtue P, Nichols P (2010) Lipid composition and partitioning of deepwater chondrichthyans: inferences of feeding ecology and distribution. Mar Biol 157: 1367–1384.
  28. 28. Pethybridge H, Daley RK, Nichols PD (2011) Diet of demersal sharks and chimaeras inferred by fatty acid profiles and stomach content analysis. J Exp Mar Biol Ecol 409: 290–299.
  29. 29. Schaufler L, Heintz R, Sigler M, Hulbert L (2005) Fatty acid composition of sleeper shark (Somniosus pacificus) liver and muscle reveals nutritional dependence on planktivores ICES CM: Elasmobranch Fisheries Science. 19.
  30. 30. Rohner C, Couturier L, Richardson A, Pierce SJ, Preddle C, et al. (in press) Diet of whale sharks Rhincodon typus inferred from stomach content and signature fatty acid analyses. Mar Ecol Prog Ser. 10.3354/meps10500.
  31. 31. Parrish CC (2009) Essential fatty acids in aquatic food webs. In: Arts MT, Brett MT, Kainz M, editors. Lipids in aquatic ecosystems: Springer Science. 309–326.
  32. 32. Iverson SJ (2009) Tracing aquatic food webs using fatty acids: from qualitative indicators to quantitative determination. In: Arts MT, Brett MT, Kainz M, editors. Lipids in aquatic ecosystems: Springer Science. 281–308.
  33. 33. Dalsgaard J, St John M, Kattner G, Müller-Navarra D, Hagen W (2003) Fatty acid trophic markers in the pelagic marine environment. Adv Mar Biol 46: 225–340.
  34. 34. Tocher DR (2003) Metabolism and functions of lipids and fatty acids in teleost fish. Rev Fish Sci 11: 107–184.
  35. 35. McMeans BC, Arts MT, Fisk AT (2012) Similarity between predator and prey fatty acid profiles is tissue dependent in Greenland sharks (Somniosus microcephalus): Implications for diet reconstruction. J Exp Mar Biol Ecol 429: 55–63.
  36. 36. Sargent J, Parkes R, Mueller-Harvey I, Henderson R (1987) Lipid biomarkers in marine ecology. In: Sleigh M A. editor. Microbes in the sea: Ellis Horwood Limited. 119–138.
  37. 37. Whitley GP (1936) The Australian devil ray, Daemomanta alfredi (Krefft), with remarks on the Superfamily Mobuloidae (Order Batoidei). Aust Zool 8: 164–188.
  38. 38. Couturier LIE, Jaine FRA, Townsend KA, Weeks SJ, Richardson AJ, et al. (2011) Distribution, site affinity and regional movements of the manta ray, Manta alfredi (Krefft, 1868), along the east coast of Australia. Mar Freshw Res 62: 628–637.
  39. 39. Clark TB (2010) Abundance, home range, and movement patterns of manta rays (Manta alfredi, M. birostris) in Hawai’i. PhD Thesis. Mãnoa, Hawaii: The University of Hawai’i.
  40. 40. Dewar H, Mous P, Domeier M, Muljadi A, Pet J, et al. (2008) Movements and site fidelity of the giant manta ray, Manta birostris, in the Komodo Marine Park, Indonesia. Mar Biol 155: 121–133.
  41. 41. Anderson RC, Adam MS, Goes JI (2011) From monsoons to mantas: seasonal distribution of Manta alfredi in the Maldives. Fish Oceanogr 20: 104–113.
  42. 42. Marshall AD (2008) Biology and population ecology of Manta birostris in southern Mozambique. PhD Thesis. St. Lucia, QLD: The University of Queensland. 278 p.
  43. 43. Papastamatiou YP, DeSalles PA, McCauley DJ (2012) Area-restricted searching by manta rays and their response to spatial scale in lagoon habitats. Mar Ecol Prog Ser 456: 233.
  44. 44. Couturier LIE, Rohner CA, Richardson AJ, Pierce SJ, Marshall AD, et al. (in press) Unusually high levels of n-6 polyunsaturated fatty acids in whale sharks and reef manta rays. Lipids DOI 10.1007/s11745-013-3829-8.
  45. 45. Sargent J, Falk-Petersen S (1988) The lipid biochemistry of calanoid copepods. Hydrobiologia 167: 101–114.
  46. 46. Alldredge A, King J (1977) Distribution, abundance, and substrate preferences of demersal reef zooplankton at Lizard Island Lagoon, Great Barrier Reef. Mar Biol 41: 317–333.
  47. 47. Alldredge AL, King JM (1980) Effects of moonlight on the vertical migration patterns of demersal zooplankton. J Exp Mar Biol Ecol 44: 133–156.
  48. 48. Osada K (2010) Relationship of zooplankton emergence, manta ray abundance and SCUBA diver usage Kona, Hawaii. Mãnoa, Hawaii: The University of Hawai’i.
  49. 49. Melo PAMC, Silva TA, Neumann-Leitão S, Schwamborn R, Gusmão LMO, et al. (2010) Demersal zooplankton communities from tropical habitats in the southwestern Atlantic. Mar Biol Res 6: 530–541.
  50. 50. Revill AT, Young JW, Lansdell M (2009) Stable isotopic evidence for trophic groupings and bio-regionalization of predators and their prey in oceanic waters off eastern Australia. Mar Biol 156: 1241–1253.
  51. 51. Peterson BJ, Fry B (1987) Stable isotopes in ecosystem studies. Annu Rev Ecol Syst 18: 293–320.
  52. 52. Hornberger GM (1995) New manuscript guidelines for the reporting of stable hydrogen, carbon, and oxygen isotope ratio data. Water Resour Res 31: 2895–2895.
  53. 53. Post DM, Layman CA, Arrington DA, Takimoto G, Quattrochi J, et al. (2007) Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152: 179–189.
  54. 54. Syvaranta J, Rautio M (2010) Zooplankton, lipids and stable isotopes: importance of seasonal, latitudinal, and taxonomic differences. Can J Fish Aquat Sci 67: 1721–1729.
  55. 55. Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37: 911–917.
  56. 56. Phleger CF, Nelson MM, Mooney B, Nichols PD, Ritar AJ, et al. (2001) Lipids and nutrition of the southern rock lobster, Jasus edwardsii, from hatch to puerulus. Mar Freshw Res 52: 1475–1486.
  57. 57. R Development CoreTeam (2008) R: A language and environment for statistical computing. Vienna, Austria: R Foundation Statistical Computing. <>. Downloaded on 15 Ocotober 2012.
  58. 58. Clarke K, Gorley R (2006) PRIMER v6: User manual/tutorial: Plymouth. Plymouth, UK: Primer-E, Ltd. 190 p.
  59. 59. Marshall AD, Dudgeon CL, Bennett MB (2011) Size and structure of a photographically identified population of manta rays Manta alfredi in southern Mozambique. Mar Biol 158: 1111–1124.
  60. 60. Pinnegar J, Polunin N (1999) Differential fractionation of δ13C and δ15N among fish tissues: implications for the study of trophic interactions. Funct Ecol 13: 225–231.
  61. 61. Burchmore J, Pollard D, Middleton M, Bell J, Pease B (1988) Biology of four species of whiting (Pisces: Sillaginidae) in Botany Bay, NSW. Mar Freshw Res 39: 709–727.
  62. 62. Hyndes G, Platell M, Potter I (1997) Relationships between diet and body size, mouth morphology, habitat and movements of six sillaginid species in coastal waters: implications for resource partitioning. Mar Biol 128: 585–598.
  63. 63. Pitt KA, Clement AL, Connolly RM, Thibault-Botha D (2008) Predation by jellyfish on large and emergent zooplankton: implications for benthic–pelagic coupling. Estuar Coast Shelf Sci 76: 827–833.
  64. 64. Lee RF, Hagen W, Kattner G (2006) Lipid storage in marine zooplankton. Mar Ecol Prog Ser 307: 273–306.
  65. 65. Sheridan MA (1988) Lipid dynamics in fish: aspects of absorption, transportation, deposition and mobilization. Comp Biochem Physiol B 90: 679–690.
  66. 66. Beckmann CL, Mitchell JG, Seuront L, Stone DA, Huveneers C (2013) Experimental evaluation of fatty acid profiles as a technique to determine dietary composition in benthic elasmobranchs. Physiol Biochem Zool 86: 266–278.
  67. 67. Regost C, Arzel J, Robin J, Rosenlund G, Kaushik S (2003) Total replacement of fish oil by soybean or linseed oil with a return to fish oil in turbot (Psetta maxima): 1. Growth performance, flesh fatty acid profile, and lipid metabolism. Aquaculture 217: 465–482.
  68. 68. Kelly JR, Scheibling RE (2012) Fatty acids as dietary tracers in benthic food webs. Mar Ecol Prog Ser 446: 1–22.
  69. 69. Howell KL, Pond DW, Billett DSM, Tyler PA (2003) Feeding ecology of deep-sea seastars (Echinodermata: Asteroidea): a fatty-acid biomarker approach. Mar Ecol Prog Ser 255: 193–206.
  70. 70. Hudson IR, Pond DW, Billett DSM, Tyler PA, Lampitt RS, et al. (2004) Temporal variations in fatty acid composition of deep-sea holothurians: evidence of bentho-pelagic coupling. Mar Ecol Prog Ser 281: 109–120.
  71. 71. Hall D, Lee SY, Meziane T (2006) Fatty acids as trophic tracers in an experimental estuarine food chain: Tracer transfer. J Exp Mar Biol Ecol 336: 42–53.
  72. 72. Lee Chang KJ, Dunstan GA, Abell GCJ, Clementson LA, Blackburn SI, et al. (2012) Biodiscovery of new Australian thraustochytrids for production of biodiesel and long-chain omega-3 oils. Appl Microbiol Biotechnol 93: 2215.
  73. 73. Nichols DS (2003) Prokaryotes and the input of polyunsaturated fatty acids to the marine food web. FEMS Microbiol Lett 219: 1–7.
  74. 74. Raghukumar S (2002) Ecology of the marine protists, the Labyrinthulomycetes (Thraustochytrids and Labyrinthulids). Eur J Protistol 38: 127–145.
  75. 75. Stoecker DK, Capuzzo JMD (1990) Predation on protozoa: its importance to zooplankton. J Plankton Res 12: 891–908.
  76. 76. Sims D, Fox A, Merrett D (1997) Basking shark occurrence off south-west England in relation to zooplankton abundance. J Fish Biol 51: 436–440.
  77. 77. Sims DW, Quayle VA (1998) Selective foraging behaviour of basking sharks on zooplankton in a small-scale front. Nature 393: 460–464.
  78. 78. Heyman WD, Graham RT, Kjerfve B, Johannes RE (2001) Whale sharks Rhincodon typus aggregate to feed on fish spawn in Belize. Mar Ecol Prog Ser 215: 275–282.
  79. 79. Nelson JD, Eckert SA (2007) Foraging ecology of whale sharks (Rhincodon typus) within Bahía de los Angeles, Baja California Norte, México. Fish Res 84: 47–64.