Introduction

Stable isotope analyses have proven to be a powerful tool for characterizing trophic relationships between predator and prey across ecosystems. An important parameter necessary to interpret stable isotope data in ecology is the diet-tissue discrimination factor (∆¹⁵N or ∆¹³C)—the enrichment in the heavy isotope in predators’ tissues relative to the prey consumed due to preferential assimilation of the heavy isotope and preferential excretion of the light isotope [1,2]. Following an influential paper by Post [3], the across-taxa mean enrichment of 3.4‰ remains the most broadly applied ∆¹⁵N value [4]. Controlled feeding experiments, however, have demonstrated that ∆¹⁵N values vary among species [5,6], with meta-analyses and experimental work empirically showing an inverse relationship between ∆¹⁵N and dietary δ¹⁵N values [7,8]. These data suggest that taxonomic classes containing a range of species that feed on diverse prey resources will exhibit intra-class variation in ∆¹⁵N, questioning the suitability of a single ∆¹⁵N value for characterizing predator-prey relationships [7,9].

Precise methods for characterizing the ecological role of organisms are imperative to the management of aquatic ecosystems [10]. This is especially relevant for marine predators that have undergone global declines in recent decades, as many species occupy a relatively high trophic position, and are therefore assumed to play an important role in structuring marine communities [11–13]. The subclass Elasmobranchii (sharks, skates and rays) comprises ~450 species that inhabit a diverse range of habitats, from the deep ocean to exposed coral shelves. Species within this subclass range in size, from the dwarf lantern shark Etmopterus perryi (~17 cm) to the whale shark Rhincodon typus (~12 m) and feed on a broad prey base, from zooplankton to marine mammals. This considerable diversity together with the reported inverse ∆¹⁵N-dietary δ¹⁵N relationship, would suggest that variable diets among species within this subclass will result in variable mean dietary δ¹⁵N values (e.g., crustacean dominated diet vs. elasmobranch dominated diet) that would ultimately drive variable ∆¹⁵N values (Figure 1). To date, two experimentally derived ∆¹⁵N values are available for elasmobranchs; a ∆¹⁵N of 2.3‰ for large sharks fed a fish diet [14] and a ∆¹⁵N of 3.7‰ for a small shark fed a diet of squid [15]. These shark-specific ∆¹⁵N values are markedly different and when applied to dietary and/or food web analyses would result in vastly different inferences dependent on the ∆¹⁵N selected. This highlights the importance of considering the diet (including protein quality and quantity) of the consumer species examined to ensure the most accurate interpretation of stable isotope data for these purposes.

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Figure 1. The predicted relationship between nitrogen stable isotope discrimination between predator and prey consumed (∆¹⁵N) and the prey stable nitrogen isotope composition (dietary-δ¹⁵N) estimates for each shark species based on the widely reported ∆¹⁵N-dietary δ¹⁵N relationship

[7,8,40,67].

https://doi.org/10.1371/journal.pone.0077567.g001

Our objective was to conduct an assessment of a range of reported ∆¹⁵N values for describing the predator-prey relationships of four shark species that feed at different trophic levels (i.e., different mean δ¹⁵N dietary values) and in so doing, determine the most applicable surrogate ∆¹⁵N value for describing predator-prey relationships within this speciose group. Given the logistical and ethical complexities of controlled feeding experiments on large predators, field sampling to derive consumer and prey isotope values in conjunction with novel ellipse estimates were used to assess appropriate ∆¹⁵N values for four species that have well characterized diets from stomach content analysis; bonnethead (Sphyrna tiburo), Atlantic sharpnose (Rhizoprionodon terraenovae), bull (Carcharhinus leucas) and white (Carcharodon carcharias) shark. The broad range of predator-prey relationships represented by the trophic assessment of these four species presents the opportunity to assess whether species-specific diets that range from a low (i.e., bonnethead preying on crustaceans) to a high (i.e., white preying on elasmobranchs and marine mammals) δ¹⁵N value affect the choice of which ∆¹⁵N value to use.

Materials and Methods

Stomach Content and Stable Isotope Data

To characterize the diet of the four focal species, we used a combination of published literature and stomach contents sampled from animals included in this study. For bonnethead and Atlantic sharpnose sharks, stomach content data were quantified following standard methods (see 21,22, M. Fritts unpublished data). For bull and white sharks, quantified stomach content data from South African animals were taken from Cliff and Dudley [23] and Hussey et al. [24], respectively. The importance of (i) functional prey groups (e.g., crustacean, mollusk, teleost, elasmobranch, mammal) and (ii) individual prey items (identified to species level where possible) to the diet of each shark was assessed using four commonly applied dietary indices: (i) %W, the weight contribution of a prey item, (ii) %F, the frequency of occurrence of each prey item, (iii) %N, the numbers of each prey item and (iv) %IRI, the relative importance of the prey item. Specifically, %W was calculated as the weight of each prey item divided by the total weight of prey items from an individual stomach, %F was calculated as the number of stomachs containing a prey item divided by the total number of stomachs containing prey and %N was calculated as the number of each prey item divided by the total number of prey items. The index of relative importance is a compound index that incorporates the previous three indices, expressed as IRI = %F (%N + %W). This product is then expressed as a percentage (%IRI) by dividing the total IRI for each prey item by the total IRI for all prey items [22]. From the complete stomach content data (for complete diet analysis see Cliff and Dudley [23]; Hussey et al. [24]; M. Fritts unpublished data), the five most commonly occurring prey items in the diet of each shark [referenced as principal prey (PP) hereafter] were determined based on the highest percent contribution by the combination of %W, %F, %N and %IRI for each prey item (Table 1).

	Sphyrna tiburo				Rhizoprionodon terraenovae				Carcharhinus leucas				Carcharodon carcharias
	TL = 39–47 cm				TL = 34–49 cm				PCL = 145–208 cm				PCL = 185–234.9 cm
	%F	%N	%W	%IRI	%F	%N	%W	%IRI	%F	%N	%W	%IRI	%F	%N	%W	%IRI
MAMMAL									7.7	2.6	7.5	0.6	34.1	4.8	24.5	27.4
Common dolphin (Delphinus delphis)													0.8	0.1	5.5	0.4b
Cetacea									4.3	1.4	5.1	3.3d
Seal (Pinnipedia)													7.0	1.0	5.4	4.1c
ELASMOBRANCH									57.5	20.7	65.5	40.6	42.6	7.2	46.3	62.3
Guitarfish (Rhinobatidae)									9.5	3.2	17.8	11.4c
Dusky/Sharpnose shark (Carcharhinidae)									6.2	2.3	8	3.3a
Bull/Blue/Honeycomb stingray (Dasyatidae)									1.5	0.6	2.2	0.2b
Dusky shark(Carcharhinus obscurus)													10.9	2.6	18.1	20.9a
Milk shark(Rhizoprionodon acutus007A)													2.3	0.3	2.52	0.61d
Spotted eagle ray (Aetobatus narinari)													0.8	0.1	3.2	0.2e
TELEOST	7.5	3.4	4.5	0.6	47.8	33.2	58.5	49.3	71.1	74.5	25.4	58.5	22.4	5.1	5.4	6.4
Spot (Leiostomus xanthurus)					2.3	1.3	15.3	1.1a
Star drum (Stellifer lanceolatus)					2.3	1.3	13.7	1b
Spotted grunter (Pomadasys commersonnii )									4.8	1.7	1.6	1.9e
CRUSTACEAN	122.5	58.6	70.8	46	38.7	23.9	28.2	17.8	5.3	1.8	0.1	0.1
Blue crab (Callinectes sp.)	22.5	11.5	18.2	12.2a
Lady crab (Ovalipes ocellatus)	7.5	4.6	13.7	2.5b
Hermit crab (Pagurus sp.)	10	4.6	5	1.7d	6.8	5.3	7	2.4d
Shrimp (Penaeidae)	15	6.9	10.4	4.7c	18.2	10.7	15.3	13.2c
Mantis shrimp (Stomatopoda)	10	4.6	6.3	2e
MOLLUSK					2.3	1.3	11.3	0.8	5.8	2.0	3.8	0.1	23.3	5.9	0.1	3.9
Squid sp. (Teuthoidea)					2.3	1.3	11.3	0.8e

Table 1. Summarized functional prey groups and the principal prey (PP) of bonnethead Sphyrna tiburo, Atlantic sharpnose Rhizoprionodon terraenovae, bull Carcharhinus leucas, and white Carcharodon carcharias shark identified from stomach content data (see 9,23 M. Fritts unpublished data).

Data are percent frequency of occurrence (%F), number (%N), weight (%W) and the index of relative importance (%IRI). TL represents total length; PCL represents precaudal length. Subscripts represent PP species in Figure 4.

Note: Principal prey (PP) do not contribute 100% to the total of each functional prey group. A component of the diet of each of each shark species consists of unidentified species (see 9,23).

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Stable carbon and nitrogen isotopes were analyzed in muscle tissue of the four predators and the determined PP species. Additional prey species identified in the diet of each species, where samples were available from respective study regions and time periods, were also analyzed. To ensure no bias of the maternal isotope signature, only bonnethead (n = 11; 39–47 cm TL) and Atlantic sharpnose (n = 6; 34–49 cm TL) sharks that were characterized as juveniles, yet older than umbilical scar stage 5 were used [19]. To avoid complications of ontogenetic diet shifts, defined size classes of bull (n = 11) and white (n = 16) sharks were selected; sub-adults/adults ranging in length from 145–208 cm PCL and sub-adults ranging in length from 185–235 cm PCL, respectively. For complete stable isotope sample preparation, lab analyses and analytical precision, see Olin et al. [16] and Hussey et al. [25]. Briefly, muscle tissues were sub-sampled from all shark and prey species (~1.0 g), freeze-dried for 48 h, ground and lipid extracted by twice agitating the ground tissue in 2:1 chloroform/methanol solution for 24 h and decanting the solvent (modified method outlined in [26]). The relative abundances of carbon (¹³C/¹²C) and nitrogen (¹⁵N/¹⁴N) were determined on ~0.5–1.0 mg sub-samples on a Thermo Finnigan Delta^Plus mass spectrometer (Thermo Finnigan, San Jose, CA, USA) coupled with an elemental analyzer (Costech, Valencia, CA, USA). Analytical accuracy was 0.14‰ for δ¹⁵N data and 0.05‰ for δ¹³C data based on a single run of NIST standard sucrose (NIST 8542; n = 13) and ammonium sulfate (NIST 8547; n = 13).

To examine whether ∆¹⁵N values varied among species and were dependent on the diet-δ¹⁵N value for a particular shark species, a range of ∆¹⁵N values (and associated ∆¹³C) were selected from the published literature (Table 2). The δ¹⁵N and δ¹³C data for each shark species were then adjusted using each of the four chosen ∆¹⁵N and ∆¹³C pairs from Table 2, by subtracting the specific ∆¹⁵N and ∆¹³C value from each individual predator’s δ¹⁵N and δ¹³C value, respectively. For each shark species, four δ¹⁵N vs. δ¹³C bi-plots were then constructed for each ∆¹⁵N- and ∆¹³C-adjusted shark isotope data and overlaid over the raw prey stable isotope data. Raw shark isotope data were also plotted over raw prey isotope data. This enabled a visual assessment of the ∆¹⁵N and ∆¹³C-adjusted shark isotope values, i.e., it would be expected that the best fit ∆¹⁵N and ∆¹³C-adjusted shark isotope values would fall within the range of the PP it consumes (Figure 2), following Philips and Gregg [27] and Fry [28].

Diet-Tissue Discrimination Factor ± SD (‰)
∆13C	∆15N	Source
0.9 ± 0.3	2.3 ± 0.2	[14]1
0.8 ± 0.1	2.8 ± 0.1	[7]2
0.4 ± 1.3	3.4 ± 1.0	[3]2
1.7 ± 0.5	3.7 ± 0.4	[15]1

Table 2. Diet-tissue discrimination factor (∆¹³C and ∆¹⁵N are the changes in isotope ratios (δ¹³C and δ¹⁵N) between predator and prey, calculated as δ^bX_predator - δ^bX_prey = ∆^bX_{predator-prey}) derived from controlled feeding experiments specific to elasmobranchs¹ and those commonly accepted from the literature².

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Figure 2. Illustration of the expected relationship between stable isotope values of a predator and its’ prey in mixing space [27,28], employing the Bayesian approach of Jackson et al. [29], centered on multivariate ellipse based metrics.

In choosing discrimination factor (∆¹⁵N and ∆¹³C) values, it would be expected that the δ¹⁵N and δ¹³C values of the predator after adjustment to specific ∆¹⁵N and ∆¹³C values should overlay or fall within the range of δ¹⁵N values of the PP it consumes (see Table 2), indicating a best-fit scenario between predator and prey [ellipses represent prey (black) and predator (gray) respectively]. Black points represent δ¹³C and δ¹⁵N values of a predator, gray points (light and dark) represent adjusted-δ¹³C and adjusted-δ¹⁵N values with two different ∆¹⁵N and ∆¹³C values. White shapes represent mean (± variance) of prey species.

https://doi.org/10.1371/journal.pone.0077567.g002

Results

In terms of %W, %F, %N and %IRI crustaceans were the principal diet component of the bonnethead, teleosts for the Atlantic sharpnose, elasmobranchs and teleosts for the bull shark, and elasmobranch and marine mammals for the white shark (Table 1). The mean diet δ¹⁵N value for each shark species based on PP ranged from low in the bonnethead shark (8.8 ± 0.9‰; Figure 3), to high in the white shark (15.1 ± 1.7‰; Figure 3), and differed significantly among the four species (F_3,322 = 177.4, P < 0.0001; Figure 3).

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Figure 3. Box plots representing the δ¹⁵N values of all of the PP derived from stomach content data of the bonnethead Sphyrna tiburo, Atlantic sharpnose Rhizoprionodon terraenovae, bull Carcharhinus leucas, and white Carcharodon carcharias shark.

https://doi.org/10.1371/journal.pone.0077567.g003

The bonnethead shark exhibited the most enriched raw δ¹⁵N values relative to PP, while the white shark exhibited the least enriched raw δ¹⁵N values relative to PP (Figure 4a, f, k, p). For the bonnethead shark that had the lowest δ¹⁵N diet value, the largest ∆¹⁵N of 3.7‰ produced both the greatest visual and ellipse overlap between predator and the functional prey group identified from stomach contents (Figure 4e; Table 3). The most suitable ∆¹⁵N for the Atlantic sharpnose based on ellipse overlap was lower than the bonnethead, a ∆¹⁵N value of 3.4‰ (Figure 4i; Table 3), while 2.3‰ provided the highest measure of overlap for the bull shark (Figure 4l; Table 3). For the white shark, a ∆¹⁵N of 2.3‰ produced the greatest area of overlap between predator and functional prey group ellipses but observation of the data suggested that the ∆¹⁵N for this highly carnivorous species is likely lower (Figure 4p, q; Table 3). The greatest area of overlap between ∆¹⁵N-adjusted predator values and functional prey group isotope values for all four shark species generally correspond with what we expected based on the contributions estimated from stomach content analyses. For example, teleosts followed by crustaceans and mollusks contributed the highest combined percentage to the diet of the Atlantic sharpnose (%W = 58.5, 28.2 and 11.3, respectively; Table 1), which corresponded to the calculated ellipse area overlap between the Atlantic sharpnose and the functional prey groups using the ∆¹⁵N value of 3.4‰ (Table 3). These results were further supported when including stable isotope values for additional prey items identified from the broader diet with the exception of the bonnethead shark (Figure 4b, e; Table 3).

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Figure 4. Dual-plot of individual predator (■) and mean (± SD) δ¹³C and δ¹⁵N values of the PP for each predator ((a), (f), (k), (p); see Table 1).

Standard ellipse areas corrected for sample size (SEA_c) of sharks (solid black) and PP functional prey groups (Crustacean, dashed light gray; Mollusk, dotted light gray; Teleost, dashed dark gray; Elasmobranch, solid dark gray; Mammal solid light gray), and the broader diet (dotted black) following Jackson et al. [29]. Note different scales on the x- and y-axes in each species.

https://doi.org/10.1371/journal.pone.0077567.g004

Discussion

Selection of appropriate ∆¹⁵N values for interpreting stable isotope data remains a controversial point (see 7,14,34,35). The implication of using inappropriate ∆¹⁵N values when calculating trophic position or undertaking mixing model analyses will result in inaccurate inferences regarding diet reconstruction and more critically, the role of an organism within aquatic food webs [36]. Species-specific ∆¹⁵N values have been recognized [37,38] and are particularly important given the advanced isotope modeling approaches currently being adopted to examine complex feeding behavior at both the population and individual level [30,39]. These data find that the greatest area of ellipse overlap between predator and prey was a result of selecting different ∆¹⁵N values that were dependent on the diet δ¹⁵N value, indicating that ∆¹⁵N values for species within the subclass Elasmobranchii vary greatly. Although considerable progress in stable isotope controlled feeding studies has been made [5,6,8], the ∆¹⁵N-diet δ¹⁵N relationship has largely been ignored based on the fact that the mechanism driving the relationship remains unknown. Despite this, our results further corroborate this relationship and future experimental analyses should focus on identifying the mechanism driving this relationship. Whether the relationship is driven by protein quantity and quality [36,40] or the variable kinematics of ¹⁴N and ¹⁵N routing and incorporation in animal tissues [37] remains unclear, but questions the use of a single ∆¹⁵N value for this subclass.

Variation in ∆¹⁵N values dependent on the consumers’ dietary-δ¹⁵N value within this subclass of species would indicate this result is ubiquitous across a broad range of taxa in both aquatic and terrestrial ecosystems. These results were consistent across shark species when comparing both isotope data of functional prey groups and the broader identified diet, the latter with the exception of the bonnethead shark. This inconsistency in ∆¹⁵N values of the bonnethead resulted from the inclusion of teleost prey species with higher δ¹⁵N values (i.e., anchovies) in the broader diet, prey species that are minor contributors to overall diet. The bonnethead shark is known to primarily feed on crustaceans with minimal contribution from teleost prey (see 41 and references therein). Our overall results were also consistent with other studies that have reported greater isotopic discrimination between predator and prey when a low δ¹⁵N diet is assimilated [7,40]. Indeed, Kim et al. [42], observed a higher ∆¹⁵N value in a controlled laboratory study on leopard sharks (Triakis semifasciata) following a diet switch from squid (~3.7‰; δ¹⁵N of diet = ~13.3‰) to tilapia (~5.5‰; δ¹⁵N of diet = ~7.9 ‰). Similarly, Blanchet-Aurigny et al. [43] observed higher ∆¹⁵N values for brittle stars Ophiocomina nigra and Ophiothrix fragilis fed a diet of mussels and/or macroalgae (2-4‰) compared to a diet of fish (~-1‰). This demonstrates that ∆¹⁵N values can vary within a species dependent on the dietary-δ¹⁵N value the animal consumes, in agreement with previous work [6,44]. If ∆¹⁵N values vary within a species, dependent on the dietary-δ¹⁵N value, it would be expected that ∆¹⁵N values will vary among species within a subclass that have diverse diets as our data indicate.

Compound specific stable isotope analysis of amino acids (AA-CSIA) has proven to be a complimentary method for estimating ∆¹⁵N values within organisms (trophic enrichment factor —TEF). Initial experimental work suggested a TEF value of 7.6‰ was universal across species and taxa [45], similar to the widely applied value of 3.4‰ used in bulk stable isotope analysis [3]. Recently published data on marine mammals fed high δ¹⁵N diets exhibited TEFs that were lower than previously observed (4.3 vs. 7.6‰) [46]. These AA-CSIA data are consistent with the relationship observed in bulk stable isotope analysis [7,8,14] and suggest that TEF estimates may vary dependent on the diet δ¹⁵N value. Knowledge of diet ascertained through stomach content data remains critical for guiding isotopic assumptions on ∆¹⁵N values and thereby improving the efficiency of yielded results from modeling exercises, for example mixing models [47,48].

Estimating trophic position and the application of mixing models for dietary reconstruction are particularly vulnerable to the basic assumption of which ∆¹⁵N value to use [6,49], with consequences not only for ecological investigations but for management decisions [49]. This point is pertinent given the promoted use of stable isotopes to investigate threatened and endangered species [49]. Specific to the white shark, stable isotopes have recently been used to derive data on feeding behavior, movement and their role in food webs, overcoming the logistical difficulties and conservation concerns of studying these large predators. For example, Carlisle et al. [39] developed a novel isotope mixing model integrating both movement data and tissue turnover rates to estimate the relative importance of different focal habitat areas and associated prey to white shark diet, and to elucidate migratory behavior. Whereas Kim et al. [50] generated ontogenetic isotopic profiles of individual white sharks using serial sampling of vertebrae. In addition, a number of studies have presented trophic position estimates for a range of elasmobranch species based on stable isotopes (e.g., [51–53]). The integrity of data interpretation in all the above studies and the broader isotopic literature, which may ultimately influence management decisions such as conservation status listing (i.e., CITES) and the designation of critical habitat and/or marine protected areas for predator and/or prey, is dependent on assumptions over ∆¹⁵N values. This is of particular importance given the recognized use of food web models by resource managers that may incorporate parameter estimates from stable isotope analyses (e.g., [54]). Future modeling exercises focused on the trophic ecology of these large marine predators using isotopic data should consider evaluating the sensitivity of predictions generated from models to different fractionation assumptions (e.g., [55]).

It is possible that several factors may have biased our observed ∆¹⁵N-diet δ¹⁵N results. These include (i) the use of stomach content data for ranking the importance of prey species, (ii) movement of predators between isotopically distinct food webs, (iii) ontogenetic diet shifts, and, (iv) temporal variability in prey and baseline isotopic values. Stomach content data is often criticized for only providing a ‘snapshot’ of a species diet [56], biases largely resulting from varying rates of prey digestion, empty stomachs and limited sampling in spatial and temporal scope. However stomach content analysis remains one of the most important measures of an animal’s feeding ecology providing data on the actual species consumed [57] and when coupled with stable isotopes analysis have resulted in significant ecological insights, for example, reef community connectivity [55], sized-based interactions [52] and niche overlap [58] that would have been difficult to infer using a single approach. The high degree of isotopic ellipse overlap between predators and prey, ranked by importance from stomach content data, would indicate our dual approach was valid. Sharks are known to be mobile predators that can feed over variable spatial and temporal domains [39]. For this reason we targeted specific size classes of our focal species to avoid confounding ontogenetic effects. For example, juvenile bonnethead and Atlantic sharpnose sharks have relatively small home-ranges, largely inhabiting coastal bays and estuaries [59,60] and sub-adult/adult bull sharks show high site fidelity [61], with only occasional large scale movements in coastal habitats [62,63]. In the case of sub-adult white sharks used in this study, individuals make directed seasonal movements between two distinct regions, the Western Cape and KZN. Considering this point, prey species were sampled in KZN and cape fur seals, the principal diet item from the Cape region were sampled, accounting for potential spatial bias in prey isotope values. Moreover, isotopic variability across space and time might be expected to enhance variation within functional prey groups (i.e., larger ellipse areas) and therefore lead to greater overlap between predator and prey. This was not observed in our analyses. Finally, integration of prey isotopes values in shark muscle tissue is on the order of <12 months (e.g., [34,64]). Consequently shark muscle tissue isotope values represent foraging over multiple seasons and locations, thereby minimizing the influence of these variables on the results of our analysis.

In conclusion, variable ∆¹⁵N values provided a more accurate representation of the predator-prey isotopic relationship for sharks rather than the application of a single ∆¹⁵N for species that feed across different trophic levels or dietary δ¹⁵N. Controlled feeding experiments to derive experimental ∆¹⁵N values for large predators are challenging due to the ethical issues of feeding animals a single diet item and therefore require in the vast majority of cases, researchers to compromise and apply a ∆¹⁵N value based on studies of either ecologically or taxonomically similar species [49]. Moreover, most organisms such as sharks feed on multiple prey items. Consequently experimental derived values based on a single diet item could be inaccurate or misleading. Given the strength of empirical work to date, we suggest that evaluation of a range of ∆¹⁵N values is a necessary consideration for future trophic and diet reconstruction analyses and is essential for improving the understanding of a species’ ecological role in its’ environment and to enhance associated conservation and management actions. With the recent advancements in mathematical modeling in Bayesian frameworks [65,66], whereby the use of stable isotopes to examine predator-prey relationships has become more precise and approaches more refined, the novel application of SIBER that incorporates predator and prey isotopic variability combined with stomach content data provided a greater level of confidence, in the context of this analysis, in choosing an appropriate ∆¹⁵N value.

Acknowledgments

Thanks to Carolyn Belcher and KwaZulu-Natal Sharks Board laboratory staff for their assistance with sample collection and to Sandra Ellis, Anna Hussey and Tom Maddox for stable isotope preparation and analysis.