In this study, we present the first controlled feeding observations of the world's largest shark species, R. typus , with comparison to tissue isotopes measured in a wild population. Like other elasmobranchs, R. typus retain urea and TMAO for osmoregulation, but, as one of the three extant planktivorous sharks, they are low‐TP zooplanktivores rather than high‐TP carnivores like most other large‐bodied sharks, suggesting they exploit a lower quality diet (McMahon and McCarthy 2016 , Ohkouchi et al. 2017 ). Five captive individuals maintained as part of an aquarium exhibit were observed over a period of up to seven years while being fed a well characterized, realistic diet, during which time their growth varied between +21 and +350% of initial body mass. This allowed the first experimental derivations of organism and tissue‐specific discrimination factors in growing R. typus . Experimental observations were used as the basis for examining isotopic variation in eight R. typus individuals encountered off the coast of Okinawa, Japan. We demonstrate how mechanistic understanding from laboratory studies can assist in the robust application of isotope analyses and thereby enhance our individual‐ and population‐level ecological understanding of enigmatic, difficult to observe organisms like R. typus .

Elasmobranchs (sharks and rays) offer a cautionary tale of both the usefulness of isotope analyses and their pitfalls in the context of discrimination variation linked to diet quality and mode of nitrogen excretion. The physiology of elasmobranchs and their generally high trophic position (TP), and thus diet quality, have been hypothesized to make them somewhat unique among marine vertebrates in an isotopic sense (Hussey et al. 2012a ). For instance, the retention of urea and trimethylamine N‐oxide (TMAO), substances with low δ 15 N, for osmoregulation may mask enrichment in 15 N with increasing TP (Fisk et al. 2002 , Hussey et al. 2012a ). In this case, the widely used discrimination factors, e.g., approximately +3.4‰ and +0.5‰ for bulk nitrogen (Δ 15 N) and carbon (Δ 13 C) discrimination, respectively (Minagawa and Wada 1984 , Vander Zanden and Rasmussen 2001 , Post 2002 , McCutchan et al. 2003 ), would underestimate elasmobranch TP if the tissues analyzed contained high concentrations of urea relative to the total amount of nitrogen (Hussey et al. 2010 , 2012a , Dale et al. 2011 ). Similarly, theory would support the notion that, as carnivores, most large sharks feeding on high‐quality diets, i.e., with a better protein/fat/carbohydrate balance and higher amino acid (AA) similarity between diet and tissue, can meet more of their AA requirements via direct isotopic routing of dietary AAs, which should reduce discrimination compared with consumers feeding on low‐quality diets (Ohkouchi et al. 2017 ). Controlled feeding studies are very rare for large sharks, however, since they are time‐consuming and costly, and may be impossible for difficult to maintain, highly migratory, or large‐bodied organisms. Experimentally determined turnover rates (Logan and Lutcavage 2010 , Hussey et al. 2012a , Malpica‐Cruz et al. 2012 ) and discrimination factors (Hussey et al. 2010 , Kim et al. 2012a , Malpica‐Cruz et al. 2012 ) thus remain rare to non‐existent for most elasmobranch species. This limits our capacity to obtain isotopic insights into foraging strategies, since any attempt to apply universal values is risky due to high inter‐species variability in physiology and metabolism (Olin et al. 2013 ). Indeed, the use of inaccurate discrimination factors may have contributed to a misrepresentation of the trophic positions of elasmobranch predators, and the length of marine food webs globally, and thus determining more accurate discrimination factors is considered essential for understanding and conserving aquatic ecosystems (Hussey et al. 2014 ).

Effective conservation and management of enigmatic species like R. typus depends on identifying appropriate spatial scales for management, which may cross multiple jurisdictional boundaries, and priority habitats and prey species for conservation. Toward this end, improved understanding of diets, trophic positions, and migration patterns can be approached with an expanding array of isotopic techniques. Most isotope applications are based on the observation that a consumer's tissue isotopes, expressed in δ notation in permil (‰), generally reflect their food source “plus a few ‰” (DeNiro and Epstein 1976 , 1978 ). Despite wide application in ecological studies (reviewed Martínez del Rio et al. 2009 , Boecklen et al. 2011 ), there are a number of challenges to accurately interpreting tissue isotope data, many of which hinge on mechanistic understanding that remains limited. Principal among these is identifying appropriate diet‐to‐tissue isotope differences, or discrimination factors, that are more accurate, and likely dynamic, than a “few ‰” (Emmery et al. 2011 , Blanke et al. 2017 , O'Connell 2017 ). Challenges associated with accurately accounting for variations in isotopic baselines (the isotope ratios in primary producers at the base of the food web) in space and time are also well acknowledged (e.g., Woodland et al. 2012 ). Further, variability in tissue‐specific rates of isotopic change (turnover times; Cerling et al. 2007 , Martínez del Rio and Carleton 2012 , Thomas and Crowther 2015 ), or the preferential assimilation, or “routing,” of specific compounds to certain tissues (e.g., Greer et al. 2015 ), both require consideration. Even small variations in such factors may alter the ecological conclusions drawn from tissue isotopes. The accuracy and generality of meta‐analysis values compiled across studies and species thus remains a question of central importance to isotope ecology (Post 2002 , Bond and Diamond 2010 , Greer et al. 2015 ), with progress in the field likely depending on an ability to effectively integrate mechanistic understanding from laboratory experiments with theory and observations of isotope patterns in the field (Martínez del Rio and Wolf 2005 , Martínez del Rio et al. 2009 ).

The whale shark Rhincodon typus is the largest fish, and indeed the largest living non‐cetacean animal on Earth, yet surprisingly little is known about their global diets, reproduction and movement patterns. A recent review suggested that R. typus “remains an enigma 183 yr after first being described” (Rowat and Brooks 2012 ). Widely distributed, but generally restricted to tropical and warm temperate seas (Compagno 2001 ), the species is listed on the IUCN Red List due to small, and potentially declining, population sizes (Stewart and Wilson 2005 , Theberge and Dearden 2006 , Bradshaw et al. 2008 ) and has recently been upgraded from “Vulnerable” to “Threatened” (Norman and Morgan 2016 , Pierce and Norman 2016 ). Aggregations of R. typus attract increasingly large numbers of tourists and are of high economic value in a number of tropical locations globally (Gallagher and Hammerschlag 2011 , Huveneers et al. 2017 ). However, perhaps a reflection of the difficulty of lethal sampling and long‐term observations (Nozu et al. 2015 ), the drivers of aggregations remain poorly understood. Juvenile R. typus are rarely observed (Rowat et al. 2008 , Aca and Schmidt 2011 , Hsu et al. 2014b ), and mating and birthing has never been recorded. While some aggregations may be for the purpose of reproduction (Macena and Hazin 2016 ), many seem to be a response to seasonally enhanced feeding opportunities. In the Atlantic, Caribbean, and the Gulf of Mexico, R. typus aggregations have been found to coincide with fish spawning and individuals have been observed feeding on fish eggs (Heyman et al. 2001 , Hoffmayer et al. 2007 , Macena and Hazin 2016 ), while in the Red Sea (Berumen et al. 2014 ) and at Ningaloo Reef in Western Australia (Taylor 1996 ) aggregations may be a response to coral spawning, or for the latter, increased regional productivity associated with mixed layer deepening of the Leeuwin Current (Wyatt et al. 2010 , 2012a , Rousseaux et al. 2012 ). At Christmas Island in the eastern Indian Ocean, the aggregation coincides with the annual mass spawning of red land crabs ( Gecarcoidea natalis ) and fecal DNA evidence indicates R. typus consume these larvae (Hobbs et al. 2009 , Meekan et al. 2009 ). The diet of R. typus outside of coastal aggregations is generally unknown. Fatty acid analyses have suggested that they may depend on deep‐water zooplankton and fishes during long‐distance migrations (Rohner et al. 2013 , Marcus et al. 2016 ), while open ocean movements suggest utilization of prey concentrated by fronts (Ryan et al. 2017 ). Careful isotopic analyses of R. typus may thus be particularly informative for understanding and comparing diets in and outside aggregations, especially given the species’ threatened status and oceanic migrations which make lethal sampling and sustained feeding observations undesirable and difficult, respectively. One of the few isotope studies of R. typus suggested that increasing muscle δ 15 N with size indicated a diet shift toward larger zooplankton and fish with ontogeny (Borrell et al. 2011 ). Increased tissue δ 15 N potentially reflected terrestrial pollution, but the role of growth rates in tissue δ 15 N differences could not be assessed. Evidence from fatty acid profiles also suggests intra‐population variation in feeding habits may exist in R. typus , independent of size and sex (Marcus et al. 2016 ).

Apparent overall assimilation efficiencies (AE) for C and N were calculated based on the ratio of diet to fecal matter C and N content measured using the EA (e.g., Calow and Fletcherwhererepresents C or N, pDthe proportion ofin the diet three days before fecal sampling based on known diet composition for each individual and pFthe proportion ofin the feces based on measured composition of the feces with the EA. These bulk AEand AEvalues were used to derive specific AE for) and) based onwhererepresentsorthe average AE of bulk material based on the mean of AEand AEfrom Eq. 9 , pDthe proportion ofin the diet three days before fecal sampling based on known mass of each component in individual diets, and pFthe proportion ofin the feces based on informed isotope modeling using the δN, δC, and ΔC composition of each fecal sample.

Isotope mixing models were used to estimate plausible percentage contributions of individual dietary components to tissues or feces based on their measured isotope compositions. Modeling was performed using the Bayesian mixing model MixSIAR (Stock and Semmens 2013 ). Models were run based on Markov chain Monte Carlo (MCMC) parameters of 100,000, 50,000, 50, and 3 for chain length, burn‐ins, thinning, and number of chains, respectively. Concentration dependence was incorporated into the models based on the measured carbon and nitrogen content of each source (see Appendix S1 : Table S3) and a sample identifier included as a random effect. Models were run using both uninformed (generalist) and informed priors, with the later derived based on the long‐term average diet composition (for plasma and cartilage) or the diet composition three days prior to sampling (for feces). Since the fecal modeling had different priors for each sample, the modeling was run separately for each fecal sample (i.e., “Process” only; see Stock and Semmens 2016 ). Selection of a three‐day turnover for feces was based on concordance between a fecal sample from shark #2 and the chance occurrence of a single diet ( Ep ) three days prior to sampling (see Appendix S1 : Table S11). Discrimination factors used in the isotope models for each tissue were generally based on long‐term average diet–tissue discrimination factors shown in Table 2 (but see growth corrections), which include propagated error as per Eq. 4 . Discrimination for Δ 14 C is, by definition, 0‰ (see Eq. 2 ). In the case of feces, negligible isotopic alteration during passage through the gut was also assumed for δ 15 N and δ 13 C (i.e., Δ 15 N and Δ 13 C ~ 0‰), but allowance was made in the models for an arbitrary amount of alteration in the form of a 2‰ SD for each of N and C.

Tissue turnover rates were estimated based on iterative linear regression of tissue isotopes (plasma or cartilage) against diet isotope time series (Eq. 3 ) that had been subjected to a Gaussian moving average using the following weighting function (wf):whereis time, HL the “half‐life” of the tissue isotope (i.e., turnover time = 2 × HL), and σ the standard deviation, or width, of the wf (set as HL). The resulting wf stretches over the turnover time (i.e., fromto− (2 × HL)) with a peak at− HL. The function has the effect of reducing the variation in the diet isotope time series as turnover time increases, thus reducing the power of linear regressions over longer turnover times (see Appendix S1 : Fig. S10). Potential turnover of blood plasma was considered over HL up to an arbitrarily long 300 d, and cartilage up to the maximum of the diet isotope time series, i.e., HL up to 920 d based on 5 yr of diet data before the first cartilage sample (see Appendix S1 : Fig. S10).

Compound‐specific isotope analysis of amino acids (CSIA‐AA) was performed according to Chikaraishi et al. ( 2007 , 2009 , 2010 ). Briefly, approximately 1 mg of each lyophilized sample was hydrolyzed for 12 to 24 h in HCl at 100°C, the hydrolysate washed with n ‐hexane/dichloromethane (3:2, v/v) to remove hydrophobic constituents, such as lipids, and then evaporated to dryness under a N 2 stream. After derivatization with thionyl chloride/2‐propanol (1:4, v/v) at 110°C for 2 h and with pivaloyl chloride/dichloromethane (1:4, v/v) at 110°C for 2 h, the NP/iPr derivatives of amino acids were extracted with n ‐hexane/dichloromethane (3:2, v/v). Nitrogen isotopes of AA were determined from a single injection using a programmable temperature vaporizing (PTV) injector (Gerstel, Mülheim, Germany) into a gas chromatograph (GC; Agilent Technologies 6890N, Santa Clara, CA, USA) coupled to a Delta plus XP IRMS (Thermo Fisher Scientific) via combustion and reduction furnaces (oxidation at 950°C and reduction at 550°C, respectively). The GC was equipped with an Ultra‐2 capillary column (50 m × 0.32 mm inner diameter, 0.52 μm film thickness; Agilent Technologies). The CO 2 generated in the combustion furnace was eliminated by a liquid nitrogen trap. The nitrogen isotopic composition is expressed in conventional δ notation, normalized to atmospheric N 2 based on multipoint linear regression of eight pure L‐AA standards (Wako Pure Chemical Industries Ltd, Osaka, Japan) of known δ 15 N (Skrzypek 2013 ). Standard mixtures were analyzed every four or five samples to confirm the reproducibility of isotope measurements, with analytical errors (1 SD) better than 0.5‰ with a minimum sample amount of 7 ng N. Depending on sample composition, the δ 15 N of up to 9 AAs (alanine, glycine, valine, leucine, isoleucine, proline, glutamic acid, phenylalanine, and hydroxyproline) could generally be determined by this method.

Tissue samples for δ 15 N and δ 13 C were not subjected to lipid extraction or urea removal for several reasons. First, urea extraction may significantly alter AA compositions, e.g., 36% change in absolute mole composition of plasma (see Kim and Koch 2012 ), perhaps leading to the removal of free AA and thus is not recommended for blood components, as long as isotope differences are confirmed not to simply be an artefact of urea (Kim and Koch 2012 ). Here, we demonstrate that there is no significant relationship between urea concentrations and plasma isotopes to suggest that changes in plasma isotopes are an artefact of urea. While several studies have highlighted the potentially confounding role of muscle lipids and urea in isotope analysis of elasmobranch tissues (Hussey et al. 2012b , Kim and Koch 2012 , Carlisle et al. 2017 , Marcus et al. 2017 ), the muscle biopsies in this study were too small for bulk isotope analysis of treated and untreated subsamples at sufficient levels of replication. In the case of fin cartilage, the tissue samples were considered unlikely to contain significant amounts of lipids or urea (see also Hussey et al. 2011 ).

Samples for isotope analyses were lyophilized and ground to a powder using a ball mill (μT‐12, Taitec Corporation, Saitama, Japan). The bulk nitrogen and carbon elemental compositions and stable isotope ratios of samples were determined using a continuous flow Elemental Analyzer‐Isotope Ratio Mass Spectrometer (EA‐IRMS) system consisting of a Flash 2000 EA coupled to a Delta V Advantage IRMS via a Conflo IV (Thermo Fisher Scientific, Bremen, Germany). Samples were combusted to N 2 and CO 2 in tin capsules (Säntis Analytical AG, Teufen, Switzerland) using the EA, purified by gas chromatography, and injected into the IRMS. Following normalization, N isotope ratios (δ 15 N) are reported in parts per thousand (permille, ‰) relative to air and C isotope ratios (δ 13 C) relative to Pee Dee Belemnite (V‐PDB). To normalize measured isotopes to an international scale, a working standard, L‐Alanine (SI Science, Tokyo, Japan; AZ101, Lot: SS13; δ 13 C = −19.6‰, δ 15 N = 13.7‰), was regularly (every two to three samples) analyzed across a range of C and N masses encompassing the range of C and N in the samples. The working standard was periodically checked in‐house against primary international standards (e.g., U.S. Geological Survey) using dual‐inlet IRMS. Precision (1 SD) based on repeated analysis of two different working standards treated as unknowns was always better than 0.05‰ for δ 13 C and 0.1‰ for δ 15 N.

Fin cartilage samples were collected from the trailing edge of the dorsal fin of swimming sharks using 10‐mm bone rongeur forceps (Dr Frigz International, Punjab, Pakistan). The location of sampling was consistent, as recommended by Hussey et al. ( 2011 ) for isotope analysis of shark fin samples. Samples were stored frozen at –35°C prior to lyophilization and subsampling for analysis. Subsamples consisting of cartilage (ceratotrichia) or a small amount of fin skin from the three aquarium specimens were prepared separately to confirm isotopic differences in these fin components. The pattern of significantly depleted δ 13 C and Δ 14 C in fin skin (−19.0‰ ± 0.13‰ and −84.3‰ ± 6.5‰, respectively, mean ± SE) compared to fin cartilage (−18.4‰ ± 0.10‰ and −46.5‰ ± 3.1‰, respectively) did not vary between individuals ( F 1,14 = 42,500, P < 0.001; F 1,14 = 27.42, P < 0.001), so further analyses focused on cartilage material (minus skin) from all individuals.

Rhincodon typus blood plasma was collected approximately monthly from the free‐swimming aquarium and cage sharks according to Ueda et al. ( 2017 ) using a dual syringe and needle (18G, 70 mm long) system with three‐way stopcock, generally from the pectoral fin but also from the first dorsal fin or, occasionally, second dorsal fin. The first syringe was used to purge air, water, and the first approximately 3 mL of (contaminated) blood, before collecting a sample of approximately 10 mL of blood into the second syringe for analysis. Blood samples were centrifuged (4000 rpm for 20 min) and a subsample of each plasma sample separated for immediate blood biochemistry analysis using an automated clinical analyzer (DRI‐CHEM 7000V, Fujifilm Corporation, Tokyo, Japan; see parameters in Appendix S1 : Table S9), with the remainder stored at –35°C for isotope analysis as in Sample preparation and isotope analyses, Compound‐specific isotope analysis of amino acids (CSIAAA), and Radiocarbon.

Aquarium water temperatures were recorded twice daily, morning and afternoon, and ranged between 20.2° and 29.8°C (average 24.8°C) during the period of the study. The aquarium sharks were fed a mixed diet two to three times daily and the amount of each item consumed per day recorded in kg for each individual (see Results ). Individual sharks were each fed directly using a small (~1 L) scoop attached to a long pole repeatedly placed into the open mouth of the swimming shark, thereby preventing its inadvertent consumption by other aquarium inhabitants. Diet items were purchased from established suppliers, and consisted largely (generally >90%, Appendix S1 : Table S1) of two commercially available (Hiromatsu Kyu Fishery, Fukuoka, Japan) wild‐caught krill, North Pacific krill ( Euphausia pacifica ) and Antarctic krill ( Euphausia superba ) caught off Iwate Prefecture, Japan and in the Southern Ocean, respectively. The cage shark (#5) was fed daily, but bad weather and turbid water conditions prevented feeding on up to 6 d per month (average 3.5). Reduced feeding effort by #5 was first observed on 21 January 2008 (1 d), with sluggish swimming and/or reduced feeding effort observed on 3 d in each of February and March 2008, 1 d in July 2008, 9 d in August 2009, and 1 d in each of September and October 2009. From 7 December 2009, #5 became inappetant (see Appendix S1 : Table S9 for details) and was administered intravenous fluids under anesthetic on 30 December 2009, 9 and 20 January 2010, 31 March 2010, and 4 and 6 April 2010. The shark was found moribund during daily feeding attempts on 6 May 2010.

Trophic positions (TPs) calculated based on CSIA‐AA of cartilage and Eq. 5 , using our estimates for Δ 15 N Glu‐Phe of 6.6 ± 0.2 for captive R. typus (i.e., 6.2 to −0.40; Table 2 ) and the meta‐analysis value of 3.4‰ ± 0.9‰ for β (Chikaraishi et al. 2009 , 2010 ), suggested very similar TPs across the five individuals analyzed, ranging from 2.2 to 2.6. The average TP was 2.4 ± 1.0, with the error incorporating propagated uncertainty in β and Δ 15 N Glu – Δ 15 N Phe . Using a meta‐analysis value of 7.6‰ ± 1.1‰ for Δ 15 N Glu‐Phe led to similar TP estimates of 2.2 ± 1.4 g/dL. Using Ala and Leu as trophic AAs instead of Glu also produced very similar TP estimates of 2.3 ± 1.2 and 2.2 ± 1.2, respectively (see Appendix S1: Table S4 for captive Δ 15 N Ala and Δ 15 N Leu ). By way of validation, in captivity TP based on our cartilage Δ 15 N Glu‐Phe value (6.6‰) averaged 3.8 ± 0.5 ( n = 7) compared to 2.8 ± 0.5 ( n = 2) for E. pacifica and 2.7 ± 0.5 ( n = 2) for E . superba . Similar results (less than 0.3 change in TP) were obtained using a Δ 15 N Glu‐Phe of 7.6‰ or a multi‐Δ 15 N Glu‐Phe approach (i.e., 7.6‰ for Euphausia spp. and 6.6‰ for R. typus ). Plasma CSIA‐AA was not performed for wild samples due to small sizes but, in captivity, plasma showed lower and more variable apparent discrimination (e.g., Δ 15 N Glu of 4.20‰ ± 2.2‰ and Δ 15 N Phe of 0.69‰ ± 1.6‰, Table 2 ; see Appendix S1: Table S4 for other AA), which presumably reflects temporal variation in the relative contribution of diet‐derived amino acids and peptides in the blood (Lorrain et al. 2009 , Nakashita et al. 2011 ) and suggests great caution would be required to accurately estimate TP from blood CSIA‐AA of wild individuals.

The wild R. typus off Okinawa could be divided into two groups according to tissue δ 15 N. One group (#6, #9, #10, #11) all exhibited low δ 15 N in plasma and muscle (<5‰) and cartilage (<7%; Fig. 9 a), hereafter termed “oceanic” sharks. In contrast, another four individuals (#7, #8, #12, and #13) showed high δ 15 N in plasma (>7%), muscle (>10‰), and cartilage (>11‰), hereafter “coastal” sharks. The wild δ 15 N values of #4 were also intermediate, but plasma δ 13 C in that individual was markedly lower (~−21‰). In general, isotopic differences between tissue compartments were consistent within wild individuals but showed increased variability relative to the aquarium individuals; coastal shark #13 in particular showed markedly lower muscle δ 13 C relative to both plasma and cartilage (Table 6). Using average discrimination factors observed in the aquarium R. typus , corrected for growth influences on Δ 15 N (see Fig. 7 a) based on plasma biochemistry‐derived growth levels (Table 1 ), the wild R. typus isospace suggests that oceanic individuals consistently sourced prey with δ 15 N ~ 2‰ across time (tissues), while coastal individuals were feeding on prey above 5‰, which some evidence of consumption of higher δ 15 N prey in the past (i.e., in slower turnover muscle and cartilage tissues; Fig. 9 b) for two coastal sharks (#12 and #13). Source estimations for δ 13 C were generally consistent across individuals and tissues (−20 to −21.5‰), apart from the muscle of #13 (−23‰; Fig. 9 c).

Blood biochemistry of the first pectoral fin of wildsampled off Yomitan, Okinawa during 2015–2016. Each panel shows individual mean values (+SD where both pectoral fins sampled) for each of the blood biochemistry parameters identified by dbRDA as correlating the most (>0.5, see Appendix S1: Results: Blood biochemistry variation) with progression to starvation in #5; see Appendix S1 : Table S9 for abbreviations and units. Reference lines show mean values for the three healthy aquarium sharks (#1, #3, and #4) in blue and for the one week prior to death of the cage shark (#5, 7 Dec 2009–May 2010) in orange, with shading showing standard errors where visible. The two yellow bars reflect the two consecutive dates on which #11 was sampled.

Similar to the captive individuals, the blood biochemistry of wild sharks showed a significant difference among individuals (pseudo‐ F 7,7 = 5.11, p perm < 0.001) and venipuncture site (pseudo‐ F 2,7 = 4.32, p perm < 0.001), but no interaction (pseudo‐ F 9,7 = 1.41, p perm = 0.098), with blood biochemistry of the dorsal fin different to the pectoral fin ( p perm < 0.001). There was no difference between the pectoral or pelvic fin ( p perm = 0.12), or between the left and right pectoral fin ( p perm = 0.93). Focusing on the biochemistry of pectoral fin samples, Canonical Analysis of Principal Coordinates (CAP) suggested that these wild R. typus exhibited variable growth rates during the approximately 4.5 months prior to plasma sampling (i.e., the half‐life of blood plasma, Table 4 ; see Table 1 for individual growth level estimates). Focusing on the biochemistry parameters most associated with the progression toward starvation in #5 (declines in TP, ALB, TG, Ht, TCho, Hb, IP, RBC, ALP, GPT, GOT, Ca, and increases in CRE, see above and Fig. 5 ), some consistent individual‐level variation was found in the wild individuals (Fig. 8 ). For instance, TP, ALB, TCho, Hb, and ALP were generally significantly lower in the wild than in the healthy, well‐fed captive individuals. Shark #9 was a notable exception, having ALB levels similar to healthy aquarium sharks (30.0 U/dL), and TG levels significantly higher than the aquarium average (49.0 g/dL). The plasma TG levels of the other sharks, and especially #6 and #8 (2.00 and 2.50 g/dL), were significantly lower than the aquarium average (32.3 ± 25, and close to the starvation levels for #5 (e.g., 1.71 ± 1.5 g/dL).

All captive sharks exhibited periods of fasting of differing degrees. Fasting events defined as ingestion (ING) less than basal metabolic rate (BMR) occurred between 39 and 132 times, with an average duration of 2.3–4.8 d (Appendix S1 : Table S9). In the aquarium, fasting with ING < BMR occurred for up to 19–57 d. The maximum durations of no feeding (ING = 0) in the aquarium were 2, 3, 8, and 6 d for #1, #2, #3, and #4, respectively. The cage shark #5 fasted for 151 d prior to its death, with no ingestion observed during this period. Pooled across individual there was no evidence of any seasonality in fasting defined as either ING < BMR or ING = 0 ( F 3,16 = 0.232, P = 0.87; F 3,16 = 0.351, P = 0.79). During starvation, the blood biochemistry parameters identified in Fig. 5 b as being most correlated with the starvation of #5 declined markedly (Fig. 6 c and d). Total proteins (TP) declined from an average of 2.21 ± 0.53 g/dL (mean ± SD) in the three years of captivity prior to inappetence, to 0.991 ± 0.25 g/dL in the week prior to death. Similarly, triglycerides (TG) declined from 20.1 ± 0.53 g/dL to 1.71 ± 1.5 g/dL and total cholesterol (TCho) from 69.5 ± 31 mg/dL to 19.0 ± 5.5 mg/dL. Hemoglobin (Hb), haematocrit (Ht), albumin (ALB), and average red blood cells (RBC) declined from 5.13 ±1.3 g/dL to 3.37 ± 0.93 g/dL, 16.9% ± 4.2% to 12.9% ± 2.7%, 1.27 ± 0.38 g/dL to 0.776 ± 0.20 g/dL, and 24.3 × 10 4 ± 6.7 × 10 4 cells/μL to 15.4 × 10 4 ± 4.3 × 10 4 cells/μL (data not shown in Fig. 6 , see Appendix S1 : Table S9). Plasma levels of creatine jumped an order of magnitude from an average of 0.111 ± 0.033 mg/dL to 1.24 ± 2.5 mg/dL, reaching as high as 6.93 mg/dL just prior to death, with average white blood cells increasing from 4,710 ± 1,200 cells/mL to 10,800 ± 2,400 cells/mL.

On a long‐term average basis, the aquarium specimens displayed bulk Δ 15 N and Δ 13 C means (± propagated error) of 1.71 ± 0.45 and 2.48 ± 0.45, respectively, for plasma, and 2.57 ± 0.36 and 5.28 ± 0.44, respectively, for cartilage (see Table 2 ). The regression equations used to estimate plasma turnover described in the preceding section agreed well with long‐term averages for Δ 15 N, with diet‐to‐plasma regression slopes of 1.1–2.9, but covered a lower range for Δ 13 C at 0.78–2.5 ( Appendix S1: Table S12 ). Growth appeared to significantly influence Δ 15 N but not Δ 13 C. A significant relationship was evident for Δ 15 N in both blood plasma and fin cartilage of the aquarium sharks, with increasing growth rates explaining 77% and 47% of the declines in bulk Δ 15 N in these two tissues, respectively (Fig. 7 a). A slight, but significant, increase in Δ 13 C plasma was evident for growth, but the slope was negligible and no relationship was evident for Δ 13 C cartilage (Fig. 7 b). Discrimination between diet and cartilage were also calculated on an average basis for individual amino acids (AA), suggesting Δ 15 N Glu of 6.2 ± 0.3 and Δ 15 N Phe of −0.4 ± 0.4 (Table 2 and Fig. 3 c; see Appendix S1: Table S4 for other AA). The low δ 15 N Phe in one fin sample (Fig. 3 c) that could not be re‐run due to the small sample size likely reflects the analysis of skin components along with cartilage, since our first analyses failed to adequately separate these components, which were later shown to be isotopically distinct (see Methods ), perhaps due to the routing of E . superba to skin. Other samples were rerun after removing skin and results are reported for cartilage only.

Iterative linear regression of diet isotope time series suggested a range of plausible values for plasma half‐lives of between 90 and 200 d (Table 4 ). In the case of #1, where growth had minimal influence on diet–plasma isotope differences (see Diet–tissue discrimination), a half‐life of 140 d allowed 73% of the variation in blood δ 15 N to be explained by variation in diet δ 15 N, compared to 115 d and 20% for δ 13 C (see Appendix S1: Table S12 for regression results). Turnover uncorrected for growth suggested shorter δ 15 N plasma half‐lives for #3 (123 d) and longer for #4 (152), while δ 13 C plasma turnover was longer for both of these two individuals compared to #1 (half‐lives of 140 and 150 d, respectively). Correcting for growth effects on Δ 15 N had little impact on the best estimate of turnover, being one day shorter for #3 (122 days) and #4 (151 d). The best estimates of plasma half‐lives averaged across individuals and C and N was 137 d, or a turnover time of approximately 9 months (2 × 137 = 274 d). Estimated half‐lives in fin cartilage were markedly longer, lying in the range 230–851 (Table 4 ). A very low estimate of turnover was evident for #4 (HL ~ 33 d from δ 13 C cartilage ) but also a large range (30–498), highlighting the uncertainty in estimates of turnover obtainable from a limited number of cartilage samples (here only three time points per individual, n = 9). Excluding the 33‐d estimate as an outlier, the average half‐life estimate for fin cartilage was 550 d or a turnover time of 3 yr (2 × 550 = 1100 d).

Temporal changes in pectoral fin blood plasma in #5, showing (a) nitrogen (δN, blue drops) and carbon (δC, black drops) isotopes from capture until death and (c) blood biochemistry in terms of total proteins (TP in g/dL; pink right axis), the ratio of blood urea nitrogen to TP × 10(BUN:TP, green, right axis), triglycerides and total cholesterol (TG in g/dL and TCho in mg/dL, respectively; blue and orange, right axis). The inappetant period leading to death is shown in detail in (b) and (d). Gray shading in each panel shows the ratio of total ingestion (ING) to basal metabolic rate (BMR) with the last known ING on 11 December 2009 (ING:BMR, right axes; see Appendix S1 : Table S9). The dashed blue and black lines in panels a, b, and c represent diet δN and δC, respectively. The timing of intravenous fluid administration to #5 is marked by dashed red lines in panels b and d.

Despite biochemistry differences (see effect of venipuncture site), no significant differences in plasma carbon or nitrogen isotopes were evident from pectoral, first dorsal, or second dorsal fin taken on the same date from the cage shark (#5; F 2,3 = 1.16, P = 0.42 and F 2,3 = 0.301, P = 0.76, respectively), suggesting blood may be isotopically well mixed relative to other parameters. Over time however, δ 15 N plasma varied by 1.4‰, 2.2‰, 2.0‰ in the aquarium sharks (mean ± SD of 7.72 ± 0.34, 7.08 ± 0.58, and 6.61 ± 0.55 for #1, #3, and #4, respectively; see Appendix S1: Fig. S3 ). Variation in δ 13 C plasma was also highest in #3 at 3.6‰ (mean −20.7 ± 0.83) compared to 1.5‰ and 1.6‰ in #1 (−21.6 ± 0.40) and #4 (−21.0 ± 0.44), respectively. In the cage shark #5, very large variation was evident in both δ 15 N and δ 13 C (range 5.5% and 11‰, respectively, mean 5.39% ± 1.5% and −21.3‰ ± 2.8‰), with exceedingly low isotope values observed prior to death (minimums of 1.85% and −28.8% for δ 15 N and δ 13 C, respectively; see Fig. 6 a).

Blood biochemistry differed significantly between all captive individuals (pseudo‐ F 1,320 = 4.40, p perm < 0.005) and was significantly more variable over time in the cage shark #5 and less variable in #4 (PERMDISP; pseudo‐ F 3,338 = 35.7, p perm < 0.001). A distance‐based redundancy analysis (dbRDA) including the wild R. typus samples (see Fig. 5 a) explained 41% of the total variation in blood biochemistry. All 24 replicated blood parameters were significantly correlated with the multivariate pattern, with the top five variables, hemoglobin (Hb), haematocrit (Ht), whole protein (TP), total cholesterol (TCho), and average red blood cells (RBC), explaining 23%, 22%, 19%, 17%, and 17% of the variation, respectively (overall AIC c = 155.6, r 2 = 0.941). Separation of the aquarium sharks from #5 was most pronounced along dbRDA1, which was correlated with higher Hb, Ht, TCho, TP, inorganic phosphorous (IP), RBC, triglyceride (TG), and albumin (ALB) (correlations >0.5: 0.915, 0.905, 0.816, 0.793, 0.757, 0.666, 0.646, and 0.506, respectively), and reduced white blood cells (WBC) and creatinine (−0.505 and −0.501, respectively). As significant interactions were evident between captive individuals and both venipuncture site (pseudo‐ F 3,320 = 8.15, p perm < 0.001) and growth level (pseudo‐ F 2,320 = 2.39, p perm < 0.05), further analyses focused on individuals separately. Seasonal influences were variable between the captive sharks, with evidence of overall seasonal changes in #1, #4, and #5, but not in #3. Venipuncture site was a significant factor in all sharks and, in the case of the aquarium sharks, so was growth level. Appendix S1 and Appendix S1 : Fig. S2 provide further details on individual variation in blood biochemistry.

Preferential and temperature‐dependent assimilation ofshowing (a) the contributions (%) of; green triangles) and; red triangles) to fecal material of four captivebased on informed tri‐isotope mixing models compared to the pre‐sampling diet proportions of each individual (three days prior, see), and (b) increasing assimilation efficiencies (AE) for nitrogen (AE; blue circles) and(AE; green triangles) with increasing average daily water temperatures. Averaged over individuals, changing diet proportions explained 52% and 37% of the variation in fecal proportions forand, respectively. Colored lines show least squares regressions for(green;= 0.516,= 12.8,< 0.001) and(red;= 0.366,= 6.93,< 0.05). Fecal proportions ofandwere generally lower and higher, respectively, than expected based on a one‐to‐one relationship with diet (black dashed line). Increasing temperatures explained 29% of the variation in AE, which was determined based on the difference in percent N between fecal material and pre‐sampling diets (three days) using Eq. 8 , and 37% of the variation in AE, which was determined based on the difference between informed tri‐isotope modeling of fecal materialproportions and pre‐sampling diets using Eq. 9 . Colored lines show least squares regressions against temperature for AE(blue;= 0.293,= 4.98,< 0.05; AE= 2.93 × Temp − 5.29) and AE(green;= 0.367,= 6.38,< 0.03; AE= 3.06 × Temp − 1.53).

Fecal material averaged over individuals contained approximately 2.9% ± 0.4% N and 9.8% ± 0.8% C, with molar C:N of 5.4 ± 2, equivalent to approximately 8% less N and 30% less C than average diets (Appendix S1 : Table S3). These declines in fecal N and C content relative to diets suggested overall average nitrogen (AE N ) and carbon (AE C ) assimilation efficiencies of 74% ± 9% and 76% ± 6%, respectively (Eq. 9 ; Table 3 ). Relative to diets, informed isotope modeling of carbonate‐corrected fecal material suggested greater assimilation of E. pacifica than E . superba (Fig. 4 a). Euphausia superba contributions to fecal isotopes was on average 10% higher than ingested over the three days prior to defecation (see Appendix S1: Table S6 for detail and Appendix S1 : Table S11 for model comparisons). Conversely, fecal material was estimated to contain 12% less E. pacifica than ingested (Table 3 ). In agreement, assimilation efficiencies for E. pacifica estimated with Eq. 10 were 12% higher than E . superba (AE Ep = 81 ± 9 and AE Es = 69 ± 14, respectively; Table 3 ). The higher AE Ep was supported by tissue isotope modeling of both blood plasma and fin cartilage. Averaged over individuals, modeling suggested 11% more Ep (54% ± 8%) in plasma than Es (43% ± 4%; Table 3 , see Appendix S1 : Table S6 for detail). Similarly, there was evidence of higher Ep incorporation (54% ± 6%) into fin cartilage than Es (43% ± 3%), which was 9% above and 3% below long‐term average diets, respectively (see Appendix S1 : Table S10 for details).

Ingestion rates differed significantly between seasons. As there was a significant interaction between individuals and season, seasonality in ingestion was analyzed individually (see Appendix S1: Results: Ingestion: Diet quantity ). There was no clear consistency in the seasonal changes in ingestion rates among individuals. Despite the lack of clear seasonality, individual ingestions rates did generally increase with water temperature, with increasing daily temperatures in the aquarium explaining between 2% and 7% of increases in daily ingestion, compared to 17% in the sea cage (#1, r 2 = 0.017, F 1,2953 = 50.1, P < 0.001; #2, r 2 = 0.068, F 1,123 = 8.76, P < 0.001; #4, r 2 = 0.027, F 1,2953 = 80.1, P < 0.001; #5, r 2 = 0.166, F 1,1407 = 261, P < 0.001). However, perhaps counterintuitively, #3′s ingestion rates declined slightly with increasing temperature ( r 2 = 0.006, F 1,2953 = 17.3, P < 0.001). Further, for all sharks there were significant negative relationships between growth and ingestion rates, with increasing growth explaining between 6 and 24% of reductions in ingestion (see Appendix S1 : Table S2).

The aquarium sharks ingested on average between 15 and 27 kg of food per day (Appendix S1 : Table S1), although there was large individual and day‐to‐day variation and all sharks went through periods of inappetence (see Fasting and starvation and Appendix S1 : Table S9). Maximum daily food ingestion was as high as 45 kg for #1, with up to 27, 38, and 32 kg consumed in a single day by #2, #3, and #4, respectively. This food intake averaged 0.6–0.9% (± 0.2–0.4%) of estimated BM per day (see Fig. 1 ; 1.5% ± 0.9 for #5 in its sea cage). The calorific content of the two main food items was similar: 620 kcal/kg for E. pacific and 660 kcal/kg for E. superba (see Appendix S1: Table S1 ). The estimated energy contribution from the diet was thus on average 2.45 ± 1.9 times the individuals’ basal metabolic rate (BMR), but ranged from 0 to as high as four to seven times BMR (Table 1 ).

Amino acid (AA) nitrogen isotopes (δ 15 N) in Rhincodon typus fin cartilage (from three individuals, gray‐scale symbols; one to two samples per individual) compared to feed items showing (a) phenylalanine (δ 15 N P he ) against glutamic acid (δ 15 N G lu ) and (b) the δ 15 N of all 10 AA measured compared to bulk δ 15 N, focusing on temporal variation in the two main feed items (two samples each of Euphausia pacifica and Euphausia superba are shown as greens and reds, respectively) and (c) focusing further on variation in δ 15 N G lu and δ 15 N P he . Solid orange lines in panel c denote the average R. typus diet δ 15 N G lu (14.2 ± 0.09) and δ 15 N P he (−1.3 ± 0.1), with differences to cartilage suggesting Δ 15 N G lu of 6.2 ± 0.2 and Δ 15 N P he of −0.40 ± 0.4.

Diet isotopes averaged across individuals for δ 15 N, δ 13 C, and Δ 14 C were 5.4‰ ± 0.4‰, −23‰ ± 0.5‰, and −45‰ ± 15‰, respectively (mean ± SD). The N and C content of diets averaged across individuals was 11.2% ± 0.25% and 40.1% ± 0.78%, respectively (mean ± SD; Table 2 ), with individual diet items ranging between 10% and 14% N, 37% to 50% C, and 3.4 to 5.1 molar C:N (Appendix S1 : Table S3). Reflecting their geographic origins, the major diet items ( E. pacifica and E. superba ) were isotopically distinct in terms of δ 15 N (6.4‰ compared to 3.7‰, respectively) and δ 13 C (−21‰ compared to −27‰, respectively; Fig. 2 a and Table 2 ). Radiocarbon further separated E. pacifica and E. superba according to relatively modern and aged carbon sources (Δ 14 C ~ +3.1‰ compared to −110‰; Fig. 2 b and Appendix S1 : Table S3). Compound‐specific isotope analysis of amino acids showed that δ 15 N values of both phenylalanine and glutamic acid were on average higher in E. pacifica (1.1 ± 2 and 17 ± 2, respectively) than E. superba (−1.5 ± 2 and 14 ± 2, respectively; Fig. 3 ). Fin material had distinct AA composition to all diet items (see Appendix S1 : Fig. S1).

All sharks grew significantly during captivity by between +21% and +350% of initial body mass. Body lengths (BL) ranged from 560 cm to 870 cm in the aquarium (Fig. 1 a), with estimated body masses (BM) based on Eq. 1 of between 2,700 and 3,800 kg at the end of the study. There was individual variation in growth rates, with annual average rates ranging between 6.9 and 36 cm/yr (Table 1 ). Basal metabolic rates (BMR) calculated using Eqs. 6 and 7 suggested that the sharks required on average between 16 and 36 MJ/d for basic metabolic functioning (Table 1 ). The cage shark #5 was 509 cm and weighed 534 kg at the time of its death, approximately 315 kg less than its body mass predicted using Eq. 1 (848 kg). Despite the highest average ingestion to BMR ratio while feeding (3.6, see Table 1 ), relative to a wild individual, #5 was approximately 64% of expected BM based on its BL.

Discussion

This study offers several insights pertinent to isotopic studies of enigmatic and hard to observe organisms in general and Rhincodon typus in particular. Intra‐species variability in discrimination is rarely considered when interpreting isotopic variability within wild populations, but has the potential to confound attempts to isotopically differentiate individuals (Sears et al. 2009, O'Connell 2017). The magnitude of isotopic variation observed in captive R. typus, despite a consistent diet, highlights the need to quantitatively consider changes in discrimination based on tissue type, growth rates, and caloric restriction or starvation when using isotope differences to infer foraging differences. The potential for variable discrimination becomes especially important given the sensitivity of isotope mixing models to the choice of discrimination factor (e.g., Table 5). We discuss potential mechanisms for the variation in discrimination observed, how such variation might influence isotopic conclusions from natural tissue samples, and reiterate recommendations to “embrace the variability” in discrimination (sensu McMahon and McCarthy 2016) and incorporate it into enhanced interpretation of isotopic variation in wild populations.

Table 5. Mixing model comparison using different discrimination factors: (1) meta‐analysis averages for aquatic consumers from Post ( ), (2) experimental discrimination in leopard sharks from Kim et al. ( ), (3) Rhincodon typus (Rt) discrimination averaged over individuals in the present study, and (4) individual‐specific Rt values (see Table ) Rt Pre‐sampling diet (1) Post ( 2002 15N: 3.5 ± 1; Δ13C: −0.1 ± 1 (2) Kim et al. ( 2012a 15N: 2.2 ± 0.7; Δ13C: 2.8 ± 0.6 Δ15N: 3.7 ± 0.4; Δ13C: 1.7 ± 0.5 (3) This study, Rt average Δ15N: 1.7 ± 0.5; Δ13C: 2.5 ± 0.5 Δ15N: 2.6 ± 0.4; Δ13C: 5.3 ± 0.4 (4) This study, Rt individuals see Table 2 for individual‐ and tissue‐specific Δ15N and Δ13C % Ep % Es % Ep % Es % Ep % Es % Ep % Es % Ep % Es Plasma #1 43 ± 7 49 ± 3 86 ± 4 [77–90] 13 ± 2 [10–16] 30 ± 6 [15–38] 57 ± 3 [52–63] 25 ± 7 [8–39] 56 ± 5 [47–63] 52 ± 3 [46–56] 45 ± 2 [42–49] #3 47 ± 16 41 ± 12 94 ± 7 [74–100] 3 ± 2 [0.3–8] 55 ± 3 [49–59] 44 ± 2 [41–47] 61 ± 2 [57–65] 38 ± 2 [35–42] 55 ± 5 [40–61] 42 ± 2 [38–47] #4 51 ± 6 44 ± 3 94 ± 5 [80–99] 5 ± 2 [1.1–9] 52 ± 4 [43–57] 47 ± 2 [43–52] 57 ± 4 [49–62] 42 ± 2 [38–47] 54 ± 5 [42–60] 44 ± 2 [39–49] #5 52 ± 5 45 ± 5 70 ± 22 [21.5–98] 22 ± 18 [1.1–67] 41 ± 15 [12.8–73] 57 ± 15 [26.6–85] 39 ± 17 [6.6–68] 48 ± 10 [28–69] 48 ± 16a [18–68] 51 ± 15a [21–69] Mean 47 ± 10 45 ± 7 91 ± 10b 7 ± 4b 46 ± 8b 49 ± 4b 48 ± 8b 45 ± 6b 54 ± 8b 43 ± 4b Cartilage #1 43 ± 6 49 ± 3 62 ± 5 38 ± 5 69 ± 9 31 ± 8 52 ± 8 44 ± 4 50 ± 13 [21.6–73] 45 ± 10 [24.3–67] #3 43 ± 12 46 ± 10 62 ± 5 37 ± 5 70 ± 8 30 ± 8 53 ± 8 43 ± 4 50 ± 11 [23.6–67] 44 ± 7 [29.5–60] #4 48 ± 6 44 ± 4 63 ± 5 37 ± 5 70 ± 8 29 ± 8 54 ± 8 42 ± 4 54 ± 10 [30.2–71] 43 ± 8 [26.9–59] Mean 45 ± 4 46 ± 2 62 ± 5 37 ± 5 70 ± 8 30 ± 8 53 ± 8 43 ± 4 51 ± 11 44 ± 8

Rhincodon typus foraging specialization from metabolically constrained tissue isotopes Our preliminary observations suggest that long‐term fasting may be common in wild R. typus, with blood biochemistry showing similarities to the starving captive individual (#5) in two to three of the eight wild individuals studied. Such fasting has the potential to confound interpretation of isotopic differences among individuals. However, by controlling for the potential effects of growth and nutritional history on discrimination using plasma biochemistry, we can begin to interpret population‐level isotopic differences (e.g., Fig. 9a) in the context of individual foraging specialization, rather than due to differences in relative growth rates or nutrition. Although diet is often considered to be a species‐level trait, variation in diets and foraging behavior has been documented for a number of species (Bolnick et al. 2003, Tinker et al. 2008, Quevedo et al. 2009, Kim et al. 2012c). As a result, it has been proposed that by measuring isotopes in multiple tissues with contrasting turnover rates it may be possible to identify specialists and generalists within a population (Martinez del Rio et al. 2009, Vander Zanden et al. 2010, Matich et al. 2011, Martínez del Rio and Carleton 2012). Assuming minimal changes in isotopic baselines with time, we would expect specialists to exhibit similar isotopes across tissues reflecting sustained feeding on a particular prey source over time. Conversely, generalist feeding may be evident as marked isotopic differences among tissues reflecting temporal changes in foraging, or between individuals reflecting a generalist population composed of individuals specializing on different prey sources. Our estimates of prey δ13C for the Okinawan R. typus population were generally similar across individuals and time (i.e., tissues; Fig. 9c), suggesting feeding focused on prey supported by regional phytoplankton productivity, likely across a small latitudinal range (Magozzi et al. 2017, Bird et al. 2018). If we assume surface feeding, similar radiocarbon values between individuals (Fig. 9e) allow us to discount the likelihood of very broad latitudinal foraging as a factor in inter‐individual isotope differences (e.g., McMahon et al. 2013). In contrast, there was clear separation of individuals according to two estimated δ15N sources, which varied little over time (Fig. 9b). This suggests consistent individual specialization on either oceanic or coastal prey. We can discount differences in trophic level or fractionation, since (1) we corrected for growth and nutrition, (2) TPs estimated from CSIA‐AA were similar and generally consistent with low‐TP planktivory (TP ~ 3), and (3) δ15N Phe confirmed reliance on different N sources at the base of the food web (Fig. 9d,e). Evidence of TPs averaging markedly less than 3 (i.e., 2.4 ± 1) could suggest significant ingestion, incidental or otherwise, of phytoplankton or algal debris (e.g., herbivory; see Rohner et al. 2013 and Leigh et al. 2018) but could also reflect the uncertainty in, or an overestimation of, Δ15N Glu‐Phe . Regardless, the low δ15N (“oceanic”) individuals appeared to consistently depend on prey supported by nitrogen fixation (e.g., Raes et al. 2015) over months to years prior to sampling, perhaps in the central Pacific gyre where mid‐water particle δ15N values are at their minimum of close to −2‰ (Somes et al. 2010). This mid‐ocean region is perhaps the only location with δ15N low enough to explain the estimates of prey around 2‰ (Fig. 9b). In contrast, the high δ15N (“coastal”) individuals more likely consistently consumed coastal prey, perhaps linked to high δ15N sources in the eastern Pacific (Somes et al. 2010), terrestrial runoff or deep‐water layers (Fig. 9b). Given that δ13C values reflected plankton, significant incorporation of more 13C‐depleted terrestrial C seems unlikely, and thus prey δ15N estimates close to 5–8‰ (Fig. 9b) suggest the coastal sharks were either at the end of a long‐distance migration from the eastern Pacific or were utilizing local prey supported by deep‐water nitrate in the Kuroshio Current system (Liu et al. 1996). Similar intra‐population specialization in R. typus has also been suggested by differences in muscle δ15N in the Red Sea (δ15N range 5.8‰ to 10.5‰, n = 20; A. S. J. Wyatt and B. Kuerten, unpublished data) and fatty acid compositions at Ningaloo Reef, Western Australia (Marcus et al. 2016), but in both cases, the influence of growth and baseline variation could not be determined. Such differences may reflect the relative amounts of individual feeding on benthic and deep‐water prey, with deep‐water foraging perhaps especially important during long‐distance migrations between aggregation sites (Rohner et al. 2013, Marcus et al. 2016). Continuing to unravel the relative importance of long‐distance migrations, deep ocean and shallow coastal productivity in supporting R. typus will greatly enhance our capacity to understand the response of the species to local and global perturbation. However, as we discuss further below, the potential for isotopic insights depends on detailed understanding of intra‐species variability in discrimination.

Discrimination on a “low‐quality” diet Recent work has suggested that variation in Δ15N is common and may principally reflect two main factors: diet “quality” and mode of nitrogen excretion (McMahon and McCarthy 2016, O'Connell 2017). In this context, “quality” is generally considered to be the similarity in amino acid compositions between consumer and diet (e.g., Martínez del Rio and Wolf 2005), but should also consider protein, carbohydrate, and fat balance (Chikaraishi et al. 2015) and the vitamins and trace metals that can be provided by feeding across a range of prey items. Generally speaking, carnivores, perhaps with more varied diets, would be expected to have higher quality diets than herbivores, since the former generally consume a range of other animals that have similar protein balance and amino acid compositions to their own tissues (e.g., sharks feeding on other fish). As a result, on higher quality diets, more dietary amino acids may be routed directly to tissues with little processing (e.g., deamination and transamination) and thus isotopic alteration (McMahon and McCarthy 2016, Ohkouchi et al. 2017). In the present study, although diet composition did vary with time, the major diet components did not differ significantly in terms of their amino acid composition, suggesting a relatively stable diet quality (Appendix S1: Fig. S1). Thus, while we cannot quantitatively assess the role of diet quality in captive R. typus, we can make some observations regarding discrimination on a relatively low‐quality zooplankton diet. The quality of diets fed to the captive R. typus could be considered low, relative to a carnivorous diet, given the large differences in amino acid compositions between krill and shark tissues; cartilage tissue for instance had highly distinct amino acid profiles compared to all diet items (Appendix S1: Fig. S1). Clearly, significant processing is required to produce fin cartilage, which, along with an amino acid imbalance (Appendix S1: Fig. S1), is perhaps reflected in the large Δ13C cartilage values of over 5‰ (Table 2; see also Hussey et al. 2011]). Perhaps also as a consequence of zooplanktivory, there was little evidence of markedly reduced nitrogen discrimination in R. typus suggested to occur in response to carnivorous diets in higher TP elasmobranchs (Dale et al. 2011, McMahon and McCarthy 2016), as well as bony fishes (Choy et al. 2012, Bradley et al. 2015) and marine mammals (Germain et al. 2013). While the Δ15N Glu‐Phe in cartilage (6.0–7.1‰; Table 2 and Fig. 3c) was slightly lower than the meta‐analysis value of around 7–8‰ (McClelland and Montoya 2002, Chikaraishi et al. 2009), the difference of up to 2‰ is within the range of expected variation. Indeed, in addition to ureotelism (discussed below), changes in diet quality alone may alter Δ15N Glu by ~2–7‰ (Chikaraishi et al. 2015, McMahon et al. 2015). Given such variability, the reduction in isotopic discrimination in higher order carnivores remains a significant issue for further study (Hussey et al. 2014, 2015), and must be quantitatively considered when dealing with isotope data from such organisms, particularly large predatory sharks consuming high‐quality diets. Understanding of discrimination changes with diet quality will benefit from more controlled feeding studies across trophic levels, with observations of low‐TP ureotelic organisms, like whale sharks (e.g., Δ15N Glu‐Phe ~ 6.6‰, this study) and herbivorous bears (e.g., Δ15N Glu‐Phe ~ 6.0‰; Nakashita et al. 2011), rare relative to those of high‐TP ureotelic and low‐TP ammonotelic organisms.

Reduced discrimination due to urea retention and starvation In addition to a higher quality diet, urea retention may reduce apparent nitrogen discrimination in sharks (Hussey et al. 2010, 2012a, Dale et al. 2011). In this study, there was little evidence of marked deviation in estimates of Δ15N in healthy R. typus away from averages for non‐ureotelic organisms to suggest anything unique about the role of N excretion on nitrogen discrimination. In agreement, when Logan and Lutcavage (2010) experimentally varied tissue urea content by salinity reductions, they found no influence of urea on the δ15N of blood from skates (Leucoraja spp.) or muscles from skates and spiny dogfish (Squalus acanthias). Several studies have suggested that urea extraction can significantly alter tissue δ15N estimates, including in R. typus subdermal tissue (Carlisle et al. 2017, Marcus et al. 2017), but it is unclear to what extent extraction techniques themselves alter tissue isotopes independent of urea, such as through incidental removal of low molecular weight proteins (Li et al. 2016). Indeed, in the case of blood plasma, urea removal is not recommended due to significant loss of free amino acids (Kim and Koch 2012). In the aquarium R. typus, there was no relationship between plasma blood urea nitrogen (BUN) and δ15N plasma across the range of BUN values observed (Appendix S1: Table S9; 726–1045 mg/dL). Average BUN was 877 ± 47 mg/dL, which equates to about 37% of the amount of total proteins (TP averaged 2.33 ± 1 g/dL; Appendix S1: Table S9). Total plasma nitrogen content was also relatively stable around the aquarium average (22% ± 0.5%; see Appendix S1: Table S7) but did increase during starvation (25.7% ± 0.5% at death, see Appendix S1: Table S7). During starvation urea retention might play a significant role in plasma isotopes via two potential mechanisms. First, urea may reduce tissue isotopes due to its reincorporation into tissues via the activity of gut microbes during starvation. Increased relative microbial activity during starvation (i.e., empty guts) may lead to the reincorporation of bloodstream urea (Sealy et al. 1987, Fouillet et al. 2008, Ohkouchi et al. 2017). We are unable to definitively explain the very low δ15N of one fecal sample, which was close to 0‰ (see Fig. 2a and Appendix S1: Table S6). Such low δ15N may reflect increased microbial processing and contribution from recycled urea, although there was no evidence of reduced ingestion in this individual prior to taking this sample. Second, increasing amounts of urea may lead to lower tissue δ15N estimates due to increasing urea contributions relative to the total N pool of starved sharks. While the isotopic effect of fasting is generally taken to be an increase in δ15N of body tissues due to catabolism of already 15N‐enriched tissues (Hobson et al. 1993), with increased excretion of 15N‐depleted nitrogenous wastes leaving tissues 15N enriched (DeNiro and Epstein 1981, Gannes et al. 1998, Bakovic et al. 2017), we suggest that marked reduction in δ15N plasma occurs during starvation (e.g., Fig. 6c) due to the increasing ratio of 15N‐depleted excretory components of the blood, such as urea and TMAO, relative to other nitrogenous components. Relative to the well‐fed state (0.460 ± 0.17), the ratio of plasma BUN to TP increased markedly in #5 during the final week of starvation (1.08 ± 0.69), reaching as high as 2.63 (Fig. 6d). Although increased nitrogen use efficiency during starvation could also lead to lower δ15N plasma (Sears et al. 2009), given the fact that δ15N plasma fell below δ15N diet (i.e., negative Δ15N), it seems improbable that δ15N plasma reductions can be explained by increased nitrogen use efficiency alone. Since urea is unlikely to influence tissue δ13C (Carlisle et al. 2017), or to be 13C‐enriched relative to lipids (Kim and Koch 2012, Li et al. 2016), the concomitant reduction in plasma δ13C likely reflects the incorporation of carbon from 13C‐depleted lipids during catabolism (e.g., Williams et al. 2007). Thus, while we have suggested that urea may be relatively stable and have limited influence on tissue isotopes in healthy, feeding R. typus, increasing relative amounts of urea and lipid should be carefully considered when interpreting tissue isotopes of individuals that may be, or have been, calorically limited. Starvation led to the largest variability in blood plasma isotopes in this study, reducing δ15N and δ13C in #5 as much as 3.5‰ and 7.5‰, respectively, below the estimated diet (Fig. 6a). It is worth briefly noting that the starvation of #5 in captivity may have been indicative of a pre‐existing condition, with this individual showing evidence of caloric restriction at capture (e.g., low triglyceride [TG], Fig. 6c) and several plasma indicators of ill health throughout captivity. For instance, higher WBC may have indicated an infection or disease, and high GPT and LDH possible liver problems (Fig. 5). However, there are several lines of evidence to suggest that significant fasting may also be common in otherwise apparently healthy R. typus in the wild. Sharks in general are known for eating large meals on an infrequent basis, potentially going days, or even weeks, without a meal (Leigh et al. 2017). One out of the five stranded R. typus examined by Rohner et al. (2013) had an empty stomach and, in the present study, there was evidence of caloric restriction in at least two of the eight wild individuals. The very low triglyceride (TG) values found in #5 at capture (i.e., ~10 μg/dL; recovered during feeding in captivity prior to inappetence) were also evident in several wild R. typus, with values less than 12 μg/dL found in three individuals (Fig. 8). Plasma TG has been shown to be a good indicator of body condition in large sharks, independent of body length (Gallagher et al. 2014). Based on the relationship between plasma TG and ingestion in our wild R. typus (see Appendix S1: Fig. S6), we suggest that of the eight individuals studied, most (five) were barely ingesting sufficient to meet their basal metabolic requirements in the months prior to sampling (e.g., BMR ≤ 1; Table 1). Low TG values in R. typus less than around 40 μg/dL (Fig. 8) likely indicate severe caloric restriction and tissue isotopes should be interpreted with extreme caution when there is evidence of TG below 10–15 μg/dL (Fig. 6d) due to the likelihood of prolonged starvation increasing the influence of excretory products and catabolism on tissue isotopes.

Reduced N discrimination at higher growth rates This study highlights the need for greater quantitative consideration of growth rate differences when interpreting isotope variation in wild populations, with growth appearing to be the principal factor driving changes in bulk Δ15N in both plasma and cartilage of the well‐fed aquarium R. typus. The two captive females (#3 and #4) were likely sexually immature throughout the study (Nozu et al. 2015), while hormonal levels indicate that the larger male #1 reached sexual maturity in 2012 (R. Matsumoto unpublished). This relative maturity is reflected in the differences in average growth rates among the aquarium individuals (7–36 cm/yr; Table 1). While these growth rates are of similar magnitude to estimates for wild R. typus, typically in the 20 to 40 cm/yr range (Hsu et al. 2014a, Norman and Morgan 2016), higher rates could be expected due to better nutrition in the aquarium. Indeed, growth as high as 75 cm/yr was observed for the maturing #3 in the present study, while another captive R. typus in Taiwan (67 cm/yr) also grew markedly faster than wild averages (Leu et al. 2015). Annual growth approaching such high rates are only predicted to occur in the wild in young R. typus (e.g., 60 cm/yr at age 1 yr and BL ~ 120 cm), with rates likely declining below 30 cm/yr by age 19 (BL ~ 890 cm; Hsu et al. 2014a). While the growth of the aquarium individuals may not be directly comparable to the observations of Hsu et al. (2014a) due to the relatively well‐fed status in captivity, the comparison does suggest that growth rates sufficient to significantly reduce Δ15N (see Fig. 7a) are plausible for wild R. typus, especially in younger/smaller sharks. Based on the natural growth‐at‐age estimates of Hsu et al. (2014a), we predict based on Fig. 7a that Δ15N cartilage and Δ15N plasma may vary between 0.5 and 1.5‰ and 1.8–2.4‰, respectively, over the range of growth rates predicted for this age range. In agreement, an abrupt change in the δ15N muscle vs. BL relationship observed in a wild population of R. typus at around 3–4 m BL was attributed to growth related changes in Δ15N (Borrell et al. 2011), with theoretical reductions in nitrogen fractionation associated with ontogeny (Martínez del Rio and Wolf 2005) supported by laboratory studies (e.g., Gaye‐Siessegger et al. 2003, Trueman et al. 2005, Sears et al. 2009). Differences in wild growth rates among R. typus individuals may significantly alter interpretations of population‐level δ15N variations, suggesting it is advisable to introduce variable discrimination factors based on relative growth rates into isotope mixing models.

Implications of slow turnover for tissue selection In addition to discrimination, we would expect tissue turnover rates to be strongly influenced by body mass (BM) and growth rates (Fry and Arnold 1982). The slow turnover evident in the present study is not surprising given that R. typus are the largest ectotherms on the planet and turnover is expected to be slowed by both larger BM and ectothermy (Martínez del Rio et al. 2009, Weidel et al. 2011, Kim et al. 2012b). Tissue‐specific turnover times have important implications for the selection of sampling tissue(s), which should be guided by the temporal scale of the question(s) at hand. In the case of R. typus, it seems unlikely that non‐lethal tissue sampling can provide diet information over short temporal scales, such as would be relevant to aggregations that typically last for weeks to several months (Taylor 1996, Heyman et al. 2001, Hobbs et al. 2009). The blood plasma turnover estimated here, on the order of 9 months (half‐life 137 d or 4.5 months; Table 4), reflect R. typus’ large body size (BM on the order of thousands of kilograms). As a comparison, turnover rates for plasma in smaller‐bodied leopard sharks (Triakis semifasciata) were markedly faster; over a BM range of 1–4 kg, turnover was estimated to be about 6 d for N and 60 d for C (Kim et al. 2012b). Body‐mass‐dependent plasma half‐lives from Thomas and Crowther (2015) were slightly lower but generally agreed well with the present estimates (e.g., 99 d for a 2,500 kg shark; Table 4). Blood sampling of R. typus in the wild has rarely been attempted but was recently achieved from Galapagos R. typus (R. Matsumoto unpublished). Such sampling has the potential to greatly increase the temporal resolution of diet information for this species to as low as months prior to sampling. However, the ability to detect a diet switch such as may occur during aggregations would depend on the magnitude of isotope differences between diets. As an example, for a hypothetically large diet shift from a diet supported by open ocean phytoplankton production (e.g., δ13C of −24‰) to a wholly benthic source from a coral reef producer (−16‰; e.g., Wyatt et al. 2012b, 2013), it would take 76 d to detect a 2‰ shift in plasma δ13C, assuming a plasma half‐life around the aquarium average of 137 d (Fig. 10). Thus, in the case of aggregations, plasma isotopes may provide useful information on diet changes associated with aggregations lasting several months, and thereby help elucidate the reproduction vs. provisioning role of these aggregations, but probably only when repeated blood sampling of individuals at the beginning and end of the aggregation is possible. It is also feasible that diet changes over short temporal scales could be detected using fecal samples (Wilson and Newbound 2001, Jarman and Wilson 2004), with evidence in the present study that fecal material reflects diet isotopes at semi‐daily scales; although such analysis would need to account for potential variations in assimilation efficiencies between different environments and prey items, and the associated implications for expected fecal isotope compositions (Fig. 4a,b). Figure 10 Open in figure viewer PowerPoint Rhincodon typus diet shift at time = 0 from one supported by oceanic plankton (e.g., δ13C of −24‰) to a coral reef producer (e.g., −16‰; black line) and corresponding changes with time in δ13C of plasma (red line; assuming a half‐life of 137 d) and cartilage (gray line; 550 d half‐life) tissues calculated using a Gaussian smoothing function (see Eq. Theoreticaldiet shift at time = 0 from one supported by oceanic plankton (e.g., δC of −24‰) to a coral reef producer (e.g., −16‰; black line) and corresponding changes with time in δC of plasma (red line; assuming a half‐life of 137 d) and cartilage (gray line; 550 d half‐life) tissues calculated using a Gaussian smoothing function (see Eq. 7 ). Compared to blood and fecal material, both muscle and fin tissue may be more tractable to sample in the field. However, our findings suggest these tissues can only provide an estimate of diets averaged over a much longer temporal window. While we do not have quantitative estimates for R. typus muscle turnover, based on the agreement between the theoretical body‐mass–plasma‐isotope relationships and plasma turnover estimates (Table 4), we can make some basic estimates of muscle turnover for the species. In general, we could expect muscle turnover rates to increase with BM (Weidel et al. 2011, Kim et al. 2012b) as well water, and thus body, temperatures (Thomas and Crowther 2015). For example, in a 4,000 kg shark, muscle half‐life could be expected to decrease from 461 d at the lowest temperature in the aquarium (20.2°C) down to 332 d at the highest (29.8°C) (see Table 4). Such theoretical estimates for muscle turnover at the average aquarium temperature, averaged over C and N (e.g., 1.5–2 yr for 1,000–4,000 kg sharks), are intermediate to our estimates of turnover in plasma and fin material (e.g., 9 months ~ 3 yr; Table 4). Longer turnover rates are expected in fin compared to muscle since fin material is less metabolically active (Hussey et al. 2010, 2011). Based on the present estimates, assuming other factors such as growth and starvation have been accounted for, we suggest that, of the tissues that can be sampled nonlethally, multi‐tissue isotope analysis of R. typus could reflect diets over a continuum from several months prior to sampling (plasma) up to several years (fin cartilage). These rough estimates can be used to constrain the timescales over which a tissue can be expected to reflect diet and suggest that non‐lethal, multi‐tissue sampling has the potential to provide a wealth of temporal information about diets from wild individuals. The lack of muscle samples from the captive R. typus is a limitation of the present study. Muscle sampling through the thick skin layer of R. typus requires a biopsy spear (e.g., Robbins 2006) and was not considered suitable due to the increased risk of stress and infection in captivity. However, we can make some observations regarding isotopes of the muscle compartment through comparison with our wild R. typus samples. Muscle biopsies from the wild R. typus demonstrate that, in most instances, muscle occupies an isotopic space intermediate to plasma and cartilage (Table 6). However, there was a degree of variation around the average differences, especially with regard to muscle to plasma isotope differences (e.g., standard deviations of ±1.2‰ and ±1.3‰ for Δ15N M‐Pl and Δ15N M‐Pl , respectively, see Table 6). This likely reflects the faster turnover and changes in diet‐derived compounds, and thus higher temporal variability, in plasma in response to temporal changes in diet or growth relative to muscle or cartilage. Comparison of isotopes among tissues may impart information on temporal changes in diet, or help identify samples that do not fit a temporal trend. For instance, in the case of #13, muscle tissue exhibited δ13C that was 0.8‰ lower than plasma (Table 6), compared to an average of +0.9‰. This could be taken to reflect feeding on a lower δ13C diet in the intermediate past (order of a year), but in this case is suspected to indicate the influence of muscle lipids on δ13C; #13 had higher muscle C:N (3.0) than other muscle samples (2.5 ± 0.2) perhaps due to higher lipid content. Multi‐tissue isotope comparisons may be especially useful when lipid extraction is not feasible or desirable (Carlisle et al. 2017) to compare with tissue likely to be lipid‐free (e.g., fin cartilage). Wherever possible, multi‐tissue sampling should be attempted to control for temporal diet changes that may be obscured by variations in tissue‐specific turnover and discrimination. Table 6. Tissue isotope differences in individual wild R. typus encountered off Yomitan, Okinawa showing the difference in nitrogen (Δ15N) and carbon (Δ13C) isotopes between muscle and plasma (M‐Pl), cartilage and muscle (C‐M), and cartilage and plasma (C‐Pl) Rt Δ15N M‐Pl Δ15N C‐M Δ15N C‐Pl Δ13C M‐Pl Δ13C C‐M Δ13C C‐Pl Aquarium #1 n.d. n.d. 0.6 ± 0.4 n.d. n.d. 2.9 ± 0.5 #3 n.d. n.d. 0.6 ± 0.4 n.d. n.d. 2.7 ± 0.4 #4 n.d. n.d. 1.4 ± 0.4 n.d. n.d. 2.8 ± 0.4 Mean n.d. n.d. 0.9 ± 0.4 n.d. n.d. 2.8 ± 0.5 Wild #6 n.d. n.d. 3.0 ± 0.1 n.d. n.d. 3.0 ± 0.3 #10 0.8 ± 0.2 1.4 ± 0.3 2.1 ± 0.2 2.3 ± 0.4 0.5 ± 0.5 2.8 ± 0.3 #11 0.2 ± 0.2 1.2 ± 0.0 1.4 ± 0.2 0.6 ± 0.2 1.6 ± 0.0 2.3 ± 0.2 #12 2.8 ± 0.1 1.0 ± 0.2 3.8 ± 0.2 1.6 ± 0.2 0.8 ± 0.0 2.4 ± 0.2 #13 2.2 ± 0.1 2.8 ± 0.5 5.0 ± 0.5 −0.8 ± 0.1 3.6 ± 0.2 2.8 ± 0.1 Mean 1.5 ± 1.2 1.6 ± 0.8 3.1 ± 1.6 0.9 ± 1.3 1.6 ± 1.4 2.6 ± 0.3

Temperature‐ and prey‐dependent assimilation Changes in assimilation efficiencies (AE; Fig. 4) were small in the context of captive discrimination relative to growth and nutrition but could be important in the context of wild R. typus feeding and behavior. The AE estimated for R. typus (74–76%) are similar to average AEs observed in other sharks, which commonly lie in the 70–90% range (Leigh et al. 2017). However, variable assimilation did increase variation in the estimated δ15N and C:N of diets due to seasonal variation in AE N , but not AE C , likely linked to water temperatures. It has been well established that increased stomach temperatures accelerate gastric evacuation rates in teleosts and sharks (reviewed Leigh et al. 2017) and the relationship between nitrogen assimilation and temperature adds a trophic explanation to R. typus’ preferences for warm surface water. As ectotherms, R. typus depend physiologically on warmth for thermoregulation (Thums et al. 2013). They spend most of their time near the surface (e.g., 65–95% less than 200 m) in warm water around 28°C (Eckert and Stewart 2001, Wang et al. 2012, Tyminski et al. 2015, Ramirez‐Macias et al. 2017), with the limit of their geographical distribution possibly associated with the 22°C isotherm (Afonso et al. 2014). Below this temperature, nitrogen assimilation may drop below 60%, compared to AEs above 76% at 28°C. Indeed, for every °C increase in water temperature above 24°C assimilation of nitrogen could be expected to increase by ~3% (Fig. 4b), suggesting there is a trophic advantage to digesting prey in warmer water. There is evidence that R. typus make regular deep dives, including in excess of 1000 m where they may encounter temperatures as low as 4°C (Wilson et al. 2006). Both R. typus and the planktivorous reef manta ray Mobula alfredi have been shown to perform reverse diurnal migrations, which may be linked to stochastic feeding on abundant mesopelagic zooplankton layers (Gleiss et al. 2013, Braun et al. 2014, Tyminski et al. 2015, Burgess et al. 2016, Stewart et al. 2016). In addition to thermoregulation, returning to surface waters post‐dive would aid in more efficient assimilation of prey consumed at depth. However, vertical movement patterns alone may not be directly linked to changes in foraging behavior (Gleiss et al. 2013) and direct quantification of links between vertical movements and foraging remain limited. Indeed, the behavioral selection of depth vs. water temperature, relative to the depth distribution of prey, may vary between individuals based on whether they need to optimize digestive efficiency or feeding rates (Leigh et al. 2017). In addition to digesting in warm water, targeting smaller prey should improve R. typus feeding efficiency. The higher assimilation efficiency for the smaller E. pacifica implies more N per unit consumed than provided by larger prey such as E. superba; in the present study, this led to 11% higher incorporation of E. pacifica into tissue nitrogen in both plasma and cartilage (Table 5). Although certainly useful as an isotopically distinct source in the present study, Southern Ocean E. superba are unlikely to be encountered by wild R. typus and appeared to be of lower nutritional value to the captive sharks, probably due to their larger size and reduced digestibility. Estimates of the target size of R. typus prey in the wild are limited, but they have been observed to feed on plankton through to larger prey (small fish and squid), with their filter apparatus suggesting a restriction to prey above 1.2 mm (Motta et al. 2010). It may be that wild R. typus are not particularly selective for prey over a small range in size, such as that separating E. pacifica and E. superba (tens of millimeters), but we suggest that smaller prey may offer an advantage based solely on improved digestibility. Better understanding of assimilation efficiencies in R. typus could help explain links between foraging and behavior, with information on digestion in sharks generally highly limited (Leigh et al. 2017). While the present study suggests that R. typus exhibit relatively high AE, more detailed experiments are needed to define variations in gut transit/digestion times. In the present study, only limited fecal observations were possible and the assumption of a three‐day gut transit time was based on the chance observation of fecal material from a rare single‐prey feeding event. Further, the captive sharks could be considered very well fed relatively to wild individuals, with larger meal sizes likely to lead to faster gut transit rates, and thus, lower overall digestive efficiency (Leigh et al. 2017): our captive AE could well underestimate wild rates. We cannot assess the link between consumption and assimilation in this study, but note that the low growth of lemon sharks despite high AE (76–88%) were considered to reflect low consumption rates (Wetherbee and Gruber 1993). Gastric retention may also vary between individuals, and with changes in growth and metabolism, perhaps linked to the nutritive content of prey (Leigh et al. 2017). Further work using conservative tracers added to captive diets may be useful for helping predict changes in R. typus foraging strategies in response to food availability and quality (Leigh et al. 2017).

The importance of estimating relative growth and nutritional history The complexity of nitrogen cycling in organisms’ tissues suggests that a single identifiable value for discrimination is unlikely to exist and we need to quantify variations in metabolism that could confound isotopic interpretations (Sears et al. 2009, O'Connell 2017). The potential impacts of starvation and growth highlights the need for complementary analyses such as blood biochemistry when interpreting tissue isotopes in wild populations. In our captive R. typus, blood biochemistry was clearly linked to ingestion rates and nutritional health. This suggests that blood sampling offers a means of quantifying metabolism‐linked variation in discrimination in the field. Indeed, the tissue δ15N differences in our wild R. typus population are difficult to interpret in the absence of nutritional information, since we have shown that both high growth and starvation can lower tissue δ15N by more than the intra‐individual δ15N differences observed. The present study adds important information to the range of blood biochemistry parameters useful for assessing nutritional history in R. typus populations. There was generally good agreement with corresponding parameters measured by Dove et al. (2010) in two captive female R. typus, suggesting that healthy levels may be common across environments (see Appendix S1: Table S8). We were able to discount starvation or high growth rates as a factor in low δ15N values in oceanic R. typus encountered off Okinawa (Fig. 9a) based on their blood biochemistry. For example, the low δ15N of at least one individual, #9, seems highly unlikely to reflect starvation, since this individual had many blood parameters above even the well‐fed aquarium specimens (Fig. 8). For instance, TG values were particularly high, at close to 50 μg/dL. The highest TG recorded in the aquarium was 146 μg/dL but the average was 35 μg/dL, suggesting that #9 may have been undertaking high rates of ingestion in the months prior to sampling, perhaps in excess of the average BMR:ING ratios observed in this study. Based on #9′s size (630 cm, equivalent to a BMR of 15.8 MJ/d) and assuming similar ingestion produces similar blood TG levels in wild and captive individuals, #9 would appear to have been consuming in excess of 38 MJ/d to maintain such high TG levels (based on average BMR:ING for the aquarium sharks of 2.45). If feeding on a diet of similar calorific content to E. pacifica (e.g., 62 kcal/100 g or ~2.5 MJ/kg; Appendix S1: Table S1), this would be equivalent to an ingestion of around 15 kg/day for this individual. Other estimates suggest that wild R. typus may be able to obtain 47 MJ/d by feeding on krill swarms. The high density of swarms observed at Ningaloo Reef in Western Australia, for instance, is a potential factor in driving the aggregation there and mean that only 8 min/day of ram filter feeding may be required to more than satisfy caloric requirements (Wilson et al. 2006, Gleiss et al. 2013). In contrast, in areas with lower prey densities, such as off the Yucatan Peninsula in Mexico, feeding may be required over 7.5 h to obtain estimated daily rations of 14–28 MJ/d (BL ~ 5.5–7 m; Motta et al. 2010). The estimated energy requirement for an individual such as #9, with high plasma TG, suggests consistent access to high densities of prey in the months prior to sampling off Okinawa, although no aggregation sites are known in the region and isotopes suggest it was of oceanic origin, perhaps supporting the role of deep‐water plankton layers (Rohner et al. 2013, Marcus et al. 2016). Conversely, evidence of low TG levels in several of the wild R. typus off Okinawa suggested prolonged caloric restriction. Low TG was particularly evident for #6 and #8, and to a lesser extent #7 and #12 (Fig. 8). The former two individuals showed plasma TG close to starvation levels of #5, suggesting that these individuals may not have not fed in the approximately 4 months prior to sampling (see Fig. 6d), if we assume that they had TG levels similar to well‐fed individuals prior to a cessation of feeding. If feeding sparsely or sporadically, the low TG values could reflect a longer period of reduced feeding, up to our estimates of blood turnover (~9 months; twice the average plasma half‐life of 137 d, Table 4). While the swimming speed of the captive R. typus has been estimated to be intermediate to values in the wild (Taniuchi et al. 2004), wild R. typus could be expected to expend more energy than individuals in the aquarium, which, although in constant motion, do not have to contend with the metabolic costs of factors such as opposing water currents (Sleeman et al. 2010). Thus, starvation states may be reached more quickly in wild individuals than we have observed in captivity. While starvation could explain the low δ15N space of oceanic shark #6, the other apparently starving individual, coastal shark #8, showed intermediate plasma δ15N with evidence that high growth rates may have led to decreased Δ15N in this individual; source δ15N estimates from #8 plasma were thus similar to the other coastal individuals once Δ15N was corrected for its apparently high growth (~6‰; Fig. 9b). Further, despite low TG, costal shark #7 had the second highest plasma δ15N, suggesting links between δ15N and nutritive state in the wild are not straight forward. Indeed, none of the wild sharks showed high BUN:TP ratios that might be indicative of increased relative urea concentrations lowering plasma δ15N during starvation (Fig. 8c), as was suggested for starvation in #5, with the lowest plasma δ15N observed in the apparently well‐fed oceanic shark #9. While we suggest that blood plasma biochemistry may be required to robustly interpret tissue isotopes of wild R. typus, caution is required to ensure a common venipuncture site between individuals. Although our limited assessment suggested that plasma isotopes are well mixed, differences in blood biochemistry based on venipuncture site, as was evident between pectoral and dorsal fins, may be common (e.g., Mylniczenko et al. 2006, Dove et al. 2010), suggesting that attempts to define relative growth and nutritional health in the wild should focus on a consistent venipuncture site.