Introduction

Interspecific resource competition is one of the primary biotic drivers modifying species’ fundamental niches towards their realized niches within a community1,2,3. Classical ecological theory suggests that niche space must be divided among sympatric organisms in order for species to coexist without competitive exclusion4,5,6. These divisions can result in species utilizing different spatial or trophic resources, with the degree of overlap inversely proportional to the intensity of their competition for resources7,8. Sympatric predators often appear to share niche space; however, studies demonstrate that resource partitioning does occur within predator guilds9,10. This resource partitioning allows for a more biodiverse predator assemblage, which may help buffer ecosystems from major changes caused by top-down forcing in the case of individual species loss due to environmental fluctuation11.

Since ecological niches are complex, empirically determining niche partitioning in natural systems is nontrivial. Traditionally, niche partitioning is evaluated using tagging and/or gut content studies, where separation of species’ trophic or spatial resources can be identified through their movements12,13 or prey composition14,15,16. These methods are, however, logistically challenging in marine environments where organisms spend the majority of their time underwater and can be difficult to observe (tagging) or are dependent on capture directly after a meal (gut contents). To overcome these challenges, other tools are being used in conjunction with traditional methods. Chemical analyses, such as stable isotope analysis (SIA), are widely used to indirectly determine trophic and spatial resource partitioning10,17. However, depending on the tissues sampled, SIA can only give a snapshot of weeks to months, a similar temporal limitation of other ecological tools.

As the use of “non-traditional methods” is becoming more common place in ecological studies, toxicological analyses offer an alternative and complementary perspective for understanding how animals interact with their environments18,19,20,21,22. The release and spread of anthropogenic contaminants worldwide, in particular legacy organic contaminants such as polychlorinated biphenyls (PCBs) and pesticides, coupled with their high resistance to degradation, has resulted in contamination of even the most pristine environments23,24. Thus, these legacy hydrophobic organic contaminants represent reliably detectable chemical markers, particularly for long-lived predatory species that have a high propensity to accumulate these contaminants25,26,27. Similar to stable isotopes, organic contaminants are acquired mainly through diet and are incorporated into tissues based on each contaminant’s physio-chemical properties28. In addition, organic contaminants can offer some degree of spatial resolution as different areas have unique chemical signatures based on the history of direct contaminant release or atmospheric deposition29. These contaminant signatures are then incorporated into local biota, which will generally reflect the environment, assuming a majority of contaminants from the diet are absorbed and metabolism by the predator is limited so as to not alter these contaminant signatures. For instance, organisms utilizing the Southern California Bight (SCB) have a strong signature of a dichlorodiphenyltrichloroethane (DDT) metabolite (e.g. 4,4′-DDE) due to the high degree of DDT release into the marine environment in the 1900s30. Since species’ contaminant loads vary based on their use of spatial and trophic resources, chemical analysis is a potential tool to examine resource partitioning in sympatric species while taking aspects of their physiology into consideration22.

In the California Current Large Marine Ecosystem (CCMLE), Isurus oxyrinchus (Shortfin Mako), Alopias vulpinus (Common Thresher Shark), and Prionace glauca (Blue Shark) comprise a sympatric predator guild12,31, making them excellent candidates to determine whether toxicological tools can aid in our understanding of resource partitioning. Within the CCMLE, the SCB represents an important nursery area for these sharks, as juveniles of all three species are relatively abundant in the region31,32. Previous tagging and dietary analyses indicate that some degree of niche partitioning occurs among these three species. Spatially, A. vulpinus are the most coastal32, whereas P. glauca and I. oxyrinchus make more regular movements offshore12. With respect to diet, all three species demonstrate some degree of prey overlap, but species can be distinguished based on stomach content composition16. As such, we would predict these differences to be reflected in their contaminant signatures. In an accompanying study33, we previously demonstrate how aspects of species’ physiology and ecology can result in different lifetime contaminant accumulation trajectories by measuring a suite of legacy contaminants in liver tissue across a range of animal sizes. P. glauca, the species with the lowest estimated metabolic rate and least time spent nearshore, demonstrated low potential for lifetime accumulation, although adult sampling was limited. While A. vulpinus and I. oxyrinchus both demonstrated patterns of growth dilution during the juvenile stage, only I. oxyrinchus exhibited significant increases in liver contaminant concentration in adults, likely due to their high metabolic rate and relatively high trophic position. Species characteristics that may influence contaminant input (i.e. where animals feed, what they feed on or how often they feed) may also have the potential to influence species’ unique contaminant signatures. Therefore, we aimed to demonstrate the applicability of organic contaminants as a tool to examine the degree of niche partitioning among three species of sympatric sharks within the same predatory guild using contaminant information collected previously33.

Methods

Sample collection

Liver samples were opportunistically obtained from frozen samples archived at the Southwest Fisheries Science Center’s (SWFSC) from the juvenile Shortfin Mako/Blue Shark survey31 and Common Thresher Shark survey, the National Marine Fisheries Service (NMFS) West Coast Region Fishery Observer Program, participating drift gillnet fishermen, and recreational fishermen. A subset of these archived samples was then analyzed for organic contaminants (see below) in individuals selected to represent a range of sizes and sexes for I. oxyrinchus, P. glauca, and A. vulpinus sampled from the years 2011–2013. Archived samples were also supplemented with contaminant data from previously published reports34,35,36 from animals sampled in the same time frame (i.e., 2011–2013).

Organochlorine contaminant analysis

Organic contaminants were analyzed at California State University Long Beach’s IIRMES facility following previously published methods34. Liver subsamples (0.5–1.0 g wet weight) from each animal were extracted for 14–16 hrs via a Soxhlet apparatus in 100% methylene chloride solution, followed by subsequent evaporation and sample purification by elution through an Alumina-B/Silica gel. Extracts were spiked with internal standards (4,4′-Dibromobiphenyl and 2,2′,5,5′-Tetrabromobiphenyl, Accustandard, Inc, New Haven, USA) and injected onto an Agilent gas chromatograph (GC; 6890N series) equipped with a mass selective detector (MSD; Agilent 5973 inert series, Santa Clara, USA). Ion peaks were then identified using gas chromatography mass spectrometry (GCMS) software for 54 PCB congeners (sum = tPCBs), DDT and its metabolites (4,4′-DDT, 4,4′-DDE, 4,4′-DDD, 2,4′-DDT, 2,4′-DDE, 2,4′-DDD; sum = tDDXs), and non-DDT chlorinated pesticides (24 compounds screened; sum = tPEST); total concentrations were expressed as the sum of all contaminant groups (tOCs). The limit of detection for all compounds was 1 ng/g. Part way through sample processing, the method at the laboratory where chemical analysis took place to include 4,4′-DDMU, a downstream break down product of 4,4′-DDE; thus, only a subset of samples (n = 44) included this metabolite and no samples from previous studies had this metabolite analyzed. Concentrations of this metabolite are reported for samples where it was measured, but were not included in overall analyses due to incongruency in measured congeners among samples. Lipid content was determined gravimetrically from split aliquots of the extracts; however, since neither wet nor lipid normalized concentrations were correlated with liver lipid content for any species (p ≥ 0.16) we reported values on a wet weight concentration basis.

To ensure quality control/quality assurance, all samples were spiked with recovery surrogate compounds (TCMX, PCB30, PCB112, PCB198) prior to extraction to measure extraction and recovery efficiency of procedures (recovery ranges for all four surrogates: 75 ± 10%). In addition, a certified reference material (Lake Michigan Trout tissue 1947, National Institute of Standards and Technology, Gaithersburg, USA), a pair of blank spikes (i.e. 400 ng of each measured congener), a blank (spiked only with recovery surrogates), and a liver sample replicate were run in tandem with each batch (i.e., ~every 24 samples). All compounds in the certified reference material were within ±30% of the reference value37. Recovery of measured compounds (n = 83) in blank spikes (n = 4) were 108 ± 7% for PCBs and 89 ± 20% for pesticides, with 4.2 ± 3.3% relative standard deviation between blank spike replicates. Blanks had high recovery of recovery surrogates (90 ± 28%) and showed little to no contamination during procedures (e.g. <2 ng/g of PCB153/PCB138); therefore, liver sample values were not blank-corrected. For liver samples where a replicate was run, the median relative significant difference between concentrations of quantifiable compounds within replicate pairs was 9.9 ± 2.8%, and mean concentrations were used for samples where replicates were available.

Data analysis

Organic contaminants were compared among species in two ways to determine if species could be distinguished based on their contaminant concentrations or signatures. For our comparison of tOCs, individual contaminant concentrations were summed across compounds for each shark sample and expressed on a wet weight (ww) basis. tOCs were then compared among I. oxyrinchus, P. glauca, and A. vulpinus using a Kruskal-Wallis (KW) test followed by a Wilcoxon rank-sum test, with all sizes and sexes combined within species. Contribution of each contaminant group (i.e. tPCBs, tDDXs, and tPEST) was calculated as the proportion that each contaminant group contributed to the total contaminant load (tOCs). Differences in contaminant proportion was assessed using a regression modeling approach for dependent data with a beta distribution such as proportional data implemented in the betareg package in R (v 3.2.0).

To determine if species could be distinguished based on their contaminant signatures (i.e. relative differences in standardized contaminant proportion), measured contaminants for each individual were standardized by dividing each individual contaminant concentration by the sample’s own PCB153 concentration (a contaminant that is consistently detected in all species38) to obtain a ratio for each contaminant per individual. Since some contaminants had much larger concentrations than others (e.g. 4,4′-DDE), ratios were standardized via a z-score transformation by species. A classification random forest analysis was then used to determine if individual shark samples could be correctly assigned by species based on their z-score transformed contaminant profiles alone. Random forest analysis was conducted using the randomForest package in R. To determine species assignment, we set the number of trees (ntree) equal to 10,000, and to avoid biases caused by unequal sample sizes among the different species, we set the number of individuals used in the training set (sampsize) equal to eleven, which is half the number of individuals in the smallest group (P. glauca). To visualize the separation of species based on contaminant signatures we performed a non-metric multidimensional scaling (nMDS; vegan package in R) using contaminant ratios where each contaminant was standardized to the samples own PCB153 concentration and k was set to two.

Results

Species showed significant differences in tOCs (KW, p < 0.001; Fig. 1) despite the inclusion of both sexes and a broad range of sizes, particularly for I. oxyrinchus and A. vulpinus that encompassed young-of-the-year through mature animals. I. oxyrinchus (n = 47, 53 to 337 cm fork length [FL]) had the highest tOCs (mean ± SD: 55,186 ± 66,968 ng−1 g ww), which were approximately 15 and 90 times greater than mean tOCs in A. vulpinus (n = 51, 63 to 283 cm FL, 3,781 ± 2,226 ng−1 g ww) and P. glauca (n = 22, 50 to 178 cm FL, 611 ± 382 ng−1 g ww), respectively. Besides having the lowest tOCs, P. glauca also had the fewest number of detectable contaminants measured (approximately 33 consistently measured contaminants out of a possible 84 screened), whereas A. vulpinus and I. oxyrinchus had much higher numbers (48 and 57, respectively).

Figure 1
figure 1

Comparison of sum tOCs measured in livers of P. glauca, A. vulpinus, and I. oxyrinchus. Different letters indicate significant differences, with an approximate 10-fold step-wise difference between species in terms of contaminant concentration. Crosses denote group means, and 25-50-75 quantiles are shown with whiskers and outliers plotted using the Tukey method.

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While no information is available on the health of individuals sampled for this study, species risk to the potential negative effects of contaminant exposure may not be similar due to the magnitude differences in PCB concentrations across species. With respect to tPCB concentrations eliciting negative effects in mammals39,40,41,42, mean I. oxyrinchus consistently exceeded these thresholds regardless of whether wet or lipid normalized values were used, while mean P. glauca concentrations fell under these thresholds. Depending on the study, tPCBs concentrations in A. vulpinus either exceeded or fell below mammalian effect thresholds. Although studies are limited, environmental PCB exposure has the capability of eliciting negative physiological responses in elasmobranchs43,44. Compared to these studies, mean tPCBs in all three shark species exceed the lowest wet weight tPCB value in U. halleri and two of the three species exceed lipid normalized concentrations43. Considering the disparity in tPCB concentrations, it is likely that physiological risk to contaminant exposure is not equitable across these shark species.

For all species, tDDXs comprised the greatest proportion of the tOC. DDX contribution was highest in A. vulpinus (69.7 ± 2.8%) and was significantly greater than both P. glauca and I. oxyrinchus (Pseudo R-squared: 0.06283, z-value = 2.57, p = 0.009), which had similar DDX contributions to the total load (63.5 ± 3% and 63.2 ± 2.5%, respectively). While 4,4′-DDE contributed the most to tDDX concentrations (Av = 87 ± 8%; Pg = 90 ± 4%; Io = 96 ± 5%) and was the most frequently detected DDX (100% for each), species had differing patterns for the next most frequently detected DDXs. For example, P. glauca had the lowest frequency of detection for 2,4′-DDX compounds (21% of the time) compared to A. vulpinus, which had the highest (69%), with I. oxyrinchus intermediate (53%). For samples where 4,4′-DDMU was measured, P. glauca had the lowest frequency of detection (1/3) and concentrations (1.67 ng/g ww), whereas both A. vulpinus and I. oxyrinchus had high rates of detection (18/22 and 18/19, respectively) and comparable mean concentrations (42 ± 13 ng/g ww and 57 ± 85 ng/g ww), although concentrations varied by an order of magnitude among I. oxyrinchus.

PCB compounds were the next highest contributor to tOCs, and contributions were not significantly different among the three species. I. oxyrinchus had the most variability in PCB contribution (33 ± 12.6%), followed by P. glauca (25 ± 8.9%) then A. vulpinus (27 ± 7.8%). Among PCBs that contributed the most to total PCB loads, PCB153 and PCB138 consistently ranked first and second among species; however, the relative contribution of these congeners to tPCBs differed (Table 1). Of the top 10 contributing PCBs, species differed in both PCB congener rank order and relative contributions of these congeners to tPCB loads (Table 1). Detection frequencies also varied among species. I. oxyrinchus had the most congeners detected in one or more samples (91% of 54 congeners measured) as well as consistently higher detection frequencies of congeners across samples (71 ± 30% of samples), followed by A. vulpinus (% congeners detected = 87%; average detection frequency = 60 ± 32%) with the least number (83%) and lowest frequency of detections (36 ± 36%) in P. glauca.

Table 1 Mean ± standard deviation contributions to total PCB loads of the top ten most frequently detected PCB congeners for each species.
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Of the non-DDT pesticides, trans-nonachlor consistently contributed the most to concentrations for all three species (Io: 54 ± 14%, Av:49 ± 15%, Pg: 40 ± 13%). However, for the next two chemicals contributing to non-DDT pesticide concentrations, species varied in both the type of contaminant and its proportion. For example, in I. oxyrinchus mirex (22 ± 12%) and cis-nonachlor (15 ± 5%) ranked second and third in proportional contribution, whereas cis-nonachlor (20 ± 7%) and alpha-BHC (19 ± 6%) were important to A. vulpinus pesticide concentrations and oxychlordane (23 ± 15%) and alpha-chlordane (20 ± 7%) to P. glauca.

Categorical random forest analysis showed that species were distinguishable based on their contaminant signature and were correctly assigned 96% of the time (Table 2). Contaminants that contributed to species separation can be found in the Supplemental Table 1. nMDS showed clear separation among the three species in their contaminant signatures (Fig. 2; stress = 0.16).

Table 2 Confusion Matrices for Random Forest Analysis using 10,000 trees with each column demonstrating the number of individuals identified as P. glauca, I. oxyrinchus or A. vulpinus by the analysis.
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Figure 2
figure 2

When contaminant signatures were compared along two dimensions, species showed clear separation from each other (stress = 0.16), demonstrating that contaminant signatures can be a useful ecological tool. I. oxyrinchus are represented with a dark grey triangles, A. vulpinus with medium grey squares and P. glauca with a light grey circles. Shark illustration credit: P. Dimens.

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Discussion

Several ecological tools are available for studying habitat use and sympatric species resource partitioning. The most appropriate tool depends on the question of interest, the nature of the ecological community, and the available resources. Exploiting the fact that an animal accrues different chemicals signatures based on its distribution and foraging ecology allows us to use chemical tools to determine resource partitioning in seemingly overlapping species and subsequently make inferences on species’ ecological niches. Accumulation of contaminants by predators has consequences for the transport of these chemicals in the environment and the health of their populations. In turn, contaminant accumulation is a product of the interaction of species’ niche, life history, and physiological characteristics. Here, we highlight the ability to use contaminants as an ecological tool to assess niche partitioning and in an accompanying study33, we further examine how ecological and physiological factors synergistically influence contaminant accumulation trajectories in three species.

Despite spatial and temporal overlap of I. oxyrinchus, P. glauca, and A. vulpinus16,45 in the CCMLE, we were able to discriminate among these three species with high accuracy using both total contaminant loads and profiles. This study corroborates prior findings showing significant differences in ecological niches among these three species in the same area16. Preti et al.16 found differences in stomach contents with A. vulpinus relying mainly on small epipelagic fish, P. glauca on squid and other organisms associated with the deep scattering layer and I. oxyrinchus being intermediate to A. vulpinus and P. glauca. Therefore, contaminants can be used as an ecological tool to study niche partitioning in species with varying degrees of spatial, temporal or dietary overlap18,19,46.

The strong southern California contaminant signal in all three species (i.e. DDX proportion) indicates the relative importance of the CCLME to these species’ forage bases and the persistence of DDX in liver tissue. A. vulpinus are considered to be more coastally oriented32,47 than either P. glauca or I. oxyrinchus. Therefore, it was not surprising that A. vulpinus had greater contaminant contributions from DDXs than both P. glauca and I. oxyrinchus. Note that details on ontogenetic-related changes in DDX signatures can be found in Lyons et al.33. A similar patterns relating to DDXs with respect to California coastal residency was found in pinnipeds sampled from southern California48, with more coastally associated species having higher DDX proportions and more pelagic species having lower DDX proportions. In comparison, P. glauca and I. oxyrinchus are known to spend protracted periods offshore in open waters12. However, despite the time spent offshore, DDXs still contributed to a large proportion of their total contaminant burden (>60% on average), suggesting that prey items with strong southern California influences are prominent in the diets of I. oxyrinchus, P. glauca, and A. vulpinus. By contrast, DDX signatures in Lamna ditropis (Salmon Sharks), another eastern North Pacific shark species that also utilizes offshore waters and occasionally is found in the CCLME, were much lower than that in P. glauca or I. oxyrinchus, with DDXs contributing to only 49% of the total load34.

Not only does geographic distribution and diet influence contaminant signatures, but these factors will also influence total contaminant concentrations, which also were distinguishable among species. There was a clear hierarchy in contaminant concentrations among the three species, with I. oxyrinchus having the highest, followed by A. vulpinus and then P. glauca. It is well known that the trophic positions of prey plays a significant role in predator concentrations, with higher trophic level predators having higher accumulated concentrations49,50. Of the three species, I. oxyrinchus has the highest trophic level16. Considering the relationship between contaminant concentrations and trophic positioning49,51, we confirm previous findings that contaminant concentration can be used to make inferences about ecology.

In addition to foraging ecology and distribution, physiological factors are also expected to influence contaminant accumulation52,53; these can, at times, be less emphasized compared to trophic position or trophic linkages in influencing contaminant concentrations among organisms54,55. The physiological influence on contaminant concentration is particularly exemplified in the separation between I. oxyrinchus and P. glauca, despite overlap in their diets. Although stable isotope analysis has confirmed that juvenile P. glauca forage at a lower trophic level than juvenile I. oxyrinchus56, adults of both species have been documented to feed on marine mammals16. Therefore, while diets overlap, the magnitudes greater contaminant concentrations in I. oxyrinchus, suggests that other, physiological factors also have an influence on contaminant accumulation. For example, metabolic rates are higher in regionally endothermic I. oxyrinchus than in ectothermic P. glauca57, which will likely influence contaminant accumulation. In contrast, while I. oxyrinchus and A. vulpinus are more physiologically similar (both are regional endotherms and utilize similar matrotrophic reproductive strategies), the discrepancy between them are more likely attributed to trophic differences16. Thus, while it is apparent that diet and habitat will influence contaminant signatures and concentrations, to fully explain the differences among species we need to also consider inherent differences in their physiology.

Corroborating the findings of traditional methods, this paper demonstrates that organochlorine contaminants can be used as an additional tool to study niche partitioning in ecological studies18,19,20. Since contaminants are acquired through diet, factors that influence feeding are hypothesized to play an important role in accumulation, and thus niche discrimination. While physiological characteristics dictate energetic demands, ecological characteristics influence where and upon what animals feed. Therefore, contaminant analyses are a tool whereby both ecological and physiological characteristics of a species are incorporated. Recognizing that species may undergo ontogenetic shifts in their ecology or physiology, it is noteworthy that despite the fact that we pooled individuals by species regardless of their sex or age class, we were still able to distinctly discriminate among species. However, details on how contaminant accumulation varied among these species across ontogeny can be found in Lyons et al.33. Nevertheless, contaminants as ecological markers may serve as a useful tool to provide insight into differential resource usage and physiology within species as well as among them.