Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Otolith chemical fingerprints of skipjack tuna (Katsuwonus pelamis) in the Indian Ocean: First insights into stock structure delineation

Otolith chemical fingerprints of skipjack tuna (Katsuwonus pelamis) in the Indian Ocean: First insights into stock structure delineation

  • Iraide Artetxe-Arrate, 
  • Igaratza Fraile, 
  • Jessica Farley, 
  • Audrey M. Darnaude, 
  • Naomi Clear, 
  • Naiara Rodríguez-Ezpeleta, 
  • David L. Dettman, 
  • Christophe Pécheyran, 
  • Iñigo Krug, 
  • Anaïs Médieu


The chemical composition of otoliths (earbones) can provide valuable information about stock structure and connectivity patterns among marine fish. For that, chemical signatures must be sufficiently distinct to allow accurate classification of an unknown fish to their area of origin. Here we have examined the suitability of otolith microchemistry as a tool to better understand the spatial dynamics of skipjack tuna (Katsuwonus pelamis), a highly valuable commercial species for which uncertainties remain regarding its stock structure in the Indian Ocean. For this aim, we have compared the early life otolith chemical composition of young-of-the-year (<6 months) skipjack tuna captured from the three main nursery areas of the equatorial Indian Ocean (West, Central and East). Elemental (Li:Ca, Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca) and stable isotopic (δ13C, δ18O) signatures were used, from individuals captured in 2018 and 2019. Otolith Sr:Ca, Ba:Ca, Mg:Ca and δ18O significantly differed among fish from different nurseries, but, in general, the chemical signatures of the three nursery areas largely overlapped. Multivariate analyses of otolith chemical signatures revealed low geographic separation among Central and Eastern nurseries, achieving a maximum overall random forest cross validated classification success of 51%. Cohort effect on otolith trace element signatures was also detected, indicating that variations in chemical signatures associated with seasonal changes in oceanographic conditions must be well understood, particularly for species with several reproductive peaks throughout the year. Otolith microchemistry in conjunction with other techniques (e.g., genetics, particle tracking) should be further investigated to resolve skipjack stock structure, which will ultimately contribute to the sustainable management of this stock in the Indian Ocean.


Skipjack tuna (Katsuwonus pelamis) is a cosmopolitan species inhabiting tropical and subtropical waters of the Indian, Pacific and Atlantic Oceans [1]. This species is, by far, the most commonly caught tuna worldwide [2, 3]. Currently, skipjack tuna stocks are considered to be in a healthy status in all oceans, both in terms of stock abundance and fishing mortality [3]. Five stocks of skipjack tuna are considered globally for management, two in the Atlantic Ocean, two in the Pacific Ocean and one in the Indian Ocean. Regional studies indicate a more complex stock structure than currently assumed for assessment and management purposes of skipjack tuna in the Indian Ocean [4, 5]; however, lack of understanding of population dynamics and connectivity at oceanic scale do not allow separation of stocks [6].

Stock structure understanding is essential to determine a suitable spatial scale for management, as the way a stock will respond to management decisions cannot be accurately predicted if the boundaries that characterize a stock are not correctly defined [7]. Skipjack tuna spawns throughout the year in large warm water tropical areas, but as those areas are often poor feeding grounds, they move towards adjacent colder and more productive subtropical areas to feed [810]. However, not all skipjack tuna seem to perform these long-distance movements, but rather, some remain around the tropical spawning area [10, 11]. As a result, the spatial dynamics of skipjack tuna and, hence, its implications on the stock structure of the species needs to be evaluated. Besides, skipjack tuna are fast growing, early maturing species, and have high reproductive potential, which makes this species more resilient to fishing pressure than many other tuna species [1214]. However, fishing pressure may vary spatially due to factors such as fleet operational constrains, variations in population densities, and differences in the value of fish caught, among others [15]. Thus, excessive local fishing effort and catches, may lead to situations of local overfishing and depletion when skipjack movements between areas are limited [16]. The understanding of exchange rates and connectivity among recruits from different regions is therefore, essential to achieve a sustainable and effective management of this species [17].

There are several methods that can be used to study fish stock structure, which can provide information at different spatial and/or temporal scales [18]. Among them, analyses of otolith (i.e., calcified structures found in the inner ear of the fish) chemical composition is widely used to explore movements and habitat use of fish [19]. This method relies on the premise that ambient water chemistry and environmental conditions (but also other intrinsic factors such as physiology, diet or genetics) affect the elemental incorporation (at minor and trace quantities) into the concentric growth of the otolith during daily increment formation [20, 21]. Since otoliths are both acellular and metabolically inert, material accreted during otolith formation is preserved as fish grows [22]. As such, the chemical composition of the material accreted during early life stages, serves as a natural marker to identify fish that have inhabited environments with distinct physicochemical characteristics [23]. Otolith microchemistry proved to be a powerful tool to discriminate among nursery fish groups for other tropical tuna species [2426], and also to depict different movements and life history patters of skipjack tuna in the Pacific Ocean [27].

Here, we examined otoliths of young-of-the-year (YOY) skipjack tuna collected across the equatorial strip of the Indian Ocean, where high larvae concentrations and spawning activity of this species have been observed; off Seychelles, Somalia and Mozambique Channel in the western Indian Ocean, off Maldives in the central Indian Ocean, and off Sumatra in the eastern Indian Ocean [9, 28, 29]. Besides, those areas are also important fishery grounds of skipjack tuna in the Indian Ocean [6]. Our aim was to determine whether YOY skipjack tuna captured within these three regions could be spatially discriminated based on their early life otolith microchemistry composition. If so, the characterized nursery origin signature can then be used as a baseline sample to predict older skipjack tuna origin, by analyzing the same early life portion of the otolith [30, 31]. This in turn, will contribute to improve our understanding of the connectivity and mixing rates and, ultimately, the stock structure of this species in the Indian Ocean.

Materials and methods

Fish sampling

Skipjack tuna were collected from three distinct nursery areas in the Indian Ocean: West (10°S-10°N, 40°E-60°E), Central (0°-10°N, 65°E-75°E), and East (5°S-10°N, 85°E-95°E) (Fig 1).

Fig 1. Skipjack tuna (Katsuwonus pelamis) sampling locations across the equatorial Indian Ocean.

Squares represent nursery areas where YOY skipjack tuna were captured from, referred to as West (orange), Central (purple) and East (green).

Samples were obtained directly by scientist or scientific observers on-board purse seine vessels or at port during two consecutive years (2018 and 2019), as part of a collaborative research project on Population Structure of Tuna, Billfish and Sharks of the Indian Ocean [32]. Fish size ranged between 24.5 and 35.0 cm fork length (FL) (Tables 1 and 2), and were estimated to be about 4–6 months old following the age-length relationship described by Eveson et al. [33]. Thus, we assumed that capture locations represent nursery areas where fish reside during the early juvenile stage. Note that fish were sampled at different years/time periods, which implies that the otolith collection available for this study comprised fish from different cohorts (S1 Fig). Those spawned from mid (August) 2017 to early (April) 2018 were assigned to the 2017 cohort, hereafter “2017”. Those spawned from mid (May) 2018 to early (January) 2019 were assigned to the 2018 cohort, hereafter “2018”.

Table 1. Number, size, sampling period and mean values of element: Ca ratios measured in otoliths of skipjack tuna (Katsuwonus pelamis) at each of the three nursery areas sampled.

Table 2. Number, size, sampling period and of carbon and oxygen stable isotope ratios mean values measured in otoliths of skipjack tuna (Katsuwonus pelamis) at each of the three nursery areas sampled.

Otolith preparation and analyses

Sagittal otoliths were extracted, cleaned of adhering organic tissue, rinsed with ultrapure water (Milli-Q), and stored dry in plastic vials. In the laboratory, one otolith of each specimen was embedded in two-part epoxy resin (Araldite 2020, Huntsman Advanced Materials, Switzerland). Blocks were polished using 3M® silicon carbide sandpaper (particle size = 220 μm) and a lapping wheel with a series of decreasing grain diameter (30, 15, 9, 3 and 1 μm) 3M® silicon carbide lapping discs, moistened with ultrapure water, to obtain a transverse section where the core was exposed. Sections were ultrasonically cleaned using ultrapure (Milli-Q) water for 10 minutes. Following sonication, otolith sections were left to air dry in loosely capped vials for 24 h before being glued in a sample plate using Crystalbond thermoplastic glue (Crystalbond 509; Buehler). When a single otolith was available only trace element analyses were conducted. When both pairs of otoliths were available, the second one was used for carbon and oxygen stable isotope analyses.

Trace element analyses.

Otoliths (n = 128) were analysed for trace element chemistry using a high resolution inductively coupled plasma mass spectrometer (HR-ICP-MS, Element XR, Thermo Scientific, Bremen, Germany), coupled to a high repetition rate 1030 nm femtosecond laser (fs-LA) system (Alfamet, Neseya, Canejan, France) available at the Institut des Sciences Analytiques et de Physico-Chimie pour l’Environnement et les Matériaux, Université de Pau et des Pays de l’Adour/CNRS (Pau, France). Each otolith was ablated 200 μm apart from the primordium along the ventral arm (S2 Fig). This spot was considered to be representative of the chemical signature corresponding to the first 13–15 days of life of the fish according to direct daily microincrement counts, and thus to exclude the potential maternal effects the primordium (i.e., the initial structure of an otolith) may incorporate [34]. Laser ablation conditions were 200 Hz, a laser beam size of 15 μm and 30 μJ per pulse energy corresponding to a fluence of 14 J/cm2, until the depth limit of ablation (< 30 μm). During ablation the small laser beam was continuously and rapidly moved (500 μm s-1) at the surface of the sample due to a 2D galvanometric scanner in order to create a trajectory made of 6 concentric circles [35]. This resulted in the ablation of a crater 30 μm in diameter and 30 μm deep. The ablation cell was flushed with argon to transport laser-induced particles to the HR-ICP-MS. The fsLA-HR-ICP-MS was tuned daily to reach optimal particle atomization conditions and minimal elemental fractionation. This was obtained for a U/Th signal ratio of 1 ± 0,05 using NIST 612. The mass spectrometer was used in the medium-resolution mode (R = 4000) to ensure a complete polyatomic interference removal for the isotopes of interest. Relative abundances of five isotopes (7Li, 88Sr, 138Ba, 24Mg and 55Mn) were estimated, as well as 43Ca, which was used as the internal standard. The concentration of 43Ca in the otolith was assumed to be constant at 38.3% [36]. Data reduction including background subtraction, conversion to ppm and standardization to calcium (element:Ca μmol mol-1) was done using an in-lab developed software FOCAL 2.27. National Institute Standards and Technology (NIST) 610 and 612 glass standards with known chemical composition were used for calibration. Measurement accuracy was determined based on an otolith certified reference material for trace elements (FEBS-1, NRC-CNRC, Canada). To correct for short-term instrumental drift, standards and reference material were measured threefold two times a day; at the beginning and the end of each session. Trace element measurements of the blank sample gases were recorded for 20-30s before each sample ablation of ~40s. Mean relative standard deviation (RSD) for NIST 612 and 610 were (n = 9): 2.9% and 8.5% (Li), 2.1% and 8.9% (Sr), 1.8% and 4.8% (Ba), 2.5% and 9.4% (Mg), 2.3% and 3.7% (Mn), respectively. The elemental ratios of Li:Ca, Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca exceeded the detection limits of the fs-LA-ICP-MS for all samples.

Stable isotopes analyses.

For stable isotope analyses (n = 56), microsampling of otolith powder for carbon (δ13C) and oxygen (δ18O) stable isotope analysis was performed using a high-resolution computerised micromill (New Wave MicroMill System, NewWave Research G. C. Co., Ltd, Cambs, UK). The area of analysis on the smallest skipjack tuna otolith (24.5 cm FL) was used to create a standard template that was then applied to the remaining otoliths, to ensure that the same portion of the otolith was analyzed in every fish (S2 Fig). Therefore, the drill path covered the area of the otolith corresponding to the first ~4 months of life (according to Eveson et al. [33] age-length relationship), with a larger time period of the otolith sampled for stable isotopes than trace elements due to differences in sample material requirements. Ten drill passes were run at a depth of 50 μm per pass over a preprogrammed drill path using a 300-μm diameter carbide bit (Komet dental; Gebr. Basseler, Lemgo, Germany). Powdered material was then analysed for δ13C and δ18O on an automated carbonate preparation device (KIEL-III, Thermo- Fisher Scientific, Waltham, MA, USA) coupled to a gas-ratio mass spectrometer (Finnigan MAT 252, ThermoFisher Scientific) at the Environmental Isotope Laboratory of the University of Arizona. All isotope values were reported according to standards of the International Atomic Energy Agency in Vienna. The isotope ratio measurement was calibrated based on repeated measurements of NBS-19 and NBS-18 (International Atomic Energy Agency standards). Measurement precision was ± 0.10 ‰ for δ18O and ±0.08‰ for δ13C (1 sigma).

Statistical analyses

All statistical analyses were performed using open access R software [37]. Prior to all multivariate analyses, otolith chemistry data was standardized (i.e., for each element, the data was centered by subtracting the mean and scaled by dividing by the standard deviation) to give the same weight to all elements and stable isotopes.

Elements were first analyzed individually. Parametric assumptions were violated by Ba:Ca, Mg:Ca and Mn:Ca. Therefore, non-parametric tests were used for trace element analyses, to apply a consistent approach for all elements. To determine whether the otolith chemistry of YOY skipjack tuna varied spatially and/or temporally, a permutational multivariate analysis of variance (PERMANOVA) was used to test for differences in element:Ca ratios between nurseries and cohorts [38, 39]. Nursery and cohort were fixed factors in the full factorial model. The resemblance matrix was based on euclidean distance dissimilarities and the number of unrestricted permutations was set to 999 random repeats using adonis {vegan}. Statistical significance was determined based on adjusted p-values after the Benjamini-Hochberg correction [40], and post hoc pairwise comparisons were applied to identify the source of differentiation between group means using lincon {WRS2}. Parametric assumptions of normality and homoscedasticity were met for δ13C and δ18O data, and therefore one-way ANOVA test was used for comparisons among nurseries, aov {stats}. When significant differences were detected, post hoc comparison were performed to the source of differences between means, using TukeyHSD {stats}.

Both trace elements and stable isotopes were them combined for multivariate analyses. The resultant dataset was limited by the number of individuals for which both types of data were available (n = 56). A multilevel pairwise comparison was performed using pairwise.adonis {pairwiseAdonis} to test for differences in the multielemental signature between nursery areas. The resemblance matrix was based on euclidean distance dissimilarities and the number of unrestricted permutations was set to 999 random repeats. Statistical significance was determined based on adjusted p-values after the Benjamini-Hochberg correction [40]. Then, multivariate data were reduced to two-dimensions and visualized by a canonical analysis of principal coordinates (CAP) using CAPdiscrim {BiodiversityR} function [41]. Random forest (RF) classification algorithm (number of trees = 500, mtry = 2) was implemented to test the ability of trace elements and stable isotope signatures to discriminate among fish belonging to different nursery areas [42]. Data was split into a training dataset (70%) and a testing dataset (30%), and this procedure was randomly repeated 1000 times. At each time, the rate of classification success (i.e., rate of correct predicted membership to nursery areas in which the fish were collected) was calculated, and mean values were extracted. Cohen´s Kappa (κ) statistic was also calculated, which is a method that accounts chance-corrected percentage of agreement between actual and predicted group memberships. Values of κ range from 0 to 1, where 0 indicates that the RF resulted in no improvement over chance, and 1 indicates perfect agreement [43]. Random forest was performed for each cohort, as well as for pooled data from both cohorts in the case of trace element data. For stable isotope data, and combined trace element and stable isotope data, pooled data from both cohorts was only used.


Individual elements

Significant differences were detected in the chemical signatures of skipjack tuna collected from different nurseries, but also between cohorts in the case of trace element data (Table 3). The concentrations of Sr and Mn differed significantly between years, concentrations being higher in 2018 (Fig 2). Significant interactions between nursery and cohort were also detected for Ba, Mg and Mn, meaning that the pattern of variation was different among nurseries at each cohort (Table 3, Fig 2). Concentrations of Sr, Ba and Mg differed among nurseries in 2017, being lower for skipjack tuna collected from the West nursery (Fig 2). For fish belonging to the 2018 cohort no significant differences across regions were detected for any of the trace elements analyzed.

Fig 2. Trace element otolith composition of young-of-the-year (YOY) skipjack tuna (Katsuwonus pelamis) in the Indian Ocean.

Boxplots compare otolith Li:Ca, Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca composition between nursery areas in the Indian Ocean in 2017 cohort (left panel), 2018 cohort (rigth panel). Letters identify significant differences (heteroscesadic anova, p<0.05) between group means. Inter quartile range (25th and 75th percentile) is shown by extent of boxes and error bars represent 10th and 90th percentiles.

Table 3. Summary of two-factor PERMANOVA for the effect of nursery and cohort on individual and combined trace element data of skipjack tuna (Katsuwonus pelamis) otoliths.

Otolith δ18O values also varied among nurseries (anova, p<0.001), with significantly higher values found in the West nursery with respect to the central and eastern nurseries (Fig 3). No significant differences were detected in otolith δ13C composition between nursery areas.

Fig 3. Contour plots of δ13C and δ18O values in otoliths of young-of-the-year (YOY) skipjack tuna (Katsuwonus pelamis) in the Indian Ocean.

The composition of each nursery area is shown: West (orange), Central (purple) and East (green). Bivariate kernel density estimated at ten levels (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%).

Multielemental signatures

When individual trace elements and carbon and oxygen stable isotopes were combined, significant regional differences were found among nursery areas (PERMANOVA, p = 0.005). Specifically, multielemental signatures from YOY skipjack tuna from the western nursery were differentiated from the central and eastern nurseries, as supported by posterior pairwise comparisons (Table 4, and Fig 4).

Fig 4. Canonical analysis of principal coordinates (CAP) variate plot of the multielemental chemistry of skipjack tuna (Katsuwonus pelamis) otoliths.

Ellipses show 95% confidence around nursery means, shown as West (triangles, orange), Central (squares, purple) and East (circles, green).

Table 4. Results of pairwise comparisons of multielemental (Li:Ca, Sr:Ca, Ba:Ca, Mg:Ca, Mn:Ca, δ13C and δ18O) signatures among three nursery areas in the Indian Ocean.

Classification accuracy was generally low regardless of the elemental combination used. Classification success using cohort pooled cohort trace element data was of 44% and κ = 0.16 (Table 5). Overall classification success of fish back to their nursery area was higher in 2017 (50%, κ = 0.33) than in 2018 (44%, κ = 0.15). Nursery specific signatures also varied considerably between cohorts. For YOY skipjack tuna from the West nursery classification accuracy decreased from 71% in 2017 to 49% in 2018, while for the Central nursery increased from 23% in 2017 to 40% in 2018. Cohort pooled classification accuracy of YOY skipjack tuna from East nursery remained similar (45% and 42% in 2017 and 2018, respectively).

Table 5. Random forest discriminant function analysis classification success (%) assigning young-of-the-year (YOY) skipjack tuna (Katsuwonus pelamis) to their nursery area in the Indian Ocean.

Carbon and oxygen stable isotopes provided higher classification success than trace elements alone (51%, κ = 0.44, Table 5). The combination of trace elements and stable isotopes did not improve the overall classification accuracy with respect to the use of carbon and stable isotopes alone (51%, κ = 0.40). However, nursery specific classification did vary, YOY from the West nursery were better classified with stable isotopes alone, while for YOY from the Central nursery greater classification accuracy was obtained when stable isotope data was combined with trace element data (Table 5, Fig 4). Nevertheless, samples from the Central and East nurseries were often misclassified (<50%), regardless on the elemental combination used.


Otolith chemical fingerprints are powerful discriminators of groups as long as differences exist, but of negligible value when differences cannot be detected, as the absence of difference does not necessarily imply that groups own a common origin [20]. The high overlap in the otolith chemical signatures of YOY from the central and eastern nurseries, may be at least partly explained by the homogeneity in the physicochemical properties of ambient water fish were exposed to during their early life. Depending on the period, the regions of Maldives (Central) and eastern Indian Ocean are quite homogeneous in terms of sea surface temperature (SST), salinity (SSS), and dissolved oxygen (DO) [44, 45]. Another potential reason to the observed overlap of the chemical signatures, is that important transoceanic mixing may have occurred prior to capture. However, it is unlikely that transoceanic migrations occur before 4 to 6 months of life. To our knowledge, transoceanic movements of skipjack tuna within the first 6 months have not been documented, since tagging studies are generally not available for these size ranges [11]. Moreover, skipjack tuna predominantly shows diffusion type movements rather than long longitudinal migrations, as they move towards colder and richer subtropical areas adjacent to their spawning areas, where they can temporarily found higher productivity and prey availability than in warm equatorial waters [8, 10]. Besides, the sub-mesoscale activity present in the nursery areas can minimize long-distance dispersal, by retaining the larvae near their spawning area [46, 47]. For all the above, we think that the observed results in the chemical signatures of YOY skipjack tuna are more likely attributed to the fact that biochemical properties of the water masses were relatively homogeneous during the early life of the fish, leading to a low discrimination capacity among fish captured in different nursery areas.

Strong interannual effect was detected in the otolith trace element signatures of fish belonging to two different cohorts. While significant differences were detected among nurseries in Sr, Ba and Mg concentrations in 2017, differences were not significant in 2018. Barium and strontium are incorporated into the otolith via Ca substitution, and may be appropriate markers of environmental history [4850]. Mg incorporation into the otolith by contrast, has been related with metabolic activity [51, 52]. Due to sampling constrains, YOY skipjack tuna were hatched during different year periods (S1 Fig). North of 10°S the Indian Ocean is characterized by two seasons with distinct wind regimes, the monsoon system, which drives the ocean circulation and climate in the tropics and northern subtropic region of the Indian Ocean [53, 54]. The southwest monsoon, also known as summer monsoon, takes place from June to October with a maximum of intensity during the months of July, August, and September. During this season there is little difference in rainfall intensity within the equatorial region, while temperatures are colder west of 60 °E due to the coastal upwelling that takes place off Somalia and Oman Coasts [44, 45, 55]. The northeast Monsoon, or the winter monsoon, takes place between December and April with a maximum of intensity in the months of January, February, and March. Upwelling events take place off northwest Australian shelf and in open ocean at 5–10 °S region [53]. During the winter monsoon waters off Somalia are colder (26.5°C), whereas SSTs are above 28°C over the rest of the equatorial region [45]. Besides, during this season rainfall intensity is stronger in the west south (4.5°S) of the equator off Madagascar and off Sumatra [55]. During the transition periods between the monsoons (i.e., May and November), a strong equatorial downwelling occurs. For skipjack tuna belonging to the 2017 cohort, those collected from the West nursery were estimated to be hatched during the summer monsoon, while those from Central and East nurseries were considered to be hatched during the winter monsoon and the autumn intermonsoon periods. All skipjack tuna belonging to the 2018 cohort were estimated to be hatched within the summer monsoon period and the two intermonsoons. As seasonal climatology strongly influences the biochemical variability throughout the northern Indian Ocean, it may be possible that observed regional differences in Sr, Ba and Mg in 2017 are due to the seasonal changes in water physicochemical properties that could have masked regional differences [56, 57].

Otolith δ18O signatures were significantly higher from individuals of the West nursery than the Central and the East nurseries. The isotopic composition of ambient seawater is dependent on evaporation, and largely controlled by salinity [58]. As otolith δ18O is incorporated into otolith aragonite close to equilibrium with seawater, the isotopic composition of otolith reflects seawater δ18O values, being inversely correlated with ambient seawater temperature [59, 60]. Otolith composition of YOY skipjack tuna from the Indian Ocean followed the expected trend for δ18O signatures, presenting higher values in the West (expected due to lower water temperatures, under the influence of the seasonal Somali upwelling) and decreasing towards the east (expected due to higher water temperatures, as confirmed by the overall sea surface temperature pattern in the Indian Ocean). Similar trends have been found in a preliminary study for yellowfin tuna from the Indian Ocean [61]. No differences were detected in otolith δ13C signatures between fish captured in the three different nursery areas. Otolith δ13C can be a proxy for metabolic rate in fish, and is influenced by the environment (dissolved inorganic carbon in water, DIC) and diet [62, 63]. The observed absence of difference in δ13C signatures followed the expected trend for surface water carbon isotope composition, which is relatively homogenous within the equatorial Indian Ocean [64], and therefore providing little value for stock discrimination.

The overall low regional variability in chemical signatures from skipjack tuna resulted in a low classification of YOY individuals to the three nursery areas analyzed in the Indian Ocean. To date, there are no studies aiming to retrospectively determine skipjack tuna to their nursery origin based on otolith chemical signatures. However, studies done with other tuna species inhabiting tropical environments (e.g. yellowfin and bigeye) reported higher classification accuracies of fish back to their nursery area than those reported in this study [2426, 65]. The use of stable isotopes alone was sufficient for nursery discrimination of yellowfin tuna in the Pacific Ocean, whereas trace elements proved to be more efficient for discriminating among yellowfin nursery areas in the Atlantic and Pacific and Indian Oceans [24, 26, 65]. Here, the use of carbon and oxygen stable isotope values alone, proved to be effective to differentiate skipjack tuna from the western nursery, but the high overlap in the chemical signature of skipjack tuna from the Central and East nurseries resulted in an overall low classification. The use of trace element data or the combination of stable isotope and trace element data did not improve the overall classification success. It is also possible that differences in classification success between trace elemental ratios and stable isotopes is due to the different the time period of the otolith represented by each tracer (i.e. ~day 13 to 15 for trace elements vs. 4–6 months for stable isotopes) [24]. One possible solution would be to analyze with LA-ICPMS the integrated signal of an ablation square that corresponds to the same otolith portion milled for stable isotopes analyses (i.e. ~4–6 months of life). However, this data should be interpreted with caution as it has been proved that ontogeny strongly influences trace element uptake into the otolith [52, 66].


Overall, the differences in the chemical signature of YOY skipjack tuna from the three nursery areas in the Indian Ocean were not strong enough to describe a reference baseline for each nursery that allows the assignment of older individuals to their origin nursery. Temporal differences in the physicochemical characteristics of the Indian Ocean seem to strongly influence otolith trace element composition. Considering also that classification success for models based on otolith trace element data compared to those using only otolith stable isotopes was lower, the effort and cost to incorporate elemental ratios into Indian Ocean skipjack tuna otolith chemistry baseline delineation may not be worthwhile [25]. Until there is a proper understanding of the stock structure of skipjack tuna, the uncertainty in relation to the response of this stock to management decisions adopted considering a single stock will be maintained. Further research on skipjack population structure using otolith microchemistry should rely on younger (<4 months) individuals to reduce the possibility of movements between nursery areas and consider temporal stratification of sampling so that seasonal differences in oceanography can be disentangled from potential regional differences. Finally, an holistic approach may provide a more accurate overview to properly define management units, as single technical approaches may not be sufficient to delineate complex stock structures [67, 68]. Recent studies combining different stock delineation techniques (e.g. otolith microchemistry, genetics, biophysical models…), proved to be more effective to fully understand the spatial ecology of highly migratory fish species, and, hence increased the resolving power of stock discrimination [6971]. For instance, including passive drift trajectory simulations may also be helpful to understand potential patterns of skipjack larval dispersal in the Indian Ocean [72]. The combination of otolith chemistry data coupled with genetic analyses can increased details on connectivity patterns, as both techniques provide information on complementary timescales (individual for otolith chemistry, and evolutionary for genetics), unravelling otherwise hidden patterns [73]. Ultimately, a better understanding of the stock structure and spatial connectivity of skipjack tuna in the Indian Ocean will be essential to implement and enforce management strategies that ensure long-term sustainable fisheries of this important species.

Supporting information

S1 Fig. Back-calculated hatch period.

Frequency histograms show the distribution of estimated hatching dates (year_month) of young-of-the-year (YOY) skipjack tuna (Katsuwonus pelamis) collected in three nursery areas West (orange), central (purple) and East (green) of the Indian Ocean.


S2 Fig. Chemical analyses location in the otolith.

Transverse section of a 33 cm FL skipjack tuna (Katsuwonus pelamis) otolith. Approximate location of laser ablation spot for trace element analyses (left) and the MicroMill drilling path used for stable isotope analyses (right) are shown.


S1 Table. Raw data.

Values of trace element ratios (Ba:Ca and Sr:Ca, ppm) and stable isotopes (δ13C and δ18O, ‰) for the young-of-the-year (YOY) skipjack tuna (Katsuwonus pelamis) analysed in this study.



Authors want to thank to all the people that were involved in the collection of the otoliths used for this study; we are grateful for all their efforts. We would like to thank the many vessel owners, skippers, observers, crews, and processors who provided fish for otolith sampling, and the Seychelles Fishing Authority for facilitating access to sample processing in the Seychelles. Thanks also to Gaelle Barbotin for her assistance in trace element analyses. Finally, many thanks to Paul de Bruyn for the support during the project. This paper is the contribution 1023 from AZTI, Marine Research, Basque Research and Technology Alliance (BRTA).


  1. 1. Collette BB, Nauen CE. Scombrids of the World. An Annotated and Illustrated Catalogue of Tunas, Mackerels, Bonitos and Related Species Known to Date. FAO, Rome. 1983. is made to pertinent literature.
  2. 2. Galland G, Rogers A, Nickson A. Netting Billions: A Global Valuation of Tuna. 2016.
  3. 3. ISSF. Status of the world fisheries for tuna. Mar. 2020. ISSF Technical Report 2020–12. Washington, D.C., USA; 2020.
  4. 4. Dammannagoda S, Hurwood D, Mather P. Genetic analysis reveals two stocks of skipjack tuna (Katsuwonus pelamis) in the northwestern Indian Ocean. Can J Fish Aquat Sci. 2011;68: 210–223.
  5. 5. Menezes M, Kumar G, Kunal S. Population genetic structure of skipjack tuna Katsuwonus pelamis from the Indian coast using sequence analysis of the mitochondrial DNA D‐loop region. J Fish Biol. 2012;80: 2198–2212. pmid:22551177
  6. 6. IOTC. Skipjack Tuna Supporting Information. In: Status Summary for Species of Tuna and Tuna-Like Species Under IOTC Mandate, as well as Other Species Impacted by IOTC Fisheries [Internet]. 2017 [cited 15 May 2017].
  7. 7. Kerr L, Hintzen N, Cadrin S. Lessons learned from practical approaches to reconcile mismatches between biological population structure and stock units of marine fish. ICES J Mar Sci. 2017;74: 1708–1722.
  8. 8. Fonteneau A, Pallares-Soubrier P. Interactions between tuna fisheries: A global review with specific examples from the Atlantic Ocean. In: Shomura RS., Majkowski J, Harman RF, editors. Status of Interactions of Pacific Tuna Fisheries in 1995 Proceedings of the second FAO Expert Consultation Interactions of Pacific Tuna Fisheries Shimizu, Japan, 23–31 January 1995 FAO Fisheries Technical Paper. Rome, Italy: FAO; 1995.: of this document
  9. 9. Grande M, Murua H, Zudaire I, Goni N, Bodin N. Reproductive timing and reproductive capacity of the Skipjack Tuna (Katsuwonus pelamis) in the western Indian Ocean. Fish Res. 2014;156: 14–22.
  10. 10. Hilborn R, Sibert J. Is international management of tuna necessary? Mar Policy. 1988;12: 31–39.
  11. 11. Fonteneau A, Hallier JP. Fifty years of dart tag recoveries for tropical tuna: A global comparison of results for the western Pacific, eastern Pacific, Atlantic, and Indian Oceans. Fish Res. 2015;163: 7–22.
  12. 12. Murua H, Rodriguez-Marin E, Neilson JD, Farley JH, Juan-Jordá MJ. Fast versus slow growing tuna species: age, growth, and implications for population dynamics and fisheries management. Rev Fish Biol Fish. 2017; 1–41.
  13. 13. Grande M, Murua H, Zudaire I, Korta M. Oocyte development and fecundity type of the skipjack, Katsuwonus pelamis, in the Western Indian Ocean. J Sea Res. 2012;73: 117–125.
  14. 14. Grande M, Murua H, Zudaire I, Arsenault-Pernet EJ, Pernet F, Bodin N. Energy allocation strategy of skipjack tuna Katsuwonus pelamis during their reproductive cycle. J Fish Biol. 2016;89: 2434–2448. pmid:27730635
  15. 15. Hoyle SD, Langley A. Scaling factors for multi-region stock assessments, with an application to Indian Ocean tropical tunas. Fish Res. 2020;228: 105586.
  16. 16. Fonteneau A. A Comparative overview of skipjack fisheries and stocks worlwide. IOTC Proc no 6. 2003;WPTT-03-02: 8–21.
  17. 17. Bosley K, Goethel D, Berger A, Deroba J, Fenske K, Hanselman D, et al. Overcoming challenges of harvest quota allocation in spatially structured populations. Fish Res. 2019;220: 105344.
  18. 18. Cadrin SX, Kerr LA, Mariani S. Stock identification methods: applications in fishery science. Second. San Diego, California: Academic Press; 2014.
  19. 19. Tanner S, Reis-Santos P, Cabral H. Otolith chemistry in stock delineation: A brief overview, current challenges and future prospects. Fish Res. 2016;173: 206–213.
  20. 20. Campana S. Chemistry and composition of fish otoliths: pathways, mechanisms and applications. Mar Ecol Prog Ser. 1999;188: 263–297.
  21. 21. Elsdon TS, Wells BK, Campana SE, Gillanders BM, Jones CM, Limburg KE, et al. Otolith chemistry to describe movements and life-history parameters of fishes: hypotheses, assumptions, limitations and inferences. Elsdon T, Wells B, Campana S, editors. Oceanogr Mar Biol an Annu Rev. 2008;46: 297–330.
  22. 22. Campana S, Neilson J. Microstructure of fish otoliths. Can J Fish Aquat Sci. 1985;42: 1014–1032.
  23. 23. Kerr L, Campana S. Chemical Composition of Fish Hard Parts as a Natural Marker of Fish Stocks. Second Edi. In: Cadrin S, Kerr L, Mariani S, editors. Stock Identification Methods: Applications in Fishery Science. Second Edi. Academic Press; 2014. pp. 205–234.
  24. 24. Kitchens L, Rooker J, Reynal L, Falterman B, Saillant E, Murua H. Discriminating among yellowfin tuna Thunnus albacares nursery areas in the Atlantic Ocean using otolith chemistry. Mar Ecol Prog Ser. 2018;603: 201–213.
  25. 25. Rooker JR, David Wells RJ, Itano DG, Thorrold SR, Lee JM. Natal origin and population connectivity of bigeye and yellowfin tuna in the Pacific Ocean. Fish Oceanogr. 2016;25: 277–291.
  26. 26. Wells RD, Rooker JR, Itano DG. Nursery origin of yellowfin tuna in the Hawaiian Islands. Mar Ecol Prog Ser. 2012;461: 87–196.
  27. 27. Arai T, Kotake A, Kayama S. Movements and life history patterns of the skipjack tuna Katsuwonus pelamis in the western Pacific, as revealed by otolith Sr: Ca ratios. J Mar Biol Assoc United Kingdom. 2005;85: 1211–1216.
  28. 28. Stéquert B, Marsac F. Tropical tuna–surface fisheries in the Indian Ocean. FAO Fish Tech Pap. 1989; 238.
  29. 29. Stéquert B, Rodriguez J, Cuisset B. Gonadosomatic index and seasonal variations of plasma sex steroids in skipjack tuna (Katsuwonus pelamis) and yellowfin tuna (Thunnus albacares) from the western Indian Ocean. Aquat Living Resour. 2001;14: 313–318.
  30. 30. Kitchens L. Origin and Population Connectivity of Yellowfin Tuna (Thunnus albacares) in the Atlantic Ocean. Dr Diss Texas A M Univ. 2017.
  31. 31. Thorrold SR, Latkoczy C, Swart PK, Jones CM. Natal Homing in a Marine Fish Metapopulation. Science (80-). 2001;291: 297–299.
  32. 32. Davies C, Marsca F, Murua H, Fraile I, Fahmi Z, Farley J, et al. Study of population structure of IOTC species and sharks of interest in the Indian Ocean using genetics and microchemistry: 2020 Final Report to IOTC. 2020.
  33. 33. Eveson J, Million J, Sardenne F, Croizier GL. Estimating growth of tropical tunas in the Indian Ocean using tag-recapture data and otolith-based age estimates. Fish Res. 2015;163: 58–68.
  34. 34. Hegg JC, Kennedy BP, Chittaro P. What did you say about my mother? The complexities of maternally derived chemical signatures in otoliths. Can J Fish Aquat Sci. 2018;76: 81–94.
  35. 35. Claverie F, Fernández B, Pécheyran C, Alexis J, Donard OFX. Elemental fractionation effects in high repetition rate IR femtosecond laser ablation ICP-MS analysis of glasses. J Anal At Spectrom. 2009;24: 891–902.
  36. 36. Sturgeon RE, Willie SN, Yang L, Greenberg R, Spatz RO, Chen Z, et al. Certification of a fish otolith reference material in support of quality assurance for trace element analysis. J Anal At Spectrom. 2005;20: 1067–1071.
  37. 37. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL Vienna, Austria: R Foundation for Statistical Computing; 2019.
  38. 38. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26: 32–46.
  39. 39. Rogers TA, Fowler A, Steer M, Gillanders BM. Discriminating Natal Source Populations of a Temperate Marine Fish Using Larval Otolith Chemistry. Front Mar Sci. 2019;6:
  40. 40. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57: 289–300.
  41. 41. Anderson M, Willis T. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84: 511–525.
  42. 42. Breiman L. Random Forests. Mach Learn. 2001;45: 5–32.
  43. 43. Titus K, Mosher JA, Williams BK. Chance-corrected classification for use in discriminant analysis: ecological applications. Am Midl Nat. 1984;111: 1–7.
  44. 44. Schott FA, Xie S-P, McCreary JP. Indian Ocean circulation and climate variability. Rev Geophys. 2009;47: RG1002.
  45. 45. Tomczak M, Godfrey J. The Indian Ocean. 2nd ed. Regional Oceanography: An introduction. 2nd ed. Tasmania, Australia: Elsevier; 2003.
  46. 46. Bakun A. Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage. Sci Mar 2006; 105–122.
  47. 47. Reglero P, Tittensor D, Álvarez-Berastegui D, Aparicio-González A, Worm B. Worldwide distributions of tuna larvae: revisiting hypotheses on environmental requirements for spawning habitats. Mar Ecol Prog Ser. 2014;501: 207–224.
  48. 48. Thomas O, Ganio K, Roberts B, Swearer S. Trace element–protein interactions in endolymph from the inner ear of fish: implications for environmental reconstructions using fish otolith chemistry. Metallomics. 2017;9: 239–249. pmid:28091665
  49. 49. Hüssy K, Limburg K, Pontual H, Thomas O, Cook P, Heimbrand Y, et al. Trace Element Patterns in Otoliths: The Role of Biomineralization. Rev Fish Sci Aquac. 2020;
  50. 50. Izzo C, Doubleday Z, Gillanders B. Where do elements bind within the otoliths of fish? Mar Freshw Res. 2016;67: 1072–1076.
  51. 51. Limburg KE, Wuenschel MJ, Hüssy K, Heimbrand Y, Samson M. Making the Otolith Magnesium Chemical Calendar-Clock Tick: Plausible Mechanism and Empirical Evidence. Rev Fish Sci Aquac. 2018;26: 479–493.
  52. 52. Thomas ORB, Thomas KV, Jenkins GP, Swearer S. Spatio-temporal resolution of spawning and larval nursery habitats using otolith microchemistry is element dependent. Mar Ecol Prog Ser. 2020;636: 169–187.
  53. 53. Schott F, McCreary J. The monsoon circulation of the Indian Ocean. Prog Oceanogr. 2001;51: 1–123.
  54. 54. Shankar D, Vinayachandran PN, Unnikrishnan AS. The monsoon currents in the north Indian Ocean. Prog Oceanogr. 2002; 63–120.
  55. 55. Keshtgar B, Alizadeh-Choobari O, Irannejad P. Seasonal and interannual variations of the intertropical convergence zone over the Indian Ocean based on an energetic perspective. Clim Dyn. 2020;54: 3627–3639.
  56. 56. Wiggert J, Murtugudde R, Christian J. Annual ecosystem variability in the tropical Indian Ocean: Results of a coupled bio-physical ocean general circulation model. Deep Sea Res Part II Top Stud Oceanogr. 2006;53: 644–676.
  57. 57. Bao S, Wang H, Zhang R, Yan H, Chen J. Spatial and temporal scales of sea surface salinity in the tropical Indian Ocean from SMOS, Aquarius and SMAP. J Oceanogr. 2020.
  58. 58. LeGrande AN, Schmidt GA. Global gridded data set of the oxygen isotopic composition in seawater. Geophys Res Lett. 2006;33. pmid:19122778
  59. 59. Kitagawa T, Ishimura T, Uozato R, Shirai K, Amano Y, Shinoda A, et al. Otolith δ18O of Pacific bluefin tuna Thunnus orientalis as an indicator of ambient water temperature. Mar Ecol Prog Ser. 2013;481: 199–209.
  60. 60. Trueman CN, MacKenzie K. Identifying migrations in marine fishes through stable-isotope analysis. J Fish Biol. 2012;81: 826–847. pmid:22803737
  61. 61. Artetxe-Arrate I, Fraile I, Farley J, Clear N, Darnaude AM, Dettman D, et al. Otolith δ18O as a tracer of yellowfin tuna (Thunnus albacares) nursery origin in the Indian Ocean. 2020. Report No.: IOTC-2020-WPTT22(AS)-06_Rev1.
  62. 62. Chung M, Trueman C, Godiksen J, Grønkjær P. Otolith δ13C values as a metabolic proxy: approaches and mechanical underpinnings. Mar Freshw Res. 2019;70: 1747–1756.
  63. 63. Martino JC, Doubleday ZA, Gillanders BM. Metabolic effects on carbon isotope biomarkers in fish. Ecol Indic. 2019;97: 10–16.
  64. 64. Magozzi S, Yool A, Vander Zanden HB, Wunder MB, Trueman CN. Using ocean models to predict spatial and temporal variation in marine carbon isotopes. Ecosphere. 2017;8: Article e01763.
  65. 65. Artetxe-Arrate I, Fraile I, Crook D, Zudaire I, Arrizabalaga H, Greig A, et al. Otolith microchemistry: a useful tool for investigating stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean. Mar Freshw Res. 2019;70: 1708–1721.
  66. 66. Macdonald JI, Drysdale RN, Witt R, Cságoly Z, Marteinsdóttir G. Isolating the influence of ontogeny helps predict island-wide variability in fish otolith chemistry. Rev Fish Biol Fish. 2020;30: 173–202.
  67. 67. Pita A, Casey J, Hawkins SJ, Villarreal MR, Gutiérrez MJ, Cabral H, et al. Conceptual and practical advances in fish stock delineation. Fish Res. 2016;173: 185–193.
  68. 68. Leis JM, Van Herwerden L, Patterson H. Estimating connectivity in marine fish populations: what works best? Oceanogr Mar Biol an Annu Rev. 2011;49: 193–234.
  69. 69. Brophy D, Rodríguez-Ezpeleta N, Fraile I, Arrizabalaga H. Combining genetic markers with stable isotopes in otoliths reveals complexity in the stock structure of Atlantic bluefin tuna (Thunnus thynnus). Nat Sci Reports. 2020;10: 1–17. pmid:32895410
  70. 70. Taillebois L, Barton DP, Crook DA, Saunders T, Taylor J, Hearnden M, et al. Strong population structure deduced from genetics, otolith chemistry and parasite abundances explains vulnerability to localized fishery collapse in a large Sciaenid fish, Protonibea diacanthus. Evol Appl. 2017;10: 978–993. pmid:29151854
  71. 71. Reis-Santos P, Tanner SE, Aboim M. A. Vasconcelos R. P. Laroche J, Charrier G, Perez M, Presa P, et al. Reconciling differences in natural tags to infer demographic and genetic connectivity in marine fish populations. Sci Rep. 2018;8: 1–122. pmid:29311619
  72. 72. Nikolic N, Montes I, Lalire M, Puech A, Bodin N, Arnaud-Haond S, et al. Connectivity and population structure of albacore tuna across southeast Atlantic and southwest Indian Oceans inferred from multidisciplinary methodology. Nat Sci Reports. 2020;10. pmid:32973260
  73. 73. Hoey JA, Fodrie FJ, Walker QA, Hilton EJ, Kellison GT, Targett TE, et al. Using multiple natural tags provides evidence for extensive larval dispersal across space and through time in summer flounder. Mol Ecol. 2020;29: 1421–1435. pmid:32176403