dataesr/scientific-paragraphs-categorization huggingface dataset:
00 and π = 0. 00000 ± 0. 00000) and highest values for AUS1 (Western Australia; h = 0. 78 ± 0. 44 and π = 0. 00115 ± 0. 00032) for CR and New Caledonia (h = 0. 61 ± 0. 03 and π = 0. 00099 ± 0. 00018) for cytb. For COI, nearly all locations showed null haplotype and nucleotide diversities, except for South Africa (h = 0. 07 ± 0. 01 and π = 0. 00010 ± 0. 00004), New Caledonia (h = 0. 09 ± 0. 02 and π = 0. 00013 ± 0. 00006), and Reunion Island (h = 0. 21 ± 0. 01 and π = 0. 00032 ± 0. 00007; Appendix S4). The CR-COI-cytb dataset (2,452 bp; N S = 127) resolved 22 haplotypes and 22 polymorphic sites, with an overall haplotype diversity of 0. 78 ± 0. 00 and a nucleotide diversity of 0. 00053 ± 0. 00000 (Table  3a ). No partitioning schemes were detected within the CR-COI-cytb sequence, and the HKY85 model of substitution was selected.
Bayesian analysis on the CR-COI-cytb dataset showed good convergence and mixing, with ESS above 200 (Appendix S5). Nevertheless, no lineages were strongly supported as no internal nodes showed high support. This was retrieved on the TCS statistical parsimony network built from the CR-COI-cytb dataset, with all haplotypes separated by only one or two mutations, and no clear geographic structuring (Figure  3a ; Appendix S6). Three main haplotypes were identified, represented by 50, 25, and 9 individuals from distinct locations.
| Mitochondrial diversity on the CR dataset Within the CR dataset, we analyzed 538 CR sequences and resolved 25 haplotypes and 16 polymorphic sites, with an overall haplotype diversity h of 0. 74 ± 0. 00 and a nucleotide diversity π of 0. 00280 ± 0. 00000 (Table  3b ). We retrieved only two new haplotypes compared to the study of  Bernard et al. (2016) , represented by three individuals (from the western Indian Ocean). From locations in common in both studies, South Africa and AUS1 (Western Australia), h and π for the CR did not vary when adding individuals from  Bernard et al. (2016)  (Table  3b ; Appendix S4). As shown by the TCS network built, samples collected for this study presented the three main haplotypes identified by  Bernard et al. (2016)  in the Indian and Pacific Oceans and none of the haplotypes identified in the western Atlantic (Figure  3b ).
| Population genetic structure Performing Bayesian clustering analyses without the LOCPRIOR model, no distinct genetic clusters were retrieved in either the 27msat or the 8-msat datasets (Figure  4a, c ). Using the LOCPRIOR model on these both microsatellite datasets, a single mode regrouping the five runs was retrieved for each K. For the 8-msat dataset, the highest averaged log likelihood of observing the data  (-15,986. 26 ± 11. 59 ) was retrieved for K = 3. F I G U R E Individuals from the western Indian Ocean (ZAN, SEY, SAF, and RUN) belonged to one genetic cluster, those from the eastern Indian Ocean (AUS1), the western (AUS2, AUS3, AUS4, COR, and NCA), and Central (HAW) Pacific to a second one, and those from the western Atlantic (BRA) to a third one (Figure  4d ). Nevertheless, with the 27-msat dataset, the two clusters previously identified did not appear (Figure  4b ).
Indeed, while K = 4 presented the highest likelihood of observing the data (-16,235. 32 ± 66. 01), from K = 2 each new cluster was mostly uninformative, with individual membership proportions in new clusters extremely low (respectively, 3. 43% ± 0. 82%, 4. 13% ± 0. 74%, and 2. 00% ± 0. 45% for K = 2, K = 3, and K = 4). Discriminant analyses of principal components performed on both 27-msat and 8-msat datasets did not retrieve distinct genetic clusters, with 95% confidence ellipses for each location overlapping (Figure  5a , b; BIC in Appendix S7). For the analysis on the 27-msat dataset, the first axis explained 32. 94% and the second 30. 38% of total inertia (Figure  5a ) and no clear groups were identified. For the 8-msat dataset, when assigning individuals to five clusters (i. e. , defining K = 5), the first axis explained 19.
58% of total inertia and the second axis 18. 01% and ellipses for each location overlapped, with no clear clusters identified (Figure  5b ). Nevertheless, with the same dataset, for lower values of K (3 and 4), individuals seemed to cluster into three groups, not related to their geographical origins Concerning the 27-msat dataset, no pairwise F ST values were found to be significantly different from 0 (F ST = [0. 000, 0. 012], all p > 0. 05 after FDR correction; Table  4a ). Nevertheless, weak but   4a ). Considering the 8-msat dataset, significant pairwise F ST values were only found between: Brazil and all other locations   4b ). (F ST = [0. Pairwise Φ ST values for the CR-COI-cytb dataset were highly significant between: Reunion Island and AUS1 (West Australia); between AUS2 (East Australia) and New Caledonia; as well as between South Africa and AUS1 (West Australia), AUS2 (East Australia), and New Caledonia (Φ ST = [0.
165, 0. 387], all p < 0. 001 after FDR correction; Table  5 ). Values were lower but also significant between the Seychelles and South Africa and between the Seychelles and Reunion Island (Φ ST = 0. 104 and Φ ST = 0. 110, all p < 0. 01 after FDR correction; Table  5 ). When using the CR dataset, highly significant values were observed between locations of the northwestern Atlantic Ocean (i. e. , GOM, FLE, BAH and USVI) and locations from the Indian and Pacific Oceans (Φ ST = [0. 697, 0. 954], all p < 0. 001 after FDR correction; Table  5 ), and also between Brazil and locations from the Indian and Pacific Oceans (Φ ST = [0. 485, 0. 793], all p < 0. 001 after FDR correction). Within the Indo-Pacific and the western Atlantic, some locations were significantly differentiated from each other, but this was not related to geographical distance or separation by coastline (see Table  5 ).
| D ISCUSS I ON Ideally, every population genetic study needs the greatest sampling coverage and the greatest number of microsatellite loci  (Koskinen, Hirvonen, Landry, & Primmer, 2004; Meirmans, 2015). Nevertheless, achieving an extensive sampling is difficult, especially for elusive species, while elaborating numerous molecular markers remains expensive. Thus, researchers continually strive to find a balance between both strategies but ultimately have to settle for what they can afford. Hence, some datasets encompass a great sampling coverage while others strive for a greater number of markers. In this study, we combined datasets to improve both sample sizes and number of markers to improve the power of analyses of tiger shark population structure. Comparing results from the datasets, they were not always congruent: the dataset with the greatest number of samples but the lowest number of molecular markers highlighted potential differentiation between the western Indian Ocean and the eastern Indian Ocean/Pacific, which was not identified with the dataset with larger number of markers but fewer samples.
| Connectivity between the western Atlantic and the Indo-Pacific Genetic differentiation between tiger sharks from the western Atlantic and the Indo-Pacific could only be investigated with the 8-msat and CR datasets as we did not obtain samples from the western Atlantic. By adding new sampling sites from the western Indian Ocean, the present study confirmed a genetic differentiation between the two re- however, this was contradicted by some of the other results. Indeed, pairwise differentiation indices were higher for comparisons between  Thus, altogether, weak genetic differentiation was highlighted between locations in the Indo-Pacific, both with microsatellite and mitochondrial markers. Tiger sharks are known to cross large oceanic expanses  (Ferreira et al. , 2015; Hammerschlag et al. , 2012; Holmes et al.
above). Methods traditionally used to detect the most likely number of clusters within a dataset (Pr(X/K), Pritchard et al. 2000;     the ΔK method, Evanno, Regnaut, & Goudet, 2005;   the Deviance Information Criterion (DIC), Gao, Bryc, & Bustamante, 2011; the Bayesian Information Criterion (BIC), Jombart et al. , 2010; the Thermodynamics Integration (TI), Verity & Nichols, 2016) may provide different outputs and do not always reflect the biological truth. To cope with these inconsistencies, we tested different K-values and different numbers of clusters in structure and DAPC, and chose to consider the number of clusters and the individual assignments that were retrieved by both types of analyses and that seemed biologically meaningful.
were estimated from the CR-COI-cytb dataset with arlequin v. 3. 5. 1. 2 (Excoffier & Lischer, 2010) , with significance tested implementing 10 5 simulated samples. Furthermore, to identify effective population size variations in the Indo-Pacific, as no population structure was highlighted between both the western Indian and the western Pacific Oceans, a coalescent framework was used through approximate Bayesian computation (ABC) using DiyaBc v. 2. 1. 0 (Cornuet et al. , 2014)  with both the 27-msat dataset and the CR-COI-cytb dataset. We defined N 0 the actual effective population size, N 1 the ancestral effective population size, N b the effective population size during a bottleneck, and N e the effective population size during an expansion. Then, seven scenarios (Figure2) were tested: (Scenario 1) a recent (<500 generations) decrease (N 0 < N 1 ), (Scenario 2) a more ancient (between 10 3 and 5 × 10 5 generations in the past) decrease, (Scenario 3) a recent.
I G U R E 4 Galeocerdo cuvier assignment probabilities of individuals to putative clusters assuming correlated allele frequencies and admixture as performed by STRUCTURE. (a) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27-msat dataset and no a priori sampling location information. (b) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27-msat dataset and the LOCPRIOR model. (c) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 3, with the 8-msat dataset and no a priori sampling location information. (d) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 4, with the 8-msat dataset and the LOCPRIOR model.
AUS1, Western Australian coast; AUS2, Queensland, Australia; AUS3, New South Wales, Australia; AUS4, Northern Territories, Australia; BRA, Brazil; COR, Coral Sea; HAW, Hawaii; NCA, New Caledonia; RUN, Reunion Island; SEY, Seychelles; SAF, South Africa; ZAN, Zanzibar ). As DAPC analyses were not performed in the studies of Holmes et al. (2017) and Bernard et al. (2016), we performed them on both Holmes 8-msat and Bernard 8-msat datasets, to compare the results obtained (Figure 5c, d, BIC in Appendix S7). Using Holmes 8-msat dataset, we observed the same patterns with the 8-msat dataset (i. e. , three clusters not related to the individual geographical origin for K varying from 2 to 4, and for higher K, no clustering pattern). With the Bernard 8-msat dataset, individuals were clearly clustered into two groups, one from the western Atlantic and one from the Indo-Pacific (Figure 5d).
The three clusters identified with the 8-msat and Holmes 8-msat datasets were not observed with the Bernard 8-msat dataset for any K. In conclusion, for both 27-msat and 8-msat datasets, neither the structure analysis without the LOCPRIOR model nor the DAPC highlighted clear genetic clusters, even among locations within the Indo-Pacific nor within the western Atlantic. Only the structure analysis with the LOCPRIOR model succeeded to identify different genetic clusters in the Indo-Pacific, but only using the 8-msat dataset. These clusters were not identified in any of the other analyses, including the same analysis with the 27-msat dataset, which, if this pattern clearly exists, should have attributed individuals into two different genetic clusters. Thus, as no clear geographic structuring was retrieved using these two microsatellite datasets, we did not perform analyses of molecular variances, and instead directly calculated pairwise differentiation estimates.
I G U R E 5 Galeocerdo cuvier scatterplot output from DAPC analyses performed using the four microsatellite datasets, and using the first and second components (a) 27-msat dataset, (b) 8-msat dataset, (c) Holmes 8-msat dataset, (d) Bernard 8-msat dataset. Dots represent individuals colored by their sampling location (AS: Andaman Sea; AUS1: Western Australian coast; AUS2: Queensland, Australia; AUS3: New South Wales, Australia; AUS4: Northern Territories, Australia; BAH: Bahamas; BRA: Brazil; COR: Coral Sea; HAW: Hawaii; GOM: Gulf of Mexico; FLE: Florida East Coast; NCA: New Caledonia; RUN: Reunion Island; SEY: Seychelles; SAF: South Africa; USVI:US Virgin Islands; ZAN: Zanzibar) significant D est values were retrieved between New Caledonia and all other locations (D est = [0. 030, 0. 052], all p < 0.
Galeocerdo cuvier sampling locations estimated with Weir and Cockerham's Φ ST for the CR-COI-cytb dataset (upper-right matrix; number of individuals indicated in parentheses on the first line) and for the CR dataset (lower-left matrix; number of individuals indicated in parentheses on the first column) Sea; AUS1: Western Australian coast; AUS2: Queensland: Australia; AUS3: New South Wales: Australia; BAH: Bahamas; BRA: Brazil; FLE: Florida East Coast; GOM: Gulf of Mexico; HAW: Hawaii; NCA: New Caledonia; RUN: Reunion Island; SAF: South Africa; SEY: Seychelles; USVI: US Virgin Islands; ZAN: Zanzibar. Tests significance were assessed after FDR correction and values significantly different from zero are indicated in bold. *P < 0. 05; **P < 0. 01; ***P < 0. 001.
First, considering the 8-mast dataset, structure with the LOCPRIOR model identified two genetic clusters, one grouping individuals from the western Indian Ocean (i. e. , ZAN, SEY, SAF, and RUN) and the other, locations from the eastern Indian Ocean (Australia) and from the Pacific (i. e. , AUS1, AUS2, AUS3, AUS4, COR, NCA, and HAW). However, structure without the LOCPRIOR model and DAPC did not partition the individuals into distinct clusters. Considering the 27-msat dataset, none of the analyses performed identified distinct clusters within the Indo-Pacific. Increasing the number of markers has improved the resolution, highlighting the need of sufficient number of microsatellite loci for population genetics analyses (Meirmans, 2015; Putman & Carbone, 2014). Thus, it is likely that the clustering pattern identified by structure (LOCPRIOR model) with the 8-msat dataset does not reflect a biological pattern, but instead the microsatellite characteristics when used in low number.
Microsatellite differentiation estimates between population pairs from the Indo-Pacific were dependent on the differentiation index used (F ST or D est ) and were not related to whether locations were connected by coastlines or separated by large oceanic expanses. If we only take into account differentiation values significantly different from zero for both indices (F ST and D est ), genetic differentiation was only highlighted for the 8-msat dataset and not particularly between very distant locations: South Africa and Reunion Island (not identified with the 27-msat dataset despite similar numbers of individuals in both datasets), South Africa and Western Australia (AUS1)or South Africa and the northeastern Australia (AUS3). When using the 27-msat dataset, none of the population pairs were significantly differentiated, supporting the hypothesis of one genetic cluster in the Indo-Pacific.
The same pattern was established by Bernard et al. (2016) , except for Hawaii that was significantly differentiated from all other locations. This discrepancy concerning Hawaii could be due to the lower sample size from eastern Australia (21 samples) used by Bernard et al. (2016)  to assess F ST and D est and to the structure analysis. The TCS haplotype networks constructed in this study and the one of Bernard et al. (2016) , using either only the CR sequence or the CR-COI-cytb sequence, showed shared haplotypes among all locations sampled in the Indo-Pacific. Only three main haplotypes were identified in both studies, separated from one another by one mutation event. This confirms the absence of differentiation among locations from the Indo-Pacific. Some of the mitochondrial differentiation estimates calculated for both datasets (CR or CR-COI-cytb) were nevertheless significantly different from zero, which would point to some level of genetic differentiation.
de la Recherche Agronomique (INRA, Clermont-Ferrand, France) for guidance and technical support. AP was supported by a doctoral fellowship from the region Reunion. VR received financial support by the French Agropolis Fondation (Labex Agro E-SPACE project number 1504-004), the European Union (ERDF), and the "Conseil Régional de La Réunion. " Samples in Zanzibar were collected as part of the BYCAM project supported by the Western Indian Ocean Marine Science Association (WIOMSA) (Grant Number MASMA/CP/2014/01). , NEH, JEGN, AJT, PB, LV, and SJ provided samples. AP and HM did lab work. AP, HM, and VR analyzed the data. All authors contributed to the writing of the manuscript. HM and SJ designed research.
Bernard et al. (2016)atistics for each sampling location of Galeocerdo cuvier for (a) the 2,452 bp CR-COI-cytb dataset and (b) the CR dataset: N S , number of individuals sequenced in this study; N Bernard , number of individuals sequenced inBernard et al. (2016); N S+Bernard , total number of individuals sequenced; H, number of haplotypes, h, haplotype diversity; S, number of polymorphic sites; π, nucleotide diversity.
F I G U R E 4 Galeocerdo cuvier assignment probabilities of individuals to putative clusters assuming correlated allele frequencies and admixture as performed by STRUCTURE. (a) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27-msat dataset and no a priori sampling location information. (b) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27-msat dataset and the LOCPRIOR model. (c) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 3, with the 8-msat dataset and no a priori sampling location information. (d) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 4, with the 8-msat dataset and the LOCPRIOR model.
F I G U R E 5 Galeocerdo cuvier scatterplot output from DAPC analyses performed using the four microsatellite datasets, and using the first and second components (a) 27-msat dataset, (b) 8-msat dataset, (c) Holmes 8-msat dataset, (d) Bernard 8-msat dataset. Dots represent individuals colored by their sampling location (AS: Andaman Sea; AUS1: Western Australian coast; AUS2: Queensland, Australia; AUS3: New South Wales, Australia; AUS4: Northern Territories, Australia; BAH: Bahamas; BRA: Brazil; COR: Coral Sea; HAW: Hawaii; GOM: Gulf of Mexico; FLE: Florida East Coast; NCA: New Caledonia; RUN: Reunion Island; SEY: Seychelles; SAF: South Africa; USVI:US Virgin Islands; ZAN: Zanzibar) significant D est values were retrieved between New Caledonia and all other locations (D est = [0. 030, 0. 052], all p < 0.
TGR47; TGR212; TGR348; TGR891; TGR943; TGR1033; TGR1157; TGR1185 e TGR233 Mitochondrial loci CR; COI CR; COI cytb Microsatellite datasets 27-msat (n = 275) Holmes 8-msat (n = 355) Bernard 8-msat (n = 380) 8-msat (n = 606) Mitochondrial datasets CR-COI-cytb (n = 127) CR (n = 538) CR (n = 538) Notes. In bold are indicated samples or markers in common in several studies. a Pirog et al. (2015). b Pirog et al. (2016). c Keeney and Heist (2003). d Mendes et al. (2016). e Bernard et al. (2015).
Posterior distributions of all parameters were then estimated from the selected model, based on the 1% of simulated datasets closest to the empirical data. More details on the ABC analysis are provided in Appendix S3. 3 | RE SULTS 3. 1 | Genetic diversity analyses 3. 1. 1 | 27-msat dataset Null alleles were detected for several loci in several sampling loca- TA B L E 2 Summary statistics for each sampling location (AUS1, Western N A r A rp H O H E F IS Australian coast; AUS2, Queensland, 27 loci Australia; AUS3, New South Wales, ZAN 8 3. 01 (0. 33) 0. 14 (0. 05) 0. 60 (0. 06) 0. 60 (0. 05) 0. 01 Australia; AUS4, Northern Territories, Australia; BRA, Brazil; COR, Coral Sea; SEY 24 2. 96 (0. 31) 0. 14 (0. 04) 0. 57 (0. 05) 0. 57 (0. 05) 0. 01 HAW, Hawaii; NCA, New Caledonia; RUN, RUN 167 3. 02 (0.
28) 0. 19 (0. 04) 0. 50 (0. 05) 0. 52 (0. 06) 0. 05 Reunion Island; SEY, Seychelles; SAF, SAF 34 3. 02 (0. 30) 0. 16 (0. 04) 0. 49 (0. 05) 0. 55 (0. 05) 0. 12 South Africa; ZAN, Zanzibar) for the 27-msat and 8-msat datasets AUS1 AUS2 9 10 2. 86 (0. 30) 2. 99 (0. 30) 0. 11 (0. 04) 0. 18 (0. 06) 0. 55 (0. 05) 0. 53 (0. 05) 0. 58 (0. 05) 0. 59 (0. 05) 0. 05 0. 1 NCA to 0. 60 ± 0. 05 in Zanzibar and H O from 0. 49 ± 0. 05 in South Africa 23 3. 05 (0. 28) 0. 18 (0. 03) 0. 53 (0. 06) 0. 58 (0. 05) 0. 09 8 loci to 0. 60 ± 0. 06 in Zanzibar (Table 2). No significant deviation from ZAN HWE was revealed for any location. The mean rarified private al-8 5. 75 (1. 10) 0. 25 (0. 15) 0. 61 (0. 12) 0. 67 (0. 11) 0. 08 SEY lelic richness varied from 0. 11 ± 0. 04 in AUS1 (Western Australia) to 24 5. 66 (1.
Bernard et al. (2016)atistics for each sampling location of Galeocerdo cuvier for (a) the 2,452 bp CR-COI-cytb dataset and (b) the CR dataset: N S , number of individuals sequenced in this study; N Bernard , number of individuals sequenced inBernard et al. (2016); N S+Bernard , total number of individuals sequenced; H, number of haplotypes, h, haplotype diversity; S, number of polymorphic sites; π, nucleotide diversity (a) CR_COI_cytb N S H S h π ZAN 8 2 1 0. 43 (0. 06) 0. 00018 (0. 00007) SEY 20 8 8 0. 76 (0. 02) 0. 00053 (0. 00009) SAF 23 6 5 0. 62 (0. 02) 0. 00035 (0. 00006) RUN 39 9 8 0. 77 (0. 01) 0. 00049 (0. 00006) AUS1 9 5 4 0. 86 (0. 03) 0. 00050 (0. 00013) AUS2 10 4 3 0. 78 (0. 03) 0. 00042 (0. 00011) NCA 18 8 9 0. 82 (0. 02) 0. 00062 (0. 00010) Total 127 22 22 0. 78 (0. 00) 0. 00053 (0.
16-E 1t -PhytoP (PPE 1 -I) and 9-D 1t -PhytoP (PPE 1 -II) Are Bound to Glycerol GC/MS analysis of APE B/D performed without any hydrolysis provided evidence that 16-E 1t -PhytoP (PPE 1 -I) and 9-D 1t -PhytoP (PPE 1 -II) are present not in a free form but bound to Gro. To further prove this, we run ESI MS analysis of fraction 3, and indeed the highest molecular ion 459. 29 m/z corresponded to Gro-PhytoP-E 1 (Figure  2 ). As in GC/MS we have found both stereoisomers of PhytoP-E 1 , namely 16-E 1t -PhytoP and 9-D 1t -PhytoP, both of them can be present in APE bound to Gro. We attempted to enrich this compound, and purified fraction 3 further on HPLC. This led to the isolation of Gro-16-E 1t -PhytoP and Gro-9-D 1t -PhytoP as determined by GC/MS (data not shown).
AUTHOR CONTRIBUTIONS NG and KD contributed to the conception and design of the study and interpretation of the data. NG performed dendritic maturation test and the statistical analysis. SD performed mast cell degranulation test. ZO and FK performed mast cell degranulation test, chemotaxis and measured IL-6 production. RE prepared and fractionated APE and APE B/D extracts, performed GC/MS analyses. KJ performed ESI MS analyses. J-MG and TD prepared synthetic phytoprostanes. CV and CO performed LC/MS analyses. KD wrote the first draft of the manuscript. UJ contributed to the interpretation of the data and critically revised the manuscript. NG, KD, and CV prepared figures. KD, NG, CV, and TD wrote sections of the manuscript. All authors contributed to the manuscript revision, read and approved the submitted version.