Bull Mar Sci. 97(2):257–280. 2021 research paper https://doi.org/10.5343/bms.2019.0114

Phylogeography of two marine predators, giant trevally (Caranx ignobilis) and bluefin trevally (Caranx melampygus), across the Indo-Pacific

1 Yale University, Department Jessica R Glass 1, 2 * of Ecology and Evolutionary Scott R Santos 3 Biology, PO Box 208106, New 4 Haven, Connecticut 06520 John SK Kauwe 4 2 South African Institute for Brandon D Pickett Aquatic Biodiversity, Private Thomas J Near 1, 5 Bag 1015, Grahamstown, South Africa 6140 3 Auburn University, Department of Biological Sciences, 101 Rouse ABSTRACT.—For economically valuable marine fishes, Life Science Building, Auburn, Alabama 36849 identifying biogeographic barriers and estimating the extent of gene flow are critical components of fisheries management. 4 Brigham Young University, We examined the population genetic structure of two Department of Biology, 4102 LSB, Provo, Utah 84602 commercially important reef-associated predators, the giant trevally (Caranx ignobilis) and bluefin trevally Caranx( 5 Yale Peabody Museum of melampygus). We sampled 225 individuals and 32,798 single Natural History, Division of nucleotide polymorphisms (SNPs) of C. ignobilis, and 74 Vertebrate Zoology, New Haven, Connecticut 06520 individuals and 43,299 SNPs of C. melampygus. Analyses of geographic population structure indicate the two species * Corresponding author email: display subtly different phylogeographic patterns. Caranx ignobilis comprises two to three putative populations—one in the Central Pacific, one inhabiting the Western Pacific and Eastern Indian oceans, and one in the Western Indian Ocean—with some restricted gene flow between them. Caranx melampygus shows evidence of restricted gene flow from Hawaii to the West Pacific and Indian oceans, as well as limited genetic connectivity across the Indo- Pacific Barrier. Both species exhibit patterns characteristic of other large, reef-associated predators such as deepwater snappers and the great barracuda. This study contributes to ongoing assessments of the role of the Indo-Pacific Barrier in shaping patterns of phylogeography for large reef-associated fishes. Furthermore, by identifying putative populations of C. ignobilis and C. melampygus in the Central Pacific, our Handling Editor: John F Walter III findings serve to improve future management measures for these economically important, data-limited species, Date Submitted: 12 December, 2019. Date Accepted: 3 March, 2021. particularly in light of historic and contemporary overfishing Available Online: 3 March, 2021. in Hawaii.

Understanding geographic patterns of genetic variation for economically impor- tant marine fishes is critical for determining stock structure, barriers to migration, patterns of recruitment, and other biological components necessary to manage the

Bulletin of Marine Science 257 © 2021 Rosenstiel School of Marine & Atmospheric Science of the University of Miami 258 Bulletin of Marine Science. Vol 97, No 2. 2021 exploitation of populations in a spatial context (Hellberg et al. 2002, Palumbi 2004, Bradbury et al. 2008). For example, the design of effective marine reserves relies on models of gene flow, spawning time and location, and physical and biological vari- ables of larval dispersal (Apostolaki et al. 2002, Sala et al. 2002, Palumbi 2003, Green et al. 2015). Gathering relevant biological data for transboundary fisheries manage- ment is of utmost importance, especially for predatory fishes, which are the most exploited in the world’s oceans (Pauly et al. 1998, Sibert et al. 2006, Fenner 2014). Tunas, swordfish, and other pelagic, predatory fishery species have received sig- nificant genetic research attention because of their high commercial value (Ward 1995, Lu et al. 2006, Jackson et al. 2014, Antoniou et al. 2017, Pecoraro et al. 2018). However, large knowledge gaps remain for species that are primarily harvested for artisanal and small-scale commercial fisheries (Salas et al. 2007, Jacquet and Pauly 2008, Pilling et al. 2009, Evans and Andrew 2011). Giant trevally (Caranx ignobilis) and bluefin trevally Caranx( melampygus; Fig. 1) are top-predator marine species (Glass et al. 2020) that are economically important for recreational, artisanal, and small-scale commercial fisheries across their ranges (Abdussamad et al. 2008, FAO 2014). They are frequently targeted by sportfishing charters in locations such as Hawaii, Japan, South Africa, Kenya, Indian Ocean is- lands (e.g., Seychelles, Maldives), as well as in the Red Sea and Gulf of Oman (Gaffeny 2000, Meyer et al. 2001, Maggs 2013, EAD 2014). The two species hold historical cultural significance for Native Hawaiians, but due to overharvesting and dangers to humans of ciguatera poisoning, the fishery for C. ignobilis and C. melampygus has been limited by state-mandated closures (Gaffeny 2000, Friedlander and Dalzell 2004, Nadon 2017). Both species share a distribution in tropical and subtropical wa- ters in the Indian and Western Pacific oceans, while C. melampygus is also resident in the Eastern Pacific, extending to the western coasts of Central and South America. Although C. ignobilis has a body size larger than that of C. melampygus—reach- ing up to 180 cm fork length (FL) compared to 117 cm FL—both species are often seen schooling together and have been reported to hybridize (Sudekum et al. 1991, Honebrink 2000, Murakami et al. 2007, Santos et al. 2011). Studies on the movement behavior of C. ignobilis indicate high adult residency, with daily vertical migrations and periodic to seasonal excursions of tens to hun- dreds of kilometers, presumably for feeding and spawning (Meyer et al. 2007, Lédée et al. 2015, Papastamatiou et al. 2015, Filous et al. 2017, Daly et al. 2019). To date, large spawning aggregations (>1000 individuals) of C. ignobilis have been identified in northern and southern Mozambique and the Philippines (von Westernhagen 1974, da Silva et al. 2015, Daly et al. 2018). While less studied than C. ignobilis, individu- als of C. melampygus in Hawaii exhibit similar movement behavior, specifically diel movement patterns and site fidelity (Holland et al. 1996, Meyer and Honebrink 2005, Filous et al. 2017). The extent to which both species travel to spawn seems depen- dent on geographic location as well as habitat and oceanographic features (Holland et al. 1996, Daly et al. 2019). Both species spawn in summer months, and multiple spawning events within a season have been reported (Sudekum et al. 1991, Meyer et al. 2007, Daly et al. 2018). Both C. ignobilis and C. melampygus associate with coral and rocky reefs, yet are sometimes also classified as semipelagic, similar to other large fishes such as barracudas Sphyraena( spp.) and yellowtail amberjacks (Seriola spp.; Gold and Richardson 1998, Babbucci et al. 2006, Daly-Engel et al. 2012). The widespread distributions of C. ignobilis and C. melampygus imply high dispersal Glass et al.: Phylogeography of giant and bluefin trevallies 259

Figure 1. (A) Giant trevally (Caranx ignobilis) and (B) bluefin trevally Caranx( melampygus) adults and juveniles. Illustrations by Elaine Heemstra, courtesy of the South African Institute for Aquatic Biodiversity. capacities, but the territorial behaviors and site-fidelity patterns observed in these two species convolute the expectations of long-distance connectivity. Because of their Indo-Pacific distributions, large body sizes, and life histories, characterizing the phylogeographic patterns of C. ignobilis and C. melampygus is relevant for ongoing work assessing the role and importance of major biogeographic barriers, such as the Indo-Pacific Barrier (IPB). The IPB, comprised of the Indonesian Archipelago, is a biogeographic barrier for marine organisms between the Indian and Pacific oceans (Briggs 2009), and much research has focused on how the IPB has facilitated and maintained high levels of species diversity in the Coral Triangle (Cowman and Bellwood 2013, Crandall et al. 2019). The Coral Triangle serves as both a biological diversity hotspot and a scientific hotspot for phylogeographic studies (Barber et al. 2011, Carpenter et al. 2011, Crandall et al. 2019). The extent to which patterns of gene flow and larval dispersal across the IPB are consistent among marine fishes and invertebrates remains an ongoing topic of research (Horne 2014, Keyse et al. 2014). Most studies have examined the influence of the IPB on shallow-water coral 260 Bulletin of Marine Science. Vol 97, No 2. 2021 reef fishes (Bowen et al. 2013, Keyse et al. 2014, Crandall et al. 2019), although re- search on deeper water and pelagic fishes has contributed to characterizing the com- plexity of the IPB (Reece et al. 2010, Gaither et al. 2011, Jackson et al. 2014). While the IPB serves as a barrier to population connectivity for many coral reef fishes (Bowen et al. 2016), other species exhibit gene flow across the IPB, facilitated by life history characteristics, directional flow, and magnitude of ocean currents (Carpenter et al. 2011, Horne 2014, Keyse et al. 2014, Crandall et al. 2019). Caranx ignobilis and C. melampygus are members of the clade Carangoidei (Girard et al. 2020), whose bulk of species diversity is located in the Coral Triangle. Some carangoid species (e.g., Seriola spp. and Trachurus spp.) have received substantial research attention due to their economic importance (Gold and Richardson 1998, Cárdenas et al. 2005, Purcell et al. 2015, Moreira et al. 2019). For many of these species, genetic studies using mitochondrial and higher-resolution microsatellite loci have shown stock structure within and between ocean basins (Arnaud et al. 1999, Nugroho et al. 2001, Perrin and Borsa 2001, Cárdenas et al. 2009, Miller et al. 2011, Purcell et al. 2015). Population genetic information on Caranx ignobilis and C. melampygus, however, is sparse. In the only prior analysis of the population genetics of C. ignobilis and C. melampygus, which focused on individuals from the Hawaiian Islands, no geographic structuring of either species was observed from two mtDNA (i.e., ATPase6 and ATPase8) markers (Santos et al. 2011). Yet, haplotype analyses for both species revealed hierarchical structuring, possibly indicating multiple colo- nization events in this region. The demographic histories of the two species were estimated to be incongruous, with C. ignobilis displaying nucleotide diversity three times that of C. melampygus (Santos et al. 2011). As that study focused on only a small portion of these species’ ranges and used two relatively low-resolution mito- chondrial markers, further research is needed to assess larger patterns of connectiv- ity and genetic diversity across the Indo-Pacific distributions of C. ignobilis and C. melampygus. Genome-wide sequencing approaches allow for fine-scale, cost-effective detection of genetic variation for numerous individuals of widespread marine species (Reitzel et al. 2013, Willette et al. 2014, Benestan et al. 2015, Andrews et al. 2016). In this study, we employed double digest restriction site-associated DNA (ddRAD) sequenc- ing to examine the population genetic structure of C. ignobilis and C. melampygus across multiple Indo-Pacific sampling sites. Based on prior studies, we predicted high levels of admixture and sought to test this hypothesis using genomic sequencing methods, with the goals of characterizing geographic signal in population structure and quantifying genetic diversity across the Indo-Pacific ranges of C. ignobilis and C. melampygus.

Materials and Methods

ddRAD Sample Collection and DNA Extraction.—We obtained samples of muscle tissue or fin clips collected during 2006–2018 through field collection via hook and line fishing as well as from museum tissue collections. All samples were preserved in 95% EtOH at −20 °C. Samples and associated permit numbers are cata- logued in the Yale Peabody Museum of Natural History (New Haven, Connecticut, USA). We obtained samples from a total of 13 sites across the Indo-Pacific to analyze; we collected C. ignobilis from 12 of these sites and C. melampygus from nine sites Glass et al.: Phylogeography of giant and bluefin trevallies 261

Figure 2. Sampling sites with boxes indicating number of individuals analyzed for Caranx ignobilis (black) and Caranx melampygus (blue) following data filtering. Sites are abbreviated as follows: RSA (South Africa), MOZ (Mozambique), SAU (Saudi Arabia), SEY (Seychelles), MAD (Madagascar), MAU (Mauritius), MAL (Maldives), AUS (Australia), JPN (Japan), NMI (Northern Mariana Islands), FIJI (Fiji), HWI (Hawaii), KIR (Kiribati).

(Fig. 2, Online Table S1). We extracted total DNA using Qiagen DNeasy Tissue kits following the manufacturer’s protocol (Qiagen, Inc., Valencia, California, USA) and examined the quality of extractions visually using gel electrophoresis and by quanti- fying 1 µL of isolated DNA using a Qubit fluorometer (Life Technologies, Carlsbad, California, USA).

Library Preparation.—We prepared samples for ddRADseq using the pro- tocol developed by Peterson et al. (2012). This method used the rare cutterPstI (5´-CTGCAG-3´ recognition site) and common cutter MspI (5´-CCGG-3´ recognition site). We carried out double digests of 300–400 ng total DNA per sample using the two enzymes in the manufacturer’s supplied buffer (New England Biolabs, Ipswich, Massachusetts, USA) for 8 hrs at 37 °C. Samples from different localities and collec- tion years were extracted separately and distributed across multiple plates to mini- mize bias during library preparation. We determined digestion success by visually examining samples using gel electrophoresis, then ligated barcoded Illumina adapt- ers onto the fragments (Peterson et al. 2012). Following ligation, we pooled samples into 12 libraries and performed a clean-up using the QIAquick PCR Purification Kit. We then performed PCR using Phusion Taq (New England Biolabs) and Illumina indexed primers (Peterson et al. 2012). Library DNA concentration was checked us- ing a Qubit fluorometer, followed by normalization, a second round of pooling into four libraries and an additional QIAquick cleanup. We then remeasured DNA con- centration using a Qubit fluorometer and combined equal amounts from each of the four pools into one. We analyzed this final pool using a BioAnalyzer (Agilent, Santa Clara, California, USA) and performed size-selection using a Pippin Prep (Sage Science, Beverly, Massachusetts, USA), selecting for fragments between 300 and 500 bp. This was followed by a final measure of concentration using a BioAnalyzer. We 262 Bulletin of Marine Science. Vol 97, No 2. 2021 sent all libraries to the University of Oregon Genomics and Cell Characterization Core Facility (https://gc3f.uoregon.edu/) where concentrations were verified via qPCR before 100 bp single-end sequencing on an Illumina HiSeq 4000 (Illumina, San Diego, California, USA).

Genome Sequencing and Assembly.—Using hook and line fishing, we collected heart tissue from one C. melampygus individual from Kaneohe, Oahu, Hawaii, USA in April 2018 and one C. ignobilis individual from Kaunakakai, Molokai, Hawaii, USA in April 2019. We flash-froze tissue samples in liquid nitrogen and packaged them in dry ice for transportation to Brigham Young University (BYU; Provo, UT, USA), where they were stored at −80 °C until sequencing. We prepared DNA with Pacific Biosciences (PacBio; Menlo Park, California, USA) SMRTbell Library kits, follow- ing recommended protocols. For C. ignobilis we followed the protocol, “Procedure & Checklist – Preparing gDNA Libraries Using the SMRTbell Express Template Preparation Kit 2.0.” For C. melampygus we followed the protocol, “Procedure & Checklist – Preparing >30 kb SMRTbell Libraries Using Megaruptor Shearing and BluePippin Size-Selection for PacBio RS II and Sequel Systems.” Sequencing was per- formed on the PacBio Sequel at the BYU DNA Sequencing Center (https://dnasc. byu.edu). We used seven SMRT cells of continuous long reads for C. ignobilis and 10 for C. melampygus. We chose these SMRT cell numbers based on the assumed genome sizes obtained from the Animal Genome Size Database (Gregory 2018) and prior studies (Hardie and Hebert 2004). A C-value of 0.64 (625.92 Mb) was used for C. ignobilis (Hardie and Hebert 2004). A C-value was not listed in the database for C. melampygus; we used 0.80 (782.4 Mb) as an upper limit based on recorded genome size values for other Caranx species. We generated 58.3 Gb and 52.7 Gb of sequence data for C. ignobilis and C. melampygus, respectively, resulting in an approximate physical coverage of 93× for C. ignobilis and 67× for C. melampygus. Errors in raw reads were corrected using Canu v1.8 and Canu v1.6 (Koren et al. 2017) for C. ignobilis and C. melampygus, respectively. Draft genome assembly was also performed with Canu de novo. For C. ignobilis, contig NG50 was 7.4 Mb and LG50 was 24 while those for C. melampygus were 1.18 Mb and 147. Additional details about assembly commands can be found in Online Appendix 1, and all raw sequencing reads used in the two assemblies are archived on the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Accession Numbers SRR13036367 and SRR13036357).

Sequence Assembly and Filtering.—We assembled all ddRAD data using the program ipyrad v0.9.31 (Eaton 2014). The input parameters for ipyrad are included in the Online Supplementary Material (Online Table S2). Both C. ignobilis and C. melampygus reads were mapped to their respective draft genome assemblies. In step one of the ipyrad workflow, we demultiplexed sequences by identifying individual sample barcode sequences and restriction overhangs. We combined demultiplexed samples into a single directory for C. ignobilis and a directory for C. melampygus. During step two, we trimmed barcodes and adapters from reads, which were then quality filtered using a q-score threshold of 20. Any base with a score below 20 was converted into an “N.” Step three consisted of clustering reads, with a minimum depth of coverage of six to retain clusters in the ddRAD assembly. During step four, we jointly estimated sequencing error rate and heterozygosity from site patterns across Glass et al.: Phylogeography of giant and bluefin trevallies 263 the clustered reads assuming a maximum of two consensus alleles per individual. In step five, we determined consensus base calls for each allele using the parameters from step four and removed consensus sequences with more than five Ns. During step six, we clustered consensus sequences and aligned reads for each sample. During step seven, we filtered the data by the maximum number of alleles per locus, maximum number of shared heterozygous sites per locus, and other criteria (Eaton 2014) and formatted output files for downstream analyses. Both C. ignobilis and C. melampygus datasets included all loci shared by at least 10 individuals. After running ipyrad, we performed additional filtering steps to account for miss- ing data and rare alleles. First, we subsampled individuals with high sequencing quality from highly-sampled sites (Seychelles and Northern Mariana Islands) to ac- count for unevenness across sampling sites, which can interfere with estimates of linkage disequilibrium and neutrality statistics (Hill and Robertson 1968, Städler et al. 2009, Subramanian 2016). Using VCFtools v0.1.16 (Danecek et al. 2011) and BCFtools v1.6 (Li et al. 2009), we then removed individuals missing more than 99% of genotype calls. We subsequently retained only biallelic single nucleotide polymor- phisms (SNPs) and removed (1) indels, (2) loci with minor allele frequencies <0.01 to exclude singletons and false polymorphic loci due to potential sequencing errors, (3) alleles with a minimum count of 2, and (4) loci with high mean depth values (>100). We then implemented an iterative series of filtering steps based on missing data and genotype call rates to maximize genomic coverage per individual (O’Leary et al. 2018; Online Table S3). Following this, we removed loci out of Hardy–Weinberg Equilibrium to filter for excess heterozygosity. We then used PLINK v1.9 (Purcell et al. 2007) to perform linkage disequilibrium pruning by calculating the squared coef- ficient of correlation r( 2) on all SNPs within a 1 kb window (Hill and Robertson 1968). We removed all SNPs with an r2 value >0.6. The ddRADseq datasets utilized in this study can be downloaded from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (Accession number: PRJNA716671).

Detection of Loci Under Selection.—We followed recent guidelines and em- ployed multiple methods to identify loci under selection (Hoban et al. 2016, Ahrens et al. 2018). First, we used BayeScan v2.1 (Foll and Gaggiotti 2008), which is broadly categorized as an FST outlier approach. The method applies linear regression to gen- erate population- and locus-specific FST estimates and calculates subpopulation FST coefficients by taking the difference in allele frequency between each population and the common gene pool. BayeScan incorporates uncertainties in allele frequencies due to small sample sizes, as well as imbalances in the number of samples between populations (Foll and Gaggiotti 2008). We assigned each sampling locality as a popu- lation. Our analyses were based on 1:50 prior odds and included 100,000 iterations and a false discovery rate of 10%. We used the default values for the remaining pa- rameters and visualized results in R v3.6.1 (R Core Team 2019). The second outlier identification method used was pcadapt v4.3.3 (Luu et al. 2017, Privé et al. 2020) as implemented in R. This program uses Principal Component Analysis (PCA) to ascertain population structure and does not require any a prio- ri population information. Loci are considered under selection if they are strongly correlated with population structure. Simulation studies indicate that pcadapt still shows high statistical power for admixed individuals and those with continuous population structure (Luu et al. 2017). We used a scree plot showing the percent of 264 Bulletin of Marine Science. Vol 97, No 2. 2021 variance explained to identify the appropriate value of K principal components for C. ignobilis and C. melampygus, and used a minimum minor allele frequency value of 0.01 and a cutoffq -value threshold of 10% to identify outliers. After running both programs, we used R to assess the number of outliers unique to, and shared by, both programs and created outlier and neutral datasets. We considered a locus an outlier if it was identified by either program.

Spatial Structure, Population Differentiation, and Summary Statistics.—To assess population structure, we used a distance-based approach (PCA) and a model-based clustering method on the combined dataset (neutral and outlier loci), as well as on neutral and outlier datasets individually. The model-based method reconstructs the genetic ancestry of individuals using sparse nonnegative matrix factorization (sNMF) and least-squares optimization. Model-based analyses were performed using the package LEA v2.6.0 in R, which estimates individual ancestry coefficients representing proportions of the genome originating from multiple ancestral gene pools (Frichot et al. 2014; Frichot and François 2015). We calculated and visualized cross-entropy scores of K population clusters ranging from 1 to 10 with 10 replicates. For sNMF and PCA analyses, no populations were assigned a priori. To interpret the results of sNMF and PCA in a geographic context, we visualized output by assigning each sampling locality as a population (12 populations of C. ignobilis and nine populations of C. melampygus). We then compared summary statistics between multiple population scenarios based on the sNMF and PCA results.

To quantify population differentiation, we calculated average pairwise FST (Weir and Cockerham 1984) values between sampling localities for neutral and outlier da- tasets using the R package StAMPP v1.6.1 with 1000 bootstrap replicates (Pembleton et al. 2013). Pairwise FST values range from zero (i.e., no differentiation) to one (i.e., alleles are fixed in subpopulations). To compare the genetic divergence of sampling sites and putative populations, we calculated population genetic summary statistics, including estimates of observed (HO) and expected (HS) heterozygosity using the R package hierfstat v0.04-22 (Goudet 2005). We calculated these summary statistics using the neutral datasets for each putative population after performing sNMF and

PCA. We compared values of HO and HS between putative populations to assess ge- netic variability and evidence of population dynamics (e.g., bottlenecks, nonrandom mating) that would lead to heterozygote deficits (i.e., the Wahlund effect). To test for evidence of population bottlenecks, we calculated Tajima’s D (Tajima 1989) and Fu and Li’s F* (Fu and Li 1993) in the R package PopGenome v2.7.5 (Pfeifer et al. 2014). Finally, to assess isolation by distance, we calculated pairwise genetic dis- tances (Edwards’) and geographic distances (Euclidean) between sampling sites and performed Mantel tests implemented in adegenet v2.1.2 with 999 permutations (Jombart 2008).

Results

ddRAD Sequencing and Outlier Detection.—After processing in ipyrad, we recovered a mean of 3.121 M reads individual−1 for C. ignobilis with an average of 111,764 loci individual−1, and 3.078 M reads individual−1 for C. melampygus with an average of 100,156 loci individual−1. Following filtering (e.g., missing data, minor al- lele frequency, linkage disequilibrium, etc.), the dataset for C. ignobilis consisted of Glass et al.: Phylogeography of giant and bluefin trevallies 265

Figure 3. (A) Individual ancestry plots for Caranx ignobilis generated using sNMF, indicating K = 2, K = 3, and K = 4 populations from the combined dataset. (B) PCA biplot of the combined da- taset showing the first two principal components. Sites are colored and abbreviated as in Figure 2.

225 individuals and 32,798 SNPs while the dataset for C. melampygus consisted of 74 individuals and 43,299 SNPs. BayeScan was more conservative in identifying outliers than pcadapt (Online Fig. S1), and we considered a locus an outlier if it was identified by either program. Subsequent filtering of outliers resulted in a C. ignobilis neutral dataset containing 25,837 SNPs and an outlier dataset containing 6,961 SNPs. The C. melampygus neutral dataset included 41,763 SNPs, and the outlier dataset included 1536 SNPs.

Caranx ignobilis: Population Structure and Summary Statistics.—The model-based population differentiation analysis, sNMF, indicated support for both K = 2 and K = 3 populations of C. ignobilis for all datasets, as inferred from indi- vidual ancestry plots (Fig. 3A, Online Fig. S2) and cross-entropy scores by population 266 Bulletin of Marine Science. Vol 97, No 2. 2021

(Online Fig. S3). Similar patterns of population clustering were evident in PCA vi- sualizations (Fig. 3B, Online Figs. S2, S4). For the scenario of K = 2 populations, vi- sual inspection of admixture and PCA plots revealed one Central Pacific population (Hawaii, Kiribati) and one consisting of all other Indo-Pacific sites (Fig. 3, Online Fig. S2). Notably, two individuals from Hawaii reflected ancestry from the widespread population (Fig. 3, Online Fig. S2). Admixture plots for the neutral dataset showed a gradient of mixed ancestry moving westward from Fiji to the larger Indo-Pacific population (Online Fig. S2). The scenario of K = 3 also supported a Central Pacific population consisting of Hawaii and Kiribati, an Eastern Indian–Western Pacific (EIWP) population inclusive of individuals from Fiji, Japan, Australia, and the Maldives, and a Western Indian Ocean population including Seychelles, Madagascar, Mauritius, Saudi Arabia, Mozambique, and South Africa (Fig. 3, Online Fig. S2). The EIWP and Western Indian Ocean populations displayed more genetic admixture than with the Central Pacific population (Fig. 3, Online Fig. S2).

Neutral and outlier pairwise FST values indicated greater genetic differentiation between C. ignobilis individuals from Hawaii and Kiribati compared to the other ten sampling sites (Fig. 4A, Online Table S4). Pairwise FST results also indicated subtle population structure between Japan, Fiji, Australia, and Maldives compared to the Western Indian Ocean, reflective of the K = 3 population scenario (Fig. 4A, Online Table S4). These results were in congruence with the individual ancestry plots (Fig. 3, Online Fig. S2), particularly with Maldives serving as an admixture zone between the Western and Central Indian Ocean (Fig. 4A). Pairwise FST comparisons between putative population groupings showed the greatest genetic differentiation between the Central Pacific and Western Indian Ocean samples (Online Table S4).

We observed lower expected heterozygosity (HS) and observed heterozygosity (HO) of individuals from Hawaii and Kiribati compared to the remaining sampling locali- ties (Table 1A). A permutation test of expected heterozygosity (HS) using the neutral dataset and 999 permutations showed the empirical difference in means between the Central Pacific population and remaining Indo-Pacific localities was significantly different than the permuted null distribution (Online Fig. S5). Values of observed and expected heterozygosity were similar between the Indian and Western Pacific Ocean sites and did not provide evidence for metapopulation processes such as in- breeding or the mixing of two previously isolated populations (Table 1A). Values of both Tajima’s D and Fu and Li’s F* were negative for the Central Pacific population (Table 1A). Tajima’s D was also negative for the EIWP population (Table 1A). The Mantel test of isolation by distance for C. ignobilis, measured with the neutral data- set, was not significant P( = 0.175).

Caranx melampygus: Population Structure and Summary Statistics.— Visual inspections of sNMF admixture (Fig. 5A) and PCA results (Fig. 5B, Online Figs. S4, S6) revealed geographic patterns of genetic ancestry for C. melampygus suggestive of both K = 2 and K = 3 populations. A separate population of Hawaiian samples was evident in admixture plots across all datasets (Fig. 5, Online Fig. S6). Cross-entropy scores from the sNMF analyses, however, supported K = 1 population for the combined and neutral datasets and K = 2 for the outlier dataset (Online Fig. S3). PCA biplots showed geographic clustering similar to the ancestry plots, with evidence for K = 3 populations in the combined dataset (Fig. 5B, Online Fig. S4) Glass et al.: Phylogeography of giant and bluefin trevallies 267

Figure 4. Pairwise FST heatmap for (A) Caranx ignobilis and (B) Caranx melampygus. The up- per (blue) triangular matrix is the outlier dataset, and lower (red) matrix is the neutral dataset. Significant comparisons P( < 0.05) are indicated with an asterisk. Solid black lines indicate the putative Central Pacific populations and dashed lines indicate the (A) Western Indian Ocean and (B) Indian Ocean populations. Sites are abbreviated as in Figure 2. Note the difference in mag- nitudes of FST values between outlier and neutral datasets. and K = 2 populations in the neutral dataset (Online Fig. S6). PCA results from the neutral dataset indicated restricted gene flow across the IPB, with separate Indian and Pacific Ocean populations (Online Fig. S6B). The scenario of K = 3 was evi- dent in the ancestry plots for both the combined (Fig. 5A) and neutral (Online Fig. S6) datasets, including one Hawaiian population, one inclusive of the remaining Pacific Ocean sites (Kiribati, Northern Mariana Islands and Japan), and one consist- ing of the Indian Ocean sites (Maldives, Seychelles, Mauritius, Saudi Arabia, and South Africa). Similar to C. ignobilis, three individuals from Hawaii did not reflect Hawaiian ancestry, but that of the Indian and Western Pacific populations.Caranx 268 Bulletin of Marine Science. Vol 97, No 2. 2021

Table 1. Summary statistics for two- and three-population scenarios for (A) Caranx ignobilis and (B) Caranx melampygus using neutral SNP datasets. Table shows Tajima’s D, Fu and Li’s F*, observed heterozygosity

(HO), and expected heterozygosity (HS). Sites are abbreviated as in Figure 2.

Population Scenarios Tajima’s D Fu and Li’s F* HO HS (A) Caranx ignobilis Two population scenario Central Pacific (HWI, KIR) −0.6373 −0.6297 0.0911 0.0882 Indo-West Pacific (all remaining sites) 0.1918 2.0419 0.1196 0.1206 Three population scenario Central Pacific (HWI, KIR) −0.6373 −0.6297 0.0905 0.0878 Eastern Indo–western Pacific (JPN, FIJI, AUS, MAL) −0.3677 0.4019 0.1185 0.1168 Western Indian Ocean (SEY, MAD, MAU, 0.0210 1.6847 0.1206 0.1225 SAU, MOZ, RSA) (B) Caranx melampygus Two population scenario Hawaii (HWI) −0.3359 −0.1600 0.1199 0.1239 Indo-Pacific (all remaining sites) −0.3486 0.3187 0.1316 0.1349 Two population scenario Pacific Ocean (HWI, KIR, JPN, NMI, FIJI) −0.3935 −0.1307 0.1286 0.1319 Indian Ocean (MAL, SEY, MAU, RSA) −0.3511 −0.0566 0.1304 0.1341 Three population scenario Hawaii (HWI) −0.3359 −0.1600 0.1199 0.1239 Central-Western Pacific (KIR, JPN, NMI, FIJI) −0.4060 −0.2067 0.1326 0.1349 Indian Ocean (MAL, SEY, MAU, RSA) −0.3511 −0.0566 0.1304 0.1341 melampygus individuals from Kiribati did not group with the Hawaiian population as observed in C. ignobilis. However, the sample size from Kiribati was small (N = 2) and additional sampling is necessary to further quantify genetic similarity between Kiribati and Hawaii for C. melampygus.

Pairwise FST comparisons indicated larger genetic differentiation between Hawaii and other sites, as well as a slight gradient moving westward towards the Indian Ocean (Fig. 4B, Online Table S5). After grouping individual sites by putative popu- lations to mitigate the effect of small sample sizes, the strongest signal of genetic differentiation still occurred between Hawaii and the Indian Ocean sites (Online Table S5). Similar to C. ignobilis, the mean within-population neutral genetic diver- sity (HS) of the C. melampygus Hawaiian population was lower than the mean HS of the putative Indo-Pacific population, as indicated by a permutation test (Table 1B, Online Fig. S5). Values of Tajima’s D were negative for all putative populations, so we could not distinguish between demographic scenarios. Fu and Li’s F* was nega- tive for Hawaiian samples compared to the Indo-Western Pacific sites (Table 1B). No evidence of isolation by distance was observed for C. melampygus via the Mantel test using the neutral dataset (P = 0.6270).

Discussion

Population Structure of C. ignobilis and C. melampygus.—With datasets encompassing thousands of SNPs, we observed subtly different patterns of population structure between two closely-related, reef-associated, predatory marine fishes sampled from 13 localities throughout the Indian and Pacific oceans. Both Glass et al.: Phylogeography of giant and bluefin trevallies 269

Figure 5. (A) Individual ancestry plots for Caranx melampygus generated using sNMF, indicat- ing K = 2 and K = 3 populations from the combined dataset. (B) PCA biplot of the combined data- set showing the first two principal components. Sites are colored and abbreviated as in Figure 2.

C. ignobilis and C. melampygus exhibited evidence of restricted gene flow between the Central Pacific and populations to the west. For C. ignobilis, this putative Central Pacific population included individuals from Hawaii and Kiribati, whereas C. melampygus individuals from Hawaii were genetically distinct from those in Kiribati. The phylogeographic signal from the model-based test was stronger for C. ignobilis than C. melampygus. Caranx ignobilis also exhibited a starker gradient of genetic divergence moving east to west and more genetic connectivity across the IPB, with Maldives individuals showing genetic similarity to Western Pacific localities. Caranx melampygus, on the other hand, exhibited signs that the IPB may restrict gene flow between Indian and Pacific Ocean populations. In addition to evidence for putative Central Pacific populations, someC. ignobilis and C. melampygus individuals from Hawaii were found to reflect the genetic ancestry of the more-widespread population. Moreover, one C. ignobilis individual from Japan affiliated with the Hawaii and Kiribati population. These results suggest connectivity between these localities is restricted but was not absent at some time in the past. One scenario is the sympatric cohabitation of two genetic populations in Hawaii that are 270 Bulletin of Marine Science. Vol 97, No 2. 2021 prevented from interbreeding by reproductive barriers, such as prezygotic isolation or differences in spawning times. Indeed, two separate colonizations of Hawaii by both C. ignobilis and C. melampygus have been suggested based on analyses of mitochondrial loci (Santos et al. 2011). Evidence for relict Hawaiian populations is corroborated by our findings of reduced genetic diversity ofC. ignobilis in Hawaii and Kiribati and C. melampygus in Hawaii. Our observations of negative values of Tajima’s D and Fu and Li’s F* for C. ignobilis, and Fu and Li’s F* for C. melampygus, though subtle, could imply a population bottleneck at the Central Pacific localities. More individuals of C. ignobilis and C. melampygus collected from Hawaii reflected ancestry of the widespread population (N = 5) than individuals in the widespread population reflected Hawaiian ancestry (N = 1). Isolation of individuals in Hawaii is a common biogeographic scenario due to the archipelago’s remoteness and its geographic location relative to major oceanic currents that promote larval transport between other Pacific islands (Hourigan and Reese 1987). Yet, evidence of connectivity between Hawaii and southern Japan via the Kuroshio and North Equatorial currents has been established for some coral reef fish species (Randall 1998). Similarly, the North Equatorial Current may serve to connect Hawaii with Kiribati and other Western Pacific populations (Craig et al. 2010). Scenarios of K = 3 populations for both C. ignobilis and C. melampygus consisted of a popu- lation comprising individuals from Western Pacific sites and, for C. ignobilis, the Maldives. There are several cases of marine and brackish water taxa, including ca- ridean shrimp (Page et al. 2007, Weese et al. 2013) and coastal fishes (Mukai et al. 2009, Shen et al. 2011), exhibiting genetic connectivity between the north and south- west Pacific, thought to be facilitated by the Kuroshio and North Equatorial cur- rents. Many of these contemporary distributions are hypothesized to be caused by the recolonization of island localities after sea level fluctuations during the Pliocene and Pleistocene, given observations of distinct genetic lineages inhabiting sympatric locations (Shen et al. 2011, Weese et al. 2013).

Neutral Processes vs Local Adaption.—Our results suggest complex demo- graphic histories supported by multiple population delineation scenarios and differ- ent signals of genetic connectivity in neutral loci vs those presumably under selection. For example, the signal for putative Hawaiian populations was more prominent in the combined and outlier datasets of both species. Differences between neutral and outlier datasets likely reflect the influence of underlying demographic processes, from genetic drift to spatial diffusion constraints across the IPB, to potentially adap- tive outlier loci (Horne 2014). Indeed, evidence of local adaptation in outlier loci lead- ing to population differentiation in widespread marine species is increasingly being detected (Horne 2014, Gagnaire et al. 2015, Phair et al. 2019). The strong signal of population structure in Hawaii and Kiribati by loci presumably under selection may be suggestive of local adaptation in these Central Pacific populations. Future work examining the function and signature of local adaptation in outlier loci would eluci- date the differences between the neutral and outlier datasets and also aid in teasing apart demographic and adaptive processes related to the isolation of individuals in the Central Pacific. Furthermore, examining how Tajima’s D varies across the ge- nome would inform genome-wide variability in selective processes, as calculating mean values may mask signals of selection on specific regions of the genome. Glass et al.: Phylogeography of giant and bluefin trevallies 271

Dispersal Potential of C. ignobilis and C. melampygus.—Given their large body sizes and association with seaweed and manmade rafts as adults and juveniles (Luiz et al. 2015), C. ignobilis and C. melampygus are assumed to have high dispersal potential. Dispersal may be habitat dependent, with close affinities to home ranges on coral reefs in island and archipelago environments (Meyer et al. 2007, Lédée et al. 2015) and more extensive migrations to spawning sites along rocky reefs and coast- lines (Daly et al. 2019). The longest distance recorded to date of C. ignobilis traveling to spawn is only about 600 km, where one individual hugged the coastline of South Africa (Daly et al. 2019). However, in 2011, three adult individuals of C. ignobilis were observed in the Galápagos Islands, providing evidence that adults either par- take in open-ocean migration or are susceptible to spontaneous long-distance dis- persal events (Acuña-Marrero and Salinas-De-León 2013). Notably, 2009–2010 were moderate El Niño years0, and El Niño events have been reported to impact tuna dis- tribution and migration in the Pacific Ocean due to warmer water temperatures and changes in the magnitude of currents (Kimura et al. 1997, Kumar et al. 2014). Similar to many marine fishes, including large groupers and snappers, C. ignobilis is known to use temperature and lunar cues to migrate to spawning locations (Graham and Castellanos 2005, Heyman et al. 2005, Meyer et al. 2007, Nanami et al. 2014, Lédée et al. 2015, Daly et al. 2019). The range ofC. melampygus is larger than that of C. ignobilis, extending to the Galápagos and the western coast of Central and South America. While the larger range of C. melampygus might suggest increased dispersal capacity compared to C. ignobilis, our observations weakly support this, as the neutral dataset indicated evi- dence of dispersal across the IPB but the combined dataset did not. Prior research on C. melampygus movement indicates strong site fidelity, similar to C. ignobilis (Holland et al. 1996). The longest distance recorded of C. melampygus, based on a tag-and-recapture study in Hawaii, was 72 km (Holland et al. 1996). In light of empirical studies showing high site fidelity and small core home ranges displayed by adults of C. ignobilis and C. melampygus, gene flow across the Indo- Pacific is presuambly maintained via larval or juvenile dispersal. Although no study has measured pelagic larval duration for either species, one analysis estimated a du- ration of about 140 d for both species in Hawaii based on timing between spawning events (Conklin et al. 2018). Mean larval dispersal distances were also estimated to be equivalent (about 87 km) for both species on Molokai Island in Hawaii (Conklin et al. 2018). In a laboratory setting, small individuals (8–10 mm) of C. ignobilis dis- played well-developed swimming abilities and orientation behavior (Leis et al. 2006). However, the lack of empirical, comparative information on early life history behav- ior makes it difficult at this time to infer differences in dispersal potential between C. ignobilis and C. melampygus.

Comparative Patterns of Phylogeography.—Our results suggest C. ignobilis and C. melampygus exhibit population genetic structure reminiscent of some large reef-associated predatory fishes, which display similar patterns of connectivity across the IPB or show population structure in the Central Pacific. For example, the crimson jobfish Pristipomoides( filamentosus), a large, reef-associated deepwater snapper, displays extensive connectivity spanning the Western Indian Ocean to the Central Pacific, with an isolated population in Hawaii (Gaither et al. 2011). Likewise, the great barracuda (Sphyraena barracuda) exhibits restricted gene flow between 272 Bulletin of Marine Science. Vol 97, No 2. 2021

Hawaii and the Western Pacific, but is panmictic across the Indo-West Pacific in spite of close associations with coral reefs as lie-in-wait predators (Daly-Engel et al. 2012). Furthermore, two closely related species of predatory snapper, Lutjanus kasmira and Lutjanus fulvus, display contrasting patterns of panmictic and geographically structured population genetic differences across the Indo-Pacific, but both species exhibit isolation in the Central Pacific (Gaither et al. 2010). Other species of Carangoidei also exhibit varying patterns of phylogeographic structure. Carangoid fishes are known for their extensive ecological and morpho- logical diversity (Price et al. 2015, Frederich et al. 2016), but phylogenetic conserva- tism in dispersal has not been examined. The torpedo scad Megalaspis( cordyla) is nested within the Caranx clade (Glass 2019) and an analysis using microsatellite loci revealed population structure between the Western Indian Ocean (Tanzania) and Western Pacific Ocean (Vietnam; Kempter et al. 2017). A strong intraspecific genetic break between Indian Ocean and Pacific Ocean sites was observed in two other spe- cies of carangoid scads, Decapterus russelli and Decapterus macrosoma, with the Indo-Malay archipelago acting as a biogeographic barrier (Perrin and Borsa 2001, Rohfritsch and Borsa 2005). Meanwhile, open ocean barriers delineate populations of yellowtail amberjack (Seriola lalandi) across the North Pacific Ocean (Purcell et al. 2015). In contrast, the widespread jack mackerel species, Trachurus murphyi and Trachurus picturatus, both constitute one population across their Pacific and Atlantic ranges, respectively (Cárdenas et al. 2009, Moreira et al. 2019). These examples suggest the importance of open-ocean barriers in shaping the phylogeographic structure of large-bodied, reef-associated fishes, such as many carangoids, and reveal similar patterns of population connectivity across the IPB. Although some smaller reef fish species maintain extensive connectivity across the Indian and Pacific oceans (Craig et al. 2007, Horne et al. 2008, Reece et al. 2010, DiBattista et al. 2015), the IPB remains a well-established inhibitor of gene flow often attributed to repeated uplifting of the Indonesian Archipelago caused by sea level changes during the Pleistocene (Gaither et al. 2011, Briggs and Bowen 2013, Liu et al. 2014, Crandall et al. 2019). A recent phylogenetic analysis of Carangiformes es- timated the ages of C. ignobilis and C. melampygus at about 5.04 MY and 3.53 MY, respectively, providing evidence that both species originated during the Pliocene and thus were subjected to parallel changes in sea level throughout the Pleistocene (Glass 2019). Thus, the subtle differences in connectivity we observed between C. ignobilis and C. melampygus may reflect differences in life history characteristics. Knowledge of C. ignobilis and C. melampygus life histories would benefit from future research on pelagic larval duration, juvenile settlement, and other biological and physiological drivers of population connectivity. Identifying more spawning locations and con- ducting studies on movement behavior in other Indo-Pacific localities will elucidate the extent to which adults migrate and contribute to more-informed estimates of dispersal potential for these two species. Ongoing work on outlier loci and the extent to which they are under selection or locally adapted in the Central Pacific will also be useful to fisheries managers. Both C. ignobilis and C. melampygus are considered data-limited, in spite of being commercially fished throughout their ranges, with most commercial fishing occur- ring in Hawaii. In light of past overfishing of C. ignobilis in Hawaii (Nadon 2017) and our findings of restricted gene flow and reduced genetic diversity, this study provides evidence for the need for precautionary fisheries management in the Central Pacific Glass et al.: Phylogeography of giant and bluefin trevallies 273 and additional research to delineate clear stocks and geographical boundaries. We encourage fishery managers in Hawaii and Kiribati to consider management mea- sures that preserve genetic diversity, since low genetic diversity can result in inbreed- ing depression, lower the fitness of a population, and reduce resilience to changing environmental conditions (Markert et al. 2010, Frankham et al. 2014).

Acknowledgments

We thank FlyCastaway, S Holzhausen, G Heyer, the Seychelles Sports Fishing Club, S Vidot, D Hoenings, M Dugasse, R Bradley, D Thevenau, D Howell, F Meyer, Getaway Tours, the Seychelles Fishing Authority, P Cowley, R Daly, C Daly, J Filmalter, R Bennett, B Mann, R Jauhary, S Talma and the Talma family, as well as K Johnston, M Yamashita, and J Ching for assistance with sample collection. We are also grateful to the Save Our Seas Foundation D’Arros Research Centre and M Gonçalves of the Ponta do Ouro Partial Marine Reserve. In addition, we thank J O’Malley of the NOAA Pacific Islands Fisheries Science Center, and the Kagoshima University Museum. We are indebted to G Watkins-Colwell of the Yale Peabody Museum of Natural History and R Bills of the South African Institute for Aquatic Biodiversity for their support. We are grateful to the DNA Sequencing Center and Office of Research Computing at Brigham Young University for continuously enabling our work. Lastly, we are grateful to R Beebe, K Caldwell, C Ching, C Gerken, R Hozaki, R Kikuta, P Nelson Nagamine, G Oyamot, R Takeuchi, C Tam, R Tanoue, K Walters, J Yamagata, and the participants of the S Tokunaga Store 2007 Ulua Challenge Tournament for assistance in collecting samples from Hawaii that were utilized in both this study and Santos et al. (2011). This research was supported by the Yale Institute for Biospheric Studies, a National Science Foundation (NSF) Doctoral Dissertation Improvement Grant (DDIG; DEB 1701597), the NSF Graduate Research Fellowship Program (DGE-1122492), the US Agency for International Development Research and Innovation Fellowship, the Yale Department of Ecology and Evolutionary Biology Chair’s Fund, the Yale MacMillan Center International Dissertation Fellowship, the South African Institute for Aquatic Biodiversity (saiab.ac.za; all to JR Glass), and the Hawaii Department of Land and Natural Resources (HDLNR)-PO C21927 (to SR Santos).

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