marine turtle mitogenome phylogenetics and evolution

Marine turtle mitogenome phylogenetics and evolution

Sebastián Duchene a, Amy Frey a, Alonzo Alfaro-Núñez b, Peter H. Dutton a, M. Thomas P. Gilbert b,Phillip A. Morin a,!a Protected Resources Division, Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 9801 La Jolla Shores Dr., La Jolla, CA 92037, USAbCentre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, 1350 Copenhagen, Denmark

a r t i c l e i n f o

Article history:Received 7 March 2012Revised 11 June 2012Accepted 13 June 2012Available online xxxx

Keywords:Sea turtleMolecular clockMitogenomeMolecular adaptive evolutionMitochondrial phylogenetics

a b s t r a c t

The sea turtles are a group of cretaceous origin containing seven recognized living species: leatherback,hawksbill, Kemp’s ridley, olive ridley, loggerhead, green, and flatback. The leatherback is the single mem-ber of the Dermochelidae family, whereas all other sea turtles belong in Cheloniidae. Analyses of partialmitochondrial sequences and some nuclear markers have revealed phylogenetic inconsistencies withinCheloniidae, especially regarding the placement of the flatback. Population genetic studies based on D-Loop sequences have shown considerable structuring in species with broad geographic distributions,shedding light on complex migration patterns and possible geographic or climatic events as driving forcesof sea-turtle distribution. We have sequenced complete mitogenomes for all sea-turtle species, includingsamples from their geographic range extremes, and performed phylogenetic analyses to assess sea-turtleevolution with a large molecular dataset. We found variation in the length of the ATP8 gene and a highlyvariable site in ND4 near a proton translocation channel in the resulting protein. Complete mitogenomesshow strong support and resolution for phylogenetic relationships among all sea turtles, and reveal phy-logeographic patterns within globally-distributed species. Although there was clear concordancebetween phylogenies and geographic origin of samples in most taxa, we found evidence of more recentdispersal events in the loggerhead and olive ridley turtles, suggesting more recent migrations (<1 Myr) inthese species. Overall, our results demonstrate the complexity of sea-turtle diversity, and indicate theneed for further research in phylogeography and molecular evolution.

Published by Elsevier Inc.

1. Introduction

The sea turtles comprise seven extant species grouped into twofamilies: Dermochelidae, with the leatherback (Dermochelys coria-cea) as the single extant species, and Cheloniidae, with six species:hawksbill, Kemp’s ridley, olive ridley, loggerhead, green, and flat-back turtles (Eretmochelys imbricata, Lepidochelys kempii, L. oliva-cea, Caretta caretta, Chelonia mydas, and Natator depressus,respectively). Their phylogenetic placement has been somewhatdebated, with different molecular data sets supporting differentgroupings within Cheloniidae. The placement of N. depressus hasbeen particularly problematic, with different data supporting itas the sister taxon either to a clade comprising the genera Eretm-ochelys, Caretta, and Lepidochelys (Dutton et al., 1996; Iversonet al., 2007), or to Chelonia only (Naro-Maciel et al., 2008).

Most sea turtles (except L. kempii and N. depressus) have a pan-tropical distribution across a wide latitudinal range from Canada to

South Africa, Southern Argentina and Chile (Hirth et al., 1997). Ge-netic studies based on the mitochondrial D-Loop of C. mydas (Enca-lada et al., 1996), D. coriacea (Dutton et al., 1999), and L. olivacea(Bowen et al., 1991; Karl and Bowen, 1999) suggest differentiationof Indo-Pacific and Atlantic groups. This implies that South andCentral America and the Isthmus of Panama represents a strongergeographic barrier to gene flow than do colder waters in the south-ern tip of Africa (Avise et al., 1992; Dutton et al., 1999), at least inthese three species.

Recent advances in DNA sequencing technologies have mademore molecular markers available for turtle phylogenetics. Previ-ous studies have used as many as 14 nuclear markers across se-lected turtle lineages (including freshwater and terrestrialturtles; Barley et al., 2010), and five nuclear and two mitochondrialmarkers in marine turtles (Naro-Maciel et al., 2008). However, interms of mitochondrial phylogenetics, only cytochrome b (Cytb)(Bowen et al., 1993), D-Loop, ND4 (Dutton et al., 1996) and 12Sand 16S (Naro-Maciel et al., 2008) regions have been used, produc-ing highly supported trees for contrasting topologies (see Naro-Maciel et al., 2008).

In other vertebrate groups, complete mitogenomes havedemonstrated an increase in phylogenetic performance in termsof branch support and divergence-time estimation relative to

1055-7903/$ - see front matter Published by Elsevier Inc.http://dx.doi.org/10.1016/j.ympev.2012.06.010

! Corresponding author.E-mail addresses: [email protected] (S. Duchene), amy.frey@noaa.

gov (A. Frey), [email protected] (A. Alfaro-Núñez), [email protected](P.H. Dutton), [email protected] (M. Thomas P. Gilbert), [email protected] (P.A. Morin).

Molecular Phylogenetics and Evolution xxx (2012) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Molecular Phylogenetics and Evolution

journal homepage: www.elsevier .com/ locate /ympev

Please cite this article in press as: Duchene, S., et al. Marine turtle mitogenome phylogenetics and evolution. Mol. Phylogenet. Evol. (2012), http://dx.doi.org/10.1016/j.ympev.2012.06.010

http://dx.doi.org/10.1016/j.ympev.2012.06.010

mailto:[email protected]









http://www.sciencedirect.com/science/journal/10557903

http://www.elsevier.com/locate/ympev



individual mitochondrial regions, and even nuclear markers (forexamples in other vertebrates see Duchene et al., 2011; Okajimaand Kumazawa, 2010; Wang and Yang, 2011).

Although phylogenetic analyses using nuclear markers havemade important contributions in uncovering evolutionary relation-ships in many taxa, branch support is sometimes low (Ducheneet al., 2011). In contrast, complete mitogenomes often providehighly supported trees and precise date estimates, often congruentwith nuclear data, allowing for well-supported hypotheses for thetrue evolutionary histories of species. However, incomplete lineagesorting, hybridization, and past gene flow can obscure evolutionaryrelationships, and in some taxa the addition of independent lines ofevidence, such as nuclear markers or morphology is crucial to ob-tain reliable phylogenetic resolution. In some cases, mitochondrialtrees can be poorly supported even when using complete mitoge-nomes (Talavera and Vila, 2011); in sea turtles, however, this hasnot been the case (Drosopoulou et al., 2012; Shamblin et al., 2012).

In addition to phylogenetic relationships and divergence-timeestimation, evolutionary reconstructions based on the entiremitogenome can benefit from genome characterization, identifica-tion of rates of evolution, and characterization of how these ratesvary along particular genomic regions. Although some non-codingregions of the mitogenome are often assumed to evolve neutrally,it is important to highlight the molecule’s crucial role in cellularrespiration. Therefore, the finding that some sites may be underpositive selection and play an important role in environmentaladaptation in other animals (Foote et al., 2011; Garvin et al.,2011) must be taken into account in phylogenetic reconstructionsand inferences of evolutionary processes.

We address several important topics concerning sea turtle evo-lution with large amounts of new data. The first is the phylogeneticrelationships among species and the distinction of several groups,including the placement of N. depressus in relation to Chelonia andthe concordant phylogeographic patterns of some globally distrib-uted species. Secondly, the timing of sea turtle speciation events iskey in understanding the timescale of turtle evolution and its rela-tion to origins of geographic barriers such as the establishment ofthe Isthmus of Panama and changes in temperature of southernocean currents around Africa, as has been previously suggestedfor turtles (Dutton et al., 1999, 1996; Encalada et al., 1996; Naro-Maciel et al., 2008) and other marine organisms (Rosen, 1988).Lastly, particular genomic features have been found in a wide vari-ety of taxa, and have not been thoroughly investigated in sea tur-tles, such as an extra base pair not translated in ND3 in birds andterrestrial turtles (Mindell et al., 1998), as well as variation inselective constraints across the mitogenome.

We have sequenced completemitogenomes for a set of samples ofall extant sea-turtle species, and collected sequences available fromGenBank to produce a large mitogenome phylogeny of these taxa.Samples from across geographic ranges have been included forseveral species to compile the genetic diversity and elaborate onintra-specific phylogeographic patterns and diversification events.

Different molecular clock and phylogenetic frameworks weretested, and provide a basis for further mitogenomic studies in thesetaxa in the form of secondary calibrations (for a discussion on sec-ondary calibrations see: Ho and Phillips, 2009; Ho et al., 2008). Fur-thermore, we explore particular characteristics of the mitogenomeand scan for codon sites under different selective constraints froma structural and phylogenetic perspective.

2. Methods

2.1. Sampling and geographic coverage

A total of 24 sea turtle samples from known localities were se-quenced and combined with additional GenBank sequences for L.

olivacea (Tandon et al., 2006), E. imbricata (Tandon et al., 2006),and C. mydas (Okajima and Kumazawa, 2010). The geographicprovennance of GenBank sequences were not publically available,so the D-Loop was compared to a stock assessment database to as-sign the most likely geographic region for these data. Table 1 listsall samples including outgroups, geographic origin, GenBank acces-sion numbers, and bibliographic reference.

2.2. Sequencing

The complete mtDNA genomes of a green turtle from Tortugero,Costa Rica (haplotype Cmydas T CR); and a leatherback (haplotypeD coriacea O CR) and Olive ridley (haplotype L olivacea O CR) fromOstional, Costa Rica were generated through Roche (454) FLXsequencing of PCR amplicons. The mtDNA genome was first PCR-amplified in two long overlapping 2 kb and 15 kb fragments. Sub-sequently the PCR products were purified, fragmented throughnebulization, converted into MID-tagged sequencing libraries andsequenced as a partial fraction of an LR70 GS-FLX (Roche) run.The generated sequences were assembled into the completemitogenome using the previous green (Chelonia mydas; GenbankID AB012104), hawksbill (E. imbricata; Genbank ID DQ533485)and Olive ridley (L. olivacea; Genbank ID DQ486893) mitogenomesas reference sequences.

Genomic DNA libraries for the rest of the samples were pre-pared and given individual indexing sequences for multiplexingprior to pooling, library enrichment and sequencing as describedin Hancock-Hanser et al. (submitted for publication). Sample li-braries were pooled prior to capture array enrichment, and samplelibraries for all species were enriched using sequence baits fromthe published mitochondrial genome of Chelonia mydas (Okajimaand Kumazawa, 2010). The pooled, enriched library was sequencedon the Illumina Genome Analyzer II (Illumina Inc., La Jolla, CA).

2.3. Sequence assembly and mitogenome annotation

Contigs for the 24 mitogenomes were assembled using refer-ence sea turtle mitogenomes for L. olivacea (Tandon et al., 2006),C. mydas (Okajima and Kumazawa, 2010) and E. imbricata (Tandonet al., 2006) found in GenBank (see Table 1) using Geneious v 4.7(Drummond et al., 2009). All mitogenomes including GenBank ref-erences were then aligned using ClustalW v 2 (Larkin et al., 2007).

Gene identification and annotation were performed by import-ing GenBank sequence annotations into the newly assembledmitogenomes, followed by a complete inspection of individualgene coverage and reading-frame matching in each of the newmitogenomes.

2.4. Phylogenetic models and mitogenome characterization

Complete mitogenomes for terrestrial turtles to be used as out-groups, Chelydra serpentina and Macrochelys temminckii (Nie andYan, 2006), were downloaded from GenBank and aligned with allsea turtles (including GenBank sequences) using Clustal W v2, pro-ducing a 17056 bp alignment.

Individual genes and non-coding regions were extracted fromthe alignment (producing 39 partitions) according to the importedGenBank annotations. Reading frames were visually inspected andbase frequencies and proportions of variable sites were estimatedusing the APE package v2.8 (Paradis et al., 2004). In order to avoidpossible frameshift due to gene overlap (between 3 and 10 bp)such as in ATP8 and ATP6, extracted regions were concatenatedafter verifying their reading frames, producing a final alignmentof 17094 bp. Although this procedure artificially increased thealignment length by 38 bp, due to overlapping sites, it is effective

2 S. Duchene et al. /Molecular Phylogenetics and Evolution xxx (2012) xxx–xxx




in assuring correct model assignment in subsequent phylogeneticanalyses.

Base frequencies, numbers of haplotypes, and proportions ofvariable sites and nucleotide diversities were estimated for allalignments using the APE v2.8 and PEGAS 0.3-3 (Paradis, 2010)packages, then the Phangorn package v1.4-0 (Schliep, 2011) was

used to test models of DNA sequence evolution for the completemitogenome, each individual region, and the three codon positions.The best-fitting models were selected and used for all phylogeneticanalyses according to the Bayesian Information Criterion (BIC)(sample size of 100) since it has been shown to perform better thanother similar methods (Luo et al., 2010).

Table 1List of all sequences used, geographic origin, Sample ID, haplotype name, sequence length, sequence accession number (ACCN), and bibliographic reference. Locations markedwith ! represent the inferred geographic origin of the sample using a turtle D-Loop stock assignment database.

Species Geographic origin Sample ID Haplotype name (Fig. 2key)

Sequencelength

Reference ACCN

Lepidochelysolivacea

North Pacific, Pelagic (NE Pacific!) SWFSC-55352

L olivacea HI (A) 16719 This study


Caribbean (International Waters) SWFSC-78920

L olivacea Car (B) 16719 This study


Ostional, Costa Rica CGG-01 L olivacea OCR (C) 16718 This study


Indo-Pacific!, SeeReference

L olivacea Ind1 (D) 16808 Tandon et al. (2006) AM258984


Indo-Pacific! SeeReference

L olivacea Ind2 (E) 16808 Tandon et al. (2006) DQ486893

Eretmochelysimbricata

Kamehame, Hawaii, USA SWFSC-5787

E imbricata HI (F) 16474 This study


Singapore SWFSC-61392

E imbricata S (G) 16489 This study


Tortuguero, Costa Rica SWFSC-72489

E imbricata T CR (H) 16472 This study


Pacific/Indian ocean! SeeReference

E imbricata Pa In (I) 16478 Tandon et al. (2006) DQ533485


Pacific/Indian ocean! SeeReference

E imbricata Pa In (I) 16478 Tandon et al. (2006) NC_012398

Chelonia mydas Yap, Federated States of Micronesia SWFSC-71270

C mydas Mi Mal (J) 16435 This study

Chelonia mydas Archipielago de Revillagigedo, Mexico SWFSC-13768

C mydas RMX (K) 16435 This study

Chelonia mydas French Frigate Shoals, Hawaii, USA SWFSC-8855

C mydas HI (L) 16435 This study

Chelonia mydas Galapagos Islands, Ecuador SWFSC-54903

C mydas Ec (M) 16435 This study

Chelonia mydas Karpaz, Cyprus SWFSC-9277

C mydas Cyp (N) 16440 This study

Chelonia mydas Sipadan, Malaysia SWFSC-28666

C mydas Mi Mal (J) 16435 This study

Chelonia mydas Tortuguero, Costa Rica CGG-01 C mydas T CR (O) 16495 This studyChelonia mydas Atlantic ocean! See

ReferenceC mydas ATL (P) 16497 Okajima and Kumazawa

(1999)NC_000886

Chelonia mydas Atlantic ocean! SeeReference

C mydas ATL (P) 16497 Okajima and Kumazawa(1999)

AB012104

Dermochelyscoriacea

Guerrero (nesting site), Mexico SWFSC-5718

D coriacea GMX (Q) 16680 This study

Dermochelyscoriacea

Maputaland (nesting site), South Africa SWFSC-9790

D coriacea USVI MSA (R) 16681 This study

Dermochelyscoriacea

St. Croix (nesting site), US VirginIslands, USA

SWFSC-88903

D coriacea USVI MSA (R) 16676 This study

Dermochelyscoriacea

Ostional, Costa Rica CGG-03 D coriacea O CR (S) 16680 This study

Lepidochelys kempii South Padre Island (nesting site), Texas,USA

SWFSC-68090

L kempii SPI1 (T) 16715 This study

Lepidochelys kempii South Padre Island (nesting site), Texas,USA

SWFSC-68091

L kempii SPI2 (P) 16715 This study

Caretta caretta Hawaii (Pelagic), USA SWFSC-46603

C caretta HI Pe (U) 16411 This study

Caretta caretta Coastal waters, Florida, USA SWFSC-69599

C caretta FL1 (V) 16454 This study

Caretta caretta Coastal waters, Florida, USA SWFSC-69611

C caretta FL2 (W) 16337 This study

Caretta caretta Peruvian coast, Peru SWFSC-87410

C caretta HI Pe (U) 16411 This study

Natator depressus Australia SWFSC-21684

N depressus Au (X) 16281 This study

Chelydra serpentina Unknown SeeReference

Chelydra serpentina 16631 Nie and Yan (2006) EF122793

Macrochelystemminckii

Unknown SeeReference

Macrochelys temminckii 16569 Nie and Yan (2006) NC_009260

S. Duchene et al. /Molecular Phylogenetics and Evolution xxx (2012) xxx–xxx 3




2.5. Phylogenetic analyses

Maximum likelihood and neighbor-Joining trees for the com-plete mitogenome were obtained in Garli v2.0 (Zwickl, 2006) andthe APE package v2.8 (Paradis et al., 2004), respectively. In bothcases the GTR+G model was used (as selected with the BIC), and100 bootstrap replicates were performed to assess statistical sup-port of taxonomic groups, and the maximum likelihood analysiswas repeated 10 times to ensure convergence.

In order to determine the most reliable divergence times andevolutionary rates, several Bayesian molecular-clock frameworksas implemented in BEAST v1.6.2 (Drummond and Rambaut,2007) were compared using Bayes Factors (Kass and Raftery,1995) of the harmonic mean of the marginal log-likelihoods as pro-duced in Tracer v1.5 (Rambaut and Drummond, 2005). In all anal-yses the following fossil-derived maximum and minimumdivergence times were used as calibrations, as suggested by Bowenet al. (1993); Dermochelidae–Cheloniidae 100–150 Ma (Weems,1988; Zangerl, 1980), Cheloniidae 50–75 Ma (Ernst and Barbour,1989; Weems, 1988), Caretta–Lepidochelys 12–20 Ma (Carr Jr andMarchand, 1942; Zangerl, 1980) and Lepidochelys 4.5–5 Ma (DoddJr. and Morgan, 1992; Hendrickson, 1980). All calibrations were de-fined using truncated uniform prior distributions since there wasno a priori information on the probability functions for any of thefossil data.

The Dermochelidae–Cheloniidae divergence is of special impor-tance given that this date is inconsistent (59.76 Myr younger thanfossil date) with fossil-derived calibrations using a bootstrap meth-od across a turtle tree, including terrestrial taxa, and spanning backto 210 Ma (Near et al., 2005). However, more recent discussionssuggest that calibrations are not necessarily consistent acrossbroad arrays of taxa due to rate heterogeneity in long timeframes(Parham and Irmis, 2008) (but see Near et al., 2008). The molecu-lar-clock method we used accounts for rate heterogeneity andencompasses sea turtles only (time span of 100–150 Myr); there-fore, we consider this calibration and the fossil-derived estimate(Weems, 1988; Zangerl, 1980) appropriate.

A total of three molecular-clock analyses were conducted withthe following settings:

(1) Single substitution model (as selected by the BIC) and strictclock for the complete mitogenome (no partitioning).

(2) Single substitution model (as selected by the BIC) andrelaxed log-normal clock (no partitioning).

(3) Five partitions corresponding to the combined 12S and 16Sregions, the three codon positions for all coding genes andthe tRNA regions (as individual partitions), and the com-bined D-Loop and Stem-Loop (a common feature of reptilemitogenomes; Jung et al., 2006). Each partition was assigneda separate substitution model and relaxed log-normal clockto incorporate rate heterogeneity across the mitogenome, asthis has been shown to increase performance of Bayesianphylogenetic analyses (Ho and Lanfear, 2010).

BEAST analyses described above were run in the University ofOslo Bioportal web-based service (Kumar et al., 2009) with the fol-lowing settings: Chain length of 150,000,000, sampling every 600,and additional second runs to ensure convergence and ESS valuesof at least 1000 for all parameters.

Since the harmonic mean of the marginal likelihood has beencriticized for favoring parameter-richer models (Beerli and Pal-czewski, 2010; Lartillot and Philippe, 2006), we employed across-validation procedure to assess reliability of Bayes-factorcomparisons. The nucleotide sites for the complete mitogenomealignment were shuffled (sampled without replacement) threetimes using custom R code (Ihaka and Gentleman, 1996; R

Development Core Team, 2006) (available from the authors uponrequest), and each shuffled alignment was analyzed under the par-titioned framework (that with most parameters). This procedureassigns sites randomly to any of the five partitions, making thespecified substitution model and partitioning scheme inappropri-ate, which should lead to lower marginal likelihood. If this wasnot the case one could infer that higher likelihoods are due to anincrease in the number of parameters and not better overall fit ofthe model.

2.6. Phylogenetic resolution of mitochondrial regions

Phylogenetic resolution was assessed for individual genes andthe complete mitogenome to estimate the amount of phylogeneticstructure and diversity detected by the different mitochondrial re-gions. This procedure entailed estimating the majority consensustree of 100 neighbor-Joining bootstrap replicates for every individ-ual region as well as the complete mitogenome, to construct genetrees (and a mitogenomic tree) with polytomies in clades withbootstrap values below 50%. In every case the substitution modelwas chosen according to the BIC before tree estimation. We thenused the Penny and Hendy (1985) method to estimate the topolog-ical distance between every pair of trees, thereby quantifying thedifferences among all topologies. This method was performedusing a custom script in R (available from the authors uponrequest).

2.7. Detection of possible sites under selective constraints

An alignment of all coding genes was used to determinewhether selective constraints were uniform throughout the mitog-enome and whether any particular sites showed evidence of posi-tive selection. The first step was to test codon models; M0, M1a,M2a, M3, M7 and M8, as implemented in PAML v4.4. (Yang,2007) (for a mitogenome example see Garvin et al., 2011). The con-catenated alignment of coding genes and the complete mitoge-nomic ML topology were used while allowing PAML to estimatebranch lengths, transition/transversion ratio (dN/dS), and x valuesacross sites (x < 1 suggests purifying selection,x = 1 suggests neu-trality, and x > 1 suggests positive selection). Nested models werethen compared (M0 vs. M1a, M1a vs. M2a, M0 vs. M3, and M7 vs.M8) using a likelihood-ratio test (LRT), and codon sites under posi-tive selection were determined based on the best-fitting models.

Since the PAML approach focuses on estimation of dN/dS ratiosper site and does not identify significant changes in protein struc-ture and function, we conducted an additional statistical analysisbased on the magnitude of changes in amino-acid properties dueto non-synonymous mutations. This method was employed asimplemented in the TreeSaap v 3.2 program (Woolley et al.,2003), which estimates a score (Z) and statistical significance ofamino-acid changes for 20 chemical properties including polarity,structure tendencies, bulkiness, isoelectric point, and others.

2.8. Mapping of variable sites in proteins

In order to find the location of potential positively-selected sitesin the proteins coded by each gene, we constructed 3D modelsusing homology modeling. A high resolution (3 Å) crystallographicstructure for the membrane domain of the respiratory Complex Ifor Escherichia coli (Efremov and Sazanov, 2011) (PDB ID: 3RKO)was used as a template, where domains NuoA, NuoJ, NuoK, NuoN,NuoM, and NuoL are homologous to ND3, ND4L, ND6, ND2, ND4,and ND5, respectively. Protein models were obtained in theSWISS-MODEL (Schwede et al., 2003) tool in the ExPASy Bioinfor-matics resource portal (Gasteiger et al., 2003), and viewed usingthe Swiss PDB Viewer v 4.0.1 (Guex and Peitsch, 1997) software.





Superimposition of protein template and models was performedusing the ‘‘Magic Fit’’ function in the Swiss PDB viewer, and trans-membrane domains were identified according to Efremov andSazanov (2011).

3. Results

3.1. Phylogenetic analyses

Contig assembly for the 24 mitogenomes produced in this studyyielded complete mitogenome lengths between 16281 and16719 bp (Table 1). The complete mitogenome alignment of the32 sequences (24 from this study plus eight GenBank sequences)revealed a total of five shared haplotypes within the species C.caretta, E. imbricata, C. mydas, and D. coriacea (see Table 1 formitogenomic haplotype naming in this study). This revealed a pro-portion of 0.83 (25/30) unique sea turtle haplotypes (number ofunique haplotypes/total number of samples), and nucleotide diver-sities (mean proportion of variable sites in pairwise comparison/alignment length) of 0.0078 (variance = 0.0033) for C. caretta,0.014 (variance = 0.005) for L. olivacea, 0.0002 (variance = 0.0002),for L. Kempii, 0.011 (variance = 0.004) for E. imbricata, 0.006 (vari-ance = 0.0017) for C. mydas, and 0.00036 (variance = 0.00016) forD. coriacea.

Model testing for the complete mitogenome showed a prefer-ence for GTR+G as the best substitution model. The proportion ofvariable sites was different among regions, the D-Loop having thehighest variability, and the Stem-loop having the lowest (seeFig. S1 for gene positions). Base frequencies were not homoge-neous among regions; the G content was particularly variable(Table S1).

Maximum likelihood and all Bayesian phylogenetic analysesrevealed the same topology with comparable support values

(bootstrap and posterior probabilities) (Fig. 1). This topology sup-ported major relationships found in previous studies based oncombined nuclear and mitochondrial data (Naro-Maciel et al.,2008), but it was inconsistent with phylogenetic reconstructionsusing mitochondrial D-Loop and ND4L (Dutton et al., 1996), Cytb(Bowen et al., 1993), and morphology (Zangerl, 1980). All nodesin Fig. 1 within and between species had bootstrap and poster-ior-probability supports of 100% and 1.00, respectively, exceptwithin D. coriacea (within node VI), where the intra-specific haplo-type relationships had a low support of 47% and 0.44 (Fig. 1).

One finding of particular importance was high support for N.depressus as the sister taxon to C. mydas. Previous studies basedon mitochondrial 12S and 16S, and nuclear markers BDNF, Cmos,R35, Rag1, and Rag 1 (Naro-Maciel et al., 2008) have supported thisrelationship, whereas D-Loop, ND4, and tRNA data have placed thisspecies as the sister taxon to the clade containing Eretmochelys,Lepidochelys and Caretta (Dutton et al., 1999).

There was a common phylogeographic pattern for three of fiveglobally distributed species (E. imbricata, C. mydas and D. coriacea).Phylogenetic groupings show two clades consisting of haplotypesfrom the geographic range extremes: The Atlantic and Indian,and Pacific ocean regions (see color coding in Figs. 1 and 2), as sug-gested by previous studies (Bowen et al., 1998, 1994; Bowen andKarl, 2007; Dutton et al., 1999; Encalada et al., 1996). In contrast,C. caretta did not display phylogenetic concordance with currentgeographic distributions, given the high support in node XII(Fig. 1 and Table 2) for the Pacific haplotype (C caretta HI Pe) beingnested within two Atlantic samples (C caretta FL1 and C carettaFL2), with a median TMRCA of 2.37 Million Years Before Present(Ma) (1.24–3.89 Highest Posterior Density (HPD)), as shown inTable 2. Within L. olivacea, the major split was approximately2.7 Ma (2.40–3.36 HPD) between Indian Ocean samples and allothers, with samples from the Pacific clustering with high support

Fig. 1. Chronogram for complete mitogenomic analysis with haplotype key for Fig. 2. Branch support is shown for Posterior probability/Bootstrap support (maximumlikelihood) only for branches where these values were below 0.99 and 95, respectively. Roman numbering corresponds to nodes listed in Table 2. Tip label colors representhaplotype geographic distribution as shown in Fig. 2.





(node I in Table 2 and Fig. 1) with a sample from the Caribbean(Fig. 1), a pattern that has also been described based on shortermitochondrial sequences (Bowen et al., 1998; Shanker et al., 2004).

Of the three molecular clocks tested, the five-partition modelwas selected according to a log Bayes Factors of "375.11 comparedto the relaxed-non-partitioned model (second best performing;Table S2). Reliability of the Bayes Factors clock comparison wasconfirmed by the 3 randomized-site analyses, where the log-likeli-hoods were lowest among relaxed-clock analyses (log Bayes Fac-tors between "483.69 and "476.72).

Using the five-partitioned clockmodel, we estimated the time tothe most recent common ancestor (TMRCA) for all nodes in Table 2and Fig. 1. Median TMRCAS for species were between 0.066 Ma(0.0082–0.17 HPD) for L. kempii, to 5.63 (3.44–8.85 HPD) for E.imbricata, whereas that for all marine turtles (families Cheloniidaeand Dermochelyidae) was 102.63 (100.00–111.58 HPD).

Molecular-clock analyses also permitted estimation of meanrates (substitutions/site/Myr) and a measure of clock-like behavior(coefficient of variation) of mitogenomic partitions (Table 3). Thefastest rate was found in the non-coding D-Loop + Stem-Loop re-gions at 3.24 # 10"3 (2.67–3.81 HPD), and the slowest in thenon-coding 12S + 16S at 0.88 # 10"3 (0.7–0.99 HPD). Since the cod-ing regions were partitioned by codon positions, our estimatesindicate that mean rate of third positions is the fastest(1.86 # 10"3 (1.56–2.13 HPD)) and second positions is the slowest(1.19 # 10"3 (1.00–1.36 HPD)), as expected from previous studieson mitochondrial rates in reptiles (Jiang et al., 2007) and other ver-tebrates (Nabholz et al., 2009).

Previous estimates of mitochondrial substitution rates in mar-ine turtles range from 4 # 10"3 using Cytb (Bowen et al., 1993),to 1.2 # 10"3 (Encalada et al., 1996) and 6.7 # 10"3 Mutations/Site/Myr for D-Loop (Dutton et al., 1999). Although our methods

Fig. 2. Map with key for mitogenomic haplotypes in Fig. 1 and shaded with corresponding colors from Fig. 1 for the two main geographic regions: Atlantic and Indian, andPacific oceans. Haplotypes marked with ! represent the inferred geographic origin of the sample using a turtle D-Loop stock assignment database.

Table 2Calibrations used in molecular clock analyses and posterior TMRCA estimates.

Taxonomic group Node in Fig. 1 Prior (MYBP) Median (MYBP) 95% HPD

Lepidochelys olivacea (haplotypes L_olivacea_O_CR, L_olivacea_HI, and L_olivacea_Car) I !Tree prior 0.23 0.077–0.50Chelonia mydas II !Tree prior 3.09 1.76–4.87Chelonia + Natator III !Tree prior 36.43 21.92–52.51Chelonidae IV 50.00–75.00 55.68 50.00–67.44Dermochelidae + Chelonidae V 100.00–150.00 102.63 100.00–111.58Dermochelys coriacea VI !Tree prior 0.17 0.06–0.35Eretmochelys imbricata VII !Tree prior 5.63 3.44–8.85Lepidochelys kempii VIII !Tree prior 0.066 0.0082–0.17Lepidochelys olivacea IX !Tree prior 2.71 2.40–3.36Lepidochelys X 4.50–5.00 4.84 4.56–5.00Caretta caretta XI !Tree prior 4.09 2.38–6.43Caretta caretta (haplotypes C caretta HI PE and C caretta FL1) XII !Tree prior 2.37 1.24–3.89Caretta + Lepidochelys XIII 12.00–20.00 18.62 15.50–20.00

Table 3Mean rate and coefficient of variation for partitions in Bayesian phylogenetic analyses.

Partition Mean rate (10"3 mutations/site #MY) Coefficient of variation

Median 95% HPD Median 95% HPD

12S + 16S 0.88 0.70–0.99 0.85 0.49–1.58First codon sites 1.71 1.44–1.97 0.88 0.64–1.18Second codon sites 1.19 1.00–1.36 1.59 1.02–2.71Third codon sites 1.86 1.56–2.13 0.67 0.46–0.93D-Loop + Stem Loop 3.24 2.67–3.81 1.11 0.83–1.53





employ more mitochondrial data (complete mitogenomes vs. sin-gle genes) and different clock models (relaxed vs. strict), our rateestimates are comparable to those in previous reports (except forthe 12S + 16S regions), but exhibit a narrower range than thosebased on smaller individual segments.

When comparing phylogenetic resolution of the different mito-chondrial genes we observed that information content in terms oftopologies is not uniformly distributed throughout the mitoge-nome (a dendrogam of the distances among all topologies is shownin Fig. S2). Given each topology was estimated with a differentdataset (partition) we could not determine whether any of the dif-ferences were significant according to topology tests such as Shi-modaira and Hasegawa (1999), however we could establishinformation content for partitions relative to the complete mitog-enome. The D-Loop, ND2, and 16S produced the most similar treesto the mitogenome tree. Other genes, such as ATP8, COX2, 12S, andND4 produced very different topologies and lower resolution.

3.2. Mitogenome features and possible selective constraints

Gene annotations as applied from published turtle mitoge-nomes in GenBank revealed several important features of the mar-ine-turtle mitogenome. As previously reported in birds and someturtles (Mindell et al., 1998), there was an extra nucleotide causinga frame shift in ND3 at nucleotide site 175 in most samples(Table S3). However, the most striking finding was a change inthe length of ATP8 caused by a point mutation in all D. coriacea,four C. mydas (from the Caribbean, Cyprus, and Atlantic ocean),two E. imbricata (from the Pacific–Indian region), and two L. oliva-cea (Indo-Pacific). This mutation eliminates a stop codon and in-creases the range of the gene until the next stop codon in theATP8 reading frame (site 186 in Fig. S3, common to all sequences),implying an additional 21 bp in ATP8 in the overlapping region be-tween ATP8 and ATP6 (as shown in Fig. S3 and Table S3). To date, nohigh-resolution crystallographic structures are available for pro-teins coded by these genes, so it is not possible to show how thismutation would affect structure and function.

PAML model comparisons provided evidence for possible sitessubject to adaptive evolution. The Likelihood Ratio Test (LRT) formodel comparisons M0 (same x for all sites) vs. M1a (x variesamong sites with two discrete categories x0 < 1 and x1 = 1), andM0 vs. M3 (x varies among sites with three discrete categoriesof estimated x, x0, x1, x2) demonstrated better performance formodels M1 and M3, respectively, suggesting heterogeneity inselective constraints across the coding regions. Positive selectionwas detected in one out of the two LRT model comparisons that ac-counted forx > 1: M1a vs. M2a, and M7 vs. M8. Model M2a (x var-ies among sites with three categories of x: x0 < 1, x1 = 1, x2 > 1)was not statistically distinguishable from M1a, but in the case ofM7 (x has a beta distribution for x < 1) vs M8 (x has a beta distri-bution like M7, and an additional class for x > 1), model M8 per-formed better (Table S4).

The only site in model M8 with a significant x value suggestiveof positive selection was found in codon site 169 of the ND4 gene(nucleotides 505–507) (Fig. S4), with a probability of positiveselection (x > 1) of 0.97 and an x estimate of 1.480 ± 0.127. Anal-ysis of changes in amino-acid properties in TreeSaap revealed thatthis codon site had 20 amino-acid properties with significant Zscores (P < 0.001), the highest of any codon site (Fig. S5).

SWISS-MODEL was used to obtain a structural prediction for theND4 subunit. The structure obtained using the E. coli NuoM tem-plate had a mean Z-score of "6.75, an E-value of 2.48 # 10"11,and 65% sequence identity. Although the Z-score and E-value ob-tained suggest a poor structural prediction (Bowie et al., 1991),superimposition of the template and model showed remarkablesimilarities in the alpha helices and beta sheets, supported by a

low RMS value of 0.7 Å. In both cases, codon site 169 was withinan alpha helix (Fig. 3a). Fig. 3b and c show the predicted (ND4)and template (NuoM) structures, the approximate path of the pro-ton translocation channel (Efremov and Sazanov, 2011), and thelocation of site 169 (according to colors in Fig. S4). Although thissite is spatially close to the channel, the predicted structure isnot considered a precise assessment of the effect of mutations atthis site.

4. Discussion

Our results support previously suggested relationships amongsea turtle species, notably the placing of N. depressus as the sistertaxon to Chelonia (Naro-Maciel et al., 2008), rather than to theclade comprising Eretmochelys, Lepidochelys, and Caretta (Duttonet al., 1999). This result is relevant in supporting phylogenetic rela-tionships within the family Cheloniidae, particularly the exclusionof N. depressus from the subfamily Carettini.

Our exploration of the phylogenetic resolution of differentmitochondrial regions (Fig. S2) potentially explains the contrastingphylogenetic relationships inferred from other mitochondrialmarkers in previous studies (Dutton et al., 1999; Naro-Macielet al., 2008). The congruence of the mitogenomic and nuclear–mitochondrial topology from this study and Naro-Maciel et al.(2008), respectively, demonstrates the importance of largeamounts of molecular data in resolving the turtle evolutionarytree. Although mitochondrial markers are linked (Rand, 2001)and represent a single gene tree, using single regions or small por-tions of the mitogenome can produce poorly supported trees andsometimes apparently conflicting estimates of that gene tree(Duchene et al., 2011). Therefore, complete mitogenomes as usedhere should provide a valuable source of data for phylogeneticreconstructions with more reliable phylogenetic structure.

We found some differences between our TMRCA estimates andthose of other studies, notably Naro-Maciel et al. (2008), who esti-mated the Pacific and Atlantic C. mydas divergence time at 7 Ma(1.92–13.47 HPD), whereas our estimate was 3.09 (1.76–4.87). Thisdifference in the estimates of the TMRCA could reflect different lin-eages that may have been sampled in both studies. Nevertheless,given that calibrations were very similar, any differences and in-crease in accuracy are likely due to the addition of molecular data(Duchene et al., 2011). Although an increase in accuracy (narrowererror bars) does not necessarily imply higher precision (reliabilityin the estimation of the true divergence time), complete mitoge-nomes have been shown provide more reliable divergence timesthan single mitochondrial markers (Duchene et al., 2011; Ho andLanfear, 2010).

Although our sampling consisted of 10 or fewer samples perspecies, the wide geographic coverage permitted phylogeographicinferences across all of the globally-distributed species. Therewas clear differentiation between Indo-Pacific and Atlantic popula-tions in three out of five of the species with cosmopolitan distribu-tions (all but N. depressus and L. kempii), as observed by otherauthors (Dutton et al., 1999; Naro-Maciel et al., 2008), pointingto the Americas as a barrier for migration in some sea turtles (E.imbricata, C. mydas, and D. coriacea, but see below). D. coriacea dis-played this distinction with poor branch support, likely due to itsmore recent range expansion and little genetic differentiation, assuggested by Dutton et al. (1999) (median TMRCA of 0.17 Ma, com-pared to a median of 3.09 Ma in Chelonia).

C. caretta samples were more divergent within the Atlantic thanbetween the Atlantic and Pacific, and in L. olivacea the only Atlanticsample was nested within those from the Pacific. It is likely thatthe current distribution of these two species was not shaped bythe same geographic barriers as the three other cosmopolitan





species. The phylogentic relationships between haplotypes of thesespecies is evidence of a possible connection between Atlantic andPacific groups, possibly across the southern tip of Africa after theclosing of the Panama isthmus. These contrasting patterns indicatethat the biogeographic history of marine turtles has been shapedby different events; and barriers to geneflow are not the samefor all species. Changes in climatic conditions over the past 1 Mahave been particularly influential for geographic genetic patternsin C. caretta (Bowen et al., 1994; Encalada et al., 1998), and proba-bly L. olivacea (Bowen et al., 1998; Shanker et al., 2004). Geographicgenetic variation is much older in the other species (except for D.coriacea, as discussed above). For example, divergence dates asold as 5.63 Ma (3.44–8.85 HPD) are needed to explain geographicgenetic variation in E. imbricata.

Timing of speciation and diversification events narrows hypoth-eses of possible geologic events driving evolution of some sea-tur-tle species. Notably, the divergence between Pacific and Atlanticclades of Chelonia mydas and E. imbricata occurred after the coolingof southern ocean waters in the mid to late Miocene (between 17and 6 Ma) (Rogl, 1998), and possibly coincided with the closing ofthe Panama isthmus (between 5 and 2.5 Ma) (Farrell et al., 1995).Our evidence also supports L. olivacea and D. coriacea divergingafter the closing of the Panama isthmus in the Pliocene. Eventssuch as these could have shaped current geographic distributionsby restricting gene flow between Atlantic and Pacific populations.

Although some mitogenomic features may be the result ofadaptive evolution, most of the adaptive variation, as detected bythe software used, refers to fixed differences among populationsor species. Therefore, the possibility that this variation is the resultof random fixed differences from stochastic processes cannot beruled out. Interestingly, the vast majority of coding-sites in themitogenome have very low variability, suggesting strong selectiveconstraints in a large proportion of the molecule and low overallvariation.

Our data support previous rate estimates of single mitochon-drial regions (Bowen et al., 1993; Dutton et al., 1999; Encaladaet al., 1996), and although the rate is not constant throughoutthe mitogenome, it ranges between 0.7 # 10"3 and 3.81 # 10"3

substitutions/site/Myr (inferred from HPD extremes of 12S–16Sand D-Loop). These estimates indicate that canonical substitutionrates for mitochondrial regions used to calibrate clocks, such asthe standard 0.01 (Shields and Wilson, 1987) and 0.075 (Randi

et al., 2001) inferred for birds and widely used in other vertebrates,are an overestimate for the turtle mitochondrial clock by as muchas an order of magnitude. This supports previous claims of a slowermolecular clock for the Testunidates (Avise et al., 1992). Molecularclocks that use these canonical rates therefore tend to underesti-mate divergence times (Ho et al., 2008).

Moreover, studies in cetaceans have found similar mitogenomicrates, ranging from to 2.6 # 10"3 in baleen whales (Jackson et al.,2009) to 3.9 # 10"3 in killer whales (Duchene et al., 2011; Morinet al., 2010). Summing these estimates from independent lines ofevidence may indicate that long-lived organisms with relativelylarge sizes may have more slowly evolving mitochondrial genomes(Jackson et al., 2009).

Complete mitochondrial genomes reveal several new featuresof sea turtle evolution. Most importantly, phylogeography of seaturtles follows a clear geographic differentiation for some species,yet migration between ocean basins is likely for L. olivacea and C.caretta. Although more data is likely to yield similar phylogeneticpatterns to those we report (Karl et al., 2012), future researchcan uncover past migration patterns that may have shaped thegeographic genetic distribution of these species with nuclearmarkers.

Acknowledgments

Samples archived in the US National Marine Fisheries Service(NMFS) Marine Turtle Molecular Research Sample Collection atthe Southwest Fisheries Science Center were collected under therespective National authorizations and CITES permit conditions.We thank Erin LaCasella, Amanda Bowman, Gabriela Serra-Valentefor laboratory assistance. For providing samples we thank theHawaiian Islands National Wildlife Refuge, US Fish and WildlifeService, NMFS Pacific Islands Fisheries Science Center, NMFS-Paci-fic Islands Regional Office Fisheries Observer Program, GalapagosNational Park, Charles Darwin Research Foundation, SEMARNAT,MINAE, US National Park Service, Padre Island National Seashore,Sandy Point National Wildlife Refuge, US Virgin Islands Depart-ment of Planning and Natural Resources, Natal Parks Board, GeorgeBalazs, Ana Barragan, Brian Bowen, Arturo Ceron, Jennifer Cruce,Cheong Hoong Diong, Sheryan Epperly, Nancy Fitzsimmons, Bren-dan Godley, Stacy Hargrove, Emma Harrison, George Hughes, Push-pa Palaniappan, Nelly de la Paz, Rotney Piedra, Laura Sarti, Donna

(a) (b) (c)

Fig. 3. (a) Superimposition of structures. White corresponds to complete superimposition. Red and blue are respectively, turtle and E. coli residues only. (b and c) Turtle andEscherichia coli protein structures. Helixes are colored from blue to red, representing N-C terminal direction. In all proteins the red arrow points to amino-acid site 169.





Shaver, Lesley Stokes, Wendy Teas, Sebastian Troeng, Shelby Walk-er, and Patricia Zarate. We are grateful to Bill Perrin, Michael Jen-sen and two anonymous reviewers for helpful comments on themanuscript. This study was funded by NMFS.

Appendix A. Supplementary material

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.ympev.2012.06.010.

References

Avise, J.C., Bowen, B.W., Lamb, T., Meylan, A.B., Bermingham, E., 1992. MitochondrialDNA evolution at a turtle’s pace: evidence for low genetic variability andreduced microevolutionary rate in the Testudines. Mol. Biol. Evol. 9, 457–473.

Barley, A.J., Spinks, P.Q., Thomson, R.C., Shaffer, H.B., 2010. Fourteen nuclear genesprovide phylogenetic resolution for difficult nodes in the turtle tree of life. Mol.Phylogenet. Evol. 55, 1189–1194.

Beerli, P., Palczewski, M., 2010. Unified framework to evaluate panmixia andmigration direction among multiple sampling locations. Genetics 109 (112532),v112531.

Bowen, B., Karl, S., 2007. Population genetics and phylogeography of sea turtles.Mol. Ecol. 16, 4886–4907.

Bowen, B.W., Meylan, A.B., Avise, J.C., 1991. Evolutionary distinctiveness of theendangered Kemp’s ridley sea turtle. Nature 352, 709–711.

Bowen, B.W., Nelson, W.S., Avise, J.C., 1993. A molecular phylogeny for marineturtles: trait mapping, rate assessment, and conservation relevance. Proc. Natl.Acad. Sci. USA 90, 5574.

Bowen, B.W., Kamezaki, N., Limpus, C.J., Hughes, G.R., Meylan, A.B., Avise, J.C., 1994.Global phylogeography of the loggerhead turtle (Caretta caretta) as indicated bymitochondrial DNA haplotypes. Evolution, 1820–1828.

Bowen, B., Clark, A., Abreu-Grobois, F., Chaves, A., Reichart, H., Ferl, R., 1998. Globalphylogeography of the ridley sea turtles (Lepidochelys spp.) as inferred frommitochondrial DNA sequences. Genetica 101, 179–189.

Bowie, J.U., Luthy, R., Eisenberg, D., 1991. A method to identify protein sequencesthat fold into a known three-dimensional structure. Science 253, 164.

Carr Jr., A.F., Marchand, L.J., 1942. A new turtle from the Chipola River, Florida. In:Proceedings of the New England Zoology Club, pp. 95–100.

Dodd Jr., C.K., Morgan, G.S., 1992. Fossil sea turtles from the early Pliocene BoneValley Formation, central Florida. J. Herpetol., 1–8.

Drosopoulou, E., Tsiamis, G., Mavropoulou, M., Vittas, S., Katselidis, K.A., Schofield,G., Palaiologou, D., Sartsidis, T., Bourtzis, K., Pantis, J., 2012. The completemitochondrial genome of the loggerhead turtle Caretta caretta (Testudines:Cheloniidae): genome description and phylogenetic considerations. Mit. DNA23, 1–12.

Drummond, A., Rambaut, A., 2007. BEAST: Bayesian evolutionary analysis bysampling trees. BMC Evol. Biol. 7, 214.

Drummond, A., Ashton, B., Cheung, M., Heled, J., Kearse, M., Moir, R., Stones-Havas,S., Thierer, T., Wilson, A., 2009. Geneious v4. 7. Biomatters, Ltd., Auckland, NewZealand.

Duchene, S., Archer, F.I., Vilstrup, J., Caballero, S., Morin, P.A., 2011. Mitogenomephylogenetics: the impact of using single regions and partitioning schemes ontopology, substitution rate and divergence time estimation. PLoS One 6, e27138.

Dutton, P.H., Davis, S.K., Guerra, T., Owens, D., 1996. Molecular phylogeny formarine turtles based on sequences of the ND4-leucine tRNA and control regionsof mitochondrial DNA. Mol. Phylogenet. Evol. 5, 511–521.

Dutton, P.H., Bowen, B.W., Owens, D.W., Barragan, A., Davis, S.K., 1999. Globalphylogeography of the leatherback turtle (Dermochelys coriacea). J. Zool. 248,397–409.

Efremov, R.G., Sazanov, L.A., 2011. Structure of the membrane domain of respiratorycomplex I. Nature 476, 414–420.

Encalada, S., Lahanas, P., Bjorndal, K., Bolten, A., Miyamoto, M., Bowen, B., 1996.Phylogeography and population structure of the Atlantic and Mediterraneangreen turtle Chelonia mydas: a mitochondrial DNA control region sequenceassessment. Mol. Ecol. 5, 473–483.

Encalada, S.E., Bjorndal, K.A., Bolten, A.B., Zurita, J.C., Schroeder, B., Possardt, E.,Sears, C.J., Bowen, B.W., 1998. Population structure of loggerhead turtle (Carettacaretta) nesting colonies in the Atlantic and Mediterranean as inferred frommitochondrial DNA control region sequences. Mar. Biol. 130, 567–575.

Ernst, C.H., Barbour, R.W., 1989. Turtles of the World. Smithsonian Institution Press,Washington, DC.

Farrell, J., Raffi, I., Janecek, T., Murray, D., Levitan, M., Dadey, K., Emeis, K.C., Lyle, M.,Flores, J.A., Hovan, S., 1995. Late Neogene Sedimentation Patterns in the EasternEquatorial Pacific Ocean. Ocean Drilling, Program, pp. 717–756.

Foote, A.D., Morin, P.A., Durban, J.W., Pitman, R.L., Wade, P., Willerslev, E., Gilbert,M.T.P., da Fonseca, R.R., 2011. Positive selection on the killer whalemitogenome. Biol. Lett. 7, 116–118.

Garvin, M.R., Bielawski, J.P., Gharrett, A.J., 2011. Positive Darwinian selection in thepiston that powers proton pumps in complex I of the mitochondria of pacificsalmon. PLoS One 6, e24127.

Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D., Bairoch, A., 2003.ExPASy: the proteomics server for in-depth protein knowledge and analysis.Nucleic Acids Res. 31, 3784–3788.

Guex, N., Peitsch, M.C., 1997. SWISS-MODEL and the Swiss-Pdb Viewer: anenvironment for comparative protein modeling. Electrophoresis 18, 2714–2723.

Hancock-Hanser, B., Frey, A., Leslie, M., Dutton, P.H., Archer, E.I., Morin, P.A.,submitted for publication. Targeted multiplex next-generation sequencing:advances in techniques of mitochondrial and nuclear sequencing for populationgenomics. Mol. Ecol. Resour.

Hendrickson, J.R., 1980. The ecological strategies of sea turtles. Am. Zool. 20, 597.Hirth, H.F., Fish, U., Service, W., 1997. Synopsis of the Biological Data on the Green

Turtle Chelonia mydas (Linnaeus 1758). Fish andWildlife Service, US Dept. of theInterior.

Ho, S.Y.W., Lanfear, R., 2010. Improved characterisation of among-lineage ratevariation in cetacean mitogenomes using codon-partitioned relaxed clocks. Mit.DNA 21, 138–146.

Ho, S.Y.W., Phillips, M.J., 2009. Accounting for calibration uncertainty inphylogenetic estimation of evolutionary divergence times. Syst. Biol. 58, 367–380.

Ho, S.Y.W., Saarma, U., Barnett, R., Haile, J., Shapiro, B., 2008. The effect ofinappropriate calibration: three case studies in molecular ecology. PLoS One 3,e1615.

Ihaka, R., Gentleman, R., 1996. R: a language for data analysis and graphics. J.Comput. Graph. Stat., 299–314.

Iverson, J.B., Brown, R., Akre, T., Near, T., Le, M., Thomson, R., Starkey, D., 2007. Insearch of the tree of life for turtles. Chel. Res. Mono. 4, 85–106.

Jackson, J., Baker, C., Vant, M., Steel, D., Medrano-González, L., Palumbi, S., 2009. Bigand slow: phylogenetic estimates of molecular evolution in baleen whales(Suborder Mysticeti). Mol. Biol. Evol. 26, 2427–2440.

Jiang, Z., Castoe, T., Austin, C., Burbrink, F., Herron, M., McGuire, J., Parkinson, C.,Pollock, D., 2007. Comparative mitochondrial genomics of snakes:extraordinary substitution rate dynamics and functionality of the duplicatecontrol region. BMC Evol. Biol. 7, 123.

Jung, S.O., Lee, Y.M., Kartavtsev, Y., Park, I.S., Kim, D.S., Lee, J.S., 2006. The completemitochondrial genome of the Korean soft-shelled turtle Pelodiscus sinensis(Testudines, Trionychidae). Mit. DNA 17, 471–483.

Karl, S.A., Bowen, B.W., 1999. Evolutionary significant units versus geopoliticaltaxonomy: molecular systematics of an endangered sea turtle (genus Chelonia).Conserv. Biol. 13, 990–999.

Karl, S.A., Toonen, R.J., Grant, W.S., Bowen, B.W., 2012. Common misconceptions inmolecular ecology: echoes of the modern synthesis. Mol. Ecol. http://dx.doi.org/10.1111/j.1365-294X.2012.05576.x.

Kass, R.E., Raftery, A.E., 1995. Bayes factors. J. Am. Stat. Assoc., 773–795.Kumar, S., Skjaeveland, A., Orr, R., Enger, P., Ruden, T., Mevik, B.H., Burki, F., Botnen,

A., Shalchian-Tabrizi, K., 2009. AIR: a batch-oriented web program package forconstruction of supermatrices ready for phylogenomic analyses. BMC Bioinf. 10,357.

Larkin, M., Blackshields, G., Brown, N., Chenna, R., McGettigan, P., McWilliam, H.,Valentin, F., Wallace, I., Wilm, A., Lopez, R., 2007. Clustal W and Clustal Xversion 2.0. Bioinformatics 23, 2947.

Lartillot, N., Philippe, H., 2006. Computing Bayes factors using thermodynamicintegration. Syst. Biol. 55, 195.

Luo, A., Qiao, H., Zhang, Y., Shi, W., Ho, S., Xu, W., Zhang, A., Zhu, C., 2010.Performance of criteria for selecting evolutionary models in phylogenetics: acomprehensive study based on simulated datasets. BMC Evol. Biol. 10, 242.

Mindell, D.P., Sorenson, M.D., Dimcheff, D.E., 1998. An extra nucleotide is nottranslated in mitochondrial ND3 of some birds and turtles. Mol. Biol. Evol. 15,1568.

Morin, P.A., Archer, F.I., Foote, A.D., Vilstrup, J., Allen, E.E., Wade, P., Durban, J.,Parsons, K., Pitman, R., Li, L., 2010. Complete mitochondrial genomephylogeographic analysis of killer whales (Orcinus orca) indicates multiplespecies. Genome Res. 20, 908–916.

Nabholz, B., Glémin, S., Galtier, N., 2009. The erratic mitochondrial clock: variationsof mutation rate, not population size, affect mtDNA diversity across birds andmammals. BMC Evol. Biol. 9, 54.

Naro-Maciel, E., Le, M., Fitzsimmons, N.N., Amato, G., 2008. Evolutionaryrelationships of marine turtles: a molecular phylogeny based on nuclear andmitochondrial genes. Mol. Phylogenet. Evol. 49, 659–662.

Near, T.J., Meylan, P.A., Shaffer, H.B., 2005. Assessing concordance of fossilcalibration points in molecular clock studies: an example using turtles. Am.Nat. 165, 137–146.

Near, T.J., Meylan, P.A., Shaffer, H.B., 2008. Caveats on the use of fossil calibrationsfor molecular dating: a reply to Parham and Irmis. Am. Nat. 171, 137–140.

Nie, L.W., Yan, L., 2006. The Complete Sequence of Chelydra serpentina. College ofLife Science, Anhui Normal University, East Beijing Road 1, Wuhu, Anhui241000, China.

Okajima, Y., Kumazawa, Y., 2010. Mitochondrial genomes of acrodont lizards:timing of gene rearrangements and phylogenetic and biogeographicimplications. BMC Evol. Biol. 10, 141.

Paradis, E., 2010. Pegas: an R package for population genetics with an integrated,Äìmodular approach. Bioinformatics 26, 419–420.

Paradis, E., Claude, J., Strimmer, K., 2004. APE: analyses of phylogenetics andevolution in R language. Bioinformatics 20, 289.

Parham, J.F., Irmis, R.B., 2008. Caveats on the use of fossil calibrations for moleculardating: a comment on Near et al. Am. Nat. 171, 132–136.





http://dx.doi.org/10.1111/j.1365-294X.2012.05576.x

http://dx.doi.org/10.1111/j.1365-294X.2012.05576.x



Penny, D., Hendy, M., 1985. The use of tree comparison metrics. Syst. Zool. 34, 75–82.

R Development Core Team, R., 2006. R: a Language and Environment for StatisticalComputing. R Foundation for Statistical Computing.

Rambaut, A., Drummond, A., 2005. Tracer: a Program for Analysing Results fromBayesian MCMC Programs such as BEAST & MrBayes. <http://evolve.zoo.ox.ac.uk/software.html>.

Rand, D.M., 2001. The units of selection of mitochondrial DNA. Ann. Rev. Ecol. Syst.,415–448.

Randi, E., Lucchini, V., Hennache, A., Kimball, R.T., Braun, E.L., Ligon, J.D., 2001.Evolution of the mitochondrial DNA control region and cytochrome b genes andthe inference of phylogenetic relationships in the avian genus Lophura(Galliformes). Mol. Phylogenet. Evol. 19, 187–201.

Rogl, F., 1998. Palaeogeographic considerations for Mediterranean and Paratethysseaways (Oligocene to Miocene). Ann. Nat. Mus. Wien. 99, 279–310.

Rosen, B.R., 1988. Progress, problems and patterns in the biogeography of reef coralsand other tropical marine organisms. Helg. Mar. Res. 42, 269–301.

Schliep, K.P., 2011. Phangorn: phylogenetic analysis in R. Bioinformatics 27, 592.Schwede, T., Kopp, J., Guex, N., Peitsch, M.C., 2003. SWISS-MODEL: an automated

protein homology-modeling server. Nucleic Acids Res. 31, 3381–3385.Shamblin, B.M., Bjorndal, K.A., Bolten, A.B., Hillis, A.Z.M., Lundgren, I., Naro-Maciel,

E., Nairn, C.J., 2012. Mitogenomic sequences better resolve stock structure ofsouthern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2430.

Shanker, K., Ramadevi, J., Choudhury, B.C., Singh, L., Aggarwal, R.K., 2004.Phylogeography of olive ridley turtles (Lepidochelys olivacea) on the east coastof India: implications for conservation theory. Mol. Ecol. 13, 1899–1909.

Shields, G.F., Wilson, A.C., 1987. Calibration of mitochondrial DNA evolution ingeese. J. Mol. Evol. 24, 212–217.

Shimodaira, H., Hasegawa, M., 1999. Multiple comparisons of log-likelihoods withapplications to phylogenetic inference. Mol. Biol. Evol. 16, 1114–1116.

Talavera, G., Vila, R., 2011. What is the phylogenetic signal limit frommitogenomes? The reconciliation between mitochondrial and nuclear data inthe Insecta class phylogeny. BMC Evol. Biol. 11, 315.

Tandon, M., Trivedi, R., Kashyap, V.K., 2006. The Complete Nucleotide Sequence ofOlive Ridley Seaturtle (Lepidochelys olivacea) Mitochondrial Genome. NationalDNA Analysis Centre, Central Forensic Science Laboratory, 30, Gorachand Road,Kolkata, W. Bengal 700014, India.

Wang, J., Yang, G., 2011. The complete mitogenome of the snakehead Channa argus(Perciformes: Channoidei): genome characterization and phylogeneticimplications. Mit. DNA 22, 120–129.

Weems, R., 1988. Paleocene turtles from the Aquia and Brightseat formations, witha discussion of their bearing on sea turtle evolution and phylogeny. Proc. Biol.Soc. Wash. 101, 109–145.

Woolley, S., Johnson, J., Smith, M.J., Crandall, K.A., McClellan, D.A., 2003. TreeSAAP:selection on amino acid properties using phylogenetic trees. Bioinformatics 19,671.

Yang, Z., 2007. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol.Evol. 24, 1586–1591.

Zangerl, R., 1980. Patterns of phylogenetic differentiation in the toxochelyid andcheloniid sea turtles. Am. Zool. 20, 585.

Zwickl, D., 2006. GARLI: Genetic Algorithm for Rapid Likelihood Inference. <http://www.bio.utexas.edu/faculty/antisense/garli/Garli.html>.



http://evolve.zoo.ox.ac.uk/software.html

http://evolve.zoo.ox.ac.uk/software.html

http://www.bio.utexas.edu/faculty/antisense/garli/Garli.html

http://www.bio.utexas.edu/faculty/antisense/garli/Garli.html



marine turtle mitogenome phylogenetics and evolution

Documents