Spatial and temporal patterns of genetic variation in the widespread antitropical deep-sea coral Paragorgia arborea

Spatial and temporal patterns of genetic variation in thewidespread antitropical deep-sea coral Paragorgiaarborea

S . HERRERA,*† T. M. SHANK‡ and J . A. SANCHEZ†

*Massachusetts Institute of Technology, Woods Hole Oceanographic Institution, Joint Program in Oceanography, 266 Woods

Hole Road, Woods Hole, MA 02543, USA, †Laboratorio de Biologia Molecular Marina (BIOMMAR), Departamento Ciencias

Biologicas, Universidad de los Andes, Carrera 1E No 18A – 10, Bogota, Colombia, ‡Biology Department, Woods Hole

Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA 02543, USA

Abstract

Numerous deep-sea species have apparent widespread and discontinuous distribu-

tions. Many of these are important foundation species, structuring hard-bottom benthic

ecosystems. Theoretically, differences in the genetic composition of their populations

vary geographically and with depth. Previous studies have examined the genetic diver-

sity of some of these taxa in a regional context, suggesting that genetic differentiation

does not occur at scales of discrete features such as seamounts or canyons, but at larger

scales (e.g. ocean basins). However, to date, few studies have evaluated such diversity

throughout the known distribution of a putative deep-sea species. We utilized

sequences from seven mitochondrial gene regions and nuclear genetic variants of the

deep-sea coral Paragorgia arborea in a phylogeographic context to examine the global

patterns of genetic variation and their possible correlation with the spatial variables of

geographic position and depth. We also examined the compatibility of this morphospe-

cies with the genealogical-phylospecies concept by examining specimens collected

worldwide. We show that the morphospecies P. arborea can be defined as a genealogi-

cal-phylospecies, in contrast to the hypothesis that P. arborea represents a cryptic

species complex. Genetic variation is correlated with geographic location at the basin-

scale level, but not with depth. Additionally, we present a phylogeographic hypothesis

in which P. arborea originates from the North Pacific, followed by colonization of the

Southern Hemisphere prior to migration to the North Atlantic. This hypothesis is

consistent with the latest ocean circulation model for the Miocene.

Keywords: coral, deep sea, DNA barcoding, phylogeography, species, widespread

Received 1 June 2012; revision received 28 August 2012; accepted 1 September 2012

Introduction

Several marine species, particularly from deep-sea envi-

ronments, have apparent widespread yet discontinuous

distributions (e.g. review by Roberts et al. 2009; Bik et al.

2012). Various mechanisms have been suggested to

explain the apparent existence of such species, including

recent connectivity among populations mediated by

long-distance dispersal (Bucklin et al. 1987; France &

Kocher 1996; Darling et al. 2000; Won et al. 2003;

Pawlowski et al. 2007; Lecroq et al. 2009; Etter et al.

2011), large population sizes and similar selective pres-

sures in a stable environment (Bisol et al. 1984; Brink-

meyer et al. 2003; Etter et al. 2011), relatively recent

events of colonization mediated by jump dispersal over

barriers (Darling et al. 2000; Etter et al. 2011), and cryptic

speciation (France & Kocher 1996; Howell et al. 2004). A

number of deep-sea coral morphospecies are among

these widespread species with discontinuous distribu-

tions, for example Lophelia pertusa, Solenosmilia variabilis

and Madrepora oculata (Roberts et al. 2009).Correspondence: Juan A. Sanchez, Fax: +57 1 3394949*2817;

E-mail: [email protected]

© 2012 Blackwell Publishing Ltd

Molecular Ecology (2012) doi: 10.1111/mec.12074

Deep-sea corals are some of the most conspi-

cuous invertebrate inhabitants of hard-bottom benthic

environments worldwide. They are not only more

diverse, in terms of number of species, than their shal-

low counterparts (Cairns 2007), but also they play a

fundamental role as foundation species and ecosystem

engineers, creating three-dimensional habitats that are

occupied by a high diversity of associate species (Buhl-

Mortensen & Mortensen 2005; Costello et al. 2005;

Etnoyer & Morgan 2007; Buhl-Mortensen et al. 2010;

Shank 2010). Coral ecosystems also support fisheries

(D’Onghia et al. 2011; Soeffker et al. 2011) and have

been identified as important sources of marine natural

products (Leal et al. 2012). Deep-sea corals have evolved

in a relatively stable and energy-poor environment; they

tend to have slow growth rates (Roberts et al. 2009; Sun

et al. 2010), great longevity (Roark et al. 2009) and

size-dependent fecundity (Cordes et al. 2001). These

characteristics make deep-sea coral ecosystems highly

susceptible to disturbance events, especially those gen-

erated by human activities, that is, bottom-trawling,

deep-sea mining, hydrocarbon extraction, waste dis-

posal, climate change and ocean acidification (reviewed

in Ramirez-Llodra et al. 2011). The characterization of

spatial distribution patterns of genetic types is of funda-

mental importance to identify the factors that shape the

ranges of deep-sea taxa, and that ultimately drive biodi-

versity patterns in the ocean (McClain & Mincks 2010).

Widespread taxa thus can be used as models to under-

stand how the effects of these factors operate at a global

scale. Such information provides critical baseline data

with which the potential effects of disturbances on

populations inhabiting earth’s largest biome can be

assessed.

A handful of studies have examined the genetic

diversity of deep-sea coral taxa in a regional context (Le

Goff-Vitry et al. 2004; Smith et al. 2004; Thoma et al.

2009; Morrison et al. 2011). These have suggested that

genetic differentiation does not seem to occur at small

geographic scales often associated with discrete features

such as individual seamounts or canyons, but presum-

ably at larger scales, that is, broader oceanic regions.

However, no studies to date have evaluated such

hypotheses throughout the entire known distribution of

a putative deep-sea coral species (see Pante & Watling

2012; for a comparison between two distant regions).

In this study, we examined the spatial patterns of

genetic variation in the widespread bubblegum coral

Paragorgia arborea (Linnaeus, 1758) (Octocorallia: Parag-

orgiidae), which is one of the most prominent coral

morphospecies in cold-water sublittoral and bathyal

hard-substrate habitats.

Paragorgia arborea plays an important ecological role

generating microhabitats for numerous species; they are

the structural analog of large trees in a rain forest

(Buhl-Mortensen & Mortensen 2005; Metaxas & Davis

2005; Watanabe et al. 2009; Buhl-Mortensen et al. 2010).

Single colonies of P. arborea can harbour hundreds of

individuals from dozens of associated species (e.g.

ophiuroids, copepods, shrimp, anemones, polychaetes,

ostracods, barnacles, amphipods, hydroids and forami-

niferans) (Buhl-Mortensen et al. 2010). The fauna associ-

ated with this coral can be two to three times richer

than the fauna associated with equivalent shallow-water

tropical gorgonians (Buhl-Mortensen & Mortensen 2004,

2005).

Paragorgia arborea has been reported from polar,

subpolar and subtropical regions of all of the world’s

oceans. This conspicuous and locally abundant species

can grow massive colonies, which can reach up to 8 m

in height (Sanchez 2005). Paragorgia arborea lives in

regions of high productivity (Sarmiento & Gruber 2006

depth-integrated primary production > 10 mol/C/m2/year) and high export fluxes (Sarmiento & Gruber

2006 particle export at 100 m > 2 mol/C/m2/year), water

temperatures lower than 12 °C and relatively high

local current velocities of 5–30 cm/s (Mortensen &

Buhl-Mortensen 2004; Bryan & Metaxas 2006; Etnoyer &

Morgan 2007; Roberts et al. 2009; Watanabe et al. 2009).

The known distribution of P. arborea in the Northern

Hemisphere includes numerous observations in both

eastern and western North Atlantic waters and also in

the eastern and western North Pacific (WNP), from

Japan to the Aleutian Islands and to the Californian

seamounts. In the Southern Hemisphere, it has been

reported around the Crozet Islands, the Patagonian

Shelf and the western South Pacific off New Zealand

(Grasshoff 1979; Tendal 1992). Since the publication of

these records, both fishing pressure and scientific

research in the deep sea have increased significantly,

and the number of new records for this species has

increased in tandem. Some of these records can now be

found in biodiversity databases such as the Ocean

Biogeographic Information System (OBIS, http://www.

iobis.org) and Global Biodiversity Information Facility

(GBIF, http://data.gbif.org); however, many others

remain unconsolidated in scattered publications and

local databases. Thus, an updated picture of the global

distribution of this species is in order.

In this study, we provide an up-to-date summary of

the global distribution of P. arborea and genetic insights

into the global phylogeography of this species. By

examining the genealogy of mitochondrial and nuclear

genetic variants from specimens collected over nearly

its entire known distribution, we tested the compatibil-

ity of the morphospecies P. arborea with the genealogi-

cal-phylospecies concept. We evaluated the hypothesis

that the morphospecies P. arborea is a complex of


2 S . HERRERA, T . M. SHANK and J . A . SANCHEZ

cryptic species in a barcoding framework. We also

examined the global patterns of genetic variation and

their possible correlation with the spatial variables of

geographic position and depth. We propose a scenario

that could explain the observed evolutionary and

present-day patterns in this and other species.

Methods

Global distribution

To illustrate the currently known global distribution of

Paragorgia arborea, we plotted on a gridded geographic

map all the unique records available to date from the

databases of the OBIS, the GBIF, the Smithsonian

Institution National Museum of Natural History

(http://www.mnh.si.edu/rc/), the Yale University

Peabody Museum of Natural History (www.peabody.

yale.edu), the Harvard University Museum of Compar-

ative Zoology (www.mcz.harvard.edu), the Museum

National d’Histoire Naturelle France (www.mnhn.fr),

the National Institute of Water & Atmospheric Research

(www.niwa.cri.nz) and several local databases and

publications (Bruntse & Tendal 2000; Wareham &

Edinger 2007; Mortensen et al. 2008; Roberts et al. 2008;

Hibberd & Moore 2009; Laptikhovsky 2011). Geographic

coordinates were reconstructed using Google Earth for

records with known collection locality, but no latitude

and longitude information. Similarly, missing depth

data were reconstructed using data from the global

GEBCO_08 30 arc-second grid.

Molecular methods

We analysed a total of 130 specimens of P. arborea avail-

able from various museum and laboratory collections

(see Table S1, Supporting information). The examined

material, collected since 1878, covers most to the known

geographic distribution as well as the entire depth dis-

tribution of P. arborea (see Table S1, and Figs S1–S4,

Supporting information for comparison). For more

details on the sequencing of old specimens, see the

Appendix S1 (Supporting information). Additional

material from other paragorgiid morphospecies was

included for comparisons. Total DNA was extracted

from dry or ethanol-preserved (70–96%) samples using

a CTAB–proteinase K–PCI protocol (Coffroth et al. 1992)

or using an automated extraction system (AutoGenprep

965; AutoGen Inc.) as described in the study by Herrera

et al. (2010). DNA was eluted in TE buffer and stored at

�70 °C.Mutation rates in octocoral mitochondria are signifi-

cantly lower than in most other organisms (Bilewitch &

Degnan 2011). The implication of this lower mutation

rate is that mitochondrial markers in octocorals are use-

ful to infer phylogeographic patterns and connectivity

at broader spatial and temporal scales. Thus, to maxi-

mize the amount of variability captured from the

genome of this organelle, we obtained sequences from

seven gene regions, amplified by five primer pairs (Her-

rera et al. 2010), adding up to approximately 3000 base

pairs (bp). These regions include the 3′-end of the

NADH dehydrogenase subunit 6 (nad6), the nad6-nad3

intergenic spacer (int), the 5′-end of the NADH dehydro-

genase subunit 3 (nad3), the 3′-end of the cytochrome c

oxidase subunit I (cox1), the 5′-end of the DNA

mismatch repair protein – mutS – homolog (mtMutS),

two different regions of the large subunit ribosomal

RNA (16S) and the 5′-end of the NADH dehydrogenase

subunit 2 (nad2).

We also sequenced the nuclear ribosomal internal

transcribed spacer 2 (ITS2). In octocorals, ITS has been

assessed in a number of groups providing enough reso-

lution for diverse phylogenetic inferences (Alcyoniidae

McFadden et al. 2001; McFadden & Hutchinson 2004;

Nephtheidae van Ofwegen & Groenenberg 2007). ITS2

has also provided enough resolution for intraspecific

and phylogeographic studies in Caribbean shallow-

water octocorals (Sanchez et al. 2007; Gutierrez-Rodri-

guez et al. 2009). Furthermore, ITS2 has also provided

valuable information for the analysis of genetic struc-

ture of deep-sea corals (Le Goff-Vitry et al. 2004; Miller

et al. 2011).

Polymerase chain reactions (PCR) and sequencing

reactions for mitochondrial gene regions were per-

formed following the protocols used by Herrera et al.

(2010). ITS2 PCR amplicons, from a subset of 19 geo-

graphically representative individuals, were examined

to assess the possibility of intragenomic variants

through denaturing gradient gel electrophoresis

(DGGE). Gels contained 8% polyacrylamide, 19 TAE

buffer and a linear urea–formamide denaturing gradient

from 45% to 80%. The gels were pre-ran at 60 °C and

90 V for 30 min, followed by 13 h at 60 °C and 90 V.

Gels were stained with ethidium bromide for 15 min

and visualized using a Bio-Rad Chemidoc system. PCR

products from DGGE-excised bands were subsequently

cleaned and sequenced. Complementary chromato-

grams were assembled and edited using the SEQUEN-

CHERTM 4.8 software (Gene Codes Corp.).

Sequences of each region were aligned independently

using MAFFT 6.8 (Katoh et al. 2002). The G-INS-i and

Q-INS-i algorithms (gap opening penalty = 1.53, offset

value = 0.07) were employed for the protein coding and

ribosomal regions, respectively. Secondary structures of

ribosomal regions were inferred to improve the align-

ments, following the protocols used in the study by

Herrera et al. (2010). To correct possible mistakes, all


PHYLOGEOGRAPHY OF DEEP-SEA CORAL PARAGORGIA ARBOREA 3

alignments of protein coding sequences were visually

inspected and translated to amino acids in GENEIOUS 5.3

(Drummond et al. 2010), using the genetic code of

Hydra attenuata (Pont-Kindon et al. 2000). No unusual

stop codons or suspicious substitutions were identified,

suggesting that no nuclear pseudogenes were

sequenced (Lopez et al. 1994; Bensasson et al. 2001).

Mitochondrial sequences were concatenated for each

individual and treated as one single locus in most sub-

sequent analyses, given that the mitochondrial genome

is assumed to be nonrecombining. Mitochondrial and

ITS2 genetic variants, with alignment gaps included as

an informative state (Giribet & Wheeler 1999), were

identified using DNASP 5.0 (Librado & Rozas 2009) and

will be referred hereafter as haplotypes.

Gene trees and molecular clock

To evaluate the compatibility of P. arborea with the

functional definition of genealogical-phylospecies sensu

De Queiroz (2007), that is, all alleles of a given locus in

individuals of P. arborea being ‘descended from a com-

mon ancestral allele not shared with those of other

species’ (Avise & Ball 1990; Baum & Shaw 1995), we

performed independent phylogenetic analyses of the

mitochondrial and ITS2 haplotypes. Homologous

sequences from eight other paragorgiid morphospecies

were included as outgroups (see Table S1, Supporting

information). Phylogenetic estimation was performed

using Bayesian inference (BI) in MRBAYES 3.12 (Huelsen-

beck & Ronquist 2001; Ronquist & Huelsenbeck 2003)

as implemented in the CIPRES portal (http://www.phylo.

org). Most likely nucleotide substitution models were

selected for each region based on the Akaike Informa-

tion Criteria (AIC) as implemented in JMODELTEST 2.0.

Models for the mitochondrial regions are shown in

Table S2 (Supporting information). The general time

reversible model with a gamma-distributed rate varia-

tion across sites (GTR+G) was selected for the ITS2.

Default prior distribution settings were assumed for all

parameters. Four independent analyses of 10 000 000

Monte Carlo Markov chain (MCMC) generations (94

chains) were run with a sampling frequency of 1000

generations (burn-in = 25%). Combined BI analysis of

the mitochondrial locus was performed with explicit

character partitions for each concatenated region, along

with their independently selected models of evolution.

To account for the rate variation among partitions (Mar-

shall et al. 2006), we allowed the rates to vary under a

flat Dirichlet prior distribution (ratepr = variable). The

parameters of nucleotide frequencies, substitution rates,

gamma shape and invariant site proportion were

unlinked across partitions. MCMC runs were analysed

in the program TRACER 1.5 (Rambaut & Drummond

2007). Convergence was indicated by the ‘straight hairy

caterpillar’ (Drummond et al. 2007) shape of the station-

ary posterior-distribution trace (generations vs. log-like-

lihood) of each parameter. Other examined convergence

and mixing diagnostics included the standard deviation

of partition frequencies (<0.01), the potential scale

reduction factor (PSRF) (ca. 1.00), the effective sample

sizes (EES) (>200) and the similitude of posterior proba-

bilities of specific nodes between different runs in the

program AWTY (http://ceb.csit.fsu.edu/awty) (Nylander

et al. 2008). High correlations between runs and no

obvious trends in the split frequency plots were

observed. Tree files for each run were combined, after

burn-in, using the program LOGCOMBINER v1.7.1 (Drum-

mond et al. 2012). The most probable trees were

summarized into a maximum clade credibility tree

using TREEANNOTATOR v1.7.1 (Drummond et al. 2012).

A Bayesian-MCMC joint estimation of gene genealogy

and divergence times was performed in BEAST 1.7.1

(Drummond et al. 2012) for the mitochondrial marker

assuming the same substitution model mentioned

above. We assumed an uncorrelated relaxed lognormal

molecular clock model, which allows for the variation

in mutation rates among branches, with the Yule model

of constant speciation rate (Yule 1925; Gernhard 2008)

and the coalescent model of constant population size

(Kingman 1982), as the tree priors. Additional

sequences from specimens of the sister family, Corallii-

dae, were added to estimate divergence time within the

phylogeny as this family contains some of the few fos-

sils available for Octocorallia. The coralliid node was

calibrated implementing a normal prior distribution for

the time to the most recent common ancestor (TMRCA)

with a mean of 83.5 million years before present (Myr

BP) and a standard deviation of 0.7, corresponding to

Campanian age stratum, in which the oldest known

fossil in this family has been found (Schlagintweit &

Gawlick 2009). Three MCMC independent analyses

were run for 30 000 000 generations with a sampling

frequency of 3000 (burn-in = 25%). Convergence diag-

nostics (generations plot and EES) were also examined

for the combined runs in TRACER 1.5 (Rambaut &

Drummond 2007) as mentioned above. The most proba-

ble trees were summarized into a maximum clade

credibility tree with median node heights using TREEAN-

NOTATOR v1.7.1 (Drummond et al. 2012).

To infer the historical patterns of dispersal in

P. arborea, we used the Bayesian phylogeography

framework proposed by Lemey et al. (2009), as imple-

mented in BEAST 1.7.1 (Drummond et al. 2012). We

mapped the geographic ocean region where each haplo-

type was sampled to the time-scaled mitochondrial

gene genealogy, which was inferred with the assump-

tion of an uncorrelated relaxed lognormal molecular



clock and the coalescent constant-population-size tree

prior, as explained above. This framework allows the

reconstruction of discrete states of geographic location

for ancestral nodes by posterior probability estimation.

Barcoding and species delimitation

To test for the possibility of cryptic species in the mor-

phospecies P. arborea, we calculated pairwise uncor-

rected distances among individuals of P. arborea and

other paragorgiid morphospecies for the mtMutS and

cox1 sequences, as proposed by McFadden et al. (2011),

in PAUP* 4.0b10 (Swofford 2002). Neighbour-joining trees

were built using the calculated distances. We also

examined the ITS2 secondary structures for the

presence of compensatory base changes (CBCs) using

the visualization program 4SALE (Seibel et al. 2006);

CBCs are altered pairings in a helix of the secondary

structure of the ITS2 RNA transcript, and empirical

work has suggested that they could be used as indica-

tors of species boundaries in most metazoans (Muller

et al. 2007; Coleman 2009).

We also used the coalescent-based species delimita-

tion method described by Pons et al. (2006) and

Monaghan et al. (2009), as implemented in the SPLITS

R-package (available from http://r-forge.r-project.org/

projects/splits/). This likelihood method is based on a

general mixed Yule-coalescent (GMYC) model, which

estimates phylospecies boundaries in a clock-con-

strained calibrated tree by identifying increases in

branching rates (looking forward in time). Such

increases are assumed to be characteristic of transition

points between interspecific speciation–extinction pro-

cesses and intraspecific coalescent processes, that is,

populations (Pons et al. 2006; Monaghan et al. 2009). Sin-

gle- and multiple-threshold models with explicit and

upper and lower limits for the estimation of scaling

parameters (0 and 10, respectively) were used in the

analysis of the time-calibrated trees obtained with the

Yule and coalescent models tree priors.

Genetic variability

Genetic variability among individuals and populations

was measured for each locus according to the haplotype

diversity (h) and genetic diversity (average number of

pairwise differences hp) indices (Tajima 1983) using

ARLEQUIN 3.5 (Excoffier & Lischer 2010). Fu’s Fs statistic

was calculated to determine whether the observed

pattern of polymorphism was consistent with a neutral

model of evolution (Tajima 1989; Fu 1997). Global FSTstatistics were calculated to evaluate for possible differ-

entiation in the genetic composition among populations

worldwide. Pairwise comparisons of population differ-

entiation were made in Arlequin and significance

values estimated after 1000 permutations. To visualize

the spatial patterns of genetic variation for each marker,

the specimens were colour-coded according to haplo-

type, their geographic collection coordinates were

plotted using IMAP v3.5 (Biovolution), and their collec-

tion depth was plotted on an X-Y scatter plot. To assess

the amount of variability in the populations of P. arbo-

rea that was represented in our samples, we generated

haplotype accumulation curves (Gotelli & Colwell 2001)

by calculating estimates of the mean and variance for

the number of accumulated haplotypes through 1000

random permutations, using the program R-package

SPIDER v1.1 (Brown et al. 2012).

Results

Global distribution

A total of 341 high-confidence geographic location

records of Paragorgia arborea were gathered (see Fig. S3,

Supporting information). Paragorgia arborea is an

antitropical taxon, occupying a band between 30° and

70° degrees of latitude in both hemispheres. These

bands are, in general, areas of high surface primary

productivity and export (Sarmiento & Gruber 2006).

Most records of P. arborea are from depths shallower

than 1000 m, indicating a preference for upper-bathyal

environments. Despite the fact that other coral species

that share part of their ranges with P. arborea have been

commonly observed in tropical and subtropical regions

(e.g. Lophelia pertusa and Madrepora oculata), P. arborea

has never been found in these areas. This suggests that

the currently known distribution of P. arborea is not a

result of undersampling at lower latitudes.

Molecular data

A total of 92 specimens were positively screened for the

mitochondrial marker. The concatenated mitochondrial

alignment for the morphospecies P. arborea had a length

of 2922 bp, of which 2881 were invariable sites (pair-

wise identity of 99.7%); eight sites were parsimony-

informative. The mean ungapped sequence length was

2917.9 bp (SD = 2.8 bp), with a range of 2910 and

2921 bp. The G-C content was 39.5%. The only noncod-

ing region in the mitochondrial locus data set, the nad6-

nad3 intergenic spacer (int), contained one indel, but no

nucleotide substitutions. The ITS2 was successfully

sequenced for 48 specimens, of which 83% overlapped

with the mitochondrial set. The ITS2 alignment had a

length of 312 bp, of which 301 were invariable (pairwise

identity of 99.2%); 35 sites were parsimony-informative.

The mean ungapped sequence length for ITS2 was



282 bp (SD 0.9 bp), ranging between 280 and 284 bp.

The G-C content was 41.1%. No intragenomic variability

was revealed, using DGGE, in the ITS2. The predicted

secondary structure of ITS2 showed the characteristic

shape of a helicoidal ring with four helixes (Coleman

2007); stems III and IV were particularly long in this

species (Fig. S5, Supporting information). The number

of haplotypes and genetic diversity estimates for each

population with both loci are shown in Figs 1 and 2.

The nad6-int-nad3 region (hereafter referred as nad6 for

simplicity) contained most of the variable sites (21) and

the greatest number of haplotypes (11) found in the

individuals with complete mitochondrial data sets.

Haplotype differences were also located in the ITS2:

one at helix I, three at helix III and five at helix IV; the

remaining two were free nucleotides at the structure

main ring (Fig. S5, Supporting information).

Gene genealogies and phylogeographic history

Individual mitochondrial gene trees were largely con-

gruent, although resolution was generally low (Fig. S6,

Supporting information). The inferred phylogeny based

on each independent loci (i.e. concatenated mitochon-

drial and ITS2) highly supported the monophyly of

P. arborea (Fig. 3) and had much greater clade resolu-

tion. Both Bayesian and neighbour-joining analyses

inferred the same evolutionary relationships. Branch

lengths were appreciably shorter within the clade of

P. arborea, when compared to the ones among morpho-

species. Relationships within Paragorgia were not fully

resolved, particularly among Paragorgia wahine, Paragor-

gia yutlinux and Paragorgia sp. 1. The systematic

relationships of Paragorgia spp. are outside of the scope

of this study and will not be further discussed.

The time-scaled trees estimated assuming the coales-

cent model of constant population size had, in general,

shorter shallower and longer deeper branches than the

tree estimated assuming the Yule model of constant

speciation rate (Figs S7 and S8, Supporting informa-

tion). Consequently, the time to the TMRCA of the

genus Paragorgia was estimated to be 61 Myr BP (95%

CI: 31–101) under the coalescent model and 54 Myr BP

(95% CI: 41–94) under the Yule model. The TMRCA of

P. arborea based on the coalescent model and the Yule

model was 10.1 Myr BP (95% CI: 4.4–18.8) and

14.1 Myr BP (95% CI: 6.7–26.3), respectively.

The Bayesian phylogeographic analysis indicates that

the lineage of P. arborea likely originated in the North

Pacific (posterior probability 0.38, see Fig. 4). Dispersal

to the South Pacific and subsequent colonization of

the North Atlantic likely occurred between the mid-

Miocene and early Pliocene.

Genetic distances, CBCs and GMYC

Maximum uncorrected genetic distances among conspe-

cifics and minimum uncorrected genetic distances

among congeners were used to measure the intraspe-

cific and interspecific variation, respectively, of mtMutS

and cox1 sequences as in the study by McFadden et al.

(2011). The maximum distances within P. arborea, 0.3%

for mtMutS and 0.9% for cox1, were in general smaller

than the distances among morphospecies of Paragorgia,

Fig. 1 The global geographic distribution

of mitochondrial haplotypes in Paragorgia

arborea. The gene tree in the centre of the

figure shows the inferred relationships

among haplotypes. Each haplotype is

indicated by a different colour. Framed

circles represent individuals. Pie charts

indicate the frequency of haplotypes in

each population (global region): North

Atlantic (NA), South Atlantic (SA), South

Pacific (SP), western North Pacific (WNP)

and eastern North Pacific (ENP). The size

of each pie is proportional to the number

of samples from each population (n). The

number of haplotypes (H), haplotype

diversity (h), genetic diversity (hp) and

the Fu’s Fs statistic are also indicated.



which ranged between 0.5–6.1% and 0–2.3% for mtMutS

and for cox1, respectively (Table S3, Supporting infor-

mation). The minimum distances between P. arborea

and the other morphospecies of Paragorgia ranged

between 1.8–5.1% for mtMutS and 0.6–1.8% for cox1. A

similar pattern was observed for ITS2 distances, 0.6% in

Fig. 2 The global geographic distribution

of nuclear internal transcribed spacer 2

haplotypes in Paragorgia arborea. The

gene tree in the centre of the figure

shows the inferred relationships among

haplotypes. Each haplotype is indicated

by a different colour. Framed circles rep-

resent individuals. Pie charts indicate the

frequency of haplotypes in each popula-

tion: North Atlantic (NA), South Indian

Ocean (SI), South Pacific (SP), western

North Pacific (WNP) and eastern North

Pacific (ENP). The size of each pie is

proportional to the number of samples

from each population (n). The number of

haplotypes (H), haplotype diversity (h),

genetic diversity (hp) and the Fu’s Fsstatistic are also indicated.

Fig. 3 Unrooted gene tree hypotheses for the mitochondrial

(top) and nuclear internal transcribed spacer 2 (bottom) mark-

ers in Paragorgia.

NA

SPNP

SA

Time (million years)

NP

NP

NP

NP

NPNP

NP

NP

SP

SP

SP

SP

NA

NA

NA

Fig. 4 Maximum clade credibility ultrametric time-scaled mito-

chondrial gene tree for Paragorgia arborea. Branch colours show

the most probable location states: North Atlantic (NA) in blue,

South Pacific (SP) in green, South Atlantic (SA) in violet and

North Pacific (NP) in orange. Pie charts show the posterior

probabilities of location states for each ancestral node (total pie

area = 1). The most probable location state of each node is also

indicated.



intraspecific comparisons within P. arborea (maximum

distances) and 3.3–7.4% in interspecific comparisons

among morphospecies of Paragorgia. The minimum

distances between P. arborea and the other morphospe-

cies ranged between 3.3 and 5.6%. The predicted ITS2

secondary structure was essentially the same for all

haplotypes of P. arborea and no CBCs or hemi-CBCs

were observed.

A transition point between species- and population-

level branching patterns was identified, by the single-

threshold GMYC method, at ca. 21 Myr BP for the

time-scaled mitochondrial gene genealogy estimated

with the Yule model tree prior (Fig. S7, Supporting

information). For the time-scaled gene genealogy esti-

mated with the coalescent model tree prior, this transi-

tion point was inferred to be at ca. 16 Myr BP (Fig. S8,

Supporting information). In both cases, the GMYC

model showed a marginally significant (i.e. a = 0.05)

better fit to the data than the null model of uniform

coalescent branching rates (LR = 4.84, d.f. = 3, P = 0.18,

compared to LR = 5.84, d.f. = 3, P = 0.12, respectively).

The implementation of a multiple-threshold GMYC

model did not yield a significantly (i.e. a = 0.05) better

fit than the single-threshold GMYC for either case

(v2 = 2.55, d.f. = 3, P = 0.47 and v2 = 0.83, d.f. = 3,

P = 0.84, respectively).

Spatial patterns of genetic variation

Overall, in P. arborea, 16 haplotypes were defined based

on the mitochondrial locus and 11 based on the ITS2.

The genealogical relationships among haplotypes

inferred by the Bayesian inference did not reveal reci-

procal monophyly of the specimens from Northern and

Southern Hemispheres, or from different oceans, for

example Pacific vs. Atlantic (Figs 1 and 2). In fact, a

number of haplotypes were shared across large

geographic spans.

Genetic diversity, as measured by the haplotype

diversity (h, the probability that two randomly chosen

haplotypes are different in a population sample) and

the average number of nucleotide differences between

all pairs of haplotypes in the sample (hp), was highest

in the western North Pacific Ocean (mitochondrial

h = 0.74, SD = 0.11, and hp = 7.44, SD = 0.11; nuclear

h = 0.75, SD = 0.14, and hp = 2.68, SD = 1.81) and South

Pacific Ocean (SP; mitochondrial h = 0.79, SD = 0.03,

and hp = 6.37, SD = 3.10; nuclear h = 0.73, SD = 0.09,

and hp = 1.64, SD = 1.15) regions, intermediate in the

North Atlantic Ocean (NA; mitochondrial h = 0.26,

SD = 0.12, and hp = 0.46, SD = 0.42; nuclear h = 0.71,

SD = 0.11, and hp = 1.02, SD = 0.82) and lowest in the

eastern North Pacific Ocean (ENP; mitochondrial

hp = 0.00; nuclear h = 0.49, SD = 0.18, and hp = 1.16,

SD = 0.91) (Figs 1 and 2). Overall, the relative levels of

genetic diversity for both loci were highly similar (see

Figs 1 and 2). No significant deviations from neutrality

were found in either locus (i.e. a = 0.05).

Mitochondrial haplotypes m15 and m12 were shared

between the NA and SP regions, although their

frequencies were dissimilar (Fig. 1). Haplotype m15 was

the dominant form in the NA, with a frequency of 0.86,

whereas only one specimen was found having the m12

variant. In the SP, these haplotypes represented two of

the three most common ones, with frequencies of 0.26

for m15 and 0.31 for m12. The rest of the haplotypes in

these regions represented private alleles, that is, variants

exclusive to a particular area. The haplotype from the

single specimen from the South Atlantic Ocean (SA)

region was a private allele. No haplotypes from the

North Pacific Ocean (NP) were shared with other

regions. Within the NP region, there was a clear break

between western and eastern subregions, separated by

the Alaska Peninsula. All haplotypes within these

subregions represented private alleles. Two dominant

haplotypes were found in the WNP, m2 and m7, with

frequencies of 0.5 and 0.21, respectively. In the ENP,

there was a single haplotype. In the ITS2 data set,

regional differences were less pronounced than in the

mitochondrial data set but, similarly, the haplotype

frequencies varied greatly among regions (Fig. 2).

Haplotypes i1 and i6 had near-cosmopolitan distribu-

tions. Haplotype i1 was found within all regions with

frequencies of 0.23 (NA), 0.67 [South Indian Ocean (SI)],

0.07 (SP), 0.25 (WNP) and 0.09 (ENP). Haplotype i6 was

found in specimens from the NA, SP and ENP, with fre-

quencies of 0.15, 0.07 and 0.73, respectively. Haplotype

i2 was shared between NA and SP, being dominant in

both regions, with frequencies of 0.54 and 0.43, respec-

tively. Lastly, haplotype i5 was found in both the NA

(frequency = 0.09) and the SI (frequency = 0.33). Private

alleles were found in the Pacific, with high frequencies

in the SP (i7, frequency = 0.36) and the NWP (i8,

frequency = 0.5), but none were found in the NA or SI.

No detectable differentiation in the haplotype distribu-

tions could be explained by depth differences (Fig. 5).

Haplotype accumulation curves revealed that the

global mitochondrial and nuclear ITS2 diversities have

not been fully sampled, as indicated by the steep slopes

of both lines (Fig. S9, Supporting information). Both

markers showed similar levels of diversity at given

sampling efforts, ITS2 being slightly lower than the

mitochondrial. When the individual genes in the

mitochondrial data set were examined individually, it

was clear that there are significant differences in their

contributions to overall diversity estimates and that a

single one does not capture the diversity found in the

combined mitochondrial marker. By far, the gene that



captures the largest mitochondrial diversity in terms of

haplotypes in P. arborea is nad6 (11), followed by nad2

(6), mtMutS (5), 16S (4) and cox1 (3). The combination of

nad6 and 16S captures 14 haplotypes, and the addition

of nad2 to these two regions captures all 16 haplotypes

found in the combined mitochondrial marker.

Discussion

The data and analyses generated in this study showed

that the morphospecies Paragorgia arborea can be defined

as a genealogical-phylospecies, in contrast to the

hypothesis that P. arborea represents a cryptic species

complex. Genetic variation in this lineage is correlated

with geographic location at the basin-scale level, but

not with depth. We present a phylogeographic hypothe-

sis for P. arborea in which this independently evolving

lineage originates from the North Pacific, followed by

colonization of the Southern Hemisphere prior to

migration to the North Atlantic. We argue that this

hypothesis is consistent with the latest ocean circulation

model for the Miocene.

A globally distinct evolving lineage?

The distinction among species, incipient species and

structured populations in many deep-sea invertebrates

remains contentious due to the difficulties in defining

the species boundaries for certain groups and to the

paucity of the genetic, ecological and taxonomic data

available to date (Vrijenhoek 2009; McFadden et al.

2011). Commonly used species concepts (e.g. biological

species concept) have been traditionally developed in

terrestrial models. However, the biological and ecologi-

cal information required to apply such concepts to deep-

sea organisms (e.g. reproductive success, behaviour) is,

at this time, impracticable to obtain. It is now recognized

that a combination of morphologic and phylogenetic cri-

terions is most practical to discern among deep-sea coral

species (e.g. Herrera et al. 2010; Pante & Watling 2012).

Here, we examined, for the first time, the compatibil-

ity of traditional taxonomical identifications and molec-

ular information in a putative deep-sea coral species

at a global scale. The mitochondrial and nuclear gene

trees have congruent topologies, showing that alleles of

P. arborea have a common ancestor not shared with

other paragorgiid morphospecies. Thus, the morphospe-

cies P. arborea is compatible with the genealogical-

phylospecies concept. Furthermore, the branch lengths

among haplotypes of P. arborea are much shorter than

the branches among other putative species, which is to

be expected for genetic variability within a phylospe-

cies. The only consistent morphological variant corre-

sponds to the populations found in the NP, which were

previously referred as Paragorgia pacifica Verrill, 1922

but synonymized with P. arborea by Grasshoff (1979).

Individuals from these populations seem to have

reduced sclerite size and ornamentation when com-

pared to the characteristics that defined P. arborea prior

to Grasshoff synonymizing the two (Sanchez 2005).

Our data do not support P. pacifica as a valid species

(see discussion below); we rather suggest that it may

represent a subspecies. Taken together, this evidence

indicates that P. arborea is a globally distinct lineage,

implying that identifications based on morphology can

accurately distinguish this taxon.

A complex of cryptic species?

The presence of cryptic species complexes has been

detected in various presumed widespread marine mor-

phospecies (e.g. clams Goffredi et al. 2003; isopods

Raupach et al. 2007; limpets Johnson et al. 2008; gastro-

pods Duda et al. 2009; Vrijenhoek 2009). Despite being

morphological indistinguishable, cryptic species have

been detected through molecular data, on the basis of

genetic dissimilarity. Here, we tested for the possibility

of cryptic species within the specimens of P. arborea, by

analysing the pairwise uncorrected genetic distances

among haplotypes using a DNA barcoding framework

Dep

th (m

)

Mitochondrial haplotype1 2 3 5 6 7 8 9 10 11 12 13 14 15 16

Dep

th (m

)

0

500

1000

1500

0

500

1000

1500

ITS2 haplotype1 2 3 4 5 6 7 8 9 10 11

Fig. 5 The distribution of mitochondrial (left) and nuclear internal transcribed spacer 2 ITS2 (right) haplotypes of Paragorgia arborea

with depth. Individuals are represented by dots. Each haplotype is indicated by a different colour, as in Figs 1 and 2. The prefix m

denotes mitochondrial haplotypes, and the prefix i denotes nuclear ITS2 haplotypes (haplotype numbers are equivalent to the ones

in Table S1, Supporting information).



based on the cox1 and mtMutS gene regions (see

McFadden et al. 2011). Under this framework, pairwise

uncorrected distances greater than 1% for mtMutS or

cox1 genes can be confidently used to indicate cryptic

species (McFadden et al. 2011). Based on this threshold,

the maximum intraclade distances (0.3% for mtMutS

and 0.9% for cox1) among specimens of P. arborea do

not suggest the presence of cryptic species. These

distances are consistent with the intraspecific distances

found within other paragorgiid species, for example

0.8% for mtMutS in Sibogagorgia cauliflora (Herrera et al.

2010). However, the suggested threshold is unidirec-

tional, meaning that distances smaller than 1% do not

imply the absence of species boundaries. Additional

genetic and biological data are needed to test for this

possibility. The uniformity of predicted ITS2 secondary

structures and the absence of CBCs or hemi-CBCs are

also consistent with intraspecific levels of variation

(Muller et al. 2007; Coleman 2009; Ruhl et al. 2010).

However, similar to the mitochondrial barcoding

threshold discussed above, this criterion is also unidi-

rectional; thus, the absence of cryptic species is not

implied (Coleman 2009). Lastly, the branching transition

points inferred by the GMYC likelihood method

indicate that the lineage of P. arborea is independently

evolving with a branching pattern characteristic of a

population-level coalescent process (Figs S7 and S8,

Supporting information). In summary, the levels and

patterns of genetic variability in mitochondrial and ITS2

loci do not provide actual evidence for cryptic species

boundaries within P. arborea.

Global patterns of genetic variation

Genetic diversity in P. arborea is not randomly distrib-

uted, as it would be expected under a scenario of global

panmixia. It is highest at the ENP and SP populations

and lowest at WNP and NA populations, as indicated

by the haplotype diversity and the average pairwise dif-

ferences among alleles (Figs 1 and 2). The significantly

high global FST value (0.61 for the mitochondrial locus

and 0.39 for the ITS2, see Table 1) indicates that there

are significant differences in the genetic composition

among worldwide populations, when defined at the

basin/regional scale. This is consistent with the results

from other studies of deep-sea corals (e.g. Smith et al.

2004; Thoma et al. 2009; Miller et al. 2011; Morrison et al.

2011). However, to test over scales smaller than regional

for genetic structuring, it will be necessary to examine

larger numbers of independent, highly variable markers

(e.g. Le Goff-Vitry et al. 2004 and Morrison et al. 2011).

Regional geographic differences were sorted out by

comparing the FST values of genetic differentiation

among populations (Table 1). The pairwise differences

among populations for both markers suggest strong

differentiation between the EPN population and all the

other populations, including the neighbouring WNP.

There is also a significant break between North and

South Pacific populations. South Pacific and North

Atlantic populations are the less dissimilar, which

suggests a more recent connection between them.

Gene genealogies of P. arborea showed no reciprocal

monophyly of alleles among populations. Two nonex-

clusive and equally plausible mechanisms could have

lead to this observed pattern: (i) gene flow between

populations for which recent connectivity could be

conceived given a temporal continuity of favourable

environmental conditions, and (ii) incomplete lineage

sorting caused by a rapid succession of divergence

events among populations, combined with large ances-

tral effective population sizes (Maddison 1997; Edwards

2009). The earliest, divergent lineage of alleles as well

as the highest genetic diversity was found in the WNP,

which lends support to the idea that P. arborea origi-

nated in this region.

The nuclear ITS2 showed signs of lower genetic

differentiation among populations than the mitochon-

drial locus. The effects of differing effective population

sizes on processes such as genetic drift and genetic

sweeps could explain this difference given that the mito-

chondrial genome has one quarter the effective popula-

tion size of the nuclear genome (given that it is haploid

and assuming maternal inheritance only). Similarly, the

nuclear gene tree had much lower resolution compared

Table 1 Global and pairwise FST values for the mitochondrial

(top) and nuclear ITS2 (bottom) markers among populations of

Paragorgia arborea

Mitochondrial

FST global 0.61

NA SP WNP ENP

NA

SP 0.27

WNP 0.67 0.39

ENP 0.98 0.67 0.74

Nuclear ITS2

FST global 0.39

NA SP WNP ENP SI

NA

SP 0.16

WNP 0.47 0.51

ENP 0.27 0.43 0.48

SI 0.13 0.31 0.24 0.26

NA, North Atlantic; SA, South Atlantic; SP, South Pacific;

WNP, western North Pacific; ENP, eastern North Pacific; SI,

South Indian Ocean; ITS2, internal transcribed spacer 2.

All values are significant (i.e. a = 0.05).



tiagohe

Highlight

to the mitochondrial one, which is likely due to the

smaller number of phylogenetically informative sites

present in the short ITS2 sequence.

In contrast to patterns observed in other deep-sea

organisms (Cho & Shank 2010; Etter et al. 2011; Miller

et al. 2011), depth does not appear to be an important

large-scale structuring factor in populations of

P. arborea. This is perhaps not surprising given the

widespread distribution of this organism, which sug-

gests that it is capable of living under a relatively broad

range of conditions. Alternatively, as mentioned above,

small-scale genetic structuring related to depth could be

revealed with higher-resolution markers.

The mitochondrial nad6 gene contained the greatest

amount diversity in terms of haplotypes in this data

set, that is, was the most variable mitochondrial marker.

This result contrasts with previous studies, in which the

mtMutS gene has been found to be significantly more

variable than any other mitochondrial gene region

(France & Hoover 2001; McFadden et al. 2004, 2010;

Herrera et al. 2010). We suggest that the levels of varia-

tion among different mitochondrial gene regions in

octocorals vary among taxa (see McFadden et al. 2010,

2011; Bilewitch & Degnan 2011), and thus, there is not a

single universal region that provides the largest amount

of variability. For the samples of P. arborea examined

here, we found that the combination of nad6 + 16S

+nad2 is the most informative. The nuclear ITS2 still

seems to be a good cost-effective alternative to detect

genetic variation among individuals, in the absence of

intragenomic variants.

Phylogeographic hypothesis

Here, we suggest a phylogeographic scenario in which

P. arborea originated in the North Pacific, possibly in

the WNP followed by colonization of the South Pacific

and spreading eastward around the Southern Hemi-

sphere in a stepping stone fashion (possibly via the

Antarctic Circumpolar Current). The colonization of the

North Atlantic seems to have occurred through a more

recent dispersal event from the South Pacific, via the

Central American Seaway, or from the SA. Similarities

between other deep-sea coral taxa from the South

Pacific and the North Atlantic have been independently

observed (Thoma et al. 2009; Pante & Watling 2012),

which gives support to the idea of a more recent

connection between South Pacific and North Atlantic

deep-sea communities. This scenario is an alternative to

the trans-Arctic interchange hypothesis (Vermeij 1991),

which suggests a recent North Pacific and North

Atlantic connection as indicated by the distributions of

several shallow-water taxa (e.g. red algae Vanoppen

et al. 1995; asteroids, bivalves, gastropods, barnacles

Wares & Cunningham 2001; seagrass Olsen et al. 2004;

cnidarians Govindarajan et al. 2005).

Paragorgia arborea shares a similar phylogeographic

history and genetic diversity patterns with the spiny

dogfish Squalus acanthias (Verissimo et al. 2010) and the

bryozoan Membranipora membranacea (Schwaninger

2008), both of which have modern antitropical distribu-

tions. The time of divergence between the WNP and

South Pacific populations of the spiny dogfish has been

estimated to be around 7.8 Myr BP and approximately

13.3 Myr BP (9.9–21.9) for the bryozoan, which is com-

parable to our estimates of 4.5 Myr BP (95% CI = 2.0–

8.3 using the coalescent model) and 8.1 Myr BP (95%

CI = 3.6–15.3 using the Yule model) for P. arborea

(Fig. 4, Figs S7 and S8, Supporting information). The

timing of colonization of the North Atlantic has been

estimated to be between 3.6 and 5.3 Myr BP for the

dogfish, 6.2 Myr BP (95% CI = 4.6–10.2) for the bryo-

zoan and between 1.7 Myr BP (95% CI = 0.4–3.6) and

4.0 Myr BP (95% CI = 1.3–8.3) for P. arborea. The similar

and independently estimated times for these events

give support to the idea that a common set of oceano-

graphic conditions in the Miocene and early Pliocene

lead to the current distributions of these species. The

latest Miocene ocean circulation models (Butzin et al.

2011) indicate that there was a dominant southward

horizontal flow that carried deep waters from the WNP

to the South Pacific, passing along the eastern side of

the New Zealand landmass, during the mid- to late

Miocene (~5–15 Myr BP). This flow decreased during

the late Miocene. The Antarctic Circumpolar Current

started to develop during the mid- to late Eocene (ca. 37

–40 Myr BP) (Scher 2006) and thus was already well

established as the dominant feature of ocean circulation

during the Miocene, transporting massive amounts of water

eastward. At the same time, during the mid-Miocene,

the deep-water formation in the North Atlantic and its

southward flow were absent or weak, likely due to the

dominant barotropic water flux from the Pacific to the

Atlantic. The formation of deep water in this time per-

iod mainly took place in the Southern Ocean. Deep-

water formation in the North Atlantic and the dominant

southward flow, as we know them today, were later

established during the late Miocene as the Central

American Seaway closed (Butzin et al. 2011). Evolution-

ary migrations inferred from genetic diversity patterns

presented here for P. arborea are consistent with this

history of ocean circulation. Historical changes in the

global patterns of ocean circulation and climate may

have caused shifts in the habitat and thus the distribu-

tion of P. arborea. Widespread ocean cooling during gla-

cial periods in the late Miocene–early Pliocene (Mercer

& Sutter 1982) and throughout the Quaternary (Ehlers

et al. 2011) could have aided the trans-equatorial



exchange by increasing the area of suitable habitat for

stepping stone populations towards the tropics (McIn-

tyre et al. 1989). Isolated relict low-latitude populations

might still exist. We hypothesize that the described set

of conditions could explain the current distribution

patterns of many other marine taxa (e.g. deep-sea coral

associates, such as ophiuroids and chirostylid crabs)

and thus might have played an important role shaping

extant deep-sea faunal diversity.

Acknowledgements

Support for this study was generously provided by a

mini-grant from the Global Census of Marine Life on

Seamounts Project (CenSeam) to J.A.S. and S.H., a grant from

the Facultad de Ciencias, Department of Biological Sciences of

the Universidad de los Andes to J.A.S, the National Systemat-

ics Laboratory of NOAA’s National Marine Fisheries Service,

a Smithsonian Graduate Student Fellowship to S.H., an award

from the Systematics Research Fund of the Systematics Associ-

ation and the Linnean Society of London to S.H., and a Grant-

in-Aid of Research from the Sigma Xi Research Society to S.H.

We are especially thankful to S.D. Cairns, A.G. Collins, C.L.

Agudelo, N. Ardila, L. Duenas, A. Ormos, J. Hunt, L. Weigt,

L. Monroy, M. Herrera and M. Sangrey for their generous

support, assistance and advise. Laboratory work was per-

formed at the Laboratories of Analytical Biology NMNH,

Smithsonian Institution and BIOMMAR, Universidad de los

Andes. Samples were generously provided by P. Alderslade

(CSIRO), A. Andouche (MNHN), A. Andrews (MLML), A.

Baco (FSU), A. Baldinger (MCZ), J. A. Boutillier (DFO), S.D.

Cairns (USNM), S. Davies (DFO), M. Eriksson (UUZM), Y.

Imahara (WPMNH), D. Janussen (SMF), E. Lazo-Wasem

(YPM), P. Lozouet (MNHN), L. Lundsten (MBARI), S. Mills

(NIWA), K. Schnabel (NIWA), and B. Stone (NOAA), D. Tra-

cey (NIWA), and R. Weber (Te Papa Tongarewa). We also

thank J. McDermott, N. Roterman and C. Munro for their

comments on earlier versions of the manuscript. We are grate-

ful for the helpful input from the editor and two anonymous

reviewers.

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Data accessibility

Sample information and locations are provided as

supporting information in Table S1 (Supporting infor-

mation).

DNA sequences are available in GenBank and acces-

sion numbers appear in Table S1 (Supporting informa-

tion).

DNA sequence alignments are available in DRYAD

doi:10.5061/dryad.ns23j.

Supporting information

Additional Supporting Information may be found in the online ver-

sion of this article.

Appendix S1 Sequencing of old samples.

Table S1 Collection and sequence information for the speci-

mens used in this study.

Table S2 Nucleotide substitution models for mitochondrial

gene partitions, as selected by the AIC criterion in JMODELTEST.

Table S3 Interspecific and intraspecific (i.e. coalescent depths)

uncorrected pairwise distances (%) among haplotypes of spe-

cies of Paragorgia and Sibogagorgia.

Fig. S1 Sampling location of specimens of Paragorgia arborea

examined in this study.

Fig. S2 Depth distribution of samples shown in Fig. S1 (Sup-

porting information).

Fig. S3 The revised geographic distribution of Paragorgia arbo-

rea.

Fig. S4 Depth distribution of records shown in Fig. S3 (Sup-

porting information).

Fig. S5 Predicted ITS2 secondary structure of Paragorgia arbo-

rea.

Fig. S6 Individual mitochondrial gene tree hypotheses in Para-

gorgia.

Fig. S7 Fit of the GMYC single-threshold model to the mito-

chondrial time-calibrated gene tree generated with the Yule

model tree prior.

Fig. S8 Fit of the GMYC single-threshold model to the mito-

chondrial time-calibrated gene tree generated with the coales-

cent model tree prior.

Fig. S9 Haplotype accumulation curves in Paragorgia arborea.



Appendix S1. Sequencing of old samples

- We arbitrarily define samples as ‘old’ if they predate the year of 1979, as this is the year of collection of the oldest specimens of P. arborea in a 40-year time window before present (as suggested by the reviewer).

- All the samples at the Smithsonian Institution in Washington DC (main source of the old NA samples, i.e. >90%) are stored in separate glass jars or individual cabinets (in the case of dry samples) and bags, there's no 'common pool' storage. The same is true for the SP samples, which came from NIWA, Wellington, New Zealand.

- During the sub-sampling process of specimens for molecular work, all dissecting instruments and working surfaces were carefully sterilized in between samplings. Furthermore, this subsampling took place in a different building than where the DNA extractions and PCR took place (buildings are separated by several kilometers).

- DNA was extracted with an automated robotic system (Autogen), which minimizes human error (the only step that requires hands-on work is the insertion of the actual samples into the 96-well extraction plate).

- This extraction system is a state-of-the-art instrument that is routinely used by the barcoding facility at the Smithsonian (thousands of samples per week), with extremely rare reports of malfunctioning. There was no reported malfunctioning of the system during the period in which the DNA from these specimens was extracted.

- No previous work on this species (or even family) had been performed at the laboratories where molecular work took place, before this study.

- DNA extraction was performed in a different room (in a different floor) than the room used forPCRs.

- All pipette tips utilized for this study had filters and aerosol barriers. Extreme caution was used while setting up PCR reactions.

- All extraction plates and PCR reactions contained negatives. None of these amplified in any reaction.

- Given that the old specimens represent only 40% of all the NA specimens, excluding their sequences from our analysis would not change the interpretations, i.e. definition of P. arborea as genealogical-phylospecies, absence of cryptic species, phylogeographic history, shared haplotypes with the South Pacific, low genetic diversity in the NA, genetic differentiation in the species at the oceanic basin-scale.

- The following figure shows the plan of the 96-well extraction/PCR/sequencing plate containing the great majority of the old specimens (98%). There are 40 old samples in this plate (36/40 of these came from the NA): 13 dry-preserved and 27 wet-preserved (most likely originally fixed in formalin before preservation in ethanol). 8/13 of the dry specimens were successfully sequenced (all from the NA), but only 1/27 of there wet specimens 1/27 of wet specimens amplified.

1 2 3 4 5 6 7 8 9 10 11 12

AParborea98080

Parborea4569

Parborea30238

Parborea80937

Parboreap17265

Ppacifica52432

Parborea100817

Parboreap30241

Parborea3310

Parborea28123

Parborea28160

ParboreaSMF4588

BParborea1016643

Parborea17264

Parborea17262

Parborea100818

Parborea4911

Parborea15874

Parborea80828

Parborea1092764

Parborea3311

Parborea28154

Parborea28392

ParboreaSMF4605

CParborea4089

Parborea4910

Parborea100843

Parborea1002420

Parborea54497

Parborea30239

Parborea1011360

Parborea4242

Palisonae3312

Parborea28155

Parborea28422

ParboreaSMF4600

DParborea5116

ParboreaNew568

ParboreaNew100

Parborea1092765

Parborea100846

Parborea17260

Parborea80838

Parborea4091

Parborea17969

Parborea28156

Parborea28423

ParboreaSMF9554

EParborea16937

ParboreaNew545

Parborea80936

Parborea1010787

Parborea33559

Parborea1011097

Parborea1011098

Parborea21855

Palisonae3312

Parborea28157

Parborea28425

ParboreaSMF9559

FParborea33733

Parborea1092766

Parborea1011079

Parboreap33560

Parborea1011014

Parborea50890

Parborea30240

Parborea1011360

Palisonae3313

Parborea28158

ParboreaZ11166

PcfarboreJapan

GParborea100758

Parborea17263

Parborea33562

Parborea17261

Parborea15880

Parborea54298

Parborea33561

Parborea3308

Parborea17970

Parborea28159

ParboreaZC0706

PcfarboreJapan

HParboreap5107

Parborea4238

Parboreap17266

Parboreap4573

Parboreap4143

Parborea50028

Parborea4599

Parborea3309

Parborea25527

Parborea28161

ParboreaSMF1246

Blank

1 2 3 4 5 6 7 8 9 10 11 12

AParborea98080

Parborea4569

Parborea30238

Parborea80937

Parboreap17265

Ppacifica52432

Parborea100817

Parboreap30241

Parborea3310

Parborea28123

Parborea28160

ParboreaSMF4588

BParborea1016643

Parborea17264

Parborea17262

Parborea100818

Parborea4911

Parborea15874

Parborea80828

Parborea1092764

Parborea3311

Parborea28154

Parborea28392

ParboreaSMF4605

CParborea4089

Parborea4910

Parborea100843

Parborea1002420

Parborea54497

Parborea30239

Parborea1011360

Parborea4242

Palisonae3312

Parborea28155

Parborea28422

ParboreaSMF4600

DParborea5116

ParboreaNew568

ParboreaNew100

Parborea1092765

Parborea100846

Parborea17260

Parborea80838

Parborea4091

Parborea17969

Parborea28156

Parborea28423

ParboreaSMF9554

EParborea16937

ParboreaNew545

Parborea80936

Parborea1010787

Parborea33559

Parborea1011097

Parborea1011098

Parborea21855

Palisonae3312

Parborea28157

Parborea28425

ParboreaSMF9559

FParborea33733

Parborea1092766

Parborea1011079

Parboreap33560

Parborea1011014

Parborea50890

Parborea30240

Parborea1011360

Palisonae3313

Parborea28158

ParboreaZ11166

PcfarboreJapan

GParborea100758

Parborea17263

Parborea33562

Parborea17261

Parborea15880

Parborea54298

Parborea33561

Parborea3308

Parborea17970

Parborea28159

ParboreaZC0706

PcfarboreJapan

HParboreap5107

Parborea4238

Parboreap17266

Parboreap4573

Parboreap4143

Parborea50028

Parborea4599

Parborea3309

Parborea25527

Parborea28161

ParboreaSMF1246

Blank

Figure showing the plan of the 96-well plate configuration used to extract, amplify and sequence 98% of the old specimens. TOP shows the distribution of old samples in the plate (underlined names with colored backgrounds). Blue background indicates that the specimen was wet-preserved and red indicates that the specimen was dry-preserved. BOTTOM shows the distribution of all sequenced samples in the plate. Colored backgrounds indicate the mitochondrial haplotype of the specimens of Paragorgia arborea (as in figures 1, 3 and 5 and table S1 of the manuscript): Orange for haplotype m15, light blue for haplotype m3, light green for haplotype m14, light orange for haplotype m16, aquamarine for haplotype m2, violet for haplotype m4, dark green for haplotype m12, brown for haplotype m5, fuchsia for haplotype m13, yellow-green for haplotype m10, yellow for haplotype m11, and pink for haplotype m6. Cells with white background and black font indicate non-NA specimens of Paragorgia cf. arborea that did not amplify. Cells with white background and maroon font indicate modern samples (post-1979) from other species that were sequenced for all genes. Cells with white font indicate NA specimens of Paragorgia cf. arborea, and those underlined indicate old specimens. Cells with black background indicate old NA specimens that did not sequence.

- The ‘old’ specimens (predating 1979) from the NA that were successfully sequenced represented 3 mitochondrial haplotypes:

m15 (orange)Globally 28 individuals had this haplotype. 9 from the South Pacific (SP) and 19 from the North Atlantic (NA). All the old samples (collected before 1979) with this haplotype were collected in the NA, and add up to 7 specimens (37% of the total m15 NA samples).

m12 (dark green)Globally 10 individuals had this haplotype. 9 from NZ and 1 from the NA, the later is an old sample.

m16 (light orange)

This is a unique haplotype from an old NA sample (therefore no contamination is possible in this case).

- This plate also contained modern samples from P. arborea specimens representing 12 mt haplotypes, plus representatives from at least 5 other species.

- It is thus extremely unlikely that if contamination occurred it was restricted to 2 particular haplotypes, and that it was strongly biased towards dry specimens from the NA

- We are aware of two publications that have successfully sequenced old octocoral specimens:

- The Aguilar and Sanchez 2008 Bull Mar Sci paper has an ITS2 sequences from the octocoral Calyptrophora japonica collected in 1900s from the USNM collection.

- The Aguilar and Sanchez 2007 MPE has several octocoral specimens collected from the Atlantide expedition 1945-1946. Their ITS2 sequences and RNA secondary structures are unique compared to any other octocoral.

In conclusion, all these lines of evidence strongly indicate that our sequences from old specimens are legitimate and are not products of contamination. It is plausible that DNA in old specimens of Paragorgia can be still viable for the sequencing of mitochondrial and nuclear ribosomal DNA if the tissue material is fixed and preserved correctly. Wet specimens were traditional fixed in formalin, which is well known to damage DNA, thus these are mostly unusable for genetic purposes. Dry preservation of specimens of Paragorgia and other octocorals is an acceptable, although not ideal, way to preserve mitochondrial and nuclear ribosomal DNA.

Sp

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Co

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1998

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and

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2001

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2008

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2008

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2007

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2007

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2008

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2008

New

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2007

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2008

New

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318

2008

New

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4559

JX12

8561

N/A

m16

USN

M33

560

Fish

ing

Bank

s, N

orth

Car

olin

a, U

SA45

.00

-53.

50A

. Ver

rill

Parb

orea

p335

60JX

1284

10JX

1284

99JX

1246

26JX

1245

72JX

1286

35N

/Am

15U

SNM

3356

1O

ff N

E N

orth

Am

eric

a, U

SAA

. Ver

rill

Parb

orea

3356

1JX

1283

58JX

1285

08JX

1246

33JX

1245

62JX

1285

54N

/Am

15U

SNM

3356

2O

ff N

E N

orth

Am

eric

a, U

SAA

. Ver

rill

Parb

orea

3356

2N

/AN

/AN

/AN

/AN

/AJX

1286

65i2

USN

M50

890

1927

Burd

woo

d Ba

nk, S

Of F

alkl

and

Isla

nds,

Scot

ia S

ea-5

4.50

-59.

10F.

Baye

rPa

rbor

ea50

890

JX12

8412

JX12

8467

JX12

4651

JX12

4521

JX12

8630

N/A

m4

USN

M80

838

1979

Balti

mor

e C

anyo

n, O

ff Ea

ster

n Sh

ore,

Mar

ylan

d, U

SA48

038

.17

-73.

84F.

Baye

rPa

rbor

ea80

838

JX12

8416

JX12

8517

JX12

4676

JX12

4544

JX12

8553

JX12

8661

m15

i2U

SNM

8093

619

79Ly

doni

a C

anyo

n, M

assa

chus

etts

, USA

680-

370

40.3

8-6

7.66

F. Ba

yer

Parb

orea

8093

6JX

1283

62JX

1285

18JX

1246

22JX

1245

43JX

1285

56N

/Am

15U

SNM

8093

719

79Ly

doni

a C

anyo

n, M

assa

chus

etts

, USA

613-

430

40.3

8-6

7.66

F. Ba

yer

Parb

orea

8093

7JX

1283

85JX

1284

69JX

1246

13JX

1245

99JX

1285

57N

/Am

15U

SNM

1007

5819

94A

leut

ian

Isla

nds

52.0

0-1

70.0

0F.

Baye

rPa

rbor

ea10

0758

JX12

8355

JX12

8534

JX12

4634

JX12

4584

JX12

8547

N/A

m3

USN

M10

0817

1994

Atk

a Is

land

, And

rean

of Is

land

s, A

leut

ian

Isla

nds

53.0

0-1

74.0

0F.

Baye

rPa

rbor

ea10

0817

JX12

8404

JX12

8494

JX12

4669

JX12

4539

JX12

8626

N/A

m3

USN

M10

0818

1994

Sem

isop

ochn

oi Is

land

, Rat

Isla

nds,

Ale

utia

n Is

land

s52

.17

179.

72F.

Baye

rPa

rbor

ea10

0818

JX12

8433

JX12

8536

JX12

4700

JX12

4597

JX12

8546

JX12

8675

m3

i8U

SNM

1008

4319

94Ta

naga

Isla

nd, A

ndre

anof

Isla

nds,

Ale

utia

n Is

land

s52

.00

-178

.00

F. Ba

yer

Parb

orea

1008

43JX

1283

82JX

1284

59JX

1246

74JX

1245

29JX

1285

84JX

1286

60m

3i8

USN

M10

0846

1994

Yuna

ska

Isla

nd, I

slan

ds o

f Fou

r M

ount

ains

, Ale

utia

n Is

land

s53

.00

-171

.00

F. Ba

yer

Parb

orea

1008

46JX

1284

11JX

1284

89JX

1246

79JX

1245

38JX

1285

70N

/Am

3U

SNM

1007

340

2001

Vanc

ouve

r Is

land

, Can

ada

1168

48.4

4-1

26.3

8F.

Baye

rPa

rago

sp10

0734

0JX

1283

64JX

1285

00JX

1246

50JX

1245

56JX

1285

72JX

1286

78m

9i6

USN

M10

1078

720

00N

orfo

lk C

anyo

n, V

irgi

nia,

USA

375-

489

37.0

7-7

4.66

F. Ba

yer

Parb

orea

1010

787

JX12

8368

JX12

8488

JX12

4639

JX12

4528

JX12

8617

JX12

8671

m15

i1U

SNM

1011

097

2002

Buld

ir R

eef,

Rat

Isla

nds,

Ale

utia

n Is

land

s16

051

.96

176.

83R

. Sto

nePa

rbor

ea10

1109

7JX

1283

57JX

1285

28JX

1246

59JX

1245

45JX

1285

60JX

1286

74m

2i8

USN

M10

1136

020

01of

f Um

nak

Isla

nd, F

ox Is

land

s, A

leut

ian

Isla

nds

102

53.6

8-1

69.1

1F.

Baye

rPa

rbor

ea10

1136

0JX

1283

78JX

1284

62JX

1246

31JX

1245

82JX

1285

43N

/Am

3U

SNM

1014

919

2003

Dav

idso

n Se

amou

nt, C

alifo

rnia

, USA

1313

35.7

0-1

22.7

0F.

Baye

rPa

rago

sp10

1491

9JX

1283

73JX

1284

95JX

1246

68JX

1245

78JX

1286

22N

/Am

9U

SNM

1016

320

2002

Briti

sh C

olum

bia,

Can

ada

1152

-119

253

.70

-133

.42

S. C

airn

sPp

acifi

ca10

1632

0JX

1283

61JX

1285

19JX

1246

80JX

1245

81JX

1286

12N

/Am

9U

SNM

1027

060

2003

Pion

eer

Seam

ount

, Sou

th o

f far

allo

n Is

land

s, C

alifo

rnia

, USA

1712

37.4

0-1

23.4

4S.

Cai

rns

Para

gosp

1027

060

JX12

8446

JX12

8463

JX12

4620

JX12

4560

JX12

8594

JX12

8643

m9

i6U

SNM

1075

738

2004

Dic

kins

Sea

mou

nt, G

ulf o

f Ala

ska,

USA

760

54.5

5-1

36.8

4S.

Cai

rns

Para

gosp

1075

738

JX12

8365

JX12

8502

JX12

4649

JX12

4526

JX12

8623

JX12

8676

m9

i6U

SNM

1075

744

2004

Dic

kins

Sea

mou

nt, G

ulf o

f Ala

ska,

USA

851

54.5

1-1

36.9

1S.

Cai

rns

Para

gosp

1075

744

JX12

8432

JX12

8453

JX12

4690

JX12

4565

JX12

8607

N/A

m9

USN

M10

7574

520

04D

icki

ns S

eam

ount

, Gul

f of A

lask

a, U

SA84

954

.51

-136

.91

S. C

airn

sPa

rago

sp10

7574

5JX

1283

66JX

1285

35JX

1246

75JX

1246

02JX

1285

99N

/Am

9U

SNM

1075

746

2004

Wel

ker

Seam

ount

, Gul

f of A

lask

a, U

SA78

055

.05

-140

.31

S. C

airn

sPa

rago

sp10

7574

6JX

1284

45JX

1284

71JX

1246

30JX

1245

40JX

1285

80JX

1286

46m

9i6

USN

M10

7575

320

04W

elke

r Se

amou

nt, G

ulf o

f Ala

ska,

USA

1112

55.0

7-1

40.4

1S.

Cai

rns

Para

gosp

1075

753

JX12

8356

JX12

8526

JX12

4685

JX12

4527

JX12

8605

JX12

8682

m9

i11

USN

M10

7575

420

04W

elke

r Se

amou

nt. G

ulf o

f Ala

ska,

USA

1084

55.0

7-1

40.4

1S.

Cai

rns

Para

gosp

1075

754

JX12

8405

JX12

8468

JX12

4665

JX12

4593

JX12

8624

N/A

m9

USN

M10

7576

020

04Pr

att

Seam

ount

, Gul

f of A

lask

a, U

SA95

956

.17

-142

.70

S. C

airn

sPa

rago

sp10

7576

0JX

1283

93JX

1284

47JX

1246

99JX

1245

30JX

1285

45JX

1286

45m

9i6

Ge

nB

an

k A

cce

sio

n N

um

be

rsTa

ble

S1. C

olle

ctio

n an

d se

quen

ce in

form

atio

n fo

r the

spec

imen

s use

d in

this

stud

y.

Hap

loty

pe

s

Acr

onym

s as f

ollo

ws:

Nat

iona

l Mus

eum

of N

atur

al H

isto

ry, S

mith

soni

an In

stitu

tion,

USA

(USN

M);

The

Nat

iona

l Ins

titut

e of

Wat

er a

nd A

tmos

pher

ic R

esea

rch,

New

Zea

land

(NIW

A);

Mus

eum

of C

ompa

rativ

e Zo

olog

y, H

arva

rd U

nive

rsity

, USA

(MC

Z); M

uséu

m N

atio

nal d

'His

toire

Nat

urel

le, P

aris

, Fr

ance

(MN

HN

); Se

ncke

nber

g R

esea

rch

Inst

itute

And

Nat

ural

His

tory

Mus

eum

Fra

nkfu

rt, G

erm

any

(SM

F); U

ppsa

la U

nive

rsity

Evo

lutio

nsm

usee

t, Sw

eden

(UU

ZM);

Wak

ayam

a Pr

efec

tura

l Mus

eum

of N

atur

al H

isto

ry, J

apan

(WPM

NH

); Ya

le P

eabo

dy M

useu

m o

f Nat

ural

His

tory

, USA

(YPM

).

USN

M10

7576

120

04Pr

att

Seam

ount

, Gul

f of A

lask

a, U

SA94

156

.17

-142

.70

S. C

airn

sPa

rago

sp10

7576

1JX

1283

74JX

1285

29JX

1246

44JX

1245

76JX

1285

59N

/Am

9U

SNM

1075

766

2004

Wel

ker

Seam

ount

, Gul

f of A

lask

a, U

SA11

1455

.07

-140

.41

S. C

airn

sPa

rago

sp10

7576

6JX

1284

00JX

1285

11JX

1246

18JX

1245

41JX

1286

14JX

1286

84m

9i6

USN

M10

9276

420

00Ea

st o

f Vir

gini

a Be

ach,

Vir

gini

a, U

SA37

5-48

937

.07

-74.

66J.

Sánc

hez

Parb

orea

1092

764

JX12

8395

JX12

8525

JX12

4688

JX12

4589

JX12

8569

N/A

m15

USN

M10

9276

520

00Ea

st o

f Vir

gini

a Be

ach,

Vir

gini

a, U

SA37

5-48

937

.07

-74.

66J.

Sánc

hez

Parb

orea

1092

765

JX12

8377

JX12

8541

JX12

4655

JX12

4520

JX12

8627

JX12

8667

m14

i5U

SNM

1092

766

2000

East

of V

irgi

nia

Beac

h, V

irgi

nia,

USA

375-

489

37.0

7-7

4.66

J. Sá

nche

zPa

rbor

ea10

9276

6JX

1283

53JX

1284

77JX

1246

40JX

1245

54JX

1285

86JX

1286

40m

15i2

USN

M11

2044

420

08of

f Mar

ylan

d, U

SA40

037

.06

-74.

62S.

Cai

rns

Para

gosp

nizi

nski

JX12

8422

JX12

8479

JX12

4663

JX12

4547

JX12

8552

N/A

m15

USN

M11

2223

320

02Sa

n Ju

an S

eam

ount

, Cal

iforn

ia, U

SA13

60.8

32.9

7-1

21.0

4J.

Sánc

hez

Parb

orea

T66

2A29

JX12

8409

JX12

8530

JX12

4662

JX12

4586

JX12

8628

N/A

m9

USN

M11

2223

720

02R

odri

guez

Sea

mou

nt, C

alifo

rnia

, USA

894.

534

.06

-121

.08

J. Sá

nche

zPa

rbor

eaT

661A

10JX

1284

01JX

1285

27JX

1246

23JX

1245

90JX

1285

65N

/Am

9U

SNM

1122

240

2002

San

Juan

Sea

mou

nt, C

alifo

rnia

, USA

1362

.932

.97

-121

.04

J. Sá

nche

zPa

rbor

eaT

662A

28JX

1284

02JX

1284

52JX

1246

91JX

1245

22JX

1285

64N

/Am

9U

SNM

1123

932

2002

Sout

h of

Tri

nity

Isla

nds,

Ale

utia

n Is

land

s74

655

.87

-154

.06

J. Sá

nche

zPa

rbor

ea41

78B

JX12

8360

JX12

8481

JX12

4628

JX12

4524

JX12

8637

JX12

8677

m9

i1U

SNM

1123

935

2004

Am

lia Is

land

, And

rean

of Is

land

s, A

leut

ian

Isla

nds

843

51.8

1-1

73.8

3J.

Sánc

hez

Parb

orea

J209

5272

JX12

8399

JX12

8516

JX12

4696

JX12

4580

JX12

8583

N/A

m7

USN

M11

2393

620

04A

mlia

Isla

nd, A

ndre

anof

Isla

nds,

Ale

utia

n Is

land

s84

351

.81

-173

.83

J. Sá

nche

zPa

rbor

eaJ2

0952

71JX

1284

07JX

1284

60JX

1246

93JX

1245

32JX

1286

02JX

1286

59m

6i1

USN

M11

2393

720

04A

dak

Can

yon,

And

rean

of Is

land

s, A

leut

ian

Isla

nds

1269

51.5

1-1

77.0

4J.

Sánc

hez

Parb

orea

J209

9211

JX12

8443

JX12

8484

JX12

4653

JX12

4604

JX12

8604

JX12

8639

m7

i9U

SNM

1123

938

2004

Am

chitk

a Pa

ss, A

ndre

anof

Isla

nds,

Ale

utia

n Is

land

s74

751

.72

-179

.58

J. Sá

nche

zPa

rbor

eaJ2

1044

1JX

1284

03JX

1285

33JX

1246

47JX

1245

36JX

1286

08JX

1286

70m

1i1

0W

PMN

HW

PMN

H20

05O

ff Ya

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shi,

Shiju

oka

Prof

., Ja

pan

760-

800

33.0

013

8.40

Pcfa

rbor

eJap

anJX

1284

42JX

1284

86JX

1246

54JX

1245

57JX

1285

93JX

1286

50m

8i1

YPM

2700

2A20

00A

tlant

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, Bea

r Se

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nt, n

ear

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1439

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039

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44J. A

. Moo

rePa

rbor

ea27

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N/A

N/A

N/A

N/A

N/A

JX12

8647

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rgia

cor

allo

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USN

M98

785

1995

East

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ific

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ff M

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5012

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F. Ba

yer

Pcfc

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lo98

785

JX12

8428

JX12

8504

JX12

8346

JX12

8350

JX12

8576

N/A

Para

gorg

ia jo

hnso

niU

SNM

7376

719

84Li

ttle

Bah

ama

Bank

, Bah

amas

608

27.1

0-7

9.70

F. Ba

yer

Pjoh

nson

i737

67JX

1284

17JX

1285

03JX

1283

44JX

1283

48JX

1286

00N

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rago

rgia

kau

peka

NIW

A33

2019

89O

ff ea

st c

oast

, New

Zea

land

820

-36.

1617

6.81

J. Sá

nche

zPk

aupe

ka33

20G

Q29

3254

GQ

2933

32G

Q29

3283

GQ

2933

13G

Q29

3351

GQ

2932

92Pa

rago

rgia

sp.

USN

M11

2230

520

04Br

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Col

umbi

a, C

anad

a51

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.14

J. Bo

utill

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Para

gosp

121

JX12

8390

JX12

8490

JX12

8343

JX12

8347

JX12

8638

N/A

Para

gorg

ia w

ahin

eN

IWA

3326

2001

Off

east

coa

st,

New

Zea

land

900

-42.

7917

9.99

J. Sá

nche

zPw

ahin

e332

6G

Q29

3255

GQ

2933

33G

Q29

3284

GQ

2933

14G

Q29

3352

GQ

2932

96Pa

rago

rgia

yut

linux

USN

M10

7348

020

03O

ff V

anco

uver

Isl.,

Briti

sh C

olum

bia,

Can

ada

846–

861

50.2

3-1

28.5

8J.

Sánc

hez

Pyut

linux

1073

480

GQ

2932

56G

Q29

3334

GQ

2932

85G

Q29

3315

GQ

2933

53G

Q29

3297

Sibo

gago

rgia

cau

liflo

raU

SNM

1122

229

2006

Dav

idso

n Se

amou

nt, C

alifo

rnia

, USA

3042

.435

.63

-122

.83

S. H

erre

raSi

boga

sp1T

947A

9G

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3258

GQ

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36G

Q29

3287

GQ

2933

17G

Q29

3355

GQ

2932

90

Acr

onym

s as f

ollo

ws:

Nat

iona

l Mus

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of N

atur

al H

isto

ry, S

mith

soni

an In

stitu

tion,

USA

(USN

M);

The

Nat

iona

l Ins

titut

e of

Wat

er a

nd A

tmos

pher

ic R

esea

rch,

New

Zea

land

(NIW

A);

Mus

eum

of C

ompa

rativ

e Zo

olog

y, H

arva

rd U

nive

rsity

, USA

(MC

Z); M

uséu

m N

atio

nal d

'His

toire

Nat

urel

le, P

aris

, Fr

ance

(MN

HN

); Se

ncke

nber

g R

esea

rch

Inst

itute

And

Nat

ural

His

tory

Mus

eum

Fra

nkfu

rt, G

erm

any

(SM

F); U

ppsa

la U

nive

rsity

Evo

lutio

nsm

usee

t, Sw

eden

(UU

ZM);

Wak

ayam

a Pr

efec

tura

l Mus

eum

of N

atur

al H

isto

ry, J

apan

(WPM

NH

); Ya

le P

eabo

dy M

useu

m o

f Nat

ural

His

tory

, USA

(YPM

).

Tabl

e S1

. Col

lect

ion

and

sequ

ence

info

rmat

ion

for t

he sp

ecim

ens u

sed

in th

is st

udy.

Model Partition -lnL AIC deltaAIC weight cumweight uDelta16S TPM2+I 10212 1086.8644 2271.7289 0 0.1562 0.1562 -coxI TPM2+I 10212 853.9125 1805.8249 0 0.1728 0.1728 -nad3 TPM1 12210 239.1497 574.2994 0 0.0933 0.0933 115.8078nad2 TPM1+I 12210 1168.0114 2434.0228 0 0.0698 0.0698 242.8336mtMutS TPM2+I 10212 1581.1612 3260.3225 0 0.1328 0.1328 216.9732nad6 TVMef+G 12314 1106.6466 2315.2932 0 0.1708 0.1708 319.3048int TIM3ef 12032 68.6581 235.3162 0 0.1623 0.1623 -

Table S2. Nucleotide substitution models for mitochondrial gene partitions, as selected by the AIC criterion in jModeltest.

mtM

utS

S. c

aulif

lora

P. ka

upek

aP.

cora

lloid

esP.

john

soni

Para

gorg

ia s

p.1

P. yu

tlinu

xP.

aliso

nae

P. w

ahin

eP.

arbo

rea

S. c

aulif

lora

-P.

kaup

eka

7.1

-P.

cora

lloid

es5.2

4.1

-P.

john

soni

6.6

5.4

2.1

-Pa

rago

rgia

sp.

17.3

6.1

2.8

1.0

-P.

yutli

nux

6.6

5.5

2.2

0.5

1.2

-P.

aliso

nae

6.8

5.6

2.3

0.7

1.4

0.7

-P.

wah

ine

6.6

5.5

2.2

0.6

1.3

0.6

0.7

-P.

arbo

rea

6.3a

5.1a

1.8a

2.1a

2.8a

2.1a

2.2a

2.1a

0.3b

cox1

S. c

aulif

lora

P. ka

upek

aP.

cora

lloid

esP.

john

soni

Para

gorg

ia s

p.1

P. yu

tlinu

xP.

aliso

nae

P. w

ahin

eP.

arbo

rea

S. c

aulif

lora

-P.

kaup

eka

2.9

-P.

cora

lloid

es3.2

2.0

-P.

john

soni

2.8

1.5

0.4

-Pa

rago

rgia

sp.

13.2

2.0

0.9

0.4

-P.

yutli

nux

2.8

1.5

0.4

0.0

0.4

-P.

aliso

nae

3.5

2.0

0.9

0.6

1.1

0.6

-P.

wah

ine

3.9

2.3

1.5

1.1

1.5

1.1

1.7

-P.

arbo

rea

3.2a

1.8a

1.1a

0.6a

1.1a

0.6a

1.3a

1.3a

0.9b

ITS2

S. c

aulif

lora

P. ka

upek

aP.

wah

ine

P. yu

tlinu

xP.

arbo

rea

S. c

aulif

lora

-P.

kaup

eka

5.8

-P.

wah

ine

5.9

5.3

-P.

yutli

nux

8.0

7.4

6.4

-P.

arbo

rea

6.2a

5.6a

4.7a

3.3a

0.6b

a =

min

imum

pai

rwis

e di

stan

ce a

mon

g co

ngen

ers,

b =

max

imum

pai

rwis

e di

stan

ce a

mon

g co

nspe

cific

s.

Tabl

e S3

. Int

ersp

ecifi

c an

d in

trasp

ecifi

c (i.

e. c

oale

scen

t dep

ths)

unc

orre

cted

pai

rwis

e di

stan

ces (

%) a

mon

g ha

plot

ypes

of s

peci

es o

f Par

agor

gia

and

Sibo

gago

rgia

.

0°

10°

20°

30°

40°

50°

60°

70°

80°

-10°

-20°

-30°

-40°

-50°

-70°

-60°

-80°

0° 20° 40° 60° 80° 100° 120° 140° 160° 180°-20°-40°-60°-80°-100°-120°-140°-160°-180°

Figure S1. Sampling location of the specimens of Paragorgia arborea examined in this study. Colors indicate depth ranges: yellow from 0 to 500 m, orange from 500 to 1000 m, red from 1000 to 1500 m, and maroon from 1500 to 2000 m. Blue indicates records for which depth data was unavailable.

Num

ber o

f Sam

ples

0

10

20

30

40

50

Depth Range (m)NA 0-500 500-1000 1000-15001500-2000

Figure S2. Depth distribution of samples shown in Figure S1.

★★

★

0°

10°

20°

30°

40°

50°

60°

70°

80°

-10°

-20°

-30°

-40°

-50°

-70°

-60°

-80°

0° 20° 40° 60° 80° 100° 120° 140° 160° 180°-20°-40°-60°-80°-100°-120°-140°-160°-180°

Figure S3. The revised geographical distribution of Paragorgia arborea. Circles = high-confidence records, squares = moderate confidence, and stars = low-confidence (confidence qualitatively assessed based on abundance of records in the neighboring area). Colors indicate depth ranges: yellow from 0 to 500 m, orange from 500 to 1000 m, red from 1000 to 1500 m, and maroon from 1500 to 2000 m. Blue indicates records for which depth data was unavailable.

Num

ber o

f Rec

ords

0

50

100

150

Depth Range (m)NA 0-500 500-1000 1000-1500 1500-2000

Figure S4. Depth distribution of records shown in Figure S3.

i1i2i3i4i5i6i7i8i9i10i11

Figure S5. Predicted ITS2 secondary structure of Paragorgia arborea. The arrows and associated numbers correspond to the 11 haplotypes (as seen in the upper left table) found in the examined specimens. Dashes indicate absence of change relative to a reference 75%-consensus sequence.

Fig. S6. Individual mitochondrial gene-tree hypotheses in Paragorgia. Trees are 50% consensus cladograms of the sampled trees, after burnin, in the Bayesian analyses.

16S

1

P. kaupekaP. coralloides

m3m1m2

P. johnsoni

m16m5m11m8m12m7m13m4m6m15m9

P. yutlinuxP. wahineP. alisonaeParagorgia sp.

m14m10

S. cauli!ora

coxI

P. kaupeka

P. johnsoniP. coralloidesP. yutlinuxP. alisonaeParagorgia sp.

P. wahine

m3m2m1

m14m6m12m11m5m15m8m13m16m7m10m4m9

S. cauli!ora S. cauli!oraP. coralloidesP. kaupeka

m4m2m1

P. yutlinuxP. wahineParagorgia sp.

m12m3m13

m11m10

m9

P. alisonaeP. johnsoni

m14m15m16

m5m6m7m8

nad6

S. cauli!oraP. kaupekaP. coralloides

m15m1m4m6m9m3m7m14m16m12m10m2m8m5m13m11Paragorgia sp.P. johnsoniP. yutlinuxP. wahineP. alisonae

nad3


m9

m11m6m8m12m7m5m10m3m2m1m4

m14m15m13m16

Paragorgia sp.P. wahineP. yutlinux

P. johnsoniP. alisonae

nad2m1m3m2


m15m13m5m12m16m9m14

P. wahineP. yutlinuxP. alisonae

m4m7m6m8m10m11

P. johnsoniParagorgia sp.

mtMutS

Paragorgia sp.

S. cauli!oraP. kaupeka

m5m1m11m14m16P. wahineP. johnsonim8m2m12m3m9m10m4m15P. alisonaem7m13P. yutlinuxm6P. coralloides

int

posterior probability > 0.95

0.020.040.060.080.0100.0120.0140.0

Parborea1011360

Parborea28425

ParboreaJ2099211

ParboreaMCZ51244

Ckishinouyei1072441

Claauense1071433

PcfarboreJapan

Paragosp121

Pkaupeka3320

Paragosp46316

Pwahine3326

Palisonae3312

Pjohnsoni73767

Parborea1011097

Paragosp1075754

Coralliumsp1075800

Claauense1072452

Csecundum1010758

Parborea33559

ParboreaJ2095271

Pcfcorallo98785

Parborea42001

Parborea3311

Parborea28392

Paracora1089600

Parborea1092765

Sibogasp1T947A9

ParboreaJ210441

Parborea50890

Pyutlinux1073480

-100 -80 -60 -40 -20 0

12

510

20


N

-100 -80 -60 -40 -20 0

-45.

5-4

5.0

-44.

5-4

4.0

-43.

5-4

3.0

Time (million years)Lo

g-lik

elih

ood

Log-likelihood of null model: -45.14365 Maximum log-likelihood of GMYC model: -42.71979 Likelihood ratio: 4.847715 Result of LR test: 0.1832939 n.s. ML number of clusters: 3 95% Con!dence interval: 2-5 ML number of entities: 9 95% Con!dence interval: 2-23 Threshold time estimate: -21.03428 Ma BP


Figure S7. Fit of the GMYC single-threshold model to the mitochondrial time-calibrated gene tree generated with the Yule-model tree prior. Ultrametric tree shows the estimated times of divergence under this model. Node bars represent the 95% highest posterior density intervals. Clades with red branches indicate the inferred, independently evolving, lineages. Dotted lines indicate the ML inferred time for the speciation-coalescent threshold for species delimitation. Top boxes show the lineage-through-time plot (left) and the log-likelihood plot (right).

0.020.040.060.080.0100.0120.0140.0160.0

ParboreaJ2099211

Parborea28392

Claauense1072452

Pyutlinux1073480

ParboreaJ210441

ParboreaJ2095271

Parborea33559

Parborea42001

Parborea1092765

Pcfcorallo98785

Parborea1011360

Palisonae3312

Parborea28425

ParboreaMCZ51244

Paragosp121

Pjohnsoni73767

Parborea50890

Sibogasp1T947A9

Ckishinouyei1072441

Parborea3311

Pkaupeka3320

Paracora1089600

Csecundum1010758

Pwahine3326

Claauense1071433

PcfarboreJapan

Paragosp46316

Coralliumsp1075800

Paragosp1075754

Parborea1011097

-100 -80 -60 -40 -20 0

12

510

20


N

-100 -80 -60 -40 -20 0

-39.

5-3

9.0

-38.

5-3

8.0

-37.

5-3

7.0

-36.

5


Log

likel

ihoo

d

Log-likelihood of null model: -39.40713 Maximum log-likelihood of GMYC model: -36.48733 Likelihood ratio: 5.839602 Result of LR test: 0.1196801 n.s. ML number of clusters: 3 95% Con!dence interval: 2-5 ML Number of entities: 9 95% Con!dence interval: 2-19 Threshold time estimate: -16.07062 Ma BP


Figure S8. Fit of the GMYC single-threshold model to the mitochondrial time-calibrated gene tree generated with the coalescent-model tree prior. Ultrametric tree shows the estimated times of divergence under this model. Node bars represent the 95% highest posterior density intervals. Clades with red branches indicate the inferred, independently evolving, lineages. Dotted lines indicate the ML inferred time for the speciation-coalescent threshold for species delimitation. Top boxes show the lineage-through-time plot (left) and the log-likelihood plot (right).

0 20 40 60 80

Haplotypes

510

15

0 20 40 60 80

24

68

10

0 20 40 60 80

24

68

1012

14Haplotypes

Individuals0 20 40 60 80

510

15

Individuals

Figure S9. Haplotype accumulation curves in Paragorgia arborea. (Top left) black line indicates mitochondrial haplotypes, and gray line indicates nuclear ITS2 haplotypes. The other graphs show the contribution of individual genes to the mitochondrial diversity. (Top right) ND6 haplotypes (green), ND2 haplotypes (blue), mtMutS haplotypes (red), 16S haplotypes (purple), and COI haplotypes (yellow). (Bottom left) ND6+16S haplotypes (purple), ND6+ND2 (green), ND6+COI (yellow), ND6+mtMutS (red). (Bottom right) ND6+16S+ND2 (green), ND6+16S+COI (yellow), and ND6+16S+mtMutS (red).

Spatial and temporal patterns of genetic variation in the widespread antitropical deep-sea coral Paragorgia arborea

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