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Biogeosciences, 17, 1845–1876, 2020
https://doi.org/10.5194/bg-17-1845-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Unexpected high abyssal ophiuroid diversity in polymetallic
nodule fields of the northeast Pacific Ocean and
implications for conservation
Magdalini Christodoulou1, Timothy O’Hara2, Andrew F. Hugall2, Sahar Khodami1, Clara F. Rodrigues3,
Ana Hilario3, Annemiek Vink4, and Pedro Martinez Arbizu1
1German Centre for Marine Biodiversity Research (DZMB), Senckenberg am Meer, 26382 Wilhelmshaven, Germany
2Museums Victoria, GPO Box 666, Melbourne, Vic. 3001, Australia
3Centre for Environmental and Marine Studies (CESAM), Department of Biology, University of Aveiro,
Aveiro, 3810-193, Portugal
4Federal Institute for Geosciences and Natural Resources, Marine Geology, 30655 Hanover, Germany
Correspondence: Magdalini Christodoulou (magdalini.christodoulou@senckenberg.de)
Received: 6 September 2019 – Discussion started: 26 November 2019
Revised: 8 February 2020 – Accepted: 11 February 2020 – Published: 7 April 2020
Abstract. The largest and commercially appealing mineral
deposits can be found in the abyssal sea floor of the Clarion-
Clipperton Zone (CCZ), a polymetallic nodule province, in
the NE Pacific Ocean, where experimental mining is due
to take place. In anticipation of deep-sea mining impacts, it
has become essential to rapidly and accurately assess bio-
diversity. For this reason, ophiuroid material collected dur-
ing eight scientific cruises from five exploration licence areas
within CCZ, one area being protected from mining (APEI3,
Area of Particular Environmental Interest) in the periphery
of CCZ and the DISturbance and re-COLonisation (DIS-
COL) Experimental Area (DEA), in the SE Pacific Ocean,
was examined. Specimens were genetically analysed using
a fragment of the mitochondrial cytochrome c oxidase sub-
unit I (COI). Maximum-likelihood and neighbour-joining
trees were constructed, while four tree-based and distance-
based methods of species delineation (automatic barcode gap
discovery, ABGD; barcode index numbers, BINs; general
mixed Yule–coalescent, GMYC; multi-rate Poisson tree pro-
cess, mPTP) were employed to propose secondary species
hypotheses (SSHs) within the ophiuroids collected. The
species delimitation analyses’ concordant results revealed
the presence of 43 deep-sea brittle star SSHs, revealing an
unexpectedly high diversity and showing that the most con-
spicuous invertebrates in abyssal plains have been so far con-
siderably underestimated. The number of SSHs found in each
area varied from five (IFREMER area) to 24 (BGR (Fed-
eral Institute for Geosciences and Natural Resources, Ger-
many) area) while 13 SSHs were represented by singletons.
None of the SSHs were found to be present in all seven ar-
eas while the majority of species (44.2 %) had a single-area
presence (19 SSHs). The most common species were Ophi-
oleucidae sp. (Species 29), Amphioplus daleus (Species 2)
and Ophiosphalma glabrum (Species 3), present in all areas
except APEI3. The biodiversity patterns could be mainly at-
tributed to particulate organic carbon (POC) fluxes that could
explain the highest species numbers found in BGR (German
contractor area) and UKSRL (UK Seabed Resources Ltd,
UK contractor area) areas. The five exploration contract ar-
eas belong to a mesotrophic province, while conversely the
APEI3 is located in an oligotrophic province, which could
explain the lowest diversity as well as very low similarity
with the other six study areas. Based on these results the
representativeness and the appropriateness of APEI3 to meet
its purpose of preserving the biodiversity of the CCZ fauna
are questioned. Finally, this study provides the foundation
for biogeographic and functional analyses that will provide
insight into the drivers of species diversity and its role in
ecosystem function.
Published by Copernicus Publications on behalf of the European Geosciences Union.
1846 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
1 Introduction
The deep sea holds the vastest and least explored ecosystems
on Earth and has justifiably being characterized as “Earth’s
last frontier” since research and exploration in these areas
is still incomplete at the very best (Ramirez-Llodra et al.,
2010; Danovaro et al., 2017). Deep-sea habitats cover more
than 65 % of the Earth’s surface and can plunge from water
depths of 200 m (below the continental shelf) to as deep as
11 km in the Mariana Trench (Gage and Tyler, 1991; Carney,
2005; Jamieson et al., 2009; Ramirez-Llodra, et al., 2011).
Abyssal ecosystems, found between 3000 and 6000 m, cover
54 % of the Earth’s surface and constitute a network of plains
and hills and seamounts, segmented by mid-ocean ridges, is-
land arcs and ocean trenches (Gage and Tyler, 1991; Car-
ney, 2005; Smith et al., 2008). The abyssal plains repre-
sent perhaps the single largest contiguous ecosystem of our
planet; nevertheless, because of its enormous size and seclu-
sion it has been the least studied (Smith et al., 2008; Ramirez-
Llodra et al., 2010). The sea floor of the abyssal plains is
mostly covered by fine sediments, while hard substrates of-
ten occur in the form of polymetallic nodules (Smith et al.,
2008; Ramirez-Llodra et al., 2011).
Metal-rich (polymetallic) nodules from the deep-sea floor
were described and their potential economic importance ac-
knowledged as early as 1873, during the HMS Challenger
expedition (Murray and Renard, 1891; Lusty and Murton,
2018). However, it was in the 1960s that economic interest
in these deposits was ignited after polymetallic nodule re-
sources in the Pacific Ocean were estimated to be so abun-
dant as to be an essentially endless supply of metals such as
Mn, Cu, Ni and Co (Mero, 1965; Lusty and Murton, 2018).
Despite the optimism in the 1970s and 1980s and the widely
held belief that deep-sea mining would commence before
the end of 2000, subsequent progress has been slow and
unsteady. The adequate supply of metals from land-based
mines, unfavourable economic conditions (e.g. rising energy
costs, lower metal prices), technological challenges, increas-
ing environmental awareness and legal obligations to inter-
national organizations (e.g. lack of a mining legislation for
the deep sea) were some of the reasons slowing down deep-
sea mining (Lusty and Murton, 2018). However, the growing
global demand for these metals coupled with the increasing
challenges of land-based mining (Calas, 2017), and the ad-
vances in mining technology, drove a renewed interest in the
exploitation of deep-sea mineral deposits (Ramirez-Llodra et
al., 2011; Lusty and Murton, 2018; Miller et al., 2018).
The greatest known accumulations of economically inter-
esting Ni and Cu and Co-rich polymetallic (Fe–Mn) nodules
occur in the Clarion-Clipperton Zone (CCZ), extending over
an area of approximately 6×106km2in size, between Hawaii
and Mexico from 120◦W to approximately 160◦W and from
20◦N to 6◦S. Additional important occurrences have been
found in the Central Indian Ocean Basin, the Cook Islands
area and the Peru Basin off South America (e.g. the DIS-
COL Experimental Area, DEA) (Mukhopadhyay et al., 2008;
Hein et al., 2013; Miller et al., 2018). The CCZ lies in the
Areas Beyond National Jurisdiction and thus falls under the
legal mandate of the International Seabed Authority (ISA;
Wedding et al., 2013). So far, 16 licence areas for the ex-
ploration of polymetallic nodules have been approved by the
ISA within the CCZ, each up to 75 000km2in size (Wedding
et al., 2013). In its environmental management plan for the
CCZ (Lodge et al., 2014), the ISA adopted nine large pro-
tection areas defined as Areas of Particular Environmental
Interest (APEIs), where mining will not be permitted (Lodge
et al., 2014). These APEIs are large enough (each of them
400 km ×400 km) and far enough away from potential min-
ing areas that they will not be affected by deep-sea mining
(Wedding et al., 2013). In order to be effective as source
populations for the recolonization of impacted areas, how-
ever, APEIs should harbour a representative subset of the
fauna found in the potential fields. In addition to these protec-
tion measures, the ISA has stipulated that prior to exploita-
tion, a benthic biological baseline study must be undertaken
for each exploration contract area, and the possible environ-
mental impacts arising from exploration should be assessed.
Nodule mining carries significant environmental concerns,
including negative direct and indirect impacts on the biodi-
versity (Ramirez-Llodra et al., 2011; Vanreusel et al., 2016;
Van Dover et al., 2017; Niner et al., 2018). The removal of the
nodules and associated organisms could result in habitat loss,
fragmentation or modification while the generation of sedi-
ment plumes may bury the organisms or clog their feeding
apparatuses and thus disrupt the food webs (Ramirez-Llodra
et al., 2011; Vanreusel et al., 2016; Van Dover et al., 2017;
Niner et al., 2018; Stratmann et al., 2018). Unfortunately, ac-
curate documentation of species diversity, which comprises
the first step in understanding patterns and structures in dif-
ferent levels of biodiversity and biogeographical and ecolog-
ical processes and is essential for marine ecosystems’ man-
agement, remains poor across the CCZ (Amon et al., 2016).
To date, Taboada et al. (2018), although dealing with a sin-
gle hexactinellid sponge species, are the only ones who have
assessed the effectiveness of an APEI (no. 6) or investigated
connectivity with the adjacent potential mining areas. Thus,
prior to exploitation, there is an urgent need to obtain base-
line data on faunal biodiversity at local and regional scales in
order to assess and predict the effects of mining on deep-sea
organisms.
The Ophiuroidea (brittle stars and basket stars) are
amongst the most emblematic mobile megafaunal inhabitants
of the deep sea regarding species diversity and individuals’
numbers (Gage and Tyler, 1991; Rex and Etter, 2010; Van-
reusel et al., 2016). They constitute the most diverse echino-
derm class, numbering more than 2064 species found in all
oceans, from intertidal to hadal depths (Stöhr et al., 2012;
Jamieson, 2015). Since then, at least 1412 species have been
recorded from the deep sea of which only 109 are from
abyssal depths, despite abyssal plains being the most ex-
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M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1847
tensive ecosystem in the world (Stöhr et al., 2012). Studies
describing the tropical abyssal northeast Pacific ophiuroid
fauna are scarce but include a few historical studies resulting
from the great expeditions of the late nineteenth and early
twentieth centuries such as the HMS Challenger (Lyman,
1878, 1879, 1882; Ludwig, 1898, 1899) and the Albatross
(Clark, 1911, 1949). Limited recent studies exist (Amon et
al., 2016, 2017; Glover et al., 2016), reporting only a small
number of species. Consequently, the diversity of the deep-
sea ophiuroid fauna in the CCZ is only poorly known. Thus
the main objectives of this study were to (1) ensure the future
molecular species identification for all different life-cycle
stages by matching morphology-based species identifications
of adult ophiuroids with molecular species assignments us-
ing c oxidase subunit I (COI) sequences and consequently
compiling a comprehensive reference library; (2) determine
species ranges; (3) describe the ophiuroid biodiversity pat-
terns of the CCZ and the DEA; and (4) explore the usefulness
of APEI3 for the preservation of the ophiuroid nodule fauna
in the CCZ in the case of deep-sea mining.
2 Materials and methods
2.1 Study areas
The study areas are located within the Clarion and Clip-
perton Fracture Zone (CCZ) in the northeast equatorial Pa-
cific Ocean and at the DISCOL Experimental Area (DEA)
in the Peru Basin (Fig. 1), at depths varying from 4050
to 4933 m (Hein et al., 2013). The CCZ is characterized
by gradual changes in environmental conditions (e.g. dif-
ferences in surface-water productivity, depth and sediment
characteristics) across an east–west and a north–south axis
that correspond to a variation in nodule size and coverage, as
well as variations in faunal composition along these gradients
(Wedding et al., 2013). Ophiuroid samples were collected
from CCZ during six scientific cruises from five different
exploration licence areas and one area protected from min-
ing (APEI3). Specifically ophiuroid samples were collected
during the following cruises: BioNod on R/V L’Atalante
(29 March–10 May 2012) to the eastern IFREMER (Insti-
tut Français de Recherche pour l’Exploitation de la Mer,
France) licence area and to the eastern BGR (Federal Insti-
tute for Geosciences and Natural Resources, Germany) li-
cence area; two ABYSSLINE research cruises, AB01 on the
R/V Melville (3–27 October 2013) and the AB02 cruise on
the R/V Thompson (12 February–25 March 2015) to the UK-
SRL licence area (UK Seabed Resources Ltd, United King-
dom); two MANGAN cruises, MANGAN 2013 on R/V Kilo
Moana (1 April–13 May 2013) and MANGAN 2014 on R/V
Kilo Moana (15 April–3 June 2014) to the eastern BGR li-
cence area; the scientific cruise EcoResponse on R/V Sonne
(SO239) (11 March–30 April 2015) to the licence areas of
BGR, GSR (G-TEC Sea Mineral Resources NV, Belgium),
IOM (Interoceanmetal Joint Organization, a country consor-
tium of Bulgaria, Cuba, the Czech Republic, Poland, the
Russian Federation, and Slovakia), IFREMER and APEI3.
Furthermore, the DISCOL Experimental Area (DEA) in the
Peru Basin, in which the German project DISCOL (DIStur-
bance and re-COLonisation experiment) was performed in
the late 1980s (Thiel and Schriever, 1990; Thiel et al., 2001),
was recently revisited in the framework of the JPIO pilot ac-
tion “Ecological Aspects of Deep-Sea Mining”. Ophiuroid
samples were collected from the DEA during two cruises,
SO242/1 and SO242/2 on the R/V Sonne from 28 July to
25 August 2015 and 28 August to 1 October 2015, respec-
tively.
2.2 Specimen sampling and processing
Small-sized ophiuroid samples were collected using a
Brenke-type epibenthic sledge (EBS; Brenke, 2005) from the
UKSRL (five deployments), BGR (15 deployments), IFRE-
MER (four deployments), GSR (four deployments) and IOM
(one deployment) licence areas and the APEI3 (three de-
ployments) and the DEA (nine deployments) (Fig. 1), fol-
lowing standard deployment procedures (Brenke, 2005). The
cod ends of the supra- and epi-net were sieved through a 500
and 300 µm mesh with cold (+10 ◦C) seawater and immedi-
ately transferred to pre-cooled (−20 ◦C) 96 % EtOH. Large-
sized ophiuroid samples were collected with a remotely op-
erated vehicle (ROV Kiel 6000, GEOMAR) using either the
ROV’s suction sampler or the ROV’s manipulator arm by di-
rect picking, manipulating scoops, shovels and nets. Large
specimens were also preserved in pre-cooled 96 % EtOH.
For all specimens the ethanol was decanted after 24 h and
replaced with new 96 % EtOH to guarantee high ethanol con-
centration for preservation of high-quality DNA and subse-
quently stored at −20 ◦C. In the laboratory at Senckenberg
am Meer, Germany, an integrative molecular-morphological
approach was implemented for the identification of the ophi-
uroid specimens. In total, DNA was extracted from 525 spec-
imens. For species delimitation analyses 300 sequences were
selected (see below). All the ROV-collected specimens were
photographed on board, while the EBS-collected specimens
were photographed in the lab using a Leica binocular stereo-
microscope or a Keyence digital microscope, VHX-5000.
The voucher specimens are stored in Senckenberg am Meer,
DZMB, Wilhelmshaven, Germany.
2.3 Morphological species identification
All ophiuroid individuals collected were morphologically
identified to the lowest possible taxonomic level (primary
species hypotheses, PSHs; Puillandre et al., 2012; Boissin
et al., 2017). Where possible, individuals were assigned to
named species; however, in many cases because of their very
small size, their early developmental stage (post-larval indi-
viduals) or their unique morphology assignment in a mor-
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1848 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 1. Compilation of study areas in the Clarion-Clipperton Zone (CCZ) and in the DISCOL Experimental Area (DEA, Peru Basin). Insets
represent detailed maps of sampling locations in the IFREMER, GSR, IOM, BGR and UKSRL exploration licence areas for polymetallic
nodules as well as in APEI3 (ISA protected area) and the DEA. Copyright for shapefiles (for CCZ area): © International Seabed Authority
2009–2019; copyright for raster file (bathymetry): © Natural Earth 2009–2020.
phological operational taxonomic unit was possible only at
a higher taxonomic level, i.e. genus or family level. For a
small number of damaged specimens, morphological identi-
fication beyond class was not possible. Following the DNA
analyses (see below), all individuals within the same mor-
phospecies that appeared to be genetically distinct from one
another were re-examined and if necessary reassigned to dif-
ferent morphospecies, while some were considered to be
true cryptic species in which clear morphological differences
were not identified. Finally, the integrated approach allowed
the assignment of damaged specimens into different opera-
tional taxonomic units. Taxonomic and systematic remarks
for each SSH are given in the Supplement.
2.4 Barcoding data collection
2.4.1 DNA extraction, amplification and sequencing
For the mtDNA COI analyses genomic DNA was extracted
from arm tissue in individuals larger than 1–2 mm or from
whole individuals when smaller than 1–2 mm. DNA ex-
tractions were carried out using 30 µL Chelex (InstaGene
Matrix, Bio-Rad) according to the protocol of Estoup et
al. (1996) and directly used as a DNA template for poly-
merase chain reaction (PCR). All DNA samples were stored
at −20 ◦C. In the cases where the whole individual was used,
20–25 µL of the supernatant was first separated from the
ophiuroid’s voucher specimen, while the individual, which
was generally intact, was transferred to 96 % ethanol and
stored as a voucher for morphological identifications. A
fragment of 657 bp of the mitochondrial cytochrome c oxi-
dase subunit (COI) was amplified by polymerase chain re-
action (PCR). Amplifications were performed using Illus-
tra PuReTaq Ready-To-Go PCR Beads (GE Healthcare) in
a 25 µL volume containing 22 µL ddH2O, 0.5 µL of each
primer (10 pmol µL−1) and 2 µL of DNA template or AccuS-
tart PCR SuperMix (Thermo Fisher Scientific) in a 25 µL vol-
ume containing PCR SuperMix (9.5 µL ddH2O, 12.5 µL Ac-
cuStart), 0.5 µL of each primer (10 pmol µL−1) and 2 µL of
DNA template. For the COI amplification the forward primer
LCOech1aF1 and the reverse primer HCO2198, tailed with
M13F and M13R-pUC, respectively (Folmer et al., 1994;
Layton et al., 2016), were used. The amplification conditions
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M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1849
consisted of an initial denaturation step of 3 min at 94 ◦C,
35 cycles of 30 s at 94 ◦C, 60 s at 42–47 ◦C and 1 min at
72 ◦C, followed by a final extension step of 5 min at 72 ◦C.
All PCR products were purified using ExoSAP-IT (Thermo
Fisher Scientific). The amplified fragments were sequenced
in both directions at Macrogen Europe Laboratory (Amster-
dam, the Netherlands).
2.4.2 Alignment, genetic divergence
The obtained COI sequences were searched against the Gen-
Bank nucleotide database using BLASTN (Altschul et al.,
1990). Forward and reverse sequences for each individual
were assembled and edited using Geneious v.9.1.7 (https:
//www.geneious.com, last access: 30 March 2020; Kearse
et al., 2012). The edited COI sequences were aligned us-
ing MAFFT v7.308 under the E-INS-i and G-INS-I algo-
rithms (Katoh et al., 2002), while alignments were further
manually edited. Our dataset was supplemented with 18 COI
ophiuroid sequences from the study of Glover et al. (2016).
Sequence data are available in GenBank (accession
numbers MN088035–MN088083, MT103664–MT103870,
MT160426–MT160451). The sequences, trace files, col-
lection data and photos for each specimen are listed in
the datasets CCZ_Ophiuroidea (https://doi.org/10.5883/DS-
CCZ1, Christodoulou et al., 2020a) and DEA_Ophiuroidea
in BOLD (https://doi.org/10.5883/DS-DEA1, Christodoulou
et al., 2020b).
2.5 Putative species delimitation
Congruent support across a range of species delimitation ap-
proaches assumedly provides more reliable results than a sin-
gle method (Carstens et al., 2013; Fontaneto et al., 2015).
Therefore, five different species delimitation analyses, in-
cluding both distance- and tree-based approaches, were per-
formed on the COI dataset, to allocate sequences into genetic
species (secondary species hypotheses, SSHs; Puillandre et
al., 2012; Boissin et al., 2017). Distance-based approaches
detect the distance at which the “barcode gap” occurs and
sort the sequences into putative species based on this dis-
tance, whereas tree-based approaches use a phylogenetic tree
from which the fit of speciation and coalescent processes is
modelled to delineate species based on the branching rate of
the tree (Carstens et al., 2013; Tang et al., 2014).
2.5.1 Distance-based approaches
A neighbour-joining tree was constructed in MEGA7 using
ap-distance substitution model, treating gaps and missing
data with “pairwise deletion” and by running 1000 bootstrap
replicates. Automatic barcode gap discovery (ABGD) anal-
ysis was implemented on the web interface http://wwwabi.
snv.jussieu.fr/public/abgd/ (last access: 26 July 2019) with
default parameters, under the p-distance model (Puillan-
dre et al., 2012). Barcode index numbers (BINs) were as-
signed to the registered DNA dataset automatically using the
BOLD v.4 workbench (http://www.boldsystems.org, last ac-
cess: 14 October 2019; Ratnasingham and Hebert, 2013).
2.5.2 Maximum-likelihood tree
The COI barcode data available for all 300 samples were ad-
equate to show genetic diversity patterns within and among
closely related species but were not sufficient to accurately
reconstruct relationships and genetic distances among the
many divergent lineages in this biota. Hence, to provide an
all-barcode-sample maximum-likelihood tree better reflect-
ing these divergences, the barcode samples were appended
to a powerful phylogenetic framework: the 48475-site exon-
28SrDNA-COI super-matrix dataset used by Christodoulou
et al. (2019). This dataset comprised 200 species that out-
lined the ophiuroid family-level phylogeny (O’Hara et al.,
2017) and 49 CCZ-DEA barcode samples with both COI
and 28S sequences included (Christodoulou et al., 2019). The
COI and 28S allowed the barcode-only samples to be linked
to the phylogenomic exon data. A maximum-likelihood tree
was constructed by IQ-TREE 1.6.9 (Nguyen et al., 2015;
Hoang et al., 2018) using a five partition (exon codon po-
sitions, 28S, COI) HKY+G model and 1000 ultra-fast boot-
strap replicates (with nearest-neighbour interchange (NNI)
optimization). Then the 200 “super-matrix backbone” sam-
ples were pruned out to leave only the 300 barcode samples,
node support bootstrap values were recalculated and the tree
was rooted according to O’Hara et al. (2017).
2.5.3 Tree-based approaches
The general mixed Yule–coalescent (GMYC; Pons et al.,
2006) method was implemented using the R package
SPLITS (Fujisawa and Barraclough, 2013), under the single-
threshold model (stGMYC), and with the required ultramet-
ric tree being produced in BEAST v.2.5. Settings were as
follows: strict clock, Yule speciation model, GTR+G sub-
stitution site model, two independent Markov chain Monte
Carlo (MCMC) runs for 50 000 000 generations, sampling
every 1000 steps (10 % was discarded as burn-in period).
The multi-rate Poisson tree processes (mPTPs; Kapli et al.,
2017) analysis used the rooted super-matrix backbone IQ-
TREE phylogeny (see above). The mPTP was implemented
on the web server https://mptp.h-its.org (last access: 6 Au-
gust 2019) using the multi-rate Poisson tree process model
and following default settings.
2.6 Genetic distances
Sequence divergences (Tables 1 and S1–S2 in the Supple-
ment) were estimated using uncorrected pdistances and un-
der the K2P model using MEGA7 according to the secondary
species hypotheses.
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1850 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Table 1. Mean genetic distance values (pdistance) and range of intraspecific distances for the ophiuroid species. Nindicates the number
of sampled individuals followed by H, the number of unique haplotypes, and values following the mean genetic distance represent standard
deviations.
No. Species Family N H Mean Range
Species 1 Ophiotholia sp. Ophiohelidae 20 19 0.013 ±0.00384 0.000–0.024
Species 2 Amphioplus daleus Amphiuridae 35 31 0.006 ±0.00382 0.000–0.021
Species 3 Ophiosphalma glabrum Ophiosphalmidae 34 28 0.009 ±0.00392 0.000–0.021
Species 4 Amphioplus cf. daleus Amphiuridae 2 2 0.055 –
Species 5 Amphilepis sp. Amphilepididae 6 6 0.016 ±0.00675 0.005–0.024
Species 6 Ophiuroglypha cf. polyacantha Ophiuridae 10 9 0.004 ±0.00215 0.000–0.008
Species 7 Ophiuroglypha sp. Ophiuridae 1 1 – –
Species 8 Ophiopyrgidae sp. Ophiopyrgidae 1 1 – –
Species 9 Ophiuroglypha sp. Ophiuridae 14 14 0.009 ±0.00375 0.002–0.018
Species 10 Anophiura sp. Ophiopyrgidae 1 1 – –
Species 11 Ophiuroglypha sp. Ophiuridae 1 1 – –
Species 12 Asteroschema sp. Euryalidae 1 1 – –
Species 13 Perlophiura profundissima Ophiosphalmidae 2 2 0.003 –
Species 14 Ophiuroglypha sp. Ophiuridae 1 1 – –
Species 15 Ophiophyllum sp. Ophiopyrgidae 2 1 0.000 –
Species 16 Amphiophiura bullata Ophiopyrgidae 11 11 0.006 ±0.00316 0.002–0.014
Species 17 Ophioscolecidae sp. Ophioscolecidae 3 1 0.000 0.000–0.000
Species 18 Ophioscolecidae sp. Ophioscolecidae 3 3 0.012 ±0.00643 0.005–0.017
Species 19 Ophioscolecidae sp. Ophioscolecidae 1 1 – –
Species 20 Ophioscolecidae sp. Ophioscolecidae 4 2 0.003 ±0.00329 0.000–0.006
Species 21 Ophiotoma sp. Ophioscolecidae 3 2 0.002 ±0.00173 0.000–0.003
Species 22 Ophioleucidae sp. Ophioleucidae 4 4 0.005 ±0.00117 0.003–0.006
Species 23 Ophioleuce gracilis Ophioleucidae 1 1 – –
Species 24 Ophiocymbium sp. Ophioscolecidae 7 3 0.010 ±0.00992 0.000–0.021
Species 25 Ophiocymbium sp. Ophioscolecidae 2 2 0.028 –
Species 26 Ophiomyces sp. Ophiohelidae 8 8 0.005 ±0.00179 0.002–0.008
Species 27 Ophiacantha cosmica Ophiacanthidae 19 11 0.003 ±0.00212 0.000–0.009
Species 28 Ophiotholia sp. Ophiohelidae 7 5 0.031 ±0.02874 0.000–0.076
Species 29 Ophioleucidae sp. Ophioleucidae 28 12 0.004 ±0.00555 0.000–0.002
Species 30 Ophiotypa simplex Ophiolepididae 6 5 0.004 ±0.00234 0.000–0.009
Species 31 Ophiernus sp. Ophiernidae 4 3 0.019 ±0.00501 0.002–0.024
Species 32 Ophiohelidae sp. Ophiohelidae 1 1 – –
Species 33 Ophioleucidae sp. Ophioleucidae 1 1 – –
Species 34 Ophioleucidae sp. Ophioleucidae 1 1 – –
Species 35 Ophioleucidae sp. Ophioleucidae 10 5 0.002 ±0.00172 0.000–0.005
Species 36 Ophiosphalma cf. glabrum Ophiosphalmidae 22 21 0.012 ±0.00426 0.000–0.024
Species 37 Ophioleucidae sp. Ophioleucidae 5 4 0.007 ±0.00405 0.000–0.014
Species 38 Ophioscolecidae sp. Ophioscolecidae 4 3 0.004 ±0.00245 0.000–0.006
Species 39 Ophiocymbium sp. Ophioscolecidae 2 2 0.011 –
Species 40 Ophiocymbium sp. Ophioscolecidae 6 5 0.026 ±0.01113 0.000–0.040
Species 41 Ophiotholia sp. Ophiohelidae 1 1 – –
Species 42 Ophioleucidae sp. Ophioleucidae 1 1 – –
Species 43 Ophiuroglypha cf. polyacantha Ophiuridae 4 3 0.004 ±0.00205 0.000–0.008
2.7 Assemblage structure and diversity analyses
Comparison of the ophiuroid assemblages between areas was
performed in R using the package “vegan” (Oksanen et al.,
2008). As the sampling effort was very different between ar-
eas, the species composition table (Table 2), including the
specimens of each species found in each area, was subjected
to “chord” transformation to explore differences in relative
abundance and “presence–absence” transformation related to
faunistic differences. After transformation non-metric multi-
dimensional scaling (nMDS) ordination was achieved with
Euclidean distance (Legendre and Gallagher, 2001). As the
number of specimens found differs greatly between areas,
diversity comparison was achieved using rarefaction curves,
together with standard Shannon (H0), Simpson (D) and Jac-
card’s evenness (J) diversity indices. The expected number
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of species per area was inferred using the extrapolation meth-
ods Chao1 (Chao, 1984; Colwell and Coddington, 1994) and
ACE (Chazdon et al., 1998). Chao1 uses the proportions of
singletons and doubletons in the sample to estimate expected
species richness, while ACE is an abundance-based cover-
age estimator. For the analysis of beta (regional) diversity,
the total multiple-site beta diversity βSOR was calculated us-
ing the modified Sørensen index (Sørensen, 1948; Baselga
and Orme, 2012), and βSOR was decomposed into its additive
components “multiple-site species turnover” βSIM (Simpson
index: Simpson, 1943) and “multiple-site nestedness” βSNE
using the R package “betapart” (Baselga, 2010; Baselga and
Orme, 2012). In order to explore the relative contribution of
every area to species turnover and nestedness, these values
were calculated taking one area out each time in a jackknife
approach. Changes in turnover and nestedness were then at-
tributable to the area that was excluded from the analysis.
3 Results
3.1 Species delineation
The species delineation dataset was comprised of 300
barcode sequences (Fig. 2), out of which 287 were
novel sequences (BOLD datasets: CCZ_Ophiuroidea,
DEA_Ophiuroidea), ranging from 547 to 657 bp in length
(92 % have a length of 657 bp).
Trees produced by both neighbour-joining (NJ, Fig. 2)
and maximum likelihood (ML, Fig. 3) showed a broad pat-
tern in which SSHs were separated by long branches, while
branches within species were shorter. The 300 DNA barcodes
clustered into 42 monophyletic clades in NJ and into 40 in
ML, supported by high bootstrap values (>90).
The ABGD analysis yielded a total of 35 SSHs based on
initial partitioning over the range of prior values for maxi-
mum intraspecific divergence (Figs. 2 and S1 in the Supple-
ment). Identical results were produced based on JC69 and
K80 corrected distances. The number of SSHs varied be-
tween 37 and 50 after the application of recursive partition-
ing. Low threshold values of 0.0010–0.0028 and 0.0046–
0.0077 prompted 50 and 47 SSHs, respectively (Fig. S1).
Moderate threshold values of 0.0129 and 0.0215 resulted in
43 and 42 SSHs, respectively (Fig. S1). Finally, higher prior
threshold values of 0.0359–0.0599, and 0.1000 provided 40
and 37 SSHs, respectively (Fig. S1). To be conservative, we
focus primarily on the results of initial partitioning (35 SSHs)
as they were consistent across the parameter settings and
congruent with other species delimitation methods (Puillan-
dre et al., 2012; Kekkonen and Hebert, 2014). Nevertheless,
for comparative reasons, the results of the recursive partition
with prior divergence 0.0359–0.0599 and which suggested
40 SSHs are also presented here (Figs. 2, S1).
In BOLD, the 300 barcodes were assigned to 49 BINs
(Fig. 2), of which 22 BINs had a single record and three BINs
had two records (CCZ_Ophiuroidea DEA and Ophiuroidea
datasets, BOLD).
Single-threshold general mixed Yule–coalescent calcula-
tions (stGMYC) yielded 47 SSHs (entities) with a confi-
dence interval ranging from 46 to 49 (Supplement, Result
of GMYC).
The mPTP model produced a more conservative number
of clusters (42 SSHs) compared to the GMYC method (Sup-
plement, Results of mPTP).
Depending on the applied method, the numbers of dif-
ferent putative species ranged from 35 to 49. Arranging
the implemented methods by increasing conservativeness
gives the following: BINs (49)<stGMYC (47) <mPTP
(42) <ABGD (35). In the present study a consensus dataset
of species that were delineated by at least three of the four
above-mentioned approaches was selected, as species delin-
eation methods tend to overestimate the number of species
present in a dataset. In the few cases that the methods were
inconsistent, the most conservative approach was adopted
after taking into account the genetic distance between the
potential species. The results were cross-referenced with
the topology produced by both the NJ and ML trees. It is
worth mentioning that 27 SSHs were congruent throughout
all methods and 34 SSHs were consistent when excluding
ABGDi (initial), which was the most conservative method. In
total 43 SSHs were recovered from the CCZ and the DEA,
of which some were PSHs split from two up to five SSHs
each. Noticeably, the PSHs Amphioplus daleus,Ophiuro-
glypha cf. polyacantha,Ophiosphalma glabrum and Ophio-
cymbium sp. revealed cryptic lineages between their popula-
tions in the CCZ and the DEA. The 43 SSHs (Figs. 5–16) are
grouped in 11 families, Amphilepididae, Amphiuridae, Eu-
ryalidae, Ophiernidae, Ophiohelidae, Ophiolepididae, Ophi-
oleucidae, Ophiopyrgidae, Ophioscolecidae, Ophiosphalmi-
dae and Ophiotomidae, attributed to all the current ophiuroid
orders (Fig. 3), Amphilepidida, Euryalida, Ophiacanthida,
Ophioscolecida and Ophiurida (see also Taxonomic and sys-
tematic remarks, Supplement).
3.2 Genetic distances
Summaries of uncorrected pairwise distances for the ophi-
uroid species (SSHs) are shown in Table 1 and Fig. 4, with
the full data available in the Supplement (Tables S1–S2).
Mean interspecific genetic distances ranged from 0.050 to
0.370 (pdistance) and 0.052 to 0.512 (K2P distance) with the
lowest divergence value observed between Ophiosphalma
glabrum and Ophiosphalma cf. glabrum (Species 3 vs. 36)
and the highest between Ophiacantha cosmica (Species 27)
and Ophiohelidae sp. (Species 32). Mean intraspecific vari-
ability ranged from 0.00 to 0.055 (pdistance) and 0.00 to
0.057 (K2P distance), with the highest values observed in the
ophiuroid Amphioplus cf. daleus. It should be mentioned that
there were 13 SSHs represented by only one sample (single-
tons).
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1852 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 2. Neighbour-joining tree (pdistance) based on 300 brittle star COI DNA barcodes. Black circles on branches represent bootstrap
supports ≥90 %. The results of species delimitation analyses (ABGD, BINs, stGMYC and mPTP) are shown on the right-hand margin of
the tree.
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Figure 3. Maximum-likelihood phylogenetic tree based on 300 brittle star COI DNA barcodes calculated using an IQ tree. Black circles on
branches represent bootstrap support (≥90 %).
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1854 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Table 2. Species composition table showing the number of specimens from each species found adding up all samples for a given area.
Species UKSRL BGR IFREMER GSR IOM APEI3 DISCOL
Ophiotholia_sp1 Species 1 16 28 0 0 0 0 0
Amphioplus daleus_sp2 Species 2 64 95 8 5 15 0 19
Ophiosphalma glabrum_sp3 Species 3 12 27 1 13 11 0 1
Amphioplus cf. daleus_sp4 Species 4 2 0 0 0 0 0 0
Amphilepis_sp5 Species 5 0 4 0 0 0 0 2
Ophiuroglypha cf. polyacantha_sp6 Species 6 5 4 0 0 0 0 1
Ophiuroglypha_sp7 Species 7 0 0 0 0 0 0 1
Ophiopyrgidae_sp8 Species 8 0 1 0 0 0 0 0
Ophiuroglypha_sp9 Species 9 0 0 0 0 0 14 0
Anophiura_sp10 Species 10 0 1 0 0 0 0 0
Ophiuroglypha_sp11 Species 11 0 1 0 0 0 0 0
Asteroschema_sp12 Species 12 0 0 0 0 0 1 0
Perlophiura profundissima_sp13 Species 13 1 0 0 0 0 0 4
Ophiuroglypha_sp14 Species 14 0 1 0 0 0 0 0
Ophiophyllum_sp15 Species 15 0 0 0 1 0 1 0
Amphiophiura bullata_sp16 Species 16 2 1 1 7 0 0 0
Ophioscolecidae_sp17 Species 17 0 0 0 0 0 3 0
Ophioscolecidae_sp18 Species 18 0 2 0 0 0 0 1
Ophioscolecidae_sp19 Species 19 1 0 0 0 0 0 0
Ophioscolecidae_sp20 Species 20 0 0 1 0 0 3 0
Ophiotoma_sp21 Species 21 0 1 0 0 0 0 2
Ophioleucidae_sp22 Species 22 1 3 0 0 0 0 0
Ophioleuce gracilis_sp23 Species 23 0 1 0 0 0 0 0
Ophiocymbium_sp24 Species 24 5 0 0 0 0 2 0
Ophiocymbium_sp25 Species 25 2 0 0 0 0 0 0
Ophiomyces_sp26 Species 26 8 0 0 0 0 0 0
Ophiacantha cosmica_sp27 Species 27 1 16 0 1 0 0 2
Ophiotholia_sp28 Species 28 5 1 0 0 0 1 0
Ophioleucidae_sp29 Species 29 5 10 2 3 4 0 4
Ophiotypa simplex_sp30 Species 30 3 1 0 1 1 0 0
Ophiernus_sp31 Species 31 0 1 0 0 0 0 3
Ophiohelidae_sp32 Species 32 0 0 0 0 0 1 0
Ophioleucidae_sp33 Species 33 1 0 0 0 0 0 0
Ophioleucidae_sp34 Species 34 0 1 0 0 0 0 0
Ophioleucidae_sp35 Species 35 2 3 0 3 2 0 0
Ophiosphalma cf.glabrum_sp36 Species 36 19 11 0 1 0 0 4
Ophioleucidae_sp37 Species 37 0 0 0 1 0 4 0
Ophioscolecidae_sp38 Species 38 0 0 0 0 0 0 4
Ophiocymbium_sp39 Species 39 1 1 0 0 0 0 0
Ophiotholia_sp40 Species 40 1 4 0 0 0 0 1
Ophiotholia_sp41 Species 41 0 0 0 0 1 0 0
Ophioleucidae_sp42 Species 42 0 0 0 0 0 1 0
Ophiuroglypha cf. polyacantha_sp43 Species 43 1 0 0 2 1 0 0
3.3 Ophiuroid assemblages and diversity
The species composition table (Table 2) shows the counts
of each species by area. The diversity values are summa-
rized in Table 3. A total of 55 sites were sampled in seven
areas. Sampling effort was uneven, with most samples deriv-
ing from the BGR area (18) and the DEA (14) in the Peru
Basin. For all other areas, three to six sites were sampled.
A total of 543 specimens were assigned to the 43 species.
None of the species was recorded in all seven areas, while
the most common species were Species 29 (Ophioleucidae),
Species 2 (Amphioplus daleus) and Species 3 (Ophiosphalma
glabrum), which were found in six areas, with all of them
absent in APEI3. It is worth mentioning that the majority
of species (44.2 %) were present only in one of the areas
(19 SSHs). The highest species numbers were found in the
BGR and UKSRL areas (24 and 22, respectively), where
the highest number of specimens was also recorded (219
and 158, respectively). The lowest values were found in the
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Figure 4. Histogram showing the percentage of genetic pdistances
within and between brittle star species based on the 657 bp “bar-
code” fragment of the COI gene. Intraspecific and interspecific vari-
ations are shown in yellow and red, respectively.
IFREMER area, with 13 specimens being attributable to five
species. While the number of species was a function of the
number of specimens, fewer species were recorded in the
IFREMER and IOM areas than would be expected if they
were to follow the same pattern as at other sites (Fig. 17).
This was corroborated by the rarefaction analysis (Fig. 18),
which shows that for the same number of specimens, the
IFREMER and IOM areas have fewer species. The rarefac-
tion curves of all other areas were very similar. Low diver-
sity in the IFREMER and IOM areas was also indicated by
the lowest Shannon diversity, Simpson diversity and even-
ness values, while the highest diversity values were recorded
in the UKSRL, BGR and DISCOL areas (Table 3). The ex-
trapolation analyses predicted a total of 57 species (Chao1 in-
dex) and 53.5 species (ACE index) for all areas together. The
lowest extrapolated numbers of species were again obtained
for the IFREMER and IOM areas (6.5–12 and 8.5–11.5, re-
spectively), whereas the highest numbers were obtained for
the BGR and UKSRL areas (57–51.1 and 27.2–30.5, respec-
tively). The highest number of unique species (species found
only in one area) was found in the BGR (six species), UK-
SRL (five species) and APEI3 (five species) areas, while no
unique species were observed in the IFREMER and GSR ar-
eas.
The faunistic similarity is summarized in Table 4, showing
the number of shared and unshared species between areas.
APEI3 showed the lowest numbers of shared species (zero
to two) and the highest number of unshared species (13–32)
compared with other areas. The most distant area, DISCOL
in the Peru Basin, shared 3–11 species with CCZ exploration
areas, but none with APEI3.
Beta diversity decomposition is shown in Fig. 19. The to-
tal multiple-site beta diversity was high (βSOR =0.782), with
a higher component of turnover (βSIM =0.640) versus nest-
edness (βSNE =0.142). To explore the relative contribution
of each area to total beta diversity, each area was taken out
once and beta diversity was recalculated. The relative change
in turnover and nestedness was then attributable to the omit-
ted area. Results of this exercise are shown graphically in
Fig. 19 and numerically in Table 3. Removing most of the
areas one by one (excluding APEI3) did not result in a dras-
tic change in turnover and nestedness (βSIM =0.604–0.663;
βSNE =0.121–0.167). Only the exclusion of APEI3 resulted
in a substantial reduction of turnover and increase in nested-
ness (βSIM =0.488; βSNE =0.229).
The nMDS plot in Fig. 20 shows the quantitative assem-
blage analysis using chord distance (relative abundance). The
BGR and UKSRL areas were close together but also close
to the areas of DISCOL, IFREMER and IOM, while greater
dissimilarity occurs with the GSR and APEI3 areas. The box
plot in Fig. 21 shows the variation in chord distance of each
area to other areas, evidencing that APEI3 was most different
from any other area (see median and extent of whiskers) than
other areas were among each other.
The ordination using presence–absence-transformed data
placed the areas with fewer unique species (IFREMER, GSR
and IOM) in the middle of the plot and spread the areas with
the highest number of unique species at the outer margins and
apart from each other (Fig. 22). The box plot in Fig. 23 shows
that APEI3 was the most dissimilar in terms of presence–
absence of species, but the median value (black horizontal
bar inside the boxes) was as high as that for the UKSRL and
BGR areas, which, however, displayed less variation.
4 Discussion
4.1 Species delimitation method performance
The results obtained here were consistent with many other
studies showing that different species delimitation methods
can produce different delimitation scenarios when employing
single-locus data (Hofmann et al., 2019). The single-locus
species delimitation methods tested here, although they are
extensively used throughout the literature, including for the
Ophiuroidea (Khodami et al., 2014; Laakman et al., 2016;
Boissin et al., 2017), are each subject to potential biases and
differing conditions inherent in the empirical datasets (Hof-
mann et al., 2019). The five species delimitation methods
used here generally recovered the same number of SSHs.
despite some degree of incongruence observed in the num-
bers of SSHs, they were consistent in recovering more SSHs
than the number of species originally recognized. Given the
lack of information regarding the biodiversity and of the re-
lationships between deep-sea ophiuroids, it was not surpris-
ing that more lineages were inferred than are currently rec-
ognized. It is likely that many of these SSHs correspond to
undescribed cryptic species, but simultaneously some may
be the result of genetic drift or isolated populations cur-
rently undergoing speciation. Noticeably, the BIN method in
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1856 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 5. Amphilepis sp. (sp5): (a) dorsal and ventral view, MA13_85_32; (b) dorsal and ventral view, MA14_39_9. Amphioplus (Unioplus)
daleus (sp2): (c) dorsal and ventral view, AB2_EB1_16_13; (d) dorsal and ventral view, AB1_EB5_10_4; (e) dorsal and ventral view,
SO239_81_07. Scale bars: 0.5 mm (a, c–d); 1 mm (b); 2 mm (e).
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Figure 6. Amphioplus (Unioplus) daleus (sp2): (a) dorsal and ventral view, MA14_38_01. Amphioplus (Unioplus) cf. daleus (sp4): (b) dorsal
and ventral view, AB2_EB1_14_27. Ophiernus sp. (sp31): (c) dorsal and ventral view of disc and detached arms, MA14_21_12. Ophiotypa
simplex (sp30): (d) dorsal and ventral view, AB2_EB2_12_3; (e) dorsal and ventral view, AB1_EB5_4. Scale bars: 0.5 mm (a, d); 2 mm (b);
1 mm (c, e).
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1858 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 7. Ophiotypa simplex (sp30): (a) dorsal and ventral view, SO239_118_1. Ophioleuce gracilis (sp23): (b) dorsal and ventral view,
SO239_397. Ophioleucidae sp. (sp22): (c) dorsal and ventral view, SO239_24_17; (d) dorsal and ventral view, SO239_59_1. Ophioleucidae
sp. (sp29): (e) dorsal and ventral view, SO239_24_12. Scale bars: 2 mm (a, b); 1 mm (d); 0.5 mm (e, c).
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Figure 8. Ophioleucidae sp. (sp29): (a) dorsal and ventral view, SO239_24_3. Ophioleucidae sp. (sp33): (b) dorsal and ventral view,
AB1_EB5_10_9. Ophioleucidae sp. (sp34): (c) dorsal and ventral view, MA14_21_3. Ophioleucidae sp. (sp35): (d) dorsal and ventral
view, SO239_118_14; (e) dorsal and ventral view, SO239_24_5; (f) dorsal and ventral view, SO239_133_2. Scale bars: 1 mm (a, c, e, f);
0.5 mm (b, d).
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1860 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 9. Ophioleucidae sp. (sp37): (a) dorsal and ventral view, SO239_139_2. Asteroschema sp. (sp12): (b) in situ (upper left and right),
specimen collected with the ROV KIEL 6000 in dorsal (lower left) and ventral (lower right) view, SO239_2113. Ophiocantha cosmica:(c) in
situ (left), specimen collected with the ROV KIEL 6000 in dorsal view (right), SO239_130. Scale bars: 2 mm (a); 1 cm (b, c). Copyright (for
in situ photos) ROV KIEL 6000 Team/GEOMAR Kiel.
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M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1861
Figure 10. Ophiocantha cosmica (sp27): (a) dorsal and ventral view, MA14_20_4; (b) specimen collected with the ROV KIEL 6000 in dorsal
and ventral view (up), in situ (down), SO242-2_191_F5. Ophiotoma sp. (sp21): (c) dorsal and ventral view, SO239_20_12. Ophiocymbium
sp. (sp39): (d) dorsal and ventral view, AB2_EB1_13_41. Ophiocymbium sp. (sp24): (e) dorsal and ventral view, AB2_EB1_13_8. Scale
bars: 0.5 cm (d); 1 mm (a, c); 1 cm (b); 2 mm (e). Copyright (for in situ photos) ROV KIEL 6000 Team/GEOMAR Kiel.
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1862 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 11. Ophiocymbium sp. (sp40): (a) dorsal and ventral view, SO239_24_19; (b) dorsal and ventral view, MA14_21_10. Ophiohelidae
sp. (sp32): (c) dorsal and ventral view, SO239_192_06. Ophiomyces sp. (sp26): (d) dorsal and ventral view, AB1_EB5_ 10_3; (e) dorsal and
ventral view, AB1_EB4_11_24; (f) lateral view, AB1_EB4_11_22. Ophiotholia sp. (sp1): (g) dorsal and ventral view, MA14_38_13. Scale
bars: 0.5 mm (a, c); 1 mm (b, e, f); 0.2 mm (d).
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M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1863
Figure 12. Ophiotholia sp. (sp1): (a) lateral view, MA13_85_3; (b) lateral view, MA13_90_18. Ophiotholia sp. (sp28): (c) dorsal and
ventral view, MA14_66_10. Ophioscolecidae sp. (sp17): (d) dorsal and ventral view, SO239_197_ 4. Ophioscolecidae sp. (sp18): (e) dorsal
and ventral view, SO239_24_21. Ophioscolecidae sp. (sp19): (f) dorsal and ventral view, AB2_EB2_ 12_ 10. Ophioscolecidae sp. (sp20):
(g) dorsal and ventral view, SO239_192_2; (h) dorsal and ventral view, SO239_192_8. Amphiophiura bullata (sp16): (i) dorsal and ventral
view, SO239_118_13. Scale bars: 1 mm (a–d, g, h); 0.5 mm (e–f, i).
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1864 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 13. Amphiophiura bullata (sp16): (a) dorsal view (upper) and dorsal (lower left) and ventral view (lower right) of disc, SO239_133_3;
(b) dorsal and ventral view, MA13_90_16. Anophiura sp. (sp10): (c) dorsal view and detailed dorsal and ventral view of disc, SO239_396.
Ophiophyllum sp. (sp15): (d) dorsal and ventral view, SO239_139_1. Ophiopyrgidae sp. (sp8): (e) dorsal and ventral view, SO239_59_10.
Scale bars: 0.5 mm (a, d); 2 mm (b); 1 mm (c, e).
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Figure 14. Ophiuroglypha cf. polyacantha (sp6): (a) dorsal and ventral view, MA14_66_7; (b) dorsal and ventral view, SO242-2_222_F1.
Ophiuroglypha cf polyacantha (sp43): (c) dorsal and ventral view, SO239_118_6; (d) in situ (right), dorsal and ventral view (left),
SO239_2037. Ophiuroglypha sp. (sp14): (e) dorsal and ventral view, SO239_59_18. Scale bars: 0.5mm (a, e); 1 mm (c); 1 cm (b, d).
Copyright (for in situ photos) ROV KIEL 6000 Team/GEOMAR Kiel.
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1866 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 15. Ophiuroglypha sp. (sp11): (a) dorsal and ventral view, SO239_395. Ophiuroglypha sp. (sp7): (b) dorsal and ventral view, SO242-
2_176_F8. Ophiuroglypha sp. (sp9): (c) in situ (upper right), dorsal and ventral view, detailed ventral view of disc (lower right), SO239_2059.
Ophiosphalma glabrum (sp3): (d) dorsal and ventral view, AB1_EB5_10_7. Scale bars: 1 cm (a–e); 0.5 mm (d). Copyright (for in situ photos)
ROV KIEL 6000 Team/GEOMAR Kiel.
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Figure 16. Ophiosphalma glabrum (sp3): (a) dorsal and ventral view, SO239_24_4; (b) dorsal and ventral view, SO239_50_2; (c) dorsal and
ventral view, SO242-2_222_F2. Ophiosphalma cf. glabrum (sp36): (d) dorsal and ventral view, SO239_24_18; (e) dorsal and ventral view,
AB2_EB1_14_2; (f) dorsal and ventral view, SO239_2014. Perlophiura profundissima:(g) dorsal and ventral view, SO242-1_387_A7. Scale
bars: 0.5 mm (d); 1 mm (b, e, g); 2 mm (a, e); 1 cm (c, f).
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1868 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Table 3. Summary of diversity parameters per sampled area. Sites: number of collection sites; N: number of specimens; S: number of
species; Usp: number of unique species; Chao ±SE: Chao estimated number of species with standard error; ACE ±SE: ACE estimated
number of species with standard error; H0: Shannon diversity; 1-D: Simpson diversity; J: Jaccard’s evenness. βSOR,βSIM and βSNE express
multiple-site total beta diversity, multiple-site species turnover and multiple-site nestedness, respectively. Note that in the rows of each area
the value is the result of excluding this area, except for the row “Total”, which includes all areas.
AREA Sites N S Usp Chao ±SE ACE ±SE H01-D J βSOR βSIM βSNE
UKSRL 5 158 22 5 27.2±5.3 30.5±2.7 2.18 0.79 0.70 0.786 0.656 0.130
BGR 18 219 24 6 57 ±26.3 51.1±4.9 2.04 0.76 0.64 0.784 0.663 0.121
IFREMER 4 13 5 0 6.5±2.5 12 ±1.8 1.17 0.57 0.73 0.756 0.634 0.122
GSR 5 38 11 0 16 ±5.9 17.1±2 1.97 0.81 0.82 0.782 0.635 0.146
IOM 3 35 7 1 8.5±2.5 11.5±1.5 1.44 0.69 0.74 0.759 0.620 0.138
APEI3 6 31 10 5 15 ±5.9 15.2±1.8 1.80 0.75 0.78 0.717 0.488 0.229
DISCOL 14 49 14 2 16.5±3.1 17 ±1.8 2.14 0.81 0.81 0.771 0.604 0.167
Total 55 543 43 – 57 53.5 2.50 0.82 0.66 0.782 0.640 0.142
Table 4. Faunistic similarity between areas. Left of slash: number of unshared species; right of slash: number of shared species. Bold numbers
indicate the number of species in each area.
UKSRL BGR IFREMER GSR IOM APEI3 DISCOL
UKSRL 0\22 14 4 9 6 2 8
BGR 18 0\24 4 8 5 1 11
IFREMER 19 21 0\54 3 1 3
GSR 15 19 8 0\11 6 2 5
IOM 17 21 6 6 0\70 3
APEI3 28 32 13 17 17 0\10 0
DISCOL 20 16 13 13 15 24 0\14
Figure 17. Relationship between number of specimens (N) and
number of species (S) in the areas examined.
Figure 18. Sample-based rarefaction curves of the examined areas.
The inset shows a close-up for the minimum shared number of spec-
imens (12).
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M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1869
Figure 19. Resulting beta diversity, when each area is excluded
from calculation, decomposed into its additive component species
turnover (light blue) and nestedness (orange). Excluding APEI3 has
the greatest impact on beta diversity. “Total” shows the values in-
cluding all areas.
Figure 20. The nMDS based on chord distance between areas.
BOLD recovered a higher number of species than all other
approaches. BOLD and specifically BINs can greatly im-
prove the Linnaean taxonomic assignment in many animal
groups, including echinoderms (Layton et al., 2016; Laak-
man et al., 2016). The low intra-cluster divergence (2.2 %)
at the initial cluster step of refined single linkage (RESL)
methodology (Ratnasingham and Hebert, 2013; Song et al.,
2018) could be the reason why in some cases the BIN method
overestimated species number, especially since there appears
to be a small overlap between intraspecific and interspecific
distance in our data (Fig. 4). This could be the case in the
delimited Ophiocymbium spp. (Species 24, 25, 40; Fig. 2,
Table S2), which were separated into numerous lineages de-
spite the relatively low divergence between them. Generally,
barcodes are well defined when the lowest interspecific dis-
tance exceeds the highest intraspecific distance, and in such
Figure 21. Box-and-whisker plot of the chord distance of each area
to other areas.
Figure 22. The nMDS based on Euclidean distance with the pres-
ence or absence of data.
cases a species delineation “threshold” will be clear. But, as
the threshold can be lineage-specific, a universal threshold
that fits all the branches may not exist, as coalescent depths
among species will vary greatly due to differences in pop-
ulation size, mutation rate and speciation times (Colins and
Cruickshank, 2012). Similarly, GMYC recovered a relatively
high number of species (47 vs. 49 BINs). Arguably, GMYC,
especially the single-threshold version of the method, is a ro-
bust species delimitation method (Fujisawa and Barraclough,
2013). GMYC performance depends on a single-locus ultra-
metric tree which tends to compress the coalescent events
towards the tips of the tree, making it especially difficult to
distinguish closely related species (Boissin et al., 2017). It
has been argued that the Poisson tree process (PTP) methods
generate diversity estimates that are more robust to differ-
ent phylogenetic methods, while GMYC is more sensitive,
but provide consistent estimates for BEAST trees (Tang et
al., 2014). Nevertheless, unresolved nodes can affect both
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1870 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
Figure 23. Box-and-whisker plot of the Euclidean distance of each
area to other areas with the presence or absence of data.
GMYC and PTP estimates, although they seem to have a
greater effect on GMYC estimates (Tang et al., 2014). In
contrast it seems that ABGD (initial partition) has underes-
timated the species number in this study, although the per-
formance of the method improved when the recursive parti-
tion option was used. ABGD has been reported to over-lump
speciose datasets with high speciation rates (Dellicour and
Flot, 2018). ABGD’s conservatism and GMYC’s overesti-
mation have also been shown on reef brittle stars (Boissin
et al., 2017) while indicating that PTP methods show a small
advantage as the most stable, suggesting the presence of ad-
ditional cryptic species but without over-splitting taxa. Sum-
marizing, despite the differences in the number of delim-
ited species, overall the methods recovered a broadly similar
number of SSHs. Congruence among different delimitation
methods is a strong indication that the delimitation is correct,
allowing the designation of cryptic species and rectification
of taxonomic problems (Dellicour and Flot, 2018), while al-
ways when possible taking into account the morphology.
4.2 Taxonomic implications
The abyssal eastern Pacific harbours a highly diverse ecosys-
tem. The number of ophiuroid species reported from the
polymetallic nodule fields of the Pacific has now increased
by 433 % from 10 (Glover et al., 2016; Amon et al., 2017) to
43 in this paper. This is the largest collection of any megafau-
nal taxon in the CCZ and the only one that has been studied in
such detail using a comprehensive combination of morpho-
logical and genetic evidence. Remarkably, from the species
reported here, 32 (75 %) are probably new to science, and
some represent hitherto unknown old evolutionary lineages
(see also Christodoulou et al., 2019). The discovery of new
species is the direct result of increased sampling effort, in
which a greater number of specimens deriving from a larger
sampling surface were collected than during any previous
studies in the DEA or in the CCZ, spanning five exploration
contract areas and one APEI. Furthermore, the use of new
sampling gears, i.e. epibenthic sledge (EBS), in relation to
past historical expeditions that took place in the area, per-
mitted the collection of fragile and minute specimens, while
new DNA barcoding approaches allowed the identification
of post-larvae and juveniles that lacked adult morphologi-
cal characters. Overall, these data show that the brittle star
biodiversity in the deep sea is still greatly underestimated,
while supporting the use of DNA barcoding as an effective
and time-efficient method of species delimitation to com-
plement morphological studies. Although we do not wish to
suggest that single mitochondrial locus data should be used
on their own to draw taxonomic conclusions, in much the
same way as using single morphological characters is dis-
couraged (DeSalle, 2006; Hofmann et al., 2019), we do ar-
gue that single gene barcoding could be the first step in iden-
tifying previously overlooked species, while also providing
a guide in cases where morphological identification is diffi-
cult. It was only recently that a transcriptome-based analysis
of Ophiuroidea (O’Hara et al., 2014) instigated a major re-
evaluation of morphology-based classifications (Smith et al.,
1995), proving that there is still a lot to be discovered and re-
evaluated within this group. Specifically in our study, DNA
barcoding proved to be necessary since a significant propor-
tion of the specimens are post-larvae juveniles, making their
identification based on morphological characteristics quite
challenging. DNA barcoding not only allowed species de-
limitation but also aided in matching post-larvae individuals
with their corresponding adults, as for example in the case
of Ophiosphalma glabrum where individuals ranging from
0.5 mm to 20 cm were collected (Fig. 23). Furthermore, the
large-sized brittle stars collected with the remotely operated
vehicle were matched with their in situ photos, allowing a
more accurate estimation of morphospecies which in turn
could facilitate the more accurate annotation of photos and
video transects used in various biodiversity assessment stud-
ies (Tilot et al., 2018).
Mean COI genetic intraspecific distances (K2P) of brittle
stars (0.00–0.057) were concordant with previous ophiuroid
studies (0.00–0.042: Khodami et al., 2014 and 0.00–0.064:
Boissin et al., 2017), while the mean COI interspecific ge-
netic distances (0.052–0.512) were found to be noticeably
higher. This could be attributed both to the great phylodi-
versity of ophiuroids collected from the polymetallic fields,
spanning 11 families and five orders, and to the discovery
of previously undescribed diversity up to the family level
(Christodoulou et al., 2019).
4.3 Ophiuroid diversity and assemblage structure and
implications for conservation in light of possible
nodule mining
Mining of abyssal polymetallic nodules in the CCZ could re-
sult in severe habitat disruption and loss of benthic commu-
nities in and directly around the mined sites (Vanreusel et al.,
Biogeosciences, 17, 1845–1876, 2020 www.biogeosciences.net/17/1845/2020/
M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1871
2016). An attempt to foresee the potential impact of deep-sea
mining on abyssal communities requires a profound knowl-
edge of natural background biodiversity and ecosystem func-
tioning, such as how many and which species are living there
now, how large the species ranges are, and whether there are
natural changes in diversity along environmental gradients.
However, our knowledge of abyssal benthic communities is
still so poor that even these simple questions remain unan-
swered for many groups of organisms in what is nonetheless
considered to be an economically important and potentially
endangered deep-sea region like the CCZ. We can now pro-
vide partial answers to these questions for the ophiuroids,
one of the dominant megafaunal groups in the CCZ.
Our initial assumption was that ophiuroid diversity would
be low (we expected around 10 species) based on the previ-
ous studies in the region (Glover et al., 2016; Amon et al.,
2017) and on a recent review of global ophiuroid distribu-
tion, in which only 28 species were recorded for the whole
tropical east Pacific at abyssal depths (Stöhr et al., 2012).
Coupled with expectations of low diversity, we assumed that
connectivity would be high and that most beta diversity be-
tween sites would be composed of nestedness (high) rather
than species turnover (low). Under these circumstances, the
APEI3 could be a good region to host most of the CCZ
species and serve as a source for most of the populations.
Also, we assumed that the most distant DISCOL area would
display a low similarity to the CCZ.
The results of our study do not support any of these ini-
tial assumptions; in fact, they show exactly the opposite.
We recorded a 4-fold higher number of species than ex-
pected, and rarefaction curves show no sign of reaching an
asymptote (Fig. 18). Chao1 and ACE estimators predicted
between 53 and 57 species across the region (Table 3). The
ophiuroid communities were characterized by high beta di-
versity that is mainly composed of high turnover between
areas, rather than nestedness. This means that there was a
high proportion of rare species (19 species were present in
only one area and 12 species were present in only two ar-
eas), which reduces nestedness and increases the potential
for damage to natural populations caused by deep-sea min-
ing at the local scale. Food availability regulated by partic-
ulate organic carbon (POC) flux seems to strongly influence
diversity and abundance in abyssal ecosystems (Smith et al.,
2008). The CCZ licence areas for exploration despite being
all in a mesotrophic zone are not all the same. The POC flux
in the CCZ shows a southeast-to-northwest gradient increas-
ing towards its eastern edge (Vanreusel et al., 2016; Volz et
al., 2018, 2020). Volz et al. (2018, 2020), after studying four
of the CCZ areas studied herein and the APEI3, found that
they differ in POC fluxes to the sea floor ranging from as low
as 1 mg Corg m−2d−1in APEI3 to 2 mg Corg m−2d−1in the
BGR area. Within this study the highest diversity values were
recorded in the UKSRL, BGR and DISCOL areas. The BGR
and UKSRL areas are located in the far east of the CCZ, in
a region of higher POC flux (Vanreusel et al., 2016; Volz at
al., 2018, 2020), which could explain higher standing stocks
and higher species diversity and abundance. Food availabil-
ity seems to justify why the communities of the very distant
and eutrophic DISCOL area (Haeckel et al., 2001) resemble
the eastern CCZ more than the geographically closer APEI3.
The DISCOL area shares 11 species with the BGR and eight
species with the UKSRL area, while no species are shared
with APEI3. In contrast to the CCZ areas, APEI3 lies within
an oligotrophic zone, exhibiting 2-fold lower POC fluxes and
subsequently 2-fold lower aerobic respiration rates in com-
parison to the BGR area (Volz et al., 2018, 2020). APEI3 dif-
fers significantly from the other areas in additional aspects
such as lower chloroplastic pigment equivalents (CPEs) and
total organic carbon (TOC) values and lower sedimentation
rates resulting in finer sediments with higher clay content
(Hauquier et al., 2019; Volz et al., 2018, 2020). In conclusion
the APEI3 biogeochemical features differ considerably from
the other areas (Volz et al., 2018, 2020) and could explain the
fact that APEI3 has a very different assemblage sharing only
up to two species with the CCZ areas and DISCOL. Further-
more, APEI3 is mainly located outside the CCZ, north of the
Clarion Fracture, a submarine mountain range characterized
by a peak and trough surpassing 1800 m difference in eleva-
tion (Hall and Gurnis, 2005), which may act as a dispersal
barrier for abyssal fauna.
This raised the critical question of whether the APEI3
fauna is representative for the exploration licence areas in
the CCZ, especially as ISA created the APEIs based on en-
vironmental conditions but in the absence of any biolog-
ical data (Wedding et al., 2013). Our results suggest that
APEI3 may not be a good surrogate area for the CCZ nodule
fauna. Only a small fraction of the total registered ophiuroid
fauna was recorded in APEI3. This area is the most differ-
ent in terms of species composition and assemblage struc-
ture (Figs. 17, 19). Removing the APEI3 from beta analysis
results in a great reduction of turnover and increase in nest-
edness. This means that the remaining areas become more
similar to each other, the total beta diversity decreases and
differences between sites due to missing species out of a
common species pool (nestedness) increase when APEI3 is
excluded. Lower species richness and abundance in APEI3
as well as very low similarity between the APEI3 and the ex-
ploration areas, independent of distance, were also found by
Vanreusel et al. (2016), Hauquier et al. (2019) and Bonifácio
et al. (2020) after studying megafaunal, nematode and poly-
chaete assemblages, respectively. Volz et al. (2018, 2020),
after studying the (bio)geochemical characteristics of APEI3
as well as four eastern CCZ areas, concluded that the preser-
vation area APEI3 does not represent the depositional con-
ditions and biogeochemical processes that are dominating
in the investigated CCZ licence areas. The results of these
studies converge with ours in finding that APEI3 is ill-suited
as a representative area of the recovery of the potentially
mined areas. Furthermore, Taboada et al. (2018) found that
APEI6 is inadequate to act as a population source for a hex-
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1872 M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields
actinellid sponge species and suggest the designation of a
new APEI closer to the exploration areas studied. The large
geographic distance between the APEIs and the explorations
areas may hinder the exchange of individuals and the genetic
flow among remaining populations after mining. Therefore,
we strongly advocate in favour of incorporating no-mining
sites within the core CCZ area with a similar nodule compo-
sition as the potential mining areas, rather than only on the
periphery of the CCZ, as was already suggested by Vanreusel
et al. (2016), in order to prevent the loss of biodiversity.
Biodiversity studies that focus only on known, nominal
species are problematic, as they likely overlook cryptic or
undiscovered lineages involved in diversification. As shown
herein, most of the brittle stars recorded in the CCZ and Peru
Basin lack formal Linnaean scientific names, thus widening
the gap between described species and actual biodiversity,
which appears to be far greater than previously estimated.
Not recognizing these cryptic or undescribed taxa ensures
that they remain in the shadows of research and conserva-
tion policies. These taxa could be locally endemic or rare
and thus more vulnerable to human impacts such as deep-
sea mining. Biodiversity studies, such as presented here, aim-
ing to develop reference libraries while using an integrative
taxonomic approach, such as presented here, will provide
much-needed comprehensive and time-efficient assessments
of “missed” diversity. These, in turn, may fill the gaps for ad-
equate baseline assessment at the onset of commercial-scale
mining and thus, through adequate management schemes,
prevent serious species declines before they have been ad-
equately described or even discovered.
In conclusion, it is important to note that the present study
explored only a part of the polymetallic fields of the CCZ
and DEA. Our dataset on ophiuroid communities in the CCZ
is the largest available to date but still too small to allow for
comprehensive conclusions. The conclusions for APEI3 can-
not be extrapolated to other APEIs in the region. Also, our
dataset is biased toward the eastern areas of the CCZ where
the sampling effort was higher. Thus, although a large num-
ber of specimens were examined, it is highly likely that the
true biodiversity is even much higher. Broader efforts, espe-
cially those that will include samples from the western parts
of the CCZ, or from other APEIs, are likely to result in the
discovery of additional diversity and will allow us to obtain a
better understanding of connectivity and patterns of distribu-
tion across the CCZ. This will in turn refine our perception of
the marine biodiversity of the abyssal plains and specifically
of polymetallic nodule fields.
5 Conclusions
Four methods of species delineation showed concordant re-
sults and revealed 43 deep-sea ophiuroid species in the
Clarion-Clipperton Zone and the DISCOL Experimental
Area (Pacific Ocean), revealing an unexpectedly high diver-
sity and showing that the most conspicuous invertebrates in
the abyssal plains have been so far considerably underesti-
mated. This study increases the number of ophiuroid species
reported from polymetallic nodule fields of the Pacific by
433 %.
A comprehensive reference library including 287 novel
ophiuroid sequences allocated to 43 species is produced. This
reference library can facilitate the assessment of potential
impacts and biodiversity loss due to deep-sea mining. It is
the first time such an integrated reference library has been
produced for the CCZ and the DISCOL area including both
genetic and morphological information about the most em-
blematic mobile megafaunal inhabitants.
The biodiversity patterns observed within CCZ could be
mainly attributed to differences in POC fluxes explaining the
higher species numbers found in BGR and UKSRL areas.
The five exploration contract areas belong to a mesotrophic
province, while in contrast the APEI3 (Area of Particular En-
vironmental Interest) is located in an oligotrophic province,
which could explain the lowest diversity as well as very low
similarity to the other six study areas.
Based on the results of our study, the representativeness
and the appropriateness of APEI3 (Area of Particular Envi-
ronmental Interest) to meet its purpose of preserving the bio-
diversity of the CCZ fauna are questioned, and the creation
of no-mining sites within the core CCZ area is suggested.
Data availability. DNA sequences, trace files, collection data and
taxonomic remarks are available in the datasets CCZ_Ophiuroidea
(https://doi.org/10.5883/DS-CCZ1, Christodoulou et al., 2020a)
and DEA_Ophiuroidea (https://doi.org/10.5883/DS-DEA1,
Christodoulou et al., 2020b) in BOLD. Furthermore, the DNA
sequences and their accompanying collection data are also available
in GenBank.
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/bg-17-1845-2020-supplement.
Author contributions. PMA and AV designed the sampling. PMA,
AV, SK, CFR and AH carried out the sampling and processed the
specimens on board. MC performed the sequencing programme.
AFH performed the phylogenomic analyses. MC, TO’H, AFH, CFR
and PMA analysed and interpreted the data. MC took the lead in
writing the manuscript in collaboration with TO’H and PMA, to
which all other authors contributed.
Competing interests. The authors declare that they have no conflict
of interest.
Biogeosciences, 17, 1845–1876, 2020 www.biogeosciences.net/17/1845/2020/
M. Christodoulou et al.: Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields 1873
Special issue statement. This article is part of the special issue
“Assessing environmental impacts of deep-sea mining – revisiting
decade-old benthic disturbances in Pacific nodule areas”. It is not
associated with a conference.
Acknowledgements. The authors would like to thank Carsten Rüh-
lemann (Federal Institute for Geosciences and Natural Resources,
BGR, Hanover) for making the material from the BGR cruises
BIONOD, MANGAN 2013 and MANGAN 2014 available. Spe-
cial thanks are due to Nicol Mahnken for her help in photographing
the specimens. This is publication number 67 from the Senckenberg
am Meer Metabarcoding and Molecular Laboratory.
Financial support. The cruises SO239 and SO242 were financed
by the German Ministry of Education and Science (BMBF) as a
contribution to the European project JPI Oceans “Ecological As-
pects of Deep-Sea Mining”. This research has been supported by
BMBF under contract 03F0707E. The ABYSSLINE cruises were
funded by UK Seabed Resources Ltd. CESAM was financially
supported by the Portuguese Foundation for Science and Tech-
nology, Ministry of Science, Technology and Higher Education
(FCT/MCTES), through national funds (UID/AMB/50017/2019).
Clara F. Rodrigues was funded by Portuguese national funds (OE),
through FCT, I.P., in the scope of the framework contract foreseen
in numbers 4, 5 and 6 of article 23 of the decree law 57/2016, on
29 August, changed by law 57/2017, on 19 July.
Review statement. This paper was edited by Tina Treude and re-
viewed by Chris Mah and two anonymous referees.
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