The first UKSR ABYSSLINE cruise (AB01), sampling the UK-1 exploration contract area, took place in October 2013 aboard the RV Melville and the second cruise (AB02), sampling the UK-1 and OMS-1 (Ocean Minerals Singapore) exploration contract areas and one Area of Particular Environmental Interest (APEI), APEI-6, took place in February-March 2015 onboard RV Thomas G. Thompson. Sampling locations are as indicated in Fig. 1.

Figure 1.  

Map of ABYSSLINE sampling locations used in this study. a) The Clarion-Clipperton Zone, central Pacific Ocean, with contract areas and Areas of Particular Environmental Interest highlighted; b) UK-1 and OMS-1 sampling locations used in this study; c-e) Bathymetric data for sites in UK-1 and OMS-1 claim areas, with yellow circles showing sampling locations.

A comprehensive description of our methods is provided in Glover et al. (2015). In summary, deep-sea benthic specimens from the targeted CCZ areas were collected using a range of oceanographic sampling gear including box core, epibenthic sledge (EBS), ROV and multiple core. Geographic data from sampling activities were recorded on a central GIS database. Live-sorting of specimen samples was carried out aboard both vessels in a ‘cold-chain’ pipeline, in which material was constantly maintained in chilled, filtered seawater held at 2–4°C. Specimens were preliminarily identified at sea and imaged live using stereomicroscopes with attached digital cameras. Only annelid specimens with a visible head end were counted and included in taxonomic and biodiversity analyses. The specimens were then stored in individual microtube vials containing an aqueous solution of 80% non-denatured ethanol, numbered and barcoded into a database and kept chilled until return to the Natural History Museum (NHM), London, UK.

In the laboratory, specimens were re-examined using stereomicroscopes, identified to the best possible taxonomic level and a small tissue sample was taken for DNA extraction. In most of the specimens in this study, the material was too poor or degraded to make species-level identifications.

Extraction of DNA was done with DNeasy Blood and Tissue Kit (Qiagen) using a Hamilton Microlab STAR Robotic Workstation. About 450 bp of 16S and 650 bp of cytochrome c oxidase subunit I (COI) were amplified using primers listed in Table 2. PCR mixtures contained 1 μl of each primer (10 μM), 2 μl template DNA and 21 μl of Red Taq DNA Polymerase 1.1X MasterMix (VWR) in a mixture of total 25 μl. The PCR amplification profile consisted of initial denaturation at 95°C for 5 min, 35 cycles of denaturation at 94°C for 45 s, annealing at 55°C for 45 s, extension at 72°C for 2 min and a final extension at 72°C for 10 min. PCR products were purified using Millipore Multiscreen 96-well PCR Purification System and sequencing was performed on an ABI 3730XL DNA Analyser (Applied Biosystems) at NHM Sequencing Facility, using the same primers as in the PCR reactions (Table 2). Overlapping sequence fragments were merged into consensus sequences using Geneious (Kearse et al. 2012) and aligned using MAFFT (Katoh 2002) for 16S and MUSCLE (Edgar 2004) for COI.

Future studies of biogeographic and bathymetric ranges, gene-flow, extinction risks, natural history, reproductive ecology, functional ecology and geochemical interactions of CCZ species are dependent on accurate taxonomic identifications. Our more recent taxonomic studies (e.g. Glover et al. (2016), Dahlgren et al. (2016), Wiklund et al. (2017), Wiklund et al. (2019), Drennan et al. (2021), Neal et al. (2022a), Neal et al. (2022b)), applied an integrative DNA taxonomy approach, with species delimitation principally based on a phylogenetic species concept, sensu Donoghue (1985) coupled with morphological interpretations. However, in this broader study of all of the annelids, including many poorly-preserved or fragmented individuals, we have relied more heavily on genetic data.

Taxonomic assignments and nomenclature

Almost all species discriminated by the analyses could be identified to family level from morphology and this was cross-checked with the genetic data through a BLAST search. Given the poor preservation of morphology and the goals of this paper to make specimen and genetic data rapidly available for any future studies, we did not attempt to force species into genera in most instances and used an open nomenclature whereby the species are named with the best taxonomic level and a species epithet that refers to the best voucher specimen of that species. For example, Pilargidae sp. (NHM_015) is the informal species name for the species that is represented by two specimens, specimen NHM_015 being the better representative (an informal type specimen). This avoids confusion with the use of sp. A, B, C etc. where informal and confusing synonyms can easily arise.

Species were delimited, based on well-supported clades recovered from phylogenetic trees estimated separately for 16S and COI sequences. Best nucleotide substitution models for each alignment (i.e. 16S and COI) and best-fit partitions were inferred using PartitionFinder 2.0 (Lanfear et al. 2017), utilising a greedy clustering algorithm (Lanfear et al. 2012) and PhyML v.3 (Guindon et al. 2010). Phylogenetic trees were estimated using Bayesian Inference in BEAST v.2.6.2 (Bouckaert et al. 2019) under a Yule speciation model, uncorrelated relaxed clock, GTR+I+G substitution model for each partition of COI (codon positions 1, 2 and 3) and for 16S and a chain length of 100 M, sampling every 10,000. The MCMC runs were inspected convergence in Tracer v.1.7.1. The first 50 M states were discarded as burn-in and a consensus median heights tree was generated from the remaining posterior trees. Trees were visualised using FigTree v.1.4.4 and species clusters were mostly defined from very similar sequences recovered in monophyletic clades with a posterior probability of 1.

The field and laboratory work created a series of databases and sample sets that are integrated into a data-management pipeline. This includes the transfer and management of data and samples between a central collections database, a molecular collections database and external repositories (GenBank, WoRMS, OBIS, GBIF, GGBN, ZooBank) through DwC archives. This provides a robust data framework to support DNA taxonomy, in which openly available data and voucher material is key to quality data standards. All DwC data present in the taxon treatments of the checklist herein are also provided as a supplementary data file to allow for ease of usage (Suppl. material 2). A further elaboration of the data pipeline is published in Glover et al. (2015).

A list of the species in this checklist and number of records is provided here in Table 1.