Molecular diversity within the genus Laeonereis (Annelida, Nereididae) along the west Atlantic coast: paving the way for integrative taxonomy

Molecular diversity within the genus Laeonereis (Annelida, Nereididae) along the west Atlantic coast: paving the way for integrative taxonomy Bruno R. Sampieri 1,2,3 , Pedro E. Vieira 1,3 , Marcos A. L. Teixeira 1,3 , Victor C. Seixas 4 , Paulo R. Pagliosa 5 , Antonia Cecília Z. Amaral 6 and Filipe O. Costa 1,3 1Centro de Biologia Molecular e Ambiental, Universidade do Minho, Braga, Portugal 2Museu de Zoologia, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil 3Institute of Science and Innovation for Bio-Sustainability (IB-S), Universidade do Minho, Braga, Portugal 4Programa de Pós-Graduação em Ecologia, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil 5Laboratório de Biodiversidade e Conservação Marinha, Universidade Federal de Santa Catarina, Florianopólis, Santa Catarina, Brazil 6Departamento de Biologia Animal, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil ABSTRACT The polychaete genus Laeonereis (Annelida, Nereididae) occurs over a broad geographic range and extends nearly across the entire Atlantic coast of America, from the USA to Uruguay. Despite the research efforts to clarify its diversity and systematics, mostly by morphological and ecological evidence, there is still uncertainty, mainly concerning the species Laeonereis culveri, which constitutes an old and notorious case of taxonomic ambiguity. Here, we revised the molecular diversity and distribution of Laeonereis species based on a multi-locus approach, including DNA sequence analyses of partial segments of the cytochrome c oxidase subunit I (COI), 16S rRNA, and 28S rRNA genes. We examined Laeonereis specimens collected from 26 sites along the American Atlantic coast from Massachusetts (USA) to Mar del Plata (Argentina). Although no comprehensive morphological examination was performed between different populations, the COI barcodes revealed seven highly divergent MOTUs, with a mean K2P genetic distance of 16.9% (from 6.8% to 21.9%), which was confirmed through four clustering algorithms. All MOTUs were geographically segregated, except for MOTUs 6 and 7 from southeastern Brazil, which presented partially overlapping ranges between Rio de Janeiro and São Paulo coast. Sequence data obtained from 16S rRNA and 28S rRNA markers supported the same MOTU delimitation and geographic segregation as those of COI, providing further evidence for the existence of seven deeply divergent lineages within the genus. The extent of genetic divergence between MOTUs observed in our study fits comfortably within the range reported for species of polychaetes, including Nereididae, thus providing a strong indication that they How to cite this article Sampieri BR, Vieira PE, Teixeira MAL, Seixas VC, Pagliosa PR, Amaral ACZ, Costa FO. 2021. Molecular diversity within the genus Laeonereis (Annelida, Nereididae) along the west Atlantic coast: paving the way for integrative taxonomy. PeerJ 9:e11364 DOI 10.7717/peerj.11364 Submitted 6 August 2020 Accepted 7 April 2021 Published 27 May 2021 Corresponding author Bruno R. Sampieri, [email protected] Academic editor Christopher Glasby Additional Information and Declarations can be found on page 15 DOI 10.7717/peerj.11364 Copyright 2021 Sampieri et al. Distributed under Creative Commons CC-BY 4.0 might constitute separate species. These results may therefore pave the way for integrative taxonomic studies, aiming to clarify the taxonomic status of the Laeonereis MOTUs herein reported. Subjects Biodiversity, Biogeography, Marine Biology, Molecular Biology Keywords Polychaeta, Multi-locus, DNA barcode, Molecular identification INTRODUCTION The genus Laeonereis Hartman, 1945 occurs over a broad geographic range and extends nearly across the entire western Atlantic Coast, from North Carolina (USA) to Canelones (Uruguay) (Jesús-Flores, Salazar-Gonzáles & Salazar-Vallejo, 2016;De León-González, Méndez & Navedo, 2018). A single species occurs from El Salvador to Costa Rica on the Pacific Coast of Central America, while another one was described occurring on the Gulf of California, Mexico (Jesús-Flores, Salazar-Gonzáles & Salazar-Vallejo, 2016; De León-González, Méndez & Navedo, 2018). The monospecificity of Laeonereis proposed by Pettibone (1971) has been questioned, resulting in the validation of six species that occur along the Atlantic and Pacific American coasts, namely Laeonereis culveri (Webster, 1879) (North America coast; Orensanz & Gianuca, 1974), L. nota (Treadwell, 1941) (Central America coast; Jesús-Flores, Salazar-Gonzáles & Salazar-Vallejo, 2016), L. acuta (Treadwell, 1923) (South America coast; Orensanz & Gianuca, 1974;Santos & Lana, 2001; Liñero-Arana & Díaz-Díaz, 2007;Pamplin, Almeida & Silva-Filho, 2007), L. pandoensis (Monro, 1937), L. watsoni De Léon-González, Mendez & Navedo, 2018 (Gulf of California; De León-González, Méndez & Navedo, 2018), and L. brunnea (Central America Pacific coast; Dean, 2001;Dean, Sibaja-Cordero & Cortés, 2012). The uncertainties about the actual diversity of this genus come from scarcity of molecular data and difficult species identification (mainly by non-taxonomists), which is due to intraspecific morphological variability and subtle diagnostic characters (Jesús-Flores, Salazar-Gonzáles & Salazar-Vallejo, 2016;De León-González, Méndez & Navedo, 2018;Oliveira et al., 2010), besides a lack of updated information in reference biological and genetic databases, such as Zoobank, GenBank, and BOLD. In addition to the taxonomic issues addressed in several studies, the case of Laeonereis merits particular attention because of its relevance in biomonitoring. As a well-represented polychaete in estuarine environments from the western Atlantic Ocean (including the Gulf of Mexico and the Caribbean Sea), it has been used as a target species in population ecology and ecotoxicological studies, as well as in water and sediment quality monitoring surveys (Omena & Amaral, 2001;Rosa et al., 2005;Weis et al., 2017;Carricavur et al., 2018; Checon et al., 2018). Estuaries constitute a prominent environment throughout the coastal region where invertebrate populations can be subject to some degree of confinement (Seixas, Paiva & Russo, 2018). However, this has not prevented many typical estuarine species from being considered cosmopolitan or at least widely distributed (Virgilio et al., 2009;Tomioka et al., 2016;Silva et al., 2017). Estuarine polychaete species have received special attention, Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 2/22 mainly for their roles in communities and potential for biomonitoring surveys of benthic fauna (Winter, Devictor & Schweiger, 2013;Nygren, 2014;Carricavur et al., 2018). Therefore, to guarantee the success and accuracy of benthic surveys and allow their replication worldwide (Winter, Devictor & Schweiger, 2013), the correct species identification within these communities is fundamental. Moreover, estuarine Laeonereis species can occur in very high densities, which are stimulated by a small freshwater inflow from nearby streams. For example, L. culveri is one of the most common annelids in Alligator (Florida) and in the Mystic River estuary (Connecticut), where it occurs in aggregates, reaching up to 10 cm deep in fine sand and withstands variations in a salinity range of 0.5–30‰(Mazurkiewicz, 1975). In the southwest coast of Brazil (São Paulo), high densities were found at the high intertidal areas of beaches and close to streams; in the Porto do Saco da Ribeira (Ubatuba), it reached more than 5,600 individuals/m 2 and a biomass wet weight above 150 g/m², which constitutes a significant contribution to the area (Amaral, 1979). Given the sustained difficulties faced by researchers throughout decades to elucidate Laeonereis taxonomy using morphology, and considering its relevance in biomonitoring, the present study aimed to apply a molecular approach to examine the diversity of Laeonereis populations collected along the western Atlantic Ocean and investigate their geographic distribution. For this purpose, we used multi-locus sequence data generated by two mitochondrial genes, namely the 5’end of the cytochrome c oxidase subunit I (COI) and 16S rDNA (16S), and one nuclear locus, the 28S rDNA (28S). Molecular approaches have been successfully applied to contribute and elucidate the diversity of polychaetes, particularly over the last decade (Brasier et al., 2017;Lobo et al., 2016;Nygren et al., 2018;Langeneck et al., 2020;Teixeira et al., 2020). Here, we aimed to generate the basal molecular assessment of west Atlantic Laeonereis to pave the way for future integrative taxonomy studies on this genus. MATERIALS AND METHODS Specimen sampling Specimens of Laeonereis were collected from 26 localities between August 2016 and December 2017, two on the east coast of the United States (USA), 22 along the coast of Brazil (BRA: Amapá State [AP]; Bahia State [BA]; Ceará State [CE]; Paraná State [PR]; Piauí State [PI]; Rio de Janeiro State [RJ]; Rio Grande do Sul State [RS]; Santa Catarina State [SC]; São Paulo State [SP]), plus one in Uruguay (URU) and one in Argentina (ARG) (Fig. 1). To this end, soft-bottom samples were taken with shovels or sediment corers in the intertidal zone during syzygy tide. All specimens were identified morphologically and confirmed as belonging to the Laeonereis genus, according to a set of morphological characters available in the literature (Hartman, 1938;1945;Pettibone, 1971;Orensanz & Gianuca, 1974;Dean, 2001;Santos & Lana, 2001;Liñero-Arana & Díaz-Díaz, 2007; Pamplin, Almeida & Silva-Filho, 2007;Oliveira et al., 2010;Dean, Sibaja-Cordero & Cortés, 2012;Jesús-Flores, Salazar-Gonzáles & Salazar-Vallejo, 2016), as follow: sub-pyriform prostomium; pair of frontal antennae; four pairs of frontal tentacular cirri; eversible pharynx with a pair of mandibles and tufts of soft papillae within proximal and distal rings; Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 3/22 biramous parapodia with small ventral and dorsal cirri; and pygidium with a pair of cirri. Figure S1 presents photomicrographs and illustrations of the main characters used for the genus identification. DNA extraction, amplification, and sequencing DNA was extracted from a small piece of tissue from each specimen, using the E.Z.N.AÒ Mollusc DNA Kit (Omega Bio-Tek, Norcross, GA, USA) and following the manufacturer’s protocol. The following molecular markers were used in this study: cytochrome c oxidase subunit I gene (COI), 16S ribosomal DNA gene (16S), and 28S ribosomal DNA gene (28S). Amplification reactions were performed in a MyCyclerTM (Bio-Rad, Hercules, CA, USA) thermocycler using the following protocol: 2.5 ml of 10× PCR buffer, 1.5 ul (10 mM COI) or 2.5 ul (10 nM 16S/28S), 2.5 ul of 25 mM of MgCL 2 , 0.5 ml of 10uM dNTP mixture, 0.2 ml of DNA Taq polymerase (Thermo Scientific, Waltham, MA, USA). PCR conditions and primer sequences for each genetic marker are listed in Table S1. PCR Figure 1 Sample sites where specimens of Laeonereis species were collected. (A) Map with the total extent of the sampling. (B and C). Approximate maps of samples from southeastern BRA. Dotted ellipse indicates samples from the same location. Sites: 1. Connecticut, USA; 2. Maryland, USA; 3. Amapá, BRA; 4. Pará, BRA; 5. Piauí, BRA; 6. Ceará, BRA; 7. Bahia, BRA; 8. Lagoa do Visgueiro, Rio de Janeiro, BRA; 9. Ilha do Japonês, Rio de Janeiro, BRA; 10. Lagoa de Saquarema, Rio de Janeiro, BRA; 11. Lagoa de Jaconé, Rio de Janeiro, BRA; 12. Lagoa de Guarapiná, Rio de Janeiro, BRA; 13. Lagoa de Maricá, Rio de Janeiro, BRA; 14. Lagoa de Itaipú, Rio de Janeiro, BRA; 15. Lagoa de Piratininga, Rio de Janeiro, BRA; 16. Praia da Coroa, Rio de Janeiro, BRA; 17. Ilha do Fundão, Rio de Janeiro, BRA; 18. Ubatuba, São Paulo, BRA; 19. São Sebastião, São Paulo, BRA; 20. Guarujá, São Paulo, BRA; 21. Cananéia, São Paulo, BRA; 22. Paraná, BRA; 23. Santa Catarina, BRA; 24. Rio Grande do Sul, BRA; 25. Montevideo, URU; 26. Mar del Plata, ARG. Full-size  DOI: 10.7717/peerj.11364/fig-1 Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 4/22 products were verified on 1.5% agarose gel and cleaned up using ExoSap (Thermo Scientific, Waltham, MA, USA). Cleaned-up amplicons were sent to an external sequencing service for bidirectional sequencing (Macrogen, Spain and STAB Vida, Portugal). Table 1 details the specimen collection and sampling locations. Table S2 lists the GenBank and BOLD accession numbers for all sequences retrieved from databases and the Table 1 List of species used in the present study, source of sequences and geographical coordinates. Species MOTU Locality Source Latitude Longitude Laeonereis culveri MOTU 1 Cheesepeak Bay, Maryland, USA GenBank 3888′71″N 3888′99″N 7652′64″W 7653′09″W Laeonereis culveri MOTU 1 Stonington, Connecticut, USA Present study 4119’00.0″N71 58’00.0″W Laeonereis sp. MOTU 2 Delta do Parnaíba, Parnaíba, PI, Brazil Present study 251′45.09″S41 39′57.70″W Laeonereis sp. MOTU 3 Praia do Goiabal, Calçoene, AP, Brazil Present study 235′34.7″N50 50′46.06″W Laeonereis sp. MOTU 3 Delta do Parnaíba, Parnaíba, PI, Brazil Present study 251′45.09″S41 39′57.70″W Laeonereis sp. MOTU 4 Praia de Ajuruteua, Bragança, PA, Brazil Present study 050′19.16″S46 35′55.15″W Laeonereis sp. MOTU 4 Rio Pacoti, Fortaleza, CE, Brazil Present study 349′51.53″S38 25′11.03″W Laeonereis sp. MOTU 5 Praia do Forte, Mata de São João, BA, Brazil Present study 1234′59.24″S38 0′52.94″W Laeonereis sp. MOTU 6 Ilha do Japonês, Cabo Frio, RJ, Brazil Present study 2252′50.80″S42 0′10.80″W Laeonereis sp. MOTU 6 Saco da Ribeira, Ubatuba, SP, Brasil Present study 2330′16.72″S45 7′19.99″W Laeonereis sp. MOTU 6 Baía do Araçá, São Sebastião, SP, Brasil Present study 2348′45.36″S45 24′18.33″W Laeonereis sp. MOTU 6 Praia do Perequê, Guarujá, SP, Brasil Present study 2356′30.51″S46 10′25.27″W Laeonereis sp. MOTU 6 Canal de Cananéia, Cananéia, SP, Brasil Present study 250′49.36″S47 55′35.69″W Laeonereis sp. MOTU 6 Ilha da Cotinga, Paranaguá, PR, Brasil Present study 2531′9.74″S48 27′11.74″W Laeonereis sp. MOTU 7 Lagoa de Guarapiná, Maricá, RJ, Brazil Genbank 2256′26.21″S42 44′28.87″W Laeonereis sp. MOTU 7 Baía de Guanabara, RJ, Brazil Present Study 2271′84.53″S43 16′46.47″W Laeonereis sp. MOTU 7 Ilha do Fundão, Baía de Guanabara, RJ, Brazil GenBank 2250′22.44″S43 13′28.19″W Laeonereis sp. MOTU 7 Lagoa de Guarapiná, Maricá, RJ, Brazil GenBank 2256′26.21″S42 44′28.87″W Laeonereis sp. MOTU 7 Lagoa de Itaipú, Niterói, RJ, Brazil GenBank 2257′57.86″S43 2′39.69″W Laeonereis sp. MOTU 7 Lagoa de Jaconé, Saquarema, RJ, Brazil GenBank 2255′55.13″S42 38′5.86″W Laeonereis sp. MOTU 7 Lagoa de Maricá, Maricá, RJ, Brazil GenBank 2255′53.34″S42 49′41.59″W Laeonereis sp. MOTU 7 Lagoa de Saquarema, Saquarema, RJ, Brazil GenBank 2255′53.12″S42 29′33.01″W Laeonereis sp. MOTU 7 Lagoa de Piratininga, Niterói, RJ, Brazil GenBank 2257′1.62″S43 5′34.71″W Laeonereis sp. MOTU 7 Lagoa do Visgueiro, Quissamã, RJ, Brazil GenBank 2211′47.36″S41 25′59.84″W Laeonereis sp. MOTU 7 Canal de Cananéia, Cananéia, SP, Brasil Present study 250′49.36″S47 55′35.69″W Laeonereis sp. MOTU 7 Lagoa do Imaruí, Laguna, SC, Brasil Present study 2827′43.2″S48 47′50.7″W Laeonereis sp. MOTU 7 Saco do Justino, Rio Grande, RS, Brasil Present study 325′5.68″S52 13′7.45″W Laeonereis sp. MOTU 7 Canelones, Montevideo, Uruguai Present study 3447′41.41″S55 52′32.75″W Laeonereis sp. MOTU 7 Rio Quequén, Mar del Plata, Argentina Present study 3833′51.60″S58 42′41.36″W Ceratocephale cf. loveni OUTGROUP Nova Scotia, Canada GenBank –– Micronereis nanaimoensis OUTGROUP British Columbia, Canada GenBank 5340′26.4"N 13222′37.2"W Allita succinea OUTGROUP –GenBank –– Allita succinea OUTGROUP Maryland, USA GenBank 3888′06"N 7654′02"W Allita succinea OUTGROUP Mar Chiquita, Mar del Plata, Argentina Present study 3744′27.47"S 5725′13.97"W Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 5/22 voucher number in the Polychaete Collection of the National Museum of Rio de Janeiro and the Zoological Museum of the University of Campinas (ZUEC-UNICAMP). All samples from the states of Connecticut (USA) and Amapá (Brazil) were used for DNA extraction. However, both populations are well represented among the lineages (MOTU) detected in this study; meanwhile, they also have representative specimens deposited in scientific collections. All sequences, trace files, and metadata were deposited in the BOLD system’s database within the dataset “DS –LCSC: Laeonereis culveri species complex” available at DOI: dx.doi.org/10.5883/DS-LCSC. Sequence data treatment The forward and reverse trace files of the three DNA markers were checked and edited in MEGA 7.0 software (Kumar, Stecher & Tamura, 2016). COI and 16S sequences were aligned by Clustal W software (Thompson, Higgins & Gibson, 1994) and 28S sequences by Muscle software (Edgar, 2004). A total of 81, 58, and 45 original sequences of COI (589 bp), 16S (442 bp), and 28S (339 bp) were considered representatives of the genus Laeonereis, respectively. The 5’and 3’ends were pruned according to the quality, and all sequences were “normalized”to the minimum-length sequence. For the 28S marker, the selected primer amplified an 800 bp fragment, which included the hypervariable region in the fragment (Nygren et al., 2018). Therefore, the hypervariable zone was excluded for phylogenetic analyses and genetic distance estimation, resulting in a 339 bp fragment. Given the presence of gaps and nucleotide positions with poor 28S alignment quality, the online tool Gblocks 0.91b (Castresana, 2000)(http://molevol.cmima.csic.es/castresana/ Gblocks.html) was used to optimize alignment blocks for phylogenetic analyses. After optimization, nearly 15% of the bases were removed from original sequences, resulting in a fragment of 293 bp. Phylogenetic reconstruction The phylogenetic relationships between populations were reconstructed through Bayesian inference (BI) and maximum likelihood (ML). The alignments of each locus were analysed individually. The best-fit substitution models for each locus were determined using the best-fit model tool in MEGA 7.0 (for ML) and jModelTest (for BI), which are based on the Bayesian Information Criterion (BIC) (Guindon & Gascuel, 2003;Darriba et al., 2012). For COI, specific models were determined for each position of the codon in the BI analysis. In other words, the Hasegawa-Kishino-Yano model with gamma distribution and the invariant sites (HKY+G+I) model were selected for the first two codon positions, while the General Time Reversible (GTR+G+I) model for the third codon position. For the ML analysis with COI, the GTR+G+I model was used. For 16S and 28S, the GTR+G+I and HKY models were applied for both methods, respectively. Bayesian inference was performed in MrBayes v.3.1.2 (Ronquist & Huelsenbeck, 2003) with two parallel runs, using 10 million generations and sampling parameters every 500 generations. One-quarter of the trees were discarded as burn-in. The average standard deviation of split frequencies was confirmed for each analysis, with values below 0.02, indicating tree convergence (Ronquist & Huelsenbeck, 2003). The ML phylogenies were Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 6/22 estimated in MEGA 7.0 software, using the NNI heuristic method and branch supports estimated with 1000 bootstraps replications. The resulting trees were analysed in the software FigTree 1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/) to interpret and confirm branch supports and appropriate clade bifurcation, according to the model used. When low support or inadequate clade bifurcation were identified, a new run with other models was performed. The final versions of the trees were edited in Adobe Illustrator CC software (https://adobe.com/products/illustrator). Two COI sequences of Alitta succinea (Leuckart, 1847) from Argentina were produced following the above protocol and used as an outgroup. In addition, four sequences of A. succinea (two COI and two 16S), one of Micronereis nananimoensis Berkeley & Berkeley, 1953 (COI), one of Ceratocephale cf. loveni Malmgren, 1867 (16S), and one of Nereis succinea (Alitta succinea) (28S) were retrieved from GenBank and BOLD to be used as an outgroup in the phylogeny of each locus (Table 1). Raw data (alignment and phylogenetic trees) were deposited and are publicly available at Figshare (Dataset - https://doi.org/10.6084/m9.figshare.12733148.v1; Trees - https://doi.org/10.6084/m9.figshare.12733043.v1). Delimitation of Molecular Operational Taxonomic Units (MOTU) The molecular dataset was subjected to four MOTU delimitation methods as previously described in Vieira et al. (2019),Desiderato et al. (2019), and Teixeira et al. (2020); two distance-based (BIN and ABGD) and two phylogeny-based (GMYC and bPTP). These methods were applied to all studied loci except the BIN method, which is implemented within the BOLD system (Ratnasingham & Hebert, 2013) and applies only to COI data. This approach clusters barcode sequences algorithmically to calculate MOTUs that show high concordance to species (Ratnasingham & Hebert, 2013). The Automatic Barcode Gap Discovery (ABGD) species delimitation tool was performed on a web interface and applied with default settings, using the genetic distance matrix of Kimura-2Parameters (K2P). This tool allows the sorting of DNA sequences into hypothetical species, based on barcode gap detection (Puillandre et al., 2012). The Generalized Mixed Yule Coalescent method (GYMC; Fujisawa & Barraclough, 2013) is based on the examination of the branching patterns of an ultrametric tree and recognition of their transitions attributable to speciation (one lineage per species) to those that can be attributed to the interspecies coalescence process (multiple lineages per species). The single-threshold variant of this method was applied (Pons et al., 2006). The Bayesian ultrametric tree was generated in BEAST 2.4.6 (Bouckaert et al., 2014) with the appropriate best model (based on AIC criteria, GTR+I) and four independent series of 50,000,000 Monte Carlo Markov Chain (MCMC) generations, sampled every 5,000 generations. Quality control analysis was performed in the Tracer 1.6 software (Rambaut et al., 2014) evaluating the ESS (Effective Sample Size) (ESSs > 200 for all parameters) and parameter estimation convergence. A consensus tree was obtained using TreeAnnotator v.2.4.6 (Bouckaert et al., 2014) and visualized in FigTree 1.4.3. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 7/22 The Poisson Tree Processes (bPTP) method incorporates the number of substitutions in the model of speciation and assumes that the probability that a substitution gives rise to a speciation event follows a Poisson distribution. The branch lengths of the input tree are supposed to be generated by two independent classes of the Poisson process, one corresponding to speciation and the other to coalescence (Zhang et al., 2013). In contrast to GMYC, bPTP accepts non-ultrametric trees; thus, the previously estimated ML tree was used. Both GMYC and bPTP analyses were performed on a web interface (https://species.h-its.org/). Genetic diversity and structure The genetic distances between and within MOTUs were calculated in the MEGA 7.0, using the K2P model. To evaluate the relationship between haplotypes and their geographical distribution, haplotype networks were built using the TCS method (Clement, Snell & Wlaker, 2001) as implemented in PopART software (Leigh & Bryant, 2015). For this purpose, the data from the six different sites in Rio de Janeiro, as well as the four in São Paulo, were grouped in two locations, RJ and SP, respectively. Indices of genetic diversity, namely haplotype diversity (h) and nucleotide diversity (π), were estimated for each MOTU based on COI data and using DNASP 5.10 (Table S3)(Librado & Rozas, 2009). RESULTS Phylogenetic inference and MOTU delimitation Sampling sites and geographic sources of sequences retrieved from databases are illustrated and detailed in Fig. 1, in which the distribution of MOTUs along the North and South America coasts can be observed (Fig. 1). Phylogenetic inference and the four automated delimitation methods are congruent for all molecular markers (except the BIN method which is available only for COI). The phylogenetic tree of COI recovered seven reciprocally monophyletic groups, with branch support above 0.95 (BI) and 90 (ML) (Fig. 2A). MOTU 1 included specimens from the USA only; MOTUs 2 (BRA –PI), 3 (BRA –AP and PI), 4 (BRA –PA and CE), and 5 (BRA –BA) were exclusive to the North and Northeast regions of Brazil; and MOTUs 6 (BRA –RJ, SP and PR) and 7 (BRA –RJ, SP, SC, and RS; URU; and ARG) were exclusive to the Southeastern and Southern regions of Brazil; with the latter also being found in Uruguay and Argentina. MOTU 2 contained only one sequence from a specimen caught at the same site as that of MOTU 3 (PI). All different delimitation methods applied to COI resulted in the same partitioning, also separating L. culveri into seven MOTUs, except for the BIN method in which MOTU 6 was split into three different groups, resulting in nine MOTUs. The number of specimens and localities represented in 16S (Fig. 2B) and 28S (Fig. 2C) databases are lower than those in COI, and so were the numbers of clades and MOTUs. The clade recovery in the 16S and 28S trees matches that found in the COI tree, and in all cases, node support was higher than 0.9 (BI) or 90 (ML). All delimitation methods Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 8/22 corroborated the phylogenetic results for both markers. The 16S sequences represented MOTUs 1, 3, 6, and 7, while the 28S sequences represented MOTUs 3, 4, 6, and 7. Genetic distance and diversity The overall mean genetic distance (K2P) within the 113 Laeonereis sequences of COI was 11.6%. Table 2 shows the genetic distances for each marker, within and among MOTUs. All MOTUs showed low within-MOTU genetic variability (<1%) but high between-MOTU genetic distance. For the COI dataset, the mean genetic distances within and among MOTUs were 0.7% (0.0–1.7%) and 16.9% (6.8–21.9%), respectively. In the 16S dataset, these values were, respectively, 0.3% (0.0–0.3%) and 9.2% (4.5–11.3%), while for the 28S they were 0.7% (0.0–1.2%) and 5.4% (1.4–7.5%). Regarding genetic diversity, MOTUs 1 and 2 were monomorphic, MOTU 6 (RJ, SP, PR) presented the highest nucleotide diversity (0.02183) and segregating sites (105), while MOTU 7 (RJ, SP, SC, RS, URU, and ARG) had the highest haplotype diversity (0.869) (Table S3). 0.99 / 88 0.99 / 100 1 / 100 1 / 96 0.99 / 97 0.96 / 78 0.99 / 100 0.05 0.005 1 / 100 1 / 100 1 / 99 0.95 / 51 0.95 / 86 0.87 / 82 0.03 1 / 100 1 / 100 1 / 99 0.92 / 73 0.97 / 88 0.98 / 98 BIN ABGD bPTP GMYCs Outgroup 0.98 / - 1 / 100 0.99 / - Outgroup ABGD bPTP GMYCs ABGD bPTP GMYCs MOTU 1 AB C 1 / 100 1 / 100 1 / 100 1 / 100 MOTU 2 MOTU 3 MOTU 4 MOTU 5 MOTU 6 MOTU 7 Laeonereis sp. Figure 2 Bayesian phylogenetic trees for Laeonereis species based on COI (A), 16S (B) and 28S (C). Node values represents posterior probability (Bayesian inference) and bootstrap support (maximum likelihood). Nodes with a dash were not recovered in maximum likelihood analysis. Vertical coloured bars represent the results of molecular species delimitation methods, BIN (only for COI), ABGD, bPTP and GYMC single threshold. Full-size  DOI: 10.7717/peerj.11364/fig-2 Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 9/22 Pedro Vieira conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. Marcos A.L. Teixeira conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. Victor C. Seixas analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. Paulo Pagliosa analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. Antonia Amaral conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. Filipe Costa conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. DNA Deposition The following information was supplied regarding the deposition of DNA sequences: The sequences are available at BOLD: LCCS083-20, LCCS085-20, LCCS073-20, LCCS074-20, LCCS075-20, LCCS037-19, LCCS038-19, LCCS039-19, LCCS040-19, LCCS041-19, LCCS042-19, LCCS043-19, LCCS044-19, LCCS079-20, LCCS045-19, LCCS046-19, LCCS047-19, LCCS048-19, LCCS049-19, LCCS050-19, LCCS051-19, LCCS076-20, LCCS077-20, LCCS078-20, LCCS080-20, LCCS081-20, LCCS084-20, LCCS086-20, LCCS087-20, LCCS088-20, LCCS001-19, LCCS002-19, LCCS003-19, LCCS005-19, LCCS006-19, LCCS095-20, LCCS052-19, LCCS004-19, LCCS007-19, LCCS008-19, LCCS009-19, LCCS010-19, LCCS011-19, LCCS012-19, LCCS013-19, LCCS014-19, LCCS015-19, LCCS016-19, LCCS017-19, LCCS018-19, LCCS019-19, LCCS020-19, LCCS021-19, LCCS022-19, LCCS023-19, LCCS024-19, LCCS025-19, LCCS026-19, LCCS027-19, LCCS028-19, LCCS029-19, LCCS030-19, LCCS031-19, LCCS032-19, LCCS033-19, LCCS034-19, LCCS035-19, LCCS036-19, LCCS082-20, LCCS053-19, LCCS054-19, LCCS055-19, LCCS056-19, LCCS057-19, LCCS058-19, LCCS059-19, LCCS060-19, LCCS061-19, LCCS062-19, LCCS063-19, LCCS064-19, LCCS065-19, LCCS066-19, LCCS068-19, LCCS069-19, LCCS070-19, LCCS071-19, LCCS028-19, LCCS029-19, LCCS030-19, LCCS031-19, LCCS032-19, LCCS033-19, LCCS034-19, LCCS035-19, LCCS036-19, LCCS082-20, LCCS053-19, LCCS054-19, LCCS055-19, LCCS056-19, LCCS057-19, LCCS058-19, LCCS059-19, LCCS060-19, LCCS061-19, LCCS062-19, LCCS063-19, LCCS064-19, LCCS065-19, LCCS066-19, LCCS068-19, LCCS069-19, LCCS070-19, LCCS071-19, LCCS028-19, LCCS029-19, LCCS030-19, LCCS031-19, LCCS032-19, LCCS033-19, LCCS034-19, LCCS035-19, LCCS036-19, LCCS082-20, LCCS053-19, LCCS054-19, LCCS055-19, LCCS056-19, LCCS057-19, LCCS058-19, LCCS059-19, LCCS060-19, LCCS061-19, LCCS062-19, LCCS063-19, LCCS064-19, LCCS065-19, LCCS066-19, LCCS068-19, LCCS069-19, LCCS070-19, LCCS071-19, LCCS072-19. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 16/22 Data Availability The following information was supplied regarding data availability: Sampieri, Bruno (2020): Alignment dataset. figshare. Dataset. DOI 10.6084/m9.figshare.12733148.v1. Sampieri, Bruno (2020): Bayesian Trees COI / 16S / 28S. figshare. Figure. DOI 10.6084/m9.figshare.12733043.v1. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.11364#supplemental-information. REFERENCES Álvarez-Campos P, Giribet G, Riesgo A. 2017. The Syllis gracilis species complex: A molecular approach to a difficult taxonomic problem (Annelida, Syllidae). Molecular Phylogenetics and Evolution 109(1):138–150 DOI 10.1016/j.ympev.2016.12.036. Amaral ACZ. 1979. Ecologia e contribuição dos anelídeos poliquetos para a biomassa bêntica da xona das marés, no litoral norte do Estado de São Paulo. Boletim do Instituto Oceanográfico 28(1):1–52 DOI 10.1590/S0373-55241979000100001. Barroso R, Klautau M, Solé-Cava AM, Paiva PC. 2010. Eurythoe complanata (Polychaeta: Amphinomidae), the ‘cosmopolitan’fireworm, consists of at least three cryptic species. Marine Biology 157(1):69–80 DOI 10.1007/s00227-009-1296-9. Bilton DT, Paula J, Bishop JDD. 2002. Dispersal, genetic differentiation and speciation in estuarine organisms. Estuarine, Coastal and Shelf Science 55(6):937–952 DOI 10.1006/ecss.2002.1037. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, Suchard MA, Rambaut A, Drummond AJ. 2014. BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. PLOS Computational Biology 10(4):e1003537 DOI 10.1371/journal.pcbi.1003537. Bowen BW, Rocha LA, Toonen RJ, Karl SA. 2013. The origins of tropical marine biodiversity. Trends in Ecology and Evolution 28(6):359–366 DOI 10.1016/j.tree.2013.01.018. Brasier MJ, Harle J, Wiklund H, Jeffreys RM, Linse K, Ruhl HA, Glover AG. 2017. Distributional patterns of polychaetes across the West Antarctic based on DNA barcoding and particle tracking analyses. Frontiers in Marine Science 4:356 DOI 10.3389/fmars.2017.00356. Capa M, Murray A. 2015. Integrative taxonomy of Parasabella and Sabellomma (Sabellidae: Annelida) from Australia: description of new species, an indication of cryptic diversity, and translocation of some species out of their natural distribution range. Zoological Journal of the Linnean Society 175(4):764–811 DOI 10.1111/zoj.12308. Carr CM, Hardy SM, Brown TM, Macdonald TA, Hebert PD. 2011. A tri-oceanic perspective: DNA barcoding reveals geographic structure and cryptic diversity in Canadian polychaetes. PLOS ONE 6(7):e22232 DOI 10.1371/journal.pone.0022232. Carricavur AD, Boudet LC, Romero MB, Polizzi P, Marcovecchio JE, Gerpe M. 2018. Toxicological responses of Laeonereis acuta (Polychaeta, Nereididae) after acute, subchronic and chronic exposure to cadmium. Ecotoxicology and Environmental Safety 149:217–224 DOI 10.1016/j.ecoenv.2017.11.048. Castresana J. 2000. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution 17(4):540–552 DOI 10.1093/oxfordjournals.molbev.a026334. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 17/22 Checon HH, Corte GN, Muniz P, Brauko KM, Di Domenico M, Bícego MC, Siegle E, Figueira RCL, Amaral ACZ. 2018. Unraveling the performance of the benthic index AMBI in a subtropical bay: the effects of data transformations and exclusion of low-reliability sites. Marine Pollution Bulletin 126:438–448 DOI 10.1016/j.marpolbul.2017.11.059. Clement M, Snell Q, Wlaker P. 2001. TCS: estimating gene genealogies Version 1.13. Provo: Bringham Young University Press. Coelho-Souza SA, López MS, Guimarães JRD, Coutinho R, Candella RN. 2012. Biophysical interactions in the Cabo Frio upwelling system, Southeastern Brazil. Brazilian Journal of Oceanography 60(3):353–365 DOI 10.1590/S1679-87592012000300008. Darriba D, Taboada GL, Doallo R, Posada D. 2012. JModelTest 2: more models, new heuristics and parallel computing. Nature Methods 9(8):772 DOI 10.1038/nmeth.2109. De León-González JA, Méndez N, Navedo JG. 2018. Laeonereis watsoni (Annelida, Nereididae), a new species from western Mexico. Journal of the Marine Biological Association of the United Kingdom 98(6):1347–1353 DOI 10.1017/S0025315417000583. Dean HK. 2001. Some Nereididae (Annelida: Polychaeta) from the Pacific coast of Costa Rica. Revista de Biología Tropical 49:37–67. Dean HK, Sibaja-Cordero JA, Cortés J. 2012. Polychaetes (Annelida: Polychaeta) of Cocos Island National Park, Pacific Costa Rica. Pacific Science 66(3):347–386 DOI 10.2984/66.3.8. DelićT, Trontelj P, RendošM, Fišer C. 2017. The importance of naming cryptic species and the conservation of endemic subterranean amphipods. Scientific Reports 7(1):e3391 DOI 10.1038/s41598-017-02938-z. DeSalle R, Goldstein P. 2019. Review and interpretation of trends in DNA barcoding. Frontiers in Ecology and Evolution 7:302 DOI 10.3389/fevo.2019.00302. Desiderato A, Costa FO, Serejo CS, Abbiati M, Queiroga H, Vieira PE. 2019. Macaronesian islands as promoters of diversification in amphipods: the remarkable case of the family Hyalidae (Crustacea, Amphipoda). Zoologica Scripta 48(3):359–375 DOI 10.1111/zsc.12339. Dias RM, Lima SMQ, Mendes LF, Almeida DF, Paiva PC, Britto MR. 2019. Different speciation process in a cryptobenthic reef fish from the western tropical Atlantic. Hydrobiologia 837(1):133–147 DOI 10.1007/s10750-019-3966-z. Dominguez JML. 2009. The coastal zone of Brazil, Geology and geomorphology of Holocene coastal barriers of Brazil. In: Dillenburg S, Hesp P, eds. Lecture Notes in Earth Sciences. Berlin: Springer-Verlag, 17–51. Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32(5):1792–1797 DOI 10.1093/nar/gkh340. Ehlers J, Gibbard PL. 2007. The extent and chronology of Cenozoic global glaciation. Quaternary International 164:6–20 DOI 10.1016/j.quaint.2006.10.008. Floeter SR, Soares-Gomes A. 1999. Biogeographic and species richness patterns of Gastropoda on the southwestern Atlantic. Revista Brasiliera de Biologia 59(4):567–575 DOI 10.1590/S0034-71081999000400006. Fujisawa T, Barraclough TG. 2013. Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach: a revised method and evaluation on simulated data sets. Systematic Biology 62(5):707–724 DOI 10.1093/sysbio/syt033. Glasby CJ, Wei NWV, Gibb KS. 2013. Cryptic species of Nereididae (Annelida: Polychaeta) on Australian coral reefs. Invertebrate Systematics 27(3):245–264 DOI 10.1071/IS12031. Guindon S, Gascuel O. 2003. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Systematic Biology 52(5):696–704 DOI 10.1080/10635150390235520. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 18/22 Hartman O. 1938. Nomenclatorial changes involving types of polychaetous annelids of the family Nereidae in the United States National Museum. Journal of the Washington Academy of Sciences 28:13–15. Hartman O. 1945. The marine annelids of North Carolina. Duke University Marine Station Bulletin 2:1–51. Hoorn C, Wesselingh FP, Ter Steege H, Bermudez MA, Mora A, Sevink J, Sanmartín I, Sanchez-Meseguer A, Anderson CL, Figueiredo JP, Jaramillo C, Riff D, Negri FR, Hooghiemstra H, Lundberg J, Stadler T, Särkinen T, Antonelli A. 2010. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330(6006):927–931 DOI 10.1126/science.1194585. Hupało K, Teixeira MAL, Rewicz T, Sezgin M, Lannillie V, Karaman GS, Grabowski M, Costa FO. 2019. Persistence of phylogeographic footprints helps to understand cryptic diversity detected in two marine amphipods widespread in the Mediterranean basin, Morphological distinction between estuarine polychaetes: Laeonereis culveri and L. nota (Phyllodocida: Nereididae). Molecular Phylogenetics and Evolution 132(3):53–66 DOI 10.1016/j.ympev.2018.11.013. Jesús-Flores C, Salazar-Gonzáles SA, Salazar-Vallejo AS. 2016. Morphological distinction between estuarine polychaetes: Laeonereis culveri and L. nota (Phyllodocida: Nereididae). International Journal of Tropical Biology 64:189–201 DOI 10.15517/rbt.v64i1.18297. Klesch WL. 1970. The reproductive biology and larval development of Laeonereis culveri Webster (Polychaeta; Nereididae). Contributions in Marine Sciences, University of Texas 15:71–75. Kumar S, Stecher G, Tamura K. 2016. MEGA7: molecular evolutionary genetics analysis Version 7.0 for bigger datasets. Molecular Biology and Evolution 33(7):1870–1874 DOI 10.1093/molbev/msw054. Langeneck J, Scarpa F, Maltagliati F, Sanna D, Barbieri M, Cossu P, Mikac B, Galletti MC, Castelli A, Casu M. 2020. A complex species complex: the controversial role of ecology and biogeography in the evolutionary history of Syllis gracilis Grube, 1840 (Annelida, Syllidae). Journal of Zoological and Systematics and Evolutionary Research 58(1):66–78 DOI 10.1111/jzs.12336. Leigh JW, Bryant D. 2015. PopART: full-feature software for haplotype network construction. Methods in Ecology and Evolution 6(9):1110–1116 DOI 10.1111/2041-210X.12410. Leuckart R. 1847. Verzeichniss der zur Fauna Helgoland’s gehörenden wirbellosen Seethiere. In: Frey H, Leuckart R, eds. Beiträge zur Kenntniss wirbelloser Thiere. 136–168. Available at http://www.biodiversitylibrary.org/bibliography/2128. Librado P, Rozas J. 2009. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11):1451–1452 DOI 10.1093/bioinformatics/btp187. Liñero-Arana I, Díaz-Díaz O. 2007. Nuevas adiciones de Nereididae (Annelida: Polychaeta) para las costas de Venezuela. Boletín del Instituto Oceanográfico Venezuela 46(2):153–163. Lobo J, Teixeira MAL, Borges LMS, Ferreira MSG, Hollatz C, Gomes PA, Sousa R, Ravara A, Costa MH, Costa FO. 2016. Starting a DNA barcode reference library for shallow water polychaetes from the southern European Atlantic coast. Molecular Ecology 16(1):298–313 DOI 10.1111/1755-0998.12441. Loeza-Quintana T, Carr CM, Khan T, Bhatt YA, Lyon SP, Hebert PDN, Adamowicz SJ. 2019. Recalibrating the molecular clock for Arctic marine invertebrates based on DNA barcodes. Genome 62(3):200–216 DOI 10.1139/gen-2018-0107. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 19/22 Mattos G, Seixas VC, Paiva PC. 2019. Comparative phylogeography and genetic connectivity of two crustacean species with contrasting life histories on South Atlantic sandy beaches. Hydrobiologia 826(1):319–330 DOI 10.1007/s10750-018-3744-3. Mazurkiewicz M. 1975. Larval development and habits of Laeonereis culveri (Webster) (Polychaeta: Nereidae). The Biological Bulletin 149(1):186–204 DOI 10.2307/1540489. Mazzini PLF, Barth JA. 2013. A comparison of mechanisms generating vertical transport in the Brazilian coastal upwelling regions. Journal of Geophysical Research: Oceans 118(11):5977–5993 DOI 10.1002/2013JC008924. Nygren A. 2014. Cryptic polychaete diversity: a review. Zoologica Scripta 43(2):172–183 DOI 10.1111/zsc.12044. Nygren A, Parapar J, Pons J, Meiûner K, Bakken T, Kongsrud JA, Oug E, Gaeva D, Sikorski A, Johansen RA, Hutchings PA, Lavesque N, Capa M. 2018. A mega-cryptic species complex hidden among one of the most common annelids in the North-East Atlantic. PLOS ONE 13(6):e0198356 DOI 10.1371/journal.pone.0198356. Oliveira VM, Santos CSG, Lana PC, Camargo MG. 2010. Morphological variations caused by fixation techniques may lead to taxonomic confusion in Laeonereis (Polychaeta: Nereididae). Zoologia 27(1):146–150 DOI 10.1590/S1984-46702010000100022. Omena EP, Amaral ACZ. 2001. Morphometric study of the nereidid Laeonereis acuta (Annelida: Polychaeta). Journal of Marine Biological Association of the United Kingdom 81(3):423–426 DOI 10.1017/S0025315401004040. Orensanz JM, Gianuca NM. 1974. Contribuição ao conhecimento dos anelídeos poliquetas do Rio Grande do Sul, Brasil, 1. Lista sistemática preliminar e descrição de três novas espécies. Comunicaciones du Museo de Ciencias 4:1–37. Paiva PC, Mutaquilha BF, Coutinho MCL, Santos CSG. 2019. Comparative phylogeography of two coastal species of Perinereis Kinberg, 1865 (Annelida, Polychaeta) in the South Atlantic. Marine Biodiversity 49(3):1537–1551 DOI 10.1007/s12526-018-0927-0. Pamplin PAZ, Almeida TCMD, Silva-Filho JPD. 2007. New record of Laeonereis acuta (Treadwell, 1923) (Nereididae: Polychaeta) in northeast coast of Brazil. Biota Neotropica 7(3):353–355 DOI 10.1590/S1676-06032007000300035. Peluso L, Tascheri V, Nunes FLD, Castro CB, Pires DO, Zilberberg C. 2018. Contemporary and historical oceanographic processes explain genetic connectivity in a Southwestern Atlantic coral. Scientific Reports 8(1):2684 DOI 10.1038/s41598-018-21010-y. Pettibone MH. 1971. Revision of some species referred to Leptonereis,Nicon, and Laeonereis (Polychaeta: Nereididae). Smithsonian Contributions to Zoology 104:1–53. Pons J, Barraclough TG, Gomez-Zurita J, Cardoso A, Duran DP, Hazell S, Kamoun S, Sumlin WD, Vogler AP. 2006. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Systematic Biology 55(4):595–609 DOI 10.1080/10635150600852011. Puillandre N, Lambert A, Brouillet S, Achaz G. 2012. ABGD, automatic barcode gap discovery for primary species delimitation. Molecular Ecology 21(8):1864–1877 DOI 10.1111/j.1365-294X.2011.05239.x. Rambaut A, Suchard M, Xie D, Drummond A. 2014. Tracer v1.6. Available at http://beast.bio.ed. ac.uk/Tracer. Ratnasingham S, Hebert PDN. 2013. A DNA-based registry for all animal species: the Barcode Index Number (BIN) system. PLOS ONE 8(7):e66213 DOI 10.1371/journal.pone.0066213. Ravara A, Ramos D, Teixeira MAL, Costa FO, Cunha MR. 2017. Taxonomy, distribution and ecology of the order Phyllodocida (Annelida, Polychaeta) in deep-sea habitats around the Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 20/22 Iberian margin. Deep Sea Research Part II: Topical Studies in Oceanography 137(5):207–231 DOI 10.1016/j.dsr2.2016.08.008. Rodrigues AM, Pires A, Mendo S, Quintino V. 2009. Diopatra neapolitana and Diopatra marocensis from the Portuguese coast: morphological and genetic comparison. Estuarine Coastal and Shelf Science 85(4):609–617 DOI 10.1016/j.ecss.2009.10.004. Ronquist F, Huelsenbeck JP. 2003. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19(12):1572–1574 DOI 10.1093/bioinformatics/btg180. Rosa CED, Iurman MG, Abreu PC, Geracitano LA, Monserrat JM. 2005. Antioxidant mechanisms of the Nereidid Laeonereis acuta (Anelida: Polychaeta) to cope with environmental hydrogen peroxide. Physiological and Biochemical Zoology 78(4):641–649 DOI 10.1086/430229. Sales JBL, Rodrigues-Filho LFS, Ferreira YS, Carneiro J, Asp NE, Shaw PW, Haimovici M, Markaida U, Ready J, Schneider H, Sampaio I. 2017. Divergence of cryptic species of Doryteuthis plei Blainville, 1823 (Loliginidae, Cephalopoda) in the Western Atlantic Ocean is associated with the formation of the Caribbean Sea. Molecular Phylogenetics and Evolution 106(7):44–54 DOI 10.1016/j.ympev.2016.09.014. Santos CSG, Lana PC. 2001. Nereididae (Annelida: Polychaeta) da costa Nordeste do Brasil. II. Gêneros Namalycastis,Ceratochephale,Laeonereis eRullierinereis.Ilheringia Série Zoológica II:137–149 DOI 10.1590/S0073-472120 01000200020. Seixas VC, Paiva PC, Russo CAM. 2018. Comparative population genetics and demographic history of two polychaeta species suggest that coastal lagoon populations evolve under alternate regimes of gene flow. Marine Biology 165(11):179 DOI 10.1007/s00227-018-3437-5. Seixas VC, Steiner TM, Solé-Cava AM, Amaral ACZ, Paiva PC. 2020. Hidden diversity within the Diopatra cuprea complex (Annelida: Onuphidae): morphological and genetics analyses reveal four new species in the south-west Atlantic. Zoological Journal of The Linnean Society 191(3):637–671 DOI 10.1093/zoolinnean/zlaa032. Silva CF, Seixas VC, Barroso R, DiDomenico M, Amaral ACZ, Paiva PC. 2017. Demystifying the Capitella capitata complex (Annelida, Capitellidae) diversity by morphological and molecular data along the Brazilian coast. PLOS ONE 12(5):e0177760 DOI 10.1371/journal.pone.0177760. Spalding MD, Fox HE, Allen GR, Davidson N, Ferdaña ZA, Finlayson M, Halpern BS, Jorge MA, Lombana A, Lourie SA, Martin KD, McManus E, Molnar J, Recchia CA, Robertson J. 2007. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57(7):573–583 DOI 10.1641/B570707. Stein ED, Martinez MC, Stiles S, Miller PE, Zakharov EV. 2014. Is DNA barcoding actually cheaper and faster than traditional morphological methods: results from a survey of freshwater bioassessment efforts in the United States? PLOS ONE 9(4):e95525 DOI 10.1371/journal.pone.0095525. Teixeira MA, Vieira PE, Pleijel F, Sampieri BR, Ravara A, Costa FO, Nygren A. 2020. Molecular and morphometric analyses identify new lineages within a large Eumida (Annelida) species complex. Zoologica Scripta 49(2):222–235 DOI 10.1111/zsc.12397. Thompson JD, Higgins DG, Gibson TJ. 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22(22):4673–4680 DOI 10.1093/nar/22.22.4673. Tomazelli LJ, Dillenburg SR, Villwock JA. 2017. Late Quaternary geological history of Rio Grange do Sul coastal plain, southern Brazil. Revista Brasileira de Geociências 30(3):474–476 DOI 10.25249/0375-7536.2000303474476. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 21/22 Tomioka S, Kondoh T, Sato-Okoshi W, Ito K, Kakui K, Kajihara H. 2016. Cosmopolitan or Cryptic Species? A case study of Capitella teleta (Annelida: Capitellidae). Zoological Science 33(5):545–554 DOI 10.2108/zs160059. Tosuji H, Bastrop R, Götting M, Park T, Hong JS, Sato M. 2019. Worldwide molecular phylogeny of common estuarine polychaetes of the genus Hediste (Annelida: Nereididae), with special reference to interspecific common haplotypes found in southern Japan. Marine Biodiversity 49(3):1385–1402 DOI 10.1007/s12526-018-0917-2. Vieira PE, Desiderato A, Holdich DM, Soares P, Creer S, Carvalho GR, Costa FO, Queiroga H. 2019. Deep segregation in the open ocean: Macaronesia as an evolutionary hotspot for low dispersal marine invertebrates. Molecular Ecology 28(7):1784–1800 DOI 10.1111/mec.15052. Virgilio M, Fauvelot C, Constantini F, Abbiati M, Backeljau T. 2009. Phylogeography of the common ragworm Hediste diversicolor (Polychaeta: Nereididae) reveals cryptic diversity and multiple colonization events across its distribution. Molecular Ecology 18(9):1980–1994 DOI 10.1111/j.1365-294X.2009.04170.x. Webster HE. 1879. The Annelida Chaetopoda of New Jersey. Annual Report of the New York State Museum of Natural History 32:101–128. Weis WA, Soares CHL, De Quadros DPC, Scheneider M, Pagliosa PR. 2017. Urbanization effects on different biological organization levels of an estuarine polychaete tolerant to pollution. Ecological Indicators 73:698–707 DOI 10.1016/j.ecolind.2016.10.029. Winter M, Devictor V, Schweiger O. 2013. Phylogenetic diversity and nature conservation: where are we? Trends in Ecology & Evolution 28(4):199–204 DOI 10.1016/j.tree.2012.10.015. Zhang J, Kapli P, Pavlidis P, Stamatakis A. 2013. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29(22):2869–2876 DOI 10.1093/bioinformatics/btt499. Sampieri et al. (2021), PeerJ, DOI 10.7717/peerj.11364 22/22