Meiofaunal diversity at a seamount in the pacific ocean: A comprehensive study using environmental DNA and RNA

https://doi.org/10.1016/j.dsr.2020.103253Get rights and content

Highlights

  • Meiofaunal assemblages at JA06 Seamount were examined using the conventional and metabarcoding methods.

  • The conventional methods failed to detect the heterogeneity of meiofaunal assemblages.

  • The metabarcoding methods provided invaluable information on meiofaunal assemblages.

  • The metabarcoding analysis is useful to reveal meiofaunal assemblages in areas with low productivity and low faunal density.

Abstract

In deep-sea environments, which have low densities of large benthic animals, meiofauna are a useful indicator of biodiversity. However, in low-latitudes and in low-productivity areas, meiofaunal density is low, but is higher than that of megafaunal or macrofaunal density. Therefore, it is difficult to collect a sufficient number of specimens for statistical analysis. In addition, because faunal classification has been based on conventional microscopic observations, the use of meiofauna to estimate biodiversity in deep-sea environments is time-consuming. However, metabarcoding analyses focused on environmental DNA (eDNA) or RNA (eRNA) have recently been used to examine deep-sea eukaryotic diversity and communities. Here, we examined meiofaunal assemblages using microscopic, eDNA-, and eRNA-based methods at Xufu Guyot (JA06 Seamount), off the southeastern coast of Minami-Torishima Island in the North Pacific Ocean. Microscopic analysis failed to detect a significant difference in diversity or community structure between the seamount terrace and base. This was likely because of the low abundance of meiofauna, which was caused by the low surface productivity at the study area. However, eDNA/eRNA-based metabarcoding analyses revealed spatial variations in diversity and community structures within a single seamount. Therefore, metabarcoding analysis might be useful to elucidate meiofaunal assemblages in areas with low productivity and low faunal density.

Introduction

Seamounts are oases of productivity and hotspots of biodiversity, making them important environments in deep-sea ecosystems (Rowden et al., 2010; George et al., 2018). Megafaunal and macrofaunal density and diversity are higher at seamount summits than at seamount slopes or comparable areas (Rowden et al., 2010; Sautya et al., 2011). There are 232 seamounts that have been surveyed for biological studies; however, meiofauna had only been surveyed at eight seamounts (George, 2013), although they are more abundant than macro and megafauna (Wei et al., 2010) and important components of deep-sea environments (e.g. Giere, 2009). There are two factors causing the limited knowledge on seamount meiofaunal assemblage. The first factor is difficulties in quantitative sampling and identification. Varieties of bottom structures (slope, even, and uneven) and bottom types (sediment, sand, and rock) make it difficult to quantitatively collect meiofaunal samples (George, 2013). The other one is research difficulties in identifying the complete species composition in a community, which are caused by the complexity of morphological characteristics. The solution is DNA signature for identifying taxon or phylogenetic position.

Environmental DNA (eDNA) and environmental RNA (eRNA) can be used to analyze the biodiversity of the phylogenetic assemblages of prokaryotes and small eukaryotes such as meiofauna and foraminifera, although many novel DNA signature emerges without taxonomical nomenclature. The reverse-taxonomy approach using genetic analysis enables straightforward classification of individual genotypes instead of taxon names (Markmann and Tautz, 2005). Recent advances in sequencing technology have afforded metagenomic methods (also referred to as “metabarcoding”) based on high-throughput sequencing, which has been commonly used for evaluating deep-sea eukaryotic diversity and communities (Pawlowski et al., 2011; Bik et al., 2012; Guardiola et al., 2016).

The emerging issue is the discrepancy between the results from eDNA and eRNA (Pochon et al., 2017). DNA extracted from environmental samples contains detailed information on the structure and functions of in situ communities, including prokaryotes and small eukaryotes, although physiologically active, dormant, and dead cells are simultaneously present in such samples. DNA, which has been preserved for a long time in deep-sea sediments, can maintain its genetic information (Dell’Anno and Danovaro, 2005; Corinaldesi et al., 2011). Therefore, eDNA approaches does not exclude data from the dead cells remained in the environment. In contrast, eRNA approaches targeting ribosomal RNA can distinguish between dead and live cells, as RNA decomposes quickly after cells die (Yamamoto et al., 1996; Takishita et al., 2010; Lejzerowicz et al., 2013). Thus, the common expectation that RNA is the most ephemeral molecule in environment, may change. Recent evidence suggest that RNA may be abundantly excreted by organisms and is sufficiently persistent in the environment to reconstruct community composition and gene expression (Cristescu, 2019). This assumption, however, remains largely untested in environmental studies, because comparative analysis with the conventional identification method using microscopic characterization of meiofaunal morphology requires time-consuming examination. In this study, we examined meiofaunal community compositions using co-extracted eDNA/eRNA collected from seamount habitats, and compared the results with those obtained using the conventional method.

The study was conducted at Xufu Guyot (JA06 Seamount) located in the high seas off Minami-Torishima Island in the Western Pacific Ocean (Fig. 1). We anticipated that the seamount formation from bottom to summit would provide habitats for various meiofauna, and that it may be a suitable sampling site for this comparative study of meiofauna communities. In this study, we addressed the following two questions:

  • 1)

    Are there any differences in meiofaunal assemblages in JA06 Seamount?

  • 2)

    Is there any discrepancy between the results obtained using the conventional microscopic method and those obtained using eDNA- and eRNA-based metabarcoding methods?

Section snippets

Sampling and sample processing

The R/V Hakurei underwent a cruise conducted by the Japan Oil, Gas and Metals National Corporation (JOGMEC) in May 2016. Sampling and observation were carried out at four stations on the base (B02, B02–2, B03, and B04) and two stations on the terrace (T02 and T02-2) of JA06 Seamount (Fig. 1b, Table 1). Sediment core samples were collected using a multiple corer (inner diameter: 7.4 cm) that could simultaneously collect up to eight sediment cores (Barnett et al., 1984). At B02, the collected

Microscopic analysis

Meiofaunal composition and diversity indices at each station are shown in Table 2, Table 3, respectively. Nematoda (including Desmoscolecid nematodes) was the most dominant taxon at all stations, and Copepoda was the second-most abundant taxon in all stations, except for B02 and B02-2 (Table 2). The meiofaunal density in terrace stations tended to be higher than that in base stations (Table 2), although the differences were not significant (t-test, p > 0.05). There were no significant

Evaluation of meiofaunal assemblage by conventional methods

We failed to detect differences in meiofaunal density, diversity, and assemblage structures between stations in JA06 Seamount using the conventional methods. Wei et al. (2010) compiled meiofaunal densities worldwide and elucidated the relationship between meiofaunal density and water depth. According to their equation, the predicted meiofaunal densities at the base (4500 m) and terrace (1300 m) of JA06 Seamount are 147 and 369 ind./10 cm2, respectively. However, the meiofaunal densities

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors are grateful to the officers, crew, and the Chief Scientist, Dr. Masashi Hayakawa of the R/V Hakurei cruise. Special thanks go to Dr. Kazuya Naito, Dr. Hideki Sugishima (JOGMEC), Dr. Hiroshi Amakawa, Dr. Yuya Tada (JAMSTEC), and Dr. Yohei Taketomo (Ocean Engineering & Development Co., Ltd.) who helped with sampling onboard. We would like to thank to the two anonymous reviewers who provided helpful comments on an earlier draft of the manuscript. Generic Mapping Tools (GMT, Wessel and

References (43)

  • J.G. Caporaso et al.

    PyNAST: a flexible tool for aligning sequences to a template alignment

    Bioinformatics

    (2010)
  • J.G. Caporaso et al.

    QIIME allows analysis of high-throughput community sequencing data

    Nat. Methods

    (2010)
  • A. Chao et al.

    A new statistical approach for assessing similarity of species composition with incidence and abundance data

    Ecol. Lett.

    (2005)
  • M.R. Clark et al.

    The ecology of seamounts: structure, function, and human impacts

    Annu. Rev. Mar. Sci.

    (2010)
  • C. Corinaldesi et al.

    Preservation, origin and genetic imprint of extracellular DNA in permanently anoxic deep-sea sediments

    Mol. Ecol.

    (2011)
  • M. Curini-Galletti et al.

    Patterns of diversity in soft-bodied meiofauna: dispersal ability and body size matter

    PloS One

    (2012)
  • R. Danovaro

    Methods for the Study of Deep-Sea Sediments, Their Functioning and Biodiversity

    (2010)
  • A. Dell'Anno et al.

    Extracellular DNA plays a key role in deep-sea ecosystem functioning

    Science

    (2005)
  • K.H. George

    Faunistic research on metazoan meiofauna from seamounts-a review

    Meiofauna Marina

    (2013)
  • O. Giere

    Meiobenthology

    (2009)
  • M. Guardiola et al.

    Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA

    PeerJ

    (2016)
  • Cited by (0)

    View full text