Metabarcoding the Antarctic Peninsula biodiversity using a multi-gene approach

Abstract

Marine sediment communities are major contributors to biogeochemical cycling and benthic ecosystem functioning, but they are poorly described, particularly in remote regions such as Antarctica. We analysed patterns and drivers of diversity in metazoan and prokaryotic benthic communities of the Antarctic Peninsula with metabarcoding approaches. Our results show that the combined use of mitochondrial Cox1, and 16S and 18S rRNA gene regions recovered more phyla, from metazoan to non-metazoan groups, and allowed correlation of possible interactions between kingdoms. This higher level of detection revealed dominance by the arthropods and not nematodes in the Antarctic benthos and further eukaryotic diversity was dominated by benthic protists: the world’s largest reservoir of marine diversity. The bacterial family Woeseiaceae was described for the first time in Antarctic sediments. Almost 50% of bacteria and 70% metazoan taxa were unique to each sampled site (high alpha diversity) and harboured unique features for local adaptation (niche-driven). The main abiotic drivers measured, shaping community structure were sediment organic matter, water content and mud. Biotic factors included the nematodes and the highly abundant bacterial fraction, placing protists as a possible bridge for between kingdom interactions. Meiofauna are proposed as sentinels for identifying anthropogenic-induced changes in Antarctic marine sediments.

Spatio‐temporal patterns of eukaryotic biodiversity in shallow hard‐bottom communities from the West Antarctic Peninsula revealed by DNA metabarcoding

“…Here we present a pioneer study to establish the molecular biodiversity baselines of shallow hardbottom habitats from the WAP. This preliminary study, similar to what Fonseca et al (2017Fonseca et al ( , 2022 found in soft-bottom habitats, reveals that most of the benthic biodiversity found in Antarctic…”

“…Local patchiness and structure within these communities are probably a consequence of a combination of several biotic and abiotic factors (i.e. ice disturbance, food supply, competition, seabed topography and life-cycle strategies; Clarke et al, 2021;Fonseca et al, 2022).…”

“…Different DNA metabarcoding approaches have been recently applied to assess biodiversity from the SO. These studies have mainly focussed on microbial communities (Flaviani et al, 2018;Luria et al, 2014), fungal diversity (Ogaki et al, 2021), sponge microbiomes (Castro-Fernández et al, 2023;Sacristán-Soriano et al, 2020), metazoans (Clarke et al, 2021;Vause et al, 2019) and benthic meiofauna (Brannock et al, 2018;Fonseca et al, 2017Fonseca et al, , 2022. However, studies analysing community DNA extracted from hard-bottom communities are lacking, which would provide a powerful tool to complement existing approaches, and a timely opportunity to gain insight into alpha and beta-diversity patterns of Antarctic hard-bottom communities, by providing the needed baselines to assess the resilience of these ecosystems in the context of global warming.…”

Environmental RNA as a Tool for Marine Community Biodiversity Assessments

“…A study evaluating arctic benthic diversity similarly found that COI detected fewer taxa than 18S using eDNA metabarcoding, and there was only ~ 40% taxa overlap between markers at the class level 42 . In the current study, the COI marker using both nucleic acid templates yielded a higher percentage of unassigned taxa after filtering for presence in the majority of mesocosms compared to 18S.…”

State-of-the-art of data analyses in environmental DNA approaches towards its applicability to sustainable fisheries management

“…Case-study examples of the applicability of both eDNA approaches to these four tasks are presented in BOX 1. eDNA metabarcoding can be used to describe species richness (e.g. in Leray and Knowlton, 2015;McClenaghan et al, 2020;Boulanger et al, 2021;Liu and Zhang, 2021;Sato et al, 2021;Yu et al, 2021;Fonseca et al, 2022;Good et al, 2022;Keck et al, 2022), surveillance of the presence/absence of target species such as invasive, or endangered (e.g. in Jerde et al, 2011;Boussarie et al, 2018;Holman et al, 2019;Bonfil et al, 2021;Manfrin et al, 2022), and monitor changes in species composition (e.g.…”