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Metabarcoding techniques have revolutionized ecological research in recent years, facilitating the differentiation of cryptic species and revealing previously hidden diversity. In the current scenario of climate change and ocean acidification, biodiversity loss is one of the main threats to marine ecosystems. Here, we explored the effects of ocean acidification on marine benthic communities using DNA metabarcoding to assess the diversity of algae and metazoans. Specifically, we examined the natural pH gradient generated by the Fuencaliente CO2 vent system, located near La Palma Island (Canary Islands). High-resolution COI metabarcoding analyses revealed high levels of taxonomic diversity in an acidified natural area for the first time. This high number of species arises from the detection of small and cryptic species that were previously undetectable by other techniques. Such species are apparently tolerant to the acidification levels expected in future oceans. Hence and following our results, future subtropical communities are expected to keep high biodiversity values under an acidification scenario, although they will tend toward overall miniaturization due to the dominance of small algal and invertebrate species, leading to changes in ecosystem functions.

Additional file 1 Format: .pdf. Details of the dataset used in the analysis of the article, including information of the sampling localities in the Iberian Peninsula and the sample processing steps prior to sequencing.

Additional file 2 Format: .pdf. Comparison of DADA2 on paired and unpaired reads.

Additional file 3 Format: .pdf. Details of the taxonomic benchmarking.

Ecosystem biomonitoring is crucial for proper management of natural communities during the Anthropocene era. With the advent of new sequencing technologies, DNA metabarcoding has been proposed as a game-changing tool for biomonitoring. In this Thesis we plead for the use of metabarcoding of a highly variable marker to infer not only the interspecies but also the intraspecies variability to assess both biogeographic, at the species level, and metaphylogeographic patterns, at the haplotype level. We focused on highly complex hard-substratum benthic littoral communities. The term "Metaphylogeography", coined in this Thesis, refers to the study of phylogeographic patterns of many species at the same time using metabarcoding data. However, as of the start of this Thesis, only a few studies had tested the metabarcoding method to directly characterize the whole eukaryotic community in highly diverse benthic ecosystems. This required to set up and calibrate methods for these communities as a prior step. We first evaluated both the sampling methods and the bioinformatic pipelines. We assessed the viability of detecting the environmental DNA released from the benthic community into the adjacent water layer using metabarcoding of COI with highly degenerated primers targeting the whole eukaryotic community. We sampled water from 0 to 20m from shallow rocky benthic communities and compared the DNA signal with the results obtained from metabarcoding directly the benthic communities by traditional quadrat sampling. We also designed a pipeline combining clustering and denoising methods to treat metabarcoding data of COI. We considered the entropy of each codon position of this coding fragment both to improve the detection of spurious sequences and to calibrate the best performing parameters of the software used. In addition, we created our own denoising program, DnoisE, to incorporate information on the codon position. This new code and parameter calibration were required as the commonly used bioinformatic pipelines had been designed and tested mostly for less variable ribosomal fragments and, particularly, in prokaryotes. Results showed that the DNA signal from the benthos decreased with the distance but was too weak for a correct assessment of benthic biodiversity. The proportion of eukaryotic DNA sequenced was also very low in water samples due to the amplification of prokaryotic DNA. We thus concluded that the benthos must be sampled directly to properly assess its biodiversity composition. The new bioinformatic developments allowed us to propose new methods for processing metabarcoding reads, combining clustering and denoising steps, and to set optimal values for the parameters used at each step. These contributions effectively expanded the field to the novel analysis of inter- and intraspecies genetic variability with metabarcoding data. Finally, we applied this methodology to 12 localities of the Western Iberian Coast along two well studied fronts, the Almeria-Oran Front (AOF) and the Ibiza Channel (IC). We analysed the species and haplotypes using the COI barcode. From a biogeographical perspective, the AOF had a strong effect in separating regions, while IC effect was less marked, but still half of the MOTUs were found in only one side of this divide. For the metaphylogeographic analysis, only 10% of the MOTUs could be used. However, they showed a good separation between populations of the three regions with a strong effect of the AOF break. The IC, on the other hand, seemed to be more a transitional zone than a fixed break. This Thesis laid the ground for the efficient use of metabarcoding in the biomonitoring of benthic reef habitats, allowing community composition, β-diversity, and biogeographic patterns to be analysed in a fast, repeatable, and cost-efficient way. We also developed the metaphylogeography approach as a new tool to assess population genetic structure at the community-wide level., This Thesis has been supported by a grant (PRE2018-085664) associated to the PopCOmics project (CTM2017-88080-C2-1-R) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/501100011033) and by “ESF Investing in your future”. The research performed has been funded by projects PopCOmics and MARGECH (PID2020-118550RB, MCIN/AEI/10.13039/501100011033), and by project BIGPARK (OAPN, 2462/2017) of the Spanish Ministry of Ecological Transtion and Demographic Challenge.

Marine biodiversity underpins ecosystem health and societal well-being. Preservation of biodiversity hotspots is a global challenge. Molecular tools, like DNA barcoding and metabarcoding, hold great potential for biodiversity monitoring, possibly outperforming more traditional taxonomic methods. However, metabarcoding-based biodiversity assessments are limited by the availability of sequences in barcoding reference databases; a lack thereof results in high percentages of unassigned sequences. In this study we (i) present the current status of known vs. barcoded marine species at a global scale based on online taxonomic and genetic databases; and (ii) compare the current status with data from ten years ago. Then we analyzed occurrence data of marine animal species from five Large Marine Ecosystems (LMEs) classified as biodiversity hotspots, to identify any consistent disparities in COI barcoding coverage between geographic regions and at phylum level. Barcoding coverage varied among LMEs (from 36.8% to 62.4% COI-barcoded species) and phyla (from 4.8% to 74.7% COI-barcoded species), with Porifera, Bryozoa and Platyhelminthes being highly underrepresented, compared to Chordata, Arthropoda and Mollusca. We demonstrate that although barcoded marine species increased from 9.5% to 14.2% since the last assessment in 2011, about 15,000 (corresponding to 7.8% increase) new species were described from 2011 to 2021. The next ten years will thus be crucial to enroll concrete collaborative measures and long term initiatives (e.g., Horizon 2030, Ocean Decade) to populate barcoding libraries for the marine realm., the Department of Biological, Geological and Environmental Sciences (BiGeA) of the University of Bologna (UniBo). The CoMBoMed initiative was supported by the European Marine Research Network (EUROMARINE Network), the Inter-Departmental Research Centre for Environmental Sciences (CIRSA – UniBo), the Cultural Heritage Department (DBC - UniBo, https://beniculturali.unibo.it/it), the Fondazione Flaminia and the ERANet Mar-Tera Project SEAMoBB (Solutions for sEmi-Automated Monitoring of Benthic Biodiversity).

The development of high-throughput sequencing technologies has provided ecologists with an efficient approach to assess biodiversity in benthic communities, particularly with the recent advances in metabarcoding technologies using universal primers. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of obtained sequences into Molecular Operational Taxonomic Units (MOTUs). The inferred MOTUs can then be used to estimate species diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into MOTUs, relatively little guidance is available on their relative performance. We focused our study in the benthic community from a natural CO2 vent present in the Canary Islands, as it can be used as a natural laboratory in which to investigate the impacts of chronic ocean acidification. Here, we propose a pipeline for studying this community using a fragment of the mitochondrial cytochrome c oxidase I (COI) sequence. We compared two DNA extraction methods, two clustering methods and validated a robust method to eliminate false positives. We found that we can obtain optimal results purifying DNA from 0.3 g of sample. Using the step-by-step aggregation algorithm implemented in SWARM for clustering yields similar results as using the Bayesian clustering method of CROP, in much less time. We introduced the new algorithm MINT (Multiple Intersection of N Tags), in order to eliminate false positives due to random errors produced before or after the sequencing. Our results show that a fully-automated analysis pipeline can be used for assessing biodiversity of marine benthic communities using COI as a metabarcoding marker in an objective, accurate and affordable manner.