Nowadays metabarcoding arises as a valuable alternative for biodiversity assessment because it combines extreme sensitivity with, potentially, the highest taxonomic resolution in a both cost- and time-effective methodology. In order to evaluate its capacity for estuarine plankton monitoring we performed a comparison between this approach and microscopy; the V9 region of the 18S rDNA gene was selected because of its broad amplification range among eukaryotes and previous success in marine plankton global studies, such as Tara Oceans and Biomarks initiatives [1, 2]. The estuary of Bilbao was one of the most contaminated in Europe but since 1979 it has undergone a water recovery program; this transition has allowed the recolonization by a mixture of neritic and estuarine species. Among them, there are Non-Indigenous Species (NIS) such as Acartia tonsa, that was first described in the this estuary in 2001 and became dominant the following year displacing other congeneric species [3, 4], and Pseudodiaptomus marinus, which was recently cited for the first time in this estuary [5] and whose effect cannot be predicted yet.

Validating Metabarcoding as a Tool for Eukaryotic Plankton Monitoring in Estuaries

David Abad1, Mikel Aguirre1, Aitor Albaina1, Aitor Laza-Martínez2, Ibon Uriarte3, Andone Estonba1

1Department of Genetics, Physical Anthropology & Animal Physiology. Faculty of Science and Technology. University of the Basque Country, UPV/EHU. Leioa, Spain. 2Phytoplankton group,Department of Plant Biology and Ecology. Faculty of Science and Technology. University of the Basque Country, UPV/EHU. Leioa, Spain. 3Zooplankton group, Department of Plant Biology and Ecology. Faculty of Science and Technology. University of the Basque Country, UPV/EHU. Gasteiz, Spain.

Introduction Results

Conclusions

1. Massana R, Gober A, Audic S, Bass D, Bittner L, Boutte C, et al. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environmental Microbiology. 2015. DOI: 10.1111/1462-2920.12955 2. de Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015. DOI: 10.1126/science.1261605 3. Albaina A, Villate F, Uriarte I. Zooplankton communities in two contrasting Basque estuaries (1999–2001): reporting changes associated with ecosystem health. J Plankton Res. 2009;31: 739–752. 4. Aravena G, Villate F, Uriarte I, Iriarte A, Ibanez B. Response of Acartia populations to environmental variability and effects of invasive congenerics in the estuary of Bilbao, Bay of Biscay. Estuar Coast Shelf Sci. 2009;83: 621-628. 5. 29. Albaina A, Uriarte I, Aguirre M, Abad D, Iriarte A, Villate F, Estonba A. Insights on the origin of invasive copepods colonizing Basque estuaries; a DNA barcoding approach. Mar Biodivers Rec. Submitted.6. Cristescu ME. From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends Ecol Evol. 2014;29: 566-571. 7. Feinstein LM, Sul WJ, Blackwood CB. Assessment of bias associated with incomplete extraction of microbial DNA from soil. J Appl Environ Microbiol. 2009;75: 5428-54433. 8. Engelbrektson A, Kunin V, Wrighton K, Zvenigorodsky N, Chen F, Ochman H, et al. Experimental factors affecting PCR-based estimates of microbialspecies richness and evenness. ISME J. 2010 May;4(5): 642-647 9. Clare EL. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evol Appl: 2014;7: 1144-1157. 10. Amend AS, Seifert KA, Bruns TD. Quantifying microbial communities with 454 pyrosequencing: does read abundance count? Mol Ecol. 2010;19: 5555-5565. 11. 13. Zhan A, Hulak M, Sylvester F, Huang X. Adebayo AA, Abbott CL, et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol Evol. 2013;4: 558-565. 12. Pochon X, Bott NJ, Smith KF, Wood SA. Evaluating detection limits of next-generation sequencing for the surveillance and monitoring of international marine pests. PLoS ONE. 2013;8: e73935.

Material and methodsSampling was carried out during summer and autumn in the 30 and 35 salinities of the estuary of Bilbao. Water was filtered through 200, 20 and 0.22 μm meshes and the 18S V9 (~150 bp) amplified and sequenced according to the Earth Microbiome Project protocols. Qiime v1.9 was used to assign reads to Operational Taxonomic Units (OTUs); 831 were identified with a 99% similarity threshold. 1) OTUs were classified into 33 categories, including one for not assigned reads (Fig 1). 2) Forty-one OTUs were included in the multivariate analysis condensing the three size-factions together (Fig 2). 3) Correlations between metabarcoding and microscopy when comparing relative abundances of every taxon within a particular sample (Fig 3).4) Comparison of metabarcoding and microscopy when assessing abundances for two NIS: Acartia tonsa and Pseudodiaptomus marinus (Fig 4).

The somewhat reduced performance of this approach for the lowest size fractions is mainly related to 18s V9 database incompleteness for these organisms. This highlights that DNA-barcoding is necessary and complementary to metabarcoding [6]. Metabarcoding replicated the Bilbao estuary plankton community temporal and spatial patterns. The lack of correlation between relative abundances could be explained by technical biases introduced during the DNA extraction [7] or PCR amplification step [8]. The Copy Number Variation (CNV) associated to multi-copy genes, such as rRNA ones, has been suggested as one of the main factors limiting the quantitative value of metabarcoding [9]. In the meantime, metabarcoding targeting multi-copy genes will remain as a semi-quantitative approach [10].The present study demonstrated the suitability of metabarcoding for early detection of NIS at extremely low abundances (Fig 4), confirming previous studies [11, 12]. The reasons behind this are a) the ability to analyze higher sample volumes and b) the capacity to take into account individuals at any life stage, such as eggs or nauplius larvae. All of this suggests that metabarcoding could be a powerful tool, if implemented in plankton monitoring, for early detection of NIS or plankton biodiversity shifts.

The percentage of not assigned reads was lower at the higher size-fractions. While maxillopoda predominated at those size fractions, a more diverse assemblage characterized the 0.22-20 µm one. Copepods represented 48.6, 36 and 2.3% while phytoplankton groups <0.1, 1.6 and 32.7% for each size-fraction respectively. The CCA explained 57.7% of variance. Main environmental factors were salinity and date. While a reduced number of brackish water species, such as the copepods A. tonsa and Calanipeda aquaedulcischaracterized the 30 community, a higher number of OTUs encompassing mostly neritic taxa conformed the 35 water mass. Correlations were significant in most of the cases and with no noticeable effect when comparing against microscopy-based counts or biomass. Whilst similar relative abundances were found for A. tonsa in the 30 water mass by both approaches (Fig 4a), it was only detected by metabarcoding in the 35 salinity (Fig 4b). Regarding P. marinus, detection was favorable to metabarcoding in six out of eight cases meanwhile only in two its presence was detected by microscopy (Fig 4c, d).

Bibliography

Environmental Sample (e.g. Filtered Seawater)

DNA extraction

PCR Amplification

Taxonomic Compositon Bioinformatics Sequencing

Scan me and get the

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Figure 1. Proportion of taxonomic groups in each sample based on the metabarcoding approach. Figure 2. CCA of the most abundant OTUs.

Figure 4. Comparison of metabarcoding and microscopy when assessing abundances for NIS.

Figure 3. Among taxa comparisons.

Blue arrows and green ellipses indicate temporal and spatial cycles respectively.