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Data from: Fine-tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches

Citation

Pereira, Cátia Lúcio; Gilbert, M. Thomas P.; Araújo, Miguel Bastos; Matias, Miguel Graça (2021), Data from: Fine-tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches, Dryad, Dataset, https://doi.org/10.5061/dryad.k6djh9w71

Abstract

Accurate quantification of biodiversity can be demanding and expensive. Although environmental DNA (eDNA) metabarcoding can facilitate biodiversity assessments through non-invasive, cost-efficient, and rapid surveys, the approach struggles to outperform traditional morphological approaches in providing reliable quantitative estimates for surveyed species (e.g., abundance and biomass).

We present an integrated methodology for improving biodiversity surveys that pairs eDNA metabarcoding with morphological data, following a series of taxonomic and geographic filters. We demonstrate its power by applying it to a new spatiotemporal dataset generated on an Iberian-wide distributed aquatic mesocosm infrastructure that spans a wide biogeographic gradient.

By building upon the strengths that these two approaches offer, our framework improved taxonomic resolution for 30% of the taxa and enabled species’ traits (e.g., body-size) and abundance to be assigned to 85% of the taxa in hybrid datasets.

These results indicate that eDNA-based assessments can complement, but not always replace, conventional approaches. Integrating conventional and modern eDNA metabarcoding approaches, already available in the ecologist’s toolbox, will greatly enhance biodiversity assessments.

Methods

Fieldwork was conducted in the Iberian Ponds Network (IPN), a multi-region experimental facility using 192 freshwater pond mesocosms (hereafter ponds) distributed across six locations in the Iberian Peninsula, ranging from southern semi-arid (Murcia, Toledo), temperate (Évora, Porto) and alpine environments (Jaca and Madrid; Fig. S1), varying in annual average temperature and total precipitation (more details on the IPN and climate of each region in Supporting Information). Fieldwork was carried out in the six locations once a year every Spring from 2016 to 2018. Three different trophic groups (phytoplankton, zooplankton, and macroinvertebrates) were surveyed using standard sampling procedures for both eDNA metabarcoding and morphology. For details on sample collection and processing see Supporting Information.

Raw sequences resulted from eDNA metabarcoding approach were then analysed using DADA2 (Callahan et al., 2016). Taxonomic assignment was performed using BLASTn and the NCBI nt database (Benson et al., 2005) at 97% similarity, and classification was attributed using the software MEGAN Community Edition (Huson et al., 2016). Taxonomic assignments, and their associated amplicon sequence variants (ASVs), that returned incomplete taxonomy or unknown identifiers were excluded from further analysis. An additive strategy was used regarding the number of PCR replicates (Alberdi et al., 2017), i.e., through combining the sequences of all PCR replicates from one sample to maximize diversity detection (e.g., Leray & Knowlton, 2015).

Morphological identification was done to the lowest taxonomic level possible for all three trophic groups. Individuals’ enumeration was done using a light microscope (phytoplankton and rotifers) and a stereomicroscope (cladocerans, copepods and macroinvertebrates) using standard methods for each trophic group. Species’ biomasses were calculated by their dry weights, which was estimated from published allometric relationships using individual measurements.
Further details in paper and Supporting Information.

Funding

Fundação para a Ciência e a Tecnologia, Award: PTDC/BIA-BIC/0352/2014

Fundação para a Ciência e a Tecnologia, Award: PTDC/AAG-MAA/3764/2014

Fundação para a Ciência e a Tecnologia, Award: PTDC/CTA-AMB/30793/2017

FEDER, Award: POCI-01-0145-FEDER-007688

Horizon 2020 Framework Programme, Award: 731065

Horizon 2020 Framework Programme, Award: 871081

Fundação para a Ciência e a Tecnologia, Award: SFRH/BD/102020/2014