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Data from: From environmental DNA sequences to ecological conclusions: how strong is the influence of methodological choices?

Citation

Calderón-Sanou, Irene et al. (2020), Data from: From environmental DNA sequences to ecological conclusions: how strong is the influence of methodological choices?, Dryad, Dataset, https://doi.org/10.5061/dryad.0t39970

Abstract

Aim: Environmental DNA (eDNA) is increasingly used for analysing and modelling all-inclusive biodiversity patterns. However, the reliability of eDNA-based diversity estimates is commonly compromised by arbitrary decisions for curating the data from molecular artefacts. Here, we test the sensitivity of common ecological analyses to these curation steps, and identify the crucial ones to draw sound ecological conclusions. Location : Valloire, French Alps. Taxon: Vascular plants and Fungi. Methods: Using soil eDNA metabarcoding data for plants and fungi from twenty plots sampled along a 1000-m elevation gradient, we tested how the conclusions from three types of ecological analyses: (i) the spatial partitioning of diversity, (ii) the diversity-environment relationship, and (iii) the distance-decay relationship, are robust to data curation steps. Since eDNA metabarcoding data also comprise erroneous sequences with low frequencies, diversity estimates were further calculated using abundance-based Hill numbers, which penalize rare sequences through a scaling parameter, namely the order of diversity q (Richness with q=0, Shannon diversity with q~1, Simpson diversity with q=2). Results: We showed that results from different ecological analyses had varying degrees of sensitivity to data curation strategies and that the use of Shannon and Simpson diversities led to more reliable results. We demonstrated that MOTU clustering, removal of PCR errors and of cross-sample contaminations had major impacts on ecological analyses. Main conclusions: In the Era of Big Data, eDNA metabarcoding is going to be one of the major tools to describe, model and predict biodiversity in space and time. However, ignoring crucial data curation steps will impede the robustness of several ecological conclusions. Here, we propose a roadmap of crucial curation steps for different types of ecological analyses.

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