DNA metabarcoding is becoming the tool of choice for biodiversity assessment across taxa and environments. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines to remove experimental noise exist. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and chosen filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools. Here, we present metabaR, an r package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, that is, contaminants and tag‐jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitates assessment of the appropriateness of data curation filtering thresholds. metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre‐analysed with any bioinformatic pipeline. Its outputs are easily usable for downstream analyses with any ecological r package. metabaR complements existing bioinformatics pipelines by providing scientists with a variety of functions to effectively clean DNA metabarcoding data and avoid serious misinterpretations. It thus offers a promising platform for automatised data quality assessments of DNA metabarcoding data for environmental research and biomonitoring.

Leaf-inhabiting fungal and bacterial endophytes are at their most diverse in tropical rainforest plant hosts, with some influencing host plant fitness as either symbionts or pathogens. Endophyte activity and community composition is thought to depend on competition amongst co-occurring species for resources. Here, we reveal the strength of competitive interactions between endophytes by combining e-DNA metabarcoding to characterize the community with culturing assays to ascertain their potential activity. The endophyte community associated with the understory palm Astrocaryum sciophilum was determined by extracting eDNA from 120 leaflets of eight plants located in a lowland rainforest in French Guiana. This revealed 516 fungal and 606 bacterial Operational Taxonomic Units (OTUs). Co-occurrence analysis of the most abundant OTUs revealed that direct fungal-fungal interactions were overall more negative than bacterial-bacterial interactions. This was confirmed by C-score calculations for the whole endophyte community, revealing significantly greater levels of species segregation for fungi but not bacteria when compared with simulated random communities. Following the culturing of 131 fungal, and 66 bacterial Astrocaryum endophytes collected from the same leaves, Minimal Inhibitory Concentrations of ethyl acetate culture extracts were evaluated against the fungus Trichophyton rubrum and the bacterium Staphylococcus aureus. Overall, a minority of extracts demonstrated antipathogen activity, with greater antifungal activity expression for both fungi and bacteria when compared with antibacterial activity. In order to explore whether this activity translated into competitive structuring of the community, a BLAST was conducted to match metabarcoding sequences with Sanger derived culture sequences. This demonstrated that cultures displaying antipathogen activity were significantly more likely to co-occur with fungi if they were bacteria, and bacteria if they were fungi. Nevertheless, overall correlation values averaged around zero. These results indicate that for Astrocaryum palms, fungal endophytes are likely to play a greater role in determining colonization success of subsequent endophytes whether these be fungal or bacterial, but that overall endophyte communities do not display strong patterns of community structuring through competition. Endophyte communities are thus likely to reach relative stasis in mature leaves, where the diverse community of fungi and bacteria amongst other factors prevent the subsequent establishment of pathogens.

DNA metabarcoding is becoming the tool of choice for biodiversity studies across taxa and large-scale environmental gradients. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines removing experimental noise have been designed to address this issue. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and the appropriateness of filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools. Here, we present metabaR , an R package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, i.e. reagent contaminants and tag-jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitate assessment of the appropriateness of data curation filtering thresholds. metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre-analysed with any bioinformatic pipeline. Its outputs are easily usable for downstream analyses with any ecological R package. metabaR complements existing bioinformatics pipelines by providing scientists with a variety of functions with customisable methods that will allow the user to effectively clean DNA metabarcoding data and avoid serious misinterpretations. It thus offers a promising platform for automatised data quality assessments of DNA metabarcoding data for environmental research and biomonitoring.