Environmental DNA (eDNA) metabarcoding is a promising approach to identify species within communities and can be used to evaluate biodiversity through a variety of estimators (Boulanger et al., 2021;Deiner et al., 2020;Pawlowski et al., 2018). The approach is based on the collection of environmental samples (e.g., soil, air or water) that contain the target organisms' DNA. After DNA extraction, DNA amplification with primers designed for a specific taxonomic group is performed and submitted to high-throughput sequencing (Deiner

In developed countries, dogs and cats frequently suffer from obesity. Recently, gut microbiota composition in humans has been related to obesity and metabolic diseases. This study aimed to evaluate changes in body composition, and gut microbiota composition in obese Beagle dogs after a 17-wk BW loss program. A total of six neutered adult Beagle dogs with an average initial BW of 16.34 ± 1.52 kg and BCS of 7.8 ± 0.1 points (9-point scale) were restrictedly fed with a hypocaloric, low-fat and high-fiber dry-type diet. Body composition was assessed with dual-energy X-ray absorptiometry scan, before (T0) and after (T1) BW loss program. Individual stool samples were collected at T0 and T1 for the 16S rRNA analyses of gut microbiota. Taxonomic analysis was done with amplicon-based metagenomic results, and functional analysis of the metabolic potential of the microbial community was done with shotgun metagenomic results. All dogs reached their ideal BW at T1, with an average weekly proportion of BW loss of -1.07 ± 0.03% of starting BW. Body fat (T0, 7.02 ± 0.76 kg) was reduced by half (P < 0.001), while bone (T0, 0.56 ± 0.06 kg) and muscle mass (T0, 8.89 ± 0.80 kg) remained stable (P > 0.05). The most abundant identified phylum was Firmicutes (T0, 74.27 ± 0.08%; T1, 69.38 ± 0.07%), followed by Bacteroidetes (T0, 12.68 ± 0.08%; T1, 16.68 ± 0.05%), Fusobacteria (T0, 7.45 ± 0.02%; T1, 10.18 ± 0.03%), Actinobacteria (T0, 4.53 ± 0.02%; T1, 3.34 ± 0.01%), and Proteobacteria (T0, 1.06 ± 0.01%; T1, 1.40 ± 0.00%). At genus level, the presence of Clostridium, Lactobacillus, and Dorea, at T1 decreased (P = 0.028), while Allobaculum increased (P = 0.046). Although the microbiota communities at T0 and T1 showed a low separation level when compared (Anosim's R value = 0.39), they were significantly biodiverse (P = 0.01). Those differences on microbiota composition could be explained by 13 genus (α = 0.05, linear discriminant analysis (LDA) score > 2.0). Additionally, differences between both communities could also be explained by the expression of 18 enzymes and 27 pathways (α = 0.05, LDA score > 2.0). In conclusion, restricted feeding of a low-fat and high-fiber dry-type diet successfully modifies gut microbiota in obese dogs, increasing biodiversity with a different representation of microbial genus and metabolic pathways.

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.