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Arthropods play a vital role in ecosystems; yet, their distributions remain poorly understood, particularly in mountainous regions. This study delves into the modelling of the distribution of 31 foliar arthropod genera in the French Alps, using a comprehensive approach encompassing multi-trophic sampling, community DNA metabarcoding and random forest models. The results underscore the significant importance of vegetation structure, such as herbaceous vegetation density, and forest density and heterogeneity, along with climate, in shaping the distributions of most arthropods. These responses to environmental gradients are consistent across trophic groups, with the exception of nectarivores, whose distributions are more sensitive to landscape structure and water availability. By leveraging community DNA metabarcoding, this study sheds light on the understudied drivers of arthropod distributions, emphasizing the importance of modelling across diverse trophic groups to anticipate arthropod responses to global change.

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Bioinformatic analysis of eDNA metabarcoding data is a crucial step toward rigorously assessing biodiversity. Many programs are now available for each step of the required analyses, but their relative abilities at providing fast and accurate species lists have seldom been evaluated. We used simulated mock communities and real fish eDNA metabarcoding data to evaluate the performance of 13 bioinformatic programs and pipelines to retrieve fish occurrence and read abundance using the 12S mt rRNA gene marker. We used four indices to compare the outputs of each program with the simulated samples: sensitivity, F-measure, root-mean-square error (RMSE) on read relative abundances, and execution time. We found marked differences among programs only for the taxonomic assignment step, both in terms of sensitivity, F-measure and RMSE. Running time was highly different between programs for each step. The fastest programs with best indices for each step were assembled into a pipeline. We compared this pipeline to pipelines constructed from existing toolboxes (OBITools, Barque, and QIIME 2). Our pipeline and Barque obtained the best performance for all indices and appear to be better alternatives to highly used pipelines for analysing fish eDNA metabarcoding data when a complete reference database is available. Analysis on real eDNA metabarcoding data also indicated differences for taxonomic assignment and execution time only. This study reveals major differences between programs during the taxonomic assignment step. The choice of algorithm for the taxonomic assignment can have a significant impact on diversity estimates and should be made according to the objectives of the study.

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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.

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