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      Feeding sites promoting wildlife-related tourism might highly expose the endangered Yunnan snub-nosed monkey ( Rhinopithecus bieti) to parasite transmission

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          Abstract

          An increasing number of studies have found that the implementation of feeding sites for wildlife-related tourism can affect animal health, behaviour and reproduction. Feeding sites can favour high densities, home range overlap, greater sedentary behaviour and increased interspecific contacts, all of which might promote parasite transmission. In the Yunnan snub-nosed monkey ( Rhinopithecus bieti), human interventions via provisioning monkeys at specific feeding sites have led to the sub-structuring of a group into genetically differentiated sub-groups. The fed subgroup is located near human hamlets and interacts with domesticated animals. Using high-throughput sequencing, we investigated Entamoeba species diversity in a local host assemblage strongly influenced by provisioning for wildlife-related tourism. We identified 13 Entamoeba species or lineages in faeces of Yunnan snub-nosed monkeys, humans and domesticated animals (including pigs, cattle, and domestic chicken). In Yunnan snub-nosed monkeys, Entamoeba prevalence and OTU richness were higher in the fed than in the wild subgroup. Entamoeba polecki was found in monkeys, pigs and humans, suggesting that this parasite might circulates between the wild and domestic components of this local social–ecological system. The highest proportion of faeces positive for Entamoeba in monkeys geographically coincided with the presence of livestock and humans. These elements suggest that feeding sites might indirectly play a role on parasite transmission in the Yunnan snub-nosed monkey. The implementation of such sites should carefully consider the risk of creating hotspots of disease transmission, which should be prevented by maintaining a buffer zone between monkeys and livestock/humans. Regular screenings for pathogens in fed subgroup are necessary to monitor transmission risk in order to balance the economic development of human communities dependent on wildlife-related tourism, and the conservation of the endangered Yunnan snub-nosed monkey.

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Contributors
                eve.afonso@univ-fcomte.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 August 2021
                4 August 2021
                2021
                : 11
                : 15817
                Affiliations
                [1 ]GRID grid.493090.7, ISNI 0000 0004 4910 6615, UMR CNRS 6249 Chrono-Environnement, , Université Bourgogne Franche-Comté, ; 16 Route de Gray, 25030 Besancon, France
                [2 ]GRID grid.464506.5, ISNI 0000 0000 8789 406X, Laboratory of Wildlife Management and Ecosystem Health, , Yunnan University of Finance and Economics, ; Kunming, China
                [3 ]GRID grid.15140.31, ISNI 0000 0001 2175 9188, Département de Biologie, , ENS Lyon, ; 46 allée d’Italie, 69007 Lyon, France
                [4 ]National Nature Reserve of BaiMa XueShan, Tacheng, China
                [5 ]Archéozoologie, archeobotanique: societes, pratiques et environnements (AASPE), Muséum National d’Histoire Naturelle, CNRS, CP55, 57 rue Cuvier, 75005 Paris, France
                Article
                95166
                10.1038/s41598-021-95166-5
                8339071
                34349189
                ef08b87c-ee55-4c24-9daa-b96beeffa24e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 March 2021
                : 7 July 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004794, Centre National de la Recherche Scientifique;
                Award ID: GDRI EHEDE
                Funded by: FundRef http://dx.doi.org/10.13039/501100004514, Yunnan University of Finance and Economics;
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                conservation biology,infectious diseases
                Uncategorized
                conservation biology, infectious diseases

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