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      Linking migration and microbiota at a major stopover site in a long-distance avian migrant

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          Abstract

          Migration is one of the most physical and energetically demanding periods in an individual bird’s life. The composition of the bird’s gut or cloacal microbiota can temporarily change during migration, likely due to differences in diets, habitats and other environmental conditions experienced en route. However, how physiological condition, migratory patterns, and other drivers interact to affect microbiota composition of migratory birds is still unclear. We sampled the cloacal bacterial microbiota of a long-distance migrant, the steppe buzzard ( Buteo buteo vulpinus), at an important spring stopover bottleneck in Eilat, Israel, after crossing the ca. 1800 km Sahara Desert. We examined whether diversity and composition of the cloacal microbiota varied with body condition, sex, movement patterns (i.e., arrival time and migration distance), and survival. Early arrival to Eilat was associated with better body condition, longer post-Eilat spring migration distance, higher microbial α-diversity, and differences in microbiota composition. Specifically, early arrivals had higher abundance of the phylum Synergistota and five genera, including Jonquetella and Peptococcus, whereas the phylum Proteobacteria and genus Escherichia-Shigella (as well as three other genera) were more abundant in later arrivals. While the differences in α-diversity and Escherichia-Shigella seem to be mainly driven by body condition, other compositional differences associated with arrival date could be indicators of longer migratory journeys (e.g., pre-fueling at wintering grounds or stopover habitats along the way) or migratory performance. No significant differences were found between the microbiota of surviving and non-surviving individuals. Overall, our results indicate that variation in steppe buzzard microbiota is linked to variation in migratory patterns (i.e., capture/arrival date) and body condition, highlighting the importance of sampling the microbiota of GPS-tracked individuals on multiple occasions along their migration routes to gain a more detailed understanding of the links between migration, microbiota, and health in birds.

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          The online version contains supplementary material available at 10.1186/s40462-022-00347-0.

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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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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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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
                nikki.thie@mail.huji.ac.il
                ran.nathan@mail.huji.ac.il
                Journal
                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                2051-3933
                7 November 2022
                7 November 2022
                2022
                : 10
                : 46
                Affiliations
                [1 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Movement Ecology Lab, , The Hebrew University of Jerusalem, ; Jerusalem, Israel
                [2 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Museum of Vertebrate Zoology, , University of California, Berkeley, ; Berkeley, CA USA
                [3 ]GRID grid.21106.34, ISNI 0000000121820794, School of Food and Agriculture, , University of Maine, ; Orono, ME USA
                [4 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Environmental Science Policy and Management, , University of California, Berkeley, ; Berkeley, CA USA
                [5 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, School of Mathematical Sciences, , University of KwaZulu-Natal, ; Durban, South Africa
                [6 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Integrative Biology, , University of California, Berkeley, ; Berkeley, CA USA
                [7 ]GRID grid.22098.31, ISNI 0000 0004 1937 0503, Present Address: Azrieli Faculty of Medicine, , Bar-Ilan University, ; Safed, Israel
                [8 ]GRID grid.7489.2, ISNI 0000 0004 1937 0511, Present Address: Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, , Ben-Gurion University of the Negev, ; Midreshet Ben-Gurion, Israel
                Article
                347
                10.1186/s40462-022-00347-0
                9641824
                36345043
                5fd13c3b-e1a1-467f-9380-e548a6636481
                © The Author(s) 2022

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 June 2022
                : 19 October 2022
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                Research
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                © The Author(s) 2022

                cloacal microbiota,long-distance migration,steppe buzzard,stopover bottleneck,gps-tracking

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