ORCID Profile
0000-0002-9096-3008
Current Organisation
Staatliches Museum für Naturkunde Stuttgart
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Publisher: Wiley
Date: 07-09-2022
Abstract: The microbiomes associated with bee nests influence colony health through various mechanisms, although it is not yet clear how honeybee congeners differ in microbiome assembly processes, in particular the degrees to which floral visitations and the environment contribute to different aspects of ersity. We used DNA metabarcoding to sequence bacterial 16S rRNA from honey and stored pollen from nests of 4 honeybee species ( Apis cerana , A. dorsata , A. florea , and A. laboriosa ) s led throughout Yunnan, China, a global bio ersity hotspot. We developed a computational pipeline integrating multiple databases for quantifying key facets of ersity, including compositional, taxonomic, phylogenetic, and functional ones. Further, we assessed candidate drivers of observed microbiome dissimilarity, particularly differences in floral visitations, habitat disturbance, and other key environmental variables. Analyses revealed that microbiome alpha ersity was broadly equivalent across the study sites and between bee species, apart from functional ersity which was very low in nests of the reclusive A. laboriosa . Turnover in microbiome composition across Yunnan was driven predominantly by pollen composition. Human disturbance negatively impacted both compositional and phylogenetic alpha ersity of nest microbiomes, but did not correlate with microbial turnover. We herein make progress in understanding microbiome ersity associated with key pollinators in a bio ersity hotspot, and provide a model for the use of a comprehensive informatics framework in assessing pattern and drivers of ersity, which enables the inclusion of explanatory variables both subtly and fundamentally different and enables elucidation of emergent or unexpected drivers.
Publisher: Springer Science and Business Media LLC
Date: 06-01-2020
Publisher: Cold Spring Harbor Laboratory
Date: 02-07-2023
DOI: 10.1101/2023.06.30.547152
Abstract: Species occurrence data are foundational for research, conservation, and science communication. But the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeDC , a new R package, and a global bee occurrence dataset to address this issue. We combined .7 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeDC R -workflow. Specifically, we harmonised species names following established global taxonomy, country, and collection date and we added record-level flags for a series of potential quality issues. These data are provided in two formats, “completely-cleaned” and “flagged-but-uncleaned”. Our data cleaning process is open and documented for transparency and reproducibility. The BeeDC package and R Markdown are provided, and will be improved and updated regularly. By publishing reproducible R workflows and globally cleaned datasets we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.
Publisher: Authorea, Inc.
Date: 19-09-2023
Publisher: Elsevier BV
Date: 07-2020
Location: United States of America
No related grants have been discovered for Michael Orr.