Plant–pollinator network architecture does not impact intraspecific microbiome variability

Gordon P. Smith; Hamutahl Cohen; Jocelyn F. Zorn; Quinn S. McFrederick; Lauren C. Ponisio

doi:10.1111/mec.17306

journal article Feb 27, 2024

Plant–pollinator network architecture does not impact intraspecific microbiome variability

Gordon P. Smith

Hamutahl Cohen

Jocelyn F. Zorn Quinn S. McFrederick Lauren C. Ponisio

Molecular Ecology Vol. 33 No. 7 · Wiley

View at Publisher Save 10.1111/mec.17306

Abstract

AbstractVariation in how individuals interact with food resources can directly impact, and be affected by, their microbial interactions due to the potential for transmission. The degree to which this transmission occurs, however, may depend on the structure of forager networks, which determine the community‐scale transmission opportunities. In particular, how the community‐scale opportunity for transfer balances individual‐scale barriers to transmission is unclear. Examining the bee–flower and bee–microbial interactions of over 1000 individual bees, we tested (1) the degree to which individual floral visits predicted microbiome composition and (2) whether plant–bee networks with increased opportunity for microbial transmission homogenized the microbiomes of bees within that network. The pollen community composition carried by bees was associated with microbiome composition at some sites, suggesting that microbial transmission at flowers occurred. Contrary to our predictions, however, microbiome variability did not differ based on transfer opportunity: bee microbiomes in asymmetric networks with high opportunity for microbial transfer were similarly variable compared to microbiomes in networks with more evenly distributed links. These findings suggest that microbial transmission at flowers is frequent enough to be observed at the community level, but that community network structure did not substantially change the dynamics of this transmission, perhaps due to filtering processes in host guts.

Topics

No keywords indexed for this article. Browse by subject →

References

71

[1]

10.1139/cjb-2013-0166

[2]

10.1098/rstb.2017.0102

[3]

10.1111/j.1574-6941.2012.01329.x

[4]

Multivariate dispersion as a measure of beta diversity

Marti J. Anderson, Kari E. Ellingsen, Brian H. McArdle

Ecology Letters 10.1111/j.1461-0248.2006.00926.x

[5]

10.1371/journal.pone.0090785

[6]

10.3732/apps.1600110

[7]

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

Nicholas A. Bokulich, Benjamin D. Kaehler, Jai Ram Rideout et al.

Microbiome 10.1186/s40168-018-0470-z

[8]

10.1016/j.tree.2011.01.009

[9]

10.1098/rstb.2013.0255

[10]

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Evan Bolyen, Jai Ram Rideout, Matthew R. Dillon et al.

Nature Biotechnology 10.1038/s41587-019-0209-9

[11]

10.21105/joss.01635

[12]

10.1111/syen.12470

[13]

10.1073/pnas.1307438110

[14]

DADA2: High-resolution sample inference from Illumina amplicon data

Benjamin J Callahan, Paul J McMurdie, Michael J Rosen et al.

Nature Methods 10.1038/nmeth.3869

[15]

10.1093/bioinformatics/bts342

[16]

10.1186/s40168-018-0530-4

[17]

10.1007/s00248-020-01549-y

[18]

10.1098/rspb.2021.1369

[19]

10.1038/s41559-017-0402-5

[20]

10.1016/j.jrurstud.2018.10.007

[21]

10.1098/rspb.1994.0176

[22]

10.1128/mbio.02164-15

[23]

10.1126/science.1227412

[24]

PHYLUCE is a software package for the analysis of conserved genomic loci

Brant C. Faircloth

Bioinformatics 10.1093/bioinformatics/btv646

[25]

10.1098/rspb.2019.0603

[26]

10.1086/721023

[27]

10.18637/jss.v022.i07

[28]

Bee declines driven by combined stress from parasites, pesticides, and lack of flowers

Dave Goulson, Elizabeth Nicholls, Cristina Botías et al.

Science 10.1126/science.1255957

[29]

10.1126/science.aat6016

[30]

10.1007/s10592-017-0937-7

[31]

10.1098/rspb.2013.2286

[32]

10.1016/j.mimet.2013.08.007

[33]

10.1016/j.tim.2017.11.002

[34]

10.1073/pnas.1905666116

[35]

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

K. Katoh, D. M. Standley

Molecular Biology and Evolution 10.1093/molbev/mst010

[36]

10.1016/j.cois.2020.09.007

[37]

10.1073/pnas.1216057111

[38]

10.3389/fevo.2018.00170

[39]

10.1016/j.cub.2020.06.011

[40]

10.1016/j.tree.2019.07.011

[41]

UniFrac: a New Phylogenetic Method for Comparing Microbial Communities

Catherine Lozupone, Rob Knight

Applied and Environmental Microbiology 10.1128/aem.71.12.8228-8235.2005

[42]

10.1111/j.1365-294x.2010.04959.x

[43]

10.1111/mec.13608

[44]

10.1007/s00248-018-1232-y

[45]

10.1007/s00248-016-0838-1

[46]

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

Bui Quang Minh, Heiko A Schmidt, Olga Chernomor et al.

Molecular Biology and Evolution 10.1093/molbev/msaa015

[47]

Oksanen J. Simpson G. L. Blanchet F. G. Kindt R. Legendre P. Minchin P. R. O'Hara R. Solymos P. Stevens M. H. H. Szoecs E. Wagner H. Barbour M. Bedward M. Bolker B. Borcard D. Carvalho G. Chirico M. De Caceres M. Durand S. …Weedon J.(2022).vegan: Community Ecology Package.https://CRAN.R‐project.org/package=vegan.

[48]

10.1146/annurev-ecolsys-110316-022919

[49]

APE: Analyses of Phylogenetics and Evolution in R language

Emmanuel Paradis, Julien Claude, Korbinian Strimmer

Bioinformatics 10.1093/bioinformatics/btg412

[50]

10.1111/brv.12366

Showing 50 of 71 references

Metrics

3

Citations

71

References

Details

Published: Feb 27, 2024
Vol/Issue: 33(7)
License: View

Authors

G

Gordon P. Smith

Department of Biology Williams College Williamstown Massachusetts USA; Department of Biology, Institute of Ecology and Evolution University of Oregon Eugene Oregon USA

H

Hamutahl Cohen

Department of Biology, Institute of Ecology and Evolution University of Oregon Eugene Oregon USA; University of California Cooperative Extension Ventura County, University of California Agriculture and Natural Resources Ventura California USA

J

Jocelyn F. Zorn

Department of Biology, Institute of Ecology and Evolution University of Oregon Eugene Oregon USA

Q

Quinn S. McFrederick

Department of Entomology University of California, Riverside Riverside California USA

L

Lauren C. Ponisio

Department of Biology, Institute of Ecology and Evolution University of Oregon Eugene Oregon USA

Funding

Foundation for Food and Agriculture Research Award: CA18‐SS‐0000000009

Cite This Article

Gordon P. Smith, Hamutahl Cohen, Jocelyn F. Zorn, et al. (2024). Plant–pollinator network architecture does not impact intraspecific microbiome variability. Molecular Ecology, 33(7). https://doi.org/10.1111/mec.17306

Plant–pollinator network architecture does not impact intraspecific microbiome variability

You May Also Like