Using Spatial-Temporal filtering and improved barcoding tools to improve
the ecological relevance of pollen meta-barcoding
- Reed Benkendorf,
- Emily Woodworth,
- Paul Caradonna,
- Jane Ogilvie,
- Sophie Taddeo,
- Jeremie Fant
Reed Benkendorf
Chicago Botanic Garden
Corresponding Author:reedbenkendorf2021@u.northwestern.edu
Author ProfileAbstract
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1) DNA metabarcoding has been successful for the rapid identification of
species in ecological assemblages, including identifying interspecific
interactions among species. However, advances in metabarcoding plants
have been hampered due to a lack of universal gene regions that work
across all taxa, limiting the applications of metagenomics in ecology
more broadly. 2) To circumvent these limitations, we propose a
spatio-temporal approach that combines multi-gene barcoding with
existing plant occurrence databases, species distribution models, and
phenological analyses to generate a shortened list of candidate species
to increase metabarcoding accuracy. To validate the ecological accuracy
of our methodological framework, we compared the results of the DNA
metabarcoding from pollen loads of wild bumble bees to long-term field
observations of bee-plant interactions, and visual pollen
identification. 3) We show that DNA metabarcoding of the plant species
included in bumble bee pollen loads was most accurate when combined with
a candidate taxa list of plant species flowering in the area when the
bumble bees were foraging, which improved the accuracy and taxonomic
precision of 77.5% of samples. 4) With the recent proliferation of
species occurrence and phenology data in tandem with advances in
computing and software development, we believe that spatio-temporal
filtering provides a simple approach for interpreting metagenomic
studies globally. Additionally, we demonstrate that the Angiosperms 353
probes offer significant promise for metagenomics projects globally,
including metabarcoding to reveal species interactions within complex
communities. Further, our approach demonstrates that integrating DNA
metabarcoding is most accurate and powerful when combined with local
ecological data.