Comparing the Seasonal Diets of Buff-tailed Bumblebees and Honey Bees in Forest Landscapes: A Metabarcoding Approach
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PROVIDER: PRJEB83440 | ENA |
REPOSITORIES: ENA
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Project description:Honeydew produced from the excretion of plant-sucking insects (order Hemiptera) is a carbohydrate-rich material that is foraged by honey bees to integrate their diets. In this study, we used DNA extracted from honey as a source of environmental DNA to disclose its entomological signature determined by honeydew producing Hemiptera that was recovered not only from honeydew honey but also from blossom honey. We designed PCR primers that amplified a fragment of mitochondrial cytochrome c oxidase subunit 1 (COI) gene of Hemiptera species using DNA isolated from unifloral, polyfloral and honeydew honeys. Ion Torrent next generation sequencing metabarcoding data analysis assigned Hemiptera species using a customized bioinformatic pipeline. The forest honeydew honeys reported the presence of high abundance of Cinara pectinatae DNA, confirming their silver fir forest origin. In all other honeys, most of the sequenced reads were from the planthopper Metcalfa pruinosa for which it was possible to evaluate the frequency of different mitotypes. Aphids of other species were identified from honeys of different geographical and botanical origins. This unique entomological signature derived by environmental DNA contained in honey opens new applications for honey authentication and to disclose and monitor the ecology of plant-sucking insects in agricultural and forest landscapes.
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Project description:Stingless bees are major flower visitors in the tropics, but their foraging preferences and behavior are still poorly understood. Studying stingless bee interactions with angiosperms is methodologically challenging due to the high tropical plant diversity and inaccessibility of upper canopy flowers in forested habitats. Pollen DNA metabarcoding offers an opportunity of assessing floral visitation efficiently and was applied here to understand stingless bee floral resources spectra and foraging behavior. We analyzed pollen and honey from nests of three distantly related stingless bee species, with different body size and social behavior: Melipona rufiventris, Scaptotrigona postica and Tetragonisca angustula. Simultaneously, we evaluate the local floristic components through seventeen rapid botanical surveys conducted at different distances from the nests. We discovered a broad set of explored floral sources, with 46.3 plant species per bee species in honey samples and 53.67 in pollen samples. Plant families Myrtaceae, Asteraceae, Euphorbiaceae, Melastomataceae and Malpighiaceae dominated the records, indicating stingless bee preferences for abundant resources that flowers of these families provide in the region. Results also reinforce the preference of stingless bees for forest trees, even if only available at long distances. Our high-resolution results encourage future bee-plant studies using pollen and honey metabarcoding in hyper-diverse tropical environments.
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Project description:Managed honey bee colonies used for crop pollination are fed artificial diets to offset nutritional deficiencies related to land-use intensification and climate change. In this study, we formulated novel microalgae diets using Chlorella vulgaris and Arthrospira platensis (spirulina) biomass and fed them to young adult honey bee workers. Diet-induced changes in bee metabolite profiles were studied relative to a natural pollen diet using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics. Untargeted analyses of pollen- and microalgae-fed bees revealed significant overlap, with 248 shared features determined by LC-MS and 87 shared features determined by GC-MS. Further metabolomic commonalities were evident upon subtraction of unique diet features. Twenty-five identified metabolites were influenced by diet, which included complex lipids, essential fatty acids, vitamins, and phytochemicals. The metabolomics results are useful to understand mechanisms underlying favorable growth performance as well as increased antioxidant and heat shock protein gene expression in bees fed the microalgae diets. We conclude that the tested microalgae have potential as sustainable feed additives and as a source of bee health-modulating natural products. Metabolomics-guided diet development could eventually help tailor feed interventions to achieve precision nutrition in honey bees and other livestock animals.
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Project description:With the decline of bee populations worldwide, studies determining current wild bee distributions and diversity are increasingly important. Wild bee identification is often completed by experienced taxonomists or by genetic analysis. The current study was designed to compare two methods of identification including: (1) morphological identification by experienced taxonomists using images of field-collected wild bees and (2) genetic analysis of composite bee legs (multiple taxa) using metabarcoding. Bees were collected from conservation grasslands in eastern Iowa in summer 2019 and identified to the lowest taxonomic unit using both methods. Sanger sequencing of individual wild bee legs was used as a positive control for metabarcoding. Morphological identification of bees using images resulted in 36 unique taxa among 22 genera, and >80% of Bombus specimens were identified to species. Metabarcoding was limited to genus-level assignments among 18 genera but resolved some morphologically similar genera. Metabarcoding did not consistently detect all genera in the composite samples, including kleptoparasitic bees. Sanger sequencing showed similar presence or absence detection results as metabarcoding but provided species-level identifications for cryptic species (i.e., Lasioglossum). Genus-specific detections were more frequent with morphological identification than metabarcoding, but certain genera such as Ceratina and Halictus were identified equally well with metabarcoding and morphology. Genera with proportionately less tissue in a composite sample were less likely to be detected using metabarcoding. Image-based methods were limited by image quality and visible morphological features, while genetic methods were limited by databases, primers, and amplification at target loci. This study shows how an image-based identification method compares with genetic techniques, and how in combination, the methods provide valuable genus- and species-level information for wild bees while preserving tissue for other analyses. These methods could be improved and transferred to a field setting to advance our understanding of wild bee distributions and to expedite conservation research.
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Project description:PREMISE OF THE STUDY:Difficulties inherent in microscopic pollen identification have resulted in limited implementation for large-scale studies. Metabarcoding, a relatively novel approach, could make pollen analysis less onerous; however, improved understanding of the quantitative capacity of various plant metabarcode regions and primer sets is needed to ensure that such applications are accurate and precise. METHODS AND RESULTS:We applied metabarcoding, targeting the ITS2, matK, and rbcL loci, to characterize six samples of pollen collected by honey bees, Apis mellifera. Additionally, samples were analyzed by light microscopy. We found significant rank-based associations between the relative abundance of pollen types within our samples as inferred by the two methods. CONCLUSIONS:Our findings suggest metabarcoding data from plastid loci, as opposed to the ribosomal locus, are more reliable for quantitative characterization of pollen assemblages. Furthermore, multilocus metabarcoding of pollen may be more reliable than single-locus analyses, underscoring the need for discovering novel barcodes and barcode combinations optimized for molecular palynology.
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