Ecological and evolutionary drivers of stingless bee honey variation at the global scale
Data files
Apr 29, 2025 version files 329.27 MB
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Chemodiv_package_Compounds.csv
2.39 KB
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Data_Global_EcoEvo_Honey.csv
84.52 KB
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Environmental_variables.zip
3.61 MB
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map.zip
325.55 MB
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README.md
10.21 KB
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SBphylo.con
12.42 KB
Abstract
Stingless bee honey (SBH) is a prime natural product consumed and used for diverse medicinal and traditional purposes by local communities across the (sub-)tropics. The drivers of its compositional variation within and among species remain poorly understood, although this could inform broader and less explored eco-evolutionary theories. In our study, we aimed to disentangle the roles of different drivers of SBH compositional variation at both continental and global scales. Using a comparative approach involving parallel analyses of honeys produced by Apis mellifera as reference points, we specifically aimed to characterize the relative importance of SBH variation in relation to stingless bee species and evolutionary history, environmental conditions and biogeography. We collected and analysed 100 honey samples from A. mellifera around the world and 150 samples from twenty-three genera of stingless bees equally distributed in the Afrotropics (n = 50), the Neotropics (n = 50) and the Indo-Malayan region/Oceania (n = 50). We performed honey profiling using H1-NMR spectroscopy to quantify 36 compounds grouped into five categories : sugars, organic acids, amino acids, fermentation markers and anti-microbial compounds. Our results showed a clear differentiation between the chemical composition and functional diversity of A. mellifera and stingless bee honeys, mainly due to the production of a range of bioproducts during sugar fermentation. The study of compositional variation of stingless bee honey showed that the role of ecological and evolutionary drivers and their joint effects varied within each tropical region, preventing the identification of a clear continental, phylogenetic or ecological pattern. Additionally, a significant part of the variation remained unexplained, presumably reflecting the various natural factors and human colony management that can affect honey properties. We provide the first global and comprehensive characterisation of stingless bee honey composition, a prerequisite for defining and accepting SBH in the different Codex Alimentarius. The chemical complexity of this product highlighted in this study requires either broad international standards or precise local quality labels. We also highlight the need for more interdisciplinary ethnographic studies on non-food uses of honeys, and to encourage trans-sectoral research adopting a holistic approach to investigate stingless bee honey characteristics.
Dataset overview
This README outlines the data files, scripts, and figures used in the analysis, allowing for replication of results and further exploration. In this study, we aimed to disentangle the roles of evolutionary and environmental drivers of SBH compositional variation using a sampling design that combines honey profiling by H1-NMR spectroscopy with the collection of honeys from honey bees and stingless bees. The main dataset includes the compositional profiles of 240 honeys quantified by Quality Services International GmbH (QSI, Bremen, Germany) following the method described in Noiset et al. (2022; see below).
Corresponding author information
Name: Pierre Noiset
ORCID: https://orcid.org/0000-0003-0141-6051
Affiliation: Agroecology Lab, Université Libre de Bruxelles, Belgium
email: pierre.noiset@ulb.be
Related publication
Noiset, P., Cabirol, N., Rojas-Oropeza, M., Warrit, N., Nkoba, K., & Vereecken, N. J. 2022. Honey compositional convergence and the parallel domestication of social bees. Scientific Reports, 12(1), 18280. https://doi.org/10.1038/s41598-022-23310-w
Funding information
TThis work was supported by the Fonds National de la Recherche Scientifique
(F.R.S-FNRS, T.0255.20), the Fonds David and Alice Van Buuren
& Fondation Jaumotte-Demoulin.
DESCRIPTION OF DIRECTORIES AND FILES
The repository is organized into the following subdirectories:
/data: Contains the primary data used in the analysis, including metadata and compound identifiers.
/scripts: Contains R scripts necessary for reproducing the analysis.
/figures: Contains all main text and supplementary figures referenced in the manuscript.
Sub-directories
1.Data
Data sub-directory include 3 files and 2 compressed directory :
- Data_Global_EcoEvo_Honey.csv (Main dataset)
- Chemodiv_package_Compounds.csv
- SBphylo.con
- Map.zip
- Environmental_variables.zip
Data_Global_EcoEvo_Honey.csv
This excel file is the main dataset produced in our study. It contains the metadata of each honey samples associated with the quantification data for 36 compounds by H1-NMR spectroscopy. The compounds were grouped in five categories : sugars (n=10), organic acids (n=3), amino acids (n=8), fermentation markers (i.e., all the compounds involved in sugar transformation trough alcoholic, acetic and lactic fermentation; n=10) and anti-microbial compounds (n=5).
The following columns are included:
- Index : Index number attributed to each sample, used in random forest analysis
- LABEL : Unique label associated with NMR spectra
- Continent/Country/Region/site: Geographic location of the sample.
- Lat/long : Geographic coordinates of the samples
- Genus : Accepted Genus name of the bee associated with the sample
- Species : AcceptedSpecies name of the bee associated with the sample
- Compound names :
| Sugars (g/100g) | Organic acids (mg/kg) | Amino acids (mg/kg) | Fermentation markers (mg/kg) | Anti-microbial compounds (mg/kg) | |||
|---|---|---|---|---|---|---|---|
| Fructose | Melezitose | Citric acid | Alanine | Valine | 2,3-butanediol | Lactic acid | Dihydroxyacetone |
| Glucose | Maltotriose | Malic Acid | Aspartic acid | Tyrosine | 5-HMF | Formic acid | Methylglyoxal |
| Sucrose | Gentiobiose | Quinic acid | Glutamine | Phenylalanine | Acetic acid | Fumaric acid | 3-phenyllactic acid |
| Turanose | Raffinose | Leucine | Acetoin | Pyruvic acid | Kynurenic acid | ||
| Maltose | Mannose | Proline | Ethanol | Succinic acid | Shikimic acid |
Chemodiv_package_Compounds.csv
This dataset contains SMILES and InChIKey PubChem identifiers for each quantified compound. SMILES and are chemical identifiers that are easily obtained for each compound by searching for compounds in PubChem. They were used in the chemodiv R package (version (0.3.0); Petrén et al., 2023). for analysing phytochemical data. This package includes a number of functions that makes it straightforward to quantify and visualize phytochemical diversity and dissimilarity for any type of phytochemical samples.
The following columns are included :
- Compound name
- SMILE identifier
- InChiKey identifier
SBphylo.con
This .con file was edited based on the stingless bee phylogeny at the genus level established by Rasmussen et Cameron (2010). The file includes the phylogenetic tree with information on the topology, branch lengths (if present), probability of the partition indicated by the branch & mean of the posterior probability density. We removed the genera for which we did not collect honey samples.
Map.zip
This compressed directory contains all the files needed to produce Figure 1. It includes :
- NE2_HR_LC_SR_W_DR.tif : Global natural earth map at 10m resolution. (source : http://www.naturalearthdata.com/)
- TM_WORLD_BORDERS_SIMPL-0.3.prj/.shp/.dbf/.shx : 4 files used to plot country borders on the global map
- worldmap_Apis.csv : It contains the number of Apis mellifera honey samples per country.
- Regions_apis.csv : It contains geographic coordinates of Apis mellifera honey samples for specific region (e.g. La Réunion in France)
Location of stingless bee honey samples are included in Data_Global_EcoEvo_Honey.csv.
Environmental_variables.zip
This compressed directory contains Raster files of environmental predictors with a CC0 license waiver used in this study. Environmental dissimilarities between locations were computed based on 8 variables impacting nectar production (soil moisture, precipitation*, solar radiation*) and composition (Tree cover, moisture, temperature*, forest density, plant biodiversity). It includes the following files :
*we used the monthly values for these predictors.
- crop_current_30arcsec_topoWet.tif : SAGA-GIS topographic wetness index from ENVIREM, see Title & Bemmels (2018).
- crop_current_30arcsec_climaticMoistureIndex.tif : relative wetness and aridity metric from ENVIREM
- crop_fden9.tif : Global-Scale Patterns of Forest density from Riitters et al. (2000).
- crop_plantbiodiv.tif : Global native species richnes from Ellis et al. (2012).
Global raster datasets of historical monthly temperature (°C), precipitation (mm), and solar radiation (kJ m-2 day-1) for 1960-2018 were sourced from Worldclim (Fick & Hijmans, 2017), available at https://www.worldclim.org/data/worldclim21.html. Additionally, global-scale forest cover rasters were obtained from Crowther et al. (2015) and can be accessed at https://elischolar.library.yale.edu/yale_fes_data/1/.
1.2 Scripts
This folder contains the 3 script necessary to the analysis described in
the manuscript
- Global_EcoEvo_honey.R
- mytoolbox.R
- scores.rda.R
Global_EcoEvo_honey.R
This is the main script used to produce the results and plot of this study. It is divided in 9 section :
- List of packages and custom function
- Data import
- Script to produce the map (Fig.1)
- Comparison of Apis and SB honeys using multivariate analysis (Fig.2)
- Chemical diversity analysis (Supplementary Fig. 2)
- Random forest analysis to compare Apis and SB honeys
- Comparison of SB honeys among Tropical regions (Supplementary Fig. 3)
- Random forest analysis to compare SB honeys among Tropical regions (Table 1 & Figure 3)
- Ecology and evolution of stingless bee honey (Fig. 4 & 5).
mytoolbox.R
This script includes a set of custom function used in Global_EcoEvo_honey.R, particularly NbClust2.R & mypalette.R
scores.rda.R
This is a function downloaded from the set of function provided in Borcard & Legendre (2018), see doi:10.1007/978-3-319-71404-2
1.3 Figures
This folder contain the main figures and supplementary table and figures
referenced in the manuscript
Figures:
- Figure 1: Geographical distribution of honey samples from honey bees(n=100) and stingless bees (n=150) across the Afrotropics, Neotropics, and Indo-Malayan regions.
- Figure 2: NMDS plot showing differentiation of stingless bee honey (SBH) into two distinct clusters by bee tribe.
- Figure 3: Random forest analysis plot highlighting key variables influencing honey composition, color-coded by significance.
- Figure 4: Stacked bar plot showing the contribution of different drivers to SBH compositional variation at global and regional scales.
- Figure 5: Mantel correlograms depicting correlations between honey composition and
phylogenetic/environmental dissimilarities.
Supporting Information:
All supporting information are available in Supporting_information.docx, which includes :
- Supplementary Tables 1 & 2: Distribution of honey samples from stingless bees and honey bees across different countries.
- Supplementary Figure 1: Phylogeny of honey sample genera.
- Supplementary Table 3: Compounds identified and quantified in honey samples.
- Supplementary Figure 2: Comparison of chemodiversity indices between Apis mellifera and
stingless bee honeys. - Supplementary Figure 3: Hierarchical clustering of honey samples based on geographic origin.
- Supplementary Table 4: db-RDA analysis results linking honey composition to bee genus and environmental factors.
Honey profiling by 1H‑NMR spectroscopy To characterize the compounds and properties of our honey samples, we used H1 Nuclear Magnetic Resonance spectroscopy (hereafter H1-NMR spectroscopy), a state-of-the-art analytical technique increasingly used alongside chemometrics statistical approaches for the qualitative and quantitative control of honeys, as well as to assess the botanical origin of honeys and to quantify their major constituting compounds (Schievano et al., 2012; Ohmenhaeuser et al., 2013). H1-NMR spectroscopy was carried out on all 240 samples described above at the laboratories of Quality Services International GmbH (QSI, Bremen, Germany) following the method described in Noiset et al, 2022. We quantified 36 compounds grouped in five categories : sugars (n=10), organic acids (n=3), amino acids (n=8), fermentation markers (i.e., all the compounds involved in sugar transformation trough alcoholic, acetic and lactic fermentation; n=10) and anti-microbial compounds (n=5) (Supplementary Table 3). Another set of physicochemical data of honey samples of Apis mellifera (n = 10) from Mexico analysed according to the protocol described previously at the QSI laboratories were pooled with our dataset of 90 Apis spp. samples; this provided a better balance between the number of honey bee and stingless bee samples analysed in this study. Statistical analyses All the statistical analyses presented here were performed in RStudio for R. We combined multivariate and model-based approach using R packages that are listed in the Scripts directory and produce plots that can be found in the Figure directory.