Data from: Geographic biases and gaps in the sampling of plant-pollinator networks
Data files
May 08, 2026 version files 1.34 MB
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code-ecography.zip
1.34 MB
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README.md
3.44 KB
Abstract
This dataset supports the manuscript “Geographic biases and gaps in the sampling of plant–pollinator networks” and contains compiled global information on plant–pollinator networks, their reuse, and associated country-level socioeconomic and biodiversity variables. The dataset integrates multiple sources to characterize spatial patterns in the availability and reuse of plant–pollinator network data. It includes metadata on ecological networks, records of dataset reuse and citations, and country-level predictors such as research investment and bee species richness. The accompanying code provides a fully reproducible workflow for data processing, statistical modeling, and figure generation. These data were used to investigate geographic biases in ecological network research, assess the influence of socioeconomic factors on biodiversity data availability, and support global syntheses of plant–pollinator interactions.
README for analysis code
Raw datasets and licensing note
This Dryad package contains only data generated or compiled by the authors that can be released under Dryad’s required CC0 waiver.
The following files are included in the Dryad deposit (code-ecography.zip):
webs.csv ( ; ) — Plant–pollinator network metadata compiled by the authors for this study.
network_reuse.csv ( ; ) — Records of reuse/citations of plant–pollinator networks compiled by the authors for this study.
Missing values
Empty cells and missing values were standardized as n/a in the CSV files.
In this dataset, n/a indicates that the information was either not available from the original source or not applicable to the corresponding record, depending on the variable.
External data
The analyses also use external country-level datasets. These files are not redistributed in the Dryad package because their original licenses or terms of use are not necessarily compatible with Dryad’s CC0 waiver. Users should obtain these data directly from the original providers:
1. World Bank Open Data:
- GDP, current US$; series NY.GDP.MKTP.CD.
- Research and development expenditure, % of GDP; series GB.XPD.RSDV.GD.ZS.
Source: World Bank Open Data.
License: World Bank datasets are generally distributed under CC BY 4.0 and associated terms of use.
2. Orr et al. (2021):
- Country-level bee species richness and country area data.
Citation:
Orr, M. C. et al. (2021). Global Patterns and Drivers of Bee Distribution. Current Biology.
https://doi.org/10.1016/j.cub.2020.10.053\
3. United Nations Statistics Division:
- Country/region/continent classification.
Source: UN Statistics Division, M49 Standard Country or Area Codes for Statistical Use.
The scripts in this repository indicate where these external files should be placed locally if users download them from the original sources.
Workflow summary
Initialization (src/)
Run these in order to set paths, load packages, prepare data, fit models, and build figures.
1) Required packages
source("src/initialize_packages.R")
2) Data: load, standardize, merge (writes data/webs_complete.csv)
source("src/initalize_data.R") # filename intentionally 'initalize'
3) Models: fit and export LaTeX tables
source("src/initalize_models.R") # filename intentionally 'initalize'
4) Figures: predictions, maps, panels (saves manuscript figures)
source("src/initalize_figure.R") # filename intentionally 'initalize'
Macro pipeline (consolidated scripts, exec/)
After the src step completes, run the end-to-end pipeline with the numbered orchestrators:
1) Data preparation (wraps/organizes the data prep stage)
source("exec/1_dataPrep.R")
2) Modeling (NB-GLM for country counts; LMM for reuse)
source("exec/2_models.R")
3) Figures (prediction curves, maps, patchwork panels)
source("exec/3_figures.R")
Citation & data acknowledgments
World Bank Open Data: GDP and R&D expenditure.
Orr, M. C., et al. (2021). Global Patterns of Bee Diversity. Current Biology. https://doi.org/10.1016/j.cub.2020.10.053
UN Statistics Division: country/region classifications.