Marcel Glück | Oliver Bossdorf | Henri A. Thomassen

• What the pipeline can do for you: Features and functionalities
• How to set up the pipeline on your system: Setting-up
• How to operate the pipeline and parse processing information: Release publication
• Found a bug? Report a bug

The wealth of biotic and abiotic environmental data generated in the Biodiversity Exploratories continues to grow steadily, and so does the effort of implementing always the newest data into our statistical frameworks. Unsurprisingly, many BE projects restrict their analyses to a handful of frequently used data sets, neglecting the wealth of information at their fingertips. Oftentimes, this might be caused by the need for stringent quality control and (pre-)processing that many environmental data sets still require. However, this restraint might often prevent us from obtaining a more complete understanding of our complex study systems. To remedy this issue, this project provides a comprehensive user-friendly, flexible, scalable, reproducible and easy-to-expand R pipeline that permits for the streamlined processing of (a)biotic EP-level data generated by the Exploratories. We are convinced that such a framework will benefit many scientists in the Exploratories, as the generated data might be used as input in many types of environmental association studies. Additionally, with modifications, this pipeline might be readily adopted to process other types of plot-based data.

This project is a registered Biodiversity Exploratories synthesis project.

✔️ Flexibility: One pipeline, three modes. Switch between forest, grassland, and combined (forest & grassland) mode effortlessly.

✔️ Ease of use: Simply parse aggregation information through csv parameters files. No coding required.

✔️ Customizability: Easily adapt the pipeline to your own needs by e.g. subsetting the template for your plots of interest.

✔️ Deployability: Effortlessly run this pipeline on both Linux and Windows operation systems thanks to reproducible environments.

✔️ Under active development: Shape the future of this project by either providing suggestions for implementations or participate by coding.

1. Data preparation and wrangling: Template creation, plot locations harmonization, correction of suspicious (NA) values, subsetting, fallbacks to more basal (taxonomic) levels, data reshaping, normalization by variable (for e.g. sampling effort)
2. Quality control: Multi-mode outlier detection
3. Data aggregation: Both within and across data sets (mean, median, SD, MAD); processing of yearly climate aggregates (incl. the removal of poorly-supported data points)
4. Diversity indices: Normalization by repeated rarefaction; calculating species richness, Simpson/Shannon-Wiener/Margalef/Menhinick index, ...)
5. Post-processing: Data joining, quality control, variables selection by variance inflation factor (VIF) analyses
6. Data export and metadata compilation: Export of composite data sets and VIF-produced subsets; fetching metadata to the variables produced to assist in preparing the data for publication, submission to BExIS, etc ...

Please cite this pipeline as (replace the X.X.X with the actual pipeline version used):

Glück M., O. Bossdorf and H. A. Thomassen (2024). BEpipeR: a user-friendly, flexible, scalable, and easily expanded pipeline for a streamlined processing of biotic and abiotic data in R. vX.X.X. Zenodo. https://zenodo.org/doi/10.5281/zenodo.10683384

Please do so if you use the pipeline or parts of it in your own work. If you use data produced through this pipeline, please cite both the data set and this pipeline.

People and/or institutions we are indebted to:

• Founders and staff of the Biodiversity Exploratories: For the envisioning, setting up, and maintenance of the research platform.
• Executive department on German Copyright Law, Tübingen University: For assisting in finding a suitable license for this pipeline.
• Open Access Publishing Fund, Tübingen University: For covering publication fees.