Database content

The VegVault database v1.0.0 is an SQLite database of a size of ~ 110 GB

Currently, the SQLite database consists of 29 interconnected tables with 87 fields (variables). See Databse Structure for more information.

Datasets	> 480 000
Samples	> 13 000 000
Taxa	> 110 000
Vegetation Traits	6
Trait values	> 11 000 000
Abiotic variables	8

See Database Access for more information on how to access the database.

!!! There should be spatio-temporal overview of the database content !!!

Assembly details of VegVault 1.0.0

The VegVault version 1.0.0 has been assembled from other publicly available databases.

Assembly process

Here is a general visualisation of the assembly process:

Figure legend:

1. Neotoma Paleoecology Database - open, community-curated data and services for paleoecological and paleoenvironmental data
   • source of fossil pollen data
2. sPlotOpen - The open-access version of sPlot
   • source of current vegetation plot data
3. Botanical Information and Ecology Network - global patterns of plot inventories and surveys
   • source of current vegetation plot and vegetation trait data
4. TRY Plant Trait Database - open access plant trait data
   • source of vegetation trait data
5. Climatologies at High resolution for the Earth’s Land Surface Area - Long-term, transient modern- and paleo-climate data
   • source of abiotic data
6. World Soil Information Service - harmonised soil profile database
   • source of abiotic data
7. VegVault-FOSSILPOL GitHub repo
   • a Tag (v1.0.0) to obtain and process fossil pollen data
8. VegVault-Vegetation_data GitHub repo
   • a Tag (v1.0.0) to process current vegetation plot data
9. VegVault-Trait_data GitHub repo
   • a Tag (v1.1.0) to process vegetation trait data
10. VegVault-abiotic_data GitHub repo
    • a Tag (v1.1.0) to process abiotic data
11. VegVault GitHub repo
    • a Tag (v1.0.0) to transfer the data into the SQLite database. S
    • ee details below.
12. VegVault - SQLite database (v1.0.0)

Note on GitHub Tags

…Tags are ref’s that point to specific points in Git history. Tagging is generally used to capture a point in history…

Here, the Tags have been used to ensure reproducibility of this version of VegVault. GitHub repo can change but the specific Tag used here (and also used in the code to source the data) ensure the transparency of the state of the database.

VegVault GitHub repo

In addition to the consolidation of all processed data into a unified SQLite database, the final VegVault “migration” repository performs three additional procedures: (i) taxa classification, (ii) grouping of traits into Trait Domains, and (iii) creation of gridpoints for abiotic data.

• (i) Taxa classification

  As VegVault consist of data on taxa from various sources, the {taxospace} R package is used to classify the diverse taxa into a unifying backbone in the VegVault database. The {taxospace} tool automatically aligns taxa names with the taxonomical backbone of the GBIF. Specifically, we find the best match of the raw names of taxa using Global Names Resolver that is then aligned with GBIF. Taxonomic information, detailed up to the family level, is stored for each taxon, ensuring consistency and facilitating comparative analyses across different datasets.

• (ii) Grouping of Traits into Trait Domains

  As there are differences in trait names across sources of data (e.g., “Leaf nitrogen (N) content per leaf dry mass” and “leaf nitrogen content per leaf dry mass”), we added a new variable Trait Domain that groups traits together following the trait selection of Diaz et al. (2016). For example, trait “Plant height vegetative” from TRY and trait “whole plant height” from BIEN are both grouped under “Plant height” Trait Domain.

• (iii) Creation of gridpoints of abiotic data

  The abiotic data in the VegVault database provide essential information on environmental factors affecting vegetation distribution and traits, namely climate and soil variables. We developed a data structure that provides readily available environmental context for each vegetation (and trait) record by creating spatio-temporal links between these records and abiotic information. We first unified the resolution of the original rasters by resampling all abiotic data into ~ 25km resolution at 500-year time slices, reducing the amount of data. Next, as the rasters are not suitable to be stored in an SQLite database, we created artificial points, called gridpoints, located in the middle of each raster cell. This resulted in a uniform spatio-temporal matrix of gridpoints holding the abiotic information. Next, we linked gridpoints and other non-gridpoint Samples, namely vegetation_plot, fossil_pollen_archive, and traits (see database structure), and calculated the spatial and temporal distances between them. We discarded any gridpoint Sample beyond 50 km and/or 5000 years to any other non-gridpoint Sample. In VegVault, users can select the information for each non-gridpoint Sample from the closest spatio-temporal abiotic data or get the average from all surrounding gridpoints. By providing such comprehensive and well-linked structure between vegetation and abiotic data, VegVault enhances the ability to study the interactions between vegetation and their environment, facilitating the workflow to advanced ecological research and modelling efforts.