Fast and Easy Compilation of Vascular Plants Occurrence Records from GBIF

GBIF hosts over 500 million tracheophyte (vascular plant) occurrence records, but processing them typically involves complex workflows and substantial computational overhead. VasGBIF is a fast, plant-optimised R package that parses, validates, and consolidates GBIF occurrence records into analysis-ready datasets.

VasGBIF integrates taxonomic resolution via the TNRS, spatial validation via CoordinateCleaner, duplicate consolidation, and native-range annotation against WCVP and WGSRPD within a single high-performance pipeline. Its vectorised design and selective parallelisation let it process one million records on a laptop in under 15 minutes without specialised hardware.

Details

Pipeline overview

VasGBIF provides a reproducible workflow organised into four sequential stages built around seven core functions. The core pipeline typically retains approximately 30% of input records as high-quality, non-redundant data.

Stage 1 — Import and taxonomic standardisation

• import_records(): loads a GBIF occurrence download ZIP, extracts the occurrence table, and parses GBIF issue flags.

• check_taxon(): submits species- and infraspecific-rank names to the Taxonomic Name Resolution Service (TNRS; Boyle et al. 2013) for resolution against the World Checklist of Vascular Plants (WCVP). Synonyms are resolved to accepted names; names that cannot be matched are flagged for review.

Stage 2 — Duplicate detection via collection-event keys

• get_collections(): builds a composite key for each record from its accepted taxon name, collection date, and rounded coordinates. Records sharing the same key are treated as potential duplicates from a single gathering event.

• set_vouchers(): scores each record on metadata completeness (nine fields) and geospatial quality (GBIF issue flags) and selects the highest-scoring record in each duplicate group as the master digital voucher. Records with incomplete keys are kept as independent vouchers.

Stage 3 — Coordinate validation and native-status annotation

• refine_records(): restores missing metadata from duplicate records via restore_duplicates(), then validates coordinates with CoordinateCleaner::clean_coordinates() (Zizka et al. 2019) to flag spatial errors such as centroids, capitals, marine coordinates, and zero coordinates. Matches validated coordinates to WGSRPD Level 3 and WCVP distribution data via detect_native_status() to classify each record as native, introduced, extinct, location_doubtful, or unknown.

Stage 4 — Export and visualisation

• export_records(): writes the refined records to disk as three gzip-compressed CSV files: all usable records, the native subset, and records that failed coordinate validation.

• map_records(): renders refined records on interactive maps via mapView(), with geohash-based decluttering and colour-coding by native status. Supports multiple basemap layers (OpenStreetMap, Esri World Imagery, and others).

Quick start

The built-in example dataset lets you run the full pipeline without a GBIF download:

library(VasGBIF)

# Step 1: Import (built-in example, or use your own ZIP)
gbif_file <- system.file(
  "extdata", "0003386-260721160103020.zip",
  package = "VasGBIF"
)
occ_import <- import_records(path = gbif_file)

# Step 2: Resolve taxon names against WCVP
taxa_checked <- check_taxon(occ_import = occ_import, accuracy = 0.9)

# Step 3: Build collection-event keys
collection_keys <- get_collections(
  occ_import = occ_import,
  taxa_checked = taxa_checked,
  precision = 2L
)

# Step 4: Select digital vouchers
voucher <- set_vouchers(
  occ_import = occ_import,
  taxa_checked = taxa_checked,
  collection_keys = collection_keys
)

# Step 5: Validate coordinates and annotate native status
refined_records <- refine_records(
  voucher = voucher,
  threads = 4
)

# Step 6: Export records
export_records(refined_records = refined_records,
  export_path = getwd())

# Optional: visualise on an interactive map
map_records(refined_records = refined_records, precision = 3,
  cex = 3)

Performance

VasGBIF achieves its speed through several architectural choices:

• C/C++ backends: core operations are delegated to data.table, stringi, and terra — packages written in C/C++ that bypass R's per-iteration interpretive overhead.

• Vectorisation over explicit loops: functions such as set_vouchers() use vectorised string matching stri_detect_fixed() and conditional assignment fcase() to process entire columns in a single compiled call rather than iterating in R.

• SIMD exploitation: vectorised routines in stringi and terra::extract() allow the compiler to emit SIMD instructions (AVX, AVX-512) that process multiple data elements per CPU cycle.

• Memory-efficient design: data.table's in-place modification :=, set()) avoids unnecessary copies, and contiguous memory access patterns improve CPU cache utilisation.

• Selective parallelisation: refine_records() partitions the dataset into chunks and distributes CoordinateCleaner validation across workers via foreach and doParallel, combining vectorised processing within each chunk with parallel execution across chunks.

On a standard laptop, VasGBIF compiles one million occurrence records within 15 minutes.

References

1. Appelhans, Tim, Florian Detsch, Christoph Reudenbach, and Stefan Woellauer. 2023. "Mapview: Interactive Viewing of Spatial Data in R." https://CRAN.R-project.org/package=mapview.

2. Boyle, Brad, Nicole Hopkins, Zhenyuan Lu, Juan Antonio Raygoza Garay, Dmitry Mozzherin, Tony Rees, Naim Matasci, et al. 2013. "The Taxonomic Name Resolution Service: An Online Tool for Automated Standardization of Plant Names." BMC Bioinformatics 14 (1): 16. doi:10.1186/1471-2105-14-16 .

3. Chirico, Michael. 2023. "geohashTools: Tools for Working with Geohashes." https://CRAN.R-project.org/package=geohashTools.

4. De Melo, Pablo Hendrigo Alves, Nadia Bystriakova, Eve Lucas, and Alexandre K. Monro. 2024. "A New R Package to Parse Plant Species Occurrence Records into Unique Collection Events Efficiently Reduces Data Redundancy." Scientific Reports 14 (1): 5450. doi:10.1038/s41598-024-56158-3 .

5. Vilela, Bruno, and Fabricio Villalobos. 2015. "letsR: A New R Package for Data Handling and Analysis in Macroecology." Edited by Timothée Poisot. Methods in Ecology and Evolution 6 (10): 1229-34. doi:10.1111/2041-210x.12401 .

6. Zizka, Alexander, Daniele Silvestro, Tobias Andermann, Josue Azevedo, Camila Duarte Ritter, Daniel Edler, Harith Farooq, et al. 2019. "CoordinateCleaner: Standardized Cleaning of Occurrence Records from Biological Collection Databases." Edited by Tiago Quental. Methods in Ecology and Evolution 10 (5): 744-51. doi:10.1111/2041-210X.13152 .

Examples

if (FALSE) { # interactive()
# Three vignettes walk through the entire VasGBIF workflow, from
# data acquisition to final export:
vignette("GetRecords",   package = "VasGBIF")  # search & download GBIF data
vignette("Example",      package = "VasGBIF")  # quick-start with built-in data
vignette("Application",  package = "VasGBIF")  # real-world full-pipeline walk-through
}