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CafriplotsR

R package for managing and exploring the Central African forest plot database cafriplot network

Overview

CafriplotsR provides tools for querying a PostgreSQL database containing forest inventories data from Tropical Africa. The package offers functions and shiny apps for (1) managing individual tree measurements on which either taxa or stem level traits sensus largo measurements (or observations) can be aggregated, (2) standardizing taxonomic information en enrich with taxa level traits.
The advantage of this package is allow managing inventories, traits and observations under the same taxonomic backbone, facilitating data integration, reproductibility in data analysis and manipulation, data reusability.

Key features: - Query plot data, individual tree measurements, and ecological features - Access and aggregate species-level traits sensus largo - Shiny app for standardize and correct your own list of taxonomic names

Installation 

# Install from GitHub
install.packages(c("tidyverse", "dbplyr", "devtools"))
devtools::install_github("umr-amap/cafriplotsR", upgrade = "never")

In case of slow internet connection, the installation from github above may fail. You may try to first launch this code line in the console, it will increase the time for trying to install :

options(timeout = max(3000, getOption("timeout")))

Note: Access to the database is restricted and requires appropriate credentials.

Package Logic & Access Control

The CafriplotsR package offers tools to manipulate, export, visualize, standardize, and enrich plant inventory data from Central Africa.

Access Model

The package implements a two-tier access system:

  1. Plot inventories (row-level security):
    • Each user has access to their own plots, controlled by database row-level security policies
    • Policies define which specific plots each user can query and update
    • Ensures data providers maintain control over their contributed inventories
    • Some inventories are accessible to all users
  2. Species-level traits (access across all users):
    • All users have read access to the taxa database
    • These data are grafted and aggregated to inventories

This design ensures data sovereignty for plot owners while enabling the research community to benefit from shared taxonomic and trait knowledge.

Future Development

  • Species occurrence data: Open access to occurrence records across Central Africa (not yet implemented). The RAINBIO database (only for shrub and trees) will be accessible and interoperable with inventories.

Database Architecture

The package connects to two PostgreSQL databases:

  1. Main database (plots_transects): Plot, subplot, and individual tree data
  2. Taxa database (rainbio): Taxonomic information and species-level traits

Quick Start 

library(CafriplotsR)

# Connect to databases
mydb <- call.mydb()
mydb_taxa <- call.mydb.taxa()

# Query plots
plots <- query_plots(id_plot = c(1, 2, 3))

# Query plots
plots <- query_plots(country = "GABON")

# Visualize database structure
get_database_fk(mydb)

Herbarium Specimen Linking: Improving Data Quality Through Time

A unique feature for long-term data quality improvement

Field identifications in forest inventories, while valuable, often suffer from taxonomic uncertainty. Botanical specimens collected from the same individual trees and deposited in herbaria undergo expert taxonomic revision over time, resulting in more accurate identifications. However, this improved knowledge typically remains isolated in herbarium databases, disconnected from the ecological inventory data.

The CafriplotsR solution: Formal specimen-individual links

This package implements a specimen linking system that creates formal, persistent connections between: - Individual trees in forest inventories (with their ecological measurements) - Herbarium specimens collected from those same individuals (with their expert-revised taxonomy)

Key advantages:

  1. Automatic taxonomic updates: When a specimen’s identification is revised by taxonomists, the linked inventory individual automatically inherits the updated taxonomy. No manual re-identification needed.

  2. Improved data quality over time: Your inventory data becomes progressively more accurate as specimen identifications are refined, without requiring field revisits or additional effort.

  3. Traceability: Each inventory record maintains a clear link to its voucher specimen, providing scientific evidence and enabling verification.

  4. Taxonomic confidence: Distinguish between field identifications (subject to uncertainty) and specimen-backed identifications (expert-verified).

  5. Data longevity: Inventory data remains connected to the evolving taxonomic knowledge, ensuring long-term scientific value.

📖 For detailed instructions on how to link specimens to individuals, see the vignette: Linking Herbarium Specimens to Inventory Individuals

Core Functions

Connection Management

Data Querying

Documentation

  • Function help: Use ?function_name for detailed documentation
  • Changelog: See NEWS.md for version history and updates

Recent Updates

See NEWS.md for the latest changes, including: - Breaking changes and migration guides - New features and enhancements - Bug fixes and improvements

Package Metadata

  • Authors: Gilles Dauby, Hugo Leblanc, Pierre Ploton
  • Maintainer: Gilles Dauby ()
  • License: GPL-2
  • Minimum R version: 4.0

Contributing

This package follows a git branching workflow: - All code changes are made on feature branches - Changes are documented in NEWS.md - Pull requests are reviewed before merging to master

Support

For issues, questions, or feature requests, contact the package maintainer.

Citation

To cite CafriplotsR in publications, use:

citation("CafriplotsR")

Or manually:

Dauby, G., Leblanc, H., & Ploton, P. (2024). CafriplotsR: Tools for Exploring, Managing and Standardizing Vegetation Inventories in Central Africa. R package version 1.8.0. https://umr-amap.github.io/cafriplotsR/

BibTeX entry:

@Manual{cafriplotsr,
  title = {CafriplotsR: Tools for Exploring, Managing and Standardizing Vegetation Inventories in Central Africa},
  author = {Gilles Dauby and Hugo Leblanc and Pierre Ploton},
  year = {2024},
  note = {R package version 1.8.0},
  url = {https://umr-amap.github.io/cafriplotsR/}
}