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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

Version française disponible ici / French version available here

Why CafriplotsR?

The Challenge

Many researchers inventory woody vegetation in Central African forests (CAF), targeting diverse objectives: dynamics (mortality and growth), floristic and functional diversity, resource assessment and management, effects of both historical and contemporary disturbances, fauna-flora interactions, and more.

However, these initiatives and the research groups conducting them suffer from insufficient visibility:

  • Within the regional community: Limited visibility among scientists and managers working on these forests restricts collaboration opportunities, experience sharing, protocol harmonization, and identification of complementarities in data and expertise.

  • At the international level: This leads to the frequent claim that “we know almost nothing about Congo Basin forests.” While there are indeed knowledge gaps compared to other major tropical forest blocks, asserting that our understanding relies solely on a handful of visible international initiatives is reductive.

The Data Accessibility Problem

Unlike species occurrence data, which has become increasingly accessible (e.g., through GBIF), it remains difficult to get a comprehensive view of inventory data in Central Africa. This includes both recent inventories and ‘historical’ inventories dating back decades. These historical inventories sometimes exist only in paper format (when they haven’t disappeared entirely!), yet they document plant biodiversity in localities that may have become inaccessible.

Root causes include: 1. Poor data archiving practices 2. Lack of resources to maintain accessibility beyond project lifetimes 3. Insufficient willingness to make data accessible

The Integration Challenge

Another major difficulty is combining different data types (e.g., species conservation status, functional traits, etc.) with inventory data, even though this compilation is essential for investigating numerous research questions. These compilations are regularly performed, but the methods lack reproducibility. If 10 people were asked to perform such a compilation independently, we would likely get 10 different results, depending on:

  • Data accessible at the time of compilation (which varies greatly with each person’s context)
  • How taxonomy is standardized between databases

The CafriplotsR Solution

CafriplotsR addresses these challenges through shared infrastructure and inventory data management while guaranteeing data sovereignty for each user or research group.

The package aims to: - Improve visibility of fieldwork conducted by different teams in Central Africa - Facilitate data management (encoding, cleaning, consolidation, queries, etc.) - Improve documentation and reproducibility of data processing - Boost scientific collaborations in the region through controlled and deliberate data sharing

How CafriplotsR Differs from Global Initiatives

While comparable to other data ‘centralization’ initiatives with global approaches (e.g., ForestPlots.net), CafriplotsR distinguishes itself through:

  1. Regional, not global: Focuses on Central Africa—a geographic and human scale that enables interactions between actors involved in collecting, managing, and using this reference data

  2. Transparent multi-data management: Manages different types of data associated with woody plant species (occurrences, traits, relevant attributes) in a transparent manner

  3. Data sovereignty over strict centralization: Each user remains sovereign in managing their data. CafriplotsR aims to federate research groups involved in woody inventories in Central Africa, not to centralize control

Next Steps

With the goal of making data import, management, and standardization accessible through interactive and user-friendly applications, the next development steps involve co-construction to identify and respond to the concrete needs of potential users in Central Africa.

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)

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/}
}