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B3gbi

B-Cubed General Biodiversity Indicators

Install / Use

/learn @b-cubed-eu/B3gbi
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

<!-- README.md is generated from README.Rmd. Please edit that file -->

b3gbi <a href="https://b-cubed-eu.github.io/b3gbi/"><img src="man/figures/logo.png" align="right" height="120" alt="b3gbi website"/></a>

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Analyze biodiversity trends and spatial patterns from GBIF data cubes, using flexible indicators like richness, evenness, and more.

Overview

Biodiversity researchers need robust and standardized tools to analyze the vast amounts of data available on platforms like GBIF. The b3gbi package leverages the power of data cubes to streamline biodiversity assessments. It helps researchers gain insights into:

  • Changes Over Time: How biodiversity metrics shift throughout the years.
  • Spatial Variations: Differences in biodiversity across regions, identifying hotspots or areas of concern.
  • The Impact of Factors: How different environmental variables or human activities might affect biodiversity patterns.

Key Features

b3gbi empowers biodiversity analysis with:

  • Standardized Workflows: Simplify the process of calculating common biodiversity indicators from GBIF data cubes.
  • Flexibility: Calculate richness, evenness, rarity, taxonomic distinctness, Shannon-Hill diversity, Simpson-Hill diversity, completeness, and more.
  • Analysis Options: Explore temporal trends or create spatial maps.
  • Visualization Tools: Generate publication-ready plots of your biodiversity metrics.

Installation

Install b3gbi in R:

install.packages("b3gbi", repos = c("https://b-cubed-eu.r-universe.dev", "https://cloud.r-project.org"))

Example: Three-Step Workflow

This basic example demonstrates the core workflow: preparing the data cube, calculating an indicator, and plotting the result as a spatial map of species richness for mammals in Denmark.

# Load package
library(b3gbi)

# 1. Load and prepare the GBIF data cube
cube_name <- system.file("extdata", "denmark_mammals_cube_eqdgc.csv", package = "b3gbi")
mammal_data <- process_cube(cube_name)

# 2. Calculate a map of observed richness
map_obs_rich_mammals <- obs_richness_map(mammal_data, level = "country", region = "Denmark", ne_scale = "medium")

# 3. Plot the indicator map
plot(map_obs_rich_mammals, title = "Observed Species Richness: Mammals in Denmark")
<img src="man/figures/README-example-1.png" width="100%" />

For a more in-depth introduction, see the tutorial: https://b-cubed-eu.github.io/b3gbi/articles/b3gbi.html.

Related Skills

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GitHub Stars9
CategoryDevelopment
Updated14d ago
Forks2

Languages

R

Security Score

75/100

Audited on Mar 27, 2026

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