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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:

Key Features

b3gbi empowers biodiversity analysis with:

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 bryophytes in Montenegro.

# Load package
library(b3gbi)

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

# 2. Calculate a map of observed richness
map_rich_bryo <- obs_richness_map(bryophyte_data, level = "country", region = "Montenegro", ne_scale = "medium", cell_size = 10)

# 3. Plot the indicator map
plot(map_rich_bryo, title = "Observed Species Richness: Bryophytes in Montenegro")

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