Identify taxonomic gaps against Flora e Funga do Brasil

This guide demonstrates how to use the jabot_gaps() function in the jabotR package to identify species listed in the Flora e Funga do Brasil (FFB) that are absent from a JABOT-hosted herbarium collection.

Function Overview

jabot_gaps() compares the species held by a target herbarium against the full checklist of accepted species recognized by FFB. It downloads (or reads previously downloaded) FFB and JABOT data, standardizes synonym names, and produces a rich set of analyses and visualizations: summary statistics, missing taxa by taxonomic group/family/genus, genera entirely absent from the herbarium, missing taxa by phytogeographic domain, a choropleth map of missing taxa by Brazilian state, and an interactive searchable table of the missing species. A self-contained HTML report is always generated.

This tool is ideal for herbarium curators who want a quick, reproducible snapshot of which parts of the Brazilian flora/fungi are still unrepresented in their collection, and where the taxonomic effort should be prioritized.

Arguments

Argument Description
herbarium Herbarium acronym in uppercase (e.g. "RB").
jabot_path Path to a directory with previously downloaded JABOT DwC-A files. NULL downloads automatically.
priority_map If TRUE, additionally downloads pooled JABOT network records and adds a nationwide municipality-level heat map ranking collecting priority for the herbarium’s missing species.
network_herbaria Herbarium acronyms to restrict the pooled network sample to, when priority_map = TRUE. NULL uses all JABOT-hosted herbaria.
format Static formats to save individual figures: "pdf", "png", or both. The HTML report is always generated regardless.
fig_width, fig_height Dimensions (in inches) of saved figures.
open_report If TRUE, opens the HTML report in the default browser after rendering.
verbose If TRUE, prints progress messages.
dir Output directory. Defaults to "<herbarium>_gap_analysis".

Basic Use Case

gap_analysis <- jabot_gaps(herbarium = "RB",
                           verbose = TRUE,
                           open_report = TRUE,
                           dir = "RB_gap_analysis")

Exporting Individual Figures

Setting format additionally saves each figure as a static PDF and/or PNG file inside <dir>/figures/, on top of the interactive HTML report.

gap_analysis <- jabot_gaps(herbarium = "RB",
                           format = c("pdf", "png"),
                           verbose = TRUE,
                           open_report = TRUE,
                           dir = "RB_gap_analysis")

Adding a Nationwide Collecting-Priority Map

Setting priority_map = TRUE downloads pooled JABOT network records (optionally restricted with network_herbaria) and ranks Brazilian municipalities by how many of the herbarium’s missing species already have a known voucher there, collected by another herbarium in the network — a nationwide guide to where new collecting expeditions could fill the most gaps.

gap_analysis <- jabot_gaps(herbarium = "RB",
                           priority_map = TRUE,
                           network_herbaria = c("ALCB", "HUEFS", "CEPEC"),
                           verbose = TRUE,
                           open_report = TRUE,
                           dir = "RB_gap_analysis")

Reusing a Previously Downloaded Dataset

gap_analysis <- jabot_gaps(herbarium = "RB",
                           jabot_path = "jabot_download",
                           format = "pdf",
                           verbose = TRUE,
                           open_report = TRUE,
                           dir = "RB_gap_analysis")

Example Output

jabot_gaps() always writes a self-contained, interactive HTML report. The snapshot below shows the summary KPI boxes, the top-missing-families chart, and the choropleth map of missing taxa by Brazilian state:

Overview and KPI summary of the jabot_gaps() HTML report

Overview and KPI summary of the jabot_gaps() HTML report

Choropleth map of missing taxa by Brazilian state

Choropleth map of missing taxa by Brazilian state

The function also returns (invisibly) a named list with every computed data frame and ggplot2 object, so results can be explored directly in R:

names(gap_analysis)
head(gap_analysis$family_gap)

Tips

  • Run jabot_summary() first to confirm the herbarium acronym exists in JABOT.
  • Supply jabot_path on repeated runs to avoid re-downloading FFB and JABOT data.
  • Use priority_map = TRUE sparingly — it downloads the pooled JABOT network sample, which can be slow on the first run; reuse jabot_path afterwards.
  • For a region-scoped version of the same idea (missing species expected in a specific state/municipality, with a map of candidate collecting localities), see jabot_missing().

See Also