ignobioR vignette

ignobioR

Version 2.0.0 - Next Generation Floristics toolkit for R

Overview

The ignobioR package implements Next Generation Floristics (NGF) methodology to explicitly account for spatial and temporal uncertainties in botanical occurrence records. It provides two core analytical tools:

  1. Map of Relative Floristic Ignorance (MRFI) - Quantifies and visualizes knowledge gaps across your study area
  2. Virtual Floristic List (VFL) - Generates probabilistic species lists accounting for spatial and temporal uncertainty

Installation 

# Install from GitHub
if(!require(devtools)) install.packages("devtools")
devtools::install_github("interacquas/ignobioR")

Data Requirements

Both functions require occurrence data with these fields: - Taxon: Species name - Long, Lat: Coordinates (WGS84 or specify CRS) - uncertainty: Spatial uncertainty radius in meters - year: Year of observation

library(ignobioR)

# Load example data
data(floratus)      # Occurrence records
data(park)          # Study area polygon
data(unsuitablezone) # Areas to exclude (optional)

Map of Relative Floristic Ignorance (MRFI)

The MRFI quantifies floristic knowledge gaps by creating uncertainty buffers around occurrences and computing spatio-temporal ignorance scores. Higher values indicate less knowledge.

Key Parameters

  • tau: Temporal decay rate (0-100%). Typical values: 10-30%. Represents % knowledge loss per 100 years.
  • cellsize: Resolution in meters. Choose based on study area size (e.g., 1000m-5000m).
  • excl_areas: Areas to exclude (e.g., water bodies for terrestrial flora).
  • site_buffer: Expand analysis beyond site boundary (default: FALSE).
  • use_coverage_weighting: TRUE (accurate, slower) or FALSE (fast approximation).

Example 1: Basic MRFI 

# Standard analysis with moderate temporal decay
mrfi_basic <- ignorance_map(
  data_flor = floratus,
  site = park,
  excl_areas = unsuitablezone,
  tau = 20,           # 20% knowledge loss per century
  cellsize = 2000     # 2 km resolution
)

# View results
terra::plot(mrfi_basic$MRFI, main = "Floristic Ignorance")
terra::plot(mrfi_basic$RICH, main = "Species Richness")
print(mrfi_basic$Statistics)

Example 2: High-Resolution Analysis 

# Finer resolution for detailed mapping
mrfi_detailed <- ignorance_map(
  data_flor = floratus,
  site = park,
  excl_areas = unsuitablezone,
  tau = 15,                    # Conservative temporal decay
  cellsize = 1000,             # 1 km resolution
  use_coverage_weighting = TRUE # Maximum accuracy
)

Example 3: Analysis with Expanded Area 

# Extend analysis beyond park boundaries
# Analyzes a larger region to map floristic knowledge in surrounding areas
mrfi_buffered <- ignorance_map(
  data_flor = floratus,
  site = park,
  excl_areas = unsuitablezone,
  tau = 20,
  cellsize = 2000,
  site_buffer = TRUE,    # Analyze beyond boundaries
  buffer_width = 5000    # 5 km expansion
)

Output Files (saved to ./output/): - 4-page PDF report with maps, diagnostics, and statistics - GeoTIFF raster (MRFI_map.tif) - Species list CSV

Virtual Floristic List (VFL)

The VFL estimates taxon occurrence probabilities using spatial overlap and temporal decay, aggregated through the inclusion-exclusion principle.

Key Parameters

  • tau: Temporal decay rate (same interpretation as MRFI).
  • upperlimit: Max records per taxon. 10 = fast, 20 = default, 30 = slow but accurate.
  • min_probability: Filter taxa below threshold (0-100%).

Example 1: Standard VFL 

# Generate probabilistic species list
vfl_standard <- virtual_list(
  data_flor = floratus,
  site = park,
  excl_areas = unsuitablezone,
  tau = 20,
  upperlimit = 20,        # Balanced speed/accuracy
  min_probability = 5     # Exclude taxa < 5%
)

# View results
head(vfl_standard$VFL, 10)
summary(vfl_standard$VFL$Estimated_Spatiotemporal_probability)

Example 2: Fast Exploratory Analysis 

# Quick analysis for initial exploration
vfl_fast <- virtual_list(
  data_flor = floratus,
  site = park,
  tau = 20,
  upperlimit = 10,         # Fast computation
  min_probability = 0      # Include all taxa
)

Example 3: High-Accuracy Conservation Analysis 

# Maximum accuracy for conservation decisions
vfl_conservation <- virtual_list(
  data_flor = floratus,
  site = park,
  excl_areas = unsuitablezone,
  tau = 10,                # Conservative decay
  upperlimit = 30,         # Maximum accuracy
  min_probability = 10     # Only confident predictions
)

# Identify poorly-documented but likely-present species
high_priority <- vfl_conservation$VFL[
  vfl_conservation$VFL$Estimated_Spatiotemporal_probability > 50 &
  vfl_conservation$VFL$Number_of_records < 5, 
]
print(high_priority)

Output Files (saved to ./output/): - 2-page PDF report with distribution histograms and statistics - Probability table CSV

What’s New in 2.0.0

  • Modern spatial packages: Migrated from raster/sp to sf/terra
  • Enhanced visualizations: Automatic PDF reports with quantile and continuous scales
  • Improved accuracy: Coverage-weighted rasterization option

Citation

If you use this package, please cite:

D’Antraccoli, M., Bedini, G., & Peruzzi, L. (2022). Maps of relative floristic ignorance and virtual floristic lists: An R package to incorporate uncertainty in mapping and analysing biodiversity data. Ecological Informatics, 67, 101512. https://doi.org/10.1016/j.ecoinf.2021.101512

Authors

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