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Calculate Game Browsing Pressure Index (R4)

Source: R/indicators-risk.R
indicator_risk_browsing.Rd

Computes browsing pressure risk from ungulates (deer, wild boar) based on species palatability from BD Foret, stand vulnerability from LiDAR, edge exposure, and local game density from hunting statistics.

Usage

indicator_risk_browsing(
  units,
  layers = NULL,
  bdforet = NULL,
  game_density = NULL,
  edge_buffer = 50
)

Arguments

units

An sf object with forest parcels.

layers

A nemeton_layers object. Used to extract BD Foret and LiDAR MNH.

bdforet

An sf object with BD Foret V2 polygons, or NULL (resolved from layers).

game_density

SpatRaster with game density index (0-100), or NULL (auto-computed from hunting data if available).

edge_buffer

Numeric. Buffer distance (m) for edge effect calculation. Default 50.

Value

The input sf object with added columns:

  • R4: Browsing pressure risk (0-100). Higher = higher risk.

  • R4_palatability: Species palatability score (0-100).

  • R4_vulnerability: Stand vulnerability score (0-100).

Details

Aligned with tuto 03 methodology: BD Foret intersection for palatability, LiDAR MNH for vulnerability, hunting data (data.gouv.fr) for density.

**Formula**: R4 = 0.35*palatability + 0.30*vulnerability + 0.20*edge + 0.15*density

**Components**:

  • palatability: From BD Foret species intersection (pattern matching on essence names). Quercus=90, Abies=85, Fagus=70, Pinus=30.

  • vulnerability: From LiDAR MNH mean height per parcel. <2m = 100, 2-10m = decreasing, >10m = 0.

  • edge_exposure: Proportion of parcel within buffer of forest edge.

  • game_density: From departmental hunting harvest statistics (data.gouv.fr, OFB). Auto-fetched via get_game_pressure_raster.

See also

Other risk-indicators: indicator_risk_drought(), indicator_risk_fire(), indicator_risk_storm()

Examples

if (FALSE) { # \dontrun{
library(nemeton)

data(massif_demo_units)
units <- massif_demo_units

result <- indicator_risk_browsing(units)
summary(result$R4)
} # }