EmpericalPatternR: Forest Stand Pattern Simulation

Simulates realistic forest stand patterns using simulated annealing optimization to match empirical targets for spatial patterns, canopy structure, species composition, and fire behavior metrics.

Main Functions

**Simulation:**

• simulate_stand: Run complete stand simulation

• simulate_mortality: Add post-disturbance mortality

**Stand Metrics:**

• calc_stand_metrics: Compute all stand-level metrics

• calc_tree_attributes: Calculate tree attributes from DBH

• calc_canopy_cover: Canopy cover with overlap handling

**Allometric Equations:**

• calc_crown_radius: Crown radius from DBH

• calc_height: Tree height from DBH

• calc_crown_base_height: Crown base height

• calc_canopy_fuel_mass: Foliage biomass

• get_default_allometric_params: Default pinyon-juniper parameters

• get_ponderosa_allometric_params: Ponderosa pine parameters

**Perturbation Operations:**

• perturb_move: Move tree to new location

• perturb_species: Change tree species

• perturb_dbh: Adjust tree diameter

• perturb_add: Add new tree

• perturb_remove: Remove tree

• perturb_add_with_nurse: Add tree with nurse effect

Optimization Weight Guidelines

All optimization weights range from 0-100:

0

Ignore this metric completely

1-20

Low priority - let it emerge from other constraints

20-50

Moderate priority - balance with other metrics

50-80

High priority - actively optimize toward target

80-100

Critical - dominates optimization

Customization

**Allometric Equations:**

Create custom allometric parameters for your forest type:

my_params <- list(
  crown_radius = list(SPECIES = list(a = 0.5, b = 0.10)),
  height = list(SPECIES = list(a = 20, b = 0.03)),
  crown_ratio = list(SPECIES = list(a = 0.70, b = 0.10)),
  crown_mass = list(SPECIES = list(a = 0.20, b = 2.1))
)

**Target Parameters:**

Modify targets to match your field data:

targets <- list(
  density_ha = YOUR_DENSITY,
  species_props = c(SP1 = 0.6, SP2 = 0.4),
  mean_dbh = YOUR_MEAN,
  canopy_cover = YOUR_COVER,
  cfl = YOUR_CFL
)

Examples

Complete working examples in inst/examples/:

• example_01_pinyon_juniper.R - P-J woodland (Huffman 2009)

• example_02_ponderosa_pine.R - Ponderosa pine forest

References

**Empirical Data:**

Huffman et al. (2009). A comparison of fire hazard mitigation alternatives in pinyon-juniper woodlands of Arizona. Forest Ecology and Management 257:628-635.

**Allometric Equations:**

Grier et al. (1992). Biomass distribution and productivity of Pinus edulis-Juniperus monosperma woodlands. Forest Ecology and Management 50:331-350.

Miller et al. (1981). Biomass of singleleaf pinyon and Utah juniper. USDA Forest Service Research Paper INT-273.

**Crown Fire Methods:**

Van Wagner (1977). Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7:23-34.

Scott & Reinhardt (2001). Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service Research Paper RMRS-RP-29.

See also

Useful links:

• https://bi0m3trics.github.io/EmpericalPatternR

• https://github.com/bi0m3trics/EmpericalPatternR

• Report bugs at https://github.com/bi0m3trics/EmpericalPatternR/issues

Author

Maintainer: Andrew Sánchez Meador andrew.sanchezmeador@nau.edu