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MinPatch for R

Note: This is still a work in progress and significant bugs may be present. Use with caution

More information on the original implemention of MinPatch for Marxan and QGIS is available here: https://cluz-systematic-conservation-planning.github.io

Overview

An R implementation of the MinPatch algorithm for post-processing conservation planning solutions to ensure minimum protected area sizes.

MinPatch is a post-processing tool for conservation planning solutions that ensures all protected areas meet user-defined minimum size thresholds. This R package implements the methodology described in Smith et al. (2010) and is designed to work with solutions from the prioritizr package, though it can work with any binary conservation solution.

The Problem

Conservation planning software like Marxan and prioritizr can produce solutions with many small, fragmented protected areas. While these solutions may be mathematically optimal, small protected areas are often:

  • Less ecologically viable
  • More expensive to manage
  • More vulnerable to edge effects
  • Less resilient to disturbances

The Solution

MinPatch addresses this by post-processing conservation solutions through three stages:

  1. Remove Small Patches: Eliminate protected areas smaller than a minimum size threshold
  2. Add New Patches: Add new areas to meet conservation targets using the BestPatch algorithm
  3. Simulated Whittling: Remove unnecessary planning units while maintaining constraints

Installation 

# Install from GitHub
pak::pak("SpatialPlanning/minpatch")

Key Features

  • Full MinPatch Algorithm: Complete implementation of all three stages
  • prioritizr Integration: Seamless workflow with prioritizr solutions
  • Flexible Parameters: Control minimum patch sizes, patch radius, and boundary penalties
  • Comprehensive Reporting: Detailed statistics and comparisons
  • Visualization Support: Plot results with ggplot2 (optional)
  • Well Documented: Extensive documentation and examples

Algorithm Details

Stage 1: Remove Small Patches

Identifies connected components (patches) in the solution and removes those smaller than the minimum size threshold. Only removes patches that weren’t originally designated as conserved areas.

Stage 2: Add New Patches (BestPatch Algorithm)

Uses the BestPatch scoring system to add new patches:

  1. Calculate current conservation levels for each feature
  2. Identify features with unmet targets
  3. Score potential patches based on their contribution to targets relative to cost
  4. Add the highest-scoring patch and repeat

The BestPatch score is calculated as:

Score = Σ(feature_contribution / target_gap) / patch_cost

Stage 3: Simulated Whittling

Removes unnecessary planning units through an iterative process:

  1. Identify edge units (on the boundary of selected areas)
  2. Calculate whittling scores based on feature importance
  3. Remove units that don’t violate constraints:
    • Must not cause targets to be unmet
    • Must not make patches too small
    • Must not increase total cost (if boundary penalty > 0)
    • Must not split patches into non-viable pieces

Citation

If you use this package, please cite both the original paper and this implementation:

Smith, R.J., Di Minin, E., Linke, S., Segan, D.B., Possingham, H.P. (2010). 
An approach for ensuring minimum protected area size in systematic conservation planning. 
Biological Conservation, 143(10), 2525-2531.

Everett J.D., Richardson A.J., Smith R.J. (2025). _minpatch: Post-Processing for Conservation Planning Solutions to Ensure Minimum Patch
  Sizes_. R package version 0.1.0, <https://github.com/SpatialPlanning/minpatch>.

License

GPL (>= 3)

References

Smith, R.J., Di Minin, E., Linke, S., Segan, D.B., Possingham, H.P. (2010). An approach for ensuring minimum protected area size in systematic conservation planning. Biological Conservation, 143(10), 2525-2531.

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