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Systematic conservation prioritization with the prioritizr R package.

Jeffrey O Hanson1,2, Richard Schuster2,3, Matthew Strimas-Mackey4

  • 1Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Queensland, Australia.

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Summary
This summary is machine-generated.

The prioritizr R package aids systematic conservation prioritization by optimizing protected area selection. It efficiently identifies priority areas, balancing conservation goals with practical constraints for effective land management.

Keywords:
biodiversidadbiodiversityconservation planninginteger programingoptimizaciónoptimizationplaneación de la conservaciónprogramación enteraprotected areasáreas protegidas

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Area of Science:

  • Conservation Science
  • Spatial Planning
  • Computational Ecology

Background:

  • Protected area system expansion requires balancing conservation objectives with economic and land-use constraints.
  • Existing decision support tools for conservation prioritization often have limitations hindering practical application.
  • Systematic conservation prioritization is crucial for effective biodiversity protection and resource management.

Purpose of the Study:

  • To introduce and demonstrate the capabilities of the prioritizr R package for systematic conservation prioritization.
  • To showcase how prioritizr can be applied to real-world conservation planning problems, considering various ecological and economic factors.
  • To evaluate the performance of different solvers within the prioritizr package for large-scale conservation planning.

Main Methods:

  • Utilized the prioritizr R package, a flexible decision support tool for conservation planning.
  • Integrated spatially explicit and tabular data, including land acquisition costs and existing protected areas.
  • Applied exact algorithm solvers (commercial and open-source) to identify optimal protected area solutions.
  • Conducted a case study in Washington state focusing on improving protected area coverage for native avifauna, incorporating fragmentation analysis.

Main Results:

  • The prioritizr package successfully generated a conservation prioritization for Washington state, identifying 12,400 km² of priority areas.
  • Both open-source and commercial solvers efficiently handled large-scale conservation planning problems.
  • Commercial solvers demonstrated superior performance for complex, large-scale prioritization tasks.
  • The study highlighted the package's utility in assessing the relative importance of selected areas.

Conclusions:

  • The prioritizr R package provides a robust and flexible platform for systematic conservation prioritization.
  • It effectively balances conservation objectives with practical considerations like cost and land suitability.
  • The package supports diverse applications beyond reserve selection, including habitat restoration and connectivity enhancement.
  • prioritizr aids real-world decision-making by informing best practices in conservation planning.