prioritizr {prioritizr}R Documentation

prioritizr: Systematic Conservation Prioritization in R

Description

The prioritizr R package uses mixed integer linear programming (MILP) techniques to provide a flexible interface for building and solving conservation planning problems (Rodrigues et al. 2000; Billionnet 2013). It supports a broad range of objectives, constraints, and penalties that can be used to custom-tailor conservation planning problems to the specific needs of a conservation planning exercise. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. In contrast to the algorithms conventionally used to solve conservation problems, such as heuristics or simulated annealing (Ball et al. 2009), the exact algorithms used here are guaranteed to find optimal solutions. Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. Finally, this package has the functionality to read input data formatted for the Marxan conservation planning program (Ball et al. 2009), and find much cheaper solutions in a much shorter period of time than Marxan (Beyer et al. 2016). See the online code repository for more information.

Details

This package contains several vignettes that are designed to showcase its functionality. To view them, please use the code vignette("name", package = "prioritizr") where "name" is the name of the desired vignette (e.g., "gurobi_installation").

prioritizr

Background information on systematic conservation planning, package intallation instructions and citation, and a demonstration of the main package features.

package_overview

Comprehensive introduction to the package and example workflows for the main package features.

calibrating_trade-offs_tutorial

Examples of balancing different criteria to identify candidate prioritizations.

connectivity_tutorial

Examples of incorporating and evaluating connectivity in prioritizations using a range of approaches.

management_zones_tutorial

Tutorial on using multiple management actions or zones to create detailed prioritizations.

gurobi_installation

Instructions for installing and setting up the Gurobi optimization software for use with the package.

solver_benchmark

Reports run times for solving conservation planning problems of varying size and complexity using different solvers.

publication_record

List of publications that have cited the package.

Author(s)

Authors:

References

Ball IR, Possingham HP, and Watts M (2009) Marxan and relatives: Software for spatial conservation prioritisation in Spatial conservation prioritisation: Quantitative methods and computational tools. Eds Moilanen A, Wilson KA, and Possingham HP. Oxford University Press, Oxford, UK.

Beyer HL, Dujardin Y, Watts ME, and Possingham HP (2016) Solving conservation planning problems with integer linear programming. Ecological Modelling, 228: 14–22.

Billionnet A (2013) Mathematical optimization ideas for biodiversity conservation. European Journal of Operational Research, 231: 514–534.

Rodrigues AS, Cerdeira OJ, and Gaston KJ (2000) Flexibility, efficiency, and accountability: adapting reserve selection algorithms to more complex conservation problems. Ecography, 23: 565–574.

See Also

Useful links:

[Package prioritizr version 8.0.4 Index]