prioritizr | R Documentation |
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.
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"
).
Background information on systematic conservation planning, package intallation instructions and citation, and a demonstration of the main package features.
Comprehensive introduction to the package and example workflows for the main package features.
Examples of balancing different criteria to identify candidate prioritizations.
Examples of incorporating and evaluating connectivity in prioritizations using a range of approaches.
Tutorial on using multiple management actions or zones to create detailed prioritizations.
Instructions for installing and setting up the Gurobi optimization software for use with the package.
Reports run times for solving conservation planning problems of varying size and complexity using different solvers.
List of publications that have cited the package.
Authors:
Jeffrey O Hanson jeffrey.hanson@uqconnect.edu.au (ORCID)
Richard Schuster richard.schuster@glel.carleton.ca (ORCID, maintainer)
Nina Morrell nina.morrell@ubc.ca
Matthew Strimas-Mackey mstrimas@gmail.com (ORCID)
Brandon P M Edwards brandonedwards3@cmail.carleton.ca (ORCID)
Matthew E Watts m.watts@uq.edu.au
Peter Arcese peter.arcese@ubc.ca (ORCID)
Joseph Bennett joseph.bennett@carleton.ca (ORCID)
Hugh P Possingham hugh.possingham@tnc.org (ORCID)
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.
Useful links:
Package website (https://prioritizr.net)
Source code repository (https://github.com/prioritizr/prioritizr)
Report bugs at (https://github.com/prioritizr/prioritizr/issues)
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