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prioritizr: Systematic Conservation Prioritization in R

Source: R/package.R
prioritizr.Rd

The prioritizr R package uses mixed integer linear programming (MILP) techniques to provide a flexible interface for building and solving conservation planning problems (Hanson et al. 2024). 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 (e.g., Rodrigues et al. 2000; Billionnet 2013). 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 (Schuster et al. 2020). 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 Hanson et al. (2024) and 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

Brief introduction to systematic conservation planning and demonstration of the main package features.

package_overview

Comprehensive introduction to systematic conservation planning and detailed overview of the 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.

Citation

Please cite the prioritizr R package when using it in publications. To cite the package, please use:

Hanson JO, Schuster R, Strimas‐Mackey M, Morrell N, Edwards BPM, Arcese P, Bennett JR, and Possingham HP (2024) Systematic conservation prioritization with the prioritizr R package. Conservation Biology, In press: doi:10.1111/cobi.14376 .

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.

Hanson JO, Schuster R, Strimas‐Mackey M, Morrell N, Edwards BPM, Arcese P, Bennett JR, and Possingham HP (2024) Systematic conservation prioritization with the prioritizr R package. Conservation Biology, In press: doi:10.1111/cobi.14376 .

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.

Schuster R, Hanson JO, Strimas-Mackey M, and Bennett JR (2020) Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ 8: e9258

See also

Useful links:

Author

Authors: