Multi-Action Conservation Planning

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in < https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here < https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the 'rcbc' R package (available at < https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (< https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples.

Multi-Action Conservation Planning

This project was financed by the National Agency of Research and Development, ANID, Chile, through the grant FONDECYT N.1180670 and through the Complex Engineering Systems Institute PIA/BASAL AFB180003. Also it has received funding from the European Union’s H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement N.691149 (SuFoRun).

Overview

The prioriactions package allows you to create and solve conservation planning problems that involve multiple threats and actions. This uses techniques of integer linear programming (ILP), obtaining optimal solutions or with a certain degree of guaranteed quality (gap). Due to its flexibility, the package offers the possibility of creating different mathematical models with multiple requirements (spatial, budget requirements, etc.). All the included models are presented in detail in Salgado-Rojas et al. (2020). The package has a variety of commercial and open-source exact algorithm solvers that guarantee to find optimal solutions.

Installation

Package prioriactions can be found at CRAN, where it is updated every few months. Installation from CRAN can be done via:

install.packages("prioriactions")

Also, the latest development version of prioriactions can be installed from GitHub using the following code (If you are using Windows, it is necessary to install Rtools beforehand).

if (!require(remotes)) install.packages("remotes")
remotes::install_github("prioriactions/prioriactions")

Usage

You can browse the package documentation online at https://prioriactions.github.io/prioriactions/.

If this is your first time using prioriactions, we strongly recommend reading the Introduction to prioriactions vignette.

If you believe you’ve found a bug in prioriactions, please file a bug (and, if possible, a reproducible example) at https://github.com/prioriactions/prioriactions/issues.

References

• Salgado-Rojas J, Alvarez-Miranda E, Hermoso V, Garcia-Gonzalo J, Weintraub A. A mixed integer programming approach for multi-action planning for threat management. Ecological Modelling 2020; 418:108901. DOI: https://doi.org/10.1016/j.ecolmodel.2019.108901.