Prioriactions: Multi‐action management planning in R

Abstract

AbstractDesigning effective conservation strategies requires deciding not only where to locate conservation actions (i.e. which territorial units should be priortized), but also which type actions should be deployed. For most of conservation planning contexts, deciding where and what to do usually yields a complex and computationally challenging decision‐making setting. Although the resulting optimization problems have typically been tackled using heuristic approaches, recent advances in mixed integer programming (MIP) solver technology have turned MIP‐based approaches into a practical alternative for solving complex conservation planning problems.We introduce theRpackageprioriactions, which allows solving complex conservation planning problems comprising prioritization and action deployment decisions.prioriactionsfeatures a MIP approach that allows formulating and solving optimally (or nearly optimally) a wide class of conservation planning problems (characterized by different spatial and functional constraints and requirements). Furthermore, the package allows using a variety of commercial and open‐source exact solvers enhancing its usability as well as its practical effectiveness.Here, we present a comprehensive description of the main functions available inprioriactions. This package has a workflow of three straightforward steps: (a) validation of the input data, using theinputData()function that prepares input; (b) the creation of a prioritization model, using theproblem()function, allows the creation of two types of common models: theminimization of coststo achieve a recovery target andmaximizing the recovery benefitsgiven a limited budget; and (c) to solve of the model, using thesolve()function.Theprioriactionspackage provides a user‐friendly platform for addressing different multi‐actions management problems, allowing to identify more rigorously, transparently and in a reproducible way the spatial deployment of management actions.