prioriactions: Multi-action management planning in R
1. Designing effective conservation strategies requires deciding not only where to lo-cate conservation actions (i.e. which territorial units should be priortized), but alsowhich type actions should be deployed. For most of conservation planning con-texts, deciding where and what to do usually yield...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/469463 |
| Acceso en línea: | https://doi.org/10.1111/2041-210X.14220 https://hdl.handle.net/10459.1/469463 |
| Access Level: | acceso abierto |
| Palabra clave: | Biodiversity Management Mixed integer programming Restoration Spatial prioritization Threats |
| Sumario: | 1. Designing effective conservation strategies requires deciding not only where to lo-cate conservation actions (i.e. which territorial units should be priortized), but alsowhich type actions should be deployed. For most of conservation planning con-texts, deciding where and what to do usually yields a complex and computationallychallenging decision-making setting. Although the resulting optimization problemshave typically been tackled using heuristic approaches, recent advances in mixedinteger programming (MIP) solver technology have turned MIP-based approachesinto a practical alternative for solving complex conservation planning problems.2. We introduce the R package prioriactions, which allows solving complex conser-vation planning problems comprising prioritization and action deployment deci-sions. prioriactions features a MIP approach that allows formulating and solvingoptimally (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-sourceexact solvers enhancing its usability as well as its practical effectiveness.3. Here, we present a comprehensive description of the main functions availablein prioriactions. This package has a workflow of three straightforward steps: (a)validation of the input data, using the inputData() function that prepares input;(b) the creation of a prioritization model, using the problem() function, allows thecreation of two types of common models: the minimization of costs to achieve arecovery target and maximizing the recovery benefits given a limited budget; and (c)to solve of the model, using the solve() function.4. The prioriactions package provides a user-friendly platform for addressing differentmulti-actions management problems, allowing to identify more rigorously, transpar-ently and in a reproducible way the spatial deployment of management actions. |
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