DOI: 10.5061/dryad.vx0k6djz1
title: Data from: bringing the forest back: restoration priorities in Colombia

description: Aim: Colombia has committed to ambitious forest restoration targets which
 include a one million ha Bonn Challenge commitment and 6.47 - 8.31 million
 ha (rehabilitation and restoration, respectively) under the National
 Restoration Plan. Determining where and how to implement programs to
 achieve these targets remains a significant challenge. Location: Colombia
 Methods: We adopt a multi-objective optimisation framework for restoration
 planning and apply it to Colombia. We explore cost-effective solutions
 that leverage the potential for assisted natural regeneration benefits
 while accounting for opportunity and establishment costs of restoration
 and maximising biodiversity conservation and climate change mitigation
 benefits. We explore four politically relevant restoration areal targets
 (one, six, 6.47 and 8.31 million ha) and identify minimum cost, and suites
 of maximum benefit and cost-effective solutions. Results: We identify
 solutions that simultaneously perform well across biodiversity and carbon
 objectives, despite trade-offs between these objectives. We find that
 cost-effective solutions can achieve on average 91.1%, 90.8%, 90.5%, and
 90.1% of maximum carbon benefit and 100% of the maximum biodiversity
 benefit while significantly reducing costs. On average, the maximum
 benefit solutions reduce the cost by 16.9%, 30.2%, 31.1%, and 34.4% when
 considering the one, six, 6.47 and 8.31 million ha restoration targets
 respectively. Main conclusions: Colombia has committed to bold restoration
 and conservation targets, such as those under the new 2030 Convention on
 Biological Diversity Global Biodiversity Framework. Strategic forest
 restoration planning will play an important role in achieving Colombia’s
 climate mitigation goals. We provide quantitative evidence to inform
 planning for environmentally and economically sensible restoration policy
 and practice in the country. Our framework and results can help guide
 Colombia towards meeting its ambitious forest restoration targets
 cost-effectively.
description: We used spatial prioritisation, the process of using
 computational tools for the informed spatial allocation of actions or
 placement land uses, to achieve an objective of restoring forest to
 maximise biodiversity and carbon sequestration benefits within selected
 priorities, while considering establishment and opportunity cost. Tree
 planting and extensive site preparation are popular restoration strategies
 and can be effective, but implementation can be prohibitively expensive
 for some sites or at large scales. Where ecological conditions are such
 that forests can grow back on their own or with low-cost assistance,
 natural regeneration methods can be less costly. To leverage these
 potential costs our establishment cost estimates account for the potential
 for natural regeneration by adjusting values relative to a spatially
 explicit random forest model.
description: R - https://www.r-project.org/  Gurobi - https://www.gurobi.com/ (there is an alternative ranking algorithm provided in the code where gurobi is not needed)
description: \# WePlan Colombia [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.vx0k6djz1) Code to run and create the
 dataframes for the analysis; however, we also provide the input dataframes
 that we used to run the analysis. \## Description of the data and file
 structure The run_col_opt_submission.R is the file to run the optimisation
 (which calls in the functions.R file). The preprocessing_submission.R file
 can be used to create all of the necessary dataframes to run in the
 run_col_opt_submission.R code. Description of .RData files (which must be
 placed with the "Input data" folder) final_species.df.RData -
 Dataframe with species ID, the species class, and the path to the species
 file hat.RData - Species habitat matrix pu.df.RData - Dataframe with
 planning unit id numbers and associated values for each variable
 pu.xy.RData - The x y coordinates of the planning units puid_v3.RData -
 Planning unit id numbers species.df.RData - Dataframe with species id,
 taxon group, area of occurrence, and current area of occupancy
 species.pu.list.RData - A list of all of the planning units that each
 species occurs in The code is set up so that the name of the input
 rasters/shp files are not that important, as long as the folder names are
 correct. All of this data feeds into the preprocessing_submission.R code.
 The folders are: land_use_map - current land use map presettlement_habitat
 - historial potential ecosystem layer carbon - potential for carbon
 sequestration carbon_max - potential for carbon sequestration upper limit
 carbon_min - potential for carbon sequestration lower limit oppcost -
 opportunity cost pnr - potential for natural regeneration actcost -
 establishment cost (assuming active regeneration) species - species data
 The stages of the preprocessing_submission.R code is: 1. Land use 2.
 Presettlement ecosystems 3. Area available for restoration 4. Planning
 units 5. Area currently forested 6. Extract - Carbon and cost 7. Species
 8. Presettlement habitat 9. Create habitat matrix (which species is
 associated with which ecosystem) 10. Create species dataframe The mapdata
 folder contains some basic spatial data to create maps to display the
 results of the optimisation including country boundaries and topographic
 rasters \## Sharing/Access information Links and citations to other
 publicly accessible locations of the data (all other data is available
 through the published article, species distribution models not yet
 publicly available for download are available upon request from Instituto
 Humboldt (the Humboldt Institute) ): Land
 use IDEAM. (2018). *Mapa de Coberturas de la Tierra Metodología Corine
 Land Cover Escala 1:100.000 Periodo*.
 Presettlement ecosystems
 Etter, A., Andrade, A., Saavendra, K., Amaya, P., Arevalo, P., Andrade,
 Á., & Arévalo, P. (2017). *Risk assessment of Colombian ecosystems: An
 application of the Red List of Ecosystems methodology (Vers. 2.0)*. Carbon
 Broadbent, E., & Zambrano, A. (2021). *Global Aboveground biomass
 Potential (GAP)*.
 Opportunity cost SEPAL. (2023). *Cost data layers*. Species Velásquez-Tibatá, J., Olaya-Rodríguez, M. H., López-Lozano, D., Gutiérrez, C., González, I., & Londoño-Murcia, M. C. (2019). BioModelos: A collaborative online system to map species distributions. *PloS One*, *14*(3), e0214522. Astorquiza Onofre, J. M. (2022). Patrones biogeográficos de diversidad Alfa, Beta y funcional de especies de murciélagos (mammalia, chiroptera) y su representatividad en el sistema nacional de áreas protegidas en Colombia. *Masters Thesis, Universidad de Nariño*. Ayerbe-Quiñones, F. A. (2018). Guía Ilustrada de la Avifauna Colombiana. *Panamericana Formas e Impresos S. A, Bogota, Colombia.* Vélez, D., Tamayo, E., Ayerbe-Quiñones, F., Torres, J., Rey, J., Castro-Moreno, C., Ramírez, B., & Ochoa-Quintero, J. M. (2021). Distribution of birds in Colombia. *Biodiversity Data Journal*, *9*, e59202. Ramírez Chávez, H., Muñoz Rodríguez, C. J., Chacón Pacheco, J., Cepeda Duque, J. C., Pérez Torres, J., Vides Avilez, H. A., Castaño Salazar, J. H., Torres Martínez, M. M., Mejía Fontecha, I. Y., Mejía Correa, J. S., Concha Osbahr, D. C., Osbahr Hansen, K., Rojano Bolaño, C., Lizcano, D. J., Noguera Urbano, E. A., & Cruz Rodríguez, C. A. (2022). Atlas de la Biodiversidad de Colombia. Grandes Roedores: Mejores modelos con el apoyo de expertos. *Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Bogotá DC, Colombia.*, 33. Londoño, M., Olaya, M. H., Bello, C., González, I., Gutiérrez, C., López, D., & Velásquez, J. (2015). Regiones bióticas delimitadas utilizando como unidad de análisis los polígonos resultados del proceso de delimitación por la unidad ejecutora. Un mapa para cada grupo taxonómico: aves, mamíferos y herpetos y un mapa consenso. *Laboratorio de Biogeografía Aplicada y Bioacústica. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt.* \## Code/Software R -   Gurobi - (there is an alternative ranking algorithm provided in the code where gurobi is not needed) R packages used in code gurobi, rgdal, raster, sp, rgeos, stringr, foreign, maptools, sf, rgeos, fasterize, dplyr, terra, sjmisc, rredlist, redlistr, tidyverse, data.table, rjson, spatialEco