bringing the forest back: restoration priorities in Colombia

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..., 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., R - https://www.r-project.org/Â Gurobi - https://www.gurobi.com/ (there is an alternative ranking algorithm provided in the code where gurobi is not needed), \# 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 ...研究目标：哥伦比亚已设定极具雄心的森林修复目标，其中包含一项100万公顷的波恩挑战（Bonn Challenge）承诺，以及根据《国家修复计划》制定的647万至831万公顷修复目标（分别对应生态重建与生态修复）。如何确定项目实施的区位与方案以达成上述目标，仍是一项重大挑战。 研究区域：哥伦比亚 研究方法：本研究采用面向修复规划的多目标优化框架，并将其应用于哥伦比亚。我们探索兼具成本效益的解决方案：在权衡修复的机会成本与营建成本的同时，充分挖掘辅助自然再生（assisted natural regeneration）的效益潜力，最大化生物多样性保护与气候变化减缓收益。本研究针对四个具有政策相关性的修复面积目标（100万、600万、647万及831万公顷）展开分析，识别出最小成本方案，以及多组最优收益与成本效益兼备的解决方案。 研究结果：我们识别出可在生物多样性维度同时表现优异的解决方案……本研究采用空间优先级排序（spatial prioritisation）方法，即借助计算工具实现行动的合理空间配置或土地用途布局，以在选定优先级范围内实现森林修复目标，最大化生物多样性与碳固存（carbon sequestration）收益，同时兼顾营建成本与机会成本。植树造林与大规模场地整治是当前主流的修复策略，虽具备有效性，但在部分场地或大规模实施时成本高昂。若生态条件允许森林自然恢复，或仅需低成本辅助措施，自然再生方法的成本可显著降低。为充分挖掘此类成本潜力，本研究的营建成本估算基于空间显式随机森林模型（random forest model）调整参数，以纳入自然再生的潜在效益。相关工具与平台：R语言（https://www.r-project.org/）、Gurobi优化器（https://www.gurobi.com/，若无需使用Gurobi，代码中提供了替代的排序算法）；# WePlan Colombia 可在Dryad平台获取本数据集：https://doi.org/10.5061/dryad.vx0k6djz1 本研究提供了用于运行分析并生成分析所需数据框的代码，同时也附带了本次分析使用的输入数据框。 ## 数据与文件结构说明 `run_col_opt_submission.R`为优化运行主文件（该文件会调用`functions.R`文件）。`preprocessing_submission.R`可用于生成`run_col_opt_submission.R`代码运行所需的全部必要数据框。 .RData文件说明（需放置于"Input data"文件夹内）： final_species.df.RData：包含物种ID、物种类别以及物种文件路径的数据框 hat.RData：物种生境矩阵 pu.df.RData：包含规划单元ID及各变量对应值的数据框 pu.xy.RData：规划单元的X、Y坐标 puid_v3.RData：规划单元ID编号 species.df.RData：包含物种ID、类群、面积……的数据框