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/ 
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 ...