matthewclark1223/RegenerativeRangelandManagementABM

Repository for: 'Minimizing climate change adaptation trade-offs in African rangelands' # RegenerativeRangelandManagementABM This repository contains the code for the agent-based model and experiments used in the study "Minimizing climate change adaptation trade-offs in African rangelands," which explores how pairing forecasts with conservation programming can reduce trade-offs among vegetation cover, livestock numbers, and wealth inequality, supporting effective adaptation to climate change at scale. ## Repository structure - **RunModel.R**: Code for the agent-based model (ABM). Sources landscape files from `StylizedLandscape/` and precipitation from `PrecipTimeseries.R` to create the initial landscape and generate precipitation scenarios. Runtime for one full setting across the three climate scenarios is ~45 minutes. It also supports a toy quickstart via environment variables. - **RunToy.R**: Code for a toy run of ``RunModel.R``. - **GeneratePlots.R**: Code to reproduce visualizations for experiments 1-3 and supplementals from the manuscript. It expects ABM outputs in a Ts_Data-style folder (see below). - **ExperimentConditions.rds**, **ConditionsExp1.rds**: Predefined experimental settings for running the ABM, used in manuscript experiments 1 to 3 and supplemental material (forecast settings, provision of supplemental fodder, social learning settings, etc.). The file ``ConditionsExp1.rds`` includes the conditions for experiment 1 only to run the toy example. - **StylizedLandscape/**: Scripts and inputs for creating the stylized landscape. - **AncillaryScripts/**: Ancillary scripts and inputs used for deriving parameters and settings for the ABM. To run `PredictedRainfall.R`, download precipitation projection data. ## Prerequisites - R ≥ 4.1 - Download the file povmap-grdi-v1.tif and place it inside the `StylizedLandscape/` folder. - Packages for ABM and visualizations Run the following code within an R session (e.g., RStudio or the R console): ```r packages <- c("cowplot", "data.table", "doParallel", "doRNG", "dplyr", "foreach", "forcats", "fs" …