DOI: 10.6084/m9.figshare.19467620.v1
title: Data used in the article "Land sparing and sharing patterns in forestry: exploring even-aged and uneven-aged management at the landscape scale"

description: The dataset contains different types of data, with different origins :

 Data about preliminary simulations, which serves to determine certain parameters for LANDIS-II
 Raster data, text files and a Python Script used to create the scenario files for each LANDIS-II simulation
 
 These files contains LANDIS-II parameters derived from previous studies, such as Boulanger et al. 2017 and Tremblay et al. 2018.
 The origin of each parameter is described in the supplementary material of the article.
 
 
 The entire set of parameter files used for every simulations used in the study, created by the Python script Script_to_create_scenario_files.py
 The Python scripts and Schell scripts used to launch and monitor the simulations and analysis on the supercomputer clusters of Compute Canada
 The resulting .csv files coming from the analysis of the output files of the simulation by the scripts Landis_output_analysis.py, Landis_output_analysis_r.R and Landis_output_analysis_additional_r.R.
 The Python script used to generate the figures of the article.

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The dataset is separated into 3 sections :

 The first section contains the data used to calibrate certain parameters of LANDIS-II : in particular, the data necessary to calibrate the Base Fire extension in our study area, along with the calibration of the surface-biomass harvested relation in order to control the biomass harvested in each scenario (as LANDIS-II can only harvest a surface target, and not a biomass target), as well as the parameters files needed to create the folders with all of the parameters necessary for a LANDIS-II simulation.
 
 Users can find the files to launch the calibration scenario for the parameters of Base Fire, and a table file describing the process of empirical determination of those parameters via repeated calibration simulations (Empirical determination of the fire parameters.ods)
 Users can also find all of the summary logs of the Biomass Harvest extension from the surface-biomass calibration scenarios, along with a Python Script to transform these into a single table used to create linear model for the surface-biomass relation (Biomass harvest outputs to calibration table.py), and the corresponding table (calibration_surface_biomass.csv)
 Users can also find the biomass targets by UA determined via the PAFIT documents of the ministry of forests of Quebec and information about the biomass of the main tree species in the landscape (biomass_targets_by_UAs.csv)
 Finally, users will find the script to create the scenario folders (Script_to_create_scenario_folders.py), along with the parameter files that it uses (/Parameter files to create scenario folders), and the table describing the scenario caracteristics that is read by the script (scenarios_table.csv). Some of these files are not necessary for the simulations proper, but are necessary for the analysis of the scenario outputs.
 
 
 The second section contains all of the scenarios folders with all of the parameters files to launch all of the simulations made for the study. Users can upload those files on Compute Canada clusters to replicate our results if needed, using the job_script_python_robot.sh script to launch a job on the cluster. The job will use the Python_watcher_bot.py script, which will monitor if simulations are running as needed.
 The third section contains all of the files resulting from the simulation and of the analysis of the simulation outputs (/All csv result files), along with the files necessary to produce the figures of the article with the script Analysis and figures.py