Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)

Title: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change Contact: Viola Heinrich ([email protected]) This repository contains: Zipped folder: Fig1_data_input.zip - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<driver>_assessment_v2.csv". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g.  0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period.  The folder also contains the output regrowth models seen in Figure 1 in the format "regrowth_model_<driver_threshold>.RData" where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.Zipped folder: Fig2_regions_outline.zip - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. Zipped folder: Fig1g_2b_e_variable_importance.zip - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. Zipped folder: Fig3_data_input.zip -  all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<REGION>-Group.csv". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC.  The folder also contains the output regrowth models seen in Figure 3 in the format "regrowth_model_<region_disturbance_type>.RData" where region_disturbance refers to the region and the type of disturbance experienced.  Zipped folder: Fig4_5_carbon_sink_2017.zip - Contains two subfolders: a) Map_aggre_0.1deg -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) secondary_forest_by_region_and_disturbance - this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below).  Code: The corresponding code mentioned here can be access here: heinrichTrees/secondary-forest-regrowth-amazon-public (github.com) Data usage: When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository.  If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]

Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change

Title: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change Contact: Viola Heinrich ([email protected]) This repository contains: Zipped folder: Fig1_data_input.zip - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<driver>_assessment_v2.csv". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g.  0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. Zipped folder: Fig1_confidence_intervals.zip - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limitZipped folder: Fig2_regions_outline.zip - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. Zipped folder: Fig1g_2b_e_variable_importance.zip - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the  random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. Zipped folder: Fig3_data_input.zip -  all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<REGION>-Group.csv". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "<REGION>_whole_class.csv" these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.Zipped folder: Fig3_confidence_intervals.zip - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format <region>_number of the region_<number referring to the disturbance combination>_confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit.  Zipped folder: Fig4_5_carbon_sink_2017.zip - Contains two subfolders: a) Map_aggre_0.1deg -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) secondary_forest_by_region_and_disturbance - this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below).  Code: The corresponding code mentioned here can be access here: heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)  Data usage: When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper.  If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]

Data and code from paper: The carbon sink of secondary and degraded humid tropical forests

This repository contains the data and code produced for the following paper: Title: The carbon sink of recovering secondary and degraded humid tropical forests Contact: Viola Heinrich ([email protected]) Please note: throughout repository where files include reference to: this refers to the Central Africa region as it is termed in the main paper. the code has not been amended for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in R (v3.6) for which the code and output datasets are available here. To access the code used in Google Earth Engine that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4 This repository contains the following zipped folders: data_folder: this folder contains further folders with all the data produced for this paper. Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "regrowthModel_..RDS. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions. Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "regrowthModel_..RDS) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region. Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed. Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2. Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. code_folder.zip: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. Data and Code usage: When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository. Further source data in .xlsx format were also submitted with the main manuscript. If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]

Data and code from paper: The carbon sink of secondary and degraded humid tropical forests, Version 1

This repository contains the data and code produced for the following paper: Title: The carbon sink of recovering secondary and degraded forests Contact: Viola Heinrich ([email protected]) Please note:  throughout repository where files include reference to: <...congo_basin...> this refers to the Central Africa region as it is termed in the main paper.the code has not been amended for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run.  The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in R (v3.6) for which the code and output datasets are available here. To access the code used in Google Earth Engine that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4  This repository contains the following zipped folders: data_folder: this folder contains further folders with all the data produced for this paper. Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "regrowthModel_..RDS. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "regrowthModel_..RDS) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded.  code_folder.zip: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote.  Data and Code usage: When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository.  Further source data in .xlsx format were also submitted with the main manuscript. If you need anything else, please contact the corresponding author: Viola Heinrich ([email protected]

Benchmark maps of 33 years of secondary forest age for Brazil

Title: Benchmark maps of 33 years of secondary forest age for Brazil Contact: Celso H. L. Silva Junior ([email protected]) Data: Increment (Product 1), Extent (Product 2), Loss (Product 3), and Age (Product 4) of Brazilian Secondary Vegetation Coverage: Brazil land Period: 1986 to 2018 (1987 to 2018 for secondary forest loss) Spatial resolution: 30-meters Temporal resolution: Annual Coordinate reference system: Geographic Coordinate System with Datum WGS84 File format: 131 zip files per year (being 33 for increment, 33 for extent, 33 for age and 32 for loss). Each zip file containing eight tiles in compressed TIFF format. Code: https://github.com/celsohlsj/gee_brazil_sv Dataset usage: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration. For the use of this dataset, please cite: Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G., Broggio, I.S., Rosan, T.M., Doblas, J., Anderson, L.O., Rousseau, G.X., Shimabukuro, Y.E., Silva, C.A., House, J.I., Aragão, L.E.O.C. Benchmark maps of 33 years of secondary forest age for Brazil. Scientific Data (2020). https://doi.org/10.1038/s41597-020-00600-

Additional file 1: of Germline mutations in BRCA1 and BRCA2 in epithelial ovarian cancer patients in Brazil

Supplementary Methods. Table S1. Clinical and pathological characteristics, BRCA sequencing and MLPA results. Table S2. BRCA1 gene variants. Table S3. BRCA2 gene variants. (ZIP 73.7 kb