Access this dataset on Dryad DOI: 10.5061/dryad.cjsxksnhj

Description of the data and file structure

Each script starts with loading the required packages, reading in the data, and converting categorical variables to factors, and describes the analysis workflow. The main scripts are noted from 1 to 4.

Data files contain extracted carbon, biodiversity, and environmental values within an area of interest (rows) derived from corresponding raster data in ArcGIS with either the Zonal Statistics as Table tool or Sample tool, with Python code, variable descriptions, and source information provided below.

Data files and variables

File: anosim_and_adonis_input_data.txt

Description: For manuscript section, “Testing for top-down effects of tiger density on forest carbon stocks”. These data are to be used together with file anosim_and_adonis_code.R. Data in all columns except ‘habitat’, ‘Tigers’, and ‘carboncluster’ are mean values within an area of interest (rows) derived from corresponding raster data in ArcGIS with the Zonal Statistics as Table tool (see Zonal_Statistics_as_Table.py), with sources provided in Access Information below.

Variables

• habitat: 104: temperate forest; 105: subtropical/tropical dry forest; 106: subtropical/tropical moist lowland forest; 109: subtropical/tropical moist montane forest
• carboncluster: low/intermediate or high AGBC/BGBC carbon cluster
• agb: aboveground biomass carbon (MgC/ha)
• bgb: belowground biomass carbon (MgC/ha)
• soc: soil organic carbon (MgC/ha, 0-100 cm depth)
• Tigers: tiger density (individuals/100km2)
• MAP: mean annual precipitation (mm)
• MAT: mean annual temperature (degrees C)
• treediv: tree species richness (species/ha)

File: Cliffs Delta AGB input data.txt

Description: For manuscript section, “Comparing tiger-present and tiger-absent forests”. These data are to be used together with file Cliffs Delta AGB code.R. Column ‘AGB’ is mean values within an area of interest (rows) derived from corresponding raster data in ArcGIS with the Sample tool (see Sample.py), with sources provided in Access Information below.

Variables

• Population: A-Restoration: Tigers absent; C-Species: Tigers present
• habitat: 1.1: boreal forest; 1.4: temperate forest; 1.4NEA: temperate forest (Northeast Asia only); 1.5: subtropical/tropical dry forest; 1.6: subtropical/tropical moist lowland forest; 1.7: subtropical/tropical mangrove vegetation above high tide level; 1.8: subtropical/tropical swamp; 1.9: subtropical/tropical moist montane forest; 1.9Sum: subtropical/tropical moist montane forest (Sumatra only)
• AGB: aboveground biomass carbon (MgC/ha)

File: interaction_model_input_data.txt

Description: For manuscript section, “Indirect tiger-carbon relationships via interactions with other wildlife diversity”. These data are to be used together with file interaction_model_code.R. Data in all columns except ‘habitat’, ‘Tigers’, and ‘ungratio’ are mean values within an area of interest (rows) derived from corresponding raster data in ArcGIS with the Zonal Statistics as Table tool (see Zonal_Statistics_as_Table.py), with sources provided in Access Information below. Where the value is NA (one case, ‘Ungratio’) it indicates that the ratio of ungulates per tiger could not be calculated because the number of tigers was zero. This single row of data was excluded from analyses of ‘Ungratio’.

Variables

• habitat: 104: temperate forest; 105: subtropical/tropical dry forest; 106: subtropical/tropical moist lowland forest; 109: subtropical/tropical moist montane forest.
• AGB: aboveground biomass carbon (MgC/ha).
• SOC: soil organic carbon (MgC/ha, 0-100 cm depth).
• Tigers: tiger density (individuals/100km2). Source: 
• BIIrich: Biodiversity Intactness Index (species richness).
• BIIabund: Biodiversity Intactness Index (abundance).
• Ungulates: ungulate biomass (kg/km2).
• diversity1ungulates243: ungulate species richness.
• diversity2ungulates243lagomorphs92: combined ungulate and lagomorph species richness.
• diversity3carnivores251: carnivore species richness.
• ungratio: ungulate biomass per tiger per 100km2. 

File: Permutation input data.txt

Description: For manuscript section, “Testing for top-down effects of tiger density on forest carbon stocks”. These data are to be used together with file Permutation code.R. Data in all columns except ‘habitat’, ‘Tigers’, and ‘carboncluster’ are mean values within an area of interest (rows) derived from corresponding raster data in ArcGIS with the Zonal Statistics as Table tool (see Zonal_Statistics_as_Table.py), with sources provided in Access Information below.

Variables

• carboncluster: low: low/intermediate AGBC/BGBC forests; high: high AGBC/BGBG forests
• habitat: 104: temperate forest; 105: subtropical/tropical dry forest; 106: subtropical/tropical moist lowland forest
• Tigers: tiger density (individuals/100km2)
• Ungulates: ungulate biomass (kg/km2)
• diversity1ungulates243: ungulate species richness
• diversity2ungulates243lagomorphs92: combined ungulate and lagomorph species richness
• diversity3carnivores251: carnivore species richnessAGB: aboveground biomass carbon (MgC/ha)

Code files

1 - anosim_and_adonis_code.R

Description: Anosim and adonis tests of the input data (anosim_and_adonis_input_data.txt) to analyse drivers of forest carbon stock variation, including forest habitat type, tiger density, and other environmental factors. Code includes rescaling continuous variables by forest habitat type, creating subsets for ‘low’ and ‘high ‘ carbon forests, and permutations for adonis tests. This code can be used to reproduce results presented in the full manuscript (DOI: 10.1111/gcb.70191) under section “Testing for top-down effects of tiger density on forest carbon stocks”.

2 - Cliffs Delta AGB code.R

Description: Cliff’s Delta (δ) of the input data (Cliffs Delta AGB input data.txt) to compare aboveground biomass carbon stocks in the tiger-present and tiger-absent forests for each forest habitat type. Code first filters the data for the forest habitat type of interest, and includes calculating bootstrapped confidence intervals. Code to be run separately for each forest habitat type/region subset by adjusting the subset value to the corresponding habitat type/region of interest. This code can be used to reproduce results presented in the full manuscript (DOI: 10.1111/gcb.70191) under section “Comparing tiger-present and tiger-absent forests”.

3 - interaction_model_code.R

Description: Generalized Additive Model (GAM) with tensor product smooth terms of the input data (interaction_model_input_data.txt) to test nonlinear interaction between tiger density and diversity/abundance metrics as predictors of aboveground biomass carbon stock. Code includes model validation in the form of leave-one-out cross validation, and the calculation and printing of root mean square error (RMSE) values. Code to be run separately for each forest habitat type/region and variable of interest by adjusting the subset value to the corresponding habitat type/region of interest and replacing ‘Ungulates’ in the GAM model to alternative variables (columns in the input data). This code can be used to reproduce results presented in the full manuscript (DOI: 10.1111/gcb.70191) under section “Indirect tiger-carbon relationships via interactions with other wildlife diversity”.

4 - Permutation code.R

Description: Exact permutation test to compare wildlife diversity/abundance between forests with ‘low’ and ‘high’ carbon (respectively ‘low’ and ‘high’ carbon clusters in input data Permutation input data.txt). Code includes rescaling continuous variables by forest habitat type. This code can be used to reproduce results presented in the full manuscript (DOI: 10.1111/gcb.70191) under section “Testing for top-down effects of tiger density on forest carbon stocks”.

File: Sample.py

Description: This code extracts values of all pixels within an area of interest in a raster file and was originally run in ArcGIS 10.3 (‘Sample’ tool) for both tiger-present and tiger-absent forests in all nine forest habitat types and regions. Data obtained from each run, e.g., aboveground biomass carbon values for all pixels within tiger-present temperate forests in Northeast Asia, were composited and arranged to form Cliffs Delta AGB input data.txt which was then run with code Cliffs Delta AGB code.R to compare aboveground biomass carbon stocks in tiger-present vs. tiger-absent forests in each forest habitat type and region. 

File: Zonal_Statistics_as_Table.py

Description: This code extracts mean values of all pixels within an area of interest in a raster file and was originally run in ArcGIS 10.3 (‘Zonal Statistics as Table’ tool). Mean values obtained for each area of interest (i.e., ‘zone’), such as a specific forest habitat type or region, or tiger conservation landscape, were arranged to form anosim_and_adonis_input_data.txt, interaction_model_input_data.txt, and Permutation input data.txt as input data to the respective analyses described above.

Access information

Data were derived from the following sources, described in more detail in Tables S1 and S7 of Roberts, N.J. et al. (2025) (https://doi.org/10.1111/gcb.70191):

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