DOI: 10.5061/dryad.cjsxksnhj
title: Global tiger density linked with forest carbon stock, top-down and bottom-up

description: Tiger (Panthera tigris) survival, as apex predators in forest ecosystems,
 largely depends on abundant prey in healthy, intact forests. Because large
 herbivore prey are drivers of plant biomass, we reasoned that tiger
 distribution and density are probably also closely linked with forest
 carbon (C) stock, the management of which is critical for mitigating
 climate change. However, whether tigers exert top-down control of forest C
 stocks or are passive surrogate C indicators bottom-up is a salient
 unanswered question in conservation and management, particularly in
 trophic rewilding. Here, we compiled estimates of global tiger presence
 and density to test the top-down effects of tigers on forest C stocks and
 tiger-carbon relationships along a gradient from “empty forests” without
 tigers to “target state” ecosystems with tigers living at different
 abundances. Our results showed that tiger presence was associated with
 higher forest vegetation C stocks, lower C emissions, and higher C inputs
 globally. Top-down effects via ungulate biomass were stronger in less
 established forests. Furthermore, forest vegetation or soil C stocks
 increased with tiger density or reached tiger-carbon peaks in four forest
 habitat types covering most of the tiger range. Our findings reveal that
 tigers, represented by their presence and density, are both an indicator
 and a driver of forest ecosystem C stocks, depending on underlying
 ecological conditions, and could safeguard forests against future C
 emissions and improve our understanding of climate-C cycle feedback.
description: # Global tiger density linked with forest carbon stock, top-down and
 bottom-up Access this dataset on Dryad DOI:
 [10.5061/dryad.cjsxksnhj](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](https://doi.org/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](https://doi.org/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](https://doi.org/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](https://doi.org/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](https://doi.org/10.1111/gcb.70191)): Bisht, S., Banerjee, S., Qureshi, Q., & Jhala, Y. (2019). Demography of a high-density tiger population and its implications for tiger recovery. *Journal of Applied Ecology, 56*(7), 1725-1740. doi:10.1111/1365-2664.13410. Chanchani, P., Bista, A., Warrier, R., Nair, S., Sharma, R., Hasan, D., & Gupta, M. (2014). *Status and conservation of tigers and their prey in the Uttar Pradesh Terai*. WWF-India, New Delhi. Darman, Y., & Bardyuk, V. V. (2021). 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