DOI: 10.5061/dryad.cjsxksngf
title: Metabolic growth mechanisms and theoretical growth potential of global woody plant communities

description: Predicting the growth and maximum biomass (Mmax) of woody plant
 communities (WPCs) is challenging due to the complexity and variability of
 tree growth. While Metabolic Scaling Theory (MST) offers a promising
 concept, its current theoretical framework is still insufficient. Here, we
 applied MST principles and our previous findings to propose an iterative
 growth model for the growth and NPP of WPCs (IGMF). This model and its
 extension show that WPC growth, net primary productivity, and other carbon
 budgets - such as total primary productivity, autotrophic respiration,
 organ turnover biomass, and non-structural carbohydrates - can be
 expressed as functions of current biomass, maintenance respiration rate
 per unit biomass, and stand age or Mmax. These globally convergent
 functions allow estimation of the current (2018–2020) global
 Mmax of woody plant communities at 1,440  ±  26 Pg, based solely
 on their current state, with an additional 510 Pg of remaining biomass
 potential. However, machine learning projections suggest that this
 potential may decline by 246 Pg by 2100, primarily in evergreen broadleaf
 forests. Species richness, by promoting functional convergence, amplifies
 the negative effects of temperature and precipitation seasonality on Mmax.
 In contrast, warming in the Northern Hemisphere may enhance Mmax in open
 shrublands. Our findings reveal WPC growth kinetics and show a shift in
 the main contributor to terrestrial carbon sequestration from forests to
 shrublands.
description: # Metabolic growth mechanisms and theoretical growth potential of global
 woody plant communities #### Overview This dataset (submitCode_(2).zip) is
 a complementary compilation designed to support research on maximum
 biomass (Mₘₐₓ), carbon fluxes (e.g., NPP, GPP), and their biogeographical
 patterns, drivers, and future changes across global woody plant
 communities (WPCs). They integrate field measurements, database
 extractions, digitized figure data, and processed global raster products,
 with consistent curation to ensure compatibility for modeling, validation,
 and biogeographical analysis. The main folder submitCode.zip contains the
 following files: **keydata.csv** Integrates field-based measurements from
 multiple studies, with the core component being standardized data from
 Michaletz et al. (2014) — covering biomass, growth, mortality, and climate
 variables for 1,247 WPCs across broad climatic gradients. Supplementary
 data from Chen et al. (2016) and Sullivan et al. (2020) are included to
 enhance coverage of boreal and tropical forest dynamics. This dataset
 serves as the primary input for model fitting (e.g., IGMF
 parameterization) and initial validation. **ForC.csv** Contains harmonized
 records of ecosystem carbon stocks and fluxes extracted from the Global
 Forest Carbon (ForC) database (Anderson-Teixeira et al. 2018). Key
 variables include NPP, GPP, biomass, mortality, stand age, and mean annual
 temperature (MAT), with biomass-related values standardized to grams of
 biomass (assuming 50% carbon content). It is used for cross-validating
 model predictions of carbon fluxes. **Fig.csv** Comprises digitized data
 from published scientific figures, focusing on bioclimatic trend data such
 as biome-level tree lifespan–MAT relationships (Locosselli et al. 2020).
 These data are used to compare model-derived predictions of functional
 traits (e.g., lifespan) with observed biogeographical patterns.
 **xex.csv** Compiles field measurements of mature or old-growth forests
 extracted from the literature (e.g., Anderson-Teixeira et al. 2016; see
 Appendix S1: Table S2 for full citations). It includes variables related
 to aboveground biomass, growth, and mortality, and is used to validate
 model predictions of biomass dynamics in late-successional ecosystems.
 **global.csv** Contains site- and grid-level environmental variables
 extracted from global raster datasets via Google Earth Engine (GEE) and
 WorldClim. Variables include productivity (e.g., MODIS products), land
 cover, 19 bioclimatic variables, atmospheric CO₂ concentrations, soil
 properties (texture, moisture, organic matter), species richness, and
 future climate projections (2020–2100). All values are processed
 derivatives (spatial sampling, temporal averaging, or standardized
 products) rather than raw raster files, tailored for global-scale Mₘₐₓ
 estimation and future change projections. Unified Data Processing: All
 datasets underwent systematic curation, standardization, and harmonization
 to ensure consistency. Biomass conversion: Aboveground biomass was scaled
 to total biomass using taxon-specific ratios. WPC classification: Pixels
 were identified as WPCs if vegetation cover exceeded 0.2 (based on land
 cover datasets). NPP partitioning: NPP was split into structural (NPPₛ)
 and non-structural (NPPₙ) components to align with model requirements.
 Unit harmonization: Carbon fluxes (NPP/GPP) and biomass variables were
 standardized to consistent units for cross-dataset comparison. Note:
 Processed raster derivatives (e.g., WorldClim bioclimatic variables, MODIS
 productivity) comply with open-access license requirements and are ready
 for direct use in biogeographical analysis, ecosystem modeling, and
 climate change impact assessments. #### Data Availability **Dataset**
 **Corresponding Figures** keydata.csv: Fig.2A/B, Fig.3B/C/D ForC.csv:
 Fig.2C/D, Fig.3D Fig.csv: Fig.3A xex.csv: Fig.3C global.csv: Fig.4, Fig.5,
 Fig.6, Fig.7, Fig.S5, Fig.S6, Fig.S7, Fig.S10 #### Data Sources All
 original data sources are publicly available with the following corrected
 citations and access links (addressed link confusion, invalid paths, and
 misaligned references): 1\. Core Field & Database Data Michaletz et
 al. (2014): Convergence of terrestrial plant production across global
 climate gradients (Nature).
 [https://doi.org/10.1038/nature13470](https://doi.org/10.1038/nature13470)
 Chen et al. (2016): Climate change-associated trends in net biomass change
 are age dependent in western boreal forests of Canada (Ecology Letters).
 [https://doi.org/10.1111/ele.12649](https://doi.org/10.1111/ele.12649)
 Note: Separated from Luo et al. (2019) (previously conflated); this is the
 original research article for boreal forest dynamics, while Luo et al.
 (2019) is the associated dataset. Luo et al. (2019): Data from: Divergent
 temporal trends of net biomass change in western Canadian boreal forests
 (Dryad Dataset).
 [https://doi.org/10.5281/dryad.8bg44b0](https://doi.org/10.5281/dryad.8bg44b0) Anderson-Teixeira et al. (2018) (ForC database): ForC: A global database of forest carbon stocks and fluxes (Ecology). Database access: [https://github.com/forc-db/ForC](https://github.com/forc-db/ForC); Original article: [https://doi.org/10.1002/ecy.2408](https://doi.org/10.1002/ecy.2408) Sullivan et al. (2020): Long-term thermal sensitivity of Earth’s tropical forests (Science) & associated data. Research article: [https://doi.org/10.1126/science.aaw7578](https://doi.org/10.1126/science.aaw7578); Data package: [http://dx.doi.org/10.5521/forestplots.net/2020_2](http://dx.doi.org/10.5521/forestplots.net/2020_2) Locosselli et al. (2020): Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature (PNAS).  [https://www.pnas.org/doi/10.1073/pnas.2003873117](https://www.pnas.org/doi/10.1073/pnas.2003873117) Anderson-Teixeira et al. (2016): Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db) (Global Change Biology). [https://doi.org/10.1111/gcb.13226](https://doi.org/10.1111/gcb.13226) 2\. Global Raster Data Sources (for global.csv)\ The global raster data used to generate global.csv were extracted and processed as described in the Methods section of the manuscript, including MODIS products (productivity, land cover), WorldClim bioclimatic variables and future projections, Google Earth Engine processing, soil properties, biome classification, and species richness layers. Variable Definitions All variable names, descriptions, and units used in the datasets are provided in Variable_names.csv. This file contains the following columns: Variable. Name — the name of the variable used in the datasets. Units — the units of measurement for each variable.Description — a brief explanation of the variable, including ecological or methodological context where applicable. Readers can refer to Variable_names.csv for complete and standardized information on all variables used in keydata.csv, ForC.csv, Fig.csv, xex.csv, and global.csv. #### R Code Description Figs 2_and_3_(including S5-S7).R: Plot Fig.2-3 & Fig.S5-S7 (s=supplementary): This code loads forest productivity datasets, fits two nonlinear biomass-temperature-age models, uses them to generate NPP predictions, and produces multiple comparative plots (A–D) evaluating fitted vs. observed NPP and related ecological parameters. Figs_4_5_6_and_7_(including_s10).R: Plot Fig.4-7 & Fig.S10 (s=supplementary): This code rasterizes global point data to analyze and visualize the spatial and latitudinal patterns of *M*ₘₐₓ and its normalized ratio (*M*/*M*ₘₐₓ), comparing overall trends and differences between forest and shrub vegetation types using maps, latitudinal profiles, and boxplots.