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Heterogeneous climatic controls on tropical-forest biomass

Nature作者：Matheus Henrique Nunes2026年8月12日正文已收录本站

Data availability

The data supporting the findings of this study are publicly available via Zenodo at https://doi.org/10.5281/zenodo.19474558 (ref. 105). Version 1 contains forest structure and environmental data at the GEDI footprint level, whereas version 2 contains forest structure and environmental data aggregated to 2.5 × 2.5-km grid cells. Source datasets used in this study are publicly available and include NASA GEDI Level 4A AGB density data (https://doi.org/10.3334/ORNLDAAC/2056), WorldClim climate data (https://worldclim.org/data/worldclim21.html), the Global SPEI database (SPEIbase v.2.10 (Earth Engine Data Catalog and Google for Developers)), CHIRPS precipitation data (https://www.chc.ucsb.edu/data/chirps), the CHIRPS-derived MCWD dataset (https://doi.org/10.5281/zenodo.4903340), MODIS MOD09GA version 6 surface reflectance data (https://lpdaac.usgs.gov/products/mod09gav061/), ERA5 reanalysis data (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels), World Wide Lightning Location Network lightning data (https://doi.org/10.5281/zenodo.10725446), SoilGrids soil data (https://soilgrids.org), OpenLandMap soil data (https://openlandmap.org), SRTM elevation data from NASA Earthdata (https://earthdata.nasa.gov) and GLAD intact forest landscapes and global land cover datasets (https://glad.umd.edu). All processed data required to reproduce the analyses are available in the Zenodo repository.

Code availability

Custom R scripts used for data processing, statistical analyses and figure generation are publicly available via Zenodo at https://doi.org/10.5281/zenodo.19474558 (ref. 105).

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Acknowledgements

We thank L. B. Sagang, S. Worden, G. Reynolds, J. L. Campana Camargo, T. N. Nana, D. Rappaport and T. de Conto for suggestions and insights in the preliminary phase of the manuscript, and N. Gonçalves and D. Rappaport for comments on the final version of the manuscript.

Funding

We acknowledge funding from the NASA contract NNL 15AA03C for the development and execution of the GEDI mission, including funding to M.H.N., A.P. and R.D.

Author information

Authors and Affiliations

  1. NASA Global Ecosystem Dynamics Investigation (GEDI), Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Matheus Henrique Nunes, Adrian Pascual & Ralph Dubayah

  2. Smithsonian Tropical Research Institute, Balboa, Panama City, Panama

    Helene C. Muller-Landau

  3. Department of Forest Engineering, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, Brazil

    Eric Bastos Görgens

Authors

  1. Matheus Henrique Nunes
  2. Helene C. Muller-Landau
  3. Eric Bastos Görgens
  4. Adrian Pascual
  5. Ralph Dubayah

Contributions

Conceptualization: M.H.N. Methodology: M.H.N., H.C.M.-L., E.B.G., A.P. and R.D. Investigation: M.H.N. and H.C.M.-L. Visualization: M.H.N. Funding acquisition: R.D. Writing (original draft): M.H.N. Writing (review and editing): M.H.N., H.C.M.-L., E.B.G., A.P. and R.D.

Corresponding author

Correspondence to Matheus Henrique Nunes.

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Competing interests

The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Separate LME models for each ecoregion based on footprint-level GEDI data.

Standardized estimates (global scaling, ecoregional centring) of the top 3 most influential environmental variables based on their coefficient values based on LME models for each ecoregion. LME models incorporated climatic (aridity, drought, MAT and storm), soil (clay content and CEC), and topographical (TPI at 150 and 1,000 m, and slope) predictors, along with their interactions. LME models specified a 10 × 10 km spatial grid as random effect to account for spatial autocorrelation. Values to the left of the zero line indicate negative model estimates, and those to the right indicate positive estimates. Sample sizes: Amazon n ≈ 13,747,868; Congo Basin n ≈ 1,637,208; Southeast Asia n ≈ 397,734 footprints. Error bars represent mean ± 95% confidence intervals. We show one panel for each ecoregion, considering six ecoregions in Amazonia (Northwestern Amazon, Southwestern Amazon, Western Guiana Shield, Central Amazon, eastern Guiana Shield and Southern Amazon), five ecoregions in the Congo Basin (Atlantic coastal forests, Northwestern Congolian, Swamp forests, Central Congolian and Northeastern Congolian), and two in Southeast Asia (Sumatra and Borneo).

Extended Data Fig. 2 Aridity and storm indices derived from separate PCAs.

PCA with the three biogeographical regions (Amazon, Congo Basin and Southeast Asia) combined shows that: a) one main axis of variation corresponds to aridity, with low values corresponding to high water availability and high values corresponding to prolonged dry seasons with clear skies. The shaded areas correspond to the climatic envelope based on 10 climatic variables, namely, MAT, MAP, N° days ≥ 20 mm year−1), MCWD, lightning, CAPE, N° months ≤ 100 mm year−1, temperature seasonality, precipitation seasonality and N° of clear days. MCWD has negative values, with the highest value as 0. It takes value 0 in everwet forests in which precipitation exceeds potential evapotranspiration in every month of the year. Thus, higher MCWD values correspond to wetter conditions. Bold names indicate variables that had a higher contribution to the aridity axis, whereas the other variables did not contribute to this aridity dimension (MAT, lightning and CAPE); b) another main axis of variation corresponds to convective storms, with low values corresponding to low storm intensity and lightning density, and high values corresponding to high probability of windthrow and lightning density. We also included first- and second-order excess CAPE variables, defined as CAPE values above 1023 J kg−1, with values below this threshold set to zero, to account for disproportionately high CAPE values that have a high probability of windthrows. The shaded areas correspond to the storm index based on lightning density and CAPE. Climatic variables at the aggregated level were used for both PCAs, with sample sizes as: Amazon n ≈ 216,667; Congo Basin n ≈ 40,306; Southeast Asia n ≈ 29,436.

Extended Data Fig. 3 Correlation analysis among independent climatic, soil and topographical variables in the Amazon.

Environmental variables are MAT, the aridity index from PCA, drought calculated from SPEI, the storm index also from PCA, CEC, soil clay content (Clay), TPI in two scales, 150 and 1,000 m, as well as slope. Colour and shade intensity reflect the direction and magnitude of the correlation. The pairwise scatter plots in the lower triangle correspond to the relationship between individual environmental variables. Sample sizes: Amazon n ≈ 216,667.

Extended Data Fig. 4 Correlation analysis among independent climatic, soil and topographical variables in the Congo Basin.

Environmental variables are MAT, the aridity index from PCA, drought calculated from SPEI, the storm index also from PCA, CEC, soil clay content (Clay), TPI in two scales, 150 and 1,000 m, as well as slope. Colour and shade intensity reflect the direction and magnitude of the correlation. The pairwise scatter plots in the lower triangle correspond to the relationship between individual environmental variables. Sample sizes: Congo Basin n ≈ 40,306.

Extended Data Fig. 5 Correlation analysis among independent climatic, soil and topographical variables in Southeast Asia.

Environmental variables are MAT, the aridity index from PCA, drought calculated from SPEI, the storm index also from PCA, CEC, soil clay content (Clay), TPI in two scales, 150 and 1,000 m, as well as slope. Colour and shade intensity reflect the direction and magnitude of the correlation. The pairwise scatter plots in the lower triangle correspond to the relationship between individual environmental variables. Sample sizes: Southeast Asia n ≈ 29,436.

Extended Data Fig. 6 Effects of interactions between environmental variables on predicted AGB, relative to the regional mean AGB, in the Amazon.

Grey lines are the 10% lowest quantile and black curves are the 90% highest quantile of the variable displayed in the title (representing the modifier variable) interacting with the x-axis variable. We show the effects of a) ln(MAT) with storms; b) storms with ln(MAT); c) ln(MAT) with soil clay content; d) aridity with drought; e) drought with aridity; f) ln(MAT) with soil CEC; g) drought with slope; h) aridity with storms; i) storms with aridity; j) ln(MAT) with TPI (1000); k) ln(MAT) with TPI (150); l) ln(MAT) with aridity; m) aridity with ln(MAT); n) aridity with TPI at 150 m scale; o) drought with TPI at 150 m scale; and p) aridity with soil CEC. Sample size = 216,667. Predictions were based on LME models.

Extended Data Fig. 7 Effects of interactions between environmental variables on predicted AGB, relative to the regional mean AGB, in the Congo Basin.

Grey lines are the 10% lowest quantile and black curves are the 90% highest quantile of the variable displayed in the title (representing the modifier variable) interacting with the x-axis variable. We show the effects of a) ln(MAT) with ln(CEC); b) ln(MAT) with aridity; c) ln(MAT) with TPI at 150 m scale; d) ln(MAT) with soil clay content; e) aridity with ln(MAT); f) aridity with soil CEC; g) aridity with slope; h) aridity with TPI at 150 m scale; i) aridity with storms; j) aridity with drought; k) drought with aridity; and l) drought with TPI at 150 m scale. Sample size = 40,306. Predictions were based on LME models.

Extended Data Fig. 8 Effects of interactions between environmental variables on predicted AGB, relative to the regional mean AGB, in Southeast Asia.

Grey lines are the 10% lowest quantile and black curves are the 90% highest quantile of the variable displayed in the title (representing the modifier variable) interacting with the x-axis variable. We show the effects of a) ln(MAT) with TPI at 150 m scale; b) ln(MAT) with aridity; c) aridity with ln(MAT); d) aridity with storms; e) aridity with soil CEC; f) aridity with drought; g) aridity with slope; h) aridity with TPI at 1000 m scale; i) drought with aridity; j) drought with clay; k) storms with slope; and l) storms with aridity. Sample sizes = 29,436. Predictions were based on LME models.

Extended Data Fig. 9 Climatic, soil and topographical variability across tropical forests.

Box plot variation of environmental variables across six ecoregions in the Amazon (Northwestern Amazon, Southwestern Amazon, Western Guiana Shield, Central Amazon, eastern Guiana Shield and Southern Amazon), five ecoregions in the Congo Basin (Atlantic coastal forests, Northwestern Congolian, Swamp forests, Central Congolian and Northeastern Congolian) and two ecoregions in Southeast Asia (Sumatra and Borneo). The environmental variables depicted are the ones used explicitly in the LME models: a) MAT (°C), b) aridity [-], which is the PCA axis of climatic variables that reflect seasonality, precipitation, solar radiation and water deficit throughout the year, c) drought magnitude [-], expressed as absolute values of the cumulative SPEI, d) storms [-], based on another PCA axis of CAPE and lightning frequency, e) CEC (cmolc kg−1), f) soil clay content (%), TPI at g) 150 m and h) 1,000 m resolution [-] and i) slope (°). Sample sizes: Amazon n ≈ 216,667; Congo Basin n ≈ 40,306; Southeast Asia n ≈ 29,436. In box plots, the centre line indicates the median, box bounds the 25th and 75th percentiles (interquartile range, IQR), and whiskers extend to the most extreme values within 1.5 × IQR of the box bounds.

Extended Data Fig. 10 Large spatial variation in the number of GEDI footprints across the tropics.

The number of footprints per hexagon reflects the availability of GEDI data (orbital geometry and cloud filtering) and natural heterogeneity (i.e. rivers, floodplains, natural wetlands) across lowland (between 55 and 900 m elevation) intact landscapes. Each hexagonal point corresponds to a grid cell of ~1,770 km² (approximately 50 × 50 km).

Extended Data Fig. 11 Variation in predicted AGB in the Amazon, Congo Basin and Southeast Asia with soil and topography.

Curves are based on AGB predictions from LME modelling with environmental variables and their interactions as fixed effects, obtained by fitting the model to six ecoregions in the Amazon (western Guiana Shield, eastern Guiana Shield, northwestern, southwestern, central and southern Amazonian forests), five ecoregions the Congo Basin (Atlantic coastal forests, swamp forests, northwestern, northeastern and central Congolian forests) and two ecoregions in Southeast Asia (Sumatra and Borneo). The model included spatial random effects, implemented as random effects of 10 ×10 km grid cells nested within ecoregion, to account for spatial autocorrelation in AGB influences due to differences in species and functional composition and other terms not included in the model. Predicted AGB showed statistical variation with a) CEC, b) soil clay content, c) TPI at 150 m scale, d) TPI at 1,000 m scale, and e) slope. β is the standardized regression coefficient for their respective environmental variables and their two-sided significance level: ns = non-significant, * p-value < 0.05, ** p-value < 0.005, p-value < 0.005. Exact p-values are provided in Supplementary Tables 1–3. Sample sizes: Amazon n ≈ 216,667; Congo Basin n ≈ 40,306; Southeast Asia n ≈ 29,436. A description and discussion of these soil and topographical relationships with AGB are provided in detail in Supplementary Methods 7.

Extended Data Fig. 12 Distribution of AGB estimated from GEDI.

a) Relationships between estimated AGB and 98% relative height across three biogeographical regions in the tropics from the GEDI data for 2020. Colours in the 2D binned density plots represent point density, following a continuous gradient from grey (low density) to red (high density). b) Histograms of estimated AGB from GEDI across the Amazon, Congo Basin and Southeast Asian forests using GEDI data at the footprint level. The vertical solid red lines correspond to the median AGB values. Sample sizes: Amazon n ≈ 13,747,868; Congo Basin n ≈ 1,637,208; Southeast Asia n ≈ 397,734 footprints.

Extended Data Table 1 Environmental variables used to model AGB

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Nunes, M.H., Muller-Landau, H.C., Görgens, E.B. et al. Heterogeneous climatic controls on tropical-forest biomass. Nature (2026). https://doi.org/10.1038/s41586-026-10880-2

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