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Tropical tree growth driven by dry-season climate variability


Interannual variability in the global land carbon sink is strongly related to variations in tropical temperature and rainfall. This association suggests an important role for moisture-driven fluctuations in tropical vegetation productivity, but empirical evidence to quantify the responsible ecological processes is missing. Such evidence can be obtained from tree-ring data that quantify variability in a major vegetation productivity component: woody biomass growth. Here we compile a pantropical tree-ring network to show that annual woody biomass growth increases primarily with dry-season precipitation and decreases with dry-season maximum temperature. The strength of these dry-season climate responses varies among sites, as reflected in four robust and distinct climate response groups of tropical tree growth derived from clustering. Using cluster and regression analyses, we find that dry-season climate responses are amplified in regions that are drier, hotter and more climatically variable. These amplification patterns suggest that projected global warming will probably aggravate drought-induced declines in annual tropical vegetation productivity. Our study reveals a previously underappreciated role of dry-season climate variability in driving the dynamics of tropical vegetation productivity and consequently in influencing the land carbon sink.

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Fig. 1: Distribution and climatic representativeness of tropical tree-ring network.
Fig. 2: Four climate response groups of tropical trees and their hydroclimatic differences.
Fig. 3: Seasonal climate responses of tropical tree growth and their relation to hydroclimate.

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Data availability

The 50-year mean RWI time series of all 347 chronologies used in this study and all relevant metadata of these chronologies are included in Supplementary Data. Raw tree-ring-width data of 98 out of the 112 contributed chronologies used in the analyses have been uploaded to the ITRDB (

Code availability

R-code used for chronology construction and statistical analyses will be made available upon request from the corresponding author.


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We acknowledge financial support to the co-authors provided by Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT 2014-2797) to M.E.F.; Alberta Mennega Stichting to P.G.; BBVA Foundation to H.A.M. and J.J.C.; Belspo BRAIN project: BR/143/A3/HERBAXYLAREDD to H.B.; Confederação da Agricultura e Pecuária do Brasil - CNA to C.F.; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES, Brazil (PDSE 15011/13-5 to M.A.P.; 88881.135931/2016-01 to C.F.; 88887.199858/2018-00 to G.A.-P.; Finance Code 001 for all Brazilian collaborators); Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, Brazil (ENV 42 to O.D.; 1009/4785031-2 to G.C.; 311874/2017-7 to J.S.); CONACYT-CB-2016-283134 to J.V.-D.; CONICET to F.A.R.; CUOMO FOUNDATION (IPCC scholarship) to M.M.; Deutsche Forschungsgemeinschaft - DFG (BR 1895/15-1 to A.B.; BR 1895/23-1 to A.B.; BR 1895/29-1 to A.B.; BR 1895/24-1 to M.M.); DGD-RMCA PilotMAB to B.T.; Dirección General de Asuntos del Personal Académico of the UNAM (Mexico) to R.B.; Elsa-Neumann-Scholarship of the Federal State of Berlin to F.S.; EMBRAPA Brazilian Agricultural Research Corporation to C.F.; Equatorian Dirección de Investigación UNL (21-DI-FARNR-2019) to D.P.-C.; São Paulo Research Foundation FAPESP (2009/53951-7 to M.T.-F.; 2012/50457-4 to G.C.; 2018/01847‐0 to P.G.; 2018/24514-7 to J.R.V.A.; 2019/08783-0 to G.M.L.; 2019/27110-7 to C.F.); FAPESP-NERC 18/50080-4 to G.C.; FAPITEC/SE/FUNTEC no. 01/2011 to M.A.P.; Fulbright Fellowship to B.J.E.; German Academic Exchange Service (DAAD) to M.I. and M.R.; German Ministry of Education, Science, Research, and Technology (FRG 0339638) to O.D.; ICRAF through the Forests, Trees, and Agroforestry research programme of the CGIAR to M.M.; Inter-American Institute for Global Change Research (IAI-SGP-CRA 2047) to J.V.-D.; International Foundation for Science (D/5466-1) to M.I.; Lamont Climate Center to B.M.B.; Miquelfonds to P.G.; National Geographic Global Exploration Fund (GEFNE80-13) to I.R.; USA’s National Science Foundation NSF (IBN-9801287 to A.J.L.; GER 9553623 and a postdoctoral fellowship to B.J.E.); NSF P2C2 (AGS-1501321) to A.C.B., D.G.-S. and G.A.-P.; NSF-FAPESP PIRE 2017/50085-3 to M.T.-F., G.C. and G.M.L.; NUFFIC-NICHE programme (HEART project) to B.K., E.M., J.H.S., J.N. and R. Vinya; Peru ‘s CONCYTEC and World Bank (043-2019-FONDECYT-BM-INC.INV.) to J.G.I.; Peru’s Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (FONDECYT-BM-INC.INV 039-2019) to E.J.R.-R. and M.E.F.; Programa Bosques Andinos - HELVETAS Swiss Intercooperation to M.E.F.; Programa Nacional de Becas y Crédito Educativo - PRONABEC to J.G.I.; Schlumberger Foundation Faculty for the Future to J.N.; Sigma Xi to A.J.L.; Smithsonian Tropical Research Institute to R. Alfaro-Sánchez.; Spanish Ministry of Foreign Affairs AECID (11-CAP2-1730) to H.A.M. and J.J.C.; UK NERC grant NE/K01353X/1 to E.G. For logistical and (field) assistance, we thank Bangladesh Forest Department; Ethiopian Orthodox Tewahido Church and Congregants; Evandro Dalmaso (CEMAL logging firm); Instituto Boliviano de Investigación Forestal (IBIF; Bolivia); INPA parket Co.; Instituto Federal de Educação; Ciência e Tecnologia de Sergipe (IFS); Ministry of Minerals, Energy and Water Resources of Botswana; Reserva Natural da Vale (RNV); Sebastian Bernal; the Valere project of University of Campania “L. Vanvitelli”; the villagers of Nizanda in Oaxaca, Mexico. We are grateful for the help and supervision by D. Stahle, D. Eckstein and H. Muller-Landau.

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Authors and Affiliations



P.A.Z., P.G. and V.T. initiated the tropical tree-ring network; P.A.Z., F.B., P.G. and V.T. designed the study; all co-authors except F.B. contributed tree-ring data; F.B. and P.G. analysed the data, with important contributions from P.A.Z.; P.A.Z. and V.T. wrote the manuscript, with important contributions from F.B. and P.G. All co-authors read and approved the manuscript.

Corresponding author

Correspondence to Pieter A. Zuidema.

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Nature Geoscience thanks Amy Hessl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Table 1 Climate responses are robust to among-site variation in quality of gridded climate data
Extended Data Table 2 Characteristics of four climate response groups
Extended Data Table 3 Regional differences of climate response groups
Extended Data Table 4 Correlations of seasonal regression coefficients and site climate conditions
Extended Data Table 5 Predicted warming at network sites

Extended Data Fig. 1 Distribution and characteristics of the 415 chronologies in the network.

a, Spatial distribution of tree-ring chronologies from two sources (ITRDB and contributors) selected to be included in this study (“in”, n = 347) or not (“out”, n = 68). b, Chronology distribution in climate space and across tropical biomes following Whittaker biome classification70. c, Distribution of the most recent 50 years of selected chronologies in time and across Koeppen climate classes71. The red line indicates the average midpoint of all 347 selected chronologies. Vegetation background in a from open source data: Natural Earth (

Extended Data Fig. 2 Cross validation test of cluster analysis reveals robustness of climate response groups.

Results of cross validation tests in which selections of sites were left out of the network to test robustness of climate response patterns and correctness of site assignment. Four sets of tests were conducted: removal of a random 10% of the sites (a, repeated 10 times), of overrepresented climates (b, cold sites), of underrepresented climates (c, wet sites) and of poorly represented regions (d-e). Line graphs per climate response group are similar to those in Fig. 2 and show mean Pearson r correlation coefficients of 50-year RWI series with monthly Tmax (red) or PP (blue) for a 24-month period that covers the year of ring formation (months 1-12) and that prior to ring formation (months 13-24). Bar graphs show the percentage of correct and incorrect assignments of the left out sites to climate response groups.

Extended Data Fig. 3 Globally identified climate response groups are maintained at regional level.

Left panels: Spatial distribution for climate response groups in well-represented sections of the network: North America (n = 119), High-mountain Asia (n = 92) and South America (n = 65). In North America, climate response groups are geographically segregated and the ‘Strong positive PP response’ and ‘Positive PP response’ groups are characterized by the lowest water availability (MAP; Extended Data Table 3). Furthermore, these climate response groups differ in PP variability, whereas the groups with the weakest (positive and negative) precipitation responses differ in PP seasonality. High-mountain Asia and South America portions of the network are dominated by climate response groups with weak (positive and negative) precipitation response. As in the full network, these groups do not differ in PP variability (Extended Data Table 3). Right panels: Responses of ring-width index (RWI) to interannual variation in monthly Tmax (red) and PP (blue) of four climate response groups. Correlation coefficients (Pearson r, mean and 95% confidence intervals) are shown for a 24-month period including the year prior to ring formation and that of ring formation. Elevation data for left panels from open source data: ETOPO5, NOAA (

Extended Data Fig. 4 Evaluating the roles of full vs. late dry season on tropical tree growth.

a,c, Density plots of significant regression coefficients of PP (blue, a) and Tmax (red, c) for dry season (all dry months prior to onset of wet season), late dry season (last 2 months before onset of wet season), and wet season. Density plots are based on 279 multiple regression models that included at least one significant seasonal climate effect. Letters denote significant differences between groups for either PP or Tmax based on Wilcoxon rank test (P < 0.05, n = 581 coefficients; dry-season n = 243; late dry-season n = 161; wet-season n = 177). Horizonal lines represent medians. b,d, As panels a and c but for relative importance (only for models with >1 significant coefficient, n = 487).

Extended Data Fig. 5 Results of multiple regression analyses for dry and wet seasons.

a-c, Association of regression coefficients for wet-season PP (blue, n = 92) and Tmax (red, n = 84) with three site-specific hydroclimate variables. All hydroclimatic variables are ordered from arid (left) to humid (right). d-g, Association of regression coefficients for seasonal PP (blue) and Tmax (red) with PP variability and PP seasonality. Symbol size is proportional to scaled representativeness of sites (‘Density’) for climate variable on X-axis (Extended Data Fig. 2). Multiple regression models are based on the most recent 50 years of each chronology. Significant correlations (P < 0.05; Spearman rank correlation, weighted for density; Extended Data Table 4) are indicated; lines are shown for illustration only.

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Supplementary Data

Supplementary metadata and mean RWI of all 347 selected chronologies. Source, whether data were obtained from the ITRDB or contributed by co-authors; Rbar, mean inter-series correlation of the chronology; SOM_clusters, climate response group to which chronology belongs; SOM_clusters_PercentAssignment, the percentage of SOM-clustering runs that a chronology was assigned to the climate response group (cluster) in column ‘SOM_clusters’; columns with calendar years (‘1929’ to ‘2017’) contain mean RWI of the re-constructed chronologies, always for the 50 most recent years of available tree-ring data.

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Zuidema, P.A., Babst, F., Groenendijk, P. et al. Tropical tree growth driven by dry-season climate variability. Nat. Geosci. 15, 269–276 (2022).

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