Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Julia Valentim Tavares; Rafael S. Oliveira; Maurizio Mencuccini; Caroline Signori-Müller; Luciano Pereira; Francisco Carvalho Diniz; Martin Gilpin; Manuel J. Marca Zevallos; Carlos A. Salas Yupayccana; Martin Acosta; Flor M. Pérez Mullisaca; Fernanda de V. Barros; Paulo Bittencourt; Halina Jancoski; Marina Corrêa Scalon; Beatriz S. Marimon; Imma Oliveras Menor; Ben Hur Marimon; Max Fancourt; Alexander Chambers-Ostler; Adriane Esquivel-Muelbert; Lucy Rowland; Patrick Meir; Antonio Carlos Lola da Costa; Alex Nina; Jesus M. B. Sanchez; Jose S. Tintaya; Rudi S. C. Chino; Jean Baca; Leticia Fernandes; Edwin R. M. Cumapa; João Antônio R. Santos; Renata Teixeira; Ligia Tello; Maira T. M. Ugarteche; Gina A. Cuellar; Franklin Martinez; Alejandro Araujo-Murakami; Everton Almeida; Wesley Jonatar Alves da Cruz; Jhon del Aguila Pasquel; Luís Aragāo; Timothy R. Baker; Plinio Barbosa de Camargo; Roel Brienen; Wendeson Castro; Sabina Cerruto Ribeiro; Fernanda Coelho de Souza; Eric G. Cosio; Nallaret Davila Cardozo; Richarlly da Costa Silva; Mathias Disney; Javier Silva Espejo; Ted R. Feldpausch; Leandro Ferreira; Leandro Giacomin; Niro Higuchi; Marina Hirota; Euridice Honorio; Walter Huaraca Huasco; Simon Lewis; Gerardo Flores Llampazo; Yadvinder Malhi; Abel Monteagudo Mendoza; Paulo Morandi; Victor Chama Moscoso; Robert Muscarella; Deliane Penha; Mayda Cecília Rocha; Gleicy Rodrigues; Ademir R. Ruschel; Norma Salinas; Monique Schlickmann; Marcos Silveira; Joey Talbot; Rodolfo Vásquez; Laura Vedovato; Simone Aparecida Vieira; Oliver L. Phillips; Emanuel Gloor; David R. Galbraith

doi:10.1038/s41586-023-05971-3

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Julia Valentim Tavares^*, Rafael S. Oliveira, Maurizio Mencuccini, Caroline Signori-Müller, Luciano Pereira, Francisco Carvalho Diniz, Martin Gilpin, Manuel J. Marca Zevallos, Carlos A. Salas Yupayccana, Martin Acosta, Flor M. Pérez Mullisaca, Fernanda de V. Barros, Paulo Bittencourt, Halina Jancoski, Marina Corrêa Scalon, Beatriz S. Marimon, Imma Oliveras Menor, Ben Hur Marimon, Max Fancourt, Alexander Chambers-OstlerAdriane Esquivel-Muelbert, Lucy Rowland, Patrick Meir, Antonio Carlos Lola da Costa, Alex Nina, Jesus M. B. Sanchez, Jose S. Tintaya, Rudi S. C. Chino, Jean Baca, Leticia Fernandes, Edwin R. M. Cumapa, João Antônio R. Santos, Renata Teixeira, Ligia Tello, Maira T. M. Ugarteche, Gina A. Cuellar, Franklin Martinez, Alejandro Araujo-Murakami, Everton Almeida, Wesley Jonatar Alves da Cruz, Jhon del Aguila Pasquel, Luís Aragāo, Timothy R. Baker, Plinio Barbosa de Camargo, Roel Brienen, Wendeson Castro, Sabina Cerruto Ribeiro, Fernanda Coelho de Souza, Eric G. Cosio, Nallaret Davila Cardozo, Richarlly da Costa Silva, Mathias Disney, Javier Silva Espejo, Ted R. Feldpausch, Leandro Ferreira, Leandro Giacomin, Niro Higuchi, Marina Hirota, Euridice Honorio, Walter Huaraca Huasco, Simon Lewis, Gerardo Flores Llampazo, Yadvinder Malhi, Abel Monteagudo Mendoza, Paulo Morandi, Victor Chama Moscoso, Robert Muscarella, Deliane Penha, Mayda Cecília Rocha, Gleicy Rodrigues, Ademir R. Ruschel, Norma Salinas, Monique Schlickmann, Marcos Silveira, Joey Talbot, Rodolfo Vásquez, Laura Vedovato, Simone Aparecida Vieira, Oliver L. Phillips, Emanuel Gloor, David R. Galbraith

^*Corresponding author for this work

School of Geography & Sustainable Development

Research output: Contribution to journal › Article › peer-review

Abstract

Tropical forests face increasing climate risk^1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ₅₀) and hydraulic safety margins (for example, HSM₅₀) are important predictors of drought-induced mortality risk^3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ₅₀ and HSM₅₀ vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ₅₀ and HSM₅₀ influence the biogeographical distribution of Amazon tree species. However, HSM₅₀ was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM₅₀ are gaining more biomass than are low HSM₅₀ forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM₅₀ in the Amazon^6,7, with strong implications for the Amazon carbon sink.

Original language	English
Pages (from-to)	111-117
Number of pages	7
Journal	Nature
Volume	617
Issue number	7959
Early online date	26 Apr 2023
DOIs	https://doi.org/10.1038/s41586-023-05971-3
Publication status	Published - 4 May 2023

Keywords

Biomass
Carbon/metabolism
Droughts
Forests
Trees/growth & development
Tropical Climate
Xylem/metabolism
Rain
Climate Change
Carbon Sequestration
Stress, Physiological
Dehydration

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Tavares, J. V., Oliveira, R. S., Mencuccini, M., Signori-Müller, C., Pereira, L., Diniz, F. C., Gilpin, M., Marca Zevallos, M. J., Salas Yupayccana, C. A., Acosta, M., Pérez Mullisaca, F. M., Barros, F. D. V., Bittencourt, P., Jancoski, H., Scalon, M. C., Marimon, B. S., Oliveras Menor, I., Marimon, B. H., Fancourt, M., ... Galbraith, D. R. (2023). Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature, 617(7959), 111-117. https://doi.org/10.1038/s41586-023-05971-3

@article{8c043c8f14f3428fb95262af56c7aebc,

title = "Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests",

abstract = "Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink. ",

keywords = "Biomass, Carbon/metabolism, Droughts, Forests, Trees/growth & development, Tropical Climate, Xylem/metabolism, Rain, Climate Change, Carbon Sequestration, Stress, Physiological, Dehydration",

author = "Tavares, {Julia Valentim} and Oliveira, {Rafael S.} and Maurizio Mencuccini and Caroline Signori-M{\"u}ller and Luciano Pereira and Diniz, {Francisco Carvalho} and Martin Gilpin and {Marca Zevallos}, {Manuel J.} and {Salas Yupayccana}, {Carlos A.} and Martin Acosta and {P{\'e}rez Mullisaca}, {Flor M.} and Barros, {Fernanda de V.} and Paulo Bittencourt and Halina Jancoski and Scalon, {Marina Corr{\^e}a} and Marimon, {Beatriz S.} and {Oliveras Menor}, Imma and Marimon, {Ben Hur} and Max Fancourt and Alexander Chambers-Ostler and Adriane Esquivel-Muelbert and Lucy Rowland and Patrick Meir and {Lola da Costa}, {Antonio Carlos} and Alex Nina and Sanchez, {Jesus M. B.} and Tintaya, {Jose S.} and Chino, {Rudi S. C.} and Jean Baca and Leticia Fernandes and Cumapa, {Edwin R. M.} and Santos, {Jo{\~a}o Ant{\^o}nio R.} and Renata Teixeira and Ligia Tello and Ugarteche, {Maira T. M.} and Cuellar, {Gina A.} and Franklin Martinez and Alejandro Araujo-Murakami and Everton Almeida and {da Cruz}, {Wesley Jonatar Alves} and {del Aguila Pasquel}, Jhon and Lu{\'i}s Aragāo and Baker, {Timothy R.} and {de Camargo}, {Plinio Barbosa} and Roel Brienen and Wendeson Castro and Ribeiro, {Sabina Cerruto} and {Coelho de Souza}, Fernanda and Cosio, {Eric G.} and {Davila Cardozo}, Nallaret and {da Costa Silva}, Richarlly and Mathias Disney and Espejo, {Javier Silva} and Feldpausch, {Ted R.} and Leandro Ferreira and Leandro Giacomin and Niro Higuchi and Marina Hirota and Euridice Honorio and {Huaraca Huasco}, Walter and Simon Lewis and {Flores Llampazo}, Gerardo and Yadvinder Malhi and {Monteagudo Mendoza}, Abel and Paulo Morandi and {Chama Moscoso}, Victor and Robert Muscarella and Deliane Penha and Rocha, {Mayda Cec{\'i}lia} and Gleicy Rodrigues and Ruschel, {Ademir R.} and Norma Salinas and Monique Schlickmann and Marcos Silveira and Joey Talbot and Rodolfo V{\'a}squez and Laura Vedovato and Vieira, {Simone Aparecida} and Phillips, {Oliver L.} and Emanuel Gloor and Galbraith, {David R.}",

note = "Funding: Data collection was largely funded by the UK Natural Environment Research Council (NERC) project TREMOR (NE/N004655/1) to D.G., E.G. and O.P., with further funds from Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior—Brasil (CAPES, finance code 001) to J.V.T. and a University of Leeds Climate Research Bursary Fund to J.V.T. D.G., E.G. and O.P. acknowledge further support from a NERC-funded consortium award (ARBOLES, NE/S011811/1). This paper is an outcome of J.V.T.{\textquoteright}s doctoral thesis, which was sponsored by CAPES (GDE 99999.001293/2015-00). J.V.T. was previously supported by the NERC-funded ARBOLES project (NE/S011811/1) and is supported at present by the Swedish Research Council Vetenskapsr{\aa}det (grant no. 2019-03758 to R.M.). E.G., O.P. and D.G. acknowledge support from NERC-funded BIORED grant (NE/N012542/1). O.P. acknowledges support from an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. R.S.O. was supported by a CNPq productivity scholarship, the S{\~a}o Paulo Research Foundation (FAPESP-Microsoft 11/52072-0) and the US Department of Energy, project GoAmazon (FAPESP 2013/50531-2). M.M. acknowledges support from MINECO FUN2FUN (CGL2013-46808-R) and DRESS (CGL2017-89149-C2-1-R). C.S.-M., F.B.V. and P.R.L.B. were financed by Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior—Brasil (CAPES, finance code 001). C.S.-M. received a scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq 140353/2017-8) and CAPES (science without borders 88881.135316/2016-01). Y.M. acknowledges the Gordon and Betty Moore Foundation and ERC Advanced Investigator Grant (GEM-TRAITS, 321131) for supporting the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk), within which some of the field sites (KEN, TAM and ALP) are nested. The authors thank Brazil–USA Collaborative Research GoAmazon DOE-FAPESP-FAPEAM (FAPESP 2013/50533-5 to L.A.) and National Science Foundation (award DEB-1753973 to L. Alves). They thank Serrapilheira Serra-1709-18983 (to M.H.) and CNPq-PELD/POPA-441443/2016-8 (to L.G.) (P.I. Albertina Lima). They thank all the colleagues and grants mentioned elsewhere [8,36] that established, identified and measured the Amazon forest plots in the RAINFOR network analysed here. The authors particularly thank J. Lyod, S. Almeida, F. Brown, B. Vicenti, N. Silva and L. Alves. This work is an outcome approved Research Project no. 19 from ForestPlots.net, a collaborative initiative developed at the University of Leeds that unites researchers and the monitoring of their permanent plots from the world{\textquoteright}s tropical forests [61]. The authros thank A. Levesley, K. Melga{\c c}o Ladvocat and G. Pickavance for ForestPlots.net management. They thank Y. Wang and J. Baker, respectively, for their help with the map and with the climatic data. The authors acknowledge the invaluable help of M. Brum for kindly providing the comparison of vulnerability curves based on PAD and on PLC shown in this manuscript. They thank J. Martinez-Vilalta for his comments on an early version of this manuscript. The authors also thank V. Hilares and the Asociaci{\'o}n para la Investigaci{\'o}n y Desarrollo Integral (AIDER, Puerto Maldonado, Peru); V. Salda{\~n}a and Instituto de Investigaciones de la Amazon{\'i}a Peruana (IIAP) for local field campaign support in Peru; E. Chavez and Noel Kempff Natural History Museum for local field campaign support in Bolivia; ICMBio, INPA/NAPPA/LBA COOMFLONA (Cooperativa mista da Flona Tapaj{\'o}s) and T. I. Bragan{\c c}a-Marituba for the research support. ",

year = "2023",

month = may,

day = "4",

doi = "10.1038/s41586-023-05971-3",

language = "English",

volume = "617",

pages = "111--117",

journal = "Nature",

issn = "0028-0836",

publisher = "Nature Research",

number = "7959",

}

Tavares, JV, Oliveira, RS, Mencuccini, M, Signori-Müller, C, Pereira, L, Diniz, FC, Gilpin, M, Marca Zevallos, MJ, Salas Yupayccana, CA, Acosta, M, Pérez Mullisaca, FM, Barros, FDV, Bittencourt, P, Jancoski, H, Scalon, MC, Marimon, BS, Oliveras Menor, I, Marimon, BH, Fancourt, M, Chambers-Ostler, A, Esquivel-Muelbert, A, Rowland, L, Meir, P, Lola da Costa, AC, Nina, A, Sanchez, JMB, Tintaya, JS, Chino, RSC, Baca, J, Fernandes, L, Cumapa, ERM, Santos, JAR, Teixeira, R, Tello, L, Ugarteche, MTM, Cuellar, GA, Martinez, F, Araujo-Murakami, A, Almeida, E, da Cruz, WJA, del Aguila Pasquel, J, Aragāo, L, Baker, TR, de Camargo, PB, Brienen, R, Castro, W, Ribeiro, SC, Coelho de Souza, F, Cosio, EG, Davila Cardozo, N, da Costa Silva, R, Disney, M, Espejo, JS, Feldpausch, TR, Ferreira, L, Giacomin, L, Higuchi, N, Hirota, M, Honorio, E, Huaraca Huasco, W, Lewis, S, Flores Llampazo, G, Malhi, Y, Monteagudo Mendoza, A, Morandi, P, Chama Moscoso, V, Muscarella, R, Penha, D, Rocha, MC, Rodrigues, G, Ruschel, AR, Salinas, N, Schlickmann, M, Silveira, M, Talbot, J, Vásquez, R, Vedovato, L, Vieira, SA, Phillips, OL, Gloor, E & Galbraith, DR 2023, 'Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests', Nature, vol. 617, no. 7959, pp. 111-117. https://doi.org/10.1038/s41586-023-05971-3

TY - JOUR

T1 - Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

AU - Tavares, Julia Valentim

AU - Oliveira, Rafael S.

AU - Mencuccini, Maurizio

AU - Signori-Müller, Caroline

AU - Pereira, Luciano

AU - Diniz, Francisco Carvalho

AU - Gilpin, Martin

AU - Marca Zevallos, Manuel J.

AU - Salas Yupayccana, Carlos A.

AU - Acosta, Martin

AU - Pérez Mullisaca, Flor M.

AU - Barros, Fernanda de V.

AU - Bittencourt, Paulo

AU - Jancoski, Halina

AU - Scalon, Marina Corrêa

AU - Marimon, Beatriz S.

AU - Oliveras Menor, Imma

AU - Marimon, Ben Hur

AU - Fancourt, Max

AU - Chambers-Ostler, Alexander

AU - Esquivel-Muelbert, Adriane

AU - Rowland, Lucy

AU - Meir, Patrick

AU - Lola da Costa, Antonio Carlos

AU - Nina, Alex

AU - Sanchez, Jesus M. B.

AU - Tintaya, Jose S.

AU - Chino, Rudi S. C.

AU - Baca, Jean

AU - Fernandes, Leticia

AU - Cumapa, Edwin R. M.

AU - Santos, João Antônio R.

AU - Teixeira, Renata

AU - Tello, Ligia

AU - Ugarteche, Maira T. M.

AU - Cuellar, Gina A.

AU - Martinez, Franklin

AU - Araujo-Murakami, Alejandro

AU - Almeida, Everton

AU - da Cruz, Wesley Jonatar Alves

AU - del Aguila Pasquel, Jhon

AU - Aragāo, Luís

AU - Baker, Timothy R.

AU - de Camargo, Plinio Barbosa

AU - Brienen, Roel

AU - Castro, Wendeson

AU - Ribeiro, Sabina Cerruto

AU - Coelho de Souza, Fernanda

AU - Cosio, Eric G.

AU - Davila Cardozo, Nallaret

AU - da Costa Silva, Richarlly

AU - Disney, Mathias

AU - Espejo, Javier Silva

AU - Feldpausch, Ted R.

AU - Ferreira, Leandro

AU - Giacomin, Leandro

AU - Higuchi, Niro

AU - Hirota, Marina

AU - Honorio, Euridice

AU - Huaraca Huasco, Walter

AU - Lewis, Simon

AU - Flores Llampazo, Gerardo

AU - Malhi, Yadvinder

AU - Monteagudo Mendoza, Abel

AU - Morandi, Paulo

AU - Chama Moscoso, Victor

AU - Muscarella, Robert

AU - Penha, Deliane

AU - Rocha, Mayda Cecília

AU - Rodrigues, Gleicy

AU - Ruschel, Ademir R.

AU - Salinas, Norma

AU - Schlickmann, Monique

AU - Silveira, Marcos

AU - Talbot, Joey

AU - Vásquez, Rodolfo

AU - Vedovato, Laura

AU - Vieira, Simone Aparecida

AU - Phillips, Oliver L.

AU - Gloor, Emanuel

AU - Galbraith, David R.

N1 - Funding: Data collection was largely funded by the UK Natural Environment Research Council (NERC) project TREMOR (NE/N004655/1) to D.G., E.G. and O.P., with further funds from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001) to J.V.T. and a University of Leeds Climate Research Bursary Fund to J.V.T. D.G., E.G. and O.P. acknowledge further support from a NERC-funded consortium award (ARBOLES, NE/S011811/1). This paper is an outcome of J.V.T.’s doctoral thesis, which was sponsored by CAPES (GDE 99999.001293/2015-00). J.V.T. was previously supported by the NERC-funded ARBOLES project (NE/S011811/1) and is supported at present by the Swedish Research Council Vetenskapsrådet (grant no. 2019-03758 to R.M.). E.G., O.P. and D.G. acknowledge support from NERC-funded BIORED grant (NE/N012542/1). O.P. acknowledges support from an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. R.S.O. was supported by a CNPq productivity scholarship, the São Paulo Research Foundation (FAPESP-Microsoft 11/52072-0) and the US Department of Energy, project GoAmazon (FAPESP 2013/50531-2). M.M. acknowledges support from MINECO FUN2FUN (CGL2013-46808-R) and DRESS (CGL2017-89149-C2-1-R). C.S.-M., F.B.V. and P.R.L.B. were financed by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001). C.S.-M. received a scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq 140353/2017-8) and CAPES (science without borders 88881.135316/2016-01). Y.M. acknowledges the Gordon and Betty Moore Foundation and ERC Advanced Investigator Grant (GEM-TRAITS, 321131) for supporting the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk), within which some of the field sites (KEN, TAM and ALP) are nested. The authors thank Brazil–USA Collaborative Research GoAmazon DOE-FAPESP-FAPEAM (FAPESP 2013/50533-5 to L.A.) and National Science Foundation (award DEB-1753973 to L. Alves). They thank Serrapilheira Serra-1709-18983 (to M.H.) and CNPq-PELD/POPA-441443/2016-8 (to L.G.) (P.I. Albertina Lima). They thank all the colleagues and grants mentioned elsewhere [8,36] that established, identified and measured the Amazon forest plots in the RAINFOR network analysed here. The authors particularly thank J. Lyod, S. Almeida, F. Brown, B. Vicenti, N. Silva and L. Alves. This work is an outcome approved Research Project no. 19 from ForestPlots.net, a collaborative initiative developed at the University of Leeds that unites researchers and the monitoring of their permanent plots from the world’s tropical forests [61]. The authros thank A. Levesley, K. Melgaço Ladvocat and G. Pickavance for ForestPlots.net management. They thank Y. Wang and J. Baker, respectively, for their help with the map and with the climatic data. The authors acknowledge the invaluable help of M. Brum for kindly providing the comparison of vulnerability curves based on PAD and on PLC shown in this manuscript. They thank J. Martinez-Vilalta for his comments on an early version of this manuscript. The authors also thank V. Hilares and the Asociación para la Investigación y Desarrollo Integral (AIDER, Puerto Maldonado, Peru); V. Saldaña and Instituto de Investigaciones de la Amazonía Peruana (IIAP) for local field campaign support in Peru; E. Chavez and Noel Kempff Natural History Museum for local field campaign support in Bolivia; ICMBio, INPA/NAPPA/LBA COOMFLONA (Cooperativa mista da Flona Tapajós) and T. I. Bragança-Marituba for the research support.

PY - 2023/5/4

Y1 - 2023/5/4

N2 - Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink.

AB - Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink.

KW - Biomass

KW - Carbon/metabolism

KW - Droughts

KW - Forests

KW - Trees/growth & development

KW - Tropical Climate

KW - Xylem/metabolism

KW - Rain

KW - Climate Change

KW - Carbon Sequestration

KW - Stress, Physiological

KW - Dehydration

U2 - 10.1038/s41586-023-05971-3

DO - 10.1038/s41586-023-05971-3

M3 - Article

C2 - 37100901

SN - 0028-0836

VL - 617

SP - 111

EP - 117

JO - Nature

JF - Nature

IS - 7959

ER -

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Abstract

Keywords

UN SDGs

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