{"id":378566,"date":"2026-03-11T00:38:16","date_gmt":"2026-03-11T00:38:16","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/378566\/"},"modified":"2026-03-11T00:38:16","modified_gmt":"2026-03-11T00:38:16","slug":"amazon-rainforests-are-rejuvenating-their-canopies-by-producing-more-photosynthetically-efficient-young-leaves-under-climate-change","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/378566\/","title":{"rendered":"Amazon rainforests are rejuvenating their canopies by producing more photosynthetically efficient young leaves under climate change"},"content":{"rendered":"
  • \n

    Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351<\/b>, 972\u2013976 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chavana-Bryant, C. et al. Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees. Sci. Total Environ. 666<\/b>, 1301\u20131315 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Niinemets, \u00dc, Cescatti, A., Rodeghiero, M. & Tosens, T. Leaf internal diffusion conductance limits photosynthesis more strongly in older leaves of Mediterranean evergreen broad-leaved species. Plant Cell Environ. 28<\/b>, 1552\u20131566 (2005).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sobrado, M. A. Leaf age effects on photosynthetic rate, transpiration rate and nitrogen content in a tropical dry forest. Physiol. Plant. 90<\/b>, 210\u2013215 (1994).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    De Moura, Y. M. et al. Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations. ISPRS J. Photogramm. Remote Sens. 131<\/b>, 52\u201364 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Meir, P. et al. Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. Plant Cell Environ. 25<\/b>, 343\u2013357 (2002).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Aguirre-Guti\u00e9rrez, J. et al. Canopy functional trait variation across Earth\u2019s tropical forests. Nature 641<\/b>, 129\u2013136 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Niinemets, \u00dc Leaf age dependent changes in within-canopy variation in leaf functional traits: a meta-analysis. J. Plant Res. 129<\/b>, 313\u2013338 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reich, P. B. et al. Controls on declining carbon balance with leaf age among 10 woody species in Australian woodland: do leaves have zero daily net carbon balances when they die?. New Phytol. 183<\/b>, 153\u2013166 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Field, C. & Mooney, H. A. Leaf age and seasonal effects on light, water, and nitrogen use efficiency in a California shrub. Oecologia 56<\/b>, 348\u2013355 (1983).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Niinemets, \u00dc Photosynthesis and resource distribution through plant canopies. Plant Cell Environ. 30<\/b>, 1052\u20131071 (2007).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, H. et al. Leaf economics fundamentals explained by optimality principles. Sci. Adv. 9<\/b>, eadd5667 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329<\/b>, 834\u2013838 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Menezes, J. et al. Changes in leaf functional traits with leaf age: when do leaves decrease their photosynthetic capacity in Amazonian trees?. Tree Physiol. 42<\/b>, 922\u2013938 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bot\u00eda, S. et al. The CO2 record at the Amazon Tall Tower Observatory: a new opportunity to study processes on seasonal and inter-annual scales. Glob. Change Biol. 28<\/b>, 588\u2013611 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Song, G. et al. Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies. ISPRS J. Photogramm. Remote Sens. 183<\/b>, 19\u201333 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lopes, A. P. et al. Leaf flush drives dry season green-up of the Central Amazon. Remote Sens. Environ. 182<\/b>, 90\u201398 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531<\/b>, E4\u2013E5 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33<\/b>, L06405 (2006).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Doughty, C. E. & Goulden, M. L. Seasonal patterns of tropical forest leaf area index and CO2 exchange. J. Geophys. Res. Biogeosci. 113<\/b>, G00B06 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107<\/b>, 14685\u201314690 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Detto, M. et al. Resource acquisition and reproductive strategies of tropical forest in response to the El Ni\u00f1o\u2013Southern Oscillation. Nat. Commun. 9<\/b>, 913 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, S. et al. Estimation of leaf photosynthetic capacity from leaf chlorophyll content and leaf age in a subtropical evergreen coniferous plantation. J. Geophys. Res. Biogeosci. 125<\/b>, e2019JG005020 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xue, M. et al. Pantropical moist forests are converging towards a middle leaf longevity. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-026-68989-x<\/a> (2026)<\/p>\n<\/li>\n

  • \n

    Myneni, R. B. et al. Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl Acad. Sci. USA 104<\/b>, 4820\u20134823 (2007).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    De Weirdt, M. et al. Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model. Geosci. Model Dev. 5<\/b>, 1091\u20131108 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Albert, L. P. et al. Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest. New Phytol. 219<\/b>, 870\u2013884 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reich, P. B. et al. Leaf demography and phenology in Amazonian rain forest: a census of 40,000 leaves of 23 tree species. Ecol. Monogr. 74<\/b>, 3\u201323 (2004).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Albert, L. P. et al. Cryptic phenology in plants: case studies, implications, and recommendations. Glob. Change Biol. 25<\/b>, 3591\u20133608 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang, X. et al. A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests. Innovation 2<\/b>, 100154 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, X. et al. Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model. J. Adv. Model. Earth Syst. 12<\/b>, e2018MS001565 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, Y. et al. Coupled estimation of 500\u2009m and 8-day resolution global evapotranspiration and gross primary production in 2002\u20132017. Remote Sens. Environ. 222<\/b>, 165\u2013182 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang, H. et al. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015\/16 El Ni\u00f1o. Proc. Natl Acad. Sci. USA 119<\/b>, e2101388119 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9<\/b>, 1937\u20131958 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kapos, V. et al. in Forests in Sustainable Mountain Development: A State of Knowledge Report for 2000. Task Force on Forests in Sustainable Mountain Development https:\/\/doi.org\/10.1079\/9780851994468.0004<\/a> (CABI, 2000).<\/p>\n<\/li>\n

  • \n

    Liu, L. et al. Tropical tall forests are more sensitive and vulnerable to drought than short forests. Glob. Change Biol. 28<\/b>, 1583\u20131595 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Han, Q. et al. Leaf-age effects on seasonal variability in photosynthetic parameters and its relationships with leaf mass per area and leaf nitrogen concentration within a Pinus densiflora crown. Tree Physiol. 28<\/b>, 551\u2013558 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xu, X. et al. Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model. Ecol. Lett. 20<\/b>, 1097\u20131106 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, J. M. et al. Global datasets of leaf photosynthetic capacity for ecological and earth system research. Earth Syst. Sci. Data 14<\/b>, 4077\u20134093 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Papastefanou, P. et al. Recent extreme drought events in the Amazon rainforest: assessment of different precipitation and evapotranspiration datasets and drought indicators. Biogeosciences 19<\/b>, 3843\u20133861 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    O\u2019Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9<\/b>, 3461\u20133482 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI\u2013MIP): project framework. Proc. Natl Acad. Sci. USA 111<\/b>, 3228\u20133232 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Restrepo-Coupe, N. et al. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Glob. Change Biol. 23<\/b>, 191\u2013208 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xu, L. et al. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett. 10<\/b>, 084005 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wu, J. et al. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytol. 217<\/b>, 1507\u20131520 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1<\/b>, 14\u201327 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, X. et al. Vapor pressure deficit and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest. Glob. Biogeochem. Cycles 35<\/b>, e2020GB006893 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Santos De Lima, L. et al. Severe droughts reduce river navigability and isolate communities in the Brazilian Amazon. Commun. Earth Environ. 5<\/b>, 370 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Santos, V. A. H. F. dos et al. Causes of reduced leaf-level photosynthesis during strong El Ni\u00f1o drought in a Central Amazon forest. Glob. Change Biol. 24<\/b>, 4266\u20134279 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Green, J. K. et al. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6<\/b>, eabb7232 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428<\/b>, 821\u2013827 (2004).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kono, Y. et al. Initial hydraulic failure followed by late-stage carbon starvation leads to drought-induced death in the tree Trema orientalis. Commun. Biol. 2<\/b>, 8 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fan, Y. et al. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114<\/b>, 10572\u201310577 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tao, S. et al. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts. Proc. Natl Acad. Sci. USA 119<\/b>, e2116626119 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342<\/b>, 850\u2013853 (2013).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Su, Y. et al. Asymmetric influence of forest cover gain and loss on land surface temperature. Nat. Clim. Change 13<\/b>, 823\u2013831 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chuvieco, E. et al. ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1. Centre for Environmental Data Analysis https:\/\/doi.org\/10.5285\/58f00d8814064b79a0c49662ad3af537<\/a> (2018).<\/p>\n<\/li>\n

  • \n

    Vancutsem, C. et al. Long-term (1990\u20132019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7<\/b>, eabe1603 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gyimah, R. & Nakao, T. Early growth and photosynthetic responses to light in seedlings of three tropical species differing in successional strategies. New For. 33<\/b>, 217\u2013236 (2007).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149<\/b>, 78\u201390 (1980).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, J. M. et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124<\/b>, 99\u2013119 (1999).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10<\/b>, 4259 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13<\/b>, 1545\u20131581 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fisher, R. A. et al. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci. Model Dev. 8<\/b>, 3593\u20133619 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, J. M. et al. Leaf area index measurements at Fluxnet\u2013Canada forest sites. Agric. For. Meteorol. 140<\/b>, 257\u2013268 (2006).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, R. et al. Seasonality of leaf area index and photosynthetic capacity for better estimation of carbon and water fluxes in evergreen conifer forests. Agric. For. Meteorol. 279<\/b>, 107708 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang, X. et al. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests. Earth Syst. Sci. Data 15<\/b>, 2601\u20132622 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance: reconciling optimal and empirical stomatal models. Glob. Change Biol. 17<\/b>, 2134\u20132144 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bernacchi, C. J. et al. Modelling C3 photosynthesis from the chloroplast to the ecosystem. Plant Cell Environ. 36<\/b>, 1641\u20131657 (2013).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhao, K., Popescu, S. & Nelson, R. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113<\/b>, 182\u2013196 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chavana-Bryant, C. et al. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytol. 214<\/b>, 1049\u20131063 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Seyednasrollah, B. et al. Publisher correction: tracking vegetation phenology across diverse biomes using version 2.0 of the PhenoCam dataset. Sci. Data 6<\/b>, 261 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Derived monthly leaf demography for the 134 vine-free upper canopy tree crowns that exhibited at least two leaf flush events during Jul2013\u2013Nov2018. ATTO https:\/\/doi.org\/10.17871\/ATTO.230.4.842<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    Coley, P. D. Herbivory and defensive characteristics of tree species in a lowland tropical forest. Ecol. Monogr. 53<\/b>, 209\u2013229 (1983).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reich, P. B. et al. Leaf lifespan as a determinant of leaf structure and function among 23 amazonian tree species. Oecologia 86<\/b>, 16\u201324 (1991).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reich, P. B. et al. Generality of leaf trait relationships: a test across six biomes. Ecology 80<\/b>, 1955\u20131969 (1999).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lugo, A. E. Comparison of tropical tree plantations with secondary forests of similar age. Ecol. Monogr. 62<\/b>, 2\u201341 (1992).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Santiago, L. S. & Wright, S. J. Leaf functional traits of tropical forest plants in telation to growth form. Funct. Ecol. 21<\/b>, 19\u201327 (2007).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chave, J. et al. Above-ground biomass and productivity in a rain forest of eastern South America. J. Trop. Ecol. 24<\/b>, 355\u2013366 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kattge, J. et al. TRY plant trait database\u2014enhanced coverage and open access. Glob. Change Biol. 26<\/b>, 119\u2013188 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, F. et al. Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes. Nat. Plants 11<\/b>, 1748\u20131758 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Janssen, T. et al. Drought effects on leaf fall, leaf flushing and stem growth in the Amazon forest: reconciling remote sensing data and field observations. Biogeosciences 18<\/b>, 4445\u20134472 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, H. et al. Accurate representation of leaf longevity is important for simulating ecosystem carbon cycle. Basic Appl. Ecol. 17<\/b>, 396\u2013407 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bradley, A. V. et al. Relationships between phenology, radiation and precipitation in the Amazon region. Glob. Change Biol. 17<\/b>, 2245\u20132260 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gloor, M. et al. Recent Amazon climate as background for possible ongoing and future changes of Amazon humid forests. Glob. Biogeochem. Cycles 29<\/b>, 1384\u20131399 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Meyer, H. et al. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ. Model. Softw. 101<\/b>, 1\u20139 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11<\/b>, 4540 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hollunder, R. K. et al. Vapor pressure deficit drives the mortality of understorey woody plants during drought recovery in the Atlantic forest. J. Veg. Sci. 35<\/b>, e13222 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219<\/b>, 851\u2013869 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11<\/b>, 5515 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Breiman, L. Random forests. Mach. Learn. 45<\/b>, 5\u201332 (2001).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29<\/b>, 1189\u20131232 (2001).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Su, Y. et al. Pervasive but biome-dependent relationship between fragmentation and resilience in forests. Nat. Ecol. Evol. 9<\/b>, 1670\u20131684 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ziehn, T. et al. The Australian Earth System Model: ACCESS-ESM1.5. J. South. Hemisph. Earth Syst. Sci. 70<\/b>, 193\u2013214 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Swart, N. C. et al. CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https:\/\/doi.org\/10.22033\/ESGF\/CMIP6.1317<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Lovato, T., Peano, D. & Butensch\u00f6n, M. CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https:\/\/doi.org\/10.22033\/ESGF\/CMIP6.13168<\/a> (2021).<\/p>\n<\/li>\n

  • \n

    Voldoire, A. CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https:\/\/doi.org\/10.22033\/ESGF\/CMIP6.1384<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Voldoire, A. CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https:\/\/doi.org\/10.22033\/ESGF\/CMIP6.1388<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Seferian, R. CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https:\/\/doi.org\/10.22033\/ESGF\/CMIP6.1395<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Dunne, J. P. et al. The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst. 12<\/b>, e2019MS002015 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    NASA Goddard Institute for Space Studies (NASA\/GISS). IPCC DDC: NASA-GISS GISS-E2.1G model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPGIGEG<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Volodin, E. et al. IPCC DDC: INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPINIC8<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Volodin, E. et al. IPCC DDC: INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPINIC0<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Boucher, O. et al. IPCC DDC: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPIPICL<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Tachiiri, K. et al. IPCC DDC: MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPMIMIL<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Schupfner, M. et al. IPCC DDC: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPDKME2<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Yukimoto, S. et al. IPCC DDC: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPMRME0<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Good, P. et al. IPCC DDC: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https:\/\/doi.org\/10.26050\/WDCC\/AR6.C6SPMOU0<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geosci. Model Dev. 12<\/b>, 3055\u20133070 (2019).<\/p>\n<\/li>\n

  • \n

    Lange, S. ISIMIP3BASD (version 2.4.1). Zenodo https:\/\/doi.org\/10.5281\/zenodo.3898426<\/a> (2021).<\/p>\n<\/li>\n

  • \n

    Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods Ecol. Evol. 3<\/b>, 260\u2013267 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xueqin, Y. & Xiuzhi, C. High-resolution leaf age-dependent LAI time-series dataset across Amazon rainforests Zenodo https:\/\/zenodo.org\/records\/18271110<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    Yang, X. et al. Code to support \u2018Amazon rainforests are rejuvenating their canopies by producing more photosynthetically efficient young leaves under climate change\u2019. Zenodo https:\/\/doi.org\/10.5281\/zenodo.18269018<\/a> (2026).<\/p>\n<\/li>\n

  • \n

    Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106<\/b>, 20610\u201320615 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xu, X. et al. Climate regime shift and forest loss amplify fire in Amazonian forests. Glob. Change Biol. 26<\/b>, 5874\u20135885 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tang, H. & Dubayah, R. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl Acad. Sci. USA 114<\/b>, 2640\u20132644 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351, 972\u2013976…\n","protected":false},"author":2,"featured_media":378567,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[269],"tags":[39116,18,440,910,19,17,3544,2389,133],"class_list":["post-378566","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-climate-change-ecology","tag-eire","tag-environment","tag-general","tag-ie","tag-ireland","tag-life-sciences","tag-plant-sciences","tag-science"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/116207754217261112","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/378566","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=378566"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/378566\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/378567"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=378566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=378566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=378566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}