Ecology

Körner, C. Alpine Plant Life: Functional plant ecology of high mountain ecosystems. (Springer, 2021). https://doi.org/10.1007/978-3-030-59538-8.Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).Article 

Google Scholar 
Pepin, N. C. et al. Climate changes and their elevational patterns in the mountains of the world. Rev. Geophys. 60, e2020RG000730 (2022).Stewart, I. T. Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol. Process 23, 78–94 (2009).Article 

Google Scholar 
Vorkauf, M., Marty, C., Kahmen, A. & Hiltbrunner, E. Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack. Clim. Change 165, 44–62 (2021).Article 

Google Scholar 
Inouye, D. W. Effects of climate change on phenology, frost damage, and floral abundance of montane wildflowers. Ecology 89, 353–362 (2008).Article 
PubMed 

Google Scholar 
Vorkauf, M., Kahmen, A., Körner, C. & Hiltbrunner, E. Flowering phenology in alpine grassland strongly responds to shifts in snowmelt but weakly to summer drought. Alp. Bot. 131, 73–88 (2021).Article 

Google Scholar 
Wipf, S. & Rixen, C. A review of snow manipulation experiments in Arctic and alpine tundra ecosystems. Polar Res. 29, 95–109 (2010).Article 

Google Scholar 
Collins, C. G. et al. Experimental warming differentially affects vegetative and reproductive phenology of tundra plants. Nat. Commun. 12, 3442 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Choler, P. Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration. Biogeosciences 12, 3885–3897 (2015).Article 

Google Scholar 
Xie, J. et al. Land surface phenology and greenness in alpine grasslands driven by seasonal snow and meteorological factors. Sci. Total Environ. 725, 138380 (2020).Article 
CAS 
PubMed 

Google Scholar 
Nord, E. A. & Lynch, J. P. Plant phenology: a critical controller of soil resource acquisition. J. Exp. Bot. 60, 1927–1937 (2009).Article 
CAS 
PubMed 

Google Scholar 
Gallinat, A. S., Primack, R. B. & Wagner, D. L. Autumn, the neglected season in climate change research. Trends Ecol. Evol. 30, 169–176 (2015).Article 
PubMed 

Google Scholar 
Rosa, R. K. et al. Plant phenological responses to a long‐term experimental extension of growing season and soil warming in the tussock tundra of Alaska. Glob. Change Biol. 21, 4520–4532 (2015).Article 

Google Scholar 
Livensperger, C. et al. Earlier snowmelt and warming lead to earlier but not necessarily more plant growth. AoB Plants 8, plw021 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Körner, C. & Hiltbrunner, E. Why is the alpine flora comparatively robust against climatic warming? Diversity 13, 383–397 (2021).Article 

Google Scholar 
Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 5, 1110–1122 (2021).Article 
PubMed 

Google Scholar 
Iversen, C. M. et al. The unseen iceberg: plant roots in arctic tundra. N. Phytol. 205, 34–58 (2015).Article 

Google Scholar 
Abramoff, R. Z. & Finzi, A. C. Are above‐ and below‐ground phenology in sync? N. Phytol. 205, 1054–1061 (2015).Article 

Google Scholar 
Liu, H. et al. Phenological mismatches between above- and belowground plant responses to climate warming. Nat. Clim. Change 12, 97–102 (2022).Article 
CAS 

Google Scholar 
Rixen, C. et al. Winters are changing: snow effects on Arctic and alpine tundra ecosystems. Arct. Sci. 1–37 (2022) https://doi.org/10.1139/as-2020-0058.Johnson, M. G., Tingey, D. T., Phillips, D. L. & Storm, M. J. Advancing fine root research with minirhizotrons. Environ. Exp. Bot. 45, 263–289 (2001).Article 
PubMed 

Google Scholar 
Atkinson, J. A., Pound, M. P., Bennett, M. J. & Wells, D. M. Uncovering the hidden half of plants using new advances in root phenotyping. Curr. Opin. Biotech. 55, 1–8 (2019).Article 
CAS 
PubMed 

Google Scholar 
Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. Root phenology in a changing climate. J. Exp. Bot. 67, 3617–3628 (2016).Article 
CAS 
PubMed 

Google Scholar 
Blume-Werry, G. The belowground growing season. Nat. Clim. Change 12, 11–12 (2022).Article 

Google Scholar 
Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Clim. Change 94, 105–121 (2009).Article 

Google Scholar 
Baptist, F., Flahaut, C., Streb, P. & Choler, P. No increase in alpine snowbed productivity in response to experimental lengthening of the growing season. Plant Biol. 12, 755–764 (2010).Article 
CAS 
PubMed 

Google Scholar 
Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int J. Biometeorol. 61, 349–361 (2017).Article 
PubMed 

Google Scholar 
Blume‐Werry, G., Jansson, R. & Milbau, A. Root phenology unresponsive to earlier snowmelt despite advanced above‐ground phenology in two subarctic plant communities. Funct. Ecol. 31, 1493–1502 (2017).Article 

Google Scholar 
Darrouzet‐Nardi, A. et al. Limited effects of early snowmelt on plants, decomposers, and soil nutrients in Arctic tundra soils. Ecol. Evol. 9, 1820–1844 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Change Biol. 20, 3256–3269 (2014).Article 

Google Scholar 
Keller, F. & Körner, C. The role of photoperiodism in alpine plant development. Arct. Antarct. Alp. Res 35, 361–368 (2003).Article 

Google Scholar 
Hiltbrunner, E., Arnaiz, J. & Körner, C. Biomass allocation and seasonal non-structural carbohydrate dynamics do not explain the success of tall forbs in short alpine grassland. Oecologia 1–15 (2021) https://doi.org/10.1007/s00442-021-04950-7.Inauen, N., Körner, C. & Hiltbrunner, E. No growth stimulation by CO2 enrichment in alpine glacier forefield plants. Glob. Change Biol. 18, 985–999 (2012).Article 

Google Scholar 
Möhl, P., Hiltbrunner, E. & Körner, C. Halving sunlight reveals no carbon limitation of aboveground biomass production in alpine grassland. Glob. Change Biol. 26, 1857–1872 (2020).Article 

Google Scholar 
Porter, J. R. & Gawith, M. Temperatures and the growth and development of wheat: a review. Eur. J. Agron. 10, 23–36 (1999).Article 

Google Scholar 
Parent, B., Turc, O., Gibon, Y., Stitt, M. & Tardieu, F. Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J. Exp. Bot. 61, 2057–2069 (2010).Article 
CAS 
PubMed 

Google Scholar 
Körner, C. H. & Woodward, F. I. The dynamics of leaf extension in plants with diverse altitudinal ranges. Oecologia 72, 279–283 (1987).Article 
PubMed 

Google Scholar 
Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants 9, plx054 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Starr, G., Oberbauer, S. F. & Pop, E. W. Effects of lengthened growing season and soil warming on the phenology and physiology of Polygonum bistorta. Glob. Change Biol. 6, 357–369 (2000).Article 

Google Scholar 
Yoshie, F. Vegetative phenology of alpine plants at Tateyama Murodo-daira in central Japan. J. Plant Res. 123, 675–688 (2010).Article 
PubMed 

Google Scholar 
Jonas, T., Rixen, C., Sturm, M. & Stoeckli, V. How alpine plant growth is linked to snow cover and climate variability. J. Geophys. Res. 113, G03013 (2008).
Google Scholar 
Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frei, E. R. & Henry, G. H. R. Long-term effects of snowmelt timing and climate warming on phenology, growth, and reproductive effort of Arctic tundra plant species. Arct. Sci. 1–22 (2021) https://doi.org/10.1139/as-2021-0028.Schäppi, B. & Körner, C. Growth responses of an alpine grassland to elevated CO2. Oecologia 105, 43–52 (1996).Article 
PubMed 

Google Scholar 
Aloni, R. Role of hormones in controlling vascular differentiation and the mechanism of lateral root initiation. Planta 238, 819–830 (2013).Article 
CAS 
PubMed 

Google Scholar 
Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. Contrasting synchrony in root and leaf phenology across multiple sub‐Arctic plant communities. J. Ecol. 104, 239–248 (2016).Article 
CAS 

Google Scholar 
Nagelmüller, S., Hiltbrunner, E. & Körner, C. Critically low soil temperatures for root growth and root morphology in three alpine plant species. Alp. Bot. 126, 11–21 (2016).Article 

Google Scholar 
Woo, H. R., Kim, H. J., Lim, P. O. & Nam, H. G. Leaf senescence: systems and dynamics aspects. Annu. Rev. Plant Biol. 70, 1–30 (2019).Article 

Google Scholar 
Liu, Z., Marella, C. B. N., Hartmann, A., Hajirezaei, M. R. & Wirén, Nvon An age-dependent sequence of physiological processes defines developmental root senescence. Plant Physiol. 181, 993–1007 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ryser, P., Puig, S., Müller, M. & Munné-Bosch, S. Abscisic acid responses match the different patterns of autumn senescence in roots and leaves of Iris versicolor and Sparganium emersum. Environ. Exp. Bot. 176, 104097 (2020).Article 
CAS 

Google Scholar 
Budge, K., Leifeld, J., Hiltbrunner, E. & Fuhrer, J. Alpine grassland soils contain large proportion of labile carbon but indicate long turnover times. Biogeosciences 8, 1911–1923 (2011).Article 
CAS 

Google Scholar 
Solly, E. F. et al. Unravelling the age of fine roots of temperate and boreal forests. Nat. Commun. 9, 3006 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Trumbore, S. E., Sierra, C. A. & Pries, C. E. H. Radiocarbon and climate change, mechanisms, applications and laboratory techniques. 45–82 (2016) https://doi.org/10.1007/978-3-319-25643-6_3.Windmaißer, T. & Reisch, C. Long-term study of an alpine grassland: local constancy in times of global change. Alp. Bot. 123, 1–6 (2013).Article 

Google Scholar 
De Witte, L. C. D., Armbruster, G. F. J., Gielly, L., Taberlet, P. & Stöcklin, J. AFLP markers reveal high clonal diversity and extreme longevity in four key arctic‐alpine species. Mol. Ecol. 21, 1081–1097 (2012).Article 
PubMed 

Google Scholar 
Landolt, E. Unsere Alpenflora. (SAC-Verlag, 2012).Puşcaş, M. & Choler, P. A biogeographic delineation of the European Alpine System based on a cluster analysis of Carex curvula-dominated grasslands. Flora – Morphol. Distrib. Funct. Ecol. Plants 207, 168–178 (2012).Article 

Google Scholar 
Grabherr, G., Mahr, E. & Reisigl, H. Nettoprimärproduktion und Reproduktion in einem Krummseggenrasen (Caricetum curvulae) der Otztaler Alpen, Tirol. Oecologia Plant. 13, 227–251 (1978).
Google Scholar 
Chiang, C., Bånkestad, D. & Hoch, G. Reaching natural growth: light quality effects on plant performance in indoor growth facilities. Plants 9, 1273 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chiang, C., Olsen, J. E., Basler, D., Bånkestad, D. & Hoch, G. Latitude and weather influences on sun light quality and the relationship to tree growth. Forests 10, 610–621 (2019).Article 

Google Scholar 
Richardson, A. D. et al. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152, 323–334 (2007).Article 
PubMed 

Google Scholar 
Jiang, Y. & Li, C. Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics 2020, 4152816 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Möller, B. et al. rhizoTrak: a flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons. Plant Soil 444, 519–534 (2019).Article 

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).Article 
CAS 
PubMed 

Google Scholar 
Smith, A. G., Petersen, J., Selvan, R. & Rasmussen, C. R. Segmentation of roots in soil with U-Net. Plant Methods 16, 13–27 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Seethepalli, A. et al. Rhizovision crown: an integrated hardware and software platform for root crown phenotyping. Plant Phenomics 2020, 3074916 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.2-1. (2021).Möhl P., von Büren R. S. & Hiltbrunner E. Data from: Growth of alpine grassland will start and stop earlier under climate warming figshare. https://doi.org/10.6084/m9.figshare.20440497 (2022). More

Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
CAS 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
CAS 

Google Scholar 
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).Article 

Google Scholar 
Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30–36 (2013).Article 

Google Scholar 
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).Article 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).Article 
CAS 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).Article 
CAS 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).Article 
CAS 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402 (2017).Article 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).Article 
CAS 

Google Scholar 
Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).Article 
CAS 

Google Scholar 
Anderegg, W. R., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Change 10, 1091–1095 (2020).Article 

Google Scholar 
Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).Article 
CAS 

Google Scholar 
Engelbrecht, B. M. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).Article 
CAS 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).Article 
CAS 

Google Scholar 
Au, T. F. et al. Demographic shifts in eastern US forests increase the impact of late‐season drought on forest growth. Ecography 43, 1475–1486 (2020).Article 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
Lindenmayer, D. B., Laurance, W. F. & Franklin, J. F. Global decline in large old trees. Science 338, 1305–1306 (2012).Article 
CAS 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).Article 
CAS 

Google Scholar 
Ellsworth, D. & Reich, P. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest. Oecologia 96, 169–178 (1993).Article 
CAS 

Google Scholar 
Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).Article 
CAS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).Article 
CAS 

Google Scholar 
Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).Article 

Google Scholar 
Piovesan, G. & Biondi, F. On tree longevity. N. Phytol. 231, 1318–1337 (2021).Article 

Google Scholar 
Jucker, T. et al. Tallo: a global tree allometry and crown architecture database. Glob. Change Biol. 28, 5254–5268 (2022).Article 
CAS 

Google Scholar 
Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).Article 

Google Scholar 
D’orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).Article 

Google Scholar 
Luo, Y. & Chen, H. Y. Observations from old forests underestimate climate change effects on tree mortality. Nat. Commun. 4, 1655 (2013).Article 

Google Scholar 
Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).Article 

Google Scholar 
Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).Article 
CAS 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 
CAS 

Google Scholar 
Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).Article 
CAS 

Google Scholar 
Phillips, R. P. et al. A belowground perspective on the drought sensitivity of forests: towards improved understanding and simulation. For. Ecol. Manage. 380, 309–320 (2016).Article 

Google Scholar 
Meinzer, F. C., Lachenbruch, B. & Dawson, T. E. Size- and Age-Related Changes in Tree Structure and Function Vol. 4 (Springer, 2011).Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article 
CAS 

Google Scholar 
Klein, T. The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Funct. Ecol. 28, 1313–1320 (2014).Article 

Google Scholar 
Cavender-Bares, J. & Bazzaz, F. Changes in drought response strategies with ontogeny in Quercus rubra: implications for scaling from seedlings to mature trees. Oecologia 124, 8–18 (2000).Article 
CAS 

Google Scholar 
Gallé, A., Haldimann, P. & Feller, U. Photosynthetic performance and water relations in young pubescent oak (Quercus pubescens) trees during drought stress and recovery. N. Phytol. 174, 799–810 (2007).Article 

Google Scholar 
Keith, H., Mackey, B. G. & Lindenmayer, D. B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl Acad. Sci. USA 106, 11635–11640 (2009).Article 
CAS 

Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).Article 
CAS 

Google Scholar 
Zhao, S. et al. The International Tree‐Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).Article 

Google Scholar 
Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).Article 

Google Scholar 
Rayback, S. A. et al. The DendroEcological Network: a cyberinfrastructure for the storage, discovery and sharing of tree-ring and associated ecological data. Dendrochronologia 60, 125678 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions. Climate of the Past 16, 1901–1916 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Higher CO2 concentrations and lower acidic deposition have not changed drought response in tree growth but do influence iWUE in hardwood trees in the Midwestern USA. J. Geophys. Res. Biogeosci. 124, 3798–3813 (2019).Article 
CAS 

Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Springer, 2013).Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
Google Scholar 
Fritts, H. Tree Rings and Climate (Academic Press, 1976).
Google Scholar 
Wilson, R. et al. Last millennium Northern Hemisphere summer temperatures from tree rings: part I: the long term context. Quat. Sci. Rev. 134, 1–18 (2016).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Holmes, R. Program COFECHA User’s Manual (Univ. Arizona Laboratory of Tree-Ring Research, 1983).Palmer, J. G. et al. Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Pacific Oscillation. Environ. Res. Lett. 10, 124002 (2015).Article 

Google Scholar 
Cook, E. R. et al. Asian monsoon failure and megadrought during the last millennium. Science 328, 486–489 (2010).Article 
CAS 

Google Scholar 
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M. & Stahle, D. W. Long-term aridity changes in the western United States. Science 306, 1015–1018 (2004).Article 
CAS 

Google Scholar 
Cook, E. R. et al. Megadroughts in North America: placing IPCC projections of hydroclimatic change in a long‐term palaeoclimate context. J. Quat. Sci. 25, 48–61 (2010).Article 

Google Scholar 
Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era. Sci. Adv. 1, e1500561 (2015).Article 

Google Scholar 
Morales, M. S. et al. Six hundred years of South American tree rings reveal an increase in severe hydroclimatic events since mid-20th century. Proc. Natl Acad. Sci. USA 117, 16816–16823 (2020).Article 
CAS 

Google Scholar 
Stokes, M. & Smiley, T. An Introduction to Tree-Ring Dating. (Univ. Chicago Press, 1968).
Google Scholar 
Lockwood, B. R., Maxwell, J. T., Robeson, S. M, & Au, T. F. Assessing bias in diameter at breast height estimated from tree rings and its effects on basal area increment and biomass. Dendrochronologia 67, 125844 (2021).Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl Acad. Sci. USA 117, 33358–33364 (2020).Article 
CAS 

Google Scholar 
Rozas, V., DeSoto, L. & Olano, J. M. Sex‐specific, age‐dependent sensitivity of tree‐ring growth to climate in the dioecious tree Juniperus thurifera. N. Phytol. 182, 687–697 (2009).Article 

Google Scholar 
Carrer, M. & Urbinati, C. Age‐dependent tree‐ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85, 730–740 (2004).Article 

Google Scholar 
Gazol, A., Camarero, J., Anderegg, W. & Vicente‐Serrano, S. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).Article 

Google Scholar 
Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

Google Scholar 
Pardos, M. et al. The greater resilience of mixed forests to drought mainly depends on their composition: analysis along a climate gradient across Europe. For. Ecol. Manage. 481, 118687 (2021).Article 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: thestandardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).Rollinson, C. R. et al. Climate sensitivity of understory trees differs from overstory trees in temperate mesic forests. Ecology 102, e03264 (2021).Article 

Google Scholar 
Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: effects of successive low‐growth episodes in old ponderosa pine forests. Oikos 120, 1909–1920 (2011).Article 

Google Scholar 
Li, X. et al. Reply to: Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 736–737 (2021).Article 

Google Scholar 
Zheng, T. et al. Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 733–735 (2021).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Long, J. A. jtools: Analysis and Presentation of Social Scientific Data R Package v.2.2.0 https://cran.r-project.org/package=jtools (2022).Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on AIC R Package v.2.3-1 https://cran.r-project.org/package=AICcmodavg (2020).Au, T. F. Au_et_al_NCC.R. Figshare https://doi.org/10.6084/m9.figshare.21263676.v1 (2022). More

.readcube-buybox { display: none !important;}
Efforts to keep oil-palm plantations from crowding out tropical rainforests could threaten biodiversity in other habitats1.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to Nature and 55 other Nature journal$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-022-04179-1

References

Subjects

Conservation biology More

Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davis, K. F. et al. Assessing the sustainability of post-Green Revolution cereals in India. Proc. Natl Acad. Sci. USA 116, 25034 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoekstra, A. Y. & Wiedmann, T. O. Humanity’s unsustainable environmental footprint. Science 344, 1114 (2014).Article 
CAS 
PubMed 

Google Scholar 
O Neill, D. W., Fanning, A. L., Lamb, W. F. & Steinberger, J. K. A good life for all within planetary boundaries. Nat. Sustain. 1, 88 (2018).Article 

Google Scholar 
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).Article 
PubMed 

Google Scholar 
van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food 2, 494 (2021).Article 

Google Scholar 
Grassini, P., Eskridge, K. M. & Cassman, K. G. Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4, 2918 (2013).Article 
PubMed 

Google Scholar 
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).Article 
PubMed 

Google Scholar 
Chen, X. et al. Integrated soil–crop system management for food security. Proc. Natl Acad. Sci. USA 108, 6399 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
FAOSTAT. FAO http://www.fao.org/faostat/en/#home (2021).Liu, Z. et al. Optimization of China’s maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints. Nat. Food 2, 426 (2021).Article 

Google Scholar 
Zhang, Q. et al. Outlook of China’s agriculture transforming from smallholder operation to sustainable production. Glob. Food Secur. 26, 100444 (2020).Article 

Google Scholar 
Duan, J. et al. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nat. Food 2, 1014 (2021).Article 
CAS 

Google Scholar 
Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhou, F. et al. Deceleration of China’s human water use and its key drivers. Proc. Natl Acad. Sci. USA 117, 7702 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, H. et al. Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models. Agr. For. Meteorol. 303, 108373 (2021).Article 

Google Scholar 
Yue, Q. et al. Deriving emission factors and estimating direct nitrous oxide emissions for crop cultivation in China. Environ. Sci. Technol. 53, 10246 (2019).Article 
CAS 
PubMed 

Google Scholar 
Ju, X., Gu, B., Wu, Y. & Galloway, J. N. Reducing China’s fertilizer use by increasing farm size. Global Environ. Chang. 41, 26 (2016).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Global Environ. Chang. 26, 152 (2014).Article 

Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254 (2012).Article 
CAS 
PubMed 

Google Scholar 
Davis, K. F., Rulli, M. C., Seveso, A. & D. Odorico, P. Increased food production and reduced water use through optimized crop distribution. Nat. Geosci. 10, 919 (2017).Article 
CAS 

Google Scholar 
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486 (2014).Article 
CAS 
PubMed 

Google Scholar 
UN Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1 (2019). https://population.un.org/wpp/2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2019).Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284 (2020).Article 

Google Scholar 
Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257 (2019).Article 
CAS 
PubMed 

Google Scholar 
Price Bureau of the National Development and Reform Commission of China. China Agricultural Products Cost–Benefit Compilation of Information 2017 (in Chinese) (China Statistics Press, 2017).Fan, S., Brzeska, J., Keyzer, M. & Halsema, A. From Subsistence to Profit: Transforming Smallholder Farms. (Inter. Food Policy Res. Inst., 2013).Wang, S. et al. Urbanization can benefit agricultural production with large-scale farming in China. Nat. Food 2, 183 (2021).Article 

Google Scholar 
Yin, Y. et al. A steady-state N balance approach for sustainable smallholder farming. Proc. Natl Acad. Sci. USA 118, e2106576118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guiding opinions of the ministry of agriculture on the adjustment of maize structure in the “sickle” area. Ministry of Agriculture and Rural Affairs of the People’s Republic of China http://www.moa.gov.cn/nybgb/2015/shiyiqi/201712/t20171219_6103893.htm (2017).Zhang, F., Chen, X. & Vitousek, P. An experiment for the world. Nature 497, 33 (2013).Article 
CAS 
PubMed 

Google Scholar 
Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671 (2016).Article 
CAS 
PubMed 

Google Scholar 
Cyberspace Administration of China. State Council of the People’s Republic of China http://www.gov.cn/xinwen/2021-12/28/content_5664873.htm (2021).Kou, T. et al. Effects of long-term cropping regimes on soil carbon sequestration and aggregate composition in rainfed farmland of Northeast China. Soil Till. Res. 118, 132 (2012).Article 

Google Scholar 
Li, X. et al. Long-term increased grain yield and soil fertility from intercropping. Nat. Sustain. 4, 943 (2021).Article 

Google Scholar 
Damerau, K. et al. India has natural resource capacity to achieve nutrition security, reduce health risks and improve environmental sustainability. Nat. Food 1, 631 (2020).Article 

Google Scholar 
Kuang, W. et al. Cropland redistribution to marginal lands undermines environmental sustainability. Natl Sci. Rev. 9, 1 (2021).
Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ma, L. et al. Exploring future food provision scenarios for China. Environ. Sci. Technol. 53, 1385 (2018).Article 

Google Scholar 
National population development plan: 2016–2030. National Development and Reform Commission http://www.gov.cn/zhengce/content/2017-01/25/content_5163309.htm (2016).Ma, L. et al. Environmental assessment of management options for nutrient flows in the food chain in China. Environ. Sci. Technol. 47, 7260 (2013).Article 
CAS 
PubMed 

Google Scholar 
Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179 (2009).Article 

Google Scholar 
Yan, X., Akiyama, H., Yagi, K. & Akimoto, H., Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Global Biogeochem. Cy. https://doi.org/10.1029/2008GB003299 (2009).Smith, P., Martino, Z. & Cai, D. ‘Agriculture’, in Climate Change 2007: Mitigation (Cambridge Univ. Press, 2007).Liang, D. et al. China’s greenhouse gas emissions for cropping systems from 1978–2016. Sci. Data 8, 171 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Overview of modeling frameworkWe have used a range of econometric, economic, and agricultural land surface models to analyze the factors driving land-use change in order to assess their ecological, agricultural, climatic and economic impacts. These multi-scale models differ in their methodologies, scale of interest, and resolution, but they are very complementary and could provide a unique opportunity to analyze public policy scenario effects on land-use and resulting changes in ecosystem carbon and biodiversity.Among these models, the economic land use model Nexus Land Use (NLU)29,30 and the agricultural supply-side model Agriculture, Recomposition de l’Offre et Politique Agricole (AROPAj)31 coupled with a spatial econometric model32 have allowed us to estimate the impact on EU land-use of a scenario involving a 50% reduction in N synthetic fertilizers compared to a baseline scenario. In the present study, we use these land-use scenarios to force ORCHIDEE-crop (Organising Carbon and Hydrology in Dynamic Ecosystems), an agricultural land surface model16,33 and Projecting Responses of Ecological Diversity in Changing Terrestrial Systems (PREDICTS)34, a biodiversity model to simulate, respectively, ecosystem C and biodiversity changes across the EU covering the domain 35.25°N and 69.25°N in latitude and 9.25°W and 34.25°W in longitude. The schematic (Fig. 1) provides a brief overview of the modelling framework applied in this study.Figure 1Schematic diagram illustrating the coupling of multi-scale land-use models. The multi-scale models coupled in this study are econometric, and economic models (NLU and AROPAj), an agricultural land surface model (ORCHIDEE-crop), and a biodiversity model (PREDICTS). Coupling means, we use the output of one model as an input to other models. In addition, we have performed one-way coupling and there is no two-way interaction between models. Each economic model generates two land-use maps corresponding to Baseline and Halving-N scenario which are inputs (2 from NLU and 2 from AROPAj) to ORCHIDEE-crop and PREDICTS. The ecosystem carbon (C) sequestration is simulated by ORCHIDEE-crop and biodiversity indicators are simulated by PREDICTS model. The abbreviations ‘BaseNLU’ and ‘HaNNLU’ means Baseline and Halving-N land-use map generated by NLU model. The abbreviations ‘BaseAR’ and ‘HaNAR’ means Baseline and Halving-N land-use map generated by AROPAj model.Full size imageIn order to link the land use output data from the AROPAj and NLU models with the ORCHIDEE-crop and PREDICTS models, the first step is to match land uses and crops between the models (see Table 1). AROPAj and NLU crops are classified into ORCHIDEE-crop plant functional types (PFTs): C3 winter and summer crops, C4 summer crop and C3/C4 natural grass (see “Model descriptions” section for a detailed description of ORCHIDEE-crop PFTs). The AROPAj and NLU crops are also classified into the PREDICTS crop types: annual, perennial, N-fixing. The AROPAj and NLU “rangeland” and “pasture” categories are found in PREDICTS but in ORCHIDEE-crop they are considered to fall within the C3 natural grassland PFT. Finally, NLU and AROPAJ forest and other natural areas are classified as “primary” natural areas (with low anthropogenic use) or “secondary” (intermediate to high anthropogenic environmental use) according to the land use map of these areas35. For ORCHIDEE-crop, they are classified as natural forest PFTs. Note that the fallow areas described in AROPAj that are part of crops are classified as “grass” PFT in ORCHIDEE-crop and as “minimum” intensity annual crops in PREDICTS.Table 1 Table of correspondences between the land uses and crops represented in the AROPAJ/NLU and ORCHIDEE models and PREDICTS.Full size tableThe land-use and land cover changes described in the following sub-section are used as inputs to ORCHIDEE-crop and PREDICTS from both the NLU and AROPAj models’ output.Land-use change scenariosLand-use changes in the EU are simulated for the present day using two scenarios: (1) a business as usual scenario (Baseline) and (2) a scenario involving a policy to reduce mineral nitrogen use by 50% from the Baseline (Halving-N). The land-use changes in Halving-N and Baseline are computed by both NLU and AROPAj models. In the latter model, the computed land-use changes result from coupling between AROPAj and a spatial econometric model. Since there are differences in the nature of the models (supply-side model versus partial equilibrium model) and their underlying data, the Baseline scenarios in the NLU and AROPAj frameworks are different. A detailed description of the differences and a discussion of their implications on the production and area of different land-uses is provided in Lungarska et al.36. EU plant production is 370 and 383 MtDM (Million tons of Dry Matter) respectively based on the application of 12 TgN (Tera grams) of N fertilizer in AROPAj and NLU. Crops, grasslands, and forests cover respectively, 116, 57 and 234 Mha in NLU and respectively 94 (including fallow land), 38 and 142 Mha in AROPAj. In AROPAj and NLU, the 50% N reduction is achieved indirectly by increasing the N input price from present-day figures36.The land-use changes output from AROPAj and NLU are supplied as inputs to the ORCHIDEE-crop and PREDICTS models. The land-use changes are matched with corresponding plant functional types (PFTs) in ORCHIDEE-crop and land-uses in PREDICTS (see Table 1). “Model descriptions” section provides a detailed description of the ORCHIDEE-crop and PREDICTS models.Model descriptionsHere, we describe the ORCHIDEE-crop and PREDICTS models that quantify the impacts of halving N fertilizer consumption in the EU. Table 2 presents a brief overview of the two models.Table 2 Overview of the ORCHIDEE-crop and PREDICTS models input and output.Full size tableA detailed description of ORCHIDEE-crop: This model is a process-based agricultural land surface model that integrates crop-specific phenology based on Simulateur mulTidisciplinaire pour les Cultures Standard (STICS)37,38. Carbon allocation is based on the plant-based hybrid model from the original ORCHIDEE allocation scheme39 and a crop specific formulation of STICS providing leaf, root, and shoot biomass, grain maturity time, litter production, and litter and soil carbon decomposition. The harvest date is calculated after grains reach maturity40. The ORCHIDEE-crop model has no explicit nitrogen cycle but accounts empirically for the effect of N fertilization by increasing the maximum Rubisco- and light-limited leaf photosynthetic rates as a function of the amount of N applied, using a Michaelis–Menten function40. Also, ORCHIDEE-crop is calibrated against observations, which showed a good match between modeled observed aboveground biomass, crop yield, and daily carbon40. This version of the model currently uses three crop PFTs: C3 winter, C3 summer and C4 summer. Forests are classified as Broadleaf, Needle leaf, Deciduous, Temperate and Boreal. Up to 11 non-cropland vegetation types can co-exist with crops on a grid point of the model, according to prescribed land cover information. A gridded simulation of ORCHIDEE-crop requires 30-min time step meteorological forcing (air temperature, specific humidity, incoming shortwave and longwave radiation, rainfall), which can be interpolated in time from gridded climate analysis data or atmospheric models. In this study, this model is used to quantify the ecosystem C variables.A detailed description of PREDICTS: The PREDICTS database was collated by searching the published literature for studies where terrestrial biodiversity (including plants, fungi, vertebrates, and invertebrates) was sampled using consistent methods across multiple sites, which vary in the pressures faced. The land use and intensity of each site have been assessed and categorized in a consistent way41,42,43. Authors of studies were contacted to ask for the raw biodiversity data where this was not already available41,42. Most records in the PREDICTS database refer to the number of individuals of a species at a site; this makes it possible to compute a range of biodiversity indices. To estimate biodiversity responses to human impacts across such a global and heterogeneous dataset, linear mixed-effects models are used; random intercepts account for differences in biogeographic factors, sampling methodology and taxonomic focus, and the spatial layout of sites within studies. Using the PREDICTS database to assess the impact of human pressures on biodiversity assumes that space-for-time substitution is valid44; it assumes that the sites have reached equilibrium and so the impact of pressures on biodiversity over time can be observed across space and that the relationship between biodiversity and drivers do not vary over time.SR is calculated as the number of species at each site; it is a widely used measure of biodiversity and is both simple and intuitive. Responses of SR to land use and intensity were modelled using generalized linear mixed effects models and with a Poisson error structure; an observation-level random effect was included to account for overdispersion45. This model is then used to project SR in each grid of a 0.5° map and expressed as a percentage of the SR level in primary vegetation from land use harmonization map35.To estimate BII change with land use and intensity, two models are required. Total abundance was first calculated as the sum of all individuals at each site; it was then rescaled within the study (so that the maximum within a study is 1) and was square-root transformed before modelling as a function of land use and intensity, to account for non-normality of the model residuals (a Poisson error structure could not be used as abundance data can include non-integer data e.g. densities). Inclusion of a random slope for land use within the study was supported (based on Akaike’s Information Criterion). Compositional similarity was then calculated as the asymmetric Jaccard index, comparing each baseline site (primary vegetation) with all other sites, and logit transformed with an adjustment of 0.01 (to account for non-normality of the model residuals). Compositional similarity was then modelled as a function of land use and intensity (coarsened so that only perennial crops were allowed to differ across intensities), including the environmental and geographic distance between sites as control variables, whose effects were permitted to differ among land use and intensity levels (these variables were cube-root and log-transformed respectively to improve residual distribution). To calculate BII, total abundance (expressed as a percentage of their level in primary vegetation) and compositional similarity (expressed as a percentage of their level in primary vegetation)46 are projected for each grid of a 0.5° map; these two maps are then multiplied to give abundance-based BII19. The PREDICTS models include different levels of management (intensive, light or minimal) and different types of land cover (forest, pasture, rangeland, annual cropland, perennial cropland, and urban zones). The coefficients of these mixed-effect models and a detailed description of the link between the PREDICTS models and NLU are available in Prudhomme et al.46. The spatial predictions of biodiversity were computed using a python pipeline, which was developed specifically for the PREDICTS project (https://github.com/ricardog/raster-project).In our modeling framework, the impact of halving N fertilizer goes through two steps: (i) we calculate the effect of this reduction of N fertilizer on agricultural yield, and (ii) calculate the effect of the yield reduction on biodiversity. By keeping yield as a proxy of agricultural land use intensification as proposed in Prudhomme et al.46, we include not only the direct effect of the reduction of N fertilizer on biodiversity but also the effects correlated to this reduction of N fertilizer such as the reduction of other chemical inputs (P and K fertilizers and pesticides). While the effect of the change in N fertilization on yield is calculated by the classical concave production function in agronomy29, the effect of the change in yield is calculated by coupling the NLU land use model and the PREDICTS biodiversity model46. For each category of crops (annual, perennial, leguminous), the coupling consists of estimating (using a Generalized Additive Model [GAM]) the share of each intensity class (minimum, light, intense) as a function of the average calorie yield based on the average crop yield maps from a plant growth model. The maps describing the share of land use intensities are from Newbold et al.19 Similarly for pasture, the share of each intensity class (light, intense) is estimated with the help of a GAM as a function of ruminant density.SimulationsOur experimental design focuses on assessing the effects of a 50% reduction in present-day N fertilizer use levels across the EU. The choice of halving N fertilizer in EU agriculture is related to the “Farm to Fork” strategy, which puts forward the ambition for 2030 to reduce nutrient losses to the environment from both organic and mineral fertilizers by at least 50%. The results from NLU (and its nitrogen balance module) show that this level of reduction corresponds to a 50% reduction in nutrient losses (nitrogen and phosphorus) aimed by the Farm to Fork strategy as a part of the European Green Deal. AROPAj models exclusively the EU countries (in 2012, there were 28 member states) while NLU simulations cover the EU and the rest of the world (EU being a part of the European region as represented by the model). However, the N reduction policy implemented in the EU alone and the comparison of the results conducted only for the EU. All EU member states are considered but for some of them we present results. A total of four simulations corresponding to four land-use maps (two from AROPAj and two from NLU, see Fig. 1) are performed in the ORCHIDEE-crop model and also in the PREDICTS model. In addition to changes in the area of different land-uses, changes in mineral N input are accounted for in both models. However, changes in organic N input and crop rotations are not accounted for. In ORCHIDEE-crop 55% of the carbon harvested from croplands is exported but the remaining residues are returned to the soils.ORCHIDEE-crop simulation details: the model simulations are performed over a domain covering the EU. Four idealized simulations are carried out using the ORCHIDEE-crop model by forcing present-day meteorological data (2006–2010), levels of N fertilizer (150 KgN/ha) and atmospheric CO2 concentration (385 ppm). The four simulations include Halving-N and Baseline corresponding to AROPAj and NLU land-use scenarios (two ORCHIDEE-crop simulations per economic model). All four simulations start from the year 2010 climate and carbon cycle conditions with a recycled climate (2006–2010) for 150 years. For the year 2010, climate and carbon cycle conditions are obtained from the output of historical simulations. Historical simulations from the year 1901 to the year 2010 are performed for both AROPAj and NLU Baseline scenario land-use land cover maps. In addition, these historical simulations started from an equilibrium state of soil carbon, energy and water cycle variables corresponding to the year 1901. The 1901 equilibrium state is determined by running a 350-year spin-up simulation corresponding to a recycled climate (1901–1910). The observation-based climate forcing data from the Global Soil Wetness Project was only available starting from the year 1901. The drift in soil carbon over the last 100 years of the 350-year simulations is less than 1%. The equilibrium state simulations corresponding to the year 1901 were necessary to have stabilized biophysical and ecosystem C variables across the EU. Other forcing variables, e.g. atmospheric CO2 concentration (296.57 ppm), N-fertilization rate (32 KgN/ha), harvest index (0.25), and also the phenology parameters for short-cycle variety winter and summer crops16 corresponding to the year 1901 were prescribed.PREDICTS simulation details: the PREDICTS model represents changes in broad-sense biodiversity in different land-uses and intensities of land-use relative to a reference land-use (as the biodiversity metrics assessed include all terrestrial biodiversity for which data are present in the PREDICTS database including plants, fungi, vertebrates and invertebrates). Here the reference ecosystem is a primary natural ecosystem. Biodiversity changes are then reported as a percentage by dividing the obtained biodiversity levels by the level of biodiversity present in the primary natural ecosystem. This simulation is performed for each grid point on a map of the EU for land-use scenarios corresponding to Baseline and Halving-N for both economic models, AROPAj and NLU (Fig. 1).Breakdown method for biodiversity and carbon changesThe Halving-N and Baseline scenarios provide contrasted land-use maps according to the assumptions of economic and land-use models36. This results in different plant and animal production, and different land-uses at the European scale in each model. A price shock on inputs, as represented in the Halving-N scenario compared to the Baseline scenario, can induce (1) a spatial reallocation of production or (2) production changes47. Here, we separate out the effects of these two mechanisms on biodiversity (species richness) and carbon indicators (NPP and soil carbon) by decomposing the overall environmental differences between the Halving-N and the Baseline scenarios. The breakdown is not possible for the BII indicator because this indicator is the product of two indicators: abundance and a similarity indicator of ecological communities.First, we breakdown the carbon and biodiversity differences by land-use type. The breakdown for carbon is straightforward because the carbon changes are computed for each land-use. The biodiversity changes associated with each land-use are computed by setting no changes in the other PREDICTS model land-uses. The sum of the biodiversity changes for each land-use is thus equal to the overall change in biodiversity.For each land-use i (forest, grassland and cropland), we separate out the carbon and biodiversity differences between the Halving-N and the Baseline scenarios into two effects in accordance with Eq. (1): (i) the carbon and biodiversity difference associated with the area difference—called “Area effect”, and (ii) the carbon and biodiversity difference associated with the difference in biodiversity and carbon sequestration per unit area—called “Intensity effect”. The “Area effect” corresponds to the change in carbon sequestration and biodiversity associated with a change in the land-use area. For example, a reduction in grassland area leads to reduction in the C sequestration and biodiversity associated with this area. The “Intensity effect” corresponds to a change in the C sequestration and biodiversity per unit area. For example, a reallocation of production toward places with high soil C content leads to an increase in the carbon stock per hectare or an increase in crop yield leads to a reduction in the biodiversity per unit of cropland. Thus, the “Intensity effect” corresponds to the effect of a production reallocation on C sequestration, and the effect of land-use intensity on biodiversity.We use the Logarithmic Mean Division Index (LMDI) method, which breaks down the target values into several main influencing factors based on mathematical identity transformation48 as follows.$$Delta {E}_{i}=Delta {E}_{i}^{A}+Delta {E}_{i}^{I}$$
(1)
(Delta {E}_{i}) is the difference in the environmental indicator between the Halving-N and the Baseline scenarios. Superscript ‘A’ denotes area effect and ‘I’ denotes intensity effect. Subscript ‘i’ denotes different land-use (e.g. forests, grassland, cropland etc.). (Delta {E}_{i}^{A}) is the difference in the environmental indicator between the Halving-N and the Baseline scenarios associated with the difference in area. (Delta {E}_{i}^{I}) is the difference between the Halving-N and the Baseline scenarios associated with the different intensity per unit of area of the environmental indicator.$$Delta {E}_{i}^{A}=frac{{E}_{i}^{hN}-{E}_{i}^{b}}{ln({E}_{i}^{hN})-{ln(E}_{i}^{b})}times lnleft(frac{{A}_{i}^{hN}}{{A}_{i}^{b}}right)$$
(2)
({E}_{i}^{hN}) is the level of the environmental indicator in the Halving-N (superscript hN) scenario. ({E}_{i}^{b}) is the level of the environmental indicator in the Baseline (superscript b). ({A}_{i}^{hN}) is the area of land-use i in the Halving-N scenario. ({A}_{i}^{b}) is the area of land-use i in the Baseline$$Delta {E}_{i}^{I}=frac{{E}_{i}^{hN}-{E}_{i}^{b}}{ln({E}_{i}^{hN})-ln({E}_{i}^{b})}times lnleft(frac{{e}_{i}^{hN}}{{e}_{i}^{b}}right)$$
(3)
Equation (3) is same as Eq. (2) but for the intensity of the environmental indicator ({e}_{i}).The breakdown of the differences in the environmental indicators is performed between the Halving-N scenario and the Baseline. A positive variation ((Delta {E}_{i} >0)) indicates a higher environmental indicator in the Halving-N scenario compared to the Baseline without implying any temporal variation since the scenarios compare the environmental indicator status in 2012 in the AROPAj and in the NLU land-uses. Conversely, a negative variation ((Delta {E}_{i} More

Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn. (Academic Press, 2008).
Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: The past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 
PubMed 

Google Scholar 
Štajerová, K., Šmilauerová, M. & Šmilauer, P. Arbuscular mycorrhizal symbiosis of herbaceous invasive neophytes in the Czech Republic. Preslia 81, 341–355 (2009).
Google Scholar 
Hempel, S. et al. Mycorrhizas in the Central European flora: Relationships with plant life history traits and ecology. Ecology 94, 1389–1399 (2013).Article 
PubMed 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. New Phytol. 227, 955–966 (2020).Article 
PubMed 

Google Scholar 
Spatafora, J. W. et al. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia 108, 1028–1046 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lekberg, Y., Hammer, E. C. & Olsson, P. A. Plants as resource islands and storage units—Adopting the mycocentric view of arbuscular mycorrhizal networks. FEMS Microbiol. Ecol. 74, 336–345 (2010).Article 
CAS 
PubMed 

Google Scholar 
Newsham, K. K., Fitter, A. H. & Watkinson, A. R. Arbuscular mycorrhiza protect an annual grass from root pathogenic fungi in the field. J. Ecol. 83, 991–1000 (1995).Article 

Google Scholar 
Vigo, C., Norman, J. R. & Hooker, J. E. Biocontrol of the pathogen Phytophthora parasitica by arbuscular mycorrhizal fungi is a consequence of effects on infection loci. Plant Pathol. 49, 509–514 (2000).Article 

Google Scholar 
Pfeffer, P. E., Douds, D. D., Becard, G. & Shachar-Hill, Y. Carbon uptake and the metabolism and transport of lipids in an arbuscular mycorrhiza. Plant Physiol. 120, 587–598 (1999).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bago, B., Pfeffer, P. E. & Shachar-Hill, Y. Carbon metabolism and transport in arbuscular mycorrhizas. Plant Physiol. 124, 949–958 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ramirez, R., Mendoza, B. & Lizaso, J. I. Mycorrhiza effect on maize P uptake from phosphate rock and superphosphate. Commun. Soil Sci. Plant Anal. 40, 13–14 (2009).Article 

Google Scholar 
Pearson, J. N. & Jakobsen, I. The relative contribution of hyphae and roots to phosphorus uptake by arbuscular mycorrhizal plants, measured by dual labeling with 32P and 33P. New Phytol. 124, 489–494 (1993).Article 
CAS 

Google Scholar 
Smith, S. E., Smith, F. A. & Jakobsen, I. Functional diversity in arbuscular mycorrhizal (AM) symbioses: The contribution of the mycorrhizal P uptake pathway is not correlated with mycorrhizal responses in growth or total P uptake. New Phytol. 162, 511–524 (2004).Article 

Google Scholar 
Smith, M. D., Hartnett, D. C. & Wilson, G. W. T. Interacting influence of mycorrhizal symbiosis and competition on plant diversity in tallgrass prairie. Oecologia 121, 574–582 (1999).Article 
CAS 
PubMed 

Google Scholar 
Bennett, J. A. et al. Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).Article 
CAS 
PubMed 

Google Scholar 
Liao, H. et al. Soil microbes regulate forest succession in a subtropical ecosystem in China: Evidence from a mesocosm experiment. Plant Soil 430, 277–289 (2018).Article 
CAS 

Google Scholar 
Awaydul, A. et al. Common mycorrhizal networks influence the distribution of mineral nutrients between an invasive plant, Solidago canadensis, and a native plant, Kummerowa striata. Mycorrhiza 29, 29–38 (2019).Article 
CAS 
PubMed 

Google Scholar 
Callaway, R. M., Newingham, B., Zabinski, C. A. & Mahall, B. E. Compensatory growth and competitive ability of an invasive weed are enhanced by soil fungi and native neighbours. Ecol. Lett. 4, 429–433 (2001).Article 

Google Scholar 
Workman, R. E. & Cruzan, M. B. Common mycelial networks impact competition in an invasive grass. Am. J. Bot. 103, 1041–1049 (2016).Article 
CAS 
PubMed 

Google Scholar 
Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—The role of mutualisms. Biol. Rev. Camb. Philos. Soc. 75, 65–93 (2000).Article 
CAS 
PubMed 

Google Scholar 
Vogelsang, K. M. & Bever, J. D. Mycorrhizal densities decline in association with nonnative plants and contribute to plant invasion. Ecology 90, 399–407 (2009).Article 
PubMed 

Google Scholar 
Horton, T. R. Mycorrhizal Networks, Ecological Studies 224 (Springer, 2015).Book 

Google Scholar 
Lin, G., McCormack, M. L. & Guo, D. Arbuscular mycorrhizal fungal effects on plant competition and community structure. J. Ecol. 103, 1224–1232 (2015).Article 
CAS 

Google Scholar 
Jasper, D. A., Abbott, J. K. & Robson, A. D. The effect of soil disturbance on vesicular-arbuscular mycorrhizal fungi in soils from different vegetation types. New Phytol. 118, 471–476 (1991).Article 

Google Scholar 
Jansa, J. et al. Diversity and structure of AMF communities as affected by tillage in a temperate soil. Mycorrhiza 12, 225–234 (2002).Article 
CAS 
PubMed 

Google Scholar 
van der Heyde, M., Ohsowski, B., Abbott, L. K. & Hart, M. Arbuscular mycorrhizal fungus responses to disturbance are context-dependent. Mycorrhiza 27, 431–440 (2017).Article 
PubMed 

Google Scholar 
Verbruggen, E. & Kiers, E. T. Evolutionary ecology of mycorrhizal functional diversity in agricultural systems. Evol. Appl. 3, 547–560 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Řezáčová, V., Řezáč, M., Gryndlerová, H., Wilson, G. W. T. & Michalová, T. Arbuscular mycorrhizal fungi favor invasive Echinops sphaerocephalus when grown in competition with native Inula conyzae. Sci. Rep. 10, 20287. https://doi.org/10.1038/s41598-020-77030-0 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callaway, R. M., Thelen, G. C., Barth, S., Ramsey, P. W. & Gannon, J. E. Soil fungi alter interactions between the invader Centaurea maculosa and North American natives. Ecology 85, 1062–1071 (2004).Article 

Google Scholar 
Abhilasha, D. & Joshi, J. Enhanced fitness due to higher fecundity, increased defence against a specialist and tolerance towards a generalist herbivore in an invasive annual plant. J. Plant Ecol. 2, 77–86 (2009).Article 

Google Scholar 
Shah, M. A., Reshi, Z. A. & Khasa, D. Arbuscular mycorrhizal status of some Kashmir Himalayan alien invasive plants. Mycorrhiza 20, 67–72 (2009).Article 
PubMed 

Google Scholar 
Shah, M. A., Reshi, Z. A. & Rasool, N. Plant invasions induce a shift in Glomalean spore diversity. Trop. Ecol. 51, 317–323 (2010).
Google Scholar 
Shah, M. A., Beaulieu, M.-E., Reshi, Z. A., Qureshi, S. & Khasa, D. P. A cross-city molecular biogeographic investigation of arbuscular mycorrhizas in Conyza canadensis rhizosphere across native and non-native regions. Ecol. Process. 4, 7. https://doi.org/10.1186/s13717-015-0034-0 (2015).Article 

Google Scholar 
Řezáčová, V., Konvalinková, T. & Řezáč, M. Decreased mycorrhizal colonization of Conyza canadensis (L.) Cronquist in invaded range does not affect fungal abundance in native plants. Biologia 75, 693–699 (2020).Article 

Google Scholar 
Shah, M. A., Reshi, Z. & Rashid, I. Mycorrhizal source and neighbour identity differently influence Anthemis cotula L. invasion in the Kashmir Himalaya, India. Appl. Soil Ecol. 40, 330–337 (2008).Article 

Google Scholar 
Řezáčová, V. et al. Plant invasion alters community structure and decreases diversity of arbuscular mycorrhizal fungal communities. Appl. Soil Ecol. 167, 104039 (2021).Article 

Google Scholar 
Řezáčová, V. et al. The root-associated arbuscular mycorrhizal fungal assemblages of exotic alien plants are simplified in invaded distribution ranges, but dominant species are retained: A trans-continental perspective. Environ. Microbiol. Rep. https://doi.org/10.1111/1758-2229.13108 (2022).Article 
PubMed 

Google Scholar 
Song, U. et al. Mowing: A cause of invasion, but also a potential solution for management of the invasive, alien plant species Erigeron annuus (L.) Pers.. J. Environ. Manag. 223, 530–536 (2018).Article 

Google Scholar 
Hempel, S. et al. Mycorrhizas in the Central European flora: Relationships with plant life history traits and ecology. Ecology 94, 1389–1399 (2013).Article 
PubMed 

Google Scholar 
Řezáčová, V., Řezáč, M., Líblová, Z., Michalová, T. & Heneberg, P. Stable colonization of native plants and early invaders by arbuscular mycorrhizal fungi after exposure to recent invaders from the Asteraceae family. IPSM 14, 147–155 (2021).Article 

Google Scholar 
Wilson, G. W. T. & Hartnett, D. C. Interspecific variation in plant responses to mycorrhizal colonization in tallgrass prairie. Am. J. Bot. 85, 1732–1738 (1998).Article 
CAS 
PubMed 

Google Scholar 
Rinaudo, V., Barberi, P., Giovanneti, M. & van der Heijden, M. G. A. Mycorrhizal fungi suppress aggressive agricultural weeds. Plant Soil 333, 7–20 (2010).Article 
CAS 

Google Scholar 
Veiga, R. S. L., Jansa, J., Frossard, E. & van der Heijden, M. G. A. Can arbuscular mycorrhizal fungi reduce the growth of agricultural weeds?. PLoS ONE 6, e27825 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Boerner, R. E. J. Plant life span and response to inoculation with vesicular-arbuscular mycorrhizal fungi. Mycorrhiza 1, 153–161 (1992).Article 

Google Scholar 
Wilson, S. D. Tilman plant competition and resource availability in response to disturbance and fertilization. Ecology 74, 599–611 (1993).Article 

Google Scholar 
Xiao-Bin, W., Dian-Xiong, C. A. I., Hoogmoed, W. B., Oenema, O. & Perdok, U. D. Potential effect of conservation tillage on sustainable land use: A review of global long-term studies. Pedosphere 16, 587–595 (2006).Article 

Google Scholar 
Miransari, M. Contribution of arbuscular mycorrhizal symbiosis to plant growth under different types of soil stress. Plant Biol. 12, 563–569 (2010).CAS 
PubMed 

Google Scholar 
Latef, A. A. H. A. et al. Arbuscular mycorrhizal symbiosis and abiotic stress in plants: A review. J. Plant Biol. 59, 407–426 (2016).Article 

Google Scholar 
Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—The role of mutualisms. Biol. Rev. 75, 65–93 (2000).Article 
CAS 
PubMed 

Google Scholar 
Řezáčová, V. et al. Mycorrhizal symbiosis induces plant carbon reallocation differently in C3 and C4 Panicum grasses. Plant Soil 425, 441–456 (2018).Article 

Google Scholar 
Newman, E. I. A method of estimating total length of root in a sample. J. Appl. Ecol. 3, 139–145 (1966).Article 

Google Scholar 
Bukovská, P., Gryndler, M., Gryndlerová, H., Püschel, D. & Jansa, J. Organic nitrogen-driven stimulation of arbuscular mycorrhizal fungal hyphae correlates with abundance of ammonia oxidizers. Front. Microbiol. 7, 711. https://doi.org/10.3389/fmicb.2016.00711 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hewitt, E. J. Sand and water culture methods used in the study of plant nutrition. CAB Tech. Commun. 22, 431–432 (1966).
Google Scholar 
Řezáčová, V. et al. Imbalanced carbon-for-phosphorus exchange between European arbuscular mycorrhizal fungi and non-native Panicum grasses—A case of dysfunctional symbiosis. Pedobiologia 62, 48–55 (2017).Article 

Google Scholar 
Řezáčová, V. et al. Little cross-feeding of the mycorrhizal networks shared between C3-Panicum bisulcatum and C4-Panicum maximum under different temperature regimes. Front. Microbiol. 9, 449 (2018).
Google Scholar 
Ohno, T. & Zibilske, L. M. Determination of low concentrations of phosphorus in soil extracts using malachite green. Soil Sci. Soc. Am. J. 55, 892–895 (1991).Article 
CAS 

Google Scholar 
Thonar, C., Erb, A. & Jansa, J. Real-time PCR to quantify composition of arbuscular mycorrhizal fungal communities-marker design, verification, calibration and field validation. Mol. Ecol. Res. 12, 219–232 (2012).Article 
CAS 

Google Scholar 
McGonigle, T. P., Miller, M. H., Evans, D. G., Fairchild, G. L. & Swan, J. A. A new method which gives an objective-measure of colonization of roots by vesicular arbuscular mycorrhizal fungi. New Phytol. 115, 495–501 (1990).Article 
CAS 
PubMed 

Google Scholar 
Koske, R. E. & Gemma, J. N. A modified procedure for staining roots to detect VA-mycorrhizas. Mycol. Res. 92, 486–505 (1989).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2022) More

Environmental and competitive filtering is most important for future tree survivalWe find that individual functional traits of each tree were most important for individual tree survival (40–87%) for all study sites, followed by forest dynamics (16–28%) and functional diversity (10–26%) (Fig. 2). Nevertheless, importance proportions substantially varied in each study site. While individual functional traits were least important in the mixed mountainous forest under reference climate (no climate warming), individual functional traits showed highest influence in the mixed temperate forest under future climate change (Fig. 2).Figure 2Relative importance of functional diversity, forest dynamics and individual traits for individual tree survival under reference climate and future climate conditions (RCP 4.5). Panels correspond to alpine needle-leaved (A), mixed mountainous (B), mixed temperate (C) and temperate broad-leaved (D) forests. Left bars in each panel illustrate reference climate (no warming) and right bars future climate (RCP4.5). Colours indicate forest dynamics (grey), functional diversity (yellow) and individual functional traits (blue). Forest dynamics include the number of locally competing trees ( > 5 m in height) and local biomass as a proxy for the successional status.Full size imageTree survival depends on a mixture of environmental (e.g. climate) and natural competitive filtering, which excludes trees with trait combinations that underperform under local conditions16. Therefore, the high importance of individual functional traits across all study sites suggests a strong environmental and competitive filtering. Under future climate, the importance of individual functional traits generally increases or remains at high levels (Fig. 2). This shows that environmental and competitive filtering through functional traits are important processes to select best performing trees for the future, although being different for each forest type.Changing forest composition and trait shifts require large functional portfolio to secure forest resistanceUnder future climate, we observe trait shifts within plant functional types and strong changes of the forest composition (Figs. 3, 4). Especially in the alpine and mixed forests, the proportion of broad-leaved trees increases to at least ~ 70% towards the end of the twenty-first century (Fig. 3). The changing climate alters environmental and competitive filtering simultaneously, whereby broad-leaved trees become more productive, survive better and increasingly outcompete needle-leaved trees. For instance, in the two mixed forests survival probabilities of broad-leaved trees (high SLA) increase by about ~ 10%, whereas the survival of needle-leaved (low SLA) trees is reduced by 10% to 30% in a warmer climate (see Supplementary Figs. S7, S8, Panel A). Locally better adapted and competitive broad-leaved trees can replace needle-leaved trees if they die and secure the forest’s overall biomass in the future. Nevertheless, our simulated forests still contain significant amounts of needle-leaved trees in the year 2099 in the two coldest study areas (Fig. 3, red and blue lines). Therefore, mixed tree communities with high functional diversity, where broad- and needle-leaved trees coexist, contain a broad range of functional niches out of which the best suitable plant strategies emerge and result in better resistance to climate change.Figure 3Forest compositions and changes in the proportion of broad-leaved trees (summergreen and evergreen plant functional type combined) under climate change (RCP 4.5) from 2000 to 2099 for each study site. The fraction of broad-leaved trees, as simulated by LPJmL-FIT for each site, increases gradually in almost every forest type reaching at least about 70% by the end of the century. Pictures depict snapshots from visualization of model output in the years 2000 and 2099, respectively. For a full animation of all sites from 2000 to 2099 please see Supplementary Video 1.Full size imageFigure 4Trait distributions of specific leaf area in year 2000 and 2099, respectively, under future climate change (RCP 4.5). Arrows indicate trait shifts within plant functional types: BL-S Broad-leaved summergreen, BL-E Broad-leaved evergreen, T-NL Temperate needle-leaved, B-NL Boreal needle-leaved. For more detailed distributions see Supplementary Figs. S3 and S4.Full size imageSimultaneously, we observe strong trait shifts in SLA within plant functional types across all study sites under climate change (Fig. 4). In general, the community of broad-leaved trees shift to lower SLA, while boreal needle-leaved trees are strongly reduced or slowly replaced by their temperate equivalent with higher SLA (Supplementary Fig. S3A, dark blue colours). In contrast, wood density distributions remain relatively broad and do not shift strongly under climate change (Supplementary Fig. S4). Throughout the century, the increasingly warmer climate filters new trait combinations leading to changes in the community composition within and across PFTs (see Supplementary Discussion B). Those trait shifts emerge from changes in the composition within PFTs and newly establishing PFTs, and could be less drastic if trait adaptation of tree individuals was considered (see “Limitations and outlook” section). The points raised above show, that trait ranges within and between PFTs should be wide to cover potential future trait shifts that secure future forest resistance.All this suggests that functionally diverse forests are more resistant to future climate changes, due to their rich portfolio of traits. Broad trait distributions both within and between PFTs form the fundament for environmental and competitive filtering to select the most productive trees, securing the forest’s overall biomass under changing conditions. But can functional diversity further strengthen forest resistance beyond portfolio effects?Functional complementarity helps young trees to surviveWe find that, in addition to port-folio effects, functional diversity increases forest resistance by supporting the survival of young trees to changing climate conditions via trait complementarity. Our results indicate, that trees benefit from functional diversity if they grow in tree communities with high FR, high FDv and low FE (Supplementary Figs. S6–S9, Panel D–F in each figure). Here, functional traits lay highly separated (FDv and FE) and span a broad range in the functional trait space (FR), enabling functional complementarity. Under these conditions the survival of trees increases up to + 16.8% (± 1.6%) depending on the study site and climate (Table 1). This effect is highest in the alpine and mountainous forests (14–17%), whereas it is less prominent or has an opposite effect in the two temperate forests (− 7% to 6%). That suggests, that complementarity effects are stronger in cold-limited and mixed forests where a marked cold winter season fosters a co-existence between broadleaved and needle-leaved trees. Both PFTs are specialized in fixing carbon during different times of the annual cycle: Due to their leaf phenology, needle-leaved trees can already be productive when broad-leaved trees are still in progress of unfolding or shedding their leaves. On the other hand, broad-leaved trees are more productive than needle-leaved trees during warmer months. If coexistence is given, these phenological differences enable complementarity and reduce competition among PFTs. That overall increases tree survival, because trees can invest more carbon in their stems and defensive structures if competition is lower. Therefore, we argue that phenological complementary can enhance tree survival and thus forest resistance. An in-depth discussion of those mechanisms is further found in Supplementary Discussion A.Table 1 Additional survival probabilities for trees in each forest site under reference climate (central column) and future climate (RCP4.5, right column) in case FR and FDv are high, while FE is low.Full size tableSurprisingly, our results show that those complementary effects are much more important for small trees ( 10 m) in right panel, respectively. Functional diversity and forest dynamics are more important for small trees compared to large trees, whereas individual functional traits matter most for large trees. This pattern was found to be consistent across all sites (see Supplementary Table S6).Full size imageFunctionally diverse understoreys unlock the synergy of filtering and complementary effectsOur findings underline the role of functionally diverse trees in the understoreys for forest resistance. On the one hand, functional diversity supports the survival of understorey trees via functional trait complementarity. On the other hand, they form the fundament for competitive and environmental filtering. Only diverse tree communities have trait pools large enough to ensure that their tree portfolio holds trait combinations best suited for changing climate conditions. Therefore, we argue that functional diversity does not only support tree survival through complementarity, but is a prerequisite for filtering resistant trees in the first place.To profit constantly from functional diversity of the understory and ensure constant adaptation, a diverse age structure is a prerequisite. Depending on the forest type, trees are distributed in a broad range of different height and age classes in our study (Supplementary Fig. S5, Supplementary Video 1). This multi-aged structure is preserved under climate change (Supplementary Fig. S5) and allows gradual changes through constant environmental and competitive filtering in the future.In this study, we simulate forests without any human interference or management. Our results are therefore to be interpreted in the context of environmental and competitive filtering as observed in natural forests. Most managed forests lack this natural filtering effect as they are less dense and diverse in their age-structure. Functionally diverse trees in the understorey could provide the fundament for climate adapted multi-aged forests, as they constantly form new better adapted tree generations with natural competition and succession allowed. Therefore, we fully underline the importance of functionally diverse understorey trees and natural competition as the fundament for future forest resistance.Management implicationsThe results of this study highlight the importance of functionally diverse understorey trees. However, browsing by game might damage new tree saplings and limit tree diversity in the understorey. In addition, invasive species like Prunus serotina or herbaceous competition might hinder forest succession and the establishment of woody native species in European forests20,21. Therefore, regulating game, limiting the spread of invasive species and controlling herbaceous vegetation should be considered in future management practices where tree diversity in the understorey seeks to be increased or maintained. Moreover, insufficient dispersal of functionally different tree species might limit the establishment of functionally diverse trees in the understorey. Future forest management may consider to artificially plant functionally different tree species if dispersal from surrounding forests cannot be guaranteed. On the other hand, forests that already contain functionally diverse trees in the understorey should be preserved.In this study, a clear trend from needle-leaved to broad-leaved trees is captured at all sites, whereas within broad-leaved PFTs a shift to lower specific leaf area and higher leaf longevities indicates that future forests might especially benefit from longer vegetation periods (earlier leaf onset, later senescence). Therefore, forests containing broad-leaved tree individuals with high phenological plasticity could be more resistant. The broad simulated wood density ranges, which persist under climate change, imply beneficial effects for forest communities entailing a range of different growing strategies, i.e. early to late successional species. Therefore, we argue that forest fragmentation should be reduced or reversed to foster some natural dispersal of early and late successional species.This study intended to explore the potentials of functionally diverse forests as a possibility to stabilize forests under climate change over a large climatic gradient. The model used in this study operates on the more general level of functional traits and their diversity rather than on species level (see “Methods” section and Supplementary Methods A). Consequently, management implications regarding suitable specific tree species are beyond the scope of this study. However, we think that our results will stimulate the discussion on the importance of functional tree traits and their diversity for species selection.Limitations and outlookThis study focussed on identifying the importance of functional diversity for future tree survival to advance our understanding on the role of biodiversity for future forest resistance using the flexible-trait Dynamic Global Vegetation Model LPJmL-FIT. The general approach of LPJmL-FIT is to simulate biogeographic dynamics purely based on environmental and competitive filtering (see “Methods” section, Supplementary Methods A). Due to missing processes in the model and the ambiguity of former human influence, drawing site-specific implications on future forest dynamics must be taken with caution (see Supplementary Discussion D).Moreover, processes not yet captured in the LPJmL-FIT model, might play a role and could lower forest resistance in the future, which is why we recommend relying on a trait space as broad as possible.Including more climate scenarios would widen the envelope of possible future pathways by considering climate model uncertainties. Insect outbreaks and pathogens might put pressure to the already drought- and temperature-stressed trees and heavily accelerate mortality especially of needle-leaved trees, although functionally diverse forests are less vulnerable to bark beetle outbreaks22,23. Multi-layered forests showed higher growth resilience to structural disturbances such as wind-throw24, likely enhancing the importance of individual tree height and reduce the survival probability of large trees25. Belowground competition and trait plasticity could favour complementarity effects further. Variable rooting strategies could further reduce competition for soil water and thereby increase individual drought resistance of trees26. Trait plasticity can contribute to tree survival by widening niche, further increasing complementarity effects. However, trait plasticity remains one of the most challenging objectives in vegetation modelling as observational data and modelling approaches are scarce27, leaving it open how far trait relationships would hold under climate change. Considering more functional traits in our analysis might increase the overall predictive power of the random forest models. Even though the explained variance increased with the number of analysed traits explaining ecosystem properties in long-term grassland experiments, such improvement is limited as abiotic factors and their interactions with plant traits might be more important for prediction28. We conclude that simulating future forest dynamics dominated by environmental and natural competitive filtering requires to integrate both, abiotic and biotic drivers on forest dynamics. Machine learning techniques are increasingly used in forest ecological research—but mainly applied in the processing of field and forest inventory data29,30. Machine learning can help to understand the complexity of interactions and provide deeper insights into the underlying ecological process in a modelling study as we have shown here using LPJmL-FIT simulation results. Random forest analyses are suitable for a variety of data and applications because they are relatively robust to different data structures. Importance analysis can help to identify the role of underlying processes in complex models and to visualize their changes in a simple way. In doing so, model development is advanced by making use of large data sets, opening the door to further theory building and deeper understanding of plant trait ecology. More