Philippa Kaur

Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).Article 

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

Google Scholar 
Campbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecol. Soc. 22, 8 (2017).Article 

Google Scholar 
Hazell, P. & Wood, S. Drivers of change in global agriculture. Philos. Trans. R. Soc. B 363, 495–515 (2008).Article 

Google Scholar 
Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).Article 

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).CAS 
Article 

Google Scholar 
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).CAS 
Article 

Google Scholar 
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67, 386–391 (2017).Article 

Google Scholar 
Ecosystems and Human Well-being: Synthesis (Millenium Ecosystem Assessment, 2005); http://www.millenniumassessment.org/documents/document.356.aspx.pdfBommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28, 230–238 (2013).Article 

Google Scholar 
Kleijn, D. et al. Ecological intensification: bridging the gap between science and practice. Trends Ecol. Evol. 34, 154–166 (2018).Article 

Google Scholar 
Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).CAS 
Article 

Google Scholar 
Wezel, A. et al. Agroecology as a science, a movement and a practice. Sustain. Agric. 2, 27–43 (2009).
Google Scholar 
Garnett, T. et al. Sustainable intensification in agriculture: premises and policies. Science 341, 33–34 (2013).CAS 
Article 

Google Scholar 
Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Change 4, 1068–1072 (2014).Article 

Google Scholar 
Tittonell, P. Ecological intensification of agriculture—sustainable by nature. Curr. Opin. Environ. Sustain. 8, 53–61 (2014).Article 

Google Scholar 
Jenkinson, D. S. The impact of humans on the nitrogen cycle, with focus on temperate arable agriculture. Plant Soil 228, 3–15 (2001).CAS 
Article 

Google Scholar 
Verheijen, F. G. A., Jones, R. J. A., Rickson, R. J. & Smith, C. J. Tolerable versus actual soil erosion rates in Europe. Earth Sci. Rev. 94, 23–38 (2009).Article 

Google Scholar 
Peoples, M. B. et al. in Agroecosystem Diversity: Reconciling Contemporary Agriculture and Environmental Quality (eds Lemaire, G. et al.) 123–142 (Academic Press, 2019); https://doi.org/10.1016/B978-0-12-811050-8.00008-XStorkey, J., Bruce, T., McMillan, V. & Neve, P. in Agroecosystem Diversity: Reconciling Contemporary Agriculture and Environmental Quality (eds Lemaire, G. et al.) 199–209 (Academic Press, 2019); https://doi.org/10.1016/B978-0-12-811050-8.00012-1Schröder, J. Revisiting the agronomic benefits of manure: a correct assessment and exploitation of its fertilizer value spares the environment. Bioresour. Technol. 96, 253–261 (2005).Article 
CAS 

Google Scholar 
Mhlanga, B., Ercoli, L., Pellegrino, E., Onofri, A. & Thierfelder, C. The crucial role of mulch to enhance the stability and resilience of cropping systems in southern Africa. Agron. Sustain. Dev. 41, 29–43 (2021).Article 

Google Scholar 
Barrett, C. B. & Bevis, L. E. M. The self-reinforcing feedback between low soil fertility and chronic poverty. Nat. Geosci. 8, 907–912 (2015).CAS 
Article 

Google Scholar 
Tittonell, P. & Giller, K. E. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. Field Crops Res. 143, 76–90 (2013).Article 

Google Scholar 
Sandén, T. et al. European long-term field experiments: knowledge gained about alternative management practices. Soil Use Manage. 34, 167–176 (2018).Article 

Google Scholar 
Storkey, J. et al. The unique contribution of Rothamsted to ecological research at large temporal scales. Adv. Ecol. Res. 55, 3–42 (2016).Article 

Google Scholar 
Johnston, A. E. & Poulton, P. R. The importance of long-term experiments in agriculture: their management to ensure continued crop production and soil fertility; the Rothamsted experience. Eur. J. Soil Sci. 69, 113–125 (2018).CAS 
Article 

Google Scholar 
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, 284–293 (2020).Marini, L. et al. Crop rotations sustain cereal yields under a changing climate. Environ. Res. Lett. 15, 124011 (2020).Article 

Google Scholar 
Lal, R. Carbon emission from farm operations. Environ. Int. 30, 981–990 (2004).CAS 
Article 

Google Scholar 
Cordell, D., Drangert, J. O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).Article 

Google Scholar 
Lechenet, M., Dessaint, F., Py, G., Makowski, D. & Munier-Jolain, N. Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants 3, 17008 (2017).Article 

Google Scholar 
Bedoussac, L. et al. Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron. Sustain. Dev. 35, 911–935 (2015).Article 

Google Scholar 
Storkey, J., Mead, A., Addy, J. & MacDonald, A. J. Agricultural intensification and climate change have increased the threat from weeds. Glob. Change Biol. 27, 2416–2425 (2021).Article 

Google Scholar 
Vanlauwe, B. et al. in Integrated Plant Nutrient Management in Sub-Saharan Africa: From Concept to Practice (eds Vanlauwe, B. et al.) 173–184 (CABI, 2002).Hijbeek, R. et al. Do organic inputs matter—a meta-analysis of additional yield effects for arable crops in Europe. Plant Soil 411, 293–303 (2017).CAS 
Article 

Google Scholar 
Thierfelder, C. & Wall, P. C. Effects of conservation agriculture techniques on infiltration and soil water content in Zambia and Zimbabwe. Soil Tillage Res. 105, 217–227 (2009).Article 

Google Scholar 
Gentile, R., Vanlauwe, B., Chivenge, P. & Six, J. Interactive effects from combining fertilizer and organic residue inputs on nitrogen transformations. Soil Biol. Biochem. 40, 2375–2384 (2008).CAS 
Article 

Google Scholar 
Mupangwa, W. et al. Maize yields from rotation and intercropping systems with different legumes under conservation agriculture in contrasting agro-ecologies. Agric. Ecosyst. Environ. 306, 107170 (2021).Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).CAS 
Article 

Google Scholar 
Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric. Ecosyst. Environ. 251, 194–202 (2018).Article 

Google Scholar 
Pittelkow, C. M. et al. When does no-till yield more? A global meta-analysis. Field Crops Res. 183, 156–168 (2015).Article 

Google Scholar 
Sun, W. et al. Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Glob. Change Biol. 26, 3325–3335 (2020).Article 

Google Scholar 
Kirkegaard, J. A. et al. Sense and nonsense in conservation agriculture: principles, pragmatism and productivity in Australian mixed farming systems. Agric. Ecosyst. Environ. 187, 133–145 (2014).Article 

Google Scholar 
Thierfelder, C. et al. Complementary practices supporting conservation agriculture in southern Africa. A review. Agron. Sustain. Dev. 38, 16–37 (2018).Article 

Google Scholar 
Alignier, A. et al. Configurational crop heterogeneity increases within-field plant diversity. J. Appl. Ecol. 57, 654–663 (2020).Article 

Google Scholar 
Liebman, M. et al. Ecologically sustainable weed management: how do we get from proof-of-concept to adoption? Ecol. Appl. 26, 1352–1369 (2016).Article 

Google Scholar 
Giller, K. E. The food security conundrum of sub-Saharan Africa. Glob. Food Sec. 26, 100431 (2020).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Addy, J. W. G., Ellis, R. H., Macdonald, A. J., Semenov, M. A. & Mead, A. Changes in agricultural climate in South-Eastern England from 1892 to 2016 and differences in cereal and permanent grassland yield. Agric. For. Meteorol. 308–309, 108560 (2021).Article 

Google Scholar 
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. Parsimonious mixed models. Preprint at https://arXiv.org/abs/1506.04967v2 (2018).MacLaren, C., Glendining, M., Poulton, P., Macdonald, A. & Clark, S. Woburn Ley-Arable Experiment: Yields of Wheat as First Test Crop, 1976–2018 (e-RA Rothamsted, 2022); https://doi.org/10.23637/wrn3-wheat7618-01 .Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means: R package version 1.7.2 https://CRAN.R-project.org/package=emmeans (2020).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
Lajeunesse, M. J. On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92, 2049–2055 (2011).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar  More

IPCC Climate Change 2007: Synthesis Report (eds Pachauri, R. K. & Reisinger, A.) (IPCC, 2007).Boyd, J. & Banzhaf, S. Ecol. Econ. 63, 616–626 (2007).Article 

Google Scholar 
Ruhl, J. B. et al. Front. Ecol. Environ. 19, 519–525 (2021).Article 

Google Scholar 
Carleton, T. & Greenstone, M. Updating the United States Government’s Social Cost of Carbon Working Paper 2021-04 (Univ. Chicago, Becker Friedman Institute for Economics, 2021).Mandle, L. et al. Nat. Sustain. 4, 161–169 (2021).Article 

Google Scholar 
Druckenmiller, H. Estimating an Economic and Social Value of Forests: Evidence from Tree Mortality in the American West (Univ. California Berkeley, 2021).Burkett, V. R. et al. Ecol. Complexity 2, 357–394 (2005).Article 

Google Scholar 
Hanley, N. & Czajkowski, M. Rev. Environ. Econ. Policy 13, 248–266 (2019).Article 

Google Scholar 
Mendelsohn, R. Rev. Environ. Econ. Policy 13, 267–282 (2019).Article 

Google Scholar 
Fenichel, E. P. et al. Proc. Natl Acad. Sci. USA 113, 2382–2387 (2016).CAS 
Article 

Google Scholar 
Martin-Ortega, J. et al. Ecosyst. Serv. 50, 101327 (2021).Article 

Google Scholar 
Borrelli, P. et al. Proc. Natl Acad. Sci. USA 117, 21994–22001 (2020).CAS 
Article 

Google Scholar 
Tropek, R. et al. Science 344, 981–981 (2014).CAS 
Article 

Google Scholar 
Vardon, M., Burnett, P. & Dovers, S. Ecol. Econ. 124, 145–152 (2016).Article 

Google Scholar 
Bastien-Olvera, B. A. & Moore, F. C. Nat. Sustain. 4, 101–108 (2021).Article 

Google Scholar 
Beland, M. et al. For. Ecol. Manage. 450, 117484 (2019).Article 

Google Scholar 
Vargas, L., Willemen, L. & Hein, L. Environ. Manage. 63, 1–15 (2019).Article 

Google Scholar 
Hallgren, W. et al. Environ. Model. Softw. 76, 182–186 (2016).Article 

Google Scholar 
Rolf, E. et al. Nat. Commun. 12, 4392 (2021).CAS 
Article 

Google Scholar 
Chernozhukov, V. et al. NBER Working Paper 24678 (National Bureau of Economic Research, 2018). More

Chuine, I. Why does phenology drive species distribution? Philos. Trans. 365, 3149–3160 (2010).
Google Scholar 
Chuine, I. & Beaubien, E. G. Phenology is a major determinant of tree species range. Ecol. Lett. 4, 500–510 (2001).
Google Scholar 
Richardson, D. A. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).ADS 

Google Scholar 
Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).
Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Chang. Biol. 25, 1922–1940 (2019).ADS 
MathSciNet 
PubMed 

Google Scholar 
Fu, Y. H. et al. Three times greater weight of daytime than of night‐time temperature on leaf unfolding phenology in temperate trees. N. Phytol. 212, 590–597 (2016).CAS 

Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).ADS 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Penuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).CAS 
PubMed 

Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Lang, G. A. Dormancy: a new universal terminology. HortScience 22, 817–820 (1987).
Google Scholar 
Perry, T. O. Dormancy of trees in winter. Science 171, 29–36 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Huang, J. et al. Intra-annual wood formation of subtropical Chinese red pine shows better growth in dry season than wet season. Tree Physiol. 38, 1225–1236 (2018).PubMed 

Google Scholar 
Knowles, J. F. et al. Montane forest productivity across a semi-arid climatic gradient. Glob. Chang. Biol. 26, 6945–6958 (2020).ADS 
PubMed 

Google Scholar 
Richard, S., Kjellsen, T. D., Schaberg, P. G. & Murakami, P. F. Dynamics of low-temperature acclimation in temperate and boreal conifer foliage in a mild winter climate. Tree Physiol. 28, 1365–1374 (2008).
Google Scholar 
Roxas, A. A., Orozco, J., Guzmán-Delgado, P. & Zwieniecki, M. A. Spring phenology is affected by fall non-structural carbohydrate concentration and winter sugar redistribution in three Mediterranean nut tree species. Tree Physiol. 41, 1425–1438 (2021).CAS 

Google Scholar 
Palacio, S., Martínez, M. M. & Montserrat-Martí, G. Seasonal dynamics of non-structural carbohydrates in two species of mediterranean sub-shrubs with different leaf phenology. Environ. Exp. Bot. 59, 34–42 (2007).CAS 

Google Scholar 
Fierravanti, A., Rossi, S., Kneeshaw, D., Grandpré, L. D. & Deslauriers, A. Low non-structural carbon accumulation in spring reduces growth and increases mortality in conifers defoliated by spruce budworm. Front. For. Glob. Change. 2, 1–13 (2019).
Google Scholar 
Oberhuber, W., Gruber, A., Lethaus, G., Winkler, A. & Wieser, G. Stem girdling indicates prioritized carbon allocation to the root system at the expense of radial stem growth in Norway spruce under drought conditions. Environ. Exp. Bot. 138, 109–118 (2017).PubMed 
PubMed Central 

Google Scholar 
Pérez-de-Lis, G., Rossi, S., Vázquez-Ruiz, R. A., Rozas, V. & García-González, I. Do changes in spring phenology affect earlywood vessels? Perspective from the xylogenesis monitoring of two sympatric ring-porous oaks. N. Phytol. 209, 521–530 (2016).
Google Scholar 
Weber, R., Gessler, A. & Hoch, G. High carbon storage in carbon-limited trees. N. Phytol. 222, 171–182 (2019).CAS 

Google Scholar 
Zani, D., Crowther, T. W., Lidong, M., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Dusenge, M. E., Duarte, A. G. & Way, D. A. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. N. Phytol. 221, 32–49 (2019).CAS 

Google Scholar 
Lin, Y.-S., Medlyn, B. E. & Ellsworth, D. Temperature responses of leaf net photosynthesis: the role of component processes. Tree Physiol. 32, 219–231 (2012).CAS 
PubMed 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 

Google Scholar 
Terashima, I. & Hikosaka, K. Comparative ecophysiology of leaf and canopy photosynthesis. Plant Cell Environ. 18, 1111–1128 (1995).
Google Scholar 
Liang, J., Xia, J., Liu, L. & Wan, S. Global patterns of the responses of leaf-level photosynthesis and respiration in terrestrial plants to experimental warming. J. Plant. Ecol. 6, 437–447 (2013).
Google Scholar 
Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Chang. Biol. 23, 5189–5202 (2017).ADS 
PubMed 

Google Scholar 
Keenan, T. F., Richardson, A. D. & Hufkens, K. On quantifying the apparent temperature sensitivity of plant phenology. N. Phytol. 225, 1033–1040 (2020).
Google Scholar 
Klein, T., Vitasse, Y. & Hoch, G. Coordination between growth, phenology and carbon storage in three coexisting deciduous tree species in a temperate forest. Tree Physiol. 36, 847–855 (2016).CAS 
PubMed 

Google Scholar 
Kagawa, A., Sugimoto, A. & Maximov, T. C. Seasonal course of translocation, storage and remobilization of 13C pulse-labeled photoassimilate in naturally growing Larix gmelinii saplings. N. Phytol. 171, 793–804 (2010).
Google Scholar 
Rinne, K. T. et al. Examining the response of needle carbohydrates from Siberian larch trees to climate using compound-specific δ(13) C and concentration analyses. Plant Cell Environ. 38, 2340–2352 (2015).CAS 
PubMed 

Google Scholar 
Schädel, C., Blöchl, A., Richter, A. & Hoch, G. Short-term dynamics of nonstructural carbohydrates and hemicelluloses in young branches of temperate forest trees during bud break. Tree Physiol. 29, 901–911 (2009).PubMed 

Google Scholar 
Kaurin, A., Junttila, O. & Hanson, J. Seasonal changes in frost hardiness in cloudberry (Rubus chamaemorus) in relation to carbohydrate content with special reference to sucrose. Physiol. Plant. 52, 310–314 (1981).CAS 

Google Scholar 
Shahba, M. A., Qian, Y. L., Hughes, H. G., Koski, A. J. & Christensen, D. Relationships of soluble carbohydrates and freeze tolerance in saltgrass. Crop Sci. 43, 2148–2153 (2003).CAS 

Google Scholar 
Wang, J. et al. Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming. Glob. Chang. Biol. 27, 5084–5093 (2021).PubMed 

Google Scholar 
Marchand, L. J. et al. Inter-individual variability in spring phenology of temperate deciduous trees depends on species, tree size and previous year autumn phenology. Agric Meteorol. 290, 108031 (2020).
Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agric Meteorol. 291, 108077 (2020).
Google Scholar 
Chen, L. et al. Long-term changes in the impacts of global warming on leaf phenology of four temperate tree species. Glob. Chang. Biol. 25, 997–1004 (2019).ADS 
PubMed 

Google Scholar 
Hanninen, H. Boreal and temperate trees in a changing climate: modelling the ecophysiology of seasonality. (Springer, 2016).Dreyer, E., Le Roux, X., Montpied, P., Daudet, F. A. & Masson, F. Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species. Tree Physiol. 21, 223–232 (2001).CAS 
PubMed 

Google Scholar 
Devi, A. F. & Garkoti, S. C. Variation in evergreen and deciduous species leaf phenology in Assam. India Trees 27, 985–997 (2013).
Google Scholar 
Bai, K., He, C., Wan, X. & Jiang, D. Leaf economics of evergreen and deciduous tree species along an elevational gradient in a subtropical mountain. AoB PLANTS 7, plv064 (2015).PubMed 
PubMed Central 

Google Scholar 
Qi, J., Fan, Z., Fu, P., Zhang, Y. & Sterck, F. Differential determinants of growth rates in subtropical evergreen and deciduous juvenile trees: carbon gain, hydraulics and nutrient-use efficiencies. Tree Physiol. 41, 12–23 (2021).CAS 
PubMed 

Google Scholar 
Fyllas, N. M. et al. Functional trait variation among and within species and plant functional types in mountainous mediterranean forests. Front. Plant Sci. 11, 1–18 (2020).
Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int J. Biometeorol. 62, 1109–1113 (2018).ADS 
PubMed 

Google Scholar 
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar 
Richardson, A. D. et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci. Data. 5, 180028 (2018).PubMed 
PubMed Central 

Google Scholar 
Klosterman, S. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).ADS 

Google Scholar 
Zhang, Y. et al. Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia. Agric. For. Meteorol. 224, 1–10 (2016).ADS 

Google Scholar 
Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).ADS 

Google Scholar 
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. et al. Validation of MODIS-GPP product at 10 flux sites in northern China. Int. J. Remote Sens. 34, 587–599 (2013).
Google Scholar 
Julien, Y. & Sobrino, J. Global land surface phenology trends from GIMMS database. Int J. Remote Sens. 30, 3495–3513 (2009).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens Environ. 84, 471–475 (2003).ADS 

Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data. 7, 1–27 (2020).
Google Scholar 
Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).PubMed 

Google Scholar 
Tang, Y., Xu, X., Zhou, Z., Qu, Y. & Sun, Y. Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data. Ecol. Inform. 63, 101307 (2021).
Google Scholar 
Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U.S.A. 112, 2788–2793 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalman, D. A singularly valuable decomposition: The SVD of a matrix. Coll. Math. J. 27, 2–23 (1996).MathSciNet 

Google Scholar 
Biriukova, K. et al. Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals. J. Geophys. Res. Biogeosci. 126, e2020JG006158 (2021).ADS 
CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246 (2010).PubMed 
PubMed Central 

Google Scholar 
Wu, C. et al. Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: Evidence from long records at two contrasting forest stands. Agric. For. Meteorol. 164, 29–38 (2012).ADS 

Google Scholar 
Cornes, R., der Schrier, G. V., den Besselaar, E. J. M. V. & Jones, P. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster. R package version 3.5-15 (2022).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Erb, I. Partial correlations in compositional data analysis. Comput. Geosci. 6, 100026 (2020).
Google Scholar 
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. U.S.A. 115, 1004–1008 (2018).CAS 
PubMed 

Google Scholar 
Kim, S. ppcor: Partial and semi-partial (part) correlation. https://CRAN.R-project.org/package=ppcor. R package version 1.1 (2015).Lefcheck, J. S. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

Google Scholar 
Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Modelling species presence-only data with random forests. Ecography 44, 1731–1742 (2021).
Google Scholar 
Freeman, E. A., Moisen, G. G., Coulston, J. W. & Wilson, B. T. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Can. J. For. Res. 46, 323–339 (2016).
Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Cutler, D. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01367-2 (2022). More

Zhu, C. et al. Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Sci. Adv. 4, eaaq1012 (2018).
Google Scholar 
Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision (FAO Agricultural Development Economics Division, 2012).Arunrat, N., Pumijumnong, N., Sereenonchai, S., Chareonwong, U. & Wang, C. Assessment of climate change impact on rice yield and water footprint of large-scale and individual farming in Thailand. Sci. Total Environ. 726, 137864 (2020).CAS 

Google Scholar 
Lafferty, D. C. et al. Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. Commun. Earth Environ. 2, 196 (2021).
Google Scholar 
Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food. 2, 54–65 (2021).
Google Scholar 
Wang, X. et al. Emergent constraint on crop yield response to warmer temperature from field experiments. Nat. Sustain. 3, 908–916 (2020).
Google Scholar 
Sun, T. et al. Current rice models underestimate yield losses from short-term heat stresses. Glob. Chang. Biol. 27, 402–416 (2020).
Google Scholar 
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4, 287–291 (2014).
Google Scholar 
Iizumi, T. & Ramankutty, N. Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ. Res. Lett. 11, 034003 (2016).
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).
Google Scholar 
Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 1–10 (2020).
Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 494, 390 (2013).CAS 

Google Scholar 
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).CAS 

Google Scholar 
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).CAS 

Google Scholar 
Guo, J. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).CAS 

Google Scholar 
Galloway, J. et al. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 320, 889–892 (2008).CAS 

Google Scholar 
Xia, L., Lam, S. K., Yan, X. & Chen, D. How does recycling of livestock manure in agroecosystems affect crop productivity, reactive nitrogen losses, and soil carbon balance? Environ. Sci. Technol. 51, 7450–7457 (2017).CAS 

Google Scholar 
Zhang, T. et al. Replacing synthetic fertilizer by manure requires adjusted technology and incentives: A farm survey across China. Resour. Conserv. Recycl. 168, 105301 (2021).
Google Scholar 
Bi, L. et al. Long-term effects of organic amendments on the rice yields for double rice cropping systems in subtropical China. Agric. Ecosyst. Environ. 129, 534–541 (2009).
Google Scholar 
Du, Y. et al. Effects of manure fertilizer on crop yield and soil properties in China: A meta-analysis. Catena 193, 104617 (2020).CAS 

Google Scholar 
Wang, K., Zhang, X. & Ervin, E. Antioxidative responses in roots and shoots of creeping bentgrass under high temperature: Effects of nitrogen and cytokinin. J. Plant Physiol. 169, 492–500 (2012).CAS 

Google Scholar 
Jespersen, D. & Huang, B. Proteins associated with heat‐induced leaf senescence in creeping bentgrass as affected by foliar application of nitrogen, cytokinins, and an ethylene inhibitor. Proteomics. 15, 798–812 (2015).CAS 

Google Scholar 
Xi, Y. et al. Exogenous phosphite application alleviates the adverse effects of heat stress and improves thermotolerance of potato (Solanum tuberosum L.) seedlings. Ecotoxicol. Environ. Saf. 190, 110048 (2020).CAS 

Google Scholar 
Waraich, E. A., Ahmad, R., Halim, A. & Aziz, T. Alleviation of temperature stress by nutrient management in crop plants: a review. J. Soil Sci. Plant Nut. 12, 221–244 (2012).
Google Scholar 
Yamori, W., Noguchi, K., Hikosaka, K. & Terashima, I. Phenotypic plasticity in photosynthetic temperature acclimation among crop species with different cold tolerances. Plant Physiol. 152, 388–399 (2010).CAS 

Google Scholar 
Mittler, R. Oxidative stress, antioxidants and stress tolerance. Trends. Plant Sci. 7, 405–410 (2002).CAS 

Google Scholar 
Wang, Q., Chen, J., He, N. & Guo, F. Metabolic reprogramming in chloroplasts under heat stress in plants. Int. J. Mol. Sci. 19, 849 (2018).
Google Scholar 
Cheng, Q. et al. An alternatively spliced heat shock transcription factor, OsHSFA2dI, functions in the heat stress-induced unfolded protein response in rice. Plant Biol. 17, 419–429 (2015).CAS 

Google Scholar 
Miura, K. et al. SIZ1-mediated sumoylation of ICE1 controls CBF3/DREB1A expression and freezing tolerance in Arabidopsis. Plant Cell 19, 1403–1414 (2007).CAS 

Google Scholar 
Xie, G., Kato, H., Sasaki, K. & Imai, R. A cold-induced thioredoxin h of rice, OsTrx23, negatively regulates kinase activities of OsMPK3 and OsMPK6 in vitro. FEBS Lett. 583, 2734–2738 (2009).CAS 

Google Scholar 
Hasanuzzaman, M., Hossain, M. A. & Fujita, M. Nitric oxide modulates antioxidant defense and the methylglyoxal detoxification system and reduces salinity-induced damage of wheat seedlings. Plant Biotechnol. Rep. 5, 353 (2011).
Google Scholar 
Uchida, A., Jagendorf, A. T., Hibino, T., Takabe, T. & Takabe, T. Effects of hydrogen peroxide and nitric oxide on both salt and heat stress tolerance in rice. Plant Sci. 163, 515–523 (2002).CAS 

Google Scholar 
Khan, S. et al. Plants mechanisms and adaptation strategies to improve heat tolerance in rice. A review. Plants 8, 508 (2019).CAS 

Google Scholar 
Li, Y., Gao, Y., Xu, X., Shen, Q. & Guo, S. Light-saturated photosynthetic rate in high-nitrogen rice (Oryza sativa L.) leaves is related to chloroplastic CO2 concentration. J. Exp. Bot. 60, 2351–2360 (2009).CAS 

Google Scholar 
Xiong, D. et al. Rapid responses of mesophyll conductance to changes of CO2 concentration, temperature, and irradiance are affected by N supplements in rice. Plant. Cell Environ. 38, 2541–2550 (2015).CAS 

Google Scholar 
Waraich, E. A., Ahmad, R., Ashraf, M. Y., Saifullah & Ahmad, M. Improving agricultural water use effciency by nutrient management in crop plants. Acta Agric. Scand. Sect.-B Soil. Plant Sci. 61, 291–304 (2011).CAS 

Google Scholar 
Dias, A. S. & Lidon, F. C. Bread and durum wheat tolerance under heat stress: A synoptical overview. Emir. J. Food Agric. 22, 412–436 (2010).
Google Scholar 
Meshah, E. A. E. Effect of irrigation regimes and foliar spraying of potassium on yield, yield components and water use efficiency of wheat in sandy soils. World J. Agric. Sci. 5, 662–669 (2009).
Google Scholar 
Huang, G., Zhang, Q., Wei, X., Peng, S. & Li, Y. Nitrogen can alleviate the inhibition of photosynthesis caused by high temperature stress under both steady-state and flecked irradiance. Front. Plant Sci. 8, 945 (2017).
Google Scholar 
Zhou, Y. et al. High nitrogen input reduces yield loss from low temperature during the seedling stage in early-season rice. Field Crop. Res. 228, 68–75 (2018).
Google Scholar 
Hou, L. et al. Effects of different phosphate fertilizer application on permeability of membrane and antioxidative enzymes in rice under low temperature stress. Acta Agriculturae. Boreali-Sinica 27, 118–123 (2012).
Google Scholar 
Dong, W. et al. Effect of different fertilizer application on the soil fertility of paddy soils in red soil region of southern China. PLoS One 7, e44504 (2012).CAS 

Google Scholar 
Bertollo, A. M. et al. Precrops alleviate soil physical limitations for soybean root growth in an Oxisol from southern Brazil. Soil Till. Res. 206, 104820 (2021).
Google Scholar 
Ren, Y. et al. Functional compensation dominates plant rhizosphere microbiota assembly of plant rhizospheric bacterial community. Soil Biol. Biochem. 150, 107968 (2020).CAS 

Google Scholar 
Oka, Y. Mechanisms of nematode suppression by organic soil amendments—a review. Appl. Soil Ecol. 44, 101–115 (2010).
Google Scholar 
Rose, M. T. et al. A meta-analysis and review of plant-growth response to humic substances: Practical implications for agriculture. Adv. Agron 124, 37–89 (2014).CAS 

Google Scholar 
García, A. C. et al. Vermicompost humic acids modulate the accumulation and metabolism of ROS in rice plants. J. Plant Physiol. 192, 56–63 (2016).
Google Scholar 
Dieleman, W. I. et al. Simple additive effects are rare: A quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob. Chang. Biol. 18, 2681–2693 (2012).
Google Scholar 
Muhammad, Q. et al. Yield sustainability, soil organic carbon sequestration, and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil Till. Res. 198, 104509 (2020).
Google Scholar 
Zhang, X. et al. Benefits and trade-offs of replacing synthetic fertilizers by animal manures in crop production in China: A meta‐analysis. Glob. Chang. Biol. 26, 888–900 (2020).
Google Scholar 
Zhang, X. et al. Significant residual effects of wheat fertilization on greenhouse gas emissions in succeeding soybean growing season. Soil Till. Res. 169, 7–15 (2017).
Google Scholar 
Latare, A. M., Kumar, O., Singh, S. K. & Gupta, A. Direct and residual effect of sewage sludge on yield, heavy metals content and soil fertility under rice–wheat system. Ecol. Eng. 69, 17–24 (2014).
Google Scholar 
Zhang, J. et al. Long-term straw incorporation increases rice yield stability under high fertilization level conditions in the rice–wheat system. Crop J. 9, 1191–1197 (2021).
Google Scholar 
Pachauri, R. K. et al. Climate change 2014: Synthesis Report. Contribution of Working Groups I, II, and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2014).Choi, W. J., Lee, M. S., Choi, J. E., Yoon, S. & Kim, H. Y. How do weather extremes affect rice productivity in a changing climate? An answer to episodic lack of sunshine. Glob. Chang. Biol. 19, 1300–1310 (2013).
Google Scholar 
FAO. FAOSTAT Online Statistical Service. https://www.fao.org/faostat/en/#data/RFN, (FAO, 2016).Carlson, K. M. et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Chang. 7, 63–68 (2017).CAS 

Google Scholar 
Sheldrick, W., Syers, J. K. & Lingard, J. Contribution of livestock excreta to nutrient balances. Nutr. Cycling Agroecosyst. 66, 119–131 (2003).
Google Scholar 
Thangarajan, R., Bolan, N. S., Tian, G., Naidu, R. & Kunhikrishnan, A. Role of organic amendment application on greenhouse gas emission from soil. Sci. Total Environ. 465, 72–96 (2013).CAS 

Google Scholar 
Aryal, J. P. et al. Factors affecting farmers’ use of organic and inorganic fertilizers in South Asia. Environ. Sci. Pollut. Res. 28, 51480–51496 (2021).CAS 

Google Scholar 
Zhang, Q. et al. Targeting hotspots to achieve sustainable nitrogen management in China’s smallholder-dominated cereal production. Agronomy 11, 557 (2021).
Google Scholar 
Tyagi, V. K. et al. Anaerobic co-digestion of organic fraction of municipal solid waste (OFMSW): Progress and challenges. Renewable Sustain. Energy Rev. 93, 380–399 (2018).
Google Scholar 
Schlesinger, W. H. Carbon sequestration in soils: Some cautions amidst optimism. Agric. Ecosyst. Environ. 82, 121–127 (2000).CAS 

Google Scholar 
Potter, P., Ramankutty, N., Bennett, E. M. & Donner, S. D. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interact. 14, 1–22 (2010).
Google Scholar 
Zhao, F., Yang, L., Chen, L., Li, S. & Sun, L. Bioaccumulation of antibiotics in crops under long-term manure application: Occurrence, biomass response, and human exposure. Chemosphere 219, 882–895 (2019).CAS 

Google Scholar 
Chadwick, D. R. et al. Strategies to reduce nutrient pollution from manure management in China. Front. Agr. Sci. Eng. 7, 45–55 (2020).
Google Scholar 
Jin, S. et al. Decoupling livestock and crop production at the household level in China. Nat. Sustain 4, 48–55 (2021).
Google Scholar 
Chen, D., Yuan, L., Liu, Y., Ji, J. & Hou, H. Long-term application of manures plus chemical fertilizers sustained high rice yield and improved soil chemical and bacterial properties. Eur. J. Agron. 90, 34–42 (2017).
Google Scholar 
Siddik, M. A. et al. Responses of indica rice yield and quality to extreme high and low temperatures during the reproductive period. Eur. J. Agron. 106, 30–38 (2019).
Google Scholar 
Bates, L. S., Waldren, R. P. & Teare, I. D. Rapid determination of free proline for water stress studies. Plant Soil 39, 205–207 (1973).CAS 

Google Scholar 
Page, A. L., Miller, R. H. & Dennis, R. K. Methods of Soil Analysis. Part 2 Chemical Methods (ed Page, A. L.) (Soil Science Society of America, 1982).Black, C. A. Methods of Soil Analysis Part II. Chemical and Microbiological Properties (ed Norman, A. G.) (American Society of Agriculture, 1965).Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

Google Scholar 
Knudsen, D., Peterson, G. A. & Pratt, P. F. Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (ed Page, A. L.) (American Society of Agriculture, 1982).Olsen, S. R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate (United States Department of Agriculture Circular, 1954).Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M. F. & Nepstad, D. The 2010 amazon drought. Science 331, 554–554 (2011).CAS 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta‐analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Google Scholar 
van Groenigen, K. J., Van Kessel, C. & Hungate, B. A. Increased greenhouse-gas intensity of rice production under future atmospheric conditions. Nat. Clim. Chang. 3, 288–291 (2013).
Google Scholar 
Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, GB1022 (2008).
Google Scholar 
Laborte, A. G. et al. RiceAtlas, a spatial database of global rice calendars and production. Sci. Data 4, 170074 (2017).
Google Scholar  More

Forest complexity increases hydrological resistance to disturbancesIn general, natural forests, old forests, forests with high coverage, and forests located in low aridity regions (P/PET ≥ 1) are characterized by higher ecosystem complexity than planted forests, young forests, forests with low coverage, and forests located in arid regions (P/PET  More

May, R. M. Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos. Science 186, 645–647 (1974).CAS 
PubMed 
Article 

Google Scholar 
Beddington, J. R., Free, C. A. & Lawton, J. H. Dynamic complexity in predator–prey models framed in difference equations. Nature 255, 58–60 (1975).Article 

Google Scholar 
Hastings, A., Hom, C. L., Ellner, S., Turchin, P. & Godfray, H. C. J. Chaos in ecology: is Mother Nature a strange attractor? Annu. Rev. Ecol. Syst. 24, 1–33 (1993).Article 

Google Scholar 
Cressie, N. & Wikle, C. K. Statistics for Spatio-Temporal Data (John Wiley & Sons, 2011).The State of World Fisheries and Aquaculture 2020 (FAO, 2020).Hastings, A. & Powell, T. Chaos in a three-species food chain. Ecology 72, 896–903 (1991).Article 

Google Scholar 
Huisman, J. & Weissing, F. J. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).Article 

Google Scholar 
Doebeli, M. & Ispolatov, I. Chaos and unpredictability in evolution. Evolution 68, 1365–1373 (2014).PubMed 
Article 

Google Scholar 
Pearce, M. T., Agarwala, A. & Fisher, D. S. Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos. Proc. Natl Acad. Sci. USA 117, 14572–14583 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costantino, R. F., Desharnais, R. A., Cushing, J. M. & Dennis, B. Chaotic dynamics in an insect population. Science 275, 389–391 (1997).CAS 
PubMed 
Article 

Google Scholar 
Becks, L., Hilker, F. M., Malchow, H., Jürgens, K. & Arndt, H. Experimental demonstration of chaos in a microbial food web. Nature 435, 1226–1229 (2005).CAS 
PubMed 
Article 

Google Scholar 
Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).PubMed 
Article 
CAS 

Google Scholar 
Tilman, D. & Wedin, D. Oscillations and chaos in the dynamics of a perennial grass. Nature 353, 653–655 (1991).Article 

Google Scholar 
Turchin, P. & Ellner, S. P. Living on the edge of chaos: population dynamics of fennoscandian voles. Ecology 81, 3099–3116 (2000).Article 

Google Scholar 
Ferrari, M. J. et al. The dynamics of measles in sub-Saharan Africa. Nature 451, 679–684 (2008).CAS 
PubMed 
Article 

Google Scholar 
Benincà, E., Ballantine, B., Ellner, S. P. & Huisman, J. Species fluctuations sustained by a cyclic succession at the edge of chaos. Proc. Natl Acad. Sci. USA 112, 6389–6394 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hassell, M. P., Lawton, J. H. & May, R. M. Patterns of dynamical behaviour in single-species populations. J. Anim. Ecol. 45, 471–486 (1976).Article 

Google Scholar 
Sibly, R. M., Barker, D., Hone, J. & Pagel, M. On the stability of populations of mammals, birds, fish and insects. Ecol. Lett. 10, 970–976 (2007).PubMed 
Article 

Google Scholar 
Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effects of fishing. Proc. Natl Acad. Sci USA. 108, 7075–7080 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salvidio, S. Stability and annual return rates in amphibian populations. Amphib. Reptil. 32, 119–124 (2011).Article 

Google Scholar 
Snell, T. W. & Serra, M. Dynamics of natural rotifer populations. Hydrobiologia 368, 29–35 (1998).Article 

Google Scholar 
Gross, T., Ebenhöh, W. & Feudel, U. Long food chains are in general chaotic. Oikos 109, 135–144 (2005).Article 

Google Scholar 
Ispolatov, I., Madhok, V., Allende, S. & Doebeli, M. Chaos in high-dimensional dissipative dynamical systems. Sci. Rep. 5, 12506 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evol. 4, 75–81 (2020).CAS 
PubMed 
Article 

Google Scholar 
Sivakumar, B., Berndtsson, R., Olsson, J. & Jinno, K. Evidence of chaos in the rainfall-runoff process. Hydrol. Sci. J. 46, 131–145 (2001).CAS 
Article 

Google Scholar 
Hanski, I., Turchin, P., Korpimäki, E. & Henttonen, H. Population oscillations of boreal rodents: regulation by mustelid predators leads to chaos. Nature 364, 232–235 (1993).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. & Taylor, A. D. Complex dynamics in ecological time series. Ecology 73, 289–305 (1992).Article 

Google Scholar 
Munch, S. B., Brias, A., Sugihara, G. & Rogers, T. L. Frequently asked questions about nonlinear dynamics and empirical dynamic modelling. ICES J. Mar. Sci. 77, 1463–1479 (2020).Article 

Google Scholar 
Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).CAS 
PubMed 
Article 

Google Scholar 
Ellner, S. P. & Turchin, P. Chaos in a noisy world: new methods and evidence from time-series analysis. Am. Nat. 145, 343–375 (1995).Article 

Google Scholar 
Nychka, D., Ellner, S., Gallant, A. R. & McCaffrey, D. Finding chaos in noisy systems. J. R. Stat. Soc. B 54, 399–426 (1992).
Google Scholar 
Webber, C. L. & Zbilut, J. P. Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76, 965–973 (1994).PubMed 
Article 

Google Scholar 
Bandt, C. & Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 174102 (2002).PubMed 
Article 
CAS 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. & Luque, J. Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80, 46103 (2009).CAS 
Article 

Google Scholar 
Toker, D., Sommer, F. T. & D’Esposito, M. A simple method for detecting chaos in nature. Commun. Biol. 3, 11 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pikovsky, A. & Politi, A. Lyapunov Exponents: A Tool to Explore Complex Dynamics (Cambridge Univ. Press, 2016).Rosenstein, M. T., Collins, J. J. & De Luca, C. J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117–134 (1993).Article 

Google Scholar 
Dämmig, M. & Mitschke, F. Estimation of Lyapunov exponents from time series: the stochastic case. Phys. Lett. A 178, 385–394 (1993).Article 

Google Scholar 
Prendergast, J., Bazeley-White, E., Smith, O., Lawton, J. & Inchausti, P. The Global Population Dynamics Database (KNB, 2010); https://doi.org/10.5063/F1BZ63Z8Thibaut, L. M. & Connolly, S. R. Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics. Ecology 101, e02893 (2020).PubMed 
Article 

Google Scholar 
Knape, J. & de Valpine, P. Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? Ecol. Lett. 15, 17–23 (2012).PubMed 
Article 

Google Scholar 
Takens, F. in Dynamical Systems and Turbulence (eds Rand, D. A. & Young, L. S.) 366–381 (Springer, 1981).Sugihara, G. Nonlinear forecasting for the classification of natural time series. Philos. Trans. R. Soc. A 348, 477–495 (1994).
Google Scholar 
Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

Google Scholar 
Kendall, B. E. Cycles chaos, and noise in predator–prey dynamics. Chaos Solitons Fractals 12, 321–332 (2001).Article 

Google Scholar 
Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
PubMed 
Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Allometric scaling of Lyapunov exponents in chaotic populations. Popul. Ecol. 62, 364–369 (2020).Article 

Google Scholar 
Graham, D. W. et al. Experimental demonstration of chaotic instability in biological nitrification. ISME J. 1, 385–393 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. Nonlinear time-series modeling of vole population fluctuations. Res. Popul. Ecol. 38, 121–132 (1996).Article 

Google Scholar 
Becks, L. & Arndt, H. Different types of synchrony in chaotic and cyclic communities. Nat. Commun. 4, 1359 (2013).PubMed 
Article 
CAS 

Google Scholar 
Becks, L. & Arndt, H. Transitions from stable equilibria to chaos, and back, in an experimental food web. Ecology 89, 3222–3226 (2008).PubMed 
Article 

Google Scholar 
Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).PubMed 
Article 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).CAS 
PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2020-2 (IUCN, 2020); https://www.iucnredlist.orgFreckleton, R. P. & Watkinson, A. R. Are weed population dynamics chaotic? J. Appl. Ecol. 39, 699–707 (2002).Article 

Google Scholar 
May, R. M. Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976).CAS 
PubMed 
Article 

Google Scholar 
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the ocean? PLoS Biol. 9, e1001127 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: a global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

Google Scholar 
Boettiger, C., Harte, T., Chamberlain, S. & Ram, K. rgpdd: R Interface to the Global Population Dynamics Database. https://docs.ropensci.org/rgpdd, https://github.com/ropensci/rgpdd (2019).Brook, B. W., Traill, L. W. & Bradshaw, C. J. A. Minimum viable population sizes and global extinction risk are unrelated. Ecol. Lett. 9, 375–382 (2006).PubMed 
Article 

Google Scholar 
Baars, J. W. M. Autecological investigations of marine diatoms, 2. Generation times of 50 species. Hydrobiol. Bull. 15, 137–151 (1981).Article 

Google Scholar 
Lavigne, A. S., Sunesen, I. & Sar, E. A. Morphological, taxonomic and nomenclatural analysis of species of Odontella, Trieres and Zygoceros (Triceratiaceae, Bacillariophyta) from Anegada Bay (Province of Buenos Aires, Argentina). Diatom Res. 30, 307–331 (2015).Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Physiological constraints on long-term population cycles: a broad-scale view. Evol. Ecol. Res. 18, 693–707 (2017).
Google Scholar 
Janes, M. J. Oviposition studies on the chinch bug, Blissus leucopterus (Say). Ann. Entomol. Soc. Am. 28, 109–120 (1935).Article 

Google Scholar 
Cook, L. M. Food-plant specialization in the moth Panaxia dominula L. Evolution 15, 478–485 (1961).Article 

Google Scholar 
Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 
PubMed 
Article 

Google Scholar 
Kobayashi, A., Tanaka, Y. & Shimada, M. Genetic variation of sex allocation in the parasitoid wasp Heterospilus prosopidis. Evolution 57, 2659–2664 (2003).PubMed 
Article 

Google Scholar 
Hozumi, N. & Miyatake, T. Body-size dependent difference in death-feigning behavior of adult Callosobruchus chinensis. J. Insect Behav. 18, 557–566 (2005).Article 

Google Scholar 
Huntley, M. E. & Lopez, M. D. G. Temperature-dependent production of marine copepods: a global synthesis. Am. Nat. 140, 201–242 (1992).CAS 
PubMed 
Article 

Google Scholar 
Cohen, R. E. & Lough, R. G. Length–weight relationships for several copepods dominant in the Georges Bank–Gulf of Maine area. J. Northwest Atl. Fish. Sci. 2, 47–52 (1981).Article 

Google Scholar 
World Register of Marine Species (WoRMS, accessed 1 November 2020); https://doi.org/10.14284/170Nakamura, Y. Growth and grazing of a large heterotrophic dinoflagellate, Noctiluca scintillans, in laboratory cultures. J. Plankton Res. 20, 1711–1720 (1998).Article 

Google Scholar 
Boulding, E. G. & Platt, T. Variation in photosynthetic rates among individual cells of a marine dinoflagellate. Mar. Ecol. Prog. Ser. 29, 199–203 (1986).CAS 
Article 

Google Scholar 
Rimet, F. et al. The Observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public. J. Limnol. https://doi.org/10.4081/jlimnol.2020.1944 (2020).Rudstam, L. Zooplankton Survey of Oneida Lake, New York, 1964 to Present (KNB, 2020); https://knb.ecoinformatics.org/view/kgordon.17.99https://knb.ecoinformatics.org/knb/metacat/kgordon.17.67/defaultDumont, H. J., Van de Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).PubMed 
Article 

Google Scholar 
Geller, W. & Müller, H. Seasonal variability in the relationship between body length and individual dry weight as related to food abundance and clutch size in two coexisting Daphnia species. J. Plankton Res. 7, 1–18 (1985).Article 

Google Scholar 
Branstrator, D. K. Contrasting life histories of the predatory cladocerans Leptodora kindtii and Bythotrephes longimanus. J. Plankton Res. 27, 569–585 (2005).Article 

Google Scholar 
Rosen, R. A. Length–dry weight relationships of some freshwater zooplankton. J. Freshw. Ecol. 1, 225–229 (1981).Article 

Google Scholar 
Peters, R. H. & Downing, J. A. Empirical analysis of zooplankton filtering and feeding rates. Limnol. Oceanogr. 29, 763–784 (1984).Article 

Google Scholar 
Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973–977 (1987).Article 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. J. & Robledo, A. Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Chaos 22, 013109 (2012).PubMed 
Article 

Google Scholar 
McCaffrey, D. F., Ellner, S., Gallant, A. R. & Nychka, D. W. Estimating the Lyapunov exponent of a chaotic system with nonparametric regression. J. Am. Stat. Assoc. 87, 682–695 (1992).Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Ricker, W. E. Stock and recruitment. J. Fish. Board Can. 11, 559–623 (1954).Article 

Google Scholar  More

CIAT Global Rural-Urban Mapping Project, v1 (GRUMPv1): Urban Extents Grid (NASA SEDAC, 2011).Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector (UNEP, 2020).Harris, N. L. et al. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
Reid, W. V. et al. Ecosystems and Human Well-being: Biodiversity Synthesis (Millenium Ecosystem Assessment, World Resources Institute, 2005).Xu, C. et al. Resour. Conserv. Recycl. 151, 104478 (2019).Article 

Google Scholar 
Su, J., Friess, D. A. & Gasparatos, A. Nat. Commun. 12, 5050 (2021).CAS 
Article 

Google Scholar 
van den Berg, M. et al. Urban For. Urban Green. 14, 806–816 (2015).Article 

Google Scholar 
Aerts, R., Honnay, O. & Van Nieuwenhuyse, A. Br. Med. Bull. 127, 5–22 (2018).Article 

Google Scholar 
Lindenmayer, D. et al. Ecol. Lett. 11, 78–91 (2008).
Google Scholar 
Knapp, S., Jaganmohan, M. & Schwarz, N. in Atlas of Ecosystem Services: Drivers, Risks, and Societal Responses (eds Schröter, M. et al.) 167–172 (Springer, 2019).Kim, H. Y. Geomat. Nat. Hazards Risk 12, 1181–1194 (2021).Article 

Google Scholar 
Vargas-Hernández, J. G., Pallagst, K. & Zdunek-Wielgołaska, J. in Handbook of Engaged Sustainability (ed. Marques, J.) 885–916 (Springer, 2018).Manso, M. et al. Renew. Sustain. Energy Rev. 135, 110111 (2021).Article 

Google Scholar 
Assimakopoulos, M.-N. et al. Sustainability 12, 3772 (2020).CAS 
Article 

Google Scholar 
Mora-Melià, D. et al. Sustainability 10, 1130 (2018).Article 

Google Scholar 
IPBES. Curr. Opin. Environ. Sustain. 26, 7–16 (2017).
Google Scholar 
Schröpfer, T. & Menz, S. in Dense and Green Building Typologies: Research, Policy and Practice Perspectives (eds Schröpfer, T. & Menz, S.) 1–4 (Springer, 2019).Pedersen Zari, M. & Hecht, K. Biomimetics 5, 18 (2020).Article 

Google Scholar  More