Ecology

Watson, J. B. & Morgan, J. J. B. Emotional reactions and psychological experimentation. Am. J. Psychol. 28, 163–174 (1917).Article 

Google Scholar 
Watson, J. B. & Rayner, R. Conditioned emotional reactions. J. Exp. Psychol. 3, 1–14 (1920).Article 

Google Scholar 
LeDoux, J. Fear and the brain: where have we been, and where are we going. Biol. Psychiatry 44, 1229–1238 (1998).CAS 
PubMed 
Article 

Google Scholar 
Fendt, M. & Fanselow, M. S. The neuroanatomical and neurochemical basis of conditioned fear. Neurosci. Biobehav. Rev. 23, 743–760 (1999).CAS 
PubMed 
Article 

Google Scholar 
Maren, S. & Quirk, G. J. Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852 (2004).CAS 
PubMed 
Article 

Google Scholar 
Bouton, M. E., Mineka, S. & Barlow, D. H. A modern learning theory perspective on the etiology of panic disorder. Psychol. Rev. 108, 4–32 (2001).CAS 
PubMed 
Article 

Google Scholar 
Kim, J. J. & Jung, M. W. Neural circuits and mechanisms involved in Pavlovian fear conditioning: a critical review. Neurosci. Biobehav. Rev. 30, 188–202 (2006).PubMed 
Article 

Google Scholar 
Watson, J. B. Psychology as the behaviorist views it. Psychological Rev. 20, 158–177 (1913).Article 

Google Scholar 
Pavlov, I. P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (Oxford University Press, 1927).Guthrie, E. R. Conditioning as a principle of learning. Psychological Rev. 37, 412–428 (1930).Article 

Google Scholar 
Kamin, L. J. in Miami Symposium on the Prediction of Behavior (ed. Jones, M. R.) 9–33 (University of Miami Press, 1968).Rescorla, R. A. Probability of shock in the presence and absence of CS in fear conditioning. J. Comp. Physiol. Psychol. 66, 1–5 (1968).CAS 
PubMed 
Article 

Google Scholar 
Wagner, A. R., Logan, F. A., Haberlandt, K. & Price, T. Stimulus selection in animal discrimination learning. J. Exp. Psychol. 76, 171–180 (1968).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. & Wagner, A. R. A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement 64–99 (Appleton-Century-Crofts, 1972).Josselyn, S. A. & Tonegawa, S. Memory engrams: recalling the past and imagining the future. Science 367, https://doi.org/10.1126/science.aaw4325 (2020).Tovote, P., Fadok, J. P. & Luthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).CAS 
PubMed 
Article 

Google Scholar 
Haubensak, W. et al. Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature 468, 270–276 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foa, E. B. & Rothbaum, B. O. Treating the Trauma of Rape: Cognitive Behavioral Therapy for PTSD (Guilford Press, 1998).Butler, A. C., Chapman, J. E., Forman, E. M. & Beck, A. T. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin. Psychol. Rev. 26, 17–31 (2006).PubMed 
Article 

Google Scholar 
Delgado, M. R., Olsson, A. & Phelps, E. A. Extending animal models of fear conditioning to humans. Biol. Psychol. 73, 39–48 (2006).CAS 
PubMed 
Article 

Google Scholar 
Mahan, A. L. & Ressler, K. J. Fear conditioning, synaptic plasticity and the amygdala: implications for posttraumatic stress disorder. Trends Neurosci. 35, 24–35 (2012).CAS 
PubMed 
Article 

Google Scholar 
Craske, M. G. et al. What is an anxiety disorder? Focus 9, 20 (2011).
Google Scholar 
LeDoux, J. E. The Emotional Brain: the Mysterious Underpinnings of Emotional Life (Simon & Schuster, 1996).Fanselow, M. S. From contextual fear to a dynamic view of memory systems. Trends Cogn. Sci. 14, 7–15 (2010).PubMed 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation—a review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Bednekoff, P. A. Foraging in the Face of Danger 305–329 (University of Chicago Press, 2007).Stephens, D. W. Decision ecology: foraging and the ecology of animal decision making. Cogn. Affect Behav. Neurosci. 8, 475–484 (2008).PubMed 
Article 

Google Scholar 
Beckers, T., Krypotos, A. M., Boddez, Y., Effting, M. & Kindt, M. What’s wrong with fear conditioning? Biol. Psychol. 92, 90–96 (2013).PubMed 
Article 

Google Scholar 
Mobbs, D. & Kim, J. J. Neuroethological studies of fear, anxiety, and risky decision-making in rodents and humans. Curr. Opin. Behav. Sci. 5, 8–15 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Pellman, B. A. & Kim, J. J. What can ethobehavioral studies tell us about the Brain’s fear system. Trends Neurosci. 39, 420–431 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thorndike, E. Biological Lectures from the Marine Laboratory at Woods’ Holl, USA, for 1899. Nature 62, 411 (1900).Bolles, R. C. Species-specific defense reactions and avoidance learning. Psychol. Rev. 77, 32–48 (1970).Choi, J. S. & Kim, J. J. Amygdala regulates risk of predation in rats foraging in a dynamic fear environment. Proc. Natl Acad. Sci. USA 107, 21773–21777 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zambetti, P. R., Schuessler, B. P. & Kim, J. J. Sex differences in foraging rats to naturalistic aerial predator stimuli. iScience 16, 442–452 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).CAS 
PubMed 
Article 

Google Scholar 
Papes, F., Logan, D. W. & Stowers, L. The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).CAS 
PubMed 
Article 

Google Scholar 
Wilensky, A. E., Schafe, G. E. & LeDoux, J. E. The amygdala modulates memory consolidation of fear-motivated inhibitory avoidance learning but not classical fear conditioning. J. Neurosci. 20, 7059–7066 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, T. & Kim, J. J. Differential effects of cerebellar, amygdalar, and hippocampal lesions on classical eyeblink conditioning in rats. J. Neurosci. 24, 3242–3250 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stiedl, O. & Spiess, J. Effect of tone-dependent fear conditioning on heart rate and behavior of C57BL/6N mice. Behav. Neurosci. 111, 703–711 (1997).CAS 
PubMed 
Article 

Google Scholar 
Guimaraes, F. S., Hellewell, J., Hensman, R., Wang, M. & Deakin, J. F. Characterization of a psychophysiological model of classical fear conditioning in healthy volunteers: influence of gender, instruction, personality and placebo. Psychopharmacology 104, 231–236 (1991).CAS 
PubMed 
Article 

Google Scholar 
Mackintosh, N. J. The Psychology of Animal Learning (Academic Press, 1974).Bouton, M. E. Learning and Behavior (Sinauer Associates 2007).Sheafor, P. J. “Pseudoconditioned” jaw movements of the rabbit reflect associations conditioned to contextual background cues. J. Exp. Psychol. Anim. Behav. Process 1, 245–260 (1975).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. Behavioral studies of Pavlovian conditioning. Annu. Rev. Neurosci. 11, 329–352 (1988).CAS 
PubMed 
Article 

Google Scholar 
Thompson, R. F. & Krupa, D. J. Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17, 519–549 (1994).CAS 
PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Wassum, K. M. The origins and organization of vertebrate pavlovian conditioning. Cold Spring Harb. Perspect. Biol. 8, a021717 (2015).PubMed 
Article 

Google Scholar 
Lee, H. J., Berger, S. Y., Stiedl, O., Spiess, J. & Kim, J. J. Post-training injections of catecholaminergic drugs do not modulate fear conditioning in rats and mice. Neurosci. Lett. 303, 123–126 (2001).CAS 
PubMed 
Article 

Google Scholar 
Palgi, Y., Gelkopf, M. & Berger, R. The inoculating role of previous exposure to potentially traumatic life events on coping with prolonged exposure to rocket attacks: a lifespan perspective. Psychiatry Res. 227, 296–301 (2015).PubMed 
Article 

Google Scholar 
Somer, E. et al. Israeli civilians under heavy bombardment: prediction of the severity of post-traumatic symptoms. Prehosp. Disaster Med. 24, 389–394 (2009).PubMed 
Article 

Google Scholar 
Alexander, B. K., Beyerstein, B. L., Hadaway, P. F. & Coambs, R. B. Effect of early and later colony housing on oral ingestion of morphine in rats. Pharm. Biochem. Behav. 15, 571–576 (1981).CAS 
Article 

Google Scholar 
Gage, S. H. & Sumnall, H. R. Rat Park: how a rat paradise changed the narrative of addiction. Addiction 114, 917–922 (2019).PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Lester, L. S. A Functional Behavioristic Approach to Aversively Motivated Behavior: Predatory Imminence as a Determinant of the Topography of Defensive Behavior 185–212 (Lawrence Erlbaum Associates Inc, 1988).Cain, C. & LeDoux, J. Brain mechanisms of Pavlovian and instrumental aversive conditioning. Handb. Behav. Neurosci. 17, 103–124 (2008).Article 

Google Scholar 
Choi, J. S., Cain, C. K. & LeDoux, J. E. The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats. Learn Mem. 17, 139–147 (2014).Article 

Google Scholar 
Steimer, T. The biology of fear- and anxiety-related behaviors. Dialogues Clin. Neurosci. 4, 231–249 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. The role of learning in threat imminence and defensive behaviors. Curr. Opin. Behav. Sci. 24, 44–49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. Associative vs topographical accounts of the immediate shock freezing deficit in rats—implications for the response selection-rules governing species-specific defensive reactions. Learn. Motiv. 17, 16–39 (1986).Article 

Google Scholar 
Landeira-Fernandez, J., DeCola, J. P., Kim, J. J. & Fanselow, M. S. Immediate shock deficit in fear conditioning: effects of shock manipulations. Behav. Neurosci. 120, 873–879 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hull, C. L. A functional interpretation of the conditioned reflex. Psychol. Rev. 36, 498–511 (1929).Article 

Google Scholar 
Lazarus, A. A. Behavior Therapy and Beyond (McGraw-Hill Companies, 1971).Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001).PubMed 
Article 

Google Scholar 
Lee, H. & Kim, J. J. Amygdalar NMDA receptors are critical for new fear learning in previously fear-conditioned rats. J. Neurosci. 18, 8444–8454 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).CAS 
PubMed 
Article 

Google Scholar  More

Brand, L. E., Campbell, L. & Bresnan, E. Karenia: The biology and ecology of a toxic genus. Harmful Algae 14, 156–178 (2012).
Google Scholar 
Hetland, R. D. & Campbell, L. Convergent blooms of Karenia brevis along the Texas coast. Geophys. Res. Lett. 34, 1–5 (2007).
Google Scholar 
Liu, G., Janowitz, G. S. & Kamykowski, D. A biophysical model of population dynamics of the autotrophic dinoflagellate Gymnodinium breve. Mar. Ecol. Prog. Ser. 210, 101–124 (2001).ADS 
CAS 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why?. J. Geophys. Res. 111, C11003 (2006).ADS 

Google Scholar 
Bidle, K. D. The molecular ecophysiology of programmed cell death in marine phytoplankton. Ann. Rev. Mar. Sci. 7, 341–375 (2015).PubMed 

Google Scholar 
Bidle, K. D. & Bender, S. J. Iron starvation and culture age activate metacaspases and programmed cell death in the marine diatom Thalassiosira pseudonana. Eukaryot. Cell 7, 223–236 (2008).CAS 
PubMed 

Google Scholar 
Bidle, K. D., Haramaty, L., Barcelos, R. J. & Falkowski, P. Viral activation and recruitment of metacaspases in the unicellular coccolithophore, Emiliania huxleyi. Proc. Natl. Acad. Sci. 104, 6049–6054 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vardi, A. et al. Programmed cell death of the dinoflagellate Peridinium gatunense is mediated by CO2 limitation and oxidative stress. Curr. Biol. 9, 1061–1064 (1999).CAS 
PubMed 

Google Scholar 
Zuppini, A., Andreoli, C. & Baldan, B. Heat stress: An inducer of programmed cell death in Chlorella saccharophila. Plant Cell Physiol. 48, 1000–1009 (2007).CAS 
PubMed 

Google Scholar 
Britt, A. B. DNA damage and repair in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 75–100 (1996).CAS 
PubMed 

Google Scholar 
Jimenez, C. et al. Different ways to die: Cell death modes of the unicellular chlorophyte Dunaliella viridis exposed to various environmental stresses are mediated by the caspase-like activity DEVDase. J. Exp. Bot. 60, 815–828 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moharikar, S., D’Souza, J. S., Kulkarni, A. B. & Rao, B. J. Apoptotic-like cell death pathway is induced in unicellular chlorophyte chlamydomonas reinhardtii (Chlorophyceae) cells following UV irradiation: Detection and functional analyses. J. Phycol. 42, 423–433 (2006).CAS 

Google Scholar 
Li, Z., Wakao, S., Fischer, B. B. & Niyogi, K. K. Sensing and responding to excess light. Annu. Rev. Plant Biol. 60, 239–260 (2009).CAS 
PubMed 

Google Scholar 
Niyogi, K. K. Photoprotection revisited: Genetic and molecular approaches. Annu. Rev. Plant Physiol. Plant Mol. Biol. 50, 333–359 (1999).CAS 
PubMed 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, Oxidative Stress, and Signal Transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).CAS 
PubMed 

Google Scholar 
Müller, P., Li, X. & Niyogi, K. K. Non-photochemical quenching. A response to excess light energy. Plant Physiol. 125, 1558–1566 (2001).PubMed 
PubMed Central 

Google Scholar 
Bidle, K. D. Programmed cell death in unicellular phytoplankton. Curr. Biol. 26, R594–R607 (2016).CAS 
PubMed 

Google Scholar 
McKay, L., Kamykowski, D., Milligan, E., Schaeffer, B. & Sinclair, G. Comparison of swimming speed and photophysiological responses to different external conditions among three Karenia brevis strains. Harmful Algae 5, 623–636 (2006).CAS 

Google Scholar 
Miller-Morey, J. S. & Van Dolah, F. M. Differential responses of stress proteins, antioxidant enzymes, and photosynthetic efficiency to physiological stresses in the Florida red tide dinoflagellate, Karenia brevis. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 138, 493–505 (2004).
Google Scholar 
Tilney, C. L., Shankar, S., Hubbard, K. A. & Corcoran, A. A. Is Karenia brevis really a low-light-adapted species?. Harmful Algae 90, 101709 (2019).CAS 
PubMed 

Google Scholar 
Yuasa, K., Shikata, T., Kuwahara, Y. & Nishiyama, Y. Adverse effects of strong light and nitrogen deficiency on cell viability, photosynthesis, and motility of the red-tide dinoflagellate Karenia mikimotoi. Phycologia 57, 525–533 (2018).CAS 

Google Scholar 
Krause, G. H. & Jahns, P. Non-photochemical energy dissipation determined by chlorophyll fluorescence quenching: Characterization and function. In Chlorophyll a Fluorescence 463–495 (Springer, Netherlands, Cham, 2004).
Google Scholar 
Evens, T. J. Photophysiological responses of the toxic red-tide dinoflagellate Gymnodinium breve (Dinophyceae) under natural sunlight. J. Plankton Res. 23, 1177–1194 (2001).CAS 

Google Scholar 
Heil, C. A. et al. Influence of daylight surface aggregation behavior on nutrient cycling during a Karenia brevis (Davis) G. Hansen & Ø Moestrup bloom: Migration to the surface as a nutrient acquisition strategy. Harmful Algae 38, 86–94 (2014).CAS 

Google Scholar 
Errera, R. Response of the Toxic Dinoflagellate Karenia Brevis to Current and Projected Environmental Conditions. (Texas A&M University, PhD dissertation, 2013).Guillard, R. R. L. & Hargraves, P. E. Stichochrysis immobilis is a diatom, not a chrysophyte. Phycologia 32, 234–236 (1993).
Google Scholar 
Dingman, J. E. & Lawrence, J. E. Heat-stress-induced programmed cell death in Heterosigma akashiwo (Raphidophyceae). Harmful Algae 16, 108–116 (2012).
Google Scholar 
Lin, Q. et al. Differential cellular responses associated with oxidative stress and cell fate decision under nitrate and phosphate limitations in Thalassiosira pseudonana: Comparative proteomics. PLoS ONE 12(9), e0184849 (2017).PubMed 
PubMed Central 

Google Scholar 
Choi, C. J., Brosnahan, M. L., Sehein, T. R., Anderson, D. M. & Erdner, D. L. Insights into the loss factors of phytoplankton blooms: The role of cell mortality in the decline of two inshore Alexandrium blooms. Limnol. Oceanogr. 62, 1742–1753 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, J. G., Janech, M. G. & Van Dolah, F. M. Caspase-like activity during aging and cell death in the toxic dinoflagellate Karenia brevis. Harmful Algae 31, 41–53 (2014).CAS 
PubMed 

Google Scholar 
Jauzein, C. & Erdner, D. L. Stress-related responses in Alexandrium tamarense cells exposed to environmental Changes. J. Eukaryot. Microbiol. 60, 526–538 (2013).CAS 
PubMed 

Google Scholar 
Severin, T. & Erdner, D. L. The phytoplankton taxon-dependent oil response and its microbiome: Correlation but not causation. Front. Microbiol. 10, 1–14 (2019).
Google Scholar 
Ralph, P. J. & Gademann, R. Rapid light curves: A powerful tool to assess photosynthetic activity. Aquat. Bot. 82, 222–237 (2005).CAS 

Google Scholar 
Suzuki, N. & Mittler, R. Reactive oxygen species and temperature stresses: A delicate balance between signaling and destruction. Physiol. Plant. 126, 45–51 (2006).CAS 

Google Scholar 
Krause, G. H. & Weis, E. Chlorophyll fluorescence and photosynthesis: The basics. Annu. Rev. Plant Physiol. Plant Mol. Biol. 42, 313–349 (1991).CAS 

Google Scholar 
Gechev, T. S. & Hille, J. Hydrogen peroxide as a signal controlling plant programmed cell death. J. Cell Biol. 168, 17–20 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Miller, G., Suzuki, N., Ciftci-Yilmaz, S. & Mittler, R. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant. Cell Environ. 33, 453–467 (2010).CAS 
PubMed 

Google Scholar 
Purvis, A. C. Role of the alternative oxidase in limiting superoxide production by plant mitochondria. Physiol. Plant. 100, 165–170 (1997).CAS 

Google Scholar 
Demmig-Adams, B. & Adams Iii, W. W. Photoprotection and other responses of plants to high light stress. Annu. Rev. Plant Biol. 43, 599–626 (1992).CAS 

Google Scholar 
Cui, Y., Zhang, H. & Lin, S. Enhancement of non-photochemical quenching as an adaptive strategy under phosphorus deprivation in the Dinoflagellate Karlodinium veneficum. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
Cassell, R. T., Chen, W., Thomas, S., Liu, L. & Rein, K. S. Brevetoxin, the dinoflagellate neurotoxin, localizes to thylakoid membranes and interacts with the light-harvesting complex II (LHCII) of photosystem II. ChemBioChem 16, 1060–1067 (2015).CAS 
PubMed 

Google Scholar 
Milne, A., Davey, M. S., Worsfold, P. J., Achterberg, E. P. & Taylor, A. R. Real-time detection of reactive oxygen species generation by marine phytoplankton using flow injection-chemiluminescence. Limnol. Oceanogr. Methods 7, 706–715 (2009).CAS 

Google Scholar 
Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium trichodesmium. Science (80-) 294, 1534–1537 (2001).ADS 
CAS 

Google Scholar 
Triantaphylidès, C. et al. Singlet oxygen is the major reactive oxygen species involved in photooxidative damage to plants. Plant Physiol. 148, 960–968 (2008).PubMed 
PubMed Central 

Google Scholar 
Gao, Y. & Erdner, D. L. Dynamics of cell death across growth stages and the diel cycle in the dinoflagellate Karenia brevis. J. Eukaryot. Microbiol. https://doi.org/10.1111/jeu.12874 (2021).Article 
PubMed 

Google Scholar 
Xu, K., Jiang, H., Juneau, P. & Qiu, B. Comparative studies on the photosynthetic responses of three freshwater phytoplankton species to temperature and light regimes. J. Appl. Phycol. 24, 1113–1122 (2012).CAS 

Google Scholar 
Yamori, W., Makino, A. & Shikanai, T. A physiological role of cyclic electron transport around photosystem I in sustaining photosynthesis under fluctuating light in rice. Sci. Rep. 6, 20147 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Berman-Frank, I., Bidle, K. D., Haramaty, L. & Falkowski, P. G. The demise of the marine cyanobacterium, Trichodesmium spp., via an autocatalyzed cell death pathway. Limnol. Oceanogr. 49, 997–1005 (2004).ADS 

Google Scholar  More

Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).Article 

Google Scholar 
Via, S. et al. Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol. Evol. 10, 212–217 (1995).CAS 
PubMed 
Article 

Google Scholar 
Ghalambor, C. K. et al. Adaptive versus non‐adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
King, J. G. & Hadfield, J. D. The evolution of phenotypic plasticity when environments fluctuate in time and space. Evol. Lett. 3, 15–27 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Newman, R. A. Genetic variation for phenotypic plasticity in the larval life history of spadefoot toads (Scaphiopus couchii). Evolution 48, 1773–1785 (1994).PubMed 

Google Scholar 
Nussey, D. H. et al. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).CAS 
PubMed 
Article 

Google Scholar 
Scheiner, S. Selection experiments and the study of phenotypic plasticity 1. J. Evol. Biol. 15, 889–898 (2002).Article 

Google Scholar 
Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).CAS 
PubMed 
Article 

Google Scholar 
Reger, J. et al. Predation drives local adaptation of phenotypic plasticity. Nat. Ecol. Evol. 2, 100–107 (2018).PubMed 
Article 

Google Scholar 
Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Brakefield, P. M. & Reitsma, N. Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi. Ecol. Entomol. 16, 291–303 (1991).Article 

Google Scholar 
Rountree, D. & Nijhout, H. Hormonal control of a seasonal polyphenism in Precis coenia (Lepidoptera: Nymphalidae). J. Insect Physiol. 41, 987–992 (1995).CAS 
Article 

Google Scholar 
Scheiner, S. M. & Holt, R. D. The genetics of phenotypic plasticity. X. Variation versus uncertainty. Ecol. Evol. 2, 751–767 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Bonamour, S. et al. Phenotypic plasticity in response to climate change: the importance of cue variation. Philos. Trans. R. Soc. B 374, 20180178 (2019).Article 

Google Scholar 
Fox, R.J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2018.0174 (2019).Auld, J. R., Agrawal, A. A. & Relyea, R. A. Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc. R. Soc. B 277, 503–511 (2010).PubMed 
Article 

Google Scholar 
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yampolsky, L. Y., Schaer, T. M. & Ebert, D. Adaptive phenotypic plasticity and local adaptation for temperature tolerance in freshwater zooplankton. Proc. R. Soc. B 281, 20132744 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Schmid, M. & Guillaume, F. The role of phenotypic plasticity on population differentiation. Heredity 119, 214–225 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B., Lande, R. & Slatkin, M. A neo-Darwinian commentary on macroevolution. Evolution 36, 474–498 (1982).PubMed 

Google Scholar 
Lynch, M. The rate of morphological evolution in mammals from the standpoint of the neutral expectation. Am. Nat. 136, 727–741 (1990).Article 

Google Scholar 
Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).Article 

Google Scholar 
West-Eberhard, M. J. Developmental plasticity and the origin of species differences. Proc. Natl Acad. Sci. USA 102, 6543–6549 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turelli, M. & Barton, N. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Noble, D. W., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66-9, 2891–2902 (2012).Article 

Google Scholar 
Houle, D. How should we explain variation in the genetic variance of traits? Genetica 102, 241–253 (1998).PubMed 
Article 

Google Scholar 
Tollrian, R. Predator‐induced morphological defenses: costs, life history shifts, and maternal effects in Daphnia pulex. Ecology 76, 1691–1705 (1995).Article 

Google Scholar 
Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).CAS 
Article 

Google Scholar 
Tollrian, R. Neckteeth formation in Daphnia pulex as an example of continuous phenotypic plasticity: morphological effects of Chaoborus kairomone concentration and their quantification. J. Plankton Res. 15, 1309–1318 (1993).Article 

Google Scholar 
Dennis, S. et al. Phenotypic convergence along a gradient of predation risk. Proc. R. Soc. B 278, 1687–1696 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hammill, E. & Beckerman, A. P. Reciprocity in predator–prey interactions: exposure to defended prey and predation risk affects intermediate predator life history and morphology. Oecologia 163, 193–202 (2010).PubMed 
Article 

Google Scholar 
Hammill, E., Rogers, A. & Beckerman, A. P. Costs, benefits and the evolution of inducible defences: a case study with Daphnia pulex. J. Evol. Biol. 21, 705–715 (2008).CAS 
PubMed 
Article 

Google Scholar 
Barnard-Kubow, K. et al. Polygenic variation in sexual investment across an ephemerality gradient in Daphnia pulex. Mol. Bio. Evol. 39, msac121 (2022).Article 

Google Scholar 
Deng, H.-W. & Lynch, M. Inbreeding depression and inferred deleterious-mutation parameters in Daphnia. Genetics 147, 147–155 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seyfert, A. L. et al. The rate and spectrum of microsatellite mutation in Caenorhabditis elegans and Daphnia pulex. Genetics 178, 2113–2121 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, S. et al. High mutation rates in the mitochondrial genomes of Daphnia pulex. Mol. Biol. Evol. 29, 763–769 (2012).CAS 
PubMed 
Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hystrix 24, 75 (2013).
Google Scholar 
Adams, D.C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. et al. Geomorph: software for geometric morphometric analyses (University of New England, 2016); https://hdl.handle.net/1959.11/21330Adams, D. C. & Collyer, M. L. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution 73, 2352–2367 (2019).PubMed 
Article 

Google Scholar 
Richards, C. L., Bossdorf, O. & Pigliucci, M. What role does heritable epigenetic variation play in phenotypic evolution? BioScience 60, 232–237 (2010).Article 

Google Scholar 
Latta, L. C. IV et al. The phenotypic effects of spontaneous mutations in different environments. Am. Nat. 185, 243–252 (2015).PubMed 
Article 

Google Scholar 
Lind, M. I. et al. The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection. Proc. R. Soc. B 282, 20151651 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Laforsch, C. & Tollrian, R. Inducible defenses in multipredator environments: cyclomorphosis in Daphnia cucullata. Ecology 85, 2302–2311 (2004).Article 

Google Scholar 
Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Tollrian, R. & Harvell, C.D. The Ecology and Evolution of Inducible Defenses (Princeton Univ. Press, 1999).Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 1435–1446 (2009).PubMed 
Article 
CAS 

Google Scholar 
Via, S. & Lande, R. Genotype–environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).PubMed 
Article 

Google Scholar 
Kvist, J. et al. Temperature treatments during larval development reveal extensive heritable and plastic variation in gene expression and life history traits. Mol. Ecol. 22, 602–619 (2013).CAS 
PubMed 
Article 

Google Scholar 
Siepielski, A. M. et al. Differences in the temporal dynamics of phenotypic selection among fitness components in the wild. Proc. R. Soc. B 278, 1572–1580 (2011).PubMed 
Article 

Google Scholar 
Muschick, M. et al. Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation. BMC Evol. Biol. 11, 116 (2011).Salzburger, W. Understanding explosive diversification through cichlid fish genomics. Nat. Rev. Genet. 19, 705–717 (2018).CAS 
PubMed 
Article 

Google Scholar 
Halligan, D. L. & Keightley, P. D. Spontaneous mutation accumulation studies in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 40, 151–172 (2009).Article 

Google Scholar 
Houle, D., Morikawa, B. & Lynch, M. Comparing mutational variabilities. Genetics 143, 1467–1483 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eberle, S. et al. Hierarchical assessment of mutation properties in Daphnia magna. G3 Genes Genomes Genetics 8, 3481–3487 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).CAS 
PubMed 
Article 

Google Scholar 
Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103 (2007).CAS 
PubMed 
Article 

Google Scholar 
Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).CAS 
Article 

Google Scholar 
Reed, T. E., Schindler, D. E. & Waples, R. S. Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate. Conserv. Biol. 25, 56–63 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
ASTM, Standard Guide for Conducting Acute Toxicity Tests with Fishes, Macroinvertebrates, and Amphibians (American Society for Testing and Materials, 1988).Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J. et al. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 
Article 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).MarkDuplicates v.2.20 (Broad Institute, 2019); http://broadinstitute.github.io/picardMcKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
Article 

Google Scholar 
Naraki, Y., Hiruta, C. & Tochinai, S. Identification of the precise kairomone-sensitive period and histological characterization of necktooth formation in predator-induced polyphenism in Daphnia pulex. Zool. Sci. 30, 619–625 (2013).Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Scrucca, L. et al. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2018).Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. RRPP: an r package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).Article 

Google Scholar 
Collyer, M., Adams, D. & and Collyer, M.M. RRPP: linear model evaluation with randomized residuals in a permutation procedure. R package version 1.3 https://CRAN.R-project.org/package=RRPP (2021).Smirnov, P. robcor: Robust correlations. R package version 0.1-6.1 https://CRAN.R-project.org/package=ropcor (2014).Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Yang, J. et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).Villanueva, R., Chen, Z. & Wickham, H. ggplot2: Elegant Graphics for Data Analysis Using the Grammar of Graphics (Springer-Verlag, 2016).Wilke, C. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9. 2 https://CRAN.R-project.org/package=cowplot (2020).Dowle, M. et al. data.table: Extension of ‘data.frame‘. R package version 1.14.0 https://CRAN.R-project.org/package=data.table (2021).Daniel, M. foreach: Provides foreach looping construct. R package version 1.5.1 https://CRAN.R-project.org/package=foreach (2020).Weston, S. doMC: Foreach parallel adaptor for ‘parallel’. R package version 1.3.7 https://CRAN.R-project.org/package=doMC (2020).Clarke, E. & Sherrill-Mix, S. Ggbeeswarm: Categorical scatter (violin point) plots. R package version 0.6. 0 https://CRAN.R-project.org (2017).Garnier, S. et al. viridis: Default color maps from ‘matplotlib’. R package version 0.5.1 (2018). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Bastin, J. F. et al. The global tree restoration potential. Science 364, 76–79 (2019).Article 
CAS 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

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

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
Article 

Google Scholar 
Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen-phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Camenzind, T., Httenschwiler, S., Treseder, K. K., Lehmann, A. & Rillig, M. C. Nutrient limitation of soil microbial processes in tropical forests. Ecol. Monogr. 88, 4–21 (2018).Article 

Google Scholar 
Hou, E., Luo, Y., Kuang, Y., Chen, C. & Wen, D. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. & Follstad Shah, J. J. Ecoenzymatic stoichiometry and ecological theory. Annu. Rev. Ecol. Evol. Syst. 43, 313–343 (2012).Article 

Google Scholar 
Houghton, R. A. Balancing the global carbon budget. Annu. Rev. Earth Planet. Sci. 35, 313–347 (2007).CAS 
Article 

Google Scholar 
Chen, J. et al. Differential responses of carbon-degrading enzyme activities to warming: implications for soil respiration. Global Change Biol. 24, 4816–4826 (2018).Article 

Google Scholar 
Waring, B. G., Weintraub, S. R. & Sinsabaugh, R. L. Ecoenzymatic stoichiometry of microbial nutrient acquisition in tropical soils. Biogeochemistry 117, 101–113 (2014).CAS 
Article 

Google Scholar 
Mori, T., Lu, X., Aoyagi, R. & Mo, J. Reconsidering the phosphorus limitation of soil microbial activity in tropical forests. Funct. Ecol. 32, 1145–1154 (2018).Article 

Google Scholar 
Gallardo, A. & Schlesinger, W. H. Factors limiting microbial biomass in the mineral soil and forest floor of a warm-temperate forest. Soil Biol. Biochem. 26, 1409–1415 (1994).Article 

Google Scholar 
Feng, J. et al. Coupling and decoupling of soil carbon and nutrient cycles across an aridity gradient in the drylands of northern China: Evidence from ecoenzymatic stoichiometry. Global Biogeochem. Cycles. 33, 559–569 (2019).CAS 

Google Scholar 
Cui, Y. et al. Patterns of soil microbial nutrient limitations and their roles in the variation of soil organic carbon across a precipitation gradient in an arid and semi-arid region. Sci. Total Environ. 658, 1440–1451 (2019).CAS 
Article 

Google Scholar 
Jing, X. et al. Soil microbial carbon and nutrient constraints are driven more by climate and soil physicochemical properties than by nutrient addition in forest ecosystems. Soil Biol. Biochem. 141, 107657 (2020).CAS 
Article 

Google Scholar 
Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).Article 
CAS 

Google Scholar 
Zhou, L. et al. Soil extracellular enzyme activity and stoichiometry in China’s forests. Funct. Ecol. 34, 1461–1471 (2020).Article 

Google Scholar 
Fang, J., Chen, A., Peng, C., Zhao, S. & Ci, L. Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292, 2320–2322 (2001).CAS 
Article 

Google Scholar 
Zhu, J. et al. Carbon stocks and changes of dead organic matter in China’s forests. Nat. Commun. 8, 1–10 (2017).Article 
CAS 

Google Scholar 
Fang, J., Yu, G., Liu, L., Hu, S. & Chapin, F. S. Climate change, human impacts, and carbon sequestration in China. Proc. Natl. Acad. Sci. USA 115, 4015–4020 (2018).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of soil enzyme activity at global scale. Ecol. Lett. 11, 1252–1264 (2008).Article 

Google Scholar 
Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).CAS 
Article 

Google Scholar 
Moorhead, D. L., Sinsabaugh, R. L., Hill, B. H. & Weintraub, M. N. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biol. Biochem. 93, 1–7 (2016).CAS 
Article 

Google Scholar 
Cui, Y. et al. Stoichiometric models of microbial metabolic limitation in soil systems. Global Ecol. Biogeogr. 30, 2297–2311 (2021).Article 

Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine, and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

Google Scholar 
Schulte-Uebbing, L. & Vries, W. D. Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: a meta-analysis. Global Change Biol. 24, 416–431 (2017).Article 

Google Scholar 
Richardson, S. J., Peltzer, D. A., Allen, R. B. & Parfitt, M. G. L. Rapid development of phosphorus limitation in temperate rainforest along the Franz josef soil chronosequence. Oecologia 139, 267–276 (2004).Article 

Google Scholar 
Augusto, L., Achat, D. L., Jonard, M., Vidal, D. & Ringeval, B. Soil parent material-a major driver of plant nutrient limitations in terrestrial ecosystems. Global Change Biol. 23, 3808–3824 (2017).Article 

Google Scholar 
Yao, Q. et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat. Ecol. Evol. 2, 499–509 (2018).Article 

Google Scholar 
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).CAS 
Article 

Google Scholar 
Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol. 198, 656–669 (2013).CAS 
Article 

Google Scholar 
Cui, Y. et al. Ecoenzymatic stoichiometry and microbial nutrient limitation in rhizosphere soil in the arid area of the northern Loess Plateau, China. Soil Biol. Biochem. 116, 11–21 (2018).CAS 
Article 

Google Scholar 
Cui, Y. et al. Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region. Soil Biol. Biochem. 147, 107814 (2020).CAS 
Article 

Google Scholar 
Johnson, J. et al. The response of soil solution chemistry in european forests to decreasing acid deposition. Global Change Biol. 24, 3603–3619 (2018).Article 

Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).CAS 
Article 

Google Scholar 
Penuelas, J. et al. Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 1–10 (2013).
Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in china over the past decade. Nat. Geosci. 12, 424–429 (2019).CAS 
Article 

Google Scholar 
Cui, Y. et al. Decreasing microbial phosphorus limitation increases soil carbon release. Geoderma 419, 115868 (2022).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L., Moorhead, D. L., Xu, X. & Litvak, M. E. Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production. New Phytol. 214, 1518–1526 (2017).CAS 
Article 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Global Change Biol. 27, 2633–2644 (2021).CAS 
Article 

Google Scholar 
Friggens, N. L., Hester, A. J., Mitchell, R. J., Parker, T. C. & Wookey, P. A. Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. Global Change Biol. 26, 5178–5188 (2020).Article 

Google Scholar 
Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
Article 

Google Scholar 
Rosinger, C., Rousk, J. & Sandén, H. Can enzymatic stoichiometry be used to determine growth-limiting nutrients for microorganisms?-A critical assessment in two subtropical soils. Soil Biol. Biochem. 128, 115–126 (2019).CAS 
Article 

Google Scholar 
Mori, T. Does ecoenzymatic stoichiometry really determine microbial nutrient limitations? Soil Biol. Biochem. 146, 107816 (2020).CAS 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term nitrogen deposition on extracellular enzyme activity in an acer saccharum, forest soil. Soil Biol. Biochem. 34, 1309–1315 (2002).CAS 
Article 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).CAS 
Article 

Google Scholar 
Lindstrom, M. J. & Bates, D. M. Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data. J. Am. Stat. Assoc. 83, 1014–1022 (1988).
Google Scholar 
Legendre, P. & Legendre, L. Numerical ecology, 2nd English edition. Elsevier Science BV, Amsterdam (1998).Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R News 8/1, 20–25 (2008).
Google Scholar 
Toms, J. D. & Lesperance, M. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

Google Scholar 
Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).Article 

Google Scholar 
Sanchez, G., Trinchera, L. & Russolillo, G. plspm: Tools for Partial Least Squares Path Modeling (PLS-PM). R package version 0.4.7 edn (2016).Development Core Team R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). More

This study presents a comprehensive quantification of carbon sequestration as well as CO2/CH4/N2O emissions reductions from terrestrial ecosystems based on multiple sources of data from literature, inventories, public databases and documents. The pathways considered ecosystem restoration and protection from being converted into cropland or built-up areas, reforestation, management with improved nitrogen use in cropland, restricted deforestation, grassland recovery, reducing risk from forest wildfire and others. Here we describe the cross-cutting methods that apply across all 16 NCS pathways. The definitions, detailed methods and data sources for evaluating individual pathways can be found in the Supplementary Information.Cross-cutting methodsBaseline settingWe set 2000 as the base year because the large-scale national ecological projects, such as the Grain for Green Project, were started since then. We first evaluate the historical mitigation capacity during 2000–2020, which is the first 20 years of implementing the projects. From this procedure we can determine how much mitigation capacity has been realized through the previous projects in the past two decades and to what extent additional actions can be made after 2020. Relative to the baseline 2000–2020, we then evaluate the maximum potentials of the NCS mitigation in the future 10 (2020–2030) and 40 (2020–2060) years, corresponding to the timetable of China’s NDCs: carbon peak before 2030 and carbon neutrality by 2060.The settings of baseline in this study are different from the existing assessments (2000s–2010s as a baseline and 2010–2025/2030/2050 as scenarios)1,22,23,27,28. Baseline sets the temporal and spatial reference for NCS pathway scenarios, which may have a great impact on the NCS estimates. Notably, NCS actions during 2000–2020 will have a great impact in the future periods, which we refer to as the ‘legacy effect’. The legacy effect itself, mainly reforestation, is independent of being assessed, but it is conceptually attributed to natural flux and excluded from future NCS potential estimates.Maximum potentialThe MAMP refers to the additional CO2 sequestration or avoided GHG emissions measured in CO2 equivalents (CO2e) at given flux rates in a period on the maximum extent to which the stewardship options are applied (numbers are expressed as TgCO2e yr−1 for individual pathways and PgCO2e yr−1 for national total) (Extended Data Fig. 1 and Supplementary Table 2). ‘Additional’ means mitigation outcomes due to human actions taken beyond business-as-usual land-use activities (since 2020) and excluding existing land fluxes not attributed to direct human activities1. The MAMP of CH4 and N2O are accounted by three cropland and wetland pathways (cropland nutrient management, improved rice cultivation and peatland restoration). We adopt 100 yr global warming potential to calculate the warming equivalent for CH4 (25) and N2O (298), respectively38,39 because these values are used in national GHG inventories, although some researchers have argued that using the fixed 100 yr global warming potential to calculate the warming equivalents may be problematic because they cannot differentiate the contrasting impacts of the long- and short-lived climate pollutants39. Because the flux rate of the GHG by ecosystems may vary with the time of recovery or growth, the MAMP may also change for different periods even given the same extent.The ‘maximum’ is constrained by varied factors across the NCS pathways. We constrain forest and grassland restoration by the rate of implementation, farmland red line and tree surviving rate (Extended Data Fig. 2). Surviving rate here is the ratio of the area with increased vegetation cover due to reforestation to the total reforestation area. The farmland red line refers to ‘the minimum area of cultivated land’ given by the Ministry of Land and Resources of China. It defines the lowest limit, and the current red line is ~120 Mha. It is a rigid constraint below which the total amount of cultivated land cannot be reduced. From this total amount, there is provincial farmland red line. This red line sets a constraint on the implementation of the NCS pathways associated with land-use change. We set the future scenario of farmland area that can be used for grassland or forest restoration on the basis of the provincial farmland red line. Basic farmland is closely related to national food security. By 2050, China’s population is predicted to decrease slightly, but with economic development, the per capita demand for food may increase40. We assume that the food production in the future can meet the food demand via increasing agricultural investment and technological advancement. The N fertilizer reduction scenario is set to be below the level 60%, under which crop yield is not significantly affected19, because N fertilizer is surplus in many Chinese croplands. For timber production, we assume that the demand for timber can be met if the production level is maintained at the level of 2010–2020 (83.31 million m3 yr−1). As deforestation of natural forests is 100% forbidden since 2020, the future timber will come mainly from tree plantations. For grazing optimization, we assume that livestock production is not affected by grassland fencing due to refined livestock management such as improving feed nutrient and fine-seed breeding41.The areas of historical NCS implementation during 2000–2020 were estimated using statistical data, published literature and public documents, with a supplement from remote-sensing data. The flux rates were obtained either by directly using the values from multiple literature sources or from estimates using the empirical formulae. For the estimates of future NCS potential, the flux rate and extent of the pathway were determined on the basis of the baseline (2000–2020). The extent is assumed to be achieved by using the same rate but limited by the multiple constraints stated in the preceding unless the implementation scopes have been reported in national planning documents. We estimate the legacy effect by multiplying the implementation area in the past by the flux rates in the future two periods.SaturationThe future mitigation potential that we estimate for 2030 and 2060 will not persist indefinitely because the finite potential for natural ecosystems to store additional carbon will saturate. For each NCS pathway, we estimate the expected duration of the potential for sequestration at the maximum rate (Supplementary Table 3). Forests can continue to sequester carbon for 70–100 years or more. Restored grasslands and fenced grasslands can continue to sequester carbon for >50 years. Forest-fire management and cover crops can continue to sequester carbon for 40–50 years or more. Sea grasses and peatlands can continue to sequester carbon for millennia. Avoided pathways do not saturate as long as the business-as-usual cases indicate that there are potential areas for avoided losses of ecosystems. In this case, sea grass and salt marsh would disappear entirely after 64 years, but it would be 100–300 years or more for forest, grassland and peatland.Estimation of uncertaintiesThe extent (area or biomass amount) and flux (sequestration or reduced emission per area or biomass amount in unit time) are considered to estimate uncertainty of the historical mitigation capacity or future potential for each NCS pathway. We use the IPCC approaches to combine uncertainty42. Where mean and standard deviation can be estimated from collected literature, 95% CIs are presented on the basis of multiple published estimates. Where a sample of estimates is not available but only a range of a factor, we report uncertainty as a range and use Monte Carlo simulations (with normal distribution and 100,000 iterations) to combine the uncertainties of extent and flux (IPCC Approach 2). The overall uncertainties of the 16 NCS pathways were combined using IPCC Approach 142. If the extent estimate is based on a policy determination, rather than an empirical estimate of biophysical potential, we do not consider it a source of uncertainty.MACsThe economic/cost constraints refer to the amount of NCS that can be achieved at a given social cost. The MAC curve is fitted according to the total publicly funded investment and total mitigation capacity or potential during a period. The MAC curves are drawn to estimate the historical mitigation or MAMP at the cost thresholds of US$10, US$50 and US$100 (MgCO2e)−1, respectively. The trading price in China’s current carbon market is ~US$10 USD (as the minimum cost43), and the cost-effective price point44,45 to achieve the Paris Agreement goal of limiting global warming to below 2 °C above pre-industrial levels is US$100 (as the maximum cost). A carbon price of US$50 is regarded as a medium value1,46. For the pathways of reforestation, avoided grassland conversion, grazing optimization and grassland restoration, we collected the statistical data of investments in China from 2000 to 2020 and estimated the affordable MAMP below the three mitigation costs. Due to data limitations, the points used for fitting the MAC curve are values for cost (invested funds) and benefit (mitigation capacity) in each of the provinces. We rank the ratio of benefit to cost in a descending order to obtain the maximum marginal benefit for MAC by assuming that NCS measures are first implemented in the region with the highest cost/benefit rate. We refer to the investment standard before 2020 as the benchmark and estimate the cost of each pathway for the future periods with discount rates of 3% and 5%, respectively. The social discount rate 4–6% is usually used as a benchmark discount value in carbon price studies in China compared with lower scenarios (for example, 3.6%)46,47. In a global study for estimating country-level social cost of carbon, 3% and 5% are used for scenario analysis48. Note that the mean value from the two discount rates was used in presenting the results. For the other pathways where investment data cannot be obtained, we refer to relevant references to estimate MAC. All the cost estimates are expressed in 2015 dollars, transformed on the basis of the Renminbi and US dollar exchange rate of the same year. The year 2015 represents a relatively stable condition of economic increase over the past decade (2011–2020) in China (the increase rate of gross domestic product (GDP) in 2015 is similar to the 10 yr mean). In the cases when the MAC curves exceed the estimated maximum potentials in the period, we identify the historical capacity or the MAMP as limited by the biophysical estimates.Additional mitigation required to meet Paris Agreement NDCsOn 28 October 2021, China officially submitted ‘China’s Achievements, New Goals and New Measures for Nationally Determined Contributions’ (‘New Measures 2021’ hereafter) and ‘China’s Mid-Century Long-Term Low Greenhouse Gas Emission Development Strategy’ to the Secretariat of the United Nations Framework Convention on Climate Change as an enhanced strategy to China’s updated NDCs (first submission in 2015). The goal of China’s updated NDCs is to strive to peak CO2 emissions before 2030 and achieve carbon neutralization by 2060. It specified the goals to include the following: before 2030, China’s carbon dioxide emissions per unit of GDP are expected be more than 65% lower than that in 2005, and the forest stock volume is expected to be increased by around 6.0 (previously 4.5) billion m3 over the 2005 level. In the ‘New Measures 2021’9 and ‘Master Plan of Major Projects of National Important Ecosystem Protection and Restoration (2021–2035)’5, many NCS-related opportunities are proposed to consolidate the carbon sequestration of ecosystems and increase the future NCS potential, including protecting the existing ecosystems, implementing engineering to precisely improve forest quality, continuously increasing forest area and stock volume, strengthening grassland protection and recovery and wetland protection and improving the quality of cultivated land and the agricultural carbon sinks.Industrial CO2 emissionsThe historical CO2 emissions data from 2000 to 201749,50 are used as the benchmark of industrial CO2 emissions during 2000–2020. For future projections, we use the peak value of the A1B2C2 scenario (in the range of 10,000 to 12,000 Mt) in 2030 from ref. 11. We assume that CO2 emission increases linearly from 2017 to 2030.Characterizing co-benefitsNCS activities proposed in the future measures or plans may enhance co-benefits. Four generalized types of ecosystem services are identified: improving biodiversity, water-related, soil-related and air-related ecosystem services (Fig. 1). Biodiversity benefits refer to the increase in different levels of diversity (alpha, beta and/or gamma diversity)51. Water, soil and air benefits refer to flood regulation and water purification, improved fertility and erosion prevention, and improvements in air quality, respectively, as defined in the Millennium Ecosystem Assessment52. The evidence that each pathway produces co-benefits from one or more peer-reviewed publications was collected through reviewing the literature (see the details for co-benefits of each pathway in Supplementary Information).Mapping province-level mitigationThe data for extent of implementing forest pathways are obtained from the statistical yearbook and reported at the province level. To be consistent with the forest pathways, the other pathways were also aggregated to the provincial-level estimate from the spatial data. If the flux data were available in different climate regions, the provinces are first assigned to climate regions. When a province spans multiple climate zones, the weight value is set according to the proportion of area, and finally an estimated value of rate was calculated (for fire management, some grassland and wetland pathways). For the forest pathways, we first collected the flux-rate data from reviewing literature and then averaged these flux rates to region/province. The flux rates for reforestation and natural forest management were calculated separately by province and age group. Similarly, specified flux rates are applied for different times after ecosystem restoration or conversion for other pathways.Classification of NCS typesThree types of NCS pathways were classified: protection (of intact natural ecosystems), improved management (on managed lands) and restoration (of native cover)35. In our study, four (AVFC, AVGC, AVCI, AVPI), eight (IMP, NFM, FM, BIOC, CVCR, CRNM, IMRC, GROP) and four (RF, GRR, CWR, PTR) NCS pathways were identified as protection, management and restoration types, respectively (Supplementary Table 1). These pathways can be further divided into groups of ‘single’ type or ‘mixed’ type according to their contribution to individual pathways. Specifically, in a certain area, when the mitigation capacity of a certain pathway accounts for more than 50% of the total, it is regarded as a single or dominant NCS type; if no single pathway accounts for more than 50%, it is a mixed type, named by the top pathways whose NCS sum exceeds 50% of the total mitigation capacity. More

Uchida, H. & Baba, O. Fishery management and the pooling arrangement in the Sakura ebi fishery in Japan, 175–189. https://www.fao.org/3/a1497e/a1497e16.pdf (2008).Omori, M. The biology of a sergestid shrimp Sergestes lucens Hansen. Bull. Ocean Res. Inst. Univ. Tokyo 4, 1–83 (1969).
Google Scholar 
Gurney, R. & Lebour, M. V. Larvae of decapod crustacea. Part VI. The genus Sergestes. Discov. Rep. 20, 1–68 (1940).
Google Scholar 
Holthuis, L. B. FAO species catalogue. Vol. 1. Shrimps and prawns of the world. An annotated catalogue of species of interest to fisheries. FAO Fish. Synop. Vol. 125, 1–271 (1980).Omori, M., Ukishima, Y. & Muranaka, F. New record of occurrence of Sergia lucens (Hansen) (Crustacea, Sergestidae) off Tung-kang, Taiwan, with special reference to phylogeny and distribution of the species. J. Oceanogr. Soc. Jpn. 44, 261–267 (1988) (in Japanese with English abstract).Article 

Google Scholar 
Isshiki, T. & Tajima, Y. The research of a sergestid shrimp, Sergia lucens (Hansen) in the mouth of Tokyo Bay I. The seasonal distribution of adult and the distribution of eggs. Bull. Kanagawa Pref. Fish. Exp. Stn. 13, 73–78 (1992) (in Japanese with English abstract).
Google Scholar 
Lee, D. A., Wu, S. H., Liao, I. C. & Yu, H. P. On three species of commercially important sergestid shrimps (Decapoda: Sergestidae) in the coastal waters of Taiwan. J. Taiwan Fish. Res. Inst. 4, 1–19 (1996) (in Chinese with English abstract).CAS 

Google Scholar 
Yinji, L. & Ratana, C. Governing in an uncertain time: The case of Sakura shrimp fishery, Japan. Marit. Stud. 20, 115–126 (2021).Article 

Google Scholar 
Isono, R. S., Kita, J. & Setoguma, T. Acute effects of kaolinite suspension on eggs and larvae of some marine teleosts. Comp. Biochem. Physiol. Part C 120, 449–455 (1998).CAS 
Article 

Google Scholar 
Aoki, S. & Oinuma, K. Distribution of clay minerals in surface sediments of Suruga Bay, central Japan. J. Geol. Soc. Jpn. 87(7), 429–438 (1981) (in Japanese with English abstract).Article 

Google Scholar 
Nasnodkar, M. R. & Ganapati, N. N. Clay mineralogy and chemistry of mudflat core sediments from Sharavathi and Gurupur estuaries: Source and processes. Indian J. Geo-Mar. Sci. 48(3), 379–388 (2019).
Google Scholar 
Capper, N. The effects of suspended sediment on the aquatic organisms Daphnia magna and Pimephales promelas. All Theses. 2. https://tigerprints.clemson.edu/all_theses/2 (2006).Boyd, M. B. et al. Disposal of dredge spoil, problem identification and assessment and research program development. Technical report H-72–8, U.S. army engineer waterways experiment station, CE, Vicksburg, Miss. (1972).McFarland, V. A. & Peddicord, R. K. Lethality of a suspended clay to a diverse selection of marine and estuarine macrofauna. Arch. Environ. Contam. Toxicol. 9, 733–741 (1980).CAS 
Article 

Google Scholar 
Arakawa, H. et al. The influence of suspended particles on larval development in the Manila clam Ruditapes philippinarum. Sci. Postp. 1, e00028. https://doi.org/10.14340/spp.2014.08A0002 (2014).Article 

Google Scholar 
Davis, H. C. Effects of turbidity-producing materials in sea water on eggs and larvae of the clam (Venus (Mercenaria) mercenaria). Biol. Bull. 118, 48–54 (1960).Article 

Google Scholar 
Tabata, A., Morinaga, T. & Arakawa, H. Influences of concentration, particle-size and kind of inorganic suspended matter on feed caught by Manila clam, Ruditapes philippinarum. La Mer 37, 163–171 (2000).CAS 

Google Scholar 
Annisa, Dwiatmoko, M. U., Saismana, U. & Maulanai, R. Characteristics of kaolin clay on Alluvial formation subdistrict mataraman based on physical properties and chemical properties. In MATEC Web of Conferences Vol. 280, 03009. https://doi.org/10.1051/matecconf/201928003009 (2019).Murray, H. H. Structure and composition of clay minerals and their physical and chemical properties. Dev. Clay Sci. 2, 7–31. https://doi.org/10.1016/S1572-4352(06)02002-2 (2006).Article 

Google Scholar 
Kumari, N. & Mohan, C. Basics of clay minerals and their characteristic properties. Clay Clay Miner. 1–29 (2021).Lively, J. S., Kaufman, Z. & Carpenter, E. J. Phytoplankton ecology of a barrier island estuary: Great South Bay, New York. Estuar. Coast. Shelf Sci. 16(1), 51–68 (1983).ADS 
Article 

Google Scholar 
Lloyd, D. S. Turbidity as a water quality standard for salmonid habitats in Alaska. N. Am. J. Fish. Manag. 7, 34–45 (1987).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshw. Biol. 28, 103–109 (1992).Article 

Google Scholar 
McCabe, G. D. & O’Brien, W. J. The effects of suspended silt on feeding and reproduction of Daphnia pulex. Am. Midl. Nat. 110, 324–337 (1983).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic Rotifers and Cladocerans. Ecology 71, 1741–1755 (1990).Article 

Google Scholar 
Loosanoff, V. L. Effects of turbidity on some larval and adult bivalves. Proc. Gulf. Carib. Fish. Inst. 14, 80–95 (1961).
Google Scholar 
Arruda, J. A., Marzolf, G. R. & Faulk, R. T. The role of suspended sediments in the nutrition of zooplankton in turbid reservoirs. Ecology 64, 1225–1235 (1983).Article 

Google Scholar 
Kathyayani, S. A., Muralidhar, M., Kumar, T. S. & Alavandi, S. V. Stress quantification in Penaeus vannamei exposed to varying levels of turbidity. J. Coast. Res. 86, 177–183 (2019).CAS 
Article 

Google Scholar 
Wilber, D. H. & Clarke, D. G. Biological effects of suspended sediments: A review of suspended sediment impacts on fish and shellfish with relation to dredging activities in estuaries. N. Am. J. Fish. Manag. 21, 855–875 (2001).Article 

Google Scholar 
Lin, H., Charmantier, G., Thuet, P. & Trilles, J. Effects of turbidity on survival, osmoregulation, and gill Na+-K+ ATPase in juvenile shrimp Penaeus japonicus. Mar. Ecol. Prog. Ser. 90, 31–37 (1992).ADS 
CAS 
Article 

Google Scholar 
Davis, H. C. & Hidu, H. Effects of turbidity-producing substances in sea water on eggs and larvae of three genera of bivalve mollusks. Veliger 11, 316–323 (1969).
Google Scholar 
Nimmo, D. R., Hamaker, T. L., Matthews, E. & Young, W. T. The long-term effects of suspended particulates on survival and reproduction of the mysid shrimp, Mysidopsis bahia, in the laboratory. In Proceedings of a Symposium on the Ecological Effects of Environmental Stress, New York, 413–422 (1979).Peddicord, R. & McFarland, V. Effects of suspended dredged material on the commercial crab, Cancer magister. In Proceedings of the Specialty Conference on Dredging and Its Environmental Effects, Mobile, Alabama, 633–644 (1976).Peddicord, R. K. Direct Effects of Suspended Sediments on Aquatic Organisms. Contaminants and Sediments. Volume 1. Fate and Transport, Case Studies, Modeling, Toxicity 501–536 (Ann Arbor Science Publishers, 1980).
Google Scholar 
Wakeman, T., Peddicord, R. & Sustar, J. Effects of suspended solids associated with dredging operations on estuarine organisms. In Ocean 75 conference, 431–436 (1975).Gebauer, P., Walter, I. & Anger, K. Effects of substratum and conspecific adults on the metamorphosis of Chasmagnathus granulata (Dana) (Decapoda: Grapsidae) megalopae. J. Exp. Mar. Biol. Ecol. 223, 185–198 (1998).Article 

Google Scholar 
Carvalho, L. & Calado, R. Trade-offs between timing of metamorphosis and grow out performance of a marine caridean shrimp juveniles and its relevance for aquaculture. Aquaculture 492, 97–102 (2018).Article 

Google Scholar 
Calado, R. et al. The physiological consequences of delaying metamorphosis in the marine ornamental shrimp Lysmata seticaudata and its implications for aquaculture. Aquaculture 546, 737391. https://doi.org/10.1016/j.aquaculture.2021.737391 (2022).Article 

Google Scholar 
Murphy, R. C. Factors affecting the distribution of the introduced bivalve, Mercenaria mercenaria, in a California lagoon—The importance of bioturbation. J. Mar. Res. 43, 673–692 (1985).Article 

Google Scholar 
Bricelj, V. M. & Malouf, R. E. Influence of algal and suspended sediment concentration on the feeding physiology of the hard clam Mercenaria mercenaria. Mar. Biol. 84, 155–165 (1984).Article 

Google Scholar 
Wenger, A. S., Jacob, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth, and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48 (2012).Article 

Google Scholar 
Robinson, W. E., Wehling, W. E. & Morse, M. P. The effect of suspended clay on feeding and digestive efficiency of the surf clam Spisula solidissima (Dillwyn). J. Exp. Mar. Biol. Ecol. 74, 1–12 (1984).CAS 
Article 

Google Scholar 
Turner, E. J. & Miller, D. C. Behavior and growth of Mercenaria mercenaria during simulated storm events. Mar. Biol. 111, 55–64 (1991).Article 

Google Scholar 
Grant, J. & Thorpe, B. Effects of suspended sediment on growth, respiration, and excretion of the soft-shelled clam (Mya arenaria). Can. J. Fish. Aquat. Sci. 48, 1285–1292 (1991).Article 

Google Scholar 
Gleason, R. A., Euliss, N. H., Hubbard, D. E. & Duffy, W. G. Effects of sediment load on emergence of aquatic invertebrates and plants from wetland soil egg and seed banks. Wetlands 23, 26–34 (2003).Article 

Google Scholar 
Jacek, R., Anna, S. & Miroslaw, S. The effect of lake sediment on the hatching success of Daphnia ephippial eggs. J. Limnol. 75, 597–605 (2016).
Google Scholar 
Newcombe, C. P. & McDonald, D. D. Effects of suspended sediment on aquatic ecosystems. N. Am. J. Fish. Manag. 11, 77–82 (1991).Article 

Google Scholar 
Chutter, F. M. The effects of silt and sand on the invertebrate fauna of streams and rivers. Hydrobiologia 34, 57–76 (1968).Article 

Google Scholar 
Hellawell, J. M. Biological indicators of freshwater pollution and environmental management. In Pollution Monitoring Series (ed. Melanby, K.) https://doi.org/10.1007/978-94-009-4315-5 (1986).Makita, M. & Kondo, M. Rearing of the larvae of Seigia Lucens (Hansen). Bull. Shizuoka Pref. Fish. Exp. Stn. 16, 97–105 (1982) (in Japanese).
Google Scholar  More

China economy-wide material flow identification: system boundary, processes, and materialsThe first step is to define an economy, i.e., the economic (rather than geographical) territory of a country in which the activities and transactions of producer and consumer units are resident. Additionally, the period is a total of thirty-one years, from 1990 to 2020, for the following reasons: (1) statistics before 1990 are of poor quality and are insufficient to allow us to conduct analyses; and (2) so far, statistics have just recently been updated to cover the year of 2020. Furthermore, the analytical framework (hereinafter referred to as China EW-MFA) is developed to explore material utilisation and its environmental consequences within China’s economy.The general structure of China EW-MFA is depicted in Fig. 1, which comprises seven processes. (1) Input of extracted resources: domestic natural resources are extracted from the environment to the economy through human-controlled means. (2) Output of domestic processed materials: after being processed by manufacturers, materials are released from the economy into the environment in the form of by-products and residues, which can be classified by their destinations (i.e., air, land, and water) and pathways (dissipative use and losses). (3) Input and (4) output by cross-border trade: by imports and exports, materials are transported between China’s economy and the economies of the rest of the world. (5) Input and (6) output of balancing items (BI): sometimes, materials identified in the output processes are not considered by inputs, which needs to be balanced. For example, the utilisation of fossil energy materials by combustion causes the emission of carbon dioxide (CO2) into the air, which is identified as system output, but requirements of oxygen (O2) as system input are not counted. (7) Additions to the system: within the economy, materials would have been added to the economy in the form of buildings, infrastructures, durable goods, and household appliances, which are referred to as the net additions to stock (NAS).Fig. 1The general structure of China EW-MFA. To note, white data cells can be obtained directly from official statistics, whereas grey cells are estimated.Full size imageThe last step is to specify the materials concerned in each process. Four types (in blue boxes in Fig. 1) of natural materials are extracted and input into the economy in China, i.e., harvested biomass (33 items), mined metal ores (28 items), quarried non-metallic minerals (155 items), and mined fossil energy materials (6 items in 3 classes). Materials (green boxes) released into the air are greenhouse gases (e.g., CO2, methane (CH4), dinitrogen oxide (N2O)), air pollutants (e.g., particulate matter 10 (PM10), black carbon (BC)), and toxic contaminants of mercury (Hg) in divalent, gaseous elemental, and particulate forms. Those released into the water are inorganic matters (of nitrogen (N), phosphorus (P), Arsenic (As), and four heavy metals of lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr)) and organic matters of cyanide, petroleum, and volatile phenol. Materials released into the land are waste disposal in uncontrolled landfills, which are illegal in China. Some materials are dissipated by application, for example, fertilisers, compost, sewage sludge being applied to agricultural land, and pesticides being used to cultivate crops. Some would be unintentionally dissipated from abrasion, corrosion, erosion, and leakages. Materials (in red boxes) are BI, which includes the input of O2 and output of water vapour in the fossil energy material combustion process, the input of O2 and output of water vapour and CO2 in the respiration process of human and cultivated livestock, input and output of water in imported and exported beverages, and the output of water from domestically extracting crops.There are some messages needed to be mentioned: (1) Material of water is not included since its flow volume is more substantial than others, which needs to be independently analysed; (2) Activities of foreign tourists, cross-border transfer of emissions through natural media, etc. are excluded. (3) To be clear, we refer to a data cell as a specific flow process of a specific substance in a specific year, e.g., the number of cereals domestically extracted in 2020.Data acquisition: sources and collectionBased on our China EW-MFA, we first analyse accessibility, reliability, completeness, rules of redistribution, etc., for each data source (yellow boxes in Fig. 1), including China national database, China rural statistical yearbooks, USGS mineral yearbooks, etc. The complete list of data sources and descriptions are presented in Table 1. Then, we store the originally retrieved data source files in a semi- or unstructured format (e.g., CSV, PDF). Next, we manually collect these statistics and reorganise them according to China EW-MFA material types and processes. However, only a tiny part of retrieved statistics can be applied directly, as specified in black colour in Fig. 1.Table 1 Data sources and descriptions.Full size tableData compilation: parameter localisation and data estimationA few inconsistencies in statistics were noticed, which would result in data incompleteness. For example, the domestic extraction of vegetables has been accounted for and published since 1995, before which statistics are unavailable. The domestically harvested timber has been measured in the volume unit of cubic metres, which needs to be converted into the mass unit via density conversion factor. Therefore, acquired statistics have to be estimated, which are specified in grey colour in Fig. 1. The following section elaborates on each data cell’s estimation methods, localised parameters, references, etc. In our uploaded data files, the original statistics, data sources, and compilation methods (using formulas) are all implemented, as explained in the Data Records Section.

The input of natural resources by domestic extraction

Vegetables in crops: Statistics of vegetable production (WVegetables)16 during 1990–1994 are unavailable, which is estimated based on the relationship between the production yield (PYield) and areas (AVegetables), as shown in Eq. 1. Here, PYield is assumed to remain constant at 27.04 thousand tonnes per thousand hectares from 1990 to 1995, derived by dividing vegetable production (257,267 thousand tonnes) by areas (9,515 thousand hectares) in 1995.$${W}_{Vegetables}={P}_{Yield}times {A}_{Vegetables}$$
(1)

Nuts in crops: One of them is chestnuts. The chestnut production in 2020 is unavailable, which is assumed to be the same as in 2019.

Crop residues in biomass residues: They are referred to as that harvested production of crops that do not reach the market to be sold but are instead employed as raw materials for commercial purposes such as energy generation and livestock husbandry. This number (Wcrop residues) can be calculated by first determining the number of crop residues available from primary crop production (Wcrop) and the harvest factor (Pharvest factor), and then using the recovery rate (Precovery rate) to determine the number of crop residues used by the economy, as shown in Eq. 2. These parameters have been localized by previous studies17,18, which are adopted in this study, i.e., wheat (1.1 for Pharvest factor and 0.463 for Precovery rate), maize (1.2, 0.463), rice (0.9, 0.463), sugar cane (0.5, 0.9), beetroots (0.7, 0.9), tuber (0.5, 0.463), pulse (1.2, 0.7), cotton (3.4, 0.463), fibre crops (1.8, 0.463), silkworm cocoons (1.8, 0.463), and oil-bearing crops (1.8, 0.463).$${W}_{cropresidues}={W}_{crop}times {P}_{harvestfactor}times {P}_{recoveryrate}$$
(2)

Roughage of grazed biomass and fodder crops in biomass residues: In China, the grazed biomass for roughage includes annual forage and perennial forage, whereas fodder crops comprise straw feed, processed straw feed, and all other fodder crops. However, information19 on grazed biomass production is only accessible from 2006 to 2018, whereas fodder crop statistics are only available from 2015 to 2017. Equation 3 and Eq. 4 can be used to estimate unavailable statistics. To note, we assume that China’s domestic roughage supply structure has remained unaltered, which has two meanings. The proportion of total domestic roughage production (WDomestic production) in requirement (WRoughage requirement) has remained constant, while the proportion (PSupply fraction) of grazed biomass and fodder crop in domestic roughage production has been unchanged. The requirement (WRoughage requirement) is determined by the quantity of livestock (QLivestock) and their annual feeding amount (PAnnual intake). PAnnual intake (in tonnes per head per year) has been localised for each type of livestock4, with 4.5 for live cattle and buffaloes, 0.5 for sheep and goats, 3.7 for horses, and 2.2 for mules and asses.$${W}_{Roughagerequirement}={Q}_{Livestock}times {P}_{Annualintake}$$
(3)
$${W}_{Domesticproduction}={W}_{Roughagerequirement}times {P}_{Supplyfraction}$$
(4)

Timber in wood: As illustrated in Eq. 5, wood production16 is reported in volume units of cubic metres (VTimber), which need to be converted into mass units (WTimber) via density (PDensity). The parameter PDensity is assumed to be 0.58 tonnes per cubic metre, calculated by averaging 0.52 for coniferous types and 0.64 for non-coniferous ones4.$${W}_{Timber}={V}_{Timber}times {P}_{Density}$$
(5)

Non-ferrous metals in metal ores: Non-ferrous metal statistics are derived from two sources. China statistics20 are measured in gross ore (WMetal ores in gross ore) but are only available from 1999 to 2017, whereas the USGS statistics21 cover the period of 1990 to 2020 but they are measured in metal or concentrate content (WMetal ores in other units). Therefore, USGS statistics need to be converted with an empirical unit conversion factor (PUnit conversion factor) before being applied to estimate unavailable statistics reported by China, as shown in Eq. 6. Conversion factors are localised for each non-ferrous metal in each year from 2000 to 2017 by using USGS statistics divided by China statistics and then averaged after removing the highest value and the lowest value (i.e., trimmed mean). This factor could capture the general relationship between statistics from two separate sources, which can be used in other long time-series studies on resource management on a particular element in China.$${W}_{Metaloresingrossore}={W}_{Metaloresinotherunits}/{P}_{Unitconversionfactor}$$
(6)

Non-metallic minerals: The official China-specific information on non-metallic mineral domestic production is available between 1999 and 201720, the rest of which could be estimated from USGS statistics (1990–2020)21. Also, two differences in reporting standards are observed resulting from the material coverages and reporting units. China statistics contain eighty-eight materials in mineral ores, whereas the USGS only includes twenty in the concentrate unit. Therefore, a conversion factor is developed in this estimation, as shown in Eq. 7. This conversion factor is applied to the total amount of non-metallic mineral production, which is assumed to have been constant from 1990 to 1999 at 11.38% (1999) and 12.56% (2017) from 2017 to 2020.$${W}_{Mineralsingrossore}={W}_{Mineralsinotherunits}/{P}_{Conversionfactor}$$
(7)

Coal in fossil energy materials: Coal, mined in China, includes raw coal, peat, stone coal, and oil shale. Except for raw coal, statistics for the rest are only available from 1999 to 201720. The unavailable data (WOther coals) is estimated using Eq. 8 under the assumption that the structure of the coal supply in China barely changes. That is, the proportion (PSupply fraction) of peat, stone coal, and oil shale in raw coal production (WRaw coal) remains constant, so the 1999 proportion is applied to all years before that (earlier years of 1990–1998), while the 2017 proportion is used to the recent years between 2018 and 2020. For example, PSupply fraction for oil shale production was assumed to be 0.014% during 1990–1999, calculated by dividing raw coal production (1,250,000) by oil shale production (179) in 1999. PSupply fraction in the earlier and the recent years are 0.007% and 0.001% for peat, 0.203% and 0.031% for stone coal, and 0.014% and 0.067% for oil shale.

$${W}_{Othercoals}={W}_{Rawcoal}/{P}_{Supplyfraction}$$
(8)

The output of processed materials by release

Materials released into the air: In China, thirteen materials are released into the air, as shown in Fig. 1. The emission of sulphur dioxide (SO2) is reported in China environmental statistical yearbooks22,23, while the rest is specified in the EDGAR24. However, in EDGAR, statistics for recent years have not yet been updated, which are estimated with the value in the most recent year in our database. For example, nitrous oxide (NOx) records are only available for the years prior to 2016, with 26,365 thousand tonnes in 2015 and 26,837 in 2014. As a result of the observed decreasing trend in NOx emissions, NOx emission data for 2016–2020 is estimated to be 26,000 thousand tonnes. This estimate may be subjective due to constraints, but it would be aligned with European statistics, allowing for international comparisons. Data can be updated after the EDGAR statistics have been updated.

Materials released into the water: Ten principal materials have been found in China wastewater (both industrial and municipal) that are nitrogen (N), phosphorus (P), organic pollutants of petroleum, volatile phenol and cyanide, heavy metals of mercury (Hg), lead (Pb), cadmium (C·d), and the hexavalent chromium (Cr6+), and arsenic (As). Many statistics22,23 have been of poor quality (e.g., inconsistent material coverages between years). Given that the statistics of pollutants in industrial wastewater cover more periods and contain fewer abnormal observations, the total material emissions can be approximated from those of industrial wastewater. Equations 9 and 10 show the estimation processes. The materials in industrial wastewater (WIndustrial materials) are first identified using material mass concentration (PConcentration) and the weight of industrial wastewater (WIndustrial wastewater), and then the materials in total wastewater (WTotal materials) are identified using the proportion (PContribution) of materials in industrial wastewaters (WIndustrial materials) to the total. The assumption is that PConcentration and PContribution change gradually between years, which enables to use linear interpolation method to estimate unavailable parameters. Consider cyanide: its PConcentration was 23.61 (1‰ ppm) in 2005 and 37.31 in 2002, which was assumed to be 28.18 in 2004 and 32.74 in 2003. PConcentration was assumed to be 100% throughout the years for cyanide because all cyanide emissions in China are driven by industrial wastewater discharges. Later, the total material emissions can be derived by dividing the industrial wastewater mass by PConcentration.$${W}_{Industrialmaterials}={W}_{Industrialwastewater}times {P}_{Concentration}$$
(9)
$${W}_{Totalmaterials}={W}_{Industrialmaterials},/,{P}_{Contribution}$$
(10)

Materials released to the land: This is zero because uncontrolled landfills are illegal in China.

Materials dissipated by organic fertiliser use: In China, manure is the primary organic fertiliser, which is excreted by pigs, dairy cows, calves, sheep, horses, asses, mules, camels, chickens, and other animals. As shown in Eq. 11, the manure production (WManure) is estimated through the amounts of raised livestock (QLivestock, heads), the weight of daily manure production (PManure production, kilograms per head per day), the number of days they are raised (PFeeding period, in days per year), and the moisture content of their manure (PDry matter, %) for each type of animal. These parameters are region-specific, which have been localised by Chinese scholars25,26,27 and listed in Table 2.$${W}_{Manure}={Q}_{Livestock}times {P}_{Manureproduction}times {P}_{Feedingperiod}times {P}_{Drymatter}$$
(11)
Table 2 Localised parameters for animal manure production.Full size table

Materials dissipated by mineral fertiliser use: The mineral fertilisers used in China are four types, i.e., nitrogen (N), phosphorus (P), potash (K), and compound. Their usage (WFertiliser usage) is measured in nutrient mass (WNutrient materials), which needs to be converted into the gross mass by dividing their nutrient content (PNutrient content). Equation 12 shows the estimation. This parameter of PNutrient content is localised by the Ministry of Agriculture and Rural Affairs of China28 as 29%, 22%, 35%, and 44% for N- bearing, P- bearing, K-bearing, and compound fertilisers, respectively.$${W}_{Fertiliserusage}={W}_{Nutrientmaterials}/{P}_{Nutrientcontent}$$
(12)

Materials dissipated by sewage sludge: Sewage sludge is the residue generated by municipal wastewater treatment. As demonstrated in Eq. 13, its dissipative use (Wss, dissipation) is the untreated amount of production (Wss, production), represented by the parameter of Pss, dissipation rate. Sewage sludge production (Wss, production) statistics are only available for the years 2006–202029, and data for the remaining years can be estimated using Eq. 14 and Eq. 15. In Eq. 14, Pss, production rate represents the relationship between sewage sludge production (Wss, production, 2006–2020) and wastewater treatment (Www, treatment, 2002–2020), and in Eq. 15, Pww, treatment efficiency represents the relationship between the quantity of treated wastewater (Www, treatment, 2002–2020) and the treatment capacity (Www, treatment capacity, 1990–2020). In this estimation, three assumptions are made. The first is to estimate Www, treatment, Pww, treatment efficiency is assumed to be unchanged at 63% during 1990–2001, given it has been increasing from 63% in 2002 to ~80% in recent years. The second is that, in order to estimate Wss, production, Pss, production rate is assumed to be unchanged at 3.5 between 1990 and 2005, suggesting 3.5 tonnes of sewage sludge are generated by processing 10,000 cubic metres of wastewater. This assumption is determined by that Pss, production rate is approximately 3.5 during 2006–2010 while declines sharply and stabilises at around two during 2011–2020. The last is, to estimate the Wss,dissipation, Pss,dissipation rate is assumed to be 5% between 1990 and 2005, given it has been around 5% during 2006–2020.$${W}_{ss,dissipation}={W}_{ss,production}times {P}_{ss,dissipationrate}$$
(13)
$${W}_{ss,production}={W}_{ww,treatment}times {P}_{ss,productionrate}$$
(14)
$${W}_{ww,treatment}={W}_{ww,treatmentcapacity}times {P}_{ww,treatmentefficiency}$$
(15)

Materials dissipated by composting: Composting is a natural process that uses microbes to turn organic materials into other products, which are then used for fertilising and entering the environment. In China, composting has been used to treat two materials: feces and municipal waste, whose quantities (WComposting) were only available from 2003 to 201029. The unavailable data can be estimated using Eq. 16. The dry weight of materials treated by composting (WComposting) is proportionally related to the fresh weight of all treated materials (WTotal), the proportion treated by composting (PComposting rate), and the dry content (PDry matter). Considering that China’s composting capacity has been declining since 2001 due to the implementation of waste incineration power generation technologies30, Pcomposting rate is assumed to be the same as it was in 2003 (9.5%) between 1990 and 2002, and 1.5% in 2010 between 2011 and 2020. The parameter of PDry matter is 50%4.$${W}_{Composting}={W}_{Total}times {P}_{Compostingrate}times {P}_{Drymatter}$$
(16)

The input and output by cross-border trade. Statistics of imports and exports have been gathered since 1962 and stored in the UN Comtrade database31. However, the data quality issue of outliers, and missing values, especially in weight, is reportedly identified. In our previous work, we addressed these issues, and an improved database32 is provided. Details about our estimation methods can be found in publications33,34,35. As UN Comtrade lists 5,039 different commodity types (in 6-digit HS0 commodity code), yet only 18 material types are specified in the China EW-MFA, UN Comtrade statistics need to be aligned to the China EW-MFA framework. Therefore, we compared each commodity and each material type between them and established a correspondence table to map UN Comtrade commodity types onto our EW-MFA material types. For example, non-ferrous metal materials of China EW-MFA include commodities, such as copper ores and concentrates (260300 HS0 code), silver powder (710610), manganese, articles thereof, and waste or scrap (811100), etc., whereas biomass residues include cereal straw and husks (121300), lucerne meal and pellets (121410), and other fodder and forage products (121410). This correspondence table between HS0 and EW-MFA classification for imports and exports is provided in Supplementary File 1.

The input of balancing items

O2 required for combustion: In BI, requirements for materials can be abstracted as equalling exogenous demands minus intrinsic supplies (Eq. 17). Three parts (two demands and one supply) are considered for O2 requirements by the combustion process: (1) demanding exogenous oxygen to oxidise elements (e.g., carbon, sulphur, nitrogen, etc., except for hydrogen) released into the air, (2) demanding exogenous oxygen to oxidise the hydrogen embedded in fossil energy materials, and (3) providing intrinsic oxygen embedded in fossil energy materials. The first part can be estimated via Eq. 18 by multiplying air emissions (WEmissions) of CO2, N2O, NOx, CO, and SO2 by their oxygen content (POxygen content). For the second (Eq. 19), the oxygen demand is estimated based on the principle of mass balance by converting the hydrogen amount of domestically utilised fossil energy materials (WFossil fuel materials × PHydrogen content) via molar mass conversion factor (PMass conversion factor). PMass conversion factor equals 7.92, derived by the molar mass of one oxygen (16 g/mol) divided by that of two hydrogen atoms (2 × 1.01 g/mol). The last is the intrinsic supplies from fossil fuel materials, which is identified via Eq. 20 by multiplying the domestically utilised amount of fossil fuel materials (WFossil fuel materials) by their oxygen content (POxygen content). The parameters in this estimation are presented in Table 3. As a footnote here, the domestically utilised amount is referred to as the domestic material consumption (DMC), which equals domestic extraction (DE) plus imports (IM) and minus exports (EX).$${W}_{Requirements}={W}_{Demands}-{W}_{Supplies}$$
(17)
$${W}_{Demands}={W}_{Emissions}times {P}_{Oxygencontent}$$
(18)
$${W}_{Demands}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(19)
$${W}_{Supplies}={W}_{Fossilfuelmaterials}times {P}_{Oxygencontent}$$
(20)
Table 3 Parameters related to combustion processes4.Full size table

O2 required for respiration: O2 is required by the metabolic activities of living organisms, the majority of which are humans and livestock. Bacteria are another sort of organism, which are not included in this estimation because their O2 requirements are too small to be quantified. The respiration-required O2 is related to the total quantity (QOrganisms) and their respiration activity by organism types, as shown in Eq. 21. The respiration activity is represented by the respiration requirement coefficient (PRespiration requirement coefficient), which is the average quantity of O2 that each organism utilises to maintain the metabolic activity, as listed in Table 4.$${W}_{Demands}={Q}_{Organisms}times {P}_{Respirationrequirementcoefficient}$$
(21)
Table 4 Parameters related to respiration processes4.Full size table

Water required for the domestic production of exported beverages: The exported beverages are produced domestically using domestically extracted materials, especially a large amount of water. The weight of water is considered in the output by cross-border trade but is not included in the domestic extraction input. The resulted imbalance can be identified by specifying the water weight in beverages, i.e., multiplying the traded beverage weight (WMaterials) by a parameter of the water content (PWater content), as given in Eq. 22. Fruit and vegetable juices (2009 in HS0 code) and beverages (code 22) are covered in the improved UN Comtrade database32, with PWater content of 85% for the first and 90% for the latter4.

$${W}_{Water}={W}_{Materials}times {P}_{Watercontent}$$
(22)

The output of balancing items.

Water vapour from combustion: Water vapour emissions by domestically combusting fossil fuel materials are contributed by two paths. The direct evaporation of embedded water is the first path (Eq. 23), which can be derived by multiplying the DMC of fossil fuel materials by their moisture content (PMoisture content). The PMoisture content for each type of fossil fuel material is listed in Table 3. The other is the generation of water vapour during hydrogen oxidation, which can be calculated by converting the oxidised weight of hydrogen to the water weight using the molar mass conversion factor (PMass conversion factor), as given in Eq. 24. PMass conversion factor equals 8.92 by dividing the molar mass of water (18.02 g/mol) by that of two hydrogen atoms (2 × 1.01 g/mol).$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Moisturecontent}$$
(23)
$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(24)

Water vapour and CO2 from respiration: Respiration activities of organisms will produce water vapour and CO2, whose estimation is similar to that of O2 requirements. As shown in Eq. 25, the respiration-caused gas emissions are related to the number of organisms (QOrganisms) and the respiration activity by organism types. The latter is represented by the parameter of respiration emission coefficient (PRespiration emission coefficient), which is specified in Table 4 for water vapour and CO2 for each type of organism.$${W}_{Emissions}={Q}_{Organisms}times {P}_{Respirationemissioncoefficient}$$
(25)

Water from imported beverages: The estimation approach is the same as water by the domestic production of exported beverages, as described in Eq. 16.

Water in biomass products: Usually, the input of biomass products by domestic extraction16 has been measured in fresh weight, but their corresponding output29 by sewage sludge, composting, etc., are in dry weight, leading to an imbalance in water weight. The water weight in biomass products is calculated by multiplying their domestic extraction amount in fresh weight (WBiomass) by a parameter of moisture content at harvest (PMoisture content), as shown in Eq. 26. The values of PMoisture content by biomass products are presented in Table 5.Table 5 The moisture content at harvest for each biomass product4.Full size table

$${W}_{Water}={W}_{Biomass}times {P}_{Moisturecontent}$$
(26)
Material flow quantificationThe above attempts have quantified material inputs and outputs by flows and presented a detailed profile of material utilisation for each material in China’s economy. In order to depict the economy in a more general way, EW-MFA indicators are assessed by aggregating flows by materials or periods as below.

Domestic extraction (DE): is referred to as natural materials that are extracted from the domestic environment and are used in the domestic economy, i.e., the total input of natural materials by extraction.

Domestic processed output (DPO): is referred to as materials that are released to the domestic environment after being processed in the domestic economy, i.e., the total output of processed materials by release.

Import (IM): is referred to as all goods (in the form of raw materials, semi-finished materials, and final products) that originated from other economies and are further used in the domestic economy. It is calculated as the sum of all imported goods.

Export (EX): is referred to as all goods that originated from the domestic economy and are transported to other economies to be used. It is calculated as the sum of all exported goods.

Domestic material input (DMI): is referred to as materials that originated from the domestic environment by extraction and other economies and are available (to be used or to be stored) for the domestic economy. It is calculated as the sum of DE plus IM, as shown in Eq. 27.$$DMI=DE+IM$$
(27)

Domestic material consumption (DMC): is referred to as materials that are directly used in the domestic economy after parts of them are exported to other economies. It is calculated as the difference between DMI and EX.

Physical trade balance (PTB): is referred to as a surplus or deficit of materials for the domestic economy. It is calculated as the difference between IM and EX.

Net additions to stock (NAS): is referred to as materials that remain in the domestic economy. It is calculated by taking BI items into account, as shown in Eq. 28.

$$NAS=DMC+B{I}_{in}-DPO-B{I}_{out}$$
(28) More