Philippa Kaur

Deli, J., Matus, Z. & Tóth, G. Carotenoid composition in the fruits of asparagus officinalis. J. Agric. Food Chem. 48, 2793–2796 (2000).CAS 
PubMed 

Google Scholar 
Howard, L. R., Talcott, S. T., Brenes, C. H. & Villalon, B. Changes in phytochemical and antioxidant activity of selected pepper cultivars (Capsicum species) as influenced by maturity. J. Agric. Food Chem. 48, 1713–1720 (2000).CAS 
PubMed 

Google Scholar 
Odgerel, B. & Tserendulam, D. Effect of Chlorella as a biofertilizer on germination of wheat and barley grains. P. Mongolian Acad. Sci. 56(4), 26–31 (2016).
Google Scholar 
Sun, R. B., Guo, X. S., Wang, D. Z. & Chua, H. Y. Effects of long-term application of chemical and organic fertilizers on the abundance of microbial communities involved in the nitrogen cycle. Appl. Soil Ecol. 95(6), 171–178 (2015).
Google Scholar 
Yu, Y. Q., Luo, Z. B., Fu, H. & Jin, Y. Effect of balanced nutrient fertilizer: A case study in Pinggu District, Beijing China. Sci. Total Environ. 754, 1–8 (2021).
Google Scholar 
Ahmad, P. et al. Role of transgenic plants in agriculture and biopharming. Biotech. Adv. 30, 524–540 (2012).CAS 

Google Scholar 
Sherlock, R. & Morrey, J. D. Ethical Issues in Biotechnology (Rowman and Littlefield. Publishers, Inc., 2002).
Google Scholar 
Schiavon, M., Ertani, A. & Nardi, S. Effects of an alfalfa protein hydrolysate on the gene expression and activity of enzymes of TCA cycle and N metabolism in Zea mays L. J. Agric. Food Chem. 56, 11800–11808 (2008).CAS 
PubMed 

Google Scholar 
Muscolo, A., Sidari, M. & Nardi, S. Humic substance: relationship between structure and activity. Deeper information suggests univocal findings. J. Geochem. Explor. 129, 57–63 (2013).CAS 

Google Scholar 
Nardi, S., Carletti, P., Pizzeghello, D., Muscolo, A. Biological activities of humic substances. In Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems. PART I. Fundamentals and Impact of Mineral-Organic-Biota Interactions on the Formation, Transformation, Turnover, and Storage of Natural Nonliving Organic Matter (NOM). (Ed. Senesi, N., Xing, B., Huang, P.M.) 301–335 (John Wiley and Sons, Hoboken, 2009).Ertani, A., Nardi, S. & Altissimo, A. Review: long-term research activity on the biostimulant properties of natural origin compounds. Acta Hort. 1009, 181–188 (2013).
Google Scholar 
Ertani, A. et al. Biostimulant activity of two protein hydrolysates on the growth and nitrogen metabolism in maize seedlings. J. Plant Nutr. Soil Sci. 172, 237–244 (2009).CAS 

Google Scholar 
Vaccaro, S. et al. Effect of a compost and its water-soluble fractions on key enzymes of nitrogen metabolism in maize seedlings. J. Agric. Food Chem. 57, 11267–11276 (2009).CAS 
PubMed 

Google Scholar 
Azcona, I. et al. Growth and development of pepper are affected by humic substances derived from composted sludge. J. Plant Nutr. Soil Sci. 174, 916–924 (2011).CAS 

Google Scholar 
Schiavon, M. et al. High molecular size humic substances enhance phenylpropanoid metabolism in maize (Zea mays L.). J. Chem. Ecol. 36, 662–669 (2010).CAS 
PubMed 

Google Scholar 
Ertani, A., Schiavon, M., Muscolo, A. & Nardi, S. Alfalfa plant-derived biostimulant stimulates short-term growth of salt stressed Zea mays L. plants. Plant Soil 364, 145–158 (2013).CAS 

Google Scholar 
Pascual, I., Azcona, I., Morales, F., Aguirreolea, J. & Sanchez-Diaz, M. Growth, yield and physiology of verticillium-inoculated pepper plants treated with ATAD and composted sewage sludge. Plant Soil 319, 291–306 (2009).CAS 

Google Scholar 
Pascual, I. et al. Growth, yield and fruit quality of pepper plants amended with two sanitized sewage sludges. J. Agric. Food Chem. 58, 6951–6959 (2010).CAS 
PubMed 

Google Scholar 
Kim, M. J., Shim, C. K., Kim, Y. K., Ko, B. G. & Kim, B. H. Effect of Biostimulator Chlorella fusca on improving growth and qualities of Chinese Chives and Spinach in organic farm. Plant Pathol. J. 34(6), 567–574 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pulz, O. & Gross, W. Valuable products from biotechnology of microalgae. Appl. Microbiol. Biot. 65, 635–648 (2004).CAS 

Google Scholar 
Kim, S. J., Ko, E. J., Hong, J. K. & Jeun, Y. C. Ultrastructures of Colletotrichum orbiculare in cucumber leaves expressing systemic acquired resistance mediated by Chlorella fusca. Plant Pathol. J. 34(2), 113–120 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Faheed, F. A. & Abd-El Fattah, Z. Effect of Chlorella vulgaris as bio-fertilizer on growth parameters and metabolic aspects of Lettuce Plant. J. Agri. Soc. Sci. 4, 165–169 (2008).
Google Scholar 
Agwa, O. K., Ogugbue, C. J. & Williams, E. E. Field evidence of Chlorella vulgaris potentials as a biofertilizer for Hibiscus esculentus. Int. J. Agric. Res. 12(4), 181–189 (2017).CAS 

Google Scholar 
Ördög, V. et al. Screening microalgae for some potentially useful agricultural and pharmaceutical secondary metabolites. J. Appl. Physicol. 16, 309–401 (2004).
Google Scholar 
Stirk, W. A., Novák, O., Strnad, M. & van Staden, J. Cytokinins in macroalgae. Plant Growth Regul. 41, 13–24 (2003).CAS 

Google Scholar 
Kholssi, R., Marks, E. A. N., Montero, J. M. O., Debdoubi, A. & Rad, C. Biofertilizing effect of Chlorella sorokiniana suspensions on wheat growth. J. Plant Growth Regul. 38, 644–649 (2019).CAS 

Google Scholar 
Stirk, W. A., Ördög, V., Van Staden, J. & Jäger, K. Cytokinin-and auxin-like activity in Cyanophyta and microalgae. J. Appl. Phycol. 14, 215–221 (2002).CAS 

Google Scholar 
Park, E. R., Jo, J. O., Kim, S. M., Lee, M. Y. & Kim, K. S. Volatile flavor component of leek (Allium tuberosum Rotter). J. Korean Soc. Food Sci. Nutr. 27, 563–567 (1998) ((in Korean)).CAS 

Google Scholar 
Jin, H. et al. Ultrahigh-cell-density heterotrophic cultivation of the unicellular green microalga Scenedesmus acuminatus and application of the cells to photoautotrophic culture enhance biomass and lipid production. Biotechnol. Bioeng. 117, 96–108 (2020).CAS 
PubMed 

Google Scholar 
Kim, M. J., Shim, C. K., Kim, Y. K., Hong, S. J. & Kim, S. C. Isolation and morphological identification of fresh water green algae from organic farming habitats in Korea. Korean J. Org. Agric. 22, 743–760 (2014).
Google Scholar 
Li, L., Tian, S. L., Jiang, J. & Wang, Y. Regulation of nitric oxide to Capsicum under lower light intensities. S. Afr. J. Bot. 132, 268–276 (2020).CAS 

Google Scholar 
Cho, Y. Y., Oh, S. B., Oh, M. M. & Son, J. E. Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Sci. Hortic-Amst. 111, 330–334 (2007).
Google Scholar 
Oster, U., Tanaka, R., Tanaka, A. & Rudiger, W. Cloning and functional expression of gene encoding the key enzyme for chlorophyll b biosynthesis (CAO) from Arabidopsis thaliana. Plant J. 21(3), 305–310 (2000).CAS 
PubMed 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72(1–2), 248–254 (1976).CAS 
PubMed 

Google Scholar  More

Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710 (2000).
Google Scholar 
Turbelin, A. J., Malamud, B. D. & Francis, R. A. Mapping the global state of invasive alien species: Patterns of invasion and policy responses. Glob. Ecol. Biogeogr. 26, 78–92 (2017).
Google Scholar 
Jackson, M. C. Interactions among multiple invasive animals. Ecology 96, 2035–2041 (2015).CAS 
PubMed 

Google Scholar 
Rodriguez, L. F. Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biol. Invasions 8, 927–939 (2006).
Google Scholar 
Duenas, M. A. et al. The role played by invasive species in interactions with endangered and threatened species in the United States: A systematic review. Biodivers. Conserv. 27, 3171–3183 (2018).
Google Scholar 
Weidenhamer, J. D. & Callaway, R. M. Direct and indirect effects of invasive plants on soil chemistry and ecosystem function. J. Chem. Ecol. 36, 59–69 (2010).CAS 
PubMed 

Google Scholar 
Bajwa, A. A., Chauhan, B. S., Farooq, M., Shabbir, A. & Adkins, S. W. What do we really know about alien plant invasion? A review of the invasion mechanism of one of the world’s worst weeds. Planta 244, 39–57 (2016).CAS 
PubMed 

Google Scholar 
Tallamy, D. W., Narango, D. L. & Mitchell, A. B. Do non-native plants contribute to insect declines?. Ecol. Entomol. 46, 729–742. https://doi.org/10.1111/een.12973 (2021).Article 

Google Scholar 
Bezemer, T. M., Harvey, J. A. & Cronin, J. T. Response of native insect communities to invasive plants. Annu. Rev. Entomol. 59, 119 (2014).CAS 
PubMed 

Google Scholar 
Cheng, F. & Cheng, Z. Research progress on the use of plant allelopathy in agriculture and the physiological and ecological mechanisms of allelopathy. Front. Plant Sci. 6, 1020 (2015).PubMed 
PubMed Central 

Google Scholar 
Kalisz, S., Kivlin, S. N. & Bialic-Murphy, L. Allelopathy is pervasive in invasive plants. Biol. Invasions 23, 367–371 (2021).
Google Scholar 
Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communities and ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Change Biol. 18, 1725–1737 (2012).ADS 

Google Scholar 
Zhang, P., Li, B., Wu, J. & Hu, S. Invasive plants differentially affect soil biota through litter and rhizosphere pathways: A meta-analysis. Ecol. Lett. 22, 200–210 (2019).ADS 
PubMed 

Google Scholar 
Dudareva, N., Klempien, A., Muhlemann, J. K. & Kaplan, I. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytol. 198, 16–32 (2013).CAS 
PubMed 

Google Scholar 
Clavijo McCormick, A. Can plant–natural enemy communication withstand disruption by biotic and abiotic factors?. Ecol. Evol. 6, 8569–8582 (2016).PubMed 
PubMed Central 

Google Scholar 
Bruce, T. J., Wadhams, L. J. & Woodcock, C. M. Insect host location: A volatile situation. Trends Plant Sci. 10, 269–274 (2005).CAS 
PubMed 

Google Scholar 
Clavijo McCormick, A., Unsicker, S. B. & Gershenzon, J. The specificity of herbivore-induced plant volatiles in attracting herbivore enemies. Trends Plant Sci. 17, 303–310 (2012).CAS 
PubMed 

Google Scholar 
Baldwin, I. T., Halitschke, R., Paschold, A., Von Dahl, C. C. & Preston, C. A. Volatile signaling in plant–plant interactions: “Talking trees” in the genomics era. Science 311, 812–815 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Kegge, W. & Pierik, R. Biogenic volatile organic compounds and plant competition. Trends Plant Sci. 15, 126–132 (2010).CAS 
PubMed 

Google Scholar 
Effah, E., Holopainen, J. K. & Clavijo McCormick, A. Potential roles of volatile organic compounds in plant competition. Perspect. Plant Ecol. Evol. Syst. 38, 58–63 (2019).
Google Scholar 
Kigathi, R. N., Weisser, W. W., Reichelt, M., Gershenzon, J. & Unsicker, S. B. Plant volatile emission depends on the species composition of the neighboring plant community. BMC Plant Biol. 19, 1–17 (2019).
Google Scholar 
Karban, R., Wetzel, W. C., Shiojiri, K., Pezzola, E. & Blande, J. D. Geographic dialects in volatile communication between sagebrush individuals. Ecology 97, 2917–2924 (2016).PubMed 

Google Scholar 
Wheeler, G. S., David, A. S. & Lake, E. C. Volatile chemistry, not phylogeny, predicts host range of a biological control agent of Old-World climbing fern. Biol. Control 159, 104636 (2021).CAS 

Google Scholar 
Li, N. et al. Manipulating two olfactory cues causes a biological control beetle to shift to non-target plant species. J. Ecol. 105, 1534–1546 (2017).CAS 

Google Scholar 
Buddenhagen, C. E. Broom Control Monitoring at Tongariro National Park (Department of Conservation Wellington, 2000).
Google Scholar 
Hayes, L. et al. Biocontrol of Weeds: Achievements to Date and Future Outlook. Ecosystem services in New Zealand-conditions and trends Vol. 2, 375–385 (Manaaki Whenua Press, 2013).
Google Scholar 
Bagnall, A. Heather at Tongariro. A study of a weed introduction. Tussock Grasslands Mt. Lands Inst. Rev 41, 17–21 (1982).
Google Scholar 
Chapman, H. M. & Bannister, P. The spread of heather, Calluna vulgaris (L.) Hull, into indigenous plant communities of Tongariro National Park. N. Z. J. Ecol. 7–16 (1990).Effah, E. et al. Effects of two invasive weeds on arthropod community structure on the Central Plateau of New Zealand. Plants 9, 919 (2020).CAS 
PubMed Central 

Google Scholar 
Keesing, V. F. Impacts of invasion on community structure: habitat and invertebrate assemblage responses to Calluna vulgaris (L.) Hull invasion, in Tongariro National Park, New Zealand, Massey University Palmerston North, New Zealand, (1995).Peterson, P. G., Fowler, S. V. & Barrett, P. Is the poor establishment and performance of heather beetle in Tongariro National Park due to the impact of parasitoids predators or disease. N. Z. Plant Prot. 57, 89–93. https://doi.org/10.30843/nzpp.2004.57.6977 (2004).Article 

Google Scholar 
Ajpark. The brands and the bees: trade marks and the mānuka challenge for honey businesses, https://www.ajpark.com/insights/the-brands-and-the-bees-trade-marks-and-the-manuka-challenge-for-honey-businesses/#:~:text=M%C4%81nuka%20is%20a%20taonga%20species,may%20be%20offensive%20to%20M%C4%81ori (2021).Effah, E. et al. Seasonal and environmental variation in volatile emissions of the New Zealand native plant Leptospermum scoparium in weed-invaded and non-invaded sites. Sci. Rep. 10, 1–11 (2020).
Google Scholar 
Effah, E., Min Tun, K., Rangiwananga, N. & Clavijo McCormick, A. Mānuka clones differ in their volatile profiles: Potential implications for plant defence, pollinator attraction and bee products. Agronomy 12, 169 (2022).CAS 

Google Scholar 
Effah, E. et al. Natural variation in volatile emissions of the invasive weed Calluna vulgaris in New Zealand. Plants 9, 283 (2020).CAS 
PubMed Central 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Ripley, B. et al. Package ‘mass’. Cran r 538, 113–120 (2013).
Google Scholar 
Chen, B. M., Liao, H. X., Chen, W. B., Wei, H. J. & Peng, S. L. Role of allelopathy in plant invasion and control of invasive plants. Allelopathy J 41, 155–166 (2017).
Google Scholar 
Ninkovic, V., Markovic, D. & Rensing, M. Plant volatiles as cues and signals in plant communication. Plant Cell Environ. 44, 1030–1043 (2021).CAS 
PubMed 

Google Scholar 
Holopainen, J. K. Multiple functions of inducible plant volatiles. Trends Plant Sci. 9, 529–533 (2004).CAS 
PubMed 

Google Scholar 
Rhoades, D. F. Responses of alder and willow to attack by tent caterpillars and webworms: evidence for pheromonal sensitivity of willows. In Plant Resistance to Insects (ed. Hedin, P. A.) 55–68 (American Chemical Society, 1983).
Google Scholar 
Hedin, P. A. Plant Resistance to Insects (American Chemical Society, 1983).
Google Scholar 
Heil, M. & Karban, R. Explaining evolution of plant communication by airborne signals. Trends Ecol. Evol. 25, 137–144 (2010).PubMed 

Google Scholar 
Barbosa, P. et al. Associational resistance and associational susceptibility: Having right or wrong neighbors. Annu. Rev. Ecol. Evol. Syst. 40, 1 (2009).
Google Scholar 
Kigathi, R. N., Weisser, W. W., Veit, D., Gershenzon, J. & Unsicker, S. B. Plants suppress their emission of volatiles when growing with conspecifics. J. Chem. Ecol. 39, 537–545 (2013).CAS 
PubMed 

Google Scholar 
Peñuelas, J. & Llusià, J. Influence of intra-and inter-specific interference on terpene emission by Pinus halepensis and Quercus ilex seedlings. Biol. Plant. 41, 139–143 (1998).
Google Scholar 
Ormeno, E., Fernandez, C. & Mévy, J.-P. Plant coexistence alters terpene emission and content of Mediterranean species. Phytochemistry 68, 840–852 (2007).CAS 
PubMed 

Google Scholar 
Himanen, S. J. et al. Birch (Betula spp.) leaves adsorb and re-release volatiles specific to neighbouring plants—A mechanism for associational herbivore resistance?. New Phytol. 186, 722–732 (2010).CAS 
PubMed 

Google Scholar 
Kessler, A. & Kalske, A. Plant secondary metabolite diversity and species interactions. Annu. Rev. Ecol. Evol. Syst. 49, 115–138 (2018).
Google Scholar 
Quintana-Rodriguez, E. et al. Plant volatiles cause direct, induced and associational resistance in common bean to the fungal pathogen Colletotrichum lindemuthianum. J. Ecol. 103, 250–260 (2015).CAS 

Google Scholar 
Loreto, F. & D’Auria, S. How do plants sense volatiles sent by other plants? Trends Plant Sci. (2021).Giordano, D., Facchiano, A., D’Auria, S. & Loreto, F. A hypothesis on the capacity of plant odorant-binding proteins to bind volatile isoprenoids based on in silico evidences. Elife 10, e66741 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ninkovic, V., Markovic, D. & Dahlin, I. Decoding neighbour volatiles in preparation for future competition and implications for tritrophic interactions. Perspect. Plant Ecol. Evol. Syst. 23, 11–17 (2016).
Google Scholar 
Kegge, W. et al. Red: far-red light conditions affect the emission of volatile organic compounds from barley (Hordeum vulgare), leading to altered biomass allocation in neighbouring plants. Ann. Bot. 115, 961–970 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gershenzon, J. Metabolic costs of terpenoid accumulation in higher plants. J. Chem. Ecol. 20, 1281–1328 (1994).CAS 
PubMed 

Google Scholar 
Anderson, P., Sadek, M., Larsson, M., Hansson, B. & Thöming, G. Larval host plant experience modulates both mate finding and oviposition choice in a moth. Anim. Behav. 85, 1169–1175 (2013).
Google Scholar 
Cunningham, J. P., Moore, C. J., Zalucki, M. P. & West, S. A. Learning, odour preference and flower foraging in moths. J. Exp. Biol. 207, 87–94 (2004).PubMed 

Google Scholar 
McCormick, A. C., Reinecke, A., Gershenzon, J. & Unsicker, S. B. Feeding experience affects the behavioral response of polyphagous gypsy moth caterpillars to herbivore-induced poplar volatiles. J. Chem. Ecol. 42, 382–393 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Proffit, M., Khallaf, M. A., Carrasco, D., Larsson, M. C. & Anderson, P. ‘Do you remember the first time?’ Host plant preference in a moth is modulated by experiences during larval feeding and adult mating. Ecol. Lett. 18, 365–374 (2015).PubMed 

Google Scholar 
Mayhew, P. J. Herbivore host choice and optimal bad motherhood. Trends Ecol. Evol. 16, 165–167 (2001).PubMed 

Google Scholar 
Jackson, T. et al. Anticipating the unexpected–managing pasture pest outbreaks after large-scale land conversion (New Zealand Grassland Association, 2012).Townsend, R. J., Dunbar, J. E. & Jackson, T. A. Flight behaviour of the manuka chafers, Pyronota festiva (Fabricius) and Pyronota setosa (Given) (Coleoptera: Melolonthinae), on the flipped soils of Cape Foulwind on the West Coast of New Zealand. N. Z. Plant Prot. 71, 255–261. https://doi.org/10.30843/nzpp.2018.71.175 (2018).Article 

Google Scholar 
Ferguson, C. M. et al. Quantifying the economic cost of invertebrate pests to New Zealand’s pastoral industry. N. Z. J. Agric. Res. 62, 255–315 (2019).
Google Scholar 
Cunningham, J. Can mechanism help explain insect host choice?. J. Evol. Biol. 25, 244–251 (2012).CAS 
PubMed 

Google Scholar 
Syrett, P., Smith, L. A., Bourner, T. C., Fowler, S. V. & Wilcox, A. A European pest to control a New Zealand weed: Investigating the safety of heather beetle, Lochmaea suturalis (Coleoptera: Chrysomelidae) for biological control of heather, Calluna vulgaris. Bull. Entomol. Res. 90, 169–178. https://doi.org/10.1017/S0007485300000286 (2000).CAS 
Article 
PubMed 

Google Scholar 
Fowler, S., Harman, H., Memmott, J., Peterson, P. & Smith, L. In Proceedings of the XII International Symposium on Biological Control of Weeds (eds Julien, M. H. et al.) 495–502.Fowler, S. V. et al. Investigating the poor performance of heather beetle, Lochmaea suturalis (Thompson) (Coleoptera: Chrysomelidae), as a weed biocontrol agent in New Zealand: Has genetic bottlenecking resulted in small body size and poor winter survival?. Biol. Control 87, 32–38 (2015).
Google Scholar 
Effah, E. et al. Herbivory and attenuated UV radiation affect volatile emissions of the invasive weed Calluna vulgaris. Molecules 25, 3200 (2020).CAS 
PubMed Central 

Google Scholar 
Pearson, D. E. & Callaway, R. M. Indirect nontarget effects of host-specific biological control agents: Implications for biological control. Biol. Control 35, 288–298 (2005).
Google Scholar 
Rand, T. A. & Louda, S. M. Exotic weed invasion increases the susceptibility of native plants to attack by a biocontrol herbivore. Ecology 85, 1548–1554. https://doi.org/10.1890/03-3067 (2004).Article 

Google Scholar  More

Spatial–temporal characteristics of land use changeAs shown in Fig. 2, cultivated land was the dominant land use type in Shandong Province during the past 40 years, which accounted for 69.86% (1980), 69.98% (1990), 69.25% (2000), 68.00% (2010) and 66.88% (2020) respectively. Moreover, it was found that the area of cultivated land, forest land, grassland, unused land and ocean gradually decreased, whereas the water area and URL (urban and rural industrial and mining residential land) increased obviously. In particular, grassland decreased by 7542.87 km2 in the past 40 years with a decline rate of 37.18%, which was much higher than cultivated land and forest land. This phenomenon was attributed to the fact that cultivated land and forest land were less susceptible to encroachment as their high vegetation coverage, while grassland was easily occupied by other land types. The serious occupation by other land types has led to a significant reduction in unused land with a very high decline ratio of 64.32% from 2010 to 2020. In contrast to unused land, URL increased significantly at this period (Fig. 3), which was due to the rapidly economic development.Figure 2Land use type map of Shandong Province from 1980 to 2020.Full size imageFigure 3Sankey diagram of land use transfer in different periods.Full size imageThe total area of land use conversion in Shandong Province was 86,909 km2 during the past 40 years, the most drastic change was observed from 2010 to 2020. On the one hand, the major project of new and old kinetic energy conversion in Shandong Province had been implemented since 2000, which led to the expansion of urban land and dramatic changes in land use patterns. On the other hand, social, economic, technological and other factors had a direct impact on land use change by influencing people’s decision-making on land use (e.g., demand for land products, investment in land, protection of land resources, etc.)45,46,47,48. Statistics showed that GDP (Gross Domestic Product) and population density of Shandong Province had increased significantly since 21st century. The GDP of 2010–2020 was about 10 times that of 1980–2000 and population density had also increased by 1.4 times (Data from: Shandong statistical yearbook, http://tjj.shandong.gov.cn/col/col6279/index.html). As the most direct reflection of human activities, land use change was obviously affected by factors such as agricultural cultivation, industrial and mining construction, and urbanization driven by population growth49,50.The most significant changes of land use type were URL (increased by 17.75%), grassland (decreased by 8.72%) and cultivated land (decreased by 7.26%) over the past forty years. URL was mostly converted from cultivated land (26,306 km2) and grassland (1684 km2), which reflected the serious situation of occupying cultivated land in the process of urbanization in Shandong Province. It was caused by tight land use scale and relatively flat terrain of grassland. Besides, the range of land use type in the four periods also exhibited great variations. The conversion of land use from 1980 to 1990 was concentrated in the Yellow River Delta, Laizhou Bay and Weishan Lake, for the same as 1990–2000. At the period of 2000 to 2010, the conversion types concentrated in Bohai Bay and Yellow River Delta. The land use conversion was violent and widely distributed from 2010 to 2020, which was different from previous periods from 1980 to 2010. The conversion of cultivated land → URL and URL → cultivated land were widely distributed in Shandong Province, while another conversion of grassland → cultivated land and forest → cultivated land were concentrated in the Central and South Shandong Mountains and Jiaodong Hills. In addition, the conversion of cultivated land → water area and URL → water area were concentrated in Bohai Bay, Yellow River Delta and Laizhou Bay. Ample water, flat terrain and fertile soils in these bays and deltas facilitates agricultural cultivation and other productive activities. Therefore, the conversion of land use types from 1980 to 2010 was mainly concentrated here (Fig. 4). Specifically, the conversion of water area → URL was 1083 km2 from 1980 to 1990, unused land → water area was 925 km2 from 1990 to 2000, cultivated land → water area was 687 km2 from 2000 to 2010. However, the pattern of land use change dominated by natural factors has been broken in the process of increasing demand for social development and continuous advancement of science and technology. The conversion of land use types has become more dispersed in spatial distribution and the types of conversion have become more diverse.Figure 4Spatial distribution map of land use conversion types in different periods.Full size imageIn fact, one issue of concern in the early exploitation of water was the ecological problems caused by over-exploitation. For example, the cut-off of the Yellow River downstream made it difficult to guarantee the water security of industrial and agricultural production and residential life in the areas along the way. At the same time, the safety of coastal ecosystems was threatened and the phenomenon of soil salinization had become more serious. To alleviate these problems, government and the public have taken a series of measures such as establishing the Yellow River Delta National Nature Reserve was established in 1992, returning farmland to lakes and wetlands, and improving the landscape pattern of rivers and lakes by carrying out ecological treatment in the coastal zone of rivers and lakes51,52. By 2020, the area of water has increased by 50% compared to 1980, while many ecological security issues have been mitigated.Spatial–temporal characteristics of habitat degradationThe spatial–temporal variation of land use types were conducted to explore the variation trend of its habitat quality in Shandong Province. The InVEST-HQ was applied to obtain layers of habitat degradation in different periods. According to the interval range of 0–0.03, 0.03–0.07 and 0.07–0.18, habitat degradation was divided into three levels: slight, moderate and high degradation35,38.As shown in Fig. 5, the habitat quality in Shandong Province was dominated by moderate degradation, with the proportion of 73.30% (1980), 73.25% (1990), 72.49% (2000), 70.45% (2010) and 64.33% (2020), respectively. The spatial pattern of habitat quality was consistent with cultivated land, indicating that cultivated land who was affected by natural and anthropogenic activities exhibited moderate degradation. The proportion of moderate degradation has decreased due to cultivated land have been encroached upon for construction in the process of development, thus habitat degradation has become more and more serious. Although some of the moderate degraded areas were also converted to slight degraded areas, the area of conversion was very small compared to its conversion to high degraded areas.Figure 5Distribution map of habitat degradation in Shandong Province from 1980 to 2020.Full size imageThe proportion of slight degradation ranges from 22.38% to 24.89%, it was concentrated in the Yellow River Delta, the Central and South of Shandong Mountains, Weishan Lake and Jiaodong Hills, which was less disturbed by human activities. Compared with 1980, the proportion of slight degraded areas increased marginally in 2020, and its change was a fluctuating process. The proportion of slight degraded areas decreased from 1980 to 1990, and its proportion slowly increased from 1990 to 2020. This dynamic change process could be verified according to the spatial distribution characteristics in the Yellow River Delta. The habitat quality of the Yellow River Delta, which originally showed slight degradation, showed high degradation in 1990, 2000 and 2010.The proportion of high degradation ranges from 4.03% to 10.78%, which was concentrated in the built-up area of the city where human activities were more intensive. The proportion of high degraded areas has been increasing, indicating that the habitat has been degraded severely and its quality has declined. As the proportion of high degraded areas raised, two patterns of their spatial distribution also emerged. First spatial pattern was concentrated in urban built-up areas because of the high degree of human exploitation of land, which led to significant habitat degradation. The second pattern was a circle structure with “slight degradation” as the center and “high degradation-moderate degradation-slight degradation” outward, which was similar to the spatial distribution structure of habitat degradation in Fujian Province studied by Li et al.40. The circle structure was formed in 2010, and the distribution range was significantly expanded in 2020. The reason for the formation was that the built-up land in the city center has been severely damaged, and the possibility of re-degradation was reduced, instead showing “slight degradation”. However, the adjacent urban areas were more threatened and severely degraded, presenting “high degradation”. With the increase of distance, habitat threat and degradation decreased gradually, displaying “slight degradation”.Spatial–temporal evolution characteristics of habitat qualityThe InVEST-HQ was used to obtain layers of habitat quality in different periods. As summarized in Table 4, habitat quality was divided into five levels by the interval range: low (0–0.2), relatively low (0.2–0.4), medium (0.4–0.6), relative high (0.6–0.8), and high (0.8–1.0)35,38.Table 4 The proportion of habitat quality level at different periods in Shandong Province.Full size tableOur study concluded that the level of habitat quality in Shandong Province declined from 1980 to 2020.The results showed an overall decline of 4.75% in Shandong Province. Among them, the most significant rate of decline was observed in 2010–2020 (1.86%), which was similar to the phase change characteristics of land use types. At this period, the “Development Plan of Yellow River Delta Efficient Ecological Economic Zone” and the “Development Plan of Shandong Peninsula Blue Economic Zone” have become national development strategies. The demonstration area of “Bohai granary” and the restructuring of steel industry were carried out simultaneously. Meanwhile, the Beijing-Shanghai high-speed railway (Shandong section), Qingdao Jiaozhou Bay Bridge, Jiaozhou Bay Tunnel have strengthened the connection between Shandong Province and the outside world. As a result, rapid development has led to a rapid decline in the quality of its habitat. The rate of decline in 1980–1990 (1.43%) and 2000–2010 (1.42%) was comparable and the rate of decline in 1990–2000 was the lowest at 0.12%, which was significantly related to the development level of cities in each period. The period of 1980–1990 and 2000–2010 were in the initial and rapid promotion stages of reform and opening-up respectively. The initial stage was led by rural reform, and urban reform was launched on a pilot basis. The rapid advancement stage was led by urban reform, and economic development entered a healthy track of steady progress. Therefore, the proportion of habitat quality changes in the two periods was comparable. The period of 1990–2000 was in the exploration and transition stage of reform and opening-up, whose development process was relatively stable, resulting in the lowest rate of change in habitat quality.The average value of habitat quality in Shandong Province was 1980 (0.5091), 1990 (0.5018), 2000 (0.5012), 2010 (0.4941) and 2020 (0.4849), which decreased during the entire period. Habitat quality was dominated by medium-level throughout the whole period, with the proportion in 1980 (68.95%), 1990 (68.54%), 2000 (67.74%), 2010 (66.37%) and 2020 (65.47%). The land type in this category was mainly cultivated land (Fig. 6), which was continuous encroachment during the study period, resulting in a decrease in the percentage of medium-level habitat quality. From 1980 to 2020, the percentage of low-level habitat quality increased from 12.67% to 17.44%, and the relatively low-level decreased from 0.46% to 0.23%. The main reason was the continuous increasing of construction land and the degree of habitat threat led to the decreasing of habitat suitability. Therefore, the area of low-level habitat quality showed an increasing trend. Low and relative low-level habitat quality areas were concentrated in the urban areas of coastal and inland cities, and the Yellow River Delta. Urban areas, with a large scale of industry, commerce and population, also have a high level of urbanization. The original natural habitat has been modified during the development process, which resulted low-level habitat quality. The habitat quality of the Yellow River Delta was dynamic. The low-level pattern formed by early over-exploitation was improved in later conservation and development. The proportion of high-level habitat quality increased from 11.64% to 12.98%, and the relatively high-level decreased from 6.28% to 3.88%. In terms of spatial distribution, it was concentrated in the Central and South Shandong Mountains, Jiaodong Hills, the Yellow River Delta (2020), Weishan Lake and Wulian Mountain. These areas were dominated by mountains and well-protected water, which had high habitat suitability and were less stressed by surrounding construction land, thus maintaining high-level habitat quality. The increase of high-level habitat quality was due to the influence of water with high habitat suitability, which expanded a lot in the past 40 years, leading to the spread of high-level regional habitat quality, especially in the Yellow River Delta.Figure 6Distribution map of habitat quality in Shandong Province from 1980 to 2020.Full size imageThe value of Moran’s I was 0.3935 (1980), 0.3852 (1990), 0.4031 (2000), 0.4186 (2010) and 0.4644 (2020), respectively, which revealed that the spatial agglomeration of habitat quality in Shandong Province was characterized by agglomeration, and the trend of agglomeration increased obviously after 2000.As shown in Fig. 7, the habitat quality in Shandong Province exhibited obvious spatial heterogeneity, and spatial distribution of cold and hot spot was consistent with the topographic features. Hot spot (high-value area of habitat quality) presented “two primary and two secondary + Yellow River Delta”. Two primary hot spots distributed in the Central and South Shandong Mountains and the Jiaodong Hills, the two secondary hot spots located in Weishan Lake and Wulian Mountain. The formation of above hot spot was mainly due to high altitudes or steep slopes conferred favorable habitat quality, which was associated with the accessibility of human activities. Human accessibility at high altitudes or steep slopes was limited, so it was unlikely to cause major interference with the original environment53,54. However, the formation of other hot spot in Yellow River Delta was due to protective human activities. Cold spot (low-value area of habitat quality) was scattered in the northwestern Plain of Shandong Province, provincial capital metropolitan area and peninsula urban agglomeration which was dominated by cultivated land and built-up land in the cities that was affected by agricultural cultivation and industrial activities.Figure 7Distribution map of hot and cold spots of habitat quality in Shandong Province from 1980 to 2020.Full size imageOverall, the spatial distribution pattern of habitat quality in Shandong Province was relatively stable and affected by many factors, among which land use change was the most important one9,40,55. The most dominant land type in Shandong Province was cultivated land, which was concentrated in the northwest plain. Influenced by agricultural farming, the habitat quality of cultivated land presented medium-level category. At the same time, the habitat quality of some cultivated land has decreased due to the influence of construction land intrusion. The high vegetation coverage and rich species diversity of mountains and hills make their natural habitat quality superior. With the development of urban economy, the scale of construction land in coastal lowlands as well as inland urban areas continued to expand. The increase in population density as well as the intensity of land use activities has led to the expansion of regional dehabitatization. In addition, the dynamic changes in the habitat quality of the Yellow River Delta indicated that differences in the degree of land use change led to a variety of impacts on habitat quality. Therefore, habitat quality improvement and ecological protection should be based on local regional resource endowments and follow the concept of comprehensive, coordinated and sustainable development. Administration should formulate differentiated ecological protection strategies. For urban land development, authorities should increase the intensive utilization of construction land, limit the development boundaries of urban land and increase the greening rate inside urban land, such as equipped with urban green space park and other ecological land. In order to ensure the efficiency of agricultural production in Shandong Province, authorities should pay special attention to the conservation of cultivated land and to the development of ecological agriculture56. For natural ecosystems such as forest and grassland, authorities should improve the natural reserve system57. The vegetation ecological restoration project should be carried out according to local conditions. Drawing on the effective experience of ecological changes in the Yellow River Delta, we would take it as a typical example in future development and adopt corresponding administrative methods to coordinate the relationship between economy and habitat quality and change the dilemma of low-level habitat quality areas. Therefore, it is necessary to implement reasonable and effective territorial space planning to achieve regional sustainable development. More

MaterialsWeinan city is located in the middle reaches of the Yellow River and in the southern part of the Loess Plateau (34°13’–35°52’N, 108°58’–110°35’E) (Fig. 1). It has a temperate semihumid, semiarid climate. The modern MAT observations indicate a value of 13.8 °C, and MAP is 570 mm; these values were obtained from the China meteorological data network, comprising the meteorological data of 2000–2015 (http://data.cma.cn/). Weinan has four distinct seasons, with hot and rainy conditions occurring in the same season. Much of the annual precipitation falls from June to August. The Weinan profile contains 42.8 m of loess–paleosol sequences (LPSs), including five paleosol layers from S0–S4 and five loess layers from L1–L5 and covering five glacial–interglacial cycles. The sampling method involved collecting one sample every 10 cm without interruption. A total of 427 samples were collected from this profile.Modern brGDGTs dataset and MAP datasetPreviously published brGDGTs data from surface soil samples were extracted using an established brGDGT-MAP model. The surface soil samples contain various types of soil and cover nearly all climatic and latitudinal zones. These datasets contain 712 surface soil samples, which all have separated 5-methyl and 6-methyl brGDGTs isomers (Table 1). To reduce the errors in collecting data from different laboratories, the MAP datasets we entered into the brGDGT-MAP model were all published in their previous studies, and we calculated the fractional abundances of each brGDGTs compound for each sample (Table 1), although there were no data regarding changes in soil occurring based on the brGDGTs indices among various laboratories. To eliminate and test the error of the previous MAP dataset, in this study, we also extracted each soil site’s multiyear MAP (1990–2020) through TerraClimate, which is a dataset of high-spatial-resolution monthly climate for global terrestrial surfaces (1/24°, ∼4 km)48. TerraClimate datasets reveal significant advances in the overall mean absolute error and enhance spatial realism compared with coarser resolution gridded datasets. Supplementary Fig. 3 shows that the two MAP datasets have high correlations, with only a few sites exhibiting large deviations. In this study, we entered these two MAP datasets into the DLNN model to obtain the most suitable DLNN-MAP model.Grain-size and magnetic susceptibility measurementsSamples at 10-cm intervals were dried in an oven at 40 °C for 3 days. Then, 0.2 g of each sample was weighed using a clean beaker with an electronic balance. Then, 10 ml of 30% H2O2 and 10 ml 10% HCl were added to remove organic matter and carbonate, respectively. Before the grain-size measurement, 0.05 mol/L (NaPO3)6 was added, and the solutions were placed in an ultrasonic machine for 10 min. The magnetic susceptibility of the samples were measured with an MS-2B Bartington meter. The grain-size was measured using a Mastersizer 2000 produced by Marvern Company in the UK, with an error of less than 1%.ChronologyWe used the ages of LPS control points on the Loess Plateau to obtain the age of each sample in the Weinan profile40. We used the magnetic susceptibility as an indicator of the accumulation rate39 combined with the U–230Th-dated oxygen isotope records from Sanbao caves in central China14. Each sample’s magnetic susceptibility was analyzed at 10-cm intervals (Supplementary Fig. 7). The calculation was as follows:$${T}_{{{{{{rm{m}}}}}}}={T}_{1}+frac{left({sum }_{i=1}^{m}{a}_{i}{s}_{i}right)left({T}_{2}-{T}_{1}right)}{{sum }_{i=1}^{n}{a}_{i}{s}_{i}}$$
(1)
where T1 and T2 indicate the ages of the control points, ai indicates the thickness of the layer, and si indicates the magnetic susceptibility of the layer.GDGTs analysisLipids in a total of 238 LPS samples were extracted, including the 198 samples reported in ref. 49. Forty samples at depths from 34.9 m to 43.7 m were selected every 20 cm intervals from the Weinan profile, and dried in an oven at 40 °C for 3 days. Afterward, the loess and paleosol samples were ground into powder and passed through a 60-mesh sieve. Each sample was weighed and extracted with 80 ml of methanol: dichloromethane (DCM) (1:9, v/v) using accelerated solvent extractors (ASE 100 or 150, Dionex, USA). The temperature and pressure were set at 100 °C and 1400 psi, respectively. Then, the lipid extracts were condensed in a rotary evaporator at 40 °C and separated into apolar and polar fractions on a flash silica gel column (0.7 cm i.d. and 1.5 g activated silica gel) chromatography using n-hexane and methanol as eluents, respectively. All polar components were passed through a 0.45-µm PTFE syringe filter. All apolar and polar compositions were dried under a gentle stream of nitrogen gas.The GDGTs were analyzed by using an Agilent 1200 series liquid chromatography-atmospheric pressure chemical ionization-6460A triple quadrupole mass spectrometry (LC-APCI-MS/MS). Ten microlitres of C46 GTGTs (0.001157 μg/μl) were added to each polar fraction, and the samples were then dissolved in 300 μl of n-hexane: iso-isopropanol (IPA) (98.2:1.8, v/v)). Two silica gel columns in series (150 mm × 2.1 mm, 1.9 μm, Thermo Finnigan; USA) were used for the separation of 5-methyl and 6-methyl brGDGTs, with the column temperature kept at 40 °C. The mass spectrometry settings were as follows: the vaporizer pressure 60 psi, the vaporizer temperature 400 °C, the flow rate of dry gas (N2) 6 l/min, drying gas temperature 200 °C, the capillary voltage 3500 V, the corona current 5 μA (∼3200 V), and a single-ion monitoring mode (SIM) was used50, targeting the protonated molecular ions ([M + H]+) 1304, 1302, 1300, 1298, 1296, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1020, 1018, and 744.The MATmr proxy was calculated to identify the changes that occurred in the mean annual temperature in the Weinan section over the last 430 ka. The calculation was as follows24,51.$${{MAT}}_{{mr}} =7.17+17.1*[{Ia}]+25.9*[{Ib}]+34.4*[{Ic}]-28.6*[{IIa}],(n=222,,{R}^{2} \ =0.68,; {RM}{SE}=4.6 {deg} {{{rm{C}}}},,P ; < ; 0.01)$$ (2) $${{MAT}}_{{mr}}=5.58+17.91*[{Ia}]-18.77*[{IIa}]$$ (3) $${MAT}({SSM})= 20.9-13.4*[{IIa}+{IIa}^{{prime}}]-17.2*[{IIIa}+{IIIa}^{{prime}}]\ -17.5*[{IIb}+{IIb}^{{prime}}]+11.2*[{Ib}]$$ (4) $${MAAT}=0.81-5.67*{CBT}+31.0*{MBT}^{{prime}}$$ (5) The soil pH was calculated using the following formulas24.$${pH}=7.15+1.59*{CBT}^{{prime}}(n=221,,{R}^{2}=0.85,,{RMSE}=0.52,, P , < ,0.0001)$$ (6) $${{CBT}}^{{prime} }=-{{log }}frac{{Ic}+{II}{a}^{{prime}}+{II}{b}^{{prime}}+{{IIc}}^{{prime} }+{{IIIa}}^{{prime} }+{III}{b}^{{prime} }+{{IIIc}}^{{prime} }}{{Ia}+{Ib}+{Ic}}$$ (7) SWC is well correlated with MBT’ when IR6ME  > 0.5, and these proxies were calculated using the following expressions:$${{MBT}}^{{prime} }=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{IIa}+{{IIa}}^{{prime} }+{IIb}+{{IIb}}^{{prime} }+{IIc}+{{IIc}}^{{prime} }+{IIIa}+{IIIa}^{prime} )}$$
(8)
$${{IR}}_{6{ME}}=frac{sum (C6-{methylated; brGDGTs})}{sum {brGDGTs}}$$
(9)
$${{MBT}^{prime} }_{6{ME}}=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{{IIa}}^{{prime} }+{{IIb}}^{{prime} }+{{IIc}}^{{prime} }+{IIIa}^{prime} )}$$
(10)
where the Roman numerals indicate different brGDGTs structures (Supplementary Fig. 1).Principal component analysis (PCA)CANOCO version 5 software was utilized to reveal the relationships among various environmental factors. The first PCA figure (Fig. 3a) was generated for the environmental factors MAT, MAPc, SWC, and pH. These variables are based on the same dataset (238 LSPs samples from Weinan profile) without any data transformation. The second PCA figure (Fig. 3b) was generated for the environmental factors MAT, MAP (based on 10Be), SWC and pH, which were all in the transition of the glacial–interglacial after 430 ka BP on the CLP. As the two LSPs profile had similar sedimentation rates, we obtained the same chronological control through linear interpolation in those transition periods. All datasets passed the Gaussian distribution test in this study.Cross wavelet analysisCompared with wavelet special analysis, cross wavelet analysis is even more complicated. The wavelet cross-spectrum can be defined as follows:$${CS}left(b,, a right)={m}_{1c}(b,, a){m}_{2c}(b,, a)$$
(11)
where ({m}_{1c}) and ({m}_{2c}) describe the covarying fractions of the overall spectra given by:$${m}_{1}left(b,, a right)={m}_{1c}left(b,, a right)+{m}_{1i}(b,, a)$$
(12)
$${m}_{2}left(b,, a right)={m}_{2c}left(b,, a right)+{m}_{2i}left(b,, a right)$$
(13)
where ({m}_{1i}) and ({m}_{2i}) are independent contributions to the variance.Overall, this is a multipart function that may be decomposed into amplitude and phase:$${CS}left(b,, a right)={{{{{rm{|}}}}}}{CS}left(b,, a right){{{{{rm{|}}}}}}{{exp }}(i;{{arg }}({CS}(b,, a)))$$
(14)
In this study, a and b represent the array of reconstructed MAPc and the Sanbao speleothem δ18O, respectively.Multiple regression linear modelTo compare the precision of the DLNN-MAP model we established, we set up a multiple regression linear model based on all 6-methyl brGDGTs except Ib. The basis of the model is defined as:$$y=a+{b}_{1}{x}_{1}+{b}_{2}{x}_{2}ldots+{b}_{n}{x}_{n}$$
(15)
where y represents MAP, x represents all 6-methyl brGDGTs and Ia and Ic, and a, b1, b2…bn represent the partial regression coefficients. n represents the number of 6-methyl we entered into the model (in this study, n = 8).The multiple correlation coefficient (R) was defined as follows:$$R=sqrt{frac{{sum }_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}}{{sum }_{i=1}^{n}{({y}_{i}-bar{y})}^{2}}}$$
(16)
where ({y}_{i}) represents the actual observed value, ({hat{y}}_{i}) represents the calculation value and (bar{y}) represents the mean value of all observed data.The t statistic is used to test the validity of regression coefficients, and it can be defined as follows:$${t}_{{b}_{j}}=frac{{b}_{j}}{{s}_{{b}_{j}}}$$
(17)
$${s}_{{b}_{j}}=sqrt{{p}_{{jj}}}*s$$
(18)
$$s=sqrt{frac{1}{n-m-1}mathop{sum }limits_{i=1}^{n}{({y}_{i}-{hat{y}}_{i})}^{2}}$$
(19)
$$P={({p}_{{jj}})}^{-1}=mathop{sum }limits_{i=1}^{n}({x}_{{ij}}-{bar{x}}_{j})({x}_{{ik}}-{bar{x}}_{k})$$
(20)
where ({b}_{j}) represents the regression coefficient of ({x}_{j}), n represents the number of samples and m represents the number of variables.The F statistic is used to test the linear relationship of variables and can be defined as follows:$$F=frac{1}{m{s}^{2}}mathop{sum }limits_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}$$
(21)
The variance inflation factor (VIF) is used to measure collinearity between variables and can be defined as follows:$${{VIF}}_{j}=frac{1}{1-{R}_{j}^{2}}$$
(22)
As shown in Supplementary Fig. 5, we found no obvious collinearity between different variables. However, there are fewer contributions in IIc’, IIIa’, IIIb’, and IIIc’ in the multiple regression linear model we established, and the value of t cannot attain the 95% confidence level (Table 2). The results of both the training dataset and extrapolated experimental dataset (Supplementary Fig. 6), although they seem good (R2 = 0.44 and 0.46, respectively), still have a considerable gap compared with the DLNN-MAP model. Especially when MAP  > 1500 mm, the multiple linear model is unable to forecast the real MAP. These results all indicate that the MAP influence on the brGDGTs compounds is not a simple linear relationship; instead, we suggest that there are complex pilot processes between them.Table 2 List of the parameters of the multiple linear modelFull size tableDLNN modelsDLNNs usually contain an input layer, a few hidden layers, and an output layer. A conceptual structure of the DLNN model was established for forecasting MAP values. The first layer accepts input signals that are various combinations of brGDGTs. The relationships among different variables are processed and analyzed in the hidden layers. The final class output is presented in the output layer; in this study, the output is the MAP reconstruction at the study site.The rectified linear unit (ReLU) activation function is applied in each neuron of the hidden layer, which is computationally simpler than the traditionally applied sigmoid function. The function of the ReLU activation function is given as follows:$$fleft(xright)=left{begin{array}{c}x,, x , > , 0 \ 0,, x , le , 0end{array}right.={{{{{rm{max }}}}}}(0,, x)$$
(23)
where x represents an input signal to a neuron and f represents the activation function.Furthermore, the bias between the measured and forecasted output values is reflected by the loss function. The loss function applied herein is the MAE (mean absolute error), which is given as follows:$${MAE}=frac{1}{N}mathop{sum }limits_{i=1}^{n}{{{{{rm{|}}}}}}T-Y{{{{{rm{|}}}}}}$$
(24)
where N is the number of training data points, and T and Y represent the measured output value and the forecasted class value, respectively.To realize the backpropagation framework, the derivative of the ReLU activation function needs to be acquired. According to the definition of the ReLU, the derivative is shown as follows.$$f{^prime} left(xright)=left{begin{array}{c}1,; x , > , 0 \ 0,; x , le , 0end{array}right.$$
(25)
Given a minibatch of m training samples obtained from the training set {x(1), x(2)…, x(m)} and their corresponding targets T(i) (i = 1,2…, m), the gradient used in the backpropagation framework is shown as follows:$$f=frac{1}{m}mathop{sum }limits_{i=1}^{n}frac{partial L}{partial w}$$
(26)
where L is the loss function; w represents the network weights; and n = 1 is the number of output values (MAP).In addition, considering that the adaptive moment estimation algorithm (Adam) was proven to be an effective neural network training method with a fast convergence speed and great classification performance, we applied this algorithm to train the DLNN model for MAP forecasting in this study. Adam has two biased equations, which are shown as follows:$$a={rho }_{1}a+(1-{rho }_{1})g$$
(27)
$$b={rho }_{2}b+(1-{rho }_{2})gtimes g$$
(28)
where ({rho }_{1}=0.9) and ({rho }_{2}=0.999) are exponential decay rates; g is the gradient; and (times) represents an elementwise product operator.After this calculation, the correct biases in the above two moments are given as follows:$${a}_{c}=frac{a}{1-{rho }_{1}^{t}}$$
(29)
$${b}_{c}=frac{b}{1-{rho }_{2}^{t}}$$
(30)
where t represents the current time step.Moreover, the update of the network weights is shown as follows:$${triangle }_{w}=-lambda frac{{a}_{c}}{sqrt{{b}_{c}}+epsilon }$$
(31)
where (lambda=0.001) represents the learning rates and (epsilon={10}^{-8}) is a constant used to ensure numerical stability.Eventually, the DLNN parameters can be updated according to the following formula.$$w ,=, w ,+, {triangle }_{w}$$
(32)
brGDGT-MAP modelsWe entered 9 brGDGTs compounds (all 6-methyl brGDGTs; each compound entered in the model is the percentage of all brGDGTs in the surface soil) into the input layer of the DLNN; these compounds are closely related to soil moisture. Then, we selected 533 surface soil samples as the training dataset and 179 surface soil samples as the validation dataset, both of which satisfied the principle of randomness. We assessed the precision of the model using forecast data R2 and root mean square error (RMSE) values.Through several parameters applied in the DLNN model, we found that the frequency of training and the number of neurons play the most significant roles in the brGDGT-MAP models. In addition, four hidden layers containing the other DLNN parameters allow the model to become more stable (detailed parameters are shown in Supplementary Fig. 7). To test the best frequency of training and the number of neurons in each hidden layer, we set a series of gradients to test the model to find the most suitable combination. As shown in Supplementary Fig. 8, for the frequency of training, we set the minimum and maximum training times to 1000 and 1500, respectively, with 100 times as the interval. We also set the numbers of neurons from 160 to 260 with a 20-neuron interval.Testing the weights of different compounds in the DLNN model and determining whether it was essential to eliminate some compounds that may make the dataset redundant were also required. Based on the model in which the Ib parameter was removed, we also set a series of experiments to test the effects of the different 6-methyl isomers on the predicted MAP values. Then, we made seven attempts to test the forecast effect of the brGDGT-MAP models by removing the Ic, IIa’, IIb’, IIc’, IIIa’, IIIb’, and IIIc’ parameters (Supplementary Fig. 9). Then, we obtained the best brGDGT-MAP model (Supplementary Fig. 10).Comparison of various ANN structuresTo improve the accuracy of our brGDGT-MAP models and the models’ universality, we also tested more complex ANN structures and then compared them with our DLNN models.RNNA recurrent neuron network (RNN) is an artificial neural network in which nodes are directionally connected into loops. The essential feature of RNN is that there are both internal feedback connections and feedforward connections between processing units. The inner structure of RNN is similar to that of the human brain, which can learn to transform a lifetime of sensory input streams into an efficient sequence of motor outputs (Supplementary Fig. 11a). Therefore, the basis of the RNN is defined as follows:$${h}_{t}=fleft(U ,*, {X}_{t}+W ,*, {h}_{t-1}right)$$
(33)
$${o}_{t}={softmax}(V ,{h}_{t})$$
(34)
where Xt represents the input value at time t; ot represents the output value at time t; ht represents the memory value at time t; and U, V, and W are the parameters of this network. For the motivative function, we chose softmax.LSTMLong short-term memory networks (LSTM) are a special type of RNN that can learn long-term dependence and contain three gates (forget gate, input gate and output gate) and one memory cell. The horizontal line above the box is called the cell state, and it acts as a conveyor belt to control the flow of information to the next moment (Supplementary Fig. 11b). Therefore, the basis of LSTM is defined as follows:$${C}_{t}={f}_{t}*{C}_{t-1}+{i}_{t}*{widetilde{C}}_{t}$$
(35)
where ({C}_{t-1}) represents the knowledge state of the model at time t − 1 and ({widetilde{C}}_{t}) represents the newly acquired information after entering new observations. ({f}_{t}) and ({i}_{t}) represent the weight parameters of ({C}_{t-1}) and ({widetilde{C}}_{t}), respectively.$${f}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{f})$$
(36)
$${i}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{i})$$
(37)
$$kern0.9pc {widetilde{C}}_{t}={{tanh }}({W}_{c}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{c})$$
(38)
where ({h}_{t-1}) represents the output value at time t − 1 and ({x}_{t}) represents the new input value at time t. ({W}_{f}) represents the motivative function in this study. We used tanh as the motivative function when our model was learning. Each new input may not have a positive impact on the machine, but it may also have a negative impact., ({b}_{f}), ({b}_{i}) and ({b}_{c}) represent the random disturbances (white noise).GRUAs mentioned above, the LSTM is proposed to overcome RNN’s inability to address remote dependence and the gate recurrent unit (GRU), a variant of the LSTM, keeps the effect of the LSTM while making the structure simpler.Compared with the LSTM, the GRU only has two gates (update (zt) and reset (rt) gates). The update gate is used to control the degree to which the state information at the previous moment is brought into the current state. The larger the value of the update gate is, the more state information at the previous moment is brought in. The reset gate is used to control the degree to which the state information at the previous moment is ignored (Supplementary Fig. 11c). Therefore, the basis of the LSTM is defined as follows:$${r}_{t}=sigma ({W}_{r}cdot [{h}_{t-1},, {x}_{t}])$$
(39)
$${z}_{t}=sigma ({W}_{z}cdot [{h}_{t-1},, {x}_{t}])$$
(40)
$${widetilde{h}}_{t}={tanh }({W}_{widetilde{h}}cdot [{{r}_{t}*h}_{t-1},, {x}_{t}])$$
(41)
$${h}_{t}=left(1-{z}_{t}right)*{{r}_{t}*h}_{t-1}+{z}_{t}*{widetilde{h}}_{t}$$
(42)
$${y}_{t}=sigma ({W}_{o}cdot {h}_{t})$$
(43)
where [] represents the connection of two vectors and * represents the multiplication of matrix elements. The xt and yt represent the input and output values at time t, respectively.It can be seen from the above formula that the parameters to be learned are the weight parameters of Wr, Wz, Wh, and Wo. The first three weights are spliced; therefore, they need to be segmented during learning. These can be defined as follows:$${W}_{r}={W}_{{rx}}+{W}_{{rh}}$$
(44)
$${W}_{z}={W}_{{zx}}+{W}_{{zh}}$$
(45)
$${W}_{widetilde{h}}={W}_{widetilde{h}x}+{W}_{widetilde{h}h}$$
(46)
As we can find in the RNN, LSTM, and GRU models we reconstructed (Supplementary Fig. 12), the training datasets all show extraordinarily high R2 values (0.99, 0.99, and 0.99, respectively) and low RMSE values (0.36, 0.23, and 0.16, respectively). However, the validation datasets do not show good prediction ability compared with the DLNN. These results indicate that the two ANN structures are not suitable for MAP prediction based on brGDGTs, although their inner structures are more complex than those of the DLNN. The reason we suggested is that the RNN, LSTM and GRU are more appropriate to the massive amounts of data and the data that have obvious spatiotemporal characteristics. The great prediction precision in the training dataset and the poor performance in the extrapolated datasets indicate that the models based on the RNN, LSTM and GRU have significant overfitting. As a result, compared with other ANN structures, we concluded that our DLNN model is the most suitable one to forecast MAP based on brGDGTs.Environmental indicators of n-alkanes proxiesLong-chain n-alkanes in plant leaf waxes are universal in terrestrial environments and can deliver signals of variations in plant sources and past climate. They are widely distributed in surface soils and Quaternary sediments, especially in LPSs. In this study, due to the insufficient samples in Weinan profile, we only analyzed n-alkanes components for 40 LPS samples, which contain ages between 340 and 430 ka BP.Instrumental measurementsFor the apolar fractions, a total of 40 samples in this study, mainly containing n-alkanes, were all investigated utilizing a Shimadzu 2010 gas chromatograph (GC) equipped with a flame ionization detector (FID) and a DB-5 fused silica capillary column (60 m (times) 0.32 mm (times) 0.25 μm film thickness) with helium as the carrier gas. The temperature of the GC oven was enhanced from 70 to 300 °C at a rate of 3 °C/min. Then, this temperature (300 °C) was maintained for 30 min. Finally, the concentrations of the n-alkane homologs were evaluated by assessing the peak area of the n-alkanes to that of the internal standard (cholane).Long-term paleoclimatic changeThe carbon preference index (CPI) evaluates the relative abundances of odd vs. even-numbered n-alkanes. The CPI increases as the environmental aridity increases. The CPI indicated warm–wet periods and cold-dry periods in paleoclimate and corresponded well with the loess–paleosol cycle52. The average chain length (ACL) value is the weighted average of the different carbon chain lengths. The lower ACL value corresponds to the lower temperature in the research of LPSs. The variations in the ACL value have good coordination with the magnetic susceptibility and particle size. The n-alkane CPI53 and ACL54 are calculated as follows:$${CPI}(1)=frac{({C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31})+({C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33})}{2({C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32})}$$
(47)
$${CPI}left(2right)=frac{1}{2}left(frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}}+frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}+{C}_{34}}right)$$
(48)
$${ACL}=frac{{23C}_{23}+{25C}_{25}+{27C}_{27}+{29C}_{29}+31{C}_{31}+{33C}_{33}}{{C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}{+C}_{33}}$$
(49)
Figure 13 shows the variations in CPI (Supplementary Fig. 13a) and ACL (Supplementary Fig. 13b) values in the Weinan profile from 340 to 430 ka BP. Compared with the MAP (Supplementary Fig. 13c) and SWC (Fig. 2e) reconstructions based on brGDGTs, we found that they all had a peak at ∼350 ka BP, which indicates relatively high soil moisture at approximately 350 ka BP.MAP reconstruction in the XRD sectionIn this section, we test the brGDGT-MAP model in the Xiangride (XRD) profile, which is located in the margin region of the East Asian monsoon (Fig. 1). With robust chronological control, we reconstructed the rainfall changes in 7000 years BP (Supplementary Fig. 14b). We found that MAP was ∼200 mm in the late Holocene, which approaches multiple modern observations in this region (180 mm). Moreover, we suggest that this region experienced the most humid period in the mid-Holocene, when the rainfall reached 600 mm. Afterward, the precipitation declined from 6000 to 4000 years BP and then increased and reached a peak value at ∼3000 years BP. Then, it had a drought trend until modern times.We discovered that our brGDGT-MAP model could precisely capture rainfall dynamics based on the Weinan profile (Supplementary Fig. 14a) and XRD profile (Supplementary Fig. 14b). Combined with the most acceptable rainfall records in the Holocene (i.e., 10Be (Supplementary Fig. 14c), pollen in Gonghai (Fig. 1 and Supplementary Fig. 14d), and Dongge cave δ18O (Fig. 1 and Supplementary Fig. 14e)), we found the same precipitation peak values in the early Holocene and mid-Holocene. In addition, they all revealed a drought trend throughout the whole Holocene. We suggest that brGDGTs can become a robust proxy to reconstruct precipitation in the Holocene. More

Flemming, H.-C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).CAS 
PubMed 
Article 

Google Scholar 
Inagaki, F. et al. Exploring deep microbial life in coal-bearing sediment down to ~2.5 km below the ocean floor. Science 349, 420–424 (2015).CAS 
PubMed 
Article 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Hondt, K. et al. Microbiome innovations for a sustainable future. Nat. Microbiol. 6, 138–142 (2021).PubMed 
Article 
CAS 

Google Scholar 
Lloyd, K. G., Steen, A. D., Ladau, J., Yin, J. & Crosby, L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems 3, e00055-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lewis, W. H., Tahon, G., Geesink, P., Sousa, D. Z. & Ettema, T. J. G. Innovations to culturing the uncultured microbial majority. Nat. Rev. Microbiol. 19, 225–240 (2021).CAS 
PubMed 
Article 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).CAS 
PubMed 
Article 

Google Scholar 
Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).CAS 
PubMed 
Article 

Google Scholar 
Hugenholtz, P. Exploring prokaryotic diversity in the genomic era. Genome Biol. 3, REVIEWS0003 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Engelen, B. & Imachi, H. Cultivation of subseafloor prokaryotic life in developments in marine geology. In Earth and Life Processes Discovered from Subseafloor Environment. Vol. 7 (eds. Stein, R., Blackman, D., Inagaki, F. & Larsen, H.-L.) 197–209 (Elsevier, 2014).Baker, B. J., Appler, K. E. & Gong, X. New microbial biodiversity in marine sediments. Annu. Rev. Mar. Sci. 13, 161–175 (2021).Article 

Google Scholar 
Hoshino, T. et al. Global diversity of microbial communities in marine sediment. Proc. Natl Acad. Sci. USA 117, 27587–27597 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tahon, G., Geesink, P. & Ettema, T. J. G. Expanding archaeal diversity and phylogeny: past, present, and future. Annu. Rev. Microbiol. 75, 359–381 (2021).PubMed 
Article 
CAS 

Google Scholar 
Bhattarai, S., Cassarini, C. & Lens, P. N. L. Physiology and distribution of archaeal methanotrophs that couple anaerobic oxidation of methane with sulfate reduction. Microbiol. Mol. Biol. Rev. 83, e00074-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Krukenberg, V. et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wegener, G., Krukenberg, V., Ruff, S. E., Kellermann, M. Y. & Knittel, K. Metabolic capabilities of microorganisms involved in and associated with the anaerobic oxidation of methane. Front. Microbiol. 7, 46 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Agrawal, L. K. et al. Treatment of raw sewage in a temperate climate using a UASB reactor and the hanging sponge cubes process. Water Sci. Technol. 36, 433–440 (1997).CAS 
Article 

Google Scholar 
Tyagi, V. K. et al. Future perspectives of energy saving down-flow hanging sponge (DHS) technology for wastewater valorization—a review. Rev. Environ. Sci. Biotechnol. 20, 389–418 (2021).Article 

Google Scholar 
Namita Maharjan, N. et al. Downflow hanging sponge system: a self-sustaining option for wastewater treatment. In Wastewater Treatment (IntechOpen, London, UK, 2020) Available at https://www.intechopen.com/online-first/74120Nurmiyanto, A. & Ohashi, A. Downflow hanging sponge (DHS) reactor for wastewater treatment—a short review. MATEC Web Conf. 280, 05004 (2019).CAS 
Article 

Google Scholar 
Hatamoto, M., Okubo, T., Kubota, K. & Yamaguchi, T. Characterization of downflow hanging sponge reactors with regard to structure, process function, and microbial community compositions. Appl. Microbiol. Biotechnol. 102, 10345–10352 (2018).CAS 
PubMed 
Article 

Google Scholar 
Tandukar, M., Uemura, S., Ohashi, A. & Harada, H. Combining UASB and the ‘fourth generation’ down-flow hanging sponge reactor for municipal wastewater treatment. Water Sci. Technol. 53, 209–218 (2006).CAS 
PubMed 
Article 

Google Scholar 
Chuang, H.-P. et al. Microbial community that catalyzes partial nitrification at low oxygen atmosphere as revealed by 16S rRNA and amoA genes. J. Biosci. Bioeng. 104, 525–528 (2007).CAS 
PubMed 
Article 

Google Scholar 
Imachi, H. et al. Cultivation of methanogenic community from subseafloor sediments using a continuous-flow bioreactor. ISME J. 5, 1913–1925 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aoki, M. et al. A long-term cultivation of an anaerobic methane-oxidizing microbial community from deep-sea methane-seep sediment using a continuous-flow bioreactor. PLoS ONE 9, e105356 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kato, S. et al. Biotic manganese oxidation coupled with methane oxidation using a continuous-flow bioreactor system under marine conditions. Water Sci. Technol. 76, 1781–1795 (2017).CAS 
PubMed 
Article 

Google Scholar 
Imachi, H. et al. Cultivable microbial community in 2-km-deep, 20-million-year-old subseafloor coalbeds through ~1000 days anaerobic bioreactor cultivation. Sci. Rep. 9, 2305 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Imachi, H. et al. Pelolinea submarina gen. nov., sp. nov., an anaerobic, filamentous bacterium of the phylum Chloroflexi isolated from subseafloor sediment. Int. J. Syst. Evol. Microbiol. 64, 812–818 (2014).CAS 
PubMed 
Article 

Google Scholar 
Imachi, H. et al. Sedimentibacter acidaminivorans sp. nov., an anaerobic, amino-acid-utilizing bacterium isolated from marine subsurface sediment. Int. J. Syst. Evol. Microbiol. 66, 1293–1300 (2016).CAS 
PubMed 
Article 

Google Scholar 
Imachi, H. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature 577, 519–525 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miyazaki, M. et al. Sphaerochaeta multiformis sp. nov., an anaerobic, psychrophilic bacterium isolated from subseafloor sediment, and emended description of the genus Sphaerochaeta. Int. J. Syst. Evol. Microbiol. 64, 4147–4154 (2014).PubMed 
Article 
CAS 

Google Scholar 
Miyazaki, M. et al. Spirochaeta psychrophila sp. nov., a psychrophilic spirochaete isolated from subseafloor sediment, and emended description of the genus Spirochaeta. Int. J. Syst. Evol. Microbiol. 64, 2798–2804 (2014).CAS 
PubMed 
Article 

Google Scholar 
Nakahara, N. et al. Aggregatilinea lenta gen. nov., sp. nov., a slow-growing, facultatively anaerobic bacterium isolated from subseafloor sediment, and proposal of the new order Aggregatilineales ord. nov. within the class Anaerolineae of the phylum Chloroflexi. Int. J. Syst. Evol. Microbiol. 69, 1185–1194 (2019).CAS 
PubMed 
Article 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 593, 553–557 (2021).CAS 
PubMed 
Article 

Google Scholar 
Ruff, S. E. et al. Global dispersion and local diversification of the methane seep microbiome. Proc. Natl Acad. Sci. USA 112, 4015–4020 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chadwick, G. et al. Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea. PLoS Biol. 20, e3001508 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Cario, A., Oliver, G. C. & Rogers, K. L. Exploring the deep marine biosphere: challenges, innovations, and opportunities. Front. Earth Sci. 7, 225 (2019).Article 

Google Scholar 
Jørgensen, B. B. & Boetius, A. Feast and famine—microbial life in the deep-sea bed. Nat. Rev. Microbiol. 5, 770–781 (2007).PubMed 
Article 
CAS 

Google Scholar 
Hoehler, T. M. & Jørgensen, B. B. Microbial life under extreme energy limitation. Nat. Rev. Microbiol. 11, 83–94 (2013).CAS 
PubMed 
Article 

Google Scholar 
Schink, B. & Stams, A. J. M. Syntrophism among prokaryotes. In The Prokaryotes: Prokaryotic Communities and Ecophysiology (eds. Rosenberg, E. et al.) 471–493 (Springer, 2013).de Bok, F. A. M. et al. The first true obligately syntrophic propionate-oxidizing bacterium, Pelotomaculum schinkii sp. nov., co-cultured with Methanospirillum hungatei, and emended description of the genus Pelotomaculum. Int. J. Syst. Evol. Microbiol. 55, 1697–1703 (2005).PubMed 
Article 
CAS 

Google Scholar 
Imachi, H. et al. Pelotomaculum propionicicum sp. nov., an anaerobic, mesophilic, obligately syntrophic, propionate-oxidizing bacterium. Int. J. Syst. Evol. Microbiol. 57, 1487–1492 (2007).PubMed 
Article 

Google Scholar 
Qiu, Y.-L. et al. Syntrophorhabdus aromaticivorans gen. nov., sp. nov., the first cultured anaerobe capable of degrading phenol to acetate in obligate syntrophic associations with a hydrogenotrophic methanogen. Appl. Environ. Microbiol. 74, 2051–2058 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matsushita, S. et al. Anti-bacterial effects of MnO2 on the enrichment of manganese-oxidizing bacteria in downflow hanging sponge reactors. Microbes Environ. 35, ME20052 (2020).PubMed Central 

Google Scholar 
Momper, L. et al. Rectinema subterraneum sp. nov, a chemotrophic spirochaete isolated from the deep terrestrial subsurface. Int. J. Syst. Evol. Microbiol. 70, 4739–4747 (2020).CAS 
PubMed 
Article 

Google Scholar 
Chuang, H.-P., Ohashi, A., Imachi, H., Tandukar, M. & Harada, H. Effective partial nitrification to nitrite by down-flow hanging sponge reactor under limited oxygen condition. Water Res. 41, 295–302 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hatamoto, M., Koshiyama, Y., Kindaichi, T., Ozaki, N. & Ohashi, A. Enrichment and identification of methane-oxidizing bacteria by using down-flow hanging sponge bioreactors under low methane concentration. Ann. Microbiol. 61, 683–687 (2010).Article 
CAS 

Google Scholar 
Hatamoto, M., Yamamoto, H., Kindaichi, T., Ozaki, N. & Ohashi, A. Biological oxidation of dissolved methane in effluents from anaerobic reactors using a down-flow hanging sponge reactor. Water Res. 44, 1409–1418 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hatamoto, M., Miyauchi, T., Kindaichi, T., Ozaki, N. & Ohashi, A. Dissolved methane oxidation and competition for oxygen in down-flow hanging sponge reactor for post-treatment of anaerobic wastewater treatment. Bioresour. Technol. 102, 10299–10304 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hatamoto, M. et al. Potential of nitrous oxide conversion in batch and down-flow hanging sponge bioreactor systems. Sustain. Environ. Res. 24, 117–128 (2014).
Google Scholar 
Cao, L. T. T. et al. Biological oxidation of Mn(II) coupled with nitrification for removal and recovery of minor metals by downflow hanging sponge reactor. Water Res. 68, 545–553 (2015).CAS 
PubMed 
Article 

Google Scholar 
Yamaguchi, T. et al. A novel approach for toluene gas treatment using a downflow hanging sponge reactor. Appl. Microbiol. Biotechnol. 102, 5625–5634 (2018).CAS 
PubMed 
Article 

Google Scholar 
Matsushita, S. et al. Production of biogenic manganese oxides coupled with methane oxidation in a bioreactor for removing metals from wastewater. Water Res. 130, 224–233 (2018).CAS 
PubMed 
Article 

Google Scholar 
Onodera, T. et al. Characterization of the retained sludge in a down-flow hanging sponge (DHS) reactor with emphasis on its low excess sludge production. Bioresour. Technol. 136, 169–175 (2013).CAS 
PubMed 
Article 

Google Scholar 
Miyaoka, Y., Hatamoto, M., Yamaguchi, T. & Syutsubo, K. Eukaryotic community shift in response to organic loading rate of an aerobic trickling filter (down-flow hanging sponge reactor) treating domestic sewage. Microb. Ecol. 73, 801–814 (2016).PubMed 
Article 
CAS 

Google Scholar 
Park, M.-O., Ikenaga, H. & Watanabe, K. Phage diversity in a methanogenic digester. Microb. Ecol. 53, 98–103 (2006).PubMed 
Article 
CAS 

Google Scholar 
Wu, Q. & Liu, W.-T. Determination of virus abundance, diversity and distribution in a municipal wastewater treatment plant. Water Res. 43, 1101–1109 (2009).CAS 
PubMed 
Article 

Google Scholar 
Chien, I.-C., Meschke, J. S., Gough, H. L. & Ferguson, J. F. Characterization of persistent virus-like particles in two acetate-fed methanogenic reactors. PLoS One 8, e81040 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Girguis, P. R., Orphan, V. J., Hallam, S. J. & DeLong, E. F. Growth and methane oxidation rates of anaerobic methanotrophic archaea in a continuous-flow bioreactor. Appl. Environ. Microbiol. 69, 5472–5482 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, Y., Maignien, L., Zhao, X., Wang, F. & Boon, N. Enrichment of a microbial community performing anaerobic oxidation of methane in a continuous high-pressure bioreactor. BMC Microbiol. 11, 137 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sauer, P., Glombitza, C. & Kallmeyer, J. A system for incubations at high gas partial pressure. Front. Microbiol. 3, 25 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Bhattarai, S. et al. Enrichment of sulfate reducing anaerobic methane oxidizing community dominated by ANME-1 from Ginsburg Mud Volcano (Gulf of Cadiz) sediment in a biotrickling filter. Bioresour. Technol. 259, 433–441 (2018).CAS 
PubMed 
Article 

Google Scholar 
Cassarini, C. et al. Enrichment of anaerobic methanotrophs in biotrickling filters using different sulfur compounds as electron acceptor. Environ. Eng. Sci. 36, 431–443 (2018).Article 
CAS 

Google Scholar 
Cassarini, C. et al. Anaerobic methane oxidation coupled to sulfate reduction in a biotrickling filter: reactor performance and microbial community analysis. Chemosphere 236, 124290 (2019).CAS 
PubMed 
Article 

Google Scholar 
Adams, M. M., Hoarfrost, A. L., Bose, A., Joye, S. B. & Girguis, P. R. Anaerobic oxidation of short-chain alkanes in hydrothermal sediments: potential influences on sulfur cycling and microbial diversity. Front. Microbiol. 4, 110 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Machdar, I., Sekiguchi, Y., Sumino, H., Ohashi, A. & Harada, H. Combination of a UASB reactor and a curtain type DHS (downflow hanging sponge) reactor as a cost-effective sewage treatment system for developing countries. Water Sci. Technol. 42, 83–88 (2000).CAS 
Article 

Google Scholar 
Judd, S. The status of membrane bioreactor technology. Trends Biotechnol. 26, 109–116 (2008).CAS 
PubMed 
Article 

Google Scholar 
Smith, A. L. et al. Perspectives on anaerobic membrane bioreactor treatment of domestic wastewater: a critical review. Bioresour. Technol. 122, 149–159 (2012).CAS 
PubMed 
Article 

Google Scholar 
Meulepas, R. J. W. et al. Enrichment of anaerobic methanotrophs in sulfate-reducing membrane bioreactors. Biotechnol. Bioeng. 104, 458–470 (2009).CAS 
PubMed 
Article 

Google Scholar 
Jagersma, G. C. et al. Microbial diversity and community structure of a highly active anaerobic methane-oxidizing sulfate-reducing enrichment. Environ. Microbiol. 11, 3223–3232 (2009).CAS 
PubMed 
Article 

Google Scholar 
Lettinga, G., van Velsen, A. F. M., Hobma, S. W., de Zeeuw, W. & Klapwijk, A. Use of the upflow sludge blanket (USB) reactor concept for biological wastewater treatment, especially for anaerobic treatment. Biotechnol. Bioeng. 22, 699–734 (1980).CAS 
Article 

Google Scholar 
Sekiguchi, Y., Kamagata, Y., Nakamura, K., Ohashi, A. & Harada, H. Fluorescence in situ hybridization using 16S rRNA-targeted oligonucleotides reveals localization of methanogens and selected uncultured bacteria in mesophilic and thermophilic sludge granules. Appl. Environ. Microbiol. 65, 1280–1288 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tawfik, A., Ohashi, A. & Harada, H. Sewage treatment in a combined up-flow anaerobic sludge blanket (UASB)-down-flow hanging sponge (DHS) system. Biochem. Eng. J. 29, 210–219 (2006).CAS 
Article 

Google Scholar 
Onodera, T. et al. Development of a sixth-generation down-flow hanging sponge (DHS) reactor using rigid sponge media for post-treatment of UASB treating municipal sewage. Bioresour. Technol. 152, 93–100 (2014).CAS 
PubMed 
Article 

Google Scholar 
Tandukar, M., Ohashi, A. & Harada, H. Performance comparison of a pilot-scale UASB and DHS system and activated sludge process for the treatment of municipal wastewater. Water Res. 41, 2697–2705 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).CAS 
PubMed 
Article 

Google Scholar 
Nauhaus, K., Albrecht, M., Elvert, M., Boetius, A. & Widdel, F. In vitro cell growth of marine archaeal-bacterial consortia during anaerobic oxidation of methane with sulfate. Environ. Microbiol. 9, 187–196 (2007).CAS 
PubMed 
Article 

Google Scholar 
Wegener, G., Niemann, H., Elvert, M., Hinrichs, K. & Boetius, A. Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environ. Microbiol. 10, 2287–2298 (2008).CAS 
PubMed 
Article 

Google Scholar 
Hu, H., Natarajan, V. P. & Wang, F. Towards enriching and isolation of uncultivated archaea from marine sediments using a refined combination of conventional microbial cultivation methods. Mar. Life Sci. Technol. 3, 231–242 (2021).Article 
CAS 

Google Scholar 
Balch, W. E., Fox, G. E., Magrum, L. J., Woese, C. R. & Wolfe, R. S. Methanogens: reevaluation of a unique biological group. Microbiol. Rev. 43, 260–296 (1979).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yokooji, Y. et al. Pantoate kinase and phosphopantothenate synthetase, two novel enzymes necessary for CoA biosynthesis in the Archaea. J. Biol. Chem. 284, 28137–28145 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moench, T. & Zeikus, J. G. An improved preparation method for a titanium (III) media reductant. J. Microbiol. Methods 1, 199–202 (1983).CAS 
Article 

Google Scholar 
Miyashita, A. et al. Development of 16S rRNA gene-targeted primers for detection of archaeal anaerobic methanotrophs (ANMEs). FEMS Microbiol. Lett. 297, 31–37 (2009).CAS 
PubMed 
Article 

Google Scholar 
Sekiguchi, Y. et al. Sequence-specific cleavage of 16S rRNA for rapid and quantitative detection of particular groups of anaerobes in bioreactors. Water Sci. Technol. 52, 107–113 (2005).CAS 
PubMed 
Article 

Google Scholar 
Meechan, P. J. & Wilson, C. Use of ultraviolet lights in biological safety cabinets: a contrarian view. Appl. Biosaf. 11, 222–227 (2006).Article 

Google Scholar 
Imachi, H., Sekiguchi, Y., Kamagata, Y., Ohashi, A. & Harada, H. Cultivation and in situ detection of a thermophilic bacterium capable of oxidizing propionate in syntrophic association with hydrogenotrophic methanogens in a thermophilic methanogenic granular sludge. Appl. Environ. Microbiol. 66, 3608–3615 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stams, A. J., Grolle, K. C., Frijters, C. T. & van Lier, J. B. Enrichment of thermophilic propionate-oxidizing bacteria in syntrophy with Methanobacterium thermoautotrophicum or Methanobacterium thermoformicicum. Appl. Environ. Microbiol. 58, 346–352 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Uchino, Y. & Suzuki, K. A simple preparation of liquid media for the cultivation of strict anaerobes. J. Pet. Environ. Biotechnol. S3, 001 (2011).
Google Scholar 
Akinyemi, T. S., Shao, N. & Whitman, W. B. Genus Methanothrix. In Bergey’s Manual of Systematics of Archaea and Bacteria (John Wiley & Sons, 2020). More

Falkowski PG. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 1994;39:235–58.CAS 
PubMed 
Article 

Google Scholar 
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237.CAS 
PubMed 
Article 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bell W, Mitchell R. Chemotactic and growth response of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.Article 

Google Scholar 
Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 
Article 

Google Scholar 
Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nat Rev Microbiol. 2007;5:782–91.CAS 
PubMed 
Article 

Google Scholar 
Meyer N, Bigalke A, Kaulfuß A, Pohnert G. Strategies and ecological roles of algicidal bacteria. FEMS Microbiol Rev. 2017;41:880–99.CAS 
PubMed 
Article 

Google Scholar 
Windler M, Bova D, Kryvenda A, Straile D, Gruber A, Kroth PG. Influence of bacteria on cell size development and morphology of cultivated diatoms. Phycol Res. 2014;62:269–81.Article 

Google Scholar 
Buhmann MT, Schulze B, Forderer A, Schleheck D, Kroth PG. Bacteria may induce the secretion of mucin-like proteins by the diatom Phaeodactylum tricornutum. J Phycol. 2016;52:463–74.CAS 
PubMed 
Article 

Google Scholar 
van Tol HM, Amin SA, Armbrust EV. Ubiquitous marine bacterium inhibits diatom cell division. ISME J. 2017;11:31–42.PubMed 
Article 
CAS 

Google Scholar 
Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signaling between a cosmopolitan phytoplankton and associated bacteria. Nature 2015;522:98–101.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci. 2015;112:453–7.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol 2017;19:3500–13.CAS 
PubMed 
Article 

Google Scholar 
Grossart H-P, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 
Article 

Google Scholar 
Crenn K, Duffieux D, Jeanthon C. Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short- and long-term associations. Front Microbiol. 2018;9:2879.PubMed 
PubMed Central 
Article 

Google Scholar 
Behringer G, Ochsenkühn MA, Fei C, Fanning J, Koester JA, Amin SA. Bacterial communities of diatoms display strong conservation across strains and time. Front Microbiol. 2018;9:659.PubMed 
PubMed Central 
Article 

Google Scholar 
Schäfer H, Abbas B, Witte H, Muyzer G. Genetic diversity of ‘satellite’ bacteria present in cultures of marine diatoms. FEMS Microbiol Ecol 2002;42:25–35.PubMed 

Google Scholar 
Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci. 2020;117:3656–3662.Stock W, Blommaert L, De Troch M, Mangelinckx S, Willems A, Vyverman W, et al. Host specificity in diatom-bacteria interactions alleviates antagonistic effects. FEMS Microbiol Ecol. 2019;95:fiz171.Segev E, Wyche TP, Kim KH, Petersen J, Ellebrandt C, Vlamakis H, et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife. 2016;5:e17473.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wagner-Döbler I, Ballhausen B, Berger M, Brinkhoff T, Buchholz I, Bunk B, et al. The complete genome sequence of the algal symbiont Dinoroseobacter shibae: a hitchhiker’s guide to life in the sea. ISME J. 2010;4:61–77.PubMed 
Article 
CAS 

Google Scholar 
Frank O, Michael V, Päuker O, Boedeker C, Jogler C, Rohde M, et al. Plasmid curing and the loss of grip – the 65-kb replicon of Phaeobacter inhibens DSM 17395 is required for biofilm formation, motility and the colonization of marine algae. Syst Appl Microbiol. 2015;38:120–7.CAS 
PubMed 
Article 

Google Scholar 
Paul C, Pohnert G. Interactions of the algicidal bacterium Kordia algicida with diatoms: regulated protease excretion for specific algal lysis. PLoS One. 2011;6:e21032.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stock F, Bilcke G, De Decker S, Osuna-Cruz CM, Van den Berge K, Vancaester E, et al. distinctive growth and transcriptional changes of the diatom Seminavis robusta in response to quorum sensing related compounds. Front Microbiol. 2020;11:1240.PubMed 
PubMed Central 
Article 

Google Scholar 
Guillard RRL Culture of Phytoplankton for Feeding Marine Invertebrates. In: Smith WL, Chanley MH, editors. Culture of marine invertebrate animals: proceedings — 1st conference on culture of marine invertebrate animals greenport. Boston, MA: Springer US; 1975. p. 29–60.Rasband WS (2016). ImageJ, U.S. National Institutes of Health, Bethesda, MD, USA. Available at: http://imagej.nih.gov/ij/, 1997–2015.DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith F. Colorimetric method for determination of sugars and related substances. Anal Chem. 1956;28:350–6.CAS 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.Article 
CAS 

Google Scholar 
Alexa A, Rahnenfuhrer J (2021). topGO: Enrichment analysis for gene ontology. R package version 2.46.0.Csardi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.Wei Q, Khan IK, Ding Z, Yerneni S, Kihara D. NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinform. 2017;18:177.Article 
CAS 

Google Scholar 
Shapiro HM (2003). Physical parameters and their uses. In: Shapiro HM (ed). Practical Flow Cytometry. John Wiley & Sons, Inc.: New York, NY, USA, pp. 273-85.Clercq AD, Inzé D. Cyclin-dependent kinase inhibitors in yeast, animals, and plants: a functional comparison. Crit Rev Biochem Mol Biol. 2006;41:293–313.PubMed 
Article 
CAS 

Google Scholar 
Zinser ER. The microbial contribution to reactive oxygen species dynamics in marine ecosystems. Environ Microbiol Rep. 2018;10:412–27.CAS 
PubMed 
Article 

Google Scholar 
Whalen KE, Kirby C, Nicholson RM, O’Reilly M, Moore BS, Harvey EL. The chemical cue tetrabromopyrrole induces rapid cellular stress and mortality in phytoplankton. Sci Rep. 2018;8:15498.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sheyn U, Rosenwasser S, Ben-Dor S, Porat Z, Vardi A. Modulation of host ROS metabolism is essential for viral infection of a bloom-forming coccolithophore in the ocean. ISME J. 2016;10:1742–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finkel ZV, Irwin AJ, Schofield O. Resource limitation alters the ¾ size scaling of metabolic rates in phytoplankton. Mar Ecol Prog Ser. 2004;273:269–80.Article 

Google Scholar 
De Troch M, Chepurnov V, Gheerardyn H, Vanreusel A, Ólafsson E. Is diatom size selection by harpacticoid copepods related to grazer body size? J Exp Mar Biol Ecol. 2006;332:1–11.Article 

Google Scholar 
Finkel ZV. Light absorption and size scaling of light-limited metabolism in marine diatoms. Limnol Oceanogr. 2001;46:86–94.CAS 
Article 

Google Scholar 
Wilhelm T, Said M, Naim V. DNA replication stress and chromosomal instability: dangerous liaisons. Genes. 2020;11:642.CAS 
PubMed Central 
Article 

Google Scholar 
Gelot C, Magdalou I, Lopez BS. Replication stress in mammalian cells and its consequences for mitosis. Genes. 2015;6:267–98.Vogt E, Kirsch-Volders M, Parry J, Eichenlaub-Ritter U. Spindle formation, chromosome segregation and the spindle checkpoint in mammalian oocytes and susceptibility to meiotic error. Mutat. Res. – Genet. Toxicol. Environ. Mutagen. 2008;651:14–29.CAS 

Google Scholar 
Van de Meene AML, Pickett-Heaps JD. Valve morphogenesis in the centric diatom Rhizosolenia setigera (Bacillariophyceae, Centrales) and its taxonomic implications. Eur J Phycol. 2004;39:93–104.Article 

Google Scholar 
Pollara SB, Becker JW, Nunn BL, Boiteau R, Repeta D, Mudge MC, et al. Bacterial quorum-sensing signal arrests phytoplankton cell division and impacts virus-induced mortality. mSphere. 2021;6:e00009–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Dassow P, Petersen TW, Chepurnov VA, Virginia Armbrust E. Inter- and intraspecific relationships between nuclear DNA content and cell size in selected members of the centric diatom genus Thalassiosira (Bacillariophyceae). J Phycol. 2008;44:335–49.Article 
CAS 

Google Scholar 
Pokrzywinski KL, Tilney CL, Warner ME, Coyne KJ. Cell cycle arrest and biochemical changes accompanying cell death in harmful dinoflagellates following exposure to bacterial algicide IRI-160AA. Sci Rep. 2017;7:45102.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durkin CA, Mock T, Armbrust EV. Chitin in diatoms and its association with the cell wall. Eukaryot Cell. 2009;8:1038.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wildermuth MC. Modulation of host nuclear ploidy: a common plant biotroph mechanism. Curr Opin Plant Biol. 2010;13:449–58.CAS 
PubMed 
Article 

Google Scholar 
Cho J-C, Giovannoni SJ. Croceibacter atlanticus gen. nov., sp. nov., A Novel Marine Bacterium in the Family Flavobacteriaceae. Syst Appl Microbiol. 2003;26:76–83.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Morris JJ, Lenski RE, Zinser ER. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio. 2012;3:e00036–12.PubMed 
PubMed Central 
Article 

Google Scholar 
Ndhlovu A, Durand PM, Ramsey G. Programmed cell death as a black queen in microbial communities. Mol Ecol. 2021;30:1110–9.CAS 
PubMed 
Article 

Google Scholar 
Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
PubMed 
Article 

Google Scholar 
Sengupta A, Carrara F, Stocker R. Phytoplankton can actively diversify their migration strategy in response to turbulent cues. Nature 2017;543:555–8.CAS 
PubMed 
Article 

Google Scholar 
Levy SF, Ziv N, Siegal ML. Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol. 2012;10:e1001325.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
PubMed 
Article 

Google Scholar 
Blair PM, Land ML, Piatek MJ, Jacobson DA, Lu T-YS, Doktycz MJ, et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 2018;3:e00045–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Helfrich EJN, Vogel CM, Ueoka R, Schäfer M, Ryffel F, Müller DB, et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat Microbiol. 2018;3:909–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Long RA, Qureshi A, Faulkner DJ, Azam F. 2-n-Pentyl-4-quinolinol produced by a marine Alteromonas sp. and its potential ecological and biogeochemical roles. Appl Environ Microbiol. 2003;69:568–76.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calabrese EJ. Hormesis: from mainstream to therapy. Cell Commun Signal. 2014;8:289–91.Article 

Google Scholar 
Chen WM, Sheu FS, Sheu SY. Novel l-amino acid oxidase with algicidal activity against toxic cyanobacterium Microcystis aeruginosa synthesized by a bacterium Aquimarina sp. Enzym Microb Technol. 2011;49:372–9.CAS 
Article 

Google Scholar 
El-Aouar Filho RA, Nicolas A, De Paula Castro TL, Deplanche M, De Carvalho Azevedo VA, Goossens PL, et al. Heterogeneous family of cyclomodulins: smart weapons that allow bacteria to hijack the eukaryotic cell cycle and promote infections. Front Cell Infect Microbiol. 2017;7:364.Ricci V, Giannouli M, Romano M, Zarrilli R. Helicobacter pylori gamma-glutamyl transpeptidase and its pathogenic role. World J Gastroenterol. 2014;20:630–8.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Teeling H, Fuchs Bernhard M, Becher D, Klockow C, Gardebrecht A, Bennke Christin M, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 2012;336:608–11.CAS 
PubMed 
Article 

Google Scholar  More

Historical land use and cover changesExisting databases differed significantly in representing historical LUCC in China (Fig. 1). Generally, datasets agree on the direction of change in cropland area until 1980 in Liu and Tian18, Ramankutty19, Houghton20, and this study (Fig. 1b, c), while the magnitude of change varied greatly. Specifically, the total cropland expansion in China was comparable between our new data set and the LUH2-GCB from 1900 onwards (56 vs 60 Mha, Fig. 1b), but cropland area changes since 1980 diverged considerably (−14 vs 41 Mha, Fig. 1c). The differences were also evident across space and more distinct during the period of 1980 to 2019 (Fig. 2a–d), in which the cropland coverage was mainly declining in our reconstructed data but increasing in LUH2-GCB (Fig. 2b, d). We found that the distinct changes are derived from the abrupt cropland increases in the FAO data reported from China, upon which LUH2-GCB was based (see Supplementary Information 3).Fig. 1: Temporal, net changes of cropland and forest from 1900 (unit: Mha).Panel a–c: cropland; panel d–f: forest; the bar charts indicate the total accumulated areas (b, e) from 1900 and (c, f) from 1980 until the last available year; LUH2-GCB was the latest version of LUH2 data used in Global Carbon Budget assessments projects (LUH2 used in MsTMIP and TRENDY were showed in Supplementary Figs. S7 and S10); Houghton data were derived from Houghton and Nassikas20 and the data in 1900 were interpolated from 1850 and 1950; Liu&Tian and Ramankutty data were derived from the works of Liu and Tian16 and Ramankutty and Foley18; the open circles indicate the changes of cropland and forest areas derived from inventory-based benchmark data; details of the benchmark data for cropland and forest were presented in Yu et al.11 and Supplementary Information 1.2 of this study, respectively; error bars: one standard deviation from the mean.Full size imageFig. 2: Spatial distribution of the fractional coverage changes of cropland and forest in China (unit: %).Panels a–d: cropland; panel e–h: forest; panels a, b, e, and f indicate the results derived from this study; data in panels c, d, g, and h were from LUH2-GCB; panels a, c, e, and g show the changes from 1900 to 1980, whereas panels b, d, f, and h show the changes from 1980 to 2019; negative and positive values indicate coverage reduction and increment, respectively.Full size imageThe problems of cropland area expansion reported to FAO are likely caused by changes in the underlying database, in which the Chinese Agricultural Yearbook (CAY) was used prior to 1996, the China Land and Resources Statistical Yearbook (LRSY) from 1996 to 2007, and the National Land and Resources Bulletin (NLRB) after 2007 (Supplementary Information 3).These three datasets are not consistent with each other because surveying methods were distinct. For example, cropland area in CAY before 1982 used an extrapolation method (i.e. “production-to-acreage” approach) due to limited field survey data11. Specifically, the extrapolation method was widely adopted for convenience and for taxation purposes in the early period, such as in the framework of the first benchmark cropland survey conducted in 1953. Such methods assumed that low-productivity cropland occupied an area of 1/3–1/8 of a predetermined, “standard-productivity” cropland21, which greatly underestimates the acreages of low productivity cropland. Biases accumulated in this method persisted until the satellite era (1980s), while the 1953 surveying data were used as the baseline for CAY to update cropland area on an annual basis.Besides the survey method, policies also contributed to a bias of reported cropland area. To tackle rising food demands, cropland expansion was highly encouraged by the government before the 1980s, implementing an incentive policy to allow new tax-free cropland without reporting to the government for the first 3–5 years22,23. Even after the initial reporting free period, these newly cultivated croplands continued to be unreported due to political incentives to show increasing crop yield to the local authorities23,24.When the first comprehensive and systematic survey (i.e. the second national cropland survey conducted during 1985–1996) was completed, the cropland area was found to be larger than previously reported in CAY11. Similarly, the shift from the use of LRSY to NLRB also introduced a spurious cropland area increment from 2007 to 2010 as small, fragmented croplands were identified by better technologies adopted in NLRB, which had remained undetected previously (Supplementary Fig. S10).Thus, LUH2-GCB has inherited spurious temporal signals of abrupt cropland increment in FAO from the 1980s to 2010 (Fig. 1a and Supplementary Fig. S10). Therefore, if the areas of other land cover types (e.g. forest) are indirectly constrained from cropland area change, cropland area biases were mirrored in the area change of other land use types. This is the case for the LUH2-GCB and for Liu and Tian’s previous land use gridded datasets. Our new database, rebuilt from Yu et al.11, corrected these problems in temporal dynamics by assimilating multiple data sources (Fig. 1a). More specifically, we retrospectively reconstructed information about cropland and forest areas year by year, using tabular data from official agencies (Supplementary Information 1 and Supplementary Data 1). To further reduce the aforementioned biases, we used the most recent and authoritative record of provincial cropland and forest areas available as the benchmark, and then spatialized the cropland and forest distributions using gridded maps as ancillary data (Supplementary Information 1). The area changes were also validated using inventory-based benchmark data (Fig. 1a, d, details were presented in Yu et al.11 and Supplementary Information 1.2).Changes in forest area in China also varied dramatically among databases. Based on Ramankutty and Foley19 and LUH2-GCB, a net forest loss was found from 1900 to the last available year, at 33–108 Mha whereas Liu and Tian18 and Houghton and Nassikas20 reported a net increase of 15 Mha (1900–2005) and 70 Mha (1900–2015) in forest area, respectively (Fig. 1d, e).By assimilating multiple source records, reports, and national surveys, however, our newly reconstructed and intensively validated database (Supplementary Figs. S4, S5, and S8) with corrected biases suggests that the forest area increased by 58 Mha from 1900 to 2019 (Fig. 1e). In particular, our data suggest that there is a surprisingly large underestimation of forest expansion in all other databases (38–102 Mha) after 1980 (Fig. 1f). We performed spatial analyses and show that widespread forest expansion in our reconstructed data was represented as a forest decline in LUH2-GCB during the period 1980–2019 (Fig. 2f, h). These existing biases in the dataset during the last four decades can be simply removed using recently available and spatially explicit forest products (Supplementary Table S2).Bias in forest change might be explained by two reasons. First, gridded datasets inherited and transferred errors from the use of FAO-based cropland dataset in developing global land use databases such as HYDE and thus LUH2-GCB8. Second, the FAO forest area reported is an important reference data used in these databases. The FAO forest area is reported based on a “land use” definition, which underestimated gross “land cover” change signals between reported years (Supplementary Information 1.3). Specifically, the FAO forest area describes lands that have been forested and will continue to be used for forestry (e.g. cut-over area, fired-over area, unestablished afforestation land) (Supplementary Table S5). This approach overestimates forest area by including lands used for reforestation where no forest was yet created. Thus, for example, the FAO statistics reported a 157.2 Mha forest area in 1990 (Supplementary Fig. S7), which is ~30 Mha higher than officially released data.More importantly, newly established forests were underestimated in such an accounting approach. The forest area expansion in China reported in the FAO statistics was 61 Mha from 1990 to 2019, which is 30 Mha lower than the officially released data16. Our reconstructed dataset, in agreement with officially released forest area, uses a “land cover” definition that characterizes the distribution of annually established forests. Therefore, the FAO statistics – a data set with definition specified to describe the area of land use – should be used with caution for constraining the temporal evolution of forest cover distribution in gridded data reconstruction, and the modeling community should be alerted to treat the LUCC data appropriately.Nonetheless, the FAO and the related LUH2 products were the dominant LUCC forcing data used in multiple studies3,25, including various process-model-based intercomparison projects (e.g. MsTMIP, LUMIP, NMIP, TRENDY), annually released Global Carbon Budget reports2,26, and IPCC reports5, implying a potential bias of these assessments for the China region. In contrast, changes in forest area from our database were independently developed (Supplementary Information 1.2), intensively calibrated, and validated using officially released national forest inventories (NFIs, see Supplementary Figs. S4 and S5), which can help to reduce the potential bias of C balance assessment in China. More specifically, the total forest area and PF area in our database were compared with historical NFIs released by the National Forestry and Grassland Administration at provincial level since 1949 (Supplementary Figs. S4 and S5), which supports the reliability of our reconstructed data.Historical carbon stock changesTo illustrate the bias in the C balance of China when using previous LUCC dataset, we performed simulations with the DLEM model for the period 1900–2019 at a resolution of 0.5 × 0.5 degree forced by our new LUCC dataset. We validated the distribution and changes of C stock using published studies and previously reported inventory-based estimations (Supplementary Information 6 and 7). The model could capture well C dynamics in China using inventory-based forest C stock changes at both provincial and national levels as the validation data set (Supplementary Fig. S14).Our results show that the total C stock decreased by 6.9 ± 0.6 Pg from 1900 to 1980 and increased by 8.9 ± 0.8 Pg C from 1980 to 2019 (Fig. 3, derived from experiment S1 in Supplementary Table S10). Such a large C stock increment since the 1980s, which is dominated by vegetation biomass C accumulation, was not captured in the MsTMIP and TRENDY projects driven by different versions of the LUH2 data (Fig. 3). This is attributed to the fast expansion of forest area(s) that was not captured by this land use forcing (Fig. 1).Fig. 3: Temporal changes of carbon storage from 1900 to 2010s in China.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively. Results derived from experiment designed to have all environmental factors vary historically from 1900 to the 2010s, for model design details of this study see Supplementary Information 8); pink color: MsTMIP (1900–2010); blue color: TRENDY (1900–2019); dark color: this study (1900–2019); the shade areas represent the ranges of 1 standard deviation; unit: Pg C.Full size imageWe found that the large-scale forest expansion in China alone has caused a substantial C accumulation since 1980 (0.21 ± 0.006 Pg C per year, Table 1). In contrast, the forest C sink of the TRENDY models is negligible (−0.02 ± 0.05 Pg C per year, Table 1). A moderate C source (0.10 ± 0.08 Pg C per year, Table 1) was even found in the MsTMIP models, since these models were driven by continuous forest area loss and cropland expansion since the 1980s (Supplementary Fig. S7).Table 1 Comparison of reported carbon fluxes from various biomes in ChinaFull size tableA recent atmospheric inversion-based study reported that China’s land ecosystems were a large CO2 sink of −1.11 ± 0.38 Pg C per year27, which seems to be ecologically implausible and critically sensitive to the assimilation of the CO2 record from one station28. The compilation of previous studies from inventory- and satellite-based estimation, atmospheric inversion, and process-based models suggested that the Chinese C sink was much smaller (−0.18– −0.45 Pg C per year; Table 1). Our model-simulated terrestrial sink (~−0.28 ± 0.06 Pg C per year) was in this range (Table 1).While our simulated C balance in different categories or biomes is close to previous estimations, three major differences are observed (Table 1). First, because the LUCC data used in previous global models suffered from biases as shown above, the national C sink was generally underestimated in these simulations (Table 1). Second, our estimation of the forest sink is around two to three times larger than the previous one during 1949–199829. This was mainly because forest area was underestimated by over 33% (53 Mha) in the previous study29 compared to the national forest inventory (NFI)16. This underestimation may stem from exclusion of economic and bamboo forests. The third major difference is the role of grassland soils in C balance during the period 1980–2000. China’s grassland soils were previously reported as a minor sink of −0.007–−0.022 Pg C per year from the 1980s to the 2000s (Table 1), while our simulations suggest that grassland soils were a C source of 0.062–0.066 Pg C per year. This discrepancy lies in the approaches used and the accounting boundaries between studies (i.e. whether the transitions of grassland were considered), in which LUCC impacts were represented differently. For example, impervious surfaces (part of urbanized area) expanded into ~15 Mha of natural lands in China from 1978 to 201730, which further drove redistribution of cropland into marginal lands with the majority converted from grassland, causing wind erosion, habitat loss, and more water and fertilizer consumption31. Earlier studies using a static grassland map exclude the C stock loss in the land-use transition32. Thus, the distinct roles of grassland soils (i.e. sink vs source) derived from our simulations and earlier studies are not contradictory but are due to differences in accounting boundaries.LUCC impacts on carbon stock changesOur DLEM simulation indicates that LUCC induced a C loss of 5.1 ± 0.7 Pg C from 1900 to 2010s (Fig. 4a), which is substantially lower than that from MsTMIP (13.8 ± 7.7 Pg C, 1900–2010) and TRENDY (9.4 ± 3.3 Pg C, 1900–2019; Fig. 4e, f and Supplementary Fig. S18d, g). From 1980 onward, LUCC increased C storage by 4.3 ± 0.7 Pg C, with the major contribution from vegetation biomass C increment in the southwestern and northeastern regions (Fig. 4d and Supplementary Fig. S19a). Nonetheless, this C increase in biomass was not captured in MsTMIP and TRENDY models (Fig. 4e, f and Supplementary Fig. S19d, g), which simulated that LUCC continued to reduce C stock by 7.5 ± 1.6 and 5.3 ± 2.3 Pg C during the period 1980 to the 2010s, respectively (Fig. 4 and Supplementary Fig. S20).Fig. 4: Spatial distribution of LUCC impacts on ecosystem carbon storage.Panel a–c: LUCC impacts for period of 1900–2019; panel d–f: LUCC impacts for period of 1980–2019 (d–f). Panels a and d are from this study; data in panels b and e are from MsTMIP; data in panels c and f are from TRENDY; negative and positive values indicate sink and source, respectively; green and yellow bar stacked in the insert indicate LUCC impacts on vegetation and soil organic carbon in Pg C; spatial map unit: g C m−2; error bars: one standard deviation from the mean of LUCC impacts on total carbon storage.Full size imageTo confirm that such discrepancy was induced by LUCC data but not the DLEM model, we set up additional DLEM simulations using the LUH2-GCB database (Supplementary Information 8). The simulated C losses induced by LUCC when DLEM was driven with LUH2-GCB were 6.5 ± 0.4 and 11.4 ± 0.6 Pg C during the periods of 1980–2019 and 1900–2019, which are close to MsTMIP and TRENDY simulations. These results confirm that the LUCC forcing database is the major contributor to the difference between our simulations and the MsTMIP and TRENDY projects. An earlier study reported that global LUCC-induced C emissions are substantially underestimated due to underrepresented tree harvesting and land clearing from shifting cultivation33. Our simulation revealed that regional LUCC-induced C emission could also be overestimated in China due to a bias in the LUCC data.There are also disputes over whether the LUCC induced a C sink in China since the 1990s or not (Supplementary Table S8). By using an updated LUCC database, our simulations revealed that LUCC was a strong C sink in China, and that its magnitude was larger than previous estimates since the 1990s (Supplementary Table S8). Our results using an improved LUCC forcing data can facilitate narrowing down the well-known, large uncertainty in LUCC-induced C change at regional scale.Attributions of different factors on C stock changes since 1980By using the DLEM model with factorial simulations (see Supplementary Information 8 for details), we examined the direct and interactive contributions of different drivers to terrestrial C stock change in China for the period 1980–2019, including LUCC, climate, forest management, N deposition, and CO2 fertilization (see Methods, Fig. 5). Note that historical C stock change is not equivalent to the sum of factorial attributions as the baseline conditions differ (see Supplementary Information 8).Fig. 5: Attributions of different environmental factors on carbon stock change in China from 1980 to 2019.Panels a–c indicate attributions of impacts on the changes of vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; CLM: climate; CO2: rising atmospheric CO2 concentration; Ndep: N deposition; Man: forest management; Nfer: N fertilizer and manure application.Full size imageOverall, 81.9% (6.5 Pg C) of the terrestrial C sink during this period was attributed to direct impacts of all major factors, while the interactive effect contributed 18.1% (1.43 Pg C; Fig. 5c). Among all the factors examined, LUCC was the dominant driver accounting for 50.3% (3.96 Pg C) of the total C increment during the period 1980–2019 (Fig. 5c), which was largely attributed to biomass C accumulation (70.0%; Fig. 5a, c). Tian et al.13 reported that LUCC’s contribution to the sink in China was at 0.05 Pg C yr−1 since the 1980s – an amount that is only about 30% of our simulations. The discrepancy is attributed to the different representation of forest expansion in model simulations, which was 65 Mha from 1980 to 2005 in our database but only ~14 Mha in Tian et al.13. Similarly, the increase in the global land sink during the recent period (1998–2012) was also mainly attributed to LUCC (i.e. decreased tropical forest area loss and increased afforestation in northern temperate regions), instead of CO2 or climate change34.Climate change enhanced biomass C stocks by 1.63 Pg but caused a soil C loss of 0.30 Pg, thus contributing to land sink of 1.41 Pg C (18.0% of the total with all factors) since 1980 (Fig. 5). Other global change factors, such as N fertilizer application, atmospheric N deposition, and rising CO2, had a relatively minor contribution (0.1–9.54%) to the terrestrial C sink. Therefore, conversely to previous studies13,35,36,37, we showed that LUCC was the dominant driver of the recent land C sink in China, and other factors including climate change, rising CO2, and N deposition, contributed much less (0.1–18.0%) to the C stock increment in China (Fig. 5c). Tian et al.13 pointed out that LUCC effects in China should not be ignored and that the CO2 fertilization effect might be overestimated in Piao et al.38.Our simulations confirm these statements, and further show that LUCC was actually the largest contributor to land sink in China since 1980 (Fig. 5). In those studies which did not account for the influence of LUCC separately, the effects of other global change factors may have been overestimated by including LUCC impacts. For example, Chen et al.39 and He et al.37 attributed China’s C sink into different components including climate change, leaf area index (LAI) change, rising CO2, and N deposition. Such partition inevitably masked the separate contribution from LUCC, because LAI changes are closely related to land-cover changes. Thus, the accurate representation of the LUCC should be prioritized in future modeling attribution studies.Carbon stock changes in each land cover type since 1980The contribution of the establishment of young and new forest plantations to C sink has received increasing attention3,40,41,42. Our simulation (experiment S1, see Methods section) revealed that the increase in terrestrial C stock was dominantly contributed by biomass C accumulation (76.3%) (Fig. 5), in which the natural and planted forests accounted for 65% (2.9 Pg C) and 35% (1.6 Pg C) during the last four decades. We examined the LUCC effect (i.e. the largest contributor of C stock increment in Fig. 5) on the C stock of different biomes and confirmed that forest was the major contributor of the net C accumulation in China since 1980, while other biomes, including cropland, grassland, shrubland, and wetland, were relatively stable, varying from −0.3 to 0.3 Pg C during the same period (Fig. 6). A recent study documented that forest expansion was essential for a large C sink in southern China during 2002–2017, where newly-established and existing forests contributed to 32% and 34% of land C sink in the region43. In comparison to the large biomass C increase since 1980 (3.0 Pg C, Fig. 6a), the SOC increase was much lower (0.7 Pg C) during the concurrent period, although SOC changes in each biome varied greatly (–3.4–8.6 Pg C; Fig. 6b) due to area change from land conversions. The biome-level analyses further revealed that the LUCC-induced C stock increment was dominantly contributed from forest and by area expansion, while C storage in grassland and shrubland was reduced by LUCC (Fig. 6).Fig. 6: LUCC-induced carbon storage changes by land cover types based on model simulations during 1980–2019.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; the widths of the red blocks indicate the estimation ranges of net changes in model simulations; purple error bars indicate one standard deviation of multiple model runs; negative and positive changes indicate carbon loss and gain, respectively.Full size imageThis study highlights the dominant role of LUCC in determining the terrestrial C sink in China. Because of inaccurate representations of land cover change in China, previous estimates of the terrestrial C sink have been strongly underestimated. In contrast, forest expansion and cropland abandonment have been overestimated in the U.S., resulting in an underestimated C emission since 19807. Hence, we highlighted that the global LUCC database should be further improved, which could potentially narrow down the C imbalance reported in global C budget accounting2. In contrast to the previous studies, we showed that the contributions of factors including rising CO2, N deposition, and climate change to the land C sink in China were much smaller than LUCC over the past four decades (1980-present time). Thus, reforestation projects could represent important climate change mitigation pathways, with co-benefits for biodiversity33. To achieve the ‘C neutrality’ goal as the Chinese government declared, future climate policy should be directed to improve land management, especially forest ecosystems.Implications for future LUCC data improvementsThis study provides a novel reconstruction of recent land use change in China and assesses its implications in quantifying for terrestrial C storage dynamics. The improved dataset more accurately depicts the spatiotemporal dynamics of LUCC in China because the historically contradictory surveying records were identified, which helped to correct the biased temporal signals. Specifically, the improved surveying methods and the socioeconomic factors have greatly shaped the LUCC signals. We advocate that these impacts should be considered in the reconstruction of the national and global LUCC dataset, especially in the areas that have been intensively disturbed by human activities as is the case of China. These endeavours will be worthwhile, as demonstrated by the large impact that these bias corrections have on China’s C dynamic assessments since 1900. Thus, accurate delineation of LUCC forcing should be stressed in global simulations, including C budget accounting, biodiversity assessments, and ecosystem services evaluations. More

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Chocolate, a serious contender for the world’s most beloved food, is made from the seed kernels of the cacao tree (Theobroma cacao). But despite its popularity, Justine Vansynghel at the University of Würzburg in Germany and her colleagues found that nobody had quantified how species living on small-scale cacao farms collectively affect production1.

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doi: https://doi.org/10.1038/d41586-022-02908-0

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