Ecology

Thakur MP, van der Putten WH, Cobben MMP, van Kleunen M, Geisen S. Microbial invasions in terrestrial ecosystems. Nat Rev Microbiol. 2019;17:621–31.CAS 
PubMed 
Article 

Google Scholar 
Mooney HA, Cleland EE. The evolutionary impact of invasive species. Proc Natl Acad Sci USA. 2001;98:5446–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mallon CA, Elsas JDV, Salles JF. Microbial invasions: the process, patterns, and mechanisms. Trends Microbiol. 2015;23:719–29.CAS 
PubMed 
Article 

Google Scholar 
O’Brien S, Hodgson DJ, Buckling A. Social evolution of toxic metal bioremediation in Pseudomonas aeruginosa. Proc R Soc B Biol Sci. 2014;281:20140858.Walter J, Maldonado-Gómez MX, Martínez I. To engraft or not to engraft: an ecological framework for gut microbiome modulation with live microbes. Curr Opin Biotechnol. 2018;49:129–39.CAS 
PubMed 
Article 

Google Scholar 
van der Goot E, van Spronsen FJ, Falcão Salles J, van der Zee EA. A microbial community ecology perspective on the gut-microbiome-brain axis. Front Endocrinol. 2020;11:611.Article 

Google Scholar 
Williamson M, Fitter A. The varying success of invaders. Ecology. 1996;77:1661–6.Article 

Google Scholar 
Catford JA, Jansson R, Nilsson C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers Distrib. 2009;15:22–40.Article 

Google Scholar 
Simberloff D. The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst. 2009;40:81–102.Article 

Google Scholar 
Acosta F, Zamor RM, Najar FZ, Roe BA, Hambright KD. Dynamics of an experimental microbial invasion. Proc Natl Acad Sci USA. 2015;112:11594–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barney JN, Ho MW, Atwater DZ. Propagule pressure cannot always overcome biotic resistance: the role of density-dependent establishment in four invasive species. Weed Res. 2016;56:208–18.Article 

Google Scholar 
Ketola T, Saarinen K, Lindström L. Propagule pressure increase and phylogenetic diversity decrease community’s susceptibility to invasion. BMC Ecol. 2017;17:1–7.Article 

Google Scholar 
Vila JCC, Jones ML, Patel M, Bell T, Rosindell J. Uncovering the rules of microbial community invasions. Nat Ecol Evol. 2019;3:1162–71.PubMed 
Article 

Google Scholar 
Dressler MD, Conde J, Eldakar OT, Smith RP. Timing between successive introduction events determines establishment success in bacteria with an Allee effect. Proc R Soc B Biol Sci. 2019;286:20190598.CAS 
Article 

Google Scholar 
Jones ML, Rivett DW, Pascual-Garria A, Bell T. Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms. eLife. 2021;10:e71811.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rivett DW, Jones ML, Ramoneda J, Mombrikotb SB, Ransome E, Bell T. Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. Ecol Lett. 2018;21:516–24.PubMed 
Article 

Google Scholar 
Case TJ. Invasion resistance arises in strongly interacting species-rich model competition communities. Proc Natl Acad Sci USA. 1990;87:9610–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jousset A, Schulz W, Scheu S, Eisenhauer N. Intraspecific genotypic richness and relatedness predict the invasibility of microbial communities. ISME J. 2011;5:1108–14.PubMed 
PubMed Central 
Article 

Google Scholar 
Eisenhauer N, Scheu S, Jousset A. Bacterial diversity stabilizes community productivity. PLoS ONE. 2012;7:e34517.Wei Z, Yang T, Friman VP, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Comm. 2015;6:8413.CAS 
Article 

Google Scholar 
Amalfitano S, Coci M, Corno G, Luna GM. A microbial perspective on biological invasions in aquatic ecosystems. Hydrobiologia. 2015;746:13–22.Article 

Google Scholar 
Li SP, Tan J, Yang X, Ma C, Jiang L. Niche and fitness differences determine invasion success and impact in laboratory bacterial communities. ISME J. 2019;13:402–12.PubMed 
Article 

Google Scholar 
Baumgartner M, Pfrunder-Cardozo KR, Hall AR. Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples. Proc R Soc B Biol Sci. 2021;288:20203106.CAS 
Article 

Google Scholar 
Kurkjian HM, Akbari MJ, Momeni B. The impact of interactions on invasion and colonization resistance in microbial communities. PLoS Comp Biol. 2021;17:e1008643.CAS 
Article 

Google Scholar 
Tilman D. Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc Natl Acad Sci USA. 2004;101:10854–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Elsas JD, Chiurazzi M, Mallon CA, Elhottova D, Kristufek V, Salles JF. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA. 2012;109:1159–64.PubMed 
PubMed Central 
Article 

Google Scholar 
Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50.CAS 
PubMed 
Article 

Google Scholar 
Mitri S, Foster KR. The genotypic view of social interactions in microbial communities. Annu Rev Microbiol. 2013;43:247–73.
Google Scholar 
Kehe J, Ortiz A, Kulesa A, Gore J, Blainey PC, Friedman J. Positive interactions are common among culturable bacteria. Sci Adv. 2021;7:7159.Article 
CAS 

Google Scholar 
Connell JH, Slatyer RO. Mechanisms of succession in natural communities and their role in community stability and organization. Am Nat. 1977;111:1119–44.Article 

Google Scholar 
Debray R, Socolar Y, Kaulbach G, Guzman A, Hernandez CA, Curley R, et al. Priority effects in microbiome assembly. Nat Rev Microbiol. 2022;20:109–21.CAS 
PubMed 
Article 

Google Scholar 
Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun. 2016;7:11965.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Datta MS, Schwartzman J, Cermak N, Schmitz D, Barrere J, et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr Biol. 2019;29:1528–35.e6.CAS 
PubMed 
Article 

Google Scholar 
Mazumdar V, Amar S, Segrè D. Metabolic proximity in the order of colonization of a microbial community. PLoS ONE. 2013;8:e77617.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka M, Szabo R, Stocker R, Cordero OX. Trophic interactions and the drivers of microbial community assembly. Curr Biol. 2020;30:R1176–88.CAS 
PubMed 
Article 

Google Scholar 
Rickard AH, Gilbert P, High NJ, Kolenbrander PE, Handley PS. Bacterial coaggregation: an integral process in the development of multi-species biofilms. Trends Microbiol. 2003;11:94–100.CAS 
PubMed 
Article 

Google Scholar 
Kolenbrander PE, Palmer RJ, Periasamy S, Jakubovics NS. Oral multispecies biofilm development and the key role of cell–cell distance. Nat Rev Microbiol. 2010;8:471–80.CAS 
PubMed 
Article 

Google Scholar 
Monier JM, Lindow SE. Aggregates of resident bacteria facilitate survival of immigrant bacteria on leaf surfaces. Micro Ecol. 2005;49:343–52.Article 

Google Scholar 
Poza-Carrion C, Suslow T, Lindow S. Resident bacteria on leaves enhance survival of immigrant cells of Salmonella enterica. Phytopathology. 2013;103:341–51.PubMed 
Article 

Google Scholar 
Li M, Wei Z, Wang J, Jousset A, Friman V, Xu Y, et al. Facilitation promotes invasions in plant-associated microbial communities. Ecol Lett. 2019;22:149–58.PubMed 
Article 

Google Scholar 
Estrela S, Vila JCC, Lu N, Bajić D, Rebolleda-Gómez M, Chang CY, et al. Functional attractors in microbial community assembly. Cell Syst. 2022;13:29–42.e7.CAS 
PubMed 
Article 

Google Scholar 
Bertness MD, Callaway R. Positive interactions in communities. Trends Ecol Evol. 1994;9:191–3.CAS 
PubMed 
Article 

Google Scholar 
Zaneveld JR, McMinds R, Thurber RV. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:1–8.Article 
CAS 

Google Scholar 
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:3033.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen LTT, Broughton K, Osanai Y, Anderson IC, Bange MP, Tissue DT, et al. Effects of elevated temperature and elevated CO2 on soil nitrification and ammonia-oxidizing microbial communities in field-grown crop. Sci Total Environ. 2019;675:81–9.CAS 
PubMed 
Article 

Google Scholar 
Piccardi P, Vessman B, Mitri S. Toxicity drives facilitation between 4 bacterial species. Proc Natl Acad Sci USA. 2019;116:15979–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34.PubMed 
PubMed Central 
Article 

Google Scholar 
Urban MC, De Meester L. Community monopolization: Local adaptation enhances priority effects in an evolving metacommunity. Proc R Soc B Biol Sci. 2009;276:4129–38.Article 

Google Scholar 
Vanoverbeke J, Urban MC, De Meester L. Community assembly is a race between immigration and adaptation: eco-evolutionary interactions across spatial scales. Ecography. 2016;39:858–70.Article 

Google Scholar 
De Meester L, Vanoverbeke J, Kilsdonk LJ, Urban MC. Evolving perspectives on monopolization and priority effects. Trends Ecol Evol. 2016;31:136–46.PubMed 
Article 

Google Scholar 
Loeuille N, Leibold MA. Evolution in metacommunities: on the relative importance of species sorting and monopolization in structuring communities. Am Nat. 2008;171:788–99.PubMed 
Article 

Google Scholar 
Nadeau CP, Farkas TE, Makkay AM, Papke RT, Urban MC. Adaptation reduces competitive dominance and alters community assembly. Proc R Soc B Biol Sci. 2021;288:20203133.Article 

Google Scholar 
Faillace CA, Morin PJ. Evolution alters the consequences of invasions in experimental communities. Nat Ecol Evol. 2017;1:0013.Article 

Google Scholar 
Castledine M, Sierocinski P, Padfield D, Buckling A. Community coalescence: an eco-evolutionary perspective. Philos Trans R Soc B Biol Sci. 2020;375:20190252.Article 

Google Scholar 
Amor DR, Ratzke C, Gore J. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci Adv. 2020;6:eaay8676.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van der Gast CJ, Thompson IP. Effects of pH amendment on metal working fluid wastewater biological treatment using a defined bacterial consortium. Biotechnol Bioeng. 2005;89:357–66.PubMed 
Article 
CAS 

Google Scholar 
Saha R, Donofrio RS. The microbiology of metalworking fluids. Appl Microbiol Biotechnol. 2012;94:1119–30.CAS 
PubMed 
Article 

Google Scholar 
Ratzke C, Barrere J, Gore J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat Ecol Evol. 2020;4:376–83.PubMed 
Article 

Google Scholar 
Estrela S, Libby E, Van Cleve J, Débarre F, Deforet M, Harcombe WR, et al. Environmentally mediated social eilemmas. Trends Ecol Evol. 2019;34:6–18.PubMed 
Article 

Google Scholar 
Enke TN, Leventhal GE, Metzger M, Saavedra JT, Cordero OX. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat Commun. 2018;9:2743.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Furman O, Shenhav L, Sasson G, Kokou F, Honig H, Jacoby S, et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat Commun. 2020;11:1–13.Article 
CAS 

Google Scholar 
Coyte KZ, Rao C, Rakoff-Nahoum S, Foster KR. Ecological rules for the assembly of microbiome communities. PLoS Biol. 2021;19:e3001116.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Molinero N, Ruiz L, Sánchez B, Margolles A, Delgado S. Intestinal bacteria interplay with bile and cholesterol metabolism: implications on host physiology. Front Physiol. 2019;10:185.PubMed 
PubMed Central 
Article 

Google Scholar 
Ruiz L, Margolles A, Sánchez B. Bile resistance mechanisms in Lactobacillus and Bifidobacterium. Front Microbiol. 2013;4:396.PubMed 
PubMed Central 
Article 

Google Scholar 
Gérard P. Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens. 2013;3:14–24.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Amarnath K, Narla AV, Pontrelli S, Dong J, Caglar T, Taylor BR, et al. Stress-induced cross-feeding of internal metabolites provides a dynamic mechanism of microbial cooperation. bioRxiv. 2021. https://doi.org/10.1101/2021.06.24.449802.Fukami T, Beaumont HJ, Zhang XX, Rainey PB. Immigration history controls diversification in experimental adaptive radiation. Nature. 2007;446:436–9.CAS 
PubMed 
Article 

Google Scholar 
Bell T, Newman JA, Silverman BW, Turner SL, Lilley AK. The contribution of species richness and composition to bacterial services. Nature. 2005;436:1157–60.CAS 
PubMed 
Article 

Google Scholar 
Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
PubMed 
Article 

Google Scholar 
Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.Article 

Google Scholar  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

Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371, eaba4658 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bailey, H., Brookes, K. L. & Thompson, P. M. Assessing environmental impacts of offshore wind farms: Lessons learned and recommendations for the future. Aquat. Biosyst. 10, 1–13 (2014).Article 

Google Scholar 
Dahl, P. H., de Jong, C. A. & Popper, A. N. The underwater sound field from impact pile driving and its potential effects on marine life. Acoust. Today. 11, 18–25 (2015).
Google Scholar 
Mooney, T. A., Andersson, M. H. & Stanley, J. Acoustic impacts of offshore wind energy on fishery resources. Oceanography 33, 82–95 (2020).Article 

Google Scholar 
Madsen, P. T., Wahlberg, M., Tougaard, J., Lucke, K. & Tyack, A. P. Wind turbine underwater noise and marine mammals: implications of current knowledge and data needs. Mar. Ecol. Prog. Ser. 309, 279–295 (2006).ADS 
Article 

Google Scholar 
Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).PubMed 
Article 

Google Scholar 
Jones, I. T., Stanley, J. A. & Mooney, T. A. Impulsive pile driving noise elicits alarm responses in squid (Doryteuthis pealeii). Mar. Pollut. Bull. 150, 110792 (2020).CAS 
PubMed 
Article 

Google Scholar 
Roberts, L. & Elliott, M. Good or bad vibrations? Impacts of anthropogenic vibration on the marine epibenthos. Sci. Total. Environ. 595, 255–268 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hawkins, A. D., Hazelwood, R. A., Popper, A. N. & Macey, P. C. Substrate vibrations and their potential effects upon fishes and invertebrates. J. Acoust. Soc. Am. 149, 2782–2790 (2021).ADS 
PubMed 
Article 

Google Scholar 
Popper, A. N. et al. Offshore wind energy development: Research priorities for sound and vibration effects on fishes and aquatic invertebrates. J. Acoust. Soc. Am. 151, 205–215 (2022).PubMed 
Article 

Google Scholar 
Williams, R. et al. Impacts of anthropogenic noise on marine life: Publication patterns, new discoveries, and future directions in research and management. Ocean. Coast. Manag. 115, 17–24 (2015).Article 

Google Scholar 
Roberts, L., Cheesman, S., Breithaupt, T. & Elliott, M. Sensitivity of the mussel Mytilus edulis to substrate-borne vibration in relation to anthropogenically generated noise. Mar. Ecol. Prog. Ser. 538, 185–195 (2015).ADS 
CAS 
Article 

Google Scholar 
Day, R. D., McCauley, R. D., Fitzgibbon, Q. P., Hartmann, K. & Semmens, J. M. Exposure to seismic air gun signals causes physiological harm and alters behavior in the scallop Pecten fumatus. Proc. Natl. Acad. Sci. 114, E8537–E8546 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Newell, R. I. Ecosystem influences of natural and cultivated populations of suspension-feeding bivalve molluscs: a review. J. Shellfish. Res. 23, 51–62 (2004).
Google Scholar 
Wijsman, J.W.M., Troost, K., Fang, J. & Roncarati, A. Global production of marine bivalves. Trends and challenges. Goods and services of marine bivalves, (Eds. Small, A.D., Ferrerira, J.G., Grant, J., Petersen, J.K., Strand, O.) 7–26 (Springer, Cham, 2019).Perveen, R., Kishor, N. & Mohanty, S. R. Off-shore wind farm development: Present status and challenges. Renew. Sust. Energ. Rev. 29, 780–792 (2014).Article 

Google Scholar 
Vaissière, A. C., Levrel, H., Pioch, S. & Carlier, A. Biodiversity offsets for offshore wind farm projects: The current situation in Europe. Mar. Policy. 48, 172–183 (2014).Article 

Google Scholar 
Musial, W.D., Beiter, P.C., Spitsen, P., Nunemaker, J. & Gevorgian, V. 2018 offshore wind technologies market report. US Department of Energy (2019).Lacroix, D. & Pioch, S. The multi-use in wind farm projects: more conflicts or a win-win opportunity?. Aquat. Living. Resour. 24, 129–135 (2011).Article 

Google Scholar 
FishstatJ. FishStatJ-Software for Fishery and Aquaculture Statistical Time Series. FAO Fisheries Division [online], Rome. Accessed April 10, 2022. (2020).Flanders Marine Institute. Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at https://www.marineregions.org/ (2019).Kallehave, D., Byrne, B. W., LeBlanc Thilsted, C. & Mikkelsen, K. K. Optimization of monopiles for offshore wind turbines. Philos. Trans. R. Soc. A 373, 20140100 (2015).ADS 
Article 

Google Scholar 
Bruns, B., Stein, P., Kuhn, C., Sychla, H. & Gattermann, J. Hydro sound measurements during the installation of large diameter offshore piles using combinations of independent noise mitigation systems. Proceedings of the Inter-noise Conference 1–10 (Melbourne, Australia, 2014).Hunt, H. L. & Scheibling, R. E. Role of early post-settlement mortality in recruitment of benthic marine invertebrates. Mar. Ecol. Prog. Ser. 155, 269–301 (1997).ADS 
Article 

Google Scholar 
Pilditch, C. A. & Grant, J. Effect of variations in flow velocity and phytoplankton concentration on sea scallop (Placopecten magellanicus) grazing rates. J. Exp. Mar. Biol. Ecol. 240, 111–136 (1999).Article 

Google Scholar 
Chauvaud, L., Thouzeau, G. & Paulet, Y. M. Effects of environmental factors on the daily growth rate of Pecten maximus juveniles in the Bay of Brest (France). J. Exp. Mar. Biol. Ecol. 227, 83–111 (1998).Article 

Google Scholar 
Rheuban, J. E., Doney, S. C., Cooley, S. R. & Hart, D. R. Projected impacts of future climate change, ocean acidification, and management on the US Atlantic Sea scallop (Placopecten magellanicus) fishery. PLoS ONE 13, e0203536 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hawkins, A. D., Pembroke, A. E. & Popper, A. N. Information gaps in understanding the effects of noise on fishes and invertebrates. Rev. Fish. Biol. Fish. 25, 39–64 (2015).Article 

Google Scholar 
Neo, Y. Y. et al. Temporal structure of sound affects behavioural recovery from noise impact in European seabass. Biol. Conserv. 178, 65–73 (2014).Article 

Google Scholar 
Sabet, S. S., Neo, Y. Y. & Slabbekoorn, H. The effect of temporal variation in sound exposure on swimming and foraging behaviour of captive zebrafish. Anim. Behav. 107, 49–60 (2015).Article 

Google Scholar 
Radford, A. N., Lèbre, L., Lecaillon, G., Nedelec, S. L. & Simpson, S. D. Repeated exposure reduces the response to impulsive noise in European seabass. Glob. Change. Biol. 22, 3349–3360 (2016).ADS 
Article 

Google Scholar 
Solan, M. et al. Anthropogenic sources of underwater sound can modify how sediment-dwelling invertebrates mediate ecosystem properties. Sci. Rep. 6, 1–9 (2016).Article 
CAS 

Google Scholar 
Hubert, J., Booms, E., Witbaard, R. & Slabbekoorn, H. Responsiveness and habituation to repeated sound exposures and pulse trains in blue mussels. J. Exp. Mar. Biol. Ecol. 547, 151668 (2022).Article 

Google Scholar 
Robson, A. A., Chauvaud, L., Wilson, R. P. & Halsey, L. G. Small actions, big costs: the behavioural energetics of a commercially important invertebrate. J. R. Soc. Interface. 9, 1486–1498 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas, G. E. & Gruffydd, L. D. The types of escape reactions elicited in the scallop Pecten maximus by selected sea-star species. Mar. Biol. 10, 87–93 (1971).Article 

Google Scholar 
Livingstone, D. R., Dezwaan, A. & Thompson, R. J. Aerobic metabolism octopine production and phosphoarginine as sources of energy in the phasic and catch adductor muscles of the giant scallop Placopecten magellanicus during swimming and the subsequent recovery period. Comp. Biochem. Physiol. B. Biochem. Mol. Biol. 70, 35–44 (1981).Article 

Google Scholar 
Comeau, L. A., Babarro, J. M., Longa, A. & Padin, X. A. Valve-gaping behavior of raft-cultivated mussels in the Ría de Arousa Spain. Aquac. Rep. 9, 68–73 (2018).Article 

Google Scholar 
Wilson, R., Reuter, P. & Wahl, M. Muscling in on mussels: new insights into bivalve behaviour using vertebrate remote-sensing technology. Mar. Biol. 147, 1165–1172 (2005).Article 

Google Scholar 
Comeau, L. A. & Babarro, J. M. Narrow valve gaping in the invasive mussel Limnoperna securis: implications for competition with the indigenous mussel Mytilus galloprovincialis in NW Spain. Aquac. Int. 22, 1215–1227 (2014).CAS 
Article 

Google Scholar 
Comeau, L. A., Mayrand, E. & Mallet, A. Winter quiescence and spring awakening of the Eastern oyster Crassostrea virginica at its northernmost distribution limit. Mar. Biol. 159, 2269–2279 (2012).Article 

Google Scholar 
Palmer, B. A. et al. The image-forming mirror in the eye of the scallop. Science 358, 1172–1175 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chappell, D. R., Horan, T. M. & Speiser, D. I. Panoramic spatial vision in the bay scallop Argopecten irradians. Proc. R. Soc. B. 288, 20211730 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Mat, A. M., Massabuau, J. C., Ciret, P. & Tran, D. Evidence for a plastic dual circadian rhythm in the oyster Crassostrea gigas. Chronobiol. Int. 29, 857–867 (2012).PubMed 
Article 

Google Scholar 
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods. Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
Dickie, L. M. & Medcof, J. C. Causes of mass mortalities of scallops (Placopecten magellanicus) in the southwestern Gulf of St Lawrence. J. Fish. Res. Board. Can. 20, 451–482 (1963).Article 

Google Scholar 
Coleman, S., Cleaver, C., Morse, D., Brady, D. C. & Kiffney, T. The coupled effects of stocking density and temperature on Sea Scallop (Placopecten magellanicus) growth in suspended culture. Aquac. Rep. 20, 100684 (2021).Article 

Google Scholar 
Methratta, E. T. Monitoring fisheries resources at offshore wind farms: BACI vs. BAG designs. ICES. J. Mar. Sci. 77, 890–900 (2020).Article 

Google Scholar 
ISO, 18406. Underwater acoustics measurement of radiated underwater sound from percussive pile driving. International Organization for Standardization (Geneva, Switzerland), 1–33 (2017).Madsen, P. T. Marine mammals and noise: Problems with root mean square sound pressure levels for transients. J. Acoust. Soc. Am. 117, 3952–3957 (2005).ADS 
CAS 
PubMed 
Article 

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

Google Scholar 
Lenth, R.V. emmeans: Estimated marginal means, aka least squares means. R package version 1.3.5.1. Retrieved from http://CRAN.R-project.org/package=emmeans (2019).Kragh, I. M. et al. Signal-specific amplitude adjustment to noise in common bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 222, jeb216606 (2019).PubMed 
Article 

Google Scholar 
Warner, R. M. Spectral Analysis of Time-Series Data (Guilford Press, 1998).
Google Scholar 
Fisher, R. A. Tests of significance in harmonic analysis. Proc. Math. Phys. Eng. Sci. 125, 54–59 (1929).MATH 

Google Scholar  More

Cole, J., Findlay, S. & Pace, M. Bacterial production in fresh and saltwater ecosystems: a cross-system overview. Mar. Ecol. Prog. Ser. 43, 1–10 (1988).ADS 
Article 

Google Scholar 
Azam, F. et al. The Ecological Role of Water-Column Microbes in the Sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
Article 

Google Scholar 
Cotner, J. B. & Biddanda, B. A. Small players, large role: Microbial influence on biogeochemical processes in pelagic aquatic ecosystems. Ecosystems. 5, 105–121 (2002).CAS 
Article 

Google Scholar 
Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science. 320, 1034–1039 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A Guide to the Natural History of Freshwater Lake Bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coleman, M. L. et al. Genomic islands and the ecology and evolution of Prochlorococcus. Science. 311, 1768–1770 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Franzosa, E. A. et al. Sequencing and beyond: Integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hanson, C., Fuhrman, J., Horner-Devine, M. & Martiny, J. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dai, A. & Trenberth, K. E. Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol. 3, 660–687 (2002).ADS 
Article 

Google Scholar 
White, W. R. World water: resources, usage and the role of man-made reservoirs. Report No. FR/R0012. Fundation for Water Research, (2010).Clark, E. A., Sheffield, J., van Vliet, M. T. H., Nijssen, B. & Lettenmaier, D. P. Continental runoff into the oceans (1950–2008). J. Hydrometeorol. 16, 1502–1520 (2015).ADS 
Article 

Google Scholar 
Stevaux, J. C., Paes, R. J., Franco, A. A., Mário, M. L. & Fujita, R. H. Morphodynamics in the confluence of large regulated rivers: The case of Paraná and Paranapanema Rivers. Lat. Am. J. Sedimentol. Basin Anal. 16, 101–109 (2009).
Google Scholar 
Brêda, J. P. L. F. et al. Climate change impacts on South American water balance from a continental-scale hydrological model driven by CMIP5 projections. Clim. Change 159, 503–522 (2020).ADS 
Article 

Google Scholar 
Llames, M. E. & Zagarese, H. E. Lakes and Reservoirs of South America. In Encyclopedia of Inland Waters vol.2 (ed. Linkens, G. E.). (Oxford: Elsevier, 2009).Cabrera, A. L. & Willink, A. Biogeografia De America Latina 2da edn (Organización de los Estados Americanos, 1980).Morrone, J. J. Biogeografía de América Latina y el Caribe 1st edn. (Nature, 2001).Morrone, J. J. Biogeographical regionalisation of the neotropical region. Zootaxa 3782, 1–110 (2014).PubMed 
Article 

Google Scholar 
Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. USA 115, 6034–6039 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sarmento, H. New paradigms in tropical limnology: The importance of the microbial food web. Hydrobiologia 686, 1–14 (2012).Article 

Google Scholar 
Meerhoff, M. et al. Environmental Warming in Shallow Lakes. A Review of Potential Changes in Community Structure as Evidenced from Space-for-Time Substitution Approaches. Adv. Ecol. Res. 46, 259–349 (2012).Article 

Google Scholar 
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Metz, S. & Huber, P. et al. A georeferenced rRNA amplicon database of aquatic microbiomes from South America (Dataset), Zenodo, https://doi.org/10.5281/zenodo.6802178 (2022).Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000 Research 5, 1492 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).Article 

Google Scholar 
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at https://www.biorxiv.org/content/10.1101/081257v1 (2016).Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Griffith, G. E., Omernik, J. M. & Azevedo, S. H. Ecological classification of the Western Hemisphere http://ecologicalregions.info/htm/ecoregions.htm (1998).Salcedo, J. C. R. South America: Argentina, Bolivia, and Peru https://www.worldwildlife.org/ecoregions/nt1002 Accessed (2018).Vidal, J. Geografía del Perú: las ocho regiones naturales, la regionalización transversal, la microregionalización 9th edn (PEISA, 1987).Paruelo, J. M., Beltran, A., Jobbagy, E., Sala, O. E. & Golluscio, R. A. The climate of Patagonia: General patterns and controls on biotic processes. Ecol. Austral 8, 85–101 (1998).
Google Scholar 
Iriondo, M. Quaternary lakes of Argentina. Palaeogeogr. Palaeoclimatol. Palaeoecol. 70, 81–88 (1989).Article 

Google Scholar 
Soto, D. & Campos, H. in Ecología de los bosques templados de Chile vol. 1 (eds. Khalin, J. M. & Villagrán, C.) (Editorial Universitaria, 1995).Modenutti, B. et al. Structure and dynamic of food webs in Andean North Patagonian freshwater systems: Organic matter, light and nutrient relationships. Ecol. Austral 20, 95–114 (2010).
Google Scholar 
Modenutti, B. E. et al. Structure and dynamics of food webs in Andean lakes. Lakes Reserv. Res. Manag. 3, 179–186 (1998).Article 

Google Scholar 
Quirós, R. & Drago, E. The environmental state of Argentinean lakes: An overview. Lakes Reserv. Res. Manag. 4, 55–64 (1999).Article 

Google Scholar 
Morris, D. P. et al. The attenuation of solar UV radiation in lakes and the role of dissolved organic carbon. Limnol. Oceanogr. 40, 1381–1391 (1995).ADS 
CAS 
Article 

Google Scholar 
Bastidas Navarro, M., Balseiro, E. & Modenutti, B. Bacterial Community Structure in Patagonian Andean Lakes Above and Below Timberline: From Community Composition to Community Function. Microb. Ecol. 68, 528–541 (2014).PubMed 
Article 

Google Scholar 
Modenutti, B. et al. Environmental changes affecting light climate in oligotrophic mountain lakes: The deep chlorophyll maxima as a sensitive variable. Aquat. Sci. 75, 361–371 (2013).CAS 
Article 

Google Scholar 
Bastidas Navarro, M., Martyniuk, N., Balseiro, E. & Modenutti, B. Effect of glacial lake outburst floods on the light climate in an Andean Patagonian lake: implications for planktonic phototrophs. Hydrobiologia 816, 39–48 (2018).CAS 
Article 

Google Scholar 
Sioli, H. Hydrochemistry and Geology in the Brazilian Amazon Region. Amazoniana 1, 267–277 (1968).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Salati, E. & Vose, P. B. Amazon Basin: A system in equilibrium. Science. 225, 129–138 (1984).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Melack, J. M. & Forsberg, B. R. In The Biogeochemistry of the Amazon Basin Vol. 1 (eds. MacCLain, M. E., Victoria, R. & Richey, J. E.). (Oxford Scholarship Online, 2001).Junk, W. J., Bayley, P. B. & Sparks, R. E. The flood pulse concept in river-floodplain systems. Can. J. Fish. Aquat. Sci. 106, 110–127 (1989).
Google Scholar 
Ratter, J. A., Ribeiro, J. F. & Bridgewater, S. The Brazilian cerrado vegetation and threats to its biodiversity. Ann. Bot. 80, 223–230 (1997).Article 

Google Scholar 
Haridasan, M. Nutritional adaptations of native plants of the cerrado biome in acid soils. Braz. J. Plant Physiol. 20, 183–195 (2008).Article 

Google Scholar 
Vasconcelos, V., de Carvalho Júnior, O. A., de Souza Martins, É. & Couto Júnior, A. F. in World Geomorphological Landscapes. Vol. 1 (eds. Vieira, B., Salgado, A. & Santos, L.) (Springer, 2015).Bichsel, D. et al. Water quality of rural ponds in the extensive agricultural landscape of the Cerrado (Brazil). Limnology 17, 239–246 (2016).CAS 
Article 

Google Scholar 
Cunha, D. G. F., Calijuri, M., do, C. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).Article 

Google Scholar 
Morellato, L. P. C. & Haddad, C. F. B. Introduction: The Brazilian atlantic forest. Biotropica 32, 786–792 (2000).Article 

Google Scholar 
Galindo-Leal, C. & Câmara, I. de G. The Atlantic Forest of South America: Biodiversity status, threats, and outlook 1st edn (Island Press, 2003).Joly, C. A., Metzger, J. P. & Tabarelli, M. Experiences from the Brazilian Atlantic Forest: Ecological findings and conservation initiatives. New Phytologist 204, 459–473 (2014).PubMed 
Article 

Google Scholar 
Caliman, A. et al. Temporal coherence among tropical coastal lagoons: A search for patterns and mechanisms. Brazilian J. Biol. 70, 803–814 (2010).CAS 
Article 

Google Scholar 
Junger, P. C. et al. Salinity Drives the Virioplankton Abundance but Not Production in Tropical Coastal Lagoons. Microb. Ecol. 75, 52–63 (2018).CAS 
PubMed 
Article 

Google Scholar 
Depetris, P. J., Probst, J. L., Pasquini, A. I. & Gaiero, D. M. The geochemical characteristics of the Paraná River suspended sediment load: An initial assessment. Hydrol. Process. 17, 1267–1277 (2003).ADS 
Article 

Google Scholar 
Orfeo, O. & Stevaux, J. Hydraulic and morphological characteristics of middle and upper reaches of the Paraná River (Argentina and Brazil). Geomorphology 44, 309–322 (2002).ADS 
Article 

Google Scholar 
Neiff, J. J. Large rivers of South America: toward the new approach. Verh. Internat. Verein. Limnol 26, 167–180 (1996).
Google Scholar 
Unrein, F. Changes in phytoplankton community along a transversal section of the Lower Paraná floodplain, Argentina. Hydrobiologia 468, 123–134 (2002).Article 

Google Scholar 
Devercelli, M. Changes in phytoplankton morpho-functional groups induced by extreme hydroclimatic events in the Middle Paraná river (Argentina). Hydrobiologia 639, 5–19 (2010).CAS 
Article 

Google Scholar 
Huber, P. et al. Environmental heterogeneity determines the ecological processes that govern bacterial metacommunity assembly in a floodplain river system. ISME J. 14, 2951–2966 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).ADS 
Article 

Google Scholar 
Conde, D., Arocena, R. & Recursos, R.-G. L. acuáticos superficiales de Uruguay: ambientes, algunas problemáticas y desafios para la gestión. Ambios 10, 1–7 (2003).
Google Scholar 
Martin, L. & Suguio, K. Variation of coastal dynamics during the last 7000 years recorded in beach-ridge plains associated with river mouths: example from the central Brazilian coast. Palaeogeogr. Palaeoclimatol. Palaeoecol. 99, 119–140 (1992).Article 

Google Scholar 
Alonso, C. et al. Environmental dynamics as a structuring factor for microbial carbon utilization in a subtropical coastal lagoon. Front. Microbiol. 4, 1664–302X (2013).Article 
CAS 

Google Scholar 
Amaral, V., Graeber, D., Calliari, D. & Alonso, C. Strong linkages between DOM optical properties and main clades of aquatic bacteria. Limnol. Oceanogr. 61, 906–918 (2016).ADS 
Article 

Google Scholar 
Rennella, A. M. M., Quiro, R. & Quirós, R. The effects of hydrology on plankton biomass in shallow lakes of the Pampa Plain. Hydrobiologia 556, 181–191 (2006).Article 

Google Scholar 
Diaz, M., Pedrozo, F. & Baccala, N. Summer classification of Southern Hemisphere temperate lakes (Patagonia, Argentina). Lakes Reserv. Res. Manag. 5, 213–229 (2000).Article 

Google Scholar 
Izaguirre, I. et al. Influence of fish introduction and water level decrease on lakes of the arid Patagonian plateaus with importance for biodiversity conservation. Glob. Ecol. Conserv. 14, e00391 (2018).Article 

Google Scholar 
Porcel, S., Saad, J. F., Sabio y García, C. A. & Izaguirre, I. Microbial planktonic communities in lakes from a Patagonian basaltic plateau: influence of the water level decrease. Aquat. Sci. 81, 51 (2019).Article 
CAS 

Google Scholar 
Bernal, M. C. et al. Spatial variation of picoplankton communities along a cascade reservoir system in Patagonia, Argentina. J. Limnol. 80, 84–99 (2021).
Google Scholar 
Leinonen, R. et al. The European nucleotide archive. Nucleic Acids Res. 39, 44–47 (2011).Article 
CAS 

Google Scholar 
ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA217932 (2013).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA302313 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA294718 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA309832 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA326475 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB48609 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA289691 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA414894 (2018).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA323673 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA356055 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA390178 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA411849 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA725228 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA292014 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA411849 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA316315 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA406945 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA515842 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA321235 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:SAMN07998328 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:SAMN07998330 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB36116 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB29989 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA788397 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB48353 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB37379 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB46122 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40710 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40864 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40854 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA268541 (2015). More

Incentivizing farmers to shift from conventional to regenerative practices could help fulfil the United Nations Food Systems commitments to transform food supply chains — as well as reducing carbon emissions (see L. A. Schulte et al. Nature Sustain. 5, 384–388; 2022).
Competing Interests
The authors declare no competing interests. More

Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, 13 (2010).
Google Scholar 
Leifeld, J., Wüst-Galley, C. & Page, S. Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nat. Clim. Change 9, 945–947 (2019).CAS 
Article 

Google Scholar 
Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).CAS 
Article 

Google Scholar 
Evans, C. D. et al. Overriding water table control on managed peatland greenhouse gas emissions. Nature 593, 548–552 (2021).CAS 

Google Scholar 
Bonn, A. et al. Investing in nature: Developing ecosystem service markets for peatland restoration. Ecosyst. Serv. 9, 54–65 (2014).Article 

Google Scholar 
Martin-Ortega, J., Allott, T. E., Glenk, K. & Schaafsma, M. Valuing water quality improvements from peatland restoration: evidence and challenges. Ecosyst. Serv. 9, 34–43 (2014).Article 

Google Scholar 
Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Change 11, 70–77 (2021).Article 

Google Scholar 
Chimner, R. A., Cooper, D. J., Wurster, F. C. & Rochefort, L. An overview of peatland restoration in North America: where are we after 25 years? Restor. Ecol. 25, 283–292 (2017).Article 

Google Scholar 
Andersen, R. et al. An overview of the progress and challenges of peatland restoration in Western Europe. Restor. Ecol. 25, 271–282 (2017).Article 

Google Scholar 
Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).Article 

Google Scholar 
Humpenöder, F. et al. Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. 15, 104093 (2020).Article 

Google Scholar 
Drever, C. R. et al. Natural climate solutions for Canada. Sci. Adv. 7, https://doi.org/10.1126/sciadv.abd6034 (2020).Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1–7 (2018).CAS 
Article 

Google Scholar 
Gunderson, L. H. Ecological resilience—in theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).Article 

Google Scholar 
Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Indic. 57, 395–408 (2015).Article 

Google Scholar 
Scheffer, M. Critical transitions in nature and society (Princeton University, 2009).Alexandrov, G. A., Brovkin, V. A., Kleinen, T. & Yu, Z. The capacity of northern peatlands for long-term carbon sequestration. Biogeosciences 17, 47–54 (2020).CAS 
Article 

Google Scholar 
Page, S. E. & Baird, A. J. Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41, 35–57 (2016).Article 

Google Scholar 
Rydin, H., Jeglum, J. K. & Bennett, K. D. The biology of peatlands, 2nd edition (Oxford University Press, 2013).Kim, J. et al. Water table fluctuation in peatlands facilitates fungal proliferation, impedes Sphagnum growth and accelerates decomposition. Front. Earth Sci. 8, 717 (2021).
Google Scholar 
IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability (Cambridge University Press, In Press).Belyea, L. R. Non-linear dynamics of peatlands and potential feedbackson the climate system, in Northern Peatlands and Carbon Cycling (A, Baird. et al. eds), pp 5–18 (American Geophysical Union Monograph Series, 2009).Holden, J. et al. Overland flow velocity and roughness properties in peatlands. Water Resour. Res. 44, https://doi.org/10.1029/2007WR006052 (2008).Holden, J., Wallage, Z. E., Lane, S. N. & McDonald, A. T. Water table dynamics in undisturbed, drained and restored blanket peat. J. Hydrol. 402, 103–114 (2011).Article 

Google Scholar 
Glaser, P. H. et al. Surface deformations as indicators of deep ebullition fluxes in a large northern peatland. Glob. Biogeochem. Cycles 18, GB1003 (2004).Article 
CAS 

Google Scholar 
Belyea, L. R. & Baird, A. J. Beyond “the limits to peat bog growth”: cross‐scale feedback in peatland development. Ecol. Monogr. 76, 299–322 (2006).Article 

Google Scholar 
Waddington, J. M. et al. Hydrological feedbacks in northern peatlands. Ecohydrology 8, 113–127 (2015).Article 

Google Scholar 
Holden, J., Evans, M. G., Burt, T. P. & Horton, M. Impact of land drainage on peatland hydrology. J. Environ. Qual. 35, 1764–1778 (2006).CAS 
Article 

Google Scholar 
Liu, H. & Lennartz, B. Hydraulic properties of peat soils along a bulk density gradient—a meta study. Hydrol. Process. 33, 101–114 (2019).Article 

Google Scholar 
Gałka, M., Tobolski, K., Górska, A. & Lamentowicz, M. Resilience of plant and testate amoeba communities after climatic and anthropogenic disturbances in a Baltic bog in Northern Poland: implications for ecological restoration. Holocene 27, 130–141 (2017).Article 

Google Scholar 
Lamentowicz, M. et al. Unveiling tipping points in long-term ecological records from Sphagnum-dominated peatlands. Biol. Lett. 15, https://doi.org/10.1098/rsbl.2019.0043 (2019).van der Velde, Y. Emerging forest-peatland bistability and resilience of European peatland carbon stores. Proc. Natl Acad. Sci. 118, https://doi.org/10.1073/pnas.210174211 (2021).Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
Article 

Google Scholar 
Minayeva, T. Y. & Sirin, A. A. Peatland biodiversity and climate change. Biol. Bull. Rev. 2, 164–175 (2012).Article 

Google Scholar 
Minayeva, T. Y., Bragg, O. & Sirin, A. A. Towards ecosystem-based restoration of peatland biodiversity. Mires Peat 19, 1–36 (2017).
Google Scholar 
Andersen, R., Chapman, S. J. & Artz, R. R. Microbial communities in natural and disturbed peatlands: a review. Soil Biol. Biochem. 1, 979–994 (2013).Article 
CAS 

Google Scholar 
van Breemen, N. How Sphagnum bogs down other plants. Trends Ecol. Evol. 10, 270–275 (1995).Article 

Google Scholar 
Hugron, S. & Rochefort, L. Sphagnum mosses cultivated in outdoor nurseries yield efficient plant material for peatland restoration. Mires Peat 20, 1–6 (2018).
Google Scholar 
Vitt, D. H. Peatlands: ecosystems dominated by bryophytes. In: Shaw A. J. & Goffinet B. (eds) Bryophyte biology, pp 312–343 (Cambridge University Press, 2002).Yu, Z. et al. Carbon sequestration in western Canadian peat highly sensitive to Holocene wet-dry climate cycles at millennial timescales. Holocene 13, 801–808 (2003).Article 

Google Scholar 
Chiapusio, G. et al. Sphagnum species module their phenolic profiles and mycorrhizal colonization of surrounding Andromeda polifolia along peatland microhabitats. J. Chem. Ecol. 44, 1146–1157 (2018).CAS 
Article 

Google Scholar 
Sherwood, J. H. et al. Effect of drainage and wildfire on peat hydrophysical properties. Hydrol. Process. 27, 1866–1874 (2013).Article 

Google Scholar 
Tanneberger, F., Flade, M., Preiksa, Z. & Schröder, B. Habitat selection of the globally threatened aquatic warbler Acrocephalus paludicola at the western margin of its breeding range and implications for management. Ibis 152, 347–358 (2010).Article 

Google Scholar 
Kreyling, J. Rewetting does not return drained fen peatlands to their old selves. Nat. Commun. 12, 1–8 (2021).Article 
CAS 

Google Scholar 
Ritson, J. P. et al. Towards a microbial process-based understanding of the resilience of peatland ecosystem service provisioning–a research agenda. Sci. Total Environ. 759, https://doi.org/10.1016/j.scitotenv.2020.143467 (2021).Secco, E. D., Haapalehto, T., Haimi, J., Meissner, K. & Tahvanainen, T. Do testate amoebae communities recover in concordance with vegetation after restoration of drained peatlands? Mires Peat 18, https://doi.org/10.19189/MaP.2016.OMB.231 (2016).Basiliko, N. et al. Controls on bacterial and archaeal community structure and greenhouse gas production in natural, mined, and restored Canadian peatlands. Front. Microbiol. 31, https://doi.org/10.3389/fmicb.2013.00215 (2013).Barber, K. E. Peat stratigraphy and climatic change. vol 219, (AA Balkema, 1981).Quinton, W. L. & Roulet, N. T. Spring and summer runoff hydrology of a subarctic patterned wetland. Arctic Alpine Res. 30, 285–294 (1998).Article 

Google Scholar 
Eppinga, M. B., Rietkerk, M., Wassen, M. J. & De Ruiter, P. C. Linking habitat modification to catastrophic shifts and vegetation patterns in bogs. Plant Ecol. 200, 53–68 (2009).Article 

Google Scholar 
Bragazza, L., Parisod, J., Buttler, A. & Bardgett, R. D. Biogeochemical plant– soil microbe feedback in response to climate warming in peatlands. Nat. Clim. Change 3, 273–277 (2013).CAS 
Article 

Google Scholar 
Fenton, N. J. Applied ecology in Canada’s boreal: a holistic view of the mitigation hierarchy and resilience theory. Botany 94, 1009–1014 (2016).Article 

Google Scholar 
Xu, L. X. et al. Maintain spatial heterogeneity, maintain biodiversity—a seed bank study in a grazed alpine fen meadow. Land Degrad. Dev. 28, 1376–1385 (2017).Article 

Google Scholar 
Laine, J., Vasander, H. & Laiho, R. Long-term effects of water level drawdown on the vegetation of drained pine mires in southern Finland. J. Appl. Ecol. 1, 785–802 (1995).
Google Scholar 
Gatis, N. et al. The effect of drainage ditches on vegetation diversity and CO2 fluxes in a Molinia caerulea‐dominated peatland. Ecohydrology 9, 407–420 (2016).CAS 
Article 

Google Scholar 
Swindles, G. T. et al. Resilience of peatland ecosystem services over millennial timescales: evidence from a degraded British bog. Journal of Ecology 104, 621–636 (2016).Article 

Google Scholar 
Liu, H., Gao, C. & Wang, G. Understand the resilience and regime shift of the wetland ecosystem after human disturbances. Sci. Total Environ. 643, 1031–1040 (2018).CAS 
Article 

Google Scholar 
Couwenberg, J. et al. Assessing greenhouse gas emissions from peatlands using vegetation as a proxy. Hydrobiologia 674, 67–89 (2011).CAS 
Article 

Google Scholar 
Tiemeyer, B. et al. High emissions of greenhouse gases from grasslands on peat and other organic soils. Glob. Change Biol. 22, 4134–4149 (2016).Article 

Google Scholar 
Strack, M. et al. Controls on plot-scale growing season CO2 and CH4 fluxes in restored peatlands: do they differ from unrestored and natural sites? Mires Peat 17, 1–18 (2016).
Google Scholar 
Nugent, K. A., Strachan, I. B., Strack, M., Roulet, N. T. & Rochefort, L. Multi-year net ecosystem carbon balance of a restored peatland reveals a return to carbon sink. Global Change Biol. 24, 5751–5768 (2018).Article 

Google Scholar 
Hambley, G. et al. Net ecosystem exchange from two formerly afforested peatlands undergoing restoration in the Flow Country of northern Scotland. Mires Peat 23, https://doi.org/10.19189/MaP.2018.DW.346 (2019).Schwieger, S. et al. Wetter is better: rewetting of minerotrophic peatlands increases plant production and moves them towards carbon sinks in a dry year. Ecosystems 24, 1093–1109 (2021).CAS 
Article 

Google Scholar 
Poulin, M., Andersen, R. & Rochefort, L. A new approach for tracking vegetation change after restoration: a case study with peatlands. Restor. Ecol. 21, 363–371 (2013).Article 

Google Scholar 
Gonzalez, E. & Rochefort, L. Drivers of success in 53 cutover bogs restored by a moss layer transfer technique. Ecol. Eng. 68, 279–290 (2014).Article 

Google Scholar 
Karofeld, E., Müür, M. & Vellak, K. Factors affecting re-vegetation dynamics of experimentally restored extracted peatland in Estonia. Environ. Sci. Pollut. Res. 23, 13706–13717 (2016).Article 

Google Scholar 
Karofeld, E., Kaasik, A. & Vellak, K. Growth characteristics of three Sphagnum species in restored extracted peatland. Restor. Ecol. 28, 1574–1583 (2020).Article 

Google Scholar 
Purre, A. H., Ilomets, M., Truus, L., Pajula, R. & Sepp, K. The effect of different treatments of moss layer transfer technique on plant functional types biomass in revegetated milled peatlands. Restor. Ecol. 28, 1584–1595 (2020).Article 

Google Scholar 
Beyer, F. et al. Drought years in peatland rewetting: rapid vegetation succession can maintain the net CO2 sink function. Biogeosciences 18, 917–935 (2021).CAS 
Article 

Google Scholar 
Ketcheson, S. J. & Price, J. S. The impact of peatland restoration on the site hydrology of an abandoned block-cut bog. Wetlands 31, 1263–1274 (2011).Article 

Google Scholar 
McCarter, C. P. R. & Price, J. S. The hydrology of the Bois-des-Bel bog peatland restoration: 10 years post-restoration. Ecol. Eng. 55, 73–81 (2013).Article 

Google Scholar 
Koebsch, F. et al. The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens. Philos. Transac. R. Soc. B 375, https://doi.org/10.1098/rstb.2019.0685 (2020).Blier‐Langdeau, A., Guêné‐Nanchen, M., Hugron, S. & Rochefort, L. The resistance and short‐term resilience of a restored extracted peatland ecosystems post‐fire: an opportunistic study after a wildfire. Restor. Ecol. 30, https://doi.org/10.1111/rec.13545 (2022).Rochefort, L., Quinty, F., Campeau, S., Johnson, K. & Malterer, T. North American approach to the restoration of Sphagnum dominated peatlands. Wetlands Ecol. Manage. 11, 3–20 (2003).CAS 
Article 

Google Scholar 
Lavoie, C., St-Louis, A. & Lachance, D. Vegetation dynamics on an abandoned vacuum-mined peatland: Five years of monitoring. Wetlands Ecol. Manage. 13, 621–633 (2005).Article 

Google Scholar 
Poulin, M., Rochefort, L., Quinty, F. & Lavoie, C. Spontaneous revegetation of mined peatlands in eastern Canada. Can. J. Botany 83, 539–557 (2005).Article 

Google Scholar 
Quinty, F., LeBlanc, M.-C. & Rochefort, L. Peatland Restoration Guide—PERG, CSPMA and APTHQ (Université Laval, 2020).Wagner, D. J. & Titus, J. E. Comparative desiccation tolerance of two Sphagnum mosses. Oecologia 62, 182–187 (1984).Article 

Google Scholar 
Gonzalez, E. & Rochefort, L. Declaring success in Sphagnum peatland restoration: identifying outcomes from readily measurable vegetation descriptors. Mires Peat 24, 1–16 (2019).
Google Scholar 
Scotland National Peatland Plan. Working for our future. https://www.nature.scot/doc/scotlands-national-peatland-plan-working-our-future#:~:text=The%202020%20Challenge%20for%20Scotland’s,more%20resilient%20to%20climate%20change (2020).Wilkie, N. M. & Mayhew, P. W. The management and restoration of damaged blanket bog in the north of Scotland. Bot. J. Scotl. 55, 125–133 (2003).Article 

Google Scholar 
Hancock, M. H., Klein, D., Andersen, R. & Cowie, N. R. Vegetation response to restoration management of a blanket bog damaged by drainage and afforestation. Appl. Veg. Sci. 21, 167–178 (2018).Article 

Google Scholar 
Harris, A. & Baird, A. J. Microtopographic drivers of vegetation patterning in blanket peatlands recovering from erosion. Ecosystems 22, 1035–1054 (2019).Article 

Google Scholar 
Bradley, A. V., Andersen, R., Marshall, C., Sowter, A. & Large, D. J. Identification of typical ecohydrological behaviours using InSAR allows landscape-scale mapping of peatland condition. Earth Surf. Dyn. 10, 261–277 (2022).Article 

Google Scholar 
Gaffney, P. P., Hancock, M. H., Taggart, M. A. & Andersen, R. Measuring restoration progress using pore-and surface-water chemistry across a chronosequence of formerly afforested blanket bogs. J. Environ. Manage. 219, 239–251 (2018).CAS 
Article 

Google Scholar 
Hermans, R. et al. Climate benefits of forest-to-bog restoration on deep peat–Policy briefing. Climate X Change 1–5, https://www.climatexchange.org.uk/media/3654/climate-benefits-of-forest-to-bog-restoration-on-deep-peat.pdf (2019).Wilson, D. et al. Greenhouse gas emission factors associated with rewetting of organic soils. Mires Peat 17, 1–28 (2016).
Google Scholar 
Günther, A. et al. Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat. Commun. 11, 1–5 (2020).Article 
CAS 

Google Scholar 
Young, D. M. et al. Misinterpreting carbon accumulation rates in records from near-surface peat. Sci. Rep. 9, 1–8 (2019).Article 
CAS 

Google Scholar 
Young, D. M., Baird, A. J., Gallego-Sala, A. V. & Loisel, J. A cautionary tale about using the apparent carbon accumulation rate (aCAR) obtained from peat cores. Sci. Rep. 11, 9547 (2021).CAS 
Article 

Google Scholar 
Klimkowska, A. et al. Are we restoring functional fens? The outcomes of restoration projects in fens re-analysed with plant functional traits. PLoS One 14, https://doi.org/10.1371/journal.pone.0215645 (2019).Huth, V. et al. The climate benefits of topsoil removal and Sphagnum introduction in raised bog restoration. Restor. Ecol. 30, https://doi.org/10.1111/rec.13490 (2022).Schimelpfenig, D., Cooper, D. J. & Chimner, R. A. Effectiveness of ditch blockage for restoring hydrologic and soil processes in mountain peatlands. Restor. Ecol. 22, 257–265 (2014).Article 

Google Scholar 
Laine, A. M., Tolvanen, A., Mehtätalo, L. & Tuittila, E. S. Vegetation structure and photosynthesis respond rapidly to restoration in young coastal fens. Ecol. Evol. 6, 6880–6891 (2016).Article 

Google Scholar 
Gallego-Sala, A. V. & Prentice, I. C. Blanket peat biome endangered by climate change. Nat. Clim. Change 3, 152–155 (2013).Article 

Google Scholar 
Schneider, R. R., Devito, K., Kettridge, N. & Bayne, E. Moving beyond bioclimatic envelope models:50 integrating upland forest and peatland processes to predict ecosystem transitions under climate change in the51 western Canadian boreal plain: Western boreal ecosystem transitions under climate change. Ecohydrology 9, 899–908 (2016).Article 

Google Scholar 
Blundell, A. & Holden, J. Using palaeoecology to support blanket peatland management. Ecol. Indic. 49, 110–120 (2005).Article 

Google Scholar 
Newman, S. et al. Drivers of landscape evolution: multiple regimes and their influence on carbon sequestration in a sub‐tropical peatland. Ecol. Monogr. 87, 578–599 (2017).Article 

Google Scholar 
Wilkinson, S. L., Moore, P. A., Flannigan, M. D., Wotton, B. M. & Waddington, J. M. Did enhanced afforestation cause high severity peat burn in the Fort McMurray Horse River wildfire? Environ. Res. Lett. 13, https://doi.org/10.1088/1748-9326/aaa136 (2018).Hokanson, K. J. et al. A hydrogeological landscape framework to identify peatland wildfire smouldering hot spots. Ecohydrology 11, https://doi.org/10.1002/eco.1942 (2018).IPCC. Global warming of 1.5 °C (IPCC, 2018).Glenk, K., Faccioli, M., Martin-Ortega, J., Schulze, C. & Potts, J. The opportunity cost of delaying climate action: Peatland restoration and resilience to climate change. Glob. Environ. Change 70, https://doi.org/10.1016/j.gloenvcha.2021.102323 (2021).Tanneberger, F. et al. The power of nature‐based solutions: how peatlands can help us to achieve key EU sustainability objectives. Adv. Sustain. Syst. 5, https://doi.org/10.1002/adsu.202000146 (2021).Loisel, J. & Walenta, J. Carbon parks could secure essential ecosystems for climate stabilization. Nat. Ecol. Evol. 6, 486–488 (2022).Article 

Google Scholar 
Morecroft, M. D. et al. Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366, eaaw9256 (2019).Terzano, D. Community‐led peatland restoration in Southeast Asia: 5Rs approach. Restor. Ecol. 3, https://doi.org/10.1111/rec.13642 (2022). More

Galloway, J. N., Townsend, W. H., Erisman, J. W., Bekunda, M. & Cai, Z. Transformation of the nitrogen cycle: Recent trends, questions and potential solutions. Science 320, 889–892 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Phoenix, G. K. et al. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Change Biol. 18, 1197–1215 (2012).ADS 
Article 

Google Scholar 
Pons, T. L., van der, Werf, A. & Lambers, H. Photosynthetic nitrogen use efficiency of inherently slow- and fast-growing species: Possible explanations for observed differences. In A Whole Plant Perspective on Carbon-Nitrogen Interactions (eds Roy, J., Garnier, E.) 61–77 (SPB Academic Publishing, The Hague, 1994).Ai, Z. M., Xue, S., Wang, G. L. & Liu, G. B. Responses of Non-structural carbohydrates and C:N: P stoichiometry of Bothriochloa ischaemum to nitrogen addition on the Loess Plateau, China. J. Plant Growth Regul. 36, 714–722 (2017).CAS 
Article 

Google Scholar 
Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. New Phytol. 193, 696–704 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dietze, M. C. et al. Nonstructural carbon in woody plants. Annu. Rev. Plant Biol. 65, 667–687 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees -from what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).CAS 
PubMed 
Article 

Google Scholar 
Yang, Q. P. et al. Different responses of non-structural carbohydrates in above-ground tissues/organs and root to extreme drought and re-watering in Chinese fir (Cunninghamia lanceolata) saplings. Trees 30, 1863–1871 (2016).CAS 
Article 

Google Scholar 
Peng, Z. T. et al. Non-structural carbohydrates regulated by nitrogen and phosphorus fertilization varied with organs and fertilizer levels in Moringa oleifera Seedlings. J. Plant Growth Regul. 40, 1777–1786 (2021).CAS 
Article 

Google Scholar 
Nardini, A. et al. Rooting depth, water relations and non-structural carbohydrate dynamics in three woody angiosperms deferentially affected by an extreme summer drought. Plant Cell Environ. 39, 618–627 (2016).CAS 
PubMed 
Article 

Google Scholar 
Wang, F. C. et al. Effects of experimental nitrogen addition on nutrients and nonstructural carbohydrates of dominant understory plants in a Chinese Fir plantation. Forests 10, 155 (2019).Article 

Google Scholar 
Elser, J. J. et al. Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).Article 

Google Scholar 
Jinm, X. M. et al. Ecological stoichiometry and biomass response of Agropyron michnoi under simulated N deposition in a sandy grassland, China. J. Arid Land. 12, 741–751 (2020).Article 

Google Scholar 
Jing, H. et al. Nitrogen addition changes the stoichiometry and growth rate of different organsin pinus tabuliformis seedlings. Front. Plant Sci. 8, 1922 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhan, S. X., Wang, Y., Zhu, Z. C., Li, W. H. & Bai, Y. F. Nitrogen enrichment alters plant N: P stoichiometry and intensifies phosphorus limitation in a steppe ecosystem. Environ. Exp. Bot. 134, 21–32 (2017).CAS 
Article 

Google Scholar 
Stiling, P. & Cornelissen, T. How does elevated carbon dioxide (CO2) affect plant–herbivore interactions? A field experiment and meta-analysis of CO2 -mediated changes on plant chemistry and herbivore performance. Glob. Change Biol. 13, 1823–1842 (2007).ADS 
Article 

Google Scholar 
Wang, X. G. et al. Responses of C:N: P stoichiometry of plants from a Hulunbuir grassland to salt stress, drought and nitrogen addition. Phyton-Int. J. Exp. Bot. 87, 123–132 (2018).
Google Scholar 
Liu, X. J. et al. Enhanced nitrogen deposition over China. Nature 494, 459–462 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huang, W. J., Houlton, B. Z., Marklein, A. R., Liu, J. X. & Zhou, G. Y. Plant stoichiometric responses to elevated CO2 vary with nitrogen and phosphorus inputs: Evidence from a global-scale meta-analysis. Sci. Rep. 5, 18225 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. Effects of nutrient addition on nitrogen, phosphorus and non-structural carbohydrates concentrations in leaves of dominant plant species in a semiarid steppe. Chin. J. Ecol. 33, 1795–1802 (2014).
Google Scholar 
Yang, D. X., Song, L. & Jin, G. Z. The soil C:N: P stoichiometry is more sensitive than the leaf C:N: P stoichiometry to nitrogen addition: A four-year nitrogen addition experiment in a Pinus koraiensis plantation. Plant Soil 442, 183–198 (2019).CAS 
Article 

Google Scholar 
Chong, P. F., Zhan, J., Li, Y. & Jia, X. Y. Carbon dioxide and precipitation alter Reaumuria soongorica root morphology by regulating the levels of soluble sugars and phytohormones. Acta Physiol. Plant 41, 184 (2019).Article 
CAS 

Google Scholar 
Ma, X. Z. & Wang, X. P. Biomass partitioning and allometric relations of the Reaumuria soongorica shrub in Alxa steppe desert in NW China. For. Ecol. Manag. 468, 118–178 (2020).Article 

Google Scholar 
He, F. L., Bao, A. K., Wang, S. M. & Jin, H. X. NaCl stimulates growth and alleviates drought stress in the salt-secreting xerophyte Reaumuria soongorica. Environ. Exp. Bot. 162, 433–443 (2019).CAS 
Article 

Google Scholar 
Xu, D. H. et al. Photosynthetic parameters and carbon reserves of a resurrection plant Reaumuria soongorica during dehydration and rehydration. Plant Growth Regul. 60, 183–190 (2010).CAS 
Article 

Google Scholar 
Zhang, H. et al. miRNA–mRNA integrated analysis reveals roles for miRNAs in a typical halophyte, Reaumuria soongorica, during seed germination under salt stress. Plants 9, 351 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Bai, Y. M., Li, Y., Shan, L. S., Su, M. & Zhang, W. T. Effects of precipitation change and nitrogen addition on root morphological characteristics of Reaumuria soongorica. Arid Zone Res. 37, 1284–1292 (2020).
Google Scholar 
Hedwall, P. O., Nordin, A., Strengbom, J., Brunet, J. & Olsson, B. Does background nitrogen deposition affect the response of boreal vegetation to fertilization?. Oecologia 173, 615–624 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, G., Fahey, T. J., Xue, S. & Liu, F. Root morphology and architecture respond to N addition in Pinus tabuliformis, west China. Oecologia 171, 583–590 (2013).ADS 
PubMed 
Article 

Google Scholar 
Grechi, I. et al. Effect of light and nitrogen supply on internal C: N balance and control of root-to-shoot biomass allocation in grapevine. Environ. Exp. Bot. 59, 139–149 (2007).CAS 
Article 

Google Scholar 
Xiao, L., Liu, G., Li, P. & Xue, S. Nitrogen addition has a stronger effect on stoichiometries of non-structural carbohydrates, nitrogen and phosphorus in Bothriochloa ischaemum than elevated CO2. Plant Growth Regul. 83, 325–334 (2017).CAS 
Article 

Google Scholar 
Quentin, A. G. et al. Non-structural carbohydrates in woody plants compared among laboratories. Tree Physiol. 35, 1146–1165 (2015).CAS 
PubMed 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. J. Range Manag. 26, 13–18 (1973).CAS 
Article 

Google Scholar 
Millard, P., Sommerkorn, M. & Grelet, G. A. Environmental change and carbon limitation in trees: A biochemical, ecophysiological and ecosystem appraisal. New Phytol. 175, 11–28 (2007).CAS 
PubMed 
Article 

Google Scholar 
Zhang, T., Cao, Y., Chen, Y. M. & Liu, G. B. Non-structural carbohydrate dynamics in Robinia pseudoacacia saplings under three levels of continuous drought stress. Trees 29, 1837–1849 (2015).CAS 
Article 

Google Scholar 
Chapin, F. S., Schulze, E. D. & Mooney, H. A. The ecology and economics of storage in plants. Annu. Rev. Ecol. Syst. 21, 423–447 (1990).Article 

Google Scholar 
Sardans, J., Rivas-Ubach, A. & Peñuelas, J. The C:N: P stoichiometry of organisms and ecosystems in a changing world: A review and perspectives. Perspect. Plant Ecol. Evolut. Syst. 14, 33–47 (2012).Article 

Google Scholar 
Xia, J. Y. & Wan, S. Q. Global response patterns of terrestrial plant species to nitrogen addition. New Phytol. 179, 428–439 (2008).CAS 
PubMed 
Article 

Google Scholar 
Yang, Y. H., Luo, Y. Q., Lu, M., Schädel, C. & Han, W. X. Terrestrial C: N stoichiometry in response to elevated CO2 and N addition: a synthesis of two meta-analyses. Plant Soil 343, 393–400 (2011).CAS 
Article 

Google Scholar 
Mayor, J. R., Wright, S. J. & Turner, B. L. Species-specific responses of foliar nutrients to long-term nitrogen and phosphorus additions in a lowland tropical forest. J. Ecol. 102, 36–44 (2014).CAS 
Article 

Google Scholar 
Koerselman, A. & Meuleman, A. F. The vegetation ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).Article 

Google Scholar 
Gusewell, S. N: P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004).PubMed 
Article 

Google Scholar 
Wang, S., Shan, L. S., Li, Y., Zhang, Z. Z. & Ma, J. Effect of Precipitation on the Stoichiometric Characteristics of Carbon, Nitrogen and Phosphorus of Reaumuria soongarica and Salsola passerina. Acta Bot. Boreal. Occident. Sin. 40, 0335–0344 (2020).ADS 

Google Scholar 
Niu, D. C., Li, Q., Jiang, S. G., Chang, P. J. & Fu, H. Seasonal variations of leaf C:N: P stoichiometry of six shrubs in desert of China’s Alxa Plateau. Chin. J. Plant Ecol. 37, 317–325 (2013).Article 

Google Scholar 
Kleyer, M. & Minden, V. Why functional ecology should consider all plant organs: An allocation-based perspective. Basic Appl. Ecol. 16, 1–9 (2015).Article 

Google Scholar 
Yemm, E. & Willis, A. The estimation of carbohydrates in plant extracts by anthrone. Biochem. J. 57, 508–514 (1954).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bao, S. D. Soil and Agricultural Chemistry Analysis 3rd edn. (China Agriculture Press, Beijing, 2000).
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