Ecology

Departamento de Biología, Escuela Politécnica Nacional del Ecuador, Ladrón de Guevara E11-253 y Andalucía, Quito, EcuadorSelene BáezBiology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Calle Tulipán s/n, Móstoles, Madrid, SpainLuis Cayuela & Guillermo Bañares de DiosDepartamento de Biología, Área de Botánica, Universidad Autónoma de Madrid, Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. Macía, Celina Ben Saadi, Julia G. de Aledo & Laura Matas-GranadosCentro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Universidad Autónoma de Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. MacíaEscuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, Universidad Nacional Abierta a Distancia de Colombia, Sede José Celestino Mutis, Cl. 14 Sur 14-23, Bogotá, ColombiaEsteban Álvarez-DávilaInstituto Experimental de Biología Luis Adam Briancon, Universidad Mayor Real y Pontificia San Francisco Xavier de Chuquisaca, Dalence 235, Sucre, BoliviaAmira Apaza-QuevedoDepartamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Ecuador. San Cayetano Alto s/n. Paris y Marcelino Chamagnat, 1101608, Loja, EcuadorItziar Arnelas & Carlos Iván EspinosaDepartamento de Biología. Grupo de Biología de Páramos y Ecosistemas Andinos, Universidad de Nariño, Calle 18 # 50-02 Ciudadela Universitaria Torobajo, Pasto, ColombiaNatalia Baca-Cortes, Marian Cabrera & María Elena Solarte-CruzDepartment of Environment, CAVElab – Computational and Applied Vegetation Ecology, Ghent University, Coupure links 653, B-9000, Gent, BelgiumMarijn Bauters & Hans VerbeeckInstituto de Ecología Regional, Universidad Nacional de Tucumán, CONICET, Residencia Universitaria Horco Molle, Edificio Las Cúpulas, 4107, Tucumán, ArgentinaCecilia BlundoHerbario UIS, Escuela de Biología, Universidad Industrial de Santander, Carrera. 27, calle 9a, Bucaramanga, ColombiaFelipe CastañoHerbario Nacional de Bolivia, Instituto de Ecología, Universidad Mayor de San Andrés, Calle 27 s/n, La Paz, BoliviaLeslie Cayola, Alfredo Fuentes, M. Isabel Loza & Carla MaldonadoCenter for Conservation and Sustainable Development, Missouri Botanical Garden, 4344 Shaw Blvd., St. Louis, MO, 63110, USALeslie Cayola, William Farfán-Rios, Alfredo Fuentes, M. Isabel Loza & J. Sebastián TelloSchool of Geography, University of Leeds, Leeds, LS2 9JT, UKBelén FadriqueLiving Earth Collaborative, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAWilliam Farfán-RiosDepartment of Biology, University of Florida, 876 Newell Drive, ZIP 32611, Gainesville, Florida, USAClaudia Garnica-DíazInstituto de Investigación de Recursos Biológicos Alexander von Humboldt, Calle 28 A # 15-09, Bogotá, ColombiaMailyn González, Ana Belén Hurtado & Natalia NordenConservación Internacional, Colombia, Carrea 13 # 71-41, Bogotá, ColombiaDiego GonzálezInstitute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, D-06108, Halle, GermanyIsabell Hensen & Denis LippokEscuela de Ingeniería Agronómica, Universidad de Cuenca, Av. 12 de Abril y Av. Loja s/n, Cuenca, EcuadorOswaldo JadánGlobal Tree Conservation Program and the Center for Tree Science, The Morton Arboretum, Lisle, IL, 60532-1293, USAM. Isabel LozaFacultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Alberdi 47, San Salvador de Jujuy, CP 4600, Jujuy, ArgentinaLucio MaliziaDepartment of Biology, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAJonathan A. MyersAMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université  de Montpellier, TA-A51/PS, Boulevard de la Lironde, 34398 cedex 5, Montpellier, FranceImma Oliveras MenorEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UKImma Oliveras Menor & Greta WeithmannPlant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073, Goettingen, GermanyKerstin Pierick & Jürgen HomeierInstituto de Investigaciones para el Desarrollo Forestal (Indefor), Vía los Chorros de Milla, Mérida, VenezuelaHirma Ramírez-AnguloDepartamento de Biología, Universidad Nacional de Colombia, Cra 45 #26-85, Bogotá, ColombiaBeatriz Salgado-NegretSenckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325, Frankfurt, GermanyMatthias SchleuningDepartment of Biology, Wake Forest University, Winston-Salem, NC, 27109, USAMiles SilmanWildlife Conservation Society (WCS), 2300 Southern Boulevard Bronx, New York, 10460, USAEmilio VilanovaFaculty of Resource Management, HAWK University of Applied Sciences and Arts, Büsgenweg 1 A, 37077, Goettingen, GermanyJürgen HomeierCentre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, GermanyJürgen HomeierL.C., J.H., M.J.M. and S.B. conceived the idea. S.B., L.C., M.J.M., J.A.M. and J.S.T. obtained funding and coordinated the L.E.C. and iDiv workshops. L.C., S.B., J.H. and K.P. compiled the data sets and performed data quality checks. L.C., S.B., J.H. and K.P. conceived and developed the figures. S.B., J.H. and L.C. wrote the manuscript. The rest of authors (ordered alphabetically) contributed data, revised and agreed on the final version of the manuscript. More

Site descriptionThe field experiment was initiated in 2013 at the Yongchun County, Fujian Province, China (25°12′37″ N, 118°10′24″ E), using the two rotations of vegetables and rice (Fig. 1). The site is in the north of the Tropic of Cancer, with a typical subtropical marine monsoon climate, sufficient sunshine, and average annual solar radiation 462.26 kJ/cm2. The climate is mild and humid, with average annual temperature 16–21 °C and average annual rainfall about 1400 mm. Agricultural production allows for the cultivation of three crops annually. The soil of the test field was lateritic red soil.Figure 1Location of the field experiment site.Full size imageExperiment designThe experiment was conducted over 9 years from 2013 to 2021. Soil samples were collected before the experiment began to determine the main physical and chemical properties of the soil in the test plot, which were: organic matter content 19.96 g/kg, total nitrogen 2.25 g/kg, total phosphorus 1.31 g/kg, total potassium 27.86 g/kg, alkaline hydrolyzable nitrogen 107.73 mg/kg, available phosphorus 60.35 mg/kg, available potassium 116 mg/kg and soil pH 5.54. The test site was a rectangular field, 26 m long and 9 m wide, divided into 15 test blocks, each 5 m long and 2.8 m wide. Cement ridges were used to separate the test blocks, and irrigation drainage ditches were set outside the blocks. A protective isolation strip 1 m wide was formed around the test site. The experiment included two crop rotations: (I) rotation P–B–O: P, kidney bean (Phaseolus vulgaris L.), B, mustard (Brassica juncea L.), O, rice (Oryza sativa L.); and II) rotation P–B–V: P, kidney bean (P. vulgaris L.), B, mustard (B. juncea L.), V, cowpea (Vigna unguiculata L.). Four fertilizer treatments were selected: (1) recommended fertilization (RF) used with rotation P–B–O; (2) recommended fertilization (RF) used with P–B–V; (3) conventional fertilization (CF) used with P–B–O; (4) conventional fertilization (CF) used with P–B–V. A randomized complete block experimental design with three replications was used in the field study. The fertilization amounts used for treatments RF and CF are shown in Table 1. Under the CF, the amount of fertilizer applied to crops in each season is determined according to the years of fertilization habits of local farmers. The fertilization amount of crops in each season under the RF was calculated according to the measured basic soil fertility combined with the fertilization model of previous studies. The fertilization amount of crops in each season under the CF in this study is obtained by investigating the local farmers. The data on the fertilization amount of crops in each season under the RF is cited from the research report of Zhang et al.23. Urea (N 46%) was the nitrogen fertilizer, calcium superphosphate (P2O5 12%) was the phosphorus fertilizer, and potassium chloride (K2O 60%) was the potassium fertilizer. All phosphorus fertilizer applied to crops in each season was used as base fertilizer, and nitrogen and potassium fertilizer were applied separately as base fertilizer (40% of the total fertilization) and topdressing (60% of the total fertilization). The topdressing method was that nitrogen and potassium fertilizer for kidney bean and cowpea were applied twice, 30% of the fertilization amount each time; nitrogen and potassium fertilizer for mustard was applied three times, 20% of the fertilization amount each time; nitrogen fertilizer for rice was applied at two different growing stages, 50% of the fertilization amount at the tillering stage and 10% of the fertilization amount at the panicle stage; potassium fertilizer was applied once, using 60% of the fertilization amount. The first crop, kidney bean, was sown in early September and harvested in November. The second crop mustard, was sown in early December and harvested in February of the following year. The third crop, rice or cowpea, was sown in early April and harvested in July.Table 1 Fertilization rate of each treatment in the long term crop rotation experiment (kg/hm2).Full size tableData analysis and methodsYield stability analysis was conducted for the 9 years period using three different approaches. First, the coefficient of variation (CV) was calculated to give a measure of the temporal variability of yield for each treatment:$$CV=frac{upsigma }{Y}*100 {%}$$
(1)
where σ is the standard deviation of average crop yield in each year, and Y is the average crop yield in each year. A low value of CV indicates little variation, which implies that interannual difference in crop yield in the experimental plot is small and the yield is relatively stable over the years of the experimental period.A second yield stability indicator is the sustainable yield index (SYI), which is calculated by Singh et al.25:$$SYI=frac{mathrm{Y}-upsigma }{{Y}_{max}}$$
(2)
where Y is the average annual crop yield, σ is the standard deviation of the average annual crop yield, and YMax is the maximum annual crop yield. A high value of SYI indicates a greater capacity of the soil to sustain a particular crop yield over time.The third stability measure is Wricke’s ecovalence index (Wi2), which was calculated individually for each crop management system by Wricke26:$${Wi}^{2}={sum }_{j=1}^{q}({x}_{ij}-{{m}_{i}-{m}_{j}+m)}^{2}$$
(3)
where xij is the yield for treatment i in year j, mi is the yield for treatment i across all years, mj is the yield for year j across all treatments, and m is the average yield for all treatments across all years. When Wi2 is close to 0, the yield for treatment i is very stable.Analysis of crop yield trendsA simple linear regression analysis of grain yield (slopes and P values) over the years was performed to identify the yield trend (Choudhary et al.27):$$Y=a+bt$$
(4)
where Y is the crop yield (t/ha), a is a constant, t is the time in years, and b is the slope, or magnitude of the yield trend (annual rate of change in yield).Analysis of variance (ANOVA) was performed using MATLAB R2019b in order to compare crop yields in the long term experiment. Yield stability and univariate linear regression equations were created and statistically analyzed using the software toolbox. The coefficients of variation for yields, yield sustainability indexes, and graphs presented in this paper were calculated and drawn using MATLAB; differences were considered to be significant when P  More

Henneron, L., Cros, C., Picon-Cochard, C., Rahimian, V. & Fontaine, S. Plant economic strategies of grassland species control soil carbon dynamics through rhizodeposition. J. Ecol. 108, 528–545 (2020).CAS 
Article 

Google Scholar 
Arft, A. M. et al. Responses of tundra plants to experimental warming: meta-analysis of the international tundra experiment. Ecol. Monogr. 69, 491–511 (1999).
Google Scholar 
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L. & Williams, M. The decadal state of the terrestrial carbon cycle: global retrievals of terrestrial carbon allocation, pools, and residence times. Proc. Natl Acad. Sci. USA 113, 1285–1290 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).ADS 
Article 

Google Scholar 
Shipley, B. & Meziane, D. The balanced-growth hypothesis and the allometry of leaf and root biomass allocation. Funct. Ecol. 16, 326–331 (2002).Article 

Google Scholar 
Eziz, A. et al. Drought effect on plant biomass allocation: a meta‐analysis. Ecol. Evolution 7, 11002–11010 (2017).Article 

Google Scholar 
Yan, Z. et al. Biomass allocation in response to nitrogen and phosphorus availability: Insight from experimental manipulations of Arabidopsis thaliana. Front. Plant Sci. 10, 598 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, C. et al. Precipitation and nitrogen addition enhance biomass allocation to aboveground in an alpine steppe. Ecol. Evol. 9, 12193–12201 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kim, J.-S. et al. Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nat. Geosci. 10, 572–576 (2017).ADS 
CAS 
Article 

Google Scholar 
Lin, D., Xia, J. & Wan, S. Climate warming and biomass accumulation of terrestrial plants: a meta-analysis. N. Phytol. 188, 187–198 (2010).Article 

Google Scholar 
Fernandez, C. W. et al. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Change Biol. 23, 1598–1609 (2017).ADS 
Article 

Google Scholar 
Keller, J. A. & Shea, K. Warming and shifting phenology accelerate an invasive plant life cycle. Ecology 102, e03219 (2020).PubMed 

Google Scholar 
Cavagnaro, R. A., Oyarzabal, M., Oesterheld, M. & Grimoldi, A. A. Screening of biomass production of cultivated forage grasses in response to mycorrhizal symbiosis under nutritional deficit conditions. Grassl. Sci. 60, 178–184 (2014).Article 

Google Scholar 
Rasheed, M. U. et al. The responses of shoot-root-rhizosphere continuum to simultaneous fertilizer addition, warming, ozone and herbivory in young Scots pine seedlings in a high latitude field experiment. Soil Biol. Biochem. 114, 279–294 (2017).CAS 
Article 

Google Scholar 
Xu, M., Liu, M., Xue, X. & Zhai, D. Warming effects on plant biomass allocation and correlations with the soil environment in an alpine meadow, China. J. Arid Land 8, 773–786 (2016).Article 

Google Scholar 
Zhou, X., Talley, M. & Luo, Y. Biomass, litter, and soil respiration along a precipitation gradient in southern great plains, USA. Ecosystems 12, 1369–1380 (2009).CAS 
Article 

Google Scholar 
Hertel, D., Strecker, T., Mueller-Haubold, H. & Leuschner, C. Fine root biomass and dynamics in beech forests across a precipitation gradient – is optimal resource partitioning theory applicable to water-limited mature trees? J. Ecol. 101, 1183–1200 (2013).Article 

Google Scholar 
Zhou, L. et al. Responses of biomass allocation to multi-factor global change: a global synthesis. Agriculture, Ecosyst. Environ. 304, 107115 (2020).CAS 
Article 

Google Scholar 
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. N. Phytol. 193, 30–50 (2012).CAS 
Article 

Google Scholar 
Gorka, S., Dietrich, M., Mayerhofer, W., Gabriel, R. & Kaiser, C. Rapid transfer of plant photosynthates to soil bacteria via ectomycorrhizal hyphae and its interaction with nitrogen availability. Front. Microbiol. 10, 168 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, W. et al. Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc. Natl Acad. Sci. USA 113, 8741–8746 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Change Biol. 24, 4544–4553 (2018).ADS 
Article 

Google Scholar 
Cheng, L. et al. Mycorrhizal fungi and roots are complementary in foraging within nutrient patches. Ecology 97, 2815–2823 (2016).PubMed 
Article 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 
Article 

Google Scholar 
Hollister, R. D. & Flaherty, K. J. Above- and below-ground plant biomass response to experimental warming in northern Alaska. Appl. Vegetation Sci. 13, 378–387 (2010).
Google Scholar 
Johnson, N. C., Rowland, D. L., Corkidi, L. & Allen, E. B. Plant winners and losers during grassland N-eutrophication differ in biomass allocation and mycorrhizas. Ecology 89, 2868–2878 (2008).PubMed 
Article 

Google Scholar 
Xia, J., Yuan, W., Wang, Y. P. & Zhang, Q. Adaptive carbon allocation by plants enhances the terrestrial carbon sink. Sci. Rep. 7, 3341 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Litton, C. M. & Giardina, C. P. Below-ground carbon flux and partitioning: global patterns and response to temperature. Funct. Ecol. 22, 941–954 (2008).Article 

Google Scholar 
Wang, P. et al. Belowground plant biomass allocation in tundra ecosystems and its relationship with temperature. Environ. Res. Lett. 11, 055003 (2016).ADS 
Article 
CAS 

Google Scholar 
Hovenden, M. J. et al. Warming and elevated CO2 affect the relationship between seed mass, germinability and seedling growth in Austrodanthonia caespitosa, a dominant Australian grass. Glob. Change Biol. 14, 1633–1641 (2008).ADS 
Article 

Google Scholar 
Olszyk, D. M. et al. Whole-seedling biomass allocation, leaf area, and tissue chemistry for Douglas-fir exposed to elevated CO2 and temperature for 4 years. Can. J. For. Res. 33, 269–278 (2003).CAS 
Article 

Google Scholar 
Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Hagedorn, F., Gavazov, K. & Alexander, J. M. Above- and belowground linkages shape responses of mountain vegetation to climate change. Science 365, 1119-+ (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pinto, R. S. & Reynolds, M. P. Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat. Theor. Appl. Genet. 128, 575–585 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rasse, D. P., Rumpel, C. & Dignac, M. F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269, 341–356 (2005).CAS 
Article 

Google Scholar 
Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).CAS 
Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).ADS 
CAS 
Article 

Google Scholar 
Wang, P., Huang, K. & Hu, S. Distinct fine‐root responses to precipitation changes in herbaceous and woody plants: a meta‐analysis. N. Phytol. 225, 1491–1499 (2020).Article 

Google Scholar 
Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Prieto, I., Armas, C. & Pugnaire, F. I. Water release through plant roots: new insights into its consequences at the plant and ecosystem level. N. Phytol. 193, 830–841 (2012).Article 

Google Scholar 
Bai, W. et al. Increased temperature and precipitation interact to affect root production, mortality, and turnover in a temperate steppe: implications for ecosystem C cycling. Glob. Change Biol. 16, 1306–1316 (2010).ADS 
Article 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Turner, B. L. Resource partitioning for soil phosphorus: a hypothesis. J. Ecol. 96, 698–702 (2008).CAS 
Article 

Google Scholar 
Phillips, L. A., Ward, V. & Jones, M. D. Ectomycorrhizal fungi contribute to soil organic matter cycling in sub-boreal forests. ISME J. 8, 699–713 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gonzalez-Meler, M. A., Silva, L. B. C., Dias-De-Oliveira, E., Flower, C. E. & Martinez, C. A. Experimental air warming of a stylosanthes capitata, vogel dominated tropical pasture affects soil respiration and nitrogen dynamics. Front. Plant Sci. 8, 46 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Carrillo, Y., Pendall, E., Dijkstra, F. A., Morgan, J. A. & Newcomb, J. M. Response of soil organic matter pools to elevated CO2 and warming in a semi-arid grassland. Plant Soil 347, 339–350 (2011).CAS 
Article 

Google Scholar 
An, J. et al. Physiological and growth responses to experimental warming in first-year seedlings of deciduous tree species. Turkish J. Agriculture Forestry 41, 175–182 (2017).CAS 
Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Liu, R., Li, Y., Wang, Y., Ma, J. & Cieraad, E. Variation of water use efficiency across seasons and years: Different role of herbaceous plants in desert ecosystem. Sci. Total Environ. 647, 827–835 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Duarte, A. G. & Maherali, H. A meta-analysis of the effects of climate change on the mutualism between plants and arbuscular mycorrhizal fungi. Ecol. Evol. 12, https://doi.org/10.1002/ece3.8518 (2022).Bastos, A. & Fleischer, K. Fungi are key to CO2 response of soil. Nature 591, 532–534 (2021).ADS 
Article 
CAS 

Google Scholar 
Wang, X., Peng, L. & Jin, Z. Effects of AMF inoculation on growth and photosynthetic physiological characteristics of Sinocalycanthus chinensis under conditions of simulated warming. Acta Ecologica Sin. 36, 5204–5214 (2016).CAS 

Google Scholar 
Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, 6480 (2020).Article 
CAS 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159–8159 (2015).ADS 
PubMed 
Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 
Article 
CAS 

Google Scholar 
IPCC. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change 1535 (Cambridge University Press, 2021).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 
Task, G. Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS) (2000).Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Luo, Y. Q., Hui, D. F. & Zhang, D. Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: A meta-analysis. Ecology 87, 53–63 (2006).PubMed 
Article 

Google Scholar 
Rosenberg, M. S., Adams, D. C. & Gurevitch, J. MetaWin: Statistical Software for Meta-analysis (Sinauer Associates, Incorporated, 2000).Kembel, S. W. et al. Picante: integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2018).Article 
CAS 

Google Scholar 
Calcagno, V. & De, C. M. Glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, https://doi.org/10.18637/jss.v034.i12 (2010).Pinheiro, J. C., Bates, D. J., Debroy, S. D. & Sakar, D. nlme: Linear and nonlinear mixed effects models. R. package version 3, 1–117 (2009).
Google Scholar 
Viechtbauer, W. Metafor: meta-analysis package for R. J. Stat. Softw. 2010, 1–10 (2010).
Google Scholar 
Rosseel, Y. Lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, https://doi.org/10.18637/jss.v048.i02 (2012). More

All vertebrates examined in this study and histologically sampled (Supplementary Table 1) exhibit polyphyodonty and dentine growth lines (Figs. 2–4 and Supplementary Figs. 2–9) that are morphologically consistent with the incremental lines of von Ebner of extant mammalian and crocodilian teeth: alternating opaque zones, line trajectories paralleling the pulp cavity, and widths ranging between 1 and 30 mm18. All functional teeth were continuously replaced through the development of the replacement tooth, lingual to the functional tooth, resulting in resorption of its base and shedding.Fig. 2: Incremental lines of Mesenosaurus efremovi.a ROMVP 85502, lingual view of fragmented dentary with dashed red lines through the plane of the LL section of the functional and replacement teeth. b Whole view of tooth family LL section near crown apex. c Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. d Closeup view of replacement tooth TR cross-section showing incremental lines, white arrows.Full size imageFig. 3: Incremental lines of Dimetrodon cf. D. limbatus.a Lateral view of Dimetrodon. b ROMVP 85510, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near the crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. e Closeup view of replacement tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Reisz42 and Brink and Reisz43.Full size imageFig. 4: Incremental lines of Edaphosaurus sp.a Lateral view of Edaphosaurus. b USNM PAL 706602, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Romer and Price41 and Modesto44.Full size imageReplacement pattern in Mesenosaurus efremovi
Replacement in the gracile predator Mesenosaurus efremovi from the Richards Spur locality (Fig. 1) appears to occur as a wave in alternating tooth positions, with every other functional tooth in a sequence undergoing replacement during one event. Gaps in the tooth row represent stages in the replacement cycle when the old tooth has been shed, but the replacement tooth has not yet become functional and is not ankylosed to the jawbone. Frequently, these small replacement teeth are lost during fossilization, but in the case of the Dolese Mesenosaurus, preservation is so exquisite that these unattached replacement teeth are preserved, often in place (Fig. 1e). We found that numerous specimens of M. efremovi have tooth families containing a functional tooth and a single replacement tooth lingual to it, but one maxilla (ROMVP 85456) was observed to have a tooth family containing a functional tooth and two successive replacement teeth (Fig. 1c).The replacement rate found in one tooth family within an M. efremovi dentary was 39 days (ROMVP 85502; Fig. 2), and 34 days for the left maxilla (ROMVP 85443; Supplementary Fig. 2). Replacement rates of three tooth families (mx10, mx12, and mx15) for ROMVP 85457 were estimated to be 46, 36, and 35 days. Thus, the replacement rate for M. efremovi does not appear to vary significantly in one specimen across tooth position, size, or ontogenetic age of tooth.Replacement pattern in other synapsidsIn contrast to the availability of many Mesenosaurus specimens for destructive sampling, other taxa are exceedingly rare, and few specimens were available for destructive analysis. Thus, only a single maxilla of the apex predator Dimetrodon with a replacement tooth in position was available (Fig. 3). The functional tooth had a total of 459 incremental lines, whereas the replacement tooth had a total of 354 lines, resulting in a replacement rate of 105 days. In contrast, the maxillary tooth for the basal sphenacodont Haptodus, was calculated to have functional tooth longevity of approximately 152 days and since neither a replacement tooth nor a resorption pit was present, the minimum replacement rate is 152 days.Similarly, relatively little material was available for the larger varanopid predator Watongia meieri which is only known from the holotype material, with a resorption pit on one of the two teeth (mx19) on a maxillary fragment, but both teeth were missing the crown apex; thus, only a minimum age could be determined using the incremental line counts. The tooth with the resorption pit was determined to be a minimum of 81 days old, while the adjacent tooth not in the process of being replaced was approximately 68 days old. A second maxillary tooth with a resorption pit at mx18 was determined to be 145 days old. Additionally, one complete tooth with no resorption pit was longitudinally LL sectioned and estimated to be 108 days old.One maxilla of the small, very rare herbivorous caseid Oromycter was available for destructive sampling (Supplementary Fig. 3). The tooth with a resorption pit in position mx07 was determined to have a total of 506 incremental lines, whereas the tooth without a resorption pit (mx09) had a total of 426 incremental lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 115 incremental lines, resulting in an approximate replacement rate of 391 days.The left dentary of the large herbivorous caseid Ennatosaurus, known only from five specimens, exhibited two posterior teeth with resorption pits on positions d08 and d07 (Supplementary Fig. 4). Tooth position d08 had a visibly larger and more developed resorption pit, with the functional tooth having a total of 628 incremental lines, whereas d07 had a smaller resorption pit and a total of 567 incremental lines. The missing replacement teeth for both d07 and d08 were estimated to have 136 and 169 incremental lines, resulting in a replacement rate of approximately 431 and 459 days, respectively.One maxilla of the herbivorous edaphosaurid Edaphosaurus had a resorption pit at tooth position mx09 (Fig. 4) and was estimated to have a total of 506 incremental lines. The adjacent tooth at position mx10 had no resorption pit and was determined to have a total of 429 lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 131 incremental lines, resulting in a replacement rate of 381 days.Replacement pattern in early and extant reptilesFor the insectivorous parareptile Delorhynchus the functional tooth had a total of 147 incremental lines, while the replacement tooth had 43 lines (Supplementary Fig. 5), resulting in a replacement rate of 104 days. For the other parareptile Colobomycter the premaxillary functional tooth had a total of 157 incremental lines, whereas the replacement tooth had a total of 59 lines, resulting in a replacement rate of 98 days (Supplementary Fig. 6). For the omnivorous eureptile Captorhinus, the functional tooth was 146 days, and the replacement tooth was 69 days, resulting in a replacement rate of approximately 77 days. For the other eureptile, the highly specialized insectivore Opisthodontosaurus, the maximum tooth age for positions d04 to d07 was 151, 155, 206, and 258, respectively (Supplementary Fig. 7). Although no replacement teeth were present, it was possible to use the resorption pit heights to estimate the replacement rates of 182 and 193 days for d06 and d07, respectively. These rates, although different from Captorhinus are not unexpected since this small, close relative of Captorhinus has a very odd, unusual dentition, specialized for feeding on harder shelled invertebrates.In addition to the above Paleozoic amniotes, two skulls were examined for the extant varanid lizards, Varanus bengalensis and Varanus komodoensis, as well as shed teeth of the latter were also available for study and comparison. The maxillary bone of Varanus bengalensis carried dentition showing six replacement events, but only the mx04 tooth position was sectioned. The functional tooth was determined to have 188 incremental lines, and since a continuous record for the replacement tooth’s incremental lines was not visible, the replacement rate was estimated based on its entire dentine area divided by the functional tooth’s mean line width. The estimated replacement rate for V. bengalensis was approximately 110 days. Unlike M. efremovi, the base of the teeth is characterized by plicidentine, and neither tooth serrations (ziphodonty; Supplementary Fig. 8) nor resorption pits were observed for V. bengalensis.Similar to Mesenosaurus, Varanus komodoensis, a highly endangered varanid lizard, exhibits ziphodonty on both the mesial and distal tooth surfaces and provides a valuable comparison with the fossil taxon. Two isolated teeth of an adult individual that were in the process of attachment, but not yet ankylosed with the jawbone, were sectioned. The age of the first tooth was determined to have 106 lines, and the second tooth had approximately 135 lines. A third isolated shed tooth (due to resorption from replacement tooth or from the processing of food)29 provided by the Toronto Zoo was determined to have approximately 227 incremental lines. Thus, from the age of initial tooth attachment to the age of shedding, a tooth appears to be functional for an average of 107 days. Additionally, as in Mesenosaurus, the adult skull of V. komodoensis (ROM R7565) showed that each tooth position exhibited multiple replacement teeth for both the dentary and the maxilla, also confirmed by the data from Auffenberg30.Replacement pattern in a stem amnioteFor the representative carnivorous stem amniote Seymouria (Supplementary Fig. 9) the functional tooth was determined to have a maximum of 171 incremental lines, while the missing replacement tooth was estimated to have had approximately 36 lines. Thus, the estimated replacement rate for Seymouria was calculated to be 135 days.Replacement rate and body massThere seems to be no significant relationship between replacement rate and body mass (kg) for the taxa examined (Supplementary Fig. 10). Although the largest body sized taxon Ennatosaurus had the longest replacement rate, but the other large species had varying rates, while the smallest taxa (Captorhinus, Delorhynchus, Colobomycter, and Opisthodontosaurus) all have varying replacement rates. Instead, replacement rates appear to be related to feeding behaviour since the herbivorous synapsids all exhibited long replacement rates and great tooth longevities (Fig. 5).Fig. 5: Rates of tooth replacement and age across a range of taxa.a Relationship between the total number of incremental lines of von Ebner (age) for the functional tooth and the tooth families replacement rate or period (days). The symbols indicate the type of feeding behaviour, with circles representing carnivory, triangles representing herbivory, square representing insectivory, and diamond representing omnivory. b Phylogenetic tree of all taxa (n = 11) used in the analyses, displaying the age in millions of years ago (length of bars) and tooth longevity (gradient in branch colours). c Phylogenetic tree of all taxa (n = 9) used in the analyses, displaying the age in millions of years ago (mya) (length of bars) and tooth replacement rate (gradient in branch colours). Reconstructed using the ‘contMap’ function in the ‘phytools’ R package. The tree was modified from Maddin, Evans, and Reisz45 and Reisz and Sues12. Source data are provided as a Source Data file.Full size image More

Wiegand, T., Gunatilleke, S. & Gunatilleke, N. Species Associations in a Heterogeneous Sri Lankan Dipterocarp Forest. Am. Nat. 170, E77–E95. https://doi.org/10.1890/06-1350.1 (2007).Article 
PubMed 

Google Scholar 
Zhang, J. et al. Spatial patterns and associations of six congeneric species in an old-growth temperate forest. Acta Oecol. 11, 29–38. https://doi.org/10.1016/j.actao.2009.09.005 (2010).ADS 
Article 

Google Scholar 
Pretzsch, H. et al. Comparison between the productivity of pure and mixed stands of Norway spruce and European beech along an ecological gradient. Ann. For. Sci. 67, 712–712. https://doi.org/10.1051/forest/2010037 (2010).Article 

Google Scholar 
Zhu, J., Kang, H., Tan, H., Xu, M. & Wang, J. Natural regeneration characteristics ofPinus sylvestris var.mongolica forests on sandy land in Honghuaerji, China. J. For. Res. 16, 253–259. https://doi.org/10.1007/BF02858184 (2005).Felton, A., Felton, A. M., Wood, J. & Lindenmayer, D. B. Vegetation structure, phenology, and regeneration in the natural and anthropogenic tree-fall gaps of a reduced-impact logged subtropical Bolivian forest. For. Ecol. Manage. 235, 186–193. https://doi.org/10.1016/j.foreco.2006.08.011 (2006).Article 

Google Scholar 
Man, R., Kayahara, G. J., Rice, J. A. & MacDonald, G. B. Eleven-year responses of a boreal mixedwood stand to partial harvesting: Light, vegetation, and regeneration dynamics. For. Ecol. Manage. 255, 697–706. https://doi.org/10.1016/j.foreco.2007.09.043 (2008).Article 

Google Scholar 
Xiang, W., Lei, X. & Zhang, X. Modelling tree recruitment in relation to climate and competition in semi-natural Larix-Picea-Abies forests in northeast China. For. Ecol. Manage. 382, 100–109. https://doi.org/10.1016/j.foreco.2016.09.050 (2016).Article 

Google Scholar 
Zhang, M., Liu, Y., Guo, W., Kang, X. & Zhao, H. Spatial associations and species collocation of dominant tree spscies in a natural spruce-fir mixed forest of Changbai Mountains in Northeastern China. Appl. Ecol. Env. Res. 17, 6213–6225. https://doi.org/10.15666/aeer/1703_62136225 (2019).Garbarino, M., Weisberg, P. J. & Motta, R. Interacting effects of physical environment and anthropogenic disturbances on the structure of European larch (Larix decidua Mill.) forests. For. Ecol. Manag. 257, 1794–1802. https://doi.org/10.1016/j.foreco.2008.12.031 (2009).Gourlet-Fleury, S. et al. Silvicultural disturbance has little impact on tree species diversity in a Central African moist forest. For. Ecol. Manage. 304, 322–332. https://doi.org/10.1016/j.foreco.2013.05.021 (2013).Article 

Google Scholar 
Yu, D. & Han, S. Ecosystem service status and changes of degraded natural reserves—A study from the Changbai Mountain Natural Reserve China. Ecosyst. Serv. 20, 56–65. https://doi.org/10.1016/j.ecoser.2016.06.009 (2016).Article 

Google Scholar 
Moreau, G. et al. Long-term tree and stand growth dynamics after thinning of various intensities in a temperate mixed forest. For. Ecol. Manage. 473, 118311. https://doi.org/10.1016/j.foreco.2020.118311 (2020).Article 

Google Scholar 
Yan, Y., Zhang, C., Wang, Y., Zhao, X. & Gadow, K. Drivers of seedling survival in a temperate forest and their relative importance at three stages of succession. Ecol. Evol. 5, 4287–4299. https://doi.org/10.1002/ece3.1688 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Bai, F. et al. Long-term protection effects of national reserve to forest vegetation in 4 decades: Biodiversity change analysis of major forest types in Changbai Mountain Nature Reserve China. Sci. China Ser. C 51, 948–958. https://doi.org/10.1007/s11427-008-0122-9 (2008).Article 

Google Scholar 
Liu, Q., Li, X., Ma, Z. & Takeuchi, N. Monitoring forest dynamics using satellite imagery—a case study in the natural reserve of Changbai Mountain in China. For. Ecol. Manage. 210, 25–37. https://doi.org/10.1016/j.foreco.2005.02.025 (2005).Article 

Google Scholar 
Hao, H. et al. Patches structure succession based on spatial point pattern features in semi-arid ecosystems of the water-wind erosion crisscross region. Glob. Ecol. Conserv. 12, 158–165. https://doi.org/10.1016/j.gecco.2017.11.001 (2017).Article 

Google Scholar 
Das Gupta, S. & Pinno, B. D. Spatial patterns and competition in trees in early successional reclaimed and natural boreal forests. Acta Oecol. 92, 138–147. https://doi.org/10.1016/j.actao.2018.05.003 (2018).Hao, Z., Zhang, J., Song, B., Ye, J. & Li, B. Vertical structure and spatial associations of dominant tree species in an old-growth temperate forest. For. Ecol. Manage. 252, 1–11. https://doi.org/10.1016/j.foreco.2007.06.026 (2007).Article 

Google Scholar 
Zhao, H., Kang, X., Guo, Z., Yang, H. & Xu, M. Species interactions in spruce-fir mixed stands and implications for enrichment planting in the Changbai Mountains China. Mount. Res. Dev. 32, 187–196. https://doi.org/10.1659/MRD-JOURNAL-D-11-00125.1 (2012).Article 

Google Scholar 
Li, Y., Hui, G., Wang, H., Zhang, G. & Ye, S. Selection priority for harvested trees according to stand structural indices. iForest 10, 561–566, DOI: https://doi.org/10.3832/ifor2115-010 (2017).Zhang, Y., Drobyshev, I., Gao, L., Zhao, X. & Bergeron, Y. Disturbance and regeneration dynamics of a mixed Korean pine dominated forest on Changbai Mountain North-Eastern China. Dendrochronologia 32, 21–31. https://doi.org/10.1016/j.dendro.2013.06.003 (2014).Article 

Google Scholar 
Zhang, M. et al. Community stability for spruce-fir forest at different succession stages in Changbai Mountains, Northeast China. Chin. J. Appl. Ecol. 26, 1609–1616. https://doi.org/10.13287/j.1001-9332.20150331.024 (2015).Gong, Z., Kang, X. & Gu, L. Quantitative division of succession and spatial patterns among different stand developmental stages in Changbai Mountains. J. Mt. Sci. 16, 2063–2078. https://doi.org/10.1007/s11629-018-5142-8 (2019).Article 

Google Scholar 
Hu, Y., Min, Z., Gao, Y. & Feng, Q. Effects of selective cutting on stand growth and structure for natural mixed spruce (Picea koraiensis )-Fir (Abies nephrolepis) forests. Scientia Silvae Sinicae 47, 15–24. https://doi.org/10.11707/j.1001-7488.20110203 (2011).Article 

Google Scholar 
Hubbell, S. P. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).Seidler, T. G. & Plotkin, J. B. Seed dispersal and spatial pattern in tropical trees. PLoS Biol. 4, e344. https://doi.org/10.1371/journal.pbio.0040344 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ghalandarayeshi, S., Nord-Larsen, T., Johannsen, V. K. & Larsen, J. B. Spatial patterns of tree species in Suserup Skov—a semi-natural forest in Denmark. For. Ecol. Manage. 406, 391–401. https://doi.org/10.1016/j.foreco.2017.10.020 (2017).Article 

Google Scholar 
Harms, K. E., Wright, S. J., Calderón, O., Hernández, A. & Herre, E. A. Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404, 493–495. https://doi.org/10.1038/35006630 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wiegand, T., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N. & Huth, A. How individual species structure diversity in tropical forests. Proc. Natl. Acad. Sci. 104, 19029–19033. https://doi.org/10.1073/pnas.0705621104 (2007).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, T., Yan, Q., Wang, J. & Zhu, J. Restoring temperate secondary forests by promoting sprout regeneration: Effects of gap size and within-gap position on the photosynthesis and growth of stump sprouts with contrasting shade tolerance. For. Ecol. Manage. 429, 267–277. https://doi.org/10.1016/j.foreco.2018.07.025 (2018).Article 

Google Scholar 
Zhang, M., Kang, X., Meng, J. & Zhang, L. Distribution patterns and associations of dominant tree species in a mixed coniferous-broadleaf forest in the Changbai Mountains. J. Mt. Sci. 12, 659–670. https://doi.org/10.1007/s11629-013-2795-1 (2015).Article 

Google Scholar 
Navarro-Cerrillo, R. M. et al. Structure and spatio-temporal dynamics of cedar forests along a management gradient in the Middle Atlas Morocco. For. Ecol. Manag. 289, 341–353. https://doi.org/10.1016/j.foreco.2012.10.011 (2013).Article 

Google Scholar 
Condit, R. Spatial patterns in the distribution of tropical tree species. Science 288, 1414–1418. https://doi.org/10.1126/science.288.5470.1414 (2000).del Río, M. et al. Characterization of the structure, dynamics, and productivity of mixed-species stands: Review and perspectives. Eur. J. For. Res. 135, 23–49. https://doi.org/10.1007/s10342-015-0927-6 (2016).Article 

Google Scholar 
Wiegand, K., Jeltsch, F. & Ward, D. Do spatial effects play a role in the spatial distribution of desert-dwelling Acacia raddiana ?. J. Veg. Sci. 11, 473–484. https://doi.org/10.2307/3246577 (2000).Article 

Google Scholar 
Hui, G. & Pommerening, A. Analysing tree species and size diversity patterns in multi-species uneven-aged forests of Northern China. For. Ecol. Manage. 316, 125–138. https://doi.org/10.1016/j.foreco.2013.07.029 (2014).Article 

Google Scholar 
Graz, F. P. The behaviour of the species mingling index M sp in relation to species dominance and dispersion. Eur. J. For. Res. 123, 87–92. https://doi.org/10.1007/s10342-004-0016-8 (2004).Article 

Google Scholar 
Zhang, M. Spatial association and optimum adjacent distribution of trees in a mixed coniferous-broadleaf forest in northeastern China. Appl. Ecol. Environ. Res. 15, 1551–1564. https://doi.org/10.15666/aeer/1503_15511564 (2017).Hou, J. H., Mi, X. C., Liu, C. R. & Ma, K. P. Spatial patterns and associations in a Quercus-Betula forest in northern China. J. Veg. Sci. 15, 407–414. https://doi.org/10.1111/j.1654-1103.2004.tb02278.x (2004).Article 

Google Scholar 
Boyden, S., Binkley, D. & Shepperd, W. Spatial and temporal patterns in structure, regeneration, and mortality of an old-growth ponderosa pine forest in the Colorado Front Range. For. Ecol. Manage. 219, 43–55. https://doi.org/10.1016/j.foreco.2005.08.041 (2005).Article 

Google Scholar 
Li, J., Niu, S. & Liu, Y. Forest Ecology. Higher Education Press, (2010).Hui, G. et al. Theory and practice of structure-based forest management. Science Press, (2020).Gong, Z. et al. Interspecific association among arbor species in two succession stages of spruce-fir conifer and broadleaved mixed forest in Changbai Mountains, northeastern China. J. Beijing For. Univ. 33, 28–33 (2011).
Google Scholar 
Suzuki, S. N., Kachi, N. & Suzuki, J.-I. Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies Forest: Analyses using spatial autocorrelation and the mark correlation function. Ann. Bot. 102, 435–441. https://doi.org/10.1093/aob/mcn113 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Shao, G. et al. Integrating stand and landscape decisions for multi-purposes of forest harvesting. For. Ecol. Manage. 207, 233–243. https://doi.org/10.1016/j.foreco.2004.10.029 (2005).Article 

Google Scholar 
Dai, L. et al. Changes in forest structure and composition on Changbai Mountain in Northeast China. Ann. For. Sci. 68, 889–897. https://doi.org/10.1007/s13595-011-0095-x (2011).Article 

Google Scholar 
Liu, Y. et al. Determining suitable selection cutting intensities based on long-term observations on aboveground forest carbon, growth, and stand structure in Changbai Mountain, Northeast China. Scand. J. For. Res. 29, 436–454. https://doi.org/10.1080/02827581.2014.919352 (2014).CAS 
Article 

Google Scholar 
K. von Gadow and & Hui, G. Y. Characterizing Forest spatial structure and diversity. Proc. of an international workshop organized at the University of Lund, Sweden, 20–30 (2001).Baddeley, A. & Turner, R. spatstat: An R Package for Analyzing Spatial Point Patterns. J. Stat. Soft. 12, 1–42. https://doi.org/10.18637/jss.v012.i06 (2005).Illian, J., Penttinen, A., Stoyan, H. & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns: Illian/Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470725160 (2007).Wiegand, T. & Moloney, K. A. Handbook of Spatial Point-Pattern Analysis in Ecology. Chapman and Hall/CRC. https://doi.org/10.1201/b16195 (2013).Martínez, I., Wiegand, T., González-Taboada, F. & Obeso, J. R. Spatial associations among tree species in a temperate forest community in North-western Spain. For. Ecol. Manage. 260, 456–465. https://doi.org/10.1016/j.foreco.2010.04.039 (2010).Article 

Google Scholar 
Wang, X. et al. Species associations in an old-growth temperate forest in north-eastern China. J. Ecol. 98, 674–686. https://doi.org/10.1111/j.1365-2745.2010.01644.x (2010).Article 

Google Scholar 
Getzin, S., Wiegand, T. & Hubbell, S. P. Stochastically driven adult–recruit associations of tree species on Barro Colorado Island. Proc. R. Soc. B. 281, 20140922. https://doi.org/10.1098/rspb.2014.0922 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Nakashizuka, T. Species coexistence in temperate, mixed deciduous forests. Trends Ecol. Evol. 16, 205–210 (2001).CAS 
Article 

Google Scholar 
Mugglestone, M. & Renshaw, E. Spectral tests of randomness for spatial point patterns. Environ. Ecol. Stat. 237–251. https://doi.org/10.1023/A:1011339607376 (2001).Stoyan, D. & Stoyan, H. Fractals, random shapes, and point fields: methods of geometrical statistics. Wiley, (1994).Liu, P. et al. Competition and facilitation co-regulate the spatial patterns of boreal tree species in Kanas of Xinjiang, northwest China. For. Ecol. Manage. 467, 118167. https://doi.org/10.1016/j.foreco.2020.118167 (2020).Article 

Google Scholar 
Wiegand, T., Moloney, A. & Rings, K. circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

Google Scholar  More

Analysis of carbon sources in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in carbon emissions from farmland ecosystems in GuangdongGuangdong’s carbon emissions from farmland ecosystems showed an increasing trend year by year during 2001–2017 (Fig. 1a), with carbon emissions gradually reaching a peak of 4.153 million t a−1 in 2016 from 3.554 million t a−1 in 2001, but decreasing year by year from 2017 onwards. Eventually it’s decreasing to 3.533 million t a−1 by 2020. Showing that Guangdong’s farmland ecosystem carbon emissions have remained relatively flat over the past 20 years, with an average annual carbon emission of 3.7624 million t a−1. The carbon emissions per unit arable land area of Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1b), from 1.12 t ha−1 in 2001 to 2.03 t ha−1 in 2020, an increase of 81.25% over 20 years, with an average annual carbon emission per unit arable land area of 1.43 t ha−1. While the carbon emissions per unit sown area show the opposite trend to the total carbon emissions, from 2001 to 2016, showing a decreasing trend year by year. The carbon emissions per unit of sown area decreased from 1.50 t ha−1 in 2001 to 1.01 t ha−1 in 2016 and then started to increase year by year from 2017 to 1.26 t ha−1 in 2020, with an overall decrease of 16% and an average annual carbon emission per unit of sown area of 1.19 t ha−1.Figure 1(a) Inter-annual variation of carbon emissions from farmland ecosystems in Guangdong; (b) inter-annual variation in carbon emissions per unit area of farmland ecosystems in Guangdong.Full size imageAnalysis of carbon sources in Guangdong farmland ecosystemsThe carbon emissions from agricultural production power (estimated by the total power of agricultural diesel and agricultural machinery) in Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1a), from 411,000 t a−1 in 2001 to 513,000 t a−1 in 2020, an increase of nearly 25% in 20 years. Carbon emissions from tillage and irrigation inputs are relatively flat, from 116,000 t a−1 in 2001 to 109,000 t a−1 in 2020, with an average of 107,000 t a−1 over the last 20 years. Carbon emissions from chemicals in agricultural production (estimated by fertilizer, pesticide, and agricultural film inputs) have the greatest impact on the overall emissions, with carbon emissions from agricultural chemicals reaching 2.9097 million t a−1 in 2020, accounting for 82.36% of total carbon emissions from farmland ecosystems, but a relatively flat trend. Although the share of carbon emissions from agricultural production power is increasing year by year, the contribution of carbon emissions due to inputs of agricultural chemicals is still in an absolute position. The use of agricultural chemicals directly affects the carbon emissions of Guangdong’s farmland ecosystems. Therefore, a more detailed analysis of the carbon emissions of various agricultural chemicals is necessary in order to make carbon reduction proposals.Depending on Fig. 2a, although the proportion of carbon emissions caused by agricultural films has been increasing year by year, chemical fertilizers still occupy an absolute position, with their carbon emissions accounting for 78.45% of agricultural chemicals on average in the past 20 years. Which the average proportions of carbon emissions caused by pesticides and agricultural films are 15.17% and 6.38% respectively. Among the carbon emissions from various fertilizers (Fig. 2b), the annual average share of carbon emissions in the past 20 years is distributed from the largest to the smallest: 81.63% from nitrogen fertilizers, 9.57% from compound fertilizers, 5.60% from phosphate fertilizers and 3.20% from potash fertilizers. From the trend of carbon emissions of various types of fertilizers, we can learn that the carbon emissions of nitrogen fertilizers have been decreasing year by year, from 85.63% in 2001 to 78.10% in 2020, and the emissions have slowly risen from 2.061 million t a−1 in 2001 to a peak of 2.1276 million t a−1 in 2016, then gradually decreased to 1.7797 million t a−1 in 2020. Compound fertilizers, on the other hand, rose from 6.17% in 2001 to 11.40% in 2020, an increase of nearly 85%, and their carbon emissions rose year by year from 148,600 t a−1 to a peak of 305,200 t a−1 in 2016 and then gradually fell to 259,900 t a−1 in 2020, an increase of 74.90%. The share of carbon emissions from potash is relatively stable, rising from 2.89% to 3.29%, reaching a peak of 91,800 t a−1 in 2016 and then gradually decreasing to 75,500 t a−1 in 2020. The share of carbon emissions from phosphate fertilizers is also on a year-on-year rise, from 5.31% to 7.20%, an increase of 37.47%. However, the carbon emissions from phosphate fertilizers do not produce a peak in 2016 but keep increasing in a relatively stable trend, with its carbon emissions rising from 127,800 t a−1 in 2001 to 164,100 t a−1 in 2020, an increase of 28.40%.Figure 2(a) Proportion of carbon emissions from various types of agricultural chemicals in Guangdong farmland ecosystems; (b) proportion of carbon emissions from different fertilizer types in Guangdong farmland ecosystems.Full size imageAnalysis of carbon sequestration in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in the carbon sequestration function of Guangdong farmland ecosystemsIn the inter-annual variation of carbon sequestration function of farmland ecosystems in Guangdong (Fig. 3a), although there are fluctuations in the variation of total carbon sequestration in farmland ecosystems, the overall decrease is not significant. With the total carbon sequestration decreasing from 21.3176 million t a−1 in 2001 to 19.1178 million t a−1 in 2020, a decrease of 10.32% in the last 20 years, and the average annual carbon sequestration is 19.0363 million t a−1, among which the total carbon sequestration in 2008 is the lowest, only 17.2033 million t a−1.Figure 3(a) Carbon sequestration function of farmland ecosystems in Guangdong; (b) inter-annual variation of carbon sequestration function per unit area of farmland ecosystems in Guangdong.Full size imageThe total carbon sequestered in 2008 was the lowest at 17.2033 million t a−1. The inter-annual variation of carbon sequestration by food crops (paddy, wheat, corn, legumes, yams, and other food crops) is similar to that of farmland ecosystems, decreasing from 13.9742 million t a−1 to 10.209 million t a−1, a decrease of 27%. The inter-annual variation of carbon sequestration by cash crops (sugarcane, peanuts, Canola, and tobacco) and vegetables generally shows a stable upward trend, with carbon sequestration increasing by 15.54% and 55.54% respectively over the past 20 years. Meanwhile, the amount of carbon sequestered per unit sown area in Guangdong’s farmland ecosystems was generally flat (Fig. 3b), with an average annual carbon sequestration per unit sown area of 4.31 t ha−1. While the amount of carbon sequestered per unit arable land area showed an increasing trend, especially in 2017, when it started to rise rapidly, from 6.82 t ha−1 per unit arable land area in 2001 to 10.97 t ha−1 per unit arable land area in 2020, an increase of 60.85%. The average annual carbon sequestration per arable area is 7.25 t ha−1, an increase of 56.71% in the 4 years from 2017 to 2020.Analysis of the role of crop carbon sequestrations in Guangdong’s farmland ecosystemsAs can be seen from Fig. 4a, food crops play the largest role in carbon sequestration in Guangdong’s farmland ecosystems, with an average share of 56.95% of the total carbon sequestration in the past 20 years. Its share tends to decline over time, but the amount of carbon sequestered by food crops in Guangdong still reaches 10.209 million t a−1 in 2020. The carbon sequestration role of cash crops is next, rising from 29.43% in 2001 to 37.92% in 2020, with an average share of 36.17%, an increase of 28.85%, and average annual carbon sequestration of 6.8863 million t a−1. The inter-year variation of vegetables carbon sequestration also shows an increasing trend, rising from 5.02 to 8.73%, with an increase of 73.90%, and average annual carbon sequestration of 1.13112 million t a−1.Figure 4(a) Proportion of carbon sequestered by various crops in Guangdong farmland ecosystems. (b) Proportion of carbon sequestered by various food crops in Guangdong farmland ecosystems; (c) proportion of carbon sequestered by various cash crops in Guangdong farmland ecosystems.Full size imageWhen the carbon sequestration capacity of food (Fig. 4b) and cash crops (Fig. 4c) in Guangdong’s farmland ecosystems is broken down, it is easy to see that paddy is in an absolute position in terms of carbon sequestration among food crops, with an average share of 83.81% over the past 20 years and average annual carbon sequestration of 8.8946 million t a−1. Especially since 2017, the carbon sequestration share of paddy has risen to over 87% and will remain until 2020. Also, sugarcane’s share of carbon sequestration in cash crops is absolute, with average annual share of 86.73% and an average annual carbon sequestration of 5.9712 million t a−1. while, peanut’s share of carbon sequestration in cash crops is also not small, with average annual share of 12.47% and an average annual carbon sequestration of 0.8606 million t a−1.An analysis of the inter-annual variation in carbon sequestration of various crops (Fig. 5) shows that paddy and sugar cane play the largest role in carbon sequestration in Guangdong’s farmland ecosystems. Their combined annual average carbon sequestration amounting to 14.8658 million t a−1, accounting for 78.09% of the total annual average carbon sequestration in Guangdong’s farmland ecosystems. Vegetables, peanuts, and yams also play a significant role in carbon sequestration, with the combined annual average carbon sequestration of the three species being 2.9936 million t a−1, accounting for 15.73% of the total annual average carbon sequestration.Figure 5Comparison of carbon sequestration by various crops in Guangdong farmland ecosystems.Full size imageAnalysis of the carbon footprint of Guangdong’s farmland ecosystems under the “dual carbon” targetThe carbon footprint of Guangdong’s farmland ecosystem ((CEF)) is 531,100 ha a−1 per year, showing a general decrease (Fig. 6), with a 59.65% decrease from 513,900 ha a−1 in 2001 to 321,900 ha a−1 in 2020. The carbon footprint of Guangdong’s farmland ecosystems in the past 20 years (the peak value is 611,500 in 2008 ha a−1) is smaller than the ecological carrying capacity (i.e. the arable land area, the lowest value is 1.7421 million ha a−1 in 2020), and is in a state of carbon ecological surplus. Guangdong’s farmland carbon surplus ((CS)) shows a decreasing trend year by year (Fig. 6), from 2.1611 million ha a−1 in 2001 to 1.4202 million ha a−1 in 2020, a decrease of 45.61%. Although the carbon footprint and the inter-annual variation of the carbon surplus both show a decreasing trend, the productive area required to absorb the carbon emissions from farmland (i.e. the carbon footprint) rises from 16.44 to 18.48% of the arable land area in the same period.Figure 6Inter-annual variation of carbon footprint and ecological surplus of farmland ecosystems in Guangdong.Full size imageAn overview of the interannual variability of carbon emissions, sequestrations and footprints of farmland ecosystems in GuangdongIn the above analysis of the inter-annual variation of carbon emissions, sequestration, and footprint of Guangdong’s farmland ecosystems, it was found that 2017 was a special year. After 2017, which the total carbon emissions from Guangdong’s farmland ecosystems and carbon emissions due to agricultural chemicals (Fig. 1a), carbon emissions per unit of arable land area and sown area (Fig. 1b), carbon sequestration per unit of arable land area (Fig. 3b) and carbon footprint and carbon surplus (Fig. 6) all show a large turnaround. Based on the analysis of the factors after 2017 in Table 3, it can be seen that the number of various fertilizers using is gradually decreasing after 2017, especially the number of nitrogen fertilizers decreased by 149,600 t a−1 in 2018 compared with the amount of the previous year, a decrease of 14.44% in a single year, and the carbon emission decreased by 316,600 t a−1. The arable land area in Guangdong is decreasing after 2017, from 2017 to 2019, it decreased by 697,800 ha, a decrease of 26.84%, but the total carbon sequestration still remains above 19 million t a−1 (Fig. 3a), and while the area of arable land in Guangdong is decreasing, the area sown is climbing. The ratio of sown area to arable land area is used as the number of tillage per unit of arable land area in the paper, and the number of tillage per unit of the arable land area rises from 1.63 ha ha−1 in 2017 to 2.56 ha ha−1 in 2020.Table 3 Inter-annual variation of selected factors in Guangdong agro-ecosystems, 2017–2020.Full size tableBased on the conclusions obtained, the author looked up the agriculture-related policies of Guangdong Province in 2016 and 2017. And found that on 30 December 2016, the Guangdong Provincial People’s Government, in response to the soil prevention and control plan of the Central Government, formulated and issued to the cities and counties under its jurisdiction the Implementation Plan of the Guangdong Provincial Soil Pollution Prevention and Control Action Plan (here in after referred to as the “Plan”). The Plan encourages farmers in all areas to reduce the number of chemical fertilizers and apply pesticides scientifically. The effectiveness of the implementation of the Plan in Guangdong Province is remarkable as seen through the changes in the application of various fertilizers, which in the aspect of reducing fertilizer application alone resulted in a 344,900 t ha−1 reduction in carbon emissions from fertilizer inputs in 2018 compared to 2017. At the same time, the number of farmland tillage has increased, and the area of arable land has been reduced, but the total sown area of crops has remained relatively constant. In 2019, while the area of arable land in Guangdong (actual data on arable land in 2020 is missing, and the forecast alone may cause too much error, so 2019 is used as an example) is 69.78 ha less than that in 2017, the total sown area has increased by 22.43 ha, and the total agricultural output value still increased by RMB 64 billion. Which shows that the utilization rate of arable land and the output value per unit of arable land in Guangdong have both increased. More

Pascolini-Campbell, M., Reager, J. T., Chandanpurkar, H. A. & Rodell, M. A 10 per cent increase in global land evapotranspiration from 2003 to 2019. Nature 593, 543–547 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. 117, 9216–9222 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 2016GL071921 (2017).
Google Scholar 
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Liu, Y., Kumar, M., Katul, G. G., Feng, X. & Konings, A. G. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Change 10, 691–695 (2020).ADS 
CAS 
Article 

Google Scholar 
Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).ADS 
CAS 
Article 

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

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Humphrey, V. et al. Sensitivity of atmospheric CO 2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).PubMed 
Article 

Google Scholar 
Hsu, J. S., Powell, J. & Adler, P. B. Sensitivity of mean annual primary production to precipitation. Glob. Change Biol. 18, 2246–2255 (2012).ADS 
Article 

Google Scholar 
Zuidema, P. A. et al. Recent CO2 rise has modified the sensitivity of tropical tree growth to rainfall and temperature. Glob. Change Biol. 26, 4028–4041 (2020).ADS 
Article 

Google Scholar 
Bansal, S., James, J. J. & Sheley, R. L. The effects of precipitation and soil type on three invasive annual grasses in the western United States. J. Arid Environ. 104, 38–42 (2014).ADS 
Article 

Google Scholar 
Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).ADS 
CAS 
Article 

Google Scholar 
O’Connor, J. C. et al. Forests buffer against variations in precipitation. Glob. Change Biol., 27, 4686–4696 (2021).Schuldt, B. et al. Change in hydraulic properties and leaf traits in a tall rainforest tree species subjected to long-term throughfall exclusion in the perhumid tropics. Biogeosciences 8, 2179–2194 (2011).ADS 
Article 

Google Scholar 
Zhang, W. et al. Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nat. Commun. 10, 671 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adams, M. A., Buckley, T. N., Binkley, D., Neumann, M. & Turnbull, T. L. CO2, nitrogen deposition and a discontinuous climate response drive water use efficiency in global forests. Nat. Commun. 12, 5194 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abel, C. et al. The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nat. Sustain. 4, 25–32 (2021).Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).ADS 
MathSciNet 
CAS 
PubMed 
Article 

Google Scholar 
Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).ADS 
Article 

Google Scholar 
Zhang, W., Brandt, M., Guichard, F., Tian, Q. & Fensholt, R. Using long-term daily satellite based rainfall data (1983–2015) to analyze spatio-temporal changes in the sahelian rainfall regime. J. Hydrol. 550, 427–440 (2017).ADS 
Article 

Google Scholar 
Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).ADS 
Article 

Google Scholar 
Huntzinger, D. N. et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project – part 1: overview and experimental design. Geosci. Model Dev. 6, 2121–2133 (2013).ADS 
Article 

Google Scholar 
Porporato, A., Daly, E. & Rodriguez-Iturbe, I. Soil water balance and ecosystem response to climate change. Am. Naturalist 164, 625–632 (2004).Article 

Google Scholar 
Good, S. P., Moore, G. W. & Miralles, D. G. A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts. Nat. Ecol. Evol. 1, 1883 (2017).PubMed 
Article 

Google Scholar 
Donohue, R. J., Roderick, M. L., McVicar, T. R. & Yang, Y. A simple hypothesis of how leaf and canopy-level transpiration and assimilation respond to elevated CO 2 reveals distinct response patterns between disturbed and undisturbed vegetation: vegetation responses to elevated CO2. J. Geophys. Res. Biogeosci. 122, 168–184 (2017).CAS 
Article 

Google Scholar 
Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946 (2016).ADS 
Article 

Google Scholar 
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO 2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).ADS 
Article 
CAS 

Google Scholar 
Wolf, A., Anderegg, W. R. L. & Pacala, S. W. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl Acad. Sci. 113, E7222–E7230 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
González de Andrés, E. et al. Tree-to-tree competition in mixed European beech-Scots pine forests has different impacts on growth and water-use efficiency depending on site conditions. J. Ecol. 106, 59–75 (2018).Article 
CAS 

Google Scholar 
Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).ADS 
CAS 
Article 

Google Scholar 
Gonsamo, A. et al. Greening drylands despite warming consistent with carbon dioxide fertilization effect. Glob. Change Biol. 27, 3336–3349 (2021).Article 

Google Scholar 
Mankin, J. S., Smerdon, J. E., Cook, B. I., Williams, A. P. & Seager, R. The curious case of projected twenty-first-century drying but greening in the American West. J. Clim. 30, 8689–8710 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc. Natl Acad. Sci. 113, 12757–12762 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions: Photosynthesis and stomatal conductance responses to rising [CO2]. Plant, Cell Environ. 30, 258–270 (2007).CAS 
Article 

Google Scholar 
Morgan, J. A. et al. C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature 476, 202–205 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duursma, R. A. et al. On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. N. Phytologist 221, 693–705 (2019).Article 

Google Scholar 
Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2015).ADS 
Article 

Google Scholar 
Thompson, S. E., Harman, C. J., Heine, P. & Katul, G. G. Vegetation-infiltration relationships across climatic and soil type gradients: vegetation-infiltration relationships. J. Geophys. Res. 115, G02023 (2010).ADS 

Google Scholar 
Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).Article 

Google Scholar 
Fatichi, S., Leuzinger, S. & Körner, C. Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. N. Phytologist 201, 1086–1095 (2014).CAS 
Article 

Google Scholar 
Cui, J. et al. Vegetation forcing modulates global land monsoon and water resources in a CO2-enriched climate. Nat. Commun. 11, 5184 (2020).Gedney, N. et al. Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439, 835–838 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cui, J. et al. Vegetation response to rising CO2 amplifies contrasts in water resources between global wet and dry land Areas. Geophys. Res. Lett. 48, e2021GL094293 (2021).Yang, Y. et al. Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: Accounting for aboveground and belowground vegetation-CO2 effects. Hydrol. Earth Syst. Sci. 25, 3411–3427 (2021).ADS 
CAS 
Article 

Google Scholar 
Keenan, T. F. et al. A constraint on historic growth in global photosynthesis due to increasing CO2. Nature 600, 253–258 (2022).ADS 
Article 
CAS 

Google Scholar 
Sang, Y. et al. Comment on “Recent global decline of CO 2 fertilization effects on vegetation photosynthesis”. Science 373, eabg4420 (2021).PubMed 
Article 
CAS 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought‐induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).ADS 
Article 

Google Scholar 
Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).ADS 
PubMed 
Article 
CAS 

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

Google Scholar 
Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).ADS 
Article 

Google Scholar 
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).ADS 
CAS 
Article 

Google Scholar 
Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).ADS 
Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl Climatol. 115, 15–40 (2014).ADS 
Article 

Google Scholar 
Prado, R. & West, M. Time series: modeling, computation, and inference (CRC Press, 2010).West, M. & Harrison, J. Bayesian forecasting and dynamic models (Springer, 1997).Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Chang. 9, 880–885 (2019).ADS 
Article 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).ADS 
Article 

Google Scholar  More

Zhao, B. H. et al. Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau China. Geoderma 296, 10–17 (2017).ADS 
CAS 
Article 

Google Scholar 
Shi, P. et al. Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau China. Sci. Total Environ. 707, 135507 (2019).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Zhang, Y. et al. Effects of farmland conversion on the stoichiometry of carbon, nitrogen, and phosphorus in soil aggregates on the Loess Plateau of China. Geoderma 351, 188–196 (2019).ADS 
CAS 
Article 

Google Scholar 
Chang, E. H. et al. Using water isotopes to analyze water uptake during vegetation succession on abandoned cropland on the Loess Plateau China. CATENA 181, 104095 (2019).Article 

Google Scholar 
Chang, E. H. et al. The impact of vegetation successional status on slope runoff erosion in the Loess Plateau of China. Water 11, 2614 (2019).CAS 
Article 

Google Scholar 
Sun, L. Y., Zhou, J. L., Cai, Q. G., Liu, S. X. & Xiao, J. G. Comparing surface erosion processes in four soils from the Loess Plateau under extreme rainfall events. Int. Soil Water Conse. 9, 520–531 (2021).Article 

Google Scholar 
Wang, R. et al. Effects of gully head height and soil texture on gully headcut erosion in the Loess Plateau of China. CATENA 207, 105674 (2021).Article 

Google Scholar 
Wei, H., Zhao, W. W. & Wang, H. Effects of vegetation restoration on soil erosion on the Loess Plateau: A case study in the Ansai watershed. Int. J. Environ. Res. Pub He. 18, 6266 (2021).Article 

Google Scholar 
Zhang, X., Li, P., Li, Z. B., Yu, G. Q. & Li, C. Effects of precipitation and different distributions of grass strips on runoff and sediment in the loess convex hillslope. CATENA 162, 130–140 (2018).Article 

Google Scholar 
Foster, G. R., Huggins, L. F. & Meyer, L. D. A laboratory study of rill hydraulics: II Shear Stress Relationships. T Asabe. 27, 797–804 (1984).Article 

Google Scholar 
Zhu, B. B., Zhou, Z. C. & Li, Z. B. Soil erosion and controls in the slope-gully system of the Loess Plateau of China: A review. Front. Environ. Sci. 9, 657030 (2021).Article 

Google Scholar 
Wang, H., Wang, J. & Zhang, G. H. Impact of landscape positions on soil erodibility indices in typical vegetation-restored slope-gully systems on the Loess Plateau of China. CATENA 201, 105235 (2021).Article 

Google Scholar 
Chang, X. G. et al. Determining the contributions of vegetation and climate change to ecosystem WUE variation over the last two decades on the Loess Plateau China. Forests 12, 1442 (2021).Article 

Google Scholar 
Li, B. B. et al. Deep soil moisture limits the sustainable vegetation restoration in arid and semi-arid Loess Plateau. Geoderma 399, 115122 (2021).ADS 
Article 

Google Scholar 
Dong, L. B. et al. Effects of vegetation restoration types on soil nutrients and soil erodibility regulated by slope positions on the Loess Plateau. J. Environ. Manage. 302, 113985 (2022).CAS 
PubMed 
Article 

Google Scholar 
Shi, P. et al. Effects of grass vegetation coverage and position on runoff and sediment yields on the slope of Loess Plateau China. Agric. Water Manage. 259, 107231 (2022).Article 

Google Scholar 
Xia, L. et al. Soil moisture response to land use and topography across a semi-arid watershed: Implications for vegetation restoration on the Chinese Loess Plateau. J. Mt Sci. 19, 103–120 (2022).Article 

Google Scholar 
Chen, Y. X. et al. Soil enzyme activities of typical plant communities after vegetation restoration on the Loess Plateau China. Appl. Soil Ecol. 170, 104292 (2022).Article 

Google Scholar 
Qiu, L. J. et al. Quantifying spatiotemporal variations in soil moisture driven by vegetation restoration on the Loess Plateau of China. J. Hydrol. 600, 126580 (2021).Article 

Google Scholar 
Fang, H. Y., Li, Q. Y. & Cai, Q. G. A study on the vegetation recovery and crop pattern adjustment on the Loess Plateau of China. Afr. J. Microbiol. Res. 5, 1414–1419 (2011).Article 

Google Scholar 
Hu, C. J., Fu, B. J., Liu, G. H., Jin, T. T. & Guo, L. Vegetation patterns influence on soil microbial biomass and functional diversity in a hilly area of the Loess Plateau China. J. Soil Sedim. 10, 1082–1091 (2010).CAS 
Article 

Google Scholar 
Sun, C. L., Chai, Z. Z., Liu, G. B. & Xue, S. Changes in species diversity patterns and spatial heterogeneity during the secondary succession of grassland vegetation on the Loess Plateau China. Front. Plant Sci. 8, 1465 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, J. X. Threholds in vegetation-precipitation relationship and the implications in restoration of vegetation on the Loesee Plateau China. Acta Ecol. Sin. 25, 1233–1239 (2005).
Google Scholar 
Yang, X., Shao, M. A., Li, T. C. G, M. & Chen, M. Y. Community characteristics and distribution patterns of soil fauna after vegetation restoration in the northern Loess Plateau. Ecol. Indic. 122, 107236 (2021).Bullock, M. S., Nelson, S. D. & Kemper, W. D. Soil cohesion as affected by freezing, water content, time and tillage. Soil Sci. Soc. Am. J. 52, 70–776 (1988).Article 

Google Scholar 
Wang, T. et al. Effects of freeze-thaw on soil erosion processes and sediment selectivity under simulated rainfall. J. Arid Land. 9, 34–243 (2017).
Google Scholar 
Su, Y. Y., Li, P., Ren, Z. P., Xiao, L. & Zhang, H. Freeze–thaw effects on erosion process in loess slope under simulated rainfall. J. Arid Land. 12, 937–949 (2020).Article 

Google Scholar 
Slattery, M. C. & Burt, T, P. Particle size characteristics of suspended sediment in hillslope runoff and stream flow. Earth Surf. Proc. Land. 22, 705–719 (1997).Wu, F. Z., Shi, Z. H., Yue, B. J. & Wang, L. Particle characteristics of sediment in erosion on hillslope. Acta Pedol. Sin. 49, 1235–1240 (2012).
Google Scholar 
Issa, O. M., Bissonnais, Y. L. & Planchon, O. Soil detachment and transport on field-and laboratory-scale interrill areas: Erosion processes and the size-selectivity of eroded sediment. Earth Surf. Proc. Land. 31, 929–939 (2006).ADS 
Article 

Google Scholar 
Shi, Z. H. et al. Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J. Hydrol. 454–455, 123–130 (2012).Article 

Google Scholar 
Koiter, A. J., Owens, P. N. & Petticrew, E. L. The behavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth Sci. Rev. 125, 24–42 (2013).ADS 
CAS 
Article 

Google Scholar 
Pan, C. Z. & Shang, G. Z. P. Runoff hydraulic characteristics and sediment generation in sloped grassplots under simulated rainfall conditions. J. Hydrol. 331, 178–185 (2006).ADS 
Article 

Google Scholar 
Pan, C. Z. & Shang, G. Z. P. The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. CATENA 2007(70), 350–355 (2007).
Google Scholar 
Zheng, M. G., Cai, Q. G., Wang, C. F. & Liu, J. G. Effect of vegetation and other measures for soil and water conservation on runoff-sediment relationship in watershed scale. J. Hydraul. Eng. 38, 47–53 (2007).
Google Scholar 
Wei, X. et al. Flow characteristics of convex composite slopes of loess under vegetation cover. Trans. Chin. Soc. Agric. Eng. 30, 147–154 (2014).CAS 

Google Scholar 
Wang, L. et al. Rainfall kinetic energy controlling erosion processes and sediment sorting on steep hillslopes: A case study of clay loam soil from the Loess Plateau China. J. Hydrol. 512, 168–176 (2014).ADS 
Article 

Google Scholar 
Li, M., Yao, W. Y., Ding, W. F., Yang, J. F. & Chen, J. N. Effect of grass coverage on sediment yield in the hillslope-gully side erosion system. J. Geogr. Sci. 19, 321–330 (2009).Article 

Google Scholar 
Benito, E., Santiago, J. L., Blas, E. D. & Varela, M. E. Deforestation of water-repellent soils in Galicia (NW Spain): Effects on surface runoff and erosion under simulated rainfall. Earth Surf. Proc. Land. 28, 145–155 (2003).ADS 
Article 

Google Scholar 
Han, P. & Li, X. X. Study on soil erosion and vegetation effect on soil conservation in the Yellow River Basin. J. Basic Sci. Eng. 16, 181–190 (2008).
Google Scholar 
Bissonnais, Y. L. Aggregate stability and assessment of soil crustability and erodibility: I. Theory and methodology. Eur. J. Soil Sci. 47, 425–437 (1996).Zhang, X., Yu, G. Q., Li, Z. B. & Li, P. Experimental study on slope runoff, erosion and sediment under different vegetation types. Water Resour. Manag. 28, 2415–2433 (2014).Article 

Google Scholar 
Xu, G. C. et al. Temporal and spatial characteristics of soil water content in diverse soil layers on land terraces of the Loess Plateau China. CATENA 158, 20–29 (2017).Article 

Google Scholar 
Yu, Y. et al. Land preparation and vegetation type jointly determine soil conditions after long-term land stabilization measures in a typical hilly catchment, Loess Plateau of China. J. Soil Sedim. 17, 144–156 (2017).CAS 
Article 

Google Scholar 
Dou, Y. X., Yang, Y., An, S. S. & Zhu, Z. L. Effects of different vegetation restoration measures on soil aggregate stability and erodibility on the Loess Plateau China. CATENA 185, 104294 (2020).CAS 
Article 

Google Scholar 
He, J., Shi, X. Y. & Fu, Y. J. Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience. J. Environ. Manage. 289, 112562 (2021).PubMed 
Article 

Google Scholar 
Qiu, D. X., Gao, P., Mu, X. M. & Zhao, B. L. Vertical variations and transport mechanism of soil moisture in response to vegetation restoration on the Loess Plateau of China. Hydrol. Process. 35, e14397 (2021).
Google Scholar 
Zhang, G. H., Liu, G. B., Wang, G. L. & Wang, Y. X. Effects of Vegetation cover and rainfall intensity on sediment-bound nutrient loss, size composition and volume fractal dimension of sediment particles. Pedosphere 21, 676–684 (2011).CAS 
Article 

Google Scholar 
Gu, Z. J. et al. Estimating the effect of Pinus massoniana Lamb plots on soil and water conservation during rainfall events using vegetation fractional coverage. CATENA 109, 225–233 (2013).Article 

Google Scholar 
Comprehensive analysis of relationship between vegetation attributes and soil erosion on hillslopes in the Loess Plateau of China. Environ Earth Sci. 72, 1721–1731 (2014).Zhao, G. J., Mu, X. M., Wen, Z. M., Wang, F. & Gao, P. Soil erosion, conservation, and eco-environment changes in the loess plateau of China. Land Degrad. Dev. 24, 499–510 (2013).Article 

Google Scholar 
Zhang, L., Wang, J. M., Bai, Z. K. & Lv, C. J. Effects of vegetation on runoff and soil erosion on reclaimed land in an opencast coal-mine dump in a loess area. CATENA 128, 44–53 (2015).Article 

Google Scholar 
Wei, W., Pan, D. L. & Feng, J. Tradeoffs between soil conservation and soil-water retention: The role of vegetation pattern and density. Land Degrad. Dev. 33, 18–27 (2021).Article 

Google Scholar 
Asadi, H., Ghadiri, H., Rose, C. W., Yu, B. & Hussein, J. An investigation of flow-driven soil erosion processes at low streampowers. J. Hydrol. 342, 134–142 (2007).ADS 
Article 

Google Scholar 
Shi, Z. H., Yan, F. L., Li, L., Li, Z. X. & Cai, C. F. Interrill erosion from disturbed and undisturbed samples in relation to topsoil aggregate stability in red soils from subtropical China. CATENA 81, 240–248 (2010).Article 

Google Scholar 
Zhou, J. et al. Effects of precipitation and restoration vegetation on soil erosion in a semi-arid environment in the Loess Plateau China. CATENA 137, 1–11 (2016).Article 

Google Scholar 
Han, Z. M. et al. Effects of vegetation restoration on groundwater drought in the Loess Plateau China. J. Hydrol. 591, 125566 (2020).Article 

Google Scholar 
Liang, Y., Jiao, J. Y., Tang, B. Z., Cao, B. T. & Li, H. Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region China. J. Hydrol. 584, 124694 (2020).Article 

Google Scholar  More