Philippa Kaur

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

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

Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25, 1–45 (1950).CAS 
PubMed 
Article 

Google Scholar 
Weersing, K. & Toonen, R. J. Population genetics, larval dispersal, and connectivity in marine systems. Mar. Ecol. Progr. Ser. 393, 1–12 (2009).ADS 
Article 

Google Scholar 
Hedgecock, D. Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates?. Bull. Mar. Sci. 39, 550–564 (1986).
Google Scholar 
Jenkins, S. R. & Hawkins, S. J. Barnacle larval supply to sheltered rocky shores: a limiting factor?. Hydrobiologia 503, 143–151 (2003).Article 

Google Scholar 
Pineda, J., Hare, J. A. & Sponaugle, S. Consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

Google Scholar 
Shanks, A. L. Mechanisms of cross-shelf dispersal of larval invertebrates and fish. In Ecology of Marine Invertebrate Larvae (ed. McEdward, L. R.) 324–367 (CRC, Boca Raton, 1995).
Google Scholar 
Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).PubMed 
Article 

Google Scholar 
Bradford, R. W., Griffin, D. & Bruce, B. D. Estimating the duration of the pelagic phyllosoma phase of the southern rock lobster, Jasus edwardsii (Hutton). Mar. Freshw. Res. 66, 213–219 (2015).Article 

Google Scholar 
Mileikovsky, S. A. Speed of active movement of pelagic larvae of marine bottom invertebrates and their ability to regulate their vertical position. Mar. Biol. 23, 11–17 (1973).Article 

Google Scholar 
Garrison, L. P. Vertical migration behavior and larval transport in brachyuran crabs. Mar. Ecol. Progr. Ser. 176, 103–113 (1999).ADS 
Article 

Google Scholar 
Morgan, S. G. & Fisher, J. L. Larval behavior regulates nearshore retention and offshore migration in an upwelling shadow and along the open coast. Mar. Ecol. Progr. Ser. 404, 109–126 (2010).ADS 
Article 

Google Scholar 
Cowen, R. K. & Castro, L. R. Relation of coral reef fish larval distributions to island scale circulation around Barbados, west indies. Bull. Mar. Sci. 54, 228–224 (1994).
Google Scholar 
Rudorff, C. A. G., Lorenzzetti, J. A., Gherardia, D. F. M. & Lins-Oliveira, J. E. Modeling spiny lobster larval dispersion in the Tropical Atlantic. Fish. Res. 96, 206–215 (2009).Article 

Google Scholar 
Allee, W. C. Studies in marine ecology. IV. The effect of temperature in limiting the geographic range of invertebrates of the Woods Hole littoral. Ecology 4, 341–354 (1923).Article 

Google Scholar 
Burton, R. S. Intraspecific phylogeography across the Point Conception biogeographic boundary. Evolution 52, 734–745 (1998).PubMed 
Article 

Google Scholar 
Lancellotti, D. A. & Vasquez, J. A. Biogeographical patterns of benthic macroinvertebrates in the southeastern Pacific littoral. J. Biogeogr. 26, 1001–1006 (1999).Article 

Google Scholar 
Hormazabal, S., Shaffer, G. & Leth, O. Coastal transition zone off Chile. J. Geophys. Res. 109, C01021 (2004).ADS 

Google Scholar 
Mcdonald, A. M. The global ocean circulation: a hydrographic estimate and regional analysis. Prog. Oceanogr. 41, 281–382 (1998).ADS 
Article 

Google Scholar 
Montecino, V. & Lange, C. B. The Humboldt Current System: Ecosystem components and processes, fisheries, and sediment studies. Progr. Oceanogr. 83, 65–79 (2009).ADS 
Article 

Google Scholar 
Haye, P. A. et al. Phylogeographic structure in benthic marine invertebrates of the southeast Pacific Coast of Chile with differing dispersal potential. PLoS ONE 9, e88613 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kelly, R. P. & Palumbi, S. R. Genetic structure among 50 species of the northeastern Pacific rocky intertidal community. PLoS ONE 5, e8594 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gaylord, B. & Gaines, S. D. Temperature or transport? Range limits in marine species mediated solely by flow. Am. Nat. 155, 769–789 (2000).PubMed 
Article 

Google Scholar 
Wares, J. P., Gaines, S. D. & Cunningham, C. W. A comparative study of asymmetric migration events across a marine biogeographic boundary. Evolution 55, 295–306 (2001).CAS 
PubMed 
Article 

Google Scholar 
Rumrill, S. S. Natural mortality of marine invertebrate larvae. Ophelia 32, 163–198 (1990).Article 

Google Scholar 
Jenkins, S. R., Marshall, D. & Fraschetti, S. Settlement and recruitment. In Marine Hard Bottom Communities, Ecological Studies Vol. 206 (ed. Wahl, M.) 177–190 (Springer, Berlin, 2009).Chapter 

Google Scholar 
Marino, I. A. M. et al. Genetic heterogeneity in populations of the Mediterranean shore crab, Carcinus aestuarii (Decapoda, Portunidae), from the Venice Lagoon. Estuar. Coast. Shelf. Sci. 87, 135–144 (2010).ADS 
CAS 
Article 

Google Scholar 
Sernapesca. Estadística de pesca de Chile. http://www.sernapesca.cl/informes/estadisticas (2022).Nation JD (1975) The Genus Cancer: Crustacea: Brachyura): Systematics, biogeography and fossil record. Nat. Hist. Mus. Los Angeles County Sci, Bull. 23 (1975).Pardo, L. M., Fuentes, J. P., Olguin, A. & Orensanz, J. M. L. Reproductive maturity in the edible Chilean crab Cancer edwardsii: methodological and management considerations. J. Mar. Biol. Assoc. U. K. 89, 1627–1634 (2009).Article 

Google Scholar 
Rojas-Hernández, N., Veliz, D. & Pardo, L. M. Use of novel microsatellite markers for population and paternity analysis in the commercially important crab Metacarcinus edwardsii. Mar. Biol. Res. 10, 839–844 (2014).Article 

Google Scholar 
Pardo, L. M., Riveros, M. P., Fuentes, J. P., Rojas-Hernández, N. & Veliz, D. An effective sperm competition avoidance strategy in crabs drives genetic monogamy despite evidence of polyandry. Behav. Ecol. Sociobiol. 70, 73–81 (2016).Article 

Google Scholar 
Pardo, L. M. et al. High fishing intensity reduces females’ sperm reserve and brood fecundity in a eubrachyuran crab subject to sex- and size biased harvest. ICES J. Mar. Sci. 74, 2459–2469 (2017).Article 

Google Scholar 
Pardo, L. M., Mora-Vásquez, P. & Garcés-Vargas, J. Asentamiento diario de megalopas de jaibas del género Cancer en un estuario micromareal. Lat. Am. J. Aquat. Res. 40, 142–152 (2012).Article 

Google Scholar 
Pardo, L. M., Rubilar, P. R. & Fuentes, J. P. North Patagonian estuaries appear to function as nursery habitats for marble crab (Metacarcinus edwardsii). Reg. Stud. Mar. Sci. 36, 101315 (2020).
Google Scholar 
Quintana, R. Larval development of the Edible crab, Cancer edwardsi Bell, 1835 under laboratory conditions (Decapoda, Brachyura). Rep. USA Mar. Biol. Inst. 5, 1–19 (1983).
Google Scholar 
Rojas-Hernández, N., Veliz, D., Riveros, M. P., Fuentes, J. P. & Pardo, L. M. Highly connected populations and temporal stability in allelic frequencies of a harvested crab from southern Pacific. PLoS ONE 11, e0166029 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Strub, P. T., James, C., Montecino, V., Rutllant, J. A. & Blanco, J. L. Ocean circulation along the southern Chile transition region (38°–46°S): Mean, seasonal and interannual variability, with a focus on 2014–2016. Progr. Oceanogr. 172, 159–198 (2019).ADS 
Article 

Google Scholar 
Beerli, P., Mashayekhi, S., Sadeghi, M., Khodaei, M. & Shaw, K. Population genetic inference with MIGRATE. Curr. Protoc. Bioinform. 68, e87 (2019).Article 

Google Scholar 
Kilian, A. et al. Diversity arrays technology: A generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89 (2012).PubMed 
Article 

Google Scholar 
Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Weiss, M. et al. Influence of temperature on the larval development of the edible crab, Cancer pagurus. J. Mar Biol. Assoc. UK 89, 753–759 (2009).CAS 
Article 

Google Scholar 
Pampoulie, C. et al. A pilot genetic study reveals the absence of spatial genetic structure in Norway lobster (Nephrops norvegicus) on fishing grounds in Icelandic waters. ICES J. Mar. Sci. 68, 20–25 (2011).Article 

Google Scholar 
Costlow, J. D. J. & Bookhout, C. G. The larval development of Callinectes sapidus Rathbun reared in the laboratory. Biol. Bull. 116, 373–396 (1959).Article 

Google Scholar 
Ungfors, A., McKeown, N. J., Shaw, P. W. & Andre, C. Lack of spatial genetic variation in the edible crab (Cancer pagurus) in the Kattegat – Skagerrak area. ICES J. Mar. Sci. 66, 462–469 (2009).Article 

Google Scholar 
Lacerda, A. L. F. et al. High connectivity among blue crab (Callinectes sapidus) populations in the Western South Atlantic. PLoS ONE 11, e0153124 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Taylor, M. S. & Hellberg, M. E. Comparative phylogeography in a genus of coral reef fishes: biogeographic and genetic concordance in the Caribbean. Mol. Ecol. 15, 695–707 (2006).CAS 
PubMed 
Article 

Google Scholar 
Arranz, V., Fewster, R. M. & Lavery, S. D. Geographic concordance of genetic barriers in New Zealand coastal marine species. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3607–3625 (2021).Article 

Google Scholar 
Ayre, D. J., Minchinton, T. E. & Perrin, C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier?. Mol. Ecol. 18, 1887–1903 (2009).CAS 
PubMed 
Article 

Google Scholar 
Barber, P. H., Erdmann, M. V. & Palumbi, S. R. Comparative phylogeography of three codistributed stomatopods: origins and timing of regional lineage diversification in the coral triangle. Evolution 60, 1825–1839 (2006).PubMed 
Article 

Google Scholar 
Macaya, E. C. & Zuccarello, G. C. Genetic structure of the giant kelp Macrocystis pyrifera along the southeastern Pacific. Mar. Ecol. Progr. Ser. 420, 103–112 (2010).ADS 
Article 

Google Scholar 
Ruiz, M., Tarifeño, E., Llanos-Rivera, A., Padget, C. & Campos, B. Efecto de la temperatura en el desarrollo embrionario y larval del mejillón, Mytilus galloprovincialis (Lamarck 1819). Rev. Biol. Mar. Oceanogr. 431, 51–61 (2008).
Google Scholar 
Toro, J. E., Castro, G. C., Ojeda, J. A. & Vergara, A. M. Allozymic variation and differentiation in the Chilean blue mussel, Mytilus chilensis, along its natural distribution. Genet. Mol. Biol. 29, 174–179 (2006).CAS 
Article 

Google Scholar 
Araneda, C., Larraín, M. A., Hecht, B. & Narum, S. Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments. Ecol. Evol. 6, 3632–3644 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Disalvo, L. H. Observations on the larval and post-metamorphic life of Concholepas concholepas (Bruguière, 1789) in laboratory culture. Veliger 30, 358–368 (1988).
Google Scholar 
Cardenas, L., Castilla, J. C. & Viard, F. Hierarchical analysis of the population genetic structure in Concholepas concholepas, a marine mollusk with a long-lived dispersive larva. Mar. Ecol. 37, 359–369 (2016).ADS 
Article 

Google Scholar 
Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Genetic structure of Carcinus maenas within its native range: larval dispersal and oceanographic variability. Mar. Ecol. Progr. Ser. 410, 111–123 (2010).ADS 
Article 

Google Scholar 
Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Temporal genetic homogeneity among shore crab (Carcinus maenas) larval events supplied to an estuarine system on the Portuguese northwest coast. Heredity 106, 832–840 (2011).CAS 
PubMed 
Article 

Google Scholar 
Vadopalas, B., Pietsch, T. & Friedman, C. The proper name for the geoduck: resurrection of Panopea generosa Gould, 1850, from the synonymy of Panopea abrupta (Conrad, 1849) (Bivalvia: Myoida: Hiatellidae). Malacologia 52, 169–173 (2010).Article 

Google Scholar 
Cassista, M. C. & Hart, M. W. Spatial and temporal genetic homogeneity in the Arctic surfclam (Mactromeris polynyma). Mar. Biol. 152, 569–579 (2007).Article 

Google Scholar 
Li, G. & Hedgecock, D. Genetic heterogeneity, detected by PCR-SSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55, 1025–1033 (1998).CAS 
Article 

Google Scholar 
Schmidt, P. S., Phifer-Rixey, M., Taylor, G. M. & Christner, J. Genetic heterogeneity among intertidal habitats in the flat periwinkle, Littorina obtusata. Mol. Ecol. 16, 2393–2404 (2007).CAS 
PubMed 
Article 

Google Scholar 
Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344 (2016).ADS 
CAS 
Article 

Google Scholar 
Reid, K. et al. Secondary contact and asymmetrical gene flow in a cosmopolitan marine fish across the Benguela upwelling zone. Heredity 117, 307–315 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hu, Z.-M., Zhang, J., Lopez-Bautista, J. & Duan, D.-L. Asymmetric genetic exchange in the brown seaweed Sargassum fusiforme (Phaeophyceae) driven by oceanic currents. Mar. Biol. 160, 1407–1414 (2013).Article 

Google Scholar 
Xuereb, A. et al. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus). Mol. Ecol. 27, 2347–2364 (2018).PubMed 
Article 

Google Scholar 
Becker, R. A. & Wilks, A. R. R version by Ray Brownrigg. mapdata: Extra Map Databases. R package version 2.3.0. (2018b).Becker, R.A. & Wilks, A. R. R version by Ray Brownrigg. Enhancements by TP Minka and A Deckmyn.maps: Draw Geographical Maps. R package version 3.3.0. https://CRAN.R-project.org/package=maps (2018a).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar 
Grube, B., Unmack, P. J., Berry, O. F. & Georges, A. dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).Article 

Google Scholar 
Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Flanagan, S. P. & Jones, A. G. Constraints on the Fst-heterozygosity outlier approach. J. Hered 108, 561–573 (2017).PubMed 
Article 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodohl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol 4, 782–788 (2013).Article 

Google Scholar 
Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. & Bonhomme, F. GENETIX 4.05, Logiciel sous Windows pour la Genetique des Populations. Laboratoire Genome, Populations, Interactions, CNRS UMR 5000 (Université de Montpellier II, Montpellier, France, 2000).Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pritchard, J. K., Wen, X. & Falush, D. Documentation for Structure Software: Version 2.3. University of Oxford http://pritch.bsd.uchicago.edu/structure.html (2010).Beerli, P. & Felsenstein, J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. U.S.A. 98, 4563–4568 (2001).ADS 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94–100 (2016).CAS 
PubMed 
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

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

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

Data collectionVirus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al4., Pandit et al.3, and Johnson et al.13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez.esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed20,21. NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias3,13. The virus-host association data source is presented in supplementary code and data files (https://zenodo.org/record/5899054).$$ searchterm= (+virus_name+,[Title/Abstract])\ ANDleft(host,OR,hosts,OR,reservoir,OR,reservoirs,OR right.\ wild,OR,wildlife,OR,domestic,OR,animal,OR,animals,OR\ mammal,OR,bird,OR,birds,OR,aves,OR,avian,OR,avians\ left. OR,vertebrate,OR,vertebrates,OR,surveillance,OR,sylvaticright)$$Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of well-recognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig. S32).Development of ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})
a. Centrality measures of observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}}))To test if centrality measures (degree centrality, betweenness centrality, eigenvector centrality, clustering coefficient) for viral nodes in the observed network (({G}_{c})) vary significantly between viral families, we firstly used the Kolmogorov-Smirnov (KS) test. KS test is routinely used to identify distances between cumulative distribution functions of two probability distributions and is largely used to compare degree distributions of networks22,23. For each viral family, distributions of centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) and clustering coefficient within the observed network (({G}_{c})) were compared with the distribution of all nodes in the network using the two-tailed KS test. Secondly, a linear regression model with virus family as a categorical variable and the number of PubMed hits as a covariate to adjust for sampling bias were fitted to understand associations of viral families with centrality measures.$${centrality},{measure}={beta }_{0}{intercept}+{{beta }_{1}{Viral}{family}}_{{categorical}}+{beta }_{2}{PubMed},{hits}$$After fitting the model, node-level permutations were implemented. For each random permutation, the output variable was randomly assigned to covariate values and the model was re-fitted. Finally, a p-value was calculated by comparing the distribution of coefficients from permutations with the original model coefficient.Network topology feature selectionUsing the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})), multiple network topological features for all node (virus) pairs were calculated. The following are topological network features calculated. Features data type, definition and methods to calculate these features are presented in Table S3.1. The Jaccard coefficient: a commonly used similarity metric between nodes in information retrieval, is also called an intersection of over the union for two nodes in the network. In the unipartite network generated here, it represents the proportion of common neighbor viruses from the union of neighbor viruses for two nodes. Neighbor viruses are defined as viruses with which the virus shares at least a single host.2. Adamic/Adar (Frequency-Weighted Common Neighbors): Is the sum of inverse logarithmic degree centrality of the neighbors shared by two nodes in the network24. The concept of Adamic Adar index is a weighted common neighbors for viruses in the network. Within network prediction, the index assumes that viruses with large neighborhoods have a less significant impact while predicting a connection between two viruses compared with smaller neighborhoods.Both Jaccard and Adamic Adar coefficients have been routinely used for generalized network prediction and have shown high accuracy in predicting missing links in networks, specifically bipartite networks25, the information flowing through neighborhoods formed by two nodes might not always be enough to have similar predictive power in an unipartite network. This warrants use of other topology features along with neighborhood-based features.3. Resource allocation: Similarity score of two nodes defined by the weights of common neighbors of two nodes. Resource allocation is another measure to quantify the closeness of two nodes in the network and hence to understand the similarity of hosts they infect.4. Preferential attachment coefficients: The mechanism of preferential attachment can be used to generate evolving scale-free networks, where the probability that a new link is connected to node x is proportional to k26.5. Betweenness centrality: For a node in the network betweenness centrality is the sum of the fraction of all-pairs shortest paths that pass through it. The feature that we used for training the supervised learning model was the absolute difference between of betweenness centralities of two nodes. The difference between the betweenness centrality represents the difference in the sharing observed by two viruses in the pair.6. Degree centrality: The degree centrality for a node v is the fraction of nodes it is connected to. The feature that we used for training the supervised learning model was the absolute difference between degree centralities of two nodes. Unlike the difference in the betweenness centrality, the difference in degree centrality only looks at the difference in the number of observed host sharing.7. Network clustering: All nodes were classified into community clusters using Louvain methods27. A binary feature variable was generated to describe if both the nodes in the pair were part of the same cluster or not. If both viruses are from the same cluster, it represents a similar host predilection than when both viruses are not from the same cluster hence accounting for the evolutionary predilection of viruses (or virus families) to infect a certain type of host.These topological network characteristics come with certain limitations when it comes to the unipartite network of viruses with links formed due to shared hosts and might not truly represent the flow of information between nodes as compared to a bipartite network. Therefore, to account for these limitations, we use multiple network features as weak learners in our model building characteristics summarizing the network through the use of several quantitative metrics. In addition to this, we estimated the feature importance of these metrics in predicting missing links between viruses to quantify the information pasting through these links.Pearson’s correlation coefficients were calculated to identify highly correlated features and for choosing features for model training (Fig. S33). Virological features included in model training were categorical variables describing the virus family of both the nodes in the pair, followed by a binary variable if both the viruses belong to the same virus family. During the model development, PubMed hits generated three predictive features for each pair of viruses on which model training and predictions were conducted. These included two features representing PubMed hits for the two viruses in the pair (PubMedV1, PubMedV2) and the absolute difference between PubMedV1 and PubMedV2 to account for differences in sampling bias between the two viruses.Cross-validation and fitting generalized boosting machine (GBMs) modelsA nested-cross-validation was implemented for the binary model while simple cross-validation was implemented for the multiclass model (multiple output categories). The parameters of the binary model were first hyper-tuned using a cross-validated grid-search method. Values were tested using a grid search to find the best-performing model parameters that showed the highest sensitivity (recall). The parameters tested for hypertuning and their performance are provided in the supplementary material (supplementary results and Table S5). For further cross-validation of the overall binary model, all the viruses were randomly assigned to five groups. For each fold, the viruses assigned to a group were dropped from the data, and a temporary training network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}{{{{{boldsymbol{)}}}}}}) was constructed, assuming that this represented the current observed status of the virus-host community. For all possible pairs in ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}) (both that sharing and not sharing any hosts) ten topological and viral characteristics were calculated as training features (Table S4). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8).The multiclass model was implemented in the same way, creating an observed network (({G}_{c})) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (({G}_{t})) to train the XGboost model. The output variables were generated based on the taxonomical orders of shared hosts. A pair of viruses can share multiple hosts, hence we trained a multioutput-multiclass model. Humans were considered an independent category of taxonomical order (label) and were given a separate label from primates. For fine-tuning the multiclass model, we started with the best performing parameters of the binary model and manually tested 5 combinations of model parameters by adjusting values of the learning rate, number of estimators, maximum depth, and minimum child weight (Supplementary code and results).We used three methods to estimate the importance of features for our binary model. Specifically, improvement in accuracy brought by branching based on the feature (gain), the percentage of times the feature appears in the XGboost tree model (weight), and the relative number of observations related to the specific feature (cover). Results for feature importance are shown in supplementary results (Fig. S10).Missing links for novel viruses, binary and multiclass predictionThe wildlife surveillance data represented a sampling of 99,379 animals (94,723 wildlife, 4656 domesticated animals) conducted in 34 countries around the world between 2009–2019 (Table S6)1. Specimens were tested using conventional Rt-PCR, Quantitative PCR, Sanger sequencing, and Next Generation Sequencing protocols to detect viruses from 28 virus families or taxonomic groups (Table S7). Testing resulted in 951 novel monophyletic clusters of virus sequences (referred to as novel viruses henceforth). Within 951 novel viruses, 944 novel viruses had vertebrate hosts that were identified with certainty based on barcoding methods and field identification. Host species identification was confirmed by cytochrome b (cytb) DNA barcoding using DNA extracted from the samples28. We predicted the shared host links between novel viruses and known viruses using binary and multiclass models in the following steps. Out of 944 novel viruses discovered in the last ten years, we were able to generate predictions for 531 novel viruses that were detected in species already classified as hosts within the network. The remaining 413 viruses were the first detection of any virus in that species and thus host associations could not be informed by the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{C}}}}}}})) data.1. A new node representing the novel virus was inserted in the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})). Using the list of species in which the novel virus was detected, new edges were created with known viruses that are also known to be found in those hosts. This generated a temporary network for the novel virus (({{{{{{boldsymbol{G}}}}}}}_{{temp}})). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed.2. Using ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) a feature dataset was generated (Jaccard coefficient(novel virus, known virus), the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality(novel virus, known virus), if the novel virus and known virus were from same virus family, the difference in PubMed hits(novel virus, known virus), PubMed hits for the novel virus, PubMed hits for the known virus). Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed’s Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed’s database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed’s Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses. To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families.$${{PubMed}}_{{Search}}left({log }right)={beta }_{0}{intercept}+{{beta }_{1}{Virus}{family}}_{{categorical}}+{beta }_{2}{Genbank},{hits},({log })$$The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0.72, p  More