Ecology

1.
Slobodkin, L. B. Growth and regulation of animal populations (Holt, Rinehart and Winston, 1961).
2.
Thompson, J. N. Rapid evolution as an ecological process. Trends Ecol. Evol. 13, 329–332 (1998).
CAS  PubMed  Article  Google Scholar 

3.
Hendry, A. P. A critique for eco-evolutionary dynamics. Funct. Ecol. 33, 84–94 (2019).
Article  Google Scholar 

4.
Turcotte, M. M., Reznick, D. N. & Hare, J. D. The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics. Ecol. Lett. 14, 1084–1092 (2011).
PubMed  Article  Google Scholar 

5.
Hairston, N. G. Jr, Ellner, S. P., Geber, M. A., Yoshida, T. & Fox, J. A. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8, 1114–1127 (2005).
Article  Google Scholar 

6.
Tan, J., Rattray, J. B., Yang, X. & Jiang, L. Spatial storage effect promotes biodiversity during adaptive radiation. Proc. R. Soc. Lond. B 284, 20170841 (2017).
Google Scholar 

7.
Hart, S. P., Turcotte, M. M. & Levine, J. M. Effects of rapid evolution on species coexistence. Proc. Natl Acad. Sci. USA 116, 2112–2117 (2019).
CAS  PubMed  Article  Google Scholar 

8.
Faillace, C. A. & Morin, P. J. Evolution alters the consequences of invasions in experimental communities. Nat. Ecol. Evol. 1, 0013 (2017).
Article  Google Scholar 

9.
Vanbergen, A. J., Espíndola, A. & Aizen, M. A. Risks to pollinators and pollination from invasive alien species. Nat. Ecol. Evol. 2, 16–25 (2018).
PubMed  Article  Google Scholar 

10.
Hendry, A. P. Eco-evolutionary dynamics (Princeton Univ. Press, 2016).

11.
Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019).
PubMed  PubMed Central  Article  Google Scholar 

12.
Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
terHorst, C. P. & Zee, P. C. Eco-evolutionary dynamics in plant–soil feedbacks. Funct. Ecol. 30, 1062–1072 (2016).
Article  Google Scholar 

14.
Soto, M. J., Domínguez‐Ferreras, A., Pérez‐Mendoza, D., Sanjuán, J. & Olivares, J. Mutualism versus pathogenesis: the give‐and‐take in plant–bacteria interactions. Cell. Microbiol. 11, 381–388 (2009).
CAS  PubMed  Article  Google Scholar 

15.
Marchetti, M. et al. Experimental evolution of a plant pathogen into a legume symbiont. PLoS Biol. 8, e1000280 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Saikkonen, K., Wäli, P., Helander, M. & Faeth, S. H. Evolution of endophyte–plant symbioses. Trends Plant Sci. 9, 275–280 (2004).
CAS  PubMed  Article  Google Scholar 

17.
Reese, A. T. & Dunn, R. R. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. mBio 9, e01294-18 (2018).
PubMed  PubMed Central  Article  Google Scholar 

18.
Miller, E. T., Svanbäck, R. & Bohannan, B. J. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).
PubMed  Article  Google Scholar 

19.
Griffin, E. A. et al. Plant host identity and soil macronutrients explain little variation in sapling endophyte community composition: is disturbance an alternative explanation? J. Ecol. 107, 1876–1889 (2019).
CAS  Article  Google Scholar 

20.
Acosta, K. et al. Duckweed hosts a taxonomically similar bacterial assemblage as the terrestrial leaf microbiome. PLoS ONE 15, e0228560 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Sandler, G., Bartkowska, M., Agrawal, A. F. & Wright, S. I. Estimation of the SNP mutation rate in two vegetatively propagating species of duckweed. G3 10, 4191–4200 (2020).
PubMed  Article  Google Scholar 

22.
Ishizawa, H., Kuroda, M., Morikawa, M. & Ike, M. Evaluation of environmental bacterial communities as a factor affecting the growth of duckweed Lemna minor. Biotechnol. Biofuels 10, 62 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Zhang, Y. et al. Duckweed (Lemna minor) as a model plant system for the study of human microbial pathogenesis. PLoS ONE 5, e13527 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Rainey, P. B. & Travisano, M. Adaptive radiation in a heterogeneous environment. Nature 394, 69–72 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Tan, J., Yang, X., He, Q., Hua, X. & Jiang, L. Earlier parasite arrival reduces the repeatability of host adaptive radiation. ISME J. 14, 2358–2360 (2020).
PubMed  PubMed Central  Article  Google Scholar 

26.
Tan, J., Yang, X. & Jiang, L. Species ecological similarity modulates the importance of colonization history for adaptive radiation. Evolution 71, 1719–1727 (2017).
PubMed  Article  Google Scholar 

27.
Meyer, J. R., Schoustra, S. E., Lachapelle, J. & Kassen, R. Overshooting dynamics in a model adaptive radiation. Proc. R. Soc. Lond. B 278, 392–398 (2011).
Google Scholar 

28.
Tan, J., Kelly, C. K. & Jiang, L. Temporal niche promotes biodiversity during adaptive radiation. Nat. Commun. 4, 2102 (2013).
PubMed  Article  CAS  Google Scholar 

29.
Spiers, A. J., Buckling, A. & Rainey, P. B. The causes of Pseudomonas diversity. Microbiology 146, 2345–2350 (2000).
CAS  PubMed  Article  Google Scholar 

30.
Spiers, A. J., Bohannon, J., Gehrig, S. M. & Rainey, P. B. Biofilm formation at the air–liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose. Mol. Microbiol. 50, 15–27 (2003).
CAS  PubMed  Article  Google Scholar 

31.
Bantinaki, E. et al. Adaptive divergence in experimental populations of Pseudomonas fluorescens. III. Mutational origins of wrinkly spreader diversity. Genetics 176, 441–453 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
McDonald, M. J., Gehrig, S. M., Meintjes, P. L., Zhang, X.-X. & Rainey, P. B. Adaptive divergence in experimental populations of Pseudomonas fluorescens. IV. Genetic constraints guide evolutionary trajectories in a parallel adaptive radiation. GENETICS 183, 1041–1053 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Bailey, S. F., Dettman, J. R., Rainey, P. B. & Kassen, R. Competition both drives and impedes diversification in a model adaptive radiation. Proc. R. Soc. Lond. B 280, 20131253 (2013).
Google Scholar 

34.
Hansen, S. K., Rainey, P. B., Haagensen, J. A. & Molin, S. Evolution of species interactions in a biofilm community. Nature 445, 533–536 (2007).
CAS  PubMed  Article  Google Scholar 

35.
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).
CAS  PubMed  Article  Google Scholar 

36.
Ahmad, F., Ahmad, I. & Khan, M. S. Screening of free-living rhizospheric bacteria for their multiple plant growth promoting activities. Microbiol. Res. 163, 173–181 (2008).
CAS  PubMed  Article  Google Scholar 

37.
El-Sayed, W. S., Akhkha, A., El-Naggar, M. Y. & Elbadry, M. In vitro antagonistic activity, plant growth promoting traits and phylogenetic affiliation of rhizobacteria associated with wild plants grown in arid soil. Front. Microbiol. 5, 651 (2014).
PubMed  PubMed Central  Article  Google Scholar 

38.
Gómez, P. & Buckling, A. Real-time microbial adaptive diversification in soil. Ecol. Lett. 16, 650–655 (2013).
PubMed  Article  Google Scholar 

39.
Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).
CAS  PubMed  Article  Google Scholar 

40.
Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl Acad. Sci. USA 115, 7368–7373 (2018).
PubMed  PubMed Central  Article  Google Scholar 

41.
Veach, A. M. et al. Rhizosphere microbiomes diverge among Populus trichocarpa plant-host genotypes and chemotypes, but it depends on soil origin. Microbiome 7, 76 (2019).
PubMed  PubMed Central  Article  Google Scholar 

42.
Lennon, J. T. & Martiny, J. B. Rapid evolution buffers ecosystem impacts of viruses in a microbial food web. Ecol. Lett. 11, 1178–1188 (2008).
PubMed  Article  Google Scholar 

43.
Pantel, J. H., Duvivier, C. & Meester, L. D. Rapid local adaptation mediates zooplankton community assembly in experimental mesocosms. Ecol. Lett. 18, 992–1000 (2015).
PubMed  Article  Google Scholar 

44.
Faillace, C. A. & Morin, P. J. Evolution alters post-invasion temporal dynamics in experimental communities. J. Anim. Ecol. 89, 285–298 (2020).
PubMed  Article  Google Scholar 

45.
Omilian, A. R., Cristescu, M. E. A., Dudycha, J. L. & Lynch, M. Ameiotic recombination in asexual lineages of Daphnia. Proc. Natl Acad. Sci. USA 103, 18638–18643 (2006).
CAS  PubMed  Article  Google Scholar 

46.
Mao, Y., Botella, J. R., Liu, Y. & Zhu, J.-K. Gene editing in plants: progress and challenges. Natl Sci. Rev. 6, 421–437 (2019).
CAS  Article  Google Scholar 

47.
Horvath, P. & Barrangou, R. CRISPR/Cas, the immune system of Bacteria and Archaea. Science 327, 167–170 (2010).
CAS  PubMed  Article  Google Scholar 

48.
Yang, L. et al. Promotion of plant growth and in situ degradation of phenol by an engineered Pseudomonas fluorescens strain in different contaminated environments. Soil Biol. Biochem. 43, 915–922 (2011).
CAS  Article  Google Scholar 

49.
Zabłocka-Godlewska, E., Przystaś, W. & Grabińska-Sota, E. Decolourization of diazo Evans blue by two strains of Pseudomonas fluorescens isolated from different wastewater treatment plants. Water Air Soil Pollut. 223, 5259–5266 (2012).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Paulsen, I. T. et al. Complete genome sequence of the plant commensal Pseudomonas fluorescens Pf-5. Nat. Biotechnol. 23, 873–878 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Rainey, P. B. Adaptation of Pseudomonas fluorescens to the plant rhizosphere. Environ. Microbiol. 1, 243–257 (1999).
CAS  PubMed  Article  Google Scholar 

52.
Gilbert, S. et al. Bacterial production of indole related compounds reveals their role in association between duckweeds and endophytes. Front. Chem. 6, 265 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Bailey, M. J., Lilley, A. K., Thompson, I. P., Rainey, P. B. & Ellis, R. J. Site directed chromosomal marking of a fluorescent pseudomonad isolated from the phytosphere of sugar beet; stability and potential for marker gene transfer. Mol. Ecol. 4, 755–764 (1995).
CAS  PubMed  Article  Google Scholar 

54.
Spiers, A. J. & Rainey, P. B. The Pseudomonas fluorescens SBW25 wrinkly spreader biofilm requires attachment factor, cellulose fibre and LPS interactions to maintain strength and integrity. Microbiology 151, 2829–2839 (2005).
CAS  PubMed  Article  Google Scholar 

55.
Lind, P. A., Libby, E., Herzog, J. & Rainey, P. B. Predicting mutational routes to new adaptive phenotypes. eLife 8, e38822 (2019).
PubMed  PubMed Central  Article  Google Scholar 

56.
O’Brien, P. A., Webster, N. S., Miller, D. J. & Bourne, D. G. Host–microbe coevolution: applying evidence from model systems to complex marine invertebrate holobionts. mBio 10, e02241-18 (2019).
PubMed  PubMed Central  Article  Google Scholar 

57.
Theis, K. R. et al. Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. mSystems 1, e00028-16 (2016).
PubMed  PubMed Central  Article  Google Scholar 

58.
Landolt, E. Biosystematic Investigations in the Family of Duckweeds (Lemnaceae), Volume 2. The Family of Lemnaceae, A Monographic Study, Volume 1 (Geobotanical Institute, ETH Zurich, 1986).

59.
Ziegler, P., Sree, K. S. & Appenroth, K.-J. Duckweeds for water remediation and toxicity testing. Toxicol. Environ. Chem. 98, 1127–1154 (2016).
CAS  Article  Google Scholar  More

Plant and insect materials
A total of 56 apple plants were grown from seeds and sampled for this study. Cultivated apple plants resulting from crosses between various cultivated apple varieties were used (M. domestica, referred to as “Dom”, N = 14, Table S1). The seeds were kindly provided by INRAE IRHS Angers that performed every year crosses for apple breeding programs. A total of 42 M. sylvestris plants were grown from field-collected seeds. These wild apple seeds originated from three out of the five known European wild apple populations (referred to as Danish: Syl_Dk, French: Syl_Fr and Romanian: Syl_Ro, N = 14 per population). Each population was represented by a single sampling site, and within each site, each seed was sampled on a single mother tree, so that each seedling has a different parental origin. Though M. domestica is usually grafted, new plants were grown from seed to eliminate the rootstock effect.
After field sampling, seeds were stored at -20 °C before vernalization for the experiment. Seeds were then vernalized for three months at 4 °C in the dark, then grown in controlled conditions for two months before being individually transferred to 3 L pots containing commercial sterilized potting soil. Potted plants were grown in a growth chamber for four weeks under the following conditions: 20 ± 1 °C, 75 ± 5% Relative Humidity (RH), and a 16:8 light:dark (L:D) photoperiod. The 56 plants were then genotyped using 13 previously published microsatellite markers (see below) to confirm their genetic status (i.e., belonging to one of the M. sylvestris European populations or crop-to-wild/wild-to-wild hybrid).
A single colony of D. plantaginea (Hemiptera: Aphididae) was used and provided by INRAE which were sampled as a population in spring 2018 from an apple tree at the Agrocampus Ouest orchard (Angers, France) (Philippe Robert, personal communication). This aphid population was mass reared without differentiating individual aphid clones on M. domestica cv. “Jonagold” plants obtained by in vitro multiplication21. Pots containing three plants (90 × 90 × 70 mm) were placed in a Plexiglas cube (50 cm). Mass rearing and experiments were performed in growth chambers under 20 ± 1 °C, 60 ± 5% RH, and a 16:8 L:D cycle.
Synchronized first instar nymphs were obtained by placing parthenogenetic adult females on plantlets for 24 h before removing them. They were then reared on M. domestica cv. “Jonagold” plants inside Plexiglas aerated boxes (36 × 24 × 14 cm) for ten days then used as the young adult RAA for the behavioral/performance experiments.
Apple population genetic diversity and structure
Genomic DNA was extracted with the NucleoSpin plant DNA extraction kit II (Macherey & Nagel, Düren, Germany) according to the manufacturer’s instructions. Microsatellites were amplified by multiplex PCR, with the Multiplex PCR Kit (QIAGEN, Inc.). We used 13 microsatellite markers, Ch01f02, Ch01f03, Ch01h01, Ch01h10, Ch02c06, Ch02c09, Ch02c11, Ch02d08, Ch03d07, Ch04c07, Ch05f06, GD12, and Hi02c07 in four multiplexes (MP01, MP02, MP03, MP04)4. PCR were performed in a final reaction volume of 15 ml (7.5 ml of QIAGEN Multiplex Master Mix, 10–20 mM of each primer, with the forward primer labelled with a fluorescent dye and 10 ng of template DNA) (See4 for more details). The final volume was achieved with distilled water. A touch-down PCR program (initial annealing temperature of 60 °C, decreasing by 1 °C per cycle down to 55 °C) was used. Genotyping was performed on the GENTYANE platform (INRAE Clermont-Ferrand) using an ABI PRISM X3730XL, with 2 ml of GS500LIZ size standard (Applied Biosystems). Alleles were scored with GENEMAPPER 4.0 software (Applied Biosystems). Only multilocus genotypes with  0.1 were classified as crop-to-wild hybrids (i.e., introgressed by M. domestica). Once crop-wild hybrids removed, plants assigned to a given wild gene pool with a cumulated membership coefficient  > 0.9 were defined as “pure wild” individuals. Plants assigned to the wild gene pool with a cumulated membership coefficient  More

1.
Gu, B., Zhang, X., Bai, X., Fu, B. & Chen, D. Four steps to food security for swelling cities. Nature 566, 31–33 (2019).
ADS  CAS  Article  Google Scholar 
2.
Godfray, H. C. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).
ADS  CAS  Article  Google Scholar 

3.
Bren D Amour, C. et al. Future urban land expansion and implications for global croplands. Proc. Natl Acad. Sci. USA 114, 8939–8944 (2017).
Article  Google Scholar 

4.
Gardi, C., Panagos, P., Van Liedekerke, M., Bosco, C. & De Brogniez, D. Land take and food security: assessment of land take on the agricultural production in Europe. J. Environ Plann. Manag. 58, 898–912 (2015).
Article  Google Scholar 

5.
Shi, K. et al. Urban expansion and agricultural land loss in China: a multiscale perspective. Sustainability 8, 790 (2016).
Article  Google Scholar 

6.
Bai, X., Shi, P. & Liu, Y. Society: realizing China’s urban dream. Nature 509, 158–160 (2014).
Article  Google Scholar 

7.
World Urbanization Prospects 2018 (United Nations, 2018); https://population.un.org/wup/Download/

8.
Zhai, Z., Chen, J. & Li, L. Future trends of China’s population and aging from 2015 to 2100 [in Chinese]. Popul. Res. 41, 60–71 (2017).
Google Scholar 

9.
Van Vliet, J., Eitelberg, D. A. & Verburg, P. H. A global analysis of land take in cropland areas and production displacement from urbanization. Glob. Environ. Change 43, 107–115 (2017).
Article  Google Scholar 

10.
Chen, J. Rapid urbanization in China: a real challenge to soil protection and food security. Catena 69, 1–15 (2007).
Article  Google Scholar 

11.
Martellozzo, F. et al. Urbanization and the loss of prime farmland: a case study in the Calgary–Edmonton corridor of Alberta. Reg. Environ. Change 15, 881–893 (2015).
Article  Google Scholar 

12.
Yan, H., Liu, J., He, Q. H., Bo, T. & Cao, M. Assessing the consequence of land use change on agricultural productivity in China. Glob. Planet. Change 67, 13–19 (2009).
ADS  Article  Google Scholar 

13.
Bai, X., Chen, J. & Shi, P. Landscape urbanization and economic growth in China: positive feedbacks and sustainability dilemmas. Environ. Sci. Technol. 46, 132–139 (2012).
ADS  CAS  Article  Google Scholar 

14.
Statistical yearbooks of prefecture-level cities in 2015 [in Chinese]. National Bureau of Statistics http://www.stats.gov.cn/tjsj/ (2016).

15.
Zuo, L. et al. Progress towards sustainable intensification in China challenged by land-use change. Nat. Sustain. 1, 304–313 (2018).
Article  Google Scholar 

16.
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).
ADS  CAS  Article  Google Scholar 

17.
Zhang, X. et al. Effects of enhancing soil organic carbon sequestration in the topsoil by fertilization on crop productivity and stability: evidence from long-term experiments with wheat–maize cropping systems in China. Sci. Total Environ. 562, 247–259 (2016).
ADS  CAS  Article  Google Scholar 

18.
Wu, Y. et al. Policy distortions, farm size, and the overuse of agricultural chemicals in China. Proc. Natl Acad. Sci. USA 115, 7010–7015 (2018).
ADS  CAS  Article  Google Scholar 

19.
Zou, B., Mishra, A. K. & Luo, B. Aging population, farm succession, and farmland usage: evidence from rural China. Land Use Policy 77, 437–445 (2018).
Article  Google Scholar 

20.
Guidance on Accelerating the Development of Agricultural Productive Services (Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2017).

21.
Ju, X., Gu, B., Wu, Y. & Galloway, J. N. Reducing China’s fertilizer use by increasing farm size. Glob. Environ. Change 41, 26–32 (2016).
Article  Google Scholar 

22.
Ren, C. et al. The impact of farm size on agricultural sustainability. J. Clean Prod. 220, 357–367 (2019).
Article  Google Scholar 

23.
Adamopoulos, T. & Restuccia, D. The size distribution of farms and international productivity differences. Am. Econ. Rev. 104, 1667–1697 (2014).
Article  Google Scholar 

24.
Wang, J., Chen, K. Z., Gupta, S. D. & Huang, Z. Is small still beautiful? A comparative study of rice farm size and productivity in China and India. China Agr. Econ. Rev. 7, 484–509 (2015).
Article  Google Scholar 

25.
Lu, H., Xie, H., He, Y., Wu, Z. & Zhang, X. Assessing the impacts of land fragmentation and plot size on yields and costs: a translog production model and cost function approach. Agr. Syst. 161, 81–88 (2018).
Article  Google Scholar 

26.
Syp, A., Faber, A., Borzecka-Walker, M. & Osuch, D. Assessment of greenhouse gas emissions in winter wheat farms using data envelopment analysis approach. Pol. J. Environ. Stud. 24, 2197–2203 (2015).
CAS  Article  Google Scholar 

27.
Li, G., Feng, Z., You, L. & Fan, L. Re-examining the inverse relationship between farm size and efficiency. China Agr. Econ. Rev. 5, 473–488 (2013).
Article  Google Scholar 

28.
Fan, L. et al. Decreasing farm number benefits the mitigation of agricultural non-point source pollution in China. Environ. Sci. Pollut. Res. 26, 464–472 (2019).
Article  Google Scholar 

29.
Cassman, K. G., Dobermann, A., Walters, D. T. & Yang, H. Meeting cereal demand while protecting natural resources and improving environmental quality. Annu. Rev. Env. Resour. 28, 315–358 (2003).
Article  Google Scholar 

30.
Pellegrini, P. & Fernández, R. J. Crop intensification, land use, and on-farm energy-use efficiency during the worldwide spread of the green revolution. Proc. Natl Acad. Sci. USA 115, 2335–2340 (2018).
CAS  Article  Google Scholar 

31.
Resource and Environment Data Cloud Platform (Resource and Environment Science and Data Center, 2018); http://www.resdc.cn/Default.aspx

32.
Laborde, D., Martin, W., Swinnen, J. & Vos, R. COVID-19 risks to global food security. Science 369, 500–502 (2020).
ADS  CAS  Article  Google Scholar 

33.
Shi, Q., Jin, H. & Zhuo, J. Does land expropriation definitely reduce farmers’ income: a survey of 7 villages in Shanghai: the defects and reforms of the current land expropriation system [in Chinese]. Manage. World 3, 77–82 (2011).
Google Scholar 

34.
Liu, Y. & Li, Y. Revitalize the world’s countryside. Nature 548, 275–277 (2017).
ADS  CAS  Article  Google Scholar 

35.
Liu, Y., Fang, F. & Li, Y. Key issues of land use in China and implications for policy making. Land Use Policy 40, 6–12 (2014).
CAS  Article  Google Scholar 

36.
Measures for Land Acquisition Compensation and Social Security for Land-Expropriated Farmers in Jiangsu Province Provincial Government Order No. 93 (Jiangsu Provincial People’s Government, 2013).

37.
Wu, Y., Chen, Y., Deng, X. & Hui, E. C. M. Development of characteristic towns in China. Habitat Int. 77, 21–31 (2018).
Article  Google Scholar 

38.
Yu, Y., Huang, Y. & Zhang, W. Modeling soil organic carbon change in croplands of China, 1980–2009. Glob. Planet Change 82–83, 115–128 (2012).
ADS  Article  Google Scholar 

39.
No. 1 Central Document (Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2020); http://www.moa.gov.cn/ztzl/jj2020zyyhwj/

40.
Güneralp, B. et al. Global scenarios of urban density and its impacts on building energy use through 2050. Proc. Natl Acad. Sci. USA 114, 8945–8950 (2017).
Article  Google Scholar  More

1.
Grigg, G. & Kirshner, D. Biology and Evolution of Crocodylians (CSIRO Publishing, 2015). https://doi.org/10.1071/9781486300679.
Google Scholar 
2.
Singh, L. A. K. Ecological studies on the Indian gharial Gavialis gangeticus (Gmelin) (Reptilia, Crocodilia). PhD Thesis, Utkal University, Odisha (1978).

3.
Whitaker, R. The management of crocodilians in India. In Wildlife Management; Crocodiles and Alligators (eds Webb, G. J. W. et al.) 63–72 (Surrey Beatty and Sons, 1987).
Google Scholar 

4.
Hussain, S. A. Reproductive success, hatchling survival and rate of increase of gharial Gavialis gangeticus in National Chambal Sanctuary, India. Biol. Conserv. 87, 261–268 (1999).
Article  Google Scholar 

5.
Bustard, H. R. A future for the Gharial. Cheetal 17, 3–8 (1975).
Google Scholar 

6.
Hussain, S. A. Basking site and water depth selection by gharial Gavialis gangeticus Gmelin 1789 (Crocodylia, Reptilia) in National Chambal Sanctuary, India and its implication for river conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 127–133 (2009).
Article  Google Scholar 

7.
Lang, J. W., Chowfin, S. & Ross, J. P. Gavialis gangeticus (errata version published in 2019). IUCN Red List Threat. Species 2019 (2019).

8.
Basu, D. Saving the gharial. Indian Wildlifer 1, 7–15 (1981).
Google Scholar 

9.
Singh, V. B. The status of the gharial (Gavialis gangeticus) in U.P. and its rehabilitation. J. Bombay Nat. Hist. Soc. 75, 668–683 (1978).
Google Scholar 

10.
Stevenson, C. & Whitaker, R. Indian Gharial Gavialis gangeticus. In Crocodiles. Status Survey and Conservation Action Plan (eds Manolis, S. C. & Stevenson, C.) 139–143 (Crocodile Specialist Group, 2010).
Google Scholar 

11.
Whitaker, R. & Basu, D. The gharial (Gavialis gangeticus) a review. J. Bombay Nat. Hist. Soc. 79, 531–548 (1982).
Google Scholar 

12.
Whitaker, R. The gharial: Going extinct again. Iguana 14, 25–33 (2007).
Google Scholar 

13.
Lang, J. W., Jailabdeen, A. & Kumar, P. Gharial ecology project—Update 2018–2019. IUCN-SSC Crocodile Spec. Gr. Newsl. 37, 15–17 (2018).
Google Scholar 

14.
IUCN/SSC. Guidelines for Reintroductions and Other Conservation Translocations IUCN. Version 1.0. Gland, Switzerland: IUCN Species Survival Commission viiii + 57 pp. (2013).

15.
Schwartz, M. K. Guidelines on the use of molecular genetics in reintroduction programs. EU LIFE-Nature Proj. to Guidel. reintroduction Threat. species 51–58 (2005).

16.
White, L. C., Moseby, K. E., Thomson, V. A., Donnellan, S. C. & Austin, J. J. Long-term genetic consequences of mammal reintroductions into an Australian conservation reserve. Biol. Conserv. 219, 1–11 (2018).
Article  Google Scholar 

17.
Weeks, A. R. et al. Assessing the benefits and risks of translocations in changing environments: A genetic perspective. Evol. Appl. 4, 709–725 (2011).
PubMed  PubMed Central  Article  Google Scholar 

18.
Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).
PubMed  Article  Google Scholar 

19.
Katdare, S. et al. Gharial (Gavialis gangeticus) populations and human influences on habitat on the River Chambal, India. Aquat. Conserv. Mar. Freshw. Ecosyst. 21, 364–371 (2011).
Article  Google Scholar 

20.
Nair, T., Thorbjarnarson, J. B., Aust, P. & Krishnaswamy, J. Rigorous gharial population estimation in the Chambal: Implications for conservation and management of a globally threatened crocodilian. J. Appl. Ecol. 49, 1046–1054 (2012).
Article  Google Scholar 

21.
Hussain, S. A. Ecology of gharial (Gavialis gangeticus) in National Chambal Sanctuary. MPhil Thesis, Aligarh Muslim University, Uttar Pradesh (1991).

22.
Sharma, S. P. et al. Mitochondrial DNA analysis reveals extremely low genetic diversity in a managed population of the Critically Endangered Gharial (Gavialis gangeticus, Gmelin 1789). Herpetol. J. 30, 202–206 (2020).
Article  Google Scholar 

23.
Jogayya, K. N., Meganathan, P. R., Dubey, B. & Haque, I. Novel microsatellite DNA markers for Indian Gharial (Gavialis gangeticus). Conserv. Genet. Resour. 5, 787–790 (2013).
Article  Google Scholar 

24.
Zhu, H., Wu, X., Xue, H., Wei, L. & Hu, Y. Isolation of polymorphic microsatellite loci from the Chinease alligator (Alligator sinensis). Mol. Ecol. Resour. 9, 892–894 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Glenn, T. C. et al. Characterization of microsatellite DNA loci in American alligators. Copeia 3, 591–601 (1998).
Article  Google Scholar 

26.
Ojeda, G. N., Amavet, P. S., Rueda, E. C., Siroski, P. A. & Larriera, A. Mating system of Caiman yacare (Reptilia: Alligatoridae) described from microsatellite genotypes. J. Hered. 108, 135–141 (2017).
PubMed  PubMed Central  Google Scholar 

27.
Yu, D. et al. Analysis of genetic variation and bottleneck in a captive population of Siamese crocodile using novel microsatellite loci. Conserv. Genet. Resour. 3, 217–220 (2011).
Article  Google Scholar 

28.
Hinlo, M. R. P. et al. Population genetics implications for the conservation of the Philippine Crocodile Crocodylus mindorensis Schmidt, 1935 (Crocodylia: Crocodylidae). J. Threat. Taxa 6, 5513–5533 (2014).
Article  Google Scholar 

29.
Mcvay, J. D. et al. Evidence of multiple paternity in Morelet’s Crocodile (Crocodylus moreletii) in Belize, CA, inferred from microsatellite markers. J. Exp. Zool. Part A Ecol. Genet. Physiol. 309, 643–648 (2008).
Article  Google Scholar 

30.
Dever, J. A., Strauss, R. E., Rainwater, T. R., McMurry, S. T. & Densmore, I. L. D. Genetic diversity, population subdivision, and gene flow in Morelet’s crocodile (Crocodylus moreletii) from Belize, Central America. Copeia 4, 1078–1091 (2002).
Article  Google Scholar 

31.
Aggarwal, R. K., Lalremruata, A. & Dubey, B. Development of fourteen novel microsatellite markers of Crocodylus palustris, the Indian mugger, and their cross-species transferability in ten other crocodilians. Conserv. Genet. Resour. 7, 197–200 (2014).
Article  Google Scholar 

32.
Campos, J. C., Mobaraki, A., Abtin, E., Godinho, R. & Brito, J. C. Preliminary assessment of genetic diversity and population connectivity of the Mugger Crocodile in Iran. Amphib. Reptil. 39, 126–131 (2018).
Article  Google Scholar 

33.
Garner, A., Rachlow, J. L. & Hicks, J. F. Patterns of genetic diversity and its loss in mammalian populations. Conserv. Biol. 19, 1215–1221 (2005).
Article  Google Scholar 

34.
Rossi, N. A. et al. High levels of population genetic differentiation in the American crocodile (Crocodylus acutus). PLoS ONE 15, e0235288 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
van Asch, B. et al. Phylogeography, genetic diversity, and population structure of Nile crocodile populations at the fringes of the southern African distribution. PLoS ONE 14, 1–20 (2019).
Google Scholar 

36.
Luck, N. L. et al. Mitochondrial DNA analyses of the saltwater crocodile (Crocodylus porosus) from the Northern Territory of Australia. Aust. J. Zool. 60, 18–25 (2012).
Article  Google Scholar 

37.
Russello, M. A., Brazaitis, P., Gratten, J., Watkins-Colwell, G. J. & Caccone, A. Molecular assessment of the genetic integrity, distinctiveness and phylogeographic context of the Saltwater crocodile (Crocodylus porosus) on Palau. Conserv. Genet. 8, 777–787 (2007).
CAS  Article  Google Scholar 

38.
Ray, D. A. et al. Low levels of nucleotide diversity in Crocodylus moreletiiand evidence of hybridization with C. acutus. Conserv. Genet. 5, 449–462 (2004).
CAS  Article  Google Scholar 

39.
Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
CAS  PubMed  Article  Google Scholar 

41.
Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Blackwell Publishing, 2007).
Google Scholar 

43.
Guries, R. P. & Ledig, F. T. Genetic structure of populations and differentiation in forest trees. in Conkle, MT (tech. coord.) Proceedings of the symposium on isozymes of North American forest trees and forest insects. USDA For. Serv. Gen. Tech. Rep. PSW-48 42–47 (1979).

44.
Biebach, I. & Keller, L. F. Inbreeding in reintroduced populations: The effects of early reintroduction history and contemporary processes. Conserv. Genet. 11, 527–538 (2010).
Article  Google Scholar 

45.
Wang, J. Estimating pairwise relatedness in a small sample of individuals. Heredity (Edinb). 119, 302–313 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Degiorgio, M. & Rosenberg, N. A. An unbiased estimator of gene diversity in samples containing related individuals p. Mol. Biol. Evol. 26, 501–512 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
PubMed  PubMed Central  Article  Google Scholar 

48.
Girod, C., Vitalis, R., Leblois, R. & Fréville, H. Inferring population decline and expansion from microsatellite data: A simulation-based evaluation of the msvar method. Genetics 188, 165–179 (2011).
PubMed  PubMed Central  Article  Google Scholar 

49.
Luikart, G. & Cornuet, J. M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv. Biol. 12, 228–237 (1998).
Article  Google Scholar 

50.
Keller, L. F. et al. Immigration and the ephemerality of a natural population bottleneck: Evidence from molecular markers. Proc. R Soc. London. Ser. B Biol. Sci. 268, 1387–1394 (2001).
CAS  Article  Google Scholar 

51.
Cristescu, R., Sherwin, W. B., Handasyde, K., Cahill, V. & Cooper, D. W. Detecting bottlenecks using BOTTLENECK 1.2.02 in wild populations: The importance of the microsatellite structure. Conserv. Genet. 11, 1043–1049 (2010).
Article  Google Scholar 

52.
Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418 (2012).
PubMed  Article  PubMed Central  Google Scholar 

53.
Hoban, S. M., Gaggiotti, O. E. & Bertorelle, G. The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: A simulation-based study. Mol. Ecol. 22, 3444–3450 (2013).
PubMed  Article  PubMed Central  Google Scholar 

54.
Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Sambrook, J., Fritsch, E. F. & Maniatis, T. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 1989).
Google Scholar 

56.
Miquel, C. et al. Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. Mol. Ecol. Notes 6, 985–988 (2006).
Article  Google Scholar 

57.
Oaks, J. R. A time-calibrated species tree of Crocodylia reveals a recent radiation of the true crocodiles. Evolution (N.Y.) 65, 3285–3297 (2011).
Google Scholar 

58.
Broquet, T. & Petit, E. Quantifying genotyping errors in noninvasive population genetics. Mol. Ecol. 13, 3601–3608 (2004).
CAS  PubMed  Article  Google Scholar 

59.
Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
CAS  PubMed  Article  Google Scholar 

60.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  CAS  Google Scholar 

61.
Valière, N. GIMLET: A computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379 (2002).
Google Scholar 

62.
Peakall, R. & Smouse, P. E. GenALEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  PubMed Central  Google Scholar 

64.
Kalinowski, S. T. HP-RARE 1.0—A computer program for performing rarefaction on measures of allelic richness.pdf. Mol. Ecol. Notes 5, 187–189 (2005).
CAS  Article  Google Scholar 

65.
Weir, B. S. & Cockerham, C. Estimating F-statistics for the analysis of population structure. Evolution (N. Y.). 38, 1358–1370 (1984).
CAS  Google Scholar 

66.
Hedrick, P. W. A standardized genetic differentiation measure. Evolution (N. Y.). 59, 1633–1638 (2005).
CAS  Google Scholar 

67.
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).
PubMed  Article  PubMed Central  Google Scholar 

68.
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: An r package for manipulating, summarizing and analysing population genetic data. Mol. Ecol. Resour. 17, 5–11 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).
PubMed  PubMed Central  Article  Google Scholar 

71.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  Google Scholar 

72.
Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Article  Google Scholar 

73.
Rosenberg, N. A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).
Article  Google Scholar 

74.
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).
PubMed  PubMed Central  Google Scholar 

75.
Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinforma. Online. 1, 47–50 (2005).
CAS  Google Scholar 

76.
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).
Article  Google Scholar 

77.
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).
CAS  PubMed  Article  Google Scholar 

78.
Di Rienzo, A. et al. Mutational processes of simple-sequence repeat loci in human populations. Proc. Natl. Acad. Sci. USA 91, 3166–3170 (1994).
ADS  PubMed  Article  Google Scholar 

79.
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acid Symp. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

81.
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS  Article  Google Scholar  More

1.
D’Odorico, P. & Porporato, A. Dryland Ecohydrology (Springer, 2019).
2.
Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).
Article  Google Scholar 

3.
Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).
CAS  Article  Google Scholar 

4.
Ahlström, 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).
Article  CAS  Google Scholar 

5.
Middleton, N. & Thomas, D. S. G. World Atlas of Desertification 2nd edn (Wiley, 1997).

6.
Budyko, M. I. & Miller, D. H. International Geophysics Series: Climate and Life Vol. 18 (Academic Press, 1974).

7.
Feng, S. & Fu, Q. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13, 10081–10094 (2013).
CAS  Article  Google Scholar 

8.
Fu, Q. & Feng, S. Responses of terrestrial aridity to global warming. J. Geophys. Res. Atmos. 119, 7863–7875 (2014).
Article  Google Scholar 

9.
Scheff, J. & Frierson, D. M. W. Terrestrial aridity and its response to greenhouse warming across CMIP5 climate models. J. Clim. 28, 5583–5600 (2015).
Article  Google Scholar 

10.
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).
Google Scholar 

11.
Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).
Article  Google Scholar 

12.
Park, C.-E. et al. Keeping global warming within 1.5 °C constrains emergence of aridification. Nat. Clim. Change 8, 70–74 (2018).
Article  Google Scholar 

13.
Koutroulis, A. G. Dryland changes under different levels of global warming. Sci. Total Environ. 655, 482–511 (2019).
CAS  Article  Google Scholar 

14.
Park, C. E. et al. Inequal responses of drylands to radiative forcing geoengineering methods. Geophys. Res. Lett. 46, 14011–14020 (2019).
Article  Google Scholar 

15.
Wei, Y. et al. Drylands climate response to transient and stabilized 2 °C and 1.5 °C global warming targets. Clim. Dyn. 53, 2375–2389 (2019).
Article  Google Scholar 

16.
Yao, J. et al. Accelerated dryland expansion regulates future variability in dryland gross primary production. Nat. Commun. 11, 1665 (2020).
CAS  Article  Google Scholar 

17.
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).
CAS  Article  Google Scholar 

18.
Rajaud, A. & de Noblet-Ducoudré, N. Tropical semi-arid regions expanding over temperate latitudes under climate change. Climatic Change 144, 703–719 (2017).
Article  Google Scholar 

19.
Yang, Y. et al. Disconnection between trends of atmospheric drying and continental runoff. Water Resour. Res. 54, 4700–4713 (2018).
Article  Google Scholar 

20.
Greve, P., Roderick, M. L., Ukkola, A. M. & Wada, Y. The aridity index under global warming. Environ. Res. Lett. 14, 124006 (2019).
CAS  Article  Google Scholar 

21.
Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).
Article  Google Scholar 

22.
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).
Article  CAS  Google Scholar 

23.
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 

24.
Berg, A. & Sheffield, J. Soil moisture–evapotranspiration coupling in CMIP5 models: relationship with simulated climate and projections. J. Clim. 31, 4865–4878 (2018).
Article  Google Scholar 

25.
Mahowald, N. et al. Projections of leaf area index in Earth system models. Earth Syst. Dyn. 7, 211–229 (2016).
Article  Google Scholar 

26.
Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).
CAS  Article  Google Scholar 

27.
Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).
Article  Google Scholar 

28.
Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).
Article  Google Scholar 

29.
Lavergne, A. et al. Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems. Glob. Change Biol. 25, 2242–2257 (2019).
Article  Google Scholar 

30.
Friedlingstein, P. Carbon cycle feedbacks and future climate change. Phil. Trans. R. Soc. A 373, 20140421 (2015).
Article  CAS  Google Scholar 

31.
Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).
CAS  Article  Google Scholar 

32.
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018).
CAS  Article  Google Scholar 

33.
Berg, A. & Sheffield, J. Evapotranspiration partitioning in CMIP5 models: uncertainties and future projections. J. Clim. 32, 2653–2671 (2019).
Article  Google Scholar 

34.
Cao, L., Bala, G., Caldeira, K., Nemani, R. & Ban-Weiss, G. Importance of carbon dioxide physiological forcing to future climate change. Proc. Natl Acad. Sci. USA 107, 9513–9518 (2010).
CAS  Article  Google Scholar 

35.
Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).
Article  CAS  Google Scholar 

36.
Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).
Article  Google Scholar 

37.
Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).
Article  Google Scholar 

38.
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
CAS  Article  Google Scholar 

39.
He, B., Wang, S., Guo, L. & Wu, X. Aridity change and its correlation with greening over drylands. Agric. For. Meteorol. 278, 107663 (2019).
Article  Google Scholar 

40.
Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 0081 (2017).
Article  Google Scholar 

41.
Burrell, A. L., Evans, J. P. & De Kauwe, M. G. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat. Commun. 11, 3853 (2020).
CAS  Article  Google Scholar 

42.
Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).
Article  Google Scholar 

43.
Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).
CAS  Article  Google Scholar 

44.
Liu, Y. et al. Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization. Nat. Geosci. 12, 809–814 (2019).
CAS  Article  Google Scholar 

45.
Zeng, Z. et al. Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models. Environ. Res. Lett. 11, 104006 (2016).
Article  Google Scholar 

46.
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).
Article  Google Scholar 

47.
Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).
CAS  Article  Google Scholar 

48.
Scheff, J., Seager, R., Liu, H. & Coats, S. Are glacials dry? Consequences for paleoclimatology and for greenhouse warming. J. Clim. 30, 6593–6609 (2017).
Article  Google Scholar 

49.
Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).
CAS  Article  Google Scholar 

50.
Berg, A. & Sheffield, J. Historic and projected changes in coupling between soil moisture and evapotranspiration (ET) confounded by the role of different ET components. J. Geophys. Res. Atmos. 124, 5791–5806 (2019).
Google Scholar 

51.
Berg, A. & McColl, K. R code for ‘No global drylands expansion under greenhouse warming’. Zenodo https://doi.org/10.5281/zenodo.4490414 (2021). More

1.
Dixon, T. & Pretorius, I. S. Drawing on the past to shape the future of synthetic yeast research. Int. J. Mol. Sci. 21, 7156 (2020).
CAS  PubMed Central  Article  PubMed  Google Scholar 
2.
Dixon, T., Curach, N. & Pretorius, I. S. Bio-informational futures: the convergence of artificial intelligence and synthetic biology. EMBO Rep. 21, e50036 (2020a). 1–5.
CAS  Article  Google Scholar 

3.
Dixon, T., Williams, T. C. & Pretorius, I. S. Sensing the future of bio-informational engineering. Nat. Commun. 12, 388 (2021).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Layeghifard, M., Hwang, D. W. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).
CAS  PubMed  Article  Google Scholar 

5.
Pretorius, I. S. Tasting the terroir of wine yeast innovation. FEMS Yeast Res. 20, foz084 (2020).
CAS  PubMed  Article  Google Scholar 

6.
Gibson, D. G. et al. Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329, 52–56 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Pretorius, I. S. & Boeke, J. D. Yeast 2.0 − Connecting the dots in the construction of the world’s first functional synthetic eukaryotic genome. FEMS Yeast Res. 18, foy032 (2018).
PubMed Central  Article  CAS  PubMed  Google Scholar 

8.
Richardson, S. M. et al. Design of a synthetic yeast genome. Science 355, 1040–1044 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

9.
Muller, E. E. L. et al. Using metabolic networks to resolve ecological properties of microbiomes. Curr. Opin. Syst. Biol. 8, 73–80 (2018).
Article  Google Scholar 

10.
Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).
CAS  Article  Google Scholar 

11.
Roume, H. et al. Comparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks. npj Biofilms Microbiomes 1, 15007 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

13.
McCarty, N. S. & Ledesma-Amaro, R. Synthetic Biology tools to engineer microbial communities for Biotechnology. Trends Biotechnol. 37, 181–197 (2018).
PubMed  Article  CAS  Google Scholar 

14.
Peris, D. et al. Synthetic hybrids of six yeast species. Nat. Commun. 11, 2085 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Yi, X. & Dean, A. M. Adaptive landscapes in the age of synthetic biology. Mol. Biol. Evol. 36, 890–907 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Goel, A., Wortel, M. T., Molenaar, D. & Teusink, B. Metabolic shifts: a fitness perspective for microbial cell factories. Biotechnol. Lett. 34, 2147–2160 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
De Vrieze, J. & Verstraete, W. Perspectives for microbial community composition in anaerobic digestion: from abundance and activity to connectivity. Environ. Microbiol. 18, 2797–2809 (2016).
PubMed  Article  CAS  Google Scholar 

18.
Wolfe, B. E. & Dutton, R. J. Fermented foods as experimentally tractable microbial ecosystems. Cell 161, 49–55 (2015).
CAS  PubMed  Article  Google Scholar 

19.
Lurgi, M., Thomas, T., Wemheuer, B., Webster, N. S. & Montoya, J. M. Modularity and predicted functions of the global sponge-microbiome network. Nat. Commun. 10, 992 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Cao, H., Gibson, T., Bashan, A. & Liu, Y. Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons. BioEssays 39, 1600188 (2016).
Article  Google Scholar 

21.
Liu, Z. et al. Network analyses in microbiome based on high-throughput multi-omics data. Brief. Bioinform. 00, 1–17 (2020).
Google Scholar 

22.
Danczak, R. E. et al. Using metacommunity ecology to understand environmental metabolomes. Nat. Commun. 11, 6369 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Dini-Andreote, F. et al. Dynamics of bacterial community succession in a saltmarsh chronosequence: evidences for temporal niche partitioning. ISME J. 8, 1989–2001 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Heintz-Buschart, A. et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol. 2, 16180 (2016).
CAS  PubMed  Article  Google Scholar 

25.
Toju, H. et al. Scoring species for synthetic community design: Network analyses of functional core microbiomes. Front. Microbiol. 11, 1361 (2020).
PubMed  PubMed Central  Article  Google Scholar 

26.
Medlock, G. L. et al. Inferring metabolic mechanisms of interaction within a defined gut microbiota. Cell Syst. 7, 245–257 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Gibson, D. G. Programming biological operating systems: genome design, assembly and activation. Nat. Meth 11, 521–526 (2014).
CAS  Article  Google Scholar 

28.
Hillson, N. et al. Building a global alliance of biofoundries. Nat. Commun. 10, 2040 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Palluk, S. et al. De novo DNA synthesis using polymerase-nucleotide conjugates. Nat. Biotechnol. 36, 645–650 (2018).
CAS  PubMed  Article  Google Scholar 

30.
Coradini, A. L. V., Hull, C. B. & Ehrenreich, I. M. Building genomes to understand biology. Nat. Commun. 11, 6177 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Bartley, B. A. et al. Organizing genome engineering for the gigabase scale. Nat. Commun. 11, 689 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Lau, Y. H. et al. Large-scale recoding of a bacterial genome by iterative recombineering of synthetic DNA. Nucleic Acids Res. 45, 6971–6980 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Kouprina, N. & Larionov, V. Transformation-associated recombination (TAR) cloning for genomics studies and synthetic biology. Chromosoma 125, 621–632 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Benders, G. A. et al. Cloning whole bacterial genomes in yeast. Nucleic Acids Res. 38, 2558–2569 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Mortimer, R. K. Radiobiological and genetic studies on a polyploid series (haploid to hexaploid) of Saccharomyces cerevisiae. Radiat. Res. 9, 312–326 (1958).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Shao, Y. et al. Creating a functional single-chromosome yeast. Nature 560, 331–335 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Hossain, A. et al. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat. Biotechnol. 38, 1466–1475 (2020).
PubMed  Article  CAS  Google Scholar 

38.
Decoene, T., Peters, G., De Maeseneire, S. L. & De Mey, M. Toward predictable 5′UTRs in Saccharomyces cerevisiae: Development of a yUTR calculator. ACS Synth. Biol. 7, 622–634 (2018).
CAS  PubMed  Article  Google Scholar 

39.
Weenink, T., van der Hilst, J., McKiernan, R. M. & Ellis, T. Design of RNA hairpin modules that predictably tune translation in yeast. Synth. Biol. 3, ysy019 (2018).
CAS  Article  Google Scholar 

40.
Kotopka, B. J. & Smolke, C. D. Model-driven generation of artificial yeast promoters. Nat. Commun. 11, 2113 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Curran, K. A. et al. Short synthetic terminators for improved heterologous gene expression in yeast. ACS Synth. Biol. 4, 824–832 (2015).
CAS  PubMed  Article  Google Scholar 

42.
MacPherson, M. & Saka, Y. Short synthetic terminators for assembly of transcription units in vitro and stable chromosomal integration in yeast S. cerevisiae. ACS Synth. Biol. 6, 130–138 (2017).
CAS  PubMed  Article  Google Scholar 

43.
Morse, N. J., Gopal, M. R., Wagner, J. M. & Alper, H. S. Yeast terminator function can be modulated and designed on the basis of predictions of nucleosome occupancy. ACS Synth. Biol. 6, 2086–2095 (2017).
CAS  PubMed  Article  Google Scholar 

44.
Gräslund, S. et al. Structural Genomics Consortium: Protein production and purification. Nat. Methods 5, 135–146 (2008).
PubMed  Article  Google Scholar 

45.
Lin, Y., Zou, X., Zheng, Y., Cai, Y. & Dai, J. Improving chromosome synthesis with a semiquantitative phenotypic assay and refined assembly strategy. ACS Synth. Biol. 8, 2203–2211 (2019).
CAS  PubMed  Article  Google Scholar 

46.
Mitchell, L. A. et al. Synthesis, debugging, and effects of synthetic chromosome consolidation: synVI and beyond. Science 355, eaaf4831 (2017).
PubMed  Article  CAS  Google Scholar 

47.
Wu, Y. et al. Bug mapping and fitness testing of chemically synthesized chromosome X. Science 355, eaaf4706 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Salinas, F. et al. Fungal light-oxygen-voltage domains for optogenetic control of gene expression and flocculation in yeast. mBio 9, e00626–18 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Shen, Y. et al. SCRaMbLE generates designed combinatorial stochastic diversity in synthetic chromosomes. Genome Res. 26, 36–49 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Jin, J., Jia, B. & Yuan, Y. J. Yeast chromosomal engineering to improve industrially-relevant phenotypes. Curr. Opin. Biotechnol. 66, 165–170 (2020).
CAS  PubMed  Article  Google Scholar 

51.
Dymond, J. & Boeke, J. The Saccharomyces cerevisiae SCRaMbLE system and genome minimization. Bioeng. Bugs 3, 168–171 (2012).
PubMed  PubMed Central  Google Scholar 

52.
Lee, D., Lloyd, N. D. R., Pretorius, I. S. & Borneman, A. R. Heterologous production of raspberry ketone in the wine yeast Saccharomyces cerevisiae via pathway engineering and synthetic enzyme fusion. Micro. Cell Fact. 15, 49 (2016).
Article  CAS  Google Scholar 

53.
Williams, T. C., Pretorius, I. S. & Paulsen, I. T. Synthetic evolution of metabolic productivity using biosensors. Trends Biotechnol. 34, 371–381 (2016).
CAS  PubMed  Article  Google Scholar 

54.
Williams, T. C., Xu, X., Ostrowski, M., Pretorius, I. S. & Paulsen, I. T. Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast. Synth. Biol. 2, ysw002 (2017).
CAS  Article  Google Scholar 

55.
Zhou, K., Qiao, K., Edgar, S. & Stephanopoulos, G. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Belda, I. et al. Unraveling the enzymatic basis of wine ‘flavorome’: a phylo-functional study of wine related yeast species. Front. Microbiol. 7, 1–13 (2016).
Article  Google Scholar 

57.
Belda, I. et al. Microbial contribution to wine aroma and its intended use for wine quality improvement. Molecules 22, 1–29 (2017).
Article  CAS  Google Scholar 

58.
Bokulich, N. A. et al. Associations among wine grape microbiome, metabolome, and fermentation behaviour suggest contribution to regional wine characteristics. mBio 7, 1–12 (2016).
Article  Google Scholar 

59.
Liu, D., Chen, Q., Zhang, P., Chen, D. & Howell, K. S. The fungal microbiome is an important component of vineyard ecosystems and correlates with regional distinctiveness of wine. mSphere 5, e00534–20 (2020).
CAS  PubMed  PubMed Central  Google Scholar  More

1.
Estrada, B., Aroca, R., Maathuis, F. J., Barea, J. M. & Ruiz-Lozano, J. M. Arbuscular mycorrhizal fungi native from a mediterranean saline area enhance maize tolerance to salinity through improved ion homeostasis. Plant Cell Environ. 36, 1771–1782 (2013).
CAS  PubMed  Article  Google Scholar 
2.
Uva, R. H. & Whitlow, T. H. Beach plum (Prunus maritima Marsh.): Small farm sustainability through crop diversification and value added products. HortScience 38, 793 (2003).
Google Scholar 

3.
Yan, D. L., Wang, G., Fang, K., Zai, X. M. & Qin, P. Introduction, cultivation and utilization of salt-tolerance beach plum. China For. Sci. Technol. 20, 67–69 (2006).
Google Scholar 

4.
Zhang, H. S., Wu, X. H. & Li, G. Interactions between arbuscular mycorrhizal fungi and phosphate solubilizing fungus (Mortierella sp.) and their effects on Kostelelzkya virginica growth and enzyme activities of rhizosphere and bulk soils at different salinities. Biol. Fert. Soils 47, 543–554 (2011).
CAS  Article  Google Scholar 

5.
Ait-El-Mokhtar, M. et al. Alleviation of detrimental effects of salt stress on date palm (Phoenix dactylifera L.) by the application of arbuscular mycorrhizal fungi and/or compost. Front. Sustain. Food Syst. 4, 131 (2020).
Article  Google Scholar 

6.
Porcel, R., Redondo-Gómez, S. & Mateos-Naranjo, E. Arbuscular mycorrhizal symbiosis ameliorates the optimum quantum yield of photosystem II and reduces non-photochemical quenching in rice plants subjected to salt stress. J. Plant Physiol. 185, 75–83 (2015).
CAS  PubMed  Article  Google Scholar 

7.
Sheng, M., Tang, M. & Chen, H. Influence of arbuscular mycorrhizae on photosynthesis and water status of maize plants under salt stress. Mycorrhiza 18, 287–296 (2008).
CAS  PubMed  Article  Google Scholar 

8.
Harbinson, J. Improving the accuracy of chlorophyll fluorescence measurements. Plant Cell Environ. 36, 1751–1754 (2013).
PubMed  Article  Google Scholar 

9.
Zhu, X. C., Song, F. B., Liu, S. Q. & Liu, T. D. Arbuscular mycorrhizae improves photosynthesis and water status of Zea mays L. under drought stress. Plant Soil Environ. 58, 186–191 (2012).
CAS  Article  Google Scholar 

10.
Wang, F., Sun, Y. & Shi, Z. Arbuscular mycorrhiza enhances biomass production and salt tolerance of sweet sorghum. Microorganisms 7, 289 (2019).
CAS  PubMed Central  Article  PubMed  Google Scholar 

11.
Qiu, Y. J. et al. Mediation of arbuscular mycorrhizal fungi on growth and biochemical parameters of Ligustrum vicaryi in response to salinity. Physiol. Mol. Plant Pathol. 112, 101522 (2020).
CAS  Article  Google Scholar 

12.
Zhang, H. S., Qin, P. & Zhang, W. M. Effects of inoculation of arbuscular mycorrhizal fungus and Apophysomyces spartina on P-uptake of castor oil plant (Ricinus communis L.) and rhizosphere soil enzyme activities under salt stress. Agri. Sci. Technol. 15, 659 (2014).
CAS  Google Scholar 

13.
Ghorchiani, M., Etesami, H. & Alikhani, H. A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70 (2018).
CAS  Article  Google Scholar 

14.
Augé, R. M., Toler, H. D., Sams, C. E. & Nasim, G. Hydraulic conductance and water potential gradients in squash leaves showing mycorrhiza-induced increases in stomatal conductance. Mycorrhiza 18, 115–121 (2008).
PubMed  Article  Google Scholar 

15.
Sharma, S., Compant, S., Ballhausen, M. B., Ruppel, S. & Franken, P. The interaction between Rhizoglomus irregulare and hyphae attached phosphate solubilizing bacteria increases plant biomass of Solanum lycopersicum. Microbiol. Res. 240, 126556 (2020).
CAS  PubMed  Article  Google Scholar 

16.
Vassilev, N., Eichler-Löbermann, B. & Vassileva, M. Stress-tolerant P-solubilizing microorganisms. Appl. Microbiol. Biot. 95, 851–859 (2012).
CAS  Article  Google Scholar 

17.
Ait-El-Mokhtar, M. et al. Use of mycorrhizal fungi in improving tolerance of the date palm (Phoenix dactylifera L.) seedlings to salt stress. Sci. Hort. 253, 429–438 (2019).
Article  Google Scholar 

18.
Zai, X. M., Zhu, S. N., Qin, P., Che, L. & Luo, F. X. Effect of Glomus mosseae on chlorophyll content, chlorophyll fluorescence parameters, and chloroplast ultrastructure of beach plum (Prunus maritima) under NaCl stress. Photosynthetica 50, 323–328 (2012).
CAS  Article  Google Scholar 

19.
Navarro, J. M., Pérez-Tornero, O. & Morte, A. Alleviation of salt stress in citrus seedlings inoculated with arbuscular mycorrhizal fungi depends on the rootstock salt tolerance. J. Plant Physiol. 171, 76–85 (2014).
CAS  PubMed  Article  Google Scholar 

20.
Toro, M., Azcon, R. & Herrera, R. Effects on yield and nutrition of mycorrhizal and nodulated Pueraria phaseolides exerted by P-solubilizing rhizobacteria. Biol. Fertil. Soils 21, 23–29 (1996).
Article  Google Scholar 

21.
Singh, S. & Kapoor, K. K. Inoculation with phosphate-solubilizing microorganisms and a vesicular-arbuscular mycorrhizal fungus improves dry matter yield and nutrient uptake by wheat grown in a sandy soil. Biol. Fertil. Soils 28, 139–144 (1999).
CAS  Article  Google Scholar 

22.
Osorio, N. W. & Habte, M. Synergistic influence of an arbuscular mycorrhizal fungus and a P solubilizing fungus on growth and P uptake of Leucaena leucocephala in an Oxisol. Arid Land Res. Manag. 15, 263–274 (2001).
CAS  Article  Google Scholar 

23.
Khan, M. S., Zaidi, A. & Wani, P. A. Role of phosphate-solubilizing microorganisms in sustainable agriculture—A review. Agron. Sustain Dev. 27, 29–43 (2007).
Article  Google Scholar 

24.
Saxena, J., Saini, A., Ravi, I., Chandra, S. & Garg, V. Consortium of phosphate-solubilizing bacteria and fungi for promotion of growth and yield of chickpea (Cicer arietinum). J. Crop Improv. 29, 353–369 (2015).
CAS  Article  Google Scholar 

25.
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis (Academic Press, 2008).
Google Scholar 

26.
Ben-Laouane, R., Baslam, M., Ait-El-Mokhtar, M., Anli, M. & Meddich, A. Potential of native arbuscular mycorrhizal fungi, rhizobia, and/or green compost as alfalfa (Medicago sativa) enhancers under salinity. Microorganisms 8, 1695 (2020).
CAS  PubMed Central  Article  PubMed  Google Scholar 

27.
Hodge, A., Campbell, C. D. & Fitter, A. H. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature 413, 297–299 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Johansen, A., Finlay, R. D. & Olsson, P. A. Nitrogen metabolism of external hyphae of the arbuscular mycorrhizal fungus Glomus intraradices. New Phytol. 133, 705–712 (1996).
CAS  Article  Google Scholar 

29.
Vicente-Sánchez, J. et al. Arbuscular mycorrhizal symbiosis alleviates detrimental effects of saline reclaimed water in lettuce plants. Mycorrhiza 24, 339–348 (2014).
PubMed  Article  CAS  Google Scholar 

30.
Abdel-Fattah, G. M. & Asrar, A. W. A. Arbuscular mycorrhizal fungal application to improve growth and tolerance of wheat (Triticum aestivum L.) plants grown in saline soil. Acta Physiol. Plant. 34, 267–277 (2012).
CAS  Article  Google Scholar 

31.
Marschner, P. Rhizosphere biology. In Marschner’s Mineral Nutrition of Higher Plants 3rd edn (ed. Marschner, P.) 369–388 (Academic Press, 2012).
Google Scholar 

32.
Abd-Allah, E. F. & Egamberdieva, D. Arbuscular mycorrhizal fungi enhance basil tolerance to salt stress through improved physiological and nutritional status. Pak. J. Bot. 48, 37–45 (2016).
Google Scholar 

33.
Van den Driessche, R. Effects of nutrients on stock performance in the forest. In Mineral Nutrition of Conifer Seedlings (ed. van den Driessche, R.) 229–260 (CRC Press, 1991).
Google Scholar 

34.
Ebel, R. C., Duan, X., Still, D. W. & Augé, R. M. Xylem sap abscisic acid concentration and stomatal conductance of mycorrhizal Vigna unguiculata in drying soil. New Phytol. 135, 755–761 (1997).
CAS  Article  Google Scholar 

35.
Ruiz-Lozano, J. M. & Aroca, R. Host response to osmotic stresses: Stomatal behaviour and water use efficiency of arbuscular mycorrhizal plants. In Arbuscular Mycorrhizas: Physiology and Function 239–256 (Springer, 2010).
Google Scholar 

36.
Birhane, E., Sterck, F. J., Fetene, M., Bongers, F. & Kuyper, T. W. Arbuscular mycorrhizal fungi enhance photosynthesis, water use efficiency, and growth of frankincense seedlings under pulsed water availability conditions. Oecologia 169, 895–904 (2012).
ADS  PubMed  PubMed Central  Article  Google Scholar 

37.
Evelin, H., Giri, B. & Kapoor, R. Ultrastructural evidence for AMF mediated salt stress mitigation in Trigonellafoenum graecum. Mycorrhiza 23, 71–86 (2012).
PubMed  Article  CAS  Google Scholar 

38.
Aroca, R. et al. Arbuscular mycorrhizal symbiosis influences strigolactone production under salinity and alleviates salt stress in lettuce plants. J. Plant Physiol. 170, 47–55 (2013).
CAS  PubMed  Article  Google Scholar 

39.
Jungklang, J. Physiological and biochemical mechanisms of salt tolerance in Sesbania rostrata Berm and Obem. PhD Thesis (Agric Univ Teckuba, 2005).

40.
Baker, N. R. & Rosenqvist, E. Applications of chlorophyll fluorescence can improve crop production strategies: Examination of future possibilities. J. Exp. Bot. 55, 1607–1621 (2004).
CAS  PubMed  Article  Google Scholar 

41.
Nwugo, C. C. & Huerta, A. J. Effects of silicon nutrition on cadmium uptake, growth and photosynthesis of rice plants exposed to low-level cadmium. Plant Soil 311, 73–86 (2008).
CAS  Article  Google Scholar 

42.
Henriques, F. S. Leaf chlorophyll fluorescence: Background and fundamentals for plant biologist. Bot. Rev. 75, 249–270 (2009).
Article  Google Scholar 

43.
Gong, M. G., Tang, M., Chen, H., Zhang, Q. & Feng, X. Effects of two Glomus species on the growth and physiological performance of Sophor davidii seedlings under water stress. New For. 44, 399–408 (2013).
Article  Google Scholar 

44.
Kaschuk, G., Kuyper, T. W., Leffelaar, P. A., Hungria, M. & Giller, K. E. Are the rate of photosynthesis stimulated by the carbon sink strength of rhizobial and arbuscular mycorrhizal symbioses. Soil Biol. Biochem. 41, 1233–1244 (2009).
CAS  Article  Google Scholar 

45.
Hoagland, D. R. & Arnon, D. I. The water-culture method for growing plants without soil. Univ. Calif. Agric. Res. Stn. Circ. 347, 1–39 (1950).
Google Scholar 

46.
Bradstreet, R. B. The kjeldahl method of organic nitrogen. Anal. Chem. 26, 185–187 (1965).
Article  Google Scholar 

47.
Li, Z. G., Luo, Y. M. & Teng, Y. Research Methods of Soil and Environmental Microorganisms 64–83 (Science Press, 2008).
Google Scholar 

48.
Mcgonigle, T. P., Miller, M. H., Evans, D. G., Fairchild, G. L. & Swan, J. A. A new method which gives an objective measure of colonization of roots by vesicular-arbuscular mycorrhizal fungi. New Phytol. 115, 495–501 (1990).
Article  Google Scholar 

49.
Xie, Z., Song, F., Xu, H., Shao, H. & Song, R. Effects of silicon on photosynthetic characteristics of maize (Zea mays L.) on alluvial soil. Sci. World J. 2014, 1–6 (2014).
Google Scholar 

50.
Chen, X. L., Li, S. Q., Ren, X. L. & Li, S. X. Effect of atmospheric NH3 and hydroponic solution nitrogen levels on chlorophyll fluorescence of corn genotypes with different nitrogen use efficiencies. Acta Ecol. Sin. 28, 1026–1032 (2008).
CAS  Google Scholar  More

1.
Millennium Ecosystem Assessment (Program). Ecosystems and human well-being: our human planet: summary for decision-makers. The Millennium Ecosystem Assessment series. https://doi.org/10.1196/annals.1439.003 (2005).
2.
Mulder, C. et al. 10 Years later: revisiting priorities for science and society a decade after the millennium ecosystem assessment. Adv. Ecol. Res. 53, 1–53 (2015).

3.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270 (2018).

4.
Díaz, S. et al. The IPBES Conceptual Framework—connecting nature and people. Curr. Opin. Environ. Sustainab. 14, 1–16 (2015).

5.
Hungate, B. A. et al. Linking biodiversity and ecosystem services: current uncertainties and the necessary next steps. Bioscience 64, 49–57 (2014).
Article  Google Scholar 

6.
Díaz, S., Fargione, J., Chapin, F. S. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).
PubMed  Article  Google Scholar 

8.
Harvey, E., Gounand, I., Ward, C. L. & Altermatt, F. Bridging ecology and conservation: from ecological networks to ecosystem function. J. Appl. Ecol. 54, 371–379 (2017).
Article  Google Scholar 

9.
Jacob, U. et al. Valuing biodiversity and ecosystem services in a complex marine ecosystem. (eds Belgrano, A., Woodward, G. & Jacob U.) in Aquatic Functional Biodiversity. 189–207 (Academic Press, 2015).

10.
Dee, L. E. et al. Operationalizing network theory for ecosystem service assessments. Trends Ecol. Evol. 32, 118–130 (2017).
PubMed  Article  Google Scholar 

11.
Bohan, D. et al. Networking our way to better ecosystem service provision. Trends Ecol. Evol. 31, 105–115 (2016).
Article  Google Scholar 

12.
Dunne, A. J. et al. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Article  Google Scholar 

13.
Binzer, A. et al. The susceptibility of species to extinctions in model communities. Basic Appl. Ecol. 12, 590–599 (2011).
Article  Google Scholar 

14.
Eklöf, A., Tang, S. & Allesina, S. Secondary extinctions in food webs: a Bayesian network approach. Methods Ecol. Evol. 4, 760–770 (2013).
Article  Google Scholar 

15.
Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1711–1723 (2009).
Article  Google Scholar 

16.
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Dobson, A. Food-web structure and ecosystem services: insights from the Serengeti. Philos. Trans. R. Soc. B Biol. Sci. 364, 1665–1682 (2009).
Article  Google Scholar 

18.
Estrada, E. Food webs robustness to biodiversity loss: the roles of connectance, expansibility and degree distribution. J. Theor. Biol. 244, 296–307 (2007).
MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

19.
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).

20.
Curtsdotter, A. et al. Robustness to secondary extinctions: comparing trait-based sequential deletions in static and dynamic food webs. Basic Appl. Ecol. 12, 571–580 (2011).
Article  Google Scholar 

21.
Srinivasan, U. T., Dunne, J. A., Harte, J. & Martinez, N. D. Response of complex food webs to realistic extinction sequences. Ecology 88, 671–682 (2007).

22.
Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning. Ecol. Lett. 8, 538–547 (2005).

23.
Dee, L. E., De Lara, M., Costello, C. & Gaines, S. D. To what extent can ecosystem services motivate protecting biodiversity? Ecol. Lett. 20, 935–946 (2017).
PubMed  Article  Google Scholar 

24.
Dunne, J., Williams Richard, J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Article  Google Scholar 

25.
Allesina, S., Bodini, A. & Pascual, M. Functional links and robustness in food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1701–1709 (2009).
Article  Google Scholar 

26.
Staniczenko, P. P. A., Lewis, O. T., Jones, N. S. & Reed-Tsochas, F. Structural dynamics and robustness of food webs. Ecol. Lett. 13, 891–899 (2010).
PubMed  Article  Google Scholar 

27.
Kremen, C. Managing ecosystem services: what do we need to know about their ecology? Ecol. Lett. 8, 468–479 (2005).
PubMed  Article  Google Scholar 

28.
Allesina, S. et al. The robustness and restoration of a network of ecological networks. Science 5, 1–8 (2013).
Google Scholar 

29.
Bane, M. S., Pocock, M. J. O. & James, R. Effects of model choice, network structure, and interaction strengths on knockout extinction models of ecological robustness. Ecol. Evol. 8, 10794–10804 (2018).
PubMed  PubMed Central  Article  Google Scholar 

30.
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and robustness of marine food webs. Mar. Ecol. Prog. Ser. 273, 291–302 (2004).
ADS  Article  Google Scholar 

31.
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).
PubMed  Article  Google Scholar 

32.
Thellmann, K. et al. Tipping points in the supply of ecosystem services of a mountainous watershed in Southeast Asia. Sustain 10, 1–15 (2018).
Article  Google Scholar 

33.
Cardinale, B. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–68 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

34.
Eklöf, A. & Ebenman, B. Species loss and secondary extinctions in simple and complex model communities. J. Anim. Ecol. 75, 239–246 (2006).
PubMed  Article  Google Scholar 

35.
Vieira, M. C. & Almeida-Neto, M. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecol. Lett. 18, 144–152 (2015).
PubMed  Article  Google Scholar 

36.
Wilmers, C. C., Estes, J. A., Edwards, M., Laidre, K. L. & Konar, B. Do trophic cascades affect the storage and flux of atmospheric carbon? An analysis of sea otters and kelp forests. Front. Ecol. Environ. 10, 409–415 (2012).
Article  Google Scholar 

37.
Estes, J. A. et al. Trophic downgrading of planet earth. Science https://doi.org/10.1126/science.1205106 (2011).

38.
He, Q. & Silliman, B. R. Consumer control as a common driver of coastal vegetation worldwide. Ecol. Monogr. 86, 278–294 (2016).

39.
Ives, A. R. & Cardinale, B. J. Food-web interactions govern the resistance of communities after non-random extinctions. Nature 429, 174–177 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Brose, U. Complex food webs prevent competitive exclusion among producer species. Proc. R. Soc. B Biol. Sci. 275, 2507–2514 (2008).

41.
Rudolf, V. H. W. & Lafferty, K. D. Stage structure alters how complexity affects stability of ecological networks. Ecol. Lett. 14, 75–79 (2011).
CAS  PubMed  Article  Google Scholar 

42.
De Visser, S. N., Freymann, B. P. & Olff, H. The Serengeti food web: empirical quantification and analysis of topological changes under increasing human impact. J. Anim. Ecol. 80, 484–494 (2011).
PubMed  Article  Google Scholar 

43.
Perry, G. L. W., Moloney, K. A. & Etherington, T. R. Using network connectivity to prioritise sites for the control of invasive species. J. Appl. Ecol. 54, 1238–1250 (2017).
Article  Google Scholar 

44.
Gross, K. & Cardinale, B. J. The functional consequences of random vs. ordered species extinctions. Ecol. Lett. 8, 409–418 (2005).
Article  Google Scholar 

45.
Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).

46.
Gaston, K. J. et al. Population abundance and ecosystem service provision: the case of birds. Bioscience 68, 264–272 (2018).
PubMed  PubMed Central  Article  Google Scholar 

47.
Davies, T. W. et al. Dominance, biomass and extinction resistance determine the consequences of biodiversity loss for multiple coastal ecosystem processes. PLoS ONE 6, e28362 (2011).

48.
Balvanera, P., Kremen, C. & Martínez-Ramos, M. Applying community structure analysis to ecosystem function: examples from pollination and carbon storage. Ecol. Appl. 15, 360–375 (2005).

49.
Xiao, H. et al. Win-wins for biodiversity and ecosystem service conservation depend on the trophic levels of the species providing services. J. Appl. Ecol. 55, 2160–2170 (2018).
Article  Google Scholar 

50.
Dobson, A., Allesina, S., Lafferty, K. & Pascual, M. The assembly, collapse and restoration of food webs. Philos. Trans. R. Soc. B Biol. Sci. 364, 1803–1806 (2009).
Article  Google Scholar 

51.
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 1–8 (2016).
Article  CAS  Google Scholar 

52.
Hechinger, R. F. et al. Food webs including parasites, biomass, body sizes, and life stages for three California/Baja California estuaries. Ecology 92, 791 (2011).
Article  Google Scholar 

53.
California Department of Fish and Wildlife. 2018-2019 California Saltwater Sport Fishing Regulations. p. 12–14 (2018).

54.
eBird. eBird: an online database of bird distribution and abundance. https://ebird.org. (2012).

55.
Dee, L. E. et al. When do ecosystem services depend on rare species? Trends Ecol. Evol. xx, 1–13 (2019).
Google Scholar 

56.
Strimas-Mackey, M., Miller, E. & Hochachka, W. Cornell Lab of Ornithology. eBird Data Extraction and Processing in R [R package auk version 0.3.2]. (Comprehensive R Archive Network (CRAN), 2019).

57.
Secretaria de Medio Ambiente Y Recursos Naturales. Secretaria de Medio Ambiente Y Recursos Naturales. Subsecretaria de gestion para la proteccion ambiental. (2018).

58.
Kones, J. K., Soetaert, K., van Oevelen, D. & Owino, J. O. Are network indices robust indicators of food web functioning? A Monte Carlo approach. Ecol. Modell. 220, 370–382 (2009).

59.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst. 1695, 426 (2006).
Google Scholar 

60.
Booth, J. E., Gaston, K. J., Evans, K. L. & Armsworth, P. R. The value of species rarity in biodiversity recreation: a birdwatching example. Biol. Conserv. 144, 2728–2732 (2011).
Article  Google Scholar 

61.
Wang, H., Zhewei, W., Junhao, G., Wang, S. & Huang, Z. Personalized PageRank to a Target Node (Cornell University, 2020).

62.
Bryan, K. & Leise, T. The linear algebra behind Google. SIAM Rev. 3, 13 (2009).
MATH  Google Scholar 

63.
Allesina, S. & Pascual, M. Googling food webs: can an eigenvector measure species’ importance for coextinctions? PLoS Comput. Biol. 5, e1000494 (2009).

64.
Rabinwitz, D. Seven forms of rarity. (ed Sygne, H.) in The Biological Aspects of Rare Plant Conservation. 205–217 (John Wiley & Sons Ltd., 1981).

65.
Pimm, S. L., Jones, H. L. & Diamond, J. On the risk of extinction. Am. Nat. 132, 757–785 (1988).
Article  Google Scholar 

66.
Lyons, K. G., Brigham, C. A., Traut, B. H. & Schwartz, M. W. Rare species and ecosystem functioning. Conserv. Biol. 19, 1019–1024 (2005).
Article  Google Scholar 

67.
Smith, M. D. & Knapp, A. K. Dominant species maintain ecosystem function with non-random species loss. Ecol. Lett. 6, 509–517 (2003).

68.
Jacob, U. et al. The role of body size in complex food webs. A cold case. Adv. Ecol. Res. 45, 181–223 (2011).

69.
Lafferty, K. D. et al. Parasites in food webs: the ultimate missing links. Ecol. Lett. 11, 533–546 (2008).
PubMed  PubMed Central  Article  Google Scholar  More