Philippa Kaur

1.
Sakschewski B, von Bloh W, Boit A, Poorter L, Peña-Claros M, Heinke J, et al. Resilience of Amazon forests emerges from plant trait diversity. Nat Clim Change. 2016;6:1032–6.
Article  Google Scholar 
2.
Gruber N, Galloway JN. An Earth-system perspective of the global nitrogen cycle. Nature. 2008;451:293–6.
CAS  PubMed  Article  Google Scholar 

3.
Kicklighter DW, Melillo JM, Monier E, Sokolov AP, Zhuang Q. Future nitrogen availability and its effect on carbon sequestration in Northern Eurasia. Nat Commun. 2019;10:1–19.
CAS  Article  Google Scholar 

4.
Liu L, Greaver TL. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol Lett. 2010;13:819–28.
PubMed  Article  Google Scholar 

5.
Maaroufi NI, Nordin A, Hasselquist NJ, Bach LH, Palmqvist K, Gundale MJ. Anthropogenic nitrogen deposition enhances carbon sequestration in boreal soils. Glob Chang. Glob Change Biol. 2015;21:3169–80.
Article  Google Scholar 

6.
Lu M, Zhou X, Luo Y, Yang Y, Fang C, Chen J, et al. Minor stimulation of soil carbon storage by nitrogen addition: a meta-analysis. Agr Ecosyst Environ. 2011;140:234–44.
CAS  Article  Google Scholar 

7.
Chen J, Luo Y, van Groenigen KJ, Hungate BA, Cao J, Zhou X, et al. A keystone microbial enzyme for nitrogen control of soil carbon storage. Sci Adv. 2018;4:q1689.
Article  CAS  Google Scholar 

8.
Cusack DF, Torn MS, Mcdowell WH, Silver WL. The response of heterotrophic activity and carbon cycling to nitrogen additions and warming in two tropical soils. Glob Change Biol. 2010;16:2555–72.
Google Scholar 

9.
Zhang J, Balkovic J, Azevedo LB, Skalsky R, Bouwman AF, Xu G, et al. Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China. Sci Total Environ. 2018;627:361–72.
CAS  PubMed  Article  Google Scholar 

10.
Bending GD, Putland C, Rayns F. Changes in microbial community metabolism and labile organic matter fractions as early indicators of the impact of management on soil biological quality. Biol Fertil Soils. 2000;31:78–84.
CAS  Article  Google Scholar 

11.
Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:13630.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Leinweber P, Jandl G, Baum C, Eckhardt K, Kandeler E. Stability and composition of soil organic matter control respiration and soil enzyme activities. Soil Biol Biochem. 2008;40:1496–505.
CAS  Article  Google Scholar 

13.
Roth V, Lange M, Simon C, Hertkorn N, Bucher S, Goodall T, et al. Persistence of dissolved organic matter explained by molecular changes during its passage through soil. Nat Geosci. 2019;12:755–61.
CAS  Article  Google Scholar 

14.
Kallenbach CM, Grandy AS, Frey SD, Diefendorf AF. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol Biochem. 2015;91:279–90.
CAS  Article  Google Scholar 

15.
Chen L, Liu L, Qin S, Yang G, Fang K, Zhu B, et al. Regulation of priming effect by soil organic matter stability over a broad geographic scale. Nat Commun. 2019;10:5112.
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Sollins P, Kramer MG, Swanston C, Lajtha K, Filley T, Aufdenkampe AK, et al. Sequential density fractionation across soils of contrasting mineralogy: evidence for both microbial- and mineral-controlled soil organic matter stabilization. Biogeochemistry. 2009;96:209–31.
CAS  Article  Google Scholar 

17.
Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.
CAS  PubMed  Article  Google Scholar 

18.
Ding X, Chen S, Zhang B, Liang C, He H, Horwath WR. Warming increases microbial residue contribution to soil organic carbon in an alpine meadow. Soil Biol Biochem. 2019;135:13–9.
CAS  Article  Google Scholar 

19.
Ni X, Liao S, Tan S, Peng Y, Wang D, Yue K, et al. The vertical distribution and control of microbial necromass carbon in forest soils. Glob Ecol Biogeogr. 2020;29:1829–39.
Article  Google Scholar 

20.
Shao P, Lynch L, Xie H, Bao X, Liang C. Tradeoffs among microbial life history strategies influence the fate of microbial residues in subtropical forest soils. Soil Biol Biochem. 2021;153:108112.
CAS  Article  Google Scholar 

21.
Ma T, Zhu S, Wang Z, Chen D, Dai G, Feng B, et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat Commun. 2018;9:3480.
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Malik AA, Puissant J, Buckeridge KM, Goodall T, Jehmlich N, Chowdhury S, et al. Land use driven change in soil pH affects microbial carbon cycling processes. Nat Commun. 2018;9:3591.
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Nocker A, Sossa-Fernandez P, Burr MD, Camper AK. Use of propidium monoazide for live/dead distinction in microbial ecology. Appl Environ Microbiol. 2007;73:5111–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Castro HF, Classen AT, Austin EE, Norby RJ, Schadt CW. Soil microbial community responses to multiple experimental climate change drivers. Appl Environ Microbiol. 2010;76:999–1007.
CAS  PubMed  Article  Google Scholar 

25.
Rinnan R, MichelsenI A, Bååth E, Jonasson S. Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem. Glob Change Biol. 2007;13:28–39.
Article  Google Scholar 

26.
Liang Y, Xiao X, Nuccio EE, Yuan M, Zhang N, Xue K, et al. Differentiation strategies of soil rare and abundant microbial taxa in response to changing climatic regimes. Environ Microbiol. 2020;22:1327–40.
CAS  PubMed  Article  Google Scholar 

27.
Wang M, Ding J, Sun B, Zhang J, Wyckoff KN, Yue H, et al. Microbial responses to inorganic nutrient amendment overridden by warming: consequences on soil carbon stability. Environ Microbiol. 2018;20:2509–22.
CAS  PubMed  Article  Google Scholar 

28.
Zhao M, Xue K, Wang F, Liu S, Bai S, Sun B, et al. Microbial mediation of biogeochemical cycles revealed by simulation of global changes with soil transplant and cropping. ISME J. 2014;8:2045–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Liang Y, Jiang Y, Wang F, Wen C, Deng Y, Xue K, et al. Long-term soil transplant simulating climate change with latitude significantly alters microbial temporal turnover. ISME J. 2015;9:2561–72.
PubMed  PubMed Central  Article  Google Scholar 

30.
Sun B, Wang F, Jiang Y, Li Y, Dong Z, Li Z, et al. A long-term field experiment of soil transplantation demonstrating the role of contemporary geographic separation in shaping soil microbial community structure. Ecol Evol. 2014;4:1073–87.
PubMed  PubMed Central  Article  Google Scholar 

31.
Fernandez I, Álvarez-González JG, Carrasco B, Ruíz-González AD, Cabaneiro A. Post-thinning soil organic matter evolution and soil CO2 effluxes in temperate radiata pine plantations: impacts of moderate thinning regimes on the forest C cycle. Can J Res. 2012;42:1953–64.
CAS  Article  Google Scholar 

32.
Baldock JA, Oades JM, Waters AG, Peng X, Vassallo AM, Wilson MA. Aspects of the chemical structure of soil organic materials as revealed by solid-state 13C NMR spectroscopy. Biogeochemistry. 1992;16:1–42.
CAS  Article  Google Scholar 

33.
Coxall HK, Wilson PA, Pälike H, Lear CH, Backman J. Rapid stepwise onset of Antarctic glaciation and deeper calcite compensation in the Pacific Ocean. Nature. 2005;433:53–7.
CAS  PubMed  Article  Google Scholar 

34.
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N, et al. is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242.
PubMed  Article  CAS  Google Scholar 

35.
Li H, Bi Q, Yang K, Zheng B, Pu Q, Cui L. D2O-isotope-labeling approach to probing phosphate-solubilizing bacteria in complex soil communities by single-cell raman spectroscopy. Anal Chem. 2019;91:2239–46.
CAS  PubMed  Article  Google Scholar 

36.
Cui L, Butler HJ, Martin-Hirsch PL, Martin FL. Aluminium foil as a potential substrate for ATR-FTIR, transflection FTIR or Raman spectrochemical analysis of biological specimens. Anal Methods. 2016;8:481–7.
CAS  Article  Google Scholar 

37.
Bååth E, Pettersson M, Söderberg KH. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol Biochem. 2001;33:1571–4.
Article  Google Scholar 

38.
Trivedi P, Delgado-Baquerizo M, Trivedi C, Hu H, Anderson IC, Jeffries TC, et al. Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships. ISME J. 2016;10:2593–604.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Zhang X, Amelung W. Gas chromatographic determination of muramic acid, glucosamine, mannosamine, and galactosamine in soils. Soil Biol Biochem. 1996;28:1201–6.
CAS  Article  Google Scholar 

40.
Schermelleh-Engel K, Moosbrugger H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Methods Psychological Res Online. 2003;8:23–74.
Google Scholar 

41.
Clarke KR. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 1993;18:117–43.
Article  Google Scholar 

42.
Kruskal JB. Nonmetric multidimensional scaling: a numerical method. Psychometrika. 1964;29:115–29.
Article  Google Scholar 

43.
Legendre P, Legendre L. Numerical Ecology, 3rd English Edition. Oxford, UK: Elsevier; 2012.

44.
Liaw A, Wiener M. Classification and regression by randomForest. R N. 2002;2:18–22.
Google Scholar 

45.
Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8:e1002606.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Wu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat Microbiol. 2019;4:1183–95.
CAS  PubMed  Article  Google Scholar 

47.
Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, et al. Quantifying global soil carbon losses in response to warming. Nature. 2016;540:104–8.
CAS  PubMed  Article  Google Scholar 

48.
Hicks Pries CE, Castanha C, Porras RC, Torn MS. The whole-soil carbon flux in response to warming. Science. 2017;355:1420–3.
CAS  PubMed  Article  Google Scholar 

49.
Peñuelas J, Ciais P, Canadell JG, Janssens IA, Fernández-Martínez M, Carnicer J, et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat Ecol Evol. 2017;1:1438–45.
PubMed  Article  Google Scholar 

50.
Sokol NW, Bradford MA. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat Geosci. 2019;12:46–53.
CAS  Article  Google Scholar 

51.
Trumbore SE, Chadwick OA, Amundson R. Rapid exchange between soil carbon and atmospheric carbon dioxide driven by temperature change. Science. 1996;272:393–6.
CAS  Article  Google Scholar 

52.
Liang C, Cheng G, Wixon DL, Balser TC. An Absorbing Markov Chain approach to understanding the microbial role in soil carbon stabilization. Biogeochemistry. 2011;106:303–9.
Article  Google Scholar 

53.
Liang C, Amelung W, Lehmann J, Kästner M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob Change Biol. 2019;00:1–13.
Google Scholar 

54.
Tian J, Zong N, Hartley IP. Microbial metabolic response to winter warming stabilizes soil carbon. Glob Change Biol. 2021;00:1–18.
Google Scholar 

55.
Coxall HK, Wilson PA, Pälike H, Lear CH, Backman J. Rapid stepwise onset of Antarctic glaciation and deeper calcite compensation in the Pacific Ocean. Nature. 2005;433:53–7.
CAS  PubMed  Article  Google Scholar 

56.
Ding X, Chen S, Zhang B, He H, Filley TR, Horwath WR. Warming yields distinct accumulation patterns of microbial residues in dry and wet alpine grasslands on the Qinghai-Tibetan Plateau. Biol Fertil Soils. 2020;56:881–92.
CAS  Article  Google Scholar 

57.
Jia J, Feng X, He J, He H, Lin L, Liu Z. Comparing microbial carbon sequestration and priming in the subsoil versus topsoil of a Qinghai-Tibetan alpine grassland. Soil Biol Biochem. 2017;104:141–51.
CAS  Article  Google Scholar 

58.
Zhou X, Chen C, Wang Y, Xu Z, Duan J, Hao Y, et al. Soil extractable carbon and nitrogen, microbial biomass and microbial metabolic activity in response to warming and increased precipitation in a semiarid Inner Mongolian grassland. Geoderma. 2013;206:24–31.
CAS  Article  Google Scholar 

59.
Chen J, Ji C, Fang J, He H, Zhu B. Dynamics of microbial residues control the responses of mineral-associated soil organic carbon to N addition in two temperate forests. Sci Total Environ. 2020;748:141318.
CAS  PubMed  Article  Google Scholar 

60.
Fontaine S, Barot S, Barré P, Bdioui N, Mary B, Rumpel C. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature. 2007;450:277–80.
CAS  PubMed  Article  Google Scholar 

61.
Wang C, Wang X, Pei G, Xia Z, Peng B, Sun L, et al. Stabilization of microbial residues in soil organic matter after two years of decomposition. Soil Biol Biochem. 2020;141:107687.
CAS  Article  Google Scholar 

62.
Zhou J, Xue K, Xie J, Deng Y, Wu L, Cheng X, et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Change. 2011;2:106–10.
Article  CAS  Google Scholar 

63.
Chen R, Senbayram M, Blagodatsky S, Myachina O, Dittert K, Lin X, et al. Soil C and N availability determine the priming effect: microbial N mining and stoichiometric decomposition theories. Glob Change Biol. 2014;20:2356–67.
Article  Google Scholar 

64.
Fontaine S, Henault C, Aamor A, Bdioui N, Bloor JMG, Maire V, et al. Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biol Biochem. 2011;43:86–96.
CAS  Article  Google Scholar 

65.
Jones DL, Nguyen C, Finlay RD. Carbon flow in the rhizosphere: carbon trading at the soil–root interface. Plant Soil. 2009;321:5–33.
CAS  Article  Google Scholar 

66.
Zang H, Wang J, Kuzyakov Y. N fertilization decreases soil organic matter decomposition in the rhizosphere. Appl Soil Ecol. 2016;108:47–53.
Article  Google Scholar 

67.
Mason-Jones K, Schmücker N, Kuzyakov Y. Contrasting effects of organic and mineral nitrogen challenge the N-Mining Hypothesis for soil organic matter priming. Soil Biol Biochem. 2018;124:38–46.
CAS  Article  Google Scholar 

68.
Carreiro MM, Sinsabaugh RL, Repert DA, Parkhurst DF. Microbial enzyme shifts explain litter decay responses to simulated nitrogen deposition. Ecology. 2000;81:2359–65.
Article  Google Scholar 

69.
Sinsabaugh RL, Hill BH, Follstad Shah JJ. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature. 2009;462:795–98.
CAS  PubMed  Article  Google Scholar 

70.
Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, et al. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol Biochem. 2013;58:216–34.
CAS  Article  Google Scholar 

71.
Tian D, Niu S. A global analysis of soil acidification caused by nitrogen addition. Environ Res Lett. 2015;10:24019.
Article  CAS  Google Scholar 

72.
Zhou Z, Wang C, Zheng M, Jiang L, Luo Y. Patterns and mechanisms of responses by soil microbial communities to nitrogen addition. Soil Biol Biochem. 2017;115:433–41.
CAS  Article  Google Scholar  More

Spatial map of Chinese protected areas
The database of protected areas (PA) distribution in China and a digitized spatial map thereof were compiled from Zhao et al.40 and Zhang et al.41. In total, we obtained the information of 2622 protected areas in China, which also included marine reserves. In order to evaluate the representation of terrestrial protected areas, we excluded marine reserves from our analyses. We also excluded Taiwan because we did not have the spatial distribution data for nature reserves in Taiwan. Finally, we had the boundary information of 2572 protected areas covering about 15.2% land area in China.
Species’ range maps
Range maps of threatened vertebrates (birds, mammals, amphibians and reptiles) were obtained from the IUCN’s Red List42. Distribution data of threatened plants were compiled from Flora of China, Atlas of woody plants in China, provincial and local floras, checklists of nature reserves, various inventory reports across China and peer-reviewed papers. We obtained the information for critically endangered (CR), endangered (EN) and vulnerable (VU) species. The conservation status of vertebrates was obtained from IUCN Red List42, while that of plants was obtained from Qin et al.43. In total, we obtained the distribution information of 103 birds, 86 mammals, 134 amphibians, 50 reptiles, and 2983 plants in China (see Supplementary Data 1). We estimated the number of species in each PA by overlaying the map of PA with the species’ range maps in ArcGIS 10.2 (ESRI, Redlands, CA). In order to validate the distribution of species, we further verified the presence of species in respective PA by checking their inventory reports.
Human footprint data
In order to measure the extent of human pressure on the protected areas, we obtained the most comprehensive global map of human pressure i.e., human footprint (HFP) from https://wcshumanfootprint.org. The human footprint measures the cumulative impact of direct pressures on environment from human activities and is based on data from built environments, agricultural lands, pasture lands, human population density, night-time lights, railways, roads and navigable waterways44. It is one of the most complete and finest terrestrial datasets on cumulative human pressure on the environment. The human footprint maps of two time periods (1993 and 2009) are available at present. We downloaded the maps of both time periods at the spatial resolution of 1 km × 1 km to quantify the change in human pressure within Chinese protected areas over a 16-year period. It should, however, be noted that any point estimate of the change in HFP might include errors due to the resolution and reliability of the component layers. For example, one of the components of HFP is the night-time lights, which changed over time from incandescent to mercury vapor to light emitting diode. This means that the change in night light is due to more than development. As a result, the systemic bias in regional economy could likely cause low HFP in wealthy as compared to rural areas. While this issue does not invalidate our analyses, such comparisons should be applied with caution.
Climate data
In order to represent the climatic conditions of the past and the present, we obtained 20 years climate data comprising monthly minimum temperature, monthly maximum temperature and monthly precipitation for two time periods (past: 1961–1970 and present: 2010–2019). We used 10 years window for each time period to capture the variability in climatic conditions in order to prevent over- or underestimation of the past and present climate. In total, we obtained 12 months × 10 years × 3 variables × 2 time periods = 720 global raster layers from the Climate Research Unit (CRU TS v. 4.04) database (http://www.cru.uea.ac.uk/data) at the spatial resolution of 0.5° × 0.5°45. We then calculated the monthly mean values for the three variables for each time period separately. From these monthly mean values, we estimated mean annual temperature (MAT), mean temperature of warmest quarter (MTWQ), mean temperature of coldest quarter (MTCQ), mean annual precipitation (MAP), precipitation of driest quarter (PDQ) and precipitation of wettest quarter (PWQ) for each time period using biovars function in the R package ‘dismo’46. We then estimated the average value of climate variables in each PA using zonal.stats function in the R package ‘spatialEco’47. In order to reduce dimensionality and collinearity of the 6 climate variables, we performed principal component analysis (PCA) using prcomp function in R v4.0.248. Following Carroll et al.37, we used climate data for both past and present based on the first 2 PCA axes, which explained 89.4–89.8 % of the variance (Supplementary Tables 1-2). Additionally, we also estimated the change in mean annual temperature to identify PAs that have experienced climate warming higher than the Paris Agreement threshold of 1.5 °C. All the analyses were performed in R version 4.0.248.
Vulnerability mapping
We calculated three indices of vulnerability within each protected area: (i) species vulnerability, (ii) anthropogenic vulnerability, and (iii) climate vulnerability. In order to measure species vulnerability, we first assigned numerical value to each IUCN threat category using a geometric progression49,50. We gave scores of 2, 4, and 8 to species belonging to categories VU, EN, and CR, respectively. We, then, summed the score of all species in each PA and standardized the value to the range of 0–1 using minimum–maximum normalization. We performed these steps separately for birds, mammals, amphibians, reptiles and plants to calculate the vulnerability score of each group. We also calculated the cumulative score by combining the total scores of all five groups. Values close to 0 indicated low species vulnerability and values close to 1 indicated high species vulnerability. Although species diversity is highly correlated with the species vulnerability metric used herein (Pearson’s correlation coefficient = 0.94 − 0.99, p  More

1.
Blair JMA, Webber MA, Baylay AJ, Ogbolu DO, Piddock LJV. Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol. 2015;13:42–51.
CAS  Article  Google Scholar 
2.
Palmer AC, Kishony R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat Rev Genet. 2013;14:243–8.
CAS  Article  Google Scholar 

3.
Blaser MJ. Antibiotic use and its consequences for the normal microbiome. Science. 2016;352:544–5.
CAS  Article  Google Scholar 

4.
Modi SR, Collins JJ, Relman DA. Antibiotics and the gut microbiota. J Clin Investig. 2014;124:4212–8.
Article  Google Scholar 

5.
Lawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138:1–11.
CAS  Article  Google Scholar 

6.
Libertucci J, Young VB. The role of the microbiota in infectious diseases. Nat Microbiol. 2019;4:35–45.
CAS  Article  Google Scholar 

7.
Bhalodi AA, Engelen TSR, van, Virk HS, Wiersinga WJ. Impact of antimicrobial therapy on the gut microbiome. J Antimicrobial Chemother. 2019;74:i6–i15.
CAS  Article  Google Scholar 

8.
Klümper U, Recker M, Zhang L, Yin X, Zhang T, Buckling A, et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J. 2019;13, 2927–37.
Article  Google Scholar 

9.
Baumgartner M, Bayer F, Pfrunder-Cardozo KR, Buckling A, Hall AR. Resident microbial communities inhibit growth and antibiotic-resistance evolution of Escherichia coli in human gut microbiome samples. PLOS Biol. 2020;18:e3000465.
CAS  Article  Google Scholar 

10.
Ianiro G, Tilg H, Gasbarrini A. Antibiotics as deep modulators of gut microbiota: between good and evil. Gut. 2016;65:1906–15.
CAS  Article  Google Scholar 

11.
Spaulding CN, Klein RD, Schreiber HL, Janetka JW, Hultgren SJ. Precision antimicrobial therapeutics: The path of least resistance? npj Biofilms Microbiomes. 2018;4:1–7.
Article  Google Scholar 

12.
Moya A, Ferrer M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016;24:402–13.
CAS  Article  Google Scholar 

13.
Jernberg C, Löfmark S, Edlund C, Jansson JK. Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology. 2010;156:3216–23.
CAS  Article  Google Scholar 

14.
Stecher B, Chaffron S, Käppeli R, Hapfelmeier S, Freedrich S, Weber TC, et al. Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. 2010;6:e1000711.
Article  Google Scholar 

15.
Van Der Waaij D, Berghuis-de Vries JM, Lekkerkerk-Van Der Wees JEC. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J Hyg. 1971;69:405–11.
Article  Google Scholar 

16.
Kim S, Covington A, Pamer EG. The intestinal microbiota: antibiotics, colonization resistance, and enteric pathogens. Immunological Rev. 2017;279:90–105.
CAS  Article  Google Scholar 

17.
Mathieu A, Fleurier S, Frénoy A, Dairou J, Bredeche MF, Sanchez-Vizuete P, et al. Discovery and function of a general core hormetic stress response in E. coli induced by sublethal concentrations of antibiotics. Cell Rep. 2016;17:46–57.
CAS  Article  Google Scholar 

18.
Pamer EG. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science. 2016;352:535–8.
CAS  Article  Google Scholar 

19.
Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evolution. 2018;2:936–43.
Article  Google Scholar  More

1.
FAO, ITPS. Status of the World’s Soil Resources – Main report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, 2015. Rome, Italy. https://www.fao.org/3/a-i5199e.pdf.
2.
UN (United Nations). Sustainable Development Goals [online]. 2015. https://www.un.org/sustainabledevelopment/sustainabledevelopment-goals/.

3.
FAO. 2019. Soil erosion: the greatest challenge for sustainable soil management. Rome: Food and Agriculture Organization of the United Nations; 2019. p. 104.
Google Scholar 

4.
Pimentel D, Harvey C, Resosudarmo P, Sinclair K, Kurz D, McNair M, et al. Environmental and economic costs of soil erosion and conservation benefits. Science. 1995;267:1117–23.
CAS  PubMed  Article  Google Scholar 

5.
Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, et al. An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun. 2017;8:1–13.
CAS  Article  Google Scholar 

6.
UN (United Nations). World Soil Day [online]. 2019. https://www.un.org/en/observances/world-soil-day.

7.
Van Oost K, Bakker MM. Soil productivity and erosion. In: Wall DH, Bardgett RD, Behan-Pelletier V, Herrick JE, Jones H, Ritz K, et al. (eds.). Soil ecology and ecosystem services. Oxford, UK: Oxford University Press; 2012. 301–14.

8.
Gregorich EG, Greer KJ, Anderson DW, Liang BC. Carbon distribution and losses: erosion and deposition effects. Soil Res. 1998;47:291–302.
Google Scholar 

9.
Lal R, Pimentel D. Soil erosion: a carbon sink or source? Science. 2008;319:1040–2.
CAS  PubMed  Article  Google Scholar 

10.
Mendonça R, Müller RA, Clow D, Verpoorter C, Raymond P, Tranvik LJ, et al. Organic carbon burial in global lakes and reservoirs. Nat Commun. 2017;8:1694.
PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Quinton JN, Govers G, Van Oost K, Bardgett RD. The impact of agricultural soil erosion on biogeochemical cycling. Nat Geosci. 2010;3:311–4.
CAS  Article  Google Scholar 

12.
Smith RW, Bianchi TS, Allison M, Savage C, Galy V. High rates of organic carbon burial in fjord sediments globally. Nat Geosci. 2015;8:450–U46.
CAS  Article  Google Scholar 

13.
Van Oost K, Quine TA, Govers G, De Gryze S, Six J, Harden JW, et al. The impact of agricultural soil erosion on the global carbon cycle. Science. 2007;318:626–9.
PubMed  Article  CAS  Google Scholar 

14.
Maestre FT, Quero JL, Gotelli NJ, Escudero A, Ochoa V, Delgado-Baquerizo M, et al. Plant species richness and ecosystem multifunctionality in global drylands. Science. 2012;335:214–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Fanin N, Gundale MJ, Farrell M, Ciobanu M, Baldock JA, Nilsson MC, et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat Ecol Evol. 2018;2:269–78.
PubMed  Article  Google Scholar 

17.
Garland G, Banerjee S, Edlinger A, Oliveira EM, Herzog C, Wittwer R, et al. A closer look at the functions behind ecosystem multifunctionality: a review. J Ecol. 2020, https://doi.org/10.1111/1365-2745.13511.

18.
Bardgett RD, Van Der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505–11.
CAS  PubMed  Article  Google Scholar 

19.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
CAS  PubMed  Article  Google Scholar 

20.
Wall H, Nielsen UN, Six J. Soil biodiversity and human health. Nature. 2015;528:69–76.
CAS  PubMed  Article  Google Scholar 

21.
Saleem M, Hu J, Jousset A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu Rev Ecol Evol Syst. 2019;50:145–68.
Article  Google Scholar 

22.
Crowther TW, Van Den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.
CAS  PubMed  Article  Google Scholar 

23.
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:3033.
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Zhou J, Deng Y, Luo F, He Z, Yang Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio. 2011;2:e00122–11.
PubMed  PubMed Central  Article  Google Scholar 

25.
Bragazza L, Parisod J, Buttler A, Bardgett RD. Biogeochemical plant–soil microbe feedback in response to climate warming in peatlands. Nat Clim Change. 2013;3:273–7.
CAS  Article  Google Scholar 

26.
Crowther TW, Thomas SM, Maynard DS, Baldrian P, Covey K, Frey SD, et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc Natl Acad Sci USA. 2015;112:7033–8.
CAS  PubMed  Article  Google Scholar 

27.
Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci USA. 2015;112:15684–9.
CAS  PubMed  Article  Google Scholar 

28.
Guo X, Feng J, Shi Z, Zhou X, Yuan M, Tao X, et al. Climate warming leads to divergent succession of grassland microbial communities. Nat Clim Change. 2018;8:813–8.
Article  Google Scholar 

29.
Li Z, Tian D, Wang B, Wang J, Wang S, Chen H, et al. Microbes drive global soil nitrogen mineralization and availability. Glob Change Biol. 2019;25:1078–88.
Article  Google Scholar 

30.
Wieder WR, Bonan GB, Allison SD. Global soil carbon projections are improved by modelling microbial processes. Nat Clim Change. 2013;3:909–12.
CAS  Article  Google Scholar 

31.
Chen Q, Dong J, Zhu D, Hu H, Delgado-Baquerizo M, Ma Y, et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. Soil Biol Biochem. 2020;141:107686.
CAS  Article  Google Scholar 

32.
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.
PubMed  Article  Google Scholar 

33.
Wagg C, Schlaeppi K, Banerjee S, Kuramae EE, Van Der Heijden MGA. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun. 2019;10:4841.
PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Van der Heijden MGA, Bardgett RD, Van Straalen NM. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett. 2008;11:296–310.
Article  Google Scholar 

35.
Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.
Article  CAS  Google Scholar 

36.
Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 2019;13:1722–36.
PubMed  PubMed Central  Article  Google Scholar 

37.
Freilich MA, Wieters E, Broitman BR, Marquet PA, Navarrete SA. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities? Ecology. 2018;99:690–9.
PubMed  Article  Google Scholar 

38.
Fuhrman JA. Microbial community structure and its functional implications. Nature. 2009;459:193–9.
CAS  PubMed  Article  Google Scholar 

39.
Banerjee S, Schlaeppi K, Van Der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.
CAS  PubMed  Article  Google Scholar 

40.
Herren CM, McMahon KD. Keystone taxa predict compositional change in microbial communities. Environ Microbiol. 2018;20:2207–17.
PubMed  Article  Google Scholar 

41.
Ochoa-Hueso R, Collins SL, Delgado-Baquerizo M, Hamonts K, Pockman WT, Sinsabaugh RL, et al. Drought consistently alters the composition of soil fungal and bacterial communities in grasslands from two continents. Glob Change Biol. 2018;24:2818–27.
Article  Google Scholar 

42.
Mabuhay JA, Nakagoshi N, Isagi Y. Influence of erosion on soil microbial biomass, abundance and community diversity. Land Degrad Dev. 2004;15:183–95.
Article  Google Scholar 

43.
Li Z, Xiao H, Tang Z, Huang J, Nie X, Huang B, et al. Microbial responses to erosion-induced soil physico-chemical property changes in the hilly red soil region of southern China. Eur J Soil Biol. 2015;71:37–44.
CAS  Article  Google Scholar 

44.
Hou S, Xin M, Wang LL, Jiang H, Li N, Wang Z. The effects of erosion on the microbial populations and enzyme activity in black soil of northeastern China. Acta Ecologica Sin. 2014;34:295–301.
Article  Google Scholar 

45.
Zhang Y, Wu Y, Liu B, Zheng Q, Yin J. Characteristics and factors controlling the development of ephemeral gullies in cultivated catchments of black soil region, Northeast China. Soil Res. 2007;96:28–41.
Google Scholar 

46.
Li H, Zhu H, Qiu L, Wei X, Liu B, Shao M. Response of soil OC, N and P to land-use change and erosion in the black soil region of the Northeast China. Agr Ecosyst Environ. 2020;302:107081.
CAS  Article  Google Scholar 

47.
Zheng F. Effect of vegetation changes on soil erosion on the Loess Plateau. Pedosphere 2006;16:420–7.
Article  Google Scholar 

48.
Page A, Miller R, Keeney D. Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. Madison, Wisconsin, American Society of Agronomy, Inc., Soil Science Society of America, Inc, 1982.

49.
Brookes P, Landman A, Pruden G, Jenkinson D. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem. 1985;17:837–42.
CAS  Article  Google Scholar 

50.
Lefcheck JS, Byrnes JEK, Isbell F, Gamfeldt L, Griffin JN, Eisenhauer N, et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat Commun. 2015;6:6936.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Maestre FT, Castillo-Monroy AP, Bowker MA, Ochoa-Hueso R. Species richness effects on ecosystem multifunctionality depend on evenness, composition and spatial pattern. J Ecol. 2012;100:317–30.
CAS  Article  Google Scholar 

52.
Wang Z, Zhang Q, Staley C, Gao H, Ishii S, Wei X, et al. Impact of long-term grazing exclusion on soil microbial community composition and nutrient availability. Biol Fertil Soils. 2019;55:121–34.
CAS  Article  Google Scholar 

53.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Mueller RC, Paula FS, Mirza BS, Rodrigues JLM, Nuesslein K, Bohannan BJM. Links between plant and fungal communities across a deforestation chronosequence in the Amazon rainforest. ISME J. 2014;8:1548–50.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Al-Ghalith GA, Hillmann B, Ang K, Shields-Cutler R, Knights D. SHI7 is a self-learning pipeline for multipurpose short-read DNA quality control. mSystems. 2018;3:e00202–17.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Al-Ghalith GA, Montassier E, Ward HN, Knights D. NINJA-OPS: fast accurate marker gene alignment using concatenated ribosomes. PLoS Comput Biol. 2016;12:e1004658.
PubMed  PubMed Central  Article  CAS  Google Scholar 

57.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.
CAS  PubMed  Article  Google Scholar 

59.
Bray JR, Curtis JT. An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr. 1957;27:326–49.
Article  Google Scholar 

60.
Anderson MJ, Willis TJ. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84:511–25.
Article  Google Scholar 

61.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, et al. Vegan: community ecology package. R package version 2.3-1. 2015, http://CRAN.R-project.org/package=vegan.

62.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.
Article  CAS  Google Scholar 

63.
Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J. Application of random matrix theory to biological networks. Phys Lett A. 2006;357:420–3.
CAS  Article  Google Scholar 

64.
Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.
Article  Google Scholar 

65.
Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. Complex Syst. 2006;1695:1–9.
Google Scholar 

66.
Ma B, Wang HZ, Dsouza M, Lou J, He Y, Dai ZM, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5:219.
PubMed  PubMed Central  Article  Google Scholar 

68.
Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. ICWSM Conf. 2009;8:361–2.
Google Scholar 

69.
Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Qiu L, Zhu H, Liu J, Yao Y, Wang X, Rong G, et al. Soil erosion significantly reduces organic carbon and nitrogen mineralization in a simulated experiment. Agr Ecosyst Environ. 2021;307:107232.
CAS  Article  Google Scholar 

71.
Crits-Christoph A, Robinson CK, Barnum T, Fricke WF, Davila AF, Jedynak B, et al. Colonization patterns of soil microbial communities in the Atacama Desert. Microbiome. 2013;1:28.
PubMed  PubMed Central  Article  Google Scholar 

72.
Tiemann LK, Billings SA. Changes in variability of soil moisture alter microbial community C and N resource use. Soil Biol Biochem. 2011;43:1837–47.
CAS  Article  Google Scholar 

73.
Banerjee S, Misra A, Sar A, Pal S, Chaudhury S, Dam B. Poor nutrient availability in opencast coalmine influences microbial community composition and diversity in exposed and underground soil profiles. Appl Soil Ecol. 2020;152:103544.
Article  Google Scholar 

74.
Abu-Hamdeh NH, Reeder RC. Soil thermal conductivity: effects of density, moisture, salt concentration, and organic matter. Soil Sci Soc Am J. 2000;64:1285–90.
CAS  Article  Google Scholar 

75.
Bajracharya RM, Lal R, Kimble JM. Diurnal and seasonal CO2-C flux from soil as related to erosion phases in central Ohio. Soil Sci Soc Am J. 2000;64:286–93.
CAS  Article  Google Scholar 

76.
Liang Y, Lal R, Guo S, Liu R, Hu Y. Impacts of simulated erosion and soil amendments on greenhouse gas fluxes and maize yield in Miamian soil of central Ohio. Sci Rep. 2018;8:520.
PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Van Der Voort M, Kempenaar M, Van Driel M, Raaijmakers JM, Mendes R. Impact of soil heat on reassembly of bacterial communities in the rhizosphere microbiome and plant disease suppression. Ecol Lett. 2016;19:375–82.
PubMed  Article  PubMed Central  Google Scholar 

78.
García-Palacios P, Vandegehuchte ML, Shaw EA, Dam M, Post KH, Ramirez KS, et al. Are there links between responses of soil microbes and ecosystem functioning to elevated CO2, N deposition and warming? A global perspective. Glob Change Biol. 2015;21:1590–1600.
Article  Google Scholar 

79.
Karimi B, Terrat S, Dequiedt S, Saby NPA, Horriguel W, Lelievre M, et al. Biogeography of soil bacteria and archaea across France. Sci Adv. 2018;4:eaat1808.
PubMed  PubMed Central  Article  Google Scholar 

80.
Janssen PH. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl Environ Microb. 2006;72:1719–28.
CAS  Article  Google Scholar 

81.
Spain AM, Krumholz LR, Elshahed MS. Abundance, composition, diversity and novelty of soil Proteobacteria. ISME J. 2009;3:992–1000.
CAS  PubMed  Article  Google Scholar 

82.
Zhang C, Liu G, Xue S, Wang G. Soil bacterial community dynamics reflect changes in plant community and soil properties during the secondary succession of abandoned farmland in the Loess Plateau. Soil Biol Biochem. 2016;97:40–49.
CAS  Article  Google Scholar 

83.
Wolińska A, Kuzniar A, Zielenkiewicz U, Izak D, Szafranek-Nakonieczna A, Banach A, et al. Bacteroidetes as a sensitive biological indicator of agricultural soil usage revealed by a culture-independent approach. Appl Soil Ecol. 2017;119:128–37.
Article  Google Scholar 

84.
DeBruyn LM, Nixon LT, Fawaz MN, Johnson AM, Radosevich M. Global biogeography and quantitative seasonal dynamics of gemmatimonadetes in soil. Appl Environ Microb. 2011;77:6295–6300.
CAS  Article  Google Scholar 

85.
Bouskill NJ, Lim HC, Borglin S, Salve R, Wood TE, Silver WL, et al. Pre-exposure to drought increases the resistance of tropical forest soil bacterial communities to extended drought. ISME J. 2013;7:384–94.
CAS  PubMed  Article  Google Scholar 

86.
Naylor D, DeGraaf S, Purdom E, Coleman-Derr D. Drought and host selection influence bacterial community dynamics in the grass root microbiome. ISME J. 2017;11:2691–704.
PubMed  PubMed Central  Article  Google Scholar 

87.
Santos-Medellin C, Edwards J, Liechty Z, Nguyen B, Sundaresan V. Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. mBio. 2017;8:e00764–17.
PubMed  PubMed Central  Article  Google Scholar 

88.
Chowdhury TR, Lee JY, Bottos EM, Brislawn CJ, White RA, Bramer LM, et al. Metaphenomic responses of a native prairie soil microbiome to moisture perturbations. mSystems. 2019;4:e00061–19.
Google Scholar 

89.
Mickan BS, Abbott LK, Solaiman ZM, Mathes F, Siddique KHM, Jenkins SN. Soil disturbance and water stress interact to influence arbuscular mycorrhizal fungi, rhizosphere bacteria and potential for N and C cycling in an agricultural soil. Biol Fert Soils. 2019;55:53–66.
CAS  Article  Google Scholar 

90.
Van Horn DJ, Okie JG, Buelow HN, Gooseff MN, Barrett JE, Takacs-Vesbach CD. Soil microbial responses to increased moisture and organic resources along a salinity gradient in a polar desert. Appl Environ Microb. 2014;80:3034–43.
Article  CAS  Google Scholar 

91.
Kielak A, Pijl AS, Van Veen JA, Kowalchuk GA. Phylogenetic diversity of Acidobacteria in a former agricultural soil. ISME J. 2009;3:378–82.
CAS  PubMed  Article  Google Scholar 

92.
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.
CAS  PubMed  Article  Google Scholar 

93.
Wolińska A, Kuzniar A, Zielenkiewicz U, Banach A, Blaszczyk M. Indicators of arable soils fatigue Bacterial – families and genera: a metagenomic approach. Ecol Indic. 2018;93:490–500.
Article  Google Scholar 

94.
Yang F, Niu KC, Collins CG, Yan XB, Ji YG, Ling N. Grazing practices affect the soil microbial community composition in a Tibetan alpine meadow. Land Degrad Dev. 2019;30:49–59.
Article  Google Scholar 

95.
Vitousek PM, Menge DNL, Reed SC, Cleveland CC. Biological nitrogen fixation: rates, patterns and ecological controls in terrestrial ecosystems. Philos Trans R Soc B Biol Sci. 2013;368:1621.
Article  CAS  Google Scholar 

96.
Fan KK, Delgado-Baquerizo M, Guo XS, Wang DZ, Wu YY, Zhu M, et al. Suppressed N fixation and diazotrophs after four decades of fertilization. Microbiome. 2019;7:143.
PubMed  PubMed Central  Article  Google Scholar 

97.
Ryu MH, Zhang J, Toth T, Khokhani D, Geddes BA, Mus F, et al. Control of nitrogen fixation in bacteria that associate with cereals. Nat Microbiol. 2020;5:314–30.
CAS  PubMed  Article  Google Scholar 

98.
Banerjee S, Kirkby CA, Schmutter D, Bissett A, Kirkegaard JA, Richardson AE. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol Biochem. 2016;97:188–98.
CAS  Article  Google Scholar 

99.
Guo J, Ling N, Chen Z, Xue C, Li L, Liu L, et al. Soil fungal assemblage complexity is dependent on soil fertility and dominated by deterministic processes. N Phytol. 2020;226:232–43.
Article  Google Scholar 

100.
Qi G, Ma G, Chen S, Lin C, Zhao X. Microbial network and soil properties are changed in bacterial wilt-susceptible soil. Appl Environ Microb. 2019;85:e00162–19.
CAS  Google Scholar 

101.
Xue L, Ren H, Brodribb TJ, Wang J, Yao X, Li S. Long term effects of management practice intensification on soil microbial community structure and co-occurrence network in a non-timber plantation. For Ecol Manag. 2020;459:117805.
Article  Google Scholar 

102.
Ling N, Zhu C, Xue C, Chen H, Duan YH, Peng C. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis. Soil Biol Biochem 2016;99:137–49.
CAS  Article  Google Scholar 

103.
Deng Y, Jiang YH, Yang YF, He ZL, Luo F, Zhou JZ. Molecular ecological network analyses. BMC Bioinforma. 2012;13:113.
Article  Google Scholar 

104.
Bai YX, She WW, Miao L, Qin SG, Zhang YQ. Soil microbial interactions modulate the effect of Artemisia ordosica on herbaceous species in a desert ecosystem, northern China. Soil Biol Biochem. 2020;150:108013.
CAS  Article  Google Scholar 

105.
Szoboszlay M, Dohrmann AB, Poeplau C, Don A, Tebbe CC. Impact of land-use change and soil organic carbon quality on microbial diversity in soils across Europe. FEMS Microbiol Ecol. 2017;93:fix146.
Article  CAS  Google Scholar 

106.
Marcos MS, Bertiller MB, Olivera NL. Microbial community composition and network analyses in arid soils of the Patagonian Monte under grazing disturbance reveal an important response of the community to soil particle size. Appl Soil Ecol. 2019;138:223–32.
Article  Google Scholar 

107.
Hamamura N, Olson SH, Ward DM, Inskeep WP. Microbial population dynamics associated with crude-oil biodegradation in diverse soils. Appl Environ Microb. 2006;72:6316–24.
CAS  Article  Google Scholar 

108.
Acosta‐Martínez V, Cotton J, Gardner T, Moore‐Kucera J, Zak J, Wester D, et al. Predominant bacterial and fungal assemblages in agricultural soils during a record drought/heat wave and linkages to enzyme activities of biogeochemical cycling. Appl Soil Ecol. 2014;84:69–82.
Article  Google Scholar 

109.
Peng M, Jia H, Wang Q. The effect of land use on bacterial communities in Saline-Alkali soil. Curr Microbiol. 2017;74:325–33.
CAS  PubMed  Article  Google Scholar 

110.
Navarrete AA, Tsai SM, Mendes LW, Faust K, de Hollander M, Cassman NA, et al. Soil microbiome responses to the short-term effects of Amazonian deforestation. Mol Ecol. 2015;24:2433–48.
CAS  PubMed  Article  Google Scholar 

111.
Byers AK, Condron L, Donavan T, O’Callaghan M, Patuawa T, Waipara N, et al. Soil microbial diversity in adjacent forest systems—contrasting native, old growth kauri (Agathis australis) forest with exotic pine (Pinus radiata) plantation forest. FEMS Microbiol Ecol. 2020;96:fiaa047.
CAS  PubMed  PubMed Central  Article  Google Scholar 

112.
Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Trumbore, Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.
CAS  PubMed  Article  PubMed Central  Google Scholar 

113.
Lehmann J, Kleber M. The contentious nature of soil organic matter. Nature. 2015;528:60–68.
CAS  PubMed  Article  PubMed Central  Google Scholar 

114.
Buzzard V, Michaletz ST, Deng Y, He Z, Ning D, Shen L, et al. Continental scale structuring of forest and soil diversity via functional traits. Nat Ecol Evol. 2019;3:1298–308.
PubMed  Article  PubMed Central  Google Scholar 

115.
Wei X, Shao M, Gale W, Li L. Global pattern of soil carbon losses due to the conversion of forests to agricultural land. Sci Rep. 2014;4:4062.
PubMed  PubMed Central  Article  CAS  Google Scholar 

116.
Delgado‐Baquerizo M, Reith F, Dennis PG, Hamonts K, Powell JR, Young A, et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology. 2018;99:583–96.
PubMed  Article  PubMed Central  Google Scholar 

117.
Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.
CAS  PubMed  Article  Google Scholar 

118.
Nottingham AT, Fierer N, Turner BL, Whitaker J, Ostle NJ, McNamara NP, et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology. 2018;99:2455–66.
PubMed  PubMed Central  Article  Google Scholar 

119.
Zhou J, Deng Y, Shen L, Wen C, Yan Q, Ning D, et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat Commun. 2016;7:12083.
CAS  PubMed  PubMed Central  Article  Google Scholar 

120.
Allison SD, Martiny JBH, et al. Resistance resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA. 2008;105:11512–9.
CAS  PubMed  Article  Google Scholar 

121.
Hartmann M, Niklaus PA, Zimmermann S, Schmutz S, Kremer J, Abarenkov K, et al. Resistance and resilience of the forest soil microbiome to logging-associated compaction. ISME J. 2014;8:226–44.
CAS  PubMed  Article  Google Scholar  More

Overview
In earlier work34, epidemiological models are broadly divided into two large categories, called forecasting and mechanistic. The former models fit a specific curve to the data and then attempt to predict the dynamics of the quantity under consideration. The most well known mechanistic models are the SIR-type models. As noted by Holmadahl and Buckee34, the mechanistic models involve substantially more complicated mathematical machinary than the forecasting models, but they have the advantage that they can make predictions even when the relevant circumstances change. In our case, since our goal is to make predictions after the situation changes due to the lifting of the lockdown measures, we need to consider a mechanistic model. However, it is widely known that the main limitation of mechanistic models is the difficulty of determining the parameters specifying such models. In this direction, a methodological advance was presented by the authors35, filling an important gap in the relevant literature: it was shown in35 that from the knowledge of the most reliable data of the epidemic in a given country, namely the cumulative number of deaths, it is possible to determine suitable combinations of the constant parameters (of the original model) which specify the differential equation characterizing the death dynamics. Furthermore, a robust numerical algorithm was presented for obtaining these parameters. One of these constants, denoted by c, is particularly important for the analysis of the effect of easing the lockdown conditions, because it is proportional to the number of contacts between asymptomatic individuals that are infected by SARS-CoV-2 and susceptible ones. Specifically, as the equations presented below will indicate, this coefficient is measured in units of inverse population (where the population represents the number of individuals to which we assign no units) times inverse days. This constant reflects the probability of infection given a contact which is proportional to the viral load (i.e., the viral concentration in the respiratory-tract fluid) of expelled respiratory droplets36. Easing the lockdown will lead to an increase of the value of this constant. Thus, in order to quantify this effect we assumed that the post-lockdown situation could be described by the same model but with c multiplied by an integer number (zeta), such as (zeta =2), or 3, etc. Assuming a fixed viral load emission (i.e., no face mask or similar protective measures), this would be tantamount to doubling or tripling the number of contacts per day. To put things into perspective, it is relevant to mention here that in the relevant literature a ballpark estimate for daily contacts of an individual is about 13.437.
We first applied the above algorithm to the case of the COVID-19 epidemic in Greece. However, the novelty and the main interest of the present work consists of the extension and application of the above methodology to two subpopulations. This situation is significantly more complicated than that of2 and is described by 12 ODEs involving 18 parameters (details are discussed in the “Methods” section). Using this extended formulation, we analysed the effect of easing the lockdown measures under two distinct possible scenarios: in the first, we examined what would happen if the interactions between older persons, namely persons above 40 years of age, as well as between older and younger persons, namely those below 40, continue to be dictated by the same restrictions as those of the lockdown period. However, we assumed that the interaction among the young was progressively more free. In the second case, we analysed the effect of easing the lockdown measures in the entire population without distinguishing the older from the young. In principle, the effect on deaths in the above two scenarios could be analyzed by the extension of the rigorous results of35. However, due to the sparsity of the deaths data (especially for the younger population), this approach is practically not possible at present. Thus, we supplemented the data for deaths for the two subpopulations with data for the cumulative numbers of reported infected.
Using four sets of data, namely the number of deaths and the number of reported infected for the older and the younger population we found that the above two alternatives would result in very different outcomes: in the first case, the total number of deaths of the two sub-populations and the number of total infections would be relatively small. In the second case, these numbers would be prohibitively high. Specifically, in the case of Greece, if the lockdown was to be continued indefinitely, our analysis suggests that the total numbers of deaths and infections would finally be around 165 and 2550, respectively. These numbers would remain essentially the same even if the lockdown measures for the interaction between the young people were eased substantially, provided that the interactions of older-older and older-young would remain the same as during the lockdown period. For example, even if the parameter measuring the effect of the lockdown restrictions on the young-young interactions were increased fourfold, the number of deaths and infections would be (according to the model extrapolation) 184 and 3585, respectively. On the other hand, even if the parameters characterizing all three interactions were increased only threefold, the relevant numbers would be 48144 and 1283462. It is clear that the latter numbers are prohibitive, suggesting that a generic release of the lockdown may be catastrophic.
In our view, the explanations provided in the “Methods” section for the assumptions of our model, which show that these assumptions are typical in the standard epidemiological models, substantiate the qualitative conclusions (and notes of caution) regarding the impact of the above two different types of exit policies. This may provide a sense of how a partial restoration of regular life activities can be achieved without catastrophic consequences, while the race for pharmacological or vaccine-based interventions that will lead to an end of the current pandemic is still ongoing. Importantly, we also offer some caveats emphasizing the qualitative nature of our conclusions and possible factors that may substantially affect the actual outcome of the lifting of lockdown measures.
Model setup: single population versus two age groups
We divide the population in two subpopulations, the young (y) and the older (o). In order to explain the basic assumptions of our model we first consider a single population, and then discuss the needed modifications in our case which involves two subpopulations. Let E(t) denote the exposed (but not infectious) population. An individual in this population, after a median 4-day period (required for incubation — see e.g.38) will either become sick or will be asymptomatic; an interval of 3-10 days captures 98% of the cases. The sick (infected) and asymptomatic populations will be denoted, respectively, by I(t) and A(t). The rate at which an exposed person becomes asymptomatic is denoted by a; this means that each day aE(t) persons leave the exposed population and enter the asymptomatic population. Similarly, each day sE(t) leave the exposed population and enter the sick population. These processes, as well as the subsequent movements are depicted in the flowchart of Fig. 1.
Figure 1

Flowchart of the populations considered in the model and the rates of transformation between them. The corresponding dynamical equations are Eqs. (1)–(6).

Full size image

The asymptomatic individuals recover with a rate (r_1), i.e., each day (r_1A(t)) leave the asymptomatic population and enter the recovered population, which is denoted by R(t). The sick individuals either recover with a rate (r_2) or they become hospitalized, H(t), with a rate h. In turn, the hospitalized patients also have two possible destinations; either they recover with a rate (r_3), or they become deceased, D(t), with a rate d.
It is straightforward to write the above statements in the language of mathematics; this gives rise to the equations (1)–(5) below:

$$begin{aligned} frac{dA}{dt}= a E – r_1 A end{aligned}$$
(1)

$$begin{aligned} frac{dI}{dt}= s E – (h + r_2) I end{aligned}$$
(2)

$$begin{aligned} frac{dH}{dt}= h I – (r_3+d) H end{aligned}$$
(3)

$$begin{aligned} frac{dR}{dt}= r_1 A + r_2 I + r_3 H end{aligned}$$
(4)

$$begin{aligned} frac{dD}{dt}= d H end{aligned}$$
(5)

$$begin{aligned} frac{dE}{dt}= c left[ T – (E+I+A+H+R+D)right] left( A + b Iright) – (a+s) E end{aligned}$$
(6)

It is noted that our model is inspired by various expanded versions of the classic SIR model adapted to the particularities of COVID-19 (such as the key role of the asymptomatically infected). It is, in particular, inspired by, yet not identical with that of14. In order to complete the system of equations (1)–(6), it is necessary to describe the mechanism via which a person can become infected. For this purpose we adopt the standard assumptions made in the typical epidemiological models, such as the SIR (susceptible, infected, recovered) model: let T denote the total population and let c characterize the number of contacts per day made by an individual with the capacity to infect (c is thought of as being normalized by T). Such a person belongs to I, A or H. However, for simplicity we assume that the hospitalized population cannot infect; this assumption is based on two considerations: first, the strict protective measures taken at the hospital, and second, the fact that hospitalized patients are infectious only for part of their stay in the hospital. The latter fact is a consequence of the relevant time scales of virus shedding in comparison to the time to hospitalization and the duration of hospital stay. The asymptomatic individuals are (more) free to interact with others, whereas the (self-isolating) sick persons are not. Thus, we use c to characterize the contacts of the asymptomatic persons and b to indicate the different infectiousness (due to reduced contacts/self-isolation) of the sick in comparison to the asymptomatic individuals.
The number of people available to be infected (i.e., the susceptible population) is (T-(E+I+A+H+R+D)). Indeed, the susceptible individuals consist of the total population minus all the individuals that are going or have gone through the course of some phase of infection, namely they either bear the infection at present ((E+A+I+H)) or have died from COVID-19 (D) or are assumed to have developed immunity to COVID-19 due to recovery (R). Hence, if we call the total initial individuals T, this susceptible population is given by the expression written earlier. The rate by which each day individuals enter E is given by the product of the above expression with (c(A+bI)). At the same time, as discussed earlier, every day ((a+s)E) persons leave the exposed population. It is relevant to note here that within this simpler model, it is possible to calculate the basic reproduction number (R_0), which is a quantity of substantial value in epidemiological studies32,33. In this model, this can be found to be33:

$$begin{aligned} R_0=frac{c T}{a+s}left[ frac{a}{r_1} + frac{b s}{r_2+h} right] . end{aligned}$$
(7)

This will be useful below for the purposes of finding the change in c (under lockdown) needed in order for transmission to cross the threshold of (R_0=1) and thus to lead to growth of the epidemic. In the particular case of the data shown in Table 1, (R_0=0.4084), in accordance with the lockdown situation associated with a controlled epidemic.
It is straightforward to modify the above model so that it can describe the dynamics of the older and younger subpopulations. Each subpopulation satisfies the same set of equations as those described above, except for the last equation which is modified as follows: the people available to be infected in each subpopulation are described by the expression given above where T, E, I, A, H, R, D have the superscripts (^o) or (^y), denoting older and young, respectively; (A+bI) is replaced in both cases by (A^o+A^y+b(I^o+I^y)) where for simplicity we have assumed that the infectiousness of the older and the young is the same. We have already considered the implications of the generalisation of the above model by allowing different parameters to describe the interaction of the older and young populations; this will be discussed in the “Methods” section. In what follows, we will discuss the results of this simpler “isotropic” interaction model.
Quantitative model findings
The parameters of the model are given in the flowchart of Fig. 1. Naturally, for the two-age model considered below, there is one set of such parameters associated with the younger population and one associated with the older one. The optimization routine used for the identification of these parameters is explained in detail in the “Methods” section. The parameters resulting from this optimization for the single population model are shown in Table 1, whereas for each of the two populations are given in Table 2. Clearly, many of these parameters are larger for the older population in comparison to the young, leading to a larger number of both infections and deaths in the older than in the young population.
Table 1 Optimized model parameters for the single population model, and the variation interval of each parameter within the optimization process (for further details, see “Methods” section).
Full size table

Table 2 Optimized (isotropic) model parameters for the young and older populations, and the variation interval of each parameter within the optimization process (for further details, see “Methods” section).
Full size table

Support for the validity of our model is presented in Fig. 2, which depicts its comparison (using the above optimized parameters) with the available data. The situation corresponding to keeping the lockdown conditions indefinitely, is the one illustrated in Fig. 2. In this case, the number of deaths and cumulative infections rapidly reaches a plateau, indicating the elimination of the infection. Here, we have optimized the model on the basis of data used from Greece39 between April 3rd and May 4th. It is noted that daily updates occurred at 3pm for the country of Greece, hence it is not clear up to what time the data are collected that are included in the daily report. We have assumed that the data reflect the infections and deaths present on that particular day. This possibly shifts the starting point of our count by a few hours, but should not change the overall result trends.
We next explain the implications of the model when different scenarios of ‘exit’ from the lockdown state are implemented. The relevant results are illustrated in Figs. 3, 4 and the essential conclusions are summarized in Table 3 for the numbers of deaths and cumulative infections, respectively. First, we need to explain the meaning of the parameter (zeta) appearing in the above tables: this parameter reflects the magnitude of the easing of the lockdown restrictions. Indeed, since the main effect of the lessening of these restrictions is that the number of contacts increases, we model the effect of easing the lockdown restrictions by multiplying the parameter c with a factor that we refer to as (zeta). The complete lockdown situation corresponds to (zeta)=1; the larger the value of (zeta), the lesser the restrictions imposed on the population. By employing the above quantitative measure of easing the lockdown restrictions, we consider in detail two distinct scenarios. In the first, which corresponds to the top rows of the Figures 3 and 4, we only allow the number of contacts of “young individuals with young individuals” (corresponding to the parameter (c^{yy}) mentioned in the “Methods” section) to be multiplied by the factor (zeta). This means that the lockdown measures are eased only with respect to the interaction of young individuals with other young individuals, while the interactions of the young individuals with the older ones, as well as the interactions among older individuals remain in the lockdown state. In the second scenario, corresponding to the bottom rows of the Figures 3 and 4, the restrictions of the lockdown are simultaneously eased in both the young and the older population; in this case all contacts are increased by the factor (zeta). It is noted that while we change c by this factor, we maintain the product cb at its previous value (i.e., we concurrently transform (crightarrow zeta c) and (brightarrow b/zeta)) considering that the sick still operate under self-isolation conditions and thus do not accordingly increase their number of contacts.
Table 3 Deaths D(t) and cumulative infections C(t) in the case of increasing of the number of contacts by (zeta). The second and fourth columns refer to the case for which the lockdown measures are eased for the young population, whereas the third and fifth column refer to the one where this occurs for both the young and older populations.
Full size table

Figure 2

Evolution of the current situation of deaths D(t) (left) and cumulative infections C(t) (right) in Greece, under the case of an indefinite continuation of the lockdown conditions. In this and all the figures that follow, the blue curve corresponds to the young population, while the red curve to the older population. The data for Greece from the 3rd of April to the 4th of May 2020 are depicted by dots. For the latter, alternate colors have been used (i.e., blue dots for the older population and red for the younger for clearer visualization).

Full size image

Fig. 3 corresponds to the case where the parameter (zeta) associated with the number of contacts between susceptible and asymptomatic individuals doubles. In this case, as also shown in Table 3, the situation does not worsen in a dramatic way. In particular, the number of deaths increases by 1, whereas the cumulative infections only increase by the small number of 58. In the second scenario where the number of contacts is doubled for both the young and the older populations, we find slightly larger (but not totally catastrophic) effects: the number of deceased individuals increases by 58 and the total number of infections grows by 1550.
Figure 3

Again the deaths D(t) and the cumulative infections C(t) are given for the case where the c factor (characterizing the number of contacts) amongst young individuals is doubled, but those of the older individuals (and of the young-older interaction) are kept fixed. This is shown in the top panels. In the bottom panels, the c’s of both young and old individuals are doubled.

Full size image

The situation becomes far more dire when the number of contacts is multiplied by a factor of 3 for both the young and older populations, meaning that the lockdown restrictions are eased significantly for the entire population. As shown in Table 3 and in Fig. 4, if the c’s of the young population only are multiplied by a factor of 3, then the deaths are increased by 3 and the infections by 198 (black line in the Figure and 3rd row of the Tables). This pales by comparison to the dramatic scenario when the c’s associated with both the young and older sub-populations are multiplied by 3; in this case, the number of deaths jumps dramatically to 48144, while the number of infections is a staggering 1283462, growing by about 500 times.
Figure 4

Same as reported in Fig. 3 but now where the contacts are multiplied by factors 3, 4 and 5. Full (dashed) lines hold for the young (older) population.

Full size image

An example corroborating the above qualitative trend can also be found in Fig. 4 and in the 4th and 5th rows of Table 3. Here, for e.g. (zeta =5), even the effect of releasing solely the young population leads to very substantial increases, namely to 6044 deaths and 306219 infections although of course it is nowhere near the scenarios of releasing both young and older populations. In the second scenario, the numbers are absolutely daunting: using the parameters of Table 2 we find that the number of deaths jumps to 83274 and the number of cumulative infections to 2221296.
Figure 5

Hospitalizations when only the young population (left) or both the young and older (right) population are released. Full (dashed) lines hold for the young (older) population.

Full size image

Finally, we show the prediction of the easing measures in the hospitalizations (i.e. daily occupied beds in hospitals). This is a crucial point to assess in order that the health system does not collapse because of COVID-19 patients. Figure 5 shows these trends for the above mentioned values of (zeta). In the case of releasing solely the young population (see left panel of the Figure), it is observed that the number of hospitalizations decreases monotonically except for (zeta =5), where the hospitalization peak is 523 for the young population and 1426 for the older one (values that are affordable by Greek health system); however, if both the young and older population are released (see right panel of the Figure), there is a monotonically decreasing behaviour only for (zeta =1) and 2. For higher (zeta) we observe that the height of the peak obviously increases with (zeta), while this peak also occurs earlier when the number of contacts is increased; for instance, for (zeta =3), the hospitalization peak number of the young population is 3844 whereas this value is 37030 for the older one, numbers that are, unfortunately, unaffordable for the Greek health system. These figures grow even further to 16869 and 163648 if (zeta =5).
In light of the above results, the significance of preserving the lockdown restrictions of the sensitive groups of the older population is naturally emerging. It can be seen that in the case where the number of contacts is roughly doubled, the behavior of release of young or young and older individuals is not dramatic (although even in this case releasing only the young population is, of course, preferable). Nevertheless, a more substantial release of the young population is still not catastrophic. On the other hand, the higher rates of infection, hospitalization and proneness to death of senior individuals may bring about highly undesirable consequences, should both the young and older members of the population be allowed to significantly increase (by 3 times or more) their number of contacts. More

1.
Longhurst A, Sathyendranath S, Platt T, Caverhill C. An estimate of global primary production in the ocean from satellite radiometer data. J Plankton Res. 1995;17:1245–71.
Article  Google Scholar 
2.
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Falkowski PG, Raven JA. Aquatic photosynthesis. Princeton, NJ: Princeton University Press; 2013.

4.
Falkowski PG, Barber RT, Smetacek V. Biogeochemical controls and feedbacks on Ocean primary production. Science. 1998;281:200–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Palmer JR, Totterdell IJ. Production and export in a global ocean ecosystem model. Deep Sea Res Part Oceanogr Res Pap. 2001;48:1169–98.
CAS  Article  Google Scholar 

6.
Legendre L, Rivkin RB. Fluxes of carbon in the upper ocean: regulation by food-web control nodes. Mar Ecol Prog Ser. 2002;242:95–109.
Article  Google Scholar 

7.
Guidi L, Stemmann L, Jackson GA, Ibanez F, Claustre H, Legendre L, et al. Effects of phytoplankton community on production, size, and export of large aggregates: a world-ocean analysis. Limnol Oceanogr. 2009;54:1951–63.
Article  Google Scholar 

8.
Michaels AF, Silver MW. Primary production, sinking fluxes and the microbial food web. Deep Sea Res Part Oceanogr Res Pap. 1988;35:473–90.
Article  Google Scholar 

9.
Calbet A, Landry MR. Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr. 2004;49:51–57.
CAS  Article  Google Scholar 

10.
Jin X, Gruber N, Dunne JP, Sarmiento JL, Armstrong RA. Diagnosing the contribution of phytoplankton functional groups to the production and export of particulate organic carbon, CaCO3, and opal from global nutrient and alkalinity distributions. Glob Biogeochem Cycles. 2006;20:GB2015.
Article  CAS  Google Scholar 

11.
Richardson TL, Jackson GA. Small phytoplankton and carbon export from the surface ocean. Science. 2007;315:838–40.
CAS  PubMed  Article  Google Scholar 

12.
Uitz J, Claustre H, Griffiths FB, Ras J, Garcia N, Sandroni V. A phytoplankton class-specific primary production model applied to the Kerguelen Islands region (Southern Ocean). Deep Sea Res Part Oceanogr Res Pap. 2009;56:541–60.
CAS  Article  Google Scholar 

13.
Uitz J, Claustre H, Gentili B, Stramski D. Phytoplankton class-specific primary production in the world’s oceans: seasonal and interannual variability from satellite observations. Glob Biogeochem Cycles. 2010;24:GB3016.

14.
Poulin FJ, Franks PJS. Size-structured planktonic ecosystems: constraints, controls and assembly instructions. J Plankton Res. 2010;32:1121–30.
PubMed  PubMed Central  Article  Google Scholar 

15.
Ward BA, Dutkiewicz S, Jahn O, Follows MJ. A size-structured food-web model for the global ocean. Limnol Oceanogr. 2012;57:1877–91.
Article  Google Scholar 

16.
Stoecker DK, Hansen PJ, Caron DA, Mitra A. Mixotrophy in the marine plankton. Annu Rev Mar Sci. 2017;9:311–35.
Article  Google Scholar 

17.
Tréguer P, Bowler C, Moriceau B, Dutkiewicz S, Gehlen M, Aumont O, et al. Influence of diatom diversity on the ocean biological carbon pump. Nat Geosci. 2018;11:27.
Article  CAS  Google Scholar 

18.
Lancelot C, Hannon E, Becquevort S, Veth C, De, Baar HJW. Modeling phytoplankton blooms and carbon export production in the Southern Ocean: dominant controls by light and iron in the Atlantic sector in Austral spring 1992. Deep Sea Res Part Oceanogr Res Pap. 2000;47:1621–62.
CAS  Article  Google Scholar 

19.
Wang S, Moore JK. Incorporating Phaeocystis into a Southern Ocean ecosystem model. J Geophys Res Oceans 2011;116:C01019.

20.
Worthen DL, Arrigo KR. A coupled ocean-ecosystem model of the Ross Sea. Part 1: Interannual variability of primary production and phytoplankton community structure. In: DiTullio GR, Dunbar RB, editors. Biogeochemistry of the Ross Sea. Antarct Res Ser. 2003;78:93–105.

21.
Li WKW. Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.
CAS  Article  Google Scholar 

22.
Jardillier L, Zubkov MV, Pearman J, Scanlan DJ. Significant CO2 fixation by small prymnesiophytes in the subtropical and tropical northeast Atlantic Ocean. ISME J. 2010;4:1180–92.
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Rii YM, Duhamel S, Bidigare RR, Karl DM, Repeta DJ, Church MJ. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the South East Pacific Ocean: Picophytoplankton diversity and productivity in the S. Pacific. Limnol Oceanogr. 2016;61:806–24.
Article  Google Scholar 

24.
Musat N, Halm H, Winterholler B, Hoppe P, Peduzzi S, Hillion F, et al. A single-cell view on the ecophysiology of anaerobic phototrophic bacteria. Proc Natl Acad Sci. 2008;105:17861–6.
CAS  PubMed  Article  Google Scholar 

25.
Foster RA, Kuypers MMM, Vagner T, Paerl RW, Musat N, Zehr JP. Nitrogen fixation and transfer in open ocean diatom–cyanobacterial symbioses. ISME J. 2011;5:1484.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Olofsson M, Robertson EK, Edler L, Arneborg L, Whitehouse MJ, Ploug H. Nitrate and ammonium fluxes to diatoms and dinoflagellates at a single cell level in mixed field communities in the sea. Sci Rep. 2019;9:1424.
PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Berthelot H, Duhamel S, L’Helguen S, Maguer J-F, Wang S, Cetinić I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651–62.
CAS  PubMed  Article  Google Scholar 

28.
Ploug H, Musat N, Adam B, Moraru CL, Lavik G, Vagner T, et al. Carbon and nitrogen fluxes associated with the cyanobacterium Aphanizomenon sp. in the Baltic Sea. ISME J. 2010;4:1215–23.
CAS  PubMed  Article  Google Scholar 

29.
Olofsson M, Kourtchenko O, Zetsche E-M, Marchant HK, Whitehouse MJ, Godhe A, et al. High single-cell diversity in carbon and nitrogen assimilations by a chain-forming diatom across a century. Environ Microbiol. 2019;21:142–51.
CAS  PubMed  Article  Google Scholar 

30.
Zaoli S, Giometto A, Marañón E, Escrig S, Meibom A, Ahluwalia A, et al. Generalized size scaling of metabolic rates based on single-cell measurements with freshwater phytoplankton. Proc Natl Acad Sci. 2019;116:17323–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Frölicher TL, Sarmiento JL, Paynter DJ, Dunne JP, Krasting JP, Winton M. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J Clim. 2014;28:862–86.
Article  Google Scholar 

32.
Landschützer P, Gruber N, Haumann FA, Rödenbeck C, Bakker DCE, van Heuven S, et al. The reinvigoration of the Southern Ocean carbon sink. Science. 2015;349:1221–4.
PubMed  Article  CAS  PubMed Central  Google Scholar 

33.
Martin JH. Glacial-interglacial CO2 change: the iron hypothesis. Paleoceanography. 1990;5:1–13.
Article  Google Scholar 

34.
de Baar HJW, Jong JTM, de, Bakker DCE, Löscher BM, Veth C, Bathmann U, et al. Importance of iron for plankton blooms and carbon dioxide drawdown in the Southern Ocean. Nature. 1995;373:412.
Article  Google Scholar 

35.
de Baar HJW, Boyd PW, Coale KH, Landry MR, Tsuda A, Assmy P, et al. Synthesis of iron fertilization experiments: from the iron age in the age of enlightenment. J Geophys Res Oceans. 2005;110:C09S16.

36.
Weber LH, El-Sayed SZ. Contributions of the net, nano- and picoplankton to the phytoplankton standing crop and primary productivity in the Southern Ocean. J Plankton Res. 1987;9:973–94.
Article  Google Scholar 

37.
Froneman PW, Laubscher RK, Mcquaid CD. Size-fractionated primary production in the South Atlantic and Atlantic Sectors of the Southern Ocean. J Plankton Res. 2001;23:611–22.
CAS  Article  Google Scholar 

38.
Blain S, Quéguiner B, Armand L, Belviso S, Bombled B, Bopp L, et al. Effect of natural iron fertilization on carbon sequestration in the Southern Ocean. Nature. 2007;446:1070–4.
CAS  PubMed  Article  Google Scholar 

39.
Pollard R, Sanders R, Lucas M, Statham P. The Crozet Natural Iron Bloom and Export Experiment (CROZEX). Deep Sea Res Part II Top Stud Oceanogr. 2007;54:1905–14.
CAS  Article  Google Scholar 

40.
Korb RE, Whitehouse MJ, Atkinson A, Thorpe SE. Magnitude and maintenance of the phytoplankton bloom at South Georgia: a naturally iron-replete environment. Mar Ecol Prog Ser. 2008;368:75–91.
CAS  Article  Google Scholar 

41.
Kopczyńska EE, Fiala M, Jeandel C. Annual and interannual variability in phytoplankton at a permanent station off Kerguelen Islands, Southern Ocean. Polar Biol. 1998;20:342–51.
Article  Google Scholar 

42.
Mosseri J, Quéguiner B, Armand L, Cornet-Barthaux V. Impact of iron on silicon utilization by diatoms in the Southern Ocean: a case study of Si/N cycle decoupling in a naturally iron-enriched area. Deep Sea Res Part II Top Stud Oceanogr. 2008;55:801–19.
Article  Google Scholar 

43.
Park Y-H, Roquet F, Durand I, Fuda J-L. Large-scale circulation over and around the Northern Kerguelen Plateau. Deep Sea Res Part II Top Stud Oceanogr. 2008;55:566–81.
Article  Google Scholar 

44.
Holmes RM, Aminot A, Kérouel R, Hooker BA, Peterson BJ. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Can J Fish Aquat Sci. 1999;56:1801–8.
CAS  Article  Google Scholar 

45.
Aminot A, Kérouel R. Dosage automatique des nutriments dans les eaux marines: méthodes en flux continu. Editions Versailles, France: Quae; 2007.

46.
Ras J, Claustre H, Uitz J. Spatial variability of phytoplankton pigment distributions in the Subtropical South Pacific Ocean: comparison between in situ and predicted data. Biogeosciences. 2008;5:353–69.
CAS  Article  Google Scholar 

47.
Mackey MD, Mackey DJ, Higgins HW, Wright SW. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser. 1996;144:265–83.
CAS  Article  Google Scholar 

48.
Wright SW, van den Enden RL, Pearce I, Davidson AT, Scott FJ, Westwood KJ. Phytoplankton community structure and stocks in the Southern Ocean (30–80 E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep Sea Res Part II Top Stud Oceanogr. 2010;57:758–78.
CAS  Article  Google Scholar 

49.
van Leeuwe MA, Visser RJW, Stefels J. The pigment composition of Phaeocystis antarctica (Haptophyceae) under various conditions of light, temperature, salinity, and iron. J Phycol. 2014;50:1070–80.
PubMed  Article  CAS  PubMed Central  Google Scholar 

50.
Szymczak-Żyla M, Kowalewska G, Louda JW. The influence of microorganisms on chlorophyll a degradation in the marine environment. Limnol Oceanogr. 2008;53:851–62.
Article  Google Scholar 

51.
Strom SL. Production of pheopigments by marine protozoa: results of laboratory experiments analysed by HPLC. Deep Sea Res Part Oceanogr Res Pap. 1993;40:57–80.
CAS  Article  Google Scholar 

52.
Roca-Martí M, Puigcorbé V, Iversen MH, van der Loeff MR, Klaas C, Cheah W, et al. High particulate organic carbon export during the decline of a vast diatom bloom in the Atlantic sector of the Southern Ocean. Deep Sea Res Part II Top Stud Oceanogr. 2017;138:102–15.
Article  CAS  Google Scholar 

53.
Bower SM, Carnegie RB, Goh B, Jones SR, Lowe GJ, Mak MW. Preferential PCR amplification of parasitic protistan small subunit rDNA from metazoan tissues. J Eukaryot Microbiol. 2004;51:325–32.
CAS  PubMed  Article  Google Scholar 

54.
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–D604.
CAS  PubMed  Article  Google Scholar 

55.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. https://doi.org/https://www.biorxiv.org/content/10.1101/299537v1.article-info. 2018;299537.

57.
Not F, Simon N, Biegala IC, Vaulot D. Application of fluorescent in situ hybridization coupled with tyramide signal amplification (FISH TSA) to assess eukaryotic picoplankton composition. Aquat Micro Ecol. 2002;28:157–66.
Article  Google Scholar 

58.
Sun J, Liu D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J Plankton Res. 2003;25:1331–46.
Article  Google Scholar 

59.
Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME. Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr. 1992;37:1434–46.
CAS  Article  Google Scholar 

60.
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.

61.
Lafond A, Leblanc K, Legras J, Cornet V, Quéguiner B. The structure of diatom communities constrains biogeochemical properties in surface waters of the Southern Ocean (Kerguelen Plateau). J Mar Syst. 2020;212:103458.
Article  Google Scholar 

62.
Chisholm SW. Phytoplankton size. In: Falkowski PG, Woodhead AD, Vivirito K, editors. Primary productivity and biogeochemical cycles in the sea. US, Boston, MA: Springer; 1992. p. 213–37.

63.
Marchetti A, Cassar N. Diatom elemental and morphological changes in response to iron limitation: a brief review with potential paleoceanographic applications. Geobiology. 2009;7:419–31.
CAS  PubMed  Article  Google Scholar 

64.
Alderkamp A-C, Kulk G, Buma AGJ, Visser RJW, Van Dijken GL, Mills MM, et al. The effect of iron limitation on the photophysiology of Phaeocystis antarctica (prymnesiophyceae) and Fragilariopsis cylindrus (bacillariophyceae) under dynamic irradiance. J Phycol. 2012;48:45–59.
CAS  PubMed  Article  Google Scholar 

65.
Marañón E. Inter-specific scaling of phytoplankton production and cell size in the field. J Plankton Res. 2008;30:157–63.
Article  CAS  Google Scholar 

66.
Huete-Ortega M, Cermeño P, Calvo-Díaz A, Marañón E. Isometric size-scaling of metabolic rate and the size abundance distribution of phytoplankton. Proc R Soc B Biol Sci. 2012;279:1815–23.
Article  Google Scholar 

67.
Hogle SL, Dupont CL, Hopkinson BM, King AL, Buck KN, Roe KL, et al. Pervasive iron limitation at subsurface chlorophyll maxima of the California Current. Proc Natl Acad Sci. 2018;115:13300–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Marchetti A, Parker MS, Moccia LP, Lin EO, Arrieta AL, Ribalet F, et al. Ferritin is used for iron storage in bloom-forming marine pennate diatoms. Nature. 2009;457:467–70.
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Lampe RH, Mann EL, Cohen NR, Till CP, Thamatrakoln K, Brzezinski MA, et al. Different iron storage strategies among bloom-forming diatoms. Proc Natl Acad Sci. 2018;115:E12275–E12284.
CAS  PubMed  Article  Google Scholar 

70.
Litchman E, Klausmeier CA. Trait-based community ecology of phytoplankton. Annu Rev Ecol Evol Syst. 2008;39:615–39.
Article  Google Scholar 

71.
Luxem KE, Ellwood MJ, Strzepek RF. Intraspecific variability in Phaeocystis antarctica’s response to iron and light stress. PLOS One. 2017;12:e0179751.
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Closset I, Lasbleiz M, Leblanc K, Quéguiner B, Cavagna A-J, Elskens M, et al. Seasonal evolution of net and regenerated silica production around a natural Fe-fertilized area in the Southern Ocean estimated with Si isotopic approaches. Biogeosciences. 2014;11:5827–46.
Article  Google Scholar 

73.
Egge J, Aksnes D. Silicate as regulating nutrient in phytoplankton competition. Mar Ecol Prog Ser. 1992;83:281–9.
CAS  Article  Google Scholar 

74.
Lam PJ, Bishop JKB. High biomass, low export regimes in the Southern Ocean. Deep Sea Res Part II Top Stud Oceanogr. 2007;54:601–38.
Article  Google Scholar 

75.
Maiti K, Charette MA, Buesseler KO, Kahru M. An inverse relationship between production and export efficiency in the Southern Ocean. Geophys Res Lett. 2013;40:1557–61.
Article  Google Scholar 

76.
Le Moigne FAC, Henson SA, Cavan E, Georges C, Pabortsava K, Achterberg EP, et al. What causes the inverse relationship between primary production and export efficiency in the Southern Ocean? Geophys Res Lett. 2016;43:4457–66.
Article  CAS  Google Scholar 

77.
Christaki U, Lefèvre D, Georges C, Colombet J, Catala P, Courties C, et al. Microbial food web dynamics during spring phytoplankton blooms in the naturally iron-fertilized Kerguelen area (Southern Ocean). Biogeosciences. 2014;11:6739–53.
Article  Google Scholar 

78.
Christaki U, Gueneugues A, Liu Y, Blain S, Catala P, Colombet J, et al. Seasonal microbial food web dynamics in contrasting Southern Ocean productivity regimes. Limnol Oceanogr. 2021;66:108–22.

79.
Planchon F, Ballas D, Cavagna A-J, Bowie AR, Davies D, Trull T, et al. Carbon export in the naturally iron-fertilized Kerguelen area of the Southern Ocean based on the 234Th approach. Biogeosciences. 2015;12:3831–48.
Article  Google Scholar 

80.
Cassar N, Wright SW, Thomson PG, Trull TW, Westwood KJ, Salas Mde, et al. The relation of mixed-layer net community production to phytoplankton community composition in the Southern Ocean. Glob Biogeochem Cycles. 2015;29:446–62.
CAS  Article  Google Scholar 

81.
Sassenhagen I, Irion S, Jardillier L, Moreira D, Christaki U. Protist interactions and community structure during early autumn in the Kerguelen Region (Southern Ocean). Protist. 2020;171:125709.
CAS  PubMed  Article  Google Scholar 

82.
Henschke N, Blain S, Cherel Y, Cotte C, Espinasse B, Hunt BPV, et al. Population demographics and growth rate of Salpa thompsoni on the Kerguelen Plateau. J Marine Systems. 2021;214:103489.
Article  Google Scholar 

83.
Moline MA, Claustre H, Frazer TK, Schofield O, Vernet M. Alteration of the food web along the Antarctic Peninsula in response to a regional warming trend. Glob Change Biol. 2004;10:1973–80.
Article  Google Scholar 

84.
Iversen MH, Pakhomov EA, Hunt BPV, van der Jagt H, Wolf-Gladrow D, Klaas C. Sinkers or floaters? Contribution from salp pellets to the export flux during a large bloom event in the Southern Ocean. Deep Sea Res Part II Top Stud Oceanogr. 2017;138:116–25.
CAS  Article  Google Scholar 

85.
Irion S, Jardillier L, Sassenhagen I, Christaki U. Marked spatiotemporal variations in small phytoplankton structure in contrasted waters of the Southern Ocean (Kerguelen area). Limnol Oceanogr. 2020;65:2835–52.

86.
Le Moigne FAC, Poulton AJ, Henson SA, Daniels CJ, Fragoso GM, Mitchell E, et al. Carbon export efficiency and phytoplankton community composition in the Atlantic sector of the Arctic Ocean. J Geophys Res Oceans. 2015;120:3896–912.
Article  Google Scholar 

87.
DiTullio GR, Grebmeier JM, Arrigo KR, Lizotte MP, Robinson DH, Leventer A, et al. Rapid and early export of Phaeocystis antarctica blooms in the Ross Sea, Antarctica. Nature. 2000;404:595–8.
CAS  PubMed  Article  Google Scholar 

88.
Pauthenet E, Roquet F, Madec G, Guinet C, Hindell M, McMahon CR, et al. Seasonal meandering of the Polar Front upstream of the Kerguelen Plateau. Geophys Res Lett. 2018;45:9774–81.
Article  Google Scholar  More

1.
Locey KJ, Lennon JT. Scaling laws predict global microbial diversity. Proc Natl Acad Sci U S A. 2016;113:5970–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci U S A. 1998;95:6578–83.
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Singh JS, Gupta VK. Soil microbial biomass: a key soil driver in management of ecosystem functioning. Sci Total Environ. 2018;634:497–500.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Bardgett RD, van der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci U S A. 2007;104:11436–40.
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Ettema CH, Wardle DA. Spatial soil ecology. Trends Ecol Evol. 2002;17:177–83.
Article  Google Scholar 

8.
Terrat S, Horrigue W, Dequietd S, Saby NPA, Lelièvre M, Nowak V, et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE. 2017;12:e0186766.
PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Ladau J, Shi Y, Jing X, He J-S, Chen L, Lin X, et al. Existing climate change will lead to pronounced shifts in the diversity of soil prokaryotes. mSystems. 2018;3:e00167–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science- Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany: IPBES Secretariat; 2019.
Google Scholar 

11.
Guisan A, Broennimann O, Buri A, Cianfrani C, D’Amen M, Di Cola V, et al. Climate change impacts on mountain biodiversity. In: Lovejoy TE, Hannah L, editors. Biodiversity and climate change. Yale, USA: Yale University Press; 2019. p. 221–33.

12.
Yashiro E, Pinto-Figueroa E, Buri A, Spangenberg JE, Adatte T, Niculita-Hirzel H, et al. Local environmental factors drive divergent grassland soil bacterial communities in the western Swiss Alps. Appl Environ Microbiol. 2016;82:6303–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Karimi B, Terrat S, Dequiedt S, Saby NPA, Horrigue W, Lelièvre M, et al. Biogeography of soil bacteria and archaea across France. Sci Adv. 2018;4:eaat1808.
PubMed  PubMed Central  Article  Google Scholar 

14.
King AJ, Freeman KR, McCormick KF, Lynch RC, Lozupone C, Knight R, et al. Biogeography and habitat modelling of high-alpine bacteria. Nat Commun. 2010;1:53.
PubMed  Article  CAS  PubMed Central  Google Scholar 

15.
Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. Proc Natl Acad Sci U S A. 2006;103:626–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Trumbore SE, Czimczik CI. An uncertain future for soil carbon. Science. 2008;321:1455–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Hettelingh JP, Posch M, Slootweg J, Reinds GJ, Spranger T, Tarrason L. Critical loads and dynamic modelling to assess European areas at risk of acidification and eutrophication. In: Brimblecombe P, Hara H, Houle D, Novak M, editors. Acid rain—deposition to recovery. Dordrecht: Springer; 2007. p. 379–84.
Google Scholar 

18.
IPCC (2014). Climate Change 2014: synthesis report. Contribution of working groups I, II and III to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Core Writing Team, Pachauri RK, Meyer LA, editors. Geneva, Switzerland: IPCC. p. 151.

19.
Hagedorn F, Gavazov K, Alexander JM. Above- and belowground linkages shape responses of mountain vegetation to climate change. Science. 2019;365:1119–23.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Monteith DT, Evans CD. The United Kingdom Acid Waters Monitoring Network: a review of the first 15 years and introduction to the special issue. Environ Pollut. 2005;137:3–13.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Augustin S, Achermann B. Deposition von Luftschadstoffen in der Schweiz: Entwicklung, aktueller Stand und Bewertung. Schweizerische Z fur Forstwes. 2012;163:323–30.
Article  Google Scholar 

22.
Blaser P, Zysset M, Zimmermann S, Luster J. Soil acidification in southern Switzerland between 1987 and 1997: A case study based on the critical load concept. Environ Sci Technol. 1999;33:2383–9.
CAS  Article  Google Scholar 

23.
McGovern ST, Evans CD, Dennis P, Walmsley CA, Turner A, McDonald MA. Resilience of upland soils to long term environmental changes. Geoderma. 2013;197-198:36–42.
CAS  Article  Google Scholar 

24.
Kirk GJD, Bellamy PH, Lark RM. Changes in soil pH across England and Wales in response to decreased acid deposition. Glob Change Biol. 2010;16:3111–9.
Google Scholar 

25.
Kosonen Z, Schnyder E, Hiltbrunner E, Thimonier A, Schmitt M, Seitler E, et al. Current atmospheric nitrogen deposition still exceeds critical loads for sensitive, semi-natural ecosystems in Switzerland. Atmos Environ. 2019;211:214–25.
CAS  Article  Google Scholar 

26.
Tipping E, Davies JAC, Henrys PA, Kirk GJD, Lilly A, Dragosits U, et al. Long-term increases in soil carbon due to ecosystem fertilization by atmospheric nitrogen deposition demonstrated by regional-scale modelling and observations. Sci Rep. 2017;7:1890.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Bond-Lamberty B, Bailey VL, Chen M, Gough CM, Vargas R. Globally rising soil heterotrophic respiration over recent decades. Nature. 2018;560:80–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Walker TWN, Kaiser C, Strasser F, Herbold CW, Leblans NIW, Woebken D, et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat Clim Change. 2018;8:885–9.
CAS  Article  Google Scholar 

29.
Kirschbaum MUF. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol Biochem. 1995;27:753–60.
CAS  Article  Google Scholar 

30.
Streit K, Hagedorn F, Hiltbrunner D, Portmann M, Saurer M, Buchmann N, et al. Soil warming alters microbial substrate use in alpine soils. Glob Change Biol. 2014;20:1327–38.
Article  Google Scholar 

31.
Lettens S, Van Orshoven J, Van Wesemael B, Muys B, Perrin D. Soil organic carbon changes in landscape units of Belgium between 1960 and 2000 with reference to 1990. Glob Change Biol. 2005;11:2128–40.
Article  Google Scholar 

32.
Yang Y, Fang J, Smith P, Tang Y, Chen A, Ji C, et al. Changes in topsoil carbon stock in the Tibetan grasslands between the 1980s and 2004. Glob Change Biol. 2009;15:2723–9.
Article  Google Scholar 

33.
Yang Y, Li P, Ding J, Zhao X, Ma W, Ji C, et al. Increased topsoil carbon stock across China’s forests. Glob Change Biol. 2014;20:2687–96.
Article  Google Scholar 

34.
Smith P. Soils and climate change. Curr Opin Environ Sustain. 2012;4:539–44.
Article  Google Scholar 

35.
Davidson EA, Janssens IA. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature. 2006;440:165–73.
CAS  PubMed  Article  Google Scholar 

36.
Glenn AR, Dilworth MJ. Soil acidity and the microbial population: survival and growth of bacteria in low pH. In: Wright RJ, Baligar VC, Murrmann RP, editors. Developments in plant and soil sciences. Dordrecht: Springer; 1991. p. 567–79.
Google Scholar 

37.
Xue P-P, Carrillo Y, Pino V, Minasny B, McBratney AB. Soil properties drive microbial community structure in a large scale transect in South Eastern Australia. Sci Rep. 2018;8:11725.
PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Castro HF, Classen AT, Austin EE, Norby RJ, Schadt CW. Soil microbial community responses to multiple experimental climate change drivers. Appl Environ Microbiol. 2010;76:999–1007.
CAS  PubMed  Article  Google Scholar 

39.
Fierer N, Ladau J, Clemente JC, Leff JW, Owens SM, Pollard KS, et al. Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science. 2013;342:621–4.
CAS  PubMed  Article  Google Scholar 

40.
Evans SE, Wallenstein MD. Climate change alters ecological strategies of soil bacteria. Ecol Lett. 2014;17:155–64.
PubMed  Article  Google Scholar 

41.
Zhang X, Zhang G, Chen Q, Han X. Soil bacterial communities respond to climate changes in a temperate steppe. PLoS ONE. 2013;8:e78616.
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Guisan A, Thuiller W, Zimmermann NE. Habitat suitability and distribution models: with applications in R. Cambridge, UK: Cambridge University Press; 2017.

43.
D’Amen M, Rahbek C, Zimmermann NE, Guisan A. Spatial predictions at the community level: from current approaches to future frameworks. Biol Rev Camb Philos Soc. 2017;92:169–87.
PubMed  Article  Google Scholar 

44.
Guisan A, Rahbek C. SESAM—a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr. 2011;38:1433–44.
Article  Google Scholar 

45.
Dubuis A, Pottier J, Rion V, Pellissier L, Theurillat J-P, Guisan A. Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. Divers Distrib. 2011;17:1122–31.
Article  Google Scholar 

46.
Randin CF, Dirnböck T, Dullinger S, Zimmermann NE, Zappa M, Guisan A. Are niche-based species distribution models transferable in space? J Biogeogr. 2006;33:1689–703.
Article  Google Scholar 

47.
Buri A, Grand S, Yashiro E, Adatte T, Spangenberg JE, Pinto‐Figueroa E, et al. What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps. J Biogeogr. 2020;47:1143–53.
Article  Google Scholar 

48.
Bouët M. Climat et météorologie de la Suisse romande. Lausanne: Payot edn; 1985.
Google Scholar 

49.
Zingg B. Modélisation de la réserve hydrique des sols dans les Alpes vaudoises méridionales. Master thesis. Lausanne, Switzerland: University of Lausanne; 2015.
Google Scholar 

50.
Swisstopo. Geological map of Switzerland. 2019.

51.
Hirzel A, Guisan A. Which is the optimal sampling strategy for habitat suitability modelling. Ecol Model. 2002;157:331–41.
Article  Google Scholar 

52.
Lazarevic V, Whiteson K, Huse S, Hernandez D, Farinelli L, Østerås M, et al. Metagenomic study of the oral microbiota by Illumina high-throughput sequencing. J Microbiol Methods. 2009;79:266–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Yashiro E, Pinto-Figueroa E, Buri A, Spangenberg JE, Adatte T, Niculita-Hirzel H, et al. Meta-scale mountain grassland observatories uncover commonalities as well as specific interactions among plant and non-rhizosphere soil bacterial communities. Sci Rep. 2018;8:5758.
PubMed  PubMed Central  Article  CAS  Google Scholar 

54.
Edgar RC. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics. 2018;34:2371–5.
CAS  PubMed  Article  Google Scholar 

55.
Myers EW, Miller W. Optimal alignments in linear space. Bioinformatics. 1988;4:11–17.
CAS  Article  Google Scholar 

56.
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–8.
CAS  Article  Google Scholar 

57.
Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.
CAS  Article  Google Scholar 

58.
McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108:4516–22.
CAS  Article  Google Scholar 

63.
Dubuis A, Giovanettina S, Pellissier L, Pottier J, Vittoz P, Guisan A. Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables. J Veg Sci. 2013;24:593–606.
Article  Google Scholar 

64.
Zubler EM, Fischer AM, Liniger MA, Croci-Maspoli M, Scherrer SC, Appenzeller C. Localized climate change scenarios of mean temperature and precipitation over Switzerland. Clim Change. 2014;125:237–52.
Article  Google Scholar 

65.
Buri A. Above- and belowground biogeography: spatial modelling of a hidden system. PhD thesis. Lausanne: University of Lausanne; 2019.
Google Scholar 

66.
Guisan A, Theurillat J-P. Assessing alpine plant vulnerability to climate change: a modeling perspective. Integr Assess. 2000;1:307–20.
Article  Google Scholar 

67.
Wood SN. Generalized additive models: an introduction with R. Boca Raton, USA: Chapman and Hall/CRC; 2017.

68.
Greenwell B, Boehmke B, Cunningham J, Developers G. gbm: Generalized boosted regression models, 2.1.5. edn. 2019.

69.
Ver Hoef JM, Boveng PL. Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology. 2007;88:2766–72.
PubMed  Article  Google Scholar 

70.
Hartig F. DHARMa: residual diagnostics for hierarchical (Multi-Level/Mixed) regression models. R package, 0.2.4 edn. 2019.

71.
Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232.
Article  Google Scholar 

72.
Scherrer D, D’Amen M, Fernandes RF, Mateo RG, Guisan A. How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods Ecol Evol. 2018;9:2155–66.
Article  Google Scholar 

73.
Evans JD. Straightforward statistics for the behavioral sciences. Pacific Grove, USA: Thomson Brooks/Cole Publishing Co; 1996.

74.
Elith J, Ferrier S, Huettmann F, Leathwick J. The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. Ecol Model. 2005;186:280–9.
Article  Google Scholar 

75.
Bradie J, Leung B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J Biogeogr. 2017;44:1344–61.
Article  Google Scholar 

76.
Pacifici M, Foden WB, Visconti P, Watson JEM, Butchart SHM, Kovacs KM, et al. Assessing species vulnerability to climate change. Nat Clim Change. 2015;5:215.
Article  Google Scholar 

77.
Fierer N, Schimel JP, Holden PA. Influence of drying–rewetting frequency on soil bacterial community structure. Micro Ecol. 2003;45:63–71.
CAS  Article  Google Scholar 

78.
Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340.
Article  Google Scholar 

79.
Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.
PubMed  Article  PubMed Central  Google Scholar 

81.
Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology. 2012;93:1867–79.
PubMed  Article  PubMed Central  Google Scholar 

82.
Wiens JJ, Ackerly DD, Allen AP, Anacker BL, Buckley LB, Cornell HV, et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol Lett. 2010;13:1310–24.
PubMed  Article  PubMed Central  Google Scholar 

83.
Pearman PB, Guisan A, Broennimann O, Randin CF. Niche dynamics in space and time. Trends Ecol Evol. 2008;23:149–58.
PubMed  Article  PubMed Central  Google Scholar 

84.
Bååth E, Anderson TH. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol Biochem. 2003;35:955–63.
Article  CAS  Google Scholar 

85.
Nottingham AT, Baath E, Reischke S, Salinas N, Meir P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob Change Biol. 2019;25:827–38.
Article  Google Scholar 

86.
Li L, Xu M, Eyakub Ali M, Zhang W, Duan Y, Li D. Factors affecting soil microbial biomass and functional diversity with the application of organic amendments in three contrasting cropland soils during a field experiment. PLoS ONE. 2018;13:e0203812.
PubMed  PubMed Central  Article  CAS  Google Scholar 

87.
Eilers KG, Debenport S, Anderson S, Fierer N. Digging deeper to find unique microbial communities: the strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem. 2012;50:58–65.
CAS  Article  Google Scholar 

88.
Galloway JN. Acid deposition: perspectives in time and space. Water Air Soil Pollut. 1995;85:15–24.
CAS  Article  Google Scholar 

89.
Tian D, Niu S. A global analysis of soil acidification caused by nitrogen addition. Environ Res Lett. 2015;10:024019.
Article  CAS  Google Scholar 

90.
Falkengren-Grerup U, Brink D-Jt, Brunet J. Land use effects on soil N, P, C and pH persist over 40–80 years of forest growth on agricultural soils. For Ecol Manage. 2006;225:74–81.
Article  Google Scholar 

91.
Saby NPA, Arrouays D, Antoni V, Lemercier B, Follain S, Walter C, et al. Changes in soil organic carbon in a mountainous French region, 1990–2004. Soil Use Manage. 2008;24:254–62.
Article  Google Scholar 

92.
Cianfrani C, Buri A, Verrecchia E, Guisan A. Generalizing soil properties in geographic space: approaches used and ways forward. PLoS ONE. 2018;13:e0208823.
CAS  PubMed  PubMed Central  Article  Google Scholar 

93.
Ren B, Hu Y, Chen B, Zhang Y, Thiele J, Shi R, et al. Soil pH and plant diversity shape soil bacterial community structure in the active layer across the latitudinal gradients in continuous permafrost region of Northeastern China. Sci Rep. 2018;8:5619.
PubMed  PubMed Central  Article  CAS  Google Scholar 

94.
Lembrechts JJ, Nijs I, Lenoir J. Incorporating microclimate into species distribution models. Ecography. 2019;42:1267–79.
Article  Google Scholar 

95.
Schink B. Synergistic interactions in the microbial world. Antonie Van Leeuwenhoek. 2002;81:257–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
Crowther TW, Thomas SM, Maynard DS, Baldrian P, Covey K, Frey SD, et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc Natl Acad Sci U S A. 2015;112:7033–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Schröder B. Challenges of species distribution modeling belowground. J Plant Nutr Soil Sci. 2008;171:325–37.
Article  CAS  Google Scholar 

98.
Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci U S A. 2012;109:21390–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
Araújo MB, Anderson RP, Márcia Barbosa A, Beale CM, Dormann CF, Early R, et al. Standards for distribution models in biodiversity assessments. Sci Adv. 2019;5:eaat4858.
PubMed  PubMed Central  Article  Google Scholar 

100.
Pinto-Figueroa EA, Seddon E, Yashiro E, Buri A, Niculita-Hirzel H, van der Meer JR, et al. Archaeorhizomycetes spatial distribution in soils along wide elevational and environmental gradients reveal co-abundance patterns with other fungal saprobes and potential weathering capacities. Front Microbiol. 2019;10:656.
PubMed  PubMed Central  Article  Google Scholar 

101.
Smith AB, Godsoe W, Rodriguez-Sanchez F, Wang HH, Warren D. Niche estimation above and below the species level. Trends Ecol Evol. 2019;34:260–73.
PubMed  Article  PubMed Central  Google Scholar 

102.
Hadly EA, Spaeth PA, Li C. Niche conservatism above the species level. Proc Natl Acad Sci U S A. 2009;106 Suppl 2:19707–14.
CAS  PubMed  PubMed Central  Article  Google Scholar 

103.
Peterson AT. Ecological niche conservatism: a time-structured review of evidence. J Biogeogr. 2011;38:817–27.
Article  Google Scholar 

104.
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N, et al. is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242.
PubMed  Article  CAS  PubMed Central  Google Scholar 

105.
Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychol Bull. 1995;118:392–404.
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Guisan A, Lehmann A, Ferrier S, Austin M, Overton JMC, Aspinall R, et al. Making better biogeographical predictions of species’ distributions. J Appl Ecol. 2006;43:386–92.
Article  Google Scholar 

107.
Elith J, Graham CH. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography. 2009;32:66–77.
Article  Google Scholar 

108.
Sites JW, Marshall JC. Delimiting species: a Renaissance issue in systematic biology. Trends Ecol Evol. 2003;18:462–70.
Article  Google Scholar 

109.
Ward DM. A macrobiological perspective on microbial species. Microbe. 2006;1:269.
Google Scholar 

110.
Ward DM, Cohan FM, Bhaya D, Heidelberg JF, Kühl M, Grossman A. Genomics, environmental genomics and the issue of microbial species. Heredity. 2008;100:207–19.
CAS  PubMed  Article  PubMed Central  Google Scholar 

111.
Vandermeer J. Niche theory. Annu Rev Ecol Syst. 1972;3:107–32.
Article  Google Scholar 

112.
Koeppel A, Perry EB, Sikorski J, Krizanc D, Warner A, Ward DM, et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc Natl Acad Sci U S A. 2008;105:2504–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

113.
Song H-K, Shi Y, Yang T, Chu H, He J-S, Kim H, et al. Environmental filtering of bacterial functional diversity along an aridity gradient. Sci Rep. 2019;9:866.
PubMed  PubMed Central  Article  CAS  Google Scholar 

114.
Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421:37–42.
CAS  PubMed  Article  PubMed Central  Google Scholar 

115.
Lenoir J, Svenning J-C. Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography. 2015;38:15–28.
Article  Google Scholar 

116.
Hoffmann AA, Sgrò CM. Climate change and evolutionary adaptation. Nature. 2011;470:479–85.
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Harrison, R. D. et al. Impacts of hunting on tropical forests in Southeast Asia. Conserv. Biol. 30, 972–981 (2016).
PubMed  Article  PubMed Central  Google Scholar 

3.
Brodie, J. F. & Aslan, C. E. Halting regime shifts in floristically intact tropical forests deprived of their frugivores. Restor. Ecol. 20, 153–157 (2012).
Article  Google Scholar 

4.
Bello, C. et al. Defaunation affects carbon storage in tropical forests. Sci. Adv. 1, e1501105 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Gardner, C. J., Bicknell, J. E., Baldwin-Cantello, W., Struebig, M. J. & Davies, Z. G. Quantifying the impacts of defaunation on natural forest regeneration in a global meta-analysis. Nat. Commun. 10, 1–7 (2019).
Article  CAS  Google Scholar 

6.
Osuri, A. M. et al. Contrasting effects of defaunation on aboveground carbon storage across the global tropics. Nat. Commun. 7, 11351 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. M. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl Acad. Sci. USA 113, 892–897 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Dantas de Paula, M. et al. Defaunation impacts on seed survival and its effect on the biomass of future tropical forests. Oikos 127, 1526–1538 (2018).
Article  Google Scholar 

9.
Chanthorn, W. et al. Defaunation of large-bodied frugivores reduces carbon storage in a tropical forest of Southeast Asia. Sci. Rep. 9, 1–9 (2019).
CAS  Article  Google Scholar 

10.
Comita, L. S. et al. Testing predictions of the Janzen-Connell hypothesis: a meta-analysis of experimental evidence for distance- and density-dependent seed and seedling survival. J. Ecol. 102, 845–856 (2014).
PubMed  PubMed Central  Article  Google Scholar 

11.
Song, X., Lim, J. Y., Yang, J. & Luskin, M. S. When do Janzen–Connell effects matter? A phylogenetic meta‐analysis of conspecific negative distance and density dependence experiments. Ecol. Lett. https://doi.org/10.1111/ele.13665 (2020).
Article  PubMed  Google Scholar 

12.
Muller-Landau, H. C. Predicting the long-term effects of hunting on plant species composition and diversity in tropical forests. Biotropica 39, 372–384 (2007).
Article  Google Scholar 

13.
Asquith, N. M., Wright, S. J. & Clauss, M. J. Does mammal community composition control recruitment in neotropical forests? Evidence from Panama. Ecology 78, 941–946 (1997).
Article  Google Scholar 

14.
DeMattia, E. A., Curran, L. M. & Rathcke, B. J. Effects of small rodents and large mammals on neotropical seeds. Ecology 85, 2161–2170 (2004).
Article  Google Scholar 

15.
Paine, C. E. T., Beck, H. & Terborgh, J. How mammalian predation contributes to tropical tree community structure. Ecology 97, 3326–3336 (2016).
PubMed  Article  Google Scholar 

16.
Wirth, R., Meyer, S. T., Leal, I. R. & Tabarelli, M. Plant herbivore interactions at the forest edge. in Progress in Botany (eds. Lüttge, U., Beyschlag, W. & Murata, J.). 69, 423–448 (Springer, Berlin, Heidelberg, 2008).

17.
Paine, C. E. T. & Beck, H. Seed predation by Neotropical rain forest mammals increases diversity in seedling recruitment. Ecology 88, 3076–3087 (2007).
PubMed  Article  Google Scholar 

18.
Jia, S. et al. Global signal of top-down control of terrestrial plant communities by herbivores. Proc. Natl Acad. Sci. USA 115, 6237–6242 (2018).
CAS  PubMed  Article  Google Scholar 

19.
Wright, S. J. & Duber, H. C. Poachers and forest fragmentation alter seed dispersal, seed survival, and seedling recruitment in the palm Attalea butyraceae, with implications for tropical tree diversity. Biotropica 33, 583–595 (2001).
Article  Google Scholar 

20.
Dracxler, C. M., Pires, A. S. & Fernandez, F. A. S. Invertebrate seed predators are not all the same: Seed predation by bruchine and scolytine beetles affects palm recruitment in different ways. Biotropica 43, 8–11 (2011).
Article  Google Scholar 

21.
Sarmiento, C. et al. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. Proc. Natl Acad. Sci. 114, 11458–11463 (2017).
CAS  PubMed  Article  Google Scholar 

22.
Kluger, C. G. et al. Host generalists dominate fungal communities associated with seeds of four Neotropical pioneer species. J. Trop. Ecol. 24, 351–354 (2008).
Article  Google Scholar 

23.
Velho, N., Isvaran, K. & Datta, A. Rodent seed predation: effects on seed survival, recruitment, abundance, and dispersion of bird-dispersed tropical trees. Oecologia 169, 995–1004 (2012).
ADS  PubMed  Article  Google Scholar 

24.
Curran, L. M. & Webb, C. O. Experimental tests of the spatiotemporal scale of seed predation in mast-fruiting Dipterocarpaceae. Ecol. Monogr. 70, 129–148 (2000).
Article  Google Scholar 

25.
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).
Article  Google Scholar 

26.
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. Dyn. Popul 298, 312 (1971).
Google Scholar 

27.
Levi, T. et al. Tropical forests can maintain hyperdiversity because of enemies. PNAS 116, 581–586 (2019).
CAS  PubMed  Article  Google Scholar 

28.
Terborgh, J. Enemies maintain hyperdiverse tropical forests. Am. Nat. 179, 303–314 (2012).
PubMed  Article  Google Scholar 

29.
Nathan, R. & Casagrandi, R. A simple mechanistic model of seed dispersal, predation and plant establishment: Janzen-Connell and beyond. J. Ecol. 92, 733–746 (2004).
Article  Google Scholar 

30.
Owen-Smith, R. N. Megaherbivores: The influence of very large body size on ecology. (Cambridge University Press, 1988).

31.
Kurten, E. L. Cascading effects of contemporaneous defaunation on tropical forest communities. Biol. Conserv. 163, 22–32 (2013).
Article  Google Scholar 

32.
Mendoza, E. & Dirzo, R. Seed-size variation determines interspecific differential predation by mammals in a Neotropical rain forest. Oikos 116, 1841–1852 (2007).
Article  Google Scholar 

33.
Wright, S. J. The myriad consequences of hunting for vertebrates and plants in tropical forests. Perspect. Plant Ecol. Evol. Syst. 6, 73–86 (2003).
Article  Google Scholar 

34.
Casula, P., Wilby, A. & Thomas, M. B. Understanding biodiversity effects on prey in multi-enemy systems. Ecol. Lett. 9, 995–1004 (2006).
PubMed  Article  PubMed Central  Google Scholar 

35.
Wright, S. J. et al. Poachers alter mammal abundance, seed dispersal, and seed predation in a Neotropical forest. Conserv. Biol. 14, 227–239 (2000).
Article  Google Scholar 

36.
Beckman, N. G. & Muller-landau, H. C. Differential effects of hunting on pre-dispersal seed predation and primary and secondary seed removal of two Neotropical tree species. Biotropica 39, 328–339 (2007).
Article  Google Scholar 

37.
Harrison, R. D. et al. Consequences of defaunation for a tropical tree community. Ecol. Lett. 16, 687–694 (2013).
PubMed  Article  PubMed Central  Google Scholar 

38.
Galetti, M., Bovendorp, R. S. & Guevara, R. Defaunation of large mammals leads to an increase in seed predation in the Atlantic forests. Glob. Ecol. Conserv 3, 824–830 (2015).
Article  Google Scholar 

39.
Culot, L., Bello, C., Batista, J. L. F., do Couto, H. T. Z. & Galetti, M. Synergistic effects of seed disperser and predator loss on recruitment success and long-term consequences for carbon stocks in tropical rainforests. Sci. Rep. 7, 1–8 (2017).
CAS  Article  Google Scholar 

40.
Rosin, C. & Poulsen, J. R. Hunting-induced defaunation drives increased seed predation and decreased seedling establishment of commercially important tree species in an Afrotropical forest. Ecol. Manag. 382, 206–213 (2016).
Article  Google Scholar 

41.
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl Acad. Sci. USA 114, E6089–E6096 (2017).
CAS  PubMed  Article  Google Scholar 

42.
Terborgh, J. Using Janzen-Connell to predict the consequences of defaunation and other disturbances of tropical forests. Biol. Conserv. 163, 7–12 (2013).
Article  Google Scholar 

43.
Brodie, J. F., Helmy, O. E., Brockelman, W. Y. & Maron, J. L. Bushmeat poaching reduces the seed dispersal and population growth rate of a mammal-dispersed tree. Ecol. Appl. 19, 854–863 (2009).
PubMed  Article  PubMed Central  Google Scholar 

44.
Dylewski, L., Ortega, Y. K., Bogdziewicz, M. & Pearson, D. E. Seed size predicts global effects of small mammal seed predation on plant recruitment. Ecol. Lett. 23, 1024–1033 (2020).
PubMed  Article  PubMed Central  Google Scholar 

45.
Bodmer, R. Strategies of seed dispersal and seed predation in Amazonian ungulates. Biotropica 23, 255–261 (1991).
Article  Google Scholar 

46.
Galetti, M. et al. Defaunation affect population and diet of rodents in Neotropical rainforests. Biol. Conserv. 190, 2–7 (2015).
Article  Google Scholar 

47.
Dirzo, R., Mendoza, E. & Ortíz, P. Size-related differential seed predation in a heavily defaunated neotropical rain forest. Biotropica 39, 355–362 (2007).
Article  Google Scholar 

48.
Luskin, M. S. et al. Cross-boundary subsidy cascades from oil palm degrade distant tropical forests. Nat. Commun. 8, 1–8 (2017).
Article  CAS  Google Scholar 

49.
Vázquez-Yanes, C. & Orozco-Segovia, A. Patterns of seed longevity and germination in the tropical rainforest. Annu. Rev. Ecol. Syst. 24, 69–87 (1993).
Article  Google Scholar 

50.
Hulme, P. E. Post-dispersal seed predation and seed bank persistence. Seed Sci. Res. 8, 513–519 (1998).
ADS  Article  Google Scholar 

51.
Franco, M. & Silvertown, J. A comparative demography of plants based upon elasticities of vital rates. Ecology 85, 531–538 (2004).
Article  Google Scholar 

52.
Howe, H. F. & Miriti, M. N. When seed dispersal matters. Bioscience 54, 651–660 (2004).
Article  Google Scholar 

53.
Cannon, P. G., O’Brien, M. J., Yusah, K. M., Edwards, D. P. & Freckleton, R. P. Limited contributions of plant pathogens to density-dependent seedling mortality of mast fruiting Bornean trees. Ecol. Evol. 10, 13154–13164 (2020).
PubMed  PubMed Central  Article  Google Scholar 

54.
Lamperty, T., Zhu, K., Poulsen, J. R. & Dunham, A. E. Defaunation of large mammals alters understory vegetation and functional importance of invertebrates in an Afrotropical forest. Biol. Conserv. 241, 10829 (2020).
Article  Google Scholar 

55.
Ewers, R. M. et al. Logging cuts the functional importance of invertebrates in tropical rainforest. Nat. Commun. 6, 1–7 (2015).
Article  CAS  Google Scholar 

56.
Peguero, G., Muller-Landau, H. C., Jansen, P. A. & Wright, S. J. Cascading effects of defaunation on the coexistence of two specialized insect seed predators. J. Anim. Ecol. 86, 136–146 (2017).
PubMed  Article  Google Scholar 

57.
Marsh, C. W. & Greer, A. G. Forest land-use in Sabah, Malaysia: an introduction to danum valley. Philos. Trans. R. Soc. B Biol. Sci. 335, 331–339 (1992).
ADS  Article  Google Scholar 

58.
Dial, R., Bloodworth, B., Lee, A., Boyne, P. & Heys, J. The distribution of free space and its relation to canopy composition at six forest sites. Science 50, 312–325 (2004).
Google Scholar 

59.
Sakai, S. General flowering in lowland mixed dipterocarp forests of South-east Asia. Biol. J. Linn. Soc. 75, 233–247 (2002).
Article  Google Scholar 

60.
Blate, G. M., Peart, D. R. & Leighton, M. Post-dispersal predation on isolated seeds: a comparative study of 40 tree species in a Southeast Asian rainforest. Oikos 82, 522–538 (1998).
Article  Google Scholar 

61.
Wong, S. T. E., Servheen, C., Ambu, L. & Norhayati, A. Impacts of fruit production cycles on Malayan sun bears and bearded pigs in lowland tropical forest of Sabah, Malaysian Borneo. J. Trop. Ecol. 21, 627–639 (2005).
Article  Google Scholar 

62.
Curran, L. M. & Leighton, M. Vertebrate responses to spatiotemporal variation in seed production of mast-fruiting Dipterocarpaceae. Ecol. Monogr. 70, 101–128 (2000).
Article  Google Scholar 

63.
Corlett, R. T. Frugivory and seed dispersal by vertebrates in tropical and subtropical Asia: an update. Glob. Ecol. Conserv. 11, 1–22 (2017).
Article  Google Scholar 

64.
Fern, K. Tropical Plants Database. (2014). Available at: tropical.theferns.info. (Accessed: 4th June 2020)

65.
O’Brien, M. J., Philipson, C. D., Tay, J. & Hector, A. The influence of variable rainfall frequency on germination and early growth of shade-tolerant dipterocarp seedlings in Borneo. PLoS ONE 8, e70287 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
Colon, C. P. & Campos-Arceiz, A. The impact of gut passage by binturongs (Arctictus binturong) on seed germination. Raffles Bull. Zool. 61, 417–421 (2013).
Google Scholar 

67.
Sowa, S., Roos, E. E. & Zee, F. Anesthetic storage of recalcitrant seed: nitrous oxide prolongs longevity of lychee and longan. HortScience 26, 597–599 (1991).
CAS  Article  Google Scholar 

68.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

69.
R Core Team. R: A language and environment for statistical computing. (2018).

70.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50, 346–363 (2008).
MathSciNet  MATH  Article  Google Scholar  More