Philippa Kaur

1.
Sreekanth, P. M., Balasundaran, M., Nazeem, P. A. & Suma, T. B. Genetic diversity of nine natural Tectona grandis L.f. populations of the Western Ghats in Southern India. Conserv. Genet.13, 1409–1419. https://doi.org/10.1007/s10592-012-0383-5 (2012).
Article  Google Scholar 
2.
Zhang, Y., Zhang, X., Chen, X., Sun, W. & Li, J. Genetic diversity and structure of tea plant in Qinba area in China by three types of molecular markers. Hereditas155, 22. https://doi.org/10.1186/s41065-018-0058-4 (2018).
Article  PubMed  PubMed Central  Google Scholar 

3.
Yang, S., Xue, S., Kang, W., Qian, Z. & Yi, Z. Genetic diversity and population structure of Miscanthus lutarioriparius, an endemic plant of China. PLoS ONE14, e0211471–e0211471. https://doi.org/10.1371/journal.pone.0211471 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Abebe, T. D., Bauer, A. M. & Léon, J. Morphological diversity of Ethiopian barleys (Hordeum vulgare L.) in relation to geographic regions and altitudes. Hereditas147, 154–164. https://doi.org/10.1111/j.1601-5223.2010.02173.x (2010).
Article  PubMed  Google Scholar 

5.
Rinaldi, R. et al. The influence of a relict distribution on genetic structure and variation in the Mediterranean tree, Platanus orientalis. AoB Plants https://doi.org/10.1093/aobpla/plz002 (2019).
Article  PubMed  PubMed Central  Google Scholar 

6.
Goudarzi, F. et al. Geographic separation and genetic differentiation of populations are not coupled with niche differentiation in threatened Kaiser’s spotted newt (Neurergus kaiseri). Sci. Rep.9, 6239. https://doi.org/10.1038/s41598-019-41886-8 (2019).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

7.
Tóth, E. G., Tremblay, F., Housset, J. M., Bergeron, Y. & Carcaillet, C. Geographic isolation and climatic variability contribute to genetic differentiation in fragmented populations of the long-lived subalpine conifer Pinus cembra L. in the western Alps. BMC Evol. Biol.19, 190. https://doi.org/10.1186/s12862-019-1510-4 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv.5, eaay9969. https://doi.org/10.1126/sciadv.aay9969 (2019).
Article  PubMed  PubMed Central  ADS  Google Scholar 

9.
Hamilton, J. A., Royauté, R., Wright, J. W., Hodgskiss, P. & Ledig, F. T. Genetic conservation and management of the California endemic, Torrey pine (Pinus torreyana Parry): Implications of genetic rescue in a genetically depauperate species. Ecol. Evol.7, 7370–7381. https://doi.org/10.1002/ece3.3306 (2017).
Article  PubMed  PubMed Central  Google Scholar 

10.
Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Wiley, New York, 2009).
Google Scholar 

11.
Cohen, J. I., Williams, J. T., Plucknett, D. L. & Shands, H. Ex situ conservation of plant genetic resources: global development and environmental concerns. Science253, 866–872. https://doi.org/10.1126/science.253.5022.866 (1991).
CAS  Article  PubMed  ADS  Google Scholar 

12.
Li, M., Zhao, Z., Miao, X. & Zhou, J. Genetic diversity and population structure of Siberian apricot (Prunus sibirica L.) in China. Int. J. Mol. Sci.15, 377–400. https://doi.org/10.3390/ijms15010377 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Bao, W. et al. Genetic diversity and population structure of Prunus mira (Koehne) from the Tibet plateau in China and recommended conservation strategies. PLoS ONE12, e0188685–e0188685. https://doi.org/10.1371/journal.pone.0188685 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Zhang, X. Vegetation Regionalization Map of China (Geological Publishing House, Beijing, 2007).
Google Scholar 

15.
Huang, D., Wang, J. & Li, D. Biological characteristics and planting technology of Acer ginnala Maxim. Mod. Agric. Sci.16, 112–113 (2009).
Google Scholar 

16.
Tian, X., Jin, Q., Li, D., Wei, Z. & Xu, T. Pollen morphology of Aceraceae and its systematic implication. Acta Bot. Yunnanica23, 457–465 (2001).
Google Scholar 

17.
Zhou, Y. et al. Inhibiting effects of 3 antioxidants on contamination and browning of tissue culture of Acer ginnala Maxim. Acta Agric. Shanghai23, 5–7 (2007).
Google Scholar 

18.
Li, H. Y., Song, J. Y., Dong, J. & Zhan, Y. G. Establishment of callus regeneration system for Acer ginnala maxim and determination of gallic acid in callus. Chin. Bull. Bot.25, 212–219 (2008).
CAS  Google Scholar 

19.
Wang, R. B., Wang, C. Q., Liu, X. L. & Li, L. H. Advances in the research of chemical constituents and medicine and edible function of Acer ginnala. J. Anhui Agric. Sci.39, 5387–5388+5517 (2011).

20.
Xie, Y. F., Li, Q., Zou, H. & Yuan, H. L. Analysis of polyphenols from the leaves of Acer ginnala Maxim by reversed-phase high performance liquid chromatography. J. Anal. Sci.27, 443–446 (2011).
CAS  Article  Google Scholar 

21.
Dong, J., Zhan, Y. & Ren, J. Kinetics in suspension culture of Acer ginnala. Sci. Silvae Sin.48, 18–23 (2012).
CAS  Google Scholar 

22.
Park, K. H. et al. Antioxidative and anti-inflammatory activities of galloyl derivatives and antidiabetic activities of Acer ginnala. Evid. Based Complement Altern. Med.2017, 6945912. https://doi.org/10.1155/2017/6945912 (2017).
Article  Google Scholar 

23.
Ma, Z. H., Zhang, M. S., Ma, C. E. & Hao, Y. H. Key points of cultivation technology of special economic forest of Acer ginnala. Spec. Econ. Anim. Plant5, 22 (2005).

24.
Wang, D., Pang, C. H., Gao, Y. H., Hao, X. J. & Wang, Y. L. Phenotypic diversity of Acer ginnala (Aceraceae) populations at different altitude. Acta Bot. Yunnanica32, 117–125 (2010).
CAS  Article  Google Scholar 

25.
Yan, N., Wang, D., Gao, Y. H., Hao, X. J. & Wang, Y. L. Genetic diversity of Acer ginnala populations at different elevation in Qiliyu based on ISSR markers. Sci. Silvae Sin.46, 50–56 (2010).
Google Scholar 

26.
Chiang, T. Y. et al. Evolution of SSR diversity from wild types to U.S. advanced cultivars in the Andean and Mesoamerican domestications of common bean (Phaseolus vulgaris). PLoS ONE https://doi.org/10.1371/journal.pone.0211342 (2019).
Article  PubMed  PubMed Central  Google Scholar 

27.
Zane, L., Bargelloni, L. & Patarnello, T. Strategies for microsatellite isolation: a review. Mol. Ecol.11, 1–16. https://doi.org/10.1046/j.0962-1083.2001.01418.x (2002).
CAS  Article  PubMed  Google Scholar 

28.
Geng, Q. et al. Understanding population structure and historical demography of Litsea auriculata (Lauraceae), an endangered species in east China. Sci. Rep.7, 17343. https://doi.org/10.1038/s41598-017-16917-x (2017).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

29.
Abdul-Muneer, P. M. Application of microsatellite markers in conservation genetics and fisheries management: recent advances in population structure analysis and conservation strategies. Genet. Res. Int.2014, 691759. https://doi.org/10.1155/2014/691759 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Li, G. & Quiros, C. F. Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor. Appl. Genet.103, 455–461. https://doi.org/10.1007/s001220100570 (2001).
CAS  Article  Google Scholar 

31.
Ferriol, M., Picó, B. & Nuez, F. Genetic diversity of a germplasm collection of Cucurbita pepo using SRAP and AFLP markers. Theor. Appl. Genet.107, 271–282. https://doi.org/10.1007/s00122-003-1242-z (2003).
CAS  Article  PubMed  Google Scholar 

32.
Budak, H. et al. Molecular characterization of Buffalograss germplasm using sequence-related amplified polymorphism markers. Theor. Appl. Genet.108, 328–334. https://doi.org/10.1007/s00122-003-1428-4 (2004).
CAS  Article  PubMed  ADS  Google Scholar 

33.
Masmoudi, M. B. et al. Contrasted levels of genetic diversity in a benthic Mediterranean octocoral: consequences of different demographic histories?. Ecol. Evol.6, 8665–8678. https://doi.org/10.1002/ece3.2490 (2016).
Article  PubMed  PubMed Central  Google Scholar 

34.
Li, X. et al. De novo transcriptome assembly and population genetic analyses for an endangered Chinese endemic Acer miaotaiense (Aceraceae). Genes9, 378 (2018).
Article  Google Scholar 

35.
Goudet, J. FSTAT (version 1.2): a computer program to calculate F-statistics. J. Hered.86, 485–486 (1995).
Article  Google Scholar 

36.
Gomory, D., Szczecińska, M., Sramko, G., Wołosz, K. & Sawicki, J. Genetic diversity and population structure of the rare and endangered plant species Pulsatilla patens (L.) Mill in East Central Europe. PLoS ONE https://doi.org/10.1371/journal.pone.0151730 (2016).
Article  Google Scholar 

37.
Lowrey, B. et al. Characterizing population and individual migration patterns among native and restored bighorn sheep (Ovis canadensis). Ecol. Evol.9, 8829–8839. https://doi.org/10.1002/ece3.5435 (2019).
Article  PubMed  PubMed Central  Google Scholar 

38.
Wang, S. H., Bao, L., Wang, T. M., Wang, H. F. & Ge, J. P. Contrasting genetic patterns between two coexisting Eleutherococcus species in northern China. Ecol. Evol.6, 3311–3324. https://doi.org/10.1002/ece3.2118 (2016).
Article  PubMed  PubMed Central  Google Scholar 

39.
Matsui, K. Pollination ecology of four Acer species in Japan with special reference to bee pollinators. Plant Spec. Biol.6, 117–120. https://doi.org/10.1111/j.1442-1984.1991.tb00218.x (1991).
Article  Google Scholar 

40.
Rosado, A., Vera-Vélez, R. & Cota-Sánchez, J. H. Floral morphology and reproductive biology in selected maple (Acer L.) species (Sapindaceae). Braz. J. Bot.41, 361–374. https://doi.org/10.1007/s40415-018-0452-1 (2018).
Article  Google Scholar 

41.
Lönn, M. & Prentice, H. C. Gene diversity and demographic turnover in central and peripheral populations of the perennial herb Gypsophila fastigiata. Oikos99, 489–498. https://doi.org/10.1034/j.1600-0706.2002.11907.x (2002).
Article  Google Scholar 

42.
Kearns, C. A., Inouye, D. W. & Waser, N. M. Endangered mutualisms: the conservation of plant-pollinator interactions. Annu. Rev. Ecol. Syst.29, 83–112. https://doi.org/10.1146/annurev.ecolsys.29.1.83 (1998).
Article  Google Scholar 

43.
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science313, 351–354. https://doi.org/10.1126/science.1127863 (2006).
CAS  Article  ADS  Google Scholar 

44.
Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol.25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).
Article  Google Scholar 

45.
Booy, G., Hendriks, R. J. J., Smulders, M. J. M., Van Groenendael, J. M. & Vosman, B. Genetic diversity and the survival of populations. Plant Biol.2, 379–395. https://doi.org/10.1055/s-2000-5958 (2000).
Article  Google Scholar 

46.
Nosil, P., Funk, D. J. & Ortiz-Barrientos, D. Divergent selection and heterogeneous genomic divergence. Mol. Ecol.18, 375–402. https://doi.org/10.1111/j.1365-294X.2008.03946.x (2009).
Article  PubMed  Google Scholar 

47.
Shih, K. M., Chang, C. T., Chung, J. D., Chiang, Y. C. & Hwang, S. Y. Adaptive genetic divergence despite significant isolation-by-distance in populations of Taiwan Cow-Tail Fir (Keteleeria davidiana var. formosana). Front. Plant Sci. https://doi.org/10.3389/fpls.2018.00092 (2018).
Article  PubMed  PubMed Central  Google Scholar 

48.
Lu, Z., Wang, Y., Peng, Y., Korpelainen, H. & Li, C. Genetic diversity of Populus cathayana Rehd populations in southwestern china revealed by ISSR markers. Plant Sci.170, 407–412. https://doi.org/10.1016/j.plantsci.2005.09.009 (2006).
CAS  Article  Google Scholar 

49.
Liu, C. et al. Genetic structure and hierarchical population divergence history of Acer mono var. mono in South and Northeast China. PLoS ONE9, e87187. https://doi.org/10.1371/journal.pone.0087187 (2014).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

50.
Tian, H. Z. et al. Genetic diversity in the endangered terrestrial orchid Cypripedium japonicum in East Asia: insights into population history and implications for conservation. Sci. Rep.8, 6467. https://doi.org/10.1038/s41598-018-24912-z (2018).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

51.
Rasmussen, I. R. & Brødsgaard, B. Gene flow inferred from seed dispersal and pollinator behaviour compared to DNA analysis of restriction site variation in a patchy population of Lotus corniculatus L. Oecologia89, 277–283. https://doi.org/10.1007/BF00317228 (1992).
CAS  Article  PubMed  ADS  Google Scholar 

52.
Šmídová, A., Münzbergová, Z. & Plačková, I. Genetic diversity of a relict plant species, Ligularia sibirica (L.) Cass. (Asteraceae). Flora206, 151–157. https://doi.org/10.1016/j.flora.2010.03.003 (2011).
Article  Google Scholar 

53.
Ilves, A., Lanno, K., Sammul, M. & Tali, K. Genetic variability, population size and reproduction potential in Ligularia sibirica (L.) populations in Estonia. Conserv. Genet.14, 661–669. https://doi.org/10.1007/s10592-013-0459-x (2013).
Article  Google Scholar 

54.
Tian, Z. & Jiang, D. Revisiting last glacial maximum climate over China and East Asian monsoon using PMIP3 simulations. Paleogeogr. Paleoclimatol. Paleoecol.453, 115–126. https://doi.org/10.1016/j.palaeo.2016.04.020 (2016).
Article  ADS  Google Scholar 

55.
Manel, S., Poncet, B. N., Legendre, P., Gugerli, F. & Holderegger, R. Common factors drive adaptive genetic variation at different spatial scales in Arabis alpina. Mol. Ecol.19, 3824–3835. https://doi.org/10.1111/j.1365-294X.2010.04716.x (2010).
Article  PubMed  Google Scholar 

56.
Manel, S. et al. Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol. Ecol.21, 3729–3738. https://doi.org/10.1111/j.1365-294X.2012.05656.x (2012).
Article  PubMed  PubMed Central  Google Scholar 

57.
Bothwell, H. et al. Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach. Conserv. Genet.14, 467–481. https://doi.org/10.1007/s10592-012-0411-5 (2013).
Article  Google Scholar 

58.
Fang, J. Y. et al. Divergent selection and local adaptation in disjunct populations of an endangered conifer, Keteleeria davidiana var. formosana (Pinaceae). PLoS ONE. https://doi.org/10.1371/journal.pone.0070162 (2013).
Article  PubMed  PubMed Central  Google Scholar 

59.
Hsieh, Y. C. et al. Historical connectivity, contemporary isolation and local adaptation in a widespread but discontinuously distributed species endemic to Taiwan, Rhododendron oldhamii (Ericaceae). Heredity111, 147–156. https://doi.org/10.1038/hdy.2013.31 (2013).
Article  PubMed  PubMed Central  Google Scholar 

60.
Huang, C. L. et al. Genetic relationships and ecological divergence in Salix species and populations in Taiwan. Tree Genet. Genomes11, 39. https://doi.org/10.1007/s11295-015-0862-1 (2015).
Article  Google Scholar 

61.
Li, Y. L., Xue, D. X., Zhang, B. D. & Liu, J. X. Population genomic signatures of genetic structure and environmental selection in the Catadromous Roughskin Sculpin Trachidermus fasciatus. Genome Biol. Evol.11, 1751–1764. https://doi.org/10.1093/gbe/evz118 (2019).
Article  PubMed  PubMed Central  Google Scholar 

62.
Xue, X. X., Li, W. H. & Liu, L. Y. The northward shift of Weihe river and the uplift of Qinling Mountains. J. Northwest Univ. (Nat. Sci. Ed.)32, 451–454 (2002).
Google Scholar 

63.
Zhang, Y. Q., Yang, N. & Ma, Y. S. Neotectonics in the southern part of the Taihang uplift, North China. J. Geomech.9, 313–329 (2003).
Google Scholar 

64.
Xue, X. X., Li, H. H., Li, Y. X. & Liu, H. J. The new data of the uplifting of Qinling Mountains since the Middle Pleistocene. Quat. Sci.24, 82–87 (2004).
Google Scholar 

65.
Zhang, T.-C., Comes, H. P. & Sun, H. Chloroplast phylogeography of Terminalia franchetii (Combretaceae) from the eastern Sino-Himalayan region and its correlation with historical river capture events. Mol. Phylogenet. Evol.60, 1–12. https://doi.org/10.1016/j.ympev.2011.04.009 (2011).
CAS  Article  PubMed  Google Scholar 

66.
Ye, J. W., Zhang, Z. K., Wang, H. F., Bao, L. & Ge, J. P. Phylogeography of Schisandra chinensis (Magnoliaceae) reveal multiple refugia with ample gene flow in Northeast China. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.00199 (2019).
Article  PubMed  PubMed Central  Google Scholar 

67.
Doyle, J. J., Doyle, J. L., Doyle, J. A. & Doyle, F. J. A rapid DNA isolation procedure for small quantities of fresh leaf material. Phytochem. Bull.19, 11–15 (1987).
MATH  Google Scholar 

68.
Russell, D. W. & Sambrook, J. Molecular Cloning: A Laboratory Manual 3rd edn. (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 2001).
Google Scholar 

69.
Kikuchi, S. & Shibata, M. Permanent genetic resources: development of polymorphic microsatellite markers in Acer mono Maxim. Mol. Ecol. Resour.8, 339–341. https://doi.org/10.1111/j.1471-8286.2007.01948.x (2008).
CAS  Article  PubMed  Google Scholar 

70.
Terui, H., Lian, C. L., Saito, Y. & Ide, Y. Development of microsatellite markers in Acer capillipes. Mol. Ecol. Notes6, 77–79. https://doi.org/10.1111/j.1471-8286.2005.01144.x (2006).
CAS  Article  Google Scholar 

71.
Liu, X. H. Genetic diversity and relationship of Acer L.germplasm resources detected by SRAP markers. College of Horticulture and Plant Protection, Yangzhou University. The Master Degree of Agricultural Science (2009).

72.
Peakall, R. & Smouse, P. E. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes6, 288–295. https://doi.org/10.1111/j.1471-8286.2005.01155.x (2006).
Article  Google Scholar 

73.
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics163, 1177–1191 (2003).
PubMed  PubMed Central  Google Scholar 

74.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol.14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
CAS  Article  PubMed  Google Scholar 

75.
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour.4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).
Article  Google Scholar 

76.
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol.25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).
Article  Google Scholar 

78.
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography37, 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x (2014).
Article  Google Scholar 

79.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan (2019).

80.
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci.14, 927–930. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x (2003).
Article  Google Scholar 

81.
Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics89, 583–590 (1978).
CAS  PubMed  PubMed Central  Google Scholar 

82.
Wang, I. J. Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution67, 3403–3411. https://doi.org/10.1111/evo.12134 (2013).
Article  PubMed  Google Scholar 

83.
Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol.5, 384–387. https://doi.org/10.1111/2041-210X.12158 (2014).
Article  Google Scholar  More

European and Mediterranean Plant Protection Organization (EPPO)-Euphresco, Paris, France
Baldissera Giovani & Nico Horn

Austrian Agency for Health and Food Safety (AGES), Institute for Sustainable Plant Production, Vienna, Austria
Sylvia Blümel

Food Department, Ministry of Agriculture and Forestry of Finland, Helsinki, Finland
Ralf Lopian

Better Border Biosecurity (B3), Plant and Food Research, Christchurch, New Zealand
David Teulon

North American Plant Protection Organization (NAPPO), Raleigh, NC, USA
Stephanie Bloem

Comite Regional de Sanidad Vegetal del Cono Sur (COSAVE), Dirección de Protección Vegetal, del Servicio Nacional y Sanidad Vegetal y Semillas, Asuncion, Paraguay
Cristina Galeano Martínez

Comunidad Andina (CAN), Secretaría General de la Comunidad Andina, Lima, Peru
Camilo Beltrán Montoya

Organismo Internacional Regional de Sanidad Agropecuaria (OIRSA), San Salvador, El Salvador
Carlos Ramon Urias Morales

Asia and Pacific Plant Protection Commission (APPPC), Bangkok, Thailand
Sridhar Dharmapuri

Pacific Plant Protection Organization (PPPO), Pacific Community Land Resources Division, Suva, Fiji
Visoni Timote

Near East Plant Protection Organization (NEPPO), Rabat, Morocco
Mekki Chouibani

African-Union Interafrican Phytosanitary Council (IAPSC), Yaoundé, Cameroon
Jean Gérard Mezui M’Ella

Ministry of Primary Industries (MPI), Wellington, New Zealand
Veronica Herrera & Aurélie Castinel

Department of Agriculture, Water and the Environment (DAWE), Canberra, Australian Capital Territory, Australia
Con Goletsos, Carina Moeller & Ian Naumann

European Food Safety Authority (EFSA), Parma, Italy
Giuseppe Stancanelli, Stef Bronzwaer & Sara Tramontini

Canadian Food Inspection Agency (CFIA), Ottawa, Ontario, Canada
Philip MacDonald & Loren Matheson

French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Plant Health Laboratory, Angers, France
Géraldine Anthoine

Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke, Belgium
Kris De Jonghe

Netherlands Food and Consumer Product Safety Authority (NVWA), Wageningen, the Netherlands
Martijn Schenk

Julius Kühn Institute (JKI), Braunschweig, Germany
Silke Steinmöller

National Institute for Agricultural and Food Research and Technology (INIA), Madrid, Spain
Elena Rodriguez

National Institute for Agriculture and Veterinary Research (INIAV), Oeiras, Portugal
Maria Leonor Cruz

Plant Biosecurity Research Initiative (PBRI), Hort Innovation, Melbourne, Victoria, Australia
Jo Luck

Plant Health Australia (PHA), Deakin, Canberra, Australian Capital Territory, Australia
Greg Fraser

International Plant Protection Convention (IPPC), Food and Agriculture Organization of the United Nations, Rome, Italy
Sarah Brunel, Mirko Montuori, Craig Fedchock & Jingyuan Xia

Department for Environment, Food & Rural Affairs (DEFRA), London, UK
Elspeth Steel & Helen Grace Pennington

Centre for Agriculture and Bioscience International (CABI), Nairobi, Kenya
Roger Day

French National Institute for Agricultural Research (INRA), INRA-Montpellier-CBGP, Montferrier-sur-Lez, France
Jean Pierre Rossi

B.G. wrote the manuscript. S.B., R.L., D.T., S.B., C.G.M., C.B.M., C.R.U.M., S.D., V.T., N.H., M.C., J.G.M.M., V.H., A.C., C.G., C.M., I.N., G.S., S.B., S.T., P.M.D., L.M., G.A., K.D.J., M.S., S.S., E.R., M.L.C., J.L., G.F., S.B., M.M., C.F., E.S., H.G.P., R.D., J.P.R. and J.X. contributed to the manuscript. More

1.
Birkeland, C. & Grosenbaugh, D. Ecological interactions between tropical coastal ecosystems. in UNEP Regional Seas Reports and Studies, Vol. 73 (PNUMA, 1985).
2.
Moberg, F. & Rönnbäck, P. Ecosystem services of the tropical seascape: interactions, substitutions and restoration. Ocean Coast. Manag.46, 27–46 (2003).
Google Scholar 

3.
Gladstone, W. Conservation and management of tropical coastal ecosystems. In Ecological Connectivity Among Tropical Coastal Ecosystems (ed. Nagelkerken, I.) 565–605 (Springer, Dordrecht, 2009). https://doi.org/10.1007/978-90-481-2406-0_16.
Google Scholar 

4.
Berkström, C. et al. Exploring ‘knowns’ and ‘unknowns’ in tropical seascape connectivity with insights from East African coral reefs. Estuar. Coast. Shelf Sci.107, 1–21 (2012).
ADS  Google Scholar 

5.
Ogden, J. C. The influence of adjacent systems on the structure and function of coral reefs. In Proceedings of the 6th International Coral Reef Symposium, Vol. 1, 123–129 (1988).

6.
Nagelkerken, I. et al. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuar. Coast. Shelf Sci.51, 31–44 (2000).
ADS  Google Scholar 

7.
Grober-Dunsmore, R., Pittman, S. J., Caldow, C., Kendall, M. S. & Frazer, T. K. A landscape ecology approach for the study of ecological connectivity across tropical marine seascapes. In Ecological Connectivity Among Tropical Coastal Ecosystems (ed. Nagelkerken, I.) 493–530 (Springer, Dordrecht, 2009). https://doi.org/10.1007/978-90-481-2406-0_14.
Google Scholar 

8.
Hemminga, M. A. & Duarte, C. M. Seagrass ecology (Cambridge University Press, Cambridge, 2000). https://doi.org/10.1017/CBO9780511525551.
Google Scholar 

9.
Short, F., Carruthers, T., Dennison, W. & Waycott, M. Global seagrass distribution and diversity: a bioregional model. J. Exp. Mar. Biol. Ecol.350, 3–20 (2007).
Google Scholar 

10.
Knowles, L. L. & Bell, S. S. The influence of habitat structure in faunal-habitat associations in a Tampa Bay seagrass system Florida. Bull. Mar. Sci.62, 781–794 (1998).
Google Scholar 

11.
Connolly, R. M. & Hindell, J. S. Review of nekton patterns and ecological processes in seagrass landscapes. Estuar. Coast. Shelf Sci.68, 433–444 (2006).
ADS  Google Scholar 

12.
Horinouchi, M. Review of the effects of within-patch scale structural complexity on seagrass fishes. J. Exp. Mar. Biol. Ecol.350, 111–129 (2007).
Google Scholar 

13.
Gacia, E., Duarte, C. M. & Middelburg, J. J. Carbon and nutrient deposition in a Mediterranean seagrass (Posidonia oceanica) meadow. Limnol. Oceanogr.47, 23–32 (2002).
ADS  CAS  Google Scholar 

14.
Hendriks, I. E., Sintes, T., Bouma, T. J. & Duarte, C. M. Experimental assessment and modeling evaluation of the effects of the seagrass Posidonia oceanica on flow and particle trapping. Mar. Ecol. Prog. Ser.356, 163–173 (2008).
ADS  Google Scholar 

15.
Beck, M. W. et al. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates: a better understanding of the habitats that serve as nurseries for marine species and the factors that create site-specific variability in nursery quality will improve conservation and management of these areas. AIBS Bull.51, 633–641 (2001).
Google Scholar 

16.
Heck, K. L. Jr., Hays, G. & Orth, R. J. Critical evaluation of the nursery role hypothesis for seagrass meadows. Mar. Ecol. Prog. Ser.253, 123–136 (2003).
ADS  Google Scholar 

17.
Boström, C., Jackson, E. L. & Simenstad, C. A. Seagrass landscapes and their effects on associated fauna: a review. Estuar. Coast. Shelf Sci.68, 383–403 (2006).
ADS  Google Scholar 

18.
Unsworth, R. K. F. & Cullen, L. C. Recognising the necessity for Indo-Pacific seagrass conservation. Conserv. Lett.3, 63–73 (2010).
Google Scholar 

19.
Leopardas, V., Uy, W. & Nakaoka, M. Benthic macrofaunal assemblages in multispecific seagrass meadows of the southern Philippines: variation among vegetation dominated by different seagrass species. J. Exp. Mar. Biol. Ecol.457, 71–80 (2014).
Google Scholar 

20.
Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. PNAS106, 12377–12381 (2009).
ADS  CAS  PubMed  Google Scholar 

21.
Short, F. T. & Wyllie-Echeverria, S. Natural and human-induced disturbance of seagrasses. Environ. Conserv.23, 17–27 (1996).
Google Scholar 

22.
Short, F. T. et al. Extinction risk assessment of the world’s seagrass species. Biol. Conserv.144, 1961–1971 (2011).
Google Scholar 

23.
Duarte, C. M. The future of seagrass meadows. Environ. Conserv.29, 192–206 (2002).
Google Scholar 

24.
Hastings, K., Hesp, P. & Kendrick, G. A. Seagrass loss associated with boat moorings at Rottnest Island, Western Australia. Ocean Coast. Manag.26, 225–246 (1995).
Google Scholar 

25.
Orth, R. J., Luckenbach, M. L., Marion, S. R., Moore, K. A. & Wilcox, D. J. Seagrass recovery in the Delmarva Coastal Bays, USA. Aquat. Bot.84, 26–36 (2006).
Google Scholar 

26.
Ruiz, J. M. & Romero, J. Effects of in situ experimental shading on the Mediterranean seagrass Posidonia oceanica. Mar. Ecol. Prog. Ser.215, 107–120 (2001).
ADS  Google Scholar 

27.
Frost, M. T., Rowden, A. A. & Attrill, M. J. Effect of habitat fragmentation on the macroinvertebrate infaunal communities associated with the seagrass Zostera marina L. Aquat. Conserv. Mar. Freshw. Ecosyst.9, 255–263 (1999).
Google Scholar 

28.
Hovel, K. A. Habitat fragmentation in marine landscapes: relative effects of habitat cover and configuration on juvenile crab survival in California and North Carolina seagrass beds. Biol. Conserv.110, 401–412 (2003).
Google Scholar 

29.
Hovel, K. A. & Lipcius, R. N. Effects of seagrass habitat fragmentation on juvenile blue crab survival and abundance. J. Exp. Mar. Biol. Ecol.271, 75–98 (2002).
Google Scholar 

30.
Thomas, C. D. & Mallorie, H. C. Rarity, species richness and conservation: butterflies of the Atlas Mountains in Morocco. Biol. Conserv.33, 95–117 (1985).
Google Scholar 

31.
Fahrig, L. Effects of habitat fragmentation on biodiversity. Ann. Rev. Ecol. Evol. Syst.34, 487–515 (2003).
Google Scholar 

32.
Fischer, J. & Lindenmayer, D. B. Landscape modification and habitat fragmentation: a synthesis. Glob. Ecol. Biogeogr.16, 265–280 (2007).
Google Scholar 

33.
Link, J. Does food web theory work for marine ecosystems?. Mar. Ecol. Prog. Ser.230, 1–9 (2002).
ADS  Google Scholar 

34.
Peterson, B. J., Thompson, K. R., Cowan, J. H. Jr. & Heck, K. L. Jr. Comparison of predation pressure in temperate and subtropical seagrass habitats based on chronographic tethering. Mar. Ecol. Prog. Ser.224, 77–85 (2001).
ADS  Google Scholar 

35.
Sweatman, J. L., Layman, C. A. & Fourqurean, J. W. Habitat fragmentation has some impacts on aspects of ecosystem functioning in a sub-tropical seagrass bed. Mar. Environ. Res.126, 95–108 (2017).
CAS  PubMed  Google Scholar 

36.
Williams, J. A. et al. Seagrass fragmentation impacts recruitment dynamics of estuarine-dependent fish. J. Exp. Mar. Biol. Ecol.479, 97–105 (2016).
Google Scholar 

37.
Bell, S. S., Brooks, R. A., Robbins, B. D., Fonseca, M. S. & Hall, M. O. Faunal response to fragmentation in seagrass habitats: implications for seagrass conservation. Biol. Conserv.100, 115–123 (2001).
Google Scholar 

38.
McCloskey, R. M. & Unsworth, R. K. F. Decreasing seagrass density negatively influences associated fauna. PeerJ3, e1053 (2015).
PubMed  PubMed Central  Google Scholar 

39.
Ubertini, M. et al. Spatial variability of benthic-pelagic coupling in an estuary ecosystem: consequences for microphytobenthos resuspension phenomenon. PLoS ONE7, e44155 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

40.
Welsh, D. T. Nitrogen fixation in seagrass meadows: regulation, plant–bacteria interactions and significance to primary productivity. Ecol. Lett.3, 58–71 (2000).
Google Scholar 

41.
Alongi, D. M. The role of bacteria in nutrient recycling in tropical mangrove and other coastal benthic ecosystems. Hydrobiologia285, 19–32 (1994).
CAS  Google Scholar 

42.
Harrison, P. G. Detrital processing in seagrass systems: a review of factors affecting decay rates, remineralization and detritivory. Aquat. Bot.35, 263–288 (1989).
Google Scholar 

43.
Mateo, M. A. & Romero, J. Detritus dynamics in the seagrass Posidonia oceanica: elements for an ecosystem carbon and nutrient budget. Mar. Ecol. Prog. Ser.151, 43–53 (1997).
ADS  CAS  Google Scholar 

44.
Barrón, C., Apostolaki, E. T. & Duarte, C. M. Dissolved organic carbon fluxes by seagrass meadows and macroalgal beds. Front. Mar. Sci.1, 42 (2014).
Google Scholar 

45.
Wetzel, R. G. & Penhale, P. A. Transport of carbon and excretion of dissolved organic carbon by leaves and roots/rhizomes in seagrasses and their epiphytes. Aquat. Bot.6, 149–158 (1979).
CAS  Google Scholar 

46.
Martin, B. C. et al. Low light availability alters root exudation and reduces putative beneficial microorganisms in seagrass roots. Front. Microbiol.8, 2667 (2018).
PubMed  PubMed Central  Google Scholar 

47.
Danovaro, R. Detritus-Bacteria-Meiofauna interactions in a seagrass bed (Posidonia oceanica) of the NW Mediterranean. Mar. Biol.127, 1–13 (1996).
CAS  Google Scholar 

48.
Lohrer, A. M., Thrush, S. F. & Gibbs, M. M. Bioturbators enhance ecosystem function through complex biogeochemical interactions. Nature431, 1092–1095 (2004).
ADS  CAS  PubMed  Google Scholar 

49.
Rosenberg, R. Marine benthic faunal successional stages and related sedimentary activity. Sci. Marina65, 107–119 (2001).
Google Scholar 

50.
Austen, M. C. et al. Biodiversity links above and below the marine sediment–water interface that may influence community stability. Biodivers. Conserv.11, 113–136 (2002).
Google Scholar 

51.
Fanjul, E., Bazterrica, M. C., Escapa, M., Grela, M. A. & Iribarne, O. Impact of crab bioturbation on benthic flux and nitrogen dynamics of Southwest Atlantic intertidal marshes and mudflats. Estuar. Coast. Shelf Sci.92, 629–638 (2011).
ADS  CAS  Google Scholar 

52.
Forster, S. & Graf, G. Impact of irrigation on oxygen flux into the sediment: intermittent pumping by Callianassa subterranea and “piston-pumping” by Lanice conchilega. Mar. Biol.123, 335–346 (1995).
Google Scholar 

53.
Snelgrove, P. V. R. The biodiversity of macrofaunal organisms in marine sediments. Biodivers. Conserv.7, 1123–1132 (1998).
Google Scholar 

54.
Hyndes, G. A. & Lavery, P. S. Does transported seagrass provide an important trophic link in unvegetated, nearshore areas?. Estuar. Coast. Shelf Sci.63, 633–643 (2005).
ADS  CAS  Google Scholar 

55.
Jones, D. A., Ghamrawy, M. & Wahbeh, M. I. Littoral and shallow subtidal environments. In Red Sea (eds Edwards, A. J. & Head, S. M.) 169–193 (Pergamon Press, London, 1987). https://doi.org/10.1016/B978-0-08-028873-4.50014-1.
Google Scholar 

56.
Ruiz-Compean, P. et al. Baseline evaluation of sediment contamination in the shallow coastal areas of Saudi Arabian Red Sea. Mar. Pollut. Bull.123, 205–218 (2017).
CAS  PubMed  Google Scholar 

57.
Bologna, P. A. X. & Heck, K. L. Impact of habitat edges on density and secondary production of seagrass-associated fauna. Estuaries25, 1033–1044 (2002).
Google Scholar 

58.
Calleja, M. L., Al-Otaibi, N. & Morán, X. A. G. Dissolved organic carbon contribution to oxygen respiration in the central Red Sea. Sci. Rep.9, 1–12 (2019).
CAS  Google Scholar 

59.
Stedmon, C. A., Markager, S. & Bro, R. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Mar. Chem.82, 239–254 (2003).
CAS  Google Scholar 

60.
Coble, P. G. Marine optical biogeochemistry: the chemistry of ocean color. Chem. Rev.107, 402–418 (2007).
ADS  CAS  PubMed  Google Scholar 

61.
Gasol, J. M. & Morán, X. A. G. Flow cytometric determination of microbial abundances and its use to obtain indices of community structure and relative activity. In Hydrocarbon and Lipid Microbiology Protocols: Single-Cell and Single-Molecule Methods (eds McGenity, T. J. et al.) 159–1870 (Springer, Berlin, 2015). https://doi.org/10.1007/8623_2015_139.
Google Scholar 

62.
Silva, L. et al. Low abundances but high growth rates of coastal heterotrophic bacteria in the Red Sea. Front. Microbiol.9, 3244 (2019).
PubMed  PubMed Central  Google Scholar 

63.
Leray, M. & Knowlton, N. DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. PNAS112, 2076–2081 (2015).
ADS  CAS  PubMed  Google Scholar 

64.
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucl. Acids Res.41, e1 (2013).
CAS  PubMed  Google Scholar 

65.
Oksanen, J. et al. Vegan: community ecology package. R package version 2.5-2, (2018).

66.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).

67.
Lê, S., Josse, J. & Husson, F. FactoMineR: a package for multivariate analysis. J. Stat. Softw.25, 1–18 (2008).
Google Scholar 

68.
Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw.22, 1–19 (2007).
Google Scholar 

69.
Chen, H. VennDiagram: Generate High-Resolution Venn and Euler Plots (2018).

70.
Clarke, K. R. & Gorley, R. N. PRIMER v7: User Manual/Tutorial, PRIMER-E: Plymouth (2015).

71.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucl. Acids Res.35, 7188–7196 (2007).
CAS  PubMed  Google Scholar 

73.
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

74.
Keeley, N., Wood, S. A. & Pochon, X. Development and preliminary validation of a multi-trophic metabarcoding biotic index for monitoring benthic organic enrichment. Ecol. Indic.85, 1044–1057 (2018).
CAS  Google Scholar 

75.
Lobelle, D., Kenyon, E. J., Cook, K. J. & Bull, J. C. Local competition and metapopulation processes drive long-term seagrass-epiphyte population dynamics. PLoS ONE8, e57072 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

76.
Ávila, E., Yáñez, B. & Vazquez-Maldonado, L. E. Influence of habitat structure and environmental regime on spatial distribution patterns of macroinvertebrate assemblages associated with seagrass beds in a southern Gulf of Mexico coastal lagoon. Mar. Biol. Res.11, 755–764 (2015).
Google Scholar 

77.
Barnes, R. S. K. & Hendy, I. W. Seagrass-associated macrobenthic functional diversity and functional structure along an estuarine gradient. Estuar. Coast. Shelf Sci.164, 233–243 (2015).
Google Scholar 

78.
York, P. H., Hyndes, G. A., Bishop, M. J. & Barnes, R. S. K. Faunal assemblages of seagrass ecosystems. In Seagrasses of Australia: Structure, Ecology and Conservation (eds Larkum, A. W. D. et al.) 541–588 (Springer, Berlin, 2018). https://doi.org/10.1007/978-3-319-71354-0_17.
Google Scholar 

79.
Magni, P., Como, S., Kamijo, A. & Montani, S. Effects of Zostera marina on the patterns of spatial distribution of sediments and macrozoobenthos in the boreal lagoon of Furen (Hokkaido, Japan). Mar. Environ. Res.131, 90–102 (2017).
CAS  PubMed  Google Scholar 

80.
Thomsen, M. S. et al. Secondary foundation species enhance biodiversity. Nat. Ecol. Evol.2, 634 (2018).
PubMed  Google Scholar 

81.
Attrill, M. J., Strong, J. A. & Rowden, A. A. Are macroinvertebrate communities influenced by seagrass structural complexity?. Ecography23, 114–121 (2000).
Google Scholar 

82.
Lee, S. Y., Fong, C. W. & Wu, R. S. S. The effects of seagrass (Zostera japonica) canopy structure on associated fauna: a study using artificial seagrass units and sampling of natural beds. J. Exp. Mar. Biol. Ecol.259, 23–50 (2001).
PubMed  Google Scholar 

83.
Nakamura, Y. & Sano, M. Comparison of invertebrate abundance in a seagrass bed and adjacent coral and sand areas at Amitori Bay, Iriomote Island, Japan. Fish. Sci.71, 543–550 (2005).
CAS  Google Scholar 

84.
Barrio Froján, C. R. S. et al. The importance of bare marine sedimentary habitats for maintaining high polychaete diversity and the implications for the design of marine protected areas. Aquat. Conserv. Mar. Freshw. Ecosyst.19, 748–757 (2009).
Google Scholar 

85.
Barnes, R. S. K. & Barnes, M. K. S. Spatial uniformity of biodiversity is inevitable if the available species are distributed independently of each other. Mar. Ecol. Prog. Ser.516, 263–266 (2014).
ADS  Google Scholar 

86.
Webster, P. J., Rowden, A. A. & Attrill, M. J. Effect of shoot density on the infaunal macro-invertebrate community within a Zostera marina seagrass bed. Estuar. Coast. Shelf Sci.47, 351–357 (1998).
ADS  Google Scholar 

87.
Bowden, D. A., Rowden, A. A. & Attrill, M. J. Effect of patch size and in-patch location on the infaunal macroinvertebrate assemblages of Zostera marina seagrass beds. J. Exp. Mar. Biol. Ecol.259, 133–154 (2001).
PubMed  Google Scholar 

88.
Turner, S. J. et al. Seagrass patches and landscapes: the influence of wind-wave dynamics and hierarchical arrangements of spatial structure on macrofaunal seagrass communities. Estuaries22, 1016–1032 (1999).
Google Scholar 

89.
Tanner, J. E. Edge effects on fauna in fragmented seagrass meadows. Austral Ecol.30, 210–218 (2005).
Google Scholar 

90.
Włodarska-Kowalczuk, M., Jankowska, E., Kotwicki, L. & Balazy, P. Evidence of season-dependency in vegetation effects on macrofauna in temperate seagrass meadows (Baltic Sea). PLoS ONE9, e100788 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

91.
Calleja, M. L., Barrón, C., Hale, J. A., Frazer, T. K. & Duarte, C. M. Light regulation of benthic sulfate reduction rates mediated by seagrass (Thalassia testudinum) metabolism. Estuar. Coasts J ERF29, 1255–1264 (2006).
CAS  Google Scholar 

92.
Barnes, R. S. K. & Barnes, M. K. S. Shore height and differentials between macrobenthic assemblages in vegetated and unvegetated areas of an intertidal sandflat. Estuar. Coast. Shelf Sci.106, 112–120 (2012).
ADS  Google Scholar 

93.
Agawin, N. S. R., Duarte, C. M., Fortes, M. D., Uri, J. S. & Vermaat, J. E. Temporal changes in the abundance, leaf growth and photosynthesis of three co-occurring Philippine seagrasses. J. Exp. Mar. Biol. Ecol.260, 217–239 (2001).
PubMed  Google Scholar 

94.
Pereg, L. L., Lipkin, Y. & Sar, N. Different niches of the Halophila stipulacea seagrass bed harbor distinct populations of nitrogen fixing bacteria. Mar. Biol.119, 327–333 (1994).
CAS  Google Scholar 

95.
Holmer, M., Duarte, C., Boschker, H. & Barrón, C. Carbon cycling and bacterial carbon sources in pristine and impacted Mediterranean seagrass sediments. Aquat. Microb. Ecol.36, 227–237 (2004).
Google Scholar 

96.
Barberá-Cebrián, C., Sánchez-Jerez, P. & Ramos-Esplá, A. Fragmented seagrass habitats on the Mediterranean coast, and distribution and abundance of mysid assemblages. Mar. Biol.141, 405–413 (2002).
Google Scholar 

97.
Ringold, P. Burrowing, root mat density, and the distribution of fiddler crabs in the eastern United States. J. Exp. Mar. Biol. Ecol.36, 11–21 (1979).
Google Scholar 

98.
Ricart, A. M. et al. Variability of sedimentary organic carbon in patchy seagrass landscapes. Mar. Pollut. Bull.100, 476–482 (2015).
CAS  PubMed  Google Scholar 

99.
Samper-Villarreal, J., Lovelock, C. E., Saunders, M. I., Roelfsema, C. & Mumby, P. J. Organic carbon in seagrass sediments is influenced by seagrass canopy complexity, turbidity, wave height, and water depth. Limnol. Oceanogr.61, 938–952 (2016).
ADS  Google Scholar 

100.
Serra, T., Oldham, C. & Colomer, J. Local hydrodynamics at edges of marine canopies under oscillatory flows. PLoS ONE13, e0201737 (2018).
PubMed  PubMed Central  Google Scholar 

101.
Choat, J. H. & Kingett, P. D. The influence of fish predation on the abundance cycles of an algal turf invertebrate fauna. Oecologia54, 88–95 (1982).
ADS  CAS  PubMed  Google Scholar 

102.
Nakamura, Y., Horinouchi, M., Nakai, T. & Sano, M. Food habits of fishes in a seagrass bed on a fringing coral reef at Iriomote Island, southern Japan. Ichthyol. Res.50, 0015–0022 (2003).
Google Scholar 

103.
Eklöf, J. S., de la Torre Castro, M., Adelsköld, L., Jiddawi, N. S. & Kautsky, N. Differences in macrofaunal and seagrass assemblages in seagrass beds with and without seaweed farms. Estuar. Coast. Shelf Sci.63, 385–396 (2005).
ADS  Google Scholar 

104.
Díaz-Cárdenas, C., Patel, B. K. C. & Baena, S. Tistlia consotensisgen. nov., sp. an aerobic, chemoheterotrophic, free-living, nitrogen-fixing alphaproteobacterium, isolated from a Colombian saline spring. Int. J. Syst. Evol. Microbiol.60, 1437–1443 (2010).
PubMed  Google Scholar 

105.
Sun, F. et al. Seagrass (Zostera marina) colonization promotes the accumulation of diazotrophic bacteria and alters the relative abundances of specific bacterial lineages involved in benthic carbon and sulfur cycling. Appl. Environ. Microbiol.81, 6901–6914 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

106.
Brown, S. M. & Jenkins, B. D. Profiling gene expression to distinguish the likely active diazotrophs from a sea of genetic potential in marine sediments. Environ. Microbiol.16, 3128–3142 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

107.
Santos, R., Lirman, D. & Pittman, S. Long-term spatial dynamics in vegetated seascapes: fragmentation and habitat loss in a human-impacted subtropical lagoon. Mar. Ecol.37(1), 200–214. https://doi.org/10.1111/maec.12259 (2015).
ADS  Article  Google Scholar 

108.
Irlandi, E. & Crawford, M. Habitats linkages: the effect of intertidal saltmarshes and adjacent habitats on abundance, movement and growth of an estuarine fish. Oecologia110, 222–230 (1997).
ADS  CAS  PubMed  Google Scholar 

109.
Boström, C., Pittman, S. J., Simenstad, C. & Kneib, R. T. Seascape ecology of coastal biogenic habitats: advances, gaps, and challenges. Mar. Ecol. Prog. Ser.427, 191–218 (2011).
ADS  Google Scholar 

110.
Mumby, P. J. Connectivity of reef fish between mangroves and coral reefs: algorithms for the design of marine reserves at seascape scales. Biol. Conserv.128, 215–222 (2006).
Google Scholar 

111.
Haila, Y. A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecol. Appl.12, 321–334 (2002).
Google Scholar 

112.
Barnes, R. S. K. Distribution patterns of macrobenthic biodiversity in the intertidal seagrass beds of an estuarine system, and their conservation significance. Biodivers. Conserv.22, 357–372 (2013).
Google Scholar 

113.
Barnes, R. S. K. & Hamylton, S. On the very edge: faunal and functional responses to the interface between benthic seagrass and unvegetated sand assemblages. Mar. Ecol. Prog. Ser.553, 33–48 (2016).
ADS  Google Scholar  More

1.
Börjesson, G., Menichetti, L., Kirchmann, H. & Kätterer, T. Soil microbial community structure affected by 53 years of nitrogen fertilisation and different organic amendments. Biol. Fertil. Soils48, 245–257 (2012).
Article  Google Scholar 
2.
Cui, J. et al. Carbon and nitrogen recycling from microbial necromass to cope with C: N stoichiometric imbalance by priming. Soil Biol. Biochem.142, 107720 (2020).
CAS  Article  Google Scholar 

3.
Xiao, D. et al. Microbial biomass, metabolic functional diversity, and activity are affected differently by tillage disturbance and maize planting in a typical karst calcareous soil. J. Soil. Sediment.19, 809–821 (2019).
CAS  Article  Google Scholar 

4.
Dangi, S., Gao, S., Duan, Y. H. & Wang, D. Soil microbial community structure affected by biochar and fertilizer sources. Appl. Soil Ecol.150, 103452 (2020).
Article  Google Scholar 

5.
Geisseler, D. & Scow, K. M. Long-term effects of mineral fertilizers on soil microorganisms: a review. Soil Biol. Biochem.75, 54–63 (2014).
CAS  Article  Google Scholar 

6.
Trivedi, P. et al. Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ. Microbiol.19, 3070–3086 (2017).
CAS  Article  Google Scholar 

7.
Jia, X., Li, X. D., Zhao, Y. H., Wang, L. & Zhang, C. Y. Soil microbial community structure in the rhizosphere of Robinia pseudoacacia L. seedlings exposed to elevated air temperature and cadmium-contaminated soils for 4 years. Sci. Total Environ.650, 2355–2363 (2019).
ADS  CAS  Article  Google Scholar 

8.
Zhong, W. et al. The effects of mineral fertilizer and organic manure on soil microbial community and diversity. Plant Soil326, 511–522 (2010).
CAS  Article  Google Scholar 

9.
Hartmann, M., Frey, B., Mayer, J., Maeder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J.9, 1177–1194 (2015).
Article  Google Scholar 

10.
Francioli, D. et al. Mineral versus organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol.14, 1446 (2016).
Google Scholar 

11.
Wang, Y. et al. Long-term no-tillage and organic input management enhanced the diversity and stability of soil microbial community. Sci. Total Environ.609, 341–347 (2017).
ADS  CAS  Article  Google Scholar 

12.
Treonis, A. M. et al. Effects of organic amendment and tillage on soil microorganisms and microfauna. Appl. Soil Ecol.46, 103–110 (2010).
Article  Google Scholar 

13.
Forge, T. A., Hogue, E. J., Neilsen, G. & Neilsen, D. Organic mulches alter nematode communities, root growth and fluxes of phosphorus in the root zone of apple. Appl. Soil Ecol.39, 15–22 (2008).
Article  Google Scholar 

14.
Ahn, J. et al. Dynamics of bacterial communities in rice field soils as affected by different long-term fertilization practices. J. Microbiol.54, 724–731 (2016).
CAS  Article  Google Scholar 

15.
Zhu, X. C. et al. Soil microbial community and activity are affected by integrated agricultural practices in China. Eur. J. Soil Sci.69, 924–935 (2018).
Article  Google Scholar 

16.
Yang, X. Y., Ren, W. D., Sun, B. H. & Zhang, S. L. Effects of contrasting soil management regimes on total and labile soil organic carbon fractions in a loess soil in China. Geoderma177–178, 49–56 (2012).
ADS  Article  Google Scholar 

17.
Chen, Z. D., Ti, F. S. & Chen, F. Soil aggregates response to tillage and residue management in a double paddy rice soil of the southern China. Nutr. Cycl. Agroecosyst.109, 103–114 (2017).
Article  Google Scholar 

18.
Wei, X., Zhu, Z., Wei, L., Wu, J. & Ge, T. Biogeochemical cycles of key elements in the paddy-rice rhizosphere: microbial mechanisms and coupling processes. Rhizosphere10, 100145 (2019).
Article  Google Scholar 

19.
Tang, H. M. et al. Effects of different soil tillage systems on soil carbon management index under double-cropping rice field in southern China. Agron. J.111, 440–446 (2019).
CAS  Article  Google Scholar 

20.
Tang, H. M. et al. Organic manure managements increases soil microbial community structure and diversity in double-cropping paddy field of southern China. Agric. Ecosyst. Environ. https://doi.org/10.1101/2020.04.08.031609 (2020).
Article  Google Scholar 

21.
Zhao, J. et al. Pyrosequencing reveals contrasting soil bacterial diversity and community structure of two main winter wheat cropping systems in China. Microb. Ecol.67, 443–453 (2014).
Article  Google Scholar 

22.
Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass by fumigation–extraction-an automated procedure. Soil Biol. Biochem.20, 1167–1169 (1990).
Article  Google Scholar 

23.
Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. PNAS110, 6548–6553 (2013).
ADS  CAS  Article  Google Scholar 

24.
Wang, Z. T., Liu, L., Chen, Q., Wen, X. X. & Liao, Y. C. Conservation tillage increases soil bacterial diversity in the dryland of northern China. Agron. Sustain. Dev.36, 28 (2016).
Article  Google Scholar 

25.
Bazzicalupo, A. L., Bálint, M. & Schmitt, I. Comparison of ITS1 and ITS2 rDNA in 454 sequencing of hyper diverse fungal communities. Fungal Ecol.6, 102–109 (2013).
Article  Google Scholar 

26.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics26, 2460–2461 (2010).
CAS  Article  Google Scholar 

27.
SAS. SAS Software of the SAS System for Windows (SAS Institute Inc, Cary, 2008).
Google Scholar 

28.
Blanco-Canqui, H., Ferguson, R. B., Shapiro, C. A., Drijber, R. A. & Walters, D. T. Does inorganic nitrogen fertilization improve soil aggregation? Insights from two long-term tillage experiments. J. Environ. Qual.43, 995–1003 (2014).
Article  Google Scholar 

29.
Neumann, D., Heuer, A., Hemkemeyer, M., Martens, R. & Tebbe, C. C. Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiol. Ecol.86, 71–84 (2013).
CAS  Article  Google Scholar 

30.
Jenkins, S. N. et al. Taxon specific responses of soil bacteria to the addition of low level C inputs. Soil Biol. Biochem.42, 1624–1631 (2010).
CAS  Article  Google Scholar 

31.
Li, H. et al. Soil bacterial communities of different natural forest types in northeast China. Plant Soil383, 203–216 (2014).
CAS  Article  Google Scholar 

32.
Pascault, N. et al. Stimulation of different functional groups of bacteria by various plant residues as a driver of soil priming effect. Ecosystems16, 810–822 (2013).
CAS  Article  Google Scholar 

33.
He, J. Z., Zheng, Y., Chen, C. R., He, Y. Q. & Zhang, L. M. Microbial composition and diversity of an upland red soil under long-term fertilization treatments as revealed by culture-dependent and culture-independent approaches. J. Soil. Sediment.8, 349–358 (2008).
CAS  Article  Google Scholar 

34.
Paungfoo-lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep.5, 8678 (2015).
CAS  Article  Google Scholar 

35.
Huang, X. M. et al. Changes of soil microbial biomass carbon and community composition through mixing nitrogen-fixing species with Eucalyptus urophylla in subtropical China. Soil Biol. Biochem.73, 42–48 (2014).
CAS  Article  Google Scholar 

36.
Desantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microb.72, 5069–5072 (2006).
CAS  Article  Google Scholar 

37.
Iovieno, P., Alfani, A. & Bååth, E. Soil microbial community structure and biomass as affected by Pinus pinea plantation in two mediterranean areas. Appl. Soil Ecol.45, 56–63 (2010).
Article  Google Scholar  More

1.
Bourke, A. F. G. & Heinze, J. The ecology of communal breeding: The case of multiple-queen Leptothoracine ants. Philos. Trans. R. Soc. Lond. B Biol. Sci.345, 359–372 (1994).
ADS  Google Scholar 
2.
Bourke, A. F. G. Principles of Social Evolution. Oxford Series in Ecology and Evolution (2011).

3.
Cockburn, A. Evolution of helping behavior in cooperatively breeding birds. Annu. Rev. Ecol. Evol. Syst.29, 141–177 (1998).
Google Scholar 

4.
Jennions, M. Cooperative breeding in mammals. Trends Ecol. Evol.9, 89–93 (1994).
CAS  PubMed  Google Scholar 

5.
Lukas, D. & Clutton-Brock, T. Life histories and the evolution of cooperative breeding in mammals. Proc. R. Soc. B279, 4065–4070 (2012).
PubMed  Google Scholar 

6.
Purcell, J. Geographic patterns in the distribution of social systems in terrestrial arthropods. Biol. Rev.86, 475–491 (2011).
PubMed  Google Scholar 

7.
Wong, M. & Balshine, S. The evolution of cooperative breeding in the African cichlid fish, Neolamprologus pulcher. Biol. Rev.86, 511–530 (2011).
PubMed  Google Scholar 

8.
Dugatkin, L. Animal cooperation among unrelated individuals. Naturwissenschaften89, 533–541 (2002).
ADS  CAS  PubMed  Google Scholar 

9.
Emlen, S. The evolution of helping. An ecological constraints model. Am. Nat.119, 29–39 (1982).
Google Scholar 

10.
Nichols, H. J. et al. Food availability shapes patterns of helping effort in a cooperative mongoose. Anim. Behav.83, 1377–1385 (2012).
Google Scholar 

11.
Riehl, C. & Strong, M. J. Stable social relationships between unrelated females increase individual fitness in a cooperative bird. Proc. R. Soc. B285, 20180130 (2018).
PubMed  Google Scholar 

12.
Sharp, S. P., English, S. & Clutton-Brock, T. H. Maternal investment during pregnancy in wild meerkats. Evol. Ecol.27, 1033–1044 (2012).
Google Scholar 

13.
Taborsky, M. Broodcare helpers in the cichlid fish Lamprologus brichardi: Their costs and benefits. Anim. Behav.32, 1236–1252 (1984).
Google Scholar 

14.
Hamilton, W. D. The genetical evolution of social behaviour. J. Theor. Biol.7, 1–52 (1964).
CAS  PubMed  Google Scholar 

15.
Bshary, R. Cooperation between unrelated individuals—a game theoretic approach. In Animal Behaviour: Evolution and Mechanisms (ed. Kappeler, P.) 213–240 (Springer, Berlin, 2010).
Google Scholar 

16.
Dugatkin, L. A. & Mesterton-Gibbons, M. Cooperation among unrelated individuals: Reciprocal altruism, by-product mutualism and group selection in fishes. Biosystems37, 19–30 (1996).
CAS  PubMed  Google Scholar 

17.
Keller, L. Queen Number and Sociality in Insects (Oxford University Press, Oxford, 1993).
Google Scholar 

18.
Matsuura, K., Fujimoto, M., Goka, K. & Nishida, T. Cooperative colony foundation by termite female pairs: Altruism for survivorship in incipient colonies. Anim. Behav.64, 167–173 (2002).
Google Scholar 

19.
Mesterton-Gibbons, M. & Dugatkin, L. A. Cooperation among unrelated individuals: Evolutionary factors. Q. Rev. Biol.67, 267–281 (1992).
Google Scholar 

20.
Bernasconi, G. & Strassmann, J. E. Cooperation among unrelated individuals: The ant foundress case. Trends Ecol. Evol.14, 477–482 (1999).
CAS  PubMed  Google Scholar 

21.
Itô, Y. Behaviour and Social Evolution of Wasps (Oxford University Press, Oxford, 1993).
Google Scholar 

22.
Packer, L. Multiple-foundress associations in sweat bees. In Queen Number and Sociality in Insects (ed. Keller, L.) 215–233 (Oxford University Press, Oxford, 1993).
Google Scholar 

23.
Schwarz, M. P., Bull, N. J. & Hogendoorn, K. Evolution of sociality in the allodapine bees: A review of sex allocation, ecology and evolution. Insectes Soc.45, 349–368 (1998).
Google Scholar 

24.
Shellman-Reeve, J. S. The spectrum of eusociality in termites. In The Evolution of Social Behavior in Insects and Arachnids (eds Choe, J. C. & Crespi, B. J.) 52–93 (Cambridge University Press, Cambridge, 1997).
Google Scholar 

25.
Thorne, B. L. Evolution of eusociality in termites. Annu. Rev. Ecol. Evol. Syst.28, 27–54 (1997).
Google Scholar 

26.
Hölldobler, B. & Wilson, E. O. The Ants (Springer, Berlin, 1990).
Google Scholar 

27.
Schmid-Hempel, P. Parasites in Social Insects (Princeton University Press, Princeton, 1998).
Google Scholar 

28.
Tschinkel, W. R. The Fire Ants (Harvard University Press, Cambridge, 2006).
Google Scholar 

29.
Cole, B. J. The ecological setting of social evolution. In Organization of Insect Societies (eds Gadau, J. & Fewell, J.) 74–104 (Harvard University Press, Cambridge, 2009).
Google Scholar 

30.
Johnson, R. A. Colony founding by pleometrosis in the semi-claustral seed-harvester ant Pogonomyrmex calfornicus (Hymenoptera: Formicidae). Anim. Behav.68, 1189–1200 (2004).
Google Scholar 

31.
Tschinkel, W. R. An experimental study of pleometrotic colony founding in the fire ant, Solenopsis invicta: What is the basis for association?. Behav. Ecol. Sociobiol.43, 247–257 (1998).
Google Scholar 

32.
Jerome, C. A., McInnes, D. A. & Adams, E. S. Group defense by colony-founding queens in the fire ant Solenopsis invicta. Behav. Ecol.9, 301–308 (1998).
Google Scholar 

33.
Helms Cahan, S. & Julian, G. E. Fitness consequences of cooperative colony founding in the desert leaf-cutter ant Acromyrmex versicolor. Behav. Ecol.10, 585–591 (1999).
Google Scholar 

34.
Adams, E. S. & Tschinkel, W. R. Effects of foundress number on brood raids and queen survival in the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol.37, 233–242 (1995).
Google Scholar 

35.
Clark, R. M. & Fewell, J. H. Social dynamics drive selection in cooperative associations of ant queens. Behav. Ecol.25, 117–123 (2014).
Google Scholar 

36.
Offenberg, J., Peng, R. & Nielsen, M. Development rate and brood production in haplo- and pleometrotic colonies of Oecophylla smaragdina. Insectes Soc.59, 307–311 (2012).
Google Scholar 

37.
Rissing, S. W. & Pollock, G. B. An experimental analysis of pleometric advantage in the desert seed-harvester ant Messor pergandei (Hymenoptera; Formicidae). Insectes Soc.38, 205–211 (1991).
Google Scholar 

38.
Sasaki, K., Jibiki, E., Satoh, T. & Obara, Y. Queen phenotype and behaviour during cooperative colony founding in Polyrhachis moesta. Insectes Soc.52, 19–25 (2005).
Google Scholar 

39.
Waloff, N. The effect of the number of queens of the ant Lasius flavus (Fab.) (Hym. Formicidae) on their survival and on the rate of development of the first brood. Insectes Soc.4, 391–408 (1957).
Google Scholar 

40.
Bartz, S. H. & Hölldobler, B. Colony founding in Myrmecocystus mimicus Wheeler (Hymenoptera, Formicidae) and the evolution of foundress associations. Behav. Ecol. Sociobiol10, 137–147 (1982).
Google Scholar 

41.
Helms Cahan, S. Ecological variation across a transition in colony-founding behavior in the ant Messor pergandei. Oecologia129, 629–635 (2001).
ADS  Google Scholar 

42.
Sommer, K. & Hölldobler, B. Colony founding by queen association and determinants of reduction in queen number in the ant Lasius niger. Anim. Behav.50, 287–294 (1995).
Google Scholar 

43.
Tschinkel, W. R. & Howard, D. F. Colony founding by pleometrosis in the fire ant, Solenopsis invicta. Behav. Ecol. Sociobiol12, 103–113 (1983).
Google Scholar 

44.
Herbers, J. M. Nest site limitation and facultative polygyny in the ant Leptothorax longispinosus. Behav. Ecol. Sociobiol19, 115–122 (1986).
Google Scholar 

45.
Nonacs, P. Queen condition and alate density affect pleometrosis in the ant Lasius pallitarsis. Insectes Soc.39, 3–13 (1992).
Google Scholar 

46.
Masoni, A. et al. Pleometrotic colony foundation in the ant Crematogaster scutellaris (Hymenoptera: Formicidae): Better be alone than in bad company. Myrmecol. News25, 51–59 (2016).
Google Scholar 

47.
Sommer, K. & Hölldobler, B. Pleometrosis in Lasius niger. In Biology and Evolution of Social Insects (ed. Billen, J.) 47–50 (Leuven University Press, Leuven, 1992).
Google Scholar 

48.
Pfennig, D. W. Absence of joint nesting advantage in desert seed harvester ants: Evidence from a field experiment. Anim. Behav.49, 567–575 (1995).
Google Scholar 

49.
Tschinkel, W. R. Brood raiding and the population dynamics of founding and incipient colonies of the fire ant Solenopsis invicta. Ecol. Entomol.17, 179–188 (1992).
Google Scholar 

50.
Helms Cahan, S. & Fewell, J. H. Division of labor and the evolution of task sharing in queen associations of the harvester ant Pogonomyrmex californicus. Behav. Ecol. Sociobiol.56, 9–17 (2004).
Google Scholar 

51.
Helmkampf, M., Mikheyev, A. S., Kang, Y., Fewell, J. & Gadau, J. Gene expression and variation in social aggression by queens of the harvester ant Pogonomyrmex californicus. Mol. Ecol.25, 3716–3730 (2016).
PubMed  Google Scholar 

52.
Overson, R. P., Gadau, J., Clark, R. M., Pratt, S. C. & Fewell, J. H. Behavioral transitions with the evolution of cooperative nest founding by harvester ant queens. Behav. Ecol. Sociobiol.68, 21–30 (2014).
Google Scholar 

53.
Shaffer, Z. et al. The foundress’s dilemma: Group selection for cooperation among queens of the harvester ant, Pogonomyrmex californicus. Sci. Rep.6, 29828 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

54.
Aron, S., Steinhauer, N. & Fournier, D. Influence of queen phenotype, investment and maternity apportionment on the outcome of fights in cooperative foundations of the ant Lasius niger. Anim. Behav.77, 1067–1074 (2009).
Google Scholar 

55.
Brütsch, T., Avril, A. & Chapuisat, M. No evidence for social immunity in co-founding queen associations. Sci. Rep.7, 16262 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

56.
Chérasse, S. & Aron, S. Measuring inotocin receptor gene expression in chronological order in ant queens. Horm. Behav.96, 116–121 (2017).
PubMed  Google Scholar 

57.
Dreier, S. & d’Ettorre, P. Social context predicts recognition systems in ant queens. J. Evol. Biol.22, 644–649 (2009).
CAS  PubMed  Google Scholar 

58.
Holman, L., Dreier, S. & d’Ettorre, P. Selfish strategies and honest signalling: Reproductive conflicts in ant queen associations. Proc. R. Soc. B277, 2007–2015 (2010).
CAS  PubMed  Google Scholar 

59.
Pull, C. D. & Cremer, S. Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour. BMC Evol. Biol.17, 219 (2017).
PubMed  PubMed Central  Google Scholar 

60.
Pull, C. D., Hughes, W. H. O. & Brown, M. J. F. Tolerating an infection: An indirect benefit of co-founding queen associations in the ant Lasius niger. Naturwissenschaften100, 1125–1136 (2013).
ADS  CAS  PubMed  Google Scholar 

61.
Bernasconi, G. & Keller, L. Phenotype and individual investment in cooperative foundress associations of the fire ant, Solenopsis invicta. Behav. Ecol9, 478–485 (1998).
Google Scholar 

62.
Bernasconi, G. & Keller, L. Effect of queen phenotype and social environment on early queen mortality in incipient colonies of the fire ant, Solenopsis invicta. Anim. Behav.57, 371–377 (1999).
CAS  PubMed  Google Scholar  More

1.
Tian, F. Q., Hu, H. C., Zhang, Z. & Hu, H. P. Secondary salinization and evapotranspiration under mulched drip irrigation condition in Tarim River basin of northwestern China. EGU Gen. Assembly Conf. Abstr. 15, EGU2013–8341 (2013).
2.
Liu, T. et al. Differentially improved soil microenvironment and seedling growth of Amorpha fruticosa by plastic, sand and straw mulching in a saline wasteland in northwest China. Ecol. Eng.122, 126–134 (2018).
Article  Google Scholar 

3.
Rewald, B., Rachmilevitch, S., McCue, M. D. & Ephrath, J. E. Influence of saline drip-irrigation on fine root and sap-flow densities of two mature olive varieties. Environ. Exp. Bot.72, 107–114 (2011).
Article  Google Scholar 

4.
Wullshleger, S. D., Meinzer, F. C. & Vertessy, R. A. A review of whole-plant water use studies in tree. Tree Physiol.18, 499–512 (1998).
Article  Google Scholar 

5.
Forster, M. A. How significant is nocturnal sap flow?. Tree Physiol.34, 757–765 (2014).
Article  Google Scholar 

6.
Chu, C. R., Hsieh, C. I., Wu, S. Y. & Phillips, N. G. Transient response of sap flow to wind speed. J. Exp. Bot.60, 249–255 (2009).
CAS  Article  Google Scholar 

7.
Ma, C. K. et al. Environmental controls on sap flow in black locust forest in Loess Plateau, China. Sci. Rep.7, 13160 (2017).
ADS  Article  Google Scholar 

8.
Zhao, C. Y., Si, J. H., Feng, Q., Yu, T. F. & Li, P. D. Comparative study of daytime and nighttime sap flow of Populus euphratica. J. Plant Growth Regul.82, 1–10 (2017).
ADS  Article  Google Scholar 

9.
Zhang, Q. Y. et al. Sap flow of black locust in response to short-term drought in southern Loess Plateau of China. Sci. Rep.8, 6222 (2018).
ADS  Article  Google Scholar 

10.
Rosado, B. H. P., Oliveira, R. S., Joly, C. A., Aidar, M. P. M. & Burgess, S. S. O. Diversity in nighttime transpiration behavior of woody species of the Atlantic Rain Forest, Brazil. Agric. For. Meteorol.159, 13–20 (2012).
ADS  Article  Google Scholar 

11.
Doronila, A. I. & Forster, M. A. Performance measurement via sap flow monitoring of three eucalyptus species for mine site and dryland salinity phytoremediation. Int. J. Phytoremediat.17, 101–108 (2015).
CAS  Article  Google Scholar 

12.
Fang, W. W., Lu, N., Zhang, Y., Jiao, L. & Fu, B. J. Responses of nighttime sap flow to atmospheric and soil dryness and its potential roles for shrubs on the Loess Plateau of China. J. Plant Ecol.11, 717–729 (2018).
Article  Google Scholar 

13.
Daley, M. J. & Phillips, N. G. Interspecific variation in nighttime transpiration and stomatal conductance in a mixed New England deciduous forest. Tree Physiol.26, 411–419 (2006).
Article  Google Scholar 

14.
Phillips, N. G., Lewis, J. D., Logan, B. A. & Tissue, D. T. Inter- and intra-specific variation in nocturnal water transport in Eucalyptus. Tree Physiol.30, 586–596 (2010).
Article  Google Scholar 

15.
Caspari, H. W., Green, S. R. & Edwards, W. R. N. Transpiration of well-watered and water-stressed Asian pear trees as determined by lysimetry, heat-pulse, and estimated by a Penman-Monteith model. Agric. For. Meteorol.67, 13–27 (1993).
ADS  Article  Google Scholar 

16.
Pfautsch, S. & Adams, M. A. Water flux of Eucalyptus regnans: Defying summer drought and a record heatwave in 2009. Oecologia172, 317–326 (2013).
ADS  Article  Google Scholar 

17.
Chang, X. X., Zhao, W. Z. & He, Z. B. Radial pattern of sap flow and response to microclimate and soil moisture in Qinghai spruce (Picea crassifolia) in the upper Heihe River Basin of arid northwestern China. Agric. For. Meteorol.187, 14–21 (2014).
ADS  Article  Google Scholar 

18.
Wang, Y. N. et al. Response of the daily transpiration of a larch plantation to variation in potential evaporation, leaf area index and soil moisture. Sci. Rep.9, 4697 (2019).
ADS  Article  Google Scholar 

19.
Prieto, I., Kikvidze, Z. & Pugnaire, F. I. Hydraulic lift: soil processes and transpiration in the Mediterranean leguminous shrub Retama sphaerocarpa (L.) Boiss. Plant Soil329, 447–456 (2010).

20.
Shen, Q., Gao, G. Y., Fu, B. J. & Lü, Y. H. Sap flow and water use sources of shelter-belt trees in an arid inland river basin of Northwest China. Ecohydrology8, 1446–1458 (2014).
Article  Google Scholar 

21.
Carter, J. L., Veneklaas, E. J., Colmer, T. D., Eastham, J. & Hatton, T. J. Contrasting water relations of three coastal tree species with different exposure to salinity. Physiol. Plantarum.127, 360–373 (2006).
CAS  Article  Google Scholar 

22.
Ma, J. X., Huang, X., Li, W. H. & Zhu, C. G. Sap flow and trunk maximum daily shrinkage (MDS) measurements for diagnosing water status of Populus euphratica in an inland river basin of Northwest China. Ecohydrology6, 994–1000 (2013).
CAS  Article  Google Scholar 

23.
Zhao, W. Z. & Liu, B. The response of sap flow in shrubs to rainfall pulses in the desert region of China. Agric. For. Meteorol.150, 1297–1306 (2010).
ADS  Article  Google Scholar 

24.
Berbigier, P. et al. Transpiration of a 64-year-old maritime pine stand in Portugal. Oecologia107, 33–42 (1996).
ADS  Article  Google Scholar 

25.
Chen, D. Y., Wang, Y. K., Liu, S. Y., Wei, X. G. & Wang, X. Response of relative sap flow to meteorological factors under different soil moisture conditions in rainfed jujube (Ziziphus jujuba Mil) plantations in semiarid Northwest China. Agric. Water. Manag.136, 23–33 (2014).
Article  Google Scholar 

26.
Shen, Q., Gao, G. Y., Fu, B. J. & Lü, Y. H. Responses of shelterbelt stand transpiration to drought and groundwater variations in an arid inland river basin of Northwest China. J. Hydrol.531, 738–748 (2015).
ADS  Article  Google Scholar 

27.
Sperry, J. S., Alder, N. N. & Eastlack, S. E. The effect of reduced hydraulic conductance on stomatal conductance and xylem cavitation. J. Exp. Bot.44, 1075–1082 (1993).
Article  Google Scholar 

28.
Barbeta, A., Ogaya, R. & Peñuelas, J. Comparative study of diurnal and nocturnal sap flow of Quercus ilex and Phillyrea latifolia in a Mediterranean holm oak forest in Prades (Catalonia, NE Spain). Trees26, 1651–1659 (2013).
Article  Google Scholar 

29.
Knapp, A. K. & Yavitt, J. B. Gas exchange characteristics of Typha latifolia L. from nine sites across North America. Aquat. Bot.49, 203–215 (1995).

30.
Wood, S. A. et al. Retraction notice to impacts of fire on forest age and runoff in mountain ash forests. Funct. Plant Biol.35, 483–492 (2008).
Article  Google Scholar 

31.
Pfautsch, S. et al. Diurnal patterns of water use in Eucalyptus victrix indicate pronounced desiccation-rehydration cycles despite unlimited water supply. Tree Physiol.31, 1041–1051 (2011).
Article  Google Scholar 

32.
Wang, Y. B. et al. The characteristics of nocturnal sap flow and stem water recharge pattern in growing season for a Larix principis-rupprechtii plantation. Acta Ecol. Sin.33, 1375–1385 (2013) ((in Chinese with English Abstract)).
CAS  Article  Google Scholar 

33.
Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics. (Springer, New York, 1998). ISBN 978-0-387-94937-6.

34.
Granier, A. Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements. Tree Physiol.3, 309–320 (1987).
CAS  Article  Google Scholar 

35.
Tie, Q., Hu, H. C., Tian, F. Q., Guan, H. D. & Lin, H. Environmental and physiological controls on sap flow in a subhumid mountainous catchment in north China. Agric. For. Meteorol.240–241, 46–57 (2017).
ADS  Article  Google Scholar 

36.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2016). https://www.R-project.org. More

1.
Smith, M. D., Knapp, A. K. & Collins, S. L. A framework for assessing ecosystem dynamics in response to chronic resource alterations induced by global change. Ecology 90, 3279–3289 (2009).
PubMed  Google Scholar 
2.
Rockstrom, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).
PubMed  Google Scholar 

3.
Morley, V. J., Woods, R. J. & Read, A. F. Bystander selection for antimicrobial resistance: implications for patient health. Trends Microbiol. 27, 864–877 (2019).
CAS  PubMed  Google Scholar 

4.
Vrancken, G., Gregory, A. C., Huys, G. R. B., Faust, K. & Raes, J. Synthetic ecology of the human gut microbiota. Nat. Rev. Microbiol. 17, 754–763 (2019).
CAS  PubMed  Google Scholar 

5.
Lässig, M., Mustonen, V. & Walczak, A. M. Predicting evolution. Nat. Ecol. Evol. 1, 0077 (2017).
Google Scholar 

6.
Robinson, J. I. et al. Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections. J. Clin. Invest. 130, 3792–3806 (2019).
Google Scholar 

7.
Oh, S., Choi, D. & Cha, C. J. Ecological processes underpinning microbial community structure during exposure to subinhibitory level of triclosan. Sci. Rep. 9, 4598 (2019).
PubMed  PubMed Central  Google Scholar 

8.
Zorner, P., Farmer, S. & Alibek, K. Quantifying crop rhizosphere microbiome ecology: the next frontier in enhancing the commercial utility of agricultural microbes. Ind. Biotechnol. 14, 116–119 (2018).
Google Scholar 

9.
Lozada-Gobilard, S. et al. Environmental filtering predicts plant-community trait distribution and diversity: kettle holes as models of meta-community systems. Ecol. Evol. 9, 1898–1910 (2019).
PubMed  PubMed Central  Google Scholar 

10.
Li, J. et al. Shared molecular targets confer resistance over short and long evolutionary timescales. Mol. Biol. Evol. 36, 691–708 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Vázquez-García, I. et al. Clonal heterogeneity influences the fate of new adaptive mutations. Cell Rep. 21, 732–744 (2017).
PubMed  PubMed Central  Google Scholar 

12.
Illingworth, C. J. R., Parts, L., Schiffels, S., Liti, G. & Mustonen, V. Quantifying selection acting on a complex trait using allele frequency time series data. Mol. Biol. Evol. 29, 1187–1197 (2012).
CAS  PubMed  Google Scholar 

13.
Leibler, S. & Kussell, E. Individual histories and selection in heterogeneous populations. Proc. Natl Acad. Sci. USA 107, 13183–13188 (2010).
CAS  PubMed  Google Scholar 

14.
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).
PubMed  Google Scholar 

15.
Wistrand-Yuen, E. et al. Evolution of high-level resistance during low-level antibiotic exposure. Nat. Commun. 9, 1599 (2018).
PubMed  PubMed Central  Google Scholar 

16.
Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).
CAS  PubMed  Google Scholar 

17.
Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11, 754 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

18.
Roodgar, M. et al. Longitudinal linked read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment. Preprint at bioRxiv https://doi.org/10.1101/2019.12.21.886093 (2019).

19.
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).
CAS  PubMed  Google Scholar 

20.
Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).
CAS  PubMed  Google Scholar 

21.
Jernberg, C., Lofmark, S., Edlund, C. & Jansson, J. K. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1, 56–66 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).
PubMed  PubMed Central  Google Scholar 

23.
Mack, I. et al. Antimicrobial resistance following azithromycin mass drug administration: potential surveillance strategies to assess public health impact. Clin. Infect. Dis. 70, 1501–1508 (2019).
Google Scholar 

24.
Tamburini, S., Shen, N., Wu, H. C. & Clemente, J. C. The microbiome in early life: implications for health outcomes. Nat. Med. 22, 713–722 (2016).
CAS  PubMed  Google Scholar 

25.
Langdon, A., Crook, N. & Dantas, G. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Med. 8, 39 (2016).
PubMed  PubMed Central  Google Scholar 

26.
Brown, J. H. & Kodricbrown, A. Turnover rates in insular biogeography: effect of immigration on extinction. Ecology 58, 445–449 (1977).
Google Scholar 

27.
Patel, R. & DuPont, H. L. New approaches for bacteriotherapy: prebiotics, new-generation probiotics, and synbiotics. Clin. Infect. Dis. 60, 108–121 (2015).
Google Scholar 

28.
Lawley, T. D. et al. Targeted restoration of the intestinal microbiota with a simple, defined bacteriotherapy resolves relapsing Clostridium difficile disease in mice. PLoS Pathog. 8, e1002995 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

29.
Ingrisch, J. & Bahn, M. Towards a comparable quantification of resilience. Trends Ecol. Evol. 33, 251–259 (2018).
PubMed  Google Scholar 

30.
Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).
PubMed  PubMed Central  Google Scholar 

31.
Trindade, S., Sousa, A. & Gordo, I. Antibiotic resistance and stress in the light of Fisher’s model. Evolution 66, 3815–3824 (2012).
PubMed  Google Scholar 

32.
Jasinska, W. et al. Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution. Nat. Ecol. Evol. 4, 437–452 (2020).
PubMed  Google Scholar 

33.
Springer, B. et al. Mechanisms of streptomycin resistance: selection of mutations in the 16S rRNA gene conferring resistance. Antimicrob. Agents Chemother. 45, 2877–2884 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

34.
Nishimura, K., Hosaka, T., Tokuyama, S., Okamoto, S. & Ochi, K. Mutations in rsmG, encoding a 16S rRNA methyltransferase, result in low-level streptomycin resistance and antibiotic overproduction in Streptomyces coelicolor A3(2). J. Bacteriol. 189, 3876–3883 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Macfarlane, E. L. A., Kwasnicka, A. & Hancock, R. E. W. Role of Pseudomonas aeruginosa PhoP-PhoQ in resistance to antimicrobial cationic peptides and aminoglycosides. Microbiology 146, 2543–2554 (2000).
CAS  PubMed  Google Scholar 

36.
Girgis, H. S., Hottes, A. K. & Tavazoie, S. Genetic architecture of intrinsic antibiotic susceptibility. PLoS ONE 4, e5629 (2009).
PubMed  PubMed Central  Google Scholar 

37.
Liu, Y., Chen, X., Pan, L. & Mao, Z. Differential protein expression of a streptomycin-resistant Streptomyces albulus mutant in high yield production of ε-poly-l-lysine: a proteomics study. RSC Adv. 9, 24092–24104 (2019).
CAS  Google Scholar 

38.
Beardmore, R. E. et al. Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community. Nat. Ecol. Evol. 2, 1312–1320 (2018).
PubMed  Google Scholar 

39.
Perron, G. G., Gonzalez, A. & Buckling, A. The rate of environmental change drives adaptation to an antibiotic sink. J. Evol. Biol. 21, 1724–1731 (2008).
CAS  PubMed  Google Scholar 

40.
Retel, C. et al. The feedback between selection and demography shapes genomic diversity during coevolution. Sci. Adv. 5, eaax0530 (2019).
PubMed  PubMed Central  Google Scholar 

41.
Day, T., Huijben, V. & Read, A. F. Is selection relevant in the evolutionary emergence of drug resistance? Trends Microbiol. 23, 126–133 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

42.
Melnyk, A. H., Wong, A. & Kassen, R. The fitness costs of antibiotic resistance mutations. Evol. Appl. 8, 273–283 (2015).
PubMed  Google Scholar 

43.
Wein, T. et al. Carrying capacity and colonization dynamics of Curvibacter in the Hydra host habitat. Front. Microbiol. 9, 443 (2018).
PubMed  PubMed Central  Google Scholar 

44.
Zhou, J. et al. Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community. mBio 4, e00584-12 (2013).
PubMed  PubMed Central  Google Scholar 

45.
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).
PubMed  PubMed Central  Google Scholar 

47.
Waples, R. S., Beechie, T. & Pess, G. R. Evolutionary history, habitat disturbance regimes, and anthropogenic changes: what do these mean for resilience of Pacific salmon populations? Ecol. Soc. 14, 3 (2009).
Google Scholar 

48.
Johnstone, J. F. et al. Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).
Google Scholar 

49.
Cairns, J. et al. Construction and characterization of synthetic bacterial community for experimental ecology and evolution. Front. Genet. 9, 312 (2018).
PubMed  PubMed Central  Google Scholar 

50.
Datta, N., Hedges, R. W., Shaw, E. J., Sykes, R. B. & Richmond, M. H. Properties of an R factor from Pseudomonas aeruginosa. J. Bacteriol. 108, 1244–1249 (1971).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Cairns, J. et al. Ecology determines how low antibiotic concentration impacts community composition and horizontal transfer of resistance genes. Commun. Biol. 1, 35 (2018).
PubMed  PubMed Central  Google Scholar 

52.
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina paired-end read merger. Bioinformatics 30, 614–620 (2014).
CAS  PubMed  Google Scholar 

53.
Martin, M. & Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar 

54.
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
CAS  PubMed  Google Scholar 

56.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
PubMed  PubMed Central  Google Scholar 

57.
Edgar, R. C. Accuracy of microbial community diversity estimated by closed- and open-reference OTUs. PeerJ 5, e3889 (2017).
PubMed  PubMed Central  Google Scholar 

58.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
PubMed  PubMed Central  Google Scholar 

60.
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Garrison, E. & Gabor, M. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://arxiv.org/abs/1207.3907 (2012).

62.
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w(1118); iso-2; iso-3. Fly 6, 80–92 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

63.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); http://www.R-project.org/

64.
Krijthe, J. Rtsne: T-distributed stochastic neighbor embedding using a Barnes-Hut implementation. R package version 0.15 https://github.com/jkrijthe/Rtsne (2015).

65.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

66.
Kuhn, M. et al. caret: Classification and regression training. R package version 6.0-84 https://CRAN.R-project.org/package=caret (2019).

67.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: Linear and nonlinear mixed effects models. R package version 3.1-142 https://CRAN.R-project.org/package=nlme (2019).

68.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics With S 4th edn (Springer, 2002)

69.
Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 https://CRAN.R-project.org/package=vegan (2019).

70.
Zapala, M. A. & Schork, N. J. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl Acad. Sci. USA 103, 19430–19435 (2006).
CAS  PubMed  Google Scholar 

71.
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
PubMed  Google Scholar 

73.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar  More

Rearing EPNs
Steinernema feltiae (SN strain) IJs were obtained from the International Entomopathogenic Nematode Collection (USDA-ARS, Byron Georgia USA). The nematodes were cultured in the laboratory using the White trap method41. Galleria mellonella (Vanderhorst Wholesale Inc. St. Marys, OH) were exposed to 100 IJs per larva. Infected G. mellonella larvae were incubated for 4 days at RT (20 ± 1 °C) and insect cadavers were transferred to White traps for IJ collection.
Why Steinernema feltiae was chosen
S. feltiae is a widely studied nematode and commercially used as a biocontrol agent in diverse systems. We selected S. feltiae for two reasons. (1) The dispersal behavior at the expected ISS temperature was the main factor in our decision. S. feltiae disperses without a quiescent period at temperatures from 15 °C to 30 °C. Some other EPNs such as S. carpocapsae IJs disperse normally at or above 25 °C, but at 20 °C and below, the nematodes have a quiescent period where IJs stay stand still for period of 40 min to 24h14. ISS ambient temperature is between 21 and 23 °C. We did not want to take chance with potential failure due to a temperature fluctuation so we chose S. feltiae, which is known to be active at the temperatures42 of the ISS. (2) S. feltiae is an intermediate forager thus incorporating behaviors of both foraging extremes43.
NASA safety certification, flight configuration, and safety experiments
The NASA safety certification was conducted by an implementation partner, NanoRacks, LLC (Houston, TX). The safety certification included filling out safety and experimental plan documents, submitting a bill of materials, and providing material safety sheets for the materials that would be flown to the ISS, their MSDS, and conducting safety and flight configuration experiments. To determine the risk of freezing tubes with moist sand on the ISS cold stowage, we sent three 15 ml conical tubes filled with 10% moist sand to NanoRacks for NASA safety testing. Briefly, play sand (Quikrete, Atlanta, GA) was washed three times in tap water and rinsed three times with MILLIQ water (MILLIPORE, Burlington, MA) using a gold pan (Garrett’s gravity trap, Garland, TX) then dried at 60 °C in the oven for 3 days or until all the moisture was evaporated. In a separate dish, dry sand was moistened with 10% MILLIQ water (w/v). Twelve ml moist sand was placed in 15 ml conical tubes that were rinsed three times with MILLIQ water and dried prior to use. We sent three tubes with moist sand to NanoRacks to be tested at NASA for safety. Next, we moved forward with flight configuration experiments for Specimens 2–6, prepared as shown in Fig. 5. Specimens 2–6 each with two replications were conducted once at 23 ± 2 °C and once at 15 ± 1 °C to determine whether nematodes can emerge and go through in the sand, infect the bait insects and reproduce within a month in a horizontal position. At the same time, we examined whether the tubes for Specimens 2–6 developed any discoloration or warp due to the byproducts produced during infection and decomposition of the cadavers for a month. Later, the flight configuration experiment was extended to six months in case return to Earth would be delayed due to lack of space in the resupply capsule. The lower temperature (15 °C) was tested as a precaution to determine whether nematodes still infect if the temperature fluctuated unexpectedly at ISS. We did one last safety test for Specimen 6 (insect only) to determine whether gasses released by decomposing insects could discolor or warp the tubes, causing a hazard on the ISS. For this experiment, two G. mellonella prepared in Fig. 5c were first placed in −80 °C overnight to kill insect larvae humanely and then the tubes incubated at 23 ± 2 °C. Two replications with a total four insect larvae were monitored monthly for over 6 months for discoloration and warping due to decomposing insect larvae. No warping or discoloration was observed, fulfilling safety requirements to move forward with the experiments.
Fig. 5: Experimental design of specimens for microgravity and their Earth control.

b Specimens 1–4. One end of the tube, designated as 1, had either two infected insect hosts for providing infective juvenile nematodes (Steinernema feltiae) or 2000 IJs in 0.5 ml of polyacrylamide gel. The other end of the tube, designated as 2, had either two healthy bait insects or infected insect. bS. feltiae IJs in gel. 4000 IJs/ml of polyacrylamide gel. c Two healthy Galleria mellonella larvae with wood shavings as shown.

Full size image

Preparing IJs for the microgravity experiments
S. feltiae IJs were removed from the White trap 4 days after the beginning of emergence and rinsed three times in deionized water to remove residual cadaver-derived metabolites and pheromones (Supplementary Table 1) as described by Kaplan et al.14. Rinsed IJs were resuspended to infect the Galleria mellonella larvae for Specimens 1, 2, and 3 and used to prepare IJs in polyacrylamide gel (75 ml of water 1 g of a polyacrylamide gel (Soil Moist, JRM Chemical, Cleveland OH) with a density of 4000 IJs/ml for Specimens 4 and 5 as shown in Fig. 5a for both microgravity experiments and their corresponding Earth controls.
Six concurrent experiments in microgravity for emergence, infection and reproduction and space flight
We designed multiple concurrent experiments that were conducted at the International Space Station U.S. National Laboratory (ISS) with controls conducted on Earth. The experiments were designated as Specimens 1–6 (Fig. 5). The concurrent experiments were conducted to capture the maximum amount of information about the EPN infection process and life cycle in microgravity and make sure that usable data was returned even if microgravity interrupted part of the EPN life cycle. The space limitations of conducting an experiment on the ISS also had to be considered. Microgravity causes changes in many variables (not just gravity), which may impact a multistep infection requiring the cooperation of two organisms (nematode and its mutualistic bacteria). Furthermore, some of the facilities on the ISS, such as cold stowage (−80 °C), have limited capacity and high demand. A timeline of the experiments (Specimens) is presented in Fig. 2 and Supplementary Table 1.
We used a modified sand column assay44,45 shown in Fig. 5. The experimental units consisted of 15 ml conical tubes (Fig. 5) filled with S. feltiae infected G. mellonella larvae or 2000 S. feltiae IJs in 0.5 ml of polyacrylamide gel were placed in one end of the tube and the healthy larvae were placed on the other end of the tube (CELLTREAT scientific products, San Diego, CA). Based on preliminary evidence, the set-up allows a 3-day time delay for the nematodes to move through the sand and infect the bait larvae. However, we did not know what delay there would be under microgravity when setting up the experiment.
Specimen 1 was prepared as shown in Fig. 5a to determine whether nematodes reproduce, form the IJ stage, emerge, disperse, travel 10 cm in moist sandy soil, find a bait insect and infect/invade the host in microgravity (Fig. 1a–d). If the infection failed, this specimen was also designed to determine whether it was due to insect host-immune response. Specimen 1 was placed in cold stowage (−80 °C) 7 days after the launch at ISS, which was after the anticipated time of IJ invasion. A corresponding Earth control was prepared for both IJ invasion and insect host immunity response. Since we did not know how freezing or a month in −80 °C storage would affect hemocyte counts, we prepared additional Earth controls to be analyzed on the day the Earth Specimen 1 controls were placed in −80 °C (Supplementary Table 1). Three replicates were prepared for each treatment; microgravity, corresponding Earth control and additional Earth control for hemocyte counts. A total of 18 insects in nine individual tubes were analyzed for IJ invasion, to determine the IJ developmental stage and hemocyte counts for both Space and Earth specimens. The hemocyte counts were done for the additional Earth control (no freezing) on the day the other set was placed in the −80 °C freezer. Mean hemocyte count for the additional Earth control (no freezing) was 2.8 ± 1.7 from three replicates.
Specimen 2 was prepared in the same manner as Specimen 1 except that the tubes were not frozen to allow IJs to develop and emerge from the bait insect. Unfortunately, they died upon return to Earth. Three replicates were prepared for each treatment; microgravity and Earth control with a total of six tubes analyzed.
Specimen 3 was prepared the same as Specimen 1 except that it did not include a bait insect and the location of the infected insect was on the opposite side (Fig. 5). This specimen was designed to determine whether nematodes can continue their development and reproduction during the flight and emerge in microgravity. Since we did not know whether IJs could emerged from consumed cadavers under microgravity, we prepared Specimen 3. Sand was included to make sure that the emerged nematodes have a place to live until the experiment was returned to Earth and to make a good comparison to Specimen 1. Furthermore, Specimen 3 was kept at ambient temperature to test IJs dispersal after returning to Earth. Again, three replicate tubes for each treatment (microgravity and Earth control) were prepared with a total of six replicate tubes.
Specimen 4 was prepared the same as specimen 1 except that it had 2000 IJs in 0.5 ml of polyacrylamide gel instead of an infected insect (Fig. 5a). This was a backup experiment just in case Specimens 1 and 2 failed to emerge in microgravity to infect bait insects. The column contained sand in the middle (causing a time delay) so the infection event would occur in space not on Earth. The experiment had to be delivered to NASA 36 h before the rocket launch. The sand provided enough time delay (2–3 days) that IJs would only find the bait insects when they reached the ISS or when the Dragon Capsule was in space. This experiment had an Earth control like the other experiments and one additional Earth control was included to determine whether the bait insects were infected before the rocket launch. Three replicates were prepared for each treatment; microgravity and two sets of Earth controls with a total of nine tubes analyzed.
The rocket launch was delayed 1 day from December 4 to 5. The IJs had 2 days, 21 h and 20 min to go through 10 cm sand to reach the bait insect as opposed to 1 day 21 h and 20 min as planned launch day December 4. This was very concerning. At the earliest time (1 and ½ h) after the rocket launch, we analyzed the additional Earth control (three replicates) for insect invasion in two steps. First, we placed the IJs in a petri dish and added water to rinse the IJs off the surface of the insects. Each replicate was analyzed on a separate petri dish. The bait insects were alive and in all three replicates had IJs on them. Next, we washed all the insects to remove all the IJs from their surface and looked inside the insects to determine whether any of the IJs were inside the insects. No IJs were found inside the bait insects suggesting that none of the insects were infected while they were on Earth.
Specimen 5 contained S. feltiae IJs in polyacrylamide gel with a density of 4000 IJ/ml (Fig. 5b). A total volume of 10 ml of IJ acrylamide suspension was placed in each of three 15 ml conical tubes. This specimen was prepared to determine how 33 days of space flight would affect IJ adjustment (movement) and infectivity after returning to Earth. Upon return to Earth at the earliest time (3 days after returning), IJ movement was compared to an Earth control and 10 days later, IJ infectivity was tested. This was also a backup experiment in case nematodes died due to the effect of sand because IJs were known to safely travel on Earth in polyacrylamide gel43. Like the other experiments, three replicate tubes (120,000 IJs) for each treatment (microgravity and Earth control) were prepared with a total of six replicate tubes (240,000 IJs).
Specimen 6 had two G. mellonella last instar larvae in 15 ml conical tubes in wood shaving (Fig. 5c). This was to determine if the infection failed in Specimen 1 due negative effects on the insect host. At the end of the space flight, insects in both microgravity and Earth control were dead at different developmental stages. Several insect stages were observed in the various tubes (larvae, pupae and adult), all of which died. A total of 12 insect larvae in six replicate tubes were analyzed.
All Specimens, except for Specimen 1, were kept at ambient temperature on the ISS, during the flight back to Earth, and for 3 days while being shipped to our laboratory for further analysis on nematode IJ fitness and infectivity.
Handing over the experiments to NanoRacks for delivery to NASA for launch
Specimens 1–4 and 6 in Fig. 5 were assembled at Kennedy Space Center (KSC) Space Station Processing Facility (SSPF) on December 2 (Supplementary Table 1). Specimen 5 in Fig. 5b was prepared on Nov 29 at the Shapiro lab in Byron, GA, and kept at 5 °C until December 2. After all of the specimens were prepared (Supplementary Fig. 1), Specimen 1 replications were placed in a sealed bag and Specimens 2–6 were placed in a separate sealed bag. They are weighed and placed in Nanotracks’ NanoLab in a Horizontal form (Supplementary Fig. 1). The Nanolab lid was sealed with Velcro. After that, the Nanolab was delivered to NASA and placed in the Dragon Capsule on SpaceX Falcon9 rocket for CRS-19 on the same day (Supplementary Table 1). The experiments in the Nanolab were placed in a horizontal position and kept 20 °C for 2 days, 21 h and 20 min in the Dragon capsule until the launch on December 5, 2019. Experiments were kept in a horizontal position during the launch, in microgravity in Space, and at ISS at ambient temperature until returned to Earth.
Earth controls: Specimen 1 for Earth control was kept in a sealed plastic bag and Specimens 2–6 were placed in a separate sealed plastic bag at ambient temperature at the KSC SSPF until launch on December 5. Specimen 1 Earth controls were taken to the Shapiro Lab by car and kept at RT until December 11. Specimens 2–6 were shipped by FedEx at ambient temperature using cool packs for maintenance of the temperature to the Pheronym R&D laboratory, Davis, CA
In orbit and return to Earth
Specimen 1 (the three 15 ml conical tubes) was transferred to cold storage on December 12 at 346/14:30 (8:30am CST) by European Space Agency Astronaut Luca Parmitano (https://blogs.nasa.gov/stationreport/2019/12/12/). Specimens 2–6 were kept in the NanoLab on the Dragon Capsule which returned to Earth on January 7, 2020 and was retrieved from the Pacific Ocean by Space X. NanoRacks received the samples on Jan 9, 2020, in their facility in Houston, TX and shipped Specimen 1 (kept frozen) in dry ice overnight by FedEx to the Shapiro Lab in Byron, GA. The Shapiro Lab in Byron GA placed the samples in −80 °C until analysis. On the same day (Jan 9), NanoRacks shipped Specimens 2–6 at RT overnight by FedEx to Pheronym, Davis, CA. On January 10, 2020 at 10 AM, Specimens 2–6 were received by Pheronym Lab, and were inspected immediately for dead, alive sluggish or active nematodes, pictures were taken, and videos recorded before removing the Teflon seals. Pictures were taken using a Nikon D60 (Nikon, Tokyo, Japan). Videos were taken with a hand-held USB Celestron microscope (Celestron, Torrance, CA) and/or iPhone 6 S (Apple, Cupertino, CA).
Immune response assessment of Specimen 1
The primary immune defense in insects against multicellular parasites including EPNs is encapsulation. Immune cells (hemocytes) bind to the nematode and one another to form a multicellular envelope; we therefore assessed immune response based on hemocyte activity and encapsulation46. G. mellonella larvae were dissected longitudinally. The presence of hemocytes adhering to the nematode body was confirmed at 400–600X. The number of nematodes showing hemocyte activity was compared between treatments.
Assessing the IJs after returning to Earth
When Specimen 2, 3, 4, and 5 were received, S. feltiae IJs were observed visually to assess whether they were alive, sluggish, or dead before removing the seals. Microgravity and corresponding Earth controls were inspected at the same time. There were several IJs visible prior to opening the tubes. Subsequently, we unsealed the tubes for Specimen 2, 3, and 4, removed half of the sand to a 10 cm petri dish and harvested the IJs by washing the sand three times with water. The IJs were then rinsed with MILLIQ water to test for dispersal behavior at Pheronym and sent to the Shapiro-Ilan’s lab for assessing symbiotic bacteria load. In case we missed any live IJs in Specimens 2, 3, and 4, we baited the other half of the sand with two G. mellonella larvae and incubated them at 22 ± 1 °C for three days before inspecting for dead or live insects. After that the samples were shipped to the Shapiro-Ilan’s lab for a second inspection and analysis of the bait insects for IJ presence.
Specimen 5 was in polyacrylamide gel which was transparent, making visually scoring easy dead or alive and taking videos (Supplementary Video 1 and 2). After inspection, three ml of IJs with a density of 4000 IJs/ml were shipped in gel at ambient temperature overnight by FedEx to the Shapiro-Ilan lab to conduct infectivity experiments.
Dispersal assays of Specimens 2 and 3
Dispersal assays were conducted as described by Kaplan et al.14,15 with a few differences. Petri dishes (6 cm) containing 0.9% agar with a gel strength: ≥900 g/cm2 (Caisson Agar, Type I, Smithfield, UT) were used for the assays. Briefly, IJs from Specimens 2 and 3 were tested for dispersal immediately by rinsing three times with MILLIQ water after the harvest from sand. The assays were conducted in the afternoon between 2 and 3 PM. Approximately ~26–55 IJs (due to a limited number of IJs) in 10 µL of water were placed in the center of an agar plate with 6 cm diameter Petri dishes and counted. The assays were run for 30 min during which IJs were free to move on agar plates. After 30 min, IJs inside and outside the 1.3 cm ring were counted. IJs remaining inside the 1.3 cm ring were considered non-dispersed, and those outside were considered to have dispersed. Percentage dispersal was calculated as the number dispersed relative to the total number of IJs on the plate. Three replications for Specimens 2 (proxy Earth control) and Specimen 3 (treatment, microgravity), a total of six plates were analyzed.
Infectivity after 33-day space flight, Specimen 5
Infectivity was assessed based on procedures describe by Mbata et al.46. IJs were extracted from gel by dilution and centrifuged at 582g to concentrate them. A total of 960 IJs per replication were pipetted onto filter paper (Whatman No. 1) in a 0.35 mm Petri dish. A single greater wax moth, Galleria mellonella (L.) larva was added to each Petri dish. Insects were incubated in the dark for at 25 °C for 72 h and then dissected under a stereomicroscope to determine the number of invading IJs. There were five insects per replicate of Specimen 5 (total six replicates microgravity and Earth controls) and insect only (no IJs) control. A total of 35 insects were analyzed.
Comparison of symbiotic bacterial load per nematode in Specimens 2 and 3
Methods to compare symbiotic nematode bacteria load among treatments were based on those described by Kaya and Stock1,5. IJs were surface sterilized with 0.5% NaClO and then washed three times with sterile distilled water (centrifuging at 582g for 2 min between each wash). The final pellet was suspended in 0.5 ml. Single IJs were homogenized for 60 s with a sterile motor-driven polypropylene pestle and then transferred onto 60 mm nutrient agar plates. The plates were incubated at 25 °C. The number of bacterial colonies per IJ was assessed at 3 and 7 days.
Statistical analysis
For comparisons of infectivity, dispersal behavior, symbiotic bacteria load, and immune response the treatments effects (from space) were compared to Earth controls using Student’s t tests (SAS version 9.4, SAS 2002). Based on the inspection of residual plots, numerical data were log transformed prior to analysis47 (SAS, 2002).
Ethics statement
We use the invertebrate model system Steinernema feltiae and Galleria mellonella for this study, in accordance, the study was exempt from ethics committee approval.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More