Ecology

1.
Kasischke, E. S. et al. Can. J. Res. 40, 1313–1324 (2010).
Article  Google Scholar 
2.
Walker, X. J. et al. Nature 572, 520–523 (2019).
Article  Google Scholar 

3.
Smolyanitsky, V. November 2019–April 2020 Arctic Seasonal Review (WMO, 2020); https://go.nature.com/2FdYUyX

4.
Hu, Y., Fernandez-Anez, N., Smith, T. E. & Rein, G. Int. J. Wildland Fire 27, 293–312 (2018).
Article  Google Scholar 

5.
Alaska Division of Forestry AK Fire Info: Alaska Wildland Fire Information https://go.nature.com/3hal6HB (2020).

6.
Prosperi, P. et al. Clim. Change 161, 415–432 (2020).
Article  Google Scholar 

7.
Kirdyanov, A. V. et al. Environ. Res. Lett. 15, 034061 (2020).
Article  Google Scholar 

8.
Olefeldt, D. et al. Nat. Commun. 7, 13043 (2016).
Article  Google Scholar 

9.
Gibson, C. M. et al. Nat. Commun. 9, 3041 (2018).
Article  Google Scholar 

10.
Usup, A., Hashimoto, Y., Takahashi, H. & Hayasaka, H. Tropics 14, 1–19 (2004).
Article  Google Scholar 

11.
Turetsky, M. R. et al. Nat. Geosci. 13, 138–143 (2020).
Article  Google Scholar 

12.
Xu, W. et al. Remote Sens. Environ. 193, 138–149 (2017).
Article  Google Scholar 

13.
Wooster, M. J., Xu, W. & Nightingale, T. Remote Sens. Environ. 120, 236–254 (2012).
Article  Google Scholar 

14.
Kirillina, K., Shvetsov, E. G., Protopopova, V. V., Thiesmeyer, L. & Yan, W. Environ. Res. Lett. 15, 035009 (2020).
Article  Google Scholar  More

Adams DC (2014) A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Syst Biol 63(5):685–697.
PubMed  Article  PubMed Central  Google Scholar 

Adams DC, Otarola-Castillo E (2013) geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol Evol 4:393–399.
Article  Google Scholar 

Berry RJ (1996) Small mammal differentiation on islands. Philos Trans R Soc Lond B 351(1341):753–764.
CAS  Article  Google Scholar 

Berry RJ, Tricker BJK (1969) Competition and extinction: the mice of Foula, with notes on those of Fair Isle and St Kilda. J Zool Lond 158:247–265.
Article  Google Scholar 

Bonhomme F, Orth A, Cucchi T, Rajabi-Maham H, Catalan J, Boursot P et al. (2011) Genetic differentiation of the house mouse around the Mediterranean basin: matrilineal footprints of early and late colonization. Proc R Soc Lond Biol Sci 278:1034–1043.
Google Scholar 

Britton-Davidian J, Caminade P, Davidian E, Pagès M (2017) Does chromosomal change restrict gene flow between house mouse populations (Mus musculus domesticus)? Evidence from microsatellite polymorphisms. Biol J Linn Soc 122(1):224–240.
Article  Google Scholar 

Cucchi T (2008) Uluburun shipwreck stowaway house mouse: molar shape analysis and indirect clues about the vessel’s last journey. J Archaeol Sci 35:2953–2959.
Article  Google Scholar 

Cucchi T, Barnett R, Martinkova N, Renaud S, Renvoisé E, Evin A et al. (2014) The changing pace of insular life: 5000 years of microevolution in the Orkney vole (Microtus arvalis orcadensis). Evolution 68(10):2804–2820.
PubMed  PubMed Central  Article  Google Scholar 

Cucchi T, Kovács ZE, Berthon R, Orth A, Bonhomme F, Evin A et al. (2013) On the trail of Neolithic mice and men towards Transcaucasia: zooarchaeological clues from Nakhchivan (Azerbaijan). Biol J Linn Soc 108:917–928.
Article  Google Scholar 

Cucchi T, Papayianni K, Cersoy S, Aznar-Cormano L, Zazzo A, Debruyne R et al. (2020) Tracking the Near Eastern origins and European dispersal of the western house mouse. Sci Rep 10:8276.
PubMed  PubMed Central  Article  CAS  Google Scholar 

Cucchi T, Vigne J-D, Auffray J-C (2005) First occurrence of the house mouse (Mus musculus domesticus Schwarz & Schwarz, 1943) in the Western Mediterranean: a zooarchaeological revision of subfossil occurrences. Biol J Linn Soc 84:429–445.
Article  Google Scholar 

Dolédec S, Chessel D (1994) Co-inertia analysis: an alternative method for studying species-environment relationships. Freshw Biol 31(3):277–294.
Article  Google Scholar 

Dray S, Dufour A-B (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22:1–20.
Article  Google Scholar 

Escoufier Y (1973) Le traitement des variables vectorielles. Biometrics 29(4):751–760.
Article  Google Scholar 

Excoffier L, Lischer HEL (2010) Arlequin suite ver 3. 5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567.
PubMed  Article  PubMed Central  Google Scholar 

Fairley JS, Smal CM (1987) Feral house mice in Ireland. Ir Naturalists’ J 22(7):284–290.
Google Scholar 

Gabriel SI, Mathias MDL, Searle JB (2013) Genetic structure of house mouse (Mus musculus Linnaeus 1758) populations in the Atlantic archipelago of the Azores: colonization and dispersal. Biol J Linn Soc 108(4):929–940.
Article  Google Scholar 

Gabriel SI, Mathias ML, Searle JB (2015) Of mice and the ‘Age of Discovery’: the complex history of colonization of the Azorean archipelago by the house mouse (Mus musculus) as revealed by mitochondrial DNA variation. J Evolut Biol 28(1):130–114.
CAS  Article  Google Scholar 

Ganem G (1998) Behavioural and physiological characteristics of standard and chromosomally divergent populations of house mice from the Orkney archipelago (Scotland). Acta Theriol 43(1):23–38.
Article  Google Scholar 

García-Rodríguez O, Andreou D, Herman JS, Mitsainas GP, Searle JB, Bonhomme F et al. (2018) Cyprus as an ancient hub for house mice and humans. J Biogeogr 45:2618–2630.
Article  Google Scholar 

Gilbert E, O’Reilly S, Merrigan M, McGettigan D, Vitart V, Joshi PK et al. (2019) The genetic landscape of Scotland and the Isles. Proc Natl Acad Sci USA 116(38):201904761.
Article  CAS  Google Scholar 

Gingerich PD, Smith BH, Rosenberg K (1982) Allometric scaling in the dentition of primates and prediction of body weight from tooth size in fossils. Am J Phys Anthropol 58:81–100.
CAS  PubMed  Article  PubMed Central  Google Scholar 

Günduz İ, Auffray J-C, Britton-Davidian J, Catalan J, Ganem G, Ramalhinho MG et al. (2001) Molecular studies on the colonization of the Madeiran archipelago by house mice. Mol Ecol 10:2023–2029.
PubMed  Article  PubMed Central  Google Scholar 

Hardouin E, Chapuis J-L, Stevens MI, van Vuuren JB, Quillfeldt P, Scavetta RJ et al. (2010) House mouse colonization patterns on the sub-Antarctic Kerguelen Archipelago suggest singular primary invasions and resilience against re-invasion. BMC Evolut Biol 10:325.
Article  Google Scholar 

Hayden L, Lochovska L, Sémon M, Renaud S, Delignette-Muller M-L, Vicot M et al. (2020) Developmental variability channels mouse molar evolution. eLife 9:e50103.
PubMed  PubMed Central  Article  Google Scholar 

Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11(1):94.
PubMed  PubMed Central  Article  Google Scholar 

Jones EP, Eager HM, Gabriel SI, Jóhannesdóttir F, Searle JB (2013) Genetic tracking of mice and other bioproxies to infer human history. Trends Genet 29(5):298–308.
CAS  PubMed  Article  PubMed Central  Google Scholar 

Jones EP, Jensen J-K, Magnussen E, Gregersen N, Hansen HS, Searle JB (2011a) A molecular characterization of the charismatic Faroe house mouse. Biol J Linn Soc 102:471–482.
Article  Google Scholar 

Jones EP, Jóhannesdóttir F, Gündüz İ, Richards MB, Searle JB (2011b) The expansion of the house mouse into north-western Europe. J Zool Lond 283(4):257–268.
Article  Google Scholar 

Jones EP, Skirnisson K, McGovern T, Gilbert M, Willerslev E, Searle JB (2012) Fellow travellers: a concordance of colonization patterns between mice and men in the North Atlantic region. BMC Evolut Biol 12(1):35.
Article  Google Scholar 

Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874.
CAS  PubMed  Article  PubMed Central  Google Scholar 

Ledevin R, Chevret P, Ganem G, Britton-Davidian J, Hardouin EA, Chapuis J-L et al. (2016) Phylogeny and adaptation shape the teeth of insular mice. Proc R Soc Lond Biol Sci 283:20152820.
Google Scholar 

Leigh JW, Bryant D (2015) POPART: full-feature software for haplotype network construction. Methods Ecol Evol 6(9):1110–1116.
Article  Google Scholar 

Leslie S, Winney B, Hellenthal G, Davison D, Boumertit A, Day T et al. (2015) The fine-scale genetic structure of the British population. Nature 519(7543):309–314.
CAS  PubMed  PubMed Central  Article  Google Scholar 

Lomolino MV (1985) Body size of mammals on islands: the island rule reexamined. Am Naturalist 125:310–316.
Article  Google Scholar 

Lomolino MV (2005) Body size evolution in insular vertebrates: generality of the island rule. J Biogeogr 32:1683–1699.
Article  Google Scholar 

Lomolino MV, Sax DF, Palombo MR, van der Geer AA (2012) Of mice and mammoths: evaluations of causal explanations for body size evolution in insular mammals. J Biogeogr 39:842–854.
Article  Google Scholar 

Losos JB, Ricklefs RE (2009) Adaptation and diversification on islands. Nature 457(7231):830–836.
CAS  PubMed  Article  Google Scholar 

Martínková N, Barnett R, Cucchi T, Struchen R, Pascal M, Pascal M et al. (2013) Divergent evolutionary processes associated with colonization of offshore islands. Mol Ecol 22:5205–5220.
PubMed  PubMed Central  Article  Google Scholar 

Millien V (2006) Morphological evolution is accelerated among island mammals. PLoS Biol 4(10):e321.
PubMed  PubMed Central  Article  Google Scholar 

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. (2013) Vegan: Community Ecology Package. R Package Version. 2.0-10. CRAN.

Peres-Neto PR, Jackson DA (2001) How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129(2):169–178.
PubMed  Article  PubMed Central  Google Scholar 

Pocock MJO, Searle JB, White PCL (2004) Adaptations of animals to commensal habitats: population dynamics of house mice Mus musculus domesticus on farms. J Anim Ecol 73:878–888.
Article  Google Scholar 

Polly PD (2004) On the simulation of the evolution of morphological shape: multivariate shape under selection and drift. Palaeontol Electron 7(2):7A:28.
Google Scholar 

Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959.
CAS  PubMed  PubMed Central  Google Scholar 

Renaud S, Chevret P, Michaux J (2007) Morphological vs. molecular evolution: ecology and phylogeny both shape the mandible of rodents. Zool Scr 36:525–535.
Article  Google Scholar 

Renaud S, Hardouin EA, Quéré J-P, Chevret P (2017) Morphometric variations at an ecological scale: seasonal and local variations in feral and commensal house mice. Mamm Biol 87:1–12.
Article  Google Scholar 

Renaud S, Ledevin R, Souquet L, Gomes Rodrigues H, Ginot S, Agret S et al. (2018) Evolving teeth within a stable masticatory apparatus in Orkney mice. Evolut Biol 45(4):405–424.
Article  Google Scholar 

Renaud S, Pantalacci S, Auffray J-C (2011) Differential evolvability along lines of least resistance of upper and lower molars in island house mice. PLoS One 6(5):e18951.
PubMed  PubMed Central  Article  CAS  Google Scholar 

Romaniuk AA, Shepherd AN, Clarke DV, Sheridan AJ, Fraser S, Bartosiewicz L et al. (2016) Rodents: food or pests in Neolithic Orkney. R Soc Open Sci 3(10):160514.
PubMed  PubMed Central  Article  Google Scholar 

Ronquist F, Teslenko M, Pvd Mark, Ayres D, Darling A, Höhna S et al. (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61(3):539–542.
PubMed  PubMed Central  Article  Google Scholar 

Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE et al. (2017) DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol 34:3299–3302.
CAS  PubMed  PubMed Central  Article  Google Scholar 

Searle JB, Jones CS, Gündüz İ, Scascitelli M, Jones EP, Herman JS et al. (2009) Of mice and (Viking?) men: phylogeography of British and Irish house mice. Proc R Soc Lond Biol Sci 276:201–207.
CAS  Google Scholar 

Sendell-Price AT, Ruegg KC, Clegg SM (2020) Rapid morphological divergence following a human-mediated introduction: the role of drift and directional selection. Heredity 124:535–549.
PubMed  PubMed Central  Article  Google Scholar 

Solano E, Franchini P, Colangelo P, Capanna E, Castiglia R (2013) Multiple origins of the western European house mouse in the Aeolian Archipelago: clues from mtDNA and chromosomes. Biol Invasions 15(4):729–739.
Article  Google Scholar 

Souquet L, Chevret P, Ganem G, Auffray J-C, Ledevin R, Agret S et al. (2019) Back to the wild: does feralization affect the mandible of non-commensal house mice (Mus musculus domesticus)? Biol J Linn Soc 126:471–486.
Article  Google Scholar 

van der Geer AA, Lyras GA, Lomolino MV, Palombo MR, Sax DF (2013) Body size evolution of palaeo-insular mammals: temporal variations and interspecific interactions. J Biogeogr 40:1440–1450.
Article  Google Scholar  More

1.
Zhou, Y. et al. Changes in element accumulation, phenolic metabolism, and antioxidative enzyme activities in the red-skin roots of Panax ginseng. J. Ginseng Res. 41, 307–315 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Rahman, M. & Punja, Z. K. Biochemistry of ginseng root tissues affected by rusty root symptoms. Plant Physiol. Biochem. 43, 1103–1114 (2005).
CAS  PubMed  Article  Google Scholar 

3.
Reeleder, R. D., Hoke, S. M. T. & Zhang, Y. Rusted root of ginseng (Panax quinquefolius) is caused by a species of rhexocercosporidium. Phytopathology 96, 1243–1254 (2006).
CAS  PubMed  Article  Google Scholar 

4.
Lu, X. H. et al. Taxonomy of fungal complex causing red-skin root of Panax ginseng in China. J. Ginseng Res. 44, 506–518 (2020).
PubMed  Article  Google Scholar 

5.
Lee, C., Kim, K. Y., Lee, J. E., Kim, S. & An, G. Enzymes hydrolyzing structural components and ferrous ion cause rusty-root symptom on ginseng (Panax ginseng). J. Microbiol. Biotechnol. 21, 192–196 (2011).
CAS  PubMed  Article  Google Scholar 

6.
Yuan, X., Song, T. J., Yang, J. S., Huang, X. G. & Shi, J. Y. Changes of microbial community in the rhizosphere soil of Atractylodes macrocephala when encountering replant disease. S. Afr. J. Bot. 127, 129–135 (2019).
CAS  Article  Google Scholar 

7.
Mazzola, M. & Manici, L. M. Apple replant disease: role of microbial ecology in cause and control. Annu. Rev. Phytopathol. 50, 45–65 (2012).
CAS  PubMed  Article  Google Scholar 

8.
Liu, X. et al. Comparison of the characteristics of artificial ginseng bed soils in relation to the incidence of ginseng red skin disease. Exp. Agric. 50, 59–71 (2014).
Article  Google Scholar 

9.
Wang, Q. X. et al. Analysis of the relationship between rusty root incidences and soil properties in Panax ginseng. 41, 012001 (2016)

10.
Liu, D., Sun, H. & Ma, H. Deciphering microbiome related to rusty roots of Panax ginseng and evaluation of antagonists against pathogenic ilyonectria. Front. Microbiol. 10, 1350 (2019).
PubMed  PubMed Central  Article  Google Scholar 

11.
Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

12.
Wan, W. et al. Responses of the rhizosphere bacterial community in acidic crop soil to pH: changes in diversity, composition, interaction, and function. Sci. Total Environ. 700, 134418 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

13.
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).
PubMed  Article  Google Scholar 

14.
Carrino-Kyker, S. R., Coyle, K. P., Kluber, L. A. & Burke, D. J. Fungal and bacterial communities exhibit consistent responses to reversal of soil acidification and phosphorus limitation over time. Microorganisms 8, 1 (2019).
Article  CAS  Google Scholar 

15.
Sun, L., Chen, S., Chao, L. & Sun, T. Effects of flooding on changes in Eh, pH and speciation of cadmium and lead in contaminated soil. Bull. Environ. Contam. Toxicol. 79, 514–518 (2007).
CAS  PubMed  Article  Google Scholar 

16.
Dordas, C. Role of nutrients in controlling plant diseases in sustainable agriculture: a review. Agron. Sustain. Dev. 28, 33–46 (2008).
CAS  Article  Google Scholar 

17.
Warren, S. L. J. H. Mineral nutrition of crops: fundamental mechanisms and implications. HortScience 39, 462–462 (2004).
Article  Google Scholar 

18.
Sun, H., Zhang, Y. Y. & Song, X. X. Study on the soil nutrients and enzyme activity of cultivate ginseng soil in the farmland and wild ginseng soil under forest by canonical correlation analysis. Acta Agriculturae Boreali-Sinica S2 (2010).

19.
Sharma, S., Duveiller, E., Basnet, R., Karki, C. B. & Sharma, R. Effect of potash fertilization on Helminthosporium leaf blight severity in wheat, and associated increases in grain yield and kernel weight. Field Crops Res. 93, 142–150 (2005).
Article  Google Scholar 

20.
Floch, C., Capowiez, Y. & Biology, S. Enzyme activities in apple orchard agroecosystems: how are they affected by management strategy and soil properties. Soil Biol. Biochem. 41, 61–68 (2009).
CAS  Article  Google Scholar 

21.
Aon, M. A. & Coloneri, A. C. II. Temporal and spatial evolution of enzymatic activities and physico-chemical properties in an agricultural soil. Appl. Soil Ecol. 18, 255–270 (2001).
Article  Google Scholar 

22.
Cai, Z. et al. Effects of the novel pyrimidynyloxybenzoic herbicide ZJ0273 on enzyme activities, microorganisms and its degradation in Chinese soils. Environ. Sci. Pollut. Res. 22, 4425–4433 (2015).
CAS  Article  Google Scholar 

23.
Antonious, G. F. Impact of soil management and two botanical insecticides on urease and invertase activity. J. Environ. Sci. Health Part B Pestic. Food Contam. Agric. Wastes 38, 479–488 (2003).
Article  CAS  Google Scholar 

24.
Makoi, J. H. J. R. & Ndakidemi, P. A. Selected soil enzymes: examples of their potential roles in the ecosystem. Afr. J. Biotechnol. 7, 181–191 (2008).
CAS  Google Scholar 

25.
Jian, S. et al. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis. Soil Biol. Biochem. 101, 32–43 (2016).
CAS  Article  Google Scholar 

26.
Schutzendubel, A. & Polle, A. Plant responses to abiotic stresses: heavy metal-induced oxidative stress and protection by mycorrhization. J. Exp. Bot. 53, 1351–1365 (2002).
CAS  PubMed  Google Scholar 

27.
Chao, A. Non-parametric estimation of the classes in a population. Scand. J. Stat. 11, 265–270 (1984).
Google Scholar 

28.
Shannon, C. E. J. B. S. T. J. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
MathSciNet  MATH  Article  Google Scholar 

29.
Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).
ADS  MATH  Article  Google Scholar 

30.
Farh, M. E., Kim, Y. J., Kim, Y. J. & Yang, D. C. Cylindrocarpon destructans/Ilyonectria radicicola-species complex: causative agent of ginseng root-rot disease and rusty symptoms. J. Ginseng Res. 42, 9–15 (2018).
PubMed  Article  Google Scholar 

31.
Lombard, L., Der Merwe, N. A. V., Groenewald, J. Z. & Crous, P. W. Generic concepts in Nectriaceae. Stud. Mycol. 80, 189–245 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Martinezbarrera, O. Y. et al. Does Beauveria bassiana (Hypocreales: Cordycipitaceae) affect the survival and fecundity of the parasitoid Coptera haywardi (Hymenoptera: Diapriidae)?. Environ. Entomol. 48, 156–162 (2019).
CAS  Article  Google Scholar 

33.
Migiro, L. N., Maniania, N. K., Chabi-Olaye, A., Wanjoya, A. & Control, J. V. J. B. Effect of infection by Metarhizium anisopliae (Hypocreales: Clavicipitaceae) on the feeding and oviposition of the pea leafminer Liriomyza huidobrensis (Diptera: Agromyzidae) on different host plants. Biol. Control 56, 179–183 (2011).
Article  Google Scholar 

34.
Tardy, V. et al. Shifts in microbial diversity through land use intensity as drivers of carbon mineralization in soil. Soil Biol. Biochem. 90, 204–213 (2015).
CAS  Article  Google Scholar 

35.
Yang, L., Lu, X., Li, S. & Wu, B. First report of common bean (Phaseolus vulgaris) root rot caused by Plectosphaerella cucumerina in China. Plant Dis. 102, 1849–1849 (2018).
Article  Google Scholar 

36.
Baldeweg, F., Warncke, P., Fischer, D. & Gressler, M. J. O. L. Fungal biosurfactants from Mortierella alpina. Org. Lett. 21, 1444–1448 (2019).
CAS  PubMed  Article  Google Scholar 

37.
Masinova, T., Yurkov, A. & Baldrian, P. J. F. E. Forest soil yeasts: decomposition potential and the utilization of carbon sources. Fungal Ecol. 34, 10–19 (2018).
Article  Google Scholar 

38.
Hu, H. et al. Fomesafen impacts bacterial communities and enzyme activities in the rhizosphere. Environ. Pollut. 253, 302–311 (2019).
CAS  PubMed  Article  Google Scholar 

39.
Barriuso, J., Marín, S. & Mellado, R. P. Effect of the herbicide glyphosate on glyphosate-tolerant maize rhizobacterial communities: a comparison with pre-emergency applied herbicide consisting of a combination of acetochlor and terbuthylazine. Environ. Microbiol. 12, 1021–1030 (2010).
CAS  PubMed  Article  Google Scholar 

40.
Zhao, J. et al. Effects of organic–inorganic compound fertilizer with reduced chemical fertilizer application on crop yields, soil biological activity and bacterial community structure in a rice–wheat cropping system. Appl. Soil Ecol. 99, 1–12 (2016).
ADS  Article  Google Scholar 

41.
Davidova, I. A., Marks, C. R., & Suflita, J. M. Anaerobic hydrocarbon-degrading Deltaproteobacteria. Handbook of Hydrocarbon and Lipid Microbiology 207–243 (2019).

42.
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365 (2020).
Article  Google Scholar 

43.
Yadav, S. et al. Cyanobacteria: role in agriculture, environmental sustainability, biotechnological potential and agroecological impact. In Plant-Microbe Interactions in Agro-Ecological Perspectives 257–277 (2017).

44.
Rossi, F., Li, H., Liu, Y. & De Philippis, R. Cyanobacterial inoculation (cyanobacterisation): perspectives for the development of a standardized multifunctional technology for soil fertilization and desertification reversal. Earth Sci. Rev. 171, 28–43 (2017).
ADS  Article  Google Scholar 

45.
Muñoz-Rojas, M. et al. Effects of indigenous soil cyanobacteria on seed germination and seedling growth of arid species used in restoration. Plant Soil 429, 91–100 (2018).
Article  CAS  Google Scholar 

46.
Speirs, L. B. M., Rice, D. T. F., Petrovski, S. & Seviour, R. J. The phylogeny, biodiversity, and ecology of the chloroflexi in activated sludge. Front. Microbiol. 10, 2015 (2019).
PubMed  PubMed Central  Article  Google Scholar 

47.
Whitman, T. et al. Dynamics of microbial community composition and soil organic carbon mineralization in soil following addition of pyrogenic and fresh organic matter. ISME J. 10, 2918–2930 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Lladó, S. et al. Functional screening of abundant bacteria from acidic forest soil indicates the metabolic potential of Acidobacteria subdivision 1 for polysaccharide decomposition. Biol. Fertil. Soils 52, 251–260 (2016).
Article  CAS  Google Scholar 

49.
Bousset, L., Ermel, M., Soglonou, B. & Husson, O. Fungal growth is affected by and affects pH and redox potential (Eh) of the growth medium. bioRxiv 401182 (2018).

50.
Mi, C. et al. Unveiling of dominant fungal pathogens associated with rusty root rot of Panax notoginseng based on multiple methods. Plant Dis. 101, 2046–2052 (2017).
CAS  PubMed  Article  Google Scholar 

51.
Wang, Q. et al. Analysis of rhizosphere bacterial and fungal communities associated with rusty root disease of Panax ginseng. Appl. Soil Ecol. 138, 245–252 (2019).
Article  Google Scholar 

52.
Li, Z., Guo, S., Tian, S., Liu, Z. & Long, B. Study on the causes for ginseng red skin sickness occurred in albic bed soil. Acta Pedol. Sin. 34, 328–335 (1997).
Google Scholar 

53.
Shi, J., Yuan, X., Lin, H., Yang, Y. & Li, Z. Y. Differences in soil properties and bacterial communities between the rhizosphere and bulk soil and among different production areas of the medicinal plant Fritillaria thunbergii. Int. J. Int. J. Mol. Sci. 12, 3770–3785 (2011).
CAS  Article  Google Scholar 

54.
Xu, Y. et al. Bacterial communities in soybean rhizosphere in response to soil type, soybean genotype, and their growth stage. Soil Biol. Biochem. 41, 919–925 (2019).
Article  CAS  Google Scholar 

55.
Pei, G., Zhu, Y., Wen, J., Pei, Y. & Li, H. Vinegar residue supported nanoscale zero-valent iron: remediation of hexavalent chromium in soil. Environ. Pollut. 256, 113407 (2019).
PubMed  Article  CAS  Google Scholar 

56.
Fawcett, J. K. The semi-micro Kjeldahl method for the determination of nitrogen. J. Med. Lab Technol. 12, 1–22 (1954).
CAS  PubMed  Google Scholar 

57.
Li, Y. et al. Humic acid fertilizer improved soil properties and soil microbial diversity of continuous cropping peanut: a three-year experiment. Sci. Rep. 9, 12014 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Article  Google Scholar 

60.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Price, M. N., Dehal, P. S. & Arkin, A. P. J. P. O. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

63.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
PubMed  PubMed Central  Article  Google Scholar 

64.
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, 1–18 (2011).
Article  Google Scholar  More

1.
Hopkins, J. B. & Ferguson, J. M. Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model. PLoS ONE 7, e28478 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. 87, 545–562 (2012).
PubMed  Article  Google Scholar 

3.
Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 835, 823–835 (2014).
Article  Google Scholar 

4.
Hopkins, J. B., Ferguson, J. M., Tyers, D. B. & Kurle, C. M. Selecting the best stable isotope mixing model to estimate grizzly bear diets in the Greater Yellowstone Ecosystem. PLoS ONE 12, e0174903 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: Coping with too much variation. PLoS ONE 5, e9672 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

6.
Ward, E. J., Semmens, B. X., Phillips, D. L., Moore, J. W. & Bouwes, N. A quantitative approach to combine sources in stable isotope mixing models. Ecosphere 2, art19 (2011).
Article  Google Scholar 

7.
Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).
PubMed  Article  Google Scholar 

8.
Stock, B. C. & Semmens, B. X. Unifying error structures in commonly used biotracer mixing models. Ecology 97, 2562–2569 (2016).
PubMed  Article  Google Scholar 

9.
Koch, P. L. & Phillips, D. L. Incorporating concentration dependence in stable isotope mixing models: A reply to Robbins, Hilderbrand and Farley (2002). Oecologia 133, 14–18 (2002).
ADS  PubMed  Article  Google Scholar 

10.
Ward, E. J., Semmens, B. X. & Schindler, D. E. Including source uncertainty and prior information in the analysis of stable isotope mixing models. Environ. Sci. Technol. 44, 4645–4650 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
MathSciNet  Google Scholar 

12.
Brown, C. J., Brett, M. T., Adame, M. F., Stewart-Koster, B. & Bunn, S. E. Quantifying learning in biotracer studies. Oecologia 187, 597–608 (2018).
ADS  PubMed  Article  Google Scholar 

13.
Bond, A. L. & Diamond, A. W. Recent Bayesian stable-isotope mixing models are highly sensitive to variation in discrimination factors. Ecol. Appl. 21, 1017–1023 (2011).
PubMed  Article  Google Scholar 

14.
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).
Article  Google Scholar 

15.
Gannes, L. Z., O’Brien, D. M. & Martinez del Rio, C. Stable isotopes in animal ecology: Assumtions, caveats, and a call for more laboratory experiments. Ecology 78, 1271–1276 (1997).
Article  Google Scholar 

16.
Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for δ13C analysis of diet. Oecologia 57, 32–37 (1983).
ADS  CAS  PubMed  Article  Google Scholar 

17.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).
ADS  CAS  Article  Google Scholar 

18.
Wessels, F. J. & Hahn, D. A. Carbon 13 discrimination during lipid biosynthesis varies with dietary concentration of stable isotopes: Implications for stable isotope analyses. Funct. Ecol. 24, 1017–1022 (2010).
Article  Google Scholar 

19.
Carleton, S. A. & del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812 (2010).
Article  Google Scholar 

20.
O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

21.
Deniro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351 (1981).
ADS  CAS  Article  Google Scholar 

22.
Martínez del Río, C. & Wolf, B. Mass-balance models for animal isotopic ecology. In Physiological and Ecological Adaptations to Feeding in Vertebrates (eds. Starck, J. M. & Wang, T.) 141–174 (Science Publishers, 2005). https://doi.org/10.1017/CBO9781107415324.004.

23.
Voigt, C. C., Rex, K., Michener, R. H. & Speakman, J. R. Nutrient routing in omnivorous animals tracked by stable carbon isotopes in tissue and exhaled breath. Oecologia 157, 31–40 (2008).
ADS  PubMed  Article  Google Scholar 

24.
Martínez Del Rio, C., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology ten years after a call for more laboratory experiments. Biol. Rev. 84, 91–111 (2009).
Article  Google Scholar 

25.
McCutchan, J. H. Jr., Lewis, W. M. Jr., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).
CAS  Article  Google Scholar 

26.
Caut, S., Angulo, E. & Courchamp, F. Caution on isotopic model use for analyses of consumer diet. Can. J. Zool. 86, 438–445 (2008).
CAS  Article  Google Scholar 

27.
Greer, A. L., Horton, T. W. & Nelson, X. J. Simple ways to calculate stable isotope discrimination factors and convert between tissue types. Methods Ecol. Evol. 6, 1341–1348 (2015).
Article  Google Scholar 

28.
Alves-Stanley, C. D. & Worthy, G. A. J. Carbon and nitrogen stable isotope turnover rates and diet-tissue discrimination in Florida manatees (Trichechus manatus latirostris). J. Exp. Biol. 212, 2349–2355 (2009).
CAS  PubMed  Article  Google Scholar 

29.
Caut, S., Angulo, E. & Courchamp, F. Variation in discrimination factors (Δ15N and Δ13C): The effect of diet isotopic values and applications for diet reconstruction. J. Appl. Ecol. 46, 443–453 (2009).
CAS  Article  Google Scholar 

30.
Bearhop, S., Waldron, S., Votier, S. C. & Furness, R. W. Factors that influence assimilation rates and fractionation of nitrogen and carbon stable isotopes in avian blood and feathers. Physiol. Biochem. Zool. 75, 451–458 (2002).
CAS  PubMed  Article  Google Scholar 

31.
Carleton, S. A., Kelly, L., Anderson-Sprecher, R. & Martinez del Rio, C. Should we use one-, or multi-compartment models to describe 13C incorporation into animal tissues?. Rapid Commun. Mass Spectrom. 22, 3008–3014 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

32.
Steinitz, R., Lemm, J. M., Pasachnik, S. A. & Kurle, C. M. Diet-tissue stable isotope ( Δ13C and Δ15N) discrimination factors for multiple tissues from terrestrial reptiles. Rapid Commun. Mass Spectrom. 30, 9–21 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Cloyed, C. S., Newsome, S. D. & Eason, P. K. Trophic discrimination factors and incorporation rates of carbon- and nitrogen-stable isotopes in adult green frogs, Lithobates clamitans. Physiol. Biochem. Zool. 88, 576–585 (2015).
PubMed  Article  Google Scholar 

34.
Neres-Lima, V. et al. Allochthonous and autochthonous carbon flows in food webs of tropical forest streams. Freshw. Biol. 62, 1012–1023 (2017).
CAS  Article  Google Scholar 

35.
Mill, A. C., Pinnegar, J. K. & Polunin, N. V. C. Explaining isotope trophic-step fractionation: Why herbivorous fish are different. Funct. Ecol. 21, 1137–1145 (2007).
Article  Google Scholar 

36.
Busst, G. M. A. & Britton, J. R. High variability in stable isotope diet–tissue discrimination factors of two omnivorous freshwater fishes in controlled ex situ conditions. J. Exp. Biol. 219, 1060–1068 (2016).
PubMed  Article  Google Scholar 

37.
Heady, W. N. & Moore, J. W. Tissue turnover and stable isotope clocks to quantify resource shifts in anadromous rainbow trout. Oecologia 172, 21–34 (2013).
ADS  PubMed  Article  Google Scholar 

38.
Busst, G. M. A., Bašić, T. & Britton, J. R. Stable isotope signatures and trophic-step fractionation factors of fish tissues collected as non-lethal surrogates of dorsal muscle. Rapid Commun. Mass Spectrom. 29, 1535–1544 (2015).
CAS  PubMed  Article  Google Scholar 

39.
Busst, G. M. A. & Britton, J. R. Tissue-specific turnover rates of the nitrogen stable isotope as functions of time and growth in a cyprinid fish. Hydrobiologia 805, 49–60 (2018).
CAS  Article  Google Scholar 

40.
Bunn, S. E., Leigh, C. & Jardine, T. D. Diet-tissue fractionation of δ15N by consumers from streams and rivers. Limnol. Oceanogr. 58, 765–773 (2013).
ADS  CAS  Article  Google Scholar 

41.
Bastos, R. F., Corrêa, F., Winemiller, K. O. & Garcia, A. M. Are you what you eat? Effects of trophic discrimination factors on estimates of food assimilation and trophic position with a new estimation method. Ecol. Indic. 75, 234–241 (2017).
Article  Google Scholar 

42.
Kambikambi, M. J., Chakona, A. & Kadye, W. T. The influence of diet composition and tissue type on the stable isotope incorporation patterns of a small-bodied southern African minnow Enteromius anoplus (Cypriniformes, Cyprinidae). Rapid Commun. Mass Spectrom. 33, 613–623 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

43.
Hobson, K. A. & Welch, H. E. Determination of trophic relationships within a high Arctic marine food web using δ13C and δ15N analysis. Mar. Ecol. Prog. Ser. 84, 9–18 (1992).
ADS  CAS  Article  Google Scholar 

44.
Healy, K. et al. SIDER: An R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness. Ecography 41, 1393–1400 (2018).
Article  Google Scholar 

45.
Soto, D. X., Gacia, E. & Catalan, J. Freshwater food web studies: A plea for multiple tracer approach. Limnetica 32, 97–106 (2013).
Google Scholar 

46.
Cucherousset, J., Bouletreau, S., Martino, A., Roussel, J. M. & Santoul, F. Using stable isotope analyses to determine the ecological effects of non-native fishes. Fish. Manag. Ecol. 19, 111–119 (2012).
Article  Google Scholar 

47.
Kadye, W. T., Chakona, A. & Jordaan, M. S. Swimming with the giant: Coexistence patterns of a new redfin minnow Pseudobarbus skeltoni from a global biodiversity hot spot. Ecol. Evol. 6, 7141–7155 (2016).
PubMed  PubMed Central  Article  Google Scholar 

48.
Skelton, P. H. A Complete Guide to the Freshwater Fishes of Southern Africa. (Struik, 2001). https://doi.org/10.2989/16085914.2002.9626577.

49.
Matley, J. K., Fisk, A. T., Tobin, A. J., Heupel, M. R. & Simpfendorfer, C. A. Diet-tissue discrimination factors and turnover of carbon and nitrogen stable isotopes in tissues of an adult predatory coral reef fish, Plectropomus leopardus. Rapid Commun. Mass Spectrom. 30, 29–44 (2016).
CAS  PubMed  Article  Google Scholar 

50.
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).
Article  Google Scholar 

51.
Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, e5096 (2018).
PubMed  PubMed Central  Article  Google Scholar 

52.
Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: A literature synthesis. PLoS ONE 10, e0116182 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Tronquart, N. H., Mazeas, L., Reuilly-Manenti, L., Zahm, A. & Belliard, J. Fish fins as non-lethal surrogates for muscle tissues in freshwater food web studies using stable isotopes. Rapid Commun. Mass Spectrom. 26, 1603–1608 (2012).
ADS  CAS  Article  Google Scholar 

54.
Cerling, T. E. et al. Determining biological tissue turnover using stable isotopes: The reaction progress variable. Oecologia 151, 175–189 (2007).
ADS  PubMed  Article  Google Scholar 

55.
Martínez Del Rio, C. & Anderson-Sprecher, R. Beyond the reaction progress variable: The meaning and significance of isotopic incorporation data. Oecologia 156, 765–772 (2008).
ADS  PubMed  Article  Google Scholar 

56.
Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 3–13 (2016) http://cran.r-project.org/package=rjags.

57.
Elzhov, T., Mullen, K., Spiess, A. & Bolker, B. minpack.lm: R interface to the Levenberg-Marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. R package version 1.2–1. https://CRAN.R-project.org/package=minpack.lm (2016).

58.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Article  Google Scholar 

59.
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).
MATH  Article  Google Scholar 

60.
Sweeting, C. J., Barry, J., Barnes, C., Polunin, N. V. C. & Jennings, S. Effects of body size and environment on diet-tissue δ15N fractionation in fishes. J. Exp. Mar. Biol. Ecol. 340, 1–10 (2007).
CAS  Article  Google Scholar 

61.
Boutton, T. W. Stable carbon isotope ratios of natural materials: II. Atmospheric, terrestrial, marine, and freshwater environments. In Carbon Isotope Techniques (eds. Coleman, D. & Fry, B.) 173–186 (Academic Press, London, 1991). https://doi.org/10.1016/b978-0-12-179730-0.50016-3.

62.
Franssen, N. R., Gilbert, E. I., James, A. P. & Davis, J. E. Isotopic tissue turnover and discrimination factors following a laboratory diet switch in Colorado pikeminnow ( Ptychocheilus lucius ). Can. J. Fish. Aquat. Sci. 74, 265–272 (2017).
CAS  Article  Google Scholar 

63.
Britton, J. R. & Busst, G. M. A. Stable isotope discrimination factors of omnivorous fishes: Influence of tissue type, temperature, diet composition and formulated feeds. Hydrobiologia 808, 219–234 (2018).
CAS  Article  Google Scholar 

64.
Roth, J. D. & Hobson, K. A. Stable carbon and nitrogen isotopic fractionation between diet and tissue of captive red fox: Implications for dietary reconstruction. Can. J. Zool. 78, 848–852 (2000).
Article  Google Scholar 

65.
Robbins, C. T., Felicetti, L. A. & Florin, S. T. The impact of protein quality on stable nitrogen isotope ratio discrimination and assimilated diet estimation. Oecologia 162, 571–579 (2010).
ADS  PubMed  Article  Google Scholar 

66.
Carter, W. A., Bauchinger, U. & McWilliams, S. R. The importance of isotopic turnover for understanding key aspects of animal ecology and nutrition. Diversity 11, 84 (2019).
CAS  Article  Google Scholar 

67.
Ishikawa, N. F. Use of compound-specific nitrogen isotope analysis of amino acids in trophic ecology: Assumptions, applications, and implications. Ecol. Res. 33, 825–837 (2018).
CAS  Article  Google Scholar 

68.
Pinnegar, J. K. & Polunin, N. V. C. Differential fractionation of δ13C and δ15N among fish tissues: Implications for the study of trophic interactions. Funct. Ecol. 13, 225–231 (1999).
Article  Google Scholar 

69.
Guelinckx, J. et al. Changes in δ13C and δ15N in different tissues of juvenile sand goby Pomatoschistus minutus: A laboratory diet-switch experiment. Mar. Ecol. Prog. Ser. 341, 205–215 (2007).
ADS  CAS  Article  Google Scholar 

70.
Shigeta, K., Tsuma, S., Yonekura, R., Kakamu, H. & Maruyama, A. Isotopic analysis of epidermal mucus in freshwater fishes can reveal short-time diet variations. Ecol. Res. 32, 643–652 (2017).
CAS  Article  Google Scholar 

71.
McIntyre, P. B. & Flecker, A. S. Rapid turnover of tissue nitrogen of primary consumers in tropical freshwaters. Oecologia 148, 12–21 (2006).
ADS  PubMed  Article  Google Scholar 

72.
Sanderson, B. L. et al. Nonlethal sampling of fish caudal fins yields valuable stable isotope data for threatened and endangered fishes. Trans. Am. Fish. Soc. 138, 1166–1177 (2009).
Article  Google Scholar 

73.
de Moor, F. C., Wilkinson, R. C. & Herbst, H. M. Food and feeding habits of Oreochromis mossambicus (Peters) in hypertrophic Hartbeespoort Dam, South Africa. South Afr. J. Zool. 21, 170–176 (1986).
Article  Google Scholar 

74.
Upadhayay, H. R. et al. Isotope mixing models require individual isotopic tracer content for correct quantification of sediment source contributions. Hydrol. Process. 32, 981–989 (2018).
ADS  Article  Google Scholar 

75.
Kambikambi, M. J., Chakona, A. & Kadye, W. T. Tracking seasonal food web dynamics and isotopic niche shifts in wild chubbyhead barb Enteromius anoplus within a southern temperate headwater stream. Hydrobiologia 837, 87–107 (2019).
CAS  Article  Google Scholar 

76.
Swan, G. J. F. et al. Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors. Methods Ecol. Evol. 2019, 1–11 (2019).
Google Scholar  More

The GM maize material used was the GM insect-resistant maize variety (line) GIF, and the maize was a yellow grain strain provided by the Lai Jinsheng Teacher Laboratory of China Agricultural University. The conventional maize variety Meiyu 11 with white kernels was selected as the pollen receptor of GM maize. The inheritance of the seed (kernel) color can be considered to be a single gene, with one pair of alleles (yellow vs. white). The yellow allele is dominant, and the white allele is recessive. The experimental site was sown at the base of the agricultural GM environmental safety assessment of the Institute of Tropical Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Wujitangxia Village, Maihao Town, Wenchang City, Hainan Province (110° 45′ 44″ E, 19° 32′ 14″ N). Transgenic insect-resistant maize was sown three times, once every other week, so that the pollination period of GM maize overlapped with the silking period of the non-GM maize. Artificial on-demand sowing with three seeds per hole and a 4–5 cm sowing depth was adopted.
Field experiments were carried out during two seasons in 2016–2017 and 2017–2018. In the first planting season of 2016–2017, the farthest investigated distance of flow frequency was 60 m (Fig. 1A, Table 1). According to the results from the first investigation, the frequency of gene flow in the eight directions beyond 30 m was very low, almost zero (Table 1). Thus, in 2017–2018, the farthest investigated distance of flow frequency was adjusted to 30 m. In the second planting season, the total area was approximately 14,000 m2 (Fig. 1B, Table 2). As in Hainan off-season reproduction regions the work of breeding research institutes is particularly intensive, it is generally difficult to meet conventional isolation conditions. At the same time, this area also provided a reference for regions around the world that need close isolation. Therefore, we added bagging measures in the treatment areas during the maize tassel pollination period in the second planting season in order to further reduce the flow frequency.
Figure 1

Design of the experimental area. (A) In the period of 2016–2017, the design of the experimental area included one control area (A) and one isolation area (B). The dimensions of control area A and isolation area B in the figure are the same. (B) In the period from 2017 to 2018, the design of the experimental area included one control area (D) and three isolation areas (A, B and C). The solid line represents the isolation area, and the dashed line represents the control area without isolation devices. A1–A8 and B1–B8 in (A) and A1–A8, B1–B8, C1–C8 and D1–D8 in (B) represent eight directions of NE, N, NW, W, SW, S, SE and E, respectively. The dimensions of control area D and isolation areas A, B and C in the figure are the same. The blue numbers represent the size of the experimental areas. The green arrows represent the main wind direction during flowering.

Full size image

In the first year of the experiment, control and treatment areas were set up. The area of the control region was 10,000 m2 (100 m × 100 m). A 100 m2 (10 m × 10 m) plot was designated in the center for GM insect-resistant maize, and non-GM maize was planted around this central area. The treatment area with isolation measures was 10,000 m2 (100 m × 100 m). A 100 m2 (10 m × 10 m) plot was designated in the center for GM insect-resistant maize, and non-GM maize was planted around this area. Colored steel plates were used as an isolation measure. The isolation height was 4 m.
A colored steel plate was the isolation material used in these experiments (Fig. 2). Colored steel plates and steel plates are two different materials. At present, there are many colors of colored steel plates. As for which color was used in our isolation experiments, there was no strict requirement, only a desire to match with the surrounding environment. Colored steel plates have the advantages of having both an organic polymer and a steel plate, and many organic polymers have good colorability, formability, corrosion resistance, decoration and high-strength. This combines with the workability of a steel plate, which can be easily finished by stamping, cutting, bending, deep drawing, and other processing to form virtually any shape. This makes the products made of colored steel plates have excellent practicability, decoration, processing and durability.
Figure 2

Isolation device for natural ecological risk control of GM maize. (A) Schematic of the isolation device; (B) partial diagram of the isolation device; (C) sectional view of figure (B); (D) structural detail diagram of the square card; 1: rectangular steel frame, 1.1: steel frame wall, 1.1a: horizontal steel rod, 1.1b: vertical steel rod, 2: inclined support rod, 3: colored steel plate, 4: door for entry and exit, 5: hot-dip galvanized steel frame. 6: structure of the square card, 6.1: screw.

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When maize was harvested after ripening, the investigated directions of control plots were NE, N, NW, W, SW, S, SE and E, labeled with A1–A8, respectively, and those of the isolation plots were labeled with B1–B8, respectively. The location of GM insect-resistant maize from 1 m, 3 m, 5 m, 10 m, 15 m, 20 m, 30 m, 40 m, 50 m and 60 m was investigated along these eight directions. The farthest investigation distances for NE, NW, SW and SE were 60 m, and other directions were 40 m. Ten maize plants were harvested randomly at each point (the first ear). Plants were marked in the order of P1, P2, P3, … P10, dried and stored for further testing. The total number of kernels harvested per corn ear was recorded.
In the second year of the experiment, one control and three treatments were set up. The control plot and the three treatment areas with isolation measures covered an area of 3500 m2 (50 m × 70 m). A 100 m2 (10 m × 10 m) plot was designated in the center of the plot to plant GM maize, and non-GM maize was planted around this central area. Colored steel plates were used as an isolation measure. Bagging of tassels of transgenic maize plants was performed during the pollination period. No bagging was conducted in the control area.
When the maize was harvested after ripening, the investigated directions of control plots were NE, N, NW, W, SW, S, SE and E, labeled D1, D2, D3, D4, D5, D6, D7 and D8, respectively. Isolation area A was marked A1, A2, A3, A4, A5, A6, A7 and A8 along the same eight directions. Isolation areas B and C were marked with B1, B2, B3, B4, B5, B6, B7 and B8, and C1, C2, C3, C4, C5, C6, C7 and C8, respectively. The location of GM insect-resistant maize from 1 m, 3 m, 5 m, 10 m, 15 m, 20 m and 30 m was investigated along these eight directions. The farthest investigation distances for NE, NW, SW and SE were 30 m, and the farthest investigation distances for N, W, S and E were 20 m. Ten maize plants were harvested randomly at each point (the first ear). Plants were marked in the order of P1, P2, P3, … P10, dried and stored for further testing. The total number of kernels harvested per corn ear was recorded.
The endosperm was identified by dominant and recessive traits. According to the number of endosperm traits of GM insect-resistant maize harvested at different directions and distances from GM insect-resistant maize, the pollen transmission distance and outcrossing rate of GM insect-resistant maize were then determined. This method can only be applied to dominant endosperm traits such as yellow or non-waxy grains.
The outcrossing rate was calculated according to formula (1):

$$ P = frac{N}{T} times 100, $$
(1)

where P is the outcrossing rate percentage (%), N is the number of corn kernels containing exogenous genes (the number of the yellow seeds) per ear of corn in units of granules, and T is the total grains (the number of the yellow seeds and white seeds) per ear in units of granules. The outcrossing rates of exogenous genes in different directions and distances were determined, and then the pollen flow distance was determined.
As descriptive statistics, the arithmetic mean as well the standard deviation of outcrossing rates were calculated. The outcrossing rate at each point (1 m, 3 m, 5 m, … 60 m) in the experiment was the mean of the outcrossing rate (P1, P2, P3, … P10) of 10 corn plants at that point.
Details of the isolation device for gene flow risk control of GM maize
The isolation device for gene flow risk control of GM maize, as shown in Fig. 2, comprises a rectangular steel frame (1). The rectangular steel frame 1 was composed of four steel frame walls (1.1), each of which was composed of multiple horizontal steel poles (1.1a) and vertical steel poles (1.1b). Each vertical steel pole was fixed 20–30 cm deep in the soil, and the angle between the inclined support pole (2) and the vertical steel pole was 30°–45°. The vertical steel pole of the four steel frame walls intersected the horizontal steel pole of the top. There were eight inclined supporting poles at the intersection of the vertical steel pole at the four corners of the rectangular steel frame and the horizontal steel pole at the top of the rectangular steel frame, and one inclined supporting pole was fixed through the square card structure (6). The four-sided steel frame wall of the rectangular steel frame was equipped with a colored steel plate (3), and one side of the isolation device was provided with an entry and exit (4). Horizontal steel bars at the top of the rectangular steel frame were provided with a hot-dip galvanized steel frame (5). The hot-dip supporting steel frame was a quadrilateral, and the four corners of the hot-dip supporting steel frame were fixed in the middle of the horizontal steel pole through the hoop. The clamp structure (6) included a side opening and a hollow rectangular frame. The top of the inclined support rod was obliquely inserted into the square clamp structure and fixed on the vertical steel rod through a screw (6.1). The dimensions of the steel rod and the inclined supporting rod were 6000 mm in length, 40 mm in diameter and 2 mm in thickness, and the colored steel plate was 0.425 mm in thickness. The size of the device and the number of inclined supporting rods were determined according to the actual situation in the field. More

1.
Forde, B. & Lorenzo, H. The nutritional control of root development. Plant Soil 232, 51–68. https://doi.org/10.1023/A:1010329902165 (2001).
CAS  Article  Google Scholar 
2.
Hodge, A. The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytol. 162, 9–24. https://doi.org/10.1111/j.1469-8137.2004.01015.x (2004).
Article  Google Scholar 

3.
Robinson, D. Tansley review no 73. The responses of plants to non-uniform supplies of nutrients. New Phytol. 127, 635–674 (1994).
CAS  Article  Google Scholar 

4.
Yu, P., White, P. J., Hochholdinger, F. & Li, C. Phenotypic plasticity of the maize root system in response to heterogeneous nitrogen availability. Planta 240, 667–678. https://doi.org/10.1007/s00425-014-2150-y (2014).
CAS  Article  PubMed  Google Scholar 

5.
Osmont, K. S., Sibout, R. & Hardtke, C. S. Hidden branches: developments in root system architecture. Annu. Rev. Plant Biol. 58, 93–113. https://doi.org/10.1146/annurev.arplant.58.032806.104006 (2007).
CAS  Article  PubMed  Google Scholar 

6.
Ahmed, S. et al. Imaging the interaction of roots and phosphate fertiliser granules using 4D X-ray tomography. Plant Soil 401, 125–134. https://doi.org/10.1007/s11104-015-2425-5 (2016).
CAS  Article  Google Scholar 

7.
Drew, M. & Saker, L. Nutrient supply and the growth of the seminal root system in barley III. Compensatory increases in growth of lateral roots, and in rates of phosphate uptake, in response to a localized supply of phosphate. J. Exp. Bot. 29, 435–451 (1978).
CAS  Article  Google Scholar 

8.
Flavel, R. J., Guppy, C. N., Tighe, M. K., Watt, M. & Young, I. M. Quantifying the response of wheat (Triticum aestivum L.) root system architecture to phosphorus in an Oxisol. Plant Soil 385, 303–310. https://doi.org/10.1007/s11104-014-2191-9 (2014).
CAS  Article  Google Scholar 

9.
Nacry, P., Bouguyon, E. & Gojon, A. Nitrogen acquisition by roots: physiological and developmental mechanisms ensuring plant adaptation to a fluctuating resource. Plant Soil 370, 1–29. https://doi.org/10.1007/s11104-013-1645-9 (2013).
CAS  Article  Google Scholar 

10.
Bloom, A. J., Frensch, J. & Taylor, A. R. Influence of inorganic nitrogen and pH on the elongation of maize seminal roots. Ann. Bot. 97, 867–873. https://doi.org/10.1093/aob/mcj605 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Bloom, A. J., Jackson, L. E. & Smart, D. R. Root-growth as a function of ammonium and nitrate in the root zone. Plant Cell Environ. 16, 199–206. https://doi.org/10.1111/j.1365-3040.1993.tb00861.x (1993).
CAS  Article  Google Scholar 

12.
Caba, J. M., Centeno, M. L., Fernandez, B., Gresshoff, P. M. & Ligero, F. Inoculation and nitrate alter phytohormone levels in soybean roots: differences between a supernodulating mutant and the wild type. Planta 211, 98–104. https://doi.org/10.1007/s004250000265 (2000).
CAS  Article  PubMed  Google Scholar 

13.
Gerendás, J. & Sattelmacher, B. Influence of nitrogen form and concentration on growth and ionic balance of tomato (Lycopersicon esculentum) and potato (Solanum tuberosum). In Plant nutrition—physiology and applications (ed. van Beusichem, M. L.) 33–37 (Springer, Berlin, 1990).
Google Scholar 

14.
Granato, T. C. & Raper, C. D. Jr. Proliferation of maize (Zea mays L.) roots in response to localized supply of nitrate. J. Exp. Bot. 40, 263–275. https://doi.org/10.1093/jxb/40.2.263 (1989).
CAS  Article  PubMed  Google Scholar 

15.
Maizlish, N., Fritton, D. & Kendall, W. Root morphology and early development of maize at varying levels of nitrogen 1. Agron. J. 72, 25–31 (1980).
CAS  Article  Google Scholar 

16.
Ogawa, S., Valencia, M. O., Ishitani, M. & Selvaraj, M. G. Root system architecture variation in response to different NH4+ concentrations and its association with nitrogen-deficient tolerance traits in rice. Acta Physiol. Plant. 36, 2361–2372. https://doi.org/10.1007/s11738-014-1609-6 (2014).
CAS  Article  Google Scholar 

17.
Sattelmacher, B. & Thoms, K. Root growth and 14C-translocation into the roots of maize (Zea mays L.) as influenced by local nitrate supply. J. Plant Nutr. Soil Sci. 152, 7–10 (1989).
CAS  Google Scholar 

18.
Schortemeyer, M., Feil, B. & Stamp, P. Root morphology and nitrogen uptake of maize simultaneously supplied with ammonium and nitrate in a split-root system. Ann. Bot. 72, 107–115. https://doi.org/10.1006/anbo.1993.1087 (1993).
CAS  Article  Google Scholar 

19.
Thoms, K. & Sattelmacher, B. Influence of nitrate placement on morphology and physiology of maize (Zea mays) root systems. In Plant nutrition—physiology and applications (ed van Beusichem, M. L.) 29–32 (Springer, Berlin, 1990).
Google Scholar 

20.
Tian, Q., Chen, F., Liu, J., Zhang, F. & Mi, G. Inhibition of maize root growth by high nitrate supply is correlated with reduced IAA levels in roots. J. Plant Physiol. 165, 942–951. https://doi.org/10.1016/j.jplph.2007.02.011 (2008).
CAS  Article  PubMed  Google Scholar 

21.
Gruber, B. D., Giehl, R. F., Friedel, S. & von Wiren, N. Plasticity of the Arabidopsis root system under nutrient deficiencies. Plant Physiol. 163, 161–179. https://doi.org/10.1104/pp.113.218453 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Lima, J. E., Kojima, S., Takahashi, H. & von Wiren, N. Ammonium triggers lateral root branching in Arabidopsis in an AMMONIUM TRANSPORTER1;3-dependent manner. Plant Cell 22, 3621–3633. https://doi.org/10.1105/tpc.110.076216 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Remans, T. et al. The Arabidopsis NRT1.1 transporter participates in the signaling pathway triggering root colonization of nitrate-rich patches. Proc. Natl. Acad. Sci. U. S. A. 103, 19206–19211. https://doi.org/10.1073/pnas.0605275103 (2006).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Zhang, H. & Forde, B. G. An Arabidopsis MADS box gene that controls nutrient-induced changes in root architecture. Science 279, 407–409. https://doi.org/10.1126/science.279.5349.407 (1998).
ADS  CAS  Article  PubMed  Google Scholar 

25.
Zhang, H., Jennings, A., Barlow, P. W. & Forde, B. G. Dual pathways for regulation of root branching by nitrate. Proc. Natl. Acad. Sci. U.S.A. 96, 6529–6534. https://doi.org/10.1073/pnas.96.11.6529 (1999).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Drew, M. Comparison of the effects of a localised supply of phosphate, nitrate, ammonium and potassium on the growth of the seminal root system, and the shoot, in barley. New Phytol. 75, 479–490 (1975).
CAS  Article  Google Scholar 

27.
Drew, M. & Saker, L. Nutrient Supply and the Growth of the Seminal Root System in Barley II. Localized, compensatory increases in lateral root growth and rates of nitrate uptake when nitrate supply is restricted to only part of the root system. J. Exp. Bot. 26, 79–90 (1975).
CAS  Article  Google Scholar 

28.
Drew, M., Saker, L. & Ashley, T. Nutrient supply and the growth of the seminal root system in barley I. The effect of nitrate concentration on the growth of axes and laterals. J. Exp. Bot. 24, 1189–1202 (1973).
CAS  Article  Google Scholar 

29.
Beeckman, F., Motte, H. & Beeckman, T. Nitrification in agricultural soils: impact, actors and mitigation. Curr. Opin. Biotechnol. 50, 166–173. https://doi.org/10.1016/j.copbio.2018.01.014 (2018).
CAS  Article  PubMed  Google Scholar 

30.
Heil, J., Vereecken, H. & Bruggemann, N. A review of chemical reactions of nitrification intermediates and their role in nitrogen cycling and nitrogen trace gas formation in soil. Eur. J. Soil Sci. 67, 23–39. https://doi.org/10.1111/ejss.12306 (2016).
CAS  Article  Google Scholar 

31.
Blume, H.-P. et al. Scheffer/Schachtschabel Soil Science (Springer, Berlin, 2015).
Google Scholar 

32.
Nieder, R., Benbi, D. K. & Scherer, H. W. Fixation and defixation of ammonium in soils: a review. Biol. Fertil. Soils 47, 1–14. https://doi.org/10.1007/s00374-010-0506-4 (2011).
CAS  Article  Google Scholar 

33.
Nommik, H. & Vahtras, K. Retention and fixation of ammonium and ammonia in soils. In Nitrogen in Agricultural Soils 22, (ed. Stevenson, F. J.) 123–171 (Wiley, Madison, Wisconsin, USA, 1982).
Google Scholar 

34.
Morris, E. C. et al. Shaping 3D root system architecture. Curr. Biol. 27, R919–R930. https://doi.org/10.1016/j.cub.2017.06.043 (2017).
CAS  Article  PubMed  Google Scholar 

35.
Anghinoni, I. & Barber, S. A. Corn root-growth and nitrogen uptake as affected by ammonium placement. Agron. J. 80, 799–802. https://doi.org/10.2134/agronj1988.00021962008000050021x (1988).
Article  Google Scholar 

36.
Anghinoni, I., Magalhaes, J. R. & Barber, S. A. Enzyme-activity, nitrogen uptake and corn growth as affected by ammonium concentration in soil solution. J. Plant Nutr. 11, 131–144. https://doi.org/10.1080/01904168809363791 (1988).
CAS  Article  Google Scholar 

37.
Pan, W. L., Madsen, I. J., Bolton, R. P., Graves, L. & Sistrunk, T. Ammonia/ammonium toxicity root symptoms induced by inorganic and organic fertilizers and placement. Agron. J. 108, 2485–2492. https://doi.org/10.2134/agronj2016.02.0122 (2016).
CAS  Article  Google Scholar 

38.
Xu, L. et al. Nitrogen transformation and plant growth in response to different urea-application methods and the addition of DMPP. J. Plant Nutr. Soil Sci. 177, 271–277. https://doi.org/10.1002/jpln.201100390 (2014).
CAS  Article  Google Scholar 

39.
Zhang, J. C. & Barber, S. A. Corn root distribution between ammonium fertilized and unfertilized soil. Commun. Soil Sci. Plant Anal. 24, 411–419. https://doi.org/10.1080/00103629309368811 (1993).
Article  Google Scholar 

40.
Maestre, F. T. & Reynolds, J. F. Small-scale spatial heterogeneity in the vertical distribution of soil nutrients has limited effects on the growth and development of Prosopis glandulosa seedlings. Plant Ecol. 183, 65–75. https://doi.org/10.1007/s11258-005-9007-1 (2006).
Article  Google Scholar 

41.
Rabbi, S. M., Guppy, C., Flavel, R., Tighe, M. & Young, I. Root plasticity not evident in N-enriched soil volumes for wheat (Triticum aestivum L.) and Barley (Hordeum vulgare L.) varieties. Commun. Soil Sci. Plant Anal. 48, 2002–2012 (2017).
CAS  Article  Google Scholar 

42.
Van Vuuren, M., Robinson, D. & Griffiths, B. Nutrient inflow and root proliferation during the exploitation of a temporally and spatially discrete source of nitrogen in soil. Plant Soil 178, 185–192 (1996).
Article  Google Scholar 

43.
Hodge, A., Robinson, D., Griffiths, B. S. & Fitter, A. H. Why plants bother: root proliferation results in increased nitrogen capture from an organic patch when two grasses compete. Plant Cell Environ. 22, 811–820. https://doi.org/10.1046/j.1365-3040.1999.00454.x (1999).
Article  Google Scholar 

44.
Hodge, A., Stewart, J., Robinson, D., Griffiths, B. S. & Fitter, A. H. Root proliferation, soil fauna and plant nitrogen capture from nutrient-rich patches in soil. New Phytol. 139, 479–494. https://doi.org/10.1046/j.1469-8137.1998.00216.x (1998).
Article  Google Scholar 

45.
Hodge, A., Stewart, J., Robinson, D., Griffiths, B. S. & Fitter, A. H. Plant, soil fauna and microbial responses to N-rich organic patches of contrasting temporal availability. Soil Biol. Biochem. 31, 1517–1530. https://doi.org/10.1016/S0038-0717(99)00070-X (1999).
CAS  Article  Google Scholar 

46.
Li, H. B. et al. Root morphological responses to localized nutrient supply differ among crop species with contrasting root traits. Plant Soil 376, 151–163. https://doi.org/10.1007/s11104-013-1965-9 (2014).
CAS  Article  Google Scholar 

47.
Abalos, D., Sanz-Cobena, A., Misselbrook, T. & Vallejo, A. Effectiveness of urease inhibition on the abatement of ammonia, nitrous oxide and nitric oxide emissions in a non-irrigated Mediterranean barley field. Chemosphere 89, 310–318. https://doi.org/10.1016/j.chemosphere.2012.04.043 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

48.
Slangen, J. H. G. & Kerkhoff, P. Nitrification inhibitors in agriculture and horticulture—a literature-review. Fertil. Res. 5, 1–76. https://doi.org/10.1007/Bf01049492 (1984).
CAS  Article  Google Scholar 

49.
Zaman, M., Zaman, S., Nguyen, M. L., Smith, T. J. & Nawaz, S. The effect of urease and nitrification inhibitors on ammonia and nitrous oxide emissions from simulated urine patches in pastoral system: a two-year study. Sci. Tot. Environ. 465, 97–106. https://doi.org/10.1016/j.scitotenv.2013.01.014 (2013).
CAS  Article  Google Scholar 

50.
Metzner, R. et al. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification. Plant Methods 11, 17. https://doi.org/10.1186/s13007-015-0060-z (2015).
Article  PubMed  PubMed Central  Google Scholar 

51.
Beuters, P., Scherer, H. W., Spott, O. & Vetterlein, D. Impact of potassium on plant uptake of non-exchangeable NH4+-N. Plant Soil 387, 37–47. https://doi.org/10.1007/s11104-014-2275-6 (2014).
CAS  Article  Google Scholar 

52.
Vetterlein, D., Kuhn, T., Kaiser, K. & Jahn, R. Illite transformation and potassium release upon changes in composition of the rhizophere soil solution. Plant Soil 371, 267–279. https://doi.org/10.1007/s11104-013-1680-6 (2013).
CAS  Article  Google Scholar 

53.
VDLUFA, M. Band 1. Die Untersuchung von Böden (VDLUFA-Verlag, Darmstad, 1991) ((in German)).
Google Scholar 

54.
Koebernick, N. et al. In situ visualization and quantification of three-dimensional root system architecture and growth using X-ray computed tomography. Vadose Zone J. https://doi.org/10.2136/vzj2014.03.0024 (2014).
Article  Google Scholar 

55.
Blaser, S. R. G. A., Schlüter, S. & Vetterlein, D. How much is too much?-Influence of X-ray dose on root growth of faba bean (Vicia faba) and barley (Hordeum vulgare). PLoS ONE 13, e0193669. https://doi.org/10.1371/journal.pone.0193669 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Schlüter, S., Blaser, S. R. G. A., Weber, M., Schmidt, V. & Vetterlein, D. Quantification of root growth patterns from the soil perspective via root distance models. Front. Plant Sci. 9, 1084. https://doi.org/10.3389/fpls.2018.01084 (2018).
Article  PubMed  PubMed Central  Google Scholar 

57.
Flavel, R. J. et al. Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography. J. Exp. Bot. 63, 2503–2511. https://doi.org/10.1093/jxb/err421 (2012).
CAS  Article  PubMed  Google Scholar 

58.
Doube, M. et al. BoneJ: free and extensible bone image analysis in ImageJ. Bone 47, 1076–1079. https://doi.org/10.1016/j.bone.2010.08.023 (2010).
Article  PubMed  PubMed Central  Google Scholar 

59.
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Kirschke, T., Spott, O. & Vetterlein, D. Impact of urease and nitrification inhibitor on NH4+ and NO3− dynamic in soil after urea spring application under field conditions evaluated by soil extraction and soil solution sampling. J. Plant Nutr. Soil Sci. 182, 441–450. https://doi.org/10.1002/jpln.201800513 (2019).
CAS  Article  Google Scholar 

61.
61Bergmann, W. Farbatlas Ernährungsstörungen bei Kulturpflanzen: visuelle und analytische Diagnose. (1986).

62.
Bennett, W. F., Pesek, J. & Hanway, J. J. Effect of nitrate and ammonium on growth of corn in nutrient solution sand culture. Agron. J. 56, 342–345 (1964).
CAS  Article  Google Scholar 

63.
Magalhaes, J. R. & Wilcox, G. E. Tomato growth and nutrient-uptake patterns as influenced by nitrogen form and light-intensity. J. Plant Nutr. 6, 941–956. https://doi.org/10.1080/01904168309363157 (1983).
CAS  Article  Google Scholar 

64.
Ganmore-Neumann, R. & Kafkafi, U. root temperature and percentage NO3−/NH4+ effect on tomato plant development I. Morphology and growth 1. Agron. J. 72, 758–761 (1980).
CAS  Article  Google Scholar 

65.
Einsmann, J. C., Jones, R. H., Pu, M. & Mitchell, R. J. Nutrient foraging traits in 10 co-occurring plant species of contrasting life forms. J. Ecol. 87, 609–619. https://doi.org/10.1046/j.1365-2745.1999.00376.x (1999).
Article  Google Scholar 

66.
Gao, W., Blaser, S. R. G. A., Schluter, S., Shen, J. B. & Vetterlein, D. Effect of localised phosphorus application on root growth and soil nutrient dynamics in situ—comparison of maize (Zea mays) and faba bean (Vicia faba) at the seedling stage. Plant Soil 441, 469–483. https://doi.org/10.1007/s11104-019-04138-2 (2019).
CAS  Article  Google Scholar 

67.
Britto, D. T. & Kronzucker, H. J. NH4+ toxicity in higher plants: a critical review. J. Plant Physiol. 159, 567–584. https://doi.org/10.1078/0176-1617-0774 (2002).
CAS  Article  Google Scholar 

68.
Adjel, F., Bouzerzour, H. & Benmahammed, A. Salt stress effects on seed germination and seedling growth of barley (Hordeum vulgare L.) Genotypes. J. Agric. Sustain. 3, 223–237 (2013).
Google Scholar 

69.
Ahmed, A. K., Tawfik, K. & Abd El-Gawad, Z. Tolerance of seven faba bean varieties to drought and salt stresses. Res. J. Agric. Biol. Sci. 4, 175–186 (2008).
Google Scholar 

70.
Link, W. et al. Genotypic variation for drought tolerance in Vicia faba. Plant Breed. 118, 477–483. https://doi.org/10.1046/j.1439-0523.1999.00412.x (1999).
Article  Google Scholar 

71.
Varshney, R. K. et al. Genome wide association analyses for drought tolerance related traits in barley (Hordeum vulgare L.). Field Crops Res. 126, 171–180. https://doi.org/10.1016/j.fcr.2011.10.008 (2012).
Article  Google Scholar 

72.
Wilcox, G. E., Magalhaes, J. R. & Silva, F. L. I. M. Ammonium and nitrate concentrations as factors in tomato growth and nutrient-uptake. J. Plant Nutr. 8, 989–998. https://doi.org/10.1080/01904168509363401 (1985).
Article  Google Scholar 

73.
Elamin, O. M. & Wilcox, G. E. Nitrogen form ratio influence on muskmelon growth, composition, and manganese toxicity. J. Am. Soc. Hortic. Sci. 111, 320–322 (1986).
Google Scholar 

74.
Handa, S., Warren, H. L., Huber, D. M. & Tsai, C. Y. Nitrogen nutrition and seedling development of normal and opaque-2 maize genotypes. Can. J. Plant Sci. 64, 885–894. https://doi.org/10.4141/cjps84-121 (1984).
CAS  Article  Google Scholar  More

We found that in breast-fed group, α diversity remained unchanged before 3 months of age, but increased significantly in 6 months of age. Previously studies have reported that faecal bacterial diversity increases with age, indicating a more complex microbial community over time8,9. Studies have shown that infants who are exclusively breast-fed have lower microbial diversity, compared with formula-fed babies whose gut microbiota is more diverse and similar to older children10,11,12. The difference of gut microbial diversity between breast-fed and formula-fed babies is also reported in animal research in tiger cubs13. We also found that among different groups, α diversity was lower in breast-fed group than formula-fed groups in 40 days of age. In adults, low gut microbial diversity has been linked to diseases in recent studies. In infants, breast milk may be the major determinant of a lower gut microbial diversity, because specific bacteria are selected for degrading particular oligosaccharides in breast milk. The predomination of infant-type Bifidobacteria during breastfeeding results in a low bacterial diversity, but it is beneficial for babies’ health. For example, the infant-type Bifidobacteria has a large impact on the maturation of the immune system, which may help reduce the incidence of infections in children. However, some diseases have been associated with a reduced microbial diversity in early life, such as eczema and asthma, which have been linked to low microbial diversity in 1 week–4 months of age. But the low microbial diversity is not coupled to Bifidobacterium abundance in these studies, and no reports have shown negative impacts of breastfeeding on development of asthma or allergies. The causality of lower diversity to diseases remains to be identified. What’s more, research has suggested that an immature gut microbial community can be “repaired” by introduction of adult-like microbes increasing greatly during introduction of solid foods in 6 months of age, which is within the development window of opportunity. Findings in adults cannot be inferred to infants regarding the association of gut microbial diversity with diseases, since the microbial ecosystem and the immune system of infants are quite different from adults4.
Bifidobacterium represented the most predominant genus and Enterobacteriaceae the second in all groups at all time-points in our study. Previous study also indicates that all infants have significant levels of Enterobacteriaceae and Bifidobacteriaceae at family level in 2 months of age. The abundance of a single genus usually constitutes the most in family level evaluation. Roger et al. have indicated that Bifidobacterium accounts for 40–60% on average of the total faecal microbiota of a 2-week old new born10. In our study, in 40 days of age, Bifidobacterium accounted for 46.2% in breast-fed group, and 32.2–33.0% in formula-fed groups, which was precisely classified according to feeding types. Bifidobacterium is present in the first few months and decreases as age goes on to almost zero by 18 months old14. Enterobacteriaceae also decreases with time7,8. This is consistent with the European study of 531 infants, which indicates the decrease trend in Bifidobacteriaceae and Enterobacteriaceae species from 6 weeks of age until 4 weeks after solid foods introduction, regardless of differences in feeding patterns15. We found that in breast-fed group, Bifidobacterium decreased from 46.2% in 40 days to 41.4% in 3 months and 29.9% in 6 months of age. In formula-fed groups, after solid foods introduction, Bifidobacterium decreased from 32.2% in 3 months to 31.7% in 6 months of age in formula A group, but increased from 33.0 to 39.0% in formula B group, indicating that different formulas may have different effects on microbiota. In our study, solid foods were introduced from 4 to 6 months of age, so they affected only the last time point in 6 m. We found that in 40 days of age, Bifidobacterium and Bacteroides were significantly higher, while Streptococcus and Enterococcus copy numbers were significantly lower in breast-fed group than they were in formula A-fed group. Lachnospiraceae was lower in breast-fed group than that in formula B-fed group. Veillonella and Clostridioides were lower in breast-fed group than that in formula A and B-fed groups. In 3 months of age there were less Lachnospiraceae and Clostridioides in breast-fed group than formula-fed groups. Other differences of microbiota were shown in Figs. 5 and 6.
After birth, the most important determinant of infant gut microbial colonization is breastfeeding. Studies have shown that breastfeeding is associated with higher levels of Bifidobacterium1,2,16, which is consistent with our study. The genus Bifidobacterium possesses multiple benefits, such as modulation of the immune system, production of vitamins, remission of atopic dermatitis symptoms in infants and decrease in rotavirus infections and lactose intolerance in children and adults10,17. Bifidobacteria is reported to be associated with diminished risk of allergic diseases18 and excessive weight gain19. Higher level of Bifidobacteria also indicates better immune responses to vaccines20.
Bacteroides is among several beneficial bacteria in the earlier neonatal phase, which has important and specific functions in the development of mucosal immune system6. The early activation of mucosal immune system may provide human body lifelong protection from health disorders6. Bacteroides is also linked with increased diversity and faster maturation of gut2. Koenig has studied 1 baby for 2.5 years after its birth and found that Bacteroides genus is absent before the introduction of solid foods21. However, Yassour M. et al. have reported that many infants present a significant Bacteroides species in the first 6 months, before the introduction of solid foods, in a longitudinal study of 39 children in their first 3 years of life14. We also found that there was Bacteroides in the first 6 months of life in all groups. Bacteroides was significantly higher in breast-fed infants, ranking third in 40 days (0.095) in breast-fed group, but decreased as time went on to 0.059 in 3 m and 0.039 in 6 m.
Besides Bacteroides, other health promoting bacteria like Clostridia has been reported to be vital to provide mucosal barrier homeostasis during the neonatal period, which is necessary in the immature intestine6. Formula-fed infants tend to have a more diverse microbial community with increased Clostridia species9,12, which is in accordance with our finding. We also found Veillonella was lower in breast-fed infants than formula-fed ones. Although there is an analysis indicating that Veillonella has been associated with a lower incidence of asthma, it has not taken feeding patterns into consideration22. So more data are needed to clarify the specific roles of certain bacteria with regard to feeding types.
Studies have shown that breast milk keeps the gut in a condition with a lower abundance of Veillonellaceae, Enterococcaceae, Streptococcaceae9,11,23 and Lachnospiraceae7, which is consistent with our results. Some researchers have indicated that higher level of Streptococcus sp. is seen in patients suffered from type 1 diabetes2. There may be other negative effects of these bacteria, but we still know little about them.
The subsequent big change in diet is the introduction of solid foods in 4–6 months of age, which is largely associated with changes in infant gut microbiota. A case study has found an increase in Bacteroidetes at phylum level after solid foods are introduced21. They have indicated that Bacteroidetes is specialized in the decomposition of complex plant polysaccharides21, and it is also associated with faster maturation of the intestinal microbial community2. In our study, after solid foods introduction, percentage of Bacteroides at genus level increased in formula A-fed group, from 0.023 to 0.028, but kept almost the same from 0.009 to 0.008 in formula B-fed group. While in breast-fed group, a decreased percentage of Bacteroides was found from 0.059 in 3 m to 0.039 in 6 m. The trends are different according to different feeding patterns. Pannaraj et al. believe that daily breastfeeding as a part of milk intake continues to affect the infant gut microbial composition, even after solid foods introduction8. But in our study, differences in gut microbiota between breast-fed group and formula-fed groups were not seen any more after solid foods were introduced. As for studies of gut microbiota, the taxonomic level of bacteria adopted in research may affect the results. We focused on microbiota mainly at genus level, resulting in certain discrepancies with some other articles at phylum or species level.
There were significant differences of microbiota between formula A-fed and formula B-fed groups in our study. We found that Pediococcus was less in formula A-fed group than that in formula B-fed group in 40 days. Many research articles have not taken the differences of formulas into consideration, especially retrospective studies. Even breast-fed group is mixed with formulas in some reports. So there must be some inaccuracies of their findings.
Except for feeding patterns, several factors are associated with the microbiota over the first year of life, which is a key period for the gut colonization, such as the mode of delivery, antibiotic exposure, geographical location, household siblings, and furry pets2,9. During the first days of life, the gut microbiota in infants born by vaginal delivery (VD) is similar to that in maternal vagina and intestinal tract, whereas in infants born by caesarean section delivery (CS) the gut microbiota shares characteristics with that of maternal skin. We noticed that the genera of Bacteroides and Parabacteroides were negatively correlated with CS. This was consistent with findings in many other studies, in which the difference of Bacteroides remains in 4 and 12 months of age7,9, and we also found the negative correlation of Bacteroides with CS existed not only in 40 days but also in 6 months of age. The increased morbidity reported extensively in infants born by CS is likely led by altered early gut colonization partially24. Accumulating data have indicated that antibiotic-mediated gut microbiota turbulence during the vital developmental window in early life period may lead to increased risk for chronic non-infectious diseases in later life24. There is a high detection rate of gut Enterococcus in antibiotic-treated infants in their early postnatal period among 26 infants born in a mean gestational age of 39 weeks25. We also found that the relative abundance of Enterococcus was positively correlated with antibiotics usage. The overgrowth of Enterococcus may be caused by antibiotic selection25.
In conclusion, by a larger cohort study than before, differences in gut microbiota among infants who were fed exclusively by breast milk or a single kind of formulas were obtained from this study, contributing further to our understanding of early gut microbial colonization, with more solid data than previous studies with mixed feeding patterns. Faecal diversity was lower in breast-fed infants than formula-fed ones in early life period, but increased significantly after solid foods introduction. A low diversity of the gut microbiota in early life appeared to characterize a healthy gut, if caused by breastfeeding, which was different from theories in adults. There were differences in bacterial composition in infants according to different feeding types, and even different formulas had different effects on microbiota, which we could not ignore in future research. This study presented initial data facilitating further research that will help us understand the importance of breastfeeding to gut microbiota in early life period. More

1.
Benoit-Bird, K. J. & Au, W. W. L. Energy: Converting from acoustic to biological resource units. J. Acoust. Soc. Am. 111, 2070 (2002).
ADS  PubMed  Article  Google Scholar 
2.
Simard, Y. & Mackas, D. L. Mesoscale aggregations of euphausiid sound scattering layers on the continental shelf of Vancouver Island. Can. J. Fish. Aquat. Sci. 46, 1238–1249 (1989).
Article  Google Scholar 

3.
Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. https://doi.org/10.1016/j.pocean.2015.03.003 (2015).
Article  Google Scholar 

4.
Ariza, A. et al. Vertical distribution, composition and migratory patterns of acoustic scattering layers in the Canary Islands. J. Mar. Syst. https://doi.org/10.1016/j.jmarsys.2016.01.004 (2016).
Article  Google Scholar 

5.
Hays, G. C. A review of the adaptive significance and ecosystem consequences of zooplankton diel vertical migrations. Hydrobiologia 503, 163–170 (2003).
Article  Google Scholar 

6.
Yatsu, A., Sassa, C., Moku, M. & Kinoshita, T. Night-time vertical distribution and abundance of small epipelagic and mesopelagic fishes in the upper 100 m layer of the Kuroshio–Oyashio Transition Zone in Spring. Fish. Sci. 71, 1280–1286 (2005).
CAS  Article  Google Scholar 

7.
Olson, R. J. et al. Bioenergetics, Trophic Ecology, and Niche Separation of Tunas. (Advances in Marine Biology, 2016).

8.
Hudson, J. M., Steinberg, D. K., Sutton, T. T., Graves, J. E. & Latour, R. J. Myctophid feeding ecology and carbon transport along the northern Mid-Atlantic Ridge. Deep Res. Part I Oceanogr. Res. Pap. https://doi.org/10.1016/j.dsr.2014.07.002 (2014).
Article  Google Scholar 

9.
Guidi, L. et al. A new look at ocean carbon remineralization for estimating deepwater sequestration. Global Biogeochem. Cy. 29, 1044–1059 (2015).
ADS  CAS  Article  Google Scholar 

10.
van Noord, J. E. Diet of five species of the family Myctophidae caught off the Mariana Islands. Ichthyol. Res. 60, 89–92 (2013).
Article  Google Scholar 

11.
Lam, V. & Pauly, D. Mapping the global biomass of mesopelagic fishes. Sea Around Us Proj. Newsl. 30, 4 (2005).
Google Scholar 

12.
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Hulley, P. A. Results of the research cruises of FRV” Walther Herwig” to South America. LVIII. Family Myctophidae (Osteichthyes, Myctophiformes). Arch. für Fischereiwiss. 31, 1–300 (1981).
Google Scholar 

14.
Hulley, P. A. Myctophidae. In Check-List of the FISHES of the Eastern Tropical Atlantic (CLOFETA) (eds Quero, J. C., Hureau, J. C., Karrer, C., Post, A. & Saldanha, L.) 398–467 (1990).

15.
Lubimova, T. G., Shust, K. V. & Popkov, V. V. Specific features in the ecology of Southern Ocean mesopelagic fish of the family Myctophidae. In Biological Resources of the Arctic and Antarctic (Collected Papers) 337 (1987).

16.
Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).
Article  Google Scholar 

17.
Cherel, Y., Fontaine, C., Richard, P. & Labat, J. P. Isotopic niches and trophic levels of myctophid fishes and their predators in the Southern Ocean. Limnol. Oceanogr. 55, 324–332 (2010).
ADS  CAS  Article  Google Scholar 

18.
Pérez-Rodríguez, A. An Integrative Study to the Functioning of the Flemish Cap Demersal Community (University of Vigo, Vigo, 2012).
Google Scholar 

19.
Battaglia, P. et al. Feeding habits of the Atlantic bluefin tuna, Thunnus thynnus (L. 1758), in the central Mediterranean Sea (Strait of Messina). Helgol. Mar. Res. 67, 97–107 (2013).
ADS  Article  Google Scholar 

20.
Rosas-Luis, R., Villanueva, R. & Sánchez, P. Trophic habits of the Ommastrephid squid Illex coindetii and Todarodes sagittatus in the northwestern Mediterranean Sea. Fish. Res. 152, 21–28 (2014).
Article  Google Scholar 

21.
Hedd, A., Montevecchi, W. A., Davoren, G. K. & Fifield, D. A. Diets and distributions of Leach’s storm-petrel (Oceanodroma leucorhoa) before and after an ecosystem shift in the Northwest Atlantic. Can. J. Zool. 87, 787–801 (2009).
CAS  Article  Google Scholar 

22.
Ohizumi, H., Kuramochi, T., Kubodera, T., Yoshioka, M. & Miyazaki, N. Feeding habits of Dall’s porpoises (Phocoenoides dalli) in the subarctic North Pacific and the Bering Sea basin and the impact of predation on mesopelagic micronekton. Deep Sea Res. Part I Oceanogr. Res. Pap. 50, 593–610 (2003).
ADS  Article  Google Scholar 

23.
Lisovenko, L. A. & Prut’ko, V. G. Reproductive biology of Diaphus suborbitalis (Myctophidae) in the equatorial part of the Indian Ocean. 2. Fecundity and reproductive potential. J. Ichthyol. 27, 1–12 (1987).
Google Scholar 

24.
Shotton, R. Lanternfishes: A potential fishery in the Northern Arabian Sea. Review of the state of world fishery resources: Marine fisheries. FAO Fisheries Circular, (920). (1997).

25
Olivar, M. P. et al. Vertical distribution, diversity and assemblages of mesopelagic fishes in the western Mediterranean. Deep Res. Part I Oceanogr. Res. Pap. 62, 53–69 (2012).
ADS  Article  Google Scholar 

26.
Battaglia, P. et al. Diet of the spothead lanternfish Diaphus metopoclampus (Cocco, 1829) (Pisces: Myctophidae) in the central Mediterranean Sea. Ital. J. Zool. 81, 530–543 (2014).
CAS  Article  Google Scholar 

27.
Lisovenko, L. A. & Prutko, V. G. Reproductive biology of Diaphus suborbitalis (Myctophidae) in the equatorial part of the Indian Ocean. 1. Nature of oogenesis and type of spawning. J. Ichthyol. 26, 619–629 (1986).
Google Scholar 

28.
Dalpadado, P. Reproductive biology of the lanternfish Benthosema pterotum from the Indian Ocean. Mar. Biol. 98, 307–316 (1988).
Article  Google Scholar 

29.
Hussain, S. M. The reproductive biology of the lantern fish Benthosema fibulatum from the northern Arabian Sea. Fish. Res. 13, 381–393 (1992).
Article  Google Scholar 

30.
Gartner, J. V. Patterns of reproduction in the dominant lanternfish species (Pisces: Myctophidae) of the eastern Gulf of Mexico, with a review of reproduction among tropical-subtropical Myctophidae. Bull. Mar. Sci. 52, 721–750 (1993).
Google Scholar 

31.
Gjosaeter, J. Life history and ecology of the myctophid fish Notoscopelus elongatus Kroeyeri from the northeast Atlantic. Ser. Havundersokelser Fisk. Skr. 17, 133–142 (1981).
Google Scholar 

32.
Kawaguchi, K., Mauchline, J. & Mauchline, J. Biology of myctophid fishes (Family Myctophidae) in the rockall trough, Northeastern Atlantic Ocean Biology of Myctophid Fishes (Family Myctophidae) in the Rockall Trough, Northeastern Atlantic Ocean. Biol. Oceanogr. 1, 337–373 (1982).
Google Scholar 

33
Hussain, S. M. & Ali-Khan, J. Fecundity of Benthosema fibulatum and Benhosema pterotum from the northern Arabian sea. Deep Sea Res. Part A Oceanogr. Res. Pap. 34, 1293–1299 (1987).
ADS  Article  Google Scholar 

34.
Young, J. W., Blaber, S. J. M. & Rose, R. Reproductive biology of three species of midwater fishes associated with the continental slope of eastern Tasmania, Australia. Mar. Biol. 95, 323–332 (1987).
Article  Google Scholar 

35.
Prosch, R. M. Reproductive biology and spawning of the myctophid Lampanyctodes hectoris and the sternoptychid Maurolicus muelleri in the Southern Benguela ecosystem. S. Afr. J. Mar. Sci. 10, 241–252 (1991).
Article  Google Scholar 

36.
Clarke, T. A. Fecundity and other aspects of reproductive effort in mesopelagic fishes from the North Central and Equatorial Pacific. Biol. Oceanogr. 3, 147–165 (1984).
Google Scholar 

37.
Flynn, A. J. & Paxton, J. R. Spawning aggregation of the lanternfish Diaphus danae (family Myctophidae) in the north-western Coral Sea and associations with tuna aggregations. Mar. Freshw. Res. 63, 1255–1271 (2012).
Article  Google Scholar 

38.
García-Seoane, E., Bernal, A. & Saborido-Rey, F. Reproductive ecology of the glacier lanternfish Benthosema glaciale. Hydrobiologia 727, 137–149 (2014).
Article  Google Scholar 

39.
Sassa, C., Ohshimo, S., Tanaka, H. & Tsukamoto, Y. Reproductive biology of Benthosema pterotum (Teleostei: Myctophidae) in the shelf region of the East China Sea. J. Mar. Biol. Assoc. UK 94, 423–433 (2014).
Article  Google Scholar 

40.
Sassa, C., Tanaka, H. & Ohshimo, S. Comparative reproductive biology of three dominant myctophids of the genus Diaphus on the slope region of the East China Sea. Deep. Res. Part I Oceanogr. Res. Pap. 115, 145–158 (2016).
ADS  Article  Google Scholar 

41.
Eschmeyer, W. N., Fricke, R. & R, van der L. Catalog of Fishes, electronic version (3 January 2017). San Francisco, CA (California Academy of Sciences). (2018).

42
Collins, M. A. et al. Latitudinal and bathymetric patterns in the distribution and abundance of mesopelagic fish in the Scotia Sea. Deep. Res. Part II Top. Stud. Oceanogr. 59–60, 189–198 (2012).
ADS  Article  Google Scholar 

43.
Albikovskaya, L. K. Some aspects of the biology and distribution of glacier lanternfish (Benthosema glaciale) over the slopes of Flemish Cap and eastern Grand Bank. NAFO Sci. Counc. Stud. 12, 37–42 (1988).
Google Scholar 

44.
Nafpaktitis, B. G. Review of the lanternfish genus Notoscopelus (family myctophidae) in the north Atlantic and the Mediterranean. Bulletin of Marine Science vol. 25 (1975).

45.
Hulley, P. A. & Paxton, J. R. Myctophiformes—Myctophidae/Neoscopelidae. In FAO Species Identification Guide for Fisheries Purposes: The Living Marine Resources of the Eastern Central Atlantic (eds Carpenter, K.E. & De Angelis, N.) 1922–1923, Rome (2016).

46.
Sarmiento-Lezcano, A. N., Triay-Portella, R., Castro, J. J., Rubio-Rodríguez, U. & Pajuelo, J. G. Age-based life-history parameters of the mesopelagic fish Notoscopelus resplendens (Richardson, 1845) in the Central Eastern Atlantic. Fish. Res. https://doi.org/10.1016/j.fishres.2018.03.016 (2018).
Article  Google Scholar 

47.
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. Part I Oceanogr. Res. Pap. https://doi.org/10.1016/j.dsr.2017.05.006 (2017).
Article  Google Scholar 

48.
Moser, H. G. & Ahlstrom, E. H. Myctophidae: lanternfishes. In The Early Stages of Fishes in the California Current Region. Cal. Coop. Ocean. Fish. (CalCOFI). (ed. Moser, H. G.) 387–475 (1996).

49.
Bordes, F. et al. Catálogo de especies meso y batipelágicas. Peces, moluscos y crustáceos. Colectadas con arrastre en las Islas Canarias, durante las campañas realizadas a bordo de B/E ‘La Bocaina’. Instituto Canarios de Ciencias Marinas (ICCM), Agencia Canaria de Investig. in 326 (2009).

50.
Bordes Caballero, F. et al. Epi- and mesopelagic fishes, acoustic data, and SST images collected off Lanzarote, Fuerteventura and Gran Canaria, Canary Islands, during cruise ‘La Bocaina 04–97’. In Informes Técnicos del Instituto Canario de Ciencias Marinas. 1–45 (1999).

51.
Wienerroither, R. M. Species composition of mesopelagic fishes in the area of the Canary Islands, Eastern Central Atlantic. Informes Técnicos del Instituto Canario de Ciencias Marinas. (2003).

52.
Fulton, T. W. The Sovereignty of the Sea: An Historical Account of the Claims of England to the Dominion of the British Seas and of the Evolution of the Territorial Waters, with Special Reference to the Rights of Fishing and the Naval Salute (W. Blackwood, Edinburgh, 1911).
Google Scholar 

53.
Brown-Peterson, N. J., Wyanski, D. M., Saborido-Rey, F., Macewicz, B. J. & Lowerre-Barbieri, S. K. A standardized terminology for describing reproductive development in fishes. Mar. Coast. Fish. 3, 52–70 (2011).
Article  Google Scholar 

54.
Brown-Peterson, N. J., Overstreet, R. M., Lotz, J. M., Franks, J. S. & Burns, K. M. Reproductive biology of cobia, Rachycentron canadum, from coastal waters of the southern United States. Fish. Bull. 99, 15–28 (2001).
Google Scholar 

55.
Luna, L. G. Manual of Histologic Staining Methods of the Armed Forces Institute of Pathology 3rd edn. (McGraw-Hill Book Company, New York, 1968).
Google Scholar 

56.
De Vlaming, V. Oocyte Developmental Pattern and Hormonal Involvement Among Teleosts. (1983).

57.
Murua, H. et al. Procedures to estimate fecundity of wild collected marine fish in relation to fish reproductive strategy. J. Northwest Atl. Fish. Sci. 33, 33–54 (2003).
Article  Google Scholar 

58.
Kiesbu, O. S. Oogenesis in cod, Gadus morhua L., studied by light and electron microscopy. J. Fish Biol. 34, 735–745 (1989).
Article  Google Scholar 

59.
Hunter, J. R., Lo, N. C. H. & Leong, J. H. Batch Fecundity in multiple spawning fishes. NOAA Tech. Rep. NMFS 36, 67–77 (1985).
Google Scholar 

60.
R Core Team. R: A Language and Environment for Statistical Computing. (2018).

61.
Sassa, C. & Hirota, Y. Seasonal occurrence of mesopelagic fish larvae on the onshore side of the Kuroshio off southern Japan. Deep. Res. Part I 81, 49–61 (2013).
Article  Google Scholar 

62.
QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. (2020). https://www.qgis.org/es/site/.

63.
Wienerroither, R., Uibleina, F., Bordes, F. & Moreno, T. Composition, distribution, and diversity of pelagic fishes around the Canary Islands, Eastern Central Atlantic. Mar. Biol. Res. 5, 328–344 (2009).
Article  Google Scholar 

64.
Olivar, M. P. et al. Variation in the diel vertical distributions of larvae and transforming stages of oceanic fishes across the tropical and equatorial Atlantic. Prog. Oceanogr. 160, 83–100 (2018).
ADS  Article  Google Scholar 

65.
Clarke, T. A. Sex ratios and sexual differences in size among mesopelagic fishes from the central Pacific Ocean. Mar. Biol. 73, 203–209 (1983).
Article  Google Scholar 

66.
Herring, P. J. The Biology of the Deep Ocean (Oxford University Press, Oxford, 2002).
Google Scholar 

67.
Dalpadado, P. Aspects of the Biology of Benthosema pterotum (Myctophidae) from the Indian Ocean (University of Bergen, Bergen, 1983).
Google Scholar 

68.
Filin, A. A. Growth, size and age composition of the Notoscopelus kroeyerii (Myctophidae). J. Ichthyol. 37, 27–32 (1997).
Google Scholar 

69.
Greely, T. M., Gartner, J. V. J. & Torres, J. J. Age and growth of Electrona antarctica (Pisces: Myctophidae), the dominant mesopelagic fish in the Southern Ocean. Mar. Biol. 133, 145–158 (1999).
Article  Google Scholar 

70.
Hulley, P. A. Myctophidae. In Fishes of the North-eastern Atlantic and the Mediterranean (eds Whitehead, P.J.P. et al.) 429–483 (1986).

71.
Kawaguchi, K. & Shimizu, H. Taxonomy and distribution of the lanternfishes, genus Diaphus (Pisces, Myctophidae) in the western Pacific, eastern Indian Oceans and the southeast Asian Seas. Bull. Ocean Res. Inst. Univ. Tokyo 10, 1–145 (1978).
Google Scholar 

72.
Herring, P. J. Sex with the lights on? A review of bioluminescent sexual dimorphism in the sea. J. Mar. Biol. Assoc. UK 87, 829–842 (2007).
CAS  Article  Google Scholar 

73.
Barnes, A. T. & Case, J. F. The luminescence of lanternfish (Myctophidae): Spontaneous activity and responses to mechanical, electrical, and chemical stimulation. J. Exp. Mar. Biol. Ecol. 15, 203–221 (1974).
Article  Google Scholar 

74.
Clarke, T. A. Some aspects of the ecology of lanternfishes (Myctophidae) in the Pacific Ocean near Hawaii. Fish. Bull. 71, 401–434 (1973).
Google Scholar 

75.
Karnella, C. The Ecology of Lanterfishes (Myctophidae) in the Bermuda ‘Ocean Acre’ (George Washington University, Washington, 1983).
Google Scholar 

76.
Sabatés, A. & Masò, M. Effect of a shelf slope front on the spatial distribution of mesopelagic fish larvae in the western Mediterranean. Deep. Res. I(37), 1085–1098 (1990).
ADS  Article  Google Scholar 

77.
Alekseyeva, Y. I. & Alekseyev, F. Y. Some aspects of reproductive biology of the lanternfishes, Myctophum punctatum and Notoscopelus resplendens (Myctophidae), from the eastern tropical Atlantic. J. Ichthyol. 23, 56–63 (1983).
Google Scholar 

78.
Davison, P. C., Checkley, D. M., Koslow, J. A. & Barlow, J. Carbon export mediated by mesopelagic fishes in the northeast Pacific Ocean. Prog. Oceanogr. https://doi.org/10.1016/j.pocean.2013.05.013 (2013).
Article  Google Scholar  More