More stories

  • in

    Genetic structure in Orkney island mice: isolation promotes morphological diversification

    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

  • in

    Protein metabolism and physical fitness are physiological determinants of body condition in Southern European carnivores

    1.
    Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123(1), 111–119 (2014).
    Article  Google Scholar 
    2.
    Wilder, S. M., Raubenheimer, D. & Simpson, S. J. Moving beyond body condition indices as an estimate of fitness in ecological and evolutionary studies. Funct. Ecol. 30(1), 108–115 (2016).
    Article  Google Scholar 

    3.
    Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32(5), 1203–1213 (2018).
    Article  Google Scholar 

    4.
    Hill, G. E. Condition-dependent traits as signals of the functionality of vital cellular processes. Ecol. Lett. 14(7), 625–634 (2011).
    PubMed  Article  Google Scholar 

    5.
    Coon, C. A., Nichols, B. C., McDonald, Z. & Stoner, D. C. Effects of land-use change and prey abundance on the body condition of an obligate carnivore at the wildland-urban interface. Landsc. Urban Plan. 192, 103648 (2019).
    Article  Google Scholar 

    6.
    Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).
    Article  Google Scholar 

    7.
    Barnett, C. A., Suzuki, T. N., Sakaluk, S. K. & Thompson, C. F. Mass-based condition measures and their relationship with fitness: in what condition is condition?. J. Zool. 296(1), 1–5 (2015).
    Article  Google Scholar 

    8.
    Warner, D. A., Johnson, M. S. & Nagy, T. R. Validation of body condition indices and quantitative magnetic resonance in estimating body composition in a small lizard. J. Exp. Zool. A. Physiol. 325(9), 588–597 (2016).
    CAS  Article  Google Scholar 

    9.
    Stevenson, R. & Woods, W. A. Condition indices for conservation: new uses for evolving tools. Int. Comp. Biol. 46(6), 1169–1190 (2006).
    CAS  Article  Google Scholar 

    10.
    Homyack, J. A. Evaluating habitat quality of vertebrates using conservation physiology tools. Wildl. Res. 37(4), 332–342 (2010).
    Article  Google Scholar 

    11.
    Hayes, J. P. & Shonkwiler, J. S. Morphometric indicators of body condition, worthwhile or wishful thinking? In Body Composition Analysis of Animals, a Handbook of Non-destructive Methods (ed. Spearman, J. R.) 8–38 (Cambridge Univ. Press, Cambridge, 2001).
    Google Scholar 

    12.
    Peig, J. & Green, A. J. The paradigm of body condition: a critical reappraisal of current methods based on mass and length. Funct. Ecol. 24(6), 1323–1332 (2010).
    Article  Google Scholar 

    13.
    Larivière, S. et al. Influence of food shortage during the summer on body composition and reproductive hormones in the red fox, Vulpes vulpes. Can. J. Zool. 79(3), 471–477 (2001).
    Article  Google Scholar 

    14.
    Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23(1), 57–69 (2009).
    Article  Google Scholar 

    15.
    Risco, D. et al. Biometrical measurements as efficient indicators to assess wild boar body condition. Ecol. Indic. 88, 43–50 (2018).
    Article  Google Scholar 

    16.
    Gosler, A. G., Greenwood, J. J. & Perrins, C. Predation risk and the cost of being fat. Nature 377(6550), 621 (1995).
    ADS  CAS  Article  Google Scholar 

    17.
    Higginson, A. D., McNamara, J. M. & Houston, A. I. The starvation-predation trade-off predicts trends in body size, muscularity, and adiposity between and within taxa. Am. Nat. 179(3), 338–350 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Houston, A. I., Stephens, P. A., Boyd, I. L., Harding, K. C. & McNamara, J. M. Capital or income breeding? A theoretical model of female reproductive strategies. Behav. Ecol. 18(1), 241–250 (2006).
    Article  Google Scholar 

    19.
    Pond, C. M. & Ramsay, M. A. Allometry of the distribution of adipose tissue in Carnivora. Can. J. Zool. 70(2), 342–347 (1992).
    Article  Google Scholar 

    20.
    Kohl, K. D., Coogan, S. C. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrients? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37(6), 701–709 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    21.
    Mangipane, L. S. et al. Dietary plasticity in a nutrient-rich system does not influence brown bear (Ursus arctos) body condition or denning. Polar Biol. 41(4), 763–772 (2018).
    Article  Google Scholar 

    22.
    Caspersen, C. J., Powell, K. E. & Christenson, G. M. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 100(2), 126 (1985).
    CAS  PubMed  PubMed Central  Google Scholar 

    23.
    Graham, A. L. et al. Fitness consequences of immune responses, strengthening the empirical framework for ecoimmunology. Func. Ecol. 25(1), 5–17 (2011).
    Article  Google Scholar 

    24.
    Martin, L. B., Weil, Z. M. & Nelson, R. J. Seasonal changes in vertebrate immune activity, mediation by physiological trade-offs. Phil. Trans. R. Soc. B 363(1490), 321–339 (2007).
    Article  Google Scholar 

    25.
    Kindermann, C., Narayan, E. J. & Hero, J. M. Does physiological response to disease incur cost to reproductive ecology in a sexually dichromatic amphibian species?. Comp. Biochem. Physiol. A. 203, 220–226 (2017).
    CAS  Article  Google Scholar 

    26.
    Whiteman, J. P. et al. Heightened immune system function in polar bears using terrestrial habitats. Physiol. Bioch. Zool 92(1), 1–11 (2019).
    Article  Google Scholar 

    27.
    Garrow, J. S. & Webster, J. Quetelet’s index (W/H2) as a measure of fatness. Int. J. Obes. 9(2), 147–153 (1984).
    Google Scholar 

    28.
    Fulton, T. W. Rate of growth of sea fishes (ed. Fulton, T. W.) (Neill & Company, Edinburgh, 1902).

    29.
    Nash, R. D., Valencia, A. H. & Geffen, A. J. The origin of Fulton’s condition factor: setting the record straight. Fisheries 31(5), 236–238 (2006).
    Google Scholar 

    30.
    Cren, E. D. The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fluviatilis). J. Anim. Ecol. 20, 201 (1951).
    Article  Google Scholar 

    31.
    Jakob, E. M., Marshall, S. D. & Uetz, G. W. Estimating fitness, a comparison of body condition indices. Oikos 77(1), 61–67 (1996).
    Article  Google Scholar 

    32.
    Jolicoeur, P. Linear regressions in fishery research, some comments. J. Fish. B. Can. 32(8), 1491–1494 (1975).
    Article  Google Scholar 

    33.
    Green, A. J. Mass/length residuals: measures of body condition or generators of spurious results?. Ecol. 82(5), 1473–1483 (2001).
    Article  Google Scholar 

    34.
    Lindsjö, J., Fahlman, Å & Törnqvist, E. Animal welfare from mouse to moose – implementing the principles of the 3Rs in wildlife research. J. Wildl. Dis. 52(2S), S65–S77 (2016).
    PubMed  Article  Google Scholar 

    35.
    Guyton, A. C., Hall, J. E. Textbook of medical physiology (ed. Guyton, A. C., Hall, J. E) 11th ed. (Elsevier Saunders, Amsterdam, 2006).

    36.
    McCue, M. D. Starvation physiology, reviewing the different strategies animals use to survive a common challenge. Comp. Bioch. Physiol. A 156, 1–18 (2010).
    Article  CAS  Google Scholar 

    37.
    Russell, K., Lobley, G. E. & Millward, D. J. Whole-body protein turnover of a carnivore, Felis silvestris catus. Br. J. Nutr. 89(1), 29–37 (2003).
    CAS  PubMed  Article  Google Scholar 

    38.
    Delgiudice, G. D., Seal, U. S. & Mech, L. D. Effects of feeding and fasting on wolf blood and urine characteristics. J. Wildl. Manage. 51, 1 (1987).
    Article  Google Scholar 

    39.
    Domingo-Roura, X., Newman, C., Calafell, F. & Macdonald, D. W. Blood biochemistry reflects seasonal nutritional and reproductive constraints in the Eurasian badger (Meles meles). Phys. Biochem. Zool. 74, 450–460 (2001).
    CAS  Article  Google Scholar 

    40.
    Karasov, W. H., del Rio, C. M. Physiological ecology, how animals process energy, nutrients, and toxins (ed. Karasov, W. H., del Rio, C. M.) 1–739 (Princeton University Press, Princeton, 2007).

    41.
    Schmidt, W., Maassen, N., Trost, F. & Böning, D. Training induced effects on blood volume, erythrocyte turnover and haemoglobin oxygen binding properties. Eur. J. Appl. Physiol. Occ. Physiol. 57(4), 490–498 (1988).
    CAS  Article  Google Scholar 

    42.
    Brocherie, F. et al. Association of hematological variables with team-sport specific fitness performance. PLoS ONE 10(12), e0144446 (2015).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    43.
    McGowan, C. Clinical pathology in the racing horse, the role of clinical pathology in assessing fitness and performance in the racehorse. Vet. Clin. N. Am. 24(2), 405–421 (2008).
    Google Scholar 

    44.
    Warton, D. I., Wright, I. J., Falster, D. S. & Westoby, M. Bivariate line-fitting methods for allometry. Biol. Rev. 81(2), 259–291 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Nussey, D. H., Froy, H., Lemaitre, J. F., Gaillard, J. M. & Austad, S. N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12(1), 214–225 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    46.
    Bowers, E. K. et al. Sex-biased terminal investment in offspring induced by maternal immune challenge in the house wren (Troglodytes aedon). Proc. R. Soc. B 279(1739), 2891–2898 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Lobo, A., Marti, J. I. & Gimenez-Cassina, C. C. Regional scale hierarchical classification of temporal series of AVHRR vegetation index. Int. J. Rem. Sens. 18(15), 3167–3193 (1997).
    Article  Google Scholar 

    48.
    Alcaraz, D., Paruelo, J. & Cabello, J. Identification of current ecosystem functional types in the Iberian Peninsula. Glob. Ecol. Biogeogr. 15(2), 200–212 (2006).
    Article  Google Scholar 

    49.
    Oftedal, O. T. & Gittleman, J. L. Patterns of energy output during reproduction in carnivores. In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman, J. L.) (Springer, New York, 1989).
    Google Scholar 

    50.
    Franzmann, A. W. & Schwartz, C. C. Evaluating condition of Alaskan black bears with blood profiles. J. Wildl. Manage. 52(1), 63–70 (1988).
    CAS  Article  Google Scholar 

    51.
    McGuire, L. P. et al. Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats. J. Mammal. 99(5), 1065–1071 (2018).
    Article  Google Scholar 

    52.
    Stocker, R., Glazer, A. N. & Ames, B. N. Antioxidant activity of albumin-bound bilirubin. Proc. Nat. Acad. Sci. 84(16), 5918–5922 (1987).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Beaulieu, M. & Costantini, D. Biomarkers of oxidative status: missing tools in conservation physiology. Conserv. Physiol. 2(1), 014 (2014).
    Article  CAS  Google Scholar 

    54.
    Tothova, C., Nagy, O. & Kovac, G. Serum proteins and their diagnostic utility in veterinary medicine, a review. Vet. Med. 61(9), 475–496 (2016).
    Article  Google Scholar 

    55.
    Peck, H. E., Costa, D. P. & Crocker, D. E. Body reserves influence allocation to immune responses in capital breeding female northern elephant seals. Funct. Ecol. 30(3), 389–397 (2016).
    Article  Google Scholar 

    56.
    Deng, P., Jones, J. C. & Swanson, K. S. Effects of dietary macronutrient composition on the fasted plasma metabolome of healthy adult cats. Metabolomics 10(4), 638–650 (2014).
    CAS  Article  Google Scholar 

    57.
    Wilkens, M. R., Firmenich, C. S., Schnepel, N. & Muscher-Banse, A. S. A reduced protein diet modulates enzymes of vitamin D and cholesterol metabolism in young ruminants. J. Ster. Biochem. Mol. Biol. 186, 196–202 (2019).
    CAS  Article  Google Scholar 

    58.
    Oliveira, R. et al. Toward a genome-wide approach for detecting hybrids: informative SNPs to detect introgression between domestic cats and European wildcats (Felis silvestris). Heredity 115(3), 195–205 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    59.
    Sikes, R. S. & Gannon, W. L. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253 (2011).
    Article  Google Scholar 

    60.
    Chinnadurai, S. K., Strahl-Heldreth, D., Fiorello, C. V. & Harms, C. A. Best-practice guidelines for field-based surgery and anesthesia of free-ranging wildlife I Anesthesia and analgesia. J. Wildl. Dis. 52, S14–S27 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    61.
    Santos, N. et al. Characterization and minimization of the stress response to trapping in free-ranging wolves (Canis lupus): insights from physiology and behavior. Stress 20(5), 513–522 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Harris, S. Age determination in the red fox (Vulpes vulpes): an evaluation of technique efficiency as applied to a sample of suburban foxes. J. Zool. 184(1), 91–117 (1978).
    Article  Google Scholar 

    63.
    Gipson, P., Ballard, W., Nowak, R. & Mech, D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manage. 64(3), 752–758 (2000).
    Article  Google Scholar 

    64.
    Anders, U., von Koenigswald, W., Ruf, I. & Smith, B. H. Generalized individual dental age stages for fossil and extant placental mammals. Paläontol. Z. 85(3), 321–339 (2011).
    Article  Google Scholar 

    65.
    Santos, N. et al. Hematology and serum biochemistry values of free-ranging Iberian wolves (Canis lupus) trapped by leg-hold snares. Eur. J. Wildl. Res. 61(1), 135–141 (2015).
    Article  Google Scholar 

    66.
    Anchinmane, V. & Sankhe, S. Evaluation of hemoglobin estimation with non-cyanide alkaline haematin D-575 method. Int. J. Res. Med. Sci. 44(10), 4297–4299 (2016).
    Article  Google Scholar 

    67.
    R Core Team. R, A language and environment for statistical computing. R Foundation for Statistical Computing (2019). https://www.R-project.org

    68.
    Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64(4), 1–34 (2015).
    Article  Google Scholar 

    69.
    Anderson, T. W. & Darling, D. A. Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 23, 193–212 (1952).
    MathSciNet  MATH  Article  Google Scholar 

    70.
    Zar, J. H. Encyclopedia of Biostatistics (ed. Zar, J. H.) (Wiley, Hoboken, 2005).

    71.
    Pouillot, R. & Delignette-Muller, M. L. Evaluating variability and uncertainty in microbial quantitative risk assessment using two R packages. Int. J. Food Microb. 142(3), 330–340 (2010).
    Article  Google Scholar 

    72.
    Vose, D. Risk analysis: a quantitative guide (ed. Vose, D.) 1–735 (Wiley, Hoboken, 2008).

    73.
    Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecol. 92, 3–10 (2011).
    Article  Google Scholar 

    74.
    Su, Y. S., Yajima, M. R2jags: Using R to run ‘JAGS’. R package version 0.5–7, 34 (2015).

    75.
    Plummer, M. JAGS version 4.3. 0 user manual [Computer software manual]. Retrieved from sourceforge.net/projects/mcmc-jags/files/Manuals/4.x, 2 (2017).

    76.
    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7(4), 457–472 (1992).
    MATH  Article  Google Scholar  More

  • in

    Factors influencing riverine utilization patterns in two sympatric macaques

    1.
    Helfield, J. M. & Naiman, R. J. Keystone interactions: salmon and bear in riparian forests of Alaska. Ecosystems 9, 167–180. https://doi.org/10.1007/s10021-004-0063-5 (2006).
    Article  Google Scholar 
    2.
    Smith, R. A. & Kennedy, M. L. Food habits of the racoon (Procyon lotor). J. Tenn. Acad. Sci. 62, 79–82 (1987).
    Google Scholar 

    3.
    Rees, E. E. et al. Assessing a landscape barrier using genetic simulation modelling: implications for raccoon rabies management. Prev. Vet. Med. 86, 107–123. https://doi.org/10.1016/j.prevetmed.2008.03.007 (2008).
    Article  PubMed  Google Scholar 

    4.
    Kempf, E. Patterns of water use in primates. Folia Primatol. (Basel) 80, 275–294. https://doi.org/10.1159/000252586 (2009).
    Article  Google Scholar 

    5.
    Nowak, K., Barnett, A. A. & Matsuda, I. Primates in Flooded Habitats: Ecology and Conservation (Cambridge University Press, Cambridge, 2019).
    Google Scholar 

    6.
    Matsuda, I. et al. in Primates in Flooded Habitats: Ecology and Conservation (eds Nowak, K., Barnett, A. A. & Matsuda, I.) 15–28 (Cambridge University Press, Cambridge, 2019).

    7.
    van Schaik, C. P. & Mirmanto, E. Spatial variation in the structure and litterfall of a Sumatran rain forest. Biotropica 17, 196–205. https://doi.org/10.2307/2388217 (1985).
    Article  Google Scholar 

    8.
    Coley, P. D. Interspecific variation in plant anti-herbivore properties: the role of habitat quality and rate of disturbance. New Phytol. 106, 251–263. https://doi.org/10.1111/j.1469-8137.1987.tb04693.x (1987).
    Article  Google Scholar 

    9.
    Chapman, C. A., Chapman, L. J., Naughton-Treves, L., Lawes, M. J. & McDowell, L. R. Predicting folivorous primate abundance: validation of a nutritional model. Am. J. Primatol. 62, 55–69. https://doi.org/10.1002/ajp.20006 (2004).
    Article  PubMed  Google Scholar 

    10.
    Matsuda, I., Tuuga, A., Bernard, H., Sugau, J. & Hanya, G. Leaf selection by two Bornean colobine monkeys in relation to plant chemistry and abundance. Sci. Rep. 3, 1873. https://doi.org/10.1038/srep01873 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Hanya, G., Kiyono, M., Takafumi, H., Tsujino, R. & Agetsuma, N. Mature leaf selection of Japanese macaques: effects of availability and chemical content. J. Zool. 273, 140–147. https://doi.org/10.1111/j.1469-7998.2007.00308.x (2007).
    Article  Google Scholar 

    12.
    Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A. Can. J. Zool. 79, 1401–1409. https://doi.org/10.1139/z01-094 (2001).
    Article  Google Scholar 

    13.
    Altendorf, K. B., Laundré, J. W., López-González, C. A. & Brown, J. S. Assessing effects of predation risk on foraging behavior of mule deer. J. Mammal. 82, 430–439. https://doi.org/10.1644/1545-1542(2001)082%3c0430:aeopro%3e2.0.co;2 (2001).
    Article  Google Scholar 

    14.
    Laundré, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7. https://doi.org/10.2174/1874213001003030001 (2010).
    Article  Google Scholar 

    15.
    Gautier-Hion, A. in Comparative Ecology and Behaviour of Primates: Proceedings of a Conference Held at the Zoological Society (eds Michael, R. P. & Crook, J. H.) 147–170 (Academic Press, Cambridge, 1973).

    16.
    van Schaik, C. P., van Amerongen, A. & van Noordwijk, M. A. in Evolution and Ecology of Macaque Societies (eds Fa, J. A. & Lindburg, D. G.) 160–181 (Cambridge University Press, Cambridge, 1996).

    17.
    Matsuda, I., Tuuga, A. & Bernard, H. Riverine refuging by proboscis monkeys (Nasalis larvatus) and sympatric primates: implications for adaptive benefits of the riverine habitat. Mamm. Biol. 76, 165–171. https://doi.org/10.1016/j.mambio.2010.03.005 (2011).
    Article  Google Scholar 

    18.
    Albert, A., Savini, T. & Huynen, M. C. Sleeping site selection and presleep behavior in wild pigtailed macaques. Am. J. Primatol. 73, 1222–1230. https://doi.org/10.1002/ajp.20993 (2011).
    Article  PubMed  Google Scholar 

    19.
    Matsuda, I., Tuuga, A. & Higashi, S. Clouded leopard (Neofelis diardi) predation on proboscis monkeys (Nasalis larvatus) in Sabah, Malaysia. Primates 49, 227–231. https://doi.org/10.1007/s10329-008-0085-2 (2008).
    Article  PubMed  Google Scholar 

    20.
    Otani, Y., Tuuga, A., Bernard, H. & Matsuda, I. Opportunistic predation and predation-related events on long-tailed macaque and proboscis monkey in Kinabatangan, Sabah, Malaysia. J. Trop. Biol. Conserv. 9, 214–218 (2012).
    Google Scholar 

    21.
    Matsuda, I., Tuuga, A., Akiyama, Y. & Higashi, S. Selection of river crossing location and sleeping site by proboscis monkeys (Nasalis larvatus) in Sabah, Malaysia. Am. J. Primatol. 70, 1097–1101. https://doi.org/10.1002/ajp.20604 (2008).
    Article  PubMed  Google Scholar 

    22.
    Ross, J., Hearn, A. J., Johnson, P. J. & Macdonald, D. W. Activity patterns and temporal avoidance by prey in response to Sunda clouded leopard predation risk. J. Zool. 290, 96–106. https://doi.org/10.1111/jzo.12018 (2013).
    Article  Google Scholar 

    23.
    Matsuda, I., Tuuga, A. & Higashi, S. Effects of water level on sleeping-site selection and inter-group association in proboscis monkeys: why do they sleep alone inland on flooded days?. Ecol. Res. 25, 475–482. https://doi.org/10.1007/s11284-009-0677-3 (2010).
    Article  Google Scholar 

    24.
    Mittermeier, R. A., Rylands, A. B. & Wilson, D. E. Handbook of the Mammals of the World. Vol. 3. Primates. (Lynx Edicions, Barcelona, 2013).

    25.
    Kurland, J. A. A natural history of kra macaques (Macaca fascicularis Raffles, 1821) at the Kutai Reserve, Kalimantan Timur, Indonesia. Primates 14, 245–262. https://doi.org/10.1007/bf01730823 (1973).
    Article  Google Scholar 

    26.
    Yeager, C. P. Feeding ecology of the long-tailed macaque (Macaca fascicularis) in Kalimantan Tengah, Indonesia. Int. J. Primatol. 17, 51–62. https://doi.org/10.1007/bf02696158 (1996).
    Article  Google Scholar 

    27.
    Choudhury, A. Ecology and behaviour of the pigtailed macaque Macaca nemestrina leonina in some forests of Assam in North-East India. J. Bombay Nat. Hist. Soc. 105, 279–291 (2008).
    Google Scholar 

    28.
    Albert, A. Feeding and ranging behavior of northern pigtailed macaques (Macaca leonina): impact on their seed dispersal effectiveness and ecological contribution in a tropical rainforest at Khao Yai National Park, Thailand PhD. thesis, Université de Liège, (2012).

    29.
    Caldecott, J. O. An Ecological and Behavioural Study of the Pig-Tailed Macaque, Vol. 21 262 (Karger, Berlin, 1985).
    Google Scholar 

    30.
    Gazagne, E. et al. When northern pigtailed macaques (Macaca leonina) cannot select for ideal sleeping sites in a degraded habitat. Int. J. Primatol. 41, 614–633. https://doi.org/10.1007/s10764-020-00173-4 (2020).
    Article  Google Scholar 

    31.
    Rodman, P. S. Structural differentiation of microhabitats of sympatric Macaca fascicularis and M. nemestrina in East Kalimantan, Indonesia. Int. J. Primatol. 12, 357–375. https://doi.org/10.1007/bf02547617 (1991).
    Article  Google Scholar 

    32.
    Matsuda, I., Otani, Y., Bernard, H., Wong, A. & Tuuga, A. Primate survey in a bornean flooded forest: evaluation of best approach and best timing. Mamm. Study 41, 101–106. https://doi.org/10.3106/041.041.0201 (2016).
    Article  Google Scholar 

    33.
    Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383. https://doi.org/10.1146/annurev.es.05.110174.001545 (1974).
    Article  Google Scholar 

    34.
    Cheney, D. L. & Wrangham, R. W. in Primate societies (eds Smuts, B. B., et al.) 227–239 (University of Chicago Press, Chicago, 1987).

    35.
    Hill, R. A. & Lee, P. C. Predation risk as an influence on group size in cercopithecoid primates: implications for social structure. J. Zool. 245, 447–456. https://doi.org/10.1111/j.1469-7998.1998.tb00119.x (1998).
    Article  Google Scholar 

    36.
    Oi, T. Population organization of wild pig-tailed macaques (Macaca nemestrina nemestrina) in West Sumatra. Primates 31, 15–31. https://doi.org/10.1007/bf02381027 (1990).
    Article  Google Scholar 

    37.
    Sackett, G. P., Ruppenthal, G. C. & Davis, A. E. Survival, growth, health, and reproduction following nursery rearing compared with mother rearing in pigtailed monkeys (Macaca nemestrina). Am. J. Primatol. 56, 165–183. https://doi.org/10.1002/ajp.1072 (2002).
    Article  PubMed  Google Scholar 

    38.
    Gouzoules, H. & Gouzoules, S. Body size effects on the acoustic structure of pigtail macaque (Macaca nemestrina) screams. Ethology 85, 324–334. https://doi.org/10.1111/j.1439-0310.1990.tb00411.x (2010).
    Article  Google Scholar 

    39.
    Matsuda, I. et al. The nose is mightier than the tooth: larger male proboscis monkeys have smaller canines. bioRxiv. https://doi.org/10.1101/848515 (2019).

    40.
    Matsuda, I., Tuuga, A. & Higashi, S. The feeding ecology and activity budget of proboscis monkeys. Am. J. Primatol. 71, 478–492. https://doi.org/10.1002/ajp.20677 (2009).
    Article  PubMed  Google Scholar 

    41.
    Matsuda, I., Kubo, T., Tuuga, A. & Higashi, S. A Bayesian analysis of the temporal change of local density of proboscis monkeys: implications for environmental effects on a multilevel society. Am. J. Phys. Anthropol. 142, 235–245. https://doi.org/10.1002/ajpa.21218 (2010).
    Article  PubMed  Google Scholar 

    42.
    Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–267. https://doi.org/10.1163/156853974×00534 (1974).
    CAS  Article  PubMed  Google Scholar 

    43.
    Kumar, D., Kumar, S., Gupta, J., Arya, R. & Gupta, A. A review on chemical and biological properties of Cayratia trifolia Linn. (Vitaceae). Pharmacogn. Rev. 5, 184–188. https://doi.org/10.4103/0973-7847.91117 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    44.
    Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).
    Google Scholar 

    45.
    Driver, C. C., Oud, J. H. L. & Voelkle, M. C. Continuous time structural equation modeling with R Package ctsem. J. Stat. Softw. https://doi.org/10.18637/jss.v077.i05 (2017).
    Article  Google Scholar 

    46.
    Driver, C. C. & Voelkle, M. C. Hierarchical Bayesian continuous time dynamic modeling. Psychol. Methods 23, 774–799. https://doi.org/10.1037/met0000168 (2018).
    Article  PubMed  Google Scholar 

    47.
    R: a language and environment for statistical computing (Foundation for Statistical Computing, Vienna, 2019).

    48.
    Tsuji, Y., Hanya, G. & Grueter, C. C. Feeding strategies of primates in temperate and alpine forests: comparison of Asian macaques and colobines. Primates https://doi.org/10.1007/s10329-013-0359-1 (2013).
    Article  PubMed  Google Scholar 

    49.
    Campbell, C. J., Fuentes, A., MacKinnon, K. C., Bearder, S. K. & Stumpf, R. M. Primates in Perspective 852 (Oxford University Press, Oxford, 2011).
    Google Scholar 

    50.
    Smith, J. A., Donadio, E., Pauli, J. N., Sheriff, M. J. & Middleton, A. D. Integrating temporal refugia into landscapes of fear: prey exploit predator downtimes to forage in risky places. Oecologia 189, 883–890. https://doi.org/10.1007/s00442-019-04381-5 (2019).
    ADS  Article  PubMed  Google Scholar 

    51.
    Miller, L. E. & Treves, A. in Primates in Perspective (eds Campbell, C. J. et al.) 525–543 (Oxford University Press, Oxford, 2007).

    52.
    Galdikas, B. M. F. & Yeager, C. P. Brief report: crocodile predation on a crab-eating macaque in Borneo. Am. J. Primatol. 6, 49–51. https://doi.org/10.1002/ajp.1350060106 (1984).
    Article  PubMed  Google Scholar 

    53.
    van Schaik, C. P., van Noordwijk, M. A., Warsono, B. & Sutriono, E. Party size and early detection of predators in sumatran forest primates. Primates 24, 211–221. https://doi.org/10.1007/bf02381083 (1983).
    Article  Google Scholar 

    54.
    Khamcha, D. & Sukumal, N. Burmese python (Python molurus) predation on a pig-tailed macaque (Macaca nemestrina) in Khao Yai National Park. Hamadryad 34, 176–178. https://doi.org/10.13140/2.1.3857.7449 (2009).
    Article  Google Scholar 

    55.
    van Schaik, C. & Mitrasetia, T. Changes in the behaviour of wild long-tailed macaques (Macaca fascicularis) after encounters with a model python. Folia Primatol. (Basel) 55, 104–108. https://doi.org/10.1159/000156506 (1990).
    Article  Google Scholar 

    56.
    Terborgh, J. Mixed flocks and polyspecific associations: costs and benefits of mixed groups to birds and monkeys. Am. J. Primatol. 21, 87–100. https://doi.org/10.1002/ajp.1350210203 (1990).
    Article  PubMed  Google Scholar 

    57.
    Hart, D. in Primate Anti-Predator Strategies, Developments in Primatology: Progress and Prospects (eds Gursky, S. L. & Nekaris, K. A. I.) 27–59 (Springer, New York, 2007).

    58.
    Fam, S. D. & Nijman, V. Spizaetus hawk-eagles as predators of arboreal colobines. Primates 52, 105–110. https://doi.org/10.1007/s10329-011-0240-z (2011).
    CAS  Article  PubMed  Google Scholar 

    59.
    Prawiradilaga, D. M. Ecology and conservation of endangered Javan Hawk-eagle Spizaetus bartelsi. Ornithol. Sci. 5, 177–186. https://doi.org/10.2326/1347-0558(2006)5[177:eacoej]2.0.co;2 (2006).
    Article  Google Scholar 

    60.
    Yeager, C. P. Possible antipredator behavior associated with river crossings by proboscis monkeys (Nasalis larvatus). Am. J. Primatol. 24, 61–66. https://doi.org/10.1002/ajp.1350240107 (1991).
    Article  Google Scholar 

    61.
    van Schaik, C. P. & van Hooff, J. A. R. A. M. On the ultimate causes of primate social systems. Behaviour 85, 91–117. https://doi.org/10.1163/156853983×00057 (1983).
    Article  Google Scholar 

    62.
    Anderson, J. R. Sleep, sleeping sites, and sleep-related activities: awakening to their significance. Am. J. Primatol. 46, 63–75. https://doi.org/10.1002/(sici)1098-2345(1998)46:1%3c63::aid-ajp5%3e3.0.co;2-t (1998).
    CAS  Article  PubMed  Google Scholar 

    63.
    Chapman, C. A., Chapman, L. J. & McLaughlin, R. L. Multiple central place foraging by spider monkeys: travel consequences of using many sleeping sites. Oecologia 79, 506–511. https://doi.org/10.1007/BF00378668 (1989).
    ADS  CAS  Article  PubMed  Google Scholar 

    64.
    Teichroeb, J. A., Holmes, T. D. & Sicotte, P. Use of sleeping trees by ursine colobus monkeys (Colobus vellerosus) demonstrates the importance of nearby food. Primates 53, 287–296. https://doi.org/10.1007/s10329-012-0299-1 (2012).
    Article  PubMed  Google Scholar 

    65.
    Vessey, S. H. Night observations of free-ranging Rhesus monkeys. Am. J. Phys. Anthropol. 38, 613–619. https://doi.org/10.1002/ajpa.1330380276 (1973).
    CAS  Article  PubMed  Google Scholar 

    66.
    Dasilva, G. L. Postural changes and behavioural thermoregulation in Colobus polykomos: the effect of climate and diet. Afr. J. Ecol. 31, 226–241. https://doi.org/10.1111/j.1365-2028.1993.tb00536.x (1993).
    Article  Google Scholar 

    67.
    Rodman, P. S. Skeletal differentiation of Macaca fascicularis and Macaca nemestrina in relation to arboreal and terrestrial quadrupedalism. Am. J. Phys. Anthropol. 51, 51–62. https://doi.org/10.1002/ajpa.1330510107 (1979).
    Article  Google Scholar  More

  • in

    Identification of volatile components from oviposition and non-oviposition plants of Gasterophilus pecorum (Diptera: Gasterophilidae)

    Volatile contents of S. caucasica shoots during the stages of oviposition by G. pecorum
    Overall, 60 volatile compounds were identified in S. caucasica shoots during the preoviposition (I), oviposition (II), and postoviposition (III) stages of G. pecorum. These comprised 16 aldehydes, 14 ketones, 12 esters, 9 alcohols, 3 alkanes, 3 aromatic hydrocarbons, 1 acid, 1 ether, and 1 other. Among them, 35 volatiles were identified in I-L, 36 in II-L, and 37 in III-L. In addition, 18 volatiles were common to I-L, II-L, and III-L; 5 to I-L and II-L; 5 to II-L and III-L; and 2 to I-L and III-L. Ten volatiles were unique to I-L, 8 to II-L, and 12 to III-L (Table 1). The main chemical classes of I-L, II-L, and III-L were alcohols, esters, and others; alcohols and others; and alcohols and esters, respectively (Fig. 1).
    Table 1 Volatiles detected from shoots of Stipa caucasica during preoviposition, oviposition, and postoviposition of Gasterophilus pecorum.
    Full size table

    Figure 1

    Volatiles classes from shoots of Stipa caucasica during preoviposition, oviposition, and postoviposition of Gasterophilus pecorum. I-L, II-L, and III-L represent Stipa caucasica shoots during the preoviposition, oviposition, and postoviposition stages of Gasterophilus pecorum. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, and (I) ethers. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05) (Fig. 1A). Of the alcohols, 3-hexen-1-ol,(Z)- had the highest relative contents, 25.68%, 55.65%, and 32.35% in I-L, II-L, and III-L, respectively, with no significant differences among these three (P  > 0.05). The relative content of 1-hexanol was higher in II-L (1.52%) than in III-L (1.01%) (P = 0.002) or I-L (0.89%) (P = 0.001), whereas III-L and I-L showed no significant difference (P  > 0.05). The relative contents of the other volatile alcohols were less than 0.8% (Table 1).
    Twelve esters were identified from the three stages of S. caucasica. Among them, three, i.e., acetic acid hexyl ester, ethyl acetate, and acetic acid phenylmethyl ester, were common to all three stages; and four, i.e., 3-cyclohexen-1-ol,acetate, 2(3H)-furanone,5-ethyldihydro-, 3-hexen-1-ol,formate,(Z)-, and acetic acid pentyl ester, were common to two of the three stages. The relative contents of esters were lower in II-L (3.16%) than in III-L (40.61%) or I-L (27.81%) (P = 0.000; P = 0.000), whereas there was no significant difference between III-L and I-L (P  > 0.05) (Fig. 1B). The relative contents of acetic acid hexyl ester in II-L (1.47%) and III-L (1.14%) were not significantly different (P  > 0.05), but were higher in both than in I-L (0.52%) (P = 0.001 and 0.005, respectively). The relative contents of 3-hexen-1-ol,acetate,(Z)- (24.8%), a specific volatile of I-L, and 3-hexen-1-ol,acetate(E)- (38.7%), which was specific to III-L, were highest in esters in stages specifically containing them. The relative content of propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester, which was detected only in I-L, was 1.12%, whereas those of the other volatiles in esters were lower than 0.8% (Table 1).
    Sixteen aldehydes were identified from the three stages of S. caucasica. Among them, seven, i.e., hexanal, nonanal, decanal, heptanal, undecanal, 2-octenal, (E)-, and 2-heptenal,(Z)-, were common to all three stages; and two, i.e., 3-hexenal and 2,4-hexadienal, (E,E)-, were common to two of the three stages. The relative contents of aldehydes in I-L, II-L, and III-L were 10.83%, 6.84%, and 9.9%, and those of hexanal were 0.62%, 2.38%, and 1.16%, respectively; none of these differences was significant (P  > 0.05) (Fig. 1C). The relative contents of nonanal in I-L (1.45%) and II-L (1.9%) did not differ significantly (P  > 0.05), and both were higher than that in III-L (0.96%) (P  > 0.05 and P = 0.018, respectively). The relative content of decanal was higher in II-L (1.20%) than in I-L (0.78%) (P = 0.043) or III-L (0.65%) (P = 0.016), but those in I-L and III-L did not differ significantly (P  > 0.05). The following two volatiles were present in two of the three stages: 3-hexenal, with higher content in I-L (7.10%) than in III-L (5.03%) (P  > 0.05); and 2,4-hexadienal,(E,E)-, with content higher in II-L (0.3%) than in III-L (0.22%) (P = 0.00). Benzaldehyde was specific to III-L (0.99%), with the relative contents of other volatile aldehydes  0.05), with no significant difference between II-L and III-L (P  > 0.05) (Fig. 1D). The content of 2(5H)-Furanone,5-ethyl- was specific to II-L (2.38%), and the relative contents of the other ketones were  0.05), and both were higher than those for III-L (12.9%) (P = 0.017 and P  > 0.05, respectively) (Fig. 1E). The relative content of acetic acid, the only volatile in the class of acids, was lower in III-L (0.61%) than in II-L (3.36%) or I-L (2.14%) (P = 0.022 and P  > 0.05, respectively); there was no significant difference between the latter two (P  > 0.05). The relative contents of alkanes, aromatic hydrocarbons, and ethers were less than 0.22% (Fig. 1G–I). These included three alkanes, one in I-L and two each in II-L and III-L; three aromatic hydrocarbons, one of them specific to each stage; and one ether, which was not found in III-L (Table 1).
    The five volatile compounds with the highest relative contents, in order, during the three stages of S. caucasica were as follows: I-L, caprolactam (30.66%)  > 3-hexen-1-ol,(Z)- (25.68%)  > 3-hexen-1-ol,acetate,(Z)- (24.8%)  > 3-hexenal (7.1%)  > acetic acid (2.14%); II-L, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl- (2.38%); III-L, 3-hexen-1-ol,acetate,(E)- (38.7%)  > 3-hexen-1-ol,(Z)- (32.35%)  > caprolactam (12.9%)  > 3-hexenal (5.03%)  > hexanal (1.16%) (Table 1). A total of eight volatiles were detected: two (i.e., 3-hexen-1-ol,(Z)- and caprolactam) were common to the three stages, and three (i.e., acetic acid, 3-hexenal, and hexanal) to two of the three stages. Finally, 2(5H)-furanone,5-ethyl- was in the top 5 volatile compounds of only II-L.
    Relative contents of volatiles in three plant species during the oviposition stage of G. pecorum
    During the oviposition stage of G. pecorum, a total of 60 volatiles were identified in S. orientalis (II-D), S. caucasica (II-L), and C. latens (II-T). These comprised 18 esters, 13 aldehydes, 11 alcohols, 10 ketones, 2 alkanes, 2 aromatic hydrocarbons, 1 acid, 1 alkene, 1 ether, and 1 other. Of these, 35 were identified in II-D, 36 in II-L, and 27 in II-T. In addition, 11 were common to II-D, II-L, and II-T, 14 to II-D and II-L, and 2 to II-L and II-T; 10 were unique to II-D, 9 to II-L, and 14 to II-T (Table 2). The main chemical classes of II-D and II-L were alcohols and others, and those of II-T were alcohols, esters, and others (Fig. 2).
    Table 2 Volatiles detected from shoots of three plant species during oviposition of Gasterophilus pecorum.
    Full size table

    Figure 2

    Volatiles classes from shoots of three plant species during oviposition of Gasterophilus pecorum. II-D, II-L, and II-T represent shoots of Stipa orientalis, Stipa caucasica, and Ceratoides latens during the oviposition stage of Gasterophilus pecorum. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, (I) ethers, and (J) alkenes. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05) (Fig. 2A). The relative content of 3-hexen-1-ol,(Z)- was lower in II-T (14.28%) than in II-L (55.65%) or II-D (44.64%) (P = 0.002 and 0.008), but there was not significant difference between II-L and II-D (P  > 0.05). The relative contents of 1-hexanol and 2-hexen-1-ol,(E)- in II-D, II-L, and II-T were 1.67%, 1.52%, 2.79%, and 0.72%, and 0.59% and 2.66%, respectively; these differences were not significant (P  > 0.05). Finally, 3-hexen-1-ol was specific to II-D (1.57%), and the relative contents of other alcohols were  0.05) (Fig. 2B). The relative content of acetic acid hexyl ester in II-D, II-L, and II-T was 0.4%, 1.47%, and 4.25%, respectively; these differences were not significant (P  > 0.05). The relative content of 2(3H)-furanone, 5-ethyldihydro- was higher in II-T (0.71%) than in II-D (0.27%) or II-L (0.26%) (P = 0.000; P = 0.000), but II-D and II-L were not significantly different (P  > 0.05). Both 3-hexen-1-ol,acetate,(Z)- (13.13%) and propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester (1.07%) were unique to II-D, and benzoic acid methyl ester (1.88%), methyl salicylate (2.52%), and cis-3-hexenyl isovalerate (8.45%) were all unique to II-T. The relative contents of other esters were  0.05) (Fig. 2C). The relative contents of hexanal, nonanal, decanal, and heptanal were 0.25–2.38% and were higher in II-L than in II-D or II-T, although the differences were not significant (P  > 0.05). Finally, 3-hexenal (6.57%) was unique to II-D, and benzaldehyde (0.94%) to II-T. The relative contents of other aldehydes were  0.05) (Fig. 2D). Five ketones, i.e., 5-hepten-2-one,6-methyl-, 2(3H)-furanone,dihydro-5-methyl-, 2-hexanone,4-methyl-, 2-undecanone,6,10-dimethyl-, and acetophenone, were common to II-D and II-L, and 2(5H)-furanone,5-ethyl- (2.38%) was unique to II-L. The relative contents of other ketones were  0.05) (Fig. 2E). Acetic acid was the only substance in the class ‘acids,’ and its relative content was lower in II-D (1.44%) than in II-T (3.62%) (P = 0.046) or II-L (3.36%) (P  > 0.05); contents in II-T and II-L did not differ significantly (P  > 0.05). The only alkene, 1,3,6-Octatriene,3,7-dimethyl-, was unique to II-T (12.67%). The relative contents of other alkanes and ethers were  caprolactam (21.76%)  > 3-hexen-1-ol,acetate,(Z)- (13.13%)  > 3-hexenal (6.57%)  > 1-hexanol (1.67%); II-L, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl- (2.38%); II-T, caprolactam (34.2%)  > 3-hexen-1-ol,(Z)- (14.28%)  > 1,3,6-octatriene,3,7-dimethyl- (12.67%)  > cis-3-hexenyl isovalerate (8.45%)  > acetic acid hexyl ester (4.25%) (Table 2). Eleven volatiles were included: two (3-hexen-1-ol,(Z)- and caprolactam) were common to all three plant species; the other nine were in the top five of only one species.
    Relative contents of volatiles from S. caucasica in different growth periods
    From S. caucasica at the early, flourishing, and late growth periods (GP1, GP2, and GP3, respectively), a total of 69 volatile compounds were identified. These comprised 17 ketones, 13 aldehydes, 11 esters, 10 alcohols, 4 alkanes, 4 aromatic hydrocarbons, 2 acids, 2 alkenes, 1 ether, and 5 others. Of these, 35 were found in GP1, 36 in GP2, and 40 in GP3. In addition, 11 were common to all three stages, 10 to both GP2 and GP3, 6 to both GP1 and GP2, and 4 to both GP1 and GP3; 14 were unique to GP1, 9 to GP2, and 15 to GP3 (Table 3). The main chemical classes of GP1 and GP2 were alcohols and others, and those of GP3 were esters and others (Fig. 3).
    Table 3 Volatiles detected from shoots of Stipa caucasica during its different growth periods.
    Full size table

    Figure 3

    Volatiles classes from shoots of Stipa caucasica during its different growth periods. GP1, GP2, and GP3 represent Stipa caucasica shoots during the early, flourishing, and late growth periods, respectively. Note that GP2 was actually the same sample as II-L in Figs. 1 and 2. Thus, the three groups had a total of seven rather than nine samples. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, (I) ethers, and (J) alkenes. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05). The 3-hexen-1-ol,(Z)- content, which was the highest among all alcohols, was lower in GP3 (15.42%) than in GP1 (49.5%) or GP2 (55.65%) (P = 0.005 and 0.002, respectively); the latter two did not differ significantly (P  > 0.05). The relative content of 1-hexanol was lower in GP3 (0.59%) than in GP1 (1.98%) or GP2 (1.52%) (P = 0.001 and 0.007, respectively); the latter two were not significantly different (P  > 0.05). The relative contents of other alcohols were  0.05) (Fig. 3B). The relative content of acetic acid hexyl ester was higher in GP2 (1.47%) than in GP1 (0.52%) (P = 0.022) or GP3 (0.98%) (P  > 0.05), with no significant difference between GP1 and GP3 (P  > 0.05). Propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester (1.48%) was unique to GP1, and 3-hexen-1-ol,acetate,(Z)- (28.42%) to GP3. The relative contents of other esters were  0.05) (Fig. 3E). The relative contents of the remaining four ‘others’ were  0.05) (Fig. 3C). The relative contents of hexanal and decanal decreased with growth period from 4.77% and 1.38% to 1.51% and 1.06%, respectively; but there were no significant differences between periods (P  > 0.05). Six volatiles were common to two of the three periods. There were no significant differences between the relative contents of 3-hexenal in GP3 (6.13%) and in GP1 (5.50%) (P  > 0.05) or between those of nonanal in GP2 (1.90%) and GP3 (1.43%) (P  > 0.05). Finally, 2-hexenal (4.51%) was unique to GP3, and the relative contents of other aldehydes were  0.05) (Fig. 3D). The relative content of 5-hepten-2-one, 6-methyl- was higher in GP1 (0.7%) than in GP3 (0.33%) (P = 0.020), with no significant difference between that in GP2 (0.45%) and that in GP1 or GP3 (both P  > 0.05). The relative content of 2-undecanone,6,10-dimethyl- was higher in GP1 (3.12%) than in GP2 (0.14%) (P = 0.05). Finally, 2(5H)-furanone,5-ethyl- (2.38%) was specific to GP2, and the relative contents of other ketones were  0.05) (Fig. 3F). The relative content of acetic acid, which was common to all three periods, was higher in GP2 (3.36%) than in GP3 (0.97%) (P = 0.035), but there was no significant difference between GP1 (1.87%) and GP2 or GP3 (both P  > 0.05). The other acid, propanoic acid,2-methyl-,2,2-dimethyl-1- (1%), was specific to GP3 (Table 3).
    Four alkanes were identified, and the relative contents of individual alkanes ranged from 0.06% to 0.89%. The relative contents of all alkanes were higher in GP1 (1.56%) than in GP3 (0.15%) (P = 0.022), with no significant difference between GP2 (0.22%) and GP1 or GP3 (both P  > 0.05) (Fig. 3G). Two alkenes were found only in GP3; they had a total relative content of 4.76% (Fig. 3J); one, 1,3,6-octatrine,3,7-dimethyl-, accounted for 4.70% of this total. The relative aromatic hydrocarbon and ether contents were  caprolactam (19.78%)  > 3-hexenal (5.5%)  > hexanal (4.77%)  > 2-undecanone,6,10-dimethyl- (3.12%); GP2, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl-(2.38%); GP3, caprolactam (28.8%)  > 3-hexen-1-ol,acetate,(Z)- (28.42%)  > 3-hexen-1-ol,(Z)- (15.42%)  > 3-hexenal (6.13%)  > 1,3,6-octatriene,3,7-dimethyl- (4.70%) (Table 3). Overall, nine volatiles were detected: two (3-hexen-1-ol,(Z)- and caprolactam) were in the top five in all three growth periods, two (3-hexenal and hexanal) in two growth periods, and the other five were in the top five of in only one of the three growth periods. More

  • in

    Comparative analysis of rhizosphere soil physiochemical characteristics and microbial communities between rusty and healthy ginseng root

    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

  • in

    Exploring source differences on diet-tissue discrimination factors in the analysis of stable isotope mixing models

    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

  • in

    A new isolation device for shortening gene flow distance in small-scale transgenic maize breeding

    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.

    Full size image

    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

  • in

    Dynamics of localised nitrogen supply and relevance for root growth of Vicia faba (‘Fuego’) and Hordeum vulgare (‘Marthe’) in soil

    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