Ecology

1.
Stien, A. et al. The impact of gastrointestinal nematodes on wild reindeer: experimental and cross-sectional studies. J. Anim. Ecol. 71, 937–945 (2002).
Article  Google Scholar 
2.
Budischak, S. A., O’Neal, D., Jolles, A. E. & Ezenwa, V. O. Differential host responses to parasitism shape divergent fitness costs of infection. Funct. Ecol. 32, 324–333 (2018).
Article  Google Scholar 

3.
Albon, S. D. et al. The role of parasites in the dynamics of a reindeer population. Proc. R. Soc. Lond. B 269, 1625–1632 (2002).
CAS  Article  Google Scholar 

4.
Festa-Bianchet, M. Numbers of lungworm larvae in feces of bighorn sheep: yearly changes, influence of host sex, and effects on host survival. Can. J. Zool. 69, 547–554 (1991).
Article  Google Scholar 

5.
Richner, H., Oppliger, A. & Christe, P. Effect of an ectoparasite on reproduction in great tits. J. Anim. Ecol. 62, 703–710 (1993).
Article  Google Scholar 

6.
Fitze, P. S., Tschirren, B. & Richner, H. Life history and fitness consequences of ectoparasites. J. Anim. Ecol. 73, 216–226 (2004).
Article  Google Scholar 

7.
Patterson, J. E. H., Neuhaus, P., Kutz, S. J. & Ruckstuhl, K. E. Parasite removal improves reproductive success of female North American red squirrels (Tamiasciurus hudsonicus). PLoS ONE 8, e55779 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Gilbert, S. F. Ecological developmental biology: developmental biology meets the real world. Dev. Biol. 233, 1–12 (2001).
CAS  PubMed  Article  Google Scholar 

9.
Monaghan, P. Early growth conditions, phenotypic development and environmental change. Philos Trans. R. Soc. Lond. B Biol. Sci. 363, 1635–1645 (2008).
PubMed  Article  Google Scholar 

10.
Bowers, E. K. et al. Neonatal body condition, immune responsiveness, and hematocrit predict longevity in a wild bird population. Ecology 95, 3027–3034 (2014).
PubMed  PubMed Central  Article  Google Scholar 

11.
Gluckman, P. D., Hanson, M. A., Morton, S. M. B. & Pinal, C. S. Life-long echoes–a critical analysis of the developmental origins of adult disease model. Neonatology 87, 127–139 (2005).
Article  Google Scholar 

12.
Gluckman, P. D., Hanson, M. A. & Beedle, A. S. Early life events and their consequences for later disease: A life history and evolutionary perspective. Am. J. Hum. Biol. 19, 1–19 (2007).
PubMed  Article  Google Scholar 

13.
Lindström, J. Early development and fitness in birds and mammals. Trends Ecol. Evol. 14, 343–348 (1999).
PubMed  Article  Google Scholar 

14.
Wu, G., Bazer, F. W., Wallace, J. M. & Spencer, T. E. Board-invited review: intrauterine growth retardation: implications for the animal sciences. J. Anim. Sci. 84, 2316–2337 (2006).
CAS  PubMed  Article  Google Scholar 

15.
Greenwood, P. L. & Bell, A. W. Prenatal nutritional influences on growth and development of ruminants. Recent Adv. Animal Nutr. Aust. 14, 57 (2003).
Google Scholar 

16.
Alexander, G. & Williams, D. Heat stress and development of the conceptus in domestic sheep. J. Agric. Sci. 76, 53–72 (1971).
Article  Google Scholar 

17.
Holland, M. D. & Odde, K. G. Factors affecting calf birth weight: a review. Theriogenology 38, 769–798 (1992).
CAS  PubMed  Article  Google Scholar 

18.
Reynolds, L. P., Ferrell, C. L., Nienaber, J. A. & Ford, S. P. Effects of chronic environmental heat stress on blood flow and nutrient uptake of the gravid bovine uterus and foetus. J. Agric. Sci. 104, 289–297 (1985).
Article  Google Scholar 

19.
Johnson, J. S. et al. The impact of in utero heat stress and nutrient restriction on progeny body composition. J. Therm. Biol. 53, 143–150 (2015).
PubMed  Article  Google Scholar 

20.
Lindström, J. & Kokko, H. Sexual reproduction and population dynamics: the role of polygyny and demographic sex differences. Proc. Biol. Sci. 265, 483–488 (1998).
PubMed  PubMed Central  Article  Google Scholar 

21.
de Figueiredo, P., Ficht, T. A., Rice-Ficht, A., Rossetti, C. A. & Adams, L. G. Pathogenesis and Immunobiology of Brucellosis: Review of Brucella-Host Interactions. Am. J. Pathol. 185, 1505–1517 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Donahoe, S. L., Lindsay, S. A., Krockenberger, M., Phalen, D. & Šlapeta, J. A review of neosporosis and pathologic findings of Neospora caninum infection in wildlife. Int. J. Parasitol. Parasites Wildl. 4, 216–238 (2015).
PubMed  PubMed Central  Article  Google Scholar 

23.
Robbins, C. T. & Robbins, B. L. Fetal and Neonatal Growth Patterns and Maternal Reproductive Effort in Ungulates and Subungulates. Am. Nat. 114, 101–116 (1979).
Article  Google Scholar 

24.
Martin, R. D. & MacLarnon, A. M. Gestation period, neonatal size and maternal investment in placental mammals.pdf. Nature 313, 220–223 (1985).
ADS  Article  Google Scholar 

25.
O’Callaghan, D. & Boland, M. P. Nutritional effects on ovulation, embryo development and the establishment of pregnancy in ruminants. Anim. Sci. 68, 299–314 (1999).
Article  Google Scholar 

26.
Blackwell, A. D. Helminth infection during pregnancy: insights from evolutionary ecology. Int. J. Womens Health 8, 651–661 (2016).
PubMed  PubMed Central  Article  Google Scholar 

27.
Booksmythe, I., Mautz, B., Davis, J., Nakagawa, S. & Jennions, M. D. Facultative adjustment of the offspring sex ratio and male attractiveness: a systematic review and meta-analysis. Biol. Rev. Camb. Philos. Soc. 92, 108–134 (2017).
PubMed  Article  Google Scholar 

28.
Trivers, R. L. & Willard, D. E. Natural selection of parental ability to vary the sex ratio of offspring. Science 179, 90–92 (1973).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Silk, J. B. Local Resource Competition and Facultative Adjustment of Sex Ratios in Relation to Competitive Abilities. Am. Nat. 121, 56–66 (1983).
Article  Google Scholar 

30.
Ryan, C. P., Anderson, W. G., Gardiner, L. E. & Hare, J. F. Stress-induced sex ratios in ground squirrels: support for a mechanistic hypothesis. Behav. Ecol. 23, 160–167 (2012).
Article  Google Scholar 

31.
Cameron, E. Z. Facultative adjustment of mammalian sex ratios in support of the Trivers-Willard hypothesis: evidence for a mechanism. Proc. Biol. Sci. 271, 1723–1728 (2004).
PubMed  PubMed Central  Article  Google Scholar 

32.
Schwanz, L. E. & Robert, K. A. Proximate and ultimate explanations of mammalian sex allocation in a marsupial model. Behav. Ecol. Sociobiol. 68, 1085–1096 (2014).
Article  Google Scholar 

33.
Silk, J. B. & Brown, G. R. Local resource competition and local resource enhancement shape primate birth sex ratios. Proc. Biol. Sci. 275, 1761–1765 (2008).
PubMed  PubMed Central  Google Scholar 

34.
Ruckstuhl, K. E., Colijn, G. P., Amiot, V. & Vinish, E. Mother’s occupation and sex ratio at birth. BMC Public Health 10, 269 (2010).
PubMed  PubMed Central  Article  Google Scholar 

35.
Flegr, J. & Kaňková, Š. The effects of toxoplasmosis on sex ratio at birth. Early Hum. Dev. 141, 104874 (2020).
CAS  PubMed  Article  Google Scholar 

36.
Kanková, S. et al. Women infected with parasite Toxoplasma have more sons. Naturwissenschaften 94, 122–127 (2007).
ADS  PubMed  Article  CAS  Google Scholar 

37.
Kanková, S. et al. Influence of latent toxoplasmosis on the secondary sex ratio in mice. Parasitology 134, 1709–1717 (2007).
PubMed  Article  Google Scholar 

38.
Simmons, N. M., Bayer, M. B. & Sinkey, L. O. Demography of Dall’s Sheep in the Mackenzie Mountains Northwest Territories. J. Wildl. Manage 48, 156–162 (1984).
Article  Google Scholar 

39.
Aleuy, O. A. et al. Diversity of gastrointestinal helminths in Dall’s sheep and the negative association of the abomasal nematode, Marshallagia marshalli, with fitness indicators. PLoS ONE 13, e0192825 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Geist, V. Mountain Sheep: A Study in Behavior and Evolution (University of Chicago Press, Chicago, 1971).
Google Scholar 

41.
Rachlow, J. L. & Bowyer, R. T. Interannual Variation in Timing and Synchrony of Parturition in Dall’s Sheep. J. Mammal. 72, 487–492 (1991).
Article  Google Scholar 

42.
Goodrowe, K. L., Smak, B., Presley, N. & Nlonfort, S. L. Reproductive, behavioral, and endocrine characteristics of the Dall’s Sheep (Ovis dalli). Zoo Biol. 15, 45–54 (1996).
Article  Google Scholar 

43.
Bunnell, F. L. & Nichols, L. Natural history of thinhorn sheep. In Mountain sheep of North America (ed. Valdez, R.) 23–77 (University of Arizona Press, Arizona, 1999).
Google Scholar 

44.
Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Chang. Biol. 20, 3256–3269 (2014).
ADS  PubMed  Article  Google Scholar 

45.
Kutz, S. J. et al. Invasion, establishment, and range expansion of two parasitic nematodes in the Canadian Arctic. Glob. Chang. Biol. 19, 3254–3262 (2013).
PubMed  Google Scholar 

46.
Kutz, S. J. et al. The Arctic as a model for anticipating, preventing, and mitigating climate change impacts on host–parasite interactions. Vet. Parasitol. 163, 217–228 (2009).
PubMed  Article  Google Scholar 

47.
Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: from evidence to a predictive framework. Science 341, 514–519 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

48.
Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69 (2009).
Article  Google Scholar 

49.
Pettorelli, N., Pelletier, F. & von Hardenberg, A. Early onset of vegetation growth vs. rapid green-up: impacts on juvenile mountain ungulates. Ecology 88(2), 381–390 (2007).
PubMed  Article  Google Scholar 

50.
Sanchez, G. PLS Path Modeling with R. (Trowchez Editions, Berkeley, 2013). http://www.gastonsanchez.com/PLSPathModelingwithR.pdf.

51.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M. & Lauro, C. PLS path modeling. Comput. Stat. Data Anal. 48, 159–205 (2005).
MathSciNet  MATH  Article  Google Scholar 

52.
Hair, J. F., Ringle, C. M. & Sarstedt, M. Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plann. 46, 1–12 (2013).
Article  Google Scholar 

53.
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 

54.
Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119 (2014).
Article  Google Scholar 

55.
Sanchez, G., Trinchera, L. & Russolillo, G. plspm: Tools for partial least squares path modeling (PLS-PM). R package version 0.4. https://doi.org/10.1111/j.1600-0706.2013.00755.x (2017).
Article  Google Scholar 

56.
Lê, S., Josse, J., Husson, F. Facto. & Mine, R. An R Package for multivariate analysis. J. Stat. Softw. https://doi.org/10.18637/jss.v025.i0 (2008).
Article  Google Scholar 

57.
Clutton-Brock, T. H., Albon, S. D. & Guinness, F. E. Maternal dominance, breeding success and birth sex ratios in red deer. Nature 308, 358–360 (1984).
ADS  Article  Google Scholar 

58.
De Roos, A. M., Galic, N. & Heesterbeek, H. How resource competition shapes individual life history for nonplastic growth: ungulates in seasonal food environments. Ecology 90, 945–960 (2009).
PubMed  Article  Google Scholar 

59.
Festa-Bianchet, M. Individual Differences, Parasites, and the Costs of Reproduction for Bighorn Ewes (Ovis canadensis). J. Anim. Ecol. 58, 785–795 (1989).
Article  Google Scholar 

60.
Festa-Bianchet, M., Jorgenson, J. T. & Wuhart, W. D. Early weaning in bighorn sheep, Ovis canadensis affects growth of males but not of females. Behav. Ecol. 5, 21–27 (1994).
Article  Google Scholar 

61.
Singer, F. J., Williams, E., Miller, M. W. & Zeigenfuss, L. C. Population Growth, Fecundity, and Survivorship in Recovering Populations of Bighorn Sheep. Restor. Ecol. 8, 75–84 (2000).
Article  Google Scholar 

62.
Simmons, N. M. Seasonal Ranges of Dall’s Sheep, Mackenzie Mountains Northwest Territories. Arctic 35, 512–518 (1982).
Article  Google Scholar 

63.
Neilsen, C. & Neiland, K. Sheep Disease Report, Project Progress Report, Federal Aid in Wildlife Restoration. (1974).

64.
Kutz, S. J. et al. Chapter 2: parasites in ungulates of Arctic North America and Greenland—a view of contemporary diversity, ecology, and impact in a world under change. In Adv Parasit (ed. Rollinson, D.) 99–252 (Academic Press, Cambridge, 2012).
Google Scholar 

65.
Moradpour, N., Borji, H., Razmi, G., Maleki, M. & Kazemi, H. The effect of Marshallagia marshalli on Serum Gastrin concentrations in experimentally infected lambs. J. Parasitol. 102, 436–439 (2016).
CAS  PubMed  Article  Google Scholar 

66.
Moradpour, N., Borji, H., Razmi, G., Maleki, M. & Kazemi, H. Pathophysiology of Marshallagia marshalli in experimentally infected lambs. Parasitology 140, 1762–1767 (2013).
PubMed  Article  Google Scholar 

67.
Simcock, D. C. et al. Hypergastrinaemia, abomasal bacterial population densities and pH in sheep infected with Ostertagia circumcincta. Int. J. Parasitol. 29, 1053–1063 (1999).
CAS  PubMed  Article  Google Scholar 

68.
Jacobs, D., Fox, M., Gibbons, L. & Hermosilla, C. Principles of Veterinary Parasitology (Wiley, Hoboken, 2015).
Google Scholar 

69.
Berger, T. Fertilization in ungulates. Anim. Reprod. Sci. 42, 351–360 (1996).
MathSciNet  Article  Google Scholar 

70.
Hayward, A. D. Causes and consequences of intra- and inter-host heterogeneity in defence against nematodes. Parasite Immunol. https://doi.org/10.1111/pim.12054 (2013).
Article  PubMed  Google Scholar 

71.
Hayward, A. D. et al. Natural selection on individual variation in tolerance of gastrointestinal nematode infection. PLoS Biol. 12, e1001917 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Reimers, E. Growth rate and body size differences in Rangifer, a study of causes and effects. Rangifer 3, 3–15 (1983).
Article  Google Scholar 

73.
Sontakke, S. D. Monitoring and controlling ovarian activities in wild ungulates. Theriogenology 109, 31–41 (2018).
PubMed  Article  Google Scholar 

74.
Festa-Bianchet, M. Birthdate and survival in bighorn lambs (Ovis canadensis). J. Zool. 214, 653–661 (1988).
Article  Google Scholar 

75.
Feder, C., Martin, J. G. A., Festa-Bianchet, M., Bérubé, C. & Jorgenson, J. Never too late? Consequences of late birthdate for mass and survival of bighorn lambs. Oecologia 156, 773–781 (2008).
ADS  PubMed  Article  Google Scholar 

76.
Hewison, A. J. M. & Gaillard, J.-M. Successful sons or advantaged daughters? The Trivers-Willard model and sex-biased maternal investment in ungulates. Trends Ecol. Evol. 14, 229–234 (1999).
CAS  PubMed  Article  Google Scholar 

77.
Leimar, O. Life-history analysis of the Trivers and Willard sex-ratio problem. Behav. Ecol. 7, 316–325 (1996).
Article  Google Scholar 

78.
Sheldon, B. C. & West, S. A. Maternal dominance, maternal condition, and offspring sex ratio in ungulate mammals. Am. Nat. 163, 40–54 (2004).
PubMed  Article  Google Scholar 

79.
Julliard, R. Sex-specific dispersal in spatially varying environments leads to habitat-dependent evolutionary stable offspring sex ratios. Behav. Ecol. 11, 421–428 (2000).
Article  Google Scholar 

80.
Schindler, S. et al. Sex-specific demography and generalization of the Trivers-Willard theory.PDF. Nature 526, 249–252 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

81.
Festa-Bianchet, M. Offspring sex ratio studies of mammals: Does publication depend upon the quality of the research or the direction of the results?. Écoscience 3, 42–44 (1996).
Article  Google Scholar 

82.
Douhard, M. Offspring sex ratio in mammals and the Trivers-Willard hypothesis: In pursuit of unambiguous evidence. Bioessays 39(9), 1700043 (2017).
Article  Google Scholar 

83.
Larson, M. A., Kimura, K., Michael Kubisch, H. & Michael Roberts, R. Sexual dimorphism among bovine embryos in their ability to make the transition to expanded blastocyst and in the expression of the signaling molecule IFN-τ. Proc. Natl. Acad. Sci. U. S. A. 98, 9677–9682 (2001).

84.
Cameron, E. Z., Lemons, P. R., Bateman, P. W. & Bennett, N. C. Experimental alteration of litter sex ratios in a mammal. Proc. Biol. Sci. 275, 323–327 (2008).
PubMed  Google Scholar 

85.
Shea-Donohue, T., Qin, B. & Smith, A. Parasites, nutrition, immune responses and biology of metabolic tissues. Parasite Immunol. 39, e12422 (2017).
Article  Google Scholar 

86.
Lafferty, K. D. The ecology of climate change and infectious diseases. Ecology 90, 888–900 (2009).
PubMed  Article  Google Scholar 

87.
Kutz, S. J., Hoberg, E. P., Molnár, P. K., Dobson, A. & Verocai, G. G. A walk on the tundra: Host–parasite interactions in an extreme environment. Int. J. Parasitol. Parasites Wildl. 3, 198–208 (2014).
PubMed  PubMed Central  Article  Google Scholar 

88.
Hoar, B. M., Ruckstuhl, K. & Kutz, S. Development and availability of the free-living stages of Ostertagia gruehneri, an abomasal parasite of barrenground caribou (Rangifer tarandus groenlandicus), on the Canadian tundra. Parasitology 139, 1093–1100 (2012).
PubMed  Article  Google Scholar 

89.
Rose, H., Hoar, B., Kutz, S. J. & Morgan, E. R. Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants. Int. J. Parasitol. Parasites Wildl. 3, 209–219 (2014).
PubMed  PubMed Central  Article  Google Scholar 

90.
Morgan, E. R. et al. Assessing risks of disease transmission between wildlife and livestock: The Saiga antelope as a case study. Biol. Conserv. 131, 244–254 (2006).
Article  Google Scholar  More

1.
Barrett, R., Kuzawa, C. W., McDade, T. & Armelagos, G. J. Emerging and re-emerging infectious diseases: the third epidemiologic transition. Annu. Rev. Anthropol. 27, 247–271 (1998).
Article  Google Scholar 
2.
McMichael, A. J. Human culture, ecological change, and infectious disease: are we experiencing history’s fourth great transition? Ecosyst. Health 7, 107–115 (2001).
Article  Google Scholar 

3.
Horby, P. W., Hoa, N. ., Pfeiffer, D. U. & Wertheim, H. F. L. Drivers of emerging zoonotic infectious diseases. Confronting Emerging Zoonoses (eds Yamada, A., Kahn, L., Kaplan, B., Monath, T., Woodall, J. & Conti, L.) (Springer Press, Tokyo, 2014).

4.
Wilcox, B. A. & Gubler, D. J. Disease ecology and the global emergence of zoonotic pathogens. Environ. Health Prev. Med. 10, 263–272 (2005).
PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–994 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

6.
Wilcox, B. A. & Colwell, R. R. Emerging and reemerging infectious diseases: biocomplexity as an interdisciplinary paradigm. Ecohealth 2, 244–257 (2005).
PubMed Central  Article  PubMed  Google Scholar 

7.
Hooper, D. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).
PubMed  Article  CAS  Google Scholar 

8.
Keesing, F., Holt, R. D. & Ostfeld, R. S. Effects of species diversity on disease risk. Ecol. Lett. 9, 485–498 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

10.
Woo, P. C. et al. Molecular diversity of coronaviruses in bats. Virology 351, 180–187 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Wolfe, N. D., Daszak, P., Kilpatrick, A. M. & Burke, D. S. Bushmeat hunting, deforestation, and prediction of zoonotic disease emergence. Emerg. Infect. Dis. 11, 1822–1827 (2005).
PubMed  PubMed Central  Article  Google Scholar 

12.
Lai, M. M. C. & Cavanagh, D. The molecular biology of coronaviruses. Adv. Virus Res. 48, 1–100 (1997).
PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Ziebuhr, J. The Coronavirus replicase. Curr. Top. Microbiol. Immunol. 287, 57–94 (2005).
PubMed  CAS  Google Scholar 

14.
Brian, D. A. & Baric, R. S. Coronavirus genome structure and replication. Curr. Top. Microbiol. Immunol. 287, 1–30 (2005).
PubMed  CAS  Google Scholar 

15.
Li, W. et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 426, 450–454 (2003).
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Walls, A. et al. Cryo-electron microscopy structure of a coronavirus spike glycoprotein trimer. Nature 531, 114–117 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

17.
Hoffmann, M., Hofmann-Winkler, H. & Poehlmann, S. Priming time: how cellular proteases arm coronavirus spike proteins, in Activation of viruses by host proteases. (eds Eva Boettger –Friebertsaeuser, Wolfgang Gartner, Hans Dieter Klenk) 71-–98 (Springer, Cham, 2018).

18.
Li, F., Li, W., Farzan, M. & Harrison, S. C. Interactions between Sars coronavirus and its receptors. Adv. Exp. Med. Biol. 581, 229–234 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Hoffmann, M. et al. SARS-CoV-2 Cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181, 1–10 (2020).
Article  CAS  Google Scholar 

20.
Hoffmann, M. et al. The novel coronavirus 2019 (2019-nCoV) uses the SARS-coronavirus receptor ACE2 and the cellular protease TMPRSS2 for entry into target cells. Preprint at BioRxiv https://doi.org/10.1101/2020.01.31.929042 (2020).

21.
Snijder, E. J., Decroly, E. & Ziebhur, J. The nonstructural proteins directing coronavirus RNA synthesis and processing. Adv. Virus Res. 96, 59–126 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Gao, Y. et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science 368, 779–782 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Yin, W. et al. Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Preprint at BioRxiv https://doi.org/10.1101/2020.04.08.032763 (2020).

24.
Zhang, X. et al. Nucleocapsid protein of SARS.CoV activates Interleukin-6 expression through cellular transcription factor NF-kB. Virology 365, 324–335 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Ge, X. Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Menachery, V. D. et al. SARS-like WIV1-CoV poised for human emergence. Proc. Natl Acad. Sci. USA 113, 3048–3053 (2016).
PubMed  Article  CAS  Google Scholar 

27.
Yang, X.-L. et al. Isolation and characterization of a novel bat coronavirus closely related to the direct progenitor of severe acute respiratory syndrome coronavirus. J. Virol. 90, 3253–3256 (2016).
PubMed Central  Article  CAS  PubMed  Google Scholar 

28.
Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).
PubMed  Article  CAS  Google Scholar 

29.
Lau, S. K. P. et al. Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proc. Natl Acad. Sci. USA 102, 14040–14045 (2005).
PubMed  Article  CAS  Google Scholar 

30.
Tang, X. C. et al. Prevalence and genetic diversity of coronaviruses in bats from China. J. Virol. 80, 7481–7490 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Cui, J., Li, F. & Shi, Z. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 17, 181–192 (2019).
PubMed  Article  CAS  Google Scholar 

32.
Zhou, H. et al. A novel bat coronavirus closely related to SARS-CoV-2 contains natural insertions at the S1/S2 cleavage site of the Spike protein. Curr. Biol. 30, 2196–2203 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Wang, N. et al. Serological evidence of bat SARS-related Coronavirus infection in humans, China. Virol. Sin. 33, 104–107 (2018).
PubMed  PubMed Central  Article  Google Scholar 

34.
Joyjinda, Y. et al. First complete genome sequence of human coronavirus HKU1 from a non hill bat guano miner in Thailand. Microbiol. Resour. Announc. 8, 1–3 (2019).
Article  Google Scholar 

35.
Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. & Garry, R. F. The proximal origin of SARS-CoV-2. Nat. Med. 26, 450–452 (2020).
PubMed  Article  CAS  Google Scholar 

36.
Guan, Y. et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).
PubMed  Article  CAS  Google Scholar 

37.
Centers for Disease Control. Prevalence of IgG antibody to SARS-associated coronavirus in animal traders: Guangdong Province, China. MMWR 52, 986–987 (2003).
Google Scholar 

38.
Normile, D. Viral DNA match spurs China’s civet roundup. Science 303, 292 (2004).
PubMed  Article  CAS  Google Scholar 

39.
Watts, J. China culls wild animals to prevent new SARS threat. Lancet 363, 134 (2004).
PubMed  PubMed Central  Article  Google Scholar 

40.
Xu, H. F. et al. An epidemiologic investigation on infection with severe acute respiratory syndrome coronavirus in wild animals traders in Guangzhou. Zhonghua Yu Fang Yi Xue Za Zhi 38, 81–83 (2004).
PubMed  Google Scholar 

41.
Wu, D. et al. Civets are equally susceptible to experimental infection by two different severe acute respiratory syndrome coronavirus isolates. J. Virol. 79, 2620–26255 (2005).
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Kan, B. et al. Molecular evolution analysis and geographic investigation of severe acute respiratory syndrome coronavirus-like virus in palm civets at an animal market and on farms. J. Virol. 79, 11892–11900 (2005).
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Wang, L. F. et al. Review of bats and SARS. Emerg. Infect. Dis. 12, 1834–1840 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Tommy, T. L. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).
Article  CAS  Google Scholar 

45.
Liu, P. et al. Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)? PLOS Pathog. 16, 1–13 (2020).
Google Scholar 

46.
Damas, J. et al. Broad host range of SARS-CoV-2 predicted Comparative and structural analysis of ACE2 in vertebrates. Preprint at BioRxiv https://doi.org/10.1101/2020.04.16.045302 (2020).

47.
Lee, J. et al. No evidence of coronaviruses or other potentially zoonotic viruses in Sunda pangolins (Manis javanica) entering the wildlife trade via Malaysia. Preprint at BioRxiv https://doi.org/10.1101/2020.06.19.158717 (2020).

48.
Xiang, X. Sichuan villager capture 33 bats isolated from their homes and have eaten them. Morning Post (February, 2020).

49.
Xu, D. Huanan market has more than a dozen of wildlife animals. China Business Network (March, 2020).

50.
Zhang, L., Zhu, G., Jones, G. & Zhang, S. Conservation of bats in China: problems and recommendations. Oryx 43, 179–182 (2009).
Article  Google Scholar 

51.
Yu, W. B., Tang, G. D., Zhang, L. & Corlett, R. T. Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-CoV-2/HCoV-19) using whole genomic data. Zool. Res. 41, 247–257 (2020).
PubMed  PubMed Central  Article  Google Scholar 

52.
Chen, W. et al. SARS-associated coronavirus transmitted from human to pig. Emerg. Infect. Dis. 11, 446–448 (2005).
PubMed  PubMed Central  Article  Google Scholar 

53.
Ling, H. Beijing Xinfa wholesale market temporarily closed! Imported salmon case board detected with new coronavirus. Science and Technology Daily Beijing (2020).

54.
Josephine M. Coronavirus: China’s first confirmed COVID-19 case traced back to November 17th. South China Morning Post (2020).

55.
Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Cohen, J. Wuhan seafood market may not be source of novel virus spreading globally. Science 367, 234–235 (2020).
PubMed  Article  CAS  Google Scholar 

57.
Deslandes, A. et al. SARS-CoV-2 was already spreading in France in late December 2019. Int. J. Antimicrob. Agents https://doi.org/10.1016/j.ijantimicag.2020.106006 (2020).
Article  PubMed  PubMed Central  Google Scholar 

58.
Forster, P., Forster, L., Renfrew, C. & Forster, M. M. Phylogenetic network analysis of SARS-CoV-2 genomes. Proc. Natl Acad. Sci. USA 117, 9241–9243 (2020).
PubMed  Article  CAS  Google Scholar 

59.
Korber, B. et al. Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2. Preprint at BioRxiv https://doi.org/10.1101/2020.04.29.069054 (2020).

60.
Bhattacharyya, C., et al. Global spread of SARS-CoV-2 subtype with spike protein mutation D614G is shaped by human genomic variations that regulate expression of TMPRSS2 and MX1 genes. Preprint at BioRxiv https://doi.org/10.1101/2020.05.04.075911 (2020).

61.
Zhang, L. et al. The D614G mutation in the SARS-CoV-2 spike protein reduces S1 shedding and increases infectivity. Preprint at BioRxiv https://doi.org/10.1101/2020.06.12.148726 (2020).

62.
Balboni, A., Palladini, A., Bogliani, G. & Battilani, M. Detection of a virus related to betacoronaviruses in Italian greater horseshoe bats. Epidemiol. Infect. 139, 216–219 (2011).
PubMed  Article  CAS  Google Scholar 

63.
Mousavizadeh, L. & Ghasemi, S. Genotype and phenotype of COVID-19: Their role in patghogenesis. J. Microbiol. Immunol. Infection, 1–5 https://doi.org/10.1016/j.jmil.2020.03.022 (2020).

64.
Ellinghaus D. et al. Genome-wide association study of severe COVID-19 with respiratory failure. N. Engl. J. Med., 1–13 https://doi.org/10.1056/NEJMoa2020283 (2020).

65.
Zeberg, H. & Paabo, S. The major genetic risk factor for severe COVID-19 is inherited from Neandertals. Preprint at BioRxiv https://doi.org/10.1101/2020.07.03.186296 (2020).

66.
Drexler, J. F. et al. Genomic characterization of severe acute respiratory syndrome-related coronavirus in European bats and classification of coronaviruses based on partial RNA-dependent RNA polymerase gene sequences. J. Virol. 84, 11336–11349 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Pfefferle, S. et al. Distant relatives of severe acute respiratory syndrome coronavirus and close relatives of human coronavirus 229E in bats. Ghana. Emerg. Infect. Dis. 15, 1377–1384 (2009).
PubMed  PubMed Central  Article  Google Scholar 

68.
Quan, P. L. et al. Identification of a severe acute respiratory syndrome coronavirus-like virus in a leaf-nosed bat in Nigeria. mBio 1(4), e00208–e00210 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Ren, W. et al. Full-length genome sequences of two SARS-like coronaviruses in horseshoe bats and genetic variation analysis. J. Gen. Virol. 87, 3355–3359 (2006).
PubMed  Article  CAS  Google Scholar 

70.
Wu, Z. et al. ORF8-related genetic evidence for Chinese horseshoe bats as the source of human severe acute respiratory syndrome coronavirus. J. Infect. Dis. 213, 579–583 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

71.
Lau, S. K. P. et al. Severe acute respiratory syndrome (SARS) coronavirus ORF8 protein is acquired from SARS-related coronavirus from greater horseshoe bats through recombination. J. Virol. 89, 10532–10547 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

1.
Marschner, H. Mineral Nutrition of Higher Plants (Academic Press, Cambridge, 1995).
Google Scholar 
2.
Bormann, F. & Likens, G. Nutrient cycling. Science 155, 424–429 (1967).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Ranger, J. & Turpault, M. P. Input–output nutrient budgets as a diagnostic tool for sustainable forest management. For. Ecol. Manage. 122, 139–154 (1999).
Article  Google Scholar 

4.
Badeau, V., Dambrine, E. & Walter, C. Propriétés des sols forestiers français: Résultats du premier inventaire systématique. Étude Gest. des Sols 6, 165 (1999).
Google Scholar 

5.
van der Heijden, G. et al. Long-term sustainability of forest ecosystems on sandstone in the Vosges Mountains (France) facing atmospheric deposition and silvicultural change. For. Ecol. Manage. 261, 730–740 (2011).
Article  Google Scholar 

6.
Johnson, J. et al. The response of soil solution chemistry in European forests to decreasing acid deposition. Glob. Change Biol. 24, 3603–3619 (2018).
ADS  Article  Google Scholar 

7.
Jonard, M. et al. Deterioration of Norway spruce vitality despite a sharp decline in acid deposition: A long-term integrated perspective. Glob. Change Biol. 18, 711–725 (2012).
ADS  Article  Google Scholar 

8.
Bailey, S. W., Horsley, S. B. & Long, R. P. Thirty years of change in forest soils of the Allegheny Plateau, Pennsylvania. Soil Sci. Soc. Am. J. 69, 681–690 (2005).
ADS  CAS  Article  Google Scholar 

9.
Hedin, L. O. et al. Steep declines in atmospheric base cations in regions of Europe and North America. Nature 367, 351–354 (1994).
ADS  CAS  Article  Google Scholar 

10.
Hedin, L. O. & Likens, G. E. Atmospheric dust and acid rain. Sci. Am. 275, 88–92 (1996).
CAS  Article  Google Scholar 

11.
Likens, G. E. et al. The biogeochemistry of calcium at Hubbard Brook. Biogeochemistry 41, 89–173 (1998).
CAS  Article  Google Scholar 

12.
Lövblad, G., Persson, C., & Roos, E. Deposition of Base Cations in Sweden. Swedish Environmental Protection Agency, Report 5119, ISBN 91-620-5119-9, ISSN 0282-7298. 60 (Stockholm, Sweden, 2000). https://www.naturvardsverket.se/Documents/publikationer/620-6145-3.pdf?pid=2834. Accessed 11 Aug 2020.

13.
Achat, D. L. et al. Quantifying consequences of removing harvesting residues on forest soils and tree growth—A meta-analysis. For. Ecol. Manage. 348, 124–141 (2015).
Article  Google Scholar 

14.
Thiffault, E. et al. Effects of forest biomass harvesting on soil productivity in boreal and temperate forests—A review. Environ. Rev. 19, 278–309 (2011).
Article  CAS  Google Scholar 

15.
Talkner, U. et al. (2019) Nutritional status of major forest tree species in Germany. In Status and Dynamics of Forests in Germany: Results of the National Forest Monitoring (eds Wellbrock, N. & Bolte, A.) 261–293 (Springer, New York, 2019).
Google Scholar 

16.
Jonard, M. et al. Tree mineral nutrition is deteriorating in Europe. Glob. Change Biol. 21, 418–430 (2015).
ADS  Article  Google Scholar 

17.
De Oliveira Garcia, W., Amann, T. & Hartmann, J. Increasing biomass demand enlarges negative forest nutrient budget areas in wood export regions. Sci. Rep. 8, 1–7 (2018).
ADS  Article  CAS  Google Scholar 

18.
Legout, A., Hansson, K., van der Heijden, G., Augusto, L. & Ranger, J. Chemical fertility of forest soils: Basic concepts. Rev. For. Française 66, 21–32 (2014).
Google Scholar 

19.
Löfgren, S., Ågren, A., Gustafsson, J. P., Olsson, B. A. & Zetterberg, T. Impact of whole-tree harvest on soil and stream water acidity in southern Sweden based on HD-MINTEQ simulations and pH-sensitivity. For. Ecol. Manage. 383, 49–60 (2017).
Article  Google Scholar 

20.
Casetou-Gustafson, S. et al. Current, steady-state and historical weathering rates of base cations at two forest sites in northern and southern Sweden: A comparison of three methods. Biogeosciences 17, 281–304 (2020).
ADS  CAS  Article  Google Scholar 

21.
van der Heijden, G. et al. Tracing and modeling preferential flow in a forest soil—Potential impact on nutrient leaching. Geoderma 195–196, 12–22 (2013).
Article  CAS  Google Scholar 

22.
van Sundert, K. et al. Towards comparable assessment of the soil nutrient status across scales—Review and development of nutrient metrics. Glob. Change Biol. 26, 392–409 (2020).
ADS  Article  Google Scholar 

23.
Hansson, K. et al. Chemical fertility of forest ecosystems. Part 1: Common soil chemical analyses were poor predictors of stand productivity across a wide range of acidic forest soils. For. Ecol. Manage. 461, 117843 (2020).
Article  Google Scholar 

24.
Legout, A. et al. Chemical fertility of forest ecosystems. Part 2: Towards redefining the concept by untangling the role of the different components of biogeochemical cycling. For. Ecol. Manage. 461, 117844 (2020).
Article  Google Scholar 

25.
Lucash, M. S., Yanai, R. D., Blum, J. D. & Park, B. B. Foliar nutrient concentrations related to soil sources across a range of sites in the northeastern United States citation details. Soil Sci. Soc. Am. J. 76, 674–683 (2012).
ADS  CAS  Article  Google Scholar 

26.
Rosenstock, N. P. et al. Base cations in the soil bank: Non-exchangeable pools may sustain centuries of net loss to forestry and leaching. Soil 5, 351–366 (2019).
CAS  Article  Google Scholar 

27.
Richardson, J. B., Petrenko, C. L. & Friedland, A. J. Base cations and micronutrients in forest soils along three clear-cut chronosequences in the northeastern United States. Nutr. Cycl. Agroecosyst. 109, 161–179 (2017).
CAS  Article  Google Scholar 

28.
van der Heijden, G., Legout, A., Pollier, B., Ranger, J. & Dambrine, E. The dynamics of calcium and magnesium inputs by throughfall in a forest ecosystem on base poor soil are very slow and conservative: Evidence from an isotopic tracing experiment (26Mg and 44Ca). Biogeochemistry 118, 413–442 (2014).
Article  CAS  Google Scholar 

29.
Smeck, N. E., Saif, H. T. & Bigham, J. M. Formation of a transient magnesium-aluminum double hydroxide in soils of southeastern Ohio. Soil Sci. Soc. Am. J. 58, 470–476 (1994).
ADS  CAS  Article  Google Scholar 

30.
van Reeuwijk, L. P. & de Villiers, J. M. Potassium fixation by amorphous aluminosilica gels. Soil Sci. Soc. Am. J. 32, 238–240 (1968).
Article  Google Scholar 

31.
Collignon, C., Ranger, J. & Turpault, M. P. Seasonal dynamics of Al- and Fe-bearing secondary minerals in an acid forest soil: Influence of Norway spruce roots (Picea abies (L.) Karst.). Eur. J. Soil Sci. 63, 592–602 (2012).
CAS  Article  Google Scholar 

32.
Hall, S. J. & Huang, W. Iron reduction: A mechanism for dynamic cycling of occluded cations in tropical forest soils?. Biogeochemistry 136, 91–102 (2017).
CAS  Article  Google Scholar 

33.
Sparks, D. L. Potassium dynamics in soils. In Advances in Soil Science (ed. Stewart, B. A.) 1–63 (Springer, New York, 1987).
Google Scholar 

34.
Hinsinger, P. & Jaillard, B. Root-induced release of interlayer potassium and vermiculitization of phlogopite as related to potassium depletion in the rhizosphere of ryegrass. J. Soil Sci. 44, 525–534 (1993).
CAS  Article  Google Scholar 

35.
Falk Øgaard, A. & Krogstad, T. Release of interlayer potassium in Norwegian grassland soils. J. Plant Nutr. Soil Sci. 168, 80–88 (2005).
Article  CAS  Google Scholar 

36.
Hamon, R. E., Bertrand, I. & McLaughlin, M. J. Use and abuse of isotopic exchange data in soil chemistry. Aust. J. Soil Res. 40, 1371–1381 (2002).
CAS  Article  Google Scholar 

37.
Ebelhar, S. A. Labile pool. In Encyclopedia of Earth Sciences Series (ed. Chesworth, W.) 425–426 (Springer, Dordrecht, 2008).
Google Scholar 

38.
Tendille, C., de Ruere, J. G. & Barbier, G. Echanges isotopiques du potassium peu mobile des sols. C.R Acad. Sci. 243, 87–89 (1956).
CAS  Google Scholar 

39.
Masozera, C. & Bouyer, S. Potassium et calicum labiles dans quelques types de sols tropicaux. in Sur l’emploi des radioisotopes et des rayonnments dans la recherche sur les relations sol-plante, vol. 12 (1971).

40.
Fardeau, J. C., Hétier, J. M. & Jappe, J. Potassium assimilable du sol: Identification au comportement des ions isotopiquement diluables. C.R Acad. Sci. 288, 1039–1042 (1979).
CAS  Google Scholar 

41.
Blume, J. M. & Smith, D. Detrmination of exchangeable calcium and cation-exchange capacity by equilibration with Ca-45. Soil Sci. 77, 9–18 (1954).
ADS  CAS  Article  Google Scholar 

42.
Newbould, P. & Russell, R. S. Isotopic equilibration of calcium-45 with labile soil calcium. Plant Soil 18, 239–257 (1963).
CAS  Article  Google Scholar 

43.
Reeve, N. G. & Sumner, M. E. Determination of exchangeable calcium in soils by isotopie dilution. Agrochemophysica 1, 13–18 (1969).
CAS  Google Scholar 

44.
van der Heijden, G., Legout, A., Mareschal, L., Ranger, J. & Dambrine, E. Filling the gap in Ca input-output budgets in base-poor forest ecosystems: The contribution of non-crystalline phases evidenced by stable isotopic dilution. Geochim. Cosmochim. Acta 209, 135–148 (2017).
ADS  Article  CAS  Google Scholar 

45.
van der Heijden, G. et al. Measuring plant-available Mg, Ca, and K pools in the soil—An isotopic dilution assay. ACS Earth Sp. Chem. 2, 292–313 (2018).
Article  CAS  Google Scholar 

46.
Graham, E. R. & Fox, R. L. Tropical soil potassium as related to labile pool and calcium exchange equilibria calcium soil analysis. Soil Sci. 3, 318–322 (1971).
ADS  Article  Google Scholar 

47.
Ross, D. S., Matschonat, G. & Skyllberg, U. Cation exchange in forest soils: The need for a new perspective. Eur. J. Soil Sci. 59, 1141–1159 (2008).
CAS  Article  Google Scholar 

48.
Reuss, J. O. & Johnson, D. W. Soil-solution interactions. In Acid Deposition and the Acidification of Soils and Waters (eds Reuss, J. O. & Johnson, D. W.) 33–54 (Springer, New York, 1986).
Google Scholar 

49.
Salmon, R. C. Cation exchange reactions. J. Soil Sci. 15, 273–283 (1964).
CAS  Article  Google Scholar 

50.
André, J. P. & Pijarowski, L. Cation exchange properties of Sphagnumpeat: Exchange between two cations and protons. J. Soil Sci. 28, 573–584 (1977).
Article  Google Scholar 

51.
Ponette, Q. Downward movement of dolomite, kieserite or a mixture of CaCO3 and kieserite through the upper layers of an acid forest soil. Water. Air. Soil Pollut. 95, 353–379 (1997).
ADS  CAS  Google Scholar 

52.
Sparks, D. L. Inorganic soil components. In Environmental Soil Chemistry (ed. Sparks, D. L.) 43–73 (Academic Press, Cambridge, 2003).
Google Scholar 

53.
Kosmulski, M. Compilation of PZC and IEP of sparingly soluble metal oxides and hydroxides from literature. Adv. Colloid Interface Sci. 152, 14–25 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Schwertmann, U. & Fechter, H. The point of zero charge of natural and synthetic ferrihydrites and its relation to adsorbed silicate. Clay Miner. 17, 471–476 (1982).
ADS  CAS  Article  Google Scholar 

55.
Grove, J. H., Sumner, M. E. & Syers, J. K. Effect of lime on exchangeable magnesium in variable surface charge soils. Soil Sci. Soc. Am. J. 45, 497–500 (1981).
ADS  CAS  Article  Google Scholar 

56.
Kinniburgh, D. G., Jackson, M. L. & Syers, J. K. Adsorption of alkaline earth, transition, and heavy metal cations by hydrous oxide gels of iron and aluminum. Soil Sci. Soc. Am. J. 40, 796–799 (1976).
ADS  CAS  Article  Google Scholar 

57.
Myers, J. A., McLean, E. O. & Bigham, J. M. Reductions in exchangeable magnesium with liming of acid Ohio soils. Soil Sci. Soc. Am. J. 52, 131–136 (1988).
ADS  CAS  Article  Google Scholar 

58.
Rowley, M. C., Grand, S. & Verrecchia, ÉP. Calcium-mediated stabilisation of soil organic carbon. Biogeochemistry 137, 27–49 (2018).
CAS  Article  Google Scholar 

59.
Simpson, A. J. et al. Molecular structures and associations of humic substances in the terrestrial environment. Naturwissenschaften 89, 84–88 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Clarholm, M., Skyllberg, U. & Rosling, A. Organic acid induced release of nutrients from metal-stabilized soil organic matter—The unbutton model. Soil Biol. Biochem. 84, 168–176 (2015).
CAS  Article  Google Scholar 

61.
Sowers, T. D., Stuckey, J. W. & Sparks, D. L. The synergistic effect of calcium on organic carbon sequestration to ferrihydrite. Geochem. Trans. 19, 4 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

62.
Meyer, D. & Jungk, A. A new approach to quantify the utilization of non-exchangeable soil potassium by plants. Plant Soil 149, 235–243 (1993).
CAS  Article  Google Scholar 

63.
Moritsuka, N., Yanai, J. & Kosaki, T. Possible processes releasing nonexchangeable potassium from the rhizosphere of maize. Plant Soil 258, 261–268 (2004).
CAS  Article  Google Scholar 

64.
Mareschal, L. Effet des substitutions d’essences forestières sur l’évolution des sols et de leur minéralogie: Bilan après 28 ans dans le site expérimental de Breuil (Morvan) (Henri Poincaré, Nancy, 2008).
Google Scholar 

65.
York, L. M., Carminati, A., Mooney, S. J., Ritz, K. & Bennett, M. M. The holistic rhizosphere: Integrating zones, processes, and semantics in the soil influenced by roots. J. Exp. Bot. 67, 3629–3643 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Pradier, C. et al. Rainfall reduction impacts rhizosphere biogeochemistry in eucalypts grown in a deep Ferralsol in Brazil. Plant Soil 414, 339–354 (2017).
CAS  Article  Google Scholar 

67.
Nezat, C. A., Blum, J. D., Yanai, R. D. & Hamburg, S. P. A sequential extraction to determine the distribution of apatite in granitoid soil mineral pools with application to weathering at the Hubbard Brook Experimental Forest, NH, USA. Appl. Geochem. 22, 2406–2421 (2007).
CAS  Article  Google Scholar 

68.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2019). https://www.r-project.org/. Accessed 17 Mar 2019. More

1.
Pearsall, D. M. et al. Distinguishing rice (Oryza Sativa Poaceae) from wild Oryza species through phytolith analysis—results of preliminary research. Econ. Bot. 49, 183–196. https://doi.org/10.1007/Bf02862923 (1995).
Article  Google Scholar 
2.
Ball, T. et al. Phytoliths as a tool for investigations of agricultural origins and dispersals around the world. J. Archaeol. Sci. 68, 32–65 (2016).
Article  Google Scholar 

3.
Lu, H. et al. Culinary archaeology: millet noodles in Late Neolithic China. Nature 437, 967–968. https://doi.org/10.1038/437967a (2005).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Wang, Y. J. & Lu, H. Y. The Study of Phytolith and Its Application (China Ocean Press, Beijing, 1993).
Google Scholar 

5.
Piperno, D. R. Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists (AltaMira Press, Lanham, 2006).
Google Scholar 

6.
Pearsall, D. M. Paleoethnobotany: A Handbook of Procedures (Academic Press, London, 1989).
Google Scholar 

7.
Piperno, D. R. Phytolyth Analysis: An Archaeological and Geological Perspective (Academic Press, London, 1988).
Google Scholar 

8.
Prebble, M., Schallenberg, M., Carter, J. & Shulmeister, J. An analysis of phytolith assemblages for the quantitative reconstruction of late Quaternary environments of the Lower Taieri Plain, otago, South Island, New Zealand I. Modern assemblages and transfer functions. J. Paleolimnol. 27, 393–413. https://doi.org/10.1023/A:1020318803497 (2002).
ADS  Article  Google Scholar 

9.
Lu, H. Y. et al. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China I: phytolith-based transfer functions. Quatern. Sci. Rev. 25, 945–959. https://doi.org/10.1016/j.quascirev.2005.07.014 (2006).
ADS  Article  Google Scholar 

10.
Bremond, L. et al. Phytolith indices as proxies of grass subfamilies on East African tropical mountains. Global Planet Change 61, 209–224. https://doi.org/10.1016/j.gloplacha.2007.08.016 (2008).
ADS  Article  Google Scholar 

11.
Iriarte, J. & Paz, E. A. Phytolith analysis of selected native plants and modern soils from southeastern Uruguay and its implications for paleoenvironmental and archeological reconstruction. Quatern. Int. 193, 99–123. https://doi.org/10.1016/j.quaint.2007.10.008 (2009).
Article  Google Scholar 

12.
Mercader, J., Bennett, T., Esselmont, C., Simpson, S. & Walde, D. Phytoliths in woody plants from the Miombo woodlands of Mozambique. Ann. Bot. 104, 91–113. https://doi.org/10.1093/aob/mcp097 (2009).
Article  PubMed  PubMed Central  Google Scholar 

13.
Mercader, J. et al. Poaceae phytoliths from the Niassa Rift, Mozambique. J. Archaeol. Sci. 37, 1953–1967. https://doi.org/10.1016/j.jas.2010.03.001 (2010).
Article  Google Scholar 

14.
Patterer, N. I., Passeggi, E. & Zucol, A. F. Phytolith analysis of soils from the southwestern Entre Rios Province (Argentina) as a tool to understand their pedological processes. Rev. Mex. Cienc. Geol. 28, 132–146 (2011).
Google Scholar 

15.
Pearce, M. & Ball, T. A study of phytoliths produced by selected native plant taxa commonly used by Great Basin Native Americans. Veg. Hist. Archaeobot. https://doi.org/10.1007/s00334-019-00738-1 (2019).
Article  Google Scholar 

16.
Carter, J. A. Phytoliths from loess in Southland, New Zealand. N. Z. J. Bot. 38, 325–332 (2000).
Article  Google Scholar 

17.
Ball, T. B., Ehlers, R. & Standing, M. D. Review of typologic and morphometric analysis of phytoliths produced by wheat and barley. Breed. Sci. 59, 505–512. https://doi.org/10.1270/jsbbs.59.505 (2009).
Article  Google Scholar 

18.
18Lu, H., Wu, N. & Liu, K. In The state of the art of phytoliths in plants and soils (eds A. Pinilla, J. Juan-Tresseras, & J. Machado) Ch. 159, 15 (Monogra as del Centro de Ciencias Medambioentales, 1997).

19.
Lu, H. et al. Phytoliths analysis for the discrimination of Foxtail millet (Setaria italica) and common millet (Panicum miliaceum). PLoS ONE 4, e4448. https://doi.org/10.1371/journal.pone.0004448 (2009).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Ge, Y. et al. Phytolith analysis for the identification of barnyard millet (Echinochloa sp.) and its implications. Archaeol. Anthrop. Sci. 10, 61–73. https://doi.org/10.1007/s12520-016-0341-0 (2018).
Article  Google Scholar 

21.
Piperno, D. R. A comparison and differentiation of phytoliths from maize and wild grasses: use of morphological criteria. Am. Antiq. 49, 361–383. https://doi.org/10.2307/280024 (1984).
Article  Google Scholar 

22.
Ge, Y., Lu, H., Zhang, J., Wang, C. & Gao, X. Phytoliths in inflorescence bracts: preliminary results of an investigation on common Panicoideae plants in China. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01736 (2020).
Article  PubMed  PubMed Central  Google Scholar 

23.
Huan, X. et al. Bulliform phytolith research in wild and domesticated rice paddy soil in South China. PLoS ONE 10, e0141255 (2015).
Article  Google Scholar 

24.
Prebble, M. & Shulmeister, J. An analysis of phytolith assemblages for the quantitative reconstruction of late Quaternary environments of the Lower Taieri Plain, Otago, South Island, New Zealand II. Paleoenvironmental reconstruction. J. Paleolimnol. 27, 415–427. https://doi.org/10.1023/a:1020314719427 (2002).
ADS  Article  Google Scholar 

25.
Lu, H. Y., Wu, N. Q., Liu, K. B., Jiang, H. & Liu, T. S. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China II: palaeoenvironmental reconstruction in the Loess Plateau. Quatern. Sci. Rev. 26, 759–772. https://doi.org/10.1016/j.quascirev.2006.10.006 (2007).
ADS  Article  Google Scholar 

26.
Carter, J. A. & Lian, O. B. Palaeoenvironmental reconstruction from last interglacial using phytolith analysis, southeastern North Island New Zealand. J. Quatern. Sci. 15, 733–743. https://doi.org/10.1002/1099-1417(200010)15:7%3c733::Aid-Jqs532%3e3.0.Co;2-J (2000).
ADS  Article  Google Scholar 

27.
Novello, A. et al. Phytoliths indicate significant arboreal cover at Sahelanthropus type locality TM266 in northern Chad and a decrease in later sites. J. Hum. Evol. 106, 66–83. https://doi.org/10.1016/j.jhevol.2017.01.009 (2017).
Article  PubMed  Google Scholar 

28.
He, K. et al. Middle-Holocene sea-level fluctuations interrupted the developing Hemudu culture in the lower Yangtze River, China. Quatern. Sci. Rev. 188, 90–103. https://doi.org/10.1016/j.quascirev.2018.03.034 (2018).
ADS  Article  Google Scholar 

29.
Deng, Z. et al. The first discovery of Neolithic rice remains in eastern Taiwan: phytolith evidence from the Chaolaiqiao site. Archaeol. Anthrop. Sci. 10, 1477–1484. https://doi.org/10.1007/s12520-017-0471-z (2018).
Article  Google Scholar 

30.
Piperno, D. R. The origins of plant cultivation and domestication in the New World tropics. Curr. Anthropol. 52, S453–S470 (2011).
Article  Google Scholar 

31.
Yang, X. et al. Barnyard grasses were processed with rice around 10000 years ago. Sci. Rep. Uk 5, 16251. https://doi.org/10.1038/srep16251 (2015).
ADS  CAS  Article  Google Scholar 

32.
Lu, H. et al. Earliest domestication of common millet (Panicum miliaceum) in East Asia extended to 10,000 years ago. Proc. Natl. Acad. Sci. U.S.A. 106, 7367–7372. https://doi.org/10.1073/pnas.0900158106 (2009).
ADS  Article  PubMed  PubMed Central  Google Scholar 

33.
Stromberg, C. Phytoliths in Paleoecology (Springer, Berlin, 2018).
Google Scholar 

34.
Stromberg, C. A. E., Dunn, R. E., Madden, R. H., Kohn, M. J. & Carlini, A. A. Decoupling the spread of grasslands from the evolution of grazer-type herbivores in South America. Nat. Commun. 4, 1–8. https://doi.org/10.1038/Ncomms2508 (2013).
Article  Google Scholar 

35.
Nurse, A. M., Reavie, E. D., Ladwig, J. L. & Yost, C. L. Pollen and phytolith paleoecology in the St. Louis River Estuary, Minnesota, USA, with special consideration of Zizania palustris L. Rev. Palaeobot. Palyno 246, 216–231. https://doi.org/10.1016/j.revpalbo.2017.07.003 (2017).
Article  Google Scholar 

36.
Liu, H., Gu, Y., Lun, Z., Qin, Y. & Cheng, S. Phytolith-inferred transfer function for paleohydrological reconstruction of Dajiuhu peatland, central China. Holocene 28, 1623–1630. https://doi.org/10.1177/0959683618782590 (2018).
ADS  Article  Google Scholar 

37.
Li, D. et al. Holocene climate reconstruction based on herbaceous phytolith indices from an AMS 14 C-dated peat profile in the Changbai Mountains, northeast China. Quatern. Int. 447, 144–157 (2017).
Article  Google Scholar 

38.
Zuo, X. et al. Dating rice remains through phytolith carbon-14 study reveals domestication at the beginning of the Holocene. Proc. Natl. Acad. Sci. 114, 6486–6491. https://doi.org/10.1073/pnas.1704304114 (2017).
CAS  Article  PubMed  Google Scholar 

39.
Luo, W. et al. Evidence for crop structure from phytoliths at the Dongzhao site on the Central Plains of China from Xinzhai to Erligang periods. J. Archaeol. Sci. Rep. 17, 852–859. https://doi.org/10.1016/j.jasrep.2017.12.018 (2018).
Article  Google Scholar 

40.
Deng, Z., Hung, H.-C., Fan, X., Huang, Y. & Lu, H. The ancient dispersal of millets in southern China: New archaeological evidence. Holocene 28, 34–43 (2017).
ADS  Article  Google Scholar 

41.
Piperno, D. R., Holst, I., Moreno, J. E. & Winter, K. Experimenting with domestication: understanding macro- and micro-phenotypes and developmental plasticity in teosinte in its ancestral pleistocene and early holocene environments. J. Archaeol. Sci. 108, 104970. https://doi.org/10.1016/j.jas.2019.05.006 (2019).
Article  Google Scholar 

42.
Piperno, D. R., Ranere, A. J., Holst, I., Iriarte, J. & Dickau, R. Starch grain and phytolith evidence for early ninth millennium BP maize from the Central Balsas River Valley, Mexico. Proc. Natl. Acad. Sci. U.S.A. 106, 5019–5024. https://doi.org/10.1073/pnas.0812525106 (2009).
ADS  Article  PubMed  PubMed Central  Google Scholar 

43.
Wang, J. et al. Revealing a 5,000-y-old beer recipe in China. Proc. Natl. Acad. Sci. 113, 6444–6448. https://doi.org/10.1073/pnas.1601465113 (2016).
CAS  Article  PubMed  Google Scholar 

44.
Hilbert, L. et al. Evidence for mid-Holocene rice domestication in the Americas. Nat. Ecol. Evol. 1, 1693–1698. https://doi.org/10.1038/s41559-017-0322-4 (2017).
Article  PubMed  Google Scholar 

45.
Kondo, R., Childs, C. & Atkinson, I. Opal Phytoliths of New Zealand Vol. 85 (Manaaki Whenua Press, Lincoln, 1994).
Google Scholar 

46.
Geis, J. W. Biogenic silica in selected species of deciduous angiosperms. Soil Sci. 116, 113. https://doi.org/10.1097/00010694-197308000-00008 (1973).
ADS  Article  Google Scholar 

47.
Kondo, R. & Peason, T. Opal phytoliths in tree leaves: 2. Opal phytoliths in dicotyledonous angiosperm tree leaves (in Japanese). Res. Bull. Obihiro Univ. Ser. I(12), 217–229 (1981).
Google Scholar 

48.
Kealhofer, L. & Piperno, D. R. Opal phytoliths in Southeast Asian Flora (Smithsonian Institution Press, Washington, 1998).
Google Scholar 

49.
Morris, L. R., Baker, F. A., Morris, C. & Ryel, R. J. Phytolith types and type-frequencies in native and introduced species of the sagebrush steppe and pinyon-juniper woodlands of the Great Basin, USA. Rev. Palaeobot. Palyno 157, 339–357. https://doi.org/10.1016/j.revpalbo.2009.06.007 (2009).
Article  Google Scholar 

50.
Lisztes-Szabó, Z., Braun, M., Csík, A. & Pető, Á. Phytoliths of six woody species important in the Carpathians: characteristic phytoliths in Norway spruce needles. Veg. Hist. Archaeobot. https://doi.org/10.1007/s00334-019-00720-x (2019).
Article  Google Scholar 

51.
Carnelli, A. L., Theurillat, J. P. & Madella, A. Phytolith types and type-frequencies in subalpine-alpine plant species of the European Alps. Rev. Palaeobot. Palyno 129, 39–65. https://doi.org/10.1016/j.revpalbo.2003.11.002 (2004).
Article  Google Scholar 

52.
Runge, F. The opal phytolith inventory of soils in central Africa—quantities, shapes, classification, and spectra. Rev. Palaeobot. Palyno 107, 23–53. https://doi.org/10.1016/S0034-6667(99)00018-4 (1999).
Article  Google Scholar 

53.
Mercader, J., Bennett, T., Esselmont, C., Simpson, S. & Walde, D. Soil phytoliths from miombo woodlands in Mozambique. Quatern. Res. 75, 138–150. https://doi.org/10.1016/j.yqres.2010.09.008 (2011).
ADS  Article  Google Scholar 

54.
Kondo, R. Phytoliths Images by Scanning Electron Microscope—An Introduction to Phytoliths (in Japanese) (Hokkaido University Press, Hokkaido, 2010).
Google Scholar 

55.
Ge, Y., Jie, D. M., Sun, Y. L. & Liu, H. M. Phytoliths in woody plants from the northern slope of the Changbai Mountain (Northeast China), and their implication. Plant Syst. Evol. 292, 55–62. https://doi.org/10.1007/s00606-010-0406-y (2011).
CAS  Article  Google Scholar 

56.
Gao, G. et al. Phytolith characteristics and preservation in trees from coniferous and broad-leaved mixed forest in an eastern mountainous area of Northeast China. Rev. Palaeobot. Palyno 255, 43–56 (2018).
Article  Google Scholar 

57.
Bremond, L., Alexandre, A., Hely, C. & Guiot, J. A phytolith index as a proxy of tree cover density in tropical areas: calibration with Leaf Area Index along a forest-savanna transect in southeastern Cameroon. Global Planet Change 45, 277–293. https://doi.org/10.1016/j.gloplacha.2004.09.002 (2005).
ADS  Article  Google Scholar 

58.
Esteban, I. et al. Phytoliths in plants from the south coast of the Greater Cape Floristic Region (South Africa). Rev. Palaeobot. Palyno https://doi.org/10.1016/j.revpalbo.2017.05.001 (2017).
Article  Google Scholar 

59.
Scurfield, G., Anderson, C. A. & Segnit, E. R. Silica in woody stems. Aust. J. Bot. 22, 211–229. https://doi.org/10.1071/Bt9740211 (1974).
CAS  Article  Google Scholar 

60.
Collura, L. V. & Neumann, K. Wood and bark phytoliths of West African woody plants. Quatern. Int. https://doi.org/10.1016/j.quaint.2015.12.070 (2016).
Article  Google Scholar 

61.
Lu, H. Y. & Liu, K. B. Phytoliths of common grasses in the coastal environments of southeastern USA. Estuar. Coast Shelf Sci. 58, 587–600. https://doi.org/10.1016/S0272-7714(03)00137-9 (2003).
ADS  Article  Google Scholar 

62.
Neumann, K. et al. International Code for Phytolith Nomenclature (ICPN) 2.0. Ann. Bot. Lond. 124, 189–199. https://doi.org/10.1093/aob/mcz064 (2019).
Article  Google Scholar 

63.
Juggins, S. C2 Version 1.5 User Guide. Software for Ecological and Palaeoecological Data Analysis and Visualisation (Newcastle University, Newcastle, 2007).
Google Scholar 

64.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

65.
Biswas, O., Mukherjee, B., Mukherjee, M. & Bera, S. Phytolith spectra in some selected fern-allies of eastern Himalaya. J. Bot. Soc. Bengal 1, 35–39 (2015).
Google Scholar 

66.
Piperno, D. R., Holst, I., Wessel-Beaver, L. & Andres, T. C. Evidence for the control of phytolith formation in Cucurbita fruits by the hard rind (Hr) genetic locus: archaeological and ecological implications. Proc. Natl. Acad. Sci. U.S.A. 99, 10923–10928. https://doi.org/10.1073/pnas.152275499 (2002).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar  More

NEWS ROUND-UP
23 September 2020

The latest science news, in brief.

Most charcoal sold in Europe is used for barbecues — but its provenance is not always clear.Credit: Getty

Unsustainable charcoal
Nearly half of charcoal bought in Europe contains wood from tropical and subtropical forests, with little of it certified as sustainable, raising fears that some is illegally logged.
“This is just an overview but it’s absolutely enough to cause alarm,” says study leader Volker Haag, a wood anatomist at the Thünen Institute of Wood Research in Hamburg, Germany (V. Haag et al. IAWA J. https://doi.org/d9n8; 2020).
Haag’s team used a microscopy technique that digitally reconstructs sections of charcoal from irregular lumps to create images from which the wood can be identified. They analysed 4,500 samples from 150 charcoal bags bought in 11 countries. Some 46% included wood from subtropical and tropical forests, which have high rates of deforestation. Of these, only one-quarter of bags bore the logos of sustainable-certification organizations. In addition, only one-quarter of the bags specified the species or origin of the wood — and only half of these were correct. A wrongly labelled product is a strong indicator of illegality, says co-author Johannes Zahnen, a forest-policy specialist at WWF Germany in Berlin.

Source: V. Haag et al. IAWA J. https://doi.org/10.1163/22941932-bja10017 (2020).

Genomes show coronavirus spread on flight
Genetic evidence strongly suggests that at least one member of a married couple flying from the United States to Hong Kong infected two flight attendants during the trip.
Researchers led by Leo Poon at the University of Hong Kong and Deborah Watson-Jones at the London School of Hygiene & Tropical Medicine studied four people on the early-March flight (E. M. Choi et al. Emerg. Infect. Dis. https://doi.org/d9jn; 2020). Two were a husband and wife travelling in business class. The others were crew members: one in business class and one whose cabin assignment is unknown. The passengers had travelled in Canada and the United States before the flight and tested positive for the new coronavirus soon after arriving in Hong Kong. The flight attendants tested positive shortly thereafter.
The team found that the viral genomes of all four were identical and that their virus was a close genetic relative of some North American SARS‑CoV-2 samples — but not of the SARS‑CoV-2 prevalent in Hong Kong. This suggests that one or both of the passengers transmitted the virus to the crew members during the flight, the authors say. The authors add that no previous reports of in-flight spread have been supported by genetic evidence.

Blood plasma donated by people who have recovered from COVID-19 contains antibodies that could help to treat the disease.Credit: Ben Stansall/AFP via Getty

A guide to making ‘cocktails’ to treat COVID-19
A new method pinpoints every mutation that a crucial SARS‑CoV-2 protein could use to evade an attacking antibody. The results could inform the development of antibody treatments for COVID-19.
The immune system produces molecules called antibodies to fend off invaders. Antibodies that bind to an important region of the SARS-CoV-2 spike protein can inactivate the viral particles, making such antibodies attractive as therapies. But over time, viruses can accumulate mutations — and some can interfere with antibody binding and allow viral particles to ‘escape’ immune forces.
James Crowe at the Vanderbilt University Medical Center in Nashville, Tennessee, Jesse Bloom at the Fred Hutchinson Cancer Center in Seattle, Washington, and their colleagues created the most detailed map so far of the spike-protein mutations that could prevent binding by ten human antibodies (A. J. Greaney et al. Preprint at bioRxiv https://doi.org/d8zm; 2020). The team then used that information to design three antibody cocktails, each consisting of two antibodies.
In laboratory tests of the cocktails against SARS-CoV-2, the virus did not develop mutations that could escape antibody binding. The findings have not yet been peer reviewed.

Nature 585, 487 (2020)
doi: 10.1038/d41586-020-02696-5

Latest on:

Microscopy

SARS-CoV-2

An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. More

1.
Halimubieke, N. et al. Mate fidelity in a polygamous shorebird, the snowy plover (Charadrius nivosus). Ecol. Evol. 9, 10734–10745. https://doi.org/10.1002/ece3.5591 (2019).
Article  PubMed  PubMed Central  Google Scholar 
2.
Reynolds, J. D. Animal breeding systems. Trends Ecol. Evol. 11, 68–72. https://doi.org/10.1016/0169-5347(96)81045-7 (1996).
CAS  Article  PubMed  Google Scholar 

3.
Neff, B. D. & Pitcher, T. E. Genetic quality and sexual selection: An integrated framework for good genes and compatible genes. Mol. Ecol. 14, 19–38. https://doi.org/10.1111/j.1365-294X.2004.02395.x (2005).
CAS  Article  PubMed  Google Scholar 

4.
Székely, T., Thomas, G. H. & Cuthill, I. C. Sexual conflict, ecology, and breeding systems in shorebirds. Bioscience 56, 801–808. https://doi.org/10.1641/0006-3568(2006)56[801:SCEABS]2.0.CO;2 (2006).
Article  Google Scholar 

5.
Culina, A., Radersma, R. & Sheldon, B. C. Trading up: The fitness consequences of divorce in monogamous birds. Biol. Rev. Camb. Philos. Soc. 90, 1015–1034. https://doi.org/10.1111/brv.12143 (2014).
Article  PubMed  Google Scholar 

6.
Székely, T., Weissing, F. J. & Komdeur, J. Adult sex ratio variation: Implications for breeding system evolution. J. Evol. Biol. 27, 1500–1512. https://doi.org/10.1111/jeb.12415 (2014).
Article  PubMed  Google Scholar 

7.
Culina, A., Lachish, S., Pradel, R., Choquet, R. & Sheldon, B. C. A multievent approach to estimating pair fidelity and heterogeneity in state transitions. Ecol. Evol. 3, 4326–4338. https://doi.org/10.1002/ece3.729 (2013).
Article  PubMed  PubMed Central  Google Scholar 

8.
Møller, A. P. The evolution of monogamy: Mating relationships, parental care and sexual selection. In Monogamy Mating Strategies and Partnerships in Birds, Humans and Other Mammals (eds Reichard, U. H. & Boesch, C.) 29–41 (Cambridge University Press, Cambridge, 2003).
Google Scholar 

9.
Lukas, D. & Clutton-Brock, T. H. The evolution of social monogamy in mammals. Science 314, 526–530. https://doi.org/10.1126/science.1238677 (2013).
CAS  Article  ADS  Google Scholar 

10.
Black, J. M. Partnerships in birds (Oxford University Press, Oxford, 1996).
Google Scholar 

11.
Black, J. M. Fitness consequences of long-term pair bonds in barnacle geese: Monogamy in the extreme. Behav. Ecol. 12, 640–645. https://doi.org/10.1093/beheco/12.5.640 (2001).
Article  Google Scholar 

12.
Reichard, U. H. & Boesch, C. Monogamy: Mating Strategies and Partnerships in Birds, Humans and Other Mammals (Cambridge University Press, Cambridge, 2003).
Google Scholar 

13.
Sánchez-Macouzet, O., Rodríguez, C. & Drummond, H. Better stay together: Pair bond duration increases individual fitness independent of age-related variation. Proc. R. Soc. B Biol. Sci. 281, 20132843. https://doi.org/10.1098/rspb.2013.2843 (2014).
Article  Google Scholar 

14.
Botero, C. A. & Rubenstein, D. R. Fluctuating environments, sexual selection and the evolution of flexible mate choice in birds. PLoS ONE 7, e32311. https://doi.org/10.1371/journal.pone.0032311 (2012).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

15.
Blomqvist, D., Wallander, J. & Andersson, M. Successive clutches and parental roles in waders: The importance of timing in multiple clutch systems. Biol. J. Linn. Soc. 74, 549–555. https://doi.org/10.1111/j.1095-8312.2001.tb01412.x (2001).
Article  Google Scholar 

16.
Eberhart-Phillips, L. J. Plover breeding systems: Diversity and evolutionary origins. In The Population Ecology and Conservation of Charadrius Plovers (eds Colwell, M. A. & Haig, S. M.) 65–88 (CRC Press, Boca Raton, 2019).
Google Scholar 

17.
Green, G. H., Greenwood, J. J. D. & Lloyd, C. S. The influence of snow conditions on the date of breeding of wading birds in north-east Greenland. J. Zool. 183, 311–328. https://doi.org/10.1111/j.1469-7998.1977.tb04190.x (1977).
Article  Google Scholar 

18.
Saalfeld, S. T. & Lanctot, R. B. Conservative and opportunistic settlement strategies in Arctic-breeding shorebirds. Auk 132, 212–234. https://doi.org/10.1642/AUK-13-193.1 (2015).
Article  Google Scholar 

19.
Székely, T., Cuthill, I. C. & Kis, J. Brood desertion in Kentish plover sex differences in remating opportunities. Behav. Ecol. 10, 185–190. https://doi.org/10.1093/beheco/10.2.185 (1999).
Article  Google Scholar 

20.
Yasué, M. & Dearden, P. Replacement nesting and double-brooding in Malaysian plovers Charadrius peronii: Effects of season and food availability. Ardea 96, 59–72. https://doi.org/10.5253/078.096.0107 (2008).
Article  Google Scholar 

21.
Gilburn, A. S. & Day, T. H. Evolution of female choice in seaweed flies: Fisherian and good genes mechanisms operate in different populations. Proc. R. Soc. B Biol. Sci. 255, 159–165. https://doi.org/10.1098/rspb.1994.0023 (1994).
Article  ADS  Google Scholar 

22.
Candolin, U., Salesto, T. & Evers, M. Changed environmental conditions weaken sexual selection in sticklebacks. J. Evol. Biol. 20, 233–239. https://doi.org/10.1111/j.1420-9101.2006.01207.x (2007).
CAS  Article  PubMed  Google Scholar 

23.
Welch, A. M. Genetic benefits of a female mating preference in gray tree frogs are context-dependent. Evolution 57, 883–893. https://doi.org/10.1111/j.0014-3820.2003.tb00299.x (2003).
Article  PubMed  Google Scholar 

24.
Lode, T., Holveck, M. J., Lesbarreres, D. & Pagano, A. Sex-biased predation by polecats influences the mating system of frogs. Proc. R. Soc. B Biol. Sci. 271, 399–401. https://doi.org/10.1098/rsbl.2004.0195 (2004).
Article  Google Scholar 

25.
Liker, A., Freckleton, R. P. & Székely, T. Divorce and infidelity are associated with skewed adult sex ratios in birds. Curr. Biol. 24, 880–884. https://doi.org/10.1016/j.cub.2014.02.059 (2014).
CAS  Article  PubMed  Google Scholar 

26.
Parra, J. E., Beltrán, M., Zefania, S., dos Remedios, N. & Székely, T. Experimental assessment of mating opportunities in three shorebird species. Anim. Behav. 90, 83–90. https://doi.org/10.1016/j.anbehav.2013.12.030 (2014).
Article  Google Scholar 

27.
Jeschke, J. M. & Kokko, H. Mortality and other determinants of bird divorce rate. Behav. Ecol. Sociobiol. 63, 1–9. https://doi.org/10.1007/s00265-008-0646-9 (2008).
Article  Google Scholar 

28.
Bried, J., Pontier, D. & Jouventin, P. Mate fidelity in monogamous birds: A re-examination of the Procellariiformes. Anim. Behav. 65, 235–246. https://doi.org/10.1006/anbe.2002.2045 (2003).
Article  Google Scholar 

29.
Andersson, M. Sexual selection (Princeton University Press, Princeton, 1994).
Google Scholar 

30.
Choudhury, S. Divorce in birds: A review of the hypotheses. Anim. Behav. 50, 413–429. https://doi.org/10.1006/anbe.1995.0256 (1995).
Article  Google Scholar 

31.
Wheelwright, N. T. & Teplitsky, C. Divorce in Savannah sparrows: Causes, consequences and lack of inheritance. Am. Nat. 190, 557–569. https://doi.org/10.1086/693387 (2017).
Article  PubMed  Google Scholar 

32.
Adkins-Regan, E. & Tomaszycki, M. Monogamy on the fast track. Biol. Lett. 3, 617–619. https://doi.org/10.1098/rsbl.2007.0388 (2007).
Article  PubMed  PubMed Central  Google Scholar 

33.
Perfito, N., Zann, R. A., Bentley, G. E. & Hau, M. Opportunism at work: Habitat predictability affects reproductive readiness in free-living zebra finches. Funct. Ecol. 21, 291–301. https://doi.org/10.1111/j.1365-2435.2006.01237.x (2007).
Article  Google Scholar 

34.
Ens, B. J., Choudhury, S. & Black, J. M. Mate fidelity and divorce in monogamous birds. In Partnerships in Birds: The Study of Monogamy (ed. Black, J. M.) 344–401 (Oxford University Press, Oxford, 1996).
Google Scholar 

35.
Gabriel, P. O., Black, J. M. & Foster, S. Correlates and consequences of the pair bond in Steller’s Jays. Ethology 119, 178–187. https://doi.org/10.1111/eth.12051 (2013).
Article  Google Scholar 

36.
Coulson, J. C. The influence of the pair-bond and age on the breeding biology of the kittiwake gull Rissa tridactyla. J. Anim. Ecol. 35, 269–279. https://doi.org/10.2307/2394 (1966).
Article  Google Scholar 

37.
Kempenaers, B., Adriaensen, F. & Dhondt, A. A. Inbreeding and divorce in blue and great tits. Anim. Behav. 56, 737–740. https://doi.org/10.1006/anbe.1998.0800 (1998).
CAS  Article  PubMed  Google Scholar 

38.
Pyle, P., Sydeman, W. J. & Hester, M. Effects of age, breeding experience, mate fidelity and site fidelity on breeding performance in declining populations of Cassin’s auklets. J. Anim. Ecol. 70, 1088–1097. https://doi.org/10.1046/j.0021-8790.2001.00567.x (2001).
Article  Google Scholar 

39.
Flodin, L. A. & Blomqvist, D. Divorce and breeding dispersal in the dunlin Calidris alpina: Support for the better option hypothesis?. Behaviour 149, 67–80. https://doi.org/10.1163/156853912X626295 (2012).
Article  Google Scholar 

40.
Arnqvist, G. & Nilsson, T. The evolution of polyandry: Multiple mating and female fitness in insects. Anim. Behav. 60, 145–164. https://doi.org/10.1006/anbe.2000.1446 (2000).
CAS  Article  PubMed  Google Scholar 

41.
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162. https://doi.org/10.1016/S0003-3472(80)80103-5 (1980).
Article  Google Scholar 

42.
Clobert, J., Danchin, E., Dhondt, A. & Nichols, J. D. Dispersal (Oxford University Press, Oxford, 2001).
Google Scholar 

43.
Trochet, A. et al. Evolution of sex-biased dispersal. Q. Rev. Biol. 91, 297–320. https://doi.org/10.1086/688097 (2016).
Article  PubMed  Google Scholar 

44.
D’Urban Jackson, J. et al. Polygamy slows down population divergence in shorebirds. Evolution 71, 1313–1326. https://doi.org/10.1111/evo.13212 (2017).
Article  PubMed  PubMed Central  Google Scholar 

45.
Székely, T. Why study plovers? The significance of non-model organisms in avian ecology, behaviour and evolution. J. Ornithol. 160, 923–933. https://doi.org/10.1007/s10336-019-01669-4 (2019).
Article  Google Scholar 

46.
Morse, D. H. & Kress, S. W. The effect of burrow loss on mate choice in the Leach’s Storm-Petrel. Auk 101, 158–160 (1984).
Article  Google Scholar 

47.
Pietz, P. J. & Parmelee, D. F. Survival, site and mate fidelity in south polar skuas Catharacta maccormicki at Anvers Island, Antarctica. Ibis 136, 32–38. https://doi.org/10.1111/j.1474-919X.1994.tb08128.x (2014).
Article  Google Scholar 

48.
Thibault, J.-C. Nest-site tenacity and mate fidelity in relation to breeding success in Cory’s Shearwater Calonectris diomedea. Bird Study 41, 25–28. https://doi.org/10.1080/00063659409477193 (1994).
Article  Google Scholar 

49.
Dubois, F. & Cézilly, F. Breeding success and mate retention in birds: A meta-analysis. Behav. Ecol. Sociobiol. 52, 357–364. https://doi.org/10.1007/s00265-002-0521-z (2002).
Article  Google Scholar 

50.
Kosztolányi, A., Székely, T., Cuthill, I. C., Yilmaz, K. T. & Berberoǧlu, S. Ecological constraints on breeding system evolution: The influence of habitat on brood desertion in Kentish plover. J. Anim. Ecol. 75, 257–265. https://doi.org/10.1111/j.1365-2656.2006.01049.x (2006).
Article  PubMed  Google Scholar 

51.
del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive (Lynx Edicions, 2018). (retrieved from https://www.hbw.com/ on 30 October 2019).

52.
Maher, K. H. et al. High fidelity: Extra-pair fertilisations in eight Charadrius plover species are not associated with parental relatedness or social mating system. J. Avian. Biol. 48, 910–920. https://doi.org/10.1111/jav.01263 (2017).
Article  Google Scholar 

53.
Székely, T., Freckleton, R. P. & Reynolds, J. D. Sexual selection explains Rensch’s rule of size dimorphism in shorebirds. Proc. Natl. Acad. Sci. USA 101, 12224–12227. https://doi.org/10.1073/pnas.0404503101 (2004).
Article  PubMed  ADS  Google Scholar 

54.
Székely, T., Lislevand, T. & Figuerola, J. Sexual size dimorphism in birds. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) (Oxford University Press, Oxford, 2007). https://doi.org/10.1093/acprof:oso/9780199208784.003.0004
Google Scholar 

55.
Lessells, C. M. The mating system of Kentish plovers Charadrius alexandrinus. Ibis 126, 474–483. https://doi.org/10.1111/j.1474-919X.1984.tb02074.x (1984).
Article  Google Scholar 

56.
Székely, T. & Lessells, C. M. Mate change by Kentish plovers Charadrius alexandrinus. Ornis. Scand. 24, 317–322 (1993).
Article  Google Scholar 

57.
Amat, J. A., Fraga, R. M. & Arroyo, G. M. Brood desertion and polygamous breeding in the Kentish plover Charadrius alexandrinus. Ibis 141, 596–607. https://doi.org/10.1111/j.1474-919X.1999.tb07367.x (1999).
Article  Google Scholar 

58.
Carmona-Isunza, M. C., Küpper, C., Serrano-Meneses, M. A. & Székely, T. Courtship behavior differs between monogamous and polygamous plovers. Behav. Ecol. Sociobiol. 69, 2035–2042. https://doi.org/10.1007/s00265-015-2014-x (2015).
Article  Google Scholar 

59.
Warriner, J. S., Warriner, J. C., Page, G. W. & Stenzel, L. E. Mating system and reproductive success of a small population of polygamous snowy plover. Wilson Bull. 98, 15–37 (1986).
Google Scholar 

60.
Eberhart-Phillips, L. J. et al. Demographic causes of adult sex ratio variation and their consequences for parental cooperation. Nat. Commun. 9, 1651. https://doi.org/10.1038/s41467-018-03833-5 (2018).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

61.
Kappeler, P. M. & van Schaik, C. P. Evolution of primate social systems. Int. J. Primatol. 23, 707–740. https://doi.org/10.1023/A:1015520830318 (2002).
Article  Google Scholar 

62.
Avise, J. C. et al. Genetic mating systems and reproductive natural histories of fishes: Lessons for ecology and evolution. Annu. Rev. Genet. 36, 19–45. https://doi.org/10.1146/annurev.genet.36.030602.090831 (2002).
CAS  Article  PubMed  Google Scholar 

63.
Bowyer, R. T., McCullough, D. R., Rachlow, J. L., Ciuti, S. & Whiting, J. C. Evolution of ungulate mating systems: Integrating social and environmental factors. Ecol. Evol. 10, 5160–5178. https://doi.org/10.1002/ece3.62 (2020).
Article  PubMed  PubMed Central  Google Scholar 

64.
Johnson, M. & Walters, J. R. Effects of mate and site fidelity on nest survival of western sandpipers (Calidris mauri). Auk 125, 76–86. https://doi.org/10.1525/auk.2008.125.1.76 (2008).
Article  Google Scholar 

65.
Brandt, E. E., Kelley, J. P. & Elias, D. O. Temperature alters multimodal signaling and mating success in an ectotherm. Behav. Ecol. Sociobiol. 72, 191. https://doi.org/10.1007/s00265-018-2620-5 (2018).
Article  Google Scholar 

66.
Conrad, T., Stöcker, C. & Ayasse, M. The effect of temperature on male mating signals and female choice in the red mason bee, Osmia bicornis (L.). Ecol. Evol. 7, 8966–8975. https://doi.org/10.1002/ece3.3331 (2017).
Article  PubMed  PubMed Central  Google Scholar 

67.
Silva, K., Vieira, M. N., Almada, V. C. & Monteiro, N. M. The effect of temperature on mate preferences and female–female interactions in Syngnathus abaster. Anim. Behav. 74, 1525–1533. https://doi.org/10.1016/j.anbehav.2007.03.008 (2007).
Article  Google Scholar 

68.
Twiss, S. D., Thomas, C., Poland, V., Graves, J. A. & Pomeroy, P. The impact of climatic variation on the opportunity for sexual selection. Biol. Lett. 3, 12–15. https://doi.org/10.1098/rsbl.2006.0559 (2007).
Article  PubMed  Google Scholar 

69.
Olsson, M. et al. In hot pursuit: Fluctuating mating system and sexual selection in sand lizards. Evolution 65, 574–583. https://doi.org/10.1111/j.1558-5646.2010.01152.x (2011).
Article  PubMed  Google Scholar 

70.
Suzaki, Y. et al. Temperature variations affect postcopulatory but not precopulatory sexual selection in the cigarette beetle. Anim. Behav. 144, 115–123. https://doi.org/10.1016/j.anbehav.2018.08.010 (2018).
Article  Google Scholar 

71.
Eberhart-Phillips, L. J. et al. Sex-specific early survival drives adult sex ratio bias in snowy plovers and impacts mating system and population growth. Proc. Natl. Acad. Sci. USA 114, E5474–E5481. https://doi.org/10.1073/pnas.1620043114 (2017).
CAS  Article  PubMed  Google Scholar 

72.
Liker, A., Freckleton, R. P. F. & Székely, T. The evolution of sex roles in birds is related to adult sex ratio. Nat. Commun. 4, 1587 (2013).
Article  ADS  Google Scholar 

73.
Kosztolányi, A., Barta, Z., Küpper, C. & Székely, T. Persistence of an extreme male-biased adult sex ratio in a natural population of polyandrous bird. J. Evol. Biol. 24, 1842–1846. https://doi.org/10.1111/j.1420-9101.2011.02305.x (2011).
Article  PubMed  Google Scholar 

74.
Handel, C. M. & Gill, R. E. Mate fidelity and breeding site tenacity in a monogamous sandpiper, the black turnstone. Anim. Behav. 60, 471–481. https://doi.org/10.1006/anbe.2000.1505 (2000).
CAS  Article  PubMed  Google Scholar 

75.
Cruz-López, M. et al. The plight of a plover: Viability of an important snowy plover population with flexible brood care in Mexico. Biol. Conserv. 209, 440–448. https://doi.org/10.1016/j.biocon.2017.03.009 (2017).
Article  Google Scholar 

76.
Székely, T., Webb, J. N., Houston, A. I. & McNamara, J. M. An evolutionary approach to offspring desertion in birds. In Current Ornithology (eds Nolan, V. & Ketterson, E. D.) 271–330 (Springer, Berlin, 1996).
Google Scholar 

77.
McNamara, J. M., Forslund, P. & Lang, A. An ESS model for divorce strategies in birds. Philos. Trans. R. Soc. B 354, 223–236. https://doi.org/10.1098/rstb.1999.0374 (1999).
Article  Google Scholar 

78.
Houston, A. I., Székely, T. & McNamara, J. M. The parental investment models of Maynard Smith: A retrospective and prospective view. Anim. Behav. 86, 667–674. https://doi.org/10.1016/j.anbehav.2013.08.001 (2013).
Article  Google Scholar 

79.
Zann, R. A. Reproduction in a zebra finch colony in south-eastern Australia: The significance of monogamy, precocial breeding and multiple broods in a highly mobile species. Emu 94, 285–299. https://doi.org/10.1071/MU9940285 (1994).
Article  Google Scholar 

80.
Fowler, G. S. Stages of age-related reproductive success in birds: Simultaneous effects of age, pair-bond duration and reproductive experience. Am. Zool. 35, 318–328. https://doi.org/10.1093/icb/35.4.318 (1995).
Article  Google Scholar 

81.
Champion de Crespigny, F. E., Hurst, L. D. & Wedell, N. Do Wolbachia-associated incompatibilities promote polyandry?. Evolution 62, 107–122. https://doi.org/10.1111/j.1558-5646.2007.00274.x (2007).
Article  PubMed  Google Scholar 

82.
Schwensow, N., Eberle, M. & Sommer, S. Compatibility counts: MHC-associated mate choice in a wild promiscuous primate. Proc. R. Soc. B Biol. Sci. 275, 555–564. https://doi.org/10.1098/rspb.2007.1433 (2008).
Article  Google Scholar 

83.
Fraga, R. M. & Amat, J. A. Breeding biology of a Kentish plover (Charadrius alexandrinus) population in an inland saline lake. Ardeola 43, 69–85 (1996).
Google Scholar 

84.
Ferreira-Rodríguez, N. & Pombal, M. A. Predation pressure on the hatching of the Kentish plover (Charadrius alexandrinus) in clutch protection projects: A case study in north Portugal. Wildl. Res. 45, 55–63. https://doi.org/10.1071/WR17122 (2018).
Article  Google Scholar 

85.
Kubelka, V. et al. Global pattern of nest predation is disrupted by climate change in shorebirds. Science 362, 680–683. https://doi.org/10.1126/science.aat8695 (2018).
CAS  Article  PubMed  ADS  Google Scholar 

86.
Greenwood, P. J. & Harvey, P. H. The natal and breeding dispersal of birds. Annu. Rev. Ecol. Evol. Syst. 13, 1–21. https://doi.org/10.1146/annurev.es.13.110182.000245 (1982).
Article  Google Scholar 

87.
Sandercock, B. K., Lank, D. B., Lanctot, R. B., Kempenaers, B. & Cooke, F. Ecological correlates of mate fidelity in two Arctic-breeding sandpipers. Can. J. Zool. 78, 1948–1958. https://doi.org/10.1139/z00-146 (2000).
Article  Google Scholar 

88.
Liu, Y. & Zhang, Z. Research progress in avian dispersal behavior. Front. Biol. 3, 375. https://doi.org/10.1007/s11515-008-0066-2 (2008).
Article  Google Scholar 

89.
Végvári, Z. et al. Sex-biased breeding dispersal is predicted by social environment in birds. Ecol. Evol. 8, 6483–6491. https://doi.org/10.1002/ece3.4095 (2018).
Article  PubMed  PubMed Central  Google Scholar 

90.
Pearson, W. J. & Colwell, M. A. Effects of nest success and mate fidelity on breeding dispersal in a population of snowy plovers Charadrius nivosus. Bird Conserv. Int. 24, 342–353. https://doi.org/10.1017/S0959270913000403 (2013).
Article  Google Scholar 

91.
Lloyd, P. Adult survival, dispersal and mate fidelity in the white-fronted plover Charadrius marginatus. Ibis 150, 182–187. https://doi.org/10.1111/j.1474-919X.2007.00739.x (2008).
Article  Google Scholar 

92.
McNamara, J. M. & Forslund, P. Divorce rates in birds: Predictions from an optimization model. Am. Nat. 147, 609–640 (1996).
Article  Google Scholar 

93.
Székely, T., Kosztolányi, A. & Küpper, C. Practical guide for investigating breeding ecology of Kentish plover Charadrius alexandrinus. https://www.pennuti.net/wp-content/uploads/2010/08/KP_Field_Guide_v3.pdf (University of Bath, 2008).

94.
Chamberlain, S. et al. rnoaa: “NOAA” Weather data from R. R package version 0.7. 0. 2017. https://cran.r-project. org/web/packages/rnoaa/ (2017).

95.
Sparks, A. H., Hengl, T. & Nelson, A. GSODR: Global summary daily weather 800 data in R. J. Open Source Softw. https://doi.org/10.21105/joss.00177 (2017).
Article  Google Scholar 

96.
Dunning, J. B. CRC Handbook of Avian Body Masses (CRC Press, Boca Raton, 2008).
Google Scholar 

97.
Grolemund, G. & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40, 25. https://doi.org/10.18637/jss.v040.i03 (2011).
Article  Google Scholar 

98.
Searle, S. R., Speed, F. M. & Milliken, G. A. Population marginal means in the linear model: An alternative to least squares means. Am. Stat. 34, 216–221. https://doi.org/10.1080/00031305.1980.10483031 (1980).
MathSciNet  Article  MATH  Google Scholar 

99.
Vincze, O. et al. Parental cooperation in a changing climate: Fluctuating environments predict shifts in care division. Global Ecol. Biogeogr. 26, 347–358. https://doi.org/10.1111/geb.12540 (2017).
Article  Google Scholar 

100.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22. https://doi.org/10.18637/jss.v033.i02 (2010).
Article  Google Scholar 

101.
R Core Team. R: A language and environment for statistical computing in R Foundation for Statistical Computing. https://www.R-project.org (2018). More

Isolation and identification of rhizobacteria from tea rhizosphere
Soil microorganisms play a crucial role in plant health and development. Moreover, they contribute immensely to the agricultural production of different crops. In the district of Darjeeling, tea is cultivated as the major cash crop. Besides tea, a number of other crops such as rice, maize, wheat, mustard, millet, ginger, orange, large cardamom, and vegetable crops are cultivated19 (Source: https://darjeeling.gov.in/agriculture.html). Rice and maize are the most important food grain crops grown in this region. However, because of the acidic nature of the soil of this region, crop cultivation becomes increasingly difficult. Agrochemicals, including N fertilizers, make the situation even more complicated as they further assist soil acidification. In the slightly acidic soils of Darjeeling district (4.2  More

Flow and transport
In this section the governing equations are provided in a general multiphase and multicomponent formulation in which all phases are treated equally (e.g., allowing for compressibility and density changes).
The transport equations are written in terms of molar conservation of each component i out of (n_{c}) total number of components, including all reacting and non-reacting components (defined in more detail in the next subsection):

$$begin{aligned} phi frac{partial c_i}{partial t} + nabla cdot vec {U}_i= & {} F^{{mathrm {well}}}_i + F^{{mathrm {react}}}_{i}, quad i = 1, ldots , n_c, end{aligned}$$
(1)

with (phi [cdot ]) the porosity, (c_i [{mathrm {mol}}/{{mathrm {m}}}^{3}]) the molar density of component i (total molar density in the case of multiphase mixtures), (F^{{mathrm {well}}}_i [{mathrm {mol}}/({{mathrm {s}}} {{mathrm {m}}}^{3})]) a source or sink of component i (e.g., a contaminant spill site or a way to prescribe inflow and outflow conditions), and (F^{mathrm {react}}_i [{mathrm {mol}}/({mathrm {s}} {{mathrm {m}}}^{3})]) the source or sink of component i due to geochemical reactions.
The component flux (U_{i}) contains both the advective and dispersive contributions. In the most general case of (n_{mathrm {ph}}) number of phases that are labeled by (alpha = 1, ldots , n_{mathrm {ph}}), (U_{i}) is given by

$$begin{aligned} vec {U}_i= & {} sum _{alpha =1}^{n_{mathrm {ph}}} left( c_{i,alpha } vec {u}_alpha + f(phi ,tau ) S_alpha vec {J}_{i,alpha }right) ,quad i = 1, ldots , n_c, end{aligned}$$
(2)

with (c_{i,alpha } [{mathrm {mol}}/{{mathrm {m}}}^{3}]) the molar density of component i in phase (alpha), (vec {u}_alpha [{{mathrm {m}}}/{mathrm {s}}]) the fiducial Darcy velocity

$$begin{aligned} vec {u}_alpha= & {} – lambda _{alpha }{mathrm {K}} (nabla p_{alpha } – rho _alpha vec {g}), quad alpha = 1, ldots , {n_{mathrm {ph}}} end{aligned}$$
(3)

in which (p_{alpha } [{mathrm {Pa}}]) is the phase pressure, (vec {g}) is the gravitational vector, and (lambda _{alpha } [{{mathrm {m}}} {mathrm {s}}/{mathrm {kg}}]= lambda _alpha (S_alpha )) is the phase mobility, (rho _alpha [{mathrm {kg}}/{mathrm {m}}^{3}]) the phase mass density, (S_{alpha } [cdot ]) the phase saturation, and (mathrm {K} [{mathrm {m}}^{2}]) the full permeability tensor. The diffusive term (f(phi ,tau ) S_alpha vec {J}_{i,alpha }) is discussed in detail below.
For a fully compressible multiphase system, the pressure (of a reference phase) evolves as41,42:

$$begin{aligned}&phi C_{f} frac{partial p}{partial t} + sum _{i=1}^{n_c} {overline{nu }_i(nabla cdot vec {U}_i}-F^{mathrm {well}}_i – F^{mathrm {react}}_{i}) =0, end{aligned}$$
(4)

with (C_{f} [mathrm {Pa}^{{-1}}]) the total fluid compressibility of the multiphase mixture, and (overline{nu }_i [{mathrm {m}}^{3}/{mathrm {mol}}]) the total partial molar volume of each component. The algorithm to compute these parameters for multiphase mixtures is highly non-linear43.
For the case of a single aqueous phase the expressions for compressibility and partial molar volumes are considerably simpler, and (n_{mathrm {ph}} =1), (alpha = w), (c_{i,alpha } = c_{i}), (lambda _{alpha } = lambda _{w} = 1/mu _{w}) with (mu _{w} [{mathrm {m}} {mathrm {s}}/{mathrm {kg}}]) the water viscosity, (S_{w}=1), and (p_{alpha }=p) (no capillary effects).
Geochemical reactions
When several species react through a number of different reactions, the concentrations of each of the species are not independent. For example, in the equilibrium reaction (hbox {H}_{2}hbox {O} rightleftharpoons hbox {H}^{+} + hbox {OH}^{-}), if one mole of (hbox {H}_{2}hbox {O}) reacts, the increase in (hbox {H}^{+}) and (hbox {OH}^{-}) concentrations equals the decrease in (hbox {H}_{2}hbox {O}) concentration. A mathematical consequence is that not all species concentrations need to be transported explicitly. One can split the total number of species into a subset of independent primary components and a set of secondary components that can be constructed from the primary ones44. The process has been described in the literature18 but is perhaps best illustrated by example.
Consider a typical mixture in the context of geological carbon dioxide ((hbox {CO}_{2})) sequestration consisting of seven species dissolved in water: (hbox {CaCO}_{3}), (hbox {Ca}^{2+}), (hbox {CO}_{3}^{2-}), (hbox {H}^{+}), (hbox {OH}^{-}), (hbox {H}_2hbox {CO}_{3}), (hbox{HCO}^{-}_{3}) that interact through the following four equilibrium reactions:

$$begin{aligned} hbox {CaCO}_3&rightleftharpoons hbox {Ca}^{2+} + hbox {CO}_{3}^{2-} , end{aligned}$$
(5)

$$begin{aligned} hbox {HCO}^{-}_{3}&rightleftharpoons hbox {CO}_{3}^{2-} + hbox {H}^{+} , end{aligned}$$
(6)

$$begin{aligned} hbox {H}_{2}hbox {CO}_{3}&rightleftharpoons hbox {CO}_{3}^{2-} + 2 hbox{H}^{+} , end{aligned}$$
(7)

$$begin{aligned} hbox {H}^{+} + hbox {OH}^{-}&rightleftharpoons hbox {H}_{2}hbox{O}. end{aligned}$$
(8)

If we denote concentrations by square brackets, changes in concentrations (time-derivatives) by, e.g., ([hbox {CaCO}_{3}]^{prime }), and rates (R_{1}, ldots , R_{4}) for the four reactions (positive in the leftward direction), the evolution of all concentrations can be solved from

$$begin{aligned} left[ hbox {CaCO}_{3} right] ^{prime }= & {} – R_1, end{aligned}$$
(9)

$$begin{aligned} left[ hbox {HCO}^{-}_{3} right] ^{prime }= & {} -R_2, end{aligned}$$
(10)

$$begin{aligned} left[ hbox {H}_2hbox {CO}_{3} right] ^{prime }= & {} – R_3, end{aligned}$$
(11)

$$begin{aligned} left[ hbox {OH}^{-} right] ^{prime }= & {} – R_4, end{aligned}$$
(12)

$$begin{aligned} left[ {mathrm {tot}} (hbox {H}) right] ^{prime }= & {} left( [hbox {H}^{+}]+ [hbox {HCO}^{-}_{3}]+2[hbox {H}_2hbox {CO}_{3}] – [hbox {OH}^{-}]right) ^{prime } =0, end{aligned}$$
(13)

$$begin{aligned} left[ {mathrm {tot}} (hbox {Ca}) right] ^{prime }= & {} left( [hbox {Ca}^{2+}]+[hbox {CaCO}_{3}] right) ^{prime } = 0, end{aligned}$$
(14)

$$begin{aligned} left[ {mathrm {tot}} (hbox {CO}_{3}) right] ^{prime }= & {} left( [hbox {CO}_{3}^{2-}]+ [hbox {CaCO}_{3}] + [hbox {HCO}^{-}_{3}] + [hbox {H}_2hbox {CO}_{3}] right) ^{prime } = 0. end{aligned}$$
(15)

The first four equations define the primary species (hbox {CaCO}_{3}) , (hbox {HCO}^{-}_{3}), (hbox {H}_2hbox {CO}_{3}), (hbox {OH}^{-}), while the last three equations involve the secondary species (hbox {H}^{+}), (hbox {Ca}^{2+}), (hbox {CO}_{3}^{2-}), as well as defining the (conservation of) total concentrations of those elements across all species. Following common notations18 and writing (Psi _{j=1, ldots , 3}) for the total concentrations, (C_{j=1, ldots , 3}) for the secondary species, and (C_{i=1, ldots , 4}) for the primary species, Eqs. (13)–(15) can be written succinctly in terms of the stoichiometry coefficients (nu _{ij}) as

$$begin{aligned} Psi _{j} = C_{j} + sum _{i=1}^{4} nu _{ij} C_{i}, quad quad nu _{ij} = left( begin{array}{cccc} 0 &{} 1 &{} 2 &{} -1\ 1 &{} 0 &{} 0 &{} 0 \ 1 &{} 1 &{} 1 &{} 0 end{array} right) . end{aligned}$$
(16)

From the definitions Eqs. (13)–(15) it is clear that the total concentrations (or ‘total components’) are conserved in the reacting system and thus a natural choice as primary variables in the molar conservation Eq. (1) for species transport. More generally, all problems of interest involve water itself and we usually choose ({mathrm {tot}} (hbox {H})) and ({mathrm {tot}} (hbox {O})) as two of the total concentrations. We will refer to the number of total or primary components that need to be transported as (n_{p}) and note that those are, in a sense, ‘bookkeeping’ quantities, whereas we will continue to use (n_{c}) for the total number of actual molecular species in the mixture.
The different symbols (c_{i}) versus (C_{i}) refer to different unit systems: Phreeqc typically expresses all concentrations per kilogram or liter of water, whereas Eq. (1) involves intrinsic molar densities (([{mathrm {mol}}/{mathrm {m}}^{3}])). In coupling the transport and geochemistry, a unit conversion is made between Osures and Phreeqc that involves the (temperature, pressure, and composition dependent) aqueous phase mass density as computed from the CPA EOS38 (equivalently, PhreeqcRM can be provided with ([{mathrm {mol}}/mathrm {l}]) concentrations together with a mass density).
Just as in most other reactive transport codes, a (sequential non-iterative) operator splitting approach is adopted in which the flow-transport problem is solved first without considering reactions, followed by the equivalent of a batch reaction calculation for each grid-cell (or node in the case of higher-order methods). More implementation details are provided below.
Diffusion of chemical species
Molecular diffusion, as defined in irreversible thermodynamics, is driven fundamentally by gradients in chemical potentials. Under the assumptions of negligible temperature and pressure diffusion an expression is obtained in terms of gradients in compositions, which is the commonly used generalized Fick’s law. Thus, while total concentrations, as the conserved quantity, are a suitable choice for advective transport they are not natural variables for the diffusive flux45. The following equations are therefore for the (n_{c}) physical species.
Diffusion of particles through a porous medium is affected by the geometry and connectivity of the pore network, and is different from diffusion in open space. The longer pathways in a porous medium are represented empirically in Eq. (2) by the factor (f(phi ,tau ) [cdot ]), which is a function of porosity and tortuosity (tau [cdot ]). The simplest option is (f(phi ,tau )=phi).
Both molecular diffusion and mechanical dispersion are considered, e.g., (vec {J}_{i,alpha } = vec {J}_{alpha , i}^{mathrm {diff}} + vec {J}_{alpha , i}^{{mathrm {disp}}}). Mechanical dispersion is computed from

$$begin{aligned} vec {J}_{alpha , i}^{{mathrm {disp}}} = – c_alpha sum _{k=1}^{n_c -1} vec {D}^{mathrm {disp}}_{alpha } nabla x_{alpha , k}, end{aligned}$$
(17)

with the coefficients given by the tensor

$$begin{aligned}&vec {D}^{mathrm {disp}}_{alpha } = d_{t,alpha } | vec {u}_{alpha } | vec {I} + (d_{l,alpha } – d_{t,alpha }) frac{vec {u}_{alpha } vec {u}^{T}_{alpha }}{|vec {u}_{alpha }|}, end{aligned}$$
(18)

with (d_{l,alpha } [{mathrm {m}}]) and (d_{t,alpha } [{mathrm {m}}]) the longitudinal and transverse phase dispersivities, respectively, and (vec {I}) the identity matrix. There are only (n_{c}-1) independent equations because by definition (sum _{i} J_{i} = 0), such that (J_{n_{c}} = – sum _{i=1}^{n_{c}-1} J_{i}). The (n_{c}-1) equations for Fickian molecular diffusion are:

$$begin{aligned} vec {J}_{alpha , i}^{mathrm {diff}} = – c_alpha sum _{k=1}^{n_c -1} D^{mathrm {Fick}}_{alpha , ik} nabla x_{alpha , k}, end{aligned}$$
(19)

with (x_{alpha , i} [cdot ]) the phase compositions (molar fractions) and (D^{mathrm {Fick}}_{alpha , ik} [{mathrm {m}}^{2}/{mathrm {s}}]) a full matrix of composition-dependent diffusion coefficients as derived from irreversible thermodynamics30,39,46.
It can easily be shown47 that only considering diagonal (‘self’) diffusion coefficients violates molar balance, because the commonly used (J_{i} sim -D_{i} nabla x_{i}) cannot simultaneously satisfy (sum _{i} J_{i} = 0) and (sum _{i} x_{i} = 1). Specifically, for (n_{c}) species we have (sum _{i=1}^{n_c} x_{i } = 1), which means that (sum _{i=1}^{n_c}nabla x_{i} = 0). In other words, the compositional gradients are not all independent and one can be expressed in terms of the others. Choosing the last component, for instance, we have

$$begin{aligned} nabla x_{n_{c}} = – sum _{i=1}^{n_c-1}nabla x_{i} . end{aligned}$$
(20)

The diffusive fluxes are also not all independent because, by definition (i.e., diffusion being the deviation of individual species fluxes from the average advective flux) (sum _{i=1}^{n_c} J_{i} = 0). Similar to Eq. (20), we choose to express the diffusive flux of the last component in terms of the other fluxes (J_{n_{c}} = – D_{c} nabla x_{c} = – sum _{i=1}^{n_c-1} J_{i} = – sum _{i=1}^{n_c-1} D_{i} nabla x_{i}). Inserting (nabla x_{n_{c}}) from Eq. (20) that requires

$$begin{aligned} sum _{i=1}^{n_c – 1} (D_{n_c} – D_{i}) nabla x_{i} = 0. end{aligned}$$
(21)

For Eq. (21) to be true for any composition (x_{i}) requires all (D_{i}= D_{n_{c}}), i.e. all diagonal diffusion coefficients have to be the same. In other words, molar conservation is only guaranteed either for a single scalar diffusion coefficient for all components (which is not justified by experimental data) or requires a full matrix of multicomponent diffusion coefficients.
In terms of implementation, for diffusion problems Phreeqc is instructed to output not only the (n_{p}) concentrations (Psi _{j}) but also the (n_{c}) concentrations (C_{i}) and (C_{j}) (this requires more memory, but not more computational effort). Eq. (19) is then updated for each ‘real’ species across each grid face in the domain, and the contributions to the molar densities of (n_{p}) total components follows from the stoichiometry (using Eq. (16)). An operator splitting step is used in the implementation: first the diffusive fluxes are computed as described, then, in updating Eq. (1) the divergence of the diffusive flux is essentially treated as a sink-source term of the total number of moles of (c_{i}) entering or leaving the grid cell through all its faces in a given time-step.
Nernst–Planck electromigration
Electrochemical migration refers to electrostatic forces coupling to charged particles that diffuse at different rates, which causes charge imbalance. Electric fields can force charged particles to diffuse when there are no compositional gradients or even diffuse from low to high concentrations, due to interaction with other species. Similar effects have been observed even in charge-neutral non-ideal mixtures such as hydrocarbon fluids48. Because the flux of one species can depend on the compositional gradients in all other species, this is another reason that a full matrix of diffusion coefficients is required.
The following expression has been used to model both Fickian ((J^{{mathrm {Fick}}}_{i})) and electrochemical ((J^{mathrm {EK}}_{i})) diffusion in the absence of externally induced currents and advective fluxes49:

$$begin{aligned} J_{i} = J^{{mathrm {Fick}}}_{i} + J^{mathrm {EK}}_{i} = – D_{i} nabla C_{i} + D_{i} C_{i} q_{i} frac{sum _{k} D_{k} q_{k} nabla C_{k} }{sum _{k} D_{k} q^2_{k} C_{k}} end{aligned}$$
(22)

with (q_{k}) the species charge, (C_{i}) concentrations, and summations over all dissolved species. Eq. (22) is a simplified form of the Nernst-Planck equation.
To be consistent with the molar balance equation (1) and allowing for variable aqueous densities (compressibility), Eq. (22) is written in terms of aqueous phase molar density c and molar fractions (x_{i}=c_{i}/c), similar to Eq. (19), as

$$begin{aligned} J_{i} = – c D_{i} nabla x_{i} + D_{i} x_{i} q_{i} frac{sum _{k} c D_{k} q_{k} nabla x_{k} }{sum _{k} D_{k} q^2_{k} x_{k}}, end{aligned}$$
(23)

which assumes that diffusion coefficients have already been corrected for porosity and tortuosity effects.
As discussed in the previous section, this type of relation for diffusion in multicomponent mixtures is physically inconsistent. However it can be a reasonable approximation (when off-diagonal diffusion coefficients are small) and is implemented in this work as an option to allow comparisons to other reactive transport codes that rely on this formulation.
Implementation
The numerical implementation of the mathematical framework described in the previous sections relies heavily on operator splitting, which permits choosing the most suitable numerical method for each subproblem. First, diffusive fluxes (Eqs. (17)–(19)) are computed using compositions, molar densities, and advective fluxes from the previous time-step. Second, the flow problem Eqs. (3)–(4) is simultaneously solved for pressures and fluxes by the implicit MHFE method. Third, the transport equations (Eqs. (1)–(2)) are updated by the DG method, using the previously computed diffusive fluxes. Other than the interpretation of total components (Eq. (16)) and the implementation of the Nernst-Planck Eq. (23) for electromigration, the implementation is identical to prior (non-reactive) works20,32,33, and is thus not repeated here in further detail.
After the transport equations have been updated for all components, PhreeqcRM is invoked to update the geochemistry. The geochemistry computations alter the compositions of reactive species, which is indicated by the (F^{mathrm {react}}_i) term in Eq. (1). As discussed above, PhreeqcRM is requested to output both the total component concentrations that are advected in Eq. (1) as well as all the physical species concentrations that are used to compute the diffusive fluxes (Eqs. (17)–(19)). The diffusive flux contributions of each species to the total component transport is derived using the stoichiometry as in Eq. (16).
The full reactive transport step is followed by an EOS-based update of fluid properties (molar and mass densities, compressibility, viscosity), as well as rock properties (porosity, permeability, fracture apertures) when dissolution and precipitation reactions are considered. For multiphase problems this would also involve phase stability and phase split computations that are iteratively coupled to the PhreeqcRM geochemistry update.
Explicit, implicit, and adaptive implicit Euler time-discretizations have been implemented, where the adaptive method uses an implicit update for grid cells that have a small Courant-Friedrichs-Lewy (CFL) time-step constraint50 and an explicit update elsewhere33. The advantage of implicit methods is that they are unconditionally stable and thus allow for larger time-steps. However, implicit methods are also known to exhibit excessive numerical dispersion. Moreover, (1) larger time-steps imply bigger changes in concentrations, which results in numerical convergence issues for PhreeqcRM, and (2) rock-fluid interactions and kinetic reactions are quite sensitive to time-step sizes. For these reasons, unless a fully coupled approach is used, an explicit transport update appears to provide the most accurate results (smaller time-steps also reduce the decoupling errors inherent to any operator splitting approach). The cost of using relatively small time-steps can be alleviated by (1) faster convergence of the non-linear geochemistry (similar to phase-split computations), and (2) the more trivial parallelization of an element-wise explicit transport and geochemistry update. The numerical examples, presented next, therefore all rely on the common implicit-pressure-explicit-composition (IMPEC) scheme. More