Ecology

1.
West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford University Press, Oxford, 2003).
Google Scholar 
2.
Beldade, P., Mateus, A. R. A. & Keller, R. A. Evolution and molecular mechanisms of adaptive developmental plasticity. Mol. Ecol. 20, 1347–1363 (2011).
PubMed  Article  PubMed Central  Google Scholar 

3.
Dall, S. R. X., McNamara, J. M. & Leimar, O. Genes as cues: Phenotypic integration of genetic and epigenetic information from a Darwinian perspective. Trends Ecol. Evol. 30, 327–333 (2015).
PubMed  Article  PubMed Central  Google Scholar 

4.
Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).
Article  Google Scholar 

5.
Ovaskainen, O. & Meerson, B. Stochastic models of population extinction. Trends Ecol. Evol. 25, 643–652 (2010).
PubMed  Article  PubMed Central  Google Scholar 

6.
Lawson, C. R., Vindenes, Y., Bailey, L. & van de Pol, M. Environmental variation and population responses to global change. Ecol. Lett. 18, 724–736 (2015).
PubMed  Article  PubMed Central  Google Scholar 

7.
Mayr, E. The growth of biological thought: Diversity, evolution, and inheritance. Am. Biol. Teach. 46, 462–463 (1984).
Article  Google Scholar 

8.
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).
ADS  Article  Google Scholar 

9.
Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Riddell, E. A., Odom, J. P., Damm, J. D. & Sears, M. W. Plasticity reveals hidden resistance to extinction under climate change in the global hotspot of salamander diversity. Sci. Adv. 4, eaar5471 (2018).
ADS  PubMed  PubMed Central  Article  Google Scholar 

11.
Noble, D. Claude Bernard, the first systems biologist, and the future of physiology. Exp. Physiol. 93, 16–26 (2008).
PubMed  Article  PubMed Central  Google Scholar 

12.
Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: Systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Whitehead, A. & Crawford, D. L. Variation within and among species in gene expression: Raw material for evolution. Mol. Ecol. 15, 1197–1211 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Regulatory changes underlying expression differences within and between Drosophila species. Nat. Genet. 40, 346–350 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Gonzalez, E. G. et al. Population proteomics of the European hake (Merluccius merluccius). J. Proteome Res. 9, 6392–6404 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Papakostas, S. et al. A proteomics approach reveals divergent molecular responses to salinity in populations of European whitefish (Coregonus lavaretus ). Mol. Ecol. 21, 3516–3530 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Chevalier, F. et al. Proteomic investigation of natural variation between Arabidopsis ecotypes. Proteomics 4, 1372–1381 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Mueller, R. S. et al. Ecological distribution and population physiology defined by proteomics in a natural microbial community. Mol. Syst. Biol. 6, 374 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119–125 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Bradshaw, A. D. Evolutionary significance of phenotypic plasticity in plants. In Advances in Genetics Vol. 13 (eds Caspari, E. W. & Thoday, J. M.) 115–155 (Academic Press, New York, 1965).
Google Scholar 

21.
Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Fisher, M. A. & Oleksiak, M. F. Convergence and divergence in gene expression among natural populations exposed to pollution. BMC Genomics 8, 108 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Pujolar, J. M. et al. Surviving in a toxic world: transcriptomics and gene expression profiling in response to environmental pollution in the critically endangered European eel. BMC Genomics 13, 507 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Davies, S. W., Marchetti, A., Ries, J. B. & Castillo, K. D. Thermal and pCO2 stress elicit divergent transcriptomic responses in a resilient coral. Front. Mar. Sci. 3, 112 (2016).

25.
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Bludau, I. & Aebersold, R. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/s41580-020-0231-2 (2020).
Article  PubMed  PubMed Central  Google Scholar 

29.
Lodish, H. et al. Molecular Cell Biology (W. H. Freeman, New York, 2000).
Google Scholar 

30.
Watson, J. D. Molecular Biology of the Gene (Pearson Education, London, 2004).
Google Scholar 

31.
Giardi, M. T., Masojídek, J. & Godde, D. Effects of abiotic stresses on the turnover of the D1 reaction centre II protein. Physiol. Plant. 101, 635–642 (1997).
CAS  Article  Google Scholar 

32.
Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell 135, 216–226 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: From theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).
PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Nikinmaa, M., Tiihonen, K. & Paajaste, M. Adrenergic control of red cell pH in salmonid fish: Roles of the sodium/proton exchange, Jacobs-Stewart cycle and membrane potential. J. Exp. Biol. 154, 257–271 (1990).
CAS  Google Scholar 

35.
Pavlov, M. Y. & Ehrenberg, M. Optimal control of gene expression for fast proteome adaptation to environmental change. Proc. Natl. Acad. Sci. USA 110, 20527–20532 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Adams, S. M., Giesy, J. P., Tremblay, L. A. & Eason, C. T. The use of biomarkers in ecological risk assessment: recommendations from the Christchurch conference on Biomarkers in Ecotoxicology. Biomarkers 6, 1–6 (2001).
CAS  PubMed  Article  Google Scholar 

37.
Diz, A. P., Truebano, M. & Skibinski, D. O. F. The consequences of sample pooling in proteomics: An empirical study. Electrophoresis 30, 2967–2975 (2009).
CAS  PubMed  Article  Google Scholar 

38.
Karp, N. A. & Lilley, K. S. Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics 9, 388–397 (2009).
CAS  PubMed  Article  Google Scholar 

39.
Bennike, T. B. et al. Comparing the proteome of snap frozen, RNAlater preserved, and formalin-fixed paraffin-embedded human tissue samples. EuPA Open Proteomics 10, 9–18 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Johnsen, I. K. et al. Evaluation of a standardized protocol for processing adrenal tumor samples: Preparation for a European adrenal tumor bank. Horm. Metab. Res. 42, 93–101 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Kruse, C. P. S., Basu, P., Luesse, D. R. & Wyatt, S. E. Transcriptome and proteome responses in RNAlater preserved tissue of Arabidopsis thaliana. PLoS ONE 12, e0175943–e0175943 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Article  Google Scholar 

43.
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.3-13. https://CRAN.R-project.org/package=raster. (2020).

44.
Cox, J. et al. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Ebner, J. N., Ritz, D. & von Fumetti, S. Comparative proteomics of stenotopic caddisfly Crunoecia irrorata identifies acclimation strategies to warming. Mol. Ecol. 28, 4453–4469 (2019).
PubMed  PubMed Central  Article  Google Scholar 

47.
Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Jimenez-Morales, D., Campos, A.R. & Von Dollen, J. artMS: Analytical R tools for Mass Spectrometry. R package version 1.5.3. https://github.com/bioadavidjm/artMS. (2020).

49.
Chen, H. VennDiagram: Generate High-Resolution Venn and Euler Plots. R package version 1.6.20. https://CRAN.R-project.org/package=VennDiagram. (2018).

50.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. (2019).

51.
Carrillo, B., Yanofsky, C., Laboissiere, S., Nadon, R. & Kearney, R. E. Methods for combining peptide intensities to estimate relative protein abundance. Bioinform. Oxf. Engl. 26, 98–103 (2010).
CAS  Article  Google Scholar 

52.
Bolstad, B. preprocessCore: A collection of pre-processing functions. R package version 1.48.0. https://github.com/bmbolstad/proprocessCore. (2019).

53.
Hastie, T., Tibshirani, R., Balasubramanian, N. & Chu, G. impute: Imputation for microarray data. R package version 1.60.0. (2019).

54.
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).
Article  Google Scholar 

55.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan. (2019).

56.
Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 559–559 (2008).
Article  CAS  Google Scholar 

57.
Campbell-Staton, S. C. et al. Winter storms drive rapid phenotypic, regulatory, and genomic shifts in the green anole lizard. Science 357, 495–498 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Campbell-Staton, S. C., Bare, A., Losos, J. B., Edwards, S. V. & Cheviron, Z. A. Physiological and regulatory underpinnings of geographic variation in reptilian cold tolerance across a latitudinal cline. Mol. Ecol. 27, 2243–2255 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Horvath, S. Weighted network analysis: Applications in genomics and systems biology (Springer, New York, 2011).
Google Scholar 

60.
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).
MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

61.
Smyth, G. K. limma: Linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds Gentleman, R. et al.) 397–420 (Springer, New York, 2005). https://doi.org/10.1007/0-387-29362-0_23.
Google Scholar 

62.
Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.17.4. (Comprehensive R Archive Network (CRAN), 2018).

63.
Stearns, S. C. & Koella, J. C. The evolution of phenotypic plasticity in life-history traits: Predictions of reaction norms for age and size at maturity. Evolution 40, 893–913 (1986).
PubMed  Article  PubMed Central  Google Scholar 

64.
Schmalhausen, I. I. Factors of Evolution: The Theory of Stabilizing Selection (Blakiston, Philadelphia, 1949).
Google Scholar 

65.
Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).
Article  Google Scholar 

66.
Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5-10. https://CRAN.R-project.org/package=geosphere. (2019).

67.
Rieder, V. et al. DISMS2: A flexible algorithm for direct proteome-wide distance calculation of LC-MS/MS runs. BMC Bioinform. 18, 148 (2017).

68.
Grüning, B. et al. Bioconda: Sustainable and comprehensive software distribution for the life sciences. Nat. Methods 15, 475–476 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

69.
Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Huerta-Cepas, J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment Analysis for Gene Ontology. R package version 2.38.1. (2019).

75.
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Fang, H. & Gough, J. dcGO: Database of domain-centric ontologies on functions, phenotypes, diseases and more. Nucleic Acids Res. 41, D536–D544 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Kuhn, N. J., Setlow, B. & Setlow, P. Manganese(II) activation of 3-phosphoglycerate mutase of Bacillus megaterium: pH-Sensitive interconversion of active and inactive forms. Arch. Biochem. Biophys. 306, 342–349 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Chander, M., Setlow, B. & Setlow, P. The enzymatic activity of phosphoglycerate mutase from gram-positive endospore-forming bacteria requires Mn2+ and is pH sensitive. Can. J. Microbiol. 44, 759–767 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Ferrer, M., Chernikova, T. N., Yakimov, M. M., Golyshin, P. N. & Timmis, K. N. Chaperonins govern growth of Escherichia coli at low temperatures. Nat. Biotechnol. 21, 1266–1267 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Strocchi, M., Ferrer, M., Timmis, K. N. & Golyshin, P. N. Low temperature-induced systems failure in Escherichia coli: Insights from rescue by cold-adapted chaperones. Proteomics 6, 193–206 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Visudtiphole, V., Watthanasurorot, A., Klinbunga, S., Menasveta, P. & Kirtikara, K. Molecular characterization of Calreticulin: A biomarker for temperature stress responses of the giant tiger shrimp Penaeus monodon. Aquaculture 308, S100–S108 (2010).
CAS  Article  Google Scholar 

82.
Wehrly, K. E., Wang, L. & Mitro, M. Field-based estimates of thermal tolerance limits for trout: Incorporating exposure time and temperature fluctuation. Trans. Am. Fish. Soc. 136, 365–374 (2007).
Article  Google Scholar 

83.
Alberts, B. et al. Molecular Biology of the Cell (Garland Science, New York, 2002).
Google Scholar 

84.
Hagner-Holler, S., Pick, C., Girgenrath, S., Marden, J. H. & Burmester, T. Diversity of stonefly hexamerins and implication for the evolution of insect storage proteins. Insect Biochem. Mol. Biol. 37, 1064–1074 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

85.
Descazeaud, V., Mestre, E., Marquet, P. & Essig, M. Calcineurin regulation of cytoskeleton organization: A new paradigm to analyse the effects of calcineurin inhibitors on the kidney. J. Cell. Mol. Med. 16, 218–227 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

86.
Urra, H. et al. IRE1α governs cytoskeleton remodelling and cell migration through a direct interaction with filamin A. Nat. Cell Biol. 20, 942–953 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

87.
Tong, M. & Jiang, Y. FK506-binding proteins and their diverse functions. Curr. Mol. Pharmacol. 9, 48–65 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

88.
Miranti, C. K. & Brugge, J. S. Sensing the environment: A historical perspective on integrin signal transduction. Nat. Cell Biol. 4, E83–E90 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

89.
Walsh, C. T., Garneau-Tsodikova, S. & Gatto, G. J. Protein posttranslational modifications: The chemistry of proteome diversifications. Angew. Chem. Int. Ed. 44, 7342–7372 (2005).
CAS  Article  Google Scholar 

90.
Snape, J. R., Maund, S. J., Pickford, D. B. & Hutchinson, T. H. Ecotoxicogenomics: The challenge of integrating genomics into aquatic and terrestrial ecotoxicology. Aquat. Toxicol. 67, 143–154 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

91.
Nikinmaa, M. & Rytkönen, K. T. Functional genomics in aquatic toxicology—Do not forget the function. Aquat. Toxicol. 105, 16–24 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

92.
Kearney, E. B., Ackrell, B. A. C., Mayr, M. & Singer, T. P. Activation of succinate dehydrogenase by anions and pH. J. Biol. Chem. 249, 2016–2020 (1974).
CAS  PubMed  PubMed Central  Google Scholar 

93.
Bissoli, G. et al. Peptidyl-prolyl cis-trans isomerase ROF2 modulates intracellular pH homeostasis in Arabidopsis. Plant J. Cell Mol. Biol. 70, 704–716 (2012).
CAS  Article  Google Scholar 

94.
Simčič, T. & Brancelj, A. Effects of pH on electron transport system (ETS) activity and oxygen consumption in Gammarus fossarum, Asellus aquaticus and Niphargus sphagnicolus. Freshw. Biol. 51, 686–694 (2006).
Article  CAS  Google Scholar 

95.
Kadrmas, J. L. & Beckerle, M. C. The LIM domain: From the cytoskeleton to the nucleus. Nat. Rev. Mol. Cell Biol. 5, 920–931 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
van der Flier, A. & Sonnenberg, A. Structural and functional aspects of filamins. Biochim. Biophys. Acta BBA Mol. Cell Res. 1538, 99–117 (2001).
Article  Google Scholar 

97.
Sun, H. Q., Yamamoto, M., Mejillano, M. & Yin, H. L. Gelsolin, a multifunctional actin regulatory protein. J. Biol. Chem. 274, 33179–33182 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

98.
Diskin, S. et al. Galectin-8 promotes cytoskeletal rearrangement in trabecular meshwork cells through activation of rho signaling. PLoS ONE 7, e44400 (2012).

99.
Motizuki, M., Yokota, S. & Tsurugi, K. Effect of low pH on organization of the actin cytoskeleton in Saccharomyces cerevisiae. Biochim. Biophys. Acta 1780, 179–184 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

100.
Wang, F., Sampogna, R. V. & Ware, B. R. pH dependence of actin self-assembly. Biophys. J. 55, 293–298 (1989).
CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Sperelakis, N. Cell Physiology Source book (Academic Press, Amsterdam, 2012).
Google Scholar 

102.
Tomanek, L. Proteomics to study adaptations in marine organisms to environmental stress. J. Proteomics 105, 92–106 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

103.
Tomanek, L., Zuzow, M. J., Ivanina, A. V., Beniash, E. & Sokolova, I. M. Proteomic response to elevated PCO2 level in eastern oysters, Crassostrea virginica: Evidence for oxidative stress. J. Exp. Biol. 214, 1836–1844 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

104.
Koritzinsky, M. et al. Two phases of disulfide bond formation have differing requirements for oxygen. J. Cell Biol. 203, 615–627 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

105.
L’Haridon, F. et al. A permeable cuticle is associated with the release of reactive oxygen species and induction of innate immunity. PLoS Pathog. 7, e1002148 (2011).

106.
Richards, A. G. Studies on arthropod cuticle—XIII: The penetration of dissolved oxygen and electrolytes in relation to the multiple barriers of the epicuticle. J. Insect Physiol. 1, 23–39 (1957).
CAS  Article  Google Scholar 

107.
Wang, K. et al. Redox homeostasis: The linchpin in stem cell self-renewal and differentiation. Cell Death Dis. 4, e537–e537 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

108.
Szablowska-Gadomska, I., Zayat, V. & Buzanska, L. Influence of low oxygen tensions on expression of pluripotency genes in stem cells. Acta Neurobiol. Exp. (Warsz.) 71, 86–93 (2011).
Google Scholar 

109.
Dreffs, A., Henderson, D., Dmitriev, A. V., Antonetti, D. A. & Linsenmeier, R. A. Retinal pH and acid regulation during metabolic acidosis. Curr. Eye Res. 43, 902–912 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

110.
Raeker, M. Ö et al. Targeted deletion of the zebrafish obscurin A RhoGEF domain affects heart, skeletal muscle and brain development. Dev. Biol. 337, 432 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

111.
Serafim, A. et al. Application of an integrated biomarker response index (IBR) to assess temporal variation of environmental quality in two Portuguese aquatic systems. Ecol. Indic. 19, 215–225 (2012).
CAS  Article  Google Scholar 

112.
Berra, E., Forcella, M., Giacchini, R., Rossaro, B. & Parenti, P. Biomarkers in Caddisfly Larvae of the Species Hydropsyche pellucidula (Curtis, 1834) (Trichoptera: Hydropsychidae) measured in natural populations and after short term exposure to fenitrothion. Bull. Environ. Contam. Toxicol. 76, 863–870 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

113.
Wernersson, A.-S. et al. The European technical report on aquatic effect-based monitoring tools under the water framework directive. Environ. Sci. Eur. 27, 7 (2015).
Article  CAS  Google Scholar 

114.
Ryan, J. A. & Hightower, L. E. Stress proteins as molecular biomarkers for environmental toxicology. In Stress-Inducible Cellular Responses (eds Feige, U. et al.) (Birkhäuser, Basel, 1996). https://doi.org/10.1007/978-3-0348-9088-5_28.
Google Scholar 

115.
Sanders, B. M. Stress proteins in aquatic organisms: An environmental perspective. Crit. Rev. Toxicol. 23, 49–75 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

116.
Barata, C. et al. Combined use of biomarkers and in situ bioassays in Daphnia magna to monitor environmental hazards of pesticides in the field. Environ. Toxicol. Chem. 26, 370–379 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

117.
Dorts, J. et al. Ecotoxicoproteomics in gills of the sentinel fish species, Cottus gobio, exposed to perfluorooctane sulfonate (PFOS). Aquat. Toxicol. 103, 1–8 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

118.
Daborn, P. J. et al. A single P450 allele associated with insecticide resistance in Drosophila. Science 297, 2253–2256 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

119.
Amichot, M. et al. Point mutations associated with insecticide resistance in the Drosophila cytochrome P450 Cyp6a2 enable DDT metabolism. Eur. J. Biochem. 271, 1250–1257 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

120.
Dunkov, B. C. et al. The Drosophila cytochrome P450 gene Cyp6a2: Structure, localization, heterologous expression, and induction by phenobarbital. DNA Cell Biol. 16, 1345–1356 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

121.
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat. Preced. https://doi.org/10.1038/npre.2010.4282.2 (2010).
Article  Google Scholar 

122.
Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 14, 91 (2013).
Article  Google Scholar 

123.
Kim, S. & Coulombe, P. A. Emerging role for the cytoskeleton as an organizer and regulator of translation. Nat. Rev. Mol. Cell Biol. 11, 75–81 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

124.
Parker, A. L., Kavallaris, M. & McCarroll, J. A. Microtubules and their role in cellular stress in cancer. Front. Oncol. 4, 153 (2014).
PubMed  PubMed Central  Article  Google Scholar 

125.
Skelly, D. A., Ronald, J. & Akey, J. M. Inherited variation in gene expression. Annu. Rev. Genomics Hum. Genet. 10, 313–332 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

126.
Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

127.
Arias, M. B., Poupin, M. J. & Lardies, M. A. Plasticity of life-cycle, physiological thermal traits and Hsp70 gene expression in an insect along the ontogeny: Effect of temperature variability. J. Therm. Biol. 36, 355–362 (2011).
CAS  Article  Google Scholar 

128.
Place, S. P. & Hofmann, G. E. Constitutive expression of a stress-inducible heat shock protein gene, hsp70, in phylogenetically distant Antarctic fish. Polar Biol. 28, 261–267 (2005).
Article  Google Scholar 

129.
Hotaling, S. et al. Mountain stoneflies may tolerate warming streams: evidence from organismal physiology and gene expression. bioRxiv 2019.12.16.878926 (2019). https://doi.org/10.1101/2019.12.16.878926.

130.
Cuellar, J. et al. Assisted protein folding at low temperature: Evolutionary adaptation of the Antarctic fish chaperonin CCT and its client proteins. Biol. Open 3, 261–270 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

131.
Cantonati, M., Füreder, L., Gerecke, R., Jüttner, I. & Cox, E. J. Crenic habitats, hotspots for freshwater biodiversity conservation: Toward an understanding of their ecology. Freshw. Sci. 31, 463–480 (2012).
Article  Google Scholar 

132.
Hofmann, G. E. & Todgham, A. E. Living in the now: Physiological mechanisms to tolerate a rapidly changing environment. Annu. Rev. Physiol. 72, 127–145 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

133.
Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: An integrative view. Philos. Trans. R. Soc. B Biol. Sci. 362, 2233–2258 (2007).
Article  CAS  Google Scholar 

134.
Shah, A. A. et al. Climate variability predicts thermal limits of aquatic insects across elevation and latitude. Funct. Ecol. https://doi.org/10.1111/1365-2435.12906 (2018).
Article  Google Scholar 

135.
Treanor, H. B., Giersch, J. J., Kappenman, K. M., Muhlfeld, C. C. & Webb, M. A. H. Thermal tolerance of meltwater stonefly Lednia tumana nymphs from an alpine stream in Waterton-Glacier International Peace Park, Montana, USA. Freshw. Sci. 32, 597–605 (2013).
Article  Google Scholar 

136.
Forsman, A. & Wennersten, L. Inter-individual variation promotes ecological success of populations and species: Evidence from experimental and comparative studies. Ecography 39, 630–648 (2016).
Article  Google Scholar 

137.
Cogne, Y. et al. Comparative proteomics in the wild: Accounting for intrapopulation variability improves describing proteome response in a Gammarus pulex field population exposed to cadmium. Aquat. Toxicol. 214, 105244 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

138.
Gotelli, N. J., Ellison, A. M. & Ballif, B. A. Environmental proteomics, biodiversity statistics and food-web structure. Trends Ecol. Evol. 27, 436–442 (2012).
PubMed  PubMed Central  Article  Google Scholar 

139.
Liu, F. et al. New Perspectives on host-parasite interplay by comparative transcriptomic and proteomic analyses of Schistosoma japonicum. PLOS Pathog. 2, e29 (2006).
PubMed  PubMed Central  Article  Google Scholar 

140.
Nold, S. C. & Zwart, G. Patterns and governing forces in aquatic microbial communities. Aquat. Ecol. 32, 17–35 (1998).
CAS  Article  Google Scholar 

141.
Pass, D. A. et al. The effect of anthropogenic arsenic contamination on the earthworm microbiome. Environ. Microbiol. 17, 1884–1896 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

142.
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: Improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
CAS  Article  Google Scholar  More

1.
Smith, P. L. Play fighting and real fighting. Perspectives on their relationship. In New Aspects of Human Ethology (eds Schmitt, A. et al.) 47–64 (Plenum Press, New York, 1997).
Google Scholar 
2.
Burghardt, G. M. The Genesis of Animal Play: Testing the Limits (MIT Press, Cambridge, 2005).
Google Scholar 

3.
Byers, J. A. Play in ungulates. In Play in Animals and Humans (ed. Smith, P. K.) 43–65 (Blackwell, New York, 1984).
Google Scholar 

4.
Thompson, K. V. Self assessment in juvenile play. In Animal Play: Evolutionary, Comparative, and Ecological Perspectives (eds Bekoff, M. & Byers, J. A.) 183–204 (Cambridge University Press, Cambridge, 1998).
Google Scholar 

5.
Špinka, M., Newberry, R. C. & Bekoff, M. Mammalian play: training for the unexpected. Q. Rev. Biol. 76, 141–168 (2001).
PubMed  Article  Google Scholar 

6.
Pellis, S. M. & Pellis, V. C. What is play fighting and what is it good for?. Learn. Behav. 45, 355–366 (2017).
PubMed  Article  Google Scholar 

7.
Smith, P. K. Does play matter? Functional and evolutionary aspects of animal and human play. Behav. Brain Sci. 5, 139–184 (1982).
Article  Google Scholar 

8.
Rothstein, A. & Griswold, G. R. Age and sex preferences for social partners by juvenile bison bulls, Bison bison. Anim. Behav. 41, 227–237 (1991).
Article  Google Scholar 

9.
Watson, D. M. & Croft, D. B. Playfighting in captive red-necked wallabies, Macropus rufogriseus banksianus. Behaviour 126, 219–245 (1993).
Article  Google Scholar 

10.
Bekoff, M. Social play behaviour: cooperation, fairness, trust and the evolution of morality. J. Consciousness Stud. 8, 81–90 (2001).
Google Scholar 

11.
Petit, O., Bertrand, F. & Thierry, B. Social play in crested and Japanese macaques: testing the covariation hypothesis. Dev. Psychobiol. 50, 399–407 (2008).
PubMed  CAS  Article  Google Scholar 

12.
Horback, K. Nosing around: play in pigs. Anim. Behav. Cogn. 1, 186–196 (2014).
Article  Google Scholar 

13.
Pellis, S. M. & Pellis, V. C. Play-fighting in the Syrian golden hamster Mesocricetus auratus Waterhouse, and its relationship to serious fighting during postweaning development. Dev. Psychobiol. 21, 323–337 (1988).
PubMed  CAS  Article  Google Scholar 

14.
Sharpe, L. L. Play fighting does not affect subsequent fighting success in wild meerkats. Anim. Behav. 69, 1023–1029 (2005).
Article  Google Scholar 

15.
Blumstein, D. T., Chung, L. K. & Smith, J. E. Early play may predict later dominance relationships in yellow-bellied marmots (Marmota faviventris). Proc. R. Soc. B. 280, 20130485 (2013).
PubMed  Article  Google Scholar 

16.
Weller, J. E., Camerlink, I., Turner, S. P., Farish, M. & Arnott, G. Socialisation and its effect on play behaviour and aggression in the domestic pig (Sus scrofa). Sci. Rep. 9, 4180 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

17.
Cenni, C. & Fawcett, T. W. The co-evolution of juvenile play-fighting and adult competition. Ethology 124, 290–301 (2017).
Article  Google Scholar 

18.
Pellis, S. M. & Pellis, V. C. Play fighting in Visayan warty pigs (Sus cebifrons): insights on restraint and reciprocity in the maintenance of play. Behaviour 153, 727–747 (2016).
Article  Google Scholar 

19.
Šilerová, J., Špinka, M., Šarova, R. & Algers, B. Playing and fighting by piglets around weaning on farms, employing individual or group housing of lactating sows. Appl. Anim. Behav. Sci. 124, 83–89 (2010).
Article  Google Scholar 

20.
Mauget, R. Behavioural and reproductive strategies in wild forms of Sus scrofa (European wild boar and feral pigs). In The Welfare of Pigs (ed. Sybesma, W.) 3–13 (Martinus Nijhoff, The Hague, 1981).
Google Scholar 

21.
Mendl, M. The social behaviour of non-lactating sows and its implications for managing sow aggression. Pig J. 34, 9–20 (1995).
Google Scholar 

22.
Gonyou, H. W. The social behaviour of pigs. In Social Behaviour in Farm Animals (eds Keeling, L. J. & Gonyou, H. W.) 147–176 (CABI, Wallingford, 2001).
Google Scholar 

23.
Turner, S. P. et al. The accumulation of skin lesions and their use as a predictor of individual aggressiveness in pigs. Appl. Anim. Behav. Sci. 96, 245–259 (2006).
Article  Google Scholar 

24.
Jensen, P. & Yngvesson, J. Aggression between unacquainted pigs: sequential assessment and effects of familiarity and weight. Appl. Anim. Behav. Sci. 58, 49–61 (1998).
Article  Google Scholar 

25.
Camerlink, I., Turner, S. P., Farish, M. & Arnott, G. The influence of experience on contest assessment strategies. Sci. Rep. 7, 14492 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Martin, J. E., Ison, S. H. & Baxter, E. M. The influence of neonatal environment on piglet play behaviour and post-weaning social and cognitive development. Appl. Anim. Behav. Sci. 163, 69–79 (2015).
Article  Google Scholar 

27.
Govindarajulu, P., Hunte, W., Vermeer, L. A. & Horrocks, J. A. The ontogeny of social play in a feral troop of vervet monkeys (Cercopithecus aethiops sabaeus): the function of early play. Int. J. Primatol. 14, 701–719 (1993).
Article  Google Scholar 

28.
Thompson, K. V. Play-partner preferences and the function of social play in infant sable antelope, Hippotragus niger. Anim. Behav. 52, 1143–1155 (1996).
Article  Google Scholar 

29.
Llamazares-Martín, C., Scopa, C., Guillén-Salazar, F. & Palagi, E. Strong competition does not always predict play asymmetry: the case of South American sea lions (Otaria flavescens). Ethology 123, 270–282 (2017).
Article  Google Scholar 

30.
Lutz, M. C., Ratsimbazafy, J. & Judge, P. G. Use of social network models to understand play partner choice strategies in three primate species. Primates https://doi.org/10.1007/s10329-018-00708-7 (2018).
Article  Google Scholar 

31.
Foister, S. et al. Social network properties predict chronic aggression in commercial pig systems. PLoS ONE 13, e0205122 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Stanton, M. A. & Mann, J. Early social networks predict survival in wild bottlenose dolphins. PLoS ONE 7, e47508 (2012).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Gilby, I. C. et al. Fitness benefits of coalitionary aggression in male chimpanzees. Behav. Ecol. Sociobiol. 67, 373 (2013).
PubMed  Article  Google Scholar 

34.
Cheney, D. L., Silk, J. B. & Seyfarth, R. M. Network connections, dyadic bonds and fitness in wild female baboons. R. Soc. Open Sci. 3, 160255 (2016).
ADS  PubMed  PubMed Central  Article  Google Scholar 

35.
Lehmann, J., Majolo, B. & McFarland, R. The effects of social network position on the survival of wild Barbary macaques, Macaca sylvanus. Behav. Ecol. 27, 20–28 (2016).
Article  Google Scholar 

36.
Camerlink, I., Turner, S. P., Farish, M. & Arnott, G. Advantages of social skills for contest resolution. R. Soc. Open Sci. 6, 181456 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

37.
D’Eath, R. B. & Pickup, H. E. Behaviour of young growing pigs in a resident-intruder test designed to measure aggressiveness. Aggress. Behav. 28, 401–415 (2002).
Article  Google Scholar 

38.
Koolhaas, J. M. et al. The resident-intruder paradigm: a standardized test for aggression, violence and social stress. J. Vis. Exp. 77, e4367 (2013).
Google Scholar 

39.
Luescher, U. A., Friendship, R. M. & McKeown, D. B. Evaluation of methods to reduce fighting among regrouped gilts. Can. J. Anim. Sci. 70, 363–370 (1990).
Article  Google Scholar 

40.
Turner, S. P., Sinclair, A. G. & Edwards, S. A. The interaction of liveweight and the degree of competition on drinking behaviour of growing pigs at different group sizes. Appl. Anim. Behav. Sci. 67, 321–334 (2000).
PubMed  CAS  Article  Google Scholar 

41.
Newberry, R. C., Wood-Gush, D. G. M. & Hall, J. W. Playful behaviour of piglets. Behav. Process. 17, 205–216 (1988).
CAS  Article  Google Scholar 

42.
Fraser, D., Phillips, P. A., Thompson, B. K. & Tennessen, T. Effect of straw on the behavior of growing pigs. Appl. Anim. Behav. Sci. 30, 307–318 (1991).
Article  Google Scholar 

43.
Brown, S. M., Peters, R., Nevison, I. M. & Lawrence, A. B. Playful pigs: evidence of consistency and change in play depending on litter and developmental stage. Appl. Anim. Behav. Sci. 198, 36–43 (2018).
PubMed  PubMed Central  CAS  Article  Google Scholar 

44.
Brown, S. M., Klaffenböck, M., Nevison, I. M. & Lawrence, A. B. Evidence for litter differences in play behaviour in pre-weaned pigs. Appl. Anim. Behav. Sci. 172, 17–25 (2015).
PubMed  PubMed Central  Article  Google Scholar 

45.
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).
PubMed  PubMed Central  Article  Google Scholar 

46.
Asher, L. et al. Recent advances in the analysis of behavioural organization and interpretation as indicators of animal welfare. J. R. Soc. Interface 6, 1103–1119 (2009).
PubMed  PubMed Central  Article  Google Scholar 

47.
Büttner, K., Scheffler, K., Czycholl, I. & Krieter, J. Network characteristics and development of social structure of agonistic behaviour in pigs across three repeated rehousing and mixing events. Appl. Anim. Behav. Sci. 168, 24–30 (2015).
Article  Google Scholar 

48.
Freeman, L. C. Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1978).
Article  Google Scholar 

49.
Bonacich, P. Some unique properties of eigenvector centrality. Soc. Netw. 29, 555–564 (2007).
Article  Google Scholar 

50.
Madden, J. R., Drewe, J. A., Pearce, G. P. & Clutton-Brock, T. H. The social network structure of a wild meerkat population: 3. Position of individuals within networks. Behav. Ecol. Sociobiol. 65, 1857–1871 (2011).
Article  Google Scholar 

51.
Farine, D. assortnet: calculate the assortativity coefficient of weighted and binary networks. R package version 0.12 (2016).

52.
Shizuka, D. & Farine, D. R. Measuring the robustness of network community structure using assortativity. Anim. Behav. 112, 237–246 (2016).
PubMed  PubMed Central  Article  Google Scholar 

53.
Farine, D. R. A guide to null models for animal social network analysis. Methods Ecol. Evol. 8, 1309–1320 (2017).
PubMed  PubMed Central  Article  Google Scholar 

54.
Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 package. R package version 2(1), 74 (2007).
Google Scholar 

55.
Ripley, B., Venables, B., Bates, D. M., Hornik, K., Gebhardt, A. & Firth, D. Package ‘mass’. Cran R 538 (2013).

56.
Taylor, G. T. Fighting in juvenile rats and the ontogeny of agonistic behavior. J. Comp. Physiol. Psychol. 94, 953–961 (1980).
Article  Google Scholar 

57.
Pellis, S. M., Pellis, V. C. & Kolb, B. Neonatal testosterone augmentation increases juvenile play fighting but does not influence the adult dominance relationships of male rats. Aggress. Behav. 18, 437–447 (1992).
CAS  Article  Google Scholar 

58.
Chaloupková, H., Illman, G., Bartoš, L. & Špinka, M. The effect of pre-weaning housing on the play and agonistic behaviour of domestic pigs. Appl. Anim. Behav. Sci. 103, 25–34 (2007).
Article  Google Scholar 

59.
Flack, J. C., Girvan, M., de Waal, F. B. M. & Krakauer, D. C. Policing stabilizes construction of social niches in primates. Nature 439, 426–429 (2006).
ADS  PubMed  CAS  Article  Google Scholar 

60.
Abell, J. et al. A social network analysis of social cohesion in a constructed pride: implications for ex situ reintroduction of the African lion (Panthera leo). PLoS ONE 8, e82541 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Shimada, M. & Sueur, C. Social play among juvenile wild Japanese macaques (Macaca fuscata) strengthens their social bonds. Am. J. Primatol. 80, e22728 (2018).
Article  Google Scholar 

62.
Shimada, M. & Sueur, C. The importance of social play networks for wild chimpanzees at Mahale Mountains National Park, Tanzania. Am. J. Primatol. 76, 1025–1036 (2014).
PubMed  Article  Google Scholar 

63.
Camerlink, I., Arnott, G., Farish, M. & Turner, S. P. Complex contests and the influence of aggressiveness in pigs. Anim. Behav. 121, 71–78 (2016).
Article  Google Scholar 

64.
Antonacci, D., Norscia, I. & Palagi, E. Stranger to familiar: wild strepsirhines manage xenophobia by playing. PLoS ONE 5, e13218 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Gentsch, C., Lichtsteiner, M., Frischknecht, H. R., Feer, H. & Siegfried, B. Isolation-induced locomotor hyperactivity and hypoalgesia in rats are prevented by handling and reversed by resocialization. Physiol. Behav. 43, 13–16 (1988).
PubMed  CAS  Article  Google Scholar 

66.
Potegal, M. & Einon, D. Aggressive behaviors in adult rats deprived of playfighting experience as juveniles. Dev. Psychobiol. 22, 159–172 (1989).
PubMed  CAS  Article  Google Scholar 

67.
Drea, C. M., Hawk, J. E. & Glickman, S. E. Aggression decreases as play emerges in infant spotted hyaenas: preparation for joining the clan. Anim. Behav. 51, 1323–1336 (1996).
Article  Google Scholar 

68.
Newberry, R. C. & Wood-Gush, D. G. M. Development of some behaviour patterns in piglets under semi-natural conditions. Anim. Prod. 46, 103–109 (1988).
Google Scholar 

69.
Petersen, H. V., Vestergaard, K. & Jensen, P. Integration of piglets into social groups of free-ranging domestic pigs. Appl. Anim. Behav. Sci. 23, 223–236 (1989).
Article  Google Scholar 

70.
Ellis, L. et al. Sex Differences: Summarizing More Than a Century of Scientific Research (Psychology Press, New York, 2008).
Google Scholar 

71.
Carter, R. N., Romanow, C. A., Pellis, S. M. & Lingle, S. Play for prey: do deer fawns play to develop species-typical anti-predator tactics or to prepare for the unexpected?. Anim. Behav. 156, 31–40 (2019).
Article  Google Scholar 

72.
Byers, J. A. Play partner preferences in Siberian ibex, Capra ibex sibirica. Z. Tierpsychol. 53, 23–40 (1980).
Article  Google Scholar 

73.
Berger, J. The ecology, structure and functions of social play in bighorn sheep (Ovis canadensis). J. Zool. 192, 531–542 (1980).
Article  Google Scholar 

74.
Sachs, B. D. & Harris, V. S. Sex differences and developmental changes in selected juvenile activities (play) of domestic lambs. Anim. Behav. 26, 678–684 (1978).
Article  Google Scholar 

75.
Reinhardt, V., Mutiso, F. & Reinhardt, A. Social behaviour and social relationships between female and male prepubertal bovine calves (Bos indicus). Appl. Anim. Behav. Sci. 4, 43–54 (1978).
Google Scholar 

76.
Crowell-Davis, S. L., Houpt, K. A. & Kane, L. Play development in Welsh pony (Equus caballus) foals. Appl. Anim. Behav. Sci. 18, 119–131 (1987).
Article  Google Scholar 

77.
Cameron, E. Z., Linklater, W. L., Stafford, K. J. & Minot, E. O. Maternal investment results in better foal condition through increased play behaviour in horses. Anim. Behav. 76, 1511–1518 (2008).
Article  Google Scholar 

78.
Dobao, M. T., Rodribanez, J. & Siliŏ, L. Choice of companions in social play in piglets. Appl. Anim. Behav. Sci. 13, 259–266 (1985).
Article  Google Scholar 

79.
D’Eath, R. D. & Turner, S. P. The natural behaviour of the pig. In The Welfare of Pigs (ed. Marchant-Forde, J. N.) 13–45 (Springer, Berlin, 2007).
Google Scholar  More

1.
Asfaw, Z. & Tadesse, M. Prospects for sustainable use and development of wild food plants in Ethiopia. Econ. Bot. 55, 47–62 (2001).
Article  Google Scholar 
2.
Della, A., Paraskeva-Hadjichambi, D. & Hadjichambis, A. C. An ethnobotanical survey of wild edible plants of Paphos and Larnaca countryside of Cyprus. J. Ethnobiol. Ethnomed. 2, 34 (2006).
PubMed  PubMed Central  Article  Google Scholar 

3.
WHO. Health of Indigenous Peoples. Factsheets No 326 (World Health Organisation, Geneva, 2007).
Google Scholar 

4.
Kawarty, A. M. A. M. A., Behçet, L. & Cakilcioğlu, U. An ethnobotanical survey of medicinal plants in Ballakayati (Erbil, North Iraq). Turk. J. Bot. 44, 345–357 (2020).
Article  Google Scholar 

5.
Satıl, F. & Selvi, S. Ethnobotanical features of Ziziphora L. (Lamiaceae) Taxa in Turkey. Int. J. Nat. Life Sci. 4, 56–65 (2020).
Google Scholar 

6.
Baytop, T. Therapy with Medicinal Plants in Turkey (Past and Present) (Nobel Medicine Publication, Istanbul, 1999).
Google Scholar 

7.
Nikbakht, A., Kafi, M. & Haghighi, M. The abilities and potentials of medicinal plants production and herbal medicine in Iran. Acta Hortic. 790, 259–262. https://doi.org/10.17660/actahortic.2008.790.38 (2008).
Article  Google Scholar 

8.
Zeder, M. A. & Hesse, B. The initial domestication of goats (Capra hircus) in the Zagros mountains 10,000 years ago. Science 287, 2254–2257 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Rÿser, R. C. Indigenous people and traditional knowledge. Berkshire Encyclopedia of Sustainability. https://www.academia.edu/841635/Indigenous_and_Traditional_Knowledge (2011).

10.
Gemedo-Dalle, T., Maass, B. L. & Isselstein, J. Plant biodiversity and ethnobotany of Borana pastoralists in Southern Oromia, Ethiopia. Econ. Bot. 59, 43–65 (2005).
Article  Google Scholar 

11.
Little, P. D. Pastoral ecologies: Rethinking interdisciplinary paradigms and the political ecology of pastoralism in East Africa. In African Savannas: Global Narratives and Local Knowledge of Environmental Change (eds Bassett, T. J. & Crummey, D.) 161–177 (James Currey, Oxford, 2003).
Google Scholar 

12.
Boardman, J., Poesen, J. & Evans, R. Socio-economic factors in soil erosion and conservation. Environ. Sci. Policy 6, 1–6 (2003).
Article  Google Scholar 

13.
Gaikwad, J. et al. Combining ethnobotany and informatics to discover knowledge from data. In Ethnomedicinal Plants: Revitalizing of Traditional Knowledge of Herbs (eds Rai, M. et al.) 447–457 (Science Publishers, Enfield, 2011).
Google Scholar 

14.
Brouwer, N. et al. An ethnopharmacological study of medicinal plants in New South Wales. Molecules 10, 1252–1262 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Lambert, J., Srivastava, J. P. & Vietmeyer, N. Medicinal plants. World Bank Technical Papers (1997).

16.
Walter, K. S. & Gillett, H. J. 1997 IUCN Red List of Threatened Plants (IUCN, World Conservation Union, Cambridge, 1998).
Google Scholar 

17.
Ansari-Renani, H. R., Rischkowsky, B., Mueller, J. P., Momen, S. M. S. & Moradi, S. Nomadic pastoralism in southern Iran. Pastor. Res. Policy Pract. 3, 11 (2013).
Article  Google Scholar 

18.
Tashakkori, A. & Teddlie, C. SAGE Handbook of Mixed Methods in Social & Behavioral Research (SAGE, Thousand Oaks, 2010).
Google Scholar 

19.
Rechinger, K.H. (ed.) Flora Iranica (Graz, 1963–2012).

20.
Assadi, M. et al. (eds.). Flora of Iran: No 1-89 (Iran Research Institute of Forests and Rangelands, Tehran , 1989–2016).

21.
Napagoda, M. T., Sundarapperuma, T., Fonseka, D., Amarasiri, S. & Gunaratna, P. An ethnobotanical study of the medicinal plants used as anti-inflammatory remedies in Gampaha District, Western Province, Sri Lanka. Scientifica (Cairo) 2018, 9395052 (2018).
Google Scholar 

22.
Bano, A. et al. Quantitative ethnomedicinal study of plants used in the skardu valley at high altitude of Karakoram-Himalayan range, Pakistan. J. Ethnobiol. Ethnomed. 10, 43 (2014).
PubMed  PubMed Central  Article  Google Scholar 

23.
Reyes-García, V., Huanca, T., Vadez, V., Leonard, W. & Wilkie, D. Cultural, practical, and economic value of wild plants: A quantitative study in the Bolivian Amazon. Econ. Bot. 60, 62–74 (2006).
Article  Google Scholar 

24.
Tardío, J. & Pardo-de-Santayana, M. Cultural importance indices: A comparative analysis based on the useful wild plants of Southern Cantabria (Northern Spain). Econ. Bot. 62, 24–39 (2008).
Article  Google Scholar 

25.
Parthasarathy, N. & Karthikeyan, R. Biodiversity and population density of woody species in a tropical evergreen forest in Courtallum reserve forest, Western Ghats, India. Trop. Ecol. 38, 297–306 (1997).
Google Scholar 

26.
González-Hernández, M. P., Mouronte, V., Romero, R., Rigueiro-Rodríguez, A. & Mosquera-Losada, M. R. Plant diversity and botanical composition in an Atlantic heather-gorse dominated understory after horse grazing suspension: Comparison of a continuous and rotational management. Glob. Ecol. Conserv. 23, e01134 (2020).
Article  Google Scholar 

27.
Davies, K. W., Bates, J. D. & Boyd, C. S. Response of planted sagebrush seedlings to cattle grazing applied to decrease fire probability. Rangel. Ecol. Manag. https://doi.org/10.1016/j.rama.2020.05.002 (2020).
Article  Google Scholar 

28.
Hailu, H. Analysis of vegetation phytosociological characteristics and soil physico-chemical conditions in Harishin Rangelands of Eastern Ethiopia. Land 6, 4 (2017).
Article  Google Scholar 

29.
Spellmeier, J., Périco, E., Haetinger, C., Freitas, E. M. & Morás, A. P. B. Effect of grazing on the plant community of a southern Brazilian swamp. Floresta e Ambiente 26, e20180339 (2019).
Article  Google Scholar 

30.
Curtis, J. T. & McIntosh, R. P. An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 32, 476–496 (1951).
Article  Google Scholar 

31.
Mishra, R. Ecology Workbook (IBH Publishing Company, Oxford, 1968).
Google Scholar 

32.
Murphy, K. P. Machine Learning a Probabilistic Perspective (MIT Press, Cambridge, 2012).
Google Scholar 

33.
Tang, C., Yi, Y., Yang, Z. & Sun, J. Risk analysis of emergent water pollution accidents based on a Bayesian network. J. Environ. Manag. 165, 199–205 (2016).
Article  Google Scholar 

34.
Taylor, D., Hicks, T. & Champod, C. Using sensitivity analyses in Bayesian networks to highlight the impact of data paucity and direct future analyses: A contribution to the debate on measuring and reporting the precision of likelihood ratios. Sci. Justice 56, 402–410 (2016).
PubMed  Article  PubMed Central  Google Scholar 

35.
Alimirzaei, F., Mohammadi Kalayeh, A., Shahraki, M. R. & Behmanesh, B. Local knowledge of medicinal plants from the point of view of nomads in the rangelands of Chehel-Kaman, North Khorasan province. J. Indig. Knowl. 4, 156–201 (2017).
Google Scholar 

36.
Hosseini, M., Forouzeh, R. & Barani, H. Identification and investigation of ethnobotany of some medicinal plants in Razavi Khorasan Province. J. Med. Plants 18, 212–231 (2019).
Google Scholar 

37.
Okoye, T. C., Uzor, P. F., Onyeto, C. A. & Okereke, E. K. Safe African medicinal plants for clinical studies. In Toxicological Survey of African Medicinal Plants (ed. Kuete, V.) 535–555 (Elsevier, Amsterdam, 2014).
Google Scholar 

38.
Freidin, B. & Timmermans, S. Complementary and alternative medicine for children’s asthma: Satisfaction, care provider responsiveness, and networks of care. Qual. Health Res. 18, 43–55 (2008).
PubMed  Article  PubMed Central  Google Scholar 

39.
Simbo, D. J. An ethnobotanical survey of medicinal plants in Babungo, Northwest Region, Cameroon. J. Ethnobiol. Ethnomed. 6, 8 (2010).
PubMed  PubMed Central  Article  Google Scholar 

40.
Tangjitman, K., Wongsawad, C., Kamwong, K., Sukkho, T. & Trisonthi, C. Ethnomedicinal plants used for digestive system disorders by the Karen of northern Thailand. J. Ethnobiol. Ethnomed. 11, 27 (2015).
PubMed  PubMed Central  Article  Google Scholar 

41.
Chen, Y. et al. Phytochemical profiles and antioxidant activities in six species of ramie leaves. PLoS ONE 9, e108140–e108140 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Bahmani, M., Baharvand-Ahmadi, B., Tajeddini, P., Rafieian-Kopaei, M. & Naghdi, N. Identification of medicinal plants for the treatment of kidney and urinary stones. J. Ren. Inj. Prev. 5, 129–133 (2016).
PubMed  Article  Google Scholar 

43.
Ahmed, H. M. Ethnopharmacobotanical study on the medicinal plants used by herbalists in Sulaymaniyah Province, Kurdistan, Iraq. J. Ethnobiol. Ethnomed. 12, 8 (2016).
PubMed  PubMed Central  Article  Google Scholar 

44.
Nimrouzi, M. & Zarshenas, M. M. Phytochemical and pharmacological aspects of Descurainia sophia Webb ex Prantl: Modern and traditional applications. Avicenna J. Phytomed. 6, 266–272 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Miraj, S. & Kiani, S. Pharmacological activities of Carum carvi L. Der. Pharm. Lett. 8, 135–138 (2016).
CAS  Google Scholar 

46.
de Lucena, R. F. P., de Lima Araújo, E. & de Albuquerque, U. P. Does the local availability of woody Caatinga plants (Northeastern Brazil) explain their use value. Econ. Bot. 61, 347–361 (2007).
Article  Google Scholar 

47.
Thomas, E., Vandebroek, I. & Van Damme, P. valuation of forests and plant species in Indigenous Territory and National Park Isiboro-Sécure, Bolivia. Econ. Bot. 63, 229–241 (2009).
Article  Google Scholar 

48.
Berlin, B. The common flora = the medicinal flora: Theoretical implications of a comparison of medical ethnobotanical and general floristic surveys in the Chiapas Highlands. In Symposium “Ethnobotany of southern Mexico” (Society of Economic Botany, 2003).

49.
Guèze, M. et al. Are ecologically important tree species the most useful? A case study from indigenous people in the Bolivian Amazon. Econ. Bot. 68, 1–15 (2014).
PubMed  PubMed Central  Article  Google Scholar 

50.
Ouarghidi, A., Powell, B., Martin, G. J. & Abbad, A. Traditional sustainable harvesting knowledge and distribution of a vulnerable wild medicinal root (A. pyrethrum var. pyrethrum) in Ait M’hamed Valley, Morocco. Econ. Bot. 71, 83–95 (2017).
Article  Google Scholar 

51.
Posthouwer, C., Verheijden, T. M. S. & van Andel, T. R. A rapid sustainability assessment of wild plant extraction on the Dutch Caribbean Island of St. Eustatius. Econ. Bot. 70, 320–331 (2016).
Article  Google Scholar 

52.
Papageorgiou, D., Bebeli, P. J., Panitsa, M. & Schunko, C. Local knowledge about sustainable harvesting and availability of wild medicinal plant species in Lemnos Island, Greece. J. Ethnobiol. Ethnomed. 16, 36 (2020).
PubMed  PubMed Central  Article  Google Scholar 

53.
Donaghy, D. J. & Fulkerson, W. J. The importance of water-soluble carbohydrate reserves on regrowth and root growth of Lolium perenne (L.). Grass Forage Sci. 52, 401–407 (1997).
CAS  Article  Google Scholar 

54.
González-Tejero, M. R. et al. Medicinal plants in the Mediterranean area: Synthesis of the results of the project Rubia. J. Ethnopharmacol. 116, 341–357 (2008).
PubMed  Article  Google Scholar 

55.
Tuttolomondo, T. et al. Ethnobotanical investigation on wild medicinal plants in the Monti Sicani Regional Park (Sicily, Italy). J. Ethnopharmacol. 153, 568–586 (2014).
PubMed  Article  Google Scholar 

56.
Weber, K. T. & Horst, S. Desertification and livestock grazing: The roles of sedentarization, mobility and rest. Pastor. Res. Policy Pract. 1, 19 (2011).
Article  Google Scholar 

57.
Miara, M. D., Bendif, H., Ait Hammou, M. & Teixidor-Toneu, I. Ethnobotanical survey of medicinal plants used by nomadic peoples in the Algerian steppe. J. Ethnopharmacol. 219, 248–256 (2018).
PubMed  Article  PubMed Central  Google Scholar 

58.
Rana, D., Bhatt, A. & Lal, B. Ethnobotanical knowledge among the semi-pastoral Gujjar tribe in the high altitude (Adhwari’s) of Churah subdivision, district Chamba, Western Himalaya. J. Ethnobiol. Ethnomed. 15, 10 (2019).
PubMed  PubMed Central  Article  Google Scholar  More

1.
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Hird, S. M. Evolutionary biology needs wild microbiomes. Front. Microbiol. 8, 1–10 (2017).
Article  Google Scholar 

3.
Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: An integrative view. Cell 148, 1258–1270 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Marchesi, J. R. et al. The gut microbiota and host health: A new clinical frontier. Gut 65, 330–339 (2016).
PubMed  Article  PubMed Central  Google Scholar 

5.
Lee, W. J. & Hase, K. Gut microbiota-generated metabolites in animal health and disease. Nat. Chem. Biol. 10, 416–424 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Visconti, A. et al. Interplay between the human gut microbiome and host metabolism. Nat. Commun. 10, 4505 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Belkaid, Y. & Hand, T. W. Role of microbiota in immunity and inflammation. Cell 157, 121–141 (2018).
Article  CAS  Google Scholar 

8.
Pickard, J. M., Zeng, M. Y., Caruso, R. & Núñez, G. Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunol. Rev. 279, 70–89 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Pickard, J. M. & Núñez, G. Pathogen Colonization Resistance in the Gut and Its Manipulation for Improved Health. Am. J. Pathol. 189, 1300–1310 (2019).
PubMed  PubMed Central  Article  Google Scholar 

10.
Nguyen, T. L. A., Vieira-Silva, S., Liston, A. & Raes, J. How informative is the mouse for human gut microbiota research? Dis. Model. Mech. 8, 1–16 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

11.
Rosshart, S. P. et al. Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance. Cell 171, 1015–1028 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Blanga-Kanfi, S. et al. Rodent phylogeny revised: analysis of six nuclear genes from all major rodent clades. BMC Evol. Biol. 9, 71 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Kreisinger, J., Bastien, G., Hauffe, H. C., Marchesi, J. & Perkins, S. E. Interactions between multiple helminths and the gut microbiota in wild rodents. Philos. T. Roy. Soc. B 370, 20140295 (2015).
Article  Google Scholar 

14.
Maurice, C. F. et al. Marked seasonal variation in the wild mouse gut microbiota. ISME J. 9, 2423–2434 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Weldon, L. et al. The gut microbiota of wild mice. PLoS ONE 10, 1–15 (2015).
Article  CAS  Google Scholar 

16.
Lavrinienko, A., et al. Environmental radiation alters the gut microbiome of the bank vole Myodes glareolus. ISME J 12 (2018).

17.
Lavrinienko, A., Tukalenko, E., Mappes, T. & Watts, P. C. Skin and gut microbiomes of a wild mammal respond to different environmental cues. Microbiome 6, 209 (2018).
PubMed  PubMed Central  Article  Google Scholar 

18.
Lavrinienko, A. et al. Applying the Anna Karenina principle for wild animal gut microbiota: temporal stability of the bank vole gut microbiota in a disturbed environment. J. Anim. Ecol. In press, https://doi.org/10.1111/1365-2656.13342 (2020).

19.
Xiao, L. et al. A catalog of the mouse gut metagenome. Nat. Biotech. 33, 1103–1108 (2015).
CAS  Article  Google Scholar 

20.
Pan, H. et al. A gene catalogue of the Sprague-Dawley rat gut metagenome. GigaScience 7, 1–8 (2018).
CAS  Google Scholar 

21.
Hutterer, R., et al. Myodes glareolus. The IUCN Red List of Threatened Species e.T4973A115070929 (2016); erratum (2017).

22.
Lonn, E. et al. Balancing selection maintains polymorphisms at neurogenetic loci in field experiments. Proc. Natl. Acad. Sci. USA 114, 3690–3695 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Van Cann, J., Koskela, E., Mappes, T., Sims, A. & Watts, P. C. Intergenerational fitness effects of the early life environment in a wild rodent. J. Anim. Ecol. 88, 1355–1365 (2019).
PubMed  Article  PubMed Central  Google Scholar 

24.
Kohl, K. D., Sadowska, E. T., Rudolf, A. M., Dearing, M. D. & Koteja, P. Experimental evolution on a wild mammal species results in modifications of gut microbial communities. Front. Microbiol. 7, 1–10 (2016).
Google Scholar 

25.
Ormerod, K. L. et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome 4, 1–17 (2016).
Article  Google Scholar 

26.
Lagkouvardos, I. et al. Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome 7, 1–15 (2019).
Article  Google Scholar 

27.
Tonteri, E. J. et al. Tick-borne encephalitis virus in wild rodents in winter, Finland, 2008–2009. Emerg. Infect. Dis. 17, 72–75 (2011).
PubMed  PubMed Central  Article  Google Scholar 

28.
Vaheri, A. et al. Hantavirus infections in Europe and their impact on public health. Rev. Med. Virol. 23, 35–49 (2013).
PubMed  Article  PubMed Central  Google Scholar 

29.
Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc Natl. Acad. Sci. USA 112, 7039–7044 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Van Duijvendijk, G., Sprong, H. & Takken, W. Multi-trophic interactions driving the transmission cycle of Borrelia afzelii between Ixodes ricinus and rodents: A review. Parasite. Vector. 8, 13–15 (2015).
Article  Google Scholar 

31.
Lavrinienko, A. et al. Two hundred and fifty-four metagenome-assembled bacterial genomes from the bank vole gut microbiota. NCBI Sequence Read Archive https://identifiers.org/insdc.sra:SRP254056 (2020).

32.
Didion, J. P., Martin, M. & Collins, F. S. Atropos: Specific, sensitive, and speedy trimming of sequencing reads. PeerJ 5, e3720 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 1–8 (2018).
Article  CAS  Google Scholar 

35.
Li, D. et al. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Li, W. et al. Ultrafast clustering algorithms for metagenomic sequence analysis. Brief. Bioinform. 13, 656–668 (2012).
PubMed  PubMed Central  Article  Google Scholar 

37.
Sommer, D. D. et al. Minimus: A fast, lightweight genome assembler. BMC Bioinform. 8, 1–11 (2007).
Article  CAS  Google Scholar 

38.
Graham, E. D., Heidelberg, J. F. & Tully, B. J. Binsanity: Unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ 5, e3035 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Parks, D. H. et al. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).
PubMed  PubMed Central  Article  Google Scholar 

41.
Delmont, T. O. & Eren, A. M. Identifying contamination with advanced visualization and analysis practices: Metagenomic approaches for eukaryotic genome assemblies. PeerJ 4, e1839 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Campbell, J. H. et al. UGA is an additional glycine codon in uncultured SR1 bacteria from the human microbiota. Proc. Natl. Acad. Sci. USA. 110, 5540–5545 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).
PubMed  PubMed Central  Article  Google Scholar 

44.
Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Article  CAS  Google Scholar 

45.
Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI Reference Sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, 501–504 (2005).
Article  CAS  Google Scholar 

47.
Potter, S. C. et al. HMMER web server: 2018 update. Nucleic Acids Res. 46, W200–W204 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Edgar, R. C. MUSCLE: A multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 1–19 (2004).
Article  CAS  Google Scholar 

50.
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 5, e9490 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

52.
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Lavrinienko, A. et al. Two hundred and fifty-four metagenome-assembled bacterial genomes from the bank vole gut microbiota. figshare https://doi.org/10.6084/m9.figshare.c.4910601 (2020).

54.
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Ecol. Evol. 27, 105–117 (2019).
CAS  Google Scholar  More

1.
Campbell, B. M. et al. Reducing risks to food security from climate change. Glob. Food Secur. 11, 34–43 (2016).
Article  Google Scholar 
2.
Nair, P. K. R. Grand challenges in agroecology and land use systems. Front. Environ. Sci. 2, 1 (2014).
Google Scholar 

3.
Connolly-Boutin, L. & Smit, B. Climate change, food security, and livelihoods in sub-Saharan Africa. Reg. Environ. Change 16, 385–399 (2016).
Article  Google Scholar 

4.
Maxwell, D. The political economy of urban food security in Sub-Saharan Africa. World Dev. 27, 1939–1953 (1999).
Article  Google Scholar 

5.
IPCC. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds. Shukla, P. R. et al.) (2019).

6.
Altieri, M. A., Nicholls, C. I., Henao, A. & Lana, M. A. Agroecology and the design of climate change-resilient farming systems. Agron. Sust. Dev. 35, 869–890 (2015).
Article  Google Scholar 

7.
Tadross, M. et al. Growing-season rainfall and scenarios of future change in southeast Africa: Implications for cultivating maize. Clim. Res. 40, 147–161 (2009).
Article  Google Scholar 

8.
Challinor, A., Wheeler, T., Garforth, C., Craufurd, P. & Kassam, A. Assessing the vulnerability of food crop systems in Africa to climate change. Clim. Change 83, 381–399 (2007).
ADS  Article  Google Scholar 

9.
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

10.
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. USA 114, 9326–9331 (2017).
CAS  PubMed  Article  Google Scholar 

11.
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).
ADS  Article  Google Scholar 

12.
Hammond, S. T. et al. Food spoilage, storage, and transport: Implications for a sustainable future. Bioscience 65, 758–768 (2015).
Article  Google Scholar 

13.
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
PubMed  Article  Google Scholar 

14.
Newton, A. C., Johnson, S. N. & Gregory, P. J. Implications of climate change for diseases, crop yields and food security. Euphytica 179, 3–18 (2011).
Article  Google Scholar 

15.
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

16.
Lee, J. G. & Kang, M. Geospatial big data: Challenges and opportunities. Big Data Res. 2, 74–81 (2015).
Article  Google Scholar 

17.
Serra-Diaz, J. M. & Franklin, J. What’s hot in conservation biogeography in a changing climate? Going beyond species range dynamics. Divers. Distrib. 25, 492–498 (2019).
Article  Google Scholar 

18.
Snyder, K. A., Miththapala, S., Sommer, R. & Braslow, J. The yield gap: Closing the gap by widening the approach. Exp. Agric. 53, 445–459 (2017).
Article  Google Scholar 

19.
Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

20.
Fernández, M., Hamilton, H. & Kueppers, L. M. Characterizing uncertainty in species distribution models derived from interpolated weather station data. Ecosphere 4, 1–17 (2013).
Article  Google Scholar 

21.
Grabowski, P. et al. Assessing adoption potential in a risky environment: The case of perennial pigeonpea. Agric. Syst. 171, 89–99 (2019).
PubMed  PubMed Central  Article  Google Scholar 

22.
Habib-Mintz, N. Biofuel investment in Tanzania: Omissions in implementation. Energy Policy 38, 3985–3997 (2010).
Article  Google Scholar 

23.
Shiferaw, B. A., Okello, J. & Reddy, R. V. Adoption and adaptation of natural resource management innovations in smallholder agriculture: Reflections on key lessons and best practices. Environ. Dev. Sustain. 11, 601–619 (2009).
Article  Google Scholar 

24.
Kwesiga, F., Akinnifesi, F. K., Mafongoya, P. L., McDermott, M. H. & Agumya, A. Agroforestry research and development in southern Africa during the 1990s: Review and challenges ahead. Agrofor. Syst. 59, 173–186 (2003).
Article  Google Scholar 

25.
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).
Article  Google Scholar 

26.
Fischer, G. et al. Global agro-ecological zones (GAEZ v3. 0)-model documentation. In International Institute for Applied Systems Analysis/Food and Agriculture Organization of the United Nations (2012).

27.
Heal, G. & Millner, A. Reflections: Uncertainty and decision making in climate change economics. Rev. Environ. Econ. Policy 8, 120–137 (2014).
Article  Google Scholar 

28.
Harth, A., Knoblock, C. A., Stadtmüller, S., Studer, R. & Szekely, P. On-the-fly integration of static and dynamic linked data. In Proceedings of the Fourth International Workshop on Consuming Linked Data (2013).

29.
Ginige, A., Javadi, B., Calheiros, R. N. & Hendriks, S. L. A smart computing framework centered on user and societal empowerment to achieve the sustainable development goals. In International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas (eds. Bassioni, G., Kebe, C. M. F., Gueye, A. & Ndiaye, A.) 158–172 (Springer, Cham, 2019).

30.
Ramirez-Cabral, N. Y. Z., Kumar, L. & Taylor, S. Crop niche modeling projects major shifts in common bean growing areas. Agric. For. Meteorol. 218, 102–113 (2016).
ADS  Article  Google Scholar 

31.
Mejias, P., & Piraux, M. AquaCrop, the crop water productivity model. In Food and Agriculture Organization of the United Nations (2017).

32.
Hijmans, R. J., Guarino, L., Cruz, M. & Rojas, E. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl. 127, 15–19 (2001).
Google Scholar 

33.
Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265 (2003).
Article  Google Scholar 

34.
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. & Freebairn, D. M. APSIM: A novel software system for model development, model testing, and simulation in agricultural systems research. Agric. Syst. 50, 255–271 (1996).
Article  Google Scholar 

35.
Dragićević, S. The potential of Web-based GIS. J. Geogr. Syst. 6, 79–81 (2004).
Article  Google Scholar 

36.
Kraak, M. J. The role of the map in a Web-GIS environment. J. Geogr. Syst. 6, 83–93 (2004).
Article  Google Scholar 

37.
Moore, R. Introducing Google Earth Engine. The Official google.org blog https://blog.google.org/2010/12/introducing-google-earth-engine_57.html (2010).

38.
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
ADS  Article  Google Scholar 

39.
Agapiou, A. Remote sensing heritage in a petabyte-scale: Satellite data and heritage Earth Engine© applications. Int. J. Digit. Earth. 10, 82–102 (2017).
Article  Google Scholar 

40.
HarvestChoice-International Food Policy Research Institute (IFPRI). Agro-Ecological Zones for Africa South of the Sahara V3. Harvard Dataverse https://doi.org/10.7910/DVN/M7XIUB (2015).

41.
Kane, D. A., Roge, P. & Snapp, S. S. A systematic review of perennial staple crops literature using topic modeling and bibliometric analysis. PLoS ONE 11, e0155788 (2016).
PubMed  PubMed Central  Article  Google Scholar 

42.
Thornton, P. K. & Herrero, M. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nat. Clim. Change. 5, 830–836 (2015).
ADS  Article  Google Scholar 

43.
Mayes, S. et al. The potential for underutilized crops to improve security of food production. J. Exp. Bot. 63, 1075–1079 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

44.
Peter, B. G., Mungai, L. M., Messina, J. P. & Snapp, S. S. Nature-based agricultural solutions: Scaling perennial grains across Africa. Environ. Res. 159, 283–290 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Hannah, L. et al. Global climate change adaptation priorities for biodiversity and food security. PLoS ONE 8, e72590 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Snapp, S. S., Blackie, M. J., Gilbert, R. A., Bezner-Kerr, R. & Kanyama-Phiri, G. Y. Biodiversity can support a greener revolution in Africa. Proc. Natl. Acad. Sci. USA 107, 20840–20845 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

47.
Sanchez, P. A. Soil fertility and hunger in Africa. Science 295, 2019–2020 (2002).
CAS  PubMed  Article  Google Scholar 

48.
Foyer, C. H. et al. Neglecting legumes has compromised human health and sustainable food production. Nat. Plants 2, 16112 (2016).
PubMed  Article  Google Scholar 

49.
Kole, C. et al. Application of genomics-assisted breeding for generation of climate resilient crops: Progress and prospects. Front. Plant Sci. 6, 563 (2015).
PubMed  PubMed Central  Article  Google Scholar 

50.
Sinha, P. et al. 2016 Identification and validation of selected universal stress protein domain containing drought-responsive genes in Pigeonpea (Cajanus cajan L.). Front. Plant Sci. 6, 1065 (2016).
PubMed  PubMed Central  Article  Google Scholar 

51.
Choudhary, A. K., Sultana, R., Pratap, A., Nadarajan, N. & Jha, U. C. Breeding for abiotic stresses in pigeonpea. J. Food Legum. 24, 165–174 (2011).
Google Scholar 

52.
Ehlers, J. D. & Hall, A. E. Cowpea (Vigna unguiculata L. walp.). Field Crops Res. 53, 187–204 (1997).
Article  Google Scholar 

53.
De Ron, A. M. et al. 2019 Common bean genetics, breeding, and genomics for adaptation to changing to new agri-environmental conditions. In Genomic Designing of Climate-Smart Pulse Crops (ed. Kole, C.) 1–106 (Springer, Cham, 2019).
Google Scholar 

54.
Smýkal, P. et al. Legume crops phylogeny and genetic diversity for science and breeding. Crit. Rev. Plant Sci. 34, 43–104 (2015).
Article  Google Scholar 

55.
Snapp, S. S., Cox, C. M. & Peter, B. G. Multipurpose legumes for smallholders in sub-Saharan Africa: Identification of promising ‘scale out’ options. Glob. Food Secur. 23, 22–32 (2019).
Article  Google Scholar 

56.
Ramírez-Villegas, J. & Thornton, P. K. Climate change impacts on African crop production. In CCAFS Working Paper No. 119. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (2015).

57.
Robertson, C. C. Black, white, and red all over: Beans, women, and agricultural imperialism in twentieth-century Kenya. Agric. Hist. 71, 259–299 (1997).
Google Scholar 

58.
Rusinamhodzi, L., Corbeels, M., Nyamangara, J. & Giller, K. E. Maize–grain legume intercropping is an attractive option for ecological intensification that reduces climatic risk for smallholder farmers in central Mozambique. Field Crops Res. 136, 12–22 (2012).
Article  Google Scholar 

59.
Bezner-Kerr, R., Snapp, S., Chirwa, M., Shumba, L. & Msachi, R. Participatory research on legume diversification with Malawian smallholder farmers for improved human nutrition and soil fertility. Exp. Agric. 43, 437–453 (2007).
Article  Google Scholar 

60.
Jones, A. D., Shrinivas, A. & Bezner-Kerr, R. Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy 46, 1–12 (2014).
Article  Google Scholar 

61.
Ojiewo, C. et al. The role of vegetables and legumes in assuring food, nutrition, and income security for vulnerable groups in Sub-Saharan Africa. World Med. Health Policy 7, 187–210 (2015).
Article  Google Scholar 

62.
Wood, S., Sebastian, K., Nachtergaele, F., Nielsen, D. & Dai, A. Spatial aspects of the design and targeting of agricultural development strategies. In Environment and Production Technology Division, International Food Policy Research Institute, Washington, DC, EPTD Discussion Paper No. 44 (1999).

63.
Chivenge, P., Mabhaudhi, T., Modi, A. T. & Mafongoya, P. The potential role of neglected and underutilised crop species as future crops under water scarce conditions in Sub-Saharan Africa. International Journal of Environmental Research and Public Health 12, 5685–5711 (2015).
PubMed  PubMed Central  Article  Google Scholar 

64.
Dakora, F. D. Biogeographic distribution, nodulation and nutritional attributes of underutilized indigenous African legumes. In II International Symposium on Underutilized Plant Species: Crops for the Future-Beyond Food Security, 53–64 International Society for Horticultural Science, ISHS Acta Horticulturae 979 (2011).

65.
Traub, J. et al. Screening for heat tolerance in Phaseolus spp. using multiple methods. Crop Sci. 58, 2459–2469 (2018).
CAS  Article  Google Scholar 

66.
Knight, A. T. et al. Knowing but not doing: Selecting priority conservation areas and the research–implementation gap. Conserv. Biol. 22, 610–617 (2008).
PubMed  Article  Google Scholar 

67.
Funk, C. et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).
Article  Google Scholar 

68.
Vizy, E. K., Cook, K. H., Chimphamba, J. & McCusker, B. Projected changes in Malawi’s growing season. Clim. Dyn. 45, 1673–1698 (2015).
Article  Google Scholar 

69.
Jayanthi, H. et al. Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study. Int. J. Disast. Risk Res. 4, 71–81 (2013).
Google Scholar 

70.
FAO. ECOCROP, Crop Environmental Requirements Database. Food and Agriculture Organization of the United Nations (1991).

71.
Peter, B. G., Messina, J. P. & Lin, Z. Web-based GIS for spatiotemporal crop climate niche mapping https://doi.org/10.7910/DVN/UFC6B5,HarvardDataverse,V2 (2019).
Article  Google Scholar 

72.
Beebe, S. et al. Genetic improvement of common beans and the challenges of climate change. In Crop Adaptation to Climate Change (eds. Yadav, S. S., Redden, R. J., Hatfield, J. L., Lotze-Campen, H. & Hall, A. E.) Ch. 16, 356–369 (Wiley-Blackwell, 2011).

73.
de Jong, R. & de Bruin, S. Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences 9, 71–77 (2012).
ADS  Article  Google Scholar 

74.
Swist, T. & Magee, L. Academic publishing and its digital binds: Beyond the paywall towards ethical executions of code. Cult.s Unbound J. Curr. Cult. Res. 9, 240–259 (2018).
Article  Google Scholar 

75.
Hedding, D. W. Comments on “Factors affecting global flow of scientific knowledge in environmental sciences” by Sonne et al. (2020). Sci. Total Environ. 705, 135933 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

76.
Rippke, U. et al. Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nat. Clim. Change 6, 605–609 (2016).
ADS  Article  Google Scholar 

77.
Sinclair, T. R., Marrou, H., Soltani, A., Vadez, V. & Chandolu, K. C. Soybean production potential in Africa. Glob. Food Secur. 3, 31–40 (2014).
Article  Google Scholar 

78.
Hajjarpoor, A. et al. Characterization of the main chickpea cropping systems in India using a yield gap analysis approach. Field Crops Res. 223, 93–104 (2018).
Article  Google Scholar 

79.
Ortega, D. L., Waldman, K. B., Richardson, R. B., Clay, D. C. & Snapp, S. Sustainable intensification and farmer preferences for crop system attributes: Evidence from Malawi’s central and southern regions. World Dev. 87, 139–151 (2016).
Article  Google Scholar 

80.
Simtowe, F., Asfaw, S. & Abate, T. Determinants of agricultural technology adoption under partial population awareness: The case of pigeonpea in Malawi. Agric. Food Econ. 4, 7 (2016).
Article  Google Scholar 

81.
Norris, K. Agriculture and biodiversity conservation: Opportunity knocks. Conservation Letters 1, 2–11 (2008).
Article  Google Scholar 

82.
Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Change 6, 810–813 (2016).
ADS  Article  Google Scholar 

83.
Pekel, J., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

84.
Allen, R. G. et al. EEFlux: A Landsat-based evapotranspiration mapping tool on the Google Earth Engine. In 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation-A Tribute to the Career of Terry Howell, Sr. Conference Proceedings, 1–11. American Society of Agricultural and Biological Engineers (2015).

85.
Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day L3 global 1km SIN grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MOD11A2.006 (2015).
Article  Google Scholar 

86.
Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land Process.. DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).
Article  Google Scholar 

87.
Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+aqua land cover type yearly L3 global 500m SIN grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MCD12Q1.006 (2019).
Article  Google Scholar 

88.
Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
ADS  Article  Google Scholar 

89.
Teluguntla, P. G. et al. Global cropland area database (GCAD) derived from remote sensing in support of food security in the twenty-first century: Current achievements and future possibilities. In Remote Sensing Handbook, Land Resources: Monitoring, Modelling, and Mapping Vol 2, Ch. 7 (CRC Press, 2015).

90.
Arino, O., Ramos, J. R., Kalogirou, V., Defourny, P. & Achard, F. GlobCover 2009. In ESA Living Planet Symposium 1–3. European Space Agency (2010).

91.
Hengl, T. & MacMillan, R. A. Predictive Soil Mapping with R, https://www.soilmapper.org (OpenGeoHub foundation, Wageningen, The Netherlands, 2019).

92.
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
ADS  Article  Google Scholar 

93.
Rossel, R. A. V. & Bouma, J. Soil sensing: A new paradigm for agriculture. Agric. Syst. 148, 71–74 (2016).
Article  Google Scholar 

94.
Herrick, J. E. et al. The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing. J. Soil Water Conserv. 68, 5A-12A (2013).
Article  Google Scholar 

95.
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change. 9, 758–763 (2019).
ADS  Article  Google Scholar 

96.
ESRI. ArcGIS Desktop: Release 10.8. (Environmental Systems Research Institute, CAs, 2020). More

1.
Garcia-Pichel F. Desert environments: biological soil crusts. encycl environ microbiol vol 6. New York, NY, USA: Set. Wiley-Interscience; 2003. p. 1019–23.
Google Scholar 
2.
Belnap J, Büdel B, Lange OL. Biological soil crusts: characteristics and distribution. biological soil crust: structure, function, and management. Berlin: Springer-Verlag; 2001. p. 3–30.
Google Scholar 

3.
Prăvălie R. Drylands extent and environmental issues. A global approach. Earth Sci Rev. 2016;161:259–78.
Article  CAS  Google Scholar 

4.
Garcia-Pichel F, Pringault O. Cyanobacteria track water in desert soils. Nature. 2001;413:380–1.
CAS  PubMed  Article  Google Scholar 

5.
Pringault O, Garcia-Pichel F. Hydrotaxis of cyanobacteria in desert crusts. Microb Ecol. 2004;47:366–73.
CAS  PubMed  Article  Google Scholar 

6.
Soule T, Anderson IJ, Johnson SL, Bates ST, Garcia-Pichel F. Archaeal populations in biological soil crusts from arid lands in North America. Soil Biol Biochem. 2009;41:2069–74.
CAS  Article  Google Scholar 

7.
Nunes da Rocha U, Cadillo-Quiroz H, Karaoz U, Rajeev L, Klitgord N, Dunn S, et al. Isolation of a significant fraction of non-phototroph diversity from a desert biological soil crust. Front Microbiol. 2015;6:1–14.
Article  Google Scholar 

8.
Hu C, Zhang D, Huang Z, Liu Y. The vertical microdistribution of cyanobacteria and green algae within desert crusts and the development of the algal crusts. Plant Soil. 2003;257:97–111.
CAS  Article  Google Scholar 

9.
Bates ST, Nash TH, Garcia-Pichel F. Patterns of diversity for fungal assemblages of biological soil crusts from the southwestern United States. Mycologia. 2012;104:353–61.
CAS  PubMed  Article  Google Scholar 

10.
Ullmann I, Büdel B. Ecological determinants of species composition of biological soil crusts on a landscape scale. In: Belnap J, Lange OL, editors. Biological soil crusts: structure, function, and management, 1st ed. Berlin: Springer-Verlag; 2001. p. 203–13.
Google Scholar 

11.
Lange OL, Belnap J, Reichenberger H, Meyer A. Photosynthesis of green algal soil crust lichens from arid lands in southern Utah, USA: role of water content on light and temperature responses of CO2 exchange. Flora. 1997;192:1–15.
Article  Google Scholar 

12.
Garcia-Pichel F, López-Cortés A, Nübel U. Phylogenetic and morphological diversity of cyanobacteria in soil desert crusts from the Colorado Plateau. Appl Environ Microbiol. 2001;67:1902–10.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Couradeau E, Karaoz U, Lim HC, Nunes Da Rocha U, Northen T, Brodie E, et al. Bacteria increase arid-land soil surface temperature through the production of sunscreens. Nat Commun. 2016;7:1–7.
Article  CAS  Google Scholar 

14.
Yeager C, Kornosky J, Housman DC, Grote EE, Belnap J, Kuske CR. Diazotrophic community structure and function in two successional stages of biological soil crusts from the Colorado Plateau and Chihuahuan Desert. Appl Environ Microbiol. 2004;70:973–83.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Yeager CM, Kornosky JL, Morgan RE, Cain EC, Garcia-Pichel F, Housman DC, et al. Three distinct clades of cultured heterocystous cyanobacteria constitute the dominant N2-fixing members of biological soil crusts of the Colorado Plateau, USA. FEMS Microbiol Ecol. 2007;60:85–97.
CAS  PubMed  Article  Google Scholar 

16.
Yeager CM, Kuske CR, Carney TD, Johnson SL, Ticknor LO, Belnap J. Response of biological soil crust diazotrophs to season, altered summer precipitation, and year-round increased temperature in an arid grassland of the Colorado Plateau, USA. Front Microbiol. 2012;3:1–14.
Article  Google Scholar 

17.
Garcia-Pichel F. Cyanobacteria. In: Schaechter M, editor. Encyclopedia of microbiology. 3rd ed. 2009. New York: Elsevier Inc.; 2009. p. 107–24.

18.
Fernandes VMC, Machado de Lima NM, Roush D, Rudgers J, Collins SL, Garcia-Pichel F. Exposure to predicted precipitation patterns decreases population size and alters community structure of cyanobacteria in biological soil crusts from the Chihuahuan Desert. Environ Microbiol. 2018;20:259–69.
PubMed  Article  Google Scholar 

19.
Garcia-Pichel F, Wojciechowski MF. The evolution of a capacity to build supra-cellular ropes enabled filamentous cyanobacteria to colonize highly erodible substrates. PLoS ONE. 2009;4:4–9.
Article  CAS  Google Scholar 

20.
Belnap J, Gardner J. Soil microstructure in soils of the Colorado Plateau: the role of the cyanobacterium Microcoleus vaginatus. West N Am Nat. 1993;53:40–7.
Google Scholar 

21.
Starkenburg SR, Reitenga KG, Freitas T, Johnson S, Chain PSG, Garcia-Piche F, et al. Genome of the cyanobacterium Microcoleus vaginatus FGP-2, a photosynthetic ecosystem engineer of arid land soil biocrusts worldwide. J Bacteriol. 2011;193:4569–70.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Rajeev L, Nunes U, Klitgord N, Luning EG, Fortney J, Axen SD, et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J. 2013;7:2178–91.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Hooper DU, Johnson L. Nitrogen limitation in dryland ecosystems: responses to geographical and temporal variation in precipitation. Biogeochemistry. 1999;46:247–93.
CAS  Google Scholar 

24.
James JJ, Tiller RL, Richards JH. Multiple resources limit plant growth and function in a saline-alkaline desert community. J Ecol. 2005;93:113–26.
CAS  Article  Google Scholar 

25.
Neff JC, Reynolds RL, Belnap J, Lamothe P. Multi-decadal impacts of grazing on soil physical and biogeochemical properties in southeast Utah. Ecol Appl. 2005;15:87–95.
Article  Google Scholar 

26.
Schlesinger WH, Raikks JA, Hartley AE, Cross AF. On the spatial pattern of soil nutrients in desert ecosystems. Ecology. 1996;77:364–74.
Article  Google Scholar 

27.
Beraldi-Campesi H, Hartnett HE, Anbar A, Gordon GW, Garcia-Pichel F. Effect of biological soil crusts on soil elemental concentrations: Implications for biogeochemistry and as traceable biosignatures of ancient life on land. Geobiology. 2009;7:348–59.
CAS  PubMed  Article  Google Scholar 

28.
Johnson SL, Budinoff CR, Belnap J, Garcia-pichel F. Relevance of ammonium oxidation within biological soil crust communities. Environ Microbiol. 2005;7:1–12.
CAS  PubMed  Article  Google Scholar 

29.
Pepe-Ranney C, Koechli C, Potrafka R, Andam C, Eggleston E, Garcia-Pichel F, et al. Non-cyanobacterial diazotrophs mediate dinitrogen fixation in biological soil crusts during early crust formation. ISME J. 2016;10:287–98.
CAS  PubMed  Article  Google Scholar 

30.
Couradeau E, Giraldo-Silva A, De Martini F, Garcia-Pichel F. Spatial segregation of the biological soil crust microbiome around its foundational cyanobacterium, Microcoleus vaginatus, and the formation of a nitrogen-fixing cyanosphere. Microbiome. 2019;7:111–22.
Article  Google Scholar 

31.
Baran R, Brodie EL, Mayberry-lewis J, Hummel E, Nunes U, Rocha D, et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat Commun. 2015;6:1–9.
Article  CAS  Google Scholar 

32.
Baran R, Ivanova N, Jose N, Garcia-Pichel F, Kyrpides N, Gugger M, et al. Functional genomics of novel secondary metabolites from diverse cyanobacteria using untargeted metabolomics. Mar Drugs. 2013;11:3617–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Velasco Ayuso S, Giraldo-Silva A, Nelson C, Barger NN, Garcia-pichel F. Microbial nursery production of high quality biological soil crust biomass for restoration of degraded dryland soils. Appl Environ Microbiol. 2017;83:1–16.
Article  Google Scholar 

34.
Giraldo-Silva A, Nelson C, Barger N, Garcia-Pichel F. Nursing biocrusts: isolation, cultivation and fitness test of indigenous cyanobacteria. Restor Ecol. 2019;27:793–803.
Article  Google Scholar 

35.
Büdel B, Darienko T, Deutschewitz K, Dojani S, Friedl T, Mohr KI, et al. Southern african biological soil crusts are ubiquitous and highly diverse in drylands, being restricted by rainfall frequency. Microb Ecol. 2009;57:229–47.
PubMed  Article  Google Scholar 

36.
Ferreira D, Garcia-Pichel F. Mutational studies of putative biosynthetic genes for the cyanobacterial sunscreen scytonemin in Nostoc punctiforme ATCC 29133. Front Microbiol. 2016;7:1–10.
Article  Google Scholar 

37.
Garcia-Pichel F, Loza V, Marusenko Y, Mateo P, Potrafka RM. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science. 2013;340:1574–77.
CAS  PubMed  Article  Google Scholar 

38.
Caporaso JG, Lauber CL, Walters WA, Berg-lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Gilbert JA, Meyer F, Jansson J, Gordon J, Pace N, Tiedje J, et al. The Earth Microbiome Project: meeting report of the ‘1 EMP meeting on sample selection and acquisition’ at Argonne National Laboratory October 6 2010. Stand Genom Sci. 2010;3:249–53.

40.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.
CAS  PubMed  Article  Google Scholar 

43.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:1–10.
Google Scholar 

45.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:242–5.
Article  CAS  Google Scholar 

48.
Clarke KR, Gorley RN. PRIMER v6: user manual/tutorial. Prim Plymouth UK. 2006;7:192.
Google Scholar 

49.
Nübel U, Garcia-Pichel F, Muyzer G. PCR primers to amplify 16S rRNA genes from cyanobacteria PCR primers to amplify 16S rRNA genes from cyanobacteria. Appl Environ Microbiol. 1997;63:3327–32.
PubMed  PubMed Central  Article  Google Scholar 

50.
Ando S, Goto M, Meunchang S, Thongra-ar P, Fujiwara T, Hayashi H, et al. Detection of nifH Sequences in Sugarcane (Saccharum officinarum L.) and Pineapple (Ananas comosus [L.] Merr.). Soil Sci Plant Nutr. 2005;51:303–8.
CAS  Article  Google Scholar 

51.
Van Dorst J, Siciliano SD, Winsley T, Snape I, Ferrari BC. Bacterial targets as potential indicators of diesel fuel toxicity in subantarctic soils. Appl Environ Microbiol. 2014;80:4021–33.
PubMed  PubMed Central  Article  CAS  Google Scholar 

52.
R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2015. http://www.R-project.org/.
Google Scholar 

53.
Mirza BS, Rodrigues JLM. Development of a direct isolation procedure for free-living diazotrophs under controlled hypoxic conditions. Appl Environ Microbiol. 2012;78:5542–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Döbereiner J, Marriel IE, Nery M. Ecological distribution of Spirillum lipoferum Beijerinck. Can J Microbiol. 1976;22:1464–73.
PubMed  Article  Google Scholar 

55.
Dobereiner J, Urquiaga S, Boddey RM. Alternatives for nitrogen nutrition of crops in tropical agriculture. Fertil Res. 1995;42:339–46.
CAS  Article  Google Scholar 

56.
Wilson PW, Knight SG. Experiments in Bacterial Physiology, 3rded. Minneapolis, Minnesota: Burgess; 1952 p. 62.

57.
Stanier RY, Kunisawa R, Mandel M. Purification and properties of unicellular blue-green algae (Order Chroococcales). Bacteriological Reviews. 1971;35:171–205.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Dowd SE, Callaway TR, Wolcott RD, Sun Y, McKeehan T, Hagevoort RG, et al. Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). BMC Microbiol. 2008;8:1–8.
Article  CAS  Google Scholar 

59.
Roesch LFW, Fulthorpe RR, Riva A, Casella G, Hadwin AKM, Kent AD, et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 2007;4:283–90.
Article  CAS  Google Scholar 

60.
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–9.
PubMed  PubMed Central  Article  Google Scholar 

61.
Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000;7:203–14.
CAS  Article  Google Scholar 

62.
Ofek M, Hadar Y, Minz D. Ecology of root colonizing Massilia (Oxalobacteraceae). PLoS ONE. 2012;7:1–12.
Article  CAS  Google Scholar 

63.
De Souza RSC, Okura VK, Armanhi JSL, Jorrín B, Lozano N, Da Silva MJ, et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci Rep. 2016;6:1–15.
Article  CAS  Google Scholar 

64.
Jones D, Keddie RM. The Genus Arthrobacter. In: Dworkin M, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E, editors. The prokaryotes: volume 3: archaea. bacteria: firmicutes, actinomycetes. New York, NY: Springer New York; 2006. p. 945–60.
Google Scholar 

65.
Baldani JI, Rouws L, Cruz LM, Olivares FL, Schmid M, Hartmann A. The family oxalobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: alphaproteobacteria and betaproteobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 919–74.
Google Scholar 

66.
Mayilraj S, Stackebrandt E. The family paenibacillaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: firmicutes and tenericutes. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 267–80.
Google Scholar 

67.
Slepecky RA, Hemphill HE. The Genus Bacillus–Nonmedical. In: Dworkin M, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E, editors. The prokaryotes: volume 4: bacteria: firmicutes, cyanobacteria. New York, NY: Springer US; 2006. p. 530–62.
Google Scholar 

68.
Carareto Alves LM, de Souza JAM, Varani A, de M, Lemos EG, de M. The family rhizobiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: alphaproteobacteria and betaproteobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 419–37.
Google Scholar 

69.
Normand P, Daffonchio D, Gtari M. The family geodermatophilaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: actinobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 361–79.
Google Scholar 

70.
Kämpfer P, Glaeser SP, Parkes L, van Keulen G, Dyson P. The family streptomycetaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: actinobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 889–1010.
Google Scholar 

71.
Octavia S, Lan R. The family enterobacteriaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: gammaproteobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 225–86.
Google Scholar 

72.
Poly F, Monrozier LJ, Bally R. Improvement in the RFLP procedure for studying the diversity of nifH genes in communities of nitrogen fixers in soil. Res Microbiol. 2001;152:95–103.
CAS  PubMed  Article  Google Scholar 

73.
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Schulz-Bohm K, Gerards S, Hundscheid M, Melenhorst J, de Boer W, Garbeva P. Calling from distance: attraction of soil bacteria by plant root volatiles. ISME J. 2018;12:1252–62.
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Zhalnina K, Louie KB, Hao Z, Mansoori N, Da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.
CAS  PubMed  Article  Google Scholar 

76.
Garcia-Pichel F. A model for internal self-shading in planktonic organisms and its implications for the usefulness of ultraviolet sunscreens. Limnol Oceanogr. 1994;39:1704–17.
Article  Google Scholar 

77.
Garcia-Pichel F, Belnap J. Small-scale environments and distribution of biological soil crusts. biological soil crusts: structure, function, and management. Berlin Heidelberg: Springer; 2001. p. 193–201.
Google Scholar 

78.
Paerl HW, Bebout BM. Direct measurement of O2-depleted microzones in marine oscillatoria: relation to N2 fixation. Science. 1988;241:442–5.
CAS  PubMed  Article  Google Scholar 

79.
Barger NN, Webber B, Garcia-Pichel F, Zaady E, Belnap J. Patterns and controls on nitrogen cycling of biological soil crusts. In: Weber B, Caldwell MM, Jayne B, Bettina W, Büdel B, Belnap J, et al. editors. Biological soil crusts: an organizing principle in drylands, 2nd ed. Switzerland: Springer; 2016. p. 257–85.

80.
Sancho L, Belnap J, Colesie C, Raggio J. Carbon budgets of biological soil crusts at micro-, meso-, and global scales. In: Belnap J, Weber B, Burkhard B, editors. Biological soil crusts: an organizing principle in drylands. Switzerland: Springer; 2016. p. 287–304.

81.
Weber B, Wu D, Tamm A, Ruckteschler N, Rodríguez-Caballero E, Steinkamp J, et al. Biological soil crusts accelerate the nitrogen cycle through large NO and HONO emissions in drylands. Proc Natl Acad Sci USA. 2015;112:15384–9.
CAS  PubMed  Article  Google Scholar 

82.
Garcia-Pichel F, Belnap J, Neuer S, Schanz F. Estimates of global cyanobacterial biomass and its distribution. Arch Hydrobiol Suppl Algol Stud. 2003;109:213–27.
Google Scholar 

83.
Belnap J, Eldridge D. Disturbance and recovery of biological soil crusts. In: Belnap J, Lange O, editors. Biological soil crusts: structure, function and management. Berlin: Springer; 2001. p. 363–83.
Google Scholar 

84.
Zaady E, Eldridge DJ, Bowker MA. Effect of local-scale disturbance on biocrusts. In: Weber B, Büdel B, Belnap J, editors. Biological soil crusts: an organizing principle in drylands. Cham: Springer International Publishing; 2016. p. 429–50.
Google Scholar 

85.
Williams WJ, Eldridge DJ, Alchin BM. Grazing and drought reduce cyanobacterial soil crusts in an Australian Acacia woodland. J Arid Environ. 2008;72:1064–75.
Article  Google Scholar  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