Philippa Kaur

1.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Google Scholar 
2.
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883 (2002).
Google Scholar 

3.
Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments: behavioural responses to urban environments. Biol. Rev. 88, 537–549 (2013).
PubMed  Google Scholar 

4.
Sih, A. Understanding variation in behavioural responses to human-induced rapid environmental change: a conceptual overview. Anim. Behav. 85, 1077–1088 (2013).
Google Scholar 

5.
Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change: behaviour and evolution. Evol. Appl. 4, 367–387 (2011).
PubMed  PubMed Central  Google Scholar 

6.
Lapiedra, O., Chejanovski, Z. & Kolbe, J. J. Urbanization and biological invasion shape animal personalities. Glob. Change Biol. 23, 592–603 (2017).
ADS  Google Scholar 

7.
Sih, A., Stamps, J., Yang, L. H., McElreath, R. & Ramenofsky, M. Behavior as a key component of integrative biology in a human-altered world. Integr. Comp. Biol. 50, 934–944 (2010).
PubMed  Google Scholar 

8.
Alberti, M. Eco-evolutionary dynamics in an urbanizing planet. Trends Ecol. Evol. 30, 114–126 (2015).
PubMed  Google Scholar 

9.
Sol, D., Lapiedra, O. & González-Lagos, C. Behavioural adjustments for a life in the city. Anim. Behav. 85, 1101–1112 (2013).
Google Scholar 

10.
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
PubMed  Google Scholar 

11.
Seferta, A., Guay, P.-J., Marzinotto, E. & Lefebvre, L. Learning differences between feral pigeons and zenaida doves: the role of neophobia and human proximity. Ethology 107, 281–293 (2001).
Google Scholar 

12.
Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172, S63–S71 (2008).
PubMed  Google Scholar 

13.
Sol, D., Duncan, R. P., Blackburn, T. M., Cassey, P. & Lefebvre, L. Big brains, enhanced cognition, and response of birds to novel environments. Proc. Natl. Acad. Sci. 102, 5460–5465 (2005).
ADS  CAS  PubMed  Google Scholar 

14.
Webster, S. J. & Lefebvre, L. Problem solving and neophobia in a columbiform–passeriform assemblage in Barbados. Anim. Behav. 62, 23–32 (2001).
Google Scholar 

15.
Lowry, H., Lill, A. & Wong, B. B. M. Tolerance of auditory disturbance by an avian urban adapter, the noisy miner: tolerance of auditory disturbance by an avian urban adapter. Ethology 117, 490–497 (2011).
Google Scholar 

16.
Rodríguez-Prieto, I., Martín, J. & Fernández-Juricic, E. Individual variation in behavioural plasticity: direct and indirect effects of boldness, exploration and sociability on habituation to predators in lizards. Proc. R. Soc. B 278, 266–273 (2011).
PubMed  Google Scholar 

17.
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).
PubMed  Google Scholar 

18.
Gosling, S. D. From mice to men: what can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).
CAS  PubMed  Google Scholar 

19.
Koolhaas, J. M. et al. Coping styles in animals: current status in behavior and stress-physiology. Neurosci. Biobehav. Rev. 23, 925–935 (1999).
CAS  PubMed  Google Scholar 

20.
Sih, A., Cote, J., Evans, M., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes: ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289 (2012).
PubMed  Google Scholar 

21.
Wolf, M. & Weissing, F. J. Animal personalities: consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).
PubMed  Google Scholar 

22.
Hardman, S. I. & Dalesman, S. Repeatability and degree of territorial aggression differs among urban and rural great tits (Parus major). Sci. Rep. 8, 5042 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

23.
Wilson, D. S., Coleman, K., Clark, A. B. & Biederman, L. Shy-bold continuum in pumpkinseed sunfish (Lepomis gibbosus): an ecological study of a psychological trait. J. Comp. Psychol. 107, 250–260 (1993).
Google Scholar 

24.
Wilson, A. D. M. & Godin, J.-G.J. Boldness and intermittent locomotion in the bluegill sunfish Lepomis macrochirus. Behav. Ecol. 21, 57–62 (2010).
Google Scholar 

25.
Ward, A. J. W., Hart, P. J. B. & Webster, M. M. Boldness is influenced by social context in threespine sticklebacks (Gasterosteus aculeatus). Behaviour 144, 351–371 (2007).
Google Scholar 

26.
Dammhahn, M. & Almeling, L. Is risk taking during foraging a personality trait? A field test for cross-context consistency in boldness. Anim. Behav. 84, 1131–1139 (2012).
Google Scholar 

27.
Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: an integrative overview. Q. Rev. Biol. 79, 241–277 (2004).
PubMed  Google Scholar 

28.
Smith, B. R. & Blumstein, D. T. Fitness consequences of personality: a meta-analysis. Behav. Ecol. 19, 448–455 (2008).
Google Scholar 

29.
Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. Lond. B 271, 847–852 (2004).
Google Scholar 

30.
Sinn, D. L., Apiolaza, L. A. & Moltschaniwskyj, N. A. Heritability and fitness-related consequences of squid personality traits. J. Evol. Biol. 19, 1437–1447 (2006).
CAS  PubMed  Google Scholar 

31.
Ariyomo, T. O., Carter, M. & Watt, P. J. Heritability of boldness and aggressiveness in the zebrafish. Behav. Genet. 43, 161–167 (2013).
PubMed  Google Scholar 

32.
van Oers, K., de Jong, G., Drent, P. J. & van Noordwijk, A. J. A genetic analysis of avian personality traits: correlated response to artificial selection. Behav. Genet. 34, 611–619 (2004).
PubMed  Google Scholar 

33.
Réale, D. & Festa-Bianchet, M. Predator-induced natural selection on temperament in bighorn ewes. Anim. Behav. 65, 463–470 (2003).
Google Scholar 

34.
Grand, T. C. Risk-taking behaviour and the timing of life history events: consequences of body size and season. Oikos 85, 467 (1999).
Google Scholar 

35.
Fraser, D. F., Gilliam, J. F., Daley, M. J., Le, A. N. & Skalski, G. T. Explaining leptokurtic movement distributions: intrapopulation variation in boldness and exploration. Am. Nat. 158, 124–135 (2001).
CAS  PubMed  Google Scholar 

36.
Mazza, V., Jacob, J., Dammhahn, M., Zaccaroni, M. & Eccard, J. A. Individual variation in cognitive style reflects foraging and anti-predator strategies in a small mammal. Sci. Rep. 9, 10157 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

37.
Patrick, S. C. & Weimerskirch, H. Personality, foraging and fitness consequences in a long lived seabird. PLoS ONE 9, e87269 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

38.
Brown, J. S. & Kotler, B. P. Hazardous duty pay and the foraging cost of predation: foraging cost of predation. Ecol. Lett. 7, 999–1014 (2004).
Google Scholar 

39.
Godin, J. G. & Dugatkin, L. A. Female mating preference for bold males in the guppy, Poecilia reticulata. Proc. Natl. Acad. Sci. 93, 10262–10267 (1996).
ADS  CAS  PubMed  Google Scholar 

40.
Ariyomo, T. O. & Watt, P. J. Disassortative mating for boldness decreases reproductive success in the guppy. Behav. Ecol. 24, 1320–1326 (2013).
Google Scholar 

41.
Collins, S. M., Hatch, S. A., Elliott, K. H. & Jacobs, S. R. Boldness, mate choice and reproductive success in Rissa tridactyla. Anim. Behav. 154, 67–74 (2019).
Google Scholar 

42.
Mettke-Hofmann, C., Winkler, H. & Leisler, B. The Significance of ecological factors for exploration and neophobia in parrots. Ethology 108, 249–272 (2002).
Google Scholar 

43.
Burstal, J., Clulow, S., Colyvas, K., Kark, S. & Griffin, A. S. Radiotracking invasive spread: are common mynas more active and exploratory on the invasion front?. Biol Invasions https://doi.org/10.1007/s10530-020-02269-7 (2020).
Article  Google Scholar 

44.
Carter, A. J., Feeney, W. E., Marshall, H. H., Cowlishaw, G. & Heinsohn, R. Animal personality: what are behavioural ecologists measuring?. Biol. Rev. 88, 465–475 (2013).
PubMed  Google Scholar 

45.
Perals, D., Griffin, A. S., Bartomeus, I. & Sol, D. Revisiting the open-field test: what does it really tell us about animal personality?. Anim. Behav. 123, 69–79 (2017).
Google Scholar 

46.
Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis ). Proc. R. Soc. B 277, 1571–1579 (2010).
PubMed  Google Scholar 

47.
Dingemanse, N. J., Both, C., Drent, P. J., van Oers, K. & van Noordwijk, A. J. Repeatability and heritability of exploratory behaviour in great tits from the wild. Anim. Behav. 64, 929–938 (2002).
Google Scholar 

48.
Dingemanse, N. J. et al. Individual experience and evolutionary history of predation affect expression of heritable variation in fish personality and morphology. Proc. R. Soc. B. 276, 1285–1293 (2009).
PubMed  Google Scholar 

49.
Careau, V. et al. Genetic correlation between resting metabolic rate and exploratory behaviour in deer mice (Peromyscus maniculatus): pace-of-life in a muroid rodent. J. Evol. Biol. 24, 2153–2163 (2011).
CAS  PubMed  Google Scholar 

50.
Korsten, P., van Overveld, T., Adriaensen, F. & Matthysen, E. Genetic integration of local dispersal and exploratory behaviour in a wild bird. Nat. Commun. 4, 2362 (2013).
ADS  PubMed  Google Scholar 

51.
Drent, P. J., van Oers, K. & van Noordwijk, A. J. Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. Lond. B 270, 45–51 (2003).
Google Scholar 

52.
Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behavior 142, 1159–1184 (2005).
Google Scholar 

53.
Both, C., Dingemanse, N. J., Drent, P. J. & Tinbergen, J. M. Pairs of extreme avian personalities have highest reproductive success. J. Anim. Ecol. 74, 667–674 (2005).
Google Scholar 

54.
Mutzel, A., Dingemanse, N. J., Araya-Ajoy, Y. G. & Kempenaers, B. Parental provisioning behaviour plays a key role in linking personality with reproductive success. Proc. R. Soc. B 280, 20131019 (2013).
CAS  PubMed  Google Scholar 

55.
Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. & Drent, P. J. Natal dispersal and personalities in great tits (Parus major). Proc. R. Soc. Lond. B 270, 741–747 (2003).
Google Scholar 

56.
Haughland, D. L. & Larsen, K. W. Exploration correlates with settlement: red squirrel dispersal in contrasting habitats. J. Anim. Ecol. 73, 1024–1034 (2004).
Google Scholar 

57.
Alford, R. A., Brown, G. P., Schwarzkopf, L., Phillips, B. L. & Shine, R. Comparisons through time and space suggest rapid evolution of dispersal behaviour in an invasive species. Wildl. Res. 36, 23 (2009).
Google Scholar 

58.
Hoset, K. S. et al. Natal dispersal correlates with behavioral traits that are not consistent across early life stages. Behav. Ecol. 22, 176–183 (2011).
Google Scholar 

59.
Debeffe, L. et al. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Anim. Behav. 86, 143–151 (2013).
Google Scholar 

60.
Schirmer, A., Herde, A., Eccard, J. A. & Dammhahn, M. Individuals in space: personality-dependent space use, movement and microhabitat use facilitate individual spatial niche specialization. Oecologia 189, 647–660 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

61.
Schirmer, A., Hoffmann, J., Eccard, J. A. & Dammhahn, M. My niche: individual spatial niche specialization affects within- and between-species interactions. Proc. R. Soc. B 287, 20192211 (2020).
PubMed  Google Scholar 

62.
Duckworth, R. A. & Badyaev, A. V. Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proc. Natl. Acad. Sci. 104, 15017–15022 (2007).
ADS  CAS  PubMed  Google Scholar 

63.
Carrete, M. & Tella, J. L. Behavioral correlations associated with fear of humans differ between rural and urban burrowing owls. Front. Ecol. Evol. 5, 54 (2017).
Google Scholar 

64.
Evans, J., Boudreau, K. & Hyman, J. Behavioural syndromes in urban and rural populations of song sparrows. Ethology 116, 588–595 (2010).
Google Scholar 

65.
Miranda, A. C., Schielzeth, H., Sonntag, T. & Partecke, J. Urbanization and its effects on personality traits: a result of microevolution or phenotypic plasticity?. Glob. Change Biol. 19, 2634–2644 (2013).
ADS  Google Scholar 

66.
Reil, D. et al. Puumala hantavirus infections in bank vole populations: host and virus dynamics in Central Europe. BMC Ecol. 17, 9 (2017).
PubMed  PubMed Central  Google Scholar 

67.
Andrzejewski, R., Babińska-Werka, J., Gliwicz, J. & Goszczyński, J. Synurbization processes in population of Apodemus agrarius. I. Characteristics of populations in an urbanization gradient. Acta Theriol. 23, 341–358 (1978).
Google Scholar 

68.
Babińska-Werka, J. Food of the striped field mouse in different types of urban green areas. Acta Theriol. 26, 285–299 (1981).
Google Scholar 

69.
Liro, A. Variation in weights of body and internal organs of the field mouse in a gradient of urban habitats. Acta Theriol. 30, 359–377 (1985).
Google Scholar 

70.
Sikorski, M. D. Craniometric variation of Apodemus agrarius (Pallas, 1771) in urban green areas. Acta Theriol. 27, 71–81 (1982).
Google Scholar 

71.
Babińska-Werka, J., Gliwicz, J. & Goszczyński, J. Demographic processes in an urban population of the striped field mouse. Acta Theriol. 26, 275–283 (1981).
Google Scholar 

72.
Gortat, T., Rutkowski, R., Gryczynska-Siemiatkowska, A., Kozakiewicz, A. & Kozakiewicz, M. Genetic structure in urban and rural populations of Apodemus agrarius in Poland. Mamm. Biol. 78, 171–177 (2013).
Google Scholar 

73.
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Google Scholar 

74.
Moule, H., Michelangeli, M., Thompson, M. B. & Chapple, D. G. The influence of urbanization on the behaviour of an Australian lizard and the presence of an activity-exploratory behavioural syndrome: impact of urbanization on the delicate skink. J. Zool. 298, 103–111 (2016).
Google Scholar 

75.
Boon, A. K., Réale, D. & Boutin, S. Personality, habitat use, and their consequences for survival in North American red squirrels Tamiasciurus hudsonicus. Oikos 117, 1321–1328 (2008).
Google Scholar 

76.
Wolf, M., van Doorn, G. S., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584 (2007).
ADS  CAS  PubMed  Google Scholar 

77.
Fischer, J. D., Cleeton, S. H., Lyons, T. P. & Miller, J. R. Urbanization and the predation paradox: the role of trophic dynamics in structuring vertebrate communities. Bioscience 62, 809–818 (2012).
Google Scholar 

78.
Shochat, E. Credit or debit? Resource input changes population dynamics of city-slicker birds. Oikos 106, 622–626 (2004).
Google Scholar 

79.
Bateman, P. W. & Fleming, P. A. Big city life: carnivores in urban environments: urban carnivores. J. Zool. 287, 1–23 (2012).
Google Scholar 

80.
Kettel, E. F., Gentle, L. K., Quinn, J. L. & Yarnell, R. W. The breeding performance of raptors in urban landscapes: a review and meta-analysis. J. Ornithol. 159, 1–18 (2018).
Google Scholar 

81.
Vines, A. & Lill, A. Boldness and urban dwelling in little ravens. Wildl. Res. 42, 590 (2015).
Google Scholar 

82.
Uchida K, Shimamoto T, Yanagawa H, Koizumi I (2020) Comparison of multiple behavioral traits between urban and rural squirrels. Urban Ecosyst. 1, 1–10 (2020).

83.
Atwell, J. W. et al. Boldness behavior and stress physiology in a novel urban environment suggest rapid correlated evolutionary adaptation. Behav. Ecol. 23, 960–969 (2012).
PubMed  PubMed Central  Google Scholar 

84.
Bókony, V., Kulcsár, A., Tóth, Z. & Liker, A. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS ONE 7, e36639 (2012).
ADS  PubMed  PubMed Central  Google Scholar 

85.
Greenberg, R. The Role of Neophobia and Neophilia in the Development of Innovative Behaviour of Birds. In Animal Innovation (eds Reader, S. M. & Laland, K. N.) 175–196 (Oxford University Press, Oxford, 2003). https://doi.org/10.1093/acprof:oso/9780198526223.003.0008.
Google Scholar 

86.
delBarco-Trillo, J. Shyer and larger bird species show more reduced fear of humans when living in urban environments. Biol. Lett. 14, 20170730 (2018).
PubMed  PubMed Central  Google Scholar 

87.
Greggor, A. L., Clayton, N. S., Fulford, A. J. C. & Thornton, A. Street smart: faster approach towards litter in urban areas by highly neophobic corvids and less fearful birds. Anim. Behav. 117, 123–133 (2016).
PubMed  PubMed Central  Google Scholar 

88.
Seress, G., Bókony, V., Heszberger, J. & Liker, A. Response to predation risk in urban and rural house sparrows: response to predation risk in house sparrows. Ethology 117, 896–907 (2011).
Google Scholar 

89.
Rymer, T., Pillay, N. & Schradin, C. Extinction or survival? Behavioral flexibility in response to environmental change in the african striped mouse rhabdomys. Sustainability 5, 163–186 (2013).
Google Scholar 

90.
Martin, J. G. A. & Réale, D. Temperament, risk assessment and habituation to novelty in eastern chipmunks Tamias striatus. Anim. Behav. 75, 309–318 (2008).
Google Scholar 

91.
Carere, C. & Locurto, C. Interaction between animal personality and animal cognition. Curr. Zool. 57, 491–498 (2011).
Google Scholar 

92.
Sih, A. & Del Giudice, M. Linking behavioural syndromes and cognition: a behavioural ecology perspective. Philos. Trans. R. Soc. B 367, 2762–2772 (2012).
Google Scholar 

93.
Mettke-Hofmann, C. & Gwinner, E. Differential assessment of environmental information in a migratory and a nonmigratory passerine. Anim. Behav. 68, 1079–1086 (2004).
Google Scholar 

94.
Mettke-Hofmann, C., Rowe, K. C., Hayden, T. J. & Canoine, V. Effects of experience and object complexity on exploration in garden warblers (Sylvia borin). J Zool. 268, 405–413 (2006).
Google Scholar 

95.
Boyer, N., Réale, D., Marmet, J., Pisanu, B. & Chapuis, J.-L. Personality, space use and tick load in an introduced population of Siberian chipmunks Tamias sibiricus. J. Anim. Ecol. 79, 538–547 (2010).
PubMed  Google Scholar 

96.
Barber, I. & Dingemanse, N. J. Parasitism and the evolutionary ecology of animal personality. Philos. Trans. R. Soc. B 365, 4077–4088 (2010).
Google Scholar 

97.
Jones, K. A. & Godin, J.-G.J. Are fast explorers slow reactors? Linking personality type and anti-predator behaviour. Proc. R. Soc. B 277, 625–632 (2010).
PubMed  Google Scholar 

98.
Sol, D., Griffin, A. S., Bartomeus, I. & Boyce, H. Exploring or avoiding novel food resources? The novelty conflict in an invasive bird. PLoS ONE 6, e19535 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

99.
Couchoux, C. & Cresswell, W. Personality constraints versus flexible antipredation behaviors: how important is boldness in risk management of redshanks (Tringa totanus) foraging in a natural system?. Behav. Ecol. 23, 290–301 (2012).
Google Scholar 

100.
Patergnani, M. et al. Environmental influence on urban rodent bait consumption. J. Pest Sci. 83, 347–359 (2010).
Google Scholar 

101.
Lehrer, E. W., Schooley, R. L. & Whittington, J. K. Survival and antipredator behavior of woodchucks (Marmota monax) along an urban-agricultural gradient. Can. J. Zool. 90, 12–21 (2012).
Google Scholar 

102.
Niemelä, P. T., Vainikka, A., Forsman, J. T., Loukola, O. J. & Kortet, R. How does variation in the environment and individual cognition explain the existence of consistent behavioral differences?. Ecol. Evol. 3, 457–464 (2013).
PubMed  Google Scholar 

103.
Garamszegi, L. Z. et al. Among-year variation in the repeatability, within- and between-individual, and phenotypic correlations of behaviors in a natural population. Behav. Ecol. Sociobiol. 69, 2005–2017 (2015).
PubMed  PubMed Central  Google Scholar 

104.
Stewart, I. D. & Oke, T. R. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93, 1879–1900 (2012).
ADS  Google Scholar 

105.
Semenov, M., Donatelli, M., Stratonovitch, P., Chatzidaki, E. & Baruth, B. ELPIS: a dataset of local-scale daily climate scenarios for Europe. Clim. Res. 44, 3–15 (2010).
Google Scholar 

106.
Hall, S. J. et al. Convergence of microclimate in residential landscapes across diverse cities in the United States. Landsc. Ecol. 31, 101–117 (2016).
Google Scholar 

107.
Janković, V. A historical review of urban climatology and the atmospheres of the industrialized world: review of urban climatology and the atmospheres of the industrialized world. WIREs Clim. Change 4, 539–553 (2013).
Google Scholar 

108.
Grimmond, S. Urbanization and global environmental change: local effects of urban warming. Geogr. J. 173, 83–88 (2007).
Google Scholar 

109.
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
ADS  Google Scholar 

110.
Charmantier, A., Demeyrier, V., Lambrechts, M., Perret, S. & Grégoire, A. Urbanization is associated with divergence in pace-of-life in great tits. Front. Ecol. Evol. 5, 53 (2017).
Google Scholar 

111.
Badyaev, A. V., Young, R. L., Oh, K. P. & Addison, C. Evolution on a local scale: developmental, functional, and genetic bases of divergence in bill form and associated changes in song structure between adjacent habitats. Evolution 62, 1951–1964 (2008).
PubMed  Google Scholar 

112.
Alberti, M., Marzluff, J. & Hunt, V. M. Urban driven phenotypic changes: empirical observations and theoretical implications for eco-evolutionary feedback. Philos. Trans. R. Soc. B 372, 20160029 (2017).
Google Scholar 

113.
Buchholz, S., Hannig, K., Möller, M. & Schirmel, J. Reducing management intensity and isolation as promising tools to enhance ground-dwelling arthropod diversity in urban grasslands. Urban Ecosyst. 21, 1139–1149 (2018).
Google Scholar 

114.
Seress, G., Lipovits, Á, Bókony, V. & Czúni, L. Quantifying the urban gradient: a practical method for broad measurements. Landsc. Urban Plan. 131, 42–50 (2014).
Google Scholar 

115.
Senatsverwaltung für Umwelt, Verkehr und Klimaschutz. Berlin Environmental Atlas—05.08 Biotopes (2016). https://fbinter.stadt-berlin.de/fb/index.jsp?loginkey=showMap&mapId=k_fb_berlinbtk@senstadt. Accessed 15 Dec 2019.

116.
GIS, E. A. v10. Environmental Systems Research Institute. Inc., Redlands, CA, USA (2011).

117.
Herde, A. & Eccard, J. A. Consistency in boldness, activity and exploration at different stages of life. BMC Ecol. 13, 49 (2013).
PubMed  PubMed Central  Google Scholar 

118.
Young, R. & Johnson, D. N. A fully automated light/dark apparatus useful for comparing anxiolytic agents. Pharmacol. Biochem. Behav. 40, 739–743 (1991).
CAS  PubMed  Google Scholar 

119.
Hall, C. S. Emotional behavior in the rat. I. Defecation and urination as measures of individual differences in emotionality. J. Comp. Psychol. 18, 385–403 (1934).
Google Scholar 

120.
Archer, J. Tests for emotionality in rats and mice: a review. Anim. Behav. 21, 205–235 (1973).
CAS  PubMed  Google Scholar 

121.
Walsh, R. N. & Cummins, R. A. The open-field test: a critical review. Psychol. Bull. 83, 482–504 (1976).
CAS  PubMed  Google Scholar 

122.
Cavigelli, S. A., Michael, K. C. & Ragan, C. M. Behavioral, physiological, and health biases in laboratory rodents: a basis for understanding mechanistic links between human personality and health. In Animal Personalities: Behavior, Physiology, and Evolution (eds Carere, C. & Maestripieri, D.) 441–498 (University of Chicago Press, Chicago, 2013).
Google Scholar 

123.
Gharnit, E., Bergeron, P., Garant, D. & Réale, D. Exploration profiles drive activity patterns and temporal niche specialization in a wild rodent. Behav. Ecol. 31, 772–783 (2020).
Google Scholar 

124.
Weiss, A. & Adams, M. J. Differential behavioral ecology. In Animal personalities: behavior, physiology and evolution (eds Carere, C. & Maestripieri, D.) 96–123 (University of Chicago Press, Chicago, 2013).
Google Scholar 

125.
Russell, P. A. Fear-evoking stimuli. In Fear in Animals and Man (ed. Sluckin, W.) 86–124 (Van Nostrand Reinhold Company, New York, 1979).
Google Scholar 

126.
Grossen, N. E. & Kelley, M. J. Species-specific behavior and acquisition of avoidance behavior in rats. J. Comp. Physiol. Psychol. 81, 307–310 (1972).
CAS  PubMed  Google Scholar 

127.
Mazza, V., Eccard, J. A., Zaccaroni, M., Jacob, J. & Dammhahn, M. The fast and the flexible: cognitive style drives individual variation in cognition in a small mammal. Anim. Behav. 137, 119–132 (2018).
Google Scholar 

128.
Geng, R. et al. Diet and prey consumption of breeding common Kestrel (Falco tinnunculus) in Northeast China. Prog. Nat. Sci. 19, 1501–1507 (2009).
Google Scholar 

129.
Jedrzejewska, B. & Jedrzejewski, W. Predation in Vertebrate Communities: The Bialowieza Primeval Forest as a Case Study, vol. 135 (Springer, Berlin, 2013).
Google Scholar 

130.
Sándor, A. D. & Ionescu, D. T. Diet of the eagle owl (Bubo bubo) in Braşov Romania. N.-West. J. Zool. 5, 170–178 (2009).
Google Scholar 

131.
Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A. & McGregor, I. S. The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144 (2005).
PubMed  Google Scholar 

132.
Adibi, M. Whisker-mediated touch system in rodents: from neuron to behavior. Front. Syst. Neurosci. 13, 40 (2019).
PubMed  PubMed Central  Google Scholar 

133.
Lavenex, P. & Schenk, F. Olfactory cues potentiate learning of distant visuospatial information. Neurobiol. Learn. Mem. 68, 140–153 (1997).
CAS  PubMed  Google Scholar 

134.
Tomlinson, W. T. & Johnston, T. D. Hamsters remember spatial information derived from olfactory cues. Anim. Learn. Behav. 19, 185–190 (1991).
Google Scholar 

135.
Casarrubea, M. et al. Temporal structure of the rat’s behavior in elevated plus maze test. Behav. Brain Res. 237, 290–299 (2013).
CAS  PubMed  Google Scholar 

136.
Takahashi, A., Kato, K., Makino, J., Shiroishi, T. & Koide, T. Multivariate analysis of temporal descriptions of open-field behavior in wild-derived mouse strains. Behav. Genet. 36, 763–774 (2006).
PubMed  Google Scholar 

137.
Krupa, D. J., Matell, M. S., Brisben, A. J., Oliveira, L. M. & Nicolelis, M. A. L. Behavioral properties of the trigeminal somatosensory system in rats performing whisker-dependent tactile discriminations. J. Neurosci. 21, 5752–5763 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

138.
von Heimendahl, M., Itskov, P. M., Arabzadeh, E. & Diamond, M. E. Neuronal activity in rat barrel cortex underlying texture discrimination. PLoS Biol. 5, e305 (2007).
Google Scholar 

139.
Morita, T., Kang, H., Wolfe, J., Jadhav, S. P. & Feldman, D. E. Psychometric curve and behavioral strategies for whisker-based texture discrimination in rats. PLoS ONE 6, e20437 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

140.
Lavenex, P. & Schenk, F. Integration of olfactory information in a spatial representation enabling accurate arm choice in the radial arm maze. Learn. Mem. 2, 299–319 (1996).
CAS  PubMed  Google Scholar 

141.
Rangassamy, M., Dalmas, M., Féron, C., Gouat, P. & Rödel, H. G. Similarity of personalities speeds up reproduction in pairs of a monogamous rodent. Anim. Behav. 103, 7–15 (2015).
Google Scholar 

142.
Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. 85, 935–956 (2010).
Google Scholar 

143.
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 

144.
Faraway, J. J. Extending the Linear Model with R (Chapman & Hall/CRC, Boca Raton, 2006).
Google Scholar 

145.
Zuur, A. F. Mixed Effects Models and Extensions in Ecology with R (Springer, Berlin, 2009).
Google Scholar 

146.
Bates, D. et al. Package ‘lme4’. Convergence 12, 2 (2015).
Google Scholar 

147.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: linear and nonlinear mixed effects models. R Package Vers. 3, 111 (2013).
Google Scholar 

148.
Tabachnick, B. G. & Fidell, L. S. Principal components and factor analysis. Using Multivar. Stat. 4, 582–633 (2001).
Google Scholar 

149.
Kaiser, H. F. Unity as the universal upper bound for reliability. Percept. Mot. Skills 72, 218–218 (1991).
Google Scholar 

150.
Hadfield, J. D., Wilson, A. J., Garant, D., Sheldon, B. C. & Kruuk, L. E. B. The misuse of BLUP in ecology and evolution. Am. Nat. 175, 116–125 (2010).
PubMed  Google Scholar 

151.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar  More

Imagination is critical to sustainable and just futures for life on Earth8,13. Writing after the West African Ebola outbreak, Professor Michael Osterholm and colleagues called for more “creative imagination” to consider future pandemic scenarios14. This feels particularly salient five years on. Purely technocratic approaches fail to engage with the emotions that motivate action towards alternative futures: fear, hope, grief and agency8,15. By building new ways of thinking about longstanding problems, inclusive and creative processes can generate positive stories about the future in ways that are empowering8,10. Imagining the future can drive societies towards change by shaping common practices, aspirations and institutions16.
Methods for imagining, such as scenarios analysis, strategic foresight and speculative fiction are commonplace in research, investment and planning8,13,17. They can help the biodiversity community address the bleak futures that are projected for biodiversity. Research can play an important role in embracing imagination by fostering novel participatory methods that enable society to explore what is possible, plausible and desirable13. All models and scenarios are wrong, some are helpful: they contain assumptions about what matters, what is known and what is unknown. Embracing and communicating these assumptions and uncertainties builds trust in science, opening up spaces for deliberation about values, trade-offs and desirable futures18.
Imagination can build the anticipatory capacity to get ahead of the curve, rather than react to crisis17. Decision makers must learn to provide anticipatory leadership that fosters shared responsibility for actions that may have greater costs now, to avert harm in the future. Enabling transformations also requires those who benefit from the status quo to acknowledge the need for change. Policy frameworks need to consider the distribution of costs and benefits over longer timescales when setting current priorities. Ultimately, society needs to accept that the future is unknowable and uncertain, but that action is needed now.
These anticipatory capacities start with asking: what are the short- and long-term drivers of change? What values should be maintained into the future? What can be done differently over the next five years? Over the next 30 years? What do we need to know and what will we never know? How can options be created and traps avoided? What are the ethical implications of action and inaction? Considering these types of questions can provide a foundation for decision making despite uncertainty.
Our stories show that choices have consequences. Some close down options. Some open up multiple pathways. Either way, choices create winners and losers. The critical challenges of the Anthropocene require humility19 and the ability to respond20. Imagination can help the biodiversity community grapple with these challenges by embracing diverse ways of thinking, listening, being and knowing. And such diversity can be the foundation of more just and sustainable futures for life on Earth. More

The Cooper bundles were found just beneath the sand surface ~15 m up from the waterline. A sand slope angle of 10∘ was measured during a site investigation which would place the burial site ~3 m vertically above the water line. This location would only be immersed during times of high water and wave action. Dredging operations took place on the river and the sand was dumped slightly upstream of the burial location and could have contributed to additional sand on top of the bills. Sand is no longer deposited on the beach and it has undergone severe erosion. Rubber bands found intact but degraded on the bundles suggests they were initially buried without any significant exposure to the elements which is known to rapidly degrade them25.
In order to determine if a seasonal diatom timeline can be used to constrain the burial of the Cooper money, the first question to be answered is: can diatoms penetrate a bundle of money buried in sand? The diatom saturated water experiment showed that penetration is possible but only for the smaller range of diatoms and only a limited distance in from the edge on the order of millimeters. No “tide lines” of diatoms or small sand fragments were found on the Cooper bill. Since we know from the experiment that diatom accumulations were likely to happen on the edges, the lack of aggregations suggests they were destroyed with the severe degradation around the edges of the bundle. The inner degraded edge where the SEM samples were taken from showed no accumulations, suggesting the bills had congealed into a solid lump (consistent with the condition that the bills were found in), preventing any further diatom infiltration.
A second line of evidence that would signal diatom infiltration while buried would be an abundance of diatoms in the bills that were also found in the surrounding sand. The extraction of the diatoms from the Tena Bar sand showed a predominance of small forms on the order of 3–5 µm. These small diatoms are consistent with species that can survive in sand due to their ability to situate in the interstitial crevices of a single sand grain26,27. Larger diatoms, of which Asterionella and Fragilaria are among the largest, have low survivability in the proportionally boulder size sand grains26. The lack of predominantly smaller diatoms on the Cooper bill suggests little to no diatom infiltration to the inner portions of the stack occurred while buried. While similar small diatoms were found on the bills, they were not a dominant category as would be expected if they were the primary source of infiltration.
If the Cooper bill used in this examination was from the top of the stack, then one could expect to find a variety of diatoms from all sources. Figure 2C indicates conclusively that the examined bill is from the middle of the stack by finding an intact Fragilaria sandwiched between two bills. Due to the congealed nature of the bills, it was not uncommon to find intact fragments of other bills adhered to the larger bill. Fragilaria at ~80 µm28 is considered a larger diatom in the Columbia River system29. It is planktonic30 and therefore has no ability to move through sand. Its size and location interior to the stack (Fig. 1) and notably with no smaller diatoms surrounding it, suggests that it came to rest there while the bill was completely exposed to river water.
If the previous experiments and investigations rule out diatom infiltration while buried, then the findings suggest that diatoms found their way onto the bills during water immersion. As shown in Fig. 4, a stack of bills once saturated, will fan out in water exposing all surfaces to micro-particles in the water environment. The exposure of the fanned out stack to the river, suggests the simplest way for large, intact but fragile diatoms to be found alone interior to the bill stack. This would have occurred prior to burial and be in the water long enough for fan out to occur.
Figure 4

(A) Stack of bills bound with a rubber band immediately after placing in still water. (B) After several minutes, the stack becomes saturated and fans out exposing individual bills to the water. Shortly thereafter the entire stack will sink to the bottom.

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The Columbia River has seasonal blooms of diatoms with different species found in winter vs summer19. If the bills were submerged for an extended period covering multiple seasons, then diatom species found on the bill should also represent multiple seasons. Table 1 shows the genera found on the Cooper bill and the dollar bill soaked in the Columbia in November. The first notable observation is that there is little overlap in genera between the two seasons.
Asterionella followed by Fragilaria are key indicators in this study. Asterionella are relatively large up to 100 µm31, planktonic diatoms that undergo radical changes in population in the Columbia River (Fig. 5) of up to 10 × during the course of the year20. They assemble into star shaped colonies that are susceptible to damage. Asterionella were found broken but associated on the Cooper bill as shown in Fig. 2A. Although in pieces, the relatively complete association of parts suggests that the diatoms landed intact on the bill and were subsequently crushed and broken after the fact. Similar associations were found elsewhere on the Cooper samples.
Figure 5

Monthly abundance of Asterionella showing population bloom in May and June. Extremely low numbers are apparent for winter months. Data compiled from three sources19,20,21 graph shows relative numbers.

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Several examples Asterionella were found on the Cooper bills and this diatom is nearly absent in November when the jump occurred20,21. There is however a very large bloom of Asterionella in early summer during the months of May and June19,21. The other diatoms identified on the Cooper bill such as Stephanodiscus are also more prevalent in the summer season21. The diatoms found on the November bill are not consistent with species found on the Cooper bill. This suggests that the Cooper bill was immersed during the summer Asterionella bloom and the length of submersion did not extend into subsequent seasons.
Trace elements are incorporated into the diatom frustule during growth and elemental availability varies in rivers during the year17. Krivtsov et al. 2000 studied the elemental variation in A. formosa and found that it varied by the season5. There were not enough recovered Asterionella from the November time frame to do a direct comparison but elemental signatures from a variety of specimens were compared between the November and Cooper bills. Figure 6 shows the diatom’s elemental spectra of calcium and sodium overlaid. The spectra were normalized to silicon and show relative abundances. The detected levels were small and near the limit of EDS sensitivity so this data is provided as qualitative. Elemental differences between the two groups showed slightly enriched calcium and a lack of sodium in the November diatoms while showing the complete opposite for the Cooper diatoms. A single fragment potentially from Asterionella or Fragilaria was found in the November sand from Tena Bar (Fig. 4B). This spectrum showed elevated levels of calcium and sodium again suggesting a difference from the A. formosa found on the Cooper bill which only showed enriched sodium. The single diatom spectrum from the March bill showed no increase in either sodium or calcium suggesting the March time frame has a different elemental abundance in the water from either the winter or Cooper sample suspected to have summer diatoms. The reproductive lifetime of a diatom is on the order of days32 so a difference in elemental abundance suggests that these three assemblages were from different seasonal periods.
Figure 6

(A) EDS spectra overlay showing the sodium line. Red lines are spectra from the Cooper bill diatoms showing elevated sodium levels, green lines are from November samples. Blue line is the single Asterionella spectra from the November sand sample showing no enrichment in either sodium or calcium. (B) Calcium line showing elevated presence of calcium for November diatoms while Cooper samples show lower levels. Each group of diatoms showed opposite enrichment of sodium and calcium. Data is relative and qualitative.

Full size image More

Affiliations

ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia
T. H. Morrison, C. Huchery & T. P. Hughes

College of Life and Environmental Sciences, University of Exeter, Exeter, UK
W. N. Adger & K. Brown

Environment and Disaster Management Program, World Wildlife Fund, Washington, DC, USA
M. Hettiarachchi

School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
M. C. Lemos

Authors
T. H. Morrison

W. N. Adger

K. Brown

M. Hettiarachchi

C. Huchery

M. C. Lemos

T. P. Hughes

Corresponding author
Correspondence to T. H. Morrison. More

1.
Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett.7, 1225–1241 (2004).
Google Scholar 
2.
Cushman, S. A. et al. Editorial: the least cost path from landscape genetics to landscape genomics: challenges and opportunities to explore NGS data in a spatially explicit context. Front. Genet.9, 215 (2018).
PubMed  PubMed Central  Google Scholar 

3.
Pereira, A. Plant abiotic stress challenges from the changing environment. Front. Plant Sci.7, 1123 (2016).
PubMed  PubMed Central  Google Scholar 

4.
Rellstab, C. et al. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol.24, 4348–4370 (2015).
PubMed  Google Scholar 

5.
Zhu, J. K. Abiotic stress signaling and responses in plants. Cell167, 313–324 (2016).
PubMed  PubMed Central  CAS  Google Scholar 

6.
Allendorf, F. W., Hohenlohe, P. A. & Luikart, G. Genomics and the future of conservation genetics. Nat. Rev. Genet.11, 697–709 (2010).
PubMed  CAS  Google Scholar 

7.
Radwan, J. & Babik, W. The genomics of adaptation. Proc. Biol. Sci.279, 5024–5028 (2012).
PubMed  PubMed Central  Google Scholar 

8.
Li, Y. et al. Ten years of landscape genomics: challenges and opportunities. Front. Plant Sci.8, 2136 (2017).
PubMed  PubMed Central  Google Scholar 

9.
Cushman, S. A. Grand challenges in evolutionary and population genetics: the importance of integrating epigenetics, genomics, modeling, and experimentation. Front. Genet.5, 197 (2014).
PubMed  PubMed Central  Google Scholar 

10.
Guggisberg, A. et al. The genomic basis of adaptation to calcareous and siliceous soils in Arabidopsis lyrata. Mol. Ecol.27, 5088–5103 (2018).
PubMed  Google Scholar 

11.
Brennan, R. S. et al. Integrative population and physiological genomics reveals mechanisms of adaptation in killifish. Mol. Biol. Evol.35, 2639–2653 (2018).
PubMed  CAS  Google Scholar 

12.
Chen, C. et al. Population genomics provide insights into the evolution and adaptation of the eastern honey bee (Apis cerana). Mol. Biol. Evol.35, 2260–2271 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

13.
Dittberner, H. et al. Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Mol. Ecol.27, 4052–4065 (2018).
PubMed  CAS  Google Scholar 

14.
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol.35, 792–806 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

15.
Ahrens, C. W., Byrne, M. & Rymer, P. D. Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. Mol. Ecol.28, 2502–2516 (2019).
PubMed  CAS  Google Scholar 

16.
Wright, S. Evolution in Mendelian populations. Genetics16, 97–159 (1931).
PubMed  PubMed Central  CAS  Google Scholar 

17.
Miao, C. Y. et al. Landscape genomics reveal that ecological character determines adaptation: a case study in smoke tree (Cotinus coggygria Scop.). BMC Evol. Biol.17, 202 (2017).
PubMed  PubMed Central  Google Scholar 

18.
Li, J. X. et al. Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics. BMC Plant Biol.18, 306 (2018).
PubMed  PubMed Central  Google Scholar 

19.
Arciero, E. et al. Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations. Mol. Biol. Evol.35, 1916–1933 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

20.
Friis, G. et al. Genome-wide signals of drift and local adaptation during rapid lineage divergence in a songbird. Mol. Ecol.27, 746–760 (2018).
Google Scholar 

21.
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett.18, 1–16 (2015).
PubMed  Google Scholar 

22.
Guerrero, J. et al. Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics. N. Phytol.219, 378–390 (2018).
CAS  Google Scholar 

23.
Keller, S. R. et al. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol. Biol. Evol.29, 3143–3152 (2012).
PubMed  CAS  Google Scholar 

24.
Manel, S. et al. Genome assemblies, genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol. Ecol.25, 170–184 (2016).
PubMed  CAS  Google Scholar 

25.
Fu, Z. Z. et al. Molecular data and ecological niche modeling reveal population dynamics of widespread shrub Forsythia suspensa (Oleaceae) in China’s warm-temperate zone in response to climate change during the Pleistocene. BMC Evol. Biol.14, 114 (2014).
PubMed  PubMed Central  Google Scholar 

26.
Hamrick, J. L. & Godt, M. J. Plant Population Genetics, Breeding, and Genetic Resources (Sinauer, Sunderland, 1990).

27.
Hewitt, G. M. Genetic consequences of climatic oscillations in the quaternary. Philos. Trans. R. Soc. Lond. B Biol. Sci.359, 183–195 (2004).
PubMed  PubMed Central  CAS  Google Scholar 

28.
Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol.28, 614–621 (2013).
PubMed  Google Scholar 

29.
Balkenhol, N. et al. Current status, future opportunities, and remaining challenges in landscape genetics. In (eds Balkenhol, N. C., et al.). Landscape Genetics: Concepts, Methods, Applications (Wiley, Hoboken, 2015).

30.
Yang, J. et al. Landscape population genomics of forsythia (Forsythia suspensa) reveal that ecological habitats determine the adaptive evolution of species. Front. Plant Sci.8, 481 (2017).
PubMed  PubMed Central  Google Scholar 

31.
Sun, X. et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE8, e58700 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

32.
Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature541, 212–216 (2017).
PubMed  CAS  Google Scholar 

33.
Unver, T. et al. Genome of wild olive and the evolution of oil biosynthesis. Proc. Natl Acad. Sci. USA114, E9413–E9422 (2017).
PubMed  CAS  Google Scholar 

34.
Yang, X. et al. The chromosome-level quality genome provides insights into the evolution of the biosynthesis genes for aroma compounds of Osmanthus fragrans. Hortic. Res.5, 72 (2018).
PubMed  PubMed Central  Google Scholar 

35.
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res.19, 1655–1664 (2009).
PubMed  PubMed Central  CAS  Google Scholar 

36.
Wallander, E. & Albert, V. A. Phylogeny and classification of Oleaceae based on rps16 and trnL-F sequence data. Am. J. Bot.87, 1827–1841 (2000).
PubMed  CAS  Google Scholar 

37.
Wang, T. Q. et al. TCM treatment of anemopyretic cold rule analysis. J. Tianjin Univ. Tradit. Chin. Med.37, 113–117 (2018).
Google Scholar 

38.
Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet.48, 134–143 (2016).
PubMed  CAS  Google Scholar 

39.
Najafi, S., Sorkheh, K. & Nasernakhaei, F. Characterization of the APETALA2/Ethylene-responsive factor (AP2/ERF) transcription factor family in sunflower. Sci. Rep.8, 11576 (2018).
PubMed  PubMed Central  Google Scholar 

40.
Xie, Z. et al. AP2/ERF transcription factor regulatory networks in hormone and abiotic stress responses in Arabidopsis. Front. Plant Sci.10, 228 (2019).
PubMed  PubMed Central  Google Scholar 

41.
Chakraborty, U. & Pradhan, B. Drought stress-induced oxidative stress and antioxidative responses in four wheat (Triticum aestivum L.) varieties. Arch. Agron. Soil Sci.58, 617–630 (2012).
CAS  Google Scholar 

42.
Noureddine, Y. Changes of peroxidase activities under cold stress in annuals populations of medicago. Mol. Plant Breed.6, 5 (2015).
Google Scholar 

43.
Gong, L. et al. Transcriptome profiling of the potato (Solanum tuberosum L.) plant under drought stress and water-stimulus conditions. PLoS ONE10, e0128041 (2015).
PubMed  PubMed Central  Google Scholar 

44.
Wang, M. et al. Comparative transcriptome analysis to elucidate the enhanced thermotolerance of tea plants (Camellia sinensis) treated with exogenous calcium. Planta249, 775–786 (2019).
PubMed  CAS  Google Scholar 

45.
Schöttler, M. A. et al. Photosynthetic complex stoichiometry dynamics in higher plants: biogenesis, function, and turnover of ATP synthase and the cytochrome b6f complex. J. Exp. Bot.66, 2373–2400 (2015).
PubMed  Google Scholar 

46.
Collakova, E. & DellaPenna, D. The role of homogentisate phytyltransferase and other tocopherol pathway enzymes in the regulation of tocopherol synthesis during abiotic stress. Plant Physiol.133, 930–940 (2003).
PubMed  PubMed Central  CAS  Google Scholar 

47.
Gavalas, N. A. & Clark, H. E. On the role of manganese in photosynthesis: kinetics of photoinhibition in manganese-deficent and 3-(4-chlorophenyl)-1, 1-dimethylurea-inhibited Euglena gracilis. Plant Physiol.47, 139–143 (1971).
PubMed  PubMed Central  CAS  Google Scholar 

48.
Chen, C. Y. et al. Structural basis of jasmonate-amido synthetase FIN219 in complex with glutathione S-transferase FIP1 during the JA signal regulation. Proc. Natl Acad. Sci. USA114, E1815–E1824 (2017).
PubMed  CAS  Google Scholar 

49.
Nisar, N. et al. Carotenoid metabolism in plant. Mol. Plant8, 68–82 (2015).
PubMed  CAS  Google Scholar 

50.
Landguth, E. L. et al. Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework. Mol. Ecol. Resour.20, 605–615 (2020).
PubMed  Google Scholar 

51.
Ram, S. Role of alcohol dehydrogenase, malate dehydrogenase and malic enzyme in flooding tolerance in Brachiaria Species. J. Plant Biochem. Biot.9, 45–47 (2000).
CAS  Google Scholar 

52.
Butsayawarapat, P. et al. Comparative transcriptome analysis of waterlogging-sensitive and tolerant zombi pea (Vigna vexillata) reveals energy conservation and root plasticity controlling waterlogging tolerance. Plants8, 264 (2019).
PubMed Central  CAS  Google Scholar 

53.
Ohsawa, T. & Ide, Y. Global patterns of genetic variation in plant species along vertical and horizontal gradients on mountains. Glob. Ecol. Biogeogr.17, 152–163 (2008).
Google Scholar 

54.
Yang, J. et al. Landscape genomics analysis of Achyranthes bidentata reveal adaptive genetic variations are driven by environmental variations relating to ecological habit. Popul. Ecol.59, 355–362 (2017).
Google Scholar 

55.
Fu, Z. Z. et al. Population genetics of the widespread shrub Forsythia suspensa (Oleaceae) in warm-temperate China using microsatellite loci: implication for conservation. Plant Syst. Evol.302, 1–9 (2016).
Google Scholar 

56.
Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics27, 764–770 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

57.
Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol.36, 338–345 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

58.
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive κ-mer weighting and repeat separation. Genome Res.27, 722–736 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

59.
Chakraborty, M. et al. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res.44, e147 (2016).
PubMed  PubMed Central  Google Scholar 

60.
Vaser, R. et al. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res.27, 737–746 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

61.
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE9, e112963 (2014).
PubMed  PubMed Central  Google Scholar 

62.
Simão, F. A. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics31, 3210 (2015).
PubMed  Google Scholar 

63.
Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics23, 1061–1067 (2007).
PubMed  CAS  Google Scholar 

64.
Zhang, J. et al. High-density genetic map construction and identification of a locus controlling weeping trait in an ornamental woody plant (Prunus mume Sieb. et Zucc). DNA Res.22, 1–9 (2015).
Google Scholar 

65.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics25, 1754–1760 (2009).
PubMed  PubMed Central  CAS  Google Scholar 

66.
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303 (2010).
PubMed  PubMed Central  CAS  Google Scholar 

67.
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009).
PubMed  PubMed Central  Google Scholar 

68.
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet.38, 904–909 (2006).
PubMed  CAS  Google Scholar 

69.
Kumar, S. et al. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evol.35, 1547–1549 (2018).
PubMed  PubMed Central  CAS  Google Scholar 

70.
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour.10, 564–567 (2010).
PubMed  Google Scholar 

71.
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet.8, e1002967 (2012).
PubMed  PubMed Central  CAS  Google Scholar 

72.
Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics180, 977–993 (2008).
PubMed  PubMed Central  Google Scholar 

73.
Hijmans, R. J. et al. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl.127, 15–19 (2001).
Google Scholar 

74.
Frichot, E. et al. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol.30, 1687–1699 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

75.
Joost, S. et al. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol. Ecol.16, 3955–3969 (2007).
PubMed  CAS  Google Scholar 

76.
Stucki, S. et al. High performance computation of landscape genomic models including local indicators of spatial association. Mol. Ecol. Resour.17, 1072–1089 (2017).
PubMed  CAS  Google Scholar 

77.
Oksanen, J. et al. Vegan: Community Ecology Package. R. Package Version 2.4-5 (2017).

78.
Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol.215, 403–410 (1990).
CAS  Google Scholar  More

Here we tested the capacity of VHR imagery to provide estimates useful for monitoring whale distribution and densities, using a direct comparison with a ship-based line transect survey to gauge the relative sighting rates obtained by the satellite platform in comparison to that of the ship. Our results show that density estimates derived from satellite imagery (0.13 whales per km2, CV = 0.38—taken from calm waters) are approximately 0.39 of those estimated from the ship-based survey (0.33 whales per km2, CV = 0.09); an encouraging result suggesting that data from satellite imagery has potential to detect whales at similar levels to a traditional survey method. These results match our expectation that image derived densities would be lower than that of the ship-survey, with the instantaneous nature of the image acquisition on the satellite platform likely a strong driver of these differences, in addition to limitations in image resolution and the potential for random fluctuations in local whale densities during the time between acquisition of satellite images and the vessel-based survey. However they also demonstrate that satellite surveys have sufficient whale detection capacity that they can provide a complementary approach to monitoring whale presence in remote regions where regular surveys are difficult.
In setting up this study, we chose an area that (1) is of specific scientific interest in terms of whales; (2) is remote and relatively difficult to access, but has had some whale survey effort; (3) where the environmental conditions are changing; and (4) where whale density and habitat use patterns are required to understand population recovery from exploitation and spatial overlap with the regional fishery for Antarctic krill. We focused on an area where one whale species very strongly predominates (humpback whales) in order that our results have potential use for inference about the density patterns of this species, and as there is a smaller likelihood that species mis-identification would introduce bias. We also chose a sea channel which is relatively sheltered, reducing the likelihood of turbulent sea conditions (particularly wind on sea), which can make satellite images useless for survey. Our site selection considerations highlight the limitations still facing development of VHR as a platform, and we consider these limitations and next steps to address them in the following sections. We propose that this method can be used to investigate spatial and temporal patterns of whale distribution and densities, supplementing existing methods, providing that the limitations of this new method are carefully considered during design and implementation.
Weather conditions, specifically the sea state, impact detectability of whales at sea. Sea state is known to influence the ability of observers to detect animals, with worsening conditions reducing the detection probability. Consequently, effort is typically halted when conditions exceed a predefined limit. In all at-sea surveys, sea state increases the likelihood that the assumption of perfect detection on the track line will be violated. If detection off the track line is impacted by environmental conditions, inclusion of covariates in the detection function can take account of this bias44 (up to a cut off, normally 5). However, if poor sighting conditions impact detection on the track line, alternative methods such as a double-observer/platform study or a mark recapture approach can be implemented to account for and quantify this bias. For an image-based survey, poorer weather conditions will also reduce the ability of the observer to differentiate FOIs from background noise (i.e. breaking waves, wind lines, etc.)30. This results in fewer features being identified, and lower reported densities. Poor sea state, and associated wind conditions, typically ground aerial surveys, whether manned or UAS-based, or force them to be aborted inflight. Here we show that worsening sea states in the south of the study area on the day that the image was taken (Fig. 2), correspond to lower perceived and estimated densities in these regions. Compared to the northern area, the surface conditions of the southern image were less conducive to the visual detection of FOIs, showing an increased frequency of white-caps and wind lines, possibly because this region is prone to katabatic winds sweeping into the channel. Densities in the south of the survey area, where the sea state was poorer, were 0.4 of those from calmer regions (0.05 versus 0.13 whales per km2, CV = 0.58 and 0.38, respectively, Table 2). To address this effect in the future, an adapted version of a Mark-Recapture Distance Sampling (MRDS) analysis, such as45 using multiple observers to review images33, could be applied to assess variations in detectability as a function of covariates (i.e. sea state), and investigate the impact of perception bias on whale detection. However, to accurately parameterise a multi-covariate model, several tens, if not hundreds of whale detections would be needed. Another approach could be to collect multiple images of the same area very close in time (within several seconds to a minute of each other), to quantify the variation in whale detections according to sea state when variation in true whale density is likely to be negligible. In the present study, density comparisons were made using data from the northern (calmer) portion of the imagery only (0.13 whales per km2, CV = 0.38, Table 2).
When planning satellite imagery analysis, species composition of the focal area needs to be carefully considered, because at present this approach has very limited capacity to differentiate between species when compared to in situ surveys, due to the resolution of the images (~ 30 cm in this study). Our density estimates most likely reflect the density of humpback whales using the area of the Gerlache Strait in summer, because these are the most commonly sighted species in this region, both in terms of previous surveys, where they comprise  > 80% of sightings15,16, and during the present ship-based survey ( > 95% of the groups were identified as humpback whales). During summer periods, other larger baleen whale species tend to be seen further offshore, exhibiting affinity for the more open waters of the Bransfield Strait15. Smaller cetacean species (e.g. Antarctic minke whales, Balaenoptera bonarensis and both Type A and B killer whales46,47,48, Orcinus orca), co-occur with humpback whales in the Gerlache Strait but are unlikely to be misidentified as humpback whales, either by ship or imagery surveys, because of their differing size, surface behaviours and morphology. Southern right whales Eubalaena australis are occasionally sighted in this region too16. However, head callosities are normally visible in overhead imagery of this species, and offer a clear means of differentiation30,31. Since other species likely reflect at best a very small fraction of the image-survey detections, they are unlikely to comprise a significant component of the overall density estimates.
Obtaining reliable whale density estimates require adjustments for biases. In addition to perception bias, as mentioned above, another key bias is availability bias45. Availability bias is the underestimation of density that occurs as a result of a proportion of animals being underwater, or too deep in the water for detection by the survey platform as it passes a point in the ocean. In the present study, we applied an estimate of surface availability49 (where availability is 1-availability bias), which was derived by taking dive-recording suction cup tag data from humpback whales in the same region and time, to estimate the proportion of time a whale spends at the surface, versus its dive. Applying this correction, density was initially estimated as 0.12 whales per km2 (CV = 0.38) over the whole region surveyed, and as 0.13 whales per km2 (CV = 0.38) in calmer waters. However, we note that when tag data are processed, the analyst determines the threshold at which the animal transitions from being present at the surface, to when it dives50. Typically, for baleen whales, dives are classified as such when the whale is  > 4–5 m for  > 20 s. However, with such a threshold, shallow dives of  More

1.
Chang, P. et al. Oceanic link between abrupt changes in the North Atlantic Ocean and the African monsoon. Nat. Geosci. 1, 444 (2008).
ADS  CAS  Google Scholar 
2.
Kushnir, Y., Robinson, W. A., Chang, P. & Robertson, A. W. The physical basis for predicting Atlantic sector seasonal-to-interannual climate variability. J. Clim. 19, 5949–5970 (2006).
ADS  Google Scholar 

3.
Brandt, P. et al. Equatorial upper-ocean dynamics and their interaction with the West African monsoon. Atmos. Sci. Lett. 12, 24–30 (2011).
ADS  Google Scholar 

4.
Sultan, B. & Janicot, S. Abrupt shift of the ITCZ over West Africa and intra-seasonal variability. Geophys. Res. Lett. 27, 3353–3356 (2000).
ADS  Google Scholar 

5.
Liebmann, B. & Mechoso, C. R. The South American Monsoon System. In The Global Monsoon System: Research and Forecast 2nd edn, (eds Chang, C.‐P. et al.) Ch. 8, pp. 137–157, (World Scientific Publishing, 2010).

6.
Nobre, P. & Shukla, J. Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J. Clim. 9, 2464–2479 (1996).
ADS  Google Scholar 

7.
Foltz, G. R. et al. The Tropical Atlantic Observing System. Front. Mar. Sci. 6, 206 (2019).
ADS  Google Scholar 

8.
Jochum, M. et al. The impact of oceanic near-inertial waves on climate. J. Clim. 26, 2833–2844 (2013).
ADS  Google Scholar 

9.
Johns, W. E., Brandt, P. & Chang, P. Tropical Atlantic variability and coupled model climate biases: results from the Tropical Atlantic Climate Experiment (TACE). Clim. Dyn. 43, 2887–2887 (2014).
Google Scholar 

10.
Toniazzo, T. & Woolnough, S. Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts. Clim. Dyn. 43, 2889–2913 (2014).
Google Scholar 

11.
Zuidema, P. et al. Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific Oceans: the U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group. Bull. Am. Meteorol. Soc. 97, 2305–2328 (2016).
ADS  Google Scholar 

12.
Dutkiewicz, S., Hickman, A. E. & Jahn, O. Modelling ocean-colour-derived chlorophyll a. Biogeosciences 15, 613–630 (2018).
ADS  CAS  Google Scholar 

13.
Siegel, D. A. et al. Solar radiation, phytoplankton pigments and the radiant heating of the equatorial Pacific warm pool. J. Geophys. Res. 100, 4885–4891 (1995).
ADS  Google Scholar 

14.
Strutton, P. G. & Chavez, F. P. Biological heating in the equatorial Pacific: observed variability and potential for real-time calculation. J. Clim. 17, 1097–1109 (2004).
ADS  Google Scholar 

15.
Bourlès, B. et al. PIRATA: a sustained observing system for tropical Atlantic climate research and forecasting. Earth Space Sci. 6, 577–616 (2019).
ADS  Google Scholar 

16.
Foltz, G. R., Grodsky, S. A., Carton, J. A. & McPhaden, M. J. Seasonal mixed layer heat budget of the tropical Atlantic Ocean. J. Geophys. Res. 108, 3146 (2003).
ADS  Google Scholar 

17.
Foltz, G. R., Schmid, C. & Lumpkin, R. Seasonal cycle of the mixed layer heat budget in the northeastern tropical Atlantic ocean. J. Clim. 26, 8169–8188 (2013).
ADS  Google Scholar 

18.
Foltz, G. R., Schmid, C. & Lumpkin, R. An enhanced PIRATA dataset for tropical Atlantic ocean–atmosphere research. J. Clim. 31, 1499–1524 (2018).
ADS  Google Scholar 

19.
Hummels, R., Dengler, M. & Bourles, B. Seasonal and regional variability of upper ocean diapycnal heat flux in the Atlantic cold tongue. Prog. Oceanogr. 111, 52–74 (2013).
ADS  Google Scholar 

20.
Hummels, R., Dengler, M., Brandt, P. & Schlundt, M. Diapycnal heat flux and mixed layer heat budget within the Atlantic Cold Tongue. Clim. Dyn. 43, 3179–3199 (2014).
Google Scholar 

21.
Wade, M. et al. A one-dimensional modeling study of the diurnal cycle in the equatorial Atlantic at the PIRATA buoys during the EGEE-3 campaign. Ocean Dyn. 61, 1–20 (2011).
ADS  Google Scholar 

22.
Wade, M., Caniaux, G. & Du Penhoat, Y. Variability of the mixed layer heat budget in the eastern equatorial Atlantic during 2005–2007 as inferred using Argo floats. J. Geophys. Res. 116, 17 (2011).
Google Scholar 

23.
McPhaden, M. J., Cronin, M. F. & McClurg, D. C. Meridional structure of the seasonally varying mixed layer temperature balance in the eastern Tropical Pacific. J. Clim. 21, 3240–3260 (2008).
ADS  Google Scholar 

24.
Moum, J. N., Perlin, A., Nash, J. D. & McPhaden, M. J. Seasonal sea surface cooling in the equatorial Pacific cold tongue controlled by ocean mixing. Nature 500, 64 (2013).
ADS  CAS  PubMed  Google Scholar 

25.
Schlundt, M. et al. Mixed layer heat and salinity budgets during the onset of the 2011 Atlantic cold tongue. J. Geophys. Res. 119, 7882–7910 (2014).
ADS  Google Scholar 

26.
Smyth, W. D., Moum, J. N., Li, L. & Thorpe, S. A. Diurnal shear instability, the descent of the surface shear layer, and the deep cycle of equatorial turbulence. J. Phys. Oceanogr. 43, 2432–2455 (2013).
ADS  Google Scholar 

27.
Giordani, H., Caniaux, G. & Voldoire, A. Intraseasonal mixed-layer heat budget in the equatorial Atlantic during the cold tongue development in 2006. J. Geophys. Res. 118, 650–671 (2013).
ADS  Google Scholar 

28.
Huang, B., Xue, Y., Zhang, D., Kumar, A. & McPhaden, M. J. The NCEP GODAS ocean analysis of the tropical Pacific mixed layer heat budget on seasonal to interannual time scales. J. Clim. 23, 4901–4925 (2010).
ADS  Google Scholar 

29.
Jouanno, J., Marin, F., du Penhoat, Y., Sheinbaum, J. & Molines, J.-M. Seasonal heat balance in the upper 100 m of the equatorial Atlantic Ocean. J. Geophys. Res. 116, C09003 (2011).
ADS  Google Scholar 

30.
Pham, H. T., Sarkar, S. & Winters, K. B. Large-eddy simulation of deep-cycle turbulence in an equatorial undercurrent model. J. Phys. Oceanogr. 43, 2490–2502 (2013).
ADS  Google Scholar 

31.
Pham, H. T., Smyth, W. D., Sarkar, S. & Moum, J. N. Seasonality of deep cycle turbulence in the eastern equatorial Pacific. J. Phys. Oceanogr. 47, 2189–2209 (2017).
ADS  Google Scholar 

32.
Brandt, P. et al. On the role of circulation and mixing in the ventilation of oxygen minimum zones with a focus on the eastern tropical North Atlantic. Biogeosciences 12, 489–512 (2015).
ADS  Google Scholar 

33.
MacKinnon, J. A. et al. Climate process team on internal wave–driven ocean mixing. Bull. Am. Meteorol. Soc. 98, 2429–2454 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

34.
Alford, M. H., MacKinnon, J. A., Simmons, H. L. & Nash, J. D. Near-inertial internal gravity waves in the ocean. Annu. Rev. Mar. Sci. 8, 95–123 (2016).
ADS  Google Scholar 

35.
Pollard, R. T. & Millard, R. C. Comparison between observed and simulated wind-generated inertial oscillations. Deep Sea Res. Oceanogr. Abstr. 17, 813–821 (1970).
ADS  Google Scholar 

36.
Falkowski, P. G., Barber, R. T. & Smetacek, V. Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200 (1998).
CAS  PubMed  Google Scholar 

37.
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701 (2013).
ADS  CAS  Google Scholar 

38.
Schafstall, J., Dengler, M., Brandt, P. & Bange, H. Tidal-induced mixing and diapycnal nutrient fluxes in the Mauritanian upwelling region. J. Geophys. Res. 115, C10014 (2010).
ADS  Google Scholar 

39.
Peters, H., Gregg, M. C. & Toole, J. M. On the parameterization of equatorial turbulence. J. Geophys. Res. 93, 1199–1218 (1988).
ADS  Google Scholar 

40.
Leaman, K. D. & Sanford, T. B. Vertical energy propagation of inertial waves: a vector spectral analysis of velocity profiles. J. Geophys. Res. (1896–1977) 80, 1975–1978 (1975).
ADS  Google Scholar 

41.
Thorpe, S. A. Turbulence and mixing in a Scottish Loch. Philos. Trans. R. Soc. Lond. Ser. A 286, 125–181 (1977).
ADS  Google Scholar 

42.
Fischer, T. Microstructure Measurements during METEOR Cruise M119, PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.920592 (2020).

43.
Ozmidov, R. V. On the turbulent exchange in a stably stratified ocean. Izv. Acad. Sci. USSR Atmos. Ocean Phys., 1, 861–871 (1965).
Google Scholar 

44.
Smyth, W. D. & Moum, J. N. Ocean mixing by Kelvin–Helmholtz instability. Oceanography 25, 140–149 (2012).
Google Scholar 

45.
Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–106 (1967).
ADS  Google Scholar 

46.
Plueddemann, A. J. & Farrar, J. T. Observations and models of the energy flux from the wind to mixed-layer inertial currents. Deep Sea Res. Part II 53, 5–30 (2006).
ADS  Google Scholar 

47.
Large, W. G. & Pond, S. Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr. 11, 324–336 (1981).
ADS  Google Scholar 

48.
Donelan, M. A. et al. On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett. 31, L18306 (2004).
ADS  Google Scholar 

49.
D’Asaro, E. A. The energy flux from the wind to near-inertial motions in the surface mixed layer. J. Phys. Oceanogr. 15, 1043–1059 (1985).
ADS  Google Scholar 

50.
Atlas, R. et al. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteorol. Soc. 92, 157–174 (2011).
ADS  Google Scholar 

51.
Wentz, F. et al. Remote Sensing Systems Cross-calibrated Multi-platform (CCMP) 6-hourly Ocean Vector Wind Analysis Product on 0.25 deg Grid, Version 2.0 (Remote Sensing Systems, Santa Rosa, CA, Google Scholar, 2015).

52.
Moisan, J. R. & Niiler, P. P. The seasonal heat budget of the North Pacific: net heat flux and heat storage rates (1950–1990). J. Phys. Oceanogr. 28, 401–421 (1998).
ADS  Google Scholar 

53.
Stevenson, J. W. & Niiler, P. P. Upper ocean heat budget during the Hawaii-to-Tahiti shuttle experiment. J. Phys. Oceanogr. 13, 1894–1907 (1983).
ADS  Google Scholar 

54.
Belanger, J. I., Jelinek, M. T. & Curry, J. A. A climatology of easterly waves in the tropical Western Hemisphere. Geosci. Data J. 3, 40–49 (2016).
ADS  Google Scholar 

55.
Burpee, R. W. The origin and structure of easterly waves in the lower troposphere of North Africa. J. Atmos. Sci. 29, 77–90 (1972).
ADS  Google Scholar 

56.
Carlson, T. N. Synoptic histories of three African disturbances that developed into Atlantic hurricanes. Mon. Weather Rev. 97, 256–276 (1969).
ADS  Google Scholar 

57.
Pytharoulis, I. & Thorncroft, C. The low-level structure of African easterly waves in 1995. Mon. Weather Rev. 127, 2266–2280 (1999).
ADS  Google Scholar 

58.
Kiladis, G. N., Thorncroft, C. D. & Hall, N. M. J. Three-dimensional structure and dynamics of African easterly waves. Part I: Observations. J. Atmos. Sci. 63, 2212–2230 (2006).
ADS  Google Scholar 

59.
Thorncroft, C. & Hodges, K. African easterly wave variability and its relationship to Atlantic tropical cyclone activity. J. Clim. 14, 1166–1179 (2001).
ADS  Google Scholar 

60.
Mickett, J. B., Serra, Y. L., Cronin, M. F. & Alford, M. H. Resonant forcing of mixed layer inertial motions by atmospheric easterly waves in the northeast tropical Pacific. J. Phys. Oceanogr. 40, 401–416 (2010).
ADS  Google Scholar 

61.
Wu, M.-L. C., Reale, O., Schubert, S. D., Suarez, M. J. & Thorncroft, C. D. African easterly jet: barotropic instability, waves, and cyclogenesis. J. Clim. 25, 1489–1510 (2012).
ADS  Google Scholar 

62.
Wu, M.-L. C., Reale, O. & Schubert, S. D. A characterization of African easterly waves on 2.5–6-day and 6–9-day time scales. J. Clim. 26, 6750–6774 (2013).
ADS  Google Scholar 

63.
Large, W. G., McWilliams, J. C. & Doney, S. C. Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32, 363–403 (1994).
ADS  Google Scholar 

64.
Foltz, G. R., Evan, A. T., Freitag, H. P., Brown, S. & McPhaden, M. J. Dust accumulation biases in PIRATA shortwave radiation records. J. Atmos. Ocean. Technol. 30, 1414–1432 (2013).
ADS  Google Scholar 

65.
Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139, 1132–1161 (2013).
ADS  Google Scholar 

66.
Morel, A. & Antoine, D. Heating rate within the upper ocean in relation to its bio-optical state. J. Phys. Oceanogr. 24, 1652–1665 (1994).
ADS  Google Scholar 

67.
Sweeney, C. et al. Impacts of shortwave penetration depth on large-scale ocean circulation and heat transport. J. Phys. Oceanogr. 35, 1103–1119 (2005).
ADS  Google Scholar 

68.
Foltz, G. R. & McPhaden, M. J. Impact of barrier layer thickness on SST in the central tropical North Atlantic. J. Clim. 22, 285–299 (2009).
ADS  Google Scholar 

69.
Fischer, J., Brandt, P., Dengler, M., Müller, M. & Symonds, D. Surveying the upper ocean with the ocean surveyor: a new phased array Doppler current profiler. J. Atmos. Ocean. Technol. 20, 742–751 (2003).
ADS  Google Scholar 

70.
Wolk, F., Yamazaki, H., Seuront, L. & Lueck, R. G. A new free-fall profiler for measuring biophysical microstructure. J. Atmos. Ocean. Technol. 19, 780–793 (2002).
ADS  Google Scholar 

71.
Merckelbach, L., Berger, A., Krahmann, G., Dengler, M. & Carpenter, J. R. A dynamic flight model for slocum gliders and implications for turbulence microstructure measurements. J. Atmos. Ocean. Technol. 36, 281–296 (2019).
ADS  Google Scholar 

72.
Osborn, T. R. Estimates of the local rate of vertical diffusion from dissipation measurements. J. Phys. Oceanogr. 10, 83–89 (1980).
ADS  Google Scholar 

73.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 2007).

74.
Hoyer, S. et al. pydata/xarray v0.10.8. Version v0.10.8 (Zenodo, 2018).

75.
Hoyer, S. & Hammam, J. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. 5, 10 (2017).
Google Scholar 

76.
Rocklin, M. Dask: Parallel Computation with Blocked algorithms and Task Scheduling, in Proceedings of the 14th Python in Science Conference. (eds  Huff, K. & Bergstra, J.) (2015).

77.
Oliphant T. E. A Guide to NumPy (Trelgol Publishing, 2006).

78.
Lam, S. K., Pitrou, A. & Seibert, S. Numba: a LLVM-based Python JIT compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (Association for Computing Machinery, 2015).

79.
Mertens, C. & Schott, F. Interannual variability of deep-water formation in the Northwestern Mediterranean. J. Phys. Oceanogr. 28, 1410–1424 (1998).
ADS  Google Scholar 

80.
Lascaratos, A., Williams, R. G. & Tragou, E. A mixed-layer study of the formation of Levantine intermediate water. J. Geophys. Res. 98, 14739–14749 (1993).
ADS  Google Scholar 

81.
Schmidtko, S., Johnson, G. C. & Lyman, J. M. MIMOC: a global monthly isopycnal upper-ocean climatology with mixed layers. J. Geophys. Res. 118, 1658–1672 (2013).
ADS  Google Scholar  More

Physical and mechanical properties of wood
The AD (0.62 to 0.68 g/cm3) and OD (0.58 to 0.65 g/cm3) values obtained this study were similar with respect to basic density33 in the same sample trees (Table 2), whereas our results for mean AD values were relatively higher than those for L. sibirica reported by Ishiguri et al.27 and lower than those reported by Koizumi et al.5. Radial variations of AD and OD showed similar patterns to those reported by other researchers of L. sibirica5 and L. kaempferi16. Meanwhile, Cáceres et al.3 reported an influence of extractives on density in L. kaempferi. They found that the hot-water extractive content of L. kaempferi varied between 2.9 to 6.9% among 20 provenances, suggesting that actual wood density might be about 5% lower than AD. As shown in Table 3, cold-water extractive content ranged from 7.3 to 16.1%, and the mean values of EOD (0.54 g/cm3, Table 2) were about 10% lower values compared to OD (0.61 g/cm3, Table 2). These results indicated that the effect of cold- or hot-water extractives on wood density might be greater in L. sibirica compared to other Larix species.
Ishiguri et al.27 reported that radial shrinkage at 1% moisture content change showed almost constant values from pith to bark, whereas tangential shrinkage increased up to 4 cm from pith and then became constant at around 0.3%. The mean values and radial variation patterns examined in this study for shrinkage in both the radial and tangential directions in L. sibirica were similar to those of L. sibirica examined by Ishiguri et al.27.
Although the tree ages varied, the mean values of MOE, MOR, and CS of the L. sibirica trees in the present study (Table 4) were similar to those found in a previous study for L. sibirica that grow naturally in Mongolia27 but lower than those for L. sibirica that grow naturally in Russia5 and higher than those for L. kaempferi planted in Japan22,24. The mean SS was higher than that of L. sibirica planted in Finland9 and L. kaempferi planted in Japan24. In the radial variation, similar radial trends were found in L. sibirica that grow naturally in Mongolia27 and in L. kaempferi planted in Japan22.
Based on the obtained results, the mean values of the physical and mechanical properties of L. sibirica collected from five different provenances in Mongolia are similar to those of L. sibirica and other Larix species found in other countries. Thus, wood resources from L. sibirica harvested in Mongolia can be used for similar purposes to other Larix species, such as construction materials.
Juvenile and mature wood
The boundary between juvenile and mature wood ranged from the 17th to 24th annual rings from the pith (Table 6). The results were similar to those reported for L. kaempferi trees17,22,42. However, Ishiguri et al.27 showed that juvenile wood might exist within 4 cm from the pith in L. sibirica. In the present study, the boundary was within 2 to 5 cm from the pith among the provenances, suggesting that juvenile wood formation in L. sibirica trees that grow naturally in Mongolia is not only affected by tree age but also by growing conditions.
We previously reported that mean values of annual ring width were 1.55, 2.47, 0.49, 1.86, and 1.74 for Khentii, Arkhangai, Zavkhan, Khuvsgul, and Selenge, respectively33. This result indicates that the radial growth rate was extremely slow in Zavkhan compared to other four provenances. Shiokura and Watanabe28 reported that suppressed radial growth in the initial stage of tree growth resulted in prolonging the juvenile wood formation period in Picea jezoensis and Abies sachalinensis. Although significant differences among provenances were also found in annual ring number from the pith in the boundary between juvenile and mature wood (Table 6), the difference in the earliest (17th) and the latest (24th) annual ring number from the pith in the boundary was only 7 years. Thus, the radial growth rate in L. sibirica does not have a strong effect on the cambial age at which the production of mature wood cells begins. However, further research is needed to clarify the relationship between the radial growth rate and annual ring number from the pith in the boundary between juvenile and mature wood in this species.
As shown in Table 7, significant differences between juvenile and mature wood were found in the mean values of physical properties, tracheid length, and mechanical properties, except for SS: the values of the physical and mechanical properties of juvenile wood were lower than those of mature wood. These lower values can be explained by shorter tracheid length and lower wood density. Similar results were obtained by several researchers of softwood species17,22,24,28,29. For example, Koizumi et al.24 found that, in L. kaempferi, the mean MOE, MOR, CS, and SS values were 8.2 GPa, 93.3 MPa, 54.0 MPa, and 11.5 MPa in juvenile wood and 9.5 GPa, 97.2 MPa, 55.1 MPa, and 11.4 MPa in mature wood, respectively. Bao et al.25 reported that the mechanical properties of juvenile wood were significantly lower than those of mature wood in Larix olgenis and L. kaempferi. We also found lower mechanical properties, basic density, and shorter latewood tracheid length of juvenile wood in 67-year-old L. kaempferi22. Thus, the presence of juvenile wood should be considered when utilizing wood resources of this species as construction materials requiring higher strength properties.
Correlation among physical and mechanical properties of wood
Figure 4 shows the correlation coefficients of the physical and mechanical properties of three different wood types (all types of wood, juvenile wood, and mature wood). In general, wood density is positively related to shrinkage in the radial and tangential directions44. The results of this study showed significant correlations between radial shrinkage at 1% moisture content and EOD in mature wood and all wood, suggesting that EOD can predict shrinkage in the radial direction in this species. Wood density is also positively correlated with many types of mechanical properties of wood45,46. CS was positively correlated with all types of wood densities measured in this study. The MOE and MOR in mature wood and all wood only exhibited a significant positive correlation with EOD. These results indicate that MOE and MOR values were correlated with wood substances without extractives, and these values in juvenile wood might be related to other properties, such as microfibril angle. Luostarinen and Heräjärvi10 reported that water-soluble arabinogalactan contents were weakly correlated with SS in L. sibirica. SS was significantly correlated with AD, but not with EOD, suggesting that cold water-soluble extractives, such as arabinogalactan, might be affected on the SS in this species.
Based on these results, strength properties (e.g., bending properties and compressive strength) can be estimated with each other and predicted by EOD. In addition, SS might be influenced by the presence of cold water-soluble extractives, such as arabinogalactan.
Among-provenance variations
Cáceres et al.3 reported that significant among-provenance differences were not found in basic and oven-dry densities, whereas hot-water extractive content was significantly affected by provenances in L. kaempferi. We also previously demonstrated that no significant differences among provenances were found in the basic density of L. sibirica naturally grown in Mongolia33. Although the cold-water extractive content significantly differed among provenances in this study, all examined densities, such as AD, OD, and EOD, showed no significant differences among the five provenances (Tables 2 and 3), indicating that wood density might not vary greatly among provenances. Thus, it can be concluded that genetic variations in relation to wood density might be small in L. sibirica trees naturally grown in Mongolia.
In half-sib families of P. jezoensis, F-values obtained by an ANOVA test for AD, MOE, and MOR among families gradually decreased from juvenile to mature wood47. In addition, Kumar et al.48 reported that estimates of narrow-sense heritability for MOE were generally higher in the corewood than in the outer wood in Pinus radiata. For Larix species, significant differences in wood density, CS, and SS but not in MOE and MOR were found in outer wood among 23 provenances for 31-year-old L. kaempferi24. Thus, genetic variations in the physical and mechanical properties of juvenile wood were higher than in mature wood in many softwood species. Significant differences were also found in most of the mechanical properties among provenances, except for CS (Table 4). In addition, significant differences were found in all examined physical and mechanical properties except for CS in mature wood among the five provenances, while no differences were found in juvenile wood for many properties (Table 7). Similar results were obtained in estimated MOE and MOR values at the 10th and 30th annual rings from the pith: no significant among-provenance variations were found in MOE and MOR at the 10th annual ring from the pith, but significant differences were found in the 30th annual ring from the pith (Table 5). Although the environmental conditions in the five provenances were not the same, the genetic variations in physical and mechanical properties among provenances were large in mature wood compared to juvenile wood for L. sibirica grown naturally in Mongolia. Further research is needed to clarify the genetic factors of the physical and mechanical properties of wood in L. sibirica.
Based on the results, there are significant among-provenance differences in the physical and mechanical properties of wood, especially in mature wood, in L. sibirica grown naturally in Mongolia. The physical and mechanical properties of wood in this species, especially in mature wood, can be improved by establishing tree breeding programs: families or clones with higher mechanical properties can be produced to achieve sustainable forestry in Mongolia. More