Philippa Kaur

1.
Lawrence, W. S. Dispersal: an alternative mating tactic conditional on sex ratio and body size. Behav. Ecol. Sociobiol. 21, 367–373 (1987).
Article  Google Scholar 
2.
Russell, E. M. & Rowley, I. Philopatry or dispersal: competition for territory vacancies in the splendid fairy-wren, Malurus splendens. Anim. Behav. 45, 519–539 (1993).
Article  Google Scholar 

3.
Herzig, A. L. Effects of population density on long-distance dispersal in the goldenrod beetle Trirhabda virgata. Ecology 76, 2044–2054 (1995).
Article  Google Scholar 

4.
Verhulst, S., Perrins, C. M. & Riddington, R. Natal dispersal of great tits in a patchy environment. Ecology 78, 864–872 (1997).
Article  Google Scholar 

5.
Johnson, M. L. & Gaines, M. S. Evolution of dispersal: theoretical models and empirical tests using birds and mammals. Annu. Rev. Ecol. Evol. Sys. 21, 449–480 (1990).
Article  Google Scholar 

6.
Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. Dispersal (Oxford University Press, Oxford, 2001).
Google Scholar 

7.
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).
PubMed  Article  PubMed Central  Google Scholar 

8.
Szulkin, M. & Sheldon, B. C. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275, 703–711 (2008).
PubMed  Article  PubMed Central  Google Scholar 

9.
Cornwallis, C. K. Cooperative breeding and the evolutionary coexistence of helper and nonhelper strategies. Proc. Nat. Acad. Sc. USA 115, 1684–1686 (2018).
CAS  Article  Google Scholar 

10.
Nelson-Flower, M. J., Ridley, A. R., Wiley, E. M. & Flower, T. P. Individual dispersal delays in a cooperative breeder: ecological constraints, the benefits of philopatry and the social queue for dominance. J. Anim. Ecol. 87, 1227–1238 (2018).
PubMed  Article  PubMed Central  Google Scholar 

11.
Komdeur, J. Importance of habitat saturation and territory quality for evolution of cooperative breeding in the Seychelles warbler. Nature 358, 493 (1992).
ADS  Article  Google Scholar 

12.
Kokko, H. & Lundberg, P. Dispersal, migration, and offspring retention in saturated habitats. Am. Nat. 157, 188–202 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Kokko, H. & Ekman, J. Delayed dispersal as a route to breeding: territorial inheritance, safe havens, and ecological constraints. Am. Nat. 160, 468–484 (2002).
PubMed  Article  PubMed Central  Google Scholar 

14.
Lemel, J. Y., Belichon, S., Clobert, J. & Hochberg, M. E. The evolution of dispersal in a two-patch system: some consequences of differences between migrants and residents. Evol. Ecol. 11, 613–629 (1997).
Article  Google Scholar 

15.
Forero, M. G., Donázar, J. A. & Hiraldo, F. Causes and fitness consequences of natal dispersal in a population of black kites. Ecology 83, 858–872 (2002).
Article  Google Scholar 

16.
Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: perspectives for a changing world. Science 313, 789–791 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Bonte, D. et al. Costs of dispersal. Biol. Rev. 87, 290–312 (2012).
PubMed  Article  PubMed Central  Google Scholar 

18.
Serrano, D. & Tella, J. L. Lifetime fitness correlates of natal dispersal distance in a colonial bird. J. Anim. Ecol. 81, 97–107 (2012).
PubMed  Article  PubMed Central  Google Scholar 

19.
Kubisch, A., Holt, R. D., Poethke, H. J. & Fronhofer, E. A. Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. Oikos 123, 5–22 (2014).
Article  Google Scholar 

20.
Drobniak, S. M., Wagner, G., Mourocq, E. & Griesser, M. Family living: an overlooked but pivotal social system to understand the evolution of cooperative breeding. Behav. Ecol. 26, 805–811 (2015).
Article  Google Scholar 

21.
Taborsky, M. Sneakers, satellites, and helpers: parasitic and cooperative behavior in fish reproduction. Adv. Study Behav. 23, e100 (1994).
Google Scholar 

22.
Clutton-Brock, T. H. & Lukas, D. The evolution of social philopatry and dispersal in female mammals. Mol. Ecol. 21, 472–492 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Pruett-Jones, S. G. & Lewis, M. J. Sex ratio and habitat limitation promote delayed dispersal in superb fairy-wrens. Nature 348, 541–542 (1990).
ADS  Article  Google Scholar 

24.
Bergmüller, R., Heg, D. & Taborsky, M. Helpers in a cooperatively breeding cichlid stay and pay or disperse and breed, depending on ecological constraints. Proc. R. Soc. B 272, 325–331 (2005).
PubMed  Article  PubMed Central  Google Scholar 

25.
Carrete, M., Donázar, J. A., Margalida, A. & Bertran, J. Linking ecology, behaviour and conservation: does habitat saturation change the mating system of bearded vultures?. Biol. Lett. 2, 624–627 (2006).
PubMed  PubMed Central  Article  Google Scholar 

26.
Covas, R., Doutrelant, C. & du Plessis, M. A. Experimental evidence of a link between breeding conditions and the decision to breed or to help in a colonial cooperative bird. Proc. R. Soc. B 271, 827–832 (2004).
PubMed  Article  PubMed Central  Google Scholar 

27.
Baglione, V. et al. Does yearround territoriality rather than habitat saturation explain delayed natal dispersal and cooperative breeding in the carrion crow?. J. Anim. Ecol. 74, 842–851 (2005).
Article  Google Scholar 

28.
Hatchwell, B. J. & Komdeur, J. Ecological constraints, life history traits and the evolution of cooperative breeding. Anim. Behav. 59, 1079–1086 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Pen, I. & Weissing, F. J. Towards a unified theory of cooperative breeding: the role of ecology and life history re-examined. Proc. R. Soc. B 267, 2411–2418 (2000).
Article  Google Scholar 

30.
Covas, R. & Griesser, M. Life history and the evolution of family living in birds. Proc. R. Soc. B 274, 1349–1357 (2007).
PubMed  Article  PubMed Central  Google Scholar 

31.
Griesser, M. & Barnaby, J. Role of Nepotism Cooperation and Competition in the Avian Families. Birds-Evolution, Behavior and Ecology Series (Nova Science Pub Inc, New York, 2010).
Google Scholar 

32.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tchabovsky, A. & Bazykin, G. Females delay dispersal and breeding in a solitary gerbil, Meriones tamariscinus. J. Mammal. 85, 105–112 (2004).
Article  Google Scholar 

34.
Ellis, E. C., Fuller, D. Q., Kaplan, J. O. & Lutters, W. G. Dating the Anthropocene: Towards an empirical global history of human transformation of the terrestrial biosphere. Elem. Sci. Anth 1, 18 (2013).
Article  Google Scholar 

35.
Bernardo-Madrid, R. et al. Human activity is altering the world’s zoogeographical regions. Ecol. Lett. 22, 1297–1305 (2019).
PubMed  PubMed Central  Google Scholar 

36.
Rodríguez-Martínez, S., Carrete, M., Roques, S., Rebolo-Ifrán, N. & Tella, J. L. High urban breeding densities do not disrupt genetic monogamy in a bird species. PLoS ONE 9, e91314 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Article  Google Scholar 

38.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).
CAS  PubMed  Article  Google Scholar 

40.
Sol, D., González-Lagos, C., Moreira, D., Maspons, J. & Lapiedra, O. Urbanisation tolerance and the loss of avian diversity. Ecol. Lett. 17, 942–950 (2014).
PubMed  Article  PubMed Central  Google Scholar 

41.
Carrete, M. & Tella, J. L. Inter-individual variability in fear of humans and relative brain size of the species are related to contemporary urban invasion in birds. PLoS ONE 6, e18859 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Díaz, M. et al. The geography of fear: a latitudinal gradient in anti-predator escape distances of birds across Europe. PLoS ONE 8, e64634 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S. & Martínez-Abraín, A. Ecological and evolutionary implications of food subsidies from humans. Ecol. Lett. 16, 1501–1514 (2013).
PubMed  Article  PubMed Central  Google Scholar 

44.
Marzluff, J. M. Worldwide urbanization and its effects on birds. Avian ecology and conservation in an urbanizing world (pp. 19–47) (Springer, Boston, MA. 2001).

45.
Haskell, D. G., Knupp, A. M. & Schneider, M. C. Nest predator abundance and urbanization. Avian ecology and conservation in an urbanizing world (pp. 243–258). Springer, Boston, MA. (2001).

46.
Luna, Á., Romero-Vidal, P., Hiraldo, F. & Tella, J. L. Cities may save some threatened species but not their ecological functions. PeerJ 6, e4908 (2018).
PubMed  PubMed Central  Article  Google Scholar 

47.
Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L. & Musiani, M. Human activity helps prey win the predator–prey space race. PLoS ONE 6, e17050 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Rebolo-Ifrán, N., Tella, J. L. & Carrete, M. Urban conservation hotspots: predation release allows the grassland-specialist burrowing owl to perform better in the city. Sci. Rep. 7, 3527 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
PubMed  Article  PubMed Central  Google Scholar 

50.
Rodewald, A. D., Kearns, L. J. & Shustack, D. P. Anthropogenic resource subsidies decouple predator-prey relationships. Ecol. Appl. 21, 936–943 (2011).
PubMed  Article  PubMed Central  Google Scholar 

51.
Sih, A., Ferrari, M. C. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).
PubMed  PubMed Central  Article  Google Scholar 

52.
Carrete, M. & Tella, J. L. Behavioural correlations associated with fear of humans differ between rural and urban burrowing owls. Front. Ecol. Evol. 5, 54 (2017).
Article  Google Scholar 

53.
Moore, J. A., Kamarainen, A. M., Scribner, K. T., Mykut, C. & Prince, H. H. The effects of anthropogenic alteration of nesting habitat on rates of extra-pair fertilization and intraspecific brood parasitism in Canada geese branta Canadensis. Ibis 154, 354–362 (2012).
Article  Google Scholar 

54.
Ryder, T. B., Fleischer, R. C., Shriver, W. G. & Marra, P. P. The ecological–evolutionary interplay: density-dependent sexual selection in a migratory songbird. Ecol. Evol. 2, 976–987 (2012).
PubMed  PubMed Central  Article  Google Scholar 

55.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Personality-dependent breeding dispersal in rural but not urban burrowing owls. Sci. Rep. 9, 2886 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Sex, personality and conspecific density influence natal dispersal with lifetime fitness consequences in urban and rural burrowing owls. PLoS ONE 15, e0226089 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Mueller, J. C. et al. Evolution of genomic variation in the burrowing owl in response to recent colonization of urban areas. Proc. R. Soc. B 285, 20180206 (2018).
PubMed  Article  PubMed Central  Google Scholar 

58.
Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).
PubMed  Article  PubMed Central  Google Scholar 

59.
Henger, C. S. et al. Genetic diversity and relatedness of a recently established population of eastern coyotes (Canis latrans) in New York City. Urban Ecosyst. 23, 1–12 (2019).
Google Scholar 

60.
Carrete, M. & Tella, J. L. Individual consistency in flight initiation distances in burrowing owls: a new hypothesis on disturbance-induced habitat selection. Biol. Lett. 6, 167–170 (2010).
PubMed  Article  PubMed Central  Google Scholar 

61.
Cockburn, A. Evolution of helping behavior in cooperatively breeding birds. Annu. Rev. Ecol. Syst. 29, 141–177 (1998).
Article  Google Scholar 

62.
Clutton-Brock, T. Breeding together: kin selection and mutualism in cooperative vertebrates. Science 296, 69–72 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Browning, L. E. et al. Career provisioning rules in an obligate cooperative breeder: prey type, size and delivery rate. Behav. Ecol. Sociobiol. 66, 1639–1649 (2012).
Article  Google Scholar 

64.
Richardson, D. S., Burke, T. & Komdeur, J. Direct benefits and the evolution of female-biased cooperative breeding in Seychelles warblers. Evolution 56, 2313–2321 (2002).
PubMed  Article  PubMed Central  Google Scholar 

65.
Gamero, A., Székely, T. & Kappeler, P. M. Delayed juvenile dispersal and monogamy, but no cooperative breeding in white-breasted mesites (Mesitornis variegata). Behav. Ecol. Sociobiol. 68, 73–83 (2014).
Article  Google Scholar 

66.
Brown, J. L. Territorial behavior and population regulation in birds: a review and re-evaluation. Wilson Bull. 81, 293–329 (1969).
Google Scholar 

67.
Emlen, S. T. The evolution of helping. I. An ecological constraints model. Am. Nat. 119, 29–39 (1982).
Article  Google Scholar 

68.
Emlen, S. T. An evolutionary theory of the family. Proc. Natl. Acad. Sci. USA 92, 8092–8099 (1995).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Ekman, J., Eggers, S., Griesser, M. & Tegelström, H. Queuing for preferred territories: delayed dispersal of Siberian jays. J. Anim. Ecol. 70(2), 317–324. https://doi.org/10.1111/j.1365-2656,2001.00490.x (2001).
Article  Google Scholar 

70.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc. B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

71.
Gardner, J. L., Magrath, R. D. & Kokko, H. Stepping stones of life: natal dispersal in the group-living but noncooperative speckled warbler. Anim. Behav. 66, 521–530 (2003).
Article  Google Scholar 

72.
Russell, E. M., Yom-Tov, Y. & Geffen, E. Extended parental care and delayed dispersal: northern, tropical, and southern passerines compared. Behav. Ecol. 15, 831–838 (2004).
Article  Google Scholar 

73.
Lucia, K. E. et al. Philopatry in prairie voles: an evaluation of the habitat saturation hypothesis. Behav. Ecol. 19, 774–783 (2008).
Article  Google Scholar 

74.
Clobert, J., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–220 (2009).
PubMed  Article  PubMed Central  Google Scholar 

75.
Bocedi, G., Heinonen, J. & Travis, J. M. Uncertainty and the role of information acquisition in the evolution of context-dependent emigration. Am. Nat. 179, 606–620 (2012).
PubMed  Article  PubMed Central  Google Scholar 

76.
Bergmüller, R., Taborsky, M., Peer, K. & Heg, D. Extended safe havens and between-group dispersal of helpers in a cooperatively breeding cichlid. Behaviour 142, 1643–1667 (2005).
Article  Google Scholar 

77.
Tanaka, H., Frommen, J. G., Engqvist, L. & Kohda, M. Task-dependent workload adjustment of female breeders in a cooperatively breeding fish. Behav. Ecol. 29, 221–229 (2018).
Article  Google Scholar 

78.
Suárez-Seoane, S., Osborne, P. E. & Alonso López, J. C. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species–habitat responses using generalized additive models. J. Appl. Ecol. 39, 755–771 (2002).
Article  Google Scholar 

79.
Boyce, M. S. et al. Can habitat selection predict abundance?. J. Anim. Ecol. 85, 11–20 (2016).
PubMed  Article  PubMed Central  Google Scholar 

80.
Muller, K. L., Stamps, J. A., Krishnan, V. V. & Willits, N. H. The effects of conspecific attraction and habitat quality on habitat selection in territorial birds (Troglodytes aedon). Am. Nat. 150, 650–661 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: implications for the study of coloniality. Ecology 79, 2415–2428 (1998).
Article  Google Scholar 

82.
Brown, C. R., Brown, M. B. & Danchin, E. Breeding habitat selection in cliff swallows: The effect of conspecific reproductive success on colony choice. J. Anim. Ecol. 69, 133–142 (2000).
Article  Google Scholar 

83.
Danchin, E., Giraldeau, L. A., Valone, T. J. & Wagner, R. H. Public information: from nosy neighbors to cultural evolution. Science 305, 487–491 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Serrano, D., Forero, M. G., Donázar, J. A. & Tella, J. L. Dispersal and social attraction affect colony selection and dynamics of lesser kestrels. Ecology 85, 3438–3447 (2004).
Article  Google Scholar 

85.
Farrell, S. L., Morrison, M. L., Campomizzi, A. J. & Wilkins, R. N. Conspecific cues and breeding habitat selection in an endangered woodland warbler. J. Anim. Ecol. J. Anim. Ecol. 81, 1056–1064 (2012).
PubMed  Article  PubMed Central  Google Scholar 

86.
Pärt, T. The importance of local familiarity and search costs for age-and sex-biased philopatry in the collared flycatcher. Anim. Behav. 49, 1029–1038 (1995).
Article  Google Scholar 

87.
Clarke, A. L., Sæther, B. E. & Røskaft, E. Sex biases in avian dispersal: a reappraisal. Oikos 79, 429–438 (1997).
Article  Google Scholar 

88.
Piper, W. H., Walcott, C., Mager, J. N. & Spilker, F. J. Nestsite selection by male loons leads to sex-biased site familiarity. J. Anim. Ecol. J. Anim. Ecol. 77, 205–210 (2008).
PubMed  Article  PubMed Central  Google Scholar 

89.
Stacey, P. B. & Ligon, J. D. The benefits-of-philopatry hypothesis for the evolution of cooperative breeding: variation in territory quality and group size effects. Am. Nat. 137, 831–846 (1991).
Article  Google Scholar 

90.
Goldstein, J. M., Woolfenden, G. E. & Hailman, J. P. A same-sex stepparent shortens a prebreeder’s duration on the natal territory: tests of two hypotheses in Florida scrub-jays. Behav. Ecol. Sociobiol. 44, 15–22 (1998).
Article  Google Scholar 

91.
Ekman, J. & Griesser, M. Why offspring delay dispersal: experimental evidence for a role of parental tolerance. Proc. R. Soc. B 269, 1709–1713 (2002).
PubMed  Article  PubMed Central  Google Scholar 

92.
Griesser, M. & Ekman, A. Nepotistic alarm calling in the Siberian jay, Perisoreus infaustus. Anim. Behav. 67, 933–939 (2004).
Article  Google Scholar 

93.
Griesser, M. Referential calls signal predator behavior in a group-living bird species. Curr. Biol. 18, 69–73 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
Emlen, S. T. & Wrege, P. H. Breeding biology of white-fronted bee-eaters at Nakuru: the influence of helpers on breeder fitness. J. Anim. Ecol. 60, 309–326 (1991).
Article  Google Scholar 

95.
Cockburn, A. et al. Can we measure the benefits of help in cooperatively breeding birds: the case of superb fairy-wrens Malurus cyaneus?. J. Anim. Ecol. J. Anim. Ecol. 77, 430–438 (2008).
PubMed  Article  PubMed Central  Google Scholar 

96.
Kingma, S. A., Hall, M. L., Arriero, E. & Peters, A. Multiple benefits of cooperative breeding in purple-crowned fairy-wrens: a consequence of fidelity?. J. Anim. Ecol. 79, 757–768 (2010).
PubMed  PubMed Central  Google Scholar 

97.
Fitch, M. A. & Shugart, G. W. Comparative biology and behavior of monogamous pairs and one male-two female trios of Herring Gulls. Behav. Ecol. Sociobiol. 14, 1–7 (1983).
Article  Google Scholar 

98.
Del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the Birds of the World. Barn Owls to Hummingbirds. Vol. 5.Lynx Edicions, Barcelona (1999).

99.
Clayton, K. M. & Schmutz, J. K. Is the decline of Burrowing Owls Speotyto cunicularia in prairie Canada linked to changes in Great Plains ecosystems?. Bird Conserv. Int. 9, 163–185 (1999).
Article  Google Scholar 

100.
Haug, E. A., Millsap, B. A. & Martell, M. S. The burrowing owl (Speotyto cunicularia). In Poole, A. & F. Gill (editors). The birds of North America. Philadelphia: The Academy of Natural Sciences; Washington, D. C. The American Ornithologists’ Union. Washington, D. C. The American Ornithologists’ Union (1993).

101.
Carrete, M. & Tella, J. L. High individual consistency in fear of humans throughout the adult lifespan of rural and urban burrowing owls. Sci. Rep. 3, 3524 (2013).
ADS  PubMed  PubMed Central  Article  Google Scholar 

102.
Rebolo-Ifrán, N. et al. Links between fear of humans, stress and survival support a non-random distribution of birds among urban and rural habitats. Sci. Rep. 5, 13723 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

103.
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: Software for estimating relatedness and relationship from multilocus genotypes. Mol. Ecol. Notes 6, 576–579 (2006).
CAS  Article  Google Scholar 

104.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  PubMed Central  Google Scholar 

105.
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Oliehoek, P. A., Windig, J. J., Van Arendonk, J. A. & Bijma, P. Estimating relatedness between individuals in general populations with a focus on their use in conservation programs. Genetics 173, 483–496 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

107.
Laake, J. L. RMark: an R interface for analysis of capture-recapture data with MARK (2013).

108.
Zar, J. H. Statistical significance of mutation frequencies, and the power of statistical testing, using the Poisson distribution. Biometr. J. 26, 83–88 (1984).
Article  Google Scholar 

109.
Labocha, M. K. & Hayes, J. P. Morphometric indices of body condition in birds: a review. J. Ornithol. 153, 1–22 (2012).
Article  Google Scholar 

110.
Choquet, R., Lebreton, J. D., Gimenez, O., Reboulet, A. M. & Pradel, R. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture–REcapture data. Ecography 32, 1071–1074 (2009).
Article  Google Scholar 

111.
Choquet, R., Rouan, L., Pradel, R. Program E-SURGE: a software application for fitting multievent models. In Modeling demographic processes in marked populations. pp. 845–865. Springer, Boston, MA, 2009.

112.
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Article  Google Scholar 

113.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

114.
Mollie, E. B. et al. glmmTMB balances speed and flexibility among Packages for zeroinflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Article  Google Scholar 

115.
Hartig F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package. 2018. https://www.cran.r-project.org/package=DHARMa, Version 0. 2. 0.

116.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

117.
Barton, K. MuMIn: Multi-Model Inference. R package version 1. 40. 0. https://CRANhttps://CRAN.R-project.org/package=MuMIn (2017). More

1.
Castro, J. I. The shark nursery of Bulls Bay, South Carolina, with a review of the shark nurseries of the Southeastern coast of the United States. Environ. Biol. Fishes 38, 37–48 (1993).
Article  Google Scholar 
2.
Heupel, M. R. & Simpfendorfer, C. A. Estuarine nursery areas provide a low mortality environment for young bull sharks Carcharhinus leucas. Mar. Ecol. Prog. Ser. 433, 237–244. https://doi.org/10.3354/meps09191 (2011).
ADS  Article  Google Scholar 

3.
Heupel, M. R., Carlson, J. & Simpfendorfer, C. A. Shark nursery areas: concepts, definition, characterization and assumptions. Mar. Ecol. Prog. Ser. 337, 287–297 (2007).
ADS  Article  Google Scholar 

4.
Martins, A. P. B., Heupel, M. R., Chin, A. & Simpfendorfer, C. A. Batoid nurseries: definition, use and importance. Mar. Ecol. Prog. Ser. 595, 253–267. https://doi.org/10.3354/meps12545 (2018).
ADS  Article  Google Scholar 

5.
Simpfendorfer, C. A. & Milward, N. Utilization of a tropical bay as a nursery area by sharks of the families Carcharhinidae and Sphyrnidae. Environ. Biol. Fishes 37, 337–345. https://doi.org/10.1007/BF00005200 (1993).
Article  Google Scholar 

6.
Heupel, M. R. & Simpfendorfer, C. A. Estimation of mortality of juvenile blacktip sharks, Carcharhinus limbatus, within a nursery area using telemetry data. Can. J. Fish. Aquat. Sci. 59(4), 624–632 (2002).
Article  Google Scholar 

7.
Yokota, L. & Lessa, R. P. A nursery area for sharks and rays in Northeastern Brazil. Environ. Biol. Fishes 75(3), 349–356. https://doi.org/10.1007/s10641-006-0038-9 (2006).
Article  Google Scholar 

8.
Cerutti-Pereyra, F. et al. Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia, evidence for the existence of a nursery. Environ. Biol. Fishes 97(4), 371–383. https://doi.org/10.1007/s10641-013-0158-y (2014).
Article  Google Scholar 

9.
Stewart, J. D., Nuttall, M., Hickerson, E. L. & Johnston, M. A. Important juvenile manta ray habitat at flower garden banks national marine sanctuary in the northwestern Gulf of Mexico. Mar. Biol. 165, 111. https://doi.org/10.1007/s00227-018-3409-9 (2018).
CAS  Article  Google Scholar 

10.
Childs, J. N. The Occurrence, Habitat Use, and Behavior of Sharks and Rays Associating with Topographic Highs in the Northwestern Gulf of Mexico (Texas A&M University, College town, 2001).
Google Scholar 

11.
Pate, J. H. & Marshall, A. D. Urban manta rays: potential manta ray nursery habitat along a highly developed Florida coastline. Endang. Species Res. 43, 51–64. https://doi.org/10.3354/esr01054 (2020).
Article  Google Scholar 

12.
Germanov, E. S. et al. Contrasting habitat use and population dynamics of reef manta rays within the Nusa Penida Marine Protected Area Indonesia. Front. Mar. Sci. 6, 215. https://doi.org/10.3389/fmars.2019.00215 (2019).
Article  Google Scholar 

13.
Gaskins, L. C. Pregnant giant devil ray (Mobula mobular) bycatch reveals potential Northern Gulf of California pupping ground. Ecology 100, 7. https://doi.org/10.1002/ecy.2689 (2019).
Article  Google Scholar 

14.
Couturier, L. I. E. et al. Biology, ecology and conservation of the Mobulidae. J. Fish. Biol. 80, 1075–1119. https://doi.org/10.1111/j.1095-8649.2012.03264.x (2012).
CAS  Article  PubMed  Google Scholar 

15.
Stewart, J. D. et al. Research priorities to support effective manta and devil ray conservation. Front. Mar. Sci. 5, 314. https://doi.org/10.3389/fmars.2018.00314 (2018).
Article  Google Scholar 

16.
Stevens, J. D., Bonfil, R., Dulvy, N. K. & Walker, P. A. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57, 476–494. https://doi.org/10.1006/jmsc.2000.0724 (2000).
Article  Google Scholar 

17.
Dulvy, N. K., Pardo, S. A., Simpfendorfer, C. A. & Carlson, J. K. Diagnosing the dangerous demography of manta rays using life history theory. PeerJ 2, e400. https://doi.org/10.7717/peerj.400 (2014).
Article  PubMed  PubMed Central  Google Scholar 

18.
Notarbartolo di Sciara, G. Natural history of the rays of the genus Mobula in the Gulf of California. Fish Bull. 86(1), 45–66 (1988).
Google Scholar 

19.
Dulvy, N. K. & Reynolds, J. D. Evolutionary transitions among egg-laying, live-bearing and maternal inputs in sharks and rays. Proc. Phys. Soc. B 264, 1309–1315. https://doi.org/10.1098/rspb.1997.0181 (1997).
Article  Google Scholar 

20.
Marshall, A. D. & Bennett, M. B. Reproductive ecology of the reef manta ray Manta alfredi in southern Mozambique. J. Fish. Biol. 77, 169–190. https://doi.org/10.1111/j.1095-8649.2010.02669.x (2010).
CAS  Article  PubMed  Google Scholar 

21.
Croll, D. A. et al. Vulnerabilities and fisheries impacts: the uncertain future of manta and devil rays. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 562–657. https://doi.org/10.1002/aqc.2591 (2016).
Article  Google Scholar 

22.
White, W., Giles, J. & Potter, I. Data on the bycatch fishery and reproductive biology of mobulid rays (Myliobatiformes) in Indonesia. Fish. Res. 82, 65–73. https://doi.org/10.1016/j.fishres.2006.08.008 (2006).
Article  Google Scholar 

23.
Rohner, C. et al. Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Mar. Ecol. Prog. Ser. 482, 153–168. https://doi.org/10.3354/meps10290 (2013).
ADS  Article  Google Scholar 

24.
Paulin, C. D., Habib, G., Carey, C. L., Swanson, P. M. & Voss, G. J. New records of Mobula japanica and Masturus lanceolatus, and further records of Luvaris imperialis. N. Z. J. Mar Freshw. Res. 16, 11–17 (1982).
Article  Google Scholar 

25.
IUCN. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org/ (2020). Accessed 20 July 2020.

26.
Ward-Paige, C. A., Davis, B. & Worm, B. Global population trends and human use patterns of manta and mobula rays. PLoS ONE 8(9), e74835. https://doi.org/10.1371/journal.pone.0074835 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
White, E. R., Myers, M. C., Flemming, J. M. & Baum, J. K. Shifting elasmobranch community assemblage at Cocos Island an isolated marine protected area. Conserv. Biol. 29, 186–1197. https://doi.org/10.1111/cobi.12478 (2015).
Article  Google Scholar 

28.
Stevens, G., Fernando, D., Dando, M. & Sciara, G. Guide to the Manta and Devil Rays of the World (Wild Nature Press, Plymout, 2018).
Google Scholar 

29.
Marshall, A.D. et al. Mobula munkiana. The IUCN Red List of Threatened Species 2019: e.T60198A124450956. (2019). Accessed 8 March 2020.

30.
Hobro, F. The Feeding Ecology, Foraging Behavior and Conservation of Manta Rays (Mobulidae) in Baja California, Mexico (University of Wales, Cardiff, 2002).
Google Scholar 

31.
Broadhurst, M. K., Laglbauer, B. J. L. & Bennett, M. B. Gestation and size at parturition for Mobula kuhlii cf. eregoodootenkee. Environ. Biol. Fish. 102, 1009–1014. https://doi.org/10.1007/s10641-019-00886-3 (2019).
Article  Google Scholar 

32.
Lopez, J. N. Estudio comparativo de la reproducción de tres especies del género Mobula (Chondrichthyes: Mobulidae) en el suroeste del Golfo de California, México (Instituto Politécnico Nacional, México). https://www.repositoriodigital.ipn.mx/bitstream/123456789/14110/1/serranol1.pdf (2009). Accessed 15 July 2020.

33.
Heinrichs, S., O’Malley, M., Medd, H. B. & Hilton, P. Manta Ray of Hope: The Global Threat to Manta and Mobula Rays. Manta Ray of Hope Project. https://wildaid.org/wp-content/uploads/2017/09/The-Global-Threat-to-Manta-and-Mobula-Rays-WEB.pdf (2011). Accessed 15 July 2020.

34.
Notarbartolo di Sciara, G. A revisionary study of the genus Mobula Rafinesque, 1810 (Chondrichthyes: Mobulidae) with the description of a new species. Zool. J. Linn. Soc. 91, 1–91. https://doi.org/10.1111/j.1096-3642.1987.tb01723.x (1987).
Article  Google Scholar 

35.
Smith, W. D., Bizzarro, J. J. & Cailliet, G. M. The artisanal elasmobranch fishery on the east coast of Baja California, Mexico: Characteristics and management considerations. Cienc. Mar. 35(2), 209–236 (2009).
Article  Google Scholar 

36.
Afonso, A. S., Cantareli, C. V., Levy, R. P. & Veras, L. B. Evasive mating behavior by female nurse sharks, Ginglymostoma cirratum (Bonnaterre, 1788), in an equatorial insular breeding ground. Neotrop Ichthyol. 14(4), e160103. https://doi.org/10.1590/1982-0224-20160103 (2016).
Article  Google Scholar 

37.
Hamlett, W. Reproductive biology and phylogeny of chondrichthyes. In Book 3, Reproductive Biology and Phylogeny (ed. Hamlett, W.) (CRC Press, Boca Raton, 2005). https://doi.org/10.1201/9781439856000.
Google Scholar 

38.
Stevens, G. M. W., Hawkins, J. P. & Roberts, C. M. Courtship and mating behaviour of manta rays Mobula alfredi and Mobula birostris in the Maldives. J. Fish. Biol. 93(2), 344–359. https://doi.org/10.1111/jfb.13768 (2018).
Article  PubMed  Google Scholar 

39.
Villavicencio-Garayzar, C. J. Observations on Mobula munkiana (Chondrichthyes: Mobulidae) in the Bahia de la Paz, B.C.S.. Mexico. Rev. Investig. Cient. 2(2), 78–81 (1991).
Google Scholar 

40.
Marshall, A. D., Compagno, L. J. V. & Bennett, M. B. Redescription of the genus Manta with resurrection of Manta alfredi. Zootaxa 28, 1–28. https://doi.org/10.5281/zenodo.191734 (2009).
Article  Google Scholar 

41.
Obeso-Nieblas, M., Gaviño-Rodríguez, J. H., Jiménez-Illescas, A. R. & Shirasago Germán, B. Simulación numérica de la circulación por marea y viento del noroeste y sur en la Bahía de La Paz B.C.S. Oceánides 11, 1–2 (2002).
Google Scholar 

42.
Bass, A. J. Problems in studies of sharks in the Southern Indian Ocean. In Sensory Biology of Sharks, Skates and Rays (ed. Hodgson, E. S.) 545–594 (Tufts University, Medford, 1978).
Google Scholar 

43.
Tenzing, P. The Eco-physiology of Two Species of Tropical Stingrays in an Era of Climate Change (James Cook University, Townsville, 2014).
Google Scholar 

44.
Brinton, E., Fleminger, A. & Siegel-Causey, D. The temperate and tropical planktonic biotas of the Gulf of California. CalCOFI Rep. 27, 228–266 (1986).
Google Scholar 

45.
Deakos, M. H. Paired-laser photogrammetry as a simple and accurate system for measuring the body size of free-ranging manta rays Manta alfredi. Aquat. Biol. 10, 1–10. https://doi.org/10.3354/ab00258 (2010).
Article  Google Scholar 

46.
Salomon-Aguilar, C. A., Villavicencio-Garayzar, C. J. & Reyes-Bonilla, H. Shark breeding grounds and seasons in the Gulf of California: fishery management and conservation strategy. Cienc. Mar. 35(4), 369–388 (2009).
Article  Google Scholar 

47.
Brinton, E. & Townsend, A. W. Euphausiids in the Gulf of California the 1957 cruises. Calif. Coop. Ocean. Fish. Investig. Rep. 21, 211–236 (1980).
Google Scholar 

48.
De Silva-Davila, R. & Palomares-Garcia, R. Unusual larval growth production of Nyctiphanes simplex in Bahia de La Paz, Baja California Mexico. J. Crustac. Biol. 18(3), 490–498. https://doi.org/10.1163/193724098X00313 (1998).
Article  Google Scholar 

49.
Gómez-Gutiérrez, J., Martínez-Gómez, S. & Robinson, C. J. Seasonal growth, molt, and egg production of Nyctiphanes simplex (Crustacea: Euphausiacea) juveniles and adults in the Gulf of California. Mar. Ecol. Prog. Ser. 455, 173–194. https://doi.org/10.3354/meps09631 (2012).
ADS  Article  Google Scholar 

50.
Uchida, S., Toda, M. & Matsumoto, Y. Captive records of manta rays in Okinawa Churaumi Aquarium. In Joint Meeting of Ichthyologists and Herpetologists (Montreal, QC), 23–28 (2008).

51.
Hidalgo-Gonzalez, R. M. & Alvarez-Borrego, S. Total and new production in the Gulf of California estimated from ocean color data from the satellite sensor SeaWIFS. Deep Sea Res. II(51), 739–752. https://doi.org/10.1016/j.dsr2.2004.05.006 (2004).
ADS  CAS  Article  Google Scholar 

52.
Santamaria-Del-Angel, E., Alvarez-Borrego, S., Millán-Nuñez, R. & Muller-Karger, F. E. Sobre el efecto de las surgencias de verano en la biomasa fitoplanctónica del Golfo de California. Rev. Soc. Mex. Hist. Nat. 49, 207–212 (1999).
Google Scholar 

53.
VUE Software Manual, Version 2.5 Vemco. Bedford, Nova Scotia, Canada (2014).

54.
Daly, R., Smale, M. J., Cowley, P. D. & Froneman, P. W. Residency patterns and migration dynamics of adult bull sharks (Carcharhinus leucas) on the east coast of southern Africa. PLoS ONE 9, e109357. https://doi.org/10.1371/journal.pone.0109357 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

55.
Smith, P.E. & Richardson, S.L. Técnicas modelo para prospecciones de huevos y larvas de peces pelágicos. In: FAO Fishing Technical Report. 175, 107 (1979).

56.
Beers, J. R. Volumetric methods. In Book 4, Zooplankton Fixation and Preservation. Monographs on Oceanography methods (ed. Steedman, H. F.) (The UNESCO Press, Paris, 1976).
Google Scholar  More

1.
Musso, D., Rodriguez-Morales, A. J., Levi, J. E., Cao-Lormeau, V.-M. & Gubler, D. J. Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America. Lancet Infect. Dis. 18, e355–e361 (2018).
PubMed  Article  Google Scholar 
2.
Mavian, C. et al. Islands as hotspots for emerging mosquito-borne viruses: a one-health perspective. Viruses 11, 11 (2018).

3.
Cao-Lormeau, V.-M. Tropical islands as new hubs for emerging arboviruses. Emerg. Infect. Dis. 22, 913–915 (2016).
PubMed  PubMed Central  Article  Google Scholar 

4.
Cassadou, S. et al. Emergence of chikungunya fever on the French side of Saint Martin island, October to December 2013. Euro Surveill. 19, 20752 (2014).

5.
Dorléans, F. et al. Outbreak of Chikungunya in the French Caribbean Islands of Martinique and Guadeloupe: findings from a Hospital-Based Surveillance System (2013–2015). Am. J. Trop. Med. Hyg. 98, 1819–1825 (2018).

6.
Halstead, S. B. Reappearance of chikungunya, formerly called dengue, in the Americas. Emerg. Infect. Dis. 21, 557–561 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Faria, N. R. et al. Zika virus in the Americas: early epidemiological and genetic findings. Science 352, 345–349 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Grubaugh, N. D., Faria, N. R., Andersen, K. G. & Pybus, O. G. Genomic insights into Zika virus emergence and spread. Cell 172, 1160–1162 (2018).
CAS  PubMed  Article  Google Scholar 

9.
Metsky, H. C. et al. Zika virus evolution and spread in the Americas. Nature 546, 411–415 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Hotez, P. J. & Murray, K. O. Dengue, West Nile virus, chikungunya, Zika-and now Mayaro? PLoS Negl. Trop. Dis. 11, e0005462 (2017).
PubMed  PubMed Central  Article  Google Scholar 

11.
Lorenz, C., Freitas Ribeiro, A. & Chiaravalloti-Neto, F. Mayaro virus distribution in South America. Acta Trop. 198, 105093 (2019).
PubMed  Article  Google Scholar 

12.
Ganjian, N. & Riviere-Cinnamond, A. Mayaro virus in Latin America and the Caribbean. Rev. Panam. Salud Publica 44, e14 (2020).
PubMed  PubMed Central  Article  Google Scholar 

13.
Weaver, S. C. & Reisen, W. K. Present and future arboviral threats. Antivir. Res. 85, 328–345 (2010).
CAS  PubMed  Article  Google Scholar 

14.
Long, K. C. et al. Experimental transmission of Mayaro virus by Aedes aegypti. Am. J. Trop. Med. Hyg. 85, 750–757 (2011).
PubMed  PubMed Central  Article  Google Scholar 

15.
Suspected dengue cases by epidemiological week for countries and territories of the America. https://www.paho.org/data/index.php/en/mnu-topics/indicadores-dengue-en/dengue-nacional-en/252-dengue-pais-ano-en.html?start=2.

16.
Five-fold increase in dengue cases in the Americas over the past decade. https://www.paho.org/hq/index.php?option=com_content&view=article&id=9657:2014-los-casos-dengue-americas- (2014).

17.
Obolski, U. et al. MVSE: an R‐package that estimates a climate‐driven mosquito‐borne viral suitability index. Methods Ecol. Evol. 10, 1357–1370 (2019).
PubMed  PubMed Central  Article  Google Scholar 

18.
Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).
PubMed  PubMed Central  Article  Google Scholar 

19.
Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Hamlet, A. et al. The seasonal influence of climate and environment on yellow fever transmission across Africa. PLoS Negl. Trop. Dis. 12, e0006284 (2018).
PubMed  PubMed Central  Article  Google Scholar 

21.
Do, T. T. T., Martens, P., Luu, N. H., Wright, P. & Choisy, M. Climatic-driven seasonality of emerging dengue fever in Hanoi, Vietnam. BMC Public Health 14, 1078 (2014).
PubMed  PubMed Central  Article  Google Scholar 

22.
Perez-Guzman, P. N. et al. Measuring mosquito-borne viral suitability in Myanmar and implications for Local Zika virus transmission. PLoS Curr. 10 (2018).

23.
Rodrigues Faria, N. et al. Epidemiology of Chikungunya Virus in Bahia, Brazil, 2014-2015. PLoS Curr. 8 (2016).

24.
Lourenço, J. et al. Epidemiological and ecological determinants of Zika virus transmission in an urban setting. eLife 6, e29820 (2017).

25.
Dengue serotypes by year for countries and territories of the Americas. https://www.paho.org/data/index.php/es/temas/indicadores-dengue/dengue-nacional/549-dengue-serotypes-es.html.

26.
Bowman, L. R., Rocklöv, J., Kroeger, A., Olliaro, P. & Skewes, R. A comparison of Zika and dengue outbreaks using national surveillance data in the Dominican Republic. PLoS Negl. Trop. Dis. 12, e0006876 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Lindsey, N. P., Staples, J. E. & Fischer, M. Chikungunya Virus disease among travelers-United States 2014–2016. Am. J. Trop. Med. Hyg. 98, 192–197 (2018).
PubMed  Article  Google Scholar 

28.
Zingman, M. A., Paulino, A. T. & Payano, M. P. Clinical manifestations of chikungunya among university professors and staff in Santo Domingo, the Dominican Republic. Rev. Panam. Salud Publica 41, e64 (2017).
PubMed  PubMed Central  Article  Google Scholar 

29.
Rosario, V. et al. Chikungunya infection in the general population and in patients with rheumatoid arthritis on biological therapy. Clin. Rheumatol. 34, 1285–1287 (2015).
CAS  PubMed  Article  Google Scholar 

30.
He, A., Brasil, P., Siqueira, A. M., Calvet, G. A. & Kwatra, S. G. The emerging Zika virus threat: a guide for dermatologists. Am. J. Clin. Dermatol. 18, 231–236 (2017).
PubMed  Article  Google Scholar 

31.
Martinez, J. D., Garza, J. A. Cla & Cuellar-Barboza, A. Going viral 2019: Zika, Chikungunya, and Dengue. Dermatol. Clin. 37, 95–105 (2019).
CAS  PubMed  Article  Google Scholar 

32.
Duffy, M. R. et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360, 2536–2543 (2009).
CAS  PubMed  Article  Google Scholar 

33.
Pineda, C., Muñoz-Louis, R., Caballero-Uribe, C. V. & Viasus, D. Chikungunya in the region of the Americas. A challenge for rheumatologists and health care systems. Clin. Rheumatol. 35, 2381–2385 (2016).
PubMed  Article  Google Scholar 

34.
Langsjoen, R. M. et al. Molecular virologic and clinical characteristics of a chikungunya fever outbreak in La Romana, Dominican Republic, 2014. PLoS Negl. Trop. Dis. 10, e0005189 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Grubaugh, N. D. et al. Tracking virus outbreaks in the twenty-first century. Nat. Microbiol. 4, 10–19 (2019).
CAS  PubMed  Article  Google Scholar 

36.
Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Liu-Helmersson, J., Stenlund, H., Wilder-Smith, A. & Rocklöv, J. Vectorial capacity of Aedes aegypti: effects of temperature and implications for global dengue epidemic potential. PLoS ONE 9, e89783 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Winokur, O. C., Main, B. J., Nicholson, J. & Barker, C. M. Impact of temperature on the extrinsic incubation period of Zika virus in Aedes aegypti. PLoS Negl. Trop. Dis. 14, e0008047 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Chan, M. & Johansson, M. A. The incubation periods of Dengue viruses. PLoS ONE 7, e50972 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).
PubMed  PubMed Central  Article  Google Scholar 

41.
Faria, N. R. et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 546, 406–410 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Cauchemez, S. et al. Local and regional spread of chikungunya fever in the Americas. Eur. Surveill. 19, 20854 (2014).
CAS  Article  Google Scholar 

43.
Nishiura, H., Kinoshita, R., Mizumoto, K., Yasuda, Y. & Nah, K. Transmission potential of Zika virus infection in the South Pacific. Int. J. Infect. Dis. 45, 95–97 (2016).
PubMed  Article  Google Scholar 

44.
Liu, Y. et al. Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones. Environ. Res. 182, 109114 (2020).
CAS  PubMed  Article  Google Scholar 

45.
Rodriguez-Barraquer, I., Salje, H. & Cummings, D. A. Opportunities for improved surveillance and control of dengue from age-specific case data. Elife 8, e45474 (2019).

46.
Plan de preparación y respuesta frente a brotes de Fiebre Chikungunya. Resolución Ministerial N° 427 – 2014/MINSA Lima, Peu (2014).

47.
Low, G. K.-K., Ogston, S. A., Yong, M.-H., Gan, S.-C. & Chee, H.-Y. Global dengue death before and after the new World Health Organization 2009 case classification: a systematic review and meta-regression analysis. Acta Trop. 182, 237–245 (2018).
PubMed  Article  Google Scholar 

48.
Freitas, A. R. R., Alarcón-Elbal, P. M., Paulino-Ramírez, R. & Donalisio, M. R. Excess mortality profile during the Asian genotype chikungunya epidemic in the Dominican Republic. 2014. Trans. R. Soc. Trop. Med. Hyg. 112, 443–449 (2018).
PubMed  Article  Google Scholar 

49.
Imai, N., Dorigatti, I., Cauchemez, S. & Ferguson, N. M. Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries. PLoS Negl. Trop. Dis. 9, e0003719 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Grubaugh, N. D. et al. Xenosurveillance: a novel mosquito-based approach for examining the human-pathogen landscape. PLoS Negl. Trop. Dis. 9, e0003628 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Fauver, J. R. et al. The use of xenosurveillance to detect human bacteria, parasites, and viruses in mosquito bloodmeals. Am. J. Trop. Med. Hyg. 97, 324–329 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Fauver, J. R. et al. Xenosurveillance reflects traditional sampling techniques for the identification of human pathogens: a comparative study in West Africa. PLoS Negl. Trop. Dis. 12, e0006348 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Grubaugh, N. D. et al. Travel surveillance and genomics uncover a hidden zika outbreak during the waning epidemic. Cell 178, 1057–1071.e11 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Vogels, C. B. F. et al. Arbovirus coinfection and co-transmission: a neglected public health concern? PLoS Biol. 17, e3000130 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Bisanzio, D. et al. Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico. PLoS Negl. Trop. Dis. 12, e0006298 (2018).
PubMed  PubMed Central  Article  Google Scholar 

56.
Freitas, L. P., Cruz, O. G., Lowe, R. & Sá Carvalho, M. Space–time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro. Proc. R. Soc. B: Biol. Sci. 286, 20191867 (2019).
Article  Google Scholar 

57.
Shioda, K. et al. Identifying signatures of the impact of rotavirus vaccines on hospitalizations using sentinel surveillance data from Latin American countries. Vaccine 38, 323–329 (2020).
PubMed  Article  Google Scholar 

58.
Blohm, G. et al. Mayaro as a Caribbean traveler: Evidence for multiple introductions and transmission of the virus into Haiti. Int. J. Infect. Dis. 87, 151–153 (2019).
CAS  PubMed  Article  Google Scholar 

59.
Lednicky, J. et al. Mayaro Virus in Child with Acute Febrile Illness, Haiti, 2015. Emerg. Infect. Dis. 22, 2000–2002 (2016).
PubMed  PubMed Central  Article  Google Scholar 

60.
Weppelmann, T. A. et al. A tale of two flaviviruses: a seroepidemiological study of dengue virus and west nile virus transmission in the ouest and sud-est departments of Haiti. Am. J. Trop. Med. Hyg. 96, 135–140 (2017).
PubMed  PubMed Central  Article  Google Scholar 

61.
Wiggins, K., Eastmond, B. & Alto, B. W. Transmission potential of Mayaro virus in Florida Aedes aegypti and Aedes albopictus mosquitoes. Med. Vet. Entomol. 32, 436–442 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Pereira, T. N., Carvalho, F. D., De Mendonça, S. F., Rocha, M. N. & Moreira, L. A. Vector competence of Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquitoes for Mayaro virus. PLoS Negl. Trop. Dis. 14, e0007518 (2020).
PubMed  PubMed Central  Article  Google Scholar 

63.
Kantor, A. M., Lin, J., Wang, A., Thompson, D. C. & Franz, A. W. E. Infection pattern of Mayaro Virus in Aedes aegypti (Diptera: Culicidae) and transmission potential of the virus in mixed infections with Chikungunya virus. J. Med. Entomol. 56, 832–843 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Komar, O. et al. West Nile virus survey of birds and mosquitoes in the Dominican Republic. Vector-Borne Zoonotic Dis. 5, 120–126 (2005).
PubMed  Article  PubMed Central  Google Scholar 

65.
Requena-Méndez, A. et al. Cases of chikungunya virus infection in travellers returning to Spain from Haiti or Dominican Republic, April-June 2014. Eur. Surveill. 19, 20853 (2014).
Article  Google Scholar 

66.
Millman, A. J. et al. Chikungunya and Dengue virus infections among united states community service volunteers returning from the Dominican Republic, 2014. Am. J. Trop. Med. Hyg. 94, 1336–1341 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Duijster, J. W. et al. Zika virus infection in 18 travellers returning from Surinam and the Dominican Republic, The Netherlands, November 2015–March 2016. Infection 44, 797–802 (2016).
PubMed  PubMed Central  Article  Google Scholar 

68.
Barzon, L. et al. Isolation of infectious Zika virus from saliva and prolonged viral RNA shedding in a traveller returning from the Dominican Republic to Italy, January 2016. Eur. Surveill. 21, 30159 (2016).
Google Scholar 

69.
Goncé, A. et al. Spontaneous abortion associated with Zika virus infection and persistent viremia. Emerg. Infect. Dis. 24, 933–935 (2018).
PubMed  PubMed Central  Article  Google Scholar 

70.
Díaz-Menéndez, M. et al. Initial experience with imported Zika virus infection in Spain. Enfermedades Infecciosas y. Microbiol.ía Cl.ínica 36, 4–8 (2018).
Google Scholar 

71.
Perez, F. et al. The decline of dengue in the Americas in 2017: discussion of multiple hypotheses. Trop. Med. Int. Health 24, 442–453 (2019).
PubMed  PubMed Central  Article  Google Scholar 

72.
Ribeiro, G. S. et al. Does immunity after Zika virus infection cross-protect against dengue?. Lancet Glob. Health 6, e140–e141 (2018).
PubMed  Article  Google Scholar 

73.
Ribeiro, G. S. et al. Influence of herd immunity in the cyclical nature of arboviruses. Curr. Opin. Virol. 40, 1–10 (2020).
CAS  PubMed  Article  Google Scholar 

74.
Gordon, A. et al. Prior dengue virus infection and risk of Zika: a pediatric cohort in Nicaragua. PLoS Med. 16, e1002726 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

75.
Rodriguez-Barraquer, I. et al. Impact of preexisting dengue immunity on Zika virus emergence in a dengue endemic region. Science 363, 607–610 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

76.
Tsang, T. K. et al. Effects of infection history on dengue virus infection and pathogenicity. Nat. Commun. 10, 1246 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Katzelnick, L. C., Montoya, M., Gresh, L., Balmaseda, A. & Harris, E. Neutralizing antibody titers against dengue virus correlate with protection from symptomatic infection in a longitudinal cohort. Proc. Natl Acad. Sci. USA 113, 728–733 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

78.
República Dominicana Chikungunya. https://www.paho.org/dor/images/stories/archivos/chikungunya/boletin_chikv_no-13_2014_8_20.pdf?ua=1 (2014).

79.
Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): a metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).
Article  Google Scholar 

80.
Vazeille, M. et al. Two Chikungunya isolates from the outbreak of La Reunion (Indian Ocean) exhibit different patterns of infection in the mosquito, Aedes albopictus. PLoS ONE 2, e1168 (2007).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Lamballerie, Xde et al. Chikungunya virus adapts to tiger mosquito via evolutionary convergence: a sign of things to come? Virol. J. 5, 33 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

82.
González, M. A. et al. Micro-environmental features associated to container-dwelling mosquitoes (Diptera: Culicidae) in an urban cemetery of the Dominican Republic. Rev. Biol. Trop. 67, 132–145 (2019).

83.
González, M. A. et al. A survey of tire-breeding mosquitoes (Diptera: Culicidae) in the Dominican Republic: considerations about a pressing issue. Biomédica 40, 507–515 (2020).

84.
IX Censo Nacional De Población Y Vivienda. vol. 1 (2012).

85.
PLISA Health Information Platform for the Americas. https://www.paho.org/data/index.php/en/.

86.
Dominican Republic: Human Development Indicators. http://hdr.undp.org/en/countries/profiles/DOM.

87.
Lipsitch, M. et al. Transmission dynamics and control of severe acute respiratory syndrome. Science 300, 1966–1970 (2003).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Petrone, M. E. et al. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic. Repository: Arbovirus_Epi_DR. (2020), https://doi.org/10.5281/zenodo.4287651. More

Nutrient, FOM, and physiochemical contents of the fecal manures
For the three main fertility nutrients (N, P and K) in the fecal manures (Table 1a) the E manure contained the highest N level at 80 µg/g, 16-fold more than the other samples. In addition, it had a high P level of 100 µg/g (along with PC1 and PC3) compared with an average of 34.66 µg/g for the others, and a moderate K level of 150 µg/g (others ranged from 125.00 to 350.00 µg/g. The FOM content ranged from 1.00 to 1.50% for all the animal manures (Table 1a). For the general physiochemical properties, chicken manures showed a relatively high salinity (Table 1b: 3.52 ± 0.28 to 4.01 ± 0.64 ppt), and these ionic salts caused a high conductivity (11.27 ± 0.69 to 12.79 ± 0.08 mS/cm) and high water saturation (73.29 ± 0.40 to 80.56 ± 3.17 mS/cm), giving less available water for plant roots. A moderate level of salinity, conductivity and water content, such as those in E, are generally preferred for farming soil. The water content of the manure was lowest in G2, goats fed with P. purpureum (23.75 ± 3.07%), but increased two-fold in G1 goats fed with D. eriantha (49.35 ± 6.08%), suggesting a different type of grass feed might confer different fecal liquid and dry contents, or the chemical composition influenced the water activity in the goat’s gut. The R manure also had a low water content (41.53 ± 5.57%).
Table 1 Characteristics of (a) NPK and FOM, and (b) physiochemical levels of the different animal fecal manures.
Full size table

The visual observation of each manure was in accord with the reported water content, with a dry and hard texture (insufficient moisture) for G1, G2, and R manures, and a wet texture for the chicken, cattle, pig, and buffalo manures. Too much water in the soil can adversely affect aeration, where the plant’s roots receive insufficient oxygen and rot. Moreover, most plants prefer a slightly acidic soil pH of 6.2–6.8, which was found in the R, MB and E composts (Table 1b). The manure pH can change through reactions such as organic matter decomposition that affects the availabilities of NPK39.
From the overall comparison of the NPK nutrients and physiochemical properties, the fecal manures from the Phuparn chicken species might be less suitable as a fertilizer because of the poor N level, and high level of salts and conductivity. Indeed, the manures from most species contained a low N level, and those from goat and rabbit also had a relatively low water content (insufficient water for a plant to absorb nutrients from the soil through the roots, to the trunk, leaves and fruits). A moderate water content in the manure was appropriate. But of course, a somewhat low water level could be adjusted by adding water. The biophysiochemical quality analyses of the manures suggested that the E manure would be appropriate as a fertilizer because of the enhanced NPK levels, low salinity, low conductivity, slightly acidic-neutral pH, and a moderate water content (Table 1b).
Quantification of total bacteria in the fecal manures
The total number of bacteria in copies/g DW fecal manure was derived from the 16S rRNA gene qPCR. Figure 1a showed the data were consistent between independent triplicate samples, and that the E and R manures had the lowest bacterial counts (at 2.1 × 109 and 2.5 × 109 copies/g DW, respectively). The other animal fecal manures contained more than 5 × 109 copies/g DW, and the greatest bacterial load was found in PC2 and MB at 2.6 × 1010 and 1.9 × 1010 copies/g DW, respectively, while PC1 and PC3 had a significantly lower level (p  98% of the sequencing coverage of taxonomic compositions at the genus and species levels, respectively (Table 2). The average Good’s coverage indices were 99.54% and 99.43% for the genus and species levels, respectively (Supplemental Table 1). This was consistent with the plateau rarefaction curves, showing the frequencies of OTUs become constant despite increasing sequencing reads, meaning a sufficient sequencing coverage was obtained (Supplemental Fig. 1).
Table 2 Good’s coverage indices (estimated sequencing coverage) and alpha diversity indices of bacterial taxonomic profiles by 16S rRNA gene sequences at the (a) genus and (b) species levels.
Full size table

Combining the count of total 16S rRNA gene copies with the OTU percent compositions gave the quantitative number of copies of each OTU in a community. These quantitative microbiota data were then used to compute the alpha and beta diversity measurements. The alpha diversity revealed that the microbiota of E had the relatively most diverse OTUs at both the genus and species levels (Chao richness, Fig. 2a,c), regardless of having the lowest total bacterial count (Fig. 1). The low total bacterial abundance but high diversity in E underlined that the various OTUs of bacteria in E might be present in small numbers when compared to the copy numbers of OTUs in the other animal manures. Note that the alpha diversity obtained by considering the distribution of general OTUs among the different animal manures were similar (Shannon diversity, Fig. 2b,d).
Figure 2

Alpha diversity measurements of OTU compositions at (a,b) genus and (c,d) species levels, by richness (Chao) and evenness (Shannon). Box plot with bar representing the mean from three sequencing replicates, and asterisk (*) indicates a statistically significant difference by one-way ANOVA at p  1% abundance). In (b), the OTUs where Mothur could not identify the genus name were denoted by small letters (p_ abbreviates phylum; o_, order; and f_, family) to the deepest taxonomic names that could be identified.

Full size image

Relative frequencies of plant symbiotic and pathogenic bacterial genera
Plant symbiosis and pathogenic bacteria were analysed across the different animal manures. Manures PC1-3, G1, G2 and CC demonstrated generally abundant symbiotic bacteria comprised of the genera Pseudomonas, Bacillus, Arthrobacter, Flavobacterium, Alcaligenes and Streptomyces (Fig. 4a). For examples, PC2 contained abundant Alcaligenes (1.16 × 1010 cells/g DW), Pseudomonas (3.35 × 109), Flavobacterium (2.82 × 108) and Arthrobacter (3.8 × 108). Streptomyces was only found in G1, G2 and E in moderate numbers (5.9–29.0 × 106 cells/g DW).
Figure 4

Comparative number of bacterial genera categorized as plant (a) symbiotic or (b) pathogenic genera across the different animal manures. Data represented mean ± S.D. List of bacteria categorized as plant symbionts and pathogens were downloaded from the Virulence Factor Database (VFDB).

Full size image

For the plant pathogenic bacteria, high levels were found in the manures that also contained high levels of plant symbiosis bacteria (PC1-3, G1, G2 and CC), except for E, plus the other manures, such as S, D and MB. The E had none of VFDB-listed plant pathogens. Thus, all manures except for E contained plant pathogenic bacteria, and these were from Escherichia, Shigella, Enterococcus, Clostridium, Acinetobacter, Treponema, Staphylococcus and Bacteroides (Fig. 4b). This finding correlated with the percent abundance of genera (Fig. 3b) where, examples, Treponema were relatively high in PCO and PC3 at 2.03 × 108 and 1.31 × 108 cells/g DW, respectively. Escherichia were relatively low in R and DC at 1.15 × 105 and 2.06 × 105 cells/g DW, respectively. In contrast, only E did not contain any VFDB-listed plant pathogenic bacteria suggesting that E offers a plant pathogen-free organic fertilizer.
Relationship among bacterial communities, and statistical correlation with the biophysiochemical properties
The NMDS demonstrated both the reproducibility of the data between the independent triplicate samples, except for R and PCO. The larger variation in bacterial communities were found generally between the manures from different animal species, while the minor variation in bacterial communities were found between the breeds, or the feeding diets. For examples, the variation in quantitative microbiota profiles between G1, G2 and E, compared with PC1-3 (p = 0.085) and MB (p = 0.59) (Supplemental Fig. 2). Indeed, the quantitative microbiota structures belonging to the six animal manures that had all the VFDB’s categorized plant pathogens (PC, S, R, D and MB, except PCO) were rather distant from E (p = 0.101, 0.052, 0.089, 0.104 and 0.099, respectively).
Seven parameters (NPK and four physiochemical properties) were analyzed for possible Pearson’s correlation with any of the quantitative microbiota structures. The N level was strongly correlated (p = 0.01) to the structures and in the same direction of the E (Fig. 5). The percent water, salinity and conductivity characteristics were significant and associated in the direction opposite to E, and also to the G1, G2 and PCO microbiota structures. On the other hand, the PC1, PC2 and MB microbiota structures were strongly associated with the water content, salinity and conductivity. These parameters are suggested to be important in controlling the diversity of microbiota structures.
Figure 5

Relationship among bacterial communities via NMDS constructed from thetayc distance coefficients among quantitative microbiota (stress value = 0.15, R2 = 0.86), and Pearson’s correlation with nutrients and physiochemical properties (AMOVA, p  More

Percoll density gradient centrifugation
The removal of the associated bacteria from KMHK cells against four Percoll density gradients, 90%, 90–50%, 90–50–30% and 90–50–30–10% were shown in Fig. 2. There was no significant difference of the remaining total bacterial counts (with an initial total bacterial count of 6.36 ± 0.04 log10 CFU/mL) between algal samples centrifuged with 90% and 90%–50% Percoll gradients, but decreased significantly from 5.02 ± 0.2 log10 CFU/mL to 4.38 ± 0.05 log10 CFU/mL when dinoflagellate samples were centrifuged with 90–50–30% Percoll gradient with no further increase on adding another layer of 10% density to the gradient (i.e., 90–50–30–10%). These suggested that the highest bacterial removal capacity was achieved by centrifugation of the KMHK cells with the three-layer discontinuous (90–50–30%) gradient. This gradient was adopted in subsequent experiments in the present study, but it was different from Cho et al. who centrifuged the small Haptphyta, Isochrysis galbana (6–12 μm) with the five-layer discontinuous gradient (50%–40%–30%–20%–10%) and harvested the algal cell in between 40 and 30% Percoll5,27. As far as we know, this is the only previous study employing discontinuous gradient for algal culture, and it is obvious the optimized gradient composition varies among algal species, probably because of the diverse algal size and morphology. Vu et al. (2018) suggested that cells with a swimming ability may swim away from the concentrated zone after centrifugation, resulting in low cell recovery efficiency11. In this study, however, the swimming ability of KMHK cells was lost only temporarily for several minutes after centrifugation. This indicated that KMHK cell recovery would not be affected if the supernatant were removed immediately after centrifugation.
Figure 2

Total bacterial count in the algal sample after centrifugation with different Percoll density gradients. All data are presented as means ± standard deviations of three independent experiments (n = 3). Different letters on the top of the bar indicate that the means were significantly different among gradients at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

Full size image

Density gradient centrifugation provides an excellent alternative to filtration and micro-pipetting to physically separate bacterial cells from the algal cells, especially for fragile cell separation11. The density medium provides padding and thus protects the algal cells from the shearing force and enhance the separation efficiency17. Although filtration is one of the commonly adopted separating techniques because of its convenience, inexpensive and easy to use11, it is infeasible for use with dinoflagellate samples. However, membrane filters can be easily clogged by the algal cells and thus prolong the processing time of the filtration. Our previous experience found that more than 1 h was required to filter 10 mL of a dinoflagellate sample with 1 L of sterile medium for washing. More, algal cell recovery becomes extremely difficult when the cells are stuck firmly to the filter, and the algal cells may be easily damaged during cell harvest. This is a very important consideration for fragile cells of the unarmored dinoflagellates as they are vulnerable to the shear force generated during filtration. Micropipetting method applied in Coscinodiscus wailesii was laborious, and a skilled operator was required to pipette the algal cells from a culture droplet and sequential wash in droplets19.
In the gradient centrifugation, the commonly used density media include Ficoll, Ludox and Percoll. Of these, Ficoll is not suitable for marine samples because it is a polysaccharide-based medium that is non-isotonic at high medium concentrations and becomes viscous liquid when dissolved in seawater28,29. Ludox and Percoll are both silicon-based density media compatible with seawater. Percoll is preferred for cell separation because it is less cytotoxic to the algal cells than is Ludox30. Few studies have demonstrated the feasibility of using Percoll density gradient centrifugation for microalgal samples17,31. Two fragile dinoflagellate species, Heterosigma carterae and Cochlodinium polykrikoides, have been efficiently harvested and recovered through centrifugation using 90% Percoll17,31.
Bacterial removal by basic and extended protocols
In the present study, Percoll density gradient centrifugation was coupled with antibiotic treatment. It is because the use of antibiotics is one of the most common bacterial killing methods but antibiotics alone rarely achieve complete bacterial elimination from the algal culture11. Antibiotic susceptibility testing results in this study reveal that the bacteria associated with KMHK culture were sensitive to the antibiotic cocktail used, that is, a combination of 100 U of penicillin, 100 µg/mL streptomycin, 100 µg/mL gentamicin and 1 µg/mL tetracycline. Similarly, Ki and Han also reported that the combination of 100 mg/L streptomycin, 150 mg/L ampicillin, 150 mg/L penicillin G and 200 mg/L gentamicin effectively killed bacteria without having detrimental effects on the dinoflagellates Peridinium bipes and A. tamarense12. Guillard demonstrated that most algal species tolerated 100 mg/L penicillin, 25 mg/L streptomycin and 25 mg/L gentamicin reasonably well32. However, many red colonies, identified as those of Rhodopirellula baltica through 16 s rDNA sequencing analysis, were observed on the antibiotic susceptibility testing plate of KMHK cells containing only penicillin, streptomycin and gentamicin in our study. Tetracycline was therefore included in the present antibiotic treatment based on previous report that Rhodopirellula sp. was highly susceptible to 0.5 ppm of tetracycline33. It is common to modify the antibiotic cocktail for different algal cultures since the antibiotics depends on the microbiome. For instance, Su et al. treated Alexandrium cultures with a combination of gentamycin, streptomycin, cephalothin and rifampicin for 7 days to obtain axenic cultures13.
The effectiveness of the basic and extended protocols to remove bacteria in the algal samples is summarized in Fig. 3. The total bacterial count significantly reduced from initial 5.79 ± 0.22 log10 CFU/mL to 4.88 ± 0.05 log10 CFU/mL (p ≤ 0.05) after centrifuged with 90% Percoll (Step 1), even though the primary aim of this step was to condense the algal cells. This is probably due to the removal of numerous free-living bacteria in the supernatant, which was discarded. In the subsequent two gradient centrifugation using 90–50–30% density layers (Steps 2 and 3), approximately 12% of bacteria were further removed at p ≤ 0.05. The bacterial count did not show any additional reduction even when a step of a 90–50–30% density gradient centrifugation was added between Steps 3 and 4 (data not shown). After Step 4 with 48-h antibiotic treatment, the bacterial count was significantly reduced by 31% (Fig. 3a). These results indicated that numerous algae-associated bacteria could be effectively inhibited using the antibiotics but these steps could not completely eradicate the bacteria. No further decline in the bacterial count was found after Step 5 but decreased significant after Step 6, although both steps employed the same gradient centrifugation (90–50–30%). This could probably be the killing effect of the extended incubation of the bacteria with the remaining antibiotics, and/or the bacterial count significantly reduced by the gradient centrifugation in Step 5 was offset by the intracellular bacteria released from algal cell lysis during the antibiotic treatment. The effect of antibiotic exposure time on bacterial removal efficiency was shown in Table 1 below, while the existence of intracellular bacteria inside the dinoflagellate cells remains controversial6 and deserves more in-depth studies. The residue antibiotics, bacteria and algal cell debris were reported to suppress the algal cell growth17,31, these suppressions could be effectively removed in the present study as shown by algal regrowth (Fig. 3).
Figure 3

Bacterial removal in the KMHK sample using the basic and extended protocols. (a) Total bacterial count after different steps in the basic protocol. Initial: the initial bacterial count in KMHK samples at the beginning; Supplementary Fig. 1 illustrate the Step 1 to 6 of basic protocol. (b) Total bacterial count after the basic and extended protocols. Extended protocol refers to the descriptions in material and method. (c) Total bacterial count in the treated KMHK cultures at different days of cultivation. (d) Algal cell concentration during the regrowth of the treated KMHK cultures. All data are presented as mean ± standard deviations of three independent experiments (n = 3). Different letters on the top of the bar indicate that means were significantly different among samples at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

Full size image

After all six steps of the present protocol, the total bacterial count remaining in the KMHK culture was 1.13 ± 0.07 log10 CFU/mL (equivalent to approximately 13 CFU/mL), indicating that  > 99.9% of the bacteria were removed. Nevertheless, the bacterial count increased significantly to 5.75 ± 0.14 log10 CFU/mL (equivalent to approximately 5.86 × 105 CFU/mL) in day 7 KMHK regrow culture after the protocol (Fig. 3c). We used the extended protocol to remove the remaining bacteria by repeating the 48-h antibiotic incubation and Steps 4–6 after the basic protocol (Fig. 3b). However, the total bacterial count did not have any significant change between the basic and extended protocols (Fig. 3b), demonstrating that the additional steps, that is, repeating the 48-h antibiotic incubation and Steps 4–6, in the extended protocol failed to eliminate the few remaining bacteria in the KMHK culture. Similar to the basic protocol, the remaining bacteria regrew significantly in day 3 and day 7 KMHK cultures after the extended one (Fig. 3c). More, the additional steps of antibiotic treatment and centrifugation even damaged the algal cells, as reflected by the regrowth of KMHK cells was strongly inhibited by the extended protocol (Fig. 3d). After the extended protocol, the KMHK cell density at day 7 was only half of that after the basic protocol that regrew normally and reached the cell density of approximately 10,000 cells/mL after 7 days, comparable to that of a routine culture. The algal growth rates (µ) after the basic and extended protocols were 1.29 and 1.14, respectively. This implied that the additional steps of antibiotic treatment in the extended protocol damaged the algal cells. It has been reported that antibiotics treatment could interfere the peptidoglycan biosynthesis and eventually inhibited chloroplast division34. The extended protocol was also ineffective in removing bacteria, probably due to limited bacterial removal capacity and/or insufficient dose and exposure time of antibiotics.
Effect of initial algal cell density and antibiotic exposure times on bacterial removal
Bacterial cell concentration, antibiotic dose and antibiotic exposure time are critical factors affecting the efficiency of bacterial removal in algal samples. We hypothesized that the incomplete bacterial removal after the basic protocol was attributable to (1) the bacterial concentration in the algal culture exceeded the treatment capacity and (2) the dose and exposure time used in the antibiotic treatment were insufficient. To test insufficient dosing, a double antibiotic dose was used but there was no significant difference in the amount of bacterial removal between the normal and double doses of antibiotics used in the treatments (data not shown). Our observations also showed that the cell density of the 7-day KMHK culture treated with a double dose of antibiotics decreased from 10,000 to 489 cells/mL, clearly indicating a high antibiotic dose severely damaged the algal cells.
Both the amount and percentage of bacterial removal were independent of the initial algal cell density (Table 1). At least 94.49% of bacteria were removed in all treatments. With a 48-h antibiotic exposure time, the percentage of bacterial removal did not have any significant changes with increases of initial algal cell density, and were 94.49%, 99.84% and 99.93% at high, moderate and low densities, respectively. Similarly, no significant difference in the amount of bacterial removal (in terms of Log10 CFU/mL) was observed between low and moderate initial algal cell densities but were significantly higher than that at high initial algal cell density. With 96-h of antibiotic exposure, bacterial removal percentage was 100% at both high and low algal densities and 98.53% at moderate algal density. Similarly, bacterial removal of low and moderate initial algal cell densities were significantly higher than that at high initial algal cell density. These results indicated that the bacterial removal ability was independent of the initial algal cell density.
Table 1 Bacterial removal in the algal cultures with different initial algal cell densities and antibiotic exposure times in the basic protocol.
Full size table

When compare the antibiotic exposure time, bacterial removal with 48-h antibiotic treatment was significantly higher than that with other exposure times, regardless of the initial algal density (Table 1). A complete bacterial removal (100%) was detected under three conditions, that is, high and low initial densities with 96-h exposure time and moderate initial algal density with 72-h exposure time. The antibiotic exposure times ranging from several hours to 1 week have been reported in pervious researches, depending on the method used, and the bacterial cell count and composition in the algal sample13,32. The exposure time should be considered and controlled carefully because prolonged exposure to antibiotics may damage the algal cells and suppress their growth. However, the present study reveals antibiotic exposure time was not a critical factor for achieving 100% bacterial removal. On the other hand, the bacterial removal might be related to the bacterial cell count and composition present in the algal culture at the beginning. The microbiome can also change frequently during the routine cultivation of the algal cultures35.
Algal cells obtained from different protocols were then cultured for 7 days and then subjected to bacterial counting. A substantial number of bacteria was found in all treatments at the end of the algal regrow cultures, including the 72-h exposure achieving 100% bacterial removal, except two 96-h antibiotic exposure treatments (Table 1). Even in these two 96-h exposure treatments with 100% bacteria-free algal cultures, bacteria were detected after several generations of the algal culture (data not shown). These indicated that the axenicity of algal culture may not be guaranteed even when no bacteria are detected after treating the algal samples with our developed protocol.
The reappearance of bacterial growth may be due to the existence of a very small proportion of bacteria attached onto the algal cells after the protocol which was too little to be detected. Dinoflagellates are covered with complex and irregular cell surface structures36. Steric hindrance from parts of these algal surface areas may protect the firmly attached bacteria, making their detachment from the algal cells during gradient centrifugation difficult and decreasing antibiotic accessibility. Another possible explanation is that a few bacteria may have developed antibiotic resistance11, but this is unlikely in the present study as antibiotic resistance usually develops when the bacteria are continuously exposed to a nonlethal dose of an antibiotic37. Although why a few bacteria remained on the algal cells and regrew rapidly along with algal cell growth are poorly understood, it is necessary to have additional treatments such as serial dilution selection to ensure a true axenic algal culture is obtained.
Selection of axenic algal cells through serial dilution
After 7 days of cultivation, KMHK cells from all dilutions described in Fig. 1 survived. No bacterial growth was observed in 42 KMHK cultures (out of total 45 cultures), although bacterial colonies were observed in one of the day 7 KMHK cultures (one culture in the 100 dilution) and in another two of the day 21 KMHK cultures (one in 100 dilution and one in 10–1 dilution) in the first trial (Supplementary Table 1). The results reiterate that a high proportion of the KMHK cells in the population is in fact axenic and the ratio of axenic to non-axenic clones in the algal cell population is high after the basic protocol, it is therefore highly feasible to obtain the axenic clones from the population through such serial dilution approach. Similar approaches have been reported in previous studies5,12,38. For instance, Ki and Han dispersed the algal cells in a 96-well plate after filtration and antibiotic treatment12. Sena et al. also serially diluted the cyanobacterial sample, Arthrospira spp., after antibiotics treatment38. Although the axenic clone could be obtained by plating the algal cells on agar5, this approach was not feasible for marine dinoflagellates because the cells were unable to grow on a solid medium.
Verification of axenicity of algal cultures
It has been reported that some marine bacteria grow very slowly on agar, something like 50 days, and some of them are unculturable39,40. Therefore, confirming the axenic state of the algal cultures is paramount. The axenic state of the two selected KMHK cultures was tested and results of DAPI epifluorescence microscopy show that no bacteria were observed in the treated KMHK cells (Fig. 4b) while bacteria were found in the untreated cells (Fig. 4a). For the rDNA sequencing analysis, a 1500-bp PCR product was obtained after bacterial 16S rDNA amplification (Fig. 5a). The BLAST of the sequence reveal that it shared 99.59% similarity with 16 s rDNA sequence in the plastid gene of K. mikimotoi (accession no. AB027236). Similarly, the 600-bp amplicon observed in the amplification of fungal ITS shared 99.67% similarity with the ITS sequence of K. mikimotoi (accession no. KT733616; Fig. 5c). These results confirm the absence of both culturable and unculturable bacteria and fungi in the treated KMHK cultures. It has been reported that the algal cultures must continually be treated with antibiotics in order to maintain their axenic status15, but algal cells may die after several sub-cultures because of prolonged antibiotic exposure. When this happens, it is usually too late to recover the cultures. In the present study, regular monitoring of the axenicity of the cultures was performed through bacterial colony counting, DAPI epifluorescence microscopy and rDNA sequence analysis. The established axenic cultures were maintained generations after generations without adding any antibiotics, and no bacteria were found in any of the sub-cultures being tested even after 30 generations (data not shown). The axenic cultures of KMHK were established successfully and maintained sustainably, indicating this methodology was a promising approach applicable to other unarmored dinoflagellates. To the best of our knowledge, this is the first successful establishment of an axenic culture for the unarmored dinoflagellate K. mikimotoi.
Figure 4

DAPI epifluorescence microscopic images of KMHK and AT6 samples under ×1000 magnification: (a) untreated (control) and (b) treated KMHK samples; (c) untreated (control) and (d) treated AT6 samples.

Full size image

Figure 5

PCR amplification of bacterial 16 s rDNA for (a) KMHK and (b) AT6 samples and that of fungal ITS region for (c) KMHK and (d) AT6 samples obtained from basic protocol and serial dilution. +ve: positive control, -ve: negative control.

Full size image

Development of axenic culture of A. tamarense using our established methodology
The established method was applied to obtain the axenic cultures of another dinoflagellate species, A. tamarense (AT6), a well-known paralytic shellfish toxin–producing agent which has been extensively studied in the past decades41,42,43,44. The AT6 culture with an initial bacterial count of 7.9 ± 0.08 log10 CFU/mL was subjected to the basic protocol and the total bacterial counts recorded after Steps 3, 4 and 6 were shown in Fig. 6a. The result was generally similar to that of KMHK culture using the basic protocol (Fig. 3a), except no bacteria were detected in the AT6 culture after Step 6. Even though 100% bacterial removal was achieved, bacterial regrowth was observed on days 3 and 7 of the treated AT6 culture (Fig. 6b). The bacterial count regrew significantly to 6.71 ± 0.08 log10 CFU/mL after 7 days of cultivation (Fig. 6c). The regrowth of bacteria from the treated AT6 culture achieving 100% bacterial removal confirmed that few bacteria attached at some points onto the algal surface were shielded. Biegala et al. reported that associated bacteria were attached onto the cell surface within the sulci and cingula of A. tamarense6.
Figure 6

Bacterial removal in the Alexandrium tamarense (AT6) samples using the basic protocol. (a) Total bacterial count against different steps. Initial: the initial bacterial count present in AT6 at the beginning. (b) Total bacterial count in the AT6 culture obtained after the protocol at different days of cultivation. (c) Algal cell concentration during the regrowth of the AT6 culture obtained after the protocol. All data are presented as means ± standard deviations of three independent experiments (n = 3). N.D.: not detected. Different letters on the top of the bar indicate that means were significantly different among different samples at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

Full size image

For the serial dilution selection of AT6 cells after the basic protocol, culturable bacteria were observed in 11 of the 15 cultures in 100 dilution (5/5 in trial 1; 4/5 in trial 2 and 2/5 in trial 3) after 7 days of algal cultivation. All these 15 cultures showed bacterial regrowth after 21 days of algal cultivation, but the number of cultures with bacterial regrowth decreased to 1 and 4 of the 15 cultures in 10–1 dilutions after 7 and 21 days of algal cultivation, respectively. In 10−2 dilutions, no culturable bacteria were observed in all the cultures after both 7 and 21 days of algal cultivation (Supplementary Table 1). These results further demonstrate the feasibility of using the stepwise serial dilution method to select axenic algae, and 10–2 dilutions offer the highest probability in acquiring the axenic clones. The bacterial status of two of these potential axenic AT6 cultures was further assessed through DAPI epifluorescence microscopy and bacterial rDNA and fungal ITS sequencing analysis. No bacteria were observed in the DAPI epifluorescence image of the treated AT6 cultures compared to the untreated control cultures (Figs. 4c,d). Neither bacterial 16 s rDNA band (Fig. 5b) nor fungal ITS region (Fig. 5d) was amplified in the treated AT6 samples. These results confirmed the axenic status of the AT6 cultures.
Our established methodology
The present results demonstrate the potential of our methodology to be used in the establishment of axenic cultures for both armored and unarmored dinoflagellates. Figure 7 summarizes the workflow and procedures of our methodology. This promising approach combines three techniques, Percoll density gradient centrifugation, antibiotic treatment and serial dilution. Density gradient centrifugation considerably reduces the bacterial population by the physical separation between the associated bacteria, mainly the free-living and loosely attached bacteria, and the dinoflagellate cells on the basis of cell size. Percoll density layers not only provide a matrix for separating the two types of cells effectively but also cushion the dinoflagellate cells against the impact of the mechanical force. The Percoll density layers together with the bactericidal action of the antibiotic treatment typically eradicate  > 99% of the associated bacteria from the dinoflagellate culture. Our strategies not target at removing the remaining  More

1.
Batalha, L. S., Silva Filho, D. F. & Martius, C. Using termite nests as a source of organic matter in agrosilvicultural production systems in Amazonia. Scientia Agricola 52, 318–325 (1995).
Article  Google Scholar 
2.
Ayuke, F. O. et al. Soil fertility management: Impacts on soil macrofauna, soil aggregation and soil organic matter allocation. Appl. Soil. Ecol. 48, 53–62 (2011).
Article  Google Scholar 

3.
Jouquet, P., Chaudhary, E. & Kumar, A. R. V. Sustainable use of termite activity in agro-ecosystems with reference to earthworms A review. Agron. Sustain. Dev. 38, 3 (2018).
Article  Google Scholar 

4.
Deligne, J., Quennedey, A. & Blum, M. S. The enemies and defense mechanisms of termites. In Social Insects (ed. Hermann, H. R.) 1–76 (Academic Press, Cambridge, 1981).
Google Scholar 

5.
Grasse, P. P. Termitologia, Tome III (Masson, Paris, 1986).
Google Scholar 

6.
Prestwich, G. D. Defense mechanisms of termites. Annu. Rev. Entomol. 29(1), 201–232 (1984).
CAS  Article  Google Scholar 

7.
Chuah, C. H. Chemical Weapons and Defense Mechanism of Malaysian Termites. In Chemistry in Malaysia 4–11 (Institut Kimia Malaysia, 2010).

8.
Iida, M. & Akino, T. Defensive effect of soldier-specific secretion by Reticulitermes speratus (Isoptera: Rhinotermitidae) on the facultative termitophagous ponerine ant, Brachyponera chinensis (Hymenoptera: Ponerinae). Appl. Entomol. Zool. 51, 111–116 (2016).
Article  Google Scholar 

9.
Kori, N. S. M. & Arumugam, N. Termites of Agropark, Universiti Malaysia Kelantan, Jeli Campus: Diversity and pest composition. J. Trop. Resour. Sustain. Sci. 5, 104–108 (2017).
Google Scholar 

10.
Alia-Diyana, M. H., Appalasamy, S. & Arumugam, N. Termite species and structural pest identification in selected rural areas of Kelantan, Malaysia. IOP Conf. Ser. Earth and Environ. Sci. https://doi.org/10.1088/1755-1315/549/1/012053 (2020).
Article  Google Scholar 

11.
Sillam-Dussès, D. et al. Comparative Study of the Labial Gland Secretion in Termites (Isoptera). PLoS ONE 7(10), e46431 (2012).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

12.
Wyatt, T. D. Pheromones and other chemical communication in animals. In Encyclopedia of Neuroscience (ed. Squire, L. R.) 611–616 (Academic Press, Oxford, 2017).
Google Scholar 

13.
Ahmad, N. & Kamarudin, N. Pheromone Trapping in Controlling Key Insect Pests: Progress and Prospects (Malaysia Palm Oil Board, Kajang, 2016).
Google Scholar 

14.
Matthews, G. Pesticides: Health, Safety and the Environment (Wiley Blackwell, Chichester, 2015).
Google Scholar 

15.
Desneux, N., Decourtye, A. & Delpuech, J.-M. The sublethal effects of pesticides on beneficial arthropods. Ann. Rev. Entomol. 52(1), 81–106 (2007).
CAS  Article  Google Scholar 

16.
Rattan, R. Mechanism of action of insecticidal secondary metabolites of plant origin. Crop Prot. 29(9), 913–920 (2010).
CAS  Article  Google Scholar 

17.
Regnault-Roger, C., Vincent, C. & Arnason, J. T. Essential oils in insect control: Low-risk products in a high-stakes world. Annu. Rev. Entomol. 57, 405–424 (2012).
CAS  PubMed  Article  Google Scholar 

18.
Pavela, R. & Benelli, G. Essential oils as ecofriendly biopesticides? Challenges and constraints. Trends Plant Sci. 21(12), 1000–1007 (2016).
CAS  PubMed  Article  Google Scholar 

19.
Chouvenc, T., Su, N. & Kenneth, G. J. Fifty years of attempted biological control of termites—Analysis of a failure. Biol. Control 59(2), 69–82 (2011).
Article  Google Scholar 

20.
Meikle, W. G. et al. Evaluation of an entomopathogenic fungus, Paecilomyces fumosoroseus (Wize) Brown and Smith (Deuteromycota: Hyphomycetes) obtained from Formosan subterranean termites (Isop, Rhinotermitidae). J. Appl. Entomol. 129(6), 315–322 (2005).
Article  Google Scholar 

21.
Tho, Y. P. Termites of Peninsular Malaysia (Forest Research Institute Malaysia, Selangor, 1992).
Google Scholar 

22.
Krasulova, J. et al. Chemistry and anatomy of the frontal gland in soldiers of the sand termite Psammotermes hybostoma. J. Chem. Ecol. 38(5), 557–565 (2012).
CAS  PubMed  Article  Google Scholar 

23.
Bakaruddin, N. H., Dieng, H., Sulaiman, S. F. & Ab Majid, A. H. Evaluation of the toxicity and repellency of tropical plant extract against subterranean termites, Globitermes sulphureus and Coptotermes gestroi. Inf. Process. Agric. 5(3), 298–307 (2018).
Google Scholar 

24.
Lee, C. C. & Lee, C. Y. A laboratory maintenance regime for a fungus-growing termite Macrotermes gilvus (Blattodea: Termitidae). J. Econ. Entomol. 108(3), 1243–1250 (2015).
CAS  PubMed  Article  Google Scholar 

25.
Zibaee, I. & Pooya, B. K. Evaluation of repellent activity of two essential oils and their mixed formulation against cockroaches (Dictyoptera: Blattidae, Blattellidae) in Iran. J. Entomol. Zool. Stud. 4, 106 (2016).
Google Scholar 

26.
OECD. Guidance Document on Assays for Testing the Efficacy of Baits Against Cockroaches Health and Safety Publications (OECD Environment, Paris, 2013).
Google Scholar 

27.
Syed, R., Manzoor, F., Adalat, R., Abdul-Sattar, A. & Syed, A. Laboratory evaluation of toxicity of insecticide formulations from different classes against American cockroach (Dictyoptera: Blattidae). J. Arthropod-Borne Dis. 8(1), 21–34 (2014).
PubMed  Google Scholar 

28.
Ohta, M., Matsuura, F., Henderson, G. & Laine, R. A. Novel free ceramides as components of the soldier defense gland of the Formosan subterranean termite (Coptotermes formosanus). J. Lipid Res. 48(3), 656–664 (2007).
CAS  PubMed  Article  Google Scholar 

29.
McDonald, L. L., Guy, R. H. & Speirs, R. D. Preliminary evaluation of new candidate materials as toxicants, repellents, and attractants against stored-product insects-1 (Agriculture Research Service, 1970).

30.
Johnson, R. A., Thomas, R. J., Wood, T. G. & Swift, M. J. The inoculation of the fungus comb in newly founded colonies of some species of the Macrotermitinae (Isoptera) from Nigeria. J. Nat. Hist. 15(5), 751–756 (2007).
Article  Google Scholar 

31.
de Mello, A. P., Azevedo, N. R., da Silva, A. M. B. & Gusmão, M. A. B. Chemical composition and variability of the defensive secretion in Nasutitermes corniger (Motschulsky, 1885) in urban area in the Brazilian semiarid region. Entomotropica 31, 82–90 (2016).
Google Scholar 

32.
Bordereau, C., Robert, A., Van Tuyen, V. & Peppuy, A. Suicidal defensive behaviour by frontal gland dehiscence in Globitermes sulphureus Haviland soldiers (Isoptera). Insectes Soc. 44(3), 289–297 (1997).
Article  Google Scholar 

33.
Touchard, A., Dejean, A., Escoubas, P. & Orivel, J. Intraspecific variations in the venom peptidome of the ant Odontomachus haematodus (Formicidae: Ponerinae) from French Guiana. J. Hymenoptera Res. 47, 87–101 (2015).
Article  Google Scholar 

34.
Aguilera-Olivares, D., Burgos-Lefimil, C., Melendez, W., Flores-Prado, L. & Niemeyer, H. M. Chemical basis of nestmate recognition in a defense context in a one-piece nesting termite. Chemoecology 26(5), 163–172 (2016).
Article  Google Scholar 

35.
Kuswanto, E., Ahmad, I., Putra, R. E. & Harahap, I. S. Two novel volatile compounds as the key for intraspecific colony recognition in Macrotermes gilvus (Isoptera: Termitidae). J. Entomol. 12(2), 87–94 (2015).
CAS  Article  Google Scholar 

36.
Ismanto, A. & Baedowi, A. Efikasi ekstrak akar tuba dalam mengendalikan rayap tanah Macrotermes gilvus hagen pada pertanaman kayu putih. Jurnal Ecogreen. 5(1), 57–62 (2019).
Google Scholar 

37.
Jones, T. H. et al. The chemistry of exploding ants, Camponotus spp. (cylindricus complex). J. Chem. Ecol. 30(8), 1479–1492 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Laciny, A. et al. Colobopsis explodens sp. n., model species for studies on “exploding ants” (Hymenoptera, Formicidae), with biological notes and first illustrations of males of the Colobopsis cylindrica group. ZooKeys 751, 1–40 (2018).
Article  Google Scholar 

39.
Kuwahara, Y. Chemical Ecology of Astigmatid Mites (Cambridge University Press, Cambridge, 2004).
Google Scholar 

40.
Iqbal, N. & Saeed, S. Toxicity of six new chemical insecticides against the termite, Microtermes mycophagus D. (Isoptera: Termitidae: Macrotermitinae). Pak. J. Zool. 45(3), 709–713 (2013).
CAS  Google Scholar 

41.
Lihoreau, M. & Rivault, C. Kin recognition via cuticular hydrocarbons shapes cockroach social life. Behav. Ecol. 20, 46–53 (2009).
Article  Google Scholar 

42.
Emanuel, S. & Libersat, F. Nociceptive Pathway in the cockroach Periplaneta americana. Front. Physiol. https://doi.org/10.3389/fphys.2019.01100 (2019).
Article  PubMed  PubMed Central  Google Scholar 

43.
Deisig, N., Dupuy, F., Anton, S. & Renou, M. Responses to pheromones in a complex odor world: Sensory processing and behavior. Insects. 5(2), 399–422 (2014).
PubMed  PubMed Central  Article  Google Scholar 

44.
Nishino, H. et al. Spatial receptive fields for odor localization. Curr. Biol. 28(4), 600–608 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Sandoz, J. C., Pham, D. M., Renou, M. & Wadhams, L. Asymmetrical generalisation between pheromonal and floral odours in appetitive olfactory conditioning of the honey bee (Apis mellifera L.). J. Comp. Physiol. 187, 559–568 (2001).
CAS  Article  Google Scholar 

46.
Kreher, S. A., Kwon, J. Y. & Carlson, J. R. The molecular basis of odor coding in the Drosophila larva. Neuron 46(3), 445–456 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Grosjean, Y. et al. An olfactory receptor for food-derived odours promotes male courtship in Drosophila. Nature 478(7368), 236 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Vosshall, L. B. & Hansson, B. S. A unified nomenclature system for the insect olfactory coreceptor. Chem. Senses 36(6), 497–498 (2011).
PubMed  Article  PubMed Central  Google Scholar 

49.
Peschke, K. & Eisner, T. Defensive secretion of the tenebrionid beetle, Blaps mucronata: Physical and chemical determinants of effectiveness. J. Comp. Physiol. 161(3), 377–388 (1987).
CAS  Article  Google Scholar 

50.
Dettner, K. Solvent-dependent variablity of effectiveness of quinone-defensive systems of Oxytelinae beetles (Coleoptera: Staphylinidae). Entomologia Generalis. 15, 275–292 (1991).
Article  Google Scholar 

51.
Roth, L. & Eisner, T. Chemical defenses of arthropods. Ann. Rev. Entomol. 7, 107–136 (2003).
Article  Google Scholar 

52.
Li, J. et al. Odoriferous defensive stink gland transcriptome to identify novel genes necessary for quinone synthesis in the red flour beetle Tribolium castaneum. PLoS Genet. 9(7), 1003596–1003596 (2013).
Article  CAS  Google Scholar 

53.
Delattre, O. et al. Complex alarm strategy in the most basal termite species. Behav. Ecol. Sociobiol. 69(12), 1945–1955 (2015).
Article  Google Scholar 

54.
Prestwich, G. D. & Chen, D. Soldier defense secretions of Trinervitermes bettonianus (Isoptera, Nasutitermitinae): Chemical variation in allopatric populations. J. Chem. Ecol. 7(1), 147–157 (1981).
CAS  PubMed  Article  Google Scholar 

55.
Piper, R. Extraordinary Animals: An Encyclopedia of Curious and Unusual Animals (Greenwood Publishing Group, Westport, 2007).
Google Scholar 

56.
Costa-Leonardo, A. A new interpretation of the defense glands of neotropical Ruptitermes (Isoptera, Termitidae, Apicotermitinae). Sociobiology 44, 391–402 (2004).
Google Scholar 

57.
Reinhard, J., Lacey, M. & Lenz, M. Application of the natural phagostimulant hydroquinone in bait systems for termite management (Isoptera). Sociobiology 39(2), 213–230 (2002).
Google Scholar 

58.
Hasyim, A., Istianto, M. & de Kogel, W. Male fruit fly, Bactrocera tau (Diptera; Tephritidae) attractants from Elsholtzia pubescens Bth. Asian J. Plant Sci. 6(1), 181–183 (2007).
Article  Google Scholar 

59.
Chen, Z. Y. et al. Insecticidal and repellent activity of essential oil from Amomum villosum Lour. and its main compounds against two stored-product insects. Int. J. Food Prop. 21(1), 2265–2275 (2018).
CAS  Article  Google Scholar 

60.
Reisenman, C. E., Lei, H. & Guerenstein, P. G. Neuroethology of olfactory-guided behavior and its potential application in the control of harmful insects. Front. Physiol. 7, 271–271 (2016).
PubMed  PubMed Central  Article  Google Scholar 

61.
Raina, A. K., Bland, J. M. & Osbrink, W. Hydroquinone is not a phagostimulant for the Formosan subterranean termite. J. Chem. Ecol. 31(3), 509–517 (2005).
CAS  PubMed  Article  Google Scholar 

62.
Bagnères, A.-G. & Hanus, R. Communication and social regulation in termites. In Social Recognition in Invertebrates: The Knowns and the Unknowns (eds Aquiloni, L. & Tricarico, E.) 193–248 (Springer, Cham, 2015).
Google Scholar 

63.
Alia Diyana, M. H., Appalasamy, S., Arumugam, N. & Boon, J. G. A study of a termite chemical defense fluid compound of Macrotermes carbonarius. IOP Conf. Ser. Earth Environ. Sci. https://doi.org/10.1088/1755-1315/269/1/012009 (2019).
Article  Google Scholar 

64.
Environmental Protection Agency. Furanone. Prevention P A T S (National Center for Environmental Publications and Information, 1993).

65.
Igwe, O. U. & Udofia, D. E. Secondary metabolites of the cuticular abdominal glands of variegated grasshopper (Zonocerus variegatus L.). Int. J. Spectrosc. 2015, 1–6 (2015).
Article  CAS  Google Scholar 

66.
Neoh, K. B., Yeap, B.-K., Tsunoda, K., Yoshimura, T. & Lee, C.-Y. Do termites avoid carcasses? Behavioral responses depend on the nature of the carcasses. PLoS ONE 7(4), 36375 (2012).
ADS  Article  CAS  Google Scholar 

67.
Blassioli-Moraes, M. C., Laumann, R. A., Michereff, M. F. F. & Borges, M. Semiochemicals for integrated pest management. In Sustainable Agrochemistry: A Compendium of Technologies (ed. Vaz, S., Jr.) 85–112 (Springer, Cham, 2019).
Google Scholar 

68.
de Melo, A. R. et al. Toxicity of different fatty acids and methyl esters on Culex quinquefasciatus larvae. Ecotoxicol. Environ. Saf. 154, 1–5 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

69.
Xie, Y., Wang, K., Huang, Q. & Lei, C. Evaluation toxicity of monoterpenes to subterranean termite, Reticulitermes chinensis Snyder. Ind. Crops Prod. 53, 163–166 (2014).
CAS  Article  Google Scholar 

70.
Xie, Y. et al. Antitermitic and antifungal activities of eugenol and its congeners from the flower buds of Syzgium aromaticum (clove). Ind. Crops Prod. 77, 780–786 (2015).
CAS  Article  Google Scholar 

71.
Zhang, Z., Yang, T., Zhang, Y., Wang, L. & Xie, Y. Fumigant toxicity of monoterpenes against fruitfly, Drosophila melanogaster. Ind. Crops Prod. 81, 147–151 (2016).
CAS  Article  Google Scholar 

72.
Silva, L. N. D. et al. The influence of fatty acid methyl esters (FAMEs) in the biochemistry and the Na+/K+-ATPase activity of Culex quinquefasciatus Larvae. J. Membr. Biol. 249(4), 459–467 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Normile, D. Reinventing rice to feed the world. Science 321, 330–333 (2008).
MathSciNet  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Marschner, P. Marschner’s Mineral Nutrition of Higher Plants (Academic Press, London, 2012).
Google Scholar 

3.
Vigani, G., Tarantino, D. & Murgia, I. Mitochondrial ferritin is a functional iron-storage protein in cucumber (Cucumis sativus) roots. Front. Plant Sci. 4, 316 (2013).
PubMed  PubMed Central  Google Scholar 

4.
Violante, A., Barberis, E., Pigna, M. & Boero, V. Factors affecting the formation, nature, and properties of iron precipitation products at the soil-root interface. J. Plant Nutr. 26, 1889–1908 (2003).
CAS  Article  Google Scholar 

5.
Pradhan, S. K. et al. Genetic regulation of homeostasis, uptake, bio-fortification and efficiency enhancement of iron in rice. Environ. Exp. Bot. 177, 104066 (2020).
CAS  Article  Google Scholar 

6.
Kilcoyne, S. H., Bentley, P. M., Thongbai, P., Gordon, D. C. & Goodman, B. A. The application of 57Fe Mössbauer spectroscopy in the investigation of iron uptake and translocation in plants. Nucl. Instrum. Meth B 160, 157–166 (2000).
ADS  CAS  Article  Google Scholar 

7.
Zhang, A. et al. Effect of biochar amendment on yield and methane and nitrous oxide emissions from a rice paddy from Tai Lake plain, China. Agric. Ecosyst. Environ. 139, 469–475 (2010).
CAS  Article  Google Scholar 

8.
Huang, M., Yang, L., Qin, H., Jiang, L. & Zou, Y. Quantifying the effect of biochar amendment on soil quality and crop productivity in Chinese rice paddies. Field Crops Res. 154, 172–177 (2013).
Article  Google Scholar 

9.
Zhang, A. et al. Effects of biochar amendment on soil quality, crop yield and greenhouse gas emission in a Chinese rice paddy: A field study of 2 consecutive rice growing cycles. Field Crops Res. 127, 153–160 (2012).
Article  Google Scholar 

10.
Kim, S. & Dale, B. E. Global potential bioethanol production from wasted crops and crop residues. Biomass Bioenerg. 26, 361–375 (2004).
Article  Google Scholar 

11.
Wang, Y., Xiao, X., Xu, Y. & Chen, B. Environmental effects of silicon within Biochar (Sichar) and carbon–silicon coupling mechanisms: A critical review. Environ. Sci. Technol. 53, 13570–13582 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Chan, K. Y., Van Zwieten, L., Meszaros, I., Downie, A. & Joseph, S. Agronomic values of greenwaste biochar as a soil amendment. Soil Res. 45, 629 (2007).
CAS  Article  Google Scholar 

13.
Van Zwieten, L. et al. Effects of biochar from slow pyrolysis of papermill waste on agronomic performance and soil fertility. Plant Soil 327, 235–246 (2009).
Article  CAS  Google Scholar 

14.
Joseph, S. et al. Shifting paradigms: Development of high-efficiency biochar fertilizers based on nano-structures and soluble components. Carbon Manag. 4, 323–343 (2013).
CAS  Article  Google Scholar 

15.
Chew, J. et al. Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice. Sci. Total Environ. 713, 136431 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Irshad, M. K. et al. Goethite-modified biochar ameliorates the growth of rice (Oryza sativa L.) plants by suppressing Cd and As-induced oxidative stress in Cd and As co-contaminated paddy soil. Sci. Total Environ. 717, 137086 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Zhang, J.-Y. et al. Effects of nano-Fe3O4-modified biochar on iron plaque formation and Cd accumulation in rice (Oryza sativa L.). Environ. Pollut. 260, 113970 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Chen, Z. et al. Mitigation of Cd accumulation in paddy rice (Oryza sativa L.) by Fe fertilization. Environ. Pollut. 231, 549–559 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Küpper, H., Zhao, F. J. & McGrath, S. P. Cellular compartmentation of zinc in leaves of the hyperaccumulator Thlaspi caerulescens. Plant Physiol. 119, 305–312 (1999).
PubMed Central  Article  Google Scholar 

20.
Blackwell, P. et al. Influences of biochar and biochar-mineral complex on mycorrhizal colonisation and nutrition of wheat and sorghum. Pedosphere 25, 686–695 (2015).
CAS  Article  Google Scholar 

21.
Rodriguez, N., Menendez, N., Tornero, J., Amils, R. & de la Fuente, V. Internal iron biomineralization in Imperata cylindrica, a perennial grass: Chemical composition, speciation and plant localization. New Phytol. 165, 781–789 (2005).
CAS  PubMed  Article  Google Scholar 

22.
Neumann, D., Nieden, U. Z., Lichtenberger, O. & Leopold, I. How does Armeria maritima tolerate high heavy metal concentrations?. J. Plant Physiol. 146, 704–717 (1995).
CAS  Article  Google Scholar 

23.
Liu, D. H., Adler, K. & Stephan, U. W. Iron-containing particles accumulate in organelles and vacuoles of leaf and root cells in the nicotianamine-free tomato mutantchloronerva. Protoplasma 201, 213–220 (1998).
CAS  Article  Google Scholar 

24.
Alkhatib, R., Alkhatib, B., Abdo, N., Al-Eitan, L. & Creamer, R. Physio-biochemical and ultrastructural impact of (Fe3O4) nanoparticles on tobacco. BMC Plant Biol. 19, 253 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Fuente, V. et al. Formation of biomineral iron oxides compounds in a Fe hyperaccumulator plant: Imperata cylindrica (L.) P. Beauv. J. Struct. Biol. 193, 23–32 (2016).
CAS  PubMed  Article  Google Scholar 

26.
Graham, U. M. et al. Tissue specific fate of nanomaterials by advanced analytical imaging techniques—A review. Chem. Res. Toxicol. 33, 1145–1162 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Aoki, D. et al. Distribution of coniferin in freeze-fixed stem of Ginkgo biloba L. by cryo-TOF-SIMS/SEM. Sci. Rep. 6, 31525 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Martin, R. R. et al. Time of flight secondary ion mass spectrometry studies of the distribution of metals between the soil, rhizosphere and roots of Populus tremuloides Minchx growing in forest soil. Chemosphere 54, 1121–1125 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Saito, K. et al. Aluminum localization in the cell walls of the mature xylem of maple tree detected by elemental imaging using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Holzforschung 68, 85–92 (2014).
CAS  Article  Google Scholar 

30.
Hanć, A., Piechalak, A., Tomaszewska, B. & Barałkiewicz, D. Laser ablation inductively coupled plasma mass spectrometry in quantitative analysis and imaging of plant’s thin sections. Int. J. Mass spectrom. 363, 16–22 (2014).
Article  CAS  Google Scholar 

31.
Shi, J., Gras, M. A. & Silk, W. K. Laser ablation ICP-MS reveals patterns of copper differing from zinc in growth zones of cucumber roots. Planta 229, 945–954 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Guizani, C., Haddad, K., Limousy, L. & Jeguirim, M. New insights on the structural evolution of biomass char upon pyrolysis as revealed by the Raman spectroscopy and elemental analysis. Carbon 119, 519–521 (2017).
CAS  Article  Google Scholar 

33.
Joseph, S. et al. An investigation into the reactions of biochar in soil. Soil Res. 48, 501–515 (2010).
CAS  Article  Google Scholar 

34.
Prendergast-Miller, M. T., Duvall, M. & Sohi, S. P. Biochar-root interactions are mediated by biochar nutrient content and impacts on soil nutrient availability. Eur. J. Soil Sci. 65, 173–185 (2014).
CAS  Article  Google Scholar 

35.
Nielsen, S. et al. Comparative analysis of the microbial communities in agricultural soil amended with enhanced biochars or traditional fertilisers. Agric. Ecosyst. Environ. 191, 73–82 (2014).
Article  Google Scholar 

36.
Hansel, C. M., Fendorf, S., Sutton, S. & Newville, M. Characterization of Fe plaque and associated metals on the roots of mine-waste impacted aquatic plants. Environ. Sci. Technol. 35, 3863–3868 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Gloter, A., Zbinden, M., Guyot, F., Gaill, F. & Colliex, C. TEM-EELS study of natural ferrihydrite from geological–biological interactions in hydrothermal systems. Earth Planet. Sci. Lett. 222, 947–957 (2004).
ADS  CAS  Article  Google Scholar 

38.
Rajendran, M. et al. Effect of sulfur and sulfur-iron modified biochar on cadmium availability and transfer in the soil-rice system. Chemosphere 222, 314–322 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Wu, C. et al. The effect of silicon on iron plaque formation and arsenic accumulation in rice genotypes with different radial oxygen loss (ROL). Environ. Pollut. 212, 27–33 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Linke, R., Schreiner, M., Demortier, G. & Alram, M. Determination of the provenance of medieval silver coins: potential and limitations of X-ray analysis using photons, electrons or protons. X-ray Spectrom. 32, 373–380 (2003).
ADS  CAS  Article  Google Scholar 

41.
Haynes, R. J. A contemporary overview of silicon availability in agricultural soils. J. Plant Nutr. Soil Sci. 177, 831–844 (2014).
CAS  Article  Google Scholar 

42.
Kostic, L. et al. Liming of anthropogenically acidified soil promotes phosphorus acquisition in the rhizosphere of wheat. Biol. Fertility Soils 51, 289–298 (2014).
Article  CAS  Google Scholar 

43.
Acosta-Martinez, V. & Tabatabai, M. Enzyme activities in a limed agricultural soil. Biol. Fertility Soils 31, 85–91 (2000).
CAS  Article  Google Scholar 

44.
Chan, K., Van Zwieten, L., Meszaros, I., Downie, A. & Joseph, S. Using poultry litter biochars as soil amendments. Soil Res. 46, 437–444 (2008).
Article  Google Scholar 

45.
Khan, N. et al. Root iron plaque on wetland plants as a dynamic pool of nutrients and contaminants. Adv. Agron. 138, 1–96 (2016).
Article  Google Scholar 

46.
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: Concept and review. Soil Biol. Biochem. 83, 184–199 (2015).
CAS  Article  Google Scholar 

47.
Ma, J., Cai, H., He, C., Zhang, W. & Wang, L. A hemicellulose-bound form of silicon inhibits cadmium ion uptake in rice (Oryza sativa) cells. New Phytol. 206, 1063–1074 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Wang, Y., Stass, A. & Horst, W. J. Apoplastic binding of aluminum is involved in silicon-induced amelioration of aluminum toxicity in maize. Plant Physiol. 136, 3762–3770 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Wang, P., Lombi, E., Zhao, F.-J. & Kopittke, P. M. Nanotechnology: A new opportunity in plant sciences. Trends Plant Sci. 21, 699–712 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Garvie, L. A. & Buseck, P. R. Ratios of ferrous to ferric iron from nanometre-sized areas in minerals. Nature 396, 667–670 (1998).
ADS  CAS  Article  Google Scholar 

51.
Goya, G. F., Berquó, T. S., Fonseca, F. C. & Morales, M. P. Static and dynamic magnetic properties of spherical magnetite nanoparticles. J. Appl. Phys. 94, 3520–3528 (2003).
ADS  CAS  Article  Google Scholar 

52.
Yao, C. et al. Developing more effective enhanced biochar fertilisers for improvement of pepper yield and quality. Pedosphere 25, 703–712 (2015).
CAS  Article  Google Scholar 

53.
Rawal, A. et al. Mineral-biochar composites: Molecular structure and porosity. Environ. Sci. Technol. 50, 7706–7714 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

54.
Mitchell, D. R. Contamination mitigation strategies for scanning transmission electron microscopy. Micron 73, 36–46 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Dong, F. et al. Ice age unfrozen: severe effect of the last interglacial, not glacial, climate change on East Asian avifauna. BMC Evol. Biol. 17, 244 (2017).
PubMed  PubMed Central  Article  Google Scholar 
2.
Hewitt, G. M. Genetic consequences of climatic oscillations in the quaternary. Philo. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 183–195 (2004).
CAS  Article  Google Scholar 

3.
Zheng, B., Xu, Q. & Shen, Y. The relationship between climate change and quaternary glacial cycles on the Qinghai-Tibetan Plateau: review and speculation. Quat. Int. 97, 93–101 (2002).
Article  Google Scholar 

4.
Wei, Z., Zhijiu, C. & Yonghua, L. Review of the timing and extent of glaciers during the last glacial cycle in the bordering mountains of Tibet and in East Asia. Quat. Int. 154, 32–43 (2006).
Article  Google Scholar 

5.
Zhou, S., Wang, X., Wang, J. & Xu, L. A preliminary study on timing of the oldest Pleistocene glaciation in Qinghai-Tibetan Plateau. Quat. Int. 154, 44–51 (2006).
Article  Google Scholar 

6.
Ma, J., Wang, Y., Jin, C., Hu, Y. & Bocherens, H. Ecological flexibility and differential survival of Pleistocene Stegodon orientalis and Elephas maximus in mainland southeast Asia revealed by stable isotope (C, O) analysis. Quat. Sci. Rev. 212, 33–44 (2019).
ADS  Article  Google Scholar 

7.
Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, Cambridge, 2000).
Google Scholar 

8.
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Wiens, J. J. Speciation and ecology revisited: phylogenetic niche conservatism and the origin of species. Evolution 58, 193–197 (2004).
PubMed  Article  Google Scholar 

10.
Srinivasan, U., Tamma, K. & Ramakrishnan, U. Past climate and species ecology drive nested species richness patterns along an east-west axis in the Himalaya. Glob. Ecol. Biogeogr. 23, 52–60 (2014).
Article  Google Scholar 

11.
Carstens, B. C. & Knowles, L. L. Shifting distributions and speciation: species divergence during rapid climate change. Mol. Ecol. 16, 619–627 (2007).
PubMed  Article  Google Scholar 

12.
Yang, S., Dong, H. & Lei, F. Phylogeography of regional fauna on the Tibetan Plateau: a review. Prog. Nat. Sci. 19, 789–799 (2009).
CAS  Article  Google Scholar 

13.
Qu, Y., Lei, F., Zhang, R. & Lu, X. Comparative phylogeography of five avian species: implications for Pleistocene evolutionary history in the Qinghai-Tibetan plateau. Mol. Ecol. 19, 338–351 (2010).
CAS  PubMed  Article  Google Scholar 

14.
Zhao, N. et al. Pleistocene climate changes shaped the divergence and demography of Asian populations of the great tit Parus major: evidence from phylogeographic analysis and ecological niche models. J. Avian Biol. 43, 297–310 (2012).
Article  Google Scholar 

15.
Lei, F., Qu, Y. & Song, G. Species diversification and phylogeographical patterns of birds in response to the uplift of the Qinghai-Tibet Plateau and Quaternary glaciations. Curr. Zool. 60, 149–161 (2014).
Article  Google Scholar 

16.
McKinney, M. L. Extinction vulnerability and selectivity: combining ecological and paleontological views. Annu. Rev. Ecol. System. 28, 495–516 (1997).
Article  Google Scholar 

17.
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

18.
Colles, A., Liow, L. H. & Prinzing, A. Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches. Ecol. Lett. 12, 849–863 (2009).
PubMed  PubMed Central  Article  Google Scholar 

19.
Glatston, A., Wei, F., Zaw, T. & Sherpa, A. Ailurus fulgens. The IUCN Red. List of Threatened Species. (2015).

20.
Choudhury, A. An overview of the status and conservation of the red panda Ailurus fulgens in India, with reference to its global status. Oryx 35, 250–259 (2001).
Article  Google Scholar 

21.
Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).
PubMed  PubMed Central  Article  Google Scholar 

22.
Wei, F., Feng, Z., Wang, Z., Zhou, A. & Hu, J. Use of the nutrients in bamboo by the red panda (Ailurus fulgens). J. Zool. 248, 535–541 (1999).
Article  Google Scholar 

23.
Roberts, M. S. & Gittleman, J. L. Ailurus fulgens repository.si.edu. Mamm. Species Acc. 222, 1–8 (1984).
Google Scholar 

24.
Su, B., Fu, Y., Wang, Y., Jin, L. & Chakraborty, R. Genetic diversity and population history of the red panda (Ailurus fulgens) as inferred from mitochondrial DNA sequence variations. Mol. Biol. Evol. 18, 1070–1076 (2001).
CAS  PubMed  Article  Google Scholar 

25.
Li, M. et al. Mitochondrial phylogeography and subspecific variation in the red panda (Ailurus fulgens): implications for conservation. Mol. Phylogenet. Evol. 36, 78–89 (2005).
CAS  PubMed  Article  Google Scholar 

26.
Hu, Y. et al. Genetic structuring and recent demographic history of red pandas (Ailurus fulgens) inferred from microsatellite and mitochondrial DNA. Mol. Ecol. 20, 2662–2675 (2011).
PubMed  Article  Google Scholar 

27.
Hu, Y. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6, eaax5751 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Dalui, S. et al. Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India. Sci. Rep. 10, 1–12 (2020).
Article  Google Scholar 

29.
Liu, Z. F. et al. Variations ofδ18O in precipitation of the Yarlung Zangbo River Basin. Acta Geograph. Sin. (Chin.) 17, 317–326 (2007).
Google Scholar 

30.
Wang, X. D. et al. Regional assessment of environmental vulnerability in the Tibetan Plateau: development and application of a new method. J. Arid Environ. 721, 929–939 (2008).
Google Scholar 

31.
Zeng, C. et al. Improving sediment load estimations: The case of the Yarlung Zangbo River (the upper Brahmaputra, Tibet Plateau). CATENA 160, 210–211 (2018).
Article  Google Scholar 

32.
Du, Z. et al. Mountain Geoecology and Sustainable Development of the Tibetan Plateau 312 (Kluwer, Riverwoods, 2000).
Google Scholar 

33.
Choudhury, A. Primates in northeast India: an overview of their distribution and conservation status. ENVIS Bull. Wildl. Prot. Areas 1, 92–101 (2001).
Google Scholar 

34.
Meijaard, E. & Groves, C. P. The geography of mammals and rivers in mainland Southeast Asia. In Primate Biogeography (eds Lehman, S. M. & Fleagle, J. G.) 305–329 (Springer, Boston, 2006).
Google Scholar 

35.
Fordham, G., Shanee, S. & Peck, M. Effect of river size on Amazonian primate community structure: a biogeographic analysis using updated taxonomic assessments. Am. J. Primatol. 82, e23136 (2020).
PubMed  Article  Google Scholar 

36.
Bazin, E. et al. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Heller, R., Chikhi, L. & Siegismund, H. R. The confounding effect of population structure on Bayesian skyline plot inferences of demographic history. PLoS ONE 8(5), e62992 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Hu, Y., Qi, D., Wang, H. & Wei, F. Genetic evidence of recent population contraction in the southernmost population of giant pandas. Genetica 138, 1297–1306 (2010).
PubMed  Article  Google Scholar 

39.
Chung, S.-L. et al. Diachronous uplift of the Tibetan plateau starting 40? Myr ago. Nature 394, 769–773 (1998).
ADS  CAS  Article  Google Scholar 

40.
Tapponnier, P. et al. Oblique stepwise rise and growth of the Tibet Plateau. Science 294, 1671–1677 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

41.
Royden, L. H., Burchfiel, B. C. & van der Hilst, R. D. The geological evolution of the Tibetan Plateau. Science 321, 1054–1058 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Kapp, P., DeCelles, P. G., Gehrels, G. E., Heizler, M. & Ding, L. Geological records of the Lhasa-Qiangtang and Indo-Asian collisions in the Nima area of central Tibet. Geol. Soc. Am. Bull. 119, 917–933 (2007).
ADS  Article  Google Scholar 

43.
Schmidt, F., Franke, F. A., Shirley, M. H., Vliet, K. A. & Villanova, V. L. The importance of genetic research in zoo breeding programmes for threatened species: the African dwarf crocodiles (genus Osteolaemus) as a case study. Int. Zoo Yearb. 49, 125–136 (2015).
Article  Google Scholar 

44.
Gippoliti, S., Cotterill, F. P., Zinner, D. & Groves, C. P. Impacts of taxonomic inertia for the conservation of African ungulate diversity: an overview. Biol. Rev. 93, 115–130 (2018).
PubMed  Article  Google Scholar 

45.
McClenachan, L., Ferretti, F. & Baum, J. K. From archives to conservation: why historical data are needed to set baselines for marine animals and ecosystems. Conserv. Lett. 5, 349–359 (2012).
Article  Google Scholar 

46.
Grace, M. et al. Using historical and palaeoecological data to inform ambitious species recovery targets. Philos. Trans. R. Soc. Lond. B 374, 20190297 (2019).
Article  Google Scholar 

47.
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).
CAS  Article  Google Scholar 

48.
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).
PubMed  PubMed Central  Article  Google Scholar 

49.
Nylander, J. A. A. MrModeltest ver. 2. 2004: Evolutionary Biology Centre (Uppsala University, Sweden, 2004).
Google Scholar 

50.
Sato, J. J. et al. Deciphering and dating the red panda’s ancestry and early adaptive radiation of Musteloidea. Mol. Phylogenet. Evol. 53, 907–922 (2009).
CAS  PubMed  Article  Google Scholar 

51.
Rambaut, A. & Drummond, A. J. Tracer version 1.5 [computer program]. (2009).

52.
Rambaut, A. FigTree version 1.4. 0. Available at http://tree.bio.ed.ac.uk/software/figtree. Accessed October (2016).

53.
Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).
CAS  PubMed  Article  Google Scholar 

54.
Corander, J. & Marttinen, P. Bayesian identification of admixture events using multilocus molecular markers. Mol. Ecol. 15, 2833–2843 (2006).
PubMed  Article  Google Scholar 

55.
Corander, J., Marttinen, P., Sirén, J. & Tang, J. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinform. 9, 539 (2008).
Article  Google Scholar 

56.
Dupanloup, I., Schneider, S. & Excoffier, L. A simulated annealing approach to define the genetic structure of populations. Mol. Ecol. 11, 2571–2581 (2002).
CAS  PubMed  Article  Google Scholar 

57.
Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).
CAS  PubMed  Google Scholar 

58.
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Excoffier, L. & Lischer, H. E. 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  Article  Google Scholar  More