Ecology

1.
Robert Feldmeth, C., Stone, E. A. & Brown, J. H. An increased scope for thermal tolerance upon acclimating pupfish (Cyprinodon) to cycling temperatures. J. Comp. Physiol. 89, 39–44 (1974).
Google Scholar 
2.
Hertz, P. E. Adaptation to altitude in two West Indian anoles. Animals 195, 25–37 (1981).
Google Scholar 

3.
Hertz, P. E. & Huey, R. B. Compensation for altitudinal changes in the thermal environment by some anolis lizards on Hispaniola. Ecology 62, 515–521 (1981).
Google Scholar 

4.
Hertz, P. E., Huey, R. B. & Nevo, E. Fight versus flight: body temperature influences defensive responses of lizards. Anim. Behav. 30, 676–679 (1982).
Google Scholar 

5.
Stillman, J. H. Acclimation capacity underlies susceptibility to climate change. Science 301, 65 (2003).
CAS  PubMed  Google Scholar 

6.
Chown, S. L., Gaston, K. J. & Robinson, D. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).
Google Scholar 

7.
Chown, S. L. et al. Adapting to climate change: a perspective from evolutionary physiology. Clim. Res. 43, 3–15 (2010).
Google Scholar 

8.
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).
PubMed  Google Scholar 

9.
Bowler, K. & Terblanche, J. S. Insect thermal tolerance: what is the role of ontogeny, ageing and senescence?. Biol. Rev. 83, 339–355 (2008).
PubMed  Google Scholar 

10.
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).
PubMed  Google Scholar 

11.
Waldschmidt, S. & Tracy, C. R. Interactions between a lizard and its thermal environment: implications for sprint performance and space utilization in the lizard Uta stansburiana. Ecology 64, 476–484 (1983).
Google Scholar 

12.
Huey, R. B. & Bennett, A. F. Phylogenetic studies of coadaptation: preferred temperatures versus optimal performance temperature of lizards. Evolution 41, 1098–1115 (1987).
PubMed  Google Scholar 

13.
Huey, R. B. & Stevenson, R. D. Intergrating thermal physiology and ecology of ecotherms: a discussion of approaches. Am. Zool. 19, 357–366 (1979).
Google Scholar 

14.
Huey, R. B. & Kingsolver, J. G. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4, 131–135 (1989).
CAS  PubMed  Google Scholar 

15.
Huey, R. B. & Kingsolver, J. G. Evolution of resistance to high temperature in ectotherms. Am. Nat. 142, 21–46 (1993).
Google Scholar 

16.
Angilletta, M. J., Wilson, R. S., Navas, C. A. & James, R. S. Tradeoffs and the evolution of thermal reaction norms. Trends Ecol. Evol. 18, 234–240 (2003).
Google Scholar 

17.
Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, Oxford, 2009).
Google Scholar 

18.
Kingsolver, J. G. et al. Complex life cycles and the responses of insects to climate change. Integr. Comp. Biol. 51, 719–732 (2011).
PubMed  Google Scholar 

19.
Logan, M. L., Cox, R. M. & Calsbeek, R. Natural selection on thermal performance in a novel thermal environment. Proc. Natl. Acad. Sci. 111, 14165–14169 (2014).
ADS  CAS  PubMed  Google Scholar 

20.
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures?. Ecol. Lett. 19, 1372–1385 (2016).
PubMed  Google Scholar 

21.
Izem, R. & Kingsolver, J. G. Variation in continuous reaction norms: quantifying directions of biological interest. Am. Nat. 166, 277–289 (2005).
PubMed  Google Scholar 

22.
Frazier, M. R., Huey, R. B. & Berrigan, D. Thermodynamics constrains the evolution of insect population growth rates: “warmer is better”. Am. Nat. 168, 512–520 (2006).
CAS  PubMed  Google Scholar 

23.
Kingsolver, J. G. The well-temperatured biologist. Am. Nat. 174, 755–768 (2009).
PubMed  Google Scholar 

24.
Bennett, A. F. Thermal dependence of locomotor capacity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 259, 253–258 (1990).
Google Scholar 

25.
van Damme, R., Bauwens, D. & Verheyen, R. F. Evolutionary rigidity of thermal physiology: the case of the cool temperate lizard Lacerta vivipara. Oikos 57, 61 (1990).
Google Scholar 

26.
Swoap, S. J., Johnson, T. P., Josephson, R. K. & Bennett, A. F. Temperature, muscle power output and limitations on burst locomotor performance of the lizard Dipsosaurus dorsalis. J. Exp. Biol. 174, 199–213 (1993).
Google Scholar 

27.
Vicenzi, N., Corbalán, V., Miles, D., Sinervo, B. & Ibargüengoytía, N. Range increment or range detriment? Predicting potential changes in distribution caused by climate change for the endemic high-Andean lizard Phymaturus palluma. Biol. Conserv. 206, 151–160 (2017).
Google Scholar 

28.
Vicenzi, N., Kubisch, E., Ibargüengoytía, N. & Corbalán, V. Thermal sensitivity of performance of Phymaturus palluma (Liolaemidae) in the highlands of Aconcagua : vulnerability to global warming in the Andes. Amphibia-Reptilia 01, 1–12 (2018).
Google Scholar 

29.
Brattstrom, B. H. Thermal acclimation in anuran amphibians as a function of latitude and altitude. Comp. Biochem. Physiol. 24, 93–111 (1968).
CAS  PubMed  Google Scholar 

30.
Bauwens, D., Castilla, A. M., Van Damme, R. & Verheyen, R. F. Field body temperatures and thermoregulatory behavior of the high altitude lizard, Lacerta bedriagae. J. Herpetol. 24, 88–91 (1990).
Google Scholar 

31.
Adolph, S. C. & Porter, W. P. Temperature, activity, and lizard life histories. Am. Nat. 142, 273–295 (1993).
CAS  PubMed  Google Scholar 

32.
Díaz, J. A. & Cabezas-Díaz, S. Seasonal variation in the contribution of different behavioural mechanisms to lizard thermoregulation. Funct. Ecol. 18, 867–875 (2004).
Google Scholar 

33.
Munoz, M. M. et al. Evolutionary stasis and lability in thermal physiology in a group of tropical lizards. Proc. R. Soc. B 281, 20132433–20132433 (2014).
PubMed  Google Scholar 

34.
Cei, J. M. Reptiles del centro, centro-oeste y sur de la Argentina. Herpetofauna de las zonas áridas y semiáridas. Mitt. zool. Mus. vol. 64 (Torino: Museo Regionale di Scienze Naturali, 1988).

35.
Scolaro, J. A. Reptiles Patagónicos Sur: Una Guía de Campo (Universidad Nacional de la Patagonia, Comodoro Rivadavia, 2005).
Google Scholar 

36.
Cecchetto, N. R., Medina, S. M., Taussig, S. & Ibargüengoytía, N. R. The lizard abides: cold hardiness and winter refuges of Liolaemus pictus argentinus in Patagonia, Argentina. Can. J. Zool. 782, 773–782 (2019).
Google Scholar 

37.
Ibargüengoytía, N. R. et al. Thermal biology of the southernmost lizards in the world: Liolaemus sarmientoi and Liolaemus magellanicus from Patagonia, Argentina. J. Therm. Biol. 35, 21–27 (2010).
Google Scholar 

38.
Fernández, J., Smith, J., Scolaro, A. & Ibargüengoytía, N. R. Performance and thermal sensitivity of the southernmost lizards in the world, Liolaemus sarmientoi and Liolaemus magellanicus. J. Therm. Biol. 36, 15–22 (2011).
Google Scholar 

39.
Piantoni, C., Ibargüengoytía, N. R. & Cussac, V. E. Age and growth of the Patagonian lizard Phymaturus patagonicus. Amphibia-Reptilia 27, 385–392 (2006).
Google Scholar 

40.
Boretto, J. M. & Ibargüengoytía, N. R. Phymaturus of Patagonia, Argentina: reproductive biology of Phymaturus zapalensis (Liolaemidae) and a comparison of sexual dimorphism within the genus. J. Herpetol. 43, 96–104 (2009).
Google Scholar 

41.
Gutiérrez, J. A., Piantoni, C. & Ibargüengoytía, N. R. Altitudinal effects on life history parameters in populations of Liolaemus pictus argentinus (Sauria:Liolaemidae). Acta Herpetol. 8, 9–17 (2013).
Google Scholar 

42.
Pianka, E. R. Comparative autecology of the lizard Cnemidophorus tigris in different parts of its georgraphic range. Ecology 51, 703–720 (1970).
Google Scholar 

43.
James, C. & Shine, R. Life-history strategies of australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).
ADS  PubMed  Google Scholar 

44.
Piantoni, C., Navas, C. A. & Ibargüengoytía, N. R. A real tale of Godzilla: impact of climate warming on the growth of a lizard. Biol. J. Linn. Soc. 126, 768–782 (2019).
Google Scholar 

45.
Gutiérrez, J. A., Krenz, J. D. & Ibargüengoytía, N. R. Effect of altitude on thermal responses of Liolaemus pictus argentinus in Argentina. J. Therm. Biol. 35, 332–337 (2010).
Google Scholar 

46.
Medina, M. et al. Thermal biology of genus Liolaemus: a phylogenetic approach reveals advantages of the genus to survive climate change. J. Therm. Biol. 37, 579–586 (2012).
Google Scholar 

47.
Huey, R. B. Physiological consequences of habitat selection. Am. Nat. 137, 91–115 (1991).
Google Scholar 

48.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).
PubMed  Google Scholar 

49.
Irschick, D. J. & Meyers, J. J. An analysis of the relative roles of plasticity and natural selection in the morphology and performance of a lizard (Urosaurus ornatus). Oecologia 153, 489–499 (2007).
ADS  PubMed  Google Scholar 

50.
Strobbe, F., McPeek, M. A., De Block, M., De Meester, L. & Stoks, R. Survival selection on escape performance and its underlying phenotypic traits: a case of many-to-one mapping. J. Evol. Biol. 22, 1172–1182 (2009).
CAS  PubMed  Google Scholar 

51.
Lima, S. L. Putting predators back into behavioral predator–prey interactions. Trends Ecol. Evol. 17, 70–75 (2002).
Google Scholar 

52.
Herczeg, G. et al. Experimental support for the cost–benefit model of lizard thermoregulation: the effects of predation risk and food supply. Oecologia 155, 1–10 (2008).
ADS  PubMed  Google Scholar 

53.
Lopez-Darias, M., Schoener, T. W., Spiller, D. A. & Losos, J. B. Predators determine how weather affects the spatial niche of lizard prey: exploring niche dynamics at a fine scale. Ecology 93, 2512–2518 (2012).
PubMed  Google Scholar 

54.
Bakken, G. S. & Angilletta, M. J. How to avoid errors when quantifying thermal environments. Funct. Ecol. 28, 96–107 (2014).
Google Scholar 

55.
Zagar, A., Carretero, M. A., Marguc, D., Simcic, T. & Vrezec, A. A metabolic syndrome in terrestrial ectotherms with different elevational and distribution patterns. Ecography 41, 1728–1739 (2018).
Google Scholar 

56.
Bartheld, J. L., Artacho, P. & Bacigalupe, L. Thermal performance curves under daily thermal fluctuation: a study in helmeted water toad tadpoles. J. Therm. Biol. 70, 80–85 (2017).
PubMed  Google Scholar 

57.
Kingsolver, J. G. & Huey, R. B. Introduction: the evolution of morphology, performance, and fitness. Integr. Comp. Biol. 43, 361–366 (2006).
Google Scholar 

58.
Bonino, M. F. et al. Running in cold weather: Morphology, thermal biology, and performance in the southernmost lizard clade in the world (Liolaemus lineomaculatus section: Liolaemini: Iguania). J. Exp. Zool. A. 315, 495–503 (2011).
Google Scholar 

59.
Kubisch, E. L., Fernández, J. & Ibargüengoytía, N. R. Is locomotor performance optimised at preferred body temperature? A study of Liolaemus pictus argentinus from northern Patagonia, Argentina. J. Therm. Biol. 36, 328–333 (2011).
Google Scholar 

60.
Angilletta, M. J. Jr. Thermal and physiological constraints on energy assimilation in a widespread lizard (Sceloporus undulatus). Ecology 82, 3044–3056 (2001).
Google Scholar 

61.
Buckley, L. B., Ehrenberger, J. C. & Angilletta, M. J. Jr. Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change. Funct. Ecol. 29, 1038–1047 (2015).
Google Scholar 

62.
Taucare-Rios, A., Veloso, C. & Bustamante, R. O. Thermal niche conservatism in an environmental gradient in the spider Sicarius thomisoides (Araneae: Sicariidae): implications for microhabitat selection. J. Therm. Biol. 78, 298–303 (2018).
CAS  PubMed  Google Scholar 

63.
Medina, M., Scolaro, J. A., Méndez-de la Cruz, F., Sinervo, B. & Ibargüengoytía, N. R. Thermal relationships between body temperature and environment conditions set upper distributional limits on oviparous species. J. Therm. Biol. 36, 527–534 (2011).
Google Scholar 

64.
Ibargüengoytía, N. R., Renner, M. L. & Boretto, J. M. Thermal effects on locomotion in the nocturnal gecko Homonota darwini (Gekkonidae). Amphibia-Reptilia 28, 235–246 (2007).
Google Scholar 

65.
Medina, S. M. & Ibargüengoytía, N. R. How do viviparous and oviparous lizards reproduce in Patagonia? A comparative study of three species of Liolaemus. J. Arid Environ. 74, 1024–1032 (2010).
ADS  Google Scholar 

66.
Boretto, J. M., Cabezas-Cartes, F. & Ibargüengoytía, N. R. Slow life histories in lizards living in the highlands of the Andes Mountains. J. Comp. Physiol. B 188, 491–503 (2018).
PubMed  Google Scholar 

67.
Nussey, D. H., Wilson, A. J. & Brommer, J. E. The evolutionary ecology of individual phenotypic plasticity in wild populations. J. Evol. Biol. 20, 831–844 (2007).
CAS  PubMed  Google Scholar 

68.
Artacho, P., Jouanneau, I. & Le Galliard, J.-F. Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard. Physiol. Biochem. Zool. 86, 458–469 (2013).
PubMed  Google Scholar 

69.
Bonino, M. F., Moreno Azócar, D. L., Schulte, J. A. & Cruz, F. B. Climate change and lizards: changing species’ geographic ranges in Patagonia. Reg. Environ. Chang. 15, 1121–1132 (2015).
Google Scholar 

70.
Gvozdík, L. & Castilla, A. M. A comparative study of preferred body temperatures and critical thermal tolerance limits among populations of Zootoca vivipara (Squamata: Lacertidae) along an altitudinal gradient. J. Herpetol. 35, 486–492 (2001).
Google Scholar 

71.
Angilletta, M. J. Estimating and comparing thermal performance curves. J. Therm. Biol. 31, 541–545 (2006).
Google Scholar 

72.
Hertz, P. E., Huey, R. B. & Nevo, E. Homage to Santa Anita: thermal sensitivity of sprint speed in agamid lizards. Evolution 37, 1075–1084 (1983).
PubMed  Google Scholar 

73.
Zamora-Camacho, F. J., Rubiño-Hispán, M. V., Reguera, S. & Moreno-Rueda, G. Thermal dependence of sprint performance in the lizard Psammodromus algirus along a 2200-meter elevational gradient: Cold-habitat lizards do not perform better at low temperatures. J. Therm. Biol. 52, 90–96 (2015).
PubMed  Google Scholar 

74.
Huey, R. B. & Slatkin, M. Cost and benefits of lizard thermoregulation. Q. Rev. Biol. 51, 363–384 (1976).
CAS  PubMed  Google Scholar 

75.
Logan, M. L., Fernandez, S. G. & Calsbeek, R. Abiotic constraints on the activity of tropical lizards. Funct. Ecol. 29, 694–700 (2015).
Google Scholar 

76.
Sears, M. W. & Angilletta, M. J. Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs. Am. Nat. 185, E94–E102 (2015).
PubMed  Google Scholar 

77.
Basson, C. H., Levy, O., Angilletta, M. J. & Clusella-Trullas, S. Lizards paid a greater opportunity cost to thermoregulate in a less heterogeneous environment. Funct. Ecol. 31, 856–865 (2017).
Google Scholar 

78.
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B 272, 2627–2634 (2005).
PubMed  Google Scholar 

79.
Stephens, D. W. & Charnov, E. L. Optimal foraging: some simple stochastic models. Behav. Ecol. Sociobiol. 10, 251–263 (1982).
Google Scholar 

80.
Kacelnik, A. & Bateson, M. Risky theories: the effects of variance on foraging decisions. Am. Zool. 36, 402–434 (1996).
Google Scholar 

81.
Lister, B. C. & Aguayo, A. G. Seasonality, predation, and the behaviour of a tropical mainland anole. J. Anim. Ecol. 61, 717–733 (1992).
Google Scholar 

82.
Broeckhoven, C. & Nortier, F. Some like it hot: camera traps unravel the effects of weather conditions and predator presence on the activity levels of two lizards. PLoS ONE 10, 1–15 (2015).
Google Scholar 

83.
Angilletta, M. J., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).
Google Scholar 

84.
Ibargüengoytía, N. R. et al. Volcanic ash from Puyehue-Cordon Caulle eruptions affects running performance and body condition of Phymaturus lizards in Patagonia, Argentina. Biol. J. Linn. Soc. 118, 842–851 (2016).
Google Scholar 

85.
Geng, J., Dong, W., Wu, Q. & Lu, H.-L. Thermal tolerance for two cohorts of a native and an invasive freshwater turtle species. Acta Herpetol. 13, 83–88 (2018).
Google Scholar 

86.
Thompson, M. E., Halstead, B. J. & Donnelly, M. A. Thermal quality influences habitat use of two Anolis species. J. Therm. Biol. 18, 54–61 (2018).
Google Scholar 

87.
Bakken, G. S. Measurement and application of operative and standard operative temperatures in ecology. Am. Zool. 32, 194–216 (1992)

88.
Medina, S. M. Adaptaciones morfológicas y fisiológicas ligadas a la transición oviparidad-viviparidad en lagartos de climas fríos: reproducción y fisiología térmica. (PhD thesis, Universidad Nacional del Comahue, 2010).

89.
Lindsey, A. A. & Newman, J. E. Use of official wather data in spring time: temperature analysis of an Indiana phenological record. Ecology 37, 812–823 (1956).
Google Scholar 

90.
Guisan, A. & Hofer, U. Predicting reptile distributions at the mesoscale: relation to climate and topography. J. Biogeogr. 30, 1233–1243 (2003).
Google Scholar 

91.
Schwanz, L. E. & Janzen, F. J. Climate change and temperature-dependent sex determination: can individual plasticity in nesting phenology prevent extreme sex ratios?. Physiol. Biochem. Zool. 81, 826–834 (2008).
PubMed  Google Scholar 

92.
Murphy, M. A., Evans, J. S. & Storfer, A. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252–261 (2010).
PubMed  Google Scholar 

93.
Boyero, L. et al. A global experiment suggests climate warming will not accelerate litter decomposition in streams but might reduce carbon sequestration. Ecol. Lett. 14, 289–294 (2011).
PubMed  Google Scholar 

94.
Graae, B. J. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19 (2012).
Google Scholar 

95.
Mitchell, N. et al. Linking eco-energetics and eco-hydrology to select sites for the assisted colonization of Australia’s rarest reptile. Biology 2, 1–25 (2012).
PubMed  PubMed Central  Google Scholar 

96.
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).
Google Scholar 

97.
Legendre, P. lmodel2: Model II Regression. (2014).

98.
R Core Team. R: A Language and Environment for Statistical Computing. (R Core Team, Vienna, 2019).

99.
Wood, S. & Wood, M. S. Package ‘mgcv’. R Packag. version 1–7 (2015).

100.
Hastie, T. & Tibshirani, R. Generalized additive models: some applications. J. Am. Stat. Assoc. 82, 371–386 (1987).
MATH  Google Scholar  More

1.
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).
CAS  PubMed  Google Scholar 
2.
Panetta, A. M., Stanton, M. L. & Harte, J. Climate warming drives local extinction: evidence from observation and experimentation. Sci. Adv. 4, eaaq1819 (2018).

3.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261 (2017).
ADS  CAS  PubMed  Google Scholar 

4.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270 (2018).
ADS  PubMed  Google Scholar 

5.
Rees, S. E., Foster, N. L., Langmead, O., Pittman, S. & Johnson, D. E. Defining the qualitative elements of Aichi Biodiversity Target 11 with regard to the marine and coastal environment in order to strengthen global efforts for marine biodiversity conservation outlined in the United Nations Sustainable Development Goal 14. Mar. Policy 93, 241–250 (2018).
Google Scholar 

6.
Gray C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).

7.
Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116 (2018).
CAS  PubMed  Google Scholar 

8.
Rasolofoson, R. A. et al. Impacts of community forest management on human economic well-being across Madagascar. Conserv. Lett. 10, 346–353 (2017).
Google Scholar 

9.
Ban, N. C. et al. Well-being outcomes of marine protected areas. Nat. Sustain. 2, 524–532 (2019).
Google Scholar 

10.
Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30, 133–141 (2016).
CAS  PubMed  Google Scholar 

11.
Roberts, C. M. et al. Marine reserves can mitigate and promote adaptation to climate change. Proc. Natl Acad. Sci. USA 114, 6167–6175 (2017).
ADS  CAS  PubMed  Google Scholar 

12.
Gurney, G. G. et al. Poverty and protected areas: an evaluation of a marine integrated conservation and development project in Indonesia. Glob. Environ. Change 26, 98–107 (2014).
Google Scholar 

13.
Andam, K. S., Ferraro, P. J., Sims, K. R. E., Healy, A. & Holland, M. B. Protected areas reduced poverty in Costa Rica and Thailand. Proc. Natl Acad. Sci. USA 107, 9996 (2010).
ADS  CAS  PubMed  Google Scholar 

14.
Keppel, G., Morrison, C., Watling, D., Tuiwawa Marika, V. & Rounds Isaac, A. Conservation in tropical Pacific Island countries: why most current approaches are failing. Conserv. Lett. 5, 256–265 (2012).
Google Scholar 

15.
Gill, D. A. et al. Social synergies, tradeoffs, and equity in marine conservation impacts. Annu. Rev. Environ. Resour. 44, 347–372 (2019).
Google Scholar 

16.
Dureuil, M., Boerder, K., Burnett, K. A., Froese, R. & Worm, B. Elevated trawling inside protected areas undermines conservation outcomes in a global fishing hot spot. Science 362, 1403–1407 (2018).
ADS  CAS  PubMed  Google Scholar 

17.
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).
CAS  PubMed  Google Scholar 

18.
Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti–Mara ecosystem. Science 363, 1424–1428 (2019).
ADS  CAS  PubMed  Google Scholar 

19.
Ewers, R. M. & Rodrigues, A. S. L. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116 (2008).
PubMed  Google Scholar 

20.
Lewison, R. L. et al. Accounting for unintended consequences of resource policy: connecting research that addresses displacement of environmental impacts. Conserv. Lett. 12, e12628 (2019).
PubMed  PubMed Central  Google Scholar 

21.
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).
ADS  CAS  PubMed  Google Scholar 

22.
Grorud-Colvert, K. et al. High-profile international commitments for ocean protection: empty promises or meaningful progress? Mar. Policy 105, 52–66 (2019).
Google Scholar 

23.
Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

24.
Chertow, M., Fugate, E. & Ashton W. In Long Term Socio-Ecological Research. Studies in Society-Nature Interactions across Spatial and Temporal Scales (eds Sing, S. J. et al.) Vol. 2, 315–338 (Springer, Dordrecht, 2013).

25.
Brodie, G., Pikacha, P. & Tuiwawa, M. In Conservation Biology: Voices from the Tropics, Ch. 21 (eds Navjot, S., Luke, G. & Peter, R.), 181–187 (Wiley, 2013).

26.
Coulthard, S. et al. Exploring ‘islandness’ and the impacts of nature conservation through the lens of wellbeing. Environ. Conserv. 44, 298–309 (2017).
Google Scholar 

27.
Reenberg, A., Birch-Thomsen, T., Mertz, O., Fog, B. & Christiansen, S. Adaptation of human coping strategies in a small island society in the SW Pacific—50 years of change in the coupled human–environment system on Bellona, Solomon Islands. Hum. Ecol. 36, 807–819 (2008).
Google Scholar 

28.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
PubMed  Google Scholar 

29.
Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl Acad. Sci. USA 113, 11261 (2016).
CAS  PubMed  Google Scholar 

30.
Cooper, W. E., Pyron, R. A. & Garland, T. Island tameness: living on islands reduces flight initiation distance. Proc. R. Soc. Ser. B 281, 20133019 (2014).
Google Scholar 

31.
Tershy, B. R., Shen, K. W., Newton, K. M., Holmes, N. D. & Croll, D. A. The importance of islands for the protection of biological and linguistic diversity. Bioscience 65, 592–597 (2015).
Google Scholar 

32.
Spatz, D. R. et al. Globally threatened vertebrates on islands with invasive species. Sci. Adv. 3, e1603080 (2017).

33.
O’Leary, B. C. et al. Addressing criticisms of large-scale marine protected areas. Bioscience https://doi.org/10.1093/biosci/biy021 (2018).

34.
Visconti, P. et al. Protected area targets post-2020. Science eaav6886 (2019).

35.
Sala, E. et al. Assessing real progress towards effective ocean protection. Mar. Policy 91, 11–13 (2018).
Google Scholar 

36.
Noss, R. F. et al. Bolder thinking for conservation. Conserv. Biol. 26, 1–4 (2012).
PubMed  Google Scholar 

37.
Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).
Google Scholar 

38.
Watson, J. E. M. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).
CAS  PubMed  Google Scholar 

39.
Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904 (2018).
ADS  CAS  PubMed  Google Scholar 

40.
Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, eaat2616 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

41.
Schleicher, J., Peres, C. A. & Leader-Williams, N. Conservation performance of tropical protected areas: how important is management? Conserv. Lett. 12, e12650 (2019).
Google Scholar 

42.
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209 (2019).
ADS  CAS  PubMed  Google Scholar 

43.
Fox, H. E. et al. Explaining global patterns and trends in marine protected area (MPA) development. Mar. Policy 36, 1131–1138 (2012).
Google Scholar 

44.
Marinesque, S., Kaplan, D. M. & Rodwell, L. D. Global implementation of marine protected areas: is the developing world being left behind? Mar. Policy 36, 727–737 (2012).
Google Scholar 

45.
Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. Ser. B 278, 1633–1638 (2011).
Google Scholar 

46.
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 480–504 (2015).
Google Scholar 

47.
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Naturalist 163, 192–211 (2004).
Google Scholar 

48.
Kinlock Nicole, L. et al. Explaining global variation in the latitudinal diversity gradient: meta‐analysis confirms known patterns and uncovers new ones. Glob. Ecol. Biogeogr. 27, 125–141 (2017).
Google Scholar 

49.
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).
ADS  CAS  PubMed  Google Scholar 

50.
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, 2602–2610 (2013).
ADS  Google Scholar 

51.
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
ADS  PubMed  PubMed Central  Google Scholar 

52.
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141 (2017).
ADS  CAS  PubMed  Google Scholar 

53.
Grenié, M. et al. Functional rarity of coral reef fishes at the global scale: hotspots and challenges for conservation. Biol. Conserv. 226, 288–299 (2018).
Google Scholar 

54.
Luck, G. W. A review of the relationships between human population density and biodiversity. Biol. Rev. 82, 607–645 (2007).
PubMed  Google Scholar 

55.
Maffi, L. Linguistic, cultural, and biological diversity. Annu. Rev. Anthropol. 34, 599–617 (2005).
Google Scholar 

56.
Gorenflo, L. J., Romaine, S., Mittermeier, R. A. & Walker-Painemilla, K. Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas. Proc. Natl Acad. Sci. USA 109, 8032 (2012).
ADS  CAS  PubMed  Google Scholar 

57.
Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H. & Bromham, L. The ecological drivers of variation in global language diversity. Nat. Commun. 10, 2047 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

58.
Butchart, S. H. M. et al. Protecting important sites for biodiversity contributes to meeting global conservation targets. PLoS ONE 7, e32529 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

59.
O’Hara, C. C., Villaseñor-Derbez, J. C., Ralph, G. M. & Halpern, B. S. Mapping status and conservation of global at-risk marine biodiversity. Conserv. Lett. e12651 (2019).

60.
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).
ADS  CAS  PubMed  Google Scholar 

61.
Cámara-Leret, R., Fortuna, M. A. & Bascompte, J. Indigenous knowledge networks in the face of global change. Proc. Natl Acad. Sci. USA 116, 9913 (2019).
PubMed  Google Scholar 

62.
Wang, C. & Steiner, B. Can ethno-linguistic diversity explain cross-country differences in social capital?: a global perspective. Econ. Rec. 91, 338–366 (2015).
Google Scholar 

63.
Page, S. E. How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press, 2008).

64.
Weeks, R. & Adams, V. M. Research priorities for conservation and natural resource management in Oceania’s small‐island developing states. Conserv. Biol. 32, 72–83 (2017).
PubMed  Google Scholar 

65.
Benitez-Capistros, F., Camperio, G., Hugé, J., Dahdouh-Guebas, F. & Koedam, N. Emergent conservation conflicts in the Galapagos Islands: human-giant tortoise interactions in the rural area of Santa Cruz Island. PLoS ONE 13, e0202268 (2018).
PubMed  PubMed Central  Google Scholar 

66.
McCrea-Strub, A. et al. Understanding the cost of establishing marine protected areas. Mar. Policy 35, 1–9 (2011).
Google Scholar 

67.
Balmford, A., Gaston, K. J., Blyth, S., James, A. & Kapos, V. Global variation in terrestrial conservation costs, conservation benefits, and unmet conservation needs. Proc. Natl Acad. Sci. USA 100, 1046 (2003).
ADS  CAS  PubMed  Google Scholar 

68.
Cetas, E. R. & Yasué, M. A systematic review of motivational values and conservation success in and around protected areas. Conserv. Biol. 31, 203–212 (2017).
PubMed  Google Scholar 

69.
Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 1–6 (2013).

70.
Leclerc, C., Courchamp, F. & Bellard, C. Insular threat associations within taxa worldwide. Sci. Rep. 8, 6393 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

71.
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).

72.
Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).
Google Scholar 

73.
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).
ADS  CAS  PubMed  Google Scholar 

74.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 

75.
Broxton, P. D., Zeng, X., Sulla-Menashe, D. & Troch, P. A. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).
ADS  Google Scholar 

76.
Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World 20th edn (SIL International, 2017).

77.
Burnham, K. P. & Anderson D. R. Model Selection and Inference. A Practical Information-Theoretic Approach (Springer, 1998).

78.
Estrella, A. A new measure of fit for equations with dichotomous dependent variables. J. Bus. Econ. Stat. 16, 198–205 (1998).
Google Scholar 

79.
Azen, R. & Traxel, N. Using dominance analysis to determine predictor importance in logistic regression. J. Educ. Behav. Stat. 34, 319–347 (2009).
Google Scholar 

80.
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285 (1988).
ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

81.
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).
Google Scholar 

82.
Romdal, T. S., Araújo, M. B. & Rahbek, C. Life on a tropical planet: niche conservatism and the global diversity gradient. Glob. Ecol. Biogeogr. 22, 344–350 (2013).
Google Scholar 

83.
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323 (2019).
ADS  Google Scholar 

84.
Pina, T. E. N., Carvalho, W. D., Rosalino, L. M. C. & Hilario. R. R. Drivers of mammal richness, diversity and occurrence in heterogeneous landscapes composed by plantation forests and natural environments. For. Ecol. Manag. 449, 117467 (2019).

85.
Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
Google Scholar 

86.
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).
PubMed  Google Scholar 

87.
Bohm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).
Google Scholar 

88.
Farneda, F. Z. et al. Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography. Ecography 43, 97–106 (2020).
Google Scholar 

89.
MacArthur, R. H. & Wilson, E. O. Equilibrium-theory of insular zoogeography. Evolution 17, 373-& (1963).
Google Scholar 

90.
Stratford, E. Island Geographies: Essays and Conversations (Routledge, London, 2017).

91.
Steinbauer, M. J., Otto, R., Naranjo-Cigala, A., Beierkuhnlein, C. & Fernández-Palacios, J.-M. Increase of island endemism with altitude – speciation processes on oceanic islands. Ecography 35, 23–32 (2012).
Google Scholar 

92.
Haddaway, N. R., Styles, D. & Pullin, A. S. Environmental impacts of farm land abandonment in high altitude/mountain regions: a systematic map of the evidence. Environ. Evid. 2, 18 (2013).
Google Scholar 

93.
Johnson, M. P., Frost, N. J., Mosley, M. W. J., Roberts, M. F. & Hawkins, S. J. The area-independent effects of habitat complexity on biodiversity vary between regions. Ecol. Lett. 6, 126–132 (2003).
Google Scholar 

94.
Darling, E. S. et al. Social-environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).
PubMed  Google Scholar  More

1.
Patterson EI, Villinger J, Muthoni JN, Dobel-Ober L, Hughes GL. Exploiting insect-specific viruses as a novel strategy to control vector-borne disease. Curr Opin Insect Sci. 2020;39:50–56. https://doi.org/10.1016/j.cois.2020.02.005.
Article  PubMed  Google Scholar 
2.
Tawidian P, Rhodes VL, Michel K. Mosquito-fungus interactions and antifungal immunity. Insect Biochem Mol Biol. 2019;111:103182. https://doi.org/10.1016/j.ibmb.2019.103182.
CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Guégan M, Zouache K, Démichel C, Minard G, Tran-Van V, Potier P, et al. The mosquito holobiont:. fresh insight into mosquito-microbiota interactions. Microbiome. 2018;6:49. https://doi.org/10.1186/s40168-018-0435-2.
Article  PubMed  PubMed Central  Google Scholar 

4.
Hegde S, Khanipov K, Albayrak L, Golovko G, Pimenova M, Saldana MA, et al. Microbiome interaction networks and community structure from laboratory-reared and field-collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquito vectors. Front Microbiol. 2018;9:2160. https://doi.org/10.3389/fmicb.2018.02160.
Article  PubMed  PubMed Central  Google Scholar 

5.
Osei-Poku J, Mbogo CM, Palmer WJ, Jiggins FM. Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya. Mol Ecol. 2012;21:5138–50. https://doi.org/10.1111/j.1365-294X.2012.05759.x.
CAS  Article  PubMed  Google Scholar 

6.
Ricci I, Cancrini G, Gabrielli S, D’Amelio S, Favia G. Searching for Wolbachia (Rickettsiales: Rickettsiaceae) in mosquitoes (Diptera: Culicidae): large polymerase chain reaction survey and new identifications. J Med Entomol. 2002;39:562–7.
Article  Google Scholar 

7.
Rasgon JL, Scott TW. An initial survey for Wolbachia (Rickettsiales: Rickettsiaceae) infections in selected California mosquitoes (Diptera: Culicidae). J Med Entomol. 2004;41:255–7.
Article  Google Scholar 

8.
Gloria-Soria A, Chiodo TG, Powell JR. Lack of evidence for natural wolbachia infections in Aedes aegypti (Diptera: Culicidae). J Med Entomol. 2018;7:e1002415. https://doi.org/10.1093/jme/tjy084.
Article  Google Scholar 

9.
Goindin D, Cannet A, Delannay C, Ramdini C, Gustave J, Atyame C, et al. Screening of natural Wolbachia infection in Aedes aegypti, Aedes taeniorhynchus and Culex quinquefasciatus from Guadeloupe (French West Indies). Acta Trop. 2018. https://doi.org/10.1016/j.actatropica.2018.06.011.

10.
Mitri C, Bischoff E, Belda Cuesta E, Volant S, Ghozlane A, Eiglmeier K, et al. Leucine-rich immune factor APL1 Is associated with specific modulation of enteric microbiome Taxa in the Asian malaria mosquito Anopheles stephensi. Front Microbiol. 2020;11:289. https://doi.org/10.3389/fmicb.2020.00306.
Article  Google Scholar 

11.
Short SM, Mongodin EF, MacLeod HJ, Talyuli OAC, Dimopoulos G. Amino acid metabolic signaling influences Aedes aegypti midgut microbiome variability. PLoS Negl Trop Dis. 2017;11:e0005677. https://doi.org/10.1371/journal.pntd.0005677.
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Pang X, Xiao X, Liu Y, Zhang R, Liu J, Liu Q, et al. Mosquito C-type lectins maintain gut microbiome homeostasis. Nat Microbiol. 2016;1:16023. https://doi.org/10.1038/nmicrobiol.2016.23.
CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Xiao X, Yang L, Pang X, Zhang R, Zhu Y, Wang P, et al. A Mesh-Duox pathway regulates homeostasis in the insect gut. Nat Microbiol. 2017;2:17020. https://doi.org/10.1038/nmicrobiol.2017.20.
Article  PubMed  PubMed Central  Google Scholar 

14.
Zhao B, Lucas KJ, Saha TT, Ha J, Ling L, Kokoza VA, et al. MicroRNA-275 targets sarco/endoplasmic reticulum Ca2+ adenosine triphosphatase (SERCA) to control key functions in the mosquito gut. PLoS Genet. 2017;13:e1006943–19. https://doi.org/10.1371/journal.pgen.1006943.
Article  PubMed  PubMed Central  Google Scholar 

15.
Dennison NJ, Saraiva RG, Cirimotich CM, Mlambo G, Mongodin EF, Dimopoulos G. Functional genomic analyses of Enterobacter, Anopheles and Plasmodium reciprocal interactions that impact vector competence. Malar J. 2016;15:425. https://doi.org/10.1186/s12936-016-1468-2.
Article  PubMed  PubMed Central  Google Scholar 

16.
Hegde S, Nilyanimit P, Kozlova E, Anderson ER, Narra HP, Sahni SK, et al. CRISPR/Cas9-mediated gene deletion of the ompA gene in symbiotic Cedecea neteri impairs biofilm formation and reduces gut colonization of Aedes aegypti mosquitoes. PLoS Negl Trop Dis. 2019;13:e0007883. https://doi.org/10.1371/journal.pntd.0007883.
CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Ramirez JL, Short SM, Bahia AC, Saraiva RG, Dong Y, Kang S, et al. Chromobacterium Csp_P reduces malaria and dengue infection in vector mosquitoes and has entomopathogenic and in vitro anti-pathogen activities. PLoS Pathog. 2014;10:e1004398. https://doi.org/10.1371/journal.ppat.1004398.
Article  PubMed  PubMed Central  Google Scholar 

18.
Hughes GL, Dodson BL, Johnson RM, Murdock CC, Tsujimoto H, Suzuki Y, et al. Native microbiome impedes vertical transmission of Wolbachia in Anopheles mosquitoes. Proc Natl Acad Sci USA. 2014;111:12498–503. https://doi.org/10.1073/pnas.1408888111.
CAS  Article  PubMed  Google Scholar 

19.
Hegde S, Rasgon JL, Hughes GL. The microbiome modulates arbovirus transmissionin mosquitoes. Curr Opin Virol. 2015;15:97–102. https://doi.org/10.1016/j.coviro.2015.08.011.
CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Dennison NJ, Jupatanakul N, Dimopoulos G. The mosquito microbiota influences vector competence for human pathogens. Curr Opin Insect Sci. 2014;3:6–13. https://doi.org/10.1016/j.cois.2014.07.004.
Article  PubMed  PubMed Central  Google Scholar 

21.
Wong AC-N, Wang Q-P, Morimoto J, Senior AM, Lihoreau M, Neely GG, et al. Gut microbiota modifies olfactory-guided microbial preferences and foraging decisions in drosophila. Curr Biol. 2017;27:2397–.e4. https://doi.org/10.1016/j.cub.2017.07.022.
CAS  Article  PubMed  Google Scholar 

22.
Fischer CN, Trautman EP, Crawford JM, Stabb E, Handelsman J, Broderick NA. Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior. eLife. 2017;6:e18855. https://doi.org/10.7554/eLife.18855.001.
Article  PubMed  PubMed Central  Google Scholar 

23.
Saldana MA, Hegde S, Hughes GL. Microbial control of arthropod-borne disease. Mem Inst Oswaldo Cruz Fundação Oswaldo Cruz. 2017;112:81–93. https://doi.org/10.1590/0074-02760160373.
CAS  Article  Google Scholar 

24.
Gao H, Cui C, Wang L, Jacobs-Lorena M, Wang S. Mosquito microbiota and implications for disease control. Trends Parasitol. 2020;36:98–111. https://doi.org/10.1016/j.pt.2019.12.001.
Article  PubMed  Google Scholar 

25.
Briones-Roblero CI, Hernandez-Garcia JA, Gonzalez-Escobedo R, Soto-Robles LV, Rivera-Orduna FN, Zuniga G. Structure and dynamics of the gut bacterial microbiota of the bark beetle, Dendroctonus rhizophagus (Curculionidae: Scolytinae) across their life stages. PLoS ONE. 2017;12:e0175470. https://doi.org/10.1371/journal.pone.0175470.
Article  PubMed  PubMed Central  Google Scholar 

26.
Medina F, Li H, Vinson SB, Coates CJ. Genetic transformation of midgut bacteria from the red imported fire ant (Solenopsis invicta). Curr Microbiol. 2009;58:478–82.
CAS  Article  Google Scholar 

27.
Muhammad A, Fang Y, Hou Y, Shi Z. The Gut Entomotype of Red Palm Weevil Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae) and their effect on host nutrition metabolism. Front Microbiol. 2017;8:2291. https://doi.org/10.3389/fmicb.2017.02291.
Article  PubMed  PubMed Central  Google Scholar 

28.
Renoz F, Noel C, Errachid A, Foray V, Hance T. Infection dynamic of symbiotic bacteria in the pea aphid Acyrthosiphon pisum gut and host immune response at the early steps in the infection process. PLoS ONE. 2015;10:e0122099.
Article  Google Scholar 

29.
Wang A, Yao Z, Zheng W, Zhang H. Bacterial communities in the gut and reproductive organs of Bactrocera minax (Diptera: Tephritidae) based on 454 pyrosequencing. PLoS ONE. 2014;9:e106988. https://doi.org/10.1371/journal.pone.0106988.
Article  PubMed  PubMed Central  Google Scholar 

30.
Lin X-L, Pan Q-J, Tian H-G, Douglas AE, Liu T-X. Bacteria abundance and diversity of different life stages of Plutella xylostella (Lepidoptera: Plutellidae), revealed by bacteria culture-dependent and PCR-DGGE methods. Insect Sci. 2015;22:375–85. https://doi.org/10.1111/1744-7917.12079
CAS  Article  PubMed  Google Scholar 

31.
Gumiel M, da Mota FF, Rizzo V, de S, Sarquis O, de Castro DP, et al. Characterization of the microbiota in the guts of Triatoma brasiliensis and Triatoma pseudomaculata infected by Trypanosoma cruzi in natural conditions using culture independent methods. Parasit Vectors. 2015;8:245. https://doi.org/10.1186/s13071-015-0836-z.
Article  PubMed  PubMed Central  Google Scholar 

32.
Gupta AK, Rastogi G, Nayduch D, Sawant SS, Bhonde RR, Shouche YS. Molecular phylogenetic profiling of gut-associated bacteria in larvae and adults of flesh flies. Med Vet Entomol. 2014;28:345–54. https://doi.org/10.1111/mve.12054.
CAS  Article  PubMed  Google Scholar 

33.
da Mota FF, Marinho LP, Moreira CJ, de C, Lima MM, Mello CB, et al. Cultivation-independent methods reveal differences among bacterial gut microbiota in triatomine vectors of Chagas disease. PLoS Negl Trop Dis. 2012;6:e1631. https://doi.org/10.1371/journal.pntd.0001631.
Article  PubMed  PubMed Central  Google Scholar 

34.
Kelly PH, Bahr SM, Serafim TD, Ajami NJ, Petrosino JF, Meneses C, et al. The gut microbiome of the vector Lutzomyia longipalpisIs essential for survival of Leishmania infantum. MBio. 2017;8:e01121–16. https://doi.org/10.1128/mBio.01121-16.
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Vieira CS, Waniek PJ, Castro DP, Mattos DP, Moreira OC, Azambuja P. Impact of Trypanosoma cruzi on antimicrobial peptide gene expression and activity in the fat body and midgut of Rhodnius prolixus. Parasit Vectors. 2016;9:119. https://doi.org/10.1186/s13071-016-1398-4.
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Zink S, Van Slyke G, Palumbo M, Kramer L, Ciota A. Exposure to West Nile virus increases bacterial diversity and immune gene expression in Culex pipiens. Viruses. 2015;7:5619–31. https://doi.org/10.3390/v7102886.
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Thongsripong P, Chandler JA, Green AB, Kittayapong P, Wilcox BA, Kapan DD, et al. Mosquito vector‐associated microbiota: metabarcoding bacteria and eukaryotic symbionts across habitat types in Thailand endemic for dengue and other arthropod‐borne diseases. Ecol Evolution. 2017;16:118. https://doi.org/10.1002/ece3.3676.
Article  Google Scholar 

38.
Seitz HM, Maier WA, Rottok M, Becker-Feldmann H. Concomitant infections of Anopheles stephensi with Plasmodium berghei and Serratia marcescens: additive detrimental effects. Zentralbl Bakteriol Mikrobiol Hyg A. 1987;266:155–66.
CAS  PubMed  Google Scholar 

39.
Muturi EJ, Bara JJ, Rooney AP, Hansen AK. Midgut fungal and bacterial microbiota of Aedes triseriatus and Aedes japonicus shift in response to La Crosse virus infection. Mol Ecol. 2016;25:4075–90. https://doi.org/10.1111/mec.13741.
CAS  Article  PubMed  Google Scholar 

40.
Bennett KL, Gómez-Martínez C, Chin Y, Saltonstall K, McMillan WO, Rovira JR, et al. Dynamics and diversity of bacteria associated with the disease vectors Aedes aegypti and Aedes albopictus. Sci Rep. 2019;9:12160–12. https://doi.org/10.1038/s41598-019-48414-8.
Article  PubMed  PubMed Central  Google Scholar 

41.
Dickson LB, Jiolle D, Minard G, Moltini-Conclois I, Volant S, Ghozlane A, et al. Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector. Sci Adv. 2017;3:e1700585. https://doi.org/10.1126/sciadv.1700585.
Article  PubMed  PubMed Central  Google Scholar 

42.
Zouache K, Raharimalala FN, Raquin V, Tran-Van V, Raveloson LHR, Ravelonandro P, et al. Bacterial diversity of field-caught mosquitoes, Aedes albopictus and Aedes aegypti, from different geographic regions of Madagascar. FEMS Microbiol Ecol. 2010. https://doi.org/10.1111/j.1574-6941.2010.01012.x.

43.
Dickson LB, Ghozlane A, Volant S, Bouchier C, Ma L, Vega-Rua A, et al. Diverse laboratory colonies of Aedes aegypti harbor the same adult midgut bacterial microbiome. Parasit Vectors. 2018;3:e1700585. https://doi.org/10.1101/200659.
Article  Google Scholar 

44.
David MR, Santos LMBD, Vicente ACP, Maciel-de-Freitas R. Effects of environment, dietary regime and ageing on the dengue vector microbiota: evidence of a core microbiota throughout Aedes aegypti lifespan. Mem Inst Oswaldo Cruz. 2016;111:577–87. https://doi.org/10.1590/0074-02760160238.
Article  PubMed  PubMed Central  Google Scholar 

45.
de O, Gaio A, Gusmão DS, Santos AV, Berbert-Molina MA, Pimenta PF, et al. Contribution of midgut bacteria to blood digestion and egg production in Aedes aegypti (diptera: culicidae) (L.). Parasit Vectors. 2011;4:105. https://doi.org/10.1186/1756-3305-4-105.
Article  Google Scholar 

46.
Gusmão DS, Santos AV, Marini DC, Russo E, de S, Peixoto AMD, et al. First isolation of microorganisms from the gut diverticulum of Aedes aegypti (Diptera: Culicidae): new perspectives for an insect-bacteria association. Mem Inst Oswaldo Cruz. 2007;102:919–24.
Article  Google Scholar 

47.
Gusmão DS, Santos AV, Marini DC, Bacci M, Berbert-Molina MA, Lemos FJA. Culture-dependent and culture-independent characterization of microorganisms associated with Aedes aegypti (Diptera: Culicidae) (L.) and dynamics of bacterial colonization in the midgut. Acta Trop. 2010;115:275–81. https://doi.org/10.1016/j.actatropica.2010.04.011.
Article  PubMed  Google Scholar 

48.
Coon KL, Vogel KJ, Brown MR, Strand MR. Mosquitoes rely on their gut microbiota for development. Mol Ecol. 2014;23:2727–39. https://doi.org/10.1111/mec.12771.
CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Kozlova EV, Khajanchi BK, Sha J, Chopra AK. Quorum sensing and c-di-GMP-dependent alterations in gene transcripts and virulence-associated phenotypes in a clinical isolate of Aeromonas hydrophila. Micro Pathog. 2011;50:213–23. https://doi.org/10.1016/j.micpath.2011.01.007.
CAS  Article  Google Scholar 

50.
Rose WA, McGowin CL, Spagnuolo RA, Eaves-Pyles TD, Popov VL, Pyles RB. Commensal bacteria modulate innate immune responses of vaginal epithelial cell multilayer cultures. PLoS ONE 2012;7:e32728. https://doi.org/10.1371/journal.pone.0032728.
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Kozlova EV, Khajanchi BK, Popov VL, Wen J, Chopra AK. Impact of QseBC system in c-di-GMP-dependent quorum sensing regulatory network in a clinical isolate SSU of Aeromonas hydrophila. Micro Pathog. 2012;53:115–24. https://doi.org/10.1016/j.micpath.2012.05.008.
CAS  Article  Google Scholar 

52.
Williams RP, Gott CL. Inhibition by streptomycin of the biosynthesis of prodigiosin. Biochem Biophys Res Commun. 1964;16:47–52. https://doi.org/10.1016/0006-291X(64)90209-8.
CAS  Article  PubMed  Google Scholar 

53.
Jeffries CL, Lawrence GG, Golovko G, Kristan M, Orsborne J, Spence K, et al. Novel Wolbachia strains in Anopheles malaria vectors from Sub-Saharan Africa. Wellcome Open Res. 2018;3:113. https://doi.org/10.12688/wellcomeopenres.14765.2.
Article  PubMed  PubMed Central  Google Scholar 

54.
Tourlousse DM, Yoshiike S, Ohashi A, Matsukura S, Noda N, Sekiguchi Y. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 2017;45:e23. https://doi.org/10.1093/nar/gkw984.
Article  PubMed  Google Scholar 

55.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2012;41:e1.
Article  Google Scholar 

56.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6. https://doi.org/10.1093/nar/gks1219.
CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:1–7. https://doi.org/10.3402/mehd.v26.27663.
Article  Google Scholar 

58.
Zhu H, Sun S-J, Dang H-Y. PCR detection of Serratia spp. using primers targeting pfs and luxS genes involved in AI-2-dependent quorum sensing. Curr Microbiol. 2008;57:326–30. https://doi.org/10.1007/s00284-008-9197-6.
CAS  Article  PubMed  Google Scholar 

59.
Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:e1005595. https://doi.org/10.1371/journal.pcbi.1005595.
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. https://doi.org/10.1093/bioinformatics/btp352
CAS  Article  PubMed  PubMed Central  Google Scholar 

61.
Kinosita Y, Kikuchi Y, Mikami N, Nakane D, Nishizaka T. Unforeseen swimming and gliding mode of an insect gut symbiont, Burkholderia sp. RPE64, with wrapping of the flagella around its cell body. ISME J. 2018;12:838–48. https://doi.org/10.1038/s41396-017-0010-z.
Article  PubMed  Google Scholar 

62.
Wiles TJ, Schlomann BH, Wall ES, Betancourt R, Parthasarathy R, Guillemin K. Swimming motility of a gut bacterial symbiont promotes resistance to intestinal expulsion and enhances inflammation. PLoS Biol. 2020;18:e3000661. https://doi.org/10.1371/journal.pbio.3000661.
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Bando H, Okado K, Guelbeogo WM, Badolo A, Aonuma H, Nelson B, et al. Intra-specific diversity of Serratia marcescens in Anopheles mosquito midgut defines Plasmodium transmission capacity. Sci Rep. 2013;3:1641–9. https://doi.org/10.1038/srep01641.
Article  PubMed  PubMed Central  Google Scholar 

64.
Abreo E, Altier N. Pangenome of Serratia marcescens strains from nosocomial and environmental origins reveals different populations and the links between them. Sci Rep. 2019;9:46–8. https://doi.org/10.1038/s41598-018-37118-0.
Article  PubMed  PubMed Central  Google Scholar 

65.
Chavda KD, Chen L, Fouts DE, Sutton G, Brinkac L, Jenkins SG, et al. Comprehensive genome analysis of carbapenemase-producing Enterobacter spp.: new insights into phylogeny, population structure, and resistance mechanisms. MBio. 2016;7. https://doi.org/10.1128/mBio.02093-16.

66.
Wyres KL, Lam MMC, Holt KE. Population genomics of Klebsiella pneumoniae. Nat Rev Micro. 2020;66:1–16. https://doi.org/10.1038/s41579-019-0315-1.
Article  Google Scholar 

67.
Pumpuni CB, Demaio J, Kent M, Davis JR, Beier JC. Bacterial population dynamics in three anopheline species: the impact on Plasmodium sporogonic development. Am J Epidemiol. 1996;54:214–8.
CAS  Google Scholar 

68.
Wang Y, Gilbreath TM III, Kukutla P, Yan G, Xu J. Dynamic gut microbiome across life history of the malaria mosquito Anopheles gambiae in Kenya. PLoS ONE. 2011;6:e24767. https://doi.org/10.1371/journal.pone.0024767.
CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Wang S, Ghosh AK, Bongio N, Stebbings KA, Lampe DJ, Jacobs-Lorena M. Fighting malaria with engineered symbiotic bacteria from vector mosquitoes. Proc Natl Acad Sci USA. 2012;109:12734–9. https://doi.org/10.1073/pnas.1204158109.
CAS  Article  PubMed  Google Scholar 

70.
Bai L, Wang L, Vega-Rodriguez J, Wang G, Wang S. A Gut Symbiotic Bacterium Serratia marcescens renders mosquito resistance to plasmodium infection through activation of mosquito immune responses. Front Microbiol. 2019;10:1580. https://doi.org/10.3389/fmicb.2019.01580.
Article  PubMed  PubMed Central  Google Scholar 

71.
Muturi EJ, Dunlap C, Ramirez JL, Rooney AP, Kim C-H. Host blood-meal source has a strong impact on gut microbiota of Aedes aegypti. FEMS Microbiol Ecol. 2019;95. https://doi.org/10.1093/femsec/fiy213.

72.
Yao Z, Wang A, Li Y, Cai Z, Lemaitre B, Zhang H. The dual oxidase gene BdDuox regulates the intestinal bacterial community homeostasis of Bactrocera dorsalis. ISME J. 2016;10:1037–50. https://doi.org/10.1038/ismej.2015.202.
CAS  Article  PubMed  Google Scholar 

73.
Andersen K, Kesper MS, Marschner JA, Konrad L, Ryu M, SK VR, et al. Intestinal dysbiosis, barrier dysfunction, and bacterial translocation account for CKD–related systemic inflammation. JASN. 2017;28:76–83. https://doi.org/10.1681/ASN.2015111285.
CAS  Article  PubMed  Google Scholar 

74.
Schaubeck M, Clavel T, Calasan J, Lagkouvardos I, Haange SB, Jehmlich N, et al. Dysbiotic gut microbiota causes transmissible Crohn’s disease-like ileitis independent of failure in antimicrobial defence. Gut. 2016;65:225–37. https://doi.org/10.1136/gutjnl-2015-309333.
CAS  Article  PubMed  Google Scholar 

75.
Peng W, Huang J, Yang J, Zhang Z, Yu R, Fayyaz S, et al. Integrated 16S rRNA sequencing, metagenomics, and metabolomics to characterize gut microbial composition, function, and fecal metabolic phenotype in non-obese type 2 diabetic Goto-Kakizaki rats. Front Microbiol. 2019;10:3141. https://doi.org/10.3389/fmicb.2019.03141.
Article  PubMed  Google Scholar 

76.
Li F, Wang P, Chen Z, Sui X, Xie X, Zhang J. Alteration of the fecal microbiota in North-Eastern Han Chinese population with sporadic Parkinson’s disease. Neurosci Lett. 2019;707:134297. https://doi.org/10.1016/j.neulet.2019.134297
CAS  Article  PubMed  Google Scholar 

77.
Hegde S, Voronin D, Casas-Sanchez A, Saldaña M, Acosta-Serrano A, Popov VL, et al. Gut-associated bacteria invade the midgut epithelium of Aedes aegypti and stimulate innate immunity and suppress Zika virus infection in cells. bioRxiv. 2019;37:866897. https://doi.org/10.1101/866897.
Article  Google Scholar 

78.
Xi Z, Ramirez JL, Dimopoulos G. The Aedes aegypti toll pathway controls dengue virus infection. PLoS Pathog. 2008;4:e1000098–49.
Article  Google Scholar 

79.
Ramirez JL, Souza-Neto J, Torres Cosme R, Rovira J, Ortiz A, Pascale JM, et al. Reciprocal tripartite interactions between the Aedes aegypti midgut microbiota, innate immune system and dengue virus influences vector competence. PLoS Negl Trop Dis. 2012;6:e1561. https://doi.org/10.1371/journal.pntd.0001561.
Article  PubMed  PubMed Central  Google Scholar 

80.
Koosha M, Vatandoost H, Karimian F, Choubdar N, Abai MR, Oshaghi MA. Effect of Serratia AS1 (Enterobacteriaceae: Enterobacteriales) on the fitness of Culex pipiens (Diptera: Culicidae) for paratransgenic and RNAi approaches. J Med Entomol. 2019;56:553–9. https://doi.org/10.1093/jme/tjy183.
CAS  Article  PubMed  Google Scholar 

81.
Adair KL, Bost A, Bueno E, Kaunisto S, Kortet R, Peters-Schulze G, et al. Host determinants of among-species variation in microbiome composition in drosophilid flies. ISME J. 2020;14:217–29. https://doi.org/10.1038/s41396-019-0532-7.
Article  PubMed  Google Scholar 

82.
Early AM, Shanmugarajah N, Buchon N, Clark AG. Drosophila genotype influences commensal bacterial levels. PLoS ONE. 2017;12:e0170332–15.
Article  Google Scholar  More

1.
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).
CAS  PubMed  Google Scholar 
2.
Worden, A. Z. et al. Environmental science. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347, 1257594 (2015).
PubMed  Google Scholar 

3.
Brum, J. R. & Sullivan, M. B. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat. Rev. Microbiol. 13, 147–159 (2015).
CAS  PubMed  Google Scholar 

4.
Selosse, M.-A., Charpin, M. & Not, F. Mixotrophy everywhere on land and in water: the grand écart hypothesis. Ecol. Lett. 20, 246–263 (2017).
PubMed  Google Scholar 

5.
Weitz, J. S. et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 9, 1352–1364 (2015).
PubMed  PubMed Central  Google Scholar 

6.
Mojica, K. D., Huisman, J., Wilhelm, S. W. & Brussaard, C. P. Latitudinal variation in virus-induced mortality of phytoplankton across the North Atlantic Ocean. ISME J. 10, 500–513 (2016).
CAS  PubMed  Google Scholar 

7.
Suttle, C. A. Marine viruses–major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).
CAS  PubMed  Google Scholar 

8.
Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

9.
Laber, C. P. et al. Coccolithovirus facilitation of carbon export in the North Atlantic. Nat. Microbiol. 3, 537–547 (2018).
CAS  PubMed  Google Scholar 

10.
Colson, P. et al. “Megavirales”, a proposed new order for eukaryotic nucleocytoplasmic large DNA viruses. Arch. Virol. 158, 2517–2521 (2013).
PubMed  PubMed Central  Google Scholar 

11.
Fischer, M. G. Giant viruses come of age. Curr. Opin. Microbiol. 31, 50–57 (2016).
PubMed  Google Scholar 

12.
Koonin, E. V. & Yutin, N. Evolution of the large nucleocytoplasmic DNA viruses of eukaryotes and convergent origins of viral gigantism. Adv. Virus Res. 103, 167–202 (2019).
PubMed  Google Scholar 

13.
Monier, A., Claverie, J. M. & Ogata, H. Taxonomic distribution of large DNA viruses in the sea. Genome Biol. 9, R106 (2008).
PubMed  PubMed Central  Google Scholar 

14.
Hingamp, P. et al. Exploring nucleo-cytoplasmic large DNA viruses in Tara Oceans microbial metagenomes. ISME J. 7, 1678–1695 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

15.
Clerissi, C. et al. Deep sequencing of amplified Prasinovirus and host green algal genes from an Indian Ocean transect reveals interacting trophic dependencies and new genotypes. Environ. Microbiol. Rep. 7, 979–989 (2015).
CAS  PubMed  Google Scholar 

16.
Li, Y. et al. The earth is small for “Leviathans”: long distance dispersal of giant viruses across aquatic environments. Microbes Environ. 34, 334–339 (2019).
PubMed  PubMed Central  Google Scholar 

17.
Mihara, T. et al. Taxon richness of “Megaviridae” exceeds those of bacteria and archaea in the ocean. Microbes Environ. 33, 162–171 (2018).
PubMed  PubMed Central  Google Scholar 

18.
Li, Y. et al. Degenerate PCR primers to reveal the diversity of giant viruses in coastal waters. Viruses 10, 496 (2018).
PubMed Central  Google Scholar 

19.
Schulz, F. et al. Giant virus diversity and host interactions through global metagenomics. Nature 578, 432–436 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Moniruzzaman, M., Martinez-Gutierrez, C. A., Weinheimer, A. R. & Aylward, F. O. Dynamic genome evolution and complex virocell metabolism of globally-distributed giant viruses. Nat. Commun. 11, 1710 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Cottrell, M. T. & Suttle, C. A. Wide-spread occurrence and clonal variation in viruses which cause lysis of a cosmopolitan, eukaryotic marine phytoplankter, Micromonas pusilla. Mar. Ecol. Prog. Ser. 78, 1–9 (1991).

22.
Bratbak, G., Egge, J. K. & Heldal, M. Viral mortality of the marine alga Emiliania huxleyi (Haptophyceae) and termination of algal blooms. Mar. Ecol. Prog. Ser. 93, 39–48 (1993).
Google Scholar 

23.
Kenji, T., Keizo, N., Shigeru, I. & Mineo, Y. Isolation of a virus infecting the novel shellfish-killing dinoflagellate Heterocapsa circularisquama. Aquat. Microb. Ecol. 23, 103–111 (2001).
Google Scholar 

24.
Fischer, M. G., Allen, M. J., Wilson, W. H. & Suttle, C. A. Giant virus with a remarkable complement of genes infects marine zooplankton. Proc. Natl Acad. Sci. USA 107, 19508–19513 (2010).
CAS  PubMed  Google Scholar 

25.
Needham, D. M. et al. A distinct lineage of giant viruses brings a rhodopsin photosystem to unicellular marine predators. Proc. Natl Acad. Sci. USA 116, 20574–20583 (2019).
CAS  PubMed  Google Scholar 

26.
Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, e1114 (2019).
Google Scholar 

28.
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, e1021 (2019).
Google Scholar 

29.
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 11, 538 (2010).
Google Scholar 

30.
Gallot-Lavallee, L., Blanc, G. & Claverie, J. M. Comparative genomics of Chrysochromulina ericina virus and other microalga-infecting large DNA viruses highlights their intricate evolutionary relationship with the established Mimiviridae family. J. Virol. 91, e00230–17 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, e1021 (2019).
Google Scholar 

32.
Mihara, T. et al. Linking virus genomes with host taxonomy. Viruses 8, 66 (2016).
PubMed  PubMed Central  Google Scholar 

33.
Ogata, H. et al. Remarkable sequence similarity between the dinoflagellate-infecting marine girus and the terrestrial pathogen African swine fever virus. Virol. J. 6, 178 (2009).
PubMed  PubMed Central  Google Scholar 

34.
Andreani, J. et al. Pacmanvirus, a new giant icosahedral virus at the crossroads between Asfarviridae and Faustoviruses. J. Virol. 91, e00212–e00217 (2017).
PubMed  PubMed Central  Google Scholar 

35.
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Science 327, 1509–1511 (2010).
CAS  PubMed  Google Scholar 

36.
Lima-Mendez, G. et al. Ocean plankton. Determinants of community structure in the global plankton interactome. Science 348, 1262073 (2015).
PubMed  Google Scholar 

37.
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, e00002–e00017 (2017).
PubMed  PubMed Central  Google Scholar 

38.
Chow, C. E. & Suttle, C. A. Biogeography of viruses in the sea. Annu Rev. Virol. 2, 41–66 (2015).
CAS  PubMed  Google Scholar 

39.
Yoshida, T. et al. Locality and diel cycling of viral production revealed by a 24 h time course cross-omics analysis in a coastal region of Japan. ISME J. 12, 1287–1295 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).
Google Scholar 

41.
Syed, T. H., Famiglietti, J. S., Zlotnicki, V. & Rodell, M. Contemporary estimates of Pan-Arctic freshwater discharge from GRACE and reanalysis. Geophys. Res. Lett. 34, L19404 (2007).
Google Scholar 

42.
Wommack, K. E. & Colwell, R. R. Virioplankton: viruses in aquatic ecosystems. Microbiol. Mol. Biol. Rev. 64, 69–114 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Bellec, L. et al. Cophylogenetic interactions between marine viruses and eukaryotic picophytoplankton. BMC Evol. Biol. 14, 59 (2014).
PubMed  PubMed Central  Google Scholar 

44.
Brussaard, C. P. D., Kempers, R. S., Kop, A. J., Riegman, R. & Heldal, M. Virus-like particles in a summer bloom of Emiliania huxleyi in the North Sea. Aquat. Microb. Ecol. 10, 105–113 (1996).
Google Scholar 

45.
Stephan, J. et al. Flow cytometric analysis of an Emiliana huxleyi bloom terminated by viral infection. Aquat. Microb. Ecol. 27, 111–124 (2002).
Google Scholar 

46.
Hurwitz, B. L., Westveld, A. H., Brum, J. R. & Sullivan, M. B. Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proc. Natl Acad. Sci. USA 111, 10714–10719 (2014).
CAS  PubMed  Google Scholar 

47.
Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nat. Geosci. 6, 718–724 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

48.
Giering, S. L. et al. Reconciliation of the carbon budget in the ocean’s twilight zone. Nature 507, 480–483 (2014).
CAS  PubMed  Google Scholar 

49.
Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature 568, 327–335 (2019).
CAS  PubMed  Google Scholar 

50.
Janice, E. L. & Curtis, A. S. Effect of viral infection on sinking rates of Heterosigma akashiwo and its implications for bloom termination. Aquat. Microb. Ecol. 37, 1–7 (2004).
Google Scholar 

51.
Close, H. G. et al. Export of submicron particulate organic matter to mesopelagic depth in an oligotrophic gyre. Proc. Natl Acad. Sci. USA 110, 12565–12570 (2013).
CAS  PubMed  Google Scholar 

52.
Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl Acad. Sci. USA 115, E6799–E6807 (2018).
CAS  PubMed  Google Scholar 

53.
Hurwitz, B. L., Brum, J. R. & Sullivan, M. B. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 9, 472–484 (2015).
CAS  PubMed  Google Scholar 

54.
Sancetta, C., Villareal, T. & Falkowski, P. Massive fluxes of rhizosolenid diatoms: a common occurrence? Limnol. Oceanogr. 36, 1452–1457 (1991).
Google Scholar 

55.
Kawakami, H. & Honda, M. C. Time-series observation of POC fluxes estimated from 234Th in the northwestern North Pacific. Deep Sea Res. I 54, 1070–1090 (2007).
Google Scholar 

56.
Richardson, T. L. & Jackson, G. A. Small phytoplankton and carbon export from the surface ocean. Science 315, 838–840 (2007).
CAS  PubMed  Google Scholar 

57.
Blanc-Mathieu, R. et al. Viruses of the eukaryotic plankton are predicted to increase carbon export efficiency in the global sunlit ocean. Preprint at bioRxiv https://doi.org/10.1101/710228 (2019).

58.
Iversen, M. H. & Ploug, H. Ballast minerals and the sinking carbon flux in the ocean: carbon-specific respiration rates and sinking velocity of marine snow aggregates. Biogeosciences 7, 2613–2624 (2010).
CAS  Google Scholar 

59.
Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998).
CAS  PubMed  Google Scholar 

61.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).
CAS  PubMed  Google Scholar 

63.
Koonin, E. V. & Yutin, N. Multiple evolutionary origins of giant viruses. F1000Res. 7, 1840 (2018).

64.
Yoshikawa, G. et al. Medusavirus, a novel large DNA virus discovered from hot spring water. J. Virol. 93, e02130–18 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

65.
Longhurst, A. R. in Ecological Geography of the Sea 2nd edn (ed. Longhurst, A. R.) Ch. 6 (Academic Press, 2007).

66.
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Besemer, J., Lomsadze, A. & Borodovsky, M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 29, 2607–2618 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Keeling, P. J. et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889 (2014).
PubMed  PubMed Central  Google Scholar 

69.
Jain, C., Rodriguez, R. L., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).
PubMed  PubMed Central  Google Scholar 

70.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 

71.
de Vargas, C. et al. Ocean plankton. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).
PubMed  Google Scholar 

72.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar  More

1.
Raven, J. A. Carbon dioxide fixation. in Algal Physiology and Biochemistry (ed Stewart, W. D. P.) 434–455 (Blackwell Scientific Publications, Oxford, 1974).
2.
Cooper, T. G., Filmer, D., Wishnick, M. & Lane, M. D. The active species of “CO2” utilized by ribulose diphosphate carboxylase. J. Biol. Chem. 244, 1081–1083 (1969).
CAS  PubMed  Google Scholar 

3.
Badger, M. R. et al. The diversity and coevolution of Rubisco, plastids, pyrenoids, and chloroplast-based CO2-concentrating mechanisms in algae. Can. J. Bot. 76, 1052–1071 (1998).
CAS  Google Scholar 

4.
Burkhardt, S., Amoroso, G., Riebesell, U. & Sültemeyerl, D. CO2 and HCO3− uptake in marine diatoms acclimated to different CO2 concentrations. Limnol. Oceanogr. 46, 1378–1391 (2001).
CAS  Article  Google Scholar 

5.
Giordano, M., Beardall, J. & Raven, J. A. CO2 concentrating mechanisms in algae: mechanisms, environmental modulation, and evolution. Ann. Rev. Plant Biol. 56, 99–131 (2005).
CAS  Article  Google Scholar 

6.
Reiskind, J. B. & Bowes, G. The role of phosphoenolpyruvate carboxykinase in a marine macroalga with C4-like photosynthetic characteristics. Proc. Natl Acad. Sci. USA 88, 2883–2887 (1991).
CAS  Article  Google Scholar 

7.
Reinfelder, J. R., Kraepiel, A. M. L. & Morel, F. M. M. Unicellular C4 photosynthesis in a marine diatom. Nature 407, 996–999 (2000).
CAS  Article  Google Scholar 

8.
Reinfelder, J. R., Milligan, A. J. & Morel, F. M. M. The role of the C4 pathway in carbon accumulation and fixation in a marine diatom. Plant Physiol. 135, 2106–2111 (2004).
CAS  Article  Google Scholar 

9.
Shao, H. et al. Responses of Ottelia alismoides, an aquatic plant with three CCMs, to variable CO2 and light. J. Exp. Bot. 68, 3985–3995 (2017).
CAS  Article  Google Scholar 

10.
Han, S. et al. Structural basis for C4 photosynthesis without Kranz anatomy in leaves of the submerged freshwater plant Ottelia alismoides. Ann. Bot. 125, 869–879 (2020).
Article  Google Scholar 

11.
Liu, D., Keesing, J. K., Xing, Q. & Shi, P. Worldʼs largest macroalgal bloom caused by expansion of seaweed aquaculture in China. Mar. Poll. Bull. 58, 888–895 (2009).
CAS  Article  Google Scholar 

12.
Keesing, J. K., Liu, D. Y., Fearns, P. & Garcia, R. Inter-and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007–2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China. Mar. Poll. Bull. 62, 1169–1182 (2011).
CAS  Article  Google Scholar 

13.
Liu, D. et al. The worldʼs largest macroalgal bloom in the Yellow Sea, China: formation and implications. Estuar. Coast. Shelf Sci. 129, 2–10 (2013).
CAS  Article  Google Scholar 

14.
Zhang, J. H., Kim, J. K., Yarish, C. & He, P. The expansion of Ulva prolifera O.F. Müller macroalgal blooms in the Yellow Sea, PR China, through asexual reproduction. Mar. Poll. Bull. 104, 101–106 (2016).
CAS  Article  Google Scholar 

15.
Xu, J. et al. Evidence of coexistence of C3 and C4 photosynthetic pathways in a green-tide-forming alga, Ulva prolifera. PLoS ONE 7, e37438 (2012).
CAS  Article  Google Scholar 

16.
Valiela, I., Liu, D., Lloret, J., Chenoweth, K. & Hanacek, D. Stable isotopic evidence of nitrogen sources and C4 metabolism driving the worldʼs largest macroalgal green tides in the Yellow Sea. Sci. Rep. 8, 17437 (2018).
Article  Google Scholar 

17.
Hatch, M. D. C4 photosynthesis: a unique blend of modified biochemistry, anatomy and ultrastructure. Biochim. Biophys. Acta Rev. Bioenerg. 895, 81–106 (1987).
CAS  Article  Google Scholar 

18.
Haimovich-Dayan, M. et al. The role of C4 metabolism in the marine diatom Phaeodactylum tricornutum. N. Phytol. 197, 177–185 (2013).
CAS  Article  Google Scholar 

19.
OʼLeary, M. H. Carbon isotopes in photosynthesis. BioScience 38, 328–336 (1988).
Article  Google Scholar 

20.
Fry, B. 13C/12C fractionation by marine diatoms. Mar. Ecol. Prog. Ser. 134, 283–294 (1996).
CAS  Article  Google Scholar 

21.
Carvalho, M. C. & Eyre, B. D. Carbon stable isotope discrimination during respiration in three seaweed species. Mar. Ecol. Prog. Ser. 437, 41–49 (2011).
CAS  Article  Google Scholar 

22.
Cornwall, C. E. et al. Inorganic carbon physiology underpins macroalgal responses to elevated CO2. Sci. Rep. 7, 46297 (2017).
CAS  Article  Google Scholar 

23.
Carvalho, M. C., Hayashizaki, K. & Ogawa, H. Short-term measurement of carbon stable isotope discrimination in photosynthesis and respiration by aquatic macrophytes, with marine macroalgal examples. J. Phycol. 45, 761–770 (2009).
Article  Google Scholar 

24.
Raven, J. A., Giordano, M., Beardall, J. & Maberly, S. C. Algal evolution in relation to atmospheric CO2: carboxylases, carbon-concentrating mechanisms and carbon oxidation cycles. Philos. Trans. Roy. Soc. B 367, 493–507 (2012).
CAS  Article  Google Scholar 

25.
Roberts, K., Granum, E., Leegood, R. C. & Raven, J. A. C3 and C4 pathways of photosynthetic carbon assimilation in marine diatoms are under genetic, not environmental control. Plant Physiol. 145, 230–235 (2007).
CAS  Article  Google Scholar 

26.
Roberts, K., Granum, E., Leegood, R. C. & Raven, J. A. Carbon acquisition by diatoms. Photosynth. Res. 93, 79–88 (2007).
CAS  Article  Google Scholar 

27.
Beardall, J. & Giordano, M. Ecological implications of microalgal and cyanobacterial CO2 concentrating mechanisms, and their regulation. Funct. Plant Biol. 29, 335–347 (2002).
CAS  Article  Google Scholar 

28.
Palmqvist, K., Yu, J. W. & Badger, M. R. Carbonic anhydrase activity and inorganic carbon fluxes in low- and high-Ci cells of Chlamydomonas reinhardtü and Scenedesmus obliquus. Physiol. Plant. 90, 537–547 (1994).
CAS  Article  Google Scholar 

29.
Reinfelder, J. R. Carbon concentrating mechanisms in eukaryotic marine phytoplankton. Ann. Rev. Mar. Sci. 3, 291–315 (2011).
Article  Google Scholar 

30.
Beardall, J. Effects of photon flux density on the CO2-concentrating mechanism of the cyanobacterium Anabaena variabilis. J. Plankton Res. 13, 133–141 (1991).
Google Scholar 

31.
Kargul, J. & Barber, J. Photosynthetic acclimation: structural reorganization of light harvesting antenna-role of redox-dependent phosphorylation of major and minor chlorophyll a/b binding proteins. FEBS J. 275, 1056–1068 (2008).
CAS  Article  Google Scholar 

32.
Zhao, X., Tang, X., Zhang, H., Qu, T. & Wang, Y. Photosynthetic adaptation strategy of Ulva prolifera floating on the sea surface to environmental changes. Plant Physiol. Biochem. 107, 116–125 (2016).
CAS  Article  Google Scholar 

33.
Xu, J. & Gao, K. Future CO2-induced ocean acidification mediates the physiological performance of a green tide alga. Plant Physiol. 160, 1762–1769 (2012).
CAS  Article  Google Scholar 

34.
Li, J., Sun, X. & Zheng, S. In situ study on photosynthetic characteristics of phytoplankton in the Yellow Sea and East China Sea in summer 2013. J. Mar. Syst. 160, 94–106 (2016).
Article  Google Scholar 

35.
Qin, B. Y., Tao, Z., Li, Z. W. & Yang, X. F. Seasonal changes and controlling factors of sea surface pCO2 in the Yellow Sea. IOP Conf. Ser. 17, 012025 (2014).
Article  Google Scholar 

36.
Krause-Jensen, D., McGlathery, K., Rysgaard, S. & Christensen, P. B. Production within dense mats of the filamentous macroalga Chaetomorpha linum in relation to light and nutrient availability. Mar. Ecol. Prog. Ser. 134, 207–216 (1996).
Article  Google Scholar 

37.
Keesing, J. K., Liu, D., Shi, Y. & Wang, Y. Abiotic factors influencing biomass accumulation of green tide causing Ulva spp. on Pyropia culture rafts in the Yellow Sea, China. Mar. Poll. Bull. 105, 88–97 (2016).
CAS  Article  Google Scholar 

38.
Pierrot, D., Lewis, E. & Wallace, D. W. R. MS Excel Program Developed for CO2System Calculations. ORNL/CDIAC−105a. (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee, 2006).

39.
Wilbur, K. M. & Anderson, N. G. Electronic and colorimetric determination of carbonic anhydrase. J. Biol. Chem. 176, 147–154 (1948).
CAS  PubMed  Google Scholar  More

Our study on a shallow tropical lake identified fluctuations and interactions of rotifer assemblage, based on trophic guild analysis, comparable to those found in temperate lakes. We also highlighted that trophic guilds, based on trophi structure, has broad application to both temperate and tropical water bodies, which shows the universality of this approach. In addition, our analysis on the interaction between the exotic species Kellicottia bostoniensis and other microphagous rotifers were sufficient to demonstrate that it does not have invasive characteristics.
The Guild Ratio (GR), based on the density of raptorial and microphagous functional groups of rotifers, revealed to be an appropriate tool in the evaluation of possible interactions with other planktonic groups, as well as in the evaluation of temporal changes of functional groups. Unlike Obertegger and Manca5 and Obertegger et al.11, we used the database of densities of functional groups instead of biomass, according to Smith et al.10. The significant correlation between GR and cladocerans showed that GR, based on number of individuals, indicated interaction between microphagous rotifers and cladocerans like that reported by Obertegger and Manca5 and Obertegger et al.11 in temperate lakes, based on biomass (GR′). The relationship GR-cladocerans showed a similar trend with monthly and bimonthly data, indicating its adequacy even when data are less frequently obtained, which agrees with results from Obertegger et al.11 in Lake Washington, USA. Given this point, our findings reinforce that other studies may be designed with lower sampling frequency and certainly achieve satisfactory results, allowing a cheaper logistic planning in further research.
The significant positive correlation between GR and cladoceran densities indicates competition between the groups, corroborating the initial hypothesis. The predominance of microphagous rotifers (i.e. lower GR values) when cladoceran densities decreased, represented mainly by Daphnia gessneri (max. size 1.22 mm) and Ceriodaphnia richardi (max. 0.70 mm)29, is a sign of competition between both groups. Therefore, when cladocerans were more abundant during the cool season (May–September), the raptorial rotifers species predominated, which coexist with filtering cladocerans, similarly to results obtained by Obertegger et al.11, in lakes Washington (USA) and Caldonazzo (Italy). Exploitative competition between cladocerans and rotifers, particularly microphagous species, which occupy a similar niche, may even lead to competitive exclusion of rotifers. Herbivorous cyclopoid nauplii could compete with rotifers and make our analysis meaningful, however there was no evidence of interaction between them and microphagous rotifers, which does not support our hypothesis.
Several studies report the competitive superiority of cladocerans4,30,31. The inferiority of rotifers may be partly due to lower clearance rate (1–10 µL ind.−1 h−1) than cladocerans (10–150 µL ind.−1 h−1) as well as a more limited size food range (ca. 4–17 µm)1. The maximum clearance rate of cladocerans may be much higher than that already mentioned by Nogrady et al.1 and dependent on various factors such as temperature, food concentration and body size9. Rotifer populations may be suppressed by more efficient cladocerans through exploitative competition, although rotifers may also suffer effects from interference competition32,33. Cladocerans larger than 1.2 mm may suppress small rotifer populations by interference34. In Lake Monte Alegre, cladoceran species are relatively small and probably exploitative competition is the most important interaction in this community.
The increase in algal carbon and temperature in the Lake Monte Alegre during the warm season (October–April) was not followed by increase of the total rotifer densities, indicating a preponderant influence of another factor. However, as mentioned above, there was an increase in the abundance of microphagous species and a decrease in densities of raptorial species in this season. Raptorial species, particularly large species (e.g., Synchaeta spp.), prefer larger items ( > 50 µm) such as algae, ciliates and other rotifers13. Species of the genus Ascomorpha feed on dinophytes, such as Peridinium and Ceratium, which are grasped, and the content sucked1. In Lake Monte Alegre, an increase of Peridinium in the fall and winter (March–September) was already reported35,36, which would benefit some raptorial rotifers, including Ascomorpha. However, in this study in 2011–2012, dinophytes were not abundant (L.H.S. Silva, unpublished data), representing about 1.4% of the total phytoplankton density, chlorophytes predominating, increasing the contribution of cyanobacteria in the warm season. Therefore, higher densities of raptorial species in the cool season were unrelated to phytoplankton composition and, on the other hand, higher temperatures in the warm season did not favor the increase in populations of this group.
The distribution of organisms can be a strategy to avoid competition and predation. In Lake Monte Alegre, several species of Colotheca, Keratella, Polyarthra, and Trichocerca occupied the entire water column in the cool season (A. J. Meschiatti et al. unpublished data). In the warm season, species of these genera, in addition to Brachionus, Hexarthra and Ptygura were limited to the oxygenated layer, avoiding the anoxic hypolimnion. Another feature of the vertical distribution of rotifers in this lake was the frequent occupation of the most superficial layer, even during the day, which is rarely occupied by cladocerans37, reducing overlap and possible interactions with other organisms.
Direct predation on rotifers by chaoborid larvae is low in Lake Monte Alegre, representing 9% of the prey number for instars I and II, 4% for instar III, not being preyed on by instar IV38. In an experiment with mesocosm in this lake, no predation effect by Chaoborus larvae on Keratella spp. densities was detected39. Zooplankton predation by fish in the lake is mainly exerted by adult of the exotic cichlid Tilapia rendalli (current name Coptodon rendalli), a pump filter-feeder40, which collects organisms with lower evasion to the filtering current, which, however, are not abundant in the lake. Although Keratella sp. was not rejected by tilápia, its consumption is low by this fish species, whose predation is higher on cladocerans40.
Temporal variations of functional groups of rotifers in Lake Monte Alegre indicated the indirect effect of cladoceran predation by invertebrates, such as Chaoborus brasiliensis larvae and the aquatic mite, Krendowskia sp., in 2011–201241. Predation pressure by invertebrates is generally higher in the warm season when their populations increased, resulting in declining cladoceran populations29,41. Consequently, there is a decrease in exploitative competition by cladocerans and the possibility of competitive exclusion when resources are limiting. Predation by invertebrates has emerged as the main structuring factor of the lake zooplankton29, and this study highlights the indirect effect of this factor on rotifers.
The high frequency of occurrence of the exotic species Kellicottia bostoniensis in the present study, combined with the weekly sampling strategy adopted, demonstrates the great persistence capacity of this species in the environment, with rare occasions when it is excluded from the water column. This feature indicates success of the exotic species in the new habitat22. The characteristics of an invasive species are not always scientifically proven, and many failures are not reported in publications, introducing a bias in evaluating the success of exotic species19. The presence of this exotic species in Lake Monte Alegre had not been detected in previous studies conducted in the 1980s42,43. Although very common, according to Josefsson and Andersson18 it is not invasive in the lake, as it did not constitute a threat to the local community of rotifers. It does not outcompete other microphagous rotifers and, on the contrary, there is evidence of being competitively inferior, as its population decreased in periods of dominance of other microphagous species. A laboratory experiment showed that K. bostoniensis had no effect on zooplankton composed of native copepods, cladocerans, and rotifers, affecting only ciliates, which are part of its food resources23, reinforcing the idea that it does not constitute a threat to the whole planktonic community.
This exotic species was caught in lakes from River Doce valley44 and in Furnas Reservoir45, in Brazil, at lower densities than those of Lake Monte Alegre (max. 127 ind. L−1). The vertical distribution in Nado Reservoir, located in Brazil, showed its highest abundance in the anoxic hypolimnion, on a diel cycle46, indicating resistance of this species to adverse conditions. In some Swedish lakes, the exotic species K. bostoniensis was also found in deeper layers18, as well as in Mirror Lake, United States, where the production of K. bostoniensis, a native species, was higher at the bottom47. Apparently, this species maintains similar distribution in its original habitat and a new habitat. The ability to occupy lower layers, often anoxic, where few microcrustaceans and rotifers are found, would lower negative interactions with other populations and even constitutes a defense strategy against predation by most invertebrates and filtering fish48. More

1.
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).
CAS  Google Scholar 
2.
Dulvy, N. K. et al. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J. Appl. Ecol. 45, 1029–1039 (2008).
Google Scholar 

3.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Google Scholar 

4.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).
CAS  Google Scholar 

5.
Hutchins, L. W. The bases for temperature zonation in geographical distribution. Ecol. Monogr. 17, 325–335 (1947).
Google Scholar 

6.
Pineda, J., Reyns, N. B. & Starczak, V. R. Complexity and simplification in understanding recruitment in benthic populations. Pop. Ecol. 51, 17–32 (2009).
Google Scholar 

7.
Morgan, S. G., Shanks, A. L., MacMahan, J. H., Reniers, A. J. H. M. & Feddersen, F. Planktonic subsidies to surf-zone and intertidal communities. Annu. Rev. Mar. Sci. 10, 345–369 (2018).
Google Scholar 

8.
Gaylord, B. & Gaines, S. D. Temperature or transport? Range limits in marine species mediated solely by flow. Am. Nat. 155, 769–789 (2000).
Google Scholar 

9.
García Molinos, J., Burrows, M. T. & Poloczanska, E. S. Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep. 7, 1332 (2017).
Google Scholar 

10.
Kumagai, N. H. et al. Ocean currents and herbivory drive macroalgae-to-coral community shift under climate warming. Proc. Natl Acad. Sci. USA 115, 8990–8995 (2017).
Google Scholar 

11.
Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).
Google Scholar 

12.
Strathmann, M. F. Reproduction and Development of Marine Invertebrates of the Northern Pacific Coast (Univ. of Washington Press, 1987).

13.
Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25, 1–45 (1950).
CAS  Google Scholar 

14.
Olive, P. J. W. Annual breeding cycles in marine invertebrates and environmental temperature: probing the proximate and ultimate causes of reproductive synchrony. J. Therm. Biol. 20, 79–90 (1995).
Google Scholar 

15.
Philippart, C. J. M. et al. Climate-related changes in recruitment of the bivalve Macoma balthica. Limnol. Oceanogr. 48, 2171–2185 (2003).
Google Scholar 

16.
Asch, R. G. Climate change and decadal shifts in the phenology of larval fishes in the California Current Ecosystem. Proc. Natl Acad. Sci. USA 112, E4065–E4074 (2015).
CAS  Google Scholar 

17.
Shearman, R. K. & Lentz, S. J. Long-term sea surface temperature variability along the U.S. East Coast. J. Phys. Oceanogr. 40, 1004–1017 (2010).
Google Scholar 

18.
Saba, V. S. et al. Enhanced warming of the northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans 121, 118–132 (2016).
Google Scholar 

19.
Castelao, R., Glenn, S. & Schofield, O. Temperature, salinity, and density variability in the central Middle Atlantic Bight. J. Geophys. Res. 115, C10005 (2010).
Google Scholar 

20.
Richaud, B., Kwon, Y.-O., Joyce, T. M., Fratantoni, P. S. & Lentz, S. J. Surface and bottom temperature and salinity climatology along the continental shelf off the Canadian and U.S. East Coasts. Cont. Shelf Res. 124, 165–181 (2016).
Google Scholar 

21.
Roughgarden, J., Gaines, S. & Possingham, H. Recruitment dynamics in complex life cycles. Science 241, 1460–1466 (1988).
CAS  Google Scholar 

22.
Connolly, S. R., Menge, B. A. & Roughgarden, J. A latitudinal gradient in recruitment of intertidal invertebrates in the northeast Pacific Ocean. Ecology 82, 1799–1813 (2001).
Google Scholar 

23.
Ma, H., Grassle, J. P. & Chant, R. J. Vertical distribution of bivalve larvae along a cross-shelf transect during summer upwelling and downwelling. Mar. Biol. 149, 1123–1138 (2006).
Google Scholar 

24.
Shanks, A. L. & Brink, L. Upwelling, downwelling, and cross-shelf transport of bivalve larvae: test of a hypothesis. Mar. Ecol. Prog. Ser. 302, 1–12 (2005).
Google Scholar 

25.
Drake, P. T., Edwards, C. A., Morgan, S. G. & Dever, E. P. Influence of larval behavior on transport and population connectivity in a realistic simulation of the California Current System. J. Mar. Res. 71, 317–350 (2013).
Google Scholar 

26.
Shanks, A. L. & Morgan, S. G. Testing the intermittent upwelling hypothesis: upwelling, downwelling, and subsidies to the intertidal zone. Ecol. Monogr. 88, 22–35 (2018).
Google Scholar 

27.
Menge, B. A. & Menge, D. N. L. Testing the intermittent upwelling hypothesis: comment. Ecology 100, e02476 (2019).
Google Scholar 

28.
Lentz, S. J. Seasonal variations in the circulation over the Middle Atlantic Bight continental shelf. J. Phys. Oceanogr. 38, 1486–1500 (2008).
Google Scholar 

29.
Gong, D., Kohut, J. T. & Glenn, S. M. Seasonal climatology of wind-driven circulation on the New Jersey Shelf. J. Geophys. Res. 115, C04006 (2010).
Google Scholar 

30.
Whitney, M. M. & Garvine, R. W. Wind influence on a coastal buoyant outflow. J. Geophys. Res. 110, C03014 (2005).
Google Scholar 

31.
Largier, J. L. Considerations in estimating larval dispersal distances from oceanographic data. Ecol. Appl. 13, S71–S89 (2003).
Google Scholar 

32.
Byers, J. E. & Pringle, J. M. Going against the flow: retention, range limits and invasions in advective environments. Mar. Ecol. Prog. Ser. 313, 27–41 (2006).
Google Scholar 

33.
Fuchs, H. L., Gerbi, G. P., Hunter, E. J. & Christman, A. J. Waves cue distinct behaviors and differentiate transport of congeneric snail larvae from sheltered versus wavy habitats. Proc. Natl Acad. Sci. USA 115, E7532–E7540 (2018).
CAS  Google Scholar 

34.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).
Google Scholar 

35.
Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change. Ecol. Lett. 8, 1138–1146 (2005).
Google Scholar 

36.
Freeman, B. G., Scholer, M. N., Ruiz-Guttierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).
CAS  Google Scholar 

37.
Free, C. M. et al. Impacts of historical warming on fisheries production. Science 363, 979–983 (2019).
CAS  Google Scholar 

38.
Young, I. R. & Ribal, A. Multiplatform evaluation of global trends in wind speed and wave height. Science 364, 548–552 (2019).
CAS  Google Scholar 

39.
Ocean Biogeographic Information System (Intergovernmental Oceanographic Commission of UNESCO, 2018); www.iobis.org

40.
Tingley, M. W. & Beissinger, S. R. Detecting range shifts from historical species occurrences: new perspectives on old data. Trends Ecol. Evol. 24, 625–633 (2009).
Google Scholar 

41.
Wigley, R. L. & Theroux, R. B. Atlantic Continental Shelf and Slope of the United States; Macrobenthic Invertebrate Fauna of the Middle Atlantic Bight Region; Faunal Composition and Quantitative Distribution Professional Paper No. 529-N (USGS, 1981).

42.
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis Technical Memorandum NESDIS NGDC-24 (National Geophysical Data Center, NOAA, 2009); https://doi.org/10.7289/V5C8276M

43.
Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1057 (2009).
CAS  Google Scholar 

44.
Kang, D. & Curchitser, E. N. Gulf stream eddy characteristics in a high-resolution ocean model. J. Geophys. Res. Oceans 118, 4474–4487 (2013).
Google Scholar 

45.
Narváez, D. A. et al. Long-term dynamics in Atlantic surfclam (Spisula solidissima) populations: the role of bottom water temperature. J. Mar. Sys. 141, 136–148 (2015).
Google Scholar 

46.
Chen, Z., Curchitser, E., Chant, R. & Kang, D. Seasonal variability of the cold pool over the Mid-Atlantic Bight continental shelf. J. Geophys. Res. Oceans 123, 8203–8226 (2018).
Google Scholar 

47.
D’Errico, J. inpaint_nans (MATLAB Central File Exchange, 2019); https://www.mathworks.com/matlabcentral/fileexchange/4551-inpaint_nans

48.
Gypaets trigradient2 (GitHub, 2020); https://www.github.com/Gypaets/trigradient2

49.
Yeager, S. & NCAR Staff The Climate Data Guide: COREv2 Air-Sea Surface Fluxes (UCAR, 2016); https://climatedataguide.ucar.edu/climate-data/corev2-air-sea-surface-fluxes

50.
National Water Information System Data (USGS, 2016); http://waterdata.usgs.gov/nwis/

51.
Lentz, S. J. Observations and a model of the mean circulation over the Middle Atlantic Bight continental shelf. J. Phys. Oceanogr. 38, 1203–1221 (2008).
Google Scholar 

52.
Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).
Google Scholar  More

1.
USDA. Coffee Annual Coffee. https://gain.fas.usda.gov/RecentGAINPublications/LOCK-UPREPORT_Pretoria_SouthAfrica-Republicof_10-29-2009.pdf (2019).
2.
Carvalho Guarçoni, R. et al. Influence of solar radiation and wet processing on the final quality of Arabica coffee. J. Food Qual. https://doi.org/10.1155/2018/6408571 (2018).
Article  Google Scholar 

3.
Iamanaka, B. T. et al. Reprint of ‘The mycobiota of coffee beans and its influence on the coffee beverage’. Food Res. Int. 61, 33–38. https://doi.org/10.1016/j.foodres.2014.05.023 (2014).
Article  Google Scholar 

4.
Barnes, E. C., Jumpathong, J., Lumyong, S., Voigt, K. & Hertweck, C. Daldionin, an unprecedented binaphthyl derivative, and diverse polyketide congeners from a fungal orchid endophyte. Chem. A Eur. J. 22, 4551–4555. https://doi.org/10.1002/chem.201504005 (2016).
Article  CAS  Google Scholar 

5.
Descroix, F. & Snoeck, J. Environmental factors suitable for coffee cultivation. In Coffee: Growing, Processing, Sustainable Production 164–177, https://doi.org/10.1002/9783527619627.ch6 (2008).

6.
De Bruyn, F. et al. Exploring the impacts of postharvest processing on the microbiota and metabolite profiles during green coffee bean production. Am. Soc. Microbiol. https://doi.org/10.1128/AEM.02398-16 (2016).
Article  Google Scholar 

7.
Hamdouche, Y. et al. Discrimination of post-harvest coffee processing methods by microbial ecology analyses. Food Control 65, 112–120. https://doi.org/10.1016/j.foodcont.2016.01.022 (2016).
Article  CAS  Google Scholar 

8.
Zhao, Q. et al. Long-term coffee monoculture alters soil chemical properties and microbial communities. Sci. Rep. 8, 1–11. https://doi.org/10.1038/s41598-018-24537-2 (2018).
ADS  Article  CAS  Google Scholar 

9.
Júnior, P. P. et al. Agroecological coffee management increases arbuscular mycorrhizal fungi diversity. PLoS ONE 14, 1–19. https://doi.org/10.1371/journal.pone.0209093 (2019).
Article  CAS  Google Scholar 

10.
Melloni, R. et al. Sistemas Agroflorestais cafeeiro-araucária e seu efeito na microbiota do solo e seus processos. Ciência Florest. 28, 784–795. https://doi.org/10.5902/1980509832392 (2018).
Article  Google Scholar 

11.
Oliveira, M. N. V. et al. Endophytic microbial diversity in coffee cherries of Coffea arabica from southeastern Brazil. Can. J. Microbiol. 59, 221–230. https://doi.org/10.1139/cjm-2012-0674 (2013).
Article  PubMed  CAS  Google Scholar 

12.
Nasanit, R. & Satayawut, K. Microbiological study during coffee fermentation of Coffea arabica var chiangmai 80 in Thailand. Kasetsart J. Nat. Sci. 49, 32–41 (2015).
CAS  Google Scholar 

13.
Evangelista, S. R. et al. Improvement of coffee beverage quality by using selected yeasts strains during the fermentation in dry process. Food Res. Int. 61, 183–195. https://doi.org/10.1016/j.foodres.2013.11.033 (2014).
Article  CAS  Google Scholar 

14.
Pereira, G. V. D. M. et al. Potential of lactic acid bacteria to improve the fermentation and quality of coffee during on-farm processing. Int. J. Food Sci. Technol. 51, 1689–1695. https://doi.org/10.1111/ijfs.13142 (2016).
Article  CAS  Google Scholar 

15.
Sahu, N., Duraisamy, V., Sahu, A., Lal, N. & K. Singh, S. Strength of microbes in nutrient cycling: A key to soil health. In Agriculturally Important Microbes for Sustainable Agriculture 69–86, https://doi.org/10.1007/978-981-10-5589-8_4 (2017).

16.
Zhang, S. J. et al. Following coffee production from cherries to cup: Microbiological and metabolomic analysis of wet processing of Coffea arabica. Appl. Environ. Microbiol. 85, 1–22. https://doi.org/10.1128/AEM.02635-18 (2019).
Article  CAS  Google Scholar 

17.
Ramos, C. L. et al. Determination of dynamic characteristics of microbiota in a fermented beverage produced by Brazilian Amerindians using culture-dependent and culture-independent methods. Int. J. Food Microbiol. 140, 225–231. https://doi.org/10.1016/j.ijfoodmicro.2010.03.029 (2010).
Article  PubMed  CAS  Google Scholar 

18.
Faoro, H. et al. Influence of soil characteristics on the diversity of bacteria in the Southern Brazilian Atlantic Forest. Appl. Environ. Microbiol. 76, 4744–4749. https://doi.org/10.1128/AEM.03025-09a (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

19.
Defelipo, B. V. & Ribeiro, A. C. Análise química do solo (metodologia). Bol. Extensão 28, 1–26 (1997).
Google Scholar 

20.
Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. Am. Soc. Microbiol. https://doi.org/10.1128/msystems.00009-15 (2015).
Article  Google Scholar 

21.
Pylro, V. S. et al. Data analysis for 16S microbial profiling from different benchtop sequencing platforms. J. Microbiol. Methods 107, 30–37. https://doi.org/10.1016/j.mimet.2014.08.018 (2014).
Article  PubMed  CAS  Google Scholar 

22.
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604 (2013).
Article  CAS  Google Scholar 

23.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

24.
Edgar, R. C. UCHIME2: Improved chimera prediction for amplicon sequencing. BioRxiv https://doi.org/10.1101/074252 (2016).
Article  Google Scholar 

25.
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
Article  PubMed  PubMed Central  CAS  Google Scholar 

26.
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 5, 169–172. https://doi.org/10.1038/ismej.2010.133 (2011).
Article  PubMed  Google Scholar 

27.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

28.
Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
Article  Google Scholar 

29.
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264. https://doi.org/10.1093/nar/gky1022 (2019).
Article  PubMed  CAS  Google Scholar 

30.
Oksanen, J. et al. Community Ecology Package. 1–296, https://cran.r-project.org/web/packages/vegan/vegan.pdf (2019).

31.
R Core Team. R: A Language and Environment for Statistical Computing. https://www.r-project.org/ (2018).

32.
Borcard, D. et al. Canonical ordination. In Numerical Ecology with R 153–225, https://doi.org/10.1007/978-1-4419-7976-6_6 (2011).

33.
Gomes, D. G. E. et al. Bats perceptually weight prey cues across sensory systems when hunting in noise. Science 353, 1277–1280. https://doi.org/10.1126/science.aaf7934 (2016).
ADS  Article  PubMed  CAS  Google Scholar 

34.
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLOS Comput. Biol. 8, 1–11. https://doi.org/10.1371/journal.pcbi.1002687 (2012).
Article  CAS  Google Scholar 

35.
Watts, S. C., Ritchie, S. C., Inouye, M. & Holt, K. E. FastSpar: Rapid and scalable correlation estimation for compositional data. Bioinformatics 35, 1064–1066. https://doi.org/10.1093/bioinformatics/bty734 (2019).
Article  PubMed  CAS  Google Scholar 

36.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst 1695, 1–9 (2006).
Google Scholar 

37.
Avelino, J. et al. Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota. J. Sci. Food Agric. 85, 1869–1876. https://doi.org/10.1002/jsfa.2188 (2005).
Article  CAS  Google Scholar 

38.
Wei, L., Wai, M., Curran, P., Yu, B. & Quan, S. Coffee fermentation and flavor—An intricate and delicate relationship. Food Chem. 185, 182–191. https://doi.org/10.1016/j.foodchem.2015.03.124 (2015).
Article  CAS  Google Scholar 

39.
Fulthorpe, R., Martin, A. R. & Isaac, M. E. Root endophytes of coffee ( Coffea arabica): Variation across climatic gradients and relationships with functional traits. Phytobiomes J. 4, 27–39. https://doi.org/10.1094/PBIOMES-04-19-0021-R (2020).
Article  Google Scholar 

40.
Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234. https://doi.org/10.1016/S1002-0160(15)60037-6 (2016).
Article  Google Scholar 

41.
Karungi, J. et al. Elevation and cropping system as drivers of microclimate and abundance of soil macrofauna in coffee farmlands in mountainous ecologies. Appl. Soil Ecol. 132, 126–134. https://doi.org/10.1016/J.APSOIL.2018.08.003 (2018).
Article  Google Scholar 

42.
Ferreira, W. P. M., Queiroz, D. M., Silvac, S. A., Tomaz, R. S. & Corrêa, P. C. Effects of the orientation of the mountainside, altitude and varieties on the quality of the coffee beverage from the “Matas de Minas” region, Brazilian Southeast. Am. J. Plant Sci. 7, 1291–1303. https://doi.org/10.4236/ajps.2016.78124 (2016).
Article  Google Scholar 

43.
Velmourougane, K. Impact of organic and conventional systems of coffee farming on soil properties and culturable microbial diversity. Scientifica 1–9, 2016. https://doi.org/10.1155/2016/3604026 (2016).
Article  CAS  Google Scholar 

44.
Siles, J. A. & Margesin, R. Abundance and diversity of bacterial, archaeal, and fungal communities along an altitudinal gradient in alpine forest soils: What are the driving factors?. Soil Microbiol. 72, 207–220. https://doi.org/10.1007/s00248-016-0748-2 (2016).
Article  Google Scholar 

45.
Frank, A., Saldierna Guzmán, J. & Shay, J. Transmission of bacterial endophytes. Microorganisms 5, 70. https://doi.org/10.3390/microorganisms5040070 (2017).
Article  PubMed Central  CAS  Google Scholar 

46.
Haile, M. & Kang, W. H. The role of microbes in coffee fermentation and their impact on coffee quality. J. Food Qual. 2019, 6. https://doi.org/10.1155/2019/4836709 (2019).
Article  CAS  Google Scholar 

47.
Decazy, F. et al. Quality of different Honduran coffees in relation to several environments. J. Food Sci. 68, 2356–2361. https://doi.org/10.1111/j.1365-2621.2003.tb05772.x (2003).
Article  CAS  Google Scholar 

48.
de Melo Pereira, G. V. et al. Conducting starter culture-controlled fermentations of coffee beans during on-farm wet processing: Growth, metabolic analyses and sensorial effects. Food Res. Int. 75, 348–356. https://doi.org/10.1016/j.foodres.2015.06.027 (2015).
Article  PubMed  CAS  Google Scholar 

49.
Zhang, W. et al. Microbial diversity in two traditional bacterial douchi from Gansu province in northwest China using Illumina sequencing. PLoS ONE 13, 1–16. https://doi.org/10.1371/journal.pone.0194876 (2018).
Article  CAS  Google Scholar 

50.
Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857. https://doi.org/10.1002/jsfa.8114 (2017).
Article  PubMed  CAS  Google Scholar 

51.
Batista, D. et al. Legitimacy and implications of reducing Colletotrichum kahawae to subspecies in plant pathology. Front. Plant Sci. 7, 1–9. https://doi.org/10.3389/fpls.2016.02051 (2017).
Article  CAS  Google Scholar 

52.
Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 1–9. https://doi.org/10.1038/ncomms9413 (2015).
ADS  Article  CAS  Google Scholar  More