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    Sex, age, and parental harmonic convergence behavior affect the immune performance of Aedes aegypti offspring

    1.Centers for Disease Control and Prevention https://www.cdc.gov/dengue/areaswithrisk/index.html (2021).2.Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Centers for Disease Control and Prevention https://www.cdc.gov/parasites/malaria/index.html (2021)5.Gatton, M. L. et al. The importance of mosquito behavioural adaptations to malaria control in Africa. Evolution 67, 1218–1230 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Sokhna, C., Ndiath, M. O. & Rogier, C. The changes in mosquito vector behaviour and the emerging resistance to insecticides will challenge the decline of malaria. Clin. Microbiol. Infect. 19, 902–907 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Hemingway, J., Hawkes, N. J., McCarroll, L. & Ranson, H. The molecular basis of insecticide resistance in mosquitoes. Insect Biochem. Mol. Biol. 34, 653–665 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Alphey, L. et al. Sterile-insect methods for control of mosquito-borne diseases: an analysis. Vector Borne Zoonotic Dis. 10, 295–311 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Oliva, C. F., Damiens, D. & Benedict, M. Q. Male reproductive biology of Aedes mosquitoes. Acta Tropica 132, S12–S19 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Benelli, G. Research in mosquito control: current challenges for a brighter future. Parasitol. Res. 114, 2801–2805 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Lees, R. S., Gilles, J. R. L., Hendrichs, J., Vreysen, M. J. B. & Bourtzis, K. Back to the future: the sterile insect technique against mosquito disease vectors. Curr. Opin. Insect Sci. 10, 156–162 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Wilke, A. B. & Marrelli, M. T. Genetic control of mosquitoes: population suppression strategies. Rev. Inst. Med. Trop. Sao Paulo 54, 287–292 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Alphey, L., Nimmo, D., O’Connell, S. & Alphey, N. Insect population suppression using engineered insects. Adv. Exp. Med. Biol. 627, 93–103 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Carvalho, D. O. et al. Suppression of a field population of Aedes aegypti in Brazil by sustained release of transgenic male mosquitoes. PLoS Negl. Trop. Dis. 9, e0003864 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Wilke, A. B. B. & Marrelli, M. T. Paratransgenesis: a promising new strategy for mosquito vector control. Parasit. Vectors 8, 342 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Hegde, S. & Hughes, G. L. Population modification of Anopheles mosquitoes for malaria control: pathways to implementation. Pathog. Glob. Health 111, 401–402 (2017).PubMed 
    Article 

    Google Scholar 
    17.Carballar-Lejarazu, R. & James, A. A. Population modification of Anopheline species to control malaria transmission. Pathog. Glob. Health 111, 424–35. (2017).PubMed 
    Article 

    Google Scholar 
    18.Li, Y. & Liu, X. A sex-structured model with birth pulse and release strategy for the spread of Wolbachia in mosquito population. J. Theor. Biol. 448, 53–65 (2018).PubMed 
    Article 

    Google Scholar 
    19.Farkas, J. Z., Gourley, S. A., Liu, R. & Yakubu, A. A. Modelling Wolbachia infection in a sex-structured mosquito population carrying West Nile virus. J. Math. Biol. 75, 621–47. (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Zhang, X., Tang, S., Liu, Q., Cheke, R. A. & Zhu, H. Models to assess the effects of non-identical sex ratio augmentations of Wolbachia-carrying mosquitoes on the control of dengue disease. Math. Biosci. 299, 58–72 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Almeida, L., Privat, Y., Strugarek, M. & Vauchelet, N. Optimal releases for population replacement strategies, application to Wolbachia. SIAM Journal on Mathematical Analysis, Society for Industrial and Applied Mathematics 51, 3170–3194 (2019).Article 

    Google Scholar 
    22.Clements, A. N. The Biology of Mosquitoes: Sensory Reception and Behaviour (Chapman & Hall, 1999).23.Downes, J. A. The swarming and mating flight of Diptera. Annu. Rev. Entomol. 14, 271–98. (1969).Article 

    Google Scholar 
    24.Yuval, B. Mating systems of blood-feeding flies. Annu Rev. Entomol. 51, 413–440 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Charlwood, J. D. & Jones, M. D. R. Mating in the mosquito, Anopheles gambiae s.l. Physiol. Entomol. 5, 315–20. (1980).Article 

    Google Scholar 
    26.Pitts, R. J., Mozuraitis, R., Gauvin-Bialecki, A. & Lemperiere, G. The roles of kairomones, synomones and pheromones in the chemically-mediated behaviour of male mosquitoes. Acta Tropica 132, S26–S34 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Hartberg, W. K. Observations on the mating behaviour of Aedes aegypti in nature. Bull. World Health Organ. 45, 847–850 (1971).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Sawadogo P. S. et al. Swarming behaviour in natural populations of Anopheles gambiae and An. coluzzii: review of 4 years survey in rural areas of sympatry, Burkina Faso (West Africa). Acta Tropica 132, S42-52 https://doi.org/10.1016/j.actatropica.2013.12.011 (2014).29.South, A. C. F. Progress in Mosquito Research (Elsevier Science, 2016).30.Cator, L. J. & Harrington, L. C. The harmonic convergence of fathers predicts the mating success of sons in Aedes aegypti. Anim. Behav. 82, 627–633 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Benelli, G. The best time to have sex: mating behaviour and effect of daylight time on male sexual competitiveness in the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae). Parasitol. Res. 114, 887–94. (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Benelli, G., Romano, D., Messing, R. H. & Canale, A. First report of behavioural lateralisation in mosquitoes: right-biased kicking behaviour against males in females of the Asian tiger mosquito, Aedes albopictus. Parasitol. Res. 114, 1613–1617 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Cator, L. J. & Zanti, Z. Size, sounds and sex: interactions between body size and harmonic convergence signals determine mating success in Aedes aegypti. Parasites Vectors 9, 622 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.South, S. H., Steiner, D. & Arnqvist, G. Male mating costs in a polygynous mosquito with ornaments expressed in both sexes. Proc. R. Soc. B 276, 3671–3678 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Roth, L. M. A study of mosquito behavior. An experimental laboratory study of the sexual behavior of Aedes aegypti (Linnaeus). Am. Midl. Nat. 40, 265–352 (1948).Article 

    Google Scholar 
    36.Wishart, G., van Sickle, G. R. & Riordan, D. F. Orientation of the males of Aedes aegypti (L.) (Diptera: Culicidae) to sound. Can. Entomol. 94, 613–26. (1962).Article 

    Google Scholar 
    37.Belton, P. Attraction of male mosquitoes to sound. J. Am. Mosq. Control Assoc. 10, 297–301 (1994).CAS 
    PubMed 

    Google Scholar 
    38.Simões, P. M. V., Ingham, R. A., Gibson, G. & Russell, I. J. A role for acoustic distortion in novel rapid frequency modulation behaviour in free-flying male mosquitoes. J. Exp. Biol. 219, 2039–2047 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    39.Simoes, P. M., Gibson, G. & Russell, I. J. Pre-copula acoustic behaviour of males in the malarial mosquitoes Anopheles coluzzii and Anopheles gambiae s.s. does not contribute to reproductive isolation. J. Exp. Biol. 220, 379–85. (2017).PubMed 
    Article 

    Google Scholar 
    40.Gibson, G. & Russell, I. Flying in tune: sexual recognition in mosquitoes. Curr. Biol. 16, 1311–1316 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Cator, L. J., Arthur, B. J., Harrington, L. C. & Hoy, R. R. Harmonic convergence in the love songs of the dengue vector mosquito. Science 323, 1077–1079 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Warren, B., Gibson, G. & Russell, I. J. Sex recognition through midflight mating duets in culex mosquitoes is mediated by acoustic distortion. Curr. Biol. 19, 485–491 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Pennetier, C., Warren, B., Dabiré, K. R., Russell, I. J. & Gibson, G. “Singing on the Wing” as a mechanism for species recognition in the malarial mosquito Anopheles gambiae. Curr. Biol. 20, 131–136 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Aldersley, A. & Cator, L. J. Female resistance and harmonic convergence influence male mating success in Aedes aegypti. Sci. Rep. 9, 2145 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.League, G. P., Baxter, L. L., Wolfner, M. F. & Harrington, L. C. Male accessory gland molecules inhibit harmonic convergence in the mosquito Aedes aegypti. Curr. Biol. 29, R196–r7. (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Villarreal, S. M. et al. Male contributions during mating increase female survival in the disease vector mosquito Aedes aegypti. J. Insect Physiol. 108, 1–9 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Dobson, A. P. & Hudson, P. J. Regulation and stability of a free-living host–parasite system: Trichostrongylus tenuis in Red Grouse. II. Population models. J. Anim. Ecol. 61, 487–498 (1992).Article 

    Google Scholar 
    48.Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: a role for parasites? Science 218, 384–387 (1982).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Hillyer, J. F., Schmidt, S. L. & Christensen, B. M. Hemocyte-mediated phagocytosis and melanization in the mosquito Armigeres subalbatus following immune challenge by bacteria. Cell Tissue Res. 313, 117–127 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Moreno-García, M., Córdoba-Aguilar, A., Condé, R. & Lanz-Mendoza, H. Current immunity markers in insect ecological immunology: assumed trade-offs and methodological issues. Bull. Entomol. Res. 103, 127–139 (2012).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    51.Schoenle, L. A., Downs, C. J. & Martin L. B. An introduction to ecoimmunology. In Advances in Comparative Immunology (ed. Cooper, E. L.). 901–932 (Springer International Publishing, 2018).52.Barthel, A., Staudacher, H., Schmaltz, A., Heckel, D. G. & Groot, A. T. Sex-specific consequences of an induced immune response on reproduction in a moth. BMC Evol. Biol. 15, 282 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Hillyer, J. F. & Strand, M. R. Mosquito hemocyte-mediated immune responses. Curr. Opin. Insect Sci. 3, 14–21 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Chun, J., Riehle, M. & Paskewitz, S. M. Effect of mosquito age and reproductive status on melanization of sephadex beads in Plasmodium-refractory and -susceptible strains of Anopheles gambiae. J. Invertebr. Pathol. 66, 11–17 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Li, J., Tracy, J. W. & Christensen, B. M. Relationship of hemolymph phenol oxidase and mosquito age in Aedes aegypti. J. Invertebr. Pathol. 60, 188–191 (1992).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Rolff, J. & Siva-Jothy, M. T. Copulation corrupts immunity: a mechanism for a cost of mating in insects. Proc. Natl Acad. Sci. USA 99, 9916–9918 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Schwenke, R. A. & Lazzaro, B. P. Juvenile hormone suppresses resistance to infection in mated female Drosophila melanogaster. Curr. Biol. 27, 596–601 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Reavey, C. E., Warnock, N. D., Cotter, S. C. & Vogel, H. Trade-offs between personal immunity and reproduction in the burying beetle, Nicrophorus vespilloides. Behav. Ecol. 25, 415–23. (2014).Article 

    Google Scholar 
    59.Christensen, B. M., Li, J. Y., Chen, C. C. & Nappi, A. J. Melanization immune responses in mosquito vectors. Trends Parasitol. 21, 192–199 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Harris, K. L., Christensen, B. M. & Miranpuri, G. S. Comparative studies on the melanization response of male and female mosquitoes against microfilariae. Dev. Comp. Immunol. 10, 305–310 (1986).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Syed, Z. A., Gupta, V., Arun, M. G., Dhiman, A., Nandy, B. & Prasad, N. G. Absence of reproduction-immunity trade-off in male Drosophila melanogaster evolving under differential sexual selection. BMC Evol. Biol. 20, 13 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Schwenke R. A., Lazzaro B. P., Wolfner M. F. Reproduction-immunity trade-offs in insects. Annu. Rev. Entomol. 61, 239–256 (2016).63.Schmid-Hempel, P. Evolutionary ecology of insect immune defenses. Annu. Rev. Entomol. 50, 529–551 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Armitage, S. A., Thompson, J. J., Rolff, J. & Siva-Jothy, M. T. Examining costs of induced and constitutive immune investment in Tenebrio molitor. J. Evol. Biol. 16, 1038–1044 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Schwartz, A. & Koella, J. C. The cost of immunity in the yellow fever mosquito, Aedes aegypti depends on immune activation. J. Evol. Biol. 17, 834–840 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Rauw, W. M. Immune response from a resource allocation perspective. Front. Genet. 3, 267 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    67.Levashina, E. A., Moita, L. F., Blandin, S., Vriend, G., Lagueux, M. & Kafatos, F. C. Conserved role of a complement-like protein in phagocytosis revealed by dsRNA knockout in cultured cells of the mosquito, Anopheles gambiae. Cell 104, 709–718 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Strand, M. R. The insect cellular immune response. Insect Sci. 15, 1–14 (2008).CAS 
    Article 

    Google Scholar 
    69.Das, S., Dong, Y., Garver, L. & Dimopoulos, G. Specificity of the Innate Immune System: a Closer Look at the Mosquito Pattern-recognition Receptor Repertoire. (Oxford University Press, 2009).
    Google Scholar 
    70.King, J. G. & Hillyer, J. F. Infection-induced interaction between the mosquito circulatory and immune systems. PLoS Pathog. 8, e1003058–e1003058 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Murdock, C. C., Paaijmans, K. P., Bell, A. S., King, J. G., Hillyer, J. F. & Read, A. F. et al. Complex effects of temperature on mosquito immune function. Proc. R. Soc. B 279, 3357–3366 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    72.Liu, W.-T., Tu, W.-C., Lin, C.-H., Yang, U.-C. & Chen, C.-C. Involvement of cecropin B in the formation of the Aedes aegypti mosquito cuticle. Sci. Rep. 7, 16395 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    73.Hillyer, J. F., Schmidt, S. L., Fuchs, J. F., Boyle, J. P. & Christensen, B. M. Age-associated mortality in immune challenged mosquitoes (Aedes aegypti) correlates with a decrease in haemocyte numbers. Cell. Microbiol. 7, 39–51 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Coggins, S., Estévez-Lao, T. & Hillyer, J. Increased survivorship following bacterial infection by the mosquito Aedes aegypti as compared to Anopheles gambiae correlates with increased transcriptional induction of antimicrobial peptides. Dev. Comp. Immunol. 37, 390–401 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Luckhart, S., Vodovotz, Y., Cui, L. & Rosenberg, R. The mosquito Anopheles stephensi limits malaria parasite development with inducible synthesis of nitric oxide. Proc. Natl Acad. Sci. USA 95, 5700–5705 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Graça-Souza, A. V., Maya-Monteiro, C., Paiva-Silva, G. O., Braz, G. R., Paes, M. C. & Sorgine, M. H. et al. Adaptations against heme toxicity in blood-feeding arthropods. Insect Biochem. Mol. Biol. 36, 322–335 (2006).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    77.Cirimotich, C. M., Ramirez, J. L. & Dimopoulos G. Native microbiota shape insect vector competence for human pathogens. Cell Host Microbe 10, 307–310 (2011).78.Sánchez-Vargas, I., Scott, J. C., Poole-Smith, B. K., Franz, A. W., Barbosa-Solomieu, V. & Wilusz, J. et al. Dengue virus type 2 infections of Aedes aegypti are modulated by the mosquito’s RNA interference pathway. PLoS Pathog. 5, e1000299 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    79.Souza-Neto, J. A., Sim, S. & Dimopoulos, G. An evolutionary conserved function of the JAK-STAT pathway in anti-dengue defense. Proc. Natl Acad. Sci. 106, 17841–17846 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Castillo, J., Brown, M. R. & Strand, M. R. Blood feeding and insulin-like peptide 3 stimulate proliferation of hemocytes in the mosquito Aedes aegypti. PLoS Pathog. 7, e1002274 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Bryant, W. B. & Michel, K. Blood feeding induces hemocyte proliferation and activation in the African malaria mosquito, Anopheles gambiae Giles. J. Exp. Biol. 217, 1238–1245 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    82.Xi, Z., Ramirez, J. L. & Dimopoulos, G. The Aedes aegypti Toll pathway controls dengue virus infection. PLoS Pathog. 4, e1000098 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    83.Bottino-Rojas, V., Talyuli, O. A., Jupatanakul, N., Sim, S., Dimopoulos, G. & Venancio, T. M. et al. Heme signaling impacts global gene expression, immunity and dengue virus infectivity in Aedes aegypti. PLoS ONE 10, e0135985 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    84.Oliveira, J. H. M., Talyuli, O. A. C., Goncalves, R. L. S., Paiva-Silva, G. O., Sorgine, M. H. F. & Alvarenga, P. H. et al. Catalase protects Aedes aegypti from oxidative stress and increases midgut infection prevalence of Dengue but not Zika. PLoS Negl. Trop. Dis. 11, e0005525 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    85.Bernstein, E., Caudy, A. A., Hammond, S. M. & Hannon, G. J. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 409, 363 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    86.Elbashir, S. M., Lendeckel, W. & Tuschl, T. RNA interference is mediated by 21- and 22-nucleotide RNAs. Genes Dev. 15, 188–200 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Miyoshi, K., Tsukumo, H., Nagami, T., Siomi, H. & Siomi, M. C. Slicer function of Drosophila Argonautes and its involvement in RISC formation. Genes Dev. 19, 2837–2848 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    88.Okamura, K., Ishizuka, A., Siomi, H. & Siomi, M. C. Distinct roles for Argonaute proteins in small RNA-directed RNA cleavage pathways. Genes Dev. 18, 1655–1666 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Rand, T. A., Ginalski, K., Grishin, N. V. & Wang, X. Biochemical identification of Argonaute 2 as the sole protein required for RNA-induced silencing complex activity. Proc. Natl Acad. Sci. USA 101, 14385–14389 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    90.Ramos-Castaneda, J., Gonzalez, C., Jimenez, M. A., Duran, J., Hernandez-Martinez, S. & Rodriguez, M. H. et al. Effect of nitric oxide on dengue virus replication in Aedes aegypti and Anopheles albimanus. Intervirology 51, 335–341 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Xiao, X., Liu, Y., Zhang, X., Wang, J., Li, Z. & Pang, X. et al. Complement-related proteins control the flavivirus infection of Aedes aegypti by inducing antimicrobial peptides. PLoS Pathog. 10, e1004027 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    92.Waldock, J., Olson, K. E. & Christophides, G. K. Anopheles gambiae antiviral immune response to systemic O’nyong-nyong infection. PLoS Negl. Trop. Dis. 6, e1565 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    93.Colpitts, T. M., Cox, J., Vanlandingham, D. L., Feitosa, F. M., Cheng, G. & Kurscheid, S. et al. Alterations in the Aedes aegypti transcriptome during infection with West Nile, dengue and yellow fever Viruses. PLoS Pathog. 7, e1002189 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    94.Moon, A. E., Walker A. J. & Goodbourn S. Regulation of transcription of the Aedes albopictus cecropin A1 gene: a role for p38 mitogen-activated protein kinase. Insect Biochem. Mol. Biol. 41, 628–636 (2011).95.Jordan, T. X. & Randall, G. Dengue virus activates the AMP kinase-mTOR axis to stimulate a proviral lipophagy. J. Virol. 91, e02020–16 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    96.Urbanowski, M. D. & Hobman, T. C. The West Nile virus capsid protein blocks apoptosis through a phosphatidylinositol 3-kinase-dependent mechanism. J. Virol. 87, 872–881 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    97.Lazzaro, B. P., Flores, H. A., Lorigan, J. G. & Yourth, C. P. Genotype-by-environment interactions and adaptation to local temperature affect immunity and fecundity in Drosophila melanogaster. PLoS Pathog. 4, e1000025 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    98.Jupatanakul, N. et al. Engineered Aedes aegypti JAK/STAT pathway-mediated immunity to dengue virus. PLoS Negl. Trop. Dis. 11, e0005187 (2017).99.Martin-Acebes M. A. et al. The composition of West Nile virus lipid envelope unveils a role of sphingolipid metabolism in flavivirus biogenesis. J. Virol. 88, 12041–12054 (2014).100.Barletta, A. B., Alves, L. R., Silva, M. C., Sim, S., Dimopoulos, G. & Liechocki, S. et al. Emerging role of lipid droplets in Aedes aegypti immune response against bacteria and dengue virus. Sci. Rep. 6, 19928 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    101.Fu, Q., Inankur, B., Yin, J., Striker, R. & Lan, Q. Sterol carrier protein 2, a critical host factor for dengue virus infection, alters the cholesterol distribution in mosquito Aag2 Cells. J. Med. Entomol. 52, 1124–1134 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    102.Jupatanakul, N., Sim, S. & Dimopoulos, G. Aedes aegypti ML and Niemann-Pick type C family members are agonists of dengue virus infection. Dev. Comp. Immunol. 43, 1–9 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    103.Evans, M. V. et al. Carry-over effects of urban larval environments on the transmission potential of dengue-2 virus. Parasites Vectors 11, 426 (2018).104.Salazar, M. I., Richardson, J. H., Sánchez-Vargas, I., Olson, K. E. & Beaty, B. J. Dengue virus type 2: replication and tropisms in orally infected Aedes aegypti mosquitoes. BMC Microbiol. 7, 9 (2007).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    105.Gloria-Soria, A., Soghigian, J., Kellner, D. & Powell, J. R. Genetic diversity of laboratory strains and implications for research: the case of Aedes aegypti. PLoS Negl. Trop. Dis. 13, e0007930 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    106.Souza-Neto, J. A., Powell, J. R. & Bonizzoni, M. Aedes aegypti vector competence studies: a review. Infect. Genet. Evol. 67, 191–209 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    107.Franz, A. W. et al. Fitness impact and stability of a transgene conferring resistance to dengue-2 virus following introgression into a genetically diverse Aedes aegypti strain. PLoS Negl. Trop. Dis. 8, e2833 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    108.Irvin, N., Hoddle, M. S., O’Brochta, D. A., Carey, B. & Atkinson, P. W. Assessing fitness costs for transgenic Aedes aegypti expressing the GFP marker and transposase genes. Proc. Natl Acad. Sci. USA 101, 891–896 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    109.Pompon, J. & Levashina, E. A. A new role of the mosquito complement-like cascade in male fertility in Anopheles gambiae. PLoS Biol. 13, e1002255–e1002255 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    110.Mitchell, S. N., Kakani, E. G., South, A., Howell, P. I., Waterhouse, R. M. & Catteruccia, F. Mosquito biology. Evolution of sexual traits influencing vectorial capacity in anopheline mosquitoes. Science 347, 985–988 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    111.League G. P. et al. Sexual selection theory meets disease vector control: Testing harmonic convergence as a “good genes” signal in Aedes aegypti mosquitoes. Preprint at bioRxiv https://doi.org/10.1101/2020.10.29.360651 (2020).112.Hillyer, J. F. & Estevez-Lao, T. Y. Nitric oxide is an essential component of the hemocyte-mediated mosquito immune response against bacteria. Dev. Comp. Immunol. 34, 141–149 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    113.Warburg, A., Shtern, A., Cohen, N. & Dahan, N. Laminin and a Plasmodium ookinete surface protein inhibit melanotic encapsulation of Sephadex beads in the hemocoel of mosquitoes. Microbes Infect. 9, 192–199 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    114.Lambrechts, L., Vulule, J. M. & Koella, J. C. Genetic correlation between melanization and antibacterial immune responses in a natural population of the malaria vector Anopheles gambiae. Evolution 58, 2377–2381 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    115.Lambrechts, L., Morlais, I., Awono-Ambene, P. H., Cohuet, A., Simard, F. & Jacques, J.-C. et al. Effect of infection by Plasmodium falciparum on the melanization immune response of Anopheles gambiae. Am. J. Tropic. Med. Hyg. 76, 475–480 (2007).Article 

    Google Scholar 
    116.Boëte, C., Paul, R. E. L. & Koella, J. C. Direct and indirect immunosuppression by a malaria parasite in its mosquito vector. Proc. R. Soc. Lond. Ser. B 271, 1611–1615 (2004).Article 

    Google Scholar 
    117.Ramakrishnan, M. A. Determination of 50% endpoint titer using a simple formula. World J. Virol. 5, 85–86 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    118.Tesla, B., Demakovsky, L. R., Mordecai, E. A., Ryan, S. J., Bonds, M. H. & Ngonghala, C. N. et al. Temperature drives Zika virus transmission: evidence from empirical and mathematical models. Proc. R. Soc. B 285, 20180795 (2018).PubMed 
    Article 

    Google Scholar 
    119.Franz, A. W., Kantor, A. M., Passarelli, A. L. & Clem, R. J. Tissue barriers to arbovirus infection in mosquitoes. Viruses 7, 3741–3767 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    120.Lanciotti, R. S., Calisher, C. H., Gubler, D. J., Chang, G. J. & Vorndam, A. V. Rapid detection and typing of dengue viruses from clinical-samples by using reverse transcriptase-polymerase chain-reaction. J. Clin. Microbiol 30, 545–551 (1992).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    121.RStudio Team. RStudio: Integrated Development Environment for R (RStudio, Inc., 2016).122.R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).123.Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W. & Nielsen, A. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).Article 

    Google Scholar 
    124.Christensen, R. H. B. Ordinal-Regression Models for Ordinal Data. R package version 2015.6-28 (R Foundation for Statistical Computing, 2015).125.Bates, D., Mächler, M., Bolker, B. & Walker S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 48 (2015).Article 

    Google Scholar 
    126.Bolker, B. R Development Core Team. bbmle: Tools for General Maximum Likelihood Estimation. R package version 1.0.20 (CRAN, 2017).127.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.2.4 ed (CRAN, 2019).128.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.3.3, ed (CRAN, 2019). More

  • in

    Adapted tolerance to virus infections in four geographically distinct Varroa destructor-resistant honeybee populations

    1.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science (80–.) 351, 594–597 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    3.Levin, S., Sela, N. & Chejanovsky, N. Two novel viruses associated with the Apis mellifera pathogenic mite Varroa destructor. Sci. Rep. 6, 37710 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Tentcheva, D. et al. Prevalence and seasonal variations of six bee viruses in Apis mellifera L. and Varroa destructor mite populations in France. Appl. Environ. Microbiol. 70, 7185–7191 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Martin, S. The role of Varroa and viral pathogens in the collapse of honeybee colonies: A modeling approach. J. Appl. Ecol. 38, 1082–1093 (2001).Article 

    Google Scholar 
    6.Mordecai, G. J., Wilfert, L., Martin, S. J., Jones, I. M. & Schroeder, D. C. Diversity in a honey bee pathogen: First report of a third master variant of the Deformed Wing Virus quasispecies. ISME J. 10, 1264–1273 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.de Miranda, J. R., Cordoni, G. & Budge, G. The Acute bee paralysis virus—Kashmir bee virus—Israeli acute paralysis virus complex. J. Invertebr. Pathol. 103, S30–S47 (2010).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    8.de Miranda, J. R. & Genersch, E. Deformed wing virus. J. Invertebr. Pathol. 103, 48–61 (2010).Article 
    CAS 

    Google Scholar 
    9.Bowen-Walker, P. L., Martin, S. J. & Gunn, A. The transmission of deformed wing virus between honeybees (Apis mellifera L.) by the ectoparasitic mite Varroa jacobsoni Oud. J. Invertebr. Pathol. 73, 101–106 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Yue, C., Schroeder, M., Gisder, S. & Genersch, E. Vertical-transmission routes for deformed wing virus of honeybees (Apis mellifera). J. Gen. Virol. 88, 2329–2336 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.de Miranda, J. R. & Fries, I. Venereal and vertical transmission of deformed wing virus in honeybees (Apis mellifera L.). J. Invertebr. Pathol. 98, 184–189 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Genersch, E. & Aubert, M. Emerging and re-emerging viruses of the honey bee (Apis mellifera L). Vet. Res. 41, 54 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.de Miranda, J. R. et al. Standard methods for virus research in Apis mellifera. J. Apic. Res. 52, 1–56 (2013).ADS 
    Article 
    CAS 

    Google Scholar 
    14.Amiri, E. et al. Quantitative patterns of vertical transmission of deformed wing virus in honey bees. PLoS ONE 13, e0195283 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Moeckel, N., Gisder, S. & Genersch, E. Horizontal transmission of deformed wing virus: Pathological consequences in adult bees (Apis mellifera) depend on the transmission route. J. Gen. Virol. 92, 370–377 (2011).CAS 
    Article 

    Google Scholar 
    16.Boecking, O. & Genersch, E. Varroosis—The ongoing crisis in bee keeping. J. für Verbraucherschutz und Leb. 3, 221–228 (2008).Article 

    Google Scholar 
    17.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).Article 

    Google Scholar 
    18.Locke, B. & Fries, I. Characteristics of honey bee colonies (Apis mellifera) in Sweden surviving Varroa destructor infestation. Apidologie 42, 533–542 (2011).Article 

    Google Scholar 
    19.Locke, B., Le Conte, Y., Crauser, D. & Fries, I. Host adaptations reduce the reproductive success of Varroa destructor in two distinct European honey bee populations. Ecol. Evol. 2, 1144–1150 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Oddie, M. A. Y., Dahle, B. & Neumann, P. Norwegian honey bees surviving Varroa destructor mite infestations by means of natural selection. PeerJ 5, e3956 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Panziera, D., van Langevelde, F. & Blacquière, T. Varroa sensitive hygiene contributes to naturally selected varroa resistance in honey bees. J. Apic. Res. 56, 635–642 (2017).Article 

    Google Scholar 
    22.Schmid-Hempel, P. Parasites and their social hosts. Trends Parasitol. 33, 453–462 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Thaduri, S., Stephan, J. G., de Miranda, J. R. & Locke, B. Disentangling host–parasite–pathogen interactions in a varroa-resistant honeybee population reveals virus tolerance as an independent, naturally adapted survival mechanism. Sci. Rep. 9, 6221 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Locke, B., Forsgren, E. & de Miranda, J. R. Increased tolerance and resistance to virus infections: A possible factor in the survival of Varroa destructor-resistant honey bees (Apis mellifera). PLoS ONE 9, e99998 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Thaduri, S., Locke, B., Granberg, F. & de Miranda, J. R. Temporal changes in the viromes of Swedish Varroa-resistant and Varroa-susceptible honeybee populations. PLoS ONE 13, e0206938 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Le Conte, Y. et al. Honey bee colonies that have survived Varroa destructor. Apidologie 38, 566–572 (2007).Article 

    Google Scholar 
    27.Fries, I., Imdorf, A. & Rosenkranz, P. Survival of mite infested (Varroa destructor) honey bee (Apis mellifera) colonies in a Nordic climate. Apidologie 37, 564–570 (2006).Article 

    Google Scholar 
    28.Dietemann, V. et al. Standard methods for varroa research. J. Apic. Res. 52, 1–54 (2013).
    Google Scholar 
    29.Meeus, I., de Miranda, J. R., de Graaf, D. C., Wäckers, F. & Smagghe, G. Effect of oral infection with Kashmir bee virus and Israeli acute paralysis virus on bumblebee (Bombus terrestris) reproductive success. J. Invertebr. Pathol. 121, 64–69 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Carrillo-Tripp, J. et al. In vivo and in vitro infection dynamics of honey bee viruses. Sci. Rep. 6, 22265 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Aupinel, P. et al. Improvement of artificial feeding in a standard in vitro method for rearing Apis mellifera larvae. Bull. Insectol. 58, 107–111 (2005).
    Google Scholar 
    32.Crailsheim, K. et al. Standard methods for artificial rearing of Apis mellifera larvae. J. Apic. Res. 52, 1–16 (2013).Article 

    Google Scholar 
    33.Forsgren, E., Locke, B., Semberg, E., Laugen, A. T. & de Miranda, J. R. Sample preservation, transport and processing strategies for honeybee RNA extraction: Influence on RNA yield, quality, target quantification and data normalization. J. Virol. Methods 246, 81–89 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Williams, G. R. et al. Standard methods for maintaining adult Apis mellifera in cages under in vitro laboratory conditions. J. Apic. Res. 52, 1–36 (2013).Article 

    Google Scholar 
    35.Locke, B., Forsgren, E., Fries, I. & de Miranda, J. R. Acaricide treatment affects viral dynamics in Varroa destructor-infested honey bee colonies via both host physiology and mite control. Appl. Environ. Microbiol. 78, 227–235 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Lourenco, A. P., Mackert, A., Cristino, A. D. S. & Simoes, Z. L. P. Validation of reference genes for gene expression studies in the honey bee, Apis mellifera, by quantitative real-time RT-PCR. Apidologie 39, 372–385 (2008).CAS 
    Article 

    Google Scholar 
    37.R Core Team. R: A language and environment for statistical computing (2017).38.Kuznetsova, A., Brockhoff, P. & Christensen, R. H. B. Package ‘lmerTest’: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    39.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    40.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50, 346–363 (2008).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    41.Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. Ser. B 34, 187–202 (1972).MathSciNet 
    MATH 

    Google Scholar 
    42.Therneau, T. M. & Grambsch, P. M. The Cox model 39–77 (Springer, 2000). https://doi.org/10.1007/978-1-4757-3294-8_3.Book 
    MATH 

    Google Scholar 
    43.Schoenfeld, D. Chi-squared goodness-of-fit tests for the proportional hazards regression model. Biometrika 67, 145–153 (1980).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    44.Therneau, T. M. Package ‘coxme’: Mixed effects Cox models. R package version 2.2-10; 2018 (2018).45.De Jong, P. S., De Jong, L. & Goncalves, D. H. Weight loss and other damage to developing worker honeybees from infestation with Varroa Jacobsoni. J. Apic. Res. https://doi.org/10.1080/00218839.1982.11100535 (1983).Article 

    Google Scholar 
    46.Sumpter, D. J. T. & Martin, S. J. The dynamics of virus epidemics in Varroa-infested honey bee colonies. J. Anim. Ecol. 73, 51–63 (2004).Article 

    Google Scholar 
    47.Mondet, F., de Miranda, J. R., Kretzschmar, A., Le Conte, Y. & Mercer, A. R. On the front line: Quantitative virus dynamics in honeybee (Apis mellifera L.) colonies along a new expansion front of the parasite Varroa destructor. PLoS Pathog. 10, e1004323 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    48.Mondet, F. et al. Specific cues associated with honey bee social defence against Varroa destructor infested brood. Sci. Rep. 6, 25444 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Brutscher, L. M., Daughenbaugh, K. F. & Flenniken, M. L. Antiviral defense mechanisms in honey bees. Curr. Opin. Insect Sci. 10, 71–82 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Martin, S. J. & Brettell, L. E. Deformed wing virus in honeybees and other insects. Annu. Rev. Virol. 6, annurev-virology-092818-015700 (2019).Article 
    CAS 

    Google Scholar 
    51.Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Amiri, E., Meixner, M. D. & Kryger, P. Deformed wing virus can be transmitted during natural mating in honey bees and infect the queens. Sci. Rep. 6, 33065 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Yue, C. & Genersch, E. RT-PCR analysis of deformed wing virus in honeybees (Apis mellifera) and mites (Varroa destructor). J. Gen. Virol. 86, 3419–3424 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Chen, Y., Evans, J. & Feldlaufer, M. Horizontal and vertical transmission of viruses in the honey bee, Apis mellifera. J. Invertebr. Pathol. 92, 152–159 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Gauthier, L. et al. Viruses associated with ovarian degeneration in Apis mellifera L. queens. PLoS ONE 6, e16217 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Nordström, S., Fries, I., Aarhus, A., Hansen, H. & Korpela, S. Virus infections in Nordic honey bee colonies with no, low or severe Varroa jacobsoni infestations. Apidologie 30, 475–484 (1999).Article 

    Google Scholar 
    57.Biesmeijer, K. Report Honeybee Surveillance Program the Netherlands 2006–2017. (2017).58.Strauss, U. et al. Seasonal prevalence of pathogens and parasites in the savannah honeybee (Apis mellifera scutellata). J. Invertebr. Pathol. 114, 45–52 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Khongphinitbunjong, K. et al. Responses of Varroa-resistant honey bees (Apis mellifera L.) to deformed wing virus. J. Asia Pac. Entomol. 19, 921–927 (2016).Article 

    Google Scholar 
    60.Råberg, L., Graham, A. L. & Read, A. F. Decomposing health: Tolerance and resistance to parasites in animals. Philos. Trans. R. Soc. B 364, 37–49 (2009).Article 

    Google Scholar 
    61.Thompson, J. N. The Coevolutionary Process (University of Chicago Press, 1994).Book 

    Google Scholar 
    62.Ongus, J. R. et al. Complete sequence of a picorna-like virus of the genus Iflavirus replicating in the mite Varroa destructor. J. Gen. Virol. 85, 3747–3755 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Gisder, S., Aumeier, P. & Genersch, E. Deformed wing virus: Replication and viral load in mites (Varroa destructor). J. Gen. Virol. 90, 463–467 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Nazzi, F. et al. Synergistic parasite–pathogen interactions mediated by host immunity can drive the collapse of honeybee colonies. PLoS Pathog. 8, e1002735 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Yang, X. & Cox-Foster, D. L. Impact of an ectoparasite on the immunity and pathology of an invertebrate: Evidence for host immunosuppression and viral amplification. Proc. Natl. Acad. Sci. 102, 7470–7475 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Yang, X. & Cox-Foster, D. Effects of parasitization by Varroa destructor on survivorship and physiological traits of Apis mellifera in correlation with viral incidence and microbial challenge. Parasitology 134, 405 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Ryabov, E. V. et al. A virulent strain of deformed wing virus (DWV) of honeybees (Apis mellifera) prevails after Varroa destructor-mediated, or in vitro transmission. PLoS Pathog. 10, e1004230 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    68.Ryabov, E. V., Fannon, J. M., Moore, J. D., Wood, G. R. & Evans, D. J. The Iflaviruses Sacbrood virus and Deformed wing virus evoke different transcriptional responses in the honeybee which may facilitate their horizontal or vertical transmission. PeerJ 4, e1591 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    69.Desai, S. D., Eu, Y.-J., Whyard, S. & Currie, R. W. Reduction in deformed wing virus infection in larval and adult honey bees (Apis mellifera L.) by double-stranded RNA ingestion. Insect Mol. Biol. 21, 446–455 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Maori, E. et al. IAPV, a bee-affecting virus associated with Colony Collapse Disorder can be silenced by dsRNA ingestion. Insect Mol. Biol. 18, 55–60 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Di Prisco, G. et al. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health. Proc. Natl. Acad. Sci. 113, 3203–3208 (2016).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar  More

  • in

    Novel clades of soil biphenyl degraders revealed by integrating isotope probing, multi-omics, and single-cell analyses

    1.Singer E, Wagner M, Woyke T. Capturing the genetic makeup of the active microbiome in situ. ISME J. 2017;11:1949–63.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Hall EK, Bernhardt ES, Bier RL, Bradford MA, Boot CM, Cotner JB, et al. Understanding how microbiomes influence the systems they inhabit. Nat Microbiol. 2018;3:977–82.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Lloyd KG, Steen AD, Ladau J, Yin J, Crosby L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems 2018;3:e00055–18.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Lewis WH, Tahon G, Geesink P, Sousa DZ, Ettema TJG. Innovations to culturing the uncultured microbial majority. Nat Rev Microbiol. 2021;19:225–40.CAS 
    Article 

    Google Scholar 
    5.Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Spang A, Caceres EF, Ettema TJG. Genomic exploration of the diversity, ecology, and evolution of the archaeal domain of life. Science. 2017;357:eaaf3883.7.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
    Article 

    Google Scholar 
    8.Chen S-C, Musat N, Lechtenfeld OJ, Paschke H, Schmidt M, Said N, et al. Anaerobic oxidation of ethane by archaea from a marine hydrocarbon seep. Nature 2019;568:108–11.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol. 2021;39:499–509.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;18:241–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    12.Abraham WR, Nogales B, Golyshin PN, Pieper DH, Timmis KN. Polychlorinated biphenyl-degrading microbial communities in soils and sediments. Curr Opin Microbiol. 2002;5:246–53.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Galbán-Malagón C, Berrojalbiz N, Ojeda M-J, Dachs J. The oceanic biological pump modulates the atmospheric transport of persistent organic pollutants to the Arctic. Nat Commun 2012;3:862.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    14.Pieper DH. Aerobic degradation of polychlorinated biphenyls. Appl Microbiol Biotechnol. 2005;67:170–91.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Chain PSG, Denef VJ, Konstantinidis KT, Vergez LM, Agulló L, Reyes VL, et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. Proc Natl Acad Sci USA. 2006;103:15280.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Furukawa K, Suenaga H, Goto M. Biphenyl dioxygenases: functional versatilities and directed evolution. J Bacteriol. 2004;186:5189–96.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.McLeod MP, Warren RL, Hsiao WWL, Araki N, Myhre M, Fernandes C, et al. The complete genome of Rhodococcus sp. RHA1 provides insights into a catabolic powerhouse. Proc Natl Acad Sci USA. 2006;103:15582.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Lee TK, Lee J, Sul WJ, Iwai S, Chai BC, Tiedje JM, et al. Novel biphenyl-oxidizing bacteria and dioxygenase genes from a Korean tidal mudflat. Appl Environ Microbiol. 2011;77:3888–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Sul WJ, Park J, Quensen JF, Rodrigues JLM, Seliger L, Tsoi TV, et al. DNA-stable isotope probing integrated with metagenomics for retrieval of biphenyl dioxygenase genes from polychlorinated biphenyl-contaminated river sediment. Appl Environ Microbiol. 2009;75:5501–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Uhlik O, Jecna K, Mackova M, Vlcek C, Hroudova M, Demnerova K, et al. Biphenyl-metabolizing bacteria in the rhizosphere of horseradish and bulk soil contaminated by polychlorinated biphenyls as revealed by stable isotope probing. Appl Environ Microbiol. 2009;75:6471.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Jiang LF, Luo CL, Zhang DY, Song MK, Sun YT, Zhang G. Biphenyl-Metabolizing microbial community and a functional operon revealed in e-waste-contaminated soil. Environ Sci Technol. 2018;52:8558–67.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Tillmann S, Strompl C, Timmis KN, Abraham WR. Stable isotope probing reveals the dominant role of Burkholderia species in aerobic degradation of PCBs. FEMS Microbiol Ecol. 2005;52:207–17.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Leigh MB, Pellizari VH, Uhlik O, Sutka R, Rodrigues J, Ostrom NE, et al. Biphenyl-utilizing bacteria and their functional genes in a pine root zone contaminated with polychlorinated biphenyls (PCBs). ISME J. 2007;1:134–48.CAS 
    Article 

    Google Scholar 
    24.Chen S-C, Duan G-L, Ding K, Huang F-Y, Zhu Y-G. DNA stable-isotope probing identifies uncultivated members of Pseudonocardia associated with biodegradation of pyrene in agricultural soil. FEMS Microbiol Ecol. 2018;94:fiy026.25.Neufeld JD, Dumont MG, Vohra J, Murrell JC. Methodological considerations for the use of stable isotope probing in microbial ecology. Micro Ecol. 2007;53:435–42.CAS 
    Article 

    Google Scholar 
    26.Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Mohn WW, Westerberg K, Cullen WR, Reimer KJ. Aerobic biodegradation of biphenyl and polychlorinated biphenyls by Arctic soil microorganisms. Appl Environ Microbiol. 1997;63:3378–84.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Wagner-Dobler I, Bennasar A, Vancanneyt M, Strompl C, Brummer I, Eichner C, et al. Microcosm enrichment of biphenyl-degrading microbial communities from soils and sediments. Appl Environ Microbiol. 1998;64:3014–22.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Allen MB. Studies with cyanidium caldarium, an anomalously pigmented chlorophyte. Arch Mikrobiol. 1959;32:270–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Rabus R, Widdel F. Anaerobic degradation of ethylbenzene and other aromatic hydrocarbons by new denitrifying bacteria. Arch Microbiol. 1995;163:96–103.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Zhou J, Bruns MA, Tiedje JM. DNA recovery from soils of diverse composition. Appl Environ Microbiol. 1996;62:316.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012;28:1823–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.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–D6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Ouyang WY, Su JQ, Richnow HH, Adrian L. Identification of dominant sulfamethoxazole-degraders in pig farm-impacted soil by DNA and protein stable isotope probing. Environ Int. 2019;126:118–26.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Tischer K, Zeder M, Klug R, Pernthaler J, Schattenhofer M, Harms H, et al. Fluorescence in situ hybridization (CARD-FISH) of microorganisms in hydrocarbon contaminated aquifer sediment samples. Syst Appl Microbiol. 2012;35:526–32.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS–a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Stryhanyuk H, Calabrese F, Kümmel S, Musat F, Richnow HH, Musat N. Calculation of single cell assimilation rates from SIP-NanoSIMS-derived isotope ratios: a comprehensive approach. Front Microbiol. 2018;9:2342.38.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019;7:e7359–e.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–7.CAS 

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

    Google Scholar 
    44.Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 
    CAS 

    Google Scholar 
    45.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D30.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D14. (D1)CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.Article 
    CAS 

    Google Scholar 
    49.Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Budhraja R, Karande S, Ding C, Ullrich MK, Wagner S, Reemtsma T, et al. Characterization of membrane-bound metalloproteins in the anaerobic ammonium-oxidizing bacterium “Candidatus Kuenenia stuttgartiensis” strain CSTR1. Talanta. 2021;223:121711.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20:1466–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016;13:741–8.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    54.Sachsenberg T, Herbst F-A, Taubert M, Kermer R, Jehmlich N, von Bergen M, et al. MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res. 2015;14:619–27.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Liu J, He XX, Lin XR, Chen WC, Zhou QX, Shu WS, et al. Ecological effects of combined pollution associated with e-waste recycling on the composition and diversity of soil microbial communities. Environ Sci Technol. 2015;49:6438–47.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Kumamaru T, Suenaga H, Mitsuoka M, Watanabe T, Furukawa K. Enhanced degradation of polychlorinated biphenyls by directed evolution of biphenyl dioxygenase. Nat Biotechnol. 1998;16:663–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Garrido-Sanz D, Manzano J, Martín M, Redondo-Nieto M, Rivilla R. Metagenomic analysis of a biphenyl-degrading soil bacterial consortium reveals the metabolic roles of specific populations. Front Microbiol. 2018;9:232.58.Kikuchi Y, Nagata Y, Ohtsubo Y, Koana T, Takagi M. Pseudomonas fluorescens KKL101, a benzoic acid degrader in a mixed culture that degrades biphenyl and polychlorinated biphenyls. Biosci Biotechnol Biochem. 1995;59:2303–4.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Musat N, Halm H, Winterholler B, Hoppe P, Peduzzi S, Hillion F, et al. A single-cell view on the ecophysiology of anaerobic phototrophic bacteria. Proc Natl Acad Sci USA. 2008;105:17861.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Calabrese F, Voloshynovska I, Musat F, Thullner M, Schlömann M, Richnow HH, et al. Quantitation and comparison of phenotypic heterogeneity among single cells of monoclonal microbial populations. Front Microbiol. 2019;10:2814.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Robertson BR, Button DK, Koch AL. Determination of the biomasses of small bacteria at low concentrations in a mixture of species with forward light scatter measurements by flow cytometry. Appl Environ Microbiol. 1998;64:3900–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Troussellier M, Bouvy M, Courties C, Dupuy C. Variation of carbon content among bacterial species under starvation condition. Aquat Micro Ecol. 1997;13:113–9.Article 

    Google Scholar 
    63.Furukawa K, Miyazaki T. Cloning of a gene cluster encoding biphenyl and chlorobiphenyl degradation in Pseudomonas pseudoalcaligenes. J Bacteriol. 1986;166:392–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Seeger M, Timmis KN, Hofer B. Conversion of chlorobiphenyls into phenylhexadienoates and benzoates by the enzymes of the upper pathway for polychlorobiphenyl degradation encoded by the bph locus of Pseudomonas sp. strain LB400. Appl Environ Microbiol. 1995;61:2654–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Chadhain SM, Moritz EM, Kim E, Zylstra GJ. Identification, cloning, and characterization of a multicomponent biphenyl dioxygenase from Sphingobium yanoikuyae B1. J Ind Microbiol Biotechnol. 2007;34:605–13.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Hofer B, Backhaus S, Timmis KN. The biphenyl/polychlorinated biphenyl-degradation locus (bph) of Pseudomonas sp. LB400 encodes four additional metabolic enzymes. Gene 1994;144:9–16.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Harwood CS, Parales RE. The beta-ketoadipate pathway and the biology of self-identity. Annu Rev Microbiol. 1996;50:553–90.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Rather LJ, Knapp B, Haehnel W, Fuchs G. Coenzyme A-dependent aerobic metabolism of benzoate via epoxide formation. J Biol Chem. 2010;285:20615–24.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Stegen JC, Fredrickson JK, Wilkins MJ, Konopka AE, Nelson WC, Arntzen EV, et al. Groundwater-surface water mixing shifts ecological assembly processes and stimulates organic carbon turnover. Nat Commun. 2016;7:1–12.70.Corteselli EM, Aitken MD, Singleton DR. Rugosibacter aromaticivorans gen. nov., sp. nov., a bacterium within the family Rhodocyclaceae, isolated from contaminated soil, capable of degrading aromatic compounds. Int J Syst Evol Microbiol 2017;67:311–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Fernandez H, Prandoni N, Fernandez-Pascual M, Fajardo S, Morcillo C, Diaz E, et al. Azoarcus sp. CIB, an anaerobic biodegrader of aromatic compounds shows an endophytic lifestyle. PLoS ONE. 2014;9:e110771.72.Iwai S, Johnson TA, Chai BL, Hashsham SA, Tiedje JM. Comparison of the specificities and efficacies of primers for aromatic dioxygenase gene analysis of environmental samples. Appl Environ Microbiol. 2011;77:3551–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Top EM, Springael D. The role of mobile genetic elements in bacterial adaptation to xenobiotic organic compounds. Curr Opin Biotechnol. 2003;14:262–9.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Dombrowski N, Donaho JA, Gutierrez T, Seitz KW, Teske AP, Baker BJ. Reconstructing metabolic pathways of hydrocarbon-degrading bacteria from the Deepwater Horizon oil spill. Nat Microbiol. 2016;1:1–7.75.de Lorenzo V. Systems biology approaches to bioremediation. Curr Opin Biotechnol. 2008;19:579–89.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    76.Rabus R, Wöhlbrand L, Thies D, Meyer M, Reinhold-Hurek B, Kämpfer P. Aromatoleum gen. nov., a novel genus accommodating the phylogenetic lineage including Azoarcus evansii and related species, and proposal of Aromatoleum aromaticum sp. nov., Aromatoleum petrolei sp. nov., Aromatoleum bremense sp. nov., Aromatoleum toluolicum sp. nov. and Aromatoleum diolicum sp. nov. Int J Syst Evol Microbiol. 2019;69:982–97.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Vogt C, Richnow HH. Bioremediation via in situ microbial degradation of organic pollutants. Adv Biochem Engin/Biotechnol. 2014;142:123–46.
    Google Scholar 
    78.Cunningham JA, Rahme H, Hopkins GD, Lebron C, Reinhard M. Enhanced in situ bioremediation of BTEX-contaminated groundwater by combined injection of nitrate and sulfate. Environ Sci Technol. 2001;35:1663–70.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Mondello FJ, Turcich MP, Lobos JH, Erickson BD. Identification and modification of biphenyl dioxygenase sequences that determine the specificity of polychlorinated biphenyl degradation. Appl Environ Microbiol. 1997;63:3096–103.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Gomez-Gil L, Kumar P, Barriault D, Bolin JT, Sylvestre M, Eltis LD. Characterization of biphenyl dioxygenase of Pandoraea pnomenusa B-356 as a potent polychlorinated biphenyl-degrading enzyme. J Bacteriol. 2007;189:5705–15.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Experimental warming differentially affects vegetative and reproductive phenology of tundra plants

    1.Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5, 424–430 (2015).ADS 

    Google Scholar 
    2.Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637 (2014).ADS 
    CAS 

    Google Scholar 
    3.Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: adaptation and mitigation time scales. Earth’s Future 2, 68–74 (2014).ADS 

    Google Scholar 
    4.Oberbauer, S. F. et al. Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment. Philos. Trans. R. Soc. B Biol. Sci. 368, 1624 (2013).5.Prevéy, J. S. et al. Warming shortens flowering seasons of tundra plant communities. Nat. Ecol. Evol. 3, 45–52 (2019).PubMed 

    Google Scholar 
    6.Jabis, M. D., Winkler, D. E. & Kueppers, L. M. Warming acts through earlier snowmelt to advance but not extend alpine community flowering. Ecology https://doi.org/10.1002/ecy.3108 (2020).7.Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34, 885–888 (2019).PubMed 

    Google Scholar 
    8.Gallinat, A. S., Primack, R. B. & Wagner, D. L. Autumn, the neglected season in climate change research. Trends Ecol. Evol. 30, 169–176 (2015).PubMed 

    Google Scholar 
    9.Semenchuk, P. R. et al. High Arctic plant phenology is determined by snowmelt patterns but duration of phenological periods is fixed: an example of periodicity. Environ. Res. Lett. 11, 125006 (2016).10.Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: Implications for predictive models. Glob. Chang. Biol. 21, 2634–2641 (2015).ADS 
    PubMed 

    Google Scholar 
    11.Diepstraten, R. A. E., Jessen, T. D., Fauvelle, C. M. D. & Musiani, M. M. Does climate change and plant phenology research neglect the Arctic tundra? Ecosphere 9, e02362 (2018).12.Savage, J. A. A temporal shift in resource allocation facilitates flowering before leaf out and spring vessel maturation in precocious species. Am. J. Bot. 106, 113–122 (2019).PubMed 

    Google Scholar 
    13.Neuner, G. Frost resistance in alpine woody plants. Front. Plant Sci. 5, 654 (2014).14.Kuprian, E., Briceño, V. F., Wagner, J. & Neuner, G. Ice barriers promote supercooling and prevent frost injury in reproductive buds, flowers and fruits of alpine dwarf shrubs throughout the summer. Environ. Exp. Bot. 106, 4–12 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    15.Vitasse, Y., Lenz, A. & Körner, C. The interaction between freezing tolerance and phenology in temperate deciduous trees. Front. Plant Sci. 5, 1–12 (2014).
    Google Scholar 
    16.Maron, J. L., Agrawal, A. A. & Schemske, D. W. Plant–herbivore coevolution and plant speciation. Ecology 100, 1–11 (2019).
    Google Scholar 
    17.Rafferty, N. E. & Ives, A. R. Effects of experimental shifts in flowering phenology on plant-pollinator interactions. Ecol. Lett. 14, 69–74 (2011).PubMed 

    Google Scholar 
    18.Fitter, A. H. & Fitter, R. S. R. Rapid changes in flowering time in British plants. Science 296, 1689–1691 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    19.Post, E. Time in Ecology: A Theoretical Framework (Princeton University Press, 2019).20.Kharouba, H. M., Vellend, M., Sarfraz, R. M. & Myers, J. H. The effects of experimental warming on the timing of a plant-insect herbivore interaction. J. Anim. Ecol. 84, 785–796 (2015).PubMed 

    Google Scholar 
    21.Zohner, C. M., Mo, L. & Renner, S. S. Global warming reduces leaf-out and flowering synchrony among individuals. Elife 7, 1–15 (2018).
    Google Scholar 
    22.Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Clim. Change 94, 105–121 (2009).ADS 

    Google Scholar 
    23.Bjorkman, A. D., Elmendorf, S. C., Beamish, A. L., Vellend, M. & Henry, G. H. R. Contrasting effects of warming and increased snowfall on Arctic tundra plant phenology over the past two decades. Glob. Chang. Biol. 21, 4651–4661 (2015).ADS 
    PubMed 

    Google Scholar 
    24.Assmann, J. J. et al. Local snow melt and temperature—but not regional sea ice—explain variation in spring phenology in coastal Arctic tundra. Glob. Chang. Biol. 25, 2258–2274 (2019).ADS 
    PubMed 

    Google Scholar 
    25.Cooper, E. J., Dullinger, S. & Semenchuk, P. Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. Plant Sci. 180, 157–167 (2011).CAS 
    PubMed 

    Google Scholar 
    26.Kelsey, K. C. et al. Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities. Glob. Chang. Biol. 1–15 https://doi.org/10.1111/gcb.15505 (2020).27.Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).ADS 

    Google Scholar 
    28.Panchen, Z. A. & Gorelick, R. Prediction of Arctic plant phenological sensitivity to climate change from historical records. Ecol. Evol. 7, 1325–1338 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    29.Livensperger, C. et al. Earlier snowmelt and warming lead to earlier but not necessarily more plant growth. AoB Plants 8, 1–15 (2016).
    Google Scholar 
    30.Livensperger, C. et al. Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence. Ecosphere 10, e02677 (2019).31.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Chang. Biol. 1922–1940 https://doi.org/10.1111/gcb.14619 (2019).32.Panchen, Z. A. et al. Substantial variation in leaf senescence times among 1360 temperate woody plant species: implications for phenology and ecosystem processes. Ann. Bot. 865–873 https://doi.org/10.1093/aob/mcv015 (2015).33.Wu, C. et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nat. Clim. Chang. 8, 1092–1096 (2018).ADS 
    CAS 

    Google Scholar 
    34.Zhu, W. et al. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Glob. Ecol. Biogeogr. 21, 260–271 (2012).
    Google Scholar 
    35.Liu, Q. et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Glob. Chang. Biol. 22, 3702–3711 (2016).ADS 
    PubMed 

    Google Scholar 
    36.Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. Meteorol. 169, 156–173 (2013).
    Google Scholar 
    37.Marchand, F. L. et al. Climate warming postpones senescence in High Arctic Tundra. Arct. Antarct. Alp. Res. 36, 390–394 (2004).
    Google Scholar 
    38.Steltzer, H. & Post, E. Seasons and life cycles. Science 324, 886–887 (2009).PubMed 

    Google Scholar 
    39.Jiang, L. L. et al. Relatively stable response of fruiting stage to warming and cooling relative to other phenological events. Ecology 97, 1961–1969 (2016).CAS 
    PubMed 

    Google Scholar 
    40.Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).CAS 
    PubMed 

    Google Scholar 
    41.Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, 1–11 (2007).
    Google Scholar 
    42.Wookey, P. A. et al. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Glob. Chang. Biol. 15, 1153–1172 (2009).ADS 

    Google Scholar 
    43.Arft, A. M. et al. Responses of Tundra plants to experimental warming: meta-analysis of the International Tundra Experiment. Ecol. Monogr. 69, 491–511 (1999).
    Google Scholar 
    44.Buttler, A. et al. Experimental warming interacts with soil moisture to discriminate plant responses in an ombrotrophic peatland. J. Veg. Sci. 26, 964–974 (2015).
    Google Scholar 
    45.Healy, N. C., Oberbauer, S. F. & Hollister, R. D. Examination of surface temperature modification by open-top chambers along moisture and latitudinal gradients in Arctic Alaska using thermal infrared photography. Remote Sens. 1–19 https://doi.org/10.3390/rs8010054 (2016).46.Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol. Lett. 15, 164–175 (2012).PubMed 

    Google Scholar 
    47.Post, E., Steinman, B. A. & Mann, M. E. Acceleration of phenological advance and warming with latitude over the past century. Sci. Rep. 1–8 https://doi.org/10.1038/s41598-018-22258-0 (2018).48.Iler, A. M., Høye, T. T., Inouye, D. W. & Schmidt, N. M. Nonlinear flowering responses to climate: Are species approaching their limits of phenological change? Philos. Trans. R. Soc. B Biol. Sci. 368, 13–16 (2013).
    Google Scholar 
    49.Prevéy, J. et al. Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes. Glob. Chang. Biol. 23, 2660–2671 (2017).ADS 
    PubMed 

    Google Scholar 
    50.Wipf, S. & Rixen, C. A review of snow manipulation experiments in Arctic and Alpine Tundra ecosystems. Polar Res. 29, 95–109 (2010).
    Google Scholar 
    51.Bokhorst, S. et al. Variable temperature effects of open top chambers at polar and alpine sites explained by irradiance and snow depth. Glob. Chang. Biol. 19, 64–74 (2013).ADS 
    PubMed 

    Google Scholar 
    52.Zhu, J., Zhang, Y. & Wang, W. Interactions between warming and soil moisture increase overlap in reproductive phenology among species in an alpine meadow. Biol. Lett. 12, 1–4 (2016).ADS 

    Google Scholar 
    53.Kemppinen, J., Niittynen, P., Aalto, J., le Roux, P. C. & Luoto, M. Water as a resource, stress and disturbance shaping tundra vegetation. Oikos 128, 811–822 (2019).
    Google Scholar 
    54.Panchen, Z. A. & Gorelick, R. Canadian arctic archipelago conspecifics flower earlier in the high arctic than the mid-arctic. Int. J. Plant Sci. 177, 661–670 (2016).
    Google Scholar 
    55.Barrett, R. T. & Hollister, R. D. Arctic plants are capable of sustained responses to long-term warming. Polar Res. 35, 1–9 (2016).
    Google Scholar 
    56.Carbognani, M., Bernareggi, G., Perucco, F., Tomaselli, M. & Petraglia, A. Micro-climatic controls and warming effects on flowering time in alpine snowbeds. Oecologia 182, 573–585 (2016).ADS 
    PubMed 

    Google Scholar 
    57.Hollister, R. D., Webber, P. J. & Tweedie, C. E. The response of Alaskan Arctic Tundra to experimental warming: Differences between short- and long-term responses. Glob. Chang. Biol. 11, 525–536 (2005).ADS 

    Google Scholar 
    58.Mulder, C. P. H., Iles, D. T. & Rockwell, R. F. Increased variance in temperature and lag effects alter phenological responses to rapid warming in a subarctic plant community. Glob. Chang. Biol. 23, 801–814 (2017).ADS 
    PubMed 

    Google Scholar 
    59.Marion, G. M. et al. Open-top designs for manipulating field temperature in high-latitude ecosystems. Glob. Chang. Biol. 3, 20–32 (1997).
    Google Scholar 
    60.Walker, M. D. et al. Plant community responses to experimental warming across the tundra biome. Proc. Natl Acad. Sci. USA 103, 1342–1346 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    61.Hollister, R. D. & Webber, P. J. Biotic validation of small open-top chambers in a tundra ecosystem. Glob. Chang. Biol. 6, 835–842 (2000).ADS 

    Google Scholar 
    62.Henry, G. H. R. & Molau, U. Tundra plants and climate change: The International Tundra Experiment (ITEX). Glob. Chang. Biol. 3, 1–9 (1997).ADS 

    Google Scholar 
    63.Welker, J. M., Molau, U., Parsons, A. N., Robinson, C. H. & Wookey, P. A. Responses of Dryas octopetala to ITEX environmental manipulations: a synthesis with circumpolar comparisons. Glob. Chang. Biol. 3, 61–73 (1997).ADS 

    Google Scholar 
    64.Basnett, S., Nagaraju, S. K., Ravikanth, G. & Devy, S. M. Influence of phylogeny and abiotic factors varies across early and late reproductive phenology of Himalayan Rhododendrons. Ecosphere 10, e02581 (2019).65.Panchen, Z. A. et al. Leaf out times of temperate woody plants are related to phylogeny, deciduousness, growth habit and wood anatomy. N. Phytol. 203, 1208–1219 (2014).CAS 

    Google Scholar 
    66.Davis, C. C., Willis, C. G., Primack, R. B. & Miller-Rushing, A. J. The importance of phylogeny to the study of phenological response to global climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 3202–3213 (2010).
    Google Scholar 
    67.Hänninen, H. et al. Experiments are necessary in process-based tree phenology modelling. Trends Plant Sci. 24, 199–209 (2019).PubMed 

    Google Scholar 
    68.Hanson, P. J. & Walker, A. P. Advancing global change biology through experimental manipulations: Where have we been and where might we go? Glob. Chang. Biol. 26, 287–299 (2020).ADS 
    PubMed 

    Google Scholar 
    69.Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, 1–17 (2016).
    Google Scholar 
    70.Ettinger, A. K. et al. Winter temperatures predominate in spring phenological responses to warming. Nat. Clim. Chang. 10, 1137–1142 (2020).71.Augspurger, C. K. Reconstructing patterns of temperature, phenology, and frost damage over 124 years: Spring damage risk is increasing. Ecology 94, 41–50 (2013).PubMed 

    Google Scholar 
    72.Caradonna, P. J. & Bain, J. A. Frost sensitivity of leaves and fl owers of subalpine plants is related to tissue type and phenology. J. Ecol. 55–64 https://doi.org/10.1111/1365-2745.12482 (2016).73.Gezon, Z. J., Inouye, D. W. & Irwin, R. E. Phenological change in a spring ephemeral: Implications for pollination and plant reproduction. Glob. Chang. Biol. 22, 1779–1793 (2016).ADS 
    PubMed 

    Google Scholar 
    74.Iler, A. M. et al. Reproductive losses due to climate change-induced earlier flowering are not the primary threat to plant population viability in a perennial herb. J. Ecol. 107, 1931–1943 (2019).
    Google Scholar 
    75.CaraDonna, P. J. & Waser, N. M. Temporal flexibility in the structure of plant–pollinator interaction networks. Oikos 129, 1369–1380 (2020).
    Google Scholar 
    76.Fründ, J., Dormann, C. F. & Tscharntke, T. Linné’s floral clock is slow without pollinators – flower closure and plant-pollinator interaction webs. Ecol. Lett. 14, 896–904 (2011).PubMed 

    Google Scholar 
    77.Song, C. & Saavedra, S. Structural stability as a consistent predictor of phenological events. Proc. R. Soc. B Biol. Sci. 285, 20180767 (2018).78.Saavedra, S., Rohr, R. P., Olesen, J. M. & Bascompte, J. Nested species interactions promote feasibility over stability during the assembly of a pollinator community. Ecol. Evol. 6, 997–1007 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    79.Mosbacher, J. B., Michelsen, A., Stelvig, M., Hjermstad-Sollerud, H. & Schmidt, N. M. Muskoxen modify plant abundance, phenology, and nitrogen dynamics in a high Arctic Fen. Ecosystems 22, 1095–1107 (2019).
    Google Scholar 
    80.Barboza, P. S., Van Someren, L. L., Gustine, D. D. & Syndonia Bret-Harte, M. The nitrogen window for arctic herbivores: Plant phenology and protein gain of migratory caribou (Rangifer tarandus). Ecosphere 9, e02073 (2018).81.Gougherty, A. V. & Gougherty, S. W. Sequence of flower and leaf emergence in deciduous trees is linked to ecological traits, phylogenetics, and climate. N. Phytol. 220, 121–131 (2018).
    Google Scholar 
    82.Bjorkman, A. D. et al. Status and trends in Arctic vegetation: evidence from experimental warming and long-term monitoring. Ambio 49, 678–692 (2020).PubMed 

    Google Scholar 
    83.Loe, L. E. et al. The neglected season: Warmer autumns counteract harsher winters and promote population growth in Arctic reindeer. Glob. Chang. Biol. 993–1002 https://doi.org/10.1111/gcb.15458 (2020).84.Ueyama, M. et al. Growing season and spatial variations of carbon fluxes of Arctic and boreal ecosystems in Alaska (USA). Ecol. Appl. 23, 1798–1816 (2013).PubMed 

    Google Scholar 
    85.White, M. A., Running, S. W. & Thornton, P. E. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. Int. J. Biometeorol. 42, 139–145 (1999).86.Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9, 852–857 (2019).ADS 
    CAS 

    Google Scholar 
    87.Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 3–7 (2008).
    Google Scholar 
    88.Radville, L., Post, E. & Eissenstat, D. M. On the sensitivity of root and leaf phenology to warming in the Arctic. Arctic Antarct. Alp. Res. 50, S100020 (2018).89.Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. Contrasting synchrony in root and leaf phenology across multiple sub-Arctic plant communities. J. Ecol. 104, 239–248 (2016).CAS 

    Google Scholar 
    90.Danby, R. K. & Hik, D. S. Responses of white spruce (Picea glauca) to experimental warming at a subarctic alpine treeline. Glob. Chang. Biol. 13, 437–451 (2007).ADS 

    Google Scholar 
    91.Dabros, A., Fyles, J. W. & Strachan, I. B. Effects of open-top chambers on physical properties of air and soil at post-disturbance sites in northwestern Quebec. Plant Soil 333, 203–218 (2010).92.Finger Higgens, R. A. et al. Changing Lake Dynamics indicate a drier Arctic in Western Greenland. J. Geophys. Res. Biogeosci. 124, 870–883 (2019).
    Google Scholar 
    93.Leuzinger, S. et al. Do global change experiments overestimate impacts on terrestrial ecosystems? Trends Ecol. Evol. 26, 236–241 (2011).PubMed 

    Google Scholar 
    94.Molau, U. & MØlgaard, P. ITEX Manual (1996).95.Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).96.Cayuela, L., Granzow-de la Cerda, Í., Albuquerque, F. S. & Golicher, D. J. Taxonstand: An r package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).
    Google Scholar 
    97.C3S. ERA5: fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://cds.climate.copernicus.eu/cdsapp#!/home%0A (2017).98.Kittel, T. G. F. et al. Contrasting long-term alpine and subalpine precipitation trends in a mid-latitude North American mountain system, Colorado Front Range, USA. Plant Ecol. Divers. 8, 607–624 (2015).
    Google Scholar 
    99.Therneau, T. A package for survival analysis in S. Citeseer 1–83 (2020).100.R Core Team. R: A Language and Environment for Statistical Computing (2019).101.Bürkner, P.-C. brms: An R package for bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).102.van de Pol, M. & Wright, J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).
    Google Scholar 
    103.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences linked references are available on JSTOR for this article: inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

    Google Scholar 
    104.Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).ADS 

    Google Scholar 
    105.Pebesma, E. Simple features for R: standardized support for spatial vector data. R J 10, 439–446 (2018).
    Google Scholar 
    106.Wickham, H. Elegant Graphics for Data Analysis Media Vol. 35 (Springer Publishing Company, Incorporated, 2009).107.Collins, C. cour10eygrace/OTC_synthesis_analyses: release for Nature Communications manuscript (Version v1.0.3). Zenodo https://doi.org/10.5281/zenodo.4763165 (2021). More

  • in

    Irradiation-induced sterility in an egg parasitoid and possible implications for the use of biological control in insect eradication

    1.DeBach, P. & Rosen, D. Biological Control by Natural Enemies (Cambridge University Press, 1991).
    Google Scholar 
    2.Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Economic value of biological control in integrated pest management of managed plant systems. Annu. Rev. Entomol. 60, 621–645 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    3.Walker, J. T. S., Suckling, D. M. & Wearing, C. H. Past, present, and future of integrated control of apple pests: The New Zealand experience. Annu. Rev. Entomol. 62, 231–248 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    4.van Lenteren, J. C., Bale, J., Bigler, F., Hokkanen, H. M. T. & Loomans, A. J. M. Assessing risks of releasing exotic biological control agents of arthropod pests. Annu. Rev. Entomol. 51, 609–634 (2006).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    5.Bale, J. S., van Lenteren, J. C. & Bigler, F. Biological control and sustainable food production. Phil. Trans. R. Soc. Lond. B 363, 761–776 (2008).Article 
    CAS 

    Google Scholar 
    6.Sheppard, A. W. et al. A global review of risk-benefit-cost analysis for the introduction of classical biological control agents against weeds: A crisis in the making?. Biocontrol News Inf. 24, 91N-108N (2003).
    Google Scholar 
    7.Barratt, B. I. P., Blossey, B. & Hokkanen, H. M. Post-release evaluation of non-target effects of biological control agents. In Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds Bigler, F. et al.) 166–186 (CABI Publishing, 2006).Chapter 

    Google Scholar 
    8.Barratt, B. I. P., Moeed, A. & Malone, L. A. Biosafety assessment protocols for new organisms in New Zealand: Can they apply internationally to emerging technologies?. Environ. Impact Assess. Rev. 26, 339–358 (2006).Article 

    Google Scholar 
    9.Klassen, W. & Curtis, C. F. History of the sterile insect technique. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 3–38 (Springer, 2021).
    Google Scholar 
    10.Hendrichs, J., Kenmore, P., Robinson, A. S. & Vreyson, M. J. B. Area-wide integrated pest management (AW-IPM): principles, practice and prospects. In Area-Wide Control of Insect Pests (eds Vreysen, M. J. B. et al.) 3–34 (Springer, 2007).
    Google Scholar 
    11.Knipling, E. F. Possibilities of insect control or eradication through the use of sexually sterile males. J. Econ. Entomol. 48, 459–462 (1955).Article 

    Google Scholar 
    12.Brockerhoff, E. G., Liebhold, A. M., Richardson, B. & Suckling, D. M. Eradication of invasive forest insects: Concepts, methods, costs and benefits. NZ J. For. Sci. 40, S117–S135 (2010).
    Google Scholar 
    13.Suckling, D. M., Tobin, P. C., McCullough, D. G. & Herms, D. A. Combining tactics to exploit Allee effects for eradication of alien insect populations. J. Econ. Entomol. 105, 1–13 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Hendrichs, J., Enkerlin, W. R. & Pereira, R. Invasive insect pests: challenges and the role of the sterile insect technique in their prevention, containment, and eradication. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management 885–922 (Springer, 2021).Chapter 

    Google Scholar 
    15.Nagel, P. & Peveling, R. Environment and the sterile insect technique. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 499–519 (Springer, 2021).
    Google Scholar 
    16.Knipling, E. F. The Basic Principles of Insect Population Suppression and Management (U.S. Department of Agriculture, 1979).
    Google Scholar 
    17.Barclay, H. J. Models for pest control: Complementary effects of periodic releases of sterile pests and parasitoids. Theor. Popul. Biol. 32, 76–89 (1987).Article 

    Google Scholar 
    18.Soller, M. & Lanzrein, B. Polydnavirus and venom of the egg-larval parasitoid Chelonus inanitus (Braconidae) induce developmental arrest in the prepupa of its host Spodoptera littoralis (Noctuidae). J. Insect Physiol. 42, 471–481 (1996).Article 
    CAS 

    Google Scholar 
    19.Tillinger, N. A., Hoch, G. & Schopf, A. Effects of parasitoid associated factors of the endoparasitoid Glyptapanteles liparidis (Hymenoptera: Braconidae). Eur. J. Entomol. 101, 243–249 (2004).Article 

    Google Scholar 
    20.Tunçbilek, A. S., Canpolat, U. & Ayvaz, A. Effects of gamma radiation on suitability of stored cereal pest eggs and the reproductive capability of the egg parasitoid Trichogramma evanescens (Trichogrammatidae: Hymenoptera). Biocontrol Sci. Techn. 19, 179–191 (2009).Article 

    Google Scholar 
    21.Lynch, L. D. et al. Insect biological control and non-target effects: a European perspective. In Evaluating Indirect Ecological Effects of Biological Control (eds Wajnberg, E. et al.) 99–126 (Springer, 2001).
    Google Scholar 
    22.van Lenteren, J. C. V. et al. Environmental risk assessment of exotic natural enemies used in inundative biological control. Biocontrol 48, 3–38 (2003).Article 

    Google Scholar 
    23.Horrocks, K. J., Avila, G. A., Holwell, G. I. & Suckling, D. M. Integrating sterile insect technique with the release of sterile classical biocontrol agents for eradication: Is the Kamikaze Wasp Technique feasible?. Biocontrol 65, 257–271 (2020).Article 

    Google Scholar 
    24.Welsh, T. J., Stringer, L. D., Caldwell, R., Carpenter, J. E. & Suckling, D. M. Irradiation biology of male brown marmorated stink bugs: Is there scope for the sterile insect technique?. Int. J. Radiat. Biol. 93, 1357–1363 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    25.Suckling, D. M. et al. The competitive mating of irradiated brown marmorated stink bugs, Halyomorpha halys, for the sterile insect technique. Insects 10, 411 (2019).PubMed Central 
    Article 

    Google Scholar 
    26.Larivière, M.-C. Fauna of New Zealand (Manaaki Whenua Press, 1995).
    Google Scholar 
    27.Martin, N. A. Green vegetable bug – Nezara viridula. Interesting insects and other invertebrates. New Zealand arthropod factsheet number 47 https://nzacfactsheets.landcareresearch.co.nz/factsheet/InterestingInsects/Green-vegetable-bug—Nezara-viridula.html (2018). Accessed 16 Sept 2020.28.Powell, J. E. & Shepard, M. Biology of Australian and United States strains of Trissolcus basalis, a parasitoid of the green vegetable bug Nezara viridula. Austr. Ecol. 7, 181–186 (1982).Article 

    Google Scholar 
    29.Cantón-Ramos, J. M. & Callejón-Ferre, Á. J. Raising Trissolcus basalis for the biological control of Nezara viridula in greenhouses of Almería (Spain). Afr. J. Agric. Res. 5, 3207–3212 (2010).
    Google Scholar 
    30.Loch, A. D. & Walter, G. H. Mating behavior of Trissolcus basalis (Wollaston) (Hymenoptera: Scelionidae): Potential for outbreeding in a predominantly inbreeding species. J. Insect Behav. 11, 2 (2002).
    Google Scholar 
    31.Johns, H. F. & Cunningham, J. R. The interaction of single beams of x and gamma rays with a scattering medium. In The Physics of Radiology 349–350 (Charles C Thomas, 1983).
    Google Scholar 
    32.Bin, F., Vinson, S. B., Strand, M. R., Colazza, S. & Jones, W. A. Source of an egg kairomone for Trissolcus basalis, a parasitoid of Nezara viridula. Physiol. Entomol. 18, 7–15 (1993).Article 

    Google Scholar 
    33.Mahmoud, A. M. A. & Lim, U. T. Evaluation of cold-stored eggs of Dolycoris baccarum (Hemiptera: Pentatomidae) for parasitization by Trissolcus nigripedius (Hymenoptera: Scelionidae). Biol. Control 43, 287–293 (2007).Article 

    Google Scholar 
    34.Haye, T. et al. Fundamental host range of Trissolcus japonicus in Europe. J. Pest Sci. 93, 171–182 (2020).Article 

    Google Scholar 
    35.Cusumano, A. et al. First extensive characterization of the venom gland from an egg parasitoid: Structure, transcriptome and functional role. J. Insect Physiol. 107, 68–80 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    36.Bundy, C. S. & McPherson, R. M. Morphological examination of stink bug (Heteroptera: Pentatomidae) eggs on cotton and soybeans, with a key to genera. Ann. Entomol. Soc. Am. 93, 616–624 (2000).Article 

    Google Scholar 
    37.Favetti, B. M., Butnariu, A. R. & Doetzer, A. K. Storage of Euschistus heros eggs (Fabricius) (Hemiptera: Pentatomidae) in liquid nitrogen for parasitization by Telenomus podisi Ashmead (Hymenoptera: Platygastridae). Neotrop. Entomol. 43, 291–293 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    38.Kazmer, D. J. & Luck, R. F. Field tests of the size-fitness hypothesis in the egg parasitoid Trichogramma pretiosum. Ecology 76, 412–425 (1995).Article 

    Google Scholar 
    39.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
    Book 

    Google Scholar 
    40.Bates, D., Machler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    41.Chapman, T., Miyatake, T., Smith, H. K. & Partridge, L. Interactions of mating, egg production and death rates in females of the Mediterranean fruit fly, Ceratitis capitata. Proc. R. Soc. Lond. B 265, 1879–1894 (1998).Article 
    CAS 

    Google Scholar 
    42.Grosch, D. S. & Sullivan, R. L. The quantitative aspects of permanent and temporary sterility induced in female Habrobracon by x-rays and β radiation. Radiat. Res. 1, 294–320 (1954).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    43.Colazza, S. & Wajnberg, E. Effects of host egg mass size on sex ratio and oviposition sequence of Trissolcus basalis (Hymenoptera: Scelionidae). Environ. Entomol. 27, 329–336 (1998).Article 

    Google Scholar 
    44.Rosi, M. C., Isidoro, N., Colazza, S. & Bin, F. Source of the host marking pheromone in the egg parasitoid Trissolcus basalis (Hymenoptera: Scelionidae). J. Insect Physiol. 47, 989–995 (2001).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    45.Abram, P. K., Brodeur, J., Burte, V. & Boivin, G. Parasitoid-induced host egg abortion: An underappreciated component of biological control services provided by egg parasitoids. Biol. Control 98, 52–60 (2016).Article 

    Google Scholar 
    46.Kuske, S. et al. Dispersal and persistence of mass released Trichogramma brassicae (Hymenoptera: Trichogrammatidae) in non-target habitats. Biol. Control 27, 181–193 (2003).Article 

    Google Scholar 
    47.Draz, K. A., Tabikha, R. M., El-Aw, M. A. & Darwish, H. F. Impact of gamma radiation doses on sperm competitiveness, fecundity and morphometric characters of peach fruit fly Bactrocera zonata (Saunders) (Diptera: Tephiritidae). J. Radiat. Res. Appl. Sci. 9, 352–362 (2016).Article 
    CAS 

    Google Scholar 
    48.Ali, A., Rashid, M. A., Huang, Q. Y. & Lei, C.-L. Effect of UV-A radiation as an environmental stress on the development, longevity, and reproduction of the oriental armyworm, Mythimna separata (Lepidoptera: Noctuidae). Environ. Sci. Pollut. Res. 23, 17002–17007 (2016).Article 
    CAS 

    Google Scholar 
    49.Liebhold, A. M. et al. Eradication of invading insect populations: From concepts to applications. Annu. Rev. Entomol. 61, 335–352 (2016).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    50.Tobin, P. C. et al. Determinants of successful arthropod eradication programs. Biol. Invasions 16, 401–414 (2014).Article 

    Google Scholar 
    51.Pluess, T. et al. Which factors affect the success or failure of eradication campaigns against alien species?. PLoS ONE 7, e48157 (2012).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Colunga-Garcia, M., Magarey, R. A., Haack, R. A., Gage, S. H. & Qi, J. Enhancing early detection of exotic pests in agricultural and forest ecosystems using an urban-gradient framework. Ecol. Appl. 20, 303–310 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Myers, J. H., Savoie, A. & van Randen, E. Eradication and pest management. Annu. Rev. Entomol. 43, 471–491 (1998).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    54.Lance, D. R. & McInnis, D. O. Biological basis of the sterile insect technique. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 69–94 (Springer, 2021).
    Google Scholar 
    55.Godfray, H. C. J. Oviposition behaviour. In Parasitoids: Behavioural and Evolutionary Ecology Vol. 67 83–150 (Princeton University Press, 1994).Chapter 

    Google Scholar 
    56.Ravuiwasa, K. T., Lu, K.-H., Shen, T.-C. & Hwang, S.-Y. Effects of irradiation on Planococcus minor (Hemiptera: Pseudococcidae). J. Econ. Entomol. 102, 1774–1780 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Bloem, S., Bloem, K. A. & Knight, A. L. Oviposition by sterile codling moths, Cydia pomonella (Lepidoptera: Tortricidae) and control of wild populations with combined releases of sterile moths and egg parasitoids. J. Entomol. Soc. 95, 99–109 (1998).
    Google Scholar 
    58.Hasaballah, A. I. Impact of gamma irradiation on the development and reproduction of Culex pipiens (Diptera; Culicidae). Int. J. Radiat. Biol. 94, 844–849 (2018).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    59.Sagarra, L. A., Vincent, C. & Stewart, R. K. Body size as an indicator of parasitoid quality in male and female Anagyrus kamali (Hymenoptera: Encyrtidae). Bull. Entomol. Res. 91, 363–367 (2001).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    60.Bertin, A., Pavinato, V. A. C. & Parra, J. R. P. Effects of intraspecific hybridization on the fitness of the egg parasitoid Trichogramma galloi. Biocontrol 63, 555–563 (2018).Article 

    Google Scholar 
    61.Bloem, S., Bloem, K. A., Carpenter, J. E. & Calkins, C. O. Inherited sterility in codling moth (Lepidoptera: Tortricidae): Effect of substerilizing doses of radiation on insect fecundity, fertility, and control. Ann. Entomol. Soc. Am. 92, 222–229 (1999).Article 

    Google Scholar 
    62.Bloem, S., Carpenter, J. E. & Hofmeyr, J. H. Radiation biology and inherited sterility in false codling moth (Lepidoptera:Tortricidae). J. Econ. Entomol. 96, 1724–1731 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.El-Kholy, E. M. S. Biological and biochemical effects of vitamin ‘c’ on the normal and irradiated mediterranean fruit fly, Ceratitis capitata (wied). J. Radiat. Res. Appl. Sci. 2, 197–212 (2009).
    Google Scholar 
    64.Rempoulakis, P., Castro, R., Nemny-Lavy, E. & Nestel, D. Effects of radiation on the fertility of the Ethiopian fruit fly, Dacus ciliatus. Entomol. Exp. Appl. 155, 117–122 (2015).Article 

    Google Scholar 
    65.Würgler, F. E. & Lütolf, H.-U. Radiosensitivity of oocytes of Drosophila I. sensitivity of class-a oocytes of triploid and diploid females. Int. J. Radiat. Biol. 21, 455–463 (1972).
    Google Scholar 
    66.Field, S. A. Patch exploitation, patch-leaving and pre-emptive patch defence in the parasitoid wasp Trissolcus basalis (Insecta: Scelionidae). Ethology 104, 323–338 (1998).Article 

    Google Scholar 
    67.Sked, S. L. & Calvin, D. D. Temporal synchrony between Macrocentrus cingulum (Hymenoptera: Braconidae) with its preferred host, Ostrinia nubilalis (Lepidoptera: Crambidae). Environ. Entomol. 34, 344–352 (2005).Article 

    Google Scholar 
    68.Jiang, N., Zhou, G., Overholt, W. A., Muchugu, E. & Schulthess, F. The temporal correlation and spatial synchrony in the stemborer and parasitoid system of Coast Kenya with climate effects. Ann. Soc. Entomol. Fr. 42, 381–387 (2006).Article 

    Google Scholar 
    69.Whitten, M. & Mahon, R. Misconceptions and constraints. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 601–626 (Springer, 2021).
    Google Scholar 
    70.Lee, Y. J. & Ducoff, H. S. Radiation-enhanced resistance to oxygen: A possible relationship to radiation-enhanced longevity. Mech. Ageing Dev. 27, 101–109 (1984).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    71.Suckling, D. M., Wee, S. L. & Pedley, R. Assessing competitive fitness of irradiated painted apple moth Teia anartoides (Lepidoptera: Lymantriidae). N.Z. Plant Prot. 57, 171–176 (2004).
    Google Scholar 
    72.Wee, S. L. et al. Effects of substerilizing doses of gamma radiation on adult longevity and level of inherited sterility in Teia anartoides (Lepidoptera: Lymantriidae). J. Econ. Entomol. 98, 732–738 (2005).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    73.Vilca Mallqui, K. S., Vieira, J. L., Guedes, R. N. C. & Gontijo, L. M. Azadirachtin-induced hormesis mediating shift in fecundity-longevity trade-off in the Mexican bean weevil (Chrysomelidae: Bruchinae). J. Econ. Entomol. 107, 860–866 (2014).Article 

    Google Scholar 
    74.Monroy Kuhn, J. M. & Korb, J. Editorial overview: Social insects: Aging and the re-shaping of the fecundity/longevity trade-off with sociality. Curr. Opin. Insect Sci. 16, 7–10 (2016).
    Google Scholar 
    75.Blacher, P., Huggins, T. J. & Bourke, A. F. G. Evolution of ageing, costs of reproduction and the fecundity–longevity trade-off in eusocial insects. Proc. R. Soc. B-Biol. Sci. 284, 20170380 (2017).Article 

    Google Scholar 
    76.Flatt, T. Survival costs of reproduction in Drosophila. Exp. Gerontol. 46, 369–375 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Vogt, E. & Nechols, J. R. The influence of host deprivation and host source on the reproductive biology and longevity of the squash bug egg parasitoid Gryon penssylvanicum (Ashmead) (Hymenoptera: Scelionidae). Biol. Control 3, 148–154 (1993).Article 

    Google Scholar 
    78.Ramesh, B. & Manickavasagam, S. Tradeoff between longevity and fecundity in relation to host availability in a thelytokous oophagous parasitoid, Trichogramma brasiliensis Ashmead (Trichogrammatidae: Hymenoptera). Int. J. Trop. Insect Sci. 23, 207–210 (2003).Article 

    Google Scholar 
    79.Gurr, G. M. & Kvedaras, O. L. Synergizing biological control: scope for sterile insect technique, induced plant defences and cultural techniques to enhance natural enemy impact. Biol. Control 52, 198–207 (2010).Article 

    Google Scholar 
    80.Knipling, E. F. Principles of Insect Parasitism Analyzed from New Perspectives: Practical Implications for Regulating Insect Populations by Biological Means (United States Department of Agriculture, 1992).
    Google Scholar 
    81.Orozco, D., Domínguez, J., Reyes, J., Villaseñor, A. & Gutiérrez, J. M. SIT and biological control of Anastrepha fruit flies in Mexico. in Proceedings of the 6th International Fruit Fly Symposium 245–249 (Isteg Scientific Publications, 2002).82.Wong, T. T. Y., Ramadan, M. M., Herr, J. C. & McInnis, D. O. Suppression of a Mediterranean fruit fly (Diptera: Tephritidae) population with concurrent parasitoid and sterile fly releases in Kula, Maui, Hawaii. J. Econ. Entomol. 85, 1671–1681 (1992).Article 

    Google Scholar 
    83.Cossentine, J. E. & Jensen, L. B. M. Releases of Trichogramma platneri (Hymenoptera: Trichogrammatidae) in apple orchards under a sterile codling moth release program. Biol. Control 18, 179–186 (2000).Article 

    Google Scholar 
    84.Carpenter, J. E., Bloem, S. & Hofmeyr, J. H. Acceptability and suitability of eggs of false codling moth (Lepidoptera: Tortricidae) from irradiated parents to parasitism by Trichogrammatoidea cryptophlebiae (Hymenoptera: Trichogrammatidae). Biol. Control 30, 351–359 (2004).Article 

    Google Scholar 
    85.Carpenter, J. E., Bloem, S. & Hofmeyr, J. H. Area-wide control tactics for the false codling moth Thaumatotibia leucotreta in South Africa: a potential invasive species. In Area-Wide Control of Insect Pests (eds Vreysen, M. J. B. et al.) 351–359 (Springer, 2007).
    Google Scholar 
    86.Faúndez, E. I. & Rider, D. A. The brown marmorated stink bug Halyomorpha halys (Stål, 1855) (Heteroptera: Pentatomidae) in Chile. Arquivos Entomol. 17, 305–307 (2017).
    Google Scholar 
    87.Kriticos, D. J. et al. The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity. J. Pest Sci. 90, 1033–1043 (2017).Article 

    Google Scholar 
    88.Kiwifruit Vine Health. KVH information sheet: BMSB risk update January 2019 (Kiwifruit Vine Health, 2019).89.Vandervoet, T. F., Bellamy, D. E., Anderson, D. & MacLellan, R. Trapping for early detection of the brown marmorated stink bug, Halyomorpha halys New Zealand. N.Z. Plant Prot. 72, 36–43 (2019).
    Google Scholar 
    90.Laing, K., Belton, D. & Taylor, J. Decision on releasing Trissolcus japonicus from containment. (Environmental Protection Authority, 2018).91.Charles, J. G. et al. Experimental assessment of the biosafety of Trissolcus japonicus in New Zealand, prior to the anticipated arrival of the invasive pest Halyomorpha halys. Biocontrol 64, 367–379 (2019).Article 
    CAS 

    Google Scholar  More

  • in

    Population structure and genetic diversity of non-native aoudad populations

    1.Blackburn, T. M. & Duncan, R. P. Establishment patterns of exotic birds are constrained by non-random patterns in introduction. J. Biogeogr. 28, 927–939 (2001).Article 

    Google Scholar 
    2.Long, J. L. Introduced Mammals of the World: Their History, Distribution and Abundance (CABI Publishing, 2003).Book 

    Google Scholar 
    3.Stuwe, M. & Scribner, K. T. Low genetic variability in reintroduced alpine ibex (Capra ibex ibex) populations. J. Mammal. 70, 370–373 (1989).Article 

    Google Scholar 
    4.Allendorf, F. W. & Lundquist, L. L. Introduction: Population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).Article 

    Google Scholar 
    5.Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).Article 

    Google Scholar 
    6.Michael Reed, J. et al. Emerging issues in population viability analysis. Conserv. Biol. 16, 7–19 (2002).Article 

    Google Scholar 
    7.Carpio, A. J. et al. Hunting as a source of alien species: A European review. Biol. Invasions 19, 1197–1211 (2017).Article 

    Google Scholar 
    8.Linnell, J. D. C. & Zachos, F. E. Status and distribution patterns of European ungulates: genetics, population history and conservation. In Ungulate Management in Europe: Problems and Practices (eds Putman, R. et al.) 12–53 (Cambridge University Press, 2011).Chapter 

    Google Scholar 
    9.Šprem, N., Gančević, P., Safner, T., Jerina, K. & Cassinello, J. Barbary sheep (Ammotragus lervia, Pallas 1777). In Handbook of the Mammals of Europe (eds Hackländer, K. & Zachos, F. E.) (Springer, 2021).
    Google Scholar 
    10.Cassinello, J. Ammotragus lervia: A review on systematics, biology, ecology and distribution. Ann. Zool. Fennici 35, 149–162 (1998).
    Google Scholar 
    11.Cassinello, J. Ammotragus lervia (aoudad). Invasive species compendium. http://www.cabi.org/isc (2015).12.Bounaceur, F., Benamor, N., Bissaad, F. Z., Abdi, A. & Aulagnier, S. Is there a future for the last populations of aoudad (Ammotragus lervia) in northern Algeria?. Pak. J. Zool. 48, 1727–1731 (2016).
    Google Scholar 
    13.Cassinello, J. et al. Ammotragus lervia. The IUCN Red List of Threatened Species. www.iucnredlist.org (2008).14.Lazarus, M. et al. Barbary sheep tissues as bioindicators of radionuclide and stabile element contamination in Croatia: Exposure assessment for consumers. Environ. Sci. Pollut. Res. 26, 14521–14533 (2019).CAS 
    Article 

    Google Scholar 
    15.Mori, E., Mazza, G., Saggiomo, L., Sommese, A. & Esattore, B. Strangers coming from the Sahara: An update of the worldwide distribution, potential impacts and conservation opportunities of alien aoudad. Ann. Zool. Fennici 54, 373–386 (2017).Article 

    Google Scholar 
    16.Gančević, P., Šprem, N. & Jerina, K. Space use and activity patterns of introduced Barbary sheep (Ammotragus lervia) in Southern Dinarides, Croatia in Abstract book of 6th World Congress on Mountain Ungulates and 5th International Symposium on Mouflon (ed. Hadjisterkotis, E.) 41 (2016).17.Bartoš, L., Kotrba, R. & Pintíř, J. Ungulates and their management in the Czech Republic. In European Ungulates and their Management in the 21st Century (eds Apollonio, M. et al.) 243–261 (Cambridge University Press, 2010).
    Google Scholar 
    18.Cassinello, J., Serrano, E., Calabuig, G. & Pérez, J. M. Range expansion of an exotic ungulate (Ammotragus lervia) in southern Spain: Ecological and conservation concerns. Biodivers. Conserv. 13, 851–866 (2004).Article 

    Google Scholar 
    19.Anadón, J. D., Pérez-García, J. M., Pérez, I., Royo, J. & Sánchez-Zapata, J. A. Disentangling the effects of habitat, connectivity and interspecific competition in the range expansion of exotic and native ungulates. Landsc. Ecol. 33, 597–608 (2018).Article 

    Google Scholar 
    20.Cassinello, J. Misconception and mismanagement of invasive species: The paradoxical case of an alien ungulate in Spain. Conserv. Lett. 11, e12440. https://doi.org/10.1111/conl.12440 (2018).Article 

    Google Scholar 
    21.Prentis, P. J., Wilson, J. R. U., Dormontt, E. E., Richardson, D. M. & Lowe, A. J. Adaptive evolution in invasive species. Trends Plant Sci. 13, 288–294 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Derouiche, L. et al. Deep mitochondrial DNA phylogeographic divergence in the threatened aoudad Ammotragus lervia (Bovidae, Caprini). Gene 739, 144510. https://doi.org/10.1016/j.gene.2020.144510 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Fernando, P., Vidya, T. N. C., Rajapakse, C., Dangolla, A. & Melnick, D. J. Reliable noninvasive genotyping: Fantasy or reality?. J. Hered. 94, 115–123 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Cassinello, J. Ammotragus free-ranging population in the south-east of Spain: A necessary first account. Biodivers. Conserv. 9, 887–900 (2000).Article 

    Google Scholar 
    26.Moravčíková, N. et al. Identification of genetic families based on mitochondrial D-loop sequence in population of the Tatra chamois (Rupicapra rupicapra tatrica). Biologia 75, 121–128 (2019).Article 
    CAS 

    Google Scholar 
    27.Cassinello, J. Ammotragus lervia Aoudad (Barbary Sheep, Arui). In Mammals of Africa. Volume VI: Pigs, Hippopotamuses, Chevrotain, Giraffes, Deer and Bovids (eds Kingdon, J. & Hoffmann, M.) 595–599 (Bloomsbury Publishing, 2013).
    Google Scholar 
    28.Nei, M., Maruyama, T. & Chakraborty, R. The bottleneck effect and genetic variability in populations. Evolution 29, 1–10 (1975).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Šprem, N. & Buzan, E. The genetic impact of chamois management in the dinarides. J. Wildl. Manag. 80, 783–793 (2016).Article 

    Google Scholar 
    30.Pascual-Rico, R. et al. Ecological niche overlap between co-occurring native and exotic ungulates: Insights for a conservation conflict. Biol. Invasions 22, 2497–2508 (2020).Article 

    Google Scholar 
    31.Dlugosch, K. M. & Parker, I. M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol. 17, 431–449 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Beja-Pereira, A. et al. Twenty polymorphic microsatellites in two of North Africa’s most threatened ungulates: Gazella dorcas and Ammotragus lervia (Bovidae; Artiodactyla). Mol. Ecol. Notes 4, 452–455 (2004).CAS 
    Article 

    Google Scholar 
    33.Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Mereu, P., Palici di Suni, M., Manca, L. & Masala, B. Complete nucleotide mtDNA sequence of Barbary sheep (Ammotragus lervia). DNA Seq. 19, 241–245 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

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

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

    Google Scholar 
    38.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

    Google Scholar 
    39.van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICROCHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
    CAS 

    Google Scholar 
    40.Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39, 1–22 (1977).MathSciNet 
    MATH 

    Google Scholar 
    41.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    43.Belkhir K, Borsa P, Goudet, J., Chikhi, L. & Bonhomme, F. Genetix 4.05, logiciel sous Windows TM pour la genetique des populations. Available at: http://www.genetix.univ-montp2.fr/genetix/genetix.htm (2004)44.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 

    Google Scholar 
    45.Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Kalinowski, S. T. Counting alleles with rarefaction: Private alleles and hierarchical sampling designs. Conserv. Genet. 5, 539–543 (2004).CAS 
    Article 

    Google Scholar 
    47.Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7, 167–184 (2006).Article 

    Google Scholar 
    48.Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    52.Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Rosenberg, N. A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).Article 

    Google Scholar  More

  • in

    Redefining the oceanic distribution of Atlantic salmon

    Our study extends the known geographic area used by salmon during their migration in the North Atlantic Ocean and Barents Sea as reported by earlier studies based on conventional tagging and sampling surveys15,16,20. An extended use of the North Atlantic Ocean and Barents Sea was also suggested in recent studies using archival tags12,23,24,25,26,28, but these studies have concentrated on single populations or been restricted by low sample sizes. The present study indicated that multiple individuals from the Norwegian and Danish populations survived to migrate northward from their home river and reached latitudes as high as 80° N. This is to our knowledge the furthest north any Atlantic salmon has ever been recorded, extending previously assumed northern limits8,30. These results confirm that the foraging areas of Atlantic salmon currently extend to more northerly latitudes than previously thought. For populations in Denmark and Norway, the marine distribution is probably limited by the northern boundary of Atlantic currents. In contrast, the populations from Iceland, Ireland and Spain did not travel as far north, but instead crossed the main North Atlantic current and migrated towards southern Greenland, indicating a difference in ocean distribution for these populations. The less directed migration displayed by most of the North American salmon tagged at Greenland was likely due to these fish already being present at their assumed main ocean feeding grounds at the west coast of Greenland15 when tagged.Despite the fact that salmon from different areas used different migration routes and ocean areas, they consistently migrated to and aggregated in assumed highly productive areas at the boundaries between large-scale frontal water masses where branches of the North Atlantic current lie adjacent to cold polar waters31. In these areas, previous analyses demonstrated frequent diving activity by tagged individuals28. The duration and diving profile of these dives suggested foraging behaviour, rather than predator escape, because the dives were U-shaped, typically lasted a few hours, and diving depths were related to the depth of the mixed layer during the different seasons28. Thus, the increased diving frequency is most likely an indication of increased feeding activity, emphasizing the importance of these productive regions as feeding areas for Atlantic salmon. In contrast to Atlantic salmon from the other areas, the two northernmost populations displayed a high diving frequency close to the shore immediately after sea entrance, as also shown by Hedger et al.28. These rivers are located closer to the frontal water masses, and these fish may have started extensive feeding earlier in their sea migration. This assumption is further supported by a study of Norwegian post-smolts, where the northernmost populations were feeding more extensively just after leaving their rivers than fish from southern populations32. Thus, the northern populations may benefit from a shorter migration route to the main feeding areas for salmon. However, given that many kelts are in poor condition when they enter the sea, it is likely that tagged fish from all populations were feeding pelagically in the first weeks at sea during the transit away from the coast when prey were available.Migration from the rivers to the assumed foraging areas (i.e., the most distant areas they migrated to) was fast and direct for individuals from southern populations, while salmon from the northern Norway did not display similar direct migration routes. Our results are similar to those reported by an earlier study26 on the same North-Western Norwegian population as in the present study, and are likely related to the greater proximity to ocean frontal areas and rich food resources.The results in the present study may have been influenced by the relatively large size of the tag compared to the size of the fish. Hedger et al.33 assessed tagging effects of PSATs on post-spawned Atlantic salmon by comparing their behaviour with salmon tagged with much smaller archival tags. They found that the overall depth distribution, ocean migration routes based on temperature recordings and return rates did not differ between salmon tagged with PSATs and smaller archival tags and concluded that PSATs are suitable for use in researching large-scale migratory behaviour of adult salmon at sea. However, salmon with PSATs dived less frequently and to slightly shallower depths33. Based on this, we believe the conclusions of the present study are valid despite potential tagging effects, but the diving depths and frequencies might be underestimated compared to non-tagged fish.Diet data from adult salmon in the ocean are limited but show that salmon feed on a variety of prey taxa. Typically, herring (Clupea harengus), sand eels (Ammodytes spp.), capelin (Mallotus villosus) and myctophids dominate as fish prey, while euphausiids and amphipods often dominate as crustacean prey34,35,36. Although there exist some data of adult herring and capelin during parts of the year, there is limited information on the spatial and temporal distribution of crustaceans in these ocean areas, and it is therefore difficult to relate the salmon diving behaviour to availability of all their main prey items. However, salmon appeared to be able to forage on prey far below the surface, indicated by the frequent dives, and salmon at sea have also previously been shown to feed on the mesopelagic community37,38. Hedger et al.28 found that the diving depth increased with the depth of the mixed layer and hypothesised that stratification affected the aggregation of prey and thereby the salmon diving behaviour. They also showed that when the stratification disappeared during the dark winter months, the salmon dived less but their dives were deeper. Nevertheless, the possibilities to feed at different depths28, expand the foraging niche of salmon compared to feeding merely near the surface.Dadswell et al.11 published the “merry-go-round hypothesis”, which implies that both first-time migrants and previous spawners from all salmon populations enter the North Atlantic Subpolar Gyres and move counter clockwise within these gyres until returning to their natal rivers. Although the full migration from river outrun to return was not followed in the present study (most tags popped off half-way into the migration), and some individuals indicated a counter clockwise migration pattern, most of the populations and individuals in this study clearly did not follow the North Atlantic Subpolar Gyres during the first months at sea. Therefore, most of our data did not support the merry-go-round hypothesis. However, some individuals from northern Norway seemed to follow the currents to a larger extent than individuals from other populations during the first months at sea. Previous studies on Atlantic salmon from Canada also documented that adults migrated either independently or against prevailing currents while at sea, indicating that the horizontal movement of adults are primarily governed by other factors12,24.Due to the size limit of the pop-up-tags, we primarily tracked large post-spawned individuals that are more mobile than smaller first-time migrants. Although some studies have shown that first-time migrants can be found in the same areas as post-spawners from the same populations8,30 is not known to which extent the migration pattern and distribution of post-spawners represent the same migration pattern of first-time migrants. Due to a larger body size, it is possible that the migration of post-spawners depends to a lesser degree on ocean currents and gyres than do the movements of first-time migrants, especially in the first part of the migration. For example, we observed that the Irish and Spanish post-spawned individuals all crossed the main North Atlantic current towards Greenlandic waters. However, Irish and other southern European post-smolts have frequently been captured in the Norwegian Sea20, indicating that some of these individuals migrate and follow the main ocean current in a northward direction. It is possible that many of these post-smolts later migrate southwest towards Greenland and feed in these waters as maiden salmon before they return to rivers. This corresponds to the observation that it is mostly large (two sea-winter) southern European salmon (including Irish individuals) that are found in the southern Greenland feeding areas20. Therefore, it might be that the post-spawned salmon from these populations return to their primary feeding areas where they were feeding as maiden salmon from their first sea migration, and not necessarily to the same area as they started their feeding migration as post-smolts.Populations differed in their ocean distribution, but the distribution also overlapped to some degree between or among populations, with more overlap between geographically proximate than distant populations. Some populations never overlapped in geographical distribution during the study. The populations from Ireland and Spain did not overlap with the Norwegian and Danish salmon, but there was a small spatial overlap between the Irish salmon and the North American salmon tagged at Greenland, although area use by these populations did not overlap in time. It is known that populations from North America and Europe largely use different parts of the North Atlantic, with more North American salmon in the western part and more European salmon in the eastern part of the ocean although they have been shown to mix at the feeding grounds at the Faroes and at Greenland12,15,16,18,20. For the Spanish population, it should be noted that tagged individuals were followed for a relatively short period, and a larger sample size over a longer period might have shown some overlap with the northern European populations, based on the initial northward direction of two individuals. At the same time as populations differed in their ocean distribution, there were also relatively large within-population differences in migration routes and geographic distribution. Individual differences in migration routes and ocean distribution of salmon from the same population, even within the same year, were also shown by Strøm et al.12,26. Collectively, these results imply that salmon from different populations will experience highly different ecological conditions, potentially contributing to between-and within-population variation in growth and survival. Since our data are limited by a varying number of individuals among the studied populations, and restricted mainly to post-spawned salmon, our results represent a minimum overlap among the populations so the actual overlap may be larger. Nevertheless, this strongly indicates a varying degree of geographical separation in ocean feeding areas. Thus, geographically close populations will to a larger extent be influenced by similar conditions in the ocean than more distant populations.The study was carried out over several years, with not all sites having tagging undertaken in the same years. There is a possibility that geographic area use and overlap among populations may vary among years, according to variation in environmental conditions among years29. However, data from multiple years for some populations suggest consistent population specific migration routes and area use among years, indicating that the principal patterns are stable over time for particular salmon populations.The differing distributions of salmon from particular populations in different oceanic regions might simply be a function of distance to appropriate feeding grounds from the different home rivers, with individuals from the different rivers mainly adapted to seek the closest feeding areas. The route selection during the migration might in addition be a result of each individuals’ opportunistic behaviour and which food resources and environmental conditions they encounter along the journey. As discussed above, the experience and learning during the first ocean migration might also impact individuals’ route choice and area use. Salmon from southern populations used more southern ocean areas, and hence stayed in warmer water, than salmon from the northern populations. We cannot rule out that salmon from different populations have different temperature preferences due to different thermal selection regimes in their home rivers, but similar to a previous study29, we suggest that the differences in thermal habitat among populations utilising different areas at sea are mainly driven by availability of prey fields. There is generally little support for the hypothesis that variation in salmonid growth rates reflects thermal adaptations to their home stream39.Despite the variation in migration patterns among and within populations, most individuals seemed to migrate to distant ocean frontal areas. This suggests that climate change may have greater impact on populations originating further south, because the distances and time required to travel to feeding areas will increase if the boundary between Atlantic and Arctic waters move northward because of ocean warming. Our study has shown that several populations are able to migrate over large distances, but the capacity for populations to adapt to an increased migration distance is unknown. Given increased migration time, especially for southern populations, the time available for accumulating important energy reserves will likely be reduced. In addition, increased water temperatures in the North Atlantic may also increase the energy expenditure that the individual fish spend per unit of distance when migrating from their home rivers towards the feeding areas. This may affect all populations to some degree, and may contribute to an additional burden for Atlantic salmon populations that are already in a poor state. This will also add to the hypothesized negative effect of climate change in freshwater for the southern populations, where temperatures will have a greater likelihood of reaching to growth inhibiting levels compared to more northern populations39.Taking advantage of the development of electronic tags, we have shown an extended use of the North Atlantic Ocean by Atlantic salmon, including the Barents Sea, which contrasts to the earlier strong focus on feeding areas at the Faroes, West Greenland and in the Norwegian Sea in previous studies. These results expand the knowledge on the marine foraging and habitat niche of Atlantic salmon, in terms of geography, migration behaviour and thermal niche. The existence of feeding areas at the boundaries between Atlantic and Arctic surface currents suggests that salmon have a strong link to Arctic oceanic frontal systems. We have further shown that salmon from different populations may migrate to different ocean frontal areas in the North Atlantic Ocean and Barents Sea and therefore be impacted by different ecological conditions that may contribute to within-population variation in growth and survival. We also conclude that climate induced changes in oceanographic conditions, which will likely alter the location of and distance to polar frontal feeding areas, may have region-specific influences on the length and cost of the Atlantic feeding migrations, particularly affecting the southern populations most. As the polar oceans get warmer and current patterns shift, changes in the location and productivity of high latitude fronts will become evident. As migration distances become longer, or more variable, and the time accumulating energy is reduced, the variation in the marine survival and productivity of different populations are likely to become more marked. Combined, our results help to shed light on important ecological process that shape Atlantic salmon population dynamics within most of its distribution area. More

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    Diversity and interactions among triatomine bugs, their blood feeding sources, gut microbiota and Trypanosoma cruzi in the Sierra Nevada de Santa Marta in Colombia

    1.Hotez, P. J. et al. An unfolding tragedy of chagas disease in North America. PLoS Negl. Trop. Dis. 7(10), e2300. https://doi.org/10.1371/journal.pntd.0002300 (2013) (PMID: 24205411).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Hotez, P. J., Bottazzi, M. E., Franco-Paredes, C., Ault, S. K. & Periago, M. R. The neglected tropical diseases of Latin America and the Caribbean: A review of disease burden and distribution and a roadmap for control and elimination. PLoS Negl. Trop. Dis. 2(9), e300. https://doi.org/10.1371/journal.pntd.0000300 (2008) (PMID: 18820747).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Lee, B. Y., Bacon, K. M., Bottazzi, M. E. & Hotez, P. J. Global economic burden of Chagas disease: A computational simulation model. Lancet Infect. Dis. 13(4), 342–348. https://doi.org/10.1016/S1473-3099(13)70002-1 (2013) (PMID: 23395248).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.WHO. Chagas disease in Latin America: An epidemiological update based on 2010 estimates. Wkly. Epidemiol. Rec. 90(6), 33–43 (2015) (PMID: 25671846).
    Google Scholar 
    5.Pena-Garcia, V. H., Gomez-Palacio, A. M., Triana-Chavez, O. & Mejia-Jaramillo, A. M. Eco-epidemiology of Chagas disease in an endemic area of Colombia: Risk factor estimation, Trypanosoma cruzi characterization and identification of blood-meal sources in bugs. Am. J. Trop. Med. Hyg. 91(6), 1116–1124. https://doi.org/10.4269/ajtmh.14-0112 (2014) (PMID: 25331808).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Mejia-Jaramillo, A. M. et al. Genotyping of Trypanosoma cruzi in a hyper-endemic area of Colombia reveals an overlap among domestic and sylvatic cycles of Chagas disease. Parasit. Vectors. 7, 108. https://doi.org/10.1186/1756-3305-7-108 (2014) (PMID: 24656115).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Dib, J. C., Agudelo, L. A. & Velez, I. D. Prevalencia de patologías tropicales y factores de riesgo en la comunidad indígena de Bunkwimake, Sierra Nevada de Santa Marta. DUAZARY. 3(1), 38–44 (2006).
    Google Scholar 
    8.Parra-Henao, G. et al. In search of congenital Chagas disease in the Sierra Nevada de Santa Marta, Colombia. Am. J. Trop. Med. Hyg. 101(3), 482–483. https://doi.org/10.4269/ajtmh.19-0110 (2019) (PMID: 31264558).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Guhl, F., Aguilera, G., Pinto, N. & Vergara, D. Actualización de la distribución geográfica y ecoepidemiología de la fauna de triatominos (Reduviidae: Triatominae) en Colombia. Biomedica. 27(Suppl 1), 143–162 (2007) (PMID: 18154255).Article 

    Google Scholar 
    10.Parra-Henao, G., Suarez-Escudero, L. C. & Gonzalez-Caro, S. Potential distribution of Chagas disease vectors (Hemiptera, Reduviidae, Triatominae) in Colombia, based on Ecological Niche Modeling. J. Trop. Med. 2016, 1439090. https://doi.org/10.1155/2016/1439090 (2016) (PMID: 28115946).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Rodriguez-Mongui, E., Cantillo-Barraza, O., Prieto-Alvarado, F. E. & Cucunuba, Z. M. Heterogeneity of Trypanosoma cruzi infection rates in vectors and animal reservoirs in Colombia: A systematic review and meta-analysis. Parasit. Vectors. 12(1), 308. https://doi.org/10.1186/s13071-019-3541-5 (2019) (PMID: 31221188).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Dib, J., Barnabe, C., Tibayrenc, M. & Triana, O. Incrimination of Eratyrus cuspidatus (Stal) in the transmission of Chagas’ disease by molecular epidemiology analysis of Trypanosoma cruzi isolates from a geographically restricted area in the north of Colombia. Acta Trop. 111(3), 237–242. https://doi.org/10.1016/j.actatropica.2009.05.004 (2009) (PMID: 19442641).Article 
    PubMed 

    Google Scholar 
    13.Parra Henao, G., Angulo, V., Jaramillo, N. & Restrepo, M. Triatominos (Hemiptera: Reduviidae) de ka Sierra Nevada de Santa Marta, Colombia. Aspectos epidemiológicos, entomológicos y de distribución. Rev. CES Med. 23(1), 17–26 (2009).
    Google Scholar 
    14.Hernandez, C. et al. Untangling the transmission dynamics of primary and secondary vectors of Trypanosoma cruzi in Colombia: Parasite infection, feeding sources and discrete typing units. Parasit. Vectors. 9(1), 620. https://doi.org/10.1186/s13071-016-1907-5 (2016) (PMID: 27903288).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Cantillo-Barraza, O., Chaverra, D., Marcet, P., Arboleda-Sanchez, S. & Triana-Chavez, O. Trypanosoma cruzi transmission in a Colombian Caribbean region suggests that secondary vectors play an important epidemiological role. Parasit. Vectors. 7, 381. https://doi.org/10.1186/1756-3305-7-381 (2014) (PMID: 25141852).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Weiss, B. & Aksoy, S. Microbiome influences on insect host vector competence. Trends Parasitol. 27(11), 514–522. https://doi.org/10.1016/j.pt.2011.05.001 (2011) (PMID: 21697014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Azambuja, P., Garcia, E. S. & Ratcliffe, N. A. Gut microbiota and parasite transmission by insect vectors. Trends Parasitol. 21(12), 568–572 (2005) (PMID: 16226491).Article 

    Google Scholar 
    18.Dumonteil, E. et al. Interactions among Triatoma sanguisuga blood feeding sources, gut microbiota and Trypanosoma cruzi diversity in southern Louisiana. Mol Ecol. 29(19), 3747–3761 (2020).Article 

    Google Scholar 
    19.Zingales, B. et al. A new consensus for Trypanosoma cruzi intraspecific nomenclature: Second revision meeting recommends TcI to TcVI. Mem. Inst. Oswaldo Cruz. 104(7), 1051–1054 (2009) (PMID: 20027478).CAS 
    Article 

    Google Scholar 
    20.Zingales, B. et al. The revised Trypanosoma cruzi subspecific nomenclature: Rationale, epidemiological relevance and research applications. Infect. Genet. Evol. 12(2), 240–253. https://doi.org/10.1016/j.meegid.2011.12.009 (2012) (PMID: 22226704).Article 
    PubMed 

    Google Scholar 
    21.Tibayrenc, M. & Ayala, F. J. The population genetics of Trypanosoma cruzi revisited in the light of the predominant clonal evolution model. Acta Trop. 151, 156–165. https://doi.org/10.1016/j.actatropica.2015.05.006 (2015) (PMID: 26188332).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Majeau, A., Murphy, L., Herrera, C. & Dumonteil, E. Assessing Trypanosoma cruzi parasite diversity through comparative genomics: Implications for disease epidemiology and diagnostics. Pathogens. 10, 212. https://doi.org/10.3390/pathogens10020212 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Flores-Ferrer, A., Marcou, O., Waleckx, E., Dumonteil, E. & Gourbière, S. Evolutionary ecology of Chagas disease; what do we know and what do we need?. Evol. Appl. 11(4), 470–487. https://doi.org/10.1111/eva.12582 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Tibayrenc, M., Kjellberg, F. & Ayala, F. J. A clonal theory of parasitic protozoa: The population structures of Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium, Trichomonas, and Trypanosoma and their medical and taxonomical consequences. Proc. Natl. Acad. Sci. USA 87, 2414–2418 (1990).ADS 
    CAS 
    Article 

    Google Scholar 
    25.Berry, A. S. F. et al. Sexual reproduction in a natural Trypanosoma cruzi population. PLoS Negl. Trop. Dis. 13(5), e0007392. https://doi.org/10.1371/journal.pntd.0007392 (2019) (PMID: 31107905).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Schwabl, P. et al. Meiotic sex in Chagas disease parasite Trypanosoma cruzi. Nat. Commun. 10(1), 3972. https://doi.org/10.1038/s41467-019-11771-z (2019) (PMID: 31481692).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Falla, A. et al. Haplotype identification within Trypanosoma cruzi I in Colombian isolates from several reservoirs, vectors and humans. Acta Trop. 110(1), 15–21 (2009) (PMID: 19135020).CAS 
    Article 

    Google Scholar 
    28.Cura, C. I. et al. Trypanosoma cruzi I genotypes in different geographical regions and transmission cycles based on a microsatellite motif of the intergenic spacer of spliced-leader genes. Int. J. Parasitol. 40(14), 1599–1607. https://doi.org/10.1016/j.ijpara.2010.06.006 (2010) (PMID: 20670628).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Rodriguez, I. B. et al. Transmission dynamics of Trypanosoma cruzi determined by low-stringency single primer polymerase chain reaction and southern blot analyses in four indigenous communities of the Sierra Nevada de Santa Marta, Colombia. Am. J. Trop. Med. Hyg. 81(3), 396–403 (2009) (PMID: 19706903).CAS 
    Article 

    Google Scholar 
    30.Waleckx, E., Gourbière, S. & Dumonteil, E. Intrusive triatomines and the challenge of adapting vector control practices. Mem. Inst. Oswaldo Cruz. 110(3), 324–338 (2015).CAS 
    Article 

    Google Scholar 
    31.Dumonteil, E. et al. Detailed ecological associations of triatomines revealed by metabarcoding and next-generation sequencing: Implications for triatomine behavior and Trypanosoma cruzi transmission cycles. Sci. Rep. 8(1), 4140. https://doi.org/10.1038/s41598-018-22455-x (2018) (PMID: 29515202).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Dumonteil, E. et al. Interactions among Triatoma sanguisuga blood feeding sources, gut microbiota and Trypanosoma cruzi diversity in southern Louisiana. Mol. Ecol. https://doi.org/10.1111/mec.15582 (2020) (PMID: 32749727).Article 
    PubMed 

    Google Scholar 
    33.O’Connor, O., Bosseno, M. F., Barnabe, C., Douzery, E. J. & Breniere, S. F. Genetic clustering of Trypanosoma cruzi I lineage evidenced by intergenic miniexon gene sequencing. Infect. Genet. Evol. 7(5), 587–593. https://doi.org/10.1016/j.meegid.2007.05.003 (2007) (PMID: 17553755).CAS 
    Article 
    PubMed 

    Google Scholar 
    34.Villanueva-Lizama, L., Teh-Poot, C., Majeau, A., Herrera, C. & Dumonteil, E. Molecular genotyping of Trypanosoma cruzi by next-generation sequencing of the mini-exon gene reveals infections with multiple parasite DTUs in Chagasic patients from Yucatan, Mexico. J. Inf. Dis. 219(12), 1980–1988 (2019).CAS 
    Article 

    Google Scholar 
    35.Parra-Henao, G., Angulo, V. M., Osorio, L. & Jaramillo, O. N. Geographic distribution and ecology of Triatoma dimidiata (Hemiptera: Reduviidae) in Colombia. J. Med. Entomol. 53(1), 122–129. https://doi.org/10.1093/jme/tjv163 (2016) (PMID: 26487247).Article 
    PubMed 

    Google Scholar 
    36.Angulo, V. M., Esteban, L. & Luna, K. P. Attalea butyracea proximas a las viviendas como posible fuente de infestacion domiciliaria por Rhodnius prolixus (Hemiptera: Reduviidae) en los Llanos Orientales de Colombia. Biomedica. 32(2), 277–285. https://doi.org/10.1590/S0120-41572012000300016 (2012) (PMID: 23242302).Article 
    PubMed 

    Google Scholar 
    37.Feliciangeli, M. D., Sanchez-Martin, M., Marrero, R., Davies, C. & Dujardin, J. P. Morphometric evidence for a possible role of Rhodnius prolixus from palm trees in house re-infestation in the State of Barinas (Venezuela). Acta Trop. 101(2), 169–177. https://doi.org/10.1016/j.actatropica.2006.12.010 (2007) (PMID: 17306204).Article 
    PubMed 

    Google Scholar 
    38.Fitzpatrick, S., Feliciangeli, M. D., Sanchez-Martin, M. J., Monteiro, F. A. & Miles, M. A. Molecular genetics reveal that silvatic Rhodnius prolixus do colonise rural houses. PLoS Negl. Trop. Dis. 2(4), e210. https://doi.org/10.1371/journal.pntd.0000210 (2008) (PMID: 18382605).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Lopez, G. & Moreno, J. Genetic variability and differentiation between populations of Rhodnius prolixus and R. pallescens, vectors of Chagas’ disease in Colombia. Mem. Inst. Oswaldo Cruz. 90, 353–357 (1995).CAS 
    Article 

    Google Scholar 
    40.Dumonteil, E. et al. Detailed ecological associations of triatomines revealed by metabarcoding based on next-generation sequencing: linking triatomine behavioral ecology and Trypanosoma cruzi transmission cycles. Sci. Rep. 8(1), 4140. https://doi.org/10.1038/s41598-018-22455-x (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Hernández-Andrade, A., Moo-Millan, J., Cigarroa-Toledo, N., Ramos-Ligonio, A., Herrera, C., Bucheton, B., et al. Metabarcoding: A powerful yet still underestimated approach for the comprehensive study of vector-borne pathogen transmission cycles and their dynamics. in Vector-Borne Diseases: Recent Developments in Epidemiology and Control (ed. Claborn, D.) 1–6. (Intechopen, 2020). https://doi.org/10.5772/intechopen.8311042.Flores-Ferrer, A., Waleckx, E., Rascalou, G., Dumonteil, E. & Gourbière, S. Trypanosoma cruzi transmission dynamics in a synanthropic and domesticated host community. PLoS Negl. Trop. Dis. 13(12), e0007902. https://doi.org/10.1371/journal.pntd.0007902 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Llewellyn, M. S. et al. Genome-scale multilocus microsatellite typing of Trypanosoma cruzi discrete typing unit I reveals phylogeographic structure and specific genotypes linked to human infection. PLoS Pathog. 5(5), e1000410. https://doi.org/10.1371/journal.ppat.1000410 (2009) (PMID: 19412340).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Herrera, C. et al. Genetic variability and phylogenetic relationships within Trypanosoma cruzi I isolated in Colombia based on Miniexon Gene Sequences. J. Parasitol. Res. https://doi.org/10.1155/2009/897364 (2009) (PMID: 20798881).Article 
    PubMed 

    Google Scholar 
    45.Zumaya-Estrada, F. A. et al. North American import? Charting the origins of an enigmatic Trypanosoma cruzi domestic genotype. Parasit. Vectors. 5, 226. https://doi.org/10.1186/1756-3305-5-226 (2012) (PMID: 23050833).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Montoya-Porras, L. M., Omar, T. C., Alzate, J. F., Moreno-Herrera, C. X. & Cadavid-Restrepo, G. E. 16S rRNA gene amplicon sequencing reveals dominance of Actinobacteria in Rhodnius pallescens compared to Triatoma maculata midgut microbiota in natural populations of vector insects from Colombia. Acta Trop. 178, 327–332. https://doi.org/10.1016/j.actatropica.2017.11.004 (2018) (PMID: 29154947).CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Kieran, T. J. et al. Regional biogeography of microbiota composition in the Chagas disease vector Rhodnius pallescens. Parasit. Vectors. 12(1), 504. https://doi.org/10.1186/s13071-019-3761-8 (2019) (PMID: 31665056).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Rodriguez-Ruano, S. M. et al. Microbiomes of North American Triatominae: The grounds for Chagas Disease epidemiology. Front. Microbiol. 9, 1167. https://doi.org/10.3389/fmicb.2018.01167 (2018) (PMID: 29951039).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Eichler, S. & Schaub, G. A. Development of symbionts in triatomine bugs and the effects of infections with trypanosomatids. Exp. Parasitol. 100(1), 17–27 (2002).CAS 
    Article 

    Google Scholar 
    50.Waltmann, A. et al. Hindgut microbiota in laboratory-reared and wild Triatoma infestans. PLoS Negl. Trop. Dis. 13(5), e0007383. https://doi.org/10.1371/journal.pntd.0007383 (2019) (PMID: 31059501).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Herren, J. K. et al. A microsporidian impairs Plasmodium falciparum transmission in Anopheles arabiensis mosquitoes. Nat. Commun. 11(1), 2187. https://doi.org/10.1038/s41467-020-16121-y (2020) (PMID: 32366903).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Moreira, L. A. et al. A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139(7), 1268–1278. https://doi.org/10.1016/j.cell.2009.11.042 (2009) (PMID: 20064373).Article 
    PubMed 

    Google Scholar 
    53.Angulo, V. M. & Esteban, L. Nueva trampa para la captura de triatominos en habitats silvestres y peridomesticos. Biomedica. 31(2), 264–268. https://doi.org/10.1590/S0120-41572011000200015 (2011) (PMID: 22159544).Article 
    PubMed 

    Google Scholar 
    54.Lent, H. & Wygodzinsky, P. Revision of Triatominae (Hemiptera: Reduviidae), and their significance as vectors of Chagas’ disease. Bull. Am. Mus. Nat. His. 163, 123–520 (1979).
    Google Scholar 
    55.Monteiro, F. A. et al. Molecular phylogeography of the Amazonian Chagas disease vectors Rhodnius prolixus and R. robustus. Mol. Ecol. 12(4), 997–1006. https://doi.org/10.1046/j.1365-294x.2003.01802.x (2003) (PMID: 12753218).CAS 
    Article 
    PubMed 

    Google Scholar 
    56.Baker, G. C., Smith, J. J. & Cowan, D. A. Review and reanalysis of domain-specific 16s primers. J. Microbiol. Meth. 55, 541–555 (2003).CAS 
    Article 

    Google Scholar 
    57.Heuer, H., Krsek, M., Baker, P., Smalla, K. & Wellington, E. M. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl. Environ. Microbiol. 63(8), 3233–3241 (1997).CAS 
    Article 

    Google Scholar 
    58.Souto, R. P., Fernandes, O., Macedo, A. M., Campbell, D. A. & Zingales, B. DNA markers define two major phylogenetic lineages of Trypanosoma cruzi. Mol. Biochem. Parasitol. 83(2), 141–152 (1996) (PMID: 9027747).CAS 
    Article 

    Google Scholar 
    59.Majeau, A., Herrera, C. & Dumonteil, E. An improved approach to Trypanosoma cruzi molecular genotyping by next-generation sequencing of the mini-exon gene. Methods Mol. Biol. 1955, 47–60 (2019).CAS 
    Article 

    Google Scholar 
    60.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2200. https://doi.org/10.1093/bioinformatics/btr381 (2011) (PMID: 21700674).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint. (arXiv:1207.3907 [q-bio.GN]), 1–9. https://arxiv.org/abs/1207.3907v2 (2012).62.Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45(W1), W180–W188. https://doi.org/10.1093/nar/gkx295 (2017) (PMID: 28449106).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5(3), e9490. https://doi.org/10.1371/journal.pone.0009490 (2010) (PMID: 20224823).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 15(4), e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019) (PMID: 30958812).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Torres-Silva, C. F. et al. Assessment of genetic mutation frequency induced by oxidative stress in Trypanosoma cruzi. Genet Mol Biol. 41(2), 466–474. https://doi.org/10.1590/1678-4685-GMB-2017-0281 (2018) (PMID: 30088612).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 9 (2001).
    Google Scholar 
    67.Cole, J. R. et al. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42(Database issue), D633–D642. https://doi.org/10.1093/nar/gkt1244 (2014) (PMID: 24288368).CAS 
    Article 
    PubMed 

    Google Scholar  More