Philippa Kaur

1.Dynowska, M., Meissner, W. & Pacyńska, J. Mallard duck (Anas platyrhynchos) as a potential link in the epidemiological chain mycoses originating from water reservoirs. Bull. Vet. Inst. Pulawy 57, 323–328 (2013).
Google Scholar 
2.Georgopoulou, I. & Tsiouris, V. The potential role of migratory birds in the transmission of zoonoses. Vet. Ital. 44, 671–677 (2008).PubMed 

Google Scholar 
3.Hubálek, Z. An annotated checklist of pathogenic microorganisms associated with migratory birds. J. Wildl. Dis. 40, 639–659 (2004).PubMed 

Google Scholar 
4.Korniłłowicz, T. K. I. Diversity of fungi in nests and pellets of Montagu’s harrier (Circus pygargus) from eastern Poland—Importance of chemical and ecological factors. Ecol. Chem. Eng. 16, 453–471 (2009).
Google Scholar 
5.Korniłłowicz-Kowalska, T. & Kitowski, I. Aspergillus fumigatus and other thermophilic fungi in nests of wetland birds. Mycopathologia 175, 43–56 (2013).PubMed 

Google Scholar 
6.Kiziewicz B. The occurrence fungi and zoosporic fungi-like organisms on feathers of birds Corvidae. in Corvids of Poland (ed. Jerzak, L.). 147–154. (Bogucki Wydawnictwo Naukowe Poznan, 2005).7.Kasprzykowski, Z. Habitat preferences of foraging Rooks Corvus frugilegus during the breeding period in the agricultural landscape of eastern Poland. Acta Ornithol. 38, 27–31 (2003).
Google Scholar 
8.Czarnecka, J. & Kitowski, I. Seed dispersal by the rook Corvus frugilegus l. In agricultural landscape—Mechanisms and ecological importance. Polish J. Ecol. 58, 511–523 (2010).
Google Scholar 
9.Czarnecka, J. et al. Seed dispersal in urban green space—Does the rook Corvus frugilegus L. contribute to urban flora homogenization?. Urban For. Urban Green. 12, 359–366 (2013).
Google Scholar 
10.Gromadzka, J. Food composition and food consumption of the Rook Corvus frugilegus in agrocoenoses in Poland. Acta Ornithol. 17, 11 (1980).
Google Scholar 
11.Green, A. J., Elmberg, J. & Lovas-Kiss, Á. Beyond scatter-hoarding and frugivory: European corvids as overlooked vectors for a broad range of plants. Front. Ecol. Evolut. 7, 133 (2019).
Google Scholar 
12.Jędrzejewski, S., Majewska, A., Zduniak, P. & Graczyk, T. Parasites of Polish corvids—Knowledge and potential risk for human. in Corvids of Poland (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 137–145. (Bogucki Wydawnictwo Naukowe, 2005).13.Kiziewicz, B. The occurrenceof fungy and zoosporic fungus like organisms on feathers of birds Corvids. in Corvids in Poland. (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 147–154. (Bogucki Wydawnictwo Naukowe, 2005).14.Camin, A. M., Chabasse, D. & Guiguen, C. Keratinophilic fungi associated with starlings (Sturnus vulgaris) in Brittany, France. Mycopathologia 143, 9–12 (1998).
Google Scholar 
15.Hubálek, Z. Keratinophilic fungi associated with free-living mammals and birds. Biol. Dermatophytes Keratinophilic Fungi 93, 1036 (2000).
Google Scholar 
16.Mandeel, Q., Nardoni, S. & Mancianti, F. Keratinophilic fungi on feathers of common clinically healthy birds in Bahrain. Mycoses 54, 71–77 (2011).PubMed 

Google Scholar 
17.Ciesielska, A., Kawa, A., Kanarek, K., Soboń, A. & Szewczyk, R. Metabolomic analysis of Trichophyton rubrum and Microsporum canis during keratin degradation. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
18.Leibner-Ciszak, J., Dobrowolska, A., Krawczyk, B., Kaszuba, A. & Sta̧czek, P. Evaluation of a PCR melting profile method for intraspecies differentiation of Trichophyton rubrum and Trichophyton interdigitale. J. Med. Microbiol. 59, 185–192 (2010).19.Ciesielska, A., Oleksak, B. & Stączek, P. Reference genes for accurate evaluation of expression levels in Trichophyton interdigitale grown under different carbon sources, pH levels and phosphate levels. Sci. Rep. 9, 1–9 (2019).CAS 

Google Scholar 
20.Calvo, A., Vidal, M. & Guarro, J. Keratinophilic fungi from urban soils of Barcelona, Spain. Mycopathologia 85, 145–147 (1984).
Google Scholar 
21.R.S/, C. Taxonomy of the Onygenales: Arthrodermataceae, Gymnoasceae, Myxotrichaceae and Onygenaceae. Mycotaxon 24, 1–216 (1985).22.Korniłłowicz-Kowalska, T. Studies on the decomposition of keratin wastes by saprotrophic microfungi. P. I. Criteria for evaluating keratinolytic activity. Acta Mycol. 175, 43–56 (1997).
Google Scholar 
23.van Oorschot, C. A. N. A revision of Chrysosporium and allied genera. Stud. Mycol. 20, 1–89 (1980).
Google Scholar 
24.Domsch, K. H. & Gams, W. A. T. H. Compedium of Soil Fungi (Academic, 1980).
Google Scholar 
25.Gan, G. G. et al. Non-sporulating Chrysosporium: An opportunistic fungal infection in a neutropenic patient. Med. J. Malaysia 57, 118–122 (2002).CAS 
PubMed 

Google Scholar 
26.de Hoog, G. S., Guarro, J. & Gene, J. Atlas of clinical fungi. Int. Microbiol 2, 51–52 (2001).
Google Scholar 
27.Manzano-Gayosso, P. et al. Onychomycosis incidence in type 2 diabetes mellitus patients. Mycopathologia 166, 41–45 (2008).PubMed 

Google Scholar 
28.Palma, M. A. G., Espín, L. A. & Pérez, A. F. Invasine sinusal mycosis due to Chrysosporium tropicum. Acta Otorrinolaringol. Esp. 58, 164–166 (2007).
Google Scholar 
29.Stillwell, W. T. & Rubin, B. O. Chrysosporium, a new causative agent in osteomycelitis. Clin. Orthopaed. Relat. Res. 184, 190–192 (1984).
Google Scholar 
30.Gueho, E. V. J. G. R. A new human case of Anixiopsis stercomia mycosis: Discussion of its taxonomy and pathogenicity. Mycoses 28, 430–436 (1985).CAS 

Google Scholar 
31.Nieuwenhuis, B. P. S. & James, T. Y. The frequency of sex in fungi. Philos. Trans. R. Soc. B Biol. Sci. 371, 0540 (2016).
Google Scholar 
32.Neubauer, G. & Sikora, A. C. T. Monitoring populacji ptaków Polski w latach 2008–2009. Biuletyn Monitoringu Przyrody 8, 1–40 (2011).
Google Scholar 
33.Jackson, C. J., Barton, R. C. & Evans, E. G. V. Species identification and strain differentiation of dermatophyte fungi by analysis of ribosomal-DNA intergenic spacer regions. J. Clin. Microbiol. 37, 931–936 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Mochizuki, T. et al. Restriction fragment length polymorphism analysis of ribosomal DNA intergenic regions is useful for differentiating strains of Trichophyton mentagrophytes. J. Clin. Microbiol. 41, 4583–4588 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Garg, A. P., Gandotra, S., Mukerji, K. G. & Pugh, G. J. F. Ecology of keratinophilic fungi. Proc. Plant Sci. 94, 149–163 (1985).
Google Scholar 
36.Abulreesh, H. H., Goulder, R. & Scott, G. W. Wild birds and human pathogens in the context of ringing and migration. Ringing Migr. 23, 193–200 (2007).
Google Scholar 
37.Prinzinger, R., Preßmar, A. & Schleucher, E. Body temperature in birds. Comp. Biochem. Physiol. Part A Physiol. 99, 499–506 (1991).
Google Scholar 
38.Summerbell, R. C. Form and function in the evolution of dermatophytes. Rev. Iberoam. Micol. 44, 30–43 (2000).
Google Scholar 
39.Warwick, A., Ferrieri, P., Burke, B. & Blazar, B. R. Presumptive invasive Chrysosporium infection in a bone marrow transplant recipient. Bone Marrow Transplant 8, 319–322 (1991).CAS 
PubMed 

Google Scholar 
40.Kitowski, I., Ciesielska, A., Korniłłowicz-Kowalska, T., Bohacz, J., & Świetlicki, M. Estimation of Chrysosporium keratinophilum Dispersal by the Rook Corvus frugilegus in Chełm (East Poland) in Urban Fauna-Animal, Man, and the City—Interactions and Relationships. (Indykiewicz, P. & Böhner, J. eds). 263–269. (Art Studio, 2014)41.Gopal, K. A., Kalaivani, V. & Anandan, H. Pulmonary infection by Chrysosporium species in a preexisting tuberculous cavity. Int. J. Appl. Basic Med. Res. 10, 62 (2020).PubMed 
PubMed Central 

Google Scholar 
42.Krawczyk, B., Samet, A., Leibner, J., Śledzińska, A. & Kur, J. Evaluation of a PCR melting profile technique for bacterial strain differentiation. J. Clin. Microbiol. 44, 2327–2332 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Ciesielska, A. et al. Application of microsatellite-primed PCR (MSP-PCR) and PCR melting profile (PCR-MP) method for intraspecies differentiation of dermatophytes. Pol. J. Microbiol. 63, 283–290 (2014).PubMed 

Google Scholar 
44.Orłowski, G. & Czapulak, A. Different extinction risks of the breeding colonies of rooks Corvus frugilegus in rural and urban areas of SW Poland. Acta Ornithologica 42, 145–155 (2007).
Google Scholar 
45.Bohacz, J. & Korniłłowicz-Kowalska, T. Species diversity of keratinophilic fungi in various soil types. Cent. Eur. J. Biol. 7, 259–266 (2012).
Google Scholar 
46.Papini, R., Mancianti, F., Grassotti, G. & Cardini, G. Survey of keratinophilic fungi isolated from city park soils of Pisa, Italy. Mycopathologia 143, 17–23 (1998).CAS 
PubMed 

Google Scholar 
47.Singh, I. K. R. Dermatophytes and related keratinophilic fungi in soil of parks and agricultural fields of Uttar Pradesh, India. Indian J. Dermatol. 55, 306–308 (2010).PubMed 
PubMed Central 

Google Scholar 
48.Gungnani, H. C., Sharma, S. & Gupta, B. Keratinophilic fungi recovered from feathers of different species of birds in St Kitts and Nevis. West Indian Med. J. 61, 912–915 (2012).CAS 
PubMed 

Google Scholar 
49.Jadczyk P, J. Z. Wintering of rooks Corvus frugilegus in Poland. in Corvids of Poland (ed. Jerzak, L.). 541–556. (Bogucki Wydawnictwo Naukowe Poznan, 2005).50.Wilk, T., Chodkiewicz, T., Sikora, A., Chylarecki, P. & Kuczyński, L. Red List of Polish Birds. (OTOP, 2020).51.Oke, T.R. The heat island of the urban boundary layer: Characteristics, causes and effects. in eWind Climate in Cities. NATO ASI Series E (ed. JE, C.). 81–107. (Kluwer Academy, 1995).52.Vidal, P., de Vinuesa, M., Los, A., Sánchez-Puelles, J. M. & Guarro, J. Phylogeny of the anamorphic genus Chrysosporium and related taxa based on rDNA internal transcribed spacer sequences. Rev. Iberoam. Micol. 17, 22–29 (2000).
Google Scholar 
53.Korniłłowicz, T. Studies on mycoflora colonizing raw keratin wastes in arable soil. Mycologica 27, 231–245 (1991).
Google Scholar 
54.Orłowski, G., Kasprzykowski, Z., Zawada, Z. & Kopij, G. Stomach content and grit ingestion by rook Corvus frugilegus nestlings. Ornis Fennica 86, 117–122 (2009).
Google Scholar 
55.Luniak, M. Consumption and digestion of food in the rook, Corvus frugilegus, in the condition of an aviary. Acta Ornithol. 16, 213–234 (1977).
Google Scholar 
56.Liu, D., Coloe, S., Baird, R. & Pedersen, J. Rapid mini-preparation of fungal DNA for PCR [5]. J. Clin. Microbiol. 38, 471 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Hunter, P. R. & Gaston, M. A. Numerical index of the discriminatory ability of typing systems: An application of Simpson’s index of diversity. J. Clin. Microbiol. 26, 2465–2466 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Greenwell, J. R. Introduction to biostatistics, 2nd edn. By R. R. Sokal and F. J. Rohlf. pp. 363. F. H. Freeman and Co., 1987. £44.99 hardback. ISBN 0 7167 18057. Exp. Physiol. 80, 681 (1995) More

1.Reiter, P. & Sprenger, D. The used tire trade: a mechanism for the worldwide dispersal of container breeding mosquitoes. J. Am. Mosq. Control Assoc. 3, 494–501 (1987).CAS 
PubMed 

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

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

Google Scholar 
4.Bonizzoni, M., Gasperi, G., Chen, X. & James, A. A. The invasive mosquito species Aedes albopictus: Current knowledge and future perspectives. Trends Parasitol. 29, 460–468 (2013).PubMed 
PubMed Central 

Google Scholar 
5.Sprenger, D. & Wuithiranyagool, T. The discovery and distribution of Aedes albopictus in Harris County, Texas. J. Am. Mosq. Control Assoc. 2, 217–219 (1986).CAS 
PubMed 

Google Scholar 
6.Yee, D. A. Thirty years of Aedes albopictus (Diptera: Culicidae) in America: An introduction to current perspectives and future challenges. J. Med. Entomol. 53, 989–991 (2016).PubMed 

Google Scholar 
7.Hahn, M. B. et al. Reported Distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995–2016 (Diptera: Culicidae). J. Med. Entomol. 53, 1169–1175 (2016).PubMed 

Google Scholar 
8.Hahn, M. B. et al. Reported distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995–2016. J. Med. Entomol. 54, 1420–1424 (2017).PubMed 
PubMed Central 

Google Scholar 
9.Egizi, A., Healy, S. P. & Fonseca, D. M. Rapid blood meal scoring in anthropophilic Aedes albopictus and application of PCR blocking to avoid pseudogenes. Infect. Genet. Evol. 16, 122–128 (2013).CAS 
PubMed 

Google Scholar 
10.Paupy, C., Delatte, H., Bagny, L., Corbel, V. & Fontenille, D. Aedes albopictus, an arbovirus vector: From the darkness to the light. Microbes Infect. 11, 1177–1185 (2009).CAS 
PubMed 

Google Scholar 
11.Grard, G. et al. Zika virus in Gabon (Central Africa)—2007: A new threat from Aedes albopictus?. PLoS Negl. Trop. Dis. 8, e2681 (2014).PubMed 
PubMed Central 

Google Scholar 
12.McKenzie, B. A., Wilson, A. E. & Zohdy, S. Aedes albopictus is a competent vector of Zika virus: A meta-analysis. PLoS ONE 14, e0216794 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Claborn, D. M., Poiry, M., Famutimi, O. D., Duitsman, D. & Thompson, K. R. A survey of mosquitoes in southern and western missouri. J. Am. Mosq. Control Assoc. 34, 131–133 (2018).CAS 
PubMed 

Google Scholar 
14.Janousek, T. E., Plagge, J. & Kramer, W. L. Record of Aedes albopictus in Nebraska with notes on its biology. J. Am. Mosq. Cont. Control Assoc. 17, 265–267 (2001).CAS 

Google Scholar 
15.Richards, T. et al. First detection of Aedes albopictus (Diptera: Culicidae) and expansion of Aedes japonicus japonicus in Wisconsin, United States. J. Med. Entomol. 56, 291–296 (2019).PubMed 

Google Scholar 
16.Stone, C. M. et al. Spatial, temporal, and genetic invasion dynamics of Aedes albopictus (Diptera: Culicidae) in Illinois. J. Med. Entomol. 57, 1488–1500 (2020).CAS 
PubMed 

Google Scholar 
17.Dunphy, B. M., Rowley, W. A. & Bartholomay, L. C. A taxonomic checklist of the mosquitoes of Iowa. J. Am. Mosq. Control Assoc. 30, 119–121 (2014).PubMed 

Google Scholar 
18.Johnson, T. L. et al. Modeling the environmental suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the contiguous United States. J. Med. Entomol. 54, 1605–1614 (2017).PubMed 

Google Scholar 
19.Kovach, K. B. & Smith, R. C. Surveillance of mosquitoes (Diptera: Culicidae) in southern Iowa, 2016. J. Med. Entomol. 55, 1341–1345 (2018).PubMed 

Google Scholar 
20.Braks, M. A. H., Honório, N. A., Lourenço-De-Oliveira, R., Juliano, S. A. & Lounibos, L. P. Convergent habitat segregation of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in southeastern Brazil and Florida. J. Med. Entomol. 40, 785–794 (2003).PubMed 

Google Scholar 
21.Delatte, H. et al. Evidence of habitat structuring Aedes albopictus populations in Réunion Island. PLoS Negl. Trop. Dis. 7, e2111 (2013).PubMed 
PubMed Central 

Google Scholar 
22.Zhong, D. et al. Genetic analysis of invasive Aedes albopictus populations in Los Angeles County, California and its potential public health impact. PLoS ONE 8, e68586 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Lee, E. J. et al. Geographical genetic variation and sources of Korean Aedes albopictus (Diptera: Culicidae) populations. J. Med. Entomol. 57, 1057–1068 (2020).CAS 
PubMed 

Google Scholar 
24.Nawrocki, S. J. & Hawley, W. A. Estimation of the northern limits of distribution of Aedes albopictus in North America. J. Am. Mosq. Control Assoc. 3, 314–317 (1987).CAS 
PubMed 

Google Scholar 
25.Moore, C. G. Aedes albopictus in the United States: Current status and prospects for further spread. J. Am. Mosq. Control Assoc. 15, 221–227 (1999).CAS 
PubMed 

Google Scholar 
26.Armstrong, P. M., Andreadis, T. G., Shepard, J. J. & Thomas, M. C. Northern range expansion of the Asian tiger mosquito (Aedes albopictus): Analysis of mosquito data from Connecticut, USA. PLoS Negl. Trop. Dis. 11, e0005623 (2017).PubMed 
PubMed Central 

Google Scholar 
27.Rochlin, I., Ninivaggi, D. V., Hutchinson, M. L. & Farajollahi, A. Climate change and range expansion of the Asian tiger mosquito (Aedes albopictus) in Northeastern USA: Implications for public health practitioners. PLoS ONE 8, e60874 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Hanson, S. M. & Craig, G. B. Aedes albopictus (Diptera: Culicidae) eggs: Field survivorship during northern Indiana winters. J. Med. Entomol. 32, 599–604 (1995).CAS 
PubMed 

Google Scholar 
29.Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).ADS 
CAS 
PubMed 

Google Scholar 
30.Yang, J. & Bou-Zeid, E. Should cities embrace their heat islands as shields from extreme cold?. J. Appl. Meteorol. Climatol. 57, 1309–1320 (2018).ADS 

Google Scholar 
31.Macintyre, H. L., Heaviside, C., Cai, X. & Phalkey, R. Comparing temperature-related mortality impacts of cool roofs in winter and summer in a highly urbanized European region for present and future climate. Environ. Int. 154, 106606 (2021).PubMed 
PubMed Central 

Google Scholar 
32.Ward, T. B. Influence of an Urban Heat Island on Mosquito Development and Survey of Biting Midge Species Associated with White-Tailed Deer Farms (Oklahoma State University, 2011).
Google Scholar 
33.Dunphy, B. M. et al. Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus. Sci. Rep. 9, 6637 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
34.Paupy, C., Girod, R., Salvan, M., Rodhain, F. & Failloux, A. B. Population structure of Aedes albopictus from La Réunion Island (Indian Ocean) with respect to susceptibility to a dengue virus. Heredity 87, 273–283 (2001).CAS 
PubMed 

Google Scholar 
35.Vazeille, M. et al. Population genetic structure and competence as a vector for dengue type 2 virus of Aedes aegypti and Aedes albopictus from Madagascar. Am. J. Trop. Med. Hyg. 65, 491–497 (2001).CAS 
PubMed 

Google Scholar 
36.Chouin-Carneiro, T. et al. Differential susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika virus. PLoS Negl. Trop. Dis. 10, e0004543 (2016).PubMed 
PubMed Central 

Google Scholar 
37.Faraji, A. & Unlu, I. The eye of the tiger, the thrill of the fight: Effective larval and adult control measures against the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae), North America. J. Med. Entomol. 53, 1029–1047 (2016).PubMed 

Google Scholar 
38.Lambrechts, L., Scott, T. W. & Gubler, D. J. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Negl. Trop. Dis. 4, e646 (2010).PubMed 
PubMed Central 

Google Scholar 
39.Vega-Rua, A., Zouache, K., Girod, R., Failloux, A.-B. & Lourenco-de-Oliveira, R. High level of vector competence of Aedes aegypti and Aedes albopictus from ten American countries as a crucial factor in the spread of chikungunya virus. J. Virol. 88, 6294–6306 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Vega-Rúa, A. et al. Chikungunya virus transmission potential by local Aedes mosquitoes in the Americas and Europe. PLoS Negl. Trop. Dis. 9, e0003780 (2015).PubMed 
PubMed Central 

Google Scholar 
41.Fikrig, K. & Harrington, L. C. Understanding and interpreting mosquito blood feeding studies: The case of Aedes albopictus. Trends Parasitol. 37, 959–975 (2021).CAS 
PubMed 

Google Scholar 
42.Gerhardt, R. R. et al. First isolation of La Crosse virus from naturally infected Aedes albopictus. Emerg. Infect. Dis. 7, 807–811 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Sardelis, M. R., Turell, M. J., O’Guinn, M. L., Andre, R. G. & Roberts, D. R. Vector competence of three North American strains of Aedes albopictus for West Nile virus. J. Am. Mosq. Control Assoc. 18, 284–289 (2002).PubMed 

Google Scholar 
44.Eiras, A. E., Buhagiar, T. S. & Ritchie, S. A. Development of the gravid Aedes trap for the capture of adult female container-exploiting mosquitoes (Diptera: Culicidae). J. Med. Entomol. 51, 200–209 (2014).PubMed 

Google Scholar 
45.Maciel-de-Freitas, R., Eiras, Á. E. & Lourenço-de-Oliveira, R. Field evaluation of effectiveness of the BG-Sentinel, a new trap for capturing adult Aedes aegypti (Diptera: Culicidae). Mem. Inst. Oswaldo Cruz 101, 321–325 (2006).PubMed 

Google Scholar 
46.Farajollahi, A. et al. Field efficacy of BG-Sentinel and industry-standard traps for Aedes albopictus (Diptera: Culicidae) and West Nile virus surveillance. J. Med. Entomol. 46, 919–925 (2009).PubMed 

Google Scholar 
47.Meeraus, W. H., Armistead, J. S. & Arias, J. R. Field comparison of novel and gold standard traps for collecting Aedes albopictus in Northern Virginia. J. Am. Mosq. Control Assoc. 24, 244–248 (2008).PubMed 

Google Scholar 
48.Johnson, B. J. et al. Field comparisons of the Gravid Aedes Trap (GAT) and BG-Sentinel Trap for Monitoring Aedes albopictus (Diptera: Culicidae) populations and notes on indoor GAT collections in Vietnam. J. Med. Entomol. 54, 340–348 (2018).
Google Scholar 
49.Darsie, R. & Ward, R. Identification and Geographical Distribution of the Mosquitoes of North America (North of Mexico. University Press of Florida, 2005).
Google Scholar 
50.Multi-Resolution Land Characteristics Consortium. NLCD 2016 Land Cover (CONUS).51.Bonnet, D. D. & Worcester, D. J. The dispersal of Aedes albopictus in the territory of Hawaii. Am. J. Trop. Med. Hyg. 26, 465–476 (1946).CAS 
PubMed 

Google Scholar 
52.Niebylski, M. L. & Craig, G. B. Dispersal and survival of Aedes albopictus at a scrap tire yard in Missouri. J. Am. Mosq. Control Assoc. 10, 339–343 (1994).CAS 
PubMed 

Google Scholar 
53.Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): A metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).
Google Scholar 
54.Post, R. J., Flook, P. K. & Millest, A. L. Methods for the preservation of insects for DNA studies. Biochem. Syst. Ecol. 21, 85–92 (1993).CAS 

Google Scholar 
55.Field, E. N., Gehrke, E. J., Ruden, R. M., Adelman, J. S. & Smith, R. C. An improved multiplex Polymerase Chain Reaction (PCR) assay for the identification of mosquito (Diptera: Culicidae) blood meals. J. Med. Entomol. 57, 557–562 (2020).CAS 
PubMed 

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

Google Scholar 
57.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
58.Ogden, R., Shuttleworth, C., McEwing, R. & Cesarini, S. Median-joining networks for inferring intraspecific phylogenies. Conserv. Genet. 6, 37–48 (2005).
Google Scholar  More

Evidence for biological and chemical isoprene consumption in coastal seawaterThe time course of isoprene concentration in coastal seawater samples incubated in closed glass bottles at the in situ temperature and in the dark demonstrated sustained loss for at least 45 h (Fig. 1a). Enclosure without headspace prevented isoprene loss by ventilation, and darkness was assumed to arrest all or most of the biological production25 and any photochemical production15 or degradation. Thus, the measured loss was considered the result of microbial degradation and chemical oxidation. In most cases an exponential function fitted better the decay than a linear function (Supplementary Table 1), indicating first-order (concentration-dependent) kinetics for isoprene loss.Fig. 1: Isoprene loss in dark incubations of coastal seawater.a Time course of isoprene concentration in 2 L dark incubations of non-filtered seawater samples from the back-reef lagoon of Mo’orea in April (blue) and the coastal Mediterranean in March (red) and May (green). Filled and open symbols correspond to duplicate incubations. Exponential fits to the data are shown by lines. See Supplementary Table 1 for fit equations and metrics, water temperatures and chlorophyll a concentrations. b Time course of isoprene concentration in series of 30 mL dark incubations of coastal Mediterranean seawater. Dark blue: non-filtered; red: filtered through 0.2 µm; green: filtered + 10 µmol L−1 H2O2; purple: filtered + 0.0025 units mL−1 bromoperoxidase (BrPO); light blue: filtered + H2O2 + BrPO. Exponential fit results in Supplementary Table 2.Full size imageIncubation of microorganism-devoid (filtered through 0.2 µm) coastal seawater sampled next to seaweeds showed an isoprene loss (0.12 d−1) that was half the loss in non-filtered water (0.20 d−1; Fig. 1b and Supplementary Table 2), implying that chemical oxidation accounted for half the total loss. Oxidation by OH·, the fastest amongst isoprene reactions with oxidative transients for which reaction rate data exist19, could account for the observed chemical loss. However, the possibility of oxidation by hitherto overlooked, pervasive oxidants like H2O2 deserved consideration. The addition of unrealistically high concentrations of either H2O2 or the enzyme bromoperoxidase (BrPO), substantially speeded up the chemical loss (0.91 d−1 with 10 µmol H2O2 L−1, 0.31 d−1 with 0.0025 units BrPO mL−1; Fig. 1b and Supplementary Table 2). Isoprene could have reacted with H2O2 in seawater as it does in acidic aerosols26. Besides, should dissolved27 BrPOs from seaweeds or outer-membrane-bound28 BrPOs from phytoplankton occur, they would have reacted with added H2O2 to produce hypobromous acid (HOBr), a strong oxidant29 that would further remove isoprene. Indeed, the addition of BrPO consumed isoprene because it produced HOBr by reaction with the naturally occurring H2O2. Confirming this interpretation, large HOBr production by simultaneous addition of BrPO and H2O2 caused complete isoprene removal in less than 4 h (Fig. 1b). Therefore, the results shown in Fig. 1b indicate that isoprene is reactive to pervasive H2O2 either directly or through the formation of enzymatically derived HOBr. All in all, first-order total isoprene loss (Fig. 1a) is expected to depend on photochemically-produced oxidants30 like H2O2, OH· and 1O2 as well as on microbiota through (a) microbial uptake and catabolism11 and (b) reaction with biologically produced oxidants26,31,32 like HOBr, H2O2 or superoxide.Variability of isoprene loss rate constants in the open oceanTen of the eleven offshore experimental sites were located in the open ocean, and one was located on the Southwestern Atlantic Shelf. Altogether they covered wide ranges of latitude (40°N–61°S), sea surface temperature (−0.8–28.6 °C), daily-averaged wind speed (3–12 m s−1), fluorometric chlorophyll-a (chla) concentration (0.1–5.8 mg m−3), and isoprene concentration (4–104 nmol m−3) (Fig. 2, Table 1 and Supplementary Table 3). Unfiltered seawater samples from the surface ocean were incubated in glass bottles for 24 h, at the in situ temperature and in the dark, and first-order loss rate constants were determined from initial and final isoprene concentrations (see Methods). Note that loss was determined under the assumption that isoprene production was arrested in the dark25. There is published evidence that residual isoprene production may occur in the dark33, but in our incubations, it was insufficient to counteract loss. Thus, isoprene losses caused by processes other than ventilation may have been underestimated.Fig. 2: Geographical distribution of the offshore experiments.Location of the sampling and incubation sites are shown by circles, coloured for isoprene concentration.Full size imageTable 1 Measured biological variables and isoprene process rate constants.Full size tableLoss rate constants (kloss = kbio + kchem) varied over an order of magnitude, ranging 0.03–0.64 d−1 with a median of 0.08 d−1 (Table 1). They did not show any significant relationship to sea surface temperature (SST) (Supplementary Fig. 1) but showed proportionality to the chla concentration (Fig. 3a) that was best described by the following linear regression equation:$${k}_{{{{{{rm{loss}}}}}}}=0.10; (pm 0.01),{{{{{rm{x}}}}}}, [{{{{{rm{chl}}}}}}a]+0.05; (pm 0.01)$$
(1)
Fig. 3: Isoprene processes and their main drivers.a Rate constant of isoprene loss in dark incubations (kloss, considered to be microbial and chemical consumption) vs. chlorophyll-a concentration. The linear regression equation is kloss = 0.10 × [chla] + 0.05 (R2 = 0.96, p = 10−7, n = 11). The standard error of the slope is 0.01 L mg−1 d−1, and the standard error of the intercept is 0.01 d−1. Error bars represent the experimentally determined standard error of kloss. The colour scale of the circles indicates bacterial abundances. b Specific (chla-normalised) rate of isoprene production vs seawater temperature (SST) across the sample series. The dashed line is the general smoothed trend. The blue line is the exponential adjustment at SST , 1000)$$
(2)
Substitution in Eq. (1) results in:$${k}_{{{{{{rm{loss}}}}}}}=0.14,{{{{{rm{x}}}}}}, {[{{{{{rm{chl}}}}}}{a}_{{{{{{rm{sat}}}}}}}]}^{1.28}+0.05$$
(3)
which is our recommended equation for kloss prediction from satellite chla. Note that only the variable term (kbio) changes from Eq. (1), while the intercept (kchem) is maintained at 0.05 d−1.Comparison of isoprene sinks and total turnover timeThe change of isoprene concentration ([iso]) in the surface mixed layer over time can be described as the budget of sources and sinks:$$varDelta [{{{{{rm{iso}}}}}}]/varDelta {{{{{rm{t}}}}}}=[{{{{{rm{iso}}}}}}]cdot ({k}_{{{{{{rm{prod}}}}}}} – {k}_{{{{{{rm{loss}}}}}}} – {k}_{{{{{{rm{vent}}}}}}} – {k}_{{{{{{rm{mix}}}}}}})$$
(4)
where kprod, kvent and kmix are the rate constants of isoprene production, ventilation to the atmosphere and vertical downward mixing by turbulent diffusion, respectively.We calculated kvent from our sampling sites over a period of 24 h (Table 1). Ventilation has been considered the main isoprene sink from the upper mixed layer of the ocean18. In our sampling sites, kloss was 0.4 to 10 times the kvent (median factor: 1.2). That is, loss through microbial + chemical consumption was of the same order as ventilation, sometimes considerably faster. Vertical mixing, kmix, was estimated to be one order of magnitude lower than the other process rates (Table 1), and in all cases but one it was calculated or assumed not to be a loss term but an import term into the mixed layer, because vertical profiles generally show maximum isoprene concentrations below the mixed layer and turbulent diffusion causes upward transport14,17. Altogether, the microbial, chemical, ventilation, and, where relevant, mixing losses resulted in total turnover times (1/(kloss + kvent + kmix)) of isoprene between 1.4 and 16 days, median 5 days (Table 1).Isoprene productionAssuming steady-state for isoprene concentrations over 24 h (Supplementary Fig. 2), i.e. Δ[iso]/Δt = 0 in Eq. (4), the sum of the daily rate constants of all sinks (kloss + kvent) equals the rate constant of isoprene production (kprod), with kmix adding to either side depending on whether it is an import to or an export from the mixed layer (Table 1). Note that kprod was the highest coinciding with higher [chla]. This is consistent with a recent study44 where measurement of the net biological isoprene production (i.e. production — consumption rates) across seasons in the open ocean was attempted; net production rates increased in May, coinciding with a large increase in [chla] and phytoplankton cell abundance.The product of kprod by the isoprene concentration gives the daily isoprene production rate, which can be normalised by dividing it by the chla concentration. In our study, this specific isoprene production rate varied between 1 and 38 nmol (mg chla)−1 d−1 (Table 1), median 8 nmol (mg chla)−1 d−1. These values are within the broad range reported across phytoplankton taxa from laboratory studies with monocultures41,45 (0.3–32, median 3 nmol (mg chla)−1 d−1, n = 124). Five of the eleven sites gave values >13 nmol (mg chla)−1 d−1, i.e. in the higher end of the laboratory data range. This is not unexpected, since measurements in monoculture experiments are typically conducted before reaching nutrient limitation, below light saturation and in the absence of UV radiation, to mention three stressors commonly occurring in the surface open ocean. If isoprene biosynthesis and release is enhanced by any of these stressors, as is the case in vascular plants7,10, then monoculture-derived results will easily render underestimates of isoprene production in the open ocean. Production by heterotrophic bacteria46 could have also contributed to increase apparent specific isoprene production rates, but the occurrence and importance of this process in the marine environment is unknown.When plotted against the SST, which was also the temperature of the incubations, specific isoprene production rates increased exponentially between −0.8 and 23 °C and dropped drastically at higher SST (Fig. 3b). Several studies with phytoplankton monocultures have reported positive dependence of specific isoprene production rates on temperature45,47,48,49,50. One of these studies45 described that the increase with temperature reaches an optimum for production that varies among phytoplankton strains and with light intensity, but falls around 23–26 °C. The most detailed study47 was conducted with a Prochlorococcus strain; remarkably, the shape of the specific production rate vs. temperature curve for this cyanobacterium strain was almost identical to that of Fig. 3b, with an exponential increase until 23 °C and a drop thereafter. This is the canonical curve type of enzymatic activities, but the thermal behaviour of the enzymes for isoprene synthesis in marine unicellular algae has not yet been characterised12.Revising the magnitude and players of the marine isoprene cycleOur results allow redrawing the isoprene cycle in the surface mixed layer of the ocean. Figure 4 sketches the magnitude of the rate constants for production and sinks presented in Table 1, averaged according to a chla concentration threshold: the blue and green arrows correspond to the experiments in waters with [chla] lower and higher than 0.4 mg m−3, respectively. Isoprene production in productive (chla-richer) waters is faster than in oligotrophic (chla-poorer) waters. Vertical mixing is assumed to majorly constitute an input into the mixed layer, yet very small. Photochemical production and emission from surfactants15 in the surface microlayer of productive waters is depicted as uncertain. Among sinks, the microbiota-dependent consumption is much faster in productive waters; actually, the statistical uncertainty of Eq. (1) and the uneven distribution of incubation results along the [chla] axis hamper resolving kbio in phytoplankton-poor waters ( More

1.Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 

Google Scholar 
2.Hellberg, M. E. Gene flow and isolation among populations of marine animals. Annu. Rev. Ecol. Evol. Syst. 40, 291–310 (2009).
Google Scholar 
3.Selkoe, K. A. et al. Taking the chaos out of genetic patchiness: Seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species. Mol. Ecol. 19, 3708–3726 (2010).PubMed 

Google Scholar 
4.Guo, X. et al. Phylogeography of the rock shell Thais clavigera (Mollusca): Evidence for long-distance dispersal in the Northwestern Pacific. PLoS ONE 10, e0129715 (2015).PubMed 
PubMed Central 

Google Scholar 
5.Hoffman, J. I., Clarke, A., Linse, K. & Peck, L. S. Effects of brooding and broadcasting reproductive modes on the population genetic structure of two Antarctic gastropod molluscs. Mar. Biol. 158, 287–296 (2011).
Google Scholar 
6.Modica, M. V., Russini, V., Fassio, G. & Oliverio, M. Do larval types affect genetic connectivity at sea? Testing hypothesis in two sibling marine gastropods with contrasting larval development. Mar. Environ. Res. 127, 92–101 (2017).CAS 
PubMed 

Google Scholar 
7.Je Lee, H. & Boulding, E. G. Spatial and temporal population genetic structure of four northeastern Pacific littorinid gastropods: The effect of mode of larval development on variation at one mitochondrial and two nuclear DNA markers. Mol. Ecol. 18, 2165–2184 (2009).
Google Scholar 
8.Barbosa, S. S., Klanten, S. O., Puritz, J. B., Toonen, R. J. & Byrne, M. Very fine-scale population genetic structure of sympatric asterinid sea stars with benthic and pelagic larvae: Influence of mating system and dispersal potential. Biol. J. Linn. Soc. 108, 821–833 (2013).
Google Scholar 
9.Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).PubMed 

Google Scholar 
10.Riginos, C., Buckley, Y. M., Blomberg, S. P. & Treml, E. A. Dispersal capacity predicts both population genetic structure and species richness in reef fishes. Am. Nat. 184, 52–64 (2014).PubMed 

Google Scholar 
11.Wort, E. J. G. et al. Contrasting genetic structure of sympatric congeneric gastropods: Do differences in habitat preference, abundance and distribution matter?. J. Biogeogr. 46, 369–380 (2019).
Google Scholar 
12.Ayre, D. J., Minchinton, T. E. & Perrin, C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier?. Mol. Ecol. 18, 1887–1903 (2009).CAS 
PubMed 

Google Scholar 
13.Meyer, C. P., Geller, J. B. & Paulay, G. Fine scale endemism on coral reefs: Archipelagic differentiation in turbinid gastropods. Evolution (N. Y.) 59, 113–125 (2005).
Google Scholar 
14.White, C. et al. Ocean currents help explain population genetic structure. Proc. R. Soc. B Biol. Sci. 277, 1685–1694 (2010).
Google Scholar 
15.Marko, P. B. ‘What’s larvae got to do with it?’ Disparate patterns of post-glacial population structure in two benthic marine gastropods with identical dispersal potential. Mol. Ecol. 13, 597–611 (2004).CAS 
PubMed 

Google Scholar 
16.Edmands, S. Phylogeography of the intertidal copepod Tigriopus californicus reveals substantially reduced population differentiation at northern latitudes. Mol. Ecol. 10, 1743–1750 (2001).CAS 
PubMed 

Google Scholar 
17.Ni, G., Li, Q., Kong, L. & Yu, H. Comparative phylogeography in marginal seas of the northwestern Pacific. Mol. Ecol. 23, 534–548 (2014).PubMed 

Google Scholar 
18.Vendrami, D. L. J. et al. RAD sequencing sheds new light on the genetic structure and local adaptation of European scallops and resolves their demographic histories. Sci. Rep. 9, 1–13 (2019).CAS 

Google Scholar 
19.Sandoval-Castillo, J., Robinson, N. A., Hart, A. M., Strain, L. W. S. & Beheregaray, L. B. Seascape genomics reveals adaptive divergence in a connected and commercially important mollusc, the greenlip abalone (Haliotis laevigata), along a longitudinal environmental gradient. Mol. Ecol. 27, 1603–1620 (2018).PubMed 

Google Scholar 
20.Hirai, J. Insights into reproductive isolation within the pelagic copepod Pleuromamma abdominalis with high genetic diversity using genome-wide SNP data. Mar. Biol. 167, 1–6 (2020).CAS 

Google Scholar 
21.Hosoya, S. et al. Random PCR-based genotyping by sequencing technology GRAS-Di (genotyping by random amplicon sequencing, direct) reveals genetic structure of mangrove fishes. Mol. Ecol. Resour. 19, 1153–1163 (2019).CAS 
PubMed 

Google Scholar 
22.Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature https://doi.org/10.1038/nature07893 (2009).Article 
PubMed 

Google Scholar 
23.Savolainen, V. et al. Sympatric speciation in palms on an oceanic island. Nature https://doi.org/10.1038/nature04566 (2006).Article 
PubMed 

Google Scholar 
24.Parent, C. E. & Crespi, B. J. Ecological opportunity in adaptive radiation of Galápagos endemic land snails. Am. Nat. https://doi.org/10.1086/646604 (2009).Article 
PubMed 

Google Scholar 
25.Chiba, S. & Cowie, R. H. Evolution and extinction of land snails on oceanic islands. Annu. Rev. Ecol. Evol. Syst. 47, 123–141 (2016).
Google Scholar 
26.Grant, P. R. & Grant, B. R. Unpredictable evolution in a 30-year study of Darwin’s finches. Science (80-.) 296, 707–711 (2002).CAS 
ADS 

Google Scholar 
27.Scheltema, R. The relevance of passive dispersal for the biogeography of Caribbean mollusks. Am. Malacol. Bull. 11, 95–115 (1995).
Google Scholar 
28.Bernardi, G. et al. Darwin’s fishes: Phylogeography of Galápagos Islands reef fishes. Bull. Mar. Sci. 90, 533–549 (2014).
Google Scholar 
29.Eble, J. A., Toonen, R. J. & Bowen, B. W. Endemism and dispersal: Comparative phylogeography of three surgeonfishes across the Hawaiian Archipelago. Mar. Biol. 156, 689–698 (2009).
Google Scholar 
30.Tomokuni, M. M. Aquatic and Semiaquatic Insects of the Bonin Islands (including the Volcano Islands). Mem. Natl. Sci. Museum (1978).31.Sugawara, T., Watanabe, K., Kato, H. & Yasuda, K. Dioecy in Wikstroemia pseudoretusa (Thymelaeaceae) endemic to the Bonin (Ogasawara) islands. APG Acta Phytotaxon. Geobot. https://doi.org/10.18942/apg.KJ00004622804 (2004).Article 

Google Scholar 
32.Chiba, S. Species diversity and conservation of Mandarina, an endemic land snail of the Ogasawara Islands. In Restoring the Oceanic Island Ecosystem: Impact and Management of Invasive Alien Species in the Bonin Islands (eds Kawakami, K. & Okochi, I.) 117–125 (Springer, 2010). https://doi.org/10.1007/978-4-431-53859-2_18.Chapter 

Google Scholar 
33.Mukai, T., Nakamura, S., Suzuki, T. & Nishida, M. Mitochondrial DNA divergence in yoshinobori gobies (Rhinogobius species complex) between the Bonin Islands and the Japan-Ryukyu Archipelago. Ichthyol. Res. 52, 410–413 (2005).
Google Scholar 
34.Shih, H. T., Komai, T. & Liu, M. Y. A new species of fiddler crab from the Ogasawara (Bonin) Islands, Japan, separated from the widely-distributed sister species Uca (Paraleptuca) crassipes (White, 1847) (Crustacea: Decapoda: Brachyura: Ocypodidae). Zootaxa 3746, 175–193 (2013).PubMed 

Google Scholar 
35.Yamazaki, D. et al. Genetic diversification of intertidal gastropoda in an archipelago: The effects of islands, oceanic currents, and ecology. Mar. Biol. https://doi.org/10.1007/s00227-017-3207-9 (2017).Article 

Google Scholar 
36.Nakano, T., Takahashi, K. & Ozawa, T. Description of an endangered new species of Lunella (Gastropoda:Turbinidae) from the Ogasawara Islands, Japan. Venus J. Malacol. Soc. Japan 66, 1–10 (2007).
Google Scholar 
37.Nakano, T., Yazaki, I., Kurokawa, M., Yamaguchi, K. & Kuwasawa, K. The origin of the endemic patellogastropod limpets of the Ogasawara Islands in the northwestern Pacific. J. Molluscan Stud. 75, 87–90 (2009).
Google Scholar 
38.González-Wevar, C. A., Nakano, T., Palma, A. & Poulin, E. Biogeography in cellana (patellogastropoda, nacellidae) with special emphasis on the relationships of southern hemisphere oceanic island species. PLoS ONE 12, 1–16 (2017).
Google Scholar 
39.Tenggardjaja, K. A., Bowen, B. W. & Bernardi, G. Reef fish dispersal in the Hawaiian Archipelago: Comparative phylogeography of three endemic damselfishes. J. Mar. Sci. https://doi.org/10.1155/2016/3251814 (2016).Article 

Google Scholar 
40.Tenggardjaja, K. A., Bowen, B. W. & Bernardi, G. Comparative phylogeography of widespread and endemic damselfishes in the Hawaiian Archipelago. Mar. Biol. 165, 1–21 (2018).
Google Scholar 
41.Kurozumi, T. & Asakura, A. Marine molluscs from the northern Mariana Islands, Micronesia. Nat. Hist. Res. Spec. Issue 1, 121–168 (1994).
Google Scholar 
42.Nakano, D. & Makoto, N. Age structure and growth in a population of Monodonta labio (Linnaeus) at Shima Peninsula, Japan. Venus J. Malacol. Soc. Japan 40, 34–40 (1981).
Google Scholar 
43.Hashino, T. & Tomiyama, K. Life history of Monodonta labio confusa Tapprone-Canefri, 1874 in Kagoshima Bay, Kyushu, Japan and age estimation based on annual ring analysis of shell. Nat. Kagoshima 39, 143–155 (2013).
Google Scholar 
44.Yoh, A. & Sakurai, I. Reproductive cycle and food habits of the herbivorous snail Monodonta confusa off the coast of Suttsu Bay in southwestern Hokkaido, Japan. Proc. Sch. Biol. Sci. Tokai Univ. 6, 17–23 (2017).
Google Scholar 
45.Sasaki, R. Larval identification and occurrence of ezo abalone, Haliotis discus hannai, in the adjacent waters of Kesennuma Bay, Miyagi Prefecture. Suisan Zoushoku 32, 199–206 (1985).
Google Scholar 
46.Yamazaki, D., Miura, O., Uchida, S., Ikeda, M. & Chiba, S. Comparative seascape genetics of co-distributed intertidal snails Monodonta spp. in the Japanese and Ryukyu archipelagoes. Mar. Ecol. Prog. Ser. 657, 135–146 (2020).ADS 

Google Scholar 
47.Ballard, J. W. O. & Whitlock, M. C. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744 (2004).PubMed 

Google Scholar 
48.Parham, J. F. et al. Genetic introgression and hybridization in Antillean freshwater turtles (Trachemys) revealed by coalescent analyses of mitochondrial and cloned nuclear markers. Mol. Phylogenet. Evol. 67, 176–187 (2013).CAS 
PubMed 

Google Scholar 
49.Hirano, T. et al. Enigmatic incongruence between mtDNA and nDNA revealed by multi-locus phylogenomic analyses in freshwater snails. Sci. Rep. 9, 6223 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
50.Funk, D. J. & Omland, K. E. Species-level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annu. Rev. Ecol. Evol. Syst. 34, 397–423 (2003).
Google Scholar 
51.Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
PubMed 

Google Scholar 
52.Hirase, S. et al. Integrative genomic phylogeography reveals signs of mitonuclear incompatibility in a natural hybrid goby population. Evolution (N.Y.) 75, 176–194 (2021).
Google Scholar 
53.Zhao, D., Li, Q., Kong, L. & Yu, H. Cryptic diversity of marine gastropod Monodonta labio (Trochidae): Did the early Pleistocene glacial isolation and sea surface temperature gradient jointly drive diversification of sister species and/or subspecies in the Northwestern Pacific?. Mar. Ecol. https://doi.org/10.1111/maec.12443 (2017).Article 

Google Scholar 
54.Mukai, T., Nakamura, S. & Nishida, M. Genetic population structure of a reef goby, Bathygobius cocosensis, in the northwestern Pacific. Ichthyol. Res. 56, 380–387 (2009).
Google Scholar 
55.Keith, S. A., Herbert, R. J. H., Norton, P. A., Hawkins, S. J. & Newton, A. C. Individualistic species limitations of climate-induced range expansions generated by meso-scale dispersal barriers. Divers. Distrib. 17, 275–286 (2011).
Google Scholar 
56.Faurby, S. & Barber, P. H. Theoretical limits to the correlation between pelagic larval duration and population genetic structure. Mol. Ecol. 21, 3419–3432 (2012).PubMed 

Google Scholar 
57.Funk, W. C. et al. Adaptive divergence despite strong genetic drift: Genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Whiteley, A. R. et al. Genetic variation and effective population size in isolated populations of coastal cutthroat trout. Conserv. Genet. 11, 1929–1943 (2010).
Google Scholar 
59.Riginos, C., Douglas, K. E., Jin, Y., Shanahan, D. F. & Treml, E. A. Effects of geography and life history traits on genetic differentiation in benthic marine fishes. Ecography (Cop.) 34, 566–575 (2011).
Google Scholar 
60.Kuriiwa, K., Chiba, S. N., Motomura, H. & Matsuura, K. Phylogeography of Blacktip Grouper, Epinephelus fasciatus (Perciformes: Serranidae), and influence of the Kuroshio Current on cryptic lineages and genetic population structure. Ichthyol. Res. 61, 361–374 (2014).
Google Scholar 
61.Tachikawa, H. Nature profile of the isolated oceanic island, the Bonin Islands. Midoriishi 5, 27–29 (1994).
Google Scholar 
62.Setsuko, S. et al. Genetic diversity, structure, and demography of Pandanus boninensis (Pandanaceae) with sea drifted seeds, endemic to the Ogasawara Islands of Japan: Comparison between young and old islands. Mol. Ecol. 29, 1050–1068 (2020).PubMed 

Google Scholar 
63.Asakura, A. & Nishihama, S. Studies on the biology and ecology of the intertidal animals of Chichijima Island in the Ogasawara (Bonin) Islands III: Description, form and habitat of the trochid snail, Monodonta perplexa boninensis n. subsp. in comparison with those in Monodonta perpl. Venus J. Malacol. Soc. Japan 46, 194–201 (1987).
Google Scholar 
64.Nakano, T. & Minato, R. Marine organisms in the intertidal zone of Nishinoshima Island. Ogasawara Res. 46, 109–121 (2019).
Google Scholar 
65.Sasaki, T. & Horikoshi, K. Marine animals of Minami-Iw-To lsland. Ogasawara Res. 33, 155–171 (2008).
Google Scholar 
66.Williams, S., Apte, D., Ozawa, T., Kaligis, F. & Nakano, T. Speciation and dispersal along continental coastlines and island arcs in the indo-west pacific turbinid gastropod genus lunella. Evolution (N. Y.) 65, 1752–1771 (2011).
Google Scholar 
67.Siddall, M. et al. Sea-level fluctuations during the last glacial cycle. Nature 423, 853–858 (2003).CAS 
PubMed 
ADS 

Google Scholar 
68.Setsuko, S. et al. Genetic variation of pantropical Terminalia catappa plants with sea-drifted seeds in the Bonin Islands: Suggestions for transplantation guidelines. Plant Species Biol. 32, 13–24 (2017).
Google Scholar 
69.Hedgecock, D. Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates?. Bull. Mar. Sci. 39, 550–564 (1986).
Google Scholar 
70.Parsons, K. E. The genetic effects of larval dispersal depend on spatial scale and habitat characteristics. Mar. Biol. 126, 403–414 (1996).CAS 

Google Scholar 
71.Pechenik, J. A. On the advantages and disadvantages of larval stages in benthic marine invertebrate life cycles. Mar. Ecol. Prog. Ser. 177, 269–297 (1999).ADS 

Google Scholar 
72.Scheltema, R. S. Larval dispersal as a means of genetic exchange between geographically separated populations of shallow-water benthic marine gastropods. Biol. Bull. 140, 284–322 (1971).
Google Scholar 
73.Wright, L. I., Tregenza, T. & Hosken, D. J. Inbreeding, inbreeding depression and extinction. Conserv. Genet. 9, 833–843 (2008).
Google Scholar 
74.Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
75.Johannesson, K. The paradox of Rockall: Why is a brooding gastropod (Littorina saxatilis) more widespread than one having a planktonic larval dispersal stage (L. littorea)?. Mar. Biol. 99, 507–513 (1988).
Google Scholar 
76.Nakajima, Y., Nishikawa, A., Iguchi, A. & Sakai, K. Regional genetic differentiation among northern high-latitude island populations of a broadcast-spawning coral. Coral Reefs 31, 1125–1133 (2012).ADS 

Google Scholar 
77.Bowen, B. W. et al. Comparative phylogeography of the ocean planet. Proc. Natl. Acad. Sci. U. S. A. 113, 7962–7969 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Funk, W. C., McKay, J. K., Hohenlohe, P. A. & Allendorf, F. W. Harnessing genomics for delineating conservation units. Trends Ecol. Evol. 27, 489–496 (2012).PubMed 
PubMed Central 

Google Scholar 
79.Palumbi, S. R. Population genetics, demographic connectivity, and the design of marine reserves. Ecol. Appl. 13, 146–158 (2003).
Google Scholar 
80.Jones, G., Srinivasan, M. & Almany, G. Population connectivity and conservation of marine biodiversity. Oceanography 20, 100–111 (2007).
Google Scholar 
81.Colgan, D. J., Ponder, W. F., Beacham, E. & Macaranas, J. M. Gastropod phylogeny based on six segments from four genes representing coding or non-coding and mitochondrial or nuclear DNA. Molluscan Res. https://doi.org/10.1071/MR03002 (2003).Article 

Google Scholar 
82.Griekspoor, A. & Groothuis, T. 4peaks. Ver. 1.7.1. http://nucleobytes.com/4peaks/ (2005).83.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. https://doi.org/10.1093/nar/22.22.4673 (1994).Article 
PubMed 
PubMed Central 

Google Scholar 
84.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. https://doi.org/10.1093/nar/gkh340 (2004).Article 
PubMed 
PubMed Central 

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

Google Scholar 
86.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
88.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
89.Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
90.Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics https://doi.org/10.1093/bioinformatics/btz966 (2020).Article 
PubMed 

Google Scholar 
91.Meirmans, P. G. & Van Tienderen, P. H. genotype and genodive: Two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4, 792–794 (2004).
Google Scholar 
92.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
93.Mussmann, S. M., Douglas, M. R., Chafin, T. K. & Douglas, M. E. BA3-SNPs: Contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol. Evol. 10, 1808–1813 (2019).
Google Scholar 
94.Rambaut, A. & Drummond, A. J. Tracer v1.6. http://tree.bio.ed.ac.uk/software/tracer/ (2013).95.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
PubMed Central 

Google Scholar  More

1.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
ADS 

Google Scholar 
2.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
3.Giovannoni, S. J. & Stingl, U. Molecular diversity and ecology of microbial plankton. Nature 437, 343–348 (2005).CAS 
PubMed 
ADS 

Google Scholar 
4.Kujawinski, E. B. The impact of microbial metabolism on marine dissolved organic matter. Ann. Rev. Mar. Sci. 3, 567–599 (2011).PubMed 

Google Scholar 
5.Moran, M. A. The global ocean microbiome. Science 350, aac8455 (2015).PubMed 

Google Scholar 
6.Pedrós-Alió, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 15.1-15.18 (2012).
Google Scholar 
7.Salazar, G. et al. Global diversity and biogeography of deep-sea pelagic prokaryotes. ISME J. 10, 596–608 (2015).PubMed 
PubMed Central 

Google Scholar 
8.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc. Natl. Acad. Sci. 103, 12115–12120 (2006).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
9.Kirchman, D. L. Growth rates of microbes in the oceans. Ann. Rev. Mar. Sci. 8, 285–309 (2016).PubMed 

Google Scholar 
10.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl. Acad. Sci. 108, 12776–12781 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
11.Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360 (2015).CAS 
PubMed 

Google Scholar 
12.Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
PubMed Central 

Google Scholar 
13.Våge, S., Storesund, J. E. & Thingstad, T. F. Adding a cost of resistance description extends the ability of virus-host model to explain observed patterns in structure and function of pelagic microbial communities. Environ. Microbiol. 15, 1842–1852 (2012).
Google Scholar 
14.Våge, S., Storesund, J. E. & Thingstad, T. F. SAR11 viruses and defensive host strains. Nature 499, 9–11 (2013).
Google Scholar 
15.Giovannoni, S., Temperton, B. & Zhao, Y. Giovannoni et al. reply. Nature 499, 9–11 (2013).
Google Scholar 
16.Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).CAS 
PubMed 
ADS 

Google Scholar 
17.Herndl, G. J. et al. Contribution of Archaea to total prokaryotic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
18.Teira, E., Lebaron, P., Van Aken, H. & Herndl, G. J. Distribution and activity of Bacteria and Archaea in the deep water masses of the North Atlantic. Limnol. Oceanogr. 51, 2131–2144 (2006).CAS 
ADS 

Google Scholar 
19.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78, 51–63 (2016).
Google Scholar 
20.Hamasaki, K., Taniguchi, A., Tada, Y., Long, R. A. & Azam, F. Actively growing bacteria in the Inland Sea of Japan, identified by combined bromodeoxyuridine immunocapture and denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 73, 2787–2798 (2007).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
21.Tada, Y., Makabe, R., Kasamatsu-Takazawa, N., Taniguchi, A. & Hamasaki, K. Growth and distribution patterns of Roseobacter/Rhodobacter, SAR11, and Bacteroidetes lineages in the Southern Ocean. Polar Biol. 36, 691–704 (2013).
Google Scholar 
22.Suttle, C. A. Marine viruses—Major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 

Google Scholar 
23.Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).CAS 
PubMed 

Google Scholar 
24.Mena, C. et al. Seasonal niche partitioning of surface temperate open ocean prokaryotic communities. Front. Microbiol. 11, 1749 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Mena, C. et al. Dynamic prokaryotic communities in the dark western Mediterranean Sea. Sci. Rep. 11, 17859 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
26.Urbach, E., Vergin, K. L. & Giovannoni, S. J. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl. Environ. Microbiol. 65, 1207–1213 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
27.Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ. Microbiol. 16, 2568–2590 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Emerson, J. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Micobiome 5, 86 (2017).
Google Scholar 
29.Smriga, S., Samo, T., Malfatti, F., Villareal, J. & Azam, F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat. Microb. Ecol. 72, 269–280 (2014).
Google Scholar 
30.Reichart, N. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—Roseobacters are the most active component. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
32.Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).CAS 
PubMed 
ADS 

Google Scholar 
33.Giovannoni, S. J. SAR11 Bacteria: The most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

Google Scholar 
34.Clifford, E. L. et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian Peninsula. Microb. Ecol. 78, 299–312 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Tripp, H. J. et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452, 741–744 (2008).CAS 
PubMed 
ADS 

Google Scholar 
36.Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295 (2009).CAS 
PubMed 

Google Scholar 
37.Winter, C., Bouvier, T., Weinbauer, M. G. & Thingstad, T. F. Trade-offs between competition and defense specialists among unicellular planktonic organisms: The ‘Killing the Winner’ hypothesis revisited. Microbiol. Mol. Biol. Rev. 74, 42–57 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Hugoni, M. et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc. Natl. Acad. Sci. 110, 1–6 (2013).
Google Scholar 
40.Qin, W. et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc. Natl. Acad. Sci. 111, 12504–12509 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
41.Pollard, P. C. & Moriarty, D. J. W. Validity of the tritiated thymidine method for estimating bacterial growth rates: Measurement of isotope dilution during DNA synthesis. Appl. Environ. Microbiol. 48, 1076–1083 (1984).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
42.Wellsbury, P., Herbert, R. A. & John Parkes, R. Incorporation of [methyl-3H]thymidine by obligate and facultative anaerobic bacteria when grown under defined culture conditions. FEMS Microbiol. Ecol. 12, 87–95 (1993).CAS 

Google Scholar 
43.Clausen, A., Matakos, A., Sandrini, M. & Piskur, J. Thymidine kinases in Archaea. Nucleosides Nucleotides Nucleic Acids 25, 1159–1163 (2006).CAS 
PubMed 

Google Scholar 
44.Hamasaki, K., Long, R. A. & Azam, F. Individual cell growth rates of marine bacteria, measured by bromodeoxyuridine incorporation. Aquat. Microb. Ecol. 35, 217–227 (2004).
Google Scholar 
45.Qin, W. et al. Influence of oxygen availability on the activities of ammonia-oxidizing Archaea. Environ. Microbiol. Rep. 9, 250–256 (2017).CAS 
PubMed 

Google Scholar 
46.Reji, L., Tolar, B. B., Smith, J. M., Chavez, F. P. & Francis, C. A. Differential co-occurrence relationships shaping ecotype diversification within Thaumarchaeota populations in the coastal ocean water column. ISME J. 13, 1144–1158 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Sebastián, M. et al. Deep ocean prokaryotic communities are remarkably malleable when facing long-term starvation. Environ. Microbiol. 20, 713–723 (2018).PubMed 

Google Scholar 
48.Vergin, K. L., Done, B., Carlson, C. A. & Giovannoni, S. J. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat. Microb. Ecol. 71, 1–13 (2013).
Google Scholar 
49.Tada, Y., Taniguchi, A., Sato-Takabe, Y. & Hamasaki, K. Growth and succession patterns of major phylogenetic groups of marine bacteria during a mesocosm diatom bloom. J. Oceanogr. 68, 509–519 (2012).
Google Scholar 
50.Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl. Acad. Sci. 115, E6799–E6807 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Ruiz-González, C. et al. Major imprint of surface plankton on deep ocean prokaryotic structure and activity. Mol. Ecol. 29, 1820–1838 (2020).PubMed 

Google Scholar 
52.Chen, X., Ma, R., Yang, Y., Jiao, N. & Zhang, R. Viral regulation on bacterial community impacted by lysis-lysogeny switch: A microcosm experiment in eutrophic coastal waters. Front. Microbiol. 10, 1763 (2019).PubMed 
PubMed Central 

Google Scholar 
53.McCarren, J. et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc. Natl. Acad. Sci. 107, 16420–16427 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
54.Reintjes, G., Arnosti, C., Fuchs, B. & Amann, R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation. ISME J. 13, 1119–1132 (2019).CAS 
PubMed 

Google Scholar 
55.Middelboe, M. Bacterial growth rate and marine virus-host dynamics. Microb. Ecol. 40, 114–124 (2000).CAS 
PubMed 

Google Scholar 
56.Buchan, A., Lecleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
57.Mou, X. et al. Bromodeoxyuridine labelling and fluorescence-activated cell sorting of polyamine-transforming bacterioplankton in coastal seawater. Environ. Microbiol. 17, 876–888 (2014).PubMed 

Google Scholar 
58.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 

Google Scholar 
59.Yilmaz, P., Yarza, P., Rapp, J. Z. & Glöckner, F. O. Expanding the world of marine bacterial and archaeal clades. Front. Microbiol. 6, 1524 (2016).PubMed 
PubMed Central 

Google Scholar 
60.Coe, A. et al. Survival of Prochlorococcus in extended darkness. Limnol. Oceanogr. 61, 1375–1388 (2016).ADS 

Google Scholar 
61.Cottrell, M. T. & Kirchman, D. L. Photoheterotrophic microbes in the arctic ocean in summer and winter. Appl. Environ. Microbiol. 75, 4958–4966 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
62.Zeder, M., Peter, S., Shabarova, T. & Pernthaler, J. A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake. Environ. Microbiol. 11, 2676–2686 (2009).PubMed 

Google Scholar 
63.Cottrell, M. T. & Kirchman, D. L. Natural assemblages of marine proteobacteria and members of the Cytophaga-flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl. Environ. Microbiol. 66, 1692–1697 (2000).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
64.Banning, E. C., Casciotti, K. L. & Kujawinski, E. B. Novel strains isolated from a coastal aquifer suggest a predatory role for flavobacteria. FEMS Microbiol. Ecol. 73, 254–270 (2010).CAS 
PubMed 

Google Scholar 
65.Uchimiya, M. et al. Coupled response of bacterial production to a wind-induced fall phytoplankton bloom and sediment resuspension in the chukchi sea shelf, Western Arctic Ocean. Front. Mar. Sci. 3, 1–12 (2016).
Google Scholar 
66.Ivancic, I. et al. Seasonal variations in extracellular enzymatic activity in marine snow-associated microbial communities and their impact on the surrounding water. FEMS Microbiol. Ecol. 94, fiy198 (2018).CAS 

Google Scholar 
67.Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

Google Scholar 
68.Manca, B. et al. Physical and biochemical averaged vertical profiles in the Mediterranean regions: An important tool to trace the climatology of water masses and to validate incoming data from operational oceanography. J. Mar. Syst. 48, 83–116 (2004).
Google Scholar 
69.Puig, P. & Palanques, A. Temporal variability and composition of settling particle fluxes on the Barcelona continental margin (Northwestern Mediterranean). J. Mar. Res. 56, 639–654 (1998).
Google Scholar 
70.Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).CAS 
ADS 

Google Scholar 
71.Alonso-González, I. J., Arístegui, J., Lee, C. & Calafat, A. Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: A biomarker diagnosis. Biogeosciences 7, 2101–2115 (2010).ADS 

Google Scholar 
72.Hunt, D. E. et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl. Environ. Microbiol. 79, 177–184 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
73.Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 

Google Scholar 
74.Alderkamp, A. C., Sintes, E. & Herndl, G. J. Abundance and activity of major groups of prokaryotic plankton in the coastal North Sea during spring and summer. Aquat. Microb. Ecol. 45, 237–246 (2006).
Google Scholar 
75.De Corte, D., Sintes, E., Yokokawa, T. & Herndl, G. J. Comparison between MICRO-CARD-FISH and 16S rRNA gene clone libraries to assess the active versus total bacterial community in the coastal Arctic. Environ. Microbiol. Rep. 5, 272–281 (2013).PubMed 

Google Scholar 
76.Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc. Natl. Acad. Sci. 115, E400–E408 (2018).CAS 
PubMed 

Google Scholar 
77.Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K. W. & Walsh, D. A. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 8, 1301–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
79.Wu, X. et al. Culturing of ‘unculturable’ subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 610001 (2020).PubMed 
PubMed Central 

Google Scholar 
80.Alonso-Sáez, L., Díaz-Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780 (2015).PubMed 

Google Scholar 
81.Liu, J., Meng, Z., Liu, X. & Zhang, X.-H. Microbial assembly, interaction, functioning, activity and diversification: A review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 

Google Scholar 
82.Long, R. A. & Azam, F. Antagonistic interactions among marine pelagic bacteria. Appl. Environ. Microbiol. 67, 4975–4983 (2001).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
83.López-Jurado, J. L. et al. The RADMED monitoring programme as a tool for MSFD implementation: Towards an ecosystem-based approach. Ocean Sci. 11, 897–908 (2015).ADS 

Google Scholar 
84.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
85.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis (Verlag Chemie GmbH, 1983). https://doi.org/10.1002/iroh.19850700232.Book 

Google Scholar 
86.Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

Google Scholar 
87.Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
88.Gasol, J. M. & del Giorgio, P. A. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci. Mar. 64, 197–224 (2000).
Google Scholar 
89.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
90.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Callahan, B. J. et al. Dada2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
93.Eren, A. M. et al. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).PubMed Central 

Google Scholar  More

1.Thomas, D. W., Fenton, M. B. & Barclay, R. M. R. Social-behavior of the little brown bat, myotis-lucifugus. 1. mating-behavior. Behav. Ecol. Sociobiol. 6, 129–136. https://doi.org/10.1007/bf00292559 (1979).Article 

Google Scholar 
2.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals-ecological implications. Trends Ecol. Evol. 7, 384–388. https://doi.org/10.1016/0169-5347(92)90009-z (1992).CAS 
Article 
PubMed 

Google Scholar 
3.Becker, N. I., Encarnação, J. A., Kalko, E. K. V. & Tschapka, M. The effects of reproductive state on digestive efficiency in three sympatric bat species of the same guild. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 162, 386–390. https://doi.org/10.1016/j.cbpa.2012.04.021 (2012).CAS 
Article 
PubMed 

Google Scholar 
4.Becker, N. I., Encarnação, J. A., Tschapka, M. & Kalko, E. K. V. Energetics and life-history of bats in comparison to small mammals. Ecol. Res. 28, 249–258. https://doi.org/10.1007/s11284-012-1010-0 (2012).CAS 
Article 

Google Scholar 
5.Ruf, T. & Bieber, C. Physiological, behavioral, and life-history adaptations to environmental fluctuations in the edible dormouse. Front. Physiol. https://doi.org/10.3389/fphys.2020.00423 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Scholander, P. F., Hock, R., Walters, V. & Irving, L. Adaptation to cold in arctic and tropical mammals and birds in relation to body temperature, insulation, and basal metabolic rate. Biol. Bull. 99, 259–271. https://doi.org/10.2307/1538742 (1950).CAS 
Article 
PubMed 

Google Scholar 
7.Geiser, F. & Ruf, T. Hibernation versus daily torpor in mammals and birds-physiological variables and classification of torpor patterns. Physiol. Zool. 68, 935–966. https://doi.org/10.1086/physzool.68.6.30163788 (1995).Article 

Google Scholar 
8.Aschoff, J. Thermal conductance in mammals and birds-its dependence on body size and circadian phase. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 69, 611–619. https://doi.org/10.1016/0300-9629(81)90145-6 (1981).Article 

Google Scholar 
9.McNab, B. K. The economics of temperature regulation in neotropical bats. Comp. Biochem. Physiol 31, 227–268. https://doi.org/10.1016/0010-406X(69)91651-X (1969).CAS 
Article 
PubMed 

Google Scholar 
10.Speakman, J. R. & Thomas, D. W. in Bat ecology (ed Thomas H. Kunz and M. Brock Fenton) 430–490 (University of Chicago Press, 2003).11.Wang, L. C. H. & Wolowyk, M. W. Torpor in mammals and birds. Can. J. Zool.-Rev. Can. Zool. 66, 133–137. https://doi.org/10.1139/z88-017 (1988).CAS 
Article 

Google Scholar 
12.Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274. https://doi.org/10.1146/annurev.physiol.66.032102.115105 (2004).CAS 
Article 
PubMed 
ADS 

Google Scholar 
13.Geiser, F. & Masters, P. Torpor in relation to reproduction in the mulgara, dasycercus-cristicauda (dasyuridae, marsupialia). J. Therm. Biol. 19, 33–40. https://doi.org/10.1016/0306-4565(94)90007-8 (1994).Article 

Google Scholar 
14.Wojciechowski, M. S., Jefimow, M. & Tęgowska, E. Environmental conditions, rather than season, determine torpor use and temperature selection in large mouse-eared bats (Myotis myotis). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 147, 828–840. https://doi.org/10.1016/j.cbpa.2006.06.039 (2007).CAS 
Article 
PubMed 

Google Scholar 
15.Ruf, T. & Geiser, F. Daily torpor and hibernation in birds and mammals. Biol. Rev. 90, 891–926. https://doi.org/10.1111/brv.12137 (2015).Article 
PubMed 

Google Scholar 
16.Tuttle, M. D. Population ecology of the gray bat (Myotis grisescens): factors Iifluencing growth and survival of newly volant young. Ecology 57, 587–595. https://doi.org/10.2307/1936443 (1976).Article 

Google Scholar 
17.Racey, P. A. & Swift, S. M. Variations in gestation length in a colony of Pipistrelle bats (Pipistrellus pipistrellus) from year to year. J. Reprod. Fertil. 61, 123–129. https://doi.org/10.1530/jrf.0.0610123 (1981).CAS 
Article 
PubMed 

Google Scholar 
18.Audet, D. & Fenton, M. B. Heterothermy and the use of torpor by the bat Eptesicus fuscus (Chiroptera, Vespertilionidae)-a field study. Physiol. Zool. 61, 197–204. https://doi.org/10.1086/physzool.61.3.30161232 (1988).Article 

Google Scholar 
19.Barnes, B. M., Kretzmann, M., Licht, P. & Zucker, I. The influence of hibernation on testis growth and spermatogenesis in the golden mantled ground squirrel, Spermophilus lateralis. Biol. Reprod. 35, 1289–1297. https://doi.org/10.1095/biolreprod35.5.1289 (1986).CAS 
Article 
PubMed 

Google Scholar 
20.Gagnon, M. F., Lafleur, C., Landry-Cuerrier, M., Humphries, M. M. & Kimmins, S. Torpor expression is associated with differential spermatogenesis in hibernating eastern chipmunks. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R455–R465. https://doi.org/10.1152/ajpregu.00328.2019 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.McLean, J. A. & Speakman, J. R. Energy budgets of lactating and non-reproductive Brown Long-Eared Bats (Plecotus auritus) suggest females use compensation in lactation. Funct. Ecol. 13, 360–372. https://doi.org/10.1046/j.1365-2435.1999.00321.x (1999).Article 

Google Scholar 
22.Wilde, C. J., Knight, C. R. & Racey, P. A. Influence of torpor on milk protein composition and secretion in lactating bats. J. Exp. Zool. 284, 35–41. https://doi.org/10.1002/(sici)1097-010x(19990615)284:1%3c35::aid-jez6%3e3.0.co;2-z (1999).CAS 
Article 
PubMed 

Google Scholar 
23.Racey, P. A. The prolonged storage and survival of spermatozoa in Chiroptera. J. Reprod. Fertil. 56, 391–402. https://doi.org/10.1530/jrf.0.0560391 (1979).CAS 
Article 
PubMed 

Google Scholar 
24.Racey, P. A. The reproductive cycle in male noctule bats, Nyctalus noctula. J. Reprod. Fertil. 41, 169–182. https://doi.org/10.1530/jrf.0.0410169 (1974).CAS 
Article 
PubMed 

Google Scholar 
25.Gustafson, A. W. Male reproductive patterns in hibernating bats. J. Reprod. Fertil. 56, 317–0 (1979).CAS 
Article 

Google Scholar 
26.Komar, E., Dechmann, D. K. N., Fasel, N. J., Zegarek, M. & Ruczyński, I. Food restriction delays seasonal sexual maturation but does not increase torpor use in male bats. J. Exp. Biol. https://doi.org/10.1242/jeb.214825 (2020).Article 
PubMed 

Google Scholar 
27.Wilkinson, G. S. & McCracken, G. F. in Bat ecology (eds Thomas H. Kunz & M. Brock Fenton) 128–155 (University of Chicago Press, 2003).28.Pescovitz, O. H., Srivastava, C. H., Breyer, P. R. & Monts, B. A. Paracrine control of spermatogenesis. Trends Endocrinol. Metab. 5, 126–131. https://doi.org/10.1016/1043-2760(94)90094-9 (1994).CAS 
Article 
PubMed 

Google Scholar 
29.Sharpe, R. M., Kerr, J. B., McKinnell, C. & Millar, M. Temporal relationship between androgen-dependent changes in the volume of seminiferous tubule fluid, lumen size and seminiferous tubule protein secretion in rats. J. Reprod. Fertil. 101, 193–198 (1994).CAS 
Article 

Google Scholar 
30.Becker, N. I., Tschapka, M., Kalko, E. K. V. & Encarnacao, J. A. Balancing the energy budget in free ranging male Myotis daubentonii bats. Physiol. Biochem. Zool. 86, 361–369. https://doi.org/10.1086/670527 (2013).Article 
PubMed 

Google Scholar 
31.Entwistle, A. C., Racey, P. A. & Speakman, J. R. The reproductive cycle and determination of sexual maturity in male brown long eared bats, Plecotus auritus (Chiroptera: Vespertilionidae). J. Zool. 244, 63–70. https://doi.org/10.1111/j.1469-7998.1998.tb00007.x (1998).Article 

Google Scholar 
32.Fasel, N. J., Kołodziej-Sobocińska, M., Komar, E., Zegarek, M. & Ruczyński, I. Penis size and sperm quality, are all bats grey in the dark?. Curr. Zool. 65, 697–703. https://doi.org/10.1093/cz/zoy094 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Dietz, M. & Kalko, E. K. V. Reproduction affects flight activity in female and male Daubenton’s bats, Myotis daubentoni. Can. J. Zool.-Rev. Can. Zool. 85, 653–664. https://doi.org/10.1139/z07-045 (2007).Article 

Google Scholar 
34.Encarnação, J. A. Spatiotemporal pattern of local sexual segregation in a tree dwelling temperate bat Myotis daubentonii. J. Ethol. 30, 271–278. https://doi.org/10.1007/s10164-011-0323-8 (2012).Article 

Google Scholar 
35.Safi, K. & Kerth, G. Comparative analyses suggest that information transfer promoted sociality in male bats in the temperate zone. Am. Nat. 170, 465–472. https://doi.org/10.1086/520116 (2007).Article 
PubMed 

Google Scholar 
36.Hałat, Z., Dechmann, D. K. N., Zegarek, M. & Ruczyński, I. Male bats respond to adverse conditions with larger colonies and increased torpor use during sperm production. Mamm. Biol. 22, 2109 (2020).
Google Scholar 
37.Dietz, M. & Horig, A. Thermoregulation of tree dwelling temperate bats-a behavioural adaptation to force live history strategy. Folia Zool. 60, 5–16. https://doi.org/10.25225/fozo.v60.i1.a2.2011 (2011).Article 

Google Scholar 
38.Ruczyński, I., Zahorowicz, P., Borowik, T. & Hałat, Z. Activity patterns of two syntopic and closely related aerial-hawking bat species during breeding season in Bialowieza Primaeval Forest. Mammal Res. 62, 65–73. https://doi.org/10.1007/s13364-016-0298-5 (2017).Article 

Google Scholar 
39.Jolly, S. E. & Blackshaw, A. W. Prolonged epididymal sperm storage, and the temporal dissociation of testicular and accessory gland activity in the common sheath-tail bat, Taphozous georgianus, of tropical Australia. J. Reprod. Fertil. 81, 205–211. https://doi.org/10.1530/jrf.0.0810205 (1987).CAS 
Article 
PubMed 

Google Scholar 
40.Boyles, J. G., Dunbar, M. B., Storm, J. J. & Brack, V. Energy availability influences microclimate selection of hibernating bats. J. Exp. Biol. 210, 4345–4350. https://doi.org/10.1242/jeb.007294 (2007).Article 
PubMed 

Google Scholar 
41.Ruczyński, I., Hałat, Z., Zegarek, M., Borowik, T. & Dechmann, D. K. N. Camera transects as a method to monitor high temporal and spatial ephemerality of flying nocturnal insects. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13339 (2020).Article 

Google Scholar 
42.Safi, K. Social bats: the males’ perspective. J. Mammal. 89, 1342–1350. https://doi.org/10.1644/08-mamm-s-058.1 (2008).Article 

Google Scholar 
43.Webb, P. I., Speakman, J. R. & Racey, P. A. The implication of small reductions in body temperature for radiant and convective heat loss in resting endothermic brown long eared bats (Pecotus auritus). J. Therm. Biol. 18, 131–135. https://doi.org/10.1016/0306-4565(93)90026-p (1993).Article 

Google Scholar 
44.Boratyński, J. S., Iwińska, K. & Bogdanowicz, W. An intrapopulation heterothermy continuum: notable repeatability of body temperature variation in food deprived yellow necked mice. J. Exp. Biol. 222, 197152. https://doi.org/10.1242/jeb.197152 (2019).Article 

Google Scholar 
45.Christian, N. & Geiser, F. To use or not to use torpor? Activity and body temperature as predictors. Naturwissenschaften 94, 483–487. https://doi.org/10.1007/s00114-007-0215-5 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
46.Smith, L. B. & Walker, W. H. The regulation of spermatogenesis by androgens. Semin. Cell Dev. Biol. 30, 2–13. https://doi.org/10.1016/j.semcdb.2014.02.012 (2014).CAS 
Article 
PubMed 

Google Scholar 
47.Macdonald, J. & Harrison, R. G. Effect of low temperatures on rat spermatogenesis. Fertil. Steril. 5, 205–216 (1954).CAS 
Article 

Google Scholar 
48.Fowler, P. A. & Racey, P. A. Relationship between body and testis temperatures in the European hedgehog, Erinaceus europaeus, during hibernation and sexual reactivation. Reproduction 81, 567. https://doi.org/10.1530/jrf.0.0810567 (1987).CAS 
Article 

Google Scholar 
49.Davis, J. R., Firlit, C. F. & Hollinger, M. A. Effect of temperature on incorporation of l-lysine-U-C14 into testicular proteins. Am. J. Physiol. 204, 696–698. https://doi.org/10.1152/ajplegacy.1963.204.4.696 (1963).CAS 
Article 
PubMed 

Google Scholar 
50.LeVier, R. R. & Spaziani, E. The influence of temperature on steroidogenesis in the rat testis. J. Exp. Zool. 169, 113–120. https://doi.org/10.1002/jez.1401690113 (1968).CAS 
Article 
PubMed 

Google Scholar 
51.Geiser, F. & Brigham, R. M. in Living in a seasonal world (eds Thomas Ruf, Claudia Bieber, Walter Arnold, & Eva Millesi) 109–121 (Springer, 2012).52.Safi, K. Die Zweifarbfledermaus in der Schweiz: Status und Grundlagen zum Schutz. (Haupt Verlag, 2006).53.Hałat, Z., Dechmann, D. K. N., Zegarek, M., Visser, A. F. J. & Ruczyński, I. Sociality and insect abundance affect duration of nocturnal activity of male parti-colored bats. J. Mammal. 99, 1503–1509. https://doi.org/10.1093/jmammal/gyy141 (2018).Article 

Google Scholar 
54.Ruczyński, I. Influence of temperature on maternity roost selection by noctule bats (Nyctalus noctula) and Leisler’s bats (N-leisleri) in Biaowieza Primeval Forest, Poland. Can. J. Zool. 84, 900–907. https://doi.org/10.1139/z06-060 (2006).Article 

Google Scholar 
55.Ruczyński, I. & Bartoń, K. A. Seasonal changes and the influence of tree species and ambient temperature on the fission-fusion dynamics of tree-roosting bats. Behav. Ecol. Sociobiol. 74, 63. https://doi.org/10.1007/s00265-020-02840-1 (2020).Article 

Google Scholar 
56.Linton, D. M. & Macdonald, D. W. Phenology of reproductive condition varies with age and spring weather conditions in male Myotis daubentonii and Myotis nattereri (Chiroptera: Vespertilionidae). Sci. Rep. 10, 6664. https://doi.org/10.1038/s41598-020-63538-y (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
57.Dammhahn, M., Landry-Cuerrier, M., Reale, D., Garant, D. & Humphries, M. M. Individual variation in energy-saving heterothermy affects survival and reproductive success. Funct. Ecol. 31, 866–875. https://doi.org/10.1111/1365-2435.12797 (2017).Article 

Google Scholar 
58.Boyles, J. G., Johnson, J. S., Blomberg, A. & Lilley, T. M. Optimal hibernation theory. Mammal. Rev. 50, 91–100. https://doi.org/10.1111/mam.12181 (2020).Article 

Google Scholar 
59.Boratyński, J. S., Willis, C. K. R., Jefimow, M. & Wojciechowski, M. S. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 179, 125–132. https://doi.org/10.1016/j.cbpa.2014.09.035 (2015).CAS 
Article 
PubMed 

Google Scholar 
60.Ruczyński, I., Kalko, E. K. V. & Siemers, B. M. The sensory basis of roost finding in a forest bat, Nyctalus noctula. J. Exp. Biol. 210, 3607–3615. https://doi.org/10.1242/jeb.009837 (2007).Article 
PubMed 

Google Scholar 
61.Lovegrove, B. G. Modification and miniaturization of Thermochron iButtons for surgical implantation into small animals. J. Comp. Physiol. B 179, 451–458. https://doi.org/10.1007/s00360-008-0329-x (2009).Article 
PubMed 

Google Scholar 
62.Willis, C. K. R., Lane, J. E., Liknes, E. T., Swanson, D. L. & Brigham, R. M. Thermal energetics of female big brown bats (Eptesicus fuscus). Can. J. Zool. 83, 871–879. https://doi.org/10.1139/z05-074 (2005).Article 

Google Scholar 
63.Willis, C. K. R. An energy-based body temperature threshold between torpor and normothermia for small mammals. Physiol. Biochem. Zool. 80, 643–651. https://doi.org/10.1086/521085 (2007).Article 
PubMed 

Google Scholar 
64.Krutzsch, P. H. in Reproductive Biology of Bats (ed Academic Press) 91–155 (2000).65.Wood, S. N. Generalized Additive Models: An Introduction With R. Vol. 66 (2006).66.Jackman, S. Bayesian Analysis for the Social Sciences. (Wiley, 2009).67.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455. https://doi.org/10.2307/1390675 (1998).MathSciNet 
Article 

Google Scholar 
68.Kellner, K. jagsUI: A Wrapper Around ‘rjags’ to Streamline ‘JAGS’ Analyses. v.R package version 1.5.1. (2019). More

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