Philippa Kaur

1.Becker, L. J. S. & Gabor, C. R. Effects of turbidity and visual vs. chemical cues on anti-predator response in the endangered fountain darter (Etheostoma fonticola). Ethology 118, 994–1000. https://doi.org/10.1111/eth.12002 (2010).Article 

Google Scholar 
2.Hettyey, A., Roelli, F., Thürlimann, N., Zürcher, A. & Van Buskirk, J. Visual cues contribute to predator detection in anuran larvae. Biol. J. Linn. Soc. 106, 820–827. https://doi.org/10.1111/j.1095-8312.2012.01923.x (2012).Article 

Google Scholar 
3.Sánchez-González, B., Barja, I. & Navarro-Castilla, Á. Wood mice modify food intake under different degrees of predation risk: influence of acquired experience and degradation of predator’s faecal volatile compounds. Chemoecoly. 27, 115–122. https://doi.org/10.1007/s00049-017-0237-1 (2017).CAS 
Article 

Google Scholar 
4.Pereira, A. & Moita, M. A. Is there anybody out there? Neural circuits of threat detection in vertebrates. Curr. Opin. Neurobiol. 41, 179–187. https://doi.org/10.1016/j.conb.2016.09.011 (2016).CAS 
Article 
PubMed 

Google Scholar 
5.Hernández, M. C., Navarro-Castilla, A., Piñeiro, A. & Barja, I. Wood mice agressiveness and flight response to human handling: Effect of individual and environmental factors. Ethology 124, 559–569. https://doi.org/10.1111/eth.12760 (2018).Article 

Google Scholar 
6.Sánchez-González, B., Planillo, A., Navarro-Castilla, Á. & Barja, I. The concentration of fear: mice’s behavioural and physiological stress responses to different degrees of predation risk. Sci. Nat. 105, 16. https://doi.org/10.1007/s00114-018-1540-6 (2018).CAS 
Article 

Google Scholar 
7.Verdolin, J. L. Meta-analysis of foraging and predation risk trade-offs in terrestrial systems. Behav. Ecol. Sociobiol. 60, 457–464. https://doi.org/10.1007/s00265-006-0172-6 (2006).Article 

Google Scholar 
8.Barja, I., Silván, G., Martínez-Fernández, L. & Illera, J. C. Physiological stress responses, fecal marking behavior, and reproduction in wild European pine martens (Martes martes). J. Chem. Ecol. 37, 253–259. https://doi.org/10.1007/s10886-011-9928-1 (2011).CAS 
Article 
PubMed 

Google Scholar 
9.Barja, I., Silván, G. & Illera, J. C. Relationships between sex and stress hormone levels in feces and marking behavior in a wild population of Iberian wolves (Canis lupus signatus). J. Chem. Ecol. 34, 697–701. https://doi.org/10.1007/s10886-008-9460-0 (2008).CAS 
Article 
PubMed 

Google Scholar 
10.Barja, I. Decision making in plant selection during the faecal-marking behavior of wild wolves. Anim. Behav. 77, 489–493 (2009).Article 

Google Scholar 
11.Barja, I. Winter distribution of European pine marten (Martes martes) scats in a protected area of Galicia, Spain. Mammalia 69, 435–438 (2005).Article 

Google Scholar 
12.Berzins, R. & Helder, R. Olfactory communication and the importance of different odour sources in the ferret (Mustela putorius f. furo). Mamm. Biol. 73, 379–387. https://doi.org/10.1016/j.mambio.2007.12.002 (2008).Article 

Google Scholar 
13.Barja, I. & List, R. Faecal marking behavior in ringtails (Bassariscus astutus) during the non-breeding period: spatial characteristics of latrines and single faeces. Chemoecoly. 16, 2019–2222. https://doi.org/10.1007/s00049-006-0352-x (2006).Article 

Google Scholar 
14.Lowry, A. C., Frank, L. & Moore, L. F. Regulation of behavioral responses by corticotropin-releasing factor. Gen. Comp. Endocr. 146, 19–27. https://doi.org/10.1016/j.ygcen.2005.12.006 (2006).CAS 
Article 
PubMed 

Google Scholar 
15.Romero, L. M. & Gormally, B. M. G. How truly conserved is the “well-conserved” vertebrate stress response?. Integr. Comp. Biol. 59, 273–281. https://doi.org/10.1093/icb/icz011 (2019).CAS 
Article 
PubMed 

Google Scholar 
16.Réale, D. & Festa-Bianchet, M. Predator-induced natural selection on temperament in bighorn ewes. Anim. Behav. 65, 463–470. https://doi.org/10.1006/anbe.2003.2100 (2003).Article 

Google Scholar 
17.Hernández, M. C., Navarro-Castilla, Á. & Barja, I. Wood mouse feeding effort and decision-making when encountering a restricted unknown food source. PLoS ONE https://doi.org/10.1371/journal.pone.0212716 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Creel, S., Christianson, D., Liley, S. & Winnie, J. A. Predation risk affects reproductive physiology and demography of elk. Science 315, 960. https://doi.org/10.1126/science.1135918 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
19.Navarro-Castilla, Á. & Barja, I. Antipredatory response and food intake in wood mice (Apodemus sylvaticus) under simulated predation risk by resident and novel carnivorous predators. Ethology 120, 90–98. https://doi.org/10.1111/eth.12184 (2014).Article 

Google Scholar 
20.Navarro-Castilla, Á. & Barja, I. Does predation risk, through moon phase and predator cues, modulate food intake, antipredatory and physiological responses in wood mice (Apodemussylvaticus)?. Behav. Ecol. Sociobiol. 68, 1505–1512. https://doi.org/10.1007/s00265-014-1759-y (2014).Article 

Google Scholar 
21.Navarro-Castilla, Á., Díaz, D. M. & Barja, I. Does ungulate disturbance mediate behavioural and physiological stress responses in Algerian mice (Mus spretus)? a wild exclosure experiment. Hystrix. 28, 165–172 (2017).
Google Scholar 
22.Brown, J. S. & Kotler, B. P. Hazardous duty pay and the foraging cost of predation. Ecol. Lett. 7, 999–1014. https://doi.org/10.1111/j.1461-0248.2004.00661.x (2004).Article 

Google Scholar 
23.Navarro-Castilla, Á. & Barja, I. Stressful living in lower-quality habitats? Body mass, feeding behaviour and physiological stress responses in wild wood mouse populations. Integr. Zool. 4, 114–126. https://doi.org/10.1111/1749-4877.12351 (2018).Article 

Google Scholar 
24.Navarro-Castilla, Á., Sánchez-González, B. & Barja, I. Latrine behaviour and faecal corticosterone metabolites as indicators of habitat-related responses of wild rabbits to predation risk. Ecol. Indic. 97, 175–182. https://doi.org/10.1016/j.ecolind.2018.10.016 (2019).Article 

Google Scholar 
25.Clarke, E., Reichard, H. U. & Zuberbühle, K. The anti-predator behavior of wild white-handed gibbons (Hylobates bar). Behav. Ecol. Sociobiol. 66, 85–96 (2012).Article 

Google Scholar 
26.Hughes, K. K., Kelley, J. L. & Banks, P. B. Dangerous liaisons: the predation risks of receiving social signals. Ecol. Lett. 15, 11326–11339. https://doi.org/10.1111/j.1461-0248.2012.01856.x (2012).Article 

Google Scholar 
27.MacLean, S. A. & Bonter, D. N. The sound of danger: Threat sensitivity to predator vocalizations, alarm calls, and novelty in gulls. PLoS ONE 8, e82384. https://doi.org/10.1371/journal.pone.0082384 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Barber, J. R., Crooks, K. R. & Fristrup, K. M. The costs of chronic noise exposure for terrestrial organisms. Trends Ecol. Evol. 25, 180–189. https://doi.org/10.1016/j.tree.2009.08.002 (2010).Article 
PubMed 

Google Scholar 
29.Sillero, N. Amphibian mortality levels on Spanish country roads: Descriptive and spatial analysis. Amphibia-Reptilia 29, 337–347. https://doi.org/10.1163/156853808785112066 (2008).Article 

Google Scholar 
30.Taylor, B. D. & Goldingay, R. L. Roads and wildlife: Impacts, mitigation and implications for wildlife management in Australia. Wildl. Res. 37, 320–331. https://doi.org/10.1071/WR09171 (2010).Article 

Google Scholar 
31.Iglesias-Merchán, C., Diaz-Balteiro, L. & de la Puente, J. Road traffic noise impact assessment in a breeding colony of cinereous vultures (Aegypius monachus) in Spain. J. Acoust. Soc. Am. or JASA. 139, 1124. https://doi.org/10.1121/1.4943553 (2016).ADS 
Article 
PubMed 

Google Scholar 
32.Iglesias-Merchan, C. et al. A new large-scale index (AcED) for assessing traffic noise disturbance on wildlife: Stress response in a roe deer (Capreolus capreolus) population. Environ. Monit. Assess. 190, 185. https://doi.org/10.1007/s10661-018-6573-y (2018).Article 
PubMed 

Google Scholar 
33.Ortiz-Urbina, E., Diaz-Balteiro, L. & Iglesias-Merchan, C. Influence of anthropogenic noise for predicting cinereous vulture nest distribution. Sustainability. 12, 503. https://doi.org/10.3390/su12020503 (2020).Article 

Google Scholar 
34.Bamford, A. J., Monadjem, A. & Hardy, I. C. W. Nesting habitat preference of the African White-backed Vulture Gyps africanus and the effects of anthropogenic disturbance. Ibis 151, 51–62. https://doi.org/10.1111/j.1474-919X.2008.00878.x (2009).Article 

Google Scholar 
35.Zwijacz-Kozica, T. et al. Concentration of fecal cortisol metabolites in chamois in relation to tourist pressure in Tatra National Park (South Poland). Acta Theriol. 58, 215–222. https://doi.org/10.1007/s13364-012-0108-7 (2013).Article 

Google Scholar 
36.Barja, I. et al. Stress physiological responses to tourist pressure in a wild population of European pine marten. J. Steroid Biochem. 104, 136–142. https://doi.org/10.1016/j.jsbmb.2007.03.008 (2007).CAS 
Article 

Google Scholar 
37.Piñeiro, A., Barja, I., Silván, G. & Illera, J. C. Effects of tourist pressure and reproduction on physiological stress response in wildcats: Management implications for species conservation. Wildl. Res. 39, 532–539. https://doi.org/10.1071/WR10218 (2012).Article 

Google Scholar 
38.Tarjuelo, R. et al. Effects of human activity on physiological and behavioral responses of an endangered steppe bird. Behav. Ecol. 26, 828–838. https://doi.org/10.1093/beheco/arv016 (2015).Article 

Google Scholar 
39.Beale, C. M. & Monaghan, P. Behavioural responses to human disturbance: A matter of choice?. Anim. Behav. 68, 1065–1069 (2004).Article 

Google Scholar 
40.Thiel, D., Jenni-Eiermann, S., Braunisch, V., Palme, R. & Jenni, L. Ski tourism affects habitat use and evokes a physiological stress response in capercaillie Tetrao urogallus: A new methodological approach. J. Appl. Ecol. 45, 845–853 (2008).Article 

Google Scholar 
41.Casas, F., Mougeot, F., Viñuela, J. & Bretagnolle,. Effects of hunting on the behaviour and spatial distribution of farmland birds: Importance of hunting-free refuges in agricultural areas. Anim. Conserv. 12, 346–354. https://doi.org/10.1111/j.1469-1795.2009.00259.x (2009).Article 

Google Scholar 
42.Wang, Z., Li, Z., Beuchamp, G. & Jiang, Z. Flock size and human disturbance affect vigilance of endangered red-crowned cranes (Grus japonensis). Biol. Conserv. 144, 101–105. https://doi.org/10.1016/j.biocon.2010.06.025 (2011).Article 

Google Scholar 
43.Maran, T. et al. Mustela lutreola. IUCN. (2010). e.T14018A4381596.44.Gómez, A., Oreca, S., Podra, M., Sanz, B. & Palazón, S. Expansión del visón europeo Mustela lutreola (Linnaeus, 1761) hacia el este de su área de distribución en España: primeros datos en Aragón. Galemys. 23, 37–45 (2011).
Google Scholar 
45.Amstislavsky, S. & Ternovskaya, Y. Reproduction in mustelids. Anim. Reprod. Sci. 60–61, 571–581 (2000).Article 

Google Scholar 
46.Harrington, L. A., Harrigton, A. L. & Macdonald, D. W. The smell of new competitors: the response of American mink Mustela vison, to the odours of otter, Lutra lutra and polecat, Mustela putorius. Ethology 115, 421–428. https://doi.org/10.1111/j.1439-0310.2008.01593.x (2008).Article 

Google Scholar 
47.Caro, T. M. & Stoner, C. J. The potential for interspecific competition among African carnivores. Biol. Conserv. 110, 67–75 (2003).Article 

Google Scholar 
48.Maran, T., Põdra, M., Põlma, M. & Macdonald, D. W. The survival of captive-born animals in restoration programmes—Case study of the endangered European mink Mustela lutreola. Biol. Conserv. 142, 1685–1692. https://doi.org/10.1016/j.biocon.2009.03.003 (2009).Article 

Google Scholar 
49.Palazón, S. (2017). Visón europeo – Mustela lutreola. In: Enciclopedia Virtual de los Vertebrados Españoles. Salvador, A., Barja, I. (Eds.). Museo Nacional de Ciencias Naturales, Madrid. http://www.vertebradosibericos.org/50.Gorman, M. L. & Trowbridge, B. J. The role of odor in the social lives of carnivores. In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman, J. L.) (Springer, 1989). https://doi.org/10.1007/978-1-4757-4716-4_3.
Google Scholar 
51.Pruitt, C. H. & Burghardt, G. M. Communicationin terrestrial carnivores: Mustelidae, Procyonidae, and Ursidae. In How Animals Communicate (ed. Seboek, T. A.) 767–793 (Indiana University Press, 1977).
Google Scholar 
52.Zschille, J., Stier, N. & Roth, M. Gender differences in activity patterns of American mink Neovison vison in Germany. Eur. J. Wildl. Res. 56, 187–194. https://doi.org/10.1007/s10344-009-0303-2 (2010).Article 

Google Scholar 
53.Hall, K. L. et al. Vigilance of kit foxes at water sources: A test of competing hypotheses for a solitary carnivore subject to predation. Behav. Process. 94, 76–82. https://doi.org/10.1016/j.beproc.2012.12.007 (2013).Article 

Google Scholar 
54.Maji, C. Dynamical analysis of a fractional-order predator–prey model incorporating a constant prey refuge and nonlinear incident rate. Model. Earth Syst. Environ. https://doi.org/10.1007/s40808-020-01061-9 (2021).Article 

Google Scholar 
55.Li, D., Zhou, Q., Tang, X., Huang, H. & Huang, C. Sleeping site use of the white-headed langur Trachypithecus leucocephalus: The role of predation risk, territorial defense, and proximity to feeding sites. Curr. Zool. 57, 260–268. https://doi.org/10.1093/czoolo/57.3.260 (2011).Article 

Google Scholar 
56.Kats, B. L. & Dill, M. L. The scent of death: Chemosensory assessment of predation risk by prey animals. Écoscience. 5, 361–394. https://doi.org/10.1080/11956860.1998.11682468 (1998).Article 

Google Scholar 
57.Šlipogor, V., Gunhold-de Oliveira, T., Tadić, Z., Massen, J. J. & Bugnyar, T. Consistent inter-individual differences in common marmosets (Callithrix jacchus) in boldness-shyness, stress-activity, and exploration-avoidance. Am. J. Primatol. 78, 961–973. https://doi.org/10.1002/ajp.22566 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Hall, B. A., Melfi, V., Burns, A., McGill, D. M. & Doyle, R. E. Curious creatures: A multi-taxa investigation of responses to novelty in a zoo environment. Peer J. 6, e4454. https://doi.org/10.7717/peerj.4454 (2018).Article 
PubMed 

Google Scholar 
59.Fernández-Lázaro, G., Latorre, R., Alonso-García, E. & Barja, I. Nonhuman primate welfare: Can there be arelationship between personality, lateralization and physiological indicators?. Behav. Proc. 166, 103897 (2019).Article 

Google Scholar 
60.de Miguel, J & Barja, I. Manual de métodos de estudio del comportamiento en carnívoros. Técnicas de Biología de la Conservación – Nº5. (ed. Tundra Ediciones) (2015).61.le Roux, A., Cherry, M. I., Gygax, L. & Manser, M. B. Vigilance behaviour and fitness consequences: Comparing a solitary foraging and an obligate group-foraging mammal. Behav. Ecol. Sociobiol. 63, 1097–1107. https://doi.org/10.1007/s00265-009-0762-1 (2009).Article 

Google Scholar 
62.Hayes, R. A., Morelli, T. L. & Wright, P. C. Volatile components of lemur scent secretions vary throughout the year. Am. J. Primatol. 68, 1202–1207. https://doi.org/10.1002/ajp.20319 (2006).CAS 
Article 
PubMed 

Google Scholar 
63.Scordato, S. E. & Drea, M. C. Scents and sensibility: Information content of olfactory signals in the ringtailed lemur, Lemur catta. Anim. Behav. 7, 301–314. https://doi.org/10.1016/j.anbehav.2006.08.006 (2007).Article 

Google Scholar 
64.Martín, J., Barja, I. & López, P. Chemical scent constituents in feces of wild Iberian wolves (Canis lupus signatus). Biochem. Syst. Ecol. 38, 1096–1102. https://doi.org/10.1016/j.bse.2010.10.014 (2010).CAS 
Article 

Google Scholar 
65.Sánchez-González, B., Planillo, A., Navarro-Castilla, Á. & Barja, I. The concentration of fear: Mice’s behavioural and physiological stress responses to different degrees of predation risk. Sci Nat. 105, 16. https://doi.org/10.1007/s00114-018-1540-6 (2018).CAS 
Article 

Google Scholar 
66.Brawata, L. R. & Neeman, T. Is water the key? Dingo management, intraguild interactions and predator distribution around water points in arid Australia. Wildl. Res. 38, 426–436. https://doi.org/10.1071/WR10169 (2011).Article 

Google Scholar 
67.Erlinge, S., Sandell, M. & Brinck, C. Scent-marking and its territorial significance in stoats, Mustela erminea. Anim. Behav. 30, 811–818. https://doi.org/10.1016/S0003-3472(82)80154-1 (1982).Article 

Google Scholar 
68.Brown, J. A., Harris, S. & Cheeseman, C. L. The development of field techniques for studying potential modes of transmission of bovine tuberculosis from badgers to cattle. (ed. Hayden, T. J,) Royal Irish Academy (1993).69.Roper, T. J. et al. Territorial marking with faeces in badgers (Meles meles): A comparison of boundary and hinterland use. Behaviour 127, 289–307 (1993).Article 

Google Scholar 
70.Hutchings, M. R. & White, P. C. L. Mustelid scent-marking in managed ecosystems: Implications for population management. Mammal Rev. 30, 157–169. https://doi.org/10.1046/j.1365-2907.2000.00065.x (2000).Article 

Google Scholar 
71.McCormick, M. I. & Manassa, R. Predation risk assessment by olfactory and visual cues in a coral reef fish. Coral Reefs 27, 105–113. https://doi.org/10.1007/s00338-007-0296-9 (2008).ADS 
Article 

Google Scholar 
72.Palazón, S. & Gómez, A. (2007). Mustela lutreola (Linnaeus, 1761). Atlas y libro rojo de los mamíferos terrestres de España. Chapter: Mustela lutreola: ficha roja. (ed. Palomo J., Gisbert, J. & Blanco, J. C.) (Dirección General para la Biodiversidad-SECEM-SECEMU 2007).73.Laundre, W. J., Hernandez, L. & Ripple, J. W. The landscape of fear: Ecological implications of being afraid. Open J. Ecol. 3, 1–7 (2010).Article 

Google Scholar 
74.Steven, R., Pickering, C. & Castley, J. G. A review of the impacts of nature based recreation on birds. J. Environ. Manag. 92, 2287–2294. https://doi.org/10.1016/j.jenvman.2011.05.005 (2011).Article 

Google Scholar 
75.Lima, L. S. & Dill, M. L. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640. https://doi.org/10.1139/z90-092 (1990).Article 

Google Scholar 
76.Lima, L. S., Blackwell, F. B., DeVault, L. T. & Fernández-Juricic, E. Animal reactions to oncoming vehicles: A conceptual review. Biol. Rev. 90, 60–76. https://doi.org/10.1111/brv.12093 (2015).Article 
PubMed 

Google Scholar 
77.Kotler, B. P. et al. Forag-ing games between gerbils and their predators: Temporal dynamics of resource depletion and apprehension in gerbils. Evol. Ecol. Res. 4, 495–518 (2002).ADS 

Google Scholar 
78.Palazón, S. et al. Causes and patterns of human-induced mortality in the critically endangered European mink Mustela lutreola in Spain. Oryx 46, 614–616. https://doi.org/10.1017/S0030605312000920 (2012).Article 

Google Scholar 
79.De Bellefroid, M.D.N. & Rosoux, R. (2005) Le Vison d’Europe. BELIN Eveil Nature Collection (2005).80.Griffin, A. S., Blumstein, D. T. & Evans, C. S. Training captive-bred or translocated animals to avoid predators. Conserv. Biol. 14, 1317–1326 (2000).Article 

Google Scholar 
81.Palazón, S. Distribución, morfología y ecología del visón europeo (Mustela lutreola L. 1761) en la Península Ibérica. Tesis Doctoral. Universidad de Barcelona, Barcelona (1998).82.Palazón, S.; Ruíz-Olmo, J. (1998). A preliminary study of behaviour of the European mink (Mustela lutreola), by means of radio-tracking. In: Dustone, N.; Gorman, M. L. (eds). Behaviour and ecology of riparian mammals: 93–105. Cambridge University Press.83.Garin, I. et al. Home ranges of European mink Mustela lutreola in southwestern Europe. Acta Theriol. 47, 55–62. https://doi.org/10.1007/BF03193566 (2002).Article 

Google Scholar 
84.Iglesias, C., Mata, C. & Malo, J. E. The influence of traffic noise on vertebrate road crossing through underpasses. Ambio 41, 193–201. https://doi.org/10.1007/s13280-011-0145-5 (2012).Article 
PubMed 

Google Scholar 
85.Palazón, S., Ruiz-Olmo, J. & Gosàlbez, J. Diet of European mink (Mustela lutreola) in Northern Spain. Mammalia 68, 159–165. https://doi.org/10.1515/mamm.2004.016 (2004).Article 

Google Scholar 
86.Fey, K., Banks, P. B., Ylönen, H. & Korpimäki, E. Behavioural responses of voles to simulated risk of predation by a native and an alien mustelid: An odour manipulation experiment. Wild. Res. 37, 273–282 (2010).Article 

Google Scholar 
87.Foster, S. A. The geography of behaviour: An evolutionary perspective. Trends Evol. Ecol. 14, 190–195 (1999).CAS 
Article 

Google Scholar 
88.Ellis, R. & Heimbach, R. Bugs and birds: Children’s acquisition of second language vocabulary through interaction. System 25, 247–259. https://doi.org/10.1016/S0346-251X(97)00012-2 (1997).Article 

Google Scholar 
89.Miller, B. et al. Development of survival skills in captive-raised Siberian polecats (Mustela eversmanni) II: Predator avoidance. J. Ethol. 8, 95–104. https://doi.org/10.1007/BF02350280 (1990).CAS 
Article 

Google Scholar 
90.McLean, I. G., Lundie-Jenkins, G. & Jarman, P. J. Teaching an endangered mammal to recognise predators. Biol. Conserv. 75, 51–62. https://doi.org/10.1016/0006-3207(95)00038-0 (1996).Article 

Google Scholar 
91.Rhoznov V. & Petrin, A. New hypothesis on the reasons of disappearance of European mink based on the study of behavioral interactions. International Conference on Conservation of European mink (2003). Logroño, Spain, Proceedings Book 209–221 (2006).92.Cole, D. N. & Landres, P. B. Threats to wilderness ecosystems: Impacts and research needs. Ecol. Appl. 6, 168–184. https://doi.org/10.2307/2269562 (1996).Article 

Google Scholar 
93.Juutinen, A. et al. Combining ecological and recreational aspects in national park management: A choice experiment application. Ecol. econ. 70, 1231–1239. https://doi.org/10.1016/j.ecolecon.2011.02 (2011).Article 

Google Scholar  More

1.Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 171–185 (2007).Article 

Google Scholar 
2.Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).Article 

Google Scholar 
3.Drake, T. W., Raymond, P. A. & Spencer, R. G. M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2/.10055 (2017).4.Lauerwald, R., Laruelle, G. G., Hartmann, J., Ciais, P. & Regnier, P. A. G. Spatial patterns in CO2 evasion from the global river network. Global Biogeochem. Cycles 29, 534–554 (2015).Article 

Google Scholar 
5.Borges, A. V. et al. Globally significant greenhouse-gas emissions from African inland waters. Nat. Geosci. 8, 637–642 (2015).Article 

Google Scholar 
6.Sawakuchi, H. O. et al. Carbon dioxide emissions along the lower Amazon River. Front. Mar. Sci. 4, 76 (2017).7.Hastie, A., Lauerwald, R., Ciais, P. & Regnier, P. Aquatic carbon fluxes dampen the overall variation of net ecosystem productivity in the Amazon basin: an analysis of the interannual variability in the boundless carbon cycle. Glob. Change Biol. 25, 2094–2111 (2019).Article 

Google Scholar 
8.Horgby, Å. et al. Unexpected large evasion fluxes of carbon dioxide from turbulent streams draining the world’s mountains. Nat. Commun. 10, 4888 (2019).9.Peter, H. et al. Scales and drivers of temporal (p_{{mathrm{CO}}_2}) dynamics in an Alpine stream. J. Geophys. Res. Biogeosci. 119, 1078–1091 (2014).Article 

Google Scholar 
10.Rocher-Ros, G., Sponseller, R. A., Bergstr, A., Myrstener, M. & Giesler, R. Stream metabolism controls diel patterns and evasion of CO2 in Arctic streams. Glob. Change Biol. https://doi.org/10.1111/gcb.14895 (2020).11.Wallin, M. B., Audet, J., Peacock, M., Sahlée, E. & Winterdahl, M. Carbon dioxide dynamics in an agricultural headwater stream driven by hydrology and primary production. Biogeosciences 17, 2487–2498 (2020).12.Crawford, J. T., Stanley, E. H., Dornblaser, M. M. & Striegl, R. G. CO2 time series patterns in contrasting headwater streams of North America. Aquat. Sci. 79, 473–486 (2017).Article 

Google Scholar 
13.Reiman, J. & Xu, Y. J. Diel variability of (p_{{mathrm{CO}}_2}) and CO2 outgassing from the lower Mississippi River: implications for riverine CO2 outgassing estimation. Water 11, 43 (2018).Article 

Google Scholar 
14.Hensley, R. T. & Cohen, M. J. On the emergence of diel solute signals in flowing waters. Water Resour. Res. 52, 759–772 (2016).Article 

Google Scholar 
15.Odum, H. T. Primary production in flowing waters. Limnol. Oceanogr. 1, 102–117 (1955).Article 

Google Scholar 
16.Johnson, M. S. et al. Direct and continuous measurement of dissolved carbon dioxide in freshwater aquatic systems—method and applications. Ecohydrology 3, 68–78 (2010).
Google Scholar 
17.Stets, E. G. et al. Carbonate buffering and metabolic controls on carbon dioxide in rivers. Global Biogeochem. Cycles 31, 663–677 (2017).Article 

Google Scholar 
18.Cory, R. M., Ward, C. P., Crump, B. C. & Kling, G. W. Sunlight controls water column processing of carbon in Arctic fresh waters. Science 345, 925–928 (2014).Article 

Google Scholar 
19.Riml, J., Campeau, A., Bishop, K. & Wallin, M. B. Spectral decomposition reveals new perspectives on CO2 concentration patterns and soil–stream linkages. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2018JG004981 (2019).20.Hartmann, J., Lauerwald, R. & Moosdorf, N. A brief overview of the GLObal RIver CHemistry Database, GLORICH. Procedia Earth Planet. Sci. 10, 23–27 (2014).Article 

Google Scholar 
21.Hotchkiss, E. R. et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nat. Geosci. 8, 696–699 (2015).Article 

Google Scholar 
22.Demars, B. O. L. & Manson, J. R. Temperature dependence of stream aeration coefficients and the effect of water turbulence: a critical review. Water Res. 47, 1–15 (2013).Article 

Google Scholar 
23.Koenig, L. E. et al. Emergent productivity regimes of river networks. Limnol. Oceanogr. 4, 173–181 (2019).Article 

Google Scholar 
24.Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).Article 

Google Scholar 
25.Raymond, P. A. et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ. 2, 41–53 (2012).Article 

Google Scholar 
26.Mulholland, P. J. et al. Inter-biome comparison of factors controlling stream metabolism. Freshw. Biol. 46, 1503–1517 (2001).Article 

Google Scholar 
27.Roberts, B. J., Mulholland, P. J. & Hill, W. R. Multiple scales of temporal variability in ecosystem metabolism rates: results from 2 years of continuous monitoring in a forested headwater stream. Ecosystems 10, 588–606 (2007).Article 

Google Scholar 
28.Vanote, R. L., Minshall, W. G., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).Article 

Google Scholar 
29.Finlay, J. C. Stream size and human influences on ecosystem production in river networks. Ecosphere 2, art87 (2011).Article 

Google Scholar 
30.Kirk, L., Hensley, R. T., Savoy, P., Heffernan, J. B. & Cohen, M. J. Estimating benthic light regimes improves predictions of primary production and constrains light-use efficiency in streams and rivers. Ecosystems https://doi.org/10.1007/s10021-020-00552-1 (2020).31.Julian, J. P., Doyle, M. W., Powers, S. M., Stanley, E. H. & Riggsbee, J. A. Optical water quality in rivers. Water Resour. Res. 44, W10411 (2008).32.Aitkenhead, J. A. & McDowell, W. H. Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales. Global Biogeochem. Cycles 14, 127–138 (2000).Article 

Google Scholar 
33.Harrison, J. A., Caraco, N. & Seitzinger, S. P. Global patterns and sources of dissolved organic matter export to the coastal zone: results from a spatially explicit, global model. Global Biogeochem. Cycles 19, GB4S04 (2005).34.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).Article 

Google Scholar 
35.Liu, S., Butman, D. E. & Raymond, P. A. Evaluating CO2 calculation error from organic alkalinity and pH measurement error in low ionic strength freshwaters. Limnol. Oceanogr. Methods 18, 606–622 (2020).36.Abril, G. et al. Technical Note: Large overestimation of (p_{{mathrm{CO}}_2}) calculated from pH and alkalinity in acidic, organic-rich freshwaters. Biogeosciences 12, 67–78 (2015).Article 

Google Scholar 
37.Duvert, C., Butman, D. E., Marx, A., Ribolzi, O. & Hutley, L. B. CO2 evasion along streams driven by groundwater inputs and geomorphic controls. Nat. Geosci. 11, 813–818 (2018).Article 

Google Scholar 
38.Rocher‐Ros, G., Sponseller, R. A., Lidberg, W., Mörth, C. & Giesler, R. Landscape process domains drive patterns of CO2 evasion from river networks. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10108 (2019).39.Richey, J. E., Melack, J. M., Aufdenkampe, A. K., Ballester, V. M. & Hess, L. L. Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2. Nature 416, 617–620 (2002).Article 

Google Scholar 
40.Guth, P. L. Drainage basin morphometry: a global snapshot from the shuttle radar topography mission. Hydrol. Earth Syst. Sci. 15, 2091–2099 (2011).Article 

Google Scholar 
41.Schneider, C. L. et al. Carbon dioxide (CO2) fluxes from terrestrial and aquatic environments in a high-altitude tropical catchment. J. Geophys. Res. Biogeosci. 125, e2020JG005844 (2020).Article 

Google Scholar 
42.Rocher‐Ros, G. et al. Metabolism overrides photo-oxidation in CO2 dynamics of Arctic permafrost streams. Limnol. Oceanogr. https://doi.org/10.1002/lno.11564 (2020).43.Dinsmore, K. J., Billett, M. F. & Dyson, K. E. Temperature and precipitation drive temporal variability in aquatic carbon and GHG concentrations and fluxes in a peatland catchment. Glob. Change Biol. 19, 2133–2148 (2013).Article 

Google Scholar 
44.Lynch, J. K., Beatty, C. M., Seidel, M. P., Jungst, L. J. & DeGrandpre, M. D. Controls of riverine CO2 over an annual cycle determined using direct, high temporal resolution (p_{{mathrm{CO}}_2}) measurements. J. Geophys. Res. Biogeosci. 115, G03016 (2010).45.Teodoru, C. R. et al. Dynamics of greenhouse gases (CO2, CH4, N2O) along the Zambezi River and major tributaries, and their importance in the riverine carbon budget. Biogeosciences 12, 2431–2453 (2015).Article 

Google Scholar 
46.Borges, A. V. et al. Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial–wetland connectivity. Biogeosciences 16, 3801–3834 (2019).Article 

Google Scholar 
47.Le, T. P. Q. et al. CO2 partial pressure and CO2 emission along the lower Red River (Vietnam). Biogeosciences 15, 4799–4814 (2018).Article 

Google Scholar 
48.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
49.Ulseth, A. J. et al. Distinct air–water gas exchange regimes in low- and high-energy streams. Nat. Geosci. 12, 259–263 (2019).Article 

Google Scholar 
50.Lapierre, J.-F., Guillemette, F., Berggren, M. & del Giorgio, P. A. Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems. Nat. Commun. 4, 2972 (2013).Article 

Google Scholar  More

1.Hansson, B. S. & Stensmyr, M. C. Evolution of insect olfaction. Neuron 72, 698–711 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Breer, H., Fleischer, J., Pregitzer, P. & Krieger, J. Molecular mechanism of insect olfaction: olfactory receptors. in Olfactory Concepts of Insect Control-Alternative to insecticides 93–114 (Springer, 2019).3.Robertson, H. M. Molecular evolution of the major arthropod chemoreceptor gene families. Annu. Rev. Entomol. 64, 227–242 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Yan, H. et al. Evolution, developmental expression and function of odorant receptors in insects. J. Exp. Biol. 223, jeb20821 (2020).Article 

Google Scholar 
5.Zhu, J., Iovinella, I., Dani, F. R., Pelosi, P. & Wang, G.  Chemosensory proteins: a versatile binding family. in Olfactory Concepts of Insect Control-Alternative to insecticides 147–169 (Springer, 2019).6.Larsson, M. C. et al. Or83b encodes a broadly expressed odorant receptor essential for Drosophila olfaction. Neuron 43, 703–714 (2004).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
8.Stengl, M. & Funk, N. W. The role of the coreceptor Orco in insect olfactory transduction. J. Comp. Physiol. A. 199, 897–909 (2013).CAS 
Article 

Google Scholar 
9.Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Annu. Rev. Entomol. 58, 373–391 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Rogers, M. E., Sun, M., Lerner, M. R. & Vogt, R. G. Snmp-1, a novel membrane protein of olfactory neurons of the silk moth Antheraea polyphemus with homology to the CD36 family of membrane proteins. J. Biol. Chem. 272, 14792–14799 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Jin, X., Ha, T. S. & Smith, D. P. SNMP is a signaling component required for pheromone sensitivity in Drosophila. Proc. Natl. Acad. Sci. 105, 10996–11001 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Scott, K. et al. A chemosensory gene family encoding candidate gustatory and olfactory receptors in Drosophila. Cell 104, 661–673 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Vosshall, L. B. & Stocker, R. F. Molecular architecture of smell and taste in Drosophila. Annu. Rev. Neurosci. 30, 505–533 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Clyne, P. J. et al. A novel family of divergent seven-transmembrane proteins: candidate odorant receptors in Drosophila. Neuron 22, 327–338 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Gao, Q. & Chess, A. Identification of candidate Drosophila olfactory receptors from genomic DNA sequence. Genomics 60, 31–39 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Vosshall, L. B., Amrein, H., Morozov, P. S., Rzhetsky, A. & Axel, R. A spatial map of olfactory receptor expression in the Drosophila antenna. Cell 96, 725–736 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Montagné, N., de Fouchier, A., Newcomb, R. D. & Jacquin-Joly, E. Advances in the identification and characterization of olfactory receptors in insects. in Progress in molecular biology and translational science, Vol. 130 55–80 (Elsevier, 2015).18.Liu, Y., Gu, S., Zhang, Y., Guo, Y. & Wang, G. Candidate olfaction genes identified within the Helicoverpa armigera antennal transcriptome. PLoS ONE 7, e48260 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Bengtsson, J. M. et al. Putative chemosensory receptors of the codling moth, Cydia pomonella, identified by antennal transcriptome analysis. PLoS ONE 7, e31620 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Gonzalez, F., Witzgall, P. & Walker, W. B. Antennal transcriptomes of three tortricid moths reveal putative conserved chemosensory receptors for social and habitat olfactory cues. Sci. Rep. 7, 41829 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Cao, D. et al. Identification of candidate olfactory genes in Chilo suppressalis by antennal transcriptome analysis. Int. J. Biol. Sci. 10, 846 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Andersson, M. N. et al. Antennal transcriptome analysis of the chemosensory gene families in the tree killing bark beetles, Ips typographus and Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae). BMC Genomics 14, 1–16 (2013).Article 
CAS 

Google Scholar 
23.Liu, S. et al. Identification of candidate chemosensory genes in the antennal transcriptome of Tenebrio molitor (Coleoptera: Tenebrionidae). Comp. Biochem. Physiol. D: Genomics Proteomics 13, 44–51 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Hu, P., Wang, J., Cui, M., Tao, J. & Luo, Y. Antennal transcriptome analysis of the Asian longhorned beetle Anoplophora glabripennis. Sci. Rep. 6, 1–12 (2016).Article 
CAS 

Google Scholar 
25.Engsontia, P. et al. The red flour beetle’s large nose: an expanded odorant receptor gene family in Tribolium castaneum. Insect Biochem. Mol. Biol. 38, 387–397 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Mitchell, R. F. et al. Sequencing and characterizing odorant receptors of the cerambycid beetle Megacyllene caryae. Insect Biochem. Mol. Biol. 42, 499–505 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Bin, S.-Y., Qu, M.-Q., Pu, X.-H., Wu, Z.-Z. & Lin, J.-T. Antennal transcriptome and expression analyses of olfactory genes in the sweetpotato weevil Cylas formicarius. Sci. Rep. 7, 1–14 (2017).ADS 
Article 
CAS 

Google Scholar 
28.Antony, B. et al. Identification of the genes involved in odorant reception and detection in the palm weevil Rhynchophorus ferrugineus, an important quarantine pest, by antennal transcriptome analysis. BMC Genomics 17, 69. https://doi.org/10.1186/s12864-016-2362-6 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Tang, Q. F. et al. Antennal transcriptome analysis of the maize weevil Sitophilus zeamais: Identification and tissue expression profiling of candidate odorant-binding protein genes. Arch. Insect Biochem. Physiol. 101, e21542 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
30.Hallett, R. et al. Aggregation pheromones of two Asian palm weevils, Rhynchophorus ferrugineus and R. vulneratus. Naturwissenschaften 80, 328–331 (1993).ADS 
CAS 
Article 

Google Scholar 
31.Peri, E. et al. Rhynchophorus ferrugineus: behavior, ecology, and communication. in Handbook of Major Palm Pests: Biology and Management, 105–130 (2017).32.Oehlschlager, A., Chinchilla, C. & Gonzalez, L. Optimization of a pheromone-baited trap for the American palm weevil Rhynchophorus palmarum (L.). in Proceedings of International Oil Palm Congress 645–660 (Kuala Lumpur, September, 1993).33.Gonzalez, F., Kharrat, S., Rodríguez, C., Calvo, C. & Oehlschlager, A. Research paper (integrated management: insects) red palm weevil (Rhynchophorus ferrugineus Olivier): recent advances. Arab J. Pl. Prot. 37, 178–187 (2019).
Google Scholar 
34.Hagley, E. A. The role of the palm weevil, Rhynchophorus palmarum, as a vector of red ring disease of coconuts. I. Results of preliminary investigations. J. Econ. Entomol. 56, 375–380 (1963).Article 

Google Scholar 
35.Chinchilla, C. M. The red ring-little leaf syndrome in oil palm and coconut. Bol. Tec Opo-CB 2, 113–136 (1988).
Google Scholar 
36.Gerber, K. & Giblin-Davis, R. M. Association of the red ring nematode and other nematode species with the palm weevil, Rhynchophorus palmarum. J. Nematol. 22, 143 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Oehlschlager, A. C., Chinchilla, C., Castillo, G. & Gonzalez, L. Control of red ring disease by mass trapping of Rhynchophorus palmarum (Coleoptera: Curculionidae). Florida Entomol. 85, 507–513 (2002).Article 

Google Scholar 
38.Rodríguez, C., Oehlschlager, A. & Chinchilla, C. Examination of critical components of Rhynchophorus palmarum pheromone traps. ASD Oil Palm Papers 46, 15 (2016).
Google Scholar 
39.Oehlschlager, C. Optimizing trapping of palm weevils and beetles. Acta Hortic. 736, 347–368. https://doi.org/10.17660/ActaHortic.2007.736.33 (2007).40.Rochat, D. et al. Rhynchophorus ferrugineus: Taxonomy, distribution, biology, and life cycle. in Handbook of Major Palm Pests: Biology and Management, 69–104 (2017).41.Antony, B. et al. Global transcriptome profiling and functional analysis reveal that tissue-specific constitutive overexpression of cytochrome P450s confers tolerance to imidacloprid in palm weevils in date palm fields. BMC Genomics 20, 1–23 (2019).CAS 
Article 

Google Scholar 
42.Antony, B., Johny, J. & Aldosari, S. A. Silencing the odorant binding protein RferOBP1768 reduces the strong preference of palm weevil for the major aggregation pheromone compound ferrugineol. Front. Physiol. 9, 252 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Antony, B. et al. Pheromone receptor of the globally invasive quarantine pest of the palm tree, the red palm weevil (Rhynchophorus ferrugineus). Mol. Ecol. 30, 1–15. https://doi.org/10.1111/mec.15874 (2021).44.Nagnan-Le Meillour, P., François, M.-C. & Jacquin-Joly, E. Identification and molecular cloning of putative odorant-binding proteins from the American palm weevil, Rhynchophorus palmarum L.. J. Chem. Ecol. 30, 1213–1223 (2004).CAS 
PubMed 
Article 

Google Scholar 
45.Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Boil. Evolut. 30, 2725–2729 (2013).CAS 
Article 

Google Scholar 
50.Edler, D., Klein, J., Antonelli, A. & Silvestro, D. raxmlGUI 2.0 beta: a graphical interface and toolkit for phylogenetic analyses using RAxML. BioRxiv, 800912 (2019).51.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Wojtasek, H., Hansson, B. S. & Leal, W. S. Attracted or repelled?—A matter of two neurons, one pheromone binding protein, and a chiral center. Biochem. Biophys. Res. Commun. 250, 217–222 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Diakite, M. M., Wang, J., Ali, S. & Wang, M.-Q. Identification of chemosensory gene families in Rhyzopertha dominica (Coleoptera: Bostrichidae). Can. Entomol. 148, 8–21 (2016).Article 

Google Scholar 
54.Vogt, R. G. et al. The insect SNMP gene family. Insect Biochem. Mol. Biol. 39, 448–456 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Mitchell, R. F., Schneider, T. M., Schwartz, A. M., Andersson, M. N. & McKenna, D. D. The diversity and evolution of odorant receptors in beetles (Coleoptera). Insect Mol. Biol. 29, 77–91 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Oehlschlager, A. C. et al. Development of a pheromone-based trapping system for Rhynchophorus palmarum (Coleoptera: Curculionidae). J. Econ. Entomol. 86, 1381–1392 (1993).Article 

Google Scholar 
57.Rochat, D. et al. Ecologie chimique des charançons des palmiers, Rhynchophorus spp.(Coleoptera). Oléagineux 48, 225–236 (1993).58.Hoddle, M. & Hoddle, C. Palmageddon: the invasion of California by the South American palm weevil is underway. CAPCA Advis 20, 40–44 (2017).
Google Scholar 
59.Witzgall, P., Kirsch, P. & Cork, A. Sex pheromones and their impact on pest management. J. Chem. Ecol. 36, 80–100 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Venthur, H. & Zhou, J.-J. Odorant receptors and odorant-binding proteins as insect pest control targets: A comparative analysis. Front. Physiol. 9, 1163 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Andersson, M. N., Keeling, C. I. & Mitchell, R. F. Genomic content of chemosensory genes correlates with host range in wood-boring beetles (Dendroctonus ponderosae, Agrilus planipennis, and Anoplophora glabripennis). BMC Genomics 20, 1–17 (2019).CAS 
Article 

Google Scholar 
62.Yang, H. et al. Molecular characterization, expression pattern and ligand-binding properties of the pheromone-binding protein gene from Cyrtotrachelus buqueti. Physiol. Entomol. 42, 369–378 (2017).CAS 
Article 

Google Scholar 
63.Jacquin-Joly, E., Vogt, R. G., François, M.-C. & Nagnan-Le Meillour, P. Functional and expression pattern analysis of chemosensory proteins expressed in antennae and pheromonal gland of Mamestra brassicae. Chem. Senses 26, 833–844 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Gu, S.-H. et al. Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition. PLoS ONE 7, e42871 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Liu, Y.-L., Guo, H., Huang, L.-Q., Pelosi, P. & Wang, C.-Z. Unique function of a chemosensory protein in the proboscis of two Helicoverpa species. J. Exp. Biol. 217, 1821–1826 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Peng, Y. et al. Identification of odorant binding proteins and chemosensory proteins in Microplitis mediator as well as functional characterization of chemosensory protein 3. PLoS ONE 12, e0180775 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Nomura, A., Kawasaki, K., Kubo, T. & Natori, S. Purification and localization of p10, a novel protein that increases in nymphal regenerating legs of Periplaneta americana (American cockroach). Int. J. Dev. Biol. 36, 391–398 (2002).
Google Scholar 
68.Maleszka, J., Foret, S., Saint, R. & Maleszka, R. RNAi-induced phenotypes suggest a novel role for a chemosensory protein CSP5 in the development of embryonic integument in the honeybee (Apis mellifera). Dev. Genes. Evol. 217, 189–196 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Benton, R., Vannice, K. S. & Vosshall, L. B. An essential role for a CD36-related receptor in pheromone detection in Drosophila. Nature 450, 289–293 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Zhang, H.-J. et al. A phylogenomics approach to characterizing sensory neuron membrane proteins (SNMPs) in Lepidoptera. Insect Biochem. Mol. Biol. 118, 103313 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Liu, S. et al. Molecular characterization of two sensory neuron membrane proteins from Chilo suppressalis (Lepidoptera: Pyralidae). Ann. Entomol. Soc. Am. 106, 378–384 (2013).CAS 
Article 

Google Scholar 
72.Liu, S. et al. Identification and characterization of two sensory neuron membrane proteins from Cnaphalocrocis medinalis (Lepidoptera: Pyralidae). Arch. Insect Biochem. Physiol. 82, 29–42 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Zhang, J., Liu, Y., Walker, W. B., Dong, S. L. & Wang, G. R. Identification and localization of two sensory neuron membrane proteins from Spodoptera litura (Lepidoptera: Noctuidae). Insect Sci. 22, 399–408 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
74.Giblin-Davis, R. M., Weissling, T. J., Oehlschlager, A. & Gonzalez, L. M. Field response of Rhynchophorus cruentatus (Coleoptera: Curculionidae) to its aggregation pheromone and fermenting plant volatiles. Florida Entomol. 77, 164–177 (1994).CAS 
Article 

Google Scholar 
75.Jaffé, K. et al. Chemical ecology of the palm weevil Rhynchophorus palmarum (L.) (Coleoptera: Curculionidae): Attraction to host plants and to a male-produced aggregation pheromone. J. Chem. Ecol. 19, 1703–1720 (1993).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Saïd, I., Renou, M., Morin, J.-P., Ferreira, J. M. & Rochat, D. Interactions between acetoin, a plant volatile, and pheromone in Rhynchophorus palmarum: Behavioral and olfactory neuron responses. J. Chem. Ecol. 31, 1789–1805 (2005).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
77.Mansourian, S. & Stensmyr, M. C. The chemical ecology of the fly. Curr. Opin. Neurobiol. 34, 95–102 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Andersson, M. N., Löfstedt, C. & Newcomb, R. D. Insect olfaction and the evolution of receptor tuning. Front. Ecol. Evol. 3, 53 (2015).
Google Scholar 
79.Caballero-Vidal, G. et al. Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
80.Yuvaraj, J. K. et al. Putative ligand binding sites of two functionally characterized bark beetle odorant receptors. BMC Biol. 19, 16 (2021). More

1.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 569–578. https://doi.org/10.1007/s00360-011-0631-x (2012).Article 

Google Scholar 
2.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).Article 
PubMed 

Google Scholar 
3.Reynolds, D. S., Shoemaker, K., von Oettingen, S. & Najjar, S. High rates of winter activity and arousals in two New England bat species: Implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208 (2017).Article 

Google Scholar 
4.Kunz, T. H. & Martin, R. A. Plecotus townsendii. Mamm. Species 175, 1–6 (1982).
Google Scholar 
5.Twente, J. W. Aspects of a population study of cavern-dwelling bats. J. Mamm. 36, 379–390 (1955).Article 

Google Scholar 
6.Humphrey, S. R. & Kunz, T. H. Ecology of a Pleistocene relict, the western big-eared bat (Plecotus townsendii), in the southern Great Plains. J. Mamm. 57, 470–494. https://doi.org/10.2307/1379297 (1976).Article 

Google Scholar 
7.Czenze, Z. J., Park, A. D. & Willis, C. K. R. Staying cold through dinner: Cold-climate bats rewarm with conspecifics but not sunset during hibernation. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 183, 859–866. https://doi.org/10.1007/s00360-013-0753-4 (2013).Article 

Google Scholar 
8.Pearson, O. P., Koford, M. R. & Pearson, A. K. Reproduction of the lump-nosed bat (Corynorhinus rafinesquei) in California. J. Mamm. 33, 273–320 (1952).Article 

Google Scholar 
9.Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lausen, C. L. & Barclay, R. M. R. Winter bat activity in the Canadian prairies. Can. J. Zool.-Rev. Can. Zool. 84, 1079–1086. https://doi.org/10.1139/z06-093 (2006).Article 

Google Scholar 
11.Thomas, D. W. & Cloutier, D. Evaporative water-loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

Google Scholar 
12.Ben-Hamo, M., Munoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577. https://doi.org/10.1242/jeb.078790 (2013).Article 
PubMed 

Google Scholar 
13.Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

Google Scholar 
14.Choate, J. R. & Anderson, J. M. Bats of jewel cave national monument, South Dakota. Prairie Nat. 29, 39–47 (1997).
Google Scholar 
15.Klüg-Baerwald, B. J., Gower, L. E., Lausen, C. L. & Brigham, R. M. Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada. Can. J. Zool. 94, 829–836. https://doi.org/10.1139/cjz-2016-0055 (2016).Article 

Google Scholar 
16.Johnson, J. S. et al. Migratory and winter activity of bats in Yellowstone National Park. J. Mamm. 98, 211–221. https://doi.org/10.1093/jmammal/gyw175 (2017).Article 

Google Scholar 
17.Norquay, K. & Willis, C. Hibernation phenology of Myotis lucifugus. J. Zool. 294, 85–92 (2014).Article 

Google Scholar 
18.Barclay, R. M. et al. Variation in the reproductive rate of bats. Can. J. Zool. 82, 688–693 (2004).Article 

Google Scholar 
19.Jonasson, K. A. & Willis, C. K. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061. https://doi.org/10.1371/journal.pone.0021061 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).Article 

Google Scholar 
21.Reeder, D. M., Field, K. A. & Slater, M. H. Balancing the costs of wildlife research with the benefits of understanding a panzootic disease, white-nose syndrome. ILAR J. 56, 275–282. https://doi.org/10.1093/ilar/ilv035 (2015).CAS 
Article 

Google Scholar 
22.Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

Google Scholar 
23.Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mamm. 76, 940–946. https://doi.org/10.2307/1382764 (1995).Article 

Google Scholar 
24.Furey, N. M. & Racey, P. A. Bats in the Anthropocene: Conservation of Bats in a Changing World 463–500 (Springer, 2016).
Google Scholar 
25.Sheffield, S. R., Shaw, J. H., Heidt, G. A. & McClenaghan, L. R. Guidelines for the protection of bat roosts. J. Mamm. 73, 707–710 (1992).
Google Scholar 
26.Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R. & Racey, P. A. Carpe noctem: The importance of bats as bioindicators. Endang. Species Res. 8, 93–115 (2009).Article 

Google Scholar 
27.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
Article 
PubMed 

Google Scholar 
28.Foley, J., Clifford, D., Castle, K., Cryan, P. & Ostfeld, R. S. Investigating and managing the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231. https://doi.org/10.1111/j.1523-1739.2010.01638.x (2011).Article 
PubMed 

Google Scholar 
29.Ingersoll, T. E., Sewall, B. J. & Amelon, S. K. Effects of white-nose syndrome on regional population patterns of 3 hibernating bat species. Conserv. Biol. 30, 1048–1059. https://doi.org/10.1111/cobi.12690 (2016).Article 
PubMed 

Google Scholar 
30.Minnis, A. M. & Lindner, D. L. Phylogenetic evaluation of Geomyces and allies reveals no close relatives of Pseudogymnoascus destructans, comb. nov., in bat hibernacula of eastern North America. Fungal Biol. 117, 638–649. https://doi.org/10.1016/j.funbio.2013.07.001 (2013).Article 
PubMed 

Google Scholar 
31.Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).ADS 
CAS 
Article 

Google Scholar 
32.Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

Google Scholar 
33.Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. U.S.A. 109, 6999–7003. https://doi.org/10.1073/pnas.1200374109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lilley, T. M. et al. White-nose syndrome survivors do not exhibit frequent arousals associated with Pseudogymnoascus destructans infection. Front. Zool. https://doi.org/10.1186/s12983-016-0143-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
35.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 313, R680–R686. https://doi.org/10.1152/ajpregu.00058.2017 (2017).CAS 
Article 

Google Scholar 
36.Knudsen, G. R., Dixon, R. D. & Amelon, S. K. Potential spread of white-nose syndrome of bats to the Northwest: Epidemiological considerations. Northwest Sci. 87, 292–306. https://doi.org/10.3955/046.087.0401 (2013).Article 

Google Scholar 
37.Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Cheng, T. L. et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. J. Anim. Ecol. 88, 591–600 (2019).Article 

Google Scholar 
39.Turner, J. M. et al. Conspecific disturbance contributes to altered hibernation patterns in bats with white-nose syndrome. Physiol. Behav. 140, 71–78 (2015).CAS 
Article 

Google Scholar 
40.Blazek, J. et al. Numerous cold arousals and rare arousal cascades as a hibernation strategy in European Myotis bats. J. Therm. Biol 82, 150–156. https://doi.org/10.1016/j.jtherbio.2019.04.002 (2019).Article 
PubMed 

Google Scholar 
41.Lorch, J. M. et al. First detection of bat white-nose syndrome in Western North America. mSphere 1(4), e00148. https://doi.org/10.1128/mSphere.00148-16 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS ONE https://doi.org/10.1371/journal.pone.0205647 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Whiting, J. C. et al. Bat hibernacula in caves of southern Idaho: Implications for monitoring and management. West. N. Am. Nat. 78, 165–173 (2018).Article 

Google Scholar 
44.Whiting, J. C. et al. Long-term bat abundance in sagebrush steppe. Sci. Rep. 8, 12288 (2018).ADS 
Article 

Google Scholar 
45.Call, R. S. et al. Maternity roosts of Townsend’s big-eared bats in lava tube caves of southern Idaho. Northwest Sci. 92, 158–165 (2018).ADS 
Article 

Google Scholar 
46.Clark, B. S., Clark, B. K. & Leslie, D. M. Seasonal variation in activity patterns of the endangered Ozark big-eared bat (Corynorhinus townsendii ingens). J. Mamm. 83, 590–598. https://doi.org/10.1644/1545-1542(2002)083%3c0590:sviapo%3e2.0.co;2 (2002).Article 

Google Scholar 
47.French, A. R. The patterns of mammalian hibernation. Am. Sci. 76, 568–575 (1988).ADS 

Google Scholar 
48.Reynolds, T. D., Connelly, J. W., Halford, D. K. & Arthur, W. J. Vertebrate fauna of the Idaho National Environmental Research Park. Gt. Basin Nat. 46, 513–527 (1986).
Google Scholar 
49.Genter, D. L. Wintering bats of the upper Snake River Plain: Occurrence in lava-tube caves. Gt. Basin Nat. 46, 241–244 (1986).
Google Scholar 
50.Gillies, K. E., Murphy, P. J. & Matocq, M. D. Hibernacula characteristics of Townsend’s big-eared bats in southeastern Idaho. Nat. Areas J. 34, 24–30 (2014).Article 

Google Scholar 
51.Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97(663–688), 2016. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

Google Scholar 
52.Schwab, N. A. & Mabee, T. J. Winter acoustic activity of bats in Montana. Northwest. Nat. 95, 13–27 (2014).Article 

Google Scholar 
53.Britzke, E. R., Slack, B. A., Armstrong, M. P. & Loeb, S. C. Effects of orientation and weatherproofing on the detection of bat echolocation calls. J. Fish Wildl. Manage. 1, 136–141. https://doi.org/10.3996/072010-jfwm-025 (2010).Article 

Google Scholar 
54.Skalak, S. L., Sherwin, R. E. & Brigham, R. M. Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods Ecol. Evol. 3, 490–502. https://doi.org/10.1111/j.2041-210X.2011.00177.x (2012).Article 

Google Scholar 
55.Miller, B. W. A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring. Acta Chiropt. 3, 93–105 (2001).
Google Scholar 
56.Nocera, T., Ford, W. M., Silvis, A. & Dobony, C. A. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort drum army installation, New York. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00633 (2019).Article 

Google Scholar 
57.Britzke, E. R., Gillam, E. H. & Murray, K. L. Current state of understanding of ultrasonic detectors for the study of bat ecology. Acta Theriol. 58, 109–117. https://doi.org/10.1007/s13364-013-0131-3 (2013).Article 

Google Scholar 
58.O’Farrell, M. J., Miller, B. W. & Gannon, W. L. Qualitative identification of free-flying bats using the Anabat detector. J. Mamm. 80, 11–23. https://doi.org/10.2307/1383203 (1999).Article 

Google Scholar 
59.Whiting, J. C., Doering, B. & Pennock, D. Acoustic surveys for local, free-flying bats in zoos: An engaging approach for bat education and conservation. J. Bat Res. Conserv. 12, 94–99. https://doi.org/10.14709/BarbJ.12.1.2019.12 (2019).Article 

Google Scholar 
60.O’Farrell, M. J. & Gannon, W. L. A comparison of acoustic versus capture techniques for the inventory of bats. J. Mamm. 80, 24–30. https://doi.org/10.2307/1383204 (1999).Article 

Google Scholar 
61.Stahlschmidt, P. & Bruhl, C. A. Bats as bioindicators—The need of a standardized method for acoustic bat activity surveys. Methods Ecol. Evol. 3, 503–508. https://doi.org/10.1111/j.2041-210X.2012.00188.x (2012).Article 

Google Scholar 
62.Avery, M. I. Winter activity of pipistrelle bats. J. Anim. Ecol. 54, 721–738. https://doi.org/10.2307/4374 (1985).Article 

Google Scholar 
63.McCulloch, C. E. & Neuhaus, J. M. Generalized linear mixed models. In Encyclopedia of Biostatistics (eds Armitage, P. & Colton, T.) (Wiley, 2005).
Google Scholar 
64.Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (Gen.) 135, 370–384 (1972).Article 

Google Scholar 
65.Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, 2007).
Google Scholar 
66.Consul, P. & Famoye, F. Generalized Poisson regression model. Commun. Stat. Theory Methods 21, 89–109 (1992).Article 

Google Scholar 
67.Aho, K. A. Foundational and Applied Statistics for Biologists using R (CRC Press, 2013).
Google Scholar 
68.Akaike, H. Selected Papers of Hirotugu Akaike 199–213 (Springer, 1998).
Google Scholar 
69.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A practical Information-Theoretic Approach 2nd edn. (Springer, 2002).
Google Scholar 
70.RCoreTeam. R: A Language and Environment for Statistical Computing (2020).71.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer, 2013).
Google Scholar 
72.Brooks, M. E. 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 
73.Perkins, J. M., Barss, J. M. & Peterson, J. Winter records of bats in Oregon and Washington. Northwest. Nat. 71, 59–62. https://doi.org/10.2307/3536594 (1990).Article 

Google Scholar 
74.Nagorsen, D. W. et al. Winter bat records for British Columbia. Northwest Nat. 74, 61–66 (1993).Article 

Google Scholar 
75.Hayman, D. T., Cryan, P. M., Fricker, P. D. & Dannemiller, N. G. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol. Evol. 8, 1813–1821 (2017).Article 

Google Scholar 
76.Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

Google Scholar 
77.Speakman, J. R. & Racey, P. A. Hibernal ecology of the pipistrelle bat: Energy expenditure, water requirements and mass-loss, implications for survial and the function of winter emergence flights. J. Anim. Ecol. 58, 797–813. https://doi.org/10.2307/5125 (1989).Article 

Google Scholar 
78.Lawrence, B. D. & Simmons, J. A. Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats. J. Acoust. Soc. Am. 71, 585–590 (1982).ADS 
CAS 
Article 

Google Scholar 
79.Dunbar, M. B. & Tomasi, T. E. Arousal patterns, metabolic rate, and an energy budget of eastern red bats (Lasiurus borealis) in winter. J. Mamm. 87, 1096–1102. https://doi.org/10.1644/05-mamm-a-254r3.1 (2006).Article 

Google Scholar 
80.Ford, W. M., Britzke, E. R., Dobony, C. A., Rodrigue, J. L. & Johnson, J. B. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. J. Fish Wildl. Manage. 2, 125–134. https://doi.org/10.3996/042011-jfwm-027 (2011).Article 

Google Scholar 
81.Bernard, R. F., Foster, J. T., Willcox, E. V., Parise, K. L. & McCracken, G. F. Molecular detection of the causative agent of white-nose syndrome on Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and two species of migratory bats in the southeastern USA. J. Wildl. Dis. 51, 519–522. https://doi.org/10.7589/2014-08-202 (2015).Article 
PubMed 

Google Scholar 
82.Dzal, Y., McGuire, L. P., Veselka, N. & Fenton, M. B. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biol. Lett. 7, 392–394 (2010).Article 

Google Scholar 
83.Brooks, R. T. Declines in summer bat activity in central New England 4 years following the initial detection of white-nose syndrome. Biodivers. Conserv. 20, 2537–2541. https://doi.org/10.1007/s10531-011-9996-0 (2011).Article 

Google Scholar 
84.Holloway, G. L. & Barclay, R. M. R. Myotis ciliolabrum. Mamm. Species 670, 1–5. https://doi.org/10.1644/1545-1410(2001)670%3c0001:mc%3e2.0.co;2 (2001).Article 

Google Scholar 
85.Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big brown bats during winter in a building hibernaculum. J. Mamm. 93, 497–503 (2012).Article 

Google Scholar 
86.Paige, K. N. Bats and barometric pressure: conserving limited energy and tracking insects from the roost. Funct. Ecol. 9, 463–467 (1995).Article 

Google Scholar 
87.Frick, W. F. Acoustic monitoring of bats, considerations of options for long-term monitoring. Therya 4, 69–78 (2013).ADS 
Article 

Google Scholar 
88.Whitaker, J. O. & Rissler, L. J. Winter activity of bats at a mine entrance in Vermillion County, Indiana. Am. Midl. Nat. 127, 52–59. https://doi.org/10.2307/2426321 (1992).Article 

Google Scholar  More

1.Carlson, A. K., Phelps, Q. E. & Graeb, B. D. S. Chemistry to conservation: Using otoliths to advance recreational and commercial fisheries management. J. Fish Biol. 90, 505–527 (2017).CAS 
PubMed 
Article 

Google Scholar 
2.Ward, R. D. Genetics in fisheries management. Hydrobiologia 420, 191–201 (2000).CAS 
Article 

Google Scholar 
3.Tracey, S. R., Lyle, J. M. & Duhamel, G. Application of elliptical Fourier analysis of otolith form as a tool for stock identification. Fish. Res. 77, 138–147 (2006).Article 

Google Scholar 
4.Ferguson, G. J., Ward, T. M. & Gillanders, B. M. Otolith shape and elemental composition: Complementary tools for stock discrimination of mulloway (Argyrosomus japonicus) in southern Australia. Fish. Res. 110, 75–83 (2011).Article 

Google Scholar 
5.Campana, S. E. & Casselman, J. M. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50(5), 1062-1083 (1993).Article 

Google Scholar 
6.Begg, G. A., Overholtz, W. J. & Munroe, N. J. The use of internal otolith morphometrics for identification of haddock (Melanogrammus aeglefinus) stocks on Georges Bank. Fish. Bull. 99, 1–1 (2001).
Google Scholar 
7.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Ansari, N. G. Truss morphometry and otolith microchemistry reveal stock discrimination in Clarias batrachus (Linnaeus, 1758) inhabiting the Gangetic river system. Fish. Res. 173, 294–302 (2016).Article 

Google Scholar 
8.Nazir, A. & Khan, M. A. Spatial and temporal variation in otolith chemistry and its relationship with water chemistry: Stock discrimination of Sperata aor. Ecol. Freshw. Fish 28, 499–511 (2019).Article 

Google Scholar 
9.Bird, J. L., Eppler, D. T. & Checkley, D. M. Jr. Comparisons of herring otoliths using Fourier series shape analysis. Can. J. Fish. Aquat. Sci. 43(6), 1228-1234 (1986).Article 

Google Scholar 
10.Castonguay, M., Simard, P. & Gagnon, P. Usefulness of Fourier analysis of otolith shape for Atlantic Mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48(2), 296-302 (1991).Article 

Google Scholar 
11.Friedland, K. D. & Reddin, D. G. Use of otolith morphology in stock discriminations of Atlantic Salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 51(1), 91-98 (1994).Article 

Google Scholar 
12.Vignon, M. & Morat, F. Environmental and genetic determinant of otolith shape revealed by a non-indigenous tropical fish. Mar. Ecol. Prog. Ser. 411, 231–241 (2010).ADS 
Article 

Google Scholar 
13.Campana, S. E., Chouinard, G. A., Hanson, J. M., Fréchet, A. & Brattey, J. Otolith elemental fingerprints as biological tracers of fish stocks. Fish. Res. 46, 343–357 (2000).Article 

Google Scholar 
14.Elsdon, T. S. & Gillanders, B. M. Reconstructing migratory patterns of fish based on environmental influences on otolith chemistry. Rev. Fish Biol. Fish. 13, 217–235 (2003).Article 

Google Scholar 
15.Stransky, C. Geographic variation of golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella) in the North Atlantic based on otolith shape analysis. ICES J. Mar. Sci. 62, 1691–1698 (2005).Article 

Google Scholar 
16.Grammer, G. L. et al. Coupling biogeochemical tracers with fish growth reveals physiological and environmental controls on otolith chemistry. Ecol. Monogr. 87, 487–507 (2017).Article 

Google Scholar 
17.Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454 (2018).Article 

Google Scholar 
18.Elsdon, T. S. & Gillanders, B. M. Fish otolith chemistry influenced by exposure to multiple environmental variables. J. Exp. Mar. Biol. Ecol. 313, 269–284 (2004).CAS 
Article 

Google Scholar 
19.Khan, M. A., Miyan, K., Khan, S., Patel, D. K. & Ansari, G. Studies on the elemental profile of otoliths and truss network analysis for stock discrimination of the threatened stinging catfish Heteropneustes fossilis (Bloch 1794) from the Ganga river and its tributaries. Zool. Stud. 51, 1195–1206 (2012).
Google Scholar 
20.Miyan, K., Khan, M. A. & Khan, S. Stock structure delineation using variation in otolith chemistry of snakehead, Channa punctata (Bloch, 1793), from three Indian rivers. J. Appl. Ichthyol. 30, 881–886 (2014).CAS 
Article 

Google Scholar 
21.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Prasad, S. Otolith fingerprints reveal stock discrimination of Sperata seenghala inhabiting the Gangetic river system. Ichthyol. Res. 63, 294–301 (2016).Article 

Google Scholar 
22.Fowler, A. M., Macreadie, P. I., Bishop, D. P. & Booth, D. J. Using otolith microchemistry and shape to assess the habitat value of oil structures for reef fish. Mar. Environ. Res. 106, 103–113 (2015).CAS 
PubMed 
Article 

Google Scholar 
23.Schilling, H. T. et al. Evaluating estuarine nursery use and life history patterns of Pomatomus saltatrix in eastern Australia. Mar. Ecol. Prog. Ser. 598, 187–199 (2018).ADS 
Article 

Google Scholar 
24.Biolé, F. G. et al. Fish stocks of Urophycis brasiliensis revealed by otolith fingerprint and shape in the Southwestern Atlantic Ocean. Estuar. Coast. Shelf Sci. 229, 106406 (2019).Article 
CAS 

Google Scholar 
25.Maguffee, A. C., Reilly, R., Clark, R. & Jones, M. L. Examining the potential of otolith chemistry to determine natal origins of wild Lake Michigan Chinook salmon. Can. J. Fish. Aquat. Sci. 76(11), 2035-2044 (2019).Article 

Google Scholar 
26.Tanner, S. E., Vasconcelos, R. P., Cabral, H. N. & Thorrold, S. R. Testing an otolith geochemistry approach to determine population structure and movements of European hake in the northeast Atlantic Ocean and Mediterranean Sea. Fish. Res. 125–126, 198–205 (2012).Article 

Google Scholar 
27.Andrade, H. et al. Ontogenetic movements of cod in Arctic fjords and the Barents Sea as revealed by otolith microchemistry. Polar Biol. 43, 409–421 (2020).Article 

Google Scholar 
28.Warton, D. I. Why you cannot transform your way out of trouble for small counts. Biometrics 74, 362–368 (2018).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
29.Foster, S. D. & Bravington, M. V. A Poisson-Gamma model for analysis of ecological non-negative continuous data. Environ. Ecol. Stat. 20, 533–552 (2013).MathSciNet 
Article 

Google Scholar 
30.Taylor, L. R. Aggregation, variance and the mean. Nature 189, 732–735 (1961).ADS 
Article 

Google Scholar 
31.Kendal, R. L., Coolen, I. & Laland, K. N. The role of conformity in foraging when personal and social information conflict. Behav. Ecol. 15, 269–277 (2004).Article 

Google Scholar 
32.Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).Article 

Google Scholar 
33.Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J. & Dunstan, P. K. Model-based thinking for community ecology. Plant Ecol. 216, 669–682 (2015).Article 

Google Scholar 
34.Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund– an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).Article 

Google Scholar 
35.Niku, J., Warton, D. I., Hui, F. K. C. & Taskinen, S. Generalized linear latent variable models for multivariate count and biomass data in ecology. J. Agric. Biol. Environ. Stat. 22, 498–522 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
36.Dunn, P. K. & Smyth, G. K. Randomized quantile residuals. J. Comput. Graph. Stat. 5, 236–244 (1996).
Google Scholar 
37.Dunn, P. K. & Smyth, G. K. Chapter 8: generalized linear models: Diagnostics. In Generalized Linear Models With Examples in R (eds. Dunn, P. K. & Smyth, G. K.) 297–331 (Springer, 2018). https://doi.org/10.1007/978-1-4419-0118-7_8.38.Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D. & Warton, D. I. Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411 (2015).Article 

Google Scholar 
39.Hui, F. K. C. Boral–Bayesian ordination and regression analysis of multivariate abundance Data in r. Methods Ecol. Evol. 7, 744–750 (2016).Article 

Google Scholar 
40.Popovic, G. C., Warton, D. I., Thomson, F. J., Hui, F. K. C. & Moles, A. T. Untangling direct species associations from indirect mediator species effects with graphical models. Methods Ecol. Evol. 10, 1571–1583 (2019).Article 

Google Scholar 
41.Jones, C. M., Palmer, M. & Schaffler, J. J. Beyond Zar: The use and abuse of classification statistics for otolith chemistry. J. Fish Biol. 90, 492–504 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Rahman, M. A. & Awal, S. Development of captive breeding, seed production and culture techniques of snakehead fish for species conservation and sustainable aquaculture. Int. J. Adv. Agric. Environ. Eng. 3, 117–120 (2016).
Google Scholar 
43.Khan, M. A., Khan, S. & Miyan, K. Stock identification of the Channa striata inhabiting the Gangetic River System using Truss Morphometry. Russ. J. Ecol. 50, 391–396 (2019).Article 

Google Scholar 
44.Phen, C., Thang, T. B., Baran, E. & Vann, L. S. Biological reviews of important Cambodian fish species, based on FishBase 2004. Volume 1: Channa striata; Channa micropeltes; Barbonymus altus; Barbonymus gonionotus; Cyclocheilichthys apogon; Cyclocheilichthys enoplos; Henicorhynchus lineatus; Henicorhynchus siamensis; Pangasius hypophthalmus; Pangasius djambal. (WorldFish Center and Inland Fisheries Research and Development Institute, 2005).45.War, M. & Haniffa, M. A. Growth and survival of larval snakehead Channa striatus (Bloch, 1793) fed different live feed organisms. Turk. J. Fish. Aquat. Sci. 11, 523–528 (2011).
Google Scholar 
46.Cagauan, A. G. Exotic aquatic species introduction in the Philippines for aquaculture—A threat to biodiversity or a boon to the economy?. J. Environ. Sci. Manag. 10, 48–62 (2007).
Google Scholar 
47.Jayaram, K. C. The Freshwater Fishes of the Indian Region (Narendra Publishing House, 1999).
Google Scholar 
48.Talwar, P. K. & Jhingran, A. G. Inland fishes of India and adjacent countries Vol. 2 (CRC Press, 1991).
Google Scholar 
49.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).50.Libungan, L. A. & Pálsson, S. ShapeR: An R package to study otolith shape variation among fish populations. PLoS ONE 10, e0121102 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).Article 

Google Scholar 
52.Turan, C. The use of otolith shape and chemistry to determine stock structure of Mediterranean horse mackerel Trachurus mediterraneus (Steindachner). J. Fish Biol. 69, 165–180 (2006).CAS 
Article 

Google Scholar 
53.Oksanen, J. vegan: Community Ecology Package. (2019).54.Venables, W. N. & Ripley, B. D. Modern applied statistics with S-PLUS (Springer Science & Business Media, 2013).
Google Scholar 
55.Warton, D. I. Raw data graphing: An informative but under-utilized tool for the analysis of multivariate abundances. Austral. Ecol. 33, 290–300 (2008).Article 

Google Scholar 
56.Begg, G. A., Friedland, K. D. & Pearce, J. B. Stock identification and its role in stock assessment and fisheries management: An overview. Fish. Res. 43, 1–8 (1999).Article 

Google Scholar 
57.Sengupta, B. Water Quality Status of Yamuna River (1999-2005), Assessment and Development of River Basin Series: ADSORBS/41/2006-07. Cent. Pollut. Control Board Delhi (2006).58.Bhardwaj, R., Gupta, A. & Garg, J. K. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Sci. 31, 52–66 (2017).Article 

Google Scholar  More

1.MacArthur, R. H., Wilson, E. O. The theory of island biogeography. in Monographs in Population Biology (Princeton University Press, Princeton, NJ, 1967)2.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography. in Monographs in Population Biology, Vol. 32 (Princeton University Press, Princeton, NJ, 2001).3.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
4.Ryther, J. Photosynthesis and fish production in the sea. Science 166, 72–76 (1969).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Cushing, D. A difference in structure between ecosystems in strongly stratified waters and in those that are only weakly stratified. J. Plankton Res. 11, 1–13 (1989).Article 

Google Scholar 
6.Barber, R. T. & Hiscock, M. R. A rising tide lifts all phytoplankton: growth response of other phytoplankton taxa in diatom‐dominated blooms. Glob. Biogeoch. Cycl. 20, GB4S03 (2006).
Google Scholar 
7.Siegel, D. A. et al. Global assessment of ocean carbon export by combining satellite observations and food-web models. Global Biogeochem. Cycl. 28, 181–196 (2014).CAS 
Article 

Google Scholar 
8.Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Irwin, A. J., Finkel, Z. V., Schofield, O. M. & Falkowski, P. G. Scaling-up from nutrient physiology to the size-structure of phytoplankton communities. J. Plankt. Res. 28, 459–471 (2006).Article 

Google Scholar 
10.Litchman, E., Klausmeier, C. A. & Yoshiyama, K. Contrasting size evolution in marine and freshwater diatoms. Proc. Natl Acad. Sci. USA 106, 2665–2670 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Tozzi, S., Schofield, O. & Falkowski, P. Historical climate change and ocean turbulence as selective agents for two key phytoplankton functional groups. Mar. Ecol. Prog. Ser. 274, 123–132 (2004).Article 

Google Scholar 
12.Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Gregg, W. W., Casey, N. W. & Rousseaux, C. S. Global surface ocean carbon estimates in a model forced by MERRA NASA Technical Report Series on Global Modeling and Data Assimilation. NASA TM-2013-104606, Vol. 31, 39 (2013).14.Hulburt, E. M. Competition for nutrients by marine phytoplankton in oceanic, coastal, and estuarine regions. Ecology 51, 475–484 (1970).Article 

Google Scholar 
15.Siegel, D. A. Resource competition in a discrete environment: why are plankton distributions paradoxical? Limnol. Oceanogr. 43, 1133–1146 (1998).Article 

Google Scholar 
16.Cyr, H., Peters, R. H. & Downing, J. A. Population density and community size structure: comparison of aquatic and terrestrial systems. Oikos 80, 139–149 (1997).Article 

Google Scholar 
17.White, E. P., Ernest, S. M., Kerkhoff, A. J. & Enquist, B. J. Relationships between body size and abundance in ecology. Trends Ecol. Evol. 22, 323–330 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
18.McCauley, D. J. et al. On the prevalence and dynamics of inverted trophic pyramids and otherwise top-heavy communities. Ecol. Lett. 21, 439–454 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
19.West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.West, G. B., Brown, J. H. & Enquist, B. J. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284, 1677–1679 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Sheldon, R. W., Prakash, A. & Sutcliffe, W. Jr The size distribution of particles in the Ocean 1. Limnol. Oceanogr. 17, 327–340 (1972).Article 

Google Scholar 
22.Jonasz, M. & Fournier, G. Light Scattering by Particles in Water: Theoretical and Experimental Foundations. (Elsevier, 2011).23.Huete-Ortega, M., Cermeno, P., Calvo-Díaz, A. & Maranon, E. Isometric size-scaling of metabolic rate and the size abundance distribution of phytoplankton. Proc. Royal Soc. B 279, 1815–1823 (2012).Article 

Google Scholar 
24.Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).Article 

Google Scholar 
25.Riley, G. A., Stommel, H. M., Bumpus, D. F. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection 12 (Yale Univ., New Haven, CT, 1949)26.Evans, G. T. & Parslow, J. S. A model of annual plankton cycles. Biol. Oceanogr. 3, 327–347 (1985).
Google Scholar 
27.Margalef, R. Perspectives in Ecological Theory. 111 pp (Univ. Chicago Press, Chicago, Ill, 1968).28.Behrenfeld, M. J. & Boss, E. S. Resurrecting the ecological underpinnings of ocean plankton blooms. Ann. Rev. Mar. Sci. 6, 167–194 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context of phytoplankton annual cycles. Glob. Change Biol. 24, 55–77 (2018).Article 

Google Scholar 
30.Strom, S. L. & Buskey, E. J. Feeding, growth, and behavior of the thecate heterotrophic dinoflagellate Oblea rotunda. Limnol. Oceanogr. 38, 965–977 (1993).Article 

Google Scholar 
31.Strom, S. L., Macri, E. L. & Olson, M. B. Microzooplankton grazing in the coastal Gulf of Alaska: Variations in top-down control of phytoplankton. Limnol. Oceanogr. 52, 1480–1494 (2007).Article 

Google Scholar 
32.Wirtz, K. W. Who is eating whom? Morphology and feeding type determine the size relation between planktonic predators and their ideal prey. Mar. Ecol. Progr. Ser. 445, 1–12 (2012).Article 

Google Scholar 
33.Kiørboe, T. How zooplankton feed: mechanisms, traits and trade-offs. Biol. Rev. 86, 311–339 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Hansen, B., Bjornsen, P. K. & Hansen, P. J. The size ratio between planktonic predators and their prey. Limnol. Oceanogr. 39, 395–403 (1994).Article 

Google Scholar 
35.Sommer, U. & Sommer, F. Cladocerans versus copepods: the cause of contrasting top–down controls on freshwater and marine phytoplankton. Oecologia 147, 183–194 (2006).PubMed 
Article 

Google Scholar 
36.Hébert, M.-P., Beisner, B. E. & Maranger, R. Linking zooplankton communities to ecosystem functioning: Toward an effect-trait framework. J. Plankton Res. 39, 3–12 (2017).Article 
CAS 

Google Scholar 
37.Fuchs, H. L. & Franks, P. J. Plankton community properties determined by nutrients and size-selective feeding. Mar. Ecol. Progr. Ser. 413, 1–15 (2010).Article 

Google Scholar 
38.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. USA 107, 15129–15134 (2010).CAS 
PubMed 
Article 

Google Scholar 
39.Dadon-Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
40.Antoine, D., Andre, J. M. & Morel, A. Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll. Global Biogeochem. Cycl. 10, 57–69 (1996).CAS 
Article 

Google Scholar 
41.Brewin, R. J. W. et al. A three-component model of phytoplankton size class for the Atlantic Ocean. Ecol. Model. 221, 1472–1483 (2010).CAS 
Article 

Google Scholar 
42.Marañón, E., Cermeño, P., Latasa, M. & Tadonléké, R. D. Temperature, resources, and phytoplankton size structure in the ocean. Limnol. Oceanogr. 5, 1266–1278 (2012).Article 

Google Scholar 
43.Kerr, S. R., Dickie, L. M. The Biomass Spectrum: a Predator-prey Theory of Aquatic Production (Columbia University Press, 2001).44.Behrenfeld, M. J., et al. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nat. Geosci. 2017; https://doi.org/10.1038/NGEO2861.45.Kiorboe, T. Turbulence, phytoplankton cell size, and the structure of pelagic food-webs. Adv. Mar. Biol. 29, 1–72 (1993).Article 

Google Scholar 
46.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer–resource interaction parameters. J. Animal Ecol. 81, 1193–1201 (2012).Article 

Google Scholar 
47.Smetacek, V. Diatoms and the ocean carbon cycle. Protist 150, 25–32 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Smetacek, V., Assmy, P. & Henjes, J. The role of grazing in structuring Southern Ocean pelagic ecosystems and biogeochemical cycles. Antarct. Sci. 16, 541–558 (2004).Article 

Google Scholar 
49.Behrenfeld, M. J., Halsey, K. H., Boss, E., Karp-Boss, L., Milligan, A. J. & Peers, G. Thoughts on the evolution and ecological niche of diatoms. Ecol. Monogr. 2021; in press.50.Glibert, P. M. Margalef revisited: a new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology. Harmful Algae 55, 25–30 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanolog. Acta 1, 493–509 (1978).
Google Scholar 
52.Cullen, J. J. & MacIntyre, J. G. Behavior, physiology and the niche of depth-regulating phytoplankton. Nato ASI Ser. G Ecol. Sci. 41, 559–580 (1998).53.Kemp, A. E. & Villareal, T. A. The case of the diatoms and the muddled mandalas: Time to recognize diatom adaptations to stratified waters. Prog. Oceanogr. 167, 138–149 (2018).Article 

Google Scholar 
54.Kudela, R. M. Does horizontal mixing explain phytoplankton dynamics? Proc. Natl Acad. Sci. USA 107, 18235–18236 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Wyatt, T. Margalef’s mandala and phytoplankton bloom strategies. Deep Sea Res. II 101, 32–49 (2014).Article 

Google Scholar 
56.Waite, A., Fisher, A., Thompson, P. A. & Harrison, P. J. Sinking rate versus cell volume relationships illuminate sinking rate control mechanisms in marine diatoms. Mar. Ecol. Prog. Ser. 157, 97–108 (1997).Article 

Google Scholar 
57.Moore, J. K. & Villareal, T. A. Size-ascent rate relationships in positively buoyant marine diatoms. Limnol. Oceanogr. 41, 1514–1520 (1996).Article 

Google Scholar 
58.Bienfang, P. & Szyper, J. Effects of temperature and salinity on sinking rates of the centric diatom Ditylum brightwellii. Biol. Oceanogr. 1, 211–223 (1982).
Google Scholar 
59.Bienfang, P., Szyper, J. & Laws, E. Sinking rate and pigment responses to light-limitation of a marine diatom – implications to dynamics of chlorophyll maximum layers. Oceanolog. Acta 6, 55–62 (1983).CAS 

Google Scholar 
60.Villareal, T. A., Pilskaln, C. H., Montoya, J. P. & Dennett, M. Upward nitrate transport by phytoplankton in oceanic waters: balancing nutrient budgets in oligotrophic seas. PeerJ 2, e302 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Irigoien, X., Flynn, K. J. & Harris, R. P. Phytoplankton blooms: a “loophole” in micozooplankton grazing impact? J. Plankton Res. 27, 313–321 (2005).Article 

Google Scholar 
62.Bolaños, L. M., et al. Small phytoplankton dominate western North Atlantic biomass. ISME J: 1–12, https://doi.org/10.1038/s41396-020-0636-0 (2020).63.Guillard, R., Kilham, P. The ecology of marine planktonic diatoms. in The Biology of Diatoms, Vol. 13, 372–469 (Blackwell Oxford, 1977).64.Malviya, S. et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc. Natl Acad. Sci. USA 113, E1516–E1525 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Barton, A. D., Finkel, Z. V., Ward, B. A., Johns, D. G. & Follows, M. J. On the roles of cell size and trophic strategy in North Atlantic diatom and dinoflagellate communities. Limnol. Oceanogr. 58, 254–266 (2013).Article 

Google Scholar 
66.Edwards, K. F. Mixotrophy in nanoflagellates across environmental gradients in the ocean. Proc. Natl Acad. Sci. USA 116, 6211–6220 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean. J. Phycol. 38, 844–861 (2002).Article 

Google Scholar 
68.Fauchereau, N., Tagliabue, A., Bopp, L. & Monteiro, P. M. The response of phytoplankton biomass to transient mixing events in the Southern Ocean. Geophys. Res. Lett. 38, L17601 (2011).Article 

Google Scholar 
69.Wolfe, G. V., Steinke, M. & Kirst, G. O. Grazing-activated chemical defence in a unicellular marine alga. Nature 387, 894–897 (1997).CAS 
Article 

Google Scholar 
70.Colin, S. P. & Dam, H. G. Effects of the toxic dinoflagellate Alexandrium fundyense on the copepod Acartia hudsonica: a test of the mechanisms that reduce ingestion rates. Mar. Ecol. Prog. Ser. 248, 55–65 (2003).Article 

Google Scholar 
71.Van Donk, E., Ianora, A. & Vos, M. Induced defences in marine and freshwater phytoplankton: a review. Hydrobiol. 668, 3–19 (2011).Article 
CAS 

Google Scholar 
72.Pohnert, G., Steinke, M. & Tollrian, R. Chemical cues, defense metabolites and the shaping of pelagic interspecific interactions. Trends Ecol. Evol. 22, 198–204 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
73.DeMott, W. R. & Moxter, F. Foraging cyanobacteria by copepods: responses to chemical defense and resource abundance. Ecology 72, 1820–1834 (1991).Article 

Google Scholar 
74.Ger, K. A., Naus-Wiezer, S., De Meester, L. & Lürling, M. Zooplankton grazing selectivity regulates herbivory and dominance of toxic phytoplankton over multiple prey generations. Limnol. Oceanogr. 64, 1214–1227 (2019).Article 

Google Scholar 
75.Smayda, T. J. & Reynolds, C. S. Community assembly in marine phytoplankton: application of recent models to harmful dinoflagellate blooms. J. Plankt. Res. 23, 447–461 (2001).Article 

Google Scholar 
76.Acevedo-Trejos, E., Brandt, G., Bruggeman, J. & Merico, A. Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean. Sci. Rep 5, 8918 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Cuesta, J. A., Delius, G. W. & Law, R. Sheldon spectrum and the plankton paradox: two sides of the same coin—a trait-based plankton size-spectrum model. J. Math. Biol. 76, 67–96 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
78.Hutchinson, G. E. Ecological aspects of succession in natural populations. Amer. Nat. 75, 406–418 (1941).Article 

Google Scholar 
79.Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).CAS 
Article 

Google Scholar 
80.Tilman, D., Mattson, M. & Langer, S. Competition and nutrient kinetics along a temperature gradient: An experimental test of a mechanistic approach to niche theory 1. Limnol. Oceanogr. 26, 1020–1033 (1981).Article 

Google Scholar 
81.Sommer, U. Nutrient competition between phytoplankton species in multispecies chemostat experiments. Archiv hydrobiol. 96, 399–416 (1983).
Google Scholar 
82.Sommer, U. Comparison between steady state and non-steady state competition: experiments with natural phytoplankton. Limnol. Oceanogr. 30, 335–346 (1985).CAS 
Article 

Google Scholar 
83.Tilman, D. Resource Competition and Community Structure (Princeton University Press, 1982).84.Sommer, U. The role of competition for resources in phytoplankton succession. in Plankton Ecology. Berlin, Heidelberg: Springer. 1989, pp. 57-106.85.Burd, A. B. & Jackson, G. A. Particle aggregation. Annu. Rev. Mar. Sci. 1, 65–90 (2009).Article 

Google Scholar 
86.Kahl, L. A., Vardi, A. & Schofield, O. Effects of phytoplankton physiology on export flux. Mar. Ecol. Prog. Ser. 354, 3–19 (2008).CAS 
Article 

Google Scholar 
87.Guidi, L. et al. Effects of phytoplankton community on production, size and export of large aggregates: a world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963 (2009).Article 

Google Scholar 
88.Kiørboe, T., Lundsgaard, C., Olesen, M. & Hansen, J. L. S. Aggregation and sedimentation processes during a spring phytoplankton bloom: a field experiment to test coagulation theory. J. Mar. Res. 52, 297–323 (1994).Article 

Google Scholar 
89.Prairie, J. C., Montgomery, Q. W., Proctor, K. W. & Ghiorso, K. S. Effects of phytoplankton growth phase on settling properties of marine aggregates. J. Mar. Sci. Engineer. 7, 265 (2019).Article 

Google Scholar 
90.Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 6237 (2015).Article 
CAS 

Google Scholar 
91.Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Ann. Rev. Mar. Sci. 6, 339–367 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Chisholm, S. W. et al. A novel free-living prochlorophyte abundant in the oceanic euphotic zone. Nature 334, 340–343 (1988).Article 

Google Scholar 
94.Caputo, A., Nylander, J. A. & Foster, R. A. The genetic diversity and evolution of diatom-diazotroph associations highlights traits favoring symbiont integration. FEMS Microbiol. Lett. 366, fny297 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
95.Decelle, J. et al. An original mode of symbiosis in open ocean plankton. Proc. Natl Acad. Sci. USA 109, 18000–18005 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Decelle, J. et al. Algal remodeling in a ubiquitous planktonic photosymbiosis. Curr. Biol. 29, 968–978 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): science motive and mission overview. Front. Mar. Sci. 6, 122 (2019).Article 

Google Scholar 
98.Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).CAS 
Article 

Google Scholar  More

1.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).CAS 
Article 

Google Scholar 
2.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809 (1992).Article 

Google Scholar 
3.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. 124, 1056–1070 (2019).Article 

Google Scholar 
4.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California Current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).5.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424 (2020).6.Hobson, E. S. & Chess, J. Trophic relations of the blue rockfish, Sebastes mystinus, in a coastal upwelling system off northern California. in Fishery Bulletin, Vol. 86, 715–743 (National Marine Fisheries Service, 1988).7.Bulman, C. M., He, X. & Koslow, J. A. Trophic ecology of the mid-slope demersal fish community off Southern Tasmania, Australia. Mar. Freshw. Res. 53, 59–72 (2002).Article 

Google Scholar 
8.Harbison, G. R. The parasites and predators of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).9.James, G. D. & Stahl, J. -C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. N. Z. J. Mar. Freshw. Res. 34, 435–454 (2000).Article 

Google Scholar 
10.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90 (2001).Article 

Google Scholar 
11.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions (Phocarctos hookeri) at the Auckland Islands. Wildl. Res. 28, 291–298 (2001).Article 

Google Scholar 
12.Lindley, J. A., Hernández, F., Scatllar, J. & Docoito, J. Funchalia sp. (Crustacea: Penaeidae) associated with Pyrosoma atlanticum (Thaliacea: Pyrosomidae) off the Canary Islands. J. Mar. Biol. Assoc. UK 81, 173–174 (2001).Article 

Google Scholar 
13.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).CAS 
Article 

Google Scholar 
14.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
15.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Sherr E. & Sherr B. Understanding roles of microbes in marine pelagic food webs: a brief history. in Microbial Ecology of the Oceans 27–44 (John Wiley & Sons Ltd, 2008).17.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743 (2019).Article 

Google Scholar 
19.Gauns, M., Mochemadkar, S., Pratihary, A., Roy, R. & Naqvi, S. W. A. Biogeochemistry and ecology of Pyrosoma spinosum from the Central Arabian Sea. Zool. Stud. 54, 3 (2015).Article 
CAS 

Google Scholar 
20.Bowlby, M. R., Widder, E. A. & Case, J. F. Patterns of stimulated bioluminescence in two pyrosomes (Tunicata: Pyrosomatidae). Biol. Bull. 179, 340–350 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493 (2010).Article 

Google Scholar 
22.Swift, E., Biggley, W. H. & Napora, T. A. The bioluminescence emission spectra of Pyrosoma atlanticum, P. spinosum (Tunicata), Euphausia tenera (Crustacea) and Gonostoma sp. (Pisces). J. Mar. Biol. Assoc. UK 57, 817–823 (1977).23.Martínez‐García, M. et al. Ammonia-oxidizing Crenarchaeota and nitrification inside the tissue of a colonial ascidian. Environ. Microbiol. 10, 2991–3001 (2008).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
24.Donia, M. S. et al. Complex microbiome underlying secondary and primary metabolism in the tunicate-Prochloron symbiosis. Proc. Natl Acad. Sci. 108, E1423–E1432 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kwan, J. C. et al. Host control of symbiont natural product chemistry in cryptic populations of the tunicate Lissoclinum patella. PLoS ONE 9, e95850 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Purcell, J. E. & Arai, M. N. Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia. 451, 27–44 (2001).Article 

Google Scholar 
27.Delannoy, C. M. J., Houghton, J. D. R., Fleming, N. E. C. & Ferguson, H. W. Mauve stingers (Pelagia noctiluca) as carriers of the bacterial fish pathogen Tenacibaculum maritimum. Aquaculture. 311, 255–257 (2011).Article 

Google Scholar 
28.Lee, M. D., Kling, J. D., Araya, R. & Ceh, J. Jellyfish life stages shape associated microbial communities, while a core microbiome is maintained across all. Front. Microbiol. 9, 1534 (2018).29.Troussellier, M., Escalas, A., Bouvier, T. & Mouillot, D. Sustaining rare marine microorganisms: macroorganisms as repositories and dispersal agents of microbial diversity. Front. Microbiol. 8 (2017).30.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the Great Pyrosome Bloom of 2017. PICES Press; Sidney Vol. 26, 22–27 (Winter, 2018).31.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Miller, R. R. et al. Distribution of pelagic Thaliaceans, Thetys vagina and Pyrosoma Atlanticum, during a period of mass occurrence within the California current. CalCOFI Rep. 60, (2019).33.Guigand, C. M., Cowen, R. K., Llopiz, J. K. & Richardson, D. E. A coupled asymmetrical multiple opening closing net with environmental sampling system. Mar. Technol. Soc. J. 39, 22–24 (2005).34.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 
Article 
PubMed Central 

Google Scholar 
35.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
Article 

Google Scholar 
37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
39.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
42.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Duperron, S. Microbial Symbioses 168 p. (Elsevier, 2016).45.Schmitt, S. et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 6, 564–576 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8 (2017).47.Urbanczyk, H., Ast, J. C., Higgins, M. J., Carson, J. & Dunlap, P. V. Reclassification of Vibrio fischeri, Vibrio logei, Vibrio salmonicida and Vibrio wodanis as Aliivibrio fischeri gen. nov., comb. nov., Aliivibrio logei comb. nov., Aliivibrio salmonicida comb. nov. and Aliivibrio wodanis comb. nov. Int. J. Syst. Evol. Microbiol. 57, 2823–2829 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Stecher, G., Tamura, K. & Kumar, S. Molecular Evolutionary Genetics Analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Booth, B. C. Marine phytoplankton. A guide to naked flagellates and coccolithophorids (C. R. Tomas [ed.]). Limnol. Oceanogr. 39, 982–983 (1994).Article 

Google Scholar 
51.Halse, G. R. & Syvertsen, E. E. Chapter 2—marine diatoms. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 5–385 (Academic Press, 1996).52.Steidinger, K. A. & Tangen, K. Chapter 3—dinoflagellates. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 387–584 (Academic Press, 1996).53.Daniels, C. & Breitbart, M. Bacterial communities associated with the ctenophores Mnemiopsis leidyi and Beroe ovata. FEMS Microbiol. Ecol. 82, 90–101 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Kramar, M. K., Tinta, T., Lučić, D., Malej, A. & Turk, V. Bacteria associated with moon jellyfish during bloom and post-bloom periods in the Gulf of Trieste (northern Adriatic). PLoS ONE 14, e0198056 (2019).Article 
CAS 

Google Scholar 
55.Hernandez-Agreda, A., Leggat, W., Bongaerts, P., Herrera, C. & Ainsworth, T. D. Rethinking the coral microbiome: simplicity exists within a diverse microbial biosphere. mBio 9, e00812–18 (2018).56.Webster, N. S. & Bourne, D. Bacterial community structure associated with the Antarctic soft coral, Alcyonium antarcticum. FEMS Microbiol. Ecol. 59, 81–94 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Rodrigues, C. F., Hilário, A., Cunha, M. R., Weightman, A. J. & Webster, G. Microbial diversity in Frenulata (Siboglinidae, Polychaeta) species from mud volcanoes in the Gulf of Cadiz (NE Atlantic). Antonie Van Leeuwenhoek 100, 83–98 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
58.McCann, J., Stabb, E. V., Millikan, D. S. & Ruby, E. G. Population dynamics of Vibrio fischeri during Infection of Euprymna scolopes. Appl. Environ. Microbiol. 69, 5928–5934 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Hammann, S., Moss, A. & Zimmer, M. Sterile surfaces of Mnemiopsis leidyi; (Ctenophora) in bacterial suspension—a key to invasion success? Open J. Mar. Sci. 05, 237–246 (2015).Article 

Google Scholar 
60.Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 https://doi.org/10.1093/femsle/fnz117 (2019).61.Nedashkovskaya, O. I., Kukhlevskiy, A. D., Zhukova, N. V. & Kim, S. B. Amylibacter ulvae sp. nov., a new alphaproteobacterium isolated from the Pacific green alga Ulva fenestrata. Arch. Microbiol. 198, 251–256 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Burke, C., Thomas, T., Lewis, M., Steinberg, P. & Kjelleberg, S. Composition, uniqueness and variability of the epiphytic bacterial community of the green alga Ulva australis. ISME J. 5, 590–600 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Catão, E. C. P. et al. Shear stress as a major driver of marine biofilm communities in the NW Mediterranean Sea. Front. Microbiol. 10 (2019).64.Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Bondoso, J. et al. Roseimaritima ulvae gen. nov., sp. nov. and Rubripirellula obstinata gen. nov., sp. nov. two novel planctomycetes isolated from the epiphytic community of macroalgae. Syst. Appl. Microbiol. 38, 8–15 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Zhu, P., Li, Q. & Wang, G. Unique microbial signatures of the Alien Hawaiian marine sponge Suberites zeteki. Microb. Ecol. 55, 406–414 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Pimentel-Elardo, S., Wehrl, M., Friedrich, A. B., Jensen, P. R. & Hentschel, U. Isolation of planctomycetes from Aplysina sponges. Aquat. Microb. Ecol. 33, 239–245 (2003).Article 

Google Scholar 
68.da Silva Oliveira, F. A. et al. Microbial epibionts of the colonial ascidians Didemnum galacteum and Cystodytes sp. Symbiosis 59, 57–63 (2013).Article 

Google Scholar 
69.Yakimov, M. M. et al. Phylogenetic survey of metabolically active microbial communities associated with the deep-sea coral Lophelia pertusa from the Apulian plateau, Central Mediterranean Sea. Deep Sea Res. A Oceanogr. Res. Pap. 53, 62–75 (2006).Article 

Google Scholar 
70.Duque-Alarcón, A., Santiago-Vázquez, L. Z. & Kerr, R. G. A microbial community analysis of the octocoral Eunicea fusca. Electron. J. Biotechnol. 15, 15–15 (2012).
Google Scholar 
71.Wiegand, S., Jogler, M. & Jogler, C. On the maverick Planctomycetes. FEMS Microbiol. Rev. 42, 739–760 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Lage, O. M. & Bondoso, J. Planctomycetes and macroalgae, a striking association. Front. Microbiol. 5 (2014).73.Ward, A. C. & Bora, N. Diversity and biogeography of marine Actinobacteria. Curr. Opin. Microbiol. 9, 279–286 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Hahn, M. W. Description of seven candidate species affiliated with the phylum Actinobacteria, representing planktonic freshwater bacteria. Int. J. Syst. Evol. Microbiol. 59, 112–117 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Gandhimathi, R. et al. Antimicrobial potential of sponge associated marine actinomycetes. J. Mycol. Méd. 18, 16–22 (2008).Article 

Google Scholar 
76.Abdelmohsen, U. R., Bayer, K. & Hentschel, U. Diversity, abundance and natural products of marine sponge-associated actinomycetes. Nat. Prod. Rep. 31, 381–399 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Wu, Z. et al. A new tetrodotoxin-producing actinomycete, Nocardiopsis dassonvillei, isolated from the ovaries of puffer fish Fugu rubripes. Toxicon. 45, 851–859 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Reichenbach, H. The ecology of the myxobacteria. Environ. Microbiol. 1, 15–21 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Marshall, R. C. & Whitworth, D. E. Is “Wolf-Pack” predation by antimicrobial bacteria cooperative? Cell behaviour and predatory mechanisms indicate profound selfishness, even when working alongside Kin. BioEssays 41, 1800247 (2019).Article 

Google Scholar 
80.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Wang, Z., Kadouri, D. E. & Wu, M. Genomic insights into an obligate epibiotic bacterial predator: Micavibrio aeruginosavorus ARL-13. BMC Genomics 12, 453 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Garcia, G. D. et al. Metagenomic analysis of healthy and white plague-affected Mussismilia braziliensis corals. Microb. Ecol. 65, 1076–1086 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Rosales, S. M. et al. Microbiome differences in disease-resistant vs. susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Evans, A. G. L. et al. Predatory activity of Myxococcus xanthus outer-membrane vesicles and properties of their hydrolase cargo. Microbiology 158, 2742–2752 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Sudo, S. & Dworkin, M. Bacteriolytic enzymes produced by Myxococcus xanthus. J. Bacteriol. 110, 236–245 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Tessler, M. et al. A putative chordate luciferase from a cosmopolitan tunicate indicates convergent bioluminescence evolution across phyla. Sci. Rep. 10, 17724 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Berger, A. et al. Microscopic and Genetic Characterization of Bacterial Symbionts With Bioluminescent Potential in Pyrosoma Atlanticum. Frontiers in Marine Science. 8 https://doi.org/10.3389/fmars.2021.606818 (2021).88.Leisman, G., Cohn, D. H. & Nealson, K. H. Bacterial origin of luminescence in marine animals. Science 208, 1271–1273 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Mackie, G. O. & Bone, Q. Luminescence and associated effector activity in Pyrosoma (Tunicata: Pyrosomida). Proc. R. Soc. Lond. B Biol. Sci. 202, 483–495 (1978).Article 

Google Scholar 
90.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Takemura, A. F., Chien, D. M. & Polz M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5 (2014).92.Barnes, E. M., Carter, E. L. & Lewis, J. D. Predicting microbiome function across space is confounded by strain-level differences and functional redundancy across taxa. Front. Microbiol. 11 (2020).93.Tian, L. et al. Deciphering functional redundancy in the human microbiome. bioRxiv 176313 https://doi.org/10.1101/176313 (2017).94.Kaeding, A. J. et al. Phylogenetic diversity and cosymbiosis in the bioluminescent symbioses of “Photobacterium mandapamensis”. Appl. Environ. Microbiol. 73, 3173–3182 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Baker, L. J. et al. Diverse deep-sea anglerfishes share a genetically reduced luminous symbiont that is acquired from the environment. eLife 8 e47606 (2019).96.Godeaux, J. E. A., Bone, Q. & Braconnot, J. C. Anatomy of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).97.Alldredge, A. L. & Madin, L. P. Pelagic tunicates: unique herbivores in the marine plankton. BioScience. 32, 655–663 (1982).Article 

Google Scholar 
98.Bone, Q., Carre, C. & Ryan, K. P. The endostyle and the feeding filter in salps (Tunicata). J. Mar. Biol. Assoc. UK 80, 523–534 (2000).Article 

Google Scholar 
99.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. 107, 15129–15134 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Dadon-Pilosof, A. et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat. Microbiol. 2, 1608–1615 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Larson, R. J. Daily ration and predation by medusae and ctenophores in Saanich Inlet, B.C., Canada. Neth. J. Sea Res. 21, 35–44 (1987).Article 

Google Scholar 
102.Suchman, C. L., Daly, E. A., Keister, J. E., Peterson, W. T. & Brodeur, R. D. Feeding patterns and predation potential of scyphomedusae in a highly productive upwelling region. Mar. Ecol. Prog. Ser. 358, 161–172 (2008).Article 

Google Scholar 
103.Bennke, C. M. et al. The distribution of phytoplankton in the Baltic Sea assessed by a prokaryotic 16S rRNA gene primer system. J. Plankton Res. 40, 244–254 (2018).CAS 
Article 

Google Scholar 
104.Green, B. R. Chloroplast genomes of photosynthetic eukaryotes. Plant J. 66, 34–44 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles. 34, e2020GB006704 (2020).106.Dadon‐Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
107.Brand, A., Liz, A., Micah, A., Marjorie, H. & Jo, S. Beyond Authorship: Attribution, Contribution, Collaboration, and Credit. Learned Publishing. 28, 151–155 (2015).Article 

Google Scholar  More

1.Stanley, D. & Stout, J. Pollinator sharing between mass-flowering oilseed rape and co-flowering wild plants: implications for wild plant pollination. Plant Ecol. 215, 315–325. https://doi.org/10.1007/s11258-014-0301-7 (2014).Article 

Google Scholar 
2.Kovacs-Hostyanszki, A. et al. Contrasting effects of mass-flowering crops on bee pollination of hedge plants at different spatial and temporal scales. Ecol. Appl. 23, 1938–1946. https://doi.org/10.1890/12-2012.1 (2013).Article 
PubMed 

Google Scholar 
3.Holzschuh, A. et al. Mass-flowering crops dilute pollinator abundance in agricultural landscapes across Europe. Ecol. Lett. 19, 1228–1236. https://doi.org/10.1111/ele.12657 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).Article 
PubMed 

Google Scholar 
5.Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc Natl Acad Sci U S A 99, 16812–16816. https://doi.org/10.1073/pnas.262413599 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Ollerton, J., Erenler, H., Edwards, M. & Crockett, R. Pollinator declines: extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346, 1360–1362. https://doi.org/10.1126/science.1257259 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Kovacs-Hostyanszki, A. et al. Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecol. Lett. 20, 673–689. https://doi.org/10.1111/ele.12762 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity: ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

Google Scholar 
9.Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe: a review. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0434-x (2017).Article 

Google Scholar 
10.Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072. https://doi.org/10.1111/j.1461-0248.2011.01669.x (2011).Article 
PubMed 

Google Scholar 
11.Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328. https://doi.org/10.7717/peerj.328 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Holzschuh, A., Dudenhoffer, J. H. & Tscharntke, T. Landscapes with wild bee habitats enhance pollination, fruit set and yield of sweet cherry. Biol. Conserv. 153, 101–107. https://doi.org/10.1016/j.biocon.2012.04.032 (2012).Article 

Google Scholar 
13.Marini, L. et al. Crop management modifies the benefits of insect pollination in oilseed rape. Agric. Ecosyst. Environ. 207, 61–66. https://doi.org/10.1016/j.agee.2015.03.027 (2015).Article 

Google Scholar 
14.Persson, A. S. & Smith, H. G. Seasonal persistence of bumblebee populations is affected by landscape context. Agric. Ecosyst. Environ. 165, 201–209. https://doi.org/10.1016/j.agee.2012.12.008 (2013).Article 

Google Scholar 
15.Rundlof, M., Persson, A. S., Smith, H. G. & Bommarco, R. Late-season mass-flowering red clover increases bumble bee queen and male densities. Biol. Conserv. 172, 138–145. https://doi.org/10.1016/j.biocon.2014.02.027 (2014).Article 

Google Scholar 
16.Westphal, C., Steffan-Dewenter, I. & Tscharntke, T. Mass flowering oilseed rape improves early colony growth but not sexual reproduction of bumblebees. J. Appl. Ecol. 46, 187–193. https://doi.org/10.1111/j.1365-2664.2008.01580.x (2009).Article 

Google Scholar 
17.Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058. https://doi.org/10.1890/11-1006.1 (2012).Article 
PubMed 

Google Scholar 
18.Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432. https://doi.org/10.2307/3071954 (2002).Article 

Google Scholar 
19.Steffan-Dewenter, I., Münzenberg, U. & Tscharntke, T. Pollination, seed set and seed predation on a landscape scale. Proc. Natl. Acad. Sci. USA 268, 1685–1690. https://doi.org/10.1098/rspb.2001.1737 (2001).CAS 
Article 

Google Scholar 
20.Bartual, A. et al. The potential of different semi-natural habitats to sustain pollinators and natural enemies in European agricultural landscapes. Agric. Ecosyst. Environ. 279, 43–52. https://doi.org/10.1016/j.agee.2019.04.009 (2019).Article 

Google Scholar 
21.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. Camb. Philos. Soc. 81, 117–142. https://doi.org/10.1017/s1464793105006949 (2006).Article 
PubMed 

Google Scholar 
22.Blaauw, B. R. & Isaacs, R. Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wild flowers. Basic Appl. Ecol. 15, 701–711. https://doi.org/10.1016/j.baae.2014.10.001 (2014).Article 

Google Scholar 
23.Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094. https://doi.org/10.1111/ele.13265 (2019).Article 
PubMed 

Google Scholar 
24.Bihaly, Á., Dóra, V., Lajos, K. & Sárospataki, M. Effect of semi-natural habitat patches on the pollinator assemblages of sunflower in an intensive agricultural landscape. Tájökológiai Lapok 16, 45–52 (2018).
Google Scholar 
25.Foldesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75. https://doi.org/10.1111/afe.12135 (2016).Article 

Google Scholar 
26.Sárospataki, M. et al. The role of local and landscape level factors in determining bumblebee abundance and richness. Acta Zool. Acad. Sci. Hung. 62, 387–407. https://doi.org/10.17109/AZH.62.4.387.2016 (2016).Article 

Google Scholar 
27.Schellhorn, N. A., Gagic, V. & Bommarco, R. Time will tell: resource continuity bolsters ecosystem services. Trends Ecol. Evol. 30, 524–530. https://doi.org/10.1016/j.tree.2015.06.007 (2015).Article 
PubMed 

Google Scholar 
28.Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. Camb. Philos. Soc. 87, 661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x (2012).Article 
PubMed 

Google Scholar 
29.Stephens, A. E. A. & Myers, J. H. Resource concentration by insects and implications for plant populations. J. Ecol. 100, 923–931. https://doi.org/10.1111/j.1365-2745.2012.01971.x (2012).Article 

Google Scholar 
30.Tscheulin, T., Neokosmidis, L., Petanidou, T. & Settele, J. Influence of landscape context on the abundance and diversity of bees in Mediterranean olive groves. Bull. Entomol. Res. 101, 557–564. https://doi.org/10.1017/S0007485311000149 (2011).CAS 
Article 
PubMed 

Google Scholar 
31.Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599. https://doi.org/10.1111/ele.12082 (2013).Article 
PubMed 

Google Scholar 
32.Eurostat. Archive: Main annual crop statistics, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Main_annual_crop_statistics&oldid=389868#Oilseeds (2018).33.KSH. STADAT tables – Agriculture. http://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_omn007b.html. (KSH, 2019).34.Hevia, V. et al. Bee diversity and abundance in a livestock drove road and its impact on pollination and seed set in adjacent sunflower fields. Agric. Ecosyst. Environ. 232, 336–344. https://doi.org/10.1016/j.agee.2016.08.021 (2016).Article 

Google Scholar 
35.Silva, C. et al. Bee pollination highly improves oil quality in sunflower. Sociobiology 65, 583–590. https://doi.org/10.13102/sociobiology.v65i4.3367 (2018).Article 

Google Scholar 
36.Terzić, S., Miklič, V. & Čanak, P. Review of 40 years of research carried out in Serbia on sunflower pollination. OCL 24, D608 (2017).Article 

Google Scholar 
37.Perrot, T. et al. Experimental quantification of insect pollination on sunflower yield, reconciling plant and field scale estimates. Basic Appl. Ecol. 34, 75–84. https://doi.org/10.1016/j.baae.2018.09.005 (2019).Article 

Google Scholar 
38.Martin, C. S. & Farina, W. M. Honeybee floral constancy and pollination efficiency in sunflower (Helianthus annuus) crops for hybrid seed production. Apidologie 47, 161–170 (2016).Article 

Google Scholar 
39.DeGrandi-Hoffman, G. & Watkins, J. C. The foraging activity of honey bees Apis mellifera and non—Apis bees on hybrid sunflowers (Helianthus annuus) and its influence on cross—pollination and seed set. J. Apic. Res. 39, 37–45. https://doi.org/10.1080/00218839.2000.11101019 (2000).Article 

Google Scholar 
40.Cerrutti, N. & Pontet, C. Differential attractiveness of sunflower cultivars to the honeybee Apis mellifera L. OCL 23, D204 (2016).Article 

Google Scholar 
41.Chambó, E. D., Garcia, R. C., Oliveira, N. T. E. D. & Duarte-Júnior, J. B. Honey bee visitation to sunflower: effects on pollination and plant genotype. Sci. Agric. 68, 647–651 (2011).Article 

Google Scholar 
42.Oz, M., Karasu, A., Cakmak, I., Goksoy, A. T. & Turan, Z. M. Effects of honeybee (Apis mellifera) pollination on seed set in hybrid sunflower (Helianthus annuus L.). Afr. J. Biotechnol. 8 (2009).43.Puškadija, Z. et al. Influence of weather conditions on honey bee visits (Apis mellifera carnica) during sunflower (Helianthus annuus L.) blooming period. Poljoprivreda 13, 230–233 (2007).
Google Scholar 
44.Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. Proc. Natl. Acad. Sci. USA 103, 13890–13895 (2006).ADS 
CAS 
Article 

Google Scholar 
45.Nderitu, J., Nyamasyo, G., Kasina, M. & Oronje, M. Diversity of sunflower pollinators and their effect on seed yield in Makueni District, Eastern Kenya. Span. J. Agric. Res. 6, 271–278 (2008).Article 

Google Scholar 
46.Carvalheiro, L. G. et al. Natural and within-farmland biodiversity enhances crop productivity. Ecol. Lett. 14, 251–259. https://doi.org/10.1111/j.1461-0248.2010.01579.x (2011).Article 
PubMed 

Google Scholar 
47.Sardiñas, H. S. & Kremen, C. Pollination services from field-scale agricultural diversification may be context-dependent. Agric. Ecosyst. Environ. 207, 17–25 (2015).Article 

Google Scholar 
48.Riedinger, V., Renner, M., Rundlof, M., Steffan-Dewenter, I. & Holzschuh, A. Early mass-flowering crops mitigate pollinator dilution in late-flowering crops. Landscape Ecol. 29, 425–435. https://doi.org/10.1007/s10980-013-9973-y (2014).Article 

Google Scholar 
49.Bennett, A. B. & Isaacs, R. Landscape composition influences pollinators and pollination services in perennial biofuel plantings. Agric. Ecosyst. Environ. 193, 1–8. https://doi.org/10.1016/j.agee.2014.04.016 (2014).Article 

Google Scholar 
50.Lowenstein, D. M., Huseth, A. S. & Groves, R. L. Response of wild bees (Hymenoptera: Apoidea: Anthophila) to surrounding land cover in Wisconsin pickling cucumber. Environ. Entomol. 41, 532–540. https://doi.org/10.1603/EN11241 (2012).CAS 
Article 
PubMed 

Google Scholar 
51.Pfister, S. C. et al. Dominance of cropland reduces the pollen deposition from bumble bees. Sci. Rep. 8, 13873. https://doi.org/10.1038/s41598-018-31826-3 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764. https://doi.org/10.1046/j.1365-2656.2002.00641.x (2002).Article 

Google Scholar 
53.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596. https://doi.org/10.1007/s00442-007-0752-9 (2007).ADS 
Article 
PubMed 

Google Scholar 
54.Lihoreau, M., Chittka, L., Le Comber, S. C. & Raine, N. E. Bees do not use nearest-neighbour rules for optimization of multi-location routes. Biol. Lett. 8, 13–16. https://doi.org/10.1098/rsbl.2011.0661 (2012).Article 
PubMed 

Google Scholar 
55.Berger-Tal, O. & Bar-David, S. Recursive movement patterns: review and synthesis across species. Ecosphere 6, 149. https://doi.org/10.1890/es15-00106.1 (2015).Article 

Google Scholar 
56.Wesserling, J. Habitatwahl und Ausbreitungsverhalten von Stechimmen (Hymenoptera: Aculeata) in Sandgebieten unterschiedlicher Sukzessionsstadien, University of Karlsruhe, (1996).57.Hagler, J. R., Mueller, S., Teuber, L. R., Machtley, S. A. & Van Deynze, A. Foraging range of honey bees, Apis mellifera, in alfalfa seed production fields. J. Insect Sci. 11, 144 (2011).PubMed 
PubMed Central 

Google Scholar 
58.Couvillon, M. J. et al. Honey bee foraging distance depends on month and forage type. Apidologie 46, 61–70. https://doi.org/10.1007/s13592-014-0302-5 (2015).Article 

Google Scholar 
59.Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee, Apis mellifera L.. Funct. Ecol. 14, 490–496. https://doi.org/10.1046/j.1365-2435.2000.00443.x (2000).Article 

Google Scholar 
60.Gary, N. E., Witherell, P. C. & Lorenzen, K. Effect of age on honey bee foraging distance and pollen collection. Environ. Entomol. 10, 950–952 (1981).Article 

Google Scholar 
61.Walther-Hellwig, K. & Frankl, R. Foraging habitats and foraging distances of bumblebees, Bombus spp. (Hym., Apidae), in an agricultural landscape. J. Appl. Entomol. 124, 299–306. https://doi.org/10.1046/j.1439-0418.2000.00484.x (2000).Article 

Google Scholar 
62.Dramstad, W. E. Do bumblebees (Hymenoptera: Apidae) really forage close to their nests?. J. Insect Behav. 9, 163–182. https://doi.org/10.1007/bf02213863 (1996).Article 

Google Scholar 
63.Knight, M. E. et al. An interspecific comparison of foraging range and nest density of four bumblebee (Bombus) species. Mol. Ecol. 14, 1811–1820 (2005).CAS 
Article 

Google Scholar 
64.Wolf, S. & Moritz, R. F. Foraging distance in Bombus terrestris L. (Hymenoptera: Apidae). Apidologie 39, 419–427 (2008).Article 

Google Scholar 
65.Osborne, J. L. et al. Bumblebee flight distances in relation to the forage landscape. J. Anim. Ecol. 77, 406–415 (2008).Article 

Google Scholar 
66.Zurbuchen, A. et al. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol. Conserv. 143, 669–676 (2010).Article 

Google Scholar 
67.Hopfenmuller, S., Steffan-Dewenter, I. & Holzschuh, A. Trait-specific responses of wild bee communities to landscape composition, configuration and local factors. PLoS ONE 9, e104439. https://doi.org/10.1371/journal.pone.0104439 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Hung, K. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc R Soc Biol Sci Ser B 285, 20172140. https://doi.org/10.1098/rspb.2017.2140 (2018).Article 

Google Scholar 
69.Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds. Ecol. Appl. 25, 881–890. https://doi.org/10.1890/14-1011.1 (2015).Article 
PubMed 

Google Scholar 
70.Bonoan, R. E., Gonzalez, J. & Starks, P. T. The perils of forcing a generalist to be a specialist: lack of dietary essential amino acids impacts honey bee pollen foraging and colony growth. J. Apic. Res. 59, 95–103. https://doi.org/10.1080/00218839.2019.1656702 (2020).Article 

Google Scholar 
71.Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, e72016. https://doi.org/10.1371/journal.pone.0072016 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Di Pasquale, G. et al. Variations in the availability of pollen resources affect honey bee health. PLoS ONE 11, e0162818. https://doi.org/10.1371/journal.pone.0162818 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Alaux, C., Ducloz, F., Crauser, D. & Le Conte, Y. Diet effects on honeybee immunocompetence. Biol. Lett. 6, 562–565. https://doi.org/10.1098/rsbl.2009.0986 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Colwell, M. J., Williams, G. R., Evans, R. C. & Shutler, D. Honey bee-collected pollen in agro-ecosystems reveals diet diversity, diet quality, and pesticide exposure. Ecol. Evol. 7, 7243–7253. https://doi.org/10.1002/ece3.3178 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Zhang, G., St. Clair, A. L., Dolezal, A., Toth, A. L. & O’Neal, M. Honey Bee (Hymenoptera: Apidea) pollen forage in a highly cultivated agroecosystem: limited diet diversity and its relationship to virus resistance. J. Econ. Entomol. 113, 1062–1072 (2020).76.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. http://qgis.osgeo.org. (2009).77.FÖMI. MePAR, the Hungarian Agricultural Land Parcel Identification System, accessed 22 November 2019 http://www.mepar.hu/ (2016).78.McGarigal, K., Cushman, S. & Ene, E. Spatial Pattern Analysis Program for Categorical and Continuous Maps. available from http://www.umass.edu/landeco/research/fragstats/fragstats.html. (University of Massachusetts, 2012).79.McGarigal, K. FRAGSTATS help. Documentation for FRAGSTATS, 4. (2014).80.McGarigal, K. (2017). Landscape metrics for categorical map patterns. Lecture Notes. Available online: accessed 28 Feb 2021 http://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf.81.R Core Team. R: A Language and Environment for Statistical Computing. version 3.6.0. https://www.R-project.org. (R Foundation for Statistical Computing, 2020).82.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528. https://doi.org/10.1093/bioinformatics/bty633 (2019).CAS 
Article 
PubMed 

Google Scholar 
83.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software 67, https://doi.org/10.18637/jss.v067.i01 (2015).84.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar 
85.DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. v. 0.3.3.0. (2020).86.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019). More