Berge, J. et al. Artificial light during the polar night disrupts Arctic fish and zooplankton behaviour down to 200 m depth. Commun. Biol. 3, 102. https://doi.org/10.1038/s42003-020-0807-6 (2020).
Google Scholar
Davies, T. W., McKee, D., Fishwick, J., Tidau, S. & Smyth, T. Biologically important artificial light at night on the seafloor. Sci. Rep. 10, 12545. https://doi.org/10.1038/s41598-020-69461-6 (2020).
Google Scholar
Ludvigsen, M. et al. Use of an autonomous surface vehicle reveals small-scale diel vertical migrations of zooplankton and susceptibility to light pollution under low solar irradiance. Sci. Adv. 4, eaap9887. https://doi.org/10.1126/sciadv.aap9887 (2018).
Google Scholar
Utne-Palm, A. C., Breen, M., Løkkeborg, S. & Humborstad, O. B. Behavioural responses of krill and cod to artificial light in laboratory experiments. PLoS One https://doi.org/10.1371/journal.pone.0190918 (2018).
Google Scholar
Marchesan, M., Spoto, M., Verginella, L. & Ferrero, E. A. Behavioural effects of artificial light on fish species of commercial interest. Fish. Res. 73, 171–185. https://doi.org/10.1016/j.fishres.2004.12.009 (2005).
Google Scholar
Stickney, A. P. Factors influencing the attraction of Atlantic Herring. Fish. Bull. 68, 73–85 (1969).
Nguyen, K. Q. et al. Application of luminescent netting in traps to improve the catchability of the snow crab Chionoecetes opilio. Mar. Coast. Fish. 11, 295–304. https://doi.org/10.1002/mcf2.10084 (2019).
Google Scholar
Wiebe, P. H. et al. Using a high-powered strobe light to increase the catch of Antarctic krill. Mar. Biol. 144, 493–502. https://doi.org/10.1007/s00227-003-1228-z (2004).
Google Scholar
Nguyen, T. T. et al. Artificial light pollution increases the sensitivity of tropical zooplankton to extreme warming. Environ. Technol. Innov. 20, 101179. https://doi.org/10.1016/j.eti.2020.101179 (2020).
Google Scholar
Kaartvedt, S., Røstad, A., Opdal, A. F. & Aksnes, D. L. Herding mesopelagic fish by light. Mar. Ecol. Prog. Ser. 625, 225–231 (2019).
Google Scholar
Underwood, M. J., Utne Palm, A. C., Øvredal, J. T. & Bjordal, Å. The response of mesopelagic organisms to artificial lights. Aquac. Fish. https://doi.org/10.1016/j.aaf.2020.05.002 (2020).
Google Scholar
Peña, M., Cabrera-Gámez, J. & Domínguez-Brito, A. C. Multi-frequency and light-avoiding characteristics of deep acoustic layers in the North Atlantic. Mar. Environ. Res. 154, 104842. https://doi.org/10.1016/j.marenvres.2019.104842 (2020).
Google Scholar
Ryer, C. H., Stoner, A. W., Iseri, P. J. & Spencer, M. L. Effects of simulated underwater vehicle lighting on fish behavior. Mar. Ecol. Prog. Ser. 391, 97–106 (2009).
Google Scholar
Bicknell, A. W. J., Godley, B. J., Sheehan, E. V., Votier, S. C. & Witt, M. J. Camera technology for monitoring marine biodiversity and human impact. Front. Ecol. Environ. 14, 424–432. https://doi.org/10.1002/fee.1322 (2016).
Google Scholar
Picheral, M. et al. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Meth. 8, 462–547. https://doi.org/10.4319/lom.2010.8.462 (2010).
Google Scholar
Herman, A. W. & Harvey, M. Application of normalized biomass size spectra to laser optical plankton counter net intercomparisons of zooplankton distributions. J. Geophys. Res. Oceans. https://doi.org/10.1029/2005JC002948 (2006).
Google Scholar
Basedow, S. L., Tande, K. S., Norrbin, M. F. & Kristiansen, S. A. Capturing quantitative zooplankton information in the sea: Performance test of laser optical plankton counter and video plankton recorder in a Calanus finmarchicus dominated summer situation. Prog. Oceanogr. 108, 72–80. https://doi.org/10.1016/j.pocean.2012.10.005 (2013).
Google Scholar
Sainmont, J. et al. Inter- and intra-specific diurnal habitat selection of zooplankton during the spring bloom observed by Video Plankton Recorder. Mar. Biol. 161, 1931–1941. https://doi.org/10.1007/s00227-014-2475-x (2014).
Google Scholar
Schulz, J. et al. Imaging of plankton specimens with the lightframe on-sight key species investigation (LOKI) system. J. Eur. Opt. Soc. 5, 10017s (2010).
Google Scholar
Schmid, M. S., Aubry, C., Grigor, J. & Fortier, L. The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Meth. Oceanogr. 15–16, 129–160. https://doi.org/10.1016/j.mio.2016.03.003 (2016).
Google Scholar
Williams, K., Rooper, C. N. & Towler, R. Use of stereo camera systems for assessment of rockfish abundance in untrawlable areas and for recording pollock behavior during midwater trawls. Fish. Bull. 108, 352–362 (2010).
Boldt, J. L., Williams, K., Rooper, C. N., Towler, R. H. & Gauthier, S. Development of stereo camera methodologies to improve pelagic fish biomass estimates and inform ecosystem management in marine waters. Fish. Res. 198, 66–77. https://doi.org/10.1016/j.fishres.2017.10.013 (2018).
Google Scholar
Mallet, D. & Pelletier, D. Underwater video techniques for observing coastal marine biodiversity: A review of sixty years of publications (1952–2012). Fish. Res. 154, 44–62. https://doi.org/10.1016/j.fishres.2014.01.019 (2014).
Google Scholar
Easton, R. R., Heppell, S. S. & Hannah, R. W. Quantification of habitat and community relationships among nearshore temperate fishes through analysis of drop camera video. Mar. Coast. Fish. 7, 87–102. https://doi.org/10.1080/19425120.2015.1007184 (2015).
Google Scholar
McLean, D. L. et al. Using industry ROV videos to assess fish associations with subsea pipelines. Cont. Shelf Res. 141, 76–97. https://doi.org/10.1016/j.csr.2017.05.006 (2017).
Google Scholar
Devine, B. M., Wheeland, L. J., de Moura Neves, B. & Fisher, J. A. D. Baited remote underwater video estimates of benthic fish and invertebrate diversity within the eastern Canadian Arctic. Polar Biol. 42, 1323–1341. https://doi.org/10.1007/s00300-019-02520-5 (2019).
Google Scholar
Trenkel, V. M., Lorance, P. & Mahévas, S. Do visual transects provide true population density estimates for deepwater fish?. ICES J. Mar. Sci. 61, 1050–1056. https://doi.org/10.1016/j.icesjms.2004.06.002 (2004).
Google Scholar
Widder, E. A., Robison, B. H., Reisenbichler, K. R. & Haddock, S. H. D. Using red light for in situ observations of deep-sea fishes. Deep-Sea Res. Part I(52), 2077–2085. https://doi.org/10.1016/j.dsr.2005.06.007 (2005).
Google Scholar
Benoit-Bird, K. J., Moline, M. A., Schofield, O. M., Robbins, I. C. & Waluk, C. M. Zooplankton avoidance of a profiled open-path fluorometer. J. Plankton Res. 32, 1413–1419. https://doi.org/10.1093/plankt/fbq053 (2010).
Google Scholar
Doya, C. et al. Diel behavioral rhythms in sablefish (Anoplopoma fimbria) and other benthic species, as recorded by the Deep-sea cabled observatories in Barkley canyon (NEPTUNE-Canada). J. Mar. Syst. 130, 69–78. https://doi.org/10.1016/j.jmarsys.2013.04.003 (2014).
Google Scholar
Stoner, A. W., Ryer, C. H., Parker, S. J., Auster, P. J. & Wakefield, W. W. Evaluating the role of fish behavior in surveys conducted with underwater vehicles. Can. J. Fish. Aquat. Sci. 65, 1230–1243. https://doi.org/10.1139/f08-032 (2008).
Google Scholar
Rooper, C. N., Williams, K., De Robertis, A. & Tuttle, V. Effect of underwater lighting on observations of density and behavior of rockfish during camera surveys. Fish. Res. 172, 157–167. https://doi.org/10.1016/j.fishres.2015.07.012 (2015).
Google Scholar
Hop, H. et al. The marine ecosystem of Kongsfjorden, Svalbard. Polar Res. 21, 167–208 (2002).
Google Scholar
Bandara, K. et al. Seasonal vertical strategies in a high-Arctic coastal zooplankton community. Mar. Ecol. Prog. Ser. 555, 49–64 (2016).
Google Scholar
Hop, H. et al. In The Ecosystem of Kongsfjorden, Svalbard (eds Hop, H. & Wiencke, C.) 229–300 (Springer International Publishing, 2019).
Google Scholar
Cusa, M., Berge, J. & Varpe, Ø. Seasonal shifts in feeding patterns: Individual and population realized specialization in a high Arctic fish. Ecol. Evol. 9, 11112–11121. https://doi.org/10.1002/ece3.5615 (2019).
Google Scholar
Sakshaug, E., Johnsen, G. & Volent, Z. In Ecosystem Barents Sea (eds Sakshaug, E. et al.) 117–138 (Tapir Academic Press, 2009).
Gordon, H. R. Can the Lambert–Beer law be applied to the diffuse attenuation coefficient of ocean water?. Limnol. Oceanogr. 34, 1389–1409. https://doi.org/10.4319/lo.1989.34.8.1389 (1989).
Google Scholar
McKee, D., Cunningham, A. & Craig, S. Estimation of absorption and backscattering coefficients from in situ radiometric measurements: Theory and validation in case II waters. App. Opt. 42, 2804–2810. https://doi.org/10.1364/AO.42.002804 (2003).
Google Scholar
Demer, D. A. et al. Calibration of acoustic instruments. ICES Cooperative Research Report No. 326. 133 (2015).
Mackenzie, K. V. Nine-term equation for sound speed in the oceans. J. Acoust. Soc. Am. 70, 807 (1981).
Google Scholar
François, R. E. & Garrison, G. R. Sound absorption based on ocean measurements. Part II: Boric acid contribution and equation for total absorption. J. Acoust. Soc. Am. 72, 1879–1890 (1982).
Google Scholar
De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291. https://doi.org/10.1093/icesjms/fsm112 (2007).
Google Scholar
Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493. https://doi.org/10.1093/icesjms/fsv121 (2015).
Google Scholar
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Google Scholar
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. TREE 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).
Google Scholar
Berge, J. et al. Unexpected levels of biological activity during the polar night offer new perspectives on a warming Arctic. Curr. Biol. 25, 2555–2561. https://doi.org/10.1016/j.cub.2015.08.024 (2015).
Google Scholar
Dalpadado, P. et al. Distribution and abundance of euphausiids and pelagic amphipods in Kongsfjorden, Isfjorden and Rijpfjorden (Svalbard) and changes in their relative importance as key prey in a warming marine ecosystem. Polar Biol. 39, 1765–1784. https://doi.org/10.1007/s00300-015-1874-x (2016).
Google Scholar
Geoffroy, M. et al. Increased occurrence of the jellyfish Periphylla periphylla in the European high Arctic. Polar Biol. 41, 2615–2619. https://doi.org/10.1007/s00300-018-2368-4 (2018).
Google Scholar
Jarms, G., Tiemann, H. & Båmstedt, U. Development and biology of Periphylla periphylla (Scyphozoa: Coronatae) in a Norwegian fjord. Mar. Biol. 141, 647–657. https://doi.org/10.1007/s00227-002-0858-x (2002).
Google Scholar
Pepin, P., Colbourne, E. & Maillet, G. Seasonal patterns in zooplankton community structure on the Newfoundland and Labrador Shelf. Prog. Oceanogr. 91, 273–285. https://doi.org/10.1016/j.pocean.2011.01.003 (2011).
Google Scholar
Cohen, J. H. & Epifanio, C. E. In Developmental Biology and Larval Ecology, Ch. 12 (eds Anger, K. et al.) 332–359 (Oxford University Press, 2020).
Orr, M. H. Remote acoustic detection of zooplankton response to field processes, oceanographic instrumentation, and predators. Can. J. Fish. Aquat. Sci. 38, 1096–1105. https://doi.org/10.1139/f81-149 (1981).
Google Scholar
Farmer, D. D., Crawford, G. B. & Osborn, T. R. Temperature and velocity microstructure caused by swimming fish1. Limnol. Oceanogr. 32, 978–983. https://doi.org/10.4319/lo.1987.32.4.0978 (1987).
Google Scholar
Koslow, J. A., Kloser, R. & Stanley, C. A. Avoidance of a camera system by a deepwater fish, the orange roughy (Hoplostethus atlanticus). Deep-Sea Res Part I 42, 233–244. https://doi.org/10.1016/0967-0637(95)93714-P (1995).
Google Scholar
Raymond, E. H. & Widder, E. A. Behavioral responses of two deep-sea fish species to red, far-red, and white light. Mar. Ecol. Prog. Ser. 350, 291–298 (2007).
Google Scholar
Bassett, D. K. & Montgomery, J. C. Investigating nocturnal fish populations in situ using baited underwater video: With special reference to their olfactory capabilities. J. Exp. Mar. Biol. Ecol. 409, 194–199. https://doi.org/10.1016/j.jembe.2011.08.019 (2011).
Google Scholar
Brill, R., Magel, C., Davis, M., Hannah, R. & Rankin, P. Effects of rapid decompression and exposure to bright light on visual function in black rockfish (Sebastes melanops) and Pacific halibut (Hippoglossus stenolepis). Fish. Bull. 106, 427–437 (2008).
Turner, J. R., White, E. M., Collins, M. A., Partridge, J. C. & Douglas, R. H. Vision in lanternfish (Myctophidae): Adaptations for viewing bioluminescence in the deep-sea. Deep-Sea Res. Part I 56, 1003–1017. https://doi.org/10.1016/j.dsr.2009.01.007 (2009).
Google Scholar
de Busserolles, F. & Marshall, N. J. Seeing in the deep-sea: Visual adaptations in lanternfishes. Philos. Trans. R Soc. Lond. B Biol. Sci. 372, 20160070. https://doi.org/10.1098/rstb.2016.0070 (2017).
Google Scholar
Valen, R., Edvardsen, R. B., Søviknes, A. M., Drivenes, Ø. & Helvik, J. V. Molecular evidence that only two opsin subfamilies, the blue light- (SWS2) and green light-sensitive (RH2), drive colour vision in Atlantic cod (Gadus morhua). PLoS One 9, e115436. https://doi.org/10.1371/journal.pone.0115436 (2015).
Google Scholar
Anthony, P. D. & Hawkins, A. D. Spectral sensitivity of the cod, Gadus morhua L. Mar. Behav. Physiol. 10, 145–166. https://doi.org/10.1080/10236248309378614 (1983).
Google Scholar
Govardovskii, V. I., Fyhrquist, N., Reuter, T., Kuzmin, D. G. & Donner, K. In search of the visual pigment template. Vis. Neurosci. 17, 509–528. https://doi.org/10.1017/s0952523800174036 (2000).
Google Scholar
Frank, T. M. & Widder, E. A. Comparative study of the spectral sensitivities of mesopelagic crustaceans. J. Comp. Physiol. A 185, 255–265. https://doi.org/10.1007/s003590050385 (1999).
Google Scholar
Båtnes, A. S., Miljeteig, C., Berge, J., Greenacre, M. & Johnsen, G. Quantifying the light sensitivity of Calanus spp. during the polar night: Potential for orchestrated migrations conducted by ambient light from the sun, moon, or aurora borealis?. Polar Biol. 38, 1–15. https://doi.org/10.1007/s00300-013-1415-4 (2015).
Google Scholar
Cohen, J. H. et al. Is ambient light during the high Arctic polar night sufficient to act as a visual cue for zooplankton?. PLoS ONE https://doi.org/10.1371/journal.pone.0126247 (2015).
Google Scholar
Jinks, R. N. et al. Adaptive visual metamorphosis in a deep-sea hydrothermal vent crab. Nature 420, 68–70. https://doi.org/10.1038/nature01144 (2002).
Google Scholar
Aguzzi, J. et al. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa169 (2020).
Google Scholar
Source: Ecology - nature.com
