| Summary: | This thesis focuses on the behavioural dynamics of mesopelagic fish in a fjordic environment. Acoustic data recorded with a stationary echosounder was used to primarily investigate the diel vertical migration behaviour of the mesopelagic fish Maurolicus muelleri. Particular emphasis was on the effect of daily and seasonal changes in surface light irradiance and the vertical distribution of large predator-like fish on M. muelleri. In addition the behaviour of the deeper distributed mesopelagic lantern fish Benthosema glaciale and large, individual fish were also investigated. Diel vertical migrations (DVM) are vital in maintaining the energy flow in marine ecosystem food webs, and migrating organisms thus directly and indirectly provide food/nourishment to many non migrating animals. Typically, DVM are characterised by an ascent to surface waters from deeper day time depths at dusk, followed by a nocturnal period close to the surface and a subsequent descent to daytime depths at dawn. M. muelleri and B. glaciale are abundant and ecologically important fish in several marine systems. They are part of the mesopelagic animal community that performs DVM - nature’s largest migration in terms of biomass. In the Norwegian Sea and fjords M. muelleri forms acoustically visible scattering layers (SLs), while the deeper living B. glaciale appears acoustically more scattered and less dense, not aggregating in distinct SLs. Both species are challenged with seasonally changing gradients of environmental variables including light, temperature, salinity, food abundance and piscivorous predators. Although several aspects of the behaviour and biology of M. muelleri and to a lesser extent of B. glaciale have been studied, our knowledge of short term and seasonal variation of individual fish and SL behaviour, as well as behavioural responses to seasonal changes in surface light irradiance and predator distribution is limited. To address these shortcomings we therefore utilised the fairly recent developments of stationary echosounder ...
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