| Summary: | Increased pressure of industrial development in the Arctic drives the need for a better understanding of Arctic fish and their interaction with their habitat. Environmental disturbances resulting from these developments often require off-setting facilities, particularly with respect to Arctic grayling (Thymallus arcticus) spawning activity. Furthermore, the Arctic is also expected to experience increased climate change effects resulting in adaptations to Arctic grayling behaviour in response to their changing environment. Due to the remoteness of sites, climate extremes and variability, fisheries field work in the Arctic typically requires extensive support, such as camp infrastructure, helicopters, and durable equipment. Support costs to undertake these field programs often limit data collection efforts, potentially leading to questionable habitat assumptions being made that will adversely affect fish behaviour. Using a multi-disciplinary approach, this thesis validated three approaches for improving Arctic grayling enumeration and for establishing a better basis for habitat design criteria: (1) A wildlife camera enumeration technique for Arctic streams was found to be comparable for population estimates when compared to trap boxes and visual stream surveys and able to provide longer data sets with less field time. The use of wildlife cameras is a suitable technique for remote locations but selection will depend on the specific requirements of an enumeration program; (2) Using paired values of depth and velocity, Arctic grayling spawning site selection can be described by the dimensionless Froude number. The preferred Arctic grayling mean Froude number value was found to be 0.27 (SE=0.0045) and was not significantly different between two populations of Arctic grayling in different size streams. This value is also lower than that identified for Sockeye and Atlantic salmon (Froude number = 0.34) which can likely be attributed to their larger size and different spawning behaviour and substrate selections when ...
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