| Summary: | Environmental conditions vary spatially and temporally, providing organisms with both challenges and opportunities. Animals have evolved a spectacular variety of migratory behaviours to take advantage of environmental variation, particularly in cases where this variation is predictable. The spatiotemporal pattern of migratory movements displayed by a population or species can be thought of as the evolutionary outcome of trade-offs amongst life history traits. As such, the study of animal movement in relation to environmental heterogeneity can yield valuable insights into the proximate and ultimate drivers of migratory behaviours as well as the behavioural mechanisms underpinning genetic structure. Against this background, the overarching aim of this thesis is to investigate the role of environmental heterogeneity in shaping locally-adapted migratory behaviours, finescale genetic structure and physiological performance in populations of wild brown trout (Salmo trutta) and Atlantic salmon (Salmo salar). Using telemetry data from passive integrated transponder (PIT) tags, I investigated the fine scale spatiotemporal patterns of spawning-related movements of brown trout between a feeding lake and two spawning streams (one inflowing, one outflowing, separated by < 100 m) over two spawning seasons. The timing of seasonal, daily and diel movements was strongly associated with variation in photoperiod, stream height and moon phase. Movement activity was highest at night, and particularly on nights with minimal lunar illumination and high water levels, suggesting that trout synchronise their spawning movements with environmental conditions that minimise their visibility to predators. Males began their movements between the lake and vii streams significantly earlier in the spawning season than females (protandry) and were generally more active. A substantial proportion of trout entered both spawning streams during the spawning periods, providing potential sources of gene flow between the two streams. However, Bayesian ...
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