Climate change is increasing the frequency and intensity of extreme lethal and sub-lethal temperature events in Canada's salmon-producing rivers. As a result, some salmon populations are increasingly vulnerable to in-river mortality during spawning migrations, making escapement and harvest objectives difficult to achieve. Harvest adjustments associated with river temperature forecasting are currently made on a limited basis to address temperature-related en route mortality of sockeye salmon in the Lower Fraser River in British Columbia; however, these forecast models are complex, data intensive, location specific, and costly to develop and operate. Here, I develop a Generalized Additive Mixed Modelling (GAMM) approach to provide broader spatial coverage, more flexible, and cost effective implementation of river temperature forecasting for use in in-season harvest management.
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