Annual migrations by juvenile Pacific salmon Oncorhynchus spp. smolts are predictable, presenting opportunities for predators to exploit these seasonal prey pulses. Directly observing predator–prey interactions to understand factors affecting predation may be possible via dual‐frequency identification sonar (DIDSON) acoustic imaging. Within Chilko Lake, British Columbia, prior telemetry and stomach content analyses suggested that the out‐migration of Sockeye Salmon Oncorhynchus nerka smolts influences the movements and aggregations of Bull Trout Salvelinus confluentus that feed extensively on smolts during their out‐migration. Bull Trout captured at a government‐installed counting fence exhibited high consumption of smolts, but it is only assumed that feeding occurred directly at the fence. We used DIDSON to assess fine‐scale predator–prey interactions between Sockeye Salmon smolts and Bull Trout over 10 d during the 2016 smolt out‐migration. We found that smolt–Bull Trout interactions were correlated with smolt densities at the counting fence, consistent with the prior diet studies in the system. Predator–prey interactions were also coupled with nocturnal migratory behaviors of Sockeye Salmon smolts, presumably to minimize predation risk. These results demonstrate that DIDSON technology can record interactions between predators and migrating prey at a resolution that can identify variability in space and time and provide insight on the role of anthropogenic structures (e.g., counting fences) in mediating such interactions.

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Indices of abundance used to inform stock assessment models are commonly derived from fishery-dependent data sources. However, fishery catch-per-unit-effort (CPUE) are often confounded by a myriad of factors for which corrections must be made using model-based standardization methods. The Alaska sablefish (Anoplopoma fimbria) fishery provides a fitting case study of such issues, wherein a regulatory change in 2017 disrupted historic fishery dynamics, promoting a rapid transition in use of pot gear over demersal hook-and-line gear in the Gulf of Alaska. To address this, we combined across both observer and logbook programs (data sources) and gear types to develop an intercalibrated abundance index. We first regressed observer records against vessel logbooks to understand potential biases that may arise from combining data sources during the CPUE standardization process. Here, we found that both data sources exhibited strong agreement in reported CPUEs when compared on a set-by-set basis. Therefore, we intercalibrated both CPUE data sources and developed an index of abundance that incorporated catch records from both demersal hook-and-line and pot gear fisheries for sablefish in Alaska, to account for the recent rapid change in gear use. This standardized index of abundance compared favourably with an index generated from a fishery-independent hook-and-line survey currently used in management, suggesting it is representative of sablefish population trends. Our findings not only represent a valuable contribution to the management of sablefish in Alaska, but also provide a widely applicable framework for standardizing fishery-dependent CPUE data to support the management of multi-gear fisheries.