A spatial analysis of longline survey data for improved indices of Atlantic halibut abundance

Atlantic halibut (Hippoglossus hippoglossus) support an economically important fishery on the eastern coast of Canada. Like other species that are not well sampled by trawl surveys, halibut in this area are monitored using longline surveys. These surveys present challenges that can make obtaining indices of abundance difficult. Issues include gear saturation, which can result in a non-linear relationship between catch per unit effort and local abundance. The current approach to obtain a relative index consists of fitting a multinomial exponential model to a subset of hooks from each survey station. While this approach accounts for hook competition, it does not account for the presence of spatial patterns. We therefore extend the multinomial exponential model to include spatial random fields for both Atlantic halibut and non-target species, set-specific soak time, and data from the hooks. Furthermore, we propose a method for aggregating the resulting spatially varying indices to obtain an annual index for the entirety of the modelled area. This novel approach identifies Atlantic halibut hotspots in multiple years, while simultaneously providing relative abundance indices for 2017 through 2020. These outcomes demonstrate the widespread applicability of our methods for improving the scientific advice upon which fisheries management decisions are based.