Electronic monitoring (EM) is a tool that is being developed in many fisheries to either replace or augment at-sea observations, or to provide data from previously unobserved fisheries. EM is currently used in Alaskan fixed-gear fisheries and is being investigated for some trawl fisheries. In both cases, the EM systems are likely unable to accurately estimate the catch of large species that are mostly discarded at-sea.
Sharks provide an interesting case study because most of the shark species in Alaska are large, almost entirely discarded, and have limited at-sea observer data to inform catch estimates. The assessments of large sharks in the Bering Sea/Aleutian Islands and Gulf of Alaska areas are extremely data-limited. The assessments are based on catch history alone, but there are a number of questions about the accuracy of the catch estimates. Estimates of shark catch from longline vessels are based on the numbers observed in a haul, converted to biomass by applying an average weight and extrapolated to the whole haul. Average estimated weights are likely biased low because generally larger sharks are not brought on deck and only small sharks are available to observers for biological sampling.
The proposed project will focus on accurately estimating catch of large sharks using EM methodology currently being applied in longline fisheries. The project has four objectives: 1) examine the EM systems ability to detect rare catches of large sharks that are typically not brought on board; 2) investigate if EM systems can be used to estimate the size class of large sharks; 3) Evaluate results of this study along with on-going special projects to compare EM catch estimation results to at-sea observer estimates for developing improved methods for estimating catch of large sharks in Alaskan fixed gear fisheries; 4) incorporate EM collected size data, along with size data collected in on-going observer special projects to develop improved methods for estimating catch for large sharks.
Candidates should have attained a Ph.D. in fisheries, statistics, mathematics, or data science within the last five years. Good quantitative abilities, written and oral communication skills, and experience in coding (e.g., R and/or Python) or advanced data analytics (i.e., machine learning/artificial intelligence) are required. Ideal candidates will have experience working with fisheries data and the abilities to synthesize large datasets and applying advanced data analytics. There may also be opportunities to participate in at-sea research.