The Herring Research and Monitoring (HRM) program goal is to improve the ability to predict the herring stocks through modeling. During the first phase of the HRM program (2012-2017) we focused on improving the existing age-structure-assessment (ASA) model used by Alaska Department of Fish and Game (ADF&G). One improvement was making the model Bayesian which allows us to include prior information on herring (e.g. mortality rates from other herring populations) and compute uncertainty around model estimates. Another improvement has been to modify the structure of ASA to include new data. In this way, we can determine whether histology data is influencing mortality the most, or is it food availability, or perhaps disease? Information from herring stocks around the world has also been collected to determine how the herring crash in Prince William Sound (PWS) compares to declines and recovery of other herring stocks. In the current phase of the program (2017-2021) the Bayesian model will be used to predict herring population levels in PWS as the ASA model did before but there are several new pieces of the puzzle that we are evaluating. These variables include new information about disease, specifically antibody levels in herring, testing different maturity scenarios, environmental data, exposure to oil in different life stages as well as alternative management strategies.
We will use the Bayesian age-structure analysis (BASA) model to predict the Prince William Sound herring stock using data collected by the monitoring projects in the HRM program and examine the impacts of alternative management strategies. The team will look at how adding different types of information (environmental conditions, food availability, etc.) improve the ability of the model to predict herring stocks. The incorporation of new disease information will be examined to determine a more appropriate way to use disease measurements to predict mortality. We will continue to collect information on herring stocks around the world to put the changes in catch, biomass, and recruitment of herring in PWS in the context of other stocks.
BASA was used to examine the value and sensitivities of different inputs. The most valuable pieces of information to BASA are the egg deposition and the disease data. Egg deposition data was found to be necessary to constrain the absolute amount of biomass while the disease data was found to be critical in capturing the initial crash of the population. When the crash was put into context of fluctuations in worldwide herring populations, it was found to be highly unusual. In the current phase of the project, we expect to better understand if environmental and ecological information can be used to predict changes in herring populations. So far, the team has found larger regional scale factors predict changes in herring mortality, and that Gulf of Alaska conditions may dictate current dynamics in Prince William Sound herring. New antibody measurements from the HRM disease team are being developed to predict mortality more precisely as well. In the upcoming year, we will continue to test sensitivities of existing inputs and include different inputs in the model. They also plan to simulate alternative management thresholds in the model to determine if new strategies could be implemented to allow a fishery without harming the current population.