The Herring Research and Monitoring (HRM) program's 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.
In 2020, we reconfigured the BASA model to use a more efficient Bayesian algorithm. This new approach cut the run time of the model by 60-80% which has allowed us to examine the sensitivity of the model to inputs and explore relationships between recruitment, mortality, and environmental factors. We are finding that some winter conditions along with increases in abundance of pink salmon, whales, and Arrowtooth flounders are associated with increases in mortality rates for herring.
Another major ongoing component of the modeling work involves the incorporation of viral hemorrhagic septicemia virus (VHSV) antibody data into the model to better estimate the disease component of mortality. Disease has been proposed to be one of the major reasons for failed recovery following the collapse of the PWS herring population. We developed a simulator of VHSV disease outbreaks and an estimation model to test whether it is possible to use antibody data to estimate mortality. Results based on simulated data are very promising but still preliminary. We are continuing our efforts on this component of the model and hope to be able to incorporate the VHSV antibody data collected by the disease project soon.