Simulating Anchovy's Full Life Cycle in the Black Sea: A Coupled Biogeochemical-DEB-Movement-IBM Model

European anchovy (Engraulis encrasicolus) is an abundant small pelagic fish in the Black Sea and supports a significant commercial fishery. Anchovy is highly sensitive to climate fluctuations that affect environmental and biological conditions. Presently, reconstructed biomass and population data from stock assessments lack detailed spatial resolution. We are developing a 3D full-life-cycle individual-based population model for Black Sea anchovy. The model is one-way coupled to a Black Sea hydrodynamics-biogeochemical model (BAMHBI) that provides temperature, circulation (transport), and prey for the anchovy. The IBM uses the same grid as the hydrodynamics-BAMHBI model. Individual anchovy progress daily through five life stages (egg, early larvae, late larvae, juvenile, and adult) and we follow individuals until age-4. A dynamic energy budget (DEB) submodel is used to simulate growth across all life stages and generate egg production. The model incorporates phototrophic small and large flagellates as food sources for larvae, and micro- and meso-zooplankton for juveniles and adults. Movement is based on a kinesis algorithm with temperature and food as cues; eggs are physically transported. A super-individual approach is utilized for computational efficiency. Calibration proceeded by separately tuning the DEB, movement, and density-dependent mortality, and then comparing predictions to data when all were combined into the full 3D population model. We presently are evaluating the full model using a baseline simulation of 1990 to 2022. We will then use simulation experiments to explore, under present-day and plausible future climates, the mechanisms that cause high and low recruitment years, population responses to changes in harvest, and the effects of invasive species that compete for common prey. We plan to use the modeling to provide insights into the sustainability and resilience of Black Sea anchovy population to climate, harvest, and stressors. Better understanding of the spatiotemporal variation in population dynamics can inform fishery management and conservation strategies.