Seasonal and regional variability of model-based zooplankton biomass in the Salish Sea and evaluation against observations

•Model captured seasonal and regional variability of zooplankton observation dataset.•Modelled zooplankton biomass was highest in regions adjacent to areas of high mixing.•Zooplankton grazing was high in mixed regions despite low primary productivity. We used a three-dimensional coupled biophysical model to examine zooplankton dynamics in the Salish Sea, NE Pacific. First, we evaluated the two zooplankton classes of the SalishSeaCast model using a transboundary zooplankton dataset comprised of observation data from the Canadian and United States waters of the Salish Sea from 2015 to 2019. Model zooplankton classes correspond to micro- and meso-zooplankton whose biomass is tightly coupled to phytoplankton through modelled food web dynamics (Z1) and mesozooplankton with life cycle-based seasonal grazing impacts (Z2). Overall, the model effectively captured seasonal patterns in observed biomass, although with slightly higher biomass estimates for both Z1 and Z2 (Bias = 0.10 and 0.08 g C m−2, respectively). Model fit varied regionally, with a weaker model fit being observed in nearshore regions. In addition, an autumn peak in Z2 was observed in the model, but not in the observations, suggesting some seasonal variations in model fit. Following the model evaluation, we used the model to determine seasonal and regional patterns of zooplankton grazing. Seasonally, the main peak in modelled zooplankton biomass increased in April or May in most of the regions defined within the Salish Sea and was driven by grazing on diatoms. Regionally, depth-integrated zooplankton biomass was consistently highest in areas adjacent to regions of strong tidal mixing. In addition, model-based zooplankton grazing was highest in the tidally mixed regions where phytoplankton biomass was high due to advection into the region despite low primary productivity. Our model-based results provide an opportunity to examine bottom-up food web processes at spatio-temporal scales not achievable with in situ sampling and help to elucidate key drivers of lower trophic level dynamics within the Salish Sea.