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Interpretation of zooplankton field observations on the Gulf of Alaska (GOA) shelf is complicated by the complex physical environment of currents, eddies and meanders which mix oceanic and coastal water masses to varying degrees. We therefore developed a lower trophic level model embedded in an ocean circulation model to examine the effects of wind, runoff and temperature on transport, biomass and production of microzooplankton and copepods on the northern GOA shelf and adjacent ocean. Mean microzooplankton biomass varied from 10–90 and 10–50 mg C m− 3 in the upper 50 m for field measurements and simulations respectively. Mean simulated and measured copepod biomass in the upper 100 m was about 1–40 g C m− 3. Field data indicated that Neocalanus biomass was negatively correlated with salinity in May. Simulations indicated that the negative correlation was due to elevated primary and secondary production on the shelf relative to the adjacent ocean. Floats programmed to simulate the ontogenetic vertical migrations of Neocalanus indicated that ~ 10 to 50% of Neocalanus originating near the shelf break spend at least some time on the shelf, depending on location. Residence time of floats on the shelf was affected primarily by wind, secondarily by runoff. Primary and secondary production in the simulations were highly correlated (r > 0.9). Simulated secondary production was 10–90 g C m− 2 y− 1, with highest values in Lower Cook Inlet, around Kodiak and west of Kodiak.

The Eastern Bering Sea shelf is divided into distinct hydrographic domains by structural fronts. Despite frontal obstructions to cross-shelf transport, each year large oceanic copepods—primarily Neocalanus spp.—are known to dominate the biomass of the outer-shelf zooplankton communities, and in some years are advected into the middle-shelf domain. Using ROMS (the Regional Ocean Modeling System), coupled with a float tracking model designed to represent ontogenetic vertical migration behavior of Neocalanus, we explored the mechanisms, timing, and location of the transport of oceanic zooplankton onto the eastern Bering Sea shelf from overwintering sources along the Gulf of Alaska and Bering Sea shelf breaks, under a variety of environmental conditions. Our float tracking experiments suggest that the timing of on-shelf transport and the distribution of oceanic zooplankton on the shelf can vary substantially between one year and another. The Bering, Pribilof, and Zhemchug Canyons and Cape Navarin were all regions of elevated on-shelf float transport. Wind direction was the primary factor controlling inter-annual variability in the timing, amount, and location of the on-shelf transport of our Neocalanus floats. Float transport across the northern and southern shelves responded in opposite directions to inter-annual differences in wind forcing: southeasterly wind enhanced on-shelf transport of the Neocalanus floats along the southern shelf but suppressed on-shelf transport over the northern shelf. Conversely, northwesterly wind suppressed on-shelf zooplankton transport onto the southern shelf but promoted enhanced transport around Cape Navarin on the northern shelf. Transport of the Neocalanus floats onto the shelf can be very episodic, reflecting the short duration of winds that promote on-shelf transport. Relatively short (days to weeks) periods of southeasterly wind between March and April significantly impacted the number of floats transported onto the shelf. The relative importance of different source areas to supplying oceanic zooplankton to the Bering Sea shelf does not appear to vary much from year to year. Our model results suggest that the Neocalanus found on the southern shelf most likely originate from overwintering sites in the Alaska Stream or the Eastern Bering Sea shelf break south of the Pribilof Islands, while Neocalanus found on the northern shelf most likely originate from sites north of the Pribilof Islands.

We use a series of Monte Carlo experiments to explore simultaneously the sensitivity of the BEST marine ecosystem model to environmental forcing, initial conditions, and biological parameterizations. Twenty model output variables were examined for sensitivity. The true sensitivity of biological and environmental parameters becomes apparent only when each parameter is allowed to vary within its realistic range. Many biological parameters were important only to their corresponding variable, but several biological parameters, e.g., microzooplankton grazing and small phytoplankton doubling rate, were consistently very important to several output variables. Assuming realistic biological and environmental variability, the standard deviation about simulated mean mesozooplankton biomass ranged from 1 to 14 mg C m− 3 during the year. Annual primary productivity was not strongly correlated with temperature but was positively correlated with initial nitrate and light. Secondary productivity was positively correlated with primary productivity and negatively correlated with spring bloom timing. Mesozooplankton productivity was not correlated with water temperature, but a shift towards a system in which smaller zooplankton undertake a greater proportion of the secondary production as the water temperature increases appears likely. This approach to incorporating environmental variability within a sensitivity analysis could be extended to any ecosystem model to gain confidence in climate-driven ecosystem predictions.