Late versus Early Phytoplankton Blooms
Hunt et al., 2002 hypothesize that there are 2 states for the Bering Sea ecosystem.
(1) Late ice retreat (late March or later) leads to an early phytoplankton bloom in cold water (e.g., 1995, 1997, 1999).
(2) No ice, or early ice retreat (before mid-March), leads to a late phytoplankton bloom in warm water in May or June (e.g., 1996, 1998, 2000).
Zooplankton populations are not closely coupled to the spring bloom, but are very sensitive to water temperature. In years with an early bloom (Scenario 1), low temperatures limit the growth of zooplankton so that they are unable to take full advantage of the phytoplankton bloom. Alternatively, in years when the bloom is late (Scenario 2), it occurs in warm water and zooplankton populations should grow rapidly, feasting on abundant phytoplankton. This table summarizes these relationships:
| Ice Retreat | Late | Early |
| Phytoplankton Bloom | Early | Late |
| Water Temperature | Cold | Warm |
| Zooplankton Bloom | Small | Large |
How would you change the model settings to mimic early and late phytoplankton blooms?
What additional settings might you add?
(Type your response in the box below.)
Discussion
The ecosystem model we use here does an imperfect job of simulating the conditions described in this scenario. This results from two main factors: (1) the water in the model has a uniform temperature. It is not stratified (warmer at the surface and cooling with depth). (2) The growth rate for zooplankton (based on their grazing rate) is not temperature dependent. However, with those caveats, we can simulate this scenario as follows:
In order to simulate early versus late phytoplankton blooms, we can change the amount of light available for phytoplankton growth. This is most easily done by changing the sea ice cover. Remember that early ice retreat results in a late bloom. For an early bloom, we set the sea ice concentration to 50%. Then, for a late bloom, we set the sea ice concentration to 0%.
Because the zooplankton growth rate in the model, controlled by the grazing rate, is not temperature dependent, we need to set the grazing rate manually. Early ice retreat leads to warm water, so we use a higher grazing rate (0.4). Conversely, the late ice retreat results in cold water, with a lower grazing rate (0.2).
1 of 3
Which scenario favors an increase in the abundance of foraging fish that eat zooplankton? (Choose the best answer.)
The correct answer is (b) Scenario 2.
Because zooplankton are the primary prey for larval and juvenile fish, scenario (1) will limit the survival of larval/juvenile fish. Alternatively, in periods when the phytoplankton bloom occurs in warm water, zooplankton populations should grow rapidly, providing plentiful prey for larval and juvenile fish.
2 of 3
Which scenario favors an increase in the abundance of predatory fish that eat foraging fish? (Choose the best answer.)
The correct answer is (b) Scenario 2.
Fewer zooplankton in scenario (1) will limit the survival of larval/juvenile fish that grow into large predatory fish. When continued over longer periods, approaching a decade, this will lead to a decreased biomass of predatory fish (fewer and/or smaller fish). Alternatively, abundant zooplankton will support the survival of immature fish to maturity, which will lead to abundant predatory fish that control forage fish.
3 of 3
Which scenario favors an increase in the abundance of sea mammals and seabirds that eat foraging fish? (Choose the best answer.)
The correct answer is Scenario (1).
Fish-eating sea mammals such as northern fur seals and seabirds such as kittiwakes compete with predatory fish. When the number of predatory fish increases, the abundance of forage fish decreases, which leads to population declines in fish-eating sea mammals and seabirds. Thus, fewer zooplankton may actually help populations of fish-eating sea mammals and seabirds by limiting their competition for food from large predatory fish.
Reference:
Climate change and control of the southeastern Bering Sea pelagic ecosystem. Hunt, G.L. Jr., Stabenob, P., Walters, G., Sinclaird, E., Brodeure, R.D., Nappc, J.M., Bondf, N.A. Deep-Sea Research II 49 (2002) 5821–5853. http://www.ingentaconnect.com/content/els/09670645/2002/00000049/00000026/art00321