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Zooplankton Population Dynamics on Georges Bank: Model and Data Synthesis

Zooplankton Population Dynamics on Georges Bank: Model and Data Synthesis. PIs: P.J.S. Franks, C.S. Chen, E.G. Durbin, W. Gentleman, J.M. Pringle and J. Runge With important contributions from students, postdocs, technicians. Goals.

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Zooplankton Population Dynamics on Georges Bank: Model and Data Synthesis

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  1. Zooplankton Population Dynamics on Georges Bank: Model and Data Synthesis PIs: P.J.S. Franks, C.S. Chen, E.G. Durbin, W. Gentleman, J.M. Pringle and J. Runge With important contributions from students, postdocs, technicians

  2. Goals • To improve our mechanistic understanding of the possible influences of climate variation on the population dynamics and production of the target zooplankton species through its effects on advective transport, temperature, food availability, and predator fields

  3. Questions and Hypotheses • The role of advection • Advective supply of Calanus finmarchicus and Pseudocalanus spp. copepodites to GB during January-April and the role of winds • Advective supply and loss of Calanus finmarchicus to GOM basin diapausing populations during June-January • Role of advection for copepod populations on GB • Population dynamics of zooplankton on GB and the GOM • Stratification and variability in food supply: the role of food limitation • Mortality and invertebrate predation

  4. The role of advection Advective supply and loss to GOM basin diapausing populations during June-January

  5. Deep density field mattersFVCOM surface currents and depth of s=26.97 isopycnal

  6. Deep density field changes Note change in cross-gulf density gradient This will change flow towards Georges Bank

  7. Calculate Geostrophic Cross Gulf Currents (200m reference level) • Calculate average density in boxes over one year period from BIO data. • Vertically integrate density difference from “level of no motion.” • Produce estimate of cross-gulf transport to Georges Bank. • This does not estimate coastal boundary current transport, but it may be related.

  8. Time series of cross-Gulf transport(missing years have too little data) Mean 0.6Sv Std. Deviation 0.22Sv

  9. Variability of GOM Transport • Sources of variability: (standard deviation)/(6 month mean) for transport along Pen Bay/Georges Bank axis, not including coastal current. • Hydrographic variability, 30 to 40% • Scotian Shelf inflow variability, about 20% • Winter winds, about 10%. • Open questions? (one of many!) • What is timescale of hydrographic variability? • What we know best is not what is important on 6 month or longer timescales

  10. Retention in basins GOM subregions Passive behavior Density-seeking behavior

  11. Transport among GOM sub-regions (passive behavior) Georges Basin Jordan Wilkinson 75 m 100 m Percent retained in sub-region 150 m 200 m 250 m Time (first of month)

  12. Retention Summary: • Retention in deep GOM is high. • Retention increases with depth. • Wilkinson Basin is most retentive, Georges is least. • Retention is greater for density-seeking particles than passive particles. • Vertical distribution and diapause behavior drives more uncertainty than winds and inflow, and is poorly understood.

  13. Advective supply to GB during January-April and the role of winds The role of advection

  14. FVCOM: 1999 MM5 wind forcing Day 76 Day 81 Day 66 Subtidal currents surface 20 m Scotian Shelf crossover event Day 78

  15. Lagrangian particles (advected, not mixed)

  16. Passive tracer (vertically mixed) • Surface particles released in the Browns Bank area “crossed-over" the NEC, reached NEP of the bank in less than 10 days and followed a clockwise circulation path over the southern Flank of GB. • The tracer experiment indicates that vertical mixing prevents a significant amount of blooming biomass from being advected to the southern flank.

  17. Population dynamics Stratification and variability in food supply

  18. Stratification and the spring bloom • Three zones: • The central bank in which water is shallow, vertically well-mixed, and relatively self-contained; • The mid-bank region characterized by a seasonal tidal mixing front; • The outer-flank between the seasonal tidal mixing front and the permanent shelf break front. A 1 B 2 3 ECOM-si ECOM-si FVCOM 1-D 2-D 3-D • Seasonal dynamics • Sensitivity • Model behavior • Stratification • Frontal structure • Cross-section variation • Advection • Event level

  19. Nitrate Silicate Ammonia Uptake Uptake Uptake Remineralization Dissolution Small Large Phytoplankton Phytoplankton Grazing Grazing Mortality Mortality Small Large Predation Zooplankton Zooplankton Mortality Mortality Fecal Grazing Mortality Detritus Detritus Nitrogen Silica

  20. Model-Data Comparison 1-D Model Site A Site B

  21. Sensitivity to heat flux 2-D Model Less heat dT/dz dT/dz Large P Large P Time Time

  22. Stratification summary • The light environment controls the onset of the bloom in the shallow region, while stratification plays a more significant role in the deep region. • The magnitude of bloom is modified by both light and nutrients. • N/Si ratio is an important parameter for the nutrients limitation process and succession of phytoplankton community. • The basic pattern of lower-level trophic food-web dynamics in shallow and deeper area mirrors the sites A and B in the 1D model. A unique pattern develops in the tidal mixing frontal zone. • If no impact from advection, the development of weak stratification is critical for the springtime bloom; wind and heat flux can regulate this process. • The frontal zone is a possible area for the “second” diatom bloom. • Advection may be critical in determining changes in stratification and thus bloom formation, particularly in deeper waters

  23. Population dynamics Variability in food supply

  24. Individual-based models Campbell et al., 2001 IBM Regression line used to evaluate: MDTi = time when 50% of cohort reached stage i (e.g. MDTC3 = 21.8 days) DTVi = reciprocal of slope = measure of variability (e.g. DTVC3 = 2.7 days)

  25. Durbin et al. 2003: Gulf of Maine Runge et al. (in prep.): Georges Bank Food limitation

  26. IBM with food and temperature effects: comparison with data All temperatures, food levels, and stages

  27. Diapause duration model

  28. Still to come: Further testing, simulation with FVCOM Detailed exploration of transport pathways, influence of behavior Develop offline code for tracers, biology Couple 3D physical model with ecosystem model for annual cycle Further develop and constrain IBM Model diapause behavior Couple ecosystem model with IBM

  29. Workshop Objectives Coordinate efforts Work on offline code Outline papers Plan research efforts for the next year

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