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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. - PowerPoint PPT Presentation
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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• 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
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
The role of advection
Advective supply and loss to GOM basin diapausing populations during June-
January
Deep density field mattersFVCOM surface currents and depth of =26.97 isopycnal
Deep density field changes
Note change in cross-gulf density gradientThis will change flow towards Georges Bank
Calculate Geostrophic Cross Gulf Currents (200m reference level)
1) Calculate average density in boxes over one year period from BIO data.
2) Vertically integrate density difference from “level of no motion.”
3) Produce estimate of cross-gulf transport to Georges Bank.
This does not estimate coastal boundary current transport, but it may be related.
Time series of cross-Gulf transport(missing years have too little data)
Mean 0.6Sv
Std. Deviation 0.22Sv
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
GOM subregions
Passive behavior
Density-seeking behavior
Retention in basins
Transport among GOM sub-regions (passive behavior)P
erce
nt r
etai
ned
in s
ub-r
egio
n
Time (first of month)
75 m
100 m
150 m
200 m
250 m
Georges Basin Jordan Wilkinson
Georges Basin
Jordan Basin
Wilkinson Basin
Upstream
Georges Bank
Downstream
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.
Advective supply to GB during January-April and the role of winds
The role of advection
Day 66 Day 76 Day 81Subtidal currents
surface
20 m
FVCOM: 1999 MM5 wind forcing
Scotian Shelf crossover event Day 78
Lagrangian particles (advected, not mixed)
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.
Population dynamics
Stratification and variability in food supply
1
2
3
1-D 2-D 3-D
• Stratification• Frontal structure• Cross-section variation
ECOM-si ECOM-si FVCOM
A
B
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.
• Seasonal dynamics• Sensitivity• Model behavior
• Advection• Event level
Stratification and the spring bloom
Ammonia Silicate
SmallPhytoplankton
LargePhytoplankton
SmallZooplankton
LargeZooplankton
DetritusNitrogen
DetritusSilica
Predation
Mortality
Remineralization
UptakeUptake Uptake
Dissolution
Fecal
Mortality
Grazing Grazing
Mortality
Mortality
Nitrate
Grazing
Mortality
Site A Site B
Model-Data Comparison 1-D Model
2-D Model
Sensitivity to heat flux
Less heat
Large P Large P
Time Time
dT/dz dT/dz
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
Population dynamics
Variability in food supply
Individual-based models
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)
Campbell et al., 2001 IBM
Food limitation
Durbin et al. 2003: Gulf of Maine Runge et al. (in prep.): Georges Bank
All temperatures, food levels, and stages
IBM with food and temperature effects: comparison with data
Diapause duration model
0 2 4 6 81.8
2
2.2
2.4
2.6
2.8
3Maximum Diapause Duration
T
Length
6060
9090
90
90
120
120
120
120
120
150
150
150
150
180
180
180
180
210
210
210
240
240
240
270
270
300
1.8 2 2.2 2.4 2.6 2.8 30
2500
5000
7500
10000
12500
15000June to August - Jordan Basin
Length (mm)
#C5s/m
2
Month 1Month 2Predicted Month 2
1.8 2 2.2 2.4 2.6 2.8 30
2500
5000
7500
10000
12500
15000August to November - Jordan Basin
Length (mm)
#C5s/m
2
Month 1Month 2Predicted Month 2
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
Workshop Objectives
Coordinate efforts
Work on offline code
Outline papers
Plan research efforts for the next year