An individual-based population dynamic model of seas scallop, with application to Georges Bank

1. An individual-based population dynamic model of seas scallop, with application to Georges Bank Rucheng Tian Department of Fisheries Oceanography SMAST, UMASSD Supervisors: Drs. C.S. Chen, K. Stokesbury, B. Rothschild Participants: the FVCOM group, Q.C. Xu, S. Hu, G. Cowles, B. Harris and M. Marino Outline: - Model structure - Parameterization - Model set up for application - Results - Findings

2. Scallop life cycle (Stewart, P.L. and S.H. Arnold. 1994. Can. Tech. Rep. Fish. Aquat. Sci. 2005: 1-36).

3. f1 f2 1 2 3 4 5 G1 G2 G3 G4 P1 P2 P3 P4 P5 A stage-based population model for bay scallop (EPA RI). Stage-based population model f1, f2: Reproduction; G1-4: recruitments; P1-5: survivorship (Hinchey, Chintal, & Gleason 2004 ).

4. nn m r Minimum harvest weight nk nk m m Weight n4 n4 m m n3 n3 n3 G m m m n2 n2 n2 m m m n1 n1 n1 n1 m e m e m e e t t+1 t+2 t+n Time r r Population dynamics model of mussels n: number of mussels;e: spawning; m: mortality; r: harvesting; G: growth (Gangnery et al., 2001)

5. A Lagrangian individual-based population dynamic model of scallop, coupled with an Eulerian concentration-based ecosystem model Eulerian Lagrangian F SV Z P Veliger Trochophore Water SV H N D Pediveliger Egg S F ST S R G Sediment Biodeposits Juvenile Young adult Adult D: Detritus; N: Nitrogen; P: Phytoplankton; Z: Zooplankton F: Feeding; G: Growth; H: Hatching; R: Recruitment; S: Spawning; ST: Settlement; SV: Survivorship;

6. Parameterization R : Respiration. G :Growth aS:Constant. bS : Constant. Starvation mortality: Ross and Nisbet, 1990.

7. Biological attributes of Lagrangian ensemble particles Pi(n,t): Number of eggs at t in an ensemble particle; Nscallop: Total scallop in a simulation cell; Segg: Total eggs spawned by each individual adult scallop in one season; M: Mortality (0.25 d-1; McGarvey et al., 1993) Number of larvae: Age: Height: Biomass:

8. Lagrangian trajectory A : Horizontal diffusivity. K : Vertical diffusivity; Pi : Particle i at x and t; Wm: Vertical migration; r: Random process; σ: Std of r; t :Time; u : Current; x :Spatial position. (Visser, 1997) Trajectory: Random walking: (eggs, at 1 m above the bottom) (trochphores) (veligers) (pediveligers) Behavior:

9. 42.1 42.0 41.9 41.8 41.7 41.6 41.5 41.4 67.00 66.8 66.6 66.4 66.00 66.2 Estimation of the spawning stock Thouzou et al., 1991 von Bertalanffy growth function: H(3) = 72.03 (mm) F(>age 3) = 76% (average on GB) Provided by K. Stokesbury

10. Spawning • The simulation starts on Aug 15; • tm (maximum spawning day) is assumed to be on Sep. 10; • (deviation) is assumed to be 1 week; • One adult spawns in average 50 million eggs (Langton, 1987; McGarvey et al., 1992, 1993) Abundance of scallop > age 3 (N m-2 ) The normal distribution was integrated using the error function:

11. Substrate distribution and larvae-settlement probability Settlement probability Settlement probability: Gravel: 0.2; Sand: 0.05; Fine sand: 0.01.

12. The scallop simulation was conducted with the framework of FVCOM - Surface forcing from MM5. - Tide. - River discharges. - Daily SST data assimilation. - Monthly boundary conditions.

13. Movie of simulated larval trajectory for 1995 Horizontal trajectory Vertical trajectory Larvae settlement

14. Movie of simulated larval trajectories for 1995 and 1998

15. Drifter trajectories (Lozer & Gawarkiewicz, 2001, JPO. 31: 2498-2510)

16. Total larvae settled on Georges Bank (GB), in the Great Southern Channel (GSC) and to the Middle Atlantic Bight (MAB)

17. Late spawning is unfavorable for larvae retention on Georges Bank

18. Larvae exchange between scallop subpopulations

19. Closed area selection and rotation

20. Closed area selection and rotation

21. Closed area selection and rotation

22. Closed area selection and rotation

24. Current/turbulence Temperature Predator Forcing Water column Detritus Phytoplankton Suspended sediments Sinking Mixing Sinking Mixing Boundary layer Detritus Phytoplankton Suspended sediments Sedimentation Suspension Feeding Feeding Sedimentation Suspension Biodeposits Scallop Sediment Sediment Predation Starvation Resuspension Temperature stress Natural & fishing Mortality Schematic of the scallop benthic module

25. SUMMARY • Construct your model based on your question. • Better using prognostic parameterizations than diagnostic one. • Model set up can be specific to each ecosystems. • Long-distance larval transport from GB to the MAB. • Interannual variability due to physical forcing. • Larval exchanges between scallop beds. • Closed-area selection and rotation.

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