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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

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

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12345 f1 f2 G1G2G3G4 P1P2P3P4P5 (EPA RI). Stage-based population model f1, f2: Reproduction; G1-4: recruitments; P1-5: survivorship (Hinchey, Chintal, & Gleason 2004 ). A stage-based population model for bay scallop

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r n1n1 n1n1 n1n1 n1n1 n2n2 n2n2 n2n2 n3n3 n3n3 n3n3 n4n4 n4n4 n nknk nknk tt+1t+2t+n ee ee Time mmm mm m mmm mm m m m Weight rr Minimum harvest weight G n: number of mussels; e: spawning; m: mortality; r: harvesting; G: growth (Gangnery et al., 2001) Population dynamics model of mussels

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

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Parameterization Ross and Nisbet, Starvation mortality: R : Respiration. G : Growth S : Constant. S : Constant.

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

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

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Provided by K. Stokesbury Thouzou et al., 1991 H(3) = (mm) F(>age 3) = 76% (average on GB) Estimation of the spawning stock von Bertalanffy growth function:

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The simulation starts on Aug 15; t m (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 ) Spawning The normal distribution was integrated using the error function:

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

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

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

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Movie of simulated larval trajectories for 1995 and 1998

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Drifter trajectories (Lozer & Gawarkiewicz, 2001, JPO. 31: )

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

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Late spawning is unfavorable for larvae retention on Georges Bank Temp. run79%37%47%23%26%32%36%48%74%25%16%

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Larvae exchange between scallop subpopulations

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Closed area selection and rotation

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Schematic of the scallop benthic module Phytoplankton Suspended sedimentsDetritus Sediment BiodepositsSedimentScallop Water column Boundary layer Detritus Phytoplankton Suspended sediments Mixing SedimentationSuspensionSedimentationSuspensionFeeding Forcing TemperatureCurrent/turbulencePredator Natural & fishing MortalityPredationResuspensionStarvationTemperature stress Sinking

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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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