Presentation on theme: "An individual-based population dynamic model of seas scallop, with application to Georges Bank Rucheng Tian Department of Fisheries Oceanography SMAST,"— Presentation transcript:
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
Scallop life cycle (Stewart, P.L. and S.H. Arnold Can. Tech. Rep. Fish. Aquat. Sci. 2005: 1-36).
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
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
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
Parameterization Ross and Nisbet, Starvation mortality: R : Respiration. G : Growth S : Constant. S : Constant.
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:
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)
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:
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:
Substrate distribution and larvae-settlement probability Settlement probability Settlement probability: Gravel: 0.2; Sand: 0.05; Fine sand: 0.01.
The scallop simulation was conducted with the framework of FVCOM - Surface forcing from MM5. - Tide. - Monthly boundary conditions. - Daily SST data assimilation. - River discharges.
Larvae settlement Movie of simulated larval trajectory for 1995 Horizontal trajectory Vertical trajectory
Movie of simulated larval trajectories for 1995 and 1998
Total larvae settled on Georges Bank (GB), in the Great Southern Channel (GSC) and to the Middle Atlantic Bight (MAB)
Late spawning is unfavorable for larvae retention on Georges Bank Temp. run79%37%47%23%26%32%36%48%74%25%16%
Larvae exchange between scallop subpopulations
Closed area selection and rotation
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
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.