an individual-based population dynamic model of seas scallop, with application to georges bank...
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An individual-based population dynamic model of seas scallop, with application to Georges Bank
Rucheng TianDepartment 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. 1994. Can. Tech. Rep. Fish. Aquat. Sci. 2005: 1-36).
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1 2 3 4 5
f1
f2
G1 G2 G3 G4
P1 P2 P3 P4 P5
(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
n1 n1 n1 n1
n2 n2 n2
n3 n3 n3
n4 n4
nn
nk nk
t t+1 t+2 t+n
e e e e
Time
m m m
mmm
m m m
m m
m m
m
Weigh
t
r rMinimum 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
Sed
imen
t
Biodeposits Young adultJuvenile
F
F
R G
ST S S
H
Eulerian Lagrangian
Wat
er
TrochophoreSV
SV
D: Detritus; N: Nitrogen; P: Phytoplankton; Z: ZooplanktonF: 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, 1990.
Starvation mortality:
RGwhen
RGwhenGR
MS
SSS
S
0
)( R : Respiration.G : GrowthS: Constant. S : Constant.
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)release after theMortality ( ;),(
)release thebefore Spawning(;2
1
),(
1
2
12
tMi
t
t
tt
eggscallop
i
etnP
eSN
tnP
m
ttagePtageP ii ),(),(
)1)(,(),( lii gtthPthP
)1)(,(),( wii gttwPtwP
Biological attributes of Lagrangian ensemble particles
Number of
larvae:
Age:
Height:
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)
Biomass:
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),()()(),( tPWKRARtutxP imxxxi
2/11 ('2'))( ttKKrtKKR xxxx
)(35);(7.1
)(355);(1.0
)(52);(3.0
)(2;0
),(
1
1
1
dayagewhensmm
dayagewhensmm
dayagewhensmm
dayagewhen
agePW im
Lagrangian trajectory
Trajectory:
Random walking:
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)
Behavior:
(eggs, at 1 m above the bottom)
(trochphores)
(veligers)
(pediveligers)
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41.4
66.0067.00 66.8 66.6 66.4 66.2
41.7
41.8
42.1
41.5
41.6
41.9
42.0
CAI
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
Provided by K. Stokesbury
Thouzou et al., 1991
)1(87.144)1()( )5566.0(2813.0)(max
0 tttk eeHtH
H(3) = 72.03 (mm)
F(>age 3) = 76% (average on GB)
Estimation of the spawning stock
von Bertalanffy growth function:
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22
168
600
2
1
1
72
1
1 1682
1100.5
2
1),(
t
t
tscallop
ttt
teggscallopi eNeSNtnPm
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 )
Spawning
21
2
1
2
1)(
2
2
1 xerfetF
tt
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
Hor
izon
tal t
raje
ctor
y 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: 2498-2510)
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0
2
4
6
8
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005Year
Lar
vae
(1012
)
GB GSC MAB
0
2
4
6
8
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005Year
Lar
vae
(1012
)
GB GSC MAB
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
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Temp. run 79% 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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Closed area selection and rotation
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Closed area selection and rotation
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Closed area selection and rotation
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Schematic of the scallop benthic module
Phytoplankton
Suspended sedimentsDetritus
Sediment Biodeposits SedimentScallop
Watercolumn
Boundary layer
Detritus
Phytoplankton
Suspended sediments
Mixing Mixing
Sedimentation SuspensionSedimentation Suspension Feeding Feeding
Forcing TemperatureCurrent/turbulence Predator
Natural & fishing MortalityPredation ResuspensionStarvation Temperature stress
Sinking 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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END
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