Patrick Lehodey Oceanic Fisheries Programme Secretariat of the Pacific Community Noumea, New Caledonia A spatial Ecosystem And Populations Dynamics Model (SEAPODYM)
Patrick LehodeyOceanic Fisheries Programme
Secretariat of the Pacific CommunityNoumea, New Caledonia
A spatial Ecosystem And Populations Dynamics Model (SEAPODYM)
SEAPODYM: Spatial model driven by physical and “simplified” food-web interactions
TemperatureCurrents
OxgenPrimary production
Mid-trophic levels
= predators forage
Predators
= Tunas, billfish …
Climate/environment variability
Fishing impact Fisheries
SEAPODYM
GMB (Grid, Mask and data interpolation)
Convert to format dym SeapodymView (visualization of 2D spatio-temporal
distributions)
− Temperature − Currents − Primary production − Oxygen concentration
Production and biomass of : − Epipelagic micronekton − Mesopelagic migrant micronekton − Mesopelagic non-migrant micronekton − Bathypelagic migrant micronekton − Bathypelagic highly-migrant micronekton
− Bathypelagic non- migrant micronekton For each tuna species: − Biomass of larvae − Biomass of juvenile − Biomass of young fish − Biomass of adult − Total Biomass − Predicted catch by group of fishing gear
Seapodym-RC (check running simulations
from remote machine)
Observed Fishing Effort and catch by
fleet
Coupled 3D- NPZD-GCM models
Temperature Currents
Primary production Oxygen concentration
Text files for EXCEL or equivalent (Sum by series, length frequencies, etc…)
Other data
EEZ boundaries Fishing data (text
format) Individual Tracks
Parameters.par
SEAPODYM software environment:
Reference manual :
-> Information Paper ME IP-1
Web site :
-> www.seapodym.org
Vertical structure of the forage-predators pelagic food web
The different daily vertical distribution patterns of the micronekton in the pelagic ecosystem. 1, epipelagic; 2, migrant mesopelagic; 3, non-migrant mesopelagic; 4, migrant bathy-pelagic; 5, highly-migrant bathypelagic; 6, non-migrant bathypelagic.
day
nightsunset, sunrise
Epipelagic layer
surface1 2 3 4 5 6
Mesopelagic layer
Bathypelagic layerday night su
nris
e
surface layer
suns
et
1
2
3
4
5
deep layer
inter-mediate layer
Five typical vertical movement behaviours simulated using a 3-layer and 2-type of prey pelagic system (adapted from Dagorn et al. 2000):
1- epipelagic predators (e.g., skipjack, marlins and sailfish); 2- predators moving between the surface and intermediate layers during the day (e.g., yellowfin tuna); 3- predators mainly in the intermediate layer during the day (e.g., albacore tuna); 4- predators moving between deep and intermediate layer during the day (e.g., blue shark); 5- predators mainly in the deep layer during the day (e.g., bigeye tuna and swordfish).
Modelling forage components:
3-layer 6-forage functional groups
day
nightsunset, sunrise
Epipelagic layer
surface1 2 3 4 5 6
Mesopelagic layer
Bathypelagic layer
PPEDay Length (DL) as a functionof latitude and date
En’
ponent is associated a coefficient of energy transfer from primary production (PP)To each com
Modelling forage components:3-layer 6-forage functional groups
PP
Epipelagic layer
surface1 2 3 4 5 6
Mesopelagic layer
Bathypelagic layer
E
PP
X %
100% of X%
Forage componentEF
Vertical layer
epi meso m-meso
bathy m-bathy
hm-bathy
Epi-pelagic
1 0 0 0 0 0
Meso-pelagic
0.307 0.237 0.456 0 0 0
Bathy-pelagic
0.17 0.1 0.22 0.18 0.13 0.2
day
nightsunset, sunrise
Forage functional groups: Dynamics is based on the 3T: Time, Temperature and Transport (currents)
New Primary Production
Eco
logi
cal t
rans
fer
1%
t 0
F
( )te SF
λ
λ −− = 1
( ) TrLn + − = 01 . 0
1 t λ λ 1 Tr
“mean age”
“lifespan”
t
λ S
te S λ−.
S
E
Lehodey P. et al., 1998. Fisheries Oceanography 7(3/4): 317-325.Lehodey P. 2001. Progress in Oceanography 49: 439-468.Lehodey P., Chai F., Hampton J. 2003. Fisheries Oceanography 12(4): 483-494
0
300
600
900
1200
1500
1800
2100
2400
0 5 10 15 20 25 30
Ambient Temperature (oC)
Age
at m
atur
ity (d
ay)
FISH obs (age at maturity)CEPHALOPODS obs (age at maturity)SHRIMPS obs (age at maturity)Tr + 1/lf(Tr)f(1/l)
Simulation outputs
Epi-pelagic layer (0-100m)
Meso-pelagic layer (100-400m)
Bathy-pelagic layer (400-1000m)
Day
Night
Predators dynamics modelling
Structure and dynamic of tuna populations
Spawning Larvae Juvenile Young Adult
Time/ age structure
t0 1st month 2nd and 3rd
month2nd quarter to age
of 1st maturity1st maturity to
last quarter
Size 2 mm 2 mm -5 cm 5-15 cm 15 - > 40 cm > 40 cm
Habitat factorsTo, Food (P),
Predators (F) in the epi-pelagic layer
To, Food (Zpk),
Predators (all young and adult tuna)
To, oxygen, Food (F),
Predators (all adult tuna) in
all layers
To, oxygen, Food (F) in all layers,
spawning seasonality
Transport / movement(advection-diffusion)
Currents in upper layer
1- Proportional to fish size2- Random movement (Diffusion) decreasing with increasing habitat3- Directed movement (Advection) following increasing gradient4- impact of currents
Natural mortality Independent estimates + habitat-related variability
Growth Independent estimates
highlights
• Habitats • Movement• Spawning seasonality• Variability of natural mortality• Prey-predator coupling
⎥⎦
⎤⎢⎣
⎡+⋅
⋅⋅= lFP
sss eIRH1logα
θSpawning Habitat
distribution of skipjack larvae
(Nishikawa et al.)
Predicted biomass of juvenile (age 2-3mo) Bigeye Simple match-mismatch mechanism, ie PP/ F, embedded in a dynamic system (currents, temperature) creates complex but realistic results
distribution of skipjack larvae
(Nishikawa et al.)
Predicted biomass of juvenile (age 2-3 mo) yellowfin
Defined by the accessibility to the different forage components according to preference of species (by age)
Feeding habitat
Bigeye
0.0
0.5
1.0
8 18 28oC
Inde
x
θ sθ f, age max
θ f, 1yrθ f, 3yr
0.0
0.5
1.0
0 1 2 3 4Oxygen (ml/l)
Inde
x
index O2 betFL max
Change in temperature function with age from age 0 (spawning) to maximum age (left) and habitat function for the oxygen (right).
Juvenile habitat
( )⎥⎥⎦⎤
⎢⎢⎣
⎡
+−⋅=
jipredpred
jipred
jijuvjijuv BB
BIH
,5.0
,,,
1θ
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200 250
Total Predator Biomass in cell i,j
Juve
nile
Hab
itat I
ndex
in c
ell i
,j
Bpred 0.5
Bpred 0.5 = 250 t deg-2
~ max value of biomass of
SKJ+YFT+BET
Movement
Habitat = null (no gradient)All displacement is due to kinesis with
individuals escaping at MSS in any straight direction. Population diffusion is maximal
Habitat = medium (medium gradient)Displacement is due to both kinesis and
klinotaxis. Population diffusion and advection are medium
G
G
Habitat = high (no gradient or negative gradient)
All displacement is due to kinesis, but population diffusion is low since
individuals stay in this favorable area
Population resulting diffusion
Individual movements
Habitat =low (high gradient)Displacement is mainly due to klinotaxis.
Population diffusion is low and advection is high
G
G
Advection – directed movement + current effect
Maximum (MSS * FL) for maximal value of gradient of standardized (0-1) adult habitat
Diffusion – random search behavior; maximum if both habitat and gradient of habitat is low
Low if habitat is high or if advection is high
Movement
Advection = directed movements along Habitat gradient (Klinotaxis)
In x direction: A = u + Χ . Gx
Current effect % to time spent in different layers
Gradient of Habitat
0.000.020.040.060.080.10
5
3055
80
0102030
40
50
Adv
. (nm
/d)
Habitat Gradient
FL (cm)
MSS = Maximum Sustainable Speed (in body length.s-1)
Gmax = max gradient of the standardised Habitat
MSS = 1 BL.s-1
MSSG
⋅=Χmax
1
MovementDiffusion = random movements
( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛⋅−⋅⎟⎟
⎠
⎞⎜⎜⎝
⎛⎥⎦
⎤⎢⎣
⎡+
−⋅⎜⎝
⎛⎟⎠⎞⋅=
max
2 9.01141
GG
HHtFLMSSD
a
aβ
Dmax
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.5 1.0Habitat Index (Ha)
f(Ha)
D is linked to Habitat value
ρ
D decreases when gradient increases
With FL the size (Fork Length) in m, MSS the Maximum Sustainable Speed (in Body length.s-1)
Spawning seasonality
Adult movement based on feeding habitat but switch to the spawning habitat with change in day length. The switch occurs based on a threshold value (>0.03 h/d).
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
15-D
ec
30-D
ec
14-J
an
29-J
an
13-F
eb
28-F
eb
15-M
ar
30-M
ar
14-A
pr
29-A
pr
14-M
ay
29-M
ay
13-J
un
28-J
un
Gra
dien
t of D
ay L
engt
h (h
/d)
6050403020
Natural mortality
M is represented by two functions (predation and senescence), and coefficient-at-age can vary in time and space based on habitat value.
Mmean =0.2
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.0 0.2 0.4 0.6 0.8 1.0
Habitat Index (Ha)
Mor
talit
y (M
)
M (e = 0)M (e = 0.5)M (e = 1.0)M (e = 2.0)
Skipjack
0.0
0.2
0.4
0.6
0.8
1.0
0 2 4 6 8 10 12 14 16 18 20
age (quarter)
predation q senesc. QM =sum qrtr MFCL
Yellowfin0.00.10.20.30.40.50.60.70.8
0 4 8 12 16 20 24 28 32
age (quarter)
predation senesc.M =sum MFCL05
Bigeye
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 4 8 12 16 20 24 28 32 36 40
age (quarter)
predation q senesc. QM =sum qrtr MFCL-05
Albacore
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50 60 70
age (quarter)
predation senesc.M =sum MFCL-03MFCL-05
Coupling prey (forage) and predators (tuna)it is possible to have from zero to N potential predators species explicitly described in the
model.
As a counterpart, this is relying on the assumption that the predators present an ‘ideal free distribution’, such that the total forage mortality by these species would be equal to λ = f(θ)
can be considered as an ~ equilibrium state.
'...21 λωωλ +++= spsp
Over the “specific predator area”, the mean forage mortality (for a given component) is
the sum of the mortalities due to the predator species described in the model + a
residual mortality λ’ due to all other predators
Locally, in each cell, the forage mortality due to food requirements of described predators, ωi,j is caculated according to
physical accessibility of the predator species (age) to the forage component considered and to their daily ration (% of body mass)
Outside specific predator area m = λ
Inside specific predator area m = ωi,j + λ’If sum of above -> ERROR:
biomass of predators cannot be sustained by the forage component
spω λ
Average forage consumption by species (all age classes) based on accessibility to forage components
Skipjackmigrant bathy0.6%
meso1.9%
highly migrant bathy15.7%
bathy0.0%
migrant meso23.5%
epi58.2%
Bigeyemigrant bathy6.1%
meso16.4%
highly migrant bathy12.2%
bathy0.0%
migrant meso24.9%
epi40.4%
Yellowfin
epi52.5%m igrant
m eso24.2%
bathy0.0%
highly m igrant
bathy15.3%
m eso5.9%
m igrant bathy2.1%
Running single vs multi-species simulations with SEAPODYM: What are the effect of interaction between top predator species like tuna?
change in abundance of
predators
Change in forage mortality
change inSpawning Habitat
(P/F)
Change inFeeding Habitat
Change inNatural mortality (if ε>0)
Change inSpatial distribution
Change in Juvenile Habitat
Change in natural mortality
Model outputs and evaluationMultiple Fisheries → compare Prediction vs Observation
0102030
J-72
J-74
J-76
J-78
J-80
J-82
J-84
J-86
J-88
J-90
J-92
J-94
J-96
J-98
J-00
J-02
cpue
02468obs skj_PSWFAD pred skj_PSWFAD CPUE
Spatially-disaggregated monthly catch
020
4060
80100
10 20 30 40 50 60 70 800.E+00
1.E+04
2.E+04
3.E+04
4.E+04sum_PSWFAD_skj
0
5000
10000
15000
10 20 30 40 50 60 70 800.E+002.E+044.E+046.E+048.E+041.E+051.E+05sum_PSWUNA_skj
Length-frequency distribution ( by fishery, time and space)
Application to skipjack, yellowfin and bigeye tuna
Table 2. Parameterisation of the populations structure in SEAPODYM
skipjack yellowfin Bigeye Albacore
Number of age classes (quarter) after juvenile phase
16 28 40 74
Age at first maturity (quarter) 4 7 11 17
Age (quarter) at recruitment 3 3 3 7
0
20
40
60
80
100
120
140
160
180
0 2 4 6 8 10 12 14 16 18Age (yr)
Fork
Len
gth
(cm
)
Length SKJ MFCLLength YFT MFCLLength ALB-MFCLLength BET MFCL
0
20
40
60
80
100
120
0 2 4 6 8 10 12 14 16 18Age (yr)
Wei
ght (
kg)
Weight SKJ MFCLWeight YFT MFCLWeight ALB-MFCLWeight BET MFCL
Length-at-age and weight-at-age coefficients estimated from MFCL analyses (crosses) and functions (curves) used to define the coefficient used in SEAPODYM simulations
Category code
Description / source / resolution
PURSE SEINE WPSASS Aggregated data of purse seine fisheries in the WCPO
Sets associated to animals, log or FAD WPSUNA Aggregated data of purse seine fisheries in the WCPO
Unassociated sets (i.e. free schools) EPSASS Aggregated data of purse seine fisheries in the EPO
Sets associated to animals, log or FAD EPSUNA Aggregated data of purse seine fisheries in the EPO
Unassociated sets (i.e. free schools) POLE-AND-LINE
PLTRO Aggregated data of tropical (25oN-25oS) pole-and-line fisheries data PLSUB Aggregated data of sub-tropical pole-and-line fisheries (mostly Japanese
domestic fleets) LONGLINE
LLP80 Aggregated data of longline fisheries before 1980 (The pre-1980/post-1980 categories was to (very roughly) define the change from targetting yellowfin to targetting bigeye)
LLSHW Aggregated data of longline shallow after 1980 (mainly TW and mainland Chinese LL offshore fleets
LLDEEP Aggregated data of deep longline fisheries after 1980 LLMIX Aggregated data of “mixed” longline fisheries after 1980
DIVERSE RINGNET Aggregated data of ringnet fisheries (mainly Philippines, Indonesia) ARTSURF Aggregated data of artisanal surface fisheries (including ringnet, mainly
Philippines, Indonesia) COMMHL Aggregated data of commercial handline fisheries (Philippines, Indonesia, PNG,
US) GILLNET Aggregated data of gillnet fisheries
TROLL Aggregated data of troll fisheries
Fisheries
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80Fork length (cm)
PLTROARTSURFRINGNET
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80Fork length (cm)
EPSUNAEPSASSWPSUNAWPSASSCOMMHL
Selectivity
Skipjack
00.2
0.40.6
0.81
10 20 30 40 50 60 70 80020000400006000080000100000120000sum_PLSUB_skj
020000
4000060000
80000100000
10 20 30 40 50 60 70 80050000
100000150000
200000250000sum_PLTRO_skj
00.20.40.60.8
11.2
10 20 30 40 50 60 70 800
5000
10000
15000sum_RINGNET_skj
0
5000
10000
15000
10 20 30 40 50 60 70 800
50000
100000
150000
200000sum_WPSUNA_skj
02000
40006000
800010000
10 20 30 40 50 60 70 800
100000
200000
300000
400000sum_WPSASS_skj
00.20.40.60.8
11.2
10 20 30 40 50 60 70 800
10000
20000
30000
40000sum_ARTSURF_skj
02000400060008000
10000
10 20 30 40 50 60 70 800
50000
100000
150000sum_EPSASS_skj
0
5000
10000
15000
10 20 30 40 50 60 70 800
5000
10000
15000
20000sum_EPSUNA_skj
00.20.40.60.8
11.2
10 20 30 40 50 60 70 800500
10001500
20002500sum_COMMHL_skj
Selectivity
yellowfin 0
0.2
0.4
0.6
0.8
1
0 50 100 150Fork Length (cm )
LLP80LLMIXLLDEEPLLSHLWCOMMHL
0
0.2
0.4
0.6
0.8
1
0 50 100 150Fork Length (cm )
PLTRORINGNETWPSASSWPSUNAEPSUNA
0
10000
20000
30000
40000
10 30 50 70 90 1101301501700
20000
40000
60000
80000
100000sum_WPSASS_
0
0.2
0.4
0.6
0.8
1
10 30 50 70 90 110 130 150 1700100020003000400050006000sum RINGNET yft
0
2000
4000
6000
8000
10000
10 30 50 70 90 1101301501700
5000
10000
15000
20000
25000sum WPSUNA
0
0.2
0.4
0.6
0.8
1
10 30 50 70 90 110130150170050001000015000200002500030000sum_EPSUNA_yft
0100020003000400050006000
10 30 50 70 90 110130 1501700500100015002000250030003500sum_COMMHL
0
0.2
0.4
0.6
0.8
1
10 30 50 70 90 110 1301501700
50000
100000
150000
200000
250000sum_EPSASS_yft
05000
1000015000200002500030000
10 30 50 70 90 1101301501700
2000
4000
6000
8000sum_LLP80
02000400060008000
100001200014000
10 30 50 70 90 1101301501700
500
1000
1500
2000
2500sum_LLSHW_
05000
1000015000200002500030000
10 30 50 70 90 1101301501700
1000
2000
3000
4000
5000sum_LLDEEP_
0100020003000400050006000
10 30 50 70 90 110 130 150 17002468101214sum LLMIX yft
Selectivity
bigeye 0.0
0.2
0.4
0.6
0.8
1.0
0 50 100 150 200Fork Length (cm)
WPSASSWPSUNAPLTRORINGNETARTSURF
0.0
0.2
0.4
0.6
0.8
1.0
0 50 100 150 200Fork Length (cm)
LLP80LLSHLWLLDEEPLLMIXCOMMHL
0
5000
10000
15000
20000
10 30 50 70 90 110130150170020004000600080001000012000sum_WPSASS_bet
0
500
1000
1500
10 30 50 70 90 110 130 150 1700
100
200
300
400sum WPSUNA bet
0
0.2
0.4
0.6
0.8
1
10 30 50 70 90 110 130 150 170050100150200250300350sum_RINGNET_bet
0
0.2
0.4
0.6
0.8
1
10 30 50 70 901101301501700.00E+001.00E+012.00E+013.00E+014.00E+015.00E+016.00E+01sum_EPSUNA_bet
0
50
100
150
200
250
10 30 50 70 90 110 130 150 1700
5
10
15
20
25sum_COMMHL_bet
0
0.2
0.4
0.6
0.8
1
10 30 50 70 90 110 130 150 1700
500
1000
1500
2000sum EPSASS bet
02000400060008000
100001200014000
10 30 50 70 90 110130 1501700
100
200
300
400
500sum_LLP80
0
2000
4000
6000
8000
10000
10 30 50 70 90 110130 1501700
20
40
60
80
100sum_LLSHW_
0
1000
2000
3000
4000
10 30 50 70 90 110 130 150 170050100150200250300350sum LLMIX bet
0100002000030000400005000060000
10 30 50 70 90 110130 1501700100200300400500600700sum_LLDEEP_
Predicted and observed CPUE
skipjack
01234567
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
2
4
6
8
10
12obs skj PLSUB
0
2
4
6
8
10
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
2
4
6
8
10obs C skj PLTRO
0
5
10
15
20
25
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
5
10
15
20
25obs skj WPSASS
0
5
10
15
20
25
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
5
10
15
20
25obs skj WPSUNA
0
2
4
6
8
10
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
2
4
6
8
10obs skj_EPSASSd kj EPSASS
0
2
4
6
8
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
2
4
6
8obs skj EPSUNA
0
2
4
6
8
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
2
4
6
8obs skj_RINGNETd kj RINGNET
0
1
2
3
4
5
72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0
1
2
3
4
5obs skj ARTSURF
0.00
0.01
0.02
0.03
53 58 63 68 73 78
cpue
0.00
0.01
0.02
0.03obs yft_LLP80 pred yft_LLP80
0.00
0.01
0.02
0.03
81 83 85 87 89 91 93 95 97 99 01 03 05
cpue
0.00
0.01
0.02
0.03obs yft_LLSHW pred yft_LLSHW
0.00
0.01
0.02
80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0.00
0.01
0.02
obs yft_LLDEEP pred yft_LLDEEP
0.00
0.01
0.02
0.03
80 82 84 86 88 90 92 94 96 98 00 02 04
cpue
0.00
0.01
0.02
0.03obs yft_LLMIX pred yft_LLMIX
0.0
0.2
0.4
0.6
0.8
70 75 80 85 90 95 00 05
cpue
0.0
0.2
0.4
0.6
0.8obs yft_PLSUB pred yft_PLSUB
0.0
0.2
0.4
0.6
0.8
70 75 80 85 90 95 00 05
cpue
0.0
0.2
0.4
0.6
obs yft_PLTRO pred yft_PLTRO
0123456
70 75 80 85 90 95 00 05
cpue
0123456
obs yft_WPSASS pred yft_WPSASS
02468
1012
70 75 80 85 90 95 00 05
cpue
024681012
obs yft_WPSUNA pred yft_WPSUNA
0
5
10
15
20
25
80 85 90 95 00
cpue
0
5
10
15
20
25obs yft_EPSASS pred yft_EPSASS
0
5
10
15
80 85 90 95 00
cpue
0
5
10
15obs yft_EPSUNA pred yft_EPSUNA
Predicted and observed CPUE
yellowfin
0.0
0.1
0.2
1972
1977
1982
1987
1992
1997
2002
cpue
0.0
0.1
0.2
obs bet_PLSUB pred bet_PLSUB
0.000.010.02
0.030.040.050.06
1972
1977
1982
1987
1992
1997
2002
cpue
0.000.010.02
0.030.040.050.06
obs bet_PLTRO pred bet_PLTRO
0.00.2
0.40.60.81.0
1.2
1971
1976
1981
1986
1991
1996
2001
cpue
0.0
0.20.40.60.8
1.01.2
obs bet_WPSASS pred bet_WPSASS
0.0
0.1
0.2
0.3
0.4
1979
1984
1989
1994
1999
2004
cpue
0.0
0.1
0.2
0.3
0.4obs bet_WPSUNA pred bet_WPSUNA
Predicted and observed CPUE
bigeye
0.00
0.01
0.02
0.0319
6919
7019
7119
7219
7319
7419
7519
7619
7719
7819
7919
8019
81
cpue
0.00
0.01
0.02
0.03obs bet_LLP80 pred bet_LLP80
0.00
0.01
0.01
0.02
0.02
0.03
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
cpue
0.000.000.000.01
0.010.010.01
obs bet_LLSHW pred bet_LLSHW
0.000.010.010.02
0.020.030.03
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
cpue
0.000.010.010.02
0.020.030.03
obs bet_LLDEEP pred bet_LLDEEP
0.000.01
0.010.020.020.03
0.03
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
cpue
0.000.01
0.010.020.020.03
0.03obs bet_LLMIX pred bet_LLMIX
Skipjack Yellowfin Bigeye
1950-75
1976-98
Average predicted distribution of juvenile (age 2-3 months) biomass during decadal period 1950-75 and 1976-98
There are large overlaps between spawning and juvenile feeding grounds.
What are the interactions?
change in abundance of
predators
Change in forage mortality
change in Spawning Habitat
(P/F)
Change inFeeding Habitat
Change inNatural mortality (if ε>0)
Change inSpatial distribution
Change in Juvenile Habitat
Change in natural mortality
Comparing single vs multi-species simulations
0.0E+00
1.0E+06
2.0E+06
3.0E+06
4.0E+06
5.0E+06
6.0E+06
7.0E+06
8.0E+06
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
1.0E+06
2.0E+06
3.0E+06
4.0E+06
5.0E+06
6.0E+06
7.0E+06SKJ WCPO 1sp skj 3sp no fishing
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1.4E+06
1.6E+06
1.8E+06
2.0E+06
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1.4E+06
1.6E+06
1.8E+06SKJ EPO 1sp skj 3sp no fishing
SKJ
1.0E+06
1.5E+06
2.0E+06
2.5E+06
3.0E+06
3.5E+06
4.0E+06
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
1.0E+06
1.5E+06
2.0E+06
2.5E+06
3.0E+06
3.5E+06
4.0E+06YFT WCPO 1sp 3sp B YFT WCPO
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1.4E+06
1.6E+06
1.8E+06
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1.4E+06YFT EPO 1sp 3sp B YFT EPO
YFT
BET
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06BET WCPO 3sp B BET WCPO
0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05
7.0E+05
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05B BET EPO 3sp B BET EPO
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0.0E+00
1.0E+04
2.0E+04
3.0E+04
4.0E+04
5.0E+04
6.0E+04
7.0E+04
8.0E+04
9.0E+04MFCL-glm # Region 2 Series1
0.0E+00
1.0E+04
2.0E+04
3.0E+04
4.0E+04
5.0E+04
6.0E+04
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
2.0E+04
4.0E+04
6.0E+04
8.0E+04
1.0E+05
1.2E+05MFCL-glm # Region 1 Series1
BET
total biomass
Comparison by region
MFCL (with fisheries): black curves
Seapodym (3-species, without fisheries): green curves
0.0E+002.0E+044.0E+046.0E+048.0E+041.0E+051.2E+051.4E+051.6E+051.8E+052.0E+05
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05
3.0E+05
3.5E+05
4.0E+05MFCL-glm # Region 3 Series2
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05
3.0E+05
3.5E+05
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05MFCL-glm # Region 4 Series3
0.0E+00
5.0E+03
1.0E+04
1.5E+04
2.0E+04
2.5E+04
3.0E+04
3.5E+04
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
2.0E+04
3.0E+04
4.0E+04
5.0E+04
6.0E+04
7.0E+04
8.0E+04
9.0E+04MFCL-glm # Region 5 Series3
0.0E+00
5.0E+03
1.0E+04
1.5E+04
2.0E+04
2.5E+04
3.0E+04
3.5E+04
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0E+00
2.0E+04
4.0E+04
6.0E+04
8.0E+04
1.0E+05
1.2E+05
1.4E+05
1.6E+05MFCL-glm # Region 6 Series3
Seapodym total biomass (multi-species simulation – no fishing)
0E+00
1E+05
2E+05
3E+05
4E+05
5E+05
6E+05
7E+05
8E+05
9E+05
1E+06
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
0E+00
1E+05
2E+05
3E+05
4E+05
5E+05
6E+05
7E+05
8E+05
9E+053sp B BET WCPO MFCL (fishing)
MFCL total biomass with fishing
5-yrs prediction based on climatological environmental data
Conclusions
• In absence of optimization function, a reasonable parameterization for 3 species and their fisheries was obtained.
• The model capture important changes in the population dynamics that explain a large part of time space variability in the catch and CPUE.
• Decline in bigeye stock in the late 1950’s and during 1960’s is reproduced by the model and due to natural variability AND species interactions.
• There is no sign of increase in bigeye stock biomass for 5-year projection based on environmental climatology.
• It is now possible to run “what… if” scenarios to test management options in a spatial multi-species and multi-fisheries context.