A spatial Ecosystem And Populations Dynamics Model … · A spatial Ecosystem And Populations Dynamics Model (SEAPODYM) SEAPODYM: Spatial model driven by physical and “simplified”

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


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


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


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


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


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


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


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


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


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.

Documents

A spatial Ecosystem And Populations Dynamics Model … · A spatial Ecosystem And Populations Dynamics Model (SEAPODYM) SEAPODYM: Spatial model driven by physical and “simplified”