Management Scenarios for Fished Resources of the New Caledonian Lagoon Using a Spatially-Explicit...

Management Scenarios

for Fished Resources of the New Caledonian

Lagoon Using a Spatially-Explicit Model

Bastien PREUSSDominique Pelletier and Laurent Wantiez

UNC/LIVE – IFREMER/LEAD – IRD/CoRéUs2 – ZONECO

Tuesday 23th 2014, FFMB, Nouméa

ContextIntroduction Approach Model Calibration SA Scenarios Conclusion

New-Caledonia: • 19 058 km² land• 19 954 km² lagoon• 250 000 inhabitants

Context

MPA (no take)

Legend

UNESCO World Heritage

Central area

Buffer area

New-Caledonia: • 19 058 km² land• 19 954 km² lagoon• 250 000 inhabitants

Noumea and suburbs:• 170 000 inhabitants

Important fishing pressure

Management :• MPA network (7 units)

Growing population:• Request for additional

management measures• Decision support tool

Introduction Approach Model Calibration SA Scenarios Conclusion

ISIS-Fish modelExisting knowledge

Management scenarios assessment

Sensitivity analysis

Modeled system

Calibration

Field work

Natural systemApproachIntroduction Approach Model Calibration SA Scenarios Conclusion

Model

• Isis-Fish spatially explicit population and fisheries dynamic model.

Population model Fisheries model

Management model

Simulation of the modeled system

ISIS-Fish


Population model

Population

0 1 i nC C C C

Age groups

Z1

…

…

Zk

…

…

Zm

…

…

Study site


Population model

Time

Year 1Year 0 Year 2

Change of age group

Movement

Reproduction

Recruitment

Natural mortality +Fishing mortality


Soutenance de thèse B. PREUSS - 8 octobre 2012

• Biology and ecology :

– Max length : 120 cm– Max age : 26 years– Piscivore– Territorial– Hermaphrodite

protogynous– Low mobility

• Biology and ecology :

– Max length : 62 cm– Max age : 27 years– Macrocarnivore– Non territorial– Hermaphrodite

protogynous– High mobility


Spatial structure


Spatial structure

North

South

Center

South edge

North – South gradient


Spatial structure

CoastIntermediate

Barrier

Coast – Barrier reef gradient


Spatial structure


North SouthCenter

Coas

tIn

term

edia

teBa

rrie

r

South edge

MPA

Spatial structureIntroduction Approach Model Calibration SA Scenarios Conclusion

North SouthCenter

Coas

tIn

term

edia

teBa

rrie

r

South edge

MPA

Fish mobilityIntroduction Approach Model Calibration SA Scenarios Conclusion

, * zbza zb esp

zii

SC m

S

, * zbza zb esp

za zii

SC m

S S

Dispersal

Reproduction

Fishing activities

• Professional logbook data• Recreational field work interviews

Nb of fishing strategies

Annual catch

Professional 2 3.5 t 20.5 t

Recreational 19 147.4 t 5.8 t

Data analysis=

MCA + HCA


• Minimisation of an target fonction by multiple objective calibration :

f = ( observed - modeled ) ²

Natural system

Modeled system

Observations

Model outputs

= ?Parameters X

Soutenance de thèse B. PREUSS - 8 octobre 2012Fenicia et al., 2005, 2007.

8559 simulations is 34.5 days

CalibrationIntroduction Approach Model Calibration SA Scenarios Conclusion

Validation

• Validation

050

100150200250300350400450

Capt

ure

(kg)

Régulier CS


0

20000

40000

60000

80000

100000

120000

140000

160000

GN CS

Plaisanciers

Capt

ure

annu

elle

(kg)

Simulée

Estimée

0

20000

40000

60000

80000

100000

120000

140000

160000

GN CS

Plaisanciers

Capt

ure

annu

elle

(kg)

Simulée

Estimée

Recreational

Annu

al c

ath

(kg)

0

500

1000

1500

2000

2500

3000

3500

GN CS

Professionnels

Capt

ure

annu

elle

(kg)

Simulée

Observée

0

500

1000

1500

2000

2500

3000

3500

GN CS

Professionnels

Capt

ure

annu

elle

(kg)

Simulée

Observée

Professional

SimulatedObserved estimated

Sensitivity Analysis

• Interest : highlighting main sources of uncertainty

• Method : fractional factorial simulation plan


Results

FactFrac3 Biomasse Y3

0.0

00.0

50.1

00.1

50.2

00.2

5

A50

Lin

f

Fcib

_P

lais

Fcib

_P

ro

Fstd

_F

ilet

Fstd

_Lig

neP

ro

Fstd

_P

ala

Mig

MortN

atp

r

MortN

at1

MortN

at2

Repro

Sele

c_Lig

neP

ro

Sele

c_F

ilet

Sele

c_P

ala

FactFrac3 Captures Y1

0.0

00

.05

0.1

00

.15

0.2

00

.25

A50

Lin

f

Fcib

_P

lais

Fcib

_P

ro

Fstd

_F

ilet

Fstd

_Lig

neP

ro

Fstd

_P

ala

Mig

MortN

atp

r

MortN

at1

MortN

at2

Repro

Sele

c_Lig

neP

ro

Sele

c_F

ilet

Sele

c_P

ala

Population biomass CatchesFactFrac3 Biomasse Y3

0.0

00.0

50.1

00.1

50.2

00.2

5

A50

Lin

f

Fcib

_P

lais

Fcib

_P

ro

Fstd

_F

ilet

Fstd

_Lig

neP

ro

Fstd

_P

ala

Mig

MortN

atp

r

MortN

at1

MortN

at2

Repro

Sele

c_Lig

neP

ro

Sele

c_F

ilet

Sele

c_P

ala

Indi

ce d

e se

nsib

ilité


0.0

00.0

50.1

00.1

50.2

00.2

5

A50

Lin

f

Fcib

_P

lais

Fcib

_P

ro

Fstd

_F

ilet

Fstd

_Lig

neP

ro

Fstd

_P

ala

Mig

Mort

Natp

r

Mort

Nat1

Mort

Nat2

Repro

Sele

c_Lig

neP

ro

Sele

c_F

ilet

Sele

c_P

ala

Indi

ce d

e se

nsib

ilité

Sensitive parameters

Pre-recruitement mortality

Max length (Linf)

Juvenile natural mortality

Adulte natural mortality

Nomber of recreational boats

Gear selectivity

Gear standardisation factor


Scenarios

• Scenario 1 Reference (statu quo)

• Scenario 2 Creation of 1 MPA

• Scenario 3 Legal size (maturity)

• Scenario 4 10 more professional fishing licences


H1 H2

Recruitment

Management Management

Other sources of uncertainty

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 1 Scenario 2 Scenario 3 Scenario 4

Simulation plan

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Recr

uitm

ent u

ncer

tain

ty

Other sources of uncertainty

Management scenarios assessment under uncertainty


reference optimistic

4000

6000

8000

10000

14000

Biomasse du stock

Bio

masse (

t)

An 1 An 2 An 3 An 4 An 5 An 6 An 7 An 8

H2Statu quo10 ProTaille minimaleAMP


15000

20000

25000

30000

Biomasse du stock

Bio

masse (

t)




020

40

60

80

100

Débarquements de Bec de cane

Débarq

uem

ents

(t)




0100

200

300

400

500

600

Débarquements de Saumo

Débarq

uem

ents

(t)




Population biomass Catches

Management scenarios assessment

Statu quo10 proLegal sizeMPA

H1

H2

H1

H2


Statu quo AMP Taille Minimale 10 Pro

1420

014

300

1440

014

500

Biomasse Bec de cane

Bio

mas

se (

t)


1020

3040

5060

Débarquements Bec de cane

Déb

arqu

emen

ts (

t)


40

004

100

42

004

300

44

004

500

Biomasse Saumonee

Bio

mas

se (

t)

Statu quo AMP Taille Minimale 10 Pro1

40

16

01

80

20

0

Débarquements Saumonee

Déb

arqu

emen

ts (

t)

Management scenarios assessmentIntroduction Approach Model Calibration SA Scenarios Conclusion

Zone Zone

Sud-AMP 0.2 Sud-Int-AMP 128.3

Corne Sud 0.0 Corne Sud 0.01

Sud -0.2 Sud-Int -32.3

Centre-AMP 0.0 Sud-Barr -10.5

Centre 0.0 Sud-Côte 0.0

Nord 0.0 Centre-Inter-AMP 0.0

Centre-Inter -0.1

Centre-Barr-AMP 0.0

Centre-Barr -0.1

Centre-Côte-AMP --

Centre-côte -0.2

Nord-Int -0.2

Nord-Barr -0.2

Nord-Côte -0.2

Focus on MPA

• Local effect – Biomass variation (%)

between scenario statu quo and scenario MPA


Conclusion


26Soutenance de thèse B. PREUSS - 8 octobre 2012

Fish populations Fisheries

Lack of knowledgeUncertainty

(Reproduction – recruitement ; stock assessment)

Lack of knowledgeUncertainty

(precision of data)

Knowledges review and data analysis

Research goal

Field and data analysis

Management goal

Constraint=

Data

Scenarios assessment

Constraint=

Computer capability

Populations Captures

MPA 0 0 0 0

Legal size ++ + - - -

+ 10 pro - 0 ++ 0

+-


Perspectives• Assessing more hypothesis of recruitment.

• Assessing different types of mobility (density dependence).

• Assessing different hypotheses of larval dispersal.

• Assessing more management scenarios (MPA location, MPA network, large vs small).


Thank you

1Z

t Zk

Znbzone

N

N N

N

Bon appétit !


Management scenarios assessment4000

6000

8000

10000

14000

Biomasse du stock

Bio

masse (

t)




0100

200

300

400

500

600

Débarquements de Saumo

Débarq

uem

ents

(t)




Biom

ass

Catc

h

H1

H2

H1

H2



4000

4100

4200

4300

4400

4500

Biomasse Saumonee

Biom

asse

(t)


140

160

180

200


Déba

rque

men

ts (t

)

Statu quo MPA Legal size 10 pro


4000

4100

4200

4300

4400

4500

Biomasse Saumonee

Biom

asse

(t)


140

160

180

200


Déba

rque

men

ts (t

)Statu quo MPA Legal size 10 pro



30

Introduction Démarche Modèle Espèces Pêche Calibration AS Scénarios Conclusion

Méthode d’Analyse de sensibilité

• i valeurs par paramètre • Si i = 2• Plan d’expérience factoriel fractionnaire

Modèle Sorties du modèle

Paramètre x

Soutenance de thèse B. PREUSS - 8 octobre 2012

Plan factoriel fractionnaire

2k simulations


0.000.050.100.150.200.25

A50

Linf

Fcib_P

lais

Fcib_P

ro

Fstd_F

ilet

Fstd_LigneP

ro

Fstd_P

ala

Mig

MortN

atpr

MortN

at1

MortN

at2

Repro

Selec_LigneP

ro

Selec_F

ilet

Selec_P

ala

Classement des paramètres

Indice de sensibilité 137 simulations soit 6 jours

pp

ik

SCIS

SC

Recruitment (3 months/yr)

• Stock-recruitment: 1 simulation (red line)• Random recruitment : 35 simulations (black lines)

0

5 000

10 000

15 000

20 000

25 000

30 000

janv

.-00

mai

-00

sept

.-00

janv

.-01

mai

-01

sept

.-01

janv

.-02

mai

-02

sept

.-02

janv

.-03

mai

-03

sept

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janv

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mai

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sept

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janv

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mai

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sept

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janv

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mai

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sept

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janv

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mai

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sept

.-07

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mai

-08

sept

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janv

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mai

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sept

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janv

.-10

mai

-10

sept

.-10

janv

.-11

mai

-11

sept

.-11

janv

.-12

mai

-12

sept

.-12

janv

.-13

mai

-13

sept

.-13

janv

.-14

mai

-14

sept

.-14

Bio

mas

se (t

)


H1

H2

Recruitment (1 month/yr)40

0080

0012

000

1600

0

Biomasse du stock

Biom

asse

(t)

An 1 An 2 An 3 An 4 An 5 An 6 An 7 An 8 An 9 An 10 An 11 An 12 An 13 An 14Yr 1 Yr 2 Yr 3 Yr 4 Yr 5 Yr 6 Yr 7 Yr 8 Yr 9 Yr 10 Yr 11 Yr 12 Yr 13 Yr 14

• 1 year of recruitment (1 cohort) support the population


H2

Management Scenarios for Fished Resources of the New Caledonian Lagoon Using a Spatially-Explicit...

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