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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
Introduction Approach Model Calibration SA Scenarios Conclusion
Population model
Population
0 1 i nC C C C
Age groups
Z1
…
…
Zk
…
…
Zm
…
…
Study site
Introduction Approach Model Calibration SA Scenarios Conclusion
Population model
Time
Year 1Year 0 Year 2
Change of age group
Movement
Reproduction
Recruitment
Natural mortality +Fishing mortality
Introduction Approach Model Calibration SA Scenarios Conclusion
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
Introduction Approach Model Calibration SA Scenarios Conclusion
Spatial structure
Introduction Approach Model Calibration SA Scenarios Conclusion
Spatial structure
North
South
Center
South edge
North – South gradient
Introduction Approach Model Calibration SA Scenarios Conclusion
Spatial structure
CoastIntermediate
Barrier
Coast – Barrier reef gradient
Introduction Approach Model Calibration SA Scenarios Conclusion
Spatial structure
Introduction Approach Model Calibration SA Scenarios Conclusion
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
Introduction Approach Model Calibration SA Scenarios Conclusion
• 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
Introduction Approach Model Calibration SA Scenarios Conclusion
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
Introduction Approach Model Calibration SA Scenarios Conclusion
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
FactFrac3 Captures Y1
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
Introduction Approach Model Calibration SA Scenarios Conclusion
Scenarios
• Scenario 1 Reference (statu quo)
• Scenario 2 Creation of 1 MPA
• Scenario 3 Legal size (maturity)
• Scenario 4 10 more professional fishing licences
Introduction Approach Model Calibration SA Scenarios Conclusion
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
Introduction Approach Model Calibration SA Scenarios Conclusion
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
H1Statu quo10 ProTaille minimaleAMP
15000
20000
25000
30000
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
H1Statu quo10 ProTaille minimaleAMP
020
40
60
80
100
Débarquements de Bec de cane
Débarq
uem
ents
(t)
An 1 An 2 An 3 An 4 An 5 An 6 An 7 An 8
H2Statu quo10 ProTaille minimaleAMP
H1Statu quo10 ProTaille minimaleAMP
0100
200
300
400
500
600
Débarquements de Saumo
Débarq
uem
ents
(t)
An 1 An 2 An 3 An 4 An 5 An 6 An 7 An 8
H2Statu quo10 ProTaille minimaleAMP
H1Statu quo10 ProTaille minimaleAMP
Population biomass Catches
Management scenarios assessment
Statu quo10 proLegal sizeMPA
H1
H2
H1
H2
Introduction Approach Model Calibration SA Scenarios Conclusion
Statu quo AMP Taille Minimale 10 Pro
1420
014
300
1440
014
500
Biomasse Bec de cane
Bio
mas
se (
t)
Statu quo AMP Taille Minimale 10 Pro
1020
3040
5060
Débarquements Bec de cane
Déb
arqu
emen
ts (
t)
Statu quo AMP Taille Minimale 10 Pro
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
Introduction Approach Model Calibration SA Scenarios Conclusion
Conclusion
Introduction Approach Model Calibration SA Scenarios 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
+-
Introduction Approach Model Calibration SA Scenarios Conclusion
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).
Introduction Approach Model Calibration SA Scenarios Conclusion
Thank you
1Z
t Zk
Znbzone
N
N N
N
Bon appétit !
Introduction Approach Model Calibration SA Scenarios Conclusion
Management scenarios assessment4000
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
H1Statu quo10 ProTaille minimaleAMP
0100
200
300
400
500
600
Débarquements de Saumo
Débarq
uem
ents
(t)
An 1 An 2 An 3 An 4 An 5 An 6 An 7 An 8
H2Statu quo10 ProTaille minimaleAMP
H1Statu quo10 ProTaille minimaleAMP
Biom
ass
Catc
h
H1
H2
H1
H2
Introduction Approach Model Calibration SA Scenarios Conclusion
Statu quo AMP Taille Minimale 10 Pro
4000
4100
4200
4300
4400
4500
Biomasse Saumonee
Biom
asse
(t)
Statu quo AMP Taille Minimale 10 Pro
140
160
180
200
Débarquements Saumonee
Déba
rque
men
ts (t
)
Statu quo MPA Legal size 10 pro
Statu quo AMP Taille Minimale 10 Pro
4000
4100
4200
4300
4400
4500
Biomasse Saumonee
Biom
asse
(t)
Statu quo AMP Taille Minimale 10 Pro
140
160
180
200
Débarquements Saumonee
Déba
rque
men
ts (t
)Statu quo MPA Legal size 10 pro
Statu quo10 proLegal sizeMPA
Statu quo10 proLegal sizeMPA
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
FactFrac3 Captures Y1
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
.-03
janv
.-04
mai
-04
sept
.-04
janv
.-05
mai
-05
sept
.-05
janv
.-06
mai
-06
sept
.-06
janv
.-07
mai
-07
sept
.-07
janv
.-08
mai
-08
sept
.-08
janv
.-09
mai
-09
sept
.-09
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
)
Introduction Approach Model Calibration SA Scenarios Conclusion
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
Introduction Approach Model Calibration SA Scenarios Conclusion
H2
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