S. Rochette, O. Le Pape, E. Rivot Agrocampus Ouest, Rennes...
Transcript of S. Rochette, O. Le Pape, E. Rivot Agrocampus Ouest, Rennes...
![Page 1: S. Rochette, O. Le Pape, E. Rivot Agrocampus Ouest, Rennes ...halieutique.agrocampus-ouest.fr/pdf/1024.pdf · Coupling an age-structured population model for fish dynamics with a](https://reader033.fdocuments.in/reader033/viewer/2022042008/5e70de568c539633ac5f2e6f/html5/thumbnails/1.jpg)
Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Coupling an age-structured population model for fish
dynamics with a larval dispersal model within a
Bayesian state-space modelling framework
S. Rochette, O. Le Pape, E. Rivot
Agrocampus Ouest, Rennes, France
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Outline
I. General contexta. State-Space models
b. Age-structured models
c. Spatialization
d. Integrated population model
II. Case study : Solea solea in the Eastern Channel
III. Population modelling
IV. Conclusions & Perspectives
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.a. State-Space models
A key methodological framework for fisheries sciencesFish population dynamics (management)
High dimensional, non linear, stochastic
State of the system not directly observedNoisy, incomplete observations
Process equation:Xt+1 = f(Xt,θ1,εt)
Observation equation:yt = g(Xt,θ2,ωt)
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.a. State-Space models
A key methodological framework for fisheries sciencesFish population dynamics (management)
High dimensional, non linear, stochastic
State of the system not directly observedNoisy, incomplete observations
Bayesian framework coupled with Monte-Carlo methodEasy-to-use quantification of uncertainty for risk analysis
Various sources of information and expertise (data and prior)
High dimension models, non linear SSM
Software (MCMC methods, OpenBUGS / R)
Process equation:Xt+1 = f(Xt,θ1,εt)
Observation equation:yt = g(Xt,θ2,ωt)
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Extension of Leslie Matrix models
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Process Equations
Age 1
Age 15+
AdultsNatural mortalityFishing
Na+1,t+1 = Na,t . exp(-Ma - Fa,t )
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Process Equations
Age 1
Age 15+
Eggs
Adults
Na+1,t+1 = Na,t . exp(-Ma - Fa,t )
Larvae
Natural mortalityFishing
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Process Equations with noise
JuvenilesAge 0
Age 1
Age 15+
Eggs
Adults
Larvae
Juveniles
K
Larvae
CarryingCapacity
Na+1,t+1 = Na,t . exp(-Ma - Fa,t )
Kt = Cc(Larvae).eγ t
Natural mortalityFishing
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Observations with error
JuvenilesAge 0
Age 1
Age 15+
Eggs
Adults
Larvae
Juveniles
K
Larvae
CarryingCapacity
Natural mortalityFishing
Ca,t = h(Na,t,Fa,t,Ma)⋅eωt
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.b. Age-structured models
Observations with error
JuvenilesAge 0
Age 1
Age 15+
Eggs
Adults
Larvae
Juveniles
K
Larvae
CarryingCapacity
Natural mortalityFishing
Ca,t = h(Na,t,Fa,t,Ma)⋅eωt
AIa,t = q⋅Na,t⋅eηt
AIa,t = q⋅Na,t⋅eηt
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
K
I.b. Age-structured models
Bayesian statistical catch-at-age analysis
JuvenilesAge 0
Age 1
Age 15+
Juveniles
Eggs
Adults
Larvae
Larvae
CarryingCapacity
Natural mortalityFishing
Ca,t = h(Na,t,Fa,t,Ma)⋅eωt
Na+1,t+1 = Na,t . exp(-Ma - Fa,t )
Kt = Cc(Larvae).eγ t
AIa,t = q⋅Na,t⋅eηt
Joint posterior distribution P(N, F, Cc parameters | Catches,Abundance indices)
AIa,t = q⋅Na,t⋅eηt
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.c. Spatialization
Recruitment governs populations renewalEggs → Juveniles: 6 months, survival ≈ 10-4
Adults survival : 15 years, s ≈ 5.10-2
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.c. Spatialization
Recruitment governs populations renewalEggs → Juveniles: 6 months, survival ≈ 10-4
Adults survival : 15 years, s ≈ 5.10-2
Nurseries are essential habitatsCoastal (high productivity, low predation)
Variable quality and productivity (time & space)
Highly impacted by human activities
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.c. Spatialization
Recruitment governs populations renewalEggs → Juveniles: 6 months, survival ≈ 10-4
Adults survival : 15 years, s ≈ 5.10-2
Nurseries are essential habitatsCoastal (high productivity, low predation)
Variable quality and productivity (time & space)
Highly impacted by human activities
Amount of juveniles different in each nurseryLarval dispersal -> Larval supply
Habitat quality -> Carrying capacity
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.c. Spatialization
Population dynamic model
JuvenilesAge 0
Age 1
Age 15+
Eggs
Adults
Larvae
LarvalDispersion
JuvenilesK
Larves
JuvenilesK
Larves
JuvenilesK
Larves
JuvenilesK
Larves
JuvenilesK
Larvae
A BC
D E
CarryingCapacity
Natural mortalityFishing
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
I.d. Integrated population model
A framework for coupling modelsLarval dispersion model
Oceanic circulation model
Lagrangian modelling
Spatialized age-structured population modelFitted to commercial Catches and Abundance Indices
Fishing mortality included
SpatializationNurseries with contrasted productivities
Use larval dispersion model as an INPUT
Application to sole population in the Eastern Channel
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Outline
I. General context
II. Case study : Solea solea in the Eastern Channela. Data
b. Habitat suitability
c. Larval dispersion
III. Population modelling
IV. Conclusions & Perspectives
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
II.a. Data
Adults (age ≥ 2) – not spatializedCatches
Abundance indices
(source : Sole stock assessment WG)
Catches (by age)
AI (by age)Eastern Channel
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
II.a. Data
Adults (age ≥ 2)
Juveniles (age 0 and 1)Habitat suitability model on nursery
Spatialized juvenile abundance indices
A B
C D E
Abundance indices on nurseries
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
II.b. Habitat suitability
Mapping nurseriesJuvenile densities = f (Depth, Sediment, Site)
High contrast of densities (in time and space)
Site effect : Quality ?
Larval supply ?
Nurseries & contributions
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
II.c. Larval dispersion
Larval dispersion modelOcean circulation model (Mars3D)
Particle-tracking system (Lagragian modelling)
Maps for spawning grounds
Individual based life traits (mortality, growth …)
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
II.c. Larval dispersion
Larval dispersion modelOcean circulation model (Mars3D)
Particle-tracking system (Lagragian modelling)
Maps for spawning grounds
Individual based life traits (mortality, growth …)
OutputsLarval survival
Larval repartition between nurseries
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Outline
I. General context
II. Case study : Solea solea in the Eastern Channel
III. Population modela. Simulation / Estimation
b. Results
IV. Conclusions & Perspectives
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.a. Simulation / estimation
Assess the performance of the estimation method
Cycles of simulation – estimation
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.a. Simulation / estimation
Assess the performance of the estimation method
Cycles of simulation – estimation
Scaled to the Eastern Channel sole population case studyPopulation dynamics
Age-structured : 15 age classes – 27 years
Larval dispersalRecruitment equation
5 different nurseries (K ≈ habitat model)Noisy recruitment over time
Noisy dataAbundances indices per age classCatches per age class
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.a. Simulation / estimation
2 models
JuvenilesAge 0
Age 1
Age 15+
Eggs
Adults
Larvae
Juveniles
K
Larvae
CarryingCapacity
Natural mortalityFishing
Eggs
Larvae
LarvalDispersion
Juveniles
K
Larves
Juveniles
K
Larves
Juveniles
K
Larves
Juveniles
K
Larves
Juveniles
K
Larvae
AB
CD
E
CarryingCapacity
Non-Spatial model Spatial model
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.b. Results
Spawning Stock Biomass (SSB)
Simulated value
Spatial model
Non-Spatial model
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.b. Results
Juveniles (N0)
Simulated value
Spatial model
Non-Spatial model
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.b. Results
Mean fishing mortality (F)
Simulated value
Spatial model
Non-Spatial model
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.b. Results
Productivity of each nurseryDensity-dependent mortalities (Spatial model)
Larvae
Juveniles
Simulated
Fitted
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
III.b. Results
Productivity of each nurseryDensity-dependent mortalities (Spatial model)
Comparison of K
Larvae
Juveniles Carrying capacity
Nurseries
Spatial model
Non-Spatial model
* Simulated
* Fitted
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Outline
I. General context
II. Case study : Solea solea in the Eastern Channel
III. Population modelling
IV. Conclusions & Perspectives
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
IV. Conclusions & Perspectives
Age-structured model and larval dispersion model were successfully coupled within the Bayesian SSM framework
Integration of various sources of data several sources of uncertainty
The model simultaneously capturesPopulation dynamics with random variations
Fishing pressure
Contrasted level of productivity in the different nurseries
Effects of ocean circulation on larval supply
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
IV. Conclusions & Perspectives
Applying to the Eastern Channel sole population(work in progress)
Validation of the larval dispersion model
Influence of missing data (Juvenile abundance indices)
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
IV. Conclusions & Perspectives
Applying to the Eastern Channel sole population(work in progress)
Validation of the larval dispersion model
Influence of missing data (Juvenile abundance indices)
Simulating population under different scenariosHabitat destruction
Pollution
Fishing pressure
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Sébastien ROCHETTE CMPD3, Bordeaux June 2010
Thanks for attention