reporter: hsu hsiang-jung modelling stochastic fish stock dynamics using markov chain monte carlo
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Reporter: Hsu Hsiang-Jung
Modelling stochastic fish stock dynamics using Markov Chain Monte Carlo
Introduction
The precautionary approach has become a basis concept in fish stock management.
The quantification of there uncertainties has emphasised the need for developing stochastic assessment approaches.
Estimates of parameters including process variances and predicted stock number have been obtained using likelihood Markov Chain Monte Carlo.
Population dynamics models
Catch-at-age in numbers and effort data for commercial fleets.
Catch-at-age in numbers without effort data for the remaining part of total international catches.
CPUE by age for research surveys.),( ,, yaresCcatchesresidual
)( ,,, yfyaf eandC
)( ,, yasI
Population dynamics models
)1()exp()exp(| ,,,,1,1 survivalyasurvivalyayayaya ZNNN
)2(),,exp()1(, ,
,,,,, yaresNe
yZa
FC resya
yZayaresyares
)3()exp()1( ,,,,
,,,,
, yaffyaZ
ya
afyfyaf Ne
Z
qeC ya
Population dynamics models
)4()365
exp( ,,,,,,, yasss
yayaasyas
TZNqI
)5(min)exp( minminmin,min AySSBSSBN AyrecruitAyAyyA
)6()exp(1,min initialstartAN
)7(min)exp()exp( 11,11,11, AaZNN ainitialaaa
Population dynamics models
)8(2,
2,
2 ressamplingresprocessres
)9(2,
2,
2 fsamplingfprocessf
)10(2,
2,
2 ssamplingsprocesss
Population dynamics models
“N” denotes the stock number.
“F” the fishing mortality .
“Z” the total mortality. “ “ the standard deviations for the
survival and fishing processes.
sresfsurvival and ,,
Population dynamics models
“q” the catchability.
“e” the effort.
“T” the day of year when the survey takes place.
“ “ the standardised normal distribution.s
Estimation methods
For complex models with strutural relationships between variables and parameters, such as the stochastic survival model considered, the so-called single component Metropolis-Hastings or Gibbs sampling is an MCMC method especially suitable for simulating the likelihood function.
The difference between the MLE and this estimator lies in the MLE being the maximum of the likelihood function while the new estimator being the mean.
Simulation experiments
The catch observations were simulated in the following way:1.The model used was the same as described by Equations (1)-(7).
2.The parameters , Θ, used were the values estimated applying data described in the next section.
3.Fres,a,y and Ff,a,y were calculated, the latter using effort data and catchabilities, qf,a.
4.NminA,1 was predicted by randomly darwing from the lognormal distribution,(Equation(6))
5.Na,1 a=2,…,A were predicted by randomly drawing from the lognormal distribution ,(Equation(7))
Simulation experiments
6. SSB1 was calculated.
7. For y = 2 recruitment NminA,ywas randomly drewn from Equation(5).
8. For a = 2,…,A Na,y was randomly drawn from Equation(1a) and SSBy calculated.
9. Steps 7and 8 were repeated as long as y < Y.
10. The catch observations, Cres,a,y, Cf,a,y and Is,a,y, were generated from the lognormal distributions(Equations(2)-(4)).
Materials and software used
Catch-at and effort data for the Dutch and English commercial beam trawl fleets.
Catch-at-age data for the combined fleet without effort data.
Survey indices for the Dutch beam trawl and the Sole Net Survey.
Materials and software used
The software package, WinBUGS1.4 was used to simulate the posterior distributions of the parameters.
Results
Results
Results
Results
2,
2,
2, ,
resprocess
EnglandprocessNetherlandprocess and
Results
Results
Results of simulation experiments
sfres and ,
Discussion
Our model of fish stock assessment which includes stochastic survival and recruitment.
Errors associated with the catch-at-age by fleet used in stock assessment consist of sampling errpr and other errors denoted as process errors.
Discussion
The MCMC methodology, in particular the single component Metropolis-Hastings and graphical models.
It is easy to implement such complex model in the WinBUGS program.
