Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Portuguese Market and On-boardSampling Effort Review
Working document presented to PGCCDBS, 7-11 February 2011
Jardim, E., Prista, N. & Dias, M.
February 5, 2011
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Outline
1 Introduction
2 Data
3 Methods
4 Results
5 Conclusions
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Introduction
The implementation of the metier approach resulted in:
I a decrease in the precision of the length frequenciesestimates by species, due to the spread of sampling effortto new species and the reduction of trips sampled.
I an increase in the number of strata to be sampledon-board
The objective of this work is to optimize sampling effort bycomputing the number of samples required to achieve theprecision levels defined by the DCF:
I for length frequencies of the landings sampled at themarket
I for total discards sampled on-board
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Data
I On market:I number of individuals estimated by tripI data from 2009-2010,I by REGION, GEAR, SPECIES & QUARTER
I On board:I weight discarded by tripI data from 2004-2010I by METIER (OTBDEF, OTBCRU) & QUARTER
Data is scarce and the breakdown by metier makes iteven scarcer, it was necessary to aggregate.
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Methods
I Model CV = f (N) using exponential decay models (Nbeing number of samples)
I Compute N to achive 12.5% CV for market sampling or20% for on-board
I Compute 95% percentile of N as an indicator of a highprobability to achieve the objective and cover species withmore variability than average
I Review the sampling plans
(Lots of technical details and statistical mambo-jambo to beprovided if requested)
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Data aggregation formarket sampling
I Each pair used in model refers toI the CV of the total number of individuals sampledI the number of samples collected from which the CV above
was computed
I Each pair was computed by GEAR (aggregation ofmetiers), QUARTER, REGION & SPECIES
I Each model was fit to distinct dimensions of the datacollapsing all other dimensions
I for each REGIONI for each GEARI for each combination of REGION and GEAR
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Example models formarket sampling
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0 5 10 15 20
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Trammel nets
N
TOT
CV
expstrexpexp logstrexp log
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Models for on-boardsampling
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Preliminary conclusionsfor market sampling
Region Metier SampEff.2010 SampEff.20111 North FPO MOL >=29 0 0 5 172 North GNS DEF 80-99 0 0 2 213 North GNS DEF 60-79 0 0 3 214 North GTR DEF >=100 0 0 8 125 North LLS DEF 0 0 0 1 176 North OTB DEF 65-69 0 0 6 187 North PS SPF >=16 0 0 7 138 North TBB CRU >=20 0 0 1 179 Center FPO MOL >=29 0 0 5 17
10 Center GNS DEF 80-99 0 0 3 1911 Center GTR DEF >=100 0 0 7 412 Center LLD LPF 0 0 0 2 213 Center LLS DEF 0 0 0 2 1714 Center LLS DWS 0 0 0 2 1715 Center OTB CRU >=70 0 0 6 416 Center OTB CRU 55-59 0 017 Center OTB DEF 65-69 0 0 1 418 Center PS SPF >=16 0 0 5 1319 South FPO MOL >=29 0 0 5 1720 South GNS DEF 80-99 0 0 2 2121 South LLD LPF 0 0 0 1 122 South LLS DEF 0 0 0 1 1723 South OTB CRU >=70 0 0 5 1824 South OTB CRU 55-59 0 025 South OTB DEF 65-69 0 0 2 1826 South PS SPF >=16 0 0 2 1327 Total 84 338
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Preliminary conclusionsfor on-board sampling
I Model point estimate is 15 samples per quarter for bothmetiers
I Sampling theory estimate is 18-20 samples per quarter
I 95 percentile is 48 samples per quarter
I Increase sampling effort up to 192 trips per year for eachmetier
The sampling effort is not applicable due to high costsand lack of human resources. In 2011 on-board samplingeffort will be increased up to the maximum possible,taking into account other metiers and resources available.
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
The End
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details: codes
I N, C, S = Norte, Centro Sul
I OTB, PS, GTR, GNS, FPO, LLS = trawl, purse seine,trammel nets, gill nets, traps, longliners
I Models: exp, strexp, exp log, strexp log = exponential,streched exponential, exponential with log errors, strechedexponential with log errors.
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details: Methods
I Models are fit to distinct data breakdowns, All, byREGION, by GEAR, by REGION & GEAR = 60 models(only market)
I Models are fit to both metiers merged (only on-board)
I Fits are analysed by visual inspection of residuals, AIC,likelihood, precision of parameters, precision of theestimated number of samples to achieve objective.
I Fits selected are averaged considering the inverse of theresiduals variance (only market)
I Number of samples are allocated considering the highestnumber for each combination of GEAR & REGION (onlymarket).
I Number of samples are estimated by the best model aswell as with sampling theory (only on-board).
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details CV (µ) = CV (τ)
τ̂ = C ∗ µ̂
var(τ̂) = C 2 ∗ var(µ̂)
CV (τ̂) =
√C 2 ∗ var(µ̂)
C ∗ µ̂=
√var(µ̂)
µ̂= CV (µ̂)
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details τ & var(τ)
Consider N the number of individuals, i = 1 . . . l to indexlength classes and j = 1 . . . s to index sampled trips.
N =∑i
Ni
Σ = var(N) =∑i
var(Ni ) + 2 ∗∑i
∑j=i+1
cov(Ni ,Nj)
Ni =∑j
Nij
var(Ni ) =
∑j(
Nij∗wwj
− Ni )2
s ∗ (s − 1)
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details DPUE & var(DPUE )
Let i be the index of the number of hauls sampled in trip j(i = 1, 2, .., nj , j = 1, 2, .., nt), d be total weight discarded (inkg) and h be the haul duration (in hours)
DPUE j =
∑nji=1
di,jhi,j
njandDPUE =
∑nti=1 DPUE j
nt
VAR(DPUE ) =∑nt
j=1 (DPUEj−DPUE)2
nt(nt−1)
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details residuals ofon-board model
Samplingreview
Jardim, E.,Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
Details residuals ofmarket model for trammel
nets
EXP
mod0$res
Fre
quen
cy
−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4
05
1020
30
STREXP
mod1$res
Fre
quen
cy
−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4
05
1020
30
EXP LOG
mod2$res
Fre
quen
cy
−3 −2 −1 0 1
05
1020
30
STREXP LOG
mod3$res
Fre
quen
cy
−3 −2 −1 0 1
010
2030
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Index
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