portuguese market and on-board sampling effort review
DESCRIPTION
Accurate and precise estimation of discards is a major objective of data collection programs throughout the world. Discard reduction is also a major topic of the new Common Fisheries Policy (CFP) and the future Data Collection Multi-Annual Programme (DC-MAP). Using data from the Portuguese on-board observer programme that samples two otter trawl fisheries in ICES Division IXa, we compare two different approaches for estimating the sampling effort required to attain "assessment grade" discard estimates: a model-based approach (exponential-decay models) and a probability-based approach (based on classic sampling theory). We show that both approaches attain comparable sample size estimates and that the sample size required to attain precision objectives varies across species and across fisheries being likely influenced by discard motifs. We demonstrate that sampling levels at least two fold higher than the present sampling levels would be required to attain the precision levels set in the current Data Collection Framework (DCF). We discuss the implications of these results in light of the future ability of European onboard sampling programs to detect, e.g., progressive reductions in discard levels.TRANSCRIPT
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
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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
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−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4
05
1020
30
EXP LOG
mod2$res
Fre
quen
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−3 −2 −1 0 1
05
1020
30
STREXP LOG
mod3$res
Fre
quen
cy
−3 −2 −1 0 1
010
2030
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