Portuguese Market and On-board Sampling Effort Review
Upcoming SlideShare
Loading in...5
×
 

Portuguese Market and On-board Sampling Effort Review

on

  • 243 views

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 ...

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.

Statistics

Views

Total Views
243
Views on SlideShare
243
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-ShareAlike LicenseCC Attribution-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Portuguese Market and On-board Sampling Effort Review Portuguese Market and On-board Sampling Effort Review Presentation Transcript

  • Sampling review Jardim, E.,Prista, N. & Dias, M.Introduction Portuguese Market and On-boardDataMethods Sampling Effort Review Working document presented to PGCCDBS, 7-11 February 2011ResultsConclusions Jardim, E., Prista, N. & Dias, M. February 5, 2011
  • Sampling review Jardim, E., OutlinePrista, N. & Dias, M.IntroductionData 1 IntroductionMethodsResultsConclusions 2 Data 3 Methods 4 Results 5 Conclusions
  • Sampling review Jardim, E., IntroductionPrista, N. & Dias, M.Introduction The implementation of the metier approach resulted in:Data a decrease in the precision of the length frequenciesMethods estimates by species, due to the spread of sampling effortResults to new species and the reduction of trips sampled.Conclusions an increase in the number of strata to be sampled on-board The objective of this work is to optimize sampling effort by computing the number of samples required to achieve the precision levels defined by the DCF: for length frequencies of the landings sampled at the market for total discards sampled on-board
  • Sampling review Jardim, E., DataPrista, N. & Dias, M.IntroductionDataMethods On market:Results number of individuals estimated by tripConclusions data from 2009-2010, by REGION, GEAR, SPECIES & QUARTER On board: weight discarded by trip data from 2004-2010 by METIER (OTBDEF, OTBCRU) & QUARTER Data is scarce and the breakdown by metier makes it even scarcer, it was necessary to aggregate.
  • Sampling review Jardim, E., MethodsPrista, N. & Dias, M.IntroductionData Model CV = f (N) using exponential decay models (NMethods being number of samples)ResultsConclusions Compute N to achive 12.5% CV for market sampling or 20% for on-board Compute 95% percentile of N as an indicator of a high probability to achieve the objective and cover species with more variability than average Review the sampling plans (Lots of technical details and statistical mambo-jambo to be provided if requested)
  • Sampling review Jardim, E., Data aggregation forPrista, N. & Dias, M. market samplingIntroductionDataMethods Each pair used in model refers toResults the CV of the total number of individuals sampledConclusions the number of samples collected from which the CV above was computed Each pair was computed by GEAR (aggregation of metiers), QUARTER, REGION & SPECIES Each model was fit to distinct dimensions of the data collapsing all other dimensions for each REGION for each GEAR for each combination of REGION and GEAR
  • Sampling review Jardim, E., Example models forPrista, N. & Dias, M. market samplingIntroductionData Trammel netsMethods 0.7 qResults 0.6 qConclusions exp strexp 0.5 exp log strexp log q q q 0.4 q q q q TOTCV q q q q q q q 0.3 q q q q q q q q q q q q q q 0.2 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.1 q q q q q q q q q q q q q 0.0 q 0 5 10 15 20 N
  • Sampling review Jardim, E., Models for on-boardPrista, N. & Dias, M. samplingIntroductionDataMethodsResultsConclusions
  • Sampling review Jardim, E., Preliminary conclusionsPrista, N. & Dias, M. for market samplingIntroduction Region Metier SampEff.2010 SampEff.2011Data 1 North FPO MOL >=29 0 0 5 17 2 North GNS DEF 80-99 0 0 2 21Methods 3 North GNS DEF 60-79 0 0 3 21 4 North GTR DEF >=100 0 0 8 12Results 5 North LLS DEF 0 0 0 1 17Conclusions 6 North OTB DEF 65-69 0 0 6 18 7 North PS SPF >=16 0 0 7 13 8 North TBB CRU >=20 0 0 1 17 9 Center FPO MOL >=29 0 0 5 17 10 Center GNS DEF 80-99 0 0 3 19 11 Center GTR DEF >=100 0 0 7 4 12 Center LLD LPF 0 0 0 2 2 13 Center LLS DEF 0 0 0 2 17 14 Center LLS DWS 0 0 0 2 17 15 Center OTB CRU >=70 0 0 6 4 16 Center OTB CRU 55-59 0 0 17 Center OTB DEF 65-69 0 0 1 4 18 Center PS SPF >=16 0 0 5 13 19 South FPO MOL >=29 0 0 5 17 20 South GNS DEF 80-99 0 0 2 21 21 South LLD LPF 0 0 0 1 1 22 South LLS DEF 0 0 0 1 17 23 South OTB CRU >=70 0 0 5 18 24 South OTB CRU 55-59 0 0 25 South OTB DEF 65-69 0 0 2 18 26 South PS SPF >=16 0 0 2 13 27 Total 84 338
  • Sampling review Jardim, E., Preliminary conclusionsPrista, N. & Dias, M. for on-board samplingIntroductionDataMethods Model point estimate is 15 samples per quarter for bothResults metiersConclusions Sampling theory estimate is 18-20 samples per quarter 95 percentile is 48 samples per quarter Increase sampling effort up to 192 trips per year for each metier The sampling effort is not applicable due to high costs and lack of human resources. In 2011 on-board sampling effort will be increased up to the maximum possible, taking into account other metiers and resources available.
  • Sampling review Jardim, E., The EndPrista, N. & Dias, M.IntroductionDataMethodsResultsConclusions
  • Sampling review Jardim, E., Details: codesPrista, N. & Dias, M.IntroductionDataMethodsResults N, C, S = Norte, Centro SulConclusions OTB, PS, GTR, GNS, FPO, LLS = trawl, purse seine, trammel nets, gill nets, traps, longliners Models: exp, strexp, exp log, strexp log = exponential, streched exponential, exponential with log errors, streched exponential with log errors.
  • Sampling review Jardim, E., Details: MethodsPrista, N. & Dias, M. Models are fit to distinct data breakdowns, All, byIntroduction REGION, by GEAR, by REGION & GEAR = 60 modelsDataMethods (only market)Results Models are fit to both metiers merged (only on-board)Conclusions Fits are analysed by visual inspection of residuals, AIC, likelihood, precision of parameters, precision of the estimated number of samples to achieve objective. Fits selected are averaged considering the inverse of the residuals variance (only market) Number of samples are allocated considering the highest number for each combination of GEAR & REGION (only market). Number of samples are estimated by the best model as well as with sampling theory (only on-board).
  • Sampling review Jardim, E., Details CV (µ) = CV (τ )Prista, N. & Dias, M.IntroductionDataMethodsResults τ =C ∗µ ˆ ˆConclusions var (ˆ) = C 2 ∗ var (ˆ) τ µ C 2 ∗ var (ˆ) µ var (ˆ) µ CV (ˆ) = τ = = CV (ˆ) µ C ∗µ ˆ µ ˆ
  • Sampling review Jardim, E., Details τ & var (τ )Prista, N. & Dias, M.Introduction Consider N the number of individuals, i = 1 . . . l to indexData length classes and j = 1 . . . s to index sampled trips.MethodsResults N= NiConclusions i Σ = var (N) = var (Ni ) + 2 ∗ cov (Ni , Nj ) i i j=i+1 Ni = Nij j Nij ∗w j ( wj − Ni )2 var (Ni ) = s ∗ (s − 1)
  • Sampling review Jardim, E., Details DPUE & var (DPUE )Prista, N. & Dias, M.IntroductionDataMethods Let i be the index of the number of hauls sampled in trip jResults (i = 1, 2, .., nj , j = 1, 2, .., nt ), d be total weight discarded (inConclusions kg) and h be the haul duration (in hours) nj di,j i=1 hi,j DPUE j = nj and nt DPUE j DPUE = i=1 nt nt j=1 (DPUEj −DPUE )2 VAR(DPUE ) = nt (nt −1)
  • Sampling review Jardim, E., Details residuals ofPrista, N. & Dias, M. on-board modelIntroductionDataMethodsResultsConclusions
  • Sampling review Jardim, E., Details residuals ofPrista, N. & Dias, M. market model for trammelIntroduction netsDataMethods 30 EXP STREXP EXP LOG STREXP LOG 30 30 30 20 20 Frequency Frequency Frequency FrequencyResults 20 20 10 10 5 10 10 5 5Conclusions 0 0 0 0 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 −3 −2 −1 0 1 −3 −2 −1 0 1 mod0$res mod1$res mod2$res mod3$res 0.4 0.4 1 q q q q 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q 0.2 q q q q q 0 q q q 0.2 0 q q q q q q q q q q q q q q q q q q q q qq q q q q q mod0$res mod1$res mod2$res mod3$res q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q −1 −1 q q q q q q q q q q q q q q q q q q q q q qq q q 0.0 q q q q q q q 0.0 q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q −2 q −2 q q q q q q q q q q q q q q q q −0.2 q q q q q −0.2 q q q −3 q q q q −3 0.10 0.15 0.20 0.25 0.30 0.10 0.15 0.20 0.25 0.30 0.10 0.15 0.20 0.10 0.15 0.20 0.25 mod0$pred mod1$pred mod2$pred mod3$pred q q q q q q q q q qq q q q q qq q 3 1 q 3 1 q q q q q qq q qq q qq q q qq q q q q q q q qq q q qq q q q q q qqq q q q q q q q scale(mod0$res) scale(mod1$res) scale(mod2$res) scale(mod3$res) q q q q q q q q q q q qq q q q q q q q q q q q q q q 0 q q q qq q 2 q q q q 2 q q q q q q q q q q q q qq q q q q q q q q q q q qq qq qq q q q q q q q q q q q qq q q q q qq q q q −1 q q q q 1 q q q q q q q 1 qq q q q q q q q q q q q q q q q q q q qq q q q qqq q q q q q q qq q q q q q −2 q q q q q qq qq q q q q q 0 q q q q q 0 q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q qq qq q qq qq q q q −3 qq q q q q q q q q q q q q q q q q q q q q q q q q q −4 q q q q −2 q −2 q q q q q q −5 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Index Index Index Index Normal Q−Q Plot Normal Q−Q Plot Normal Q−Q Plot Normal Q−Q Plot 0.4 0.4 1 q q q q 1 q q q qq q q q q qq q q q qq qq q qqq q q q q q qqqq q q qqq qqqqq qqq qq qqq q Sample Quantiles Sample Quantiles Sample Quantiles Sample Quantiles qqq qq qqq qqq qqq qqq qq qqqq qq qqqq 0.2 qqq qqq qqq qqq 0 qqq qq 0.2 0 qq qq qq qq qq q qqq qqq qqq qq q qq qqq qq q qq qqq q qqq qq q qq q qq q qq q qq q qq q qq qq qq qq qq qq q qqq qqq qq −1 −1 q qq q q qqq qq q q qq q qqq q qqq qqq qqq 0.0 qq qq qq qqq qqq 0.0 qq qq q q qqq qqq qqq qq qqqq qqqq qqqq q qq qq qqqq qqqq q qqq qq qq q qq q qq qq qqq qq −2 −2 qqq qqq q qq qq q qq q q qq −0.2 q qqq q −0.2 q q q −3 q q q q −3 −2 −1 0 1 2 −2 −1 0 1 2 −2 −1 0 1 2 −2 −1 0 1 2 Theoretical Quantiles Theoretical Quantiles Theoretical Quantiles Theoretical Quantiles