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  1. California For-Hire Review December 2008

  2. Logbooks • Potential to provide an effort census, but not currently used for effort estimation. • Logbook response is unreliable. • Validation does not appear to be done in an organized manner. • Consistent data quality over time must be ensured for logbook data to be useful at monitoring trends.

  3. Logbooks • Verification of logbooks should be based on probability sampling at dockside. • Logbook and dockside sampling can be coordinated using double sampling techniques to adjust logbook estimates.

  4. CRFS: PCPS • Sample allocation is based on percentage of vessels basis. • Allocation should be based on controlling sampling error for key domains of interest. • Optimal allocation to control variance will likely lead to sampling in proportion to expected number of angler trips.

  5. CRFS: PCPS • A serious effort must be made to improve response rates: • Only 50% of vessels respond, including a large number of refusals. • Non-response adjustment must be applied to reduce non-response bias. • Estimation procedures do not appear to account for stratification of sampling frame.

  6. CRFS: CA-PC • Selection of CPFV trips described as a three step process: • Selection of landing sites. • Selection of day type, CPFV type and areas fished. • Selection of CPFV at the landing. • Difficult to evaluate without frame construction and probabilities of selection at each stage. • Trip selection at landing site: • Described as systematic and proportional to past effort for day type. • Unclear how this can be implemented as vessels are returning to the landing site.

  7. CRFS: CA-PC • There is a need to formalized the sampling process: • There appears to be some probability proportional to size sampling built in, but this should implemented in a formal selection algorithm. • On large vessels, a sample of anglers may need to be taken, but there is no formal process for doing this. • Similarly, the method for sampling from an angler’s catch should be formalized.

  8. CRFS: CA-OSP • General approach is to increase sample size and reduce sampling error. • Too little attention given to controlling coverage and measurement biases. • Procedures for estimating effort and CPUE for missed boats not based on probability sampling. • Too much discretion is left to data collectors. • Documentation of landing port-day sample required.

  9. CRFS: AT-SEA • Limited to larger vessels. • No rigorous use of probability sampling. • There is a need to document procedures for combining at sea sampling with other intercept data.

  10. CRFS: General comments • Population definitions must be clearly stated: • Must clarify whether area fished or area landed is basis for reporting. • Frame undercoverage needs to be documented and examined: • Is it negligible or should coverage be extended?

  11. CRFS • The telephone survey response rate is poor but it is the major source of effort data. • Validity checks are made, but in unspecified ways. • No effort is made to systematically combine data from different sources. • A long term effort is required to: • Eliminate duplication • Get log-type data on time (perhaps on sample basis) • Increase response rates

  12. CRFS • Respondent sample is treated as the selected sample in estimation: • Selection of CPUE sample by site introduces extra stage into sampling process. • Large cluster sizes lead to higher variance. • Sampling for CPUE: • Appears based on site and time assignments with quotas for numbers of vessels and fish. • Data collectors have too much discretion in selecting the sample.

  13. CRFS • Probability proportional to size sampling could be used to control workload and improve precision. • Make greater use of optimal allocation • Probability sampling should be used at all stages of sample collection. • Estimation should be based on the probability sampling design, with correct weighting and adjustments for non-response bias and undercoverage.

  14. CRFS • Similar data are obtained from multiple sources. • It should be possible to provide timely preliminary estimates, and revise these using more reliable data or multiple-frame estimation.