WGCATCH publications

WGCATCH publications

A collection of reports and publications related to the work of WGCATCH

ICES Reports​​

ICES 2008. Report of the Workshop on Methods to Evaluate and Estimate the Accuracy of Fisheries Data used for Assessment (WKACCU), 27–30 October 2008, Bergen, Norway.

This was the first in a series of workshops aimed at quantifying and improving the accuracy of fisheries data. The report provides some useful information on detecting and avoiding bias. The workshop also produced a scorecard for bias detection which was further developed into quality assurance tables by subsequent groups.​

ICES. 2010. Report of the Workshop on methods to evaluate and estimate the precision of fisheries data used for assessment (WKPRECISE), 8-11 September 2009, Copenhagen, Denmark.​

This workshop focused on sources of variability and estimation procedures for fisheries data. The report outlines best practices in fishery sampling programmes and provides a list of key parameters and statistics used in stock assessment with their main sources of error.  

ICES 2009. Workshop on Sampling Methods for Recreational Fisheries (WKSMRF). 14-17 April 2009, Nantes, France.​​

This workshop was set up to develop sampling methods for recreational fisheries, many of the issues carry over to catch sampling in general. The report provides a useful overview of survey methods with clear explanations of key concepts. This workshop established the Planning Group on Recreational Fisheries (PGRFS), which met in 2010 and 2011 and then established the Working Group on Recreational Fisheries Surveys (WGRFS) which is still active.

This workshop addressed the need for estimating fisheries data at the metier level as required under the EU Data Collection Framework (DCF). The workshop provided guidelines for the design of sampling schemes that can provide these data. The report also contains an annex with some common formulae applied in design-based fishery surveys.
This series of workshops focused on several classes of catch sampling schemes for estimating variables such as quantities discarded, and length or age composition of catches, taking account of the many practical problems that face people trying to obtain representative, randomized samples of catches. The Workshops have provided guidelines for good practice, and explored ways of documenting the quality of sampling designs and of the data that are collected in a way that is useful for different types of end-users. WKPICS3 introduced the 4 principal design classes of catch survey designs and produced a handy glossary of terms relevant to catch sampling designs. 
SGPIDS was formed to foster an exchange of expertise on discard sampling, planning, implementation coordination and data collection procedures between countries. During the first meeting, the study group identified potential sources of bias within discard sampling programmes.
The second meeting of SGPIDS focussed on providing the practical tools to implement unbiased sampling frames, random vessel selection procedures and data quality indicators.
The last SGPIDS meeting focused on practical aspects of implementing sampling plans with participants providing case studies, worked examples, and progress reports that covered three main themes: sampling frames based on vessel lists; random vessel selection procedures; on-board sampling and estimation. 
SGPIDS developed a range of quality indicators to highlight potential problems with sampling designs. 
ICES, 2016. Working Group on Commercial Catches (WGCATCH) 7-11 November 2016, Oostende, Belgium.
Currently, an important task for WGCATCH is to improve and review sampling survey designs for commercial fisheries, particularly those for estimating quantities and size or age compositions of landings and discards and providing data quality indicators. However, the scope of WGCATCH is broader than this, covering many other aspects of collection and analysis of data on fishing activities and catches. This will be end-user driven, and coordinated with the work of other ICES data EGs such as the Working Group on Biological Parameters (WGBIOP), the Planning Group on Data Needs for Assessments and Advice (PGDATA) and the Working Group on Recreational Fisheries Surveys (WGRFS) to ensure synergy and efficiency. 
WGCATCH 2014 produced best-practice guidelines for designing an onshore sampling survey. The report also contains an overview of the development and use of quality assurance tables by various other ICES groups.
WGCATCH 2015 documented sampling practices for small-scale fisheries and by-catches of protected, endangered and threatened species. And produced guidelines for simulations of regional sampling designs.
WGCATCH 2016 produced guidelines for best-practice of small-scale fisheries as well as guidelines for the use of commercial LPUE data in stock assessments. 

Other Reports 
Besides some basic methodology and definitions, these guidelines provide practical rules, strategies and hints to lead the practitioner in the initial phase of survey planning and reporting, in the selection of the best sampling method and type of allocation, in the determination of the sample size and sampling errors and in the treatment of non-response and calculation of data estimates.
Eurostat. The European Self-Assessment Checklist for Survey Managers. DESAP.
EuroStat has developed this comprehensive checklist that forces you to consider all aspects of your survey. Some sections might not be relevant to catch surveys but most of it is generic enough to be useful. 
This annex of the fishPi report provides an overview of the guiding principles underlying a probability-based regional sampling design for the collection of fisheries data, involving both on‐shore or at‐sea sampling programmes. These principles are based on the standard survey methodologies needed to make inferences about a population from a sample of the elements of that population. The implementation of such a design is set out as a series of steps 
A good example of the widespread implementation of a sampling design across diverse sampling strata, where the implementation is by autonomous institutions but the sampling practices are harmonised to common data collection and estimation principles.
Example of a comprehensive review of regional sampling designs by an independent committee; several references to phone and mail surveys, pannel surveys, domain estimation, and dual-frame sampling.
This website has a wealth of information on their catch surveys and estimation. It is aimed at the public so it provides a high-level overview of the main concepts.
Reduced Uncertainty in Stock Assessments (REDUS) is a new research project based at the Institute of Marine Research in Norway.
The REDUS project will use both a bottom-up approach (from observations through management) and a top-down (society’s uncertainty requirements implications for observations). Thus REDUS will provide society with knowledge of how uncertainty affects stock assessment and hence quota advice, and complementary how much catches can increase if we reduce this uncertainty.

Scientific Papers 
The Horvitz‐Thompson estimator is the most widely used and versatile design‐based estimator for stratified probability based sampling. This estimator is applicable to many, if not all, of the design stages and protocols outlined here that are based on probability based selection of sampling units.
Nelson, G.A. (2014) Cluster Sampling: A Pervasive, Yet Little Recognized Survey Design in Fisheries. Trans Am Fish Soc 143 (4) 926-938.
Cluster sampling is a common survey design used pervasively in fisheries research to sample fish populations. The goal of this paper is to provide an introduction to the estimation of population attributes and analysis of fisheries data collected via cluster sampling. This article addresses the nature of clustered fisheries data, reviews the random cluster sampling estimators of population attributes, explores the implications of violating the assumption of independence in hypothesis testing, and reviews current statistical approaches that can be used to analyze appropriately clustered data.
A nice description of a survey design that explicitly goes through the steps of defining the target and study population, defining the sampling frame, running a pilot study and developing a sampling design.
R package 'survey'
This R package can be used for design-based estimation and a broad enough functionality to be used for the main design classes for catch data (as well as research survey data).
An internationally recognized statistical software package that specializes in providing efficient and accurate analysis of data from complex studies. SUDAAN is ideal for the proper analysis of data from surveys and experimental studies, since SUDAAN procedures properly account for complex design features, such as correlated observations, clustering, weighting, and stratification. The package is available in a version that runs under SAS, effectively expanding the library of function in SAS for analyzing complex survey data, including imputations. ​

Cochran, W.G. (1977) Sampling Techniques. John Wiley and Sons, New York. 
A classic reference on sampling methods. It does demand a fairly sound statistical background but the main ideas are well explained in English as well as in mathematical notation. 
Jessen, R. A. 1978. Statistical survey techniques, John Wiley and Sons, New York. 
Kish, L.  1965. Survey Sampling. John Wiley & Sons, New York.  
Lohr, S. 2010. Sampling: design and analysis 2nd Ed. Brooks/Cole, Boston.
Särndal, C.‐E., Swensson, B., Wretman, J. 1992. Model Assisted Survey Sampling. Springer‐Verlag.
Thompson, S.K. 2012. Sampling. 3rd Ed. John Wiley & Sons, New  York.
Useful texts on sampling
Lumley, T. 2010. Complex surveys, a guide to analysis using R. John Wiley & sons New Jersey.
Demonstrates the use of the “survey” package in the R statistical language for estimation from complex survey designs. outlines the use of the Horvitz‐Thompson estimator.
Moser, C.A. Kalton, G. 1979. Survey Methods in Social Investigation. Gower Publishing, Aldershot.
A non‐technical appraisal of survey sampling in a social context, which in many ways is highly applicable to the situation found in fisheries sampling

​​Vølstad J.H. et al. (2011). Probability-based surveying using self-sampling to estimate catch and effort in Norway's coastal tourist fishery. ICES JMS 68(8) 1785-1791

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WGCATCH publications

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