Course 1: Catch per unit effort data standardisation in R for fisheries biologists and practitioners
This short course is aimed at introducing researchers to fisheries data analysis using linear models (LM), generalized linear models (GLM) and generalized linear mixed models (GLMM) in the R working environment. Scientific monitoring and artisanal, commercial or recreational fish catch data is often used to assess population status, but such data are usually complex and require careful standardisation.
By the end of the course, participants should be able to:
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Undertake data exploration to avoid common pitfalls in tackling a data analysis
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Recognise data structures and fit appropriate models to CPUE data
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Understand and apply alternative approaches to model selection
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Interpret and present the results of statistical models
The sessions during November 23-24, 2022 will be a blend of interactive demonstrations, lectures and Q&A time. All materials delivered during the course - including lecture videos, R scripts and resources - will be freely available on this website for future use and independent learning.
The course GitHub page has a discussion group where you can share your challenges and solutions about R and package installations, statistics or other related topics. You will need a GitHub account to post on this discussion group, but creating a GitHub account is easy and useful anyway.
The course is led by Dr Carl Smith (Nature Research Centre, Lithuania and University of Lodz, Poland) with an extensive expertise in statistical analyses and teaching. The course is organised by Dr Asta Audzijonyte (Nature Research Centre, Lithuania & University of Tasmania, Australia). Additional technical support is provided Dr Catarina Silva (Nature Research Centre) and Dr Eglė Jakubavičiūtė (Nature Research Centre).
Course content
Part 1 - Introduction and preparation
Part 2 - Fitting linear models
Part 3 - General linear mixed models
Part 4 - Zero inflated general linear mixed models
Part 5 - Time series analysis and Bayesian inference
Feedback
If you completed the course, we greatly appreciate your feedback to help us improve and plan further potential courses. If you would like to stay informed and have not registered yet, you can fill in this registration form so that we have your contact details.
Course 2: Further points on exploring, cleaning & filtering data prior to analysis
This auxilary course provides further introduction and discussion about data exploration. This course is not directly related to the CPUE standardisation course above and can be done with or without completing the previous course. The data exploration course has been prepared and contributed by Dr Harry Gorfine, fisheries biologist at Nature Research Centre, Lithuania and Victorian Fisheries Authority, Australia. The course provides a general overview of data exploration challenges and good practices and is intended to be followed independently.
Data exploration course materials
These courses are organised as a part of the “Sustainable inland fisheries” project, funded by the European Regional Development Fund (project No 01.2.2-LMT-K-718-02-0006) under grant agreement with the Research Council of Lithuania (LMTLT).