As the lingua franca for data science in general, the R environment can handle most if not all data analysis as well as tabular and graphical visualization of the results. It has therefore become the go-to tool for a range of fisheries and marine science applications. ICES scientists use R to perform numerous tasks, including traditional stock assessment and simulating models of fisheries and ecological systems. The user often solves these tasks by using R packages such as FLR, DATRAS, MSY, SURBAR, and VMStools. Scientists can also write their own R script to create new applications which can then be transferred and picked up readily by other users. In this way, R is flexible and free-form, enabling a user to shape the analysis according to their needs.
The course will cover the fundamentals of the grammar of data and graphics, relying on packages under the umbrella of "Hadleyverse" (for example dplyr and ggplot2) that were designed to make coding of data analysis and graphical visualization as natural as possible. The basics of functions and creation of packages will also be studied.
"The course is targeted at marine scientists who already have some basic experience in R but are yet not proficient enough to write code fluently for data manipulation, exploration, and writing their own functions. Some part of the course may also be beneficial to those who are currently using R in fisheries science but may have skipped the basics or be unaware of recent advancements," said co-instructor Einar Hjörleifsson .
This course provides an overview of Management Strategy Evaluations (MSEs) by covering a range of topics with associated case studies and practical sessions. Students will be equipped with the knowledge, skills, and tools to undertake MSEs on their own fisheries resources.
As well as operating models and risk, the case studies will bring into focus harvest control rules (HCRs). As the operational heartbeat of harvest strategies, HCRs are laid down to stipulate how much fishing can take place on a certain living marine resources. Recommended under the precautionary approach to fisheries management, HCRs derive management measures (e.g. quotas) necessary for attaining targets and avoiding limits in the context of prevailing uncertainties.
Given this inherent uncertainty in the system, weighing up how effective an HCR has been in achieving what it set out to achieve is important. For this reason, MSEs are used to assess the impact of the key sources of the uncertainty. MSEs are therefore an important part of the process, giving insight into various fisheries resources and the fishing objectives set.
'Analysis and visualization of VMS and logbook data using the VMStools R package', 23–27 OctoberThere are a growing number of advice requests requiring spatial fisheries information, and improved computing power means VMS and logbook data can be processed to create informative indicators of ecosystem states and pressures which feed directly into the Terms of Reference (ToRs) of many ICES working groups.
This course will provide instruction in the use of the VMStools software package for simultaneous analyses of vessel monitoring system (VMS) and logbook data. Students will be guided through the entire process of obtaining and cleaning VMS and logbook data, making plots and tables, shapefiles and combining the datasets to enable more advanced analyses such as dispatching the landings at higher spatio-temporal resolutions, calculating indicators (e.g. area of seabed fished) and linking VMS and logbook data to other spatial data in order to calculate potential impact of area closures on fishing fleets.
Co-instructor Niels Hintzen explained how the course will be useful for those with some experience of VMS and logbook data.
"For those who already work with VMS but not yet with VMStools, they will likely enjoy working with a 'standardized' software package which has a large community of people behind it who keep on developing new tools. Also, many people will have experience with plotting and aggregating VMS data, but doing more complicated economic analyses or linking VMS to other spatial biological datasets is not something done on a regular basis."
'Bayesian Network analysis including the socio-cultural dimension', 26 November–1 DecemberBayesian networks are a useful tool for modelling, understanding, and communicating complex systems, and particularly for dealing with uncertainty. They can be used for many different types of tasks – from intelligent data analysis to integrative models spanning multiple data sources and disciplines.
This course will introduce the basics of Bayesian networking and consider how the method can be used to address issues related to social-ecological systems.
Co-instructor Laura Uusitalo outlined the usefulness of the tools and some of the practical elements of the course:
'Bayesian networks are relatively easy to communicate to stakeholders, and they also make it easy to show how the expected results and the related uncertainties change in different scenarios. The networks are especially useful for socio-economic systems' modelling, because they enable linking different sub-systems quantitatively or semi-quantitatively. The way of thinking that is used in BN modelling can also be helpful in the scoping phase of a socio-ecological study, and this conceptual model can later be quantified.'
'In addition to the theory necessary to understand the principles of the networks and case study examples of socio-ecological modelling, the course will include practical exercises to familiarize the participants with the software. An important part of the course will be hands-on work that will be the most motivating if the participants have their own research questions to work on.'