Fish stocks do not respect national boundaries or, frequently, even fisheries management areas. For highly migratory and internationally shared stocks this can lead to major difficulties regarding quota allocations between nations participating in the fishery (Fernandes & Fallon, 2020). Over time, changes in distributions and migration routes can lead to mismatches between abundances of fish and distributions of fishing quota. One extremely valuable commercial stock affected by the considerations above is Atlantic mackerel (Scomber scombrus). Mackerel have a wide distribution, with spawning in European waters occurring along the continental shelf from northern Spain to Norway, and in the northern North Sea. In May-July adults undergo a large feeding migration into the Norwegian Sea, before returning to spawning areas in the spring, resulting in the stock being present across national boundaries and jurisdictions.

To address these concerns, in this project you will develop a spatial population model for mackerel. The model will use methods originally developed for zooplankton (Speirs et al., 2006), adapted for fish (Heath et al., 2014; Dolmaire & Speirs, 2024; Speirs et al., 2024), including migratory fish (Dolmaire, 2022). The model will be used to understand the role of biological and physical drivers in determining the spawning and feeding migrations of mackerel, and to explore the consequences to spatial distribution of changes in these drivers due to climate change. This will involve a) parameterising an existing implementation of the model for mackerel b) using a Random Forest model to generate (time-varying) spatial fields of food availability and spawning suitability, and c) using the model to forecast shifting spatial distributions and migration routes over the entire geographic range of northeastern Atlantic mackerel.

Training:

You will:

Have access to training funds, valued at £16,425 for the current academic year, which can be spent to support professional development, attend meetings and conferences, and the acquisition of technical skills.

Receive training in spatial and population modelling, and random forest statistical modelling. Additionally, you will gain experience in mining data for demographic parameters, such as growth rates, mortality rates, and abundance estimates. Together with the second supervisor and the Heriot-Watt team you will explore supporting data (tags, surveys in summer feeding grounds and egg surveys). There may be possibilities for you to get involved in either the acoustic surveys or their analysis.

About SUPER:

SUPER is multi-institutional, cross-disciplinary, and interdisciplinary; helping to foster a new generation of students equipped to take on diverse careers and to manage our natural environments more sustainably. SUPER brings together the research strengths of the Universities of Aberdeen, Edinburgh Napier, Heriot-Watt, Highlands and Islands, St Andrews, Stirling, Strathclyde, and the West of Scotland. All institutional partners are members of the Marine Alliance for Science and Technology for Scotland (MASTS), whose research and training collaborations address cutting-edge scientific challenges across the Natural Environment Research Council (NERC; part of the UKRI) remit. Underpinning these research partners, providing additional training and projects, are stakeholder organisations including industry and governmental bodies.

SUPER DTP students will be part of a cohort that will develop together, forming a nurturing network, supported by bespoke events. The SUPER cohort will pursue research, engage in training, and learn from each other as part of a large multi-disciplinary group. Members are offered unparalleled opportunities to understand societal and environmental challenges and to deliver science for the benefit of wider society, with international implications.