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Unlocking the enigma of Scottish harbour porpoise fine-scale habitat use

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  • Full or part time
    Prof B Wilson
    Dr K Brookes
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Harbour porpoises (Phocoena phocoena) are the most abundant and widespread cetacean in Scottish waters but are vulnerable to anthropogenic pressures such as noise from aquaculture and wind farm construction, bycatch in fisheries, and climate change. As a result, various national and international laws and agreements aim to protect porpoises from these pressures. In addition to general protection and inclusion in decision making processes, porpoises were the reason for the designation of one of Europe’s largest protected areas, the cSAC along the west coast of Scotland in 2018.

Porpoises are found in a range of habitats throughout Scottish waters. However, these habitats are not used equally with some favoured year round and others at particular times. This project will seek to understand the drivers for these differences, making use of existing datasets collected using passive acoustic monitoring (PAM). PAM is a commonly used technique to establish presence and relative abundance of echolocating porpoises in specific locations over extended time periods, using fixed or towed underwater acoustic detectors (hydrophones). Across both the east and west coasts, several projects have been collecting PAM data from fixed stations for up to a decade. While these data have all been collected for slightly different purposes, they have all used similar methods and so this project seeks to consider them at a more holistic level to better understand porpoise ecology and how and why porpoises use different environments. Most Scottish PAM studies to date have focused on large-scale trends in usage, however their use has also revealed intriguing patterns of habitat use that vary at fine-scales (<1km). Some of these differences have been found to occur over subtly different seabed habitats, depths and time scales. Integrating these data with environmental parameters is likely to help reveal and provide insights into which fundamental ecological and/or anthropogenic drivers underpin porpoise distribution and habitat use in Scottish waters.

In this project, the student will have the opportunity to combine and explore previously independent datasets collected for a variety of reasons from across Scotland. Doing so will provide opportunities to better understand biases in standard survey methods and reveal wider patterns of habitat use by porpoises. In addition there may be the opportunity to collect new data to better test some of the environmental relationships that emerge. Discoveries associated with this project have the potential to provide information for conservation management efforts particularly in terms of improving monitoring strategies.

With this project bringing together previously disparate datasets, the student will need to be willing to deal with large datasets and the inherent challenges these bring (particularly extensive manipulation and error-checking). Thus the applicant will need to demonstrate excellent attention to detail and organisational skills. Analysis of these datasets will likely involve sophisticated statistical modelling approaches, and a strong candidate will possess excellent numeracy skills and be familiar with advanced quantitative analytical approaches. Familiarity with programming (such as R or Matlab) and multivariate statistics would be a bonus. Experience in underwater acoustics, PAM analysis and practical experience studying small cetaceans in temperate coastal environments may be beneficial but are not essential.

The student will benefit from an experienced supervisory team, which will be led by Prof. Ben Wilson (SAMS-UHI) and will also include Dr. Kate Brookes (Marine Scotland), Dr. Steven Benjamins (SAMS-UHI) and Prof. Paul Thompson (University of Aberdeen). The student will join the SAMS team working on Scottish marine vertebrates, acoustics and behaviour under Prof. Wilson, but will be expected to also spend time at the other partner institutions to facilitate analysis of PAM datasets held by them and to develop working relationships with colleagues working in different areas of marine science.

Funding notes

Funded by NERC Studentships awarded to the SUPER Doctoral Training Partnership. The SUPER DTP partner Universities are St Andrews University, Aberdeen University, Edinburgh Napier University, Heriot-Watt University, the University of the Highlands and Islands, Stirling University, University of Strathclyde and the University of the West of Scotland. Underpinning these research partners, providing additional training and projects are Marine Scotland, Scottish Natural Heritage, and the James Hutton Institute, among a total of 40 stakeholder organisations including industry and government agencies and international collaborators.

The start date of this project is: 5th October 2020

The 3½ year studentships cover:
• Tuition fees each year (for 2019/20 this is currently £4,327 for full-time study)
• A maintenance grant each of around £15,000 per annum (for full-time study)
• Funding for research training
• Part-time study is an option, with a minimum of 50% of full-time effort being required.

Applicants should normally have, or be studying for:
• A postgraduate Master’s degree from a degree-awarding body recognised by the UK government, or equivalent, or
• A first or upper second class honours degree from a degree awarding body recognised by the UK government, or equivalent, or
• Other qualifications or experience that affords sufficient evidence of an applicant’s ability to work at the academic level associated with doctoral study.

Numerate applicants who are familiar with advanced quantitative analytical approaches are encouraged to apply.

Project specific enquiries: [Email Address Removed] or [Email Address Removed]

General enquiries: Graduate School Office [Email Address Removed]


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