Multi-stressor interactions in the context of beaver-mediated recovery of natural processes within riparian habitats

This project is part of the NERC-funded Centre for Doctoral Training, ECOWILD. For more details, and for a full list of projects offered under this programme, please visit: https://ecowild.site.hw.ac.uk/

Explore the intricate dynamics of riparian wetland ecosystems with a PhD project investigating the impact of Eurasian beaver reintroduction on river health and biodiversity. As beavers engineer new wetlands, potential stressors like temperature, eutrophication, and invasive species intersect with alterations to the processes that shape riparian habitats. This study aims to uncover the most relevant stressors in beaver-created wetlands, evaluating their implications for ecosystem health, particularly on indicator species like pearl mussels and salmonids. By identifying circumstances where beaver activity may conflict with conservation goals, the project guides decisions on licensing and management. Employing cutting-edge eDNA metabarcoding and field methods, this research analyses community diversity, species abundance, and stressor quantification using multi-stressor theory.

The student will have the opportunity to formulate a project on the timely topic of reintroducing a keystone species, exploring their potential to tolerate and alleviate stressors in freshwater ecosystems and contribute to resolving the biodiversity crisis. Engaging in the design of biodiversity surveys and experiments in natural settings, the candidate will untangle the complexities of multiple stressors while gaining valuable fieldwork and laboratory experience.

The student will be based at in the multi-disciplinary Institute for Biodiversity and Freshwater Conservation - UHI Inverness at UHI Inverness with access to state-of-the art molecular facilities and close proximity close proximity to wide range of beaver-modified habitats. The supervisory team of this project is comprised by academics from UHI and the University of Oxford with expertise in freshwater ecology, eDNA based biodiversity monitoring, bioinformatics, statistical analysis and multiple stressor theory. Practitioners from the non-academic partners Natural England and the Cairngorm National Park Authority will provide expertise in beaver ecology, government policy and habitat management. 

This PhD offers not only the opportunity to develop highly relevant molecular and data analysis skills, but also a chance to apply these in a real-world context. The student will develop essential and transferable skills in cutting-edge molecular techniques like DNA metabarcoding. This will be complemented by proficiency in associated bioinformatic analysis and statistical examination of community composition data within the framework of multiple stressor theory. The close engagement with non-academic stakeholders will provide the student with the relevant policy and management context and an provides excellent opportunities to develop skills an expertise relevant for employment including a 3-month work placement.

We recognise that not every talented researcher will have had the same opportunities to advance their careers. We therefore will account for any particular circumstances that applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process.

We will be holding online ‘meet the supervisor’ events on March 25^th, 26^th and 27^th – if you can’t make these but are interested in this project, please contact the lead supervisor directly.

Links to the scheduled ‘meet the supervisor’ events, as well as the full list of projects advertised under this CDT, can be found here https://ecowild.site.hw.ac.uk/phd-training/

How to apply: You must apply via the ECOWILD website https://ecowild.site.hw.ac.uk/how-to-apply/

Interviews: expected to take place beginning June 2024.

Start date: Applicants must be available to start in October 2024.

Eligibility: standard UHI entry requirements apply

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