ACCE+ DLA programme: Habitat networks designed for all - ensuring climate-resilience for a wide diversity of species

Description

About the Project

Biodiversity is threatened by habitat degradation and fragmentation, which hampers the ability of species to successfully shift their ranges in response to climate change. Increasingly, as the world aims for ‘nature recovery’, habitat will be restored, but where should restoration be targeted to provide the greatest benefit to biodiversity? The Condatis model can identify locations to make habitat networks better connected and more climate-resilient, but there is a risk that networks based on habitat maps alone will not serve all the species that need to use them. Alongside connectivity, we must also plan for large populations, that are resistant to extinction. This project will develop novel plans to conserve hundreds of species as the climate changes, then, importantly, test whether simplifications are possible to make future prioritisation more feasible and generalisable for practitioners. The results will contribute to an influential debate in conservation: the extent to which we need to consider species individually, or whether we can generalise by analysing a set of conditions that will allow them to survive. The candidate will use butterflies and larger moths in Great Britain as a model system for the habitat network plans, in collaboration with Butterfly Conservation and Natural England.

Objectives

1)     Develop fine-scale, mapped proposals for climate-resilient habitat networks for Lepidoptera species, using detailed range and trait data. The networks must provide both habitat aggregations for stable populations and long-distance connectivity.

2)     Test what best explains synergies and conflicts between the ideal spatial plans for different species (e.g. overlap in habitat use, overlap in climate envelope or similarity of dispersal).

3)     Evaluate the diminishing-returns of analysing more species and habitat types, and the most effective way to choose a representative sample.

4)     Contribute to guidance for conservation practitioners who have limited time for analysis, and widely differing quality of information for different taxonomic groups.

This project is led by Dr Jenny Hodgson at the University of Liverpool, who developed the Condatis decision support model for better connecting habitat networks. The Hodgson group investigates how the spatial configuration of landscapes can affect biodiversity, using a combination of field studies, citizen science data and modelling. The co-supervisors are Professor Humphrey Crick from Natural England, Dr Katie Powell from Butterfly Conservation, and Dr HyeJin Kim from UKCEH, who bring an additional wealth of experience in landscape ecology, community ecology and conservation policy. Objective 1 builds on a pilot project currently underway, led by Dr Powell, which will develop useful R code for the student.

This is a primarily desk-based project utilizing national datasets on habitats, Lepidoptera and their traits, and climate change. The student will gain skills in quantitative, spatial ecology that are vital to solve both conceptual and applied conservation biology questions around the world, and highly transferrable.

The project will have both short-term and long-term implications for deciding where nature recovery efforts are targeted to provide biodiversity resilience in the face of climate change. The supervisors – who are all experienced in the translation of scientific outputs into conservation policy and action – will help the student to make the most of opportunities for impact. The project will include two placements, one within Natural England and one at a relevant employer of the student’s choice.

To thrive in this PhD, you will need some core background knowledge in ecology and conservation science. You will also need skills in Geographic Information Systems and an aptitude for quantitative analysis. Scripting and programming skills are desirable, but further Masters-level training is available, alongside bespoke training from the supervisory team.

How to Apply

Please see the ACCE website for all details of how to apply to the programme at each ACCE+ institution: https://accedtp.ac.uk/how-to-apply/

All applicants to ACCE+ must complete the ACCE+ personal statement proforma. This is instead of a personal/supporting statement or cover letter. The proforma is designed to standardise this part of the application to minimise the difference between those who are given support and those who are not. Candidates should also submit a CV and the contact details of two referees.

Part-Time Study Options

All ACCE+ PhDs are available as part time or full time, with part time being a minimum of 50% of full time. Please discuss potential part time arrangements with the primary supervisor before applying to the programme. 

Project CASE Status

This project is a CASE project. Your project will be co-supervised by the non-academic partner organisation, and you will spend 3-6 months on a placement with your CASE partner in their workplace. You will experience training, facilities and expertise not available in an academic setting, and will build business and research collaborations.

Candidate webinar

The project primary supervisor will hold a candidate Zoom webinar in December 2024 to discuss the project with interested candidates. Please register here if you would like to join!

Availability

Open to students worldwide

Funding information

Funded studentship

NERC ACCE+ DLA programme starts from October 2025.

UKRI provide the following funding for 3.5 years:

• Stipend (2024/25 UKRI rate £19,237)

• Tuition Fees at UK fee rate (2024/25 rate £4,786)

• Research support and training grant (RTSG)

Note - UKRI funding only covers UK (Home) fees. The DLA partners have various schemes which allow international students to join the DLA but only be required to pay home fees. Home fees are already covered in the UKRI funding, meaning that successful international candidates do not need to find any additional funding for fees.

Supervisors