From landscape to soundscape: what drives spatial variation in natural soundscape characteristics?

(BUTLER_UBIO23ARIES)

From landscape to soundscape: what drives spatial variation in natural soundscape characteristics?

(BUTLER_UBIO23ARIES)

Project Description

Supervisors

Dr Simon Butler (University of East Anglia, School of Biological Sciences) – Contact me

Dr James Gilroy, UEA School of Environmental Sciences

Dr Simon Gillings, British Trust for Ornithology

 

Project background

There is a growing body of evidence that spending time in nature has short- and long-term benefits for physical, mental and social well-being. Whilst we use all senses to engage with nature, sound plays a key role in the quality of the experience, with bird song in particular providing the soundtrack to time spent outdoors. Land-use change, agricultural intensification, and urbanisation are changing the abundance and distribution of bird populations and threatening the diversity and richness of our natural soundscapes. Although the importance of conserving natural soundscapes is becoming increasingly recognised, the main drivers of spatial and temporal variation in soundscape characteristics remain unknown, preventing effective targeting of natural soundscape conservation efforts.

Research methodology

This project will employ a novel framework for the large-scale reconstruction and acoustic analysis of natural soundscapes to examine the relationships between landscape structure and land-use, avian community composition and soundscape characteristics. The student will combine long-term UK Breeding Bird Survey and Bird Atlas data with recordings of individual species to reconstruct avian soundscapes for specific habitats, seasons and years. Supported by the targeted deployment of Passive Acoustic Monitoring units, the student will:

Objective 1: Quantify and map the acoustic properties of avian soundscapes across the UK.

Objective 2: Assess seasonal variation and spatial consistency in avian soundscape characteristics.

Objective 3: Determine key drivers of spatial variation in both absolute measures of, and long-term trends in, avian soundscape characteristics.

Training

The successful candidate will receive extensive training in the theoretical and practical aspects of ecoacoustics research, including study design and hypothesis testing, field-based acoustic monitoring, and the analysis of large-scale, long-term datasets. You will gain a wide range of skills in critical thinking, statistical analyses, scientific writing and science communication, and will also be encouraged to develop independent lines of research alongside the core objectives.

Person Specification

We seek an enthusiastic individual with a degree in ecology, environmental sciences or a related subject. Relevant experience in ecoacoustics, avian ecology, and/or spatial modelling will be an advantage. Please contact simon.j.butler@uea.ac.uk for further details.

References

  • Morrison, C.A. et al (2021) Bird population declines and species turnover are changing the acoustic properties of spring soundscapes. Nature Communications 12: 6217.
  • Gillings, S. et al (2019) Breeding and wintering bird distributions in Britain and Ireland from citizen science bird atlases. Global Ecology & Biogeography 28: 866-874.
  • Müller, S. et al (2022) Land-use intensity and landscape structure drive the acoustic composition of grasslands. Agriculture, Ecosystems & Environment 328: 107845
  • Sethi, S.S. et al (2020) Characterising soundscapes across diverse ecosystems using a universal acoustic feature set. PNAS 117: 17049-17055.
  • Cifuentes, E.F. et al (2021) Relationship between acoustic indices, length of recordings and processing time: a methodological test. Biota Colombiana 22: 26-35.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2023. The closing date for applications is 23:59 on 11th January 2023.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£17,668 p.a. for 2022/23) and research funding. International applicants are eligible for fully-funded ARIES studentships including fees. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK.
  • ARIES students benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis, visa requirements notwithstanding
  • For further information, please contact the supervisor. To apply for this Studentship follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential, and will be stored on the UEA sharepoint server. Data will not be shared with those involved in making decisions on the award of Studentships, and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be ask to complete the form by the University to which you apply.

Applications are open

Apply now