Coventry University Featured PhD Programmes
University of Oxford Featured PhD Programmes
University of Reading Featured PhD Programmes

Capturing signals in biodiversity data across scales in space and time

   Engineering, Environment and Emerging Technologies

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof Caroline Brophy  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

About Project:

Real-time data can be challenging to analyse and interpret as it can be highly variable (noisy), multivariate, zero-inflated and complex in structures over space and time. In this PhD project, the successful candidate will develop statistical models to analyse the big data that arises from real-time biodiversity monitoring. Applying these models will facilitate the detection of any underlying and biologically meaningful biodiversity patterns and signals in the data across multiple scales. In a truly multidisciplinary and circular approach, the knowledge gained from these statistical analyses will help to iteratively refine and improve our biodiversity monitoring processes.

PhD students will work as a team and so excellent team working and communication skills are required. Ideally, experience or interest in biodiversity and addressing environmental challenges is desirable. This project is part of a highly interdisciplinary team, and so excellent team working and communication skills are required. Each candidate will produce an independent piece of research in the form of a PhD thesis based on their individual research projects. This project is part of the Kinsella Challenge-Based E3 projects at Trinity College Dublin, and PhD students will have the opportunity to work alongside those from the other successful projects, particularly in terms of team-building and dissemination events.

The PhDs are all 4-year structured programmes, with an anticipated start date of September 2021. 

Further Project Information:

Obtaining reliable biodiversity data on the ground is expensive, requiring field surveys, sample collection and processing and consequently tends to be done at coarse spatial and temporal resolutions. This hinders our understanding and capacity to predict the impacts of human disturbances of ecosystems, causes tension between industry and environmental conservation by increasing the cost of environmental impact assessments, and undermines the effectiveness of both national and international environmental legislation and policy, including the UN Sustainable Development Goals.

Digitising Biodiversity will develop and integrate three technologies—acoustic, video and radar—to monitor biodiversity, and will use AI machine-learning and state-of-the-art statistical tools to integrate and interpret the resulting information, while also addressing explicitly what is lost and gained through this process of digital translation of information from landscapes and animals to human understanding. We have assembled an accomplished, balanced and highly-multidisciplinary supervisory team to meet the diverse combination of technological, computational, statistical, ecological and translational challenges the project poses, to bring about a step-change in the measurement, recording and translation of biological data from nature.

The technologies:

Microphone arrays allow localisation and enhancement of sound, augmenting detection and classification accuracy of mid-range 360 video sensors. We will combine these media modalities with low-power mm-wave radar technology, which will be able to detect small animals like insects and provide additional rich information, including body size, flight speed and trajectory. This has never been done before.

Data processing and analyses:

Data collection is being undertaken at unprecedented scales across the globe, including in the monitoring of biodiversity. However, biodiversity data collection is costly and delivers data at coarse spatial and temporal resolutions. Our project will dramatically improve both the quality and quantity of biodiversity data by capturing data from multiple targeted scales from small insects to larger animals and birds simultaneously, generating complex multivariate data to which we will apply statistical modelling techniques to translate our ‘big data’ collection efforts into ‘big information’ knowledge gain. The data produced will have many dimensions: multivariate responses, varying scales of measurement, and real-time spatial and temporal gradients. We will develop statistical models that identify the patterns and signals that are most meaningful across particular focal scales in space and time in the highly variable and ‘noisy’ data. In a truly multidisciplinary and circular approach, we will use the knowledge gained from these analyses to iteratively refine and improve our biodiversity monitoring processes.


There is a growing body of literature in the humanities and social sciences that is investigating the relationship between the human and the more-than-human. The emphasis is on developing a relational ontology that considers human identity in relation to the non-human world. Rarely addressed, however, is what is to be the communicative basis of this relationality? How are humans to engage with and understand the non-human? How do they translate information they receive into information that is intelligible to human communities, yet avoids the pitfalls of anthropomorphism? Our project will establish how translation can help us to think about resilient and mutually-respectful forms of human/non-human engagement in different contexts and how this can help develop more sustainable versions of human identity and self-understanding in the Anthropocene. By giving concrete expression to the development of ethical forms of human/non-human communication, it will provide a model for best practice elsewhere.

The diverse and exciting combination of technological, computational, statistical, ecological and translational challenges at the core of this project means that it aligns fully and elegantly with all of the key perspectives of E3, to bring about “Technology developed in symbiosis with the natural world to meet the challenges of our time and create a more sustainable future”.

Application Process

Applications can be made through this link; Apply - E3 - Engineering, Environment and Emerging Technologies - Trinity College Dublin ( Late applications will not be accepted. Informal enquiries should be made to the primary supervisor. Completed applications should be submitted to via the above link and will require :

1.   A curriculum vitae (including the names of two referees, one of which must be an academic referee).

2.   A cover letter (maximum 1000 words) outlining the applicant’s research interests and why they are suitable for this project.

Applications will be jointly reviewed by project supervisors. Shortlisted applicants will be invited to video-interview. Successful applicants will subsequently apply to register as PhD students through the Trinity College Dublin central portal but must meet all requirements for registration in order to be eligible for this funding award. Postgraduate admission requirements are available here: The successful applicant will be required to provide evidence of English language competence following the award offer and before registering.

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.