Mammal and bird community dynamics across habitat edges using camera traps and the MEWC workflow

About the research project

Agricultural expansion is reshaping Tasmanian landscapes into mosaics of forest, plantations and farmland, increasing the prevalence of edges that can alter wildlife communities. This PhD will quantify how mammal assemblages shift from edge to interior across these land-use boundaries, using camera-trap data processed through the MEWC workflow, an open-source, modular pipeline that automates detection, species identification and metadata export for analysis. Existing datasets will anchor a desktop-first programme of work: hierarchical multi-species occupancy/abundance models will relate distributions to remote-sensing covariates (e.g., NDVI, canopy structure) and simple microclimate, providing robust inference without heavy new field spend. Methodologically, the project will test an active-learning loop in MEWC, prioritising uncertain images for expert review, to quantify reductions in labelling effort while maintaining accuracy. Targeted field components (subject to approvals and resources) will add value rather than drive the design: short, co-located thermal-drone and camera surveys will estimate species-specific detection offsets, and a focused experiment will compare avian detectability between tree-mounted and ground cameras (baited vs unbaited) to correct bias in standard protocols. The candidate will work in Docker/R with Camelot and AddaxAI on local GPU or the Nectar cloud, producing reproducible outputs and agency-ready data products. Expected outcomes include: (i) a generalisable account of edge effects on mammal communities across working landscapes; (ii) an evidence-backed, cost-efficient AI workflow for high-throughput camera data; and (iii) three publishable papers spanning methods (MEWC active learning), ecology (edge–interior community contrasts with RS covariates), and survey design (camera configuration for birds).

Primary supervisor

Meet Professor Barry Brook

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• First Class Honours or equivalent research master's in ecology/conservation biology/quantitative biology (or closely related field).
• Demonstrated research and analytical ability (completed thesis; data-rich project; presentations).
• Strong R skills for data wrangling, visualisation, and statistical modelling (GLMMs; detection/occupancy; Bayesian or ML).
• Competence with spatial/GIS analysis and handling remote-sensing covariates.
• Experience with camera-trap workflows (data organisation, QC, metadata) and willingness to standardise processes.
• Ability to learn and use containerised tools (Docker) and run analyses on local GPU or Nectar cloud (Linux/WSL acceptable).
• Excellent written and verbal communication in English, with evidence of clear, concise scientific writing.
• High independence, organisation, and reliability; ability to plan work and meet deadlines.
• Willingness to undertake limited, targeted fieldwork in remote settings under safety protocols.

Additional desirable selection criteria specific to this project:

• Familiarity with MEWC/AddaxAI, MegaDetector/YOLO, or related AI pipelines; basic Python for model training/inference.
• Experience with active learning/uncertainty sampling or domain adaptation for image classification.
• Experience integrating Camelot and working with EXIF/CSV metadata exports.
• Thermal-drone experience (RePL/ROC) and/or interest in acquiring credentials; understanding of dawn/dusk survey design.
• Prior work with Tasmanian/Australian vertebrate fauna and landscape-ecology/edge-effects theory.
• HPC/cloud workflows and GPU acceleration.
• Clean driver's licence (4WD experience) and remote first-aid certification (or willingness to obtain).
• Evidence of engagement with stakeholders (land managers/industry/NRM) and successful collaboration.
• Record of scholarly outputs (manuscripts under review/published; conference talks/posters).

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Professor Barry Brook to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 June 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

Explore other projects