Macquarie Harbour Microbial Ecology

PhD research on marine microbial ecology along environmental gradients in a stratified Tasmanian estuary.

Overview

My PhD research at the University of Tasmania characterized the pelagic microbial community of Macquarie Harbour — a highly stratified, low-oxygen estuary — and its response to environmental gradients and salmon aquaculture.

Scientific or practical problem

Coastal aquaculture can alter microbial communities, but disentangling aquaculture effects from strong natural gradients (oxygen, salinity stratification) in an already extreme estuarine system is analytically challenging.

My role

Lead researcher (PhD). I designed the sampling and analysis approach, generated and analyzed 16S/18S rRNA sequencing data, and led all statistical and machine learning analyses and resulting publications.

Dataset or data type

16S and 18S rRNA amplicon sequencing across spatial and depth gradients in Macquarie Harbour, paired with environmental (oxygen, salinity, nutrient) measurements.

Methods and technologies

• 16S/18S amplicon sequencing and community profiling
• Network analysis of microbial co-occurrence
• Machine learning (random forest classification and regression) for biomarker/indicator identification
• Multivariate statistics: PCA, PERMANOVA, distance-based redundancy analysis (db-RDA)
• Spatial and gradient-based ecological modeling

Workflow diagram

flowchart LR
  A[16S/18S sequencing] --> B[Community profiling]
  B --> C[Multivariate stats: PCA, PERMANOVA, db-RDA]
  B --> D[Network analysis]
  B --> E[ML: Random Forest classification/regression]
  C --> F[Environmental driver identification]
  D --> F
  E --> F

Major results or outputs

Identified environmental drivers of bacterioplankton dynamics and oxygen depletion; quantified aquaculture effects on microbial assemblages; built classification models discriminating impacted vs. reference conditions (ROC-AUC up to 0.93) and regression models explaining biological outcomes from environmental variables (R² up to 0.87). Results published across four peer-reviewed papers and one preprint.

Challenges and decisions

Working in a naturally extreme, non-randomized system meant designing statistical frameworks (network + ML + multivariate stats together) that could separate natural stratification effects from aquaculture-driven change, rather than relying on any single method.

Publications

• Da Silva, R.R.P., White, C.A., Bowman, J.P., Bodrossy, L., Bissett, A., Revill, A., Eriksen, R. and Ross, D.J., 2022. Network and machine learning analyses of estuarine microbial communities along a freshwater-marine mixed gradient. Estuarine, Coastal and Shelf Science, 108026. Link
• Da Silva, R.R.P., White, C.A., Bowman, J.P. and Ross, D.J., 2022. Composition and functionality of bacterioplankton communities in marine coastal zones adjacent to finfish aquaculture. Marine Pollution Bulletin. https://doi.org/10.1016/j.marpolbul.2022.113957
• Da Silva, R.R.P., White, C.A., Bowman, J.P., Raes, E., Bisset, A., Chapman, C., Bodrossy, L. and Ross, D.J., 2021. Environmental influences shaping microbial communities in a low oxygen, highly stratified marine embayment. Aquatic Microbial Ecology, 87, 185-203. Link
• Da Silva, R.R.P., White, C.A., Bowman, J.P. and Ross, D.J., 2022. Effects of finfish farms on pelagic protist communities in a semi-closed stratified embayment. bioRxiv. https://doi.org/10.1101/2022.08.08.503163
• PhD thesis: https://doi.org/10.25959/24165534

Code and documentation

Repository link pending (analysis scripts not yet publicly organized).

Collaborators

PhD supervised at IMAS, University of Tasmania, with co-authors C.A. White, J.P. Bowman, D.J. Ross, and others named in the publications above.

Current status

Completed; published.

Limitations or confidentiality note

None — fully published doctoral research.