Learnings from the Application of Bayesian Networks and Dynamic Bayesian Networks to Fisheries Data

Learnings from the Application of Bayesian Networks and Dynamic Bayesian Networks to Fisheries Data

Presented at the 8th Annual BayesiaLab Conference on October 30, 2020.


This talk gives two examples of how data-driven Bayesian Networks were applied to fisheries datasets to untangle and represent very complex systems. For the first example, I used dynamic Bayesian networks to explore and interrogate how environmental covariates, from the past and present, may drive the abundance and recruitment of mud crab in regions of the Gulf of Carpentaria in Northern Australia. Data used included weather observations and commercial logbooks from mud-crabbers.

For the second example, I modelled the associations of species that were caught by commercial fishers in regions off the east coast of Queensland, Australia. Challenges I encountered, and insights I made during the modelling process will be discussed.

Presentation Video

About the Presenter

Amanda Northrop, Senior Fisheries Scientist
Department of Agriculture and Fisheries
Queensland Government (opens in a new tab)

I am a data scientist with over 15 years of experience in a variety of industries both in Australia and abroad. I have gained knowledge in industry, academia and government. My current role is in Queensland with the Australian Government, which involves building mathematical models of fish stocks population dynamics, and calibrating the models using observed data. Some of my previous employers include Procter and Gamble, Australian National University and the Transport Accident Commission. I am passionate about good science and translating statistical concepts for non-experts. \

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