A data-oriented predictive ecology approach to modelling fish communities

Some spectacular collapses in fish stocks have occurred in the past 20 years but the most notable is the once largest cod stock in the world, the Northern cod stock off eastern Newfoundland, which experienced a 99% decline in biomass.

Cod unfortunately, is not alone and there are stocks of various species that have been reduced to only a small percentage of stock sizes in recent history. Much of this effect is due to direct mortality on fish through fishing and subsequent indirect effects and weak linkages to other species. Some of these regions may have moved to an 'alternative stable state' or experienced a 'regime shift' and are unlikely to return to a cod dominated community for many years (possibly decades) without some chance environmental event beyond human control.

We focus on using state-of-the-art computational techniques based upon Dynamic Bayesian Networks with latent variables to both integrate human expertise with extensive empirical data and model unmeasured factors to predict collapses in different fish communities. What is more we exploit functional equivalence between different communities to identify equivalent species in different regions and therefore predict functional collapse.

Publications

Trifonova, N. , Kenny, A. , Maxwell, D. , Duplisea, D. , et al. (2015) 'Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology'. Ecological Informatics, 30 pp. 142 - 158. doi: 10.1016/j.ecoinf.2015.10.003

Trifonova, N. , Duplisea, D. , Kenny, A. and Tucker, A. (2014) 'A Spatio-Temporal Bayesian Network Approach for Revealing Functional Ecological Networks in Fisheries' , Symposium on Intelligent Data Analysis. Brussels. Springer Verlag

Trifonova, N. , Duplisea, D. , Kenny, A. , Maxwell, D. and Tucker, A. (2014) 'Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes in Fisheries Ecology' , Discovery Science. Bled. Springer Verlag

Meet the Principal Investigator(s) for the project

Dr Allan Tucker - Allan Tucker is Professor of Artificial Intelligence in the Department of Computer Science where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analytics for 21 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He was academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS, and another on detecting significant changes in Adaptive AI Models of Healthcare (£195k). He is currently academic lead on two Pioneer Funds on Explainability of AI (£168k) and In-Silico Trials (£750k). He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics. He hosted a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019 and was general chair for AI in Medicine 2021. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.

Related Research Group(s)

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.

Project last modified 21/11/2023