Ecological Informatics

About Ecological Informatics

The application of computational sciences to environmental data offers huge benefits to our understanding of the natural world. Ecological Informatics is primarily focussed on and around the theme of ecology and the environment and involves the exploration of ecosystems based upon the availability of data that is increasingly common (from field-surveys to sensors and webcams). Data science and machine learning can enable the prediction of changes taking place within the natural environment, allowing for better understanding of the underlying processes (from food-webs to factors driving climate change) and therefore the development of early warning signals.

Research Papers

• Uusitalo, L., Tomczak, MT., Müller-Karulis, B., Putnis, I., Trifonova, N. and Tucker, A. (2018) 'Hidden variables in a Dynamic Bayesian Network identify ecosystem level change'. Ecological Informatics, 45. pp. 9 - 15. ISSN: 1574-9541
• Gredin, V., Bishop, D., Broadbent, D., Tucker, A. and Williams, A. (2018) 'Experts Integrate Explicit Contextual Priors and Environmental Information to Improve Anticipation Efficiency'. Journal of Experimental Psychology: Applied, 24 (4). pp. 509 - 520. ISSN: 1939-2192
• Vitolo, C., Scutari, M., Ghalaieny, M., Tucker, A. and Russell, A. (2018) 'Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions'. Earth and Space Science, 5 (4). pp. 76 - 88. ISSN: 2333-5084
• Curtis, TY., Bo, V., Tucker, A. and Halford, NG. (2018) 'Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress'. Food and Energy Security, 7 (1). pp. e00126 - e00126. ISSN: 2048-3694
• Nicolson, N., Challis, K., Tucker, A. and Knapp, S. (2017) 'Erratum to: Impact of e-publication changes in the International Code of Nomenclature for algae, fungi and plants (Melbourne Code, 2012) - did we need to “run for our lives”?'. BMC Evolutionary Biology, 17 (1). pp. 156. ISSN: 1471-2148
• Tucker, A., Trifonova, N., Maxwell, D., Pinnegar, J. and Kenny, A. (2017) 'Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model'. ICES Journal of Marine Science, 73 (10). pp. 1334 - 1343. ISSN: 1054-3139
• Vitolo, C., Russell, A. and Tucker, A. (2016) 'rdefra: Interact with the UK AIR Pollution Database from DEFRA'. The Journal of Open Source Software, 1 (4). pp. 51 - 51.
• Franco, C., Hepburn, L., Smith, D., Nimrod, S. and Tucker, A. (2016) 'A Bayesian Belief Network to assess rate of changes in coral reef ecosystems'. Environmental Modelling and Software, 80. pp. 132 - 142. ISSN: 1364-8152
• Trifonova, N., Kenny, A., Maxwell, D., Duplisea, D., Fernandes, J. and Tucker, A. (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. ISSN: 1574-9541
• Kirkup, D. and Tucker, A. (2014) 'Extracting Predictive Models from Marked-Up Free-Text Documents at The Royal Botanic Gardens, Kew, London'.Symposium on Intelligent Data Analysis. Brussels. 1 - 1 November. Lecture Notes in Computer Science. pp. 309 - 320. ISSN: 1611-3349
• 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. 1 - 1 November. Springer Verlag. pp. 298 - 308. ISSN: 0302-9743
• 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. 6 - 6 October. Lecture Notes in Computer Science. pp. 301 - 312. ISSN: 1611-3349
• Bo, V., Lysenko, A., Saqi, M., Habash, D. and Tucker, A. (2013) 'Integrating multiple studies of wheat microarray data to identify treatment-specific regulatory networks'. Springer Berlin Heidelberg. pp. 104 - 115. ISSN: 0302-9743
• Westgarth-Smith, A., Roy, DB., Scholze, M., Tucker, A. and Sumpter, JP. (2012) 'The role of the North Atlantic Oscillation in controlling U.K. butterfly population size and phenology'. Ecological Entomology, 37 (3). pp. 221 - 232. ISSN: 0307-6946
• Tucker, A. and Duplisea, D. (2012) 'Bioinformatics tools in predictive ecology: Applications to fisheries'. Philosophical Transactions of the Royal Society: Part B, 367 (1586). pp. 279 - 290. ISSN: 0962-8436
• Tucker, A. and Duplisea, D. (2011) 'Integrating marine species biomass data by modelling functional knowledge'.Symposium on Intelligent Data Analysis 2011. Porto, Portugal. 29 - 31 October. Springer. pp. 352 - 363. ISSN: 0302-9743
• Tucker, A., Swift, S., Counsell, S., Kent, S., Dickie, J., Liu, K. and et al. (2010) 'Data mining the millennium seedbank at Kew'.Workshop on Data Mining in Agriculture (DMA 2010) at the Industrial Conference on Data Mining (ICDM). Berlin/Germany. 14 IBaI Publishing. pp. 85 - 94.