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Fernandes, JA, Irigoien, X, Lozano, JA, Inza, I, Goikoetxea, N and Pérez, A 2015 Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics, 25. 35-42. 10.1016/j.ecoinf.2014.11.004

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James, MK, Lessin, G, Al Azhar, M, Bedington, M, Clubley, CH, Somerfield, PJ and Knights, AM 2025 The ‘everything is everywhere’ framework: Holistic network analysis as a marine spatial management tool. Ecological Informatics, 87. 103105. 10.1016/j.ecoinf.2025.103105

Kroonenberg, PM, Widdicombe, S and Somerfield, PJ 2024 The relative impact of co-occurring stressors on the abundance of benthic species examined with three-way correspondence analysis. Ecological Informatics, 80. 102483. 10.1016/j.ecoinf.2024.102483

Mata, A, Moffat, D, Almeida, S, Radeta, M, Jay, W, Mortimer, N, Awty-Carroll, K, Thomas, OR, Brotas, V and Groom, SB 2024 Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas. Ecological Informatics, 82. 1-12. 10.1016/j.ecoinf.2024.102708

Trifonova, N, Wihsgott, JU and Scott, BE 2025 Propagating uncertainty from physical and biogeochemical drivers through to top predators in dynamic Bayesian ecosystem models improves predictions. Ecological Informatics. 103510. 10.1016/j.ecoinf.2025.103510 (In Press)

Trifonova, N, Kenny, A, Maxwell, D, Duplisea, D, Fernandes, JA 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. 142-158. 10.1016/j.ecoinf.2015.10.003

Wilson, RJ, Kay, S and Ciavatta, S 2024 Partitioning climate uncertainty in ecological projections: Pacific oysters in a hotter Europe. Ecological Informatics, 80. 102537. 10.1016/j.ecoinf.2024.102537