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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. https://doi.org/10.1016/j.ecoinf.2014.11.004

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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. https://doi.org/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. https://doi.org/10.1016/j.ecoinf.2024.102708

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. https://doi.org/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. https://doi.org/10.1016/j.ecoinf.2024.102537