Neural networks applications in Ecology and Oceanography: my (incomplete) reference list.

Adams, S.M., J.S. Jaworska, and K.D. Ham, Influence of ecological factors on the relationship between MFO induction and fish growth: bridging the gap using neural networks. Marine Environmental Research, 1996. 42(1-4): p. 197-201.

Aoki I., T. Komatsu, K. Hwang, Prediction of response of zooplankton biomass to climatic and oceanic changes, Ecological Modelling 120 (2-3) (1999) pp. 261-270.

Aoki, I. and T. Komatsu, Analysis and prediction of the fluctuation of the sardine abundance using a neural network. Oceanologica Acta, 1997. 20(1): p. 81-88.

Aurelle D., S. Lek, J. Giraudel, P. Berrebi, Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations, Ecological Modelling 120 (2-3) (1999) pp. 313-324.

Aussem A., D. Hill, Wedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean sea, Ecological Modelling 120 (2-3) (1999) pp. 225-236.

Baran, P., et al., Stochastic models that predict trout population density or biomass on a mesohabitat scale. Hydrobiologia, 1996. 337(1-3): p. 1-9.

Barciela R.M., E. Garcia, E. Fernandez, Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks, Ecological Modelling 120 (2-3) (1999) pp. 199-211.

Brey, T., A. Jarre-Teichmann, and O. Borlich, Artificial neural network versus multiple linear regression: predicting P/B ratios from empirical data. Marine Ecology Progress Series, 1996. 140: p. 251-256.

Brosse S., J. Guegan, J. Tourenq, S. Lek, The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake, Ecological Modelling 120 (2-3) (1999) pp. 299-311.

Brown M., S.R. Gunn, H.G. Lewis, Support vector machines for optimal classification and spectral unmixing, Ecological Modelling 120 (2-3) (1999) pp. 167-179.

Chon, T.S., et al., Patternizing communities by using an artificial neural network. Ecological Modelling, 1996. 90(1): p. 69-78.

Conversano, F., et al., Analisi preliminare dei dati di ossigeno disciolto raccolti durante la campagna LIWEX 95. Atti XII Congresso A.I.O.L., 1998, vol. 2: 359-368,

Cornuet, J.M., et al., Classifying individuals among infra-specific taxa microsatellite data and neural networks. Comptes Rendus de L'Academie des Sciences Serie III - Sciences de la Vie, 1996. 319(12): p. 1167-1177.

Dimopoulos I., J. Chronopoulos, A. Chronopoulou-Sereli, S. Lek, Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece), Ecological Modelling 120 (2-3) (1999) pp. 157-165.

Dreyfus-Leon M.J., Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning, Ecological Modelling 120 (2-3) (1999) pp. 287-297.

Foody G.M., Applications of the self-organising feature map neural network in community data analysis, Ecological Modelling 120 (2-3) (1999) pp. 97-107.

French, M. and F. Recknagel, Modeling algal blooms in freshwaters using artificial neural networks, in Computer Techniques in Environmental Studies. Vol. 2. Environmental Systems, P.e. Zannetti, Editor. 1994, Computational Mechanics Publications: Southampton, Boston. p. 87-94.

Gross L., S. Thiria, R. Frouin, Applying artificial neural network methodology to ocean color remote sensing, Ecological Modelling 120 (2-3) (1999) pp. 237-246.

Haralabous, J. and S. Georgakarakos, Artificial Neural networks as a tool for species identification of fish schools. ICES Journal of Marine Science, 1996. 53: p. 173-180.

Komatsu, T., et al., Prediction of the catch of Japanese sardine larvae in Sagami Bay using a neural network. Fisheries Science, 1994. 60: p. 385-391.

Komatsu, T., et al., Prediction of the path type and offshore distance of the Kuroshio current using neural network. Fisheries Science, 1994. 60: p. 253-260.

Lae R., S. Lek, J. Moreau, Predicting fish yield of African lakes using neural networks, Ecological Modelling 120 (2-3) (1999) pp. 325-335.

Lek S., J.F. Guegan, Artificial neural networks as a tool in ecological modelling, an introduction, Ecological Modelling 120 (2-3) (1999) pp. 65-73.

Lek, S., et al., Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 1996. 90(1): p. 39-52.

Lek, S., et al., Improved estimation, using neural networks, of the food consumption of fish populations. Marine and Freshwater Research, 1995. 46: p. 1229-1236.

Lek, S., et al., Role of some environmental variables in trout abundance models using neural networks. Aquatic Living Resources, 1996. 9: p. 23-29.

Lek, S., I. Dimopoulos, and A. Fabre, Predicting phosphorus concentration and phosphorus load from watershed characteristics using backpropagation neural networks. Acta Oecologica, 1996. 17(1): p. 43-53.

Lek-Ang S., L. Deharveng, S. Lek, Predictive models for collembolan diversity and abundance in a riparian habitat, Ecological Modelling 120 (2-3) (1999) pp. 247-260.

Manel S., J. Dias, S.J. Ormerod, Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird, Ecological Modelling 120 (2-3) (1999) pp. 337-347.

Masson M.H., S. Canu, Y. Grandvalet, A. Lynggaard-Jensen, Software sensor design based on empirical data, Ecological Modelling 120 (2-3) (1999) pp. 131-139.

Mastrorillo, S., S. Lek, and F. Dauba, Predicting the abundance of minnow Phoxinus phoxinus (Cyprinidae) in the river Ariege (France) using artificial neural networks. Aquatic Living Resources, 1997. 10(3): p. 169-176.

Moatar F., F. Fessant, A. Poirel, pH modelling by neural networks. Application of control and validation data series in the Middle Loire river, Ecological Modelling 120 (2-3) (1999) pp. 141-156.

Morlini I., Radial basis function networks with partially classified data, Ecological Modelling 120 (2-3) (1999) pp. 109-118.

Nakano, H., et al., Identification of plankton using a neural network with a function of unknown species detection. Rep. Measurement Res. Group, 1991. IM-91-30: p. 47-56.

Recknagel, F., ANNA - Artificial Neural Network model predicting blooms and succession of blue-green Algae. Hydrobiologia, 1997. 349: p. 47-57.

Recknagel, F., et al., Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling, 1996. 96(1-3): p. 11-28.

Recknagel, F., et al., Modelling and prediction of phyto- and zooplankton dynamics in Lake Kasumigaura by artificial neural networks. Lakes and Reservoirs, 1997. in press.

Scardi M., L.W. Jr. Harding, Developing an empirical model of phytoplankton primary production: a neural network case study, Ecological Modelling 120 (2-3) (1999) pp. 213-223.

Scardi, M., Artificial neural networks as empirical models of phytoplankton production. Marine Ecology Progress Series, 1996. 139: p. 289-299.

Scardi, M., Neuronal network models of phytoplankton primary production. In: Lek S, Guegan J-F [eds.], Artificial Neuronal Networks: Application to Ecology and Evolution, Springer-Verlag, Berlin/Heidelberg, 2000: p. 115-129.

Schleiter I.M., D. Borchardt, R. Wagner, T. Dapper, K. Schmidt, H. Schmidt, H. Werner, Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks, Ecological Modelling 120 (2-3) (1999) pp. 271-286.

Tourenq C., S. Aulagnier, F. Mesleard, L. Durieux, A. Johnson, G. Gonzalez, S. Lek, Use of artificial neural networks for predicting rice crop damage by greater flamingos in the Camargue, France, Ecological Modelling 120 (2-3) (1999) pp. 349-358.

van Wijk M.T., W. Bouten, Water and carbon fluxes above European coniferous forests modelled with artificial neural networks, Ecological Modelling 120 (2-3) (1999) pp. 181-197.

Vila J., V. Wagner, P. Neveu, M. Voltz, P. Lagacherie, Neural network architecture selection: new Bayesian perspectives in predictive modelling Application to a soil hydrology problem, Ecological Modelling 120 (2-3) (1999) pp. 119-130.