Estimations of trout density and biomass: A neural networks approach

Nonlinear Analysis, Theory, Methods and Applications

Volumn 30, Issue 8, 1997, Pages 4985-4990

  Lek, Sovan ^a   Baran, Philippe ^b  

^a CNRS   (France)

^b UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

Biomass; Density; Ecology; Neural networks; Nonlinear modelling; Pyrenees; Trout

Indexed keywords

EID: 0042728108     PISSN: 0362546X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0362-546X(96)00291-X     Document Type: Article

References (23)

1. MACARTHUR R.H., REICHER H. & CODY M.L., On the relation between habitat selection and bird species diversity. Am. Nat. 100, 319-332 (1966).
2. FRETWELL S.D., Populations in a seasonal environment. Monogr. Popul. Biol. No 5. Princeton University Press, PRINCETON, N.J. 217p (1972).
3. CODY M.L., Competition and the structure of bird communities. Monogr. Popul Biol. No 7. Princeton University Press, PRINCETON, N.J. 318p (1974).
4. TILMAN D., Resource competition and community structure. Monogr Popul. Biol. No 17. Princeton University Press, PRINCETON, N.J. 296p (1982).
5. MACNALLY R.C., On assessing the significance of interspecific competition to guild structure. Ecology 64, 1646-1652 (1983).
6. SCHOENER T.W., Field experiments on interspecific competition. Am. Nat. 122, 240-285 (1983).
7. GATZ A.J., SALE M.J. & LOAR J.M., Habitat shifts in rainbow trout: competitive influences of brown trout. Oecologia 74, 7-19 (1987).
8. SMITH S.J., PERRY R.I. & FANNING L.P., Relationships between water mass characteristics and estimates of fish population abundance from trawl surveys. Environ. Monit. Assess. 17, 227-245 (1981).
9. FAUSCH, K.D., HAWKES C.L. & PARSONS M.G., Models that predict the standing crop of stream fish from habitat variables. U.S. Forest Service General Technical Report PNW-GTR. 213p (1988).
10. JAMES F.C. & MCCULLOCH C.E., Multivariate analysis in ecology and systematics: panacea or Pandora's box ? Annu. Rev. Ecol. Syst. 21, 129-166 (1990).
11. SMITS J.R.M., BREEDVELD L.W., DERKSEN M.W.J., KATERMAM G., BALFOORT H.W., SNOEK J. & HOFSTRAAT J. W., Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data. Analytica Chimica Acta 258, 11-25 (1992).
12. ALBIOL J., CAMPMAJO C., CASAS C. & POCH M., Biomass estimation in plant cell cultures: a neural network approach Biotechnol. Prog 11, 88-92 (1995).
13. LERNER B., GUTERMAN H., DINSTEIN I. & ROMEM Y., Feature selection and chromosome classification using a multilayer perceptron neural network. In: Proceedings of the I.E.E.E. International Conference on Neural Networks, I.E.E.E., Orlando, 3540-3545 (1994).
14. FARAGGI D. & SIMON R., A neural network model for survival data. Statistics in Medicine, 14, 73-82 (1995).
15. SEGINER I., BOULARD T. & BAILEY B. J., Neural network models of the greenhouse climate. J. Agric. Engng Res. 59, 203-216 (1994).
16. CASSELMAN F.L., FREEMAN D.F., KERRIGAN D.A., LANE S.C., MAGLEY D.M., MILLSTROM N.H & ROY C.R., A neural network-based underwater acoustic application. In: Proceedings of the I.E.E.E. International Conference on Neural Networks, I.E.E.E., Orlando, 3409-3414 (1994).
17. LEK S, DELACOSTE M., BARAN P., DIMOPOULOS I., LAUGA J., AULAGNIER S., Application of neural networks to modelling nonlinear relationships in ecology. Ecological modelling (in press, 1996).
18. BARAN P., LEK S., DELACOSTE M. & BELAUD A., Stochastic models that predict trouts population densities or biomass on macrohabitat scale. Hydrobiologia (in press, 1996).
19. LEK S, BELAUD A., BARAN P., DIMOPOULOS I, DELACOSTE M., Role of some environmental variables in trout abundance models using neural networks. Aquat. Living Resour., 9, 23-29 (1996).
20. MAIAVOI, J.R., Typologie des faciès d'écoulement ou unités morpho-dynamiques d'un cours d'eau à haute énergie. Bull. fr. Pêche Piscic. 315, 189-210 (1989).
21. DE LURY D.B., On the planning of experiments for the estimation of fish populations. J. Fish. Res. Bd Can. 18, 281-307 (1951).
22. RUMELHART D.E., HINTON G.E. & WILLIAMS R.J., Learning representations by back-propagating error. Nature 323, 533-536 (1986).
23. SMITH M., Neural networks for statistical modelling Van Nostrand Reinhold, New York, 235p (1994).