Macro-invertebrates in a dynamic river environment: Analysis of time series from artificial substrates, using a 'white box' neural network modelling method

Aquatic Ecology

Volumn 41, Issue 3, 2007, Pages 413-425

  Jaarsma, N G ^a   Bergman, M ^a,b   Schulze, F H ^a   Vaate, A Bij De ^c  

^a WITTEVEEN BOS   (Netherlands)

^b Uni Network Consultancy BV (UNC)   (Netherlands)

^c Ministry of Transport Public Works and Water Management Netherlands   (Netherlands)

Author keywords

Corophium curvispinum; Dikerogammarus villosis; Invasive taxa; Peak discharge; Product unit networks; RF5 algorithm; Rhine

Indexed keywords

ABUNDANCE; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL INVASION; DATA SET; ENVIRONMENTAL MODELING; MACROINVERTEBRATE; RIVER; SPECIES DIVERSITY; TIME SERIES;

EURASIA; EUROPE; MEUSE RIVER; RHINE RIVER; WESTERN EUROPE;

COROPHIUM CURVISPINUM; CRUSTACEA; DIKEROGAMMARUS; INVERTEBRATA;

EID: 36148989529     PISSN: 13862588     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10452-005-9016-0     Document Type: Article

References (34)

1. Bij de Vaate A. 2003. Degradation and recovery of the freshwater fauna in the lower sections of the rivers Rhine and Meuse. Thesis, Wageningen University. Wageningen, The Netherlands
2. Bij de Vaate A, Jazdzewski K, Ketelaars HAM, Gollasch S, van der Velde G (2002) Geographical patterns in range extension of Ponto-Caspian macro-invertebrate species in Europe. Canadian Journal of Fisheries and Aquatic Sciences 59: 1159-1174
3. Brosse S, Giraudel JL, Lek S (2001) Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modelling 146: 159-166
4. Brosse S, Guégan JF, Tourenq JN, Lek S (1999) The use of artificial neural network to assess fish community structure in a mesotrophic lake. Ecological Modelling 120: 299-311
5. Cereghino R, Giraudel J L, Compin A (2001) Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps. Ecological Modelling 146: 167-180
6. Dick JTA, Platvoet D (2000) Invading predatory crustacean Dikerogammarus villosis eliminates both native and exotic species. Proceedings of the Royal Society of London B 267: 977-983
7. Directive 2000/60/EC. Official Journal of the European Communities, L327. (22 December 2000, pp. 1-72).
8. Fausch KD, Taniguchi Y, Nakano S, Grossman GD, Townsend CR (2001) Flood disturbance regimes influence rainbow trout invasion success among five Holarctic regions. Ecological Applications 11: 1438-1455
9. Gevrey M, Dimopoulos L, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160(3): 249-264
10. Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecological Modelling 146(1-3): 329-339
11. Guégan JF, Lek S, Oberdorff T (1998) Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature 391: 382-384
12. Ibarra A, Gevrey M, Park Y-S, Lin P, Lek S (2003) Modelling the composition of fish guilds in the Garonne basin (France) with a backpropagation network: an aid to decision-making. Ecological Modelling 160(3): 281-290
13. Jaarsma NG, De Boer SM, Townsend CR, Thompson RM, Edwards ED (1998) Characterising food webs in two New Zealand streams. New Zealand Journal of Marine and Freshwater Research 32: 271-286
14. Lancaster J (1996) Scaling the effects of predation and disturbance in a patchy environment. Oecologia 107: 321-331
15. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90: 39-52
16. Moody J. and Utans J. 1992. Principled architecture selection for neural networks: Application to corporate bond rating prediction. In: Moody J.E., Hanson S.J. and Lippmann R.P. (eds), Advances in Neural Information Processing Systems, (4), pp. 683-690
17. Mulholland PJ, Steinman AD, Palumbo AV, Flum T, DeAngelis DL (1991) Influence of nutrients and grazing on the response of stream periphyton communities to a scour disturbance. J. N. Am. Benthol. Soc. 10: 127-142
18. Olden JD, Jackson DA (2001) Fish-habitat relationships in lakes: Gaining predictive and explanatory insight using artificial neural networks. Transactions of the American Fisheries Society 130: 878-897
19. Olden JD, Jackson DA (2002) Illuminating the "black box": Understanding variable contributions in artificial neural networks. Ecological Modelling 154: 135-150
20. Oost E.M., Ten Hagen S. and Schulze F.H. (2002). Extracting multivariate power functions from complex data sets. In: Proceedings of the Belgian-Dutch AI Conference (BNAIC-02)
21. Park Y-S, Céréghino R, Compin A, Lek S (2003a) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling 160(3): 265-280
22. Park Y-S, Verdonschot PFM, Chon T-S, Lek S (2003b) Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. Water Research 37(8): 1749-1758
23. Reyjol Y, Lim P, Belaud P, Lek S (2001) Modelling of microhabitat used by fishes in natural and regulated flows in the Garonne river (France). Ecological Modelling 146(1-3): 131-142
24. th Int. Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1078-1083, San Francisco
25. th Int. Conference on Neural Information Processing (ICONIP-97), pp. 843-846
26. Ter Braak CJF, Smilauer P (1998) CANOCO Reference Manual and User's Guide to Canoco for Windows (version 4). Centre for Biometry, Wageningen The Netherlands
27. Ter Braak CJF, Smilauer P (1999) CANOCO for Windows (version 4.02) - a FORTRAN program for canonical community ordination. Centre for Biometry, Wageningen The Netherlands
28. Townsend CR, Riley R (1999) Assessment of river health: accounting for perturbation pathways in physical and ecological space. Freshwater Biology 41: 393-405
29. Townsend CR, Scarsbrook MR, Dolédec S (1997) The intermediate disturbance hypothesis, refugia and biodiversity in streams. Limnology and Oceanography 42: 938-949
30. Townsend CR, Thompson RM, McIntosh AR, Kilroy C, Edwards E, Scarsbrook MR (1998) Disturbance, resource supply and food-web architecture in streams. Ecology Letters 1: 200-209
31. Van den Brink FWB, van der Velde G, Bij de Vaate A (1993) Ecological aspects, explosive range extension and impact of a mass invader, Corophium curvispinum Sars, 1895 (Crustacea, Amphipoda), in the Lower Rhine (The Netherlands). Oecologia 93: 224-232
32. Van der Velde G, Paffen BGP, Van den Brink FWB, Bij de Vaate A, Jenner HA (1994) Decline of zebra mussel populations in the Rhine: Comparison between two mass invaders "(Dreissena polymorpha and Corophium curvispinum)". Naturwissenschaften 81(1): 32-34
33. Van Tongeren O (1986) "FLEXCLUS, an interactive flexible cluster program". Acta bot. Neerl. 35: 137-142
34. Wootton JT, Parker MS, Power ME (1996) The effect of disturbance on river food webs. Science 273: 1558-1560