Sensitivity analysis and stability patterns of two-species pest models using artificial neural networks

Ecological Modelling

Volumn 204, Issue 3-4, 2007, Pages 427-438

  Park, Young Seuk ^a   Rabinovich, Jorge ^b   Lek, Sovan ^c  

^a Kyung Hee University   (South Korea)

^b CENTRO DE ESTUDIOS PARASITOLÓGICOS Y DE VECTORES CEPAVE   (Argentina)

^c CNRS   (France)

Author keywords

Host parasite dynamics; Nezara viridula; Pest insects; Pinus radiata; Sirex noctilio; Stability; Trichopoda giacomellii

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION; POPULATION DYNAMICS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS;

HOST-PARASITE DYNAMICS; NEZARA VIRIDULA; PEST INSECTS; PINUS RADIATA; SIREX NOCTILIO; TRICHOPODA GIACOMELLII;

ECOLOGY;

ARTIFICIAL NEURAL NETWORK; CONIFEROUS TREE; HOST-PARASITOID INTERACTION; NUMERICAL MODEL; PEST SPECIES; SENSITIVITY ANALYSIS; STABILITY ANALYSIS;

GLYCINE MAX; HEXAPODA; NEZARA VIRIDULA; PINUS RADIATA; SIREX NOCTILIO; TRICHOPODA GIACOMELLII;

EID: 34247481904     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2007.01.021     Document Type: Article

References (46)

1. Balls G.-R., Palmer Brown D., and Sanders G.-E. Investigating microclimatic influences on ozone injury in clover (Trifolium subterraneum) using artificial neural networks. New Phytol. 132 (1996) 271-280
2. Bellman R.E. Dynamic Programming (1957), Princeton University Press, Princeton
3. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees (1984), Wadsworth and Brooks/Cole Advanced Books and Software, Monterey, CA
4. Chan K., Saltelli A., and Tarantola S. Winding stairs: a sampling tool to compute sensitivity indices. Stat. Comput. 10 (2000) 187-196
5. Chesson P.L., and Rosenzweig M. Behaviour, heterogeneity, and the dynamics of interacting species. Ecology 72 (1991) 1187-1195
6. Chon T.-S., Park Y.-S., Kim J.-M., Lee B.-Y., Chung Y.-J., and Kim Y. Use of an artificial neural network to predict population dynamics of the forest-pest pine needle gall midge (Diptera: Cecidomyiida). Environ. Entomol. 29 (2000) 1208-1215
7. Cohen J. A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20 (1960) 37-46
8. De'ath G., and Fabricius K.E. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81 (2000) 3178-3192
9. Dimopoulos I., Chronopoulos J., Chronopoulou-Sereli A., and Lek S. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecol. Model. 120 (1999) 157-165
10. Dimopoulos Y., Bourret P., and Lek S. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Proces. Lett. 2 (1995) 1-4
11. Garson G.D. Interpreting neural network connection weights. Artif. Intel. Expert 6 (1991) 47-51
12. Gevrey M., Dimopoulos I., and Lek S. Review and comparison of methods to study the contribution of variables in Artificial Neural Network models. Ecol. Model. 160 (2003) 249-264
13. Goh A.T.C. Back-propagation neural networks for modelling complex systems. Artif. Intel. Eng. 9 (1995) 143-151
14. Hassell M.P. The Spatial and Temporal Dynamics of Host-Parasitoids Interactions (2000), Oxford University Press, Oxford
15. Hassell M.P., Pacala S.W., May R.M., and Chesson M.L. The persistence of host-parasitoid association in patchy environments. I. A general criterion. Am. Natur. 138 (1991) 568-583
16. Haykin S. Neural Networks (1994), Macmillian College Publishing Company, New York
17. Hilborn R., and Walters C.J. Quantitative Fisheries Stock Assessment. Choice, Dynamics and Uncertainty (1992), Chapman and Hall, London
18. Ipinza R., and Molina M.P. Control integrado de Sirex noctilio. Ciencia e Investigación Forestal 5 (1991) 96-149
19. Kot M. Elements of Mathematical Ecology (2001), Cambridge University Press, Cambridge
20. Kung S.Y. Digital Neural Networks (1993), Prentice Hall, Eaglewood Cliffs, New Jersey
21. Lek S., and Guégan J.F. Artificial neural networks as a tool in ecological modelling an introduction. Ecol. Model. 120 (1999) 65-73
22. In: Lek S., and Guégan J.F. (Eds). Artificial Neuronal Networks: Application to Ecology and Evolution (2000), Springer, Berlin
23. Lek S., Belaud A., Dimopoulos I., Lauga J., and Moreau J. Improved estimation, using neural networks, of the food consumption of fish populations. Marine Freshwater Res. 46 (1995) 1229-1236
24. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., and Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90 (1996) 39-52
25. Liljesthröm G. Distribución de los ataques del parasitoide Trichopoda giacomellii (Diptera: Tachinidae) sobre una población de Nezara viridula (Hemiptera: Pentatomidae). Ecol. Austral 2 (1992) 29-37
26. Liljesthröm G., and Rabinovich J.E. Modelling biological control: the population regulation of Nezara viridula by the parasitoid Trichopoda giacomellii. Ecol. Appl. 14 (2004) 254-267
27. Liljesthröm G., and Bernstein C. Density dependence and regulation in the system Nezara viridula (L.) (Hemiptera: Pentatomidae), host and Trichopoda giacomelli (Blanchard) (Diptera: Tachinidae), parasitoid. Oecologia 84 (1990) 45-52
28. Liong S.Y., Lim W.H., and Paudyal G.N. River stage forecasting in Bangladesh: neural network approach. J. Comput. Civil Eng. 14 (2000) 1-8
29. Maier H.R., and Dandy G.C. The use of artificial neural networks for the prediction of water quality parameters. Water Resour. Res. 32 (1996) 1013-1022
30. May R.M. Host-parasitoid systems in patchy environments: a phenomenological model. J. Anim. Ecol. 47 (1978) 833-843
31. McCallum H. Population Parameters. Estimation for Ecological Models (2000), Blackwell Science, Oxford
32. Olden J.D. A species-specific approach to modelling biological communities and its potential for conservation. Conserv. Biol. 17 (2003) 854-863
33. Olden J.D., Joy M.K., and Death R.G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model. 178 (2004) 389-397
34. Olden J.D., and Jackson D.A. Illuminating the "black box": understanding variable contributions in artificial neural networks. Ecol. Model. 154 (2002) 135-150
35. Özesmi S., and Özesmi U. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecol. Model. 116 (1999) 15-31
36. Paruelo J.M., and Tomasel F. Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecol. Model. 98 (1997) 173-186
37. Recknagel F. Ecological Informatics: Understanding Ecology by Biologically-Inspired Computation (2003), Springer, Berlin
38. Recknagel F., French M., Harkonen P., and Yabunaka K.-L. Artificial neural network approach for modelling and prediction of algal blooms. Ecol. Model. 96 (1997) 11-28
39. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McCelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. I: Foundations (1986), MIT Press, Cambridge 318-362
40. Saltelli A., Tarantola S., and Chan K.P.S. A quantitative model independent method for global sensitivity analysis of model output. Technometrics 41 1 (1999) 39-56
41. Scardi M., and Harding L.W. Developing an empirical model of phytoplankton primary production: a neural network case study. Ecol. Model. 120 (1999) 213-223
42. Skelton P.H., Cambray J.A., Lombard A., and Benn G.A. Patterns of distribution and conservation status of freshwater fishes in South Africa. S. Afr. J. Zool. 30 (1995) 71-81
43. StatSoft, Inc. Statistica for Windows (Computer Program Manual) (1999), Statsoft Inc., Tulsa
44. Sung A.H. Ranking importance of input parameters of neural networks. Expert Syst. Appl. 15 (1998) 405-411
45. Tuma A., Haasis H.D., and Rentz O. A comparison of fuzzy expert systems, neural networks and neuro-fuzzy approaches controlling energy and material flows. Ecol. Model. 85 (1996) 93-98
46. Zurada J.M., Malinowski A., and Cloete L. Sensitivity analysis for minimization input data dimension for feedforward neural network. Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 6. London, May 28-June 2 (1994), IEEE Press 447-450