Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species

Ecological Informatics

Volumn 3, Issue 1, 2008, Pages 64-74

  Watts, Michael J ^a   Worner, S P ^b  

^a UNIVERSITY OF SYDNEY   (Australia)

^b LINCOLN UNIVERSITY   (New Zealand)

Author keywords

Artificial neural networks; Input contribution analysis; Knowledge discovery; Multi layer perceptrons

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLIMATE VARIATION; IDENTIFICATION METHOD; PEST SPECIES; PREDICTION;

HEXAPODA;

EID: 40949093593     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2007.06.004     Document Type: Article

References (56)

1. Andersen M.C. Potential applications of population viability analysis to risk assessment for invasive species. Human and Ecological Risk Assessment 11 (2005) 1083-1095
2. Andersen M.C., Adams H., Hope B., and Powell M. Risk assessment for invasive species. Risk Analysis 24 4 (2004) 787-793
3. Araujo M.B., and Pearson R.G. Equilibrium of species distributions with climate. Ecography 28 5 (2005) 693-695
4. Baker R., and MacCleod A. Pest risk assessments: tools, resources and key challenges. Invasive Alien Species and the IPPC Framework (2004) 106-132
5. Bobesberger E.J. Multivariate techniques for estimating the risk of plant pest establishment in new environments. NAPPA PRA Symposium, Mexico, March 2002 (2002)
6. Brosse S., Guegan J.-F., Tourenq J.-N., and Lek S. The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling 120 (1999) 299-311
7. Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1960) 3746
8. Crick F. The recent excitement about neural networks. Nature 337 (1989) 129-132
9. Crop Protection Compendium- Global Module, 5th Edition. c CAB International, Wallingford, UK 2003.
10. Dentener P.R., Whiting D.C., and Connolly P.G. Thrips palmi karny (Thysanoptera: Thripidae): could it survive in New Zealand?. New Zealand Plant Protection 55 (2002) 18-24
11. Dimopulos 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). Ecological Modelling 120 (1999) 157-165
12. Eyre M.D., Rushton S.P., Luff M.L., and Telfer M.G. Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude. Journal of Biogeography 32 (2005) 973-983
13. Flexer A. Statistical evaluation of neural network experiments: minimum requirements and current practice. In: Trappl R. (Ed). Cybernetics and Systems '96, Proceedings of the 13th European Meeting on Cybernetics and Systems Research. Austrian Society for Cybernetic Studies (1996) 1005-1008
14. Garson G.D. Interpreting neural-network connection weights. AI Expert 6 7 (1991) 47-51
15. Gevrey M., and Worner S.P. Prediction of global distribution of insect pest species in relation to climate using an ecological informatics method. Environmental Entomology 99 3 (2006) 979-986
16. Gevrey M., Dimopoulos I., and Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160 (2003) 249-264
17. Gotelli N.J. Null model analysis of species co-occurrence patterns. Ecology 81 9 (2000) 2606-2621
18. Guisan A., and Zimmermann N.E. Predictive habitat distribution models in ecology. Ecological Modelling 135 (2000) 147-186
19. Guisan A., Edwards T.C., and Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157 (2002) 89-100
20. Haykin S. Neural Networks: A Comprehensive Foundation (1994), MacMillan Publishing Company
21. Ibarra A.A., Gevrey M., Park Y.-S., Lim P., and Lek S. Modelling the factors that influence fish guilds composition using a back-propagation network: assessment of metrics for indices of biotic integrity. Ecological Modelling 160 (2003) 281-290
22. James F.C., and McCulloch C.E. Multivariate analysis in ecology and systematics: panacea or Pandora's box?. Annual Review Ecological Systems 21 (1990) 129-166
23. Jeong K.-S., Kim D.-K., and Joo G.-J. River phytoplankton prediction model by artificial neural network: model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system. Ecological Informatics 1 (2006) 235-245
24. Joy M.K., and Death R.G. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biology 49 (2004) 1036-1052
25. Kasabov N.K. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (1996), MIT Press
26. Kimes D.S., Nelson R.F., and Fifer S.T. Predicting ecologically important vegetation variables from remotely sensed optical/radar data using neural networks. In: Lek S., and Guegan J.-F. (Eds). Artificial Neuronal Networks: Application to Ecology and Evolution (2000)
27. Laë R., Lek S., and Moreau J. Predicting fish yield of African lakes using neural networks. Ecological Modelling 120 (1999) 325-335
28. Lehmann A., Overton J., and Leathwick J.J. GRASP: generalized regression analysis and spatial prediction. Ecological Modelling 157 (2002) 189-207
29. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., and Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90 (1996) 39-52
30. Mael S., Dias J.-M., and Ormerod S.J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distribution: a case study with a Himalayan river bird. Ecological Modelling 120 (1999) 337-347
31. Mastrorillo S., Lek S., Dauba F., and Belaud A. The use of artificial neural networks to predict the presence of small-bodied fish in a river. Freshwater Biology 38 (1997) 237-246
32. Milne L.K. Feature selection with neural networks with contribution measures. Proceedings of the Australian Conference on Artificial Intelligence AI'95, Canberra (1995)
33. Olden J.D., and Jackson D.A. Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling 154 (2002) 135-150
34. 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. Ecological Modelling 178 (2004) 389-397
35. Park Y.-S., Céréghino R., Compin A., and Lek S. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling 160 (2003) 265-280
36. Peacock L., Worner S., and Sedcole R. Climate variables and their role in site discrimination of invasive insect species distributions. Environmental Entomology 35 4 (2006) 958-963
37. Peterson A.T., and Vieglas D.A. Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. Bioscience 51 5 (2001) 363-371
38. Prechelt L. A quantitative study of experimental evaluations of neural network learning algorithms: current research practice. Neural Networks 9 3 (1996) 457-462
39. Rafoss T. Spatial stochastic simulation offers potential as a quantitative method for pest risk analysis. Risk Analysis 23 4 (2003) 651-661
40. Recknagel F., Cao H., Kim B., Takamura N., and Welk A. Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation. Ecological Informatics 1 (2006) 133-151
41. Reed R.D., and Marks R.J. Neural smithing (1999), MIT Press
42. Rossi R.E., Borth P.W., and Tollefson J.J. Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecological Applications 3 4 (1993) 719-735
43. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back-propagating errors. Nature 323 (1986) 533-536
44. Soltic S., Pang S., Peacock L., and Worner S. Evolving computation offers potential for estimation of pest establishment. International Journal of Computers, Systems and Signals 5 2 (2004) 36-43
45. Soltic S., Pang S., Kasabov N., Worner S., and Peacock L. Dynamic neuro-fuzzy inference and statistical models for risk analysis of pest insect establishment. Lecture Notes in Computer Science 3316 (2004) 971-976 (January)
46. Stynes B. Pest risk analysis: methods and approaches (2002), NAPPO PRA Symposium, Puerto Vallarta, Mexcio March 2002
47. Sutherst R.W., Maywald G.F., and Skarratt B.D. Predicting insect distributions in a changed climate. In: Harrington R., and Stoke N.E. (Eds). Insects in a changing environment (1995), Academic, London
48. Sutherst R.W. Prediction of species geographical range. Journal of Biogeography 30 (2003) 805-816
49. Tarassenko L. A Guide to Neural Computing Applications (1998), Wiley, London
50. Wagner R., Dapper T., and Schmidt H.-H. The influence of environmental variables on the abundance of aquatic insects: a comparison of ordination and artificial neural networks. Hydrobiologica 422/423 (2000) 143-152
51. Wagner R., Obach M., Werner H., and Schmidt H.-H. Artificial neural nets and abundance prediction of aquatic insects in small streams. Ecological Informatics 1 (2006) 423-430
52. Watts M.J., and Worner S.P. Comparison of artificial neural networks models for predicting insect pest establishment. International Journal of Information Technology 12 6 (2006) 35-42
53. Worner S.P. Ecoclimatic assessment of potential establishment of exotic pests. Journal of Economic Entomology 81 (1988) 973-983
54. Worner S.P. Predicting the establishment of exotic pests in relation to climate. In: Sharp J.L., and Hallman G.J. (Eds). Quarantine Treatment for Pests of Food Plants (1994), Boulder Press, Westview, CO 11-31
55. Worner S.P., and Gevrey M. Modelling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology 43 (2006) 858-867
56. Zaniewski A.E., Lehmann A., and Overton J. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling 157 (2002) 261-280