Machine learning methods without tears: A primer for ecologists

Quarterly Review of Biology

Volumn 83, Issue 2, 2008, Pages 171-193

  Olden, Julian D ^a   Lawler, Joshua J ^a   Poff, N Leroy ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Artificial neural networks; Classification and regression trees; Ecological informatics; Evolutionary algorithms; GARP; Genetic algorithms; Inductive modeling

Indexed keywords

FRESH WATER;

ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGY; CLASSIFICATION; COMPUTER PROGRAM; DECISION TREE; ECOLOGY; ECOSYSTEM; EVOLUTION; FISH; REVIEW; SPECIES DIFFERENCE; STATISTICS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DECISION TREES; ECOLOGY; ECOSYSTEM; EVOLUTION; FISHES; FRESH WATER; MODELS, GENETIC; NEURAL NETWORKS (COMPUTER); SOFTWARE; SPECIES SPECIFICITY;

EID: 49249102441     PISSN: 00335770     EISSN: None     Source Type: Journal    
DOI: 10.1086/587826     Document Type: Review

References (93)

1. Anderson R. P., Martínez-Meyer E. 2004. Modeling species' geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biological Conservation 116(2):167-179.
2. Anderson M. C., Watts J. M., Freilich J. E., Yool S. R., Wakefield G. I., McCauley J. F., Fahnestock P. B. 2000. Regression-tree modeling of desert tortoise habitat in the central Mojave desert. Ecological Applications 10(3):890-900.
3. Austin M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 200(1-2):1-19.
4. Bell J. F. 1999. Tree-based methods. Pages 89-105 in Machine Learning Methods for Ecological Applications, edited by A. H. Fielding. Boston (MA): Kluwer Academic.
5. Bishop C. M. 1995. Neural Networks for Pattern Recognition. Oxford (UK): Clarendon Press.
6. Breiman L., Friedman J., Olshen R. A., Stone C. J. 1984. Classification and Regression Trees. Belmont (CA): Wadsworth International Group.
7. Brosse S., Lek S., Townsend C. R. 2001. Abundance, diversity, and structure of freshwater invertebrates and fish communities: an artificial neural network approach. New Zealand Journal of Marine and Freshwater Research 35(1):135-145.
8. Chen D. G., Hargreaves N. B., Ware D. M., Liu Y. 2000. A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships. Canadian Journal of Fisheries and Aquatic Science 57(9):1878-1887.
9. Cho E., Chon T.-S. 2006. Application of wavelet analysis to ecological data. Ecological Informatics 1(3):229-233.
10. Clark J. S., Carpenter S. R., Barber M., Collins S., Dobson A., Foley J. A., Lodge D. M., Pascual M., Pielke R., Jr., Pizer W., Pringle C., Reid W. V., Rose K. A., Sala O., Schlesinger W. H., Wall D. H., Wear D. 2001. Ecological forecasts: an emerging imperative. Science 293:657-660.
11. Cornuet J. M., Aulagnier S., Lek S., Franck P., Solignac M. 1996. Classifying individuals among infraspecific taxa using microsatellite data and neural networks. Comptes rendus de l'Académie des sciences, Série III, Sciences de la vie 319(12):1167-1177.
12. Cushing J. B., Wilson T. 2005. Eco-informatics for decision makers advancing a research agenda. Pages 325-334 in Data Integration in the Life Sciences: Second International Workshop, DILS 2005, San Diego, CA, USA, July 20-22, 2005, Proceedings, Lecture Notes in Computer Science, Volume 3615, edited by B. Ludäscher and L. Raschid. Berlin (Germany): Springer-Verlag.
13. Cutler D. R., Edwards T. C., Jr., Beard K. H., Cutler A., Hess K. T., Gibson J., Lawler J. J. 2007. Random forests for prediction in ecology. Ecology 88(11):2783-2792.
14. D'Angelo D. J., Howard L. M., Meyer J. L., Gregory S. V., Ashkenas L. R. 1995. Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats. Canadian Journal of Fisheries and Aquatic Sciences 52:1893-1908.
15. De'ath G. 2007. Boosted trees for ecological modeling and prediction. Ecology 88(1):243-251.
16. De'ath G., Fabricius K. E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11):3178-3192.
17. Dimopoulos Y., Bourret P., Lek S. 1995. Use of some sensitivity criteria for choosing networks with good generalization. Neural Processing Letters 2:1-4.
18. Drake J. M., Lodge D. M. 2006. Forecasting potential distributions of nonindigenous species with a genetic algorithm. Fisheries 31:9-16.
19. Drake J. M., Randin C., Guisan A. 2006. Modelling ecological niches with support vector machines. Journal of Applied Ecology 43(3):424-432.
20. Elith J., Graham C. H., Anderson R. P., Dudík M., Ferrier S., Guisan A., Hijmans R. J., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29(2):129-151.
21. Elith J., Leathwick J. 2007. Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions 13(3):265-275.
22. Ferrier S., Guisan A. 2006. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology 43(3):393-404.
23. Fielding A. H., editor. 1999. Machine Learning Methods for Ecological Applications. Boston (MA): Kluwer Academic Publishers.
24. Fielding A. H., Bell J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(1):38-49.
25. Garson G. D. 1991. Interpreting neural-network connection weights. Artificial Intelligence Expert 6(4):46-51.
26. Geman S., Bienenstock E., Doursat R. 1992. Neural networks and the bias/variance dilemma. Neural Computation 4(1):1-58.
27. Gevrey M., Dimopoulos I., 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.
28. Goldberg D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading (MA): Addison-Wesley.
29. Green J. L., Hastings A., Arzberger P., Ayala F. J., Cottingham K. L., Cuddington K., Davis F., Dunne J. A., Fortin M.-J., Gerber L., Neubert M. 2005. Complexity in ecology and conservation: mathematical, statistical, and computational challenges. BioScience 55(6):501-510.
30. Guégan J.-F., Lek S., Oberdorff T. 1998. Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature 391:382-384.
31. Guisan A., Zimmermann N. E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135(2-3):147-186.
32. Haefner J. W. 2005. Modeling Biological Systems: Principles and Applications. Second Edition. New York: Springer.
33. Hastie T., Tibshirani R., Friedman J. H. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.
34. Hogeweg P. 1988. Cellular automata as a paradigm for ecological modeling. Applied Mathematics and Computation 27(1):81-100.
35. Holland J. H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor (MI): University of Michigan Press.
36. Hopfield J. J. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences USA 79(8):2554-2558.
37. Hornik K., Stinchcombe M., White H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359-366.
38. Iverson L. R., Prasad A. M. 1998. Predicting abundance of 80 tree species following climate change in the Eastern United States. Ecological Monographs 68(4):465-485.
39. Iverson L. R., Prasad A. M. 2007. Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra. Landscape Ecology 22(3):323-331.
40. Kohonen T. 2001. Self-Organizing Maps. Berlin (Germany) and New York: Springer-Verlag.
41. Koza J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge (MA): MIT Press.
42. Lamon E. C., III, Stow C. A. 1999. Sources of variability in microcontaminant data for Lake Michigan salmonids: statistical models and implications for trend detection. Canadian Journal of Fisheries and Aquatic Sciences 56(Supplement):71-85.
43. Lawler J. J., White D., Neilson R. P., Blaustein A. R. 2006. Predicting climate-induced range shifts: model differences and model reliability. Global Change Biology 12(8):1568-1584.
44. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S. 1996. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90(1):39-52.
45. Lek S., Guégan J.-F. 2000. Artificial Neuronal Networks: Application to Ecology and Evolution. Berlin (Germany): Springer-Verlag.
46. Levin S. A. 1998. Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1(5):431-436.
47. Maier H. R., Dandy G. C. 2000. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications. Environmental Modelling and Software 15(1):101-124.
48. Mastrorillo S., Lek S., Dauba F., Belaud A. 1997. The use of artificial neural networks to predict the presence of small-bodied fish in a river. Freshwater Biology 38(2):237-246.
49. McCulloch W. S., Pitts W. 1943. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115-133.
50. McKay R. I. 2001. Variants of genetic programming for species distribution modelling - fitness sharing, partial functions, population evaluation. Ecological Modelling 146(1-3):231-241.
51. McKenna J. E., Jr. 2005. Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin. Transactions of the American Fisheries Society 134(1):28-43.
52. Mercado-Silva N., Olden J. D., Maxted J. T., Hrabik T. R., Vander Zanden M. J. 2006. Forecasting the spread of invasive rainbow smelt in the Laurentian Great Lakes region of North America. Conservation Biology 20(6):1740-1749.
53. Minns C. K. 1989. Factors affecting fish species richness in Ontario lakes. Transactions of the American Fisheries Society 118:533-545.
54. Mitchell M. 1998. An Introduction to Genetic Algorithms. Cambridge (MA): MIT Press.
55. Morgan J. N., Sonquist J. A. 1963. Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association 58(302):415-434.
56. Muttil N., Lee J. H. W. 2005. Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecological Modelling 189(3-4):363-376.
57. O'Connor R. J., Jones M. T., White D., Hunsaker C., Loveland T., Jones B., Preston E. 1996. Spatial partitioning of environmental correlates of avian biodiversity in the conterminous United States. Biodiversity Letters 3(3):97-110.
58. Olden J. D. 2003. A species-specific approach to modeling biological communities and its potential for conservation. Conservation Biology 17(3):854-863.
59. Olden J. D., Jackson D. A. 2001. Fish-habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Transactions of the American Fisheries Society 130(5):878-897.
60. Olden J. D., Jackson D. A. 2002a. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology 47(10):1976-1995.
61. Olden J. D., Jackson D. A. 2002b. Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling 154(1-2):135-150.
62. Olden J. D., Joy M. K., Death R. G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 78(3-4):389-397.
63. Olden J. D., Joy M. K., Death R. G. 2006a. Rediscovering the species in community-wide modeling. Ecological Applications 16(4):1449-1460.
64. Olden J. D., Poff N. L., Bledsoe B. P. 2006b. Incorporating ecological knowledge into ecoinformatics: an example of modeling hierarchically structured aquatic communities with neural networks. Ecological Informatics 1(1):33-42.
65. Özesmi S. L., Özesmi U. 1999. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling 116(1):15-31.
66. Özesmi S. L., Tan C. O., Özesmi U. 2006. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecological Modelling 195(1-2):83-93.
67. Park Y.-S., Chon T.-S. 2007. Biologically-inspired machine learning implemented to ecological informatics. Ecological Modelling 203(1-2):1-7.
68. Peters R. H. 1991. A Critique for Ecology. Cambridge (UK): Cambridge University Press.
69. Peterson A. T. 2001. Predicting species' geographic distributions based on ecological niche modeling. Condor 103(3):599-605.
70. Peterson A. T. 2003. Predicting the geography of species' invasions via ecological niche modeling. Quarterly Review of Biology 78(4):419-433.
71. Peterson A. T. 2007. Why not WhyWhere: the need for more complex models of simpler environmental spaces. Ecological Modelling 203(3-4):527-530.
72. Peterson A. T., Vieglais D. A. 2001. Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. BioScience 51(5):363-371.
73. Phillips S. J., Anderson R. P., Schapire R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190(3-4):231-259.
74. Recknagel F. 2001. Applications of machine learning to ecological modelling. Ecological Modelling 146(1-3):303-310.
75. Recknagel F. 2003. Ecological Informatics: Understanding Ecology by Biologically-Inspired Computation. Berlin (Germany) and New York: Springer-Verlag.
76. Ripley B. D. 1996. Pattern Recognition and Neural Networks. Cambridge (UK): Cambridge University Press.
77. Rollins M. G., Keane R. E., Parsons R. A. 2004. Mapping fuels and fire regimes using sensing, ecosystem simulation, and gradient modeling. Ecological Applications 14(1):75-95.
78. Rumelhart D. E., Hinton G. E., Williams R. J. 1986. Learning representations by back-propagating error. Nature 323:533-536.
79. rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU '90, Paris, France, July 2-6, 1990, Lecture Notes in Computer Science, Volume 521, edited by B. Bouchon-Meunier et al. Berlin (Germany): Springer-Verlag.
80. Sarkar S., Pressey R. L., Faith D. P., Margules C. R., Fuller T., Stoms D. M., Moffett A., Wilson K. A., Williams K. J., Williams P. H., Andelman S. 2006. Biodiversity conservation planning tools: present status and challenges for the future. Annual Review of Environment and Resources 31:123-159.
81. Scardi M., Harding L. W., Jr. 1999. Developing an empirical model of phytoplankton primary production: a neural network case study. Ecological Modelling 120(2-3):213-223.
82. Smith S. J., Iverson S. J., Bowen W. D. 1997. Fatty acid signatures and classification trees: new tools for investigating the foraging ecology of seals. Canadian Journal of Fisheries and Aquatic Sciences 54(6):1377-1386.
83. Spitz F., Lek S. 1999. Environmental impact prediction using neural network modelling: an example in wildlife damage. Journal of Applied Ecology 36(2):317-326.
84. Stockwell D. R. B. 2006. Improving ecological niche models by data mining large environmental data-sets for surrogate models. Ecological Modelling 192(1-2):188-196.
85. Stockwell D. R. B., Noble I. R. 1992. Induction of sets of rules from animal distribution data: a robust and informative method of analysis. Mathematics and Computers in Simulation 33(5-6):385-390.
86. Stockwell D. R. B., Peters D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13(2):143-158.
87. Sutton C. D. 2005. Classification and regression trees, bagging, and boosting. Pages 303-329 in Handbook of Statistics: Data Mining and Data Visualization, Volume 24, edited by C. R. Rao et al. Amsterdam (the Netherlands): Elsevier Publishing.
88. Termansen M., McClean C. J., Preston C. D. 2006. The use of genetic algorithms and Bayesian classification to model species distributions. Ecological Modelling 192(3-4):410-424.
89. Thuiller W. 2003. BIOMOD - optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9(10):1353-1362.
90. Torres L. G., Rosel P. E., D'Agrosa C., Read A. J. 2003. Improving management of overlapping bottlenose dolphin ecotypes through spatial analysis and genetics. Marine Mammal Science 19(3):502-514.
91. Usio N. 2007. Endangered crayfish in northern Japan: distribution, abundance and microhabitat specificity in relation to stream and riparian environment. Biological Conservation 134(4):517-526.
92. Vander Zanden M. J., Olden J. D., Thorne J. H., Mandrak N. E. 2004. Predicting occurrences and impacts of smallmouth bass introductions in north temperate lakes. Ecological Applications 14(1):132-148.
93. Worner S. P., Gevrey M. 2006. Modelling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology 43(5):858-867.