Issues and challenges of incorporating fuzzy sets in ecological modeling

NATO Security through Science Series C: Environmental Security

Volumn , Issue , 2007, Pages 33-51

  Robinson, Vincent B ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Ecological geographic variables; Fuzzy sets; Geographic information system; Habitat model; Spatially explicit population model

Indexed keywords

EID: 34548667682     PISSN: 18714668     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4020-6438-8_3     Document Type: Conference Paper

References (56)

1. Arnot, C., Fisher, P. F., Wadsworth, R., and Wellens, J., 2004, Landscape metrics with ecotones: pattern under uncertainty, Landscape Ecology 19:181-195.
2. Austen, M. J., Francis, C. M., Burke, D. M., and Bradstreet, M. S. W., 2001, Landscape context and fragmentation effects on forest birds in Southern Ontario, The Condor 103:701-714.
3. Benedikt, J., Reinberg, S., and Riedl, L., 2002, A GIS application to enhance cell-based information modeling, Information Sciences 142:151-160.
4. Bezdek, J. C., Ehrlich, R., and Full, W., 1984, FCM: the fuzzy c-means clustering algorithm, Computers and Geosciences 10:191-203.
5. Bone, C., Dragicevic, S., and Roberts, A., 2006, A fuzzy-constrained cellular automata model of forest insect infestations, Ecological Modelling 192:107-125.
6. Bordogna, G., Chiesa, S., and Geneletti, D., 2006, Linguistic modelling of imperfect spatial information as a basis for simplifying spatial analysis, Information Sciences 176:366-389.
7. Bridges, L. M., 2003, Spatial Scale and Environmental Structure: Habitat Selection of Adult Grey Squirrels (Sciurus carolinensis) in Central Ontario, Master's thesis, Trent University.
8. Brotons, L., Thuiller, W., Ara-jo, M. B., and Hirzel, A. H., 2004, Presence-absence versus presence-only modelling methods for predicting bird habitat suitability, Ecography 27:437-448.
9. Burgman, M. A., Breininger, B. W., Duncan, B. W., and Ferson, S., 2001, Setting reliability bound on habitat suitability indices, Ecological Applications 11:70-78.
10. Carroll, C., Zielinski, W. J., and Noss, R. F., 1999, Using presence-absence data to build and test spatial habitat models for the Fisher in the Klamath Region, U.S.A., Conservation Biology 13:1344-1359.
11. Comber, A., Fisher, P., and Wadsworth, R., 2005, What is land cover? Environment and Planning B: Planning and Design 32:199-209.
12. DeGenst, A., Canters, F., and Gulink, H., 2001, Uncertainty modeling in buffer operations applied to connectivity analysis, Transactions in GIS 5:305-326.
13. Dettmers, R. and Bart, J., 1999, A GIS modeling method applied to predicting forest songbird habitat, Ecological Applications 9:152-163.
14. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., and Weingessel, A., 2005, The e1071 Package, R Project for Statistical Computing, http://cran.r-project.org/.
15. Dragicevic, S., 2005, Multi-dimensional interpolations with fuzzy sets, In: Fuzzy Modeling with Spatial Information for Geographic Problems, Petry, F. E., Robinson, V. B., and Cobb, M. A., eds., Springer, Berlin, pp. 143-158.
16. Eastman, J. R., 1999, Idrisi32 Guide to GIS and Image Processing, Clark Labs, Worcester, MA.
17. Equihua, M., 1990, Fuzzy clustering of ecological data, Journal of Ecology 78:519-534.
18. Feoli, E. and Zerihun, W., 2000, Fuzzy set analysis of the Ethiopian rift valley vegetation in relation to anthropogenic influences, Plant Ecology 147:219-225.
19. Fielding, A. H. and Bell, J. F., 1997, A review of methods for assessment of prediction errors in conservation presence/absence models, Environmental Conservation 24:38-49.
20. Fisher, P. F. and Pathirana, S., 1994, The evaluation of fuzzy membership of land cover classes in the suburban zone, Remote Sensing of Environment 34:121-132.
21. Fisher, P., Cheng, T., and Wood, J., in press, Higher order vaguess in geographical information: empirical geographical population of type-n fuzzy sets, GeoIinformatica 29:106-128.
22. Graniero, P. A. and Robinson, V. B., 2003, A real-time adaptive sampling method for field mapping in patchy, heterogeneous environments, Transactions in GIS 7:31-54.
23. Graniero, P. A. and Robinson, V. B., in press, A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework, International Journal of Geographical Information Science
24. Hagen-Zanker, A., Straatman, B., and Uljee, I., 2005, Further developments of a fuzzy set map comparison approach, International Journal of Geographical Information Science 19:769-785.
25. Hill, K. E. and Binford, M. W., 2002, The role of category definition in habitat models: practical and logical limitations of using Boolean, indexed, probabilistic, and fuzzy categories, In: Predicting Species Occurrences: Issues of Accuracy and Scale, Scott, J. M., Heglund, P. J., Morrison, M. L., Haufer, J. B., Raphael, M. G., Wall, W. A., and Samson, F. B., eds., Island Press, Washington, DC, pp. 97-106.
26. Hodgson, P., in progress, An Exploratory Study in the Use of Fuzzy Set to Model the Habitat of Worm-eating Warblers in the Land Between the Lakes National Recreation Area (tentative title), Master's thesis, University of Toronto.
27. Jenkins, C. N., Powell, R. D., Bass, O. L., Jr., and Pimm, S. L., 2003, Why sparrow distributions do not match model predictions, Animal Conservation 6:39-46.
28. Lima, S. L. and Zollner, P. A., 1996, Towards a behavioral ecology of ecological landscapes, Trends in Ecology and Evolution 11:131-135.
29. Liu, J., Dunning, J. B., Jr., and Pulliam, H. R., 1995, Potential effects of a forest management plan on Bachman's sparrows (Aimophila aestivalis): linking a spatially explicit model with GIS, Conservation Biology 9:62-75.
30. Lurz, P. W. W., Rushton, S. P., Wauters, L. A., Bertolino, S., Currado, I., Massoglio, P., and Shirley, M. D. F., 2001, Predicting grey squirrel expansion in North Italy: a spatially explicit modelling approach, Landscape Ecology 16:407-420.
31. Mackay, D. S., Samanta, S., Ahl, D. E., Ewers, B. E., Gower, S. T., and Burrows, S. N., 2003, Automated parameterization of land surface process models using fuzzy logic, Transactions in GIS 7:139-153.
32. Maechler, M., 2005, The cluster Package, R Project for Statistical Computing, http://cran.r-project.org/.
33. Mech, S. G. and Zollner, P. A., 2002, Using body size to predict perceptual range, Oikos 98:47-52.
34. Medellin, R. A., Equihua, M., and Amin, M. A., 2000, Bat diversity and abundance as indicators of disturbance in neotropical rainforests, Conservation Biology 14:1666-1675.
35. Minasny, B. and McBratney, A. B., 2002, FuzME version 3.5, Australian Centre for Precision Agriculture, The University of Sydney, Asutralia, December 12, 2003.
36. Nicholls, K. H. and Tudorancea, C., 2001, Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure, Canadian Journal of Fisheries and Aquatic Sciences 58:231-240.
37. Odeh, I. O. A., McBratney, A. B., and Chittleborough, D. J., 1990, Design of optimal sample spacings for mapping soil using fuzzy k-means and regionalized variable theory, Geoderma 47:93-112.
38. Oehler, J. D. and Litvaitis, J. A., 1996, The role of spatial scale in understanding responses of medium-sized carnivores, Canadian Journal of Zoology 74:2070-2079.
39. Olden, J. D., Schooley, R. L., Monroe, J. B., and Poff, N. L., 2004, Context-dependent perceptual ranges and their relevance to animal movements in landscapes, Journal of Animal Ecology 73:1190-1194.
40. Orrock, J. L., Pagels, J. F., McShea, W. J., and Harper, E. K., 2000, Predicting presence and abundance of a small mammal species: the effect of scale and resolution, Ecological Applications 10:1356-1366.
41. Pearce, J. L., Cherry, K., Drielsma, M., Ferner, S., and Whish, G., 2001, Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution, Journal of Applied Ecology 38:412-424.
42. Pearce, J. L. and Boyce, M. S., 2006, Modelling distribution and abundance with presence-only data, Journal of Applied Ecology 43:405-412.
43. Robinson, V. B., 1988, Some implications of fuzzy set theory applied to geographic databases, Computers, Environment, and Urban Systems 12:89-97.
44. Robinson, V. B., 2002, Using fuzzy spatial relations to control movement behavior of mobile objects in spatially explicit ecological models, In: Applying Soft Computing in Defining Spatial Relations, Matsakis, P. and Sztandera, L. M., eds., Physica-Verlag, Heidelberg, pp. 158-178.
45. Robinson, V. B., 2003, A perspective on the fundamentals of fuzzy sets and their use in geographic information systems, Transactions in GIS 7:3-30.
46. Robinson, V. B. and Graniero, P. A., 2005, Spatially explicit individual-based ecological modeling with mobile fuzzy agents, In: Fuzzy Modeling with Spatial Information for Geographic Problems, Petry, F. E., Robinson, V. B., and Cobb, M. A., ed., Springer, Heidelberg, pp. 299-334.
47. Robinson, V. B. and Thongs, D., 1986, Fuzzy set theory applied to the mixed pixel problem of multispectral landcover databases, In: Geographic Information Systems in Government, Opitz, B. K., ed., A. Deepak Publishing, Hampton, VA, pp. 871-885.
48. Ruckelshaus, M., Hartway, C., and Kareiva, P., 1997, Assessing the data requirements of spatially explicit dispersal models, Conservation Biology 11:1298-1306.
49. Schaefer, J. A. and Wilson, C. C., 2002, A fuzzy structure of populations, Canadian Journal ofZoology 2235-2241.
50. Seoane, J., Bustamante, J., and Diaz-Delgado, R., 2005, Effect of expert opinion on the predictive abiliyt of environmental models of bird distribution, Conservation Biology 19:512-522.
51. Skubic, M., Blisard, S., Bailey, C., Adams, J. A., and Matsakis, P., 2004, Qualitative analysis of sketched route maps: translating a sketch into linguistic descriptions, IEEE Transactions on Systems, Man, and Cybernetics 34:1275-1282.
52. Smithson, M., 2005, Fuzzy set inclusion: linking fuzzy set methods with mainstream techniques, Sociological Methods and Research 33:431-461.
53. Yanar, T. A. and Akyurek, Z., 2006, The enhancement of the cell-based GIS analyses with fuzzy processing capabilities, Information Sciences 176:1067-1085.
54. Zadeh, L. A., 1978, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1:3-28.
55. Zhu, A. X., Hudson, B., Burt, J., Lubich, K., and Simonson, D., 2001, Soil mapping using GIS, expert knowledge, and fuzzy logic, Soil Science Society of America Journal 65:1463-1472.
56. Zollner, P. A. and Lima, S. L., 2005, Behavioral tradeoffs when dispersing across a patchy landscape, Oikos 108:219-230.