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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 邱祈榮(Chyi-Rong Chiou) | |
dc.contributor.author | Chiang Yao | en |
dc.contributor.author | 姚強 | zh_TW |
dc.date.accessioned | 2021-05-20T20:29:40Z | - |
dc.date.available | 2008-08-04 | |
dc.date.available | 2021-05-20T20:29:40Z | - |
dc.date.copyright | 2008-08-04 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-30 | |
dc.identifier.citation | Anderson, R.P., A.T. Peterson, and M. Gomez-Laverde (2002b) Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. OIKOS, 98: 3–16.
Anderson, R.P., and E. Mart′ınez-Meyer (2004) Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biololical Conservation, 116: 167–179. Anderson, R.P., D. Lewc, and A.T. Peterson (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecological Modelling, 162: 211–232. Anderson, R.P., M. Gomez-Laverde, and A.T. Peterson (2002a) Geographical distributions of spiny pocket mice in South America: insights from predictive models. Global Ecology and Biogeography, 11: 131–141. Araújo, M.B., and A. Guisan (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33: 1677–1688. Austin, M.P., A.O. Nicholls and C.R. Margules (1990) Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species, Ecological Monographs, 60: 161–177. Bates, J.M. and C.W.J. Granger (1969) The combination of forecasts. Operational Research Quarterly, 20: 451–468. Beven, K.J. (1997) Distributed Modelling in Hydrology: Applications of the TOPMODEL Concepts, Wiley, Chichester. Beven, K.J., M.J. Kirkby (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24: 43–69. Box, E.O. (1981) Macroclimate and plant formm: An introduction to predictive modeling in phytogeoraphy. Volume 1 of Tasks of Vegetation Science, The Hague: Junk Publishers, Pp. 132. Breiman, L. (1996) Bagging predictors. Machine Learning, 26: 123–140. Breiman, L. (2001) Random forest. Machine Learning, 45: 5–32. Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone (1984) Classification and Regression Trees. Wadsworth and Brooks/Cole, Monterey, CA. Brovkin, V. (2002) Climate-vegetation interaction. Journal De Physique IV – Proceedings, 12:10–57. Cairns, D.M. (2001) A comparison of methods for predicting vegetation type. Plant Ecology, 156: 3–18. Cantor, S.B., C.C. Sun, G. Tortolero-Luna, R. Richards-Kortum, and M. Follen (1999) A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. Journal of Clinical Epidemiology, 52(9): 885–892. Cha, G.S. (1998) Estimation of changes in potential forest area under climate change. Journal of Korea Forest Society, 87(3): 358–365. Chang, C.R., P.F. Lee, M.L. Bai, T.T. Lin (2004) Predicting the geographical distribution of plant communities in complex terrain - a case study in Fushan Experimental Forest, northeastern Taiwan. Ecography, 27(5): 577–588. Chang, H.J. (1999) Vegetation analysis of long-term sites of Taiwan Fir at Ho-Huan mountain.Project of Taroko National Park, Construction and Plaining Agency Ministry of the Interioir, Pp. 64. (in Chinese) Chatfield, C. (1995) Model uncertainty, data mining and statistical inference. Journal of the Royal Society, Series A, 158: 419–466. Chen, Y.F. (1995) Series of Taiwan Fir reaserch (I): study of the history. Annual Journal of Museum of Taiwan. 38: 23–53. Chen, Y.F. (1997) Research on the response of vegetation to climat change: modeling. Progress in Geography, 16(3): 24–28. Chen, Y.F. (1999) China climate-vegetation model based on soil classification. Progress in Nature Science, 9(1): 54–60. Chen, Y.F. (2004) Taiwan Hemlock zone in Taiwan (I). Avangard Publisher, Taipei,. Pp. 452. (in Chinese) Chiou, C.R., C.F. Yu, and C.F. Li (2005) The planning and present situation of Taiwan Vegetation Information System. Proceeding if the Third Symposium of Vegetation Diversity in Taiwan, Forest Bureau, Council of Agriculture, Taipei, Taiwan, 202–215. (in Chinese) Chiou, C.R., J.R. Lin and C.F. Li (2006) Analysis of distribution characteristics of Taiwan Hemlock communities. Proceedings of Fourth Symposium of Vegetatoin Diversity in Taiwan Vegetation Mappong Series, 8: 280–305. (in Chinese) Chiou, C.R., Y.J. Lai, C.F. Li, and Y.C. Laing (2004) The application of GIS on the simulation of climate change impact on forest – a case study on Taiwan Cypress forest. Greater China GIS Conference and Exhibition 2004, Hong Kong GIS System Association. (in Chinese with English abstract) Chiu, C.A., K.C. Lu, P.H. Lin, and M.C. Liao (2005) Mapping Holdridge’s life zones at Taiwan. Academic Journal of Naitonal Park, 15(1): 61–78. Clemen, R. T. (1989) Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5: 559–583. Cohen, J. (1988) Statistical power analysis for the behavioral sciences (2nd ed.) Hillsdale, Lawrence Erlbaum Associates, NJ. Cramer, W.P. and R. Leemans (1993) Assessing impacts of climate change on vegetation using climate classification system. In: Lolomon A.M. and H.H. Shugart(eds.). Vegetation Dynamics and Global Change. Chapman and Hall, New York, 190–217 pp. Danijela, P.M. (2003) Predictive vegetation modeling for forest conservation and management in settled landscapes. A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Graduate Department of Faculty of Forestry, University of Toronto. De’Ath, G., and K.E. Fabricius (2000) Classificaiton and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81(11): 3178–3192. Della Pietra, S., V. Della Pietra, and J. Lafferty (1997) Inducing features of random fields. IEEE Trans. Pattern Annual Machine Intellegence, 19 (4): 1–13. Dias, E., R.B. Elias, and V. Nunes (2004) Vegetation mapping and nature conservation: a case study in Terceira Island (Azores). Biodiversity and Conservation, 13: 1519–1539. Dormann, C.F. (2007) Promising the future? Global change projections of species distributions. Basic and Applied Ecology, 8: 387–397. Elith, J. and J. Leathwick (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13: 265–275. Elith, J. and M.A. Burgman (2003) Habitat models for PVA. In: Population Viability in Plants. Conservation, Management and Modeling of Rare Plants, Springer-Verlag, New York, Pp 203–235. Elith, J., C.H. Graham, R.P. Anderson, M. Dudk, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J.McC. Overton, A.T. Peterson, S.J. Phillips, K. Richardson, R. Scachetti-Pereira, R.E. Schapire, J. Soberon, S. Williams, M.S. Wisz, and N.E. Zimmermann (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129–151. Fielding, A.H. and J.F. Bell (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1): 38–49. Foley, J.A., S. Levis, I.C. Prentice, D. Pollard, and S.L. Thompsons (1998) Coupling dynamic models of climate and vegetation. Global Change Biology, 4: 561–579. Forest Bureau (1995) The Third Forest Resource and Land-Use Inventory. Council of Agriculture, Taipei, Taiwan. (in Chinese) Franklin, J. (1995) Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress Physical Geography, 19: 474–499. Franklin, J. (1998) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal of Vegetation Science, 9: 733–748. Franklin, J. (2002) Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral. Applied Vegetation Science, 5: 135–146. Franklin, J. (2002) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal of vegetation science, 9:733–748. Friedman, J.H. (2001) Greedy function approximation: A gradient boosting machine. Annual of Statistic, 29: 1189–1232. Fu, K.M. (2002) Vegetation Ecology of Dan-da Region. Department of Forestry, National Chung Hsing University, Pp. 137. (in Chinese) Gibson, L., B. Barrett, and A. Burbidge (2007) Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Diversity and Distributions, 13: 704–713. Gilmer, B.F. (2007) Predictive Vegetation Models: A Comparison of Model Combination Approaches. Master Thesis, Department of Geology and Geography Morgantown, West Virginia University. Gotelli, N.J. and B.J. McGill (2006) Null versus neutral models: what's the difference? Ecography, 29(5): 793–800. Graham, C.H., and R.J. Hijmans (2006) A comparison of methods for mapping species ranges and species richness. Global Ecology and Biogeography, 15: 578–587. Graham, C.H., J. Elith, R.J. Hijmans, A. Guisan, A.T. Peterson, B.A. Loiselle, and The Nceas Predicting Species Distributions Working Group (2008) The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45: 239–247. Grinnell, J. (1917) Field tests of theories concerning distributional control. The American. Naturalist, 51: 115–128. Grinnell, J. (1924) Geography and Evolution, Ecology, 5: 225–229. Gu, S.L., T.T. Chen, C.W. Lai, J.R. Lin and Y.J. Hsia (2006) Predicting species spatial distributions in Li-Wu Watershed using generalized additive models. Proceedings of Fourth Symposium of Vegetatoin Diversity in Taiwan Vegetation Mappong Series, 8: 80–99. (in Chinese) Guisan, A. and N.E. Zimmerman (2000) Predictgive habitat distribution models in ecology. Ecological Modelling, 135: 147–186. Guisan, A., A. Lehmann, S. Ferrier, M.P. Austin, J. Mc. C. Overton, R. Aspinall, and T. Hastie (2006) Making better biogeographical predictions of species’ distributions. Journal of Applied Ecology, 43: 386–392. Guisan, A., and W. Thuiller (2005) Predicting species distribution: offering more than simple habitat models. Ecological Letters, 8: 993–1009. Guisan, A., C.H. Graham, J. Elith, F. Huettmann, and the NCEAS Species Distribution Modelling Group (2007) Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13: 332–340. Guisan, A., J.P. Theurillat, and F. Kienast (1998) Predicting the potential distribution of plant species in an alpine environment. Journal of vegetation science, 9:65–74. Guisan, A., N.E. Zimmermann, J. Elith, C.H. Graham, S. Phillips, And A.T. Peterson (2007) What matters for predicting the occurrences of trees: techniques, data, or species' characteristics? Ecological Monographs, 77(4): 615–630. Guisan, A., T.C. Edwards, Jr, and T. Hastie (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157: 89–100. Hardtle, W. (1995) On the theoretical concept of the potential vegetation and proposals for an up to date modification. Folia Geobotanica et Phytotaxonomica, 30(3): 263–276. Hastie, T.J., and R.J. Tibshirani (1986) Generalized additive models. Statistical Science, 1: 297–318. Hastie, T.J., and R.J. Tibshirani (1990) Generalized Additive Models. Chapman and Hall, New York. Hengeveld, R. (1990) Dynamic biogeography. Cambridge University Press, Cambridge. Hernandez P.A., C.H. Graham, L.L. Master, and D.L. Albert (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29: 773–785. Holdridge, C.T. (1947) Determination of world formations from simple climatic data. Science, 105: 367–368. Holdridge, L.R. (1967) Life Zone Ecology. San Jose, Costa Rica. Tropical Science Center, 54 pp. Hothorn, T., K. Hornik and A. Zeileis (2006) Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3): 651–674. Hsu, H.H., and C.T. Chen (2002) Observed and projected climate change in Taiwan. Meteorological Atmosphere Physics, 79: 87–104. Huang, T.C., H. Keng, W.C. Shieh, J.L. Tsai, C.F. Hsieh, J.M. Hu, C.F. Shen, K.C. Yang, and S.Y. Yang (1994) Flora of Taiwan. Vol. 1: 567–569. Huberty, C.J. (1994) Applied discriminant analysis. Wiley Interscience, New York, Pp. 466. Hutchinson, G.E. (1957) Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22: 415–427. Hutchinson, G.E. (1978) An introduction to population ecology, Yale University Press, New Haven. IPCC (2001) IPCC third assessment report, summary of policy maker: the scientific basis, WGI: Scientific aspects of climate. IPCC (2007) IPCC forth assessment report, summary of policy maker: the scientific basis, WGI: Climate Change 2007: The Physical Science Basis. James, F.C. and C.E. McCulloch (1990) Multivariate analysis in ecology and systematics: panacea or Pandora’s box? Annual Review of Ecological System, 21: 129–166. Jansen, M.E.; W. Shmidt; V. Stuber; H. Wachter; C. Neader; M. Weckesser and F.J. Knauft (2002) Modeling of nature woodland communities in Harz mountains. Spatial modeling in forest ecology and management: a case study. Berlin, Springer, New York, 162-175. Johnson, W.C. (1989) The role of Blue Jays (Cyanocitta cristata) in the postglacial dispersal of Fagaceous tree in eastern north-America. Journal of Biogeography, 16: 561–571. Kellman, M.C. (1980) Plant geography. Methuen and Co. Ltd., London, Pp. 181. Kessell, S.R. (1976) Gradient modeling: a nre approach to fire mofeling and wildness resource management. Environmental Management, 1:39–48. Kira, T. (1948) On the altitudinal arrangement of climate zone in Japan – a contribution to the rational utilization in cool highlands. Agricultural Science of the North Temperate Region, 2: 143-173. (in Japanese) Köppen, W. (1931) Grundriss der Klimakunde, Berlin: DeGruyter. Pp. 388. Kriegler, B. (2007) Cost-sensitive stochastic gradient boosting within a quantitative regression framework. Ph. D. Dissertation, University of California Los Angeles. Kuo, B.C. (1978) Relationship between distribution of forest and plants, and warmth index in Taiwan. Journal of Association of Chinese Argriculture, 105–113. (in Chinese) Landis, J.R. and G.C. Koch (1977) The measurement of observer agreement for categorical data. Biometrics, 33: 159–74. Lassueur, T., S. Joost, and F. Randin (2006) Very high resolution digital elevation models: Do they improve models of plant species distribution? Ecological Modelling, 198: 139–153. Leniha, J.M. and R.P. Neilson (1993) A rule-based vegetation formation model for Canada. Journal of Biogeography, 20: 615–628. Liang, Y.C. (2004) Studies on Zoning the Ecoregion at Domain and Division Levels in Taiwan. Master Thesis, School of Forestry and Resource Conservation, National Taiwan University. Pp. 122. (in Chinese) Lim, B. K., Petereson, A.T., Engstrom, M.D. (2002) Robustness of ecological niche modeling algorithms for mammals in Guyana. Biodiversity and Conservation, 11: 1237–1246. Lin, H.L. and Z.S. Chen (2005) Statistics: approaches and applications (II). Yeh Yeh Book Gallery, Pp. 630. (in Chinese) Lindsay, J.B. (2005) The Terrain Analysis System: a tool for hydro-geomorphic applications. Hydrological Process, 19: 1123–1130. Liu, J.C. (2003) Study of plant forms of important habitats of wild animals at Ci-Lan mountain (II). Conservation Serie, Forestry Bureau, Council of Argriculture Executice Yuan, Pp. 97. (in Chinese) Liu, J.Y. and Y.H. Tseng (1999) Study of vegetation ecology at Sha Li Shian River watershed in Yushan National Park. Report of National Park, 9(1): 11–31. (in Chinese) Liu, T.S. (1962) A phytogeographic sketch on the forest flora of Taiwan. Acta Phytotaxonomica et Geobotanica, 20:149–157. Lobo, J.M., A. Jimenez-Valverde, and R. Real (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17: 145–151. Lowell, K.E. (1991) Utilizing discriminant function analysis with a geographical information system to model ecological succession spatiality. International Journal of Geographical Information System, 5:175–191. Luckman, B. (1990) Mountain areas and global change: a view of Canadian Rockies. Mountain Research and Development, 10(2): 183–195. Mackey, B.G., and D.B. Lindenmayer (2001) Towards a hierarchical framework for modelling the spatial distribution of animals. Journal of Biogeography, 28: 1147–1166. Manel, S., H.C. Williams, and S.J. Ormerod (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38: 921–931. Manel, S., J.M. Dias, S.J. Ormerod (1999) Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling, 120: 337–347. Miller, J. (2005) Incorporating spatial dependence in predictive vegetation models: Residual interpolation methods. Professional Geographer, 57(2): 169–184. Miller, J. and J. Franklin (2002) Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157: 227–247. Miller, J., J. Franklin, R. Aspinall (2007) Incorporating spatial dependence in predictive vegetation models. Ecological Modelling, 202(3-4): 225–242. Mit′aˇsov′a, H., J. Hofierka (1993) Interpolation by regularized spline with tension: II. Applications to terrain modeling and surface geometry analysis. Mathematical Geology 25(6): 657–669. Miyawaki, A. (1988) Restoration of urban green environments based on the theories of vegetation ecology. Ecological Engineering, 11(1-4): 157–165. Miyawaki, A. and K. Fujiwara (1988) vegetation mapping in Japan. Pp. 427–442 in A.W. Kulcher and I.S. Zonneveld, eds. Vegetation Mapping. Kulwer Academic Publisher, Pp. 635. Miyawaki, A., K. Fujiwara, and S. Okuda (1987) The status of nature and re-creation of green environment in Japan pp. 357–376 in A.Miyawaki; A. Bogenrider; S. Okuda and J. White, eds. Vegetation Ecology and Creation of New Environment, Tokyo. Moisen, G. G. and T. S. Frescino (2002) Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157: 209–225. Moore, I.D., R.B. Grayson, and A.R. Ladson (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5: 3–30. Moore, I.D., T.W. Norton, and J.E. Williams (1993) Modelling environmental heterogeneity in forested landscapes. Journal of Hydrology, 150: 717–747. Muñoz, J. and A. Felicísimo (2004) Comparison of statistical methods commonly used in predictive modeling. Journal of Vegetation Science, 15: 285–292. Murphy, A.H., and R.L. Winkler (1987) A general framework for forecast verification. Monthly Weather Review, 115: 1330–1338. Murphy, A.H., and R.L. Winkler (1992) Diagnostic verification of probability forecasts. International Journal of Forecasting, 7: 435–455. Oke, T.R. (1981 ) Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. Journal of Climatology, 1(1-4): 237–254. Olmeda, I. and E. Fernández. (1997) Hybrid classifiers for financial multicriteria decision making: the case of bankruptcy prediction. Computational Economics, 10: 317–335. Ou, C.H, J.C. Liu, C.C. Wang, M.C. Chang, C.A. Chiu and C.Y. Tseng (1994) Study of vegetation ecoloy of Shuan-Kuei Lake nature preserve. Conservation Serie, Forestry Bureau, Council of Argriculture Executice Yuan, Pp 107. (in Chinese) Pape, M., and P. Gaubert (2007) Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Diversity and Distributions, 13: 890–902. Pearce, J., and S. Ferrier (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecological modeling, 133: 225–245. Pearson, R.G. and T.P. Dawson (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12: 361–371. Peterson, A.T., J. Soberon, V. Sanchez-Cordero (1999) Conservatism of ecological niches in evolutionary time. Science, 285: 1265–1267. Peterson, A.T., L.G. Ball, and K.P. Cohoon (2002) Predicting distributions of Mexican birds using ecological niche modelling methods. Ibis, 144: E27–E32. Peterson, A.T., M. Papes, and J. Soberon (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213: 63–72. Peterson, A.T.and K.C. Cohoon (1999) Sensitivity of distributional prediction algorithms to geographic data completeness, Ecological Modeling. 117: 159–164. Phillips, S.J. (2008) Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography, 31: 272–278. Phillips, S.J. et al. 2005. Maxent software for species distribution modeling. <http://www.cs.princeton.edu/~schapire/maxent/>. Phillips, S.J., and M. Dudı′k (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31: 161–175. Phillips, S.J., Dudik, M., Schapire, R.E. (2004) A maximum entropy approach to species distribution modelling. In: Proceedings ofthe Twenty-first Century International Conference on Machine Learning. ACM Press, New York, pp. 655–662. Phillips, S.J., R.P. Andersonb, and R. E. Schapired, (2006) Maximum entropy modeling of species geographic distributions, Ecological Modelling, 190:231-259. Prates-Clark, C.D.C., S.S. Saatchib, and D. Agostid (2008) Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data. Ecological Modelling, 211: 309–323. Pulliam, H.R. (2000) On the relationship between niche and distribution. Ecological Letters, 3: 349–361. Raes, N., and H. ter Steege (2007) A null-model for significance testing of presence-only species distribution models. Ecography, 30: 727–736. Reid, D. J. (1968) Combining three estimates of gross domestic product. Economica, 35: 431–444. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Rodríguez, J.P., L. Brotons, J. Bustamante and J. Seoane (2007) The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions, 13: 243–251. Scott, J.M., M. Murray, R.G. Wright, B. Csuti, P. Morgan, and R.L. Pressey (2001) Representation of natural vegetation in protected areas: capturing the geographic range. Biodiversity and Conservation 10: 1297–1301. Scott, J.M., P.J. Heglund, J.B. Haufler, M. Morrison, M.G. Raphael, and W.B.Wall (2002). Predicting Species Occurrences: Issues of Accuracy and Scale. Island Press, Covelo, CA. See L. and R.J. Abrahart (2001) Multi-modal data fusion for hydrological forecasting. Computers and Geosciences, 27: 987–994. Segurado, P., M.B. Aeaujo, and W.E. Kunin (2006) Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 43: 433–444. Seibert, P. and M. Conrad-Brauner (1995) Concept, mapping and application of the potential natural vegetation taking the PNV-map of the lower Inn-Valley as an example. Tuexenia, 15:25–43. Sen, B.Y. (1937) Study of plant communities of Abies kawakamii near sub-alpine vellege (I); (II).Animals and Plants, 6(9): 46–52. Song, G.Z.M., C.T. Lin, C.R. Chiou, and Y.C. Lu (2007) Comparing three species distribution models - Applied in Tsuga chinensis distribution in Taiwan. Proceedings of the Fifth Symposium of Vegetaion Diversity in Taiwan, Vegetation Mapping Series, 9: 49–65. Steyn, D.G. (1980) The calculation of view factors from fisheye-lens photographs. Atmosphere-Ocean, 18(3): 254–258. Stockwell, D.R.B. (2006) Improving ecological niche models by data mining large environmental datasets for surrogate models. Ecologival Modelling, 192: 188–196. Stockwell, D.R.B., and A.T. Peterson (2002) Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148: 1–13. Strasser, H. and C. Weber (1999) On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics, 8: 220–250. Su, H.J. (1984a) Studies on the climate and vegetation types of the natural forests in Taiwan (I). Analysis of the variation in climatic factors. Quarterly Journal of Chinese Forestry, 17(3): 1–14. Su, H.J. (1984b) Studies on the climate and vegetation types of the natural forests in Taiwan (II). Altitudinal vegetaion zones in relation to temperature gradient. Quarterly Journal of Chinese Forestry, 17(4): 57–73. Su, H.J. (1985) Studies on the climate and vegetation types of the nature forests in Taiwan (III): a scheme of geographical climatic resgions. Quarterly Journal of Chinese Forestry, 18(3): 33–44. Su, H.J. (1987) Forest habitat factor and its quantititative estimation. Quarterly Jounal of Chinese Forestry, 20(1): 1–14。(in Chinese) Su, H.J. (1988) Study of vegetation ecology at nature preserve of Juniperus sqyanata var. morrisonicola in Hsuehshan. Conservation Serie, Forestry Bureau, Council of Argriculture Executice Yuan, Pp 123. (in Chinese) Su, H.J. (1991) Study of vegetation ecology at nature preserve of conifer and deciduous forest in Pei Da Wu Mountain (II): estimation of representativeness of nature preserve of vegetation analysis. Study of Vegetation Ecology of Nature Preserve of Nature Forest of Taiwan, Forestry Bureau, Council of Argriculture Executice Yuan, Pp. 141. (in Chinese) Su, H.J. (1992) Vegetation of Taiwan: altitudinal vegetation zone and geographical climate regions. Institute of botany, Academia Sinica Monograph series, Academia Sinica, Taiwan, 11: 39-53. Swets, J.A. (1988) Measuring the accuracy of diagnostic system. Science, 240: 1285–1293. Taiwan Government Information Office. (2008) http://www.gio.gov.tw/ Thomas, C.D., A. Cameron, R.E. Green, M. Bakkenes, L.J. Beaumont, Y.C. Collingham, B.F.N. Erasmus,M.F. de Siqueira, A. Grainger, L. Hannah, L. Hughes, B. Huntley, A.S. van Jaarsveld, G.F. Midgley, L. Miles, M.A. Ortega-Huerta, A.T. Peterson, O.L. Phillips, S.E. Williams (2004) Extinction risk from climate change. Nature, 427: 145–148. Thuiller, W., M. Araujo, and S. Lavorel (2003) Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14: 669–680. Tsao, L.S. (2007) Using generalized additive models to establish the relationships between distribuition ranges and climatic factors for six conifer species of Taiwan. Master Thesis, School of Forestry and Resource Conservation, National Taiwan University. Pp. 76. Tsoar, A., O. Allouche, O. Steinitz, D. Rotem, and R. Kadmon (2007) A comparative evaluation of presenceonly methods for modelling species distribution. Diversity and Distributions, 13: 397–405. Tuhkanen, S., (1980) Climatic Parameters and Indices in Plant Geography. Almqvist and Wiksell International, Sweden, 110pp. Urban, D.L., G. B. Bonan, T.M. Smith, and H.H. Shugart (1991) Spatial applications of gap models. Forest Ecology and Management, 42: 95–110. Vayssières, M. P., R. E. Plant, and B.H. Allen-Diaz (2000) Classification trees: an alternative non-parametric approach for predicting species distributions. Journal of Vegetation Science, 11: 679–694. Walter, H. (2002) Walter’s vegetation of the Earth: The Ecological Systems of the Geo-Biosphere. 4th, Completely Revised and Enlarged Edition. Springer-Verlag, Berlin, 527 pp. Walther, G.R. (2004) Plants in a warmer world. Perspective in Plant Ecology, Evolution and Systematics, 6: 169-185. Wang, Y.S., B.Y. Xie, F.H. Wan, Q.M. Xiao, and L.Y. Dai (2007) The potential geographic distribution of Radopholus similis in China. Agricultural Sciences in China, 6(12): 1444–1449. Wang, Y.S., B.Y. Xie, F.H. Wang, Q.M. Xiao, and L.G. Dai (2007) Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models. Biodiversity Science, 15(4): 365–372. Whittaker, R.H. (1975) Classification of Plant Communities. Wiksell International, Sweden, 110 pp. Wilson, J.D., (1984) Determining a TOPEX score, Scottish Forestry, 38: 251–256. Wilson, K.A., M.I. Westphal, H.P. Possingham, and J. Elith (2004) Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation, 22(1): 99–112. Yang, Y.L. (1997) A study on the application of potential vegetation to planting design-as case study syn-show mountain of Taipei. Master Thesis, Department of Horticulture, National Taiwan University. Yen, S.M. (2007) Modeling species distributions of three coniferous forest type in Taiwan. Master Thesis, Department of Geography, College of Science, National Taiwan University, Pp. 96. Yen, S.M., C.R. Chiou, K.C. Chang, and J.R. Lin (2007) Development and evaluation of Taiwan Hemlock distribution model in Taiwan. Quarterly Jounal of Chinese Forestry, in publishing. Zevenbergen, L.W., C.R. Thorne (1987) Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12: 47–56. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9579 | - |
dc.description.abstract | 由於人為活動增加過量的溫室氣體,導致氣候變遷下環境也可能發生改變,在未知的變動下植群社會要如何面對氣候變遷所造成的衝擊,了解植物社會是如何適應自然環境,將是首要的任務。近年來物種分布模式(Species Distribution Models, SDM)被廣泛的使用在了解物種與環境之間的關係,並且應用在生物多樣性保育與經營上。本研究的目標物種為台灣鐵杉(Tsuga chinensis var. formosana Li and Keng)出現樣點,以16個環境因子(包括大尺度的氣候因子與中尺度的地形因子)為Maxent物種分布預測模式的輸入,並測試三種不同的輸入各是如何影響預測模式的表現:(1)以種成分分析法(principal component analysis, PCA)與分類樹(classification and regression tree, CART)和條件推論樹(conditional inference tree, CIT)分析種環境因子與台灣鐵杉的關係當作預測模式環境因子選擇的依據,(2)比較所有台灣鐵杉出現的樣點數與以矩陣群團分析法分類之台灣鐵杉次植群型單位的樣點數,(3)不同的環境因子解析度。並分析植群與優勢物種分布和環境因子對模式的貢獻程度,進一步以Maxent物種分布模式預測出機率分布圖,預測之結果以受試者工作特徵曲線面積(AUC)值來評估台灣鐵杉植群型分布模式的準確性。應用2種合併模式的方法結合機率模式的結果與門檻值的篩選產生台灣鐵杉的潛在植群圖(potential vegetation map)並以誤差矩陣(confusion matrix)來評估潛在植群圖的準確性。植群分析結果產生四群台灣鐵杉次植群型,環境分析和模式預測結果顯示影響台灣鐵杉的空間分布為主要的環境因子為海拔,次之為雨量,都屬於氣候因子;地形因子及對預測模式沒有主要的貢獻,但是仍然使預測模式更加精確。樣點數較小較且均質的植群型單位模式有著比樣點數較多的物種單位模式還高的模式預測能力。本研究中環境圖層的解析度對模式的預測能力沒有特別顯著的影響,預測的區域因為受到樣本數跟著改變的影響來無法突顯預測範圍的大小是否影響模式的表現,潛在植群圖的合成有助於應用的決策和考量,使得物種分布模式的應用更具有彈性。最後預測植群圖的可適用性能需要進一步的實驗預測的環境條件是否真的是和目標物種的生存來加以支持預測物種的空間分布。 | zh_TW |
dc.description.abstract | To know the adaptation of plant society under climate change impacts is based on knowledge of the potential distribution of vegetation distributions. Vegetation is a society of plant species. Applying combination of species distribution models (SDMs) results to establish potential vegetation maps (PVMs) need determination strategies. This article firstly analyzes the relationship between Taiwan Hemlock (Tsuga chinensis var. formosana Li and Keng) and 16 topographical and climatic variables and then to generate a probability map by Maxent to test how 3 different situations of model input affects the model performance: (i) selection and analysis of suitable environmental variables by principal component analysis (PCA), classification and regression tree (CART) and conditional inference tree (CIT) method, (ii) sample size and homogeneity of species and vegetation sub-unit occurrence data (iii) resolution for environmental layers. Model evaluated by area under receiver-operating characteristic (ROC) curve (AUC) and Kappa statistic. 2 model combination approaches is also applied in this study to aid to generate the potential vegetation map (PVM) of Taiwan Hemlock. PVM is evaluated by error matrix and its derived indices. The result of vegetation analysis by cluster analysis classified Taiwan Hemlock into 4 sub-unit vegetation type. The result of environmental analysis and modeling revealed that the environmental variable that is affecting spatial distribution of Taiwan Hemlock most is majorly elevation gradient and the secondary is precipitation and both are climatic variables. Topographical showed minor contribution to the model. Sample size test showed more accurately when input the smaller size and more homogeneous samples. Resolution of environmental layers showed no sigibificant effect on model performance in this case. Overlaying Taiwan Hemlock vegetation sub-unit probability maps with 2 deterministic combination approaches synthesizes a potential vegetation map of Taiwan Hemlock. Modification of strategy for predicting PVMs is according to local ecological theory and further study on testing the potential ability from the environmental variable is really suitable for the target species. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T20:29:40Z (GMT). No. of bitstreams: 1 ntu-97-R95625057-1.pdf: 2577857 bytes, checksum: 964b50ebc21c7646c4b0aa4d8b074453 (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 摘要 i
Abstract ii Table of Content iii Table of Figures vi Chapter 1: Introduction 1 1.1 Background 1 1.2 Objective 4 Chapter 2: Literature review 6 2.1 Climatic Factor and Vegetation Distribution in Taiwan 6 2.2 Data Mining Approach in Environmental Factor Analysis 8 2.2.1 CART 8 2.2.2 CIT 9 2.3 Ecological Niche and Species Distribution Model 11 2.3.1 Predicted Vegetation Modeling 11 2.3.2 SDM/ENM 16 2.3.3 SDMs and Ecological Theory 19 2.3.4 MAXENT 21 2.4 Model Performance Evaluation 23 2.4.1 Confusion Matrix for Measuring Discrimination Performance 25 2.4.2 Threshold Independence AUC 27 2.5 Model Comparison and Combination 31 Chapter 3: Materials and Methods 34 3.1 Study Area 34 3.2 Target Species 34 3.3 Data Preparation and Preprocessing 35 3.3.1 Occurrence Data 38 3.3.2 Environmental Layers 41 3.3.3 Vegetation Analysis 44 3.4 Environmental Factor Analysis for SDMs Performance 45 3.4.1 Avoidance of Multicollinearity 45 3.4.2 Attributes of Environmental variables 45 3.4.4 PCA Approach 47 3.4.5 Data Mining Approach 47 3.5 Predicting Species Distribution 48 3.5.1 Model Building 48 3.5.2 How Map Resolution and Environmental Variables Affect Model Performance 50 3.5.3 Vegetation and Species Units for Model Input 50 3.6 Model Evaluation 51 3.6.1 Threshold Independent AUC 51 3.6.2 Threshold Dependent Confusion Matrix 52 3.6.3 Null Model for Significant test 53 3.7 Potential Nature Vegetation Mapping 54 3.7.1 Specific Threshold to Presence 54 3.7.2 Model Combination and PNV Mapping Criteria 55 Chapter 4: Results 57 4.1 Vegetation Classification of Taiwan Hemlock Presence 57 4.2 Environmental Layers Analysis 72 4.2.1 Correlation Analysis of Environmental Variables 72 4.2.2 Attributes of Environmental Variables 75 4.2.3 PCA Approaches 80 4.2.4 Data Mining Approach: CART 83 4.2.5 Data Mining Approach: CIT 88 4.3 SDM Outputs and AUC 92 4.3.1 Resolution, Presence Unit, Environmental Variable Selection and AUC 92 4.4 SDM Assessment with Threshold and Null Model 103 4.4.1 Threshold to Presence 103 4.4.2 Threshold Dependent Indices 104 4.4.3 Null Model for Significant test 105 4.5 PNV Mapping Criteria and PVM for Taiwan Hemlock 106 Chapter 5: Discussion 111 5.1 Vegetation Analysis 111 5.2 Analysis of Species-Environment Relationship 112 5.3 Environmental Variables to Taiwan Hemlock and Model Assessment 113 5.4 Vegetation and Species Based Units and Map Resolution 115 5.5 Combination of Models for Predicting Vegetation Map 116 Chapter 6: Conclusion 119 References 122 | |
dc.language.iso | en | |
dc.title | 資料探勘技術應用於Maxent物種分布模式之變數篩選─以台灣鐵杉為例 | zh_TW |
dc.title | Applying Data Mining Approach to Variable Selection for Maxent: Taiwan Hemlock Case Study | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李培芬,王立志 | |
dc.subject.keyword | 台灣鐵杉,分類樹,條件推論樹,Maxent,AUC,誤差矩陣,潛在植群, | zh_TW |
dc.subject.keyword | Tsuga chinensis,CART,CIT,Maxent,AUC,confusion matrix,PVM, | en |
dc.relation.page | 140 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2008-08-01 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 森林環境暨資源學研究所 | zh_TW |
顯示於系所單位: | 森林環境暨資源學系 |
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