Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/66210
標題: Exploration of multi-model species ecological modeling for Elaeocarpus japonicus and Rhododendron formosanum
探討多模式物種生態模擬─以薯豆、台灣杜鵑為例
作者: Su, Sheng-Yang
蘇聖暘
關鍵字: 3S
3S技術
geographic information system
global position system
remote sensing
Elaeocarpus japonicas
Rhododendron formosanum
generalized linear model
classification tree algorithm
surface range envelope
maximum entropy
kappa statistic
true skill statistic
receiver operating characteristic analysis
area under curve
accuracy
threshold selection
species traits
地理資訊系統
全球定位系統
遙感探測
薯豆
台灣杜鵑
廣義線性模式
分類樹演算法
表面環境封包
最大熵值法
kappa統計
真技術統計
受納者操作特性分析
曲線下面積
精度
門檻選擇
物種生態特性
出版社: 森林學系所
引用: 中文圖書 王子定 (1993) 現代育林學 (下冊),南天書局。 行政院農委會林務局 (2006) 台灣的自然資源與生態資料庫III 農林漁牧,行政院農委會林務局。 何春蓀 (1986) 臺灣地質概論臺灣地質圖說明書,增訂第二版。經濟部中央地質調查所。 林震岩 (2008) 多變量分析:SPSS的操作與應用,智勝出版社。 國立中興大學實驗林管理處(1994)國立中興大學實驗林管理處簡介。國立中興大學實驗林管理處。 國立中興大學實驗林管理處 (2001) 關刀溪森林生態系。國立中興大學實驗林管理處。 游繁結(2001)水文與氣象。惠蓀林場關刀溪森林生態系。國立中興大學實驗林管理處。陳明義、許博行、吳聲海編。22-35頁。 劉堂瑞、蘇鴻傑 (1983) 森林植物生態學,臺灣商務。 劉業經、呂福原、歐辰雄,1994。台灣樹木誌。國立中興大學農學院。 中文期刊論文 王志強、歐辰雄 (2002) 惠蓀林場維管束植物名錄 (一) 蕨類植物,林業研究季刊 24 (4):1-14。 王素芬、陳永寬、鄭祈全 (2004) 森林生態系經營決策支援系統之建立與應用,航測及遙測學刊9 (1):41 – 52。 李嘉馨、應紹舜 (2000) 鳳凰山系台灣杜鵑植物社會之研究。中華林學季刊33(4): 437–455。 邱清安、林博雄 (2004) 由測站資料推估台灣之氣溫與降水之空間分布,大氣科學32 (4):329 – 350。 洪煜鈞 (2009) 臺灣南部大型猛禽棲地利用及棲地適合度分布預測,國立屏東科技大學野生動物保育研究所碩士論文。 洪淑華 (2007) 和平北溪森林植物社會沿海拔梯度之物種多樣性研究,國立宜蘭大學自然資源學系碩士論文。 黃明通 (1995) 玉山國家公園八通關越道之森林植群調查與分析,內政部營建署玉山國家公園管理處。 黃美秀、潘怡如、蔡幸蒨、郭彥仁、林冠甫 (2010) 臺灣黑熊分布預測模式及保育行動綱領之建立 (1),行政院農業委員會林務局保育研究系列98-23號。 陳明義、陳文民、陳恩倫、羅南璋、劉思謙 (2004) 北港溪南集水區天然植群之研究,林業研究季刊 26 (4):39-50。 陳恩倫、陳文民、陳宗駿、陳鳳華、俞秋豐、陳明義 (2009) 烏溪流域天然林植群多樣性分類及製圖,林業研究季刊 31 (2):1-14。 張鈺敏 (2009) 最大熵物種分布模式應用於台灣十種樹種之可轉移性研究,國立東華大學自然資源管理研究所碩士論文。 馮豐隆、黃志成 (1997) 惠蓀林場土地利用之地景排列和變遷,林業研究季刊 30(4):387-400。 曾喜育、歐辰雄、呂福原、曾麗蓉 (2004) 關刀溪森林生態系牛奶榕物候及性別分化之表現,林業研究季刊 26 (2):61-78。 劉業經、林文鎮、歐辰雄、呂金誠 (1986) 惠蓀林場闊葉樹次生林林相改良報告 (I)--伐採三十年後之植生組成及初步整理,中華林學季刊19 (3):1-11。 蔡尚惪、黃立彥、林志銓、歐辰雄、許博行、呂金誠 (2006) 惠蓀林場紅檜人工林與闊葉樹次生林植群監測,林業研究季刊 28 (4):13-28。 謝立忻、羅南璋、黃凱易 (2005) 應用3S 地球空間技術於植群空間分布型態之探討,林業研究季刊27 (4):37 – 46。 羅南璋 (1992) 惠蓀實驗林場東峰溪集水區分析。中興大學森林學研究所碩士論文。 羅南璋 (2010) 3S技術結合多變量統計模擬測繪林木之潛在生育地,國立中興大學森林學研究所博士論文。 羅南璋、張偉顗、黃凱易 (2011) 應用3S技術及多變量統計於薯豆及卡氏櫧潛在生育地之推估,林業研究季刊33 (3):55-70。 蘇鴻傑 (1987) 森林生育地因子及其定量評估,中華林學季刊 20 (1): 1-14。 蘇鴻傑 (1992) 台灣之植群:山地植群帶與地理氣候區。中央研究院植物研究所專刊 11:39-53。中央研究院植物研究所。 其他 王寗、羅南璋、張偉顗、黃凱易 (2012) GIS協同環框演算法推估台灣杜鵑之空間分布型態,101年森林資源永續經營研討會,2012年10月25-26日,台灣南投。 龔文斌、楊懿如,2010,運用志工調查資料進行台灣蛙類分布預測,2010數位典藏地理資訊學術研討會,台北市台灣大學。 西文圖書 Chesson, P. (2012) Theoretical Ecology, Lecture Material. Jensen, J. R., (2005), Introductory Digital Image Processing—A Remote Sensing Perspective, Pearson Prentice Hall, Pearson Education, Inc. McCullagh, P. and Nelder, J.A. (1989) Generalized linear models Chapman and Hall. Odum, E. P., (1971) Fundamentals of ecology, (3rd ed.), W. B. Saunders Co. Philadelphia. Paine, D. P. and J. D. Kiser, 2003. Aerial Photography and Image Interpretation, (2nd ed.). John Wiley & Sons, New York. Kellman, M. C. (1980) Plant Geography Methuen Co. Ltd. London. 西文期刊論文 Allouche, O., Tsoar, A., and R. Kadmon (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS), Journal of Applied Ecology 43: 1223-1232. Anderson, R. P. and A. Raza (2010) The effect of the extent of the study region on GIS models of species geographic distributions and estimate of niche evolution: preliminary test with montance rodents (genus Nephelomys) in Venezuela, Journal of Biography 37: 1378-1393. Araujo, M. B. and A. Guisan (2006) Five (or so) challenges for species distribution modelling, Journal of Biogeography 33: 1677-1688. Barve, N., Barve, V., Jimenez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., Soberon, J. and F. Villalobos (2011) The crucial role of the accessible area in ecological niche modelling and species distribution modelling, Ecological Modelling 222: 1810-1819. Bean, W. T., Stafford, R. and S Brashares (2012) The effects if small sample size and sample bias on threshold selection and accuracy assessment of species distribution models, Ecography 35: 250-258. Bourg, N. A., W. J. Mcshea and D. E. Gill (2005) Putting A CART Before Search: Successful Habitat Prediction for A Rare Forest Herb. Ecology 86 (10): 2793 – 2804. Breiman, L., Friedman, J. H., Olshen, R. A., and C. J. Stone (1984) Classification and regression trees, Chapmna and Hall, New York. Brown, D. G. (1994) Predicting vegetation type at treeline using topography and biophysical disturbance virables, Journal of Vegetation Science 5: 641-656. Busby JR (1991) BIOCLIM - a bioclimate analysis and prediction system. In: Margules CR, Austin MP, editors. Nature Conservation: Cost E_ective Biological Surveys and Data Analysis. Canberra, Australia: CSIRO. pp. 64-68. Cantor, S. B., Sun, C. C., Tortolero-Luna, G., Richards-Kortun, R. and M. Follen (1999) A comparison of C/B ratios from studies using receiver operating characteristic curve analysis, Journal of Clinical Epidemiology 52: 885 – 892. Cheffauoi, R. M. and J. M. Lobo (2008) Assessing the effects of pseudo-absences on predictive distribution model performance, Ecological Modelling 210: 478-486. Clark, J. S. (1991) Disturbance and tree life history on the shifting mosaic landscape, Journal of Ecology 72:1102-1118. Cohen, J. (1960) A coefficient of agreement for normal scales, Educational and Physiological Measurement 20 (1): 37-46. Congalton, R. G. (1991) A review of assessing the accuracy of classification of remotely sensed data, Remote Sensing of Environment, 37: 35-46 Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettman, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S., Richardson, K., Schachetti Pereira, R., Schapire, R. E., Sober_on, J., Williams, S. E., Wisz, M. and N. E. Zimmermann (2006) Novel methods improve predictions of species'' distributions from occurrence data, Ecography 29: 129-151. Engler, R and A. Guisan (2009) MigClim: predicting plant distribution and dispersal in a changing climate, Diversity and Distribution 15: 590-601. Fortin, M. -J., Keitt, T. H., Maurer, B. A., Taper, M. L., Kaufman, D. M. and T. M. Blackburn (2005) Species'' geographic ranges and distributional limits: pattern analysis and statistical issues, OIKOS 108: 7-17. Giam, X., Bradshaw, C. J.A., Tan, H. T.W. and N. S. Sodhi (2010) Future habitat loss and the conservation of plant biodiversity, Biological Conservation, 143: 1594-1602. Guisan, A., Graham, C. H., Elith, J., Huettmann, F. and NCEAS Predicting Species Distribution Working Group (2007) Sensitivity of predictive species distribution models to change in grain size, Diversity and Distributions 13: 332-340. Guisan, A. and N. E. Zimmermann (2000) Predictive habitat distribution models in ecology, Ecological Modelling, 135, pp.147-186. Guisan, A., Theurillat, J. –P. and F. Kienast (1998) Predicting the potential distribution of plant species in alpine environment, Journal of Vegetation Science 9: 65-74. Guisan, A., Weiss, S. B. and A. D. Weiss (1999) GLM versus CCA spatial modeling of plant species distribution, Plant Ecology 143: 107-122. Guisan, A. and W. Thuiller (2005) Predicting species distribution: offering more than simple habitat models, Ecology Letters 8:993-1009. Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S. and A. T. Peterson (2007) What matters for predicting the occurrences of trees: techniques, data, or species'' characteristics? Ecological Monographs 77 (4):615-630. Hanberry, B. B., He, H. S. and D. C. Dey (2012) Sample sizes and model comparison metrics for species distribution models, Ecological modelling 227: 29-33. Hanley, J. A. and B. J. McNeil (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve, RADIOLOGY 1: 29-36. Hanspech, J., Kuhn, I., Pompe, S. and S. Klotz (2010) Predictive performance of plant species distribution models depends on species traits, Perspectives in Plant Ecology, Evolution and Systematic 12: 219-225. Hernandez, A. P., 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. Huntley, B., Berry, P. M., Creamer, W. and A. P. McDonald (1995) Modelling present and potential future ranges of some European higher plants using climate response surfaces, Journal of Biogeography 22: 967 – 1001. Hutchinson, G. E. (1957) Concluding remarks, Cold Spring Harbor Symposia on Quantitative Biology 22 : 145-159. Landenburger, L., R. L. Lawrence, S.Podruzny, and C. C. Schwartz, (2008), Mapping Regional Distribution of a Single Tree Species: Whitebark Pine in the Greater Yellowstone Ecosystem, Sensors 8: 4983 – 4994. Lischke, H., Guisan, A., Fischlin, A. and H. Bugmann (1998) Vegetation response to climate change in the Alps-Modeling studies, In: Cebon, P., Dahinden, U., Davis, H., Imboden, D. and C. Jaeger (editors), Aview from alps: regional perspectives on climate change, MIT press, Boston: 309-350. Lobo, M. J., Jimenez-Valverde, A. and Raimundo Real (2007) AUC: a misleading measure of the performance of predictive distribution models, Global Ecology and Biography. Lohel, C. (2012) Relative frequency function models for species distribution modeling, Ecography 35: 1-12. Martinez-Meyer, E. and A. T. Peterson (2006) Conservatism of ecological niche characteristics in North American plant species over Pleistocene-to-Recent transition, Journal of Biogeography. Marquinez, J., J Lastra and P. Garcia (2003) Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis, Journal of Hydrology 270: 1-11. Miller, J. R., Turner, M. G., Smithwick, E. A. H., Dent, C. L. and E. H. Stanely (2004) Spatial extrapolation: the science of predicting ecological patterns and process, BioSceince 54 (4): 310-320. O'' connor, N. E. and T. P. Crowe (2005) Biodiversity loss and ecosystem functioning: distinguishing between number and identy of species, Ecology 86: 1783-1796. Pearson, R. G., Raxworthy, C. J., Nakamura, M. and A. T. Peterson (2007) Predicting species distribution from small numbers of occurrence records: a test case using cryptic geckos in Madagascar, Journal of Biogeography 34: 102-117. Pearson, R. G., Thuiller, W., Araujo, M. B., Martinez-Myer, E., Brotons, L., McClean, C., Miles, L., Segurado, P., Dawson, T. P. and D. C. Lee (2006) Model-based uncertainty in species range prediction, Journal of Biogeography 33: 1704-1711. Pereira, J. M. C., and R. M. Itami (1991) GIS-based habitat modeling using logistic multiple regression: A study of the Mt. Graham red squirrel, Photogrammetric Engineering and Remote Sensing 57 (11): 1475 – 1486. Peterson, A. T. (2006) Uses and requirement of ecological niche models and related distributional models, Biodiversity Informatics 3: 59-72. Peterson, A. T., Papes, M. and J. Soberon (2008) Rethinking receiver operation characteristic analysis applications in ecological niche modelling, Ecological Modelling 213: 63-72. Phillips, S. J., R. P. Anderson and R. E. Schapire (2006) Maximum entropy modeling of species geographic distributions, Ecological Modelling 190: 231–259. Prudhomme, C and D. W. Reed (1999) Mapping extreme rainfall in a mountainous region using geostatistical techniques: a case study in Scotland, International Jurnal of Climatology 19: 1337-1356. Reside, A. E., Watson, I., VanDerWal, J. and A. S. Kutt (2011) Incorporating low-resolution historic species location data decreases performance of distribution models, Ecological Modelling 222: 3444-3448. Rosenfield, G. H. and K. Fitzpatrick-Lins (1986) A coefficient of agreement as a measure of thematic classification accuracy, Photogrammetric Engineering & Remote Sensing 52 (2): 223-227. Skidmore, A. K. (1990) Terrain position as mapped from a gridded digital elevation model, International Journal of Geographical Information Science 4: 33-49. Soberon, J. and A. T. Peterson (2004) Biodiversity informatics: managing and applying primary biodiversity data, Philos. Trans. R. Soc. Lond. B 359: 689-698 Soberon, J. and A. T. Peterson (2005) Interpretation of models of fundamental ecological niches and species'' distribution, Biodiversity Informatics 2: 1-10. Stockwell, D. R. B. and A. T. Peterson (2002) Effects of sample size on accuracy of species distribution models, Ecological Modelling 148: 1-13. Thuiller, T., Lavorel, S., Araujo, M. B., Sykes, M. T. and I. C. Prentice (2005) Climate change threats to plant diversity in Europe, PANS 102 (23): 8245-8250. Van Houwelingen , J. C. and S. Le Cessie (1990) Predictive value of statistical model, Stat. Med. 9: 1303-1325. Williams, J. N., Seo C., Thorne J., Nelson J. K., Erwin S., O’Brien J. M. and M. W. Schwartz (2009) Using species distribution models to predict new occurrences for rare plants, Diversity and Distributions 15: 565-576. Wissel, C. (1992) Aims and limits of ecological modeling exemplified by island theory, Ecological Modelling 63: 1-12. Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Graham, C. H., Guisan, A. and NCEAS Predicting Species Distribution Working Group (2008) Effects of sample size on the performance of species distribution models, Diversity and Distributions 14: 763-773. Zimmermann, N. E. and F. Kienast (1999) Predictive mapping of alpine grassland in Switzerland: species versus community approach, Journal of Vegetation Science 10: 469-482.
摘要: 隨著科技快速進展,人類對於環境的影響及破壞日益嚴重,直接影響到人類賴以維生的生物圈,使生物多樣性快速減少。然而生物多樣性的相關資訊,需透過萃取單一物種相關的空間資訊,整併結合後,方能產生。傳統物種空間資訊,需透過實地生態調查取得,然而傳統生態調查除了耗時費力外,其結果往往受到樣區範圍侷限;然此限制,可透過整合3S技術進行物種生態模擬,加以克服。本研究即應用3S技術,以GIS平台,整併遙測資料以及物種定位調查之GPS資料,建立薯豆及台灣杜鵑兩樹種之生態模擬模式。推導了海拔、坡度、坡向、坡面位置、植生指標、八方位坡向及潛在植群等七項環境因子,並採用經常應用的廣義線性模式、分類樹演算法、範圍環境封包、最大熵值法等四種模式,並以kappa、TSS、AUC、A四種指標,探討不同指標適用的門檻選擇原則,並以多指標評估模擬結果。此外亦探討模擬所需的背景樣本數量、不同流域資料,及物種生態特性等三者對於生態模擬的影響。本研究模擬最佳之主體背景樣本比例約為主體的0.5至2倍。兩樹種,整體模式效力以最大熵值法最佳,範圍環境封包效力最差,而廣義線性模式及分類樹演算法則居中。由研究區域全數薯豆樣本建立四模式,皆較單一流域樣本建立者效力良好;全數台灣杜鵑樣本建立四模式,可對單一流域樣本建立之模式結果優劣,產生「截長補短」的效用。 依樹種結果而論,台灣杜鵑建立之模式皆優於薯豆建立者,此現象因兩樹種於研究區域中生態分布特性差異使然。物種特性亦影響最終預測生育地面積,薯豆預測得到較放寬而破碎的生育地格局;台灣杜鵑則得到較收斂,同時集中的生育地分布。本研究盡可能納入執行物種生態模擬時,必要的考量及檢測,並建立一套生態模擬檢測執行之流程,期望本研究可以做為後續學者研究架構擬訂的參考,進一步加速台灣物種生態模擬研究的進展。
Anthropogenic pollution and depredations to environment are increasingly degrading the biosphere human rely on. The key issue, sharp decrease of biodiversity, have caused fragile ecosystem. To explore the information of biodiversity, integrating spatial pattern of individual species is needed. In the past, species’ spatial patterns were generally obtained by time-consuming and labor-intensive ecological survey with spatial limitations. These limitations of ecological survey could be overcome by species ecological modeling apply with 3S (GIS, GPS, RS) technology recently. The aim of this study is applying GIS (geographic information system) accompany species GPS (global position system) data and RS (remote sensing) derived data layers, to build ecological modeling for target tree species, Elaeocarpus japonicas (EJ) and Rhododendron formosanum (RT). The environmental factor layers used in this study were elevation, slope, aspect, terrain position, vegetation index, eight-direction aspect, and potential flora map. We selected generalized linear models (GLM), classification tree algorithm (CTA), surface range envelope (SRE), and maximum entropy (MAXENT) to build models for EJ and RF. Then, we used kappa statistic, true skill statistic (TSS), area under cruve (AUC) of receiver operating characteristic (ROC) curve analysis, and accuracy (A) to perform multi-measure assessment for model performance evaluation. Nevertheless, we considered necessary thresholds that optimize each measure to convert logistic predictions to binary predictions. Except model building and evaluation, we also considered the other important issues of ecological modeling: (1) the optimal ratio of presence to pseudo-absence for modeling, (2) the difference among models which built by three sampling design (i.e. Tong-Feng samples, Kuan-Dau samples, and integrated samples), and (3) effects of inherent species traits of EJ ant RT to model prediction. The result showed that the optimal ratios of presence to pseudo-absence, regardless EJ and RT, were from 0.5 to 2. Performance of MAXENT was the best followed by GLM and CTA, and SRE was the worst. The models built by integrated samples were superior to others for both of species. Owing to species’ traits, the integrated samples of EJ improved model performance by increase of ample size, and the integrated sample of RT balanced advantages and disadvantages of Tong-Feng and Kuan-Dau samples. In the similar way, species’ traits made models for RT overall outperform those for EJ. The patterns of predictions were also affected by species’ traits. Prediction habitat for EJ was wider and more segmental than RT. This study included the essential procedures and critical issues of ecological modeling. We hope that the framework of this study could be a reference for related research to accelerate species’ spatial information mining by ecological modeling in Taiwan.
URI: http://hdl.handle.net/11455/66210
其他識別: U0005-1906201318505900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1906201318505900
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