Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/89291
標題: Effects of sample size on accuracy of MaxEnt : A case study of Fagus hayatae
樣本數量對最大熵物種分布模式 (MaxEnt) 準確度之影響:以臺灣水青岡為例
作者: Hong-You Jiang
江鴻猷
關鍵字: 物種分布模式;臺灣水青岡;MaxEnt;樣本數量;準確度;普及率;species distribution model;Fagus hayatae;MaxEnt;sample size;accuracy;prevalence
引用: 孔祥璿 (2011) 以葉綠體及核微衛星DNA標記探究臺灣水靑岡之族群遺傳結構。臺灣大學生態與演化生物學系研究所碩士論文。共53頁。 方精雲、費松林、趙坤、樊擁軍、庄東紅、吳鳴翔 (2000) 浙江省水青岡屬植物的解剖特徵及其分類學意義。北京大學學報 36(4): 509-516。 王亞男、林建良 (2002) 利用RAPD分子標誌研究臺灣山毛櫸之族群變異。中華林學季刊 35(3): 265-272。 王娟、倪健 (2006) 植物種分布的模式研究進展。植物生態學報 30(6):1040-1053。 王運生、謝丙炎、萬方浩、肖啟明、戴良英 (2007) ROC曲線分析在評價入侵物種分布模型中的應用。生物多樣性 15(4): 365-372。 吉成均、沈海花、方精雲 (2002) 基於RAPD標記的我國水青岡屬植物的分類研究。北京師範大學學報 (自然科學版) 28(6): 817-822。 朱容君 (2008) 應用廣義加法模式建立墾丁國家公園稀有植物之潛在分布。國立屏東科技大學森林系碩士論文。共85頁。 呂金誠、歐辰雄、邱清安 (1998) 插天山自然保留區植群之研究 (二) 臺灣水青岡之族群組成。中興大學實驗林研究彙刊 20(2): 79-91。 李建強 (1996) 山毛櫸科植物的起源和地理分布。植物分類學報 37(4): 376-396。 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。共62頁。 林世宗、巫智斌 (2011) 臺灣水青岡的物候與繁殖更新。84-95頁。冰河孑遺的夏綠林—臺灣水青岡。行政院農業委員會林務局。臺北市。共271頁。 邱宗儀 (2008) 宜蘭縣南澳溪流域之植群分類與製圖。國立宜蘭大學自然資源學系碩士論文。共200頁。 邱祈榮、陳子英、劉和義、王震哲、葉慶龍、謝長富 (2009) 臺灣現生天然植群圖集。行政院農業委員會林務局。共420頁。 邱清安 (1996) 插天山自然保留區植相與植群之研究。國立中興大學森林學研究所碩士論文。共162頁。 邱清安、陳子英、王志強、邱祈榮、賴彥任、蔡智勇 (2013) 應用BIOMOD2模式臺灣水青岡之分布。林業研究季刊 35(4): 253-271。 柳榗 (1968) 臺灣產殼斗科植物地理之研究。臺灣省林業試驗所報告 第165號。 徐嘉君 (2007) 利用生態棲位模式預測物種分布模式及其於保育生物學上之應用。林業研究專訊 14(1): 36。 翁仁憲、黃士元、廖天賜 (2004) 珍貴稀有植物—臺灣水青岡。自然保育季刊 46: 24-32。 張鈺敏 (2009) 最大熵物種分布模式應用於臺灣十種樹種之可轉性研究。國立東華大學自然資源管理研究所碩士論文。共90頁。 曹立松 (2007) 應用廣義加法模式建構六種臺灣針葉樹物種分布範圍與氣候因子之關係。國立臺灣大學生物資源暨農學院森林環境暨資源學系碩士論文。共86頁。 陳子英 (2004) 銅山地區山毛櫸林植物資源調查 (1/2)。林務局保育研究系列第92-7號。 陳子英 (2005) 銅山地區山毛櫸林植物資源調查 (2/2)。林務局保育研究系列第93-6號。 陳子英、謝長富、毛俊傑、賴玉菁、林世宗、胡哲明、徐堉峰、楊正釧、林哲榮、孔祥璿、陳品邑、邱宗儀、巫智斌 (2011) 冰河孑遺的夏綠林—臺灣水青岡。行政院農業委員會林務局。臺北市。共271頁。 陳玉峰 (1995) 臺灣植被誌 (第一卷) 總論及植被帶概論 玉山出版社。 陳品邑 (2012) 宜蘭銅山臺灣水青岡林長期動態樣區之森林動態與天然更新。國立宜蘭大學森林暨自然資源學系碩士論文。共115頁。 黃立彥 (2000) 拉拉山臺灣山毛櫸林植群生態與天然更新之研究。國立中興大學森林學系碩士論文。共91頁。 楊正釧 (2011) 臺灣水青岡種實的發芽、儲藏與育苗。96-111頁。冰河孑遺的夏綠林—臺灣水青岡。行政院農業委員會林務局。臺北市。共271頁。 劉棠瑞、蘇鴻傑 (1972) 北插天山夏綠林群落之研究。省立博物館科學年刊 15: 1-16。 劉棠瑞、蘇鴻傑 (1983) 森林植物生態學。臺灣商務印書館。共462頁。 劉業經、呂福原、歐辰雄 (1994) 臺灣樹木誌,國立中興大學農學院。第310頁。 歐辰雄、呂金誠、邱清安、王志強、張美瓊、曾喜育 (1995) 插天山自然保留區植被調查研究 (ɪ)。臺灣省林務局保育研究系列 84-02號。 蔡顯麞、劉湘川 (2009) 兩種脊迴歸模式與複線性迴歸模式之交互驗證比較。測驗統計年刊 17(下): 77-83。 蘇鴻傑 (1987) 森林生育地因子及其定量評估。中華林業季刊 20(1):1-14。 蘇鴻傑 (1988) 臺灣國有林自然自保護區植群生態之調查研究、南澳闊葉樹保護區植群生態之研究。臺灣省農林廳林務局保育研究系列。 蘇鴻傑 (1992) 山地植群帶與地理氣候區。「臺灣生物資源調查及資訊管理研習會」論文集 (彭鏡毅編)。中央研究院植物研究所專刊第十一號。第39-53頁。 龔文斌 (2011) 海岸山脈兩棲類物種分布模式之研究。國立東華大學自然資源與環境學系碩士論文。共86頁。 Allouche, O., A. Tsoar and R. Kadmon (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) Applied Ecology 43: 1223-1232. Araujo, M. B., R. G. Pearson, W. Thuiller and M. Erhard (2005) Validation of species-climate impact models under climate change. Global Change Biology 1(9): 1504-1513. Austin, M. (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200(1): 1-19. Austin, M. P. (1971) Role of regression analysis in plant ecology. Proceedings of the Ecological Society of Australia 6: 63-75. Chen, X. and Y. Lei (2011) Effects of sample size on accuracy and stability of species distribution models: a comparison of GARP and MaxEnt. Lecture Notes in Electrical Engineering 125: 601-609. Chiu, C. A., P. H. Lin and K. C. Lu (2009) GIS-based test for quality control of meteorological data and spatial interpolation of climate data. Mountain Research and Development 29(4): 339-349. Cramer, J. S. 2003. Logit models from economics and other fields. Cambridge University Press. 168 pp. Denk, T. (2003) Phylogeny of Fagus L. (Fagaceae) based on morphological data. Plant Systematics and Evolution 240: 55-81. Denk, T., G. W. Grimm and V. Hemleben (2005) Patterns of molecular and morphological differentiation in Fagus (Fagaceae): phylogenetic implications. American Journal of Botany 92: 1006-1016. Elith, J., C. Graham and the NCEAS Species Distribution Modelling Group (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151. Engler, R., A. Guisun and L. Rechsteiner (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Applied Ecology 41 (2): 263-274. 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. Guisan, A. and N. E. Zimmermann (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135(2): 147-186. Guisan, A. and J. P. Theurillat (2000) Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia 30: 353-384. Guisan, A. and W. Thuiller (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993-1009. Guisan, A., A. Lehmann, S. Ferrier, M. Austin, J. M. C. C. Overton, R. Aspinall and T. Hastie (2006) Making better biogeographical predictions of species' distributions. Journal of Applied Ecology 43: 386-392. Hanberry, B. B., H. S. He and D. C. Dey (2012) Sample sizes and model comparison metrics for species distribution models. Ecological modelling 227: 29-33. 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. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones and A. Jarvis (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. Hirzel, A. H., J. Hausser, D. Chessel and N. Perrin (2002) Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology 83: 2027-2036. Hoehler, F. K. (2000) Bias and prevalence effects on kappa viewed in terms of sensitivity and specificity. Journal of Clinical Epidemiology 53: 499-503. Holdridge, L. R. (1967) Life zone ecology. Tropical Science Center. p. 206. Hukusima, T., S. Y. Lu, T. Matsui, T. Nishio, C.L. Liu and S. Pignatti (2005) Phytosociological study of Fagus hayatae forests in Taiwan. Rendiconti Lincei 9(16): 171-189. Hutchinson, G. E. (1957) Concluding remarks. Cold Harbor symposium on quantitative biology 22: 415-427. Kato, S., T. Koike, T. T. Lei, C. F. Hsieh, K. Ueda and T. Mikami (2000) Analysis of mitochondrial DNA of an endangered beech species, Fagus hayatae Palibin ex Hayata. New Forests 19:109-114. Kira, T. (1977) A climatological interpretation of Japanese vegetation zones. In: Miyawaki, A. and R. Tuxen (eds), Vegetation Science and Environmental Protection. Maruzen, Tokyo. pp. 21-30. Lantz, C. A. and E. Nebenzahl (1996) Behavior and interpretation of the k statistic: resolution of two paradoxes. Journal of Clinical Epidemiology 49: 431-434. Liew, P. M. and S. Y. Huang (1994) Pollen analysis and their paleoclimatic implication in the Middle Pleistocene lake deposits of the Ilan district, Northeastern Taiwan. Journal Geological Society of China 37(1): 115-124. 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 and 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. Marino, J., M. Bennett, D. Cossios, A. Iriarte, M. Lucherini, P. Pliscoff, C. Sillero-Zubiri, L. Villalba and S. Walker. (2011) Bioclimatic constraints to Andean cat distribution: A modelling application for rare species. Diversity and Distribution 17(2): 311-322. Martinez-Meyer, E., A. T. Peterson, J. I. Servin and L. F. Kiff (2006) Ecological niche modeling and prioritizing areas for species relationships. Oryx 40(40): 411-418. McPherson, J. M., W. Jetz and D. Rogers (2004) The effects of species' range sizes on the accuracy of distribution models: Ecological phenomenon or statistical artifact? Journal of Applied Ecology 41: 811-823. Michael, K. and P. Warren (2009) Mechanistic niche modeling: combining physiological and data to predict species' ranges. Ecology Letters 12: 1-17. Murray, J. V., S. L. Choy, C. A. McAlpine, H. P. Possingham and A. W. Goldizen (2008) The importance of ecological scale for wildlife conservation in naturally fragmented environments: A case study of the brush-tailed rock-wallaby (Petrogale penicillata). Biological Conservation 141: 7-22. Pearce, J. and S. Ferrier (2000) An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecological Modelling 128: 127-147. Pearson, R. G. (2007) Species' Distribution Modeling for Conservation Educators and Practitioners. Synthesis. American Museum of Natural History. 50 pp. Pearson, R. G., C. J. Raxworthy, M. Nakamura and A. T. Peterson (2007) Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. Joumal of Biogeography 34: 102-117. Peters, R. (1997) Beech forests. Kluwer Academic Publishers, Dordrecht, Boston and London. 169 pp. Phillips, S. J., M. Dudik and R. E. Schapire (2005) Correcting sample selection bias in maximum entropy density estimation. Advances in Neural Information Processing Systems 18: 323-330. Phillips, S. J., M. Dudik, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick and S. Ferrier (2009) Sample selection bias and presence-only distribution model: implications for background and pseudo-absence data. Ecological Applications 19(1): 181-197. Phillips, S. J., R. P. Anderson and R. E. Schapire (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. Reese, G. C., K. R. Wilson, J. A. Hoeting and C. H. Flather (2005) Factors affecting species distribution predictions: a simulation modelling experiment. Ecological Applications 15: 556-564. Roberts, D. W. and S. V. Cooper (1989). Concepts and techniques of vegetation mapping. General technical report INT-U.S. Department of Agriculture, Forest Service, Intermountain Research Station 257: 90-96. Rushton, S. P., S. J. Ormerod and G. Kerby (2004) New paradigms for modelling species distributions? Journal of Applied Ecology 41: 193-200. Segurado, P. and M. B. Araujo (2004) An evaluation of methods for modelling species' distributions. Journal of Biogeography 31: 1555-1568. Seoane, J., L.M. Carrascal, C. L. Alonso and D. Palomino (2005) Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecological Modelling 185: 299-308. Shen C. F. (1992) A monograph of the genus Fagus Tourn. ex L. (Fagaceae). Doctoral Thesis, The City University of New York, New York. Stockwell, D. R. B. and A. T. Peterson (2002) Effects of sample size on accuracy of species distribution models. Ecological Modelling 148: 1-13. Swets, J. A. (1988) Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. Tanaka, N., T. Matsui, T. Yagihashi and H. Taoda (2006) Climatic controls on natural forest distribution and predicting the impact of climate warming: Especially referring to Buna (Fagus crenata) forests. Global Environmental Research 10(2): 151-160. Tognelli, M. F., S. A. Roig-Junent, A. E. Marvaldi, G. E. Flores and J. M. Lobo (2009) An evaluation of methods for modeling distribution of Patagonian. Revista Chjiena de Historia Natural 82: 347-360. Verbyla, D. L. and J. A. Litvaitis (1989) Resampling methods for evaluating class accuracy of wildlife habitat models. Environmental Management 13: 783-787. William, T. B., S. Robert and S. B. Justin (2012) The effects of small size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35: 250-258. Williams, J. N., C. Seo, J. Thorne, J. K. Nelson, S. Erwin, J. M. ƠBrien and W. Schwartz. (2009) Using species distribution models to predict new occurrences for rare plants. Diversity and Distribution 15(4): 565-576. Wilson, J. P. and J. C. Gallant (2000) Terrain analysis, John Wiley and Sons, Inc., 51-58. Wisz, M. S., R. J. Hijmans, J. Li, A.T. Peterson, C.H. Graham, A. Guisan and NCEAS Predicting Species Distributions Working Group (2008) Effects of sample size on the performance of species distribution models. Diversity and Distributions 14: 763-773. Zhang, J. (2012) Prediction of potential survival areas of smooth cordgrass (Spartina alterniflora) in China. Dissertation, Uppsala University. 44 pp. Zimmermann, N. E., T. C. Edwards Jr., C. H. Graham, P. B. Pearman and J. Svenning (2010) New trends in species distribution modeling. Ecography 33(6):985-989.
摘要: 
物種分布模式的準確度除了因物種、模式方法、解釋變數和研究尺度等因子而有所差異外,亦受到樣本數量的影響。本研究以40 m間距對臺灣水青岡 (Fagus hayatae) 分布區進行等距取樣,可獲取1~8,014個分布樣點,並選取8項環境預測變數,以MaxEnt為物種分布模式方法,用AUC (area under the receiver operating characters curve)、TSS (true skill statistic) 和Kappa評估MaxEnt成效,藉以比較不同樣本數量所建構之模式的差異。結果顯示隨著樣本數量增加,MaxEnt模式準確度也隨之增加,直至達到最大準確度後趨於緩和,其原因為建構模式的環境變數估計值會隨樣本數量的增加而減少其不確定性;但根據AUC指標顯示,過量的樣本 (如2,008、8,014點) 會使模式準確度略為下降,另一方面本研究也顯示部分小樣本 (10點以下) 建構的模式亦表現良好,證實小樣本在特定的取樣條件下,仍可建立具有可信度的模式;本研究結果顯示,35~200個無空間偏差之物種出現點應是合宜的樣本數量。評估模式表現之3種指標並不一致,其中Kappa明顯受到普及率之影響,並不適用於評估樣本數量對物種分布模式之影響。由折刀分析法 (jackknife analysis) 和主成分分析 (principal component analysis) 評估8個環境變數對模式的重要性之結果發現,年降水量、降水季節性、溫度季節性和溫量指數等4個環境變數對模式的貢獻度較高,證實熱量及水分因子對臺灣水青岡具有重要的影響性。棲地適宜度預測分布圖顯示,包含過去曾發現臺灣水青岡分布的部份地區,可能因競爭或氣候變遷,造成族群消失,或是該地族群過於稀少而未再發現,未來應進一步調查,以釐清臺灣水青岡的分布,或藉由氣象資料,探討環境變化,推斷臺灣水青岡分布減少之原因。

The accuracy of species distribution modeling (SDM) is affected not only by species, modeling method and scale, and environmental predictor, but also by sample size. In this study, we extracted the occurrence data of Fagus hayatae with 1~8,014 occurrence points together with 8 environmental predictors to evaluate the effect of sample size on MaxEnt through AUC, TSS and Kappa indices. The results showed that through 40 m arithmetic series of sampling scale, the accuracy of MaxEnt increased with enlarging sample size, accompanied by decreasing uncertainty, until it reached the maximum value of AUC, TSS and Kappa indices. However, based on AUC index, the exaggerated sample size such as 2,008 and 8,014 points led to a lightly reduction of MaxEnt accuracy. On the other hand, some cases using small sample size (less than 10 points) for SDM also performed well. The result represented that small sample size from a specific sampling condition could provide a reliable modeling, and for modeling F. hayatae distribution, with sample size 35 ~ 200 occurrence points unbiased spatially was appropriate. AUC, TSS and Kappa were inconsistent for evaluating the SDM performance. Because Kappa was sensitive to the prevalence, and not applicable to evaluate the effect of sample size on SDM accuracy.By the jackknife analysis and principal component analysis to assessment the important of model with eight environment variables, and we found the annual precipitation, precipitation of seasonality, temperature seasonality and warmth index had higher influence than other environment variables, that confirmed heat and moisture factors had important impact to F. hayatae. The predicted habitat suitability map showed that, some distributed areas of F. hayatae found in past decades were disappeared by competition or climate change, or couldn't be rediscovered by its limited population size. Further investigation to clarity the decreace F. hayatae distribution with he needed, probably by meteorological data to explore the environmental changes in the areas.
URI: http://hdl.handle.net/11455/89291
其他識別: U0005-1501201416581800
Rights: 同意授權瀏覽/列印電子全文服務,2017-01-17起公開。
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