Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23606
DC FieldValueLanguage
dc.contributor李培芬zh_TW
dc.contributorPei-Fen Leeen_US
dc.contributor丁宗蘇zh_TW
dc.contributorTzung-Su Dingen_US
dc.contributor.advisor吳聲海zh_TW
dc.contributor.advisorSheng-Hai Wuen_US
dc.contributor.author蘇珊慧zh_TW
dc.contributor.authorSu, Shan-Huien_US
dc.contributor.other中興大學zh_TW
dc.date2013zh_TW
dc.date.accessioned2014-06-06T07:20:46Z-
dc.date.available2014-06-06T07:20:46Z-
dc.identifierU0005-0302201218203800zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/23606-
dc.description.abstract黃緣閉殼龜,又稱食蛇龜,是台灣本土種半陸棲澤龜。近年來由於坊間謠傳其龜板中藥藥效甚佳,加上食蛇龜個性溫和好飼養,使其成為寵物市場的新寵,造成食蛇龜在野外的盜獵狀況嚴重,影響其在野外族群的存活。食蛇龜主要棲息在中低海拔次生林,除了夏季午後雷陣雨之後容易發現牠的蹤跡以外,帄時非常難發現。本研究旨在了解食蛇龜從冰河時期到2080 年的分布預測,從而了解其保育現況以及未來可行的研究及保育管理規劃。 本研究在2010 – 2011 以野外訪問、設陷阱及蒐集博物館資料等方法,蒐集了424 筆食蛇龜實際分布地點,供預測分布使用。三種分布預測模式用來計算食蛇龜分布機率,分別是基因演繹法(Genetic Algorithm of Rule-set Prediction, GARP)、最大熵函數演化法(Maximum Entropy, MaxEnt)及生態棲位因子分析(Ecological Niche Factor Analysis, ENFA)。在分析食蛇龜實際分布的點後,選取了七個相互間相關性低,但最能代表食蛇龜分布棲位的因子做為預測分布用。所有的預測模式均經過曲線下面積(area under curve)的評估值檢測。預測結果顯示大多數適合食蛇龜分布的地區均沒有在現有保育區的範圍內。 在預測了現下分布之後,本研究進一步利用現下分布狀況,預測冰河時期以及2050 年、2080 年的分布預測。結果顯示,冰河時期時,食蛇龜集中分布於台灣南部,且從冰河時期到2050 年為止,適合食蛇龜分布的區域漸漸變多;在2050年到2080 年,適合食蛇龜分布的範圍則呈現下降的趨勢。雖說現下食蛇龜適合的分布區域是增加的,但獵捕壓力越來越高加上食蛇龜多分布於人多區域的狀況下,食蛇龜的野外生存情況並不樂觀。從2050 到2080 食蛇龜的適合分布的區域雖然減少了,但分布區域漸漸往高海拔保育區及人煙稀少的區域移動。若是往後研究區域及保育計畫可以著重於適合食蛇龜分布的區域,將會有助於復育食蛇龜,並且不會需要浪費多餘的經費再另外設立食蛇龜野外分布保留區。zh_TW
dc.description.abstractThe yellow-margined box turtle (Cuora flavomarginata) is a relatively terrestrial freshwater turtle. Because of the rising pet trade requirement and Chinese medicine need, survival of the species is now under heavy pressure. Cuora flavomarginata inhabits mainly low elevation secondary forests, but is not easily spotted except after rains. Our aims are to understand the distribution of C. flavomarginata from past to future, and seek potential areas for protecting the species. We collected 424 actual presence localities of C. flavomarginata using field interviewing, trapping, and museum record in 2010 - 2011. Three species distribution modeling (SDM) methods (environmental niche factor analysis, ENFA; genetic algorithm for rule-set prediction, GARP; maximum entropy, MaxEnt) were used to predict the potential range of the species. Seven environmental factors were selected to conduct the prediction. Area under receiver operating characteristic (ROC) curve (AUC) was used to test the accuracy of the models. The AUC value of the models ranged from 0.83 - 0.9, indicating that all the models have good predictive power. The results indicate that most of the predictive presence area of the species is not under protection currently. In addition, I conducted distribution status for the species from the last glacial age to future by global circulation modeling (GCMs). The results shows that the suitable area in the last glacial maximum (LGM) were concentrated in the southern part of Taiwan, and the number of suitable area increase from LGM to 2050, but would decrease from 2050 to 2080 to higher elevations. These results provide a basis for conservation management plans, such as strengthening the law enforcement in areas with high distribution probabilities instead of setting up a new conservation area.en_US
dc.description.tableofcontentsIndex 1. Introduction……………………………………………………….…..…1 1.1 Species distribution modeling……………………………...………...1 1.2 Researching species…………………………………………….....…2 1.3 Knowing the past, predicting the future…………………………..….4 2. Materials and methods…………………………………...…………..…..5 2.1 Study area………………………………………………………..…..5 2.2 Distributional data collection…………………………………......….5 2.2.1 Field interview…………………………………………………5 2.2.2 Trapping…………………………………………………….…7 2.3 Environmental factor selection………………………........................7 2.4 Predictive methods……….………………………………..................9 2.4.1 Maximum entropy (MaxEnt)………………………….……..9 2.4.2 Genetic algorithm (GA)……………………………….……..9 2.4.3 Ecological niche factor analysis (ENFA)…………….………10 2.4.4 Ensemble and hotspot……………………………….….…….11 2.4.5 Gap analysis……………………………………………...…..11 2.5 Model evaluation…………………………………………………...11 2.6 From past to future…………………………………….....................12 3. Results……………………………………………………………….....14 3.1 Species distributional data collection…………………..…………..14 3.2 Predictive distribution of current status……….……………………14 3.2.1 Ensemble result………………………………………………15 3.2.2 Conservation status of current predicted distribution…..……16 3.3 Suitable distribution areas of glacial age and future ………………17 3.3.1 Predictive results of last glacial age……………….…………17 3.3.2 Predictive results of future by first method………………….17 3.3.3 Predictive results of future by second method………………18 3.3.4 Overlaid suitable area from current to future………………18 4. Discussion……………………………………………………………...19 5. Reference……………………………………………………………….23 List of table……….……………………………………………………….30 List of figure………………………………………………………………49 6. Supplementary material………………..……………………………….73zh_TW
dc.language.isoen_USzh_TW
dc.publisher生命科學系所zh_TW
dc.subjectAUCen_US
dc.subject曲線下區域zh_TW
dc.subjectclimate changeen_US
dc.subjectCuora flavomarginataen_US
dc.subjectfield interviewen_US
dc.subjectpredictive distributionen_US
dc.subjectspecies distribution modeling (SDM)en_US
dc.subject氣候變遷zh_TW
dc.subject食蛇龜zh_TW
dc.subject田野訪問zh_TW
dc.subject預測分布zh_TW
dc.subject物種分布模式zh_TW
dc.title台灣地區食蛇龜的分布預測zh_TW
dc.titlePredictive Distribution of Cuora flavomarginata (Testudines: Geoemydidae) in Taiwanen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
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