彭雲明臺灣大學:農藝學研究所陳嘉瑩Chen, Chia-YingChia-YingChen2010-05-052018-07-112010-05-052018-07-112009U0001-1607200923411200http://ntur.lib.ntu.edu.tw//handle/246246/180119生物族群的動態可以利用該物種之生活史參數來研究。實上,為動物的標記試驗無法長時間密集地監控,個體的正確死亡時間和地點通常無法得知。此, 可透過捕捉再看的統計模式來估計動物的存活率和有關的參數。論文提供詳盡的模式方法介紹,實際分析野外收集之數據並作解釋。章中分析黑面琵鷺 11 年的標記再看資料,鳥種的背景知識在內文中的第二章有概要性的介紹。第三章中, 我們先將完整資料分成三個群別(幼鳥/健康, 成鳥/健康, 成鳥/復原),後採用開放族群之 Cormack-Jolly-Seber 模式進行分析。方法是從個體在第一次被捕捉的條件下,計其物種的存活率和再看機率,中的計算步驟均以 MARK 分析軟體執行。後, 我們解釋;討論三群別的參數估計值。第四章中, 我們詳細地描述如何改善模式中過多參數的問題。用 likelihood-ratio test 及 Akaike''s Information Criterion選擇最佳的簡約模式。黑面琵鷺的例子中,佳的模式僅可由一個存活率, 0.8568(95%CI: 0.7287-0.9302),一個再看機率, 0.9333(0.7851-0.9817),來描述。下來,們在模式中加入限制條件的方法,估算參數受生理或環境因素的影響(年齡或感染肉毒桿菌)。個體遭受疾病感染時,存活率約下降百分之40。外,體的再看機率在幼鳥時期約比成鳥時期減少百分之10。第五章中, 我們利用前幾章所估出的存活率來建立黑面琵鷺在台灣的族群數量模式,預測其未來的族群趨勢。本章中,定性和隨機性模式都放入討論,進一步作模擬分析。模式分析結果,時間來到 2015年時,面琵鷺在台灣的數量(+-SD)將成長到 2360 +- 275今日其在台灣的數量為1104)。維持現今的條件下,面琵鷺將不會有滅亡的危機。當其數量成長兩倍時,動空間和食物將是需要關心的議題。The understanding of the dynamics of animal populations and of related ecologicalnd evolutionary issue frequently depends on analyses of individuals'' life history parameters.ecause marked individuals cannot be followed closely through time,he exact time of death is most often unknown.hus, the analysis of survival studies and experiments must be based on capture-recapture(or resighting) models.his article presents a detailed, practical example on the design, analysis,nd interpretation of capture-recapture studies.he marked-resighting data set on the black-faced spoonbill is given to illustrate the theory,nd its lifestyle of backgrounds is covered in detail in Chapter 2.n Chapter 3 we consider time-dependent Cormack-Jolly-Seber open population models with groups of animals,hich are central to the article.his approach is conditioning on first capture;ence it dose not attempt to model the initial capture of unmarked animals as functions ofopulation abundance in addition to survival and resighting probabilitieshich were developed and estimated using MARK.he fluctuations of estimates in three groups of birds (juvenile/health, adult/health and adult/recovery) are compared.n Chapter 4 we give a detailed description and demonstration of model selection.oodness of fit, likelihood-ratio test and Akaike''s Information Criterion are introducedor the selection of more parsimonious models.he best model to describe the data of BFS is one single survival rate, 0.8568(95%CI: 0.7287-0.9302),nd one single resighting probability, 0.9333(0.7851-0.9817).ext, we examine the effects of physical situation or environmental event(outbreak of botulism or age) by adding constraints in the models.uffering from diseases the survival rate of BFS drop about 40 percent.esighting probabilities in the earlier three years are 10 percent lower than latter years.n Chapter 5 we apply annual population information and life history parameters estimated in previous chapters to modelnd predict the population of BFS, and evaluate the time of extinction.eterministic and stochastic models are both considered,nd simulated results are provided.hen the time is in the year of 2015,he number(+- SD) of BFS in Taiwan will grow up to 2360 +- 275 (today''s population in Taiwan is 1104),nd in current situation the possibility to be extinct is quite small.owever,hen the number is double,pace and food will be another big issue to be considered.Thesis Oral Examination Committee Members Approval Sheet............ icknowledgements…………………………………………………… iihinese abstract ……………………………………………………….iiinglish abstract ……………………………………………………….iv Introduction ………………………………………………………….1.1 Motivation of this dissertation ……………………………………..2.2 Objective of this dissertation ………………………………………2 Background of the Black-Faced Spoonbill……………………………4.1 Distribution …………………………………………………………4.2 History of BFS studies in Taiwan…………………………………..7.3 Characteristics and habits …………………………………………10.3.1 Appearance ………………………………………………………10.3.2 Foraging………………………………………………………….10.3.3 Migration…………………………………………………………11.3.4 Threats……………………………………………………………12.3.5 Conservation …………………………………………………….12 Open-population Capture-Recapture Analysis……………………....13.1 Introduction ……………………………………………………… 13.2 Structure of Capture-Recapture study and Data …………………..14.3 Open Population Capture-Recapture Models ……………………..15.3.1 Notation of Conditional CJS Modeling …………………………15.3.2 Assumptions for the CJS model …………………………………17.4 Maximum Likelihood with CJS model ……………………………17.4.1 Deriving variance of estimates …………………………………..18.4.2 Reconstituting parameter values …………………………………19.5 The BFS example…………………………………………………...22 Model Selection ………………………………………………………31.1 Introduction …………………………………………………………31.2 Goodness of fit of a model ………………………………………….31.3 Likelihood Ratio Test(LRT) ………………………………………..32.3.1 Deviance ………………………………………………………….32.3.2 An example ………………………………………………………34.4 Akaike’s Information Criterion (AIC) ……………………………..36.4.1 AIC……………………………………………………………….36.4.2 AICc ………………………………………………………………37.4.3 QAICc ……………………………………………………………38.4.4 AIC Differences…………………………………………………..39.4.5 An example ……………………………………………………….41.5 Model selection for BFS data ………………………………………42.6 Adding constraints - the effects of disease …………………………45 Population of BFS ……………………………………………………50.1 The simple linear birth and death process ………………………….51.2 Deterministic model ………………………………………………..51.3 Stochastic model ……………………………………………………55.3.1 Probability of extinction ………………………………………….57.4 Simulated model ……………………………………………………61 Conclusion and Discussion …………………………………………...65ibliography…………………………………………………………… 67application/pdf1055009 bytesapplication/pdfen-US捕捉再看存活再看黑面琵鷺模式選擇族群數量capture-recapturesurvivalresightingblack-faced spoonbillpopulation sizemodel selectionthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/180119/1/ntu-98-D93621202-1.pdf