Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98196Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 丁宗蘇 | zh_TW |
| dc.contributor.advisor | Tzung-Su Ding | en |
| dc.contributor.author | 呂程安 | zh_TW |
| dc.contributor.author | Cheng-An Lu | en |
| dc.date.accessioned | 2025-07-30T16:17:56Z | - |
| dc.date.available | 2025-07-31 | - |
| dc.date.copyright | 2025-07-30 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-25 | - |
| dc.identifier.citation | 林佳祈（2020）。比較系統性調查與公民科學資料於鳥類物種分布模型之表現差異。國立臺灣大學碩士論文。
林春富、楊育昌、吳和瑾（2011）溫度與缺水效應對史丹吉氏小雨蛙蝌蚪存活與發育之影響。臺灣生物多樣性研究 13，225–235。https://doi.org/10.7064/TJB.201107.0225 陳宛均、羅祈鈞、蔡富安、張安瑜（2020）。運用開放資料建置臺灣陸域環境因子多時序資料集。臺灣生物多樣性研究 22，13–44。 陳建志（2015）。臺灣外來種斑腿樹蛙分布之研究。國立東華大學碩士論文。 楊懿如（2010）。臺灣蛙類的分布及棲地利用。臺灣博物季刊，29（4），46-49。 楊懿如、龔文斌（2014）。臺灣地區斑腿樹蛙族群分布探討。臺灣生物多樣性研究 16，21–32。 Anderson, O.F., Guinotte, J.M., Rowden, A.A., Clark, M.R., Mormede, S., Davies, A.J., Bowden, D.A., 2016. Field validation of habitat suitability models for vulnerable marine ecosystems in the South Pacific Ocean: Implications for the use of broad-scale models in fisheries management. Ocean & Coastal Management 120, 110–126. https://doi.org/10.1016/j.ocecoaman.2015.11.025 Bonney, R., Phillips, T.B., Ballard, H.L., Enck, J.W., 2016. Can citizen science enhance public understanding of science? Public Understanding of Science 25, 2–16. https://doi.org/10.1177/0963662515607406 Chen, I.-C., Hill, J.K., Ohlemüller, R., Roy, D.B., Thomas, C.D., 2011. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333, 1024–1026. https://doi.org/10.1126/science.1206432 Chyn, K., Lin, T.-E., Wilkinson, D.P., Tracy, J.L., Lawing, A.M., Fitzgerald, L.A., 2021. Fine-scale roadkill risk models: understanding the intersection of wildlife and roads. Biodivers Conserv 30, 139–164. https://doi.org/10.1007/s10531-020-02083-6 Costa, G.C., Nogueira, C., Machado, R.B., Colli, G.R., 2010. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biodiversity and Conservation 19, 883–899. https://doi.org/10.1007/s10531-009-9746-8 Elith, J., Leathwick, J.R., 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics 40, 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159 Elith, J., H. Graham, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., J. Phillips, S., Richardson, K., Scachetti-Pereira, R., E. Schapire, R., Soberón, J., Williams, S., S. Wisz, M., E. Zimmermann, N., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x Franklin, J., 2010. Mapping Species Distributions: Spatial Inference and Prediction, Ecology, Biodiversity and Conservation. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511810602 Garrote, G., Fernández–López, J., Real Jardín Botánico de Madrid–CSIC, Madrid, Spain, López, G., Agencia de Medio Ambiente y Agua de Andalucia, Sevilla, Spain, Ruiz, G., Agencia de Medio Ambiente y Agua de Andalucia, Sevilla, Spain, Simón, M.A., Consejería de Medio Ambiente de la Junta de Andalucía, Jaen, Spain, 2018. Prediction of Iberian lynx road–mortality in southern Spain: a new approach using the MaxEnt algorithm. Animal Biodiversity and Conservation 41, 217–225. https://doi.org/10.32800/abc.2018.41.0217 Graham, C.H., Ferrier, S., Huettman, F., Moritz, C., Peterson, A.T., 2004. New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution 19, 497–503. https://doi.org/10.1016/j.tree.2004.07.006 Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., McDonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S.A., Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M.R., Possingham, H.P., Buckley, Y.M., 2013. Predicting species distributions for conservation decisions. Ecology Letters 16, 1424–1435. https://doi.org/10.1111/ele.12189 Hernandez, P.A., Graham, C.H., Master, L.L., Albert, D.L., 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x Jarnevich, C.S., Stohlgren, T.J., Kumar, S., Morisette, J.T., Holcombe, T.R., 2015. Caveats for correlative species distribution modeling. Ecological Informatics 29, 6–15. https://doi.org/10.1016/j.ecoinf.2015.06.007 Johnston, A., Moran, N., Musgrove, A., Fink, D., Baillie, S.R., 2020. Estimating species distributions from spatially biased citizen science data. Ecological Modelling 422, 108927. https://doi.org/10.1016/j.ecolmodel.2019.108927 Kearney, M., 2006. Habitat, environment and niche: what are we modelling? Oikos 115, 186–191. https://doi.org/10.1111/j.2006.0030-1299.14908.x Lobo, J.M., Jiménez-Valverde, A., Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17, 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x Miller, J., 2010. Species Distribution Modeling. Geography Compass 4, 490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x Pagany, R., 2020. Wildlife-vehicle collisions - Influencing factors, data collection and research methods. Biological Conservation 251, 108758. https://doi.org/10.1016/j.biocon.2020.108758 Pecchi, M., Marchi, M., Burton, V., Giannetti, F., Moriondo, M., Bernetti, I., Bindi, M., Chirici, G., 2019. Species distribution modelling to support forest management. A literature review. Ecological Modelling 411, 108817. https://doi.org/10.1016/j.ecolmodel.2019.108817 Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M., Araújo, M.B., 2011. Ecological Niches and Geographic Distributions （MPB-49）, in: Ecological Niches and Geographic Distributions （MPB-49）. Princeton University Press. https://doi.org/10.1515/9781400840670 Phillips, S.J., Dudík, M., Schapire, R.E., 2004. A maximum entropy approach to species distribution modeling, in: Twenty-First International Conference on Machine Learning - ICML ’04. Presented at the Twenty-first international conference, ACM Press, Banff, Alberta, Canada, p. 83. https://doi.org/10.1145/1015330.1015412 Pocock, M.J.O., Roy, H.E., Preston, C.D., Roy, D.B., 2015. The Biological Records Centre: a pioneer of citizen science. Biological Journal of the Linnean Society 115, 475–493. https://doi.org/10.1111/bij.12548 Pontius, R.G., Schneider, L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, Predicting Land-Use Change 85, 239–248. https://doi.org/10.1016/S0167-8809（01）00187-6 Santos, R.A.L., Santos, S.M., Santos-Reis, M., Figueiredo, A.P. de, Bager, A., Aguiar, L.M.S., Ascensão, F., 2016. Carcass Persistence and Detectability: Reducing the Uncertainty Surrounding Wildlife-Vehicle Collision Surveys. PLOS ONE 11, e0165608. https://doi.org/10.1371/journal.pone.0165608 Santos, S.M., Carvalho, F., Mira, A., 2011. How Long Do the Dead Survive on the Road? Carcass Persistence Probability and Implications for Road-Kill Monitoring Surveys. PLOS ONE 6, e25383. https://doi.org/10.1371/journal.pone.0025383 Schwartz, A.L.W., Shilling, F.M., Perkins, S.E., 2020. The value of monitoring wildlife roadkill. European Journal of Wildlife Research 66, 18. https://doi.org/10.1007/s10344-019-1357-4 Syfert, M.M., Smith, M.J., Coomes, D.A., 2013. The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. PLOS ONE 8, e55158. https://doi.org/10.1371/journal.pone.0055158 Venne, S., Currie, D.J., 2021. Can habitat suitability estimated from MaxEnt predict colonizations and extinctions? Diversity and Distributions 27, 873–886. https://doi.org/10.1111/ddi.13238 West, A.M., Kumar, S., Brown, C.S., Stohlgren, T.J., Bromberg, J., 2016. Field validation of an invasive species Maxent model. Ecological Informatics 36, 126–134. https://doi.org/10.1016/j.ecoinf.2016.11.001 Wisz, M. s, Hijmans, R., Li, J., Peterson, A., Graham, C., Guisan, A., Elith, J., Dudík, M., Ferrier, S., Huettmann, F., Leathwick, J., Lehmann, A., Lohmann, L., Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J., Zimmermann, N., 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions 14, 763–773. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98196 | - |
| dc.description.abstract | 近年興起的公民科學，帶來大量的物種紀錄，為物種分布模型提供了新的資料來源，但不同的調查方式和資料特性，將會對物種分布模型的預測能力造成影響。生物為了因應全球性的環境變遷，分布範圍持續產生變化，因此了解生物分布資訊成為現今生態保育的重要議題。物種分布模型因為可以透過物種和環境間的關係，推算出可能的物種分布範圍和機率，被廣泛利用在不同領域。為了瞭解公民科學資料庫是否可以用來建立物種分布模型，且不同的資料特性及生物特性將會如何影響模型的預測能力，本研究利用了系統性調查與機會性回報，兩種不同調查方式的公民科學資料集，選取臺灣本土20種蛙類作為研究對象，並運用最大熵模型（MaxEnt）結合臺灣陸域環境因子建立物種分布模型。後續採用了AUC值、Kappa值、正確率與精確率等指標，進行統計分析比較各個模型預測能力。研究結果顯示，使用公民科學資料所建立的物種分布模型，大多擁有合理表現；資料量大且取樣偏差較小的系統性調查資料集，模型預測表現優於機會性回報資料集；狹域分布型的物種，因資料量較少因此在模型預測的精確率上不如廣泛分布型的物種。未來可將公民科學資料應用於物種分布模型，但使用時需注意資料庫的資料量大小及取樣偏差，使物種分布模型的預測能更為準確。 | zh_TW |
| dc.description.abstract | The rise of citizen science has provided abundant species records, offering new data sources for species distribution models (SDMs). However, different survey methods and data characteristics may affect SDM predictive accuracy. As species distributions continue to shift under global environmental change, understanding distribution patterns has become crucial for conservation. To investigate the feasibility of using citizen science databases for species distribution models and the impact of different data characteristics and biological traits on their predictive capabilities. This study evaluated the use of two types of citizen science data—systematic surveys and opportunistic reports—in building MaxEnt-based SDMs for 20 native frog species in Taiwan. Model performance was assessed using AUC, Kappa, accuracy, and precision. Results showed that most models performed reasonably well, with systematic survey data yielding significantly better predictions due to larger sample sizes and lower sampling bias. In contrast, narrowly distributed species showed lower predictive accuracy, likely due to limited data. These findings highlight the value of citizen science in SDMs while emphasizing the need to consider data quality and sampling design. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-30T16:17:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-30T16:17:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………………………………. i
謝辭…………………………………………………………………. ii 摘要…………………………………………………………………. iii Abstract………………………………………………………………. iv 目次………………………………………………………………. v 圖次………………………………………………………………. ix 表次………………………………………………………………. x 前言 1 材料與方法 6 一、研究範圍 6 二、物種出現資料 6 （一）系統性調查資料集 6 （二）機會性回報資料集 7 （三）物種篩選 7 三、環境因子 9 四、物種分布模型 10 五、資料分析 11 結果 14 一、不同資料集物種分布模型的模型表現力 14 （一）驗證AUC值 14 （二）最大Kappa值 14 二、比較不同資料集間20個物種的驗證AUC值、預測機率平均值及適宜棲地網格數 14 三、比較不同資料集間20個物種的最大Kappa值、正確率及精確率 15 四、比較單一物種不同資料集模型的驗證AUC值、適宜棲地網格數、最大Kappa值、正確率及精確率 15 （一）驗證AUC值 15 （二）適宜棲地網格數 16 （三）最大Kappa值 16 （四）正確率 16 （五）精確率 16 五、比較狹域分布與廣泛分布物種在相同資料集中模型評估表現16 討論 18 一、公民科學資料在物種分布模型的表現 18 二、資料樣本數對物種分布模型的影響 19 三、取樣偏差對物種分布模型的影響 20 四、調查特性對物種分布模型的影響 21 五、狹域分布型物種的物種分布模型表現 23 六、環境因子對物種分布模型之影響 24 七、未來展望 25 結論 26 參考文獻 27 附錄 45 附錄表一、用來建置物種分布模型之20個物種在系統性調查與機會性回報資料集之樣本數量及分布型態 45 附錄表二、本研究所採用的各環境因子定義及其單位 46 附錄表三、系統性調查與機會性回報資料集在20個物種的模型驗證AUC值及最大Kappa值區間 47 附錄表四、系統性調查與機會性回報資料集在各物種所建置模型之驗證AUC值、正確率、最大Kappa值、精確率與適宜棲地網格數 49 附錄一、20個物種由系統性調查資料集及機會性回報資料集所建置模型之平均預測結果圖 53 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 公民科學 | zh_TW |
| dc.subject | 兩棲類 | zh_TW |
| dc.subject | 最大熵模型 | zh_TW |
| dc.subject | 路殺 | zh_TW |
| dc.subject | 樣本數 | zh_TW |
| dc.subject | 取樣偏差 | zh_TW |
| dc.subject | MaxEnt | en |
| dc.subject | amphibian | en |
| dc.subject | sampling bias | en |
| dc.subject | sample size | en |
| dc.subject | roadkill | en |
| dc.subject | citizen science | en |
| dc.title | 系統性調查資料和機會性回報資料在蛙類物種分布模型之表現差異 | zh_TW |
| dc.title | Performance Differences of Frog Species Distribution Models when Using Systematic Survey Data and Opportunity Data | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 楊懿如;林思民;柯佳吟 | zh_TW |
| dc.contributor.oralexamcommittee | Yi-Ju Yang;Si-Min Lin;Chia-Ying Ko | en |
| dc.subject.keyword | 公民科學,兩棲類,最大熵模型,路殺,樣本數,取樣偏差, | zh_TW |
| dc.subject.keyword | amphibian,citizen science,MaxEnt,roadkill,sample size,sampling bias, | en |
| dc.relation.page | 72 | - |
| dc.identifier.doi | 10.6342/NTU202502361 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-28 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 森林環境暨資源學系 | - |
| dc.date.embargo-lift | 2025-07-31 | - |
| Appears in Collections: | 森林環境暨資源學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf | 4.4 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.