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  1. NTU Theses and Dissertations Repository
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  3. 生物多樣性國際碩士學位學程
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96444
完整後設資料紀錄
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dc.contributor.advisor柯佳吟zh_TW
dc.contributor.advisorChia-Ying Koen
dc.contributor.authorKristina Kryzhovazh_TW
dc.contributor.authorKristina Kryzhovaen
dc.date.accessioned2025-02-18T16:09:49Z-
dc.date.available2026-01-01-
dc.date.copyright2025-02-18-
dc.date.issued2025-
dc.date.submitted2025-01-22-
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96444-
dc.description.abstractNonezh_TW
dc.description.abstractThe increasing impact of climate change on marine ecosystems requires robust modeling approaches to project future species distributional shifts. These biogeographical reorganizations are fundamentally transforming the global fishing industry, as traditional fishing grounds experience declining productivity or ecological regime shifts toward novel species assemblages, while simultaneously creating emerging opportunities in previously marginal areas, though these new fishing frontiers often lack the necessary infrastructure and regulatory frameworks to support sustainable resource exploitation. Some projections that rely solely on temperature as the main parameter may fail to capture the complex interplay of multiple environmental stressors, including dissolved oxygen, pH, and their complex effects on marine ecosystems, potentially leading to oversimplified or inaccurate future projections. This study aims to investigate if adding dissolved oxygen concentration, and pH as predictor variables, can better predict the distribution shifts of 16 demersal and 5 pelagic fish species in the North Sea. To project marine fish response to abiotic factors under SSP1-2.6, SSP2-4.5, SSP5-8.5 scenarios for 2050 and 2100, the ensemble of Species Distribution Models (SDMs) was implemented. While SSP5-8.5 scenario incorporated declining oxygen levels (projected decrease of 31-34% from when to when) and ocean acidification (pH decrease of 5-6% from when to when under which scenarios) by 2100, model performance analysis revealed that temperature alone created the best ensemble model, with the best validation metrics (TSS = 0.950±0.001). Under the most pessimistic scenario - SSP5-8.5, temperature-driven models projected mean north-west shifts of distributional centroids at 245±223 km for most demersal species and eastwards 193±62 km shifts for pelagic species by 2100, while combination of parameters projected generally south and south-eastwards movements for both functional groups up to 143±57 km. When comparing single-factor and multi-factor models, the similarity comes in projection up to 2050 when the southern and central areas tend to be the most suitable ones, however later projections for 2100 showed disparity in direction and magnitude of distributional shifts, especially for the demersal species, seeking refugia in different locations. These findings suggest that while numerous stressors affect marine ecosystems, statistically, temperature’s impact is the strongest for selected species in the North Sea region based on the applied data. The findings reveal significant implications for improving climate impact assessments of marine wildlife through the integration of species distribution models into unified frameworks that enable robust analysis of migration patterns.en
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dc.description.tableofcontentsMaster’s Thesis Acceptance Certificate i
Abstract ii
1. Introduction 1
1.1 Concept of fish distributions 2
1.1 Impact of ocean warming on fish 4
1.2 Impact of acidification on fish 6
1.3 Impact of oxygen depletion on fish 7
1.4 Study Area 9
1.4.1 Geographical characteristics 9
1.4.1.1 Hydrological Characteristics 10
1.4.1.2 Currents 12
1.4.1.3 Frontal Systems 13
1.4.1.4 Atmospheric circulation 14
1.4.1.5 Temperature Regime 14
1.4.2 Current Ecological Issues 15
1.4.3 Economical Importance 18
1.4.4 Fishery Regulations in the North Sea 19
2. Materials and methods 21
2.1. Data source 21
2.1.1. Biological data 21
2.1.2 Environmental data 25
2.2 Statistical methods 28
2.2.1 Species Distribution Models 28
2.2.2 Data processing 32
2.2.3 Ensemble modelling 36
2.2.4 Future predictions 39
3. Results 41
3.1. SDMs performance 41
3.2. Projected distributional shift based on sea temperate 44
3.2.1 Distributional Centroids based on sea temperate 46
3.3 Projected distributional shift based multi-factor models 47
3.3.1 Distributional Centroids based multi-factor models 48
4. Discussion 49
4.1 Comparison of the different model experiments 52
4.2 Uncertainties about using SDMs 55
4.3 Global Fisheries’ challenges 57
5. Conclusion 61
Final results 63
References 64
Illustrations 77
Supplementary materials 97
-
dc.language.isoen-
dc.subjectModel comparisonzh_TW
dc.subjectClimate changezh_TW
dc.subjectFisheryzh_TW
dc.subjectNorth Seazh_TW
dc.subjectSpecies distribution modellingzh_TW
dc.subjectClimate changeen
dc.subjectSpecies distribution modellingen
dc.subjectNorth Seaen
dc.subjectFisheryen
dc.subjectModel comparisonen
dc.titleModelling Fish Species Distributions Under Ocean Acidification and Hypoxia in the North Seazh_TW
dc.titleModelling Fish Species Distributions Under Ocean Acidification and Hypoxia in the North Seaen
dc.typeThesis-
dc.date.schoolyear113-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee丁宗蘇;張俊偉zh_TW
dc.contributor.oralexamcommitteeTzung-Su Ding;Chun-Wei Changen
dc.subject.keywordSpecies distribution modelling,North Sea,Fishery,Model comparison,Climate change,zh_TW
dc.subject.keywordSpecies distribution modelling,North Sea,Fishery,Model comparison,Climate change,en
dc.relation.page140-
dc.identifier.doi10.6342/NTU202500202-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2025-01-23-
dc.contributor.author-college共同教育中心-
dc.contributor.author-dept生物多樣性國際碩士學位學程-
dc.date.embargo-lift2026-01-01-
顯示於系所單位:生物多樣性國際碩士學位學程

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