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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99414| 標題: | 臺灣文蛤養殖水質與微生物相對文蛤生長影響之研究 Effects of Water Quality and Microbiota on Growth of Hard Clam(Meretrix spp.)in Taiwanese Aquaculture |
| 作者: | 侯兆謙 Jhao-Cian Hou |
| 指導教授: | 陳立涵 Li-Han Chen |
| 關鍵字: | 文蛤(Meretrix spp.),特定生長速率(SGR),腸道菌相,藻類組成,廣義加乘模型(GAM),套索回歸(LASSO),網絡分析, Meretrix spp.,Specific Growth Rate (SGR),Gut microbiota,Algal composition,Generalized Additive Model (GAM),LASSO regression,Network analysis, |
| 出版年 : | 2025 |
| 學位: | 碩士 |
| 摘要: | 文蛤(Meretrix spp.)為臺灣極具經濟價值的養殖貝類,惟養殖過程常面臨季節性死亡與成長不良等問題。藻類與微生物組成被認為與貝類健康密切相關,然目前對臺灣文蛤實際攝食藻類種類與腸道菌相結構之了解有限,亦缺乏可預測水質變化與生物風險的微生物指標。本研究旨在探討不同季節與環境條件下,水體與腸道微生物組成對文蛤特定生長速率(Specific Growth Rate, SGR)的潛在影響。研究自2023年7月至2024年2月,每月兩次於彰化伸港兩處文蛤養殖池系統採樣,蒐集水體藻類、微生物、環境參數,以及文蛤腸道菌相與生長數據。分析方法包括廣義加乘模型(Generalized Additive Model, GAM)、套索回歸(Least Absolute Shrinkage and Selection Operator, LASSO)、斯皮爾曼相關係數(Spearman’s rank correlation)及微生物網絡分析。結果顯示,文蛤生長具有明顯季節性,秋季 SGR 顯著高於冬季(p < 0.01)。GAM 顯示日照量約 525 MJ/m²、硝酸鹽濃度 0.2–0.5 mg/L及中等鹽度與 SGR 呈正相關。LASSO 辨識出與文蛤 SGR 正相關的藻類與菌屬如Navicula、Tribonema。Spearman 分析顯示適度控制磷酸鹽濃度有助優化藻菌組成、促進生長。微生物功能呈現季節性差異,秋季腸道菌相有利於能量代謝與腸道穩定;冬季則反映營養限制與潛在有害藻類(如Scrippsiella、Rhodella)富集,可能釋放毒性或阻礙濾食。秋季水體 Cetobacterium益生菌顯著富集,在微生物網絡中與 Blidingia 等正向貢獻藻類呈正相關,並可能透過資源競爭抑制 Clostridium 等潛在有害菌,顯示其兼具營養提供與腸道穩定之潛力。未來可整合微生物共生關係、代謝路徑與基因功能,發展以關鍵菌種為核心的腸道微生態管理策略,以提升文蛤健康與養殖效益。 Meretrix spp., one of the most economically valuable cultured bivalves in Taiwan, often faces challenges such as seasonal mortality and poor growth performance. Shifts in algal and microbial community composition are believed to be closely linked to the health of bivalves. However, knowledge about the actual algal species consumed by Meretrix in Taiwan and the structure of its gut microbiota remains limited. Moreover, there is a lack of microbial indicators and management strategies to predict water quality changes and biological risks. This study aimed to investigate the potential effects of seasonal and environmental variation in water and gut microbial communities on the specific growth rate (SGR) of Meretrix. From July 2023 to February 2024, biweekly sampling was conducted in two clam ponds in Shengang, Changhua, to collect data on phytoplankton, microbial communities, environmental parameters, gut microbiota, and clam growth. Analytical methods included Generalized Additive Models (GAM), Least Absolute Shrinkage and Selection Operator (LASSO) regression, Spearman’s rank correlation, and microbial network analysis. Results showed a clear seasonal pattern, with significantly higher SGR in autumn than in winter (p < 0.01). GAM analysis indicated positive correlations between SGR and sunshine (~525 MJ/m²), nitrate concentration (0.2–0.5 mg/L), and moderate salinity. LASSO identified algae genera such as Navicula and Tribonema as positively associated with SGR. Spearman analysis suggested that proper phosphate control may optimize algal-bacterial composition and enhance growth. Microbial functional profiles showed seasonal variation: autumn gut microbiota supported energy metabolism and intestinal stability, while winter communities reflected nutrient limitation and enrichment of potentially harmful algae (e.g., Scrippsiella, Rhodella), which may release toxins or obstruct filter-feeding. The probiotic Cetobacterium was enriched in autumn water samples and showed a positive correlation with beneficial algae, such as Blidingia, in microbial networks. It may also suppress harmful genera like Clostridium through resource competition, suggesting its dual role in nutrient provision and gut stabilization. These findings underscore the potential of integrating microbial symbiosis, metabolic pathways, and gene functions to develop gut microbiota management strategies based on key taxa, thereby enhancing clam health and aquaculture sustainability. |
| URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99414 |
| DOI: | 10.6342/NTU202503092 |
| 全文授權: | 同意授權(全球公開) |
| 電子全文公開日期: | 2030-07-31 |
| 顯示於系所單位: | 漁業科學研究所 |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 此日期後於網路公開 2030-07-31 | 6.24 MB | Adobe PDF |
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