來一趟說走就走的旅行論文書籍站
國立東華大學 海洋生物研究所 呂明毅所指導 呂紹隆的 人工環境中黃足笛鯛(Lutjanus fulvus) (Forster, 1801)的自然產卵及初期生活史之研究 （2019），提出Guam Zoo關鍵因素是什麼，來自於自然產卵、初期發育、骨骼發生學、仔稚魚培育、黃足笛鯛。
而第二篇論文國立臺灣師範大學 生命科學系 王達益所指導 翁正軒的 探索斑腿樹蛙腸道菌以及其網絡關係 （2018），提出因為有 network、gut microbiota、artificial hibernation、probiotics、Polypedates megacephalus的重點而找出了 Guam Zoo的解答。
人工環境中黃足笛鯛(Lutjanus fulvus) (Forster, 1801)的自然產卵及初期生活史之研究
為了解決Guam Zoo 的問題，作者呂紹隆 這樣論述：
黃足笛鯛 (Lutjanus fulvus) 為一種廣泛分布於印度-太平洋海域的商業、遊釣及觀賞魚類，目前有關其完整的產卵及初期發育資訊所知有限。本研究首次描述二尾黃足笛鯛親魚自2017年9月1日至2019年2月31日期間在水溫24.1-29.0 ˚C條件下自然交配和產卵。黃足笛鯛的生殖具有明顯的月週期性，在每月的望月前後即可收集到受精卵，於朔月前後即停止產卵，每月約產卵4-9次，每次產卵量為496-124,146粒。受精卵為球形透明之浮性卵，卵徑為0.693-0.743 mm (0.720 ± 0.001) (mean ± SE)，油球徑為0.130-0.174 mm (0.153 ± 0
.001)；於26.1 ± 0.7 ˚C水溫下，孵化時間耗時16小時30分鐘。剛孵化的仔魚，體全長(total length, TL)為2.03 ± 0.04 mm，具有27-29 (10-11 + 17-18) 條肌節，單一油球位於卵黃囊前端。仔魚首先以S型輪蟲 (Brachionus ibericus) 、橢形長足水蚤 (Calanopia elliptica) 及腹針胸刺水蚤 (Centropages abdominalis) 的無節幼生餵食，接著依序投餵橢形長足水蚤及腹針胸刺水蚤的橈足幼生及成體，最終以人工飼料馴餌。仔魚於孵化後 (days post hatching, DPH) 第3
天 (2.65 ± 0.04 mm TL) 卵黃囊消耗殆盡並開口攝食，口徑大小為307.39 ± 4.41 µm；12 DPH時 (5.09 ± 0.07 mm TL) ，脊索末端開始上屈，尾下骨及尾鰭鰭條開始發育；26 DPH時 (15.11 ± 0.21 mm TL)，各鰭條數已達成魚之定數 (背鰭硬棘X，軟條14；臀鰭硬棘III，軟條8) ，進入稚魚期。初期攝食之骨骼發育方面，0 DPH至14 DPH仔魚只具備軟骨結構，顯示此時期捕食能力相對較弱，14-20 DPH仔魚上下顎各骨骼及咽齒開始骨化，有助於輾碎和破壞獵物結構，增加其捕食及吸收營養能力，按口徑推測此時期已能夠攝食橈足類成蟲。探
討不同溫度和鹽度對魚卵孵化及剛孵化仔魚畸形率之影響；結果顯示，溫度27.0˚C及鹽度34.0 psu可能為較適合的水質條件。本研究資訊可應用於改善繁殖場的種苗培育方法，有助於未來成功的培育笛鯛科魚類。
探索斑腿樹蛙腸道菌以及其網絡關係
為了解決Guam Zoo 的問題，作者翁正軒 這樣論述：
The concerted activity of intestinal microbes is crucial to the health and development of their host organisms. Studies have suggested that microbial assemblages in the intestine of animals are engines of globally important host physiological processes between hibernating and non-hibernating states
. The advances in Next Generation Sequencing (NGS) technologies facilitate our understanding of gut microbiota with high resolutions in diversity and metabolic functioning between hibernating and non-hibernating seasons. Polypedates megacephalus is an invasive species in Taiwan since 2006. The appro
ach of habitat usage and population dispersal of this invasive treefrogs across seasons have suggested a rapid expansion across counties in Taiwan. However, it still lacks an effective solution to control the expansion of invasive P. megacephalus. Due to the reciprocal interactions between gut micro
biota and host physiology, I attempt to explore gut microbiota of P. megacephalus, decipher microbial interactions to understand the potential mechanisms of microbial ecosystem, and further manipulate host response by modulating gut microbiota according to the guidance by computational network analy
sis. This study not only delineated seasonal changes of gut microbiota in composition and metabolic functioning but demonstrated the potentials of computational network inference toward practical applications on animal systems.The compositional and predicted functional changes of gut microbiota acro
ss non-hibernating and artificial hibernating seasons were identified based on 16S rRNA amplicon analysis. The abundance profile and predicted functions of microbial community significantly change between artificial hibernating (AH) and non-hibernating (NH) treefrogs. Artificial hibernation signific
antly reduces microbial diversity and the level of Firmicutes and increases the level of Proteobacteria in the treefrog gut microbiota. In addition, AH treefrogs harbor core taxonomic units that are rarely abundant in NH treefrogs. Moreover, artificial hibernation significantly increased relative ab
undance of red-leg syndrome-related genera such as Citrobacter and Aeromonas. Functional predictions via PICRUSt and Tax4Fun suggested that artificial hibernation has effects on most pathways including metabolism and signal transduction. These results suggest that artificial hibernation restructure
gut microbiota in treefrogs and significantly reduce microbial complexity of gut microbiome.The use of computational methods to decipher microbial interactions have been applied on microbiome data in a time-series fashion. A time-series microbiome data could monitor the population changes of each ba
cterium in the community over time. Due to the adjustable gut microbiome complexity of hibernating animals, the growth microbiome time-series (GMT) dataset is proposed to apply on the computational network inference methods. Among varieties of network inference tools, regression-based network model
is selected and utilized due to its better performance tested by using in silico dataset. Lotka-Volterra models, also known as predator–prey equations, are the most currently used regression-based method, and predict both dynamics of microbial communities and how communities are structured and susta
ined. The interaction network of gut microbiota at the genus level in the treefrog was constructed using Metagenomic Microbial Interaction Simulator (MetaMIS) package. The interaction network contained 1,568 commensal, 1,737 amensal, 3,777 mutual, and 3,232 competitive relationships, e.g., Lactococc
us garvieae has a commensal relationship with Corynebacterium variabile. To validate the interacting relationships, I took advantage of probiotic system to evaluate the responses of gut microbiota to the probiotic trials. The trials involved different groups including single strain (L. garvieae, C.
variabile, and Bacillus coagulans, respectively) and a combination of L. garvieae, C. variabile, and B. coagulans, because of the cooperative relationship among their respective genera identified in the interaction network. After a two-week trial, the combination of cooperative microbes yielded sign
ificantly higher probiotic concentrations than single strains, and the immune response (interleukin-10 expression) also significantly changed in a manner consistent with improved probiotic effects.