2016-08-012024-05-18https://scholars.lib.ntu.edu.tw/handle/123456789/711639摘要：體長結構 (size structure)是代表魚群狀態的重要指標，因為它可以反應魚群的穩定度、對環境的抵抗力、以及族群的生產力。過去研究顯示體長結構主要受到 (1) 漁業捕撈以及 (2) 溫度變化兩者的影響。然而，漁撈及溫度變化哪一者對體長結構有較大的影響並不清楚。另一方面，以往研究多使用單變數體長結構指標 (univariate size-based indicator)將體長結構簡化為單一指標來表示體長結構的變化（例如平均體長，體長組成的均勻度，體長95%位數），然而許多研究顯示這些指標不一定能反應體長結構整體的改變。所以本研究的目的是使用多元回歸分析中的變異分配，來分析完整的體長結構變化，藉以了解經濟魚種的體長結構如何受到溫度和捕撈影響，以及去探討兩者是否有交互作用。我們預計分析來自Northeast Pacific, Northeast Atlantic, North Sea, Baltic Sea and Arctic Sea 等多個魚種，以達到跨物種整合分析。在量化溫度和捕撈對魚種的體長結構影響強度後，我們更進一步以多變數複回歸分析探討這些強度與其他因子的連結，包含魚種的生活史(成長率，成熟年紀，極限體長)，棲地型態，棲地的水溫及其變異，漁業死亡率及其變異。最後再比較我們所提出的新方法和傳統以單變數體長結構指標分析的結果。 我們預期本研究將提出分析經濟魚種的體長結構如何受到溫度和捕撈影響的新思維。這些資訊是目前所有漁業國家對於漁業資源管理都需要的。我們的結果將對於在氣候變遷下如何經營管理海洋漁業資源有建樹。 <br> Abstract: Size structure represents a key demographic characteristic of fish population, playing an important role in maintaining population stability and reproductive output. Size structure, on one hand, is predicted to shrink due to size-selective removal caused by fishing, while on the other hand, to shrink due to warming-induced metabolic constraints. However, the relative contribution and the potential interaction of these forces have not been thoroughly examined empirically. In addition, existing analyses on size structure have thus far focused on univariate size-based indicators (SBIs), such as mean length, evenness of size classes, or the upper 95-percentile of the length frequency distribution (L95). These over-simplified univariate SBIs may not effectively quantify fishing and/or environmental effects on size structure. To bridge the gap, we propose to use variation partitioning approach to examine how the variation of size structure (composition of size classes) responds to fishing and warming and the interactive effects. We collect data of exploited stocks living in a wide range of temperature gradient, including the Northeast Pacific, Northeast Atlantic, North Sea, Baltic Sea and Arctic Sea. After obtaining the variance explained by fishing, temperature and their interaction, we then employ multivariate regression analyses to link the percentage of explained variance by fishing and temperature to the life history traits including growth rate (K), maturation age (A50), length infinity (Linf), and habitat type, fishing indices including mean fishing mortality (F) and variability of F, and habitat temperature including mean SST and changing rate of SST. Finally, to demonstrate the efficacy of our approach, we compare the performance of variation partitioning approach with the traditional univariate SBIs analyses. We aim to provide a novel approach to quantify how fishing and warming affect size structure of exploited stocks, which is a key requirement for contemporary fisheries managements in most countries. Our results likely have profound implications for fisheries managements in the context of a changing environment.體型大小結構氣候暖化漁業壓力生活史參數棲地變異分配生物指標size compositionfisheriesclimate warminglife history traitshabitatvariation partitioningsize-based indicator