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研究生:莫巴列
研究生(外文):MUBARAK MAMMEL
論文名稱:海洋環境變化對台灣沿岸海域紅甘鰺(Seriola dumerili)棲地適宜性和營養生態的影響
論文名稱(外文):Influence of oceanic conditions change on the habitat suitability and trophic ecology of greater amberjack (Seriola dumerili) in the coastal waters off Taiwan
指導教授:李明安李明安引用關係
指導教授(外文):Ming-An Lee
口試委員:曾萬年曾振德張懿張以杰藍國瑋呂學榮李明安
口試委員(外文):Wann-Nian TzengChen-Te TsengYi-ChangYi-Jay ChangKuo-Wei LanHsueh-Jung LuMing-An Lee
口試日期:2023-07-05
學位類別:博士
校院名稱:國立臺灣海洋大學
系所名稱:環境生物與漁業科學學系
學門:農業科學學門
學類:漁業學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:英文
論文頁數:200
中文關鍵詞:紅甘鰺物種分佈棲息地模型氣候變化營養生態學
外文關鍵詞:greater amberjackspecies distributionhabitat modelingclimate changetrophic ecology
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隨著環境變遷的不確定性增加,海洋魚類資源及其棲息的生態系統的海洋環境也面臨極大的挑戰,這些挑戰包括過度捕撈、棲息地破壞、氣候變化以及捕撈對整體生態的影響。值得注意的是,物種的分佈和生態從根本上取決於對合適海洋環境條件的偏好,但氣候變化後相一致的環境條件變化可能逐漸使物種暴露在其海洋棲息地適宜環境範圍之外的條件下。魚類是海洋生態系統的重要組成部分,在海洋食物網中發揮著關鍵作用,海洋食物網通過不同魚類之間以及魚類與其他海洋生物之間的捕食-獵物關係維持海洋的健康。對於世界各地數億人來說,海洋生物是食物和生計的來源。紅甘鰺(Seriola dumerili)是一種大型中上層魚類,主要分佈於溫帶至熱帶海域,是經濟和生態上最有利可圖的海洋頂級捕食者之一。在全球範圍內,海洋物種和群落受到棲息地、時空分佈、生態相互作用以及生態系統結構和動態變化的影響。儘管生物對環境變化的反應給保護規劃和漁業管理帶來了問題,但海洋物種的環境和生態之間可測量的聯繫可以支持主動的氣候適應,但台灣水域紅甘鰺的棲息環境和生態尚不清楚,也沒有製定管理或保護計劃。據此,本論文的目的是探索和增進對海洋環境因素對台灣水域紅甘鰺棲息地分佈和營養生態的影響和影響的認識和理解。
本論文的第一個目標是確定台灣海峽(TS)從南到北(20°N–29°N和115°E–127°E)的環境特徵紅甘鰺的時空分佈格局,以及航海日誌和航次的時空漁業數據。此一章節使用了廣義線性模型(GLM)和廣義加性模型(GAM),並證實 GAM 優於 GLM 模型。第二個目標是開發一個綜合的棲息地適宜性模型,以探索紅甘鰺的棲息地適宜性,並估計氣候引起的 ENSO 事件的變化。結果表明ENSO 事件調節海峽環境條件,並對 TS 中紅甘鰺的捕獲率和棲息地適宜性產生影響。第三個目標是研究台灣水域中紅甘鰺及其主要餌料生物的攝食動態。研究表明,覓食地中餌料生物物種的可用性受到環境因素空間變化的影響,從而影響目標物種的飲食偏好。研究結果表明,紅甘鰺具有普遍的攝食策略,是大型中上層物種的機會主義捕食者,捕食在台灣水域發現的多種餌料生物物種。這些發現有助於更好地了解紅甘鰺的攝食行為以及台灣水域餌料生物可用性與海洋環境因素之間的關係。論文最後一個目標為胃內容物分析、碳和氮穩定同位素分析以及貝葉斯混合模型分析,來估計生物的營養生態。結果顯示紅甘鰺營養位階與其餌料生物偏好之間呈現個體發生的轉變,應屬海洋生態系統中的頂級捕食者物種。

Marine fish species and the oceanographic environment of the ecosystems they inhabit are in decline in many regions of our ocean, this decline is caused by a variety of factors, such as overfishing, habitat destruction, climate change, and the overall ecological impact of fishing. The distribution and ecologies of species are fundamentally supported by preferences for suitable oceanographic environmental conditions. However, changes in environmental conditions that are consistent with the consequences of climate change are progressively exposing species to conditions outside of their preferred marine habitat range. Fish are an essential component of marine ecosystems and play a key role in the marine food web, which maintains the health of the ocean through predator-prey relationships between different fish species and between fish and other marine organisms. For hundreds of millions of people around the world, rely on ocean life as a source of food and a living. The greater amberjack (Seriola dumerili) is a large pelagic fish species that is a member of Carangidae family. It mainly distributed in temperate to tropical waters, and it is one of the most profitable economically and ecologically important marine top predator. Globally, marine species and communities have been impacted through changes in habitat, spatial and temporal distribution, ecological interactions, and ecosystem structure and dynamics. Although, the fact that biological responses to environmental change present issues for conservation planning and fisheries management, measurable links between the environment and ecology for marine species can support proactive climate adaptation. Despite the ecological and economic importance, especially regarding fishery management, trophic ecosystem on species and impact on climate events, therefore, the information on the current status of greater amberjack in the Taiwanese waters is unknown, and there is no management or conservation plan has been developed. However, the detailed research is needed to efficiently manage this valuable fishery. The purpose of this thesis is to explore and advance knowledge and understanding of the impacts and influences of oceanographic environmental factors on the habitat distribution and the trophic ecology of greater amberjack in Taiwanese waters.
The first objective of the thesis was to identify the spatiotemporal distribution pattern of greater amberjack with environmental characteristics in the coastal waters off Taiwan, including Taiwan Strait (TS) from south to north (20ºN–29ºN and 115ºE–127ºE), as well as spatiotemporal fisheries data from logbooks and voyage data recorders of Taiwanese fishing vessels and satellite-derived remote sensing environmental data. To examine the impact of environmental conditions and catch rates, I utilized the generalized linear model (GLM) and generalized additive model (GAM). In terms of the functional relationship of the GAM for producing a trustworthy and reliable predictive tool, the results show that the GAM outperforms the GLM model. The second objective was to develop an integrated habitat suitability model to explore the habitat suitability for the greater amberjack in the coastal waters off Taiwan, including TS and to estimate the changes in the ENSO events caused by the climate. ENSO, a significant oceanic phenomenon, causes interannual variations in the climate and ecosystem productivity of tropical and subtropical regions. For many commercial fish species, these changes have an impact on the habitat suitability. These results suggest that ENSO events regulate environmental conditions and have an impact on catch rates and habitat suitability for the greater amberjack in the coastal waters off Taiwan, including TS. The third objective was examining the feeding dynamics of the greater amberjack and its key prey species in the Taiwanese waters. The availability of prey species in the foraging ground was shown to be influenced by spatial variations in environmental factors, thereby affecting the dietary preference of the target species. Findings reveal that the greater amberjack has a generalized feeding strategy and is an opportunistic predator of large pelagic species, consuming a variety of prey species found in the Taiwanese waters. These findings contribute to a better knowledge of the greater amberjack's feeding behavior and the relationship between prey availability and oceanographic environmental factors in the Taiwanese waters. The fourth and last objective of the thesis reveal that researchers recently employed a novel method that included stomach content analysis, stable isotope analysis of carbon and nitrogen (δ13C and δ15N), as well as Bayesian mixing model analysis, to estimate the trophic ecology of the greater amberjack and their ecosystem trophic dynamics. The relationships between trophic position of greater amberjack and its prey preferences suggested an ontogenetic shift and as considered as the top-level predator species in the pelagic marine ecosystem.

Table of Contents
Acknowledgement I
摘要 II
Abstract III
Table of Contents V
List of Tables X
List of Figures XII
CHAPTER I GENERAL INTRODUCTION 1
1.1 Overview of study species 2
1.2 Habitat distribution and spatial pattern of fish species 3
1.3 Effect of climate related environmental changes on the marine ecosystem 4
1.4 Feeding dynamics and marine trophic ecology 5
1.5 Thesis outline and objectives 7
CHAPTER II SPECIES DISTRIBUTION MODEL OF GREATER AMBERJACK 11
2.1 Introduction 12
2.2 Materials and methods 14
2.2.1 Greater amberjack fishery data 14
2.2.2 Satellite-derived environmental data 14
2.2.3 Statistical models for the spatiotemporal predictions of catch rates 15
2.2.4 Predictions of greater amberjack catch rates 17
2.3 Results 17
2.3.1 Spatial and temporal distribution pattern of S. dumerili in the study area 17
2.3.2 Environmental effect on S. dumerili catch rates by statistical model 18
2.3.3 Predicted spatial distribution pattern of greater amberjack 19
2.4 Discussion 20
2.4.1 Distribution of greater amberjack in the study area 20
2.4.2 Environmental factors affecting the greater amberjack 23
2.5 Conclusion 26
CHAPTER III ENSO EVENTS IMPACT ON HABITAT OF GREATER AMBERJACK 46
3.1 Introduction 47
3.2 Materials and methods 49
3.2.1 Greater amberjack fishery data 49
3.2.2 Satellite-derived marine environmental data 50
3.2.3 Climatic Niño Index 50
3.2.4 Statistical analysis 51
3.2.5 Partial least squares regression method 51
3.2.6 Development of the habitat suitability index model 52
3.3 Results 53
3.3.1 Greater amberjack time-series analysis and spatial distribution pattern of catch rates in the study area 53
3.3.2 ENSO episodes and temporal variability of catch rates of greater amberjack 54
3.3.3 Environmental variability in ENSO event periods 54
3.3.4 Association between greater amberjack catches and environmental variables in the study area 55
3.3.5 Environmental variability and suitability index 56
3.3.6 Variability in habitat suitability during ENSO events 56
3.3.7 Spatial variability of environmental conditions and suitable habitats 57
3.4 Discussion 57
3.4.1 Seasonal catch rate variations and spatial distribution patterns 57
3.4.2 Influence of marine environmental variability on the catch rates of greater amberjack 59
3.4.3 ENSO events and habitat variations of greater amberjack 61
3.5 Conclusion 63
CHAPTER IV FEEDING STRATEGY OF GREATER AMBERJACK 80
4.1 Introduction 81
4.2 Materials and methods 83
4.2.1 Study area and sampling 83
4.2.2 Remotely sensed environmental data 83
4.2.3 Laboratory analysis 83
4.2.4 Stomach content analysis 84
4.2.5 Dietary similarity and statistical analysis 86
4.3 Results 87
4.3.1 Environmental conditions and spatial distribution of the prey species of the greater amberjack 87
4.3.2 Dietary composition of the greater amberjack 87
4.3.3 Feeding intensity and prey species diversity 89
4.3.4 Dietary similarities and feeding strategy 89
4.4 Discussion 90
4.4.1 Environmental factors and spatial variations in prey 90
4.4.2 Feeding preference and dietary composition of the greater amberjack 91
4.4.3 Feeding strategy of the greater amberjack 92
4.5 Conclusion 94
CHAPTER V TROPHIC ECOLOGY OF GREATER AMBERJACK 115
5.1 Introduction 116
5.2 Materials and methods 118
5.2.1 Conceptual framework 118
5.2.2 Data collection and sampling 118
5.2.3 Remotely sensed environmental data 118
5.2.4 Generalised additive model (GAM) 119
5.2.5 Stomach content analysis 119
5.2.6 Niche breadth Indices of greater amberjack 120
5.2.7 Investigation of SCA, niche breadth and isotopic variations by length, sex and season of greater amberjack 121
5.2.8 Stable isotope analysis 121
5.2.9 Bayesian mixing model for trophic position estimates 122
5.3 Results 123
5.3.1 Oceanographic environmental effects on habitats of greater amberjack by statistical model 123
5.3.2 Dietary composition and niche breadth of the greater amberjack 124
5.3.3 Stable isotope analysis of the greater amberjack 125
5.3.4 Stable isotope Bayesian mixing model and trophic position 126
5.4 Discussion 127
5.4.1 Environmental factors affecting the greater amberjack habitat 127
5.4.2 Feeding preference and nice breadth 128
5.4.3 Stable isotope Bayesian mixing model and trophic position of greater amberjack 129
5.5 Conclusion 132
CHAPTER VI GENERAL DISCUSSION AND CONCLUSION 150
6.1 Oceanographic environmental characteristics on greater amberjack fishery 151
6.2 Climate related environmental change in marine system 151
6.3 Trophic ecology of greater amberjack 152
6.4 Conclusion 153
REFERENCES 157




List of Tables
Table 2.1 The satellite-derived remotely sensed oceanographic environmental variables for the descriptions and data sources used in the model. 27
Table 2.2 Summary statistics for the building of the generalized linear model (GLM). Deviance explained (DE) by the model represented the goodness-of-fit of the model; approximate Akaike information criterion (AIC) was employed to identify the optimal model; are examined to find significant predictors. 28
Table 2.3 Summary statistics for the building of the generalized additive model (GAM). DE by the model represented the goodness-of-fit of the model; an approximate AIC value was employed to identify the optimal model. The model’s prediction ability was measured by the determination coefficient (R2) for greater amberjack catch rates in the study area (all p < 0.001). 29
Table 2.4 The root mean square difference (RMSD) reflects the cumulative frequency data test error metric for the observed and predicted high catch rates (>60 percent) of greater amberjack in the study area during 2019. 30
Table 3.1 Based on the VIP scores, the following variables are proportionally weighted: sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), sea surface chlorophyll-a (Chl-a), mixed layer depth (MLD), and eddy kinetic energy (EKE) in El Niño, La Niña, and normal years. 65
Table 3.2 The seasonal fitted suitability index (SI) model includes all the environmental variables; α and β are the estimated parameters of the SI model, and RMSD indicates the root mean square difference. 66
Table 4.1 Oceanographic environmental conditions observed in the Taiwan Bank and Northern Taiwan waters during the study period. 96
Table 4.2 Dietary composition of greater amberjack samples stratified by sex. %FO, percent prey-specific frequency of occurrence; %PN, percent prey-specific number; %PW, percent prey-specific weight; %PSIRI, prey-specific index of relative importance. 97
Table 4.3 Dietary composition of greater amberjack samples stratified by length. %FO, percent prey-specific frequency of occurrence; %PN, percent prey-specific number; %PW, percent prey-specific weight; %PSIRI, prey-specific index of relative importance; and TL, total length. 98
Table 4.4 Dietary composition of greater amberjack samples stratified by season. %FO, percent prey-specific frequency of occurrence; %PN, percent prey-specific number; %PW, percent prey-specific weight; and %PSIRI, prey-specific index of relative importance. 99
Table 4.5 Correlations between the key prey groups of the greater amberjack. 100
Table 4.6 Results of the one-way permutational multivariate analysis of variance performed on the basis of the Bray–Curtis similarities to analyze the percent abundance of the prey species of greater amberjack samples stratified by length, sex, and season. TL, total length. 101
Table 4.7 Results of the three-way permutational multivariate analysis of variance performed to analyze the diet of greater amberjack samples with regard to season, sex, and length. Significant results are shown in bold. 102
Table 5.1 Estimated effects of spatial variables (latitude and longitude) and oceanographic environmental factors considered in the analyses of greater amberjack in the Taiwanese waters using generalized additive models (GAMs). The table provides the smooth terms of each variables with the best-fitted model based on Akaike Information Criterion (AIC), standard error (SE) and test of the deviance explained (DE) in the GAMs with variables progressively added (first to last). 133
Table 5.2 Dietary composition of greater amberjack samples in the Taiwanese waters. %F, percentage by frequency of occurrence; %N, percentage by number; %W, percentage by weight; %IRI, percentage by the index of relative importance. 134
Table 5.3 The Isotopic (δ13C and δ15N) values of the greater amberjack samples with regards to length, sex and season in the Taiwanese waters between June 2020 and June 2022. 135
Table 5.4 The estimated percentage contributions of isotopic composition (mean and SD) by stable isotope Bayesian mixing model of the key prey species consumed by the greater amberjack. 136



List of Figures
Figure 1.1 The study species greater amberjack (Seriola dumerili), source: https://www.igfa.org 9
Figure 1.2 The conceptual framework describes the general structure of overall study designs of the habitat patterns and feeding characteristics of the greater amberjack fisheries in the coastal waters off Taiwan. 10
Figure 2.1 Flowchart illustrates of the research methods and analysis of both catch data of greater amberjack in relation to environmental factors applied in this study. 31
Figure 2.2 Spatial distribution pattern of greater amberjack (as indicated by green colour) caught using angling gear from 2014 to 2017 in the coastal waters off Taiwan, including Taiwan Strait (TS) and the current system; Kuroshio Branch Current, South China Sea Current in summer, and China Coastal Current in winter. On the right figure is a highlighted map of topographic features of bathymetry in representing the isobaths, as shown by the orange line. 32
Figure 2.3 Greater amberjack fishing locations in 0.1 spatial grids were used to analyze the annual and seasonal variations of the distribution patterns from 2014 to 2017 in the study area. 33
Figure 2.4 Left figure the monthly changes of mean (a) latitudinal and (b) longitudinal gravitational centers of observed catch rates (G) of greater amberjack (c) sea surface temperature (SST), (d) sea surface salinity (SSS), (e) sea surface height (SSH), (f) chlorophyll-a (Chl-a), (g) mixed layer depth (MLD), and (h) eddy kinetic energy (EKE) in the study area. Right figure is the correlation between latitude and longitude in relation to the environmental factors. 34
Figure 2.5 Seasonal average observed catch rates of greater amberjack overlaid with sea surface temperature: (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 35
Figure 2.6 Seasonal average observed catch rates of greater amberjack overlaid with sea surface salinity: (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 36
Figure 2.7 Seasonal average observed catch rates of greater amberjack overlaid with sea surface height: (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 37
Figure 2.8 Seasonal average observed catch rates of greater amberjack overlaid with sea surface chlorophyll-a : (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 38
Figure 2.9 Seasonal average observed catch rates of greater amberjack overlaid with mixed layer depth: (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 39
Figure 2.10 Seasonal average observed catch rates of greater amberjack overlaid with eddy kinetic energy: (a) Spring, (b) Summer, (c) Autumn, (d) Winter, from 2014 to 2017 in the study area. 40
Figure 2.11 The multiple correlation coefficients for the link between greater amberjack oceanographic habitat preferences based on satellite-observed environmental variables; sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), chlorophyll-a (Chl-a), mixed layer depth (MLD), and eddy kinetic energy (EKE), all the environmental factors with correlation coefficients ranging from r = -0.55 to 0.43. 41
Figure 2.12 The boxplot graph shows the root mean square difference (RMSD) value-based prediction performance of the generalized additive model (GAM) and generalized linear model (GLM). The model performs better if the RMSD value is close to zero. 42
Figure 2.13 The estimated environmental variable effects derived from the optimal generalized additive model (GAM) analysis of the catch rates of greater amberjack in the study area: (a) normal quantile–quantile plot (Q-Q plot), (b) latitude (Lat), (c) longitude (Lon), (d) SST, (e) SSS, (f) SSH, (g) Chl-a, (h) MLD, and (i) EKE. The solid and black dotted lines indicate the fitted GAM function and 95 percent confidence intervals in (a–i). On the x-axis, the rug plot depicts the relative density of data points, while the y-axis depicts the results of smoothing the fitted values. Moreover, s(xn) denotes each model covariate’s spline smoothing function xn. 43
Figure 2.14 Three-dimensional partial dependence plots representing the environmental variables with Log (Catch Rates) of greater amberjack in relation to SST and each of the environmental factor (a) SSS; (b) SSH; (c) Chl-a; (d) MLD; (e) EKE in the model. 44
Figure 2.15 Seasonal average spatiotemporal distribution pattern of observed catch rates for greater amberjack overlaid with predicted catch rates from selected GAMs in the 2014–2017 period in the coastal waters off Taiwan, including TS: (a) spring, (b) summer, (c) autumn, and (d) winter. 45
Figure 3.1 The methodological framework for data interpretation and analysis used in this study. 67
Figure 3.2 Monthly average time-series trend of oceanographic environmental factors from January 2014 to December 2019 in the study area. 68
Figure 3.3 The greater amberjack monthly average catches and catch rates in the southern and northern Taiwan Strait. 69
Figure 3.4 Greater amberjack seasonal average catch rates in the southern (blue color box) and northern (red color box) Taiwan Strait from 2014 to 2019. 70
Figure 3.5 The El Niño–Southern Oscillation interannual variability (a) and the association between the latitudinal gravitational centers and the monthly catch rates in the study area (b) from 2014 to 2019. 71
Figure 3.6 The Hövmöller plot of anomalies in six environmental variables; (a) sea surface temperature anomaly (SSTA), (b) sea surface salinity anomaly (SSSA), (c) sea surface height anomaly (SSHA), (d) Log chlorophyll-a anomaly (Log Chl-a A), (e) mixed layer depth anomaly (MLDA), and (f) eddy kinetic energy anomaly (EKEA) during El Niño and La Niña events in the study area in 2014–2019. 72
Figure 3.7 Box-and-whisker plots show the effect of the environmental variables on catch rates for greater amberjack in spring (a1–a7), summer (b1–b7), autumn (c1–c7), and winter (d1–d7) during ENSO events in the study area. The standard error is represented by the height of the box, and the mean is shown by the horizontal black line. The significant differences determined by the Tukey's test (p < 0.05) are indicated by the superscripted alphabetic letters. The significant differences of each box were determined using a one-way analysis of variance. 73
Figure 3.8 The suitability index (SI) curves were fitted to the relationship between fishing effort for greater amberjack and all the environmental variable, that is, (a) sea surface temperature (SST), (b) sea surface salinity (SSS), (c) sea surface height (SSH), (d) sea surface chlorophyll-a (Chl-a), (e) mixed layer depth (MLD), and (f) eddy kinetic energy (EKE), in each season. The intersections of the SI curve and the horizontal dashed lines (SI = 0.6) are the optimal range for all the environmental variable. 74
Figure 3.9 The average catch rates of greater amberjack overlaid with habitat suitability in spring (a–c), summer (d–f), autumn (g–i), and winter (j–l) during ENSO events. 75
Figure 3.10 The percentage of suitable habitat area for greater amberjack in the study area throughout each season during ENSO events. 76
Figure 3.11 The seasonal spatial distribution of greater amberjack fisheries with most preferred environmental conditions indicated with colored lines; magenta (Chlorophyll-a; Chl-a), light green (sea surface salinity; SSS), red (sea surface temperature; SST), dark green (mixed layer depth; MLD), aqua blue (eddy kinetic energy; EKE), blue (sea surface height; SSH), respectively. 77
Figure 3.12 The relationship between the spatial distribution of habitat suitability index (HSI) values and environmental variables is assessed through correlation coefficients: (a) SST versus HSI, (b) SSS versus HSI, (c) SSH versus HSI, (d) Chl-a versus HSI, (e) MLD versus HSI, and (f) EKE versus HSI. 78
Figure 3.13 Greater amberjack habitat suitability index (HSI) time-series latitudinal section plots along the southern and northern Taiwan Strait: (a) 118°E, (b) 119°E, (c) 120°E, (d) 122°E, (e) 123°E, and (f) 124°E. The maps were produced using an arithmetic mean model developed using marine environmental data from 2014–2019. The locations having an HSI value of 0.8 are delineated by a solid black line. 79
Figure 4.1 Latitudinal distribution of the catch rate of greater amberjacks (A; Mammel et al., 2022a). The field circles indicate the spatial distribution patterns of the key prey species of greater amberjack samples (B; stomach content analysis) collected from the (C) Taiwan Bank and (D) Northern Taiwan waters between June 2020 and June 2022. Lat, latitude; Lon, longitude. 103
Figure 4.2 Spatial distribution of oceanographic environmental factors at the fish sampling locations in the Taiwan Bank and Northern Taiwan waters during the study period. The dotted green circles indicate greater amberjack samples. SST, sea surface temperature; SSS, sea surface salinity; SSH, sea surface height; Chl-a, chlorophyll-a concentration; MLD, mixed-layer depth; EKE, eddy kinetic energy. 104
Figure 4.3 Seasonal cumulative curves of the target prey species: (A) spring, (B) summer, (C) autumn, (D) winter, and (E) combined (all seasons). The curves indicate the average number of prey species observed, in addition to 95% confidence interval values, with regard to the number of fish stomach samples analyzed. 105
Figure 4.4 Association of the length and weight of greater amberjack samples with the environmental conditions at the two study locations. SST, sea surface temperature; SSS, sea surface salinity; SSH, sea surface height; Chl-a, chlorophyll-a concentration; MLD, mixed-layer depth; EKE, eddy kinetic energy; TL. total length; BW, body weight. 106
Figure 4.5 Bar chart showing the prey-specific index of relative importance (%PSIRI) of key prey groups identified in the stomach contents of greater amberjack samples stratified by (A) sex, (B) length, and (C) season. 107
Figure 4.6 Box plots showing the fullness weight index (FWI) of greater amberjack samples stratified by (A) sex, (B) length, and (C) season. Different letters above the boxes denote significant between-group differences. Statistical significance was set at P < 0.05 (one-way analysis of variance with Tukey’s post hoc test). 108
Figure 4.7 Pyramid diagram showing the percent gastro-somatic index (%GaSI) and percent vacuity index (%VI) values of greater amberjack samples stratified by sex, length, and season. 109
Figure 4.8 Interpolated (solid lines) and extrapolated (dotted lines) sampling curves, with 95% confidence intervals (shaded areas), as a function of (A) sample size and (C) coverage. Separate curves are presented for each diversity order: q = 0 (species richness; left panel), q = 1 (Shannon diversity; middle panel), and q = 2 (Simpson diversity; right panel). The curves presented in (A) and (C) are associated with sample completeness curves (B). 110
Figure 4.9 Dendrogram showing the hierarchical clustering of prey species based on percent abundance (by weight) in greater amberjack samples stratified by sex, length, and season. Clusters A, B, and C had the highest Bray–Curtis similarity values. 111
Figure 4.10 Principal component analysis biplot showing the diet composition of the greater amberjack with regard to sex, length, season, and key prey groups. 112
Figure 4.11 Results of principal component analysis: screen plot (A) and loading plot (B) displaying the eigenvalues and the first two principal components of the key prey groups of the greater amberjack. 113
Figure 4.12 Graphical representation of the feeding strategy of the greater amberjack. An association was noted between prey-specific percent abundance (%PAi) and percent frequency of occurrence (%FOi;). The black circles indicate the key prey groups: the pelagic species (PS), demersal species (DS), unidentified teleosts (UT), crustaceans (CC), and cephalopods (CP). 114
Figure 5.1 A flowchart that provides a detailed overview of the data processing and analysis methods used in this study. The workflow process is shown by the bold red lines, while the relevant additional inputs into the model are shown by the black dotted lines. 137
Figure 5.2 Map of the left panel shows the bathymetry, and in the right panel shows the red color circle indicates the greater amberjack, and the different color lines represents various depths, respectively in the Taiwanese waters. 138
Figure 5.3 The white color circle indicates the greater amberjack, overlaid with monthly average oceanographic environmental factors in the Taiwanese waters between June 2020 and June 2022, corresponding (a) sea surface temperature (SST), (b) sea surface salinity (SSS), (c) sea surface height (SSH), (d) sea surface chlorophyll-a (Chl-a), (e) mixed layer depth (MLD), and (f) eddy kinetic energy (EKE), respectively. 139
Figure 5.4 Pearson correlation coefficients for the relationship between greater amberjack length and weight with the spatial variables (latitude and longitude), and oceanographic environmental factors. Lat, latitude; Lon, longitude; SST sea surface temperature; SSS, sea surface salinity; SSH, sea surface height; Chl-a, sea surface chlorophyll-a; MLD, mixed layer depth; EKE, eddy kinetic energy. 140
Figure 5.5 Estimated effects of oceanographic environmental variables from generalized additive model (GAM) analysis of greater amberjack in the Taiwanese waters: (a) latitude, (b) longitude, (c) SST; sea surface temperature (d) SSS; sea surface salinity (e) SSH; sea surface height (f) Chl-a; sea surface chlorophyll-a (g) MLD; mixed layer depth and (h) EKE; eddy kinetic energy, respectively. The lines represent the effect relative to the average and the light blue shaded areas is the 95% confidence bands. The rug plot on the x-axis indicates the relative density of the data points, and the results of smoothing the fitted values are shown on the y-axis. Additionally, s(xn) represents a spline smoothing function for each model covariate xn. 141
Figure 5.6 Donut graph showing the weight percent abundance of prey species in the stomach samples of greater amberjack by (a) length, (b) sex, (c) season, and (d) yearly, respectively. 142
Figure 5.7 The percentages by number (%N), percentage by weight (%W), and percentage by frequency of occurrence (%F) of prey species in the stomach of the greater amberjack were examined, and each colored box represents the major prey species group: (a–c) based on length, (d) combined all, (e–g) based on sex, and (h–k) based on season, respectively. 143
Figure 5.8 The niche breadth (a) Standardized Levin’s index (Bi), (b) Shannon–Wiener index (H'), and (c) Pielou’s evenness index (J), of greater amberjack analyzed with regard to length, sex, and season, respectively. Statistical significance was set at P < 0.05 (one-way analysis of variance with Tukey’s post hoc test). 144
Figure 5.9 Relationships between the greater amberjack regard with length and isotopic values (a and b), with weight and isotopic values (c and d), and with C:N (Carbon/Nitrogen) and isotopic values (e and f), respectively; TL. total length; BW, body weight. The red trendlines shown for linear regressions, with associated equations, r2, and p values shown, the shaded region shows the 95 percent confidence interval. 145
Figure 5.10 Plots generated to analyses from the carbon and nitrogen stable isotopic data of greater amberjack with regard to length (a and d), with regard to sex (b and e), and with regard to season (c and f) respectively, in the Taiwanese waters. 146
Figure 5.11 The trophic position of the greater amberjack was calculated based on length, sex, and season using the stable isotope analysis approach (a–c), and the stomach content approach (d–f). Statistical significance was set at P < 0.05 (one-way analysis of variance with Tukey’s post hoc test). 147
Figure 5.12 Bi-plot of δ13C and δ15N values of the greater amberjack and their prey species. The distribution of stable isotope ratios of carbon and nitrogen among the different prey species and target predator species that make up the food web of Taiwanese waters are depicted. The error bars display the standard deviation of the isotope values for each species. Therefore, the details on the stable isotope composition of each prey species are provided in the Table 5.4. 148
Figure 5.13 Illustrating the schematic overview of the marine ecosystem services and the energy flow for the trophic ecology of the greater amberjack and its prey species interactions within the marine environment. 149


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