人工魚礁常被使用於改善漁場環境，許多研究皆指出其能夠有效改善沿海的生態環境，提供生物棲息場所。而台灣在 1960 年代起便已開始投放人工魚礁，至今已有近百處的人工魚礁區。欲發揮人工魚礁之功效，除了適當的選址是關鍵因素以外，魚礁的結構設計、平面配置或是堆疊也相當重要。在過去對珊瑚礁之研究中，一般認為地形複雜程度越高，魚群豐度及物種豐富度也會越高。本研究
所使用的地形複雜度指標分別有：(1)表面粗糙度、(2)地形起伏度、(3)體積、(4)三維碎形維度、(5)水深標準差；並由景觀生態學的角度計算景觀指標如(1)景觀面積百分比、(2)綴塊密度、(3)最大綴塊指數、(4)平均綴塊面積、(5)面積加權平均綴塊碎形維度。
本研究利用 Reson SeaBat 7125 多音束測深系統於高雄市永安區外海四個魚礁區蒐集水深及水層資料，並使用 Echoview 水層資料處理軟體萃取魚訊。在上述四個魚礁區中，礁體堆疊、排列方式都不盡相同，故本研究試圖找出人工魚礁之配置、地形複雜度與魚群豐度之相關性。資料蒐集時期由 2014 至 2016 年，分別於清晨、中午及黃昏與午夜時段觀測多音束水層資料，發現大多數午夜時段及少數黃昏時段的水層資料非魚跡訊號頗多，故本研究僅針對清晨、中午及黃昏時段共十四個航次的資料，透過 Echoview 的水層資料處理標準化作業流程推估得各測段之魚訊數量及位置後，發現茄萣(三)魚礁區偵測到相對其他三個魚礁區為
多的魚訊數量。
由 Reson SeaBat 7125 所取得之水深資料輸出為網格，並將各魚礁區分別切割為 10m 及 30m 網格，並計算網格之地形複雜度指標與景觀指標。經整合各網格之魚訊量及上述指標後，利用 SPSS 統計分析軟體探討水層魚訊與各種地形複雜度指標的相關性，獲得下述結論：(1) 在二維平面的配置下，景觀面積百分比及最大綴塊指數與魚訊量呈現顯著且高度的正相關，意即當一魚礁區的礁體總面
積越大，或是該區最大礁體群落所佔整區比例越大時皆會偵測到越多的魚訊量。(2) 各地形複雜度指標皆與魚訊量有顯著的正相關，其中以地形起伏度及體積與魚訊量之相關性最高，也有較佳的預測能力，即魚礁若有堆疊至二至三層，且堆放體積越大的情況下，此區會有越多的魚訊量。(3) 觀察魚隻與地形的分布圖發現魚隻除了常出現在魚礁正上方以外，各魚礁區最大的魚群多在魚礁邊緣處被偵測到，此情況符合邊緣效應之理論。在網格大小為 30m 時，魚訊量與地形複雜度指標之相關性較高，原因可能是魚隻會徘徊在距離礁體一定範圍內，若將網格尺度定為 10m 可能不足以展現魚隻徘徊之特性。(4)以時段區分後可發現清晨時段所偵測到之魚訊量明顯多於中午與黃昏時段，且清晨時段偵測到的魚跡訊號大多不在礁體周圍，故推測清晨時段為魚群離開礁體活動之時段。

Artificial Reefs (ARs) had been deployed more than one hundred sites around Taiwan coast since 1960. One of important designing factors for ARs are suitable site selection and configuration in horizontal and vertical aspects. Topographic complexity is correlated with fish and species abundance according to reports from a lot of papers. The indices used for topographic complexity in this study have (1) surface rugosity, (2) relief, (3) volume, (4) 3D fractal dimension, (5) standard deviation of depths. Some indices from landscape ecology such as: (1) patch density, (2) percentage of landscape, (3) largest patch index, (4) mean patch area, (5) area-weighted average patch fractal dimension calculated by Fragstats software were also explored.
In this study, Reson SeaBat 7125 MultiBeam Echo Sounder (MBES) was used to collect water column data around four AR sites at Yungan coast, Kaohsiung, Taiwan, and to analyze the fish abundance by Echoview software. Above mentioned four AR sites have different forms of piling and arrangements, so this study tries to find the correlation between topographic complexity and fish abundance. Water column data were collected four time periods in a day, such as: dawn, noon, dusk, midnight, in each trip last for one or two continuous days. It was found most of midnight water column data were very noisy, and some of the dusk data had similar problem. After removing
those noisy data, 14 water column data sets of 2014-2016 were analyzed by Echoview to estimate amount of fishes and their position. It was found site #3 had detected more fishes than rest of the three sites.
The water depths surveyed by Reson 7125 Multibeam Echo sounding system for AR sites were gridded into sizes of 10m and 30m. Then we calculated the topographic complexity indices and landscape indices. After integrating amount of fishes and landscape indices and topographic complexity indices information, correlation analysis was performed to analyze which indices are good for topographic complexity and having higher correlation coefficients.
There are four conclusions we can make: (1) among landscape indices, percentage of landscape and largest patch indices have significant higher positive correlation with fish abundance. That means the higher the percentage of area or largest patch of an AR site have, the richer fish abundance may be detected, (2) all of the topographic complexity indices have significant positive correlation with fish abundance, but relief, standard deviation of depths and volume have higher correlation coefficients. That means if the ARs were piled up to 2-3 layers or had larger surface area, we may have higher amount of fishes, (3) it was found the detected fishes not only located right above the ARs, they also appeared around the edge of ARs. This phenomenon conforms to the theory of edge effect. The correlation coefficient for grid size of 30m is higher than
that of 10m, this may be because 10m is too small to demonstrate the characteristics of fish wandering, (4) it is also found amount of fishes detected in the dawn is higher than in the noon and dusk, and most of them were found at outside of the ARs, We may infer fishes likes to leave AR site in the dawn.

論文審定書 i
誌謝 ii
摘要 iii
Abstract v
目錄 vii
圖目錄 ix
表目錄 xii
第一章、緒論 1
1-1、研究動機與目的 1
1-2、研究方法與流程 3
第二章、文獻回顧 5
2-1、地形複雜度之相關文獻回顧 5
2-2、景觀生態學 14
第三章、實驗與資料處理方法 18
3-1、研究區背景及資料蒐集 18
3-1-1、背景資料 18
3-1-2、水層與地形資料蒐集作業說明 22
3-2、水層資料處理流程 26
3-3、地形複雜度指標之處理 30
3-4、景觀指標 34
第四章、結果與討論 40
4-1、各區魚訊之空間與時間分佈探討 40
4-1-1、各區空間特性與魚訊偵測結果 40
4-1-2、各區魚訊之時段分布 47
4-2、地形複雜度指標與魚訊量之相關分析 54
4-2-1、表面粗糙度 54
4-2-2、地形起伏度 57
4-2-3、體積 59
4-2-4、三維碎形維度 61
4-2-5、水深標準差 64
4-2-6、小結 66
4-3、景觀指標 70
4-3-1、各魚礁區之景觀指標 70
4-3-2、景觀指標與魚訊量之相關分析 71
4-4、地形複雜度指標之特性 76
第五章、結論與建議 80
5-1、結論 80
5-2、建議 84
參考文獻 86
附錄一、各區 10m 網格之地形複雜度與魚訊量之散佈圖 95
附錄二、各區 30m 網格之地形複雜度與魚訊量之散佈圖 100
附錄三、2014 年各區景觀指標與魚訊量之散佈圖 105
附錄四、2015-2016 年各區景觀指標與魚訊量之散佈圖 108

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網頁資料
76. Fragstats 軟體及操作手冊下載
http://www.umass.edu/landeco/research/fragstats/fragstats.html
77. ICAMS 軟體下載 (需取得作者同意)
http://www.rsgis.envs.lsu.edu/icams.asp