The Application of Fish Autecology Matrix to Habitat Simulation / 魚類個體生態矩陣於溪流棲息地模擬之應用

碩士 / 國立成功大學 / 水利及海洋工程學系碩博士班 / 96 / Although Taiwan is a small island, it possesses extraordinarily abundant biodiversity resources and many endemic species. Understanding the fish assemblage composition and assessing their habitat preferences is an important reference for the ecological engineering work in river restoration. This study collected fish and habitat data in Tsengwen River Basin from late 2007 to early 2008. Electrofishing was used to collect fish in grids and not positioned repeatedly sampling 74 times. By using the field survey data, fish habitat preferences can be learned, and this information can be used to establish the fish community - habitat models. A total of 622 samples was obtained, including 6 genera and 15 species, and the dominant species are Rhinogobius giurinus, Sinogastromyzon puliensis, Rhinogobius rubromaculatus, Zacco pachycephalus. Fuzzy control theory is applied to model habitats as deep pool, deep riffle, shallow riffle, and shallow pool – which are defined by surveyed depth and velocity.
In order to consider the organism requirements of the river, the model was developed primarily from an ecological point of view. Fish sampling data was analyzed by a fish autecology matrix (Suen 2005) to identify the fish habitat preferences, which were presented as percentages. This environmental requirement information was then combined with surveyed habitat data for establishing stream habitat simulation system. The cross validated habitat model can be used for large scale sampling data in a watershed. By using historical fish sampling data, the system could simulate stream habitat percentage composition, and the fish community habitat preference could be analyzed. This study can provide a reference of stream restoration and ecological engineering to maintain the ecological integrity and diversity in the rivers.

Identiferoai:union.ndltd.org:TW/096NCKU5083037
Date January 2008
CreatorsWei-che Su, 蘇瑋哲
ContributorsJian-Ping Suen, 孫建平
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format105

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