1. A geographic information system for fisheries management in American Samoa 運用地理資訊系統針對美屬薩摩亞 漁場管理之研究 Francesca Riolo Reporter ：曾千芬

2. Introduction

3. What is GIS 用來儲存與處理地理資訊的電腦系統，包括對真 實世界的空間資料進行獲取、管理、處理、分析 與展示等工作，以解決與空間相關或複雜的規劃 與管理問題 。

4. marine fisheries applications 漁場環境調查設施選址或漁區規劃漁船監控漁場魚況預測資訊服務系統漁獲量努力量

5. American Samoa Fig. 1. American Samoa and EEZ boundaries for the Pacific Islands. EEZ Main island of Tutuila

6. Catch 長鰭鮪 albacore 1 黃鰭鮪 yellowfin tuna 2 大目鮪 bigeye 3

7. Export American Samoa’s largest private industry, represent the nation economic foundation with an annual export of US$500 million worth of tuna.

8. Objective present an innovative way to utilize the geographic information associated with fisheries data to effectively visualize and quantify temporal and spatial patterns in fisheries activities.

9. Materials & Methods

11. Data and database Department of Marine and Wildlife Resources (DMWR) 海洋環境資源部門 Table fields include: 1 date, time and geographic coordinates for the beginning and end of set 2 date, time and geographic coordinates for the beginning and end of haul; 3 vessel name, length and owner 4 number of deployed hooks 5 mainline length 6 number of fishes kept and released for each pelagic species; 7 number of fishes kept and released alive and dead for each protected species.

12. Generates a point shapefile Fig. 3. Bringing tabular information (attributes and coordinates) into point vector GIS format. The points represent the fishing locations. Attributes of each fishing set are linked to the spatially referenced point.

13. Production by VB Fig. 2. Customized graphical user interface (GUI) for the fishery GIS and pre-defined map layout and symbology

14. Map layout Fig. 5. Density surfaces are created in the GIS as a grid of cell （ number of fishing points ） 5km Density analysis 取樣間距 30km ， 點與點之間做運 算, 密度越高顏色 呈現紅色。每一 網格皆有顏色。

15. Divide (GIS 影像處理工具 ) 每一網格皆有 RGB 值， 使網格呈現顏色，而將 兩張圖套疊後並做網格 計算可得輸出之網格圖。

16. Catch Per Unit of Effort (CPUE ） Fig. 4. The density of fishes in each cell in the catch density raster is multiplied by 1000 and then divided by the density of hooks in the correspondent cell in the hook density raster to obtain the catch per unit of effort raster. represents the number of fishes caught per 1000 hooks

17. Results

18. Hook density Fig. 6. Spatial distribution of fishing activities expressed in number of hooks per square kilometer (hook density) for the years 2000-2003

19. Albacore catch density Fig. 7. Albacore catch density (number of fish per square kilometer) for the years 2000e2003. Catch density patterns follow closely the hook density patterns (Fig. 6).

20. CPUE Fig. 8. CPUE (number of fish caught by 1000 hooks) for the years 2000e2003. CPUE gives information on the success of the fishing activity.

21. Fished area Fig. 9. Total fished area for the years 1996-2003. 145,000 km 2 1,272,000 km2

22. Overall CPUE Fig. 10. Trend of overall median CPUE for pairs of sequential years (1996-2003).

23. Percentages Fig. 12. Percentages of total fished area of different CPUE classes for the years 2000e2003. CPUE 值介於 20-30 間，其地區 佔有率最高

24. Monthly spatial （ hook density ） Fig. 13. Monthly spatial distribution of fishing activities in terms of hook density (number of hooks per square kilometer) for the year 2003. 季節性變動

25. Monthly spatial （ CPUE ） Fig. 14. Monthly spatial distribution of CPUE (total number of fishes caught by 1000 hooks) for the year 2003.

26. Discussion & Conclusion

27. GIS and spatial analysis as a tool and technique to achieve this objective through density analysis. The parameters that influence density analysis are the cell size and the search radius.

28. Cell size Fig. 15. Effect of changing cell size. Cell size is specified at the lower left corner of each image. Best

29. Search radius Fig. 16. Effect of changing search radius. Search radius length is specified at the lower left corner of each image. Best

30. Based on the preliminary success of this application further development and improvement of the fishery GIS is envisioned. Correlations in time and space of estimated parameters, as well as correlation with other parameters can be also computed.

31. The system is highly dynamic due to its ability to instantaneously answer any temporal and spatial questions regarding fishing activity for any vessel in the fleet or any species caught. The tool can be easily adapted to any fishery data stored in a tabular format that contains fishing location, vessel, catch and date information.