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We developed the Columbia River Basin salmonid GIS on a UNIX-based Sun SPARC station 10 with OpenWindows 3.4 and a Pentium 90 mhz PC with 16 megabytes of RAM running Windows 95. The salmonid GIS is currently composed of four separate ArcView Projects. A total of 140 megabytes hard disk space is required for the four projects, which includes over 400 separate shape (ArcView's spatial data file format) and tabular files and 43 coverages. In addition, the ArcView software requires a minimum of 13 megabytes of hard disk space. ESRI's minimum recommendation for running ArcView on a PC is a 486/66 mhz with 12 megabytes of RAM. Due to the size of our data files, consider our configuration as the minimum for developing or running this GIS.

Each Project brings together the primary benefits of a GIS, linking digitized geographic files to a tabular database (Fig. 2). Data sets used in this system were associated with the spatial data in two different ways: directly and related/linking (Fig. 3). The direct method involved adding fields to the attribute table of a particular digitized geographic file and then inputting records (i.e., stream name). The related/linking method involved the establishment of a "common related" item between the attribute table and the independent tabular data, the same principle as for a relational database. The "principle related" item we used was the River Reach Code number (RRN). The RRN number is a national river reach coding system developed by the Environmental Protection Agency (EPA). A list of River Reach Code numbers for Oregon, Washington, and Idaho rivers, can be obtained Bonneville Power Administration, Portland, OR. Each RRN consists of a hierarchical 15-digit number with one decimal which is unique within the country and represents region, river basin, sub-basin, reach segment, and reach segment mile (Fig. 4). Each RRN stream segment within the primary stream coverage has a unique RRN.

The four ArcView Projects created were named primarily for the species data they represent: 1) Coho, 2) Chinook, 3) Steelhead, and 4) Other (includes chum, pink, and sockeye salmon data). Each project was composed of Views, Tables, and Charts. The spatial (i.e., geography) data were located under Views, the tabular data was located under Tables, and Charts displayed the counts for corresponding years for each event as a histogram.

We further enhanced the GIS, adding three new categories to the ArcView menu: Stocks, Attributes, and Imagery. Under each of these categories is a list of options from which the user can choose. Each option runs a script in Avenue (ArcView's programming language) which automatically opens various views, tables, or charts related to that particular option (Table 1). For example, under the Stocks category the user can view information on escapement, presence/absence, data from Wahle and Pearson (1987), and hatchery release information.

To provide a visual geographic reference of the tabular escapement data, Arc/Info's dynamic segmentation module was used. Routes depicting salmon upriver migration were created, with each individual route terminating at the last escapement observation location (an event location). From the event tables, event markers were placed at the end of each route showing the location of the escapement observation. The escapement data were linked to the event table, and a correlated histogram was created, showing annual counts at the particular location. Table 2 shows the number of events currently in the projects. Summer steelhead had the largest number of events for a stock/species (314) and pink salmon the fewest (4). An example of the actual GIS output (on screen) for coho salmon escapement is shown in Figure 5. An example of GIS output for hatchery-release information is shown in Figure 6.

Each project has species-specific data sets under the Stocks category for spawning escapement, hatchery releases, presence/absence, and run-size data from Wahle and Pearson (1987). However, most of the spatial data are uniform within all the projects. The escapement data sets are the main focus for the individual projects and are presented similarly. Routes and Events which represent the escapement observation locations, were further separated according to the type of observation they represented. For example, within the Coho Project the user may choose between Total Live Fish (TLF), Fish Per Mile, Peak or Index Live Fish, Dam Events, Sport, or Other.

Finally, some watershed attributes, which undoubtedly play a large role in the health and viability of salmon populations in the Northwest, are included in the GIS. In each project under the Attribute category are found geo-referenced data on the following subcategories: Main Dams, Hatcheries, Land Ownership, Evolutionarily Significant Units (Waples 1991), and Hydrologic Units. Simple tabulation of Land Ownership data reveals that almost half the land in the Columbia River Basin is controlled by the Federal Government (Table 3). Further analysis would allow users to identify the primary salmonid species or stocks that inhabit these lands, trends in their abundance, or any number of other questions. 

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