8096

• UCREFRP Catalog Number: 8096
• Author: American Fisheries Society.
• Title: Proceedings of the 19th Annual Meeting, Colorado - Wyoming Chapter, American Fisheries Society.
• USFW Year: 1984.
• USFW - Doc Type: March 7-8, 1984.
• Copyright Material: NO

greatest trout standing stock, while scores between 58-100 (good-fair) were
acceptable fisheries habitat. The greatest number of catchable trout were
associated with a SRICSI score between 70-85 (good-fair) (personal
communication, R. Rainville, U.S. Forest Service, Couer d'Alene,. Idaho).
Working on two small streams in southeast Wyoming, Eifert and Wesche (1982) -
found that a SRICSI score between 65-91 (good-fair) corresponded to the best
trout habitat.
Geomorphic variables, computed from maps, have been correlated with trout
standing stock. Basin area, basin perimeter, basin relief, and compactness
coefficient were used by Heller et al. (1983) to predict fish habitat quality
in the Siuslaw National Forest, Oregon. Significant correlation between
drainage area, total stream length, mean basin elevation, forest ratio, and
trout standing stock were reported for small Wyoming streams (Burton and
Wesche 1975).
Objectives of this study were:
1. To evaluate the statistical relations between SRICSI scores, and other
variables available on the SRICSI rating form, and the standing stock of
trout in Rocky Mountain Region streams.
2. Develop predictive multiple regression models for trout standing stock
applicable to Rocky Mountain Region streams.
METHODS
Stream habitat along with concurrently sampled standing stock records
were collected from government agency and University of Wyoming personnel. A
total of 94 reaches, 47 from forest areas and 47 from nonforest areas were
collected. Model development was based upon regression analysis (Nie et al.
1975) of 1$ independent habitat variables (Table 1).
RESULTS
Simple regression analysis of SRICSI indicated that it significantly
accounted for variance in fish standing stock, but the relationship between
SRICSI and standing stock was different in forested and nonforested streams.
In 47 forested stream reaches SRICSI was positively correlated (F = 6.51.;
P < .014; r = 0.35) with standing stock, accounting for 11% of the variation.
In 47 nonforested stream reaches SRICSI was negatively correlated (F = 11.19;
P < .002; r = -0.45) with standing stock accounting for 18% of the variation.
These results led to the development of separate models for forested and
nonforested stream reaches.
In both forest and nonforest streams the use of kilograms of trout per
kilometer as the dependent variable resulted in the best regression equation.
The forest stream model, based upon 34 stream reaches with complete data for
the model variables was 3C = 0.50 (% Bedrock and Boulders) + 0.02
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