scholarly journals Monitor and Protect Wigwam River Bull Trout for Koocanusa Reservoir; Skookumchuck Creek Juvenile Bull Trout and Fish Habitat Monitoring Program, Annual Report 2002.

2003 ◽  
Author(s):  
R. Cope
Keyword(s):  
Annual Report ◽  
Fish Habitat ◽  
Monitoring Program ◽  
Bull Trout ◽  
Habitat Monitoring

Lower Granite Dam Smolt Monitoring Program, 1998 Annual Report.

10.2172/14214 ◽  
1998 ◽  
Author(s):  
Peter Verhey ◽  
Doug Ross ◽  
Charles Morrill
Keyword(s):  
Annual Report ◽  
Monitoring Program

Using confidence intervals to estimate the response of salmon populations (Oncorhynchus spp.) to experimental habitat alterations

2005 ◽  
Vol 62 (12) ◽  
pp. 2716-2726 ◽  
Author(s):  
Michael J Bradford ◽  
Josh Korman ◽  
Paul S Higgins
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Statistical Power ◽  
Habitat Restoration ◽  
Coho Salmon ◽  
Fish Habitat ◽  
Monitoring Program ◽  
Considerable Uncertainty ◽  
Monitoring Programs

There is considerable uncertainty about the effectiveness of fish habitat restoration programs, and reliable monitoring programs are needed to evaluate them. Statistical power analysis based on traditional hypothesis tests are usually used for monitoring program design, but here we argue that effect size estimates and their associated confidence intervals are more informative because results can be compared with both the null hypothesis of no effect and effect sizes of interest, such as restoration goals. We used a stochastic simulation model to compare alternative monitoring strategies for a habitat alteration that would change the productivity and capacity of a coho salmon (Oncorhynchus kisutch) producing stream. Estimates of the effect size using a freshwater stock–recruit model were more precise than those from monitoring the abundance of either spawners or smolts. Less than ideal monitoring programs can produce ambiguous results, which are cases in which the confidence interval includes both the null hypothesis and the effect size of interest. Our model is a useful planning tool because it allows the evaluation of the utility of different types of monitoring data, which should stimulate discussion on how the results will ultimately inform decision-making.

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