Brodeur et al.: Distribution, growth, condition, origin, and associations of juvenile salmonids 



29 



G = (MWt)-MWt R ))/M, 



where Wt = weight of the CWT fish at capture; 



Wt R = the average weight of fish in the CWT group 



at time of release; and 

 Ad = days between hatchery release and capture 

 in the ocean. 



Estimated growth rates of these CWT fish, of known release 

 date and known average release weight were used to vali- 

 date the growth rates estimated from scale analysis 



Our analysis of the growth of chinook salmon based on 

 scale characteristics is not far enough advanced to report 

 in this article. We plan to present these data in a later 

 article. 



Contribution of hatchery coho salmon to catches 



The total numbers, percentages of marked fish ( any exter- 

 nal fin clips or internal tags) and grand average weights 

 of hatchery coho salmon smolts released in 2000 are sum- 

 marized for different release regions in Appendix Table 1. 

 These data were used to compare the estimated average 

 weights of fish at time of ocean entry (from scale analy- 

 sis ) with the average weights of hatchery fish at time of 

 release, and also to estimate the proportions of hatchery 

 coho salmon in our catches. We calculated the expected 

 percentage (E%) of marked fish in each catch if 100% of 

 the fish were hatchery fish: 



E% 



X*.*4, 



where i?, = the proportional contribution of region i to the 

 catch (this paper for the GLOBEC catches, 

 and from Teel et al., 2003 for the plume study 

 catches); and 

 A, = the percentage of hatchery fish that were 

 marked in region i ( from Appendix Table 1 ). 



The percentage of hatchery fish in each catch sample (H%) 

 was then estimated as 



0% 



H% = — xlOO, 

 E% 



where O c A = observed percentage of marked fish. 



Genetic analysis 



The freshwater origins of juvenile chinook and coho salmon 

 and steelhead (O. my kiss) were studied by using standard 

 methods of genetic mixed stock analysis (Milner et al., 

 1985; Pella and Milner, 1987). According to the methods 

 described by Aebersold et al. (1987), samples of eye, liver, 

 heart, and skeletal muscle were extracted from frozen whole 

 juvenile salmon and analyzed with horizontal starch-gel 

 protein electrophoresis. Data from previous studies char- 

 acterizing genetic (allozyme) differences among spawning 

 populations in California and the Pacific Northwest were 

 then used as baseline data to estimate the stock composi- 

 tions of our ocean caught mixed-stock samples. Baselines 



consisted of 32 gene loci and 116 populations for chinook 

 salmon (Teel et al. 5 ), 58 loci and 49 populations for coho 

 salmon (Teel et al., 2003), and 55 loci and 57 populations 

 for steelhead (Busby et al., 1996). Estimates of stock com- 

 positions were made by using the maximum likelihood 

 procedures described by Pella and Milner (1987) and the 

 Statistical Package for Analyzing Mixtures (Debevec et al., 

 2000). Estimates of individual baseline populations were 

 then summed to estimate contributions of regional stock 

 groups. Precision of the stock composition estimates was 

 estimated by bootstrapping the estimates 100 times with 

 resampling of the baseline and mixture genetic data as 

 described in Pella and Milner (1987). 



Habitat and assemblage analysis 



The raw numbers offish and squid caught from each trawl 

 were converted to densities based on the volume filtered 

 per trawl to standardize for differences in effort between 

 tows. Density contours of juvenile salmon and other nekton 

 were produced using specialized graphics programs. We 

 then tested whether the habitat associations of the domi- 

 nant salmonids were significantly different from the total 

 habitat sampled by following the methods outlined in Perry 

 and Smith ( 1994). This procedure involved comparing the 

 cumulative distributions of salmon catch with observed 

 environmental conditions (temperature, salinity, chloro- 

 phyll-a at one meter, water depth, and neuston displace- 

 ment volume). We performed 5000 randomizations of the 

 data and used the Cramer-von Mises test statistic recom- 

 mended by Syrjala ( 1996) as being robust to the effects of 

 inordinately large catches. 



To explore the relationship between juvenile salmon and 

 other fish species and environmental variables, we used 

 several types of multivariate analyses (McCune and Grace, 

 2002 ). Original data from each of the two cruises formed 

 complimentary species and environmental matrices. The 

 June and August cruises were analyzed individually to 

 look at spatial patterns of species composition in relation to 

 environmental gradients (Gauch, 1982). To avoid spurious 

 effects of rare species, we excluded species from the data 

 matrix that had a frequency of occurrence of less than 10% 

 of the possible occurrences for each cruise (McCune and 

 Grace, 2002). To minimize the effect of very large catches, 

 the data were log transformed. Stations with no species 

 present were eliminated from the data set to allow for anal- 

 ysis of sample units in species space. Data transformations 

 and their effects on the summary statistics were examined 

 prior to analysis. Analyses of data were performed by using 

 PC-ORD version 4.28 (McCune and Mefford, 1999). 



Agglomerative hierarchical cluster analysis (AHCA) 

 using the Bray-Curtis dissimilarity measure and Wards 



Teel, D. J„ P. A. Crane. C. M. Guthrie, III, A. R. Marshall. D. 

 M. Van Doornik, W. D. Templin, N. V. Varnavskaya, and L. W. 

 Seeb. 1999. Comprehensive allozyme database discriminates 

 chinook salmon from around the Pacific Rim. (NPAFC docu- 

 ment 440), 25 p. Alaska Department of Fish and Game, Divi- 

 sion of Commercial Fisheries, 333 Raspberry Road, Ancorage, AK 

 99518. 



