1. A review of quantitative genetic components of fitness in salmonids: implications for adaptation to future change Stephanie M. Carlson 1 * and Todd R. Seamons 2 * 1 Department of Applied Mathematics and Statistics, University of California, Santa Cruz 2 School of Aquatic and Fishery Sciences, University of Washington, Seattle *equal contributionIn Press Evolutionary Applications

3. Harvested fish are getting smaller –Salmon –Cod Smaller because of slow growth or younger fish? Evolutionary response to selection against large fish?

4. Dams have changed characteristics of migration North Fork Snake River Chinook change in age-at- smoltification –Once 0+ now 1+ Evolutionary response from changed selection regime?

5. Global climate change, anthropogenic effects on fish populations http://www.globalwarmingart.com/wiki/Image:Global_Warming_Predictions_Map_jpg

6. Using quantitative genetic models we can make predictions about evolution R= response (evolution, change in trait mean) S= selection (selection coefficient) h 2 = heritability (additive genetic variance) –Ratio, ranges between 0 and 1 R = h 2 S Falconer and McKay 1996 aka: Breeder’s equation

7. Evolution of a single quantitative trait, with effects of correlated traits R X = h 2 X S X + h X h Y r G S Y For trait X –R = response (evolution) –h 2 = heritability For traits X and Y –S = selection coefficient –h = standard deviation (h 2 = variance) –r G = genetic correlation (additive genetic) Ranges from -1 to 1 Roff 2007

8. Response to selection with genetic correlations Initial trait distribution Hypothetical response Realized response With opposing selection on a genetically correlated trait Adapted from slide by K. Naish h 2 = 1 Selection coefficient Trait mean after selection

9. Objectives 2 broad goals –Summarize available data –Test for differences among categorical variables species, genera trait classes traits within trait classes source population types experimental treatment types life history types life history stages

10. Approach – do a review. Don’t try this at home! Published estimates of h 2 and r G –Oncorhynchus, Salmo, Salvelinus spp. 187 different papers total (1972 - 2007) –h 2 182 papers 3150 estimates –r G 108 papers 2284 estimates

11. h 2 values... Median = 0.22 Median = 0.27 All species O. mykiss only Heritability (narrow sense) Frequency 0 0.5 1

12. r G values... Median = 0.40 Median = 0.28 All species O. mykiss only Genetic correlation (~narrow sense) Frequency -1 0 1

13. parameter estimates were not distributed equally among categories Heritability data distribution O. mykiss Species Genus Trait class Trait within trait class Source population type Experimental treatment type Life history type Life history stage

14. parameter estimates for behavioral traits were nearly absent from the literature Heritability data distribution none for O. mykiss

15. parameter estimates were rare for wild fish reared in the wild none for O. mykiss Heritability data distribution

16. 1 Excluded life history stage specific traits 2 Excluded smolt specific traits h2h2 FactorTraitInteraction term Species P = 0.245P < 0.001 Genus P = 0.471P < 0.001P = 0.480 Life History Stage 1 P = 0.619P < 0.001 Diadromy 2 P = 0.035P < 0.001P = 0.012 Parity P = 0.538P < 0.001P = 0.007 Treatment P < 0.001 P = 0.863 Broodstock P = 0.495P < 0.001 Treatment x Broodstock P = 0.891P = 0.836

17. 1 Excluded life history stage specific traits 2 Excluded smolt specific traits rGrG FactorTraitInteraction term Species P < 0.001P = 0.001P < 0.001 Genus P = 0.662P < 0.001P = 0.020 Life History Stage 1 P = 0.392P < 0.001P = 0.924 Diadromy 2 P = 0.904P < 0.001P = 0.057 Parity P = 0.625P < 0.001P = 0.129 Treatment P = 0.450P < 0.001P = 0.247 Broodstock P = 0.378P < 0.001P = 0.004 Treatment x Broodstock P = 0.017P = 0.917

18. R Iteroparity = h 2 Itero S Itero + h Itero h Y r G S Y Heritability data for ‘Iteroparity’? None

19. Iteroparity = survival Heritability Genetic correlation Median = 0.31

20. Steelhead repeat spawning rates RiverRun% x 1% x 2% x 3 SkagitWinter9271 SnohomishWinter9261 GreenWinter937 PuyallupWinter8910 NisquallyWinter9361 QuillayuteWinter9171 CowlitzWinter964 KalamaWinter936 KalamaSummer946 Snow CreekWinter88102 Source: Busby et al. 1996

21. http://www.globalwarmingart.com/wiki/Image:Global_Warming_Predictions_Map_jpg

22. Many Thanks… Funding –National Science Foundation –Bonneville Power Administration For general consultation –Dr. Jeff Hard, NWFSC –Dr. Kerry Naish, UW For translations of papers –Nathalie Hamel, UW – French –Jocelyn Lin, UW - Japanese For help obtaining copies of papers –Dr. Christina Ramirez, WSU

23. Some final take-home points Making accurate predictions will be difficult –Selection and heritability may be correlated –Heritability and environment may be correlated –Never measure all correlated traits –Lots of data lacking –Cant necessarily use published data –Difficult to get accurate/precise parameter estimates

24. Does tell us something about relative rates of evolution

25. Selection on two correlated traits Trait 1 Distribution Trait 2 Distribution Selection Differential h2h2 corr. h 2 ParentsParentsProgeny Response Slide from WH Eldridge