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
Slide 2
Slide 3
Harvested fish are getting smaller Salmon Cod Smaller because
of slow growth or younger fish? Evolutionary response to selection
against large fish?
Slide 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?
Slide 5
Global climate change, anthropogenic effects on fish
populations
http://www.globalwarmingart.com/wiki/Image:Global_Warming_Predictions_Map_jpg
Slide 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: Breeders equation
Slide 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
Slide 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
Slide 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
Slide 10
Approach do a review. Dont 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
Slide 11
h 2 values... Median = 0.22 Median = 0.27 All species O. mykiss
only Heritability (narrow sense) Frequency 0 0.5 1
Slide 12
r G values... Median = 0.40 Median = 0.28 All species O. mykiss
only Genetic correlation (~narrow sense) Frequency -1 0 1
Slide 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
Slide 14
parameter estimates for behavioral traits were nearly absent
from the literature Heritability data distribution none for O.
mykiss
Slide 15
parameter estimates were rare for wild fish reared in the wild
none for O. mykiss Heritability data distribution
Slide 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
Slide 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
Slide 18
R Iteroparity = h 2 Itero S Itero + h Itero h Y r G S Y
Heritability data for Iteroparity? None
Slide 19
Iteroparity = survival Heritability Genetic correlation Median
= 0.31
Slide 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
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
Slide 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
Slide 24
Does tell us something about relative rates of evolution
Slide 25
Selection on two correlated traits Trait 1 Distribution Trait 2
Distribution Selection Differential h2h2 corr. h 2
ParentsParentsProgeny Response Slide from WH Eldridge