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Evolution of functional traits(Joel Kingsolver, Biology)
• Traits as functions: functional, function-valued, infinite-dimensional
• A primer in evolutionary models:– Variation, inheritance, selection, evolution
• Approaches to analysing functional traits:– understanding genetic variation– Estimating selection– Predicting evolutionary responses & constraints
Examples of functional traits
• Functions of age: – growth trajectories; life history; aging
• Functions of environmental state: – Physiological ‘reaction norms’
• Descriptions of 2-D or 3-D shape, etc
Age-specific mortality rates (Drosophila) Pletcher et al, Genetics (1999)
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Weeks on Wheels
0
10
20
30
40
50
60
70
80
90
Wh
ee
l Re
volu
tio
ns
(k
ilom
ete
rs)
GS8 Nov. 20, 2000 2:49:35 PM
ControlSelected
0
0.02
0.04
0.06
0.08
10 15 20 25 30 35 40
Temperature ( C)
Me
an
Gro
wth
Ra
te (
g/g
/h) M. sexta
P. rapae
Evolution of quantitative traits: some basics
• Individual organism:– Phenotype: observable trait with value z– Genotype: genetic ‘type’ (usually inferred)
• Population: – Phenotypic variance, P = G + E– Genetic variance, G
• Evolution = change in mean trait value per generation, z
Evolution, in 3 easy steps
P
Trait value z
Fre
quen
cy
z
Evolution, in 3 easy steps (2)
Trait value z
Fre
quen
cy
z z’
Evolution, in 3 easy steps (3)
Trait value z
Fre
quen
cy
z z’
z
A simple evolutionary model
• Variation and inheritance– Variance: P = G + E
• Selection– Selection gradient: = P-1( z’ - z )
– Also: = d[ln(W)]/dz , where W = mean population fitness
• Evolutionary response z = G
Evolution on an adaptive landscape
Mean phenotype, z
Mea
n fi
tnes
s, W
z = G
z may be:
• a scalar
• a vector
• a function
z(t) = G(t,s) sds
Analysing genetics of functional traits
• Estimating G: an example
• Biological hypotheses about G: eigenfunction analysis
• G and the response to selection
Temperature & caterpillar growth rates: Thermal performance curves (TPCs)
z(t), where t = temperature
0
0.01
0.02
0.03
0.04
10 15 20 25 30 35
Temperature ( C)
Rel
ativ
e G
row
th R
ate
(g/g
/h)
GeneticVar-Cov for RGR
35 -0.214 0.735 1.613 3.947 23.094
29 -1.027 -1.026 3.725 14.393 3.947
23 0.043 -1.099 4.505 3.725 1.613
17 -0.229 3.156 -1.099 -1.026 0.735
11 1.255 -0.229 0.043 -1.027 -0.214
Temp 11 17 23 29 35
aa
11.17.
23.29.
35.11.
17.
23.
29.
35.
05
101520
11.17.
23.29.
35.
Genetic Covariance Function
11.17.
23.29.
35.11.
17.
23.
29.
35.
05
101520
11.17.
23.29.
35.
Full Fit Smooth Fit
Basis = orthogonal polynomials
Analysing genetics of functional traits
• Estimating G: an example
• Biological hypotheses about G: eigenfunction analysis
• G and the response to selection
aa
15 20 25 30 35
A
15 20 25 30 35
Temperature °C
C
15 20 25 30 35
B
Faster-slower
Hotter-colder
Generalist-specialist
aa
15 20 25 30 35
-0.2
-0.1
0.1
0.2 A
0
15 20 25 30 35
-0.2
-0.1
0.1
0.2 B
0
Temperature °C
15 20 25 30 35
-0.2
-0.1
0.1
0.2
0
C
Eig
enfu
ncti
on
Faster-slower
Hotter-colder
Generalist-specialist
aa
Leading and Second Eigenfunctions
15 20 25 30 35
-1
-0.5
0.5
1
15 20 25 30 35
-1
-0.5
0.5
1
Temperature °CTemperature °C
Full Fit Smooth Fit
00
Caterpillar growth rates
Temperature
Per
form
ance
Hotter-Colder II
-4
-2
0
2
4 -4
-2
0
2
4
-0.1-0.05
00.050.1
-4
-2
0
2
4
G-covariance function: Variation in TPC position
First eigenfunction (65%)
-3 -2 -1 0 1 2 3 4
-1
-0.75
-0.5
-0.25
0.25
0.5
0.75
1
Second eigenfunction (25%)
-3 -2 -1 0 1 2 3 4
-1
-0.75
-0.5
-0.25
0.25
0.5
0.75
1
Analysing genetics of functional traits
• Estimating G: an example
• Biological hypotheses about G: eigenfunction analysis
• G and the response to selection dssstGtz )(),()(
aa
15 20 25 30 35
-0.2
-0.1
0.1
0.2 A
0
15 20 25 30 35
-0.2
-0.1
0.1
0.2 B
0
Temperature °C
15 20 25 30 35
-0.2
-0.1
0.1
0.2
0
C
Eig
enfu
ncti
on
Faster-slower
Hotter-colder
Generalist-specialist
aa
Leading and Second Eigenfunctions
15 20 25 30 35
-1
-0.5
0.5
1
15 20 25 30 35
-1
-0.5
0.5
1
Temperature °CTemperature °C
Full Fit Smooth Fit
00
aaa
T015 20 25 30 35
-1
-0.5
0.5
1
Evolutionary Constraints: Identifying zero eigenfunctions
Selection and evolutionary response
• Estimating selection, (s): an example
• Predicting evolutionary responses
dssstGtz )(),()(
Selection on caterpillar growth rate TPCs
• Measure z(t) for a sample of individuals in the lab --> estimate P(s,t)
• Measure fitness of those individuals in the field
• Estimate (s) (cubic splines)
Selection on Growth Rate
-0.5
-0.3
-0.1
0.1
0.3
0.5
10 15 20 25 30 35
Temperature ( C)
Sele
cti
on
Gra
die
nt **
Evolutionary Response to Selection
aaa
15 20 25 30 35
-0.2
-0.1
0.1
0.2
0.3
0.4
T0
A
15 20 25 30 35
-15
-10
-5
5
0
z
T
B
15 20 25 30 35
5
10
15
20
25z
z’
T
C
0
Selection
Evolutionary response
Evolutionary change in one generation
Challenges
• Estimation methods for G
• Hypothesis testing of eigenfunctions
• Estimating zero eigenfunctions
• Estimation methods for • Predicting evolutionary responses