Genecology and Adaptation of
Douglas-Fir to Climate Change
Brad St.Clair1, Ken Vance-Borland2 and Nancy Mandel1
1USDA Forest Service, Pacific Northwest Research Station
2Oregon State University
Corvallis, Oregon
Objectives



To explore geographic genetic structure and the
relationship between genetic variation and
climate
To evaluate the effects of changing climates on
adaptation of current populations
To consider the locations of populations that
might be expected to be best adapted to future
climates
Genecology

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
Definition: the study of intra-specific genetic variation of
plants in relation to environments (Turesson 1923)
Consistent correlations between genotypes and
environments suggest natural selection and adaptation of
populations to their environments (Endler 1986)
Methods for exploring genecology and geographic
structure – common garden studies
 Classical provenance tests
 Campbell approach
 intensive sampling scheme
 particularly advantageous in the highly
heterogeneous environments in mountains
Objective 1: Geographic structure and relationship
between genetic variation and climate
Douglas-fir common garden study
Raised beds
Distribution of parent trees
and elevation
Analysis


Canonical correlation analysis
 Determines pairs of linear combinations from two
sets of original variables such that the correlations
between canonical variables are maximized
 Trait variables
 emergence, growth, bud phenology, and
partitioning
 Climate variables
 modeled by PRISM
 annual and monthly precipitation, minimum
and maximum temperatures, seasonal ratios
Use GIS to display results
Results from CCA
Component
Canonical
Correlation
Canonical
R-squared
Proportion of
trait variance
explained by
CV for traits
Proportion of
trait variance
explained by
CV for climate
1
0.86
0.73
0.39
0.29
2
0.59
0.35
0.11
0.04
3
0.34
0.11
0.04
0.005
First component accounted for much of the variation.
First component may be called vigor – correlated with large size
(r=0.65), late bud-set (r=0.94), high shoot:root ratio (r=0.60),
and fast emergence rate (r=0.71).
Results from CCA
First CV for Traits correlated with:
Dec min temperature
0.79
Jan min temperature
0.73
Feb max temperature
0.73
Mar min temperature
0.77
Aug min temperature
0.42
Aug precipitation
0.30
Model:
trait1=-0.08+0.38*decmin –0.25*janmin+0.09*febmax
+0.13*marmin-0.12*augmin+0.02*augpre
Geographic genetic variation in first
canonical variable for traits
CV 1 for Traits
Dec Minimum Temperature
Objective 2: Effects of changing climates on
adaptation of current populations
Methods
1.
2.
3.
Develop model of the relationship between
genetic variation and environment using climate
variables.
Given model, determine set of genotypes that
may be expected to be best adapted to future
climate.
Given climate change, determine degree of
maladaptation of current population to changed
climate as determined by the mismatch between
current population and best adapted population.
Step 2: Given model, determine set of
genotypes that may be expected to be
best adapted to future climate


Some assumptions:
 A population is better adapted to its place of
origin than any other populations.
 The map of adaptive genetic variation is also a
map of the environmental complex that is
active in natural selection.
Thus, the map of the future climate is also a map
of the genotypes that may be expected to be best
adapted to that climate.
Climate change predictions

Two models:
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Canadian Center for Climate Modeling and Analysis
Hadley Center for Climate Prediction and Research
We assumed no geographic variation in
climate change
Climate change predictions
Expected Values for Climate Change (ºC)
Model/Year
Dec
Min
Temp
Jan
Min
Temp
Feb
Max
Temp
Mar
Min
Temp
Aug
Min
Temp
Aug
Precip
(ratio)
C 2030
2.5
2.5
1.8
2.0
1.0
0.9
H 2030
2.3
2.3
1.7
2.1
1.8
1.0
C 2090
6.0
6.0
5.8
5.5
4.4
1.0
H 2090
5.5
5.5
4.0
5.2
4.7
0.9
Geographic genetic variation that may be
expected to be best adapted to present and
future climates
Present
2030
2095
Step 3: Given climate change, determine degree of
maladaptation of current population to changed climate as
determined by the mismatch between current population and
best adapted population to the future climate
(risk index as proposed by Campbell 1986)
current population
future environmental complex
Degree of mismatch a function of:
difference = 0.5
additive genetic variance a= 0.52
percentage mismatch = 37 %
Maladaptation from climate change
Present
2030
2095
Model
Difference
Risk
Difference
Risk
Canadian
Hadley
0.56
0.50
0.41
0.37
1.46
1.11
0.84
0.71
Summary of Objective 2: Effects of
changing climates on adaptation of
current populations
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40% risk of maladaptation within acceptable limits
of seed transfer (Campbell, Sorensen).
71-84% risk is somewhat high.
Enough genetic variation present to allow evolution
through natural selection or migration.
Maladaptation does not necessarily mean mortality.
Trees may actually grow better, but below the
optimum possible with the best adapted populations.
Objective 3. To consider the locations of
populations that might be expected
to be best adapted to future climates
Focal Point Seed Zones
present
2030
2095
How far down in elevation do we go to
find populations adapted to future
climates?
Year
2095
3
Year
2030
Year
2000
2
CV Trait 1
1
0
-1
-2
-3
-4
-5
0
200 400 600 800 1000 1200 1400 1600 1800 2000
Elevation
r = -0.69
Conclusions
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Douglas-fir has considerable geographic genetic structure
in vigor, most strongly associated with winter minimum
temperatures.
Climate change results in some risk of maladaptation, but
current populations appear to have enough genetic
variation that they may be expected to evolve to a new
optimum through natural selection or migration.
Populations that may be expected to be best adapted to
future climates will come from much lower elevations,
and, perhaps, further south.
Forest managers should consider mixing seed from local
populations with populations that may be expected to be
adapted to future climates.