Genecology and Adaptation of Douglas-Fir to Climate Change
Brad St.ClairBrad St.Clair11, Ken Vance-Borland, Ken Vance-Borland22 and Nancy Mandel and Nancy Mandel11
11USDA Forest Service, Pacific Northwest Research StationUSDA Forest Service, Pacific Northwest Research Station22Oregon State UniversityOregon State University
Corvallis, OregonCorvallis, Oregon
Objectives
To explore geographic genetic structure and the To explore geographic genetic structure and the relationship between genetic variation and relationship between genetic variation and climateclimate
To evaluate the effects of changing climates on To evaluate the effects of changing climates on adaptation of current populationsadaptation of current populations
To consider the locations of populations that To consider the locations of populations that might be expected to be best adapted to future might be expected to be best adapted to future climatesclimates
Genecology Definition: the study of intra-specific genetic variation of Definition: the study of intra-specific genetic variation of
plants in relation to environments (Turesson 1923)plants in relation to environments (Turesson 1923) Consistent correlations between genotypes and Consistent correlations between genotypes and
environments suggest natural selection and adaptation of environments suggest natural selection and adaptation of populations to their environments (Endler 1986)populations to their environments (Endler 1986)
Methods for exploring genecology and geographic Methods for exploring genecology and geographic structure – common garden studiesstructure – common garden studies Classical provenance testsClassical provenance tests Campbell approach Campbell approach
intensive sampling schemeintensive sampling scheme particularly advantageous in the highly particularly advantageous in the highly
heterogeneous environments in mountainsheterogeneous environments in mountains
Douglas-fir common garden study
Distribution of parent trees Distribution of parent trees and elevationand elevation
Objective 1: Geographic structure and relationship between genetic variation and climate
Raised beds
Analysis Canonical correlation analysisCanonical correlation analysis
Determines pairs of linear combinations from two sets Determines pairs of linear combinations from two sets of original variables such that the correlations of original variables such that the correlations between canonical variables are maximizedbetween canonical variables are maximized
Trait variablesTrait variablesemergence, growth, bud phenology, and emergence, growth, bud phenology, and
partitioningpartitioning Climate variablesClimate variables
modeled by PRISMmodeled by PRISMannual and monthly precipitation, minimum and annual and monthly precipitation, minimum and
maximum temperatures, seasonal ratiosmaximum temperatures, seasonal ratios Use GIS to display resultsUse GIS to display results
Results from CCA
ComponentComponentCanonical Canonical
CorrelationCorrelationCanonical Canonical
R-squaredR-squared
Proportion of Proportion of trait variance trait variance explained by explained by CV for traitsCV for traits
Proportion of Proportion of trait variance trait variance explained by explained by
CV for climateCV for climate
11 0.860.86 0.730.73 0.390.39 0.290.29
22 0.590.59 0.350.35 0.110.11 0.040.04
33 0.340.34 0.110.11 0.040.04 0.0050.005
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:First CV for Traits correlated with:
Dec min temperatureDec min temperature 0.790.79
Jan min temperatureJan min temperature 0.730.73
Feb max temperatureFeb max temperature 0.730.73
Mar min temperatureMar min temperature 0.770.77
Aug min temperatureAug min temperature 0.420.42
Aug precipitationAug precipitation 0.300.30
Model: trait1=-0.08+0.38*decmin –0.25*janmin+0.09*febmax +0.13*marmin-0.12*augmin+0.02*augpre
CV 1 for Traits
Geographic genetic variation in first canonical variable for traits
Dec Minimum Temperature
Methods1.1. Develop model of the relationship between Develop model of the relationship between
genetic variation and environment using climate genetic variation and environment using climate variables.variables.
2.2. Given model, determine set of genotypes that Given model, determine set of genotypes that may be expected to be best adapted to future may be expected to be best adapted to future climate.climate.
3.3. Given climate change, determine degree of Given climate change, determine degree of maladaptation of current population to changed maladaptation of current population to changed climate as determined by the mismatch between climate as determined by the mismatch between current population and best adapted population.current population and best adapted population.
Objective 2: Effects of changing climates on adaptation of current populations
Step 2: Given model, determine set of genotypes that may be expected to be best adapted to future climate
Some assumptions: Some assumptions: A population is better adapted to its place of A population is better adapted to its place of
origin than any other populations.origin than any other populations. The map of adaptive genetic variation is also a The map of adaptive genetic variation is also a
map of the environmental complex that is map of the environmental complex that is active in natural selection.active in natural selection.
Thus, the map of the future climate is also a map Thus, the map of the future climate is also a map of the genotypes that may be expected to be best of the genotypes that may be expected to be best adapted to that climate.adapted to that climate.
Climate change predictions Two models:Two models:
Canadian Center for Climate Modeling and AnalysisCanadian Center for Climate Modeling and Analysis Hadley Center for Climate Prediction and ResearchHadley Center for Climate Prediction and Research
We assumed no geographic variation in We assumed no geographic variation in climate changeclimate change
Climate change predictions
Expected Values for Climate Change (Expected Values for Climate Change (ºC)ºC)
Model/YearModel/YearDec Dec Min Min
TempTemp
Jan Jan
Min Min TempTemp
Feb Feb Max Max
TempTemp
Mar Mar Min Min
TempTemp
Aug Aug Min Min
TempTemp
Aug Aug PrecipPrecip
(ratio)(ratio)
C 2030C 2030 2.52.5 2.52.5 1.81.8 2.02.0 1.01.0 0.90.9
H 2030H 2030 2.32.3 2.32.3 1.71.7 2.12.1 1.81.8 1.01.0
C 2090C 2090 6.06.0 6.06.0 5.85.8 5.55.5 4.44.4 1.01.0
H 2090H 2090 5.55.5 5.55.5 4.04.0 5.25.2 4.74.7 0.90.9
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
difference = 0.5
percentage mismatch = 37 %
additive genetic variance a= 0.52
Degree of mismatch a function of:
Maladaptation from climate change
ModelModel DifferenceDifference Risk Risk DifferenceDifference Risk Risk
CanadianCanadian 0.560.56 0.410.41 1.461.46 0.840.84
HadleyHadley 0.500.50 0.370.37 1.111.11 0.710.71
Present 2030 2095
Summary of Objective 2: Effects of changing climates on adaptation of current populations 40% risk of maladaptation within acceptable limits 40% risk of maladaptation within acceptable limits
of seed transfer (Campbell, Sorensen).of seed transfer (Campbell, Sorensen). 71-84% risk is somewhat high.71-84% risk is somewhat high. Enough genetic variation present to allow evolution Enough genetic variation present to allow evolution
through natural selection or migration.through natural selection or migration. Maladaptation does not necessarily mean mortality. Maladaptation does not necessarily mean mortality.
Trees may actually grow better, but below the Trees may actually grow better, but below the optimum possible with the best adapted populations.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
present 2030 2095
Focal Point Seed Zones
How far down in elevation do we go to find populations adapted to future
climates?
Elevation0 200 400 600 800 100012001400160018002000
CV
Tra
it 1
-5
-4
-3
-2
-1
0
1
2
3
Year2095
Year 2030
Year2000
r = -0.69
Conclusions Douglas-fir has considerable geographic genetic structure Douglas-fir has considerable geographic genetic structure
in vigor, most strongly associated with winter minimum in vigor, most strongly associated with winter minimum temperatures.temperatures.
Climate change results in some risk of maladaptation, but Climate change results in some risk of maladaptation, but current populations appear to have enough genetic current populations appear to have enough genetic variation that they may be expected to evolve to a new variation that they may be expected to evolve to a new optimum through natural selection or migration.optimum through natural selection or migration.
Populations that may be expected to be best adapted to Populations that may be expected to be best adapted to future climates will come from much lower elevations, future climates will come from much lower elevations, and, perhaps, further south.and, perhaps, further south.
Forest managers should consider mixing seed from local Forest managers should consider mixing seed from local populations with populations that may be expected to be populations with populations that may be expected to be adapted to future climates.adapted to future climates.