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.