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 Will discuss the results of a common garden study and the implications for adaptation to future climates. Acknowledge collaborators.

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 Three objectives. These objectives build upon each other.

Genecology 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 First, to define genecology. Why are we interested in genecology? Two methods. Campbell approach is oriented towards creating a response surface of genetic variation as a function of source environment.

Douglas-fir common garden study Objective 1: Geographic structure and relationship between genetic variation and climate Douglas-fir common garden study Describe common garden study methods. Sampling both extensive and intensive. Large number of parent trees. Sown in three different years with 70 families in common. Grown for two years. 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 Several options for analysis: individual traits, PCA, and CCA. I chose CCA because it summarizes all the variables into a few that explain most of the variation, like PCA, but also maximizes the correlation between traits and environments. Results of all three approaches are similar. CCA is essentially an extension of multiple regression in which both dependent and independent variables are multivariate combinations of the original variables. PRISM is a statistical-geographic model developed by Chris Daley at OSU in which climate parameters are predicted for a grid cell using localized regression equations of climate as a function of elevation with greater weight given to climate data from nearby weather stations of similar elevation and topographic position. Being used around the world.

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 0.005 Go over each column. Proportion of trait variance explained by CV for traits is essentially the eigenvalue from PCA. 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 Mar min temperature 0.77 Aug min temperature 0.42 Aug precipitation 0.30 Most strongly related to December min temp. Other variables included because they add something to the model. 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 Given the model derived from CCA, we can use grid algebra in ArcInfo to create genetic maps. Similar to map of dec min temperature.

Objective 2: Effects of changing climates on adaptation of current populations Methods 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. Three steps. Already have done step 1. Next move on to steps 2 and 3. Look at difference between what might best be adapted to the future environment and what is presently there.

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. First assumption is a conservative assumption. Assumption of local is best. Second assumption is essentially that genetic variation is a related to the climate.

Climate change predictions Two models: 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 1.8 2.0 1.0 0.9 H 2030 2.3 1.7 2.1 C 2090 6.0 5.8 5.5 4.4 H 2090 4.0 5.2 4.7 Canadian model a little more severe change in winter. Not much change in precip for the region.

Geographic genetic variation that may be expected to be best adapted to present and future climates 2030 2095 For any particular site, the best adapted population would be one with higher vigor. Warming results in sources no longer being appropriate for their current sites. A general movement to colder sites (higher elevations), and novel variation being most appropriate for the Coast Range and inland valleys. But this shows mean response. What about within population variation? Do populations have enough genetic variation that they may be expected to evolve via natural selection? Uses Canadian model.

Degree of mismatch a function of: 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: Leads to third step. difference = 0.5 additive genetic variance a= 0.52 percentage mismatch = 37 %

Maladaptation from climate change Present 2030 2095 Thus, by the year 2030, we might expect, on average, that about 40% of the current population would differ from the best population for that environment. But that means the 60% of the current population overlaps with a population that may be considered best adapted to the site. Within limits of acceptable levels of seed transfer. By 2095, the numbers look somewhat worse with only 16-29% of the current population overlapping with the best adapted population. Model Difference Risk Canadian 0.56 0.41 1.46 0.84 Hadley 0.50 0.37 1.11 0.71

Summary of Objective 2: Effects of changing climates on adaptation of current populations 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. So the next question is where do we go to get 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 This leads us to the third objective.

How far down in elevation do we go to find populations adapted to future climates? Need to drop considerably in elevation to find the parents best adapted to future climates. More than 300 m for populations adapted to year 2030 and more than 800 m for populations adapted to 2095. r = -0.69

Conclusions 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.