Adaptive variation

A feature of an organism that has been favoured by

natural selection because of that feature's positive effect

on relative fitness

Common garden experiments

Clines

Qst (phenotypic differentiation) versus Fst (genetic

differentiation at neutral molecular markers)

Identifying local adaptation

Annual ReviewsThe definition of local adaptation (Kawecki & Ebert 2004).

Common garden experiments

Clausen, Keck, & HieseyPotentilla glandulosa

Common gardens

Phenotypic plasticity

Gen

etic

diff

eren

ce

Annual Reviews

Transfer response functions for fitness and its components in Pinus sylvestris for a central population from latitude 60◦N and a northern population from latitude 66◦N.

Annual Reviews

Clinal variation in traits related to timing of growth i

Vw= average within population genetic variation

Vb= average between population genetic variation

Qst=Vb/(Vw+2Vb)

Note these are genetic variances, not phenotypic variances

Need estimates of heritability within populations Clonal Daphnia used by Spitze

Annual Reviews

FST and QST values of twelve tree species

Neutral processes (e.g. drift)

Exaptations - a trait may have evolved previously for

another purpose (Gould)

Pleiotropy - selection on another trait which is

controlled by the same genes

Phenotypic plasticity

Historical contingency (multiple adaptive peaks)

Not all traits are adaptations

Climate change may occur more quickly than migration

The degree of phenotypic plasticity may be less than is required

to deal with the climatic variability associated with climate change

Can plants adapt to climate change?

Effects of climate change on plant populations

Habitat fragmentation

-Ne reduced (drift increases, efficiency of selection reduced)

-reduced gene flow (m<1)

-erodes genetic variation, increased inbreeding (inbreeding

depression) -> reduced population fitness

Strong selection pressures from multiple sources may exhaust

genetic variation -> population can’t stay at fitness optima

Genetic correlations among traits can impede the response to

selection

Species with long generation times will respond slowest

Effects of climate change on plant populations: adaptation

Genetic variation and extinction risk

A small population is prone to positive feedback loops in inbreeding and genetic drift that draws the population down an extinction vortex toward smaller and smaller population size until extinction (mutational meltdown)

Thus the rate of adaptation may be outstripped by climate change for many species->extinction

Outlier FST as evidence for adaptive variation

Locations of the 6 sampled populations

Success of SNP assays

Summary statistics by population

Analysis for adaptive differentiation The program “newfst” (Beaumont & Balding 2004) was used to identify genes

subject to selection

This program relies on a Bayesian model to generate FST values through a Markov

Chain Monte Carlo (MCMC) algorithm

It can disentangle the locus effect (αi), the population effect (βj), and the

interaction between the locus and the population effects (γij).

A large positive αi indicates the presence of a positive selection on the studied

gene, while a large positive γij indicates locus–population interaction, thus a

potentially advantageous mutation that would be locally adapted to a particular

population

Loci with high positive γij values (above 0.10) possibly reflect true adaptive

differentiation

Obtain estimates of F for locus i, population j.

Fit the following linear model:

is locus effect (averaged over populations) is population effect (averaged over loci) is locus x population effect (adaptation in specific populations)

It is possible to identify the majority of loci under adaptive selection; in simulations, good discrimination for adaptively selected loci when s > 5m.(s = selection coefficient, m = migration rate among populations)

Back to Namroud et al.

Conclusions of Namroud et al. First genome-wide SNP scan of genes in a nonmodel species First to be conducted in conifer populations for which significant

genetic differentiation in quantitative traits has been demonstrated from common garden studies

Average among-population FST was very low (0.006) No strong local adaptation (no positive γij at the 95% or the 99%

confidence levels), but 49 SNPs showed a “trend” towards local adaptation (γij value > 0.10), despite low FST .

“Ascertainment bias”: Only SNPs of higher frequency were assayed, yet low frequency SNPs might contribute most to local adaptation

Clear definition of phyiological roles of these SNPs is a long way from being determined (need association, functional studies)

“Next generation” sequencing methods will make sequencing and genotyping much less expensive

Genecology and Adaptation Genecology and Adaptation of Douglas-Fir to Climate of Douglas-Fir to Climate

ChangeChange

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 of this studyObjectives of this study

To explore geographic genetic structure and To explore geographic genetic structure and the relationship between genetic variation and the 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

GenecologyGenecologyDefinition: the study of intra-specific genetic variation Definition: the study of intra-specific genetic variation of plants in relation to environments (Turesson 1923)of plants in relation to environments (Turesson 1923)Consistent correlations between genotypes and Consistent correlations between genotypes and environments suggest natural selection and environments suggest natural selection and adaptation of populations to their environments adaptation of populations to their environments (Endler 1986)(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 schemeparticularly advantageous in the highly heterogeneous particularly advantageous in the highly heterogeneous environments in mountainsenvironments in mountains

Douglas-fir common garden studyDouglas-fir common garden study

Distribution of parent Distribution of parent trees and elevationtrees and elevation

Objective 1: Geographic structure and relationship between genetic variation and climate

Raised beds

AnalysisAnalysis

Canonical correlation analysisCanonical correlation analysis– Determines pairs of linear combinations from two Determines pairs of linear combinations from two

sets of original variables such that the correlations sets of original variables such that the correlations between canonical variables are maximizedbetween canonical variables are maximized

– Trait variablesTrait variablesemergence, growth, bud phenology, and partitioningemergence, growth, bud phenology, and partitioning

– Climate variablesClimate variablesmodeled by PRISMmodeled by PRISMannual and monthly precipitation, minimum and maximum annual and monthly precipitation, minimum and maximum temperatures, seasonal ratiostemperatures, seasonal ratios

Use GIS to display resultsUse GIS to display results

Results from CCAResults from CCA

ComponentComponent Canonical Canonical CorrelationCorrelation

Canonical 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 CCAResults 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

CV 1 for Traits

Geographic genetic variation in Geographic genetic variation in first canonical variable for traitsfirst canonical variable for traits

Dec Minimum Temperature

MethodsMethods1.1. 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 (determined by the mismatch between climate (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

Climate change predictionsClimate change predictionsTwo 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 predictionsClimate change predictions

Expected Values for Climate Change (ºC)Expected Values for Climate Change (ºC)

Model/YearModel/Year Dec Min Dec Min TempTemp

Jan Jan

Min Min TempTemp

Feb Feb Max Max

TempTemp

Mar Min Mar 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 Geographic genetic variation that may be expected to be best adapted to be expected to be best adapted to

present and future climatespresent and future climatesPresent 2030 2095

Summary of Objective 2: Effects of Summary of Objective 2: Effects of changing climates on adaptation of changing climates on adaptation of

current populationscurrent populations

40% risk of maladaptation within acceptable 40% risk of maladaptation within acceptable limits of seed transfer (Campbell, Sorensen).limits of seed transfer (Campbell, Sorensen).

71-84% risk is somewhat high.71-84% risk is somewhat high.

Enough genetic variation present to allow Enough genetic variation present to allow evolution through natural selection or migration.evolution through natural selection or migration.

Maladaptation does not necessarily mean Maladaptation does not necessarily mean mortality. Trees may actually grow better, but mortality. Trees may actually grow better, but below the optimum possible with the best below the optimum possible with the best adapted populations.adapted populations.

Objective 3. To consider the locations of Objective 3. To consider the locations of populations that might be expected to be populations that might be expected to be best adapted to future climatesbest adapted to future climates

present 2030 2095

Focal Point Seed Zones

How far down in elevation do we go to find populations How far down in elevation do we go to find populations adapted to future climates?adapted to future climates?

Elevation0 200 400 600 800 1000 1200 1400 1600 1800 2000

CV

Tra

it 1

-5

-4

-3

-2

-1

0

1

2

3

Year2095

Year 2030

Year2000

r = -0.69

ConclusionsConclusions

Douglas-fir has considerable geographic genetic Douglas-fir has considerable geographic genetic structure in vigor, most strongly associated with winter structure in vigor, most strongly associated with winter minimum temperatures.minimum temperatures.Climate change results in some risk of maladaptation, Climate change results in some risk of maladaptation, but current populations appear to have enough but current populations appear to have enough genetic variation that they may be expected to evolve genetic variation that they may be expected to evolve to a new optimum through natural selection or to a new optimum through natural selection or migration.migration.Populations that may be expected to be best adapted Populations that may be expected to be best adapted to future climates will come from much lower to future climates will come from much lower elevations, and, perhaps, further south.elevations, and, perhaps, further south.Forest managers should consider mixing seed from Forest managers should consider mixing seed from local populations with populations that may be local populations with populations that may be expected to be adapted to future climates.expected to be adapted to future climates.