Adaptive variation A feature of an organism that has been favoured by natural selection because of...
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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.