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Adaptive variation

Adaptive variation. A feature of an organism that has been favoured by natural selection because of that feature's positive effect on relative fitness. Identifying local adaptation. Common garden experiments Clines Q st (phenotypic differentiation) versus F st (genetic

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Adaptive variation

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  1. Adaptive variation A feature of an organism that has been favoured by natural selection because of that feature's positive effect on relative fitness

  2. Identifying local adaptation Common garden experiments Clines Qst (phenotypic differentiation) versus Fst (genetic differentiation at neutral molecular markers)

  3. The definition of local adaptation (Kawecki & Ebert 2004). Annual Reviews

  4. Common garden experiments Clausen, Keck, & Hiesey Potentilla glandulosa

  5. Common gardens Genetic difference Phenotypic plasticity

  6. 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.

  7. Annual Reviews Clinal variation in traits related to timing of growth i

  8. 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

  9. Annual Reviews FST and QST values of twelve tree species

  10. Not all traits are adaptations 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)

  11. Effects of climate change on plant populations 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?

  12. Effects of climate change on plant populations: adaptation 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

  13. 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

  14. Outlier FST as evidence for adaptive variation

  15. Locations of the 6 sampled populations

  16. Success of SNP assays

  17. Summary statistics by population

  18. 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 αiindicates the presence of a positive selection on the studied gene, while a large positive γijindicates locus–population interaction, thus a potentially advantageous mutation that would be locally adapted to a particular population • Loci with high positive γijvalues (above 0.10) possibly reflect true adaptive differentiation

  19. 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)

  20. Back to Namroud et al.

  21. 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

  22. 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

  23. Objectives of this study • 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

  24. 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

  25. Objective 1: Geographic structure and relationship between genetic variation and climate Douglas-fir common garden study Distribution of parent trees and elevation Raised beds

  26. 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

  27. Results from CCA 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).

  28. Results from CCA Model: trait1=-0.08+0.38*decmin –0.25*janmin+0.09*febmax +0.13*marmin-0.12*augmin+0.02*augpre

  29. Dec Minimum Temperature CV 1 for Traits Geographic genetic variation in first canonical variable for traits

  30. 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 (determined by the mismatch between current population and best adapted population).

  31. 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

  32. Climate change predictions

  33. Present 2030 2095 Geographic genetic variation that may be expected to be best adapted to present and future climates

  34. 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.

  35. present 2030 2095 Objective 3. To consider the locations of populations that might be expected to be best adapted to future climates Focal Point Seed Zones

  36. How far down in elevation do we go to find populations adapted to future climates? r = -0.69

  37. 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.

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