Lecture 12.  Highlights of the course:

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I. Phylogenetic analyses: many algorithms, a few core principles

    Goal: arrangement of taxa on a "tree of life"
                understand the evolution of characters;
                process is best understood when pattern is clear --
                    that is, many evolutionary questions require a well-resolved phylogeny

    Gene trees vs. species trees
    Nuclear vs. organellar (mtDNA, cpDNA) history
    Cladistics -- monophyletic clades united by synapomorphies
    Parsimony & maximum likelihood approaches
                 bootstrapping to assess the robustness of clades (how well-supported are the nodes?)
    Molecular clock
                 linear extrapolations to estimate divergence times

II. Speciation

Allopatric vs. sympatric vs. parapatric models
Islands as natural laboratories
Biological vs. phylogenetic species concepts
Punctuated equilibrium (macroevolution) vs. gradualism (microevolution)
Role of constraints vs. adaptations in evolution
Wright’s shifting balance theorem vs. Fisher’s mass selection view

III. Population genetics
Hardy-Weinberg principle: p2 + 2pq + q2
        Ratio of homozygotes to heterozygotes
        Gene diversity/expected heterozygosity 1-·pi2
    The 5 forces that drive evolution:
      Drift (= small population size)
      Non-random mating
      Selection (neutral markers/neutral theory)
      3 additional assumptions
        (Non-overlapping generations)
        (Sexual reproduction)
    Measures of genetic structure:
      Variance-based (F-statistics)
        FIS (inbreeding) (HE - HO)/ HE = (HS - HI)/ HS
        FST = (HT - HS)/ HT ;  Hexp at pop. level vs. global 2p-bar*q-bar
        Weir and Cockerhamís Q
        (RST for stepwise mutation model - SMM)
        Many ways to derive F-statistics (PHSC, Wahlund, etc.)
      Wahlund effect -- excess homozygosity overall whenever gene frequencies
          differ across subpopulations
        Converse Wahlund effect is "isolate breaking" - excess heterozygosity when divergent
          populations interbreed
      Genetic distances:
        Geometric (no biology)
          Cavalli-Sforza chord distance, Rogersí distance
        Model-based (assumptions about mutation, drift or migration)
          Neiís (1978), measures based on stepwise mutation model (SMM; e.g., dm2), Reynolds' distance (drift)
    Concept of effective population size (Ne) and factors that affect it

      Fluctuating population size (harmonic mean)
      Sex ratio Ne (4*Nm*Nf)/( Nm + Nf) [4 males + 100 females leads to Ne of 15.4]
      Variance in offspring number (> or < Poisson?)
      Spatial dispersion (neighborhood size = 4ps2d)
      Overlapping generations can reduce Ne

IV. Conservation genetics

Where and when are genetics an appropriate tool?
Rare not equal to depauperate and vice versa
Genetic detective work (e.g., mapping migrants from wintering to breeding grounds)
Some interesting and appropriate applications:
        Delineating worthy taxa
        Assessing information content and biodiversity hotspots.
Neglected importance of quantitative genetics as an evolutionary and conservation tool

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