2. Summary AFLP, RAPDs, RFLPs, microsatellites Repeatability Test for power (PID and test progeny) Have we sampled enough? Rarefaction curves, resampling, need to be ob flat portion of curve

3. Microsatellites or SSRs AGTTTCATGCGTAGGT CG CG CG CG CG AAAATTTTAGGTAAATTT Number of CG is variable Design primers on FLANKING region, amplify DNA Electrophoresis on gel, or capillary Size the allele (different by one or more repeats; if number does not match there may be polimorphisms in flanking region) Stepwise mutational process (2 to 3 to 4 to 3 to2 repeats)

4. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count 1010011 1001011 1001000

5. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count A: 1010011 AB= 0.60.4 (1-AB) B: 1001011 BC=0.50.5 C: 1001000 AC=0.20.8

6. Now that we have distances…. Plot their distribution (clonal vs. sexual)

7. Now that we have distances…. Plot their distribution (clonal vs. sexual) Analysis: –Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA

8. Now that we have distances…. Plot their distribution (clonal vs. sexual) Analysis: –Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA –AMOVA; requires a priori grouping

9. Results: Jaccard similarity coefficients 0.3 0.900.920.94 0.960.98 1.00 0 0.1 0.2 0.4 0.5 0.6 0.7 Coefficient Frequency P. nemorosa P. pseudosyringae: U.S. and E.U. 0.3 Coefficient 0.900.920.940.960.981.00 0 0.1 0.2 0.4 0.5 0.6 0.7 Frequency

10. 0.90.910.920.930.940.950.960.970.980.99 Pp U.S. Pp E.U. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Jaccard coefficient of similarity 0.7 P. pseudosyringae genetic similarity patterns are different in U.S. and E.U.

11. P. nemorosa P. ilicis P. pseudosyringae Results: P. nemorosa

12. Results: P. pseudosyringae P. nemorosa P. ilicis P. pseudosyringae = E.U. isolate

13. AMOVA groupings Individual (within populations) Population (among populations) Region (between or among groups of populations) AMOVA: partitions molecular variance amongst a priori defined groupings

14. AMOVA Percentage of variance by grouping (%) Its statistical significance (P<0.05) PHIst: ranges between 0 and 1 (1= populations are completely different; >0.2 significantly different, 0.1-02 moderately different, <0.1=not different. Remember PHI st can only be calculated among populations, not within. It is a proxy for Fst

15. How to interpret AMOVA results Significant amount of genetic variance within populations= populations are constituted by genetically distinct individuals. Normally indication of sexual reproduction ongoing in population Significant amount of genetic variance between populations= populations are genetically different, suggesting limited gene flow between them

16. The “scale” of disease Dispersal gradients dependent on propagule size, resilience, ability to dessicate, NOTE: not linear Important interaction with environment, habitat, and niche availability. Examples: Heterobasidion in Western Alps, Matsutake mushrooms that offer example of habitat tracking Scale of dispersal (implicitely correlated to metapopulation structure)---

17. The scale of disease Curves of spore dispersal (rapid dilution effect, e.g most spores fall near source, but a long low tail, a few spores will travel long distances Genetic structure of species: the more structure the more fragmented the less dispersal Mantel tests, spatial autocorrelation: plot the genetic distance against the geographic distance

26. White mangroves: Corioloposis caperata Distances between study sites

27. Coriolopsis caperata on Laguncularia racemosa Forest fragmentation can lead to loss of gene flow among previously contiguous populations. The negative repercussions of such genetic isolation should most severely affect highly specialized organisms such as some plant- parasitic fungi. AFLP study on single spores

30. Using DNA sequences Obtain sequence Align sequences, number of parsimony informative sites Gap handling Picking sequences (order) Analyze sequences (similarity/parsimony/exhaustive/bayesian Analyze output; CI, HI Bootstrap/decay indices

31. Good chromatogram! Bad chromatogram… Pull-up (too much signal)Loss of fidelity leads to slips, skips and mixed signals Reverse reaction suffers same problems in opposite direction

32. Alignments (Se-Al)

33. Using DNA sequences Testing alternative trees: kashino hasegawa Molecular clock Outgroup Spatial correlation (Mantel) Networks and coalescence approaches

34. Using DNA sequences Bootstrap: the presence of a branch separating two groups of microbial strains could be real or simply one of the possible ways we could visualize microbial populations. Bootstrap tests whether the branch is real. It does so by trying to see through iterations if a similar branch can come out by chance for a given dataset BS value over 65 ok over 80 good, under 60 bad

37. From Garbelotto and Chapela, Evolution and biogeography of matsutakes Biodiversity within species as significant as between species