2. Summary DNA evolves leading to unique sequences that may be used to identify species, biological species, provenences of strains, genotypes, genetic or allelic richness and genetic structure Mutations and recombinations drive evolution of DNA sequences. Isolation, drift, and selection lead to unique sequences associated with different species or isolated populations Isolation: allopatric vs. sympatric. In both cases there is no gene flow between species DNA sequences can be used to identify species. They need to be aligned and compared. If each species is unequivocally found within a statistically supported clade, then that sequence can be used to identify species and provenance for that group of organisms Diagnostic sequence,narrower concept need to be from a locus that is less variable within species and more variable in between species. Alternatively fixed alleles may be the most powerful. Rare alleles or private alleles are also important in defining populations (individuals that are freely mating): allele frequencies used by assignment tests such as structure

3. Summary Sequences used to identify species either by comparison of actual sequence or by use of taxon specific PCR primers that will only amplify target organism. Need for control. I.e. primers that will amplify any organism to make sure reaction is working. If sequences are obtained and compared they can –Aligned with sequences of similar organisms to determine presence of statistically significant clades –Compared with sequences present in public databases such as GenBank. BLAST engine –Beware that a single locus may be deceiving, because history of locus (gene geneaology is not necessarily history of organism)

4. Summary If more than just species identification is needed, multiple genetic markers will be needed. These should be as much as possible unlinked. These multiple markers can be used to identify genotypes and study their distribution to understand epidemiology of a disease or perform paternity tests; determine allelic richness: this is considered an important issue in conservation biology (normally small or isolated populations tend to loose alleles); study the genetic structure of a species, I.e. Are populations genetically different (are their alleleic frequencies significantly different) and if so at what scale does the difference become significant; finally multiple genetic markers can be used to understand if species is reproducing sexually or not. This is important to understand epidemiology Genetic information can be supported by other types of information. For fungi for instance the use of somatic compatibility and of mating allele richness can be used to make inferences on genotypic composition, and relatedness of genotypes. Mitochondrial analysis can also be used to make inferences on genetic relatedness

5. Recognition of self vs. non self Intersterility genes: maintain species gene pool. Homogenic system Mating genes: recognition of “other” to allow for recombination. Heterogenic system Somatic compatibility: protection of the individual.

6. Recognition of self vs. non self It is possible to have different genotypes with the same vc alleles VC grouping and genotyping is not the same It allows for genotyping without genetic tests Reasons behing VC system: protection of resources/avoidance of viral contagion

7. Somatic incompatibility

8. More on somatic compatibility Perform calculation on power of approach Temporary compatibility allows for cytoplasmic contact that then is interrupted: this temporary contact may be enough for viral contagion

9. SOMATIC COMPATIBILITY Fungi are territorial for two reasons –Selfish –Do not want to become infected If haploids it is a benefit to mate with other, but then the n+n wants to keep all other genotypes out Only if all alleles are the same there will be fusion of hyphae If most alleles are the same, but not all, fusion only temporary

10. SOMATIC COMPATIBILITY SC can be used to identify genotypes SC is regulated by multiple loci Individual that are compatible (recognize one another as self, are within the same SC group) SC group is used as a proxy for genotype, but in reality, you may have some different genotypes that by chance fall in the same SC group Happens often among sibs, but can happen by chance too among unrelated individuals

11. Recognition of self vs. non self What are the chances two different individuals will have the same set of VC alleles? Probability calculation (multiply frequency of each allele) More powerful the larger the number of loci …and the larger the number of alleles per locus

12. Recognition of self vs. non self: probability of identity (PID) 4 loci 3 biallelelic 1 penta-allelic P= 0.5x0.5x0.5x0.2=0.025 In humans 99.9%, 1000, 1 in one million

13. INTERSTERILITY If a species has arisen, it must have some adaptive advantages that should not be watered down by mixing with other species Will allow mating to happen only if individuals recognized as belonging to the same species Plus alleles at one of 5 loci (S P V1 V2 V3)

14. INTERSTERILITY Basis for speciation These alleles are selected for more strongly in sympatry You can have different species in allopatry that have not been selected for different IS alleles

15. MATING Two haploids need to fuse to form n+n Sex needs to increase diversity: need different alleles for mating to occur Selection for equal representation of many different mating alleles

16. MATING If one individuals is source of inoculum, then the same 2 mating alleles will be found in local population If inoculum is of broad provenance then multiple mating alleles should be found

17. MATING How do you test for mating? Place two homokaryons in same plate and check for formation of dikaryon (microscopic clamp connections at septa)

18. Clamp connections

19. MATING ALLELES All heterokaryons will have two mating allelels, for instance a, b There is an advantage in having more mating alleles (easier mating, higher chances of finding a mate) Mating allele that is rare, may be of migrant just arrived If a parent is important source, genotypes should all be of one or two mating types

20. Two scenarios: A, A, B, C, D, D, E, H, I, L A, A, A,B, B, A, A

21. Two scenarios: A, A, B, C, D, D, E, H, I, L Multiple source of infections (at least 4 genotypes) A, A, A,B, B, A, A Siblings as source of infection (1 genotype)

22. SEX Ability to recombine and adapt Definition of population and metapopulation Different evolutionary model Why sex? Clonal reproductive approach can be very effective among pathogens

23. Long branches in between groups suggests no sex is occurring in between groups Fir-Spruce Pine Europe Pine N.Am.

24. Small branches within a clade indicate sexual reproduction is ongoing within that group of individuals 890 bp CI>0.9 NA S NA P EU S EU F

25. Index of association Ia= if same alleles are associated too much as opposed to random, it means sex is not occurring Association among alleles calculated and compared to simulated random distribution

26. Evolution and Population genetics Positively selected genes:…… Negatively selected genes…… Neutral genes: normally population genetics demands loci used are neutral Loci under balancing selection…..

27. Evolution and Population genetics Positively selected genes:…… Negatively selected genes…… Neutral genes: normally population genetics demands loci used are neutral Loci under balancing selection…..

28. Evolutionary history Darwininan vertical evolutionary models Horizontal, reticulated models..

33. Are my haplotypes sensitive enough? To validate power of tool used, one needs to be able to differentiate among closely related individual Generate progeny Make sure each meiospore has different haplotype Calculate P

34. RAPD combination 1 2 1010101010 1010000000 1011101010 1010111010 1010001010 1011001010 1011110101

35. Conclusions Only one RAPD combo is sensitive enough to differentiate 4 half-sibs (in white) Mendelian inheritance? By analysis of all haplotypes it is apparent that two markers are always cosegregating, one of the two should be removed

36. If we have codominant markers how many do I need IDENTITY tests = probability calculation based on allele frequency… Multiplication of frequencies of alleles 10 alleles at locus 1 P1=0.1 5 alleles at locus 2 P2=0,2 Total P= P1*P2=0.02

37. Have we sampled enough? Resampling approaches Saturation curves –A total of 30 polymorphic alleles –Our sample is either 10 or 20 –Calculate whether each new sample is characterized by new alleles

38. Saturation (rarefaction) curves 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 No Of New alleles

39. Dealing with dominant anonymous multilocus markers Need to use large numbers (linkage) Repeatability Graph distribution of distances Calculate distance using Jaccard’s similarity index

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

41. 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

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

43. 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

44. 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

45. AMOVA groupings Individual Population Region AMOVA: partitions molecular variance amongst a priori defined groupings

46. Example SPECIES X: 50%blue, 50% yellow

47. AMOVA: example v Scenario 1Scenario 2 POP 1 POP 2 v

48. Expectations for fungi Sexually reproducing fungi characterized by high percentage of variance explained by individual populations Amount of variance between populations and regions will depend on ability of organism to move, availability of host, and NOTE: if genotypes are not sensitive enough so you are calling “the same” things that are different you may get unreliable results like 100 % variance within pops, none among pops

49. Plotting distances Pairwise genetic distances can be plotted: the distribution of distances can be informative of biology of organism

50. 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

51. 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.

52. P. nemorosa P. ilicis P. pseudosyringae Results: P. nemorosa

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

54. 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)---

58. RAPDS> not used often now

59. RAPD DATA W/O COSEGREGATING MARKERS

61. PCA

62. AFLP Amplified Fragment Length Polymorphisms Dominant marker Scans the entire genome like RAPDs More reliable because it uses longer PCR primers less likely to mismatch Priming sites are a construct of the sequence in the organism and a piece of synthesized DNA

63. How are AFLPs generated? AGGTCGCTAAAATTTT (restriction site in red) AGGTCG CTAAATTT Synthetic DNA piece ligated –NNNNNNNNNNNNNNCTAAATTTTT Created a new PCR priming site –NNNNNNNNNNNNNNCTAAATTTTT Every time two PCR priming sitea are within 400-1600 bp you obtain amplification

66. White mangroves: Corioloposis caperata Distances between study sites

67. 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

70. 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

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

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

75. 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)

76. 75 MS18(AC) 38 218 bp (AC) 39 220 bp (AC) 40 222 bp MS43a(CAGA) 70 373 bp MS43a(CAGA) 71 377 bp MS43a(CAGA) 72 381 bp (220-218) 2 2 2 (222-218) 2 4 2 (377-373) 2 4 2 (381-373) 2 8 2 (39-38) 2 1 2 (40-38) 2 2 2 (71-70) 2 1 2 (72-70) 2 2 2 ACACACACACACACACAC AMOVA Analysis of Molecular Variance

77. 76 Example 1: Origins of the Sudden Oak Death Epidemic in California (Mascheretti et al., Molecular Ecology (2008) 17: 2755-2768) Photo: UC Davis Photo: www.membranetransport.orgwww.membranetransport.org Photo: Northeast Plant Diagnostic Network

78. 77 Spatial autocorrelation Geographical distance (m) 10 100 1000 Moran’s I 0 Within approx. 100 meters the genetic structure correlates with the geographical distance

79. 78 Spatial autocorrelation Moran’s I (coefficient of departure from spatial randomness) correlates with distance up to Distribution of genotypes (6 microsatellite markers) in different populations of P.ramorum in California

80. 79 NJ tree of P. ramorum populations in California SC-1 MA-4 NURSERY SC-3 MA-3 SO-1 SO-2 MA-5 SC-2 MO-1 MO-2 MA-2 MA-1 HU-1 HU-2

81. 80 Phytophthora ramorum (Oomycete) –causal agent of Sudden Oak Death (SOD) first reported in California in 1994 –SOD affects tanoak (Lithocarpus densiflora), coast live oak (Quercus agrifolia), Californian black oak (Quercus kelloggii), and Canyon live oak (Quercus chrysolepis) –P.ramorum also cause a disease characterized mostly by leaf blight and/or branch dieback in over 100 species of both wild and ornamental plants, including California bay laurel (Umbellularia cailfornica), California redwood (Sequoia sempervirens), Camellia and Rhododrendron species Example: microsatellites genotyping of P. ramorum isolates Collection of infected bay leaves from several forests in Sonoma, Monterey, Marin, Napa, Alameda, San Mateo

82. 81 Microsatellites (I) mating type A1 (EU) and mating type A2 (US) A2 (US)A1 (EU) Locus 29325/ - 325/337 -/337 Locus 33315/337325/337 Locus 65234/252 236/244 220/222

83. 82 Ind.MS39aMS39bMS43aMS43bMS45MS18MS64 Mating type 1129-129246-246369-369486-486167-187220-278342-374A1 2129-129246-246369-369486-486167-187220-278342-374A1 3129-129246-246373-373486-486167-187220-274342-374A1 4129-129246-246373-373 486-486167-187220-274342-378A1 5129-129246-246373-373 486-486167-187220-274342-378A1 6129-129246-246373-373 486-486167-187220-274342-378A1 7129-129246-246373-373 486-486167-187220-278342-378A1 8129-129246-246373-373 486-486167-187220-278342-374A1 9129-129250-250369-369486-486167-187220-278342-374A1 10129-129250-250 369-369486-486167-187220-278342-374A1 11129-129250-250369-369486-486167-187220-278342-374A1 12129-129250-250 377-377490-490167-187220-278342-374A1 13129-129250-250 377-377 490-490 167-187220-278342-381A1 14129-129250-250 377-377 490-490 167-187220-278342-381A1 15129-129250-250 377-377 490-490 167-187220-278342-381A1 16129-129246-246377-377 490-490 167-187220-278342-374A1 17129-129246-246377-377 486-486167-187220-278342-374A1 18129-129246-246369-369486-486167-187220-278342-374A1 19129-129246-246381-381486-486167-187222-null342-374A2 20129-129246-246381-381494-494167-187222-null342-374A2

84. Genetic analysis requires variation at loci, variation of markers (polymorphisms) How the variation is structured will tell us –Does the microbe reproduce sexually or clonally –Is infection primary or secondary –Is contagion caused by local infectious spreaders or by a long- disance moving spreaders –How far can individuals move: how large are populations –Is there inbreeding or are individuals freely outcrossing

85. CASE STUDY A stand of adjacent trees is infected by a disease: How can we determine the way trees are infected?

86. CASE STUDY A stand of adjacent trees is infected by a disease: How can we determine the way trees are infected? BY ANALYSING THE GENOTYPE OF THE MICROBES: if the genotype is the same then we have local secondary tree-to-tree contagion. If all genotypes are different then primary infection caused by airborne spores is the likely cause of Contagion.

87. CASE STUDY WE HAVE DETERMINED AIRBORNE SPORES (PRIMARY INFECTION ) IS THE MOST COMMON FORM OF INFECTION QUESTION: Are the infectious spores produced by a local spreader, or is there a general airborne population of spores that may come from far away ? HOW CAN WE ANSWER THIS QUESTION?

88. If spores are produced by a local spreader.. Even if each tree is infected by different genotypes (each representing the result of meiosis like us here in this class)….these genotypes will be related HOW CAN WE DETERMINE IF THEY ARE RELATED?

89. By using random genetic markers we find out the genetic similarity among these genotypes infecting adjacent trees is high If all spores are generated by one individual –They should have the same mitochondrial genome –They should have one of two mating alleles

90. WE DETERMINE INFECTIOUS SPORES ARE NOT RELATED QUESTION: HOW FAR ARE THEY COMING FROM? ….or…… HOW LARGE IS A POPULATION? Very important question: if we decide we want to wipe out an infectious disease we need to wipe out at least the areas corresponding to the population size, otherwise we will achieve no result.

91. HOW TO DETERMINE WHETHER DIFFERENT SITES BELONG TO THE SAME POP OR NOT? Sample the sites and run the genetic markers If sites are very different: –All individuals from each site will be in their own exclusive clade, if two sites are in the same clade maybe those two populations actually are linked (within reach) –In AMOVA analysis, amount of genetic variance among populations will be significant (if organism is sexual portion of variance among individuals will also be significant) –F statistics: Fst will be over ) 0.10 (suggesting sttong structuring) –There will be isolation by distance

92. Levels of Analyses  Individual identifying parents & offspring– very important in zoological circles – identify patterns of mating between individuals (polyandry, etc.)  In fungi, it is important to identify the "individual" -- determining clonal individuals from unique individuals that resulted from a single mating event.

93. Levels of Analyses cont… Families – looking at relatedness within colonies (ants, bees, etc.) Population – level of variation within a population. –Dispersal = indirectly estimate by calculating migration –Conservation & Management = looking for founder effects (little allelic variation), bottlenecks (reduction in population size leads to little allelic variation) Species – variation among species = what are the relationship between species. Family, Order, ETC. = higher level phylogenies

94. What is Population Genetics?  About microevolution (evolution of species)  The study of the change of allele frequencies, genotype frequencies, and phenotype frequencies

95. Natural selection (adaptation) Chance (random events) Mutations Climatic changes (population expansions and contractions) … To provide an explanatory framework to describe the evolution of species, organisms, and their genome, due to: Assumes that: the same evolutionary forces acting within species (populations) should enable us to explain the differences we see between species evolution leads to change in gene frequencies within populations Goals of population genetics

96. Pathogen Population Genetics must constantly adapt to changing environmental conditions to survive –High genetic diversity = easily adapted –Low genetic diversity = difficult to adapt to changing environmental conditions – important for determining evolutionary potential of a pathogen If we are to control a disease, must target a population rather than individual Exhibit a diverse array of reproductive strategies that impact population biology

97. Analytical Techniques –Hardy-Weinberg Equilibrium p 2 + 2pq + q 2 = 1 Departures from non-random mating –F-Statistics measures of genetic differentiation in populations –Genetic Distances – degree of similarity between OTUs Nei’s Reynolds Jaccards Cavalli-Sforza –Tree Algorithms – visualization of similarity UPGMA Neighbor Joining

98. Allele Frequencies Allele frequencies (gene frequencies) = proportion of all alleles in an all individuals in the group in question which are a particular type Allele frequencies:  p + q = 1 Expected genotype frequencies:  p 2 + 2pq + q 2

99. Evolutionary principles: Factors causing changes in genotype frequency Selection = variation in fitness; heritable Mutation = change in DNA of genes Migration = movement of genes across populations –Vectors = Pollen, Spores Recombination = exchange of gene segments Non-random Mating = mating between neighbors rather than by chance Random Genetic Drift = if populations are small enough, by chance, sampling will result in a different allele frequency from one generation to the next.

100. The smaller the sample, the greater the chance of deviation from an ideal population. Genetic drift at small population sizes often occurs as a result of two situations: the bottleneck effect or the founder effect.

102. Founder Effects; typical of exotic diseases Establishment of a population by a few individuals can profoundly affect genetic variation –Consequences of Founder effects Fewer alleles Fixed alleles Modified allele frequencies compared to source pop GREATER THAN EXPECTED DIFFERENCES AMONG POPULATIONS BECAUSE POPULATIONS NOT IN EQUILIBRIUM (IF A BLONDE FOUNDS TOWN A AND A BRUNETTE FOUND TOWN B ANDF THERE IS NO MOVEMENT BETWEEN TOWNS, WE WILL ISTANTANEOUSLY OBSERVE POPULATION DIFFERENTIATION)

104. The bottleneck effect occurs when the numbers of individuals in a larger population are drastically reduced By chance, some alleles may be overrepresented and others underrepresented among the survivors Some alleles may be eliminated altogether Genetic drift will continue to impact the gene pool until the population is large enough Bottleneck Effect

105. Founder vs Bottleneck

106. Northern Elephant Seal: Example of Bottleneck Hunted down to 20 individuals in 1890’s Population has recovered to over 30,000 No genetic diversity at 20 loci

107. Hardy Weinberg Equilibrium and F-Stats In general, requires co-dominant marker system Codominant = expression of heterozygote phenotypes that differ from either homozygote phenotype. AA, Aa, aa

108. Hardy-Weinberg Equilibrium Null Model = population is in HW Equilibrium –Useful –Often predicts genotype frequencies well

109. if only random mating occurs, then allele frequencies remain unchanged over time. After one generation of random-mating, genotype frequencies are given by AAAaaa p 2 2pqq 2 p = freq (A) q = freq (a) Hardy-Weinberg Theorem

110. The possible range for an allele frequency or genotype frequency therefore lies between ( 0 – 1) with 0 meaning complete absence of that allele or genotype from the population (no individual in the population carries that allele or genotype) 1 means complete fixation of the allele or genotype (fixation means that every individual in the population is homozygous for the allele -- i.e., has the same genotype at that locus). Expected Genotype Frequencies

111. 1) diploid organism 2) sexual reproduction 3) Discrete generations (no overlap) 4) mating occurs at random 5) large population size (infinite) 6) No migration (closed population) 7) Mutations can be ignored 8) No selection on alleles ASSUMPTIONS

112. If the only force acting on the population is random mating, allele frequencies remain unchanged and genotypic frequencies are constant. Mendelian genetics implies that genetic variability can persist indefinitely, unless other evolutionary forces act to remove it IMPORTANCE OF HW THEOREM

113. Departures from HW Equilibrium Check Gene Diversity = Heterozygosity –If high gene diversity = different genetic sources due to high levels of migration Inbreeding - mating system “leaky” or breaks down allowing mating between siblings Asexual reproduction = check for clones –Risk of over emphasizing particular individuals Restricted dispersal = local differentiation leads to non-random mating

114. Pop 1 Pop 2 Pop 3 Pop 4 F ST = 0.02 F ST = 0.30

115. Pop1Pop2Pop3 Sample size 20 AA1050 Aa4108 aa6512

116. Pop1Pop2Pop3 Freq p(20 + 1/2*8)/40 = 0.60 (10+1/2*20)/40 =.50 (0+1/2*16)/40 = 0.20 q(12 + 1/2*8)/40 = 0.40 (10+1/2*20)/40 =.50 (24+1/2*16)/40 = 0.80

117. Calculate H OBS – Pop1: 4/20 = 0.20 – Pop2: 10/20 = 0.50 – Pop3: 8/20 = 0.40 Calculate H EXP (2pq) – Pop1: 2*0.60*0.40 = 0.48 – Pop2: 2*0.50*0.50 = 0.50 – Pop3: 2*0.20*0.80 = 0.32 Calculate F = (H EXP – H OBS )/ H EXP Pop1 = (0.48 – 0.20)/(0.48) = 0.583 Pop2 = (0.50 – 0.50)/(0.50) = 0.000 Pop3 = (0.32 – 0.40)/(0.32) = -0.250 Local Inbreeding Coefficient

118. F Stats Proportions of Variance F IS = (H S – H I )/(H S ) F ST = (H T – H S )/(H T ) F IT = (H T – H I )/(H T )

119. PopHsHs HIHI pqHTHT F IS F ST F IT 10.480.200.600.40 20.50 30.320.400.200.80 Mea n 0.430.370.430.570.49- 0.14 0.120.24

120. Important point Fst values are significant or not depending on the organism you are studying or reading about: – Fst =0.10 would be outrageous for humans, for fungi means modest substructuring

121. R E S E A R C H A R T I C L E Isolation by landscape in populations of a prized edible mushroom Tricholoma matsutake Anthony Amend Æ Matteo Garbelotto Æ Zhendong Fang Æ Sterling Keeley Conserv Genet DOI 10.1007/s10592-009-9894-0

122. 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)

123. Rhizopogon vulgaris Rhizopogon occidentalis Host islands within the California Northern Channel Islands create fine-scale genetic structure in two sympatric species of the symbiotic ectomycorrhizal fungus Rhizopogon

125. Rhizopogon sampling & study area Santa Rosa, Santa Cruz –R. occidentalis –R. vulgaris Overlapping ranges –Sympatric –Independent evolutionary histories

126. Sampling

127. Bioassay – Mycorrhizal pine roots

129. B T NE W Local Scale Population Structure Rhizopogon occidentalis F ST = 0.26 F ST = 0.33 F ST = 0.24 Grubisha LC, Bergemann SE, Bruns TD Molecular Ecology in press. F ST = 0.17 Populations are differentPopulations are similar 8-19 km 5 km

130. NE W Local Scale Population Structure Rhizopogon vulgaris F ST = 0.21 F ST = 0.25 F ST = 0.20 Grubisha LC, Bergemann SE, Bruns TD Molecular Ecology in press Populations are different

134. How do we know that we are sampling a population? We actually do not know Mostly we tend to identify samples from a discrete location as a population, obviously that’s tautological Assignment tests will use the data to define population, that is what Grubisha et al. did using the program STRUCTURE

135. Four phases of INVASION TRANSPORT SURVIVAL AND ESTABLISHMENT (LAG PHASE) INVASION POST-INVASION

136. TRANSPORT Biology will determine how Normally very few organisms will make it Use phylogeographic approach to determine origin ( Armillaria, Heterobasidion) Use population genetic approach (Cryphonectria, Certocystis fimbriata)

137. TRANSPORT-2 Need to sample source pop or a pop that is close enough Need markers that are polymorphic and will differentiate genotypes haplotypes Need analysis that will discriminate amongst individuals and identify relationships ( similarity clusterying, parsimony, Fst & N, coalescent)

138. ESTABLISHMENT LAG PHASE; normally effects not noticed because mortality are masked by background normal mortality By the time the introduction is discovered, normally too late to eradicate Short lag phase= aggressive pathogen Long lag phase= less aggressive pathogen

139. ESTABLISHMENT NORMALLY REDUCED GENETIC VARIABILITY

140. INVASION Because of lack of equilibrium, high Fst values, I.e. strong genetic structuring among populations Normally dominance of a few genotypes Spatial autocorrelation analyses to tell us exten of spread

141. INVASION-2 Later phase: genetic differentiation Higher genetic difference in areas of older establishment