Sanchez-Ramirez_Santiago_201511_PhD_thesis.pdf - TSpace

Scaling macro- and microevolutionary dynamics in the Caesar’s mushrooms (Amanita sect. Caesareae)

by

Santiago Sánchez-Ramírez

A thesis submitted in conformity with the requirements for the degree of PhD in Ecology and Evolutionary Biology

Ecology and Evolutionary Biology University of Toronto

© Copyright by Santiago Sánchez-Ramírez 2015

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Scaling macro- and microevolutionary dynamics in the Caesar’s

mushrooms (Amanita sect. Caesareae)

Santiago Sánchez-Ramírez

PhD in Ecology and Evolutionary Biology

Ecology and Evolutionary Biology University of Toronto

2015

Abstract

Biological diversity is the product of evolutionary and ecological factors that operate at different

spatial, temporal, and genomic scales. Phylogenies and genomes have the potential to uncover

the mechanisms leading to such diversity, with clues imprinted throughout the evolution of DNA

sequences. There are several challenges to study fungal evolution, some of which include

inaccurate species delimitations, unknown distributions, sampling biases, and a poor fossil

record. However, most fungi have small genomes, making them practical for studying

evolutionary genomics. This thesis includes four Chapters that use phylogenetic and genomic

approaches to answer questions related to the ecology and evolution of the Caesar’s mushrooms

(Amanita sect. Caesareae). Chapters 2 and 3 show how diversification and historical

biogeogaphical models explain the distribution, geographic disjunctions, and diversity of

temperate and tropical taxa worldwide. Results indicate that land-mediated dispersal and high

diversification rates in the New World and in the temperate region have played important roles,

which explain, in part, “shifted” latitudinal diversity gradients within the group. Chapter 4

describes new genomic data from A. jacksonii, and comparing it with that of other species.

Chapters 5 and 6 deal with the interrelationships between past climate change, regional

geography, species richness, genomic variation, selection, and historical demography. Gene

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genealogies and coalescent-based multi-locus species delimitations reveal high levels of cryptic

diversity in North America (NA). Higher speciation rate in climatically stable areas and recent

population expansions highlight the importance of Pleistocene glacial dynamics in shaping

diversity patterns within NA Caesar’s mushrooms. Finally, both natural selection on coding

DNA and demography explain genome-wide diversity across 500 single-copy genes within eight

species from a recently diverged species complex. This work evidences the spatial dynamics of

populations and species of a gastronomically and culturally important group of mushrooms over

millions of years, adding significant insights onto the evolution of symbiotic fungi.

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Acknowledgments

Being married and having a child while being a graduate student is a mixed blessing. On one side

it helps staying focused and on track, and on the other, it demands time and commitment. But

overall, it makes days amenable, interesting, and memorable. Sol (my wife) and Julian (my boy)

deserve substantial credit, first for their inexhaustible (though some times compromised)

patience; for being my everyday companions and friends; and lastly, for being my engines to try

harder every time. I love you guys!

I’m lucky to have such great parents. They are the kind that always backs you up no matter what.

Their support and advice throughout my life have been paramount, not only financially, but also

ideologically. My father, Victor Sanchez Sotomayor, is a Mexican conservation biologist striving

to maintain the Baja California pronghorn population. He has taught me not only the ups and

downs about biology, but about life (is there any difference?). My mother, Beatriz Ramirez Ruiz

de Velazco, a great (retired?) schoolteacher, is a devoted wife that gives her children more than

just care and love. I thank you both!

A great quality from my supervisor, Prof. Jean-Marc Moncalvo, is that (if he trusts you) he’ll

give you the freedom to explore any area (within the kingdom Fungi) you deem worthy. When I

started the PhD he said “Hey, you know what? We should look into genomics”. Both him and I

weren’t sure what we were getting into, but we went on anyways. Anyhow, it convoluted to a

mix beyond that, for which I am grateful. That and his endless support and trust.

This thesis would not have been possible without the extensive collaboration with Dr. Rodham

E. Tulloss, one of my mentors on Amanita biology. He not only provided most of the material

used in this thesis, but also made important intellectual contributions. I cherish the long hours we

spent talking about Amanita-related topics in his research headquarters (his home !).

When inspiration was short, the ever-helpful discussions with Prof. Jason T. Weir were always

instrumental. I owe him the urge to go beyond phylogenies and think more about evolutionary

patterns and processes. Pretty much every time I met with him we talked about a new model that

we could test. I’m glad to have invited him to my thesis committee, and to have met some

fantastic people in his lab (Paola, Maya, Alfredo, Diogo, Adam). In a similar way, Prof. Rampal

S. Etienne at the University of Groningen (Netherlands) aided and instructed me in the intricacies

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of diversification analyses. I would also like to thank Prof. Sa!a Stefanovic for all his

constructive critiques during committee meetings, and for always being available when needed.

The road to finalize a dissertation is long and harsh. Luckily there were always friends around to

make the edges less rough. I thank mis amigos, the latino crew at the ROM, not only for always

keeping the fun level up, but also for sharing intellectual and cultural thoughts. To be more

specific, I owe, mi tocayo (Santiago Sánchez-Pacheco), Julio Rivera, Ruben Cordero, Kevin

Konga (honorary latino), and Pedro Bernardo a big thanks. Another former ROM-UofT student

who was instrumental in my formation is Chris Blair (now an Assistant Professor at CUNY).

Although we overlapped briefly, Chris introduced me to the latest trends in molecular

phylogenetics, including the R system (which I didn’t know before I started as a PhD student).

We still communicate and collaborate every now and then.

I want to thank Simona Margaritescu for her help and support in the Molecular Systematics Lab

at the ROM, and for dealing with sample-related issues. All my collaborators in Mexico deserve

credit for helping me with the sampling. Specifically, I’m indebt to mycologists Drs. Laura

Guzmán-Dávalos, Joaquín Cifuentes, Ricardo Valenzuela, Raúl Díaz, Fortunato Garza, Florencia

Ramírez, Felipe Ruán (and two of his students), and students Nallely Hernández and Mariano

Torres. I must thank many Amanita collectors that provided personal samples (US: Erin

Feldman; Canada: Renée Lebeuf, Greg Thorn; Europe: Mario Amalfi), and various Fungaria

personnel throughout North America.

Finally, the Mexican National Council of Science and Technology (CONACyT) deserves credit

for awarding me a full scholarship. I would also like to extend my gratitude to Dr. Daniel Piñero

for providing financial aid in times of need. The Centre for Global Change Science (CGCS) at

UofT also funded three months of fieldwork in Mexico and the eastern US. Both SciNet and

CAGEF provided access to high-performance computing infrastructure. Dr. Dax Torti at the

Donelly Sequencing Centre also provided advice and performed all wet lab procedures on exon-

targeted sequencing.

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Table of Contents ACKNOWLEDGMENTS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!#$!

TABLE OF CONTENTS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!$#!

LIST OF TABLES!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!%!

LIST OF FIGURES!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!%#!

LIST OF APPENDICES!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!%$!

CHAPTER 1!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&!

! INTRODUCTION!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&!1

1.1! BIOLOGICAL DIVERSITY AND EVOLUTIONARY SCALES!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&!1.2! THE GENOMIC ERA!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!'!1.3! WHY FUNGI?!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!(!1.4! THE SYSTEM!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!)!1.5! QUESTIONS, OBJECTIVES, RESEARCH HYPOTHESES, AND SCIENTIFIC PRODUCTION!"""""""""""""""""""""!)!"#$#"! %&'()*+!,!#######################################################################################################################################################################!$!"#$#,! %&'()*+!-!#######################################################################################################################################################################!$!"#$#-! %&'()*+!.!#######################################################################################################################################################################!/!"#$#.! %&'()*+!$!#######################################################################################################################################################################!/!"#$#$! %&'()*+!/!#######################################################################################################################################################################!0!

CHAPTER 2!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!*!

! EVOLUTIOANRY HISTORY AND GLOBAL BIOGEOGRAPHY OF AMANITA SECT. 2

CAESAREAE!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!*!2.1! INTRODUCTION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!*!2.2! MATERIAL AND METHODS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&&!,#,#"! 1'23+')3+4!(+3)3%315!'67!7')'!8')+9%*5!###################################################################################################!""!,#,#,! %'192+')936!5)+')*:9*5!'67!79;*+:*6%*<)98*!*5)98')936!######################################################################!",!,#,#-! '6%*5)+'1<'+*'!+*%365)+=%)936!'67!:*3795(*+5'1!837*15!##################################################################!"-!,#,#.! 79;*+59>9%')936!+')*!)*5)5!###################################################################################################################################!".!2.3! RESULTS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&+!

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,#-#"! (&413:*6*)9%5!'67!79;*+:*6%*<)98*!*5)98')*5!##########################################################################################!"/!,#-#,! 293:*3:+'(&4!'67!'6%*5)+'1!'+*'5!#################################################################################################################!"/!,#-#-! 79;*+59>9%')936!+')*5!#############################################################################################################################################!"?!2.4! DISCUSSION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&*!,#.#"! &95)3+9%'1!293:*3:+'(&4!######################################################################################################################################!"?!,#.#,! 79;*+59>9%')936!#########################################################################################################################################################!,-!2.5! CONCLUSIONS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!'+!

CHAPTER 3!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!',!

! LATITUDINAL DIVERSITY GRADIENTS AND DIVERSIFICATION RATES IN THE 3

CAESAR’S MUSHROOMS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!',!3.1! INTRODUCTION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!',!3.2! MATERIAL AND METHODS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!(&!-#,#"! )'@36!5'8(196:A!1')9)=7*!)+'9)5!'67!7')'!###################################################################################################!-"!-#,#,! )98*<;'+496:!5(*%9')936!+')*!*5)98')936!'67!)&*!+*%365)+=%)936!3>!'6%*5)+'1!1')9)=7*5!##!-"!-#,#-! )*5)96:!)&*!*>>*%)!3>!1')9)=7*!36!5(*%9')936!+')*5!#################################################################################!-,!-#,#.! 29+)&<7*')&!79;*+59)4<7*(*67*6)!79;*+59>9%')936!###################################################################################!--!3.3! RESULTS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!()!-#-#"! )98*<;'+496:!5(*%9')936!+')*5!'67!1')9)=796'1!)+'9)5!###########################################################################!-.!-#-#,! 5(*%9')936!+')*!%&'6:*5!'5!'!>=6%)936!3>!1')9)=7*!##################################################################################!-/!-#-#-! 79;*+59)4<7*(*67*6%*!746'89%5!########################################################################################################################!.-!3.4! DISCUSSION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!)(!-#.#"! )98*<;'+496:!5(*%9')936!+')*5!'67!'6%*5)+'1!1')9)=7*5!#######################################################################!.-!-#.#,! &9:&*+!5(*%9')936!+')*5!')!)*8(*+')*!1')9)=7*5!*@(1'96!79>>*+*6%*5!96!79;*+59)4!###################!./!-#.#-! 79;*+59)4<7*(*67*6%*!'67!9)5!%1'7*<1*;*1!*>>*%)!36!)&*!17:!############################################################!.0!-#.#.! )&*!+31*!3>!*@)96%)936!##########################################################################################################################################!.?!-#.#$! (3)*6)9'1!(9)>'115!###################################################################################################################################################!.?!3.5! CONCLUSIONS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-.!

CHAPTER 4!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-&!

! WHOLE-GENOME SEQUENCING AND ANNOTATION OF AMANITA JACKSONII AND 4

PARTIAL GENOMIC SEQUENCES OF AMANITA BASII!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-&!4.1! INTRODUCTION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-&!

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4.2! SEQUENCED STRAIN AND ACCESSION NUMBERS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-'!4.3! MATERIAL AND METHODS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-(!4.4! RESULTS AND DISCUSSION!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-(!4.5! PARTIAL GENOMIC SEQUENCES FROM AMANITA BASII!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-)!4.6! DEVELOPMENT OF NOVEL GENOMIC LOCI!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!--!

CHAPTER 5!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-+!

! SPECIES DELIMITATION, COMPARATIVE PHYLOGEOGRAPHY AND GLACIAL 5

REFUGIA IN THE NORTH AMERICAN CAESAR’S MUSHROOM SPECIES COMPLEX!"""""""!-+!5.1! INTRODUCTION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-+!5.2! MATERIAL AND METHODS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!-,!$#,#"! 5'8(196:!######################################################################################################################################################################!$B!$#,#,! 1'23+')3+4!(+3)3%315!##########################################################################################################################################!$B!$#,#-! 7')'!8')+9%*5A!(&'596:!3>!&*)*+3C4:3)*5A!'67!76'!837*15!################################################################!$?!$#,#.! :*6*!)+**5A!%36%')*6')936A!'67!:*6*'13:9%'1!53+)96:!###########################################################################!$?!$#,#$! 2'4*59'6!5(*%9*5!7*1989)')936!############################################################################################################################!/D!$#,#/! 5(*%9*5!)+**!*5)98')936!#########################################################################################################################################!/"!$#,#0! 5(*%9*5!795)+92=)936!837*196:!###########################################################################################################################!/"!$#,#B! '6%*5)+'1!'+*'!+*%365)+=%)936!'67!79;*+59>9%')936!+')*5!#################################################################!/,!$#,#?! 5(*%9*5E!(3(=1')936!59C*!*5)98')936!>+38!)&*!8=1)9<5(*%9*5!%3'1*5%*6)!#########################################!/-!$#,#"D! +*:+*559365!'67!7*83:+'(&9%!&95)3+9*5!#####################################################################################################!/.!$#,#""! (3(=1')936!:*6*)9%!8*)+9%5!#############################################################################################################################!/$!5.3! RESULTS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!+-!$#-#"! :*6*!)+**5!'67!%36%')*6')*7!7')'!#################################################################################################################!/$!$#-#,! 5(*%9*5!)+**!*5)98')936!'67!5(*%9*5!7*1989)')936!#####################################################################################!/B!$#-#-! 69%&*!837*196:A!293:*3:+'(&9%!+*%365)+=%)936!'67!79;*+59>9%')936!+')*5!##################################!/?!$#-#.! &95)3+9%'1!7*83:+'(&4!'67!(3(=1')936!:*6*)9%5!#####################################################################################!/?!5.4! DISCUSSION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!/(!$#.#"! 5(*%9*5!7*1989)')936!'67!%+4()9%!79;*+59)4!##################################################################################################!0-!$#.#,! +*>=:9'1!8'%+3!'67!89%+3<*;31=)936'+4!746'89%5!##################################################################################!00!$#.#-! (3)*6)9'1!%';*')5!###################################################################################################################################################!B,!5.5! CONCLUSIONS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!,(!

CHAPTER 6!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!,)!

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! COMPARATIVE POPULATION GENOMICS AND MOLECULAR ADAPTATION IN THE 6

AMANITA JACKSONII COMPLEX!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!,)!6.1! INTRODUCTION!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!,)!6.2! MATERIAL AND METHODS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!,+!/#,#"! 5'8(1*5!'67!:*6389%!7')'!###################################################################################################################################!B/!/#,#,! 97*6)9>9%')936!3>!596:1*<%3(4!3+)&313:5A!(+32*!7*59:6A!'67!:*6*!5*1*%)936!#################################!B0!/#,#-! 5*F=*6%*!%'()=+*A!192+'+4!(+*('+')936A!'67!5*F=*6%96:!######################################################################!B0!/#,#.! 29396>3+8')9%5!##########################################################################################################################################################!BB!/#,#$! '6'145*5!######################################################################################################################################################################!B?!6.3! RESULTS!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!*(!/#-#"! )'+:*)5A!5*F=*6%*!7')'A!'67!29396>3+8')9%5!###############################################################################################!?-!/#-#,! 76'!(31483+(&958A!6*=)+'19)4A!'67!'11*1*!5&'+96:!################################################################################!?.!/#-#-! &95)3+9%'1!(3(=1')936!59C*5!'67!79;*+:*6%*!##############################################################################################!"D"!/#-#.! :*6*!>=6%)9365!3>!(359)9;*14!5*1*%)*7!:*6*5!############################################################################################!"D.!6.4! DISCUSSION!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&.)!/#.#"! )&*!*@36<)'+:*)*7!5*F=*6%*<%'()=+*!'((+3'%&!#####################################################################################!"D/!/#.#,! *>>*%)5!3>!5*1*%)936!'67!7*83:+'(&4!36!:*6389%!;'+9')936!#############################################################!"D0!/#.#-! >=6%)936'19)4!3>!'7'()9;*!:*6*5!'67!)&*!*8!&'29)!###############################################################################!"""!6.5! CONCLUSIONS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&&'!

CHAPTER 7!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&&(!

! CONCLUDING REMARKS AND PERSPECTIVES!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&&(!7

REFERENCES!"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&&/!

! APPENDICES!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!&).!8

! COPYRIGHT PERMISSIONS!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""!'&.!9

!

x

List of Tables )'21*!,#"!19G*19&337<+')93!)*5)!%38('+9536!3>!:*3795(*+5'1!837*15!96!!"!#$%!!5*%)#!&!'(!)'!'#!#############################!,D!)'21*!,#,!%38('+9536!3>!296'+4<5)')*!5(*%9')936!'67!*@)96%)936!H2955*I!837*15!2'5*7!36!'6!'6'14595!3>!;'+9'6%*!

H'63;'I!2*)J**6!6*J!J3+17!'67!317!J3+17!5(*%9*5!3>!!"!#$%!!5*%)#!&!'(!)'!'!HK3JA!5(*%9')936!+')*!96!)&*!317!J3+17L!K6JA!5(*%9')936!+')*!96!)&*!6*J!J3+17L!M3JA!*@)96%)936!+')*!96!)&*!317!J3+17L!M6JA!*@)96%)936!+')*!96!)&*!6*J!J3+17I#!#########################################################################################################################################!,D!

)'21*!-#"#!>9))*7!F='55*!837*15!36!)&*!(&413:*64!3>!!"!#$%!!5*%)#!&!'(!)'!'!H5*%)936<1*;*1I#!81!3()989C')9365!J*+*!(*+>3+8*7!36!)&*!8'@9=8<%1'7*<%+*792919)4!H8%%I!)+**#!#####################################################################################!-0!

)'21*!-#,#!)+**!598=1')9365!=67*+!)&*!F='55*!837*1!>3+!5(*%9')936!'5!'!837'1!>=6%)936!3>!1')9)=7*#!*'%&!598=1')936!;'+9*7!96!)&*!79>>*+*6%*!96!5(*%9')936!%&'6:*!'67!)&*!6=82*+!3>!)9(5#!################################################!-?!

)'21*!-#-#!>9))*7!F='55*!837*15!>3+!%1'7*!N%'*5'+*'N#!;'1=*5!'+*!';*+':*7!3;*+!"DD!(35)*+93+!5=2)+**5#!)&*!*@)96%)936!+')*!J'5!*;'1=')*7!'5!'!%365)'6)!96!'11!837*15#!##########################################################################################!.D!

)'21*!-#.#!>9))*7!79;*+59)4<7*(*67*6)!79;*+59>9%')936!H777I!837*15!>3+!%1'7*!N%'*5'+*'N#!;'1=*5!'+*!';*+':*7!3;*+!"DD!(35)*+93+!5=2)+**5#!#######################################################################################################################################################!..!

)'21*!$#"#!5(*%9*5!:*6*'13:9%'1!53+)96:!9679%*5!H*($I!(*+!:*6*!'67!%1'7*<1*;*1!(35)*+93+!(+32'2919)9*5!H((I#!5(*%9*5!&9:&19:&)*7!96!:+*4!J*+*!>3=67!)3!2*!+*%9(+3%'114!8363(&41*)9%!'67!>=114!5=((3+)*7#!6'6A!63)!'(19%'21*!##############################################################################################################################################################################################!/0!

)'21*!$#,#!POPULATION GENETIC SUMMARY STATISTICS PER SPECIES PER GENE.!#####################################################################!0D!TABLE 6.1. SUMMARIZED POLYMORPHISM DATA FOR 502 GENES IN THE A. JACKSONII COMPLEX. N, SAMPLE SIZE; S,

SEGREGATING SITES; !, NUCLEOTIDE DIVERSITY; ", WATTERSON’S THETA, DTAJ, TAJIMA’S D. IN THE TABLE

BELOW, POLYMORPHISM DATA IS SEPARATED BY SITE CLASS.!#############################################################################################!?$!

xi

List of Figures >9:=+*!,#"#!:+'(&9%'1!+*(+*5*6)')936!3>!)&*!:*3795(*+5'1!837*15!96!!+,5*%)#!&!'(!)'!'!96>*++*7!J9)&!1':+'6:*#!

)&*!)3(!>3=+!('6*15!'+*!6=11!837*15!3>!795(*+5936!J9)&!79>>*+*6)!795(*+5'1<+')*!%365)+'96)5#!)&*!837*1!96!)&*!13J*+!('6*1!95!'!)98*<5)+=%)=+*7!837*1!2'5*7!36!J31>*E5!23+*3)+3(9%'1!&4(3)&*595#!2317!'67!9)'19%!>36)5!9679%')*!317!J3+17!'67!6*J!J3+17!'+*'5A!+*5(*%)9;*14!H(6JA!('%9>9%!63+)&!J*5)L!*6'A!*'5)*+6!63+)&!'8*+9%'L!8*@A!8*@9%3A!96%1=796:!6*J!8*@9%3!'67!'+9C36'!)3!)&*!63+)&!'67!2*19C*!)3!)&*!53=)&L!%+A!!%3+7911*+'!7*!)'1'8'6%'A!%35)'!+9%'L!*=+A!8*79)*++'6*'6!*=+3(*L!5*'A!53=)&*'5)!'59'!'67!)&*!5963<&98'1'4'5L!O'(A!O'('6L!3G9A!3G96'J'!951'67L!G*+A!G*+'1'A!9679'L!'>+A!5=2<5'&'+'6!'>+9%'L!'=5A!*'5)*+6!'=5)+'19'I#!##########################################################################################################################################################################################!"$!

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

>9:=+*!,#-!196*':*5<)&+3=:&<)98*!(13)5!3>!"DD!(35)<2=+6<96!(35)*+93+!)+**5!2'5*7!36!7')'5*)!-!H!"!#$%!!5*%)#!&!'(!)'!'!5(*%9*5<1*;*1!(&413:*64I#!)&*!-<'@95!95!13:<5%'1*7!'67!)&*!.<'@95!+*(+*5*6)5!)98*#!)&*!23@(13)!)3!)&*!1*>)!5&3J5!)&*!*5)98')*7!R<5)')95)9%!3>!)&*!5'8*!"DD!(35)*+93+!)+**5#!)&*!;*+)9%'1!73))*7!196*5!36!)&*!23@(13)!9679%')*!)&*!%36>97*6%*!1989)5!>3+!)J3<)'91*7!HS"#?/A!"#?/I!'67!36*<)'91*7!HS"#/$I!)*5)5!3>!59:69>9%'6%*!HT!U!D#D$I#!####################################################################################################################################################################!,.!

>9:=+*!,#.!23@(13)5!3>!8'+G3;!%&'96!836)*!%'+13!*5)98')*5!3>!5)')*<2'5*7!5(*%9')936!HKI!'67!*@)96%)936!HVI!3>!)&*!2*5)!296'+4<5)')*!5(*%9')936!'67!*@)96%)936!837*1!HK3J!W!K6JA!M3J!W!M6JI!96!!"!#$%!!5*%)#!&!'(!)'!'#!)&*!1'5)!)J3!23@(13)5!)3!)&*!+9:&)!'+*!)&*!6*)!79;*+59>9%')936!+')*5!%'1%=1')*7!'5!)&*!79>>*+*6%*!2*)J**6!)&*!5(*%9')936!'67!*@)96%)936!+')*5!HK!S!VI#!3JA!317!J3+17L!6JA!6*J!J3+17#!#################################!,$!

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

xii

X%'*5'+*'E#!)&*!966*+!19:&)*+!:+*4!23@!7*1989)5!)&*!)*8(*+')*!5=2%1'7*#!'11!3)&*+!+*8'9696:!196*':*5!96!%1'7*!X%'*5'+*'E!'+*!%36597*+*7!'5!)&*!)+3(9%'1!5=2%1'7*A!96!J&9%&!)&*!)*8(*+')*!%1'7*!95!6*5)*7#!')!)&*!)+**!)9(5A!5(*%9*5E!1')9)=7*!)+'9)5!'+*!5&3J6!J9)&!)&*9+!%36)96*6)'1!13%')936#!6'A!63+)&!'8*+9%'L!'5A!'59'L!'=A!'=5)+'19'L!'>A!'>+9%'A!*=A!*=+3(*#!##########################################################################################################################!-$!

>9:=+*!-#,#!8'@98=8<19G*19&337!*5)98')936!3>!)&*!>=6%)936!)&')!837*15!)&*!5(*%9')936!+')*!'5!'!837'1Y63+8'1!>=6%)936!3>!1')9)=7*!H)&9%G!21'%G!196*!'67!1*>)!597*!'@95I!36!)&*!5*%)936<1*;*1!(&413:*64!H!+,5*%)#!&!'(!)'!'I#!:+*4!196*5!H+9:&)!597*!'@95I!5&3J!)&*!5(*%9*5!+9%&6*55!H*#:#!6=82*+!3>!5(*%9*5I!3>!*@)'6)!H53197I!'67!'6%*5)+'1Z*@)'6)!H73))*7I!5(*%9*5!(*+!1')9)=7*!296!H$!7*:+**5I#!#######################################################################!-B!

>9:=+*!-#-#!2'+!(13)5!H1*>)!597*I!'+*!)&*!'G'9G*!J*9:&)5!36!*'%&!3>!)&*!837*15!)*5)*7#!'G'9G*!J*9:&)5!'+*!*55*6)9'114!'9%!5%3+*5!J9)&!'+*!+*1')9;*!)3!"#!*'%&!3>!)&*!2'+5!36!)&*!.<'@95!%3++*5(3675!)3!36*!(35)*+93+!5=2)+**#!96!)&*!>9+5)!2'+!(13)!H'IA!%313+5!9679%')*!)&*!+*1')9;*!>9)!3>!696*!F='55*!837*15#!*'%&!837*1!79>>*+*7!*9)&*+!36!)&*!5(*%9')936!+')*!>=6%)936!3+!36!)&*!96)*+6'1!5=2%1'7*!'++'6:*8*6)!H5=2%1'7*!7*%3=(196:I[!%1'7*<J97*!196*'+!H+*7<2+3J6IL!%1'7*<7*%3=(1*7!%365)'6)!H(=+(1*IL!%1'7*<J97*!59:8397!H2*9:*IL!%1'7*<7*%3=(1*7!196*'+!36!)&*!)*8(*+')*!5=2%1'7*!H19:&)!:+**6IL!%1'7*<7*%3=(1*7!196*'+!36!)&*!)+3(9%'1!5=2%1'7*!H7'+G!:+**6IL!%1'7*<J97*!%365)'6)!H2+3J6IL!%1'7*<J97*!837'1!H(96GIL!%1'7*<7*%3=(1*7!196*'+!36!23)&!%1'7*5!H21=*IL!'67!%1'7*!7*%3=(1*7!63+8'1!36!)&*!)*8(*+')*!5=2%1'7*!H:+*4I#!)&*!(13)!2*13J!H2I!5&3J5!)&*!8'@98=8<19G*19&337!*5)98')936!3>!)&*!196*'+!5(*%9')936!+')*!>=6%)936!H1*>)!597*!'@95I!J9)&!9)5!(35)*+93+!795)+92=)936!H>+38!"DD!(35)*+93+!5=2)+**5I#!36!)&*!5'8*!:+'(&A!)&*!23@<(13)!5&3J5!5(*%9*5!H96%1=796:!'6%*5)+'1!5(*%9*5I!+9%&6*55!H+9:&)!597*!'@95I!(*+!1')9)=7*!296!H$!7*:+**5I#!###########!."!

>9:=+*!-#.#!(13)5!3>!F='55*E5!196*'+!5(*%9')936!+')*!>=6%)936!J&*6!%36597*+96:!96%38(1*)*!)'@36!5'8(196:#!*'%&!196*!+*(+*5*6)5!'!596:1*!(35)*+93+!5=2)+**#!################################################################################################################!.,!

>9:=+*!-#$#!)&*!2'+<(13)!H'I!5&3J5!)&*!+*1')9;*!>9)!3>!79;*+59)4<7*(*67*6)!837*15!%3++*5(36796:!)3[!5*('+')*!79;*+59)4<7*(*67*6)!746'89%5!J&*+*!/)+3(!'67!/)*8(!'+*!*F='1!H+*7I!3+!967*(*67*6)!H3+'6:*I!'67!*F='1!5(*%9')936!'67!*@)96%)936!+')*5!2*)J**6!5=2%1'7*5L!5*('+')*!79;*+59)4<7*(*67*6)!746'89%5!(1=5!;'+496:!96)+9659%!5(*%9')936!+')*!H7'+G!21=*IA!*@)96%)936!+')*!H19:&)!21=*I!3+!23)&!+')*5!H%4'6I!'%+355!*%313:9%'1!5=2%1'7*5L!63!79;*+59)4<7*(*67*6%*!2=)!;'+496:!+')*5!'%+355!5=2%1'7*5!H:+**6IL!63!7*%3=(196:!3>!('+'8*)*+5!'%+355!%1'7*5!H21'%GIL!'67!79;*+59)4<967*(*67*6%*!HJ&9)*I#!)&*!23@<(13)!2*13J!H2I!5&3J5!)&*!%1'7*<1*;*1!%'++496:!%'('%9)9*5!/!3>!)&*!)J3!2*5)!837*15!H3+'6:*!'67!+*7L!3+!837*15!"!'67!,!96!)'21*!-#-I#!##################################################################################################################################################################################################################!.$!

>9:=+*!.#"#!!"!#$%!!0!&/(1#$$!(38*+1#!HF-I!>+38!'!%135*!(3(=1')936!96!F=\2*%A!%'6'7'#!(&3)3!24!+*6\*!1*2*=>!H%88I#!##################################################################################################################################################################################################!$,!

>9:=+*!$#"#!2'4*59'6!8'@98=8<%1'7*<%+*792919)4!:*6*!)+**5!3>!(&'5*7!'11*1*5!9679%')96:!7*1989)*7!5(*%9*5!24!%313+!')!)&*!)9(5#!)&*!1'5)!)+**!)3!)&*!1*>)!95!2'5*7!36!%36%')*6')*7!7')'#!)&9%G*6*7!2+'6%&*5!'+*!%1'7*5!J9)&!P!D#?!((!5=((3+)#!)&*!:+*4!5&'(*5!9679%')*!)&*!)J3!8'O3+!6'!%1'7*5!X2'599E!'67!XO'%G53699E#!#####################!/0!

>9:=+*!$#,#!MAXENT SPECIES DISTRIBUTION MODELING OF NORTH AMERICAN CAESAR’S MUSHROOMS IN PRESENT (A)

AND LAST GLACIAL MAXIMUM (B; C. 22 KA) CLIMATIC CONDITIONS. THE SCALE IN A INDICATES HABITAT

SUITABILITY SCORES. C AND D ARE DISTRIBUTION MAPS OF SAMPLES INCLUDED IN THIS STUDY COLORED BY

SPECIES GROUP. GREY SHADES IN C AND D REPRESENT FORESTED (TEMPERATE MIXED AND CONIFEROUS

xiii

FORESTS) AREAS ACCORDING TO WWF TERRESTRIAL ECOREGIONS (OLSON ET AL. 2001). THE DOTTED LINES IN

C AND D DELINEATE THE BOUNDARIES BETWEEN REFUGIAL AND EXPANSION AREAS.!################################################!0-!>9:=+*!$#-#!RECONSTRUCTED BAYESIAN SPECIES TREE FROM *BEAST SHOWING MEAN NODE HEIGHTS, AND

ANCESTRAL AREA RECONSTRUCTIONS FROM RASP (PIE CHARTS) AS WELL AS STOCHASTIC CHARACTER MAPPING

FROM DIVERSITREE. COLOR CODES REPRESENT SPECIES IN REFUGIAL (BLUE), WIDESPREAD (GREEN), OR IN

EXPANSION (RED) AREAS. TERMINALS IN GREY AND DOTTED LINEAGES WERE IGNORED AS THEY EITHER

REPRESENT EURASIAN OUTGROUPS OR WERE DEEMED OUTSIDE NORTH AMERICA (SANCHEZ-RAMIREZ ET AL.

2015). PLI = PLIOCENE, PLE = PLEISTOCENE. THE BOXPLOT ON THE LEFT SHOWS PER AREA MCMC ESTIMATES

OF SPECIATION (#), EXTINCTION (µ) AND DISPERSAL ($) RATES FROM MUSSE (DIVERSITREE).!########################!0.!>9:=+*!$#.#!(A) A PLOT OF SPECIES’ LATITUDES VERSUS THE MAGNITUDE OF POPULATION EXPANSION (M). THE DOTTED

LINE INDICATES M = 1. (B) PLOT OF LATITUDE VERSUS THE POPULATION SIZE AT PRESENT TIME FOR EXTANT

SPECIES AND BEFORE SPECIATION FOR ANCESTRAL SPECIES (!1). IN A AND B, THE THICK SOLID LINE

REPRESENTS THE BEST GAUSSIAN FIT TO THE DATA. (C) PLOT OF THE EXTENT OF RANGE SHIFT VERSUS M. IN C

THE SOLID LINE REPRESENTS THE BEST LINEAR FIT TO THE DATA (R2 = 0.23, P-VALUE = 0.002). IN ALL PLOTS,

GREY AND BLACK CIRCLES REPRESENT EXTANT AND ANCESTRAL SPECIES, RESPECTIVELY, AND THINNER SOLID

LINES REPRESENT VARIATION WITHIN THE DATA (E.G. POSTERIOR ESTIMATES AND SAMPLED LATITUDES). B,

AMANITA BASII; C, A. SP. ‘COCHISEANA’; J, A. JACKSONII; J2, A. SP-JACK2; J3, A. SP-JACK3; J5 A. SP-JACK5; J6 A. SP-

JACK6. * MEDIAN, † MEAN.!##############################################################################################################################################################!0?!>9:=+*!$#$#!PLOT OF DEMOGRAPHIC CHANGE AS A FUNCTION OF TIME FOR ANCESTRAL AND EXTANT SPECIES. THE

FIRST UPPER ROW SHOWS FOUR EXTANT SPECIES WITH FAST DEMOGRAPHIC CHANGE. THE SECOND ROW

INCLUDES FOUR EXTANT SPECIES WITH NEAR CONSTANT HISTORICAL DEMOGRAPHY. THE TWO LAST ROWS SHOW

EIGHT ANCESTRAL SPECIES WITH RELATIVELY FAST DEMOGRAPHIC CHANGE. IN THESE THE NUMBERS REPRESENT

NODES SHOWN IN FIG. 5.3.!###############################################################################################################################################################!B"!>9:=+*!/#"#!BOX-PLOTS OF TAJIMA’S D OVER 502 GENES SEPARATED BY SITE-CLASSES (NON-SYNONYMOUS, LIGHT

GREY; SYNONYMOUS, WHITE; INTRONS, DARK GREY) FOR THE A. JACKSONII COMPLEX.!##############################################!?0!>9:=+*!/#,#!BOX-PLOTS OF % RATIOS FOR DIVERGENCE (%D) AND POLYMORPHISM (%P) (UPPER PANEL), AND & VALUES

THE STANDARD MKT TEST (GREY) AND A MODIFIED VERSION THAT TAKES INTO ACCOUNT SITE-FREQUENCY

SPECTRA (WHITE) (MESSER AND PETROV 2013A).!################################################################################################################!"DD!>9:=+*!/#-#!DISTRIBUTIONS OF 'GSI AND VARGSI FOR ALL 502 GENES. 'GSI INDICATES HOW WELL SPECIES CLADES ARE

RESOLVED, AND VARGSI SHOWS THE EXTENT OF DEVIATION FROM THE MAXIMUM GSI VALUE POSSIBLE, 1.!########!"DD!>9:=+*!/#.#!SCATTER-PLOTS SHOWING THE RELATIONSHIP BETWEEN TAJIMA’S D AND GSI FOR EACH GENE, FOR EACH

SPECIES.!##############################################################################################################################################################################################!"D,!>9:=+*!/#$#!DEMOGRAPHIC TRENDS THROUGH TIME FOR EACH SPECIES BASED ON THE EBSP AND *BEAST MODELS.

GREY SHADING REPRESENTS 95% POSTERIOR CONFIDENCE INTERVALS (CI). IN EBSP, SOLID LINES REPRESENT

MEAN POPULATION SIZE VALUES AND DOTTED LINES REPRESENT MEDIAN VALUES. IN *BEAST PLOTS, THE

DOTTED LINE REPRESENT MEAN VALUES. THE LOWER PHYLOGENETIC TREE IS THE MAXIMUM-CLADE-

CREDIBILITY SPECIES TREE. HORIZONTAL BARS REPRESENT DIVERGENCE 95% CI. NUMBERS OVER NODES ARE

xiv

CLADE POSTERIOR PROBABILITIES. VERTICAL DOTTED LINES POINT TO THE START OF THE PLEISTOCENE EPOCH

AND THE ONSET OF DEMOGRAPHIC EXPANSIONS.!##################################################################################################################!"D-!>9:=+*!/#/#!GENE ONTOLOGY TERMS WORDLES FOR GENES UNDER POSITIVE SELECTION (A: & > 0, B: PAML LTR)

COMBINED FOR ALL SPECIES. THE SIZE OF THE TERM INCLUDES THE EXTENT OF SELECTION AND HOW OFTEN THE

TERM WAS FOUND ACROSS GENES AND SPECIES.!###################################################################################################################!"D$!>9:=+*!/#0#!SPECIES DISTRIBUTION MAPS BASED ON GEOGRAPHIC DATA FROM SÁNCHEZ-RAMÍREZ ET AL. (2015) AND

MAXENT (PHILLIPS ET AL. 2006). THE DOTTED LINE MARKS AN HYPOTHESIZED LIMIT FOR GLACIAL REFUGIA.

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xv

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>9:=+*!%B#$#!*BEAST SPECIES TREES USED FOR SPECIES DELIMITATION WITH CLADE POSTERIOR PROBABILITIES

(UPPER), BP&P SPECIES PROBABILITIES (LOWER AND BOLD), AND BRANCHING TIMES.!###########################################!"?.!>9:=+*!%B#/#!REGRESSION ANALYSES ON THE LATITUDE VERSUS POPULATION SIZE AND EXPANSION DATA. THE BLUE

LINE REPRESENTS THE LINEAR MODEL, WHILE THE GREEN AND RED REPRESENT THE GAUSSIAN AND

POLYNOMIAL MODELS, RESPECTIVELY.!####################################################################################################################################!"?$!>9:=+*!7B#0#!6=82*+!3>!;'+9'21*!59)*5!H:+*4I!'67!(*+%*6)':*!3>!895596:!7')'!H21'%GI!;*+5=5!:*6*!1*6:)&#!#######!,D"!>9:=+*!7B#B#!)&*!J'))*+536!*5)98')3+A!)&*)'A!5*('+')*7!24!59)*!%1'55*5#!########################################################################!,D,!>9:=+*!7B#?#!*5)98')*5!3>!6=%1*3)97*!79;*+59)4!3;*+!'11!:*6*5!5*('+')*7!24!%1'55!59)*5#!###########################################!,D-!>9:=+*!7B#"D#!*5)98')*5!3>!*>>*%)9;*!(3(=1')936!59C*#!###############################################################################################################!,D.!>9:=+*!7B#""#!5%'))*+(13)5!3>!:59!;'1=*5!;*+5=5!38*:'!H(31483+(&958I#!##########################################################################!,D$!>9:=+*!7B#",#!5%'))*+(13)5!3>!:59!;'1=*5!;*+5=5!'1(&'#!############################################################################################################!,D/!>9:=+*!7B#"-#!6*=)+'19)4!967*@!;*+5=5!)&*!5%'1*7!6=82*+!3>!5)3(!%37365#!######################################################################!,D0!

xvi

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1

Chapter 1

INTRODUCTION 1

1.1 Biological diversity and evolutionary scales

Organisms are vastly diverse. Biological diversity results from the interaction of many

evolutionary processes such as speciation, extinction, dispersal, migration, natural selection and

adaptation, gene flow, mutation, recombination, and genetic drift (Gillespie 1991). Two of the

main dimensions on which evolution operates are time and space. For instance, although

speciation can occur rapidly over short time periods (Kocher 2004), this process generally takes

few millions of years (Myr) in most plants and animals (Hedges et al. 2015). Extinction can also

occur over drastically short time intervals, for instance, during abrupt mass extinction events

(Jablonski 1986). Dispersal –the movement of a species in geographical space– can occur

quickly over long distances in adaptable species that naturally disperse by seeds or spores (Cain

et al. 2000; Moncalvo and Buchanan 2008) or gradually in species that are restricted ecologically

(Davis et al. 2002, 2004; Donoghue and Smith 2004; Sánchez-Ramírez et al. 2015a; Chapter 2).

Furthermore, population biology theory suggests that at any point in time species behave as

populations of individuals, in which processes such as selection, mutation, genetic drift, and gene

flow, can all operate within few generations. In some organisms a generational timeframe may

represent many decades (e.g. large mammals), while in others less than a year (e.g.

microorganisms).

Most evolutionary processes also entail spatial limits. These often relate to ecological boundaries

to which organisms are adapted. Two of the most common factors responsible for speciation are

geographic barriers, which results in allopatric speciation, and geographic distance, which may

result in parapatric speciation (Coyne and Orr 2004). Similarly, geographic subdivision of

populations generally leads to genetic differentiation, which may lead to speciation in the

absence of gene flow (Rousset 1997). Species and populations also adapt to different

environments, in some cases within the same area, causing sympatric or ecological speciation

(Schluter 2001; Rundle and Nosil 2005; Schluter and Conte 2009). The spatial dimension of

biodiversity can be as wide as Earth itself or as small as centimeters depending on the organisms

2

(Godfray and Lawton 2001). Diversity can also vary within environmental or geographic

gradients (Ricklefs 2004; Mittelbach et al. 2007; Weir and Schluter 2007), or through time due to

climatic, ecological, or phenotypic shifts (Weir and Schluter 2004; Moyle et al. 2009 Stadler

2011; Etienne et al. 2012; Etienne and Haegeman 2012; Silvestro et al. 2014). For these reasons,

knowing the dimensions on which evolutionary processes operate is crucial for understanding

how organisms change and diversify.

Molecular phylogenies (phylogenetic trees) aim at reconstructing the historical relationships of

organisms using data encoded in the DNA. The branches of a tree directly reflect the amount of

molecular change (substitutions), which can be scaled to evolutionary time, while internal nodes

in the tree may represent branching (speciation) events or ancestors. Modern phylogenetic

models include a suite of approaches where many biological features of terminal nodes (tips or

extant units) and evolutionary processes can be temporally scaled. For instance, based on

coalescent theory and gene genealogies, the time-to-the-most-recent-concestor and effective

population size can be estimated for any sample of alleles from a population (Fu and Li 1999).

Methods with the same theoretical foundations can measure past population dynamics

(Drummond et al. 2003; Ho and Shapiro 2011). Moreover, biogeographic or trait histories can be

modeled and time-scaled over phylogenies (O’Meara 2012), and rates of speciation and

extinction quantified (Nee 2006; Rabosky 2013).

Historically, phylogenetics and population genetics were two separate fields that had little

interaction. However, recent models that are built at the interface of coalescent and phylogenetic

theory are attempting to bridge the gap between the two disciplines (Edwards 2009; Liu et al.

2009). One of these models is known as the multi-species coalescent. This promising,

biologically sound model aims at reconstructing species trees from independent gene histories of

allele populations across genomes (Heled and Drummond 2008). In the process, it estimates

species divergence times and scales historical population sizes in both extant and ancestral

species. The blending of micro- and macroevolutionary disciplines in a genomic context are

proving to be the next paradigm in evolutionary biology (Cutter 2013; Losos et al. 2013).

1.2 The genomic era

The emerging of the –omics era and the so-called next-generation sequencing techniques are

making the access to whole-genome data increasingly affordable. These techniques were

3

developed initially for the completion of the Human Genome Project at the turn of the 21st

century (Zhang et al. 2011), at a time when even the sequencing of a single gene was expensive

and time-consuming (Metzker 2010). Nowadays, depending on its size, a genome can be

sequenced for few thousand dollars over a couple of days (Ekblom and Gallindo 2010; Ellegren

2014). However, having a glance at genome-wide data at reduced time and cost does not require

the production of whole-genome sequences. Several other approaches, based on reduced

representation libraries, such as restriction-site-associated DNA (RAD-tags), genotype-by-

sequencing (GBS), targeted-amplicon sequencing, transcriptome sequencing, and hybrid

enrichment (i.e. exon- or ultraconserved element-targeted sequencing), are proving to be useful

tools for exploring genome-wide patterns in species without a reference genome (Lemmon and

Lemmon 2013).

In parallel, computational infrastructure for science is quickly improving (Averly 2002), and

bioinformatics software are becoming efficient enough to analyze complex genomic data

(Kanehisa and Bork 2003). Some bioinformatics tools make possible “reverse ecology”

approaches where functional traits can be mined from annotated genomes. These tools can be

useful for exploring genome-scale evolutionary and ecological patterns in organisms that remain

hidden from the scientific spotlight.

1.3 Why fungi?

Historically, fungi were considered as non-photosynthetic plants, and thus studied by botanists as

part of the Cryptogamic Flora. Current phylogenies place them closer to animals and indicate

that they represent a whole independent Kingdom of Life (Doolittle 1999; James et al. 2006).

Fungi not only reinvented multicellularity (Medina et al. 2003; James et al. 2006), they also are

extremely diverse taxonomically and ecologically (Hawksworth 2004; Blackwell 2011). Fungi

thrive in virtually every environment, where they perform activities as decomposers, pathogens,

or symbionts. Unfortunately, in spite of the biological importance and vastness of this realm,

knowledge on how fungi evolve is still scarce. Specifically, we know little about the scales and

dimensions on which evolutionary processes operate in fungi. This could be attributed to (1) a

depauperate fossil record, (2) convergent evolution, (3) cryptic and unknown diversity, and (4)

poorly sampled regions. Nonetheless, fungi also have many life history traits that make them

4

ideal and amenable biological models in genetics and genomics (Anderson and Kohn 1998;

Gladieux et al. 2013).

1.4 The system

The Caesar’s mushrooms belong to a diverse group –the genus Amanita Pers. (Agaricales,

Basidiomycota), which includes c. 500 described (Kirk et al. 2008) and an estimate of c. 1000

species (Tulloss 2005). The genus is best known for contradictingly including deadly poisonous,

hallucinogenic, and edible species (Zhang et al. 2015). Within Amanita, the Caesar’s mushrooms

belong to Section Caesareae Singer, a group that is well defined morphologically and

phylogenetically (Bas 1969; Tulloss et al. 1992; Yang 1997; Weiß et al. 1998; Drehmel et al.

1999; Moncalvo et al. 2000; Sánchez-Ramírez et al. 2015a). Amanita caesarea (Scop.:Fr.) Pers.

is the type species of section Caesareae, and at least since Roman times it is considered a

gastronomical asset in Europe (Pegler 2002). Similarly, many species of Caesar’s mushrooms

throughout the world have high commercial and cultural value as wild edible mushrooms (Yun

and Hall, 2004; Boa, 2004; Garibay-Origel et al., 2007; Härkönen, 2002; Christensen et al.,

2008). Like most Amanita, the Caesar’s mushrooms are ectomycorrhizal (EM) –root-associated

mutualists– with species of most ectotrophic tree families, such as Fagaceae and Pinaceae (Smith

and Read 2008). About 80 species are recognized in the section by morphology (Tulloss 2015).

They can be found in most temperate forests in the Northern Hemisphere, including

Mediterranean, Balkan, Carpathian, and Sino-Himalayan-Japanese regions, and in North

America. Their distribution also reaches some subtropical and tropical woodlands and rainforests

in the Paleotropics, including leguminous forests in Africa, dipterocarp and Castanopsis forests

in Southeast Asia, and with some Myrtaceae in tropical and temperate Australia. A peculiarity of

their distribution is that no member of sect. Caesareae has been found in South America, with

the Talamanca Corrdillera being their southern limit in the Americas (Tulloss 2005).

1.5 Questions, objectives, research hypotheses, and scientific production

The goal of this thesis is to illuminate evolutionary patterns and processes in a group of EM

fungi by scaling the dynamics of species, populations, and genes through time and space.

4

ideal and amenable biological models in genetics and genomics (Anderson and Kohn 1998;

Gladieux et al. 2013).

1.4 The system

The Caesar’s mushrooms belong to a diverse group –the genus Amanita Pers. (Agaricales,

Basidiomycota), which includes c. 500 described (Kirk et al. 2008) and an estimate of c. 1000

species (Tulloss 2005). The genus is best known for contradictingly including deadly poisonous,

hallucinogenic, and edible species (Zhang et al. 2015). Within Amanita, the Caesar’s mushrooms

belong to Section Caesareae Singer, a group that is well defined morphologically and

phylogenetically (Bas 1969; Tulloss et al. 1992; Yang 1997; Weiß et al. 1998; Drehmel et al.

1999; Moncalvo et al. 2000; Sánchez-Ramírez et al. 2015a). Amanita caesarea (Scop.:Fr.) Pers.

is the type species of section Caesareae, and at least since Roman times it is considered a

gastronomical asset in Europe (Pegler 2002). Similarly, many species of Caesar’s mushrooms

throughout the world have high commercial and cultural value as wild edible mushrooms (Yun

and Hall, 2004; Boa, 2004; Garibay-Origel et al., 2007; Härkönen, 2002; Christensen et al.,

2008). Like most Amanita, the Caesar’s mushrooms are ectomycorrhizal (EM) –root-associated

mutualists– with species of most ectotrophic tree families, such as Fagaceae and Pinaceae (Smith

and Read 2008). About 80 species are recognized in the section by morphology (Tulloss 2015).

They can be found in most temperate forests in the Northern Hemisphere, including

Mediterranean, Balkan, Carpathian, and Sino-Himalayan-Japanese regions, and in North

America. Their distribution also reaches some subtropical and tropical woodlands and rainforests

in the Paleotropics, including leguminous forests in Africa, dipterocarp and Castanopsis forests

in Southeast Asia, and with some Myrtaceae in tropical and temperate Australia. A peculiarity of

their distribution is that no member of sect. Caesareae has been found in South America, with

the Talamanca Corrdillera being their southern limit in the Americas (Tulloss 2005).

1.5 Questions, objectives, research hypotheses, and scientific production

The goal of this thesis is to illuminate evolutionary patterns and processes in a group of EM

fungi by scaling the dynamics of species, populations, and genes through time and space.

5

1.5.1 Chapter 2

Chapter 2 seeks to explore the biogeographic history of the Caesar’s mushrooms, built upon a

solid and well-sampled phylogenetic foundation. I am interested in inferring the place of origin

of the group and explaining how continental disjunctions between and within tropical and

temperate regions came to be. One hypothesis that I test predicts out-of-the-tropics land-

mediated dispersal during the Tertiary (Wang, 1961; Wolfe, 1975, 1980; Tiffney, 1985; Lavin

and Luckow, 1993). The implications of the investigations pertain to the geographic and

temporal origins of the group in relation to EM taxa. In addition, I further investigate lineage

diversification in the group after the prominent continental colonization of North America. My

objectives in this Chapter are the following:

• Reconstruct the biogeographical history of A. sect. Caesareae.

• Test Wolfe’s boreotropical hypothesis as a possible explanation for

tropical–temperate relationships in the group.

• Contrast rates of diversification between the New World and Old World.

Chapter 2 was published in the Journal of Biogeography:

Sánchez-Ramírez, S., R. E. Tulloss, M. Amalfi and J.-M. Moncalvo. 2015. Palaeotropical origins,

boreotropical distribution, and increased rates of diversification in a clade of edible ectomycorrhizal fungi

(Amanita sect. Caesareae). Journal of Biogeography 42(2): 351–363.

1.5.2 Chapter 3

In this Chapter, I investigate the evolution of latitudinal ranges in the Caesar’s mushrooms,

exploring speciation rate gradients with respect to latitude. I make use of macroevolutionary

models to test hypotheses about the nature of latitudinal diversity gradients in A. sect. Caesareae.

This work shows the importance of accounting for macroevolutionary processes for explaining

the temperate-shifted latitudinal diversity gradient (LDG) in EM fungi (Tedersoo and Nara 2010;

Tedersoo et al. 2012, 2014). The objectives are the following:

• Investigate the effects of macroevolutionary processes, such as speciation

and extinction, on the shifted LDG in the Caesar’s mushrooms.

6

• Use macroevolutionary models to test Tedersoo and Nara’s hypotheses on

the shifted EM LDG.

Chapter 3 was published in the journal Evolution:

Sánchez-Ramírez, S., R. S. Etienne and J.-M. Moncalvo. 2015. High speciation rate at temperate latitudes

explains unusual diversity gradients in a clade of ectomycorrhizal fungi. Evolution 69(8): 2196–2209.

1.5.3 Chapter 4

Given that Chapters 5 and 6 incorporate genomic data from novel loci, Chapter 4 reports the

description of the genome of A. jacksonii, which was sequenced de novo, and partial genomic

sequences from A. basii. The objectives of this Chapter are to:

• Sequence, assemble, and characterize the genome of Amanita jacksonii.

• Develop novel primers/probes for novel and informative loci.

The draft genome of A. jacksonii was published in IMA Fungus:

Sánchez-Ramírez, S., M. Stata and J.-M. Moncalvo. 2014. Draft genome of the edible ectomycorrhizal

basidiomycete Amanita jacksonii TRTC168611 from Awenda Provincial Park, Ontario, Canada. In: van

der Nest et al. IMA Genome-F 3: Draft genomes of Amanita jacksonii, Ceratocystis albifundus,

Fusarium circinatum, Huntiella omanensis, Leptographium procerum, Rutstroemia sydowiana,

and Sclerotinia echinophila. IMA Fungus 5(2): 473-486.

Raw 454 reads of A. basii were deposited in the Sequence Read Archive (NCBI).

1.5.4 Chapter 5

This Chapter explores the extent of diversity (taxonomic and genetic) in the North American

Caesar’s mushrooms. I make use of multi-locus coalescent methods to delimit species. Later, I

test hypotheses about evolutionary processes and climatic events (Hewitt 1996, 2000, 2004a,b)

that have lead to such diversity, within and among species. The objectives of this Chapter are the

following:

• Delimit species in the North American Caesar’s mushrooms throughout

their distribution range.

7

• Unravel the relative contribution of speciation, extinction, and dispersal to

explain the make-up of North American diversity in the context of

Pleistocene glacial refugia.

• Examine the relationships between population size, genetic diversity, and

the geographic distribution of species

As presented here, Chapter 5 corresponds to a manuscript accepted for publication

in the journal Molecular Ecology:

Sánchez-Ramírez, S., R. E. Tulloss, J. Cifuentes-Blanco, L. Guzmán-Dávalos, R. Valenzuela, A. Estrada-

Torres, F. Ruán-Soto, R. Díaz-Moreno, N. Hernández-Rico, M. Torres-Gómez, H. León and J.-M.

Moncalvo. Accepted. In and out of refugia: historical patterns of diversity and demography in the North

American Caesar’s mushroom species complex. Molecular Ecology.

1.5.5 Chapter 6

In this Chapter I look into patterns of genome-wide variation in eight cryptic species of the A.

jacksonii complex in North America. I explore how selection, demographic history, and lineage

divergence has shaped this genomic diversity. I hypothesize that climate-driven population

dynamics during the Pleistocene may have deeply shaped the genomic variation in some species.

In addition, as a process of regional adaptation to local environments, there will be differences in

functional genomic traits under selection. This study can help understand better how

evolutionary forces operate in EM fungi, and potentially identify functionally important genomic

traits. My objectives here are to:

• Generate protein-coding DNA data for hundreds of genes for eight species

in the Amanita jacksonii complex using exon-targeted sequencing.

• Compare DNA variation, natural selection in protein-coding regions, and

demographic history among species.

• Identify and characterize candidate genes under positive selection.

As presented here, this Chapter corresponds to a manuscript in preparation for the

journal Molecular Biology and Evolution:

8

Sánchez-Ramírez, S. and J.-M. Moncalvo. Selection and demography shape genome-

wide diversity in eight recently diverged Amanita Species.

9

Chapter 2

EVOLUTIOANRY HISTORY AND GLOBAL BIOGEOGRAPHY OF 2AMANITA SECT. CAESAREAE

2.1 Introduction

Studies on the natural history and biogeographical relationships of fungi are still in their infancy

(Lumbsch et al. 2008). A number of factors can explain the slow pace of progress, including: (1)

the limited fossil record (Berbee and Taylor 2010); (2) taxonomical difficulties due to

morphological convergence and cryptic diversity (Taylor et al. 2000); (3) poor sampling in many

regions of the world (Schmit and Mueller 2007); and (4) the assumption that most species are

widely distributed (Peay et al. 2010). Additionally, the distribution patterns of many fungi have

often been assumed to be similar to the plants with which they are associated (e.g. as in

Sanmartín and Ronquist 2004), probably because of analogous dispersal mechanisms. Studies on

fungal dispersal and colonization success at a local scale have shown differences and varying

levels of spore yield between fungi of different ecological habits and within species (Galante et

al. 2011). Furthermore, studies on fungal dispersal over geological time-scales have reported that

long-distance intercontinental dispersal occurs but is rather episodical (Moyersoen et al. 2003;

Moncalvo and Buchanan 2008; Geml et al. 2012). Overall, inter- and intra-continental

populations usually exhibit strong phylogenetic structures (James et al. 2001; Geml et al. 2006,

2008; Jeandroz et al. 2008; Jargeat et al. 2010), and vicariance and dispersal events should both

be considered when explaining the current distribution patterns (Hosaka et al. 2008; Matheny et

al. 2009; Du et al. 2012; Wilson et al. 2012).

Ectomycorrhizal (EM) fungi are root-associated mutualists of many tropical, temperate and

boreal tree species in families such as Pinaceae, Fagaceae, Betulaceae, Salicaceae, Fabaceae,

Myrtaceae and Dipterocarpaceae (Smith and Read 2008). Recent studies based on molecular

phylogenies and ancestral-area/host reconstructions (Matheny et al. 2009; Wilson et al. 2012)

support the Palaeotropics as the area of origin of certain groups (Alexander 2006), but the time

and place of origin remains unknown for most EM clades. At least two hypotheses have been

proposed to explain the extant distributions at different geographical scales: (1) host

10

comigrations, at regional levels (Kennedy et al. 2011; Põlme et al. 2013); and (2) the

boreotropical hypothesis, at a global scale (Wilson et al. 2012). This latter hypothesis was

conceived from the observation that tropical broad-leaved forests existed during the Tertiary at

higher latitudes, in regions that are presently temperate (Wang 1961; Wolfe 1975, 1980; Tiffney

1985; Lavin and Luckow 1993), and that many contemporary temperate angiosperm taxa have

evergreen relatives in subtropical rain forests (Axelrod 1966). This could be an appealing

hypothesis for explaining the distribution of EM fungi, mainly because (1) most species are host-

generalists, (2) most EM clades include species that have either a tropical or a temperate

distribution (Tedersoo and Nara 2010), (3) they associate with tree families and communities that

dominate in both temperate and tropical forests (Alexander 2006; Smith and Read 2008), and (4)

many such families have boreotropical distributions.

There have been increasing efforts to explore patterns and processes of diversification in EM

lineages in addition to their biogeography. Recent studies have invoked the possibility of

putative ecological limits (sensu Rabosky, 2009) to EM species diversity across evolutionary

time. Ryberg and Matheny (2012) looked for signatures of adaptive radiation early in the

evolutionary history of nine EM lineages within the Agaricales and found a pattern that was not

significantly different from a constant rate of diversification, suggesting limited ecological niche

constraints at the genus/family level. On the other hand, Kennedy et al. (2012) noted that

diversification rates differed between temperate and tropical lineages of an EM clade of the

genus Clavulina, suggesting that biogeographical effects might be contributory factors to global

EM species diversity.

One particularly diverse EM lineage is the genus Amanita Pers. (Agaricales; Basidiomycota),

with an estimated 500–1000 species (Tulloss, 2005). This is a well-studied and well-defined

group of agarics – gilled mushrooms – and puffballs in terms of both morphotaxonomy and

molecular phylogenetics (Bas, 1969; Tulloss et al. 1992; Yang, 1997; Weiß et al. 1998; Drehmel

et al. 1999; Justo et al. 2010), and is subdivided into two subgenera and seven sections. Amanita

sect. Caesareae, the focus of this study, includes c. 90 putative species (Tulloss and Yang, 2014)

that were informally grouped into stirps by Tulloss (1998) with the purpose of facilitating the

discovery and description of taxa. Species of A. sect. Caesareae are known from temperate and

tropical regions of Africa, Asia, Australia, Europe, and Central and North America (Beeli, 1935;

11

Bas, 1977; Reid, 1980; Tulloss et al. 1992, 2011; Wood, 1997; Yang, 1997; Guzmán and

Ramírez-Guillén, 2001; Villanueva-Jiménez et al. 2006), but not South America.

2.2 Material and Methods

Most species identifications and morphological descriptions are available in Tulloss and Yang

(2014) (see Appendix Table A8.1). A small number of lesser-known species have no description

yet, as they were found to be cryptic in the present study. Although my taxonomical sampling

represents about 70% of the known species in A. sect. Caesareae, I considered the sampled

coverage to be 60% for analytical purposes, to allow for unknown and cryptic taxa.

2.2.1 Laboratory protocols and data matrices

DNA was extracted using various standard protocols including a recently described extraction

method using 96-well plates (Dentinger et al. 2010). Primer pairs for PCR were LR0R and LR5

(Vilgalys and Hester, 1990) for the 28S nuclear long subunit (LSU), EF1-983F and EF1-1567R

(Rehner, 2001) for translation elongation factor alpha (tef1), and b6F and b7.1R (Matheny et al.

2007) for RNA polymerase II subunit II (rpb2). PCR reactions consisted of 10-!L reactions

containing EH buffer (100 mM Tris–HCl, pH 8.3; 500 mM KCl; 2.5 mM MgCl2; 0.1% gelatin;

1.6 mg mL"1 bovine serum albumin), dNTPs (1.24 mM each), primers (10 !M), 0.5 U of

Platinum Taq (Life Technologies, Carlsbad, CA, USA), and 1–10 ng of template DNA.

Thermocycler (Mastercycler EP Gradient, Eppendorf, Hamburg, Germany) conditions consisted

of two-step (LSU: 60 °C, 55 °C) and four-step (tef1, rpb2: 60 °C, 58 °C, 56 °C, 54 °C)

‘touchdown’ PCR. Sequencing reactions were conducted using BigDye Terminator kit and run in

a 3730 DNA Analyzer (Life Technologies, Carlsbad, CA, USA). All sequences were edited

using GENEIOUS PRO 5.6.3 (Biomatters, Auckland, New Zealand) by assembling paired strands

into contigs and trimming their ends.

Sequences from each gene region were aligned using MUSCLE 3.6 (Edgar, 2004) and visualized

with SE-AL (Rambaut, 2002). Ambiguously aligning regions in LSU and introns in tef1 and rpb2

were removed. Nucleotide substitution models and partition schemes were selected from 24

models of evolution in PARTITIONFINDER (Lanfear et al. 2012) using the Akaike information

criterion (AIC).

12

Three data matrices were used for different analyses. Dataset 1 was used for fossil calibration;

dataset 2 covered A. sect. Caesareae + outgroup (sect. Vaginatae and sect. Amanita), including

all samples found in Table A8.1; dataset 3 was the same as dataset 2, but reduced to species-level

sampling (one terminal per species). For dataset 3, species were primarily delimited on the tree

produced from dataset 2, using the following two criteria for recognition as separate species: (1)

clade support of at least 95% for taxa with multiple samples; and (2) divergence of at least 1.5

Myr since the last common ancestor. In a few cases, putative species were also recognized when

they were geographically and/or morphological distinct.

2.2.2 Calibration strategies and divergence-time estimation

For calibration, I used two agaricomycete fossils: Archaeomarasmius leggetti (Hibbett et al.

1997) as the minimum age of the Agaricales; and Quatsinoporites cranhamii (Smith et al. 2004)

as the minimum age of the Hymenochaetales. The former is an agaricoid fruiting body that is

comparable to the extant genera Mycena, Marasmius, Marasmiellus, Collybia and

Phaeomarasmius, preserved in Dominican amber from the mid-Cretaceous (c. 90 Ma). In this

calibration, I used the most recent common ancestor (MRCA) of the tricholomatoid, marasmioid

and hygrophoroid clades, which include 17–22 species each. The second fossil is a poroid

fruiting body from Apple Bay on Vancouver Island that dates to the Early Cretaceous (c.

113 Ma). This specimen can be assigned to the Hymenochaetales and is comparable to extant

genera such as Phaeolus in the Polyporales and Cyclomyces and Inonotus in the

Hymenochaetales. Other clades included were the Boletales + Atheliales as the sister clade of the

Agaricales, Pluteus and Volvopluteus as sister taxa of the Amanitaceae in the pluteoid clade, and

Gautieria othii as the outgroup. The Hymenochaetales were not constrained as monophyletic,

based on results from Matheny et al. (2007) (see Appendix Table A8.2 for a comprehensive table

with all the taxa used for preliminary calibrations).

Divergence times were estimated using BEAST 1.8 (Drummond et al. 2012), using an

uncorrelated log-normally distributed clock model. XML files were constructed using BEAUTI

1.8 by importing separate NEXUS files of each gene partition. The gene partitions were set to be

unlinked for substitution and molecular-clock models and linked for gene trees (e.g.

concatenation). The substitution model GTR+#+I was used for all genes in dataset 1 and for

LSU in datasets 2 and 3; SYM+#+I was selected for tef1 and rpb2 in datasets 2 and 3. Orders

13

and known clades within the Agaricales (dataset 1; Appendix Table A8.2) were constrained to be

monophyletic except for the Hymenochaetales. Different tree-model priors were applied to the

separate datasets: Yule for dataset 1, constant coalescent for dataset 2, and birth–death with

incomplete sampling for dataset 3. The prior for the ucld.mean parameter was gamma-distributed

(shape = 1.0; scale = 0.001; offset = 0.0) for all genes. For fossil node calibrations, I used

gamma-distributed priors (shape = 1.0; scale = 50.0; offset = 90.0 for Agaricales or 113.0 for

Hymenochaetales) with an arbitrarily ‘long tail’. Subsequent analyses (datasets 2 and 3) were

conducted using a normally distributed calibration of the treeModel.rootHeight parameter (mean,

75; SD, 1), according to the highest posterior density (HPD) of the time to the MRCA of the

subgenus Amanita. Four independent Markov chain Monte Carlo (MCMC) chains of 50,000,000

generations and sampling frequency of 5000 were conducted for each dataset. Convergence and

mixing were assessed by comparing log files in TRACER 1.6 (Rambaut and Drummond, 2009)

and confirming that effective sample size (ESS) values were at least 200. An ultrametric

maximum clade credibility (MCC) tree with mean and 95% HPD node ages and per-clade

posterior probabilities was summarized using TREEANNOTATOR 1.8, with 10% burn-in and a 0.8

posterior limit. XML, log and tree files are available on request to the corresponding author.

2.2.3 Ancestral-area reconstruction and geodispersal models

Geographical areas were defined based on discrete units marked by the presence of natural

barriers deemed to present limits to dispersal. Individually, these areas are also viewed as

continuous forested regions where within-area dispersal is possible. Although many of these

areas could be split into smaller subunits, I considered such subunits to be irrelevant for the

evolutionary time-scale and scope of the study. The New World (NW) areas were: Pacific

Northwest (PNW); eastern USA (ENA); Mexico (MEX), including New Mexico and Arizona to

the north and Belize to the south; and the Cordillera de Talamanca in Costa Rica (CR). The Old

World (OW) areas were: Mediterranean Europe (EUR); Southeast Asia and the Sino-Himalayas

(SEA); Japan (JAP); Okinawa Island (OKI); the state of Kerala, India (KER); sub-Saharan

Africa (AFR); and eastern Australia (AUS). For ancestral-area reconstruction, a Bayesian binary

MCMC (BBM) analysis was conducted using RASP 2.1 (Yu et al. 2011) and the 95% HPD trees

(c. 10,000) from BEAST under dataset 2. All parameters were left as default, except for ‘state

frequencies’ and ‘rate variation’, which were set to F80 and gamma, respectively. Because

14

members of Amanita sections Vaginatae and Amanita can be found in any of the aforementioned

areas, the root was treated as ‘widespread’.

In order to explicitly assess the likelihoods of different geodispersal scenarios, I used the

dispersal-extinction-cladogenesis model implemented in LAGRANGE (Ree and Smith, 2008) to

reconstruct ancestral ranges by constraining dispersal in ad hoc biogeographical settings. I

considered four null geodispersal models: (1) a permissive model allowing high dispersal rates

across all areas, which assumes that there are no limits to dispersal, such that most species are

deemed to be widespread; (2) a permissive model allowing moderate dispersal rates between

different areas; (3) a restrictive model allowing low dispersal rates between different areas,

which entails high levels of endemism and range restriction; and (4) a model with randomly

assigned dispersal rates across areas, assuming that dispersal rates vary randomly between areas.

Models were compared using maximum likelihood (ML) and $2 likelihood-ratio tests against a

‘boreotropical’ model, where dispersal rates were temporally constrained to be compatible with

the boreotropical hypothesis. In this model, I allowed dispersal to increase between

Palaeotropical areas during the Oligocene and the Early Miocene, between Palaeotropical and

temperate OW areas during the Late Miocene, and between temperate areas in the OW and NW

during the Pliocene and Pleistocene (Fig. 2.1).

2.2.4 Diversification rate tests

Lineage accumulation through time (LTT) plots were constructed to initially assess the

distribution of nodes over time. I used 100 trees chosen evenly from the 95% HPD trees from the

BEAST analysis of dataset 3 (species-level sampling). I also estimated the %-statistic (Pybus and

Harvey, 2000) as a quantitative measure of deviation from rate-constancy. The %-statistic and

LTT plots were estimated and plotted using APE (Paradis et al. 2004) in R 3.0.1 (R Core Team,

2014). Moreover, I was interested in exploring the implications of geographically determined

variation in diversification rates. Because my biogeographical analyses suggested that the NW

was colonized at least eight times independently (see Results), I asked whether these

colonization events resulted in different diversification rates. For this purpose, I used the binary-

state speciation and extinction (BiSSE) model (Maddison et al. 2007; FitzJohn et al. 2009)

implemented in DIVERSITREE (FitzJohn, 2012), which integrates the likelihoods of

15

Figure 2.1. Graphical representation of the geodispersal models in A. sect. Caesareae

inferred with LAGRANGE. The top four panels are null models of dispersion with different

dispersal-rate constraints. The model in the lower panel is a time-structured model based

on Wolfe’s boreotropical hypothesis. Bold and italic fonts indicate Old World and New

World areas, respectively (PNW, Pacific North West; ENA, Eastern North America; MEX,

Mexico, including New Mexico and Arizona to the north and Belize to the south; CR,

Cordillera de Talamanca, Costa Rica; EUR, Mediterranean Europe; SEA, Southeast Asia

and the Sino-Himalayas; JAP, Japan; OKI, Okinawa Island; KER, Kerala, India; AFR,

sub-Saharan Africa; AUS, eastern Australia).

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

5-0 Ma15-5 Ma34-15 Ma

0.1 (low) 0.5 (moderate) 1.0 (high)Dispersal rates

Permissive (high) Restrictive

Nul

l m

odel

sB

oreo

tropi

cal

mod

el

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

56-34 Ma

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

Permissive (moderate)

AFRSEAAUSKEROKIEURJAPPNWENAMEXCR

AFRSEA

AUS

KER

OKI

EUR

JAP

PNW

ENA

MEX

CR

Random

56-0 Ma56-0 Ma56-0 Ma56-0 Ma

16

speciation (&), extinction (µ) and continuous-time state transition (q) into a single function. A

binary geographical state was assigned to each terminal in the MCC tree (dataset 3) based on its

location (OW / NW). ML analyses evaluated a full six-rate model (&OW, &NW, !OW, !NW, qOW and

qNW); two five-rate models – one with &OW constrained to approximate &NW (&OW ' &NW), and one

with !OW constrained to approximate !NW (!OW ' !NW) – and a four-rate model with &OW and

!OW constrained to approximate &NW and !NW (&OW ' &NW, !OW ' !NW), respectively. The fit of

all models was compared by ANOVA in R. In order to have a broader view of the parameter

space, I conducted MCMC estimations of the full model. In all analyses, the proportion of

missing species was taken as 30% in the OW and 10% in the NW.

2.3 Results

2.3.1 Phylogenetics and divergence-time estimates

The topology of the dataset 1 tree was largely consistent with other published phylogenies of the

Agaricomycetes, showing the known relationships between clades (Appendix Fig. A8.1). MCC

trees from datasets 2 and 3 were also largely consistent with one another, in terms of both

topology and divergence times. Figure 2 shows the phylogenetic relationships between taxa in A.

sect. Caesareae, revealing five major clades. The node-height posterior age estimates (mean;

95% HPD in parentheses) of the different clades in dataset 1 were 146 (113.0–203.2) million

years ago (Ma) for the hymenochaetoid clade, 119.0 (78.1–170.6) Ma for the Boletales, 132.6

(90.2–186.2) Ma for the Agaricales, 115.5 (77.2–165.7) Ma for the tricholomatoid clade, 120.9

(82.3–172.5) Ma for the hygrophoroid clade; 115.6 (78.8–164.9) Ma for the marasmioid clade,

112.0 (73.5–158.6) Ma for the pluteoid clade, and 75.4 (48–109) Ma for the subgenus Amanita.

Posterior node-height estimates from dataset 2 and 3 indicated that A. sect. Caesareae probably

originated between Palaeocene and the Eocene, 56 (39–73) Ma, and diversified throughout the

Miocene and Pliocene (24–3 Ma).

2.3.2 Biogeography and ancestral areas

Based on the BBM result (Fig. 2.2, Appendix Fig. A8.2), three of the largest clades (‘Caesarea’,

‘Spreta’ and ‘Zambiana’) were estimated to have either an AFR or SEA ancestral area.

Divergence-time estimates together with ancestral-area reconstruction (Fig. 2.2) suggest that

some dispersal events were contemporary. For instance, in clades ‘Caesarea’ and ‘Zambiana’,

17

Figure 2.2 Maximum-clade-credibility tree of Amanita sect. Caesareae based on dataset 2.

Only clades with > 0.8 posterior probability (number on branches) are annotated with the

most likely ancestral area (rectangle with area code), the 95% highest posterior density of

divergence-time estimation (horizontal bars) and clade posterior probabilities. Certain

clades are named for discussion purposes. The grey vertical box near the tips delimits the

divergence threshold for species delimitation. The top-left world map delimits the

distribution areas and shows arrows indicating major biogeographical events. The

A. jacksonii | RET 393-6 | USA, North Carolina

A. affin. mafingensis | MA898 | Congo Republic

A. affin. jacksonii | XAL Chacon 5895 | Mexico, Ver.

A. vernicoccora | RET 281-9 | USA, Washington

A. cinnamomescens | RET 290-5 | Pakistan

A. arkansana | RET 283-5 | USA, Texas

A. jacksonii | RET 315-8 | USA, Connecticut

A. calyptroderma | RET 389-9 | USA, WashingtonA. calyptroderma | RET 389-4 | USA, Washington

A. hemibapha s. l. | RET 258-3 | China, Yunnan

A. jacksonii | RET 393-7 | USA, North Carolina

A. hemibapha var. ochracea | RET 258-1 | China, Yunnan

A. sp-F11 | RET 138-1 | USA, Florida

A. hemibapha s. l. | TRTC157250 | Viet Nam, Ca Tien

A. affin. masasiensis | RET 348-2 | ZambiaA. affin. mafingensis | MA1069 | Congo RepublicA. affin. mafingensis | RET 345-9 | Zambia

A. sp-W15 | DEWV 9597 | USA, West Virginia

A. cochiseana | RET 498-1 | USA, Arizona

A. mafingensis | RET 348-9 | Zambia

A. sp-T31 | RET 365-1 | USA, Texas

A. affin. mafingensis | MA1065 | Congo Republic

A. cinnamomescens | RET 317-5 | Pakistan

A. (Uncultured EM clone c45) | USA, Georgia

A. hemibapha | RET 342-8 | India, Kerala

A. cochiseana | RET 072-5 | USA, New Mexico

A. affin. masasiensis | De Kesel 3579 | Benin

A. calyptroderma | RET 092-6 | USA, California

A. affin. jacksonii | F1132188 | USA, Texas

A. hemibapha s. l. | TRTC150314 | Thailand, Chiang Mai

A. vernicoccora | RET 084-7 | USA, California

A. sp-AUS3 | Halling 6815 | Australia, Queensland

A. garabitoana | RET 333-6 | Costa Rica, San Jose

A. sp-F11 | RET 138-2 | USA, Florida

A. sp-W15 | DEWV 400 | USA, West Virginia

A. vernicoccora | RET 385-2 | USA, Washington

A. affin. jacksonii | RET 354-8 | USA, Texas

A. caesarea | RET 450-3 | Italy, Cozenza

A. basii | RET 260-6 | Mexico, Estado de Mexico

A. jacksonii | RET 109-4 | USA, Massachusetts

A. hemibapha s. l. | TRTC161171 | Viet Nam, Da Lat

A. vernicoccora | RET 385-6 | USA, Oregon

A. caesarea | RET 036-2 | Italy, Cozenza

A. affin. jacksonii | RET 252-9 | Mexico, Hidalgo

A. cochiseana | RET 497-10 | US, Arizona

A. hemibapha s. l. | TRTC150422 | Thailand, Chiang Mai

A. hemibapha s. l. | TRTC150286 | Thailand, Chiang Mai

A. jacksonii | RET 393-8 | USA, North Carolina

A. sp-AR01 | RET 373-9 | USA, Arkansas

A. affin. hayalyuy | FCME 15194 | Mexico, Oaxaca

A. calyptroderma | RET 385-7 | USA, Washington

A. caesarea | RET 142-9 | Italy, Cozenza

A. vernicoccora | RET 385-8 | USA, California

A. hemibapha s. l. | TRTC161164 | Viet Nam, Da Lat

A. basii | FCME Sanchez S38 | Mexico, Michoacan

A. affin. jacksonii | FCME 21550 | Mexico, Oaxaca

A. hemibapha s. l. | BPI HPUB 560 India Himachal Pradesh

A. jacksonii | RET 154-10 | USA, New York

A. affin. masasiensis | Sharp 607/97 | Zimbabwe

A. calyptroderma | RET 385-4 | USA, Washington

A. rubromarginata | RET 383-1 Japan Okinawa

A. hemibapha s. l. | RET 257-10 | China, Yunnan

A. cochiseana | FCME Sanchez S31 | Mexico, Chih.

A. affin. caesaroides | RET 356-10 | China, Yunnan

A. hemibapha s. l. | TRTC161134 | Viet Nam, Da Lat

A. hemibapha s. l. | RET 349-5 | Thailand, Chiang Mai

A. arkansana | RET 139-10 | USA, Texas

A. affin. javanica | HKAS56863 | China

A. masasiensis | RET 344-5 | ZambiaA. affin. tanzanica | RET 346-7 | Zambia

A. basii | FCME Sanchez S44 | Mexico, Puebla

A. calyptroderma | RET 385-3 | USA, Oregon

A. affin. jacksonii | FCME 21652 | Mexico, Oaxaca

A. affin. masasiensis | RET 348-3 | Zambia

A. sp-AUS2 | Halling 6814 | Australia, Queensland

A. basii | FCME Sanchez S32 | Mexico, DF

A. calyptroderma | RET 389-5 | USA, Washington

A. arkansana | RET 354-9 | USA, Texas

A. caesarea | RET 427-1 | Italy, Cozenza

A. hemibapha s. l. | RET 456-7 | Japan

1.0

0.81

0.911.0

0.99

1.0

1.0

1.0

1.01.0

1.0

1.0

0.83

1.0

0.99

1.0

0.83

0.9

0.93

0.99

0.94

0.82

0.97

1.0

0.97

0.92

1.0

1.0

0.99

1.0

1.0

0.97

0.91

1.0

1.0

MEXMEXMEXMEXMEXMEXMEXMEXMEXCR

ENAENAENAPNWPNWPNWPNWPNWPNWPNWPNWPNWPNWPNWPNWSEASEASEASEAENAENAENAENAMEXMEXMEXMEXENAENAENAENAENAENAENAEUREUREUREURJAPSEAENAENAENAENASEASEASEASEASEASEAOKISEAAUSSEASEASEASEAAUSKERAFRAFRAFRAFRAFRAFRAFRAFRAFRAFRAFR

MEX

ENA

SEA

SEA

SEA

ENA

SEA

ENA

‘Caesarea’

SEA

SEA

SEA

SEASEA

SEASEAAFR

AFR

SEA

EUR

ENA

SEA

SEA

AFR

AFR

AFR

AFR

AFR

ENA

PNW

PNW

ENA

JAP

EUR

MEX

‘basii’ clade‘jacksonii’ clade

20-16 Ma

c. 4 Mac. 6 Ma

c. 8-4 Ma

12-7 Ma

>3 Ma

2.7 Ma

c. 13 Ma

c. 12 Mac. 3 Ma

JAP

OKI

AUS

KER

EUR

ENA

PNW

MEX

CR

SEA

AFR

ENA

ENA

1.0

0.81

1.0

0102030405060Eocene Oligocene MiocenePalaeocene Plio Ple

Continued

18

thickness of the arrow implies recurrence. Probabilities of ancestral area estimations can

be found in Fig. A8.2 in Appendix A (PNW, Pacific North West; ENA, Eastern North

America; MEX, Mexico, including New Mexico and Arizona northwards and Belize

southwards; CR, Cordillera de Talamanca, Costa Rica; EUR, Mediterranean Europe;

SEA, Southeast Asia and the Sino-Himalayas; JAP, Japan; OKI, Okinawa Island; KER,

the state of Kerala, India; AFR, sub-Saharan Africa; AUS, eastern Australia). Plio,

Pliocene; Ple, Pleistocene.

Figure 2.2. Continued.

8 My

01020304050607080

A. affin. chepangiana HKAS2772 | China, Sichuan

A. sp-M36 | RET 293-4 | Mexico, Tlaxcala

A. egregia | RET 136-7 | Australia, QueenslandA. chepangiana | UTC Shrethsa 154P | Nepal

A. calyptratoides | RET 273-7 | USA, California

A. ristichii | RET 124-10 | USA, Maine

A. affin. princeps HKAS75788 China

A. longistriata | Bas 9040 (L) | China

A. zambiana | De Kesel 3714 | Benin

A. virginiana | RET 374-8 | USA, Tennessee

A. affin. esculenta | TRTC150406 | Thailand, Chiang Mai

A. muscaria subsp. guessowii | RET 303-4

A. recutita sensu Coker | Ovrebo 4809B | USA, Oklahoma

A. sp-Thai03 | RET 356-5 | Thailand

A. ristichii | RET 096-1 Canada Quebec

A. murrilliana | RET 374-2 | USA, Tennessee

A. yuaniana | RET 257-8 | China, Yunnan

A. affin. princeps | RET 357-5 | China, Yunnan

A. affin. vaginata | TRTC150325

A. sp-53 | RET 383-2 | USA, New York

A. murrilliana | RET 278-1 | USA, North Carolina

A. zambiana | RET 261-3 | Burundi

A. tlaxcandela | RET 292-1 | Mexico, Tlaxcala

A. zambiana | MA861 | Congo Republic

A. affin. princeps | TRTC150309 | Thailand, Chiang Mai

A. affin. virginiana | RET 361-6 | USA, Massachusetts

A. roseolamellata | RET 476-4 | Australia, New South Wales

A. spreta | RET 296-10 | USA, New Jersey

A. sp-Thai03 | RET 351-7 | Thailand, Nakhon

A. sp-M36 | RET 293-3 | Mexico, Tlaxcala

A. banningiana | RET 065-5 | USA, Connecticut

A. affin. esculenta | TRTC150410 | Thailand, Chiang Mai

A. banningiana | RET 030-4 | USA, New Jersey

A. belizeana | RET 094-5 | Belize

A. spreta | RET 296-2 | USA, New York

A. murrilliana | RET 251-4 | Canada Quebec

A. virginiana | RET 268-10 | USA, Connecticut

A. torrendii | LOU Fungi 18202 | Spain

A. affin. esculenta | TRTC150413 | Thailand, Chiang Mai

A. sp-Thai03 | RET 350-1 | Laos

A. calyptratoides | MEXU 24314 | USA, California

A. zambiana | De Kesel 3227 | BeninA. zambiana | De Kesel 4378 | Togo

A. zambiana | RET 343-10 | Zambia

A. spreta | RET 315-10 | USA, Connecticut

A. affin. princeps | RET 359-9 | Thailand, Chiang Mai

A. incarnatifolia | HKAS29519 | China

A. affin. calyptratoides | RET 382-8 | USA, California

1.0

0.99

0.97

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

0.97

0.89

1.0

1.0

1.0

1.0

SEASEAENAENAENAENAENAENAENAENAEURAUSENAENAENAENAMEXSEASEASEASEASEASEASEASEAAUSSEASEASEASEASEAAFRAFRAFRAFRAFRAFRPNWPNWPNWMEXENAENAENAMEXMEX

Eocene Oligocene MiocenePalaeoceneCretaceous Plio Ple

‘Spreta’

‘Zambiana’

‘Calyptratoides’

‘Murrilliana’

Section Caesareae

ENA

SEA

SEA

ENA

SEA

AFR

AFR

MEX

ENA

SEA

ENA

ENA

SEA

SEA

SEA

AFR

MEX

PNW

1.0ENA

Continued

Time (Ma)

19

AFR ( SEA and SEA ( AUS both occurred during the Early Miocene, and in clades

‘Caesarea’ and ‘Spreta’, SEA ( NW occurred during the Late Miocene and the Pliocene. I

found that the NW was colonized from an OW ancestor at least eight times and that SEA was a

pivotal region for dispersal events between continental and insular areas (Fig. 2.2). Furthermore,

dispersal–extinction–cladogenesis analyses supported my boreotropical model as compared to

other null models (Table 2.1), indicating in part that Neotropical taxa (e.g. in CR and MEX) were

established relatively recently from northern populations.

2.3.3 Diversification rates

The LTT plot shows a nearly linear trajectory, with a slight decrease in lineage accumulation

during the Oligocene (Fig. 2.3). Although this could have been caused by a ‘massive’ extinction

event (see Crisp and Cook, 2009, for LTT plot interpretation), I did not test for significance due

to the lack of fossil records with which to make comparisons. The %-statistic across 100 trees

from the posterior distribution (see boxplot in Fig. 2.3) suggests that most trees approximate a

more or less constant diversification-rate model, with a mean % of 0.40. According to Pybus et al.

(2002), the closer % approaches 0, the more the phylogeny approximates a constant pure-birth

model, and can only significantly deviate from this distribution (at a 0.05 confidence level) if % >

1.96, % < "1.96 (two-tailed test) or % < "1.65 (one-tailed test). In short, both metrics suggested

that A. sect. Caesareae diversified rather constantly (Fig. 2.3). Nevertheless, my BiSSE model

comparison indicated that diversification rates differed significantly between the NW and OW

lineages in A. sect. Caesareae (Table 2.2). My results support NW lineages having higher

speciation and extinction rates than OW lineages (ML estimates in lineages per million years:

&OW = 0.133 Myr"1, &NW = 1.453 Myr"1, µOW = 0.0174 Myr"1, µNW = 1.3058 Myr"1; Table 2.2,

Fig. 2.4).

2.4 Discussion

2.4.1 Historical biogeography

Fungal distributions are mostly determined by genetic, ecological and morphological constraints

(Pringle et al. 2009). For instance, ecological and phylogenetic studies have shown that some

species of fungi might be virtually free from dispersal barriers (Queloz et al. 2011), whereas

others have strong biogeographical structures (Geml et al. 2006, 2008; Matheny et al. 2009)

20

Table 2.1 Likelihood-ratio test comparison of geodispersal models in Amanita sect.

Caesareae. Each null model was compared to the Boreotropical.

Models d.f. ln-likelihood $2 P

Boreotropical 1 "163.171 — —

Null

Permissive with moderate dispersal 1 "167.581 8.82 0.00297**

Restrictive except for within-area dispersal

1 "167.581 8.82 0.00297**

Permissive with high dispersal 1 "169.366 12.39 4.31639 ) 10"4***

Randomly assigned dispersal rates 1 "175.040 23.74 1.10381 ) 10"6***

*P < 0.05, ** P < 0.01, *** P < 0.001.

Table 2.2 Comparison of binary-state speciation and extinction (BiSSE) models based on

an analysis of variance (ANOVA) between New World and Old World species of Amanita

sect. Caesareae (!OW, speciation rate in the Old World; !NW, speciation rate in the New

World; "OW, extinction rate in the Old World; "NW, extinction rate in the New World).

Simpler models were compared to the most parameter rich.

Models d.f. ln-likelihood AIC $2 P

&OW * &NW, !OW * !NW 6 "247.24 506.48 — —

&OW ' &NW 5 "256.64 523.28 18.798 1.453 ) 10"5***

!OW ' !NW 5 "265.73 541.46 36.978 1.195 ) 10"9***

&OW ' &NW, !OW ' !NW 4 "277.29 562.58 60.093 8.926 ) 10"14***

AIC, Akaike information criterion.

*P < 0.05, ** P < 0.01, *** P < 0.001.

21

that may be related to host and habitat limitations. My biogeographical analyses test plausible

dispersal scenarios in a ML framework by comparing null models –loosely based on generalized

assumptions on dispersal constraints – to an explicit and structured boreotropical model (Fig.

2.1). My results indicate that A. sect. Caesareae has a strong biogeographical structure,

evidenced by a probable AFR origin (Palaeocene–Eocene), followed by dispersal to SEA (Early

Miocene), from where dispersal occurred (Late Miocene and Pliocene) towards temperate

regions in both Southern (AUS) and Northern hemispheres (JAP, PWN, ENA, MEX and CR)

(Fig. 2.2, Table 2.1). This pattern is largely consistent with the boreotropical hypothesis. This

hypothesis has been useful for explaining disjunct distributions of Asian and American sister

taxa as well as Palaeotropical and Neotropical sister taxa (Donoghue et al. 2001; Davis et al.

2002; Donoghue and Smith, 2004; Milne, 2006).

The boreotropical model also supports the idea that dispersal between tropical areas such as AFR

and SEA was possible during the Oligocene and Early Miocene (Fig. 2.2). This premise stems

from geological evidence on the collision of the African and Eurasian plates during the

Oligocene and the Miocene (Allen and Armstrong 2008; Potter and Szatmari 2009), creating

land-bridges between both continents. In addition, similar biogeographical patterns with

coinciding dates have been recently observed and discussed in plants (Couvreur et al. 2011;

Zhou et al. 2011; Bacon et al. 2012), mammals (Bibi, 2011), birds (Irestedt et al. 2011) and

insects (Kodandaramaiah and Wahlberg 2007).

Asian–American pairs of sister taxa are mostly present in the ‘Spreta’ and ‘Caesarea’ clades

(Fig. 2.2). Node ages and dispersal events from Asia to America suggest that these events

occurred late in the Miocene and the Pliocene, primarily eastwards from Asia to America. Two

of the five most likely periods of floristic interchange between Asia and America proposed by

Tiffney (1985) were the Miocene and the Pliocene–Pleistocene (Wen 1999). This is also in

accordance with molecular dating analyses of the floristical disjunction between Asia and North

America (Xiang et al. 2000), suggesting that dispersal during this time was also feasible for EM

fungi. The most plausible explanation is that these events occurred through Beringia (Fig. 2.2). I

find that there were at least eight such events – three associated with sister taxa in clade

‘Caesarea’, two in clade ‘Spreta’, one in clade ‘Calyptratoides’, and one more in clade

‘Murrilliana’. Although my data do not support this explicitly, my results suggest that the PNW

and ENA probably served as Plio-Pleistocene refugia. This stems from the observation that

22

multiple lineages colonized these areas independently (Fig. 2.2; ENA: A. spreta, A. banningiana,

A. ristichii, A. jacksonii, A. murrilliana and A. arkansana; PNW: A. calyptratoides,

A. vernicoccora and A. calyptroderma) before the onset of the Pleistocene glaciations. Similarly,

Geml et al. (2006, 2008) found common Beringian connections between Eurasian and American

populations of A. muscaria, as well as distinct phylogenetic lineages endemic to the PNW and

ENA.

The two largest North American subclades within clade ‘Caesarea’ (‘basii’ and ‘jacksonii’; Fig.

2.1) continued to expand southwards, eventually reaching highlands in Central America, such as

Costa Rica, Mexico (Chiapas) and Guatemala (Guzmán and Ramírez-Guillén 2001; Tulloss et al.

2011). The time to the MRCA [3.69 (1.96–5.53) Ma] of A. garabitoana, A. basii, A. cochiseana

and A. affin. hayalyuy (Fig. 2.2) is consistent with the uplift of the Cordillera de Talamanca

during the late Pliocene, between 3.5 and 5 Ma (Coates and Obando 1996; Gräfe et al. 2002),

during which time this area probably became a suitable habitat for EM fungi. It would be

interesting to examine whether this pattern is recurrent in other EM fungi found in Costa Rican

forests. To date, no taxa assignable to sect. Caesareae have been found in South America, and

other species of Amanita are also poorly represented (Tulloss 2005). This suggests the possibility

that geographical barriers and limited host availability constrain dispersal towards South

America (Tulloss 2005). Northwards, a plausible explanation for the reduced representation of

taxa in the Rocky Mountains could be that most lineages went extinct there during Pleistocene

glacial cycles, as is thought to have occurred in certain groups of plants (Wen 1999; Qian and

Ricklefs 2000; Donoghue and Smith 2004).

Other events during the Pliocene resulted from westward dispersal from SEA to EUR, leading to

the extant distribution of Amanita caesarea (s. str.) (Fig. 2.2). The Alpine and Himalayan

orogenic systems were already in place during the Miocene (Potter and Szatmari 2009), and

more temperate and alpine forest elements probably dominated the highlands towards the

Pliocene (Tiffney and Manchester 2001). This observation is supported by reports of

A. caesarea-like species in pine forests of Turkey (Köstekci et al. 2005) and Iran (Bahram et al.

2006). Although it is not explicitly discussed, the MCC tree in Du et al. (2012) shows numerous

disjunctions between Asia and Europe that date to the Late Miocene and Pliocene in morels

(Morchella), which is also consistent with my results.

23

Disjunct insular distributions include vicariant taxa from JAP and SEA (Fig. 2.2). Mycological

studies based on morphology have already noted the high level of affinity between JAP and SEA

Amanita species (Tulloss 2005). Moreover, the phylogeny indicates consistent disjunctions

between SEA and AUS taxa in the clades ‘Zambiana’ and ‘Caesarea’, all with Miocene MRCAs

(Fig. 2.2). Although boreotropical distributions are a phenomenon exclusive to the Northern

Hemisphere, I incorporate into the model the possibility of taxa also dispersing southwards from

SEA to AUS, in agreement with other biogeographical patterns of ‘Gondwanan’ flora (Sanmartín

and Ronquist 2004).

Overall, I observe a large number of biogeographical similarities across clades, but also strong

differences in terms of the presence and absence of taxa worldwide. Clade ‘Zambiana’, for

instance, seems to be restricted to lower latitudes in more tropical environments and no members

are known to exist in EUR or the NW. Clade ‘Spreta’ has a better representation of taxa at higher

latitudes and more temperate environments than clade ‘Zambiana’. In contrast, clade ‘Caesarea’

is definitely the most widespread and most species-rich within A. sect. Caesareae.

2.4.2 Diversification

There is still much debate about the underlying causes of observed patterns and processes of

species diversity (Rabosky 2009; Wiens 2011). Phylogenetic trees are often interpreted as

signature representations of past evolutionary histories, including modes of diversification (Nee

2006). These signatures are often associated with speciation and extinction dynamics, which can

be constant or variable through time (Stadler 2013). Empirical studies in animals and plants have

suggested that decreasing levels of diversification towards the present is a fairly common and

widespread pattern (e.g. McPeek 2008), and has been explained by clade-level diversity-

dependence (Rabosky and Lovette 2008; Etienne et al. 2012), time-dependency of climatic

fluctuations (Stadler 2013) and protracted speciation (Etienne and Rosindell 2012). In spite of

the wealth of theoretical and empirical studies, little is known about the diversification patterns,

processes and signatures in microorganisms. Studies conducted by Ryberg and Matheny (2011,

2012) have shown that a large number of EM agaric lineages show no significant signatures of

rate shifts through time, suggesting that these lineages might be evolving constantly and,

therefore, are probably less limited by niche availability. Although results partly agree with

24

Figure 2.3 Lineages-through-time plots of 100 post-burn-in posterior trees based on dataset

3 (Amanita sect. Caesareae species-level phylogeny). The y-axis is log-scaled and the x-axis

represents time. The boxplot to the left shows the estimated #-statistic of the same 100

posterior trees. The vertical dotted lines on the boxplot indicate the confidence limits for

two-tailed ($1.96, 1.96) and one-tailed ($1.65) tests of significance (% = 0.05).

-60 -50 -40 -30 -20 -10 0

12

510

2050

Time (Ma)

Log-

linea

ges

-2 -1 0 1 2

25

Figure 2.4 Boxplots of Markov chain Monte Carlo estimates of state-based speciation (!)

and extinction (µ) of the best binary-state speciation and extinction model (!OW & !NW, "OW

& "NW) in Amanita sect. Caesareae. The last two boxplots to the right are the net

diversification rates calculated as the difference between the speciation and extinction rates

(! $ µ). OW, Old World; NW, New World.

OW NW OW NW OW NW

0.0

0.5

1.0

Line

ages

per

mill

ion

year

s

26

this view (Fig. 2.3), I show that diversification processes (e.g. speciation and extinction) have not

occurred evenly through time and space when viewed in a geographical context (Fig. 2.4).

BiSSE suggests that diversification rates in A. sect. Caesareae are significantly higher in the NW

than in the OW (Fig. 2.4, Table 2.2). Surprisingly, this rate difference only occurs in the

relatively recent past, beginning with dispersal events in the late Miocene and Pliocene (Figs 2.1

and 2.2). In support of this idea, Kennedy et al. (2012) showed that speciation rates in tropical

and temperate EM lineages of Clavulina are different, being higher in a single temperate clade

(C. cristata group). In addition, results are in agreement with Qian and Ricklefs (2000), who

argue that extinction has been more prevalent in North America than in Asia during the

Quaternary, resulting in differences in plant diversity between the regions. Although lower

diversification rates in the OW (Fig. 2.4) may be linked to many factors, I would like to point out

two possible explanations: (1) lower extinction rates may relate to the fact that a large portion of

the OW is tropical and thus taxa there are less prone to extinction; and (2) lower speciation rates

may relate to longer waiting times to speciation completion, as in protracted speciation (Etienne

and Rosindell, 2012), in the OW. To exemplify the last point, whereas it takes 7.5 Myr

(0.133 Myr"1) on average for a speciation event to occur in the OW, it takes only 0.68 Myr

(1.453 Myr"1) in the NW. This suggests that diversification processes in the NW are more

dynamic and recent. Speciation caused by Plio-Pleistocene climatic oscillations has been well

documented in many groups of organisms in North America (Hewitt, 2000; Knowles, 2001;

Weir and Schluter, 2004). I speculate that these events are a likely causal explanation for the

higher speciation and extinction rates in the NW (Fig. 2.4). This study shows that although the

sampled species richness is similar in the NW and in the OW (Appendix Table A8.1), it results

from different tempos and modes of diversification.

2.5 Conclusions

I present the first comprehensive phylogeny of species of Amanita sect. Caesareae and show

evidence from molecular dating and ancestral-area reconstruction that this group probably

originated between the Palaeocene and Eocene in a Palaeotropical setting, most likely in Africa.

Subsequent dispersal during the Miocene and Pliocene into other temperate and tropical areas

probably led to extant distributions in the section. Results support the idea that the evolutionary

history of these EM basidiomycetes has a structured biogeography, largely consistent with

Wolfe’s boreotropical hypothesis, in agreement with the history of other organisms that present

27

tropical, subtropical and temperate disjunct elements across the globe. Furthermore, although the

overall diversification rate of the clade has been rather constant, recently derived New World

taxa appear to diverge at a faster rate.

28

Chapter 3

LATITUDINAL DIVERSITY GRADIENTS AND DIVERSIFICATION 3RATES IN THE CAESAR’S MUSHROOMS

3.1 Introduction

A major question in ecology and evolutionary research pertains to how biodiversity has arisen

and is maintained throughout evolutionary history. Tropical regions are generally viewed as

biodiversity hotspots, harboring far more species than in any other region (Gaston 2000). This

observation dates back at least to the 19th century (Brown 2013), and is still of major interest to

many scientists (Sherratt and Wilkinson 2009). The general latitudinal diversity gradient (LDG)

is a global well-documented phenomenon, which describes a decline in species richness from the

equator towards the poles (Hillebrand 2004; Mittelbach et al. 2007; Schemske 2009). Although

most taxonomic groups of plants and animals follow the general LDG, there are other groups that

deviate from the pattern (Hillebrand and Azovsky 2001; Kindlmann et al. 2007), and others, for

which the pattern is still elusive.

Microbial biogeography and macroecology are still in their early stages (Martiny et al. 2006),

however, in fungi, some patterns have started to appear. Fungi are present in virtually every

ecosystem and play diverse ecological roles as nutrient recyclers, symbionts, pathogens,

parasites, predators, and prey. In spite of their ubiquity, taxonomic richness, and ecological

diversity, global biogeographic and evolutionary studies are still scarce. Nonetheless, substantial

progress has been achieved recently in a comprehensive assessment of their global diversity and

biogeography, showing that in general, soil fungi conform to the general LDG, with several

notable exceptions among taxonomic or ecological groups (Tedersoo et al. 2014). One of these

exceptions includes ectomycorrhizal (EM) fungi, for which it has been shown that diversity

peaks at temperate rather than tropical latitudes (Tedersoo and Nara 2010; Tedersoo et al. 2012,

2014). EM fungi are widespread root-associated mutualists in symbiosis with woody perennial

trees from families such as Pinaceae, Fagaceae, Betulaceae, Salicaceae, Ericaceae, Rosaceae,

Myrtaceae, Dipterocarpaceae and Fabaceae, some of which are predominant in tropical and/or

temperate environments (Taylor and Alexander 2005; Alexander 2006; Smith and Read 2008).

29

They are also polyphyletic across the fungal kingdom, with at least 78-82 independent origins

from saprobic or wood-decaying ancestors with no noticeable reversals (Tedersoo and Smith

2013). This implies that diversity estimates that account for the EM LDG have been pooled from

multiple fungal lineages. From this line of reasoning, I pose two questions: (1) did the EM LDG

arise independently within each EM lineage? and (2) what processes are namely responsible for

the EM LDG? My analyses focus on answering the latter question. Tedersoo and Nara (2010)

provide three plausible explanations: (1) historical and biogeographical effects (for instance, high

dispersal from tropical to temperate environments or higher net diversification in the temperate

region could potentially lead to a reduced representation of taxa in the tropics); (2) fewer

available niches in the tropics potentially caused by lower EM plant taxonomic diversity and

abundance, as well as less differentiated soil profiles; and (3) reduced host availability (i.e. less

diverse root systems) and habitat fragmentation (i.e. “islands” of EM host trees) potentially

leading to smaller populations sizes, and thus higher risk of extinction in the tropics.

The general LDG has been explained by a variety of mechanisms including biotic and abiotic

interactions, population biology, molecular and metabolic evolution, niche conservatism,

biogeography, and macroevolutionary rates (Pianka 1966; Rohde 1992; Gillooly et al. 2001;

Wiens and Donoghue 2004; Jablonsky et al. 2006; Weir and Schluter 2007; Mittelbach et al.

2007; Schemske 2009; Dowle et al. 2013). Speciation and extinction are the ultimate factors that

regulate species diversity through time; and the growing availability of phylogenetic data, in

conjunction with mathematical models, allow for increasingly accurate estimations of their rates

(Barraclough and Nee 2001; Nee 2006; Rabosky 2013; Stadler 2011, 2013a, b). The simplest

models assume that speciation and extinction are constant through time (Nee et al. 1994).

However, empirical data from phylogenies (McPeek 2008) and fossil records (Alroy 2009;

Huang et al. 2014; Silvestro et al. 2015) often suggest that macroevolutionary rates vary. In order

to account for rate-variation, more complex equations model rates that are diversity- (Rabosky

and Lovette 2008; Etienne and Haegeman 2012; Etienne et al. 2012), state- (Maddison et al.

2006; Fitzjohn 2010; Goldberg et al. 2010, 2011), and time-dependent (Stadler 2011b; Rabosky

2014), while other assume speciation is protracted (Etienne and Rosindell 2012; Etienne et al.

2014; Lambert et al. 2015). It has also been found that rate-variation is generally associated with

decreasing speciation rates towards the present (McPeek 2008; Morlon et al. 2010; Rabosky et

al. 2012; Moen and Morlon 2014). Overall, it is still difficult to disentangle competing

30

evolutionary and ecological processes or scenarios from the use of current models alone

(Rabosky 2009; Wiens 2011; Rabosky et al. 2012; Moen and Morlon 2014), particularly when

supporting evidence is scarce. Not surprisingly most empirical studies focus on plants and

animals (McPeek 2008; Butlin et al. 2009), while microorganisms remain somewhat overlooked

(Curtis et al. 2009).

Many clades exhibit an initial rapid burst of diversification after the appearance of a key

innovation or after the colonization of a new region (Rabosky and Lovette 2008; Phillimore and

Price 2008; Etienne et al. 2012; Etienne and Haegeman 2012). Phylogenetic data commonly

shows a decrease in lineage accumulation towards the present, which can be explained,

according to adaptive radiation theory (Schluter 2000), by the saturation of ecological niches (but

see Moen and Morlon 2014). Many EM lineages have high species diversity. For instance,

Amanita (ca. 500 spp.), Cortinarius (ca. 2,000 spp.), the Inocybaceae (ca. 500 spp.), Russula (ca.

750) and Lactarius (ca. 450) (Kirk et al. 2008), are examples of species-rich EM groups. To

investigate if such diversity originated from an early burst of diversification, Ryberg and

Matheny (2012) fitted rate-variable and rate-constant models to nine time-calibrated phylogenies

of EM Agaricales finding no support for diversification slowdowns, where rate-constancy could

not be rejected. Kennedy et al. (2012) further indicate the lack of relationship between taxonomic

diversity, clade age, and rates of diversification between tropical and temperate lineages in the

EM genus Clavulina.

In this work, I examine the LDG from a macroevolutionary perspective focusing on a group of

EM Amanita –A. sect. Caeasareae (the Caesars's mushrooms), for which a comprehensive

worldwide phylogeny was recently produced (Sánchez-Ramírez et al. 2015a; Chapter 2). The

Caesar’s mushrooms are found in most tropical, subtropical, and temperate regions of the world.

Based on a blunt 20/-20 degree cut-off, 39 species can be found at temperate (including 3 in the

Southern Hemisphere) and 28 at tropical latitudes. Here, the gradient conforms from south to

north; thus, higher species richness can be found in the Northern Hemisphere. No species from

this section has been found, or is known, from South America and they are rarely present in

boreal regions (e.g. > 50 degrees latitude). We make use of various macroevolutionary models to

measure rates of diversification, taking into account the reconstruction of ancestral latitudes. My

aim is to look at how macroevolutionary rates, such as the rate of speciation, vary in time and

latitudinal space within this group of mushrooms. Finally, I discuss competing hypotheses

31

addressing the influence of ecological and evolutionary factors on the observed patterns of EM

diversity in the Caesar’s mushrooms.

3.2 Material and Methods

3.2.1 Taxon sampling, latitude traits and data

Amanita sect. Caesareae comprises nearly 80 known species

(http://www.amanitaceae.org/?section%20Caesareae), of which 67 are represented in the

phylogeny of Sánchez-Ramírez et al. (2015a) (Chapter 2). However, given the cryptic nature of

fungi, I considered the sampling to represent about 60% of the total diversity. As proxies for the

latitudinal distribution of the species, I used the sampled latitude for species represented by a

single record, and the average of the sampled latitude for those with more than one record. I

avoided the use of absolute latitudes in my analyses in order to elude biases in ancestral state

reconstruction (e.g. if sister taxa are found at northern and southern temperate latitudes,

respectively, by using absolute latitudes the ancestral latitude would most likely be temperate,

however, by the positive and negative latitudes the ancestor would most likely be inferred to be

tropical, which has more biogeographical sense) and geographic sampling biases in the Southern

Hemispere (see Sánchez-Ramírez et al. 2015a; Chapter 2). After the first analysis (next section),

I focused on the most species-rich and best-sampled clade (Fig. 3.1) –clade ‘Caesarea’. I

considered complete taxon sampling for this clade, as it includes all the known species

assignable to it (26 described in http://www.amanitaceae.org/?section%20Caesareae; 42

sampled, including undescribed taxa). For analyses based on the phylogeny of Amanita sect.

Caesarea, I employed the maximum-clade-credibility tree produced by BEAST (Drummond et al.

2012) in Sánchez-Ramírez et al. (2015a; Chapter 2). For analyses conducted on clade ‘Caesarea’,

I used 100 post-burnin posterior subtrees (subsampled at a 10% frequency). These subtrees were

obtained by using the function extract.clade from the R package ‘ape’ (Paradis et al. 2004).

3.2.2 Time-varying speciation rate estimation and the reconstruction of ancestral latitudes

In order to detect intrinsic changes in diversification rates within the data, without relying on

trait-based models, I used the Bayesian Analysis of Macroevolutionary Mixtures (BAMM) and

BAMMtools (Rabosky 2014) to estimate time-varying speciation and extinction rates across the

phylogeny, and to produce visual representations of the data. The BAMM algorithm is based on

32

Markov Chain Monte Carlo (MCMC) simulations that produce posterior estimates of these rates

and how they change as a function of time. It uses a reversible-jump (rjMCMC) sampling to

explore efficiently multiple configurations of diversification rate changes throughout the

phylogeny (Rabosky 2014). One hundred thousand generations were run, sampling every 10th

state. Posterior and prior samples (“event data”) were then imported and analysed in BAMMtools.

I compared this result to trait-based approaches of how latitudinal ranges evolve. Under the

assumption of Brownian motion (BM) evolution, I reconstructed ancestral latitudes by using the

function ace in the APE (Paradis et al. 2004) package, under a Maximum Likelihood (ML)

framework. Further, under the assumption of ‘constrained’ evolution of latitudinal ranges, I

considered an Orstein-Uhlenbeck (OU) model with multiple ‘selective regimes’. For this I used a

step-wise AIC method implemented in the package SURFACE (Ingram and Mahler 2013). This

method finds the best configuration of trait regimes by sequential model fitting. It can also infer

in which lineage a shift in latitudinal optima occurred and its respective value.

3.2.3 Testing the effect of latitude on speciation rates

We used the ML-based quantitative-state speciation and extinction (QuaSSE) model (FitzJohn

2010) implemented in DIVERSITREE (FitzJohn 2012) to evaluate the effect of latitude –as a

continuous trait– on dynamics of speciation and extinction. I explored four clade-wide trait-

dependent models of diversification (constant, sigmoid, linear, and modal speciation rate

functions), which were fitted to the section-level (MCC) phylogeny. To measure the effect of

extinction, I allowed the rate to vary as a linear and modal function, respectively, in the best

fitting speciation rate model. Five additional models, which included subclade decoupling (e.g.

separate parameters for each temperate/tropical sublcades, hereon called ‘ecological’ subclades),

were also fitted to clade ‘Caesarea’ posterior subtrees. Both the linear and sigmoid functions

expect a directional change in speciation with latitude; however, the sigmoid function allows the

speciation rate to be constant before and after the change occurs. In the modal function, the

speciation rate is expected to increase, peak at a given latitude, and then decrease. I used 20

degrees as initial parameter for the inflection point in the sigmoid function and the midpoint in

the modal function (xmid). In addition, the assumed per-species standard deviation of latitudinal

ranges (5 degrees) was used as a starting parameter for the steepness (r) and variance (+2x) in the

sigmoid and modal functions, respectively. In analyses where ecological subclades were

decoupled (i.e. independent), I assumed that the diffusion parameter (,2) was equal for both

33

clades (,2trop ' ,2

temp) and I fitted (1) the linear function to each subclade simultaneously, (2) the

linear function to the temperate subclade and the constant function to the tropical subclade, (3)

the constant function to the temperate subclade and the linear function to the tropical subclade,

and (4) the modal function to the temperate subclade and the constant function to the tropical

subclade.

In order to show the power to detect significant speciation rate gradients with moderate-sized

phylogenies (50-100 tips), I simulated phylogenies of 50 and 100 tips under QuaSSE’s modal

function, with similar parameters as the ones found in the section-level phylogeny (xmid = 28

degrees, +2x = 5 degrees, ! constant = 1.0E-8, ,2 = 15). Besides the tip number, I carried out 4

simulations with different speciation rate conditions: (1) a control simulation with no speciation

rate change (%y0 = %y1), (2) a simulation with 1-fold increase in speciation rate (%y0 = 0.05, %y1 =

0.1), (3) with 2-fold increase (%y0 = 0.05, %y1 = 0.15), and (4) 3-fold increase (%y0 = 0.05, %y1 =

0.2). The simulated phylogenies and their associated traits were then fitted to a null model where

the trait has no effect on the speciation rate (constant function) and to the modal function model.

Both optimizations were compared using AIC and a likelihood ratio test. Furthermore, although

the taxonomic sampling included all described species (as well as additional cryptic and

undescribed species) in clade ‘Caesarea’, I wanted to test the potential effect of missing taxa on

the latitude/speciation rate gradient. Thus, I repeated estimations of the best model, but specified

20, 40, 60, or 80 % of taxa missing, accordingly. The “directionality” parameter (“drift”) was

ignored for all models tested, and the extinction rate was assumed constant across latitude for all

clade ‘Caesarea’ models. This was because QuaSSE generally has little power to estimate

accurate extinction rates unless the effect of the trait and the number of taxa are high (FitzJohn

2010).

3.2.4 Birth-death diversity-dependent diversification

Etienne and Haegeman (2012) recently provided a likelihood framework under which the effect

of key ecological innovations can be studied in the context of diversity-dependent

macroevolution. Their method can be used to detect a negative rate shift caused by diversity-

dependence, as well as changes in speciation and extinction rates, and clade-wide shifts in clade-

level carrying capacity, and shifts caused by decoupling of diversity-dependence dynamics of a

subclade from the clade in which it is nested (Etienne and Haegeman 2012). Their likelihood

34

function allows for the estimation, testing and fitting of a number of models ranging from the

two-rate standard birth-death model (Nee et al. 1994) to more parameterized models, which can

include independent per clade speciation and extinction rates, clade-level carrying capacities, and

the time of decoupling. I fitted eight models to all 100 posterior subtrees of clade ‘Caesarea’

using the function dd_KI_ML in the R package DDD (Etienne et al. 2012, Etienne and Haegeman

2012). Most models considered temperate and tropical subclades decoupled. The models

included the following: model 1: equivalent speciation and extinction rates, but independent

clade-level carrying capacities (Ktemp * Ktrop) for each ecological subclade; model 2: model 1

with equivalent clade-level carrying capacities (Ktemp ' Ktrop); model 3: model 1 considering

separate intrinsic speciation rates; model 4: model 1 considering separate extinction rates; model

5: model 1 considering both separate speciation and extinction rates; model 6: considers no

diversity-dependence (i.e. Ktemp ' Ktrop ' -) but separate speciation and extinction rates for both

subclades; model 7: birth-death diversity-dependence with no subclade separation (Etienne et al.

2012); and model 8, the clade-wide standard constant-rate birth-death model (Nee et al. 1994; i.e.

no diversity-dependence and no subclade separation). The advantage of this method is that it

computes a single ML evaluation and estimates separate parameters when the subclade of

interest is nested within another clade.

3.3 Results

3.3.1 Time-varying speciation rates and latitudinal traits

The BAMM analysis revealed that speciation rates gradually increase from the base of clade

‘Caesarea’ towards the tips (Fig. 3.1). Within the set of most credible rate shift configurations,

BAMM found comparatively higher speciation rates in clade ‘Caesarea’ (Appendix Fig. B8.3).

The rate shift configuration in which the temperate subclade in clade ‘Caesarea’ had a higher rate

of speciation was found within the 95% credible sets, albeit with a very low frequency (2.5%,

Appendix Fig. B8.3). Ancestral state reconstructions support that early lineages were

tropical/subtropical, later becoming temperate (Fig. 3.1). The temperate subclade in clade

‘Caesarea’ was found by SURFACE to have had shift in latitudinal optima from 20.48 to 33.09

degrees (Fig. 3.1). Three tropical lineages, ancestral to this clade, were found to have had a shift

in latitudinal optima from 20.48 to -6.93 and to -15.45 degrees. According to a BM model,

ancestral latitudinal states

35

Figure 3.1. The left side figure shows BAMM’s time-varying speciation rate estimates along

the branches of the time-calibrated phylogeny of Amanita sect. Caesareae. The colors on the

branches and the density plot (left) are temperature based, where black are low and red

are high speciation rates. The right side phylogeny shows on its nodes ML ancestral

latitude traits based on a Brownian motion model. The color of the branches represents the

latitudes of the OU optima found by SURFACE. The latitude colors are based on a rainbow

scale shown on the right side corner. The outer darker grey box delimits clade ‘Caesarea’.

The inner lighter grey box delimits the temperate subclade. All other remaining lineages in

clade ‘Caesarea’ are considered as the tropical subclade, in which the temperate clade is

nested. At the tree tips, species’ latitude traits are shown with their continental location.

NA, North America; AS, Asia; AU, Australia; AF, Africa, EU, Europe.

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36

averaged 28.21 degrees for the temperate subclade, and 3.9 degrees for the tropical. Both results

show that clade ‘Caesarea’ is ancestrally tropical and includes a single dominantly temperate

subclade. Within the temperate subclade another shift was detected in a group of neotropical taxa

(Fig. 3.1) found mostly in Mexico and Central America. Speciation rate shifts were also detected

by BAMM (Appendix Fig. B8.3). Although these taxa could be regarded as tropical based on their

latitude, they are restricted to highland mountains above 2000 masl, which make them in essence

“ecologically” temperate. Markedly, comparing both results from BAMM and the ancestral

latitude reconstruction (in both cases of BM or OU evolution) in Fig. 3.1 suggest a latitude-

driven gradient in speciation rates in clade ‘Caesarea’.

3.3.2 Speciation rate changes as a function of latitude

Analyses on section-level phylogeny supported the modal/normal speciation rate function as the

best model (Table 3.1). Here, results show that the speciation rate increases after c. 20 degrees

and peaks at c. 28 degrees, decreasing to a constant rate before c. 40 degrees (Fig. 3.2). Changes

in speciation rate were compared against the number of extant and ancestral species per 5-degree

unit of latitude (Fig. 3.2). In addition, I found that allowing the extinction rate to vary with

latitude did not improve the likelihood significantly (Table 3.1). Simulations show that

moderately sized phylogenies (50-100 tips) have enough power to detect significant changes in

speciation rates, in particular if the effect of the trait on the speciation rate is large (Table 3.2).

From the nine QuaSSE models tested on clade ‘Caesarea’ posterior subtrees, the one with clade-

wide linear speciation rate function resulted the most likely (Table 3.3 and Fig. 3.3A). ML

optimizations across all posterior subtrees indicate a positive slope (m) (Fig. 3B), for which the

mean m was 2.4E-3 [min 1.68E-3, max 3.6E-3]. A positive m implies a positive correlation

between speciation rates and latitude. Moreover, I found little variation in extinction rates across

posterior subtrees, with mean and standard deviation of 1.7E-4 Myr-1 and 7.1E-4 Myr-1,

respectively. Running the linear function model for clade ‘Caesarea’ and accounting for missing

taxa did not seem to affect the m parameter as the slope was positive across all treatments (Fig.

3.4). Another model that also received a relatively good fit was the constant function on

speciation with ecological subclades decoupled (model 2 in Table 3.3, Fig. 3.3A). This model

resulted in higher speciation rates in the temperate region than in the tropics, and slightly higher,

but close to zero, extinction rates in the tropics compared to the temperate region (Appendix Fig.

B8.4).

37

Table 3.1. Fitted QuaSSE models on the phylogeny of Amanita sect. Caesareae (section-

level). ML optimizations were performed on the maxium-clade-credibility (MCC) tree.

No. Model D.F. lnL AIC† AICc $2 P-value

1 Speciation rate as a modal function with constant extinction rate

6 -463.47 938.93 940.33 14.15 2.71E-3**

2 Speciation rate as a modal function with linear extinction rate

7 -463.27 940.54 942.43 14.55 5.74E-3**

3 Speciation rate as a linear function and constant extinction rate

4 -466.80 941.6 942.25 7.48 6.24E-3**

4 Speciation rate as a sigmoid function and constant extinction rate

6 -465.94 943.89 945.28 9.20 2.68E-2*

5 Speciation rate as a modal function with modal extinction rate

9 -463.38 944.76 947.92 14.32 2.62E-2*

6 Speciation rate as a constant function and constant extinction rate

3 -470.54 947.08 947.46 -- --

†Akaike Information Criterion

*P < 0.05, ** P < 0.01, *** P < 0.001

38

Figure 3.2. Maximum-likelihood estimation of the function that models the speciation rate

as a modal/normal function of latitude (thick black line and left side axis) on the section-

level phylogeny (A. sect. Caesareae). Grey lines (right side axis) show the species richness

(e.g. number of species) of extant (solid) and ancestral+extant (dotted) species per latitude

bin (5 degrees).

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39

Table 3.2. Tree simulations under the QuaSSE model for speciation as a modal function of

latitude. Each simulation varied in the difference in speciation change and the number of

tips.

Treatment Model D.F. lnL AIC† $2 P-value

Control, 50 tips Constant 3 -399.97 805.95 -- --

Modal 6 -398.54 809.08 2.86 4.13E-01

Control, 100 tips Constant 3 -819.45 1644.89 -- --

Modal 6 -818.57 1649.15 1.74 6.28E-01

1-fold, 50 tips Constant 3 -390.48 786.96 -- --

Modal 6 -384.81 781.62 11.34 1.00E-02*

1-fold, 100 tips Constant 3 -808.39 1622.79 -- --

Modal 6 -802.20 1616.39 12.40 6.14E-03**

2-fold, 50 tips Constant 3 -382.88 771.77 -- --

Modal 6 -377.07 766.14 11.63 8.77E-03**

2-fold, 100 tips Constant 3 -775.94 1557.88 -- --

Modal 6 -768.49 1548.98 14.90 1.90E-03**

3-fold, 50 tips Constant 3 -369.90 745.81 -- --

Modal 6 -363.65 739.30 12.51 5.83E-03**

3-fold, 100 tips Constant 3 -750.44 1506.87 -- --

Modal 6 -741.06 1494.12 18.75 3.08E-04***

†Akaike Information Criterion

*P < 0.05, ** P < 0.01, *** P < 0.001

40

Table 3.3. Fitted QuaSSE models for clade 'Caesarea'. Values are averaged over 100

posterior subtrees. The extinction rate was evaluated as a constant in all models.

No. Models D.F. lnL AIC† AICc

1 Speciation rate as a linear function 4 -301.37 610.75 611.82

2 Speciation rate as a constant function in both decoupled ecological subclades

5 -301.74 613.49 615.15

3 Speciation rate as a linear function in the tropical subclade and constant in temperate subclade

6 -301.29 614.59 616.98

4 Speciation rate as a linear function in the temperate subclade and constant in tropical subclade

6 -301.32 614.65 617.04

5 Speciation rate as a sigmoid function 6 -301.46 614.93 617.33

6 Speciation rate as a constant function 3 -304.57 615.15 615.78

7 Speciation rate as a linear function in both decoupled ecological subclades

7 -300.96 615.92 619.21

8 Speciation rate as a modal function 6 -302.50 617.01 619.41

9 Speciation rate as a modal function in the temperate subclade and constant in tropical subclade

8 -300.54 617.08 621.44

†Akaike Information Criterion

41

Figure 3.3. Bar plots (left side) are the Akaike weights on each of the models tested. Akaike

weights are essentially AIC scores with are relative to 1. Each of the bars on the x-axis

corresponds to one posterior subtree. In the first bar plot (A), colors indicate the relative fit

of nine QuaSSE models. Each model differed either on the speciation rate function or on

the internal subclade arrangement (subclade decoupling): clade-wide linear (red-brown);

clade-decoupled constant (purple); clade-wide sigmoid (beige); clade-decoupled linear on

the temperate subclade (light green); clade-decoupled linear on the tropical subclade (dark

green); clade-wide constant (brown); clade-wide modal (pink); clade-decoupled linear on

both clades (blue); and clade decoupled normal on the temperate subclade (grey). The plot

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42

below (B) shows the maximum-likelihood estimation of the linear speciation rate function

(left side axis) with its posterior distribution (from 100 posterior subtrees). On the same

graph, the box-plot shows species (including ancestral species) richness (right side axis) per

latitude bin (5 degrees).

Figure 3.4. Plots of QuaSSE’s linear speciation rate function when considering incomplete

taxon sampling. Each line represents a single posterior subtree.

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43

3.3.3 Diversity-dependence dynamics

DDD models 1 and 2 performed nearly equally well, with a slightly better fit of model 1 (Table

3.4, Fig. 3.5A). In 57 out of 100 posterior subtrees model 1 performed better than model 2 (mean

logL = -125.59, mean AIC = 261.17), leaving 43 trees better fitting model 2 (mean logL = -

126.69, mean AIC = 261.38). For model 1 (Table 3.4), K values were measured independently

for both ecological subclades (Fig. 3.5B), which resulted in higher K values in the temperate

(mean ML Ktemp = 57.75 lineages) compared to the tropical subclade (mean ML Ktrop = 19.012

lineages). Although there was less support for more parameterized models that allowed

macroevolutionary rates to vary (models 3, 4 and 5 in Table 3.4), models that generally

considered separate diversity-dependence dynamics for the two clades (models 1, 2, 3, 4 and 5 in

Table 3.4) together explained on average 94.5% of the data (Fig. 3.5A). This left very little

support for widely applied models, such as the constant birth-death model (Nee et al. 1994,

model 8 in Table 3.4), which explained on average 0.7% of the data (Fig. 3.2B).

3.4 Discussion

Macroevolution is defined as the patterns and processes affecting the birth, death, and

persistence of species (Lieberman and Eldrege 2014). Phylogenies provide a generalized

framework to study macroevolution (Barraclough and Nee 2001) and can be extended through

mathematical models to express how species and their associated traits have evolved (O’Meara

2012). Many of these approaches have proven useful for testing evolutionary hypotheses, for

instance, on trait evolution (Sanger et al. 2012; Collar et al. 2010), ecomorphological radiations

(Harmon et al. 2003; Mahler et al. 2013), clade-level radiations (Rabosky and Lovette 2008;

Etienne et al. 2012; Etienne and Haegeman 2012; Silvestro et al. 2014), clade age and clade size

disparity (Rabosky et al. 2012), and macroecology (Hernandez et al. 2013; Pyron and Wiens

2013).

3.4.1 Time-varying speciation rates and ancestral latitudes

Time-varying rates of diversification ignore trait-dependency, which allowed us to detect

changes in speciation rate gradients without integrating latitude data into the analysis. However,

I compared these results to the reconstruction of ancestral latitudes to have an idea of how these

changes in the speciation rate fluctuated in latitudinal space. BM and OU are two

44

Table 3.4. Fitted diversity-dependent diversification (DDD) models for clade 'Caesarea'.

Values are averaged over 100 posterior subtrees.

No. Models D.F. lnL AIC† AICc

1 Clade-decoupled diversity-dependence dynamics (Ktrop * Ktemp)

5 -125.58 261.17 262.84

2 Clade-decoupled diversity-dependence dynamics (Ktrop ' Ktemp)

4 -126.69 261.38 262.47

3 Clade-decoupled diversity-dependence and speciation rates

6 -125.81 263.63 266.03

4 Clade-decoupled diversity-dependence and extinction rates

6 -125.91 263.83 266.24

5 Clade-decoupled diversity-dependence, speciation and extinction rates

7 -125.01 264.02 267.32

6 Clade-decoupled speciation and extinction rates 4 -129.11 266.23 267.32

7 Clade-wide diversity-dependence, speciation and extinction rates .

3 -131.42 268.85 269.48

8 Clade-wide speciation and extinction rates* 2 -132.82 269.64 269.95

†Akaike Information Criterion

( Birth-death diversity-dependence model (sensu Etienne et al. 2012)

* Standard birth-death model (sensu Nee et al. 1994)

45

Figure 3.5. The bar-plot (A) shows the relative fit of diversity-dependent models

corresponding to: separate diversity-dependent dynamics where Ktrop and Ktemp are equal

(red) or independent (orange) and equal speciation and extinction rates between subclades;

separate diversity-dependent dynamics plus varying intrinsic speciation rate (dark blue),

extinction rate (light blue) or both rates (cyan) across ecological subclades; no diversity-

dependence but varying rates across subclades (green); no decoupling of parameters across

clades (black); and diversity-independence (white). The box-plot below (B) shows the clade-

level carrying capacities K of the two best models (orange and red; or models 1 and 2 in

Table 3.3).

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46

commonly used trait evolution models in comparative phylogenetics. BM assumes that traits are

normally distributed allowing them to ‘wiggle’ through evolutionary time (O’Meara 2012). OU

models extend BM by constraining the ‘wiggle’, with a given strength (/), towards an optimum

(0) value (O’Meara 2012; Butler and King 2004). BM models have been used in a

phylogeographic context by using individual cartographical coordinates to reconstruct ancestral

ranges or dispersal events (Lemmon and Lemmon 2008; Lemey et al. 2009). In my analyses,

both models evidence an ancestrally tropical origin of Amanita sect. Caesarea and clade

‘Caesarea’ (Fig. 3.1). This is also in agreement with biogeographic reconstructions suggesting

that the group is of Paleotropical origin (Sánchez-Ramírez et al. 2015a; Chapter 2). Results show

that in clade ‘Caesarea’ ancestrally temperate portions of the tree have a relatively higher

speciation rate than ancestrally tropical portions (Fig. 3.1). Although OU models have been

poorly explored in a geographic context, with SURFACE, I was able to infer several shifts in

latitudinal optima along the phylogeny. Most of these shifts involve switching from a subtropical

(ca. 20 degrees) to a temperate (ca. 33/38 degrees) or tropical (ca. -6/-15 degrees) optima. More

importantly, one of the shifts to temperate optima in clade ‘Caesarea’ coincides with a speciation

rate increase (Fig. 3.1).

3.4.2 Higher speciation rates at temperate latitudes explain differences in diversity

Macroevolutionary processes, such as speciation, extinction and dispersal, are the ultimate

factors that regulate species diversities. In this context, Mittelbach et al. (2007) review two major

hypotheses that explain the general LDG: (1) is a time and area hypothesis, which postulates that

tropical lineages are older and have had longer time to accumulate species; this hypothesis also

supports the idea that tropical regions are historically more stable than temperate regions; and (2)

a diversification rate hypothesis, which holds that speciation rates are higher in the tropics and

extinction rates are lower. It is important to note that both hypotheses are not mutually exclusive.

For EM fungi, my data and other studies (Alexander 2006; Matheny et al. 2009; Kennedy et al.

2012; Wilson et al. 2012; Sánchez-Ramírez et al. 2015a) give support for tropical origins of EM

taxa, favoring the time and area hypothesis. However, if tropical lineages are considered to be

older than temperate ones, and constant diversification rate is assumed, I would certainly expect

to have higher species diversity in the tropics. Kennedy et al. (2012) already rejected this

hypothesis in Clavulina, which is ancestrally tropical, but has a single temperate clade

47

diversifying at a faster rate. The EM LDG proposed by Tedersoo et al. (2012, 2014) and the

LDG found the Caesar’s mushrooms have similar patterns (Fig. 3.2). In both cases, diversity is

higher in the northern temperate region compared to the tropics. Based on the best-supported

models I show that the EM LDG, at least in the Caesar’s mushrooms, is driven by latitudinal

differences in speciation rates. When considering the whole section-level phylogeny I find that

speciation rates vary in a modal/normal mode (Table 3.1, Fig. 3.2). In this model, latitude has

little effect on speciation rates below 20 degrees, but has a high effect on latitudes ranging from

25 to 35 degrees. This view agrees with both the EM LDG and the Caesar’s mushroom’s LDG.

Furthermore, results for clade ‘Caesarea’ best support a model where the speciation rate varies

linearly as a function of latitude (Table 3.3, Fig. 3.3). ML evaluations across 100 posterior

subtrees indicate a positive slope m parameter, which implies a positive linear correlation of

speciation rates with increasing latitude. The fact that this model was preferred over models

where tropical and temperate subclades were considered decoupled (e.g. models 2, 3, 4, 7, 9 in

Table 3.4, Fig. 3.3A) suggests that the gradient is smooth rather than abrupt. I also show that

these patterns are also robust to missing data and moderate tip sampling (Fig. 3.4, Table 3.2).

When discussing the potential causes of the EM LDG, Tedersoo and Nara (2010) never account

for macroevolutionary processes such as differences in diversification rates. However, their

hypotheses (e.g. differentiated soil profiles and higher host diversity) may reflect factors that can

contribute to differential diversification rates, as pointed out by Kennedy et al. (2012).

3.4.3 Diversity-dependence and its clade-level effect on the LDG

For EM fungi, the temperate region should –in theory– support a higher number of ecological

‘niches’ consisting of more diverse and complex soil horizons, as well as more phylogenetically

diverse EM tree host families. In contrast, tropical soils are less stratified and the organic matter

has a high turnover. There is also less phylogenetic EM host diversity, which can potentially lead

to a lower number of niches available (Tedersoo and Nara 2010; Tedersoo et al. 2012).

Diversity-dependence is a key concept in adaptive radiation theory, postulating that ecological

opportunity triggers species-level carrying capacities, which in turn regulate diversification rate

dynamics within a clade (Schluter 2000; Glor 2010; Rabosky et al. 2012; Etienne and Haegeman

2012). If niche number were a limitation for diversification in temperate and tropical regions,

there would be differences in clade-level carrying capacities of taxa with those distributions. In

particular, “temperate” clades would have a higher clade-level carrying capacity because they

48

need more species to reach niche saturation. Conversely, “tropical” environments would have a

lower clade-level carrying capacity because niches become saturated more quickly, leading for

instance to strong diversity-dependence signatures. DDD analyses suggest that diversity-

dependence has a strong effect in temperate and tropical subclades of clade ‘Caesarea’ (Table

3.4, Fig. 3.5A). While it is difficult to distinguish between the two best models (Table 3.4),

where clade-level carrying capacities differ or are equal across ecological subclades (Fig. 3.5B),

results suggest that differences in speciation and extinction rates between ecological subclades

are not significant (Table 3.4). Nevertheless, the precise ecological mechanisms behind “carrying

capacity” dynamics are not well understood (Rabosky et al. 2012), making niche number

conditioning arguable. Studies have shown that fungal communities are vertically stratified

within soil horizons (Buée et al. 2007; Taylor et al. 2014; Vo1í2ková et al. 2014) and that the

communities are dynamic with respect to season (Vo1í2ková et al. 2014), making the idea of

niche driven diversification appealing for EM fungi. Nevertheless, it is neither clear nor has it

been studied how soil niche partitioning affects fungal diversification from an evolutionary

perspective.

The idea of niche driven diversification has its roots in adaptive radiation theory, where,

individuals from an ancestral population adapt to thrive in a ‘new niche’. If niche partitioning

drives diversification in EM fungi, it would most likely be a sympatric process. However, phylo-

and biogeographic studies in EM fungi have shown that most species and populations are

geographically restricted (Martin et al. 2002; Geml et al. 2006, 2008; Carriconde et al. 2008;

Jeandroz et al. 2008; Jargeat et al. 2011; Sánchez-Ramírez et al. 2015a), suggesting a prevalence

of allopatric over sympatric speciation. In addition, it has been shown that endemicity plays a

major role in structuring fungal communities over continental scales (Talbot et al. 2014).

Disentangling geographical from ecological effects –such as niche partitioning– is certainly a

difficult endeavor, and fine-scale studies would be needed to truly discern between both.

Nevertheless, my concerns about the underlying mechanisms of diversification in the Caesar’s

mushrooms are in line with Moen and Morlon’s (2014) review of causes of diversification

slowdowns. They point out numerous factors that may equally contribute to diversification

slowdowns, such as of niche differentiation, geographic factors, peripatric speciation,

environment-driven diversification, and protracted speciation. It is important to note that both

time- and trait-dependent models support increases in diversification rates, while diversity-

49

dependent models imply diversification slowdowns. Unfortunately, these models have different

implementations and assumptions, making them incomparable under a single framework. I

suggest that caution must be exercised when deciding which models best explain the data with

my cross-application approach.

3.4.4 The role of extinction

The third hypothesis of Tedersoo and Nara (2010) states that resource availability and

fragmentation, in the form of less diverse root systems and scattered EM host “islands”, also

accounts for the EM LDG pattern by increasing competition and limiting gene flow (Peay et al.

2007; Tedersoo et al. 2010a). Competition and dispersal restrictions may in turn reduce

population sizes, ultimately leading to an increased risk of extinction (Liao et al. 2013). Long-

term extinction in the tropics is controversial because the general LDG predicts and finds higher

species richness at equatorial latitudes, and because empirical studies have demonstrated that

higher extinction rates are generally expected at higher rather than lower absolute latitudes (Weir

and Schluter 2007; Pyron and Wiens 2013; Rolland et al. 2014). Although extinction rate

estimates from molecular phylogenies may be dubious (Rabosky 2010; Quental and Marshall

2010; although see Beaulieu and O’Meara 2015), the analyses could not detect any changes in

extinction rates associated to latitude (Table 3.1) or any significant differences on extinction

rates between both ecological subclades (Table 3.4). Results provide a limited view on the effect

of latitude on extinction rates. However, it might be worth pointing out that the second best

QuaSSE model (Table 3.3) – with constant speciation and extinction rates in both decoupled

subclades– supports slightly higher, although close to zero, extinction rates in the tropical clade

than in the temperate (Appendix Fig. B8.4), leaving space for further analyses regarding the role

of extinction in tropical EM taxa.

3.4.5 Potential pitfalls

It has been shown that trait-dependent diversification analyses can be misleading. Some of the

issues that have been detected indicate high Type I error rates due to phylogenetic

psudoreplication and the effect of correlated and “neutral” traits on diversification rates

(Maddison and Fitzjohn 2015; Rabosky and Golberg 2015). Although model inadequacy and

potential pitfalls associated with state-based diversification analyses are conceivable in the data,

I strive to deal with some of the caveats by: (1) including other diversification models that are

50

not trait-dependent (Fig. 3.1); (2) comparing many models ranging from simple to complex

(Table 3.1); (3) assessing the variation in speciation rate (linear model) at various levels of

incomplete taxon sampling (Fig. 3.4); and using simulations to show the power in the data (Table

3.2).

3.5 Conclusions

I apply different macroevolutionary models to the Caesar’s mushrooms phylogeny in order to

test hypotheses about their distribution and diversification with respect to latitude, which I

extrapolate to the LDG observed for EM fungi as a whole (higher temperate diversity). Although

I explored potential evolutionary explanations from many analytical perspectives, my most

conclusive result indicates that higher speciation rates at temperate latitudes explain LDGs

within the Caesar’s mushrooms. The pattern could not be explained by higher extinction rates in

the tropics. Further, I also show support for significant effects of diversity-dependent dynamics

for between ecological subclades in clade ‘Caesarea’, which may suggest potential differences in

diversity limits between both regions. Finally, while some of the hypothetical explanations for

the EM LDG are in line with results in the Caesar’s mushrooms, many questions remain to be

answered as to which are the main contributing factors and processes responsible for the

observed EM diversity. This study highlights the need of complementing macroecological

generalizations with macroevolutionary approaches to better explain patterns and processes of

diversification in microorganisms.

51

Chapter 4

WHOLE-GENOME SEQUENCING AND ANNOTATION OF AMANITA 4JACKSONII AND PARTIAL GENOMIC SEQUENCES OF AMANITA BASII

4.1 Introduction

The genus Amanita (Agaricales, Basidiomycota, Fungi) is primarily known for species that

produce deadly toxic compounds such as phallotoxins and amatoxins (Vetter 1998). However, a

few taxa from this genus –such as Amanita jacksonii, A. hemibapha, A. caesarea and allies – are

also traditionally and culturally known to be excellent wild edible mushrooms in many regions of

the world (Pegler 2002; Boa 2004). Most species of the genus are known to be ectomycorrhizal

(EM) –living in a mutualistic symbiosis with many members of woody tree families (Taylor and

Alexander 2005)–, while few others are saprotrophic. The latter condition is known to be

ancestral, meaning that the EM habit probably evolved once within the genus (Wolfe et al.

2011).

Currently, only two Amanita genomes are publicly available from the Joint Genome Institute

(JGI) (http://genome.jgi.doe.gov/agaricomycotina/agaricomycotina.info.html) and the

Mycorrhizal Genomics Initiative (http://mycor.nancy.inra.fr/IMGC/MycoGenomes) as

components of sequencing the Fungal Tree of Life (Martin et al. 2011): a strain from the

saprotrophic A. thiersii, and a strain from the ectomycorrhizal (EM) A. muscaria var. guessowii

(from here on referred to as A. muscaria). In addition, the partial genome of A. bisporigera has

been sequenced with the purpose of isolating genes producing toxic compounds (Hallen et al.

2007). Furthermore, three other Amanita genomes have been recently sequenced –A.

brunnescens, A. polypyramis, and A. inopinata– with the aim of assessing the dynamics of

transposable elements in EM and asymbiotic species within the genus (Hess et al. 2014).

Amanita muscaria has also been used as a model for understanding the biosynthetic pathways of

betalain pigments, which are commercially used to dye food and shown to have antioxidant

properties (Hinz et al. 1997; Strack et al. 2003).

52

Amanita jacksonii is a non-toxic EM member of the genus (Fig. 4.1), which also produces

betalains. The genomic data here presented should facilitate further comparative genomic

analyses between members of the genus Amanita. It will also shed light into the evolution of

toxicity, the EM habit, and of betalain biosynthetic pathways.

Figure 4.1. Amanita jacksonii Pomerl. (Q3) from a population in Québec, Canada. Photo by

Renée Lebeuf (CMM).

4.2 Sequenced strain and accession numbers

CANADA: Ontario: collected from soil in a mixed conifer (Picea)– broadleaf (Fagus, Acer,

Betula, Quercus) forest in Awenda Provincial Park, Ontario, Canada (N 44.84620, W 079.97507,

elev. 222m), August 27, 2011, S. Sánchez-Ramírez and J.-M. Moncalvo (TRTC168611 – dried

basidiome). The draft genome sequence of A. jacksonii (TRTC168611) has been deposited in

EMBL/ DDBJ/GenBank under the accession no. AYNK00000000. This submission represents

the first draft version.

53

4.3 Material and methods

Genomic DNA was isolated from the context of the stipe of a fresh specimen by removing the

surface tissue with a clean razor blade. Pieces (~200 mg) of the stipe context were then frozen at

-20oC until the extraction step, for which I used a 2% CTAB protocol (modified from Zolan and

Pukkila (1986)). This protocol included a proteinase-K digestion step followed by a

chloroform:isoamylalcohol (1:24) extraction, RNA denaturation, and isopropanol precipitation (a

document with the details can be found at

https://sites.google.com/site/santiagosnchezrmirez/home/amanita-jacksonii-genomics/genomic-

dna-extraction. A whole-genome shotgun approach was used to produce one library for Roche

454 pyrosequencing (standard single-ended) and one TruSeq for Illumina HiSeq 2000 (pair-

ended, insert size: 350-500 bp) (conducted at the Duke Genome Sequencing and Analysis Core

Resource; http://www.genome.duke.edu/cores/sequencing/). The libraries were run in half of a

454 pico-titer plate (PTP) and half of an Illumina lane, respectively. RAY v1.7 (Boisvert et al.

2010) was used to assemble all reads combined in multi-threaded mode. Gene prediction was

conducted with AUGUSTUS v2.5.5 (Stanke et al. 2004) using the hidden-markov-model (HMM)

profile of Laccaria bicolor. BLAST2GO v2.6.6 (Conesa et al. 2005) was used for protein

annotation. I used the CEGMA (Core Eukaryotic Genes Mapping Approach) pipeline to assess the

level of genome completeness based on the qualitative and quantitative conditions of eukaryotic

clusters of orthologous groups and core eukaryotic genes (CEGs) (Parra et al. 2007, 2009). Gene

orthology and comparative genomic analyses were performed using custom Python scripts and

BLAST, based on reciprocal best hits (Moreno-Hagelsieb and Latimer 2008).

4.4 Results and discussion

The 454 run yielded c. 1.4 million reads ranging from ~100 to ~1000 bp, whereas the Illumina

platform produced c. 157 million reads after quality control filtering. Both runs had read yields

within their platform standards (Buermans and den Dunnen 2014). The combined read assembly

produced a 30,285,912 bp draft genome with 2,988 contigs (>1000 bp), of which the largest was

504,181 bp. The average contig length was 10,139 bp, and N50 and N90 stats were 26,643 and

3,566 bp, respectively. According to CEGMA, the genome completeness based on 248 CEGs

resulted in 93.15% and 95.97% for complete and partial alignments (e.g. complete and

incomplete proteins), respectively. Similar genome statistics have been found for other recent

54

Amanita genome sequencing projects (Hess et al. 2014; http://genome.jgi.doe.gov/). For

instance, the genome size of A. muscaria, A. thiersii, A. brunnescens, A. inopinata, and A.

polypyramis is 40.7, 33.7, 57.6, 22.1, 23.5 Mbp, respectively (Hess et al. 2014). The HMM-

based gene prediction found 8,511 structural protein-coding genes, which represent 60% of the

genome (48% exons and 12% introns). The hypothetical proteome ranged from 47 to 5,455

amino acids in length. In contrast, the genomes of A. muscaria and A. thiersii include 18,153 and

10,354 structural genes, respectively (http://genome.jgi-psf.org/). As expected, A. thiersii has a

higher number of genes encoding a glycoside hydrolase (EC:3.2.1.) domain (128), compared to

A. jacksonii (80) and A. muscaria (61). However, the number of cellulases (GH5) was

comparable with 12 (A. jacksonii), 8 (A. muscaria), and 10 (A. thiersii) genes each. Furthermore,

results from BLAST2GO protein annotation suggest that the genome of A. jacksonii is enriched

with proteins related to metabolic and cellular processes including: oxido-reduction,

biosynthetic, nitrogen compound processes, as well as primary, cellular and macromolecule

metabolic processes (carbohydrate, lipid, phosphorus and DNA metabolism, gene expression).

Finally, the reciprocal best-hits method described in Moreno-Hagelsieb and Latimer (2008)

indicate that A. jacksonii shares 4,408 to 5,178 putative orthologs with the other Agaricales

species presently available in the JGI database, sharing the most with A. muscaria and the least

with Agaricus bisporus. Interestingly, this method suggests that more putative orthologs are

shared with other non-congeneric ectomycorrhizal species, such as Laccaria bicolor, Hebeloma

cylindrosporum, and Tricholoma matsutake, than with the congeneric Amanita thiersii, which is

saprobic.

4.5 Partial genomic sequences from Amanita basii

In parallel to A. jacksonii TRTC168611, DNA extracted from a closely related species was

submitted to the Duke Genome Sequencing facility (A. basii strain AB01). While the 454 reads

from A. basii were of good quality, most Illumina reads were contaminated and subsequently

discarded, which prevented a good quality genome assembly for this strain. The 454 run yielded

663,526 reads with a mean read length of 592.8 bp. About 70% of these reads were successfully

mapped to the A. jacksonii genome using BWA (Li and Durbin 2010). Gene prediction with

AUGUSTUS resulted in the retrieval of 5,878 genes.

55

4.6 Development of novel genomic loci

In order to develop new probes and primers for targeting specific genomic regions in the

Caesar’s mushrooms, I compared by BLAST the 8,511 and 5,878 genes obtained from the partial

genomes of A. jacksonii and A. basii, respectively. I designed a perl code (rbh.pl, available at:

https://sites.google.com/site/santiagosnchezrmirez/home/software/perl) to retrieve single copy

genes across both species. For Chapter 5, I designed and tested primers for PCR amplification of

several genes, of which two (AJ0000-2 and AJ0103-132) were eventually selected on the basis

of both their reliability in PCR amplifications and genetic informativeness. For Chapter 6, I

designed c. 4,000 probes for exon-targeted sequencing of 502 genes. The details of both

procedures can be found in Chapters 5 and 6, respectively.

56

Chapter 5

SPECIES DELIMITATION, COMPARATIVE PHYLOGEOGRAPHY 5AND GLACIAL REFUGIA IN THE NORTH AMERICAN CAESAR’S MUSHROOM SPECIES COMPLEX

5.1 Introduction

The effects of past climatic changes on biological diversity have been relatively well studied in

certain groups of plants and animals but not in fungi. It has been shown that climatically stable

areas, such as refugia, generally harbor higher species richness and are considered diversification

foci (Quian and Ricklefs 2000; Jetz et al. 2004; Svenning and Skov 2007; Keppel et al. 2012).

The Pleistocene glaciations are known to be an important climate-based diversity catalyst during

which many species not only persisted in refugia, but also had complex dynamics in their

demography and divergence history (Hewitt 1996, 2000, 2004a,b; Taberlet and Cheddadi 2002).

This epoch is characterized by abrupt temperature oscillations occurring in intervals of thousands

of years, not only causing the advance and retreat of glaciers but also greatly modifying biomes

in relatively short time periods (Pileou 1991; Webb and Bartlein 1992). As a response to climate

change during the Quaternary, many organisms were driven to extinction (Alroy 2001; Barnosky

et al. 2004), adapted to colder environments (Stuart 1991; Churchill 1998; Álvarez-Lao and

García 2011), or retreated to southern refugia (Bennett et al. 1991; Lessa et al. 2003; Soltis et al.

2006; Waltari et al. 2007; Provan and Bennett 2008; Beatty and Provan 2010). A common notion

in studies focused on Quaternary range dynamics is that when local climate became unsuitable

for species their ranges contracted and expanded after conditions became favorable (Keppel et al.

2012). This expansion-contraction model predicts higher genetic diversity in refugial areas

compared to expansion areas (Petit et al. 2003). In this prediction the expectation is that northern

populations will suffer from genetic erosion during bottlenecks and/or population expansions,

while those persisting in refugia will conserve more allelic diversity (Hewitt 1996, 2000,

2004a,b). During interglacial periods species can respond to climate change in different ways,

namely by expanding, shifting, or persevering their range. However, during glacial periods

ranges may contract, and given enough time and isolation promote speciation (Knowles 2001;

Barraclough and Vogler 2002; Weir and Schluter 2004).

57

Species are considered fundamental units in many biological disciplines and are key to draw

conclusions about macro- and microevolutionary processes (Sites and Marshall 2004; Agapow et

al. 2004; Knowles 2009). However, a universal species recognition criterion is far from

attainable (Hey 2006), and therefore different species concepts are commonly applied (de

Queiroz 2007). Traditionally, boundaries between species were determined based upon evidence

from morphological, ethological and/or ecological observations alone. More recent studies

incorporate genetic data, both because of the increasing accessibility and scalability of DNA

genotyping and sequencing, and the development of statistically solid analytical methods (Wiens

2007; Fujita et al. 2012). DNA sequence data can be useful to uncover cryptic diversity

(Bickford et al. 2006) and delimit boundaries between incipient species (Mulcahy 2007), while

also conveying information on evolutionary processes occurring at the species and population

levels (e.g. phylogenetic relationships, ancestral states/areas, past diversity dynamics, population

size changes, selection, recombination, and gene flow) (Carbone and Kohn 2004; Coyne and Orr

2004; Butlin et al. 2009, 2012; Drummond and Bouckaert 2015). One model that has received

much attention recently in molecular systematics, phylogenetics, and phylogeography is the

multi-locus multi-species coalescent (Edwards 2009; Fujita et al. 2012; Cutter 2013). One of the

benefits of this model is the inference of species relationships, while assuming independent gene

histories and stochastic coalescent processes (Liu et al. 2009; Yang and Rannala 2010). Further,

this model can integrate population size estimations of extant and ancestral species, as well their

divergence times (Heled and Drummond 2010).

In this study, I use different implementations of the multi-species coalescent model to delimit

species and test hypotheses about the consequences of glacial cycles on the spatial arrangement

of diversity in a group of closely related mushroom species. I focus on the North American (NA)

Caesar’s mushrooms (related to the European Caesar’s mushroom –Amanita caesarea). These

species are edible ectomycorrhizal (EM) –root-associated mutualists– fungi that can be found

from mixed temperate forests in southeastern Canada (Ontario and Québec) to the Talamanca

highlands (Costa Rica), including the Pacific Costal Mountains in western USA. They are also

considered a species complex (Guzmán and Ramírez-Guillén 2001) and their diversity and

geographical boundaries are still not well understood, in spite of being valuable cultural and

culinary assets (Boa 2004; Garibay-Orijel et al. 2007). Moreover, it has been recently shown that

the American Caesar’s mushrooms stem from multiple dispersal events from Asian to NA during

58

the Miocene and Pliocene, in which at least two lineages (‘jacksonii’ and ‘basii’) diversified in

situ (Sánchez-Ramírez et al. 2015a).

5.2 Material and Methods

5.2.1 Sampling

Two hundred and eighty specimens belonging to sect. Caesareae (clade Caesarea, Sánchez-

Ramírez et al. 2015a; Chapter 2) were sampled from 142 localities throughout North and Central

America. These localities cover the entire distribution of the group, with few exceptions. Some

field collections were sampled with Wattman® PlantSaver FTA® cards (GE Healthcare Life

Sciences, Piscataway, NJ, USA) and later dried, while others were taken from dried herbarium

specimens. All collections can be found in mycological collection repositories located in Mexico

(CETH, FCME, IBUG, TLAX, XAL), The United States (DBG, DEWV, F, NYBG, MICH,

RET, TENN, UMO), and Canada (TRTC, CMMF, UWO) (Appendix Table C8.3). Since NA

lineages are intermixed with other Eurasian lineages, I included four species that represent sister

lineages to the largest clades (‘jacksonii’ and ‘basii’). The taxa were A. caesarea s. str. from

Europe and A. hemibapha s. lat. from Japan as sister lineage to clade ‘jacksonii’, and A. sp

‘cinnamomescens’ from Pakistan and A. hemibapha var. ochracea from China as sister lineage to

clade ‘basii’. The only purpose for the inclusion of these taxa was to polarize geographic states

in ancestral area reconstruction analyses, all other information was ignored.

5.2.2 Laboratory protocols

Genomic DNA was extracted from c. 20 mg of dried tissue (gills) or from FTA cards following

(Dentinger et al. 2010). For dried tissue samples I used a standard CTAB + proteinase K protocol

with an isoamylic alcohol:chlorophorm (1:24) extraction (modified from Zolan and Pukkila

1986). PCR amplifications were carried out using translation elongation factor 1-alpha (tef1)

primers EF1-983F and EF1-1567R (Rehner 2001), and two novel primer pairs designed to target

two loci (AJ0103-132 and AJ0000-2) from predicted genes in draft genome assemblies of A.

jacksonii (van der Nest et al. 2014; Chapter 4) and A. basii (Sánchez-Ramírez and Moncalvo,

unpub. data). Primers AJ0103-132F (GCAAACCATCGCCGTCATCTGG) and AJ0103-132R

(TGCCAGCGCCTCCTAGCTTC) can be found within the genomic coordinates

AYNK01003324.1:34483-34462 and AYNK01003324.1:33846-33865, respectively. Likewise,

59

primers AJ0000-2F (TTCGGCTTGCAACGCCCTCC) and AJ0000-2R

(AGTGCCGCTACCTTGCGCTG) can be found at AYNK01000001.1:6742-6761 and

AYNK01000001.1:7512-7493, respectively. The PCR chemistry, thermocycler conditions, and

DNA sequencing followed Sánchez-Ramírez et al. (2015a; Chapter 2). In a few cases, cloning

(TOPO PCR cloning kit, Life Technologies, Carlsbad, CA, USA) was required for AJ0103-132,

in which allelic length variants were found. These three genes were preferred over other

conventional genes used in fungal phylogenetics, such as the ribosomal 28S long subunit (LSU),

the internal transcribed spacer region (ITS), and the RNA polymerase II subunit II (rpb2), for the

following reasons: insufficient DNA variation, violations of the assumptions of the coalescent

process in multi-copy genes (Innan 2003), and inconsistent PCR amplification across samples.

5.2.3 Data matrices, phasing of heterozygotes, and DNA models

Sequences for each gene were aligned with MUSCLE (Edgar 2004) and intron regions in tef1 and

AJ0103-132 were visually inspected and corrected by eye when necessary in SE-AL (Rambaut

2002). Once sequences were aligned, I used SEQPHASE (Flot 2010) part 1 script to generate

input files for PHASE v2.1 (Stephens and Donnelly 2003), in which heterozygous alleles were

phased using the “M” algorithm, only keeping alleles that had a probability higher or equal to

90%. Afterwards, SEQPHASE part 2 script produced FASTA files with haploid alleles. In order

to reduce the amount of data, I only kept a single sequence for homozygotes. The best DNA

substitution model was inferred using jMODELTEST v2 (Darriba et al. 2012) based on AIC scores

and a set of 88 candidate models.

5.2.4 Gene trees, concatenation, and genealogical sorting

Individual gene trees and the concatenated gene tree were estimated using BEAST v1.8

(Drummond et al. 2012). Phased gene matrices were uploaded to BEAUTI v1.8, setting clock,

nucleotide substitution and tree models as unlinked (e.g. independently estimated for each

partition) for individual gene trees. In the case of concatenation, I collapsed phased alleles to

their respective IUPAC ambiguity codes using the Perl script unPhaseAgain.pl (available at:

https://sites.google.com/site/santiagosnchezrmirez/home/software/perl), and only the tree model

was set unlinked. Substitution models were set as GTR+#+I, TNef93+#+I, and F81+#+I, for

tef1, AJ0103-132, and AJ0000-2, respectively. I used a constant coalescent model as tree prior

and a strict molecular clock. Trees were calibrated using a global substitution rate of 0.00194

60

Myr-1 for tef1 (Sánchez-Ramírez et al. 2015a; Sánchez-Ramírez unpub. data; Chapter 2).

Substitution rates for AJ0103-132 and AJ0000-2 were co-estimated and scaled based on tef1’s

rate and a gamma distributed prior (shape: 1.0, scale: 1.0E-3). Two Monte Carlo Markov Chains

(MCMC) were run for 50 million generations independently for the gene trees and concatenated

gene tree XMLs. States/trees were sampled every 10,000 generations. Log files were viewed in

TRACER v1.6 (Rambaut et al. 2013), where convergence and mixing was assessed by making

sure that both runs converged at similar parameters and effective sample sizes (ESS) were at 200

or higher. I discarded 10% of the initial states as burnin. Trees from both MCMC runs were

combined using LOGCOMBINER v1.8 for a total of 10,000 trees (minus 1,000 discarded as

burnin) and summarized on the maximum-clade-credibility (MCC) trees with TREEANNOTATOR

v1.8 selecting mean node heights. Trees and posterior parameters were inspected in FIGTREE

v1.4 (http://tree.bio.ed.ac.uk/software/figtree/).

Building individual and concatenated gene trees helped us identify potential species units. For

which I measured the extent of lineage sorting on the MCC trees using the genealogical sorting

index (gsi) function in the R package GENEALOGICALSORTING (Cummings et al. 2008). The gsi

can be calculated for any predefined group of phylogenetic tree terminals, where an index of 1

represents monophyly.

5.2.5 Bayesian species delimitation

In order to infer the best species delimitation model, I used the reversible-jump (rj)MCMC

algorithm 1 and the nearest-neighbor interchange sampler for species tree proposals (Yang and

Rannala 2014) in BP&P v3.1 (Rannala and Yang 2003, 2013; Yang and Rannala 2010). The new

species tree sampler avoids the need to provide an initial species guide tree. Phylogenetic species

(de Queiroz 2007) were delimited separately on the two largest NA clades: ‘jacksonii’ and

‘basii’. This method takes into account the species phylogeny and considers that any potential

incongruence between the embedded gene trees stems from lineage sorting due to ancestral

polymorphism. I applied a gamma distribution prior G(2, 1000), with mean 2/1000 = 0.002, on

the species’ population size parameters (0s). Since in BP&P divergence times (3) are calculated as

the time of divergence times the mutation rate, I assigned a gamma prior G(25, 5000) to the age

of the root in the species tree (30). For this distribution the mean is 25/5000 = 0.005, which was

calculated based on a root age of ~5 Myr (Sánchez-Ramírez et al. 2015a; Chapter 2) times a

61

mutation rate of ~0.001 substitutions per site Myr-1. Divergence times other than 30 were

assigned a Dirichlet prior (Yang and Rannala 2010: equation 2). These priors were chosen based

on population and divergence times previously estimated in *BEAST. In each case, I ran BP&P

twice, first using a prior on ‘uniform rooted trees’, which assumes equal prior probabilities for

internal nodes, and afterwards, with ‘user defined’ probabilities, based on node pps from

*BEAST. Both analyses yielded the same results. In every run, I used the algorithm 1 [described

in Yang and Rannala (2010) and Rannala and Yang (2013)] with gamma distribution for fine-

tuning (/=2, m=0.5) of population size parameters. The MCMCs were 200,000 generations long,

discarding 10% (20,000) of the initial states and sampling every 10th state, for a total of 18,000

posterior samples.

5.2.6 Species tree estimation

Bayesian species trees were estimated using the multi-species coalescent model implemented in

*BEAST (Heled and Drummond 2010) on phased alignments. Species units were defined based

on the best species delimitation model supported by BP&P for clades ‘basii’ and ‘jacksonii’. The

species tree reconstruction comprised all sampled lineages, including extra-American species.

Population sizes were modeled using a piecewise linear demographic function. This function

allows population sizes to change linearly with time, while respecting that the sum of the

population size of the daughter species equals the population size of the ancestral species at the

time of the split (Heled and Drummond 2010). MCMC conditions and tree summarization were

performed similarly as previously described for Bayesian gene trees.

5.2.7 Species distribution modeling

Rasterized (gridded) climatic data was downloaded from the WorldClim website

(www.worldclim.org) at a resolution of 2.5 arc-minutes, equivalent to a grid size of roughly 5

km. Data representing an average over the years between 1960 and 1990 (Hijmans et al. 2005)

was considered as ‘present’ climatic variables. Last glacial maximum (LGM, ~ 22,000 ya)

climatic data was obtained from two general climate models (GCMs): MIROC-ESM (A Model

for Interdisciplinary Research On Climate – Earth System Model) and CCSM4 (Community

Climate System Model) (Braconnot et al. 2007; Watanabe et al. 2010). Both present and LGM

data consisted of 19 ‘bioclimatic’ variables, which represent annual trends (e.g. mean annual

temperature/precipitation), seasonality (e.g. annual range in temperature/precipitation), and

62

extreme or limiting environmental factors (e.g. temperature of the coldest and warmest month,

and precipitation of the wet and dry quarters) that better reflect ecological conditions (Hijmans et

al. 2005). All files were imported into R as stack layers using the rasterize and stack functions in

the package RASTER (Hijmans and van Etten 2012), and cropped to an extent of –128, –65

degrees W longitude and 5, 50 N degrees latitude, on which all projections were made. The

geographic coordinates in decimal degrees of all individual samples (Appendix Table C8.3) were

first filtered for duplicated locations and then converted to a SpatialPoints object using the

coordinates function from the package SP (Bivand et al. 2013). I used the learning machine

algorithm MaxEnt –maximum entropy– (Phillips et al. 2006), as implemented in the package

DISMO (Hijmans et al. 2011), to model habitat suitability scores across the study region, and then

project them to present and LGM climatic conditions. LGM predictions based on MIROC-ESM

and CCSM4 systems were averaged onto a single raster object using the calc function of the

package RASTER.

5.2.8 Ancestral area reconstruction and diversification rates

I reconstructed ancestral areas on the final species tree from *BEAST using RASP (Yu et al. 2015).

I defined areas of ‘expansion’ (E) and ‘refugia’ (R) based on climatic niche modeling during the

LGM assigning to each terminal one or the other category if they were found exclusively that

area, or a third ‘widespread’ (W) category if the species was found in both areas. All (four)

extra-American (Eurasian, parameters are denoted with the subscript ‘O’) taxa were defined in

an additional category, which was also considered as the ancestral state of the group based on

Sánchez-Ramírez et al. (2015a). I intentionally added these taxa because they indeed represent

natural relationships, polarizing states more accurately in ancestral reconstructions. In RASP, I

uploaded 9,000 species trees from the posterior distribution and summarized estimations on the

MCC species tree. Model parameters included estimated state frequencies and state rates

modeled using a gamma distribution (F81+G). Default values were used for MCMC settings

(50000 generations, 10 chains, 100 sample frequency, 100 burnin, temperature 0.1).

To infer diversification rates within areas I used the (discrete) multi-state speciation and

extinction (MuSSE) model implemented in the R package (R Core Team 2014) DIVERSITREE

(FitzJohn 2012). This model co-estimates speciation and extinction rate estimates together with a

continuous-time transition matrix of multiple areas (or states) (Pagel 1994; FitzJohn 2012). In

63

this framework I set up a “full” geographic model with 12 parameters (four speciation rate

parameters [&R, &W, &E, &O], four extinction rate parameters [µR, µW, µE, µO], four dispersal rate

parameters [4R->W, 4W->R, 4W->E, 4O->W]). In this model, I constrained dispersal from outside NA

4O->W to result only in ‘widespread’ states; I did not allow ‘expansion’ states to transition to

either ‘widespread’ or ‘refugial’ states (4E->W = 4E->R = 0); finally, I considered a gradual

expansion process where ‘refugial’ states were not allowed to transition directly to ‘expansion’

states, meaning that ‘expansion’ states could only be reached through either W->E or R->W->E.

In order to evaluate the relative contribution of speciation, extinction, and dispersal rates, to

observed diversity patterns, I compared using likelihood-ratios, three submodels, in which each

of the three macroevolutionary parameters was constrained respectively. For instance, in the

“constrained speciation” model I have &R = &W = &E, in the “constrained extinction” model I have

µR = µW = µE, and in the “constrained dispersal” model I have 4R->W = 4W->R = 4W->E. By doing

this, the submodel with a significantly different likelihood from the “full” model should indicate

which macroevolutionary process better explains the pattern. Since the whole species tree

actually includes non-American species I estimated parameters with extra-American areas (&O,

µO, 4O->W), but ignored their values. Similarly to RASP, I constrained the root of the tree to be an

extra-American area. To explore the parameter space I performed 1,000 MCMC simulations,

using an exponential exp(0.5) prior, and 0.1 tuning (w). Initial parameters were taken from

previous maximum-likelihood estimations.

Additionally, I reconstructed the ancestral latitude of all nodes in the species tree in a similar

way as in Sánchez-Ramírez et al. (2015b; Chapter 3). Tip (extant species) traits (latitudes) for

100 post-burnin trees were sampled from a uniform distribution limited by maximum and

minimum sampled latitudes. To infer ancestral states I used a Brownian motion model in a

maximum-likelihood (ML) framework, as implemented in the ace function of the package APE

(Paradis et al. 2004). Nodes that were found to be outside were ignored.

5.2.9 Species’ population size estimation from the multi-species coalescent

Ideal models for species tree estimations should not only allow you to reconstruct the most

optimal species tree topology, but give you estimates of divergence times and historical

population sizes. While most studies using species tree estimation have focused on topology and

divergence times results, little attention has been given to discuss population size dynamics in a

64

phylogeographical context, that includes ancestral species, even when this goal was of primary

interest in the first developments of the multi-species coalescent model (Rannala and Yang

2003). The current (v 1.8) Bayesian implementation of the multi-species coalescent in *BEAST

includes two demographic models, one that assumes constant population sizes and a second that

allows population sizes to change linearly with time (Heled and Drummond 2010). In *BEAST

notation, the stepwise linear model estimates three parameters for each node in the species tree

(including terminal nodes): dmt, standing for demographic time (similar to the TMRCA of the

species); dmv1 standing for demographic values (01) near the present; and dmv2 for demographic

values (02) near the root. For terminal nodes, dmv1 is the population size 0 at time 0 (present),

whereas for internal nodes dmv1 is the population size 0 before speciation. For every node, dmv2

is the initial (from past to present) population size 0 at time dmt after speciation. The notations 1

and 2 in dmv are ordered from present to past. Based on linear function algebra (i.e. y = f(x) =

mx + c), I used the following formula:

! ! ! !!! ! !!!!!! ! !!!

to estimate m, the slope in line, as a proxy of the magnitude of population size change. Here, y1

and y2 represent dmv2 and dmv1, respectively. The variables x1 and x2 represent the times of the

start and end of a lineage. For terminal nodes, x2 was always 0. In order to have m > 0 represent

increases and m < 0 decreases in population size, branching times were always considered as

negative values. Hereon, dmv1 and dmv2 will be referred to as 01 and 02, respectively, for

consistency. Values were extracted for 100 post-burnin trees (the same used for ancestral

reconstruction of latitudes) using the read.annotated.trees function from the package

OUTBREAKTOOLS (Aanensen et al. 2015). m was calculated for each tree and then all values

were summarized.

5.2.10 Regressions and demographic histories

I fitted three regression models (standard linear, polynomial, and Gaussian) to the latitude and

population size data (m and 01) from 100 posterior trees. I used species’ latitudes (as proxy for

geographic location) as predictor variables. These consisted of average (mean) values based on

uniformly sampled latitudes for tips, and ML estimates for nodes (see section on ancestral state

reconstruction). The population size data (response variables) consisted of median values for m

65

and 01 summarized from posterior samples. The polynomial regression consisted in a fourth

degree polynomial with two inflection points (! ! !!! ! !!! ! !!! ! !" ! !). For the

Gaussian regression I used a non-linear four-parameter model,

! ! ! ! !!!"# ! !!! ! !!

!! !

where n is the midpoint of the function, b is the baseline, s defines the rate at which the curve

drops, and a the magnitudes relative to y. In both cases, ! stands for error. All three models were

compared using AIC. Additionally, I computed the extent of range expansions (!!!!!!!!! ) –

measured as the proportion of the absolute difference between a species latitude !! and its

ancestral state !!, to the amount of time elapsed ! (branch length)–, and compared it to the

magnitude of population expansion m. In this case, I only applied a standard linear regression

model. Inferred demographic histories (e.g. change of population size through time) were plotted

for species (extant and ancestral) with expanding and constant population size. Confidence

intervals were estimated using the 1st and 3rd quartiles (25-75% range) of the posterior

distribution.

5.2.11 Population genetic metrics

I measured several molecular population genetics metrics for each species and each locus (tef1,

AJ0103-132, AJ0000-2) with DNASP v5 (Librado and Rozas 2009). The metrics included the

number of segregating sites, 5 nucleotide diversity (i.e. the average number of pairwise

nucleotide differences per site, Nei and Li 1979), and Tajima’s D (Tajima 1989). The first three

give different measures of intra-population diversity, while the fourth tests for neutrality

deviation.

5.3 Results

5.3.1 Gene trees and concatenated data

The gene tree analyses allowed us to view topological differences between gene trees and

compare differences in divergences times and rates of evolution. All three genes performed

equally, resolving phylogenetic relationships among most species with high support (Fig. 5.1,

66

!"#$

$%&'(

)*+$+,-$,.

$%&'(

)*++++-.

$%&'(

/0123!"13!401

$%&'(

!"#$%&#'(#)%%!5%67%8*+*,%)-#$#9!5%67%83##415%(#)%%9!5%67%83##415%:3'3;'<'9!5%,#.#/.0.12#3#(%&+#$#!5%#34#$)#$#!5%67-&=$!5%*#/.5&3+6-3"#!7'8-3$%*+**+3#!5%67->32?@!5%67->32?,!5%67->32?A!5%67->32?.!5%9#*4)+$%%!5%67-B$$!5%67-C,$!5%67->32?$!5%67-)D+$!5%67-E$A

9#*4)+$%%%2;3F"

(#)%%%2;3F"

67

Figure 5.1. Bayesian maximum-clade-credibility gene trees of phased alleles indicating

delimited species by color at the tips. The last tree to the left is based on concatenated data.

Thickened branches are clades with > 0.9 pp support. The grey shapes indicate the two

major NA clades ‘basii’ and ‘jacksonii’.

Table 5.1. Species genealogical sorting indices (gsi) per gene and clade-level posterior

probabilities (pp). Species highlighted in grey were found to be reciprocally monophyletic

and fully supported. NaN, not aplicable

Species tef1 AJ0103-132 AJ0000-2 Species'

average gsi

Concatenation

gsi pp gsi pp gsi pp gsi pp

Amanita arkansana 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. basii 0.99 NaN 0.97 NaN 1.00 0.99 0.98 1.00 0.94

A. calyptroderma 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. hayalyuy 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. jacksonii 1.00 1.00 1.00 1.00 1.00 0.92 1.00 1.00 1.00

A. sp affin. basii 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp affin. hayalyuy 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-AR01 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp cochiseana 1.00 0.19 0.91 NaN 1.00 1.00 0.97 1.00 0.99

A. sp-F11 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-MO1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-T31 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-W15 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-jack1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-jack2 0.91 NaN 1.00 0.63 0.97 NaN 0.96 1.00 0.97

68

A. sp-jack3 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

A. sp-jack5 1.00 0.99 0.76 NaN 0.93 NaN 0.90 1.00 1.00

A. sp-jack6 0.92 NaN 0.83 NaN 0.90 NaN 0.89 0.88 NaN

A. vernicoccora 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Gene's average gsi 0.99 0.97 0.99 0.99

Table 5.1). Divergences times were roughly similar; AJ0000-2 had the shallowest time to the

most common ancestor (TMRCA) with 15.83 [95% posterior density (HPD) 12.61–19.14] Ma,

followed by tef1 with 16.05 [HPD 12.78–19.33] Ma, and by AJ0103-132 with 16.27 [HPD 12.9–

19.93] Ma. However, compared to tef1, AJ0000-2 and AJ0103-132 had higher substitution rates,

with 0.00214 [HPD 0.0017–0.0026] and 0.00247 [HPD 0.00192–0.003] substitutions/site/Myr,

respectively. Fourteen out of 19 (74%) proposed species were reciprocally monophyletic across

all genes with clade-level pps higher than 0.99. The least supported species was A. sp-jack6 with

an average gsi of 0.89, followed by A. sp-jack5 with 0.90, A. sp-jack2 with 0.96, A. sp.

“cochiseana” with 0.97, and A. basii with 0.98 (Table 5.1, Fig. 5.1). The concatenated gene tree

(Fig. 5.1) resolved phylogenetic relationships with higher support than individual gene trees. In

addition to shallow incongruences at the species-level, I also detected differences in deep

coalescent events among gene trees (Fig. 5.1).

5.3.2 Species tree estimation and species delimitation

The topologies of the reconstructed species tree and the concatenated gene tree varied slightly.

The species tree had divergence times closer to the present (14.37 [11.92–18.04]) than any of the

gene trees or the concatenated gene tree (15.95 [12.62–18.95]). Clade-level support ranged from

0.43 to 1.0. Twelve out of 22 (55%) nodes were resolved with ! 0.95 pp; 4 (18%) more had pps

between 0.95 and 0.8; while 6 (27%) other had pps between 0.6 and 0.4. The unguided rjMCMC

algorithm in BP&P gave full support for species initially defined in clades ‘basii’ (0.85, 8-species

model) and ‘jacksonii’ (0.66, 8-species) (Appendix Fig. C8.5). A total of 19 species were

delimited (including three lineages outside clades ‘basii’ and ‘jacksonii’), of which only seven

69

had been previously described based on their morphological distinctiveness (Figs. 5.1 and

Appendix Fig. C8.5).

5.3.3 Niche modeling, biogeographic reconstruction and diversification rates

MaxEnt training performed well with an AUC of 0.98. The model used 500 iterations and a total

of 8810 points (133 presence points). The variables that contributed the most to the model were

bio12 (annual precipitation), bio1 (annual mean temperature), and bio2 (mean diurnal range, i.e.

mean of monthly [max temp - min temp]). Present distribution predictions coincided well with

mixed temperate forest environments in Mexico, Central America, eastern and northwestern

USA, and with known absences in the Central Rocky Mountains (Figs. 5.3A,C,D). LGM

distribution predictions indicated substantial habitat reductions in the eastern USA, confining

most of the suitable habitats below c. 35 degrees latitude (Fig. 5.2B). In contrast, habitats in

Mexico, Central America and California were more geographically stable, even wider in

southern Mexico and Central America (Fig. 5.2B). From the 19 extant species, 10 were deemed

exclusively refugial, five widespread, and three exclusively in expansion areas (Figs. 5.2C,D and

5.3). Fourteen ancestral species (nodes) were deemed NA, of which seven were most likely

ancestrally widespread and seven other exclusively ancestrally refugial (Fig. 5.3). The MuSSE

submodel comparison supported the “constrained speciation” model as the least optimal model,

with a significantly (P-value = 0.04) worse likelihood (lnL -84.86, !2 = 6.2) than the “full” model

(lnL -81.76), compared to the “constrained extinction” (lnL -81.76, !2 = 0.0, P-value = 0.99) and

“constrained dispersal” models (lnL -82.66, !2 = 1.8, P-value = 0.4). This indicates that the

speciation rate has had larger contribution to the observed pattern (Fig. 5.3).

5.3.4 Historical demography and population genetics

Population parameters were only considered for nodes (including terminal nodes) that were

inferred to be in NA (Fig. 5.3). The highest "1 was found in A. basii with 8.01 [5.76–10.49] and

lowest in A. arkansana with 0.57 [0.17–1.03]. In ancestral species (i.e. internal nodes), "1 varied

from 0.76 [0.13–1.54] to 2.04 [0.45–3.81]. Per area, species (extant and ancestral) had on

average a "1 of 1.84 [0.58–8] in refugial areas, of 1.66 [0.57–5.3] for widespread, and 2.79

[1.99–3.58] for those in expansion areas. The magnitude of population expansion m varied from

5.32 in A. basii to –0.016 in A. sp-jack1. I found that the linear model performed poorly for both

m (AICm = 117.58) and "1 (AIC" = 122.08) compared to polynomial (AICm = 116.97, AIC" =

70

Table 5.2. Population genetic summary statistics per species per gene.

Species N No. Segregating Sites Nucleotide diversity (!) Tajima’s D

tef1 AJ000-2 AJ010-132 tef1 AJ0000-2 AJ0103-132 tef1 AJ0000-2 AJ0103-132

Refugial

A. sp affin hayalyuy 5 9 7 4 0.006 0.007 0.004 0.324 0.211 -1.030

A. basii 74 46 42 32 0.034 0.072 0.181 -2.037 -1.773 -1.360

A. sp affin basii 12 4 0 3 0.014 0.000 0.017 -0.042 NaN 0.197

A. hayalyuy 7 2 6 2 0.005 0.012 0.004 0.121 0.190 -0.040

A. sp-jack3 9 1 6 3 0.001 0.004 0.006 -0.942 -1.543 -0.505

A. sp-jack5 11 3 10 6 0.009 0.013 0.008 -0.028 -0.992 -0.749

A. sp-jack6 17 5 10 13 0.021 0.049 0.010 -0.205 -0.505 -1.987

A. calyptroderma 6 3 4 5 0.003 0.003 0.004 -0.730 -0.539 -0.670

A. vernicoccora 5 12 5 0 0.022 0.009 0.000 2.035 1.203 NaN

A. sp-F11 6 5 32 3 0.005 0.008 0.003 0.818 -0.147 -1.137

Widespread

A. arkansana 7 4 2 1 0.003 0.007 0.002 -0.759 0.779 -0.790

A. sp cochiseana 33 30 13 15 0.019 0.006 0.018 -1.501 -1.188 -0.903

71

A. sp-AR01 8 8 17 2 0.002 0.005 0.007 -0.324 -0.836 -0.069

A. sp-T31 5 9 7 2 0.004 0.005 0.004 1.170 1.146 -0.245

A. sp-jack1 8 2 3 3 0.002 0.008 0.009 -1.124 -0.363 0.106

A. sp-jack2 23 14 18 24 0.017 0.014 0.011 -1.775 -0.725 -1.825

Expansion

Amanita jacksonii 31 21 24 14 0.037 0.020 0.021 -1.388 -2.141 -2.020

A. sp-MO1 4 11 12 3 0.004 0.006 0.003 0.100 -1.606 -0.704

A. sp-W15 6 9 5 4 0.004 0.003 0.002 0.704 0.818 0.687

72

73

Figure 5.2. MaxEnt species distribution modeling of North American Caesar’s mushrooms in present (A) and last glacial maximum (B; c. 22 ka) climatic conditions. The scale in A

indicates habitat suitability scores. C and D are distribution maps of samples included in this study colored by species group. Grey shades in C and D represent forested (temperate

mixed and coniferous forests) areas according to WWF terrestrial ecoregions (Olson et al. 2001). The dotted lines in C and D delineate the boundaries between refugial and expansion

areas.

124.24) and Gaussian (AICm = 114.30, AIC! = 117.41) regressions (Appendix Fig. C8.6). For

both parameters, a Gaussian function was comparatively the best descriptor (Akaike weight for

m = 0.68 and for !1 = 0.88) (Fig. 5.4A,B). The least-squares estimation for m resulted in three

out of four parameters with a significant (alpha = 0.05) fit (n = 24.52, P-value = 2x10-15; s =

5.57, P-value = 0.02; a = 2.18, P-value = 5x10-3). A significant positive correlation was found

between the extent of range expansions and m (Fig. 5.4C, R2 = 0.23, P-value = 0.002). I detected

at least 12 population size changes with m > 1 (Fig. 5.4A, 5), of which four occurred in extant

species, and six other in ancestral species (Fig. 5.3). Moreover, six of these demographic changes

occurred in species found to be in exclusively refugial, five in widespread regions, and one in

exclusively expansion areas (Fig. 5.3-5). Nine out of 12 population size changes occurred during

the Pleistocene (Fig. 5.5 only shows seven, plots of A. sp-jack5 and A. sp-jack6 are not shown).

The average number of segregating sites in tef1, AJ0000-2, and AJ0103-132, were 7, 10, and 12,

respectively, with the highest values found in A. basii (32, 46, and 42, respectively). In the same

order, the average " per site for each gene was 0.016, 0.011, 0.013, being the highest in A. basii

for tef1 (0.181) and AJ0103-132 (0.072), and in A. sp-jack2 for AJ0000-2 (0.037). The average

Tajima’s D for each gene was -0.72, -0.29, -0.44, respectively. Values ranged from 2.03 to -2.41

across all genes. Amanita jacksonii had the lowest Tajima’s D for tef1 (-2.02) and AJ0103-132 (-

2.41), and A. basii for AJ0000-2 (-2.03). Details can be found in Table 5.2.

5.4 Discussion

5.4.1 Species delimitation and cryptic diversity

Cryptic species are defined as “two or more distinct species that are erroneously classified (and

hidden) under one species name” (Bickford et al. 2006). In most cases, the underestimation of

74

Figure 5.3. Reconstructed Bayesian species tree from *BEAST showing mean node heights,

and ancestral area reconstructions from RASP (pie charts) as well as stochastic character

mapping from DIVERSITREE. Color codes represent species in refugial (blue), widespread

(green), or in expansion (red) areas. Terminals in grey and dotted lineages were ignored as

they either represent Eurasian outgroups or were deemed outside North America

(Sanchez-Ramirez et al. 2015). Pli = Pliocene, Ple = Pleistocene. The boxplot on the left

shows per area MCMC estimates of speciation (!), extinction (µ) and dispersal (") rates

from MuSSE (DIVERSITREE).

75

species diversity is due to little or imperceptible morphological, ecological, or ethological

differences among cryptic taxa. Macrofungi themselves are cryptic in nature; remaining hidden

underground most their lifetime, only being conspicuous through fruiting bodies, on which most

taxonomic work is focused. Their fruiting bodies also have overall simple anatomy, which make

species boundaries based on morphology difficult to assess (Taylor et al. 2000, 2006). Since

mycologists started adopting molecular methods (Yang 2011) for species delimitation, many

cryptic species are now recognized (Vilgalys and Sun 1994; James et al. 1999; Matute et al.

2006; Arnold 2007; Le Gac et al. 2007; Carriconde et al. 2008; Geml et al. 2008; Crespo and

Lumbsch 2010; Sánchez-Ramírez et al. 2015a). Previous morphotaxonomic work (e.g. Guzmán

and Ramírez-Guillén 2001; Tulloss 2015) already pointed out suspicions of multiple cryptic

species in the A. caesarea-complex, but lacked genetic confirmation. This study not only

confirms part of this diversity genetically, it shows that cryptic lineages have different temporal

and spatial origins. Nineteen species were delimited from the combined information from

individual and concatenated gene trees, the gsi, and the multi-locus species delimitation based on

the multi-species coalescent. From these, only seven have been described, and four (A. sp.

“cochiseana”; A. sp.-AR01, A. sp.-F11, A. sp.-T31) other have been provisionally segregated

based on distinctive morphological features (Tulloss 2015). This means that between 63 and 42%

of species diversity was unknown for the NA Caesar’s mushrooms. In the Caesar’s mushroom

system, A. jacksonii, which was previously thought to have an ample distribution ranging from

Canada to Central America (Guzmán and Ramírez-Guillén 2001; Tulloss 2015), sets a good

example. Based on my results, I now recognize at least nine other cryptic species throughout the

range resembling A. jacksonii (Fig. 5.2-4). Some of these show complex speciation patterns with

no clear geographic separation (A. sp-AR01, A. sp-jack1, A. sp-jack2, A. sp-T31 and A. sp-W15

in SE USA), others have some degree of geographical structure (A. sp-jack3, A. sp-jack5, A. sp-

jack6 in southern Mexico). Except for A. sp-jack2, which can be found from Indiana (USA) to

Chiapas (Mexico), most species are range restricted. In the true A. jacksonii Pomerl., I delimit

both its genetic and geographic boundaries, showing that there is little geographic and ecological

overlap with other species.

A case that merits further exploration concerns the relationship between A. hayalyuy D. Arora

and G. H. Shepard and A. garabitoana Tulloss, Halling and G. M. Muell (Shepard et al. 2008;

Tulloss et al. 2011). These two names have been applied to material described from Chiapas

76

(Mexico) and Costa Rica, respectively. Both have similar morphological features and inhabit

similar habitats (e.g. pine-oak forests) (Shepard et al. 2008; Tulloss et al. 2011). Based on my

initial gene tree/gsi assessment, I considered both as a single entity, however I encourage further

analyses with larger genetic and geographic sampling in A. hayalyuy/garabitoana. Overall, part

of my up coming work is to formally publish the newly found species, and to match up genetic

entities to type specimens.

Incorporating the information of multiple unlinked loci can help improve species delimitations

(Carstens and Knowles 2007). In part, this has led to the use and development of multi-locus

approaches in phylogenetic and phylogeographic analyses (Knowles 2009; Fujita et al. 2012).

Some of the benefits of Bayesian species delimitation models based on the multi-species

coalescent are: (1) the assumption of independent gene histories and stochastic coalescent

processes, which take into account potential sources of gene tree incongruence such as

incomplete lineage sorting, (2) the multi-dimensional sampling of species delimitation models,

and (3) the quantification of uncertainty (Yang and Rannala 2010, 2014; Rannala and Yang

2003, 2013). Nonetheless, gene matrix concatenation (e.g. Rokas et al. 2003) is still one of the

most widely used approaches in phylogenetic inference, in spite of the adverse effects reported in

some groups (Degnan and Rosenberg 2006, 2009; Pollard et al. 2006; Carstens and Knowles

2007; Heled and Drummond 2010). The concatenation analysis was mostly consistent with

BP&P’s multi-locus delimitation (Fig. 5.1, Appendix Fig. C8.5). There were some minor

topological differences across gene trees, in particular at deeper nodes (Fig. 5.1), however, 14

out of 19 (74%) species were reciprocally monophyletic with posterior probability of > 0.99

(Table 5.1). Most inconsistencies were detected either at deeper nodes (deep coalescence) or

between species with shallow divergences (from 1.48 [1.08–2.13] to 0.41 [0.26–0.64] Ma) and/or

large population sizes (Table 5.1: A. basii and A. sp. “cochiseana” in clade ‘basii’; A. sp-jack2,

A. sp-jack5, A. sp-jack6 in clade ‘jacksonii’).

Incomplete lineage sorting within recently diverged species is expected, particularly if species

(and ancestral species) have large effective population sizes (Maddison and Knowles 2006). On

this front, I took advantage of the multi-species coalescent model in order to (1) mitigate the

effects of incomplete lineage sorting in phylogenetic reconstruction and divergence time

estimations, and to (2) estimate historical population sizes based on multi-locus data. The multi-

species coalescent model is considered a paradigmatic model, bridging between micro- and

77

macroevolutionary processes (Edwards 2009). Nonetheless, its demographic inferences are

rarely used in a hypothesis-testing context. Here, I intend to extend the range of its applications,

not only to delimit species and estimate species trees, but to integrate both macro- (speciation,

extinction, dispersal) and microevolutionary processes (population sizes) to better understand

diversity patterns in the context of Quaternary climate change.

5.4.2 Refugial macro and micro-evolutionary dynamics

Higher species richness in refugia is expected (Keppel et al. 2012). Most of the diversity in the

NA Caesar’s mushrooms was found in refugial areas, which together with species in widespread

areas make up about 80% of the total diversity (Fig. 5.1-3). This observation is consistent with

other studies that show high diversity patterns in refugia (Médail and Diadema 2009; Tzedakis

2009; Keppel et al. 2012). The nature of high refugial diversity is debatable, especially because

there are many factors, such as speciation, extinction, and dispersal which contribute to diversity

build-up (Birand et al. 2012). Some authors suggest that refugia harbour high diversity due to

ecological, geographic, and climatic conditions that accelerate diversification (Quian and

Ricklefs 2000; Jetz et al. 2004; Svenning and Skov 2007). MuSSE results show that high

speciation rates in refugial areas –but not high extinction in expansion areas or higher dispersal

to refugial areas– better explains current diversity patterns in the Caesar’s mushrooms (Fig. 5.3).

Particularly, most refugial species are found in Mexico and Central America, which are

important centers of diversification (Perry et al. 1998; Escalante et al. 2004; Zarza et al. 2008;

Bryson et al. 2011; De-Nova et al. 2012), and diverged relatively recently (Figs. 5.2 and 5.4)

suggesting a role of Pleistocene glaciations in promoting genetic isolation. A recent review by

Mastretta-Yanes et al. (2015) highlights many of the natural processes that promote divergence

and speciation in the Mexican highlands. In particular, they point out co-occurring climatic and

topographical changes in the Trans-Mexican Volcanic Belt during the Pleistocene, and their

implications for recent diversification. Another area with high relative diversity is SE USA,

which is considered a well-known refugium (Soltis et al. 2006). This view is also supported by

paleoclimatic modeling (Fig. 5.2A,B). In this region most species are sympatric, with the

exception of A. sp-F11, which is confined to temperate forests in Florida (Fig. 5.2). To show the

degree of overlap, I sampled four species of this complex (little morphological differences)

within few meters of each other in Duke Forest, North Carolina (J.-M. Moncalvo pers. obs.).

78

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79

Figure 5.4. (A) A plot of species’ latitudes versus the magnitude of population expansion

(m). The dotted line indicates m = 1. (B) Plot of latitude versus the population size at

present time for extant species and before speciation for ancestral species (!1). In A and B,

the thick solid line represents the best Gaussian fit to the data. (C) Plot of the extent of

range shift versus m. In C the solid line represents the best linear fit to the data (R2 = 0.23,

P-value = 0.002). In all plots, grey and black circles represent extant and ancestral species,

respectively, and thinner solid lines represent variation within the data (e.g. posterior

estimates and sampled latitudes). b, Amanita basii; c, A. sp. ‘cochiseana’; j, A. jacksonii; j2,

A. sp-jack2; j3, A. sp-jack3; j5 A. sp-jack5; j6 A. sp-jack6. * median, † mean.

Divergence times in the species tree (Fig. 5.3) indicate that most species within SE USA

diverged before the Pleistocene.

Regression models did not support my prediction of a linear relationship of population size with

latitude. Nonetheless, we found an interesting Gaussian relationship on both metrics (m and !1)

showing that population sizes and expansions are larger at intermediate latitudes (Fig. 5.4A,B).

This observation suggests that historical and contemporary population sizes have been large

within a range between 20 and 35 degrees latitude. This finding gives some support to my “high

genetic diversity” hypothesis, showing that high genetic variation can be found within a refugial

range. We also note that ancestral species with this type of response are concentrated within this

range, which might be related to the high speciation rate found in refugial areas (Fig. 5.3), and/or

to high rates of speciation at worldwide temperate latitudes (Sánchez-Ramírez et al. 2015b). It is

interesting that predictions about Pleistocene refugia theory are mostly based on European

studies (Taberlet et al. 1998; Hewitt 1999, 2004a; Petit et al. 2003; Schmitt 2007). Notably

differences in topography (e.g. east-west versus north-south orogeny) can have implications for

different diversity patterns in both continents (Anderson et al. 2013). For instance, north-south

oriented mountain chains are more likely to cause refugia and endemisms along costal

environments (Brunsfeld et al. 2001). Also higher connectivity between temperate and tropical

environments (e.g. southern US and Mexico/Central America) make the extent of refugial limits

broader in North America, compared to Europe, where fragmentation is larger. Nonetheless,

patterns in the NA Caesar’s mushrooms suggest that high population sizes at lower latitudes (e.g.

southern end of the distribution, Central America) are probably less tenable because of less forest

coverage. Less frequent population size expansions towards higher latitudes might reflect range

80

81

Figure 5.5. Plot of demographic change as a function of time for ancestral and extant

species. The first upper row shows four extant species with fast demographic change. The

second row includes four extant species with near constant historical demography. The two

last rows show eight ancestral species with relatively fast demographic change. In these the

numbers represent nodes shown in Fig. 5.3.

shifts in species that move northward as glaciers retreated, without changes in population size. In

contrast, broader niches at mid-latitudes during glacial cycles (Fig. 5.2) may allow in part for

populations to both expand and conserve allele diversity (Fig. 5.4A,C). Additionally, we find that

the range shift extent is significantly correlated with population expansions (Fig. 5.4C). This

means that faster and farther movements northward or southward away from the ancestral range

are likely to influence population size changes in this group of fungi. This is a common

prediction of population genetic dynamics in response to Quaternary climate change (Hewitt

1996, 2000, 2004a,b).

Outliers in the data are likely to be driven by intrinsic environmental limitations leading to

species-specific local adaptation and colonization responses (Savolainen et al. 2007). To

exemplify, A. jacksonii has a broad range extending to the northern distribution limit (Fig. 5.2D),

and a demographic history that suggests a fast population expansion (Fig. 5.5). These

characteristics suggest that this species is a hardy colonizer. In contrast, other species with

similar latitudes, such as A. sp-W15, have narrower ranges (Fig. 5.2C,D) and lower and more

constant population sizes (Fig. 5.5). Similarly, A. sp-jack3, A. sp. ‘affin. hayalyuy’, and A. sp

‘affin. basii’ occur within the same latitude as A. basii, but do not show signs of population

expansions and have relatively smaller population size (Fig. 5.4A,B, 6). In widespread species,

only two (A. sp. ‘cochiseana’ and A. sp-jack2) species show signs of population expansions (Fig.

5.5), suggesting that they recolonized northern habitats from southern ‘source’ populations. In

both cases, genetic diversity statistics (Table 5.2) and population sizes (Fig. 5.4B) are not

comparatively low, which may suggest that population bottlenecks were not severe. According to

metapopulation theory, metapopulations that have sufficiently large ‘source’ populations and

high dispersal capabilities are likely to overcome high rates of extinction (Levin 1995), which

were likely during glacial cycles.

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Several species (A. basii, A. sp-jack2, A. sp-jack5, A. sp-jack6) experienced population

expansions exclusively within refugial areas (Fig. 5.4A,B, 5), all of them occurring in Mexico

(Fig. 5.2C,D). This pattern may be attributed to the presence of microrefugia in this region (e.g.

Ornelas et al. 2010; Gutiérrez-Rodríguez et al. 2011; Ruiz-Sánchez and Ornelas 2014), coupled

with broader environmental niches during glacial periods (Fig. 5.2B), which may have facilitated

population expansions (Mastretta-Yanes et al. 2015). Moreover, I detected eight ancestral

demographic expansions, three occurring in widespread areas between the Miocene and

Pliocene, and three more occurring later within refugial areas during the Pleistocene (Fig. 5.3-5).

The former results are consistent with NA colonization patterns in Amanita sect. Caesareae

(Sánchez-Ramírez et al. 2015a), supporting the view of population expansion following the

colonization of a new region (Sakai et al. 2001); while the later events may also be attributed to

glacial-interglacial expansions. I find a higher number of population expansions occurring during

the Pleistocene (Fig. 5.5), which advocates to the importance of this epoch in shaping diversity

patterns in the NA Caesar’s mushrooms.

5.4.3 Potential caveats

Some results shown here should be interpreted with caution. (1) Population size (! ! !!!!)

estimates based on the coalescent often have several assumptions –for instance non-overlapping

generations, neutral evolution, infinite-site mutation models, random mating, and free

recombination between, but not within, loci–, which can be easily violated leading to biased

results. It has been shown that increasing the number of independent loci can improve the

accuracy of population size estimation, however, a large number (> 16) of unlink genetic loci is

often recommended (Heled and Drummond 2008). This is probably one of the reasons why

posterior population size estimations have large confidence intervals for some species (Fig. 4).

(2) The geographic limits of some of the newly discovered species are poorly known. This means

that their climatic demands are also poorly understood. Although I strive to include as much data

and as much uncertainty as possible for characterizing species distributions, we might later

discover, with more data, novel distributions or range shifts that might alter the discussed

interpretations. Finally, (4) certain flaws in BiSSE-type diversification models have been

recently pointed out. In particular, I am concerned about phylogenetic sudo-replication, the effect

of neutral traits, and low power in small-sized phylogenies (Maddison and FitzJohn 2015;

Rabosky and Goldberg 2015; Davis et al. 2013).

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5.5 Conclusions

By applying multi-locus coalescent-based species tree models and broad geographic sampling, I

have performed a thorough assessment of the diversity in the Caesar’s mushrooms (A. affin.

caesarea) species complex in North America. With implementations of the multi-species

coalescent model in BP&P I delimited 19 species and inferred their divergence times and

historical population sizes across multiple single-copy genes. Results uncovered unexpected

levels of cryptic diversity (68%). High speciation rate in refugial areas and more abundant

population expansions during the Pleistocene are indicators of intricate dynamics during glacial

cycles, which evidence their role in explaining current and historical diversity patterns. Complex

population dynamics and high genetic diversity at mid-range (20-35 degrees latitude)

distributions point out Mexican highlands as a diversity pump. Also, I show that abrupt range

shifts are followed by population size expansions, which is expected under Quaternary climate

change. Finally, this study highlights the potential of the multi-species coalescent model in

comparative phylogeographic analyses and diversity assessments that include ancestral species.

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Chapter 6

COMPARATIVE POPULATION GENOMICS AND MOLECULAR 6ADAPTATION IN THE AMANITA JACKSONII COMPLEX

6.1 Introduction

Understanding the nature of genomic variation can help illustrate how natural selection and

demography balance out throughout the history of a species. The neutral theory of molecular

evolution (Kimura 1986; Ohta 1992) postulates that most mutations contributing substantially to

the genetic pool of a population will be either neutral or nearly so. Behind this reasoning is the

assumption that changes that alter the amino acid composition of a protein will tend to be

deleterious and selected against, while those that are advantageous will be rare. In this

framework, one can test whether DNA variation at a given locus fits the expectations under the

neutral equilibrium model (Tajima 1989; Fu and Li 1993, 1999). These models primarily allow

for explicit testing of neutrality departures, however different kinds of evolutionary processes,

such as selective sweeps, recombination, and demographic changes, can mimic similar results

(Bachtrog and Andolfatto 2006; Ramírez-Soriano et al. 2008). Nonetheless, selection is likely to

affect specific regions through the fixation of linked neutral variation –a process known as

hitchhiking or selective sweeps–, while demographic changes are more likely to have genome-

wide effects (Andolfatto 2001; Nielsen 2001).

Natural selection at the molecular level is detectable in protein-coding DNA. This can be

accomplished by looking at the relationship between functional (amino acid-changing or non-

synonymous) and silent (neutral or synonymous) variation. A widely use test of selection is the

McDonald-Kreitman test (MKT, McDonald and Kreitman 1991), which is based on the

comparison between the ratio of functional !!!! and silent !!!! mutations within a species

(polymorphism) and the ratio of fixed !! and !! mutations between species (divergence). If both

polymorphism and divergence were solely driven by mutation and drift, one would expect their

ratio to equal one. This model assumes that all !! mutations are neutral and that !! mutations

are either strongly deleterious, neutral or strongly advantageous (Smith and Eyre-Walker 2002).

In this framework, advantageous mutations are more likely to contribute to divergence due to

85

their high fixation probability, while deleterious mutations are likely to be found at lower

frequencies due to the effects of purifying selection. On the other hand, the vast majority of

segragating mutations are likely to be neutral, as they will tend to persist in the population for

longer periods until they eventually go to fixation by random genetic drift (Sella et al. 2009).

However, the proportion of adaptive mutations can be under- or overestimated under certain

demographic scenarios combined with weak selection (Smith and Eyre-Walker 2002; Eyre-

Walker 2006; Messer and Petrov 2013a). For instance, in species with reduced population sizes,

slightly deleterious mutations are effectively neutral, thus they are at higher frequency in the

population resulting in an underestimation of the proportion of adaptive mutations. Similarly, a

recent population expansion will tend to decrease polymorphism, confounding sweep-driven

positive selection (Nielsen 2001; Nielsen et al. 2005; Wright and Gaut 2005). Furthermore,

another approach is to quantify selection directly as the ratio of substitution rates at !! and !!

sites (Kimura 1977; Huges and Nei 1988; Goldman and Yang 1994). In a neutral scenario,

!!!!! should approximate 1, with values above indicating diversifying (positive) selection, and

values below indicating purifying (negative) selection (Nielsen 1997; Yang and Nielsen 2000;

Nielsen 2001).

Detecting genome-wide patterns of selection and demography is now feasible through the

growing availability and accessibility of whole-genome data; the field now known as population

genomics (Charlesworth 2010). Most studies addressing questions such as genome-wide effects

of selection have usually focus on a number of model organisms (Hough et al. 2013; Ellegren

2014), most of which have good reference genomes and well-documented annotations, while for

the vast majority of species, empirical evidence is still lacking. Nonetheless, lowering

sequencing costs and more efficient bioinformatics tools are making these types of approaches

accessible to non-model organisms (Ekblom and Galindo 2011; Aguileta et al. 2010).

Fungi play multiple essential roles in the environment, mainly as decomposers of organic matter,

but also as pathogens, commensals, and mutualists. Not only due to their diverse ecology

(Selbmann et al. 2013), but also because of their genetic machinery (Anderson et al. 1992;

Schoustra et al. 2007), fungi are regarded as highly adaptable organisms. Most evidence comes

from experimental studies in yeasts (Suutari et al. 1990; Davies et al. 1995; Piper et al. 2001; Liu

2006; Dettman et al. 2007; Anderson et al. 2010; Gerstein et al. 2014), drug resistance

(Kontoyiannis and Lewis 2002; Anderson 2005), and the evolution of virulence in pathogens

86

(Bentrup and Russell 2001; Becher et al. 2010; Fisher et al. 2012). In spite of being great

candidates (mostly due to relatively smaller genomes and short generation times) to study

evolutionary genomics (Gladieux et al. 2014), adaptation in wild fungal populations and species

has been scarcely explored. However, recent work in few fungal species has evidenced adaptive

mechanisms molecularly and experimentally in the wild. For instance in Neurospora,

environmental factors such as temperature and latitude are contributing components to genome

divergence and adaptation in wild populations (Ellison et al. 2011). Moreover, Aguileta et al.

(2012) and Gladieux et al. (2013) highlight the importance of selection and molecular adaptation

during host switches and host specialization in Botrys and Microbotryum. Finally, Branco et al.

(2015) explores patterns of genome divergence in two populations of the ectomycorrhizal (EM)

–root-associated symbionts– fungus Suillus adapted coastal and montane environments. These

studies are few recent examples that evidence genome-wide adaptive mechanisms in wild fungal

populations.

The genus Amanita comprises 500-1000 species (Tulloss 2005), most of which live as EM

symbionts (Wolfe et al. 2012). The Caesar’s mushrooms (sect. Caesareae) are a clade of edible

EM Amanita distributed worldwide. Two studies by Sánchez-Ramírez et al. (2015a, accepted)

have shown that North America harbors high diversity in this group, which has derived, in part,

from recent continental radiations during the Plio-Pleistocene. With its eight species, the A.

jacksonii complex is an example of one of such radiations (Sánchez-Ramírez et al. 2015a,

accepted). I hypothesize that climatic dynamics during the Pleistocene may be responsible for

population size fluctuations and local adaptation to glacial refugia. Here, I explore the relative

contributions of natural selection and demography in explaining genome-wide diversity patterns

in the recently diverged A. jacksonii complex.

6.2 Material and Methods

6.2.1 Samples and genomic data

Forty-six dried specimens, representing eight species (Sánchez-Ramírez et al. accepted; Chapter

5) were processed from collections made in various locations in North America, all deposited in

fungal herbaria (Appendix Table D8.4). DNA was extracted from c. 30-50 mg of dried fungal

tissue (gill) and extracted using a modified standard CTAB/proteinase K/chorophrom:isoamilic

alcohol protocol (van der Nest et al. 2014; Chapter 4). In addition, whole-genome 454 sequences

87

were produced by the Duke Center for Genomic and Computational Biology for a North

American species outside the A. jacksonii complex –A. basii (Chapter 4). These sequences were

mapped to the A. jacksonii TRTC168611 draft genome (van der Nest et al. 2014), with the

purpose of building a whole-genome draft assembly of an outgroup species.

6.2.2 Identification of single-copy orthologs, probe design, and gene selection

Structural genes were predicted in A. jacksonii and A. basii using AUGUSTUS v3.0.3 (Stanke et al.

2006). All CDS in each species were reciprocally aligned against each other using command-line

(standalone) BLASTn, creating two files in tabular format (-outfmt 7). Next, a custom perl script

(rbh.pl, available at: https://sites.google.com/site/santiagosnchezrmirez/home/software/perl) was

used to find reciprocal single-copy hits, putting each pair in a single FASTA file. Each single-

copy pair was aligned using MUSCLE v3.6 (Edgar 2004). Probes for target hybridization were

designed based on conserved (identical) CDS regions, 60bp long, across both species. Only

genes with four or more non-overlapping regions were chosen. Two PCR primers (5’-

TAATACGACTCACTATAGGG-3’ and 5’-CTATAGTGTCACCTAAATC-3’) were added to

the 5’ and 3’ ends of the 60bp probe target, for a total of 100bp per oligonucleotide probe.

6.2.3 Sequence capture, library preparation, and sequencing

Custom oligos probes were synthesized by LCScience

(http://www.lcsciences.com/applications/genomics/oligomix/) using DNA microchip technology

(Gao et al. 2004) The synthesized probes were then PCR amplified as needed for downstream

applications. Probes were later transcribed into RNA using MegaScript T7 Kit (Invitrogen,

Carlsbad, CA) and Biotin-dUTP to produce labeled oligos. The DNA was sheared and a standard

Illumina TruSeq V2 DNA library kit was prepared, multiplexing all 46 samples. Sequence

captures (probe-target hybridization) were performed with 200ng of library, 500ng of

biotinylated probes, and Steptavidin beads (Invitrogen, Dynabeads M-270, CAT65305). The

capture procedure was repeated at least twice. The details of the protocols can be found in

Supplementary file 1. All target captures were directly sequenced in a single Illumina Hi-Seq

2500 lane.

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6.2.4 Bioinformatics

Read alignments, sequence phasing, and quality control. Sequences were de-multiplexed into

individual FASTQ files and each one mapped onto the targets’ whole-gene sequences from A.

jacksonii using the Burrows-Wheeler Aligner (BWA v0.7.12) algorithm BWA-MEM (Li and

Durbin 2010). The resulting SAM files were then filtered with view in SAMtools v0.1.19 (Li et

al. 2009) for off target reads, low quality mapping (< 30), and PCR duplicates, and converted to

BAM files. The reference sequences were indexed and the BAM files sorted. I used

HAPCOMPASS (Aguiar and Istrail 2012), which uses a read-based graph algorithm, to generate

haploid sequences from BAM and VCF files. VCF files were produced by “piping” mpileup,

bcftools’ view, calling the “-cg” flags for genotype likelihood computing, and varFilter in

vcfutils.pl, only keeping SNPs with Fred quality ! 20. The SAM files produced by HapCompass

were then converted to BAM and sorted. Consensus FASTQ files were produced by “piping”

mpileup, bcftools’ view, and vcf2fq in vcfutils.pl, in a similar way as above. A custom perl script

(OrderFromSamtools.pl) was used to convert FASTQ files to FASTA format and to produce by-

gene alignments from by-sample files. A second perl script (getPhasedGenome.pl) parsed those

FASTA files to produce “a” and “b” allelic variants per haploid sequence. All missing data was

scored as Ns, while unsorted polymorphisms were either left as IUPAC ambiguity codes or

scored as Ns, depending on the analysis. Scripts are available at:

https://sites.google.com/site/santiagosnchezrmirez/home/software/perl.

Structural and functional annotations. Whole-gene sequences from A. jacksonii were translated

into amino acids and then imported into BLAST2GO (Conesa et al. 2005), which was used as an

annotation tool. The main amino acid sequence annotation was taken from the non-redundant

(nr) protein database in NCBI using BLASTp, only keeping the best 10 matches. In addition, I ran

INTERPROSCAN v5 (Zdobnov and Apweiler 2001) to identify functional domains, from which I

extracted GO terms. For visualization purposes I used advanced “word clouds” in

http://www.wordle.net/advanced, where scaled GO terms were laid out. Only the molecular

function and biological processes terms were used. For the scaling proportion, I log-transformed

values using:

!"!! !"" ! ! !"#! !"!! !""

89

where ! is the number of identical GO terms found for any given list of terms found in a group

of sequences, and ! is the mean (averaged if found multiple times) proportion of sites under

positive selection, either for MKT tests or for codon-based models in PAML. The right side of the

scaling equation is to ensure that values are positive.

6.2.5 Analyses

6.2.5.1 DNA polymorphism

I measured nucleotide diversity, !, for ! samples as the average number of pairwise nucleotide

differences per site between any two random DNA sequences (Nei 1987):

! ! !! ! ! ! !!!!!!"

!

!!!

!

!!!!!!

where !!!!! are the haplotype frequencies and !!" are the number of differences between the ith

and jth sequences. The Watterson’s !! estimator (Watterson 1975) was measured as the

proportion of segregating sites ! in a sequence of length ! divided by the ! ! ! th harmonic

mean number:

!! !! !!!!

!! !!!

!!!

!!!

For DTaj I used Tajima's (1989: equation 38) formula:

! !! ! !

!!!!!!

!

! ! ! !!"!!!!!

!!!

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All statistics were measured for synonymous and non-synonymous sites per gene per species in

CDS and introns, as well as for whole-gene regions. Synonymous and non-synonymous

segregating mutations were separated into classes, considering the total number of synonymous

and non-synonymous sites, respectively for each class. In order to account for sample size in

missing data biases I excluded segregating sites present at a frequency below 70% of the total

number of sequences per species. All calculations were performed using the nuc.div.pl perl

script, available at https://sites.google.com/site/santiagosnchezrmirez/home/software/perl.

Estimations were compared to those generated in software such as DNASP v5 (Librado and

Rozas 2009) and Polymorphorama (Bachtrog and Andolfatto 2006,

http://ib.berkeley.edu/labs/bachtrog/data/polyMORPHOrama/polyMORPHOrama.html).

6.2.5.2 McDonald-Kreitman test

I estimated the degree of adaptive evolution in CDS for each species by measuring the amount of

polymorphism (within species DNA changes) and divergence (fixed DNA changes between

species) leading to non-synonymous and synonymous substitutions. I used the McDonald-

Kreitman test (MKT) (McDonald and Kreitman 1991) to estimate the Neutrality Index (NI),

!! !!!! !!

Where !! and !! are, respectively, the within-species proportion of non-synonymous and

synonymous segregating sites per non-synonymous and synonymous site, and !! and !!, the

proportion of fixed non-synonymous and synonymous substitutions per non-synonymous and

synonymous site. Values closer to 1 indicate neutrality, those close to 0 indicate positive

selection, and those > 1 suggest either negative purifying selection or balancing selection. I used

the formula derived by (Smith and Eyre-Walker 2002) to estimate the proportion of amino acid

substitutions under positive selection:

! ! !! !!!!!!!!!

This model assumes that mutations are either strongly deleterious or neutral, thus model

violations can arise in the presence of slightly deleterious mutations. Likewise, demographic

changes can also cause ! to be under or overestimated (Eyre-Walker 2006) . In order, to account

91

for demography in the estimation of ! I used the formula presented in (Messer and Petrov

2013b) which uses a site frequency spectra correction:

!!!! ! !! !!!!!!!!!!!!!!!

Where !!!!! and !!!!! are the levels of polymorphism for specific derived allele frequencies of

! in non-synonymous and synonymous sites. In a similar way as in the previous diversity

estimations, I mitigated the effects of sampling size and missing data by only considering SNP

with a frequency above 70%.

6.2.5.3 Codon-based model selection

Models that solely rely on !! !! (!!!ratios are also robust and popular methods for detecting

natural selection at the molecular level (Nielsen 2001; Yang and Nielsen 2002). Essentially, CDS

regions that have an equal number non-synonymous and synonymous mutations are thought to

evolve under effective neutrality, having a ! ! !. Deviations from this ratio indicate purifying

selection if ! ! ! and diversifying (positive) selection if ! ! ! (Yang 1998; Anisimova et al.

2001). I used likelihood ratio tests (LRT) to discriminate between three codon-based models: (1)

a site-wise model with a single ! ratio; (2) a site-wise “nearly neutral” model where there is a

proportion of sites !! with !! ! !, and a proportion of sites !! with !! ! !. This serves as a

null model for a third that considers an additional category of sites with !! with !! ! !. In the

last two models, !! and !! are fixed as !! ! !! !! and !! ! !! !! ! !!, respectively. All

models are based on Goldman and Yang's (1994) codon substitution model and were estimated

using maximum likelihood in PAML v4.8a (Yang 2007) using codeml. Because ! ratios assume

that mutations are fixed differences between species and because population polymorphisms may

potentially bias ! estimates (Kryazhimskiy and Plotkin 2008), I selected only one sequence per

species per gene.

6.2.5.4 Phylogenetics and lineage sorting

I used MRBAYES v3.2 (Ronquist et al. 2012) to generate individual gene trees based on the

general time reversible (GTR) model and among-site rate heterogeneity modeled as a gamma

distribution (+G). For each gene, I ran two parallel runs each with eight Metropolis-coupled

Markov Chain Monte Carlo (MC3) chains and 1 million generations. I sampled every 100th state

92

and discarded the 10% initial states as burnin. I assessed convergence by making sure that the

average standard deviation of split frequencies was ! 0.01. Posterior trees were summarized onto

a majority-rule consensus tree compatible with clades with frequencies " 0.5. The summary trees

were imported into R using APE (Paradis et al. 2004) where the genealogical sorting index (gsi,

Cummings et al. 2008) was calculated. The gsi measures, in a range from 0 to 1, the degree of

exclusive ancestry in labeled terminal groups, where 1 signifies group monophyly. These groups

can represent any type of biological association, such as species or populations. I further devised

a measure that quantifies the variance in gsi among groups relative to its maximum value 1.

!"#!"# !!!"#! ! !!!!

!!!!! ! !

where ! is the number of groups and !"#! is the !"# of the ith group. This variance vargsi allowed

us to quantify how much a given gene had information at the species level. This is particularly

important for selecting genes in demographic models (see below) that do not violate its

assumptions (i.e. gene flow).

6.2.5.5 Historical demography

I took two approaches to infer historical demography under the same framework: (1) I applied

the multi-locus eBSP model (Heled and Drummond 2008) individually on per species

alignments; using the Kimura-2-parameter (K80) model with gamma distribution as the

substitution model; and (2) I used the multi-species coalescent model in *BEAST (Heled and

Drummond 2010), which co-estimates the species tree and population sizes, allowing population

sizes to change linearly with time; here I used the GTR substitution model with equal base

frequencies (SYM). Both approaches are implemented in BEAST v1.8.2 (Drummond et al. 2012).

In order to use a time calibrated substitution rate, I used the program DISTR (Bevan et al. 2005)

to generate a relative rate for the largest gene based on tef1 sequences for the same taxa. The

relative rates were then scaled by a factor, based on the rate of tef1 (0.00194

substitution/site/Myr) (Sánchez-Ramírez et al. 2015a, accepted; Chapters 2 and 5). The resulting

rate for the largest gene, 0.0025 substitution/site/Myr, was used and fixed in both analyses. The

rates of other genes were co-estimated in the analyses. The data consisted in a set of selected

non-contiguous genes (Appendix Table D8.5) with a vargsi below 0.004. The eBSP analyses were

performed on per-species alignments, while the *BEAST included alignments with all eight

93

species. Each of the eBSP analyses ran for 50 million generations, with a sampling rate of 10%,

and a burnin of 10%. The *BEAST analysis ran for 100 million generations with the same state

sampling rate and burnin as the previous. I assessed convergence and mixing by looking at

likelihood per generation trace plots and effective sample size values in Tracer 1.6 (Rambaut et

al. 2013). Population size values in eBSP were produced directly from the analyses in CSV

format. For *BEAST, I generated a maximum-clade-credibility tree with summarized data and

extracted the tip annotations which corresponded to population size (!!, !!, and the time to the

most recent common ancestor).

6.3 Results

6.3.1 Targets, sequence data, and bioinformatics

Reciprocal BLAST hits between predicted proteomes in the draft genomes of A. jacksonii and A.

basii identified 3,427 putative single-copy candidate genes. The total number of structural genes

compared was of 8,511 in A. jacksonii and 5,878 in A. basii. From the set of 3,427, I selected

genes based on a criteria of a stretch of 60 bp of conserved DNA and a minimum number of four

probes per gene. Most genes exceeded the minimum number criterion by having 7 to 8 non-

overlapping probes. This resulted in 3,887 probes hybridizing to 502 genes.

The total number of reads per sample ranged between 1,787,146 and 24,316,811, with a mean of

9,157,472. The percentage of on-target reads per sample ranged between 56.6% and 96.3%, with

an average of 81%. The average number of on-target reads per sample varied between 3,078.6

and 45,760.5 per gene, with a mean of 14,720 across samples. Sequences were recovered from

all 46 samples in 498 genes, while only four genes were recovered for 45 samples. This resulted

in near complete DNA matrices for all genes.

The genes ranged in size from 1,136 to 13,720 bp, with an average length of 4,950 bp. Introns

were smaller in size than exons [coding DNA (CDS)], with an average of 68.56 compared to

258.69 bp in CDS. Each gene had about 14 introns and 15 CDS on average. Eighty percent of the

data corresponded to CDS, while only 20% corresponded to introns. In total, the data represented

2,483,742 nucleotide sites. I measured the amount of missing data in the alignments given that

low mapping quality regions and/or regions with no coverage resulted in missing data (Ns). I

94

find that both the amount of missing data and the amount of variable sites (across all 8 species) is

correlated with gene size (Appendix Fig. D8.7).

6.3.2 DNA polymorphism, neutrality, and allele sharing

The average nucleotide diversity ! over all genes varied from 0.0009 in A. sp-jack3 to 0.0028 in

A. sp-F11. Similarly, the lowest !! (hereon simply !) was found in A. sp-jack3 with 0.0011 and

the highest in A. sp-F11 with 0.0048. Mean values for !Taj ranged from –1.81 in A. jacksonii to

0.72 in A. sp-jack1 (Table 6.1). Diversity in CDS regions varied largely across species, in

particular at synonymous sites. The highest !!was found in A. sp-F11 with a mean of 0.0120,

while the lowest mean, 0.0024, was found in A. sp-jack3. As expected, !! was lower in

comparison with mean values ranging from 0.0006 in A. sp-jack3 and A. sp-jack1 to 0.0017 in A.

sp-F11 (Table 6.1, Appendix Fig. D8.8). !! and !! followed a similar pattern (Table 6.1,

Appendix Fig. D8.9). I observed that four species (A. jacksonii, A. sp-jack2, A. sp-jack6, and A.

sp-jack5) had a strongly skewed polymorphism frequency at synonymous and non-synonymous

sites. This was evidenced by mean !Taj values ranging from –1.94 (A. jacksonii) to –0.77 (A. sp-

jack5) at non-synonymous sites, and from –1.46 (A. jacksonii) to –0.58 (A. sp-jack5) in

synonymous sites (Fig. 6.1). Interestingly, polymorphism frequency at intron regions tended to

vary less across species, indicating strong differences in diversity dynamics between site classes

(Fig. 6.1, Appendix Fig. D8.8-9). Based on silent diversity (!! ! !int), Tajima’s (1983) estimator,

and assuming a mutation rate per site of ~2x10-9 (Sánchez-Ramírez et al. 2015a, accepted;

Chapters 2 and 5) per generation (one generation = one year), I estimated effective population

sizes (!!), which ranged on average from ~1.5x106 in A. sp-F11 to ~3x105 in A. sp-jack3

(Appendix Fig. D8.10).

The MKT is susceptible to violations of the standard neutral model (Kimura 1986), such as

demographic changes or structure, the segregation of slightly deleterious mutations,

recombination, and gene flow (Andolfatto 2001; Nielsen 2001; Eyre-Walker 2006). In addition

to the 70% frequency cut-off for filtering polymorphism (see Material and Methods), I further

restricted MKT to genes with at least 5 polymorphic/divergent sites. In spite of these stringent

settings, results show an overall excess of polymorphism compared to divergence (Fig. 6.2). On

average !! was higher that !!, except for A. sp-F11 which had roughly similar ratios (Fig. 6.2).

The Neutrality Index (NI) was > 1 on all eight species, with mean values ranging from 1.5 [5–

95

Table 6.1. Summarized polymorphism data for 502 genes in the A. jacksonii complex. N, sample size; S, segregating sites; !,

nucleotide diversity; ", Watterson’s theta, Dtaj, Tajima’s D. In the table below, polymorphism data is separated by site class.

Species N mean quantiles mean quantiles mean quantiles mean quantiles

! 5% 95% ! 5% 95% !! 5% 95% !!"# 5% 95%

A. jacksonii 28 56 20 113 0.0015 0.0005 0.0034 0.0033 0.0010 0.0069 -1.81 -2.36 -0.99

A. sp-jack2 18 58 17 117 0.0018 0.0006 0.0037 0.0037 0.0010 0.0076 -1.11 -1.91 -0.27

A. sp-jack6 10 29 9 55 0.0012 0.0004 0.0024 0.0023 0.0005 0.0050 -0.77 -1.41 -0.10

A. sp-jack5 10 24 8 46 0.0014 0.0004 0.0032 0.0019 0.0005 0.0039 -0.73 -1.45 0.04

A. sp-jack3 6 13 2 31 0.0009 0.0002 0.0022 0.0011 0.0002 0.0029 0.29 -0.76 1.36

A. sp-jack1 6 13 4 29 0.0011 0.0003 0.0023 0.0012 0.0003 0.0028 0.72 -0.10 1.41

A. sp-F11 6 55 18 120 0.0012 0.0010 0.0055 0.0049 0.0017 0.0093 0.46 -0.42 1.26

A. sp-T31 8 26 7 55 0.0028 0.0004 0.0025 0.0020 0.0006 0.0043 -0.09 -1.13 1.05

96

Continued.

mean quantiles mean quantiles mean quantiles mean quantiles mean quantiles mean quantiles

!! 5% 95% !! 5% 95% !! 5% 95% !! 5% 95% !! 5% 95% !! 5% 95%

22 7 47 17 5 35 0.0048 0.0014 0.0116 0.0008 0.0002 0.0018 0.0067 0.0019 0.0155 0.0016 0.0005 0.0035

24 7 52 15 3 34 0.0051 0.0013 0.0115 0.0007 0.0002 0.0015 0.0079 0.0016 0.0181 0.0015 0.0003 0.0033

12 3 27 8 1 18 0.0035 0.0008 0.0075 0.0005 0.0001 0.0013 0.0056 0.0012 0.0130 0.0010 0.0002 0.0024

10 2 20 6 1 16 0.0041 0.0011 0.0098 0.0006 0.0001 0.0015 0.0045 0.0010 0.0102 0.0008 0.0001 0.0020

5 0 14 4 0 11 0.0023 0.0000 0.0064 0.0005 0.0000 0.0014 0.0024 0.0000 0.0070 0.0006 0.0000 0.0016

6 0 13 4 0 10 0.0029 0.0001 0.0070 0.0005 0.0000 0.0014 0.0029 0.0001 0.0068 0.0006 0.0000 0.0015

25 8 51 12 1 28 0.0074 0.0023 0.0145 0.0010 0.0001 0.0024 0.0120 0.0040 0.0223 0.0017 0.0002 0.0041

10 2 24 7 1 17 0.0032 0.0007 0.0074 0.0005 0.0001 0.0013 0.0045 0.0009 0.0098 0.0009 0.0001 0.0022

97

Figure 6.1. Box-plots of Tajima’s D over 502 genes separated by site-classes (non-

synonymous, light grey; synonymous, white; introns, dark grey) for the A. jacksonii

complex.

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98

95% quant. 0.15–4.74] in A. sp-F11 to 4.1 [0.49–11.34] in A. sp-jack3. A similar effect was

found with ! (the proportion of adaptive fixations), which was negative to the largest extent (Fig.

6.2). The species with the highest average proportion of adaptive fixations (Smith and Eyre-

Walker 2002) with ! values higher than zero was A. jacksoniii (35%, range 1–87%), while the

one with the lowest was A. sp-jack3 (19%, range 0.1–74%). The number of genes with at least

one adaptive mutation per 1000 bp (i.e. ! > 0.1%) was 150 in A. sp-F11, 128 in A. sp-jack2, 69

in A. jacksonii, 60 in A. sp-T31, 54 in A. sp-jack6, 52 in A. sp-jack6, 21 in A. sp-jack1, and 14 in

A. sp-jack3.

A number of different factors can cause a higher degree of polymorphism relative to divergence.

Some of them, such as introgression and/or ancestral polymorphism, can be quantified through

genealogies. I measured the gsi (Cummings et al. 2008) on Bayesian phylogenetic trees in order

to assess the relative contribution of within-species phylogenetic conflict to within-species

polymorphism. The gsi measures the degree of co-ancestry within labeled groups and cannot

distinguish between introgression and ancestral polymorphism (i.e. incomplete lineage sorting).

However, it provides an overall view of how consistent gene trees are with species divergence.

Various measures of gsi indicate that the vast majority of gene trees support the monophyly of at

least six species, with a variance from the higher value (1) less than 0.15 (Fig. 6.3). When I

looked at the association between gsi values and DTaj (Fig. 6.4) and !! (Appendix Fig. D8.11) I

found that genes with significant amounts of within-species phylogenetic conflict do not skew

estimates compared to those that fully support monophyly within species (gsi = 1). Similarly,

genes with longer than expected coalescence times (i.e. ancestral polymorphisms) may

potentially be under balancing selection (Charlesworth 2009). I looked at the relationship

between gsi and ! and found that genes with low gsi do not have lower ! values than those with

gsi = 1 (Appendix Fig. D8.12)

Aside from the effects of allele sharing, negative ! values can also be caused either by negative

purifying selection (lowering !! with respect to !!) or by balancing (diversifying) positive

selection (increasing !! with respect to !!). The MKT is unable to distinguish between both

processes (Nielsen 2001), for which I performed likelihood-ratio-test of codon-based substitution

models (Yang and Nielsen 2002). I found that the majority of genes (278, 55%) fitted a nearly-

neutral model, with a proportion of sites with ! ! !, while the minority of genes (48, 10%)

99

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100

Figure 6.2. Box-plots of ! ratios for divergence (!D) and polymorphism (!P) (upper panel),

and " values the standard MKT test (grey) and a modified version that takes into account

site-frequency spectra (white) (Messer and Petrov 2013a).

Figure 6.3. Distributions of #gsi and vargsi for all 502 genes. #gsi indicates how well species

clades are resolved, and vargsi shows the extent of deviation from the maximum gsi value

possible, 1.

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101

fitted a positive-diversifying selection model, with an additional proportion of sites with ! ! !.

For genes fitting the nearly-neutral and positive-diversifying selection models, the mean !

across sites was 0.23 ± 0.09 and 0.27 ± 0.12, respectively. The rest (176, 35%) did not

significantly (0.05 significance) fit either model, so were deemed to be under negative-purifying

selection, given that !!1 (mean ! 0.18 ± 0.1). In addition, in order to discard any potential

biases caused by annotation errors, I compared the sample-size-corrected proportion of stop

codons found within CDS regions to NI values (Appendix Fig. D8.13). I find that both metrics

are not correlated.

6.3.3 Historical population sizes and divergence

I inferred the demographic history of each species using a genealogical approach in a Bayesian

framework (BEAST, Drummond et al. 2012), including the eBSP (Heled and Drummond 2008)

on by-species data sets, and the co-estimation of the species tree and populations sizes (*BEAST,

Heled and Drummond 2010). I opted for reducing the gene data set in demographic history

estimation for several reasons: (1) it has been shown that 16 informative genes or more should be

sufficient to estimate accurate population size histories (Heled and Drummond 2008); (2) gsi

results show that some genes may be in conflict with each other (Fig. 6.3), possibly involving

introgression, which may bias the assumptions of the demographic models (Heled and

Drummond 2010); (4) some of the genes I sampled are contiguous and probably tightly linked,

biasing the assumption of free recombination between loci; and (3) to improve Bayesian

parameter mixing and convergence times, which increases with more data (Suchard and

Rambaut 2009). Based on a vargsi limit of 0.004 and excluding those contiguous/linked positions,

I selected a total of 41 genes for historical demography inference (Appendix Table D8.5).

The eBSP analyses supported at least one demographic change within the 95% posterior density

distribution (PDD) in all eight species. Amanita sp-jack2 had the highest mean number of

demographic changes with 2.25 [2, 3 95% PDD], while the lowest was found in A. sp-jack6 with

1.35 [1, 2 95% PDD]. Demographic trends of population size expansions were most notorious in

A. jacksonii, A. sp-jack2, and A. sp-jack5, and to some extent in A. sp-jack6 and A. sp-T31, and

sp-F11, while A. sp-jack3 and A. sp-jack1 had rather constant demographic dynamics (Fig. 6.5).

The most abrupt demographic expansions were found in A. jacksonii and A. sp-jack2, with a near

20-fold increase in population size within the last c. 200 kyr (Fig. 6.5). The linear demographic

102

Figure 6.4. Scatter-plots showing the relationship between Tajima’s D and gsi for each

gene, for each species.

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103

Figure 6.5. Demographic trends through time for each species based on the eBSP and

*BEAST models. Grey shading represents 95% posterior confidence intervals (CI). In eBSP,

solid lines represent mean population size values and dotted lines represent median values.

104

In *BEAST plots, the dotted line represent mean values. The lower phylogenetic tree is the

maximum-clade-credibility species tree. Horizontal bars represent divergence 95% CI.

Numbers over nodes are clade posterior probabilities. Vertical dotted lines point to the

start of the Pleistocene epoch and the onset of demographic expansions.

models from *BEAST show similar demographic trends, but include explicit information about

the time to the most recent common ancestor, based on the species tree. Amanita sp-F11

exhibited the largest population size at present time (!! = 2.1 [1.6, 2.5 95% PDD]), while the

smallest was found in A. sp-jack3 (!! = 0.3 [0.2, 0.4 95% PDD]) (Fig. 6.5). The inferred species

tree had high posterior support (> 0.95) on every node except for the A. sp-jack6/A. sp-jack5

divergence, which scored a lower value (Fig. 6.5).

6.3.4 Gene functions of positively selected genes

According to the Gene Ontology (GO) annotations found within the candidate genes under

positive selection, a large proportion included proteins involved with energetic and metabolic

processes (ATP binding, GO:0005524, Fig. 6.6) and protein-protein interactions (protein

binding, GO:0005515, Fig. 6.6). To a lesser extent proteins related directly or indirectly with

gene expression regulation (i.e. nucleotide [GO:0000166], nucleic acid [GO:0003676], and DNA

[GO:0003677] binding; zinc ion binding [GO:0008270]; regulation of transcription, DNA-

dependent [GO:0006355]), transport and signaling (i.e. protein phosphorylation [GO:0006468],

phosphorylation [GO:0016310], protein kinase activity [GO:0004672, GO:0004712], trans-

membrane transport [GO:0055085], protein transport [GO:0015031]), metabolic processes (i.e.

oxidation-reduction processes [GO:0055114], oxidoreductase activity [GO:0016491],

carbohydrate metabolic process [GO:0005975], hydrolase activity [GO:0016787]), and

homeostasis (calcium ion binding [GO:0005509]). Species-specific genes under selection varied

in their functional content and number (Appendix Fig. D8.14). A. sp-F11, confined to a unique

and humid environment (Florida), had comparatively more species-specific genes under positive

selection that also had unique GO enrichments (Appendix Fig. D8.14).

6.4 Discussion

Interpreting DNA variation from an evolutionary perspective has been a long-standing goal for

population geneticists, both doing empirical and theoretical work. The neutral equilibrium theory

105

Figure 6.6. Gene Ontology terms Wordles for genes under positive selection (A: ! > 0; B: M2a) combined for all species. The size of the term includes the extent of selection and how often the term was found across genes and species.

A

B

106

of molecular evolution (Kimura 1977, 1986) provides a null framework to test hypotheses about

natural selection, and other processes, including demography, acting on the genome. While the

core of population genomic research has develop around coalescent-based theory in a single –or

at least a pair– of species (Hough et al. 2013; Charlesworth 2010), there are clear benefits in

integrating comparative and phylogenetic data, in addition to divergence assessments, in multi-

species multi-locus genomic analyses (Cutter 2013). This study tackles common questions in

population genomics (i.e. how does selection and population size shape genome-wide

diversity?), but taking advantage of exhaustive species-level sampling, phylogeny, genealogy,

and time-scaled demographic inference.

6.4.1 The exon-targeted sequence-capture approach

Before and early into the “whole-genome sequencing” era, most population genomics studies

relied on PCR and Sanger sequencing of individual genes for data production. Some classic

studies included data ranging from tens to few hundreds of genes, in what were then called

“whole-genome” approaches. Exon-targeted sequencing takes advantage of the rapid and

massive data production of next-generation sequencing, while focusing on specific genes of

interest. Although any class of gene can be potentially targeted, I opted for single-copy genes,

attempting to avoid potential spurious DNA hybridizations with paralogues, which can bias

diversity estimates. However, targeting single-copy genes has the disadvantage of characterizing

genome-wide diversity based on a single “class” of genes, which may not necessarily represent a

heterogeneous sample from the genome. Nonetheless, the degree of variation and distribution of

the data within each species, in combination with my results, suggests that this sample of 502

genes maybe a good representation genome-wide variation. Furthermore, this method has been

proven successful when DNA material is only available in low quantities (Hancock-Hanser et al.

2013), or from degraded samples (Templeton et al. 2013). All of my samples came from dried

museum specimens and FTA plant saver cards (Dentinger et al. 2010), which would not reach

quantity/quality standards for other popular downstream genomic applications such as whole-

genome resequencing or reduced-library representation approaches (e.g. RAD-tag, GBS). In

particular, reduced-library representation approaches are practical for sampling genome-wide

SNPs, however the shortness of the fragments makes it impossible to conduct codon-based

divergence and polymorphism assessments. Therefore, exon-targeting methods are a practical

107

and efficient strategy for scoring many loci and complete structural genes in non-model

organisms.

6.4.2 Effects of selection and demography on genomic variation

One of the most striking results is the consistent excess of polymorphism compared to

divergence (Fig. 6.2), which translates into reduced adaptive amino acid substitutions. Studies in

flies (Smith and Eyre-Walker 2002; Bierne and Eyre-Walker 2004; Sella et al. 2009), plants

(Slotte et al. 2010), and humans (Fay et al. 2001) have suggested that the rate of adaptive

substitution can reach high proportions (20–40%), even in the presence of high levels of

selective constrain or purifying selection (Williamson et al. 2014; Slotte et al. 2010). The rate of

adaptive evolution is thought to be even higher in microorganisms, such as bacteria

(Charlesworth and Eyre-Walker 2006). In contrast, in these Amanita species, both MKT and

!!!!! likelihood-ratio tests indicate that the majority of genes approximate neutrality, with a

skew towards a negative alpha, and an overall small fraction of genes under adaptive evolution

(Fig. 6.2). Genome-wide diversity can arise from different evolutionary processes. Among them,

I will discuss the relative contributions of selection, population size changes, and gene flow in

explaining diversity patterns within and among members of the species complex.

Strong selective constraint is expected in protein coding DNA, largely because the majority of

amino acid changing mutations are likely to be deleterious (Ohta 1992; Andolfatto 2005). Both

polymorphism (Table 6.1, Appendix Fig. D8.8-9) and divergence (Fig. 6.2) data support overall

high levels of selective constraint in the A. jacksonii complex. One possible explanation for

values of ! ! ! and !! ! !! is purifying selection. If non-synonymous substitutions are not

being fixed between species, it is likely that they were deleterious early in the evolution of a

lineage, and are thus being purged from the population (Fay and Wu 2003). Mixed genome-wide

signals of positive and negative selection are not uncommon, and have been found in other

organisms such as plants (Williamson et al. 2014) and humans (Fay et al. 2001). An excess of

polymorphism relative to divergence can also be caused either by balancing selection (Verrelli et

al. 2002), a prevalence of soft selective sweeps (Messer and Petrov 2013b), or local adaptation in

subpopulations (Turner et al. 2010). In either case I would observe a high ratio of non-

synonymous to synonymous polymorphism, which would cause both ! ! ! and !! ! !!.

Genes under long-term balancing selection have longer than expected coalescence times due to

108

the retention of ancestral polymorphism (Edwards et al. 1997; Charlesworth 2009). Moreover,

high ! across multiple species is an indicator of positive selection on allelic diversity (Yang et

al. 2005; Aguilar and Garza 2007). Results show little support for long-term diversifying

selection. Only 10% of genes had a significant site class with ! ! !. In addition, genes with

strongly negative ! were not significantly affected by ancestral polymorphism (i.e. low gsi

scores; Appendix Fig. D8.12).

The fate of adaptive and slightly deleterious mutations is highly affected by !! (Ohta 1992;

Eyre-Walker and Keightley 2007; Charlesworth 2009). In small populations, adaptive mutations

can get lost by drift effects alone, while slightly deleterious mutations are effectively neutral and

can increase in frequency within the population. MKT-based approaches are generally

insensitive to the presence of slightly deleterious mutations (although see Eyre-Walker and

Keightley 2007 and Messer and Petrov 2013a). However, estimates of the effective population

size from silent diversity and the inference of demographic changes can help clarify if slightly

deleterious mutations might be common given a demographic history. Results from temporal

demographic models in BEAST indicate recent sharp increases in population size, particularly in

A. jacksonii, A. sp-jack2, A. sp-jack6, and A. sp-jack5 (Fig. 6.5). These changes are consistent

with skewed genome-wide frequency spectra (!Taj ! !, Fig. 6.1), also reflecting the steepness of

the population size change. From estimates of !! (Appendix Fig. D8.10) and demographic trends

(Fig. 6.5), I observe that A. sp-F11 has the largest !! and a relatively stable demography.

Amanita sp-F11 is likely to have had a stable population size throughout the glaciations confined

to a known Pleistocene refugium in the southeastern US (Fig. 6.7) (Soltis et al. 2006; Sánchez-

Ramírez et al. accepted). This explains not only the differences in !! and diversity between A.

sp-F11 and the rest of the complex, but it also allows us to use A. sp-F11 as reference species

with a constant population size. By doing so, I show that compared to A. sp-F11, species that

underwent steep demographic expansions (A. jacksonii, A. sp-jack2, A. sp-jack6, A. sp-jack5)

have lower !! (Appendix Fig. D8.10) in addition to an excess of low frequency variants (Fig.

6.1). When !! is low, slightly deleterious mutations are effectively neutral (Ohta 1992), and can

rise in frequency with demographic expansion (Nielsen 2001). This same situation can be

applied to species that did not undergo demographic expansion, but have low historical

population sizes such as A. sp-jack3, A sp-jack1, and A. sp-T31 (Fig. 6.5). Abundance of slightly

deleterious mutations can certainly underestimate MKT-based estimates of ! (Smith and Eyre-

109

Walker 2002). Since demography is more likely to affect the whole genome compared to soft

sweeps and local adaptation, I prefer the explanation that low !! caused by population

bottlenecks during the Pleistocene glaciations, followed by recent expansions, has recently

increased the frequency of slightly deleterious/effectively neutral mutations causing an excess of

polymorphism compared to divergence. This signal can be amplified by purifying selection,

further increasing the gap between divergence and polymorphism.

Adaptive responses can follow rapid environmental change or the colonization of a new

environment (Hermisson and Pennings 2005; Barrett and Schluter 2007; Hoffmann and Sgrò

2011). The Pleistocene is characterized by a series of abrupt temperature oscillations that have

deeply affected the genetic diversity of populations and species (Hewitt 1996, 2000, 2004a,b). If

I consider a situation where different alleles are selected during glacial and inter-glacial cycles, I

might expect to find a high degree of polymorphism. This is in fact a prediction during soft

selective sweeps under recurrent !! bottlenecks (Messer and Petrov 2013b). Although

demographic analysis did not support recurrent fluctuations of population size (Fig. 6.5), it is not

unlikely that these species might have suffered from recurrent fluctuations in population size

during glacial cycles. Under this view, I do not discard the effects of soft selective sweeps as a

potential cause for increased polymorphism.

Although I did not quantify gene flow directly, per gene gsi values provide an overview of the

extent of intraspecific gene sharing. Species that are more evolutionary distant (A. sp-F11, A. sp-

T31, A. sp-jack1) tend to share less alleles among species, than those that are more closely

related (A. jacksonii, A. sp-jack2, A. sp-jack3, A. sp-jack5, A. sp-jack6) (Figs. 6.4 and 6.5). In

particular, substantial allele sharing was observed (data not shown) between geographically close

species pairs (Fig. 6.7; A. jacksonii and A. sp-jack2; and A. sp-jack5 and A. sp-jack6). To less

extent A. sp-jack3 had some allele sharing with A. sp-jack2 and A. sp-jack5, which are also

geographically close, however, had more exclusive alleles. This is consistent with the view of A.

sp-jack3 being more range restricted and having a relatively small !!. Furthermore, it has been

shown that in subdivided populations different sampling strategies a can affect frequency-based

diversity estimators, such as ! and !Taj, to various degrees in the presence of migration (Städler

et al. 2009; Cutter et al. 2012). In particular, if high migration will tend to homogenize the

coalescence of samples making it converge to a similar !Taj value regardless of the sampling

110

Figure 6.7. Species distribution maps based on geographic data from Sánchez-Ramírez et

al. (accepted; Chapter 5) and MaxEnt (Phillips et al. 2006). The dotted line marks an

hypothesized limit for glacial refugia.

strategy. In contrast, low migration will tend to polarize and bias !Taj values towards high or low

values depending on the sampling (Cutter et al. 2012). I show that !Taj is not overall affected by

genes with low gsi. This means that the skew in !Taj estimates is not directly influenced by allele

111

sharing between species. Although the sampling strategy might still be a potential factor, I not

aware of any population structure within species that might bias within species diversity

estimates.

6.4.3 Functionality of adaptive genes and the EM habit

The genes that were assayed here were not selected for their specific functions. However,

comparing functional properties across species, among those genes under a given selective force,

may shed light on potential shared and local adaptive responses. Two of the most prevalent GO

terms with genes under positive directional selection (i.e. ! ! !) and positive diversifying

selection (! ! !) reflect ATP and protein binding functions. Although these terms are too

generic, probably due to the still insufficient characterization of fungal proteins, they suggest that

many of these genes are involved with energetic and metabolic cellular processes, as well as

protein-protein interactions. Overall, protein functions seem to be divided into three general

categories; those related to (1) gene transcription/expression activities, (2) signalling and

transport, and (3) metabolic oxidative processes (Fig. 6.6). Many of these functions coincide

with divergent regions in the genome of another EM fungus (S. brevipes) that are also thought to

be under selection (Branco et al. 2015). I also noted some functions related to carbohydrate

metabolism, specifically with catalytic and hydrolytic activities (Fig. 6.6, Appendix Fig. D8.14).

These may be compelling because all EM fungi depend on external carbohydrate sources that are

generally supplied by their hosts (Nehls et al. 2007). Throughout the evolution of the EM

symbiosis, many species have suffered from a convergent loss of decay mechanisms, while

retaining moderate gene repertoires with lignocellulitic capabilities (Kohler et al. 2015).

Adaptation in proteins related to carbohydrate metabolism, such as hydrolase activity, could

allow for a more efficient breakdown of complex polysaccharides during periods of stress and/or

low photosynthetic activity. Also worth mentioning, proteins with telomeric template reverse

transcriptase activity were found under diversifying selection (Fig. 6.6B). Studies suggest that

the dynamic nature of telomeric and subtelomeric regions are likely to contribute to rapid

evolution of fungi during biotic interactions (Aguileta et al. 2009).

I find that it is more common to find genes under adaptive divergence within species than genes

under diversifying selection across all species. This implies that different genes within species

have undergone adaptive evolution more commonly than a single gene across species. One of the

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species that had the most exclusive set of genes under positive selection was A. sp-F11. To my

knowledge (Sánchez-Ramírez et al. 2015a, accepted) this species is restricted to a unique

environment in semi-tropical temperate forests in Florida (USA), with virtually no overlap with

other species in the group (Fig. 6.7). This observation could be an indication of local adaptation

to a Plio-Pleistocene refugium environment in this species.

6.5 Conclusions

I produced DNA sequence data for 502 structurally complete genes across multiple samples in

eight recently diverged fungal species. Multi-locus coalescent models indicate that relatively

recent demographic expansions during the Pleistocene have strongly contributed to diversity

patterns throughout the genome. I explain that demographic expansions have increased the

frequency of rare polymorphisms in at least four species, most notoriously in the one with the

northernmost distribution (A. jacksonii). An abundance of non-synonymous variation and

purifying selection are evidenced by an excess of polymorphism relative to divergence, and

reflects a low average proportion of adaptive fixations. Nevertheless, some of the genes found to

be either under directional positive selection within species or diversifying selection among

species suggests that transcription regulation, cell transport, and oxidative metabolism have been

important factors leading to adaptive responses in the A. jacksonii complex.

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Chapter 7

CONCLUDING REMARKS AND PERSPECTIVES 7This thesis explores several aspects of the evolution of the Caesar’s mushrooms, by using a

“stepwise phylogenetic zooming” approach; from macro- to microevolution, and from global to

regional patterns (Fig. 7.1). In Chapter 2 general geographic and diversity patterns are inferred at

a global scale. Chapter 3 investigates diversity drivers in one of the most species-rich clades.

Chapter 5 broadens the geographic sampling within that clade at a more regional scale to delimit

species, and investigates relatively “recent” climate-driven diversity patterns. Finally, Chapter 6

looks into genome-wide diversity patterns within a few closely related species. The “zooming” is

both at temporal and spatial scales. To my knowledge, no other study focusing on fungi has

investigated macro- and microevolutionary patterns throughout time, space, and genomes.

Figure 7.1. Temporal and spatial scales explored within the Caesar’s mushrooms.

0102030405060 0102030405060 01020 05

!"#$%&'() !"#$%&'(* !"#$%&'(+ !"#$%&'(,

Ma

114

Macroevolutionary investigations are generally complemented and strengthened by incorporating

data from fossil records. Many animals and plants have good fossil records that can make

macroevolutionary and biogeographic hypotheses robust. However, most fungal tissue decays

quickly and lacks rigid substances, making them poor fossilizers. By using time-scaled

phylogenies –from external calibrations with some of the few fungal fossils (Hibbett et al. 2007;

Smith et al. 2004)– I was able to infer ancestral distribution and dispersal patterns in the Caesar’s

mushrooms. Some of the results shown here may have implications for geographic and

evolutionary histories in other fungi and other organisms in general. For instance, botanists

studying biogeographic patterns of ectotrophic trees might find interesting that potential

symbiotic partners may have been found in Africa as early as the Eocene. Also that “Out-of-

Africa” and “Out-of-the-Paleotropics” dispersal patterns of EM species may have assisted

Boreotropical distributions during the Tertiary (Chapter 2). This pattern has also been suggested

for other groups of ectomycorrhizal fungi (Matheny et al. 2009; Du et al. 2012; Wilson et al.

2012). Moreover, the relationship between East Asia and North American floras is of interest to

many botanists (Quian and Ricklefs 2000; Xiang et al. 2000; Smith and Donoghue 2004).

Chapter 2 also documents that recurrent dispersal between these two continents was active

during the late Miocene and Pliocene, and probably assisted and/or followed established

ectotrophic tree communities.

Some attention has been given recently to global diversity patterns in fungi (Tedersoo et al.

2012, 2014). An interesting pattern that has arisen is that the diversity of EM fungi is higher in

subtropical and temperate regions, rather than following the general LDG pattern observed in

most other group of organisms. Although several hypotheses have been proposed (Tedersoo and

Nara 2010), I show that, at least in the Caesar’s mushrooms, this diversity bias might be a result

of high rates of speciation at temperate latitudes (Chapter 3). Evidently, more data from other

EM taxa/clades is needed to give more support to this pattern at a larger scale. Likewise, future

studies would benefit from the integration of fine-scale ecological and climatical data to larger

and better sampled phylogenies.

Genome data is part of the 21st century evolutionary biology (Losos et al. 2013). Comparative

genomics and population genomics provide the means to look deeper and more closely into the

evolutionary history of species and populations. In Chapter 4, I produced, assembled, and

annotated the genome of a strain of A. jacksonii, and obtained partial genomic sequences from a

115

strain of a closely related species, A. basii. I used these genomic data to developed novel single-

copy gene markers for multilocus phylogenetic analyzes (Chapter 5) and genome-wide diversity

exploration (Chapter 6). This is the first genome sequenced from an edible species of Amanita

and may provide insights later in the exploration of mushroom toxicity/edibility, and

complement our understanding about the evolution of ectomycorrhizal symbiosis (e.g. Kohler et

al. 2015) in the genus.

Fungal phylogeography is still in its early stages (Lumbsch et al. 2008). Part of the limitations

involve the lack of sufficient geographic sampling (Schmit and Mueller 2007), the failure to

recognize distinct evolutionary units (Taylor et al. 2000, 2006), and difficulties to characterize

diversity patterns in temporal and spatial contexts. Chapter 5 seeks to overcome some of these

limitations in the limitations of diversity in the North American Caesar’s mushrooms. As already

found in other fungi (Crespo and Lumbsch 2010), I uncover high levels of cryptic diversity in

North America. Most of the cryptic diversity occurs within refugia, probably as a result of high

speciation rates during glacial oscillations. Chapter 5 also reveals many population expansions

during the Pleistocene, most of which occur within refugial latitudes. This study evidences

responses to past climate change in fungi, which have been rarely documented.

Genome-wide DNA data can provide insights into demographic and adaptive processes that

shape diversity. In Chapter 6, a sample of 502 putative single-copy genes revealed differences in

DNA variation and frequency spectra among eight closely related species in the A. jacksonii

complex. Models for detecting selection and demographic trends indicate a strong influence of

negative selection and recent expansion events during the Pleistocene. In this Chapter, I also

identify potential genes involved in adaptive processes. Some of these may prove to be part of

fine-level symbiotic relationships or abiotic conditions that allow these fungi to thrive in the

North American temperate ecosystem.

Many new and interesting questions also emerged from this thesis. For instance, compared to

Asia and America, Europe apparently holds low diversity, in spite of being mostly temperate. Is

there really low diversity? Or is it a matter of sampling? To my knowledge there are many

geographic populations of “A. caesarea” in Europe and adjacent areas. For instance, there are

records of this species in highland forests in Iran, Georgia, Turkey, the Balkans, the Carpatians,

Italy, France, Spain, and Algeria, in addition a few small populations in Switzerland that are

116

protected. It would be interesting to know if these, in fact, represent a single or multiple cryptic

species, and if they diverged as a result of relictual isolation during the Quaternary. In North

America, A. basii and A. cochiseana are two sister species with a large effective population size

and a recent history of divergence. Although A. basii is generally found in southern Mexico, I

was able to identify one specimen of A. basii in New Mexico, U.S.A. With better geographic and

genomic sampling it would be interesting to see if this species pair forms a Mesoamerican

“species ring” fueled by Pleistocene dynamics. Many questions related to “speciation genomics”

also remain unanswered. For instance, how do sympatric species develop and maintain

reproductive isolation? Amanita calyptroderma and A. vernicoccora in western North America

appear to have decoupled fruiting phenology; the former fruits in the summer while the later

fruits in the spring. But, are there any environmental or genomic cues? What about other

sympatric species in southeastern U.S.A? Continuous sampling and genotyping in poorly known

areas is likely to provide better insights. Finally, further research on A. jacksonii may also prove

to be enlightening for studying the genomics of demographic expansions, selection, local

adaptation, and recombination in populations of ectomycorrhizal fungi.

117

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Appendices 8Appendix A: Chapter 2

Table A8.1. !"#$%#&'()*#&+',-.$/#0&'12&+'3-$)4%-(&')(5'6#(7)(8')$$#&&%-('(.*9#0&'-:')33'&)*"3#&':-0';/)"4#0'<=

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

Amanita arkansana RET 139-10 USA, Texas NW KF877195 KF877098 KF877036

A. arkansana RET 283-5 USA, Texas NW KF877196 KF877099 KF877037

A. arkansana RET 354-9 USA, Texas NW KF877197 KF877100 KF877038

A. banningiana RET 030-4 USA, New Jersey NW KF877198 KF877101

A. banningiana RET 065-5 USA, Connecticut NW KF877199 KF877102

A. basii FCME Sanchez-S32 Mexico, Distrito Federal NW KF877200 KF877039

A. basii FCME Sanchez-S38 Mexico, Michoacán NW KF877201

A. basii FCME Sanchez-S44 Mexico, Puebla NW KF877202 KF877103 KF877040

A. basii RET 260-6 Mexico, Estado de Mexico NW KF877203

A. belizeana RET 094-5 Belize NW KF877204 KF877104 KF877041

A. caesarea RET 036-2 Italy, Cozenza OW KF877205 KF877105 KF877042

A. caesarea RET 142-9 Italy, Cozenza OW KF877206

141

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. caesarea RET 427-1 Italy, Cozenza OW KF877207 KF877106

A. caesarea RET 450-3 Italy, Cozenza OW KF877208

A. affin. caesareoides RET 356-10 China, Yunnan OW KF877209 KF877107

A. calyptratoides MEXU 24314 USA, California NW KF877210 KF877108

A. calyptratoides RET 273-7 USA, California NW KF877211 KF877109 KF877043

A. calyptratoides RET 382-8 USA, California OW KF877212 KF877044

A. calyptroderma RET 092-6 USA, California NW KF877213

A. calyptroderma RET 385-3 USA, Oregon NW KF877214

A. calyptroderma RET 385-4 USA, Washington NW KF877215 KF877110 KF877045

A. calyptroderma RET 385-7 USA, Washington NW KF877216 KF877111 KF877046

A. calyptroderma RET 389-4 USA, Washington NW KF877217

A. calyptroderma RET 389-5 USA, Washington NW KF877218 KF877112 KF877047

A. calyptroderma RET 389-9 USA, Washington NW KF877219

A. chepangiana UTC Shrethsa 154P Nepal OW KF877220 KF877113

A. cinnamomescens RET 290-5 Pakistan OW KF877221

142

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. cinnamomescens RET 317-5 Pakistan OW KF877222 KF877114 KF877048

A. cochiseana FCME Sanchez S31 Mexico, Chihuahua NW KF877223 KF877115

A. cochiseana RET 072-5 USA, New Mexico NW KF877224 KF877116 KF877049

A. cochiseana RET 497-10 USA, Arizona NW KF877225 KF877117 KF877050

A. cochiseana RET 498-1 USA, Arizona NW KF877226 KF877118 KF877051

A. egregia RET 136-7 Australia, Queensland OW KF877227 KF877119 KF877052

A. affin. esculenta TRTC150406 Thailand, Chiang Mai OW KF877228 KF877120

A. affin. esculenta TRTC150410 Thailand, Chiang Mai OW KF877229 KF877121 KF877053

A. affin. esculenta TRTC150413 Thailand, Chiang Mai OW KF877230

A. garabitoana RET 333-6 Costa Rica, San José NW KF877231 KF877122 KF877054

A. affin. hayalyuy FCME-15194 Mexico, Oaxaca NW KF877232 KF877123

A. hemibapha RET 342-8 India, Kerala OW KF877233 KF877124 KF877055

A. affin. hemibapha BPI HPUB 560 India, Himachal Pradesh OW KF877234 KF877125

A. affin. hemibapha RET 257-10 China, Yunnan OW KF877235 KF877056

A. affin. hemibapha RET 258-3 China, Yunnan OW KF877236 KF877126

143

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. affin. hemibapha RET 349-5 Thailand, Chiang Mai OW KF877237

A. affin. hemibapha RET 456-7 Japan OW KF877238 KF877127 KF877057

A. affin. hemibapha TRTC150286 Thailand, Chiang Mai OW KF877239 KF877128 KF877058

A. affin. hemibapha TRTC150314 Thailand, Chiang Mai OW KF877240 KF877129 KF877059

A. affin. hemibapha TRTC150422 Thailand, Chiang Mai OW KF877241 KF877130 KF877060

A. affin. hemibapha TRTC157250 Viet Nam, Cát Tiên OW KF877242 KF877131 KF877061

A. affin. hemibapha TRTC161134 Viet Nam, Da Lat OW KF877243 KF877132 KF877062

A. affin. hemibapha TRTC161164 Viet Nam, Da Lat OW KF877244 KF877133

A. affin. hemibapha TRTC161171 Viet Nam, Da Lat OW KF877245 KF877134

A. hemibapha var. ochracea RET 258-1 China, Yunnan OW KF877246 KF877135 KF877063

A. jacksonii RET 109-4 USA, Massachusetts NW KF877247 KF877136 KF877064

A. jacksonii RET 154-10 USA, New York NW KF877248 KF877137

A. jacksonii RET 315-8 USA, Connecticut NW KF877249 KF877138

A. jacksonii RET 354-8 USA, Texas NW KF877250 KF877139

A. jacksonii RET 393-6 USA, North Carolina NW KF877251 KF877140

144

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. jacksonii RET 393-7 USA, North Carolina NW KF877252 KF877141 KF877065

A. jacksonii RET 393-8 USA, North Carolina NW KF877253 KF877142

A. affin. jacksonii FCME 21550 Mexico, Oaxaca NW KF877254 KF877143

A. affin. jacksonii FCME 21652 Mexico, Oaxaca NW KF877255 KF877144 KF877066

A. affin. jacksonii F 1132188 USA, Texas NW KF877256 KF877145 KF877067

A. affin. jacksonii RET 252-9 Mexico, Hidalgo NW KF877257 KF877146

A. affin. jacksonii XAL Chacon 5895 Mexico, Veracruz NW KF877258 KF877147

A. mafingensis RET 348-8 Zambia OW KF877259 KF877148

A. affin. mafingensis MA 1065 Republic of the Congo OW KF877260 KF877149

A. affin. mafingensis MA 1069 Republic of the Congo OW KF877261 KF877150 KF877068

A. affin. mafingensis MA 898 Republic of the Congo OW KF877262 KF877151 KF877069

A. affin. mafingensis RET 345-9 Zambia OW KF877263

A. masasiensis RET 344-5 Zambia OW KF877264 KF877152 KF877070

A. affin. masasiensis De Kesel 3579 Benin OW KF877265 KF877153 KF877071

A. affin. masasiensis RET 348-2 Zambia OW KF877266 KF877154 KF877072

145

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. affin. masasiensis RET 348-3 Zambia OW KF877267 KF877155

A. affin. masasiensis Sharp 607-97 Zimbabwe OW KF877268 KF877156 KF877073

A. murrilliana RET 278-1 USA, North Carolina NW KF877269 KF877157 KF877074

A. murrilliana RET 374-2 USA, Tennessee NW KF877270

A. murrilliana RET 251-4 Canada, Quebec NW KF877271

A. affin. princeps RET 357-5 China, Yunnan OW KF877272 KF877158

A. affin. princeps RET 359-9 Thailand, Chiang Mai OW KF877273 KF877159

A. affin. princeps TRTC150309 Thailand, Chiang Mai OW KF877274 KF877160

A. recutita sensu Coker CSU CL Ovrebo (4809B)

USA, Oklahoma NW KF877275 KF877161

A. ristichii RET 096-1 Canada, Quebec NW KF877276 KF877162 KF877075

A. ristichii RET 124-10 USA, Maine NW KF877277

A. roseolamellata RET 476-4 Australia, New South Wales

OW KF877278 KF877163

A. rubromarginata RET 383-1 Japan, Okinawa OW KF877279 KF877164

A. sp-53 RET 383-2 USA, New York NW KF877280 KF877165 KF877076

146

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. sp-AR01 RET 373-9 USA, Arkansas NW KF877281 KF877166 KF877077

A. sp-AUS02 R. Halling 6814 Australia, Queensland OW KF877282 KF877167

A. sp-AUS03 R. Halling 6815 Australia, Queensland OW KF877283 KF877168

A. sp-F11 RET 138-1 USA, Florida NW KF877284 KF877169

A. sp-F11 RET 138-2 USA, Florida NW KF877285 KF877170 KF877078

A. sp-M36 RET 293-3 Mexico, Tlaxcala NW KF877286 KF877171 KF877079

A. sp-M36 RET 293-4 Mexico, Tlaxcala NW KF877287 KF877172

A. sp-T31 RET 365-1 USA, Texas NW KF877288 KF877173 KF877080

A. sp-Thai03 RET 350-1 Laos OW KF877289 KF877174 KF877081

A. sp-Thai03 RET 351-7 Thailand, Nakhon OW KF877290 KF877175 KF877082

A. sp-Thai03 RET 356-5 Thailand OW KF877291 KF877176

A. sp-W15 DEWV 400 USA, West Virginia NW KF877292 KF877177 KF877083

A. sp-W15 DEWV 9597 USA, West Virginia NW KF877293 KF877178 KF877084

A. spreta RET 296-10 USA, New Jersey NW KF877294 KF877179 KF877085

A. spreta RET 296-2 USA, New York NW KF877295

147

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. spreta RET 315-10 USA, Connecticut NW KF877180 KF877086

A. affin. tanzanica RET 346-7 Zambia OW KF877296 KF877181

A. tlaxcandela RET 292-1 Mexico, Tlaxcala NW KF877297 KF877182 KF877087

A. vernicoccora RET 084-7 USA, California NW KF877298

A. vernicoccora RET 281-9 USA, Washington NW KF877299 KF877183 KF877088

A. vernicoccora RET 385-2 USA, Washington NW KF877300 KF877184 KF877089

A. vernicoccora RET 385-6 USA, Oregon NW KF877301 KF877185 KF877090

A. vernicoccora RET 385-8 USA, California NW KF877302 KF877186

A. virginiana RET 268-10 USA, Connecticut NW KF877303 KF877091

A. virginiana RET 361-6 USA, Massachusetts NW KF877304 KF877187

A. virginiana RET 374-8 USA, Tennessee NW KF877305 KF877092

A. yuaniana RET 257-8 China, Yunnan OW KF877306 KF877188

A. zambiana De Kesel 3227 Benin OW KF877307 KF877189 KF877093

A. zambiana De Kesel 3714 Benin OW KF877308 KF877190 KF877094

A. zambiana De Kesel 4378 Togo OW KF877309 KF877191 KF877095

148

Taxon Specimen voucher Location BiSSE LSU tef1 rpb2

A. zambiana MA 861 Republic of the Congo OW KF877310 KF877192

A. zambiana RET 261-3 Burundi OW KF877311 KF877193 KF877096

A. zambiana RET 343-10 Zambia OW KF877312 KF877097

GenBank

A. chepangiana HKAS-2772 China, Sichuan OW AF024445

A. incarnatifolia HKAS-29519 China OW AF024459

A. affin. javanica HKAS-56863 China OW JX998071 JX998014

A. longistriata C Bas 9040 L China OW AF024462

A. affin. princeps HKAS-75788 China OW JX998067 JX998013

A. torrendii LOU Fungi 18202 Spain OW GQ925368

Uncultured clone c45 USA, Georgia NW KC424546

ectomycorrhizal fungus

Outgroup

A. muscaria var. guessowii RET 303-4 USA, New Jersey EU071990 EU071875

A. affin. vaginata TRTC150325 Thailand, Chiang Mai KF877313 KF877194

149

Table A8.2. GenBank sequences used for fossil-calibrated dating analysis.

Taxa (GenBank label) Order / clade LSU rpb2 tef1

Pluteus romellii Agaricales / pluteoid AY634279 AY786063 AY883433

Volvariella gloiocephala Agaricales / pluteoid DQ089020

Amanita brunnescens Agaricales / pluteoid AY631902 AY780936 AY881021

Pleurotus ostreatus Agaricales / pluteoid AY645052 AY786062 AY883432

Physalacria bambusae Agaricales / marasmioid DQ097349 DQ474123 GU187732

Flammulina velutipes Agaricales / marasmioid AY639883 AY786055 AY883423

Xerula radicata Agaricales / marasmioid AY645051 AY786067 DQ029194

Armillaria mellea Agaricales / marasmioid AY700194 AY780938 AY881023

Marasmius rotula Agaricales / marasmioid DQ457686 DQ474118 GU187723

Gymnopus dryophilus Agaricales / marasmioid AY640619 DQ472717 DQ408152

Mycetinis alliaceus Agaricales / marasmioid AY635776 AY786060 AY883430

Anthracophyllum archeri Agaricales / marasmioid AY745709 DQ385877 DQ028586

Gymnopus contrarius Agaricales / marasmioid DQ457670 DQ472716 GU187700

Mycena amabilissima Agaricales / marasmioid DQ457691 DQ474121 GU187727

Mycena aurantiidisca Agaricales / marasmioid DQ470811 DQ474122 GU187728

Hydropus cf. scabripes Agaricales / marasmioid DQ411536 DQ457634

Porotheleum fimbriatum Agaricales / marasmioid DQ457673 DQ472721

Megacollybia platyphylla Agaricales / marasmioid AY702016 DQ385887 DQ435786

150

Taxa (GenBank label) Order / clade LSU rpb2 tef1

Hemimycena gracilis Agaricales / marasmioid DQ457671 DQ472719 GU187709

Baeospora myosura Agaricales / marasmioid DQ457648 DQ470827 GU187762

Cheimonophyllum

candidissimum

Agaricales / marasmioid DQ457654 DQ470831 GU187760

Chondrostereum

purpureum

Agaricales / marasmioid AY586644 AY218477 DQ457632

Henningsomyces candidus Agaricales / marasmioid AY571008 AY218513 AY883424

Macrotyphula fistulosa Agaricales / hygrophoroid DQ071735

Typhula phacorrhiza Agaricales / hygrophoroid AY586724 AY218525

Pleurocybella porrigens Agaricales / hygrophoroid EF537894 GU187740

Phyllotopsis sp. Agaricales / hygrophoroid AY684161 AY786061 DQ059047

Mycena galericulata Agaricales / hygrophoroid AY647216 DQ385888

Mycena plumbea Agaricales / hygrophoroid DQ470813 GU187729

Sarcomyxa serotina Agaricales / hygrophoroid AY691887 DQ859892 GU187754

Xeromphalina campanella Agaricales / hygrophoroid DQ470826 GU187758

Hygrocybe coccinea Agaricales / hygrophoroid DQ457676 DQ472723 GU187705

Hygrocybe aff. conica Agaricales / hygrophoroid AY684167 AY803747 AY883425

Hygrophorus pudorinus Agaricales / hygrophoroid DQ457678 DQ472725 GU187710

Chrysomphalina grossula Agaricales / hygrophoroid EU652373 DQ470832

151

Taxa (GenBank label) Order / clade LSU rpb2 tef1

Hygrophorus

auratocephalus

Agaricales / hygrophoroid DQ457672 DQ472720

Pseudoarmillariella

ectypoides

Agaricales / hygrophoroid DQ154111 DQ474127 GU187733

Camarophyllus basidiosus Agaricales / hygrophoroid DQ457651 DQ470828

Cantharocybe gruberi Agaricales / hygrophoroid DQ234540 DQ385879 DQ059045

Tricholomopsis decora Agaricales / hygrophoroid AY691888 DQ408112 DQ029195

Asterophora agaricoides Agaricales /

tricholomatoid

AF223190 DQ367431 DQ367424

Lyophyllum decastes Agaricales /

tricholomatoid

AY207228 DQ367433 DQ367426

Tephrocybe boudieri Agaricales /

tricholomatoid

DQ825430 DQ825411 EF421070

Termitomyces sp. Agaricales /

tricholomatoid

DQ110875 EF421010 EF421078

Clitocybe candicans Agaricales /

tricholomatoid

AY645055 DQ385881 DQ408149

Clitocybe subditopoda Agaricales /

tricholomatoid

AY691889 AY780942 DQ408151

Collybia tuberosa Agaricales /

tricholomatoid

AY639884 AY787219 AY881025

Clitocybe nebularis Agaricales / DQ457658 DQ470833 EF421081

152

Taxa (GenBank label) Order / clade LSU rpb2 tef1

tricholomatoid

Lepista irina Agaricales /

tricholomatoid

DQ385885 DQ234538 DQ028591

Tricholoma myomyces Agaricales /

tricholomatoid

U76459 DQ367436 DQ367429

Nolanea sericea Agaricales /

tricholomatoid

AY207197 DQ367435 DQ367428

Entoloma prunuloides Agaricales /

tricholomatoid

AY700180 DQ385883 DQ457633

Rhodocybe mundula Agaricales /

tricholomatoid

AY700182 DQ474128

Clitopillus prunulus Agaricales /

tricholomatoid

AY700181 GU384648

Catathelasma ventricosum Agaricales /

tricholomatoid

DQ089012 DQ470830

Callistosporum

graminicolor

Agaricales /

tricholomatoid

GU187553 GU187815 GU187742

Pseudoclitocybe

cyathiformis

Agaricales /

tricholomatoid

EF551313 GU187815 GU187742

Infundibulicybe gibba Agaricales /

tricholomatoid

DQ457682 DQ472727 GU187759

Strobilomyces floccopus Boletales AY612824 AY786065 AY883428

Boletus edulis Boletales HQ326927 GU187774 HQ326860

153

Taxa (GenBank label) Order / clade LSU rpb2 tef1

Boletus projectellus Boletales AY684158 AY787218 AY879116

Paxillus vernalis Boletales AY645059 DQ457629

Hydnomerulius pinastri Boletales GU187580 GU187787 GU187708

Suillus bresadolae Boletales GU187598 GU187810 GU187746

Suillus pictus Boletales AY684154 AY786066 AY883429

Gomphidius roseus Boletales DQ534669 GU187818 GU187702

Calostoma cinnabarinum Boletales AY645054 AY780939 AY879117

Leucogyrophana

lichenicola

Boletales GU187583 GU187789 GU187715

Serpula lacrymans Boletales GU187596 GU187809 GU187752

Serpula himantioides Boletales AF518648 DQ366283 DQ059046

Serpula tignicola Boletales GU187597 GU187753

Leucogyrophana mollusca Boletales GU187584 GU187795

Leucogyrophana arizonica Boletales GU187582 GU187792 GU187714

Pseudomerulius curtisii Boletales GU187589 GU187796 GU187725

Tapinella atrotomentosa Boletales GU187603 GU187813 GU187757

Fibulorhizoctonia sp. Atheliales AY635779 AY857985 AY879115

Coltricia perennis Hymenochaetales AJ406472 AY218526 AY885147

Fomitiporia mediterranea Hymenochaetales AY684157 AY803748 AY885149

154

Taxa (GenBank label) Order / clade LSU rpb2 tef1

Peniophorella

praetermissa

Hymenochaetales AY707094 AY787221 AY885150

Cotylidia sp. Hymenochaetales AY629317 AY883422 AY885148

Gautieria otthii Gomphales AF336249 AY218486 AY883434

155

Figure A8.1. Fossil-calibrated maximum-clade-credibility tree of the beast analysis of

dataset 1 including Amanita sect. Caesareae. Horizontal bars represent highest posterior

20 Ma

0255075100125150175200

Pos

terio

r D

ensi

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156

density (HPD) intervals. White circles with letters indicate the mean age where fossil constrains were placed. Name clades were constrained as monophyletic. Only nodes with posterior probabilities greater than 0.8 are annotated. The density plot in on the top-left

corner shows age estimates from four runs on the most recent common ancestor (MRCA) of Amanita subg. Amanita.

157

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158

Figure A8.2. Bayesian binary Markov chain monte carlo (BBM) results from rasp analysis

of Amanita sect. Caesareae (dataset 2) showing the most likely reconstructed areas (left)

and pie-charts with area posterior probabilities (right). The model used in rasp was F81+G.

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159

Appendix B: Chapter 3

Figure B8.3. The set of credible diversification rate shifts from BAMM based on a Bayes

Factor criterion of 3. Each plot indicates the sampled frequency of each model and time-

varying speciation rate estimates. Colors are on a temperature scale (see Fig. 3.1).

f = 0.69 f = 0.078 f = 0.048

f = 0.029 f = 0.026 f = 0.025

f = 0.025 f = 0.02 f = 0.015

160

Figure B8.4. Boxplots of speciation and extinction rates in temperate and tropical subclades

based on the second best QuaSSE model (model 2 in Table 3.1) for the clade ‘Caesarea’

data set. In this model, speciation and extinction rates are constant functions of latitude.

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161

Appendix C: Chapter 5

Table C8.3. Species, specimens, geographic locations, and GenBank accession numbers for DNA data used in Chapter 5.

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'affin. hayalyuy' FCME-15194 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724404 KP723856 KP724128

Amanita sp. 'affin. hayalyuy' SSRH-03-A Mexico: Oaxaca, San Sebastian Rio Hondo 16.179 -96.469 KP724405 KP723857 KP724129

Amanita sp. 'affin. hayalyuy' ABM040 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724406 KP723858 KP724130

Amanita sp. 'affin. hayalyuy' ABM041 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724407 KP723859 KP724131

Amanita sp. 'affin. hayalyuy' ABM045 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724408 KP723860 KP724132

Amanita affin hemibapha RET 456-7 Japan NA NA KP724409 KP723861 KP724133

Amanita arkansana H. R. Rosen RET 136-6 USA: Arkansas, Pulaski Co., Little Rock 34.728 -92.438 KP724410 KP723862 KP724134

162

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita arkansana RET 543-8 USA: Georgia, Gwinnett Co., Beverly Lake 33.983 -84.182 KP724411 KP723863 KP724135

Amanita arkansana MO 86 USA: Missouri, Wayne Co., Mingo National Wildlife Refuge 37.018 -90.131 KP724412 KP723864 KP724136

Amanita arkansana RET 354-10 USA: Texas, Newton Co., Bleakwood 30.706 -93.843 KP724413 KP723865 KP724137

Amanita arkansana RET 354-9 USA: Texas, Newton Co., Bleakwood 30.706 -93.843 KP724414 KP724138

Amanita arkansana RET 283-5 USA: Texas, Tyler Co., Spurger 30.690 -94.189 KP724415 KP723866 KP724139

Amanita arkansana RET 139-10 USA: Texas, Warren, Hicksbaugh Rd 30.526 -94.360 KP724416 KP723867 KP724140

Amanita basii Guzmán & Ram.-Gill.

FCME-Cifuentes-2013-01-01

Mexico: Aguascalientes, La Congoja 22.166 -102.555 KP724417 KP723868 KP724141

Amanita basii RS-186 Mexico: Chiapas, San Cristobal de las Casas 16.737 -92.638 KP724418 KP723869 KP724142

163

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii ABM016 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724419 KP723870 KP724143

Amanita basii ABM020 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724420 KP723871 KP724144

Amanita basii ABM022 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724421 KP723872 KP724145

Amanita basii ABM023 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724422 KP723873 KP724146

Amanita basii ABM024 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724423 KP723874 KP724147

Amanita basii ABM026 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724424 KP723875 KP724148

Amanita basii ABM027 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724425 KP723876 KP724149

Amanita basii ABM028 Mexico: Distrito Federal, Ajusco 19.208 -99.245 KP724426 KP723877 KP724150

164

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii V-054-A Mexico: Distrito Federal, Milpa Alta, Tlacoyucan San Lorenzo 19.137 -99.028 KP724427 KP723878 KP724151

Amanita basii ABM034 Mexico: Estado de Mexico, Nevado de Toluca 19.131 -99.748 KP724428 KP723879 KP724152

Amanita basii ABM035 Mexico: Estado de Mexico, Nevado de Toluca 19.131 -99.748 KP724429 KP723880 KP724153

Amanita basii ABM001 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724430 KP723881 KP724154

Amanita basii ABM003 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724431 KP723882 KP724155

Amanita basii ABM005 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724432 KP723883 KP724156

165

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii ABM009 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724433 KP723884 KP724157

Amanita basii ABM011 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724434 KP723885 KP724158

Amanita basii ABM012 Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724435 KP723886 KP724159

Amanita basii FCME-Sanchez-S44

Mexico: Estado de Mexico, Rio Frio de Juarez 19.385 -98.678 KP724436 KP723887 KP724160

Amanita basii FCME-10383 Mexico: Guerrero, Atlixtac 17.562 -98.934 KP724437 KP723888 KP724161

Amanita basii FCME-974 Mexico: Guerrero, Chichihualco, Los Morros 17.658 -99.676 KP724438 KP723889 KP724162

Amanita basii FCME-12699 Mexico: Guerrero, Chilpancingo de los Bravos 17.519 -99.561 KP724439 KP723890 KP724163

Amanita basii ABM046 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724440 KP723891 KP724164

166

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii ABM047 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724441 KP723892 KP724165

Amanita basii ABM048 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724442 KP723893 KP724166

Amanita basii ABM043 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724443 KP723894 KP724167

Amanita basii IBUG LV-174 Mexico: Jalisco, Mascota, El Sol Oros 20.380 -104.582 KP724444 KP723895 KP724168

Amanita basii IBUG F-858 Mexico: Jalisco, Mazamitla 19.875 -103.018 KP724445 KP723896 KP724169

Amanita basii IBUG V-214 Mexico: Jalisco, Mezquitic, San Andres Cohamiata 22.194 -104.204 KP724446 KP723897 KP724170

Amanita basii IBUG OR-1414 Mexico: Jalisco, San Ignacio, Cerro Gordo 20.757 -102.583 KP724447 KP723898 KP724171

Amanita basii IBUG SJ-594 Mexico: Jalisco, Sierra de Quila 20.278 -104.085 KP724448 KP723899 KP724172

Amanita basii IBUG V-145 Mexico: Jalisco, Tapalpa de Allende, El Refugio 20.261 -104.776 KP724449 KP723900 KP724173

167

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii IBUG OG-89 Mexico: Jalisco, Tecaltitlan, El Rebaje 19.403 -103.322 KP724450 KP723901 KP724174

Amanita basii IBUG L-3338 Mexico: Jalisco, Volcan del Tequila 20.800 -103.845 KP724451 KP723902 KP724175

Amanita basii SS-MOR-MX-2013-10 Mexico: Michoacan, Charo 19.642 -101.038 KP724452 KP723903 KP724176

Amanita basii SS-MOR-MX-2013-11 Mexico: Michoacan, Charo 19.642 -101.038 KP724453 KP723904 KP724177

Amanita basii SS-MOR-MX-2013-12 Mexico: Michoacan, Charo 19.642 -101.038 KP724454 KP723905 KP724178

Amanita basii SS-MOR-MX-2013-13 Mexico: Michoacan, Charo 19.642 -101.038 KP724455 KP723906 KP724179

Amanita basii SS-MOR-MX-2013-6 Mexico: Michoacan, Charo 19.642 -101.038 KP724456 KP723907 KP724180

Amanita basii SS-MOR-MX-2013-7 Mexico: Michoacan, Charo 19.642 -101.038 KP724457 KP723908 KP724181

Amanita basii SS-MOR-MX-2013-8 Mexico: Michoacan, Charo 19.642 -101.038 KP724458 KP723909 KP724182

Amanita basii MT-MOR-MX-2013-3 Mexico: Michoacan, Morelia 19.611 -101.128 KP724459 KP723910 KP724183

Amanita basii MT-MOR-MX-2013-4 Mexico: Michoacan, Morelia 19.611 -101.128 KP724460 KP723911 KP724184

168

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii SS-PATZ-MX-2013-1

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724461 KP723912 KP724185

Amanita basii SS-PATZ-MX-2013-2

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724462 KP723913 KP724186

Amanita basii SS-PATZ-MX-2013-3

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724463 KP723914 KP724187

Amanita basii SS-PATZ-MX-2013-4

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724464 KP723915 KP724188

Amanita basii SS-PATZ-MX-2013-5

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724465 KP723916 KP724189

Amanita basii SS-PATZ-MX-2013-6

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724466 KP723917 KP724190

Amanita basii SS-PATZ-MX-2013-7

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724467 KP723918 KP724191

169

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii SS-PATZ-MX-2013-8

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724468 KP723919 KP724192

Amanita basii SS-PATZ-MX-2013-9

Mexico: Michoacan, Patzcuaro, Sta Clara del Cobre 19.450 -101.633 KP724469 KP723920 KP724193

Amanita basii MT-MOR-MX-2013-5 Mexico: Michoacan, Pino Real 19.612 -101.013 KP724470 KP723921 KP724194

Amanita basii MT-MOR-MX-2013-6 Mexico: Michoacan, Pino Real 19.612 -101.013 KP724471 KP723922 KP724195

Amanita basii SS-MOR-MX-2013-3 Mexico: Michoacan, Pino Real 19.612 -101.013 KP724472 KP723923 KP724196

Amanita basii SS-GUAC-OAX-MX-2013-2

Mexico: Oaxaca, Etla, Arroyo Guacamaya 17.268 -96.686 KP724473 KP723924 KP724197

Amanita basii FCME-V-C-1026 Mexico: Oaxaca, San Pedro Tidaa 17.326 -97.395 KP724474 KP723925 KP724198

Amanita basii CES-sn-1998 Mexico: Tlaxcala 19.398 -98.336 KP724475 KP723926 KP724199

Amanita basii M-2033 Mexico: Tlaxcala, Panotla 19.338 -98.271 KP724476 KP723927 KP724200

Amanita basii M-E-1530 Mexico: Tlaxcala, Panotla 19.338 -98.271 KP724477 KP723928 KP724201

170

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii Montoya-5-10-2009 Mexico: Tlaxcala, Panotla 19.338 -98.271 KP724478 KP723929 KP724202

Amanita basii KLME-1559 Mexico: Tlaxcala, Panotla, San Francisco Temezontla 19.348 -98.296 KP724479 KP723930 KP724203

Amanita basii M-E-1444 Mexico: Tlaxcala, Panotla, San Francisco Temezontla 19.348 -98.296 KP724480 KP723931 KP724204

Amanita basii C-C-06 Mexico: Tlaxcala, Zitlaltepec de Trinidad Sanchez Santos 19.213 -97.965 KP724481 KP723932 KP724205

Amanita basii M-E-1430 Mexico: Tlaxcala, Zitlaltepec de Trinidad Sanchez Santos 19.213 -97.965 KP724482 KP723933 KP724206

Amanita basii M-E-1461-A Mexico: Tlaxcala, Volcan La Malinche 19.263 -98.035 KP724483 KP723934 KP724207

Amanita basii RET 287-9 Mexico: Tlaxcala, Volcan La Malinche 19.263 -98.035 KP724484 KP723935 KP724208

171

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita basii ABM029 Mexico: Veracruz, Cofre de Perote 19.510 -97.177 KP724485 KP723936 KP724209

Amanita basii ABM030 Mexico: Veracruz, Cofre de Perote 19.510 -97.177 KP724486 KP723937 KP724210

Amanita basii ABM031 Mexico: Veracruz, Cofre de Perote 19.510 -97.177 KP724487 KP723938 KP724211

Amanita basii ABM032 Mexico: Veracruz, Cofre de Perote 19.510 -97.177 KP724488 KP723939 KP724212

Amanita basii ABM033 Mexico: Veracruz, Cofre de Perote 19.510 -97.177 KP724489 KP723940 KP724213

Amanita basii States-1456 USA: Arizona, Santa Cruz Co. 31.676 -110.854 KP724490 KP724214

Amanita caesarea (Scop. : Fr.) Pers. RET 036-2 Italy: Cozenza NA NA KP724491 KP723941 KP724215

Amanita calyptroderma G. F. Atk. & V. G. Ballen RET 385-3 USA: Oregon, Multnomah Co.,

Portland, Washington Park 45.511 -122.710 KP724492 KP723942 KP724216

172

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita calyptroderma RET 385-4 USA: Washington, Clark Co., Lacamas Park 45.603 -122.406 KP724493 KP723943 KP724217

Amanita calyptroderma RET 385-7 USA: Washington, Clark Co., Vancouver 45.689 -122.389 KP724494 KP723944 KP724218

Amanita calyptroderma RET 389-10 USA: Washington, Clark Co., Vancouver 45.689 -122.389 KP724495 KP723945 KP724219

Amanita calyptroderma RET 389-5 USA: Washington, Clark Co., Vancouver 45.689 -122.389 KP724496 KP723946 KP724220

Amanita calyptroderma RET 389-9 USA: Washington, Clark Co., Vancouver 45.689 -122.389 KP724497 KP723947 KP724221

Amanita sp. 'cinnamomescens' RET 317-5 Pakistan NA NA KP724498 KP723948 KP724222

Amanita sp. 'cochiseana' FCME-Sanchez-S31 Mexico: Chihuahua, Basaseachic 28.175 -108.213 KP724499 KP723949

Amanita sp. 'cochiseana' AKL-sn-A Mexico: Chihuahua, Basaseachic 28.175 -108.213 KP724500 KP723950 KP724223

Amanita sp. 'cochiseana' FCME-16259 Mexico: Chihuahua, San Juanito 27.971 -107.600 KP724501 KP723951 KP724224

Amanita sp. 'cochiseana' FCME-15333 Mexico: Chihuahua, Bocoyna 27.841 -107.585 KP724502 KP723952 KP724225

173

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'cochiseana' FCME-17764 Mexico: Chihuahua, Bocoyna 27.841 -107.585 KP724503 KP723953 KP724226

Amanita sp. 'cochiseana' AKL-4146 Mexico: Chihuahua, El Ranchito 27.950 -107.750 KP724504 KP723954 KP724227

Amanita sp. 'cochiseana' FCME-17768 Mexico: Chihuahua, Guachochi 26.820 -107.069 KP724505 KP723955 KP724228

Amanita sp. 'cochiseana' AKL-3276 Mexico: Chihuahua, Bocoyna, La Tinaja 27.841 -107.585 KP724506 KP723956 KP724229

Amanita sp. 'cochiseana' HUACJ-17 Mexico: Chihuahua, Lago Arareco 27.705 -107.590 KP724507 KP723957 KP724230

Amanita sp. 'cochiseana' HUACJ-50 Mexico: Chihuahua, Lago Arareco 27.705 -107.590 KP724508 KP723958 KP724231

Amanita sp. 'cochiseana' AKL-4011 Mexico: Chihuahua, Llano Grande 28.058 -107.767 KP724509 KP723959

Amanita sp. 'cochiseana' AKL-sn-B Mexico: Chihuahua, Llano Grande 28.058 -107.767 KP724510 KP723960 KP724232

Amanita sp. 'cochiseana' RDM-1001-A Mexico: Durango, El Salto 23.782 -105.359 KP724511 KP723961 KP724233

Amanita sp. 'cochiseana' RDM-1001-B Mexico: Durango, El Salto 23.782 -105.359 KP724512 KP723962 KP724234

Amanita sp. 'cochiseana' RDM-1001-C Mexico: Durango, El Salto 23.782 -105.359 KP724513 KP723963 KP724235

174

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'cochiseana' DGB27481 USA: Arizona 34.049 -111.094 KP724514 KP723964 KP724236

Amanita sp. 'cochiseana' RET 497-10 USA: Arizona, Cochise Co., Chiricahua Mts. 31.861 -109.295 KP724515 KP723965 KP724237

Amanita sp. 'cochiseana' RET 498-1 USA: Arizona, Cochise Co., Chiricahua Mts. 31.861 -109.295 KP724516 KP723966 KP724238

Amanita sp. 'cochiseana' RET 511-7 USA: Arizona, Cochise Co., Chiricahua Mts. 31.861 -109.295 KP724517 KP723967 KP724239

Amanita sp. 'cochiseana' States-1302 USA: Arizona, Cochise Co., Chiricahua Mts. 31.861 -109.295 KP724518 KP723968 KP724240

Amanita sp. 'cochiseana' States-999 USA: Arizona, Cochise Co., Chiricahua Mts. 31.861 -109.295 KP724519 KP723969 KP724241

Amanita sp. 'cochiseana' DGB27480 USA: Arizona, Coconino Co. 35.035 -111.852 KP724520 KP723970 KP724242

Amanita sp. 'cochiseana' States-984 USA: Arizona, Coconino Co. 35.035 -111.852 KP724521 KP723971 KP724243

Amanita sp. 'cochiseana' DGB23437 USA: New Mexico 36.027 -106.683 KP724522 KP723972 KP724244

175

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'cochiseana' RET 072-5 USA: New Mexico, Colfax Co. 36.452 -105.093 KP724523 KP723973 KP724245

Amanita sp. 'cochiseana' RET 565-3 USA: New Mexico, Grant Co. 32.969 -108.238 KP723974 KP724246

Amanita sp. 'cochiseana' DGB22388 USA: New Mexico, Los Alamos Co. 35.850 -106.323 KP724524 KP723975 KP724247

Amanita sp. 'cochiseana' DGB22171 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724525 KP723976 KP724248

Amanita sp. 'cochiseana' DGB22426 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724526 KP723977 KP724249

Amanita sp. 'cochiseana' DGB22514 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724527 KP723978 KP724250

Amanita sp. 'cochiseana' DGB22527 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724528 KP723979 KP724251

Amanita sp. 'cochiseana' DGB23436 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724529 KP723980 KP724252

Amanita sp. 'cochiseana' DBG22427 USA: New Mexico, Sandoval Co. 35.755 -106.535 KP724530 KP723981 KP724253

Amanita garabitoana Tulloss, Halling & G. M. Muell.

F1102486b Costa Rica: Cartago 9.754 -83.677 KP724531 KP723982 KP724254

Amanita garabitoana RET 333-6 Costa Rica: San Jose 9.928 -84.091 KP724532 KP723983 KP724255

Amanita garabitoana RH-8438 Costa Rica: San Jose, La Chonta 9.700 -83.942 KP724533 KP723984 KP724256

176

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita garabitoana RH-8632 Costa Rica: San Jose, La Chonta 9.700 -83.942 KP724534 KP723985 KP724257

Amanita hayalyuy Arora & G. H. Shepard RS-363 Mexico: Chiapas, Chamula 16.787 -92.688 KP724535 KP723986 KP724258

Amanita hayalyuy RS-365 Mexico: Chiapas, Chamula 16.787 -92.688 KP724536 KP723987 KP724259

Amanita hayalyuy RS-366 Mexico: Chiapas, Chamula 16.787 -92.688 KP724537 KP723988 KP724260

Amanita hemibapha var. ochracea Zhu L. Yang RET 258-1 China: Yunnan NA NA KP724538 KP723989 KP724261

Amanita jacksonii Pomerl. AW200 Canada: Ontario, Awenda Prov Park 44.844 -80.006 KP724539 KP723990 KP724262

Amanita jacksonii AW201 Canada: Ontario, Awenda Prov Park 44.844 -80.006 KP724540 KP723991 KP724263

Amanita jacksonii AW203 Canada: Ontario, Awenda Prov Park 44.844 -80.006 KP724541 KP723992 KP724264

Amanita jacksonii AW206 Canada: Ontario, Awenda Prov Park 44.844 -80.006 KP724542 KP723993 KP724265

Amanita jacksonii Thorn-ON-2009 Canada: Ontario, Exeter 43.346 -81.480 KP724543 KP723994 KP724266

177

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita jacksonii 2Q Canada: Quebec, Boucherville 45.591 -73.436 KP724544 KP723995 KP724267

Amanita jacksonii 5Q Canada: Quebec, Ille Perrot 45.393 -73.955 KP724545 KP723996 KP724268

Amanita jacksonii 3Q Canada: Quebec, Ste Anne de Bellevue 45.403 -73.950 KP724546 KP723997 KP724269

Amanita jacksonii 4Q Canada: Quebec, Ste Anne de Bellevue 45.403 -73.950 KP724547 KP723998 KP724270

Amanita jacksonii RET 315-8 USA: Connecticut, Mansfield, Storrs 41.819 -72.236 KP724548 KP723999 KP724271

Amanita jacksonii RET 315-9 USA: Connecticut, Mansfield, Storrs 41.819 -72.236 KP724549 KP724000 KP724272

Amanita jacksonii RET 489-5 USA: Connecticut, Middlesex Co., Devils Hopyard 41.474 -72.346 KP724550 KP724001 KP724273

Amanita jacksonii RET 109-4 USA: Massashusets, Franklin Co. 42.532 -72.408 KP724551 KP724002 KP724274

Amanita jacksonii RET 154-10 USA: New York, Ulster Co. 41.859 -74.312 KP724552 KP724003 KP724275

178

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita jacksonii RET 393-6 USA: Norh Carolina, Haywood Co. 35.640 -83.060 KP724553 KP724004 KP724276

Amanita jacksonii RET 393-7 USA: Norh Carolina, Haywood Co. 35.640 -83.060 KP724554 KP724005 KP724277

Amanita jacksonii RET 393-8 USA: Norh Carolina, Haywood Co. 35.640 -83.060 KP724555 KP724006 KP724278

Amanita jacksonii RET 526-5 USA: Pennsylvania, Carbon Co., Hickory Run State Park 41.046 -75.706 KP724556 KP724007 KP724279

Amanita jacksonii RET 547-4 USA: Pennsylvania, Erie Co. 41.864 -79.990 KP724557 KP724008 KP724280

Amanita jacksonii RET 551-6 USA: Pennsylvania, Luzerne Co., Ricketts Glen Park 41.335 -76.275 KP724558 KP724009 KP724281

Amanita jacksonii Feldman-20-07-13

USA: Pennsylvania, Mifflinburg Co., Raymond B. Winter State Park 40.989 -77.194 KP724559 KP724010 KP724282

179

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita jacksonii Feldman-29-06-13

USA: Pennsylvania, Petersburg Co., Shavers Creek Environmental Center

40.668 -77.914 KP724560 KP724011 KP724283

Amanita jacksonii DEWV-2410 USA: West Virginia, Barbour Co., Audra State Park 39.038 -80.082 KP724561 KP724012 KP724284

Amanita jacksonii DEWV-7136 USA: West Virginia, Blackwater Falls State Park 39.101 -79.479 KP724562 KP724013 KP724285

Amanita jacksonii DEWV-918 USA: West Virginia, Fayett Co., Babcock State Park 37.981 -80.952 KP724563 KP724014 KP724286

Amanita jacksonii DEWV-360 USA: West Virginia, Kumbrabow State Forest 38.644 -80.115 KP724564 KP724015 KP724287

Amanita jacksonii DEWV-1083 USA: West Virginia, Mogongahela National Forest, Stuart Memorial Drive

38.931 -79.726 KP724565 KP724016 KP724288

180

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita jacksonii DEWV-1893 USA: West Virginia, Monongahela National Forest, Blue Bend 37.923 -80.267 KP724566 KP724017 KP724289

Amanita jacksonii DEWV-191 USA: West Virginia, Richwood 38.236 -80.513 KP724567 KP724018 KP724290

Amanita jacksonii DEWV-702 USA: West Virginia, Seneca State Forest 38.322 -79.952 KP724568 KP724019 KP724291

Amanita jacksonii DEWV-4715 USA: West Virginia, Upshur Co., French Creek State Wildlife Center 38.863 -80.262 KP724569 KP724020 KP724292

Amanita sp.-AR01 RET 519-4 USA: Arkansas, Craighead Co., Jonesboro 36.109 -91.182 KP724570 KP724021 KP724293

Amanita sp.-AR01 RET 373-9 USA: Arkansas, Washington Co. 35.931 -94.151 KP724571 KP724022 KP724294

Amanita sp.-AR01 MO-139 USA: Missouri, Wayne Co., Latonka 37.039 -90.526 KP724572 KP724023 KP724295

181

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-AR01 MO-87 USA: Missouri, Wayne Co., Mingo National Wildlife Refuge 37.018 -90.131 KP724573 KP724024 KP724296

Amanita sp.-AR01 JM-DUKE-AJ23 USA: Norh Carolina, Durham, Eno river State Park 36.060 -79.013 KP724574 KP724025 KP724297

Amanita sp.-AR01 RV-DUKE-AJ04 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724575 KP724026 KP724298

Amanita sp.-AR01 RV-DUKE-AJ24 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724576 KP724299

Amanita sp.-AR01 RET 507-10 USA: Tennessee, Cumberland Co., Rinnie 36.155 -85.045 KP724577 KP724027 KP724300

Amanita sp. 'affin. basii' SS-CUAJ-OAX-MX-2013-1 Mexico: Oaxaca, Cuajimoloyas 17.123 -96.438 KP724578 KP724028 KP724301

Amanita sp. 'affin. basii' SS-CUAJ-OAX-MX-2013-2 Mexico: Oaxaca, Cuajimoloyas 17.123 -96.438 KP724579 KP724029 KP724302

Amanita sp. 'affin. basii' SS-CUAJ-OAX-MX-2013-3 Mexico: Oaxaca, Cuajimoloyas 17.123 -96.438 KP724580 KP724030 KP724303

182

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'affin. basii' SS-CUAJ-OAX-MX-2013-4 Mexico: Oaxaca, Cuajimoloyas 17.123 -96.438 KP724581 KP724031 KP724304

Amanita sp. 'affin. basii' SS-GUAC-OAX-MX-2013-3

Mexico: Oaxaca, Etla, Arroyo Guacamaya 17.268 -96.686 KP724582 KP724032 KP724305

Amanita sp. 'affin. basii' SS-IXTL-OAX-MX-2013-1 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724583 KP724033 KP724306

Amanita sp. 'affin. basii' SS-IXTL-OAX-MX-2013-2 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724584 KP724034 KP724307

Amanita sp. 'affin. basii' SS-IXTL-OAX-MX-2013-3 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724585 KP724035 KP724308

Amanita sp. 'affin. basii' SS-IXTL-OAX-MX-2013-4 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724586 KP724036 KP724309

Amanita sp. 'affin. basii' SSRH-03-C Mexico: Oaxaca, San Sebastian Rio Hondo 16.179 -96.469 KP724587 KP724037 KP724310

Amanita sp. 'affin. basii' G-H-26 Mexico: Oaxaca, Santa Catarina Lachatao 17.250 -96.467 KP724588 KP724038 KP724311

183

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp. 'affin. basii' O-M-35 Mexico: Oaxaca, Santa Catarina Lachatao 17.250 -96.467 KP724589 KP724039 KP724312

Amanita sp.-F11 RET 537-5 USA: Florida, Lee Co., Alva 26.797 -81.671 KP724590 KP724040 KP724313

Amanita sp.-F11 RET 537-10 USA: Florida, Lee Co., Alva 26.797 -81.671 KP724591 KP724041

Amanita sp.-F11 RET 548-8 USA: Florida, Marion, Ocala National Forest 29.258 -81.779 KP724592 KP724042 KP724314

Amanita sp.-F11 RET 138-1 USA: Florida, Marion, Ocala National Forest 29.258 -81.779 KP724593 KP724315

Amanita sp.-F11 RET 138-2 USA: Florida, Marion, Ocala National Forest 29.258 -81.779 KP724594 KP724316

Amanita sp.-F11 RET 547-10 USA: Florida, Marion, Ocala National Forest 29.258 -81.779 KP724595 KP724317

Amanita sp.-jack1 JM-DUKE-AJ12 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724596 KP724043 KP724318

184

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack1 JM-DUKE-AJ17 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724597 KP724044 KP724319

Amanita sp.-jack1 JM-DUKE-AJ19 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724598 KP724045 KP724320

Amanita sp.-jack1 JM-DUKE-AJ22 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724599 KP724046 KP724321

Amanita sp.-jack1 RV-DUKE-AJ10 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724600 KP724047 KP724322

Amanita sp.-jack1 RV-DUKE-AJ20 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724601 KP724048 KP724323

Amanita sp.-jack1 TENN-57937 USA: North Carolina, Macon Co. 35.120 -83.336 KP724602 KP724049 KP724324

Amanita sp.-jack1 TENN-66058 USA: Tennessee, Blount Co. 35.671 -83.847 KP724603 KP724050 KP724325

Amanita sp.-jack2 Amanita-2004 Mexico: Chiapas, La Trinitaria, Lagunas de Montebello 16.119 -92.052 KP724604 KP724051 KP724326

Amanita sp.-jack2 XAL-Chacon-5895 Mexico: Veracruz, Xalapa 19.514 -96.936 KP724605 KP724052 KP724327

185

Coordinates Accessions

Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack2 R-Doss-27708 USA: Arkansas 34.787 -92.876 KP724606 KP724053 KP724328

Amanita sp.-jack2 RET 531-4 USA: Indiana, Lawrence Co., Lake Monroe 39.033 -86.422 KP724607 KP724054 KP724329

Amanita sp.-jack2 RET 531-9 USA Indiana: Monroe Co., Lake Monroe 39.033 -86.422 KP724608 KP724055 KP724330

Amanita sp.-jack2 JM-DUKE-AJ18 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724609 KP724056 KP724331

Amanita sp.-jack2 RV-DUKE-AJ01 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724610 KP724057 KP724332

Amanita sp.-jack2 RV-DUKE-AJ02 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724611 KP724058 KP724333

Amanita sp.-jack2 RV-DUKE-AJ03 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724612 KP724059 KP724334

Amanita sp.-jack2 RV-DUKE-AJ05 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724613 KP724060 KP724335

Amanita sp.-jack2 RV-DUKE-AJ06 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724614 KP724061 KP724336

186

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack2 RV-DUKE-AJ07 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724615 KP724062 KP724337

Amanita sp.-jack2 RV-DUKE-AJ13 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724616 KP724063 KP724338

Amanita sp.-jack2 RV-DUKE-AJ14 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724617 KP724064 KP724339

Amanita sp.-jack2 RV-DUKE-AJ16 USA: Norh Carolina, Durham, Duke Forest 36.021 -78.983 KP724618 KP724065 KP724340

Amanita sp.-jack2 TENN-55460 USA: South Carolina, Oconee Co. 34.783 -83.155 KP724619 KP724066 KP724341

Amanita sp.-jack2 TENN-61260 USA: Tennessee, Knoxville Co. 35.943 -83.875 KP724620 KP724067 KP724342

Amanita sp.-jack2 RET 463-6 USA: Texas, Hardin Co., Big Thicket Natlional Preserve 30.476 -94.357 KP724621 KP724068 KP724343

Amanita sp.-jack2 (F) Lewis-6322 USA: Texas, Newton Co., Bleakwood 30.706 -93.843 KP724622 KP724069 KP724344

Amanita sp.-jack2 RET 354-7 USA: Texas, Newton Co., Bleakwood 30.706 -93.843 KP724623 KP724070 KP724345

187

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack2 RET 354-8 USA: Texas, Newton Co., Bleakwood 30.706 -93.843 KP724624 KP724071 KP724346

Amanita sp.-jack2 F1132183 USA: Texas, Orange Co., Vidor 30.136 -94.046 KP724625 KP724072 KP724347

Amanita sp.-jack3 RS-390 Mexico: Chiapas 16.757 -93.129 KP724626 KP724073 KP724348

Amanita sp.-jack3 RS-364 Mexico: Chiapas, Chamula 16.787 -92.688 KP724627 KP724074 KP724349

Amanita sp.-jack3 ABM049 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724628 KP724075 KP724350

Amanita sp.-jack3 ABM050 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724629 KP724076 KP724351

Amanita sp.-jack3 ABM051 Mexico: Hidalgo, Acaxochitlan 20.172 -98.199 KP724630 KP724077 KP724352

Amanita sp.-jack3 ABM042 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724631 KP724078 KP724353

Amanita sp.-jack3 ABM044 Mexico: Hidalgo, San Miguel El Cerezo 20.171 -98.718 KP724632 KP724079 KP724354

Amanita sp.-jack3 RET 252-9 Mexico: Hidalgo, Zacaultipan 20.638 -98.674 KP724633 KP724080 KP724355

Amanita sp.-jack3 VVR-01 Mexico: Hidalgo, Zacaultipan La Mojonera 20.629 -98.629 KP724634 KP724081 KP724356

188

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack3 V-15016 Mexico: Hidalgo, Zacaultipan La Mojonera 20.629 -98.629 KP724635 KP724082 KP724357

Amanita sp.-jack5 FCME-5528 Mexico: Guerrero, Taxco 18.626 -99.672 KP724636 KP724083 KP724358

Amanita sp.-jack5 FCME-12243 Mexico: Guerrero, Tixtla de Guerrero 17.556 -99.379 KP724637 KP724084 KP724359

Amanita sp.-jack5 SS-GUAC-OAX-MX-2013-1

Mexico: Oaxaca, Etla, Arroyo Guacamaya 17.268 -96.686 KP724638 KP724085 KP724360

Amanita sp.-jack5 SS-GUAC-OAX-MX-2013-4

Mexico: Oaxaca, Etla, Arroyo Guacamaya 17.268 -96.686 KP724639 KP724086 KP724361

Amanita sp.-jack5 FCME-21550 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724640 KP724087 KP724362

Amanita sp.-jack5 FCME-21652 Mexico: Oaxaca, Ixtlan de Juarez 17.360 -96.471 KP724641 KP724088 KP724363

Amanita sp.-jack5 Martinez-19 Mexico: Oaxaca, Zaachila, Magdalena Mixtepec 16.916 -96.907 KP724642 KP724089 KP724364

Amanita sp.-jack5 FCME Mexico: Oaxaca, San Pedro Tidaa 17.326 -97.395 KP724643 KP724090 KP724365

189

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack5 R-y-V-1057 Mexico: Oaxaca, San Pedro Tidaa 17.326 -97.395 KP724644 KP724091 KP724366

Amanita sp.-jack5 R-y-V-1090 Mexico: Oaxaca, San Pedro Tidaa 17.326 -97.395 KP724645 KP724092 KP724367

Amanita sp.-jack5 SSRH-03-B Mexico: Oaxaca, San Sebastian Rio Hondo 16.179 -96.469 KP724646 KP724093 KP724368

Amanita sp.-jack6 FCME-17782 Mexico: Chihuahua 27.841 -107.585 KP724647 KP724094 KP724369

Amanita sp.-jack6 L-Ruiz-5-IBUG Mexico: Jalisco 20.299 -104.047 KP724648 KP724095 KP724370

Amanita sp.-jack6 R-C-1514 Mexico: Jalisco, Atenguillo 20.431 -104.552 KP724649 KP724096 KP724371

Amanita sp.-jack6 H-12 Mexico: Jalisco, Bosque La Primavera 20.641 -103.557 KP724650 KP724097 KP724372

Amanita sp.-jack6 H-1 Mexico: Jalisco, Huaxtla, Rio Salado 20.686 -103.668 KP724651 KP724098 KP724373

Amanita sp.-jack6 H-422 Mexico: Jalisco, Huaxtla, Rio Salado 20.686 -103.668 KP724652 KP724099 KP724374

Amanita sp.-jack6 H-4 Mexico: Jalisco, Huaxtla, Rio Salado 20.686 -103.668 KP724653 KP724100 KP724375

190

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-jack6 H-191 Mexico: Jalisco, San Sebastian del Oeste 20.725 -104.847 KP724654 KP724101 KP724376

Amanita sp.-jack6 GD-7330 Mexico: Jalisco, San Sebastian del Oeste 20.725 -104.847 KP724655 KP724102 KP724377

Amanita sp.-jack6 F-241 Mexico: Jalisco, Sierra de Quila 20.278 -104.085 KP724656 KP724103 KP724378

Amanita sp.-jack6 SS-MOR-MX-2013-5 Mexico: Michoacan, Charo 19.642 -101.038 KP724657 KP724104 KP724379

Amanita sp.-jack6 SS-MOR-MX-2013-9 Mexico: Michoacan, Charo 19.642 -101.038 KP724658 KP724105 KP724380

Amanita sp.-jack6 MT-MOR-MX-2013-1 Mexico: Michoacan, Morelia 19.611 -101.128 KP724659 KP724106 KP724381

Amanita sp.-jack6 MT-MOR-MX-2013-2 Mexico: Michoacan, Morelia 19.611 -101.128 KP724660 KP724107 KP724382

Amanita sp.-jack6 SS-MOR-MX-2013-2 Mexico: Michoacan, Pino Real 19.612 -101.013 KP724661 KP724108 KP724383

Amanita sp.-jack6 SS-MOR-MX-2013-4 Mexico: Michoacan, Pino Real 19.612 -101.013 KP724662 KP724109 KP724384

Amanita sp.-jack6 FCME-3859 Mexico: Nayarit, Tepic, Reserva Ecologica de San Juan 21.499 -104.931 KP724663 KP724110 KP724385

191

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-MO1 RET 436-7 USA: Arkansas, Perry Lake, Sylvia Recreation Area 34.868 -92.822 KP724664 KP724111 KP724386

Amanita sp.-MO1 RET 532-9 USA: Indiana, Montgomery, Shades State Park 39.940 -87.096 KP724665 KP724112 KP724387

Amanita sp.-MO1 RET 517-4 USA: Indiana, Sullivan Co., Graysville 39.118 -87.556 KP724666 KP724113 KP724388

Amanita sp.-MO1 RET 563-8 USA: Missouri, Jasper Co. 37.095 -94.360 KP724667 KP724114 KP724389

Amanita sp.-MO1 RET 450-8 USA: Missouri, Stoddard Co., Mingo National Wildlife Refuge 37.018 -90.131 KP724668 KP724115 KP724390

Amanita sp.-MO1 MO-134 USA: Missouri, Wayne Co., Latonka 37.039 -90.526 KP724669 KP724116 KP724391

Amanita sp.-T31 RET 453-1 USA: Missouri, Ste. Genevieve, Hawn Satet Park 37.833 -90.242 KP724670 KP724117 KP724392

192

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita sp.-T31 RET 516-3 USA: Missouri, Ste. Genevieve, Hawn Satet Park 37.833 -90.242 KP724671 KP724118 KP724393

Amanita sp.-T31 RET 552-4 USA: Missouri, Ste. Genevieve, Hawn Satet Park 37.833 -90.242 KP724672 KP724119 KP724394

Amanita sp.-T31 F1132188 USA: Texas, Angelina Co., Zavalla 31.191 -94.433 KP724673 KP724395

Amanita sp.-T31 RET 365-1 USA: Texas, Jasper Co., Evadale 30.364 -94.086 KP724674 KP724396

Amanita sp.-W15 RET 532-7 USA: Indiana, Montgomery, Shades State Park 39.940 -87.096 KP724675 KP724120 KP724397

Amanita sp.-W15 RET 532-5 USA: Indiana, Owen Co., McCormicks Creek State Park 39.298 -86.724 KP724676 KP724121 KP724398

Amanita sp.-W15 DEWV-400 USA: West Virginia, Mercer Co., Spanishburg 37.448 -81.129 KP724677 KP724122

Amanita sp.-W15 DEWV-9597 USA: West Virginia, North Bend State Park 39.218 -81.107 KP724678 KP724123

193

Coordinates Accessions Species Voucher Location latitude longitude tef1 AJ0000-2 AJ0132-103

Amanita vernicoccora Bojantchev & R.M. Davis RET 385-9 USA: Oregon, Linn Co., Santiam

State Forest 44.718 -122.416 KP724679 KP724124 KP724399

Amanita vernicoccora RET 385-6 USA: Oregon, Marion Co., Salem 44.845 -122.593 KP724680 KP724400

Amanita vernicoccora RET 385-2 USA: Washington, Clark Co., Basket Flats Rd 45.835 -122.454 KP724681 KP724125 KP724401

Amanita vernicoccora RET 385-8 USA: Washington, Klickitat Co. 45.866 -121.251 KP724682 KP724126 KP724402

Amanita vernicoccora RET 281-9 USA: Washington, Klickitat Co., North Fork Spring Creek Rd 45.807 -121.497 KP724683 KP724127 KP724403

194

Figure C8.5. *BEAST species trees used for species delimitation with clade posterior

probabilities (upper), BP&P species probabilities (lower and bold), and branching times.

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195

Figure C8.6. Regression analyses on the latitude versus population size and expansion data.

The blue line represents the linear model, while the green and red represent the Gaussian

and polynomial models, respectively.

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196

Appendix A: Chapter 6

Table D8.4. Samples used in Chapter 6 for exon-targeted sequencing.

Species Voucher Location latitude longitude Amanita jacksonii 1Q Canada, Quebec, Boucherville 45.365 -73.423 Amanita jacksonii 2Q Canada, Quebec, Boucherville 45.591 -73.436 Amanita jacksonii 3Q Canada, Quebec, Ste. Anne de Bellevue 45.403 -73.950 Amanita jacksonii 4Q Canada, Quebec, Ste Anne de Bellevue 45.403 -73.950 Amanita jacksonii AW200 Canada, Ontario, Awenda Prov Park 44.844 -80.006 Amanita jacksonii AW201 Canada, Ontario, Awenda Prov. Park 44.844 -80.006 Amanita jacksonii AW203 Canada, Ontario, Awenda Prov Park 44.844 -80.006 Amanita jacksonii AW217 Canada, Ontario, Awenda Prov Park 44.844 -80.006 Amanita jacksonii AW228 Canada, Ontario, Awenda Prov Park 44.844 -80.006

Amanita jacksonii DEWV 1893 USA, West Virginia, Monongahela National Forest, Blue Bend 37.923 -80.267

Amanita jacksonii DEWV 2410 USA, West Virginia, Barbour, Audra State Park 39.041 -80.069

Amanita jacksonii DEWV 4715 USA, West Virginia, Upshur, French Creek, State Wildlife Center 38.886 -80.297

Amanita jacksonii DEWV 7136 USA, West Virginia, Blackwater Falls State Park 39.109 -79.492

Amanita jacksonii Feldman 20-07-13 USA, Pennsylvania, Mifflinburg, Raymond B. Winter State Park 40.992 -77.186

Amanita jacksonii RET 109-4 USA, Massashusets, Franklin 42.083 -71.397 Amanita jacksonii RET 154-10 USA, New York, Ulster 41.859 -74.312 Amanita jacksonii RET 315-9 USA, Connecticut, Mansfield, Storrs 41.808 -72.250 Amanita jacksonii RET 393-8 USA, Norh Carolina, Haywood 35.624 -82.993 Amanita jacksonii RET 489-5 USA, Connecticut, Middlesex Co, Devils Hopyard 41.476 -72.340 Amanita jacksonii RET 526-5 USA, Pennsylvania, Carbon Co, Hickory Run SP 41.036 -75.684 Amanita jacksonii RET 547-4 USA, Pennsylvania, Erie 42.086 -80.116 Amanita jacksonii RET 551-6 USA, Pennsylvania, Luzerne Co., Ricketts Glen Park 41.330 -76.291 Amanita jacksonii Thorn ON 2009 Canada, Ontario, Exeter 43.346 -81.480 Amanita sp-F11 RET 537-10 USA, Florida, Lee Co., Alva 26.797 -81.671

197

Species Voucher Location latitude longitude Amanita sp-F11 RET 537-5 USA, Florida, Lee Co., Alva 26.716 -81.610 Amanita sp-F11 RET 547-10 USA, Florida, Marion, Ocala Natl. Forest 29.241 -81.689 Amanita sp-F11 RET 548_8 USA, Florida, Marion, Ocala Natl. Forest 29.258 -81.779 Amanita sp-jack1 RV DUKE AJ20 USA, Norh Carolina, Durham 35.994 -78.899 Amanita sp-jack1 RV-DUKE-AJ10 USA, Norh Carolina, Durham, Duke Forest 36.021 -78.983 Amanita sp-jack1 TENN 57937 USA, North Carolina, Macon 36.439 -78.084 Amanita sp-jack1 TENN 66058 USA, Tennessee, Blount 35.671 -83.847 Amanita sp-jack2 Amanita 2004 Mexico, Chiapas, La Trinitaria, Lagunas de Montebello 16.757 -93.129 Amanita sp-jack2 F1132183 USA, Texas, Orange, Vidor 30.132 -94.015 Amanita sp-jack2 JM DUKE AJ18 USA, Norh Carolina, Durham 35.994 -78.899 Amanita sp-jack2 R. Doss 27708 USA, Arkansas 35.201 -91.832 Amanita sp-jack2 RET 354-8 USA, Texas, Newton, Bleakwood 30.692 -93.822 Amanita sp-jack2 RET 463_6 USA, Texas, Hardin , Big Thicket Natl. Preserve 30.474 -94.346 Amanita sp-jack2 RET 531-4 USA, Indiana, Lawrence, Lake Monroe 39.070 -86.445 Amanita sp-jack2 TENN 55460 USA, South Carolina, Oconee 34.749 -82.993 Amanita sp-jack2 TENN 61260 USA, Tennessee, Knoxville 35.961 -83.921 Amanita sp-jack3 ABM042 Mexico, Hidalgo, San Miguel El Cerezo 20.160 -98.728 Amanita sp-jack3 ABM044 Mexico, Hidalgo, San Miguel El Cerezo 20.171 -98.718 Amanita sp-jack3 ABM049 Mexico, Hidalgo, Acaxochitlan 20.158 -98.200 Amanita sp-jack3 ABM051 Mexico, Hidalgo, Acaxochitlan 20.172 -98.199 Amanita sp-jack3 RET 252-9 Mexico, Hidalgo, Zacaultipan 20.646 -98.655 Amanita sp-jack3 RS_364 Mexico, Chiapas, Chamula 16.787 -92.688 Amanita sp-jack3 V.V.R. 01 Mexico, Hidalgo, Zacaultipan, La Mojonera 20.643 -98.655 Amanita sp-jack5 FCME 12243 Mexico, Guerrero, Tixtla de Guerrero 17.556 -99.379 Amanita sp-jack5 FCME 5528 Mexico, Guerrero, Taxco 18.626 -99.672 Amanita sp-jack5 Martinez 19 Mexico, Oaxaca, Magdalena Mixtepec (Zaachila) 16.350 -96.603 Amanita sp-jack5 R y V 1090 Mexico, Oaxaca, San Pedro Tidaa 17.340 -97.379 Amanita sp-jack5 SSRH_03_B Mexico, Oaxaca, San Sebastian Rio Hondo 16.182 -96.464 Amanita sp-jack6 F. 241 Mexico, Jalisco, Sierra de Quila 20.278 -104.085

198

Species Voucher Location latitude longitude Amanita sp-jack6 FCME 17782 Mexico, Chihuahua 27.841 -107.585 Amanita sp-jack6 FCME 3859 Mexico, Nayarit, Tepic, Reserva Ecologica de San Juan 21.499 -104.931 Amanita sp-jack6 H. 191 Mexico, Jalisco, San Sebastian 20.366 -102.966 Amanita sp-jack6 H. 422 Mexico, Jalisco, Huaxtla, Rio Salado 20.727 -103.658

Amanita sp-jack6 MT MOR MX 2013 1 Mexico, Michoacan, Morelia 19.706 -101.195

Amanita sp-jack6 MT MOR MX 2013 2 Mexico, Michoacan, Morelia 19.611 -101.128

Amanita sp-jack6 SS MOR MX 2013 2 Mexico, Michoacan, Pino Real 19.650 -101.013

Amanita sp-T31 F1132188 USA, Texas, Angelina, Zavalla 31.159 -94.426 Amanita sp-T31 RET 453-1 USA, Missouri, St. Genevieve , Hawn St. Park 37.816 -90.253 Amanita sp-T31 RET 516-3 USA, Missouri, St. Genevieve , Hawn St. Park 37.816 -90.253 Amanita sp-T31 RET 552-4 USA, Missouri, St. Genevieve , Hawn St. Park 37.816 -90.253

199

Table D8.5. Genes used for demographic analyses, including a BLAST homolog, their functional annotations, as well as results from gsi and PAML analysis.

Linkage group Gene Best BLAST hit (Accession) Species Functional annotation GO terms ! vargsi "gsi LRT

scaffold-0 g0007 KIL67405.1 Amanita muscaria acyl-CoA dehydrogenase activity GO:0003995 0.232 0.000 8.000 Neu

scaffold-0 g0013 KIL67414.1 A. muscaria kinesin-like protein GO:0008017 0.186 0.000 8.000 Neu

scaffold-2 g0041 KIL69537.1 A. muscaria CMGC/DYRK domin containing protein GO:0004672 0.263 0.000 8.000 Pur

scaffold-2 g0082 KIL69598.1 A. muscaria protein prenyltransferase activity GO:0008318 0.234 0.000 8.000 Neu scaffold-8 g0162 KIL68638.1 A. muscaria hect-domain-containing protein GO:0004842 0.115 0.000 8.000 Neu scaffold-16 g0274 KIL62189.1 A. muscaria acyltransferase GO:0016747 0.344 0.000 8.000 Div

scaffold-31 g0464 KIL66181.1 A. muscaria proteophosphoglycan PPG4 protein GO:0008137 0.327 0.000 8.000 Neu

scaffold-31 g0472 KIL66211.1 A. muscaria ubiquitin-protein ligase GO:0004842 0.184 0.000 8.000 Pur scaffold-32 g0530 KIL71094.1 A. muscaria bzip-family transcription factor GO:0003700 0.263 0.000 8.000 Neu scaffold-81 g0828 KIL65846.1 A. muscaria chitin synthethase GO:0004100 0.099 0.001 7.924 Pur scaffold-102 g0981 KIL61570.1 A. muscaria phosphoketolase GO:0016832 0.197 0.000 8.000 Neu

scaffold-103 g1032 KIL65900.1 A. muscaria hypothetical protein, protein binding GO:0005515 0.404 0.000 8.000 Pur

scaffold-106 g1045 KIL69738.1 A. muscaria glycosyltransferase famlily 48 GO:0003843 0.115 0.000 8.000 Neu scaffold-106 g1074 KIL69785.1 A. muscaria STE/STE11 protein kinase GO:0004674 0.154 0.000 8.000 Pur

scaffold-106 g1111 KIL69836.1 A. muscaria kex protein, serine-type endopeptidase activity GO:0004252 0.248 0.000 8.000 Neu

scaffold-106 g1118 KIL69850.1 A. muscaria glutamate-tRNA ligase GO:0004818 0.342 0.000 8.000 Pur scaffold-106 g1153 KIL69899.1 A. muscaria vacuolar ATP synthase subunit D GO:0042626 0.093 0.000 8.000 Pur

scaffold-109 g1225 KIL70700.1 A. muscaria P-loop containing nucleoside triphosphate hydrolase GO:0004386 0.215 0.000 8.000 Neu

scaffold-116 g1367 KIL68906.1 A. muscaria hypothetical protein (palmitoyl-(protein) hydrolase activity) GO:0008474 0.386 0.000 8.000 Neu

scaffold-291 g1957 KIL63265.1 A. muscaria hypothetical protein NA 0.538 0.001 7.924 Neu

200

Linkage group Gene Best BLAST hit (Accession) Species Functional annotation GO terms ! vargsi "gsi LRT

scaffold-587 g3041 KIL67387.1 A. muscaria hypothetical protein (metal ion binding) GO:0046872 0.275 0.001 7.923 Pur

scaffold-663 g3502 KIL67827.1 A. muscaria metallopeptidase GO:0004222 0.162 0.000 8.000 Neu scaffold-666 g3586 KIL69438.1 A. muscaria ring finger ubiquitin ligase GO:0008270 0.230 0.000 8.000 Pur scaffold-666 g3629 KIL69372.1 A. muscaria carnitine acyl transferase GO:0016746 0.316 0.003 7.861 Neu scaffold-675 g3712 KIL69708.1 A. muscaria GTP binding GO:0005488 0.141 0.000 8.000 Neu

scaffold-685 g3819 KIL69245.1 A. muscaria von Willebrand ring finger domain-containing protein GO:0008270 0.126 0.003 7.861 Neu

scaffold-698 g4062 KIL68186.1 A. muscaria hypothetical protein NA 0.298 0.004 7.843 Pur scaffold-706 g4167 KIL71593.1 A. muscaria chitin synthethase GO:0004100 0.053 0.000 8.000 Pur scaffold-706 g4215 KIL71522.1 A. muscaria WD40 domain-containing protein GO:0005515 0.273 0.003 7.862 Neu

scaffold-723 g4374 KIL70861.1 A. muscaria hypothetical protein (GTPase activity) GO:0003924 0.326 0.003 7.803 Neu

scaffold-726 g4486 KJA15191.1 Hypholoma sublateritium kinase-like protein GO:0004674 0.291 0.000 8.000 Neu

scaffold-732 g4599 KIL62230.1 A. muscaria DNA mismatch repair protein GO:0030983 0.205 0.001 7.913 Neu

scaffold-742 g4668 KIL69515.1 A. muscaria pumilio RRM domin-containing protein GO:0000166 0.166 0.000 8.000 Pur

scaffold-757 g4744 KIL59086.1 A. muscaria AMP deaminase GO:0003876 0.188 0.004 7.843 Neu scaffold-831 g5023 KIL68278.1 A. muscaria growth-arrest protein GO:0007050 0.291 0.000 8.000 Neu

scaffold-871 g5212 KIL70662.1 A. muscaria chromatin remodeling complex protein NA 0.161 0.003 7.867 Neu

scaffold-8177 g7424 KIL70086.1 A. muscaria ubiquitin conjugating enzyme GO:0016881 0.131 0.000 8.000 Neu scaffold-9363 g7918 KIL70545.1 A. muscaria kinase-like protein GO:0004674 0.211 0.000 8.000 Neu

scaffold-9363 g7991 KIL68102.1 A. muscaria cellular morphogenesis-related protein NA 0.280 0.000 8.000 Neu

scaffold-9461 g8575 KIL70176.1 A. muscaria vacuolar sorting protein NA 0.318 0.000 8.000 Neu scaffold-9463 g8604 KIL71356.1 A. muscaria glycosyltransferase famlily 2 GO:0016758 0.091 0.000 8.000 Pur

201

Figure D8.7. Number of variable sites (grey) and percentage of missing data (black) versus

gene length.

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Figure D8.8. The Watterson estimator, theta, separated by site classes.

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Figure D8.9. Estimates of nucleotide diversity over all genes separated by class sites.

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Figure D8.10. Estimates of effective population size.

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Figure D8.11. Scatterplots of gsi values versus omega (polymorphism).

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Figure D8.12. Scatterplots of gsi values versus alpha.

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Figure D8.13. Neutrality Index versus the scaled number of Stop codons.

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208

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209

Figure D8.14. Wordles of GO terms for each species. These represent genes that were found under positive selection (alpha > 0), but were species-specific.

210

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