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The genetics of adaptation and speciation in threespine stickleback species pairs (Gasterosteus aculeatus… Conte, Gina Louise 2014

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	 ?The	 ?Genetics	 ?of	 ?Adaptation	 ?and	 ?Speciation	 ?in	 ?Threespine	 ?Stickleback	 ?Species	 ?Pairs	 ?(Gasterosteus	 ?aculeatus	 ?species	 ?complex)	 ?	 ?	 ?by	 ?	 ?	 ?Gina	 ?Louise	 ?Conte	 ?	 ?	 ? 	 ?A	 ?THESIS	 ?SUBMITTED	 ?IN	 ?PARTIAL	 ?FULFILLMENT	 ?OF	 ?THE	 ?REQUIREMENTS	 ?FOR	 ?THE	 ?DEGREE	 ?OF	 ?	 ?	 ?DOCTOR	 ?OF	 ?PHILOSOPHY	 ?	 ?	 ?in	 ?	 ?	 ?The	 ?Faculty	 ?of	 ?Graduate	 ?and	 ?Postdoctoral	 ?Studies	 ?	 ?	 ?(Zoology)	 ?	 ?	 ? 	 ?THE	 ?UNIVERSITY	 ?OF	 ?BRITISH	 ?COLUMBIA	 ?(Vancouver)	 ?	 ?	 ?February	 ?2014	 ?	 ?	 ??	 ?Gina	 ?Louise	 ?Conte,	 ?2014	 ?	 ?	 ? ii	 ?Abstract	 ?Ecological	 ?speciation	 ?appears	 ?to	 ?be	 ?a	 ?common	 ?process	 ?by	 ?which	 ?new	 ?species	 ?arise	 ?and	 ?the	 ?genetics	 ?underlying	 ?the	 ?process	 ?can	 ?substantially	 ?affect	 ?its	 ?outcome.	 ?Replicate	 ??benthic	 ?and	 ?limnetic?	 ?pairs	 ?of	 ?threespine	 ?stickleback	 ?species	 ?are	 ?an	 ?ideal	 ?system	 ?with	 ?which	 ?to	 ?study	 ?the	 ?genetics	 ?underlying	 ?ecological	 ?speciation.	 ?My	 ?first	 ?question	 ?was:	 ?what	 ?genetic	 ?mechanisms	 ?link	 ?divergent	 ?natural	 ?selection	 ?to	 ?reproductive	 ?isolation	 ?during	 ?ecological	 ?speciation?	 ?In	 ?chapter	 ?2,	 ?I	 ?present	 ?an	 ?experiment	 ?designed	 ?to	 ?determine	 ?what	 ?genetic	 ?mechanism	 ?links	 ?divergent	 ?selection	 ?on	 ?body	 ?size	 ?to	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?in	 ?the	 ?Paxton	 ?Lake	 ?species	 ?pair.	 ?I	 ?found	 ?that	 ?body	 ?size	 ?functions	 ?as	 ?a	 ?mate	 ?signal	 ?trait	 ?and	 ?determines	 ?female	 ?mate	 ?preference	 ?via	 ?phenotype	 ?matching.	 ?This	 ?implies	 ?that	 ?genes	 ?under	 ?divergent	 ?selection	 ?are	 ?the	 ?same	 ?as	 ?those	 ?underlying	 ?both	 ?components	 ?of	 ?assortative	 ?mating,	 ?a	 ?mechanism	 ?that	 ?should	 ?facilitate	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow.	 ?My	 ?second	 ?question	 ?was:	 ?what	 ?is	 ?the	 ?genetic	 ?architecture	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation?	 ?In	 ?chapter	 ?4,	 ?I	 ?used	 ?QTL	 ?mapping	 ?to	 ?discover	 ?the	 ?genetic	 ?architecture	 ?underlying	 ?a	 ?large	 ?number	 ?of	 ?parallel	 ?morphological	 ?differences	 ?in	 ?the	 ?Paxton	 ?and	 ?Priest	 ?Lake	 ?species	 ?pairs	 ?and	 ?found	 ?it	 ?to	 ?be	 ?polygenic	 ?and	 ?widespread	 ?throughout	 ?the	 ?genome	 ?in	 ?both.	 ?This	 ?suggests	 ?that	 ?many	 ?loci	 ?underlying	 ?ecologically	 ?important	 ?traits	 ?have	 ?diverged	 ?(and/or	 ?divergence	 ?has	 ?persisted)	 ?during	 ?ecological	 ?speciation	 ?despite	 ?homogenizing	 ?gene	 ?flow.	 ?My	 ?third	 ?question	 ?was:	 ?how	 ?predictable	 ?are	 ?the	 ?genetics	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation	 ?and	 ?in	 ?general?	 ?Chapters	 ?3	 ?and	 ?4	 ?describe	 ?the	 ?first	 ?studies	 ?to	 ?quantitatively	 ?address	 ?this	 ?question.	 ?In	 ?chapter	 ?4,	 ?I	 ?found	 ?that	 ?about	 ?50%	 ?of	 ?QTL	 ?for	 ?parallel	 ?morphological	 ?differences	 ?are	 ?parallel	 ?in	 ?the	 ?two	 ?species	 ?pairs.	 ?Also,	 ?on	 ?average,	 ?the	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?individual	 ?traits	 ?is	 ?about	 ?0.4.	 ?In	 ?Chapter	 ?3,	 ?I	 ?present	 ?the	 ?results	 ?of	 ?a	 ?meta-??analysis	 ?of	 ?the	 ?genetics	 ?underlying	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?natural	 ?populations.	 ?Using	 ?an	 ?impartial	 ?literature	 ?review,	 ?I	 ?found	 ?that	 ?the	 ?average	 ?probability	 ?of	 ?gene	 ?reuse	 ?was	 ?0.32	 ?-??	 ?0.55.	 ?I	 ?also	 ?found	 ?that	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?declined	 ?with	 ?increasing	 ?age	 ?of	 ?the	 ?taxa	 ?compared.	 ?	 ?	 ?	 ?	 ? 	 ?	 ? iii	 ?Preface	 ?A	 ?version	 ?of	 ?chapter	 ?2	 ?has	 ?been	 ?published.	 ?Conte,	 ?G.L.,	 ?and	 ?D.	 ?Schluter.	 ?2013.	 ?Experimental	 ?confirmation	 ?that	 ?body	 ?size	 ?determines	 ?mate	 ?preference	 ?via	 ?phenotype	 ?matching	 ?in	 ?a	 ?stickleback	 ?species	 ?pair.	 ?Evolution	 ?67-??5:	 ?1477-??1484.	 ?I	 ?conceived	 ?of	 ?and	 ?implemented	 ?the	 ?experiment,	 ?analyzed	 ?the	 ?data	 ?and	 ?wrote	 ?the	 ?manuscript.	 ?My	 ?supervisor	 ?and	 ?co-??author,	 ?Dolph	 ?Schluter,	 ?advised	 ?on	 ?and	 ?helped	 ?with	 ?all	 ?aspects	 ?of	 ?the	 ?project.	 ?Jacob	 ?Best,	 ?a	 ?research	 ?assistant,	 ?assisted	 ?in	 ?the	 ?set-??up	 ?and	 ?implementation	 ?of	 ?the	 ?experiment.	 ?	 ?A	 ?version	 ?of	 ?chapter	 ?3	 ?has	 ?been	 ?published.	 ?Conte,	 ?G.L.,	 ?M.E.	 ?Arnegard,	 ?C.L.	 ?Peichel,	 ?and	 ?D.	 ?Schluter.	 ?2012.	 ?The	 ?probability	 ?of	 ?genetic	 ?parallelism	 ?and	 ?convergence	 ?in	 ?natural	 ?populations.	 ?Proc.	 ?R.	 ?Soc.	 ?B	 ?279:	 ?5039-??5047.	 ?Dolph	 ?Schluter	 ?and	 ?I	 ?conceived	 ?of	 ?the	 ?study,	 ?with	 ?Catherine	 ?Peichel	 ?and	 ?Matthew	 ?Arnegard	 ?making	 ?important	 ?contributions.	 ?I	 ?conducted	 ?the	 ?literature	 ?survey	 ?and	 ?led	 ?the	 ?subsequent	 ?detailed	 ?investigations	 ?of	 ?the	 ?literature,	 ?this	 ?task	 ?being	 ?divided	 ?evenly	 ?between	 ?all	 ?co-??authors.	 ?I	 ?organized	 ?and	 ?analyzed	 ?the	 ?data	 ?with	 ?help	 ?and	 ?supervision	 ?from	 ?Dolph	 ?Schluter.	 ?I	 ?wrote	 ?a	 ?large	 ?part	 ?of	 ?the	 ?manuscript,	 ?but	 ?Dolph	 ?Schluter	 ?made	 ?significant	 ?contributions	 ?to	 ?the	 ?writing	 ?as	 ?well.	 ?Chapter	 ?4	 ?is	 ?part	 ?of	 ?a	 ?larger	 ?study	 ?to	 ?investigate	 ?the	 ?genetic	 ?architecture	 ?of	 ?ecological	 ?speciation.	 ?Catherine	 ?Peichel	 ?and	 ?Dolph	 ?Schluter	 ?are	 ?co-??principal	 ?investigators	 ?of	 ?this	 ?larger	 ?project,	 ?and	 ?Matthew	 ?Arnegard	 ?and	 ?myself	 ?are	 ?the	 ?leading	 ?researchers.	 ?For	 ?Chapter	 ?4,	 ?Dolph	 ?Schluter,	 ?Catherine	 ?Peichel,	 ?Matthew	 ?Arnegard	 ?and	 ?myself	 ?all	 ?contributed	 ?the	 ?conception	 ?of	 ?the	 ?project.	 ?I	 ?made	 ?the	 ?crosses,	 ?raised	 ?the	 ?F1	 ?hybrids	 ?and	 ?initiated	 ?the	 ?pond	 ?populations	 ?of	 ?F2	 ?hybrids.	 ?The	 ?F2	 ?hybrids	 ?specimens	 ?were	 ?collected	 ?and	 ?preserved	 ?by	 ?Matthew	 ?Arnegard	 ?and	 ?myself	 ?with	 ?help	 ?from	 ?two	 ?assistants,	 ?Jacob	 ?Best	 ?and	 ?Nicole	 ?Bedford.	 ?Wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?specimens	 ?were	 ?collected	 ?and	 ?photographed	 ?by	 ?Richard	 ?Svanback	 ?and	 ?Jennifer	 ?Gow.	 ?Jacob	 ?Best	 ?scored	 ?morphological	 ?phenotypes	 ?of	 ?all	 ?specimens	 ?with	 ?supervision	 ?and	 ?guidance	 ?from	 ?Matthew	 ?Arnegard	 ?and	 ?myself.	 ?Jacob	 ?Best	 ?and	 ?I	 ?performed	 ?all	 ?DNA	 ?extractions	 ?and	 ?prepared	 ?DNA	 ?samples	 ?for	 ?	 ? iv	 ?genotyping.	 ?Genotyping	 ?was	 ?done	 ?at	 ?the	 ?Fred	 ?Hutchinson	 ?Cancer	 ?Research	 ?Center?s	 ?Genomics	 ?Shared	 ?Resource	 ?by	 ?its	 ?supervisor,	 ?Cassie	 ?Sather,	 ?under	 ?the	 ?direction	 ?of	 ?Catherine	 ?Peichel.	 ?I	 ?curated	 ?the	 ?data,	 ?performed	 ?all	 ?data	 ?analyses,	 ?and	 ?wrote	 ?the	 ?manuscript	 ?with	 ?supervision	 ?and	 ?help	 ?from	 ?Dolph	 ?Schluter.	 ?I	 ?am	 ?certified	 ?by	 ?the	 ?Canadian	 ?Council	 ?on	 ?Animal	 ?Care	 ?(CCAC)	 ?/	 ?National	 ?Institutional	 ?Animal	 ?User	 ?Training	 ?(NIAUT)	 ?Program.	 ?My	 ?certificate	 ?number	 ?is	 ?4061	 ??	 ?11.	 ?Permission	 ?for	 ?collections	 ?of	 ?wild	 ?threespine	 ?sticklebacks	 ?(Gasterosteus	 ?aculeatus	 ?species	 ?complex)	 ?made	 ?by	 ?my	 ?collaborators	 ?and	 ?I,	 ?and	 ?used	 ?herein	 ?was	 ?granted	 ?by	 ?the	 ?following	 ?permits:	 ?BC	 ?ministry	 ?of	 ?the	 ?Environment	 ?permit	 ?numbers	 ?NA/SU08-??42033	 ?and	 ?NA/SU09-??51805;	 ?Fisheries	 ?and	 ?Oceans	 ?Canada	 ?SARA	 ?permit	 ?number	 ?SECT	 ?08	 ?SCI	 ?002	 ?and	 ?SARA-??116.	 ?Permission	 ?to	 ?care	 ?for	 ?and	 ?use	 ?threespine	 ?sticklebacks	 ?for	 ?the	 ?studies	 ?herein	 ?was	 ?granted	 ?by	 ?the	 ?University	 ?of	 ?British	 ?Columbia	 ?Animal	 ?Care	 ?Certificate	 ?A07-??0293.	 ?	 ?	 ? 	 ?	 ? v	 ?Table	 ?of	 ?Contents:	 ?	 ?Abstract	 ?.......................................................................................................................................	 ?ii	 ?Preface	 ?.......................................................................................................................................	 ?iii	 ?Table	 ?of	 ?Contents:	 ?....................................................................................................................	 ?v	 ?List	 ?of	 ?Tables	 ?............................................................................................................................	 ?vi	 ?List	 ?of	 ?Figures	 ?..........................................................................................................................	 ?vii	 ?Acknowledgements	 ?.............................................................................................................	 ?viii	 ?Dedication	 ?..................................................................................................................................	 ?x	 ?1	 ?	 ? Introduction	 ?......................................................................................................................	 ?1	 ?2	 ? Experimental	 ?Confirmation	 ?That	 ?Body	 ?Size	 ?Determines	 ?Mate	 ?Preference	 ?Via	 ?Phenotype	 ?Matching	 ?in	 ?a	 ?Threespine	 ?Stickleback	 ?Species	 ?Pair	 ?.......................	 ?9	 ?Introduction	 ?..........................................................................................................................	 ?9	 ?Methods	 ?...............................................................................................................................	 ?12	 ?Results	 ?..................................................................................................................................	 ?17	 ?Discussion	 ?...........................................................................................................................	 ?22	 ?3	 ?	 ? The	 ?Probability	 ?of	 ?Genetic	 ?Parallelism	 ?and	 ?Convergence	 ?in	 ?Natural	 ?Populations	 ?.............................................................................................................................	 ?26	 ?Introduction	 ?.......................................................................................................................	 ?26	 ?Methods	 ?...............................................................................................................................	 ?32	 ?Results	 ?..................................................................................................................................	 ?38	 ?Discussion	 ?...........................................................................................................................	 ?41	 ?4	 ?	 ? The	 ?Extent	 ?of	 ?Parallel	 ?Genetic	 ?Evolution	 ?Underlying	 ?Parallel	 ?Phenotypic	 ?Evolution	 ?in	 ?Pairs	 ?of	 ?Benthic	 ?and	 ?Limnetic	 ?Threespine	 ?Stickleback	 ?Species	 ?..	 ?46	 ?Introduction	 ?.......................................................................................................................	 ?46	 ?Results	 ?..................................................................................................................................	 ?50	 ?Discussion	 ?...........................................................................................................................	 ?56	 ?Methods	 ?...............................................................................................................................	 ?60	 ?5	 ? Conclusions	 ?......................................................................................................................	 ?70	 ?References	 ?..............................................................................................................................	 ?75	 ?Appendices	 ?...........................................................................................................................	 ?102	 ?Appendix	 ?A:	 ?Chapter	 ?2	 ?Supplementary	 ?Material	 ?.................................................	 ?102	 ?Appendix	 ?B:	 ?Chapter	 ?3	 ?Supplementary	 ?Material	 ?.................................................	 ?107	 ?Appendix	 ?C:	 ?Chapter	 ?4	 ?Supplementary	 ?Material	 ?.................................................	 ?116	 ?	 ?	 ? vi	 ?List	 ?of	 ?Tables	 ?Table	 ?2.1	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?.......................	 ?20	 ?Table	 ?2.2	 ?Effects	 ?of	 ?female	 ?treatment	 ?on	 ?male	 ?behavior	 ?.......................................................	 ?22	 ?	 ?Table	 ?A.1	 ?Effects	 ?of	 ?body	 ?size	 ?manipulation	 ?............................................................................	 ?103	 ?Table	 ?A.2	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?stringent	 ?female	 ?acceptance	 ?scores	 ?104	 ?Table	 ?A.3	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?males?	 ?first	 ?trial	 ?..........................................................................................................................................	 ?105	 ?Table	 ?A.4	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?restricted	 ?date	 ?range	 ?................................................................................................................	 ?106	 ?	 ?Table	 ?B.1	 ?Summary	 ?of	 ?cases	 ?detected	 ?by	 ?our	 ?literature	 ?search	 ?.......................................	 ?107	 ?Table	 ?B.2	 ?Node	 ?numbers	 ?...................................................................................................................	 ?111	 ?Table	 ?B.3	 ?Proportional	 ?similarity	 ?at	 ?each	 ?node	 ?......................................................................	 ?114	 ?	 ?Table	 ?C.1	 ?Trait	 ?divergence	 ?categories	 ?.........................................................................................	 ?122	 ?Table	 ?C.2	 ?Identities,	 ?map	 ?positions,	 ?and	 ?physical	 ?locations	 ?of	 ?SNPs	 ?.............................	 ?124	 ?Table	 ?C.3	 ?Paxton	 ?Lake	 ?QTL	 ?scan	 ?results	 ?.....................................................................................	 ?131	 ?Table	 ?C.4	 ?Priest	 ?Lake	 ?QTL	 ?scan	 ?results	 ?.......................................................................................	 ?133	 ?Table	 ?C.5	 ??Combined?	 ?QTL	 ?scan	 ?results	 ?.......................................................................................	 ?134	 ?Table	 ?C.6	 ?QTL	 ?effects	 ?of	 ?candidate	 ?QTL	 ?......................................................................................	 ?136	 ?Table	 ?C.7	 ?Proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?parallel	 ?traits	 ?......................	 ?139	 ?	 ? 	 ?	 ? vii	 ?List	 ?of	 ?Figures	 ?Figure	 ?2.1	 ?Standard	 ?length	 ?distributions	 ?after	 ?manipulation	 ?of	 ?body	 ?size	 ?....................	 ?18	 ?Figure	 ?2.2	 ?Female	 ?acceptance	 ?scores	 ?.............................................................................................	 ?21	 ?	 ?Figure	 ?3.1	 ?Calculating	 ?proportional	 ?similarity	 ?...........................................................................	 ?36	 ?Figure	 ?3.2	 ?Probability	 ?of	 ?gene	 ?reuse	 ?by	 ?node	 ?age	 ?.....................................................................	 ?39	 ?	 ?Figure	 ?4.1	 ?Trait	 ?divergence	 ?categories	 ?..........................................................................................	 ?51	 ?Figure	 ?4.2	 ?Examples	 ?of	 ?QTL	 ?with	 ?parallel,	 ?single-??lake	 ?and	 ?opposite	 ?effects	 ?.................	 ?53	 ?Figure	 ?4.3	 ?QTL	 ?effect	 ?categories	 ?.......................................................................................................	 ?54	 ?Figure	 ?4.4	 ?QTL	 ?PVE	 ?by	 ?QTL	 ?effect	 ?category	 ?.................................................................................	 ?55	 ?Figure	 ?4.5	 ?Entropy	 ?difference	 ?between	 ?crosses	 ?by	 ?QTL	 ?effect	 ?category	 ?.........................	 ?56	 ?	 ?Figure	 ?A.1	 ?Experimental	 ?Design	 ?....................................................................................................	 ?102	 ?	 ?Figure	 ?C.1	 ?All	 ?phenotypes	 ?scored	 ?..................................................................................................	 ?116	 ?Figure	 ?C.2	 ?Examples	 ?of	 ?mapping	 ?candidate	 ?QTL	 ?....................................................................	 ?117	 ?Figure	 ?C.3	 ?Map	 ?of	 ?candidate	 ?QTL	 ?..................................................................................................	 ?119	 ?Figure	 ?C.4	 ?Proportions	 ?of	 ?QTL	 ?effect	 ?categories	 ?per	 ?chromosome	 ?................................	 ?121	 ?	 ? 	 ?	 ? viii	 ?Acknowledgements	 ?Firstly,	 ?I	 ?thank	 ?my	 ?advisor,	 ?Dolph	 ?Schluter,	 ?without	 ?whom	 ?none	 ?of	 ?this	 ?work	 ?would	 ?have	 ?been	 ?possible.	 ?I	 ?am	 ?tremendously	 ?grateful	 ?to	 ?have	 ?done	 ?my	 ?PhD	 ?in	 ?Dolph?s	 ?lab.	 ?In	 ?my	 ?experience,	 ?Dolph?s	 ?reputation	 ?as	 ?not	 ?only	 ?a	 ?renowned	 ?evolutionary	 ?biologist,	 ?but	 ?also	 ?an	 ?outstanding	 ?advisor	 ?is	 ?more	 ?than	 ?justified.	 ?I	 ?thank	 ?Dolph	 ?for	 ?his	 ?sincere	 ?enthusiasm,	 ?for	 ?the	 ?time	 ?and	 ?effort	 ?he	 ?committed	 ?to	 ?my	 ?work,	 ?and	 ?for	 ?always	 ?challenging	 ?me.	 ?I	 ?also	 ?owe	 ?great	 ?thanks	 ?to	 ?the	 ?members	 ?of	 ?my	 ?committee,	 ?Catherine	 ?Peichel,	 ?Loren	 ?Rieseberg,	 ?and	 ?Michael	 ?Whitlock.	 ?While	 ?being	 ?an	 ?illustrious	 ?group	 ?of	 ?scientists,	 ?they	 ?are	 ?also	 ?approachable,	 ?helpful	 ?and	 ?down-??to-??earth.	 ?I	 ?sincerely	 ?enjoyed	 ?having	 ?each	 ?of	 ?them	 ?on	 ?my	 ?committee	 ?and	 ?am	 ?appreciative	 ?of	 ?the	 ?perspectives	 ?and	 ?expertise	 ?that	 ?they	 ?offered	 ?me.	 ?I	 ?extend	 ?special	 ?thanks	 ?to	 ?Catherine	 ?Peichel,	 ?with	 ?whom	 ?I	 ?collaborated	 ?on	 ?many	 ?projects,	 ?and	 ?whose	 ?influence	 ?has	 ?been	 ?instrumental	 ?in	 ?my	 ?work.	 ?	 ?To	 ?my	 ?lab	 ?mates,	 ?I	 ?am	 ?grateful	 ?for	 ?many	 ?stimulating	 ?discussions	 ?and	 ?for	 ?a	 ?great	 ?deal	 ?of	 ?helpful	 ?feedback.	 ?In	 ?particular,	 ?I	 ?am	 ?very	 ?appreciative	 ?of	 ?the	 ?wealth	 ?of	 ?perspective	 ?and	 ?guidance	 ?given	 ?by	 ?my	 ?lab	 ?mate	 ?and	 ?collaborator	 ?Matthew	 ?Arnegard,	 ?who	 ?I	 ?affectionately	 ?called	 ?my	 ??post-??doc	 ?advisor?.	 ?I	 ?am	 ?also	 ?particularly	 ?indebted	 ?to	 ?my	 ?research	 ?assistant,	 ?Jacob	 ?Best,	 ?who	 ?enthusiastically	 ?worked	 ?with	 ?me	 ?for	 ?nearly	 ?my	 ?entire	 ?PhD	 ?and	 ?whose	 ?help	 ?has	 ?been	 ?invaluable.	 ?	 ?Being	 ?a	 ?member	 ?of	 ?the	 ?Biodiversity	 ?Research	 ?Centre	 ?and	 ?the	 ?Zoology	 ?Department	 ?at	 ?UBC	 ?has	 ?been	 ?both	 ?an	 ?honor	 ?and	 ?a	 ?joy.	 ?The	 ?community,	 ?comprised	 ?of	 ??power-??house?	 ?faculty	 ?and	 ?the	 ?impressive	 ?students	 ?and	 ?post-??docs	 ?they	 ?attract,	 ?has	 ?been	 ?inspiring	 ?to	 ?say	 ?the	 ?least.	 ?Perhaps	 ?the	 ?most	 ?outstanding	 ?quality	 ?of	 ?the	 ?community	 ?is	 ?how	 ?interactive	 ?its	 ?members	 ?are,	 ?in	 ?both	 ?scientific	 ?and	 ?social	 ?realms,	 ?and	 ?for	 ?that	 ?I	 ?am	 ?grateful.	 ?My	 ?greatest	 ?thanks	 ?of	 ?all	 ?go	 ?to	 ?my	 ?family	 ?and	 ?friends,	 ?whose	 ?love	 ?and	 ?support	 ?are	 ?the	 ?very	 ?source	 ?of	 ?my	 ?motivation.	 ?I	 ?thank	 ?my	 ?mom	 ?and	 ?dad	 ?for,	 ?well,	 ?nearly	 ?everything.	 ?I	 ?thank	 ?my	 ?sister	 ?for	 ?being	 ?my	 ?closest	 ?friend.	 ?I	 ?thank	 ?my	 ?husband	 ?for	 ?his	 ?daily	 ?love,	 ?support,	 ?patience	 ?and	 ?assurance;	 ?he	 ?is	 ?my	 ?rock.	 ?I	 ?thank	 ?my	 ?extended	 ?family	 ?for	 ?their	 ?encouragement	 ?and	 ?for	 ?the	 ?tight-??knit	 ?support	 ?group	 ?they	 ?have	 ?always	 ?provided.	 ?I	 ?thank	 ?my	 ?family	 ?in-??law	 ?for	 ?their	 ?generous	 ?and	 ?warm-??hearted	 ?support	 ?and	 ?for	 ?providing	 ?me	 ?with	 ?the	 ?spark	 ?of	 ?inspiration	 ?that	 ?set	 ?me	 ?on	 ?an	 ?academic	 ?track.	 ?	 ? ix	 ?Finally,	 ?I	 ?thank	 ?my	 ?friends	 ?for	 ?my	 ?day-??to-??day	 ?well-??being.	 ?In	 ?particular,	 ?I	 ?thank	 ?my	 ?fellow	 ?graduate	 ?student	 ?friends,	 ?with	 ?whom	 ?I	 ?have	 ?shared	 ?my	 ?experience.	 ?	 ?	 ?Specific	 ?acknowledgements	 ?for	 ?Chapter	 ?2:	 ?Special	 ?thanks	 ?to	 ?Jacob	 ?Best	 ?for	 ?help	 ?with	 ?the	 ?set-??up	 ?and	 ?implementation	 ?of	 ?the	 ?experiment.	 ?Thanks	 ?to	 ?Matthew	 ?Arnegard	 ?and	 ?Gerrit	 ?Velema	 ?for	 ?help	 ?with	 ?collection	 ?of	 ?fish.	 ?Matthew	 ?Arnegard,	 ?Gwylim	 ?Blackburn,	 ?Richard	 ?Fitzjohn,	 ?Travis	 ?Ingram,	 ?Julie	 ?Lee-??Yaw,	 ?Leithen	 ?M?Gonigle,	 ?Sarah	 ?Otto,	 ?Kieran	 ?Samuk,	 ?Maria	 ?Servedio,	 ?Laura	 ?Southcott,	 ?Michael	 ?Whitlock,	 ?and	 ?Sara	 ?Via	 ?provided	 ?valuable	 ?comments	 ?on	 ?the	 ?manuscript.	 ?This	 ?research	 ?was	 ?funded	 ?by	 ?the	 ?Natural	 ?Sciences	 ?and	 ?Engineering	 ?Research	 ?Council	 ?(NSERC)	 ?of	 ?Canada.	 ?I	 ?was	 ?supported	 ?by	 ?the	 ?NSERC	 ?Collaborative	 ?Research	 ?and	 ?Training	 ?Experience	 ?Program	 ?(CREATE).	 ?	 ?Specific	 ?acknowledgements	 ?for	 ?Chapter	 ?3:	 ?I	 ?thank	 ?all	 ?of	 ?the	 ?researchers	 ?who	 ?generated	 ?data	 ?used	 ?in	 ?the	 ?meta-??analysis.	 ?I	 ?am	 ?grateful	 ?to	 ?three	 ?reviewers	 ?who	 ?provided	 ?helpful	 ?comments.	 ?This	 ?work	 ?was	 ?funded	 ?by	 ?grants	 ?from	 ?the	 ?National	 ?Institutes	 ?of	 ?Health	 ?(R01	 ?GM089733	 ?to	 ?Catherine	 ?Peichel	 ?and	 ?Dolph	 ?Schluter)	 ?and	 ?the	 ?National	 ?Science	 ?and	 ?Engineering	 ?Research	 ?Council	 ?(NSERC)	 ?of	 ?Canada	 ?(to	 ?Dolph	 ?Schluter).	 ?I	 ?was	 ?supported	 ?by	 ?the	 ?NSERC	 ?CREATE	 ?training	 ?programme.	 ?	 ?Specific	 ?acknowledgements	 ?for	 ?Chapter	 ?4:	 ?I?d	 ?like	 ?to	 ?acknowledge	 ?my	 ?co-??authors	 ?on	 ?a	 ?version	 ?of	 ?the	 ?manuscript	 ?that	 ?will	 ?eventually	 ?be	 ?submitted	 ?to	 ?a	 ?peer-??reviewed	 ?journal:	 ?Matthew	 ?Arnegard,	 ?Jacob	 ?Best,	 ?Yingguang	 ?Frank	 ?Chan,	 ?Felicity	 ?Jones,	 ?David	 ?Kingsley,	 ?Dolph	 ?Schluter	 ?and	 ?Catherine	 ?Peichel.	 ?I	 ?thank	 ?Nicole	 ?Bedford	 ?and	 ?Travis	 ?Ingram	 ?for	 ?their	 ?assistance	 ?in	 ?the	 ?collection	 ?of	 ?F2	 ?hybrid	 ?fish.	 ?I	 ?am	 ?also	 ?grateful	 ?to	 ?Richard	 ?Svanback	 ?and	 ?Jennifer	 ?Gow	 ?for	 ?allowing	 ?my	 ?use	 ?of	 ?wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?specimens	 ?(or	 ?photographs	 ?of	 ?specimens)	 ?from	 ?their	 ?own	 ?collections.	 ?This	 ?work	 ?was	 ?funded	 ?by	 ?grants	 ?from	 ?the	 ?National	 ?Institutes	 ?of	 ?Health	 ?(R01	 ?GM089733	 ?to	 ?Catherine	 ?Peichel	 ?and	 ?Dolph	 ?Schluter)	 ?and	 ?the	 ?National	 ?Science	 ?and	 ?Engineering	 ?Research	 ?Council	 ?(NSERC)	 ?of	 ?Canada	 ?(to	 ?Dolph	 ?Schluter).	 ?I	 ?was	 ?supported	 ?by	 ?the	 ?NSERC	 ?CREATE	 ?training	 ?programme	 ?and	 ?a	 ?University	 ?of	 ?British	 ?Columbia	 ?Zoology	 ?Graduate	 ?Fellowship.	 ? x	 ?	 ? Dedication	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ? To John, Mary Lynn, Richard and Jessica 	 ?	 ? 1	 ?1	 ?	 ? Introduction	 ?Ecological	 ?speciation,	 ?whereby	 ?ecologically	 ?based	 ?divergent	 ?natural	 ?selection	 ?leads	 ?to	 ?the	 ?evolution	 ?of	 ?reproductive	 ?isolation,	 ?is	 ?now	 ?thought	 ?to	 ?be	 ?responsible	 ?for	 ?the	 ?origin	 ?of	 ?a	 ?large	 ?portion	 ?of	 ?biological	 ?diversity	 ?(Schluter	 ?2009;	 ?Nosil	 ?2012).	 ?Due	 ?to	 ?the	 ?generality	 ?and	 ?simplicity	 ?of	 ?its	 ?mechanism,	 ?ecological	 ?speciation	 ?can	 ?occur	 ?in	 ?any	 ?geographic	 ?context	 ?and	 ?involve	 ?a	 ?wide	 ?variety	 ?of	 ?reproductive	 ?barriers	 ?(Schluter	 ?2001;	 ?Nosil	 ?2012).	 ?Ecological	 ?speciation	 ?can	 ?be	 ?broken	 ?down	 ?into	 ?three	 ?necessary	 ?components:	 ?1)	 ?a	 ?source	 ?of	 ?ecologically-??based	 ?divergent	 ?selection,	 ?2)	 ?a	 ?form	 ?of	 ?reproductive	 ?isolation	 ?and	 ?3)	 ?a	 ?genetic	 ?mechanism	 ?linking	 ?the	 ?two	 ?(Kirkpatrick	 ?and	 ?Ravign?	 ?2002;	 ?Rundle	 ?and	 ?Nosil	 ?2005).	 ?In	 ?the	 ?past	 ?couple	 ?of	 ?decades,	 ?we	 ?have	 ?accumulated	 ?a	 ?fair	 ?amount	 ?of	 ?data	 ?regarding	 ?the	 ?first	 ?two	 ?components,	 ?while	 ?data	 ?on	 ?the	 ?third	 ?is	 ?still	 ?scarce.	 ?My	 ?PhD	 ?work	 ?has	 ?focused	 ?on	 ?the	 ?genetics	 ?underlying	 ?the	 ?process	 ?of	 ?ecological	 ?speciation.	 ?Because	 ?ecological	 ?speciation	 ?is	 ?fundamentally	 ?based	 ?on	 ?the	 ?process	 ?of	 ?adaptation,	 ?parts	 ?of	 ?my	 ?work	 ?also	 ?have	 ?great	 ?relevance	 ?to	 ?the	 ?broader	 ?topic	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?	 ?The	 ?three	 ?primary	 ?questions	 ?addressed	 ?herein	 ?are:	 ?1)	 ?what	 ?genetic	 ?mechanisms	 ?link	 ?divergent	 ?natural	 ?selection	 ?to	 ?reproductive	 ?isolation	 ?during	 ?ecological	 ?speciation?,	 ?2)	 ?what	 ?is	 ?the	 ?genetic	 ?architecture	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation?	 ?and	 ?3)	 ?how	 ?predictable	 ?are	 ?the	 ?genetics	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation	 ?and	 ?in	 ?general?.	 ?Question	 ?1:	 ?what	 ?genetic	 ?mechanisms	 ?link	 ?divergent	 ?natural	 ?selection	 ?to	 ?reproductive	 ?isolation	 ?during	 ?ecological	 ?speciation?	 ?For	 ?a	 ?form	 ?of	 ?reproductive	 ?isolation	 ?to	 ?evolve	 ?during	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow,	 ?there	 ?must	 ?be	 ?a	 ?genetic	 ?mechanism	 ?by	 ?which	 ?it	 ?becomes	 ?associated	 ?with	 ?loci	 ?under	 ?divergent	 ?natural	 ?selection.	 ?Some	 ?forms	 ?of	 ?reproductive	 ?isolation	 ?accompany	 ?divergent	 ?natural	 ?selection	 ?by	 ?definition,	 ?such	 ?as	 ?reduced	 ?immigrant	 ?	 ? 2	 ?and	 ?hybrid	 ?fitness	 ?(a.k.a.	 ?extrinsic	 ?reproductive	 ?isolation)	 ?(Schluter	 ?2001;	 ?Schluter	 ?and	 ?Conte	 ?2009;	 ?Nosil	 ?2012).	 ?This	 ?reproductive	 ?isolation	 ?is,	 ?by	 ?nature,	 ?a	 ?result	 ?of	 ?the	 ?same	 ?traits	 ?that	 ?are	 ?under	 ?divergent	 ?selection,	 ?and	 ?therefore	 ?the	 ?same	 ?underlying	 ?loci.	 ?Thus,	 ?the	 ?genetic	 ?mechanism	 ?linking	 ?the	 ?two	 ?is	 ?unambiguous	 ?(Schluter	 ?and	 ?Conte	 ?2009).	 ?Alone,	 ?reduced	 ?immigrant	 ?and	 ?hybrid	 ?fitness	 ?resulting	 ?from	 ?divergent	 ?natural	 ?selection	 ?may	 ?often	 ?not	 ?be	 ?strong	 ?enough	 ?to	 ?result	 ?in	 ?total	 ?reproductive	 ?isolation,	 ?and	 ?speciation	 ?may	 ?thus	 ?involve	 ?the	 ?evolution	 ?of	 ?additional	 ?forms	 ?of	 ?reproductive	 ?isolation.	 ?There	 ?are	 ?two	 ?general	 ?genetic	 ?mechanisms	 ?by	 ?which	 ?this	 ?can	 ?occur.	 ?First,	 ?the	 ?very	 ?traits	 ?under	 ?divergent	 ?natural	 ?selection	 ?(and	 ?therefore,	 ?their	 ?underlying	 ?loci)	 ?may	 ?sometimes	 ?also	 ?cause	 ?other	 ?forms	 ?of	 ?reproductive	 ?isolation,	 ?such	 ?as	 ?assortative	 ?mating,	 ?habitat/spatial	 ?isolation,	 ?temporal	 ?isolation	 ?or	 ?even	 ?intrinsic	 ?incompatibilities	 ?(Mayr	 ?1942;	 ?Dobzhansky	 ?1951;	 ?Schluter	 ?2001;	 ?Nosil	 ?2012).	 ?Various	 ?sets	 ?of	 ?terminology	 ?exist	 ?to	 ?explain	 ?this	 ?phenomenon,	 ?including	 ?the	 ??by-??product?	 ?mechanism	 ?(Schluter	 ?2001),	 ?pleiotropy	 ?(Bolnick	 ?and	 ?Fitzpatrick	 ?2007),	 ?direct	 ?selection	 ?(Kirkpatrick	 ?and	 ?Ravign?	 ?2002),	 ?the	 ?magic	 ?trait	 ?mechanism	 ?(Gavrilets	 ?2004)	 ?and	 ?the	 ?one-??allele	 ?mechanism	 ?(Felsenstein	 ?1981;	 ?Servedio	 ?2009).	 ?Finding	 ?these	 ?types	 ?of	 ?relationships	 ?between	 ?traits/loci	 ?under	 ?divergent	 ?selection	 ?and	 ?those	 ?involved	 ?in	 ?additional	 ?forms	 ?of	 ?reproductive	 ?isolation	 ?is	 ?thought	 ?to	 ?be	 ?a	 ??signature?	 ?(though	 ?not	 ?a	 ?requirement)	 ?of	 ?ecological	 ?speciation	 ?(Rundle	 ?and	 ?Nosil	 ?2005).	 ?	 ?Alternatively,	 ?natural	 ?selection	 ?may	 ?drive	 ?the	 ?evolution	 ?of	 ?additional	 ?forms	 ?of	 ?prezygotic	 ?reproductive	 ?isolation,	 ?conferred	 ?by	 ?loci	 ?other	 ?than	 ?those	 ?under	 ?divergent	 ?natural	 ?selection,	 ?if	 ?they	 ?prevent	 ?mal-??adaptive	 ?hybridization;	 ?a	 ?process	 ?known	 ?as	 ?reinforcement	 ?(Kirkpatrick	 ?and	 ?Ravign?	 ?2002;	 ?Servedio	 ?and	 ?Noor	 ?2003;	 ?Bolnick	 ?and	 ?Fitzpatrick	 ?2007).	 ?However,	 ?when	 ?reproductive	 ?isolation	 ?is	 ?incomplete	 ?and	 ?individuals	 ?adapted	 ?to	 ?different	 ?environments	 ?still	 ?hybridize,	 ?recombination	 ?may	 ?prevent	 ?the	 ?build	 ?up	 ?of	 ?linkage	 ?disequilibrium	 ?between	 ?favorable	 ?combinations	 ?of	 ?alleles	 ?at	 ?different	 ?loci	 ?in	 ?the	 ?genome	 ?(Kirkpatrick	 ?and	 ?Ravign?	 ?2002;	 ?Gavrilets	 ?2004).	 ?A	 ?seminal	 ?model	 ?by	 ?Felsenstein	 ?(1981)	 ?showed	 ?that	 ?for	 ?speciation	 ?with	 ?gene	 ?flow	 ?to	 ?occur	 ?when	 ?loci	 ?under	 ?divergent	 ?selection	 ?and	 ?loci	 ?controlling	 ?assortative	 ?	 ? 3	 ?mating	 ?are	 ?unlinked,	 ?either	 ?divergent	 ?selection	 ?must	 ?be	 ?very	 ?strong	 ?or	 ?a	 ?high	 ?level	 ?of	 ?assortative	 ?mating	 ?must	 ?evolve	 ?prior	 ?to	 ?the	 ?initiation	 ?of	 ?gene	 ?flow	 ?between	 ?populations.	 ?Thus,	 ?in	 ?general,	 ?the	 ?probability	 ?of	 ?reinforcement	 ?is	 ?increased	 ?by	 ?any	 ?mechanism	 ?that	 ?enhances	 ?linkage	 ?disequilibrium	 ?between	 ?the	 ?loci	 ?under	 ?divergent	 ?selection	 ?and	 ?those	 ?conferring	 ?additional	 ?forms	 ?of	 ?prezygotic	 ?reproductive	 ?isolation,	 ?such	 ?as	 ?tight	 ?physical	 ?linkage	 ?(Via	 ?2001),	 ?regions	 ?of	 ?low	 ?recombination	 ?(Butlin	 ?2005)	 ?and	 ?chromosomal	 ?rearrangements	 ?(Rieseberg	 ?2001).	 ?	 ?The	 ?type	 ?of	 ?genetic	 ?mechanism	 ?linking	 ?divergent	 ?natural	 ?selection	 ?to	 ?other	 ?forms	 ?of	 ?reproductive	 ?isolation	 ?can	 ?have	 ?a	 ?large	 ?impact	 ?on	 ?the	 ?probability	 ?that	 ?ecological	 ?speciation	 ?will	 ?occur	 ?(Felsenstein	 ?1981;	 ?Dieckmann	 ?and	 ?Doebeli	 ?1999;	 ?Kondrashov	 ?and	 ?Kondrashov	 ?1999;	 ?Doebeli	 ?2005;	 ?Servedio	 ?2009).	 ?To	 ?ultimately	 ?understand	 ?the	 ?relative	 ?frequency	 ?and	 ?importance	 ?of	 ?different	 ?the	 ?genetic	 ?mechanisms	 ?in	 ?the	 ?origin	 ?of	 ?new	 ?species,	 ?we	 ?must	 ?discover	 ?what	 ?genetic	 ?mechanisms	 ?are	 ?involved	 ?when	 ?natural	 ?populations	 ?undergo	 ?ecological	 ?speciation.	 ?Question	 ?2:	 ?what	 ?is	 ?the	 ?genetic	 ?architecture	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation?	 ?The	 ?probability	 ?of	 ?ecological	 ?speciation	 ?may	 ?be	 ?influenced	 ?by	 ?the	 ?number	 ?of	 ?loci	 ?underlying	 ?the	 ?targets	 ?of	 ?divergent	 ?selection	 ?(Nosil	 ?2012).	 ?Some	 ?considerations	 ?lead	 ?to	 ?a	 ?prediction	 ?that	 ?ecological	 ?speciation	 ?is	 ?more	 ?likely	 ?if	 ?few	 ?loci	 ?underlie	 ?divergent	 ?adaptations,	 ?while	 ?others	 ?considerations	 ?lead	 ?us	 ?to	 ?predict	 ?the	 ?opposite.	 ?If	 ?traits	 ?under	 ?divergent	 ?selection	 ?are	 ?based	 ?on	 ?fewer	 ?loci,	 ?then	 ?there	 ?will	 ?be	 ?fewer	 ?opportunities	 ?for	 ?interspecific	 ?recombination	 ?to	 ?disintegrate	 ?associations	 ?between	 ?co-??adapted	 ?complexes	 ?(Arnegard	 ?and	 ?Kondrashov	 ?2004;	 ?Gavrilets	 ?and	 ?Vose	 ?2007).	 ?Furthermore,	 ?the	 ?waiting	 ?time	 ?to	 ?speciation	 ?may	 ?be	 ?shorter	 ?in	 ?this	 ?case,	 ?as	 ?fewer	 ?mutations	 ?may	 ?be	 ?required	 ?to	 ?produce	 ?the	 ?variation	 ?necessary	 ?for	 ?divergence	 ?(Gavrilets	 ?and	 ?Vose	 ?2007).	 ?Finally,	 ?fewer	 ?loci	 ?imply	 ?larger	 ?locus	 ?effects	 ?and	 ?therefore	 ?stronger	 ?selection	 ?on	 ?each	 ?locus	 ?(Gavrilets	 ?and	 ?Vose	 ?2007).	 ?This	 ?in	 ?turn,	 ?may	 ?increase	 ?the	 ?probability	 ?that	 ?selection	 ?can	 ?overcome	 ?migration	 ?at	 ?a	 ?given	 ?locus	 ?and	 ?cause	 ?divergence	 ?(Yeaman	 ?and	 ?Otto	 ?2011;	 ?Yeaman	 ?and	 ?Whitlock	 ?2011).	 ?On	 ?the	 ?	 ? 4	 ?other	 ?hand,	 ?the	 ?more	 ?loci	 ?that	 ?are	 ?divergent,	 ?the	 ?higher	 ?the	 ?probability	 ?that	 ?other	 ?forms	 ?of	 ?reproductive	 ?isolation	 ?will	 ?arise	 ?as	 ?a	 ?correlated	 ?response	 ?of	 ?their	 ?divergence,	 ?either	 ?via	 ?pleiotropic	 ?consequences	 ?at	 ?particular	 ?loci	 ?and/or	 ?by	 ?incompatibilities	 ?with	 ?other	 ?loci.	 ?Commonly	 ?known	 ?as	 ?the	 ?snowball	 ?effect,	 ?as	 ?the	 ?number	 ?of	 ?divergent	 ?loci	 ?increases,	 ?the	 ?number	 ?of	 ?pairwise	 ?interactions	 ?that	 ?can	 ?lead	 ?to	 ?incompatibilities	 ?increases	 ?rapidly	 ?(Orr	 ?1995;	 ?Orr	 ?and	 ?Turelli	 ?2001).	 ?	 ?Expectations	 ?for	 ?the	 ?genomic	 ?distribution	 ?of	 ?loci	 ?under	 ?divergent	 ?natural	 ?selection	 ?during	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow	 ?also	 ?vary.	 ?Under	 ?certain	 ?circumstances,	 ?we	 ?may	 ?expect	 ?a	 ?clumped	 ?genomic	 ?distribution	 ?of	 ?loci,	 ?since	 ?their	 ?cumulative	 ?fitness	 ?effects	 ?may	 ?be	 ?more	 ?likely	 ?to	 ?outweigh	 ?the	 ?opposing	 ?effects	 ?of	 ?migration	 ?(Yeaman	 ?and	 ?Whitlock,	 ?2011).	 ?According	 ?to	 ?Yeaman	 ?and	 ?Whitlock	 ?(2011),	 ?populations	 ?adapt	 ?quickly	 ?to	 ?divergent	 ?selection	 ?pressures,	 ?with	 ?the	 ?loci	 ?initially	 ?involved	 ?scattered	 ?across	 ?the	 ?genome.	 ?Given	 ?enough	 ?time	 ?though,	 ?if	 ?many	 ?loci	 ?are	 ?capable	 ?of	 ?producing	 ?a	 ?phenotype,	 ?and/or	 ?if	 ?chromosomal	 ?rearrangements	 ?are	 ?possible,	 ?clumped	 ?genetic	 ?architectures	 ?tend	 ?to	 ?replace	 ?the	 ?initial	 ?scattered	 ?ones,	 ?since	 ?divergence	 ?there	 ?is	 ?more	 ?likely	 ?to	 ?persist	 ?despite	 ?the	 ?homogenizing	 ?effects	 ?of	 ?gene	 ?flow.	 ?On	 ?the	 ?other	 ?hand,	 ?if	 ?only	 ?few	 ?loci	 ?are	 ?capable	 ?of	 ?producing	 ?a	 ?phenotype,	 ?if	 ?pleiotropic	 ?consequences	 ?of	 ?rearrangements	 ?are	 ?too	 ?negative,	 ?and/or	 ?if	 ?not	 ?very	 ?much	 ?time	 ?has	 ?passed,	 ?we	 ?may	 ?expect	 ?to	 ?see	 ?a	 ?scattered	 ?genomic	 ?distribution	 ?of	 ?loci	 ?across	 ?the	 ?genome	 ?(Yeaman	 ?2013).	 ?	 ?Clearly,	 ?there	 ?are	 ?several	 ?important	 ?factors	 ?that	 ?may	 ?differentially	 ?influence	 ?both	 ?the	 ?number	 ?of	 ?divergently	 ?selected	 ?loci	 ?and	 ?their	 ?genomic	 ?distributions	 ?during	 ?ecological	 ?speciation.	 ?Studies	 ?in	 ?natural	 ?populations	 ?are	 ?required	 ?to	 ?test	 ?these	 ?predictions	 ?and	 ?determine	 ?under	 ?what	 ?circumstances	 ?we	 ?do,	 ?in	 ?fact,	 ?see	 ?particular	 ?types	 ?of	 ?genetic	 ?architectures.	 ?Question	 ?3:	 ?how	 ?predictable	 ?are	 ?the	 ?genetics	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation	 ?and	 ?in	 ?general?	 ?One	 ?of	 ?the	 ?most	 ?important	 ?and	 ?instructive	 ?questions	 ?currently	 ?being	 ?asked	 ?within	 ?the	 ?field	 ?of	 ?evolutionary	 ?genetics	 ?is:	 ??how	 ?predictable	 ?are	 ?the	 ?genetics	 ?of	 ?	 ? 5	 ?adaptation??	 ?Studying	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?leads	 ?to	 ?understanding	 ?the	 ?core	 ?forces	 ?shaping	 ?them.	 ?A	 ?promising	 ?way	 ?to	 ?estimate	 ?how	 ?predictable	 ?the	 ?genetics	 ?of	 ?adaptations	 ?are	 ?is	 ?to	 ?look	 ?at	 ?how	 ?commonly	 ?repeated	 ?phenotypic	 ?adaptation	 ?is	 ?underlain	 ?by	 ?repeated	 ?genetic	 ?evolution.	 ?This	 ?topic	 ?has	 ?gained	 ?increasing	 ?popularity	 ?in	 ?the	 ?past	 ?several	 ?years,	 ?and	 ?many	 ?insightful,	 ?though	 ?qualitative,	 ?reviews	 ?have	 ?been	 ?written	 ?(Wood	 ?et	 ?al.	 ?2005;	 ?Arendt	 ?and	 ?Reznick	 ?2008;	 ?Gompel	 ?and	 ?Prud?homme	 ?2009;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Christin	 ?et	 ?al.	 ?2010;	 ?Manceau	 ?et	 ?al.	 ?2010;	 ?Elmer	 ?and	 ?Meyer	 ?2011;	 ?Losos	 ?2011;	 ?Martin	 ?and	 ?Orgogozo	 ?2013;	 ?Stern	 ?2013).	 ?Now,	 ?to	 ?understand	 ?exactly	 ?how	 ?predictable	 ?the	 ?genetics	 ?of	 ?adaptation	 ?are	 ?in	 ?natural	 ?populations,	 ?quantitative	 ?estimates	 ?are	 ?needed.	 ?Threespine	 ?stickleback	 ?study	 ?system	 ?As	 ?a	 ?model	 ?system	 ?of	 ?parallel	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow,	 ?the	 ??benthic	 ?and	 ?limnetic	 ?species	 ?pairs?	 ?of	 ?threespine	 ?sticklebacks	 ?(Gasterosteus	 ?aculeatus	 ?species	 ?complex)	 ?are	 ?a	 ?system	 ?uniquely	 ?well	 ?suited	 ?to	 ?addressing	 ?my	 ?primary	 ?questions.	 ?Although	 ?most	 ?lakes	 ?containing	 ?threespine	 ?sticklebacks	 ?harbor	 ?only	 ?one	 ?species,	 ?there	 ?are	 ?three	 ?lakes	 ?(in	 ?one	 ?case,	 ?a	 ?pair	 ?of	 ?connected	 ?lakes)	 ?in	 ?southwestern	 ?British	 ?Columbia	 ?that	 ?harbor	 ?two	 ?sympatric	 ?species,	 ?a	 ?limnetic	 ?zone	 ?dweller	 ?known	 ?as	 ?the	 ??limnetic?	 ?species	 ?and	 ?a	 ?littoral	 ?and	 ?benthic	 ?zone	 ?dweller	 ?known	 ?as	 ?the	 ??benthic?	 ?species	 ?(McPhail	 ?1984,	 ?1992,	 ?1994;	 ?Schluter	 ?and	 ?McPhail	 ?1992).	 ?(Historically,	 ?there	 ?were	 ?five	 ?such	 ?lakes	 ?(or	 ?pairs	 ?of	 ?connected	 ?lakes),	 ?but	 ?due	 ?to	 ?introduction	 ?of	 ?invasive	 ?species,	 ?one	 ?species	 ?pair	 ?collapsed	 ?(Taylor	 ?et	 ?al.	 ?2006)	 ?and	 ?another	 ?went	 ?extinct	 ?(Hatfield	 ?2001).)	 ?The	 ?species	 ?pair	 ?lakes	 ?were	 ?formed	 ?when	 ?glaciers	 ?receded	 ?in	 ?the	 ?late	 ?Pleistocene	 ?(10,000-??20,000	 ?years	 ?ago)	 ?(Schluter	 ?and	 ?McPhail	 ?1992),	 ?and	 ?there	 ?appears	 ?to	 ?have	 ?been	 ?two	 ?separate	 ?ancestral	 ?invasions	 ?into	 ?each	 ?of	 ?the	 ?lakes	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?McPhail	 ?1994;	 ?Taylor	 ?and	 ?McPhail	 ?2000).	 ?Subsequently,	 ?the	 ?evolution	 ?of	 ?the	 ?two	 ?forms	 ?within	 ?each	 ?lake	 ?has	 ?occurred	 ?independently	 ?from	 ?the	 ?other	 ?lakes	 ?(Taylor	 ?and	 ?McPhail	 ?1999,	 ?2000;	 ?Jones	 ?et	 ?al.	 ?2012a).	 ?	 ?	 ? 6	 ?The	 ?threespine	 ?stickleback	 ?species	 ?pairs	 ?are	 ?an	 ?ideal	 ?system	 ?in	 ?which	 ?to	 ?study	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?	 ?Despite	 ?their	 ?independent	 ?evolution,	 ?divergence	 ?within	 ?each	 ?species	 ?pair	 ?has	 ?occurred	 ?largely	 ?in	 ?parallel	 ?with	 ?the	 ?other	 ?pairs	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?and	 ?Nagel	 ?1995;	 ?McKinnon	 ?and	 ?Rundle	 ?2002;	 ?Gow	 ?et	 ?al.	 ?2008).	 ?Benthics	 ?and	 ?limnetics	 ?are	 ?consistently	 ?distinguished	 ?by	 ?not	 ?only	 ?resource	 ?and	 ?habitat	 ?use	 ?but	 ?by	 ?a	 ?large	 ?number	 ?of	 ?correlated	 ?morphological,	 ?behavioral	 ?and	 ?likely	 ?physiological	 ?differences	 ?(McPhail	 ?1992;	 ?Bell	 ?and	 ?Foster	 ?1994;	 ?Rundle	 ?et	 ?al.	 ?2000;	 ?Boughman	 ?et	 ?al.	 ?2005;	 ?Gow	 ?et	 ?al.	 ?2008).	 ?The	 ?repeated	 ?evolution	 ?of	 ?these	 ?traits	 ?in	 ?correlation	 ?with	 ?the	 ?environment	 ?is	 ?interpreted	 ?as	 ?strong	 ?evidence	 ?for	 ?a	 ?role	 ?of	 ?natural	 ?selection	 ?in	 ?their	 ?evolution,	 ?because	 ?it	 ?is	 ?very	 ?unlikely	 ?that	 ?these	 ?associations	 ?arose	 ?by	 ?stochastic	 ?evolutionary	 ?forces	 ?(Rundle	 ?et	 ?al.	 ?2000;	 ?Schluter	 ?2000).	 ?The	 ?replicated	 ?adaptation	 ?across	 ?many	 ?different	 ?traits	 ?provides	 ?us	 ?with	 ?an	 ?opportunity	 ?to	 ?accurately	 ?gauge	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?trait	 ?adaptation	 ?in	 ?a	 ?single	 ?system	 ?(i.e.	 ?holding	 ?other	 ?evolutionary	 ?parameters	 ?constant).	 ?	 ?The	 ?threespine	 ?stickleback	 ?species	 ?pairs	 ?are	 ?also	 ?an	 ?ideal	 ?system	 ?in	 ?which	 ?to	 ?study	 ?the	 ?genetics	 ?underlying	 ?ecological	 ?speciation,	 ?the	 ?third	 ?component	 ?of	 ?ecological	 ?speciation.	 ?This	 ?is	 ?in	 ?part	 ?because	 ?we	 ?have	 ?a	 ?relatively	 ?solid	 ?understanding	 ?of	 ?the	 ?first	 ?two	 ?components.	 ?Regarding	 ?the	 ?first	 ?component	 ?of	 ?ecological	 ?speciation,	 ??a	 ?source	 ?of	 ?ecologically-??based	 ?divergent	 ?selection?,	 ?evidence	 ?suggests	 ?that	 ?divergence	 ?in	 ?ecological	 ?traits	 ?likely	 ?evolved,	 ?at	 ?least	 ?in	 ?part,	 ?due	 ?to	 ?resource	 ?competition	 ?and	 ?frequency	 ?dependent	 ?natural	 ?selection	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?2003).	 ?These	 ?pressures	 ?resulted	 ?in	 ?ecological	 ?character	 ?displacement	 ?and	 ?specialization	 ?in	 ?the	 ?use	 ?of	 ?either	 ?the	 ?benthic	 ?or	 ?the	 ?limnetic	 ?habitat	 ?zone	 ?and	 ?their	 ?respective	 ?sets	 ?of	 ?resources	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?2003).	 ?Regarding	 ?the	 ?second	 ?component,	 ??a	 ?form	 ?of	 ?reproductive	 ?isolation?,	 ?the	 ?fitness	 ?trade-??offs	 ?associated	 ?with	 ?resource-??use	 ?specialization	 ?has	 ?lead	 ?to	 ?substantial	 ?extrinsic	 ?reproductive	 ?isolation	 ?(Rundle	 ?2002;	 ?Gow	 ?et	 ?al.	 ?2007).	 ?Morphological	 ?differences	 ?between	 ?benthics	 ?and	 ?limnetics,	 ?including	 ?body	 ?size	 ?and	 ?gill	 ?raker	 ?number,	 ?appear	 ?to	 ?underlie	 ?a	 ?strong	 ?trade-??off	 ?in	 ?feeding	 ?efficiency	 ?and	 ?growth	 ?rate	 ?along	 ?the	 ?benthic-??limnetic	 ?habitat	 ?gradient	 ?with	 ?morphological	 ?	 ? 7	 ?intermediates	 ?(hybrids)	 ?suffering	 ?a	 ?competitive	 ?disadvantage	 ?in	 ?either	 ?habitat	 ?(Schluter	 ?1993,	 ?1995;	 ?Hatfield	 ?and	 ?Schluter	 ?1999).	 ?Intermediate	 ?hybrids	 ?are	 ?thus,	 ?selected	 ?against	 ?in	 ?the	 ?wild	 ?(Rundle	 ?2002;	 ?Gow	 ?et	 ?al.	 ?2007).	 ?An	 ?additional	 ?form	 ?of	 ?reproductive	 ?isolation	 ?separating	 ?contemporary	 ?populations	 ?of	 ?benthic	 ?and	 ?limnetic	 ?sticklebacks	 ?is	 ?behavioral	 ?premating	 ?isolation,	 ?whereby	 ?females	 ?strongly	 ?prefer	 ?to	 ?mate	 ?with	 ?males	 ?of	 ?their	 ?own	 ?species	 ?(Nagel	 ?and	 ?Schluter	 ?1998;	 ?Rundle	 ?et	 ?al.	 ?2000;	 ?Boughman	 ?et	 ?al.	 ?2005).	 ?Body	 ?size	 ?differences	 ?appear	 ?to	 ?make	 ?a	 ?predominant	 ?contribution	 ?to	 ?female	 ?mate	 ?preference	 ?(Nagel	 ?and	 ?Schluter	 ?1998;	 ?Albert	 ?2005;	 ?Conte	 ?and	 ?Schluter	 ?2013).	 ?Given	 ?our	 ?knowledge	 ?of	 ?the	 ?sources	 ?of	 ?divergent	 ?natural	 ?selection	 ?and	 ?the	 ?forms	 ?of	 ?reproductive	 ?isolation	 ?involved	 ?in	 ?ecological	 ?speciation	 ?between	 ?the	 ?benthic	 ?and	 ?limnetic	 ?species	 ?pairs,	 ?we	 ?now	 ?have	 ?the	 ?opportunity	 ?to	 ?ask	 ?what	 ?genetic	 ?mechanisms	 ?are	 ?involved.	 ?	 ?Chapter	 ?2:	 ?In	 ?chapter	 ?2	 ?of	 ?my	 ?thesis,	 ?I	 ?describe	 ?a	 ?behavioral	 ?experiment	 ?designed	 ?to	 ?inform	 ?us	 ?of	 ?the	 ?genetic	 ?mechanism	 ?linking	 ?divergent	 ?natural	 ?selection	 ?to	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?in	 ?a	 ?young	 ?species	 ?pair	 ?of	 ?benthic	 ?and	 ?limnetic	 ?threespine	 ?stickleback	 ?(Question	 ?1).	 ?I	 ?ask	 ?whether	 ?divergent	 ?selection	 ?on	 ?body	 ?size	 ?may	 ?cause	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?as	 ?an	 ?automatic	 ?by-??product.	 ?Specifically,	 ?I	 ?use	 ?experimental	 ?manipulation	 ?of	 ?body	 ?size	 ?in	 ?mate	 ?choice	 ?trials	 ?to	 ?determine	 ?whether	 ?body	 ?size,	 ?in	 ?addition	 ?to	 ?being	 ?under	 ?divergent	 ?selection,	 ?also	 ?confers	 ?assortative	 ?mating	 ?by	 ?serving	 ?as	 ?a	 ?mate	 ?signal	 ?trait	 ?and	 ?determining	 ?female	 ?preference	 ?via	 ?phenotype	 ?matching.	 ?If	 ?so,	 ?then	 ?the	 ?genes	 ?underlying	 ?body	 ?size	 ?and	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?are	 ?one	 ?and	 ?the	 ?same,	 ?constituting	 ?a	 ?mechanism	 ?that	 ?should	 ?facilitate	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow.	 ?I	 ?am	 ?also	 ?involved	 ?in	 ?two	 ?other	 ?studies	 ?that	 ?are	 ?still	 ?in	 ?progress,	 ?and	 ?not	 ?described	 ?herein,	 ?that	 ?will	 ?contribute	 ?further	 ?to	 ?our	 ?understanding	 ?of	 ?the	 ?genetics	 ?of	 ?premating	 ?reproductive	 ?isolation	 ?and	 ?how	 ?they	 ?relate	 ?the	 ?to	 ?the	 ?genetics	 ?of	 ?traits	 ?under	 ?divergent	 ?natural	 ?selection	 ?(Question	 ?1).	 ?Specifically,	 ?we	 ?are	 ?attempting	 ?to	 ?	 ? 8	 ?directly	 ?investigate	 ?the	 ?genetic	 ?architecture	 ?of	 ?female	 ?mate	 ?choice	 ?and	 ?male	 ?nesting	 ?habitat	 ?choice	 ?using	 ?quantitative	 ?trait	 ?loci	 ?(QTL)	 ?mapping	 ?experiments.	 ?These	 ?two	 ?studies	 ?are	 ?part	 ?of	 ?a	 ?larger	 ?study	 ?to	 ?investigate	 ?the	 ?genetics	 ?of	 ?ecological	 ?speciation	 ?in	 ?the	 ?benthic	 ?and	 ?limnetic	 ?species	 ?pairs	 ?(see	 ?Preface	 ?and	 ?Chapter	 ?4).	 ?	 ?Chapters	 ?3	 ?and	 ?4:	 ?In	 ?Chapter	 ?4,	 ?we	 ?investigated	 ?how	 ?similar,	 ?and	 ?therefore,	 ?how	 ?predictable	 ?the	 ?genetic	 ?architecture	 ?of	 ?divergence	 ?is	 ?between	 ?two	 ?benthic	 ?and	 ?limnetic	 ?species	 ?pairs.	 ?However	 ?first,	 ?to	 ?develop	 ?quantitative	 ?predictions,	 ?which	 ?were	 ?previously	 ?completely	 ?lacking	 ?in	 ?the	 ?literature,	 ?we	 ?conducted	 ?a	 ?meta-??analysis,	 ?described	 ?in	 ?Chapter	 ?3,	 ?to	 ?estimate	 ?the	 ?probability	 ?of	 ?parallel	 ?and	 ?convergent	 ?genetic	 ?evolution	 ?in	 ?natural	 ?populations	 ?(Question	 ?3).	 ?Using	 ?the	 ?results	 ?of	 ?an	 ?impartial	 ?literature	 ?review,	 ?we	 ?estimated	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?in	 ?natural	 ?populations	 ?undergoing	 ?repeated	 ?phenotypic	 ?evolution.	 ?Furthermore,	 ?we	 ?asked	 ?whether	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?declines	 ?with	 ?increasing	 ?age	 ?of	 ?the	 ?taxa	 ?being	 ?compared.	 ?	 ?Then,	 ?in	 ?chapter	 ?4,	 ?I	 ?discuss	 ?our	 ?investigation	 ?of	 ?the	 ?genetic	 ?architectures	 ?of	 ?parallel	 ?differences	 ?in	 ?a	 ?large	 ?number	 ?of	 ?morphological	 ?traits	 ?in	 ?the	 ?Paxton	 ?Lake	 ?and	 ?Priest	 ?Lake	 ?species	 ?pairs.	 ?Our	 ?results	 ?are	 ?instructive	 ?of	 ?the	 ?number	 ?of	 ?loci	 ?and	 ?their	 ?genomic	 ?distributions	 ?during	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow	 ?(Question	 ?2),	 ?a	 ?topic	 ?that	 ?will	 ?be	 ?discussed	 ?further	 ?in	 ?the	 ?Conclusions	 ?chapter	 ?of	 ?this	 ?thesis.	 ?Within	 ?Chapter	 ?4,	 ?we	 ?take	 ?a	 ?step	 ?back	 ?and	 ?view	 ?our	 ?results	 ?from	 ?the	 ?more	 ?broad	 ?perspective	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?(Question	 ?3).	 ?We	 ?estimate	 ?the	 ?percent	 ?of	 ?QTL	 ?underlying	 ?parallel	 ?phenotypic	 ?evolution	 ?that	 ?are	 ?parallel	 ?and	 ?non-??parallel.	 ?As	 ?an	 ?additional	 ?metric	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?also	 ?estimate	 ?the	 ?average	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?individual	 ?parallel	 ?traits.	 ?Finally,	 ?we	 ?ask	 ?whether	 ?genetic	 ?parallelism	 ?is	 ?correlated	 ?with	 ?the	 ?effect	 ?size	 ?of	 ?QTL.	 ? 	 ?	 ? 9	 ?2	 ? Experimental	 ?Confirmation	 ?That	 ?Body	 ?Size	 ?Determines	 ?Mate	 ?Preference	 ?Via	 ?Phenotype	 ?Matching	 ?in	 ?a	 ?Threespine	 ?Stickleback	 ?Species	 ?Pair1	 ?Introduction	 ?Preference	 ?for	 ?mates	 ?having	 ?a	 ?similar	 ?phenotype	 ?to	 ?one?s	 ?self,	 ?which	 ?we	 ?term	 ??mate	 ?choice	 ?by	 ?phenotype	 ?matching?	 ?(after	 ??phenotype	 ?matching?	 ?in	 ?Lacy	 ?and	 ?Sherman	 ?1983),	 ?is	 ?interesting	 ?in	 ?the	 ?context	 ?of	 ?speciation,	 ?because	 ?of	 ?what	 ?it	 ?implies	 ?about	 ?the	 ?genetics	 ?underlying	 ?mate	 ?signal	 ?and	 ?mate	 ?preference.	 ?The	 ?evolution	 ?of	 ?assortative	 ?mating	 ?between	 ?diverging	 ?populations	 ?that	 ?still	 ?experience	 ?gene	 ?flow	 ?can	 ?be	 ?hindered	 ?by	 ?recombination	 ?between	 ?alleles	 ?for	 ?mate	 ?preference	 ?and	 ?those	 ?for	 ?mate	 ?signal	 ?(e.g.	 ?model	 ?2	 ?in	 ?Kondrashov	 ?and	 ?Kondrashov	 ?1999;	 ?Doebeli	 ?2005).	 ?When	 ?mate	 ?choice	 ?occurs	 ?by	 ?phenotype	 ?matching,	 ?however,	 ?such	 ?recombination	 ?cannot	 ?occur,	 ?since	 ?mate	 ?preference	 ?is	 ?based	 ?on	 ?one?s	 ?own	 ?signal	 ?phenotype	 ?(e.g.	 ?model	 ?2	 ?in	 ?Dieckmann	 ?and	 ?Doebeli	 ?1999,	 ?model	 ?1	 ?in	 ?Kondrashov	 ?and	 ?Kondrashov	 ?1999).	 ?This	 ?implies	 ?that	 ?the	 ?genes	 ?underlying	 ?mate	 ?signal	 ?also	 ?determine	 ?mate	 ?preference	 ?values	 ?and,	 ?therefore,	 ?recombination	 ?cannot	 ?dissociate	 ?the	 ?two.	 ?Mate	 ?choice	 ?by	 ?phenotype	 ?matching	 ?should	 ?thus	 ?facilitate	 ?the	 ?evolution	 ?of	 ?assortative	 ?mating	 ?between	 ?diverging	 ?populations.	 ?Furthermore,	 ?if	 ?mate	 ?preference	 ?is	 ?for	 ?matching	 ?mate	 ?signal	 ?traits	 ?(mate	 ?choice	 ?by	 ?phenotype	 ?matching),	 ?and	 ?divergent	 ?natural	 ?selection	 ?acts	 ?on	 ?the	 ?mate	 ?signal	 ?	 ?(a.k.a.	 ?magic	 ?trait;	 ?reviewed	 ?in	 ?Servedio	 ?et	 ?al.	 ?2011)	 ?then	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow	 ?(Schluter	 ?2001)	 ?is	 ?greatly	 ?facilitated	 ?(e.g.	 ?model	 ?1	 ?in	 ?Dieckmann	 ?and	 ?Doebeli	 ?1999).	 ?This	 ?is	 ?because	 ?a	 ?target(s)	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?1	 ?A	 ?version	 ?of	 ?chapter	 ?2	 ?has	 ?been	 ?published.	 ?	 ?Conte,	 ?G.L.,	 ?and	 ?D.	 ?Schluter.	 ?2013.	 ?Experimental	 ?confirmation	 ?that	 ?body	 ?size	 ?determines	 ?mate	 ?preference	 ?via	 ?phenotype	 ?matching	 ?in	 ?a	 ?stickleback	 ?species	 ?pair.	 ?Evolution	 ?67-??5:	 ?1477-??1484.	 ?	 ?	 ? 10	 ?of	 ?divergent	 ?selection	 ?and	 ?both	 ?components	 ?of	 ?mate	 ?recognition	 ?are	 ?all	 ?controlled/determined	 ?by	 ?the	 ?same	 ?genes	 ?and	 ?assortative	 ?mating	 ?may	 ?readily	 ?evolve	 ?as	 ?an	 ?automatic	 ?by-??product	 ?when	 ?divergent	 ?selection	 ?acts.	 ?	 ?Here	 ?we	 ?test	 ?the	 ?hypothesis	 ?that	 ?mate	 ?choice	 ?by	 ?phenotype	 ?matching	 ?based	 ?on	 ?body	 ?size	 ?(hereafter	 ??size	 ?matching?)	 ?occurs	 ?in	 ?the	 ?benthic	 ?and	 ?limnetic	 ?species	 ?pair	 ?of	 ?threespine	 ?stickleback	 ?(Gasterosteus	 ?aculeatus	 ?complex)	 ?residing	 ?in	 ?Paxton	 ?Lake	 ?on	 ?Texada	 ?Island,	 ?British	 ?Columbia	 ?(BC).	 ?For	 ?convenience,	 ?and	 ?following	 ?previous	 ?practice,	 ?we	 ?refer	 ?to	 ?benthics	 ?and	 ?limnetics	 ?as	 ?species	 ?because	 ?they	 ?are	 ?almost	 ?completely	 ?reproductively	 ?isolated	 ?in	 ?the	 ?wild	 ?(Schluter	 ?1993,	 ?1995;	 ?McPhail	 ?1994;	 ?Nagel	 ?and	 ?Schluter	 ?1998;	 ?Hatfield	 ?and	 ?Schluter	 ?1999;	 ?Vamosi	 ?and	 ?Schluter	 ?1999;	 ?Rundle	 ?et	 ?al.	 ?2000;	 ?McKinnon	 ?and	 ?Rundle	 ?2002;	 ?Boughman	 ?et	 ?al.	 ?2005;	 ?Gow	 ?et	 ?al.	 ?2007),	 ?although	 ?they	 ?have	 ?not	 ?been	 ?formally	 ?designated.	 ?The	 ?Paxton	 ?Lake	 ?pair	 ?is	 ?one	 ?of	 ?several	 ?stickleback	 ?species	 ?pairs	 ?found	 ?in	 ?small	 ?lakes	 ?of	 ?coastal	 ?BC	 ?(Schluter	 ?and	 ?McPhail	 ?1992).	 ?In	 ?each	 ?case,	 ?one	 ?of	 ?the	 ?species,	 ?the	 ?limnetic,	 ?is	 ?small-??bodied	 ?and	 ?feeds	 ?primarily	 ?on	 ?plankton	 ?in	 ?the	 ?open	 ?water	 ?whereas	 ?the	 ?second	 ?species,	 ?the	 ?benthic,	 ?is	 ?large-??bodied	 ?and	 ?feeds	 ?primarily	 ?on	 ?benthos	 ?in	 ?the	 ?littoral	 ?and	 ?benthic	 ?zones	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?1993).	 ?The	 ?lakes	 ?formed	 ?shortly	 ?after	 ?the	 ?Pleistocene	 ?glaciers	 ?receded	 ?(10,000-??12,000	 ?years	 ?ago)	 ?and	 ?likely	 ?experienced	 ?two	 ?separate	 ?invasions	 ?by	 ?ancestral	 ?stickleback.	 ?Thus,	 ?an	 ?initial	 ?period	 ?of	 ?allopatry	 ?has	 ?likely	 ?contributed	 ?to	 ?divergence	 ?between	 ?the	 ?sympatric	 ?species	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?McPhail	 ?1994;	 ?Taylor	 ?and	 ?McPhail	 ?2000).	 ?Nonetheless,	 ?since	 ?the	 ?establishment	 ?of	 ?sympatry,	 ?these	 ?pairs	 ?have	 ?been	 ?subject	 ?to	 ?gene	 ?flow	 ?(Taylor	 ?and	 ?McPhail	 ?1999;	 ?Gow	 ?et	 ?al.	 ?2006),	 ?yet	 ?they	 ?have	 ?evolved	 ?and/or	 ?maintained	 ?large	 ?differences	 ?in	 ?morphology,	 ?ecology	 ?and	 ?mate	 ?recognition	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?1996;	 ?Taylor	 ?and	 ?McPhail	 ?2000).	 ?A	 ?conspicuous	 ?body	 ?size	 ?difference	 ?is	 ?important	 ?not	 ?only	 ?because	 ?it	 ?represents	 ?adaptation	 ?to	 ?the	 ?alternate	 ?foraging	 ?habitats	 ?(big	 ?fish	 ?have	 ?higher	 ?foraging	 ?efficiency	 ?and	 ?growth	 ?rate	 ?in	 ?the	 ?littoral	 ?zone,	 ?whereas	 ?small	 ?fish	 ?have	 ?the	 ?advantage	 ?in	 ?the	 ?open-??water;	 ?(Schluter	 ?1993,	 ?1995;	 ?Hatfield	 ?and	 ?Schluter	 ?1999)),	 ?but	 ?because	 ?it	 ?also	 ?appears	 ?to	 ?be	 ?an	 ?important	 ?mate	 ?signal	 ?trait	 ?involved	 ?in	 ?assortative	 ?mating	 ?between	 ?the	 ?two	 ?species	 ?(Nagel	 ?and	 ?Schluter	 ?1998;	 ?Albert	 ?2005;	 ?Boughman	 ?et	 ?al.	 ?2005).	 ?Previous	 ?	 ? 11	 ?observational	 ?studies	 ?suggest	 ?that	 ?size	 ?matching	 ?may	 ?occur.	 ?Using	 ?no-??choice	 ?mating	 ?trials	 ?between	 ?wild-??caught	 ?individuals,	 ?Nagel	 ?and	 ?Schluter	 ?(1998)	 ?found	 ?that	 ?females	 ?hybridized	 ?only	 ?when	 ?placed	 ?with	 ?males	 ?of	 ?the	 ?opposite	 ?species	 ?who	 ?were	 ?similar	 ?to	 ?her	 ?in	 ?size,	 ?indicating	 ?that	 ?preference	 ?changes	 ?with	 ?size	 ?difference	 ?between	 ?a	 ?mating	 ?pair.	 ?However,	 ?hybridization	 ?events	 ?tended	 ?to	 ?occur	 ?late	 ?in	 ?the	 ?season	 ?when	 ?the	 ?smallest	 ?benthics	 ?and	 ?the	 ?largest	 ?limnetics	 ?were	 ?in	 ?breeding	 ?condition.	 ?Any	 ?effect	 ?of	 ?date	 ?on	 ?level	 ?of	 ?discrimination	 ?is	 ?confounded	 ?with	 ?differences	 ?in	 ?body	 ?size	 ?(Nagel	 ?and	 ?Schluter	 ?1998).	 ?Boughman,	 ?Rundle,	 ?and	 ?Schluter	 ?(2005)	 ?found	 ?the	 ?same	 ?negative	 ?correlation	 ?of	 ?size	 ?difference	 ?and	 ?mating	 ?compatibility	 ?in	 ?the	 ?Paxton	 ?lake	 ?pair	 ?and	 ?two	 ?other	 ?independently	 ?evolved	 ?species	 ?pairs.	 ?However,	 ?the	 ?effects	 ?of	 ?trial	 ?date	 ?were	 ?not	 ?investigated.	 ?Furthermore,	 ?other	 ?variables	 ?that	 ?tend	 ?to	 ?be	 ?correlated	 ?with	 ?body	 ?size	 ?in	 ?the	 ?wild,	 ?such	 ?as	 ?trophic	 ?niche	 ?and	 ?age,	 ?add	 ?uncertainty	 ?to	 ?the	 ?conclusions	 ?based	 ?on	 ?observational	 ?studies	 ?that	 ?size	 ?matching	 ?is	 ?occurring.	 ?	 ?To	 ?overcome	 ?this	 ?problem,	 ?we	 ?used	 ?experimental	 ?manipulation	 ?of	 ?body	 ?size	 ?followed	 ?by	 ?mate	 ?choice	 ?trails	 ?to	 ?determine	 ?whether	 ?size	 ?matching	 ?contributes	 ?to	 ?assortative	 ?mating	 ?between	 ?benthic	 ?and	 ?limnetic	 ?sticklebacks.	 ?This	 ?experimental	 ?approach	 ?was	 ?used	 ?previously	 ?by	 ?McKinnon	 ?et	 ?al.	 ?(2004),	 ?who	 ?found	 ?that	 ?in	 ?world-??wide	 ?pairs	 ?of	 ?stream	 ?and	 ?marine	 ?threespine	 ?stickleback	 ?populations,	 ?female	 ?preference	 ?changed	 ?when	 ?female	 ?size	 ?was	 ?manipulated;	 ?they	 ?were	 ?more	 ?likely	 ?to	 ?prefer	 ?a	 ?male	 ?of	 ?the	 ?opposite	 ?ecotype	 ?when	 ?they	 ?were	 ?manipulated	 ?to	 ?be	 ?more	 ?similar	 ?in	 ?size	 ?to	 ?him.	 ?This	 ?approach,	 ?however,	 ?has	 ?never	 ?been	 ?used	 ?for	 ?benthic	 ?and	 ?limnetic	 ?pairs,	 ?a	 ?study	 ?system	 ?that	 ?has	 ?become	 ?an	 ?important	 ?model	 ?of	 ?species	 ?divergence	 ?and	 ?persistence	 ?in	 ?the	 ?face	 ?of	 ?gene	 ?flow.	 ?Here,	 ?we	 ?compare	 ?the	 ?propensity	 ?of	 ?benthic	 ?and	 ?limnetic	 ?females	 ?to	 ?accept	 ?males	 ?of	 ?the	 ?opposite	 ?species	 ?when	 ?they	 ?were	 ?manipulated	 ?by	 ?diet	 ?either	 ?to	 ?be	 ?more	 ?similar	 ?or	 ?more	 ?different	 ?in	 ?size	 ?to	 ?males.	 ?Importantly,	 ?within	 ?each	 ?species,	 ?all	 ?females	 ?came	 ?from	 ?the	 ?same	 ?population	 ?and	 ?were	 ?randomly	 ?assigned	 ?to	 ?treatments.	 ?Thus,	 ?the	 ?effects	 ?of	 ?genotype,	 ?age,	 ?early-??life	 ?experience	 ?and	 ?other	 ?differences	 ?potentially	 ?affecting	 ?mate	 ?preferences	 ?were	 ?randomized.	 ?If	 ?size	 ?matching	 ?is	 ?not	 ?occurring,	 ?then	 ?manipulation	 ?of	 ?females?	 ?body	 ?size	 ?should	 ?not	 ?affect	 ?their	 ?probability	 ?of	 ?accepting	 ?the	 ?	 ? 12	 ?heterospecific	 ?male.	 ?However,	 ?if	 ?size	 ?matching	 ?is	 ?occurring,	 ?then	 ?females	 ?manipulated	 ?to	 ?be	 ?similar	 ?in	 ?size	 ?to	 ?the	 ?heterospecific	 ?male	 ?will	 ?be	 ?more	 ?likely	 ?to	 ?accept	 ?him	 ?than	 ?females	 ?manipulated	 ?to	 ?be	 ?different	 ?in	 ?size	 ?from	 ?him.	 ?Methods	 ?Fish	 ?Collection	 ?In	 ?the	 ?fall	 ?of	 ?2009,	 ?we	 ?collected	 ?juvenile	 ?fish	 ?from	 ?two	 ?ponds	 ?at	 ?the	 ?UBC	 ?Experimental	 ?Pond	 ?Facility,	 ?one	 ?containing	 ?only	 ?Paxton	 ?benthics	 ?and	 ?the	 ?other	 ?containing	 ?only	 ?Paxton	 ?limnetics.	 ?All	 ?individuals	 ?were	 ?thus,	 ?naive	 ?to	 ?the	 ?other	 ?species.	 ?Progenitors	 ?of	 ?the	 ?pond	 ?populations	 ?had	 ?been	 ?collected	 ?from	 ?the	 ?wild	 ?and	 ?introduced	 ?to	 ?ponds	 ?in	 ?April	 ?of	 ?2008.	 ?We	 ?collected	 ?400	 ?juveniles	 ?of	 ?each	 ?species	 ?and	 ?randomly	 ?assigned	 ?them	 ?to	 ?tanks	 ?of	 ?their	 ?own	 ?species	 ?and	 ?to	 ?size-??manipulation	 ?groups	 ?(either	 ??abundant-??food?	 ?or	 ??reduced-??food?).	 ?In	 ?total,	 ?for	 ?each	 ?of	 ?the	 ?size	 ?manipulation	 ?groups	 ?there	 ?were	 ?10	 ?benthic	 ?tanks	 ?and	 ?6	 ?limnetic	 ?tanks.	 ?Size	 ?Manipulation	 ?To	 ?generate	 ?size	 ?differences	 ?between	 ?groups	 ?within	 ?a	 ?species,	 ?we	 ?provided	 ?them	 ?with	 ?alternate	 ?amounts	 ?of	 ?food	 ?(a	 ?mix	 ?of	 ?blood	 ?worms	 ?and	 ?mysis	 ?shrimp).	 ?	 ?We	 ?took	 ?measures	 ?of	 ?standard	 ?length,	 ?body	 ?mass	 ?and	 ?condition	 ?factor	 ?(a	 ?scaled	 ?ratio	 ?of	 ?body	 ?mass	 ?to	 ?standard	 ?length	 ?(body	 ?mass	 ?x	 ?105)/standard	 ?length3	 ?(Williams,	 ?2000))	 ?periodically	 ?from	 ?a	 ?sample	 ?of	 ?the	 ?fish	 ?from	 ?each	 ?group.	 ?To	 ?determine	 ?the	 ?appropriate	 ?amount	 ?of	 ?food	 ?for	 ?the	 ?abundant-??food	 ?group	 ?that	 ?would	 ?maximize	 ?their	 ?growth	 ?rate,	 ?we	 ?periodically	 ?gave	 ?them	 ?ad	 ?libitum	 ?feedings	 ?and	 ?converted	 ?the	 ?amount	 ?consumed	 ?into	 ?the	 ?percent	 ?of	 ?the	 ?group?s	 ?estimated	 ?average	 ?body	 ?weight	 ?that	 ?was	 ?consumed	 ?per	 ?fish	 ?on	 ?average.	 ?We	 ?gave	 ?each	 ?abundant-??food	 ?tank	 ?this	 ?amount	 ?of	 ?food	 ?multiplied	 ?by	 ?the	 ?number	 ?of	 ?fish	 ?in	 ?the	 ?tank,	 ?each	 ?day.	 ?	 ?We	 ?fed	 ?the	 ?reduced-??food	 ?group	 ?2-??3	 ?times	 ?less	 ?(corrected	 ?by	 ?their	 ?groups?	 ?estimated	 ?average	 ?body	 ?weight)	 ?than	 ?that	 ?of	 ?the	 ?abundant-??food	 ?group	 ?of	 ?their	 ?same	 ?species.	 ?For	 ?any	 ?given	 ?period	 ?of	 ?time,	 ?the	 ?exact	 ?reduction	 ?in	 ?food	 ?availability	 ?to	 ?the	 ?reduced-??food	 ?	 ? 13	 ?group	 ?depended	 ?on	 ?our	 ?desired	 ?condition	 ?and	 ?growth	 ?rate	 ?for	 ?them,	 ?with	 ?the	 ?goal	 ?being	 ?that	 ?they	 ?be	 ?fed	 ?as	 ?little	 ?as	 ?possible,	 ?while	 ?preventing	 ?significant	 ?reductions	 ?in	 ?condition	 ?factor	 ?relative	 ?to	 ?the	 ?abundant-??food	 ?group.	 ?Over-??wintering	 ?We	 ?over-??wintered	 ?the	 ?fish	 ?in	 ?a	 ?temperature-??controlled	 ?chamber	 ?to	 ?synchronize	 ?the	 ?onset	 ?of	 ?breeding	 ?condition	 ?upon	 ?release	 ?from	 ?winter	 ?conditions.	 ?Over-??wintering	 ?took	 ?place	 ?from	 ?February	 ?15th	 ?through	 ?June	 ?26th	 ?2010.	 ?At	 ?the	 ?start	 ?of	 ??winter?,	 ?temperature	 ?was	 ?gradually	 ?lowered	 ?(1	 ?degree/day)	 ?from	 ?17C	 ?to	 ?8C,	 ?and	 ?photoperiod	 ?was	 ?gradually	 ?decreased	 ?(1	 ?hour/day)	 ?from	 ?16:8	 ?L:D	 ?to	 ?8:16	 ?L:D.	 ?At	 ?the	 ?end	 ?of	 ?winter,	 ?the	 ?temperature	 ?and	 ?photoperiod	 ?were	 ?gradually	 ?increased,	 ?reversing	 ?the	 ?above	 ?changes.	 ?Mating	 ?Trials	 ?We	 ?conducted	 ?no-??choice	 ?trials,	 ?in	 ?which	 ?a	 ?single	 ?female	 ?and	 ?a	 ?single	 ?male	 ?were	 ?allowed	 ?to	 ?interact.	 ?No-??choice	 ?trials	 ?result	 ?in	 ?a	 ?score	 ?reflecting	 ?female	 ?acceptance	 ?of	 ?the	 ?male	 ?and	 ?comparison	 ?of	 ?such	 ?scores	 ?between	 ?treatments	 ?is	 ?generally	 ?thought	 ?to	 ?provide	 ?a	 ?good	 ?estimate	 ?of	 ?the	 ?female	 ?preference	 ?function	 ?(Wagner	 ?1998;	 ?Bush	 ?et	 ?al.	 ?2002).	 ?Females	 ?used	 ?in	 ?the	 ?trials	 ?came	 ?from	 ?both	 ?abundant	 ?and	 ?reduced-??food	 ?tanks.	 ?All	 ?benthic	 ?males	 ?were	 ?from	 ?the	 ?reduced-??food	 ?group	 ?(making	 ?them	 ?more	 ?similar	 ?in	 ?size	 ?to	 ?limnetics),	 ?and	 ?all	 ?limnetic	 ?males	 ?were	 ?from	 ?the	 ?abundant-??food	 ?group	 ?(making	 ?them	 ?more	 ?similar	 ?in	 ?size	 ?to	 ?benthics).	 ?Thus,	 ?the	 ?two	 ?treatments	 ?for	 ?each	 ?species	 ?of	 ?female	 ?were	 ??different	 ?size?	 ?(control)	 ?and	 ??similar	 ?size?	 ?(experimental)	 ?relative	 ?to	 ?the	 ?heterospecific	 ?male	 ?(Fig	 ?A.1).	 ?In	 ?total,	 ?15	 ?successful	 ?trials	 ?(i.e.	 ?the	 ?data	 ?was	 ?kept)	 ?were	 ?conducted	 ?for	 ?benthic	 ?females	 ?similar	 ?in	 ?size	 ?to	 ?a	 ?limnetic	 ?male	 ?and	 ?19	 ?for	 ?benthic	 ?females	 ?different	 ?in	 ?size	 ?to	 ?a	 ?limnetic	 ?male.	 ?Thirteen	 ?successful	 ?trials	 ?were	 ?conducted	 ?for	 ?limnetic	 ?females	 ?similar	 ?in	 ?size	 ?to	 ?a	 ?benthic	 ?male	 ?and	 ?10	 ?for	 ?limnetic	 ?females	 ?different	 ?in	 ?size	 ?to	 ?a	 ?benthic	 ?male.	 ?	 ?As	 ?a	 ?result	 ?of	 ?providing	 ?different	 ?amounts	 ?of	 ?food	 ?to	 ?generate	 ?size	 ?differences	 ?among	 ?treatments,	 ?the	 ?comparison	 ?of	 ?female	 ?mate	 ?preference	 ?between	 ?large	 ?and	 ?	 ? 14	 ?small	 ?experimental	 ?fish	 ?was	 ?confounded	 ?with	 ?that	 ?between	 ?abundant-??food	 ?and	 ?reduced-??food	 ?fish.	 ?However,	 ?one	 ?feature	 ?of	 ?the	 ?experimental	 ?design	 ?provides	 ?a	 ?control	 ?for	 ?the	 ?effects	 ?of	 ?diet	 ?manipulation,	 ?nutrition	 ?and	 ?other	 ?correlated	 ?effects.	 ?In	 ?the	 ?larger	 ?benthic	 ?species,	 ?the	 ?control	 ?trial	 ?involved	 ?a	 ?large	 ?(abundant-??food)	 ?female	 ?and	 ?the	 ?experimental	 ?trial	 ?involved	 ?a	 ?small	 ?(reduced-??food)	 ?female.	 ?Conversely,	 ?in	 ?the	 ?smaller	 ?limnetic	 ?species,	 ?the	 ?control	 ?involved	 ?a	 ?small	 ?(reduced-??food)	 ?female	 ?and	 ?the	 ?experimental	 ?trial	 ?involved	 ?a	 ?large	 ?(abundant-??food)	 ?female.	 ?Thus,	 ?if	 ?diet/nutrition	 ?were	 ?to	 ?bias	 ?female	 ?mate	 ?preference	 ?in	 ?a	 ?predictable	 ?direction	 ?(e.g.	 ?reduced-??food	 ?fish	 ?prefer	 ?larger	 ?males)	 ?then	 ?we	 ?would	 ?expect	 ?the	 ?direction	 ?of	 ?effect	 ?to	 ?be	 ?consistent	 ?with	 ?female	 ?food	 ?amounts	 ?in	 ?both	 ?species	 ?rather	 ?than	 ?control	 ?vs.	 ?experimental	 ?group.	 ?	 ?Mating	 ?trials	 ?took	 ?place	 ?in	 ?110	 ?L	 ?tanks	 ?that	 ?were	 ?visually	 ?isolated	 ?from	 ?neighboring	 ?tanks.	 ?Each	 ?tank	 ?contained	 ?limestone	 ?gravel	 ?covering	 ?the	 ?bottom,	 ?small	 ?sprigs	 ?of	 ?plastic	 ?plants	 ?located	 ?in	 ?the	 ?two	 ?rear	 ?corners,	 ?a	 ?nesting	 ?dish	 ?filled	 ?with	 ?sand	 ?and	 ?soil	 ?located	 ?in	 ?the	 ?rear	 ?left	 ?corner,	 ?and	 ?a	 ?small	 ?bunch	 ?of	 ?java	 ?moss	 ?anchored	 ?next	 ?to	 ?the	 ?right	 ?side	 ?of	 ?the	 ?nesting	 ?dish.	 ?In	 ?addition,	 ?tanks	 ?were	 ?regularly	 ?replenished	 ?with	 ?short	 ?pine	 ?needles,	 ?which	 ?stickleback	 ?males	 ?use	 ?for	 ?structural	 ?support	 ?in	 ?nest	 ?building.	 ?	 ?We	 ?added	 ?males	 ?showing	 ?signs	 ?of	 ?breeding	 ?condition	 ?(i.e.	 ?red	 ?throat	 ?color)	 ?to	 ?trial	 ?tanks	 ?and	 ?gave	 ?them	 ?five	 ?full	 ?days	 ?to	 ?build	 ?a	 ?nest.	 ?A	 ?gravid	 ?female	 ?contained	 ?in	 ?a	 ?jar	 ?was	 ?placed	 ?in	 ?the	 ?tank	 ?of	 ?each	 ?male	 ?for	 ?at	 ?least	 ?15	 ?mins.	 ?per	 ?day.	 ?These	 ??motivator	 ?females?	 ?were	 ?not	 ?used	 ?in	 ?mating	 ?trials.	 ?If	 ?a	 ?male	 ?built	 ?no	 ?nest	 ?within	 ?five	 ?days,	 ?we	 ?returned	 ?him	 ?to	 ?his	 ?rearing	 ?tank	 ?and	 ?replaced	 ?him.	 ?	 ?Gravid	 ?females	 ?were	 ?randomly	 ?assigned	 ?to	 ?heterospecific	 ?males	 ?with	 ?nests.	 ?At	 ?the	 ?start	 ?of	 ?a	 ?trial,	 ?we	 ?released	 ?the	 ?female	 ?into	 ?the	 ?male?s	 ?tank,	 ?as	 ?far	 ?from	 ?the	 ?male	 ?as	 ?possible.	 ?In	 ?all	 ?trials	 ?conducted	 ?for	 ?this	 ?study,	 ?the	 ?male	 ?began	 ?to	 ?court	 ?the	 ?female	 ?within	 ?five	 ?minutes.	 ?Trials	 ?lasted	 ?40	 ?minutes	 ?or	 ?until	 ?spawning.	 ?Since	 ?spawning	 ?occurred	 ?only	 ?once	 ?at	 ?around	 ?39	 ?min.,	 ?all	 ?trials	 ?were	 ?about	 ?the	 ?same	 ?duration.	 ?We	 ?recorded	 ?the	 ?following	 ?well	 ?established	 ?courtship	 ?behaviors	 ?(see	 ?Tinbergen	 ?1962;	 ?Rowland	 ?1994	 ?for	 ?a	 ?more	 ?complete	 ?description)	 ?if	 ?and	 ?when	 ?they	 ?occurred	 ?using	 ?the	 ?software	 ?Event	 ?Recorder	 ?	 ?(Berger	 ?and	 ?Bleed	 ?2003):	 ?male	 ?	 ? 15	 ?behaviors	 ?-??	 ?male	 ?approaches	 ?female,	 ?male	 ?bites	 ?female,	 ?male	 ?zigzags	 ?towards	 ?female,	 ?male	 ?attempts	 ?to	 ?lead	 ?female	 ?to	 ?his	 ?nest,	 ?male	 ?performs	 ?nest-??maintenance	 ?behaviors,	 ?male	 ?creeps-??through	 ?his	 ?nest,	 ?female	 ?behaviors	 ?-??	 ?female	 ?exhibits	 ??head-??up?	 ?posture,	 ?female	 ?follows	 ?male	 ?towards	 ?his	 ?nest,	 ?female	 ?approaches	 ?male,	 ?female	 ?inspects	 ?male?s	 ?nest,	 ?and	 ?female	 ?deposits	 ?eggs	 ?in	 ?nest.	 ?	 ?Immediately	 ?after	 ?a	 ?trial,	 ?we	 ?recorded	 ?a	 ?score	 ?to	 ?visually	 ?qualify	 ?males?	 ?red	 ?nuptial	 ?color	 ?brightness/intensity,	 ?ranging	 ?from	 ?1-??3.	 ?We	 ?also	 ?measured	 ?standard	 ?length	 ?and	 ?body	 ?mass	 ?for	 ?both	 ?the	 ?female	 ?and	 ?the	 ?male.	 ?Finally,	 ?if	 ?a	 ?female	 ?did	 ?not	 ?spawn,	 ?we	 ?gently	 ?squeezed	 ?the	 ?eggs	 ?from	 ?her	 ?oviduct	 ?at	 ?the	 ?end	 ?of	 ?the	 ?trial	 ?to	 ?confirm	 ?receptivity	 ?(Nagel	 ?and	 ?Schluter	 ?1998).	 ?Any	 ?female	 ?found	 ?not	 ?ready	 ?to	 ?mate	 ?was	 ?excluded	 ?(this	 ?occurred	 ?in	 ?15	 ?out	 ?of	 ?72	 ?trials).	 ?Among	 ?those	 ?deemed	 ?ready	 ?to	 ?mate,	 ?variation	 ?in	 ?degree	 ?of	 ?readiness	 ?likely	 ?existed.	 ?However,	 ?this	 ?variation	 ?was	 ?randomized	 ?among	 ?treatments.	 ?Each	 ?female	 ?was	 ?used	 ?in	 ?only	 ?one	 ?trial.	 ?However,	 ?due	 ?to	 ?male-??limitation,	 ?we	 ?used	 ?some	 ?males	 ?in	 ?a	 ?second	 ?trial	 ?with	 ?the	 ?opposite	 ?type	 ?of	 ?female	 ?one	 ?to	 ?three	 ?days	 ?later.	 ?For	 ?males	 ?used	 ?twice,	 ?we	 ?measured	 ?standard	 ?length	 ?and	 ?body	 ?mass	 ?after	 ?their	 ?second	 ?trial.	 ?To	 ?ensure	 ?that	 ?using	 ?some	 ?males	 ?twice	 ?did	 ?not	 ?affect	 ?the	 ?results	 ?of	 ?the	 ?experiment,	 ?the	 ?data	 ?were	 ?also	 ?analyzed	 ?using	 ?only	 ?the	 ?first	 ?trial	 ?for	 ?each	 ?male.	 ?	 ?We	 ?used	 ?a	 ?dichotomous	 ?score	 ?to	 ?describe	 ?female	 ?acceptance	 ?of	 ?the	 ?heterospecific	 ?male.	 ?A	 ?score	 ?of	 ?0	 ?indicates	 ?no	 ?acceptance	 ?and	 ?a	 ?score	 ?of	 ?1	 ?indicates	 ?that	 ?some	 ?degree	 ?of	 ?acceptance	 ?was	 ?shown.	 ?More	 ?specifically,	 ?a	 ?score	 ?of	 ?1	 ?was	 ?assigned	 ?if	 ?the	 ?female	 ?reciprocated	 ?in	 ?courtship	 ?with	 ?one	 ?or	 ?more	 ?of	 ?the	 ?following	 ?behaviors:	 ?head-??up	 ?posture,	 ?female	 ?follows	 ?male	 ?to	 ?his	 ?nest,	 ?female	 ?approached	 ?male	 ?at	 ?his	 ?nest,	 ?female	 ?inspects	 ?male?s	 ?nest,	 ?female	 ?enters	 ?male?s	 ?nest/deposits	 ?eggs.	 ?Alternatively,	 ?a	 ?score	 ?of	 ?0	 ?was	 ?assigned	 ?if	 ?the	 ?female	 ?did	 ?not	 ?reciprocate	 ?with	 ?any	 ?courtship	 ?behaviors.	 ?We	 ?chose	 ?this	 ?scoring	 ?method	 ?to	 ?maximize	 ?the	 ?amount	 ?of	 ?variation	 ?available	 ?to	 ?be	 ?analyzed	 ?(spawning	 ?occurred	 ?in	 ?only	 ?1	 ?of	 ?57	 ?trials	 ?and	 ?courtship	 ?rarely	 ?proceeded	 ?to	 ?the	 ?penultimate	 ?step	 ?of	 ?mating).	 ?Female	 ?courtship	 ?behaviors	 ?are	 ?known	 ?to	 ?proceed	 ?in	 ?a	 ?fixed	 ?sequence	 ?(as	 ?listed	 ?above)	 ?and	 ?females	 ?may	 ?terminate	 ?courtship	 ?at	 ?any	 ?point	 ?along	 ?this	 ?sequence.	 ?Therefore,	 ?it	 ?can	 ?be	 ?inferred	 ?that	 ?with	 ?any	 ?given	 ?threshold	 ?for	 ?a	 ?score	 ?of	 ?1	 ?along	 ?this	 ?sequence,	 ?the	 ?	 ? 16	 ?females	 ?with	 ?a	 ?score	 ?of	 ?1	 ?would	 ?be	 ?more	 ?likely	 ?to	 ?mate	 ?than	 ?those	 ?with	 ?a	 ?score	 ?of	 ?0.	 ?To	 ?demonstrate	 ?that	 ?our	 ?results	 ?were	 ?not	 ?dependent	 ?on	 ?the	 ?particular	 ?threshold	 ?chosen,	 ?we	 ?also	 ?analyze	 ?the	 ?data	 ?using	 ?a	 ?score	 ?with	 ?a	 ?more	 ?stringent	 ?requirement	 ?for	 ?a	 ?score	 ?of	 ?1,	 ?whereby	 ?the	 ?female	 ?had	 ?to	 ?at	 ?least	 ?follow	 ?a	 ?male	 ?to	 ?his	 ?nest	 ?to	 ?be	 ?assigned	 ?a	 ?score	 ?of	 ?1.	 ?Dates	 ?of	 ?Trials	 ?For	 ?benthic	 ?females	 ?only,	 ?there	 ?was	 ?a	 ?significant	 ?difference	 ?in	 ?the	 ?dates	 ?of	 ?control	 ?and	 ?experimental	 ?trials	 ?(F1,	 ?32	 ?=	 ?14.194,	 ?p	 ?=	 ?7x10-??4),	 ?because	 ?females	 ?manipulated	 ?to	 ?be	 ?large	 ?began	 ?to	 ?come	 ?into	 ?and	 ?go	 ?out	 ?of	 ?breeding	 ?condition	 ?earlier	 ?in	 ?the	 ?season	 ?than	 ?those	 ?manipulated	 ?to	 ?be	 ?small.	 ?This	 ?difference	 ?did	 ?not	 ?exist	 ?for	 ?limnetic	 ?females	 ?(F1,21	 ?=	 ?0.1716,	 ?p	 ?=	 ?0.683).	 ?To	 ?completely	 ?eliminate	 ?trial	 ?date	 ?as	 ?a	 ?confounding	 ?variable,	 ?we	 ?also	 ?analyzed	 ?a	 ?dataset	 ?containing	 ?all	 ?trials	 ?with	 ?limnetic	 ?females	 ?and	 ?only	 ?those	 ?trials	 ?with	 ?benthic	 ?females	 ?that	 ?occurred	 ?after	 ?the	 ?first	 ?trails	 ?involving	 ?benthic	 ?females	 ?from	 ?the	 ?reduced-??food	 ?regime	 ?(?similar-??size?	 ?treatment)	 ?and	 ?before	 ?the	 ?last	 ?trails	 ?with	 ?the	 ?abundant-??food	 ?regime	 ?(?different-??size?	 ?treatment).	 ?This	 ?eliminated	 ?10/19	 ?benthic	 ?control	 ?trials	 ?and	 ?3/15	 ?benthic	 ?experimental	 ?trials.	 ?In	 ?this	 ?dataset,	 ?no	 ?statistical	 ?difference	 ?in	 ?the	 ?dates	 ?of	 ?control	 ?and	 ?experimental	 ?trails	 ?was	 ?found	 ?(F1,	 ?42	 ?=	 ?0.2275,	 ?p	 ?=	 ?0.601).	 ?Analysis	 ?We	 ?used	 ?linear	 ?models	 ?to	 ?test	 ?for	 ?the	 ?effects	 ?of	 ?size	 ?manipulation	 ?on	 ?standard	 ?length,	 ?body	 ?mass	 ?and	 ?condition	 ?factor.	 ?We	 ?used	 ?logistic	 ?regression	 ?in	 ?a	 ?generalized	 ?linear	 ?model	 ?context	 ?to	 ?test	 ?for	 ?effects	 ?on	 ?female	 ?acceptance.	 ?Our	 ?main	 ?analysis	 ?tested	 ?for	 ?the	 ?effects	 ?of	 ?experimental	 ?treatment,	 ?as	 ?well	 ?as	 ?female	 ?species	 ?and	 ?the	 ?interaction	 ?of	 ?treatment	 ?and	 ?female	 ?species.	 ?We	 ?further	 ?examined	 ?whether	 ?any	 ?other	 ?measured	 ?explanatory	 ?variables	 ?correlated	 ?with	 ?female	 ?acceptance	 ?after	 ?accounting	 ?for	 ?the	 ?effects	 ?of	 ?treatment,	 ?by	 ?including	 ?them	 ?one-??at-??a-??time	 ?as	 ?the	 ?second	 ?term	 ?in	 ?a	 ?model	 ?also	 ?including	 ?treatment	 ?as	 ?the	 ?first	 ?term.	 ?These	 ?variables	 ?included	 ?breeding	 ?date,	 ?male	 ?nuptial	 ?color	 ?and	 ?six	 ?male	 ?courtship	 ?behaviors.	 ?Finally,	 ?	 ? 17	 ?to	 ?determine	 ?the	 ?effects	 ?of	 ?treatment	 ?on	 ?male	 ?behavior,	 ?we	 ?used	 ?linear	 ?models	 ?to	 ?analyze	 ?data	 ?from	 ?only	 ?males?	 ?first	 ?trials.	 ?All	 ?analyses	 ?were	 ?performed	 ?in	 ?R	 ?v2.12.1	 ?(R	 ?Core	 ?Team	 ?2010).	 ?Results	 ?Size	 ?Manipulation	 ?The	 ?size	 ?manipulation	 ?resulted	 ?in	 ?nearly	 ?non-??overlapping	 ?female	 ?size	 ?distributions	 ?between	 ?treatments	 ?within	 ?each	 ?species,	 ?with	 ?highly	 ?significant	 ?differences	 ?in	 ?mean	 ?for	 ?both	 ?standard	 ?length	 ?(Figure	 ?2.1)	 ?and	 ?body	 ?mass	 ?(Table	 ?A.1).	 ?In	 ?addition,	 ?condition	 ?factor	 ?was	 ?only	 ?slightly	 ?greater	 ?in	 ?females	 ?manipulated	 ?to	 ?be	 ?large	 ?than	 ?in	 ?those	 ?manipulated	 ?to	 ?be	 ?small,	 ?but	 ?not	 ?significantly	 ?(Table	 ?A.1),	 ?suggesting	 ?that	 ?the	 ?manipulation	 ?successfully	 ?affected	 ?the	 ?extent	 ?of	 ?growth	 ?without	 ?greatly	 ?compromising	 ?the	 ?relative	 ?mass	 ?for	 ?a	 ?given	 ?size.	 ?	 ? 	 ?	 ? 18	 ?Figure	 ?2.1	 ?Standard	 ?length	 ?distributions	 ?after	 ?manipulation	 ?of	 ?body	 ?size	 ?(A)	 ?Benthic	 ?females	 ?manipulated	 ?to	 ?be	 ?large	 ?and	 ?small,	 ?compared	 ?with	 ?the	 ?limnetic	 ?males	 ?(manipulated	 ?to	 ?be	 ?large)	 ?that	 ?they	 ?were	 ?paired	 ?with.	 ?(B)	 ?Limnetic	 ?females	 ?manipulated	 ?to	 ?be	 ?large	 ?and	 ?small,	 ?compared	 ?with	 ?the	 ?benthic	 ?males	 ?(manipulated	 ?to	 ?be	 ?small)	 ?that	 ?they	 ?were	 ?paired	 ?with.	 ?	 ?	 ?	 ? 	 ?36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76Frequency0246810large benthic femalesmall benthic femalelimnetic male (large)A36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76Standard Length(mm)Frequency0246810small limnetic femalelarge limnetic femalebenthic male (small)B	 ? 19	 ?Female	 ?Acceptance	 ?Treatment	 ?(similar	 ?size	 ?vs.	 ?different	 ?size)	 ?had	 ?a	 ?highly	 ?significant	 ?effect	 ?on	 ?the	 ?female	 ?acceptance	 ?score	 ?(Table	 ?2.1).	 ?Females	 ?paired	 ?with	 ?a	 ?male	 ?of	 ?the	 ?opposite	 ?species	 ?were	 ?more	 ?likely	 ?to	 ?reciprocate	 ?courtship	 ?behaviors	 ?when	 ?they	 ?were	 ?manipulated	 ?to	 ?be	 ?similar	 ?in	 ?size	 ?to	 ?him	 ?than	 ?when	 ?they	 ?were	 ?manipulated	 ?to	 ?be	 ?different	 ?in	 ?size	 ?to	 ?him	 ?(Figure	 ?2.2).	 ?This	 ?treatment	 ?effect	 ?was	 ?present	 ?in	 ?all	 ?models	 ?and	 ?no	 ?other	 ?explanatory	 ?variables	 ?significantly	 ?correlated	 ?with	 ?female	 ?acceptance	 ?either	 ?before	 ?(tests	 ?not	 ?shown)	 ?or	 ?after	 ?the	 ?effects	 ?of	 ?treatment	 ?were	 ?accounted	 ?for	 ?(Table	 ?2.1).	 ?The	 ?results	 ?from	 ?the	 ?analysis	 ?using	 ?1)	 ?the	 ?more	 ?stringent	 ?female	 ?acceptance	 ?score	 ?(Table	 ?A.2),	 ?2)	 ?only	 ?males?	 ?first	 ?trials	 ?(Table	 ?A.3),	 ?and	 ?3)	 ?a	 ?subset	 ?of	 ?the	 ?data	 ?for	 ?which	 ?trial	 ?date	 ?was	 ?not	 ?a	 ?confounding	 ?variable	 ?(Table	 ?A.4),	 ?produced	 ?the	 ?same	 ?results	 ?as	 ?these.	 ?	 ? 	 ?	 ? 20	 ?Table	 ?2.1	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?Logistic	 ?regressions	 ?to	 ?test	 ?for	 ?the	 ?effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores.	 ?Each	 ?model	 ?includes	 ?treatment	 ?and	 ?the	 ?additional	 ?variable(s)	 ?indicated.	 ?Treatment	 ?was	 ?entered	 ?first	 ?and	 ?so	 ?has	 ?identical	 ?effects	 ?in	 ?all	 ?models	 ?except	 ?model	 ?1i,	 ?due	 ?to	 ?missing	 ?data	 ?points	 ?for	 ?male	 ?nuptial	 ?color.	 ?The	 ?second	 ?explanatory	 ?variable	 ?in	 ?models	 ?1c	 ??	 ?1h	 ?are	 ?male	 ?courtship	 ?behaviors.	 ?N	 ?=	 ?57	 ?mate	 ?choice	 ?trials.	 ?	 ?	 ?	 ? 	 ?Model	 ? Explanatory	 ?Variable	 ? df	 ? X2	 ? p	 ?1a	 ?-??	 ?1h	 ? treatment	 ?(similar	 ?vs.	 ?different	 ?size)	 ? 1	 ? 20.95	 ? 5x10-??6	 ?	 ?1i	 ? 1	 ? 18.48	 ? 2x10-??5	 ?	 ?1a	 ? female	 ?species	 ? 1	 ? 	 ?	 ?	 ?2.14	 ? 0.14	 ?1a	 ? treatment	 ?x	 ?female	 ?species	 ? 1	 ? 	 ?	 ?	 ?0.57	 ? 0.45	 ?1b	 ? trial	 ?date	 ? 1	 ? 	 ?	 ?	 ?0.58	 ? 0.45	 ?1c	 ? no.	 ?of	 ?approaches	 ? 1	 ? 	 ?	 ?	 ?0.62	 ? 0.43	 ?1d	 ? no.	 ?of	 ?zig-??zags	 ? 1	 ? 	 ?	 ?	 ?0.10	 ? 0.75	 ?1e	 ? no.	 ?of	 ?bites	 ? 1	 ? 	 ?	 ?	 ?0.04	 ? 0.84	 ?1f	 ? no.	 ?of	 ?leads	 ?to	 ?nest	 ? 1	 ? 	 ?	 ?	 ?0.08	 ? 0.78	 ?1g	 ? no.	 ?of	 ?nest	 ?maintenance	 ?events	 ? 1	 ? 	 ?	 ?	 ?0.23	 ? 0.63	 ?1h	 ? no.	 ?of	 ?nest	 ?creep-??throughs	 ? 1	 ? 	 ?	 ?	 ?3.28	 ? 0.07	 ?1i	 ? male	 ?nuptial	 ?color	 ? 1	 ? 	 ?	 ?	 ?0.20	 ? 0.29	 ?	 ? 21	 ?Figure	 ?2.2	 ?Female	 ?acceptance	 ?scores	 ?Mean	 ?female	 ?acceptance	 ?score	 ?for	 ?treatments	 ?in	 ?which	 ?the	 ?female	 ?was	 ?manipulated	 ?to	 ?be	 ?different	 ?in	 ?body	 ?size	 ?versus	 ?similar	 ?in	 ?body	 ?size	 ?to	 ?a	 ?male	 ?of	 ?the	 ?opposite	 ?species,	 ?with	 ?95%	 ?confidence	 ?intervals.	 ?Means	 ?for	 ?limnetic	 ?females	 ?are	 ?connected	 ?by	 ?a	 ?solid	 ?line,	 ?and	 ?for	 ?benthic	 ?females	 ?by	 ?a	 ?dashed	 ?line.	 ?	 ?Little?to?no	 ?Male	 ?Preference	 ?Males	 ?did	 ?not	 ?behave	 ?significantly	 ?differently	 ?towards	 ?heterospecific	 ?females	 ?of	 ?different	 ?size	 ?treatments	 ?except	 ?in	 ?1	 ?of	 ?the	 ?12	 ?comparisons	 ?(Table	 ?2.2):	 ?limnetic	 ?males	 ?maintained	 ?their	 ?nest	 ?slightly	 ?more	 ?often	 ?when	 ?paired	 ?with	 ?similar-??sized	 ?females	 ?than	 ?when	 ?paired	 ?with	 ?different-??sized	 ?females	 ?(Table	 ?2.2),	 ?whereas	 ?there	 ?was	 ?no	 ?detectable	 ?difference	 ?in	 ?the	 ?other	 ?five	 ?behaviors.	 ?Benthic	 ?males	 ?did	 ?not	 ?behave	 ?detectably	 ?differently	 ?towards	 ?females	 ?in	 ?different	 ?treatments.	 ?	 ?	 ?	 ?????LBDifferent Size Similar Size00.20.40.60.81.0Female Acceptance ScoreFemale Body Size Relative to Male	 ? 22	 ?Table	 ?2.2	 ?Effects	 ?of	 ?female	 ?treatment	 ?on	 ?male	 ?behavior	 ?Linear	 ?models	 ?to	 ?test	 ?for	 ?effects	 ?of	 ?female	 ?treatment	 ?(similar	 ?vs.	 ?different	 ?size)	 ?on	 ?the	 ?number	 ?of	 ?times	 ?males	 ?exhibited	 ?each	 ?of	 ?six	 ?courtship	 ?behaviors	 ?using	 ?data	 ?from	 ?	 ?their	 ?first	 ?trial	 ?only.	 ?	 ?Discussion	 ?This	 ?study	 ?provides	 ?experimental	 ?evidence	 ?that	 ?both	 ?benthic	 ?and	 ?limnetic	 ?stickleback	 ?females	 ?prefer	 ?mates	 ?of	 ?the	 ?opposite	 ?species	 ?whose	 ?body	 ?size	 ?more	 ?closely	 ?matches	 ?their	 ?own.	 ?Males	 ?appeared	 ?to	 ?exhibit	 ?little-??to-??no	 ?preference	 ?and	 ?no	 ?effect	 ?of	 ?male	 ?courtship	 ?behaviors	 ?were	 ?detected	 ?on	 ?female	 ?acceptance	 ?scores.	 ?Unlike	 ?previous	 ?studies,	 ?these	 ?results	 ?cannot	 ?be	 ?explained	 ?by	 ?timing	 ?in	 ?the	 ?mating	 ?season,	 ?or	 ?by	 ?any	 ?other	 ?traits	 ?that	 ?were	 ?not	 ?directly	 ?affected	 ?by	 ?manipulation	 ?of	 ?body	 ?size.	 ?Our	 ?results	 ?imply	 ?that	 ?body	 ?size,	 ?a	 ?trait	 ?under	 ?divergent	 ?natural	 ?selection	 ?that	 ?functions	 ?as	 ?a	 ?mate	 ?signal	 ?(Schluter	 ?2001;	 ?McKinnon	 ?and	 ?Rundle	 ?2002),	 ?also	 ?determines	 ?a	 ?female?s	 ?preferred	 ?size	 ?(referred	 ?to	 ?as	 ?simply	 ??mate	 ?preference?	 ?below)	 ?via	 ?phenotype	 ?matching.	 ?	 ?Along	 ?with	 ?body	 ?size,	 ?other	 ?traits	 ?that	 ?distinguish	 ?the	 ?species,	 ?such	 ?as	 ?shape,	 ?color	 ?and	 ?behavior	 ?are	 ?likely	 ?involved	 ?in	 ?assortative	 ?mating	 ?as	 ?well.	 ?Indeed,	 ?Southcott	 ?et	 ?al.	 ?(2013)	 ?attribute	 ?a	 ?large	 ?increase	 ?in	 ?premating	 ?reproductive	 ?isolation	 ?Model	 ? Response	 ?Variable	 ? Benthic	 ?Males	 ? Limnetic	 ?Males	 ?F(df)	 ? p	 ? F(df)	 ? p	 ?2a	 ? no.	 ?of	 ?approaches	 ? 0.06	 ?(1,14)	 ? 0.81	 ? 5x10-??4	 ?(1,25)	 ? 0.98	 ?2b	 ? no.	 ?of	 ?zig-??zags	 ? 1.03	 ?(1,14)	 ? 0.33	 ? 0.02	 ?(1,25)	 ? 0.90	 ?2c	 ? no.	 ?of	 ?bites	 ? 0.20	 ?(1,14)	 ? 0.66	 ? 1.44	 ?(1,25)	 ? 0.24	 ?2d	 ? no.	 ?of	 ?leads	 ?to	 ?nest	 ? 0.02	 ?(1,14)	 ? 0.89	 ? 0.22	 ?(1,25)	 ? 0.65	 ?2e	 ? no.	 ?of	 ?nest	 ?maintenance	 ?events	 ? 0.80	 ?(1,14)	 ? 0.39	 ? 4.23	 ?(1,25)	 ? 0.05	 ?2f	 ? no.	 ?of	 ?nest	 ?creep-??throughs	 ? 0.02	 ?(1,14)	 ? 0.90	 ? 1.10	 ?(1,25)	 ? 0.30	 ?	 ? 23	 ?to	 ?interactions	 ?of	 ?some	 ?other	 ?species-??specific	 ?trait(s),	 ?with	 ?body	 ?size	 ?and,	 ?in	 ?benthics,	 ?color.	 ?Conspicuous	 ?shape	 ?differences,	 ?which	 ?have	 ?not	 ?traditionally	 ?been	 ?quantified	 ?in	 ?stickleback	 ?mate	 ?choice	 ?studies,	 ?are	 ?an	 ?interesting	 ?candidate.	 ?Other	 ?studies	 ?too	 ?have	 ?found	 ?effects	 ?of	 ?male	 ?nuptial	 ?color	 ?on	 ?female	 ?mate	 ?preference	 ?(Boughman	 ?2001;	 ?Boughman	 ?et	 ?al.	 ?2005).	 ?However,	 ?note	 ?that	 ?any	 ?traits	 ?that	 ?were	 ?not	 ?directly	 ?affected	 ?by	 ?body	 ?size	 ?were	 ?randomized	 ?among	 ?treatments	 ?and	 ?therefore	 ?cannot	 ?explain	 ?our	 ?results.	 ?Size	 ?matching	 ?in	 ?benthic	 ?and	 ?limnetic	 ?sticklebacks	 ?is	 ?interesting	 ?because	 ?of	 ?what	 ?it	 ?implies	 ?for	 ?the	 ?genetics	 ?of	 ?divergence	 ?with	 ?gene	 ?flow	 ?between	 ?them.	 ?Size-??matching	 ?implies	 ?that	 ?the	 ?genes	 ?underlying	 ?a	 ?target	 ?of	 ?divergent	 ?natural	 ?selection,	 ?mate	 ?signal	 ?trait	 ?and	 ?mate	 ?preference	 ?are	 ?one	 ?and	 ?the	 ?same	 ?and,	 ?thus,	 ?these	 ?traits	 ?cannot	 ?be	 ?dissociated	 ?during	 ?divergence	 ?with	 ?gene	 ?flow.	 ?While	 ?the	 ?phenomenon	 ?of	 ?size	 ?matching	 ?itself	 ?may	 ?have	 ?a	 ?separate	 ?genetic	 ?basis	 ?(encoding,	 ?for	 ?example,	 ?a	 ?mate	 ?choice	 ?rule	 ?that	 ?dictates:	 ??prefer	 ?others	 ?whose	 ?size	 ?is	 ?like	 ?mine?),	 ?such	 ?loci	 ?effectively	 ?transfer	 ?the	 ?determination	 ?of	 ?mate	 ?preference	 ?to	 ?body	 ?size	 ?loci.	 ?As	 ?a	 ?result,	 ?divergent	 ?selection	 ?on	 ?body	 ?size	 ?should	 ?lead	 ?to	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?as	 ?a	 ?by-??product.	 ?Thus,	 ?the	 ?genetics	 ?of	 ?divergence	 ?between	 ?the	 ?Paxton	 ?Lake	 ?species	 ?is,	 ?in	 ?this	 ?one	 ?way,	 ?extremely	 ?favorable	 ?for	 ?the	 ?speciation	 ?process.	 ?This	 ?finding	 ?helps	 ?to	 ?explain	 ?the	 ?evolution	 ?and	 ?persistence	 ?of	 ?the	 ?species	 ?pair	 ?in	 ?the	 ?face	 ?of	 ?gene	 ?flow.	 ?These	 ?results	 ?also	 ?agree	 ?with	 ?those	 ?of	 ?a	 ?similar	 ?study	 ?of	 ?marine	 ?and	 ?stream	 ?pairs	 ?of	 ?threespine	 ?stickleback	 ?(McKinnon	 ?et	 ?al.	 ?2004).	 ?Because	 ?the	 ?marine	 ?population	 ?is	 ?ancestral	 ?to	 ?most	 ?freshwater	 ?forms,	 ?this	 ?raises	 ?the	 ?possibility	 ?that	 ?the	 ?evolution	 ?of	 ?size-??matching	 ?predated	 ?and	 ?subsequently	 ?facilitated	 ?the	 ?evolution	 ?of	 ?the	 ?benthic	 ?and	 ?limnetic	 ?species	 ?pair	 ?and,	 ?indeed,	 ?the	 ?entire	 ?adaptive	 ?radiation	 ?of	 ?threespine	 ?sticklebacks	 ?into	 ?freshwater	 ?environments	 ?around	 ?the	 ?northern	 ?hemisphere	 ?in	 ?the	 ?late	 ?Pleistocene.	 ?	 ?How	 ?do	 ?stickleback	 ?females	 ?know	 ?their	 ?own	 ?size?	 ?They	 ?may	 ?learn	 ?their	 ?own	 ?size	 ?by	 ??self-??referencing?	 ?(Hauber	 ?and	 ?Sherman	 ?2001),	 ?for	 ?example,	 ?by	 ?directly	 ?judging	 ?how	 ?well	 ?matched	 ?their	 ?own	 ?size	 ?(or	 ?some	 ?other	 ?phenotype	 ?that	 ?changes	 ?as	 ?a	 ?by-??product	 ?of	 ?changing	 ?body-??size)	 ?is	 ?with	 ?that	 ?of	 ?others.	 ?Termed	 ??self-??referent	 ?mate	 ?choice?,	 ??self-??referent	 ?assortative	 ?mating?,	 ?or	 ??self-??referent	 ?phenotype	 ?matching?	 ?	 ? 24	 ?this	 ?mechanism	 ?has	 ?been	 ?an	 ?important	 ?component	 ?in	 ?many	 ?theoretical	 ?models	 ?of	 ?speciation	 ?with	 ?gene	 ?flow	 ?(e.g.	 ?Gavrilets	 ?and	 ?Boake	 ?1998;	 ?Dieckmann	 ?and	 ?Doebeli	 ?1999;	 ?Kondrashov	 ?and	 ?Kondrashov	 ?1999;	 ?Kirkpatrick	 ?and	 ?Nuismer	 ?2004;	 ?Verzijden,	 ?Lachlan,	 ?and	 ?Servedio	 ?2005;	 ?Kisdi	 ?and	 ?Priklopil	 ?2011).	 ?However,	 ?as	 ?of	 ?yet,	 ?there	 ?have	 ?been	 ?no	 ?tests	 ?of	 ?self-??referencing	 ?mechanisms	 ?in	 ?stickleback.	 ?Alternatively,	 ?stickleback	 ?females	 ?may	 ?indirectly	 ?learn	 ?their	 ?own	 ?size	 ?via	 ?sexual	 ?imprinting	 ?or	 ?social	 ?learning	 ?by	 ?associating	 ?with	 ?other	 ?stickleback	 ?that	 ?tend	 ?to	 ?have	 ?the	 ?same	 ?phenotype	 ?as	 ?they	 ?do,	 ?such	 ?as	 ?their	 ?father	 ?or	 ?siblings	 ?(Lacy	 ?and	 ?Sherman	 ?1983;	 ?Hauber	 ?and	 ?Sherman	 ?2001).	 ?This	 ?mechanism	 ?too	 ?has	 ?received	 ?a	 ?good	 ?deal	 ?of	 ?attention	 ?in	 ?both	 ?the	 ?theoretical	 ?and	 ?empirical	 ?literature	 ?on	 ?speciation	 ?with	 ?gene	 ?flow	 ?(Laland	 ?1994;	 ?Irwin	 ?and	 ?Price	 ?1999;	 ?Owens	 ?et	 ?al.	 ?1999;	 ?Verzijden	 ?et	 ?al.	 ?2005).	 ?Ours	 ?and	 ?other	 ?studies	 ?can	 ?shed	 ?light	 ?on	 ?the	 ?role	 ?that	 ?sexual	 ?imprinting	 ?and/or	 ?social	 ?learning	 ?may	 ?play	 ?in	 ?size	 ?matching.	 ?Two	 ?independent	 ?studies	 ?in	 ?benthics	 ?and	 ?limnetics	 ?have	 ?failed	 ?to	 ?find	 ?evidence	 ?that	 ?daughters	 ?sexually	 ?imprint	 ?on	 ?their	 ?father?s	 ?body	 ?size	 ?(Albert	 ?2005;	 ?Kozak	 ?et	 ?al.	 ?2011).	 ?Furthermore,	 ?in	 ?our	 ?study,	 ?any	 ?effects	 ?of	 ?sexual	 ?imprinting	 ?were	 ?randomized	 ?between	 ?treatments.	 ?However,	 ?individuals	 ?in	 ?our	 ?study	 ?were	 ?raised	 ?with	 ?conspecifics,	 ?and	 ?from	 ?late	 ?adolescence	 ?to	 ?adulthood	 ?were	 ?kept	 ?in	 ?tanks	 ?with	 ?other	 ?individuals	 ?in	 ?the	 ?same	 ?size	 ?manipulation	 ?group.	 ?Thus,	 ?it	 ?is	 ?possible	 ?that	 ?social	 ?learning	 ?from	 ?conspecifics	 ?contributes	 ?to	 ?size	 ?matching.	 ?Since	 ?our	 ?experimental	 ?sticklebacks	 ?were	 ?na?ve	 ?to	 ?heterospecifics	 ?up	 ?until	 ?their	 ?mate	 ?choice	 ?trial,	 ?our	 ?results	 ?suggest	 ?that	 ?experience	 ?with	 ?heterospecifics	 ?is	 ?not	 ?necessary	 ?for	 ?the	 ?development	 ?of	 ?size	 ?matching.	 ?Interestingly,	 ?Kozak	 ?and	 ?Boughman	 ?(2008)	 ?found	 ?that	 ?juveniles	 ?raised	 ?with	 ?mostly	 ?conspecifics	 ?spent	 ?more	 ?time	 ?shoaling	 ?with	 ?conspecifics	 ?of	 ?similar	 ?size	 ?than	 ?those	 ?of	 ?different	 ?size,	 ?whereas,	 ?juveniles	 ?raised	 ?with	 ?mostly	 ?heterospecifics	 ?spent	 ?equal	 ?amounts	 ?of	 ?time	 ?shoaling	 ?with	 ?conspecifics	 ?of	 ?similar	 ?and	 ?different	 ?sizes.	 ?Their	 ?results	 ?suggest	 ?that	 ?juvenile	 ?experience	 ?with	 ?conspecifics	 ?may	 ?be	 ?necessary	 ?for	 ?the	 ?development	 ?of	 ?size	 ?matching	 ?and	 ?thus,	 ?a	 ?social	 ?learning	 ?component	 ?exists,	 ?at	 ?least	 ?in	 ?the	 ?case	 ?of	 ?shoal	 ?member	 ?preferences.	 ?Nonetheless,	 ?the	 ?relative	 ?contributions	 ?to	 ?size	 ?matching	 ?of	 ?self-??reference	 ?and	 ?learning	 ?from	 ?siblings	 ?or	 ?conspecifics,	 ?remain	 ?to	 ?be	 ?determined.	 ?	 ? 25	 ?In	 ?general,	 ?phenotype	 ?matching	 ?has	 ?previously	 ?been	 ?studied	 ?extensively	 ?as	 ?a	 ?means	 ?of	 ?kin	 ?recognition,	 ?inbreeding	 ?avoidance	 ?and	 ?mate	 ?choice	 ?for	 ?an	 ?optimal	 ?complementation	 ?of	 ?major	 ?histocompatibility	 ?complex	 ?(MHC)	 ?alleles	 ?(Bateson	 ?1978;	 ?Lacy	 ?and	 ?Sherman	 ?1983;	 ?Holmes	 ?1986;	 ?Heth	 ?et	 ?al.	 ?1998;	 ?Petrie	 ?et	 ?al.	 ?1999;	 ?Hauber	 ?and	 ?Sherman	 ?2001;	 ?Landry	 ?et	 ?al.	 ?2001;	 ?Mateo	 ?and	 ?Johnston	 ?2001;	 ?Milinski	 ?2006;	 ?Le	 ?Vin	 ?et	 ?al.	 ?2010).	 ?Its	 ?potential	 ?role	 ?in	 ?assortative	 ?mating	 ?and	 ?speciation	 ?has	 ?received	 ?less	 ?attention.	 ?Theoretical	 ?models	 ?of	 ?speciation	 ?have	 ?implemented	 ?various	 ?types	 ?of	 ?mate	 ?choice	 ?by	 ?phenotype	 ?matching	 ?(Laland	 ?1994;	 ?Gavrilets	 ?and	 ?Boake	 ?1998;	 ?Dieckmann	 ?and	 ?Doebeli	 ?1999;	 ?Kondrashov	 ?and	 ?Kondrashov	 ?1999;	 ?Kirkpatrick	 ?and	 ?Nuismer	 ?2004;	 ?Verzijden	 ?et	 ?al.	 ?2005;	 ?Kisdi	 ?and	 ?Priklopil	 ?2011),	 ?but	 ?empirical	 ?tests	 ?are	 ?still	 ?lacking.	 ?Our	 ?results	 ?along	 ?with	 ?those	 ?of	 ?McKinnon	 ?et	 ?al.	 ?(2004)	 ?indicate	 ?that	 ?assortative	 ?mating	 ?by	 ?size	 ?matching	 ?may	 ?have	 ?greatly	 ?facilitated	 ?the	 ?rapid	 ?and	 ?repeated	 ?diversification	 ?of	 ?sticklebacks	 ?in	 ?freshwater	 ?ecosystems	 ?where	 ?body	 ?size	 ?is	 ?under	 ?divergent	 ?selection.	 ?Other	 ?similar	 ?examples	 ?involve	 ?sexual	 ?imprinting	 ?(reviewed	 ?in	 ?Irwin	 ?and	 ?Price	 ?1999).	 ?For	 ?example,	 ?Darwin?s	 ?finches	 ?sexually	 ?imprint	 ?on	 ?their	 ?father?s	 ?song,	 ?which	 ?is	 ?directly	 ?affected	 ?by	 ?beak	 ?shape,	 ?a	 ?trait	 ?under	 ?divergent	 ?selection	 ?(Podos	 ?2001;	 ?Grant	 ?and	 ?Grant	 ?2011).	 ?Although	 ?the	 ?signal	 ?trait	 ?is	 ?actually	 ?song,	 ?a	 ?pattern	 ?of	 ?beak	 ?shape	 ?phenotype	 ?matching	 ?emerges	 ?because	 ?song	 ?changes	 ?as	 ?a	 ?by-??product	 ?of	 ?changing	 ?beak	 ?shape.	 ?Divergent	 ?selection	 ?on	 ?beak	 ?shape	 ?should	 ?readily	 ?lead	 ?to	 ?assortative	 ?mating	 ?by	 ?beak	 ?shape	 ?as	 ?a	 ?by-??product.	 ?More	 ?studies	 ?are	 ?greatly	 ?needed	 ?to	 ?determine	 ?the	 ?prevalence	 ?of	 ?similar	 ?phenomena	 ?in	 ?nature,	 ?as	 ?there	 ?have	 ?been	 ?few	 ?tests	 ?to	 ?date.	 ?For	 ?example	 ?while	 ?many	 ?studies	 ?have	 ?looked	 ?at	 ?whether	 ?a	 ?trait	 ?under	 ?divergent	 ?selection	 ?is	 ?also	 ?a	 ?mate	 ?signal,	 ?whether	 ?mate	 ?preference	 ?is	 ?determined	 ?by	 ?that	 ?trait	 ?through	 ?phenotype	 ?matching	 ?has	 ?rarely	 ?been	 ?investigated.	 ?	 ?	 ?	 ? 	 ?	 ? 26	 ?3	 ?	 ? The	 ?Probability	 ?of	 ?Genetic	 ?Parallelism	 ?and	 ?Convergence	 ?in	 ?Natural	 ?Populations2	 ?Introduction	 ?Parallel	 ?and	 ?convergent	 ?evolution	 ?of	 ?traits	 ?in	 ?independent	 ?populations	 ?inhabiting	 ?similar	 ?environments	 ?(?repeated	 ?phenotypic	 ?evolution?)	 ?implicates	 ?natural	 ?selection	 ?(Endler	 ?1986;	 ?Harvey	 ?and	 ?Pagel	 ?1991;	 ?Schluter	 ?2000;	 ?Losos	 ?2011).	 ?Processes	 ?contributing	 ?to	 ?phenotypic	 ?evolution	 ?other	 ?than	 ?selection,	 ?such	 ?as	 ?mutation	 ?and	 ?drift,	 ?are	 ?unlikely	 ?to	 ?yield	 ?the	 ?same	 ?evolutionary	 ?shifts,	 ?again	 ?and	 ?again,	 ?in	 ?correlation	 ?with	 ?environment.	 ?Conversely,	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?underlying	 ?genes	 ?during	 ?parallel	 ?and	 ?convergent	 ?phenotypic	 ?evolution	 ?is	 ?thought	 ?to	 ?reflect	 ?biases	 ?and	 ?constraints	 ?on	 ?the	 ?supply	 ?and	 ?fixation	 ?of	 ?beneficial	 ?mutations.	 ?For	 ?example,	 ?some	 ?genes	 ?might	 ?contribute	 ?to	 ?adaptation	 ?more	 ?often	 ?than	 ?others	 ?because	 ?they	 ?have	 ?more	 ?standing	 ?genetic	 ?variation,	 ?higher	 ?mutation	 ?rates,	 ?larger	 ?effect	 ?sizes,	 ?more	 ?numerous	 ?beneficial	 ?mutations,	 ?fewer	 ?pleiotropic	 ?constraints,	 ?particular	 ?linkage	 ?relationships	 ?or	 ?because	 ?they	 ?are	 ?involved	 ?in	 ?particular	 ?epistatic	 ?interactions	 ?with	 ?the	 ?genetic	 ?background	 ?(Orr	 ?2005;	 ?Weinreich	 ?et	 ?al.	 ?2006;	 ?Gompel	 ?and	 ?Prud?homme	 ?2009;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Chevin	 ?et	 ?al.	 ?2010;	 ?Christin	 ?et	 ?al.	 ?2010;	 ?Streisfeld	 ?and	 ?Rausher	 ?2011;	 ?Feldman	 ?et	 ?al.	 ?2012).	 ?	 ?Knowledge	 ?of	 ?these	 ?underlying	 ?effects	 ?and	 ?constraints	 ?might	 ?ultimately	 ?allow	 ?us	 ?to	 ?predict	 ?genetic	 ?evolution	 ?(Orr	 ?2005;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Chevin	 ?et	 ?al.	 ?2010).	 ?Instances	 ?of	 ?parallel	 ?and	 ?convergent	 ?phenotypic	 ?evolution	 ?provide	 ?an	 ?opportunity	 ?to	 ?measure	 ?the	 ?predictability	 ?of	 ?genetic	 ?changes	 ?underlying	 ?adaptation.	 ?In	 ?some	 ?cases,	 ?high	 ?molecular	 ?specificity	 ?of	 ?a	 ?selective	 ?agent,	 ?such	 ?as	 ?a	 ?toxin	 ?in	 ?the	 ?diet	 ?that	 ?interferes	 ?with	 ?the	 ?function	 ?of	 ?particular	 ?proteins,	 ?drives	 ?repeated	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?	 ?2	 ?A	 ?version	 ?of	 ?chapter	 ?3	 ?has	 ?been	 ?published.	 ?Conte,	 ?G.L.,	 ?M.E.	 ?Arnegard,	 ?C.L.	 ?Peichel,	 ?and	 ?D.	 ?Schluter.	 ?2012.	 ?The	 ?probability	 ?of	 ?genetic	 ?parallelism	 ?and	 ?convergence	 ?in	 ?natural	 ?populations.	 ?Proc.	 ?R.	 ?Soc.	 ?B	 ?279:	 ?5039-??5047.	 ?	 ? 27	 ?evolution	 ?in	 ?a	 ?small	 ?number	 ?of	 ?genes	 ?(Gompel	 ?and	 ?Prud?homme	 ?2009).	 ?For	 ?example,	 ?resistance	 ?to	 ?tetrodotoxin	 ?(TTX)	 ?in	 ?puffer	 ?fish	 ?and	 ?several	 ?snake	 ?species	 ?has	 ?repeatedly	 ?evolved	 ?by	 ?changes	 ?to	 ?a	 ?few	 ?amino	 ?acid	 ?residues	 ?in	 ?the	 ?outer	 ?pore	 ?of	 ?voltage-??gated	 ?sodium	 ?channel	 ?proteins,	 ?where	 ?the	 ?neurotoxin	 ?binds	 ?to	 ?its	 ?target,	 ?causing	 ?paralysis	 ?(Jost	 ?et	 ?al.	 ?2008;	 ?Feldman	 ?et	 ?al.	 ?2012).	 ?This	 ?specificity	 ?explanation	 ?fails	 ?when	 ?many	 ?genes	 ?influence	 ?a	 ?trait,	 ?and	 ?changes	 ?to	 ?any	 ?one	 ?may	 ?produce	 ?similar	 ?alterations	 ?in	 ?phenotype.	 ?For	 ?example,	 ?all	 ?known	 ?cases	 ?of	 ?parallel	 ?reduction	 ?of	 ?complete	 ?armor	 ?plating	 ?in	 ?freshwater	 ?populations	 ?of	 ?threespine	 ?stickleback	 ?(Gasterosteus	 ?aculeatus	 ?species	 ?complex)	 ?involve	 ?the	 ?same	 ?major	 ?gene,	 ?Eda	 ?(Ectodysplasin)	 ?(Colosimo	 ?et	 ?al.	 ?2005)	 ?(for	 ?other	 ?references	 ?see	 ?Table	 ?B.1),	 ?even	 ?though	 ?mutations	 ?in	 ?several	 ?genes	 ?of	 ?the	 ?Eda-??signaling	 ?pathway	 ?in	 ?mammals	 ?are	 ?known	 ?to	 ?cause	 ?similar	 ?phenotypic	 ?changes	 ?in	 ?hair,	 ?teeth,	 ?sweat	 ?glands	 ?and	 ?dermal	 ?bones	 ?(Knecht	 ?et	 ?al.	 ?2007).	 ?A	 ?ready	 ?supply	 ?of	 ?standing	 ?genetic	 ?variation	 ?in	 ?Eda	 ?in	 ?the	 ?ancestral	 ?population	 ?likely	 ?contributed	 ?to	 ?almost	 ?universal	 ?use	 ?of	 ?the	 ?same	 ?gene	 ?(Colosimo	 ?et	 ?al.	 ?2005).	 ?	 ?Despite	 ?a	 ?growing	 ?number	 ?of	 ?cases	 ?(reviewed	 ?in	 ?Wood	 ?et	 ?al.	 ?2005;	 ?Arendt	 ?and	 ?Reznick	 ?2008;	 ?Gompel	 ?and	 ?Prud?homme	 ?2009;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Christin	 ?et	 ?al.	 ?2010;	 ?Manceau	 ?et	 ?al.	 ?2010;	 ?Elmer	 ?and	 ?Meyer	 ?2011;	 ?Martin	 ?and	 ?Orgogozo	 ?2013;	 ?Stern	 ?2013),	 ?the	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?in	 ?natural	 ?populations	 ?has	 ?not	 ?been	 ?estimated.	 ?The	 ?number	 ?of	 ?examples	 ?of	 ?repeated	 ?gene	 ?use	 ?in	 ?the	 ?published	 ?literature	 ?gives	 ?the	 ?impression	 ?that	 ?this	 ?probability	 ?might	 ?be	 ?high.	 ?Indeed,	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?is	 ?regarded	 ?as	 ?sufficiently	 ?common	 ?in	 ?evolution	 ?that	 ?the	 ?detection	 ?of	 ?equivalent	 ?genomic	 ?signatures	 ?of	 ?selection	 ?between	 ?independent	 ?natural	 ?populations	 ?that	 ?have	 ?adapted	 ?to	 ?similar	 ?environments	 ?is	 ?a	 ?valuable	 ?tool	 ?for	 ?discovering	 ?genes	 ?involved	 ?in	 ?adaptation	 ?(Hancock	 ?et	 ?al.	 ?2010;	 ?Turner	 ?et	 ?al.	 ?2010;	 ?Chan	 ?et	 ?al.	 ?2012;	 ?Jones	 ?et	 ?al.	 ?2012b).	 ?Yet,	 ?the	 ?apparent	 ?frequency	 ?of	 ?reuse	 ?of	 ?genes	 ?might	 ?be	 ?distorted	 ?if	 ?biased	 ?methods	 ?are	 ?used	 ?to	 ?detect	 ?it	 ?or	 ?if	 ?less	 ?attention	 ?has	 ?been	 ?paid	 ?to	 ?cases	 ?in	 ?which	 ?different	 ?genes	 ?underlie	 ?repeated	 ?phenotypic	 ?evolution.	 ?	 ?Here,	 ?we	 ?conducted	 ?an	 ?objective	 ?survey	 ?of	 ?the	 ?published	 ?literature	 ?to	 ?make	 ?a	 ?quantitative	 ?estimate	 ?of	 ?the	 ?probability	 ?of	 ?reuse	 ?of	 ?genes	 ?during	 ?repeated	 ?	 ? 28	 ?phenotypic	 ?evolution	 ?in	 ?independent	 ?lineages	 ?of	 ?natural	 ?populations.	 ?We	 ?clarify	 ?different	 ?approaches	 ?and	 ?biases	 ?that	 ?may	 ?affect	 ?estimation	 ?of	 ?this	 ?probability.	 ?We	 ?focus	 ?on	 ?repeated	 ?changes	 ?to	 ?the	 ?same	 ?gene,	 ?rather	 ?than	 ?on	 ?reuse	 ?of	 ?the	 ?same	 ?mutations,	 ?because	 ?the	 ?mutations	 ?are	 ?unknown	 ?in	 ?most	 ?cases.	 ?We	 ?include	 ?both	 ?protein-??coding	 ?sequences	 ?and	 ?associated	 ?regulatory	 ?regions	 ?in	 ?our	 ?definition	 ?of	 ?a	 ??gene?.	 ?We	 ?treat	 ?paralogous	 ?genes	 ?as	 ?different	 ?genes,	 ?which	 ?is	 ?a	 ?conservative	 ?decision	 ?because	 ?considering	 ?them	 ?to	 ?be	 ?the	 ?same	 ?gene	 ?increases	 ?the	 ?overall	 ?probability	 ?of	 ?gene	 ?reuse.	 ?	 ?In	 ?addition,	 ?we	 ?test	 ?whether	 ?the	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?declines	 ?as	 ?more	 ?distantly	 ?related	 ?taxa	 ?are	 ?compared.	 ?We	 ?would	 ?expect	 ?the	 ?probability	 ?to	 ?decline	 ?if	 ?phylogenetically	 ?distant	 ?taxa	 ?use	 ?different	 ?developmental	 ?pathways	 ?and	 ?networks	 ?more	 ?often	 ?than	 ?closely	 ?related	 ?species	 ?when	 ?they	 ?adapt	 ?to	 ?similar	 ?selection	 ?pressures	 ?(Futuyma	 ?2009).	 ?Another	 ?reason	 ?to	 ?predict	 ?a	 ?decline	 ?is	 ?that	 ?pleiotropic	 ?constraints	 ?and	 ?the	 ?supply	 ?of	 ?beneficial	 ?mutations	 ?at	 ?a	 ?locus	 ?are	 ?likely	 ?to	 ?depend	 ?on	 ?its	 ?sequence	 ?and	 ?on	 ?its	 ?genetic	 ?background,	 ?both	 ?of	 ?which	 ?have	 ?had	 ?more	 ?time	 ?to	 ?diverge	 ?between	 ?taxa	 ?that	 ?are	 ?more	 ?distantly	 ?related.	 ?	 ?Counterexamples	 ?are	 ?known	 ?in	 ?which	 ?repeated	 ?phenotypic	 ?evolution	 ?of	 ?closely	 ?related	 ?taxa	 ?used	 ?different	 ?genes,	 ?and	 ?in	 ?which	 ?distantly	 ?related	 ?taxa	 ?used	 ?the	 ?same	 ?genes	 ?(Arendt	 ?and	 ?Reznick	 ?2008).	 ?Indeed,	 ?the	 ?frequency	 ?of	 ?such	 ?examples	 ?prompted	 ?Arendt	 ?and	 ?Reznick	 ?(2008)	 ?to	 ?conclude	 ?that,	 ?from	 ?a	 ?genetic	 ?perspective,	 ?there	 ?is	 ?no	 ?clear	 ?distinction	 ?between	 ??parallel	 ?evolution?	 ?and	 ??convergent	 ?evolution?.	 ?Yet,	 ?from	 ?a	 ?phylogenetic	 ?standpoint,	 ?it	 ?is	 ?useful	 ?to	 ?distinguish	 ?cases	 ?in	 ?which	 ?populations	 ?derived	 ?from	 ?the	 ?same	 ?or	 ?closely	 ?related	 ?ancestors	 ?evolved	 ?in	 ?the	 ?same	 ?direction	 ?(parallel	 ?evolution)	 ?from	 ?cases	 ?in	 ?which	 ?more	 ?distantly	 ?related,	 ?phenotypically	 ?differentiated	 ?populations	 ?evolved	 ?a	 ?similar	 ?trait	 ?(convergent	 ?evolution).	 ?This	 ?distinction	 ?is	 ?also	 ?reflected	 ?in	 ?the	 ?design	 ?of	 ?genetic	 ?studies	 ?reviewed	 ?here.	 ?Genetic	 ?studies	 ?of	 ?parallel	 ?phenotypic	 ?evolution	 ?compare	 ?multiple	 ?derived	 ?populations	 ?to	 ?the	 ?same	 ?ancestor	 ?(or	 ?to	 ?closely	 ?related	 ?populations	 ?representing	 ?their	 ?common	 ?ancestral	 ?state),	 ?whereas	 ?genetic	 ?studies	 ?of	 ?convergent	 ?evolution	 ?compare	 ?each	 ?of	 ?two	 ?or	 ?more	 ?distantly	 ?related,	 ?derived	 ?populations	 ?to	 ?different	 ?ancestral	 ?species,	 ?rather	 ?than	 ?to	 ?the	 ?common	 ?ancestor	 ?of	 ?all	 ?the	 ?taxa.	 ?Using	 ?this	 ?	 ? 29	 ?distinction,	 ?we	 ?ask	 ?whether	 ?the	 ?genetics	 ?of	 ?parallel	 ?and	 ?convergent	 ?evolution	 ?differ	 ?from	 ?one	 ?another	 ?in	 ?the	 ?probability	 ?of	 ?gene	 ?reuse.	 ?On	 ?genomic	 ?approaches	 ?for	 ?detecting	 ?repeated	 ?genetic	 ?evolution	 ?Genomic	 ?approaches	 ?hold	 ?great	 ?promise	 ?for	 ?detecting	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?underlying	 ?genes	 ?in	 ?phenotypic	 ?evolution.	 ?Counting	 ?the	 ?frequency	 ?of	 ?signatures	 ?of	 ?selection	 ?in	 ?the	 ?same	 ?genes	 ?between	 ?replicate	 ?natural	 ?populations	 ?adapting	 ?to	 ?similar	 ?environments,	 ?and	 ?exhibiting	 ?similar	 ?phenotypic	 ?changes,	 ?is	 ?straightforward	 ?in	 ?principle	 ?(Turner	 ?et	 ?al.	 ?2010;	 ?Jones	 ?et	 ?al.	 ?2012b).	 ?The	 ?approach	 ?has	 ?the	 ?advantage	 ?of	 ?broad	 ?coverage,	 ?and	 ?it	 ?allows	 ?the	 ?detection	 ?of	 ?mutations	 ?having	 ?relatively	 ?small	 ?effect	 ?sizes	 ?on	 ?fitness.	 ?For	 ?example,	 ?Jones	 ?et	 ?al.	 ?(2012)	 ?resequenced	 ?one	 ?individual	 ?from	 ?each	 ?of	 ?10	 ?marine	 ?(ancestral)	 ?and	 ?10	 ?phenotypically	 ?similar	 ?stream	 ?populations	 ?of	 ?threespine	 ?stickleback	 ?from	 ?around	 ?the	 ?northern	 ?hemisphere.	 ?Stream	 ?and	 ?marine	 ?populations	 ?were	 ?consistently	 ?distinguished	 ?at	 ?about	 ?200	 ?loci,	 ?the	 ?outcome	 ?of	 ?repeated	 ?selection	 ?on	 ?standing	 ?genetic	 ?variation.	 ?Deeper	 ?sequencing	 ?of	 ?a	 ?single	 ?stream-??marine	 ?pair	 ?found	 ?that	 ?35%	 ?of	 ?all	 ?genomic	 ?regions	 ?showing	 ?evidence	 ?of	 ?adaptive	 ?differentiation	 ?between	 ?the	 ?two	 ?populations	 ?also	 ?separated	 ?marine	 ?and	 ?stream	 ?populations	 ?globally.	 ?The	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?was	 ?thus	 ?estimated	 ?as	 ?0.35	 ?in	 ?this	 ?study.	 ?Genome	 ?scans	 ?based	 ?on	 ?genetic	 ?markers	 ?rather	 ?than	 ?complete	 ?sequences	 ?also	 ?find	 ?evidence	 ?for	 ?parallel	 ?genetic	 ?evolution,	 ?but	 ?to	 ?different	 ?extents	 ?(e.g.	 ?Campbell	 ?and	 ?Bernatchez	 ?2004;	 ?Bonin	 ?et	 ?al.	 ?2006;	 ?Egan	 ?et	 ?al.	 ?2008;	 ?Nosil	 ?et	 ?al.	 ?2008;	 ?Bradbury	 ?et	 ?al.	 ?2010;	 ?Hohenlohe	 ?et	 ?al.	 ?2010).	 ?The	 ?main	 ?limitation	 ?of	 ?genomic	 ?studies	 ?is	 ?the	 ?lack	 ?of	 ?information	 ?on	 ?phenotypic	 ?traits	 ?affected	 ?by	 ?genes.	 ?Conceivably,	 ?separate	 ?mutations	 ?in	 ?the	 ?same	 ?genes	 ?might	 ?have	 ?divergent,	 ?rather	 ?than	 ?parallel	 ?effects	 ?on	 ?a	 ?phenotypic	 ?trait,	 ?or	 ?they	 ?might	 ?affect	 ?different	 ?traits.	 ?This	 ?problem	 ?can	 ?be	 ?partly	 ?overcome	 ?with	 ?functional	 ?experiments	 ?that	 ?determine	 ?if	 ?independent	 ?mutations	 ?in	 ?the	 ?same	 ?gene	 ?have	 ?the	 ?same	 ?phenotypic	 ?effects	 ?in	 ?different	 ?populations.	 ?It	 ?will	 ?be	 ?more	 ?difficult	 ?to	 ?identify	 ?those	 ?cases	 ?in	 ?which	 ?mutations	 ?in	 ?different	 ?genes	 ?lead	 ?to	 ?similar	 ?phenotypic	 ?	 ? 30	 ?effects.	 ?One	 ?might	 ?argue	 ?that	 ?fitness	 ?itself	 ?is	 ?the	 ?phenotypic	 ?trait	 ?addressed	 ?in	 ?genomic	 ?studies,	 ?since	 ?it	 ?evolves	 ?in	 ?parallel	 ?as	 ?replicate	 ?populations	 ?adapt	 ?to	 ?similar	 ?environments.	 ?On	 ?the	 ?other	 ?hand,	 ?fitness	 ?evolves	 ?in	 ?parallel	 ?even	 ?when	 ?phenotypes	 ?diverge,	 ?and	 ?hence	 ?estimates	 ?of	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?from	 ?genomic	 ?studies	 ?will	 ?not	 ?necessarily	 ?agree	 ?with	 ?estimates	 ?based	 ?on	 ?the	 ?identity	 ?of	 ?mapped	 ?genes	 ?underlying	 ?phenotypic	 ?traits	 ?evolving	 ?in	 ?parallel.	 ?For	 ?these	 ?reasons,	 ?it	 ?will	 ?eventually	 ?be	 ?interesting	 ?to	 ?compare	 ?results	 ?from	 ?the	 ?two	 ?approaches.	 ?Here,	 ?we	 ?chose	 ?to	 ?focus	 ?on	 ?genetic	 ?studies	 ?of	 ?repeated	 ?phenotypic	 ?evolution,	 ?which	 ?are	 ?presently	 ?more	 ?common	 ?than	 ?genome	 ?sequence	 ?comparisons	 ?of	 ?populations	 ?adapted	 ?to	 ?similar	 ?environments.	 ?	 ?The	 ?genetics	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?We	 ?surveyed	 ?published	 ?genetic	 ?studies	 ?to	 ?estimate	 ?the	 ?probability	 ?of	 ?reuse	 ?of	 ?the	 ?same	 ?genes	 ?underlying	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?natural	 ?populations.	 ?These	 ?studies	 ?used	 ?either	 ?of	 ?two	 ?main	 ?approaches	 ?to	 ?assess	 ?the	 ?genetic	 ?basis	 ?of	 ?phenotypic	 ?differences:	 ?genetic	 ?crosses	 ?and	 ?analysis	 ?of	 ?candidate	 ?genes.	 ?Under	 ?a	 ?genetic	 ?cross	 ?approach,	 ?replicate	 ?populations	 ?that	 ?have	 ?independently	 ?evolved	 ?a	 ?particular	 ?phenotype	 ?are	 ?crossed	 ?to	 ?populations	 ?representing	 ?ancestral	 ?phenotypes.	 ?Quantitative	 ?trait	 ?locus	 ?(QTL)	 ?mapping	 ?methods	 ?are	 ?then	 ?used	 ?to	 ?test	 ?markers	 ?across	 ?the	 ?genome	 ?for	 ?an	 ?association	 ?with	 ?the	 ?phenotype	 ?of	 ?interest	 ?in	 ?hybrid	 ?offspring.	 ?Alternatively,	 ?mapping	 ?is	 ?carried	 ?out	 ?using	 ?admixed	 ?populations.	 ?Ideally,	 ?techniques	 ?such	 ?as	 ?fine	 ?mapping	 ?and	 ?functional	 ?assays	 ?are	 ?used	 ?subsequently	 ?to	 ?confirm	 ?gene	 ?identity	 ?(or	 ?at	 ?least	 ?to	 ?narrow	 ?the	 ?identified	 ?genomic	 ?region).	 ?The	 ?genetic	 ?cross	 ?approach	 ?has	 ?the	 ?advantage	 ?of	 ?genome-??wide	 ?coverage,	 ?allowing	 ?multiple	 ?loci	 ?contributing	 ?to	 ?the	 ?derived	 ?phenotype	 ?to	 ?be	 ?discovered	 ?and	 ?the	 ?magnitude	 ?of	 ?their	 ?phenotypic	 ?effects	 ?to	 ?be	 ?estimated.	 ?For	 ?example,	 ?this	 ?approach	 ?was	 ?used	 ?to	 ?map	 ?repeated	 ?evolution	 ?of	 ?red	 ?wing	 ?patterning	 ?in	 ?Heliconius	 ?erato	 ?and	 ?H.	 ?melpomene	 ?butterflies	 ?to	 ?the	 ?optix	 ?locus	 ?in	 ?both	 ?species	 ?(Baxter	 ?et	 ?al.	 ?2008;	 ?Papa	 ?et	 ?al.	 ?2008;	 ?Reed	 ?et	 ?al.	 ?2011).	 ?	 ? 31	 ?More	 ?commonly	 ?under	 ?this	 ?approach,	 ?researchers	 ?carried	 ?out	 ?genome-??wide	 ?mapping	 ?in	 ?one	 ?pair	 ?of	 ?populations	 ?representing	 ?derived	 ?and	 ?ancestral	 ?phenotypes,	 ?and	 ?then	 ?other	 ?methods	 ?such	 ?as	 ?complementation	 ?crosses	 ?and	 ?localized	 ?(rather	 ?than	 ?genome-??wide)	 ?mapping	 ?were	 ?used	 ?to	 ?determine	 ?whether	 ?the	 ?same	 ?gene	 ?or	 ?genomic	 ?regions	 ?were	 ?involved	 ?in	 ?other	 ?instances	 ?of	 ?repeated	 ?evolution.	 ?For	 ?instance,	 ?albinism	 ?was	 ?mapped	 ?to	 ?Oca2	 ?in	 ?two	 ?separate	 ?populations	 ?of	 ?cavefish,	 ?Astyanax	 ?mexicanus,	 ?whereas	 ?a	 ?complementation	 ?cross	 ?in	 ?a	 ?third	 ?population	 ?indicated	 ?that	 ?albinism	 ?arose	 ?through	 ?mutations	 ?in	 ?the	 ?same	 ?gene	 ?(Protas	 ?et	 ?al.	 ?2006).	 ?Or,	 ?genetic	 ?crosses	 ?were	 ?used	 ?to	 ?estimate	 ?the	 ?number	 ?of	 ?genes	 ?underlying	 ?a	 ?trait,	 ?followed	 ?by	 ?localized	 ?mapping	 ?of	 ?a	 ?candidate	 ?gene	 ?if	 ?Mendelian	 ?segregation	 ?was	 ?observed.	 ?For	 ?example,	 ?crosses	 ?between	 ?populations	 ?of	 ?the	 ?deer	 ?mouse,	 ?Peromyscus	 ?maniculatus,	 ?that	 ?have	 ?evolved	 ?on	 ?light	 ?and	 ?dark	 ?substrates	 ?revealed	 ?evidence	 ?for	 ?a	 ?single	 ?gene	 ?of	 ?major	 ?effect	 ?underlying	 ?a	 ?coat	 ?color	 ?phenotype	 ?(Linnen	 ?et	 ?al.	 ?2009).	 ?Alleles	 ?of	 ?the	 ?candidate	 ?gene	 ?Agouti	 ?were	 ?found	 ?to	 ?segregate	 ?perfectly	 ?with	 ?the	 ?phenotype	 ?(Linnen	 ?et	 ?al.	 ?2009;	 ?Manceau	 ?et	 ?al.	 ?2010).	 ?Under	 ?the	 ?alternative,	 ?candidate	 ?gene	 ?approach,	 ?one	 ?or	 ?a	 ?small	 ?number	 ?of	 ?designated	 ?genes	 ?is	 ?tested	 ?for	 ?association	 ?with	 ?phenotype.	 ?Ideally,	 ?the	 ?finding	 ?of	 ?such	 ?an	 ?association	 ?is	 ?accompanied	 ?by	 ?functional	 ?assays	 ?or	 ?other	 ?methods	 ?to	 ?confirm	 ?causality.	 ?This	 ?approach	 ?was	 ?used	 ?to	 ?demonstrate	 ?that	 ?electrical	 ?excitability	 ?of	 ?the	 ?myogenic	 ?electric	 ?organ,	 ?which	 ?has	 ?evolved	 ?independently	 ?in	 ?mormyroid	 ?and	 ?gymnotiform	 ?fishes,	 ?involved	 ?amino	 ?acid	 ?substitutions	 ?in	 ?the	 ?same	 ?functional	 ?regions	 ?of	 ?the	 ?sodium	 ?channel	 ?gene,	 ?Scn4aa,	 ?in	 ?both	 ?lineages	 ?(Zakon	 ?et	 ?al.	 ?2006;	 ?Arnegard	 ?et	 ?al.	 ?2010).	 ?The	 ?candidate	 ?gene	 ?approach	 ?determines	 ?whether	 ??this	 ?same	 ?gene	 ?is	 ?involved?	 ?when	 ?independent	 ?populations	 ?evolve	 ?a	 ?similar	 ?phenotype,	 ?but	 ?it	 ?does	 ?not	 ?provide	 ?estimates	 ?of	 ?the	 ?magnitude	 ?of	 ?the	 ?contributions	 ?of	 ?all	 ?genomic	 ?regions	 ?affecting	 ?a	 ?trait.	 ?The	 ?approach	 ?thus	 ?only	 ?provides	 ?a	 ?qualitative	 ?score	 ?of	 ?gene	 ?reuse.	 ?In	 ?turn,	 ?this	 ?may	 ?lead	 ?to	 ?higher	 ?estimates	 ?of	 ?the	 ?probability	 ?of	 ?parallel	 ?evolution	 ?compared	 ?to	 ?crossing	 ?and	 ?mapping	 ?studies.	 ?For	 ?this	 ?reason	 ?we	 ?analyze	 ?data	 ?from	 ?the	 ?two	 ?approaches	 ?separately.	 ?	 ?	 ? 32	 ?Methods	 ?Literature	 ?search	 ?We	 ?searched	 ?the	 ?literature	 ?for	 ?examples	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?natural	 ?populations	 ?that	 ?provided	 ?evidence	 ?on	 ?whether	 ?the	 ?same	 ?genes	 ?were	 ?used.	 ?To	 ?obtain	 ?a	 ?representative	 ?sample	 ?we	 ?searched	 ?the	 ?online	 ?Thomson	 ?Reuters	 ?Web	 ?of	 ?Knowledge	 ?database	 ?for	 ?all	 ?studies	 ?in	 ?the	 ?subject	 ?area	 ?of	 ?evolutionary	 ?biology	 ?(as	 ?of	 ?June	 ?17,	 ?2012)	 ?that	 ?included	 ?the	 ?topic	 ?gene*	 ?and	 ?that	 ?contained	 ?either	 ?parallel*	 ?or	 ?converg*	 ?in	 ?the	 ?title	 ?(a	 ??*?	 ?at	 ?the	 ?end	 ?of	 ?a	 ?search	 ?term	 ?includes	 ?all	 ?words	 ?beginning	 ?with	 ?the	 ?preceding	 ?letters).	 ?We	 ?reasoned	 ?that	 ?these	 ?search	 ?terms	 ?would	 ?detect	 ?many	 ?studies	 ?that	 ?had	 ?tested	 ?the	 ?genetic	 ?basis	 ?of	 ?parallel	 ?or	 ?convergent	 ?phenotypic	 ?evolution	 ?regardless	 ?of	 ?outcome.	 ?In	 ?support,	 ?our	 ?search	 ?criteria	 ?detected	 ?multiple	 ?studies	 ?in	 ?which	 ?the	 ?genetic	 ?basis	 ?was	 ?found	 ?not	 ?to	 ?be	 ?the	 ?same	 ?between	 ?independent	 ?instances	 ?of	 ?repeated	 ?phenotypic	 ?evolution.	 ?In	 ?total,	 ?the	 ?search	 ?yielded	 ?1612	 ?publications,	 ?of	 ?which	 ?25	 ?met	 ?further	 ?criteria	 ?for	 ?inclusion	 ?in	 ?the	 ?study.	 ?To	 ?be	 ?included,	 ?we	 ?required	 ?that	 ?a	 ?study	 ?addressed	 ?the	 ?genetic	 ?basis	 ?of	 ?a	 ?repeatedly	 ?evolved	 ?phenotype	 ?in	 ?natural	 ?populations	 ?rather	 ?than	 ?experimentally	 ?evolved	 ?or	 ?artificially	 ?selected	 ?populations.	 ?While	 ?the	 ?latter	 ?types	 ?of	 ?studies	 ?offer	 ?a	 ?wealth	 ?of	 ?information	 ?regarding	 ?parallel	 ?and	 ?convergent	 ?genetic	 ?evolution	 ?(e.g.	 ?Woods	 ?et	 ?al.	 ?2006;	 ?Kao	 ?and	 ?Sherlock	 ?2008;	 ?Tenaillon	 ?et	 ?al.	 ?2012)	 ?our	 ?goal	 ?was	 ?to	 ?better	 ?understand	 ?repeated	 ?genetic	 ?evolution	 ?in	 ?wild	 ?populations,	 ?which	 ?span	 ?a	 ?greater	 ?range	 ?of	 ?ages	 ?and	 ?about	 ?which	 ?less	 ?is	 ?currently	 ?known.	 ?We	 ?included	 ?only	 ?studies	 ?with	 ?original	 ?data,	 ?rather	 ?than	 ?reviews.	 ?It	 ?was	 ?also	 ?necessary	 ?that	 ?the	 ?phenotypic	 ?trait	 ?in	 ?a	 ?study	 ?be	 ?an	 ?organismal-??level	 ?trait	 ?rather	 ?than	 ?a	 ?molecular	 ?phenotype,	 ?since	 ?a	 ?protein	 ?sequence,	 ?expression	 ?pattern,	 ?or	 ?function-??based	 ?phenotype	 ?usually	 ?predetermines	 ?its	 ?underlying	 ?gene.	 ?We	 ?further	 ?required	 ?that	 ?repeated	 ?evolution	 ?in	 ?the	 ?phenotypic	 ?trait	 ?had	 ?been	 ?discovered	 ?prior	 ?to	 ?the	 ?discovery	 ?of	 ?its	 ?genetic	 ?basis.	 ?This	 ?was	 ?done	 ?to	 ?avoid	 ?an	 ?obvious	 ?bias	 ?accompanying	 ?a	 ?reverse	 ?discovery	 ?sequence	 ?in	 ?which	 ?the	 ?phenotypes	 ?were	 ?investigated	 ?only	 ?after	 ?repeated	 ?genetic	 ?changes	 ?had	 ?been	 ?found.	 ?Finally,	 ?we	 ?included	 ?only	 ?instances	 ?in	 ?	 ? 33	 ?which	 ?the	 ?direction	 ?of	 ?evolution	 ?was	 ?known	 ?or	 ?strongly	 ?suspected	 ?in	 ?independent	 ?populations,	 ?to	 ?exclude	 ?populations	 ?that	 ?might	 ?instead	 ?represent	 ?reversions	 ?to	 ?the	 ?ancestral	 ?state.	 ?This	 ?criterion	 ?meant	 ?that	 ?we	 ?could	 ?not	 ?include	 ?studies	 ?of	 ?the	 ?genetics	 ?of	 ?abdominal	 ?pigmentation	 ?in	 ?Drosophila,	 ?where	 ?the	 ?direction	 ?of	 ?evolution	 ?could	 ?not	 ?be	 ?verified	 ?(Wittkopp	 ?et	 ?al.	 ?2003),	 ?and	 ?the	 ?evolution	 ?of	 ?increased	 ?pigmentation	 ?in	 ?native	 ?American	 ?peoples,	 ?which	 ?involved	 ?evolution	 ?in	 ?the	 ?opposite	 ?direction	 ?compared	 ?to	 ?pigmentation	 ?changes	 ?in	 ?other	 ?human	 ?populations	 ?(Quillen	 ?et	 ?al.	 ?2012).	 ?Data	 ?from	 ?the	 ?25	 ?papers	 ?meeting	 ?our	 ?criteria	 ?were	 ?arranged	 ?according	 ?to	 ?phylogeny	 ?of	 ?taxa	 ?and	 ?similarity	 ?of	 ?traits	 ?(Table	 ?B.1).	 ?We	 ?then	 ?carried	 ?out	 ?an	 ?exhaustive	 ?search	 ?for	 ?all	 ?other	 ?publications	 ?on	 ?the	 ?same	 ?traits	 ?in	 ?the	 ?same	 ?species	 ?to	 ?ensure	 ?that	 ?we	 ?had	 ?the	 ?most	 ?up-??to-??date	 ?information	 ?on	 ?the	 ?genetic	 ?basis	 ?of	 ?the	 ?phenotypic	 ?traits	 ?in	 ?the	 ?examples	 ?originally	 ?identified	 ?by	 ?our	 ?objective	 ?search.	 ?We	 ?did	 ?not	 ?pursue	 ?citations	 ?found	 ?in	 ?papers	 ?included	 ?in	 ?our	 ?study	 ?that	 ?described	 ?other	 ?cases	 ?of	 ?parallel	 ?or	 ?convergent	 ?evolution	 ?not	 ?detected	 ?in	 ?our	 ?primary	 ?Web	 ?of	 ?Knowledge	 ?search.	 ?We	 ?felt	 ?that	 ?including	 ?them	 ?might	 ?produce	 ?a	 ?citation	 ?bias	 ?that	 ?would	 ?inflate	 ?the	 ?apparent	 ?probability	 ?of	 ?gene	 ?reuse.	 ?Adhering	 ?to	 ?this	 ?objective	 ?criterion	 ?forced	 ?us	 ?to	 ?leave	 ?out	 ?some	 ?well-??known	 ?studies	 ?of	 ?the	 ?genetics	 ?of	 ?phenotypic	 ?evolution.	 ?For	 ?example,	 ?our	 ?primary	 ?search	 ?turned	 ?up	 ?three	 ?study	 ?systems	 ?in	 ?which	 ?repeated	 ?evolutionary	 ?loss	 ?of	 ?pigmentation	 ?involved	 ?the	 ?gene,	 ?Mc1r:	 ?beach	 ?mice	 ?(Steiner	 ?et	 ?al.	 ?2009),	 ?White	 ?Sands	 ?lizards	 ?(Rosenblum	 ?et	 ?al.	 ?2010)	 ?and	 ?Mexican	 ?cavefish	 ?(Gross	 ?et	 ?al.	 ?2009).	 ?However,	 ?the	 ?search	 ?did	 ?not	 ?turn	 ?up	 ?other	 ?known	 ?cases	 ?of	 ?pigmentation	 ?evolution	 ?involving	 ?Mc1r	 ?(Ritland	 ?et	 ?al.	 ?2001;	 ?Theron	 ?et	 ?al.	 ?2001;	 ?Eizirik	 ?et	 ?al.	 ?2003;	 ?Nachman	 ?et	 ?al.	 ?2003;	 ?Mundy	 ?et	 ?al.	 ?2004;	 ?R?mpler	 ?et	 ?al.	 ?2006).	 ?We	 ?stress	 ?that	 ?our	 ?aim	 ?was	 ?to	 ?estimate	 ?the	 ?probability	 ?of	 ?repeated	 ?genetic	 ?evolution,	 ?which	 ?demanded	 ?an	 ?impartial	 ?survey.	 ?We	 ?do	 ?not	 ?claim	 ?to	 ?have	 ?eliminated	 ?all	 ?sources	 ?of	 ?bias,	 ?especially	 ?publication	 ?bias	 ?and	 ?the	 ?difficulty	 ?of	 ?detecting	 ?and	 ?identifying	 ?genes	 ?of	 ?small	 ?effect.	 ?	 ?	 ?With	 ?the	 ?help	 ?of	 ?TimeTree	 ?(Hedges	 ?et	 ?al.	 ?2006)	 ?we	 ?obtained	 ?phylogenies	 ?and	 ?node	 ?age	 ?estimates	 ?for	 ?all	 ?relevant	 ?taxa,	 ?including	 ?those	 ?from	 ?different	 ?study	 ?systems	 ?that	 ?had	 ?independently	 ?evolved	 ?a	 ?similar	 ?phenotype	 ?(Table	 ?B.1).	 ?This	 ?	 ? 34	 ?allowed	 ?us	 ?to	 ?compare	 ?probability	 ?of	 ?gene	 ?reuse	 ?with	 ?estimated	 ?node	 ?age	 ?of	 ?common	 ?ancestors.	 ?In	 ?cases	 ?of	 ?parallel	 ?phenotypic	 ?evolution	 ?(i.e.,	 ?independently	 ?derived	 ?forms	 ?are	 ?crossed,	 ?or	 ?compared,	 ?to	 ?the	 ?same,	 ?recent	 ?ancestral	 ?form),	 ?node	 ?ages	 ?are	 ?also	 ?approximate	 ?times	 ?of	 ?onset	 ?of	 ?phenotypic	 ?divergence.	 ?In	 ?most	 ?cases	 ?of	 ?convergent	 ?evolution	 ?(i.e.,	 ?different	 ?derived	 ?forms	 ?are	 ?compared	 ?to	 ?different	 ?ancestral	 ?forms),	 ?the	 ?onset	 ?of	 ?phenotypic	 ?divergence	 ?occurred	 ?within	 ?each	 ?lineage	 ?long	 ?after	 ?the	 ?divergence	 ?of	 ?the	 ?lineages	 ?themselves.	 ?Hence	 ?good	 ?estimates	 ?of	 ?the	 ?timing	 ?of	 ?phenotypic	 ?shifts	 ?(e.g.,	 ?Lavou?	 ?et	 ?al.	 ?2012)	 ?were	 ?often	 ?difficult	 ?to	 ?obtain,	 ?and	 ?we	 ?are	 ?unable	 ?to	 ?analyze	 ?the	 ?additional	 ?effects	 ?of	 ?trait	 ?origin	 ?times	 ?on	 ?the	 ?probability	 ?of	 ?gene	 ?reuse.	 ?Calculating	 ?the	 ?probability	 ?of	 ?repeated	 ?gene	 ?use	 ?We	 ?analyzed	 ?data	 ?from	 ?genetic	 ?crosses	 ?and	 ?candidate	 ?gene	 ?studies	 ?separately.	 ?Studies	 ?that	 ?employed	 ?genetic	 ?cross	 ?methods	 ?provided	 ?effect	 ?sizes	 ?(percent	 ?variance	 ?explained	 ?or	 ?magnitude	 ?of	 ?effect)	 ?that	 ?we	 ?used	 ?to	 ?compute	 ?the	 ?relative	 ?contribution	 ?of	 ?identified	 ?genes	 ?or	 ?QTL	 ?(if	 ?causal	 ?genes	 ?had	 ?not	 ?yet	 ?been	 ?identified)	 ?to	 ?the	 ?evolved	 ?phenotypic	 ?change	 ?in	 ?a	 ?particular	 ?cross.	 ?These	 ?effects	 ?were	 ?rescaled	 ?so	 ?that	 ?the	 ?contributions	 ?represented	 ?proportions	 ?and	 ?summed	 ?to	 ?1.0	 ?(see	 ?bar	 ?graphs	 ?at	 ?the	 ?tips	 ?of	 ?the	 ?hypothetical	 ?phylogenetic	 ?tree	 ?in	 ?Figure	 ?3.1).	 ?In	 ?a	 ?single	 ?case,	 ?effect	 ?sizes	 ?for	 ?two	 ?out	 ?of	 ?five	 ?mapped	 ?genes	 ?were	 ?not	 ?available,	 ?so	 ?the	 ?unexplained	 ?variance	 ?was	 ?split	 ?evenly	 ?between	 ?the	 ?two.	 ?Genes	 ?that	 ?were	 ?confirmed	 ?to	 ?have	 ?a	 ?major	 ?effect,	 ?either	 ?by	 ?complementation	 ?tests	 ?of	 ?shared	 ?use	 ?of	 ?a	 ?gene	 ?of	 ?major	 ?effect	 ?or	 ?localized	 ?mapping	 ?of	 ?a	 ?candidate	 ?gene	 ?in	 ?a	 ?cross	 ?in	 ?which	 ?the	 ?trait	 ?showed	 ?simple	 ?Mendelian	 ?segregation,	 ?were	 ?assigned	 ?an	 ?effect	 ?size	 ?of	 ?1.	 ?	 ?Probability	 ?of	 ?gene	 ?reuse	 ?between	 ?a	 ?pair	 ?of	 ?taxa	 ?was	 ?quantified	 ?using	 ?proportional	 ?similarity	 ?(Whittaker	 ?1952),	 ?calculated	 ?as	 ?PS = min(???, ???)? ,	 ?where	 ?pi1	 ?and	 ?pi2	 ?are	 ?the	 ?proportional	 ?contributions	 ?of	 ?gene	 ?i	 ?in	 ?the	 ?two	 ?taxa	 ?(Figure	 ?3.1).	 ?This	 ?quantity	 ?treats	 ?the	 ?distribution	 ?of	 ?contributions	 ?by	 ?genes	 ?in	 ?each	 ?of	 ?the	 ?two	 ?taxa	 ?as	 ?a	 ?frequency	 ?distribution	 ?and	 ?measures	 ?their	 ?intersection.	 ?When	 ?causative	 ?genes	 ?within	 ?QTL	 ?were	 ?not	 ?known,	 ?co-??localizing	 ?QTL	 ?were	 ?considered	 ?to	 ?represent	 ?	 ? 35	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ??gene?.	 ?It	 ?is	 ?possible	 ?that	 ?different	 ?genes	 ?within	 ?co-??localizing	 ?QTL	 ?are	 ?responsible	 ?in	 ?different	 ?cases	 ?of	 ?repeated	 ?phenotypic	 ?evolution.	 ?However,	 ?at	 ?this	 ?point,	 ?QTL	 ?represent	 ?the	 ?best	 ?available	 ?information	 ?for	 ?many	 ?taxa.	 ?Future	 ?studies	 ?will	 ?be	 ?better	 ?able	 ?to	 ?estimate	 ?the	 ?prevalence	 ?of	 ?different	 ?but	 ?tightly	 ?linked	 ?genes	 ?underlying	 ?repeated	 ?phenotypic	 ?evolution.	 ?	 ?When	 ?data	 ?were	 ?available	 ?on	 ?multiple	 ?derived	 ?populations	 ?for	 ?a	 ?given	 ?named	 ?species	 ?that	 ?independently	 ?evolved	 ?the	 ?same	 ?phenotype	 ?(e.g.,	 ?two	 ?populations	 ?above	 ?the	 ?node	 ?for	 ?species	 ?A	 ?in	 ?Figure	 ?3.1),	 ?PS	 ?was	 ?calculated	 ?between	 ?all	 ?population	 ?pairs	 ?and	 ?then	 ?averaged,	 ?yielding	 ?a	 ?single	 ?PS	 ?estimate	 ?for	 ?the	 ?species.	 ?The	 ?species	 ?value	 ?for	 ?the	 ?relative	 ?contributions	 ?of	 ?different	 ?genes	 ?was	 ?then	 ?calculated	 ?by	 ?averaging	 ?the	 ?relative	 ?contributions	 ?of	 ?its	 ?multiple	 ?populations	 ?(see	 ?bar	 ?graph	 ?below	 ?node	 ?A	 ?in	 ?Figure	 ?3.1).	 ?Our	 ?justification	 ?for	 ?using	 ?just	 ?one	 ?data	 ?point	 ?per	 ?species	 ?for	 ?a	 ?given	 ?trait	 ?was	 ?that	 ?frequency	 ?of	 ?gene	 ?use	 ?of	 ?derived	 ?populations	 ?within	 ?a	 ?species	 ?is	 ?expected	 ?to	 ?be	 ?greater	 ?than	 ?that	 ?between	 ?populations	 ?of	 ?different	 ?species,	 ?even	 ?after	 ?accounting	 ?for	 ?age	 ?differences.	 ?This	 ?is	 ?because	 ?populations	 ?within	 ?a	 ?species	 ?are	 ?typically	 ?crossed	 ?to	 ?the	 ?same	 ?ancestral	 ?form	 ?and	 ?so	 ?are	 ?not	 ?independent.	 ?They	 ?are	 ?also	 ?more	 ?likely	 ?to	 ?share	 ?standing	 ?genetic	 ?variation.	 ?This	 ?decision	 ?is	 ?conservative,	 ?because	 ?treating	 ?separate	 ?populations	 ?within	 ?species	 ?as	 ?independent	 ?replicates	 ?in	 ?the	 ?overall	 ?analysis	 ?raises	 ?the	 ?probability	 ?of	 ?repeated	 ?gene	 ?reuse.	 ?Proportional	 ?similarity	 ?was	 ?then	 ?calculated	 ?separately	 ?between	 ?each	 ?sister	 ?pair	 ?in	 ?the	 ?phylogeny	 ?(e.g.,	 ?PS	 ?is	 ?calculated	 ?between	 ?taxa	 ?A	 ?and	 ?B	 ?in	 ?Figure	 ?3.1).	 ?After	 ?calculating	 ?PS	 ?between	 ?two	 ?sister	 ?taxa	 ?at	 ?a	 ?given	 ?node,	 ?the	 ?relative	 ?contributions	 ?of	 ?genes	 ?in	 ?the	 ?two	 ?taxa	 ?were	 ?averaged	 ?(see	 ?bar	 ?graph	 ?below	 ?the	 ?node	 ?connecting	 ?A	 ?and	 ?B	 ?in	 ?Figure	 ?3.1).	 ?This	 ?average	 ?was	 ?then	 ?used	 ?to	 ?calculate	 ?proportional	 ?similarity	 ?between	 ?sister	 ?taxa	 ?at	 ?the	 ?next	 ?node	 ?down	 ?the	 ?tree	 ?(e.g.,	 ?between	 ?C	 ?and	 ?the	 ?node	 ?connecting	 ?taxa	 ?A	 ?and	 ?B	 ?in	 ?Figure	 ?3.1).	 ?	 ?	 ? 	 ?	 ? 36	 ?Figure	 ?3.1	 ?Calculating	 ?proportional	 ?similarity	 ?Hypothetical	 ?example	 ?to	 ?illustrate	 ?calculation	 ?of	 ?proportional	 ?similarity	 ?(PS)	 ?to	 ?measure	 ?probability	 ?of	 ?gene	 ?reuse	 ?between	 ?sister	 ?taxa.	 ?A,	 ?B,	 ?and	 ?C	 ?represent	 ?species	 ?having	 ?one	 ?or	 ?more	 ?populations	 ?that	 ?independently	 ?evolved	 ?a	 ?similar	 ?change	 ?in	 ?phenotype	 ?(open	 ?circles)	 ?compared	 ?with	 ?an	 ?ancestral	 ?phenotype	 ?(filled	 ?circles).	 ?Bar	 ?graph	 ?above	 ?each	 ?derived	 ?population	 ?indicates	 ?the	 ?relative	 ?contributions	 ?of	 ?each	 ?gene	 ?i	 ?to	 ?the	 ?phenotype	 ?(here,	 ?i	 ?is	 ?1,	 ?2,	 ?or	 ?3).	 ?PS	 ?is	 ?calculated	 ?between	 ?a	 ?pair	 ?of	 ?taxa	 ?as	 ?	 ?PS = min(???, ???)? ,	 ?where	 ?pi1	 ?and	 ?pi2	 ?are	 ?the	 ?proportional	 ?contributions	 ?of	 ?gene	 ?i	 ?in	 ?the	 ?two	 ?taxa.	 ?Within	 ?a	 ?species,	 ?proportional	 ?similarity	 ?is	 ?measured	 ?between	 ?all	 ?pairs	 ?of	 ?derived	 ?populations	 ?and	 ?averaged.	 ?Relative	 ?contributions	 ?of	 ?genes	 ?are	 ?then	 ?averaged	 ?among	 ?populations	 ?(illustrated	 ?for	 ?species	 ?A	 ?by	 ?the	 ?bar	 ?graph	 ?immediately	 ?below	 ?node	 ?A).	 ?PSA-??B	 ?compares	 ?the	 ?relative	 ?contributions	 ?of	 ?the	 ?three	 ?genes	 ?in	 ?species	 ?B	 ?with	 ?the	 ?average	 ?for	 ?species	 ?A	 ?(PS =  ?0.6+ 0+ 0 = 0.6).	 ?PSAB-??C	 ?compares	 ?the	 ?relative	 ?contributions	 ?of	 ?the	 ?three	 ?genes	 ?in	 ?species	 ?C	 ?with	 ?the	 ?average	 ?of	 ?A	 ?and	 ?B,	 ?shown	 ?in	 ?the	 ?bar	 ?graph	 ?below	 ?the	 ?node	 ?connecting	 ?A	 ?and	 ?B	 ?(PS =  ?0+ 0.2+ 0 =0.2).	 ?	 ?In	 ?our	 ?analysis	 ?of	 ?candidate	 ?gene	 ?studies,	 ?a	 ?given	 ?population	 ?or	 ?species	 ?received	 ?a	 ?score	 ?of	 ?1	 ?if	 ?use	 ?of	 ?the	 ?candidate	 ?gene	 ?was	 ?confirmed	 ?and	 ?a	 ?0	 ?if	 ?the	 ?assay	 ?used	 ?produced	 ?no	 ?evidence	 ?that	 ?the	 ?gene	 ?contributed	 ?to	 ?the	 ?trait.	 ?We	 ?recognize	 ?that	 ?	 ? 37	 ?assays	 ?were	 ?not	 ?always	 ?exhaustive	 ?and	 ?often	 ?could	 ?not	 ?completely	 ?rule	 ?out	 ?an	 ?effect	 ?of	 ?the	 ?gene	 ?on	 ?the	 ?trait.	 ?Proportional	 ?similarity	 ?between	 ?two	 ?taxa	 ?was	 ?calculated	 ?as	 ?PS = min(??, ??),	 ?where	 ?p1	 ?and	 ?p2	 ?are	 ?the	 ?proportional	 ?uses	 ?of	 ?the	 ?candidate	 ?gene	 ?in	 ?the	 ?two	 ?taxa.	 ?When	 ?data	 ?on	 ?multiple	 ?derived	 ?populations	 ?were	 ?available	 ?for	 ?a	 ?given	 ?named	 ?species,	 ?the	 ?species	 ?value	 ?for	 ?candidate	 ?gene	 ?reuse	 ?was	 ?calculated	 ?by	 ?averaging	 ?the	 ?values	 ?(0?s	 ?and	 ?1?s)	 ?across	 ?the	 ?populations.	 ?Proportional	 ?similarity	 ?was	 ?then	 ?calculated	 ?separately	 ?between	 ?each	 ?sister	 ?pair	 ?in	 ?the	 ?phylogeny.	 ?After	 ?calculating	 ?PS	 ?between	 ?two	 ?sister	 ?taxa	 ?at	 ?a	 ?given	 ?node,	 ?the	 ?proportional	 ?use	 ?values	 ?for	 ?candidate	 ?genes	 ?was	 ?averaged	 ?between	 ?the	 ?two	 ?taxa.	 ?This	 ?average	 ?value	 ?was	 ?then	 ?used	 ?to	 ?calculate	 ?proportional	 ?similarity	 ?between	 ?sister	 ?taxa	 ?at	 ?the	 ?next	 ?node	 ?down	 ?the	 ?tree.	 ?When	 ?more	 ?than	 ?one	 ?informative	 ?candidate	 ?gene	 ?was	 ?available	 ?at	 ?any	 ?given	 ?node,	 ?the	 ?above	 ?process	 ?was	 ?repeated	 ?for	 ?each	 ?gene	 ?and	 ?their	 ?PS	 ?values	 ?were	 ?averaged.	 ?Comparisons	 ?between	 ?two	 ?sister	 ?taxa,	 ?one	 ?of	 ?whose	 ?data	 ?was	 ?obtained	 ?using	 ?the	 ?candidate	 ?gene	 ?method,	 ?and	 ?the	 ?other	 ?of	 ?whose	 ?data	 ?came	 ?from	 ?genetic	 ?crosses,	 ?were	 ?included	 ?in	 ?the	 ?analysis	 ?of	 ?candidate	 ?genes.	 ?In	 ?such	 ?cases	 ?only,	 ?we	 ?treated	 ?the	 ?mapping	 ?data	 ?as	 ?though	 ?a	 ?candidate	 ?gene	 ?approach	 ?had	 ?been	 ?applied,	 ?assigning	 ?a	 ?score	 ?of	 ?0	 ?or	 ?1	 ?for	 ?candidate	 ?gene	 ?use.	 ?In	 ?some	 ?cases	 ?the	 ?same	 ?node	 ?is	 ?used	 ?in	 ?both	 ?mapping	 ?and	 ?candidate	 ?gene	 ?analyses.	 ?However,	 ?the	 ?populations	 ?being	 ?compared	 ?in	 ?such	 ?cases	 ?are	 ?always	 ?mutually	 ?exclusive.	 ?We	 ?repeated	 ?all	 ?analyses	 ?using	 ?a	 ?second,	 ?multiplicative	 ?measure	 ?of	 ?overlap	 ?of	 ?gene	 ?contributions	 ?between	 ?taxa,	 ?O	 ?=	 ? (??? ? ???? ) (???)? (???)?,	 ?where	 ?pi1	 ?and	 ?pi2	 ?are	 ?the	 ?proportional	 ?contributions	 ?of	 ?gene	 ?i	 ?in	 ?the	 ?two	 ?taxa	 ?(Pianka	 ?1973).	 ?O	 ?represents	 ?the	 ?probability	 ?that	 ?a	 ?random	 ?draw	 ?from	 ?the	 ?proportional	 ?use	 ?distributions	 ?of	 ?each	 ?species	 ?results	 ?in	 ?the	 ?same	 ?gene,	 ?scaled	 ?so	 ?that	 ?the	 ?measurement	 ?is	 ?insensitive	 ?to	 ?the	 ?number	 ?of	 ?genes	 ?in	 ?the	 ?distribution.	 ?Applying	 ?this	 ?measure	 ?led	 ?to	 ?virtually	 ?identical	 ?results	 ?as	 ?proportional	 ?similarity,	 ?and	 ?so	 ?we	 ?present	 ?only	 ?proportional	 ?similarity.	 ?	 ?Throughout,	 ?standard	 ?errors	 ?and	 ?P-??values	 ?should	 ?be	 ?regarded	 ?as	 ?heuristic	 ?because	 ?of	 ?uncertainty	 ?about	 ?the	 ?degree	 ?of	 ?independence	 ?of	 ?observations	 ?in	 ?the	 ?meta-??analysis.	 ?	 ? 38	 ?Results	 ?Results	 ?are	 ?plotted	 ?separately	 ?for	 ?measurements	 ?based	 ?on	 ?genetic	 ?cross	 ?methods	 ?(Figure	 ?3.2a)	 ?and	 ?candidate	 ?gene	 ?methods	 ?(Figure	 ?3.2b).	 ?Each	 ?point	 ?represents	 ?the	 ?mean	 ?of	 ?proportional	 ?similarity	 ?(PS)	 ?between	 ?pairs	 ?of	 ?populations	 ?of	 ?a	 ?single	 ?species,	 ?if	 ?multiple	 ?populations	 ?were	 ?available	 ?(parallel	 ?evolution	 ?-??	 ?open	 ?symbols),	 ?or	 ?between	 ?sister	 ?species	 ?or	 ?other	 ?sister	 ?taxa	 ?at	 ?deeper	 ?nodes	 ?of	 ?the	 ?phylogenetic	 ?trees	 ?(convergent	 ?evolution	 ?-??	 ?filled	 ?symbols).	 ?Approximate	 ?ages	 ?of	 ?nodes	 ?are	 ?given	 ?along	 ?the	 ?horizontal	 ?axis.	 ?In	 ?the	 ?case	 ?of	 ?species	 ?values	 ?(open	 ?symbols),	 ?node	 ?age	 ?represents	 ?the	 ?approximate	 ?time	 ?at	 ?which	 ?repeated	 ?trait	 ?divergence	 ?began	 ?between	 ?ancestral	 ?and	 ?derived	 ?populations.	 ?Ages	 ?of	 ?sister	 ?species	 ?and	 ?more	 ?distantly	 ?related	 ?sister	 ?taxa	 ?(filled	 ?circles)	 ?only	 ?indicate	 ?the	 ?age	 ?of	 ?their	 ?common	 ?ancestor,	 ?since	 ?repeated	 ?phenotypic	 ?evolution	 ?typically	 ?occurred	 ?long	 ?afterward	 ?(cf.	 ?Figure	 ?3.1).	 ?	 ?	 ?	 ? 	 ?	 ? 39	 ?Figure	 ?3.2	 ?Probability	 ?of	 ?gene	 ?reuse	 ?by	 ?node	 ?age	 ?Measurements	 ?of	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?based	 ?on	 ?(a)	 ?data	 ?from	 ?genetic	 ?crosses	 ?and	 ?(b)	 ?candidate	 ?gene	 ?data.	 ?Open	 ?symbols	 ?represent	 ?average	 ?of	 ?proportional	 ?similarity	 ?between	 ?all	 ?pairs	 ?of	 ?derived	 ?populations	 ?within	 ?the	 ?same	 ?species	 ?(i.e.,	 ?parallel	 ?evolution).	 ?Filled	 ?symbols	 ?represent	 ?similarity	 ?measurements	 ?between	 ?sister	 ?taxa	 ?at	 ?deeper	 ?nodes	 ?in	 ?the	 ?phylogenetic	 ?trees	 ?(i.e.,	 ?convergent	 ?evolution).	 ?Curves	 ?are	 ?best-??fit	 ?logistic	 ?regressions	 ?to	 ?the	 ?data.	 ?	 ?On	 ?the	 ?basis	 ?of	 ?data	 ?from	 ?genetic	 ?crosses,	 ?estimated	 ?similarity	 ?of	 ?gene	 ?usage	 ?between	 ?taxa	 ?undergoing	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?a	 ?trait	 ?is	 ?0.32	 ??	 ?0.10	 ?SE	 ?on	 ?average.	 ?The	 ?probability	 ?of	 ?gene	 ?reuse	 ?based	 ?on	 ?candidate	 ?gene	 ?data	 ?is	 ?0.55	 ??	 ?0.08	 ?SE.	 ?	 ? 40	 ?The	 ?results	 ?showed	 ?the	 ?predicted	 ?tendency	 ?for	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?between	 ?sister	 ?taxa	 ?to	 ?decline	 ?with	 ?the	 ?age	 ?of	 ?the	 ?node	 ?of	 ?their	 ?common	 ?ancestor	 ?(Figure	 ?3.2).	 ?Also,	 ?across	 ?the	 ?span	 ?of	 ?ages	 ?represented,	 ?estimated	 ?probability	 ?of	 ?gene	 ?reuse	 ?tended	 ?to	 ?be	 ?higher	 ?in	 ?candidate	 ?gene	 ?data	 ?than	 ?in	 ?data	 ?from	 ?genetic	 ?crosses.	 ?The	 ?predicted	 ?probability	 ?of	 ?gene	 ?reuse	 ?was	 ?high	 ?(around	 ?0.8)	 ?in	 ?both	 ?data	 ?sets	 ?at	 ?the	 ?youngest	 ?nodes.	 ?This	 ?probability	 ?declined	 ?to	 ?about	 ?0.10	 ?by	 ?about	 ?108	 ?y	 ?for	 ?mapping	 ?data,	 ?but	 ?remained	 ?higher	 ?(about	 ?0.40)	 ?at	 ?the	 ?same	 ?node	 ?age	 ?for	 ?candidate	 ?gene	 ?data	 ?(Figure	 ?3.2).	 ?	 ?To	 ?test	 ?these	 ?trends	 ?we	 ?used	 ?logistic	 ?regression	 ?to	 ?model	 ?the	 ?relationship	 ?between	 ?proportional	 ?similarity,	 ?node	 ?age,	 ?and	 ?genetic	 ?method	 ?(genetic	 ?cross	 ?vs.	 ?candidate	 ?gene).	 ?Results	 ?indicate	 ?that	 ?the	 ?decline	 ?in	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?with	 ?node	 ?age	 ?is	 ?real	 ?(???=	 ?3.04,	 ?P	 ?=	 ?0.04,	 ?one-??tailed	 ?test).	 ?The	 ?effect	 ?of	 ?genetic	 ?method	 ?was	 ?not	 ?statistically	 ?significant	 ?in	 ?a	 ?two-??tailed	 ?test	 ?(???=	 ?2.64,	 ?P	 ?=	 ?0.10),	 ?and	 ?so	 ?these	 ?data	 ?do	 ?not	 ?fully	 ?resolve	 ?the	 ?difference	 ?between	 ?mapping	 ?and	 ?candidate	 ?gene	 ?data.	 ?These	 ?tests	 ?are	 ?conservative	 ?because	 ?many	 ?values	 ?of	 ?PS	 ?lie	 ?between	 ?0	 ?and	 ?1	 ?(Figure	 ?3.2),	 ?and	 ?so	 ?have	 ?lower	 ?residual	 ?variance	 ?than	 ?assumed	 ?by	 ?logistic	 ?regression.	 ?Reanalysis	 ?using	 ?quasi-??binomial	 ?errors	 ?(McCullagh	 ?and	 ?Nelder	 ?1989)	 ?slightly	 ?strengthen	 ?the	 ?above	 ?findings.	 ?Finally,	 ?a	 ?randomization	 ?test	 ?confirmed	 ?the	 ?overall	 ?correlation	 ?between	 ?PS	 ?and	 ?node	 ?age	 ?(r	 ?=	 ??0.25,	 ?P	 ?=	 ?0.04,	 ?one-??tailed	 ?test).	 ?	 ?Points	 ?obtained	 ?from	 ?comparing	 ?multiple	 ?populations	 ?within	 ?a	 ?species	 ?to	 ?the	 ?same	 ?ancestral	 ?form	 ?(open	 ?symbols	 ?in	 ?Figure	 ?3.2,	 ?representing	 ?parallel	 ?phenotypic	 ?evolution)	 ?are	 ?younger	 ?than	 ?points	 ?obtained	 ?by	 ?comparing	 ?species	 ?and	 ?higher	 ?taxa	 ?to	 ?different	 ?ancestors	 ?(filled	 ?symbols,	 ?representing	 ?cases	 ?of	 ?convergent	 ?phenotypic	 ?evolution).	 ?The	 ?decline	 ?in	 ?proportional	 ?similarity	 ?with	 ?older	 ?nodes	 ?thus	 ?implies	 ?that	 ?the	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?underlying	 ?genes	 ?is	 ?indeed	 ?lower	 ?for	 ?convergent	 ?evolution	 ?than	 ?parallel	 ?evolution,	 ?as	 ?we	 ?defined	 ?these	 ?terms.	 ?Specifically,	 ?when	 ?estimated	 ?using	 ?data	 ?from	 ?genetic	 ?crosses,	 ?the	 ?probability	 ?of	 ?reuse	 ?of	 ?the	 ?same	 ?genes	 ?is	 ?0.47	 ??	 ?0.15	 ?SE	 ?on	 ?average	 ?in	 ?taxa	 ?undergoing	 ?parallel	 ?phenotypic	 ?evolution	 ?and	 ?0.24	 ??	 ?0.12	 ?SE	 ?on	 ?average	 ?in	 ?taxa	 ?undergoing	 ?convergent	 ?phenotypic	 ?evolution.	 ?Likewise,	 ?using	 ?candidate	 ?gene	 ?data,	 ?the	 ?probabilities	 ?of	 ?reuse	 ?of	 ?the	 ?same	 ?genes	 ?are	 ?	 ? 41	 ?0.67	 ??	 ?0.17	 ?SE	 ?and	 ?0.51	 ??	 ?0.09	 ?SE	 ?on	 ?average	 ?for	 ?parallel	 ?and	 ?convergent	 ?phenotypic	 ?evolution,	 ?respectively.	 ?	 ?Discussion	 ?Our	 ?results	 ?based	 ?on	 ?data	 ?from	 ?genetic	 ?crosses	 ?indicate	 ?that	 ?when	 ?similar	 ?traits	 ?evolve	 ?independently	 ?in	 ?different	 ?lineages,	 ?the	 ?probability	 ?that	 ?the	 ?same	 ?genes	 ?are	 ?used	 ?is	 ?estimated	 ?to	 ?be	 ?0.32,	 ?on	 ?average.	 ?The	 ?probability	 ?estimated	 ?from	 ?candidate	 ?gene	 ?studies	 ?is	 ?0.55,	 ?on	 ?average.	 ?One	 ?explanation	 ?for	 ?such	 ?a	 ?high	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?is	 ?that	 ?at	 ?the	 ?time	 ?of	 ?adaptation,	 ?effect	 ?sizes	 ?and	 ?availability	 ?of	 ?beneficial	 ?mutations	 ?were	 ?strongly	 ?biased	 ?toward	 ?a	 ?small	 ?number	 ?of	 ?genes.	 ?This	 ?result	 ?can	 ?be	 ?summarized	 ?by	 ?the	 ??effective?	 ?number	 ?of	 ?genes,	 ?equivalent	 ?to	 ?the	 ?number	 ?of	 ?loci	 ?available	 ?if	 ?all	 ?have	 ?effects	 ?of	 ?equal	 ?magnitude	 ?and	 ?the	 ?same	 ?probability	 ?of	 ?fixation.	 ?For	 ?example,	 ?imagine	 ?that	 ?in	 ?two	 ?populations	 ?there	 ?are	 ?n	 ?equivalent	 ?genes	 ?underlying	 ?a	 ?trait	 ?under	 ?selection	 ?in	 ?which	 ?new	 ?advantageous	 ?mutations	 ?might	 ?occur	 ?and	 ?fix.	 ?If	 ?a	 ?major	 ?effect	 ?mutation	 ?occurs	 ?and	 ?fixes	 ?in	 ?one	 ?gene	 ?in	 ?one	 ?of	 ?the	 ?populations,	 ?the	 ?probability	 ?is	 ?1/n	 ?that	 ?a	 ?second	 ?population	 ?experiencing	 ?similar	 ?selection	 ?fixes	 ?a	 ?mutation	 ?in	 ?the	 ?same	 ?gene.	 ?	 ?In	 ?this	 ?case,	 ?a	 ?probability	 ?of	 ?gene	 ?reuse	 ?of	 ?0.32	 ?corresponds	 ?to	 ?an	 ?n	 ?of	 ?1/0.32	 ?=	 ?3.1	 ?effective	 ?genes.	 ?A	 ?probability	 ?of	 ?gene	 ?reuse	 ?of	 ?0.55	 ?corresponds	 ?to	 ?an	 ?n	 ?of	 ?1/0.55	 ?=	 ?1.8	 ?effective	 ?genes.	 ?This	 ?rough	 ?calculation	 ?is	 ?simplistic,	 ?because	 ?real	 ?genes	 ?do	 ?not	 ?have	 ?equivalent	 ?effects.	 ?In	 ?addition,	 ?it	 ?does	 ?not	 ?indicate	 ?the	 ?cumulative	 ?number	 ?of	 ?genes	 ?that	 ?might	 ?contribute	 ?if	 ?parallel	 ?or	 ?convergent	 ?evolution	 ?is	 ?repeated	 ?many	 ?times.	 ?Nevertheless,	 ?the	 ?high	 ?probabilities	 ?of	 ?gene	 ?reuse	 ?estimated	 ?from	 ?published	 ?data	 ?indicate	 ?that	 ?the	 ?effective	 ?number	 ?of	 ?genes	 ?used	 ?in	 ?parallel	 ?and	 ?convergent	 ?phenotypic	 ?adaptation	 ?is	 ?typically	 ?small.	 ?If	 ?the	 ?causes	 ?of	 ?this	 ?low	 ?number	 ?can	 ?be	 ?elucidated,	 ?then	 ?genetic	 ?evolution	 ?may	 ?indeed	 ?be	 ?somewhat	 ?predictable	 ?(Gompel	 ?and	 ?Prud?homme	 ?2009;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Streisfeld	 ?and	 ?Rausher	 ?2011).	 ?It	 ?is	 ?difficult	 ?to	 ?judge	 ?how	 ?surprising	 ?these	 ?estimates	 ?of	 ?effective	 ?number	 ?of	 ?genes	 ?are	 ?without	 ?knowing	 ?the	 ?total	 ?number	 ?of	 ?genes	 ?available	 ?in	 ?which	 ?mutations	 ?would	 ?cause	 ?similar	 ?phenotypic	 ?changes.	 ?	 ?Some	 ?data	 ?are	 ?available	 ?to	 ?assess	 ?this.	 ?Of	 ?6	 ?	 ? 42	 ?genes	 ?of	 ?the	 ?Eda	 ?signaling	 ?pathway,	 ?mutations	 ?in	 ?most	 ?of	 ?which	 ?produce	 ?a	 ?similar	 ?phenotype	 ?in	 ?mammals,	 ?only	 ?two	 ?have	 ?been	 ?found	 ?to	 ?be	 ?associated	 ?with	 ?lateral	 ?plate	 ?variation	 ?in	 ?threespine	 ?stickleback:	 ?Eda	 ?and	 ?the	 ?receptor	 ?Edar	 ?	 ?(Knecht	 ?et	 ?al.	 ?2007).	 ?	 ?Similarly,	 ?Streisfeld	 ?and	 ?Rausher	 ?(2011)	 ?noted	 ?that	 ?changes	 ?to	 ?any	 ?of	 ?the	 ?nine	 ?enzymes	 ?of	 ?the	 ?anthocyanin	 ?biosynthetic	 ?pathway	 ?would	 ?alter	 ?pathway	 ?flux	 ?and	 ?produce	 ?a	 ?change	 ?in	 ?intensity	 ?of	 ?flower	 ?pigmentation.	 ?In	 ?accord,	 ?37	 ?spontaneous	 ?mutations	 ?affecting	 ?floral	 ?pigment	 ?intensity	 ?have	 ?been	 ?detected	 ?in	 ?5	 ?of	 ?these	 ?9	 ?genes,	 ?predominantly	 ?in	 ?coding	 ?regions	 ?(another	 ?32	 ?mutations	 ?affecting	 ?floral	 ?pigment	 ?intensity	 ?occurred	 ?in	 ?transcription	 ?factors).	 ?However,	 ?in	 ?all	 ?7	 ?cases	 ?in	 ?which	 ?evolved	 ?differences	 ?in	 ?pigment	 ?intensity	 ?were	 ?mapped,	 ?the	 ?fixed	 ?changes	 ?mapped	 ?to	 ?transcription	 ?factors	 ?that	 ?regulate	 ?the	 ?pathway	 ?genes	 ?rather	 ?than	 ?to	 ?the	 ?genes	 ?themselves,	 ?indicating	 ?a	 ?strong	 ?fixation	 ?bias	 ?away	 ?from	 ?coding	 ?mutations	 ?in	 ?pathway	 ?proteins	 ?(Streisfeld	 ?and	 ?Rausher	 ?2011).	 ?In	 ?5	 ?of	 ?these	 ?7	 ?cases,	 ?the	 ?changes	 ?occurred	 ?in	 ?a	 ?gene	 ?encoding	 ?an	 ?R2R3	 ?Myb	 ?transcription	 ?factor.	 ?Such	 ?findings	 ?suggest	 ?that	 ?the	 ?number	 ?of	 ?genes	 ?used	 ?and	 ?reused	 ?in	 ?adaptive	 ?evolution	 ?is	 ?a	 ?small	 ?subset	 ?of	 ?available	 ?genes.	 ?A	 ?host	 ?of	 ?factors	 ?may	 ?lead	 ?to	 ?much	 ?higher	 ?probabilities	 ?of	 ?certain	 ?genes	 ?being	 ?involved	 ?in	 ?phenotypic	 ?adaptation	 ?than	 ?others,	 ?including	 ?amounts	 ?of	 ?standing	 ?genetic	 ?variation,	 ?differences	 ?in	 ?mutation	 ?rates	 ?or	 ?mutation	 ?effect	 ?sizes,	 ?pleiotropic	 ?constraints,	 ?linkage	 ?relationships	 ?and	 ?epistatic	 ?interactions	 ?with	 ?the	 ?genetic	 ?background	 ?(Orr	 ?2005;	 ?Weinreich	 ?et	 ?al.	 ?2006;	 ?Gompel	 ?and	 ?Prud?homme	 ?2009;	 ?Stern	 ?and	 ?Orgogozo	 ?2009;	 ?Chevin	 ?et	 ?al.	 ?2010;	 ?Christin	 ?et	 ?al.	 ?2010;	 ?Streisfeld	 ?and	 ?Rausher	 ?2011;	 ?Feldman	 ?et	 ?al.	 ?2012).	 ?Any	 ?explanations	 ?for	 ?such	 ?a	 ?high	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?must	 ?also	 ?explain	 ?why	 ?this	 ?probability	 ?declines	 ?as	 ?more	 ?distantly	 ?related	 ?taxa	 ?are	 ?compared.	 ?First,	 ?the	 ?high	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?by	 ?young,	 ?closely	 ?related	 ?populations	 ?might	 ?result	 ?in	 ?part	 ?because	 ?they	 ?have	 ?access	 ?to	 ?the	 ?same	 ?pool	 ?of	 ?standing	 ?genetic	 ?variation	 ?(Colosimo	 ?et	 ?al.	 ?2005;	 ?Barrett	 ?and	 ?Schluter	 ?2008),	 ?an	 ?option	 ?not	 ?available	 ?to	 ?more	 ?distantly	 ?related	 ?taxa.	 ?Second,	 ?as	 ?lineages	 ?diverge,	 ?not	 ?only	 ?do	 ?the	 ?specific	 ?genes	 ?that	 ?affect	 ?the	 ?phenotypic	 ?trait	 ?diverge	 ?in	 ?sequence,	 ?but	 ?the	 ?genetic	 ?backgrounds	 ?with	 ?which	 ?they	 ?interact	 ?diverge	 ?as	 ?well.	 ?Hence,	 ?the	 ?biases	 ?that	 ?favor	 ?use	 ?of	 ?some	 ?genes	 ?over	 ?others	 ?during	 ?repeated	 ?	 ? 43	 ?phenotypic	 ?evolution	 ?themselves	 ?should	 ?evolve,	 ?in	 ?which	 ?case	 ?we	 ?would	 ?expect	 ?the	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?genes	 ?to	 ?decline	 ?with	 ?time	 ?and	 ?genetic	 ?divergence.	 ?The	 ?probability	 ?that	 ?changes	 ?to	 ?the	 ?same	 ?genes	 ?produce	 ?similar	 ?phenotypic	 ?changes	 ?is	 ?also	 ?likely	 ?to	 ?be	 ?reduced	 ?the	 ?more	 ?widely	 ?divergent	 ?the	 ?lineages	 ?(Zhang	 ?2003),	 ?unless	 ?gene	 ?functions	 ?are	 ?highly	 ?conserved.	 ?	 ?Repeated	 ?evolution	 ?can	 ?be	 ?divided	 ?into	 ?two	 ?types:	 ?parallel	 ?evolution,	 ?whereby	 ?evolution	 ?begins	 ?from	 ?the	 ?same	 ?starting	 ?point,	 ?and	 ?convergent	 ?evolution,	 ?whereby	 ?evolution	 ?begins	 ?at	 ?different	 ?starting	 ?points.	 ?Arendt	 ?and	 ?Reznick	 ?(2008)	 ?	 ?argued	 ?that	 ?from	 ?a	 ?genetic	 ?perspective	 ?there	 ?is	 ?no	 ?clear	 ?distinction	 ?between	 ?parallel	 ?and	 ?convergent	 ?evolution.	 ?We	 ?found	 ?that	 ?average	 ?proportional	 ?similarity	 ?of	 ?genes	 ?underlying	 ?parallel	 ?phenotypic	 ?evolution	 ?was	 ?greater	 ?than	 ?that	 ?underlying	 ?convergent	 ?evolution	 ?(Figure	 ?3.2).	 ?The	 ?reasons	 ?are	 ?likely	 ?similar	 ?to	 ?those	 ?described	 ?for	 ?the	 ?effect	 ?of	 ?node	 ?age,	 ?since	 ?points	 ?representing	 ?parallel	 ?evolution	 ?have	 ?younger	 ?node	 ?ages	 ?than	 ?points	 ?representing	 ?convergent	 ?evolution.	 ?If	 ?evolution	 ?is	 ?biased	 ?toward	 ?some	 ?genes	 ?over	 ?others,	 ?populations	 ?beginning	 ?from	 ?the	 ?same	 ?ancestral	 ?genome	 ?will	 ?more	 ?likely	 ?share	 ?these	 ?biases	 ?than	 ?populations	 ?beginning	 ?from	 ?divergent	 ?genomes.	 ?However,	 ?there	 ?is	 ?no	 ?sudden	 ?break	 ?in	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?between	 ?parallel	 ?and	 ?convergent	 ?evolution	 ?(Figure	 ?3.2).	 ?The	 ?distinction	 ?is	 ?one	 ?of	 ?degree	 ?rather	 ?than	 ?of	 ?kind.	 ?Our	 ?estimates	 ?based	 ?on	 ?candidate	 ?genes	 ?are	 ?higher	 ?than	 ?those	 ?based	 ?on	 ?genetic	 ?crosses.	 ?Although	 ?not	 ?statistically	 ?significant	 ?in	 ?our	 ?analysis,	 ?the	 ?difference	 ?suggests	 ?that	 ?the	 ?calculated	 ?probability	 ?of	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?gene	 ?depends	 ?on	 ?the	 ?methods	 ?used	 ?to	 ?detect	 ?it.	 ?If	 ?the	 ?difference	 ?is	 ?real,	 ?what	 ?are	 ?the	 ?possible	 ?reasons?	 ?Whereas	 ?genetic	 ?cross	 ?methods	 ?allow	 ?us	 ?to	 ?estimate	 ?the	 ?contributions	 ?of	 ?all	 ?genes	 ?(or	 ?at	 ?least	 ?all	 ?genes	 ?of	 ?moderate	 ?to	 ?major	 ?effect)	 ?to	 ?repeated	 ?phenotypic	 ?evolution,	 ?the	 ?candidate	 ?gene	 ?approach	 ?allows	 ?us	 ?to	 ?determine	 ?only	 ?whether	 ?a	 ?specific	 ?gene	 ?of	 ?interest	 ?makes	 ?a	 ?contribution	 ?in	 ?each	 ?case.	 ?This	 ?essentially	 ?lowers	 ?the	 ?bar	 ?for	 ?a	 ?positive	 ?outcome	 ?in	 ?the	 ?case	 ?of	 ?candidate	 ?genes,	 ?because	 ?the	 ?probability	 ?of	 ?reuse	 ?of	 ?a	 ?gene	 ?of	 ?interest	 ?between	 ?two	 ?taxa	 ?is	 ?likely	 ?to	 ?be	 ?higher	 ?than	 ?the	 ?proportional	 ?shared	 ?use	 ?of	 ?genes	 ?when	 ?all	 ?mapped	 ?genes	 ?are	 ?considered.	 ?Another	 ?reason	 ?is	 ?that	 ?the	 ?candidate	 ?gene	 ?method	 ?might	 ?be	 ?more	 ?strongly	 ?affected	 ?	 ? 44	 ?by	 ?publication	 ?bias	 ?than	 ?estimates	 ?based	 ?on	 ?genetic	 ?crosses.	 ?We	 ?suspect	 ?that	 ?studies	 ?that	 ?fail	 ?to	 ?confirm	 ?a	 ?role	 ?for	 ?a	 ?candidate	 ?gene	 ?are	 ?more	 ?likely	 ?to	 ?go	 ?unreported	 ?than	 ?results	 ?from	 ?mapping	 ?studies,	 ?which	 ?produce	 ?noteworthy	 ?findings	 ?if	 ?evidence	 ?for	 ?genes	 ?is	 ?found	 ?anywhere	 ?in	 ?the	 ?genome.	 ?On	 ?the	 ?other	 ?hand,	 ?estimates	 ?of	 ?proportional	 ?similarity	 ?that	 ?take	 ?magnitude	 ?of	 ?effect	 ?into	 ?account	 ?(i.e.,	 ?those	 ?based	 ?on	 ?genetic	 ?crosses)	 ?are	 ?prone	 ?to	 ?higher	 ?sampling	 ?error,	 ?which	 ?will	 ?tend	 ?to	 ?cause	 ?a	 ?downward	 ?bias	 ?in	 ?estimates	 ?for	 ?the	 ?probability	 ?of	 ?gene	 ?reuse.	 ?	 ?	 ?Caution	 ?is	 ?warranted	 ?when	 ?interpreting	 ?our	 ?results	 ?because	 ?of	 ?numerous	 ?judgments	 ?and	 ?uncertainties	 ?inherent	 ?to	 ?a	 ?meta-??analysis	 ?involving	 ?heterogeneous	 ?data	 ?collected	 ?from	 ?various	 ?organisms,	 ?traits,	 ?and	 ?genes.	 ?In	 ?some	 ?cases,	 ?we	 ?considered	 ?overlapping	 ?QTL	 ?to	 ?be	 ?reuse	 ?of	 ?the	 ?same	 ??gene?,	 ?though	 ?we	 ?may	 ?eventually	 ?learn	 ?that	 ?different	 ?genes	 ?within	 ?the	 ?QTL	 ?underlie	 ?repeated	 ?phenotypic	 ?evolution.	 ?While	 ?this	 ?and	 ?other	 ?factors	 ?already	 ?described,	 ?such	 ?as	 ?publication	 ?bias,	 ?may	 ?cause	 ?us	 ?to	 ?overestimate	 ?the	 ?probability	 ?of	 ?gene	 ?reuse,	 ?still	 ?other	 ?factors	 ?may	 ?cause	 ?an	 ?underestimation.	 ?For	 ?example,	 ?our	 ?definition	 ?of	 ?repeated	 ?genetic	 ?evolution	 ?treats	 ?paralogous	 ?genes	 ?as	 ?different	 ?(several	 ?examples	 ?were	 ?present	 ?in	 ?our	 ?dataset,	 ?including	 ?the	 ?paralogous	 ?genetic	 ?basis	 ?of	 ?convergent	 ?evolution	 ?of	 ?caerulein	 ?skin	 ?toxin	 ?in	 ?frogs	 ?(Roelants	 ?et	 ?al.	 ?2010),	 ?of	 ?digestion	 ?of	 ?foregut-??fermenting	 ?bacteria	 ?in	 ?leaf-??eating	 ?colobine	 ?monkeys	 ?and	 ?ruminant	 ?artiodactyls	 ?(Zhang	 ?2003,	 ?2006),	 ?and	 ?of	 ?red	 ?flower	 ?color	 ?in	 ?Mimulus	 ?spp.	 ?(Cooley	 ?and	 ?Willis	 ?2009;	 ?Cooley	 ?et	 ?al.	 ?2011)).	 ?In	 ?addition,	 ?multiple	 ?populations	 ?within	 ?a	 ?single	 ?named	 ?species	 ?were	 ?represented	 ?by	 ?only	 ?a	 ?single	 ?data	 ?point	 ?in	 ?our	 ?analysis	 ?(the	 ?average)	 ?to	 ?prevent	 ?rampant	 ?parallel	 ?genetic	 ?evolution	 ?within	 ?any	 ?one	 ?species	 ?from	 ?unduly	 ?affecting	 ?the	 ?results.	 ?Finally,	 ?the	 ?number	 ?of	 ?studies	 ?on	 ?which	 ?we	 ?have	 ?based	 ?our	 ?analyses	 ?is	 ?not	 ?large,	 ?which	 ?also	 ?adds	 ?uncertainty	 ?to	 ?these	 ?results.	 ?Despite	 ?these	 ?uncertainties,	 ?our	 ?aim	 ?here	 ?has	 ?been	 ?to	 ?stimulate	 ?thinking	 ?about	 ?these	 ?issues	 ?and	 ?to	 ?move	 ?towards	 ?a	 ?quantitative	 ?understanding	 ?of	 ?repeated	 ?genetic	 ?evolution,	 ?which	 ?we	 ?have	 ?attempted	 ?with	 ?the	 ?best	 ?available	 ?information.	 ?As	 ?we	 ?accumulate	 ?more	 ?studies	 ?of	 ?the	 ?genetics	 ?underlying	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?natural	 ?populations	 ?we	 ?will	 ?be	 ?better	 ?able	 ?to	 ?estimate	 ?the	 ?probability	 ?of	 ?the	 ?same	 ?genes	 ?being	 ?used.	 ?In	 ?turn,	 ?this	 ?will	 ?enhance	 ?our	 ?ability	 ?to	 ?ask	 ?	 ? 45	 ?what	 ?factors	 ?explain	 ?variability	 ?in	 ?genetic	 ?parallelism	 ?and	 ?convergence.	 ?For	 ?example,	 ?broader	 ?sampling	 ?may	 ?allow	 ?us	 ?to	 ?ask	 ?whether	 ?there	 ?is	 ?a	 ?difference	 ?in	 ?probability	 ?of	 ?gene	 ?reuse	 ?between	 ?loss-??of-??function	 ?and	 ?gain-??of-??function	 ?traits,	 ?or	 ?between	 ?genes	 ?of	 ?major	 ?and	 ?minor	 ?effect.	 ?Improved	 ?knowledge	 ?of	 ?the	 ?biochemical	 ?functions	 ?and	 ?pathway	 ?positions	 ?of	 ?genes	 ?will	 ?allow	 ?us	 ?to	 ?address	 ?whether	 ?genes	 ?that	 ?influence	 ?a	 ?greater	 ?number	 ?of	 ?other	 ?genes	 ?in	 ?developmental	 ?pathways	 ?are	 ?more	 ?or	 ?less	 ?likely	 ?to	 ?underlie	 ?repeated	 ?phenotypic	 ?evolution	 ?than	 ?genes	 ?acting	 ?at	 ?terminal	 ?points	 ?in	 ?the	 ?pathways	 ?(Stern	 ?and	 ?Orgogozo	 ?2008).	 ?Knowledge	 ?of	 ?mutations	 ?will	 ?allow	 ?us	 ?to	 ?address	 ?how	 ?properties	 ?such	 ?as	 ?dominance	 ?contribute	 ?to	 ?the	 ?probability	 ?they	 ?will	 ?repeatedly	 ?underlie	 ?evolution	 ?of	 ?a	 ?phenotype	 ?(Rosenblum	 ?et	 ?al.	 ?2010).	 ?Further	 ?tests	 ?are	 ?required	 ?of	 ?the	 ?mechanisms	 ?proposed	 ?to	 ?underlie	 ?the	 ?high	 ?rate	 ?of	 ?reuse	 ?of	 ?the	 ?same	 ?genes,	 ?such	 ?as	 ?pleiotropy	 ?and	 ?mutation	 ?bias	 ?(Streisfeld	 ?and	 ?Rausher	 ?2011).	 ?In	 ?the	 ?future,	 ?it	 ?will	 ?be	 ?interesting	 ?to	 ?compare	 ?our	 ?estimates	 ?with	 ?probabilities	 ?of	 ?gene	 ?reuse	 ?from	 ?whole-??genome	 ?sequences	 ?of	 ?populations	 ?adapting	 ?to	 ?similar	 ?environments.	 ?	 ?We	 ?feel	 ?that	 ?studies	 ?starting	 ?from	 ?purely	 ?genetic	 ?and	 ?genomic	 ?approaches	 ?must	 ?incorporate	 ?steps	 ?to	 ?understand	 ?the	 ?phenotypic	 ?effects	 ?of	 ?the	 ?genetic	 ?changes	 ?detected.	 ?This	 ?will	 ?be	 ?important	 ?to	 ?determine	 ?whether	 ?parallel	 ?genomic	 ?signatures	 ?resulted	 ?from	 ?selection	 ?on	 ?the	 ?same	 ?phenotypic	 ?traits	 ?in	 ?different	 ?populations,	 ?and	 ?to	 ?determine	 ?the	 ?mechanisms	 ?of	 ?selection.	 ?Likewise,	 ?studies	 ?of	 ?phenotypic	 ?evolution	 ?should	 ?be	 ?followed	 ?through	 ?to	 ?its	 ?genetic	 ?basis	 ?to	 ?gain	 ?a	 ?better	 ?understanding	 ?of	 ?the	 ?consequences	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?at	 ?the	 ?level	 ?of	 ?genes	 ?and	 ?mutations.	 ?	 ?With	 ?solid	 ?connections	 ?between	 ?phenotypes	 ?and	 ?genotypes,	 ?repeated	 ?phenotypic	 ?evolution	 ?provides	 ?a	 ?powerful	 ?way	 ?to	 ?study	 ?the	 ?predictability	 ?of	 ?genetic	 ?changes	 ?underlying	 ?adaptive	 ?evolution.	 ?	 ?	 ? 46	 ?4	 ?	 ? The	 ?Extent	 ?of	 ?Parallel	 ?Genetic	 ?Evolution	 ?Underlying	 ?Parallel	 ?Phenotypic	 ?Evolution	 ?in	 ?Pairs	 ?of	 ?Benthic	 ?and	 ?Limnetic	 ?Threespine	 ?Stickleback	 ?Species	 ?Introduction	 ?It	 ?is	 ?now	 ?clear	 ?that	 ?the	 ?genetics	 ?of	 ?adaptation	 ?are	 ?to	 ?some	 ?extent	 ?predictable,	 ?but	 ?we	 ?still	 ?have	 ?a	 ?poor	 ?understanding	 ?of	 ?how	 ?predictable	 ?they	 ?are	 ?(Stern	 ?and	 ?Orgogozo	 ?2008;	 ?Conte	 ?et	 ?al.	 ?2012;	 ?Martin	 ?and	 ?Orgogozo	 ?2013;	 ?Stern	 ?2013).	 ?Studying	 ?the	 ?genetics	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?allows	 ?us	 ?to	 ?estimate	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?adaptation.	 ?When	 ?organisms	 ?independently	 ?evolve	 ?similar	 ?phenotypes	 ?in	 ?response	 ?to	 ?similar	 ?selection	 ?pressures,	 ?we	 ?can	 ?ask	 ??how	 ?similar	 ?is	 ?the	 ?genetic	 ??solution?	 ?underlying	 ?those	 ?phenotypes??	 ?The	 ?more	 ?similar	 ?the	 ?genetic	 ?basis	 ?of	 ?repeatedly	 ?evolved	 ?phenotypes,	 ?the	 ?more	 ?predictable	 ?we	 ?may	 ?conclude	 ?the	 ?genetics	 ?of	 ?adaptation	 ?are.	 ?	 ?The	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?should	 ?be	 ?affected	 ?by	 ?how	 ?many	 ?genetic	 ??solutions?	 ?are	 ?available.	 ?If	 ?mutations	 ?in	 ?only	 ?a	 ?few	 ?genes	 ?tend	 ?to	 ?be	 ?capable	 ?of	 ?producing	 ?a	 ?similar	 ?phenotypic	 ?change	 ?then	 ?the	 ?genetics	 ?of	 ?adaptation	 ?should	 ?be	 ?more	 ?predictable	 ?than	 ?if	 ?mutations	 ?in	 ?many	 ?genes	 ?tend	 ?to	 ?be	 ?capable	 ?of	 ?doing	 ?so.	 ?The	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?may	 ?be	 ?further	 ?affected	 ?if	 ?some	 ?of	 ?the	 ?available	 ?genes	 ?are	 ?more	 ?likely	 ?to	 ?underlie	 ?phenotypic	 ?adaptation	 ?than	 ?others.	 ?Thus	 ?far,	 ?evidence	 ?indicates	 ?that	 ?the	 ?genes	 ?used	 ?and	 ?reused	 ?in	 ?adaptive	 ?evolution	 ?are	 ?a	 ?small	 ?subset	 ?of	 ?available	 ?genes	 ?(Conte	 ?et	 ?al.	 ?2012;	 ?Stern	 ?2013).	 ?A	 ?recent	 ?meta-??analysis	 ?based	 ?on	 ?published	 ?studies	 ?of	 ?natural	 ?populations	 ?estimates	 ?that	 ?across	 ?taxa	 ?separated	 ?by	 ?a	 ?wide	 ?range	 ?of	 ?divergence	 ?times,	 ?the	 ?average	 ?probability	 ?of	 ?gene	 ?reuse	 ?is	 ?0.32	 ?-??	 ?0.55	 ?(depending	 ?on	 ?the	 ?type	 ?of	 ?data	 ?used	 ?to	 ?calculate	 ?it)	 ?(Conte	 ?et	 ?al.	 ?2012).	 ?By	 ?taking	 ?the	 ?inverse	 ?of	 ?this	 ?probability,	 ?Conte	 ?et	 ?al.	 ?(2012)	 ?calculated	 ?a	 ?simplistic	 ?estimate	 ?of	 ?the	 ?effective	 ?number	 ?of	 ?genes	 ?available	 ?for	 ?adaptive	 ?evolution	 ?of	 ?a	 ?phenotype,	 ?if	 ?all	 ?genes	 ?have	 ?equal	 ?effects;	 ?the	 ?effective	 ?number	 ?of	 ?genes	 ?was	 ?2	 ?-??	 ?3.	 ?While	 ?it	 ?is	 ?very	 ?difficult	 ?to	 ?estimate	 ?the	 ?actual	 ?number	 ?of	 ?	 ? 47	 ?genes	 ?in	 ?which	 ?mutations	 ?may	 ?lead	 ?to	 ?a	 ?particular	 ?phenotype,	 ?some	 ?methods	 ?are	 ?available,	 ?albeit	 ?imperfect.	 ?Counting	 ?the	 ?number	 ?of	 ?genes	 ?in	 ?biosynthetic	 ?pathways	 ?that	 ?may	 ?affect	 ?a	 ?trait	 ?when	 ?mutated	 ?(Knecht	 ?et	 ?al.	 ?2007;	 ?Streisfeld	 ?and	 ?Rausher	 ?2011),	 ?or	 ?counting	 ?the	 ?number	 ?genes	 ?in	 ?which	 ?mutations	 ?affecting	 ?a	 ?trait	 ?occur	 ?in	 ?mutagenesis	 ?screens	 ?(Breidenstein	 ?et	 ?al.	 ?2008;	 ?Yeung	 ?et	 ?al.	 ?2009;	 ?Stern	 ?2013)	 ?or	 ?in	 ?breeding	 ?or	 ?horticultural	 ?records	 ?(Streisfeld	 ?and	 ?Rausher	 ?2011),	 ?suggest	 ?that	 ?the	 ?number	 ?of	 ?genes	 ?in	 ?which	 ?mutations	 ?are	 ?capable	 ?of	 ?producing	 ?a	 ?particular	 ?phenotype	 ?is	 ?often	 ?much	 ?larger	 ?than	 ?the	 ?estimated	 ?2-??3	 ?effective	 ?genes	 ?that	 ?are	 ?used	 ?in	 ?adaptive	 ?evolution	 ?of	 ?a	 ?phenotype	 ?on	 ?average	 ?(Conte	 ?et	 ?al.	 ?2012;	 ?Stern	 ?2013).	 ?These	 ?observations	 ?suggest	 ?that	 ?the	 ?probability	 ?of	 ?repeated	 ?genetic	 ?evolution	 ?is	 ?higher	 ?than	 ?we	 ?would	 ?expect	 ?if	 ?mutations	 ?in	 ?all	 ?genes	 ?potentially	 ?affecting	 ?a	 ?trait	 ?had	 ?equal	 ?chances	 ?of	 ?occurring	 ?and	 ?going	 ?to	 ?fixation.	 ?Rather,	 ?some	 ?genes	 ?might	 ?contribute	 ?to	 ?adaptation	 ?more	 ?often	 ?than	 ?others	 ?if,	 ?for	 ?example,	 ?they	 ?have	 ?fewer	 ?pleiotropic	 ?constraints,	 ?higher	 ?mutation	 ?rates	 ?or	 ?more	 ?standing	 ?genetic	 ?variation	 ?(Streisfeld	 ?and	 ?Rausher	 ?2011;	 ?Martin	 ?and	 ?Orgogozo	 ?2013;	 ?Stern	 ?2013).	 ?To	 ?ultimately	 ?understand	 ?what	 ?factors	 ?affect	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?and	 ?to	 ?what	 ?extent,	 ?we	 ?must	 ?start	 ?by	 ?estimating	 ?just	 ?how	 ?predictable	 ?they	 ?are.	 ?	 ?Studies	 ?employing	 ?forward	 ?genetic	 ?approaches,	 ?such	 ?as	 ?quantitative	 ?trait	 ?loci	 ?(QTL)	 ?mapping	 ?and	 ?association	 ?mapping	 ?are	 ?still	 ?the	 ?gold	 ?standard	 ?for	 ?investigating	 ?the	 ?genetic	 ?basis	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?(Conte	 ?et	 ?al.	 ?2012).	 ?Compared	 ?to	 ?other	 ?common	 ?approaches,	 ?including	 ?reverse	 ?genetic	 ?techniques	 ?and	 ?genome	 ?scans	 ?for	 ?signatures	 ?of	 ?selection,	 ?the	 ?advantages	 ?of	 ?QTL	 ?and	 ?association	 ?mapping	 ?are	 ?the	 ?ability	 ?to	 ?link	 ?genotypes	 ?to	 ?the	 ?phenotypes	 ?they	 ?affect,	 ?the	 ?ability	 ?to	 ?discover	 ?the	 ?entire	 ?detectable	 ?genetic	 ?architecture	 ?of	 ?phenotypes	 ?via	 ?genome-??wide	 ?scans,	 ?and	 ?the	 ?resulting	 ?ability	 ?to	 ?estimate	 ?the	 ?relative	 ?effect	 ?sizes	 ?of	 ?multiple	 ?QTL	 ?underlying	 ?phenotypes	 ?(Lynch	 ?and	 ?Walsh	 ?1998;	 ?Broman	 ?and	 ?Sen	 ?2009).	 ?	 ?Several	 ?cases	 ?exist	 ?in	 ?which	 ?a	 ?repeatedly	 ?evolved	 ?phenotype	 ?has	 ?been	 ?mapped	 ?in	 ?multiple	 ?populations	 ?(Conte	 ?et	 ?al.	 ?2012)	 ?and	 ?from	 ?these	 ?we	 ?can	 ?get	 ?an	 ?idea	 ?of	 ?how	 ?commonly	 ?repeated	 ?phenotypic	 ?evolution	 ?is	 ?underlain	 ?by	 ?repeated	 ?genetic	 ?evolution.	 ?However,	 ?there	 ?are	 ?several	 ?ways	 ?in	 ?which	 ?the	 ?data	 ?are	 ?lacking.	 ?	 ? 48	 ?First,	 ?mapping	 ?studies	 ?so	 ?far	 ?have	 ?tended	 ?to	 ?focus	 ?on	 ?a	 ?few,	 ?discrete	 ?traits.	 ?Since	 ?biological	 ?traits	 ?appear	 ?to	 ?vary	 ?continuously	 ?more	 ?often	 ?than	 ?they	 ?vary	 ?discretely,	 ?discrete	 ?traits	 ?are	 ?probably	 ?not	 ?representative	 ?of	 ?most	 ?traits	 ?involved	 ?in	 ?population	 ?differentiation	 ?(Mackay	 ?2001)	 ?and	 ?thus,	 ?it	 ?is	 ?difficult	 ?to	 ?make	 ?generalizations	 ?based	 ?on	 ?such	 ?studies.	 ?A	 ?second	 ?area	 ?in	 ?which	 ?mapping	 ?studies	 ?to	 ?date	 ?are	 ?still	 ?lacking	 ?is	 ?that,	 ?to	 ?our	 ?knowledge,	 ?no	 ?single	 ?studies	 ?have	 ?used	 ?crosses	 ?from	 ?multiple	 ?populations	 ?to	 ?explicitly	 ?test	 ?for	 ?repeated	 ?use	 ?of	 ?the	 ?same	 ?QTL.	 ?Rather	 ?multiple	 ?mapping	 ?studies	 ?have	 ?been	 ?done	 ?at	 ?disparate	 ?times	 ?and	 ?in	 ?disparate	 ?ways	 ?(Conte	 ?et	 ?al.	 ?2012),	 ?potentially	 ?introducing	 ?confounding	 ?variables.	 ?	 ?We	 ?investigated	 ?two	 ?pairs	 ?of	 ?threespine	 ?stickleback	 ?species	 ?(hereafter	 ?called	 ??species	 ?pairs?),	 ?one	 ?pair	 ?from	 ?Paxton	 ?Lake	 ?and	 ?the	 ?other	 ?from	 ?Priest	 ?Lake	 ?on	 ?Texada	 ?Island,	 ?British	 ?Columbia.	 ?Both	 ?pairs	 ?are	 ?comprised	 ?of	 ?a	 ?limnetic	 ?ecotype	 ?that	 ?specializes	 ?on	 ?use	 ?of	 ?resources	 ?in	 ?the	 ?lake?s	 ?limnetic	 ?zone,	 ?and	 ?a	 ?benthic	 ?ecotype	 ?that	 ?specializes	 ?on	 ?use	 ?of	 ?resources	 ?in	 ?the	 ?lake?s	 ?littoral	 ?and	 ?benthic	 ?zones	 ?(McPhail	 ?1984,	 ?1992,	 ?1994;	 ?Schluter	 ?and	 ?McPhail	 ?1992).	 ?Repeated	 ?genetic	 ?evolution	 ?seems	 ?relatively	 ?probable	 ?in	 ?the	 ?species	 ?pairs	 ?for	 ?several	 ?reasons.	 ?First,	 ?phenotypic	 ?divergence	 ?within	 ?each	 ?pair	 ?has	 ?occurred	 ?largely	 ?in	 ?parallel	 ?among	 ?the	 ?replicate	 ?pairs,	 ?and	 ?individuals	 ?of	 ?the	 ?same	 ?ecotype	 ?from	 ?different	 ?lakes	 ?strongly	 ?resemble	 ?one	 ?another	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?Schluter	 ?and	 ?Nagel	 ?1995;	 ?McKinnon	 ?and	 ?Rundle	 ?2002;	 ?Gow	 ?et	 ?al.	 ?2008).	 ?Second,	 ?the	 ?species	 ?pairs	 ?appear	 ?to	 ?have	 ?originated	 ?in	 ?the	 ?past	 ?10,000-??12,000	 ?years,	 ?following	 ?double	 ?invasions	 ?by	 ?ancestral	 ?populations	 ?into	 ?the	 ?post-??glacial	 ?lakes	 ?(Schluter	 ?and	 ?McPhail	 ?1992;	 ?McPhail	 ?1994;	 ?Taylor	 ?and	 ?McPhail	 ?2000).	 ?Thus,	 ?the	 ?species	 ?pairs	 ?are	 ?young	 ?and	 ?the	 ?biases	 ?potentially	 ?causing	 ?some	 ?genes	 ?to	 ?underlie	 ?adaptive	 ?evolution	 ?more	 ?frequently	 ?than	 ?others	 ?are	 ?likely	 ?to	 ?still	 ?be	 ?shared	 ?by	 ?them.	 ?Finally,	 ?evidence	 ?suggests	 ?that	 ?the	 ?evolution	 ?of	 ?many	 ?freshwater	 ?threespine	 ?stickleback	 ?populations	 ?involved	 ?repeated	 ?natural	 ?selection	 ?for	 ?shared	 ?standing	 ?genetic	 ?variation	 ?(Colosimo	 ?et	 ?al.	 ?2005;	 ?Miller	 ?et	 ?al.	 ?2007;	 ?Kitano	 ?et	 ?al.	 ?2010;	 ?Jones	 ?et	 ?al.	 ?2012b).	 ?Some	 ?genetic	 ?variation	 ?showing	 ?signatures	 ?of	 ?natural	 ?selection	 ?has	 ?been	 ?found	 ?to	 ?be	 ?shared	 ?by	 ?the	 ?species	 ?pair	 ?ecotypes,	 ?and	 ?this	 ?too	 ?may	 ?be	 ?explained	 ?by	 ?repeated	 ?use	 ?of	 ?standing	 ?variation	 ?(Jones	 ?et	 ?al.	 ?2012a).	 ?Given	 ?these	 ?considerations,	 ?we	 ?expect	 ?to	 ?find	 ?a	 ?relatively	 ?high	 ?amount	 ?	 ? 49	 ?of	 ?genetic	 ?parallelism	 ?underlying	 ?parallel	 ?phenotypic	 ?evolution	 ?between	 ?the	 ?species	 ?pairs.	 ? We	 ?used	 ?QTL	 ?mapping	 ?to	 ?map	 ?the	 ?genetic	 ?architecture	 ?of	 ?many	 ?continuously	 ?varying,	 ?quantitative	 ?traits,	 ?as	 ?well	 ?as	 ?a	 ?few	 ?discrete	 ?traits	 ?that	 ?repeatedly	 ?differentiate	 ?the	 ?species	 ?within	 ?separate	 ?pairs.	 ?We	 ?chose	 ?to	 ?focus	 ?on	 ?morphological	 ?traits,	 ?since	 ?morphological	 ?divergence	 ?has	 ?been	 ?largely	 ?parallel	 ?between	 ?the	 ?species	 ?pairs.	 ?We	 ?used	 ?identical	 ?methods	 ?for	 ?crossing,	 ?raising,	 ?phenotyping,	 ?genotyping,	 ?linkage	 ?mapping,	 ?QTL	 ?mapping,	 ?and	 ?analyzing	 ?QTL	 ?in	 ?both	 ?species	 ?pairs.	 ?Thus,	 ?our	 ?results	 ?are	 ?directly	 ?comparable.	 ?We	 ?raised	 ?our	 ?F2	 ?hybrids	 ?in	 ?controlled,	 ?semi-??natural	 ?ponds	 ?allowing	 ?natural	 ?expression	 ?of	 ?the	 ?focal	 ?phenotypes.	 ?	 ?To	 ?measure	 ?the	 ?extent	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?developed	 ?and	 ?implemented	 ?a	 ?model	 ?selection	 ?technique,	 ?using	 ?the	 ?Akaike	 ?information	 ?criterion	 ?(corrected	 ?for	 ?finite	 ?sample	 ?size;	 ?AICc)	 ?to	 ?compete	 ?alternative	 ?models	 ?of	 ?the	 ?effects	 ?of	 ?single	 ?chromosomal	 ?regions	 ?(identified	 ?as	 ?being	 ?QTL	 ?in	 ?at	 ?least	 ?one	 ?of	 ?the	 ?lakes)	 ?in	 ?the	 ?two	 ?lakes.	 ?From	 ?these	 ?we	 ?determined	 ?whether	 ?individual	 ?chromosomal	 ?regions	 ?had	 ?parallel	 ?phenotypic	 ?effects	 ?(i.e.	 ?effects	 ?in	 ?the	 ?same	 ?direction,	 ?though	 ?not	 ?necessarily	 ?of	 ?equal	 ?magnitude)	 ?or	 ?non-??parallel	 ?phenotypic	 ?effects	 ?in	 ?the	 ?two	 ?lakes,	 ?whereby	 ?two	 ?forms	 ?of	 ?non-??parallel	 ?phenotypic	 ?effects	 ?were	 ?an	 ?effect	 ?in	 ?only	 ?one	 ?of	 ?the	 ?lakes	 ?or	 ?effects	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes.	 ?We	 ?note	 ?that	 ?QTL	 ?are	 ?(often	 ?large)	 ?chromosomal	 ?regions	 ?of	 ?association	 ?with	 ?phenotypic	 ?traits	 ?that	 ?usually	 ?contain	 ?many	 ?genes	 ?in	 ?addition	 ?to	 ?the	 ?causative	 ?one(s).	 ?However,	 ?at	 ?the	 ?time	 ?being,	 ?they	 ?represent	 ?our	 ?best	 ?available	 ?information.	 ?As	 ?a	 ?second	 ?and	 ?distinct	 ?measure	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?measured	 ?the	 ?overlap	 ?in	 ?the	 ?proportional	 ?contributions	 ?of	 ?all	 ?QTL	 ?effects	 ?underlying	 ?each	 ?individual	 ?trait	 ?in	 ?the	 ?two	 ?pairs,	 ?using	 ?proportional	 ?similarity	 ?as	 ?in	 ?Conte	 ?et	 ?al.	 ?(2012)	 ?(hereafter	 ?called	 ??proportional	 ?similarity	 ?of	 ?QTL	 ?use?).	 ?While	 ?the	 ?first	 ?measure	 ?of	 ?genetic	 ?parallelism	 ?reflects	 ?how	 ?commonly	 ?QTL	 ?have	 ?phenotypic	 ?effects	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?both	 ?lakes,	 ?the	 ?second	 ?measure	 ?reflects	 ?to	 ?what	 ?extent	 ?QTL	 ?make	 ?equal	 ?contributions	 ?to	 ?the	 ?phenotypic	 ?difference	 ?in	 ?the	 ?two	 ?lakes.	 ?Finally,	 ?since	 ?our	 ?dataset	 ?contained	 ?a	 ?relatively	 ?large	 ?number	 ?of	 ?traits	 ?and	 ?QTL,	 ?we	 ?were	 ?able	 ?to	 ?ask	 ?whether	 ?the	 ?phenotypic	 ?effect	 ?sizes	 ?of	 ?QTL	 ?predict	 ?	 ? 50	 ?whether	 ?their	 ?effects	 ?are	 ?parallel	 ?or	 ?non-??parallel.	 ?All	 ?else	 ?equal,	 ?mutations	 ?(that	 ?underlie	 ?QTL)	 ?of	 ?larger	 ?beneficial	 ?effect	 ?are	 ?more	 ?likely	 ?to	 ?fix	 ?than	 ?those	 ?of	 ?smaller	 ?beneficial	 ?effect	 ?(Fisher	 ?1930;	 ?Kimura	 ?1983),	 ?and	 ?thus,	 ?they	 ?are	 ?more	 ?likely	 ?to	 ?fix	 ?repeatedly.	 ?However,	 ?mutations	 ?with	 ?larger	 ?phenotypic	 ?effects	 ?may	 ?be	 ?less	 ?likely	 ?to	 ?be	 ?beneficial	 ?if	 ?the	 ?same	 ?mutations	 ?affect	 ?other	 ?traits	 ?as	 ?well	 ?(Fisher	 ?1930).	 ?Looking	 ?at	 ?the	 ?pattern	 ?in	 ?our	 ?data	 ?may	 ?help	 ?guide	 ?our	 ?intuition	 ?regarding	 ?how	 ?the	 ?phenotypic	 ?effect	 ?size	 ?of	 ?QTL	 ?may	 ?affect	 ?the	 ?probability	 ?of	 ?genetic	 ?parallelism	 ?in	 ?natural	 ?populations.	 ?Results	 ?Parallel	 ?Phenotypic	 ?Evolution	 ?We	 ?began	 ?by	 ?determining	 ?which	 ?of	 ?our	 ?58	 ?focal	 ?phenotypic	 ?traits	 ?(Figure	 ?C.1)	 ?had	 ?diverged	 ?in	 ?parallel	 ?in	 ?the	 ?two	 ?lakes.	 ?We	 ?considered	 ?trait	 ?divergence	 ?to	 ?be	 ??parallel?	 ?when	 ?it	 ?was	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?the	 ?two	 ?lakes	 ?(i.e.	 ?benthics	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?limnetics	 ?in	 ?both	 ?lakes,	 ?or	 ?vise	 ?versa),	 ?though	 ?not	 ?necessarily	 ?of	 ?the	 ?same	 ?magnitude.	 ?There	 ?were	 ?two	 ?ways	 ?in	 ?which	 ?trait	 ?divergence	 ?was	 ?considered	 ?non-??parallel.	 ?First,	 ?trait	 ?divergence	 ?was	 ?considered	 ?to	 ?be	 ?only	 ?in	 ?a	 ??single	 ?lake?	 ?if	 ?it	 ?occurred	 ?in	 ?one	 ?lake	 ?but	 ?not	 ?the	 ?other	 ?(i.e.	 ?benthic	 ?and	 ?limnetic	 ?trait	 ?means	 ?differed	 ?in	 ?only	 ?one	 ?of	 ?the	 ?two	 ?lakes).	 ?Second,	 ?trait	 ?divergence	 ?was	 ?considered	 ??opposite?	 ?if	 ?it	 ?was	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes	 ?(i.e.	 ?benthics	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?limnetics	 ?in	 ?one	 ?lake	 ?and	 ?a	 ?lower	 ?mean	 ?than	 ?limnetics	 ?in	 ?the	 ?other).	 ?These	 ?definitions	 ?describe	 ?alternative	 ?linear	 ?models	 ?that	 ?we	 ?fitted	 ?to	 ?the	 ?data	 ?for	 ?each	 ?trait	 ?separately.	 ?We	 ?then	 ?used	 ?AICc	 ?to	 ?determine	 ?which	 ?model	 ?was	 ?best	 ?supported	 ?by	 ?the	 ?data	 ?for	 ?each	 ?trait.	 ?We	 ?found	 ?that	 ?76.2%	 ?(n=32)	 ?of	 ?traits	 ?diverged	 ?in	 ?parallel	 ?in	 ?the	 ?two	 ?lakes.	 ?Divergence	 ?in	 ?the	 ?remaining	 ?23.8%	 ?of	 ?traits	 ?was	 ?non-??parallel,	 ?whereby	 ?11.9%	 ?(n=5)	 ?diverged	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?and	 ?11.9%	 ?(n=5)	 ?diverged	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes	 ?(Figure	 ?4.1	 ?and	 ?Table	 ?C.1).	 ?Fifteen	 ?traits	 ?for	 ?which	 ?more	 ?than	 ?one	 ?trait	 ?divergence	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well	 ?were	 ?left	 ?out.	 ?Also,	 ?the	 ?total	 ?	 ? 51	 ?number	 ?of	 ?traits	 ?considered	 ?here	 ?was	 ?57	 ?rather	 ?than	 ?58,	 ?since	 ?rather	 ?than	 ?long	 ?gill	 ?raker	 ?count	 ?and	 ?short	 ?gill	 ?raker	 ?count	 ?on	 ?the	 ?first	 ?gill	 ?arch,	 ?only	 ?the	 ?total	 ?gill	 ?raker	 ?count	 ?on	 ?the	 ?first	 ?gill	 ?arch	 ?was	 ?included.	 ?This	 ?result	 ?corroborates	 ?claims	 ?that	 ?that	 ?the	 ?majority	 ?of	 ?morphological	 ?traits	 ?have	 ?diverged	 ?in	 ?parallel	 ?between	 ?the	 ?species	 ?pairs,	 ?though	 ?also	 ?demonstrates	 ?that	 ?evolution	 ?in	 ?a	 ?substantial	 ?number	 ?of	 ?traits	 ?has	 ?been	 ?non-??parallel.	 ?For	 ?the	 ?remainder	 ?of	 ?the	 ?study	 ?we	 ?focused	 ?on	 ?only	 ?the	 ?genetics	 ?underlying	 ?traits	 ?that	 ?diverged	 ?in	 ?parallel,	 ?thereby	 ?allowing	 ?us	 ?to	 ?estimate	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?Figure	 ?4.1	 ?Trait	 ?divergence	 ?categories	 ?The	 ?frequency	 ?and	 ?percentage	 ?of	 ?traits	 ?determined	 ?to	 ?have	 ?diverged	 ?in	 ?parallel	 ?in	 ?the	 ?two	 ?lakes	 ?(n=32),	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?(n=5)	 ?and	 ?in	 ?both	 ?lakes	 ?but	 ?in	 ?opposite	 ?directions	 ?(n=5).	 ?Fifteen	 ?traits	 ?for	 ?which	 ?more	 ?than	 ?one	 ?trait	 ?divergence	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well	 ?were	 ?left	 ?out.	 ?	 ?Parallel	 ?genetic	 ?evolution:	 ?We	 ?detected	 ?a	 ?total	 ?of	 ?58	 ?QTL	 ?that	 ?had	 ?an	 ?effect	 ?in	 ?one	 ?or	 ?both	 ?lakes	 ?underlying	 ?26	 ?(of	 ?the	 ?32)	 ?traits	 ?that	 ?diverged	 ?in	 ?parallel	 ?(Figure	 ?C.3).	 ?Examples	 ?of	 ?Parallel Single lake OppositeTrait divergenceNumber of Traits0510152025303576.2% 11.9% 11.9%	 ? 52	 ?QTL	 ?scan	 ?results	 ?are	 ?shown	 ?in	 ?Figure	 ?C.2.	 ?All	 ?detected	 ?QTL	 ?are	 ?shown	 ?in	 ?Tables	 ?C.3	 ?-??	 ?C.5.	 ?We	 ?considered	 ?these	 ?58	 ?QTL	 ?as	 ?candidate	 ?chromosomal	 ?positions	 ?at	 ?which	 ?to	 ?test	 ?for	 ?genetic	 ?parallelism	 ?and	 ?hereafter,	 ?refer	 ?to	 ?them	 ?as	 ??candidate	 ?QTL?.	 ?	 ?We	 ?considered	 ?the	 ?effects	 ?of	 ?a	 ?candidate	 ?QTL	 ?to	 ?be	 ??parallel?	 ?when	 ?the	 ?phenotypic	 ?effects	 ?of	 ?genotypes	 ?at	 ?the	 ?candidate	 ?QTL	 ?were	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?the	 ?two	 ?lakes,	 ?though	 ?not	 ?necessarily	 ?of	 ?identical	 ?magnitudes	 ?(i.e.,	 ?F2	 ?hybrids	 ?with	 ?benthic	 ?genotypes	 ?had	 ?a	 ?higher	 ?mean	 ?phenotype	 ?than	 ?F2	 ?hybrids	 ?with	 ?limnetic	 ?genotypes	 ?in	 ?both	 ?lakes,	 ?or	 ?vise	 ?versa).	 ?A	 ?candidate	 ?QTL	 ?was	 ?considered	 ?to	 ?have	 ?an	 ?effect	 ?in	 ?only	 ?a	 ??single	 ?lake?	 ?if	 ?the	 ?genotypes	 ?at	 ?the	 ?candidate	 ?QTL	 ?had	 ?a	 ?phenotypic	 ?effect	 ?in	 ?one	 ?lake	 ?but	 ?not	 ?the	 ?other.	 ?The	 ?effects	 ?of	 ?a	 ?candidate	 ?QTL	 ?were	 ?considered	 ??opposite?	 ?if	 ?phenotypic	 ?effects	 ?of	 ?genotypes	 ?at	 ?the	 ?candidate	 ?QTL	 ?were	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes	 ?(i.e.	 ?F2	 ?hybrids	 ?with	 ?benthic	 ?genotypes	 ?had	 ?a	 ?higher	 ?mean	 ?phenotype	 ?than	 ?F2	 ?hybrids	 ?with	 ?limnetic	 ?genotypes	 ?in	 ?one	 ?lake	 ?but	 ?the	 ?opposite	 ?in	 ?the	 ?other	 ?lake).	 ?Linear	 ?models	 ?of	 ?QTL	 ?effects	 ?representing	 ?the	 ?three	 ?possibilities	 ?were	 ?fitted	 ?to	 ?the	 ?data	 ?at	 ?each	 ?candidate	 ?QTL,	 ?and	 ?we	 ?used	 ?AICc	 ?to	 ?determine	 ?which	 ?model	 ?was	 ?best	 ?supported	 ?by	 ?the	 ?data	 ?(Table	 ?C.6).	 ?	 ?Figure	 ?4.2	 ?shows	 ?examples	 ?that	 ?illustrate	 ?our	 ?definitions.	 ?In	 ?each	 ?panel,	 ?an	 ?additive	 ?genotype	 ?score	 ?of	 ?0	 ?indicates	 ?the	 ?limnetic	 ?genotype,	 ?1	 ?the	 ?benthic	 ?genotype	 ?and	 ?0.5	 ?the	 ?heterozygote	 ?(values	 ?in	 ?between	 ?indicate	 ?uncertain	 ?genotypes,	 ?with	 ?score	 ?reflecting	 ?genotype	 ?probability).	 ?The	 ?left	 ?panel	 ?of	 ?Figure	 ?4.2	 ?shows	 ?an	 ?example	 ?of	 ?a	 ?QTL	 ?with	 ?parallel	 ?effects:	 ?in	 ?F2	 ?hybrids,	 ?the	 ?number	 ?of	 ?long	 ?gill	 ?rakers	 ?decreases	 ?with	 ?an	 ?increasing	 ?additive	 ?genotype	 ?score	 ?at	 ?a	 ?candidate	 ?QTL	 ?on	 ?linkage	 ?group	 ?7,	 ?in	 ?both	 ?lakes.	 ?In	 ?contrast,	 ?the	 ?middle	 ?panel	 ?of	 ?Figure	 ?4.2	 ?shows	 ?an	 ?example	 ?of	 ?a	 ?QTL	 ?with	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake:	 ?the	 ?trait	 ??landmark	 ?y26?	 ?(the	 ?y-??coordinate	 ?of	 ?a	 ?landmark	 ?placed	 ?on	 ?the	 ?dorsum	 ?of	 ?the	 ?trunk	 ?over	 ?the	 ?pectoral	 ?fin	 ?mid-??point)	 ?changes	 ?with	 ?the	 ?genotype	 ?of	 ?a	 ?candidate	 ?QTL	 ?on	 ?linkage	 ?group	 ?1	 ?in	 ?Priest	 ?Lake	 ?but	 ?not	 ?in	 ?Paxton	 ?Lake.	 ?Finally,	 ?the	 ?right	 ?panel	 ?of	 ?Figure	 ?4.2	 ?shows	 ?an	 ?example	 ?of	 ?a	 ?QTL	 ?with	 ?opposite	 ?effects:	 ?the	 ?trait	 ??landmark	 ?y27?	 ?(the	 ?y-??coordinate	 ?of	 ?a	 ?landmark	 ?placed	 ?at	 ?the	 ?posterior	 ?insertion	 ?of	 ?the	 ?dorsal	 ?fin	 ?at	 ?the	 ?first	 ?soft	 ?ray)	 ?changes	 ?with	 ?the	 ?genotype	 ?of	 ?a	 ?candidate	 ?QTL	 ?on	 ?linkage	 ?group	 ?17	 ?in	 ?both	 ?lakes,	 ?but	 ?in	 ?opposite	 ?directions.	 ?	 ? 53	 ?Figure	 ?4.2	 ?Examples	 ?of	 ?QTL	 ?with	 ?parallel,	 ?single-??lake	 ?and	 ?opposite	 ?effects	 ?Examples	 ?of	 ?phenotype	 ?by	 ?genotype	 ?relationships	 ?at	 ?candidate	 ?QTL	 ?in	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?(light	 ?blue)	 ?and	 ?the	 ?Priest	 ?Lake	 ?cross	 ?(purple).	 ?Phenotypes	 ?are	 ?shown	 ?on	 ?the	 ?y-??axes.	 ?The	 ?x-??axes	 ?show	 ?the	 ?additive	 ?genotype	 ?score	 ?at	 ?the	 ?candidate	 ?QTL	 ?with	 ?0	 ?indicating	 ?the	 ?limnetic	 ?genotype,	 ?1	 ?the	 ?benthic	 ?genotype	 ?and	 ?0.5	 ?the	 ?heterozygote	 ?(values	 ?in	 ?between	 ?indicate	 ?uncertain	 ?genotypes,	 ?with	 ?score	 ?reflecting	 ?genotype	 ?probability).	 ?Lines	 ?represent	 ?the	 ?fitted	 ?values	 ?of	 ?linear	 ?models	 ?fitted	 ?to	 ?the	 ?phenotype	 ?and	 ?genotype	 ?data	 ?for	 ?each	 ?lake	 ?separately	 ?(light	 ?blue:	 ?Paxton	 ?Lake	 ?cross,	 ?purple:	 ?Priest	 ?Lake	 ?cross),	 ?using	 ?family	 ?identity	 ?and	 ?sex	 ?as	 ?covariates.	 ?Phenotypic	 ?measurements	 ?shown	 ?here	 ?are	 ?corrected	 ?for	 ?family	 ?identity.	 ?	 ?	 ?	 ?For	 ?15	 ?of	 ?the	 ?58	 ?candidate	 ?QTL,	 ?more	 ?than	 ?one	 ?QTL	 ?effect	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well,	 ?so	 ?they	 ?were	 ?not	 ?included	 ?in	 ?the	 ?following	 ?calculations.	 ?Of	 ?the	 ?remaining	 ?43	 ?candidate	 ?QTL	 ?for	 ?parallel	 ?traits	 ?(n=23),	 ?48.8%	 ?(n=21)	 ?had	 ?parallel	 ?effects	 ?in	 ?the	 ?two	 ?lakes,	 ?41.9%	 ?(n=18)	 ?had	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?and	 ?9.3%	 ?(n=4)	 ?had	 ?opposite	 ?effects	 ?in	 ?the	 ?two	 ?lakes	 ?(Figure	 ?4.3).	 ?That	 ?is,	 ?almost	 ?50%	 ?of	 ?all	 ?QTL	 ?detected	 ?that	 ?underlie	 ?parallel	 ?phenotypic	 ?evolution	 ?had	 ?phenotypic	 ?effects	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?crosses	 ?from	 ?both	 ?lakes,	 ?though	 ?not	 ?necessarily	 ?of	 ?identical	 ?magnitudes.	 ?About	 ?40%	 ?of	 ?QTL	 ?had	 ?a	 ?phenotypic	 ?effect	 ?in	 ?one	 ?lake?s	 ?cross	 ?but	 ?not	 ?the	 ?other,	 ?and	 ?the	 ?remaining	 ?approximately	 ?10%	 ?of	 ?QTL	 ?had	 ?phenotypic	 ?effects	 ?in	 ?opposite	 ?directions	 ?in	 ?crosses	 ?from	 ?the	 ?two	 ?lakes.	 ?	 ?To	 ?ensure	 ?that	 ?including	 ?multiple	 ?QTL	 ?that	 ?map	 ?to	 ?the	 ?same	 ?genomic	 ?regions	 ?(and	 ?therefore	 ?are	 ?either	 ?based	 ?on	 ?same	 ?loci	 ?or	 ?separate	 ?tightly	 ?linked	 ?loci)	 ?did	 ?not	 ?bias	 ?our	 ?result,	 ?we	 ?also	 ?calculated	 ?the	 ?QTL for long gill raker count detected in the combined scan Single QTL Model Results: Theta = 5.6 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.26?????????????????? ???????????? ???????????????? ?????????????????????????????????????????? ?????????????????????????? ?????????????? ?????????????? ???? ????????????????? ??????? ????????????????????? ?????0.0 0.2 0.4 0.6 0.8 1.018202224long gill raker countAdditive genotype score LG 7 at 35.1 cM0 20 40 60051015LODLG 7 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for short gill raker count detected in the combined scan Single QTL Model Results: Theta = 4.12 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.72????????????? ?????????????????? ??????? ?????????????? ??? ?????????????????? ????????????????????????????? ???? ??????????????????????????????????????????????????? ?? ??????????????? ??? ??? ????? ??????? ?????? ???? ?????????????? ??????????? ???? ?????????? ??????????????? ?????????????0.0 0.2 0.4 0.6 0.8 1.014151617181920short gill raker countAdditive genotype score LG 1 at 21.2 cM0 10 20 30 40051015LODLG 1 Map P sition (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y25 detected in the combined scan Single QTL Model Results: Theta = 4.03 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.88??????????????????????? ?????????????? ????????????????????????????? ????????????? ?????????????????????????????????????????????????????????????????? ???????????????? ???????????????????????????????????????????????????????0.0 0.2 0.4 0.6 0.8 1.0?0.22?0.20?0.18?0.16y25Additive genotype score LG 12 at 13.2 cM0 5 10 15 20 25051015LODLG 12 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y26 detected in the combined scan Single QTL Model Results: Theta = 46.5 Two?Level Significance Category =significant in Priest only Lowest AICc Category =QTL only in Pr 2nd lowest AICc Category =full Delta.AIC = 2.66???????????????????????????????????????????????????????? ????????????????????? ??????????????????????????????????????????????????????????????????????????? ????????????? ???????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????0.0 0.2 0.4 0.6 0.8 1.00.400.450.500.55y26Additive genotype sc re LG 1 at 21.7 cM0 10 20 30 40051015LODLG 1 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y27 detected in the combined scan Single QTL Model Results: Theta = 7.94 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.55????????????????????? ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????? ??????????? ??????????????????????????????0.0 0.2 0.4 0.6 0.8 1.00.180.200.220.240.26y27Additiv  genotype score LG 12 at 13.2 cM0 5 10 15 20 25051015LODLG 12 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y27 detected in the combined scan Single QTL Model Results: Theta = 85.4 Two?Level Significance Category =non?parallel Lowest AICc Category =QTL with diff effect ? diff dir. 2nd lowest AICc Category =pr.only Delta.AIC = 10.7?????????????????????????????????????????????????????????????????????????????????????????????? ????????????????????????????????????????????????? ?????????????????????? ???????? ?????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????? ?????? ???????????????? ??0.0 0.2 0.4 .6 0.8 1.0.180.220.26y27Additive genotype sc re LG 17 at 21.7 cM0 10 20 30 40051015LODLG 17 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest Cross	 ? 54	 ?average	 ?proportion	 ?of	 ?QTL	 ?effects	 ?per	 ?chromosome,	 ?whereby	 ?each	 ?chromosome	 ?is	 ?a	 ?data	 ?point,	 ?rather	 ?than	 ?each	 ?QTL.	 ?The	 ?results	 ?of	 ?this	 ?more	 ?conservative	 ?approach	 ?(Figure	 ?C.4)	 ?are	 ?very	 ?similar	 ?to	 ?those	 ?presented	 ?above	 ?(Figure	 ?4.3).	 ?Figure	 ?4.3	 ?QTL	 ?effect	 ?categories	 ?The	 ?frequency	 ?and	 ?percentage	 ?of	 ?candidate	 ?QTL	 ?underlying	 ?traits	 ?that	 ?evolved	 ?in	 ?parallel	 ?that	 ?were	 ?determined	 ?to	 ?have	 ?parallel	 ?effects	 ?(n=21;	 ?blue),	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?(n=18;	 ?grey)	 ?and	 ?opposite	 ?effects	 ?(n=4;	 ?red).	 ?15	 ?QTL	 ?for	 ?which	 ?more	 ?than	 ?one	 ?QTL	 ?effect	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well	 ?were	 ?left	 ?out.	 ?	 ?	 ? As	 ?a	 ?distinct	 ?measure	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?calculated	 ?the	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?for	 ?each	 ?of	 ?the	 ?26	 ?traits	 ?that	 ?had	 ?undergone	 ?parallel	 ?evolution	 ?and	 ?for	 ?which	 ?candidate	 ?QTL	 ?were	 ?detected	 ?(Table	 ?C.7).	 ?Here,	 ?proportional	 ?similarity	 ?is	 ?the	 ?overlap	 ?in	 ?the	 ?proportional	 ?contributions	 ?(i.e.	 ?proportional	 ?phenotypic	 ?effects)	 ?of	 ?all	 ?QTL	 ?affecting	 ?a	 ?trait	 ?(more	 ?details	 ?in	 ?Methods).	 ?Across	 ?parallel	 ?traits,	 ?we	 ?found	 ?that	 ?the	 ?average	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?was	 ?0.38	 ?with	 ?a	 ?standard	 ?deviation	 ?of	 ?0.34.	 ?Parallel Single lake OppositeQTL effectNumber of QTL051015202548.8% 41.9% 9.3%	 ? 55	 ?Correlates	 ?of	 ?genetic	 ?parallelism	 ?We	 ?found	 ?no	 ?effect	 ?of	 ?QTL	 ?PVE	 ?(the	 ?percent	 ?of	 ?the	 ?phenotypic	 ?variance	 ?explained	 ?by	 ?QTL;	 ?a	 ?measure	 ?of	 ?a	 ?QTL?s	 ?phenotypic	 ?effect	 ?size)	 ?on	 ?whether	 ?QTL	 ?were	 ?parallel	 ?or	 ?non-??parallel	 ?(where	 ?non-??parallel	 ?includes	 ?QTL	 ?with	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?and	 ?those	 ?with	 ?opposite	 ?effects)	 ?(df=1,	 ?X2=0.43,	 ?p=0.51)	 ?(Figure	 ?4.4).	 ?Samples	 ?sizes	 ?are	 ?the	 ?same	 ?as	 ?those	 ?shown	 ?in	 ?Figure	 ?4.3.	 ?Figure	 ?4.4	 ?QTL	 ?PVE	 ?by	 ?QTL	 ?effect	 ?category	 ?Percent	 ?of	 ?the	 ?phenotypic	 ?variance	 ?explained	 ?(PVE)	 ?by	 ?QTL	 ?with	 ?parallel	 ?effects	 ?(blue),	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?(grey)	 ?and	 ?opposite	 ?effects	 ?(red).	 ?For	 ?each	 ?candidate	 ?QTL,	 ?the	 ?PVE	 ?of	 ?its	 ?associated	 ?phenotype	 ?was	 ?calculated	 ?separately	 ?for	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?and	 ?the	 ?Priest	 ?Lake	 ?cross,	 ?and	 ?the	 ?higher	 ?of	 ?the	 ?two	 ?is	 ?plotted	 ?here.	 ?Solid	 ?lines	 ?represent	 ?means	 ?for	 ?each	 ?group.	 ?Samples	 ?sizes	 ?are	 ?the	 ?same	 ?as	 ?those	 ?shown	 ?in	 ?Figure	 ?4.3.	 ?	 ?	 ? Since	 ?our	 ?markers	 ?were	 ?differentially	 ?informative	 ?in	 ?the	 ?crosses	 ?from	 ?the	 ?two	 ?lakes,	 ?we	 ?also	 ?tested	 ?whether	 ?differences	 ?in	 ?genotype	 ?information	 ?content,	 ?measured	 ?as	 ?genotype	 ?entropy	 ?(Broman	 ?and	 ?Wu	 ?2013),	 ?explained	 ?variation	 ?in	 ?Parallel Single lake Opposite024681012QTL effectQTL PVE????????????????????? ???????????????????	 ? 56	 ?whether	 ?QTL	 ?had	 ?an	 ?effect	 ?in	 ?both	 ?lakes	 ?(parallel	 ?and	 ?opposite	 ?effects)	 ?or	 ?in	 ?only	 ?a	 ?single	 ?lake.	 ?We,	 ?indeed,	 ?found	 ?evidence	 ?that	 ?entropy	 ?differences	 ?between	 ?the	 ?crosses	 ?affected	 ?whether	 ?QTL	 ?were	 ?detected	 ?in	 ?both	 ?lakes	 ?or	 ?not	 ?(df=1,	 ?X2=5.58,	 ?p=0.02)	 ?(Figure	 ?4.5).	 ?	 ?Figure	 ?4.5	 ?Entropy	 ?difference	 ?between	 ?crosses	 ?by	 ?QTL	 ?effect	 ?category	 ?The	 ?absolute	 ?value	 ?of	 ?the	 ?difference	 ?in	 ?entropy	 ?(where	 ?entropy	 ?is	 ?an	 ?index	 ?of	 ?genotype	 ?information	 ?content)	 ?between	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?and	 ?the	 ?Priest	 ?Lake	 ?cross	 ?at	 ?candidate	 ?QTL	 ?that	 ?were	 ?determined	 ?to	 ?have	 ?parallel	 ?effects	 ?(blue),	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?(grey)	 ?and	 ?opposite	 ?effects	 ?(red).	 ?Solid	 ?lines	 ?represent	 ?means	 ?for	 ?each	 ?group.	 ?Sample	 ?sizes	 ?are	 ?the	 ?same	 ?as	 ?those	 ?in	 ?Figure	 ?4.3.	 ?	 ?	 ?Discussion	 ?We	 ?found	 ?that	 ?almost	 ?50%	 ?of	 ?QTL	 ?underlying	 ?parallel	 ?phenotypic	 ?differences	 ?between	 ?the	 ?Paxton	 ?and	 ?Priest	 ?Lake	 ?species	 ?pairs	 ?were	 ?parallel,	 ?meaning	 ?their	 ?phenotypic	 ?effects	 ?were	 ?in	 ?the	 ?same	 ?direction.	 ?When	 ?considering	 ?only	 ?QTL	 ?that	 ?actually	 ?had	 ?an	 ?effect	 ?in	 ?the	 ?crosses	 ?from	 ?both	 ?lakes,	 ?84%	 ?were	 ?parallel,	 ?while	 ?the	 ?Parallel Single lake Opposite0.00.10.20.30.40.50.60.7QTL effectAbsolute value of entropy difference???????????? ???? ??????????????????? ????	 ? 57	 ?other	 ?16%	 ?had	 ?effects	 ?in	 ?opposite	 ?directions.	 ?We	 ?also	 ?found	 ?that	 ?the	 ?average	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?traits	 ?that	 ?evolved	 ?in	 ?parallel	 ?in	 ?the	 ?two	 ?lakes	 ?was	 ?about	 ?0.4.	 ?These	 ?are	 ?the	 ?first	 ?comprehensive	 ?estimates	 ?of	 ?the	 ?extent	 ?of	 ?genetic	 ?parallelism	 ?underlying	 ?repeated	 ?phenotypic	 ?evolution	 ?in	 ?a	 ?single	 ?system.	 ?Because	 ?our	 ?focal	 ?phenotypes	 ?have	 ?repeatedly	 ?evolved	 ?in	 ?correlation	 ?with	 ?the	 ?environment	 ?and	 ?are	 ?therefore	 ?likely	 ?to	 ?be	 ?adaptive	 ?(Endler	 ?1986;	 ?Harvey	 ?and	 ?Pagel	 ?1991;	 ?Schluter	 ?2000;	 ?Losos	 ?2011),	 ?our	 ?study	 ?addresses	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?Since	 ?we	 ?mapped	 ?many,	 ?continuously	 ?varying	 ?quantitative	 ?traits	 ?as	 ?well	 ?as	 ?a	 ?few	 ?discrete	 ?traits,	 ?our	 ?results	 ?may	 ?be	 ?more	 ?characteristic	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?than	 ?estimates	 ?based	 ?only	 ?on	 ?few,	 ?discrete	 ?traits.	 ?	 ?Our	 ?estimate	 ?of	 ?the	 ?average	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?parallel	 ?traits,	 ?0.4,	 ?is	 ?not	 ?far	 ?off	 ?from	 ?the	 ?estimate	 ?by	 ?Conte	 ?et	 ?al.	 ?(2012), where	 ?proportional	 ?similarity	 ?of	 ?gene/QTL	 ?use	 ?between	 ?young	 ?populations	 ?belonging	 ?to	 ?the	 ?same	 ?species	 ?and	 ?evolving	 ?in	 ?parallel	 ?was	 ?0.47.	 ?Conte	 ?et	 ?al.	 ?(2012)	 ?also	 ?showed	 ?that	 ?the	 ?probability	 ?of	 ?repeated	 ?genetic	 ?evolution	 ?appears	 ?to	 ?decrease	 ?with	 ?increasing	 ?age	 ?of	 ?the	 ?taxa	 ?being	 ?compared	 ?(Conte	 ?et	 ?al.	 ?2012).	 ?Repeated	 ?genetic	 ?evolution	 ?may	 ?be	 ?more	 ?likely	 ?to	 ?occur	 ?in	 ?younger	 ?taxa	 ?if	 ?the	 ?biases	 ?potentially	 ?causing	 ?some	 ?genes	 ?to	 ?be	 ?used	 ?for	 ?the	 ?adaptive	 ?evolution	 ?of	 ?a	 ?phenotype	 ?more	 ?frequently	 ?than	 ?others	 ?diverge	 ?over	 ?time	 ?themselves.	 ?Younger	 ?taxa	 ?are	 ?also	 ?likely	 ?to	 ?share	 ?more	 ?standing	 ?genetic	 ?variation	 ?than	 ?older	 ?taxa,	 ?increasing	 ?the	 ?probability	 ?that	 ?the	 ?same	 ?loci	 ?are	 ?repeatedly	 ?involved	 ?in	 ?adaptive	 ?evolution	 ?of	 ?a	 ?phenotype.	 ?Given	 ?the	 ?young	 ?age	 ?of	 ?the	 ?threespine	 ?stickleback	 ?species	 ?pairs	 ?being	 ?compared	 ?here	 ?and	 ?the	 ?fact	 ?that	 ?shared	 ?standing	 ?genetic	 ?variation	 ?has	 ?probably	 ?been	 ?involved	 ?in	 ?the	 ?evolution	 ?of	 ?the	 ?species	 ?pairs,	 ?our	 ?estimates	 ?likely	 ?fall	 ?on	 ?the	 ?high	 ?end	 ?of	 ?the	 ?distribution	 ?of	 ?estimates	 ?that	 ?we	 ?can	 ?expect	 ?to	 ?find	 ?among	 ?other	 ?natural	 ?populations.	 ?Our	 ?estimates	 ?may	 ?be	 ?affected	 ?by	 ?several	 ?biases	 ?stemming	 ?from	 ?the	 ?use	 ?of	 ?QTL	 ?to	 ?study	 ?repeated	 ?genetic	 ?evolution,	 ?rather	 ?than	 ?genes	 ?or	 ?mutations.	 ?Because	 ?QTL	 ?are	 ?genomic	 ?regions	 ?that	 ?are	 ?usually	 ?large	 ?and	 ?contain	 ?many	 ?genes	 ?in	 ?addition	 ?to	 ?the	 ?causal	 ?one(s),	 ?we	 ?may	 ?have	 ?overestimated	 ?the	 ?amount	 ?of	 ?genetic	 ?parallelism	 ?if	 ?different	 ?loci	 ?within/linked	 ?to	 ?QTL	 ?are	 ?responsible	 ?for	 ?parallel	 ?phenotypic	 ?	 ? 58	 ?changes.	 ?Shared	 ?local	 ?genomic	 ?landscape	 ?(Renaut	 ?et	 ?al.	 ?2014)	 ?in	 ?addition	 ?to	 ?weak	 ?selection	 ?for	 ?the	 ?clustering	 ?of	 ?loci	 ?involved	 ?in	 ?local	 ?adaptation	 ?(Yeaman	 ?2013)	 ?may,	 ?indeed,	 ?increase	 ?the	 ?probability	 ?that	 ?different	 ?loci	 ?within	 ?the	 ?same	 ?genomic	 ?regions	 ?may	 ?underlie	 ?parallel	 ?phenotypic	 ?evolution.	 ?On	 ?the	 ?contrary,	 ?QTL	 ?detection	 ?limitations	 ?may	 ?have	 ?caused	 ?us	 ?to	 ?underestimate	 ?the	 ?amount	 ?of	 ?genetic	 ?parallelism.	 ?First,	 ?the	 ?traits	 ?we	 ?mapped	 ?were	 ?mostly	 ?continuously	 ?varying,	 ?quantitative	 ?traits,	 ?underlain	 ?by	 ?mostly	 ?small	 ?effect	 ?loci.	 ?The	 ?closer	 ?the	 ?effect	 ?of	 ?a	 ?QTL	 ?was	 ?to	 ?our	 ?detection	 ?threshold	 ?(either	 ?slightly	 ?above	 ?or	 ?below	 ?it),	 ?the	 ?less	 ?likely	 ?it	 ?became	 ?that	 ?we	 ?would	 ?be	 ?able	 ?to	 ?detect	 ?that	 ?same	 ?QTL	 ?twice,	 ?due	 ?to	 ?sampling	 ?error	 ?(Beavis	 ?1998).	 ?For	 ?this	 ?reason,	 ?we	 ?may	 ?have	 ?miss-??categorized	 ?some	 ?QTL	 ?of	 ?small	 ?effect	 ?as	 ?having	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake.	 ?Interestingly	 ?though,	 ?if	 ?this	 ?were	 ?often	 ?the	 ?case,	 ?we	 ?would	 ?expect	 ?to	 ?have	 ?an	 ?enrichment	 ?of	 ?very	 ?small	 ?effect	 ?QTL	 ?in	 ?the	 ??single	 ?lake?	 ?QTL	 ?effect	 ?category,	 ?which	 ?we	 ?do	 ?not	 ?see	 ?(see	 ?Figure	 ?4.4).	 ?Second,	 ?we	 ?only	 ?used	 ?one	 ?cross	 ?per	 ?lake	 ?and	 ?therefore,	 ?one	 ?individual	 ?per	 ?species.	 ?If	 ?the	 ?effects	 ?of	 ?any	 ?morphological	 ?QTL	 ?are	 ?polymorphic	 ?within	 ?species,	 ?and	 ?our	 ?F0	 ?progenitors	 ?from	 ?one	 ?lake	 ?happened	 ?to	 ?be	 ?lacking	 ?those	 ?effects,	 ?we	 ?may	 ?have	 ?concluded	 ?that	 ?such	 ?QTL	 ?had	 ?an	 ?effect	 ?in	 ?only	 ?one	 ?lake,	 ?when,	 ?in	 ?fact,	 ?we	 ?just	 ?did	 ?not	 ?sample	 ?the	 ?effect	 ?in	 ?the	 ?other	 ?lake.	 ?	 ?Third,	 ?we	 ?may	 ?have	 ?missed	 ?some	 ?parallel	 ?(and	 ?non-??parallel)	 ?QTL	 ?if	 ?there	 ?was	 ?simply	 ?too	 ?little	 ?genotype	 ?information	 ?in	 ?the	 ?QTL	 ?region	 ?in	 ?one	 ?of	 ?the	 ?crosses.	 ?We	 ?did,	 ?in	 ?fact,	 ?find	 ?evidence	 ?for	 ?an	 ?enrichment	 ?of	 ?large	 ?differences	 ?in	 ?genotype	 ?information	 ?content	 ?(entropy)	 ?between	 ?the	 ?crosses	 ?at	 ?candidate	 ?QTL	 ?that	 ?were	 ?found	 ?to	 ?have	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake.	 ?It	 ?is,	 ?thus,	 ?possible	 ?that	 ?some	 ??single-??lake?	 ?QTL	 ?actually	 ?belong	 ?in	 ?the	 ?parallel	 ?or	 ?non-??parallel	 ?QTL	 ?effect	 ?categories,	 ?but	 ?that	 ?genotype	 ?information	 ?content	 ?was	 ?too	 ?low	 ?in	 ?one	 ?of	 ?the	 ?crosses	 ?to	 ?detect	 ?their	 ?effect.	 ?While	 ?that	 ?may	 ?be,	 ?it	 ?seems	 ?that	 ?entropy	 ?differences	 ?were	 ?high	 ?enough	 ?to	 ?have	 ?potentially	 ?mislead	 ?us	 ?in	 ?a	 ?relatively	 ?small	 ?number	 ?of	 ?cases	 ?(see	 ?Figure	 ?4.5).	 ?We	 ?found	 ?no	 ?evidence	 ?that	 ?the	 ?phenotypic	 ?effect	 ?sizes	 ?of	 ?QTL	 ?affect	 ?how	 ?likely	 ?they	 ?are	 ?to	 ?be	 ?parallel.	 ?On	 ?the	 ?one	 ?hand,	 ?we	 ?might	 ?have	 ?expected	 ?to	 ?see	 ?that	 ?larger	 ?effect	 ?QTL	 ?were	 ?more	 ?often	 ?parallel	 ?than	 ?smaller	 ?effect	 ?QTL.	 ?This	 ?is	 ?because	 ?mutations	 ?(which	 ?underlie	 ?QTL)	 ?have	 ?an	 ?increased	 ?probability	 ?of	 ?fixation	 ?with	 ?an	 ?increased	 ?beneficial	 ?effect	 ?(Fisher	 ?1930;	 ?Kimura	 ?1983).	 ?If	 ?phenotypic	 ?effect	 ?sizes	 ?of	 ?	 ? 59	 ?mutations	 ?are	 ?positively	 ?correlated	 ?with	 ?their	 ?fitness	 ?effect	 ?sizes,	 ?then	 ?mutations	 ?of	 ?large	 ?phenotypic	 ?effect	 ?should	 ?be	 ?on	 ?average	 ?more	 ?likely	 ?fix	 ?and	 ?therefore,	 ?more	 ?likely	 ?to	 ?fix	 ?repeatedly.	 ?On	 ?the	 ?other	 ?hand,	 ?if	 ?mutations	 ?affect	 ?multiple	 ?phenotypes	 ?and	 ?this	 ?results	 ?in	 ?a	 ?weak	 ?correlation	 ?between	 ?phenotypic	 ?effect	 ?sizes	 ?and	 ?fitness	 ?effect	 ?sizes,	 ?our	 ?predictions	 ?regarding	 ?parallelism	 ?may	 ?differ.	 ?For	 ?instance,	 ?according	 ?to	 ?Fisher?s	 ?geometric	 ?model,	 ?mutations	 ?with	 ?a	 ?large	 ?phenotypic	 ?effect	 ?infrequently	 ?contribute	 ?to	 ?adaptation	 ?in	 ?new	 ?or	 ?changing	 ?environments	 ?(Fisher	 ?1930;	 ?Orr	 ?2006)	 ?and	 ?typically	 ?occur	 ?early	 ?in	 ?an	 ?adaptive	 ?walk	 ?on	 ?a	 ?fitness	 ?landscape,	 ?before	 ?deleterious	 ?side-??effects	 ?on	 ?other	 ?traits	 ?are	 ?too	 ?severe	 ?(Orr	 ?2006)	 ?with	 ?distance	 ?from	 ?the	 ?optima	 ?also	 ?affecting	 ?how	 ?frequently	 ?they	 ?occur	 ?(Rogers	 ?et	 ?al.	 ?2012a).	 ?Consequently,	 ?it	 ?is	 ?possible	 ?that	 ?mutations	 ?of	 ?larger	 ?phenotypic	 ?effect	 ?are	 ?equally	 ?or	 ?less	 ?likely	 ?to	 ?fix	 ?repeatedly	 ?than	 ?mutations	 ?of	 ?smaller	 ?phenotypic	 ?effect.	 ?In	 ?our	 ?study,	 ?it	 ?is	 ?possible	 ?that	 ?opposing	 ?influences	 ?of	 ?the	 ?phenotypic	 ?effect	 ?size	 ?of	 ?QTL	 ?on	 ?the	 ?probability	 ?that	 ?their	 ?effects	 ?are	 ?parallel	 ?(such	 ?as	 ?those	 ?just	 ?discussed)	 ?result	 ?in	 ?no	 ?net	 ?effect.	 ?Another	 ?possibility	 ?is	 ?that	 ?even	 ?if	 ?a	 ?clear	 ?trend	 ?does	 ?exist,	 ?it	 ?could	 ?only	 ?be	 ?seen	 ?with	 ?greater	 ?variation	 ?in	 ?effect	 ?size	 ?and/or	 ?a	 ?larger	 ?sample	 ?size	 ?than	 ?was	 ?involved	 ?in	 ?our	 ?study.	 ?More	 ?work	 ?is	 ?clearly	 ?needed	 ?in	 ?this	 ?area.	 ?For	 ?example,	 ?studies	 ?that	 ?directly	 ?measure	 ?the	 ?fitness	 ?effect	 ?sizes	 ?of	 ?QTL	 ?will	 ?provide	 ?useful	 ?insights,	 ?as	 ?predictions	 ?regarding	 ?fitness	 ?effect	 ?sizes	 ?are	 ?more	 ?straightforward	 ?than	 ?those	 ?regarding	 ?phenotypic	 ?effect	 ?sizes.	 ?Note	 ?that,	 ?if	 ?the	 ?number	 ?of	 ?traits	 ?that	 ?map	 ?to	 ?a	 ?genomic	 ?region	 ?is	 ?a	 ?good	 ?proxy	 ?for	 ?the	 ?cumulative	 ?fitness	 ?effect	 ?size	 ?of	 ?that	 ?genomic	 ?region,	 ?then	 ?we	 ?might	 ?expect	 ?to	 ?see	 ?a	 ?relationship	 ?between	 ?this	 ?number	 ?and	 ?how	 ?parallel	 ?the	 ?genomic	 ?region	 ?is	 ?(the	 ?proportion	 ?of	 ?traits	 ?with	 ?a	 ?parallel	 ?QTL	 ?in	 ?the	 ?region).	 ?However,	 ?we	 ?saw	 ?no	 ?such	 ?relationship	 ?in	 ?our	 ?data	 ?and	 ?since	 ?that	 ?may	 ?be	 ?due	 ?to	 ?a	 ?weak	 ?correlation	 ?between	 ?number	 ?of	 ?traits	 ?that	 ?map	 ?to	 ?a	 ?region	 ?and	 ?the	 ?fitness	 ?effect	 ?size	 ?of	 ?the	 ?region,	 ?we	 ?do	 ?not	 ?formally	 ?present	 ?that	 ?result	 ?here.	 ?	 ?	 ? 60	 ?Conclusions	 ?Here	 ?we	 ?present	 ?the	 ?first	 ?comprehensive	 ?estimate	 ?of	 ?the	 ?extent	 ?of	 ?parallel	 ?genetic	 ?evolution	 ?underlying	 ?parallel	 ?phenotypic	 ?evolution,	 ?in	 ?a	 ?single	 ?system.	 ?These	 ?estimates	 ?reflect	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?in	 ?the	 ?system.	 ?In	 ?young	 ?species	 ?pairs	 ?of	 ?threespine	 ?stickleback,	 ?we	 ?find	 ?that	 ?almost	 ?50%	 ?of	 ?QTL	 ?have	 ?parallel	 ?effects	 ?and	 ?on	 ?average	 ?about	 ?40%	 ?of	 ?QTL	 ?use	 ?underlying	 ?individual	 ?traits	 ?is	 ?shared.	 ?We	 ?find	 ?no	 ?evidence	 ?that	 ?the	 ?phenotypic	 ?effect	 ?size	 ?of	 ?QTL	 ?affects	 ?how	 ?likely	 ?they	 ?are	 ?to	 ?be	 ?parallel.	 ?Future	 ?studies	 ?may	 ?improve	 ?upon	 ?our	 ?estimates	 ?by	 ?using	 ?a	 ?greater	 ?number	 ?of	 ?genetic	 ?markers	 ?that	 ?are	 ?informative	 ?in	 ?both	 ?species	 ?pairs,	 ?multiple	 ?crosses	 ?per	 ?species	 ?pair	 ?and	 ?even	 ?larger	 ?samples	 ?sizes	 ?of	 ?F2	 ?hybrids.	 ?As	 ?we	 ?obtain	 ?more	 ?and	 ?better	 ?estimates	 ?of	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?we	 ?will	 ?be	 ?better	 ?able	 ?to	 ?understand	 ?what	 ?factors	 ?influence	 ?predictability.	 ?Future	 ?studies	 ?should	 ?aim	 ?to	 ?estimate	 ?the	 ?actual	 ?number	 ?of	 ?genes	 ?in	 ?which	 ?mutations	 ?may	 ?lead	 ?to	 ?a	 ?particular	 ?phenotype,	 ?and	 ?then	 ?begin	 ?to	 ?dissect	 ?the	 ?deterministic	 ?factors	 ?that	 ?we	 ?predict	 ?will	 ?cause	 ?the	 ?effective	 ?number	 ?to	 ?be	 ?lower.	 ?Methods	 ?Genetic	 ?Crosses	 ?and	 ?Experimental	 ?Ponds	 ?In	 ?2009,	 ?we	 ?used	 ?wild-??caught	 ?adult	 ?fish	 ?to	 ?make	 ?two	 ?in	 ?vitro	 ?interspecific	 ?crosses,	 ?one	 ?using	 ?fish	 ?from	 ?Paxton	 ?Lake	 ?and	 ?the	 ?other	 ?using	 ?fish	 ?from	 ?Priest	 ?Lake.	 ?Both	 ?crosses	 ?involved	 ?a	 ?limnetic	 ?female	 ?and	 ?a	 ?benthic	 ?male.	 ?We	 ?stored	 ?their	 ?bodies	 ?in	 ?95%	 ?ethanol	 ?for	 ?DNA	 ?analysis.	 ?We	 ?reared	 ?the	 ?resulting	 ?F1	 ?hybrids	 ?in	 ?the	 ?laboratory.	 ?On	 ?May	 ?2,	 ?2010	 ?we	 ?randomly	 ?selected	 ?35	 ?F1	 ?hybrid	 ?adults	 ?(19	 ?female	 ?and	 ?16	 ?male)	 ?from	 ?the	 ?Paxton	 ?cross	 ?and	 ?25	 ?F1	 ?hybrid	 ?adults	 ?(12	 ?female	 ?and	 ?13	 ?male)	 ?from	 ?the	 ?Priest	 ?cross.	 ?We	 ?took	 ?a	 ?sample	 ?of	 ?caudal	 ?fin	 ?tissue	 ?from	 ?each	 ?individual	 ?F1	 ?hybrid	 ?for	 ?DNA	 ?analysis	 ?and	 ?then	 ?released	 ?them	 ?into	 ?separate	 ?experimental	 ?ponds	 ?at	 ?the	 ?UBC	 ?pond	 ?facility.	 ?These	 ?ponds	 ?(25	 ?x	 ?15	 ?m)	 ?were	 ?designed	 ?to	 ?harbor	 ?both	 ?benthic	 ?and	 ?limnetic	 ?habitat,	 ?containing	 ?a	 ?sloping	 ?shallow	 ?zone	 ?and	 ?a	 ?deep	 ?open-??water	 ?zone	 ?(6	 ?m	 ?deep)	 ?(Arnegard	 ?et	 ?al.	 ?in	 ?press).	 ?To	 ?establish	 ?a	 ?natural	 ?prey	 ?base,	 ?	 ? 61	 ?we	 ?inoculated	 ?the	 ?ponds	 ?with	 ?macrophytes,	 ?sediments	 ?and	 ?water	 ?full	 ?of	 ?aquatic	 ?insects,	 ?mollusks	 ?and	 ?plankton	 ?from	 ?Paxton	 ?Lake.	 ?We	 ?did	 ?this	 ?once	 ?in	 ?the	 ?spring	 ?of	 ?2009	 ?(a	 ?year	 ?before	 ?releasing	 ?our	 ?F1	 ?hybrids)	 ?and	 ?once	 ?in	 ?the	 ?spring	 ?of	 ?2011.	 ?We	 ?additionally	 ?added	 ?1.25kg	 ?of	 ?a	 ?25.5:1	 ?mix	 ?of	 ?50%	 ?pure	 ?KNO3	 ?:	 ?KH2PO4	 ?in	 ?the	 ?spring	 ?of	 ?2009	 ?and	 ?again	 ?in	 ?the	 ?spring	 ?of	 ?2010.	 ?After	 ?release,	 ?the	 ?F1	 ?hybrids	 ?were	 ?allowed	 ?to	 ?mate	 ?freely	 ?with	 ?their	 ?full-??siblings	 ?in	 ?the	 ?ponds	 ?throughout	 ?the	 ?breeding	 ?season.	 ?The	 ?following	 ?year,	 ?on	 ?September	 ?14,	 ?2011,	 ?we	 ?collected	 ?407	 ?adult	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?and	 ?324	 ?adult	 ?F2	 ?hybrids	 ?from	 ?the	 ?Priest	 ?Lake	 ?cross.	 ?We	 ?euthanized	 ?F2	 ?hybrids	 ?using	 ?buffered	 ?MS222	 ?and	 ?then	 ?took	 ?a	 ?sample	 ?of	 ?caudal	 ?fin	 ?tissue	 ?from	 ?each	 ?individual	 ?F2	 ?for	 ?DNA	 ?analysis.	 ?Then,	 ?we	 ?fixed	 ?each	 ?F2	 ?hybrid	 ?body	 ?in	 ?10%	 ?formalin	 ?for	 ?morphological	 ?measurements.	 ?During	 ?the	 ?same	 ?summer,	 ?we	 ?collected	 ?an	 ?additional	 ?180	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?and	 ?92	 ?F2	 ?hybrids	 ?from	 ?the	 ?Priest	 ?Lake	 ?cross	 ?for	 ?a	 ?separate	 ?study.	 ?These	 ?F2	 ?hybrids	 ?were	 ?not	 ?included	 ?in	 ?the	 ?QTL	 ?mapping	 ?stage	 ?of	 ?this	 ?study	 ?but	 ?were	 ?used	 ?to	 ?construct	 ?the	 ?linkage	 ?map.	 ?	 ?Wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?samples	 ?To	 ?enable	 ?us	 ?to	 ?determine	 ?whether	 ?or	 ?not	 ?our	 ?focal	 ?phenotypes	 ?diverged	 ?in	 ?parallel,	 ?we	 ?obtained	 ?high	 ?quality	 ?photos	 ?of	 ?Alizarin	 ?Red-??stained,	 ?wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?specimens	 ?from	 ?Paxton	 ?and	 ?Priest	 ?Lakes.	 ?	 ?From	 ?these	 ?collections,	 ?made	 ?in	 ?the	 ?spring	 ?of	 ?2005	 ?(Ingram	 ?et	 ?al.	 ?2012),	 ?we	 ?used	 ?25	 ?benthics	 ?and	 ?21	 ?limnetics	 ?from	 ?Paxton	 ?Lake	 ?and	 ?36	 ?benthic	 ?and	 ?22	 ?limnetics	 ?from	 ?Priest	 ?Lake.	 ?Since	 ?the	 ?Priest	 ?limnetic	 ?sample	 ?contained	 ?no	 ?females,	 ?we	 ?supplemented	 ?the	 ?collection	 ?with	 ?23	 ?additional	 ?Alizarin	 ?red	 ?stained	 ?wild-??caught	 ?Priest	 ?limnetics	 ?(10	 ?female)	 ?that	 ?were	 ?collected,	 ?stained	 ?and	 ?photographed	 ?in	 ?1999	 ?by	 ?J.	 ?Gow.	 ?	 ?Phenotype	 ?Measurements	 ?We	 ?stained	 ?the	 ?F2	 ?specimens	 ?with	 ?Alizarin	 ?Red,	 ?following	 ?the	 ?methods	 ?of	 ?Peichel	 ?et	 ?al.	 ?(2001),	 ?and	 ?then	 ?took	 ?lateral	 ?high-??resolution	 ?photographs,	 ?containing	 ?a	 ?ruler	 ?in	 ?each	 ?photograph	 ?for	 ?scale.	 ?All	 ?of	 ?the	 ?following	 ?steps	 ?were	 ?done	 ?separately	 ?for	 ?the	 ?wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?collection	 ?and	 ?for	 ?the	 ?F2	 ?hybrid	 ?collection.	 ?	 ? 62	 ?Using	 ?tpsDig	 ?(Rohlf	 ?2010)	 ?we	 ?digitized	 ?and	 ?scaled	 ?26	 ?morphological	 ?landmarks	 ?(Figure	 ?C.1)	 ?on	 ?the	 ?photos	 ?of	 ?the	 ?specimens	 ?in	 ?randomized	 ?order.	 ?We	 ?measured	 ?centroid	 ?size	 ?as	 ?the	 ?square	 ?root	 ?of	 ?the	 ?sum	 ?of	 ?squared	 ?distances	 ?of	 ?the	 ?26	 ?landmarks	 ?from	 ?their	 ?centroid.	 ?We	 ?then	 ?performed	 ?Generalized	 ?Procrustes	 ?Superimposition	 ?on	 ?the	 ?x	 ?and	 ?y	 ?coordinates	 ?of	 ?the	 ?scaled	 ?landmarks	 ?using	 ?the	 ?R	 ?package	 ??shapes?	 ?(Dryden	 ?2013),	 ?resulting	 ?in	 ?52	 ??landmark	 ?traits?.	 ?To	 ?correct	 ?for	 ?specimen	 ?bending,	 ?we	 ?followed	 ?the	 ?approach	 ?of	 ?Albert	 ?et	 ?al.	 ?(2008).	 ?	 ?We	 ?scored	 ?five	 ?meristic	 ?traits	 ?(i.e.	 ?countable	 ?quantitative	 ?traits)	 ?(Figure	 ?C.1)	 ?using	 ?the	 ?fixed	 ?and	 ?stained	 ?F2	 ?specimens	 ?themselves.	 ?In	 ?the	 ?absence	 ?of	 ?the	 ?wild-??caught	 ?reference	 ?fish	 ?specimens,	 ?we	 ?scored	 ?meristic	 ?traits	 ?using	 ?their	 ?photos.	 ?However,	 ?since	 ?photos	 ?do	 ?not	 ?show	 ?the	 ?gill	 ?rakers,	 ?we	 ?could	 ?not	 ?count	 ?the	 ?long	 ?and	 ?short	 ?gill	 ?rakers	 ?on	 ?the	 ?first	 ?gill	 ?arch	 ?for	 ?the	 ?wild-??caught	 ?benthic	 ?and	 ?limnetic	 ?samples,	 ?as	 ?was	 ?done	 ?for	 ?the	 ?F2	 ?hybrids.	 ?Instead	 ?we	 ?used	 ?counts	 ?taken	 ?by	 ?Ingram	 ?et	 ?al.	 ?(2012)	 ?of	 ?the	 ?total	 ?gill	 ?raker	 ?number	 ?on	 ?the	 ?first	 ?gill	 ?arch	 ?for	 ?the	 ?same	 ?individuals.	 ?For	 ?the	 ?23	 ?additional	 ?Priest	 ?limnetic	 ?fish,	 ?no	 ?gill	 ?raker	 ?counts	 ?were	 ?available,	 ?and	 ?thus,	 ?they	 ?were	 ?left	 ?out	 ?of	 ?the	 ?test	 ?of	 ?parallelism	 ?in	 ?gill	 ?raker	 ?divergence.	 ?We	 ?tested	 ?for	 ?and	 ?removed	 ?significant	 ?outlier	 ?data	 ?points	 ?for	 ?all	 ?traits	 ?using	 ?the	 ?function	 ??outlierTest?	 ?in	 ?the	 ?R	 ?package	 ??car?	 ?(Fox	 ?et	 ?al.	 ?2013).	 ?F2	 ?hybrids	 ?that	 ?were	 ?standard	 ?length	 ?outliers	 ?were	 ?dropped	 ?from	 ?the	 ?study	 ?(4	 ?Paxton	 ?individuals	 ?and	 ?1	 ?Priest	 ?individual).	 ?Identifying	 ?parallel	 ?phenotypic	 ?evolution	 ?We	 ?classified	 ?a	 ?trait?s	 ?divergence	 ?as	 ??parallel?	 ?when	 ?the	 ?species	 ?difference	 ?in	 ?the	 ?trait	 ?was	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?both	 ?lakes	 ?(i.e.	 ?benthics	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?limnetics	 ?in	 ?both	 ?lakes,	 ?or	 ?vise	 ?versa),	 ?though	 ?not	 ?necessarily	 ?of	 ?the	 ?same	 ?magnitude.	 ?We	 ?classified	 ?a	 ?trait?s	 ?divergence	 ?as	 ??opposite?	 ?when	 ?the	 ?species	 ?difference	 ?in	 ?the	 ?trait	 ?was	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes	 ?(i.e.	 ?benthics	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?limnetics	 ?in	 ?one	 ?lake	 ?and	 ?a	 ?lower	 ?mean	 ?than	 ?limnetics	 ?in	 ?the	 ?other).	 ?If	 ?the	 ?species	 ?differed	 ?in	 ?a	 ?trait	 ?in	 ?only	 ?one	 ?of	 ?the	 ?two	 ?lakes,	 ?we	 ?classified	 ?the	 ?trait?s	 ?divergence	 ?as	 ?only	 ?in	 ?a	 ??single	 ?lake?.	 ?Finally,	 ?if	 ?the	 ?species	 ?did	 ?not	 ?differ	 ?in	 ?a	 ?	 ? 63	 ?trait	 ?in	 ?either	 ?lake,	 ?we	 ?classified	 ?the	 ?trait?s	 ?divergence	 ?as	 ?in	 ??neither	 ?lake?.	 ?For	 ?each	 ?trait,	 ?we	 ?tested	 ?these	 ?scenarios	 ?by	 ?fitting	 ?5	 ?linear	 ?models	 ?to	 ?phenotypes	 ?of	 ?our	 ?wild	 ?caught	 ?benthics	 ?and	 ?limnetics	 ?from	 ?both	 ?lakes	 ?and	 ?then	 ?deciding	 ?which	 ?fit	 ?the	 ?data	 ?best	 ?(Table	 ?C.1).	 ?Model	 ?1:	 ??same	 ?effect?,	 ?included	 ?the	 ?main	 ?effect	 ?of	 ?species	 ?(benthic	 ?vs.	 ?limnetic).	 ?Model	 ?2:	 ??different	 ?effect?	 ?included	 ?the	 ?main	 ?effects	 ?of	 ?both	 ?species	 ?and	 ?its	 ?interaction	 ?with	 ?lake	 ?(allowing	 ?the	 ?effect	 ?of	 ?species	 ?to	 ?differ	 ?between	 ?the	 ?lakes).	 ?In	 ?Model	 ?3:	 ??effect	 ?in	 ?Paxton	 ?only?,	 ?the	 ?main	 ?effect	 ?of	 ?species	 ?was	 ?manipulated	 ?so	 ?that	 ?all	 ?Priest	 ?individuals	 ?were	 ?treated	 ?as	 ?the	 ?same	 ?species.	 ?The	 ?reverse	 ?was	 ?done	 ?in	 ?Model	 ?4:	 ??effect	 ?in	 ?Priest	 ?only?.	 ?Finally	 ?in	 ?Model	 ?5:	 ??no	 ?effect?,	 ?the	 ?effects	 ?of	 ?species	 ?were	 ?dropped	 ?completely.	 ?All	 ?models	 ?included	 ?the	 ?main	 ?effect	 ?of	 ?sex.	 ?We	 ?then	 ?grouped	 ?traits	 ?into	 ?the	 ?four	 ??Trait	 ?divergence?	 ?categories	 ?based	 ?on	 ?the	 ?model	 ?with	 ?lowest	 ?AICc	 ?value,	 ?which	 ?we	 ?term	 ?the	 ??best	 ?model?.	 ?Trait	 ?divergence	 ?was	 ?considered	 ?to	 ?be	 ?parallel	 ?if	 ?either	 ?model	 ?1	 ?was	 ?the	 ?best,	 ?or	 ?model	 ?2	 ?was	 ?the	 ?best	 ?and	 ?the	 ?effect	 ?of	 ?species	 ?was	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?the	 ?two	 ?lakes.	 ?Traits	 ?were	 ?considered	 ?to	 ?be	 ?divergent	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?if	 ?either	 ?model	 ?3	 ?or	 ?4	 ?was	 ?the	 ?best.	 ?Trait	 ?divergence	 ?was	 ?considered	 ?to	 ?be	 ?opposite	 ?if	 ?model	 ?2	 ?was	 ?the	 ?best	 ?and	 ?the	 ?effect	 ?of	 ?species	 ?was	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes.	 ?Finally,	 ?a	 ?trait	 ?was	 ?considered	 ?to	 ?be	 ?divergent	 ?in	 ?neither	 ?lake	 ?if	 ?model	 ?5	 ?was	 ?the	 ?best.	 ?	 ?For	 ?15	 ?traits,	 ?more	 ?than	 ?one	 ?trait	 ?divergence	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well.	 ?That	 ?is,	 ?the	 ?delta	 ?AICc	 ?value	 ?between	 ?the	 ?best	 ?model	 ?and	 ?the	 ?second	 ?best	 ?model	 ?was	 ?less	 ?than	 ?2	 ?and	 ?the	 ?second	 ?best	 ?model	 ?called	 ?for	 ?a	 ?different	 ?trait	 ?divergence	 ?category	 ?than	 ?the	 ?best	 ?model	 ?did	 ?(Table	 ?C.1).	 ?These	 ?15	 ?traits	 ?were,	 ?therefore	 ?left	 ?out	 ?of	 ?all	 ?calculations	 ?and	 ?analyses	 ?in	 ?which	 ?trait	 ?divergence	 ?category	 ?was	 ?a	 ?variable.	 ?Finally,	 ?because	 ?the	 ?gill	 ?raker	 ?counts	 ?for	 ?our	 ?wild-??caught	 ?reference	 ?fish	 ?were	 ?of	 ?the	 ?total	 ?number	 ?on	 ?the	 ?first	 ?gill	 ?arch,	 ?rather	 ?than	 ?the	 ?subdivided	 ?counts	 ?of	 ?long	 ?and	 ?short	 ?rakers	 ?on	 ?the	 ?first	 ?arch,	 ?as	 ?was	 ?scored	 ?in	 ?our	 ?F2	 ?hybrids,	 ?the	 ?trait	 ?divergence	 ?category	 ?determined	 ?for	 ?the	 ?total	 ?number	 ?of	 ?gill	 ?rakers,	 ?was	 ?inferred	 ?to	 ?be	 ?the	 ?trait	 ?divergence	 ?category	 ?of	 ?the	 ?subdivided	 ?counts	 ?as	 ?well.	 ?However,	 ?for	 ?calculating	 ?the	 ?proportion	 ?of	 ?traits	 ?that	 ?diverged	 ?in	 ?parallel,	 ?in	 ?a	 ?single	 ?lake	 ?and	 ?in	 ?	 ? 64	 ?opposite	 ?directions	 ?(Figure	 ?4.1),	 ?only	 ?the	 ?total	 ?gill	 ?raker	 ?count	 ?was	 ?considered	 ?(therefore,	 ?n=57	 ?rather	 ?than	 ?n=58	 ?traits	 ?for	 ?those	 ?calculations).	 ?SNP	 ?Genotyping	 ?We	 ?isolated	 ?genomic	 ?DNA	 ?from	 ?caudal	 ?fin	 ?tissue	 ?of	 ?our	 ?4	 ?F0	 ?progenitors,	 ?60	 ?F1	 ?hybrids	 ?and	 ?735	 ?F2	 ?hybrids	 ?using	 ?either	 ?Proteinase	 ?K	 ?digestion,	 ?phenol-??chloroform	 ?extraction,	 ?ethanol	 ?precipitation	 ?and	 ?re-??suspension	 ?of	 ?the	 ?precipitated	 ?DNA	 ?in	 ?30	 ??L	 ?of	 ?TE	 ?buffer	 ?(10	 ?mM	 ?Tris,	 ?1	 ?mM	 ?EDTA,	 ?pH	 ?8.0),	 ?or	 ?the	 ?DNeasy	 ?96	 ?Blood	 ?and	 ?Tissue	 ?Kit	 ?(QIAGEN),	 ?using	 ?only	 ?30?L	 ?of	 ?Buffer	 ?AE	 ?for	 ?the	 ?first	 ?elution	 ?(leading	 ?to	 ?a	 ?relatively	 ?high	 ?DNA	 ?concentration	 ?in	 ?the	 ?eluant).	 ?	 ?We	 ?then	 ?diluted	 ?an	 ?aliquot	 ?of	 ?each	 ?sample	 ?using	 ?TE	 ?buffer	 ?to	 ?a	 ?DNA	 ?concentration	 ?between	 ?3	 ?ng/?L	 ?and	 ?150	 ?ng/?L,	 ?based	 ?on	 ?the	 ?PicoGreen?	 ?assay	 ?(Life	 ?Technologies).	 ?We	 ?genotyped	 ?all	 ?F0,	 ?F1,	 ?and	 ?F2	 ?individuals	 ?using	 ?Illumina?s	 ?GoldenGate	 ?assay	 ?and	 ?a	 ?custom	 ?multiplex	 ?oligonucleotide	 ?pool	 ?developed	 ?for	 ?a	 ?recently	 ?published	 ?collection	 ?of	 ?SNPs	 ?(Jones	 ?et	 ?al.	 ?2012a).	 ?We	 ?found	 ?430	 ?of	 ?these	 ?SNPs	 ?to	 ?be	 ?polymorphic	 ?in	 ?at	 ?least	 ?one	 ?of	 ?our	 ?crosses	 ?(246	 ?were	 ?polymorphic	 ?in	 ?the	 ?Paxton	 ?cross	 ?and	 ?318	 ?were	 ?polymorphic	 ?in	 ?the	 ?Priest	 ?cross,	 ?134	 ?of	 ?which	 ?were	 ?polymorphic	 ?in	 ?both).	 ?See	 ?Table	 ?C.2	 ?for	 ?the	 ?identities,	 ?genomic	 ?locations,	 ?and	 ?U.S.	 ?National	 ?Center	 ?for	 ?Biotechnology	 ?Information	 ?(NCBI)	 ?identification	 ?numbers	 ?for	 ?all	 ?430	 ?SNP	 ?markers.	 ?	 ?The	 ?Illumina	 ?Sentrix	 ?Array	 ?Matrices	 ?used	 ?for	 ?genotyping	 ?were	 ?processed	 ?at	 ?the	 ?Genomics	 ?Shared	 ?Resource	 ?of	 ?the	 ?Fred	 ?Hutchinson	 ?Cancer	 ?Research	 ?Center	 ?(Seattle,	 ?WA,	 ?USA).	 ?We	 ?scored	 ?genotypes	 ?from	 ?the	 ?raw	 ?data	 ?using	 ?GenomeStudio	 ?software	 ?(Illumina	 ?Inc.).	 ?Linkage	 ?Mapping	 ?We	 ?created	 ?a	 ?linkage	 ?map	 ?with	 ?JoinMap	 ?ver.	 ?3.0	 ?(Ooijen	 ?and	 ?Voorrips	 ?2002),	 ?coding	 ?F2	 ?hybrid	 ?genotypes	 ?according	 ?to	 ?the	 ??cross	 ?pollinator?	 ?population	 ?code	 ?for	 ?outbred	 ?crosses	 ?between	 ?two	 ?diploid	 ?parents.	 ?To	 ?determine	 ?the	 ?identity	 ?of	 ?each	 ?F2	 ?hybrid?s	 ?F1	 ?parents,	 ?we	 ?used	 ?the	 ?R	 ?package	 ??MasterBayes?	 ?(Hadfield	 ?2013)	 ?to	 ?reconstruct	 ?pedigrees	 ?based	 ?on	 ?the	 ?full	 ?SNP	 ?dataset.	 ?For	 ?creating	 ?the	 ?linkage	 ?map,	 ?	 ? 65	 ?we	 ?included	 ?F1	 ?x	 ?F1	 ?families	 ?containing	 ?at	 ?least	 ?10	 ?F2	 ?hybrids.	 ?This	 ?included	 ?268	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?cross	 ?and	 ?261	 ?F2	 ?hybrids	 ?from	 ?the	 ?Priest	 ?cross,	 ?some	 ?of	 ?which	 ?were	 ?not	 ?used	 ?for	 ?QTL	 ?mapping,	 ?as	 ?noted	 ?above.	 ?	 ?We	 ?used	 ?JoinMap	 ?ver.	 ?3.0	 ?to	 ?compute	 ?all	 ?obtainable	 ?pairwise	 ?recombination	 ?frequencies	 ?and	 ?associated	 ?LOD	 ?(logarithm	 ?(base	 ?10)	 ?of	 ?odds)	 ?scores	 ?between	 ?SNP	 ?makers	 ?for	 ?each	 ?F1	 ?x	 ?F1	 ?family	 ?(hereafter	 ??family?)	 ?separately.	 ?We	 ?then	 ?combined	 ?them	 ?into	 ?a	 ?single	 ?pairwise	 ?data	 ?file	 ?(pwd-??file)	 ?for	 ?all	 ?families	 ?and	 ?used	 ?JoinMap	 ?to	 ?produce	 ?a	 ?single	 ?linkage	 ?map	 ?derived	 ?from	 ?the	 ?combined	 ?results	 ?of	 ?the	 ?Paxton	 ?and	 ?Priest	 ?Lake	 ?crosses.	 ?We	 ?also	 ?created	 ?separate	 ?linkage	 ?maps	 ?for	 ?each	 ?lake.	 ?We	 ?found	 ?that	 ?we	 ?detected	 ?all	 ?the	 ?same	 ?QTL	 ?when	 ?we	 ?analyzed	 ?each	 ?separate	 ?lake?s	 ?cross	 ?with	 ?its	 ?own	 ?map	 ?as	 ?we	 ?did	 ?when	 ?we	 ?analyzed	 ?each	 ?separate	 ?lake?s	 ?cross	 ?with	 ?the	 ?combined	 ?map.	 ?Therefore,	 ?we	 ?proceeded	 ?with	 ?the	 ?use	 ?of	 ?the	 ?combined	 ?map	 ?for	 ?the	 ?remaining	 ?analyses.	 ?Identifying	 ?candidate	 ?QTL	 ?To	 ?identify	 ?a	 ?set	 ?of	 ?chromosomal	 ?regions	 ?at	 ?which	 ?to	 ?conduct	 ?tests	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?carried	 ?out	 ?a	 ?round	 ?of	 ?QTL	 ?scans	 ?to	 ?identify	 ?those	 ?chromosomal	 ?regions	 ?that	 ?had	 ?a	 ?phenotypic	 ?effect	 ?in	 ?at	 ?least	 ?one	 ?of	 ?the	 ?lakes	 ?(which	 ?we	 ?termed	 ??candidate	 ?QTL?).	 ?We	 ?constructed	 ?three	 ?QTL	 ?maps	 ?for	 ?each	 ?trait	 ?by	 ?interval	 ?mapping	 ?using	 ?Haley-??Knott	 ?regression	 ?via	 ?the	 ??scanone?	 ?function	 ?in	 ?R/qtl,	 ?and	 ?F2	 ?cross	 ?genotype	 ?coding	 ?(Broman	 ?and	 ?Wu	 ?2013).	 ??scanone?	 ?includes	 ?additive	 ?and	 ?dominance	 ?components	 ?of	 ?genotypes	 ?when	 ?testing	 ?for	 ?QTL.	 ?The	 ?first	 ?QTL	 ?map	 ?was	 ?made	 ?using	 ?only	 ?the	 ?F2	 ?hybrids	 ?from	 ?the	 ?Priest	 ?Lake	 ?cross	 ?(n=323)	 ?and	 ?the	 ?second	 ?QTL	 ?map	 ?was	 ?made	 ?using	 ?only	 ?the	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?(n=403).	 ?The	 ?third	 ?QTL	 ?map	 ?was	 ?made	 ?using	 ?the	 ?F2	 ?hybrids	 ?from	 ?both	 ?crosses,	 ?and	 ?the	 ?scan	 ?included	 ?a	 ?genotype	 ?x	 ?cross	 ?interaction	 ?covariate.	 ?The	 ?main	 ?effects	 ?of	 ?family	 ?identity	 ?and	 ?sex	 ?were	 ?used	 ?as	 ?covariates	 ?in	 ?all	 ?three	 ?of	 ?the	 ?scans.	 ?We	 ?lumped	 ?all	 ?F2	 ?hybrids	 ?with	 ?no	 ?full	 ?siblings	 ?into	 ?a	 ?single	 ??pseudo-??family?.	 ?This	 ?pseudo-??family	 ?consisted	 ?of	 ?20	 ?F2	 ?hybrids	 ?in	 ?the	 ?Priest	 ?Lake	 ?cross	 ?and	 ?24	 ?F2	 ?hybrids	 ?in	 ?the	 ?Paxton	 ?Lake	 ?cross.	 ?We	 ?found	 ?that	 ?use	 ?of	 ?these	 ?pseudo-??families	 ?presented	 ?no	 ?artifacts.	 ?For	 ?each	 ?trait,	 ?we	 ?	 ? 66	 ?performed	 ?10,000	 ?permutations	 ?to	 ?determine	 ?the	 ?genome-??wide	 ?LOD	 ?threshold	 ?for	 ?significant	 ?QTL	 ?at	 ?the	 ??	 ?=	 ?0.05	 ?level.	 ?	 ?Our	 ?candidate	 ?QTL	 ?dataset	 ?included	 ?only	 ?QTL	 ?underlying	 ?traits	 ?that	 ?were	 ?determined	 ?to	 ?have	 ?diverged	 ?in	 ?parallel.	 ?Some	 ?of	 ?these	 ?QTL	 ?were	 ?discovered	 ?in	 ?both	 ?the	 ?combined	 ?scan	 ?and	 ?one	 ?or	 ?both	 ?of	 ?the	 ?single-??lake	 ?scans,	 ?as	 ?indicated	 ?by	 ?overlapping	 ?1.5	 ?LOD	 ?confidence	 ?intervals	 ?(i.e.	 ?confidence	 ?intervals	 ?for	 ?the	 ?position	 ?of	 ?QTL	 ?that	 ?extend	 ?on	 ?either	 ?side	 ?of	 ?the	 ?peak	 ?LOD	 ?score	 ?position	 ?to	 ?the	 ?position	 ?at	 ?which	 ?a	 ?1.5	 ?LOD	 ?drop	 ?relative	 ?to	 ?the	 ?peak	 ?LOD	 ?score	 ?is	 ?seen).	 ?In	 ?this	 ?case,	 ?we	 ?used	 ?the	 ?QTL	 ?position	 ?from	 ?the	 ?combined	 ?scan	 ?in	 ?our	 ?candidate	 ?QTL	 ?dataset	 ?and	 ?discarded	 ?the	 ?result(s)	 ?from	 ?the	 ?single-??lake	 ?scan	 ?(though	 ?all	 ?detected	 ?QTL	 ?are	 ?shown	 ?in	 ?Tables	 ?C.3	 ?-??	 ?C.5).	 ?	 ?Testing	 ?for	 ?parallel	 ?genetic	 ?evolution	 ?We	 ?classified	 ?a	 ?QTL?s	 ?effect	 ?as	 ??parallel?	 ?when	 ?its	 ?phenotypic	 ?effect	 ?was	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?both	 ?lakes	 ?(i.e.	 ?F2	 ?hybrids	 ?with	 ?benthic	 ?genotypes	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?F2	 ?hybrids	 ?with	 ?limnetic	 ?genotypes	 ?in	 ?both	 ?lakes,	 ?or	 ?vise	 ?versa),	 ?though	 ?not	 ?necessarily	 ?of	 ?the	 ?same	 ?magnitude.	 ?We	 ?classified	 ?a	 ?QTL?s	 ?effect	 ?as	 ??opposite?	 ?when	 ?its	 ?phenotypic	 ?effect	 ?was	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes	 ?(i.e.	 ?F2	 ?hybrids	 ?with	 ?benthic	 ?genotypes	 ?had	 ?a	 ?higher	 ?mean	 ?than	 ?F2	 ?hybrids	 ?with	 ?limnetic	 ?genotypes	 ?in	 ?one	 ?lake	 ?and	 ?the	 ?opposite	 ?in	 ?the	 ?other	 ?lake).	 ?If	 ?the	 ?phenotypic	 ?effect	 ?of	 ?a	 ?QTL	 ?was	 ?present	 ?in	 ?only	 ?one	 ?of	 ?the	 ?lakes,	 ?we	 ?classified	 ?the	 ?QTL?s	 ?effect	 ?as	 ?only	 ?in	 ?a	 ??single	 ?lake?.	 ?For	 ?each	 ?candidate	 ?QTL,	 ?we	 ?tested	 ?these	 ?scenarios	 ?by	 ?fitting	 ?5	 ?linear	 ?models	 ?to	 ?the	 ?phenotypes	 ?of	 ?F2	 ?hybrids	 ?from	 ?both	 ?lakes	 ?combined,	 ?and	 ?then	 ?deciding	 ?which	 ?fit	 ?the	 ?data	 ?best	 ?(Table	 ?C.6).	 ?	 ?When	 ?present,	 ?the	 ?main	 ?effects	 ?of	 ?QTL	 ?genotype	 ?(at	 ?the	 ?peak	 ?marker	 ?position;	 ?the	 ?position	 ?showing	 ?the	 ?strongest	 ?association	 ?with	 ?the	 ?phenotype),	 ?included	 ?both	 ?the	 ?additive	 ?and	 ?dominance	 ?components	 ?of	 ?genotypes	 ?(referred	 ?to	 ?as	 ?additive	 ?score	 ?and	 ?dominance	 ?score,	 ?respectively).	 ?The	 ?additive	 ?score,	 ?reflecting	 ?the	 ?estimated	 ?proportion	 ?of	 ?the	 ?genotype	 ?that	 ?is	 ?from	 ?the	 ?benthic	 ?grandparent,	 ?was	 ?calculated	 ?as	 ?0.5	 ??	 ?P(AA)/2	 ?+	 ?P(BB)/2,	 ?and	 ?the	 ?dominance	 ?score,	 ?reflecting	 ?the	 ?	 ? 67	 ?estimated	 ?probability	 ?that	 ?the	 ?genotype	 ?is	 ?heterozygote,	 ?was	 ?calculated	 ?as,	 ?P(AB)/2,	 ?where	 ?A	 ?and	 ?B	 ?represent	 ?the	 ?alternative	 ?alleles	 ?at	 ?the	 ?SNP	 ?marker,	 ?and	 ?P(AA),	 ?P(AB)	 ?and	 ?P(BB)	 ?represent	 ?an	 ?individual?s	 ?probability	 ?of	 ?having	 ?the	 ?respective	 ?genotype.	 ?Genotype	 ?probabilities	 ?were	 ?calculated	 ?using	 ?the	 ?R/qtl	 ?function	 ??calc.genoprob?	 ?(Broman	 ?and	 ?Wu	 ?2013).	 ?Model	 ?1:	 ??same	 ?effect?,	 ?included	 ?the	 ?main	 ?effects	 ?of	 ?QTL	 ?genotype.	 ?Model	 ?2:	 ??different	 ?effect?	 ?included	 ?both	 ?the	 ?main	 ?effects	 ?of	 ?QTL	 ?genotype	 ?and	 ?the	 ?interaction	 ?between	 ?its	 ?additive	 ?score	 ?with	 ?lake	 ?(allowing	 ?the	 ?additive	 ?genotypic	 ?effect	 ?to	 ?differ	 ?between	 ?the	 ?lakes).	 ?In	 ?Model	 ?3:	 ??effect	 ?in	 ?Paxton	 ?only?,	 ?the	 ?main	 ?effect	 ?of	 ?QTL	 ?genotype	 ?was	 ?manipulated	 ?so	 ?that	 ?the	 ?genotypes	 ?of	 ?Priest	 ?F2	 ?hybrids	 ?were	 ?fixed.	 ?The	 ?reverse	 ?was	 ?done	 ?in	 ?Model	 ?4:	 ??effect	 ?in	 ?Priest	 ?only?.	 ?Finally	 ?in	 ?Model	 ?5:	 ??no	 ?effect?,	 ?the	 ?effects	 ?of	 ?QTL	 ?genotype	 ?were	 ?dropped	 ?completely.	 ?All	 ?models	 ?included	 ?family	 ?identity	 ?and	 ?sex	 ?as	 ?covariates.	 ?We	 ?then	 ?grouped	 ?the	 ?58	 ?candidate	 ?QTL	 ?into	 ?the	 ?three	 ??QTL	 ?effect?	 ?categories	 ?based	 ?on	 ?which	 ?model	 ?had	 ?the	 ?lowest	 ?AICc	 ?value,	 ?which	 ?we	 ?term	 ?the	 ??best	 ?model?.	 ?QTL	 ?we	 ?considered	 ?to	 ?have	 ?parallel	 ?effects	 ?if	 ?either	 ?model	 ?1	 ?was	 ?the	 ?best,	 ?or	 ?model	 ?2	 ?was	 ?the	 ?best	 ?and	 ?the	 ?effect	 ?of	 ?the	 ?QTL	 ?was	 ?in	 ?the	 ?same	 ?direction	 ?in	 ?the	 ?two	 ?lakes.	 ?QTL	 ?were	 ?considered	 ?to	 ?have	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?if	 ?either	 ?model	 ?3	 ?or	 ?4	 ?was	 ?the	 ?best.	 ?QTL	 ?were	 ?considered	 ?to	 ?have	 ?opposite	 ?effects	 ?if	 ?model	 ?2	 ?was	 ?the	 ?best	 ?and	 ?the	 ?effect	 ?of	 ?the	 ?QTL	 ?was	 ?in	 ?opposite	 ?directions	 ?in	 ?the	 ?two	 ?lakes.	 ?Since	 ?model	 ?5	 ?was	 ?never	 ?the	 ?best,	 ?no	 ?QTL	 ?were	 ?considered	 ?to	 ?have	 ?an	 ?effect	 ?in	 ?neither	 ?lake.	 ?For	 ?24	 ?candidate	 ?QTL,	 ?more	 ?than	 ?one	 ?QTL	 ?effect	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well.	 ?That	 ?is,	 ?the	 ?delta	 ?AICc	 ?value	 ?between	 ?the	 ?best	 ?model	 ?and	 ?the	 ?second	 ?best	 ?model	 ?was	 ?less	 ?than	 ?2	 ?and	 ?the	 ?second	 ?best	 ?model	 ?called	 ?for	 ?a	 ?different	 ?QTL	 ?effect	 ?category	 ?than	 ?the	 ?best	 ?model	 ?did	 ?(Table	 ?C.6).	 ?These	 ?15	 ?candidate	 ?QTL	 ?were,	 ?therefore,	 ?left	 ?out	 ?of	 ?all	 ?calculations	 ?and	 ?analyses	 ?in	 ?which	 ?QTL	 ?effect	 ?category	 ?was	 ?a	 ?variable.	 ?	 ?As	 ?a	 ?second,	 ?distinct	 ?measure	 ?of	 ?genetic	 ?parallelism,	 ?we	 ?estimated	 ?the	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?each	 ?trait	 ?represented	 ?in	 ?our	 ?candidate	 ?QTL	 ?dataset	 ?that	 ?had	 ?undergone	 ?parallel	 ?phenotypic	 ?evolution	 ?(n=26)	 ?(Conte	 ?et	 ?al.	 ?2012).	 ?We	 ?accomplished	 ?this	 ?by	 ?fitting	 ?multiple	 ?QTL	 ?linear	 ?models	 ?using	 ?the	 ?R/qtl	 ?function	 ??fitqtl?	 ?(Broman	 ?and	 ?Wu	 ?2013)	 ?(see	 ?Table	 ?C.7).	 ?We	 ?did	 ?this	 ?separately	 ?for	 ?the	 ?two	 ?lakes.	 ?Of	 ?the	 ?full	 ?set	 ?of	 ?candidate	 ?QTL	 ?for	 ?any	 ?given	 ?trait,	 ?only	 ?	 ? 68	 ?those	 ?having	 ?a	 ?significant	 ?effect	 ?(?=0.05)	 ?in	 ?an	 ?ANOVA	 ?comparing	 ?a	 ?full	 ??single	 ?QTL,	 ?single	 ?lake	 ?linear	 ?model?	 ?to	 ?a	 ?reduced	 ?model	 ?from	 ?which	 ?QTL	 ?genotype	 ?terms	 ?were	 ?dropped	 ?were	 ?included	 ?in	 ?the	 ?multiple	 ?QTL	 ?model	 ?for	 ?that	 ?lake.	 ?The	 ??single	 ?lake,	 ?single	 ?QTL	 ?linear	 ?models?	 ?contained	 ?the	 ?main	 ?effects	 ?of	 ?QTL	 ?genotype,	 ?including	 ?the	 ?additive	 ?and	 ?dominance	 ?score	 ?terms,	 ?as	 ?well	 ?as	 ?the	 ?main	 ?effects	 ?of	 ?family	 ?identity	 ?and	 ?sex.	 ?The	 ?multiple	 ?QTL	 ?models	 ?included,	 ?the	 ?main	 ?effects	 ?of	 ?those	 ?QTL	 ?genotypes,	 ?as	 ?well	 ?as	 ?the	 ?main	 ?effects	 ?of	 ?family	 ?identity	 ?and	 ?sex.	 ?We	 ?calculated	 ?the	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?each	 ?trait	 ?following	 ?the	 ?methods	 ?of	 ?Conte	 ?et	 ?al.	 ?(2012).	 ?For	 ?each	 ?lake	 ?separately,	 ?the	 ?PVEs	 ?of	 ?all	 ?QTL	 ?included	 ?in	 ?the	 ?multiple	 ?QTL	 ?model	 ?for	 ?a	 ?given	 ?trait,	 ?calculated	 ?using	 ?R/qtl?s	 ??fitqtl?	 ?function	 ?via	 ??drop	 ?one	 ?QTL	 ?at	 ?a	 ?time	 ?ANOVAs?	 ?(Broman	 ?and	 ?Wu	 ?2013),	 ?were	 ?scaled	 ?so	 ?that	 ?their	 ?sum	 ?was	 ?equal	 ?to	 ?1,	 ?resulting	 ?in	 ?proportional	 ?contributions	 ?of	 ?each	 ?QTL	 ?to	 ?the	 ?phenotype	 ?(Table	 ?C.7).	 ?We	 ?then	 ?calculated	 ?proportional	 ?similarity	 ?as	 ?the	 ?minimum	 ?overlap	 ?in	 ?the	 ?distribution	 ?of	 ?proportional	 ?contributions;	 ?PS	 ?=	 ??imin(pi1,	 ?pi2),	 ?where	 ?pi1	 ?and	 ?pi2	 ?are	 ?the	 ?proportional	 ?contributions	 ?of	 ?QTLi	 ?in	 ?the	 ?two	 ?taxa.	 ?Correlates	 ?of	 ?genetic	 ?parallelism	 ?We	 ?asked	 ?whether	 ?QTL	 ?PVE	 ?(the	 ?percent	 ?of	 ?the	 ?phenotypic	 ?variance	 ?explained	 ?by	 ?QTL;	 ?a	 ?measure	 ?of	 ?a	 ?QTL?s	 ?phenotypic	 ?effect	 ?size)	 ?had	 ?an	 ?effect	 ?on	 ?whether	 ?QTL	 ?were	 ?parallel	 ?or	 ?non-??parallel.	 ?We	 ?determined	 ?PVE	 ?for	 ?each	 ?candidate	 ?QTL	 ?in	 ?each	 ?lake	 ?separately,	 ?using	 ??single	 ?lake,	 ?single	 ?QTL	 ?linear	 ?models?	 ?(described	 ?above)	 ?and	 ?then	 ?ANOVAs	 ?comparing	 ?the	 ?full	 ?model	 ?to	 ?a	 ?reduced	 ?model	 ?from	 ?which	 ?QTL	 ?genotype	 ?terms	 ?were	 ?dropped.	 ?We	 ?calculated	 ?PVE	 ?as	 ?the	 ?absolute	 ?value	 ?of	 ?the	 ?difference	 ?in	 ?the	 ?residual	 ?sum	 ?of	 ?squares	 ?explained	 ?by	 ?the	 ?full	 ?and	 ?reduced	 ?models	 ?divided	 ?by	 ?the	 ?total	 ?sum	 ?of	 ?squares	 ?explained	 ?by	 ?the	 ?full	 ?model.	 ?Using	 ?the	 ?higher	 ?PVE	 ?value	 ?of	 ?the	 ?two	 ?lakes,	 ?we	 ?then	 ?asked	 ?whether	 ?the	 ?mean	 ?PVE	 ?of	 ?QTL	 ?with	 ?parallel	 ?effects	 ?was	 ?different	 ?from	 ?the	 ?mean	 ?PVE	 ?of	 ?QTL	 ?with	 ?non-??parallel	 ?effects	 ?(including	 ?QTL	 ?with	 ?effects	 ?in	 ?only	 ?one	 ?lake	 ?and	 ?opposite	 ?effects),	 ?using	 ?binomial	 ?logistic	 ?regressions.	 ?15	 ?candidate	 ?QTL	 ?were	 ?left	 ?out	 ?of	 ?this	 ?analysis	 ?because	 ?more	 ?	 ? 69	 ?than	 ?one	 ?QTL	 ?effect	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well.	 ?The	 ?analysis	 ?included	 ?the	 ?remaining	 ?43	 ?candidate	 ?QTL	 ?underlying	 ?traits	 ?that	 ?evolved	 ?in	 ?parallel.	 ?We	 ?tested	 ?whether	 ?differences	 ?in	 ?genotype	 ?information	 ?content	 ?explained	 ?any	 ?variation	 ?in	 ?whether	 ?QTL	 ?were	 ?determined	 ?to	 ?have	 ?an	 ?effect	 ?in	 ?both	 ?lakes	 ?(parallel	 ?and	 ?opposite	 ?effects)	 ?or	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake.	 ?As	 ?a	 ?measure	 ?of	 ?genotype	 ?information	 ?content,	 ?we	 ?used	 ?the	 ?function	 ??plot.info?	 ?in	 ?R/qtl	 ?to	 ?calculate	 ?an	 ??entropy?	 ?score	 ?at	 ?each	 ?marker	 ?whereby,	 ?a	 ?lower	 ?value	 ?indicates	 ?greater	 ?genotype	 ?information	 ?content	 ?(Broman	 ?and	 ?Wu	 ?2013).	 ?When	 ?QTL	 ?peak	 ?markers	 ?were	 ?real	 ?SNP	 ?markers,	 ?we	 ?simply	 ?extracted	 ?the	 ?entropy	 ?score	 ?of	 ?each	 ?cross	 ?at	 ?that	 ?peak	 ?marker.	 ?When	 ?the	 ??peak	 ?marker?	 ?fell	 ?between	 ?two	 ?real	 ?SNP	 ?markers,	 ?we	 ?calculated	 ?entropy	 ?using	 ?linear	 ?interpolation.	 ?We	 ?then	 ?asked	 ?whether	 ?QTL	 ?with	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?were	 ?associated	 ?with	 ?larger	 ?differences	 ?in	 ?entropy	 ?between	 ?the	 ?lakes,	 ?than	 ?QTL	 ?with	 ?an	 ?effect	 ?in	 ?both	 ?lakes,	 ?using	 ?a	 ?binomial	 ?logistic	 ?regression.	 ?The	 ?same	 ?candidate	 ?QTL	 ?were	 ?included	 ?in	 ?this	 ?analysis	 ?as	 ?were	 ?included	 ?in	 ?the	 ?analysis	 ?of	 ?the	 ?effect	 ?of	 ?QTL	 ?PVE	 ?on	 ?QTL	 ?parallelism	 ?(described	 ?directly	 ?above).	 ?	 ?	 ?	 ? 	 ?	 ? 70	 ?5	 ? Conclusions	 ?The	 ?origin	 ?of	 ?species	 ?may	 ?quite	 ?often	 ?occur	 ?via	 ?the	 ?process	 ?of	 ?ecological	 ?speciation	 ?(Schluter	 ?2001;	 ?Nosil	 ?2012)	 ?and	 ?the	 ?genetics	 ?underlying	 ?the	 ?process	 ?may	 ?substantially	 ?affect	 ?its	 ?outcome	 ?(Schluter	 ?and	 ?Conte	 ?2009).	 ?The	 ?studies	 ?herein	 ?have	 ?made	 ?contributions	 ?to	 ?our	 ?understanding	 ?of	 ?the	 ?genetics	 ?of	 ?ecological	 ?speciation	 ?and	 ?of	 ?adaptation	 ?in	 ?general.	 ?In	 ?this	 ?chapter,	 ?I	 ?revisit	 ?the	 ?primary	 ?questions	 ?outlined	 ?in	 ?the	 ?introduction	 ?and	 ?discuss	 ?how	 ?our	 ?work	 ?has	 ?contributed	 ?to	 ?answering	 ?them.	 ?	 ?Question	 ?1:	 ?what	 ?genetic	 ?mechanisms	 ?link	 ?divergent	 ?natural	 ?selection	 ?to	 ?reproductive	 ?isolation	 ?during	 ?ecological	 ?speciation?	 ?In	 ?chapter	 ?2,	 ?we	 ?show	 ?that	 ?body	 ?size,	 ?a	 ?trait	 ?under	 ?divergent	 ?natural	 ?selection,	 ?also	 ?functions	 ?as	 ?a	 ?mate	 ?signal	 ?and	 ?determines	 ?female	 ?mate	 ?preference,	 ?via	 ?phenotype	 ?matching	 ?in	 ?the	 ?Paxton	 ?Lake	 ?threespine	 ?stickleback	 ?species	 ?pair.	 ?This	 ?implies	 ?that	 ?the	 ?genes	 ?controlling	 ?body	 ?size	 ?are	 ?the	 ?same	 ?as	 ?those	 ?that	 ?cause	 ?assortative	 ?mating	 ?by	 ?body	 ?size,	 ?constituting	 ?a	 ?genetic	 ?mechanism	 ?that	 ?is	 ?thought	 ?to	 ?greatly	 ?facilitate	 ?ecological	 ?speciation	 ?with	 ?gene	 ?flow.	 ?As	 ?a	 ?result,	 ?divergent	 ?selection	 ?on	 ?body	 ?size	 ?should	 ?lead	 ?to	 ?assortative	 ?mating	 ?by	 ?body	 ?size	 ?as	 ?an	 ?automatic	 ?by-??product.	 ?	 ?We	 ?see	 ?support	 ?for	 ?similar	 ?mechanisms	 ?in	 ?many	 ?natural	 ?populations.	 ?For	 ?example,	 ?divergent	 ?adaptations	 ?may	 ?often	 ?lead	 ?to	 ?spatial	 ?and/or	 ?temporal	 ?isolation	 ?that	 ?limit	 ?the	 ?probability	 ?of	 ?differentially	 ?adapted	 ?reproductive	 ?individuals	 ?encountering	 ?one	 ?another.	 ?Populations	 ?of	 ?pea	 ?aphids	 ?(Acyrthosiphon	 ?pisum)	 ?that	 ?are	 ?adapted	 ?to	 ?different	 ?host	 ?plant	 ?species	 ?experience	 ?spatial	 ?isolation	 ?as	 ?a	 ?byproduct	 ?of	 ?living	 ?and	 ?mating	 ?on	 ?their	 ?respective	 ?host	 ?plants	 ?(Via	 ?1999).	 ?Populations	 ?of	 ?apple	 ?maggot	 ?flies	 ?(Rhagoletis	 ?pomonella)	 ?that	 ?are	 ?adapted	 ?to	 ?different	 ?host	 ?plant	 ?species	 ?experience	 ?both	 ?spatial	 ?isolation	 ?as	 ?a	 ?result	 ?of	 ?living	 ?and	 ?mating	 ?on	 ?their	 ?host	 ?plants	 ?and	 ?temporal	 ?isolation	 ?as	 ?a	 ?byproduct	 ?of	 ?the	 ?different	 ?fruiting	 ?times	 ?of	 ?their	 ?respective	 ?host-??plant	 ?species	 ?(and	 ?their	 ?own	 ?co-??evolved	 ?timing	 ?of	 ?emergence)	 ?(Bush	 ?1969).	 ?Lord	 ?Howe	 ?palms	 ?(Arecacea)	 ?that	 ?are	 ?adapted	 ?to	 ?different	 ?soil	 ?types	 ?are	 ?	 ? 71	 ?temporally	 ?isolated	 ?by	 ?differences	 ?in	 ?flowering	 ?time	 ?that	 ?are	 ?thought	 ?to	 ?be	 ?caused	 ?by	 ?physiological	 ?changes	 ?induced	 ?by	 ?the	 ?soil	 ?type	 ?(Savolainen	 ?et	 ?al.	 ?2006).	 ?Populations	 ?of	 ?sunflower	 ?(Helianthus	 ?petiolaris)	 ?that	 ?are	 ?differentially	 ?adapted	 ?to	 ?dune	 ?and	 ?non-??dune	 ?habitats	 ?experience	 ?some	 ?spatial	 ?isolation	 ?due	 ?to	 ?the	 ?distance	 ?between	 ?their	 ?habitats	 ?(Andrew	 ?et	 ?al.	 ?2012).	 ?Populations	 ?of	 ?yellow	 ?monkeyflower	 ?(Mimulus	 ?guttatus)	 ?that	 ?are	 ?differentially	 ?adapted	 ?to	 ?costal	 ?vs.	 ?inland	 ?habitats	 ?are	 ?isolated	 ?both	 ?by	 ?distance	 ?between	 ?the	 ?habitats	 ?and	 ?differences	 ?in	 ?flowering	 ?time	 ?that	 ?are	 ?associated	 ?with	 ?climatic	 ?regimes	 ?within	 ?the	 ?habitats	 ?(Lowry	 ?et	 ?al.	 ?2008b).	 ? Like	 ?in	 ?our	 ?study,	 ?divergent	 ?adaptations	 ?have	 ?also	 ?been	 ?shown	 ?to	 ?affect	 ?assortative	 ?mating	 ?decisions	 ?in	 ?natural	 ?populations.	 ?In	 ?most	 ?of	 ?these	 ?cases	 ?they	 ?have	 ?been	 ?found	 ?to	 ?function	 ?as	 ?a	 ?mate	 ?signal,	 ?these	 ?constituting	 ??classic	 ?magic	 ?traits?	 ?(reviewed	 ?in	 ?Servedio	 ?et	 ?al.	 ?(2011)).	 ?Though,	 ?some	 ?examples	 ?also	 ?exist	 ?in	 ?which	 ?traits	 ?under	 ?divergent	 ?natural	 ?selection	 ?have	 ?been	 ?shown	 ?to	 ?affect	 ?mate	 ?preferences,	 ?for	 ?example	 ?in	 ?cases	 ?of	 ?sensory	 ?drive	 ?(reviewed	 ?in	 ?Boughman	 ?(2002)).	 ?Our	 ?results	 ?make	 ?an	 ?important	 ?contribution	 ?to	 ?this	 ?literature	 ?because	 ?we	 ?provide	 ?one	 ?of	 ?the	 ?first	 ?examples	 ?of	 ?a	 ?trait	 ?under	 ?divergent	 ?natural	 ?selection	 ?functioning	 ?as	 ?a	 ?mate	 ?signal	 ?and	 ?determining	 ?mate	 ?preference,	 ?via	 ?phenotype	 ?matching.	 ?The	 ?issue	 ?of	 ?building	 ?linkage	 ?disequilibrium	 ?between	 ?assortative	 ?mating	 ?and	 ?targets	 ?of	 ?divergent	 ?natural	 ?selection	 ?is	 ?not	 ?eliminated	 ?unless	 ?the	 ?genes	 ?under	 ?divergent	 ?selection	 ?control	 ?both	 ?mate	 ?signal	 ?and	 ?mate	 ?preference.	 ?This	 ?fact	 ?has	 ?been	 ?underappreciated	 ?until	 ?recently	 ?(Doebeli	 ?2005;	 ?Maan	 ?and	 ?Seehausen	 ?2012;	 ?Conte	 ?and	 ?Schluter	 ?2013)	 ?and	 ?thus,	 ?the	 ?rarity	 ?of	 ?examples	 ?is	 ?perhaps	 ?not	 ?due	 ?to	 ?rarity	 ?in	 ?nature	 ?but	 ?rather	 ?a	 ?lack	 ?of	 ?appropriate	 ?testing.	 ?These	 ?observations	 ?beg	 ?more	 ?work	 ?in	 ?the	 ?area	 ?to	 ?fill	 ?in	 ?the	 ?apparent	 ?gap.	 ?Question	 ?2:	 ?what	 ?is	 ?the	 ?genetic	 ?architecture	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation?	 ?	 ?In	 ?Chapter	 ?4,	 ?we	 ?identified	 ?a	 ?large	 ?number	 ?of	 ?QTL	 ?for	 ?morphological	 ?divergence	 ?that	 ?are	 ?widely	 ?distributed	 ?across	 ?the	 ?genome	 ?in	 ?both	 ?the	 ?Paxton	 ?and	 ?Priest	 ?Lake	 ?species	 ?pairs	 ?of	 ?threespine	 ?stickleback.	 ?Likewise,	 ?in	 ?a	 ?recently	 ?published	 ?	 ? 72	 ?study	 ?(Arnegard	 ?et	 ?al.	 ?in	 ?press)	 ?that	 ?was	 ?part	 ?of	 ?the	 ?same	 ?larger	 ?project	 ?to	 ?discover	 ?the	 ?genetic	 ?architecture	 ?of	 ?ecological	 ?speciation	 ?(see	 ?Preface),	 ?we	 ?showed	 ?that	 ?the	 ?genetic	 ?architecture	 ?of	 ?juvenile	 ?niche	 ?divergence	 ?in	 ?the	 ?Paxton	 ?Lake	 ?species	 ?pair	 ?is	 ?explained	 ?by	 ?multiple,	 ?unlinked	 ?QTL.	 ?Interestingly,	 ?this	 ?genetic	 ?architecture	 ?appeared	 ?to	 ?be	 ?largely	 ?additive,	 ?with	 ?each	 ?benthic	 ?allele	 ?making	 ?an	 ?approximately	 ?equal	 ?contribution	 ?to	 ?an	 ?overall	 ?benthic	 ?niche	 ?phenotype.	 ?Our	 ?findings	 ?in	 ?the	 ?stickleback	 ?species	 ?pairs	 ?are	 ?interesting	 ?because	 ?they	 ?suggest	 ?that	 ?many	 ?loci	 ?underlying	 ?ecologically	 ?important	 ?traits	 ?have	 ?diverged	 ?(and/or	 ?divergence	 ?has	 ?persisted)	 ?during	 ?ecological	 ?speciation	 ?despite	 ?the	 ?homogenizing	 ?effects	 ?of	 ?gene	 ?flow.	 ?	 ? In	 ?other	 ?natural	 ?populations,	 ?we	 ?see	 ?examples	 ?ranging	 ?from	 ?few	 ?to	 ?many	 ?loci	 ?underlying	 ?traits	 ?under	 ?divergent	 ?natural	 ?selection.	 ?For	 ?example,	 ?in	 ?a	 ?review	 ?of	 ?ecological	 ?speciation	 ?in	 ?phytophagous	 ?insects,	 ?three	 ?of	 ?eight	 ?studies	 ?found	 ?1-??2	 ?loci	 ?underlying	 ?divergently	 ?selected	 ?traits,	 ?three	 ?studies	 ?supported	 ?3-??5	 ?such	 ?loci	 ?and	 ?two	 ?concluded	 ?polygenic	 ?control	 ?(Matsubayashi	 ?et	 ?al.	 ?2010).	 ?Also,	 ?in	 ?a	 ?review	 ?of	 ?speciation	 ?in	 ?flowering	 ?plants,	 ?anywhere	 ?from	 ?1-??17	 ?loci	 ?were	 ?found	 ?to	 ?underlie	 ?reproductive	 ?isolation,	 ?which	 ?in	 ?many	 ?cases	 ?was	 ?a	 ?result	 ?of	 ?traits	 ?under	 ?divergent	 ?natural	 ?selection	 ?(Lowry	 ?et	 ?al.	 ?2008a).	 ?Based	 ?on	 ?a	 ?large	 ?number	 ?of	 ?genome	 ?scans	 ?(e.g.	 ?Hohenlohe	 ?et	 ?al.	 ?2010;	 ?Michel	 ?et	 ?al.	 ?2010;	 ?Jones	 ?et	 ?al.	 ?2012a,b;	 ?Renaut	 ?et	 ?al.	 ?2012;	 ?Andrew	 ?and	 ?Rieseberg	 ?2013,	 ?to	 ?name	 ?a	 ?small	 ?few)	 ?we	 ?also	 ?see	 ?that	 ?the	 ?genomic	 ?distributions	 ?of	 ?divergent	 ?loci	 ?can	 ?be	 ?anywhere	 ?from	 ?highly	 ?clustered	 ?to	 ?widespread.	 ?Although	 ?we	 ?cannot	 ?determine	 ?the	 ?role	 ?of	 ?these	 ?regions	 ?in	 ?divergence	 ?without	 ?accompanying	 ?studies	 ?to	 ?associate	 ?them	 ?with	 ?the	 ?phenotypes	 ?they	 ?affect	 ?(as	 ?is	 ?currently	 ?the	 ?case	 ?for	 ?many	 ?genome	 ?scans,	 ?but	 ?certainly	 ?not	 ?all),	 ?we	 ?nonetheless	 ?see	 ?that	 ?snapshots	 ?of	 ?genetic	 ?divergence	 ?across	 ?the	 ?genome	 ?may	 ?involve	 ?anywhere	 ?from	 ?few	 ?and/or	 ?clustered	 ?loci	 ?to	 ?many	 ?and/or	 ?widespread	 ?loci.	 ?While	 ?both	 ?theory	 ?(see	 ?Introduction)	 ?and	 ?empirical	 ?observations	 ?suggest	 ?that	 ?the	 ?number	 ?of	 ?loci	 ?underlying	 ?traits	 ?under	 ?divergent	 ?selection	 ?may	 ?range	 ?from	 ?few	 ?to	 ?many	 ?and	 ?their	 ?genomic	 ?distributions	 ?may	 ?range	 ?from	 ?clustered	 ?to	 ?widespread,	 ?more	 ?work	 ?is	 ?needed	 ?to	 ?determine	 ?whether	 ?theoretical	 ?explanations	 ?for	 ?this	 ?	 ? 73	 ?variation,	 ?such	 ?as	 ?the	 ?strength	 ?of	 ?selection,	 ?the	 ?amount	 ?of	 ?gene	 ?flow	 ?and	 ?progress	 ?towards	 ?speciation,	 ?can	 ?predict	 ?what	 ?we	 ?find	 ?in	 ?nature.	 ?	 ?Question	 ?3:	 ?how	 ?predictable	 ?are	 ?the	 ?genetics	 ?of	 ?adaptation	 ?during	 ?ecological	 ?speciation	 ?and	 ?in	 ?general?	 ?In	 ?Chapter	 ?3,	 ?we	 ?estimated	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?in	 ?natural	 ?populations	 ?undergoing	 ?repeated	 ?phenotypic	 ?evolution,	 ?informing	 ?us	 ?of	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?across	 ?a	 ?wide	 ?range	 ?of	 ?taxa.	 ?We	 ?found	 ?this	 ?estimate	 ?to	 ?be	 ?0.32-??0.55,	 ?depending	 ?on	 ?the	 ?genetic	 ?methods	 ?employed	 ?by	 ?individual	 ?studies.	 ?Furthermore,	 ?we	 ?found	 ?that	 ?the	 ?probability	 ?of	 ?gene	 ?reuse	 ?declines	 ?with	 ?increasing	 ?age	 ?of	 ?the	 ?taxa	 ?being	 ?compared.	 ?In	 ?Chapter	 ?4,	 ?we	 ?estimated	 ?the	 ?extent	 ?of	 ?genetic	 ?parallelism	 ?underlying	 ?parallel	 ?morphological	 ?divergence	 ?between	 ?the	 ?Paxton	 ?and	 ?Priest	 ?Lake	 ?species	 ?pairs,	 ?providing	 ?insight	 ?to	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation	 ?in	 ?a	 ?single	 ?system.	 ?We	 ?found	 ?that	 ?about	 ?50%	 ?of	 ?QTL	 ?for	 ?parallel	 ?morphological	 ?differences	 ?are	 ?parallel,	 ?and	 ?on	 ?average,	 ?the	 ?proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?individual	 ?morphological	 ?traits	 ?is	 ?about	 ?0.4.	 ?In	 ?addition,	 ?we	 ?find	 ?no	 ?evidence	 ?that	 ?the	 ?phenotypic	 ?effect	 ?size	 ?of	 ?QTL	 ?predicts	 ?whether	 ?or	 ?not	 ?they	 ?are	 ?parallel.	 ?Studying	 ?the	 ?genetics	 ?of	 ?repeated	 ?phenotypic	 ?evolution	 ?is	 ?important	 ?because	 ?it	 ?informs	 ?us	 ?of	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?Repeated	 ?phenotypic	 ?evolution	 ?appears	 ?to	 ?be	 ?a	 ?common	 ?phenomenon	 ?and	 ?hundreds	 ?of	 ?examples	 ?exist	 ?in	 ?the	 ?literature.	 ?In	 ?a	 ?growing	 ?number	 ?of	 ?these	 ?cases,	 ?the	 ?genetic	 ?basis	 ?is	 ?being	 ?or	 ?has	 ?been	 ?discovered,	 ?providing	 ?us	 ?with	 ?the	 ?opportunity	 ?to	 ?directly	 ?and	 ?empirically	 ?estimate	 ?the	 ?predictability	 ?of	 ?the	 ?genetics	 ?of	 ?adaptation.	 ?While	 ?this	 ?topic	 ?is	 ?of	 ?considerable	 ?interest	 ?to	 ?evolutionary	 ?biologists	 ?and	 ?has	 ?been	 ?the	 ?subject	 ?of	 ?many	 ?recent	 ?studies	 ?and	 ?reviews,	 ?our	 ?study	 ?in	 ?chapter	 ?3	 ?is	 ?the	 ?first	 ?to	 ?provide	 ?a	 ?quantitative	 ?estimate	 ?of	 ?the	 ?probability	 ?of	 ?repeated	 ?genetic	 ?evolution	 ?across	 ?a	 ?wide	 ?range	 ?of	 ?taxa.	 ?Furthermore,	 ?our	 ?study	 ?in	 ?chapter	 ?4	 ?is	 ?the	 ?first	 ?to	 ?comprehensively	 ?provide	 ?said	 ?estimates	 ?in	 ?a	 ?single	 ?system.	 ?	 ? 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	 ?Figure'S1:'Experimental,Design.,Grey,cartoon,fish,represent,individuals,for,which,size,was,manipulated,to,be,similar,to,that,of,the,opposite,species.,Black,cartoon,fish,represent,individuals,for,which,size,was,manipulated,to,be,different,from,that,of,the,opposite,species.!Treatment'' Female' Male' Predictions'if#phenotype#matching#by#size:# if#no#phenotype#matching#by#size:#Different,Size,(Control), Large,Benthic, Large,Limnetic,Mating,more,likely,in,similarEsize,treatment,than,in,differentEsize,treatment,Mating,equally,likely,in,similarEsize,and,differentEsize,treatments,, ,Similar,Size,(Experimental), Small,Benthic, Large,Limnetic,, ,Different,Size,(Control), Small,Limnetic, Small,Benthic,, ,Similar,Size,(Experimental), Large,Limnetic, Small,Benthic,, ,'''''''	 ? 103	 ?Table	 ?A.1	 ?Effects	 ?of	 ?body	 ?size	 ?manipulation	 ?Effects	 ?of	 ?body	 ?size	 ?manipulation	 ?on	 ?mean	 ?standard	 ?length	 ?(cm),	 ?body	 ?mass	 ?(g)	 ?and	 ?condition	 ?factor	 ?(g	 ?105cm-??3)	 ?of	 ?benthic	 ?and	 ?limnetic	 ?females	 ?randomly	 ?assigned	 ?to	 ?abundant-??food	 ?and	 ?reduced-??food	 ?groups.	 ?Standard	 ?are	 ?deviations	 ?in	 ?parentheses.	 ?	 ?Female	 ? Measure	 ? Abundant-??food	 ? Reduced-??food	 ? F-??value	 ? df	 ? p	 ?Benthic	 ?	 ? Standard	 ?Length	 ?(cm)	 ? 70.10	 ?(4.72)	 ? 59.72	 ?(3.17)	 ? 53.45	 ? 1	 ? 2.6x10-??8	 ?Body	 ?Mass	 ?(g)	 ? 	 ?	 ?	 ?5.3	 ?	 ?	 ?(1.0)	 ? 	 ?	 ?	 ?2.8	 ?	 ?	 ?	 ?(0.5)	 ? 72.05	 ? 1	 ? 1.1x10-??9	 ?Condition	 ?Factor	 ? 	 ?	 ?	 ?1.24	 ?(0.14)	 ? 	 ?	 ?	 ?1.17	 ?(0.12)	 ? 	 ?	 ?2.22	 ? 1	 ? 0.146	 ?Limnetic	 ? Standard	 ?Length	 ?(cm)	 ? 50.06	 ?(3.61)	 ? 42.23	 ?(3.82)	 ? 39.89	 ? 1	 ? 2.9x10-??6	 ?	 ?Body	 ?Mass	 ?(g)	 ? 	 ?	 ?	 ?1.8	 ?	 ?	 ?	 ?(0.3)	 ? 	 ?	 ?	 ?0.8	 ?	 ?	 ?	 ?(0.2)	 ? 62.38	 ? 1	 ? 1.0x10-??7	 ?Condition	 ?Factor	 ? 	 ?	 ?	 ?1.04	 ?(0.10)	 ? 	 ?	 ?	 ?0.97	 ?(0.15)	 ? 	 ?	 ?1.46	 ? 1	 ? 0.244	 ?	 ?	 ? 	 ?	 ? 104	 ?Table	 ?A.2	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?stringent	 ?female	 ?acceptance	 ?scores	 ?Logistic	 ?regressions	 ?to	 ?test	 ?for	 ?the	 ?effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?the	 ?more	 ?stringent	 ?female	 ?acceptance	 ?scores,	 ?for	 ?which	 ?females	 ?had	 ?to	 ?at	 ?least	 ?follow	 ?the	 ?male	 ?to	 ?his	 ?nest	 ?to	 ?be	 ?assigned	 ?a	 ?score	 ?of	 ?1.	 ?Each	 ?model	 ?includes	 ?treatment	 ?and	 ?the	 ?one	 ?additional	 ?variable	 ?indicated.	 ?Treatment	 ?was	 ?entered	 ?first	 ?and	 ?so	 ?has	 ?identical	 ?effects	 ?in	 ?all	 ?models	 ?except	 ?model	 ?1i,	 ?due	 ?to	 ?missing	 ?data	 ?points	 ?for	 ?male	 ?nuptial	 ?color.	 ?The	 ?second	 ?explanatory	 ?variable	 ?in	 ?models	 ?1c	 ??	 ?1h	 ?are	 ?male	 ?courtship	 ?behaviors.	 ?N	 ?=	 ?57	 ?mate	 ?choice	 ?trials.	 ?	 ?Model	 ? Explanatory	 ?Variable	 ? df	 ? X2	 ? p	 ?1a	 ??	 ?1h	 ? treatment	 ?(similar	 ?vs.	 ?different	 ?size)	 ? 1	 ? 	 ?	 ?6.44	 ? 0.01	 ?1i	 ? 1	 ? 	 ?	 ?4.75	 ? 0.03	 ?1a	 ? female	 ?species	 ? 1	 ? 	 ?	 ?2.14	 ? 0.14	 ?1a	 ? treatment	 ?x	 ?female	 ?species	 ? 1	 ? 	 ?	 ?0.14	 ? 0.70	 ?1b	 ? trial	 ?date	 ? 1	 ? 	 ?	 ?2.23	 ? 0.14	 ?1c	 ? no.	 ?of	 ?approaches	 ? 1	 ? 	 ?	 ?0.79	 ? 0.37	 ?1d	 ? no.	 ?of	 ?zig-??zags	 ? 1	 ? 	 ?	 ?2.30	 ? 0.13	 ?1e	 ? no.	 ?of	 ?bites	 ? 1	 ? 	 ?	 ?0.46	 ? 0.50	 ?1f	 ? no.	 ?of	 ?leads	 ?to	 ?nest	 ? 1	 ? 	 ?	 ?0.26	 ? 0.61	 ?1g	 ? no.	 ?of	 ?nest	 ?maintenance	 ?events	 ? 1	 ? 	 ?	 ?0.001	 ? 0.98	 ?1h	 ? no.	 ?of	 ?nest	 ?creep-??throughs	 ? 1	 ? 11.22	 ? 0.001	 ?1i	 ? male	 ?nuptial	 ?color	 ? 1	 ? 	 ?	 ?0.70	 ? 0.40	 ?	 ?	 ? 	 ?	 ? 105	 ?Table	 ?A.3	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?males?	 ?first	 ?trial	 ?Logistic	 ?regressions	 ?to	 ?test	 ?for	 ?the	 ?effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?data	 ?from	 ?only	 ?the	 ?first	 ?trial	 ?of	 ?each	 ?male	 ?(N	 ?=	 ?43	 ?trials	 ?total).	 ?Each	 ?model	 ?includes	 ?treatment	 ?and	 ?the	 ?one	 ?additional	 ?variable	 ?indicated.	 ?Treatment	 ?was	 ?entered	 ?first	 ?and	 ?so	 ?has	 ?identical	 ?effects	 ?in	 ?all	 ?models	 ?except	 ?model	 ?1i,	 ?due	 ?to	 ?missing	 ?data	 ?points	 ?for	 ?male	 ?nuptial	 ?color.	 ?The	 ?second	 ?explanatory	 ?variable	 ?in	 ?models	 ?1c	 ??	 ?1h	 ?are	 ?male	 ?courtship	 ?behaviors.	 ?	 ?Model	 ? Explanatory	 ?Variable	 ? df	 ? X2	 ? p	 ?1a	 ??	 ?1h	 ? treatment	 ?(similar	 ?vs.	 ?different	 ?size)	 ? 1	 ? 15.17	 ? 1x10-??4	 ?	 ?1i	 ? 1	 ? 12.73	 ? 4x10-??4	 ?	 ?1a	 ? female	 ?species	 ? 1	 ? 	 ?	 ?2.44	 ? 0.12	 ?1a	 ? treatment	 ?x	 ?female	 ?species	 ? 1	 ? 	 ?	 ?0.38	 ? 0.54	 ?1b	 ? trial	 ?date	 ? 1	 ? 	 ?	 ?0.93	 ? 0.34	 ?1c	 ? no.	 ?of	 ?approaches	 ? 1	 ? 	 ?	 ?0.02	 ? 0.90	 ?1d	 ? no.	 ?of	 ?zig-??zags	 ? 1	 ? 	 ?	 ?0.37	 ? 0.54	 ?1e	 ? no.	 ?of	 ?bites	 ? 1	 ? 	 ?	 ?0.89	 ? 0.35	 ?1f	 ? no.	 ?of	 ?leads	 ?to	 ?nest	 ? 1	 ? 	 ?	 ?0.30	 ? 0.58	 ?1g	 ? no.	 ?of	 ?nest	 ?maintenance	 ?events	 ? 1	 ? 	 ?	 ?1.67	 ? 0.20	 ?1h	 ? no.	 ?of	 ?nest	 ?creep-??throughs	 ? 1	 ? 	 ?	 ?0.42	 ? 0.52	 ?1i	 ? male	 ?nuptial	 ?color	 ? 1	 ? 	 ?	 ?0.53	 ? 0.47	 ?	 ?	 ? 	 ?	 ? 106	 ?Table	 ?A.4	 ?Effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?restricted	 ?date	 ?range	 ?Logistic	 ?regressions	 ?to	 ?test	 ?for	 ?the	 ?effects	 ?of	 ?explanatory	 ?variables	 ?on	 ?female	 ?acceptance	 ?scores	 ?using	 ?data	 ?from	 ?all	 ?limnetic	 ?female	 ?trials	 ?and	 ?only	 ?those	 ?benthic	 ?female	 ?trials	 ?that	 ?occurred	 ?during	 ?the	 ?restricted	 ?date	 ?range	 ?in	 ?which	 ?both	 ??large?	 ?and	 ??small?	 ?category	 ?benthic	 ?females	 ?were	 ?in	 ?breeding	 ?condition	 ?(N	 ?=	 ?44	 ?trials	 ?total).	 ?Each	 ?model	 ?includes	 ?treatment	 ?and	 ?the	 ?one	 ?additional	 ?variable	 ?indicated.	 ?Treatment	 ?was	 ?entered	 ?first	 ?and	 ?so	 ?has	 ?identical	 ?effects	 ?in	 ?all	 ?models	 ?except	 ?model	 ?1i,	 ?due	 ?to	 ?missing	 ?data	 ?points	 ?for	 ?male	 ?nuptial	 ?color.	 ?The	 ?second	 ?explanatory	 ?variable	 ?in	 ?models	 ?1c	 ??	 ?1h	 ?are	 ?male	 ?courtship	 ?behaviors.	 ?	 ?Model	 ? Explanatory	 ?Variable	 ? df	 ? X2	 ? p	 ?1a	 ??	 ?1h	 ? treatment	 ?(similar	 ?vs.	 ?different	 ?size)	 ? 1	 ? 14.80	 ? 1x10-??4	 ?	 ?1i	 ? 1	 ? 13.79	 ? 2x10-??4	 ?	 ?1a	 ? female	 ?species	 ? 1	 ? 	 ?	 ?2.29	 ? 0.13	 ?1a	 ? treatment	 ?x	 ?female	 ?species	 ? 1	 ? 	 ?	 ?0.59	 ? 0.44	 ?1b	 ? trial	 ?date	 ? 1	 ? 	 ?0.0001	 ? 0.99	 ?1c	 ? no.	 ?of	 ?approaches	 ? 1	 ? 	 ?	 ?1.12	 ? 0.29	 ?1d	 ? no.	 ?of	 ?zig-??zags	 ? 1	 ? 	 ?	 ?0.04	 ? 0.85	 ?1e	 ? no.	 ?of	 ?bites	 ? 1	 ? 	 ?	 ?2.54	 ? 0.11	 ?1f	 ? no.	 ?of	 ?leads	 ?to	 ?nest	 ? 1	 ? 	 ?	 ?0.72	 ? 0.40	 ?1g	 ? no.	 ?of	 ?nest	 ?maintenance	 ?events	 ? 1	 ? 	 ?	 ?0.27	 ? 0.61	 ?1h	 ? no.	 ?of	 ?nest	 ?creep-??throughs	 ? 1	 ? 	 ?	 ?3.02	 ? 0.08	 ?1i	 ? male	 ?nuptial	 ?color	 ? 1	 ? 	 ?	 ?0.32	 ? 0.57	 ?	 ?	 ? 107	 ?Appendix	 ?B:	 ?Chapter	 ?3	 ?Supplementary	 ?Material	 ?Table	 ?B.1	 ?Summary	 ?of	 ?cases	 ?detected	 ?by	 ?our	 ?literature	 ?search	 ?(Starts	 ?on	 ?next	 ?page)	 ?Listed	 ?node	 ?ages	 ?are	 ?the	 ?means	 ?of	 ?all	 ?available	 ?estimates,	 ?rounded	 ?to	 ?the	 ?nearest	 ?highest	 ?significant	 ?figure.	 ?Node	 ?ages	 ?are	 ?only	 ?shown	 ?between	 ?taxa	 ?that	 ?evolved	 ?a	 ?similar	 ?phenotype	 ?(individual	 ?taxa	 ?with	 ?ancestral	 ?phenotypes	 ?are	 ?not	 ?shown).	 ?Initial	 ?genetic	 ?methods:	 ?m	 ?=	 ?genome-??wide	 ?mapping;	 ?ct	 ?=	 ?complementation	 ?test;	 ?mcg	 ?=	 ?mapping	 ?of	 ?a	 ?candidate	 ?gene;	 ?cg	 ?=	 ?candidate	 ?gene.	 ?The	 ?column	 ??Informative	 ?Candidate	 ?Gene(s)	 ?Confirmed?	 ?refers	 ?to	 ?those	 ?candidate	 ?genes	 ?confirmed	 ?to	 ?contribute	 ?to	 ?the	 ?repeatedly	 ?evolved	 ?phenotype	 ?in	 ?the	 ?taxa	 ?specified	 ?in	 ?the	 ?same	 ?row,	 ?for	 ?which	 ?there	 ?is	 ?also	 ?evidence	 ?to	 ?draw	 ?upon	 ?regarding	 ?its	 ?contribution	 ?in	 ?the	 ?other	 ?taxa	 ?that	 ?evolved	 ?the	 ?same	 ?phenotype.	 ?A	 ?candidate	 ?gene	 ?was	 ?considered	 ?to	 ?have	 ?no	 ?effect	 ?if	 ?the	 ?assay	 ?used	 ?produced	 ?no	 ?evidence	 ?that	 ?the	 ?gene	 ?contributed	 ?to	 ?the	 ?trait.	 ?We	 ?recognize	 ?though,	 ?that	 ?assays	 ?were	 ?not	 ?always	 ?exhaustive	 ?and	 ?could	 ?not	 ?completely	 ?rule	 ?out	 ?an	 ?effect	 ?of	 ?the	 ?gene	 ?on	 ?the	 ?trait.	 ?Candidate	 ?genes	 ?considered	 ?to	 ?have	 ?no	 ?effect	 ?are	 ?listed	 ?(as	 ?"not	 ?___")	 ?only	 ?when	 ?nothing	 ?more	 ?is	 ?known	 ?about	 ?the	 ?genetic	 ?basis	 ?of	 ?the	 ?phenotype.	 ?	 ?	 ? 108	 ?Phenotype Independent+Origins+In Mean+Estimated+Node+Ages+(rounded+to+the+nearest+highest+significant+figure) Initial+Genetic+Method Estimated+Relative+Contribution+of+Genes+or+QTL+ Informative+Candidate+Gene(s)+Confirmed ReferencesDrosophila*montana cg ?? svbDrosophila*borealis cg ?? svbDrosophila*sechellia m svb:&1 ??Xenopus*laevis cg ?? xlcae3pLitoria*splendida cg ?? lscae1pThamnophis*couchii* cg ?? Nav1.4Thamnophis*atratus cg ?? Nav1.4Thamnophis*sirtalis cg ?? Nav1.4Amphiesma*pryeri* cg ?? Nav1.4Rhabdophis*tigrinus cg ?? Nav1.4Liophis*epinephelus cg ?? Nav1.4European*Homo*sapiens m LCT:&1 ??Saudi&Arabian&H.*sapiens cg ?? LCTKenyan&and&Tanzanian&H.*sapiens cg ?? LCTPaxton&benthic*Gasterosteus*aculeatus m Eda:&0.83;&LG7:&0.04;&LG10:&0.08;*LG21:&0.05 ??Cranby&G.*aculeatus m Eda:&0.98;&LG7:&0.02 ??Paq;&Graham;&Bear&Paw&G.*aculeatus m Eda:&1 ??Boot&Lake;&Whale&Lake&G.*aculeatus ct Eda:&1 ??Nakagawa&Creek&G.*aculeatus ct Eda:&1 ??6&Pacific&freshwater&populations&of&G.*aculeatus cg ?? Eda5&Atlantic&populations&of*G.*aculeatus cg ?? Eda5&Scandinavian&populations&of&G.*aculeatus cg ?? EdaFox&Hole&Pungitius*pungitius m LG12:&1 ??Larval&trichome&loss Dickinson&et&al.&19933;&Sucena&and&Stern&20003;&Sucena&et&al.&20031;&&Tamura&et&al.&20042;&MoralesYHojas&et&al.&20112Skin&toxin&Y&caeruleinTetrodotoxin&resistanceHedges&et&al.&20062;&Roelants&et&al.&20101;&Roelants&et&al.&20113de&Queiroz&et&al.&20022;&W?ster&et&al.&20082;&Wood&et&al.&20112;&&Feldman&et&al.&20121Orti&et&al.&19942;&Hatfield&19973;&Colosimo&et&al.&20043;&Cresko&et&al.&20041;&&Schluter&et&al.&20043;&Colosimo&et&al.&20051;&Cano&et&al.&20063;&Kitano&et&al.&20083;&Bell&et&al.&20092;&Shapiro&et&al.&20091;&Le&Rouzic&et&al.&20113;&Rogers&et&al.&20123;&Rogers&pers.&comm.3Tishkoff&et&al.&20071;&Enattah&et&al.&20083;&Ingram&et&al.&20093Lactase&persistanceReduction&in&lateral&plate&number60&mya&2&mya&0.1&mya&0.05&mya&200&mya&2&mya&0.5&mya&30&mya&60&mya&60&mya&0.01&mya&0.01&mya&0.8&mya&10&mya&	 ? 109	 ? 	 ?Phenotype Independent+Origins+In Mean+Estimated+Node+Ages+(rounded+to+the+nearest+highest+significant+figure) Initial+Genetic+Method Estimated+Relative+Contribution+of+Genes+or+QTL+ Informative+Candidate+Gene(s)+Confirmed ReferencesPaxton'benthic!G.!aculeatus m Pitx1:'0.731;!LG1:'0.073;!LG2:'0.122;'LG4:'0.074 ??Boulton!G.!aculeatus m LG4:'1 ??7'Alaska'populations'of'G.!aculeatus cg ?? Pitx1Dolomite;'Orphia!G.!aculeatus cg ?? not!Pitx1Loch'Fada!G.!aculeatus m Pitx1:'1 ??Loch'Vifilsstadavat'G.!aculeatus ct Pitx1:'1 ??Loch'Scadavay'G.!aculeatus cg ?? not!Pitx1Fox'Hole'Pungitius!pungitius m LG4:'1 ??West'African'strains'Saccharomyces!cerevisiae m GAL3:'1 ??27361N'strain'S.!cerevisiae ct GAL1:'1 ??Saccharomyces!kudriavzevii cg ?? Gal!1!B!Gal!7Candida!glabrata cg ?? Gal!1!B!Gal!7Eremothecium!gossypi!and!Kluyveromyces!waltii cg ?? Gal!1!B!Gal!7Mimulus!l.!variegatus mcg Pla2:'1 ??Mimulus!naiandinus ct Pla2:'0 ??Mimulus!cupreus mcg Pla1:'1 ??Ipomoea!Mina'clade cg ?? F3'hIpomoea!horsfalliae cg ?? F3'hIochroma!gesnerioides cg ?? DfrMormyroids cg ?? Scn4aaGymnotiforms cg ?? Scn4aaHeliconius!melpomene m optix:'1 ??Heliconius!erato m optix:'1 ??Pygathrix!nemaeus cg ?? RNase1BColobus!guereza cg ?? RNase1beta;!!RNase1gammaruminant'artiodactyls cg ?? bovine!pancreatic!RNase!geneAustralian'D.!melanogaster cg ?? InRNorth'American'D.!melanogaster cg ?? InRRed'floral'pigmentation'(system'2) Stefanovic'et'al.'20022;'Hedges'et'al.'20062;'Streisfeld'&'Rausher'20091;'Des'Marais'et'al.'20103;'Smith'&'Rausher'20111Nie'et'al.'20062;'Cooley'&'Willis'20091;'Cooley'et'al.'20111;'Grossenbacher'and'Whittall'20112Inability'to'use'galactoseOrti'et'al.'19942;'Cresko'et'al.'20043;'Shapiro'et'al.'20043;'Marks'20063;''Shapiro'et'al.'20061;'Coyle'et'al.'20073;'Bell'et'al.'20092;'Shapiro'et'al.'20091;'Chan'et'al.'20103Cliften'et'al.'20032;'Hittinger'et'al.'20041;'Beltrao'and'Serrano'20052;'Warringer'et'al.'20113Red'floral'pigmentation'(system'1)Zhang'et'al.'20023;'Zhang'20031;''Hedges'et'al.'20062;'Sterner'et'al.'20062;'Zhang'20063;'Fabre'et'al.'20092Pelvic'spine'and'girdle'reductionElectrical'activity'of'myogenic'electric'organDigestion'of'foregut[fermenting'bacteriaLife'history''(latitudinal'clines?)Red'wing'patterns'Hedges'et'al.'20062;'Zakon'et'al.'20061;'Arnegard'et'al.'20103;'Lavou?'et'al.'20122'David'and'Capy'19882;'Paaby'et'al.'20101;Baxter'et'al.'20083;'Papa'et'al.'20083;'Pohl'et'al.'20092;'Quek'et'al.'20103;'Reed'et'al.'2011150'mya'2'mya'300'mya'400'mya'90'mya'10'mya'70'mya'4'mya'300'mya'20'mya'0.0003mya'2'mya'3'mya'80'mya'0.01'mya'0.01'mya'0.8'mya'10'mya'	 ? 110	 ? 	 ?Phenotype Independent+Origins+In Mean+Estimated+Node+Ages+(rounded+to+the+nearest+highest+significant+figure) Initial+Genetic+Method Estimated+Relative+Contribution+of+Genes+or+QTL+ Informative+Candidate+Gene(s)+Confirmed ReferencesYangochiroptera-and-Yinpterochiroptera! cg ?? Prestin,)Tmc1,)Pjvk,)Cdh23,)Pcdh15,)OtofTursiops)truncatus cg ?? Prestin,)Tmc1,)Pjvk,)Cdh23,)Pcdh15,)OtofClearwater-Oncorhynchus)mykiss m OC8:-0.80;-OC9:-0.03-;OC10:-0.02;-OC14:-0.04;-OC24:-0.04;-Ocb:-0.07 ??Swanson-O.)mykiss m tthR13-(same-as-OC8-above):-0.60;-tthR9:-0.19;-tthR6:0.21 ??European)H.)sapiens m !Kitlg:-0.23;-SLC45A2:-0.0175;-SLC45A5:-0.315;-TYR:-0.14;)Oca2:-0.14 ??East-Asian)H.)sapiens cg ?? not)SLC45A2;)not-SLC45A5GulfNcoast-Peromyscus)polionotus m ?Agouti:-0.64;-Mc1r:-0.33;)LG14:-0.03 ??Atlantic-coast-Anastasia-Island-P.)polionotus cg ?? not)Mc1r)Atlantic-coast-Sotheastern-P.)polionotus cg ?? not)Mc1rSand-Hill-Peromyscus)maniculatus mcg Agouti:-1 __White-sands-Sceloporus)undulatus cg ?? Mc1rWhite-sands-Holbrookia)maculata cg ?? Mc1rWhite-sands)Aspidoscelis)inornata cg ?? Mc1rPaxton-benthic)G.)aculeatus m Kitlg:-1 ??Fishtrap-Creek-G.)aculeatus cg ?? KitlgGasterosteus)williamsoni cg ?? KitlgPach?n-cavefish)Astyanax)mexicanus m Oca2:-0.57;-Mc1r:-0.43 ??Molino-A.)mexicanus m Oca2:-1 ??Yerbaniz/Japon?s--A.)mexicanus ct Oca2:-1 ??Curva-A.mexicanus ct Mc1r:-1 ??Chica-A.)mexicanus ct Mc1r:-1 ??Piedras-A.mexicanus ct Mc1r:-1 ??Notes:?Clines-are-bidirectional.1Reference-found-by-objective-literature-search;-2Additional-reference-used-to-estimate-node-age;-3Additional-reference-for-genetics-underlying-trait"Both-albino-and-brown-phenotypes-were-considered-?reduced-pigmentation?.-The-Pach?n-population-is-polymorphic-for-these-phenotypes,-but-individuals-are-either-one-or-the-other.-Therefore,-we-set-the-relative-contributions-of-the-underlying-genes-to-the-frequency-with-which-each-gene-would-underlie-?reduced-pigmentation?-in-a-hypothetical-cross-between-heterozygotes-at-both-loci.-The-Japon?s-population-is-fixed-for-the-albino-phenotype,-but-also-harbors-a-?brown-mutation?.-Since-this-mutation-is-not-the-current-cause-of-reduced-pigmentation,-it-was-assigned-a-relative-contribution-of-0.-?Average-relative-contributions-for-all-body-regions-phenotyped.#Light-pigmentation-alleles-at-Kitlg-are-shared-between-European-and-East-Asian-humans.-For-Kitlg-and-SLC45A2-effect-size-in-melanin-units-was-converted-to-an-estimated-PVE-using-a-linear-conversion-factor-based-on-the-these-values-for-SLC45A5.-After-doing-so,-the-remaining-unexplained-variance-was-split-evenly-for-Tyr-and)Oca2.-!Yinpterochiroptera-includes-all-members-except-the-family-Pteropodidae,-as-they-do-not-exhibit-highNfrequency-hearing.-Yangochiroptera-and-Yinpterochiroptera-are-conservatively-grouped-together-here,-as-it-is-not-yet-known-whether-high-frequency-hearing-evolved-independently-in-each-lineage-or-only-once-and-then-was-lost-in-Pteropodidae.Hedges-et-al.-20062,-Liu-et-al.-20103,-Davies-et-al.-20121,-Shen-et-al.-20123Rapid-development-rate McCusker-et-al.-20002;-Robison-et-al.-20013;-Sundin-et-al.-20053;-Nichols-et-al.-20073;-Miller-et-al.-20121Reduced-pigmentation-(system-3)UltrahighNfrequency-hearing-for-echolocation-Sadoglu-and-McKee-19693;--Jeffrey-20012;-Wilkens-and-Strecker-20033;-Protas-et-al.-20061;-Gross-et-al.-20091Reduced-pigmentation-(system-4)Shriver-et-al.-20033;-Graf-et-al.-20053;-Lamason-et-al.-20053;-Soejima-et-al.-20063;-Miller-et-al.-20071;-Norton-et-al.-20073Reduced-pigmentation-(system-2)Reduced-pigmentation-(system-5)"Miller-et-al.-20071Schulte-et-al.-20002;-20032;-Vidal-and-Hedges-20093;-Rosenblum-et-al.-20101Hedges-et-al.-20062;-Hoekstra-et-al.-20063;-Degner-et-al.-20072;-Steiner-et-al.-20073;-Linnen-et-al.-20093;--Steiner-et-al.-20091;-Manceau-et-al.-20113Reduced-pigmentation-(system-1) 0.05-mya-0.07-mya-0.07-mya-7-mya-200-mya-40-mya-0.01-mya-0.5-mya-90-mya-300-mya-300-mya-400-mya-80-mya-1-mya-	 ? 111	 ?Table	 ?B.2	 ?Node	 ?numbers	 ?Nodes	 ?corresponding	 ?to	 ?data	 ?in	 ?Table	 ?B.3.	 ?Node	 ?ages	 ?are	 ?given	 ?in	 ?Table	 ?B.1.	 ?	 ?	 ?Phenotype Independent+Origins+In Node+NumberDrosophila*montanaDrosophila*borealisDrosophila*sechelliaXenopus*laevisLitoria*splendidaThamnophis*couchii*Thamnophis*atratusThamnophis*sirtalisAmphiesma*pryeri*Rhabdophis*tigrinusLiophis*epinephelusEuropean*Homo*sapiensSaudi,Arabian,H.*sapiensKenyan,and,Tanzanian,H.*sapiensPaxton,benthic*Gasterosteus*aculeatusCranby,G.*aculeatusPaq;,Graham;,Bear,Paw,G.*aculeatusBoot,Lake;,Whale,Lake,G.*aculeatusNakagawa,Creek,G.*aculeatus6,Pacific,freshwater,populations,of,G.*aculeatus5,Atlantic,populations,of*G.*aculeatus5,Scandinavian,populations,of,G.*aculeatusFox,Hole,Pungitius*pungitiusTetrodotoxin,resistanceLactase,persistanceTable&S2.!Nodes!corresponding!to!data!in!Table!S3.!Node!ages!are!given!in!Table!S1.Larval,trichome,lossSkin,toxin,L,caeruleinReduction,in,lateral,plate,number2,1,10,9,3,5,4,6,7,8,11,12,13,14,	 ? 112	 ?	 ?	 ?Phenotype Independent+Origins+In Node+NumberDrosophila*montanaDrosophila*borealisDrosophila*sechelliaXenopus*laevisLitoria*splendidaThamnophis*couchii*Thamnophis*atratusThamnophis*sirtalisAmphiesma*pryeri*Rhabdophis*tigrinusLiophis*epinephelusEuropean*Homo*sapiensSaudi,Arabian,H.*sapiensKenyan,and,Tanzanian,H.*sapiensPaxton,benthic*Gasterosteus*aculeatusCranby,G.*aculeatusPaq;,Graham;,Bear,Paw,G.*aculeatusBoot,Lake;,Whale,Lake,G.*aculeatusNakagawa,Creek,G.*aculeatus6,Pacific,freshwater,populations,of,G.*aculeatus5,Atlantic,populations,of*G.*aculeatus5,Scandinavian,populations,of,G.*aculeatusFox,Hole,Pungitius*pungitiusTetrodotoxin,resistanceLactase,persistanceTable&S2.!Nodes!corresponding!to!data!in!Table!S3.!Node!ages!are!given!in!Table!S1.Larval,trichome,lossSkin,toxin,L,caeruleinReduction,in,lateral,plate,number2,1,10,9,3,5,4,6,7,8,11,12,13,14,Paxton,benthic*G.*aculeatusBoulton*G.*aculeatus7,Alaska,populations,of,G.*aculeatusDolomite;,Orphia*G.*aculeatusLoch,Fada*G.*aculeatusLoch,Vifilsstadavat,G.*aculeatusLoch,Scadavay,G.*aculeatusFox,Hole,Pungitius*pungitiusWest,African,strains,Saccharomyces*cerevisiae27361N,strain,S.*cerevisiaeSaccharomyces*kudriavzeviiCandida*glabrataEremothecium*gossypi*and*Kluyveromyces*waltiiMimulus*l.*variegatusMimulus*naiandinusMimulus*cupreusIpomoea*Mina,cladeIpomoea*horsfalliaeIochroma*gesnerioidesMormyroidsGymnotiformsPygathrix*nemaeusColobus*guerezaruminant,artiodactylsRed,floral,pigmentation,(system,2)Inability,to,use,galactosePelvic,spine,and,girdle,reductionElectrical,activity,of,myogenic,electric,organRed,floral,pigmentation,(system,1)Digestion,of,foregutLfermenting,bacteria20,19,21,22,30,29,2625,28,23,24,27,15,16,17,18,	 ? 113	 ?	 ?	 ?Phenotype Independent+Origins+In Node+NumberDrosophila*montanaDrosophila*borealisDrosophila*sechelliaXenopus*laevisLitoria*splendidaThamnophis*couchii*Thamnophis*atratusThamnophis*sirtalisAmphiesma*pryeri*Rhabdophis*tigrinusLiophis*epinephelusEuropean*Homo*sapiensSaudi,Arabian,H.*sapiensKenyan,and,Tanzanian,H.*sapiensPaxton,benthic*Gasterosteus*aculeatusCranby,G.*aculeatusPaq;,Graham;,Bear,Paw,G.*aculeatusBoot,Lake;,Whale,Lake,G.*aculeatusNakagawa,Creek,G.*aculeatus6,Pacific,freshwater,populations,of,G.*aculeatus5,Atlantic,populations,of*G.*aculeatus5,Scandinavian,populations,of,G.*aculeatusFox,Hole,Pungitius*pungitiusTetrodotoxin,resistanceLactase,persistanceTable&S2.!Nodes!corresponding!to!data!in!Table!S3.!Node!ages!are!given!in!Table!S1.Larval,trichome,lossSkin,toxin,L,caeruleinReduction,in,lateral,plate,number2,1,10,9,3,5,4,6,7,8,11,12,13,14,Heliconius*melpomeneHeliconius*eratoAustralian,D.*melanogasterNorth,American,D.*melanogasterYangochiroptera,and,YinpterochiropteraTursiops*truncatusClearwater,Oncorhynchus*mykissSwanson,O.*mykissEuropean*H.*sapiensEast,Asian*H.*sapiensGulfLcoast,Peromyscus*polionotusAtlantic,coast,Anastasia,Island,P.*polionotusAtlantic,coast,Sotheastern,P.*polionotusSand,Hill,Peromyscus*maniculatusWhite,sands,Sc loporus*undulatusWhite,sands*Aspidoscelis*inornataWhite,sands,Holbrookia*maculataPaxton,benthic*G.*aculeatusFishtrap,Creek,G.*aculeatusGasterosteus*williamsoniPach?n,cavefish*A tyanax*mexicanusMolino,A.*mexicanusYerbaniz/Japon?s,,A.*mexicanusCurva,A.mexicanusChica,A.*mexicanusPiedras,A.mexicanusReduced,pigmentation,(system,2)Reduced,pigmentation,(system,5)UltrahighLfrequency,hearing,for,echolocationReduced,pigmentation,(system,4)Reduced,pigmentation,(system,1)Reduced,pigmentation,(system,3)Life,history,,(latitudinal,clines)Red,wing,patternsRapid,development,rate31,32,35,37,36,38,41,40,43,44,39,42,45,46,33,34,	 ? 114	 ?Table	 ?B.3	 ?Proportional	 ?similarity	 ?at	 ?each	 ?node	 ?Proportional	 ?similarity	 ?at	 ?each	 ?node	 ?in	 ?the	 ?data	 ?set.	 ?Methods:	 ?'cross'	 ?=	 ?genetic	 ?cross,	 ?'cg'	 ?=	 ?candidate	 ?gene.	 ?Node	 ?numbers	 ?are	 ?identified	 ?in	 ?Table	 ?B.2.	 ?Some	 ?nodes	 ?are	 ?represented	 ?twice	 ?(once	 ?under	 ?method	 ?'cross'	 ?and	 ?again	 ?with	 ?method	 ?'cg');	 ?in	 ?such	 ?cases	 ?there	 ?is	 ?no	 ?overlap	 ?between	 ?the	 ?populations	 ?used	 ?in	 ?the	 ?two	 ?analyses.	 ?	 ?	 ?Phenotype Node+Number Average+Proportional+Similarity Rounded+Average+Estimate+of+Node+Age+(years) Species>level? MethodReduction*in*lateral*plate*number 11 0.95 10000 y crossReduction*in*lateral*plate*number 14 0.00 10000000 n crossPelvic*spine*and*girdle*reduction 17 0.37 800000 y crossPelvic*spine*and*girdle*reduction 18 0.27 10000000 n crossInability*to*use*galactose 19 0.00 2000000 y crossRed*floral*pigmentation 23 0.00 2000000 n crossRed*floral*pigmentation 24 0.00 3000000 n crossRed*wing*patterns 31 1.00 20000000 n crossRapid*development*rate 34 0.60 1000000 y crossReduced*pigmentation* 38 0.64 7000000 n crossReduced*pigmentation* 39 0.00 90000000 n crossReduced*pigmentation* 44 0.43 500000 y crossReduced*pigmentation* 45 0.00 300000000 n crossReduced*pigmentation* 46 0.27 400000000 n crossLarval*trichome*loss 1 1.00 2000000 n cgLarval*trichome*loss 2 1.00 60000000 n cgSkin*toxin*I*caerulein 3 0.00 200000000 n cgTetrodotoxin*resistance 4 1.00 500000 n cgTetrodotoxin*resistance 5 1.00 2000000 n cgTetrodotoxin*resistance 6 1.00 30000000 n cgTetrodotoxin*resistance 7 1.00 60000000 n cgTetrodotoxin*resistance 8 1.00 60000000 n cgLactase*persistance 9 1.00 50000 y cgLactase*persistance 10 1.00 100000 y cgReduction*in*lateral*plate*number 13 1.00 800000 y cgReduction*in*lateral*plate*number 14 0.00 10000000 n cgPelvic*spine*and*girdle*reduction 17 0.34 800000 y cgPelvic*spine*and*girdle*reduction 18 0.00 10000000 n cgInability*to*use*galactose 20 0.14 50000000 n cgInability*to*use*galactose 21 0.57 300000000 n cgInability*to*use*galactose 22 0.78 400000000 n cgTable+S3:*Proportional*similarity*at*each*node*in*the*data*set.*Methods:*'cross'*=*genetic*cross,*'cg'*=*candidate*gene.*Node*numbers*are*identified*in*Table*S2.*Some*nodes*are*represented*twice*(once*under*method*'cross'*and*again*with*method*'cg');*in*such*cases*there*is*no*overlap*between*the*populations*used*in*the*two*analyses.*	 ? 115	 ?	 ?	 ?	 ?Phenotype Node+Number Average+Proportional+Similarity Rounded+Average+Estimate+of+Node+Age+(years) Species>level? MethodReduction*in*lateral*plate*number 11 0.95 10000 y crossReduction*in*lateral*plate*number 14 0.00 10000000 n crossPelvic*spine*and*girdle*reduction 17 0.37 800000 y crossPelvic*spine*and*girdle*reduction 18 0.27 10000000 n crossInability*to*use*galactose 19 0.00 2000000 y crossRed*floral*pigmentation 23 0.00 2000000 n crossRed*floral*pigmentation 24 0.00 3000000 n crossRed*wing*patterns 31 1.00 20000000 n crossRapid*development*rate 34 0.60 1000000 y crossReduced*pigmentation* 38 0.64 7000000 n crossReduced*pigmentation* 39 0.00 90000000 n crossReduced*pigmentation* 44 0.43 500000 y crossReduced*pigmentation* 45 0.00 300000000 n crossReduced*pigmentation* 46 0.27 400000000 n crossLarval*trichome*loss 1 1.00 2000000 n cgLarval*trichome*loss 2 1.00 60000000 n cgSkin*toxin*I*caerulein 3 0.00 200000000 n cgTetrodotoxin*resistance 4 1.00 500000 n cgTetrodotoxin*resistance 5 1.00 2000000 n cgTetrodotoxin*resistance 6 1.00 30000000 n cgTetrodotoxin*resistance 7 1.00 60000000 n cgTetrodotoxin*resistance 8 1.00 60000000 n cgLactase*persistance 9 1.00 50000 y cgLactase*persistance 10 1.00 100000 y cgReduction*in*lateral*plate*number 13 1.00 800000 y cgReduction*in*lateral*plate*number 14 0.00 10000000 n cgPelvic*spine*and*girdle*reduction 17 0.34 800000 y cgPelvic*spine*and*girdle*reduction 18 0.00 10000000 n cgInability*to*use*galactose 20 0.14 50000000 n cgInability*to*use*galactose 21 0.57 300000000 n cgInability*to*use*galactose 22 0.78 400000000 n cgTable+S3:*Proportional*similarity*at*each*node*in*the*data*set.*Methods:*'cross'*=*genetic*cross,*'cg'*=*candidate*gene.*Node*numbers*are*identified*in*Table*S2.*Some*nodes*are*represented*twice*(once*under*method*'cross'*and*again*with*method*'cg');*in*such*cases*there*is*no*overlap*between*the*populations*used*in*the*two*analyses.*Red*floral*pigmentation 25 1.00 4000000 n cgRed*floral*pigmentation 26 0.00 70000000 n cgRed*floral*pigmentation 27 0.00 80000000 n cgElectrical*activity*of*myogenic*electric*organ 28 1.00 300000000 n cgDigestion*of*foregutIfermenting*bacteria 29 0.00 10000000 n cgDigestion*of*foregutIfermenting*bacteria 30 0.00 90000000 n cgLife*history**(latitudinal*clines) 32 1.00 300 y cgUltrahighIfrequency*hearing*for*echolocation 33 1.00 80000000 n cgReduced*pigmentation* 35 0.00 50000 y cgReduced*pigmentation* 37 0.00 70000 y cgReduced*pigmentation* 38 0.00 7000000 n cgReduced*pigmentation* 39 0.00 90000000 n cgReduced*pigmentation* 40 1.00 40000000 n cgReduced*pigmentation* 41 1.00 200000000 n cgReduced*pigmentation* 42 0.06 300000000 n cgReduced*pigmentation* 43 1.00 10000 y cgReduced*pigmentation* 45 0.00 300000000 n cgReduced*pigmentation* 46 0.31 400000000 n cg	 ? 116	 ?Appendix	 ?C:	 ?Chapter	 ?4	 ?Supplementary	 ?Material	 ?Figure	 ?C.1	 ?All	 ?phenotypes	 ?scored	 ?Landmark	 ?numbers	 ?were	 ?made	 ?consistent	 ?with	 ?those	 ?in	 ?Arnegard	 ?et	 ?al.	 ?(in	 ?press).	 ?	 ?	 ?Landmarks)(x#and#y#coordinates)#)1.? posterior#midpoint#of#the#caudal#peduncle##2.? anterior#inser9on#of#the#anal#fin#at#the#first#so;#ray##3.? posteroventral#corner#of#the#ectocoracoid#bone##4.? posterodorsal#corner#of#the#ectocoracoid#bone##5.? anteriorAmost#corner#of#the#ectocoracoid#bone##6.? anteroventral#corner#of#the#opercle##7.? posterodorsal#corner#of#the#opercle##8.? dorsal#edge#of#the#opercleAhyomandibular#boundary##9.? dorsalAmost#extent#of#the#preopercle##10.? posteroventral#corner#of#the#preopercle##11.? anteriorAmost#extent#of#the#preopercle#along#the#ventral#silhoueHe##12.? posteroventral#extent#of#the#maxilla##13.? anterodorsal#extent#of#the#maxilla##14.?No&landmark&15.? anterior#margin#of#the#orbit#in#line#with#the#eye?s#midpoint##16.? posterior#margin#of#the#orbit#in#line#with#the#eye?s#midpoint##17.? ventral#margin#of#the#orbit#in#line#with#the#eye?s#midpoint##18.? posterior#extent#of#neurocranium#(i.e.,#supraoccipital)#along#dorsal#silhoueHe##19.? anterior#inser9on#of#the#dorsal#fin#at#the#first#so;#ray##20.? posterior#inser9on#of#the#anal#fin#at#the#first#so;#ray##21.? edge#of#the#lachrymal#at#the#naris#22.? dorsal#margin#of#the#orbit#in#line#with#the#eye?s#midpoint##23.? anteriorAmost#extent#of#the#premaxilla##24.? dorsal#inser9on#of#the#pectoral#fin##25.? ventral#inser9on#of#the#pectoral#fin#26.? dorsum#of#the#trunk#over#the#pectoral#fin#midpoint#27.? posterior#inser9on#of#the#dorsal#fin#at#the#first#so;#ray#?? centroid##size#(square#root#of#the#sum#of#squared#distances#of#the#26#landmarks#from#their#centroid#)#Meris-cs)))?? lateral#plate#count#?? 1st#dorsal#spine#presence/absence#?? 2nd#dorsal#spine#presence/absence#?? long#gill#raker#count#(on#the#first#gill#arch)#?? short#gill#raker#count#(on#the#first#gill#arch)#1#cm#	 ? 117	 ?Figure	 ?C.2	 ?Examples	 ?of	 ?mapping	 ?candidate	 ?QTL	 ?(Starts	 ?on	 ?next	 ?page)	 ?The	 ?three	 ?depicted	 ?candidate	 ?QTL	 ?were	 ?determined	 ?to	 ?have	 ?the	 ?following	 ?effects:	 ?top	 ?row	 ?-??	 ?parallel	 ?effects;	 ?middle	 ?row	 ?-??	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake;	 ?bottom	 ?row	 ?-??	 ?opposite	 ?effects.	 ?The	 ?left	 ?column	 ?(also	 ?depicted	 ?in	 ?Figure	 ?4.2)	 ?shows	 ?examples	 ?of	 ?phenotype	 ?by	 ?genotype	 ?relationships	 ?at	 ?candidate	 ?QTL	 ?in	 ?F2	 ?hybrids	 ?from	 ?the	 ?Paxton	 ?Lake	 ?cross	 ?(light	 ?blue)	 ?and	 ?the	 ?Priest	 ?Lake	 ?cross	 ?(purple).	 ?Phenotypes	 ?are	 ?shown	 ?on	 ?the	 ?y-??axes.	 ?The	 ?x-??axes	 ?show	 ?the	 ?additive	 ?genotype	 ?score	 ?at	 ?the	 ?candidate	 ?QTL	 ?with	 ?0	 ?indicating	 ?the	 ?limnetic	 ?genotype,	 ?1	 ?the	 ?benthic	 ?genotype	 ?and	 ?0.5	 ?the	 ?heterozygote	 ?(values	 ?in	 ?between	 ?indicate	 ?uncertain	 ?genotypes,	 ?with	 ?score	 ?reflecting	 ?genotype	 ?probability).	 ?Lines	 ?represent	 ?the	 ?fitted	 ?values	 ?of	 ?linear	 ?models	 ?fitted	 ?to	 ?the	 ?phenotype	 ?and	 ?genotype	 ?data	 ?for	 ?each	 ?lake	 ?separately	 ?(light	 ?blue:	 ?Paxton	 ?Lake	 ?cross,	 ?purple:	 ?Priest	 ?Lake	 ?cross),	 ?using	 ?family	 ?identity	 ?and	 ?sex	 ?as	 ?covariates.	 ?Phenotypic	 ?measurements	 ?shown	 ?here	 ?are	 ?corrected	 ?for	 ?family	 ?identity.	 ?For	 ?the	 ?same	 ?three	 ?QTL	 ?(one	 ?QTL	 ?per	 ?row),	 ?the	 ?plots	 ?in	 ?the	 ?right	 ?column	 ?show	 ?the	 ?LOD	 ?profiles	 ?(left	 ?y-??axis	 ?and	 ?bold	 ?lines)	 ?from	 ?the	 ?three	 ?distinct	 ?QTL	 ?scans	 ?across	 ?the	 ?entire	 ?linkage	 ?group	 ?on	 ?which	 ?the	 ?QTL	 ?was	 ?detected	 ?(x-??axis).	 ?They	 ?also	 ?show	 ?the	 ?Entropy	 ?scores	 ?(an	 ?index	 ?of	 ?missing	 ?genotype	 ?information)	 ?for	 ?each	 ?lake?s	 ?cross	 ?across	 ?the	 ?entire	 ?linkage	 ?group	 ?(right	 ?y-??axis	 ?and	 ?un-??bolded	 ?lines).	 ?The	 ?tick	 ?marks	 ?along	 ?the	 ?x-??axis	 ?represent	 ?the	 ?positions	 ?of	 ?SNP	 ?markers	 ?on	 ?the	 ?linkage	 ?group.	 ?The	 ?vertical	 ?grey	 ?line	 ?represents	 ?the	 ?position	 ?of	 ?the	 ?peak	 ?marker	 ?in	 ?the	 ?combined	 ?scan	 ?(gold).	 ?	 ?	 ?	 ? 118	 ?QTL for long gill raker count detected in the combined scan Single QTL Model Results: Theta = 5.6 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.26?????????????????? ???????????? ???????????????? ?????????????????????????????????????????? ?????????????????????????? ?????????????? ?????????????? ???? ????????????????? ??????? ????????????????????? ?????0.0 0.2 0.4 0.6 0.8 1.018202224long gill raker countAdditive genotype score LG 7 at 35.1 cM0 20 40 60051015LODLG 7 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for short gill raker count detected in the combined scan Single QTL Model Results: Theta = 4.12 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.72????????????? ?????????????????? ??????? ?????????????? ??? ?????????????????? ????????????????????????????? ???? ??????????????????????????????????????????????????? ?? ??????????????? ??? ??? ????? ??????? ?????? ???? ?????????????? ??????????? ???? ????????????????????????? ?????????????0.0 0.2 0.4 0.6 0.8 1.014151617181920short gill raker countAdditive genotype score LG 1 at 21.2 cM0 10 20 30 40051015LODLG 1 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y27 detected in the combined scan Single QTL Model Results: Theta = 7.94 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.55??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???? ???????????????????????0.0 0.2 0.4 0.6 0.8 1.00.180.200.220.240.26y27Additive genotype score LG 12 at 13.2 cM0 5 10 15 20 25051015LODLG 12 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y27 detected in the combined scan Single QTL Model Results: Theta = 85.4 Two?Level Significance Category =non?parallel Lowest AICc Category =QTL with diff effect ? diff dir. 2nd lowest AICc Category =pr.only Delta.AIC = 10.7?????????????????????????????????????????????????????????????????????????????????????????????? ????????????????????????????????????????????????? ?????????????????????? ???????? ?????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????? ?????? ???????????????? ??0.0 0.2 0.4 0.6 0.8 1.00.180.220.26y27Additive genotype score LG 17 at 21.7 cM0 10 20 30 40051015LODLG 17 Map Position (cM)0.00.20.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y25 detected in the combined scan Single QTL Model Results: Theta = 4.03 Two?Level Significance Category =perfectly parallel Lowest AICc Category =perfectly parallel 2nd lowest AICc Category =full Delta.AIC = 2.88??????????????????????? ??????? ?????????????????????????????????????????? ?????????????????????????????????????????????0.0 0.2 0.4 0.6 0.8 1.0?0.22?0.20?0.18?0.16y25Additive genotype score LG 12 at 13.2 cM0 5 10 15 20 25051015LODLG 12 Map Position (cM)0.0.2.4.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest CrossQTL for y26 detected in the combined scan Single QTL Model Results: Theta = 46.5 Two?Level Significance Category =significant in Priest only Lowest AICc Category =QTL only in Pr 2nd lowest AICc Category =full Delta. I  = 2.66???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????0.0 0.2 0.4 0.6 0.8 1.00.400.450.500.55y26Additive genotype score LG 1 at 21.7 cM0 10 20 30 40051015LODLG 1 Map Position (cM)0.0.2.40.60.81.0EntropyLOD Scores from:Combined ScanPaxton ScanPriest ScanEntropy within:Paxton CrossPriest Cross	 ? 119	 ?Figure	 ?C.3	 ?Map	 ?of	 ?candidate	 ?QTL	 ?	 ?(Starts	 ?on	 ?next	 ?page)	 ?Map	 ?of	 ?58	 ??candidate	 ?QTL?	 ?(i.e.	 ?QTL	 ?with	 ?an	 ?effect	 ?in	 ?one	 ?or	 ?both	 ?lakes).	 ?Linkage	 ?groups	 ?on	 ?which	 ?QTL	 ?were	 ?detected	 ?are	 ?shown.	 ?For	 ?each,	 ?the	 ?positions	 ?of	 ?SNP	 ?markers	 ?are	 ?depicted	 ?by	 ?tick	 ?marks	 ?on	 ?the	 ?left.	 ?Colored	 ?bars	 ?span	 ?the	 ?1.5	 ?LOD	 ?confidence	 ?intervals	 ?of	 ?candidate	 ?QTL.	 ?Black	 ?dots	 ?within	 ?bars	 ?represent	 ?the	 ?peak	 ?marker	 ?position.	 ?The	 ?phenotype	 ?affected	 ?by	 ?each	 ?candidate	 ?QTL	 ?is	 ?printed	 ?to	 ?the	 ?left	 ?its	 ?bar.	 ?Colors	 ?of	 ?bars	 ?represent	 ?the	 ??QTL	 ?Effect?	 ?category,	 ?as	 ?follows:	 ?parallel	 ?effects	 ??	 ?blue;	 ?effect	 ?in	 ?only	 ?one	 ?lake	 ??	 ?grey;	 ?opposite	 ?effects	 ??	 ?red.	 ?Tan	 ?colored	 ?bars	 ?represent	 ?the	 ?candidate	 ?QTL	 ?for	 ?which	 ?more	 ?than	 ?one	 ?QTL	 ?effect	 ?category	 ?fit	 ?the	 ?data	 ?nearly	 ?equally	 ?well.	 ?	 ?	 ? 120	 ?	 ?403020100chr 1Map locationshort gill raker count?x13?x16? x21?y11?y26? y10?centroid size?x20?50403020100chr 2Map locationplate count?y7?403020100chr 3Map locationlong gill raker count?706050403020100chr 4Map locationx2? x6? x20?y10? y11?y12?y18?y3?6040200chr 7Map locationplate count?long gill raker count?short gill raker count? y4? y5? x18?y3?x13?x22?x2?x6?y7?302520151050chr 8Map locationy1?y27?50403020100chr 9Map location y7?403020100chr 11Map locationy18?y11?2520151050chr 12Map locationx16?y25?y26?y27? x17?x20?2520151050chr 13Map locationx16?y12? x6? y16? 50403020100chr 14Map locationy10?x17? y26?x2? 403020100chr 16Map locationplate count?403020100chr 17Map locationy27?302520151050chr 19Map location y26?y5?y12?centroid size?403020100chr 21Map locationy16?	 ? 121	 ?Figure	 ?C.4	 ?Proportions	 ?of	 ?QTL	 ?effect	 ?categories	 ?per	 ?chromosome	 ?The	 ?average	 ?proportion	 ?of	 ?candidate	 ?QTL	 ?per	 ?chromosome	 ?(n=13)	 ?with	 ?parallel	 ?effects	 ?(blue),	 ?an	 ?effect	 ?in	 ?only	 ?a	 ?single	 ?lake	 ?(grey)	 ?and	 ?opposite	 ?effects	 ?(red)	 ?is	 ?shown	 ?with	 ?standard	 ?error	 ?bars.	 ?The	 ?43	 ?candidate	 ?QTL	 ?considered	 ?are	 ?shown	 ?in	 ?Figure	 ?C.3	 ?in	 ?blue,	 ?grey	 ?and	 ?red.	 ?	 ?	 ? 	 ?Parallel Single lake OppositeQTL EffectAverage Proportion of QTL Effects per Chromosome0.00.10.20.30.40.5	 ? 122	 ?Table	 ?C.1	 ?Trait	 ?divergence	 ?categories	 ?Trait	 ?divergence	 ?was	 ?considered	 ??parallel?	 ?when	 ?either	 ?the	 ?best	 ?model	 ?of	 ?the	 ?species	 ?effect	 ?was	 ??same	 ?effect?,	 ?or	 ?when	 ?the	 ?best	 ?model	 ?of	 ?species	 ?effect	 ?was	 ??different	 ?effect?	 ?but	 ?the	 ?direction	 ?of	 ?divergence	 ?was	 ??same?.	 ?Trait	 ?divergence	 ?was	 ?considered	 ?only	 ?in	 ?a	 ??single	 ?lake?	 ?when	 ?the	 ?best	 ?model	 ?of	 ?the	 ?species	 ?effect	 ?was	 ?either	 ??effect	 ?only	 ?in	 ?Paxton?	 ?or	 ??effect	 ?only	 ?in	 ?Priest?.	 ?Trait	 ?divergence	 ?was	 ?considered	 ??opposite?	 ?when	 ?the	 ?best	 ?model	 ?of	 ?species	 ?effect	 ?was	 ??different	 ?effect?	 ?and	 ?the	 ?direction	 ?of	 ?divergence	 ?was	 ??opposite?.	 ?The	 ?second	 ?best	 ?model	 ?of	 ?species	 ?effect	 ?and	 ?the	 ?delta	 ?AICc	 ?between	 ?it	 ?and	 ?the	 ?best	 ?model	 ?is	 ?also	 ?shown.	 ?When	 ?the	 ?delta	 ?AICc	 ?was	 ?less	 ?than	 ?two	 ?and	 ?the	 ?2nd	 ?best	 ?model	 ?called	 ?for	 ?a	 ?different	 ?trait	 ?divergence	 ?category	 ?than	 ?the	 ?best	 ?model	 ?did,	 ?we	 ?dropped	 ?the	 ?trait	 ?from	 ?further	 ?study,	 ?though	 ?all	 ?detected	 ?QTL	 ?are	 ?shown	 ?in	 ?Tables	 ?C.3	 ??	 ?C.5.	 ?	 ?Trait 'Trait'divergence''based'on'AICc'model'selection Direction'of'divergence Best'model'of'species'effect 2nd'best'model'of'species'effect Delta'AICc1st'dorsal'spine Single'lake opposite effect'in'Paxton'only different'effect 2.142nd'dorsal'spine Neither'lake same no'effect effect'in'Priest'only 0.95gill'raker'count Parallel same same'effect different'effect 1.24plate'count Parallel same different'effect same'effect 35.62x1 Neither'lake opposite no'effect effect'in'Priest'only 0.17y1 Parallel same same'effect different'effect 1.72x2 Parallel same different'effect same'effect 5.78y2 Parallel same same'effect different'effect 1.28x3 Opposite opposite different'effect effect'in'Paxton'only 3.97y3 Parallel same different'effect same'effect 1.14x4 Single'lake opposite effect'in'Priest'only different'effect 2.12y4 Parallel same different'effect same'effect 31.59x5 Single'lake opposite effect'in'Priest'only different'effect 0.02y5 Parallel same different'effect same'effect 2.77x6 Parallel same same'effect different'effect 2.20y6 Opposite opposite different'effect effect'in'Priest'only 5.36x7 Opposite opposite different'effect effect'in'Priest'only 1.47y7 Parallel same same'effect different'effect 1.90x8 Single'lake opposite effect'in'Paxton'only different'effect 0.32y8 Parallel same different'effect same'effect 0.53x9 Single'lake same effect'in'Priest'only different'effect 1.36y9 Single'lake same effect'in'Paxton'only different'effect 0.26x10 Single'lake same effect'in'Priest'only different'effect 2.18y10 Parallel same same'effect different'effect 1.01x11 Single'lake same effect'in'Paxton'only different'effect 1.69y11 Parallel same same'effect different'effect 1.83x12 Opposite opposite different'effect effect'in'Priest'only 19.50y12 Parallel same same'effect different'effect 1.03x13 Parallel same different'effect same'effect 2.97y13 Parallel same same'effect different'effect 1.72x15 Parallel same same'effect effect'in'Priest'only 1.70y15 Parallel same same'effect different'effect 1.01x16 Parallel same different'effect same'effect 0.78y16 Parallel same same'effect different'effect 0.67x17 Parallel same different'effect same'effect 3.19y17 Parallel same different'effect same'effect 0.37x18 Parallel same different'effect same'effect 0.84y18 Parallel same different'effect same'effect 0.43x19 Opposite opposite different'effect effect'in'Priest'only 1.00y19 Single'lake same effect'in'Paxton'only different'effect 2.13x20 Parallel same different'effect effect'in'Priest'only 3.10	 ? 123	 ?	 ?	 ?Trait 'Trait'divergence''based'on'AICc'model'selection Direction'of'divergence Best'model'of'species'effect 2nd'best'model'of'species'effect Delta'AICcy20 Parallel same different'effect same'effect 17.17x21 Parallel same same'effect different'effect 2.09y21 Neither'lake opposite no'effect effect'in'Priest'only 0.37x22 Parallel same same'effect different'effect 2.17y22 Single'lake same effect'in'Paxton'only different'effect 0.27x23 Opposite opposite different'effect effect'in'Paxton'only 4.95y23 Parallel same different'effect effect'in'Priest'only 0.29x24 Single'lake same effect'in'Priest'only different'effect 1.32y24 Opposite opposite different'effect effect'in'Paxton'only 17.01x25 Single'lake same effect'in'Priest'only different'effect 2.19y25 Parallel same different'effect effect'in'Paxton'only 4.15x26 Parallel same different'effect effect'in'Priest'only 1.63y26 Parallel same different'effect same'effect 3.30x27 Parallel same same'effect different'effect 2.12y27 Parallel same same'effect different'effect 2.15centroid Parallel same different'effect effect'in'Paxton'only 25.59	 ? 124	 ?	 ?Table	 ?C.2	 ?Identities,	 ?map	 ?positions,	 ?and	 ?physical	 ?locations	 ?of	 ?SNPs	 ?(Starts	 ?on	 ?next	 ?page)	 ?Identities,	 ?map	 ?positions,	 ?and	 ?physical	 ?locations	 ?of	 ?the	 ?430	 ?single	 ?nucleotide	 ?polymorphism	 ?(SNP)	 ?markers	 ?used	 ?in	 ?linkage	 ?and	 ?QTL	 ?analysis.	 ?The	 ?linkage	 ?group	 ?(LG)	 ?and	 ?map	 ?position	 ?in	 ?centimorgans	 ?(cM)	 ?are	 ?provided	 ?for	 ?each	 ?SNP.	 ?Each	 ?marker	 ?name	 ?is	 ?a	 ?combination	 ?of	 ?the	 ?chromosome	 ?number	 ?(before	 ?the	 ?colon)	 ?and	 ?the	 ?physical	 ?position	 ?in	 ?base	 ?pairs	 ?(after	 ?the	 ?colon)	 ?of	 ?the	 ?SNP	 ?in	 ?the	 ?initial	 ?stickleback	 ?genome	 ?assembly	 ?(Broad	 ?S1,	 ?Feb.	 ?2006).	 ?Markers	 ?identified	 ?from	 ?unassembled	 ?regions	 ?of	 ?the	 ?genome	 ?are	 ?indicated	 ?with	 ??chrUN?.	 ?In	 ?such	 ?cases,	 ?the	 ?position	 ?in	 ?base	 ?pairs	 ?is	 ?based	 ?on	 ?the	 ?composite	 ?chrUN	 ?in	 ?the	 ?UCSC	 ?genome	 ?browser.	 ?Marker	 ?information	 ?can	 ?be	 ?obtained	 ?from	 ?the	 ?Single	 ?Nucleotide	 ?Polymorphism	 ?Database	 ?(dbSNP,	 ?available	 ?at	 ?http://www.ncbi.nlm.nih.gov/projects/SNP/),	 ?which	 ?is	 ?hosted	 ?by	 ?the	 ?National	 ?Center	 ?for	 ?Biotechnology	 ?Information	 ?(NCBI)	 ?of	 ?the	 ?U.S.	 ?National	 ?Institutes	 ?of	 ?Health.	 ?Data	 ?for	 ?specific	 ?markers	 ?may	 ?be	 ?found	 ?by	 ?searches	 ?of	 ?the	 ?dbSNP	 ?using	 ?the	 ?submitted	 ?SNP	 ?ID	 ?numbers	 ?(ss#).	 ?Two	 ?SNPs	 ?are	 ?still	 ?awaiting	 ?ss#	 ?assignment.	 ?	 ? 125	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(1 0 chrI:27642534 418642015 2 45.24 chrII:919438 2442227811 8.45 chrUn:18660323 418642624 2 45.324 chrUn:23384875 4186426271 9.18 chrI:22716347 418642010 2 50.981 chrII:533883 1202584181 11.439 chrI:3310077 244222768 3 0 chrUn:30223426 4186426411 14.833 chrI:19946499 418642005 3 0.219 chrUn:27149198 4186426321 15.322 chrI:2718044 418641984 3 0.593 chrUn:27040022 4186426311 16.421 chrI:4219350 244222770 3 2.685 chrUn:30323959 4186426421 18.111 chrI:3494580 120258412 3 6.884 chrIII:16463929 2442227961 19.109 chrI:14261764 418641998 3 10.455 chrIII:16251071 1202584311 20.267 chrI:15145305 418642000 3 16.956 chrIII:15793968 4186420891 21.162 chrI:4171190 244222769 3 18.59 chrIII:15185662 4186420881 21.745 chrI:17306554 418642003 3 19.173 chrIII:15157782 4186420871 22.395 chrI:7545826 418641993 3 22.347 chrIII:14892994 2442227941 23.715 chrI:20584613 418642006 3 25.596 chrIII:13397314 4186420781 23.959 chrI:22899825 418642011 3 26.289 chrIII:13520975 2528411021 24.572 chrI:22361077 120258417 3 28.19 chrIII:14393183 4186420841 25.305 chrI:3538018 418641987 3 28.59 chrIII:14048561 2528410581 27.917 chrI:26879230 244222777 3 28.741 chrIII:13911180 4186420801 28.238 chrI:25560380 418642013 3 28.761 chrIII:11836494 4186420721 37.745 chrI:1550 418641979 3 29.684 chrIII:13699701 4186420791 38.432 chrUn:37631434 244223001 3 29.727 chrIII:12930427 4186420761 41.893 chrI:913033 120258411 3 29.906 chrIII:14135608 4186420812 0 chrII:22443700 244222787 3 30.523 chrIII:14456990 2528410632 1.55 chrII:22644752 418642054 3 30.9 chrIII:14248039 4186420832 10.77 chrII:21231538 244222786 3 32.208 chrIII:11302839 4186420712 17.049 chrII:21013052 418642052 3 33.049 chrIII:2376699 4186420652 21.421 chrII:19985741 244222785 3 34.228 chrIII:1968625 4186420632 31.618 chrII:5914538 418642030 3 37.992 chrIII:1198125 1202584282 32.478 chrII:10092618 418642034 3 38.662 chrIII:639237 4186420592 32.693 chrII:8305286 418642033 3 38.988 chrIII:1651721 2528410792 33.053 chrII:6475468 244222782 3 41.069 chrIII:269753 4186420572 33.629 chrII:17453243 418642042 3 42.403 chrIII:105665 4186420552 33.653 chrII:5935944 252841148 3 43.645 chrIII:186390 4186420562 33.707 chrII:12292176 120258425 4 0 chrUn:27478064 2442229932 33.978 chrII:14611516 244222784 4 2.47 chrUn:27589750 4186426332 34.026 chrII:17312835 418642041 4 5.799 chrUn:27402745 2528410682 36.632 chrII:4530808 120258423 4 11.6 chrIV:32592491 4186421502 38.498 chrII:19324477 418642044 4 12.021 chrIV:32487875 2442228122 39.102 chrII:3931852 418642025 4 13.638 chrIV:32387818 1202584472 39.262 chrII:4157699 252841112 4 14.49 chrIV:32277841 4186421462 39.701 chrII:3516452 120258422 4 15.02 chrIV:32236655 4186421452 42.057 chrII:3384330 120258421 4 16.56 chrIV:32092919 252841132	 ? 126	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(4 16.927 chrIV:32005807 120258445 5 46.025 chrV:7791830 2528410934 20.838 chrIV:31740478 244222809 5 50.516 chrUn:10540032 4186426144 21.77 chrIV:31350187 418642140 5 53.473 chrUn:10213240 4186426134 23.776 chrIV:29763654 120258443 5 53.596 chrUn:11980918 2528411364 24.599 chrIV:31611147 252841084 5 56.444 chrUn:12390868 1202585694 26.831 chrIV:30568387 252841083 6 0 chrVI:487411 4186421834 28.149 chrIV:5165268 418642111 6 3.991 chrVI:6312798 4186421874 30.064 chrIV:21232476 418642127 6 5.262 chrVI:1440771 2442228234 30.311 chrIV:21605258 252841082 6 5.672 chrVI:10415741 4186419204 33.352 chrIV:15721538 244222806 6 6.903 chrVI:11954719 4186421924 33.352 chrIV:15737291 244222807 6 7.644 chrVI:13220597 2528410444 34.536 chrIV:15530121 244222805 6 7.721 chrVI:11873663 1202584544 35.12 chrIV:15052901 244222804 6 7.97 chrVI:12427477 4186421934 36.67 chrIV:10997988 244222801 6 12.157 chrVI:3116218 2442228254 36.782 chrIV:9220132 418642120 6 12.259 chrVI:16870159 2442228344 36.984 chrIV:8545605 418642119 6 13.529 chrVI:218630 2442228204 38.029 chrIV:11367975 120258435 6 15.74 chrVI:14571427 4186422004 41.136 chrIV:4065598 244222799 6 20.547 chrVI:15413799 4186422034 45.377 chrIV:3334208 418642103 6 23.176 chrVI:14976508 4186422014 58.662 chrIV:2045971 418642099 6 24.097 chrVI:15654034 4186422044 71.359 chrIV:219384 418642093 6 24.282 chrVI:15692312 4186422055 0 chrUn:25831365 418642629 7 0 chrVII:27918897 4186422575 2.542 chrUn:25946639 244222990 7 0.743 chrUn:29400087 4186426385 8.438 chrV:11316476 252841077 7 14.707 chrVII:26769148 4186422515 9.302 chrV:11368893 418642177 7 19.214 chrVII:26538823 2442228425 13.732 chrV:11509827 418642178 7 19.55 chrVII:26448674 2528411255 17.208 chrV:11642284 418642179 7 22.7 chrVII:26227403 1202584615 19.847 chrV:11722274 418642180 7 26.614 chrVII:25662266 1202584605 20.126 chrV:10649179 252841089 7 28.341 chrVII:25193081 4186422465 23.802 chrV:10674055 418642173 7 29.302 chrVII:249883305 30.785 chrV:10028353 418642167 7 32.219 chrVII:24217606 4186422455 31.771 chrV:9884672 418642164 7 33.407 chrVII:19857837 4186422375 31.771 chrV:9911653 418642165 7 33.931 chrVII:16848769 4186422325 32.969 chrV:9768052 252841108 7 34.008 chrVII:24203557 1202584595 34.423 chrV:9157076 244222818 7 34.209 chrVII:23703797 4186422435 40.776 chrV:8327818 244222816 7 34.447 chrVII:22798737 4186422405 42.038 chrV:1238066 120258448 7 34.985 chrVII:21302029 4186422385 43.017 chrV:1727383 418642153 7 35.124 chrVII:20883742 2528410675 43.695 chrV:2528528 244222814 7 35.45 chrVII:18353106 2442228395 44.689 chrUn:11085407 418642615 7 35.809 chrVII:13452516 2442228365 45.499 chrV:5064057 418642160 7 35.815 chrVII:5552972 2528410665 45.501 chrV:4819972 418642158 7 37.029 chrVII:5936068 120258457	 ? 127	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(7 37.322 chrVII:4310181 418642225 9 32.728 chrIX:13852312 4186423117 44.369 chrVII:2559099 418642220 9 33.542 chrIX:803523 2528410657 50.636 chrVII:1569236 418642218 9 33.594 chrIX:16779825 2442228697 50.989 chrVII:1481322 418642217 9 36.679 chrIX:2360337 2442228597 59.593 chrVII:835236 252841091 9 37.317 chrIX:2310926 4186422997 70.492 chrUn:29087782 244222996 9 39.339 chrIX:2089567 2442228587 72.645 chrVII:537136 252841113 9 43.455 chrIX:1273244 2442228577 72.679 chrVII:393417 418642213 9 45.903 chrIX:1417909 4186422927 73.819 chrUn:28671327 244222995 9 46.308 chrIX:1571056 4186422948 0 chrVIII:19282658 418642286 9 52.345 chrIX:639609 2442228568 4.379 chrVIII:868226 418642258 10 0 chrX:1275840 4186423268 5.961 chrVIII:18760705 244222855 10 4.412 chrX:14831394 4186423588 12.586 chrVIII:2505620 418642263 10 4.526 chrX:14456479 2528411008 13.173 chrVIII:1929053 244222843 10 4.527 chrX:14549101 2528411228 16.606 chrVIII:2257915 418642261 10 6.198 chrX:14265366 1202584868 18.186 chrVIII:3765115 418642265 10 7.06 chrUn:14127611 4186426198 18.689 chrVIII:3627706 244222844 10 8.977 chrUn:14043112 4186426188 19.011 chrVIII:3987295 120258464 10 9.768 chrUn:24511995 4186426288 20.132 chrVIII:6680213 418642268 10 10.14 chrUn:29017220 4186426378 20.538 chrVIII:8858242 418642273 10 10.338 chrX:13132917 4186423528 20.747 chrVIII:14278829 418642277 10 16.368 chrX:10080391 4186423388 20.771 chrVIII:12472630 252841158 10 16.858 chrX:11139448 2528411288 20.929 chrVIII:13412707 244222846 10 17.302 chrX:4696470 4186423308 21.923 chrVIII:15261158 418642279 10 19.902 chrX:8703061 1202584858 23.667 chrVIII:13577518 252841097 10 20.486 chrX:7113953 1202584838 24.825 chrVIII:14472465 244222848 10 22.444 chrX:11252137 2442228758 30.855 chrVIII:16843576 418642285 10 24.019 chrX:12844036 4186423509 0 chrIX:19781202 244222870 10 28.446 chrX:12507632 2442228779 0.675 chrIX:20090929 244222871 11 0 chrXI:16701186 2442228889 7.45 chrIX:19745222 418642321 11 0.287 chrXI:16655205 1202584959 17.662 chrIX:18494397 418642317 11 8.458 chrXI:15154801 4186423829 19.295 chrIX:18826248 418642319 11 15.6 chrUn:32523521 4186426469 19.628 chrIX:19322448 418642320 11 20.238 chrXI:14631875 4186423799 24.547 chrIX:5109672 244222860 11 20.482 chrXI:14691162 4186423809 25.033 chrIX:4882924 120258472 11 20.626 chrXI:14830913 2442228859 27.249 chrIX:5403530 120258474 11 23.738 chrXI:15005173 2442228869 28.314 chrIX:5568375 244222863 11 31.588 chrXI:12097498 4186423759 30.384 chrIX:12933483 244222865 11 31.877 chrXI:10976029 2442228839 30.606 chrIX:7146708 418642304 11 34.655 chrXI:9039275 2528410949 31.139 chrIX:15670033 244222868 11 35.303 chrXI:7355052 4186423709 31.408 chrIX:7893416 418642306 11 37.738 chrXI:12746496 2442228849 31.862 chrIX:13553866 252841127 11 38.425 chrXI:3120961 244222880	 ? 128	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(11 42.418 chrXI:1017481 120258488 13 21.03 chrXIII:2632698 24422290111 43.126 chrXI:1449684 120258489 13 22.175 chrXIII:2523163 12025850511 43.715 chrXI:1266618 418642362 13 23.993 chrXIII:1909687 24422290011 46.473 chrXI:234849 120258487 13 24.529 chrXIII:1698554 41864242112 0 chrXII:17758877 244222897 13 27.703 chrXIII:1001571 12025850312 3.744 chrXII:16628544 418642412 13 28.789 chrXIII:2105469 41864242312 4.281 chrXII:2242677 418642394 14 0 chrXIV:14049917 25284109012 4.39 chrUn:26305459 244222991 14 3.337 chrUn:21213332 12025857112 4.88 chrXII:16877465 418642413 14 6.577 chrXIV:11054767 12025851712 5.223 chrXII:3026329 418642398 14 7.427 chrXIV:9742642 41864245812 5.738 chrXII:18221941 244222899 14 9.222 chrXIV:6992838 41864245612 6.258 chrXII:4123972 418642400 14 9.409 chrXIV:15137805 41864246212 6.422 chrUn:30606854 244222997 14 9.506 chrXIV:6641188 41864245512 7.731 chrUn:38378170 120258576 14 9.686 chrXIV:7313827 41864245712 9.452 chrXII:3810254 418642399 14 11.233 chrXIV:15033103 41864246112 10.731 chrXII:6012527 418642404 14 13.935 chrXIV:3414352 12025851412 10.731 chrXII:5828898 418642403 14 14.748 chrXIV:3598443 41864245212 10.863 chrXII:5521301 418642402 14 15.715 chrXIV:3534175 12025851512 11.573 chrXII:6399147 252841133 14 22.579 chrXIV:2084777 41864244612 11.61 chrXII:6924609 418642405 14 22.648 chrXIV:1798136 41864244312 11.639 chrXII:6745006 244222892 14 24.911 chrXIV:1713227 12025851312 11.825 chrXII:6913126 120258500 14 27.904 chrXIV:1641269 41864244212 12.164 chrXII:7504339 418642406 14 28.549 chrXIV:1442872 12025851212 13.016 chrXII:16454328 418642411 14 30.976 chrXIV:1383447 24422290812 13.016 chrXII:1589655 120258497 14 32.066 chrXIV:1311694 41864244112 13.016 chrXII:2157795 418642393 14 34.828 chrXIV:1087388 41864243912 13.016 chrXII:15046849 418642410 14 36.498 chrXIV:800076 41864243812 13.24 chrXII:11472159 418642407 14 38.817 chrXIV:721170 24422290712 13.498 chrXII:13045611 244222894 14 41.408 chrXIV:451065 12025851112 14.199 chrXII:14223760 244222895 14 41.654 chrXIV:348659 41864243512 15.462 chrXII:1483544 244222889 14 43.257 chrUn:35285565 41864264912 20.999 chrXII:880748 418642389 14 48.931 chrUn:36334731 24422300012 25.007 chrXII:548804 252841119 15 0 chrXV:13047331 41864248113 0 chrXIII:18470329 252841124 15 0.602 chrXV:12281774 41864248013 8.132 chrXIII:17392141 120258510 15 3.131 chrXV:6446874 41864247713 8.48 chrXIII:17249562 418642432 15 6.668 chrXV:2507809 24422291413 16.245 chrXIII:8085851 418642430 15 7.328 chrXV:3703641 41864247513 17.207 chrXIII:4401535 418642425 15 9.649 chrXV:2169610 24422291213 18.717 chrXIII:4868788 418642428 15 12.33 chrXV:1902350 24422291113 19.246 chrXIII:4621027 418642426 15 13.521 chrXV:1800560 41864246813 20.037 chrXIII:2969182 418642424 15 19.973 chrXV:414608 12025851913 20.712 chrXIII:3109522 120258506 15 20.789 chrXV:505537 418642465	 ? 129	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(15 26.144 chrXV:979445 418642466 17 27.817 chrXVII:12022612 12025853615 28.551 chrXV:215800 418642464 17 29.614 chrXVII:1264852 41864250815 29.834 chrXV:11818 418642463 17 30.48 chrXVII:12528572 25284115116 0 chrXVI:2764206 120258523 17 34.32 chrXVII:769372 24422293916 0.74 chrXVI:2650854 244222922 17 34.515 chrXVII:645029 41864250616 1.67 chrXVI:2392758 244222921 17 41.148 chrXVII:14127979 41864252816 1.674 chrXVI:2483136 252841051 18 0 chrXVIII:15478444 12025854916 3.48 chrXVI:3206769 244222923 18 19.259 chrXVIII:13773116 41864254516 5.889 chrXVI:13588796 244222930 18 23.155 chrXVIII:13753579 24422295816 6.329 chrXVI:14093156 244222931 18 24.456 chrXVIII:13193140 24422295716 7.378 chrXVI:14963879 244222933 18 24.798 chrXVIII:12818939 12025854516 9.956 chrXVI:12996432 244222929 18 26.138 chrXVIII:12273872 25284115016 9.978 chrXVI:5562355 244222924 18 26.312 chrXVIII:11896010 24422295416 10.609 chrXVI:6415385 418642487 18 27.539 chrXVIII:11765327 12025854316 11.281 chrXVI:9428786 244222926 18 27.765 chrXVIII:11702469 41864254316 12.994 chrXVI:13148331 418642492 18 27.765 chrXVIII:11641450 24422295316 14.134 chrXVI:14283264 244222932 18 28.292 chrXVIII:11504306 41864254216 16.126 chrXVI:15039503 418642494 18 29.831 chrXVIII:13352631 12025854616 16.894 chrXVI:16058672 252841101 18 31.323 chrXVIII:5765162 12025854016 19.173 chrXVI:17471373 418642502 18 31.327 chrXVIII:4836241 12025853916 19.623 chrXVI:18106789 120258529 18 34.367 chrXVIII:313722816 21.464 chrXVI:17895677 244222938 18 41.287 chrXVIII:1211531 41864253016 22.417 chrXVI:17405918 418642501 19 0 chrXIX:8190806 12025855416 24.961 chrXVI:17236926 244222936 19 0.054 chrXIX:14650559 41864197516 31.626 chrXVI:16673569 120258528 19 0.099 chrXIX:18045399 12025855816 38.499 chrUn:37016121 418642651 19 0.102 CH213.119K16:14070 41864197716 43.334 chrUn:26389255 244222992 19 0.102 CH213.21C23:188808 41864195317 0 chrXVII:1733515 418642509 19 0.434 chrXIX:3737235 41864196517 8.642 chrXVII:12666712 418642526 19 0.554 chrXIX:18043409 25284105917 10.714 chrXVII:2664810 244222940 19 15.488 chrXIX:1546489 41864195817 11.707 chrXVII:2626658 418642511 19 16.847 chrXIX:1472847 12025855117 13.517 chrXVII:2872553 418642512 19 30.53 chrXIX:897343 41864195617 18.03 chrXVII:3906379 244222942 20 0 chrXX:12622695 24422296617 18.272 chrXVII:10329401 418642524 20 0 chrXX:12810044 25284104817 18.272 chrXVII:9697366 244222947 20 0.278 chrXX:14562943 41864256917 19.473 chrXVII:3843835 120258534 20 0.588 chrXX:14462157 24422296817 20.238 chrXVII:4909843 244222944 20 0.724 chrXX:14859034 41864257117 20.598 chrUn:2474754 418642603 20 1.646 chrXX:5734841 41864255817 20.713 chrUn:2776586 120258568 20 3.948 chrXX:15996390 41864257317 20.713 chrUn:2632376 252841074 20 8.045 chrXX:16253512 25284106017 21.65 chrXVII:2999556 418642513 20 16.409 chrXX:2080510 41864255317 23.546 chrXVII:9881295 418642523 20 22.695 chrUn:30545876 120258573	 ? 130	 ?	 ?	 ?Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)( Linkage(Group Map(Position((cM) Marker(name((chromosome:(position)( NCBI(submitted(SNP(ID(numbers((ss#)(21 0 chrUn:31339987 24422299821 2.453 chrUn:28158103 41864263421 24.879 chrXXI:11060209 12025856621 26.406 chrXXI:10969152 24422298121 31.416 chrUn:23042966 41864262621 40.24 chrXXI:9820534 41864258921 42.589 chrXXI:7002178 24422297721 42.816 chrXXI:5737465 24422297321 44.076 chrXXI:3082227 41864258521 44.583 chrUn:6720054 244222987	 ? 131	 ?Table	 ?C.3	 ?Paxton	 ?Lake	 ?QTL	 ?scan	 ?results	 ?The	 ?QTL	 ?scan	 ?results	 ?for	 ?all	 ?QTL	 ?detected	 ?in	 ?our	 ?Paxton	 ?Lake	 ?scan	 ?are	 ?shown.	 ?Together,	 ?the	 ??1.5	 ?LOD	 ?C.I.	 ?low	 ?(cM)?	 ?and	 ??1.5	 ?LOD	 ?C.I.	 ?high	 ?(cM)?	 ?columns	 ?indicate	 ?the	 ?range	 ?of	 ?the	 ?1.5	 ?LOD	 ?confidence	 ?interval	 ?of	 ?the	 ?genomic	 ?location	 ?of	 ?the	 ?QTL.	 ?The	 ??LOD?	 ?column	 ?indicates	 ?the	 ?LOD	 ?score	 ?at	 ?the	 ?QTL?s	 ?peak	 ?marker	 ?(the	 ?maker	 ?at	 ?which	 ?genotypes	 ?showed	 ?the	 ?strongest	 ?association	 ?with	 ?phenotypes).	 ?The	 ??p-??value?	 ?column	 ?indicates	 ?the	 ?genome-??wide	 ?significance	 ?of	 ?the	 ?peak	 ?marker?s	 ?LOD	 ?score	 ?for	 ?the	 ?associated	 ?trait.	 ?	 ?Trait Linkage+group Peak+Marker+Position+(cM) 1.5+LOD+C.I.+low+(cM) 1.5+LOD+C.I.+high+(cM) LOD p?value Candidate+QTL?plate&count 7 33.93 33.41 34.99 16.65 <1.00E604 nolong&gill&raker&count 3 36 30.9 42 6.43 1.70E603 nolong&gill&raker&count 7 34.01 32.22 35.81 8.66 <1.00E604 noshort&gill&raker&count 1 21.16 16 23.72 6.05 3.50E603 noshort&gill&raker&count 7 34.99 32.22 35.81 5.54 7.60E603 no1st&dorsal&spine 2 33.63 22 39.26 8.11 <1.00E604 no2nd&dorsal&spine 20 1.65 0 22.7 4.39 3.68E602 nox1 1 20 16.42 21.75 5.05 1.86E602 noy1 8 18.19 10 30 5.12 1.58E602 nox2 7 0 0 14 6.38 1.70E603 yesx3 1 21.16 16.42 23.72 9.14 <1.00E604 nox3 5 52 26 56.44 5.65 6.60E603 nox3 12 13.5 12.16 24 10.23 <1.00E604 noy3 7 6 0 14 11.12 <1.00E604 nox4 3 10 4 16 5.68 5.20E603 nox4 7 34.21 32.22 35.81 7.61 4.00E604 nox4 12 20 7.73 25.01 5.09 1.50E602 noy4 7 34.99 32 37.03 5.27 1.08E602 noy5 19 2 0 10 5.11 1.55E602 yesx6 7 35.45 30 54 5.61 5.50E603 noy6 7 35.45 14.71 40 4.47 4.79E602 noy6 13 18.72 0 24.53 5.1 1.80E602 noy6 19 0.1 0 4 11.3 <1.00E604 nox10 7 40 34.45 50 5.54 7.80E603 noy10 4 58 35.12 71.36 6.47 1.10E603 yesy10 14 11.23 0 22 7.32 2.00E604 nox11 1 21.16 16.42 23.72 5.94 3.90E603 noy11 1 21.75 16.42 23.72 6.82 9.00E604 noy11 4 30 26.83 71.36 8.11 3.00E604 nox12 1 19.11 16 23.72 4.71 2.95E602 noy12 4 28.15 26 71.36 4.62 3.74E602 yesy12 19 0.1 0 8 5.13 1.59E602 nox13 1 19.11 16.42 27.92 7.15 3.00E604 nox13 7 35.12 24 40 4.58 4.24E602 nox16 1 19.11 16.42 27.92 4.76 2.85E602 nox16 13 20.04 8.48 27.7 4.44 4.93E602 yesx18 7 32.22 26.61 33.93 4.84 2.52E602 noy18 4 36 30.31 71.36 4.53 4.11E602 yesx19 10 8 6.2 24 4.96 2.00E602 noy19 8 26 19.01 30.86 6.83 1.00E603 noy19 12 12.16 10 25.01 4.84 2.73E602 nox20 1 25.31 24 34 4.36 5.30E602 yes	 ? 132	 ?	 ?Trait Linkage+group Peak+Marker+Position+(cM) 1.5+LOD+C.I.+low+(cM) 1.5+LOD+C.I.+high+(cM) LOD p?value Candidate+QTL?x21 1 20 16.42 22.4 4.9 2.32E602 yesx22 7 33.93 24 35.45 4.62 4.06E602 yesx23 1 19.11 16.42 27.92 8.51 <1.00E604 nox24 1 19.11 16 26 5.62 6.40E603 nox25 1 20 16 26 4.53 4.33E602 nox25 16 12.99 0 24 4.48 4.74E602 noy25 12 18 12.16 25.01 7.67 <1.00E604 nox26 1 19.11 16 26 5.13 1.47E602 noy26 19 0.1 0 6 4.85 2.63E602 nocentroid 19 0.1 0 6 4.67 3.42E602 yes	 ? 133	 ?	 ?Table	 ?C.4	 ?Priest	 ?Lake	 ?QTL	 ?scan	 ?results	 ?The	 ?QTL	 ?scan	 ?results	 ?for	 ?all	 ?QTL	 ?detected	 ?in	 ?our	 ?Priest	 ?Lake	 ?scan	 ?are	 ?shown.	 ?Together,	 ?the	 ??1.5	 ?LOD	 ?C.I.	 ?low	 ?(cM)?	 ?and	 ??1.5	 ?LOD	 ?C.I.	 ?high	 ?(cM)?	 ?columns	 ?indicate	 ?the	 ?range	 ?of	 ?the	 ?1.5	 ?LOD	 ?confidence	 ?interval	 ?of	 ?the	 ?genomic	 ?location	 ?of	 ?the	 ?QTL.	 ?The	 ??LOD?	 ?column	 ?indicates	 ?the	 ?LOD	 ?score	 ?at	 ?the	 ?QTL?s	 ?peak	 ?marker	 ?(the	 ?maker	 ?at	 ?which	 ?genotypes	 ?showed	 ?the	 ?strongest	 ?association	 ?with	 ?phenotypes).	 ?The	 ??p-??value?	 ?column	 ?indicates	 ?the	 ?genome-??wide	 ?significance	 ?of	 ?the	 ?peak	 ?marker?s	 ?LOD	 ?score	 ?for	 ?the	 ?associated	 ?trait.	 ?	 ?	 ?Trait Linkage+group Peak+Marker+Position+(cM) 1.5+LOD+C.I.+low+(cM) 1.5+LOD+C.I.+high+(cM) LOD p?value Candidate+QTL?plate&count 2 26 18 44 4.41 2.34E302 noplate&count 7 35.45 30 42 8.58 <1.00E304 noplate&count 16 14 6.33 22 5.5 2.70E303 nolong&gill&raker&count 7 46 32.22 58 6.13 1.70E303 noshort&gill&raker&count 1 14.83 6 32 4.67 1.69E302 nox1 2 24 18 33.63 9.52 <1.00E304 nox2 14 38.82 30.98 48.93 4.04 4.88E302 yesx3 14 38.82 28.55 48.93 4.38 2.47E302 noy3 4 71.36 66 71.36 4.67 1.51E302 noy5 7 40 34.21 56 6.16 1.40E303 nox6 4 20.84 14.49 32 3.99 5.35E302 yesx6 13 27.7 22.18 28.79 4.06 4.64E302 yesx7 3 6 0 14 5.32 4.80E303 noy7 7 35.45 30 50.64 5.58 3.50E303 noy7 9 10 0 17.66 4.32 2.97E302 yesy10 1 23.96 4 27.92 5.8 1.80E303 noy11 11 30 8.46 37.74 4.86 8.30E303 nox15 3 2.69 0 12 4.2 3.39E302 nox16 1 21.75 18.11 34 5.4 3.20E303 nox16 12 13.5 2 15.46 5.68 1.70E303 noy16 21 42.82 26 44.58 4.19 4.17E302 yesx17 12 6.42 4 13.24 6.31 7.00E304 nox17 14 34.83 24 48.93 4.1 4.36E302 yesy18 11 34.66 28 40 4.31 3.04E302 noy19 1 22 16 37.75 5.87 9.00E304 noy19 12 12.16 0 24 4.3 3.20E302 noy19 14 34.83 0 41.41 4.53 2.15E302 nox20 12 4.39 0 15.46 4.56 1.86E302 yesx23 3 4 0 12 4.69 1.54E302 noy23 21 44 20 44.58 5.35 4.70E303 noy25 12 11.57 9.45 15.46 4.09 4.82E302 noy26 1 21.75 18.11 30 7.02 <1.00E304 noy26 12 15.46 3.74 25.01 4.7 1.42E302 noy26 14 36.5 12 48.93 4.13 3.94E302 yesy27 12 4.39 0 15.46 4.47 2.19E302 noy27 17 21.65 0 27.82 4.29 2.99E302 nocentroid 1 24.57 22.4 32 6.74 3.00E304 no	 ? 134	 ?Table	 ?C.5	 ??Combined?	 ?QTL	 ?scan	 ?results	 ?The	 ?QTL	 ?scan	 ?results	 ?for	 ?all	 ?QTL	 ?detected	 ?in	 ?our	 ??combined	 ?scan?	 ?(i.e.	 ?Paxton	 ?and	 ?Priest	 ?Lakes,	 ?and	 ?including	 ?a	 ?genotype	 ?by	 ?lake	 ?interaction	 ?covariate)	 ?are	 ?shown.	 ?Together,	 ?the	 ??1.5	 ?LOD	 ?C.I.	 ?low	 ?(cM)?	 ?and	 ??1.5	 ?LOD	 ?C.I.	 ?high	 ?(cM)?	 ?columns	 ?indicate	 ?the	 ?range	 ?of	 ?the	 ?1.5	 ?LOD	 ?confidence	 ?interval	 ?of	 ?the	 ?genomic	 ?location	 ?of	 ?the	 ?QTL.	 ?The	 ??LOD?	 ?column	 ?indicates	 ?the	 ?LOD	 ?score	 ?at	 ?the	 ?QTL?s	 ?peak	 ?marker	 ?(the	 ?maker	 ?at	 ?which	 ?genotypes	 ?showed	 ?the	 ?strongest	 ?association	 ?with	 ?phenotypes).	 ?The	 ??p-??value?	 ?column	 ?indicates	 ?the	 ?genome-??wide	 ?significance	 ?of	 ?the	 ?peak	 ?marker?s	 ?LOD	 ?score	 ?for	 ?the	 ?associated	 ?trait.	 ?	 ?Trait Linkage+group Peak+Marker+Position+(cM) 1.5+LOD+C.I.+low+(cM) 1.5+LOD+C.I.+high+(cM) LOD p?value Candidate+QTL?plate&count 2 24 18 39.1 5.59 5.27E502 yesplate&count 7 33.93 33.41 34.99 24.81 <1.00E504 yesplate&count 16 9.98 4 22 5.66 4.68E502 yeslong&gill&raker&count 3 36 30.9 42 7.29 2.30E503 yeslong&gill&raker&count 7 35.12 33.41 35.81 14.12 <1.00E504 yesshort&gill&raker&count 1 21.16 14 23.72 9.43 2.00E504 yesshort&gill&raker&count 7 34.99 32.22 35.81 6.67 9.40E503 yes1st&dorsal&spine 2 33.63 24 39.26 10.39 <1.00E504 nox1 2 26 18 33.05 10.61 <1.00E504 noy1 8 18 10 30.86 5.97 3.45E502 yesx2 4 23.78 14.49 26.83 5.97 5.01E502 yesx3 1 21.16 16.42 22.4 8.16 1.40E503 nox3 5 50.52 30 56.44 6.4 1.57E502 nox3 12 18 8 25.01 8.57 6.00E504 noy3 4 71.36 66 71.36 5.8 4.04E502 yesy3 7 6 0 14 10.65 <1.00E504 yesx4 7 33.93 26.61 35.81 8.4 1.10E503 noy4 7 34.99 32.22 37.03 5.81 4.95E502 yesy5 7 35.45 34.21 42 9.77 <1.00E504 yesx6 7 34.21 30 50.99 6.18 1.92E502 yesy6 7 37.32 26.61 44 6.86 6.80E503 noy6 13 12 0 23.99 7.32 2.80E503 noy6 19 0 0 4 11.42 <1.00E504 nox7 3 6 0.22 12 7.61 1.70E503 noy7 2 33.63 30 38 6.05 2.52E502 yesy7 7 35.45 32.22 56 6.41 1.36E502 yesx9 3 4 0 10 5.89 3.10E502 nox10 2 36.63 28 42 5.71 4.66E502 noy10 1 19.11 18.11 26 8.36 5.00E504 yesy10 14 12 0 22 10.43 <1.00E504 yesx11 1 21.16 16.42 23.72 5.96 3.77E502 noy11 1 21.16 15.32 26 5.86 5.35E502 yesy11 4 30 26.83 71.36 6.31 2.86E502 yesy11 11 28 10 37.74 6.7 1.58E502 yesx12 19 0.55 0 6 6.12 4.57E502 noy12 13 27.7 24.53 28.79 6 4.21E502 yesy12 19 0 0 10 6.85 1.28E502 yesx13 1 18.11 16 30 6.51 2.34E502 yesx13 7 28 24 33.41 6.87 1.37E502 yesx16 1 21.75 18.11 23.72 9.67 <1.00E504 yesx16 12 5.22 2 15.46 6.92 5.60E503 yesy16 13 28.79 24 28.79 6.18 2.02E502 yes	 ? 135	 ?	 ?Trait Linkage+group Peak+Marker+Position+(cM) 1.5+LOD+C.I.+low+(cM) 1.5+LOD+C.I.+high+(cM) LOD p?value Candidate+QTL?x17 12 6.42 4 15.46 6.87 9.50E503 yesx18 7 32.22 29.3 37.03 6.51 1.12E502 yesy18 11 34 28 42 6.44 1.24E502 yesy19 1 21.75 14 41.89 6.25 2.17E502 noy19 4 34.54 30.31 38 6.7 1.02E502 noy19 8 26 19.01 30.86 6.97 6.50E503 noy19 12 12.16 10.86 15.46 9.25 1.00E504 noy19 14 11.23 0 36 6.01 3.22E502 noy19 19 0 0 6 6.19 2.46E502 nox20 4 20 15.02 23.78 6.42 1.28E502 yesy22 1 18 15.32 20.27 6.71 9.20E503 nox23 1 21.16 16.42 23.72 6.75 1.67E502 nox23 3 6 0 10.46 8.19 2.00E503 noy25 12 13.24 10.86 21 11.06 <1.00E504 yesy26 1 21.75 18.11 34 7.91 9.00E504 yesy26 12 13.24 10.86 22 9.37 <1.00E504 yesy26 19 0.55 0 6 6.83 9.00E503 yesy27 8 19.01 16.61 30.86 5.54 5.42E502 yesy27 12 13.24 10.73 24 7.51 2.90E503 yesy27 17 21.65 12 27.82 7.15 4.40E503 yescentroid 1 24.57 2 32 9.64 3.00E504 yes	 ? 136	 ?Table	 ?C.6	 ?QTL	 ?effects	 ?of	 ?candidate	 ?QTL	 ?(Starts	 ?on	 ?next	 ?page)	 ?QTL	 ?effect	 ?was	 ?considered	 ??parallel?	 ?when	 ?either	 ?the	 ?best	 ?model	 ?of	 ?the	 ?QTL	 ?effect	 ?was	 ??same	 ?effect?,	 ?or	 ?when	 ?the	 ?best	 ?model	 ?of	 ?QTL	 ?effect	 ?was	 ??different	 ?effect?	 ?but	 ?the	 ?direction	 ?of	 ?additive	 ?effects	 ?were	 ??same?.	 ?QTL	 ?effect	 ?was	 ?considered	 ?only	 ?in	 ?a	 ??single	 ?lake?	 ?when	 ?the	 ?best	 ?model	 ?of	 ?the	 ?QTL	 ?effect	 ?was	 ?either	 ??effect	 ?in	 ?Paxton	 ?only?	 ?or	 ??effect	 ?in	 ?Priest	 ?only?.	 ?QTL	 ?effect	 ?was	 ?considered	 ??opposite?	 ?when	 ?the	 ?best	 ?model	 ?of	 ?QTL	 ?effect	 ?was	 ??different	 ?effect?	 ?and	 ?the	 ?direction	 ?of	 ?additive	 ?effects	 ?were	 ??opposite?.	 ?The	 ?second	 ?best	 ?model	 ?of	 ?QTL	 ?effect	 ?and	 ?the	 ?delta	 ?AICc	 ?between	 ?it	 ?and	 ?the	 ?best	 ?model	 ?is	 ?also	 ?shown.	 ?When	 ?the	 ?delta	 ?AICc	 ?was	 ?less	 ?than	 ?two	 ?and	 ?the	 ?2nd	 ?best	 ?model	 ?called	 ?for	 ?a	 ?different	 ?QTL	 ?effect	 ?category	 ?than	 ?the	 ?best	 ?model	 ?did,	 ?we	 ?dropped	 ?the	 ?QTL	 ?from	 ?any	 ?analysis	 ?in	 ?which	 ?QTL	 ?effect	 ?category	 ?was	 ?a	 ?variable.	 ?PVE	 ?for	 ?each	 ?QTL	 ?in	 ?each	 ?lake	 ?was	 ?determined	 ?using	 ??single	 ?QTL,	 ?single	 ?lake	 ?linear	 ?models?.	 ?The	 ??Priest	 ?Entropy?	 ?and	 ??Paxton	 ?Entropy?	 ?columns	 ?show	 ?the	 ?entropy	 ?values	 ?(an	 ?index	 ?of	 ?genotype	 ?information	 ?content,	 ?where	 ?lower	 ?values	 ?indicate	 ?greater	 ?information	 ?content),	 ?in	 ?each	 ?lake?s	 ?cross	 ?at	 ?the	 ?QTL?s	 ?peak	 ?marker.	 ?	 ? 137	 ?TraitScan)QTL)was)detected)in Linkage)groupPeak)Marker)Position)(cM)Direction)of)additive)effects'QTL)Effect')based)on)AICc)model)selection Best)model 2nd)best)model Delta)AICc PVE)in)Priest PVE)in)Paxton Priest)entropy Paxton)entropyplate&count combined 2 24 same Parallel same&effect different&effect 2.21 4.9 1.32 0.28 0.27plate&count combined 7 33.93 same Parallel different&effect same&effect 0.94 9.1 12.09 0.04 0.03plate&count combined 16 9.98 opposite Opposite different&effect effect&in&Priest&only 2.03 6.06 0.73 0.06 0.17long&gill&raker&count combined 7 35.12 same Parallel same&effect different&effect 2.26 6.51 6.3 0.01 0.08long&gill&raker&count combined 3 36 same Single&lake effect&in&Paxton&only different&effect 1.12 1.08 5 0.14 0.14short&gill&raker&count combined 1 21.16 same Parallel same&effect different&effect 2.72 4.13 4.32 0.07 0.17short&gill&raker&count combined 7 34.99 same Parallel same&effect different&effect 0.68 1.67 3.97 0 0.08y1 combined 8 18 same Single&lake effect&in&Paxton&only same&effect 4.14 1.58 4.18 0.09 0.12x2 combined 4 23.78 same Parallel same&effect different&effect 2.99 1.48 1.13 0.08 0.21x2 Paxton 7 0 same Single&lake effect&in&Paxton&only same&effect 0.58 0.07 2.39 0.71 0.05x2 Priest 14 38.82 opposite Single&lake effect&in&Priest&only different&effect 0.39 1.92 0.37 0.08 0.16y3 combined 4 71.36 opposite Opposite different&effect effect&in&Priest&only 1.38 5.29 0.9 0.06 0.88y3 combined 7 6 opposite Single&lake effect&in&Paxton&only different&effect 2.49 0.13 8.86 0.5 0.13y4 combined 7 34.99 same Parallel same&effect different&effect 2 1.56 4.08 0 0.08y5 combined 7 35.45 same Parallel same&effect different&effect 2.42 4.34 2.51 0 0.12y5 Paxton 19 2 same Parallel same&effect different&effect 0.42 0.21 3.63 0.19 0.05x6 combined 7 34.21 opposite Opposite different&effect effect&in&Paxton&only 0.92 0.68 3.4 0.02 0.05x6 Priest 4 20.84 same Parallel same&effect different&effect 1.81 3.09 0.7 0.05 0.2x6 Priest 13 27.7 same Single&lake effect&in&Priest&only same&effect 7.08 3.15 0.08 0.1 0.2y7 combined 7 35.45 same Single&lake effect&in&Priest&only different&effect 0.94 5.9 0.52 0 0.12y7 combined 2 33.63 opposite Opposite different&effect effect&in&Paxton&only 5.61 2.41 3.14 0.03 0.04y7 Priest 9 10 same Single&lake effect&in&Priest&only same&effect 3.44 4.61 0.59 0.35 0.34y10 combined 1 19.11 opposite Opposite different&effect effect&in&Priest&only 9.03 2.77 1.63 0 0.01y10 combined 14 12 same Parallel same&effect different&effect 0.31 1.23 4.22 0.06 0.2y10 Paxton 4 58 opposite Single&lake effect&in&Paxton&only different&effect 2.57 0.09 3.76 0.03 0.74y11 combined 11 28 same Single&lake effect&in&Priest&only different&effect 0.57 2.45 0.37 0.13 0.12y11 combined 1 21.16 opposite Single&lake effect&in&Paxton&only different&effect 5.1 0.35 3.49 0.07 0.17y11 combined 4 30 same Single&lake effect&in&Paxton&only different&effect 2.4 0.38 4.13 0.01 0.24y12 combined 19 0 same Single&lake effect&in&Priest&only different&effect 13.99 2.03 3.11 0.22 0.03y12 combined 13 27.7 same Parallel same&effect different&effect 3.01 1.96 1.58 0.1 0.2y12 Paxton 4 28.15 same Single&lake effect&in&Paxton&only different&effect 2.77 0.55 2.81 0.06 0.19x13 combined 7 28 opposite Parallel same&effect different&effect 0.23 2.3 1.91 0.09 0.22x13 combined 1 18.11 same Parallel same&effect different&effect 0.31 0.57 3.71 0.08 0.17x16 combined 1 21.75 same Parallel same&effect different&effect 2.97 6.19 3.32 0.11 0.2	 ? 138	 ?	 ?TraitScan)QTL)was)detected)in Linkage)groupPeak)Marker)Position)(cM)Direction)of)additive)effects'QTL)Effect')based)on)AICc)model)selection Best)model 2nd)best)model Delta)AICc PVE)in)Priest PVE)in)Paxton Priest)entropy Paxton)entropyx16 combined 12 5.22 same Parallel different&effect same&effect 0.87 5.45 1.36 0.06 0.16x16 Paxton 13 20.04 same Parallel different&effect same&effect 1.02 0.98 3.39 0.05 0.07y16 combined 13 28.79 opposite Opposite different&effect same&effect 0.83 3.55 2.08 0.16 0.26y16 Priest 21 42.82 same Parallel same&effect effect&in&Priest&only 0.34 4.22 0.54 0.01 0.87x17 combined 12 6.42 opposite Single&lake effect&in&Priest&only different&effect 3.69 6.84 0.01 0.16 0.22x17 Priest 14 34.83 same Single&lake effect&in&Priest&only different&effect 2.58 4.52 0.29 0 0.22x18 combined 7 32.22 opposite Single&lake effect&in&Paxton&only different&effect 4.02 2.06 3.84 0.06 0.11y18 combined 11 34 same Parallel same&effect different&effect 1.45 2.71 1.27 0.03 0.03y18 Paxton 4 36 opposite Single&lake effect&in&Paxton&only different&effect 9.02 0.16 2.85 0.02 0.04x20 combined 4 20 opposite Single&lake effect&in&Priest&only effect&in&Paxton&only 3.16 3.58 2.15 0.04 0.19x20 Paxton 1 25.31 same Parallel different&effect effect&in&Paxton&only 1.62 1.18 3.32 0.13 0.15x20 Priest 12 4.39 opposite Opposite different&effect effect&in&Priest&only 1.21 4.34 0.75 0.05 0.17x21 Paxton 1 20 same Single&lake effect&in&Paxton&only same&effect 2.57 0.25 2.51 0.01 0.01x22 Paxton 7 33.93 same Parallel different&effect effect&in&Paxton&only 0.06 0.73 3.94 0.04 0.03y25 combined 12 13.24 same Parallel same&effect different&effect 2.88 4.71 5.64 0.02 0.04y26 combined 1 21.75 opposite Single&lake effect&in&Priest&only different&effect 2.66 4.71 0.07 0.11 0.2y26 combined 12 13.24 same Parallel same&effect different&effect 2.36 3.17 2.57 0.02 0.04y26 combined 19 0.55 same Parallel different&effect effect&in&Priest&only 5.04 1.18 2.79 0.19 0.04y26 Priest 14 36.5 opposite Single&lake effect&in&Priest&only different&effect 6.62 2.83 0.05 0.07 0.18y27 combined 12 13.24 same Parallel same&effect different&effect 2.55 4.16 2.91 0.02 0.04y27 combined 17 21.65 opposite Opposite different&effect effect&in&Priest&only 10.7 4.81 2.95 0.11 0.1y27 combined 8 19.01 same Single&lake effect&in&Paxton&only effect&in&Priest&only 1.4 2.76 2.53 0.01 0.1centroid combined 1 24.57 same Single&lake effect&in&Priest&only different&effect 1.14 6.4 0.91 0.07 0.13centroid Paxton 19 0.1 opposite Single&lake effect&in&Priest&only same&effect 1.28 1.24 2.96 0.21 0.02	 ? 139	 ?Table	 ?C.7	 ?Proportional	 ?similarity	 ?of	 ?QTL	 ?use	 ?underlying	 ?parallel	 ?traits	 ?For	 ?each	 ?QTL,	 ??PVE	 ?in	 ?Priest?	 ?and	 ??PVE	 ?in	 ?Paxton?	 ?were	 ?determined	 ?using	 ?a	 ??multiple	 ?QTL	 ?linear	 ?model?	 ?containing	 ?genotypic	 ?effects	 ?of	 ?each	 ?QTL	 ?affecting	 ?the	 ?same	 ?trait	 ?(as	 ?well	 ?as	 ?family	 ?identity	 ?and	 ?sex	 ?as	 ?covariates).	 ?These	 ?models	 ?were	 ?run	 ?for	 ?each	 ?lake	 ?separately.	 ?If	 ?the	 ?QTL	 ?genotype	 ?(both	 ?additive	 ?and	 ?dominant	 ?components)	 ?did	 ?not	 ?show	 ?a	 ?significant	 ?effect	 ?when	 ?dropped	 ?from	 ?a	 ??single	 ?lake,	 ?single	 ?QTL	 ?linear	 ?model?	 ?then	 ?it	 ?was	 ?not	 ?entered	 ?in	 ?the	 ?multiple	 ?QTL	 ?model	 ?for	 ?that	 ?lake.	 ?In	 ?this	 ?case,	 ?the	 ?PVE	 ?column	 ?is	 ?left	 ?blank.	 ?In	 ?each	 ?lake,	 ?proportional	 ?contributions	 ?of	 ?QTL	 ?to	 ?traits	 ?were	 ?calculated	 ?by	 ?scaling	 ?the	 ?PVEs	 ?of	 ?all	 ?QTL	 ?affecting	 ?the	 ?same	 ?trait	 ?so	 ?that	 ?they	 ?summed	 ?to	 ?1.	 ?The	 ?proportional	 ?similarity	 ?of	 ?a	 ?QTL	 ?was	 ?taken	 ?as	 ?the	 ?overlap	 ?in	 ?the	 ?proportional	 ?contributions	 ?of	 ?that	 ?QTL	 ?in	 ?the	 ?two	 ?lakes.	 ?The	 ??proportional	 ?similarity	 ?of	 ?QTL	 ?use?	 ?underlying	 ?any	 ?given	 ?trait	 ?is	 ?then	 ?the	 ?sum	 ?of	 ?the	 ?proportional	 ?similarities	 ?of	 ?all	 ?QTL	 ?affecting	 ?that	 ?trait.	 ?	 ?Trait QTL!!!!!!!!!!!!!!!(LG!#!@!position!(cM)) PVE+in+Priest Proportional+Contribution+in+Priest PVE+in+Paxton Proportional+Contribution+in+Paxton Proportional+Similarityplate!count 16@10.0 4.79 0.31 ! ! 0.00plate!count 2@24.0 3.39 0.22 1.28 0.10 0.10plate!count 7@33.9 7.32 0.47 12.04 0.90 0.47long!gill!raker!count 7@35.1 6.51 1.00 6.23 0.56 0.56long!gill!raker!count 3@36.0 ! ! 4.93 0.44 0.00short!gill!raker!count 1@21.2 4.13 1.00 4.89 0.52 0.52short!gill!raker!count 7@35.0 ! ! 4.54 0.48 0.00y1 8@18.0 1.58 1.00 4.18 1.00 1.00x2 14@38.8 1.70 0.57 ! ! 0.00x2 4@23.8 1.26 0.43 1.11 0.32 0.32x2 7@0.0 ! ! 2.38 0.68 0.00y3 4@71.4 5.29 1.00 ! ! 0.00y3 7@6.0 ! ! 8.86 1.00 0.00y4 7@35.0 ! ! 4.08 1.00 0.00y5 7@35.5 4.34 1.00 2.44 0.41 0.41y5 19@2.0 ! ! 3.57 0.59 0.00x6 13@27.7 3.11 0.50 ! ! 0.00x6 4@20.8 3.06 0.50 ! ! 0.00x6 7@34.2 ! ! 3.40 1.00 0.00y7 2@33.6 1.43 0.12 3.14 1.00 0.12y7 7@35.5 5.98 0.49 ! ! 0.00y7 9@10.0 4.74 0.39 ! ! 0.00y10 1@19.1 2.61 0.71 0.73 0.11 0.11y10 14@12.0 1.07 0.29 3.51 0.51 0.29y10 4@58.0 ! ! 2.68 0.39 0.00y11 11@28.0 2.45 1.00 ! ! 0.00y11 1@21.2 ! ! 2.60 0.44 0.00y11 4@30.0 ! ! 3.24 0.56 0.00y12 13@27.7 1.54 0.49 1.35 0.19 0.19y12 19@0.0 1.61 0.51 3.14 0.44 0.44y12 4@28.1 ! ! 2.69 0.37 0.00x13 7@28.0 2.30 1.00 1.31 0.30 0.30x13 1@18.1 ! ! 3.11 0.70 0.00x16 1@21.7 4.53 0.54 2.31 0.37 0.37x16 12@5.2 3.79 0.46 1.16 0.18 0.18x16 13@20.0 ! ! 2.83 0.45 0.00y16 13@28.8 3.03 0.45 2.08 1.00 0.45y16 21@42.8 3.70 0.55 ! ! 0.00x17 12@6.4 6.00 0.62 ! ! 0.00	 ? 140	 ?	 ?Trait QTL!!!!!!!!!!!!!!!(LG!#!@!position!(cM)) PVE+in+Priest Proportional+Contribution+in+Priest PVE+in+Paxton Proportional+Contribution+in+Paxton Proportional+Similarityx17 14@34.8 3.68 0.38 ! ! 0.00x18 7@32.2 2.06 1.00 3.84 1.00 1.00y18 11@34.0 2.71 1.00 0.88 0.26 0.26y18 4@36.0 ! ! 2.47 0.74 0.00x20 12@4.4 4.15 0.55 ! ! 0.00x20 4@20.0 3.40 0.45 1.61 0.37 0.37x20 1@25.3 ! ! 2.78 0.63 0.00x21 1@20.0 ! ! 2.51 1.00 0.00x22 7@33.9 ! ! 3.94 1.00 0.00y25 12@13.2 4.71 1.00 5.64 1.00 1.00y26 1@21.7 3.02 0.38 ! ! 0.00y26 12@13.2 2.07 0.26 3.00 0.48 0.26y26 14@36.5 1.62 0.20 ! ! 0.00y26 19@0.6 1.21 0.15 3.22 0.52 0.15y27 12@13.2 3.47 0.34 2.87 0.35 0.34y27 17@21.7 4.36 0.43 3.17 0.38 0.38y27 8@19.0 2.36 0.23 2.21 0.27 0.23centroid!size 1@24.6 6.40 1.00 ! ! 0.00centroid!size 19@0.1 ! ! 2.96 1.00 0.00

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