Insetos Herbívoros E Taxa De Fecundação Cruzada Em Espermatófitas

UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE CENTRO DE BIOCIÊNCIAS PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA

Insetos herbívoros e taxa de fecundação cruzada em espermatófitas

Tales Martins de Alencar Paiva

Orientador: Prof. Dr. Carlos Roberto Sorensen Dutra da Fonseca

Natal - RN Fevereiro | 2020 Tales Martins de Alencar Paiva

Insetos herbívoros e taxa de fecundação cruzada em espermatófitas

Dissertação de mestrado apresentada ao Programa de Pós-Graduação em Ecologia da Universidade Federal do Rio Grande do Norte como requisito parcial para obtenção do grau de Mestre em ecologia.

Orientador: Prof. Dr. Carlos Roberto Sorensen Dutra da Fonseca

Natal - RN Fevereiro | 2020

Universidade Federal do Rio Grande do Norte - UFRN Sistema de Bibliotecas - SISBI Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial Prof. Leopoldo Nelson - •Centro de Biociências - CB

Paiva, Tales Martins de Alencar. Insetos herbívoros e taxa de fecundação cruzada em espermatófitas / Tales Martins de Alencar Paiva. - Natal, 2020. 85 f.: il.

Dissertação (Mestrado) - Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-graduação em Ecologia. Orientador: Prof. Dr. Carlos Roberto Sorensen Dutra da Fonseca.

1. Herbivoria - Dissertação. 2. Modelos evolutivos - Dissertação. 3. Sistemas sexuais - Dissertação. 4. Inimigos naturais - Dissertação. 5. Hipótese da Rainha Vermelha - Dissertação. I. Fonseca, Carlos Roberto Sorensen Dutra da. II. Universidade Federal do Rio Grande do Norte. III. Título.

RN/UF/BSCB CDU 591.531.1

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Tales Martins de Alencar Paiva

Insetos herbívoros e taxa de fecundação cruzada em espermatófitas

Dissertação de mestrado apresentada ao Programa de Pós-Graduação em Ecologia da Universidade Federal do Rio Grande do Norte como requisito parcial para obtenção do grau de Mestre em ecologia.

Data da defesa: 19/02/2020

BANCA EXAMINADORA

______Dr. Carlos Roberto Sorensen Dutra da Fonseca Presidente/Orientador | UFRN

______Dra. Vanessa Graziele Staggemeier Membro interno | UFRN

______Dr. Gustavo Brant de Carvalho Paterno Membro externo | UFJF

“Uma pessoa que está sentada em vez de estar de pé; os destinos às vezes dependem disso.”

- Trecho encontrado em alguma página de Os miseráveis (Victor Hugo) durante uma noite sem sono -

DEDICATÓRIA

Vovó Terezinha e Vovô Severino, por todo o carinho e por todos os bons momentos que vivemos juntos, que deixaram em mim saudosas lembranças, dedico a vocês esta dissertação.

AGRADECIMENTOS

MINHA FAMÍLIA Antes de qualquer outra coisa, gostaria de agradecer aos meus pais - Letice e Francisco - obrigado pelo carinho incondicional e pelo apoio que sempre recebi de vocês em todos os meus planos, por mais improváveis que fossem. Não poderia desejar pessoas melhores para serem meus progenitores e meus guias nesta vida. Também agradeço por me proporcionarem uma boa educação, tanto formal quanto cotidiana, que me possibilitou poder ver e questionar muito do que o mundo nos apresenta, um privilégio que poucos têm em um país tão desigual.

Ao meu irmão, Théo, e minha irmã, Maria Helena, por estarem sempre ao meu lado e participarem da minha jornada, espero que continuem sempre sendo as pessoas incríveis que vejo que vocês estão se tornando. Apesar da minha pouca presença física nos últimos anos, podem sempre contar com seu irmão mais velho para tudo. Já conto com vocês para cuidarem de mim quando eu estiver caduco! Aos meus avós - Terezinha e Severino (In memoriam) - pelo exemplo de pessoas admiráveis que vocês sempre foram. Todo carinho, encanto, diversão, alegria e incentivo que alguém pode desejar eu recebi, e ainda recebo, de vocês. Obrigado pelos dias que passamos juntos. Acho que nunca poderei agradecer da forma que vocês merecem. Se um dia eu vier a representar para alguém o que vocês representam para mim, o brilho ainda será o de vocês. Aos meus avós - Salete (In memoriam) e Xixico - que apesar do nosso pouco tempo de convívio, em parte devido à morte e em parte devido à vida, sei do enorme carinho que sempre tiveram por mim. Gostaria de ter compartilhado com vocês algumas das histórias que me foram narradas. Não foi possível..., porém o pouco de proximidade que a vida nos permitiu foi suficiente para que fosse recheada de boas recordações. À minha madrinha Cláudia, uma pessoa especial e radiante, portadora de um enorme coração, que mal cabe dentro do peito. Obrigado pelo carinho e afeto, e por sempre direcionar a mim bons pensamentos. Sei que poderei sempre contar com você para compartilhar bons momentos de risadas e também aquela cervejinha gelada com a turma toda (ou algumas garrafas de vinho no festival gastronômico de Martins)! Agradeço a todos os demais familiares, meus tios, tias, primos e primas, que de alguma maneira sempre me apoiaram e me ajudaram neste caminho. Gratidão a todos vocês!

AMIGOS E PESSOAS ESPECIAIS

Muitas pessoas fizeram parte desta história, velhos e novos amigos foram essenciais para que eu chegasse ao fim desta importante etapa da minha jornada acadêmica. Vou tentar sintetizar em poucos parágrafos, com a certeza que não conseguirei contemplar tudo e todos que participaram desta caminhada de forma completa.

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Primeiramente, quero agradecer ao meu orientador Carlos Fonseca. Antes de tudo, pela confiança de me aceitar em seu grupo de pesquisa e me pôr à frente deste projeto, pelo qual desenvolvi um genuíno interesse e um enorme carinho. Sou grato também pelo apoio e suporte durante a elaboração e execução de todo o projeto, sempre que precisei de um norte, encontrei na sua orientação. Sem dúvidas encontrei uma ótima pessoa e um exímio pesquisador com quem espero seguir trabalhando nas próximas etapas da minha vida acadêmica! Agradeço aos meus companheiros de laboratório: Gustavão, um cientista exemplar, cujo amor pela ciência é inspirador. Sou grato por toda a ajuda, sugestões, e por ter sido pra mim um excelente co-orientador durante essa pesquisa. Andressa, minha simpática amiga, super solícita e atenciosa, a quem agradeço pelo companheirismo durante esses anos e pela disposição para me ajudar sempre que precisei. Agradeço a todos os amigos que fiz no PPGECO durante o mestrado, com os quais dividi bons momentos de ciência, conversas, almoços, ansiedades, cafés e cervejas. Gabriel, Ana Elizabeth, Fernanda, Kelly, Mery, Thayná, Priscila, Bruno, Coquinha, Marcelo, Milico, Ursão, André Yuri, Felipe Marinho, Paulo Henrique, Virgínia, Adriana Almeida, Milena, Nádia, Vitão, Kionara, Dimas, dona Marlene... Obrigado a todos vocês e desculpem se esqueci de mencionar alguém! Quero deixar um agradecimento especial para meu amigo Gabriel, obrigado pela parceria em todas as empreitadas que enfrentamos juntos durante esses anos, dentro e fora da pós-graduação. Minha história na ciência começou bem antes da pós-graduação. Quero aqui agradecer ao meu ex-orientador Gabriel Costa, pela oportunidade de ingressar em um laboratório engajado e viver, ainda na graduação, um ambiente onde se respirava ciência e companheirismo. Foi ali que conheci pessoas que me fizeram ver a beleza da ciência e foi com eles que decidi dedicar minha vida a esta linda profissão. Andre (Tritão), Juan Pablo (Juanpy), Bruno Maggi, Felipe Coelho, Bruninho e Adrián (Pichi). Valeu por tudo, galera! Não posso deixar de expressar minha imensa gratidão a Andre, o primeiro amigo que fiz quando cheguei a Natal, hoje um irmão. Obrigado por guiar meus primeiros passos na ciência, pelo incentivo, conselhos, parceria e pelas conversas de bar em todos esses anos. Espero que continuemos trabalhando juntos por muito tempo! Serei eternamente grato por você ter acreditado na minha jornada acadêmica, muitas vezes mais do que eu mesmo acreditei. Aos velhos amigos, que apesar da distância física que a vida nos impôs, sempre estiveram ao meu lado, torcendo por mim. Luiz Eduardo e João Filho, nossa amizade, que já se aproxima dos vinte anos de história, sempre será uma fonte de apoio mútuo e parceria. Meus irmãos, onde quer que estejamos me alegro em saber que posso contar com vocês. E quando a gente menos espera, um reencontro na antiga Ribeira pode encher nossa vida de alegria e vitalidade. Agradeço a Paloma, minha linda, por estar ao meu lado durante os últimos tempos, inclusive os mais difíceis. Aprendi e vivi muitas coisas bonitas graças a sua companhia. Que os melhores e mais belos seres elementais te acompanhem por toda sua vida! Te adoro, morena/ruivinha!

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UFRN Agradeço à Universidade Federal do Rio Grande do Norte, ao Centro de Biociências e ao Departamento de Ecologia, por me proporcionarem uma formação sólida, abrangente e de excelência, tanto na graduação como no mestrado, onde pude aprofundar meus conhecimentos e práticas na área a qual hoje dedico meu trabalho e esforço acadêmico com muito carinho e empenho. Agradeço ao PPGECO por esses dois anos de aprendizado, trabalho e imersão científica, que me possibilitou crescer como pesquisador e como pessoa. A todos os professores que compõem a pós-graduação que se dedicaram em tornar este programa referência internacional em Ecologia. Espero que o programa continue crescendo e formando pesquisadores com uma visão abrangente, dotados de curiosidade e preocupações reais com a investigação científica e as causas socioambientais. Tenho muito orgulho de fazer parte desta família. Agradeço ao professor Márcio Zikán pela oportunidade de realizar um ótimo estágio de docência durante o mestrado, onde pude participar do andamento de uma disciplina encantadora e fundamental para uma boa formação em Ciências Biológicas. Agradeço à professora Vanessa Staggemeier pela solicitude de sempre e pelas contribuições essenciais para este trabalho, especialmente durante os processos do exame de qualificação e da defesa de dissertação. Agradeço a todo o corpo docente que fez parte da minha trajetória desde meu ingresso na universidade, a todos os colegas que me ajudaram nessa caminhada e a todos os membros da UFRN que direta ou indiretamente contribuíram para minha formação. Para onde quer que os ventos da ciência me levem, certamente irá comigo tudo o que aprendi e vivi na UFRN. O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001.

SUMÁRIO

LISTA DE FIGURAS...... XI

LISTA DE TABELAS...... XIII

INTRODUÇÃO GERAL...... 1

SINGLE CHAPTER

Insect herbivores modulate outcrossing rates across seed plants...... 14

ABSTRACT…………………………………………………………..16

INTRODUCTION……………………………………………………...17

MATERIAL AND METHODS………………………………………….. 20

RESULTS…………………………………………………………….23

DISCUSSION…………………………………………………………25

CONCLUSIONS…………………………………………...………….29

ACKNOWLEDGEMENTS……………………………………………...30

AUTHORS’ CONTRIBUTIONS…………………………………………30

DATA AVAILABILITY………………………………………………...30

REFERENCES………………………………………………………...31

FIGURES AND TABLES……………………………………………….40

SUPPORTING INFORMATION…………………………………………43

APPENDIX 1…………………………………………………..44

APPENDIX 2…………………………………………………..62

LISTA DE FIGURAS

Figure 1. Phylonegetic regressions between outcrossing rates and hervivores richness. (A) for 136 species of seed plants under two alternative evolutionary models. (B) only for 83 European-native species under two alternative evolutionary models. (C) Only fore 53 exotic species under two alternative evolutinary models. Blue line: phylogenetic regression under Ornstein-Uhlenbeck model. Red line: phylogenetic regression under Brownian Motion model. Herbivores richeness values were extracted from a method to correct the effort bias (see methods)……………………………………………………40

Figure 2. Distribution of outcrossing rate and vegetative variables. The categorical variables: (A) outcrossing rate and life span. (B) outcrossing rate and growth form. In both cases the points represent de mean value and bars represent the standard error. The continuous variables: (C) outcrossing rate and maximum height. (D) outcrossing rate and SLA. In both case the lines express the PGLS under Brownian Motion model (red) and under Ornstein-Uhlenbeck (blue)……………………………………………………….41

Figure S1. Results of herbivores simple model (pgls.her) from the 10000 randomized datasets for both Ornstein-Uhlenbeck (PGLS - OU) and Brownian Motion (PGLS - BM) evolutionary models. Horizontal blue line indicates the significance threshold (0.05). (A) Boxplot for OU results. (B) Scatterplot for OU results. (C) Boxplot for BM results. (D) Scatterplot for BM results. For OU models 99.96% and for BM models 93.67% were below the significance threshold………………………………………………………..68

Figure S2. Results of all simple models for OU. HERB = Herbivores richness, SLA = Specific Leaf Area, MAX = Maximum height, BIE = Life span-Biennial, PER = Life span-Perennial, HEM = Growth form-Hemiparasite, HER = Growth form-Herb, LIA = Growth form-Liana, SHR = Growth form-Shrub, TRE = Growth form-Tree…………..69

Figure S3. Results of all simple models for BM. HERB = Herbivores richness, SLA = Specific Leaf Area, MAX = Maximum height, BIE = Life span-Biennial, PER = Life span-Perennial, HEM = Growth form-Hemiparasite, HER = Growth form-Herb, LIA = Growth form-Liana, SHR = Growth form-Shrub, TRE = Growth form-Tree…….…….70

Figure S4. Results of the full OU model, that includes all fixed variables. F_HERB = Herbivores richness, F_SLA = Specific Leaf Area, F_MAX = Maximum height, F_BIE = Life span-Biennial, F_PER = Life span-Perennial, F_HEM = Growth form- Hemiparasite, F_HER = Growth form-Herb, F_LIA = Growth form-Liana, F_SHR = Growth form-Shrub, TRE = Growth form-Tree……………………………………….71

Figure S5. Results of the full BM model, that includes all fixed variables. F_HERB = Herbivores richness, F_SLA = Specific Leaf Area, F_MAX = Maximum height, F_BIE = Life span-Biennial, F_PER = Life span-Perennial, F_HEM = Growth form- Hemiparasite, F_HER = Growth form-Herb, F_LIA = Growth form-Liana, F_SHR = Growth form-Shrub, TRE = Growth form-Tree………………………………………..72

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LISTA DE TABELAS

Table 1. Model averaging by AICc for Ornstein-Uhlenbeck models. Bold text marks important models in explain outcrossing rate (tm) variation (delta < 2) ……………….42

Table 2. Model averaging by AICc for Brownian Motion models. Bold text marks important models in explain outcrossing rate (tm) variation (delta < 2) ……………….42

Table S1. Models built including outcrossing rate populational variation…………….63

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Introdução geral

EVOLUÇÃO DA REPRODUÇÃO SEXUADA

A evolução da reprodução sexuada tem sido por muitas décadas uma das grandes questões da biologia evolutiva (Lively & Morran, 2014; Meirmans, 2009). Não é novidade que o sexo é comum na natureza, sendo observado na maioria das espécies. Porém, uma observação paradoxal se torna evidente quando comparamos a reprodução sexuada com mecanismos assexuais de reprodução (Otto, 2009). O sexo apresenta custos a curto prazo que, a priori, seriam desvantajosos para as espécies que assim se reproduzem. Primeiramente, ao nível da população, o sexo impõe o “custo dos machos”, que se refere ao fato dos machos não contribuírem diretamente com novos indivíduos para a próxima geração. Isso resulta em uma prole menor que a produzida por vias assexuadas, como a partenogênese, partindo de um mesmo número de genitores (Maynard Smith, 1977). Ao nível de indivíduo, os pais sexuados são penalizados com uma redução de 50% do parentesco com a sua prole em relação aos pais assexuados, que se reproduzem de forma clonal (Williams, 1975). Portanto, populações assexuadas crescem de forma mais rápida e com a uma semelhança genética elevada entre as gerações. Frente a tais custos, esperar- se-ia que uma população sexuada invadida por uma linhagem assexuada fosse rapidamente sobrepujada por esta última. Porém, espécies com reprodução estritamente assexuada são bastante raras na natureza (Vrijenhoek, 1998). A questão que surge a partir destas observações é: quais forças evolutivas são responsáveis pela persistência da reprodução sexuada na natureza? Para responder tal questão, diversos modelos e hipóteses integrando tanto aspectos genéticos quanto ecológicos têm sido propostos. (Hamilton, 1980; Bell, 1982; Muller, 1964; Williams, 1975; ver Otto, 2009).

Dentre as primeiras hipóteses formalmente propostas para explicar a vantagem do sexo em relação à reprodução assexuada podemos elencar duas. Primeiramente, temos a hipótese de Fisher-Muller (Fisher, 1930; Muller, 1932), que propõe que populações sexuadas, devido ao fenômeno da recombinação genética, podem unir em um mesmo genoma alelos benéficos ao indivíduo, enquanto populações assexuadas dependeriam de mutações, que são estocásticas, para que este alelos fossem fixados em um mesmo indivíduo. Neste cenário, as populações sexuadas gerariam indivíduos de maior fitness

1 com mais frequência e, consequentemente, teriam um maior sucesso evolutivo em relação às populações assexuadas. Uma segunda hipótese clássica, proposta para explicar as vantagens do sexo, ficou conhecida como Muller’s ratchet (“A catraca de Muller”) (Muller, 1964). A observação inicial desta teoria parte de que as mutações são na maioria das vezes deletérias, raramente ocorrendo mutações que revertem os alelos deletérios à sua forma benéfica. Deste modo, uma vez que surja uma mutação em uma população assexuada, ela tende se perpetuar pelas gerações clonais, acumulando-se com novas mutações deletérias que surgirão, resultando no declínio da viabilidade dos genomas, que poderá direcionar a população à extinção. Portanto, o sexo teria a vantagem de poder recriar os genomas benéficos, através da recombinação e do cruzamento com indivíduos não afetados pelas mutações deletérias, resultando em maior resistência às mutações deletérias ao longo do tempo. Finalmente, ambas as hipóteses têm em seu cerne processos genéticos, a nível de indivíduo e de população, como principais fatores que levam à vantagem do sexo, dando pouco enfoque aos fatores ecológicos que estão por trás da seleção diferencial dos genótipos. Nós focaremos, de agora em diante, em uma hipótese que abarca tanto fatores genéticos e como ecológicos como principais mecanismos para a evolução e persistência da reprodução sexuada, a Hipótese da Rainha Vermelha.

HIPÓTESE DA RAINHA VERMELHA

A Hipótese da Rainha Vermelha (HRV) traz a coevolução através de interações bióticas do tipo parasita-hospedeiro como principal causa da pressão seletiva que favorece a evolução e persistência da reprodução sexuada nas espécies(Jaenike & June, 1978; Hamilton, 1980; Bell, 1982). À luz da HRV, os parasitas exercem uma pressão seletiva antagônica mais forte sobre o genótipo mais comum na população de hospedeiros (Hamilton, 1980). Isso beneficiará hospedeiros com genótipos raros, que logo aumentarão sua frequência na população, culminando em uma seleção dependente de frequência sobre os genótipos. Portanto, alelos raros associados à resistência à infecção aumentam sua frequência na população, tornando-se comuns, o que jogará a seleção contra eles, direcionado os parasitas a desenvolverem meios de sobrepujar tal resistência. Dessa forma, a frequência dos alelos do parasita e do hospedeiro apresentarão uma dinâmica de oscilações cíclicas (Lively, 1996). Essa “corrida armamentista” pela sobrevivência travada entre parasita e hospedeiro na HRV requer uma fonte de variabilidade genética para o surgimento dos novos genótipos (Lively, 1996). A reprodução sexuada, apesar dos seus custos inerentes à curto prazo, já mencionados, é uma estratégia que gera

2 variabilidade de forma rápida, fornecendo à população genótipos novos e únicos a cada geração (Hartfield & Keightley, 2012). Portanto, o sexo é capaz de originar genótipos que tenham uma elevada resistência à parasitas a cada geração, diferentemente da reprodução clonal, onde a variabilidade genética na população é dependente de mutações estocásticas (Peters & Lively, 2007).

Em diversos grupos de animais, já foram encontrados resultados empíricos condizentes com a HRV, tanto em invertebrados quanto em vertebrados. O estudo de uma clássica dinâmica parasita-hospedeiro entre um caramujo de água doce (Potamopyrgus antipodarum) e um verme trematódeo (Microphallus sp.), foi encontrado que em populações onde variedades assexuadas e sexuadas do caramujo coexistem, há uma pressão seletiva contra os genótipos mais comuns de hospedeiro ao longo do tempo. Foi observado, também, que os genótipos uma vez comum na população, torna-se mais susceptível à infecção pelos trematódeos anos depois (Jokela, Dybdahl, & Lively, 2009). Um outro exemplo de sistema animal que fornece suporte para a HRV foi encontrado em Kryptolebias marmoratus, um peixe hermafrodita, com populações compostas por indivíduos puramente hermafroditas ou por machos e hermafroditas. Maiores cargas de parasitas estavam relacionadas com maiores níveis de autofecundação, enquanto cargas menores de parasitas estavam relacionadas com altos níveis de fecundação cruzada (Ellison, Cable, & Consuegra, 2011). Estes resultados indicam que a autofecundação deixa os peixes mais susceptíveis a infecção. Portanto, para vários grupos de animais a HRV surge como uma boa preditora para a ocorrência de reprodução sexuada e assexuada entre as espécies (Neiman, Meirmans, Schwander, & Meirmans, 2018). Porém, para outros grupos de seres vivos, a relação parasita-hospedeiro como principal explicação para evolução e persistência da reprodução sexuada foi pouco testada empiricamente, continuando sub explorada.

As plantas, em especial as angiospermas, possuem uma ampla diversidade de estruturas e mecanismos que garantem uma variedade de estratégias reprodutivas (Barrett, 2002). Porém, apesar deste grande potencial para estudos que investiguem a evolução da reprodução sexuada, há poucos trabalhos que exploraram explicitamente a HRV usando as plantas como modelo. Em um estudo pioneiro, foi encontrada uma relação positiva entre a taxa de recombinação genética e a pressão de inimigos naturais, como pestes, para várias espécies de plantas (Levin, 1975). Este estudo seminal antecedeu a formalização da Hipótese da Rainha Vermelha (Hamilton, 1980), mas já continha em

3 seu cerne o raciocínio biológico HRV. Estudos mais recentes têm investigado a relação entre estratégias reprodutivas em plantas e a pressão de inimigos naturais, como patógenos e herbívoros, vindo a encontrar resultados conforme o esperado pela HRV (Busch, Neiman, & Koslow, 2004; Verhoeven & Biere, 2013; Paterno et al., no prelo) ou encontrando resultados não esperados pela teoria (Hartmann et al., 2017). Portanto, a dinâmica parasita-hospedeiro como principal força direcionando as plantas à reprodução sexuada ainda são escassos e controversos, necessitando de estudos empíricos tanto a nível intra como a nível interspecífico.

SISTEMAS SEXUAIS EM PLANTAS

Tradicionalmente, as plantas têm seus sistemas sexuais categorizados segundo a relação entre os níveis de autofecundação e fecundação cruzada que cada espécie apresenta, variando dentro de um gradiente que vai de espécies totalmente autofecundantes (autógamas) até espécies totalmente cruzantes (alógamas) (Lande & Schemske, 1985). Muitos fatores evolutivos e ecológicos, tantos abióticos como bióticos, podem atuar no direcionamento desses sistemas sexuais, podendo favorecer a autofecundação ou a fecundação cruzada, ou ainda a manutenção de níveis intermediários de cada modo de reprodução, em sistemas mistos estáveis (Goodwillie, Kalisz, & Eckert, 2005). Por exemplo, é esperado que em ambientes que estão em processo de colonização, como estágios iniciais de sucessão, haja uma maior riqueza de espécies autógamas, com elevados índices de autofecundação (Baker, 1955). Em tais condições, há menor disponibilidade de indivíduos coespecíficos para troca de pólen, bem como limitação de polinizadores, portanto a autogamia é favorecida em relação a alogamia, havendo a garantia reprodutiva da espécie (Hargreaves & Eckert, 2014; Kalisz, Vogler, & Hanley, 2004). Adicionalmente, as características ligadas aos sistemas sexuais podem ser afetadas, também, por outros fatores como a composição de espécies de polinizadores (Gervasi & Schiestl, 2017) e, como predito pela HRV, a pressão de inimigos naturais (Busch, Neiman e Koslow, 2004).

Dentre alguns exemplos empíricos de fatores que direcionam a evolução dos sistemas sexuais, podemos destacar que em um estudo com Clarkia xantiana, foi encontrado que plantas com baixa hercogamia e protandria, apresentaram o fitness mais elevado quando cultivadas em populações pequenas, isoladas e sem polinizadores, se comparado com plantas com alta hercogamia e protandria (Moeller & Gebre, 2005). Esse resultado aponta para a vantagem adaptativa da autofecundação em ambientes em 4 processo de colonização, conforme mencionado acima (Baker, 1955). Em um estudo experimental recente, foi observado o efeito de variáveis abióticas no desenvolvimento de características florais em Collinsia verna. As plantas cultivadas submetidas ao tratamento com menores tempo de luminosidade e disponibilidade de água (simulando uma estação de crescimento curta), desenvolveram características ligadas à autofecundação mais acentuadas, como um menor número de flores e um menor tamanho das flores (Spigler & Kalisz, 2013). Sintetizando, existem forças evolutivas e ecológicas direcionando mudanças nos sistemas sexuais das plantas, aumentando a aptidão da autofecundação ou da fecundação cruzada. Portanto, entender como tais sistemas podem ser medidos e estimados dentro de uma escala, seja ela contínua ou categórica, é o primeiro passo para compreender como a biologia reprodutiva das plantas responde aos mais diversos fatores ecológicos que as populações vegetais podem enfrentar.

TAXA DE FECUNDAÇÃO CRUZADA DERIVADA DE MARCADORES MOLECULARES

Os sistemas sexuais podem ser mensurados de várias formas, como métricas derivadas de características florais, por exemplo o display floral (Goodwillie et al., 2010) e a hercogamia (Opedal, 2018), ou ainda derivadas do uso de marcadores moleculares, como aloenzimas e microssatélite de DNA (Cruzan, 1998). Independente da métrica utilizada, ela será uma estimativa dentro de um gradiente que vai da autogamia à alogamia, passando pelos sistemas sexuais mistos. Porém, algumas métricas podem ser mais precisas que outras em identificar eventos autofecundação ou de fecundação cruzada na prole de um indivíduo. Uma das métricas mais sensíveis a tais eventos é a taxa de fecundação cruzada calculada a partir dos marcadores moleculares, que utilizam uma comparação de múltiplos lócus gênicos polimórficos em sua estimativa (tm; Ritland & Jain, 1981). Esta taxa consiste em uma razão entre a prole gerada por vias autogâmicas e por vias alogâmicas, variando de 0 a 1, sendo 0 a autogamia total e 1 a alogamia total.

Historicamente, espécies apresentando tm entre 0 e 0,2 são consideradas como autofecundantes e entre 0,8 e 1 como espécies de fecundação cruzada (Schemske & Lande, 1985), os valores entre 0,2 e 0,8 são considerados sistemas mistos (Goodwillie,

Kalisz e Eckert, 2005). Atualmente, ferramentas comuns para estimação do valor de tm são softwares que utilizam de máxima verossimilhança para calcular esta taxa, como o MLT e MLTR (Ritland, 1990; Ritland, 2002). Há décadas que diversos estudos que analisam a ecologia e a evolução dos sistemas sexuais, sob os mais diversos aspectos, utilizam desta métrica ao invés de características morfológicas, por sua maior

5 sensibilidade e acurácia (Lande & Schemske, 1985; Goodwillie, Kalisz e Eckert, 2005; Fornoni, Ordano, Pérez-Ishiwara, Boege, & Domínguez, 2016; Moeller et al., 2017;

Whitehead, Lanfear, Mitchell, & Karron, 2018). Portanto, tm é um bom estimador do sistema sexual, por refletir com confiabilidade as taxas de fecundação cruzada apresentadas pelos indivíduos das populações estudadas.

A TAXA DE FECUNDAÇÃO CRUZADA E A RAINHA VERMELHA

Um estudo que vise a testar a HRV usando plantas como modelo deve ter em vista dois fatores principais: (I) a forma de mensuração do sistema sexual e (II) o grupo de inimigos naturais que será usado. Como exposto acima, o valor de tm é uma forma adequada e confiável de estimação do sistema sexual. Quanto aos inimigos naturais, já se sabe que dentre os muitos antagonistas que as plantas possuem, os insetos herbívoros podem exercer um efeito significativo sobre a seleção de características relacionadas aos sistemas sexuais das plantas (Carr & Eubanks, 2014), o que faz deste grupo de inimigos naturais um dos mais adequados para se testar a HRV no reino vegetal, conforme discutido abaixo.

PRESSÃO EVOLUTIVA DE INSETOS HERBÍVOROS

A relação de entre insetos herbívoros e plantas está entre as interações ecológicas mais antigas e amplamente distribuídas na natureza. Ao longo dos milhões de anos, tal interação moldou a coevolução de características tanto nos herbívoros quanto nas plantas, podendo, inclusive, ser umas das forças responsáveis pela larga diversidade encontrada nestes dois grupos (Judith X. Becerra, 2015; Ehrlich & Raven, 1964; Futuyma & Agrawal, 2009; Marquis et al., 2016). Sob a pressão dos herbívoros, as plantas desenvolveram uma ampla uma variedade de mecanismos de defesa, como a produção de metabólitos secundários (Becerra, Noge, & Venable, 2009) e a tolerância à herbivoria (Strauss & Agrawal, 1999). A relação entre os herbívoros e os mecanismos defensivos das plantas pode ser identificada tanto em escala micro quanto em escala macroevolutiva. Por exemplo, Agrawal, Hastings, Johnson, Maron, & Salminen (2012), constataram experimentalmente que uma espécie de Onagraceae apresentava, em poucos anos, um decréscimo na resistência contra herbívoros quando cultivadas na ausência de insetos fitófagos. Este resultado demonstra que o papel dos insetos herbívoros é fundamental na persistência dos mecanismos ligados à defesa das plantas, ainda que em curtos períodos de tempo. Sob uma ótica macroevolutiva, as plantas podem sofrer diferentes pressões de

6 herbivoria seguindo padrões ambientais globais, como o gradiente latitudinal. Ehrlich & Raven (1964), propuseram, em um trabalho pioneiro, que baixas latitudes estariam associadas a elevadas taxas de herbivoria e, consequentemente, a maiores pressões evolutivas para o desenvolvimento de mecanismos antiherbivoria por parte das plantas. Muitos estudos encontraram resultados consistentes com essa predição (Marquis, Ricklefs, & Abdala-Roberts, 2012; Pennings et al., 2009; Salazar & Marquis, 2012; Wieski & Pennings, 2014), embora alguns estudos tenham encontrado resultados em desacordo com esta predição geral (ver Anstett, Ahern, Johnson, & Salminen, 2018).

Podemos perceber que, mesmo em diferentes escalas de análise espacial e temporal, a coevolução entre plantas e insetos herbívoros direcionando a seleção de diferentes características é evidente para ambos os grupos. A dinâmica de coevolução entre estes grupos consiste na seguinte lógica: o surgimento de uma nova mutação, ou um novo genótipo gerado pela reprodução sexuada, em uma população de plantas pode fornecer um novo mecanismo de defesa, total ou parcial, contra uma determinada espécie ou guilda de insetos herbívoros. Tal genótipo ou mutação elevaria o fitness do seu portador e tenderia a aumentar sua frequência na população. Isso forneceria uma oportunidade para que os insetos herbívoros que sobrepujassem essa defesa tivessem um aumento em seu fitness. Consequentemente, haveria o aumento da pressão seletiva para adaptação dos herbívoros a tais defesas (Ehrlich & Raven, 1964; Becerra et al, 2009; Marquis et al, 2016; Salazar et al., 2018). Fica claro que esta dinâmica inseto herbívoro- planta é regida por uma seleção dependente de frequência, logicamente idêntica a dinâmica parasita-hospedeiro proposta pela HRV para explicar a vantagem da reprodução sexuada. Portanto, fica justificado o uso dos insetos herbívoros como um bom grupo de inimigos naturais que gerem seleção de novos genótipos em plantas em estudos que visem a testar a HRV neste reino.

SÍNTESE E PROBLEMÁTICA

Finalmente, fica claro que a intrigante evolução da reprodução sexuada nos mais variados táxons, especialmente nas plantas, que apresentam diversos mecanismos e estratégias reprodutivas, é uma questão ainda aberta. Esta questão instiga estudos que abordam diversas forças seletivas como principais direcionadores do processo evolutivo. A HRV propõe a pressão de inimigos naturais como força principal para evolução e persistência do sexo. Apesar de sólidas evidências favoráveis à hipótese em vários grupos de animais, não há muitos estudos que incluam o reino vegetal como modelo. Portanto, um estudo 7 que busque explicar o investimento em estratégias de reprodução sexuada pelas plantas, correlacionando tal investimento com a ação de antagonistas, se faz necessário para compor o arcabouço empírico e teórico sobre a evolução dos sistemas sexuais nas plantas. Um estudo em escala macroevolutiva, incluindo em sua análise comparativa táxons com diferentes histórias evolutivas, poderia preencher esta lacuna ainda existente sobre a extensão da Hipótese da Rainha Vermelha no reino vegetal.

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SINGLE CHAPTER

INSECT HERBIVORES MODULATE OUTCROSSING RATES

ACROSS SEED PLANTS

Flowering tree, unknown artist.

Tales M. A. Paiva | Martin M. Gossner | Martin Brändle | Carlos R. Fonseca

Manuscript to be submitted to Journal of Ecology

1 Insect herbivores modulate outcrossing rates across seed plants

3 Tales M. A. Paiva1*, Martin M. Gossner2, Martin Brändle3, Carlos Roberto Fonseca1

5 1 Department of Ecology, Universidade Federal do Rio Grande do Norte, Natal, 59072- 6 970, Brazil.

7 2 Forest Entomology, Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, 8 Birmensdorf, Switzerland.

9 3 Animal Ecology, Department of Ecology, Faculty of Biology, Philipps Universität 10 Marburg, Karl-von-Frisch Strasse 8, 35032 Marburg, Germany.

13 *Correspondence author: Tales Martins de Alencar Paiva

14 Address: Departamento de Ecologia, Universidade Federal do Rio Grande do Norte, 15 Centro de Biociências, Campus Universitário, Lagoa Nova, Natal, RN 59078-970, Brazil.

16 Email: [email protected]

17 ABSTRACT

18 Understanding the evolution of plant mating systems, which can be described by selfing 19 and outcrossing rates, is a great challenge in evolutionary biology. The Red Queen 20 hypothesis suggests that host-parasite biotic interaction is the major factor driving the 21 evolution and persistence of sexual reproduction in nature. Species under higher pressure 22 of natural enemies are expected to show higher investment in sex. There are support for 23 Red Queen for several groups of animals, however in plant kingdom this hypothesis 24 remains underexploited, especially using comparative approaches. Here, we test if 25 richness of insect herbivores associated to seed plants modulate their outcrossing rates. 26 Phylogenetic regressions, under two alternative evolutionary models (Brownian Motion 27 and Ornstein-Uhlenbeck) were used to test this hypothesis for all available species and 28 for native and exotic species, when controlling for key vegetative covariables (life span, 29 growth form, specific leaf area and maximum height). For all species, we showed that 30 plant species associated to more insect herbivores had higher outcrossing rates. We found 31 the same pattern for native species, but not for exotic species. We also found that tall and 32 longer-lived plants tend to be higher outcrossing rates than small and short-lived. These 33 results are in accordance with the expected by the Red Queen hypothesis, supporting the 34 still scarce empirical framework of macroevolutionary studies about the role of natural 35 enemies on evolution of mating system in seed plants.

36 Synthesis: The evolutionary pressure exerted by insect herbivores on seed plants 37 modulates the evolution of their mating system, particularly outcrossing rates, bringing 38 support to the application of the Red Queen hypothesis to the plant kingdom.

39 Key-words: Evolutionary models, herbivory, mating system, natural enemies, Red 40 Queen Hypothesis.

41 INTRODUCTION

42 The evolution of plant mating systems has been one of the major subjects in plant 43 reproductive biology (Barrett et al. 2003; Charlesworth 2006). Plants have a wide 44 diversity of reproductive strategies which reflect the ecological and evolutionary lability 45 of their mating system (Barrett, 2002). Many studies have grouped plants in two distinct 46 reproductive categories, selfers or outcrossers (Schemske & Lande, 1985). Others, have 47 subdivided further (Cruden, 1977). Modern molecular techniques, however, allow species

48 to be located along a continuum of outcrossing rate (tm), from obligatorily self-fertilized

49 (tm = 0) to obligatory cross-fertilized species (tm = 1). Nowadays, it is clear that stable 50 mixed mating systems, with intermediate outcrossing rates, are common in nature (Kalisz 51 et al. 2004; Goodwillie et al. 2005). To reveal the ecological and evolutionary forces that 52 drive the interspecific variation in outcrossing rate, an important step is to understand the 53 hypotheses that aim to explain the adaptative advantages of sexual reproduction over 54 parthenogenesis (see review in Hartfield & Keightley, 2012).

55 The Red Queen Hypothesis has been proposed to explain the emergence and 56 persistence of sexual reproduction (Bell 1982; Jaenike 1978; Hamilton 1980). Its major 57 assumption is that biotic interactions between short-lived natural enemies, generically 58 called parasites, and their hosts favor the maintenance of sex. Parasites are under selection 59 to be able to infect the most common genotype in host population while hosts are under 60 pressure to produce effective defenses against their parasites. Such selection generates a 61 dynamic frequency dependent selection favoring rare genotypes in both host and 62 parasites, since genotype rarity is related to higher success in virulence and defensive 63 development in parasites and hosts, respectively (see Lively, 1996). Interspecifically, the 64 theory predicts that species under stronger parasite pressure should favor outcrossing. 65 Additionally, longer host life span can be predicted to lead to higher levels of sexual 66 reproduction, since parasites will have more generations per host generation to break their 67 defense. Many theoretical and empirical studies found results in accordance with this 68 relationship between life span and sexual reproduction (Bell 1982; Barrett 1996; Lively 69 2010; Munoz et al, 2016; Moeller et al. 2017; Lesaffre & Billiard, 2019). The Red Queen 70 has received empirical support in several animal-parasite systems (Duncan & Little, 2007; 71 Lively, 1987; Schrag, Ndifon, & Read, 1994). However, when we look to the plant 72 kingdom, there are few studies that exploit the Red Queen dynamics as an explanation 73 for mating system determination in plant populations.

74 In his seminal paper, Levin (1975) proposed that intense pressure by natural 75 enemies could lead to higher genetic recombination rates in plants. Species or populations 76 under high pressure by their short-lived natural enemies, such as insect herbivores and 77 pathogens, would have mechanisms favoring higher genetic variation, such as dioecy and 78 self-incompatibility. There is some intraspecific empirical support for this hypothesis. A 79 study with a dandelion species (Taraxacum officinale) revealed that pressure of natural 80 enemies can be an important factor to explain the gradient of geographical distribution of 81 both sexual and asexual populations (Verhoeven & Biere, 2013). These authors found 82 that populations exclusively asexual were under less pressure of antagonists, matching 83 with the red queen hypothesis. On the other hand, in a recent study, the pressure of natural 84 enemies was not effective to explain the distribution of sexual and asexual populations of 85 Hieracium alpinum (Hartmann et al., 2017). Interspecifically, it has been demonstrated 86 that plants associated to a higher number of pathogenic/antagonist fungus species 87 exhibited higher outcrossing rates (Busch, 2004).

88 Insect herbivores are one of the most important natural enemies of plants. They 89 are ecologically and phylogenetically diverse and share more than 200 million years of 90 evolutionary history with their hosts (Ehrlich & Raven, 1964; Futuyma & Agrawal, 91 2009). As response to the pressure of insect herbivores, plant developed a great diversity 92 of defense mechanisms, such as production of secondary metabolic compounds (Becerra, 93 Noge, & Venable, 2009) and tolerance to herbivory (Strauss & Agrawal, 1999). The key- 94 mechanism to the development of new and effective defenses against insect herbivores is 95 sex. Self-fertilization reduces the heterozygosity level of offspring which can directly 96 reduce the diversity of chemical defenses. Also, leads to the expression of deleterious 97 recessive alleles (Charlesworth & Charlesworth, 1987; Crnokrak, Barrett, Journal, & 98 Organic, 2002). Both processes can reduce the resistance of self-fertilized plants to 99 herbivores (Núñez-Farfán & Cabrales-Vargas, 1996; Ridley, Hangelbroek, Wagenius, 100 Stanton-Geddes, & Shaw, 2011). The greater genetic variability induced by sexual 101 reproduction increases the probability of producing offspring carrying rare and effective 102 defense mechanisms against their herbivores (Weismann 1887; Burt 2000; Godfrey and 103 Johnson 2014). Thus, it is expected that populations with higher self-fertilization rates are 104 more susceptible to natural enemies than populations with higher outcrossing rates (Levin 105 1975; Núñez-Farfán and Cabrales-Vargas 1996; Johnson et al. 2009). For example, 106 Johnson et al. (2009), in an experimental study with Onagracreae species, found that

107 plants of exclusively asexual linages showed greater foliar damage caused by herbivorous 108 generalist insects than the exclusively sexual lineages. As expected by theory, this result 109 suggest that outcrossing is an effective strategy in the development of defense 110 mechanisms against certain groups of insects. Despite the growing number of studies that 111 exploit the relation among mating system evolution and insect herbivores (Carr & 112 Eubanks, 2014), there is a shortage of studies analyzing this relation using a 113 macroevolutionary comparative approach.

114 Evolutionary history is a key factor that must to be considered in 115 macroevolutionary studies of plants and their associated insect herbivores. Native plants 116 generally share a long evolutionary history with the local associated herbivores. Alien 117 plants, however, when are introduced to a new environment not only leave their 118 associated herbivores behind, but can possess defense mechanisms which were never 119 experienced by the local herbivores. Therefore, it is expected that local native plants are 120 under more herbivory pressure than exotics ones, the so called enemy release hypothesis 121 (Keane & Crawley, 2002; Lucero et al., 2019). This hypothesis had empirical support for 122 plants and could be an important role in mating system evolution (Schutzenhofer, 2007). 123 For example, in two different species of Lespedeza inhabiting the same region, the 124 introduced exotic species showed more than 60% less herbivory damage than native, and 125 in both species, herbivory pressure change the chasmogamous-cleistogamous flowers 126 ratio, a reliable indicative of plant mating system (Schutzenhofer, 2007). Furthermore, 127 since exotic species coevolved with their herbivores in another context, in invaded ranges, 128 one predicts a mismatch between the number of associated herbivores and their mating 129 system strategy.

130 Several vegetative traits related to plant life history can contribute to explain the 131 variation in outcrossing rates across species (Moeller et al. 2017; Lesaffre & Billiard, 132 2019). Under the Red Queen hypothesis, as mentioned before, plants with higher life span 133 are expected to invest more in sex, increasing their outcrossing rates. However, other 134 variables can influence plant mating system evolution by affecting plant-herbivore 135 dynamics. Specific leaf area (SLA), for instance, is a key vegetative trait negatively 136 correlated to investments in constitutive defenses (Cornelissen et al. 2003) which can be 137 expected to modulate long term herbivory pressure. A recent experimental study with 138 Alternanthera philoxeroides found that in treatment submitted to herbivory, plants

139 showed a decrease in SLA over generations, compared to control treatment, without 140 herbivores (Dong, Fu, Luo, & Yu, 2017).

141 Here, we use a macroevolutionary approach to test the Red Queen hypothesis in 142 higher plants. In particular, phylogenetic regressions, under two alternative evolutionary 143 models (Brownian Motion and Ornstein-Uhlenbeck), were performed to test if richness 144 of insect herbivores associated to plants, used as surrogate for its long-term herbivory 145 pressure, modulate their mating system, measured by its outcrossing rate. Also, we tested 146 additional alternative models, for both native and exotic species, containing four 147 additional explanatory variables: growth form, life span, plant height, and SLA. Under 148 the Red Queen hypothesis, we expect native plants with more associated insect herbivores 149 and longer lifespan to have higher outcrossing rates; this relationship holding for native 150 but not for exotic species.

151

152 MATERIALS AND METHODS

153 Data sets

154 Our data-set on plant outcrossing rates (tm) has been built following two principal 155 approaches. First, we compiled published databases available in the literature (four

156 studies were our principal source of tm data: Goodwillie et al. 2005; Fornoni et al. 2016; 157 Moeller et al. 2017; Whitehead et al. 2018). Second, for a more complete scan of recent 158 publications, we performed a species-specific search in Web of Science, collecting all 159 studies published by each plant species until November, 23, 2018. To optimize our effort, 160 we only searched for plant species names which were present in our global insect 161 herbivore database. The search for each species included as key-word (I) the species 162 binomial and (II) the term outcrossing rate OR mating system. Our final database, 163 contained only those species that had values for both insect herbivores richness and 164 outcrossing rate values. In total, we have 136 species of spermatophytes; 104 angiosperms 165 and 32 gymnosperms (see taxonomic identity and distributions in Appendix 1, subsection 166 1.1). We checked the taxonomy identity and synonymy for all species using The Plant 167 List as base. We built a list of the collected names and the name matched and accepted in 168 The Plant List (see Appendix 1, subsection 1.1).

169 In total, our dataset had 430 records of outcrossing rate, because for several of our 170 136 species more than one outcrossing rate measurement was available in literature. For 171 example, in studies where comparisons were made across species populations, we 172 recorded all values of outcrossing rate per species (i.e. Stojanova, Cheptou, & Maurice, 173 2014). For the main analyses, we used mean outcrossing rate per species as the response 174 variable. As an alternative approach, single populations were randomly selected to 175 characterize the species in appropriate randomization tests (see Appendix 2 for details).

176 Data of insect herbivores associated to plants were acquired from a recent dataset 177 of plant-herbivore interaction for plants species occurring in Europe nowadays, built from 178 an extensive review of insect collection and literature data. Such database includes, then, 179 information both native and exotic species. All insect herbivores species recorded was 180 included in dataset, regardless of their food guild and the part of plant consumed by them. 181 Here, we used insect herbivore richness as a proxy for evolutionary herbivory pressure 182 for plant species.

183 In addition to herbivores richness, we collected data from four vegetative variables 184 for our species: life span, growth form, specific leaf area (SLA) and maximum height. 185 We used the plant traitbase LEDA (Kleyer et al., 2008) and data found in primary 186 literature as source. We used these variables as both single and covariate predictor in our 187 phylogenetical models. We classified all 136 species in three categories of life span: 188 annuals, biennials, and perennials. In the same way, we categorized all species in six 189 categories of growth form: graminoid, hemiparasite, herb, liana, shrub, and tree. For our 190 136 species, only 88 (64.7%) presented data for SLA and maximum height. For 48 species 191 containing missing data, we estimated these values by phylogenetic imputation using 192 Rphylopars package (Goolsby, Bruggeman, & Ané, 2017). Phylogenetic imputation is a 193 reliable approach to estimate missing data (Penone et al., 2014), especially when one of 194 variables have strong phylogenetical signal, such as maximum height (Pagel’s λ = 0.83, 195 p < 0.001). We estimated phylogenetical signal using phytools package (Revell, 2012).

196 Correcting the sampling effort bias

197 Sampling effort is known to influence the number of records of insect herbivores, 198 or others natural enemies, associated to plants (e.g. Busch et al. 2004). In general, richness 199 of insect herbivores is expected to be underestimated for plants poorly recorded. In order 200 to correct for such bias, we collected for each plant species the total number of recorded

201 occurrences in the Global Biodiversity Information Facility repository [gbif.org] in June, 202 25, 2019. Then, we performed a simple linear regression using the number of occurrences 203 as predictor and observed herbivore richness as response variable. As expected, we found 204 a significant positive relationship between these two variables (slope = 0.438, p-value = 205 0.000; for details see Appendix 1, section 3). Then, we extracted the residuals of this 206 model to use it as corrected herbivore richness in our phylogenetic models (hereafter, 207 simply ‘herbivore richness’).

208 Coevolutionary time

209 To analysis how coevolutionary time modulate the effect of herbivore pressure on 210 outcrossing rates, we classified the plants in two categories: European native species 211 (hereafter ‘native’) and non-European species (hereafter ‘exotic’). This classification was 212 performed with the use of plant databases and herbaria sites (see the original sources in 213 Appendix 1, subsection 1.2). Species that naturally occurs in more than one continent and 214 cosmopolitan species were considered as exotic, since herbivores richness may be under- 215 sampled due the plant-insect interaction dataset was built only for European records. In 216 our 136 species, we had 83 natives and 53 exotics (see the list in Appendix 1, subsection 217 1.2). Beyond the models using data from all species, we also performed all phylogenetic 218 models described below separately for native and exotic species.

219 Phylogenetic models

220 To test the Red Queen Hypothesis, we initially used a phylogenetic regression model 221 using the mean outcrossing rate per species as response variable and richness of insect 222 herbivores as the single explanatory variable. Subsequently, we contrasted this simple 223 model with several simple and multiple regression models which were built with several 224 key vegetative traits as predictors (Table 1; for details see Appendix 1). We built all 225 models using Phylogenetic Generalized Least Squares (PGLS), to take into consideration 226 the phylogenetic proximity between species (Felsenstein, 1985; Grafen, 1989). The 227 analyses were performed using two alternative evolutionary models: Brownian Motion 228 (BM), which assumes that trait evolve according to a random walk process, where the 229 traits evolve around a mean value and the variance increases over time (Symonds and 230 Blomberg, 2014), and Ornstein-Uhlenbeck (OU), which assumes that traits evolve around 231 a single optimal value or multiple values (Butler and King, 2004; O’Meara and Beaulieu, 232 2014). The phylogenetic hypothesis used in our models was built by pruning a recently

233 published dated phylogeny for plants (Smith & Brown, 2018). To execute the PGLS 234 models we used the phylolm package (Ho & Ané, 2014). For OU models, the parameters 235 alpha (α) and squared sigma (σ²) were estimate by maximum likelihood. Lastly, we built 236 model averaging tables for both BM and OU models, using the corrected Akaike 237 Information Criterion (AICc; Hurvich and Tsai 1989), to rank all our 15 phylogenetical 238 models (see Table 1 and 2). We categorized our models in ‘simple model’ for those 239 containing one predictive variable and ‘additive model’ for those containing more than 240 one predictor. For model comparison and averaging, we used the MuMIn package 241 (Bartoń, 2019). All analytical procedures were performed in R (R core team, 2019).

242 Caveats

243 Seed plants comprehend a large phylogenetics groups and the species used here were 244 limited to those present simultaneously in two independent datasets (outcrossing rates and 245 insect herbivore richness), produced by two independent research groups. Because of that, 246 our final dataset was a little subset of total seed plants. Also, our dataset has many species 247 with agricultural importance which suffered an unknown level of artificial selection for 248 economic optimization which possibly affected their trait distributions. Finally, insect 249 herbivores represent only a fraction of plant’s natural enemies and other groups not 250 included here, such as fungal pathogens, can affect mating system (Busch, 2004). Future 251 studies designed to measure outcrossing rates in natural plant populations with distinct 252 antagonists’ pressure are needed to better support macroevolutionary studies.

253

254 RESULTS

255 Herbivores richness

256 In our main simple PGLS model, which only insect herbivores richness is the predictor 257 of outcrossing rate variation, we found a positive and significant relationship in both BM 258 and OU evolutionary models (Figure 1a; BM: slope = 0.148, p-value = 0.011; OU: slope 259 = 0.155, p-value = 0.009, α = 0.139, σ² = 0.03). When we ran a non-phylogenetic linear 260 regression, the result remains significant (OLS, slope = 0.169, p-value = 0.007), but due 261 the existence of phylogenetic signal in residuals (Pagel’s λ = 0.454, p-value = 0.000), we 262 accept the PGLS result. The phylogenetic signal in residuals disappeared in PGLS 263 regression (Pagel’s λ = 0.000, p-value = 0.995). This result remained significant when we

264 included outcrossing rate populational variations in our models, using randomizations 265 (see Appendix 2).

266 Native and exotic species

267 As expected, we found that in the main PGLS simple model, the positive and significative 268 phylogenetical regression was still robust when we ran only for native species (Figure 1b; 269 BM: slope = 0.227, p = 0.008; OU: slope = 0.194, p = 0.013), but not for the exotics 270 (Figure 1c; BM: slope = 0.127, p = 0.092; OU: slope = 0.159, p = 0.082).

271 Vegetative traits

272 The distribution of outcrossing rate across the life span and growth form 273 categories increases from annual to perennials plants (Figure 2a) and from graminoids to 274 trees (Figure 2b). Therefore, tall and long-lived plants tend to have higher out-crossing 275 levels than small and short-lived ones. For life span, both biennials and perennials plants 276 differ from annuals in OU model (Biennials: slope = 0.293, p-value = 0.006; Perennials: 277 slope = 0.430, p-value = 0.000). In BM model, only perennials differ from annuals 278 (perennials: slope = 0.364, p-value = 0.000) For growth form, only trees differed from 279 graminoids in OU model (slope = 0.401, p-value = 0.001). In BM model, there is no 280 difference in outcrossing rate between growth forms. We found similar results when 281 outcrossing rate populational variation was included in analysis (see Appendix 2).

282 Specific leaf area (SLA) and maximum height had significant results to explain 283 outcrossing rate variation in OU models. Maximum height showed a positive relationship 284 (slope = 0.193, p-value = 0.000), which means that tall species tend to have high

285 outcrossing rate (Figure 2c). SLA showed a negative relationship (slope = -0.433, p-value

286 = 0.000), which means that species with low SLA tend to have high outcrossing rate 287 (Figure 2d). In BM model, SLA had significant results, remaining the negative 288 relationship found in OU model (slope = -0.560, p-value = 0.000). Maximum height had 289 no effect in outcrossing rate variation in BM model. We found similar results when 290 outcrossing rate populational variation was included in analysis (see Appendix 2).

291 Model comparison

292 In OU models comparison by AICc difference, we found three important models 293 to explain outcrossing rate variation (Table 1, delta AICc < 2). The best model included 294 both insect herbivores richness and life span as predictor. The second model include only

295 life span. The third include life span and growth form as predictors. All others models 296 had not relevant importance in AICc table. In BM models comparison, only the additive 297 model with herbivore richness and life span was selected (Table 2, delta AICc < 2). 298 Comparing the tables, OU models had lower AICc values than BM models, even the 299 worst model in OU table had low AICc than the best BM model. Model comparison based 300 in Information Theory analysis, such as AICc, can be done to a model set built under 301 alternative evolutionary models (Garamszegi and Mundry, 2014). Therefore, the OU 302 models selected in our AICc analysis are more parsimonious than BM models, even using 303 the same data and predictors combinations. Thus, we assumed that OU is the more 304 realistic evolutionary model in our comparative study.

305

306 DISCUSSION

307 Herbivores richness and Red Queen

308 The Red Queen hypothesis predicts that the main driver for widespread sexual 309 reproduction in nature is the evolutionary pressure of short-lived natural enemies. Species 310 under more pressure would reproduce sexually more often to increase the genetic 311 variability and the potentially to evolve new defense pathways. We tested this assumption 312 for seed plants, using outcrossing rate and insect herbivores richness as proxies for mating 313 system and pressure of natural enemies, respectively. In this study, we used two 314 alternative evolutionary models, Brownian Motion and Ornstein-Uhlenbeck, to test our 315 hypothesis. Our results corroborated the theory’s prediction; plant species associated to 316 more insect herbivore species had higher levels of outcrossing rate in both evolutionary 317 models. The model comparison results also indicated that outcrossing rate evolution 318 under OU processes are more supported than by simply BM random walk. Therefore, our 319 finding supports the still scarce empirical background of the Red Queen hypothesis for 320 the plant kingdom. In fact, since the seminal work of Levin (1975), evolution of plant 321 mating systems related to ‘parasite-host’ dynamics has been poorly reported using 322 interspecific comparative approach (but see Busch, 2004).

323 Studies can explore the Red Queen dynamics in plant mating system ecology and 324 evolution in many ways. Firstly, there are studies that adopted a biogeographical 325 approach, aiming to explain the differential geographical distribution of sexual and 326 asexual populations observed in some species, called geographical parthenogenesis

327 (Bierzychudek, 1985), based in different pressure of natural enemies (Verhoever & Biere, 328 2013; Hartmann, et al., 2017). These intraspecific studies make clear that different 329 pressure of enemies could be determinant in selection of more adaptative sexual regime 330 in hermaphrodite species. Secondly, macroevolutionary approaches, with multi-species 331 analysis, could reveal general patterns of mating system evolution that match with those 332 found in species-specific studies. For example, Busch et al (2004) found that seed plant 333 species attacked by more fungal pathogen species had higher outcrossing rates than those 334 attacked by few pathogens. This result strictly matches with ours and it is interesting 335 because although we used insect herbivores richness instead of pathogens, the general 336 pattern remained clear, regardless of the natural enemy considered. Understanding the 337 interaction nature and mechanisms by which herbivores can affect plant mating system 338 selection is a crucial step to clearly interpret these results under the Red Queen hypothesis.

339 It is known that herbivory may have both direct and indirect effects in plant 340 reproductive traits related to mating system, especially in flowering plants (Johnson, 341 Campbell, & Barrett, 2015; Moreira, Castagneyrol, Abdala-Roberts, & Traveset, 2019). 342 Traditionally, is expected that herbivores affect negatively plant reproductive fitness 343 (Strauss, Conner, & Rush, 1996; Tsuji & Ohgushi, 2018; Moreira, Castagneyrol, Abdala- 344 Roberts, & Traveset, 2019) not only through resource limitation to seed siring but also by 345 flowers traits related to pollinator attraction and outcrossing rates, such as reducing 346 volatiles organic compounds (Goodwillie et al., 2010; Schiestl, Kirk, Bigler, Cozzolino, 347 & Desurmont, 2014) and reducing herkogamy (Chautá, Whitehead, Amaya-Márquez, & 348 Poveda, 2017). Under the Red Queen hypothesis, however, one could expect herbivory 349 to have a disproportional negative effect on female function in relation to the male 350 function. Alternatively, under herbivory, male function could be even stimulated. For 351 instance, plants of Raphanus raphanistrum experimentally subjected to herbivore 352 damaged showed higher male fitness, measured by the proportion of seeds sired on 353 another individual, than no damaged plants (Strauss, Conner, & Lehtilä, 2001). Plants of 354 Brassica nigra infested by a specific chewing herbivore had larger floral display than 355 non-infested ones, with possible consequences for outcrossing levels (Rusman, Poelman, 356 Nowrin, Polder, & Lucas-Barbosa, 2019). Some studies, however, suggest that the 357 interaction between herbivores and floral traits can change depending on the herbivore 358 guild (Rusman, Poelman, Nowrin, Polder, & Lucas-Barbosa, 2019). Therefore, future 359 studies that categorize insect species in different feeding guilds and test their effect in

360 outcrossing rates would make clearer the evolutionary relationship between herbivores 361 and plant mating system.

362 Plant-herbivore coevolutionary time and Red Queen

363 As expected by the Red Queen, we found significative effect of herbivore richness on 364 outcrossing rates only for species categorized as native. For exotic species the effect was 365 not significative. These results are supported by the enemy release hypothesis and suggest 366 that, for plant-insect herbivores, the Red Queen dynamics are observable only for long 367 coevolutionary times, such as the time of coevolution between native plant species and 368 their own herbivores in a distinct site. It is known that exotic species could experience 369 lower herbivory pressure than natives (Schutzenhofer, 2007; Lucero et al. 2019), and also 370 that exotics could attract more pollinators than natives (Woods, Jonas, & Ferguson, 2012). 371 Therefore, theoretically, exotic plants tend to be under a softer scenario than natives, with 372 less enemies pressuring for new defensive mechanisms, such as those generates by sexual 373 reproduction (Weismann 1887; Hamilton, 1980; Burt 2000; Godfrey and Johnson 2014). 374 This expectance can be clearly explained looking for coevolutionary time. Long time of 375 coevolution between plant species and herbivores in their native environment could 376 results in feed specialization. Thus, exotic plants would not be preferentially attacked by 377 local herbivores. This pattern is robust and it was observed even for generalist herbivores 378 (Lucero et al. 2019). We suggest that due the little time of coevolutionary history between 379 exotic plants and native herbivores, the Red Queen dynamics cannot be observable in 380 these species yet. Future studies exploiting the native-exotic dynamic for natural enemies- 381 outcrossing rates relationship using long-term populations, those that completed both 382 invasion and establishing processes a long time ago, would be important to a better 383 understanding about the necessary time-shared evolutionary history for the Red Queen be 384 perceptible in plant populations.

385 Vegetative traits and Red Queen

386 We found that long-lived and tall plants had higher outcrossing rate than short-lived and 387 small ones, as expected by theory. These results corroborate a recent macroecological 388 analysis which showed that lifespan and growth form were good predictors of outcrossing 389 variation (Moeller et al., 2017). Just like us, they found that perennials plants and trees 390 had higher outcrossing rate than annuals and herbs. These patterns corroborate the Red 391 Queen hypothesis, since insect herbivores can be expected to have a lifespan more similar

392 to annuals and herbs than to perennial plants and trees, making the evolutionary herbivory 393 pressure on the latter group to be stronger. In sum, long-lived inbreeder plants would 394 suffer an unbearable herbivory pressure in long term. In a theoretical study, Lively (2010) 395 found support to the influence of host life history in variations in the ‘costs of sex’ (see 396 Maynard Smith, 1977). He found that long-lived species have reduced costs of sex when 397 compared to short-lived, which means that long-lived species have a low threshold of 398 parasite virulence required to sex maintenance. Therefore, long-lived plants tend to 399 increase investments in sexual reproduction under less herbivores pressure than short- 400 lived plants. This result is in accordance with another recent theoretical study that found 401 strong support to correlation between plant life span and mating system evolution, 402 mediated by different inbreeding depression levels in distinct stages of life cycle of 403 individuals (Lesaffre & Billiard, 2019). In a large macroevolutionary empirical study, 404 Munoz, Violle & Cheptou (2016) found significant correlation between life history, such 405 as life span and growth form categories, and plant mating system using a data set that 406 contain almost two thousand species. They found that annual herbs tend to be more 407 frequently self-fertilized than perennial woody plants. Together, these theoretical and 408 empirical findings clearly suggest that sexual reproduction is expected to be more 409 common in long-lived than in annual hosts.

410 We also found a negative relationship between outcrossing rate and SLA. The Red 411 Queen hypothesis predict that sex is underlying the host’s defenses evolution (Hamilton, 412 1980). Thus, our results could be in accordance with this expectation, since low SLA 413 measurements are related to higher investment in defenses (Cornelissen et al., 2003). 414 However, we found no significant relationship in herbivore richness predicting SLA 415 (linear regression; slope = -1.674, p = 0.315), so our result could be due the relationship 416 between both growth form and life span in SLA. In general, trees and late successional 417 species have low values of SLA than others small and ephemeral growth forms (Poorter, 418 Van De Plassche, Willems, & Boot, 2004). Therefore, all our vegetative traits were 419 correlated to plant mating systems, although having distinct importance and effect (see 420 Tables 1 and 2), where the life span seems to have more influence in mating system 421 determination than other traits purely. Future studies that evaluate trade-offs between 422 growing rate and herbivory susceptibility, and finally the influence of this in mating 423 system response, would contribute to a better understand about the role of vegetative traits 424 in herbivores-hosts coevolution.

425 Sinergy between plant’s antagonists and mutualists

426 Plant mating systems are effected by several ecological and evolutionary forces (Barrett 427 & Harder, 2017; Sapir et al., 2019). Pollinators, for instance, are an important force 428 driving plant mating systems. Experimental studies found that pollinator communities 429 promote changes in floral traits related to plant mating system, leading to increasing or 430 decreasing in outcrossing rates (Gervasi & Schiestl, 2017; Newman, Manning, & 431 Anderson, 2015). In Dalechampia scandens, populations with more pollinators had 432 higher herkogamy and outcrossing rates than populations with less pollinators (Opedal et 433 al., 2016). However, high investment in attractive traits for pollinators, such as floral 434 volatiles and floral display, could also serve as a clue for antagonists (i.e. insect 435 herbivores), reducing the reproductive fitness and acting as a trade-off that limits the 436 allocation in attractiveness (Knauer & Schiestl, 2017; Theis & Adler, 2012). Studies that 437 analyze plant responses to simultaneous effects of herbivores and pollinators in natural 438 systems are scarce, but some experimental efforts have been done to help to untangle this 439 question. In a recent green-house experiment with the fast-cycling plant Brassica rapa, 440 Ramos & Schiestl (2019) found that although pollinators alone induce higher increase in 441 attractiveness traits than others treatments, the interaction between herbivores and 442 pollinators had great importance in changes in floral traits related to mating system over 443 few generations. In another experimental study, Santangelo, Thompson & Johnson (2018) 444 found that herbivores, not pollinators, are the main factor that drives selection of 445 reproductive traits in Trifolium repens. Therefore, the isolated and additive influence of 446 mutualistic and antagonistic interactions is not completely understood. Here, we found an 447 evidence of the important role of antagonists in mating system evolution. We argue that 448 futures studies focusing in the interactions between plant outcrossing rates and antagonist 449 pressure using a macroevolutionary approach could reveal this consistent positive 450 correlation, clearing the importance of natural enemies in the evolution of sexual 451 reproduction in plants.

452

453 CONCLUSIONS

454 The Red Queen hypothesis predict that antagonists’ pressure is the major driver of sexual 455 reproduction evolution. This hypothesis remains unexploited to vegetal kingdom. Using 456 a macroevolutionary analysis with two independent datasets, we found that insect

457 herbivores pressure is an effective predictor of plant outcrossing rates. Our results are an 458 important finding for the Red Queen highlight in plant science, since few studies had used 459 plants as model to test Red Queen dynamics, despite the great both reproductive strategies 460 diversity and natural enemies’ variety present in this group. Although the knowledge 461 about several biotic and abiotic factors that could have influence on plant mating system 462 evolution, the antagonists pressure demonstrates that has a determinant role, especially 463 for host-parasite that share long coevolutionary time. It must to be considered in future 464 studies that aim to understand evolutionary changes in outcrossing rates. Finally, the 465 evolution of plant mating system remains an intriguing question in vegetal biology and 466 macroevolutionary studies can revels still unclear patterns about the effects of ecological 467 and evolutionary forces behind the observable data.

468

469 ACKNOWLEDGEMENTS

470 Firstly, we thank all those researchers and institutions that generated the original raw data 471 used in this comparative study. We also thank Gustavo Brant Paterno and Vanessa 472 Graziele Staggemeier for their suggestions and critical contributions for this study. This 473 study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível 474 Superior - Brasil (CAPES) - Finance Code 001.

475

476 Authors’ contributions

477 CRF and TMAP conceived the central ideas. TMAP compiled both the outcrossing rates 478 and covariables data, performed the analysis and wrote the manuscript. CRF conceived 479 the methodological and analytical procedures. MMG and MB compiled and provided the 480 insect herbivores richness data. All authors contributed critically to the drafts and gave 481 final approval for publication.

482 Data availability

483 All data and R codes used in this paper are free and available in GitHub repository: 484 https://github.com/tales14/Paiva_et_al_2020_data. Raw data and codes used here are also 485 available for request to correspondence author.

486

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730 Figures and Tables

731

732 Figure 3. Phylogenetic regressions between outcrossing rates and hervivores richness. 733 (A) for 136 species of seed plants under two alternative evolutionary models. (B) only for 734 83 European-native species under two alternative evolutionary models. (C) Only for 53 735 exotic species under two alternative evolutinary models. Blue line: phylogenetic 736 regression under Ornstein-Uhlenbeck model. Red line: phylogenetic regression under 737 Brownian Motion model. Herbivores richeness values were extracted from a method to 738 correct the effort bias (see methods). 739

740

741 742 Figure 4. Distribution of outcrossing rate and vegetative variables. The categorical 743 variables: (A) outcrossing rate and life span. (B) outcrossing rate and growth form. In 744 both cases the points represent de mean value and bars represent the standard error. The 745 continuous variables: (C) outcrossing rate and maximum height. (D) outcrossing rate and 746 SLA. In both case the lines express the PGLS under Brownian Motion model (red) and 747 under Ornstein-Uhlenbeck (blue). 748

749 Table 1. Model averaging by AICc for Ornstein-Uhlenbeck models. Bold text marks 750 important models in explain outcrossing rate (tm) variation (delta < 2).

Model df logLik AICc delta weight tm ~ herbivores + life span 6 -13.13 38.91 0.00 0.39 tm ~ life_span 5 -14.43 39.33 0.41 0.32 tm ~ life_span + growth 10 -9.34 40.44 1.53 0.18 tm ~ herbivores + life_span + growth 11 -8.84 41.82 2.90 0.09 tm ~ herbivores + sla + height + life_span + growth 13 -8.67 46.32 7.40 0.01 tm ~ herbivores + sla + height 6 -25.71 64.07 25.16 0.00 tm ~ herbivores + height 5 -26.87 64.20 25.28 0.00 tm ~ sla + height 5 -27.06 64.57 25.66 0.00 tm ~ height 4 -28.55 65.41 26.50 0.00 tm ~ herbivores + growth 9 -23.34 66.12 27.21 0.00 tm ~ growth 8 -24.57 66.28 27.36 0.00 tm ~ herbivores + sla 5 -31.05 72.57 33.66 0.00 tm ~ sla 4 -32.97 74.25 35.34 0.00 tm ~ herbivores 4 -36.50 81.31 42.40 0.00 null 3 -39.92 86.04 47.12 0.00 751 752 Table 2. Model averaging by AICc for Brownian Motion models. Bold text marks 753 important models in explain outcrossing rate (tm) variation (delta < 2).

Model df logLik AICc delta weight tm ~ herbivores + life span 5 -83.40 177.26 0.00 0.86 tm ~ life_span 4 -86.48 181.27 4.01 0.12 tm ~ herbivores + sla + height + life_span + growth 12 -79.81 186.16 8.90 0.01 tm ~ herbivores + life_span + growth 10 -82.54 186.84 9.58 0.01 tm ~ life_span + growth 9 -85.28 189.99 12.73 0.00 tm ~ herbivores + sla 4 -94.92 198.14 20.87 0.00 tm ~ herbivores + sla + height 5 -94.39 199.23 21.97 0.00 tm ~ sla 3 -96.74 199.67 22.41 0.00 tm ~ sla + height 4 -96.31 200.92 23.66 0.00 tm ~ herbivores + height 4 -99.20 206.70 29.43 0.00 tm ~ herbivores 3 -100.40 206.98 29.72 0.00 tm ~ height 3 -102.55 211.29 34.03 0.00 null 2 -103.69 211.46 34.20 0.00 tm ~ herbivores + growth 8 -99.05 215.23 37.97 0.00 tm ~ growth 7 -102.00 218.88 41.62 0.00 754

755 Supporting information 756 Additional supporting information may be found in the online version of this article: 757 758 Appendix 1: Taxonomy, phylogeny and exploratory graphics. 759 Appendix 2: Phylogenetic models including the outcrossing rate populational variation.

Appendix 1: Taxonomy, phylogeny and exploratory graphics

Insect herbivores modulate outcrossing rates across seed plants

Tales Martins de Alencar Paiva Martin M. Gossner Martin Brändle Carlos Roberto Fonseca

Contents

1 Taxonomic informations 45 1.1 Species list ...... 45 1.2 Native-exotic status ...... 48 1.3 Checking taxonomy identity and synonymy ...... 51 1.4 Phylogeny ...... 55

2 Taxonomic distribution 56 2.1 Families sampled ...... 56

2.2 Distribution of outcrossing rate (tm)...... 57 2.3 Distribution of insect herbivores richness ...... 58 2.4 Distribution of vegetative traits ...... 60

3 Correcting the sampling eﬀort bias 61

4 References 61

44 1 Taxonomic informations

1.1 Species list

N Family Genus Species 1 Pinaceae Abies Abies_alba 2 Pinaceae Abies Abies_balsamea 3 Pinaceae Abies Abies_borisii-regis 4 Malvaceae Alcea Alcea_rosea 5 Amaranthaceae Amaranthus Amaranthus_caudatus 6 Amaranthaceae Amaranthus Amaranthus_hybridus_subsp._cruentus 7 Ranunculaceae Aquilegia Aquilegia_vulgaris 8 Brassicaceae Arabidopsis Arabidopsis_thaliana 9 Brassicaceae Arabis Arabis_alpina 10 Cactaceae Ariocarpus Ariocarpus_fissuratus 11 Poaceae Arrhenatherum Arrhenatherum_elatius 12 Apocynaceae Asclepias Asclepias_syriaca 13 Amaranthaceae Beta Beta_vulgaris 14 Asteraceae Bidens Bidens_pilosa 15 Boraginaceae Borago Borago_officinalis 16 Brassicaceae Brassica Brassica_napus 17 Poaceae Bromus Bromus_arvensis 18 Poaceae Bromus Bromus_hordeaceus 19 Poaceae Bromus Bromus_tectorum 20 Scrophulariaceae Buddleja Buddleja_davidii 21 Brassicaceae Cakile Cakile_maritima 22 Ericaceae Calluna Calluna_vulgaris 23 Brassicaceae Camelina Camelina_sativa 24 Theaceae Camellia Camellia_japonica 25 Theaceae Camellia Camellia_sinensis 26 Campanulaceae Campanula Campanula_rapunculoides 27 Asteraceae Carduus Carduus_acanthoides 28 Asteraceae Carduus Carduus_nutans 29 Pinaceae Cedrus Cedrus_atlantica 30 Pinaceae Cedrus Cedrus_libani 31 Asteraceae Centaurea Centaurea_cineraria 32 Asteraceae Centaurea Centaurea_solstitialis 33 Onagraceae Chamerion Chamerion_angustifolium 34 Asteraceae Cirsium Cirsium_palustre 35 Rosaceae Comarum Comarum_palustre 36 Rosaceae Crataegus Crataegus_crus-galli 37 Boraginaceae Cynoglossum Cynoglossum_officinale 38 Poaceae Cynosurus Cynosurus_cristatus

45 N Family Genus Species 39 Solanaceae Datura Datura_stramonium 40 Apiaceae Daucus Daucus_carota 41 Poaceae Deschampsia Deschampsia_cespitosa 42 Boraginaceae Echium Echium_plantagineum 43 Boraginaceae Echium Echium_vulgare 44 Apiaceae Eryngium Eryngium_alpinum 45 Fagaceae Fagus Fagus_sylvatica_var._atropunicea 46 Rosaceae Filipendula Filipendula_vulgaris 47 Rosaceae Fragaria Fragaria_vesca 48 Oleaceae Fraxinus Fraxinus_excelsior 49 Geraniaceae Geranium Geranium_pratense 50 Rosaceae Geum Geum_rivale 51 Rosaceae Geum Geum_urbanum 52 Fabaceae Glycine Glycine_max 53 Fabaceae Hedysarum Hedysarum_coronarium 54 Asteraceae Helianthus Helianthus_annuus 55 Poaceae Hordeum Hordeum_jubatum 56 Poaceae Hordeum Hordeum_vulgare 57 Balsaminaceae Impatiens Impatiens_capensis 58 Juglandaceae Juglans Juglans_mandshurica 59 Juglandaceae Juglans Juglans_regia 60 Lamiaceae Lamium Lamium_amplexicaule 61 Cupressaceae Larix Larix_decidua 62 Pinaceae Larix Larix_occidentalis 63 Fabaceae Lathyrus Lathyrus_latifolius 64 Fabaceae Lens Lens_culinaris 65 Poaceae Lolium Lolium_multiﬂorum 66 Malpighiaceae Malpighia Malpighia_emarginata 67 Malvaceae Malva Malva_moschata 68 Anacardiaceae Mangifera Mangifera_indica 69 Euphorbiaceae Manihot Manihot_esculenta 70 Fabaceae Medicago Medicago_polymorpha 71 Fabaceae Medicago Medicago_sativa 72 Fabaceae Medicago Medicago_truncatula 73 Euphorbiaceae Mercurialis Mercurialis_annua 74 Myrtaceae Myrtus Myrtus_communis 75 Lamiaceae Ocimum Ocimum_basilicum 76 Oleaceae Olea Olea_europaea 77 Asteraceae Onopordum Onopordum_illyricum 78 Papaveraceae Papaver Papaver_dubium 79 Papaveraceae Papaver Papaver_somniferum 80 Fabaceae Phaseolus Phaseolus_coccineus 81 Fabaceae Phaseolus Phaseolus_lunatus

46 N Family Genus Species 82 Xanthorrhoeaceae Phormium Phormium_tenax 83 Poaceae Phyllostachys Phyllostachys_edulis 84 Pinaceae Picea Picea_abies 85 Pinaceae Picea Picea_engelmannii 86 Pinaceae Picea Picea_glauca 87 Pinaceae Picea Picea_jezoensis 88 Pinaceae Picea Picea_mariana 89 Pinaceae Picea Picea_sitchensis 90 Pinaceae Pinus Pinus_banksiana 91 Pinaceae Pinus Pinus_contorta 92 Pinaceae Pinus Pinus_densiflora 93 Pinaceae Pinus Pinus_heldreichii 94 Pinaceae Pinus Pinus_parviflora 95 Pinaceae Pinus Pinus_pinaster 96 Pinaceae Pinus Pinus_ponderosa 97 Pinaceae Pinus Pinus_pungens 98 Pinaceae Pinus Pinus_resinosa 99 Pinaceae Pinus Pinus_rigida 100 Pinaceae Pinus Pinus_sibirica 101 Pinaceae Pinus Pinus_strobus 102 Pinaceae Pinus Pinus_sylvestris 103 Anacardiaceae Pistacia Pistacia_lentiscus 104 Fabaceae Pisum Pisum_sativum 105 Plantaginaceae Plantago Plantago_lanceolata 106 Plantaginaceae Plantago Plantago_major 107 Cupressaceae Platycladus Platycladus_orientalis 108 Primulaceae Primula Primula_vulgaris 109 Rosaceae Prunus Prunus_avium 110 Rosaceae Prunus Prunus_mahaleb 111 Pinaceae Pseudotsuga Pseudotsuga_menziesii 112 Fagaceae Quercus Quercus_petraea_subsp._petraea 113 Fagaceae Quercus Quercus_robur_subsp._robur 114 Orobanchaceae Rhinanthus Rhinanthus_angustifolius 115 Orobanchaceae Rhinanthus Rhinanthus_minor 116 Ericaceae Rhododendron Rhododendron_ferrugineum 117 Fabaceae Robinia Robinia_pseudoacacia 118 Lamiaceae Salvia Salvia_pratensis_subsp._pratensis 119 Rosaceae Sanguisorba Sanguisorba_officinalis 120 Caprifoliaceae Scabiosa Scabiosa_columbaria 121 Sciadopityaceae Sciadopitys Sciadopitys_verticillata 122 Asteraceae Senecio Senecio_squalidus 123 Asteraceae Senecio Senecio_vulgaris 124 Asteraceae Silybum Silybum_marianum

47 N Family Genus Species 125 Rosaceae Sorbus Sorbus_domestica 126 Poaceae Sorghum Sorghum_bicolor 127 Cupressaceae Thuja Thuja_occidentalis 128 Cupressaceae Thuja Thuja_plicata 129 Lamiaceae Thymus Thymus_vulgaris 130 Asteraceae Tragopogon Tragopogon_dubius 131 Fabaceae Trifolium Trifolium_subterraneum 132 Pinaceae Tsuga Tsuga_heterophylla 133 Ulmaceae Ulmus Ulmus_laevis 134 Scrophulariaceae Verbascum Verbascum_thapsus 135 Fabaceae Vicia Vicia_faba 136 Asparagaceae Yucca Yucca_ﬁlamentosa

1.2 Native-exotic status

We considered as “Native” all European species. Non-European species are considered as “Exotic”. The source about the native continent information is the Source column.

N Species Status Source 1 Abies_alba Native gbif 2 Abies_balsamea Exotic gymnosperm database 3 Abies_borisii-regis Native gymnosperm database 4 Alcea_rosea Native gbif 5 Amaranthus_caudatus Exotic missouri botanical garden 6 Amaranthus_hybridus_subsp._cruentus Exotic gbif 7 Aquilegia_vulgaris Native missouri botanical garden 8 Arabidopsis_thaliana Native gbif 9 Arabis_alpina Native missouri botanical garden 10 Ariocarpus_ﬁssuratus Exotic IUCN 11 Arrhenatherum_elatius Native gbif 12 Asclepias_syriaca Exotic gbif; missouri botanical garden 13 Beta_vulgaris Native missouri botanical garden 14 Bidens_pilosa Exotic gbif 15 Borago_oﬃcinalis Native gbif; missouri botanical garden 16 Brassica_napus Native gbif 17 Bromus_arvensis Native gbif 18 Bromus_hordeaceus Native invasive species compendium 19 Bromus_tectorum Native invasive species compendium 20 Buddleja_davidii Exotic missouri botanical garden 21 Cakile_maritima Native gbif 22 Calluna_vulgaris Native gbif 23 Camelina_sativa Native gbif

48 N Species Status Source 24 Camellia_japonica Exotic missouri botanical garden 25 Camellia_sinensis Exotic IUCN 26 Campanula_rapunculoides Native missouri botanical garden 27 Carduus_acanthoides Native gbif 28 Carduus_nutans Native gbif 29 Cedrus_atlantica Native gbif 30 Cedrus_libani Exotic missouri botanical garden 31 Centaurea_cineraria Native gbif 32 Centaurea_solstitialis Native gbif 33 Chamerion_angustifolium Exotic missouri botanical garden 34 Cirsium_palustre Native gbif 35 Comarum_palustre Exotic IUCN 36 Crataegus_crus-galli Exotic missouri botanical garden 37 Cynoglossum_oﬃcinale Native invasive species compendium 38 Cynosurus_cristatus Native gbif 39 Datura_stramonium Exotic gbif 40 Daucus_carota Native invasive species compendium 41 Deschampsia_cespitosa Exotic missouri botanical garden 42 Echium_plantagineum Native gbif 43 Echium_vulgare Native gbif 44 Eryngium_alpinum Native gbif 45 Fagus_sylvatica_var._atropunicea Native gbif 46 Filipendula_vulgaris Native gbif 47 Fragaria_vesca Native invasive species compendium 48 Fraxinus_excelsior Native missouri botanical garden 49 Geranium_pratense Native missouri botanical garden 50 Geum_rivale Exotic gbif 51 Geum_urbanum Native gbif 52 Glycine_max Native gbif 53 Hedysarum_coronarium Native gbif; IUCN 54 Helianthus_annuus Exotic gbif 55 Hordeum_jubatum Exotic IUCN 56 Hordeum_vulgare Native gbif; IUCN 57 Impatiens_capensis Exotic gbif; missouri botanical garden 58 Juglans_mandshurica Exotic gbif 59 Juglans_regia Native gbif 60 Lamium_amplexicaule Native gbif 61 Larix_decidua Native gbif 62 Larix_occidentalis Exotic gbif 63 Lathyrus_latifolius Native gbif; missouri botanical garden 64 Lens_culinaris Native gbif 65 Lolium_multiﬂorum Native gbif 66 Malpighia_emarginata Exotic gbif

49 N Species Status Source 67 Malva_moschata Native missouri botanical garden 68 Mangifera_indica Exotic gbif 69 Manihot_esculenta Exotic gbif 70 Medicago_polymorpha Native gbif 71 Medicago_sativa Native gbif 72 Medicago_truncatula Native gbif 73 Mercurialis_annua Native gbif 74 Myrtus_communis Native gbif 75 Ocimum_basilicum Exotic gbif 76 Olea_europaea Native gbif 77 Onopordum_illyricum Native gbif 78 Papaver_dubium Native gbif 79 Papaver_somniferum Native gbif 80 Phaseolus_coccineus Exotic gbif 81 Phaseolus_lunatus Exotic gbif 82 Phormium_tenax Exotic missouri botanical garden 83 Phyllostachys_edulis Exotic missouri botanical garden 84 Picea_abies Native gbif 85 Picea_engelmannii Exotic missouri botanical garden 86 Picea_glauca Exotic missouri botanical garden 87 Picea_jezoensis Exotic missouri botanical garden 88 Picea_mariana Exotic gymnosperm database 89 Picea_sitchensis Exotic gbif 90 Pinus_banksiana Exotic missouri botanical garden 91 Pinus_contorta Exotic gymnosperm database 92 Pinus_densiﬂora Exotic missouri botanical garden 93 Pinus_heldreichii Native gymnosperm database 94 Pinus_parviﬂora Exotic gymnosperm database 95 Pinus_pinaster Native gbif 96 Pinus_ponderosa Exotic missouri botanical garden 97 Pinus_pungens Exotic missouri botanical garden 98 Pinus_resinosa Exotic missouri botanical garden 99 Pinus_rigida Exotic gbif 100 Pinus_sibirica Exotic gymnosperm database 101 Pinus_strobus Exotic missouri botanical garden 102 Pinus_sylvestris Native gbif 103 Pistacia_lentiscus Native gbif 104 Pisum_sativum Native missouri botanical garden 105 Plantago_lanceolata Native invasive species compendium 106 Plantago_major Native invasive species compendium 107 Platycladus_orientalis Exotic missouri botanical garden 108 Primula_vulgaris Native missouri botanical garden 109 Prunus_avium Native missouri botanical garden

50 N Species Status Source 110 Prunus_mahaleb Native IUCN 111 Pseudotsuga_menziesii Exotic gbif; missouri botanical garden 112 Quercus_petraea_subsp._petraea Native gbif 113 Quercus_robur_subsp._robur Native gbif 114 Rhinanthus_angustifolius Native gbif 115 Rhinanthus_minor Native gbif 116 Rhododendron_ferrugineum Native gbif 117 Robinia_pseudoacacia Exotic gbif; missouri botanical garden 118 Salvia_pratensis_subsp._pratensis Native gbif 119 Sanguisorba_oﬃcinalis Native gbif 120 Scabiosa_columbaria Native gbif 121 Sciadopitys_verticillata Exotic missouri botanical garden 122 Senecio_squalidus Native gbif 123 Senecio_vulgaris Native global invasive species database 124 Silybum_marianum Native gbif 125 Sorbus_domestica Native gbif 126 Sorghum_bicolor Exotic missouri botanical garden 127 Thuja_occidentalis Exotic missouri botanical garden 128 Thuja_plicata Exotic missouri botanical garden 129 Thymus_vulgaris Native gbif 130 Tragopogon_dubius Native gbif 131 Trifolium_subterraneum Native gbif 132 Tsuga_heterophylla Exotic gbif; gymnosperm database 133 Ulmus_laevis Native gbif 134 Verbascum_thapsus Native missouri botanical garden 135 Vicia_faba Native gbif 136 Yucca_ﬁlamentosa Exotic missouri botanical garden

1.3 Checking taxonomy identity and synonymy

Since we collected species names from papers published in diﬀerent periods, and taxonomic names may change over the time, we checked possible synonyms in The Plant List database and put here the accepted name in nowdays. The “Collect_names” column refers those names collected in original papers. The “The_Plant_List_names” colunm refers the name matched and accepted in The Plant List database.

N Collected_name The_Plant_List_name 1 Abies_alba Abies alba 2 Abies_balsamea Abies balsamea 3 Abies_borisii-regis Abies borisii-regis 4 Alcea_rosea Alcea rosea 5 Amaranthus_caudatus Amaranthus caudatus

51 N Collected_name The_Plant_List_name 6 Amaranthus_hybridus_subsp._cruentus Amaranthus cruentus 7 Aquilegia_vulgaris Aquilegia vulgaris 8 Arabidopsis_thaliana Arabidopsis thaliana 9 Arabis_alpina Arabis alpina 10 Ariocarpus_fissuratus Ariocarpus fissuratus 11 Arrhenatherum_elatius Arrhenatherum elatius 12 Asclepias_syriaca Asclepias syriaca 13 Beta_vulgaris Beta vulgaris 14 Bidens_pilosa Bidens pilosa 15 Borago_officinalis Borago officinalis 16 Brassica_napus Brassica napus 17 Bromus_arvensis Bromus arvensis 18 Bromus_hordeaceus Bromus hordeaceus 19 Bromus_tectorum Bromus tectorum 20 Buddleja_davidii Buddleja davidii 21 Cakile_maritima Cakile maritima 22 Calluna_vulgaris Calluna vulgaris 23 Camelina_sativa Camelina sativa 24 Camellia_japonica Camellia japonica 25 Camellia_sinensis Camellia sinensis 26 Campanula_rapunculoides Campanula rapunculoides 27 Carduus_acanthoides Carduus acanthoides 28 Carduus_nutans Carduus nutans 29 Cedrus_atlantica Cedrus atlantica 30 Cedrus_libani Cedrus libani 31 Centaurea_cineraria Centaurea cineraria 32 Centaurea_solstitialis Centaurea solstitialis 33 Chamerion_angustifolium Epilobium angustifolium 34 Cirsium_palustre Cirsium palustre 35 Comarum_palustre Comarum palustre 36 Crataegus_crus-galli Crataegus crus-galli 37 Cynoglossum_officinale Cynoglossum officinale 38 Cynosurus_cristatus Cynosurus cristatus 39 Datura_stramonium Datura stramonium 40 Daucus_carota Daucus carota 41 Deschampsia_cespitosa Deschampsia cespitosa 42 Echium_plantagineum Echium plantagineum 43 Echium_vulgare Echium vulgare 44 Eryngium_alpinum Eryngium alpinum 45 Fagus_sylvatica_var._atropunicea Fagus grandifolia 46 Filipendula_vulgaris Filipendula vulgaris 47 Fragaria_vesca Fragaria vesca 48 Fraxinus_excelsior Fraxinus excelsior

52 N Collected_name The_Plant_List_name 49 Geranium_pratense Geranium pratense 50 Geum_rivale Geum rivale 51 Geum_urbanum Geum urbanum 52 Glycine_max Glycine max 53 Hedysarum_coronarium Hedysarum coronarium 54 Helianthus_annuus Helianthus annuus 55 Hordeum_jubatum Hordeum jubatum 56 Hordeum_vulgare Hordeum vulgare 57 Impatiens_capensis Impatiens capensis 58 Juglans_mandshurica Juglans mandshurica 59 Juglans_regia Juglans regia 60 Lamium_amplexicaule Lamium amplexicaule 61 Larix_decidua Larix decidua 62 Larix_occidentalis Larix occidentalis 63 Lathyrus_latifolius Lathyrus latifolius 64 Lens_culinaris Lens culinaris 65 Lolium_multiﬂorum Lolium multiﬂorum 66 Malpighia_emarginata Malpighia emarginata 67 Malva_moschata Malva moschata 68 Mangifera_indica Mangifera indica 69 Manihot_esculenta Manihot esculenta 70 Medicago_polymorpha Medicago polymorpha 71 Medicago_sativa Medicago sativa 72 Medicago_truncatula Medicago truncatula 73 Mercurialis_annua Mercurialis annua 74 Myrtus_communis Myrtus communis 75 Ocimum_basilicum Ocimum basilicum 76 Olea_europaea Olea europaea 77 Onopordum_illyricum Onopordum illyricum 78 Papaver_dubium Papaver dubium 79 Papaver_somniferum Papaver somniferum 80 Phaseolus_coccineus Phaseolus coccineus 81 Phaseolus_lunatus Phaseolus lunatus 82 Phormium_tenax Phormium tenax 83 Phyllostachys_edulis Phyllostachys edulis 84 Picea_abies Picea abies 85 Picea_engelmannii Picea engelmannii 86 Picea_glauca Picea glauca 87 Picea_jezoensis Picea jezoensis 88 Picea_mariana Picea mariana 89 Picea_sitchensis Picea sitchensis 90 Pinus_banksiana Pinus banksiana 91 Pinus_contorta Pinus contorta

53 N Collected_name The_Plant_List_name 92 Pinus_densiflora Pinus densiflora 93 Pinus_heldreichii Pinus heldreichii 94 Pinus_parviflora Pinus parviflora 95 Pinus_pinaster Pinus pinaster 96 Pinus_ponderosa Pinus ponderosa 97 Pinus_pungens Pinus pungens 98 Pinus_resinosa Pinus resinosa 99 Pinus_rigida Pinus rigida 100 Pinus_sibirica Pinus sibirica 101 Pinus_strobus Pinus strobus 102 Pinus_sylvestris Pinus sylvestris 103 Pistacia_lentiscus Pistacia lentiscus 104 Pisum_sativum Pisum sativum 105 Plantago_lanceolata Plantago lanceolata 106 Plantago_major Plantago major 107 Platycladus_orientalis Platycladus orientalis 108 Primula_vulgaris Primula vulgaris 109 Prunus_avium Prunus avium 110 Prunus_mahaleb Prunus mahaleb 111 Pseudotsuga_menziesii Pseudotsuga menziesii 112 Quercus_petraea_subsp._petraea Quercus petraea 113 Quercus_robur_subsp._robur Quercus robur 114 Rhinanthus_angustifolius Rhinanthus serotinus 115 Rhinanthus_minor Rhinanthus minor 116 Rhododendron_ferrugineum Rhododendron ferrugineum 117 Robinia_pseudoacacia Robinia pseudoacacia 118 Salvia_pratensis_subsp._pratensis Salvia pratensis 119 Sanguisorba_officinalis Sanguisorba officinalis 120 Scabiosa_columbaria Scabiosa columbaria 121 Sciadopitys_verticillata Sciadopitys verticillata 122 Senecio_squalidus Senecio squalidus 123 Senecio_vulgaris Senecio vulgaris 124 Silybum_marianum Silybum marianum 125 Sorbus_domestica Sorbus domestica 126 Sorghum_bicolor Sorghum bicolor 127 Thuja_occidentalis Thuja occidentalis 128 Thuja_plicata Thuja plicata 129 Thymus_vulgaris Thymus vulgaris 130 Tragopogon_dubius Tragopogon dubius 131 Trifolium_subterraneum Trifolium subterraneum 132 Tsuga_heterophylla Tsuga heterophylla 133 Ulmus_laevis Ulmus laevis 134 Verbascum_thapsus Verbascum thapsus

54 N Collected_name The_Plant_List_name 135 Vicia_faba Vicia faba 136 Yucca_ﬁlamentosa Yucca ﬁlamentosa

1.4 Phylogeny

The phylogeny adjusted for this study. We pruned this phylogeny by a recent published plant phylogeny (Smith and Brown, 2018).

Arabidopsis thaliana Chamerion angustifolium

Geranium pratense Cakile maritima Brassica napus Cynoglossum officinale Camelina sativa Malva moschata

Myrtus communis Arabis alpina Echium plantagineum Alcea rosea

Echium vulgare Borago officinalis LamiumOcimum amplexicauleThymus basilicum vulgaris

Rhinanthus angustifolius Pistacia lentiscus Mangifera indica Phaseolus coccineus Phaseolus lunatus Glycine max Robinia pseudoacacia Hedysarum coronarium Rhinanthus minor Lens culinaris Pisum sativum Lathyrus latifolius Vicia faba Verbascum thapsus Medicago polymorpha Medicago truncatula Medicago sativa Trifolium subterraneum Buddleja davidii Juglans mandshurica Juglans regia PlantagoPlantago lanceolata major Quercus robur subsp. robur Quercus petraea subsp. petraea Salvia pratensis subsp. pratensis Fagus sylvatica var. atropunicea Olea europaea Ulmus laevis Fraxinus excelsior Prunus mahaleb Prunus avium Sorbus domestica Asclepias syriaca Crataegus crus−galli Datura stramonium Filipendula vulgaris Geum rivale Helianthus annuus Geum urbanum Sanguisorba officinalis Bidens pilosa Fragaria vesca Senecio squalidus Comarum palustre Senecio vulgaris Malpighia emarginata Manihot esculenta Tragopogon dubius Mercurialis annua Centaurea cineraria Tsuga heterophylla Abies balsamea Centaurea solstitialis Abies alba Cirsium palustre Abies borisii−regis Silybum marianum Cedrus atlantica Cedrus libani Carduus nutans Picea glauca Picea engelmannii Carduus acanthoides Picea mariana Onopordum illyricum Picea sitchensis Picea abies Picea jezoensis Campanula rapunculoides Scabiosa columbaria Pinus strobus Daucus carota Pinus parviflora Pinus sibirica Eryngium alpinum Pinus ponderosa Primula vulgaris Pinus pungens Pinus rigida Pinus contorta Camellia japonica Pinus banksiana Camellia sinensis Pinus pinaster Pinus sylvestris Calluna vulgaris Pinus resinosa Pinus densiflora Pinus heldreichii Larix decidua Larix occidentalis Pseudotsuga menziesii Thuja plicata Thuja occidentalis Platycladus orientalis Sciadopitys verticillata Impatiens capensis Yucca filamentosa Phormium tenax Phyllostachys edulis

RhododendronAriocarpus ferrugineum fissuratusBeta vulgaris

Amaranthus caudatusPapaver dubium Papaver somniferum AquilegiaSorghum vulgaris bicolor

Lolium multiflorum

Cynosurus cristatus Amaranthus hybridus subsp. cruentus Hordeum jubatumHordeum vulgareBromus arvensis Bromus tectorum

Arrhenatherum elatius Bromus hordeaceus Deschampsia cespitosa

55 2 Taxonomic distribution

2.1 Families sampled

Group 25 Eudicot Gimnosperm 20 Monocot

10 Number of species

0 Poaceae Pinaceae Apiaceae Oleaceae Fabaceae Fagaceae Theaceae Ericaceae Ulmaceae Rosaceae Myrtaceae Cactaceae Malvaceae Lamiaceae Asteraceae Solanaceae Onagraceae Primulaceae Geraniaceae Brassicaceae Apocynaceae Boraginaceae Juglandaceae Papaveraceae Caprifoliaceae Asparagaceae Cupressaceae Malpighiaceae Anacardiaceae Euphorbiaceae Balsaminaceae Plantaginaceae Ranunculaceae Amaranthaceae Orobanchaceae Campanulaceae Sciadopityaceae Scrophulariaceae Xanthorrhoeaceae

Species are distributed into 39 families of seed plants.

56 2.2 Distribution of outcrossing rate (tm)

2.2.1 Across families and species

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3 Outcrossing rate (tm) Outcrossing rate 0.2

0.1

0.0 Poaceae Apiaceae Pinaceae Oleaceae Fabaceae Fagaceae Theaceae Ericaceae Ulmaceae Rosaceae Myrtaceae Cactaceae Malvaceae Lamiaceae Asteraceae Solanaceae Onagraceae Primulaceae Geraniaceae Brassicaceae Apocynaceae Boraginaceae Juglandaceae Papaveraceae Caprifoliaceae Asparagaceae Cupressaceae Malpighiaceae Anacardiaceae Euphorbiaceae Balsaminaceae Plantaginaceae Ranunculaceae Amaranthaceae Orobanchaceae Campanulaceae Sciadopityaceae Scrophulariaceae Xanthorrhoeaceae

1.0

0.9

0.8

0.7

0.6

0.5

0.4

Outcrossing rate (tm) Outcrossing rate 0.3

0.2

0.1

0.0

Species

This distribution shows that there are values sampled for almost all tm gradient. Vertical bars represents the standard error for species that had more than one tm value. Both red and

57 blue dashed lines represents the threshold of classical mating systems classiﬁcation, where with a tm above 0.8 species can be classiﬁed as ‘outcrosser’ and below 0.2 as ‘selfer’ (Schemske & Lande, 1985).

2.3 Distribution of insect herbivores richness

2.3.1 Across families

1 Insect herbivores richness (log) Insect herbivores

0 Poaceae Pinaceae Apiaceae Oleaceae Fagaceae Fabaceae Theaceae Ericaceae Ulmaceae Rosaceae Myrtaceae Cactaceae Malvaceae Lamiaceae Asteraceae Solanaceae Onagraceae Primulaceae Geraniaceae Brassicaceae Apocynaceae Boraginaceae Juglandaceae Papaveraceae Caprifoliaceae Asparagaceae Cupressaceae Malpighiaceae Anacardiaceae Euphorbiaceae Balsaminaceae Plantaginaceae Ranunculaceae Amaranthaceae Orobanchaceae Campanulaceae Sciadopityaceae Scrophulariaceae Xanthorrhoeaceae

58 2.3.2 Across species

1 Insect herbivores richness (log) Insect herbivores

The distribution of insect herbivores richness across species varies from species with had only one associated herbivore recorded, such as Eryngium alpinum, to those with varies herbivores associated. The higher value in our data base is for Quercus robur, that has 229 species of insect herbivores recorded. Because this high range, we present herbivore distribuion across plant families and species using log transformation.

59 2.4 Distribution of vegetative traits

60 A 100 B 60 50 91 80 40 60 20 10 10

40 Frenquency 2 4 0

Frenquency 34 20 Herb Liana Shrub Tree 11 Graminoid 0 Hemiparasite Annual Biennial Perennial Life span Growth form

C 20 D 15

15 10

Frenquency 5 Frenquency

0 0 0.5 1.0 1.5 −1 0 1 2 SLA (log) Maximum height (log)

60 3 Correcting the sampling eﬀort bias 2.0 1.5 1.0 0.5 Herbivores richness (log) Herbivores 0.0

3 4 5

Number of occurrences (log)

Linear regression between Number of occurrences and Herbivore richness. Occurrences data were collected in gbif (see methods in the paper).

4 References

Schemske DW, Lande R. 1985. The evolution of self-fertilization and inbreeding depression in plants. II. Empirical observations. Evolution 39:41–52 Smith, S. A., & Brown, J. W. (2018). Constructing a broadly inclusive seed plant phylogeny. American Journal of Botany, 105(3), 302–314. doi: 10.1002/ajb2.1019

61 Appendix 2: Phylogenetic models including the outcrossing rate populational variation

Insect herbivores modulate outcrossing rates across seed plants

Tales Martins de Alencar Paiva Martin M. Gossner Martin Brändle Carlos Roberto Fonseca

Contents

1 Methods 63 1.1 Outcrossing rate populational variation ...... 63

1.2 Phylogenetic models using tm with populational variation ...... 63 1.3 R codes ...... 64

2 Results 68 2.1 Insect herbivores richness ...... 68 2.2 Simple models ...... 69 2.3 Full model ...... 70

3 Conclusion 72

4 References 72

62 1 Methods

1.1 Outcrossing rate populational variation

For each plant species whose outcrossing rate (hereafter tm) was calculated from more than one population in source paper, we collected tm value for each population. There are no records for both insect herbivores richness and vegetative traits at populational level, matching with the same populations where tm was estimated. Therefore, in our final database we had tm at populational level and both herbivores richness and the vegetative traits at specie level. Outcrossing rate is a metric that can have wide variation among populations (Barrett & Husband, 1990; Whitehead et al. 2018). Thus, this population-level approach is a way to ensuring a more embracing view of the variation of tm within the species, avoiding the issues of use the mean value. Here, we considered that all populations of a same species are under the same herbivory pressure, that is, having the same insect herbivores richness. The vegetative traits were also at species level, being the same for all populations. Although only tm be at population level, this approach will be enough to include sensibility to populational variations in our models. Some species had more values of tm registered just because they are sampled in more populations than others. For example, for herbaceous Cirsium palustre we had studies to seven different populations with tm ranging from 0.44 to 0.98. Differently, for herbaceous Malva moschata we had tm data just for one population with value of 0.64. Therefore, the real populational variations is not known in less sampled species. Building our database in this way, we are able to perform analysis using multiple tm for species and, thus, including populational variations in our analyses. We ran all models (Table S1) using this aproach.

Table S1: Models built including outcrossing rate popualtional variation

Models Type

tm ~ herbivores Simple tm ~ sla Simple tm ~ maximum_height Simple tm ~ life_span Simple tm ~ growth_form Simple tm ~ herbivores + sla + maximum_height + life_span + growth_form Full

1.2 Phylogenetic models using tm with populational variation

Since tm (our response variable) had more than one value for specie, our ﬁrst step to proceed the analysis was to build a database with a single value per species. To avoid the populational bias by the using of mean tm values, we build 10000 databases with a single value per species, randomizing tm for the species with data collected in more than one population. All the remaining variables (herbivores richness and vegetative traits) remained with the same values/categories in all databases. Since we built multiples databases, we ran all PGLS

63 models (Table 1) for each of these databases. Therefore, in the end we obtained 10000 results for each model. To access these results, we extracted all p-values and constructed a distribution of probabilities by each model, to detect if there were signiﬁcative (< 0.05) results in any of the randomized databases. We compare these models analyzing the distribution of probabilities of each of them. No information criterions-based ranks were used. All analysis was performed in R (R Core Team, 2019).

1.3 R codes

1.3.1 Packages library(readxl) library(knitr) library(dplyr) library(ape) library(geiger) library(sensiPhy) library(phylolm)

1.3.2 Cretating 10,000 databases with single tm value per species data <- read_excel("data/dataset_complete.xlsx") dat <- select(data, Binomial, life_span, growth_form, sla, max_height, herbivores, tm) dat_rnd <- lapply(1:10000, function(x) dat %>% group_by(Binomial, life_span, growth_form, sla, max_height, herbivores) %>% sample_n(1)) length(dat_rnd)

## [1] 10000

64 Examples of tmvariation between two datasets from 10,000. The diﬀerences in tm values observed in dat.rnd[[1]] and dat_rnd[[5000]] are due sampling in diﬀerent populations. Species sampled in one population have the same tm value in all 10,000 databases.

# dat_rnd[[1]] kable(data.frame(Species = head(dat_rnd[[1]]$Binomial), tm = head(dat_rnd[[1]]$tm)))

Species tm Abies_alba 0.896 Abies_balsamea 0.870 Abies_borisii-regis 0.940 Alcea_rosea 0.979 Amaranthus_caudatus 0.173 Amaranthus_hybridus_subsp._cruentus 0.310

# dat_rnd[[5000]] kable(data.frame(Species = head(dat_rnd[[5000]]$Binomial), tm = head(dat_rnd[[5000]]$tm)))

Species tm Abies_alba 0.896 Abies_balsamea 0.990 Abies_borisii-regis 0.940 Alcea_rosea 0.972 Amaranthus_caudatus 0.173 Amaranthus_hybridus_subsp._cruentus 0.310

1.3.3 Preparing phylogeny and databases

# Importing phylogeny fil <- read.tree("phylogeny/plants_phy.tre")

# Creating comparative object dat_com <- match_dataphy(formula = tm ~ herbivores, data = as.data.frame(dat_rnd[[1]]), phy = fil)

65 1.3.4 Phylogenetic models

Each of these codes create a list of 10,000 models, using the list of 10,000 databases dat_rnd. Here, we showed only the codes for Ornstein-Uhlenbeck evolutionary model. To ﬁt these models under Brownian Motion in the phylolm package the term "OUfixedRoot" must to be replaced by "BM".

# Simple models

pgls.her <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ herbivores, data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot")) pgls.sla <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ log10(sla), data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot")) pgls.max <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ log10(max_height), data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot")) pgls.lif <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ life_span, data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot")) pgls.gro <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ growth_form, data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot"))

# Full model pgls.full <- lapply(dat_rnd, function(x)phylolm(formula = tm ~ herbivores + sla + max_height + life_span + growth_form, data = as.data.frame(x), phy = dat_com$phy, model = "OUfixedRoot"))

66 1.3.5 Extracting results of each list of models

This code extracts p-values from each of the 10,000 models. Here we represent only the code for pgls.her list. To extract the results from the other lists the same synthax is obeyed.

# pgls.her

for (i in 1:length(pgls.her)) { HERBIV <- coef(summary(pgls.her[[i]]))["herbivores",4] for (i in 1:length(pgls.her)) { HERBIV[i] <- coef(summary(pgls.her[[i]]))["herbivores",4] p_her <- as.data.frame(HERBIV) } }

length(p_her$HERBIV)

## [1] 10000

head(p_her$HERBIV)

## [1] 0.000180680 0.000109950 0.015112325 0.000279349 0.000014000 0.000704509

67 2 Results

2.1 Insect herbivores richness

For each model, we had 10000 results of PGLS performed in 10000 different randomized datasets (see methods). To make it more viewable, we plotted all p-values of each models in boxplots. First, for the Ornstein-Uhlenbeck evolutionary model, herbivores richness significance ranged from 0.000 to 0.084 (mean = 0.003) and showed significant results (< 0.05) for 9996 datasets (Figure S1). Secondly, for the Brownian Motion model the significance ranged from 0.000 to 0.306 (mean = 0.01) and showed significant results for 9367 datasets results for 9367 datasets (Figure S1). Therefore, the effect of tm populational variation is evident in our analysis, but in a general way not change our results, insect herbivores richness remains a good predictor of tm variation.

Figure S1. Results of hervibores simple model (pgls.her) from the 10000 randomized datasets for both Ornstein-Uhlenbeck (PGLS - OU) and Brownian Motion (PGLS - BM)

68 evolutionary models. Horizontal blue line indicates the signiﬁcance threshold (0.05). (A) Boxplot for OU results. (B) Scatterplot for OU results. (C) Boxplot for BM results. (D) Scatterplot for BM results. For OU models 99.96% and for BM models 93.67% were below the signiﬁcance threshold.

2.2 Simple models

2.2.1 Ornstein-Uhlenbeck

Plotting all simple OU models, we found that SLA and maximum height had all results below significance threshold (Figure S2). In categorical vegetative traits, we found that: for life span, Biennials differed from Annuals in 8977 databases and Perennials differed from Annuals plants in all results (Figure S2). For growth form analysis, only Trees differed from Graminoids in all databases (Figure S2). Shrubs only differed from Graminoids in 1750 databases. These results are in accordance with those founded in analysis using only mean tm values, where herbivores richness, SLA and maximum height are highly significant in explain tm variation.

Figure S2. Results of all simple models for OU. HERB = Herbivores richness, SLA = Speciﬁc Leaf Area, MAX = Maximum height, BIE = Life span-Biennial, PER = Life span-Perennial, HEM = Growth form-Hemiparasite, HER = Growth form-Herb, LIA = Growth form-Liana, SHR = Growth form-Shrub, TRE = Growth form-Tree.

69 2.2.2 Brownian Motion

For the BM models, we found that herbivores remains a good predictor fot tm variation. However, the vegetative traits, that were highly signiﬁcative in OU models, had less eﬀects as predictor. We found that SLA has the higher number of signifcant results (Figure S3). The slopes of SLA models were negative, corroborating with the results using mean tm.

Figure S3. Results of all simple models for BM. HERB = Herbivores richness, SLA = Speciﬁc Leaf Area, MAX = Maximum height, BIE = Life span-Biennial, PER = Life span-Perennial, HEM = Growth form-Hemiparasite, HER = Growth form-Herb, LIA = Growth form-Liana, SHR = Growth form-Shrub, TRE = Growth form-Tree.

2.3 Full model

2.3.1 Ornstein-Uhlenbeck

Plotting the full OU model, that contains all fixed variables, we found that herbivores richness, SLA, maximum height and growth form effective significant results disappeared (Figure S4). Life span was the only variable that remains significant in most of databases. Biennials differed from Annuals in 8112 database and Perennials differed from Annuals in 9976 (Figure S4).

70 Figure S4. Results of the full OU model, that includes all ﬁxed variables. F_HERB = Her- bivores richness, F_SLA = Speciﬁc Leaf Area, F_MAX = Maximum height, F_BIE = Life span-Biennial, F_PER = Life span-Perennial, F_HEM = Growth form-Hemiparasite, F_HER = Growth form-Herb, F_LIA = Growth form-Liana, F_SHR = Growth form- Shrub, TRE = Growth form-Tree.

2.3.2 Brownian Motion

Plotting BM full model, we found that herbivores richness and SLA remains the variables with higher number of signiﬁcant results (Figure S5). This result matches with that found in simple BM models (Figure S3).

71 Figure S5. Results of the full BM model, that includes all ﬁxed variables. F_HERB = Herbivores richness, F_SLA = Speciﬁc Leaf Area, F_MAX = Maximum height, F_BIE = Life span-Biennial, F_PER = Life span-Perennial, F_HEM = Growth form- Hemiparasite, F_HER = Growth form-Herb, F_LIA = Growth form-Liana, F_SHR = Growth form-Shrub, TRE = Growth form-Tree.

3 Conclusion

The outcrossing rate populational variation may cause change in phylogenetic model results in both Brownian Motion and Ornstein-Uhlenbeck scenarios. Although these changes, we found support for the Red Queen hypothesis, where herbivores richness is eﬀective in predict plant outcrossing rate, just like in results obtained in models without population variation (using only mean tm). Therefore, we can argue that our results about the Red Queen dynamics in vegetable kingdom are robust to intraspecifc variations in plant outcrossing rate.

4 References

Barrett, S. C. H., and B. C. Husband. 1990. Variation in Outcrossing Rates in Eichhornia paniculata: The Role of Demographic and Reproductive Factors. R Core Team (2019). R: A language and environment for statistical computing. R Founda- tion for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Whitehead, M. R., R. Lanfear, R. J. Mitchell, and J. D. Karron. 2018. Plant Mating Systems Often Vary Widely Among Populations. Front. Ecol. Evol. 6:1–9.