UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA
JÉFERSON BUGONI
REDES PLANTA-POLINIZADOR NOS TRÓPICOS: LIÇÕES DAS INTERAÇÕES PLANTA – BEIJA-FLOR
PLANT-POLLINATOR NETWORKS IN THE TROPICS: LESSONS FROM HUMMINGBIRD-PLANT INTERACTIONS
CAMPINAS
2017
JÉFERSON BUGONI
REDES PLANTA-POLINIZADOR NOS TRÓPICOS: LIÇÕES DAS INTERAÇÕES PLANTA – BEIJA-FLOR
PLANT-POLLINATOR NETWORKS IN THE TROPICS: LESSONS FROM HUMMINGBIRD-PLANT INTERACTIONS
Tese apresentada ao Instituto de Biologia da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do Título de Doutor em Ecologia.
Thesis presented to the Institute of Biology of the University of Campinas in partial fulfillment of the requirements for the degree of Doctor in Ecology.
ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA TESE DEFENDIDA PELO ALUNO JÉFERSON BUGONI E ORIENTADA PELA DRA. MARLIES SAZIMA.
Orientadora: MARLIES SAZIMA
Co-Orientador: BO DALSGAARD
CAMPINAS
2017
Campinas, 17 de fevereiro de 2017.
COMISSÃO EXAMINADORA
Profa. Dra. Marlies Sazima
Prof. Dr. Felipe Wanderley Amorim
Prof. Dr. Thomas Michael Lewinsohn
Profa. Dra. Marina Wolowski Torres
Prof. Dr. Vinícius Lourenço Garcia de Brito
Os membros da Comissão Examinadora acima assinaram a Ata de Defesa, que se encontra no processo de vida acadêmica do aluno.
DEDICATÓRIA
À minha família por me ensinar o amor à natureza e a natureza do amor.
Ao povo brasileiro por financiar meus estudos desde sempre, fomentando assim meus sonhos. EPÍGRAFE
“Understanding patterns in terms of the processes that produce them is the essence of science […]”
Levin, S.A. (1992). The problem of pattern and scale in ecology. Ecology 73:1943–1967. AGRADECIMENTOS
Manifestar a gratidão às tantas pessoas que fizeram parte direta ou indiretamente do processo que culmina nesta tese não é tarefa trivial. Inicialmente, agradeço aos muitos professores, familiares e amigos que desde a infância incentivaram a trilhar o caminho dos sonhos distantes e abstratos, sem perder de vista a honestidade;
Agradeço a todos os conhecidos e desconhecidos que por cerca de 23 anos permitiram que eu estudasse em escolas públicas. Agradeço especialmente às pessoas simples que humildemente pagaram com seu tempo e suor os impostos que financiaram meus estudos. Espero poder retribuir seus esforços com meu trabalho contínuo para um futuro melhor para todos
À querida orientadora Marlies, pela orientação, confiança e apoio ao meu trabalho; pelo suporte logístico, psicológico e intelectual; pelo carinho e humildade que tanto me admira e referencia; e pela compreensão às razões pessoais que motivaram minha frequente distância física do lab;
Ao coorientador e amigo Bo Dalsgaard pela orientação, amizade e estímulo às big ideas; por facilitar minha estadia e me receber no Center for Macroecology Evolution and Climate da Universidade de Copenhague; e pelos diversos jantares e cervejas, permeados de ciência e brincadeiras com a lille Fifi;
Ao grande amigo Pietro pela ajuda que foi imprescindível para a coleta intensiva dos dados, o transporte para o (e no) campo; por muitas das ideias, críticas e por ser co-responsável por parte expressiva dos eventuais méritos deste trabalho. Agradeço-o especialmente pela companhia na cerveja e papo furado dos fins-de-dia na floresta tropical chuvosa ou na gélida e cinza Escandinávia. Grande pesquisador e imenso amigo;
À Mardiore Pinheiro por estimular e facilitar a oportunidade de ingresso na Unicamp.
Aos amigos dinamarqueses, colegas de campo no Brasil e Dinamarca, Jesper Sonne e Peter Kyvsgaard;
Ao Matthias Schleuning e Bob O’hara pela sugestões analíticas do capítulo 3 e ao Jesper pela fundamental implementação de parte das análises; Aos diversos coautores dos capítulos desta tese, cujos nomes estão mencionados em cada capítulo específico;
A Grete e Jesper pela estadia em Copenhague e por proporcionarem dias incríveis nos interstícios do frio nórdico;
Aos amigos funcionários do Núcleo Santa Virgínia pela prestatividade e companheirismo, especialmente João Paulo, Wyll, Crau, Cristiano, Fernanda, Isa, Lucimara, Luciano, Hélio, Andressa, Zaira, Francisca, Luís, Valdair, Vanderlei, Valdir, Vagner, Fernando, Maria;
A Fundação Florestal pela autorização para realização da coleta de parte dos dados desta tese no Parque Estadual da Serra do Mar;
Agradeço ao meu pai (Valdir) e minha mãe (Salete) pelo incentivo aos estudos e pelo inspirador encantamento com as surpresas da natureza; Além disso, por me ensinarem com seus exemplos de vida que humildade, honestidade e trabalho árduo não são um atalho, mas o único caminho para a realização pessoal e consequentemente a felicidade plena;
Aos meus irmãos Dudu, Mila, Leandro e Eliane por compartilharem tantos momentos bons (e também os não tão bons) na minha vida;
Amigos ecólogos da casa em Barão Geraldo, especialmente Alexandre Neutzling, André Rech, Camila Islas, Carlos Coquinho Nunes, Carlos Tomate Tonhatti, Felipe Amorim, Gastão Rodrigues, Maraísa Braga, Leo Ré Jorge, Marcelo Moro, Pietro Mendonça, Samuel Schnnor e Vinicius Brito.
Aos colegas de mestrado e doutorado por tantas discussões produtivas sobre ciência e outras banalidades;
Aos irmãos por parte de Marlies com os quais tive o privilégio de conviver, especialmente, André Rech, Carlos Coquinho Nunes, Felipe Amorim, Fernanda Leite, Kayna Agostini, Leandro Freitas, Lorena Fonseca, Paulo Eugênio Oliveira, Pedro Bérgamo, Pietro Maruyama Mendonça, Vini Brito, Márcia Rocca, Mardiore Pinheiro, Marina Wolowski, Nathália Streher e Ruben Ávila;
Aos professores do PPG em Ecologia da Unicamp, dos curso de campo ou de temas específicos pelas críticas, ensinamentos sobre ecologia, ciência e análises; especialmente agradeço a André Freitas, Fernando Martins, Gustavo Romero, Ivan Sazima, José Roberto Trigo, Martín Pareja, Paulo Guimarães, Paulo Oliveira, Rafael Oliveira, Sérgio Furtado dos Reis, Thomas Lewinsohn, Wesley Silva e Woodruff Benson;
A Diego Vázquez, Luciano Cagnolo e Natasha Chacoff pela oportunidade de aprendizado no curso de redes na Argentina em 2012, a qual impulsionou minha carreira;
Ao PPG em Ecologia da Unicamp por proporcionar tantas oportunidades de aprendizado e o contato com pesquisadores excelentes do mundo inteiro;
À Unicamp pelo ensino público, gratuito e de qualidade;
À CAPES pela bolsa regular de doutorado e a bolsa PDSE (008012/2014-08);
Pedro Lorenzo pela ilustração que abre cada capítulo e a figura 1 do capítulo 3;
Em suma, gostaria de manifestar minha sincera e imensa gratidão a todos que participaram deste processo, sejam eles mencionados diretamente aqui ou não;
Por fim, preciso mencionar minha gratidão a diversos pesquisadores que me estimularam a estudar mutualismos usando a abordagem de redes complexas. Seus artigo e livros - ou mesmo conversas - transmitiram a mim o fascínio por esta parte da ciência e seu potencial para o entendimento de interações em comunidades ecológicas. Agradeço por exemplificarem como um trabalho científico pode influenciar a vida de outras pessoas, mesmo aquelas com quem nunca tivermos contato direto. Esta é uma das partes mais fascinantes e estimulantes da vida de cientista que escolhi.
RESUMO
A abordagem de redes complexas tem permitido avanços rápidos no entendimento das interações entre populações de espécies em comunidades, incluindo a descrição dos padrões e processos determinantes das interações. Apesar do crescente acúmulo de estudos sobre redes mutualísticas planta-polinizador, estudos em regiões tropicais são mais escassos, especialmente os que investigam os processos que determinam tais interações. Neste sentido, esta tese contribui para o avanço no entendimento das redes de interações planta-polinizador, com particular ênfase em comunidades Neotropicais. No capítulo 1, realizamos uma revisão da distribuição global de estudos sobre redes de polinização, bem como dos padrões e processos determinantes destas redes, com foco no entendimento de lacunas de investigação e nas diferenças das redes em regiões tropicais. Nos capítulos 2 e 3, utilizamos redes mutualísticas planta − beija-flor como um sistema-modelo. No capítulo 2, investigamos como o esforço de amostragem influencia na detecção de padrões de interação e na compreensão dos processos estruturadores de interações. Utilizamos para isso, dados coletados intensivamente ao longo de dois anos em uma comunidade de Floresta Atlântica Montana no sudeste do Brasil. No capítulo 3 expandimos estudos prévios para uma escala macroecológica através da análise de um banco de dados de 25 redes de interações quantitativas distribuídas do México ao sul do Brasil, que possuíam dados de morfologia, fenologia e abundância para cada espécie. De forma ampla, esta tese aponta lacunas geográficas e metodológicas no estudo de redes planta-polinizador nos trópicos, bem como discute generalizações sobre padrões de interação e seus processos estruturadores (capítulo 1); indica métricas robustas ao esforço para a descrição da estrutura das interações e argumenta que, no sistema estudado, a importância de atributos como estruturadores de interações em sistemas especializados pode ser identificada mesmo com esforço amostral relativamente baixo (capítulo 2); e que restrições impostas por desacoplamentos na morfologia e fenologia (mais do que a chance de encontro baseada em abundância) são processos dominantes na determinação das interações planta – beija-flor nas Américas, os quais são pouco influenciados por clima, heterogeneidade topográfica e riqueza de espécies, e que promovem estruturas similares nas redes (capítulo 3). Em suma, demonstramos que as interações entre plantas e beija-flores são estruturadas fundamentalmente por restrições impostas por nicho, o que argumentamos ocorrer devido à (frequente) alta diversidade de atributos (e.g. variação em morfologias e fenologias). Finalmente, expandimos um modelo que prevê a formação de um continuum na importância de processos baseados em nicho e neutralidade na estruturação das interações em comunidades e apresentamos evidências de que este continuum depende da diversidade de atributos funcionais na assembleia. Sugerimos que outros sistemas com alta diversidade funcional localizam-se neste mesmo extremo do continuum, i.e. onde processos baseados em nichos são determinantes fundamentais das interações. Este é provavelmente o caso de diversos grupos especializados de polinizadores e plantas nos trópicos, tais como observado aqui para beija-flores e suas flores.
ABSTRACT
Complex networks approach has promoted fast advances in the understanding of species interactions in communities, including the description of patterns and processes structuring interactive assemblages. Despite of the accumulation of studies on plant-pollinator networks, such investigations on tropical regions are still scarce, especially on the determinants of such interactions. Here we aimed to advance the understanding of tropical plant-pollinator networks in the Tropics. In the first chapter, we review the global distribution of studies on pollination networks and their patterns and structuring processes, with particular focus to understand research gaps and potential differences in tropical networks. In the next chapters we focus on mutualistic plant-hummingbirds networks as a ‘model system’. In the second chapter, we investigated the influences of sampling effort on the detection of patterns of interaction and on the identification of the main structuring processes. For that, we sampled a Montane Atlantic Rainforest community in Southeastern Brazil intensively along two years. In the third chapter, we expand our previous studies to marcroecological scale by analyzing a unique dataset of 25 quantitative networks collected from Mexico to Southern Brazil, encompassing data on species morphology, phenology and abundances. Based on our findings, we indicate geographical and methodological gaps on the studies of pollination networks in the tropics and discussed recurrent patterns and their structuring processes (chapter 1); we point out that quantitative metrics tend to be more robust to sampling effort, and we report that the importance of traits as determinants of interactions in this specialized system is evident even under little sampling effort (chapter 2). We present evidences that constraints imposed by morphological and phenological mismatching (more that random meeting driven by species abundances) are dominant processes structuring plant- hummingbird networks throughout Americas; overall, climate, topographic heterogeneity and species richness have few influence on the importance of these processes, and produce similar network structures (chapter 3). In sum, we demonstrate that plant-hummingbird networks are fundamentally structured by constraints imposed by some dimensions of the species niches, which we argue to be linked to the high trait diversity in the assemblages (i.e. variation in morphology and phenology). Finally, we expand a model which hypothesizes a continuum of importance from niche- to neutral-based processes as drivers of species interactions in communities. We present evidences that this continuum depends of the variation in species traits within assemblages. We suggest that other systems encompassing high functional diversity are located at this extreme of the continuum in which niche based-processes tend to be the most important drivers of species interactions. This is likely the case of several plants and pollinators communities in the tropics, as we observed here for hummingbirds and their plants.
SUMÁRIO
RESUMO ...... 10 ABSTRACT ...... 12 SUMÁRIO ...... 14 INTRODUÇÃO GERAL ...... 15 CAPÍTULO 1 ...... 18 CAPÍTULO 2 ...... 66 CAPÍTULO 3 ...... 118 CONSIDERAÇÕES FINAIS ...... 170 REFERÊNCIAS ...... 173 ANEXOS ...... 175
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INTRODUÇÃO GERAL
“It is interesting to contemplate an entangled bank, clothed with many plants of many kinds, with birds singing on the bushes, with various insects flitting about, and with worms crawling through the damp earth, and to reflect that these elaborately constructed forms, so different from each other, and dependent on each other in so complex a manner, have all been produced by laws acting around us.” (Darwin 1859).
Conforme ilustrado verbalmente pelo trecho do ‘On the Origin of Species’ de Charles Darwin, a complexidade das interações que conectam os organismos vivos (entangled bank) tem despertado a atenção de ecólogos desde muito tempo. Organismos interagem de diversas formas, gerando efeitos positivos, negativos ou neutros uns nos outros (Levin 2009). Dentre os efeitos positivos recíprocos, encontram-se os mutualismos entre plantas e animais, como a polinização (Jordano 1987).
Entretanto, as investigações sobre interações entre plantas e polinizadores e os processos que os geram foram até recentemente restritas (embora mais detalhadas) a pares ou conjuntos limitados de espécies. Com os avanços conceituais e analíticos das redes complexas, tem sido possível a investigação destas interações como um sistema complexo (Jordano 1987, Memmott 1999). Usando esta abordagem, diversos padrões recorrentes de interação mutualísticas entre espécies têm sido descritos em comunidades (e.g. Vázquez et al. 2009a). Desde o trabalho seminal de Jordano (1987), um corpo extenso e rapidamente crescente de literatura tem se acumulado, inclusive sobre redes de polinização. Destes avanços surgiram tentativas preliminares de síntese, que permitiram a identificação de padrões de interação recorrentes - tais como baixa conectância e topologia aninhada e modular das redes - além da identificação de um conjunto de processos ecológicos, evolutivos e históricos determinantes de tais padrões (Olesen et al. 2007, Vázquez et al. 2009a, Vizentin- Bugoni et al. 2014). Porém, ainda não houveram investigações sobre a distribuição global dos estudos de redes, sendo que é possível que a compreensão da estrutura e funcionamento das redes planta-polinizador seja enviesada para algumas áreas e ecossistemas mais bem amostrados. Além disso, a magnitude da possível influência da distribuição geográfica dos 16
estudos sobre o entendimento dos padrões de redes, e de como processos estruturadores operam em escala macroecológica são relativamente pouco compreendidos ou controversos (Vázquez et al. 2009 a, b, Vizentin-Bugoni et al. 2014).
Apesar dos avanços na compreensão das propriedades estruturais das redes de interações planta-polinizador (e.g. Vázquez et al. 2009a), comparativamente menos avanços têm ocorrido no entendimento das variações espaço-temporais nos padrões e processos que determinam a estrutura e a dinâmica destas redes (Freitas et al. 2014, Guimarães & De Deyn 2016). Particularmente, desvelar a importância relativa dos processos que originam os padrões de interações observados em redes complexas (‘laws acting around us’, nas palavras de Darwin) segue desafiador (e.g. Vázquez et al. 2009a,b, Vizentin-Bugoni et al. 2014)
Dentre os mecanismos estruturadores de redes de interações, dois conjuntos de processos principais têm sido debatidos: os ‘processos baseados em nicho’, os quais postulam que uma série de atributos relacionados às histórias de vida das espécies condicionam as interações; e os ‘processos neutros’ que postulam que as interações se dão ao acaso de modo que abundância das espécies condiciona as interações através da determinação da probabilidade de encontro entre indivíduos (detalhes conceituais em Vázquez et al. 2009b, Vizentin-Bugoni et al. 2014). Apesar da reconhecida influência de ambos os mecanismos, sua importância relativa permanece debatida (e.g. Vizentin-Bugoni et al. 2014, Olito & Fox 2014).
Neste sentido, esta tese pretende avançar no entendimento das redes de interações, através do preenchimento de algumas lacunas na investigação de redes planta-polinizador. Especificamente, no capítulo 1 da tese revisamos a literatura sobre redes de polinização publicadas até o momento. Com isso, evidenciamos e discutimos lacunas geográficas e metodológicas nos estudos destas redes com particular foco na região tropical, cujos padrões e processos estruturadores das redes foram comparados (quando possível) com redes provenientes de regiões extra-tropicais. Além disso, neste capítulo avançamos no desenvolvimento e discussão de um modelo conceitual (baseado em Gravel et al. 2006 e Canard et al. 2014) sobre a importância relativa de processos neutros e de nicho na estruturação das interações. Neste modelo hipotetizamos que a diversidade de atributos funcionais nas comunidades seria determinante da importância relativa destes processos ao longo de um gradiente (i.e. neutral-niche continuum hypothesis). 17
A partir destes resultados e da escassez de estudos sobre a importância relativa dos determinantes das interações planta-polinizador, enfocamos em um sistema de polinização específico e importante na região Neotropical (i.e. beija-flores e plantas), para investigar os efeitos do esforço de amostragem sobre padrões e processos em redes de polinização (capítulo 2; Vizentin-Bugoni et al. 2016). Neste capítulo, utilizamos uma rede de interações quantitativa de alta resolução, gerada a partir de 2716 horas de observações focais em uma comunidade na Floresta Atlântica Montana no estado de São Paulo e descrevemos os efeitos da acumulação de esforço de amostragem sobre a descrição de dez das principais métricas de redes e na identificação dos mecanismos determinantes das frequências de interação entre pares de espécies.
Por último (capítulo 3), utilizamos 25 redes de interações planta − beija-flor de um banco de dados (atualizado de Dalsgaard et al. 2011), para testar a importância relativa da sobreposição fenológica e acoplamento morfológico (i.e. processos baseados em nicho) e a abundância das espécies (i.e. um proxy para ‘neutralidade’ ou ‘processos neutros’) como determinantes de interações em redes de interações distribuídas nas Américas (do México ao sul do Brasil). Neste estudo, investigamos a prevalência destes processos como determinantes da frequência de interações entre pares de espécies; e se suas importâncias relativas estão relacionadas ao clima, topografia ou riqueza de espécies; e por fim, testamos se a estrutura das redes (i.e. especialização, modularidade e aninhamento) é determinada pela importância diferencial de processos baseados em nicho ou neutros. Discutimos estes achados no contexto da hipótese do ‘neutral-niche continuum’ (capítulo 1).
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CAPÍTULO 1
Tropical pollination networks*
Jeferson Vizentin-Bugoni1, Pietro Kiyoshi Maruyama2, Camila Souza Silveira3, Jeff Ollerton4, André Rodrigo Rech5 & Marlies Sazima2
* Manuscrito formatado de acordo com Springer Basic Style, para submissão para o livro Ecological Networks in the Tropics.
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1 Programa de Pós-Graduação em Ecologia, Universidade Estadual de Campinas (Unicamp), Cx. Postal 6109, CEP: 13083-970, Campinas, SP, Brasil ([email protected])
2 Departamento de Biologia Vegetal, Universidade Estadual de Campinas (Unicamp), Cx. Postal 6109, CEP: 13083-970, Campinas, SP, Brasil ([email protected]; [email protected])
3 Programa de Pós Graduação em Ecologia e Conservação, CCBS, Universidade Federal de Mato Grosso do Sul (UFMS), Cx. Postal 549, CEP: 79070-900, Campo Grande, MS, Brasil ([email protected])
4 Faculty of Arts, Science and Campus Technology, University of Northampton, Avenue, Northampton, UK ([email protected])
5 Licenciatura em Educação do Campo, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), CEP 39100-000 Diamantina, MG, Brasil ([email protected])
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Abstract
Most tropical plants rely on animals for pollination, thus engaging in complex interaction networks. Here we present an overview of pollination networks in the tropics and point out research gaps and emerging differences between tropical and non-tropical areas. Our review highlights an uneven global distribution of studies biased towards non-tropical areas. Moreover, within the tropics, there is a bias towards the Neotropical region where sub- networks represent 70.1% of the published studies. Additionally, most networks sampled so far (95.6%) were assembled via a phytocentric approach, i.e. inferring interactions by surveying plants. These biases may limit accurate global comparisons of the structure and dynamics of tropical and non-tropical pollination networks. Noteworthy differences of tropical networks (in comparison to the non-tropical ones) include higher species richness which, in turn, promotes lower connectance but higher modularity due to both the higher diversity as well as the integration of more vertebrate pollinators. These interaction patterns are influenced by several ecological, evolutionary and historical processes, and also sampling artifacts. We propose a neutral–niche continuum model for interactions in pollination systems. This is, arguably supported by evidence that a high diversity of functional traits promotes greater importance of niche-based processes (i.e. forbidden links caused by morphological mismatching and phenological non-overlap) in determining which interactions occur, rather than random chance of encounter based on abundances (neutrality). We conclude discussing the possible existence of a latitudinal gradient of specialization in pollination networks. 21
1. Introduction
Naturalists have long been amazed by the diversity and complexity of life in the tropics (e.g. Darwin 1859, Wallace 1869). In tropical ecosystems, a high proportion of species rely on mutualistic interactions to complete their life cycles. Pollination by animals is one of these processes and occurs when animals transfer pollen grains among flowers while visiting them, hence promoting seed set. Despite the occurrence of self-fertilization and abiotic pollination by wind and water, most angiosperms (at least 300,000 species) rely on animals for pollination (Ollerton et al. 2011), and at least 200,000 vertebrate and invertebrate animal species are estimated to engage in this interaction (Burkle & Alarcón 2011, Ollerton et al. 2011). The proportion of animal-pollinated plants is widely variable around the planet, with some tropical communities having as much as 100% of the plants partially or entirely dependent on animals (Ollerton et al. 2011, Rech et al. 2016).
Classically, plant-pollinator studies have considered a few focal species of plants or functional pollinator groups (Burkle & Alarcón 2011; next section in this chapter). However, coexisting assemblages of plants and pollinators engage in complex interactions networks encompassing sometimes hundreds of species (Jordano 1987). Studying plant- pollinator systems as ecological networks allows the investigation of the structure and dynamics of these complex interactive assemblages and facilitates the understanding of system-level phenomena that cannot be inferred by looking at the components of a community in isolation (Memmott 1999, Bascompte 2009). In doing so, network analysis offers possibilities to explore novel and long lasting questions in ecology (Bascompte 2009). Importantly, network thinking has been integrated into conservation, restoration and management (Tylianakis et al. 2010). This integration offer promising tools to cope with the urgent challenge to understand and mitigate the effects of environmental changes, biological invasions and species loss on crucial ecological processes such as pollination (Tylianakis et al. 2010, Burkle & Alarcón 2011, Maruyama et al. 2016).
Indeed, network analyses have thus promoted several advances including the description of consistent structural patterns of mutualistic assemblages and the underlying processes (reviewed in Vázquez et al. 2009a, Trøjelsgaard & Olesen 2016). Furthermore, it has stimulated research using plant-pollinator interactions as a study system to elucidate challenging questions in ecology and evolution, such as how coevolution takes place within 22
communities (Bascompte 2009, Guimarães et al. 2011) and whether there is a latitudinal gradient in specialization (Schleuning et al. 2012, Pauw & Stanway 2015).
Here we present an overview of the contribution of the network approach to pollination ecology, with particular focus on the understanding of plant-pollinator interactions in the tropics. Specifically, our goals are (1) to review the global distribution of studies on pollination networks; (2) to describe the most recurrent structural patterns and the main underlying mechanisms, discussing peculiarities of tropical pollination networks; and (3) to discuss the evidence, or the lack thereof, of a latitudinal gradient of specialization in pollination networks.
2. A profile of pollination network studies
To investigate the global distribution of research on pollination networks, we compiled the published articles on the topic using Web of Science, Scopus and Google Scholar, using the following search terms: “plant-pollinator network", "pollination network" and ‘floral visitation network’. After filtering these papers for redundancies (e.g. the same networks used in different studies), we extracted the following metadata: coordinates, altitude, country, ecosystem type and sampling methods (Online Appendix). Here we considered complete networks, i.e. those interaction matrices encompassing all plants and pollinators in a given site without any a priori cut-off; and sub-networks, which are those assembled considering a subset of interacting species from a larger pollination network in the entire community.
2.1 Global distribution of the studies
We found 206 published papers on pollination networks (last search: 2 October 2016), including 325 sampled networks. Considering the localities of these studies (Figure 1), an uneven distribution around the globe becomes evident. Indeed, most studies are from non- tropical areas (n = 178 vs. 147 tropical networks), especially Europe (n = 103) and North America (n = 34). In the tropical region, the great majority comes from the Neotropics (n = 137) especially from the Central America and Atlantic Coast of South America, with a notable gap in the Amazon and Central Neotropical Savanna areas. Studies are even more scarce in the Paleotropic, with only a few networks from tropical Africa, Asia and Indian Ocean Islands, e.g. Mauritius and Seychelles (n = 11). Temperate networks in the southern hemisphere and Asia are also comparatively few in number (n = 35). From this, a bias 23
towards northern hemisphere temperate areas and a few well sampled Neotropical areas becomes evident. The geographical gaps indicate places were future studies should be considered in order to have a more complete understanding of spatial variation in pollination networks. We consider that several historical and political reasons - more than proper biological reasons – are likely the causes of this bias, such as scarce or non-existent financial support for basic research programs in some regions, lack of tradition on the study of pollination in other places, and logistical difficulties to access and sample in remote sites. Consequently, the knowledge accumulated so far on tropical pollination networks is inherently biased towards well-sampled Neotropical areas, suggesting that generalizations have to be drawn carefully when comparing tropical and temperate regions.
Most tropical networks to date were collected at elevations between 297 and 1350 m a.s.l. (25-75% percentiles) ranging from 5 to 4200 m a.s.l. while in non-tropical regions elevations are mostly between 100 and 1300 m a.s.l. (25-75% percentiles), ranging from 5 to 3600 a.s.l. (Figure 2A; Online Appendix). Overall, this suggests that lower coastal (<100m) and higher mountain top (>2000 m a.s.l.) areas have been relatively poorly sampled. Therefore, under-sampled areas for pollination networks coincide with areas under particular threat by climatic change (IPCC 2014).
2.2 Networks and sub-networks
Overall, we found 186 complete networks and 139 sub-networks. Despite the more comprehensive nature of the network approach (Memmott 1999) obvious limitations to understand system-level phenomena arise when subsets of species are considered. The definition of the species included in sub-networks usually follows functional or taxonomic criteria, e.g. all hummingbird-pollinated flowers (e.g. Maruyama et al. 2014) or all oil- producing flowers (Bezerra et al. 2009). Even though sub-networks have been studied from both tropical and non-tropical areas (Figure 1), there is a much higher proportion of sub- networks in the tropics (70.1%; n = 103) than outside the tropics (20.2%; n = 36) (Figure 2B). This is likely a consequence of the challenge associated with sampling entire communities in the tropics arising from higher species diversity. Moreover, not only species richness is higher, but functional groups of both plants and pollinators (e.g. nocturnal versus diurnal behavior of floral visitors, numerous animal-pollinated epiphytes) and the range of pollination systems encountered are more diverse in the tropics (see Ollerton et al. 2006). In addition, the greater structural complexity in tropical vegetation is challenging, e.g. canopy in tropical 24
forest such as the Amazon is sometimes 60 m above the ground. Therefore, it is simpler to describe complete networks for temperate grasslands or tundra ecosystems, for instance, than most tropical forests. In this sense, the few tropical complete networks are generally restricted to structurally simpler ecosystems such as high-altitude or rocky outcrop grasslands (Danieli- Silva et al. 2012, Carstensen et al. 2016, but see Watts et al. 2016). Hopefully, with new technologies for sampling interactions, e.g. automated monitoring by cameras coupled with motion video detection (Weinstein 2014) and DNA sequencing techniques (Evans et al. 2016), this challenge may be overcome in the future.
Importantly, there is a need to recognize that pollination networks themselves are merged into larger, more complex networks which include other types of positive, negative and neutral interactions, such as seed dispersal, herbivory, predation, mycorrhizae, and epiphytism. However, few studies to date have undertaken such an integrative approach. This is one of the main avenues for study that is just starting to be investigated in temperate areas (e.g. Dáttilo et al. 2016). There are also plant and pollinator species that are not connected to the wider interaction web because they are largely mutually specialized, for example fig trees and their fig wasps (but see Machado et al. 2005). Not surprisingly, these have mainly been ignored in plant-pollinator studies focused on networks, but nonetheless they are an important component of these assemblages as “satellite” species or taxonomic/functional groups standing apart from the rest of the community.
2.3 Sampling methods
Pollination networks can be assembled by two major sampling approaches. The phytocentric approach consists of identifying and quantifying interactions by observing flowering plants, i.e. ‘focal observation’ or observation in spatially delimited areas. Alternatively, the zoocentric perspective consists of identifying pollen grains attached to the pollinators’ body to establish the interactions (Bosch et al. 2009, Jordano et al. 2009; Freitas et al. 2014).
Our survey indicates a clear bias toward phytocentric sampling (n = 311 out of 325 networks) as only 10 studies used a zoocentric approach and four studies used both methods. Both approaches have their merits and limitations: while phytocentric sampling is simpler and straightforward to apply in the field with little demands of later lab work, plant species can be sometimes overlooked if they are rare or occur in inaccessible spots. In contrast, the zoocentric approach is more comprehensive as it virtually encompasses pollen of 25
all plants where anthers were contacted by the pollinator. However, pollen identification tends to be time-consuming and it is especially difficult in highly diverse tropical communities, demanding comparisons with a reference collection of pollen or genetic sequencing. Furthermore, pollinators can sometimes clean their bodies, or collect pollen without facilitating pollination, i.e. bees. In addition, as individuals have distinct home ranges, the spatial scale of zoocentric studies is often unknown, in contrast to phytocentric sampling (Freitas et al. 2014, Jordano 2016b). Regarding network metrics, studies suggest that phytocentric approaches tend to overestimate specialization in tropical bird-plant networks (Ramirez-Burbano et al. in press, Zanata et al. in review). This fact associated to the observed over-representation of phytocentric approaches indicates that the high specialization detected in pollination networks to date may be at some extent a methodological consequence. However, how the prevalence of phytocentric approaches affect other network patterns remains poorly investigated. Ideally, the combination of both approaches would result in a more accurate description of the pollination networks (Bosch et al. 2009), although it may be challenging in practice owed to the difficulty of sorting pollen samples to species in some plant groups, e.g. Ericaceae (Ramirez-Burbano et al. in press). Finally, a recent study counting pollen deposition on stigmas after single visits of each visitor revealed higher specialization of networks when compared to a network based on visitation frequency only (Ballantyne et al. 2015), suggesting better accuracy in inferring the consequences related to network structure for plant reproduction (as suggested by Ollerton et al. 2003). However, although ground- breaking, Ballantyne et al. (2015) worked in a low-diversity community that included only five plants and 16 species (or groups) of flower visitors. Applying single visit pollen deposition to build networks would be far more challenging in species rich communities in the tropics.
3. Structure and drivers of pollination networks
Emerging structural patterns in plant-pollinator networks are relatively well documented and similar to other mutualistic networks (reviewed in Vázquez et al. 2009a, Trøjelsgaard & Olesen 2016). Despite the recurrence of these patterns, few of these networks are tropical and the ones from the tropics are mostly Neotropical sub-networks. Importantly, the existing geographical and sampling biases hinder accurate comparisons of network structure between tropical and non-tropical areas, and even generalization for the tropical areas is hampered. Nevertheless, studies accumulated to date do not suggest dramatic structural differences between tropical and non-tropical network structures (Trøjelsgaard & 26
Olesen 2016). Here we will describe the main patterns and later discuss peculiarities found (or expected) for tropical plant-pollinator networks.
3.1. General network patterns
Low connectance. Pollination networks usually possess low connectance, i.e. only a small proportion of potential links actually occur (Jordano 1987). Connectance is known to decrease with species richness, even in sub-networks (Jordano 1987), thus tropical hyper-diverse communities are expected to possess less connected networks. This is likely due to morphological or spatio-temporal constraints to the interactions that are imposed. Indeed, the role of these mechanisms as barriers to some interactions has been increasingly supported (e.g. Vizentin-Bugoni et al. 2014; Jordano 2016a) and will be detailed in section 3.2.
Uneven degree distribution and interaction strength. Degree is the number of partners a given species interacts with. In a pollination network, most species have few partners while few species have many partners (Waser et al. 1996, Jordano et al. 2003). Thus, extremely low or high specialization are ends of a continuum. Despite the recognized influence of sampling effort on network structure (e.g. Vizentin-Bugoni et al. 2016), this pattern was found to be relatively robust to sampling effort for a database including 18 networks, of which three were tropical (Vázquez & Aizen 2006). Also, when some measure of interaction strength is considered (quantitative networks), it becomes evident that few interactions are strong while most are weak (Bascompte et al. 2006, Vázquez et al. 2007). These uneven patterns imply that most species exhibit some degree of specialization, while only few hyper-generalists are present in the network (Jordano et al. 2006, Vázquez et al. 2009a). However, relatively low sampling intensity, often concentrated over a single season, is still commonplace and hampers our efforts to truly understand how specialized or generalized species are. Importantly, species-level data may hide potential individual differences in specialization within populations; this aspect however, remains poorly investigated.
Asymmetric interactions. This feature refers to both the degree and the interaction strength, i.e. an estimate of the impact of one species on another. Respectively, this means that species with many partners tend to interact with specialized partners, and that if species A is strongly dependent on a species B, then B tends to be less dependent on A (Vázquez et al. 2009a). However, it is important to notice that asymmetric interactions does 27
not necessarily mean asymmetric dependencies, as some plant species can set seed without pollinators, i.e. by spontaneous self-pollination or apomixy, or have few ovules which may be all pollinated even under low visitation rates which may enought pollen deposition and ensure the maximum seed set. Even though asymmetric interactions have been suggested to be pervasive, few tropical networks have been analyzed in this regard (one tropical network out of 18 pollination networks in Vázquez & Aizen 2004).
Nestedness. As with other plant-animal mutualistic interactions, pollination networks are often nested, i.e. specialists (both pollinators and plants) interacting with generalist partners, while generalists interact also with other generalist partners (Bascompte et al. 2003). Nestedness in tropical pollination networks has been comparatively less explored, but three out of the five tropical networks included in a global analysis were found significantly nested (Bascompte et al. 2003). On the other hand, several flower–visitor networks from high tropical mountain forests were non-nested (Cuartas-Hernandez & Medel 2015) as well as a plant-hummingbird sub-network (Vizentin-Bugoni et al. 2014, 2016). Nestedness notably implies that specialization only occurs on generalist partners while specialists almost never specialize on specialist partners. In this sense, nestedness can be seen as a consequence of the interaction asymmetries and uneven distribution of interactions among partners (described above). Importantly, nestedness supports the idea that co-evolution is a diffuse process (Ehrlich & Raven 1964, Endress 1994) and it is partially determined by species abundances and phenologies (Vázquez et al. 2009b).
Modularity. Modularity is the presence of subsets of species interacting more frequently among themselves than with other species in the network, which seems pervasive in pollination networks (Dicks et al. 2002, Olesen et al. 2007, Carstensen et al. 2016, Watts et al. 2016) and even in sub-networks (Maruyama et al. 2014). For plant-hummingbird sub- networks, modularity tends to increase with species richness, suggesting that competition in species rich communities generates finer niche partitioning and ultimately produces modules (Martín González et al. 2015). Accordingly, a larger analysis showed that networks containing more than 150 plants and pollinators were always modular. In addition, the number of modules and the size of each module increases with species richness (Olesen et al. 2007). However, only seven out of 51 networks were from tropical areas. Also, low sampling effort was shown to overestimate modularity and the number of modules in a tropical plant- hummingbird sub-network (Vizentin-Bugoni et al. 2016). 28
It is important to recognize that more robust conceptual and analytical frameworks to test significance of nestedness (Almeida-Neto &Ulrich 2011) and modularity (Dormann & Strauss 2014) for quantitative matrices were developed only recently. Taken together, this calls for the need to revisit both patterns using more robust tools and larger comprehensive datasets including more numerous tropical networks. In sum, despite the fact that pollination networks have been generally less studied in the tropics, most structural properties seem similar between tropical and non-tropical regions. Some fundamental differences detected (or expected) are the presence of more species which, in turn, promote less connected but highly modular networks due to the higher species richness and integration of a greater number of functional pollination groups (Ollerton et al. 2006).
3.2 Drivers of network structure and a niche-neutral continuum model for interactions
A number of ecological, evolutionary and historical processes as well as sampling artifacts influence detected patterns in pollination networks (reviewed in Vázquez et al. 2009a, b) and their relative importance is still under debate. Overall, pollination networks present many non-observed links. To understand why these ‘zeros’ occur is essential to explain virtually all of the patterns described above. A combination of factors contributes to ‘zeros’ in the interaction matrix, including biological constraints, neutrality and sampling as outlined as below.
Contemporary mechanisms. If the link occurs in nature but was not observed due to sampling, then the absence of an interaction in the matrix is a ‘missing link’. However, if any biological phenomenon prevents a pair of species from interacting, then this is a ‘structural zero’ in the matrix (Jordano 2016b). To make a distinction of true missing links and structural zeros is challenging and may demand deep natural history knowledge to determine their causes (Olesen et al. 2011, Jordano et al. 2006, Jordano 2016b). These structural zeros may be explained by three main hypotheses.
First of all, the forbidden links hypothesis postulates that inherent biological features of species define the occurrence (or not) of an interaction. Several mechanisms may cause forbidden links, for example, spatial or temporal non-overlap in species distribution or activity are some of the most obvious causes, but factors such as morphological barriers are also frequent (Jordano et al. 2006). For instance, a plant-hummingbird sub-network in the Neotropical savanna has an entire module dictated by bill-corolla matching, while other modules are defined by non-overlapping distribution of potential partners among habitats 29
(Maruyama et al. 2014). Therefore, these modules emerge from the impossibility of some interactions to occur due to biological constraints, i.e. niche-based processes. In other words, species may interact only when they are in the same place at the same time and have compatible phenotypes.
Alternatively, a neutral hypothesis postulates that interactions may be defined by random (stochastic) encounters of individuals. Therefore species abundances are expected to have an important role. If this is the case, abundant species should interact with more partners and more frequently than the rarer ones. On the other hand, when rare species match in their biological traits they may still be too rare to find each other and then interact (Canard et al. 2012). Indeed, species abundances have been shown to predict interactions in several networks, e.g. plant-insect [in general] (Vázquez et al. 2009b) and some temperate plant- hawkmoth sub-networks (Sazatornil el al. 2016). Although we refer to this as ‘neutral’, it is important to note that the relative abundance of a species may be driven also by niche-based processes (Vizentin-Bugoni et al. 2014), an the elaboration of such a mechanistic framework deserves further attention.
A third mechanism called the morphological matching hypothesis has been shown to better predict interactions in some cases. It postulates that – among the interactions that are not forbidden links – pollinators are expected to preferentially explore flowers whose morphology fits closely to pollinator mouthparts, and some plants that could potentially be accessed are not, for instance, due to competition with other pollinators (Santamaría & Rodríguez-Gironés 2007, Maglianesi et al. 2015). Indeed, this mechanism has been shown to determine interactions in some plant-hawkmoth networks from the tropics (Sazatornil et al. 2016). The influence of trait matching highlights that interactions may be determined also by (i) evolutionary adjustment of morphologies of sets of partners, (ii) species preferences and (iii) avoidance of easier-to-access resources, which presumably imply more intense competition.
A ‘neutral-niche continuum model’ for species interactions. Importantly, the three hypothetical mechanisms outlined above are not mutually exclusive and all can (potentially) be occurring in every network, though their relative importance remains debated (Vázquez et al. 2009a,b; Vizentin-Bugoni et al. 2014). In this sense, the existence of a continuum of importance from niche-based processes, i.e. forbidden links and matching hypotheses, to neutrality structuring interactions has been hypothesized (Canard et al. 2014). 30
For pollination networks, the relative importance of a process will depend on the diversity of functional traits, i.e. the extent in which traits vary in the assemblage. In an extreme of this continuum, where plants and pollinators present highly variable traits, niche-based processes such as forbidden links and morphological matching are expected to be dominant drivers of interactions, such as in highly diverse tropical areas (Figure 3; right tip of the blue line). In the opposite extreme of this continuum, random chance of encounter driven by species abundances is expected to matter more where traits are not very variable, i.e. low diversity of functional traits (Figure 3; left tip of the red line). As high trait diversity one may consider corolla tube lengths, for instance, in which discrepancies between sorter and longer corollas are expected to produce opportunities for forbidden links. The same may be expected for floral colors: in a system containing wide variety of colors (i.e. high functional diversity), certain colours may act as filters of some visitors group, such as red flower reflecting low ultraviolet light which are not visited by bees but are preferred by hummingbirds (Lunau et al. 2011. citar).
Indeed, for pollination networks, evidence accumulated so far arguably supports the expectation of this simple ‘neutral-niche continuum model’ for species interactions. Niche-based processes were shown to matter more than neutrality in systems with high trait variation from tropical areas, such as plant-hummingbird (Maruyama et al. 2014; Vizentin- Bugoni et al. 2014) and tropical plant-hawkmoth pollination systems (Sazatornil et al. 2016). On the other hand, the neutral process was found to be important in some non-tropical networks (e.g. Vázquez et al. 2009b, Olito & Fox 2014). As seasonal climate may shape species phenologies in some communities, niche-based processes related to phenology can also play a role along with species abundances to structure interactions. Indeed, phenological overlap was shown to be an important driver of interactions - along with abundances - in some non-tropical pollination networks (Vázquez et al. 2009b, Olito & Fox 2014). The continuum hypothesis we develop here does not exclude the potential existence of a hierarchy of importance among distinct mechanisms determining interactions in a system (Junker et al. 2013), but suggest that the order in this hierarchy may depend on the diversity of functional traits. Despite of the predictions of the ‘neutral-niche continuum model’, a proper test of this hypothesis relating is still missing and must directly relate the importance of distinct niche and neutral processes along a gradient of networks differing in trait diversity.
Importantly, it is necessary to highlight that the niche- and neutral-based processes outlined above originate from a number other causal processes. Such processes 31
underlie community composition and structure, i.e. species richness and relative abundances, and species distributions in time and space, which ultimately influence network structure (Vázquez et al. 2009a, Trøjelsgaard and Olesen 2016, Bartomeus et al. 2016). Some other processes include, for example, dispersal limitation, demographic processes, adaptation, local extinction and competition (Vázquez et al. 2009a).
Evolutionary history may shape interactions by its influence on species traits. One of the few attempts to describe a complete pollination network in a tropical high-altitude grassland found around 69% of interactions correctly predicted by the combination of floral traits (Danieli-Silva et al. 2012), as was found also for a temperate study (Dicks et al. 2003). Hence modules of taxonomic (presumably also functional) pollinator groups interact with plants which are adapted (both related and phenotypically convergent) to these pollinators. In other words, closely related pollinators tend to interact with a functionally similar array of flowers and, in several cases drive convergent floral evolution. An implication of these findings is that sub-networks assembled by considering particular pollination systems (or pollination syndrome) may not be just an artifact, and correspond to reasonably independent modules within a larger and more complete pollination network, especially in the tropics (Danielli-Silva 2011, Carstensen et al. 2016, Watts et al. 2016).
Historical drivers of interactions. If species coexistence is crucial for interactions, those phenomena promoting speciation or extinction must also be important for contemporary network structure. These phenomena may include catastrophic events (e.g. extinctions by volcanic activity) or gradual events such as historical climate change. In this sense, environmental (climatic) stability is expected to promote structurally more complex networks, by offering more opportunities for coexistence, co-adaptation and the evolution of narrower niches, i.e. specialization, as shown for plant-hummingbird sub-networks (Sonne et al. 2016). Indeed, pollination networks present higher modularity in areas of higher historical climate stability, such as some tropical areas, both in mainland and on islands (Dalsgaard et al. 2013) which is an indication of higher specialization in these areas. Accordingly, modularity increases with contemporary precipitation while nestedness decreases (Dalsgaard et al. 2013), which suggests that climatic condition operating on more recent time-scales is also influencing the structure of pollination networks (see also Rech et al. 2016).
4. Is there a latitudinal gradient of specialization in pollination networks? 32
The general latitudinal trend of rising species richness as one move from the poles to the tropics has long been recognized (Pianka 1966, Hillebrand 2004, Willig et al. 2003). There are around 25 tentative explanations to this latitudinal gradient (Brown and Lomolino 1998). Considering this higher diversity in the tropics, ecologists have proposed that species in the tropics are more often specialized in their interactions with other species because their niche breadths evolve to be narrower in communities that are more densely packed with species (MacArthur 1972; Janzen 1973; but see Vázquez and Stevens 2004). However, there have been rather few tests of this assumption with plant-pollinator interactions, and their findings have been mixed. Olesen and Jordano (2002) initially suggested that plant-pollinator networks were more specialized in the tropics, however Ollerton and Cranmer (2002) showed that any apparent increase in plant specialization in the tropics disappeared when sampling effort was considered, a pattern that was consistent over two large, independent datasets. Schleuning et al. (2012) later showed plant-flower visitor networks to actually be less ecologically specialized in the tropics compared to other regions; and Trøjelsgaard & Olesen (2013), found the number of pollinators per plant species peaks at mid-latitudes. More recently Pauw & Stanway (2015) have further demonstrated that there may be differences between the northern and southern hemispheres. The “opposite” latitudinal trend found by Schleuning et al. (2012) was both theoretically and intuitively unpredicted, and (controversially) suggests that ecological functions such as mutualistic seed dispersal and pollination may be most sensitive to the extinction of species in temperate ecosystems, rather than those in the tropics, as is often assumed.
Importantly, as evindence here (section 1) that most tropical network are actually sub-networks, while non-tropical are complete networks. Moreover, there is evidence that some sub-networks may be more specialized in the tropics, e.g. New World hummingbird- flower assemblages, which seems to be related mostly to precipitation (Martín González et al. 2015). This could indicate that tropical specialization may be encountered in only some sub- networks, but it is unclear why, and there is much scope for further research. What is certain, however, is that suggestions that tropical species, and the interaction networks in which they are embedded, are always more specialized is a huge over-simplification (Moles and Ollerton 2016).
It is worth mentioning that biodiversity gradients are strongly dependent upon spatial scale and sampling effort (Willig et al. 2003, see also Dalsgaard et al. 2017). Hence, considering that tropical studies are often less intensively sampled than temperate ones, it is 33
plausible to suggest that we are still far from a proper test of whether specialization is higher in tropical pollination interactions. Even though the pattern of species increase from the poles to the equator is far more common than the opposite (or the absence of pattern) and holds true for terrestrial plants (Cowling and Samways 1995, Gentry 1988), some important groups of pollinators such as bees and wasps show their highest diversity at intermediate latitudes (Janzen 1981, Ollerton et al. 2006, Michener 2007). Indeed, Trøjelsgaard and Olesen (2013), found the number of pollinators per plant species peaking at mid-latitudes. Also, among the groups showing negative correlation to latitude, not all of them increase in species at the same rate. Bats, for example, show the steepest equatorward species increment among mammals (Willig et al. 2003). Therefore, considering that different pollinator groups may show contrasting local diversities along the latitudinal gradient, it will also be interesting to go deeper into how their relative importance as pollinators and the plants relying upon each pollinator group will behave over space (Ollerton et al. 2006).
5. Concluding remarks
Our review suggests that pollination networks are structurally similar in the tropical and non-tropical areas. In tropical regions where species diversity (and presumably also functional diversity) is high, however, niche-based processes which impose barriers to species interactions via forbidden links and trait matching among partners are expected to be more important than neutral-based processes in structuring interaction networks. Importantly, the influence of sampling artifacts on pollination networks has been poorly investigated so far, especially in the tropics. Here, we show that there are some important biases which potentially limits accurate generalizations, notably, geographical and sampling biases in the distribution of pollination networks worldwide. Thus, we highlight that further advances in the understanding of plant-pollinator networks demands increasing research effort covering the Paleotropical region, consideration of the multiple functional groups interacting with flowers and sampling of more complete networks. Such studies are needed to better understand differences among tropical and non-tropical areas and whether the latitudinal gradient in species richness affects the structure of pollination networks, which may have important implications on our ability to predict and manage interactions under scenarios of increasing environmental change.
6. Acknowledgements 34
We thank Bjorn Hermansen for help with map layers and Felipe W. Amorim, Marina Wolowski, Vinicius Brito and Thomas M. Lewinsohn for comments on a first draft. Funding was provided by CAPES through Ph.D. scholarships to JVB and CCS, FAPESP through a Postdoctoral grant to PKM (proc. 2015/21457-4) and a visiting researcher grant to JO (proc. 2013/14442-5) and CNPq through a research grant to MS (proc. 303084/2011-1).
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Figure 1 - Distribution of pollination networks around the world. Complete networks, i.e. including the entire assemblages of plants and pollinators in a site, encompass 79.8% and 29.9% of the networks assembled in non-tropical and tropical studies, respectively.
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Figure 2. A - Distribution of network studies on pollination networks thoughout elevations the tropics and nontropics (grey box delimits 25-75% percentiles and the line depicts the median). B - Proportions of pollination networks assembled based on interactions of a subset of species (sub-networks) or the entire community (complete networks) in tropical and non- tropical areas. Number of networks in each category is indicated.
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Figure 3. Neutral–niche continuum model for interactions in pollination networks. In this model, species interactions are expected to be mainly structured by forbidden links and morphological matching (niche-based processes) when associated with high functional diversity, while random chance of encounter based on species abundances (neutral-based process) are expected to matter more under low functional diversity. See text for examples.
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Online Appendix
[Table]. Metadata extracted from 206 studies published on pollination networks. Abreviations: NT = Non-tropical and T = Tropical; Zoo = Zoocentrinc and Phyto = Phytocentric methods.
Net Lat Long Regi Coordi Altitude Study type Country Ecosystem type Animals Plants Sampling References ID on nates (m) included included
1 32º32'S 68º57 NT Article 1270 Single Argentina Monte Desert All insects Entire flora Phyto Chacoff, N. P. et al. 'W community 2012 - J. Anim. Ecol. 81: 190–200. 2 51º27' 2º35' NT Internet 11 Single Great Natural or semi- All insects Entire flora Phyto/Zoo Lopezaraiza–Mikel, M. N W community Britain natural vegetation in E. et al. 2007- Ecol. the city of Bristol Lett. 10: 539–550. 3 35º20'S 57º20 NT Article 900-1800 Single Argentina Xeric temperate forest All insects Entire flora Phyto Basilio, A. M. et al. 'W community 2006 - Austral Ecol. 31: 975–983. 4 34º13' 116º5 NT Article 2300 Single USA Montane meadow Hummingbir Entire flora Phyto Alarcón, R. et al. 2008 N 7'W community ds and - Oikos 117: 1796– insects 1807. 5 46º26' 9º56' NT Article 1900- Single Switzerland Morteratsch glacier All insects Entire flora Phyto Albrecht, M. et al. N E 2010 community foreland 2010 - Oikos 119: 1610–1624. 6 00º56'S 090º5 T Article 300 Single Ecuador - Sparsely vegetated All insects Entire flora Phyto Philipp, M. et al. 2006. 8'W community Galápagos lava desert - Ecography 29: 531– Islands 540. 7 39º16' 89º53 NT Article 189 Single USA Woodlands All insects Entire flora Phyto Burkle, L. A. et al. N 'W community 2013 - Science 339: 1611–1615. 9 48º17' 2º41' NT Article 60 Experimenta France Controlled experiment Bumble bees Controlled Phyto Fontaine, C. et al. 2005 N E l community and Syrphids experiment - PLoS Biol. 4 10 42º17' 3º17' NT Internet 1000 Single Spain Mediterranean All insects Entire flora Phyto Bartomeus, I. et al. N E community shrublands (but emphasis 2008 - Oecologia 155: on two invasive 761–770. species) 12 33º17'S 70º16 NT Article 2200- Compilation Chile Subandean scrub Entire Entire flora Phyto Olesen, J. M. and 'W 2600 Community Jordano, P. 2002 - Ecology 83: 2416– 45
2424. 12 33º17'S 70º16 NT Article 2700- Compilation Chile Cushion zone Entire Entire flora Phyto Idem 'W 3100 Community 12 33º17'S 70º16 NT Article 3200- Compilation Chile Subnival zone Entire Entire flora Phyto Idem 'W 3600 Community 12 68º21' 18º30 NT Article 985 Compilation Sweden Rocky slope Entire Entire flora Phyto Idem N 'W Community 12 74º28' 20º34 NT Article 25 Compilation Greenland Tundra Entire Entire flora Phyto Idem N 'W Community 12 20º25'S 57º43 T Article 5 Compilation Mauritius Coralline island Entire Entire flora Phyto Idem 'E Community 12 37ºN 6ºW NT Article 25 Compilation Spain Scrub Entire Entire flora Phyto Idem Community 12 82ºN 71ºW NT Article 500 Compilation Canada Tundra Entire Entire flora Phyto Idem Community 12 35º10' 135º5 NT Article 300-740 Compilation Japan Mixed forest Entire Entire flora Phyto Idem N 2'E Community 12 36º25'S 148º2 NT Article 1860- Compilation Australia scrub/snow gum Entire Entire flora Phyto Idem 0'E 2040 Community 12 35º02' 135º4 NT Article 60 Compilation Japan Urban area Entire Entire flora Phyto Idem N 7'E Community 12 35º39' 136º5 NT Article 45 Compilation Japan Marsh Entire Entire flora Phyto Idem N 'E Community 12 35º20' 138º NT Article 620-959 Compilation Japan Beech forest Entire Entire flora Phyto Idem N E Community 12 35º35' 138º2 NT Article 1850 Compilation Japan Subalpine Entire Entire flora Phyto Idem N 3'E forest/meadow Community 12 81º49' 71º18 NT Article 100 Compilation Canada Tundra Entire Entire flora Phyto Idem N 'W Community 12 1ºS 90ºW T Article 15-580 Compilation Ecuador - Volcanic southern Entire Entire flora Phyto Idem Galápagos slope Community Islands 12 75ºN 11ºW NT Article 100 Compilation Canada Tundra Entire Entire flora Phyto Idem Community 12 56ºN 0ºE NT Article 5 Compilation Denmark Wasteground Entire Entire flora Phyto Idem Community 12 28ºN 17ºW NT Article 1200 Compilation Spain Laurel forest Entire Entire flora Phyto Idem Community 46
12 56ºN NA NT Article 5 Compilation Denmark Beech–oak forest Entire Entire flora Phyto Idem Community 12 56ºN NA NT Article 5 Compilation Denmark Salt bog Entire Entire flora Phyto Idem Community 12 38ºN 31ºW NT Article 10 Compilation Portugal Coastal cliff Entire Entire flora Phyto Idem Community 12 19ºN 76ºW T Article 5 Compilation Jamaica Coastal scrub Entire Entire flora Phyto Idem Community 12 38ºN 23ºE NT Article 135-215 Compilation Greece Phrygana Entire Entire flora Phyto Idem Community 12 43ºS 171º NT Article 900 Compilation New Grassland Entire Entire flora Phyto Idem E Zealand Community 12 43ºS 171º NT Article 600-1800 Compilation New Grassland Entire Entire flora Phyto Idem E Zealand Community 12 43ºS 171º NT Article 1600- Compilation New Grassland Entire Entire flora Phyto Idem E 1800 Zealand Community 12 5º35'N 61º43 T Article 1350 Compilation Venezuela Open forest Entire Entire flora Phyto Idem '0 Community 12 40ºN 88ºN NT Article 300 Compilation USA Deciduous forest Entire Entire flora Phyto Idem Community 41 20ºS NA T Internet 400 Single Mauritius Coastal forest Entire Entire flora Phyto Olesen, J. M. et al. community Community 2002 - Divers. Distrib. 8: 181–192. 22 51º27' 2º35' NT Internet 100 Single England Meadow community Entire Entire flora Phyto Memmott, J. 1999 - N W community Community Ecol. Lett. 2: 276–280. 28 34º13' 116º5 NT Article 2300 Single USA Meadow community Entire Entire flora Phyto Alarcón, R. 2010 - N 7'W community Community Oikos 119: 35–44. 33 57º10' 3º44' NT Article 100 Single Scotland Scottish woodlands Entire Entire flora Phyto Devoto, M. et al. 2012 N W community Community - Ecol. Lett. 15: 319– 328. 35 61º9'N 7º10' NT Article 1200 Single Norway Temperate grassland Entire Entire flora Phyto Hegland, S.J. et al. E community Community 2010 - Biological Conservation 143: 2092-2101. 37 4º40'S 55º26 T Article 500-1200 Single Gondwanala Inselbergs Entire Entire flora Phyto Kaiser-Bunbury, C. N. 'E community nd - Mahé Community et al. 2011- J. Ecol. 99: (Seychelles) 202–213. 38 38º22' 0º38' NT Article 12 Single Spain Mediterranean Entire Entire flora Phyto Stang, M. et al. 2009 - N W community vegetation mosaic Community Ann. Bot. 103: 1459– 47
1469. 40 41ºS NA NT Article 780-870 Single Argentina Temperate rain forest; Entire Entire flora Phyto Morales, C. L. and community Mixed forest; Flor et Community Aizen, M. A. 2006 - J. Boutleje Ecol. 94: 171–180. 42 50º45' 115º1 NT Article 100 Single Canada Low-alpine meadow Entire Entire flora Phyto Olito, C. and Fox, J. N 7'W community Community W. 2015 - Oikos 124: 428–436. 45 74º30' 21º0' NT Article 25 Single Greenland Heathland All insects Entire flora Phyto Olesen, J. M. et al. N W community 2008 - Ecology 89: 1573–1582. 47 42ºS 73º38 NT Article 30 Single Chile Old-growth and Entire Entire flora Phyto Ramos-Jiliberto, R. et 'W community second-growth forest Community al. 2009- Oecologia 160: 697–706. 50 56º4'N 10º13 NT Article 40-150 Single Denmark Humid forest meadow Bees One plant Phyto Dupont, Y. L. et al. 'E community species 2014 - Oikos 123: 1301–1310. 53 38º57' 106º5 NT Internet 2900 Single USA Rocky Mountain Entire Entire flora Phyto Burkle, L. and Irwin, N 9'W community Community R. 2009 - Oikos 118: 1816–1829. 55 20º42'S 57º44 T Article 400 Single Mauritius Dwarfforest Entire Entire flora Phyto Kaiser-Bunbury, C. N. 'E community Community et al. 2010 - Ecol. Lett. 13: 442–452. 56 48º50' 2º38' NT Internet 230 Single France Gradient of All insects Entire flora Phyto Geslin, B. et al. 2013 - N E community urbanisation PloS One 8: e63421. 58 15º25' 61º20 T Article 3-847 Single Dominica Dry scrub woodland/ Hummingbir Entire flora Phyto Dalsgaard, B. et al. N 'W community Montane thicket-elfin ds 2008 - Oikos 117: woodland 789–793. 58 12º7'N 61º40 T Article 30-715 Single Grenada Coastal dry scrub Hummingbir Entire flora Phyto Dalsgaard, B. et al. 'W community woodland/ Rainforest/ ds and 2008 - Oikos 117: Montane thicket-elfin insects 789–793. woodland 94 41ºS NA NT Article 780-990 Single Argentina Temperate rain forest/ Entire Entire flora Phyto Morales, C. L. and community Mixed forest of Community (but focus on Aizen, M. A. 2006 - J. Nothofagus dombeyi/ alien species) Ecol. 94: 171–180. Flor et Boutleje forest 69 23º17'S 23º24 T Article 850-1100 Single Brazil Atlantic rainforest Hummingbir Hummingbird- Phyto Vizentin-Bugoni, J. et 'W community ds pollinated al. 2014 - Proc. R. Soc. plants B Biol. Sci. 281: 20132397. 48
70 28º0'W 28º45 NT Internet 2300 Single USA Pine savannah Entire Entire flora Phyto Spiesman, B. J. and 'W community Community Inouye, B. D. 2013 - Ecology 94: 2688– 2696. 72 58º21' 9º45' NT Article 1200 Single Norway Boreal forest Entire Entire flora Phyto Nielsen, A. and N E community Community Bascompte, J. 2007- J. Ecol. 95: 1134–1141. 73 19º20'S 43º44 T Article 1073– Single Brazil rocky outcrops Entire Entire flora Phyto Carstensen, D. W. et 'W 1260 community Community al. 2014 - Plos One 9: e112903. 75 38º22' 0º38' NT Article 12-1200 Single Spain Mediterranean All insects All nectar Phyto Stang, M. et al. 2006 - N W community vegetation mosaic producing plant Oikos 112: 111 - 121. species 78 28º21' 16º54 NT Article 12-1200 Single Spain Xerophytic shrubs All insects Entire flora Phyto Padrón, B. et al. 2009 - N 'W community PLoS ONE 4: e6275. 78 39º56' 4º15' NT Article 12-1200 Single Spain Mediterranean All insects Entire flora Phyto Padrón, B. et al. 2009 - N E community shrubland PLoS ONE 4: e6275. 79 17ºS 65ºW T Article 302 Compilation Bolivia Forest Entire Entire flora Phyto Schleuning, M. et al. Community 2012- Curr. Biol. 22: 1925–1931. 79 17ºS 63ºW T Article 411 Compilation Bolivia Forest Entire Entire flora Phyto Idem Community 79 18ºS 63ºW T Article 434 Compilation Bolivia Forest Entire Entire flora Phyto Idem Community 79 21ºS 62ºW T Article 268 Compilation Bolivia Forest Entire Entire flora Phyto Idem Community 79 39ºN 107º NT Article 3420 Compilation USA Non-forest Entire Entire flora Phyto Idem W Community 79 59ºN 17ºE NT Article 20 Compilation Sweden Non-forest Entire Entire flora Phyto Idem Community 79 46ºN 66ºW NT Article 120 Compilation Canada Forest Entire Entire flora Phyto Idem Community 79 45ºN 109º NT Article 3050 Compilation USA Non-forest Restricted Restricted Phyto Idem W 79 23ºN 48ºW T Article 700 Compilation Brazil Forest Entire Entire flora Phyto Idem Community 79 53ºN 13ºE NT Article 30 Compilation Germany Non-forest Entire Entire flora Phyto Idem Community 49
79 51ºN 10ºE NT Article 350 Compilation Germany Non-forest Entire Entire flora Phyto Idem Community 79 48ºN 9ºE NT Article 800 Compilation Germany Non-forest Entire Entire flora Phyto Idem Community 79 19ºN 105º T Article 265 Compilation Mexico Forest Restricted Restricted Phyto Idem W 79 10ºN 61ºW T Article 185 Compilation Trinidad Forest Restricted Restricted Phyto Idem
79 9ºN 83ºW T Article 3150 Compilation Costa Rica Forest Restricted Restricted Phyto Idem
79 5ºN 73ºW T Article 2400 Compilation Colombia Forest Restricted Restricted Phyto Idem
79 4ºN 73ºW T Article 2475 Compilation Colombia Forest Restricted Restricted Phyto Idem
79 0ºN 78ºW T Article 1650 Compilation Ecuador Forest Restricted Restricted Phyto Idem
79 8ºS 38ºW T Article 321 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 13ºS 41ºW T Article 940 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 20ºS 43ºW T Article 1325 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 20ºS 42ºW T Article 785 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 23ºS 45ºW T Article 850 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 52ºN 1ºE NT Article 20 Compilation UK Non-forest Entire Entire flora Phyto Idem Community 79 5ºN 117º T Article 100 Compilation Malaysia Forest Entire Entire flora Phyto Idem E Community 79 49ºN 10ºE NT Article 308 Compilation Germany Non-forest Entire Entire flora Phyto Idem Community 79 30ºN 35ºE NT Article 155 Compilation Israel Non-forest Entire Entire flora Phyto Idem Community 79 0ºN 34ºE T Article 1600 Compilation Kenya Forest Entire Entire flora Phyto Idem Community 79 29ºS 50ºW T Article 750 Compilation Brazil Forest Entire Entire flora Phyto Idem Community 50
79 51ºN 9ºE NT Article 150 Compilation Germany Non-forest Entire Entire flora Phyto Idem Community 79 36ºS 148º NT Article 1990 Compilation Australia Non-forest Entire Entire flora Phyto Idem E Community 79 60ºN 22ºE NT Article 25 Compilation Finland Non-forest Restricted Restricted Phyto Idem
79 7ºN 80ºE T Article 50 Compilation Sri Lanka Non-forest Restricted Restricted Zoo Idem
79 75ºN 115º NT Article 100 Compilation Canada Non-forest Entire Entire flora Phyto Idem W Community 79 36ºN 78ºW NT Article 100 Compilation USA Forest Entire Entire flora Phyto Idem Community 79 29ºS 30ºE NT Article 1200 Compilation South Non-forest Restricted Restricted Phyto Idem Africa 79 44ºN 1ºE NT Article 230 Compilation France Non-forest Restricted Restricted Zoo Idem
79 25ºS 48ºW T Article 150 Compilation Brazil Forest Hummingbir Bromeliads Phyto Idem ds 79 43ºN 80ºW NT Article 490 Compilation Canada Non-forest Restricted Restricted Phyto Idem
79 40ºN 88ºW NT Article 220 Compilation USA Forest Entire Entire flora Phyto Idem Community 79 45ºN 75ºW NT Article 70 Compilation Canada Non-forest Entire Entire flora Phyto Idem Community 79 53ºN 13ºE NT Article 75 Compilation Germany Non-forest Restricted Restricted Phyto Idem
79 50ºN 7ºE NT Article 160 Compilation Germany Non-forest Restricted Restricted Phyto Idem
79 10ºN 84ºW T Article 50 Compilation Costa Rica Forest Restricted Restricted Phyto Idem
79 20ºS 40ºW T Article 700 Compilation Brazil Forest Restricted Restricted Phyto Idem
79 41ºS 71ºW NT Article 969 Compilation Argentina Forest Entire Entire flora Phyto Idem Community 79 12ºS 69ºW T Article 260 Compilation Peru Forest Entire Entire flora Phyto Idem Community 79 13ºS 72ºW T Article 3526 Compilation Peru Forest Restricted Restricted Phyto Idem 51
79 38ºN 122º NT Article 203 Compilation USA Non-forest Restricted Restricted Phyto Idem W 79 7ºN 58ºW T Article 35 Compilation Guyana Forest Entire Entire flora Phyto Idem community 80 56º04' 9º16' NT Article 40-150 Single Denmark Heathland All insects Restricted Phyto Dupont, Y. L. and N E community Olesen, J. M. 2009 - J. Anim. Ecol. 78: 346– 353. 80 56º6'N 9º6'E NT Article 40-150 Single Denmark Conifer plantation and All insects Restricted Phyto Idem community deciduous forest 80 56º8'N 9º23' NT Article 40-150 Single Denmark Heathland All insects Restricted Phyto Idem E community 81 39ºN 81ºW NT Article 2300 Single USA Strip Mine Entire Entire flora Phyto Cusser, S. and community community Goodell, K. 2013 - Restor. Ecol. 21: 713– 721. 90 28º21' 16º54 NT Article 115 Compilation Spain Coastal desert Idem Entire flora Phyto Dupont, Y. L. et al. N 'W 2009 - Oikos 118: 1261–1269. 90 38º0'N 23º38 NT Article 135-215 Compilation USA Mediterranean low Idem Entire flora Phyto Idem 'W scrub 90 39º56' 4º15' NT Article 45 Compilation Spain Mediterranean scrub Idem Entire flora Phyto Idem N W 90 56º4'N 9º16' NT Article 90 Compilation Denmark Temperate dry heath Idem Entire flora Phyto Idem W 90 56º4'N 10º13 NT Article 10 Compilation Denmark Forest meadow Idem Entire flora Phyto Idem 'W 90 74º30' 21º0' NT Article 50 Compilation Greenland Arctic heath Idem Entire flora Phyto Idem N W 21 41º24' 2º09' NT Internet 340 Single Spain Mediterranean Idem Entire flora Phyto Bosch, J. et al. 2009 - N E community scrubland Ecol. Lett. 12: 409– 419. 15 50º41' 2º02' NT Article 100-500 Single England Heathland All insects All Phyto Forup, M. L. et al. N W community entomophilous 2008 - J. Appl. Ecol. plant species 45: 742–752. 15 50º41' 2º02' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 52
15 50º43' 2º03' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 15 50º42' 2º06' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 15 50º42' 2º10' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 15 50º43' 2º09' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 15 50º43' 2º07' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 15 50º43' 2º07' NT Article 100-500 Single England Heathland All insects All Phyto Idem N W community entomophilous plant species 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Gibson, R. H. et al. community 2011 - Oikos 120: 822–831. 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Idem community 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Idem community 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Idem community 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Idem community 31 51ºN 2ºW NT Internet 100-500 Single England Deciduous woodland All insects Entire flora Phyto Idem community 39 105ºS 39ºE NT Internet 500-2100 Single USA Rocky Mountains Entire Entire flora Phyto Memmott, J. et al. community Community 2004 - Proc. R. Soc. B Biol. Sci. 271: 2605– 2611. 39 41ºN 87ºW NT Internet 2300 Single USA Prairie–forest Entire Entire flora Phyto Idem community transition Community 51 35º50'S 72º30 NT Article 2700- Single Chile Ruil forest Entire Entire flora Phyto Rivera-Hutinel, A. et 'W 3100 community Community al. 2012 - Ecology 93: 53
1593–1603. 54 51ºN 2ºW NT Internet 20-800 Single UK Cropped and Entire Entire flora Phyto Pocock, M. J. O. et al. community noncropped Community 2012 - Science 335: 973–977. 65 39ºN 74ºW NT Internet 500-2100 Single USA Deciduous forest All insects Entire flora Phyto/Zoo Winfree, R. et al. 2014 community - Am. Nat. 183: 600– 611. 65 36ºN 119º NT Internet 500-2100 Single USA Native chaparral, oak All insects Entire flora Phyto/Zoo Idem W community savannah, mixed riparian vegetation, and seminatural grasslands 96 51ºN 2ºW NT Internet 100-500 Single England Semi-natural grassland All insects Entire Phyto Carvalheiro, L. G. et community and flora/Focus on al. 2008 - J. Appl. scrubland/headland Trinia glauca Ecol. 45: 1419–1427. (Apiaceae) 97 50º55' 11º35 NT Article 130 Single Germany Floodplain of the river All insects Entire flora Phyto Ebeling, A. et al. 2011 N 'E community Saale - Basic Appl. Ecol. 12: 300–309. 100 22º40'S 44º20 T Article 2100 Single Brazil High-altitude Entire Entire flora Phyto Danieli-Silva, A. et al. 'W community grassland Community 2012 - Oikos 121: 35– 43. 102 38ºN 90ºW NT Internet 500-2100 Single USA Forested matrix Entire Entire flora Phyto Burkle, L. A. and community Community Knight, T. M. 2012 - Ecology 93: 2329– 2335. 103 0º17'N 35º52 T Article 1800 Single Kenya Savanna Entire Entire flora Phyto Baldock, K. C. et al. 'E community Community 2011 - Ecology 92: 687–698. 105 30ºS 30ºE NT Article 320 Single South Forest Entire Entire flora Phyto Grass, I. et al. 2013- community Africa Community Oecologia 173: 913– 923. 106 32ºS 34ºE NT Internet 155 Single Israel Desert (Natural Only bees Entire flora Phyto Gotlieb, A. et al. 2011- community habitats and Basic Appl. Ecol. 12: Ornamental gardens) 310–320. 108 39ºN 0ºW NT Internet 1100 Single Spain Mediterranean Only All nectar Phyto Olesen, J. M. et al. community shrubland butterflies producing plant 2011- PLoS One 6: species e26455. 109 1ºN 1ºS T Internet 15-580 Single Ecuador - NA Entire Entire flora Phyto Traveset, A. et al. 2013 54
community Galápagos Community - Proc. R. Soc. B Biol. Islands Sci. 280: 20123040. 109 1ºN 1ºS T Internet 15-580 Single Ecuador - NA Entire Entire flora Phyto Idem community Galápagos Community Islands 109 1ºN 1ºS T Internet 15-580 Single Ecuador - NA Entire Entire flora Phyto Idem community Galápagos Community Islands 109 1ºN 1ºS T Internet 15-580 Single Ecuador - NA Entire Entire flora Phyto Idem community Galápagos Community Islands 109 1ºN 1ºS T Internet 15-580 Single Ecuador - NA Entire Entire flora Phyto Idem community Galápagos Community Islands 112 41ºN 1ºE NT Internet 12-1200 Single Spain Mediterranean Entire Entire flora Phyto Martín González, A. community scrubland Community M. et al. 2012- Oikos 121: 2001–2013. 116 40º09'S 71º53 NT Article 800 Single Argentina Temperate forest Entire Entire flora Phyto Devoto, M. et al. 2005 'W community Community - Oikos 109: 461–472. 116 40º44'S 71º26 NT Article 950 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 39º34'S 71º35 NT Article 1000 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 40º37'S 71º21 NT Article 872 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 40º39'S 71º12 NT Article 900 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 39º48'S 71º06 NT Article 780 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 40º48'S 71º06 NT Article 735 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 116 40º43'S 71º05 NT Article 727 Single Argentina Temperate forest Entire Entire flora Phyto Idem 'W community Community 117 27º54'S 99º38 NT Article 3300- Single China Meadow All insects Entire flora Phyto Fang, Q. and Huang, 'E 3350 community S.-Q. 2012 - PLoS ONE 7: e32663. 118 56º4'N 9º16' NT Article 500-2100 Single Denmark Dwarf shrubs Entire Entire flora Phyto Dupont, Y. L. and E community Community Olesen, J. M. 2012 - Ecol. Complex. 11: 55
84–90. 118 56º6'N 9º6'E NT Article 10 Single Denmark Dwarf shrubs Entire Entire flora Phyto Idem community Community 118 56º8'N 9º23' NT Article 10 Single Denmark Dwarf shrubs Entire Entire flora Phyto Idem E community Community 120 51º18' 2º19' NT Article 100-500 Single England Mixed lowland farm Entire Entire flora Phyto Evans, D. M. et al. N W community Community 2013 - Ecol. Lett. 16: 844–852. 121 40ºN 77ºW NT Article 500-2100 Single USA Experiment native Bees Restricted Phyto Idem. community perennial species 125 49º51' 8º35' NT Article 100-600 Single Germany Sandy ecosystems Bees Entire flora Phyto Kratochwil, A. et al. N E community 2009 - Apidologie 40: 634–650. 126 57º5'N 3º40' NT Internet 20-800 Single UK Boreal pine forest Moths Entire flora Phyto Devoto, M. et al. 2011- W community Ecol. Entomol. 36: 25– 35. 127 23º45'S 138º2 NT Article 1860- Single Australia Sand dunes Bees Entire flora Phyto Popic, T.J. et al. 2012 - 8'E 2040 community Aust. Ecol. 1 - 11. 128 55ºN 4ºW NT Internet 100-400 Single Scotland Livestock grazing All insects Entire flora Phyto Vanbergen, A. J. et al. community 2014 - Funct. Ecol. 28: 178–189. 129 12º43'S 41º42 T Article 900-1400 Single Brazil Agri-natural lands Hymenopter Entire flora Phyto Moreira, E. F. et al. 'W community a 2015 - PloS One 10: e0123628. 130 12º42'S 39º46 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Santos, G.M.M. et al. 'W community 2012- Biol Invasions 14: 2369–2378. 130 9º26'S 41º50 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Idem 'W community 130 7º22'S 36º15 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Idem 'W community 130 7º25'S 36º30 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Idem 'W community 130 6º35'S 37º20 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Idem 'W community 130 6º35'S 37º20 T Article 750-1100 Single Brazil Caatinga Bees Entire flora Phyto Idem 'W community 131 39º48' 2º47' NT Article 1100 Single Spain Mountain scrublands Entire Entire flora Phyto Tur, C. et al. 2014 - J. N E community Community Anim. Ecol. 83: 306– 56
317. 131 39º48' 2º47' NT Article 1400 Single Spain Mountain scrublands Entire Entire flora Phyto Idem N E community Community 134 49º47' 9º56' NT Internet 100-600 Single Germany NA Entire Entire flora Phyto Junker, R. R. et al. N E community Community 2013- Funct. Ecol. 27: 329–341. 136 38º5'N 2º45' NT Internet 1615 Single Spain Pine forest All insects Entomophilous Phyto Fortuna, M. A. et al. O community plant species 2008 - Ecol. Lett. 11: 490–498. 138 21º30'S 47º50 T Article 750-1100 Single Brazil Savannah Social wasps Entire flora Phyto Mello, M. A. R. et al. 'W community 2011- Acta Oecologica 37: 37–42. 138 22º15'S 47º0' T Article 750-1100 Single Brazil Savannah Social wasps Entire flora Phyto Idem W community 138 29º43'S 52º25 T Article 750-1100 Single Brazil Atlantic forest Social wasps Entire flora Phyto Idem 'W community 138 29º30'S 5º10' T Article 750-1100 Single Brazil Atlantic forest Social wasps Entire flora Phyto Idem W community 138 12º42'S 39º46 T Article 750-1100 Single Brazil Caatinga Social wasps Entire flora Phyto Idem 'W community 138 12º27'S 41º27 T Article 1000- Single Brazil rocky outcrops Social wasps Entire flora Phyto Idem 'W 1400 community 139 48ºN 8ºE NT Internet 100-600 Single Germany Grasslands All insects Entire flora Phyto Weiner, C. N. et al. community 2011 - Basic Appl. Ecol. 12: 292–299. 140 39ºN 54ºW NT Article 500-2100 Single USA Experiment native Bees Controlled Phyto MacLeod, M. et al. community perennial species experiment 2016 - Ecology in press. 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Carstensen, D.W. et al. 'W 1400 community Community 2016- Ecology 97(5): 1298–1306. 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 57
144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 144 19º21'S 43º31 T Internet 1000- Single Brazil rocky outcrops Entire Entire flora Phyto Idem 'W 1400 community Community 146 39º46' 3º7' NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Castro-Urgal, R.C. and N W community Community Traveset, A. 2014 - Bot. Jour. of the Linn. Soc. 174: 478–488. 146 39º44' 3º26' NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem N E community Community 146 29º12' 13º25 NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem N 'W community Community 146 29º16' 13º30 NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem N 'W community Community 147 15º25' 61º20 T Article 3-847 Single Dominica Coastal dry scrub Hummingbir Entire flora Phyto Dalsgaard, B. et al. N 'W community woodland/Montane ds and 2008- Oikos 117: 789– thicket-elfin woodland insects 793. 147 12º7'N 61º40 T Article 30-715 Single Grenada Idem Idem Entire flora Phyto Idem 'W community 149 22º49'S 47º49 T Article 130-1200 Single Brazil Semi-deciduous forest Entire Specific family Phyto Genini, J. et al. 2010 - 'W community Community Biol. Letters 6: 494– 497. 149 22º49'S 47º49 T Article 130-1200 Single Brazil Semi-deciduous forest Entire Specific family Phyto Idem 'W community Community 151 23ºS 46ºW T Internet NA Compilation Brazil NA Hummingbir Restricted Phyto Maruyama, M.P.K. et ds al. 2015- Oecologia 178:783–793. 152 19º36' 9º22' T Article 20 Single Mexico Dune pioneers, sand Bees Entire flora Phyto Ramírez-Flores, V. A. N W community dune scrub, tropical et al. 2015- dry and deciduous Sociobiology in press. forests, mangrove and freshwater swamps 152 19º42' 87º52 T Article 30 Single Mexico Subdeciduous tropical Bees Entire flora Phyto Idem N 'W community forest and secondary vegetation 152 18º14' 97º26 T Article 600-3000 Single Mexico Srubs with spines, Bees Entire flora Phyto Idem N 'W community columnar cacti associations, and Yucca sp. associations 58
152 19º12' 90º53 T Article 1010 Single Mexico Rustic coffee Bees Entire flora Phyto Idem N 'W community plantation, montane forest 154 27ºN 17ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Trojelsgaard, K. et al. community Community 2015- Proc. R. Soc. B Biol. Sci. 282: 20142925. 154 28ºN 17ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem community Community 154 27ºN 15ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem community Community 154 28ºN 13ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem community Community 154 28ºN 16ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem community Community 154 28ºN 16ºW NT Article 12-1200 Single Spain NA Entire Entire flora Phyto Idem community Community 154 28ºN 14ºW NT Article NA Single Western NA Entire Entire flora Phyto Idem community Sahara Community 160 55ºN 15ºW NT Internet 100-400 Single Scotland Deciduous woodland All insects Entire flora Phyto Vanbergen, A. J. et al. community 2013- Funct. Ecol. 28: 178–189. 162 19º10' 48º23 T Article 130-1200 Single Brazil Savanna Hummingbir Entire flora Phyto Maruyama, P. K. et al. N 'W community ds 2014 - Biotropica 46: 740–747. 163 22ºN 81ºW T Article 500-1200 Compilation Cuba Swamp forest Hummingbir Entire flora Phyto Maruyama, M.P.K. et ds al. 2016 - Diversity Distrib. 22: 672–681. 163 18ºN 66ºW T Article 1000- Compilation Puerto Rico Elfin forest Hummingbir Entire flora Phyto Idem 1300 ds 163 15ºN 61ºW T Article 3-847 Compilation Dominica Coastal dry scrubland Hummingbir Entire flora Phyto Idem ds 163 12ºN 61ºW T Article 30-715 Compilation Grenada Rainforest Hummingbir Entire flora Phyto Idem ds 163 10ºN 61ºW T Article 150-200 Compilation Trinidad Mixed forest Hummingbir Entire flora Phyto Idem ds 163 5ºN 73ºW T Article 2150- Compilation Colombia Andean humid Hummingbir Entire flora Phyto Idem 3100 montane forest ds 59
163 5ºN 73ºW T Article 2150- Compilation Colombia Andean humid Hummingbir Entire flora Phyto Idem 3100 montane forest ds 163 4ºN 75ºW T Article 2150- Compilation Colombia Andean second growth Hummingbir Entire flora Phyto Idem 3100 humid forest ds 163 4ºN 75ºW T Article 2150- Compilation Colombia Andean second growth Hummingbir Entire flora Phyto Idem 3100 humid forest ds 163 0ºN 78ºW T Article 15-580 Compilation Ecuador Andean rainforest Hummingbir Entire flora Phyto Idem mid-elevation ds 163 3ºN 70ºW T Article 2150- Compilation Colombia Amazonian rainforest Hummingbir Entire flora Phyto Idem 3100 ds 163 22ºN 45ºW T Article 130-1200 Compilation Brazil Montane Forest Hummingbir Entire flora Phyto Idem ds 163 23ºN 45ºW T Article 130-1200 Compilation Brazil Montante Atlantic Hummingbir Entire flora Phyto Idem forest ds 163 23ºN 44ºW T Article 130-1200 Compilation Brazil Atlantic forest Hummingbir Entire flora Phyto Idem ds 163 23ºN 45ºW T Article 130-1200 Compilation Brazil Secondary Atlantic Hummingbir Entire flora Phyto Idem forest ds 163 23ºN 44ºW T Article 130-1200 Compilation Brazil Restinga, Atlantic Hummingbir Entire flora Phyto Idem forest ds 163 23ºN 45ºW T Article 130-1200 Compilation Brazil Coastal Atlantic Forest Hummingbir Entire flora Phyto Idem ds 163 23ºN 45ºW T Article 130-1200 Compilation Brazil Coastal cloud Atlantic Hummingbir Entire flora Phyto Idem forest ds 163 25ºN 48ºW T Article 130-1200 Compilation Brazil Atlantic forest Hummingbir Entire flora Phyto Idem ds 163 27ºN 49ºW T Article 130-1200 Compilation Brazil Atlantic forest Hummingbir Entire flora Phyto Idem ds 163 31ºN 52ºW T Article 500-1600 Compilation Brazil Pampa Hummingbir Entire flora Phyto Idem ds 164 NA NA T NA NA Compilation Brazil NA Bees Entire flora Phyto Biesmeijer, J. C. et al. 2005- Biota Neotropica 5: 85–93. 165 18º34' 95º4' T Article 0-150 Single Mexico Forest fragments All insects Palm Phyto Dáttilo, W. et al. 2015- N W community PLOS ONE 10: e0121275. 167 NA NA T NA 130-1200 Compilation Brazil Tropical dry forest Bees Entire flora Phyto Giannini, T. C. et al. 2015- Plos One 10: e0137198. 60
167 NA NA T NA 130-1200 Compilation Brazil Tropical dry forest Bees Entire flora Phyto Idem
167 NA NA T NA 1073– Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem 1260 167 NA NA T NA 1073– Compilation Brazil Agroecosystem Bees Entire flora Phyto Idem 1260 167 NA NA T NA 1073– Compilation Brazil Agroecosystem Bees Entire flora Phyto Idem 1260 167 NA NA T NA 130-1200 Compilation Brazil Tropical dry forest Bees Entire flora Phyto Idem
167 NA NA T NA 1073– Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem 1260 167 NA NA T NA 1073– Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem 1260 167 NA NA T NA 1073– Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem 1260 167 NA NA T NA 900-1400 Compilation Brazil Urban area Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Tropical moist forest Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Tropical savanna Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Mangrove Bees Entire flora Phyto Idem
167 NA NA T NA 130-1200 Compilation Brazil Tropical moist forest Bees Entire flora Phyto Idem
167 NA NA T NA 900-1400 Compilation Brazil Urban area Bees Entire flora Phyto Idem
167 NA NA T NA 900-1400 Compilation Brazil Urban area Bees Entire flora Phyto Idem
167 NA NA T NA 900-1400 Compilation Brazil Urban area Bees Entire flora Phyto Idem
167 NA NA T NA 900-1400 Compilation Brazil Urban area Bees Entire flora Phyto Idem 61
167 NA NA T NA 1325 Compilation Brazil Tropical moist forest Bees Entire flora Phyto Idem
168 49ºE 9ºE NT Internet 100-600 Single Germany NA Entire Entire flora Phyto Junker, R. R. et al. community Community 2010 - J. Anim. Ecol. in press. 168 49ºE 9ºE NT Internet 100-600 Single Germany NA Entire Entire flora Phyto Idem community Community 169 35ºS 18ºE NT Article 320 Single South Wine region All insects Entire flora Phyto Kehinde, T. and community Africa Samways, M. J. 2014 - Anim. Conserv. 17: 401–409. 170 38ºN 122º NT Article 500-2100 Single USA Serpentine seep Entire Entire flora Phyto Koski, M. H. et al. W community communities Community 2015 - Arthropod-Plant Interact. 9: 9–21. 171 46º13' 10º40 NT Article 2425- Single Italy Glacier foreland All insects Two plant Phyto Losapio, G. et al. N 'E 2560 community species 2015- Ecol. Model. 314: 73–79. 172 39º10' 26º20 NT Article 135-215 Single Greece Semi-natural habitat in All insects Entire flora Phyto Lázaro, A. et al. 2016 - N 'E community the east Mediterranean Ecol. Appl. 26: 796– 807. 174 NA NA T NA 1325 Compilation Brazil Atlantic forest Oil bees Oil Flowers Phyto Mello, M. A. R. et al. 2013- Biotropica 45: 45–53. 174 NA NA T NA 750-1100 Compilation Brazil Caatinga Oil bees Oil Flowers Phyto Idem
174 NA NA T NA 1073– Compilation Brazil Savanna Oil bees Oil Flowers Phyto Idem 1260 174 NA NA T NA 750-1100 Compilation Brazil Restinga Oil bees Oil Flowers Phyto Idem
174 NA NA T NA 750-1100 Compilation Brazil Sand Dunes Oil bees Oil Flowers Phyto Idem
174 NA NA T NA 130-1200 Compilation Brazil Southern Grasslands Oil bees Oil Flowers Phyto Idem
178 32º4'S 19º04 NT Article 320 Single South Vegetation transitions Entire Entire flora Phyto Pauw, A. and Stanway, 'E community Africa Community R. 2015- J. Biogeogr. 42: 652–661. 180 53ºN 6ºW NT Internet 95-268 Single Ireland lowland permanent All insects Entire flora Phyto Power, E. F. and Stout, community grassland J. C. 2011 - J. Appl. Ecol. 48: 561–569. 62
182 35º50'S 72º30 NT Article 2700- Single Chile Deciduous forest All insects Entire flora Phyto Rivera-Hutinel, A. et 'W 3100 community al. 2012 - Ecology 93: 1593–1603. 183 49º53' 97º08 NT Article 100 Single Canada Tall-grass prairie All insects Entire flora Phyto Robson, D.B. 2008 - N 'W community Botany 86: 1266-1278. 183 49º7'N 96º40 NT Article 100 Single Canada Tall-grass prairie All insects Entire flora Phyto Idem 'W community 185 23ºS 46ºW T Internet 130-1200 Compilation Brazil Atlantic Rain forest Moths Entire flora Zoo Sazatornil, F. D. et al. 2016 - J. Anim. Ecol. 1-9. 185 23ºS 46ºW T Internet 1073– Compilation Brazil Cerrado Moths Entire flora Zoo Idem 1260 185 34ºS 34ºE NT Internet 500-1200 Compilation Argentina Transition zone Moths Entire flora Zoo Idem between western Chaco woodland and Yungas montane rain forest 185 34ºS 34ºE NT Internet 500-1200 Compilation Argentina Transition zone Moths Entire flora Zoo Idem between western Chaco woodland and Yungas montane rain forest 185 34ºS 34ºE NT Internet 500-1200 Compilation Argentina Chaco montane dry Moths Entire flora Zoo Idem woodland 186 40º9'N 8º24' NT Article 1900 Single Portugal Rural area Entire Controlled Phyto Ferrero, V. et al. 2013- W community Community experiment Biol. Invasions 15: 2347–2358. 186 40º9'N 8º24' NT Article 1900 Single Portugal Rural area Entire Controlled Phyto Idem W community Community experiment 189 2º50'S 79º9' T Article 3000- Single Ecuador Forest Hummingbir Entire flora Phyto Tinoco, B. A. et al. W 3200 community ds 2016 - Oikos in press. 189 2º50'S 79º9' T Article 3000- Single Ecuador Secondary forest Hummingbir Entire flora Phyto Idem W 3200 community ds 189 2º57'S 79º5' T Article 3000- Single Ecuador Pasture grass Hummingbir Entire flora Phyto Idem W 3200 community ds 190 3º47'S 70º25 T Article 2150- Single Colombia Amazonian rainforest Hummingbir Entire flora Zoo Rodríguez-Flores, C. et 'W 3100 community ds al. 2012- Ornitologia Neotropical 25: 85- 100. 63
191 49ºE 15ºW NT Article 100-400 Single Czech Meadow All insects Entire flora Phyto Janovsky, Z. et al. community Republic 2013- PLoS ONE 8: e77361. 192 6º5'N 10º18 T Article 2200 Single Cameroon Stream mantel Sunbirds Birds- Phyto/Zoo Janececk, S. et al. 2012 'E community vegetation pollinated - Biol. Journ. of the plants Linn. Soci. 1-13. 193 19°36' 96°2 T Article 100 Single Mexico Tropical dry and Entire Entire flora Phyto Hernández-Yáñez, H. N 2'W community deciduous forests, Community et al. 2013 – sand dune vegetation, Sociobiology. mangrove forest, freshwater marsh, and flooded deciduous forest 194 21°24’ 41°0 T Article 1200 Single Brazil Lowland seasonal Entire only Phyto Benevides, C. R. et al. S 4’W community semi-deciduous forest Community Passifloraceae 2013 – Sociobiology. 194 21°33’ 41°0 T Article 130 Single Brazil Lowland seasonal Entire only Phyto Idem S 2’ W community semi-deciduous forest Community Passifloraceae -Funil Forest 195 71°N 52°W NT Article 180 Single Greenland Mosaic of Betula- Entire Entire flora Phyto Lundgren, R. and community dominated heath and Community Olesen, J. M. 2005 - bare rock Arct. Antarct. Alp. Res. 37: 514–520. 196 19°05′ 48°2 T Article 750–1260 Single Brazil Acerola orchards Insects Plant species Zoo Vilhena, A. M. G. F. et 48″S 1′05″ community (Centridini with pollen al. 2012 - Apidologie W bees) found on the 43: 51–62. scopae of Centridini visitors 197 37°07' 08°3 NT Article 1900 Single Portugal Wetlands, dunes and Moths likely nectar Zoo Banza, P. et al. 2015- N 5'W community farmland (nocturnal sources for Insect Conserv. Divers. Lepidopteran moths 8: 538–546. ) 198 5°30'11 75°5 T Article 2150- Single Colombia Pluvial cloud forest Entire Entire flora Phyto Cuartas-Hernández, S. "N 1'7"E 3100 community Community and Medel, R. 2015- PLOS ONE 10: e0141804. 199 47°32' 12°5' NT Article 600-2000 Single Germany Grasslands Entire Entire flora Phyto Hoiss, B. et al. 2015 - N E community Community Glob. Change Biol. 21: 4086–4097. 64
200 45°00' 109° NT Article 3000 Single USA The area’s vegetation Entire Entire flora Phyto Simanonok, M. P. and N 25'W community is dominated by Geum Community Burkle, L. A. 2014 - rossii, Deschampsia Ecosphere 5: art149- spp., Carex spp., Salix art149. spp., and several different cushion plants 201 52°14' 7°55' NT Internet 95-268 Single Ireland Heathland invaded by All insects focused on Phyto Stout, J. C. and Casey, N W community Rhododendron Rhododendron L. M. 2014- Acta ponticum ponticum– Oecologica 55: 78–85. entire flora 202 37°8′0 3°21′ NT Article 1723 Single Spain Biennial, semelparous All insects Only Erysimum Phyto Valverde, J. et al. 7′′N 71′′ community herb endemic mediohispanicu 2016- Oikos 125: 468– W m(Brassicaceae 479. ) 203 30°14′ 84°3 NT Internet 500-2100 Single USA Pine savannah Entire Entire flora Phyto Spiesman, B. J. and 10″N 9′56″ community Community Gratton, C. 2016 - W Ecology 97: 1431– 1441. 203 43.07° 89.41 NT Article 500-2100 Single USA Seminatural prairies Entire Entire flora Phyto Spiesman, B. J. and N ° W community Community Gratton, C. 2016- Ecology 97: 1431– 1441. 204 52.894 6.400 NT Internet 165 Single Ireland Native mixed or oak All insects focused on Phyto Tiedeken, E. J. and N E community woodland forest sites Rhododendron Stout, J. C. 2015- invaded by R. ponticum– PLOS ONE 10: ponticum entire flora on e0119733. the sites invaded by R. ponticum 204 53.060 6.102 NT Internet 160 Single Ireland Idem All insects Idem Phyto Idem N E community 204 53.192 6.427 NT Internet 284 Single Ireland Idem All insects Idem Phyto Idem N E community 204 53.017 6.274 NT Internet 185 Single Ireland Idem All insects Idem Phyto Idem N E community 205 20º03'S 44°3 T Article 80 Single Madagascar Tropical dry Entire Entire flora Phyto Waser, N.M. et al. 9'E community deciduous forest Community 2010 -Journ. of Pollin. Ecol. 2(1): 1-6. 65
206 13º13'S 72°2' T Article 2700– Single Peru Several in the andean Entire Entire flora Phyto Watts, S. et al. 2016 - W 3200 community mountains Community Ann. of Bot. 1 - 15. 206 13º13'S 72°2' T Article 3000– Single Peru Several in the andean Entire Entire flora Phyto Idem W 3400 community mountains Community 206 13º13'S 72°2' T Article 3500– Single Peru Several in the andean Entire Entire flora Phyto Idem W 3800 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community 206 13º13'S 72°2' T Article 3700– Single Peru Several in the andean Entire Entire flora Phyto Idem W 4200 community mountains Community
66
CAPÍTULO 2
Influences of sampling effort on detected patterns and structuring processes of a Neotropical plant-hummingbird network **
Jeferson Vizentin-Bugonia,b*, Pietro Kiyoshi Maruyamaa, Vanderlei Júlio Debastianic, Leandro da Silva Duartec, Bo Dalsgaardb and Marlies Sazimaa
** Artigo publicado como: Vizentin-Bugoni, J, PK Maruyama, VJ Debastiani, LD Duarte, B Dalsgaard & M Sazima. Influences of sampling effort on detected patterns and structuring processes of a Neotropical plant-hummingbird network. Journal of Animal Ecology 85(1):262-272.
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aUniversidade Estadual de Campinas (Unicamp), Cx. Postal 6109, CEP: 13083-862, Campinas, SP, Brasil bCenter for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark cPrograma de Pós-Graduação em Ecologia, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, RS, Brasil
* Corresponding author: [email protected]
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Summary
1. Virtually all empirical ecological interaction networks to some extent suffer from undersampling. However, how limitations imposed by sampling incompleteness affect our understanding of ecological networks is still poorly explored, which may hinder further advances in the field. 2. Here, we use a plant-hummingbird network with unprecedented sampling effort (2,716 hours of focal observations) from the Atlantic Rainforest in Brazil, to investigate how sampling effort affects the description of network structure (i.e. widely used network metrics) and the relative importance of distinct processes (i.e. species abundances vs traits) in determining the frequency of pairwise interactions. 3. By dividing the network into time slices representing a gradient of sampling effort, we show that quantitative metrics, such as interaction evenness, specialization
(H2'), weighted nestedness (wNODF) and modularity (Q; QuanBiMo algorithm), were less biased by sampling incompleteness than binary metrics. Furthermore, the significance of some network metrics changed along the sampling effort gradient. Nevertheless, the higher importance of traits in structuring the network was apparent even with small sampling effort. 4. Our results (i) warn against using very poorly sampled networks as this may bias our understanding of networks, both their patterns and structuring processes, (ii) encourage the use of quantitative metrics little influenced by sampling when performing spatio-temporal comparisons, and (iii) indicate that in networks strongly constrained by species traits, such as plant-hummingbird networks, even small sampling is sufficient to detect their relative importance for the structure of interactions. Finally, we argue that similar effects of sampling are expected for other highly specialized subnetworks.
Key-words: abundance, Atlantic Rainforest, connectance, forbidden links, modularity, nestedness, network metrics, neutrality, NODF, plant-pollinator networks, QuanBiMo, specialization
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Introduction
In the last decades, the understanding of mutualistic plant-animal interactions at the community scale has greatly advanced due to an increasing use of network approaches (Jordano 1987, Bascompte 2009, Dormann et al. 2009, Vázquez et al. 2012, 2015, Schleuning et al. 2014, Kissling & Schleuning 2015). This has revealed several consistent patterns in the structure of bipartite plant-animal networks. Notably often only a small proportion of possible links are actually realized, resulting in low connectance (Jordano 1987); networks are often nested and modular (Bascompte at el. 2003, Olesen et al. 2007); the degree distribution is skewed with most species having few links and few species having many links (Jordano, Bascompte & Olesen 2003); and there is high asymmetric dependence between partners in a given network (Jordano 1987, Bascompte, Jordano & Olesen 2006). These structural properties are expected to be associated with community stability and maintenance (Bascompte, Jordano & Olesen 2006, Thébault & Fontaine 2010), ecosystem functioning (Schleuning, Fründ & Garcia 2015) and to have implications for conservation (Tylianakis et al. 2010).
An array of ecological, historical and evolutionary processes may influence network structure (Vázquez et al. 2009a, Vázquez, Chacoff & Cagnolo 2009b, Dalsgaard et al. 2013, Schleuning et al. 2014, Vizentin-Bugoni, Maruyama & Sazima 2014, Martín González et al. 2015, Renoult et al. 2015). Additionally, chance meeting governed by species abundances, i.e. neutrality, may determine the structure of networks (Stang, Klinkhamer & van der Meijden 2007, Vázquez et al. 2009b). In this regard, several recent studies have investigated the relative importance of distinct processes in structuring mutualistic networks. Most of these studies have found species abundances as a major factor determining interactions in the networks, with a complementary role of species traits (e.g. Stang, Klinkhamer & van der Meijden 2007, Vázquez et al. 2009b, González-Castro et al. 2015, Olito & Fox 2015). Nevertheless, in some more specialized systems, such as Neotropical plant-hummingbird networks, contrasting results were found with species traits predicting interactions better than abundances (e.g. Maruyama et al. 2014, Vizentin-Bugoni, Maruyama & Sazima 2014). Importantly, sampling may also influence the detected network structure making it critical to consider in order to disentangle biological processes from methodological shortcomings (Blüthgen et al. 2008, Ulrich 2009, Vázquez et al. 2009a, Chagnon 2015). Thus, limitations related to sampling may reduce our understanding of the structure of interaction networks (Vázquez et al. 2009b) and make spatio-temporal and taxonomic comparisons 70
problematic (Blüthgen et al. 2007, Chacoff et al. 2012, Fründ, McCann & Williams 2015). Despite the fact that sampling incompleteness may influence network patterns, most studies provide no estimate of sampling effort, but assume that interactions in the given community were sufficiently sampled to describe the associated network (Ollerton & Cranmer 2002, Vázquez et al. 2009a, Gibson et al. 2011, Chacoff et al. 2012, Rivera-Rutinel et al. 2012, Fründ, McCann & Williams 2015). Considering that even estimates of species making up the community suffer from difficulties of sampling (Gotelli & Colwell 2001), limitations of sampling should be analogous for interaction networks which represent distinct combinations of species.
To date, only a handful of studies have explicitly evaluated the effects of sampling incompleteness on the description of network structure. These studies found high variation in the number of detected species and total number of links and suggest that some metrics, i.e. aggregated statistics describing network patterns, are prone to sampling bias (e.g. Goldwasser & Roughgarden 1997, Martinez et al. 1999, Banašek-Richter, Cattin & Bersier 2004, Nielsen & Bascompte 2007, Blüthgen et al. 2008, Dormann et al. 2009, Rivera-Hutinel et al. 2012, Fründ, McCann & Williams 2015). Nevertheless, some important gaps remain to be addressed regarding the importance of sampling completeness. Notably, little is known about the influence of sampling effort on quantitative network structure, i.e. metrics calculated from networks that take into account the strength of interactions (but see Blüthgen, Menzel & Blüthgen 2006 and Blüthgen et al. 2008 for some indices; and Fründ, McCann & Williams 2015, for numerous metrics using simulated data) and we know of only one attempt at investigating the effects of sampling incompleteness on the understanding of the processes structuring ecological networks (Olito & Fox 2015). Notably, all information about the influence of sampling on detected network structures are based on either simulated networks (e.g. Fründ, McCann & Williams 2015) or are from less specialized and potentially incompletely sampled networks from the temperate region (e.g. Nielsen & Bascompte 2007; Hegland et al. 2010, Chacoff et al. 2012, Rivera-Hutinel et al. 2012). Although a reasonable description of plant-pollinator network structure may be possible with a sampling focused during the peak-season in temperate areas (Hegland et al. 2010), such a short sampling span is unlikely to be enough for networks from tropical areas, where many pollinators' life and/or activity span longer periods and their foodplants typically present staggered flowering all year-round. 71
By employing an unprecedent sampling effort across two years and focusing on a specialized and relatively easily sampled sub-networks of plants and hummingbirds, we built a network with unusually high sampling completeness. This characteristic of our study system offers an unique opportunity to evaluate the susceptibility of network patterns and structuring processes under little variation in the network species richness, as well as greater confidence that unobserved interactions actually do not happen. Specifically, we ask : 1) to what extent is network structure, as measured by both binary and quantitative metrics, affected by sampling effort? and 2) does sampling effort affect the relative importance of distinct processes (i.e. species abundance vs traits) in determining pairwise frequencies of interaction in the network? As a model system, we use a plant-hummingbird subnetwork embedded in a larger diverse network from an species-rich area in the Brazilian Atlantic Rainforest where forbidden links are known to play a major role in structuring the interactions (Vizentin-Bugoni, Maruyama & Sazima 2014).
Materials and methods
Study area and data collection
The data used here represents an update of our previous study (Vizentin-Bugoni, Maruyama & Sazima 2014) by covering additional plant species and one more year of sampling. Data were collected along 12,000m of trails in the Atlantic Rainforest from Santa Virgínia Field Station at Serra do Mar State Park, SE Brazil (23º17’S – 23º24’S and 45º03’W – 45º11’W) from September 2011 to August 2013, over 4–10 days per month. We sampled interaction by observing at least three individuals of ornithophilous or potentially hummingbird-pollinated plants, away 100m (or more) from each other along the trails. We included in the network all species blooming at least once during our 2-years of sampling and to which it was possible to observe for 50 hours (Table A1). Five species with slightly lower sampling were also included: Edmundoa lindeni (Regel) Leme (38h), Macrocarpaea rubra Malme, Sinningia cooperi (Paxton) Wiehler, Wittrockia superba Lindm. (44h each) and Nidularium rutilans E. Morren (46h). In total, we carried out 2,716 hours of focal observation in which we identified the visiting hummingbirds that touched anthers and stigmas (Table A2) as well as the precise moment the visits occurred across the 50h of sampling for each plant species. Individual-based rarefaction indicates that most links in the community were recorded (Figure B1) and links richness estimation suggests that we observed ca. 82% of all 72
interactions (123 observed links from 150.3 ± 16.9 expected by Chao 2 estimator; Chacoff et al. 2012).
We monitored flower and hummingbird phenologies by assigning their presence or absence monthly (Table A3-A4). We did not consider a finer time scale for phenology as it was not possible to sample all co-flowering plant species within the same hour or day. For each of the hummingbird-pollinated species, we measured corolla depth from ca. ten flowers collected in the field (Table A5) and hummingbird bill lengths were measured from museum specimens (Table A6). In order to better estimate the hummingbirds' ability to access nectar in deep flowers, we added 80% as a correction for tongue extension to bill length estimates. This correction is based on measures from Selasphorus rufus tongue extension (Grant and Temeles 1992) and keeps the proportionality in which longer-billed species tend also to have longer tongues (Paton & Collins 1989) (Table A6). Using an alternative 33% threshold to correct for tongue extension (as in Vizentin-Bugoni, Maruyama & Sazima 2014) did not alter our results (Table A10). Plant abundance was quantified as the total number of open flowers counted per species monthly along all trails (Table A1). In order to obtain data for all species, hummingbird abundances were measured as the proportion of days a species was recorded in our 130 days in the field (Table A2). We used this measure because some rarer species were not recorded during our counting of hummingbirds. The reliability of this estimation of hummingbird abundance is supported by its positive and strong correlation with the number of aural and visual contacts of species across ten transects sampled monthly (100m; Figure B2; Table A7). All other sampling details on phenological, morphological and abundances data followed Vizentin-Bugoni, Maruyama & Sazima (2014).
Data analysis
(a) Interaction networks along a sampling effort gradient. Plant-hummingbird interaction data were assembled into a quantitative bipartite network, with pairwise interaction frequencies representing the number of legitimate visits between a given hummingbird and plant species. Thus, each cell (aij) represents the number of interactions between a pollinator (i) and plant (j) species. To construct a sampling gradient, we pooled all interactions observed into time slices of 1h of sampling effort, for instance, the 1st-hour time slice was composed by all interactions recorded by plants and pollinators in the first hour of observation to each plant species. If a plant species was not visited in this interval, it was not included in this specific time slice. Then, by summing time slices sequentially, we created a 73
gradient of networks with increasing sampling effort. Therefore, the gradient was composed of 50 networks, from one to 50 h of observation for each plant species in the community.
(b) Network patterns. In order to evaluate how sampling effort influences network patterns, we computed network metrics for the 50 networks with accumulated sampling effort. We first calculated descriptors of networks including the number of plants, richness of pollinators, number of links (i.e. pairwise combinations) and number of interactions recorded. Moreover, we also calculated binary and quantitative network metrics which are widely used in the literature and cover distinct network properties: Connectance is defined as the proportion of possible links actually observed in the network; Interaction evenness is a measure of variation in frequencies of interactions, i.e. visits, among distinct links (e.g. Bersier, Banašek-Richter & Cattin 2002). Specialization was quantified by the H2' index, which is an application of information theory to quantitative networks and can be interpreted as how species partition their interactions in the network (Blüthgen, Menzel & Blüthgen 2006); Nestedness was measured by NODF index, both for binary and quantitative networks, the latter denoted as wNODF (Almeida-Neto & Ulrich 2011). Modularity (Q) was also estimated for both binary and quantitative matrices using the QuanBiMo optimization algorithm (Dormann & Strauss 2014). As the QuanBiMo algorithm is stochastic, the values found can be slightly different between runs; we followed Maruyama et al. (2014) and Schleuning et al. (2014) and account for this by choosing the higher values from 10 independent runs set to 107 swaps to each network. The significance of metrics was assessed by comparing the observed values to those obtained by 1,000 null model randomizations, with the exception of modularity, which was tested against 100 runs due to large computational time required by the algorithm. Calculation of NODF significance was performed using the software ANINHADO and tested with the null model in which interactions are distributed proportionally to the marginal totals of the network (Guimarães & Guimarães 2006). For binary Q, we used the shufle.web function that relocates entries in the matrix keeping the original dimensions and, for all quantitative indices, we used the vaznull null model which keeps the marginal totals and the connectance in the network, both ran in R package bipartite (Dormann, Gruber & Fründ 2008). Metric values were considered significant if they did not overlap 95% of confidence intervals of the randomized values.
(c) Rarefaction-like approaches for interaction networks. One can argue that the order of the focal observations in the field used to assemble the network could affect metric values, especially across the sampling gradient. For instance, if the first plant sampled was an 74
individual located in a less visible spot, then it could be less visited not by barriers related to the plant trait, but by some stochasticity. Therefore, this would affect the values of metrics calculated for each time slice. In order to account for this potential bias, we used an analysis inspired in sample-based rarefaction method used for species diversity (Gotelli & Colwell 2011). In this case, our samples were time slices of 1h of observation to each plant species, with the respective links (and number of visits) observed. As we observed 50h, each plant species also had 50 time-slices. We then randomly assembled networks with accumulating sampling effort from 1h to 50h of accumulated observation. To each class of sampling effort (1 to 50h) we generated 1000 randomized networks and calculated all network metrics to contrast to the observed variation in the metric values with increasing sampling.
We also checked the robustness of our findings by simulating an individual- based rarefaction-like gradient of sampling effort, also inspired on Gotelli & Colwell (2001). We successively removed 10% of the interactions recorded from the complete matrix (50h), from 10% to 90% of interactions removal and all metrics were recalculated for each removal level. We then performed 1000 repetitions for each removal level and calculated all above mentioned metrics. Results are presented in Figure B3 which can be directly compared with Figure 1. As the results were roughly similar regardless of the methods, we kept the "sample- based rarefaction-like method", which better reflect sampling procedures in the field, which are constructed through timed observations (see Gibson et al. 2011).
(d) Processes entangling interactions. We investigated the relative importance of phenological overlap, morphological matching, and species abundance (or combinations of them) in predicting pairwise frequencies of interactions. We used the conceptual and analytical framework proposed by Vázquez et al. (2009b) and subsequent adaptations by Vizentin-Bugoni, Maruyama & Sazima (2014) which is based on probability matrices (null models) and likelihood analysis. We produced eight probability matrices based on: Phenological overlap (P) - the probability of interaction between two species is given by the number of months a hummingbird and a flowering plant species overlapped in their occurrence (aij = 0 to 24 months); Morphological matching (M) - an interaction is allowed (aij = 1) only if the hummingbird bill + tongue length estimates exceed the floral corolla depth, otherwise the probability of interaction is zero (aij = 0); Abundances (A) - the probability of an interaction is proportional to the multiplication of the relative abundances of plant and hummingbird species in the community. In this sense, two abundant species have higher probability to interact than rarer species. Note that in A all species combinations are 75
allowed (aij > 0), while in P and M some interactions are considered "forbidden links" (aij = 0) when species do not overlap in time or have no morphological matching, respectively. In order to investigate potential effects of combined mechanisms on the network, we also multiplied these matrices (A, P and M) in all possible combinations by element-wise multiplication (Hadamard product), i.e. PM, PA, AM and APM. Finally, all matrices were normalized by dividing each cell by the matrix total. As a benchmark, we produced an additional matrix in which all species had the same probability to interact (Null).
We used likelihood analysis and model selection to compare the capacity of each probabilistic matrix (A, P, M, AP, AM, PM, APM and Null) in predicting frequencies of pairwise interactions in the observed matrix. Model matrices were considered equal when AIC ≤ 2 (Burnham & Anderson 2012). To penalize models with distinct complexities, we used as parameters the number of species contained in the matrix (plant + hummingbird species) multiplied by the number of probabilistic matrices considered in the model (i.e. A, P and M =1; AP, AM and PM = 2; and APM = 3; see Table A8). In order to identify whether the best predictor changes across the sampling gradient, we repeated this analysis in networks built with cumulative sampling effort (1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50h). Analyses on probability matrices were conducted using bipartite package in R, assuming that the probability of interactions follows a multinomial distribution (Vázquez et al. 2009b). Finally, in order to estimate the fit of the best model to the data, we performed a Spearman's rank-order correlation between the observed frequency of a pairwise interactions and its corresponding probability of interactions from the best model (i.e. the PM model, see Results). We repeated the correlation for each time-slice of sampling effort and presented the coefficient of correlation in Figure 2.
Results
Network structure and the metrics variation across sampling effort gradient
We recorded 55 hummingbird-pollinated plants and nine hummingbird species, which performed 2,793 visits distributed among 123 distinct pairwise interactions. The plant- hummingbird network presented moderate connectance (0.25), high interaction evenness
(0.85; 95% CI: 0.66-0.68), moderately high specialization (H2' = 0.47; 95% CI: 0.11-0.18); high nestedness when considering binary information (NODF = 66.15; 95% CI: 37.7-38.2), 76
but a non-nested pattern when considering quantitative data (wNODF = 29.50; 95% CI: 42.5- 55.2). Conversely, the network was marginally non-modular in the binary version (Q = 0.33; 95% CI: 0.31-0.34), but modular when considering quantitative information (Q = 0.41; 95% CI: 0.03-0.13), with four distinct modules (Figure 1; Table A9 and A11).
All nine hummingbird species were recorded with 3h of observation for all plants. Although some plant species only received visits after 33h of sampling, and were then included in the network, 96% of the species received at least one visit before completing 10h of observations (Fig 1; Table A9). In spite of the inclusion of most plants and hummingbirds early in the sampling gradient, new links were still recorded close to 50h of sampling.
Connectance, interaction evenness and H2' achieved reasonably stable values around 10-15h, and H2' became significantly higher than expected by the null model after 5h. On the other hand, values of NODF increased consistently along the sampling gradient, although already significant also after 5h. Weighted nestedness, wNODF, achieved an asymptotic value around 30h, but was only significant under very small sampling (1h of observation). Binary modularity, Q, progressively decreased with sampling completeness but was always non- significant; quantitative Q showed oscillation under small sampling but stabilized after 20h of observation. Although quantitative Q was significant after just one hour of observation, the number of identified modules stabilized only after 15h. The order of sample slices that we used to assemble the networks across the sampling gradient had no or only small influence on most metrics. Only connectance, wNODF and binary modularity presented slightly different values from those obtained in randomly reassembled networks under small sampling effort (< 15h; compare black lines with grey trends in Fig. 1, see also Fig. B3).
Processes structuring the network across sampling effort gradient
With very small sampling effort (≤ 2h) the Null model had the best predictive ability of the observed network (Figure 2, Table A10). However, after 3h of observation the model PM, which includes both phenological overlap and bill-corolla matching, had consistently the best ability to predict the frequency of pairwise interactions (Figure 2). Furthermore, after 15h of sampling effort the coefficient of correlation remained consistent with a slight increase up to r = 0.543 in the most complete network (50h).
Discussion 77
We provide a thorough evaluation on the effects of sampling effort on ecological networks by analysing widely used binary and quantitative metrics as well as on the processes defining interactions in networks. Our findings show that low sampling effort to some extent influences our understanding of interaction patterns, but has minor influence on the identification of processes structuring a tropical plant-hummingbird subnetwork. Specifically, we found that network metrics are not all equally affected by sampling: quantitative metrics tend to be more robust than binary ones, and the significance of network metrics change along the sampling gradient. These findings were consistent regardless of the methods applied to create the gradient of sampling effort, i.e. sample-based or individual-based rarefaction-like methods. Furthermore, inferences on the relative importance of processes determining frequencies of interaction were biased only under very small sampling effort, and we consistently identified the importance of traits as the main drivers of interactions as sampling increased.
Are detected network patterns biased at low sampling effort?
The Santa Virgínia network was structurally similar to other tropical plant- hummingbird networks, presenting more plants than hummingbird species, moderate connectance, intermediate specialization, low nestedness and high modularity (Maruyama et al. 2014; 2015, Vizentin-Bugoni, Maruyama & Sazima 2014, Martín González et al. 2015). The order of the focal observations, i.e. time slices, to assemble the network, had some effect on connectance, wNODF and binary modularity, at least under smaller sampling effort (ca. 15h; Figure 1). This further reinforces the idea that poorly sampled networks might lead to wrong inferences on network patterns. In addition to the methods chosen to assemble the networks (Gibson et al 2011), our findings thus reveal that the order and choice of which individual plant will be observed (and how many of them) can also influence some of the observed metrics. Since species richness in our system stabilizes after ca.10h, detected biases on the network metrics beyond 10h of sampling reflect the effect of addition of links and distribution of the interactions among links, more than the dimension of the network, as suggested previously (see below).
Effects of low sampling effort on network structure depend on the metric considered, and two different biases emerged: in the metric values per se and their significances. To understand these potential biases is crucial when comparing network metric values between webs, either to investigate geographical (e.g. Olesen and Jordano 2002, 78
Ollerton & Cranmer 2002, Banašek-Richter, Cattin & Bersier 2004, Dalsgaard et al. 2013, Schleuning et al. 2014, Martín González et al. 2015) or temporal patterns (e.g. Petanidou et al. 2008).
Connectance is known to be strongly biased by the addition of new species in the network (e.g., Jordano 1987, Banašek-Richter, Cattin & Bersier 2004, Nielsen & Bascompte 2007, Rivera-Hutinel et al. 2012, Fründ, McCann & Williams 2015, but see Martinez et al. 1999). Previous studies with plant-insect pollinator networks have found that connectance tends to decrease along the sampling gradient (Nielsen & Bascompte 2007, Rivera-Hutinel et al. 2012), which may be caused by the discovery of new species happening faster than of new links. This is not the case in our system, since network size reached a constant relatively quickly, i.e. most hummingbird species was recorded visiting a particular plant species early in the sampling gradient. This may partly be because hummingbirds are very active and need to frequently visit flowers to cope with their high metabolism (Suarez 1992), thus they are readily recorded and incorporated in the interaction network. In contrast to plant- hummingbird networks, one should expect stronger effects of sampling incompleteness on connectance in other subnetworks such as orchid-pollinator systems in which many species produce no or scarce nectar and interactions are rarely recorded (e.g. Ackerman, Rodriguez- Robles & Melendez 1994) or larger “full” networks, i.e. including several animal taxa (e.g. Danieli-Silva et al. 2012, Donatti et al. 2011).
Another widely used binary metric, NODF, consistently increased with species and links inclusion in the network (Figure 1). The same was found by Rivera-Hutinel et al. (2012), who argued that the increase in NODF could be related to the increase in network size. However, this argument is not supported here since network size did not increase much after ca. 10h of sampling, nevertheless NODF value kept increasing. This suggests that NODF is dependent on the detection of links for highly connected species, which are major contributor for high nestedness in networks (Almeida-Neto et al. 2008). Thus, the use of NODF to compare nestedness estimates across time, space or systems should be done carefully, and it is safer to compare this metric preferably for networks that had reached both a stable size and number of links. In this sense, binary nestedness seems to be more influenced by sampling incompleteness than previously thought (Nielsen & Bascompte 2007). In contrast, quantitative nestedness (wNODF) was more robust, but still tended to increase with sampling intensity, as also found by a simulation study (Fründ, McCann & Williams 2015). Even with stable network size, this metric was progressively affected by the detection 79
of new links (10-35h) and first stabilized after ca. 35h of sampling. This suggests that high sampling effort in order to accumulate most of the links may be needed before its use for spatio-temporal comparisons. The same may be expected for binary modularity (Q), though less so, since its value decreased consistently up to ca. 15h of sampling effort. Interestingly, the same trend of decreasing binary modularity along a sampling gradient was found for a plant-pollinator network from Chilean deciduous forest (Rivera-Hutinel et al. 2012). This suggests that the addition of new links had the effect of blurring the boundaries of modules.
Interaction evenness, specialization index (H2') and quantitative modularity (Q) were less prone to biases since all these weighted metrics achieved stable values already after ca. 10h of sampling, which argue for their use in comparative studies. The stabilization of all these metrics coincided with the stabilization of network size, which suggest that all are depended on network dimensions but little affected by addition of rarer links. In relation to
H2' specifically, our results agree with Blüthgen, Menzel & Blüthgen (2006) that found this metric to be less affected by sampling incompleteness; however contrary to what they suggested, it is possible that this index is also affected by network size. The influence of network size on H2' could explain the slight overestimation found by simulations of this index value on poor sampled networks (Fründ, McCann & Williams 2015). In regard to modularity, however, despite the stability of its value, modules identification under small sampling was sensitive to addition of species and new links and interactions (Table A9), suggesting that modules conformation can not be safely assessed in poorly sampled networks. Also, the high variation of the detected Q in the randomizations (grey lines in Figure 1) under small sampling effort, suggest that this metric is influenced by the identity of both species and links that are added. In sum, our empirical findings suggest that quantitative metrics are more robust to sampling effort than binary ones, as suggested previously for some food webs metrics (Bersier, Banašek-Richter & Cattin 2002, Banašek-Richter, Cattin & Bersier 2004) and simulated bipartite networks (Fründ, McCann & Williams 2015). Additionally, our results point out that network dimensions has some effect on most of the estimates metric values, since higher variation was prevalent under small sampling effort when network size was variable.
When considering metrics significance, small sampling leads to wrong conclusions depending on the metrics considered, as NODF and H2' became significant and wNODF became non-significant after ca. 5h of sampling. In short, both findings regarding the variation in metrics value and significance suggests that description of network patterns is 80
susceptible to bias by missing species, links and visits and an accumulated sampling effort of ca. 10-15 hours for each plant species seems necessary for our system consisting of plant- hummingbird interactions.
Are processes structuring networks biased at low sampling effort?
As many other tropical plant-pollinator communities, in Santa Virgínia there was strong variation in hummingbird bill and corolla length; plants presented short flowering periods and species bloomed sequentially (Tables A3-A6). All these factors together seem to be the primary mechanisms determining interactions between plants and hummingbirds, no matter how abundant species are (Wolf, Stiles & Hainsworth 1976, Maruyama et al. 2014, Vizentin-Bugoni, Maruyama & Sazima 2014). This conclusion is already apparent with only ≥ 3h of sampling, when morphological matching and temporal overlap appeared as the best predictors of the frequency of interactions. Interestingly, this happened before the stabilization of network dimensions (i.e., species richness), suggesting that when forbidden links are the most important predictors of frequencies of interactions, their importance is revealed even in incompletely sampled networks (here, with only 6% of the total sampling effort). The only previous study that also evaluated the influence of sampling on the inferences of processes determining interaction frequencies used a temperate plant-pollinator network (Olito & Fox 2015). They consistently found the phenological overlap as the best predictor of pairwise interactions. Despite of the more than two-fold sampling completeness of our tropical subnetwork (i.e., 82% here vs. 37% of the links identified in Olito & Fox 2015), both studies agree that the identification of the major processes structuring ecological networks may be possible even under small sampling effort.
Although we present an analysis for a single ecological subnetwork within a larger netwok, our findings may also apply to other types of mutualistic systems in the tropics, such as plant-frugivorous bird and plant-hawkmoth subnetworks, in which there is high morphological variation among species in traits important for determining interactions (e.g. sizes of mouth aparatus, fruits, flowers) and fruiting or flowering periods tend to be sequentially organized among species (Moermond & Denslow 1985, Cocucci et al. 2009, Amorim, Wyatt & Sazima 2014, González-Castro et al. 2015). It may also apply to antagonistic networks and entire food webs when traits are dominant structuring factors (Eklöf et al. 2014). On the other hand, larger networks such as those including multiple groups of pollinators, e.g. bees, flies, moths, birds (Danieli-Silva et al. 2012), or multiple 81
taxonomic groups of seed dispersers, e.g. birds, large and small mammals and fishes (Donatti et al. 2011), could each be influenced differently by sampling effort. This could be generated, for instance, by distinct rates of species and interactions accumulation among groups within the same network due differences in physiology, morphology and behavior, for example. Thus different taxonomic groups may demand distinct sampling efforts in order to satisfactorily describe the network structure. Geographically we may also expect differences in the influence of sampling effort as species rich communities in the tropics may be more prone to bias than temperate counterparts.
In sum, detected network patterns may be biased depending on the sampling effort employed to build the network and the metrics considered; evidence suggests that binary metrics are more influenced by sampling than quantitative metrics. However, for more specialized networks in which traits have a strong role in determining interactions, processes structuring the networks may be identified even under small sampling effort. The latter may even be the case for more generalized systems, such as temperate plant-insect pollination networks (Olito & Fox 2015). We argue that ecologists should better investigate the extent to which sampling artifacts bias network patterns, especially for lager networks in diverse tropical ecosystems. To circumvent sampling bias, we suggest using metrics little influenced by sampling (i.e. certain quantitative metrics) and gather more intensively sampled networks. This may include the use of complementary sampling methods, e.g. focal observation, spot censuses, and pollen loads analysis, which may be valuable ways to improve link detection.
Acknowledgements
We thank Instituto Florestal and Santa Virgínia Field Station staff for permission and facilities to collect the data. We also thank Carsten Dormann for suggestions on modularity calculations; Pedro Jordano and an anonymous reviewer for valuable suggestions; also Pedro J. Bergamo, Marcela Firens, Rafael Gomes, Cintia Kameyama, Raquel F. Monteiro and Marina Wolowski for assistance on plant identification; and Vinicius A. G. Bastazini for assistance with Figure 1. Financial support was provided by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) through a PDSE scholarship (processo: 99999.008012/2014-08) and a Ph.D scholarship to JVB, CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnológico) through a Ph.D scholarship to JVB and PKM 82
and a grant to MS; finally, JVB and BD thank the Danish National Research Foundation for its support of the Center for Macroecology, Evolution and Climate.
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FIGURE LEGENDS
Figure 1. Network patterns described by metrics calculated on the observed network (black line) or expected (gray overlapping lines) by randomizations (1,000 iterations) of the data across increasing sampling effort of a plant-hummingbird network in SE Brazil. Dashed black lines indicate mean and 95% CI. Note that quantitative metrics were, in general, more robust to low sampling effort. Note also the observed sequence of sampling (black lines) within the networks assembled randomly (1,000 grey lines) from a pool of observed samples (1h time slices), which suggest that random changes in the order and identity of plant individuals observed had some effect on the value of network metrics, especially under low sampling.
Figure 2. Ability of eight models to predict observed frequency of interaction between pairwise species over increasing sampling effort in a hummingbird-plant network. Models are probability matrices based on species abundance (A), phenological overlap (P), morphological matching (M), and all possible combinations among them. The Null model is a benchmark model that assumes all interactions have the same probability to occur. Note that after 5 hours of sampling effort the model PM, which include both phenological overlap and bill-corolla (morphological) matching, had the best ability to predict pairwise interaction while those models including A had worst fits, even worse than the Null model. This means that the higher importance of forbidden links in detriment of abundances was identified even under low sampling. We also show the change in coefficient of correlation between the observed frequency of pairwise interactions and the frequency predicted by the best model PM (right axe, black line with dots in the plot). Note that the correlation coefficient tends to increase with increasing sampling effort. All correlations were P<0.001.
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Figure 1.
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Figure 2. 20000 1,0 APM
AP
A 0,8 15000 AM
Null
M 0,6
10000 P
AIC AIC values PM
0,4 r r (modelPM)
5000
(modelsfit to observed interactions) 0,2
0 0,0 0 1 5 10 15 20 25 30 35 40 45 50 Cumulative sampling effort (h)
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ONLINE SUPPORTING INFORMATION
The following Supporting Information is available for this article online
APPENDIX A. Tables showing interactions and abundances, morphology and phenology to each plant and hummingbird species; the geographical coordinates of sampling transects; the results of network metrics and expected by null models; and results of model selection and parameters used to compare probabilistic models across the sampling completeness gradient.
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Table A1. Abundances of 55 hummingbird-pollinated species quantified along 12000m of trails in the Atlantic Rainforest at Santa Virgínia Field Station, southeastern Brazil. Number of flowers (or inflorescences, for Asteraceae species) was counted monthly from September 2011 to August 2013 and relative abundances indicate the relative proportion of flowers accounted by each species calculated on the total number of flowers.
Species Number of Relative Species Acronym flowers abundances
Aechmea cf. organensis Wawra Aec_org 104 0,00065 Aechmea distichantha Lem. Aec_dis 60 0,00037 Aechmea gamosepala Wittm. Aec_gam 74 0,00046 Aechmea nudicaulis (L.) Griseb. Aec_nud 19 0,00012 Aechmea vanhoutteana (Van Houtte) Mez Aec_van 37 0,00023 Alstroemeria inodora Herb. Als_ino 440 0,00273 Aphelandra colorata Wassh. Aph_col 5424 0,03366 Aphelandra longiflora (Lindl.) Profice Aph_lon 81 0,0005 Besleria longimucronata Hoehne Bes_lon 681 0,00423 Billbergia amoena (Lodd.) Lindl. Bil_amo 359 0,00223 Callianthe rufinerva (A. St.-Hil.) Donnell Cal_ruf 80 0,0005 Canistrum perplexum L.B. Sm. Can_per 65817 0,40844 Canna paniculata Ruiz & Pav. Can_pan 60 0,00037 Centropogon cornutus (L.) Druce Cen_cor 2382 0,01478 Edmundoa lindenii (Regel) Leme Edm_lin 123 0,00076 Erythrina speciosa Andrews Ery_spe 54924 0,34084 Fuchsia regia (Vell.) Munz Fuc_reg 10443 0,06481 Inga sessilis (Vell.) Mart. Ing_ses 1195 0,00742 Justicia sp.1 Jus_sp1 76 0,00047 Justicia sp.2 Jus_sp2 65 0,0004 Lantana camara L. Lan_cam 600 0,00372 Macrocarpaea rubra Malme Mac_rub 1095 0,0068 Manettia cordifolia Mart. Man_cor 1160 0,0072 93
Mendoncia velloziana Mart. Men_sp 18 0,00011 Mutisia speciosa Aiton ex Hook. Mut_spe 18 0,00011 Nematanthus cf. maculatus (Fritsch) Nem_mac Wiehler 26 0,00016
Nematanthus fluminensis (Vell.) Fritsch Nem_flu 660 0,0041 Nematanthus fritschii Hoehne Nem_fri 509 0,00316 Nematanthus gregarius D.L. Denham Nem_gre 19 0,00012 Nematanthus sericeus (Hanst.) Chautems Nem_ser 555 0,00344 Nidularium innocentii Lem. Nid_ino 1364 0,00846 Nidularium longiflorum Ule Nid_lon 207 0,00128 Nidularium procerum Lindm. Nid_pro 410 0,00254 Nidularium rutilans E. Morren Nid_rut 56 0,00035 Psittacanthus dichroos (Mart.) Mart. Psi_dic 71 0,00044 Psychotria leiocarpa Cham. & Schltdl. Psy_lei 2721 0,01689 Pyrostegia venusta (Ker Gawl.) Miers Pyr_ven 132 0,00082 Sinningia cooperi (Paxton) Wiehler Sin_coo 286 0,00177 Sinningia elatior (Kunth) Chautems Sin_ela 339 0,0021 Sinningia glazioviana (Fritsch) Chautems Sin_gla 312 0,00194 Siphocampylus convolvulaceus G. Don Sip_con 224 0,00139 Siphocampylus lauroanus Handro & M. Kuhlm. Sip_lau 163 0,00101 Siphocampylus longipedunculatus E. Wimm. Sip_lon 110 0,00068 Spirotheca rivieri (Decne.) Ulbr. Spi_riv 2405 0,01492 Tillandsia dura Baker Til_dur 6 0,00004 Tillandsia geminiflora Brongn. Til_gem 30 0,00019 Tillandsia sp. Til_sp 60 0,00037 Tillandsia stricta Sol. ex Sims Til_str 289 0,00179 Vriesea carinata Wawra Vri_car 488 0,00303 Vriesea erythrodactylon (E. Morren) E. Morren ex Mez Vri_ery 3930 0,02439 94
Vriesea incurvata Gaudich. Vri_inc 179 0,00111 Vriesea inflata (Wawra) Wawra Vri_inf 34 0,00021 Vriesea simplex (Vell.) Beer Vri_sim 58 0,00036 Vriesea sp. Vri_sp 101 0,00063 Wittrockia superba Lindm. Wit_sup 23 0,00014
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Table A2. Abundances of nine hummingbird species ocurring in the Atlantic Rainforest from September 2011 to August 2013at Santa Virgínia Field Station, southeastern Brazil. Contacts in transects indicate the total number of aural and visual contacts with individuals counted monthly across ten transects (100m each); frequency of occurrence is the proportion of days in which a species was recorded across 12000m of trails percurred and over 130 days of fieldwork; and the relative frequency is the relative proportion accounted by each species calculated on the frequency of occurences.
Acron Contacts in Frequency of Relative Species ym transects occurrence frequencies
Phaethornis eurynome Pe 86 86,923 0,288 (Lesson, 1832) Thalurania glaucopis Tg 28 78,462 0,260 (Gmelin, 1788)
Clytolaema rubricauda Cr 18 66,923 0,222 (Boddaert, 1783)
Leucochloris albicollis La 0 20,000 0,071 (Vieillot, 1818) Stephanoxis lalandi (Vieillot, Sl 0 13,077 0,066 1818)
Amazilia versicolor (Vieillot, Av 0 8,462 0,043 1818) Eupetomena macroura Em 0 0,769 0,028 (Gmelin, 1788)
Lophornis chalybeus Lc 1 5,385 0,018 (Vieillot, 1822)
Florisuga fusca (Vieillot, Ff 5 21,538 0,003 1817)
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Table A3. Plant phenology quantified by the monthly presence/absence of flowers of 55 hummingbird-pollinated species from September 2011 to August 2013 in the 12000 m of trails in the Atlantic Rainforests at Santa Virgínia Field Station, southeastern Brazil. Species acronym according Table A1. In total, 130 days of sampling were spread along the years.
2011 2012 2013
Species Se Oc No De Ja Fe Ma Ap Ma Ju Ju Au Se Oc No De Ja Fe Ma Ap Ma Ju Ju Au acronym p t v c n b r r y n l g p t v c n b r r y n l g
Aec_dis 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 Aec_gam 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 Aec_nud 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 Aec_org 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Aec_van 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 Als_ino 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 Aph_col 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Aph_lon 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 Bes_lon 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 Bil_amo 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 Cal_ruf 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 Can_pan 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 Can_per 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 97
Cen_cor 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 Edm_lin 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 Ery_spe 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 Fuc_reg 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Ing_ses 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 Jus_sp1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 Jus_sp2 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 Lan_cam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 Mac_rub 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 Man_cor 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 Men_sp 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 Mut_spe 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 Nem_flu 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 Nem_fri 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 Nem_gre 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 Nem_mac 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 Nem_ser 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 Nid_ino 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 Nid_lon 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 98
Nid_pro 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 Nid_rut 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 Psi_dic 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 Psy_lei 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 Pyr_ven 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 Sin_coo 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 Sin_ela 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 Sin_gla 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Sip_con 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Sip_lau 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 Sip_lon 0 0 0 1 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 Spi_riv 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 Til_dur 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 Til_gem 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 Til_sp 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 Til_str 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Vri_car 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 Vri_ery 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 Vri_inc 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 99
Vri_inf 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 Vri_sim 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 Vri_sp 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 Wit_sup 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0
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Table A4. Hummingbird phenology indicated by the presence/absence of flowers each month from September 2011 to August 2013 in the 12000 m of trails in the Atlantic Rainforests at Santa Virgínia Field Station, southeastern Brazil. Species acronym according Table A2.
2011 2012 2013 Species Se Oc No De Ja Fe Ma Ap Ma Ju Ju Au Se Oc No De Ja Fe Ma Ap Ma Ju Ju Au acronym p t v c n b r r y n l g p t v c n b r r y n l g Pe 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Tg 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Cr 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 La 0 1 1 0 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1 1 Sl 0 1 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 Av 0 1 0 1 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 Em 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lc 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 Ff 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0
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Table A5. Minimum corolla depth in 55 hummingbird-pollinated species in Atlantic Rainforests from September 2011 to August 2013 in the 12000 m of trails in the Atlantic Rainforest at Santa Virgínia Field Station, southeastern Brazil. Corolla depth was measured as the internal distance from the base of nectar chamber to the distal portion of the flower (i.e. effective corolla length, sensu Wolf, Stiles & Hainsworth 1976), which represents the minimum mouth apparatus length needed to a hummingbird access the nectar legitimately. Species acronym according Table A1.
Species acronym Minimum corolla depth (cm) Mean ± sd corolla depth (cm)
Aec_dis 1,22 1,29 ± 0,07 (n=3) Aec_gam 0,85 1,08 ± 0,23 (n=8) Aec_nud 0,96 1,06 ± 0,10 (n=3) Aec_org 1,04 1,21 ± 0,17 (n=4) Aec_van 0,93 1,09 ± 0,16 (n=10) Als_ino 1,00 1,00 (n=1) Aph_col 3,24 3,38 ± 0,14 (n=10) Aph_lon 3,06 3,28 ± 0,22 (n=13) Bes_lon 1,06 1,30 ± 0,24 (n=20) Bil_amo 3,28 3,70 ± 0,42 (n=6) Cal_ruf 0,38 0,58 ± 0,20 (n=3) Can_pan 2,93 3,49 ± 0,56 (n=3) Can_per 1,49 1,57 ± 0,08 (n=5) Cen_cor 3,36 3,67 ± 0,31 (n=5) Edm_lin 1,09 1,71 ± 0,62 (n=2) Ery_spe 0,9 1,04 ± 0,14 (n=11) Fuc_reg 1,37 1,58 ± 0,21 (n=9) Ing_ses 1,11 1,59 ± 0,48 (n=3) Jus_sp1 2,69 2,88 ± 0,19 (n=9) Jus_sp2 0,95 1,16 ± 0,21 (n=15) Lan_cam 0,61 0,66 ± 0,05 (n=5) 102
Mac_rub 2,14 2,34 ± 0,20 (n=17) Man_cor 4,57 5,14 ± 0,57 (n=12) Men_sp 3,06 3,23 ± 0,17 (n=4) Mut_spe 1,26 1,46 ± 0,20 (n=5) Nem_flu 3,97 4,30 ± 0,33 (n=9) Nem_fri 3,78 3,94 ± 0,16 (n=9) Nem_gre 1,38 1,52 ± 0,14 (n=7) Nem_mac 4,26 4,37 ± 0,11 (n=5) Nem_ser 2,75 3,08 ± 0,33 (n=4) Nid_ino 4,29 4,62 ± 0,33 (n=16) Nid_lon 4,44 4,66 ± 0,22 (n=8) Nid_pro 3,56 4,20 ± 0,64 (n=9) Nid_rut 3,19 3,57 ± 0,38 (n=3) Psi_dic 1,44 1, 44 (n=1) (n=1) Psy_lei 0,6 0,66 ± 0,06 (n=20) Pyr_ven 1,75 2,04 ± 0,29 (n=8) Sin_coo 2,63 2,88 ± 0,25 (n=6) Sin_ela 2,76 3,04 ± 0,28 (n=2) Sin_gla 3,08 3,19 ± 0,11 (n=11) Sip_con 3,14 3,49 ± 0,35 (n=6) Sip_lau 3,92 4,10 ± 0,18 (n=4) Sip_lon 3,19 3,88 ± 0,29 (n=5) Spi_riv 0,33 0,67 ± 0,34 (n=3) Til_dur 1,36 1,50 ± 0,14 (n=8) Til_gem 1,20 1,26 ± 0,06 (n=5) Til_sp 1,20 1,20 (n=1) Til_str 1,45 1,52 ± 0,07 (n=11) Vri_car 3,14 3,49 ± 0,35 (n=11) Vri_ery 4,28 4,28 (n=1) 103
Vri_inc 3,22 3,55 ± 0,33 (n=6) Vri_inf 3,39 3,66 ± 0,27 (n=8) Vri_sim 3,35 3,62 ± 0,27 (n=6) Vri_sp 3,35 3,80 ± 0,45 (n=3) Wit_sup 1,43 1,85 ± 0,42 (n=8)
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Table A6. Bill length (exposed culmen), estimated tongue extension and bill+tongue estimation of nine hummingbird species in the Atlantic Rainforest at Santa Virgínia Field Station, southeastern Brazil. *Tongue extension estimated based on Selasphorus rufus measures, which is around 80% percent of the bill length (Grant & Temeles 1992). Species acronym according to Table A2.
Species acronym Mean bill length ± sd (cm) Tongue extension (cm)* Bill + tongue
Pe 3,33 ± 0,12 (n=12) 2,72 6,12
Tg 1,79 ± 0,08 (n=6) 1,44 3,24
Cr 1,90 (n=1) 1,52 3,42
La 2,02 ± 0,14 (n=10) 1,60 3,60
Sl 1,45 ± 0,12 (n=8) 1,20 2,70
Av 1,61 ± 0,08 (n=8) 1,28 2,88
Em 2,23 ± 0,13 (n=7) 1,76 3,96
Lc 1,08 ± 0,10 (n=6) 0,96 2,16
Ff 2,16 ± 0,11 (n=7) 1,76 3,96
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Table A7. Geographical coordinates of starting and ending points from the ten transects (100 m long) where we counted hummingbirds monthly at Santa Virgínia Field Station, southeastern Brazil.
Starting point Ending point
Transect 1 S 23º20.299, W 45º08.965 S 23º20.313, W 45º09.020
Transect 2 S 23º20.430, W 45º09.332 S 23º20.486, W 45º09.353
Transect 3 S 23º20.575, W 45º09.318 S 23º20.658, W 45º09.337
Transect 4 S 23º20.658, W 45º09.356 S 23º20.693, W 45º09.390
Transect 5 S 23º20.744, W 45º09.487 S 23º20.786, W 45º09.470
Transect 6 S 23º20.844, W 45º09.448 S 23º20.885, W 45º09.457
Transect 7 S 23º20.968, W 45º09.397 S 23º20.981, W 45º09.344
Transect 8 S 23º20.994, W 45º09.004 S 23º20.979, W 45º08.942
Transect 9 S 23º20.767, W 45º09.806 S 23º20.747, W 45º08.765
Transect 10 S 23º20.932, W 45º09.265 S 23º20.949, W 45º09.218
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Table A8. Number of parameters used to penalize model complexity in each of eight models described in ESM 12. These numbers of parameters were defined according to number of plant and animal species in the matrix and to number of variables included in the models. Smaller networks tend to be easier to predict, so the number of species in the matrices was included in the model's penalization to account for the increasing network size along the sampling effort gradient.
Cumulative sampling effort (hours) Models 1 2 3 4 5 10 15 20 25 30 35 40 45 50
PM 86 98 100 110 112 124 126 126 126 126 128 128 128 128 P 43 49 50 55 56 62 63 63 63 63 64 64 64 64 M 43 49 50 55 56 62 63 63 63 63 64 64 64 64 Null 1 1 1 1 1 1 1 1 1 1 1 1 1 1 AM 86 98 100 110 112 124 126 126 126 126 128 128 128 128 A 43 49 50 55 56 62 63 63 63 63 64 64 64 64 AP 86 98 100 110 112 124 126 126 126 126 128 128 128 128 APM 129 147 150 165 168 186 189 189 189 189 192 192 192 192
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Table A9. Network metrics over increasing sampling completeness in the hummingbird-plant network in Santa Virgínia. Between parenthesis are shown values expected by the null model (95% confidence interval) and bold indicates significant differences between observed and expected by the null model.
Metric Cummulative sampling effort (hours observing each plant species)
m1 m5 m10 m15 m20 m25 m30 m35 m40 m45 m50
N. plants 36 47 53 54 54 54 54 55 55 55 55 N. pollinators 7 9 9 9 9 9 9 9 9 9 9 Links 44 68 81 94 102 105 111 115 115 120 123 Visits 83 466 812 1083 1437 1719 2011 2282 2474 2642 2793 Connectanc e 0.17 0.17 0.17 0.19 0.21 0.27 0.23 0.23 0.23 0.24 0.25 Interaction 0.93 0.85 0.83 0.83 0.84 0.84 0.85 0.85 0.85 0.85 0.85 evenness (0.62- (0.56- (0.56- (0.59- (0.62- (0.63- (0.63- (0.64- (0.64- (0.66- (0.66- 0.66) 0.59) 0.59) 0.62) 0.64) 0.65) 0.66) 0.66) 0.66) 0.67) 0.68) NODF 17.05 28.92 32.71 44.92 49.43 51.62 58.00 59.63 59.63 61.60 66.65 (21.54- (23.25- (25.47- (30.05- (31.65- (32.18- 34.57- (35.02- (34.91- (36.08- (37.72- 22.15) 23.65) 26.06) 30.52) 32.12) 32.68) 35.05) 35.52) 35.39) 36.58) 38.20) wNODF 13.05 15.37 15.07 19.29 22.73 24.17 26.90 29.13 28.74 29.47 (4.10- (17.10- 20.50- (27.70- (32.80- (34.20- (36.50- (38.00- (38.10- (40.40- 29.50(40. 12.47) 26.60) 30.43) 38.50) 44.50) 46.20) 49.90) 49.90) 51.40) 52.60) 70-54.64)
H2' 0.70 0.61 0.60 0.55 0.53 0.52 0.48 0.48 0.48 0.58 (0.55- (0.27- (0.23- (0.18- (0.16- (0.15- (0.14- (0.13- (0.13- (0.12- 0.47(0.11- 108
0.77) 0.50) 0.39) 0.28) 0.24) 0.24) 0.21) 0.20) 0.20) 0.19) 0.18) Q binary 0.54 0.43 0.38 0.38 0.37 0.36 0.35 0.35 0.34 (0.45- 0.48 (0 (0.20- (0.30- (0.31- (0.32- (0.34- (0.32- (0.33- (0.32- 0.33(0.31- 0.54) 25-0.48) 0.43) 0.40) 0.39) 0.39) 0.37) 0.36) 0.36) 0.35) 0.34) Q 0.63 0.51 0.48 0.45 0.44 0.43 0.42 0,41 0.41 quantitative (0.15- (0.03- 0.49(0.03- (0.03- (0.03- (0.03- (0.03- (0.03- (0.03- (0.03- 0.41(0.03- 0.61) 0.16) 0.15) 0.16) 0.15) 0.15) 0.15) 0.14) 0.13) 0.13) 0.13) N modules quantitative 5 3 or 4 3 or 4 4 4 4 4 4 4 4 4
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Table A10. AIC values indicating the ability of eight models to predict observed frequency of interaction between pairwise species over increasing sampling effort in the Santa Virgínia hummingbird-plant network. Models are probability matrices based on species abundance (A), phenological overlap (P) and morphological matching (M) and all possible combination among them. Null model is a benchmark model that assumes all interactions have the same probability to occur. Note that Null is the best predictive model under very small sampling (<2h), but after 3h of cumulative sampling the PM model, which includes both phenological overlap and bill-corolla (morphological) matching, had the best ability to predict pairwise interaction. Also note that all models including A had the worst fits, even worse than the Null model. Because data on tongue extension in hummingbirds is scarce in the literature, we also recreate a morphological model (M1) considering the tongue extension as 1/3 of the bill length. However, these models presented minor influence on the results because they performed similarly to the model M; thus we discussed just model M in the text.
Cumulative sampling effort (hours) Models 1 2 3 4 5 10 15 20 25 30 35 40 45 50
PM 531 885 1168* 1623* 2015* 3313* 4201* 5211* 6003* 6660* 7456* 7977* 8353* 8719* P 482 860 1178 1684 2130 3613 4638 5832 6771 7579 8501 9121 9584 10007 M 474 862 1192 1833 2348 4004 5025 6252 7229 8180 9241 9933 10409 10880 M1 441 795 1073 1658 2115 3545 4415 5444 6262 7048 7980 8566 8940 9337 Null 431* 842* 1214 1918 2495 4330 5509 6935 8071 9185 10357 11155 11723 12276 AM 815 1435 1948 2592 3250 5430 7370 9358 11070 13010 14713 16094 17113 17968 A 743 1367 1367 2548 3225 5458 7447 9501 11265 13260 15011 16429 17480 18362 AP 860 1432 2064 2697 3358 5598 7669 9848 11700 13683 15514 16953 18075 19001 110
APM 938 1610 2137 2766 3415 5625 7665 9802 11620 13809 15642 17081 18203 19129
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Table A11. Plant-hummingbird network from Atlantic Rainforest at Santa Virgínia Field Station, southeastern Brazil assembled with 50h of observation to each plant species (see detail in 'Materials and Methods' for detail). Plant and hummingbirds species names follow Table A1 and A2, respectively.
Pe Tg Cr Ff La Sl Av Lc Em
Aec_dis 15 21 1 0 0 0 0 0 0 Aec_gam 18 95 6 0 0 2 0 0 0 Aec_nud 1 6 15 0 0 0 0 0 0 Aec_org 12 24 0 0 0 0 0 0 0 Aec_van 95 132 0 0 0 0 0 0 0 Als_ino 30 0 0 0 2 18 0 0 0 Aph_col 18 0 0 0 0 0 0 0 0 Aph_lon 31 0 0 0 0 0 0 0 0 Bes_lon 9 19 0 0 0 0 0 0 0 Bil_amo 36 0 0 0 0 0 0 0 0 Cal_ruf 1 22 60 0 0 0 0 0 0 Can_pan 65 0 0 0 0 0 0 0 0 Can_per 14 68 18 1 0 0 0 0 0 Cen_cor 23 0 0 0 0 0 0 0 0 Edm_lin 53 26 0 0 0 0 0 0 0 Ery_spe 107 137 95 0 0 0 1 5 1 Fuc_reg 6 5 15 0 0 0 0 0 0 Ing_ses 44 32 198 41 23 0 8 2 0 Jus_sp1 7 0 0 0 0 0 0 0 0 Jus_sp2 5 0 0 0 0 0 0 0 0 Lan_cam 1 0 0 0 0 26 0 0 0 Mac_rub 6 33 2 0 1 6 30 0 0 Man_cor 50 0 0 0 0 0 0 0 0 Men_sp 2 0 14 0 0 0 16 0 0
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Mut_spe 62 0 0 0 14 0 0 0 0 Nem_flu 22 0 0 0 0 0 0 0 0 Nem_fri 36 0 0 0 0 0 0 0 0 Nem_gre 4 18 0 0 0 0 0 0 0 Nem_mac 15 0 0 0 0 0 0 0 0 Nem_ser 5 1 4 0 0 0 0 0 0 Nid_ino 36 0 2 0 0 0 0 0 0 Nid_lon 11 0 0 0 0 0 0 0 0 Nid_pro 31 0 0 0 0 0 0 0 0 Nid_rut 20 0 1 0 0 0 0 0 0 Psi_dic 1 1 53 1 10 0 1 0 0 Psy_lei 0 2 0 0 0 0 1 0 0 Pyr_ven 17 1 0 0 1 0 0 0 0 Sin_coo 35 0 0 0 0 0 0 0 0 Sin_ela 7 0 0 0 9 0 0 0 0 Sin_gla 4 0 0 0 0 0 0 0 0 Sip_con 22 1 0 0 0 0 0 0 0 Sip_lau 4 0 0 0 0 0 0 0 0 Sip_lon 7 0 0 0 0 0 0 0 0 Spi_riv 0 74 102 4 0 0 0 5 0 Til_dur 1 0 0 0 0 0 0 0 0 Til_gem 2 12 2 0 1 1 0 0 0 Til_sp 2 4 1 0 0 0 0 0 0 Til_str 1 3 1 0 0 1 0 0 0 Vri_car 16 0 0 0 0 0 0 0 0 Vri_ery 17 0 0 0 0 0 0 0 0 Vri_inc 22 0 0 0 0 0 0 0 0 Vri_inf 9 0 0 0 0 0 0 0 0 Vri_sim 24 0 0 0 0 0 0 0 0
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Vri_sp 20 0 0 0 0 0 0 0 0 Wit_sup 55 77 0 0 0 0 0 0 0
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APPENDIX B. Figures showing the asymptotic trend of links in a rarefaction and the correlation between hummingbird species abundances and their frequencies of occurrences.
Figure B1. Individual-based rarefaction curve with 95% confidence intervals (grey lines). Note the asymptotic tendency from accumulated number of links with the accumulation of visits observed in the network. We ran this analysis in EstimateS 9.1.0 (Colwell 2013) and calculated confidence intervals using unconditional variances as suggested by Colwell et al. (2012, doi:10.1093/jpe/rtr044)
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120
100
80
60
40
Number of links (IC95%) 20
0 1 501 1001 1501 2001 2501 Number of interactions (visits)
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Figure B2. Spearman correlation between frequency of occurrence during 130 days of field work and number of contacts during counts in transects. Observations were conducted over a 2 year period and include nine hummingbird species from Santa Virgínia Field Station, southeastern Brazil.
100
80
60
40
20 Frequency Frequency occurenceof (%) r = 0.827; p = 0.012
0 0 20 40 60 80 100 Number of aural/visual contacts
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Figure B3. Network metrics calculated under a simulated sampling effort gradient created by interaction removals of a plant-hummingbird network in SE Brazil. We simulated rarefaction- like sampling reduction by removing successively 10% of interactions creating class of removal from 90% to 10% removals. Network metrics were recalculated (1,000 iterations) and their values (gray overlapped lines) and mean (black line) were plotted. Dashed black lines indicate mean and 95% CI. Note that the results do not differ importantly from Figure 1 but metrics were less variable across the gradient of sampling effort using this approach.
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CAPÍTULO 3
Processes entangling interactions at local and macroscale: the importance of niche-based processes in plant-hummingbird networks across the Americas***
Authors: Jeferson Vizentin-Bugoni1,2 *, Jesper Sonne2, Pietro K. Maruyama3, Andréa C. Araújo4, Aline Coelho5, Peter Cotton6, Oscar H. Marín-Gómez7, Liliana R. Lasprilla8, Caio G. Machado5, Maria A. Maglianesi9,20, Tiago S. Malucelli10, Ana M. Martín González11, Genilda M. Oliveira12, Paulo E. Oliveira13, Raul Ortiz-Pulido14, Márcia A. Rocca15, Licléia Rodrigues16, Ana M. Rui17, Boris Tinoco18, Isabela G. Varassin10, Marcelo F. Vasconcelos19, Bob O'Hara20, Matthias Schleuning20, Carsten Rahbek2, Marlies Sazima3 and Bo Dalsgaard2
*** Manuscrito formatado nas normas de Ecology Letters.
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Affiliation
1 Programa de Pós-Graduação em Ecologia, Universidade Estadual de Campinas (Unicamp), Cx.Postal 6109, CEP: 13083-970, Campinas, SP, Brasil. ([email protected])
2 Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark. (JS: [email protected], CR: [email protected], BD: [email protected])
3 Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (Unicamp), Cx. Postal 6109, CEP:13083-970 Campinas, SP, Brasil. (PKM: [email protected], MSa: [email protected])
4 Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul, CEP: 79070-900 Campo Grande, Mato Grosso do Sul, Brasil. ([email protected])
5 Laboratório de Ornitologia, Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, Feira de Santana-BA, CEP: 44036-900, Brasil. (AGC: [email protected], CGM: [email protected])
6 Marine Biology & Ecology Research Centre, Plymouth University, Plymouth PL4 8AA, UK. ([email protected])
7 Instituto de Ciências Naturales, Universidad Nacional de Colombia, Apartado 7495 Bogotá, Colombia; and Instituto de Ecología, A.C., Carretera Antigua a Coatepec 351, ElHaya, Xalapa, Veracruz, 91070 México. ([email protected])
8 Escuela de Ciencias Biológicas, Grupo de Investigación Biología para la Conservación, Universidad Pedagógica y Tecnológica de Colombia, Tunja, Boyacá, Colombia. ([email protected])
9 Vicerrectoría de Investigación, Universidad Estatal a Distancia (UNED), San José, Costa Rica. ([email protected])
10 Laboratório de Ecologia Vegetal, Departamento de Botânica, Centro Politécnico, Cx. 19031, CEP: 81531-980, Curitiba-PR, Brasil. (TSM: [email protected], IGV: [email protected])
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11Pacific Ecoinformatics and Computational Ecology Lab, 1604McGee Ave, 94703 Berkeley, CA, USA. ([email protected])
12Instituto Federal do Triângulo Mineiro, Campus Uberlândia, Uberlândia-MG, Brasil. ([email protected])
13Instituto de Biologia, Universidade Federal de Uberlândia, Uberlândia-MG, Brasil. ([email protected])
14 Centro de Investigaciones Biológicas, Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Km 4.5, Carretera Pachuca–Tulancingo, Mineral de la Reforma, C.P. 42184 Pachuca, Hidalgo, Mexico. ([email protected])
15 Centro de Ciências Biológicas e da Saúde, Departamento de Ecologia, Universidade Federal de Sergipe, Avenida Marechal Rondon, s/n, Jardim Rosa Elze, CEP: 49100000 - São Cristóvão, SE, Brasil. ([email protected])
16 Laboratório de Ornitologia, Departamento de Zoologia, ICB, Universidade Federal de Minas Gerais. Caixa Postal 486, CEP: 31270-901, Belo Horizonte, MG, Brasil. ([email protected])
17 Departamento de Ecologia, Zoologia e Genética, Instituto de Biologia, Universidade Federal de Pelotas, CEP: 96160-000 Capão do Leão, Rio Grande do Sul, Brasil. ([email protected])
17 Escuela de Biología, Ecología y Gestión, Universidad del Azuay, Cuenca, Ecuador. ([email protected])
19 Museu de Ciências Naturais, Pontifícia Universidade Católica de Minas Gerais, Coração Eucarístico, CEP: 30535901 - Belo Horizonte, MG – Brasil. ([email protected])
20Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, 60325 Frankfurt (Main), Germany. (BO: [email protected], MSc: [email protected])
*Corresponding authors: Jeferson Vizentin-Bugoni, Instituto de Biologia, Prédio da Pós- Graduação, Bloco O, Avenida Bertrand Russel, s/n, Cidade Universitária Zeferino Vaz -
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Barão Geraldo, Caixa Postal 6109, CEP 13083-865, Campinas - SP – Brasil. PHONE: +55 19 3521-6381/6373 - FAX: +55 19 3521-6374. Email: [email protected].
AUTHORSHIP
JVB, JS, PKM, MSc, CR, MSa and BD conceived the study. JVB, PKM, ACA, AC, PC, OHMR, LRL, CGM, MAM, TSM, GMO, PEO, ROP, MAR, LC, AMR, BT, IGV and MFV collected the data. JVB, PKM, AMMG and BD jointly compiled the data. JVB, JS and PKM performed the analyses. JVB, JS, PKM, MSa, MSc and BD wrote the manuscript. BO and MSc contributed to the analyses. All authors contributed to the the final version of the manuscript.
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Abstract
The major processes structuring patterns of interactions among coexisting species may be divided into “niche-based processes” (e.g. species traits and phenlogies) and “neutral-based processes” (i.e. species abundances), but whether their relative importance is related to environmental conditions and network strucuture is virtually unknown. We evaluate this in 25 plant-hummingbird networks across the Americas, testing whether the relative importance of niche-based and neutral-based processes varies according to environmental setting, species richness, and network structure, i.e. specialization, nestedness and modularity. We document a prevalence of niche-based processes regardless of differences in species richness and environmental setting. Phenology predicted interactions more accurately towards colder, drier and more seasonal environments. The importance of niche- or neutral-based processes was largely unrelated to network structure, but specialization increases with the importance of trait matching. These results demonstrate the pervasive importance of niche-based processes in determining interactions in coevolved hummingbird-plant systems across the Americas.
Keywords: forbidden links, macroecology, modularity, nestedness, neutrality, phenologial overlap, pollination, specialization, species abundances, species traits.
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Introduction
Interactions between species influence virtually all hierarchical levels of ecological systems, from individual’s fitness and population dynamics to patterns of biodiversity and ecosystem functioning (Levin 2009; Ings et al. 2009; Schleuning et al. 2015). In an evolutionary context, species may co-adapt in response to each other through interactions, with ‘diffuse coevolution’ acting as a spatiotemporally dynamic process shaping species phenotypes and promoting biodiversity on Earth (Thompson 2009; Guimarães et al. 2011). The structural patterns of interactions between coexisting species may be represented as complex networks of species linked by interactions, such as mutualistic interactions between assemblages of flowering plants and their animal pollinators (Bascompte 2009). The structural properties of these networks may influence the stability and resilience of communities (Thébault & Fontaine 2010) and affect the dynamic and disassembly of ecological networks (Bascompte & Stouffer 2009). Several structural properties are common to different types of interaction networks, such as a low connectance, an asymmetric degree distribution, as well as a nested and modular patterns of interactions (e.g. Bascompte 2009, Vázquez et al. 2009a; Morris et al. 2014). The recurrence of these patterns suggests universality in the rules of interactions between species. Despite its importance, a persistent and unresolved challenge is to understand the processes determining patterns of interactions among coexisting species (Ings et al. 2009; Vázquez et al. 2009a; Canard et al. 2014; Vizentin-Bugoni et al. 2014).
Previous studies investigating the processes shaping network structures have focused on two main processes, namely neutral and niche-based hypotheses (e.g. Bosch et al. 2009; Vázquez et al. 2009b; Canard et al. 2014, Vizentin-Bugoni et al. 2014). Neutral hypotheses (or ‘neutrality’) propose that interactions are randomly established among coexisting species, with the encounter probability between species being proportional to their relative abundances (Vázquez et al. 2009b). In contrast, niche-based hypotheses propose that species intrinsic characteristics, such as morphological traits and phenologies, define the identity and strength of pairwise interactions via exploitation barriers and trait matching (Santamaría & Rodrígues-Gironés 2007; Vázquez et al. 2009a, Dehling et al. 2016). In previous studies of single communities, both relative abundances and species traits have been reported to play a structuring role in different network types, such as mutualistic plant-seed disperser or plant-pollinator interactions (Vázquez et al. 2009a, b; González-Castro et al.
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2015; Vizentin-Bugoni et al. 2014), plant-plant facilitation (Verdú & Valiente-Benuet 2011), commensalism (Sáyago et al. 2013), and antagonistic hosts-parasite relationships (Canard et al. 2014). It has been proposed that niche-based processes could have a higher importance in determining frequencies of interactions in tropical than in temperate communities (Maruyama et al. 2014), due to geographical differences in species richness, climate and associated level of specialization (Dalsgaard et al. 2011). In addition, it has been hypothesized that neutral- and niche-based process form a continuum of importances as drivers of interactions in host- parasite networks (Gravel et al. 2006, Canard et al. 2014). This hypothesis was further extended to mutualistic networks in which niche-based processes are arguably expected to increase in importance in communities with higher trait diversity while neutrality is expected to matter more when lower trait diversity occurs (Vizentin-Bugoni et al. 2017). However, these ideas remain untested. Contrastingly to the lack of studies investigating the spatial variation in the processes determining interactions in ecological networks, in recent decades an array of studies have identified correlations between geographical variation in interaction patterns (network structure) and species richness, environmental conditions and levels of anthropogenic disturbance (e.g. Jordano 1987; Olesen & Jordano 2002; Ollerton & Cranmer 2002; Vázquez et al. 2005; Sugiura 2010; Dalsgaard et al. 2011; Schleuning et al. 2012; 2014; Trøjelsgaard & Olesen 2013; Canard et al. 2014; Morris et al. 2014; Martín González et al. 2015; Sebastián-González et al. 2015; Sonne et al. 2016). For instance, plant-hummingbird networks have been found to be more specialized and modular in the tropics, especially in areas characterized by high temperature, rainfall and climatic stability, and in communities with high species richness and many smaller-ranged species (Dalsgaard et al. 2011; Martín González et al. 2015; Sonne et al. 2016). If the role of niche-based and neutral-based processes varies depending on network structure, then the importance of these processes structuring interactions should also change across geographical space in response to environmental conditions (Maruyama et al. 2014).
Here we test this hypothesis by exploring the large-scale spatial variation in the relative importance of neutral- and niche-based processes structuring mutualistic plant- hummingbird networks. Interactions between assemblages of hummingbirds and plants are relatively easy to observe and quantify, thus their interaction networks are well resolved and therefore good model systems for macroecological analyses (Dalsgaard et al. 2011). We analyzed a unique dataset of 25 networks distributed widely across the Americas, all
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comprising data on phenology, morphology and abundance for every species collected in situ. We ask: (1) which are the most important processes, i.e. neutral- or niche-based processes, determining frequencies of interactions among plants and hummingbirds in each community? (2) Is the importance of these processes associated to species richness, climate and topography? (3) Does the level of network specialization, modularity and nestedness differ according to the prevalence of either neutral- or niche-based processes?
Based on the long-standing knowledge on the natural history of plant- hummingbird interactions, we expect that niche-based processes associated to morphological barriers and phenological overlap of flowers and hummingbirds play a major structuring role in these interaction networks, while species abundances will be less important (e.g. Wolf et al. 1976; Stiles 1977; Feinsinger & Colwell 1978; Buzato et al. 2000; Dalsgaard et al. 2009; Maruyama et al. 2014; Vizentin-Bugoni et al. 2014; 2016). We also predict an increased importance of species' morphology and phenology towards tropical areas with higher climatic stability and higher species richness, as plant-hummingbird communities in such places tend to be more specialized, have a higher diversity of bill and corolla shapes, and flowering phenophases are often temporally more structured (e.g. Wolf et al. 1976; Stiles 1977; Buzato et al. 2000; Dalsgaard et al. 2011; Maruyama et al. 2014; Vizentin-Bugoni et al. 2014). The importance of morphology and phenology is also expected to increase with topographic heterogeneity, as topographical complex landscapes should encompass more distinct species and allow species to persist and form tighter co-evolved associations (Sandel et al. 2011, Dalsgaard et al. 2011). Finally, we expect a higher level of network specialization and modularity in communities structured by niche based-process (Vázquez et al. 2009a, Martín González et al. 2012; Maruyama et al. 2014), while higher nestedness should occur in communities determined by neutral-based processes (Ollerton et al. 2003; Krishna et al. 2008).
We found a widespread importance of niche-based processes for defining frequencies of interactions, which were more important than ‘neutrality’ regardless of differences in species richness and environmental setting. Only phenology predicted interactions more accurately towards colder, drier and more seasonal environments. Overall, network structures were not associated to the importance of neutral- or niche-based processes, suggesting that different structuring processes may lead to the same network structures. Network-level specialization was an exeption, as it increases in communities where
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morphological matching is an important determinant of frequencies of interactions. These results demonstrate the pervasive importance of niche-based processes in determining interactions in coevolved hummingbird-plant systems across the Americas.
Methods
Data collection
(a) Interaction networks. We compiled 25 quantitative interaction networks that aimed at including all hummingbird-pollinated plants. Communities were recorded in areas mostly or completely covered with native vegetation, so that urban areas were excluded (Table ESM1; updated dataset from Dalsgaard et al. 2011, Martín González et al. 2015). We considered only legitimate interactions, i.e. when the bird used the entrance of the corolla to extract nectar and likely touched anthers and stigmas. Thus, we excluded instances of nectar larceny whereby the hummingbird did not pollinate the plant (Maruyama et al. 2015). We only included networks sampled for at least (or almost) an annual cycle, covering all seasons (range: 11 to 29 months). Frequency of interactions was quantified as the number of visits per bird species on each plant species. Most studies used plant focal observation (i.e. direct observation or using video cameras) to quantify visits. The only exception was one network assembled by following hummingbirds in the field for up to ten minutes to identify the visited plant species and to count the number of visits (‘Network ID18’ in Table ESM1); this network does not show notable differences in terms of species richness and structure in comparison to the others in the dataset (Tables ESM4, ESM 5).
(b) Predictors of frequencies of interaction
Plant data. Abundance was independently measured as the total number of flowers produced per species in each community throughtout the entire sampling period; flowers were counted in plots or transects estimated regularly throughout the sampling period. One network (‘ID 25’ in Table ESM1) was an exception by having the number of plant individuals quantified as a measure of abundance. Flower morphology was characterized by the effective corolla length, i.e. distances from the internal basis to the flower opening (sensu Wolf et al. 1976) which reflects the minimum length required by a pollinator's mouthparts to access the nectar legitimately. Measurements were taken from flowers collected in the field
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(up to 10 flowers estimated per species). The few missing data were estimated from local herbarium vouchers or scaled photos (seven out of 478 plant species, i.e. 1.5 %). Temporal flowering distribution was quantified as the presence of flowers per temporal sampling slice (usually months) over the study period. For simplicity, we hereafter refer to the temporal distribution of plants as "Phenology".
Hummingbird data. Abundance was independently measured in the field by counting the number of aural and visual contacts with individuals across transects or point counts, or the number of birds captured by mist netting. For four networks that lacked independent measures of abundance, we used frequencies of interactions of each species as a proxy instead (sensu Vázquez et al. 2009b). This estimation has been shown to strongly correlate with independent measures of hummingbird abundances in previous studies (Maruyama et al. 2014; Vizentin-Bugoni et al. 2014) and was found significantly correlated for 16 out of 20 (76%) networks in our database in which independent data were available (Table ESM 2). Bill morphology was measured as the length of the exposed culmen from the captured hummingbird individuals or specimens from local museums; in cases where this information was lacking, we supplemented data with information from the literature. Temporal hummingbird distribution was measured as the presence of bird individual in transects or while observing plants during the sampling time slices (see above). For simplicity, as for plants, we hereafter refer to temporal distribution of hummingbirds as "Phenology".
(c) Environmental and richness predictors of the determinants of frequency of interaction
We represented ‘Contemporary climate’ based on the mean annual temperature and precipitation, and also seasonality in temperature (calculated as the annual standard deviation in monthly mean temperature) and precipitation (calculated as the annual coefficient of variation in monthly precipitation). Data were extracted from the WorldClim database with a resolution of 1 x 1 km (Hijmans et al. 2005; http://www.worldclim.org). To avoid risk of overfitting the models (see Data Analysis below), we reduced the number of contemporary climate variables using Principal Component Analysis (PCA): one variable for mean annual temperature and precipitation (‘mean annual climate’) and one for seasonality in temperature and precipitation (‘seasonality’). In both cases, we kept the first principal component axis, which explained 81% and 78% of the variation, respectively. We opted to make two separated
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PCAs because a single PCA including all four contemporary climate variables did not result in an easy interpretable PCA axis. ‘Topographic heterogeneity’ was also included and calculated as the variation in elevation within a 10 km buffer zone around the sampled area as well as species richness, calculated as the number of bird and plants species in a network.
Data Analysis
(a) Creating predictive models. Predictive models based on abundances, morphology and phenology were built based on Vázquez et al. (2009b) and adaptations by Vizentin-Bugoni et al. (2014, 2016): Neutral model (A) combined abundances of a given hummingbird species and plant species by multiplication of the relative abundances of each species, i.e. species are expected to interact proportionally to their relative abundances. In this model, all interactions are possible, i.e. there are no forbidden links. Morphological barrier model (M) was built considering that an interaction was only possible when the length of the bill of a hummingbird was longer than the depth of a corolla; otherwise the interaction was considered impossible, i.e. “forbidden” (probability of interaction = 0). Due to the ability of hummingbirds to extend their tongues to access nectar, we added 80% of the bill length to account for potential tongue extension (Vizentin-Bugoni et al. 2016). Phenological overlap model (P) represented the number of time slices of field expeditions in which a hummingbird and a flowering plant species co-occurred over the study period. When there was no temporal co-ocurrence, the interaction was assigned as “forbidden” (probability of interaction = 0). In order to convert these values in probabilities of interactions between species pairs, all models were normalized in further analysis by dividing the values of each cell by the sum of elements in the matrix.
(b) Processes entangling interactions at the local scale. We applied a Bayesian generalized linear mixed effect model (glmm) to measure the fit of each of the probabilistic matrices A, M and P (see section a) to the frequencies of interaction separately within each network. These ‘values of fit’ of each model (i.e. A, P or M) were measured as the posterior error distribution (PED), which was considered as a response variable for compasions among networks in a subsequent regression analysis against the environmental predictors. For this Bayesian glmm, a zero-inflated model framework was applied (Hadfield 2009; Bolker et al. 2012; 2013) as large quantities of absent interactions among partners were present within the interaction matrices. Thus, we modeled separately the binary part (occurrence or absence of interactions among partners) and the discrete count part (interaction frequencies). These two
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response variables were analyzed by a logistic model using a logit link function, and a Poisson model using a log link function, respectively. As the same species were sometimes observed repeatedly across networks, we added random effects for plant and hummingbird species identity. For the binary response variable, we did not include random effects and fixed the residual error to 1 as it was not identifiable (Bolker et al. 2012). Prior to model fitting, i.e. fitting posterior coefficients against the environmental variables in regression analysis, all variables were standardized to zero mean and unit variance. Models for each network were run by 50000 iterations with a 5000 iteration burnin. In two of the 25 networks (i.e. ‘ID 21’ and ‘ID 25’ in Table ESM1), the posterior distribution of parameters did not converge by stabilizing around a mean posterior estimate, even after trying higher number of iterations. Thus, these two networks were excluded prior to subsequent geographical analysis (section c) as it results in non-interpretable values. The reasons for the lack of convergence are hard to infer. In this case, species richness and structural properties of these networks were not notably distinct from the remaining networks (Table ESM4 and 5). From the posterior distributions of standardized regression coefficients, we extracted the mean and standard deviation (associated to the count part only) to be used as response variable on the subsequent regression analyses (section c and Table ESM 3). All Bayesian model specification and fitting were performed using the R-package MCMCglmm (Hadfield 2010).
(c) Are processes defining interaction frequencies structured at macroscale? Here, we investigate the influence of contemporary climate, topographic heterogeneity and species richness on the predictive effect of the probabilistic matrices A, M and P on the frequencies of interaction. Our response variable was the PED (predictive effect of A, M and P on the frequencies of interaction), measured as the posterior mean of the standardized coefficients using above described Bayesian models. Our predictor variables were the two PCA axes (‘mean annual climate’ and ‘seasonality’), ‘topographic heterogeneity’ and species richness (i.e. network size). PCA were used in order to avoid overfitting the models. We fitted series of weighted least squares regressions where the contribution of each network was weighted by the corresponding standard deviation in PED. Thereby, the influence of the networks in the regression analyses decreased by the increase in standard deviation of the mentioned predictors. The influence of correlating climate variables are then illustrated in partial regression plots (Figure 2). We analyzed all combination of the predictor variables and then used Akaike Information Criterion corrected for small samples (AICc) to identify the best models (∆AICc ≤ 2) (Burnham & Anderson 2002). Interactions among variables were
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not included, as we did not have any a priori hyphotesis for their inclusion. Finally, a model including only the intercept was used as beckmarck for comparisons.
(d) Does network structure relate to processes defining frequencies of interactions? To capture the overall network structure, we calculated three metrics:
Complementary specialization H2', which characterizes the specificity in the interactions for the entire network (Blüthgen et al. 2006); Weighted Nestedness, which describes a pattern where specialists interact with subsets of the partners of more generalist species and at low frequencies, while generalists interact at high frequencies among themselves (weighted NODF; Almeida-Neto & Ulrich 2011); and Weighted Modularity, which describes the level of compartmentalization in a community, with networks showing a high modularity Q when species interact at high frequencies with species located within their module and at low frequencies with species from outside their module (QuanBiMo algorithm with steps=1e7). Due to the stochastic nature of this optimization algorithm, some differences might exist among runs, so we repeated the analysis 10 times for each network and kept the highest Q value (Dormann & Strauss 2014). As networks vary in size and sampling intensity, the observed values of these metrics may not be directly comparable (Dalsgaard et al. 2017). In order to overcome these differences, we corrected metrics by subtracting the observed values from the mean values obtained from randomizations of the Patefield null model (∆- transformation), which keeps network size and total number of interaction per species fixed (Dalsgaard et al. 2017). We used 1000 randomizations for specialization and nestedness, and 100 randomizations for modularity, as calculation of the later is time consuming.
To test whether there is an association between network structure and the processes defining frequencies of interactions, we performed series of multiple linear regressions between each of the three network metrics and PED, i.e. the response variable indicating the predictive effect of A, M and P on frequencies of interation. Again, regressions were weighted by the corresponding standard deviation of errors. To account for the possible influence of sampling and among networks, we included the metric Sampling Intensity calculated for each network by dividing the total number of observed interactions (square-root transformed) with the richness for plants and hummingbirds (Schleuning et al. 2014). Finally, to account for the unequal species richness, we also included network size defined by sum of hummingbird and plant species in a network.
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Results
The 25 networks analyzed here encompass 104 hummingbird species, ca. 29% of all hummingbird species in the World (Schuchmann & Bonan 2016; Table ESM6), and 478 plant species belonging to 58 plant families (Table ESM7). Our Bayesian approach revealed that morphological barriers (M) and phenological overlap (P) produced largest predictive effects (i.e. mean standardized posterior distribution coefficients) on frequencies of interaction for 12 and 11 plant-hummingbird networks, respectively. On the other hand, species abundances never produced the largest predictive effect in a network (Figure 1; Table ESM3).
We found no predictive effects of climate, topography and species richness on the ability of species abundances and morphological matching to predict the frequencies of interactions among plants and hummingbirds, as the intercept-only model was the best selected model for both probabilistic matrices A and M (Table 1). Only the predictive effect of phenology was associated with climate. Specifically, the phenological overlap decreased in predictive importance with increasing mean annual temperature and precipitation, and increased in importance with increasing seasonality in temperature and precipitation (Table 1; Figure 2).
Regardless of the importance of species abundances, phenological overlap or morphological matching as predictors of interaction frequencies, networks were remarkably similar in terms of weighted nestedness and weighted modularity (Table 2; Figure ESM 1). However, specialization increased in communities where morphological matching is an important predictor of interaction frequencies (Table 2).
Discussion
The importance of niche-based processes
We showed that niche-based processes, i.e. morphological matching and phenological overlap, are the main mechanisms structuring interactions between plants and hummingbirds in most networks, irrespectively of environmental setting and species richness from Mexico to Southern Brazil (Figure 1; Table ESM 1). Neutral processes, i.e. random encounter based on chance related to species abundances, were overall of secondary
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importance. Interestingly, recurrent network structural patterns such as specialization, modularity and nestedness were largely similar regardless of the distinct processes driving pairwise frequencies of interactions, with exception of specialization which increase with the importance of morphology.
Bill length and shape have long been recognized as important traits determining food sharing in hummingbird communities (Feinsinger & Colwell 1978), and usually there is a high variation in these traits among co-existing hummingbird species (Maglianesi et al. 2014; Lessard et al. 2016; Vizentin-Bugoni et al. 2014; Tinoco et al. 2016). The detected large role of bill length-corolla depth in determining interactions in plant-hummingbird networks thus may reflect its strong role in allowing narrower floral niche partitioning (Wolf et al. 1976; Dalsgaard et al. 2009; Maglianesi et al. 2014; Maruyama et al. 2014; Vizentin-Bugoni et al. 2014; 2016). As here detected throughout the Americas, previous single community studies have also detected a high importance of temporal overlap of hummingbirds and flowers as determinant of species interactions (Wolf et al. 1976: Stiles 1977; Maruyama et al. 2014; Vizentin-Bugoni et al. 2014; 2016). For sympatric flowering plants, temporal niche partitioning is important and likely represents a consequence of competition or facilitation among plants species for shared pollinators (Waser 1978), resulting possibly in the staggered flowering pattern recorded in several hummingbird-pollinated communities in the Neotropics (e.g. Stiles 1977; Buzato et al. 2000; Vizentin-Bugoni et al. 2014). When associated to the temporal variation in the presence of hummingbird species in the community, phenological mismatches may lead to an important proportion of ‘forbidden links’ in the networks (Maruyama et al. 2014; Vizentin-Bugoni et al. 2014; 2016). The detected high importance of niche-based processes related to morphology and phenological overlap in determining interactions between co-existing plants and hummingbirds is thus in correspondence with natural history knowledge. Here we show that it may be pervasive across communities regardless of differences in species identity.
A secondary role of species abundances
The overall secondary role of species abundances as determinants of interactions is in line with the suggested existence of a hierarchy of importance of neutral- and niche- based processes. Mechanistically, the hierarchical nature between processes as argued by some authors (e.g. Canard et al. 2014, Junker et al. 2014) means, in this case, that hummingbird species will not interact with many plant partners if they have short bills and
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short temporal distribution, regardless of whether the species is abundant or not. However, if corollas are short and flowering is long, the frequency of interaction would be proportional to the abundance of both the plant and the hummingbird. In the same way, highly abundant plants are not expected to interact with many hummingbird species if they have long (restrictive) corollas or short flowering periods. In this sense, long-billed hummingbirds or short corolla flowers with a long temporal span can explore a wider array of partners (Tinoco et al. 2016) or intensively with some of them regardless of their rarity, hence making relative abundances a poor predictor of interactions. However, even though accessible morphology is crucial to make interactions possible, these interactions not always took place as longer-billed hummingbirds may avoid shorter tube flower, due to higher exploitation competition with shorter-billed species (Maglianesi et al. 2015). In this sense, the existence of a continuum of importance from neutral- to niche-based processes structuring interactions has been suggested (Canard et al. 2014) which may be associated to trait diversity (Vizentin-Bugoni et al. 2017). In a system with largely variable traits (i.e. bill length and corolla depth, Table ESM5) such as plant-hummingbird networks, niche-based processes are expected to be more important than neutrality as drivers of interactions, as we detected here.
These findings also contribute with insights towards prediction of interactions by proxies (Morales-Castilla et al. 2015). By knowing few relevant attributes of species in a community, such as their morphologies and phenological distribution (or trait diversity), one may forecast how often a plant or a hummingbird species will interact in a given community. For instance, at high northern latitudes, where hummingbird-pollinated plants bloom sequentially (Waser 1978) but hummingbirds present similar bill lengths, few constraints are imposed by morphology. We could expect phenologies and abundances to be important drivers of plant-hummingbird interactions in these high latitude, which were not included here. Previous studies have shown few species traits to define the occurrence of an interaction (Woodward & Hildrew 2002, Eklöf et al. 2013). Complementary, our findings add that few traits are important to define the strength of species interactions in ecological networks.
Processes entangling interactions at macroscale
Geographically, we found neither contemporary climate and topography nor species richness influencing the ability of morphology or species abundances to predict frequencies of interactions. On the other hand, phenology predicts interactions more accurately towards colder, drier and more seasonal environments (Figure 2). The higher
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ability of phenological overlap to predict interactions in these circunstances could be because some hummingbird species are induced to move around (or migrate) in part of the year, hence creating forbidden links. In addition, this may be related with the short period that species have suitable conditions to accomplish events such as flowering season and nesting in these communities, making interactions more frequent among species coexisting in these seasons. In fact, a previous study has shown that ecosystems with such characteristics, such as highlands, tend to have phylogenetically related hummingbirds because only few lineages can colonize and be present all year-round (Lessard et al. 2016). On the other hand, assemblages from stable climates maintain more hummingbird species all year-round enabling hummingbirds to interact with most of the plant species (Stiles 1977) and, hence, making phenology a poorer determinant of interactions in these areas.
It was recently shown that in northern Andes, distinct communities comprised distinct hummingbird species in terms of identity and evolutionary relationships, however, geographically distant assemblages tend to encompass similar trait composition (Weinstein et al. 2014). Considering our data, there was an expressive and consistent variation in hummingbirds’ morphologies and phenologies within communities (Table ESM5), despite of differences in species composition, climate, topographic heterogeneity and species richness among areas (Table ESM6 and ESM7). Moreover, communities where morphological matching has higher predictive power on frequencies of interaction are those where interactions are more partitioned among species, as indicated by complementary specialization. This suggests that variations in species traits promote species coexistence through niche partitioning, in which pollinators segregate plants based on these features (Maglianesi et al. 2014, Vizentin-Bugoni et al. 2014). Thus, hummingbird species identity (and presumably plant species identity) changes according to historical and environment contexts (Weinstein et al. 2014), but the processes structuring hummingbird-plant interactions are largely similar in most communities as we show here, and hence mostly independent of species taxonomic identity.
Link between processes and network structure
Contrary to our predictions, communities in which relative importance of niche- and neutral processes differed were little associated to network structure, i.e. specialization, nestedness and modularity. In fact, only specialization associated positively to the ability of morphological matching as predictor of frequencies of interaction. This may be because even
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though long-billed hummingbirds can access nectar from both short and long corollas, they specialize in a subset of long tubes species while short-billed species are able to interact legitimately only with the subset of plants with short corolla (Maglianesi et al. 2015). This highlight the role of morphological constraints for the niche partitioning (Maglianesi et al. 2015), and therefore, to define specialization in this system. As most of our cases, previous studies have found that best probabilistic models do not always succeed in predicting network metrics (Vázquez et al. 2009b, Vizentin-Bugoni et al. 2014). Thus, our findings cast uncertanties whether the processes determining pairwise frequencies of interaction between species are the same processes determining network structure. On the other hand, our evidences suggest that distinct processes (i.e. neutral or niche-based) are able to produce similar network structures; while specialization may be an exception. This calls for more explicit investigation of the mechanisms determining frequencies of interactions versus network structure.
In conclusion, we found that species do not interact by chance in plant- hummingbird communities; instead, niche-based processes that impose constraints to interactions mostly drive these interactions. This pattern is widespread across different communities, suggesting that the structuring mechanisms of plant-hummingbird networks are similar regardless of climate, topographic heterogeneity and species richness. Only the importance of phenological overlap differed across space, as it was more important predictor of pairwise species interactions in colder, drier and more seasonal communities. These findings reinforce the importance of few species traits to define the occurrence and (in our case) strength of species interactions in ecological networks (Woodward & Hildrew 2002, Eklöf et al. 2013), but also suggest that climate influences interaction networks (Dalsgaard et al. 2011). The different processes associated to frequencies of interactions seems largely unrelated to the overall network structure, reinforcing the idea that – irrespectively of climate conditions, species richness and also network structure – niche-based processes are key determinants of plant-hummingbird interactions.
Acknowledgements
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We thank Edgar Chavez-González for making available the data from Pachuca and Vinícius L. G. Brito for suggestions on a early draft. Financial support was provided by CAPES through a PDSE scholarship (processo: 008012/2014-08) and a Ph.D scholarship to JVB, CNPq through a grant to M.Sazima; FAPESP to PKM (FAPESP Proc. 2015/21457-4). JS, AMMG, CR and BD thank the Danish National Research Foundation for its support of the Center for Macroecology, Evolution and Climate.
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FIGURES
Figure 1. Barplot showing the predictive effect of abundance (white), phenology (light grey) and morphology (dark grey) for each of the 23 networks included in the Bayesian analyses. For each network, we show means (bars) and standard deviations (horizontal lines) of the posterior distributions of standardized regression coefficients associated to the predictive effects of abundance, phenology and morphology, respectively. Longer bars indicates higher importance; Networks sequence follow the importance of morphological matching. Drawing (by Pedro Lorenzo) depicts plant-pollinator interactions from a tropical forest in Brazil, where the long-billed Phaethornis eurynome interact frequently with both long- and short-tubed flowers of Vriesea simplex and Fuchsia regia, respectively, while the short-billed Thalurania glaucopis is able to extract nectar only in the later. As both P. eurynome and F. regia occurs year-round in the community, they also interact frequently. These examples illustrate how morphological barriers and phenological overlap shape the frequency of interaction in the given community.
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Figure 2. Weighted least squared regression plots showing A) the partial association between mean annual climate and the predictive effects of phenology given the associations to other variables (i.e. climate seasonality, topographic heterogeneity, and species richnnes), and B) the association between seasonality and the predictive effect of phenology given the associations to other variables (i.e. mean annual climate, topographic heterogeneity and species richness). The point coloration towards black corresponds to the standard deviation in the within-network predictive effect of phenology, i.e. lighter dots have lower influence on the regressions, i.e. dark coloured dots have a high standard deviation and light coloured dots have a low standard deviation.
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Table 1. Selected best models in multi-predictor linear models (ΔAICc ≤ 2.0) explaining the predictive effect of A, M and P on the frequencies of interaction. Predictive effects were weighted by the standard deviation of the posterior distribution associated to the coefficient of each predictor variable. Note that the best model selected for A and M was the intercept-only model, meaning that none of the predictor variables was important (climatic, topographic and species richness). By the other hand, the best model for P was the full model. Adjusted-R2 was calculated to the best model. See Methods for details.
Predictor of interactions Model description AICc ΔAICc weight R2 Abundances Intercept 36.6 0.00 0.21 - Topographic heterogeneity (TH) 37.1 0.48 0.16 - Mean annual seasonality (SEAS) 37.6 0.98 0.13 - Mean annual climate (MAC) 38.3 1.76 0.08 -
Phenological overlap MAC+SEAS+TH+Species richness 29.0 0.00 0.24 0.34 MAC+SEAS+TH 29.1 0.14 0.22 - MAC 30.8 1.84 0.09 - MAC+SEAS+ Species richness 30.9 1.92 0.09 -
Morphological matching Intercept 97.8 0.00 0.27 - Species richness 99.3 1.50 0.12 - MAC 99.6 1.74 0.11 - TH 99.6 1.78 0.11 -
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Table 2. Multiple linear regression coefficients relating network structure (specialization, modularity and nestedness) to the predictive effects of species abundances (A), phenological overlap (P) and morphological matching (M) on frequencies of interactions after accounting for ‘sampling intensity’ and ‘network size’; * P<0.05; otherwise non-significant. Weights are given by the standard deviation of the posterior distribution associated to the coefficient of each predictor variable. Note that no matter the importance of A, P and M as determiant of frequencies of interaction, network structure remains consistent, except for specialization, which increases in communities where morphological matching is a more important predictor of frequencies of interaction.
Metric Predictor Estimate R2 Conditional R2
Complementary specialization (∆H2’) A 0.17 0.16 0.02 P 0.22 0.16 0.04 M 0.62* 0.39 0.21
Modularity (∆Q) A -0.06 0.14 0.01 P 0.22 0.17 0.04 M 0.37 0.22 0.04
Nestedness (∆wNODF) A -0.23 0.31 0.08 P -0.02 0.25 <0.01 M -0.54 0.36 0.12
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SUPPLEMENTARY MATERIAL
Figure ESM 1. Weighted least squared regression plots showing the association between network specialization, modularity and nestedness and and the predictive effects of abundances, phenology and morphology. Weights are given by the standard deviation of the posterior distribution associated to the coefficient of each predictor variable.
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Table ESM 1. Coordinates, description and data source for the 25 plant-hummingbird networks across Americas.
Networ k ID Lat Long Site description and general location Source 1 20,13 -98,71 Temperated highlands, central Mexico Román Díaz-Valenzuela & Ortiz-Pulido, R. Unpublished data.
2 19,23 -98,97 Highland temperate forest, Mexico Lara C. (2006) Temporal dynamics of flower use by hummingbirds in a highland temperate forest in Mexico. Ecoscience, 13, 23-29 3 10,44 -84,01 Maglianesi M.A., Blüthgen N., Böhning-Gaese K., & Schleuning, M. Tropical rainforest, Costa Rica (2014) Morphological traits determine specialization and resource use in plant-hummingbird networks in the neotropics. Ecology, 95, 3325-3334 4 10,27 -84,08 Maglianesi M.A., Blüthgen N., Böhning-Gaese K., & Schleuning, M. Tropical rainforest, Costa Rica (2014) Morphological traits determine specialization and resource use in plant-hummingbird networks in the neotropics. Ecology, 95, 3325-3334 5 10,18 -84,11 Maglianesi M.A., Blüthgen N., Böhning-Gaese K., & Schleuning, M. Tropical rainforest, Costa Rica (2014) Morphological traits determine specialization and resource use in plant-hummingbird networks in the neotropics. Ecology, 95, 3325-3334 6 4,5 -75,6 Secondary Andean forest, Colombia Marín-Gómez, O.H. Unpublished data. 7 0,07 -72,45 Rosero-Lasprilla L. (2003) Interações planta/beija-flor em três Tropical rainforest, Costa Rica comunidades vegetais da parte sul do Parque Nacional Natural Chiribiquete, Amazonas (Colombia). PhD Thesis. Universidade Estadual de Campinas, Brasil 8 -2,84 -79,16 Tinoco, B. A., Graham, C. H., Aguilar, J. M. and Schleuning, M. (2016), Shrubland, Ecuador Effects of hummingbird morphology on specialization in pollination networks vary with resource availability. Oikos. doi: 10.1111/oik.02998 9 -2,87 -79,12 Tinoco, B. A., Graham, C. H., Aguilar, J. M. and Schleuning, M. (2016), Highland Andean forest, Ecuador Effects of hummingbird morphology on specialization in pollination networks vary with resource availability. Oikos. doi: 10.1111/oik.02998 10 -2,96 -79,1 Cattle ranching (former Andean forest), Tinoco, B. A., Graham, C. H., Aguilar, J. M. and Schleuning, M. (2016), Ecuador Effects of hummingbird morphology on specialization in pollination networks vary with resource availability. Oikos. doi: 10.1111/oik.02998
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11 -3,82 -70,27 Amazonian rainforest, SE Colombia Cotton P.A. (1998) The hummingbird community of a lowland Amazonian rainforest. Ibis, 140, 512-521 12 -13,12 -41,58 Machado C.G. (2009) Beija-flores (Aves: Trochilidae) e seus recursos Semi arid Caatingas, NE Brazil florais em uma área de caatinga da Chapada Diamantina, Bahia, Brasil. Zoologia, 26, 255-265 13 -13,12 -41,57 Machado C.G. (2014) The hummingbird community and the plants which Cerrado, NE Brazil they visit at a savannah in the Chapada Diamantina, Bahia, Brazil. Bioscience Journal, 30, 1578-1587 14 -13,81 -39,2 Coelho, A.G. (2013) A comunidade de plantas utilizada por beija-flores Atlantic rainforest, NE Brazil no sub-bosque de um fragmento de Mata Atlântica da Bahia, Brasil. PhD Thesis, Universidade Estadual de Feira de Santana, Brasil 15 -19,16 -48,39 Maruyama P.K., Vizentin-Bugoni J., Oliveira G.M., Oliveira P.E., & Cerrado, central Brazil Dalsgaard B. (2014) Morphological and spatio-temporal mismatches shape a Neotropical savanna plant-hummingbird network. Biotropica, 46, 740–747. 16 -19,25 -43,52 Rodrigues L.C. & Rodrigues M. (2014) Flowers visited by hummingbirds Rocky outcrops, central Brazil in the open habitats of the southeastern Brazilian mountaintops: species composition and seasonality. Brazilian Journal of Biology, 74, 659-676 17 -19,52 -56,98 Natural forest patches, Pantanal, SW Araujo A.C. & Sazima M. (2003) The assemblage of flowers visited by Brazil hummingbirds in the “capões” of southern Pantanal, Mato Grosso do Sul, Brazil. Flora, 198, 427-435 18 -19,95 -43,9 Vasconcelos M.F. & Lombardi J.A. (1999) Padrão sazonal na ocorrência Rocky outcrops, central Brazil de seis espécies de beija-flores (Apodiformes: Trochilidae) em uma localidade de campo rupestre na Serra do Curral, Minas Gerais. Ararajuba, 7, 71-79 19 -23,28 -45,05 Vizentin-Bugoni, J., Maruyama, P.K., Debastiani, V.J., Duarte, L.D.S., Motane Atlantic forest, SE Brazil Dalsgaard, B. & Sazima, M. (2016). Influences of sampling effort on detected patterns and structuring processes of a Neotropical plant- hummingbird network. J. Anim. Ecol., 85, 262–272. 20 -23,32 -44,94 Maruyama P.K, Vizentin-Bugoni J., Dalsgaard B., Sazima I. & Sazima. Restinga, Atlantic forest, SE Brazil M. (2015) Nectar robbery by a hermit hummingbird: association to floral phenotype and its influence on flowers and network structure. Oecologia:
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178, 783-793.
21 -23,35 -44,83 Secondary Atlantic forest, SE Brazil Maruyama P.K, Vizentin-Bugoni J., Dalsgaard B., Sazima I. & Sazima. M. (2015) Nectar robbery by a hermit hummingbird: association to floral phenotype and its influence on flowers and network structure. Oecologia: 178, 783-793. 22 -23,36 -44,85 Coastal Atlantic forest, SE Brazil Maruyama P.K, Vizentin-Bugoni J., Dalsgaard B., Sazima I. & Sazima. M. (2015) Nectar robbery by a hermit hummingbird: association to floral phenotype and its influence on flowers and network structure. Oecologia: 178, 783-793. 23 -24,18 -47,93 Atlantic forest, SE Brazil Rocca-de-Andrade M.A. (2006) Recurso floral para aves em uma comunidade de Mata Atlântica de encosta: sazonalidade e distribuição vertical . PhD Thesis. Universidade Estadual de Campinas, Brasil 24 -25,32 -48,7 Atlantic forest, S Brazil Malucelli, T.S. (2014). Fatores envolvidos na estruturação das redes de polinização beija-flor-planta em um gradiente sucessional. MSc Thesis, Universidade Federal do Paraná, Brasil. 25 -31,8 -52,42 Pampas, S Brazil Jeferson Vizentin-Bugoni and Ana M. Rui, unpublished data
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Table ESM 2. Coefficient of correlation (Spearmans’ r) between frequency of interaction and independent measures of hummingbird abundances for all networks with such independent data. Positive correlation was corroborated for 16 (76%) networks, shown in bold. For two networks (*) P=0.05 and P=0.073 was considered significant due the high value of correlation, otherwise P<0.05.
Network Spearmans' r P-value ID 1 0.72 0.033 2 0.96 0.001 3 0.56 0.016 4 0.73 0.048 5 0.25 0.516 6 0.35 0.201 7 0.85 0.013 8 0.70 0.010 9 0.92 0.002 10 0.74 0.030 11 0.34 0.211 15 0.91 >0.001 16 0.77 0.010 18 0.84 0.044 19 0.64 0.073* 20 0.91 <0.001 21 1.00 0.050* 22 0.92 <0.001 23 0.87 <0.001 25 0.54 0.210
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Table ESM 3. Means and standard deviations of the posterior distributions of standardized regression coefficients associated to the predictive effects of respectively abundance (A), phenology (P) and morphology (M) within each network. Posterior distribution of coefficients did not converge for two networks (‘ID 21’ and ‘ID 25’), therefore they are not presented here.
Abundances Phenology Morphology ID mean sd Mean sd mean sd 1 0.01 0.06 0.36 0.07 -0.55 0.18 2 -0.51 0.33 1.18 0.27 -1.74 0.46 3 0.23 0.16 0.67 0.22 -0.49 0.38 4 -0.11 0.14 0.43 0.17 0.64 0.31 5 0.19 0.13 0.63 0.21 -0.25 0.39 6 0.74 0.12 0.62 0.15 1.15 0.31 7 -0.11 0.11 0.48 0.12 0.30 0.27 8 0.77 0.25 1.87 0.23 0.72 0.40 9 0.17 0.12 1.10 0.16 0.40 0.37 10 0.49 0.13 1.34 0.16 0.38 0.46 11 -0.29 0.19 1.07 0.15 2.10 0.40 12 -0.48 0.18 0.95 0.21 -0.82 0.37 13 0.67 0.29 0.87 0.35 0.22 0.69 14 0.28 0.28 1.51 0.32 -0.01 0.51 15 0.25 0.21 1.17 0.25 3.16 0.83 16 0.18 0.13 0.04 0.18 1.41 0.51 17 -0.61 0.37 0.97 0.43 -2.35 0.78 18 0.73 0.31 1.04 0.42 -0.43 0.69 19 0.12 0.09 0.21 0.12 2.36 0.32 20 -0.17 0.15 0.56 0.17 0.44 0.44 22 0.18 0.10 0.10 0.14 4.23 0.99 23 0.08 0.07 0.44 0.10 0.36 0.24 24 0.06 0.11 1.14 0.19 1.48 0.37
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Table ESM 4. Complementary specialization (H2’), weighted nestedness (wNODF) and weighted modularity (Q) values for each network. ∆-values are the metric values corrected by randomization of Patefield null model, i.e. ∆ = observed value – mean values of ramdomizations. We used 1000 randomization to ∆H2’ and ∆wNODF, and 100 to ∆Q.
ID H2’ ∆H2’ wNODF ∆wNODF Q ∆Q 1 0.06 0.42 69.77 -26.42 0.26 0.25 2 0.19 0.49 50.87 -15.80 0.24 0.40 3 0.53 0.61 28.06 -38.95 0.47 0.41 4 0.64 0.40 27.38 -36.91 0.51 0.25 5 0.51 0.40 25.28 -40.17 0.36 0.32 6 0.32 0.62 28.21 -47.54 0.25 0.25 7 0.60 0.16 31.13 -31.60 0.46 0.01 8 0.37 0.30 35.48 -40.29 0.33 0.08 9 0.44 0.64 45.26 -25.38 0.39 0.49 10 0.45 0.49 45.17 -22.31 0.39 0.34 11 0.54 0.54 22.26 -51.81 0.48 0.47 12 0.42 0.14 37.45 -34.21 0.36 0.14 13 0.63 0.56 35.12 -38.29 0.46 0.35 14 0.55 0.40 36.04 -77.11 0.48 0.32 15 0.44 0.33 19.49 -34.59 0.44 0.27 16 0.67 0.40 11.68 -52.19 0.55 0.34 17 0.64 0.36 30.18 -41.85 0.28 0.27 18 0.33 0.44 25.69 -49.24 0.11 0.35 19 0.48 0.33 29.55 -39.33 0.41 0.13 20 0.60 0.50 22.33 -40.99 0.56 0.35 21 0.29 0.41 21.92 -15.44 0.06 0.36 22 0.46 -0.14 31.66 -38.87 0.44 -0.02 23 0.57 0.30 32.66 -30.97 0.47 0.21 24 0.47 0.53 26.18 -34.29 0.36 0.34 25 0.36 0.49 40.89 -29.01 0.18 0.40
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Table ESM 5. Number of hummingbirds and plants, and coefficients of variation in morphological traits, phenology and abundance of hummingbird and plant species to each interaction network. Note that coefficients of variation were usually high for morphological and phenological variables.
N. N. Networ Bill Corolla Hummingbird Plant Hummingbird Plant humminbird plant k ID length length phenology phenology abundance abundance s s 1 9 23 15.0 5.9 48.9 62.9 165.8 323.3 2 10 11 23.5 450.0 52.5 20.9 95.2 133.0 3 8 21 28.6 47.4 50.0 72.7 99.1 228.9 4 8 25 37.5 57.1 39.6 68.2 126.4 245.0 5 9 19 40.0 65.6 43.2 55.6 164.9 192.0 6 13 23 29.2 38.7 23.6 43.3 66.2 262.8 7 8 44 31.8 50.0 42.3 82.4 89.2 273.9 8 8 20 67.9 141.4 76.1 34.7 178.3 156.6 9 12 19 63.6 142.3 69.6 61.4 119.2 170.0 10 9 19 33.3 84.6 94.0 38.4 173.0 62.3 11 15 30 29.2 47.8 59.0 53.2 139.4 205.7 12 7 28 30.4 90.5 51.9 61.2 NA 370.6 13 8 11 38.1 93.8 83.3 54.7 NA 117.8 14 13 16 26.1 61.5 82.7 54.5 NA 106.3
155
15 8 35 30.0 55.0 34.6 47.6 84.3 175.0 16 6 50 28.0 85.0 26.3 79.5 112.6 480.0 17 4 13 16.7 68.4 80.0 74.4 NA 150.9 18 6 10 28.6 58.3 70.9 57.9 81.9 165.0 19 9 56 30.0 56.5 55.4 62.2 101.8 405.6 20 13 22 31.3 58.3 84.1 63.5 97.4 282.2 21 5 16 32.4 43.3 120.0 75.6 1650.0 176.2 22 11 28 31.3 52.0 82.4 75.0 90.1 208.6 23 12 36 27.3 48.5 97.7 67.3 191.6 341.4 24 10 24 31.8 51.9 95.1 62.1 NA 153.7 25 7 28 105.0 63.6 55.0 51.0 252.1 4128.6
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Table ESM 6. Hummingbird species in each of the 25 networks across Americas.
Species Network ID Amazilia fimbriata 11, 15, 20, 21, 22, 24 Adelomyia melanogenys 6 Aglaeactis cupripennis 10 Aglaiocercus kingi 6 Amazilia beryllina 2 Amazilia franciae 6 Amazilia lactea 12, 20 Amazilia leucogaster 14 Amazilia saucerrottei 6 Amazilia tzacatl 3, 6 Amazilia versicolor 7, 19, 20, 21, 22, 23, 24 Anopetia gounellei 12, 23 Anthracothorax nigricollis 6, 11, 23, 25 Aphantochroa cirrhochloris 14, 23, 24 Archilochus colubris 1, 2 Atthis heloisa 2 Augastes scutatus 16 Boissonneaua flavescens 6 Calliphlox amethystina 13, 20 Calothorax lucifer 1, 2 Campylopterus hemileucurus 5 Campylopterus largipennis 11, 16 Chaetocercus mulsant 8 Chlorostilbon lucidus 12, 13, 15, 16, 20, 23, 24, 25 Chlorostilbon mellisugus 11 Chlorostilbon notatus 11, 14 Chlorostilbon olivaresi 7 Chrysolampis mosquitus 12, 13, 14 Chrysuronia oenone 11 Clytolaema rubricauda 19, 20, 23 Coeligena coeligena 6 Coeligena iris 8, 9, 10 Colibri coruscans 6, 8, 9, 10 Colibri serrirostris 12, 13, 15, 16, 20 Colibri thalassinus 1, 2 Cynanthus latirostris 1 Doryfera ludovicae 4, 5, 6 Ensifera ensifera 8, 9 Eriocnemis luciani 8, 9, 10 Eugenes fulgens 1, 2, 5
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12, 13, 14, 15, 16, 17, 19, 20, Eupetomena macroura 22 Eupherusa nigriventris 4 Eutoxeres aquila 4 Florisuga fusca 19, 20, 22, 23, 24, 25 Florisuga mellivora 3, 6, 7, 11 Glaucis dohrnii 14 Glaucis hirsuta 11, 14, 20, 22 Heliactin bilophus 13 Heliangelus exortis 6 Heliangelus viola 8, 9, 10 Heliodoxa jacula 4, 5 Heliomaster squamosus 20 Heliothryx auritus 14 Hylocharis chrysura 17, 25 Hylocharis cyanus 14, 20, 22 Hylocharis leucotis 1, 2 Klais guimeti 3 Lafresnaya lafresnayi 8, 9, 10 Lampornis amethystinus 2 Lampornis calolaema 4, 5 Lampornis clemenciae 1, 2 Lampornis hemileucus 4 Lesbia nuna 10 Lesbia victoriae 8 Leucochloris albicollis 19, 20, 23, 25 Lophornis chalybeus 19, 22, 23, 24 Metallura baroni 8 Metallura tyrianthina 8, 9, 10 Ocreatus underwoodii 6 Panterpe insignis 5 Phaeochroa cuvierii 3 Phaethornis bourcieri 7, 11 Phaethornis eurynome 19, 23, 24 Phaethornis guy 4, 5, 6 Phaethornis hispidus 11 Phaethornis longirostris 3 Phaethornis malaris 7 Phaethornis margarettae 14 Phaethornis pretrei 12, 13, 14, 15, 16, 20 Phaethornis ruber 7, 11, 14, 20, 22 Phaethornis squalidus 20, 21, 22, 23, 24 Phaethornis striigularis 3, 4 Phaethornis subochraceous 17
158
Phaethornis superciliosus 11 Polytmus guainumbi 17 Pterophanes cyanopterus 8, 9, 10 Ramphodon naevius 20, 21, 22, 23, 24 Ramphomicron microrhynchum 8 Selasphorus flammulla 5 Selasphorus platycercus 1, 2 Selasphorus rufus 1, 2 Selasphorus sasin 2 Selasphorus scintilla 5 Stephanoxis lalandi 19 Stephanoxis loddigesii 25 Thalurania colombica 3 Thalurania furcata 7, 11, 15 Thalurania glaucopis 14, 19, 20, 21, 22, 23, 24, 25 Threnetes leucurus 11 Threnetes ruckeri 3 Topaza pyra 7
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Table ESM 7. Plant species in each of the 25 networks across Americas.
Family Species Network ID Acanthaceae Anisacanthus brasiliensis 12 Acanthaceae Aphelandra colorata 19 Acanthaceae Aphelandra macrostachya 7 Acanthaceae Aphelandra storkii 3 Acanthaceae Dicliptera pohliana 25 Acanthaceae Dicliptera squarrosa 15 Acanthaceae Geissomeria longiflora 15 Acanthaceae Geissomeria macrophylla 14 Acanthaceae Geissomeria sp. 19 Acanthaceae Justicia brasiliana 25 Acanthaceae Justicia carnea 21 Acanthaceae Justicia cuatrecasii 7 Acanthaceae Justicia sp. 23 Acanthaceae Justicia sp1 19 Acanthaceae Justicia sp2 19 Acanthaceae Mendoncia sp. 19 Acanthaceae Mendoncia velloziana 20, 23, 24 Acanthaceae Pachystachys coccinea 24 Acanthaceae Pseuderanthemum sp. 14 Acanthaceae Ruellia affinis 14 Acanthaceae Ruellia brevifolia 15 Acanthaceae Ruellia elegans 20 Acanthaceae Ruellia sp1 12 Acanthaceae Ruellia sp2 13 Acanthaceae Sanchezia cf. putumayensis 11 Acanthaceae Sanchezia munita 11 Acanthaceae Sanchezia nobilis 21 Acanthaceae Trichanthera gigantea 6 Alstroemeriaceae Alstroemeria inodora 19 Alstroemeriaceae Alstroemeria plantaginea 20 Alstroemeriaceae Alstroemeria rupestris 12 Alstroemeriaceae Bomarea caldasii 8 Alstroemeriaceae Bomarea carderi 6 Alstroemeriaceae Bomarea edulis 20, 22 Alstroemeriaceae Bomarea glaucescens 8, 9 Alstroemeriaceae Bomarea hirsuta 5 Alstroemeriaceae Bomarea sp. 6 Amaryllidaceae Hippeastrum puniceum 17 Amaryllidaceae Rhodophiala cipoana 16 Apocynaceae Mandevilla scabra 12
160
Apocynaceae Prestonia coalita 12, 17 Apocynaceae Rauvolfia sp. 11 Apocynaceae Tabernamontana macrocalyx 7 Apocynaceae Thevetia bicornuta 17 Asparagaceae Agave sp. 1 Asteraceae Acritopappus longifolius 16 Asteraceae Chuquiraga jussieui 10 Asteraceae Cirsium nivale 2 Asteraceae Chronopappus bifrons 16 Asteraceae Dasyphyllum candolleanum 12 Asteraceae Eremanthus crotonoides 16 Asteraceae Eremanthus erythropappus 16, 20 Asteraceae Hololepis pedunculata 16 Asteraceae Lepidaploa sp1 16 Asteraceae Lessingianthus roseus 16 Asteraceae Lychnophora rosmarinifolia 13 Asteraceae Lychnophora salicifolia 13 Asteraceae Mutisia lemanni 8, 9 Asteraceae Mutisia speciosa 19, 22 Asteraceae Neomirandea eximia 5 Asteraceae Trixis vauthieri 16 Asteraceae Verbesina latisquama 8 Asteraceae Vernonanthura phosphorica 16 Asteraceae Pipitoletis leptosmermoides 16 Balsaminaceae Impatiens walleriana 20, 22 Berberidaceae Berberis lutea 8, 10 Bignoniaceae Bignoniaceae sp1 17 Bignoniaceae Bignoniaceae sp1a 12 Bignoniaceae Campsis grandiflora 25 Bignoniaceae Jacaranda momosaefolia 25 Bignoniaceae Piriadacus erubescens 12 Bignoniaceae Pleonotoma cf. melioides 12 Bignoniaceae Setilobus simplicifolius 12 Bignoniaceae Tabebuia aurea 17 Bignoniaceae Tabebuia umbellata 24 Bignoniaceae Zeyheria montana 13 Boraginaceae Cordia glabrata 17 Boraginaceae Cordia superba 12 Bromeliaceae Aechmea bromelioides 16 Bromeliaceae Aechmea gamosepala 19 Bromeliaceae Aechmea organensis 19 Bromeliaceae Aechmea chantinii 7 Bromeliaceae Aechmea coelestis 22, 23 Bromeliaceae Aechmea contracta 7, 11
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Bromeliaceae Aechmea corymbosa 7 Bromeliaceae Aechmea distichantha 19, 22 Bromeliaceae Aechmea mariae-reginae 3 Bromeliaceae Aechmea miniata 14 Bromeliaceae Aechmea nudicaulis 19, 22, 23, 24, 25 Bromeliaceae Aechmea ornata 23 Bromeliaceae Aechmea pectinata 22, 23 Bromeliaceae Aechmea recurvata 25 Bromeliaceae Aechmea rubiginosa 7 Bromeliaceae Aechmea viridostigma 14 Bromeliaceae Billbergia amoena 16 Bromeliaceae Billbergia amoena 19 Bromeliaceae Billbergia decora 7 Bromeliaceae Billbergia pyramidalis 22 Bromeliaceae Billbergia pyramidalis 21 Bromeliaceae Billbergia vittata 16 Bromeliaceae Bromelia antiacantha 22, 25 Bromeliaceae Bromelia balansae 17 Bromeliaceae Bromelia sp1 7 Bromeliaceae Bromelia sp1a 9 Bromeliaceae Bromelia sp2 8, 10 Bromeliaceae Bromelia sp4 10 Bromeliaceae Canistropsis bilbergioides 23 Bromeliaceae Canistropsis seidelii 21, 22 Bromeliaceae Canistrum cyathiforme 23 Bromeliaceae Canistrum perplexum 19 Bromeliaceae Dyckia dissitiflora 13 Bromeliaceae Dyckia sp. 16 Bromeliaceae Edmundoa lindenii 19, 23 Bromeliaceae Encholirium subsecundum 16 Bromeliaceae Guzmania monostachia 3 Bromeliaceae Guzmania nicaraguenses 4 Bromeliaceae Guzmania sp. 6 Bromeliaceae Hohenbergia sp. 14 Bromeliaceae Lymania brachycaulis 14 Bromeliaceae Mezobromelia sp. 6 Bromeliaceae Neoregelia bahiana 16 Bromeliaceae Neoregelia johannis 20, 21 Bromeliaceae Nidularium angustifolium 21, 22 Bromeliaceae Nidularium fluminensis 21 Bromeliaceae Nidularium innocentii 19, 21, 22, 24 Bromeliaceae Nidularium krisgreeniae 23 Bromeliaceae Nidularium longiflorum 19 Bromeliaceae Nidularium procerum 19, 24
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Bromeliaceae Nidularium rutilans 19 Bromeliaceae Orthophytum lemei 12 Bromeliaceae Pepinia caricifolia 7 Bromeliaceae Pitcairnia brittoniana 4 Bromeliaceae Quesnelia sp. 19 Bromeliaceae Streptocalyx cf. williamsii 11 Bromeliaceae Tillandsia aeranthos 25 Bromeliaceae Tillandsia complanata 8, 9, 10 Bromeliaceae Tillandsia dura 19 Bromeliaceae Tillandsia gardneri 16 Bromeliaceae Tillandsia geminiflora 19, 20, 22 Bromeliaceae Tillandsia sp. 19 Bromeliaceae Tillandsia stricta 19, 23 Bromeliaceae Vriesea carinata 14, 19, 24 Bromeliaceae Vriesea chrysostachys 7 Bromeliaceae Vriesea ensiformes 14, 21, 22, 23, 24 Bromeliaceae Vriesea erythrodactylon 19 Bromeliaceae Vriesea incurvata 19, 24 Bromeliaceae Vriesea inflata 19 Bromeliaceae Vriesea medusa 16 Bromeliaceae Vriesea ororiensi 5 Bromeliaceae Vriesea phillipocoburguii 23 Bromeliaceae Vriesea platylema 23 Bromeliaceae Vriesea procera 14, 16, 20, 22 Bromeliaceae Vriesea procera tenuies 16 Bromeliaceae Vriesea rodigasiana 20, 21, 23 Bromeliaceae Vriesea simplex 19 Bromeliaceae Vriesea sp. 19 Bromeliaceae Vriesea vagans 23 Bromeliaceae Wittrockia superba 19 Burseraceae Protium sp. 16 Cactaceae Cipocereus minensis 16 Cactaceae Opuntia imbricata 1 Cactaceae Opuntia sp1 'amarilla' 1 Cactaceae Opuntia sp2 'morada' 1 Cactaceae Pilosocereus aurisetus 16 Campanulaceae Burmeistera parviflora 5 6, 12, 14, 19, 20, Campanulaceae Centropogon cornutus 22 Campanulaceae Centropogon granulosus 4 Campanulaceae Centropogon latisepalus 6 Campanulaceae Centropogon solanifolius 5 Campanulaceae Centropogon sp. 9 Campanulaceae Lobelia fistulosa 16
163
Campanulaceae Siphocampylus convolvulaceus 19 Campanulaceae Siphocampylus fimbriatus 16 Siphocampylus Campanulaceae longipediunculatus 19 Campanulaceae Siphocampylus sp. 19 Cannaceae Canna paniculata 19, 20 Caprifoliaceae Valeriana sp. 10 Caryocaraceae Caryocar brasiliense 15 Caryophyllaceae Silene laciniata 1 Clusiaceae Symphonia globulifera 7 Combretaceae Combretum llewelynii 11 Convolvulaceae Jacquemontia sp1 12 Convolvulaceae Jacquemontia sp2 12 Costaceae Costus curvibracteatus 4 Costaceae Costus pulverulentus 3, 4 Costaceae Costus scaber 3, 7, 11 Costaceae Costus sp. 6 Costaceae Costus spiralis 11, 14, 23, 24 Crassulaceae Echeveria gibbiflora 2 Curcubitaceae Gurania acuminata 14 Curcubitaceae Gurania coccinea 4 Curcubitaceae Gurania rhizantha 11 Curcubitaceae Gurania rufipila 7 Curcubitaceae Gurania spinulosa 11 Eleocarpaceae Vallea stipularis 9, 10 Ericaceae Agarista cariifolia 16, 20 Ericaceae Cavendishia bracteata 5, 6, 9 Ericaceae Cavendishia callista 4 Ericaceae Cavendishia complectens 4 Ericaceae Cavendishia quereme 4 Ericaceae Disterigma humboldtii 5 Ericaceae Gaultheria erecta 10 Ericaceae Gaultheria gracilis 5 Ericaceae Gaultheria tomentosa 10 Ericaceae Gaylussacia brasiliensis 16 Ericaceae Gaylussacia oleifolia 16 Ericaceae Gaylussacua hispida 16 Ericaceae Gonocalyx pterocarpus 5 Ericaceae Macleania rupestris 8, 9, 10 Ericaceae Psammisia ramiflora 4 Ericaceae Satyria meiantha 4 Ericaceae Satyria panurensis 7 Ericaceae Thibaudia costaricensi 4 Ericaceae Vaccinium poasanum 5
164
Fabaceae Bauhinia brevipes 15 Fabaceae Bauhinia longifolia 12 Fabaceae Bauhinia ungulata 15 Fabaceae Bionia coriacea 13 Fabaceae Calliandra brevipes 25 Fabaceae Calliandra sessillis 13 Fabaceae Calliandra sincorana 13 Fabaceae Calliandra tweedii 25 Fabaceae Camptosema croriaceum 15 Fabaceae Camptosema ellipticum 17 Fabaceae Centrosema coriaceum 20 Fabaceae Chaetocalyx subulatus 12 Fabaceae Dahlstedtia pentaphylla 23, 24 Fabaceae Dahlstedtia pinnata 20, 21, 22, 23 Fabaceae Erythrina cristagalli 25 Fabaceae Erythrina fusca 11 Fabaceae Erythrina rubrinervia 6 Fabaceae Erythrina speciosa 19, 22, 24, 25 Fabaceae Inga dumosa 11 Fabaceae Inga edulis 23, 24 Fabaceae Inga marginata 20, 21 Fabaceae Inga ornata 6 Fabaceae Inga sessilis 19 Fabaceae Inga subnuda 22 Fabaceae Inga vera 17, 15 Fabaceae Periandra coccinea 12 Fabaceae Schizolobium parahyba 24 Fabaceae Tachigali paniculata 11 Fabaceae Vigna longifolia 17 Fabaceae Zygia lathetica 7 Gentianaceae Calolisianthus pendulus 16 Gentianaceae Macrocarpaea macrophylla 5 Gentianaceae Macrocarpaea rubra 19 Gentianaceae Tachia occidentalis 7 _Unknown Gesneriaceae Gesneriaceae'PAC019' 11 Gesneriaceae Alloplectus peruvianus 9 Gesneriaceae Alloplectus savanarum 7 Gesneriaceae Besleria columneoides 3 Gesneriaceae Besleria longimucronata 19, 20, 21 Gesneriaceae Besleria notabilis 4 Gesneriaceae Besleria solanoides 5, 6 Gesneriaceae Columnea dimidiata 6 Gesneriaceae Columnea ericae 7
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Gesneriaceae Columnea magnifica 5 Gesneriaceae Columnea microcalyx 5 Gesneriaceae Columnea purpurata 4 Gesneriaceae Columnea querceti 4 Gesneriaceae Drymonia conchocalyx 4 Gesneriaceae Drymonia semicordata 7 Gesneriaceae Kohleria affinis 6 Gesneriaceae Kohleria inaequalis 6 Gesneriaceae Kohleria tigridia 5 Gesneriaceae Nematanthus corticola 14 Gesneriaceae Nematanthus fissus 22 Gesneriaceae Nematanthus fluminensis 19, 22 Gesneriaceae Nematanthus fornix 19 Gesneriaceae Nematanthus gregarius 19, 23 Gesneriaceae Nematanthus maculatus 19 Gesneriaceae Nematanthus striatus 23 Gesneriaceae Nematanthus tessmannii 24 Gesneriaceae Nematanthus strigillosus 16 Gesneriaceae Nematanthus fritschii 19 Gesneriaceae Paliavana sericiflora 16, 20 Gesneriaceae Sinningia cooperi 19 Gesneriaceae Sinningia elatior 19 Gesneriaceae Sinningia glazioviana 19 Gesneriaceae Sinningia rupicola 20 Gesneriaceae Sinningia sp. 23 Heliconiaceae Heliconia acuminata 7 Heliconiaceae Heliconia angusta 21, 22 Heliconiaceae Heliconia atropurpurea 4 Heliconiaceae Heliconia farinosa 20, 21, 24 Heliconiaceae Heliconia griggsiana 6 Heliconiaceae Heliconia imbricata 3 Heliconiaceae Heliconia juruana 11 Heliconiaceae Heliconia lankesteri 5 Heliconiaceae Heliconia latispatha 3, 6 Heliconiaceae Heliconia mariae 3 Heliconiaceae Heliconia mathiasiae 3 Heliconiaceae Heliconia pogonantha 3 Heliconiaceae Heliconia psittacorum 15 Heliconiaceae Heliconia richardiana 14 Heliconiaceae Heliconia schumanniana 11 Heliconiaceae Heliconia sp. 23 Heliconiaceae Heliconia stricta 11 Heliconiaceae Heliconia tarumaensis 7 Heliconiaceae Heliconia vaginalis 4
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Heliconiaceae Heliconia venusta 6 Heliconiaceae Heliconia wagneriana 3 Heliconiaceae Heliconia spathocircinata 20 Lamiaceae Amasonia arborea 7 Lamiaceae Hypits sp1 16 Lamiaceae Hypts leptostachys 12 Lamiaceae Prunella vulgaris 2 Lamiaceae Salvia amarissima 1 Lamiaceae Salvia chamaedryoides 1 Lamiaceae Salvia corrugata 8, 9, 10 Lamiaceae Salvia elegans 2 Lamiaceae Salvia hirta 8, 9 Lamiaceae Salvia microphylla 1 Lamiaceae Salvia mocinoi 2 Lamiaceae Salvia polystachya 1 Lamiaceae Salvia prunelloides 1 Lamiaceae Salvia sp1 1 Lamiaceae Salvia sp2 1 Lamiaceae Scutellaria caerulea 1 Lamiaceae Stachys coccinea 1 Lamiaceae Vitex cymosa 17 Loranthaceae Psittacanthus cordatus 17 Loranthaceae Psittacanthus cucularis 11 Loranthaceae Psittacanthus dichroos 19, 22, 23 Loranthaceae Psittacanthus lasianthus 7 Loranthaceae Tristerix longebracteatus 10 Lythraceae Cuphea aequipetala 1 Lythraceae Cuphea ericoides 16 Lythraceae Cuphea melvilla 11, 17 Lythraceae Cuphea procumbeus 1 Lythraceae Lafoensia glyptocarpa 12 Lythraceae Lafoensia pacari 20 Malvaceae Abutilon sp. 19 Malvaceae Eriotheca pentaphylla 22 Malvaceae Erythroxylum vaccinifolium 16 Malvaceae Helicteres brevispira 15 Malvaceae Helicteres eichleri 12 Malvaceae Helicteres guazumaefolia 17 Malvaceae Helicteres saca-rolha 15 Malvaceae Helicteres velutina 12 Malvaceae Luehea divaricata 25 Malvaceae Malvaceae sp1 16 Malvaceae Malvaviscus palmanus 5 Malvaceae Melochia parvifolia 17
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Malvaceae Pavonia sp. 12 Malvaceae Pavonia viscosa 20 Malvaceae Sida cordifolia 13 Malvaceae Spirotheca rivieri 19, 23 Marantaceae Calathea capitata 11 Marantaceae Calathea crocata 14 Marantaceae Calathea gymnocarpa 3 Marantaceae Calathea inocephala 3 Marantaceae Calathea lasiostachya 4 Marantaceae Calathea lutea 3 Marantaceae Calathea zingiberea 7 Marantaceae Callatea comunis 23 Marantaceae Ischnosiphon lasiocoleus 7 Marantaceae Monotagma secundum 7 Marcgraviaceae Schwartzia brasiliensis 22, 23, 24 Melastomataceae Brachyotum sp. 8, 10 Melastomataceae Miconia denticulata 9 Musaceae Musa ornata 20, 23 Musaceae Musa rosacea 24 Musaceae Musa velutina 6 Myrtaceae Callistemon speciosus 25 Myrtaceae Eucalyptus globulus 6 Myrtaceae Melaleuca leucadendra 25 Myrtaceae Myrcia lasiantha 16 Myrtaceae Myrcianthes sp. 9 Myrtaceae Syzygium malaccense 11 Onagraceae Fuchsia microphylla 2 Onagraceae Fuchsia regia 19, 23 Onagraceae Fuchsia vulcânica 8, 9, 10 Onagraceae Oenothera sp. 1 Orchidaceae Elleanthus aurantiacus 5, 6 Orchidaceae Elleanthus brasiliensis 23 Orobanchaceae Agalinis angustifólia 16 Orobanchaceae Castilleja scorzonerifolia 2 Orobanchaceae Castilleja tenuiflora 1, 2 Orobanchaceae Esterhazya splendida 16 Orobanchaceae Lamourouxia dasyantha 1 Passifloraceae Passiflora aff. involucrata 11 Passifloraceae Passiflora edmundoi 12 Passifloraceae Passiflora quadriglandulosa 11 Passifloraceae Passiflora skiantha 7 Passifloraceae Passiflora sp. 8 Passifloraceae Passiflora spinosa 11 Passifloraceae Passiflora vitifolia 7
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Plantaginaceae Penstemon barbatus 1 Plantaginaceae Penstemon gentianoides 2 Plantaginaceae Penstemon roseus 2 Polemoniaceae Loeselia mexicana 1 Proteaceae Oreocallis grandiflora 8, 9, 10 Rosaceae Rubus floribundus 8, 10 Rosaceae Rubus rosifolius 24 Rubiaceae Augusta longifolia 12 Rubiaceae Bouvardia ternifolia 1, 2 Rubiaceae Coussarea hydrangeifolia 15 Rubiaceae Decagonocarpus cornutus 7 Rubiaceae Faramea eurycarpa 4 Rubiaceae Ferdinandusa sprucei 7 Rubiaceae Genipa americana 11 Rubiaceae Hamelia patens 3, 6 Rubiaceae Hillia ilustris 23 Rubiaceae Hillia parasitica 16 Rubiaceae Hillia triflora 4 Rubiaceae Hoffmannia arborescens 5 Rubiaceae Isertia hypoleuca 7 Rubiaceae Isertia rosea 7 Rubiaceae Manettia cordifolia 13, 15, 19 Rubiaceae Manettia luteo-rubra 24 Rubiaceae Palicourea acetosoides 6 Rubiaceae Palicourea aff. lasiantha 11 Rubiaceae Palicourea crocea 11 Rubiaceae Palicourea gomezii 4 Rubiaceae Palicourea lasiorrhachis 5 Rubiaceae Palicourea marcgravii 15 Rubiaceae Palicourea nigricans 7 Rubiaceae Palicourea quadrifolia 7 Rubiaceae Palicourea rigida 15 Rubiaceae Palicourea sp1 11 Rubiaceae Palicourea sp2 4 Rubiaceae Palicourea sp3 9 Rubiaceae Palicourea stenoclada 11 Rubiaceae Palicourea subspicata 7 Rubiaceae Pentagonia monocaulis 3 Rubiaceae Psychotria bahiensis 7 Rubiaceae Psychotria carthagenensis 17 Rubiaceae Psychotria elata 3, 4 Rubiaceae Psychotria leiocarpa 19 Rubiaceae Psychotria nuda 20, 21, 22, 24 Rubiaceae Psychotria platypoda 7
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Rubiaceae Psychotria poeppigiana 3, 7 Rubiaceae Psychotria suterella 24 Rubiaceae Psychotria vellosiana 16 Rubiaceae Pyrostegia venusta 12, 19 Rubiaceae Retiniphyllum rhabdocalyx 7 Rubiaceae Sabicea aspera 17 Rubiaceae Sabicea grisea 20, 22 Rubiaceae Warscewiczia coccinea 3 Salicaceae Ryania pyrifera 7 Sapindaceae Paullinia pinnata 17 Sapindaceae Serjania coradinii 12 Schlegeliaceae Schlegelia fastigiata 3 Solanaceae Acnistus arborescens 20, 24 Solanaceae Brugmansia sanguinea 8, 9 Solanaceae Dyssochroma viridiflora 23 Solanaceae Markea coccínea 7 Solanaceae Nicotiana glauca 1 Solanaceae Saracha quitensis 8, 9, 10 Theaceae Laplacea fruticosa 16 Tropaeolaceae Tropaeolum pentaphyllum 25 Velloziaceae Barbacenia flava 16 Velloziaceae Barbacenia gentianoides 16 Velloziaceae Barbacenia luzilifolia 16 Velloziaceae Barbacenia williamsii 20 Velloziaceae Barnadesia arborea 8, 9, 10 Velloziaceae Vellozia cf. epidendroides 16 Verbenaceae Citharexylum myrianthum 23 Verbenaceae Lantana camara 12, 19, 20 Verbenaceae Lipia sp. 13 Verbenaceae Stachytarpheta quadrangula 12 Verbenaceae Stachytarpheta cayennensis 20, 22 Verbenaceae Stachytarpheta gesnerioides 15 Verbenaceae Stachytarpheta glabra 16, 20 Verbenaceae Stachytarpheta maximiliani 24 Verbenaceae Stachytarpheta mexie 16 Violaceae Paypayrola hulkiana 7 Violaceae Viola arguta 8, 9, 10 Vochysiaceae Qualea multiflora 15 Vochysiaceae Vochysia emarginata 16 Vochysiaceae Vochysia sp1 16 Vochysiaceae Vochysia tucanorum 15 Zingiberaceae Hedychium coronarium 20 Zingiberaceae Renealmia cernua 3, 4 Zingiberaceae Renealmia sp. 7
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CONSIDERAÇÕES FINAIS
Nesta tese demonstramos que (capítulo 1) apesar dos muitos avanços na compreensão das interações planta-polinizador em comunidades em geral, a maioria do conhecimento acumulado até o momento deriva de redes de polinização provenientes de regiões extra-tropicais. Nos trópicos, relativamente poucos estudos descrevem redes completas e a maioria das redes é proveniente da região Neotropical. Dessa forma, evidenciamos limitações na compreensão das redes de polinização e a necessidade de mais estudos no Paleotrópico, bem como amostragens de redes mais completas. Apesar disso, aparentemente a estrutura destas redes tem sido em geral similar entre áreas tropicais e extra- tropicais, sendo a presença de vertebrados uma das diferenças mais notáveis nos trópicos. Em alguns sistemas tropicais com elevada diversidade de atributos funcionais, as interações são determinadas por restrições tais como acoplamentos morfológicos e sobreposições fenológicas (processos baseados em nicho), enquanto na maioria das redes extra-tropicais a neutralidade (sozinha ou em combinação com outros processos) assume um papel importante (e.g. Vázquez et al. 2009b). Estas evidências levaram ao desenvolvimento da hipótese do ‘neutral-niche continuum’, que postula que sistemas com elevada diversidade de atributos funcionais devem possuir interações estruturadas mais por processos baseados em nicho, enquanto neutralidade deve assumir papel importante sob reduzida diversidade de atributos funcionais, já que restrições impostas por fenótipos ou distribuição no espaço e no tempo devem ser mais escassas nestas comunidades. Esta hipótese deriva de proposições prévias de Gravel et al. (2006) para a estrutura de comunidades e Canard et al. (2014) para redes de interações parasita-hospedeiro.
No capítulo 2, a análise de uma rede planta − beija-flor amostrada intensamente na Floresta Atlântica brasileira demostrou que métricas quantitativas, i.e. interaction evenness, especialização (H2’), aninhamento ponderado (wNODF) e modularidade (Q; QuanBiMo algorithm), são descritores mais robustos ao esforço amostral do que métricas binárias. Além disso, constatamos que mesmo um baixo esforço de amostragem foi suficiente para detectar a importância das restrições morfológicas e fenológicas na determinação das interações na comunidade, o que pode ser uma característica de sistemas especializados nos quais atributos funcionais desempenham papel importante na partilha de nichos entre as espécies, tais como interações entre plantas e beija-flores.
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No capítulo 3 expandimos as ideias dos nossos estudos prévios (Maruyama et al. 2014, Vizentin-Bugoni et al. 2014). Especificamente, demonstramos que restrições impostas por barreiras morfológicas e sobreposição fenológica das espécies são mecanismos fundamentais para as interações planta – beija-flor nas Américas, independente das características climáticas (médias anuais e sazonalidade na temperatura a precipitação), heterogeneidade topográfica e riqueza de espécies das comunidades. Além disso, demonstramos aqui que a sobreposição fenológica é um preditor mais importante da frequência das interações em ambientes mais frios, secos e mais sazonais. Em adição, evindenciamos que características topológicas das redes (i.e. modularidade e aninhamento) e sua especialização variam independentemente de quais mecanismos (i.e. morfologias, fenologias ou abundâncias) atuam na determinação da frequência das interações. A única exceção foi a especialização, que é aumentada em comunidades onde acoplamento morfológico é um preditor importante da frequência das interações. Argumentamos que a ampla importância de processos baseados em nicho (i.e. barreiras morfológicas e sobreposição fenológica) para a estruturação das interações neste sistema - o qual possui tipicamente elevada diversidade de atributos funcionais (i.e variação nos comprimentos de bicos e corolas e na distribuição temporal de beija-flores e florações), está de acordo com a predição proposta pela ‘neutral-niche continuum hypothesis’.
Somados aos estudos prévios sobre interações planta − beija-flor (e.g. Stiles 1975, Wolf et al. 1976, Buzato et al. 2000, Dalsgaard et al. 2009), as evidências baseadas em redes complexas apresentadas aqui demonstram a profunda importância das morfologias de flores e bicos de beija-flores, bem como das dinâmicas temporais da floração e dos movimentos das aves em pequena ou larga escala, para a partilha de nichos entre plantas e polinizadores. Propomos aqui um modelo (‘neutral-niche continuum’) através do qual sugerimos que a importância destas barreiras tende a aumentar com a variação ou diversidade dos atributos funcionais nas comunidades, de forma que é possível antecipar que em assembleias altamente diversas estes processos baseados em nicho são fatores cruciais na determinação da estrutura e dinâmica das redes de polinização. Este é provavelmente o caso da maioria dos sistemas de polinização (e.g. envolvendo esfingídeos), na maioria das florestas tropicais megadiversas. Além disso, esperamos que as predições deste modelo se apliquem a outros atributos importantes conhecidamente para as interações entre plantas e beija-flores, tais como qualidade e quantidade do néctar secretados por flores e as variações no tamanho e comportamento dos beija-flores (e.g. territoriais versus ‘trapliners’), os quais foram ainda
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pouco explorados em estudos similares a este (e.g. Maruyama et al. 2014) e merecem atenção futura.
Por fim, acreditamos que esta tese contribui para a compreensão dos processos determinantes das interações entre espécies em comunidades ecológicas. Outros esforços neste sentido, como a inclusão de outros atributos funcionais como preditores, bem como o teste explícito da hipótese do “neutral-niche continuum’ para outros tipos de interações (i.e. medindo diretamente a diversidade funcional nas comunidades e correlacionando com a importância dos diferentes mecanismos), são caminhos possíveis para o desenvolvimento de predições mais acuradas de interações bióticas a partir de proxies, tais como os atributos das espécies ou suas abundâncias relativas (Morales-Castilla et al. 2015).
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ANEXOS
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