EVOLUTION, SPECIATION, AND GENOMICS OF THE ANDEAN HUMMINGBIRDS

Coeligena bonapartei AND Coeligena helianthea

Catalina Palacios

Laboratorio de Biología Evolutiva de Vertebrados

Departamento de Ciencias Biológicas

Universidad de los Andes

Advisor

Carlos Daniel Cadena PhD

Dissertation committee

Sangeet Lamichhaney PhD

Juan Armando Sánchez PhD

Dissertation submitted in partial fulfillment of the requirements for obtaining the title of Doctor of Philosophy in Biological Sciences

Bogotá, Colombia

2020

To Tim Minchin and Trevor Noah

To Martina, Emilia, Zoe, Lucia, Antonia, Eva, Gia and Maya

Little bits of light of a better future

“Emilia aprendió a no despreciar nada. Menos que nada el lenguaje de los hechos, el que mira en cada peripecia algo único, aunque derive su conocimiento de doctrinas generales” Ángeles Mastretta. Mal de Amores. 1996

Continuidad

Las hojas caen en el oscuro silencio Desde lo alto de los árboles del bosque Sobre las aguas plateadas de los ríos corrientes Hasta el latir profundo de las sombras Al suelo húmedo donde aguardan los hongos. Mientras tanto, Los corazones rojos y húmedos laten desbocados Al paso de las alas que se agitan Las lenguas se pronuncian desde el techo de la cabeza Viajan hacia las corolas El azúcar a raudales entra a sus sistemas Atraviesa como hilos los intestinos Se deslíe y en segundos es sangre Viaja entre balsas cargadas de oxígeno Se atropella contra las paredes oscuras Atraviesa las membranas Entra al ciclo, uno, dos, tres ATPs son casi 30 Se hace energía, se va en el aire Retumban los cuerpos a cada impulso Alas adelante, alas atrás, van al vuelo Un milisegundo quietos, vuelven al vuelo Prisioneros de su historia buscan continuarse Una generación, va otra Las lenguas desde el cráneo Los insectos, el néctar, los nidos, los huevos La humedad Al interior del bosque Al borde del bosque Va otra generación y sin anuncio no son los mismos Las envolturas de unas plumas que se rompen La luz que le da paso a los colores se refleja Uno dorado, el otro rosa No son los mismos, aunque estén cerca En el laberinto de sus genes algo ha cambiado ¿Qué los diferencia? Mientras tanto, Al húmedo suelo donde los hongos aguardan Hasta el latir oscuro de las sombras Sobre las corrientes plateadas de las aguas del río Desde los árboles altos de los bosques En el profundo silencio Caen las hojas.

Index

Acknowledgments ...... 5

Abstract ...... 7

Resumen ...... 8

Introduction ...... 9

“We’re one but we are not the same”1 – Shallow genetic divergence and distinct phenotypic differences between two Andean hummingbirds: Speciation with gene flow? – Chapter 1 ...... 14

How much is too little? – Complete mitochondrial genomes do not distinguish phenotypically distinct lineages of Andean Coeligena hummingbirds – Chapter 2 ...... 48

Seeking gold and roses – Genomic differentiation and evolution of lineages of Andean Coeligena Hummingbirds – Chapter 3 ...... 76

Conclusions ...... 108

Acknowledgments

I am very grateful to all institutions and people who contributed to this work. I thank the Universidad de los Andes, the Vicerrectoría de Investigación, and the Facultad de Ciencias for funding through research grants and the teaching assistantship program. I thank Colciencias for funding through a PhD scholarship and to Colfuturo for managing it. I thank the Fundación para la promoción de la investigación y la tecnología from the Banco de la República for funding. I thank the Moore Laboratory of Zoology at the Occidental College, especially John McCormack, Eugenia Zarza, and Whitney Tsai. I thank the Lovette Lab at the Cornell Lab of Ornithology, especially Irby Lovette, Leonardo Campagna, and Bronwyn Butcher. I thank Christopher Witt at the University of New Mexico. I thank the Jarvis Lab and the Vertebrate Genome Lab at Rockefeller University, especially Erich Jarvis and Sadye Paez. I thank the Museo de Historia Natural at the Universidad de los Andes, the Instituto de Ciencias Naturales at the Universidad Nacional de Colombia, and the Instituto de Investigación de Recursos Biológicos Alexander Von Humboldt.

I am deeply grateful to my advisor Daniel Cadena; you supported my ideas no matter how unusual they were, from games to genomes, you helped me find the resources, the people, and the way to achieve our work with the best possible quality. In our discussions and lab meetings your recurrent come-back to theory and to the scientific method helped me think not only about a singular problem but about the broad picture of questions and hypotheses; having you as my advisor was a fortunate privilege. I thank all my coauthors; collaborating with you was a pleasure and your contributions to this work were essential. I am very grateful to Leonardo Campagna; I had a great time working with you, I learned a lot, and I enjoyed our conversations; It was a pleasure to find a peer to talk about genes, genomes, lab work, and so many other varied topics. I am extremely grateful to the members of my advisory committee, my qualifying exam, and my candidacy exam, Silvia Restrepo, Andrew Crawford, and Reto Burri; during each occasion I found in you the knowledge and the encouragement to continue. I thank Gary Stiles whom I owe all my love for hummingbirds. I thank the Good Will Runners with whom I ran so many kilometers. I thank my D&D fellows with whom I lived so many adventures. I thank Pedro Andrés Oróstegui, who knows how to look beyond, and whose views helped me find the way back countless times. I thank my mother, my father, my sister, and my brother, always by my side and in my heart.

With all my brain and all my heart, I owe my gratitude to my Friends. Life and discussions, scientific or not, are always more lovely and interesting with you. To Tania Pérez, Manuel Franco, María José Contreras, Camila Gómez, Nick Bayly, Andrés Quiñones, Paola Montoya, María Alejandra Meneses, Laura Céspedes, Mateo Dávila, Melanie Ramírez, Natalia Gutiérrez, Valentina Gómez, Ghislaine Cárdenas, Laura Mahecha, Santiago Herrera, Lina Gutiérrez, Luisa Dueñas, Mileidy Betancourth, Jorge Avendaño, Paulo Pulgarín, Oscar Laverde, David Ocampo, Lucas Barrientos, Ana Aldana, Vicky Flechas, Andrew Crawford, Leonardo Campagna, Erich Jarvis, Andrés Cuervo, Carlos Guarnizo, Juan Luis Parra, Catalina Cruz, Iván Páez, Fabian Salgado, Andrea Paz, Gerriet Janssen, Will Vargas, Sauk Naranjo, Alfa, Milo, and Logan Luminus Veroandi. Without your constant support and love none of this work would have ever been possible.

I thank you, for reading this document!

Abstract

The evolutionary mechanisms driving lineage differentiation in a system and the genetic basis of phenotypic differentiation remain central issues in evolutionary biology. In the Colombian Andes, two hummingbird species, Coeligena bonapartei and C. helianthea, show striking phenotypic differences in coloration but low genetic differentiation in a phylogeny of the Coeligena genus. These species comprise five allopatric and sympatric subspecies. In this thesis I studied the evolution of the lineages in C. bonapartei and C. helianthea to identify the evolutionary mechanisms driving their divergence and the genetic basis of their phenotypic differences. I sequenced for the first time the genomes of a population sample of hummingbirds and I used tools from population genetics, comparative genomics, niche modeling, and comparative morphology to clarify the evolutionary relationships among lineages of Coeligena; to test the hypothesis that they diverged in the presence of gene flow; and to identify candidate genes related to their phenotypic differences. I found that C. bonapartei and C. helianthea comprise at least four evolutionary lineages. The evolution of the lineages C. b. eos and C. b. consita was likely driven by genetic drift in the absence of gene flow, whereas C. b. bonapartei and C. helianthea diverged in the presence of gene flow driven by natural selection, sexual selection, or both. The low genetic differentiation among the mitochondrial genomes of these hummingbirds supports their recent divergence and the occurrence of introgression. The landscape of genomic differentiation among these lineages shows many peaks containing genes possibly related to their phenotypic differences. These genes support the idea that structural coloration in hummingbirds is a polygenic trait closely linked to feather development. My work contributes to understanding of comparative genomics in hummingbirds and how species are formed in megadiverse mountain systems in the tropics.

Resumen

El efecto de los mecanismos evolutivos en la divergencia de un sistema dado, y las bases genéticas de las diferencias fenotípicas son aún temas centrales en evolución. En la cordillera Oriental de los Andes colombianos, habitan las especies de colibríes Coeligena bonapartei y C. helianthea, que son muy diferentes en coloración, pero mostraron baja divergencia genética en una filogenia del género. Estas especies tienen 5 subespecies descritas entre alopátricas y simpátricas. En esta tesis estudié la evolución de C. bonapartei y C. helianthea para identificar los mecanismos evolutivos asociados a su divergencia y las bases genéticas de sus diferencias fenotípicas. Secuencié por primera vez los genomas de una muestra poblacional de colibríes, y utilicé herramientas de genómica comparativa, modelos de nicho, y comparaciones morfológicas, para aclarar las relaciones filogenéticas entre estos linajes, evaluar la hipótesis de que divergieron en presencia de flujo génico, e identificar genes candidatos asociados a sus diferencias fenotípicas. Encontré que C. bonapartei y C. helianthea comprenden al menos 4 linajes evolutivos. Los linajes C. b. eos y C. b. consita evolucionaron dirigidos principalmente por deriva genética y sin flujo génico, mientras que C. b. bonapartei y C. helianthea divergieron en presencia de flujo génico dirigidos por selección natural, sexual o ambas. La baja diferenciación genética entre los genomas mitocondriales de estos linajes soporta su reciente divergencia y eventos de introgresión. El paisaje de diferenciación genómica entre estos linajes muestra varios picos con genes posiblemente asociados a sus diferencias fenotípicas, que apoyan la idea de que la coloración estructural en colibríes es un rasgo poligénico estrechamente ligado al desarrollo de las plumas. Con esta tesis contribuí al desarrollo de la genómica comparativa en colibríes y al entendimiento de cómo se forman las especies en los sistemas montañosos megadiversos de los trópicos.

Introduction

Biodiversity is a result of the ongoing process of evolution, whereby various mechanisms playing out in lineage-specific contexts determines which lineages persist, go extinct, or form new lineages through the process of speciation. Mutation and recombination ultimately underlie the origin of differentiation, whereas drift, gene flow, and selection are evolutionary mechanisms proximately governing the persistence or loss of differentiation among lineages (Coyne & Orr, 2004; Price, 2007). Although lack of gene flow typically drives speciation when lineages are allopatric (Mayr, 1963) whereas selection is responsible for differentiation between sympatric lineages (Nosil, 2012), in any particular case the question of what evolutionary mechanisms drove lineage differentiation is a central issue in speciation research. Did recently diverged, sympatric lineages differentiate in the present of gene flow? If so, and selection is strong enough to overcome the homogenizing effect of gene flow, on which traits is selection acting on? How do different evolutionary forces shape patterns of genomic differentiation between lineages? Genomic approaches have furthered our ability to answer these kinds of questions and to understand the role that the interplay among evolutionary mechanisms has played in the differentiation and persistence of species (Lamichhaney et al., 2017; Ottenburghs et al., 2017; Toews et al., 2016a).

The genomic landscape of divergence among lineages may consist of large and few regions of high differentiation relative to background levels of differentiation (genomic continents of differentiation, Michel et al., 2010), or may involve multiple, relatively small regions of differentiation (peaks or islands of differentiation, Feder & Nosil, 2010). Genomic regions of high differentiation may have contributed to speciation (Turner, Hahn, & Nuzhdin, 2005), or may reflect processes such as genetic conflict, genetic drift, variable mutation rates, chromosomal structure and selective sweeps (Kelley et al., 2012). However, peaks of differentiation are expected to be associated with phenotypic differentiation and speciation, especially if they exist between recently diverged species having had limited time to accumulate neutral divergence (Nosil & Feder, 2012). Linking patterns seen in comparisons of the genomes of species to evolutionary processes is central to studies on the genomics of speciation. Among animals, studies in birds have led the field of genomics, since the pioneering sequencing of the first avian genome (ICGSC International Chicken Genome Sequencing Consortium, 2004) up to the consolidation of one of the largest and better-quality genomic data sets currently available (Koepfli, Paten, & Brien, 2015; Rhie et al., 2020). In particular, avian genomics has provided data relevant for understanding different scenarios of speciation (Edwards et al., 2005; Toews et al., 2016b), and new studies in closely related taxa contribute to identify genes and genomic regions related to phenotypic differentiation and speciation. However, studies on comparative genomics of neotropical birds are still required to understand how the evolutionary mechanisms drive phenotypic differentiation and speciation in this region.

In this thesis, I worked on the speciation genomics of hummingbirds, a lineage that has radiated in the Andes of tropical South America where up to 200 species exist (from 363 in the family Trochilidae, Restall, Rodner, & Lentino, 2007).Out of the nine crown-clades of the hummingbird family, two clades are mainly restricted to the high Andes: the Coquettes and the Brilliants (McGuire, Witt, Remsen, Dudley, & Altshuler, 2008). The genus Coeligena is a member of the Brilliants clade, formed by medium-sized hummingbirds with straight and long bills with a striking variation in coloration phenotypes among species (Hilty & Brown, 1986). Some species exhibit dull or dark colors (e.g. brown and black in C. coeligena, C. wilsoni, and C. prunellei), whereas others exhibit brilliant and lighter colors (e.g. green, golden and white in C. orina, C. bonapartei, and C. phalerata; Parra, 2010). Coeligena hummingbirds inhabit mostly humid montane forests in the Andes from Bolivia to Venezuela where they feed on flowers in the interior or the edge of the forest. A multilocus phylogeny of Coeligena resolved the evolutionary relationships among species of the genus (Parra, Remsen, Alvarez-Rebolledo, & McGuire, 2009). In this phylogeny the species C. bonapartei and C. helianthea stood out as unusual because their sequences revealed shallow divergence and lack of reciprocal monophyly although they show striking phenotypic differences.

C. bonapartei and C. helianthea differ strongly in plumage coloration. Both species have bright green crowns and violet gorgets, but males of C. bonapartei are largely golden green with fiery golden underparts and rumps, whereas those of C. helianthea are largely blackish with rose bellies and aquamarine rumps (Hilty & Brown, 1986). Although females are paler than males, they are also distinctly different. C. bonapartei and C. helianthea are similar in other traits such as ecology, behavior and morphology; both species feed on flowering plants in the lower levels of the forests (Hilty & Brown, 1986) and inhabit cloud forests in elevations between 2000 and 3500m in the Cordillera Oriental of the Andes in Colombia and Venezuela (Ayerbe-Quiñones, 2015). In this group, five subspecies are recognized based on differences in plumage coloration and distribution. Three subspecies are recognized in C. bonapartei: C. b. bonapartei is distributed from Cundinamarca (Sabana de Bogotá), Boyacá, and Santander departments in the western slope of the Cordillera Oriental of Colombia; C. b. consita is endemic to the Serranía de Perijá (northeastern Colombia and northwestern Venezuela); and C. b. eos is from the Cordillera de Mérida in Venezuela. The latter taxon has been treated as a different species for some authors but currently is recognized as a subspecies (Remsen et al., 2018). Two subspecies are recognized in C. helianthea: C. h. helianthea is from Cundinamarca (Sabana de Bogotá), Boyacá, Santander, and Norte de Santander departments in the eastern slope of the Cordillera Oriental; and C. h. tamai is from the Tamá Massif in the border between Colombia (south of Norte de Santander) and Venezuela. In sum, C. b. consita and C. b. eos are allopatric to all other subspecies, C. b. bonapartei and C. h. helianthea are parapatric with a zone of sympatry in the south of their ranges, and C. h. tamai is possibly parapatric to C. b. bonapartei and C. h. helianthea. The recent origin, diversity of phenotypes, and geographic ranges of these hummingbirds make them appropriate subjects for studies on the forces leading to speciation in a complex landscape and on the genetic basis of species differences.

The evolutionary history of C. bonapartei and C. helianthea is unclear due to their similarity in genetics and overall ecology, their plumage differentiation, and the combination of allopatric and parapatric geographical distributions of different lineages. In this thesis I studied the evolution of C. bonapartei and C. helianthea, with a focus on assessing the evolutionary mechanisms underlying their divergence and speciation. I employed genetic and genomic resources, niche modeling, morphological comparisons, phylogenetic reconstructions, and population genetics analyses to answer three main questions: (1) Does the previously reported lack of genetic divergence between C. bonapartei and C. helianthea hold with a larger sampling of individuals and genetic markers, and if it does, could such a pattern be consistent with the hypothesis of divergence with gene flow? (2) What are the patterns of population structure and the degree of differentiation in the mitochondrial genomes of the lineages of these hummingbirds? And (3) How is the genomic landscape of differentiation between C. bonapartei and C. helianthea, and what candidate genes for phenotypic differentiation can be identified in these species? My work contributes to understanding how the interaction among evolutionary mechanisms drives the processes of phenotypic differentiation and speciation in the complex and biodiverse landscape of the tropical Andes in which these awesome hummingbirds evolved. References

Ayerbe-Quiñones, F. (2015). Colibríes de Colombia (First). Wildlife Conservation Society. Coyne, J. A., & Orr, H. A. (2004). Speciation. Sunderland, MA: Sinauer Associates. Edwards, S. V., Kingan, S. B., Calkins, J. D., Balakrishnan, C. N., Jennings, W. B., Swanson, W. J., & Sorenson, M. D. (2005). Speciation in birds: Genes, geography, and sexual selection. Proceedings of the National Academy of Sciences, 102(Supplement 1), 6550–6557. doi: 10.1073/pnas.0501846102 Feder, J. L., & Nosil, P. (2010). The Efficacy of Divergence Hitchhiking in Generating Genomic Islands During Ecological Speciation. Evolution, 64(6), 1729–1747. doi: 10.1111/j.1558-5646.2009.00943.x Hilty, S. L., & Brown, W. L. (1986). A guide to birds of Colombia. Princeton, New Jersey: Princeton University Press. ICGSC International Chicken Genome Sequencing Consortium. (2004). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432(7018), 695–716. doi: 10.1038/nature03154 Kelley, J. L., Passow, C. N., Plath, M., Rodriguez, L. A., Yee, M.-C., & Tobler, M. (2012). Genomic resources for a model in adaptation and speciation research: characterization of the Poecilia mexicana transcriptome. BMC Genomics, 13(1), 652. doi: 10.1186/1471-2164-13-652 Koepfli, K., Paten, B., & Brien, S. J. O. (2015). The Genome 10K Project : A Way Forward. doi: 10.1146/annurev-animal-090414-014900 Lamichhaney, S., Han, F., Webster, M. T., Andersson, L., Grant, B. R., & Grant, P. R. (2017). Rapid hybrid speciation in Darwin’s finches. Science. doi: 10.1126/science.aao4593 Mayr, E. (1963). Animal Species and Evolution. Cambridge, Massachusetts: Harvard University Press. McGuire, J. A., Witt, C. C., Remsen, J. V., Dudley, R., & Altshuler, D. L. (2008). A higher- level taxonomy for hummingbirds. Journal of Ornithology, 150(1), 155–165. doi: 10.1007/s10336-008-0330-x Michel, A. P., Sim, S., Powell, T. H. Q., Taylor, M. S., Nosil, P., & Feder, J. L. (2010). Widespread genomic divergence during sympatric speciation. doi: 10.1073/pnas.1000939107/- /DCSupplemental.www.pnas.org/cgi/doi/10.1073/pnas.1000939107 Nosil, P. (2012). Ecological Speciation (1st ed.). Retrieved from http://ukcatalogue.oup.com/product/9780199587117.do#.UWMcL5PU-Ak Nosil, P., & Feder, J. L. (2012). Genomic divergence during speciation: causes and consequences. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367(1587), 332–342. doi: 10.1098/rstb.2011.0263 Ottenburghs, J., Kraus, R. H. S., van Hooft, P., van Wieren, S. E., Ydenberg, R. C., & Prins, H. H. T. (2017). Avian introgression in the genomic era. Avian Research, 8(1), 30. doi: 10.1186/s40657-017-0088-z Parra, J. L. (2010). Color evolution in the hummingbird genus Coeligena. Evolution, 64(2), 324–335. doi: 10.1111/j.1558-5646.2009.00827.x Parra, J. L., Remsen, J. V., Alvarez-Rebolledo, M., & McGuire, J. A. (2009). Molecular phylogenetics of the hummingbird genus Coeligena. Molecular Phylogenetics and Evolution, 53(2), 425–434. doi: 10.1016/j.ympev.2009.07.006 Price, T. (2007). Especiation in Birds. Boulder, Colorado: Roberts & Company. Remsen, J. V., Areta, J. I., Cadena, C. D., Claramunt, S., Jaramillo, A., Pacheco, J. F., … Zimmer, K. J. (2018). Version 1 December 2018. A classification of the bird species of South America. Retrieved from http://www.museum.lsu.edu/~Remsen/SACCBaseline.htm Restall, R., Rodner, C., & Lentino, M. (2007). Birds of Northern South America. Yale University Press. Rhie, A., Mccarthy, S. A., Fedrigo, O., Damas, J., Formenti, G., London, S. E., … Friedrich, S. R. (2020). Towards complete and error-free genome assemblies of all vertebrate species. 1–56. Toews, D. P. L., Campagna, L., Taylor, S. A., Balakrishnan, C. N., Baldassarre, D. T., Deane-Coe, P. E., … Winger, B. M. (2016a). Genomic approaches to understanding population divergence and speciation in birds. The Auk, 133(1), 13–30. doi: 10.1642/auk-15-51.1 Toews, D. P. L., Campagna, L., Taylor, S. A., Balakrishnan, C. N., Baldassarre, D. T., Deane-Coe, P. E., … Winger, B. M. (2016b). Genomic approaches to understanding population divergence and speciation in birds. The Auk, 133(1), 13–30. doi: 10.1642/AUK-15-51.1 Turner, T. L., Hahn, M. W., & Nuzhdin, S. V. (2005). Genomic islands of speciation in Anopheles gambiae. PLoS Biology, 3(9), e285. doi: 10.1371/journal.pbio.0030285

“We’re one but we are not the same”1 – Shallow genetic divergence and distinct phenotypic differences between two Andean hummingbirds: Speciation with gene flow? – Chapter 1

Developed in collaboration with Silvana Garcia1, Juan Luis Parra2, Andrés Cuervo3, Gary Stiles4, John McCormack5, and Carlos Daniel Cadena1

1 Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias Biológicas, Universidad de Los Andes, Carrera 1 No. 18 A 10, Bogotá, Colombia

2 Grupo de Ecología y Evolución de Vertebrados, Instituto de Biología, Universidad de Antioquia, Calle 67 No. 53-108, Medellín, Colombia

3 Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Villa de Leyva, Boyacá, Colombia

4 Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia

5 Moore Laboratory of Zoology, Occidental College, Los Angeles, California, USA

This chapter was published as a research paper in The Auk https://academic.oup.com/auk/article- abstract/136/4/ukz046/5556799?redirectedFrom=fulltext

Abstract

Ecological speciation can proceed despite genetic interchange when selection counteracts the homogenizing effects of migration. We tested predictions of this divergence-with-gene- flow model in Coeligena helianthea and C. bonapartei, 2 parapatric Andean hummingbirds with marked plumage divergence. We sequenced putatively neutral markers (mitochondrial DNA [mtDNA] and nuclear ultraconserved elements [UCEs]) to examine genetic structure and gene flow, and a candidate gene (MC1R) to assess its role underlying divergence in coloration. We also tested the prediction of Gloger’s rule that darker forms occur in more humid environments, and examined morphological variation to assess adaptive mechanisms potentially promoting divergence. Genetic differentiation between species was low in both ND2 and UCEs. Coalescent estimates of migration were consistent with divergence with gene flow, but we cannot reject incomplete lineage sorting reflecting recent speciation as an explanation for patterns of genetic variation. MC1R variation was unrelated to phenotypic differences. Species did not differ in macroclimatic niches but were distinct in morphology. Although we reject adaptation to variation in macroclimatic conditions as a cause of divergence, speciation may have occurred in the face of gene flow driven by other ecological pressures or by sexual selection. Marked phenotypic divergence with no neutral genetic differentiation is remarkable for Neotropical birds, and makes C. helianthea and C. bonapartei an appropriate system in which to search for the genetic basis of species differences employing genomics.

Resumen

La especiación ecológica puede ocurrir en presencia de flujo génico si la selección contrarresta el efecto homogeneizador de la migración. Evaluamos predicciones del modelo de divergencia con flujo génico en Coeligena helianthea y C. bonapartei, dos especies parapátricas de colibríes altoandinos con diferencias marcadas en su coloración. Secuenciamos marcadores putativamente neutrales (ADN mitocondrial [mtADN] y elementos ultraconservados nucleares [UCEs]) para evaluar estructura genética y flujo génico, y secuenciamos un gen candidato (MC1R) para evaluar su papel en la divergencia en coloración. También evaluamos la regla de Gloger, que señala que organismos de coloraciones más oscuras habitan ambientes más húmedos, y examinamos variación morfológica para analizar posibles mecanismos adaptativos que podrían haber promovido la divergencia entre estas especies de colibríes. Encontramos baja diferenciación genética entre las especies tanto en ND2 como en UCEs. Los estimadores de migración fueron consistentes con el modelo de divergencia con flujo génico, pero no podemos rechazar la posibilidad de que los patrones de variación genética reflejen separación incompleta de linajes debida a especiación reciente. La variación en MC1R fue también muy baja y no estuvo asociada con las diferencias en coloración. Las dos especies no se diferencian en el nicho macroclimático que ocupan, aunque son distintas morfológicamente. Aunque rechazamos la variación en condiciones macroclimáticas como una causa de la divergencia en estos colibríes, estas especies podrían haberse diferenciado en presencia de flujo génico debido a otras presiones de selección ecológicas o por selección sexual. La marcada diferenciación fenotípica sin diferenciación genética en marcadores neutrales que documentamos es excepcional en aves neotropicales y hace de C. helianthea y C. bonapartei un sistema ideal para buscar las bases genéticas de la divergencia entre especies utilizando genómica.

Introduction

New species often arise when geographic isolation of populations allows for divergence via genetic drift or selection (Mayr 1963, Coyne and Orr 2004). Central to this speciation model are the ideas that geographic isolation restricts gene flow, thus allowing for differentiation, and that speciation without geographic isolation is unlikely because gene flow homogenizes populations (Coyne and Orr 2004). Alternatively, the divergence-with- gene-flow model proposes that speciation is possible without geographic isolation if natural selection is sufficiently strong to counteract the homogenizing effect of gene flow (Gavrilets 1999, Nosil 2008, Pinho and Hey 2010, Martin et al. 2013, Morales et al. 2017). Under this model, phenotypic differentiation may develop in the face of gene flow owing to divergent selection acting on traits directly associated with reproduction or on traits associated with those involved in reproduction through pleiotropic effects (Schluter 2001, Servedio 2016). Assortative mating or selection against hybrids may further facilitate the completion of reproductive isolation (Coyne and Orr 2004, Schluter 2009, Fitzpatrick et al. 2009).

Several studies provide evidence that natural selection can promote phenotypic divergence among populations despite gene flow (e.g. Smith 1997; Morgans et al. 2014; Fitzpatrick et al. 2015) and this may lead to speciation (Hey 2006, Nosil 2008). However, documenting speciation with gene flow is complicated because of the difficulty of determining whether shared genetic variation between species is a consequence of divergence in the presence of migration, hybridization ocurring after speciation, or incomplete lineage sorting due to recent or rapid divergence (Hey 2006, Pinho and Hey 2010). This difficulty has been partly overcome thanks to the development of tools to estimate migration between pairs of populations under alternative demographic scenarios (Hey and Nielsen 2004, 2007; Beerli 2006, Kuhner 2006, Durand et al. 2011). Some studies using such tools have found incomplete lineage sorting as the cause for lack of genetic differentiation (Nosil et al. 2009, Wall et al. 2013, Suh et al. 2015), whereas others support population divergence despite gene flow (Green et al. 2010, Rheindt et al. 2014, Supple et al. 2015, Kumar et al. 2017). However, compelling evidence that population divergence has scaled up to the formation of different species in the face of gene flow remains limited. Nonetheless, the finding that the evolutionary histories of various organisms are characterized by substantial cross-species genetic exchange (e.g. Novikova et al. 2016; Zhang et al. 2016; Kumar et al. 2017) implies that attention should be devoted to understanding the selective mechanisms maintaining species as distinct entities in the face of gene flow.

In birds, plumage traits are often targets of natural selection. This results in adaptations for foraging and flight efficiency (Zink and Remsen 1986), camouflage (Zink and Remsen 1986) or conspicuousness (Endler 1993), thermoregulation (Walsberg 1983), and protection against pathogens (Burtt and Ichida 2004, Goldstein et al. 2004, Shawkey et al. 2007), among others. Because plumage traits are also critical in mate selection and species recognition, plumage divergence may drive lineage diversification (Price 2007, Servedio et al. 2011, Hugall and Stuart-Fox 2012, Maia et al. 2013). A frequently observed pattern in presumably adaptive plumage variation is Gloger’s rule, which states that birds with darker plumage coloration occur in more humid environments than lighter-colored conspecifics (Delhey 2019). This pattern is often attributed to adaptation to reduce bacterial degradation of plumage in humid conditions where bacteria are most abundant because melanin (the pigment responsible for black plumage color) confers resistance against these microbes (Goldstein et al. 2004, Peele et al. 2009, Amar et al. 2014). Because differences in melanic pigmentation can serve as cues for mate choice and species recognition (Uy et al. 2009), adaptive differentiation in plumage coloration might thus drive the origin of reproductive isolation. However, we are unaware of studies explicitly relating the evolution of melanic plumage coloration by natural selection to population divergence or speciation in the presence of gene flow (but see Cooper and Uy 2017; see also Rosenblum et al. 2017; Pfeifer et al. 2018 for examples involving skin pigmentation in other animals).

Here, we test the divergence-with-gene-flow model of speciation as an explanation for the evolution of two Andean hummingbird species, Coeligena helianthea (Blue-throated Starfrontlet) and Coeligena bonapartei (Golden-bellied Starfrontlet). We studied these species because: (1) they have largely parapatric ranges in a topographically complex area of the Andes over which environmental conditions, hence selective pressures, may differ (Figure 1); (2) They seemingly lack genetic differentiation in neutral markers (Parra et al. 2009, McGuire et al. 2014) as expected under divergence with gene flow; (3) They exhibit distinct phenotypic differences (plumage in C. helianthea is considerably darker than in C. bonapartei) and no hybrids have been reported even where they coexist locally (except perhaps for a few old specimens; Fjeldså & Krabbe, 1990); and (4), because variation in melanic pigmentation may reflect adaptation to different environments, divergence in plumage traits between these hummingbird species might have been driven by natural selection.

The apparent lack of genetic differentiation between C. helianthea and C. bonapartei (Parra et al. 2009, McGuire et al. 2014) despite their distinct differences in traits potentially under selection may reflect divergence with gene flow, contemporary hybridization, or incomplete lineage sorting (Hey 2006, Suh et al. 2015, Sonsthagen et al. 2016). We here evaluate predictions of the divergence-with-gene-flow model of speciation and consider evolutionary mechanisms driving divergence between these species by first addressing the following questions: (1) does the lack of genetic differentiation between C. helianthea and C. bonapartei persist with a much larger and geographically extensive sampling and additional molecular markers relative to earlier work (Parra et al. 2009)?, and (2) are patterns of genetic variation consistent with a model of divergence in the face of gene flow? We next asked (3) is color divergence associated with genetic variation in the MC1R gene, a candidate underlying melanic coloration in various bird species and other vertebrates? To examine possible mechanisms through which natural selection might have driven population differentiation we examined whether phenotypic divergence may be attributable to adaptation to contrasting macro-environmental conditions by asking (4) is C. helianthea with darker plumage distributed in more humid environments as predicted by Gloger’s rule? and (5) is there morphometric variation between species which may suggest adaptation to alternative microhabitats or resources?

Materials and Methods

Study system

Coeligena helianthea inhabits mostly the eastern slope of the Cordillera Oriental of the Northern Andes from western Meta in Colombia to the Táchira Depression in Venezuela, and comprises two subspecies: C. h. helianthea occupies most of the range, whereas C. h. tamai occurs in the Tamá Massif in the border between Colombia and Venezuela (Figure 1). The distribution of C. bonapartei is not continuous and three subspecies are recognized: (1) C. b. bonapartei ranges along the western slope of the Cordillera Oriental in Cundinamarca, Boyacá, and western Santander in Colombia, (2) C. b. consita is restricted to the Serranía del Perijá, and (3) C. b. eos is endemic to the Cordillera de Mérida in the Venezuelan Andes (Hilty and Brown 1986; Hilty 2003. Figure 1). Some authors consider eos a distinct species (Donegan et al. 2015, del Hoyo et al. 2018), but it is currently treated as a subspecies of C. bonapartei (Remsen et al. 2018). Although the distributions of C. helianthea and C. bonapartei are not sympatric for the most part, the nominate subspecies co-occur regionally in Cundinamarca and Boyacá (Gutiérrez-Zamora 2008).

Coeligena helianthea and C. bonapartei differ strikingly in plumage coloration. Although both species have bright green crowns and violet gorgets, males of C. helianthea are considerably darker, with a largely greenish back with a rose belly and aquamarine rump; males of C. bonapartei are largely golden green with fiery gold underparts and rump. Females are paler than males, but also differ distinctly in plumage, especially in their lower underparts (Hilty and Brown 1986, Parra 2010). The differences in coloration between species may reflect variation in the melanin content of feathers (D’Alba et al. 2014), differences in the nanostructure of feather barbules (Greenewalt et al. 1960), or both.

Tissue samples and DNA sequencing protocols

We collected specimens in Colombia and Venezuela, and obtained tissue samples from the collections of the Instituto Alexander von Humboldt (IAvH), the Museo de Historia Natural de la Universidad de los Andes (ANDES), and the Colección Ornitológica Phelps (Table S1). Our sampling included a total of 62 individuals: 38 specimens of C. bonapartei (12 C. b. bonapartei, 5 C. b. consita, and 21 C. b. eos) and 24 specimens of C. helianthea (7 C. h. helianthea, 17 C. h. tamai). Subspecies were assigned based on taxonomic determination of museum specimens or by geography. We extracted DNA from tissue samples using either a QIAGEN DNeasy Tissue Kit (Qiagen, Valencia, CA, USA) following the manufacturer’s instructions or a standard phenol/chloroform extraction protocol. For 60 specimens we amplified by PCR (Appendix A) and sequenced 1,041 base pairs (bp) of the mitochondrial ND2 gene, and used the data for range-wide phylogeographic and population genetic analyses (Genbank accession numbers: MG874354-874409). We used published sequences of C. lutetiae (McGuire et al. 2007, Parra et al. 2009) and C. orina (McGuire et al. 2014) as outgroups in phylogenetic analyses.

We used a subset of 36 individuals to assess whether color differentiation between species is associated with nucleotide substitutions in the coding region of the melanocortin-1 receptor gene (MC1R), a locus responsible for melanic pigmentation in several birds and other vertebrates (Mundy 2005, Roulin and Ducrest 2013). We amplified by PCR (Appendix A) and sequenced 788 bp of the 945 bp of the MC1R locus for 6 individuals of C. h. helianthea, 10 C. h. tamai, 8 C. b. bonapartei, 1 C. b. consita and 11 C. b. eos. All PCR products were cleaned and sequenced in both directions by Macrogen Inc. or at the sequencing facilities of the Universidad de los Andes (Genbank accession numbers: MG880079-880114). We assembled, edited, and aligned sequences of the ND2 and MC1R genes using BioEdit 7.2.5 (Hall 1999) and Geneious 9.1.5 (http://www.geneious.com/; Kearse et al., 2012), employing the MUSCLE algorithm (Edgar 2004) and manual editing.

We also employed a sequence capture approach to acquire data from regions flanking ultraconserved elements (UCEs; Faircloth et al. 2012) for a small sample of 1 individual of C. h. helianthea, 4 C. h. tamai, 1 C. b. bonapartei, and 1 C. b. consita to obtain a preliminary overview of genetic divergence between these taxa at a genomic level. We used a standard library preparation protocol (http://ultraconserved.org/; Faircloth & Glenn, 2012) and enriched the pool of samples for 5,060 UCE loci using the MYbaits_Tetrapods- UCE-5K probes. We sequenced the pool after quantification using 250 bp paired-end Illumina MiSeq. Following the PHYLUCE pipelines (Faircloth 2015), we used Illuminoprocessor (Faircloth 2013) and Trimmomatic (Bolger et al. 2014) to trim reads, discarded adapter contamination and low-quality bases, and assembled the reads into contigs using a kmer = 50 and ABySS (Simpson et al. 2009). We aligned the contigs against the original UCE probes to identify contigs matching UCE loci using LASTZ (Harris 2007). Among the individuals, we aligned unphased sequences of UCE loci using the default MAFFT v7.13 algorithm (Katoh and Standley 2013). Finally, we pulled out UCE loci from the Anna’s Hummingbird (Calypte anna) genome (Gilbert et al. 2014, Zhang et al. 2014) to use them as outgroup.

For phylogenetic analyses we used a concatenated alignment of 2,313 UCE loci shared at least among 3 individuals including the outgroup. Of these, 1,465 loci were present in all the individuals (mean locus length = 615.1 bp, mean number of individuals per locus in the incomplete matrix = 7.3). We generated a second concatenated alignment of 1,604 loci shared among all Coeligena specimens (i.e. without the outgroup). Of these, 389 loci showed no variation, 75 had only indels (informative or not), 615 had singletons and indels, and 525 (32.8%) had informative sites (polymorphic sites with each variant represented in at least two individuals). We used the latter 525 loci or a subset of these for population genetic analyses (Appendix A), but because our sample size was low we treated the results from these data as preliminary and interpreted them with caution. Phylogenetic and Population Genetic Analysis

We used maximum-likelihood and Bayesian inference methods to reconstruct phylogenies from the CIPRES Portal (http://www.phylo.org/) or locally. We selected a single partition and the TNR substition model as the best-fit for our ND2 data according to the corrected Akaike Information Criterion (cAIC) in PartionFider 2.1.1 (Guindon et al. 2010, Lanfear et al. 2017). To analyze UCE data we used a concatenated alignment of all 2,313 loci for which we specified 16 partitions and nucleotide substitution models for each partition following CloudForest analysis (Crawford and Faircloth 2011). We conducted maximum- likelihood analyses in RAxML (Stamatakis 2014) using the GTR+GAMMA model and non- parametric bootstrapping under the autoMRE stopping criterion for ND2 and UCE data. We conducted Bayesian analyses in Mr.Bayes v3.2 (Ronquist et al. 2012). The MCMC parameters consisted of two runs with four chains ran for 15 million generations sampling every 100 generations for the ND2 data, and ran for 25 million generations sampling every 500 generations for the UCE data. We discarded the first 10% generations as burn-in before estimating the consensus tree and posterior probabilities. To account for heterogeneity among gene trees, we also conducted a species-tree analysis of our data set of 525 UCE loci using the program ASTRAL (Zhang et al. 2018; details in Appendix A).

We also conducted Bayesian analyses in BEAST 2 (Bouckaert et al. 2014) using the ND2 data to estimate divergence times using a strict clock model of evolution, assuming a Yule model prior or a coalescent constant-size population prior. We used a substitution rate of 2.5% divergence per million year for ND2 (Smith and Klicka 2010). MCMC runs consisted of 50 million generations, sampling trees every 1,000 generations, and discarding the first 15,000 trees (30%) as burn-in. Convergence and effective sample sizes of parameter estimates for Mr.Bayes and Beast 2 results were examined using Tracer 1.7.1 (Rambaut et al. 2018).

To further examine relationships among ND2 haplotypes, we used an alignment of 885 bp for which complete data were available for all individuals to construct a median-joining haplotype network in Network 5.0.0.1 (http://www.fluxus-engineering.com/; Bandelt, Forster, & Röhl, 1999). To examine genetic structure between species, we calculated Fst with R package hierfstat (Goudet and Jombart 2015, R Core Team 2017) and AMOVAs with R package ade4 (Dray and Dufour 2007, R Core Team 2017) assessing significance using 10,000 permutations (Script S1). Also, we used Structure 2.3.4 (Pritchard et al. 2000) to assess population structure using our 293 SNP data set from UCE loci; because our sample size was limited, we consider population genetic analyses using UCEs preliminary and thus do not report on them in detail in the main text (Appendix A).

Testing for Divergence with Gene Flow

To examine whether there has been geen flow between C. helianthea and C. bonapartei we used Migrate 3.2.1 (Beerli 2009) to estimate the following demographic parameters: effective population size scaled by mutation rate (), time scaled by the mutation rate (T), and migration scaled by mutation rate (M = (m / µ)). If there has been gene flow between species after speciation, then the posterior distributions of M should exclude values of zero. We also tried to distinguish scenarios of divergence with gene flow and hybridization following secondary contact using the option of parameter estimation for different moments through time in Migrate, but because results were unreliable we chose not to report on these analyses (Appendix A).

For Migrate analyses we employed a ND2 alignment including 23 individuals of C. helianthea and 17 individuals of C. b. bonapartei/consita (i.e. excluding C. b. eos, which we found to be genetically distinct; see below). We also used 293 SNPs from our UCE data for Migrate analyses (Appendix A). Because inference of gene flow requires neutrally evolving markers, we first confirmed that our data sets met this assumption by calculating Tajima’s D using DNAsp 5.1 (Librado and Rozas 2009). We determined prior maximum values for the parameters  and M for each species based on several test runs. In final analyses aimed to estimate gene flow we set prior values to 0.15 for  for both species, and to 1,000 for M in both directions. We ran Migrate in the CIPRES Portal (http://www.phylo.org/) using a long chain of 3,000 million steps (sampling 1,000,000 steps recorded every 3,000 steps) with a burn-in of 1,000,000 steps.

MC1R gene analyses

We compared variable sites in MC1R sequences between our study species and translated sequences to aminoacids to check for synonymous and non-synonymous substitutions. As reference for comparisons we used sequences of Anna’s Hummingbird (Calypte anna) and Chimney Swift (Chaetura pelagica) predicted from genome annotations (Zhang et al. 2014). Because these comparisons revealed no variation potentially implied in phenotypic variation (see results), we did not conduct any additional analysis. Examining the selective regime: niches and morphological differentiation

We tested the hypothesis that natural selection underlies phenotypic divergence in color between C. heliathea and C. bonapartei through macroclimatic differences in the regions occupied by these species. Specifically, we tested the prediction of Gloger’s rule that C. helianthea (with darker plumage) occurs in environments with more humid conditions than C. bonapartei, and examined whether other macroclimatic conditions that may promote adaptation differ between environments occupied by these hummingbirds. We examined ecological differentiation among C. helianthea, C. b. bonapartei/consita and C. b. eos (which we found to be genetically distinct; see below) using occurrence data, environmental variables, and measurements of niche overlap (Broennimann et al. 2012). In addition to the locality data associated with specimens included in molecular analyses, we obtained occurrence data from eBird (http://ebird.org/content/ebird/), Vertnet (http://vertnet.org/), GBIF (http://www.gbif.org/), Xeno-canto (http://www.xeno-canto.org/), and the ornithological collection of the Instituto de Ciencias Naturales of the Universidad Nacional de Colombia (http://www.biovirtual.unal.edu.co/en/), for a total of 242 records. After eliminating duplicates and excluding non-reliable locations we retained 196 records for analysis: 85 of C. helianthea, 75 of C. b. bonapartei/consita, and 36 of C. b. eos (Table S2).

To delimit the accessible areas for each species we used ecoregions as defined by Dinerstein et al. (2017). We used all the ecoregions with occurrence records as the environmental background available for the analysis of niche overlap. We obtained climatic data from WorldClim (http://www.worldclim.org/ Hijmans et al. 2005), CliMond (https://www.climond.org/ Kriticos et al., 2012), and EarthEnv (http://www.earthenv.org/cloud Wilson and Jetz 2016). We clipped layers for ecoregions and climatic variables to our study area (i.e. longitude -76 to -70 degrees, latitude 3 to 12 degrees) and excluded variables highly correlated to others (r > 0.70) within this area using the package usdm in R (Naimi 2015, R Core Team 2017). We conducted niche overlap analyses using 11 variables: three related to temperature, three related to precipitation, four related to cloudiness, and one related to air moisture (Table S3).

We extracted climatic data from 10,000 points from the background environment and from the 196 occurrence records and performed a principal component analysis (PCA) to summarize climatic variation using the package ade4 in R (Dray and Dufour 2007, R Core Team 2017). With the two first PCA axes, we plotted the densities of each taxon in climatic space relative to the background using the package ecospat in R (Broennimann et al. 2016, R Core Team 2017). We also used this package to estimate the D statistic (Warren et al. 2008) to quantify niche overlap (D = 0 indicates different niches, and D = 1 indicates identical niches), and we performed similarity tests (1,000 iterations) to assess whether niches are less similar (niche divergence) than expected by chance given background climatic variation (Script S2). Significant niche divergence with the darker C. helianthea occupying more humid areas would be consistent with adaptive divergence following Gloger’s rule, whereas no significant differences in niches would suggest that adaptation to distinct climatic conditions cannot account for phenotypic differentiation between species.

We also assessed whether there is morphometric differentiation between species which may reflect adaptation to different microhabitats or food resources (Stiles 2008) by measuring 17 traits related to beak, wing, tail and leg morphology (Table S4). We measured morphological variables from 35 live individuals (17 females and 18 males) of C. h. helianthea and 46 individuals (23 females and 23 males) of C. b. bonapartei. Using these data we asked whether individuals of different species and sexes are distinguishable in multivariate space employing linear discriminant analysis (LDA) using the package MASS in R (Venables and Ripley 2002, R Core Team 2017). We built ANOVA models to test for mean differences in all individual variables among species and sexes simultaneously. Because a few of the variables were not normally distributed according to Shapiro-Wilk tests, we used Kruskal-Wallis tests for comparisons involving such variables. Shapiro-Wilk tests, Kruskal-Wallis tests and ANOVAs were performed using basic functions in R (R Core Team 2017).

Results

Does lack of genetic differentiation between C. helianthea and C. bonapartei persist with greater sampling and additional markers?

We found low genetic differentiation between C. b. bonapartei and C. b. consita, but both taxa were markedly differentiated from C. b. eos. Therefore, hereafter we treat C. b. bonapartei and C. b. consita as a single group, which we refer to as C. b. bonapartei/consita. Divergence in ND2 of both C. helianthea and C. b. bonapartei/consita relative to C. b. eos was high, with siginificant Fst values of 0.56 and 0.52, respectively (p < 0.001 in both cases), and relatively high fractions of genetic variance (59.4% and 52.0%, respectively) existing between groups in AMOVA. In contrast, ND2 data showed little to no differentiation between C. helianthea and C. b. bonapartei/consita. Although differentiation as measured by Fst was significant (p = 0.03), the Fst value was very low (0.07) and only 1.7% of the variance was partitioned between these two taxa in AMOVA, with 98.3% of the variance existing among individuals within taxa.

Phylogenetic analyses using ND2 data showed that C. b. eos forms a strongly supported clade (posterior probability PP = 1.0, maximum-likelihood bootstrap MLbs = 87%), which is sister to a clade lacking strong support (PP = 0.84, MLbs = 69%) formed by C. helianthea and C. b. bonapartei/consita (Figure 2A). Within the latter clade, relationships among populations appeared to be determined more by geography than by current species-level taxonomy: most sequences of the northern subspecies C. h. tamai and C. b. consita formed a strongly supported clade (PP = 1.0, MLbs = 85%), whereas the majority of sequences of southern subspecies C. h. helianthea and C. b. bonapartei formed another moderately supported clade (PP = 0.95, MLbs = 61%).

Haplotype networks confirmed the above findings (Figure 2B): (1) C. helianthea and C. b. bonapartei/consita shared haplotypes, whereas C. b. eos did not share any haplotypes with the other taxa; and (2) haplotype groups were more consistent with geography than with taxonomy. However, networks showed that the latter pattern is not perfect because two individuals of C. b. bonapartei (from the south) had the haplotype most common in the north, one C. h. tamai (from the north) had the haplotype most common in the south, and one C. b. bonapartei had an intermediate haplotype.

Likewise, UCE nuclear markers for 7 individuals did not reveal genetic differentiation between C. helianthea and C. bonapartei (no data were available for C. b. eos). The phylogeny estimated using 2,313 concatenated UCE loci showed a well-supported clade including all sequences of C. helianthea nested within a clade in which the earliest diverging branches were the samples of (1) C. b. consita and (2) C. b. bonapartei (Figure 2C). The same topology was obtained with the species-tree analysis which considers gene-tree heterogeneity (Appendix A). Because our data set of 293 SNPs obtained form UCEs met the assumption of neutrality (Tajima’s D = 1.5; p > 0.1), we were able to use them for analyses of population genetic structure. Differentiation between species in these markers was not significant (Fst = 0.2, p = 0.5), and the most likely number of genetic clusters estimated in Structure was one (K = 1, prob(k =1) = 0.99), although we caution our sample was small. We estimated that divergence between the clade formed by C. helianthea and C. bonapartei lineage and its sister group formed by C. lutetiae and C. orina occurred ca. 0.40 million years ago (95% credibility interval 0.27 to 0.58 million years ago) using a Yule model prior. Using a coalescent constant-size population prior, the time of divergence was 0.74 million years ago (95% credibility interval 0.48 to 1.01 million years ago, Figure 5 in Appendix A). Because the latter divergence time is more consistent with published estimates based on broader phylogenetic frameworks (Parra et al. 2009, McGuire et al. 2014), we focus on estimates using the coalescent constant-size population prior. Given this model, the time of divergence between the C. helianthea + C. b. bonapartei/consita clade and C. b. eos was estimated at 0.31 million years ago (95% credibility interval 0.17 to 0.46 million years ago). Because C. helianthea and C. b. bonapartei/consita wre not reciprocally monophyletic in the ND2 gene tree, we were unable to date their divergence, but it must be more recent than the time of their divergence from C. b. eos.

Are patterns of genetic variation consistent with divergence in the face of gene flow?

Our ND2 data set fit the assumption of neutrality (Tajima’s D = -0.68 p > 0.1), which allowed us to use it for gene flow inference. The analyses suggested that there has been gene flow from C. helianthea to C. b. bonapartei/consita, whereas gene flow in the other direction could not be estimated reliably. Mean estimates of migration (M = m / µ) were in all cases different from zero: M = 725.1 from C. helianthea to C. b. bonapartei/consita and 446.1 from C. b. bonapartei/consita to C. helianthea. However, the estimated posterior probability distributions of M were wide: 95% credibility intervals ranged from 284.7 to 1,000 from C. helianthea to C. b. bonapartei/consita, and from 0.0 to 628 from C. helianthea to C. b. bonapartei/consita (Figure 3). Analyses of UCE markers with our limited sample, however, were inconclusive as to whether patterns of variation are best explained by gene flow between C. helianthea and C. b. bonapartei/consita, or by incomplete lineage sorting (Appendix A).

Is color divergence associated with genetic variation in MC1R?

Of the 36 Coeligena individuals sampled for MC1R, 32 shared a haplotype (excluding ambiguous positions). Genetic variation at MC1R was limited to three individuals of C. helianthea and one individual of C. bonapartei, and involved changes in four sites. Only one change was non-synonymous (Ser275 [AGC] → Arg275 [AGG] at nucleotide site 825), but it was present in a single C. helianthea (Andes-BT 1126) with typical plumage coloration. These results reveal no association between MC1R genotype and species- specific color phenotypes in C. helianthea and C. bonapartei.

Is C. helianthea with darker plumage distributed in more humid environments as predicted by Gloger’s rule?

We found no support for the prediction that the more darkly colored C. helianthea occurs in more humid environments than C. b. bonapartei/consita: the climatic niches of these taxa overlap considerably (D = 0.65, Figure 4A) and we found no evidence for significant niche divergence relative to background climate (p = 0.99). Niche overlap between C. b. eos and C. b. bonapartei/consita and C. helianthea was considerably lower (D = 0.07 and 0.10, respectively, Figure 4B), but relative to the background niche differences were not significant (p = 0.70 and 0.76, respectively).

Is there morphometric variation between species that may suggest adaptations to alternative microhabitats or resources?

Morphometric data showed differences between C. h. helianthea and C. b. bonapartei and between females and males of each taxon: LDA analysis distinguished species/sex with a low classification error of 1.2%. The two most relevant variables in the LD function were wing loading (coefficients: LD1 = 240.2, LD2 = 287.8, and LD3 = 120.0), and wing taper (coefficients: LD1 = -39.7, LD2 = -42.6, and LD3 = -23.4). ANOVA or Kruskal-Wallis tests showed significant differences in 11 morphological variables between species, and in 14 variables between sexes (Figure 8 in Appendix A). The three variables that differed the most between species and sexes were length of extended wing (ANOVA coefficients: -3.4 species and 5.9 sex), total culmen (ANOVA coefficients: 2.3 species and 2.2 sex), and length of tail (ANOVA coefficients: 1.0 species and 3.7 sex). Coeligena b. bonapartei has longer wings, shorter bills and shorter tails than C. h. helianthea (p = < 0.001 in all cases), and females have shorter wings, longer bills and shorter tails than males in both species (p = < 0.001 in all cases). Our analyses further revealed that the magnitude of morphometric differences between sexes varied by species. For example, females of C. helianthea are the smallest of the four groups (i.e. combinations of species and sexes), but males of C. helianthea are the largest.

Discussion

Coeligena helianthea and C. bonapartei are closely related species of hummingbirds from the Northern Andes differing distinctly in plumage coloration, but we found a striking lack of genetic differentiation between them in a mitochondrial gene (ND2) and in 2,313 UCE markers broadly scattered across the genome (Figure 2). Considering that low genomic divergence is typically associated with low differentiation in coloration in other Andean birds (Winger 2017), the strong phenotypic differences between C. helianthea and C. bonapartei in the absence of neutral genetic differentiation are remarkable, and make these species an appropriate system in which to search for the genetic basis and adaptive significance of phenotypic differences involved in speciation (see Campagna et al. 2017). However, we found no evidence that MC1R (a candidate gene associated with melanic pigmentation in a variety of vertebrates) underlies phenotypic variation, and found no support for the hypothesis that Gloger’s rule (adaptation to geographic variation in humidity) or other macroclimatic niche differences (Figure 4) are associated with phenotypic divergence between these species. Nonetheless, our finding that C. h. helianthea and C. b. bonapartei differ in morphometric traits (Figure 8) potentially related to habitat and resource use is consistent with the hypothesis that natural selection may have played a role in their divergence. In addition, as we discuss below, phenotypic divergence in coloration with little genetic differentiation may reflect sexual selection.

Because coalescent estimates of migration based on ND2 and UCE data suggested that C. helianthea and C. b. bonapartei/consita may have experienced migration (Figures 3 and 7), their phenotypic divergence might have arisen or been maintained by selection in the face of gene flow. However, preliminary analyses with UCE data did not allow us to rule out incomplete lineage sorting as an explanation for the low genetic divergence between these species. However, we note that divergence with gene flow and incomplete lineage sorting are not mutually exclusive explanations of shallow genetic differentiation (Kutschera et al. 2014), especially when speciation occurs rapidly due to selection (Suh et al. 2015, McLean et al. 2016). Because our sample size for UCEs markers was admittedly small, more extensive data and analyses of genome-wide variation are required to reach more definitive conclusions. Complete genome analyses may help clarify the influence of introgression and incomplete lineage sorting on patterns of genetic variation (Suh et al. 2015), may allow reducing uncertainty in the estimation of population genetic parameters (Hey and Nielsen 2004), and may allow identifying the genetic basis of phenotypic differences (Bourgeois et al. 2016, Toews et al. 2016, Campagna et al. 2017).

We found no variation between species in the coding region of MC1R, a gene associated with variation in plumage coloration in several other birds (Theron et al. 2001, Mundy 2004, Doucet et al. 2004, Baião et al. 2007, Gangoso et al. 2011). Nevertheless, other studies have shown no association between plumage coloration differences and variation in the coding region of MC1R (MacDougall-Shackleton et al. 2003, Cheviron et al. 2006, Haas et al. 2009). As in these latter studies, our work suggests that differences in coloration between C. helianthea and C. bonapartei are controlled by other genetic mechanisms which may include genes agonists or antagonists of MC1R in the melanin metabolic pathway, regions regulating the expression of MC1R or other genes (Theron et al. 2001), and genes controlling traits of feather structure influencing the production of structural colors (Shawkey et al. 2003).

We found no support for Gloger’s rule because the darker C. helianthea does not occur in more humid environments than the more lightly colored C. bonapartei (Figure 4). Nevertheless, adaptation to different environmental conditions may occur at a finer scale, where habitat differences might select for plumage traits that, for instance, stand out from the background augmenting signal efficacy (Endler 1993, Brumfield and Braun 2001). Indeed, we found that the species differ in morphometric traits (e.g. C. bonapartei has longer wings and shorter tails than C. helianthea, Figure 8) typically associated with use of different microhabitats or foraging behaviors. Variation in such traits can affect flight speed or the relative ability to maneuver in open vs. closed environments (Altshuler et al. 2010, Ortega-Jimenez et al. 2014). To the extent that morphological differences may reflect adaptations to different resources between species (Altshuler and Dudley 2002) and between sexes within species of hummingbirds (Temeles and Kress 2010), our data are consistent with a role for selection driving morphological divergence, but the adaptive value of phenotypic variation, if any, remains to be discovered. Considering that C. bonapartei often occurs along forest edges whereas C. helianthea is more frequently found in forest interior (Hilty and Brown 1986), studies of the functional consequences of phenotypic differences would be especially useful to assess any potential role of natural selection in driving and maintaining divergence.

Knowledge of the timing of speciation also might allow one to make inferences about historical processes that could have promoted divergence between C. helianthea and C. bonapartei. We estimated that the most recent common ancestor of these species diverged from C. b. eos between 0.17 and 0.46 million years ago (Figure 5). Therefore, divergence between C. helianthea and C. bonapartei must be more recent, potentially coinciding with some of the last glaciations of the Pleistocene when high-altitude environments were uninhabitable and forests likely retreated, resulting in the isolation and divergence of populations (Vuilleumier 1969, Ramírez-Barahona and Eguiarte 2013). Under this scenario, C. helianthea and C. bonapartei may have diverged in allopatry and their lack of genetic differentiation could be a result of hybridization after secondary contact, with the existence of two groups of haplotypes reflecting geography (north and south) more than taxonomy (i.e. plumage phenotype, Figure 2) reflecting divergence in allopatry followed by range expansions by both species and subsequent hibridization in both areas. It thus remains possible that different selective regimes promoted speciation in these hummingbirds if their divergence occurred across environments with contrasting climatic conditions in the Pleistocene even if they occupy similar environments at present. Although such a hypothesis might be partly testable by modeling historical climates and potential distributions, one would still be faced with the question of what evolutionary forces might maintain C. helianthea and C. bonapartei as distinct given that they occur in regional sympatry in the same macroenvironments in the present.

Aside from natural selection, another plausible explanation for the origin and maintenance of phenotypic distinctiveness in plumage, given the strong sexual dichromatism in C. helianthea and C. bonapartei, is that their differentiation was promoted by sexual selection (Price 1998, 2007). Sexual selection is thought to be a powerful force driving speciation in birds and other organisms (Campagna et al. 2012, 2017; Harrison et al. 2015), and some examples exist of speciation due to sexual selection with gene flow (Servedio 2016). Of direct relevance to our system, a study comparing sexually selected (i.e. gorget and crown coloration) and non-sexually selected traits among Coeligena species found that sexual selection may be an important driver of phenotypic differentiation, but that it is probably insufficient for speciation to be completed unless it acts in concert with natural selection (Parra 2010; see also Servedio and Boughman 2017). To assess the plausibility of the hypothesis that sexual selection is involved in the divergence and speciation of C. helianthea and C. bonapartei, one should test for associations among components of males’ fitness, signaling traits (i.e. coloration, songs), and female preferences. Genomic analyses examining whether there are genetic and signatures of selection acting on regions associated with sexual traits (Charlesworth 2009, Huang and Rabosky 2015, Kirkpatrick 2017) would further help to test the hypothesis of divergence driven by sexual selection.

Another explanation for our results showing no genetic differentiation despite marked phenotypic differences and patterns of population genetic structure better reflecting geography than plumage phenotype is that C. helianthea and C. bonapartei are not different species but rather morphs within a single species which have become differentially sorted in different areas. While this possibility is intriguing and parapatric populations lacking neutral genetic differences yet exhibiting distinct plumages are treated as conspecific in other birds (e.g. Joseph et al., 2006; Poelstra et al., 2014; but see Aguillon et al., 2018), we stress that with the exception of a handful of old specimens there is no current evidence of hybridization that would suggest that C. helianthea and C. bonapartei are conspecific. Furthermore, because plumage differences between them are quite striking in the context of differences among undisputed species in the genus and other hummingbirds (Remsen et al. 2018), we believe they are best treated as distinct species given existing evidence.

In conclusion, our study provides evidence that the formation of two species of Andean hummingbirds likely occurred recently, rapidly and possibly in the face of gene flow, suggesting some form of selection played a role maintaining phenotypic differences and driving speciation. However, because the main selective mechanism we examined (i.e. adaptation to contrasting macroclimatic conditions) appears not to operate in C. helianthea and C. bonapartei, we conclude that ecological pressures that we did not consider directly or sexual selection may have been involved in their divergence. Future studies should thus aim to test predictions of hypotheses of natural and sexual selection acting on this system. Regardless of the selective processes involved, in line with previous research, our study suggests that selection may play an important role in maintaining phenotypic differences that could lead to speciation in tropical montane birds (Cadena et al. 2011, Winger and Bates 2015). Finally, the shallow genetic divergence that we observed between these species suggest that their genomes are unlikely to have been substantially affected by processes occurring after speciation (e.g. post-speciation divergence by drift), which makes this system especially promising for work on the genomics of speciation. Studies aiming to understand the genetic underpinnings of species differences employing genomic approaches (e.g. Campagna et al. 2017; Stryjewski and Sorenson 2017) will be an important complement to increasing knowledge of the geographic and ecological context of speciation in tropical montane birds.

Acknowledgments

We thank the Facultad de Ciencias at Universidad de Los Andes for financial support through the Proyecto Semilla program. For providing tissue samples and supporting fieldwork, we thank the Museo de Historia Natural de la Universidad de los Andes (ANDES), the Instituto Alexander von Humboldt (IAvH) and the Colección Ornitológica Phelps (Jorge Perez-Emán and Miguel Lentino). We also thank Whitney Tsai, Paola Montoya, Camila Gómez and Laura Céspedes for their help with laboratory work and analyses. The manuscript was improved thanks to comments by L. Campagna, E. Jarvis and anonymous reviewers, and discussion with Irby Lovette’s and Daniel Cadena’s lab groups. All the authors confirm that we do not have any conflicts of interest to declare. Contributions: Conceived the idea S.G.R., C.D.C. and J.L.P. Performed the experiments and analyzed the data: C.P. and S.G.R. Contributed substantial materials, resources and funding A.M.C., F.G.S., J.E.M and C.D.C. All authors contributed to write the paper lead by C.P. and C.D.C.

Figures

Figure 1. Geographical distribution and sampled localities of C. helianthea and C. bonapartei. Black dots correspond to localities of specimens sampled for genetic markers. Colored dots correspond to occurrence data obtained from public databases. All localities were used for niche overlap analyses. Polygons correspond to the likely distributions of the subspecies according to elevational limits (Ayerbe-Quiñones 2015) and occurrence data. The tealed polygon in the south corresponds to the region where species are sympatric. Illustrations courtesy of Lynx Edicions (del Hoyo et al. 2018).

Figure 2. ND2 phylogenetic reconstructions and haplotype network show lack of divergence between C. helianthea and C. b. bonapartei/consita. The ND2 (1,041bp) gene tree (A) and ND2 (885bp) haplotype network (B) show C. helianthea and C. b. bonapartei/consita in a single group separate from C. b. eos. Most specimens of the northern subspecies C. h. tamai and C. b. consita cluster together, whereas southern subspecies C. h. helianthea and C. b. bonapartei form another group, suggesting that population structure more strongly reflects geography (i.e. north-south differentiation) than taxonomy based on plumage phenotype. The phylogenetic reconstruction based on UCEs (2,313 loci shared by at least 3 individuals including the outgroup, C) shows C. helianthea nested within C. b. bonapartei/consita. Numbers at the right of the individuals in the tips of the trees correspond to the sampling locality (Table S1). Illustrations courtesy of Lynx Edicions (del Hoyo et al. 2018).

Figure 3. Posterior probability distributions of the migration parameter M = m / µ based on ND2 data (1,041bp) suggest gene flow from C. helianthea to C. b. bonapartei (right), but gene flow could not be estimated reliably from C. b. bonapartei to C. helianthea (left). Colors and lines correspond to the limits of the intervals accumulating 50% (darker colors), and 95% (medium colors) of the probability density.

Figure 4. Because C. helianthea and C. b. bonapartei/consita do not differ in climatic niches, their phenotypic differences are not attributable to Gloger’s rule. The climatic niches of C. helianthea and C. b. bonapartei/consita overlap considerably (D = 0.65) (A). The climatic niche of C. b. eos overlaps very little with C. helianthea (D = 0.10) and C. b. bonapartei/consita (D = 0.07) climatic niches (B). Nevertheless, relative to the background the differences between the niches are not significant in any case (p>0.1).

References

Aguillon, S. M., L. Campagna, R. G. Harrison, and I. J. Lovette (2018). A flicker of hope: Genomic data distinguish Northern Flicker taxa despite low levels of divergence. The Auk 135:748–766. Altshuler, D. L., and R. Dudley (2002). The ecological and evolutionary interface of hummingbird flight physiology. Journal of Experimental Biology 205:2325–2336. Altshuler, D. L., M. Princevac, H. Pan, and J. Lozano (2010). Wake patterns of the wings and tail of hovering hummingbirds. Animal Locomotion:273–284. Amar, A., A. Koeslag, G. Malan, M. Brown, and E. Wreford (2014). Clinal variation in the morph ratio of Black Sparrowhawks Accipiter melanoleucus in South Africa and its correlation with environmental variables. Ibis 156:627–638. Ayerbe-Quiñones, F. (2015). Colibríes de Colombia. First. Wildlife Conservation Society. Baião, P. C., E. Schreiber, and P. G. Parker (2007). The genetic basis of the plumage polymorphism in Red-Footed Boobies (Sula sula): a Melanocortin-1 receptor (MC1R) analysis. Journal of Heredity 98:287–92. Bandelt, H.-J., P. Forster, and A. Röhl (1999). Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution 16:37–48. Beerli, P. (2006). Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22:341–345. Beerli, P. (2009). How to use MIGRATE or why are Markov Chain Monte Carlo programs difficult to use? Population Genetics for Animal Conservation. Bolger, A. M., M. Lohse, and B. Usadel (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. Bouckaert, R., J. Heled, D. Kühnert, T. Vaughan, C. H. Wu, D. Xie, M. A. Suchard, A. Rambaut, and A. J. Drummond (2014). BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. PLoS Computational Biology 10:1–6. Bourgeois, Y. X. C., J. A. M. Bertrand, B. Delahaie, J. Cornuault, T. Duval, B. Milá, and C. Thébaud (2016). Candidate Gene Analysis Suggests Untapped Genetic Complexity in Melanin-Based Pigmentation in Birds. Journal of Heredity 107:327–335. Broennimann, O., V. Di Cola, and A. Guisan (2016). ecospat: Spatial Ecology Miscellaneous Methods. [Online.] Available at https://cran.r- project.org/package=ecospat. Broennimann, O., M. C. Fitzpatrick, P. B. Pearman, B. Petitpierre, L. Pellissier, N. G. Yoccoz, W. Thuiller, M.-J. Fortin, C. Randin, N. E. Zimmermann, C. H. Graham, and A. Guisan (2012). Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography 21:481–497. Brumfield, R. T., and M. J. Braun (2001). Phylogenetic relationships in bearded manakins (Pipridae: Manacus) indicate that male plumage color is a misleading taxonomic marker. The Condor 103:248–258. Burtt, E. H., and J. M. Ichida (2004). Gloger’s Rule, feather-degrading bacteria, and color variation among Song Sparrows. The Condor 106:681–686. Cadena, C. D., Z. A. Cheviron, and W. C. Funk (2011). Testing the molecular and evolutionary causes of a “leapfrog” pattern of geographical variation in coloration. Journal of Evolutionary Biology 24:402–414. Campagna, L., P. Benites, S. C. Lougheed, D. A. Lijtmaer, A. S. Di Giacomo, M. D. Eaton, and P. L. Tubaro (2012). Rapid phenotypic evolution during incipient speciation in a continental avian radiation. Proceedings of the Royal Society B: Biological Sciences 279:1847–1856. Campagna, L., M. Repenning, L. F. Silveira, C. Suertegaray Fontana, L. Tubaro, Pablo, and I. J. Lovette (2017). Repeated divergent selection on pigmentation genes in a rapid finch radiation. Science Advances 3:e1602404. Charlesworth, B. (2009). Fundamental concepts in genetics: Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics 10:195–205. Cheviron, Z. A., S. J. Hackett, and R. T. Brumfield (2006). Sequence variation in the coding region of the melanocortin-1 receptor gene (MC1R) is not associated with plumage variation in the blue-crowned manakin (Lepidothrix coronata). Proceedings of the Royal Society B: Biological Sciences 273:1613–8. Cooper, E. A., and J. A. C. Uy (2017). Genomic evidence for convergent evolution of a key trait underlying divergence in island birds. Molecular Ecology 26:3760–3774. Coyne, J. A., and H. A. Orr (2004). Speciation. Sinauer Associates, Sunderland, MA. Crawford, N. G., and B. C. Faircloth (2011). CloudForest. [Online.] Available at https://github.com/ngcrawford/CloudForest. D’Alba, L., C. Van Hemert, K. A. Spencer, B. J. Heidinger, L. Gill, N. P. Evans, P. Monaghan, C. M. Handel, and M. D. Shawkey (2014). Melanin-based color of plumage: role of condition and of feathers’ microstructure. Integrative and Comparative Biology 54:633–644. Delhey, K. (2019). A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biological Reviews. https://doi.org/10.1111/brv.12503 Dinerstein, E., D. Olson, A. Joshi, C. Vynne, N. D. Burgess, E. Wikramanayake, N. Hahn, S. Palminteri, P. Hedao, R. Noss, M. Hansen, et al. (2017). An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67:534–545. Donegan, T., A. Quevedo, J. C. Verhelst, O. Cortés-Herrera, T. Ellery, and P. Salaman (2015). Revision of the status of bird species occurring or reported in Colombia 2015, with discussion of BirdLife International’s new taxonomy. Conservación Colombiana 23:3–48. Doucet, S. M., M. D. Shawkey, M. K. Rathburn, H. L. Mays, and R. Montgomerie (2004). Concordant evolution of plumage colour, feather microstructure and a melanocortin receptor gene between mainland and island populations of a fairy-wren. Proceedings of the Royal Society of London. Series B: Biological Sciences 271:1663–70. Dray, S., and A. B. Dufour (2007). The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22:1–20. Durand, E. Y., N. Patterson, D. Reich, and M. Slatkin (2011). Testing for ancient admixture between closely related populations. Molecular Biology and Evolution 28:2239–2252. Edgar, R. C. (2004). MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792–1797. Endler, J. A. (1993). Some general comments on the evolution and design of animal communication systems. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 340:215–25. Faircloth, B. C. (2013). Illumiprocessor: a Trimmomatic wrapper for parallel adapter and quality trimming. Faircloth, B. C. (2015). PHYLUCE is a software package for the analysis of conserved genomic loci. Bioinformatics 32:786–788. Faircloth, B. C., and T. C. Glenn (2012). Not all sequence tags are created equal: Designing and validating sequence identification tags robust to indels. PLoS ONE 7:e42543. Faircloth, B. C., J. E. McCormack, N. G. Crawford, M. G. Harvey, R. T. Brumfield, and T. C. Glenn (2012). Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Systematic Biology 61:717–26. Fitzpatrick, B. M., J. A. Fordyce, and S. Gavrilets (2009). Pattern, process and geographic modes of speciation. Journal of Evolutionary Biology 22:2342–2347. Fitzpatrick, S. W., J. C. Gerberich, J. A. Kronenberger, L. M. Angeloni, and W. C. Funk (2015). Locally adapted traits maintained in the face of high gene flow. Ecology Letters 18:37–47. Fjeldså, J., and N. Krabbe (1990). Birds of the High Andes. Apollo Books, Svendborg, Denmark. Gangoso, L., J. M. Grande, A. L. Ducrest, J. Figuerola, G. R. Bortolotti, J. A. Andrés, and A. Roulin (2011). MC1R-dependent, melanin-based colour polymorphism is associated with cell-mediated response in the Eleonora’s falcon. Journal of Evolutionary Biology 24:2055–2063. Gavrilets, S. (1999). A dynamical theory of speciation on holey adaptive landscapes. The American Naturalist 154:1–22. Gilbert, M. P., E. D. Jarvis, B. LI, C. LI, C. V. Mello, The_Avian_Genome_Consortium, J. Wang, and G. Zhang (2014). Genomic data of the Anna’s Hummingbird (Calypte anna). [Online.] Available at http://dx.doi.org/10.5524/101004. Goldstein, G., K. R. Flory, B. A. Browne, S. Majid, J. M. Ichida, and E. H. Burtt (2004). Bacterial degradation of black and white feathers. The Auk 121:656–659. Goudet, J., and T. Jombart (2015). hierfstat: Estimation and Tests of Hierarchical F- Statistics. [Online.] Available at https://cran.r-project.org/package=hierfstat. Green, R. E., J. Krause, A. W. Briggs, T. Maricic, U. Stenzel, M. Kircher, N. Patterson, H. Li, W. Zhai, M. H. Y. Fritz, N. F. Hansen, et al. (2010). A draft sequence of the Neandertal genome. Science 328:710–722. Greenewalt, C. H., W. Brandt, and D. D. Friel (1960). The iridescent colors of hummingbird feathers. Proceedings of the American Philosophical Society 104:249–253. Guindon, S., J. F. Dufayard, V. Lefort, M. Anisimova, W. Hordijk, and O. Gascuel (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Systematic Biology 59:307–321. Gutiérrez-Zamora, A. (2008). Las interacciones ecológicas y estructura de una comunidad altoandina de colibríes y flores en la cordillera oriental de Colombia. Ornitología Colombiana 7:17–42. Haas, F., M. A. Pointer, N. Saino, A. Brodin, N. I. Mundy, and B. Hansson (2009). An analysis of population genetic differentiation and genotype-phenotype association across the hybrid zone of carrion and hooded crows using microsatellites and MC1R. Molecular Ecology 18:294–305. Hall, T. A. (1999). BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41:95–98. Harris, R. S. (2007). Improved Pairwise Alignment of Genomic DNA. Ph.D. Thesis, The Pennsylvania State University. Harrison, P. W., A. E. Wright, F. Zimmer, R. Dean, S. H. Montgomery, M. A. Pointer, and J. E. Mank (2015). Sexual selection drives evolution and rapid turnover of male gene expression. Proceedings of the National Academy of Sciences of the USA 112:4393– 4398. Hey, J. (2006). Recent advances in assessing gene flow between diverging populations and species. Current Opinion in Genetics & Development 16:592–596. Hey, J., and R. Nielsen (2004). Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167:747–60. Hey, J., and R. Nielsen (2007). Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proceedings of the National Academy of Sciences of the USA 104:2785–2790. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965–1978. Hilty, S. L. (2003). Birds of Venezuela. 2nd edition. Princeton University Press, Princeton, New Jersey. Hilty, S. L., and W. L. Brown (1986). A guide to birds of Colombia. Princeton University Press, Princeton, New Jersey. del Hoyo, J., A. Elliott, J. Sargatal, D. A. Christie, and E. de Juana (2018). Handbook of the Birds of the World Alive. [Online.] Available at http://www.hbw.com/. Huang, H., and D. L. Rabosky (2015). Sex-linked genomic variation and its relationship to avian plumage dichromatism and sexual selection. BMC Evolutionary Biology 15:199. Hugall, A. F., and D. Stuart-Fox (2012). Accelerated speciation in colour-polymorphic birds. Nature 485:631–634. Joseph, L., T. Wilke, J. Ten Have, and R. Terry Chesser (2006). Implications of mitochondrial DNA polyphyly in two ecologically undifferentiated but morphologically distinct migratory birds, the masked and white-browed woodswallows Artamus spp. of inland Australia. Journal of Avian Biology 37:625–636. Katoh, K., and D. M. Standley (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology and Evolution 30:772–780. Kearse, M., R. Moir, A. Wilson, S. Stones-Havas, M. Cheung, S. Sturrock, S. Buxton, A. Cooper, S. Markowitz, C. Duran, T. Thierer, et al. (2012). Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. Kirkpatrick, M. (2017). The evolution of genome structure by natural and sexual selection. Journal of Heredity 108:3–11. Korneliussen, T. S., A. Albrechtsen, and R. Nielsen (2014). ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15:356. Kriticos, D. J., B. L. Webber, A. Leriche, N. Ota, I. Macadam, J. Bathols, and J. K. Scott (2012). CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution 3:53–64. Kuhner, M. K. (2006). LAMARC 2.0: Maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22:768–770. Kumar, V., F. Lammers, T. Bidon, M. Pfenninger, L. Kolter, M. A. Nilsson, and A. Janke (2017). The evolutionary history of bears is characterized by gene flow across species. Scientific Reports 7:46487. Kutschera, V. E., T. Bidon, F. Hailer, J. L. Rodi, S. R. Fain, and A. Janke (2014). Bears in a forest of gene trees: Phylogenetic inference is complicated by incomplete lineage sorting and gene flow. Molecular Biology and Evolution 31:2004–2017. Lanfear, R., P. B. Frandsen, A. M. Wright, T. Senfeld, and B. Calcott (2017). Partitionfinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Molecular Biology and Evolution 34:772–773. Librado, P., and J. Rozas (2009). DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25:1451–1452. MacDougall-Shackleton, E. a, L. Blanchard, S. a Igdoura, and H. L. Gibbs (2003). Unmelanized plumage patterns in Old World leaf warblers do not correspond to sequence variation at the melanocortin-1 receptor locus (MC1R). Molecular Biology and Evolution 20:1675–81. Maia, R., D. R. Rubenstein, and M. D. Shawkey (2013). Key ornamental innovations facilitate diversification in an avian radiation. Proceedings of the National Academy of Sciences of the United States of America 110:10687–92. Martin, S. H., K. K. Dasmahapatra, N. J. Nadeau, C. Slazar, J. R. Walters, F. Simpson, M. Blaxter, A. Manica, J. Mallet, and C. D. Jiggins (2013). Genome-wide evidence for speciation with gene flow in Heliconius butterflies. Genome Research 23:1817–1828. Mayr, E. (1963). Animal Species and Evolution. Harvard University Press, Cambridge, Massachusetts. McGuire, J. A., C. C. Witt, D. L. Altshuler, and J. V. Remsen (2007). Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy. Systematic Biology 56:837–856. McGuire, J. A., C. C. Witt, J. V. Remsen, A. Corl, D. L. L. Rabosky, D. L. L. Altshuler, R. Dudley, A. Corl, D. L. L. Rabosky, D. L. L. Altshuler, and R. Dudley (2014). Molecular phylogenetics and the diversification of hummingbirds. Current Biology 24:910–916. McLean, B. S., D. J. Jackson, and J. A. Cook (2016). Rapid divergence and gene flow at high latitudes shape the history of Holarctic ground squirrels (Urocitellus). Molecular Phylogenetics and Evolution 102:174–188. Morales, A. E., N. D. Jackson, T. A. Dewey, B. C. O’Meara, and B. C. Carstens (2017). Speciation with Gene Flow in North American Myotis Bats. Systematic Biology 66:440–452. Morgans, C. L., G. M. Cooke, and T. J. Ord (2014). How populations differentiate despite gene flow: sexual and natural selection drive phenotypic divergence within a land fish, the Pacific leaping blenny. BMC Evolutionary Biology 14:97. Mundy, N. I. (2004). Conserved genetic basis of a quantitative plumage trait involved in mate choice. Science 303:1870–1873. Mundy, N. I. (2005). A window on the genetics of evolution: MC1R and plumage colouration in birds. Proceedings of the Royal Society B: Biological Sciences 272:1633–1640. Naimi, B. (2015). usdm: Uncertainty Analysis for Species Distribution Models. R package version 1.1-15. [Online.] Available at https://cran.r-project.org/package=usdm. Nosil, P. (2008). Speciation with gene flow could be common. Molecular Ecology 17:2103– 2106. Nosil, P., L. J. Harmon, and O. Seehausen (2009). Ecological explanations for (incomplete) speciation. Trends in Ecology and Evolution 24:145–156. Novikova, P. Y., N. Hohmann, V. Nizhynska, T. Tsuchimatsu, J. Ali, G. Muir, A. Guggisberg, T. Paape, K. Schmid, O. M. Fedorenko, S. Holm, et al. (2016). Sequencing of the genus Arabidopsis identifies a complex history of nonbifurcating speciation and abundant trans-specific polymorphism. Nature Genetics 48:1077– 1082. Ortega-Jimenez, V. M., N. Sapir, M. Wolf, E. A. Variano, and R. Dudley (2014). Into turbulent air: size-dependent effects of von Kármán vortex streets on hummingbird flight kinematics and energetics. Proceedings of the Royal Society B: Biological Sciences 281. Parra, J. L. (2010). Color evolution in the hummingbird genus Coeligena. Evolution 64:324–335. Parra, J. L., J. V. Remsen, M. Alvarez-Rebolledo, and J. A. McGuire (2009). Molecular phylogenetics of the hummingbird genus Coeligena. Molecular Phylogenetics and Evolution 53:425–434. Peele, A. M., E. H. Burtt, M. R. Schroeder, and R. S. Greenberg (2009). Dark color of the Coastal Plain Swamp Sparrow (Melospiza georgiana nigrescens) may be an evolutionary response to occurrence and abundance of salt-tolerant feather- degrading bacilli in its plumage. The Auk 126:531–535. Pfeifer, S. P., S. Laurent, V. C. Sousa, C. R. Linnen, M. Foll, L. Excoffier, H. E. Hoekstra, and J. D. Jensen (2018). The evolutionary history of Nebraska Deer Mice: Local adaptation in the face of strong gene flow. Molecular Biology and Evolution 35:792– 806. Pinho, C., and J. Hey (2010). Divergence with gene flow: Models and data. Annual Review of Ecology, Evolution, and Systematics 41:215–230. Poelstra, J. W., N. Vijay, C. M. Bossu, H. Lantz, B. Ryll, I. Muller, V. Baglione, P. Unneberg, M. Wikelski, M. G. Grabherr, and J. B. W. Wolf (2014). The genomic landscape underlying phenotypic integrity in the face of gene flow in crows. Science 344:1410–1414. Price, T. D. (1998). Sexual selection and natural selection in bird speciation. Philosophical Transactions of the Royal Society B: Biological Sciences 353:251–260. Price, T. D. (2007). Speciation in Birds. Roberts & Company Publishers, Greenwood Village, Colorado. Pritchard, J. K., M. Stephens, and P. Donnelly (2000). Inference of population structure using multilocus genotype data. Genetics 155:945–959. R Core Team (2017). R: A Language and Environment for Statistical Computing. [Online.] Available at https://www.r-project.org/. Rabiee, M., E. Sayyari, and S. Mirarab (2019). Multi-allele species reconstruction using ASTRAL. Molecular Phylogenetics and Evolution 130:286–296. Rambaut, A., A. J. Drummond, D. Xie, G. Baele, and M. A. Suchard (2018). Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Systematic Biology 67:901–904. Ramírez-Barahona, S., and L. E. Eguiarte (2013). The role of glacial cycles in promoting genetic diversity in the Neotropics: The case of cloud forests during the Last Glacial Maximum. Ecology and Evolution 3:725–738. Reich, D., K. Thangaraj, N. Patterson, A. L. Price, and L. Singh (2009). Reconstructing Indian population history. Nature 461:489–494. Remsen, J. V., J. I. Areta, C. D. Cadena, S. Claramunt, A. Jaramillo, J. F. Pacheco, M. B. Robbins, F. G. Stiles, D. F. Stotz, and K. J. Zimmer (2018). Version 1 December 2018. A classification of the bird species of South America. [Online.] Available at http://www.museum.lsu.edu/~Remsen/SACCBaseline.htm. Rheindt, F. E., M. K. Fujita, P. R. Wilton, and S. V. Edwards (2014). Introgression and phenotypic assimilation in Zimmerius flycatchers (Tyrannidae): Population genetic and phylogenetic inferences from genome-wide SNPs. Systematic Biology 63:134– 152. Ronquist, F., M. Teslenko, P. van der Mark, D. L. Ayres, A. Darling, S. Höhna, B. Larget, L. Liu, M. a Suchard, and J. P. Huelsenbeck (2012). MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology 61:539–42. Rosenblum, E. B., C. E. Parent, E. T. Diepeveen, C. Noss, and K. Bi (2017). Convergent Phenotypic Evolution despite Contrasting Demographic Histories in the Fauna of White Sands. The American Naturalist 190:S44–S56. Roulin, A., and A.-L. L. Ducrest (2013). Genetics of colouration in birds. Seminars in Cell and Developmental Biology 24:594–608. Schluter, D. (2001). Ecology and the origin of species. Trends in Ecology and Evolution 16:372–380. Schluter, D. (2009). Evidence for ecological speciation and its alternative. Science 323:737–41. Servedio, M. R. (2016). Geography, assortative mating, and the effects of sexual selection on speciation with gene flow. Evolutionary Applications 9:91–102. Servedio, M. R., and J. W. Boughman (2017). The role of sexual selection in local adaptation and speciation. Annual Review of Ecology, Evolution, and Systematics 48:85–109. Servedio, M. R., G. S. Van Doorn, M. Kopp, A. M. Frame, and P. Nosil (2011). Magic traits in speciation: “magic” but not rare? Trends in Ecology and Evolution 26:389–397. Shawkey, M. D., A. M. Estes, L. M. Siefferman, and G. E. Hill (2003). Nanostructure variation predicts intraspecific in ultraviolet-blue plumage colour. Proceedings of the Royal Society B: Biological Sciences 270:1455–1460. Shawkey, M. D., S. R. Pillai, G. E. Hill, L. M. Siefferman, and S. R. Roberts (2007). Bacteria as an agent for change in structural plumage color: correlational and experimental evidence. The American Naturalist 169:S112-21. Simpson, J. T., K. Wong, S. D. Jackman, J. E. Schein, S. J. M. Jones, and I. Birol (2009). ABySS : A parallel assembler for short read sequence data. Genome Research 19:1117–1123. Smith, B. T., and J. Klicka (2010). The profound influence of the late Pliocene Panamanian uplift on the exchange, diversification, and distribution of new world birds. Ecography 33:333–342. Smith, T. B. (1997). A role for ecotones in generating rainforest biodiversity. Science 276:1855–1857. Sonsthagen, S. A., R. E. Wilson, R. T. Chesser, J. M. Pons, P. A. Crochet, A. Driskell, and C. Dove (2016). Recurrent hybridization and recent origin obscure phylogenetic relationships within the ‘white-headed’ gull (Larus sp.) complex. Molecular Phylogenetics and Evolution 103:41–54. Sorenson, M. D., J. C. Ast, D. E. Dimcheff, T. Yuri, and D. P. Mindell (1999). Primers for a PCR-based approach to mitochondrial genome sequencing in birds and other vertebrates. Molecular phylogenetics and evolution 12:105–14. Stamatakis, A. (2014). RAxML version 8: A tool for phylogenetic analysis and post- analysis of large phylogenies. Bioinformatics 30:1312–1313. Stiles, F. G. (2008). Ecomorphology and phylogeny of hummingbirds: Divergence and convergence in adaptations to high elevations. Ornitologia Neotropical 19:511–519. Stryjewski, K. F., and M. D. Sorenson (2017). Mosaic genome evolution in a recent and rapid avian radiation. Nature Ecology and Evolution 1:1912–1922. Suh, A., L. Smeds, and H. Ellegren (2015). The dynamics of incomplete lineage sorting across the ancient adaptive radiation of neoavian birds. PLoS Biology 13:1–18. Supple, M. A., R. Papa, H. M. Hines, W. O. McMillan, and B. A. Counterman (2015). Divergence with gene flow across a speciation continuum of Heliconius butterflies. BMC Evolutionary Biology 15:204. Temeles, E. J., and W. J. Kress (2010). Mate choice and mate competition by a tropical hummingbird at a floral resource. Proceedings of the Royal Society B: Biological Sciences 277:1607–1613. Theron, E., K. Hawkins, E. Bermingham, R. E. Ricklefs, and N. I. Mundy (2001). The molecular basis of an avian plumage polymorphism in the wild: a melanocortin-1- receptor point mutation is perfectly associated with the melanic plumage morph of the bananaquit, Coereba flaveola. Current Biology 11:550–557. Toews, D. P. L., S. A. Taylor, R. Vallender, A. Brelsford, B. G. Butcher, P. W. Messer, and I. J. Lovette (2016). Plumage genes and little else distinguish the genomes of hibridizing warblers. Current biology 26:1–6. Uy, J. A. C., R. G. Moyle, C. E. Filardi, and Z. a Cheviron (2009). Difference in plumage color used in species recognition between incipient species is linked to a single amino acid substitution in the melanocortin-1 receptor. The American Naturalist 174:244–54. Venables, W. N., and B. D. Ripley (2002). Modern Applied Statistics with S. Fourth. Springer, New York. Vuilleumier, F. (1969). Pleistocene speciation in birds living in the high Andes. Nature 223:1179–1180. Wall, J. D., S. K. Kim, F. Luca, L. Carbone, A. R. Mootnick, P. J. de Jong, and A. Di Rienzo (2013). Incomplete lineage sorting is common in extant gibbon genera. PLoS ONE 8:1–5. Walsberg, G. E. (1983). Avian ecological energetics. VII. Academic Press, Inc., New York. Warren, D. L., R. E. Glor, and M. Turelli (2008). Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62:2868–2883. Wilson, A. M., and W. Jetz (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biology 14:e1002415. Winger, B. M. (2017). Consequences of divergence and introgression for speciation in Andean cloud forest birds. Evolution 71:1815–1831. Winger, B. M., and J. M. Bates (2015). The tempo of trait divergence in geographic isolation: Avian speciation across the Marañon Valley of Peru. Evolution 69:772–787. Zhang, C., M. Rabiee, E. Sayyari, and S. Mirarab (2018). ASTRAL-III: Polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19:15–30. Zhang, G., C. Li, Q. Li, B. Li, D. M. Larkin, C. Lee, J. F. Storz, A. Antunes, M. J. Greenwold, R. W. Meredith, A. Odeen, et al. (2014). Comparative genomics reveals insights into avian genome evolution and adaptation. Science 346:1311–1321. Zhang, W., K. K. Dasmahapatra, J. Mallet, G. R. P. Moreira, and M. R. Kronforst (2016). Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biology 17:25. Zink, R. M., and J. V. Remsen (1986). Evolutionary processes and patterns of geographic variation in birds. Current Ornithology 4:1–69.

How much is too little? – Complete mitochondrial genomes do not distinguish phenotypically distinct lineages of Andean Coeligena hummingbirds – Chapter 2

Developed in collaboration with Leonardo Campagna1,2, Juan Luis Parra3, and Carlos Daniel Cadena4

1 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA

2 Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA

3 Grupo de Ecología y Evolución de Vertebrados, Instituto de Biología, Universidad de Antioquia, Calle 67 No. 53-108, Medellín, Colombia

4 Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias Biológicas, Universidad de Los Andes, Carrera 1 No. 18 A 10, Bogotá, Colombia

This chapter is under review in the Biological Journal of the Linnean Society.

Abstract

Lack of divergence in mitochondrial DNA between species with clear phenotypic differences may be the result of low resolution of markers, incomplete lineage sorting, introgression, or the interplay of various evolutionary mechanisms. Previous work revealed that the Andean hummingbirds Coeligena bonapartei and C. helianthea lack genetic divergence in the mitochondrial ND2 gene, which shows variation discordant with coloration phenotype but consistent with geography. We sequenced and analyzed complete mitochondrial genomes for 42 individuals of C. b. bonapartei, C. b. consita, C. h. helianthea and C. h. tamai to assess whether patterns revealed by ND2 analyses hold when considering the entire mitogenome, and to shed light into the evolution of these hummingbirds. We found low genetic differentiation in mitogenomes among the four lineages. Estimates of genetic differentiation, phylogenies and haplotype network analyses of complete mitogenomes did not separate phenotypically distinct taxa, but were consistent with the pattern of northern vs. southern divergence along the Cordillera Oriental of Colombia. Mitogenomes of the nominate subspecies are indistinguishable, suggesting incomplete lineage sorting or introgression. Mitogenomes of C. b. consita and C. h. tamai are slightly differentiated, but they are more similar to each other than either is to that of its respective nominate subspecies, a result also suggestive of mtDNA introgression despite distinct phenotypic differences. Our results indicate that various evolutionary mechanisms playing out over a complex biogeographic scenario in the Colombian Andes drove divergence in phenotypes and mitochondrial genomes of Coeligena hummingbirds, and lead to alternative hypotheses to be tested with whole- genome analyses.

Resumen

La falta de divergencia en ADN mitocondrial entre especies con claras diferencias fenotípicas puede ser el resultado de baja resolución de los marcadores, sorteo incompleto de linajes, introgresión, o la interacción de varios mecanismos evolutivos. Trabajos anteriores revelaron que los colibríes andinos Coeligena bonapartei y C. helianthea carecen de divergencia en el gen mitocondrial ND2, cuya variación es discordante con el fenotipo de la coloración, pero consistente con la geografía. En este trabajo secuenciamos y analizamos el genoma mitocondrial completo de 42 individuos de C. b. consita, C. b. bonapartei, C. h. helianthea y C. h. tamai para evaluar si se mantienen los patrones presentados por los análisis con ND2 al considerar el genoma mitocondrial completo, y para informar la evolución de estos colibríes. Encontramos baja diferenciación genética entre los genomas mitocondriales de los cuatro linajes. Estimadores de diferenciación genética, análisis filogenéticos y de redes de haplotipos del genoma mitocondrial completo no separan los taxa fenotípicamente diferentes, sino que fueron consistentes con el patrón de divergencia norte sur a lo largo de la Cordillera Oriental de Colombia. Los genomas mitocondriales de las subespecies nominales son indistinguibles, sugiriendo sorteo incompleto de linajes o introgresión. Los genomas mitocondriales de C. b. consita y C. h. tamai están ligeramente diferenciados, pero son más similares entre ellos que con sus subespecies nominales, un resultado que sugiere introgresión mitocondrial a pesar de las diferencias fenotípicas. Nuestros resultados indican que varios mecanismos evolutivos dirigieron la divergencia en los fenotipos y en los genomas mitocondriales de estos Coeligena, interactuando en un complejo escenario biogeográfico en los Andes colombianos, y conducen a hipótesis alternativas que pueden ser evaluadas con análisis de genomas completos.

Introduction

In the early days of sequence-based molecular systematics, mitochondrial DNA (mtDNA) was the marker of choice for most studies of population genetics, phylogenetics and phylogeography of animals because mtDNA is a haploid non-recombinant molecule almost free of non-coding regions, inherited via the maternal line, and abundant in tissues (Avise et al., 1987; Galtier et al., 2009; Wilson et al., 1985). Also, mtDNA evolves largely neutrally at a fast rate allowing one to find distinctive haplotypes among lineages (Avise et al., 1987; Ballard and Whitlock, 2004). However, mtDNA does not always reflect the evolutionary history of lineages owing to evolutionary and demographic processes such as selection, or differences between paternal and maternal dispersal and gene flow (Ballard and Melvin, 2010; Edwards et al., 2005; James et al., 2017). Thus, researchers have turned to assaying nuclear markers alongside mtDNA to study the divergence of lineages, an approach becoming increasingly feasible with the development of sequencing technologies allowing one to assay and analyze large numbers of genetic markers at relatively low cost (Kraus and Wink, 2015; Oyler-McCance et al., 2016; Toews et al., 2016). Information on genome-wide variation has not only contributed to more robust inferences of relationships among lineages as well as insights about how evolutionary mechanisms drive such divergence, but has also shed light on how evolutionary mechanisms interact to shape patterns of genetic divergence across genomes (Bonnet et al., 2017; Toews and Brelsford, 2012).

Phenotypes, nuclear genomes and mitochondrial genomes are not always equally divergent among lineages. When divergence is mainly driven by genetic drift, mtDNA is expected to diverge at a faster rate than nuclear DNA – and nuclear-encoded phenotypes – because the effective population size of the former is lower (Ballard and Whitlock, 2004; Moore, 1995). However, when selection drives divergence among populations, mtDNA need not diverge sooner than the nuclear genome, resulting in cases where patterns of mitochondrial and nuclear differentiation are not coincident or where phenotypic differentiation exists with little to no mitochondrial differentiation. Furthermore, phenotypically distinct populations may share mtDNA haplotypes because of mitochondrial introgression due to gene flow after divergence (Irwin et al., 2009; Rheindt et al., 2011; Toews and Brelsford, 2012).

Morphology, plumage, and songs are commonly used to compare populations and inform the species-level taxonomy of birds (Edwards et al., 2005; Remsen, 2005). Morphological measurements may provide evidence of barriers to gene flow (Cadena et al., 2018), whereas visual and acoustic signals are key phenotypes for species delimitation because they are involved in species recognition and reproductive isolation (Price, 2008; Roulin, 2004; Uy et al., 2009). Studies on Neotropical birds often show concordance in differentiation among lineages in phenotype and mitochondrial markers (e.g. Gutiérrez- Pinto et al., 2012; Lovette et al., 2010; Ribas et al., 2012; Sedano and Burns, 2010; Valderrama et al., 2014; Winger and Bates, 2015), although several examples exist of groups in which mtDNA is highly structured in distinct lineages despite little variation in plumage (Cadena et al., 2019; Chesser et al., 2020; D’Horta et al., 2013; Valderrama et al., 2014). Cases documenting species with marked differences in plumage coloration and little mitochondrial genetic divergence are more scarce (Campagna et al., 2012; Lougheed et al., 2013; Luna et al., 2017).

Among hummingbirds (Trochilidae), concordance between mtDNA divergence and overall differences in coloration between species and populations appears to be the norm (Chaves et al., 2007 Adelomyia; Jiménez and Ornelas, 2016 Amazilia; McGuire et al., 2008 Trochilidae; Ornelas et al., 2014 Amazilia; Parra et al., 2009 Coeligena; Zamudio- Beltrán and Hernández-Baños, 2018, 2015 Laprolamia and Eugenes). mtDNA divergence often coincides with differences in coloration among hummingbirds even when phenotypic variation is subtle, such as in the color of the crown, gorget, or tail (Benham and Witt, 2016 Metallura; Gonzalez et al., 2011 C. curvipennis; Lozano-Jaramillo et al., 2014 Antocephala; Ornelas et al., 2016 Lampornis; but see Rodríguez-Gómez and Ornelas, 2015 Amazilia; Sornoza-Molina et al., 2018 Oreotrochilus). There are, to our knowledge, only two documented cases of hummingbirds showing lack of genetic divergence with marked differentiation in coloration (i.e. differences in color in various plumage patches; Eliason et al., 2020; Parra, 2010), both occurring in the high Andes. One case involves two species of Metallura metaltails (Benham et al., 2015; García-Moreno et al., 1999, Metallura theresiae and M. eupogon) and the other two species of starfontlets in the genus Coeligena (Palacios et al., 2019; Parra et al., 2009) which we focus on in this study.

The Golden-bellied Starfrontlet (C. bonapartei) and the Blue-throated Starfrontlet (C. helianthea) inhabit the Northern Andes of Colombia and Venezuela (Figure 1A). The nominate subspecies of these species are sympatric in the southern part of their ranges in the Cordillera Oriental, whereas subspecies C. b. consita and C. h. tamai are allopatric in the Serranía de Perijá and Tamá Massif, respectively. These species are strikingly different in structural plumage coloration (Eliason et al., 2020; Sosa et al., 2020): C. bonapartei is greenish with fiery golden underparts whereas C. helianthea is blackish with a rose belly and aquamarine rump. Despite their markedly different phenotypes, C. bonapartei and C. helianthea are not genetically distinct in a mitochondrial gene (ND2), in a gene involved in the melanogenesis pathway (Melanocortin 1 Receptor MC1R), nor in regions flanking ultra-conserved elements (UCEs) across the nuclear genome (Palacios et al., 2019). Although these hummingbirds occupy similar environments, their lack of genetic differentiation is consistent with divergence with gene flow (Palacios et al., 2019). Phylogenetic analyses of sequences of the ND2 mitochondrial gene also suggest that C. b. consita and C. h. tamai are more closely related to each other than either is to their nominate subspecies, a pattern more consistent with geography than with phenotype and taxonomy. However, it is unclear whether lack of genetic differentiation between C. bonapartei and C. helianthea is restricted to ND2 or if it is a general pattern across the mitochondrial genome. Other mitochondrial markers may be more variable owing to differences among regions in substitution rates (e.g. ND4 or the control region, Arcones et al., 2019; Eo and DeWoody, 2010) or in selective or stochastic demographic processes (Morales et al., 2015; Wort et al., 2017). Examining complete mitochondrial genomes might thus reveal heretofore undetected differences between species of Coeligena. Alternatively, if complete mitogenomes confirm lack of genetic divergence between C. bonapartei and C. helianthea, and that relationships of lineages of these species are inconsistent with their phenotype, then further consideration of mechanisms underlying evolutionary divergence in mtDNA and coloration in the group would be necessary. Such mechanisms potentially include natural and sexual selection as well as demographic processes acting during periods of geographic isolation and contact among lineages (Krosby and Rohwer, 2009; Morales et al., 2017; Pons et al., 2014; Toews et al., 2014).

We sequenced and assembled complete mitochondrial genomes of multiple individuals to address the following questions: (1) Are the sequence and structure of the mitochondrial genomes of C. bonapartei and C. helianthea like those of mitogenomes of other bird and hummingbird species? (2) Is the lack of genetic divergence between C. bonapartei and C. helianthea a general pattern across the mitochondrial genome? (3) Are phylogenetic relationships of lineages of C. bonapartei and C. helianthea based on ND2 also recovered using complete mitochondrial genomes? (4) Are different genes and regions in the mitochondrial genome equally informative about lineage relationships? And, (5) Are there substitutions in mitochondrial protein-coding genes among lineages of C. bonapartei and C. helianthea involving changes between aminoacids with different funcional characteristics which may suggest selection acting on these genes?

Materials and Methods

Samples and sequencing

We sampled 46 individuals, 23 each of C. bonapartei and C. helianthea (Supplementary Table 1), representing subspecies C. b. bonapartei, C. b. consita, C. h. helianthea, and C. h. tamai. Taxon identities were assigned by determination of specimens in the museum or by geography. Because previous work indicated that populations from the Mérida Cordillera of Venezuela often referred to C. bonapartei (subspecies C. b. eos) are genetically divergent from other populations in the complex (Palacios et al. 2019), we did not consider them in this study. Muscle tissue samples from voucher specimens were obtained from the collections of the Instituto Alexander von Humboldt (IAvH) and the Museo de Historia Natural de la Universidad de los Andes (ANDES). We employed relatively even samples sizes of each sex and subspecies of both C. bonapartei and C. helianthea.

We extracted total genomic DNA using a phenol/chloroform method and Phase-Lock Gel tubes, followed by a standard cleaning protocol employing magnetic beads. We prepared 46 Illumina TruSeq Nano DNA-enriched libraries following the manufacturer’s protocol for low-throughput configuration and 550bp insert size. We quantified the libraries using a Qubit fluorometer. Normalizing, pooling and sequencing were done by the Genomics Facility of the Institute of Biotechnology at Cornell University. Sequencing was performed using two lanes of NexSeq 500 2x150 paired end. We filtered the raw data by quality according to Illumina instructions, checked reads using Fastqc (Andrews, 2010), and cleaned them to remove adapters using AdapterRemoval (Schubert et al., 2016).

Assembly and annotation of mitochondrial genomes

Although our sequence data contained sequences originating from both the nuclear and mitochondrial genomes, here we focus specifically on the later. We used MITObim v.1.9.1 (Hahn et al., 2013) with default parameters to assemble complete mitochondrial genomes from filtered reads following two alternative assembling strategies based on using different baits: (1) two independent assemblies using as baits the complete mitochondrial genomes of Oreotrochilus melanogaster and Heliodoxa aurescens (Genbank NC027454 and KP853094, respectively), and (2) a third assembly using as bait the ND2 gene sequence for each individual -or a related one- available from previous work (Palacios et al., 2019). We expected that the first strategy would allow us to recover more complete individual mitogenome sequences because during initial iterations, reads would map to different sites on the reference mitogenome and this would allow extension from multiple edges. In turn, we expected that the gene-bait strategy would enable us to identify structural changes in genomes because it would allow extension only from the two edges of the gene, but it would likely be susceptible to recovering incomplete sequences when reads did not overlap, impeding continued extension.

The gene-bait strategy required multiple independent rounds of assembling. In each round we used as bait a new fragment obtained from the final genome assembled in the previous round. We compared the results from each strategy to determine the sequence and structure of mitogenomes of C. bonapartei and C. helianthea. In addition, we mapped the read-pool obtained from the complete-genome assembling strategy against the mitogenome sequence obtained from the gene-bait strategy using the “map to reference assemble” tool in Geneious 9.1.5 (http://www.geneious.com; Kearse et al., 2012). We used these map-to-reference assemblies to close gaps in some sequences, to check the number of repetitions at the end of the control region (see results), and to verify assigned alleles in each sequence at polymorphic sites. We aligned and edited mitochondrial genomes using ClustarO (Sievers et al., 2011) and manually in Geneious, and annotated them using MITOS beta version (http://mitos2.bioinf.uni-leipzig.de/index.py) and Geneious. In addition to the alignment of complete mitogenomes, for phylogenetic and population genetic analyses described below we generated alignments of each protein-coding gene (PCG), and a concatenated alignment of 13 PCGs (ND1, ND2, COX1, COX2, ATP8, ATP6, COX3, ND3, ND4L, ND4, ND5, CYTB, and ND6).

Population genetic, phylogenetic, and amino-acid change analyses

Using the alignment of complete mitogenomes, we calculated nucleotide diversity (Pi) for all sequences as a unit, and separately for C. bonapartei, C. helianthea, and for each of the four subspecies (C. b. bonapartei, C. b. consita, C. h. helianthea, C. h. tamai). We calculated absolute genetic divergence (Dxy) in DnaSP v6 (Rozas et al., 2017), and relative genetic divergence (Fst) between species and among subspecies assessing significance with 1,000 permutations using R package Hierfstat (Goudet and Jombart, 2015; R Core Team, 2017). We examined phylogenetic relationships among individuals based on each of our alignments using maximum-likelihood analysis and computed majority-rule consensus trees in RAxML v8.2.12 (Stamatakis, 2014). We used the GTR+GAMMA model and multiparametric bootstrapping stopped by the autoMRE criterion. We used mitochondrial genomes of Oreotrochilus melanogaster and Heliodoxa aurescens (Genbank NC027454 and KP853094, respectively) as outgroups. We also built a median-joining haplotype network (Bandelt et al., 1999) in PopArt (Leigh and Bryant, 2015) using the complete mitogenome alignment.

Finally, we assessed whether there are fixed changes in amino-acids in proteins encoded in the mitogenome of lineages of C. bonapartei and C. helianthea potentially suggestive of selection. We first calculated the number and type of substitutions in each protein coding gene in DnaSP v6 (Rozas et al., 2017). Then, for each non-synonymous substitution we examined whether amino-acid variants were from different functional groups.

Results

Sequence and structure of mitochondrial genomes in C. bonapartei and C. helianthea

We recovered very similar sequence assemblies using the gene-bait and the complete mitogenome bait strategies. However, using the complete mitogenome strategy we observed insertions in some mitogenomes not recovered with the gene-bait strategy. Additionally, we found minor differences between assemblies obtained using the two strategies mainly in the length and sequence of the control region. We used the read-pool map-to-reference assemblies to resolve discrepancies between sequences from different assemblies and to review and manually correct nucleotide assignments in variant sites.

We recovered complete mitochondrial genomes for 42 of the 46 specimens (excluding IDs 23, 24, 26 and 33 in Supplementary Table 1, which we do not consider further because the data we obtained were of low quality), with an average coverage of 127.5x for all genomes (Max 1,555.7, Min 11.8, see Supplementary Table 1 for details, GenBank accession numbers MT341527 to MT341568). The size of the mitochondrial genome of C. bonapartei and C. helianthea varied from 16,813 bp to 16,859 bp, mainly due to individual variation in length of a repetitive motive (‘AAAC’) at the end of the control region (beginning at 16,759 bp in the alignment). The 42 sequences were identical across 16,560 bp (98.2%), showed 248 variant sites (1.5%), with 51 positions having gaps or being ambiguous (0.3%). Mean pairwise identity was 99.7%, and total GC content was 44.8%. On average, the mitogenome sequences of Coeligena were identical to those of O. melanogaster across 14,555 bp (86.0%) and to those of H. aurescens across 14,450 bp (85.6%). The beginning of the control region (350 bp) was the most difficult to align between sequences of Coeligena and those of outgroups. The mitochondrial genome structure of Coeligena species followed the typical pattern observed in other birds including hummingbirds, with 2 ribosomal RNAs, 13 protein coding genes, 22 transfer RNAs, and the control region (Figure 2).

Genetic divergence and clustering patterns among lineages of C. bonapartei and C. helianthea

Across the complete mitogenome alignment including all individuals of C. bonapartei and C. helianthea, we found only 250 mutations (two sites had 3 alleles) in 248 variable sites (1.5% of the genome). Of these variable sites, 89 were singletons and 159 were parsimony-informative. Nucleotide diversity was low in the complete alignment (Pi = 0.00247, SD = 0.00013). The least diverse lineage was C. b. consita (Pi = 0.00019, 9 polymorphic sites), followed by C. h. helianthea (Pi = 0.00084, 40 polymorphic sites), C. h. tamai (Pi = 0.00124, 98 polymorphic sites), and C. b. bonapartei (Pi = 0.00254, 156 polymorphic sites). When we compared groupings based on species assignment (i.e. C. bonapartei vs C. helianthea), we found low relative genetic divergence (Fst = 0.076, p = 0.016). However, Fst values were greater when considering the four lineages separately (Table 1), with comparisons between lineages assigned to the same species showing higher relative genetic divergence than those between lineages assigned to different species (e.g. C. b. consita vs C. b. bonapartei Fst = 0.385, p-value < 0.001; C. h. helianthea vs C. h. tamai Fst = 0.518, p < 0.001; C. b. bonapartei vs C. h. helianthea Fst = 0.083, p = 0.1). All comparisons indicated low absolute genetic divergence (Dxy), supporting the general lack of genetic differentiation in the mitogenomes of these species (Table 1). However, high values of relative genetic divergence (Fst) between lineages of C. bonapartei and C. helianthea suggested genetic structure.

Phylogenetic analyses of the complete mitogenome alignment clustered all sequences of Coeligena hummingbirds in a well-supported clade (maximum-likelihood bootstrap ML-bs 100%, Figure 1). Relationships within this clade were unresolved, with a polytomy comprising (1) a clade grouping all sequences of C. b. consita (ML-bs 97%), (2) a clade grouping all but one of the sequences of C. h. tamai (ML-bs 90%), and (3) the remaining sequences (mostly of C. b. bonapartei and C. h. helianthea) scattered in smaller clades or by themselves. Phylogenies built with other alignments (each PCG and concatenated PCGs, Figure S1) showed lower resolution (i.e. more polytomies or lower support values). In most phylogenies, C. b. consita and C. h. tamai were more closely related to each other than either was to the nominate subspecies, but most support values for this grouping were lower than 80% except in the control-region phylogeny (ML-bs 88%).

All sequences of C. b. consita clustered together in the the median-joining haplotype network (Figure 1). All sequences but one of C. h. tamai clustered in another group which was close to, but distinguishable from, two sequences of C. b. bonapartei (ID 12 and 15). The remaining sequences of C. b. bonapartei, all sequences of C. h. helianthea, and the remaining sequence of C. h. tamai (ID 40) clustered in a third group (Figure 1). The network showed that sequences of C. b. consita and C. h. tamai are more similar to each other than to C. b. bonapartei and C. h. helianthea. Also, two individuals of C. b. bonapartei (ID 10 and 14) with the same haplotype were highly divergent from all other individuals. C. b. consita was the lineage with the lowest number of haplotypes (4 among 9 individuals). In the other lineages, the number of haplotypes was similar to the number of individuals: 12 haplotypes in C. b. bonapartei (13 individuals), 6 in C. h. helianthea (7 individuals), and 13 in C. h. tamai (13 individuals).

Based on the clustering patterns described above, we defined genetic groups for additional analyses in which we calculated the number of substitutions and measures of genetic divergence among groups. First, we defined (1) a northern group comprising all sequences of C. b. consita, all sequences of C. h. tamai except ID 40, and two sequences of C. b. bonapartei (ID 12 and 15); and (2) a southern group including most sequences of nominate subspecies C. b. bonapartei and C. h. helianthea (except ID 10 and 14) and one sequence of C. h. tamai (ID 40). Second, we considered separately the groups of C. b consita and C. h. tamai (excluding ID 40). There were only 27 substitutions (0.16%) yet high relative genetic divergence (Fst = 0.513, p-value < 0.001) between the northern and southern groups. Likewise, there were 14 substitutions (0.083%) and genetic divergence was high (Fst = 0.502, p-value < 0.001) between C. b. consita and C. h. tamai. The remaining 118 parsimony-informative sites existing among all sequences corresponded to intrapopulation diversity. Nucleotide diversity in the southern group (Pi = 0.0018) was higher than that of C. b. consita (Pi = 0.00019) and C. h. tamai (0.00089), but comparable to that of the northern group (Pi = 0.0012). Given a substitution rate of 0.00256 substitutions per site per lineage per million years (s/s/l/My) for the complete mitogenome of birds (Eo and DeWoody, 2010), we estimated that the northern and southern groups diverged around 310,000 years ago, and that C. b. consita and C. h. tamai diverged around 160,000 years ago. Based on 13 protein-coding genes plus the two rRNAs and a substitution rate of 0.00164 s/s/l/My (mean rate for Apodiformes; Arcones et al., 2019) estimates of divergence times are similar yet slightly older: 380,000 years ago between the northern and southern groups, and 180,000 years ago between C. b. consita and C. h. tamai.

Functional aminoacid changes

Of the total 248 variant sites, 160 were located in protein-coding genes (Table S2). The remaining 88 variant sites were in rRNAs (6 in 12SrRNA, 20 in 16SrRNA), tRNAs (11), inter-gene spacers (5), and the control region (46). Among the 160 variant sites in protein- coding genes, 123 corresponded to synonymous changes and 38 to non-synonymous changes. Most non-synonymous changes were singletons (23 sites) or varied within populations (11 sites). Of the remaining 4 variant sites, a non-synonymous change was shared between one individual of C. b. bonapartei and one individual of C. h. tamai (TC position 269 in ND5). Only three non-synonymous changes corresponded to substitutions between genetic groups. One change in ND2 and one in ND6 were fixed differences between the northern and the southern groups (GA position 475 in ND2, and GA position 112 in ND6). These non-synonymous substitutions do not imply any evident functional changes because both aminoacids involved (valine and isoleucine) are aliphatic, nonpolar, and neutral. Finally, a non-synonymous substitution between C. b. consita and all other sequences (AG position 145 in ND4) implies a functional change in aminoacids. Whereas C. b. bonapartei, C. h. helianthea and C. h. tamai had the aliphatic, nonpolar alanine, C. b. consita had the hydroxyl-containing, polar threonine. Note that this change is not between the two main mitogenome groups because C. h. tamai has the variant of the southern mitogenenome group at this position.

Discussion

We found that the complete mitochondrial genomes of two hummingbird species differing strikingly in phenotype, C. bonapartei and C. helianthea, are highly similar. Mitogenomes of a sample of 42 individuals representing both species and two subspecies recognized within each of them were 98.2% identical. Moreover, estimates of genetic differentiation and clustering analyses of mitogenome sequences were unable to recover groups corresponding to species, and suggested instead that mitogenomes of C. b. consita and C. h. tamai formed distinct clusters more similar to each other than either was to mitogenomes of the nominate subspecies C. b. bonapartei and C. h. helianthea which were, in turn, indistinguishable from each other. These results indicate that patterns of variation based on the ND2 gene (Palacios et al., 2019) are consistent across the mitochondrial genome, implying that the previously documented lack of mtDNA divergence between species does not reflect insufficient data nor atypical variation in ND2 relative to other mitochondrial markers. Instead, patterns of variation and relationships among the mitochondrial genomes of the four lineages are inconsistent with phenotypic variation and current taxonomy, but seem to agree partly with geography, considering that C. b. consita and C. h. tamai occur in the Serranía de Perijá and the north of the Cordillera Oriental whereas both nominate subspecies occur to the south along the cordillera.

The discordance between mitochondrial genomes and coloration phenotypes in C. bonapartei and C. helianthea can be accounted for by various evolutionary processes which must have acted over a relatively short period of time given divergence-time estimates for the group. Based on the ND2 gene, the clade formed by C. bonapartei and C. helianthea diverged from C. b. eos around 310,000 years ago, and the northern and southern clades comprising the four lineages of C. bonapartei and C. helianthea diverged around 240,000 years ago (Palacios et al., 2019). The latter estimate is more recent than our calculations of the divergence between the northern and southern groups at ca. 310,000 (complete mitogenome) or 380,000 years ago (PCG and rRNAs). Our estimates of divergence times must be interpreted with caution because different factors may bias them (Galtier et al., 2009; García-Moreno, 2004; Lovette, 2004), but they do suggest that the divergence between the northern and southern mitogenome groups, and the divergence between the mitogenomes of C. b. consita and C. h. tamai (160,000 estimated through complete mitogenomes and, 180,000 years ago using the PCG and rRNAs) are recent, i.e. happening within the past 500,000 years.

Ours is the first study in hummingbirds using complete mitochondrial genomes for a population-level analysis of genetic structure between species and across geography we are aware of, and few complete mitochondrial genomes of hummingbirds have been published (Morgan-Richards et al., 2008; Prosdocimi et al., 2016; Souto et al., 2016). We searched GenBank for complete mitochondrial genomes of closely related hummingbirds with more than a single individual sequenced per species to compare their divergence with the divergence we observed in Coeligena. We only found six mitogenome sequences for three subspecies of Amazilia versicolor (A. v. versicolor KF624601, NC_024156; A. v. milleri KP722042, NC033405; and A. v. rondoniae KP722041, NC_033404; Prosdocimi et al., 2016) representing populations occurring over a broad geographic range. Overall, these sequences are much more differentiated (5.1% of sites were variable) than our entire data set (1.5%). Although this comparison is far from comprehensive, it does support the idea that the mitogenomes of the lineages of Coeligena hummingbirds are highly similar and their divergence is quite recent relative to other hummingbirds with comparable data, as also indicated by analyses of individual mtDNA genes (Palacios et al., 2019; Parra et al., 2009).

In contrast to mtDNA phylogenies, nuclear markers suggest C. b. consita was the first branch to diverge in the group, whereas C. h. helianthea and C. h. tamai are reciprocally monophyletic groups forming a clade sister to C. b. bonapartei (Palacios et al., 2019; Palacios et al. unpublished). We found that complete mitogenomes of C. b. bonapartei and C. h. helianthea are undifferentiated even though both subspecies differ strikingly in phenotype and are also distinguishable using nuclear markers. Incomplete lineage sorting may explain this result because the southern mitogenome group exhibited high nucleotide diversity in comparison with C. b. consita and C. h. tamai, a pattern one would not expect due to a recent introgression (Krosby and Rohwer, 2009). However, nuclear sorting without mitochondrial sorting would be unlikely because the effective population size of the latter is ¼ that of the former. Instead, then, a scenario in which one mitogenome quickly swept through replacing the mitogenome of the other lineage and later recovered of nucleotide diversity may explain patterns of mitogenome sharing between C. b. bonapartei and C. h. helianthea.

The similarity in mitogenomes of C. b. consita and C. h. tamai appears more consistent with introgression after phenotypic differentiation in isolation. Mitochondrial introgression may often reflect selection (e.g. adaptive introgression via metabolic efficiency, Ballard and Melvin, 2010; Toews et al., 2014), but may also be due to demographic effects or to asymmetries between sexes in dispersal, mating behavior, and offspring production (Harris et al., 2018; James et al., 2016; Morales et al., 2017; Rheindt et al., 2014; Toews and Brelsford, 2012). We did not find functional changes in protein-coding genes between the northern and the southern mitogenomes suggesting adaptation, although adaptive changes related to substitutions in the control region (or in the 16SrRNA gen in the case of C. h. tamai) are possible. We are unaware of differential dispersal between sexes in Coeligena, in which dispersal and breeding biology are poorly known. Mitochondrial introgression between C. b. consita and C. h. tamai may have been facilitated by their geographical proximity and may have happened during a period of greater connectivity of forests in the Pleistocene (Flantua et al., 2019; Graham et al., 2010). Then, both lineages became isolated again and their mitogenomes diverged. The northern mitogenome may thus have evolved within C. b. consita and introgressed into C. h. tamai in a north to south direction, and such introgression may have further proceeded into C. b. bonapartei explaining why individuals ID 12 and 15 have haplotypes more closely related to the northern group.

The divergent mitogenomes of four individuals of C. b. bonapartei (ID 10, 12, 14, and 15) were unexpected considering the similarity among all other sequences. Although individuals ID 12 and 15 were closely related to the northern group, they shared 9 unique variants. Individuals ID10 and ID14 shared a mitogenome haplotype which was even more divergent (34 unique variants) sharing variants with both the northern (9) and the southern (18) groups. We can reject hybridization with other unstudied taxa as an explanation for these atypical mitogenomes because ND2 sequences placed these specimens within the clade formed by C. bonapartei and C. helianthea to the exclusion of C. b. eos (Palacios et al. 2019). These atypical sequences may instead be evidence of persistence of a relict or a “ghost” mitochondrial lineage in C. b. bonapartei (Grandcolas et al., 2014; Zhang et al., 2019), which may have arisen and remained in isolation in the western slope of the Cordillera Oriental in Boyacá (Iguaque Massif and surroundings), a region where atypical patterns in mtDNA variation have been reported in other groups (Avendaño and Donegan, 2015; Chaves et al., 2011; Chaves and Smith, 2011; Chesser et al., 2020; Guarnizo et al., 2009). Another less likely explanation for these atypical sequences may be heteroplasmy and mitochondrial recombination which have been recognized in vertebrates in some cases (Piganeau et al., 2004; Rokas et al., 2003; Sammler et al., 2011).

In sum, based on our results and earlier work (Palacios et al. 2019) we hypothesize that a plausible evolutionary scenario accounting for patterns of mtDNA and phenotypic variation in C. bonapartei and C. helianthea is as follows. Based on comparison with the outgroup and other related species (C. b. eos, C. lutetiae, C. orina), the most probably body plumage coloration of the ancestor of our study clade was green with golden/orange underparts. The first lineage to diverge was likely C. b. consita, which evolved in the Serranía de Perijá in isolation from the ancestor of the other three lineages, retaining features of the ancestral plumage coloration but diverging in mtDNA. A second divergence event involved sister clades formed by C. b. bonapartei and C. helianthea (i.e. the common ancestor of both subspecies), with the former retaining the ancestral plumage and the latter evolving darker body coloration, rose belly, and aquamarine rump. These two lineages diverged in phenotype while maintaining an undifferentiated mitogenome owing to incomplete lineage sorting or introgression, except for populations of C. b. bonapartei which became isolated in the western slope of the Cordillera Oriental and diverged in mitogenome. Third, C. h. tamai and C. h. helianthea became isolated and diverged slightly in phenotype. Finally, during a period of forest connectivity the mitogenome of C. b. consita introgressed into C. h. tamai, a process followed by subsequent isolation of these lineages resulting in some divergence in their mitogenomes. Although this is a convoluted historical scenario, it is amenable to testing using genomic data and demographic models (e.g. Aguillon et al., 2018; Benham and Cheviron, 2019; Kearns et al., 2018) and other explanations for patterns of variation would appear even more complex.

In conclusion, low genetic divergence among lineages of C. bonapartei and C. helianthea is a general pattern across their mitochondrial genomes despite their marked phenotypic differences. Mitogenomic variation in these lineages seems to more closely reflect geography and demographic history than the processes shaping their phenotypes and likely their nuclear genomes. Studying closely related lineages that diverged recently in complex topographic scenarios, such as the system of C. bonapartei and C. helianthea, might help to explain the different effects that evolutionary mechanisms may have in shaping the divergence between and within genomes. Incomplete lineage sorting, mitochondrial introgression, and demographic processes like population bottlenecks, phases of expansion and contraction, and the persistence of relict lineages have likely acted in this system resulting in marked discordance between phenotypes and mtDNA variation. A natural next step to understand the processes at work in this system is to place the results of the present study in the context of genome-wide patterns of genetic variation.

Acknowledgments

We thank the Fundación para la promoción de la investigación y la tecnología del Banco de la República, and the Lovette Lab at the Cornell Lab of Ornithology for financial support. For providing tissue samples we thank the Museo de Historia Natural de la Universidad de los Andes (ANDES) and the Instituto Alexander von Humboldt (IAvH). We exported tissues samples to the Cornell Lab of Ornithology (Ithaca, NY) thanks to CITES permit No. CO 41452 granted by the Ministerio de Ambiente y Desarrollo Sostenible of Colombia. We also thank Irby J. Lovette and Bronwyn G. Butcher for facilitating laboratory work.

Table and Figures

Table 1. Population genetic statistics and measures of genetic divergence between C. bonapartei and C. helianthea and among groups within. Nucleotide diversity Pi is lower in C. b. consita and C. h. tamai than in nominate subspecies. Absolute genetic divergence Dxy is low yet relative divergence Fst is high across comparisons. Genetic groups are derived from the clustering patterns analyses and are marked as “Gen” in the table.

Population genetic statistics # of # of Tajima's T's D p- Population Pi Seq Variants D value C. bonapartei 22 171 0.00249 -0.44 0.351 C. helianthea 20 133 0.00218 -0.09 0.481 C. b. consita 9 9 0.00019 -0.05 0.500 C. b. bonapartei 13 156 0.00254 -0.68 0.273 C. h. helianthea 7 40 0.00084 -0.75 0.274 C. h. tamai 13 98 0.00124 -1.54 0.057 C. b. consita Gen 9 9 0.00019 -0.05 0.500 C. h. tamai Gen 12 54 0.00089 -0.76 0.251 Northern group 23 92 0.00120 -0.76 0.242 Gen Southern group 17 100 0.00180 -1.63 0.043 Gen

Measures of genetic divergence Fst p- Population 1 Population 2 Fst Dxy value C. bonapartei C. helianthea 0.076 0.0160 0.0026 C. b. consita C. b. bonapartei 0.385 0.0010 0.0032 C. b. consita C. h. helianthea 0.764 0.0010 0.0032 C. b. consita C. h. tamai 0.402 0.0010 0.0017 C. b. bonapartei C. h. helianthea 0.083 0.1069 0.0019 C. b. bonapartei C. h. tamai 0.317 0.0010 0.0034 C. h. helianthea C. h. tamai 0.518 0.0010 0.0033 Northern group Southern group Gen 0.514 0.0010 0.0035 Gen C. b. consita Gen C. h. tamai Gen 0.502 0.0010 0.0016

Figure 1. The maximum-likelihood phylogeny (B) and haplotype network (C) support two main mitogenome groups in C. bonapartei and C. helianthea more related to their geographical distribution (A) than with their taxonomic or phenotypic assignation. Note that the mitogenomes of C. b. consita and C. h. tamai are differentiated whereas the mitogenomes of C. b. bonapartei and C. h helianthea are indistinguishable. Numbers on the tips of the tree, on the haplotype network and on locations in the map correspond to individual IDs in Table S1. Colors correspond to the assigned subspecies C. b. consita (orange), C. b. bonapartei (yellow), C. h. helianthea (light blue), and C. h. tamai (dark blue). In the map the teal area is the region where nominate subspecies are sympatric. In the tree, numbers on branches are ML-bootstrap values; branch lengths were set to equal.

Figure 2. The mitochondrial genome structure of Coeligena hummingbirds follows the typical organization of birds: 22 tRNAS (pink), 2 rRNAs (raspberry), 13 protein-coding genes PCGs (blue), and the control region (gray). Coding sequences CDS are in yellow. Substitutions among the three genetic groups C. b. consita (orange), C. b. tamai (blue) and the southern group (green) are represented in the inner circles (singletons and intrapopulation variant sites are not represented). Gray boxes indicate the three non- synonymous substitutions found, the box with black edges indicates the only substitution involving a change between amino-acids with different functional features.

References

Aguillon, S.M., Campagna, L., Harrison, R.G., Lovette, I.J., 2018. A flicker of hope: Genomic data distinguish Northern Flicker taxa despite low levels of divergence. Auk 135, 748–766. https://doi.org/10.1642/AUK-18-7.1 Andrews, S., 2010. FastQC: A quality control tool for high throughput sequence data. Arcones, A., Ponti, R., Vieites, D.R., 2019. Mitochondrial substitution rates estimation for molecular clock analyses in modern birds based on full mitochondrial genomes. bioRxiv 855833. https://doi.org/10.1101/855833 Avendaño, J.E., Donegan, T.M., 2015. A distinctive new subspecies of Scytalopus griseicollis (Aves, Passeriformes, Rhinocryptidae) from the northern Eastern Cordillera of Colombia and Venezuela. Zookeys 506, 137–153. https://doi.org/10.3897/zookeys.506.9553 Avise, J.C., Arnold, J., Ball, R.M., Bermingham, E., Lamb, T., Neigel, J.E., Reeb, C.A., Saunders, N.C., 1987. Intraspecific phylogeography: The mitochondrial DNA bridge between population genetics and systematics. Annu. Rev. Ecol. Syst. 18, 489–522. https://doi.org/10.1146/annurev.es.18.110187.002421 Ballard, J.W.O., Melvin, R.G., 2010. Linking the mitochondrial genotype to the organismal phenotype: Invited review. Mol. Ecol. 19, 1523–1539. https://doi.org/10.1111/j.1365- 294X.2010.04594.x Ballard, J.W.O., Whitlock, M.C., 2004. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744. https://doi.org/10.1046/j.1365-294X.2003.02063.x Bandelt, H.-J., Forster, P., Röhl, A., 1999. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. Benham, P.M., Cheviron, Z.A., 2019. Divergent mitochondrial lineages arose within a large, panmictic population of the Savannah sparrow (Passerculus sandwichensis). Mol. Ecol. 28, 1765–1783. https://doi.org/10.1111/mec.15049 Benham, P.M., Cuervo, A.M., Mcguire, J.A., Witt, C.C., 2015. Biogeography of the Andean metaltail hummingbirds: Contrasting evolutionary histories of tree line and habitat- generalist clades. J. Biogeogr. 42, 763–777. https://doi.org/10.1111/jbi.12452 Benham, P.M., Witt, C.C., 2016. The dual role of Andean topography in primary divergence: Functional and neutral variation among populations of the hummingbird, Metallura tyrianthina. BMC Evol. Biol. 16, 1–16. https://doi.org/10.1186/s12862-016- 0595-2 Bonnet, T., Leblois, R., Rousset, F., Crochet, P.A., 2017. A reassessment of explanations for discordant introgressions of mitochondrial and nuclear genomes. Evolution (N. Y). 71, 2140–2158. https://doi.org/10.1111/evo.13296 Cadena, C.D., Pérez-Emán, J.L., Cuervo, A.M., Céspedes, L.N., Epperly, K.L., Klicka, J.T., 2019. Extreme genetic structure and dynamic range evolution in a montane passerine bird: implications for tropical diversification. Biol. J. Linn. Soc. 126, 487– 506. https://doi.org/10.1093/biolinnean/bly207 Cadena, C.D., Zapata, F., Jiménez, I., 2018. Issues and perspectives in species delimitation using phenotypic data: Atlantean evolution in Darwin’s finches. Syst. Biol. 67, 181–194. https://doi.org/10.1093/sysbio/syx071 Campagna, L., Benites, P., Lougheed, S.C., Lijtmaer, D.A., Di Giacomo, A.S., Eaton, M.D., Tubaro, P.L., 2012. Rapid phenotypic evolution during incipient speciation in a continental avian radiation. Proc. R. Soc. B Biol. Sci. 279, 1847–1856. https://doi.org/10.1098/rspb.2011.2170 Chaves, J.A., Pollinger, J.P., Smith, T.B., LeBuhn, G., 2007. The role of geography and ecology in shaping the p hylogeography of the speckled hummingbird (Adelomyia melanogenys) in Ecuador. Mol. Phylogenet. Evol. 43, 795–807. https://doi.org/http://dx.doi.org/10.1016/j.ympev.2006.11.006 Chaves, J.A., Smith, T.B., 2011. Evolutionary patterns of diversification in the Andean hummingbird genus Adelomyia. Mol. Phylogenet. Evol. 60, 207–218. https://doi.org/10.1016/j.ympev.2011.04.007 Chaves, J.A., Weir, J.T., Smith, T.B., 2011. Diversification in Adelomyia hummingbirds follows Andean uplift. Mol. Ecol. 20, 4564–4576. https://doi.org/10.1111/j.1365- 294X.2011.05304.x Chesser, R.T., Isler, M.L., Cuervo, A.M., Cadena, C.D., Galen, S.C., Lane, D.F., Hosner, P.A., 2020. Conservative plumage masks extraordinary phylogenetic diversity in the Grallaria rufula (Rufous Antpitta) complex of the humid Andes. Auk In press. D’Horta, F.M., Cuervo, A.M., Ribas, C.C., Brumfield, R.T., Miyaki, C.Y., 2013. Phylogeny and comparative phylogeography of Sclerurus (Aves: Furnariidae) reveal constant and cryptic diversification in an old radiation of rain forest understorey specialists. J. Biogeogr. 40, 37–49. https://doi.org/10.1111/j.1365-2699.2012.02760.x Edwards, S. V., Kingan, S.B., Calkins, J.D., Balakrishnan, C.N., Jennings, W.B., Swanson, W.J., Sorenson, M.D., 2005. Speciation in birds: Genes, geography, and sexual selection. Proc. Natl. Acad. Sci. 102, 6550–6557. https://doi.org/10.1073/pnas.0501846102 Eliason, C.M., Maia, R., Parra, J.L., Shawkey, M.D., 2020. Signal evolution and morphological complexity in hummingbirds (Aves: Trochilidae). Evolution (N. Y). 1– 12. https://doi.org/10.1111/evo.13893 Eo, S.H., DeWoody, J.A., 2010. Evolutionary rates of mitochondrial genomes correspond to diversification rates and to contemporary species richness in birds and reptiles. Proc. R. Soc. B Biol. Sci. 277, 3587–3592. https://doi.org/10.1098/rspb.2010.0965 Flantua, S.G.A., O’Dea, A., Onstein, R.E., Giraldo, C., Hooghiemstra, H., 2019. The flickering connectivity system of the north Andean páramos. J. Biogeogr. 1808–1825. https://doi.org/10.1111/jbi.13607 Galtier, N., Nabholz, B., GlÉmin, S., Hurst, G.D.D., 2009. Mitochondrial DNA as a marker of molecular diversity: A reappraisal. Mol. Ecol. 18, 4541–4550. https://doi.org/10.1111/j.1365-294X.2009.04380.x García-Moreno, J., 2004. Is there a universal mtDNA clock for birds? J. Avian Biol. 35, 465–468. https://doi.org/10.1111/j.0908-8857.2004.03316.x García-Moreno, J., Arctander, P., Fjeldså, J., 1999. Strong Diversification at the treeline among Metallura hummingbirds. Auk 116, 702–711. González, C., Ornelas, J.F., Gutiérrez-Rodríguez, C., Gonzalez, C., Ornelas, J.F., Gutierrez-Rodriguez, C., 2011. Selection and geographic isolation influence hummingbird speciation: Genetic, acoustic and morphological divergence in the wedge-tailed sabrewing (Campylopterus curvipennis). BMC Evol. Biol. 11, 38. https://doi.org/10.1186/1471-2148-11-38 Goudet, J., Jombart, T., 2015. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Graham, C.H., Silva, N., Velásquez-Tibatá, J., 2010. Evaluating the potential causes of range limits of birds of the Colombian Andes. J. Biogeogr. 37, 1863–1875. https://doi.org/10.1111/j.1365-2699.2010.02356.x Grandcolas, P., Nattier, R., Trewick, S., 2014. Relict species: A relict concept ? Trends Ecol. Evol. 29, 655–663. https://doi.org/10.1016/j.tree.2014.10.002 Guarnizo, C.E., Amézquita, A., Bermingham, E., 2009. The relative roles of vicariance versus elevational gradients in the genetic differentiation of the high Andean tree frog, Dendropsophus labialis. Mol. Phylogenet. Evol. 50, 84–92. https://doi.org/10.1016/j.ympev.2008.10.005 Gutiérrez-Pinto, N., Cuervo, A.M., Miranda, J., Pérez-Emán, J.L., Brumfield, R.T., Cadena, C.D., 2012. Non-monophyly and deep genetic differentiation across low-elevation barriers in a Neotropical montane bird (Basileuterus tristriatus; Aves: Parulidae). Mol. Phylogenet. Evol. 64, 156–65. https://doi.org/10.1016/j.ympev.2012.03.011 Hahn, C., Bachmann, L., Chevreux, B., 2013. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads - A baiting and iterative mapping approach. Nucleic Acids Res. 41. https://doi.org/10.1093/nar/gkt371 Harris, R.B., Alström, P., Ödeen, A., Leaché, A.D., 2018. Discordance between genomic divergence and phenotypic variation in a rapidly evolving avian genus (Motacilla). Mol. Phylogenet. Evol. 120, 183–195. https://doi.org/10.1016/j.ympev.2017.11.020 Irwin, D.E., Rubtsov, A.S., Panov, E.N., 2009. Mitochondrial introgression and replacement between yellowhammers (Emberiza citrinella) and pine buntings (Emberiza leucocephalos) (Aves: Passeriformes). Biol. J. Linn. Soc. 98, 422–438. https://doi.org/10.1111/j.1095-8312.2009.01282.x James, J., Castellano, D., Eyre-Walker, A., 2017. DNA sequence diversity and the efficiency of natural selection in animal mitochondrial DNA. Heredity (Edinb). 118, 88– 95. https://doi.org/10.1038/hdy.2016.108 James, J.E., Piganeau, G., Eyre-Walker, A., 2016. The rate of adaptive evolution in animal mitochondria. Mol. Ecol. 25, 67–78. https://doi.org/10.1111/mec.13475 Jiménez, R.A., Ornelas, J.F., 2016. Historical and current introgression in a Mesoamerican hummingbird species complex: A biogeographic perspective. PeerJ 2016. https://doi.org/10.7717/peerj.1556 Kearns, A.M., Restani, M., Szabo, I., Schrøder-nielsen, A., Kim, J.A., Richardson, H.M., Marzluff, J.M., Fleischer, R.C., Johnsen, A., Omland, K.E., 2018. Genomic evidence of speciation reversal in ravens. Nat. Commun. 9. https://doi.org/10.1038/s41467- 018-03294-w Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., Thierer, T., Ashton, B., Meintjes, P., Drummond, A., 2012. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 Kraus, R.H.S., Wink, M., 2015. Avian genomics: Fledging into the wild! J. Ornithol. 156, 851–865. https://doi.org/10.1007/s10336-015-1253-y Krosby, M., Rohwer, S., 2009. A 2000 km genetic wake yields evidence for northern glacial refugia and hybrid zone movement in a pair of songbirds. Proc. R. Soc. B Biol. Sci. 276, 615–621. https://doi.org/10.1098/rspb.2008.1310 Leigh, J.W., Bryant, D., 2015. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041- 210X.12410 Lougheed, S.C., Campagna, L., Dávila, J.A., Tubaro, P.L., Lijtmaer, D.A., Handford, P., 2013. Continental phylogeography of an ecologically and morphologically diverse Neotropical songbird, Zonotrichia capensis. BMC Evol. Biol. 13. https://doi.org/10.1186/1471-2148-13-58 Lovette, I.J., 2004. Mitochondrial dating and mixed support for the “2% rule” in birds. Auk 121, 1–6. https://doi.org/10.2307/4090049 Lovette, I.J., Pérez-Emán, J.L., Sullivan, J.P., Banks, R.C., Fiorentino, I., Córdoba- Córdoba, S., Echeverry-Galvis, M., Barker, F.K., Burns, K.J., Klicka, J., Lanyon, S.M., Bermingham, E., 2010. A comprehensive multilocus phylogeny for the wood-warblers and a revised classification of the Parulidae (Aves). Mol. Phylogenet. Evol. 57, 753– 770. https://doi.org/10.1016/j.ympev.2010.07.018 Lozano-Jaramillo, M., Rico-Guevara, A., Cadena, C.D., 2014. Genetic differentiation, niche divergence, and the origin and maintenance of the disjunct distribution in the blossomcrown Anthocephala floriceps (Trochilidae). PLoS One 9. https://doi.org/10.1371/journal.pone.0108345 Luna, L.W., Rêgo, P.S. do, Sampaio, I., Schneider, H., Carneiro, L.S., Araripe, J., de Girão e Silva, W.A., Souza, T.O., 2017. Molecular data and distribution dynamics indicate a recent and incomplete separation of manakins species of the genus Antilophia (Aves: Pipridae) in response to Holocene climate change. J. Avian Biol. 48, 1177–1188. https://doi.org/10.1111/jav.01378 McGuire, J.A., Witt, C.C., Remsen, J. V., Dudley, R., Altshuler, D.L., 2008. A higher-level taxonomy for hummingbirds. J. Ornithol. 150, 155–165. https://doi.org/10.1007/s10336-008-0330-x Moore, W.S., 1995. Inferring phylogenies from mtDNA variation: Mitochondrial-gene trees versus nuclear-gene trees. Evolution (N. Y). 49, 718. https://doi.org/10.2307/2411136 Morales, H.E., Pavlova, A., Joseph, L., Sunnucks, P., 2015. Positive and purifying selection in mitochondrial genomes of a bird with mitonuclear discordance. Mol. Ecol. 24, 2820–2837. https://doi.org/10.1111/mec.13203 Morales, H.E., Sunnucks, P., Joseph, L., Pavlova, A., 2017. Perpendicular axes of differentiation generated by mitochondrial introgression. Mol. Ecol. 26, 3241–3255. https://doi.org/10.1111/mec.14114 Morgan-Richards, M., Trewick, S.A., Bartosch-Härlid, A., Kardailsky, O., Phillips, M.J., McLenachan, P.A., Penny, D., 2008. Bird evolution: Testing the Metaves clade with six new mitochondrial genomes. BMC Evol. Biol. 8, 1–12. https://doi.org/10.1186/1471-2148-8-20 Ornelas, J.F., González, C., Espinosa de los Monteros, A., Rodríguez-Gómez, F., García- Feria, L.M., 2014. In and out of Mesoamerica: Temporal divergence of Amazilia hummingbirds pre-dates the orthodox account of the completion of the Isthmus of Panama. J. Biogeogr. 41, 168–181. https://doi.org/10.1111/jbi.12184 Ornelas, J.F., González, C., Hernández-Baños, B.E., García-Moreno, J., 2016. Molecular and iridescent feather reflectance data reveal recent genetic diversification and phenotypic differentiation in a cloud forest hummingbird. Ecol. Evol. 6, 1104–1127. https://doi.org/10.1002/ece3.1950 Oyler-McCance, S.J., Oh, K.P., Langin, K.M., Aldridge, C.L., 2016. A field ornithologist’s guide to genomics: Practical considerations for ecology and conservation. Auk 133, 626–648. https://doi.org/10.1642/AUK-16-49.1 Palacios, C., Garcia-R, S., Parra, J.L., Cuervo, A.M., Stiles, F.G., McCormack, J.E., Cadena, C.D., 2019. Shallow evolutionary divergence between two Andean hummingbirds: Speciation with gene flow? Auk 136. https://doi.org//10.1101/249755 Parra, J.L., 2010. Color evolution in the hummingbird genus Coeligena. Evolution (N. Y). 64, 324–335. https://doi.org/10.1111/j.1558-5646.2009.00827.x Parra, J.L., Remsen, J. V., Alvarez-Rebolledo, M., McGuire, J.A., 2009. Molecular phylogenetics of the hummingbird genus Coeligena. Mol. Phylogenet. Evol. 53, 425– 434. https://doi.org/10.1016/j.ympev.2009.07.006 Piganeau, G., Gardner, M., Eyre-Walker, A., 2004. A broad survey of recombination in animal mitochondria. Mol. Biol. Evol. 21, 2319–2325. https://doi.org/10.1093/molbev/msh244 Pons, J.M., Sonsthagen, S., Dove, C., Crochet, P.A., 2014. Extensive mitochondrial introgression in North American Great Black-backed Gulls (Larus marinus) from the American Herring Gull (Larus smithsonianus) with little nuclear DNA impact. Heredity (Edinb). 112, 226–239. https://doi.org/10.1038/hdy.2013.98 Price, T.D., 2008. Speciation in Birds. Roberts & Company Publishers, Greenwood Village, Colorado. Prosdocimi, F., Souto, H.M., Ruschi, P.A., Furtado, C., Jennings, W.B., 2016. Complete mitochondrial genome of the versicoloured emerald hummingbird Amazilia versicolor, a polymorphic species. Mitochondrial DNA 27, 3214–3215. https://doi.org/10.3109/19401736.2015.1007352 R Core Team, 2017. R: A Language and environment for statistical computing. Remsen, J. V., 2005. Pattern, process, and rigor meet classification. Auk 122, 403. https://doi.org/10.1642/0004-8038(2005)122[0403:pparmc]2.0.co;2 Rheindt, F.E., Fujita, M.K., Wilton, P.R., Edwards, S. V., 2014. Introgression and phenotypic assimilation in Zimmerius flycatchers (Tyrannidae): Population genetic and phylogenetic inferences from genome-wide SNPs. Syst. Biol. 63, 134–152. https://doi.org/10.1093/sysbio/syt070 Rheindt, F.E., Székely, T., Edwards, S. V, Lee, P.L.M., Burke, T., Kennerley, P.R., Bakewell, D.N., Alrashidi, M., Kosztolányi, A., Weston, M. a, Liu, W.-T., Lei, W.-P., Shigeta, Y., Javed, S., Zefania, S., Küpper, C., 2011. Conflict between genetic and phenotypic differentiation: The evolutionary history of a “lost and rediscovered” shorebird. PLoS One 6, e26995. https://doi.org/10.1371/journal.pone.0026995 Ribas, C.C., Aleixo, A., Nogueira, A.C.R., Miyaki, C.Y., Cracraft, J., 2012. A palaeobiogeographic model for biotic diversification within Amazonia over the past three million years. Proc. R. Soc. B Biol. Sci. 279, 681–689. https://doi.org/10.1098/rspb.2011.1120 Rodríguez-Gómez, F., Ornelas, J.F., 2015. At the passing gate: Past introgression in the process of species formation between Amazilia violiceps and A. Viridifrons hummingbirds along the Mexican Transition Zone. J. Biogeogr. 42, 1305–1318. https://doi.org/10.1111/jbi.12506 Rokas, A., Ladoukakis, E., Zouros, E., 2003. Animal mitochondrial DNA recombination revisited. Trends Ecol. Evol. 18, 411–417. https://doi.org/10.1016/S0169- 5347(03)00125-3 Roulin, A., 2004. The evolution, maintenance and adaptive function of genetic colour polymorphism in birds. Biol. Rev. Camb. Philos. Soc. 79, 815–48. Rozas, J., Ferrer-Mata, A., Sanchez-DelBarrio, J.C., Guirao-Rico, S., Librado, P., Ramos- Onsins, S.E., Sanchez-Gracia, A., 2017. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302. https://doi.org/10.1093/molbev/msx248 Sammler, S., Bleidorn, C., Tiedemann, R., 2011. Full mitochondrial genome sequences of two endemic Philippine hornbill species (Aves: Bucerotidae) provide evidence for pervasive mitochondrial DNA recombination. BMC Genomics 12. https://doi.org/10.1186/1471-2164-12-35 Schubert, M., Lindgreen, S., Orlando, L., 2016. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 88. https://doi.org/10.1186/s13104-016-1900-2 Sedano, R.E., Burns, K.J., 2010. Are the Northern Andes a species pump for Neotropical birds? Phylogenetics and biogeography of a clade of Neotropical tanagers (Aves: Thraupini). J. Biogeogr. 37, 325–343. https://doi.org/10.1111/j.1365- 2699.2009.02200.x Sievers, F., Wilm, A., Dineen, D., Gibson, T.J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J.D., Higgins, D.G., 2011. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539. https://doi.org/10.1038/msb.2011.75 Sornoza-Molina, F., Freile, J.F., Nilsson, J., Krabbe, N., Bonaccorso, E., 2018. A striking, critically endangered, new species of hillstar (Trochilidae: Oreotrochilus) from the southwestern Andes of Ecuador. Auk 135, 1146–1171. https://doi.org/10.1642/auk- 18-58.1 Sosa, J., Parra, J.L., Stavenga, D.G., Giraldo, M.A., 2020. Sexual dichromatism of the Blue-throated Starfrontlet, Coeligena helianthea, hummingbird plumage. J. Ornithol. 161, 289–296. https://doi.org/10.1007/s10336-019-01709-z Souto, H.M., Ruschi, P.A., Furtado, C., Jennings, W.B., Prosdocimi, F., 2016. The complete mitochondrial genome of the ruby-topaz hummingbird Chrysolampis mosquitus through Illumina sequencing. Mitochondrial DNA 27, 769–770. https://doi.org/10.3109/19401736.2014.915533 Stamatakis, A., 2014. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 Toews, D.P.L., Brelsford, A., 2012. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930. https://doi.org/10.1111/j.1365- 294X.2012.05664.x Toews, D.P.L., Campagna, L., Taylor, S.A., Balakrishnan, C.N., Baldassarre, D.T., Deane- Coe, P.E., Harvey, M.G., Hooper, D.M., Irwin, D.E., Judy, C.D., Mason, N.A., McCormack, J.E., McCracken, K.G., Oliveros, C.H., Safran, R.J., Scordato, E.S.C., Stryjewski, K.F., Tigano, A., Uy, J.A.C., Winger, B.M., 2016. Genomic approaches to understanding population divergence and speciation in birds. Auk 133, 13–30. https://doi.org/10.1642/AUK-15-51.1 Toews, D.P.L., Mandic, M., Richards, J.G., Irwin, D.E., 2014. Migration, mitochondria, and the yellow-rumped warbler. Evolution (N. Y). 68, 241–255. https://doi.org/10.1111/evo.12260 Uy, J.A.C., Moyle, R.G., Filardi, C.E., 2009. Plumage and song differences mediate species recognition between incipient Flycatcher species of the Solomon Islands. Evolution (N. Y). 63, 153–164. https://doi.org/10.1111/j.1558-5646.2008.00530.x Valderrama, E., Pérez-Emán, J.L., Brumfield, R.T., Cuervo, A.M., Cadena, C.D., 2014. The influence of the complex topography and dynamic history of the montane Neotropics on the evolutionary differentiation of a cloud forest bird (Premnoplex brunnescens, Furnariidae). J. Biogeogr. 41, 1533–1546. https://doi.org/10.1111/jbi.12317 Wilson, A.C., Kathleen, M., Higuchi, R.G., Stephen, R., Prager, E.M., 1985. Mitochondrial DNA and two perspectives on evolutionary genetics. Biol. J. Linn. Soc. 26, 375–400. Winger, B.M., Bates, J.M., 2015. The tempo of trait divergence in geographic isolation: Avian speciation across the Marañon Valley of Peru. Evolution (N. Y). 69, 772–787. https://doi.org/10.1111/evo.12607 Wort, E.J.G., Fenberg, P.B., Williams, S.T., 2017. Testing the contribution of individual genes in mitochondrial genomes for assessing phylogenetic relationships in Vetigastropoda. J. Molluscan Stud. 83, 123–128. https://doi.org/10.1093/mollus/eyw044 Zamudio-Beltrán, L.E., Hernández-Baños, B.E., 2018. Genetic and morphometric divergence in the Garnet-Throated Hummingbird Lamprolaima rhami (Aves: Trochilidae). PeerJ 2018, 1–22. https://doi.org/10.7717/peerj.5733 Zamudio-Beltrán, L.E., Hernández-Baños, B.E., 2015. A multilocus analysis provides evidence for more than one species within Eugenes fulgens (Aves: Trochilidae). Mol. Phylogenet. Evol. 90, 80–84. https://doi.org/10.1016/j.ympev.2015.04.024 Zhang, D., Tang, L., Cheng, Y., Hao, Y., Xiong, Y., Song, G., Qu, Y., Rheindt, F.E., Alstro, P., Jia, C., Lei, F., 2019. “ Ghost Introgression ” as a cause of deep mitochondrial divergence in a bird species complex. Mol. Biol. Evol. 36, 2375–2386. https://doi.org/10.1093/molbev/msz170

Seeking gold and roses – Genomic differentiation and evolution of lineages of Andean Coeligena Hummingbirds – Chapter 3

Developed in collaboration with Leonardo Campagna1,2, Juan Luis Parra3, and Carlos Daniel Cadena4

1 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA

2 Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA

3 Grupo de Ecología y Evolución de Vertebrados, Instituto de Biología, Universidad de Antioquia, Calle 67 No. 53-108, Medellín, Colombia

4 Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias Biológicas, Universidad de Los Andes, Carrera 1 No. 18 A 10, Bogotá, Colombia

Introduction

Uncovering the genetic basis of phenotypes is crucial for understanding the role that different evolutionary mechanisms play in driving divergence between species. Genomic comparisons of closely related species are especially useful to inform about the role of mechanisms acting to produce evolutionary diversification, and to uncover information on candidate genes and genomic regions potentially involved in phenotypic differentiation. In particular, highly differentiated genomic regions between species showing little overall genetic differentiation may play a major role in establishing phenotypic differences relevant to speciation even if populations experience gene flow (Ellegren et al., 2012; Feder, Egan, & Nosil, 2012). However, highly differentiated genomic regions may also be the result of processes associated to genomic architecture, linked selection, and variation in recombination rates among regions (Burri, 2017; Seehausen et al., 2014). In turn, low differentiation between species in genomic regions may be a transient phenomenon if such a pattern reflects common ancestry (i.e. incomplete lineage sorting Wang et al., 2018), or may be maintained owing to recurrent gene flow (i.e. introgression Martin & Jiggins, 2017; Ottenburghs et al., 2017). Given such complexity and dynamism of the genomic landscape of differentiation between species, more genomic data of better quality are necessary to move forward in our understanding of how mutation, drift, gene flow, and selection shape the origin and evolution of species.

Genomic studies of birds have blossomed over recent years (Kraus & Wink, 2015; Zhang et al., 2014). Since the first sequencing of the chicken genome in 2004 (ICGSC International Chicken Genome Sequencing Consortium, 2004), several group initiatives like the Avian Phylogenomics Project and the B10K project (Genome 10K, 2009; Koepfli, Paten, & Brien, 2015) as well as the work of many others have provided us with one of the largest and better quality genomic data sets for any animal group existing to date (Rhie et al., 2020; Zhang et al., 2014), with more than 500 genomics projects for more than 180 bird species currently registered in Genbank [https://www.ncbi.nlm.nih.gov/bioproject/]. The development of avian genomic resources has been instrumental in connecting theory with data relevant to understanding evolution and speciation (Edwards et al., 2005; Toews, Campagna, et al., 2016). For example, genomic studies of hybridizing bird species have enabled recognizing mechanisms of isolation leading to hybrid speciation (Hermansen, Haas, Trier, & Bailey, 2014), and have provided insights into genomic regions prone to and resistant to introgression, the direction and magnitude of introgression, and discrepancies or correspondence in spatial patterns of genetic and morphological variation informative of mechanisms involved in speciation (Billerman, Cicero, Bowie, & Carling, 2019; Ottenburghs, 2020; Walsh, Shriver, Olsen, & Kovach, 2016). Comparisons of avian genomes have also helped to understand structural and functional features of genome evolution such as genomes size, chromosomal structure, gene synteny, recombination rates, and the role of transposable elements in the stability of genome structure (Kapusta & Suh, 2017; Kawakami et al., 2017; Van Doren et al., 2017; Zhang et al., 2014). An emerging pattern from genomic comparisons of many avian systems is that sexual chromosomes are often highly differentiated between species, a pattern likelyy related to both structural features of genomes, and to the presence of genes involved in phenotypic differentiation and evolving under selection (Batttey, 2020; Ellegren et al., 2012; Sigeman et al., 2019).

Avian genomic studies have also provided information on genes and genomic regions candidates to be the genetic basis of a variety of phenotypic traits. Examples include gene regions associated with the evolution of flightlessness (Burga et al., 2017; Sackton et al., 2019), the origin of distinct reproductive morphs (Küpper et al., 2015; Tuttle et al., 2016) and sexual dichromatism (Gazda et al., 2020), distinct migratory behaviors (Toews, Taylor, Streby, Kramer, & Lovette, 2019) and various genes and regulatory regions involved in bill (Bosse et al., 2017; Lamichhaney et al., 2015b, 2016; Yusuf et al., 2020). Among the many avian traits being the focus of genomic analyses, coloration has figured prominently among studies seeking to understand the genetic basis of phenotypes (Orteu & Jiggins, 2020).

Birds exhibit a remarkable diversity of colors (Stoddard & Prum, 2011) and plumage coloration is central for avian communication, playing an important role in species recognition, reproductive state signaling, sexual selection, and reproductive isolation (Price, 2008; Roulin, 2004). In birds, colors may be produced by pigments found across many taxa such as melanins and carotenoids (the latter recruited from diet), and also by more specialized and taxonomically restricted pigments such as porphyrins, pterins, spheniscins and psittacofulvins (Hill & McGraw, 2006). Alternatively, colors may be the result of the interaction between light and the structure of feathers (i.e. structural coloration, Eliason, Maia, Parra, & Shawkey, 2020; Orteu & Jiggins, 2020). Genomic studies of different avian systems have found associations between variation in coding sequences and regulatory regions of genes involved in the melanin pathway as important for phenotypic differences involving melanin coloration (Campagna et al., 2017; Cooper & Uy, 2017; Delmore et al., 2016; Poelstra et al., 2014; San-jose, Ducret, & Ducrest, 2017; Toews, Taylor, et al., 2016). Although carotenoid pigments are obtained from the diet, genomic studies have found that plumage variation involving such pigments is related to genes involved in carotenoid processing (Brelsford, Toews, & Irwin, 2017; Lopes et al., 2016; Mundy et al., 2016). Little is known about the genetic underpinnings of other pigment types, though progress is being made through genomic comparisons such as those helping to identify a gene related to the expression of psittacofulvins (Cooke et al., 2017).

In contrast to pigment-based coloration, the genetic basis of structural coloration in birds remains essentially unknown (Orteu & Jiggins, 2020). Structural coloration is considered a complex trait involving several morphological, physiological, and developmental components which in birds interact to produce the nanostructure of feathers (Eliason et al., 2020). Variation in the quantity and organization of layers of keratin, air space, and melanosomes (organelles with the pigment melanin) in feathers is linked to diversity of structural colors, but the interaction between such components is still not fully understood (Orteu & Jiggins, 2020). Because structural coloration is a complex trait, it is expected to have a polygenic basis, with involvement of several genes and regulatory factors, including both genes affecting feather nanostructure and those involved in the melanin metabolic pathway. The polygenic base of structural coloration has been supported on other animal systems (Brien et al., 2019), but studies on the topic focused on birds are lacking. Among birds, hummingbirds stand out for their remarkable diversity in iridescent plumage colors produced by structural coloration (Eliason et al., 2020). Here, we studied a system of closely related species of Andean hummingbirds to characterize their genomic landscape of differentiation and search for candidate genomic regions related to structural color likely involved in the speciation processes.

The Golden-bellied Starfrontlet (Coeligena bonapartei) and the Blue-throated Starfrontlet (Coeligena helianthea) are recently diverged species differing strikingly in their coloration. C. bonapartei males are greenish with fiery golden underparts, whereas C. helianthea males are blackish with rose bellies and aquamarine rumps. Females are paler than males, but still distinct. Despite the marked differences in coloration between species which may partly reflect melanin content (as well as structural differences, Eliason et al., 2020; Sosa, Parra, Stavenga, & Giraldo, 2020), they exhibit no differences in the coding sequence of the Melanocortin-1 receptor (MC1R) (Palacios et al., 2019), a gene involved in the metabolic pathway of melanin responsible for color variation in many vertebrate taxa. These hummingbird species comprise four subspecies: C. b. consita, C. b. bonapartei, C. h. helianthea and C. h. tamai, which diverged from their closest relative (C. b. eos) less than 500,000 years ago (Palacios et al., 2019; Palacios et al. under review) and are sympatric in part of the Cordillera Oriental in the Colombian Andes where different phenotypes persist (Figure 1 A). Patterns of genetic variation among these lineages in individual mitochondrial genes, complete mitochondrial genomes, and a set of ultra- conserved elements did not match patterns of plumage variation, suggesting speciation in the group may have proceeded in the face of gene flow, with phenotypic divergence potentially maintained by selection on loci related to traits involved in mating or ecological adaptation (Palacios et al., 2019; Palacios et al. under review). Predictions of such divergence-with-gene flow hypothesis of speciation have not been examined based on genome-wide patterns of genetic variation.

To further examine the history of speciation of C. bonapartei and C. helianthea clarify, the evolutionary relationships among its four lineages, and to look for candidate genes related to differences in the structural coloration of these hummingbirds, we adopted a population genomic approach in which we sequenced complete draft genomes of 46 individuals. We addressed the following questions: (1) Are the four lineages in C. bonapartei and C. helianthea distinguishable with whole-genome data? (2) What are the evolutionary relationships among lineages of these hummingbirds as inferred based on complete genomes? (3) How is the landscape of genomic differentiation among the four lineages of Coeligena? (4) Are there candidate genes associated with coloration differences in these hummingbirds? And (5) Are patterns of genomic differentiation between Coeligena hummingbirds consistent with introgression and selection (i.e. divergence with gene flow) as suggested by previous analyses based on mitochondrial data? By answering these questions our study furthers our understanding of the evolution of Coeligena hummingbirds, and sheds light on the role of different evolutionary mechanisms as drivers of divergence and speciation.

Materials and Methods

Samples, Genome sequencing and assembly

We obtained tissue samples (muscle) from 46 voucher specimens of C. bonapartei and C. helianthea from the collections at Instituto Alexander von Humboldt (IAvH), Museo de Historia Natural de la Universidad de los Andes (ANDES), and Instituto de Ciencias Naturales de la Universidad Nacional de Colombia (ICN-Aves). Taxon identities were assigned by museum curators according to phenotype and geography. We sampled 9 individuals of C. b. consita, 12 C. b. bonapartei, 10 C. h. helianthea and 15 C. h. tamai, and relatively even samples of each sex (Table S1).

We used a custom phenol/chloroform method coupled with Phase-Lock Gel tubes and a magnetic beads cleaning protocol to extract total genomic DNA from samples. We followed the manufacturer’s protocol to prepare Illumina TruSeq Nano DNA-enriched libraries for low-throughput configuration and 550bp insert size per sample. Libraries were quantified with a Qubit fluorometer. Normalizing, pooling and sequencing of the libraries were done by the Genomics Facility of the Institute of Biotechnology of Cornell University. Two lanes of NexSeq 500 2x150 paired end were used for sequencing. We filtered raw reads by quality following Illumina recommendations, checked reads using Fastqc (Andrews, 2010), and cleaned adapters using AdapterRemoval (Schubert, Lindgreen, & Orlando, 2016). We estimated average coverage according to the retained reads after filtered and considering the total genome size as 1.1 Gb estimated for the Anna’s Hummingbird (Calypte anna) (Zhang B; Li, C; Gilbert,M.T.P.; Mello, C.V.; Jarvis, E.D.; The Avian Genome Consortium; Wang,J;, 2014).

We used two reference genomes to assemble our data: that of C. anna provided by Erich Jarvis’ Lab at Rockefeller University (Rhie et al., 2020), and that of the Black-breasted Hillstar (Oreotrochilus melanogaster) provided by Christopher Witt’s Lab at the University of New Mexico (umpubl. Data). We employed both reference genomes because the genome of C. anna is robustly assembled and annotated; O. melanogaster is closely related to Coeligena hummingbirds (McGuire, Witt, Remsen, Dudley, & Altshuler, 2008) but its genome is partially assembled and not annotated. Because the percentage of alignment reads against to the genome of O. melanogaster was higher than to the genome of C. anna (see results) we proceeded using the genome of O. melanogaster as reference. We used Bowtie (Langmead & Salzberg, 2012), Samtools (Li, 2011; Li & Durbin, 2009), and Picardtools (MIT, 2017) to index and align the filtered reads to each reference genome and marked duplicates. We used Genome Analysis Toolkit GATK (McKenna et al., 2010) to call the variants per individual and then join them in a single vcf file. We filtered this vcf file to get sets of single nucleotide polymorphisms (SNPs) files for analyses.

Phylogenetic and Genomic Population Analyses

We assessed whether lineages of Coeligena (i.e. species and subspecies defined based on plumage coloration) are distinguishable with genomic data using a biallelic SNPs data set to perform a principal component analysis with the R package SNPRelate (R Core Team, 2017; Zheng et al., 2012). We also examined phylogenetic relationships among individuals and determined whether lineages formed clades. We used the biallelic SNPs data set to build a maximum-likelihood phylogenetic tree with the SVDquartets method (Chifman & Kubatko, 2014, 2015) in PAUP* V4 (Swofford, 2003), using 100 bootstrap replicates to assess nodal support, and including O. melanogaster as outgroup.

We characterized the genomic landscape of differentiation among lineages of Coeligena by calculating Fst values in non-overlapping 25 kb windows across the genome for all comparisons (i.e. between species and subspecies) using VCFtools 0.1.14 (Danecek et al., 2011). To identify candidate genes potentially involved in phenotypic differences between the lineages of Coeligena we initially focused on Fst comparisons between lineages differing in plumage phenotype, i.e. C. bonapartei vs. C. helianthea. We defined outlier windows as those with mean Fst values four times the standard deviation above the genomic mean. However, our finding of phylogenetic relationships inconsistent with phenotypic variation (see below) provided a unique opportunity to identify candidate regions via comparisons involving pairs of close relatives being either different (C. b. bonapartei and C. h. helianthea) or similar in coloration phenotype (C. h. helianthea and C. h. tamai). Because Fst comparisons between all lineages revealed peaks of differentiation in broadly the same regions (see results), we sought to identify outlier windows between lineages with different coloration phenotype while controlling by genomic differentiation between lineages with similar coloration phenotype. Therefore, we focused on examining genomic differentiation between C. b. bonapartei and C. h. helianthea (whith strikingly different coloration) relative to that between C. h. helianthea and C. h. tamai (whit similar coloration). For this comparison, we Z-transformed Fst values (Z-Fst = (Fst value – Fst mean)/Fst standard deviation) and then calculated Delta Z-Fst (Z-Fst) as the difference of Z-Fst values between comparisons (Vijay et al., 2016). We selected outlier windows as those with Z-Fst values at the 99th percentile. To identify loci in outliers windows we used BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and considered the first three best blast hits. We then grouped the obtained loci according functional categories based on information from the NCBI (https://www.ncbi.nlm.nih.gov/gene/).

Results

Genome sequencing and assembly

We recovered genomes with an average coverage of 3.9X (range 2.7- 6.5X, Table S1). We excluded 3 individuals from subsequent analyses due to low sequencing or assembly quality (ID 23, 24 and 33 in Table S1). Our reads assembled on average 91% against the genome of C. anna and 97% against the genome of O. melanogaster. Thus, we conducted subsequent analyses with 43 individuals and assemblies which used the genome of O. melanogaster as reference. The version we used of this reference genome was assembled in 434,731 scaffolds with scaffold N50 and N90 of 3.5 Mb and 183.8 kb, and a scaffold L50 of 87. We obtained a total of 7.3 million SNPs and 6.3 million biallelic SNPs after filtering. Considering the size of the genome of O. melanogaster (1.17 Gb) and the percentage of it covered by our reads assemblies (97%) we estimated the size of the draft genomes of Coeligena hummingbirds is roughly 1.14 Gb. Considering the total number of SNPs we obtained as the variable proportion of the genome among the lineages of Coeligena, these hummingbirds may differ in approximately 0.064% of their genomes. Genomic differentiation and phylogenetic relationships among lineages

Individuals from each lineage (i.e. subspecies) clustered together in principal component analyses (PCA, Figure 1 B). The first principal component (PC1) explained 12.44% of genetic variation and mainly separated C. b. consita from the other three lineages, whereas PC2 explained 5.79% of genetic variation and mainly separated C. b. bonapartei from both lineages of C. helianthea. We ran a second PCA excluding C. b. consita and found that PC1 explained 7.36% of genetic variation and separated individuals of C. b. bonapartei from lineages of C. helianthea. PC2 explained 4.81% of genetic variation and mainly separated C. h. helianthea from C. h. tamai. Individuals referred to C. b. bonapartei appeared to cluster in two different groups which agree with their geographical distribution, with one group from localities in the north and the other from localities in the south of this taxon’s range (Figure 1 A).

Phylogenetic analysis clustered all individuals of Coeligena in a clade with 100% bootstrap support (Figure 1 C). Within this clade, individuals of each lineage clustered together in well-supported clades (100%) yet phylogenetic relationships inferred with complete genomes were not consistent with taxonomy. Despite being considered a subspecies of C. bonapartei, C. b. consita was not sister to C. b. bonapartei, but rather was sister to a strongly supported clade including C. b. bonapartei and a clade formed by C. h. helianthea and C. h. tamai. As in the PCA analyses, C. b. bonapartei consisted of two distinct geographic groups (both with 100% bootstrap support).

Mean Fst values averaged across the genome also showed that C. b. consita is the most genetically differentiated lineage in the group (Fst values 0.239, 0.297, and 0.240 against C. bonapartei, C. h. helianthea, and C. h. tamai, respectively). Fst values among the other three lineages were lower and generally similar to each other (0.078 and 0.083 between C. b. bonapartei and C. h. helianthea and C. h. tamai respectively, and 0.054 between the two lineages of C. helianthea).

Genomic landscape of differentiation and candidate genes for phenotypic differentiation

Mean Fst values estimated for 25-kb windows across the genome were unevenly distributed among comparisons (Figure S1). Manhattan plots showed dozens of regions with high Fst values indicating marked differentiation among lineages across multiple scaffolds (Figure 1 D and Figure S2). Several of the Fst peaks were common across comparison and many mapped to the Z-chromosome (e.g. Scaffolds 19, 71, 75, 107, and 117 in Figure 1 D, see below). Scaffolds mapping to the Z-chromosome were the most differentiated between C. h. helianthea and C. h. tamai, two lineages showing similar coloration and exhibiting the lowest average genomic differentiation.

Comparisons of mean Fst values from 25 kb windows between C. bonapartei (both subspecies with similar phenotype) and C. helianthea (both subspecies) revealed 149 outlier windows (i.e. with mean Fst values four standard deviations above the genome Fst mean, Figure 1 D). However, given our interest in finding candidate loci associated with plumage variation and the overall patterns genomic differentiation and phylogenetic relationships among lineages we uncovered, we focused on identifying windows showing strong differentiation between C. b. bonapartei and C. h. helianthea (with different coloration phenotype) but low differentiation between C. h. helianthea and C. h. tamai (with similar coloration phenotype). Z-Fst values revealed 174 outlier windows (above the 99th percentile value) in 79 scaffolds across the genome (Figure 2). We found that 43 windows were identified as distinct in both the Fst outlier’s analysis comparing C. bonapartei and C. helianthea and int the Z-Fst analysis comparing phenotypically distinct and phenotypically similar taxa. These shared windows appear especially promising in terms of containing candidate genes underlying differences in coloration between lineages.

Of the 79 scaffolds showing outlier windows identified using Z-Fst, 32 were small (<500 bp), thus involved a single outlier window. Among the longer scaffolds (>25 kb), 27 scaffolds showed a single outlier window, and 20 included two or more outlier windows (Table S2). We were able to identify most of the latter 20 scaffolds because BLAST mapped most of their windows to the same chromosome in the genome of C. anna. Most scaffolds (9) mapped to the Z-chromosome (including a total of 47 outlier windows), 3 scaffolds mapped to chromosome 12 (12 total outlier windows), and each of 5 scaffolds mapped to chromosomes 6, 4, 5A, 4B and 11 (with 14, 8, 6, 4 and 3 outlier windows respectively). We were unable to identify 3 scaffolds because windows inside them mapped to different chromosomes, including scaffold 135 (ID in the ordered by size) which was the one with the highest number of outlier windows (17).

Within the total 174 outlier windows, we found 119 loci which we considered potential candidates involved in phenotypic differences between C. b. bonapartei and C. h. helianthea. 75 of these genes were related to nine main functional categories, 25 genes were scattered in other functional categories, 11 loci were unknown or uncharacterized, and 8 loci (which we called the usual suspects) mapped to different windows and scaffolds but with little coverage (Figure 2 and Table S3). The functional categories to which the 119 loci most often corresponded were cell growth, cell differentiation, cell cycle, apoptosis and autophagy, including transcription factors, homeobox genes, ubiquitin related genes, zinc finger genes, and members of the Rho and Ras gene family. Other, less frequent categories were those of genes involved in brain, neurons and neuronal signaling, genes related to thyroid hormones and a GABA receptor gene. Outlier windows also included genes related to functional categories such as endothelial cells, glycolysis, collagen, immune cells, mitochondria-related, epithelia development, and melanin.

Discussion

Relationships among Coeligena lineages and mito-nuclear discordance

Complete genome analyses clearly allowed us to distinguish four lineages of Coeligena hummingbirds, two of which are treated as subspecies of C. bonapartei and two as subspecies of C. helianthea based on coloration. However, in contrast to current taxonomy and to overall coloration similarities we found that C. b. consita is the most genetically distinct lineage in the complex, and phylogenetic analyses revealed this taxon is not sister to C. b. bonapartei but rather is the first branch to diverge in the group. Because previous work showed that C. b. eos, a taxon with coloration similar to that of C. bonapartei, is the outgroup of the four lineages in which we focused in this study (Palacios et al., 2019), orange-golden-green plumage coloration is the ancestral state in this clade, whereas the blackish-rose-aquamarine coloration of C. helianthea is derived.

Despite the marked similarity in plumage between C. b. bonapartei and C. b. consita, genomic differentiation between them and between C. b. consita and the two lineages of C. helianthea is quite high relative to genomic differentiation reported in other recently diverged avian systems (Campagna et al., 2017; Poelstra et al., 2014; Vijay et al., 2016). Such marked genomic divergence is striking given previous work in which mitochondrial genomes (and some nuclear loci) did not distinguish lineages of Coeligena corresponding to species or subspecies (Palacios et al., 2019; Palacios et al. in review). Moreover, mitogenomes suggested that C. b. consita is more closely related to C. h. tamai than to C. b. bonapartei, which in turn was indistinguishable from C. h. helianthea in its mitochondrial genome, indicating that mtDNA variation better reflects geography than taxonomy and plumage (Palacios et al. in review). These results contrast with our finding that complete nuclear genomes recover subspecies of C. helianthea as sister groups differentiated from both C. b. bonapartei and C. b. consita, implying a case of mito-nuclear discordance likely caused by gene flow and introgression. Although our biallelic SNPs data set produced a strongly supported phylogeny resolving the relationships among lineages of Coeligena, phylogenetic analyses focused on independent windows across the genome would be useful to identify discordant regions, whereas demographic analyses may inform about the role of incomplete lineage sorting, gene flow, and introgression in the history of the group (Lamichhaney et al., 2015a; Ottenburghs, 2020).

Landscape of genomic differentiation among Coeligena lineages

An emerging pattern from examining the landscape of differentiation among Coeligena lineages is that, overall, the same genetic regions seem to show peaks of differentiation in all comparisons. Because such regions were observed to exhibit divergence both in comparisons involving phenotypically similar and phenotypically distinct lineages, their patterns of variation most likely reflect effects of genomic architecture (e.g. regions standing out in all comparisons may experience low recombination) or regions responding to the same selective pressures (Vijay et al., 2016). Given such pattern, the Z-Fst analysis was especially relevant for identifying candidate regions exhibiting high differentiation between lineages with different phenotypes but low differentiation between lineages with similar phenotypes. However, many outlier windows identified in the Z-Fst analysis are in the same scaffolds where we detected peaks of differentiation, such as those mapping to sexual chromosomes. Elevated values of genetic differentiation in sexual chromosomes have been reported in many other studies of avian genomics (Batttey, 2020; Campagna et al., 2017; Elgvin et al., 2017; Ellegren et al., 2012; Poelstra et al., 2014; Sigeman et al., 2019; Toews, Taylor, et al., 2016), reflecting their smaller effective population size relative to autosomes, low recombination rates and high linkage disequilibrium, selection on sex-linked genes, and Haldane´s rule (Irwin, 2018). Recent work suggest high levels of differentiation between species in sexual chromosomes may also reflect linked selection (Burri, 2017; Kawakami et al., 2017), effects of sexual selection (i.e. variance in male reproductive success, Batttey, 2020); and female-biased gene flow (Lamichhaney et al., 2020). In Coeligena, high levels of differentiation in sexual chromosomes may be related to any of the above scenarios and thus are not necessarily coupled to differences in coloration Candidate genes for phenotypic differences between C. b. bonapartei and C. helianthea

Because C. b. bonapartei is the sister clade of C. helianthea, because taxa are sympatric in part of their ranges in areas where both coloration phenotypes persist without current evidence of intermediates (Palacios et al., 2019), and because they diverged so recently, lineages in this group are an ideal system to search for candidate genes for the genetic basis of structural coloration. In agreement with the expectation of structural coloration being polygenic given all morphological, physiological, and developmental components involved (Eliason et al., 2020), we found several genes in outlier windows that may be candidates for having a role in feather development considering their known functions, yet pointing out direct associations between specific variants and phenotypes is not yet possible. Nonetheless, we describe the type of candidate genes we uncovered and how might they relate to coloration due to their possible influence on feather development and pigmentation.

First, consistent with the hypothesis that feathers showing different structural colors may undergo different genetic programs of development, we found that several genes involved in cell growth and cell differentiation (e.g. EMB, GAB3, LIN54, LTBP2, RAD51B, and BTG1-like), as well as homeobox genes (e.g. CDX1, CDX4, and NKX2-3) differed between lineages of Coeligena with distinct plumage coloration. More specifically, some of the candidate genes we identified (e.g. FRS2 and RCAN2) are related to fibroblast growth factor genes which play an important role in the development of the feather buds (Rouzankina, Abate-Shen, & Niswander, 2004). In addition, Coeligena lineages differed in genes involved in endothelial and epithelial cell development (e.g. PTGER4, FAT1, HS3ST1-like), which may reflect that feather development involves the interaction of epidermal and dermal cells (Yu et al., 2004). Other functional categories including candidate genes in Coeligena were those related to programed cellular death (i.e. apoptosis) and degradation of cellular components (i.e. autophagy) such as SHISA5, ATG10 and ATG7; given that cell degeneration is relevant in feather morphogenesis (Alibardi, 2018), and that hummingbirds show hollow melanosomes in the nanostructure of their feathers (Eliason, Bitton, & Shawkey, 2013; Eliason et al., 2020) such processes may strongly influence the configuration of structural colors. Finally, we found two genes related to melanin as candidates for phenotypic differences: CSPG4, a cell surface proteoglycan involved in melanoma tumors in humans (Tang, Lord, Stallcup, & Whitelock, 2018; X. Wang et al., 2010), and SLC45A2, a gene associated with color variation in other animals systems including birds (Abolins-Abols et al., 2018; Domyan et al., 2014; Gunnarsson et al., 2007). Given that SLC45A2 has a role in vesicle sorting in melanocytes and variation on the d allele of in this gene is associated to lighter vs. darker plumages in pigeons (Domyan et al., 2014). All the loci above are the first candidates towards establishing the genetic basis of structural coloration in birds, but functional evidence of their role in feather development and pigment deposition is largely lacking. Detailed analyses of the genetic variation on these genes coupled to larger population studies (i.e. GWAS) will help to refine whether they are or not involved in variation of structural coloration. In addition, expression analyses (transcriptomes and real time PCR) will help to clarify the functional role that candidate genes may have in to produce structural coloration phenotypes. In particular, SLC45A2 appears to be a promising candidate to be involved in the production of darker plumages in C. helianthea, a hypothesis to be later tasted with a functional genomic approach.

Speciation and the landscape of differentiation in Coeligena hummingbirds

Understanding what drove speciation between C. bonapartei and C. helianthea was the original question that motivated this and our previous studies on this system (Palacios et al., 2019; Palacios et al. in review). Because coloration is a major trait distinguishing these hummingbirds and plumage coloration has a main role in avian communication and species recognition, we hypothesize that the evolution of a novel coloration phenotype in C. helianthea caused partly by changes in structural properties of feathers (Eliason et al., 2020; Sosa et al., 2020) was involved in the process of speciation in these hummingbirds. Because C. bonapartei and C. helianthea diverged recently and are sympatric in part of their range, their speciation likely proceeded while maintaining high rates of gene flow, and analyses of variation in the mitochondrial genome provided evidence consistent with such a divergence-with-gene-flow model of speciation (Palacios et al., 2019). Our findings of mito-nuclear discordance revealed by full genome analyses showing relationships and patterns of differentiation between these lineages differing from those inferred from mtDNA support the idea that they have experienced gene flow.

Under the divergence-with-gene-flow model of speciation (Fitzpatrick, Gerberich, Kronenberger, Angeloni, & Funk, 2015), selection is thought to counteract the homogenizing effects of gene flow, a process often resulting in genomic landscapes of divergence in which genes under selection show marked differences between species relative to a genomic background of low divergence. Regarding the role of color differences in avian speciation, evidence of such genomic landscapes in which regions controlling pigmentation stand out as exceptionally divergent is mounting across various taxa (Campagna et al., 2017; Poelstra et al., 2014; Stryjewski & Sorenson, 2017; Toews, Brelsford, Grossen, Milá, & Irwin, 2016), but we are unaware of similar patterns in cases involving structural coloration. We hypothesize that selective pressures acting on the novel phenotype of C. helianthea were likely drivers of speciation and maintain C. bonapartei and C. helianthea as distinct in the face of gene flow, but this is not manifested in a genomic landscape with overt peaks of differentiation containing genes related to coloration because of the likely polygenic basis of structural colors. Our set of candidate loci should aid future studies on the genetic basis of speciation in Coeligena via functional analyses. Also, the genomes we generated are a valuable resource one may use to link genetic variants with the structural properties of feathers involved in the production of colors.

In addition to offering clues about processes involved in speciation, the landscape of genomic divergence in Coeligena lineages undoubtedly also reflects their likely complex biogeographic history. The topography of the Andes and climatic fluctuations during the Pleistocene (Flantua, O’Dea, Onstein, Giraldo, & Hooghiemstra, 2019) likely resulted in episodic contractions and expansions of the mountain forest inhabited by lineages of Coeligena. Thus, changes in population size and population fragmentation during the history of these lineages should have also affected their landscape of genomic differentiation. Such genomic landscape thus reflects the interplay of evolutionary mechanism including high levels of gene flow, episodes involving variable levels of drift during periods of isolation, and selective pressures possibly coupled to a novel coloration phenotype in C. helianthea. Such interplay of evolutionary mechanisms is likely common to other systems in which species evolve and are maintained in the face of gene flow (Lamichhaney et al., 2015b).

Acknowledgments

We thank the Fundación para la promoción de la investigación y la tecnología del Banco de la República, the Facultad de Ciencias de la Universidad de los Andes and the Lovette Lab at Cornell Lab of Ornithology for financial support. We thank the Instituto Alexander von Humboldt, the Museo de Historia Natural de la Universidad de los Andes, and the Instituto de Ciencias Naturales de la Universidad Nacional de Colombia for providing tissue samples. We exported tissue samples thanks to the CITES permit No. CO41452 granted by the Ministerio de Ambiente y Desarrollo Sostenible of Colombia. We thank Christopher Witt and his lab, and Erich Jarvis and his lab for provided us with reference genomes. We thank Irby J. Lovette and Bronwyn G. Butcher for facilitating laboratory work. We thank Silvia Restrepo, Andrew J. Crawford, Daniel Cadena’s lab members, and anonymous reviewers, whose valuable comments helped to improve this work. Figures

Figure 1. Principal component analyses PCA (B), phylogenetic analysis (C), and Manhattan plots showing divergence across the genome (D) indicate that C. b. consita is the most genetically differentiated lineage in the group although all are genetically distinguishable. PCAs and the SVDquartets phylogeny based on complete genomes distinguish all four lineages as well as two additional two groups within C. b. bonapartei coinciding with geography as shown in (A). Colors on the map, PCA plots and the phylogeny correspond to C. b. consita (orange), C. b. bonapartei (yellow), C. h. helianthea (light blue), and C. h. tamai (dark blue); the hatched green area on the map is the region where C. b. bonapartei and C. h. helianthea are sympatric. Numbers on the map and on the phylogeny correspond to individuals ID in Table S1. Numbers on branches are bootstrap values; branch lengths were set to equal. In (D), dots are mean Fst values from 25-kb windows for different pairwise comparisons, showing several regions with high Fst values and sharing of such divergent regions across comparisons. Outlier windows (with mean Fst values 4 SD above the mean) are highlighted in the first comparison with blue dots. Hummingbird illustrations by Jesús de Orion.

Figure 2. Z-Fst values comparing divergence across the genome between a pair of taxa with different coloration (C. b. bonapartei and C. h. helianthea) and a pair with similar coloration (C. h. helianthea and C. h. tamai) identified174 outlier windows (99th percentile, blue dots) in 79 scaffolds across the genome. 119 loci identified in these windows are grouped in 12 functional categories (colored ellipses at the bottom), and examples of these loci with their location are shown on top of the Manhattan plot, with colored lines corresponding to functional categories. Scaffolds that mapped to known chromosomes in the genome of C. anna are pointed out. 43 outlier windows were shared between C. bonapartei and C. helianthea Fst analysis and the Z-Fst outlier analysis (unfilled blue dots). References

Abolins-Abols, M., Kornobis, E., Ribeca, P., Wakamatsu, K., Peterson, M. P., Ketterson, E. D., & Milá, B. (2018). Differential gene regulation underlies variation in melanic plumage coloration in the dark-eyed junco (Junco hyemalis). Molecular Ecology, 27(22), 4501–4515. doi: 10.1111/mec.14878 Alibardi, L. (2018). Transmission electron microscopic and immunohistochemical observations of resting follicles of feathers in chicken show massive cell degeneration. Anatomical Science International, 93(4), 548–558. doi: 10.1007/s12565-018-0449-7 Andrews, S. (2010). FastQC: A quality control tool for high throughput sequence data. Retrieved from https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Batttey, C. J. (2020). Evidence of linked selection on the Z chromosome of hybridizing hummingbirds. Evolution, 74(4), 699–804. doi: 10.1111/evo.13888 Billerman, S. M., Cicero, C., Bowie, R. C. K., & Carling, M. D. (2019). Phenotypic and genetic introgression across a moving woodpecker hybrid zone. Molecular Biology Reports, 28(November 2017), 1692–1708. doi: 10.1111/mec.15043 Bosse, M., Spurgin, L. G., Laine, V. N., Cole, E. F., Firth, J. A., Gienapp, P., … Slate, J. (2017). Recent natural selection causes adaptive evolution of an avian polygenic trait. Science, 358(6361), 365–368. doi: 10.1126/science.aal3298 Brelsford, A., Toews, D. P. L., & Irwin, D. E. (2017). Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration. Proceedings of the Royal Society B: Biological Sciences, 284. doi: 10.1098/rspb.2017.1106 Brien, M. N., Enciso-Romero, J., Parnell, A. J., Salazar, P. A., Morochz, C., Chalá, D., … Nadeau, N. J. (2019). Phenotypic variation in Heliconius erato crosses shows that iridescent structural colour is sex-linked and controlled by multiple genes. Interface Focus, 9(1). doi: 10.1098/rsfs.2018.0047 Burga, A., Wang, W., Ben-David, E., Wolf, P. C., Ramey, A. M., Verdugo, C., … Kruglyak, L. (2017). A genetic signature of the evolution of loss of flight in the Galapagos cormorant. Science, 356(6341). doi: 10.1126/science.aal3345 Burri, R. (2017). Linked selection, demography and the evolution of correlated genomic landscapes in birds and beyond. Molecular Ecology, 26(15), 3853–3856. doi: 10.1111/mec.14167 Campagna, L., Repenning, M., Silveira, L. F., Suertegaray Fontana, C., Tubaro, Pablo, L., & Lovette, I. J. (2017). Repeated divergent selection on pigmentation genes in a rapid finch radiation. Science Advances, 3(5), e1602404. doi: 10.1126/sciadv.1602404 Chifman, J., & Kubatko, L. (2014). Quartet inference from SNP data under the coalescent model. Bioinformatics, 30(23), 3317–3324. doi: 10.1093/bioinformatics/btu530 Chifman, J., & Kubatko, L. (2015). Identifiability of the unrooted species tree topology under the coalescent model with time-reversible substitution processes, site-specific rate variation, and invariable sites. Journal of Theoretical Biology, 374, 35–47. doi: 10.1016/j.jtbi.2015.03.006 Cooke, T. F., Fischer, C. R., Wu, P., Jiang, T., Xie, K. T., Kuo, J., … Bustamante, C. D. (2017). Article genetic mapping and biochemical basis of yellow feather pigmentation in budgerigars. Cell, 171(2), 427-432.e21. doi: 10.1016/j.cell.2017.08.016 Cooper, E. A., & Uy, J. A. C. (2017). Genomic evidence for convergent evolution of a key trait underlying divergence in island birds. Molecular Ecology, 26(14), 3760–3774. doi: 10.1111/mec.14116 Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., … Durbin, R. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156–2158. doi: 10.1093/bioinformatics/btr330 Delmore, K. E., Toews, D. P. L., Germain, R. R., Owens, G. L., Irwin, D. E., Delmore, K. E., … Irwin, D. E. (2016). The Genetics of Seasonal Migration and Plumage Report The Genetics of Seasonal Migration and Plumage Color. Current Biology, 1–7. doi: 10.1016/j.cub.2016.06.015 Domyan, E. T., Guernsey, M. W., Kronenberg, Z., Krishnan, S., Boissy, R. E., Vickrey, A. I., … Shapiro, M. D. (2014). Epistatic and Combinatorial Effects of Pigmentary Gene Mutations in the Domestic Pigeon. Current Biology : CB, 24(4), 459–464. doi: 10.1016/j.cub.2014.01.020 Edwards, S. V., Kingan, S. B., Calkins, J. D., Balakrishnan, C. N., Jennings, W. B., Swanson, W. J., & Sorenson, M. D. (2005). Speciation in birds: Genes, geography, and sexual selection. Proceedings of the National Academy of Sciences, 102(Supplement 1), 6550–6557. doi: 10.1073/pnas.0501846102 Elgvin, T. O., Trier, C. N., Tørresen, O. K., Hagen, I. J., Lien, S., Nederbragt, A. J., … Sætre, G. P. (2017). The genomic mosaicism of hybrid speciation. Science Advances, 3(6), 1–16. doi: 10.1126/sciadv.1602996 Eliason, C. M., Bitton, P. P., & Shawkey, M. D. (2013). How hollow melanosomes affect iridescent colour production in birds. Proceedings of the Royal Society B: Biological Sciences, 280(1767), 15. doi: 10.1098/rspb.2013.1505 Eliason, C. M., Maia, R., Parra, J. L., & Shawkey, M. D. (2020). Signal evolution and morphological complexity in hummingbirds (Aves: Trochilidae). Evolution, 1–12. doi: 10.1111/evo.13893 Ellegren, H., Smeds, L., Burri, R., Olason, P. I., Backström, N., Kawakami, T., … Wolf, J. B. W. (2012). The genomic landscape of species divergence in Ficedula flycatchers. Nature, 491(7426), 756–760. doi: 10.1038/nature11584 Feder, J. L., Egan, S. P., & Nosil, P. (2012). The genomics of speciation-with- gene-flow. Trends in Genetics, 28(7), 342–350. doi: 10.1016/j.tig.2012.03.009 Fitzpatrick, S. W., Gerberich, J. C., Kronenberger, J. A., Angeloni, L. M., & Funk, W. C. (2015). Locally adapted traits maintained in the face of high gene flow. Ecology Letters, 18(1), 37–47. doi: 10.1111/ele.12388 Flantua, S. G. A., O’Dea, A., Onstein, R. E., Giraldo, C., & Hooghiemstra, H. (2019). The flickering connectivity system of the north Andean páramos. Journal of Biogeography, (March), 1808–1825. doi: 10.1111/jbi.13607 Gazda, M. A., Araújo, P. M., Lopes, R. J., Toomey, M. B., Andrade, P., Afonso, S., … Carneiro, M. (2020). A genetic mechanism for sexual dichromatism in birds. Science (New York, N.Y.), 368(6496), 1270–1274. doi: 10.1126/science.aba0803 Genome 10K, C. of S. (2009). Genome 10K : A Proposal to Obtain Whole-Genome Sequence for 10 000 Vertebrate Species. Journal of Heredity, 100(6), 659–674. doi: 10.1093/jhered/esp086 Gunnarsson, U., Hellström, A. R., Tixier-Boichard, M., Minvielle, F., Bed’hom, B., Ito, S., … Andersson, L. (2007). Mutations in SLC45A2 cause plumage color variation in chicken and Japanese quail. Genetics, 175(2), 867–877. doi: 10.1534/genetics.106.063107 Hermansen, J. O. S., Haas, F., Trier, C. N., & Bailey, R. I. (2014). Hybrid speciation through sorting of parental incompatibilities in Italian sparrows. Molecular Ecology, 1– 12. doi: 10.1111/mec.12910 Hill, G. E., & McGraw, K. J. (2006). Bird coloration. Volume I. Mechanisms and measurements. Cambridge: Harvard University Press. ICGSC International Chicken Genome Sequencing Consortium. (2004). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432(7018), 695–716. doi: 10.1038/nature03154 Irwin, D. E. (2018). Sex chromosomes and speciation in birds and other ZW systems. Molecular Ecology, 27(19), 3831–3851. doi: 10.1111/mec.14537 Kapusta, A., & Suh, A. (2017). Evolution of bird genomes—a transposon’s-eye view. Annals of the New York Academy of Sciences, 1389(1), 164–185. doi: 10.1111/nyas.13295 Kawakami, T., Mugal, C. F., Suh, A., Nater, A., Burri, R., Smeds, L., & Ellegren, H. (2017). Whole-genome patterns of linkage disequilibrium across flycatcher populations clarify the causes and consequences of fine-scale recombination rate variation in birds. Molecular Ecology, 26(16), 4158–4172. doi: 10.1111/mec.14197 Koepfli, K., Paten, B., & Brien, S. J. O. (2015). The Genome 10K Project : A Way Forward. doi: 10.1146/annurev-animal-090414-014900 Kraus, R. H. S., & Wink, M. (2015). Avian genomics: Fledging into the wild! Journal of Ornithology, 156(4), 851–865. doi: 10.1007/s10336-015-1253-y Küpper, C., Stocks, M., Risse, J. E., Dos Remedios, N., Farrell, L. L., McRae, S. B., … Burke, T. (2015). A supergene determines highly divergent male reproductive morphs in the ruff. Nature Genetics, 48(1), 79–83. doi: 10.1038/ng.3443 Lamichhaney, S., Berglund, J., Almén, M. S., Maqbool, K., Grabherr, M., Martinez-Barrio, A., … Andersson, L. (2015a). Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature, 518(7539), 371–375. doi: 10.1038/nature14181 Lamichhaney, S., Berglund, J., Almén, M. S., Maqbool, K., Grabherr, M., Martinez-Barrio, A., … Andersson, L. (2015b). Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature. doi: 10.1038/nature14181 Lamichhaney, S., Han, F., Berglund, J., Wang, C., Almén, M. S., Webster, M. T., … Andersson, L. (2016). A beak size locus in Darwin’s finches facilitated character displacement during a drought. Science, 352(6284), 470–474. doi: 10.1126/science.aad8786 Lamichhaney, S., Han, F., Webster, M. T., Grant, B. R., Grant, P. R., & Andersson, L. (2020). Female-biased gene flow between two species of Darwin’s finches. Nature Ecology and Evolution. doi: 10.1038/s41559-020-1183-9 Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods, 9(4), 357–359. doi: 10.1038/nmeth.1923 Li, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987–2993. doi: 10.1093/bioinformatics/btr509 Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England), 25(14), 1754–1760. doi: 10.1093/bioinformatics/btp324 Lopes, R. J., Johnson, J. D., Toomey, M. B., Hill, G. E., Corbo, J. C., Carneiro, M., … Araujo, P. M. (2016). Genetic Basis for Red Coloration in Birds Report Genetic Basis for Red Coloration in Birds. Current Biology, 26, 1427–1434. doi: 10.1016/j.cub.2016.03.076 Martin, S. H., & Jiggins, C. D. (2017). Interpreting the genomic landscape of introgression. Current Opinion in Genetics and Development, 47, 69–74. doi: 10.1016/j.gde.2017.08.007 McGuire, J. A., Witt, C. C., Remsen, J. V., Dudley, R., & Altshuler, D. L. (2008). A higher- level taxonomy for hummingbirds. Journal of Ornithology, 150(1), 155–165. doi: 10.1007/s10336-008-0330-x McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., … DePristo, M. a. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297– 1303. doi: 10.1101/gr.107524.110 MIT, and H. B. I. (2017). Picard. Retrieved from http://broadinstitute.github.io/picard/ Mundy, N. I., Stapley, J., Bennison, C., Tucker, R., Twyman, H., Kim, K.-W., … Slate, J. (2016). Red Carotenoid Coloration in the Zebra Finch Is Controlled by a Cytochrome P450 Gene Cluster. Current Biology, 26(11), 1435–1440. doi: 10.1016/j.cub.2016.04.047 Orteu, A., & Jiggins, C. D. (2020). The genomics of coloration provides insights into adaptive evolution. Nature Reviews Genetics. doi: 10.1038/s41576-020-0234-z Ottenburghs, J. (2020). Ghost Introgression : Spooky Gene Flow in the Distant Past. BioEssays, 2000012, 1–5. doi: 10.1002/bies.202000012 Ottenburghs, J., Kraus, R. H. S., van Hooft, P., van Wieren, S. E., Ydenberg, R. C., & Prins, H. H. T. (2017). Avian introgression in the genomic era. Avian Research, 8(1), 30. doi: 10.1186/s40657-017-0088-z Palacios, C., Garcia-R, S., Parra, J. L., Cuervo, A. M., Stiles, F. G., McCormack, J. E., & Cadena, C. D. (2019). Shallow evolutionary divergence between two Andean hummingbirds: Speciation with gene flow? The Auk, 136(4). doi: /10.1101/249755 Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Muller, I., … Wolf, J. B. W. (2014). The genomic landscape underlying phenotypic integrity in the face of gene flow in crows. Science, 344(6190), 1410–1414. doi: 10.1126/science.1253226 Price, T. D. (2008). Speciation in Birds. Greenwood Village, Colorado: Roberts & Company Publishers. R Core Team. (2017). R: A Language and environment for statistical computing. Retrieved from https://www.r-project.org/ Rhie, A., Mccarthy, S. A., Fedrigo, O., Damas, J., Formenti, G., London, S. E., … Friedrich, S. R. (2020). Towards complete and error-free genome assemblies of all vertebrate species. 1–56. Roulin, A. (2004). The evolution, maintenance and adaptive function of genetic colour polymorphism in birds. Biological Reviews of the Cambridge Philosophical Society, 79(4), 815–848. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15682872 Rouzankina, I., Abate-Shen, C., & Niswander, L. (2004). Dlx genes integrate positive and negative signals during feather bud development. Developmental Biology, 265(1), 219–233. doi: 10.1016/j.ydbio.2003.09.023 Sackton, T. B., Grayson, P., Cloutier, A., Hu, Z., Liu, J. S., Wheeler, N. E., … Edwards, S. V. (2019). Convergent regulatory evolution and loss of flight in paleognathous birds. Science, 364(6435), 74–78. doi: 10.1126/science.aat7244 San-jose, L. M., Ducret, V., & Ducrest, A. (2017). Beyond mean allelic effects : A locus at the major color gene MC1R associates also with differing levels of phenotypic and genetic (co)variance for coloration in barn owls. Evolution, 71(10), 2469–2483. doi: 10.1111/evo.13343 Schubert, M., Lindgreen, S., & Orlando, L. (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 9(1), 88. doi: 10.1186/s13104-016-1900-2 Seehausen, O., Butlin, R. K., Keller, I., Wagner, C. E., Boughman, J. W., Hohenlohe, P. A., … Widmer, A. (2014). Genomics and the origin of species. Nature Reviews. Genetics, 15(3), 176–192. doi: 10.1038/nrg3644 Sigeman, H., Ponnikas, S., Chauhan, P., Dierickx, E., Brooke, M. D. L., Hansson, B., … Hansson, B. (2019). Repeated sex chromosome evolution in vertebrates supported by expanded avian sex chromosomes. Proceedings of the Royal Society B: Biological Sciences, 286. Sosa, J., Parra, J. L., Stavenga, D. G., & Giraldo, M. A. (2020). Sexual dichromatism of the Blue-throated Starfrontlet, Coeligena helianthea, hummingbird plumage. Journal of Ornithology, 161(1), 289–296. doi: 10.1007/s10336-019-01709-z Stoddard, M. C., & Prum, R. O. (2011). How colorful are birds? Evolution of the avian plumage color gamut. Behavioral Ecology, 22(5), 1042–1052. doi: 10.1093/beheco/arr088 Stryjewski, K. F., & Sorenson, M. D. (2017). Mosaic genome evolution in a recent and rapid avian radiation. Nature Ecology and Evolution, 1(12), 1912–1922. doi: 10.1038/s41559-017-0364-7 Swofford, D. L. (2003). PAUP* Phylogenetic Analysis Using Parsimony (*and Other Methods). Sunderland, MA: Sinauer Associates. Tang, F., Lord, M. S., Stallcup, W. B., & Whitelock, J. M. (2018). Cell surface chondroitin sulphate proteoglycan 4 (CSPG4) binds to the basement membrane heparan sulphate proteoglycan, perlecan, and is involved in cell adhesion. Journal of Biochemistry, 163(5), 399–412. doi: 10.1093/jb/mvy008 Toews, D. P. L., Brelsford, A., Grossen, C., Milá, B., & Irwin, D. E. (2016). Genomic variation across the Yellow-rumped Warbler species complex. The Auk, 133(4), 698– 717. doi: 10.1642/AUK-16-61.1 Toews, D. P. L., Campagna, L., Taylor, S. A., Balakrishnan, C. N., Baldassarre, D. T., Deane-Coe, P. E., … Winger, B. M. (2016). Genomic approaches to understanding population divergence and speciation in birds. The Auk, 133(1), 13–30. doi: 10.1642/AUK-15-51.1 Toews, D. P. L., Taylor, S. A., Streby, H. M., Kramer, G. R., & Lovette, I. J. (2019). Selection on VPS13A linked to migration in a songbird. Proceedings of the National Academy of Sciences of the United States of America, 116(37), 18272–18274. doi: 10.1073/pnas.1909186116 Toews, D. P. L., Taylor, S. A., Vallender, R., Brelsford, A., Butcher, B. G., Messer, P. W., & Lovette, I. J. (2016). Plumage genes and little else distinguish the genomes of hibridizing warblers. Current Biology, 26, 1–6. doi: 10.1016/j.cub.2016.06.034 Tuttle, E. M., Bergland, A. O., Korody, M. L., Brewer, M. S., Newhouse, D. J., Minx, P., … Balakrishnan, C. N. (2016). Divergence and functional degradation of a sex chromosome-like supergene. Current Biology, 26(3), 344–350. doi: 10.1016/j.cub.2015.11.069 Van Doren, B. M., Campagna, L., Helm, B., Illera, J. C., Lovette, I. J., & Liedvogel, M. (2017). Correlated patterns of genetic diversity and differentiation across an avian family. Molecular Ecology, 26, 3982–3997. doi: 10.1111/mec.14083 Vijay, N., Bossu, C. M., Poelstra, J. W., Weissensteiner, M. H., Suh, A., Kryukov, A. P., & Wolf, J. B. W. (2016). Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nature Communications, 7, 1–10. doi: 10.1038/ncomms13195 Walsh, J., Shriver, W. G., Olsen, B. J., & Kovach, A. I. (2016). Differential introgression and the maintenance of species boundaries in an advanced generation avian hybrid zone. BMC Evolutionary Biology, 16(1), 65. doi: 10.1186/s12862-016-0635-y Wang, K., Lenstra, J. A., Liu, L., Hu, Q., Ma, T., Qiu, Q., & Liu, J. (2018). Incomplete lineage sorting rather than hybridization explains the inconsistent phylogeny of the wisent. Communications Biology, 1(169). doi: 10.1038/s42003-018-0176-6 Wang, X., Wang, Y., Yu, L., Sakakura, K., Visus, C., Schwab, J. H., … Ferrone, S. (2010). CSPG4 in Cancer: Multiple Roles. Current Molecular Medicine, 10(4), 419–429. doi: 10.2174/156652410791316977 Yu, M., Yue, Z., Wu, P., Wu, D. Y., Mayer, J. A., Medina, M., … Chuong, C. M. (2004). The developmental biology of feather follicles. International Journal of Developmental Biology, 48(2–3), 181–191. doi: 10.1387/ijdb.15272383 Yusuf, L., Heatley, M. C., Palmer, J. P. G., Barton, H. J., Cooney, C. R., & Gossmann, T. I. (2020). Noncoding regions underpin avian bill shape diversification at macroevolutionary scales. Genome Research, 30(4), 553–565. doi: 10.1101/gr.255752.119 Zhang B; Li, C; Gilbert,M.T.P.; Mello, C.V.; Jarvis, E.D.; The Avian Genome Consortium; Wang,J;, G. L. (2014). Genomic data of the Anna’s Hummingbird (Calypte anna). Retrieved from http://dx.doi.org/10.5524/101004 Zhang, G., Li, C., Li, Q., Li, B., Larkin, D. M., Lee, C., … Froman, D. P. (2014). Comparative genomics reveals insights into avian genome evolution and adaptation. Science, 346(6215), 1311–1320. doi: 10.1126/science.1251385 Zheng, X., Levine, D., Shen, J., Gogarten, S., Laurie, C., & Weir, B. (2012). A High- performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics, 28(24), 3326–3328. doi: 10.1093/bioinformatics/bts606

Supplementary Material

Figure S1. Distribution of mean Fst values from 25 kb windows across the genome were different for each comparison.

Table S1. Individuals information and sequencing coverage. Paper-ID (identification number) through the paper. Sample ID corresponds to the tissue sample ID, to the specimen voucher ID or to the collector’s ID. Specimen voucher corresponds to the skin ID in collections or to collector’s ID. All samples are from Colombia. Sex F = females and M = males.

Paper Specimen Latitude and Sequencing Sample ID Species Subspecies Sex ID voucher Longitude Coverage (X) 1 ANDES-T1287 C. bonapartei consita F ICNAves36833 10.3669 -72.8975 3.5 2 ANDES-T1288 C. bonapartei consita F ICNAves36820 10.3669 -72.8975 3.6 3 ANDES-T1289 C. bonapartei consita M ICNAves36819 10.3669 -72.8975 3.8 4 ANDES-T1290 C. bonapartei consita M ICNAves36841 10.3669 -72.8975 4.0 5 ANDES-T1291 C. bonapartei consita F ICNAves36818 10.3669 -72.8975 2.7 6 ANDES-T1292 C. bonapartei consita M ICNAves36822 10.3669 -72.8975 4.1 7 IAvH-CT8567 C. bonapartei consita F ICNAves37116 10.3669 -72.8975 3.6 8 IAvH-CT8473 C. bonapartei consita M ICNAves37115 10.3640 -72.9474 3.4 9 IAvH-CT8503 C. bonapartei consita F ICNAves37104 10.3640 -72.9474 3.6 10 IAvH-CT00017312 C. bonapartei bonapartei M IAvH15365 5.8643 -73.1305 4.2 11 IAvH-CT00004191 C. bonapartei bonapartei M IAvH12581 5.7297 -73.4628 3.3 12 IAvH-CT4188 C. bonapartei bonapartei M IAvH12578 5.7297 -73.4628 4.2 13 IAvH-CT6966 C. bonapartei bonapartei F IAvH14196 5.7066 -73.4601 4.1 14 IAvH-CT6973 C. bonapartei bonapartei M IAvH14203 5.7046 -73.4572 4.0 15 IAvH-CT00002277 C. bonapartei bonapartei M IAvH12299 5.6394 -73.4872 4.9 16 IAvH-CT2265 C. bonapartei bonapartei F IAvH12290 5.6394 -73.4872 4.2 17 IAvH-CT2271 C. bonapartei bonapartei F IAvH12292 5.6394 -73.4872 3.3 18 ICN-Aves34450 C. bonapartei bonapartei M ICNAves34450 4.9333 -74.1833 3.7 19 ANDES-T2006 C. bonapartei bonapartei F JLPV74 4.9290 -74.1121 2.9 20 DCP01 C. bonapartei bonapartei F DCP01 4.8817 -74.4267 4.7 21 IAvH-CT00006791 C. bonapartei bonapartei M IAvH13986 4.6271 -74.3076 3.7 22 IAvH-CT6802 C. bonapartei bonapartei F IAvH13997 4.6271 -74.3076 4.2 23 Andes-BT 402 C. helianthea helianthea M ICNAves36409 7.0724 -72.9380 NA 24 IAvH-CT-11225 C. helianthea helianthea F IAvH8398 7.3042 -72.3711 NA 25 IAvH-CT18134 C. helianthea helianthea M ICNAves38141 5.2819 -73.3608 3.0 26 IAvH-CT-2530 C. helianthea helianthea F IAvH12633 4.4939 -73.6925 4.5 27 IAvH-CT00002569 C. helianthea helianthea F IAvH12682 4.7036 -73.8511 3.7 28 IAvH-CT2599 C. helianthea helianthea M IAvH12719 4.7036 -73.8511 6.5 29 IAvH-CT2601 C. helianthea helianthea F IAvH12722 4.6900 -73.8558 4.5 30 ANDES-T813 C. helianthea helianthea ? FGS4129 4.6667 -74.0330 3.8 31 IAvH-CT00002504 C. helianthea helianthea M IAvH12590 4.4939 -73.6925 3.9 32 ANDES-T70 C. helianthea helianthea F ICNAves36307 4.3213 -73.7768 4.2 33 Andes-BT 1126 C. helianthea tamai M IAvH 14908 7.4181 -72.4431 NA 34 ANDES-T1127 C. helianthea tamai F IAvH14906 7.4181 -72.4431 2.9 35 ANDES-T1128 C. helianthea tamai F IAvH14899 7.4181 -72.4431 5.2 36 ANDES-T1129 C. helianthea tamai F IAvH14897 7.4181 -72.4431 5.0 37 ANDES-T1130 C. helianthea tamai M IAvH14885 7.4181 -72.4431 3.4 38 ANDES-T1131 C. helianthea tamai F IAvH14884 7.4181 -72.4431 3.9 39 ANDES-T916 C. helianthea tamai M IAvH14818 7.4181 -72.4431 3.3 40 ANDES-T931 C. helianthea tamai F IAvH14836 7.4181 -72.4431 4.3 41 IAvH-CT11474 C. helianthea tamai M IAvH14915 7.4181 -72.4431 3.8 42 IAvH-CT11511 C. helianthea tamai F IAvH14912 7.4181 -72.4431 4.9 43 ANDES-T933 C. helianthea tamai M IAvH14964 7.4032 -72.4415 3.1 44 ANDES-T940 C. helianthea tamai M IAvH14971 7.4032 -72.4415 3.4 45 ANDES-T170 C. helianthea tamai M IAvHA8406 7.3042 -72.3711 3.2 46 ANDES-T1570 C. helianthea tamai M ICNAves37550 6.7308 -72.7956 3.3

Table S2. Twenty scaffolds showed more than two outliers windows identified in Z-Fst analyses, and 18 of these scaffolds mapped to known chromosomes in the genome of C. anna.

Ordered by size Scaffold ID in the No. of outlier Mapped to Scaffold ID reference genome windows 3 Chrom 4 scaffold17 8 4 Chrom 5A scaffold62 6 13 Chrom 6 scaffold90 14 19 Chrom Z scaffold68 4 71 Chrom Z scaffold297 3 75 Chrom Z scaffold385 8 107 Chrom Z scaffold715 5 115 Chrom 4B scaffold313 4 117 Chrom Z scaffold45 6 135 None scaffold101 17 140 Chrom 11 scaffold396 3 153 Chrom Z scaffold243 3 175 Chrom Z scaffold645 3 203 Chrom Z scaffold108 13 217 Chrom 12 scaffold238 2 246 None scaffold14 2 303 Chrom 12 scaffold344 2 320 Chrom Z scaffold361 2 338 None scaffold100 2 416 Chrom 12 scaffold159 8

Table S3 Using BLAST we found 119 loci in the 174 outliers windows identified in Z-Fst analyses. We grouped loci in categories related to their available function information. Main Functional Category Functional Category 1 Functional Category 2 Gene Brain* Thyroid hormones action DIO2 Brain* Neuronal proliferation Tumor DUSP26 Brain* Heart and Neurons Sour taste HCN1 Brain* ? Brain? RPP25 Brain* Sodium SLC6A2 Brain* Brain SNCAIP Brain* Brain STON2 Brain* Brain Lysosomal TMEM175 Brain* Gaba sensitivity GABA"" Cell growth** Cell growth Rho ARHGAP27 Cell growth** Autophagy Ubiquitin ATG10 Cell growth** Autophagy Transport vacuole ATG7 Cell growth** Transcription factors BATF Cell growth** Cytoskeletal structure Rho CCM2 Cell growth** Transcription factors Homeobox CDX4 Cell growth** Mitosis CENPP Cell growth** Cell growth Membrane EMB Cell growth** Calcium Extracellular matrix FBLN2 Cell growth** Phosphorylation-dependent Ubiquitin FBXO4 ubiquitination Cell growth** Cell motility Rho FGD3 Cell growth** Cell growth Macrophage differentiation GAB3 Cell growth** Immune cell differentiation Ubiquitin ITCH Cell growth** Cell growth Cell cycle genes LIN54 Cell growth** Cell growth Extracellular matrix LTBP2 Cell growth** Mitosis Kinase MOB3B Cell growth** Cell growth Heart and brain NISCH Cell growth** Transcription factors Homeobox NKX2-3 Cell growth** Osteoblast differentiation Heart OGN Cell growth** Zinc finger Cell growth PRDM5 Cell growth** Endothelial cells Epithelia development PTGER4 Cell growth** Ras Tumor RAB5A Cell growth** Cell growth Cell cycle genes RAD51B Cell growth** Ras Tumor? RASAL2 Cell growth** Ubiquitin RNF180 Cell growth** Rho Cell growth RTKN Cell growth** Apoptosis SHISA5 Cell growth** Cell growth Wnt-7a Cell growth** Cell growth Cell cycle genes BTG1-like"" Cell growth** Transcription factors Homeobox CDX-1 Cell growth** Zinc finger Cell growth KLF5"" Cell growth** Zinc finger LOC115599778 Cell growth** ? Ras LOC103534149 Collagen Chondrogenesis Collagen ASPN Collagen Collagen COL14A1 Collagen Collagen Nephritis COL4A5 Collagen Collagen Fibroblast FRS2 Collagen Collagen LOC115598664 Endothelial cells Endothelial cells Kidney ESM1 Endothelial cells Endothelial cells Calcium FLVCR2 Endothelial cells Endothelial cells Transcription factor GPBP1 Endothelial cells Endothelial cells NDNF Endothelial cells Endothelial cells Fibroblast RCAN2 Endothelial cells Endothelial cells Scavenger STAB1 Endothelial cells Blood coagulation TFPI Endothelial cells Endothelial cells Apoptosis TNFSF15 Endothelial cells Endothelial cells LOC115599371 Epithelia development Epithelia development FAT1 Epithelia development Epithelia development LOC113982597 Epithelia development ? Kinase LOC109370333 Glycolysis, Sweet taste, Insulin Transcription factors Insulin ISL1 Glycolysis, Sweet taste, Insulin Oligosaccharide processing MOGS pathway Glycolysis, Sweet taste, Insulin Supply of D-mannose derivatives MPI Glycolysis, Sweet taste, Insulin Glycolysis Fat PDPR Glycolysis, Sweet taste, Insulin Sweet taste REEP2 Glycolysis, Sweet taste, Insulin Diabetes Heart disease LOC103535721 Immune cells B cell development and activation BLNK Immune cells pre-B and pre-T lymphocytes DNTT Immune cells Gamma interferon receptor IFNGR1 Immune cells B cell and T cells LRRC23 Melanin Melanoma SLC45A2 Melanin Melanoma CSPG4 Mitochondria Dehydrogenase Mitochondria ALDH7A1 Mitochondria Mitochondria Calcium MICU3 Mitochondria Mitochondria MRPS30 Mitochondria Mitochondria QRSL1 Others Transport across membranes Cholesterol ABCA1 Others DNA damage ATRIP Others ? Kidney CTXN3 Others Nitric oxide generation DDAH1 Others Kinase Hypospadias DGKK Others Heat shock protein DNAJC15 Others Heat shock protein Protein folding DNAJC28 Others Extracellular matrix Fat ECM2 Others Membrane Tumor ENTPD1 Others Fat FAM219B Others Prenyltransferases Cisteine residue FNTA Others Endoplasmic reticulum Protein folding GPX8 Others DNA damage IPPK Others Kinase Signal transduction MAP3K1 Others Vesicular transport NIPSNAP3A Others Nuclear pore NUP210 Others Fat? Calcium? PLA2G15 Others Transport across membranes Sodium SLC12A2 Others Nanophthalmos TMEM98 Others Development Head TRABD2A Others Protein-Protein intreaction WBP1 Others Ovalbumin LOC103527411 Others ? Tumor LOC103535886 Others ? Tumor? LOC113982579 Others Antioxidant defense LOC115598252 The usual suspects CGGBP1 The usual suspects CSDC2 The usual suspects GGPS1 The usual suspects JMJD4 The usual suspects PITPNC1

The usual suspects TMED8 The usual suspects TRMT1L The usual suspects LOC103531782 Unknown and uncharacterized ? X-inactivation CHIC1 Unknown and uncharacterized ? CZH5orf51 Unknown and uncharacterized ? ENDOD1 Unknown and uncharacterized ? GPATCH2L Unknown and uncharacterized ? ? TTC33 Unknown and uncharacterized ? ? TWSG1 Unknown and uncharacterized ? LOC103534192 Unknown and uncharacterized ? ? LOC108497351 Unknown and uncharacterized ? ? LOC115598456 Unknown and uncharacterized ? ? LOC115599873 Unknown and uncharacterized ? ? LOC115600101

*Brain, neurons, and neuronal signaling **Cell growth, differentiation, cell cycle, apoptosis and autophagy

Conclusions

Evolution and speciation are ongoing processes, and they are happening right now. I began this project considering C. bonapartei and C. helianthea as two species but found that they comprise at least four recently diverged lineages: C. b. eos, C. b. consita, C. b. bonapartei and C. helianthea. My work revealed that the evolution of these lineages has been shaped by the interplay among evolutionary mechanisms whereby drift in the absence of gene flow has likely driven the differentiation of the allopatric C. b. eos and C. b. consita, whereas selection in the face of gene flow has driven the differentiation between C. b. bonapartei and C. helianthea. I found remarkably low differentiation in the mitochondrial genomes of C. b. consita, C. b. bonapartei, C. h. helianthea and C. h. tamai which is consistent with their recent divergence and with introgression. Whole genome analyses, however, readily distinguished lineages and allow me to identify peaks in the genomic landscape of differentiation between C. b. bonapartei and C. helianthea containing candidate genes potentially associated with their phenotypic differences and presumably involved in their speciation. My analyses revealed that the genetic basis of the differences in coloration between these hummingbirds is likely polygenic, with various genes and regulatory regions influencing phenotypes through feather development. Thus, patterns in gene expression, metabolic pathways, and developmental changes during feather growth involved in differences in structural coloration between these hummingbirds are yet to be characterized in detail. Such work would be facilitated with the access to references genome of high quality for these species that would complement my inferences based on draft genomes for multiple individuals to better understand differentiation among Coeligena lineages.

My findings lend support to the hypothesis that natural selection, sexual selection, or both have acted to drive and maintain phenotypic divergence on the hummingbirds I studied, but unravelling the specific mechanisms through which selection is acting requires further investigation. Testing whether coloration is adapted to microclimatic environments to enhance signal efficacy, addressing the preferences of females for different coloration phenotypes, or studying how coloration is linked to other traits like feeding or breeding behaviors will require spending time in the field. My work in genomics calls for basic studies on natural history of these species. My study, the first to generate a data set including complete genomes of multiple individuals in hummingbirds, allowed me to gain great insight into their evolutionary history and, more broadly, on how species form in megadiverse tropical mountains where historical climate changes may have isolated and reconnected populations throughout their divergence. However, my research has only opened the door to understanding the mechanisms underlying speciation and to identify the genetic basis of phenotypic differentiation in young systems evolving in the complex biogeographical scenario of the South American Andes.