Phylogeography and patterns of genetic and morphological variation in diglossa albiratera (aves: thraupidae): testing the influence of isolation and adaptive evolution on population differentiation

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(1)Phylogeography and Patterns of Genetic and Morphological Variation in Diglossa albilatera (Aves: Thraupidae): Testing the Influence of Isolation and Adaptive Evolution on Population Differentiation. Ángela Patricia Navas Berdugo. Director Carlos Daniel Cadena PhD Biology. Codirector Jorge Luis Pérez Emán PhD Biology. Universidad de los Andes Departamento de Ciencias Biológicas Laboratorio de Biología Evolutiva de Vertebrados July, 2008. i.

(2) Dedicated to my aunt Julieta, for being my inspiration and for only being “part of the air”.. ii.

(3) Abstract Neotropical mountains represent one of the hotspots of endemism, diversity, and threatened species in world. Traditionally, the historical generation of the great diversity of these mountains has been explained based on the influence of geographic barriers isolating populations that diversify in allopatry. However, steep elevational gradients and high topographic complexity in montane regions result in different environments over short distances, which could have influenced avian diversification in this region as a result of geographic variation in selective pressures. In this study, I analyze differentiation patterns in Diglossa albilatera (Aves, Thraupidae) in Colombia and Venezuela by assessing genetic (i.e mitochondrial DNA) and morphological variation across geography and in relation to ecological (i.e. climatic) variation. Phylogenetic analyses showed that D. albilatera is a monophyletic species comprising two separate monophyletic clades, one of which includes populations from the Colombian Andes and one haplotype from the Venezuelan slope of Serranía del Perijá, and the other includes populations from Venezuelan Andes, both slopes of Serranía del Perijá, and a subclade from the Sierra Nevada de Santa Marta. Population genetic analyses suggest a rapid and recent diversification of D. albilatera across Colombian mountains, accompanied by gene flow between cordilleras. Morphological analyses did not reveal consistent differences among regions, and Mantel tests indicate that the associations between genetic, morphological, and climatic variation are weak to nonexistent I conclude that time and isolation have been insufficient to allow for morphological differentiation among populations of D. albilatera, but because morphological differentiation can proceed rapidly even in the face of gene flow if selection is sufficiently strong, an alternative interpretation is that differences over iii.

(4) this part of the species’ range might be minor. Extending molecular and morphological coverage across all of the species’ range is necessary to better understand the forces influencing population differentiation in this species.. Key words: adaptation, Diglossa albilatera, Andes, morphological variation, phylogeography, Neotropical highlands, Neotropical birds.. Resumen Las montañas neotropicales representan uno de los “hotspots” de endemismo, diversidad y especies amenazadas en el mundo. Tradicionalmente, el surgimiento de la gran diversidad en estas montañas ha sido explicado por la influencia de las barreras geográficas en el aislamiento de las poblaciones que se han diversificado en alopatría. Sin embargo, los gradientes altitudinales y la complejidad topográfica en las regiones montanas lleva a la existencia de distintos ambientes en muy cortas distancias, lo cual puede haber influenciado la diversificación de las aves de esta región como resultado de variación geográfica de las presiones selectivas. En este estudio analizo los patrones de diferenciación en Diglossa albilatera (Aves, Thraupidae) en Colombia y Venezuela evaluando la variación genética (ADN mitocondrial) y morfológica a lo largo de su rango de distribución y su relación con la variación ecológica (climática). Los análisis filogenéticos muestran que D. albilatera es una especie monofilética que comprende dos clados monofiléticos, uno de estos incluye poblaciones de los Andes colombianos y un haplotipo de la pendiente venezolana de la Serranía del Perijá, y el otro clado incluye poblaciones de los Andes venezolanos, ambas pendientes de la Serranía del Perijá y un subclado de la Sierra Nevada de Santa Marta. Los análisis de genética de iv.

(5) poblaciones sugieren una diversificación rápida y reciente a lo largo de las montañas colombianas,. acompañada. por. flujo. genético. entre. cordilleras.. Los. análisis. morfológicos no revelan diferencias sustanciales entre regiones y la prueba de Mantel indica que la asociación entre variación genética, morfológica y climática es débil o inexistente. Concluyo que el tiempo y aislamiento han sido insuficientes para llevar a diferenciación morfológica entre las poblaciones de D. albilatera. Sin embargo, si la selección es lo suficientemente fuerte la diferenciación morfológica puede ocurrir rápidamente, incluso en presencia de flujo genético, una interpretación alternativa es que las diferencias en esta parte del rango de distribución de la especie son muy pocas. Ampliar la cobertura molecular y morfológica a lo largo de todo el rango de distribución de la especie es necesario para tener un mejor entendimiento de las fuerzas que están influenciando la diferenciación a nivel poblacional en esta especie.. Palabras clave: Adaptación, Diglossa albilatera, Andes, variación morfológica, filogeografía, zonas altas neotropicales, Aves neotropicales.. v.

(6) Table of contents:. ABSTRACT. iii. RESUMEN. iv. TABLE OF CONTENTS. vi. INTRODUCTION. 1. MATERIAL AND METHODS Sampling. 5. Laboratory procedures. 6. Phylogenetics and genetic variation analysis. 7. Morphological analysis. 9. Climatic analysis. 10. Testing for correlations between genetic, climate, and morphology. 10. RESULTS Phylogenetics and genetic variation analysis. 11. Phylogenetic analysis. 11. Population genetic analysis. 12. Morphological analysis. 13. Climatic analysis. 14. Correlations between genetic, climatic, and morphological variation. 14. DISCUSSION. 15. ACKNOWLEDGEMENTS. 20. REFERENCES. 22. FIGURES AND TABLES Tables. 29. Figures. 32. APPENDIX. 45. vi.

(7) Introduction Many phylogenetic and phylogeographic studies have attempted to explain the history of diversification of Neotropical species (reviewed by Moritz et al. 2000, Weir 2006), but most of the studies conducted in birds have been developed with a focus in lowland lineages (Cadena et al. 2007) despite apparent differences in diversification patterns between lowland and highland taxa (Weir 2006). In the lowlands, avian species diversification occurred predominantly during the Late Miocene and Early Pliocene (Weir 2006), and was likely driven by the emergence of geographic barriers (e.g. mountains chains), the formation of the Amazon system drainage, marine incursions, the appearance of freshwater barriers such as rivers and lakes, or the fragmentation of forest due to climatic changes (Moritz et al. 2000, Nores 2000).. In contrast, diversification in highland species apparently dates predominantly to the Late Pleistocene, when the diversification rate of highland avian clades was increased, such that approximately one third of the species originated within the last million years (Weir 2006). Glacial cycles led to habitat fragmentation and modified migration routes, thus leading to the divergence of many bird species in various regions of the world (Johnson & Cicero 2004; Ruegg et al. 2006; Chaves et al. 2007). In the Andes, glacial-driven cycles in temperature coupled with changes in precipitation and CO2 concentration in the atmosphere, led to shifts in the elevational range of different vegetation types, resulting in periods of high connectivity and periods of isolation of high elevation areas (Vuilleumier 1969a, Hooghiemstra & Van der Hammen 2004). These events presumably caused the appearance of multiple new. 1.

(8) species as a consequence of the geographic and ecological isolation that occurred in Neotropical mountain chains (Vuilleumier 1969a).. As described above, avian speciation in the Neotropical highlands is often explained based on isolation through geographic barriers. However, the wide elevational gradients in the Andes and the topographical complexity of the region expose bird populations to drastic changes in selective pressures over short distances (Vuilleumier 1969a). Thus, whereas patterns of population differentiation across geography can result from processes of genetic drift in isolation, they also likely arise from natural selection in functionally important traits that may eventually confer reproductive isolation (Schluter 2001). Accordingly, combining the now traditional phylogeographic and population genetic analyses with studies of morphological differentiation in relation to environmental variation, allows a better understanding of the processes involved in population differentiation and eventually speciation (Moritz et al. 2000; Graham et al. 2004; Chaves et al. 2007). In particular, a way to understand the mechanisms leading to genetic and phenotypic differentiation is to study intraspecific patterns of variation by comparing genetic and morphological differentiation among populations of the same species across different environments (Moritz et al. 2000). This intraspecific approach can give the bases to understand differentiation patterns and speciation at a regional scale (Chaves et al. 2007).. The Northern Andes (Venezuela, Colombia, Ecuador, and Peru) represent one of the main hotspots of endemism, diversity, and threatened species in the world (Orme et al. 2005). Thus, conducting evolutionary studies in this region is especially important to 2.

(9) understand Neotropical diversification. In order to test hypotheses about diversification in the Neotropical highlands, here I focus on population differentiation in the White-sided Flowerpiercer, Diglossa albilatera (Aves, Thraupidae). This species is an excellent model to understand speciation processes in the Neotropical mountains owing to its wide distribution across Neotropical highland forests (Graves 1982). The species occurs in the coastal mountains of Venezuela (including Serranía del Perijá), all major Colombian mountains (the three Andean cordilleras, Serranía del Perijá, and Sierra Nevada de Santa Marta), and through the Andes across Ecuador into Central Peru, from 1600 to 3100m elevation (Fig. 1; Vuileumier 1969b; Isler & Isler 1999; ABO 2000).. Phylogenetic studies at the family level in the Thraupidae indicate that bill shape is highly plastic evolutionarily; specifically, specialized bill morphologies have evolved repeatedly as adaptations to nectarivory in several tanager lineages, including the Diglossa flowerpiercers (Burns et al. 2003, Remsen 2003). Among nectar-feeding tanagers, Diglossa albilatera and other species in this genus have received attention from ecologists owing to their association with plants. Flowerpiercers are nectarivorous, but unlike many hummingbirds, they are nectar “thieves” that obtain nectar by opening a hole in the corolla of flowers, a behavior that is facilitated by the length and curvature of their maxillary hook (Bock 1985, Moynihan 1979, Schondube & Martinez del Rio 2003; Rojas-Nossa 2007; Mauck 2007). Diglossa albilatera belongs in one of two clades comprising the genus (Mauck 2007); this clade is the most diverse of the two, and its members have longer hooks than other flowerpiercers. Thus, the evolution of a large-hooked bill might have represented a key innovation that could 3.

(10) have led to an increase in diversification at a macroevolutionary scale in Diglossa (Mauck 2007). From a microevolutionary perspective, several studies have shown that bill differentiation in nectarivorous birds is a result of selection in response to flower morphology (e.g. Chaves et al. 2007, Schoundube & Martinez del Rio 2003, Wolf et al. 1976). Specifically, Schondube & Martinez del Rio (2003) experimentally demonstrated that longer maxillary hooks allow more efficient access to flowers with tubular corollas, but restrict the ability to forage on fruits in D. baritula. Thus, one might expect that if environmental variation across the distribution range of a species leads to differential availability of food, adaptative differentiation in bill morphology among populations may arise. Accordingly, because D. albilatera has a wide distributional range across which it may be exposed to very different environments, and considering the lability of bill morphology in this lineage, this species is an ideal system to examine the role of adaptive morphological variation and geographical isolation in population differentiation in Andean birds.. A way to examine the role that different environments play as selective factors acting on the differentiation of morphological characters of ecological relevance (e.g. bill morphology in Diglossa), is to compare isolated populations that exist on similar and different environments (Chaves et al. 2007). In D. albilatera, these comparisons can be done by means of studying morphological characteristics of populations occuring in different geographic regions, which presumably have been exposed to contrasting environments with dissimilar floristic composition. Moreover, comparing patterns of morphological and genetic differentiation allows assessing the role of geographic isolation and ecology in morphological diversification, which informs our 4.

(11) understanding of how might these factors lead to the formation of new species. Thus, in this study, I (1) describe and compare patterns of genetic and morphological differentiation among populations of Diglossa albilatera in a phylogenetic context, and (2) make inferences about the processes that could have generated such differentiation patterns. Ultimately, I seek to better understand the role of geographic isolation and adaptation to different environments in the diversification of D. albilatera at the population level.. Materials and Methods Sampling I included samples of a total of 53 individuals of D. albilatera (40 fresh tissues and 13 skin samples taken from museum specimens) and three individuals of the Venezuelan Flowerpiercer (D. venezuelensis, the closest relative of D. albilatera; Mauck 2007) in molecular analyses, which I supplemented with one sequence of D. albilatera and two of D. venezuelensis obtained from Mauck (2007; see below). Samples were obtained from the collections of Instituto Alexander von Humboldt (IAvH), the Museo de Historia Natural at Universidad de los Andes (ANDES), the Instituto de Ciencias Naturales at Universidad Nacional de Colombia (ICN), and the Colección Ornitológica Phelps, Venezuela (COP). These samples cover the distribution of the species thoroughly in Colombia and Venezuela (Fig. 1, Appendix 1); samples from Ecuador and Peru were not available, but will be included in a forthcoming study.. To conduct morphological analyses, I measured 177 specimens from multiple Colombian localities deposited in the ornithological collections of the ICN (103), the 5.

(12) Louisiana State University Museum of Natural Science (4), the IAvH (69), and ANDES (1) (Fig.1). These specimens provide comprehensive coverage of the species’ distribution (and thus possible geographic variation) within Colombia. I will expand geographic coverage for morphological analyses to Venezuela, Ecuador, and Peru in a future study.. Laboratory procedures I extracted DNA from tissues using the DNeasy Tissue Kit (Qiagen), following the manufacturer’s instructions. I modified the protocol when working with toe pad samples by using 30µl of ProtK during digestion, and 60µl of AE buffer that was heated to 70°C for the final elution, which was conducted with two separate elutions of 30µl.. I amplified the mitochondrial cytochrome b (cytb) gene using external primers L14996, H16064, ocassionally paired with the internal primers L15413 and H15646 (Sorenson et al. 1999). PCR constituents were: 1µl of DNA extracted (2µl for toe pad samples),16,5µl of H2O, 2,5 µl of 10X buffer, 1,5 µl of MgCl2, 1,2 µl of each primer, and 0,125 µl of Taq polymerase, in a total volume of 25 µl. Polymerase chain reactions (PCR) were run in a PTC-200 Thermal Cycler (MJ Research), beginning with an initial denaturation at 94°C for two minutes, followed by 34 cycles of denaturation at 94°C for 45 s, annealing at 52°C for 30 s, and extension at 72°C for one min; the procedure ended with a final extension at 72°C for 10 min. I cleaned PCR products using ExosapIT (USB corporation), following the manufacturer’s instructions. Each PCR product was sequenced in both directions by Macrogen Inc. I edited the sequences of each 6.

(13) individual using SeqMan II (DNAstar, Inc.), and aligned them manually using the text editor in Paup 4.0b (Swofford 2002).. Phylogenetics and population genetic analyses Based on the complete matrix of sequences obtained from a total of 54 individuals of D. albilatera and 5 of D. venezuelensis, I determined the unique haplotypes in the sample using the program DNAsp 4.0 (Rozas et al. 2003). Following this, I conducted phylogenetic analyses based on 38 unique haplotypes observed for D. albilatera, a single haplotype observed for D. venezuelensis, and one sequence of D. lafresnayii obtained from GenBank (AF006229.1; Burns 1997) as an outgroup.. I used Maximum Parsimony (MP), Maximum Likelihood (ML), and Bayesian Inference (BI) methods for phylogeny reconstruction. The Maximum Parsimony analysis consisted of a heuristic search conducted in Paup (Swofford 2002); I assessed support for branches using 1000 bootstrap replicates. I ran Maximum Likelihood and Bayesian Inference analyses in the CIPRES Portal (<http://www.phylo.org/>) using the programs RAxML (Stamatakis 2007) and MrBayes (Huelsenbeck & Ronquist 2001), respectively. For Maximum-likelihood analyses, I implemented the GTR nucleotide substitution model, and ran 10,000 bootstrap replicates to assess branch support. For Bayesian Inference, analyses employed the GTR nucleotide substitution model, and consisted of 5,000,000 generations and four chains. I discarded the first 500,000 generations as the burn-in. As an additional way to visualize relationships, I constructed a haplotype network using the Median-Joining method in the program Network 4.5.0.0 (Fluxus Technology Ldt. 2007). 7.

(14) To determine if patterns of shared and closely related haplotypes found in different geographic areas observed in phylogenetic reconstructions (see Results) could be explained by gene flow or by incomplete lineage sorting, I used the program MDIV (Nielsen & Wakeley 2001) to estimate a Bayesian posterior distribution of the effective number of female migrants exchanged per generation between selected pairs of populations. For this analysis, I used the finite sites model with 2,000,000 generations of Markov chain Monte Carlo sampling, of which the first 500,000 were discarded as burn-in.. To determine if patterns of genetic differentiation could be explained by isolation by distance, I tested for a correlation between geographical distance and genetic distance using a Mantel test approach, with 1000 permutations, implemented in the program AIS (Alleles In Space; Miller, 2005). I conducted separate analyses for the two main clades recovered by phylogenetic analyses (see below).. To determine whether populations of D. albilatera occurring in different regions had experienced sudden expansions, or if population size had been constant, I analyzed mismatch distributions (Rogers & Harpending 1992). The test was run assuming a constant population size as a null, with default parameters (Theta initial, Theta final, and Tau) in DNAsp 4.0 (Rozas et al. 2003). Additionally, I conducted Fu’s test (Fu & Li 1993) to give statistical support to results obtained through mismatch distribution analyses.. 8.

(15) Morphological Data I characterized the variation in bill morphology among Colombian populations of D. albilatera based on methods adapted from those used by Rojas-Nossa (2007), Mauck (2007), and McCormack (2007). I took photographs of the 177 specimens with a Panasonic Lumix camera DMC-LZ2 model with a resolution of 7,3 Mpixels; all photographs included a ruler to allow calibration for morphological measurements. I modified photographs with Adobe Photoshop version 8.0.1 by overlaying a grid consisting of 250 x 250-pixel squares on the specimen, and then scaling the image so that 250 pixels corresponded to 10 mm. I then took 13 different measurements on bills following Mauck (2007; see description of measurements on Fig.2) and also hook angle following Rojas-Nossa (2007) using the ImageJ 1,38x software for Windows (Rasband 2007). Furthermore, I measured bill length (total and exposed culmen), bill width and height, wing, tail, and tarsus length as described by Villarreal et al. (2004). I took these measurements only on adult individuals of both sexes; because a multivariate lineal model revealed that males and females differed significantly in some variables, I conducted analyses for males and females independently.. I tested if morphological variables were normally distributed with a Shapiro-Wilk test and transformed those that were not through a logarithmic transformation. To assess geographic variation in morphology, I separated the morphological data into populations corresponding to the different slopes of each cordillera, the Sierra Nevada de Santa Marta, the Serranía del Perijá, and the Serranía de los Yariguíes, resulting in nine categories. I then conducted a Discriminant Function Analysis (DFA) to determine whether different populations could be distinguished morphologically, and 9.

(16) conducted ANOVAs followed by Tukey post-hoc tests to test for univariate differences between populations. All analyses were executed in SPSS for Windows version 11.5 (SPSS, Inc., Chicago IL).. Climatic analysis Because direct measurements of habitat structure and resource availability are not available for different areas of the Colombian Andes, I followed Chaves et al. (2007; see also Kozak et al. 2008 for a review) in using climatic information as a proxy to characterize ecologically the different regions where D. albilatera occurs based on the 19 variables available in the Worldclim data base (Hijmans et al. 2005). I extracted the values of each climate variable for all the localities from which I measured morphological variables on specimens using the program ArcGis, and then ran a Discriminant Function Analysis (DFA) to determine if different regions could be distinguished climatically. Regions were defined as described above for morphological analyses.. Testing for Correlations between genetics, climate, and morphology To determine whether patterns of genetic, ecological (i.e., climatic), geographic, and morphological variation correlate with each other, I ran Mantel tests for combinations of these factors. I obtained Euclidean distance matrices for climate and morphology using SPSS, uncorrected pairwise genetic distances using Mega 4 (Tamura et al. 2007), and geographic distances between localities using AIS (Miller 2005). Mantel tests were ran using the PopTools application for Microsoft Excel.. 10.

(17) Results: Phylogenetic and genetic variation analysis: Phylogenetic analysis Topologies obtained using different methods of phylogeny reconstruction were consistent in revealing the following general patterns (Figure 3). First, reciprocal monophyly of D. albilatera and Diglossa venezuelensis was well supported. Second, D. albilatera consists of two monophyletic, well-supported groups. One includes all haplotypes from populations of the Colombian Andes and a single haplotype shared by three individuals from the Venezuelan slope of the Serranía del Perijá. Phylogenetic relationships within this clade are not well resolved, and it is impossible to clearly associate particular clades with particular geographic regions. The other clade includes two well-supported groups: one includes haplotypes from Aragua and the Venezuelan slope of the Serranía del Perijá, and the other includes haplotypes from the Sierra Nevada de Santa Marta, both slopes (Colombian and Venezuelan) of the Serranía del Perijá, and from other Venezuelan mountains.. The uncorrected genetic distance between D. albilatera and D. venezuelensis is approximately 4.5%. The divergence between the two main clades of D. albilatera was approximately 2.5%. Within the Andean clade, genetic distances among haplotypes averaged less that 0.5%. Within the other clade, the divergence between Venezuelan and Santa Marta populations is approximately 1%.. 11.

(18) The haplotype network shows that the two main clades of D. albilatera are separated by 22 mutational steps (Fig. 4). The group formed by haplotypes of the Sierra Nevada de Santa Marta is separated by 6 mutational steps from the group formed by haplotypes sampled in Venezuela and the Serranía del Perijá. The haplotypes of the Venezuelan side of Serranía del Perijá are separated by six mutations from those of the Venezuelan Andes. The other group includes haplotypes from the three Colombian cordilleras and one haplotypes from the Venezuelan side of Serranía del Perijá. Within this group, there is no clear pattern of grouping with respect to geography.. Population genetic analysis MDIV analyses suggest that there is migration between populations occurring in the different Colombian cordilleras; posterior probability distributions peaked at moderate to high levels of gene flow, and in one case a high probability extended to high levels of gene flow (Fig. 5a-c). This suggests that the pattern of little differentiation among cordilleras is likely not a result of incomplete sorting, but is instead caused by migration. In contrast, the haplotype sharing between the Venezuelan slope of Serranía de Perijá and the Colombian Cordillera Oriental likely reflects the retention of an ancestral polymorphism because MDIV analysis showed that migration between these two populations is restricted. However, the large confidence interval around the point estimate of migration (Fig. 5d) implies it is. 12.

(19) possible that this pattern can be a result of gene flow and not of incomplete lineage sorting.. Mismatch distribution analyses showed that in the Colombian Andes clade, patterns of genetic differentiation differ from those expected by a model of constant population size, and are suggestive of a sudden population expansion (Fig. 6a). In contrast, patterns of variation in the Venezuelan clade (excluding the Sierra Nevada de Santa Marta) are consistent with a history of constant population size (Fig. 6b).. Analyses in AIS showed that there is no evidence of isolation by distance in any of the two main clades of D. albilatera (Fig 7). In both of these clades, the correlations between genetic and geographic distance were low and non-significant.. Morphological analyses. Significant differences in several morphological variables were observed among populations (ANOVA, P<0.05; Table 1). Variables showing significant differences among populations were hook depth (HD), tooth depth (TD), bill concavity (CO), exposed culmen (EC), wing (W), bill height (BH), and tarsus length (T). The other variables did not vary significantly among populations.. 13.

(20) Despite the differences noted above, the discriminant function analysis seeking to establish whether different populations could be distinguished morphologically was significant only for female data (Females: Wilks-λ = 0.022, gl = 84, p<0.01; Males: Wilks-λ =0.445, gl = 66, p = 0.305). In addition, the percentage of correct grouping was poor and variable, and was greater for females than for males (Table 2). Although some populations appear to be morphologically differentiated, the general conclusion of these analyses is that there is no consistent overall relationship between membership in previously established populations and morphological variation (Fig. 8).. Climatic analysis In contrast to the results based on morphological measurements, most localities were classified correctly to their corresponding, previously established regions, based on climate data (Table 3; Fig 9), and the DFA was significant (Wilks-λ=0.017, gl=120, p≤0.01). Noteworthy patterns include the climatic distinctiveness of the environments occupied by D. albilatera in the Sierra Nevada de Santa Marta and in the eastern slope of the Cordillera Occidental; the latter are markedly different climatically even with respect to localities from the western slope of the same cordillera (Fig. 9). The analysis did not reveal clear dissimilarities in climate at sites occupied by the species between slopes of the Cordillera Oriental nor between slopes of the Cordillera Central. Although this analysis included only one locality for the Serranía de los Yariguíes, climate at this site, a spur of the Cordillera Oriental, appears distinct with respect to all other regions. In contrast, the localities from the Serranía del Perijá do not appear to be climatically distinct, and group with localities from the Cordillera Oriental. 14.

(21) Correlations between genetic, climatic, and morphological variation Overall, I found no clear correspondence between morphological variation and patterns of genetic, geographic, and climatic differentiation across the Colombian range of D. albilatera (Fig. 10). Some correlations were significant but the percentage of variance explained by these was very low in most of the cases; thus, significance is likely a product of large sample sizes. The correlation that explained most variation was that between genetic distance and morphological distance (19%; Fig. 10a); however, the relationship between these variables was negative, which contrasts with the prediction of the hypothesis that morphological differentiation can be explained by drift in isolation.. Discussion. In this study, all phylogenetic analyses supported the monophyly of D. albilatera with respect to its sister species D. venezuelensis, which agrees with the results obtained by Mauck (2007) based on a limited sample of individuals. Vuilleumier (1969b) proposed that differentiation of D. venezuelensis, which is endemic to extreme northeastern Venezuela (Fig.1), from a common ancestor with D. albilatera that was widespread across the mountains of northern Venezuela, was “initiated, or reinforced” by an increase in aridity during the Pleistocene that augmented the extent of unsuitable habitats between the coastal range and the Turimiquire massif. Assuming that avian cytochrome b accumulates c. 2% differentiation per million years of divergence (Lovette 2004, Weir & Schluter 2008), my results suggest these species 15.

(22) diverged earlier than hypothesized by Vuilleumier (1969b). This substitution rate would place their differentiation in the Late Pliocene, at ~2,5 mya, a period of great climatic changes and major uplift of the Andes (Adriessen et al. 1993; Colinvaux 1996; Gregory-Wodzicki 2000; Ravelo et al. 2004), processes that could have influenced the divergence of these species. This scenario rejects, in part, the Pleistocene origin of D. venezuelensis proposed by Vuilleumier (1969b). However, I note that a molecular clock has not been calibrated for Diglossa, which implies that my results need to be interpreted with care; in addition, it is possible that the isolation of D. venezuelensis and D. albilatera was reinforced by Pleistocene changes as suggested by Vuilleumier (1969b).. Diglossa albilatera consists of two reciprocally monophyletic lineages, which were well supported in all my analyses. Based on their 2.5% divergence, these two clades presumably differentiated c. 1.25 mya, in the Early Pleistocene. Population genetic analyses suggest that it is likely that D. albilatera colonized the Colombian Andes from Venezuela because (1) Colombian Andean populations appear to have suffered a rapid expansion whereas those from Venezuela appear to have been stable according to mismatch distribution analyses, and (2) nucleotide diversity is lower in the Colombian clade (0.0049) than in the Venezuelan group (0.0126) suggesting a comparatively recent origin for the populations occurring in the Colombian Andes (see Zink 2002). In addition, the Venezuelan origin of D. albilatera, which confirms the hypothesis raised by Mauck (2007), is supported by the distribution of its sister group D venezuelensis. Nevertheless, I note that these results are also consistent with a scenario in which the ancestor of the two current lineages was originally widespread 16.

(23) and then became separated as a result of a vicariant event, with a subsequent history of range contraction and expansion in the Colombian clade and stability in the Venezuelan clade (a pattern possibly resulting from the dynamics of vegetation in the Colombian Andes; Hooghiemstra et al. 2006). Including Ecuadorian and Peruvian samples in analyses may allow for a more comprehensive understanding of the biogeographic history of D. albilatera.. Within the Colombian Andes clade (which includes one haplotype from the Venezuelan slope of the Serranía del Perijá), there is no clear structuring with respect to geography, and gene flow among the three Colombian cordilleras appears high. This pattern is consistent with morphological analyses, which do not reveal clear phenotypic differences among three cordilleras, but contrasts with previous phylogeographic studies on Northern Andean birds, which revealed a close correspondence between geography and patterns of phylogenetic relationships and genetic variation. For example, the Magdalena River Valley has been proposed as a major geographical barrier isolating distinct lineages in Arremon torquatus (Cadena et al. 2007), Myadestes ralloides (Velásquez 2008), and Premnoplex brunnescens (Valderrama 2008), but this valley does not appear to have restricted genetic exchange in D. albilatera. In sum, in contrast to the history of population isolation between cordilleras within Colombia documented for these other Andean birds, the history of D. albilatera is one of high gene flow and presumably recent and rapid population expansion over the Andes as indicated by lack of clear geographic structure, shallow divergences, short length of internodes, and a mismatch distribution consistent with sudden population expansion. This is further consistent with the inclusion of a 17.

(24) haplotype from the Venezuelan slope of Serranía del Perijá in the Colombian Andean clade, which, according to MDIV analyses, appears most likely to be a consequence of incomplete lineage sorting resulting from the recent separation of this clade from its sister group.. The other lineage of D. albilatera is divided in two well supported clades, one including haplotypes from Aragua and the Venezuelan slope of the Serranía del Perijá, and the other including haplotypes from the Sierra Nevada de Santa Marta, from other Venezuelan mountains, and from the Venezuelan and Colombian slopes of the Perijá. The fact that the Sierra Nevada de Santa Marta clade is nested within a paraphyletic group of Venezuelan haplotypes (which also includes a haplotype from the Colombian Perijá) indicates that these mountains were likely colonized from Venezuela or separated by a vicariant event from populations occurring in that area, an event that occurred some 0.5 mya according to the 2% divergence per million years molecular clock. Despite this period of geographical isolation, the population from the Sierra Nevada de Santa Marta is not morphologically distinct from other Colombian populations.. Although the Magdalena River valley does not represent an important geographic barrier for D. albilatera as noted above, clearly other barriers are related to isolation among populations. These include the lowlands isolating populations occurring in the Sierra Nevada de Santa Marta from those occurring in other mountains (a pattern that has also been observed in other species; Cadena et al. 2007, Valderrama 2008), and the Táchira Depression, which separates the Colombian Andes from the 18.

(25) Venezuelan mountains. Traditionally, this depression has been considered an important barrier isolating avian populations (Vuilleumier & Ewert 1978), and is considered the southwestern boundary of the Meridian Montane center of endemism (Cracraft 1985).. Correlation between genetic, morphological, and climatic variation. As described above, diversification on Neotropical mountains has traditionally been explained based on the emergence of geographical barriers that produced isolation between populations. Alternatively, differentiation in montane areas may result from adaptation to different selection pressures over short distances resulting from steep elevational gradients and marked topographic complexity (Vuilleumier 1969a). Thus, if morphological variation within D. albilatera is simply a result of neutral evolution in isolation, I expected a positive correlation between genetic and morphological distances. Alternatively, if morphological changes reflect adaptation to different environments, I expected a positive correlation between morphological and “ecological” (i.e., climatic) distances. Finally, if morphological variation is explained by the degree of geographical isolation between populations, I expected a correlation between morphological and geographic distances.. My analyses did not reveal any strong correlation that would suggest that morphology varied in parallel with genetic, ecological, or geographic distance. In addition, patterns of genetic variation did not follow an isolation-by-distance scenario. Although geographic isolation has led to genetic differentiation in some cases (e.g. the 19.

(26) Sierra Nevada de Santa Marta are genetically distinct from others), I attribute the lack of any correlation that would support the alternative differentiation scenarios described above to the rather recent origin of Colombian populations of D. albilatera, accompanied by high levels of gene flow. Such a shallow history and high gene flow, coupled with the limited geographic variation in morphology, suggest simply that the species might have had insufficient isolation and time to diversify morphologically within Colombia. However, if selection pressures are sufficiently strong, morphological differentiation can occur rapidly, and even in the face of gene flow (Smith et al. 2005), which suggests that geographic variation in selective pressures across the range of D. albilatera might be limited. Extending geographical coverage for morphological, ecological, and molecular analyses to include populations over the entire distributional range of the species will allow me to better understand the role of geographic isolation and ecological adaptation in creating morphological variation within D. albilatera, if, in fact, such variation exists.. Acknowledgements: I first want to thank Carlos Daniel Cadena for giving me the opportunity to work with him, for always believing in me and in my abilities, and for his unconditional support, advice, and teachings during the development of this project. I thank Jorge Pérez Emán for his advice and comments, and for providing DNA samples from Venezuelan populations. Thanks to Andrés Cuervo for his appropriate comments about morphological analyses and for taking the photographs of LSUMZ specimens; J. V. Remsen authorized the access to these specimens. William Mauck kindly shared unpublished sequences and allowed me to see his unpublished manuscript describing 20.

(27) his approach to characterizing bill morphology in Diglossa, which was of great help. Juan Armando Sánchez helped me using computer programs for morphological measurements, and Orlando Martinez helped me with statistical analyses. The Instituto Alexander von Humboldt (especially Juan Diego Palacio, Javier Maldonado, Ana Maria Umaña, and Socorro Sierra) provided tissue and skin samples, and allowed me to visit their ornithological collection. The Instituto de Ciencias Naturales (especially Gary Stiles) allowed me to access the ornithological collection to take morphological measurements and toe pad samples. The Instituto de Genética de Poblaciones at Universidad de los Andes allowed me to use laboratory equipments and reagents. I want to thank my laboratory partners for their support, especially Eugenio Valderrama, Francisco Velasquez, and Korik Vargas for helping me with molecular procedures, to Carlos Pedraza for his help with maps and climatic analyses, to Jorge Enrique Avendaño for providing tissue and blood samples, and to Juan Pablo Gómez for his constant support during this process. I want to especially thank Enrique Villarraga for his initial support and his inestimable teachings. Financial support for this project was provided by the Facultad de Ciencias, Universidad de los Andes. It is important to thank my friends, especially Juan David Ramirez, Victoria Rodriguez, Angela Sánchez, and Diana García, and all the people that decided to accompany me in this incredible adventure. Finally, I want to acknowledge to the most important people in my life: thanks to my family for their unconditional support in every aspect and for always believing in me.. 21.

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(35) Figures and tables Table 1. Summary of results of one-way ANOVAs testing for differences among populations in each morphological variable. Hook length (HL), hook angle (HA), maxilla height (MH), hook depth (HD), tooth depth (TD), concavity (CO), total culmen (TC), exposed culmen (EC), bill width (BW), bill height (BH), tarsus length (T), wing length (W), and tail length (TL). Significant differences among populations (p≤0.05) are indicated in gray and with asterisk (*).. Variable HL HA MH HD TD CO TC EC BW BH T W TL. Females. Males. * * *. *. * * * *. 29.

(36) Table 2. Summary of results of discriminant function analysis based on morphological variables per region-slope. Numbers and percentages (in parenthesis) of correctly classified cases are shown for each pair of regions. Values in bold indicate correctly classified cases. Eastern Cordillera Eastern slope (E-E), Western slope (E-W). Central Cordillera Eastern slope (C-E), Western slope (C-W). Western Cordillera Eastern slope (W-E), Western slope (W-W). Sierra Nevada de Santa Marta (SNSM). Serranía del Perijá (SP). Serranía Yariguíes (SY). In general, correct classification according to regions is poor, especially for males.. Region slope Females E-E E-W C-E C-W. E-E. E-W. 9 (64.3) 3 (27.3) 1 (14.3). 1 (7.1) 4 (36.4) 1 (14.3). C-E. 1 (9.1) 5 (71.4). W-E W-W SNSM SP Males E-E E-W C-E C-W W-E W-W SNSM SY. Total. Predicted group. 1 (10) 1 (16.7). 9 (42.9) 4 (13.8) 2 (9.5) 1 (9.1) 4 (57.1) 3 (42.9) 4 (26.7). 3 (20.0). W-E. W-W. SNSM. 2 (14.3). 1 (7.1). 1 (7.1) 3 (27.3). 9 (90). 1 (10). 2 (100). 1 (10) 1 (16.7). 1 (16.7). 7 (33.3) 19 (65.5) 5 (23.8) 3 (27.3) 1 (14.2). C-W. 3 (14.3) 2 (6.9) 11 (52.4) 2 (18.2) 1 (14.3) 1 (14.3) 3 (20.0). 1 (4.8) 1 (3.4) 1 (4.8) 3 (27.3). 8 (80). 1 (3.4). 1 (14.3). 1 (4.8) 1 (9.1). 3 (50). SY 14 (100) 11 (100) 7 (100) 10 (100) 2 (100) 10 (100) 6 (100) 1 (100). 1 (100). 1 (4.8) 2 (6.9) 1 (4.8) 1 (9.1). 3 (42.9) 5 (33.3). 30. SP. 1 (100). 21 (100) 29 (100) 21 (100) 11 (100) 7 (100) 7 (100) 15 (100) 1 (100).

(37) Table 3. Summary of results of discriminant function analysis based on climatic variables per region-slope. Percentages (in parenthesis) and numbers of correctly classified cases are showed for each pair of regions. Values in bold are correctly classified cases (see Table 2 for abbreviations).. Regions lope E-E E-W C-E. E-E. E-W. 8 (72.7) 2 (9.5). 3 (27.3) 17 (81). C-W W-E W-W SNSM SP. Total. Predicted group C-E. 6 (66.7) 2 (25). C-W. 1 (4.8) 3 (33.3) 5 (62.5) 1 (33.3). W-E. W-W. SNSM. SP. SY. 1 (4.8) 1 (12.5) 6 (100). 1 (33.3). 2 (66.7) 2 (66.7). SY. 31. 1 (100). 1 (100). 11 (100) 21 (100) 9 (100) 8 (100) 6 (100) 3 (100) 3 (100) 1 (100) 1 (100).

(38) Figures Figure 1. Map of the distribution range of D. albilatera (black line) and D. venezuelensis (yellow area). Dots indicates localities for which I gathered molecular (red) and morphological (blue) data.. 32.

(39) Figure 2. Morphological measurements used to characterize the bill morphology of specimens of D. albilatera (image taken from Mauck 2007). Landmark 1 (LM1) is the anterior edge of the nostril, (LM2) is the first distal tooth, and LM3 is the end of the hook. Line 1 was drawn perpendicular to the union of the maxilla and mandible. Line 2 starts at LM3 and is perpendicular to Line 1. Line 3 is as an extension of the maxillary tomium. Maxilla height (MH) was measured from the ventral to the dorsal edges of the maxilla along line 1. Hook depth (HD) is the distance between the ventral edge of maxilla and line 2 along line 1. Tooth depth (TD) is the perpendicular distance from LM2 to line 2. Hook length (HL) is the distance between LM2 and LM3. The hook angle (HA) is the angle described by the HL and line 3 (Rojas-Nossa 2007). Concavity (CO) is the difference between TD and HD (see Mauck 2007 for more details).. 33.

(40) Figure 3. Phylogram showing relationships among haplotypes of D. albilatera and D. venezuelensis from different geographic regions inferred by the maximum likelihood method. Numbers on each node are Bayesian probabilities, and Maximum likelihood and Maximum Parsimony bootstrap values, respectively. Numbers for a particular terminal identify the individual specimens with a particular haplotype (Appendix 1); more than one number per terminal indicates the haplotype was shared by two or more individuals. Haplotypes are associated with the regions where they occur (see Table 2 for abbreviations).. 47_. 34.

(41) Figure 4. Haplotype network showing relations among haplotypes, with colors corresponding to different regions. The size of the circle is proportional to the number of individuals sharing a particular haplotype; black dots represent unsampled or extinct haplotypes. Note the grouping of a haplotype from the Venezuelan side of Serranía del Perijá with haplotypes from the Eastern cordillera.. 35.

(42) Figure 5. Probability distributions of gene flow between pairs of population estimated by MDIV. (a) Eastern cordillera and Central cordillera, (b) Eastern cordillera and Western cordillera, (c) Central cordillera and Western cordillera, and (d) Eastern cordillera and Serranía del Perijá. The insets show the same graphs extending to 10 migrants per generation.. (a). (b). 36.

(43) (c). (d). 37.

(44) Figure 6. Mismatch distributions showing the frequency of pairwise differences for (a) Colombian Andean clade and (b) Venezuelan Andean clade. The hatched lines represent observed frequencies and the continuous lines represent expected frequencies assuming a population of constant size. Data differ from the null (i.e. constant population) expectation when R2<0.05 and when Fu-D and Fu-F are negative and significant.. (a). (b). 38.

(45) Figure 7. Results of Mantel tests showing no correlation between geographic and genetic distance for (a) Colombian and (b) Venezuelan populations.. (a). (b). 39.

(46) Figure 8: Results of discriminant function analyses based morphological variables for females (a) and males (b) classified by region and slope, showing little correspondence between geographic provenance and morphology. Each axis shows the statistics associated with the corresponding function (see Table 2 for abbreviations).. (a). (b). 40.

(47) Figure 9: Results of discriminant function analysis based climatic variables classified by region and slope, showing little correspondence between geographic provenance and climatic variation. Each axis shows the statistics associated with the corresponding function (see Table 2 for abbreviations).. 41.

(48) Figure 10: Result of Mantel test showing weak or no correlations between (a) morphological and genetic data including males and females; morphological and climatic data for (b) females, and (c) males; morphological and geographic data including males and females for (d) Eastern Cordillera, (e) Central Cordillera, and (f) Western Cordillera.. (a). (b). 42.

(49) (c). (d). 43.

(50) (e). (f). 44.

(51) Appendix 1: Information about specimens and localities for 60 individuals included in the molecular study. Column “No.” identifies samples in figure 3. Museum abbreviations are: IAvH-BT (Instituto Alexander on Humboldt, tissue collection), IAvH (Instituto Alexander von Humboldt, skin collection), ANDES-BT (Museo de Historia Natural, Universidad de los Andes, tissue colection), ICN (Instituto de Ciencias Naturales, Universidad Nacional de Colombia), COP (Colección Ornitológica Phelps. No. 1. Museum number/ Source IAvH-BT 28. 2. Specie. Country. Department. Municipio. Locality. Latitude. Longitude. D. albilatera. Marker conditio n Partial. Colombia. Herrán. P.N.N. Tamá. Sector Orocué. 7,42528. -72,444. IAvH-BT 45. D. albilatera. Complete. Colombia. Herrán. P.N.N. Tamá. Sector Orocué. 7,42528. -72,444. 3. IAvH-BT 476. D. albilatera. Complete. Colombia. Norte de Santander Norte de Santander Magdalena. Santa Marta. 10,8097. -73,696. 4. IAvH-BT 477. D. albilatera. Complete. Colombia. Magdalena. Santa Marta. San Lorenzo. Sierra Nevada de santa Marta San Lorenzo, Sierra Nevada de santa Marta. 10,8097. -73,696. 5. IAvH-BT 1139. D. albilatera. Complete. Colombia. Boyacá. 5,71978. -73,561. 6 7. IAvH-BT 1141 IAvH-BT 1673. D. albilatera D. albilatera. Complete Complete. Colombia Colombia. Cundinamarca Norte de Santander. Villa de Leyva Bojacá Cucutilla. 4,65074 7,51823. -74,478 -72,69. 8. IAvH-BT 1776. D. albilatera. Complete. Colombia. Norte de Santander. Cucutilla. 7,51823. -72,69. 9. IAvH-BT 1864. D. albilatera. Complete. Colombia. Caldas. Neira. 5,21983. -75,404. 10. IAvH-BT 2329. D. albilatera. Partial. Colombia. Huila. Saladoblanco. 2,01037. -76,05. 11. IAvH-BT 4000. D. albilatera. Partial. Colombia. Risaralda. Pueblo Rico. 5,23511. -76,03. 45. Finca Macanal Carrizal. Sector Sisavita. 'Cuenca del Río Salinas, 45 min al SW de Cucutilla por la cuenca del río Cucutilla hasta el final de la carretera y a partir de allí 40 min a pié hacia W Carrizal. Sector Sisavita. 'Cuenca del Río Salinas, 45 min al SW de Cucutilla por la cuenca del río Cucutilla hasta el final de la carretera y a partir de allí 40 min a pié hacia W Vereda la Cristalina Finca La Estrella. Cuenca Alta del río Tapias PNN Puracé. Caserio El Palmar, PNN Puracé, Sector Granates La Cumbre. PNN Tatáma.

(52) 12 13 14. IAvH-BT 4037 IAvH-BT 4073 IAvH-BT 4167. D. albilatera D. albilatera D. albilatera. Complete Complete Complete. Colombia Colombia Colombia. Risaralda Risaralda Boyacá. Pueblo Rico Pueblo Rico Villa de LeyvaArcabuco Santa Rosa de Cabal Pensilvania Pensilvania. 15. IAvH-BT 4490. D. albilatera. Complete. Colombia. Risaralda. 16 17 18. IAvH-BT 4567 IAvH-BT 4637 IAvH-BT 5177. D. albilatera D. albilatera D. albilatera. Complete Partial Partial. Colombia Colombia Colombia. Caldas Caldas Antioquia. 19. IAvH-BT 5206. D. albilatera. Partial. Colombia. Antioquia. 20. IAvH-BT 6719. D. albilatera. Complete. Colombia. Cundinamarca. Fusagasuga. 21 22 23 24 25. IAvH-BT 6786 IAvH-BT 6803 IAvH-BT 6815 ANDES BT 018 ANDES BT-270. D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera. Complete Complete Complete Complete Complete. Colombia Colombia Colombia Colombia Colombia. Cundinamarca Cundinamarca Cundinamarca Cundinamarca Magdalena. Bojacá Bojacá Bojacá Bogota Santa Marta. 26. ANDES BT-278. D. albilatera. Complete. Colombia. Magdalena. Santa Marta. 27. ANDES BT(JEAC)-528. D. albilatera. Complete. Colombia. Santander. Piedecuesta. 28 29. ICN 33730 ICN 33928. D. albilatera D. albilatera. Partial Complete. Colombia Colombia. Pasto Herrán. 30. ICN 35941. D. albilatera. Complete. Colombia. Nariño Norte de Santander Cauca. Argelia. 31. ICN 35942. D. albilatera. Partial. Colombia. Cauca. Argelia. 32. ICN 35928. D. albilatera. Partial. Colombia. Cauca. Argelia. 33. ICN 36244. D. albilatera. Complete. Colombia. Cesar. 34. ICN 36456. D. albilatera. Complete. Colombia. Santander. Manaure Balcón del Cesar Piedecuesta. 35 36. IAvH 7214 IAvH 10699. D. albilatera D. albilatera. Complete Partial. Colombia Colombia. Cauca Norte de Santander. La Romelia Herrán. 46. La Cumbre. PNN Tatáma La Cumbre. PNN Tatáma Capilla y Monte Suárez. SFF Iguaque. 5,23511 5,23511 5,71978. -76,03 -76,03 -73,561. La Linda. Parque Municipal de Campoalegre Berlín Berlín Campo 1: Cordillera occidental: Páramo Frontino Campo 2: Cordillera occidental: Páramo Frontino Los Robles. Insp Policia La Aguadita, La Carbonera Fute. San Cayetano Fute. San Cayetano Fute. San Cayetano Universidad de los Andes San Lorenzo, Sierra Nevada de santa Marta San Lorenzo, Sierra Nevada de santa Marta Planadas. Sector España, Finca Jericó, margen izquierdo quebrada la Honda SFF Galeras. Vertiente Galeras P.N.N. Tamá. Sector Orocué. 4,87035. -75,625. 5,38613 5,38613 6,45. -75,161 -75,161 -76,083. 6,43333. -76,083. 4,33257. -74,363. 4,73346 4,73346 4,73346 4,60987 10,8097. -74,35 -74,35 -74,35 -74,082 -73,696. 11,1082. -74,073. 7,01364. -72,969. 1,21 7,42528. -77,36 -72,444. Serranía del Pincha. Cerro La Soledad Serranía del Pincha. Cerro La Soledad Serranía del Pincha. Cerro La soledad Vda. El Cinco. Finca Raul Barrera. 2,26389. -77,319. 2,26389. -77,319. 2,26389. -77,319. 10,3899. -73,03. Planadas. Sector España, Finca Jericó, margen izquierdo quebrada la Honda PNN Munchique PNN Tamá. Sector Orocué.. 7,01364. -72,969. 2,544 7,42528. -76,94 -72,444.

(53) 37. IAvH 11664. D. albilatera. Complete. Colombia. Boyacá. 38 39. IAvH 11677 IAvH 11843. D. albilatera D. albilatera. Partial Complete. Colombia Colombia. Cundinamarca Caldas. 40. IAvH 12226. D. albilatera. Complete. Colombia. Boyacá. 41 42 43 44 45 46 47 48 49 50 51 52 53 54. JM-470/ COP JM-501/ COP JM-505/ COP 07N0358/ COP 07N0362 / COP 07N0380/ COP 07N0415/ COP 07N0460/ COP IC-848/ COP IC-870/ COP IC-871/ COP IC-872/ COP IC-887/ COP DV1/ COP. Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete Complete complete. Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela. Dinira Dinira Dinira Merida Merida Mérida Mérida Mérida Zulia Zulia Zulia Zulia Zulia Anzoátegui. 55. DV2/ COP. complete. Venezuela. 56. DV3/ COP. D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. albilatera D. venezuelensi s D. venezuelensi s D. venezuelensi s D. albilatera. complete. D. venezuelensi s D. venezuelensi s D. lafresnayii. 57 58. AMNH_ DOT2892/ W. Mauck COP81847/ W. Mauck. 59. COP81846/ W.Mauck. 60. AF006229/GBNCBI. Villa de Leyva Bojacá Neira. SFF Iguaque. 5,63333. -73,483. Finca Macanal Vereda la Cristalina Finca La Estrella. Cuenca Alta del río Tapias SFF Iguaque. 4,65074 5,21983. -74,478 -75,404. 5,68833. -73,451. Sierra Nevada de Merida Sierra Nevada de Merida Sierra de la Culata Sierra Nevada de Merida Sierra de la Culata Sierra de Perijá Sierra de Perijá Sierra de Perijá Sierra de Perijá Sierra de Perijá El Guamal. 9,71967 9,71967 9,71967 8,63043 8,63043 8,70527 8,70527 8,70527 10,3265 10,3265 10,3265 10,3265 10,3265 10,0248. -70,043 -70,043 -70,043 -71,041 -71,041 -71,35 -71,35 -71,35 -72,591 -72,591 -72,591 -72,591 -72,591 -64,13. Anzoátegui. El Guamal. 10,0248. -64,13. Venezuela. Anzoátegui. El Guamal. 10,0248. -64,13. Complete. Venezuela. Aragua. Km 40. on El Junquito/Col. Tovar road.. 10,3333. -67,667. Complete. Venezuela. Anzoátegui. Serranía del Turimiquire, Cerro La Launa (El Guamal). 10,0248. -64,13. Complete. Venezuela. Anzoátegui. Serranía del Turimiquire, Cerro La Launa (El Guamal). 10,0248. -64,13. Complete. Perú. Cajamarca. Cerro Chinguela, 5km NE Sapalache. Villa de Leyva Lara Lara Lara. 47.

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