Evolutionary patterns of diversification in the Andean hummingbird genus Adelomyia

Molecular Phylogenetics and Evolution 60 (2011) 207–218

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Evolutionary patterns of diversification in the Andean hummingbird genus Adelomyia Jaime A. Chaves ⇑, Thomas B. Smith Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, 619 Charles E. Young Dr. South, La Kretz Hall, Suite 300, Los Angeles, CA 90095-1496, USA Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, CA 90095-1496, USA

a r t i c l e

i n f o

Article history: Received 20 July 2010 Revised 8 April 2011 Accepted 11 April 2011 Available online 4 May 2011 Keywords: Phylogenetics Morphology Biogeography Speckled hummingbird Andes Remote sensing

a b s t r a c t The patterns of genetic diversity and morphological variation are of central importance in understanding the evolutionary process that drive diversification. We use molecular, morphological, and ecological data to explore the influence of geography and ecology in promoting speciation in the widespread Andean hummingbird genus Adelomyia. Six monophyletic clades were recovered which show distributional limits at well-defined geographic barriers. Percentage sequence divergence ranged between 5.8% and 8.2% between phylogroups separated by large (>4000 km) and small (<50 km) distances respectively, suggesting that geographic isolation may be influential at very different scales. We show that morphological traits in independent phylogroups are more related to environmental heterogeneity than to geographic barriers. We provide a molecular reconstruction of relationships within Adelomyia and recommend its use in future comparative studies of historical biogeography and diversification in the Andes. Ó 2011 Elsevier Inc. All rights reserved.

1. Introduction The Andes of South America are important hotspots of avian cladogenesis (Fjeldså, 1994; Bates and Zink, 1994; Arctander and Fjeldså, 1994; Roy et al., 1997; Bleiweiss, 1998a,b; García-Moreno et al., 1999), where geographic isolation and complex environmental gradients are thought to have played important roles in the diversification of birds in the Andes (Mayr, 1942; Chesser and Zink, 1994; Fjeldså, 1994; García-Moreno and Fjeldså, 2000; Dingle et al., 2006; Brumfield and Edwards, 2007; Milá et al., 2009). Of the many avian groups found in the Andes, hummingbirds represent one of the most spectacular examples of radiations with more than 80% of the ca. 350 hummingbird species living along midmontane Andean slopes (Schuchmann, 1999; Bleiweiss, 1998a). A detailed analysis by McGuire et al. (2007) suggested that early hummingbird assemblages originated in the lowlands of South America and through repeated invasions of the Andes resulted in complex patterns of dispersal and vicariance that ultimately shaped the diversity of the group. While such interspecific analyses are valuable to understand diversification, few studies have examined the patterns and processes at the intraspecific level. Intraspecific studies that explore the geographic distributions of lineages and fitness-related traits can be particularly valuable in ⇑ Corresponding author. Fax: +1 310 8255446. E-mail addresses: [email protected] (J.A. Chaves), [email protected] (T.B. Smith). 1055-7903/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ympev.2011.04.007

understanding the proximate mechanisms driving diversification (Moritz et al., 2000; Puorto et al., 2001; Nicholls and Austin, 2005; Smith et al., 2005; Schneider and Moritz, 1999; Clegg et al., 2002; Chaves et al., 2007). While some studies of Andean hummingbirds have addressed the diversifying effects of dispersal (García-Moreno et al., 1999), natural selection (Chaves et al., 2007), and sexual selection (Parra et al., 2009), a complete picture of the diversification of the group has been hampered by the lack of broad geographic sampling (particularly from Colombia). This has limited the ability to fully resolve phylogeographic patterns and understand the evolutionary processes that have given rise to the high biodiversity. Fortunately, the recent availability of genetic material from Colombia is beginning to change this and acquiring a clearer picture of the biogeography of Andean taxa now becomes possible. The objectives of this study are to examine genetic and phenotypic variation in the speckled hummingbird (Adelomyia melanogenys) throughout its range, and attempt to relate patterns of variation to geographic and environmental features. The speckled hummingbird (A. melanogenys) is confined to the Andes of Argentina, Bolivia, Peru, Ecuador, Colombia and Venezuela and a few isolated montane forests in western Ecuador and Venezuela. This broad geographic and ecologic distribution makes it an excellent study system in which to examine patterns of genetic and phenotypic variation. Based on plumage coloration and biogeography, eight subspecies have been described (Adelomyia melanogenys melanogenys, inornata, connectens, maculata, aenosticta, chlorospila,

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debellardiana and cervina) (Fjeldså and Krabbe, 1990; Schuchmann, 1999; Hostos and Chinchilla, 1994). Molecular studies based on mitochondrial sequences (García-Moreno and Fjeldså, 2000; Chaves et al., 2007) reveal significant among-population variation in Ecuador and Bolivia, but because the sampling was limited, it remains unclear whether groupings represent single or multiple lineages. Although phenotypic variation is relatively subtle across thousands of kilometers along the Andes, biogeographic features such as river valleys and other geologic features tend to correspond with taxonomic classifications. However, despite the apparent influence of biogeographic breaks, morphological studies on fitness-related traits, particularly bill length, have supported a role for ecology over vicariance in shaping phenotypic variation (Chaves et al., 2007). In this paper we expanded our analyses across the entire Andes to test if different phenotypes are indeed correlated with specific environmental conditions in order to gain a better understanding of how environmental features shape morphological variation in Adelomyia across ecological gradients, and explore the proximate mechanisms that generate and maintain intraspecific adaptive variation. Our goals in this paper are fourfold: (1) to describe the distribution of genetic variation across the landscape, (2) to examine if lineages are consistent with current taxonomic designations, (3) to explore if genetic limits coincide with geographic barriers, and (4) to determine to what extent morphologic variation in these groups are associated with ecology. 2. Material and methods 2.1. Genetic sampling Genetic samples were obtained from multiple field trips (2005– 2007) to Andean countries and from museum collections in the US and Colombia. Geographic sampling consisted of 44 sites (Fig. 1) covering most of the range of the species, including the countries of Venezuela, Colombia, Ecuador, Peru and Bolivia, and each currently recognized subspecies (Table S1). Field capture and tissue sample protocols are described elsewhere (Chaves et al., 2007). We generated sequence data from 130 individuals of A. melanogenys and one sample of the hummingbird Aglaiocercus kingi as outgroup. Outgroup selection was guided by the analyses of multiple genes for nearly all genera in the Trochilidae (Altshuler et al., 2004; McGuire et al., 2007). To explain the variation in this complex, we defined ‘‘phylogroups’’, as genetically distinct, reciprocally monophyletic geographic subdivisions of a species (Avise and Walker, 1998; Avise et al., 1998) with good support values (>95% ML bootstrap and 1.0 Bayesian PP) (Rissler and Apodaca, 2007). 2.2. DNA amplification, sequencing, and aligning Genomic DNA was extracted from blood, feathers or tissue using a Dneasy Tissue extraction kit (Qiagen Inc.). We amplified and sequenced the two subunits of the ATPase mitochondrial gene (ATPase 6 and 8: 852 bp-treated as one gene from herein) for all our 130 samples. We increased the sample size to 162 with another 32 unique sequences from Ecuadorian sites (see Chaves et al., 2007 Acc. Nos. EF028323–EF028292). Based on this preliminary analysis we selected a subset of 36 individuals representing each monophyletic lineage of A. melanogenys maximizing geographic distribution within each clade (Fig. 3). For this subset, we sequenced two additional mitochondrial genes, cytochrome b (Cytb: 1143 bp) and NADH dehydrogenase subunit 2 (ND2: 1041 bp), and three autosomal nuclear introns, transforming growth factor b-2 intron 5 (TGFb2.5: 575 bp), Beta Fibrinogen

intron 7 (bfib7: 1037 bp) and Ornithine Decarboxylase intron 6 (OD6: 335 bp). PCR amplification was completed following previous established protocols for this species (Chaves et al., 2007), newly designed primers for this study and previously published primer pairs (Table S4). When multiple bands persisted, target bands were isolated and purified using a Zymoclean Gel DNA recovery kit (Zymo Research). Single PCR products were treated with shrimp phosphatase and exonuclease (exoSAPit) (Affymetrix) to degrade unincorporated primers and dNTP’s. Purified PCR products were sequenced directly using Big Dye Terminator sequencing reaction mix following manufacturer’s protocols (Applied Biosystems) with the corresponding primers. Sequencing reaction products were resolved on an ABI 3730 automated sequencer. All sequences are deposited in GenBank (Table S1). Data from heavy and light strands were assembled to obtain a consensus sequence for each sample using Sequencher 4.2.2 (Gene Codes Corporation 2004). For mtDNA sequences, we assessed its mitochondrial origin by examining sequence electropherograms for unexpected insertions/deletions, unique features of nuclear copies (i.e. double peaks), frameshifts or stop codons using Genious Pro 4.5.4 (Biomatters Ltd. 2009). Alleles or haplotypes of nuclear sequence origin were separated in heterozygous individuals using PHASE 2.1.1 (Stephens and Donnelly, 2003) to facilitate our analyses of population structure based on nuclear loci (Harrigan et al., 2008). Given the sexual monomorphism of this species and in particular for the morphological analysis, we genetically sexed our individuals using the methodology and primers described in Chaves et al. (2007). 2.3. Model selection and phylogenetic reconstruction The best-fitting models of molecular evolution were determined for each data partition out of 88 possible models with JModeltest v0.1.0. (Posada, 2008) via the Akaike Information Criterion (AIC, Burnham and Anderson, 2002). Phylogenetic reconstruction was conducted using maximum parsimony (MP) and maximum likelihood (ML) performed in PAUP v4b10 (Swofford, 2000), and Bayesian (BA) inference in MrBayes v3.1.2 (Ronquist and Huelsenbeck, 2003). MP analyses were performed on each marker independently as well as for the concatenated mtDNA dataset as heuristic searches with stepwise random addition of taxa with the TBR (tree-bisection-reconnection) branch-swapping algorithm with all characters equally weighted. The stability of each branch was determined using the nonparametric bootstrap (Felsenstein, 1985), with 1000 replicates and 100 random taxon additions. ML analyses were conducted using PAUP under the appropriate model parameter values obtained from JModelTest for the concatenated mtDNA dataset (GTR+C+I). We assessed clade support via 100 bootstrap pseudoreplicates, with an initial tree generated by neighbor joining. BA analyses were conducted in MrBayes, with a mixed-model with a partition by gene assigning independent models of evolution to each partition (gene). All parameters were unlinked between partitions, except topology and branch lengths on the mtDNA extended dataset. This procedure improves resolution of nodes deeper in the tree and has been shown to be a more effective than a single model of nucleotide evolution imposed on datasets of differentially evolving markers (Brandley et al., 2005). Analyses consisted of two runs of four simultaneous Markov chains each for 30 million generations, sampling a tree every 1000 generations and applying a 25% burn-in after checking for convergence using TRACER v1.4 (Rambaut and Drummond, 2007) and AWTY (Nylander et al., 2008) to confirm that the standard deviation of split frequencies approached zero. The resulting trees were kept to calculate posterior probabilities in a 50% majority-rule consensus tree.

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b

a

Clade E

43 42

debellardiana

38

aenosticta

Clade F 39

Barquisimeto Depression

cervina

Táchira Depression

34

36 33

connectens

29

28

27 26 25

Clade C maculata Colombia 15 14 13 12

Río Marañón Valley (North Peruvian Low)

Clade D melanogenys cervina connectens debellardiana

Colombia 24 23 22 Inter-Andean Valley 21 20 Ecuador

17 16

Inter-Andean Valley

Bucaramanga Fault

Venezuela 30

melanogenys

Ecuador

Barquisimeto Depression

31

Venezuela

maculata

41 40

37

35

32

44

aenosticta

19 18

Río Marañón Valley (North Peruvian Low)

11 10

8

Peru

Peru 9

melanogenys 7

Bolivia

6

Clade B melanogenys “chlorospila”

Bolivia

“chlorospila” Vilcanota Cordillera

5

inornata

Vilcanota Cordillera 4

Clade A inornata

3 2

1

500 km

500 km

Fig. 1. Geographic distribution and sampling localities of Adelomyia melanogenys. (a) Subspecies groups (names highlighted in gray) after Fjeldså and Krabbe (1990), Schuchmann (1999) and Hostos and Chinchilla (1994). (b) Proposed phylogroups (Clades A–F) based on our molecular data with their corresponding subspecific designations complemented with ranges from museum data. Arrows depict geographic barriers for dispersal and numbered dots refer to sampling sites as discussed in the paper. Dashed lines correspond to suspected range limit from which samples are missing.

2.4. Genetic structure and sequence divergence in ATPase Genetic structure analysis was performed on our largest dataset of mtDNA sequences from 132 individuals since we sequenced all available samples for ATPase. To evaluate the amount of genetic structure among A. melanogenys lineages, we conducted an analysis of molecular variance (AMOVA) by testing different population associations using the program Arlequin v3.0 (Excoffier et al., 2005). To identify larger-scale genetic populations, we grouped sampling sites into several biogeographic categories (e.g. Andes of Bolivia, Peru, east and west Ecuador, coast Ecuador Andes Colombia, Andes Venezuela, coast Venezuela) and tried several grouping combinations in order to maximize among-group variance (i.e. Uct-values). Those groupings that maximized values of Uct after 1000 random permutations of the DNA sequences were assumed to reflect the most probable geographical subdivisions (Excoffier et al., 1992).

To calculate corrected sequence divergence between clades, we used the number of nucleotide changes between haplotypes (Dxy) taking into account intraclade polymorphism (Nei, 1987), so that Dxy = dixy 0.5(dix + diy), where x and y are the groups being compared and di is uncorrected average genetic distance (Wilson et al., 1985). Sequence divergence was calculated using Arlequin v3.0 (Excoffier et al., 2005) for both the complete ATPase dataset and for the concatenate mtDNA dataset separately (Table 2). 2.5. Multilocus phylogeny and species-tree in Adelomyia Since the common approach of concatenating sequences from multiple genes of different nature (mtDNA vs nuDNA) in multigene phylogenetics may result in poor estimates and highly variable species-trees (Degnan and Rosenberg, 2006; Kubatko and Degnan, 2007; Via, 2009), we used the Bayesian phylogenetic analysis employing the coestimation of multiple individual gene trees

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embedded in a shared species-tree using BEAST (Heled and Drummond, 2010). Terminal taxa have to be defined a priori when implementing BEAST representing every possibly independently evolving lineage (i.e. population lineage). Often mtDNA lineages are good for this purpose because mtDNA evolves quickly, revealing more terminal lineages than nuDNA (McCormack et al., 2010). Here, we combined this approach with other assessments to guide delimitation of phylogroups for BEAST. First, we used the AMOVA results supporting a geographic clustering in six well-defined biogeographic categories, which agreed with mtDNA phylogroups. Second, we used highly supported clades in all phylogenetic reconstructions, and third, we checked nuclear loci for variation and additional phylogenetic structure within each of the ‘‘mtDNAdelimited terminal taxa’’. Finding none (i.e. there was no further structure towards the tips of the tree in nuDNA compared to mtDNA lineages), we concluded that the geographically-based mtDNA lineages represent the most fine-scale splitting of terminal taxa relevant to the present data. We implemented BEAST by importing the mtDNA concatenated dataset and then adding the phased nuclear sequences and ran the program for 100 million generations with a Yule Process tree prior and sampled every 10,000 generations. The mtDNA data were partitioned into three codon positions, and a relaxed uncorrelated lognormal clock was applied to this gene tree. We used the GTR+C+I model for the concatenated mtDNA and the pre-determined models of evolution for each intron. A consensus species-tree with its corresponding posterior probabilities was obtained from the 10000 generated trees, after discarding the first 2500 as burn-in. Results for the BEAST analyses were obtained by combining posterior samples from four independent chains. Again, convergence was assessed using Tracer and AWTY. We also examined if topologies guided by mtDNA results were best supported (%) when using all the available data using TreeLogAnalyser v.1.6.0 (Drummond and Rambaut, 2007). To visualize and interpreter the multiple species-trees generated in BEAST we used DensiTree v.1.37 (Bouckaert, 2010) where the majority of the trees that agree on the topology and branch length are densely shaded instead of lines in one graph.

2.7. Environmental data and regression models For each georeferenced sampling location, we determined environmental characteristics using ground-based measurements of temperature and precipitation (Worldclim data base (Hijmans et al., 2004)), as well as space-based observations of topography (Farr and Kobrick, 2000) and vegetation (Myneni et al., 2002) (Table S2). We used principal component (PC) analysis to reduce these variables using our georeferenced sampling sites corresponding to each of the phylogroups (Clade A: 28 sites, Clade B: 19 sites, Clade C-coast: 13 sites, Clade C-Andes: 29 sites, Clade D: 69 sites, and Clade E: 13 sites). To determine whether separation in environmental and thus ecological space was statistically significant, we performed a multivariate analysis of variance (MANOVA) with PC scores as the dependent variable and phylogroup as the fixed factor. Post-hoc Bonferroni’s correction for multiple comparisons were used to determine whether phylogroups differed in environmental space. To ensure that spatial patterns in morphological data were not due to similarity of habitat characteristics at sites in close proximity to one another, we ran a series of spatial autocorrelation analyses (see Supporting Information S5 and Fig. S6). As little to no spatial autocorrelation was detected in any of our morphological dataset, we did not consider autocorrelation as a potential bias in our results. To identify the relationships between morphological variation and ecological characteristics, we ran stepwise multiple regression, using the DFA morphological factors (DFA 1 and DFA 2) as response variables, and the remotely sensed variables as predictors, within the R statistical framework (R Development Core Team). To avoid over-fitting (given the large number of ecological variables we had to select from), we used the Akaike Information Criteria (AIC, Akaike, 1974) to select the simplest models that explained the maximum variation observed in DFA scores. 3. Results 3.1. Sequence variation

2.6. Morphological variation We assessed morphological variation from a total of 189 adult hummingbirds mist-netted in the field. Five measurements were all taken by JAC using dial calipers to an accuracy of 0.1 mm: culmen length from the anterior end of the nares to the tip of the upper mandible; exposed culmen from the base of the bill to the tip of the upper mandible; bill width and depth at the anterior end of the nares; and tail length from the base of the uropygial gland to the tip of the longest rectrix. Wing length was estimated by measuring the unflattened wing chord with a wing ruler to the nearest 0.5 mm. In addition, body mass was recorded using an Acculab Digital Scale. We defined groups guided by our phylogenetic analysis but kept the Clade C corresponding to the maculata subspecies separated into Andean populations and coastal Ecuador populations as suggested by Chaves et al. (2007). Morphological data were tested for normality before statistical analyses and log transformed when needed. Principal component (PC) analysis of the covariance matrix was used to examine size and shape variation. A canonical discriminant function analysis (DFA) was performed to predict group membership based on the PCA results. We did not obtained morphologic data from Clade F, so we excluded this phylogroup from our morphologic and remote sensing analysis. Because sexual dimorphism is evident in this group and larger sample sizes for males were available, analyses were done on males only. All statistical analyses were performed in SPSS 11.0 (SPSS, Inc., Chicago, IL).

We generated ATPase sequence data for 130 individuals representing 107 unique haplotypes from a total of 44 sites (Table S1). The presence of a conserved reading frame in our mtDNA protein-coding genes and the absence of extra stop codons, frameshifts, or unusual amino acid substitutions among all taxa suggest that our DNA sequences were consistent with mitochondrial origin (Mindell et al., 1997; Sorenson and Quinn, 1998). Based on the previous selected subset of individuals from the ATPase dataset (Fig. 3), we generated sequences for 35 Adelomyia samples and one A. kingi sample for each mtDNA (cytochrome b, ND2) and nuDNA marker (26 samples for bfib7, 26 for TGFb2.5F, and 33 for OD6). The combined sequences of the three-mtDNA markers created a dataset of 3036 bp, whereas the total of three nuclear introns combined created a dataset of 1947 bp. The number of parsimony-informative characters for the analyzed genes were: 132 for ATPase, 166 for cytochrome b, 186 for ND2, 137 for bfib7, 5 for TGFb2.5, and 4 for OD6. 3.2. Genetic differentiation, phylogeny and biogeography All methods of phylogenetic inference identified the same phylogroups within Adelomyia corresponding to the six reciprocally monophyletic clades; therefore we only present Bayesian (combined mtDNA: Fig. 2) and ML topologies (ATPase: Fig. 3). We found the best-fit model to be TIM2 + I for ATPase, HKY+C for Cytb and TIM3+C+I for ND2. GTR+C+I model of evolution best

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2 Clade B: Samples for this clade corresponded to individuals found from the southeastern Andes of Peru from Cuzco (Cordillera Vilcanota) along the eastern slope to the northeastern Andes of Peru (Marañón River Valley) in San Martín. This phylogroup corresponds geographically to the subspecific designation melanogenys and chlorospila recognized by Fjeldså and Krabbe (1990), Schuchmann (1999) and Zimmer (1951) (Fig. 1b sites 4–8; Fig. 2). 3 Clade C: This clade included populations from the northwestern Andes of Peru, western Andes of Ecuador, as well as populations from the isolated coastal Cordillera of Chongón-Colonche in Ecuador. This phylogroup corresponds geographically to the subspecific designation maculata recognized by Fjeldså and Krabbe (1990) and Schuchmann (1999) (Fig. 1b sites 9–17; Fig. 2). 4 Clade D: This clade corresponded to populations from the eastern cordillera of Ecuador (including samples from Cordillera del Cóndor and Cutucú) to southeastern Colombia (Caquetá), western and central Andes of Colombia, northern Andes of Colombia and Venezuela (Serranía del Perijá), and Andes Venezuela (Fig. 2; Fig. 1b sites 18 to 41 [except 35 and 36 see Clade F]). This phylogroup corresponds geographically to the subspecific designations melanogenys (Fjeldså and Krabbe, 1990; Schuchmann, 1999), cervina, connectens (Meyer de Schauensee, 1945), and debellardiana (Hostos and Chinchilla, 1994).

fit the concatenated mtDNA dataset. The best fit-model for intron bfib7 was TrN + I, HKY for TGFb2.5 and F81 for OD6. Among several possible groupings performed in the AMOVA, the highest Uct value was obtained for the same cluster of phylogroups recovered from our phylogenetic analysis (k = 6 groupings; 82.1% of variance explained) (Table 1). Sequence divergence among Adelomyia phylogroups ranged from 3.6% to 7.6% in the ATPase dataset, whereas in the concatenated mtDNA dataset sequence divergence ranged from 2.2% to 8.2% (Table 2). The largest values in both data sets were between Clade A and the rest of the northern groups such as Clade F (8.2%), Clade D (7.2%), Clade E (6.8%) and Clade B (6.8%). Based on these combined molecular analysis, A. melanogenys consist of six well-differentiated phylogroups (Fig. 1b). Here we delineate individual phylogroups based on their degree of molecular differentiation and geographically disjunct distributional areas: 1 Clade A: All analyses found the phylogroup from the Central Andes in Bolivia and southern Peru monophyletic (Fig. 1b sites 1–3; Fig. 2) with high nodal support and sister to the in-group (five nested northern clades) (Fig. 2). This phylogroup corresponds geographically to the subspecific designation inornata recognized by Fjeldså and Krabbe (1990) and Schuchmann (1999).

1.00/100/100

Clade D melanogenys cervina connectens debellardiana

1.00/100/100

78/58/1.00/83/97 1.00/100/100

Clade E 1.00/100/100

aenosticta

?

Clade F ?

1.00/100/100 1.00/100/100

74/68/73

Clade C maculata

1.00/100/100

1.00/100/100

Clade B “chlorospila” melanogenys

1.00/99/100

1.00/100/100

Clade A inornata 1.00/100/99

0.01 substitution/site Fig. 2. Bayesian tree for speckled hummingbird concatenated mtDNA haplotypes showing MRBAYES posterior probabilities, ML boostrap scores and MP bootstrap. Values in bold indicate support for the six phylogroups depicted to the right (Clades A–F) with corresponding subspecies as in Fig. 1. Question mark at nodes denotes the lack of support.

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West Andes Ecuador (Pichincha, Azuay, Loja) North Andes Perú (NW of Marañón) (Cajamarca, La Libertad) (n=14)

Clade C maculata

Coastal Ecuador (Guayas) (n=9) North, central and south Andes Perú (SE of Marañón) (San Martín, Huánuco, Pasco, Cuzco) (n=14)

Clade B “chlorospila” melanogenys

East Andes Ecuador (Cutucú-Morona Santiago, Napo) (n=13)

East Andes Ecuador (Cóndor-Zamora Chinchipe) (n=2) West Andes Colombia (Risaralda, Antioquia, Valle Cauca) Andes Venezuela (Lara, Mérida) (n=9) West Andes Colombia ( Valle Cauca) Andes Venezuela (Lara, Mérida) (n=5) Andes Venezuela (Perijá-Zulia, Mérida, Lara, Barinas) East noth Andes Colombia (N.Santander) and Central Andes Colombia (Huila, Cundinamarca) (n=13)

Clade D melanogenys cervina connectens debellardiana

South and Central Andes Colombia (Huila, Risaralda, Caquetá, Caldas) (n=6) Coastal Cordillera Venezuela (Miranda), Aroa (n=3) Coastal Cordillera Venezuela (Miranda) Serranía San Luis (Falcón), Aroa ( Yaracuy) (n=5)

Clade E aenosticta

East central Andes Colombia (Santander) (n=4)

Clade F

Andes Bolivia (La Paz, Cochabamba) (n=10)

Clade A inornata

Fig. 3. Maximum-likelihood phylogenetic tree for the extended data set (n = 190) of mtDNA gene ATPAase. Nodal support represented by asterisks (: 70–90% bootstrap, : >90% bootstrap). Clades and corresponding subspecies as in Figs. 1 and 2. Geographic information for each clade corresponds to general geographic location, country in bold followed by Provinces and Departments with sample sizes.

Table 1 Analysis of molecular variance (AMOVA) to explore geographical subdivision in populations of Adelomyia melanogenys based on mtDNA ATPase. Letter in brackets correspond to geographic location: B: Bolivia; P: Peru; WA: West Andes; CE: Coast Ecuador; EA: East Andes (Ecuador); WAC: West Andes Colombia; CAC: Central Andes Colombia; S: East Andes Colombia-Santander; AV: Andes Venezuela; CV: Coast Venezuela. Population groupings

UST

UCT

% Total variation

Sign

All [CE + WA][P + CV + EA + CAC + AV + WAC + S + B] [CE + WA + P + CV + EA + CAC + AV + WAC + S][B] [CE][WA + P][B][S][EA + CAC + AV + WAC][CV] [CE + WA + P][B][S + EA + CAC + AV + WAC][CV] [CE + WA + P][B][S][EA + CAC + AV + WAC][CV] [CE][WA][P][B][CV][EA + CAC + AV + WAC][S] [CE][WA][[P][B]EA][CV][CAC + AV + WAC][S] [CE][P][B][WA][EA][CV][AV][WAC][CAC][S] [CE + WA][B][P][EA + WAC + CAC + AV][S][CV]

0.83754 0.88026 0.91102 0.87173 0.88102 0.88006 0.86788 0.86310 0.86197 0.9292

– 0.31455 0.40037 0.46699 0.48861 0.50405 0.72970 0.78980 0.80963 0.82098

– 31.46 40.04 46.70 48.86 50.40 72.97 78.98 80.96 82.10

<0.001 <0.5 <0.08 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

5 Clade E: Samples for this clade corresponded to coastal cordilleras from Venezuela such as Serranía de San Luis, Aroa and Cordillera de la Costa (Fig. 2; Fig. 1b sites 42–44). This phylogroup corresponds geographically to the subspecific designation aenosticta (Fjeldså and Krabbe, 1990; Schuchmann, 1999).

6 Clade F: This clade included individuals from the central region of the east Andes of Colombia (Santander), corresponding geographically to melanogenys subspecies recognized by Fjeldså and Krabbe (1990) and Schuchmann (1999). This phylogroup is divergent and unique in its genetic

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Table 2 Corrected average pairwise difference between phylogroups for ATPase (852 bp) (above diagonal) and for the extended mtDNA dataset (3036 bp) (below diagonal). Percentage corrected sequence divergence in parenthesis for each phylogroup.

Clade Clade Clade Clade Clade Clade

B D E B F A

Clade B

Clade D

Clade E

Clade B

Clade F

Clade A

– 109.7 (3.6%) 100.8 (3.3%) 68.1 (2.2%) 149.7 (4.9%) 191.1 (6.2%)

57.6 (6.7%) – 127 (4.1%) 142.8 (4.7%) 178 (5.8%) 219.2 (7.2%)

43.2 (5.0%) 33.5 (3.9%) – 119.6 (3.9%) 162.3 (5.3%) 207.8 (6.8%)

44.5 (5.1%) 38.4 (4.5%) 35.9 (4.2%) – 164.6 (5.4%) 208.4 (6.8%)

44.3 (5.1%) 38 (4.5%) 34.4 (4.0%) 57.4 (6.7%) – 250 (8.2%)

65.3 57.6 56.7 30.8 62.5 –

composition compared to other melanogenys populations, suggesting independent evolutionary history (Fig. 2; Fig. 1b sites 35 and 36). Within each clade, we recovered shallow population structuring suggesting further intra-lineage divergence, which in most cases corresponded to well known geographic areas within each region (see Section 4). 3.3. Multilocus phylogeny and species-tree in Adelomyia Ten thousand species-trees were generated and are visualized as consensus tree in Fig. 5. One ‘‘cloudogram’’ based on 4983 base pairs was produced. The relationships among some of the lineages differed in the multilocus species-trees estimate compared with the other analyses (Fig. 4). A notable difference was the high support for a contradictory topology in the multilocus species-tree in the placement of Clade F. Nevertheless, the monophyly for most of the six groups was still supported (<85% posterior probabilities). Similarities between the multilocus species-tree and other analyses include overall support and topological placement for Clades A, D, E and sister relationship between Clades B and C (Fig. 4). 3.4. Morphological variation Morphological variation was evident among the five sampled phylogroups along the Andes. The PCA reduced six morphological measures to two components that explained 62% of the total variance. PC1 explained approximately 46% of the variance and was

Aglaiocercus kingi

Clade E

Clade D

0.99

Clade B

0.85 Clade C 0.98 Clade F

Clade A

Fig. 4. Species-tree with the highest posterior probability (PP > 80) superimposed upon a cloudogram of the entire posterior distribution of species-trees recovered in  BEAST. Areas where the majority of trees agree in topology and branch length are shown as darker areas (well-supported clades), while areas with little agreement as webs. Monophyly of Adelomyia highly supported (PP: 0.98).

(7.6%) (6.7%) (6.5%) (3.6%) (7.3%)

largely a measure of overall bill size (bill length, width and depth) whereas the second factor was determined by wing and tail (Table 3). Two canonical discriminant functions were calculated in the DFA and the major phylogroups of Adelomyia showed considerable separation when plotted in morphological space (Fig. 5). Lowlands Clade C-coast Ecuador (larger bills) and Venezuelan Clade E (smaller bills) were found at the two extremes, whereas Andean Clades A, Clade B, Clade C-Andes, and Clade D occupy the range corresponding to intermediate bill lengths. Higher-than-chance assignments (% of the individuals correctly assigned in their respective groups) were high for lowland Venezuelan Clade E (75%), Clade C-Andes of Ecuador (76%) and Clade C-coast Ecuador (90.9%). Individuals from Bolivian Clade A and Andean Clades B, C, D were not distinct from one another in morphological space, but the DFA did allow for higher assignments to their respective groups, indicating some degree of differentiation. For instance, 65.6% of the individuals were clustered correctly into Clade B but an additional 15.6% and 12.5% were miss-assigned to Clade A and Clade C-Andes. Similarly, 39.1% of individuals were correctly assigned into Clade A but an additional 30.4% were miss-assigned to Clade E. Only 10.8% of the Clade D individuals were correctly assigned into their class, whereas 35.4% and 27% of the time they were incorrectly assigned to Clade B and Clade C-Andes respectively (Table 4). 3.5. Environmental data and environmental correlates of morphological variation PC analyses indicated that phylogroups were predominately distributed in unique environmental space (Fig. 6). The 26 climatic and remotely sensed variables were reduced into three factors that explained 97% of the variance. The primary factor (PC1, 50.8%) of ecological variation is largely a measure of precipitation (i.e. annual precipitation, precipitation of the wettest month). The second most important factor (PC2, 29.3%) is determined mainly by temperature conditions (i.e. temperature seasonality, temperature annual range) and the third axis mainly by vegetation attributes (PC3, 16.7%) (Table S3). The PCA scores differed significantly between the phylogroups (MANOVA PC1: F5,171 = 33.5; p < 0.001; PC2: F5,171 = 16.9; p < 0.001; PC3: F5,171 = 88.4; p < 0.001), and post hoc test revealed both factor 1 and factor 2 were significant. Linear regression models for Adelomyia indicated that variance in morphological variation can be explained using relatively few ecological predictors. In the regression for DFA1 (a factor primarily composed of variation in bill length), 57% of variation observed was explained by three variables (Adj. R2 = 0. 574; p = 2.797e 5): the precipitation seasonality (Bio15), minimum temperature of coldest month (Bio6), and mean topography. Similarly, a regression for DFA2 (a factor composed of variation in wing and tail length) revealed as the best predictors; annual mean temperature (Bio1), mean temperature of the warmest quarter (Bio10), and overall surface moisture variation (Qscat_stdv), which together explained 39% of the variation (Adj. R2 = 0. 398; p = 0.0015).

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Table 3 DFA coefficients for morphologic traits and proportion of variance explained for each function for 189 males speckled hummingbirds. Trait

Function 1

Weight Wing Tail Culmen Exposed culmen Bill depth Bill width Variance Explained (%)

0.069 0.208 0.008 0.788 0.058 0.192 0.485 88.0

Function 2 0.226 0.052 0.679 0.481 0.039 0.228 0.680 9.8

Clade A Clade B Clade C-Andes Clade C-Coast Ecuador Clade D Clade E

5.0

DFA Score 2

2.5

0.0

-2.5

-5.0 -5.0

-2.5

0.0

2.5

5.0

DFA score 1 Fig. 5. Morphology data scatterplot of canonical DFA scores for Adelomyia melanogenys. Clade names correspond to each phylogroups as in previous figures. Individuals from Clade C were split into Andes and coastal Ecuador after Chaves et al. (2007) (see text for details). DFA coefficients for the phenotypic traits used in the analysis are found in Table 3.

4. Discussion 4.1. Do genetic data support independent evolutionary lineages? We have uncovered remarkable genetic differentiation among speckled hummingbird populations characterized by high sequence divergence and deep phylogenetic breaks between groups. These results suggest that Adelomyia phylogroups are evolving along independent trajectories. Here we describe our findings as they relate to the available information on existing taxonomic designations. A pair of Colombian subspecies suggested by Meyer de Schauensee (1945) and reported by Fjeldså and Krabbe (1990) and Schuchmann (1999) from the head of the Magdalena Valley (connectens type collected at La Candela, Huila), and from the Western and Central Andes of Colombia (Cordillera Occidental and Central, subspecies cervina) could not be recovered using molecular data (Fig. 1). The original taxonomic placement of these two subspecies was based on differences in plumage (see Gould, 1872; Meyer de Schauensee, 1945). Because we sampled these regions, we assume that representatives of these two lineages were included but that they could not be differentiated by any of our analyses. Similarly, the phylogroup from the East Central Andes of Colombia (here referred as Clade F) should correspond to melanogenys based on

geographic information (Fjeldså and Krabbe, 1990; Schuchmann, 1999). However, genetic results suggest this phylogroup is a distinct clade despite its close geographic proximity (<50 km) to other sampling sites in North Santander corresponding to Clade D (melanogenys). We found incongruence in the phylogroup corresponding to the Andes of Peru, Clade B (San Martín through Cuzco). After Fjeldså and Krabbe (1990) and Schuchmann (1999) these samples should correspond to melanogenys but our genetic dataset finds Clade B as a separate genetic entity reciprocally monophyletic with respect to the maculata group (Clade C). The high nodal probability estimated for the monophyly of this phylogroup supports a phylogeographic history in the Andes of Peru distinct from other groups. Zimmer (1951) placed the southernmost limit for melanogenys in the Urubamba Valley in Peru terminating with a hybrid zone between inornata and melanogenys, assigned as ‘‘chlorospila’’ by Chapman (1921). The author described the series of specimens from this southern tip of the distribution as ‘‘too unstable’’ to warrant any subspecific designation (Zimmer, 1951). The genetic data provided here in combination with precise locality data from the examined specimens by Zimmer, suggests our samples from near the type locality of ‘‘chlorospila’’ and the series examined by Zimmer correspond to Clade B. Of nine Peruvian specimens examined by Zimmer from ‘‘Inambari’’, some matched the inornata plumage characteristics (blue feathers on the throat), whereas others were indistinguishable from melanogenys. This was corroborated by the plumage description for ‘‘chlorospila’’, with an examination of a series from Cuzco and Madre de Dios (Field Museum Chicago) all of which lacked the inornata diagnostic blue gorget pattern. This Peruvian phylogroup appears to be a combination of current melanogenys and ‘‘chlorospila’’ forms, with an extended distribution in the eastern Andes of Peru to the north at the North Peruvian Low (NPL) and Marañón River Valley, to the Urubamba Valley and Cordillera Vilcanota in the south (Fig. 1). The southern clade (Clade A) corresponds geographically to the inornata subspecies (Fjeldså and Krabbe, 1990; Schuchmann, 1999) from Bolivia and Argentina. Nevertheless, without samples from southern Bolivia and northern Argentina, we can only place its northern limit to the Cordillera of Vilcanota in Peru (Fig. 1). As with Clade B, adding samples between Puno and Cuzco will confirm the distribution limits of Clade A in the Andes of Peru. Fjeldså and Krabbe (1990) and Schuchmann (1999) described the melanogenys subspecies as having the largest geographic distribution from the Andes of Venezuela to south Peru. Our sampled individuals corresponding to the northern distribution of melanogenys (Clade D) show strong support for its monophyly, clustering populations from southern Ecuador, the Andes of Colombia (except from Santander: Clade F) and the Andes of Venezuela (including debellardiana see below), but excluding populations from the Andes of Peru (Clade B). Despite previous subspecific designations for populations from the central and western Andes of Colombia (connectens and cervina; Meyer de Schauensee, 1945), and the marked biogeographic features of these cordilleras in Colombia (Cadena et al., 2007), we could not recover deep genetic structuring (to the level of the other phylogroups) across this vast region. This suggests reduced differentiation between montane species inhabiting the central and west Andes of Colombia (Cuervo et al., 2005; Cadena et al., 2007). Individuals clustered in Clade C matched geographically with the maculata classification proposed by Fjeldså and Krabbe (1990) and Schuchmann (1999). These populations are found exclusively in the western Andes of north Peru, western Andes of Ecuador (and probably southwestern Andes of Colombia in Nariño) and from the coastal cordillera of Chongón-Colonche in Ecuador. Our results confirmed the previously reported genetic split between the western Andean clades and the coastal lineage in

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J.A. Chaves, T.B. Smith / Molecular Phylogenetics and Evolution 60 (2011) 207–218 Table 4 DFA percentage of correctly classified cases in parenthesis and values for same phylogroup correspondence are in bold. Phylogroup

Clade Clade Clade Clade Clade Clade

Predicted group membership

A B D C-coast C-Andes E

4

Total

Clade A

Clade B

Clade D

Clade C-coast

Clade C-Andes

Clade E

9 (39.1) 5 (15.6) 13 (20) 0 (0) 0 (0) 2 (25)

5 (21.7) 21 (65.6) 23 (35.4) 0 (0) 6 (12) 0 (0)

2 2 7 0 2 0

0 (0) 0 (0) 0 (0) 10 (90.9) 4 (8) 0 (0)

0 (0) 4 (12.5) 18 (27.7) 1 (9.1) 38 (76) 0 (0)

7 0 4 0 0 6

(8.7) (6.3) (10.8) (0) (4) (0)

Bolivia

PC 2

2 1 Coastal Ecuador

Coastal Venezuela

0

23 (100) 32 (100) 65 (100) 11 (100) 50 (100) 8 (100)

distinct genetic groupings lack any taxonomic designation (Zink, 2004, 2010). This is the case for some of our recovered phylogroups in Adelomyia, in which genetic distinctiveness likely represents better the historical population subdivisions consistent with evolutionary history in this group. Although the lack of genetic concordance in mtDNA data with subspecific classifications might appear problematic, the use of existing subspecies (sensu Remsen, 2010: minimum diagnosable units) provides an important starting point to elucidate the pattern and process of diversification (Winker et al., 2007; Remsen, 2010). The genetic subdivisions presented here including a unique genetic group restricted to the Central Andes in Colombia (Clade F), will be relevant for revising the taxonomy of Adelomyia, although a detail reassessment of taxonomic considerations are beyond the scope of these analyses.

Clade A Clade B Clade C-Andes Clade C-Coast Ecuador Clade D Clade E

3

(30.4) (0) (6.2) (0) (0) (75)

-1 Central and North Andes

4.2. Do lineage limits coincide with geographic barriers?

-2 -2

-1

0

1

2

3

4

PC 1 Fig. 6. Scatter plot of the climatic and environmental space occupied by the different phylogroups of the speckled hummingbird in South America based on PC1 (50.8% variance explained) and PC2 (29.3% variance explained) scores. Symbols for each Clade as in Fig. 5.

Ecuador (Chaves et al., 2007) supporting our previous findings that Pleistocene climatic events shaped the pattern (after a 2% molecular clock (Weir and Schluter, 2008)). The final group recovered by our phylogenetic analysis corresponded to the aenosticta subspecies (Fjeldså and Krabbe, 1990; Schuchmann, 1999) from samples from the lowlands of Venezuela. In all instances, the high nodal support associated with the monophyly of Clade E suggests a unique evolutionary history linked to lower montane forests in this region. The extended mtDNA tree reconstruction found significant geographic structuring on the genetic distribution within this phylogroup corresponding to samples from the Serranía de Aroa (Yaracuy), Serranía de San Luis (Falcón) and Cordillera de la Costa (Miranda). Hostos and Chinchilla (1994) described a new subspecies Adelomyia melanogenys debellardiana from Venezuela. Specimens classified by these authors as aenosticta from Curimagua in Falcón (<5 km from our sampled sites in Serranía de San Luis) were supported in our analyses (Clade E), but other specimens described as debellardiana from Cabudare in Lara (Andes of Venezuela) and the rest of the localities examined for debellardiana in Venezuela correspond genetically to Clade D (Fig. 1). Remsen (2010) describes a subspecies as ‘‘a geographic population diagnosable by one or more phenotypic traits’’. In the speckled hummingbird, some of the phenotypic traits used to delimit subspecies may represent arbitrary geographic divisions of single character clines, as observed in other studies (Rising, 2001, 2007; Remsen, 2005; Zink, 2010). It is not unusual for subspecies limits not to correspond to genetic subdivisions based on mtDNA molecules (Zink, 2004; Smith and Filardi, 2007), and in many cases

Phylogeographic results indicated that limits for A. melanogenys clades coincide with well-defined biogeographic regions and punctuated by geographic barriers (Fig. 1b). These findings highlight the important role of Andean topography in defining the directionality of dispersal events and the role of genetic isolation between lineages. We can infer that the lineage limits between Clades A and B coincide geographically with the Cordillera of Vilcanota in south Peru. As mentioned before, to completely define the limit between these lineages, additional samples from the geographic gap in Puno are needed. Nevertheless, combining museum information and the information gathered by Zimmer (1951) we suggest the geographic limit to be at this mountain range (Fig. 1). Populations from our recovered Andean Clades B, C and D share close geographic proximity in the NPL and Marañón River Valley. However, these clades share no haplotypes between them and in all instances the phylogenetic reconstruction suggest strong lineage isolation by this geographic barrier. These results corroborate a pattern commonly found in other bird distributions at this region (Parker et al., 1985; Bates and Zink, 1994; Johnson and Jones, 2001; Miller et al., 2007; Weir, 2009). Furthermore, Clades D and C in Ecuador are separated east and west at each mountain system by the Ecuadorian Andean Valley shear, a pattern previously reported by Chaves et al. (2007). The question whether the split of the two cordilleras in Ecuador geographically isolated these populations, or directional dispersal events around the NPL to each mountain range took place after the valley was created needs further examination. Clade F corresponds geographically to the western slope of the Eastern Cordillera in Colombia, where the northernmost Andes bifurcate in two ranges. The major geographic break takes place along the Bucaramanga Fault (Cooper et al., 1995) closely linked to the uplift of the Eastern Cordillera (Irving, 1975; Villamil, 1999). Our two sampling localities are found on the western side of the Bucaramanga Fault, but given the lack of sampled sites south of these localities, we suspect the southern limit to correspond to another tectonic element the Boyacá Fault (Fig. 1b). The

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incomplete genetic data from this region limit our comparisons of the effect of these orogenies in avian diversification. Nevertheless, as more data becomes available from Colombia, these geologic barriers might be found to be important in affecting genetic structure in other taxa with similar distributions. The Venezuelan Clade E is isolated to lowland montane regions and separated from highland forms in the Andes (Clade D) by the Barquisimeto Depression (BD). This geographic feature marks the northern limit of Clade E reported to be an important barrier in other avian taxa (Buarremon torquatus: Cadena et al., 2007). Within this Venezuelan phylogroup, the recovered populations corresponded to Serranía de Aroa, San Luis and the Cordillera de la Costa (Fig. 2) (see next). The six major Adelomyia phylogroups have apparently maintained their structure through time, with little or no mixing, despite likely expansions and contraction of their ranges. Using a rough estimate of divergence time (2% molecular clock (Weir and Schluter, 2008)), most intraclade structure can be attributed to Pleistocene times. This pattern is consistent with other Andean birds showing genetic differentiation in response to lowering montane forest zones and glaciated high elevations of the Andes (Weir, 2009). This is evident in Clade C between Andean and coastal individuals (Chaves et al., 2007), and in Clade D between samples from the eastern Andes of Ecuador and northern Andes in Colombia and Venezuela (Fig. 2). The genetic divergence in the eastern Andes in Clade D could also be attributed to a series of low-elevation montane passes near the Rio Caquetá, which have been hypothesized to influence other bird species’ genetic distributions (Weir, 2009). Contrary to the reciprocally monophyly reported in the concatenated mtDNA tree in coastal Venezuelan sequences coinciding with defined biogeographic regions (Clade E: Fig. 2), this pattern was not recovered in the ATPase dataset (Fig. 3). Although we take the concatenated mtDNA results to better represent phylogeographic associations, we suggest future sampling in Serranía de Aroa, San Luis and the Cordillera de la Costa to fully understand patterns of diversification in this region. Several prominent barriers along the Andes known to greatly affect population structure in Andean birds were not found to impact lineage differentiation in Adelomyia, including the Táchira Depression separating the east Colombian and Venezuelan Andes, and the Huallaga and Apurimac Rivers in Peru. Perhaps not surprising, these barriers did not affect genetic patterns in the same fashion across the studied bird groups (Weir, 2009). As might also be the case in Adelomyia, these results suggest differential permeability of these barriers at different geologic times, as well as pointing out different dispersal abilities across these groups. Together, these findings suggest a protracted history of dispersal and vicariance in Adelomyia that should further be explored by accurately dating phylogroups’ origination events and time of barrier formation. 4.3. Evidence for a role of ecology in shaping morphology across phylogroups The different environmental conditions experienced by geographically isolated populations can be the drivers of divergence (Orr and Smith, 1998; Schluter, 2001; Funk et al., 2006). This seems to be the case in Adelomyia. We have shown that genetic groups are found in allopatry separated by defined geographic barriers, that most of the phylogroups inhabit unique environments, and that the morphological variation is not driven by distance alone. In addition we found that a model comprised of precipitation, temperature and topography variables was the best predictor of variation in morphology, primarily bill length. Under this scenario, two lines of evidence suggest that vicariance might not fully explain divergence in morphology and point to a role of ecology in morphologic differentiation in A. melanogenys. First, Central and

Northern Andes lineages (Clade B, Clade D and Clade C-Andes) presented incorrect groupings after the DFA despite being geographically isolated by the Río Marañón and Inter-Andean Valleys. These results are not surprising given Chaves et al. (2007) showed that Clade C-Andes (western cordillera) and Clade D (eastern cordillera) from Ecuador, isolated for more than 2 My, are similar in these same traits, suggesting the absence of divergent selection. Second, the high differentiation in bill length (DF1) between Bolivian Clade A, Clade C-coast of Ecuador and lowland Venezuelan Clade E could be an indication that these populations are found in different environments, where differential selection regimes might operate to shape bill characteristics (Chaves et al., 2007). Our analysis separated these phylogroups based on their climatic and environmental space. For example, the change in overall climatic patterns around the geographic limit between Andean Clade B and Bolivian Clade A is also the limit between very different ecological zones (Peruvian and Bolivian Yungas ecoregions as defined by WWF). The ecological boundary between these ecoregions could be placed around the Rio Inambari or at the Cordillera of Vilcanota (Fig. 1) reported to affect species distributional shifts and to influence high degree of endemism in birds (Stattersfield et al., 1998; Fjeldså et al., 1999). The steep mountain ridges in this region create a geographic pattern of climatic stability (‘‘mini-refugia’’) by blocking southern cold winds known to affect vegetation and climatic patterns in the lowlands and lower Andean slopes of Bolivia and southern Peru (Fjeldså et al., 1999). This is further corroborated by the species distribution-modeling study by Buermann et al. (2008) showing a drastic change in temperature seasonality from low (<10 °C) to high (up to 30 °C) values as populations of Adelomyia move from tropical to extratropical regions in the southern range. Nevertheless, direct measurements of floral resources and interspecific competition would be needed in each of these regions to fully understand the cause of morphological differentiation. 4.4. Multilocus phylogeny and species-tree in Adelomyia The contradictory placement of Clade F and Clade A (low posterior probabilities) in the multilocus species-trees from BEAST compared to the concatenated mtDNA tree is not surprising. Different topological differences revealed by mtDNA and multilocus species-trees are not uncommon and does not imply misleading information contained in other markers (McCormack et al., 2010). Complete concordance among mtDNA and nuDNA trees is rare even among avian lineages considered good species, and in most of the reported cases it is believed that these conflicts are the result of variation in lineage sorting rates, introgression, sex-biased dispersal or gene flow (Edwards et al., 2005; Brumfield et al., 2008; Zink and Barrowclough, 2008; Maddison and Knowles, 2006; McCormack et al., 2010). Further exploration of these events will provide a better understanding of their influence on species-tree estimation. While we acknowledge the difficulty discerning between these aspects with our data, we suggest that our structured mtDNA tree likely reflects the history of lineage divergence and geographic isolation. 5. Conclusions The present study offers novel insights into the phylogeographic structure of a broadly distributed Andean hummingbird, A. melanogenys. Results show contrasting and complex patterns of Andean topology and lineage formation in which lineages separated by more than 4000 km (Clade A and Clade E) show 8.2% sequence divergence, whereas those separated by only 50 km (Clade F and Clade D), show sequence divergence of 5.8%. These results emphasize the spatial complexity of geography on genetic

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diversification. Morphological analysis suggests that despite deep genetic lineages, ecology nevertheless shapes phenotypic characters of Adelomyia populations. The combination of multilocus phylogenies, phenotypic and environmental data proved to be a useful tool in informing patterns of diversification in this group, and a promising starting point for testing of alternative hypotheses of diversification in other Andean taxa. Disclosure statement The authors disclose no conflict of interest. Acknowledgments We are grateful to the nations of Bolivia, Peru and Venezuela for allowing us to conduct our research. For help in the field we gratefully acknowledge the assistance of José Hidalgo, Esteban Neumann, Mauricio Ugarte Lewis, Oscar Gonzalez, Jhonathan Miranda, Maya Yanover, Vanesa Serrudo, Matthew Gracey, Eberth Ledezma, Magaly Acuña and Fernando Barrantes. R. Harrigan, H. Thomassen and J. McCormack provided comments on earlier drafts of the manuscript and W. Buermann provided information on the environmental layers. We would like to thank the curators at the following museums for access to specimens and permits: The Louisiana State University Museum of Natural Science (J.V. Remsen, Robb Brumfield and Donna Dittmann), The Humboldt Institute in Colombia (Diana López), Museo Nacional de Historia Natural in Bolivia (Isabel Gomez) and finally to the Colección Ornitológica W.H. Phelps in Venezuela (Jorge Pérez). Methods for sampling were approved by UCLA’s Animal Research Protocol (A3196-01). Funding was made possible through support from: The Miguel Velez Endowment Fund, Sigma Xi, The Explorers Club, UCLA Latin American Institute, Lida Scott Brown Fund, Frank M. Chapman Memorial Fund-American Museum of Natural History, and Grants from NSF (IRCEB9977072) and NASA (IDS/03-0169-0347) to TBS. We acknowledge two anonymous referees and the editor Irby Lovette who greatly improved this manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ympev.2011.04.007. References Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723. Altshuler, D.L., Dudley, R., McGuire, J.A., 2004. Resolution of a Paradox: hummingbird flight at high elevation does not come without a cost. Proc. Natl. Acad. Sci. USA 101, 17731–17736. Arctander, P., Fjeldså, J., 1994. Andean tapaculos of the genus Scytalopus (Aves, Rhinocryptidae): a study of speciation using DNA sequence data. In: Loeschcke, V., Tomiuk, J., Jain, S.K. (Eds.), Conservation Genetics. Birkhäuser Verlag, Basel, Switzerland, pp. 205–225. Avise, J.C., Walker, D., 1998. Pleistocene phylogeographic effects on avian populations and the speciation process. Proc. Roy. Soc. Lond. Ser. B 265, 457– 463. Avise, J.C., Walker, D., Johns, G.C., 1998. Speciation durations and pleistocene effects on vertebrate phylogeography. Proc. Roy. Soc. Lond. Ser. B 265, 1707–1712. Bates, J., Zink, R.M., 1994. Evolution into the Andes: molecular evidence for species relationships in the genus Leptopogon. Auk 111, 507–515. Bleiweiss, R., 1998a. Tempo and mode of hummingbird evolution. Biol. J. Linn. Soc. 65, 63–76. Bleiweiss, R., 1998b. Origins of hummingbird faunas. Biol. J. Linn. Soc. 65, 77–97. Bouckaert, R., 2010. DensiTree: Making Sense of Sets of Phylogenetic Trees. Bioinformatics Advance Access. Oxford University Press. Brandley, M.C., Schmitzm, A., Teeder, T.W., 2005. Partitioned Bayesian analyses, partition choice, and the phylogenetic relationships of scincid lizards. Syst. Biol. 54, 373–390. Brumfield, R.T., Edwards, S.V., 2007. Evolution into and out of the Andes: a Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367.

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