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Molecular Phylogenetics and Evolution xxx (2007) xxx–xxxwww.elsevier.com/locate/ympev

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The role of geography and ecology in shaping the phylogeography of the speckled hummingbird (Adelomyia melanogenys) in Ecuador

Jaime A. Chaves a,b,c,¤, John P. Pollinger b, Thomas B. Smith b, Gretchen LeBuhn a

a Department of Biology, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94132, USAb Department of Ecology and Evolutionary Biology and Center for Tropical Research, Institute of the Environment, University of California,

Los Angeles, 619 Charles E. Young Drive East, Los Angeles, CA 90095-1496, USAc Fundación Numashir para la Conservación de Ecosistemas Amenazados, Casilla Postal 17-12-122, Quito, Ecuador

Received 16 May 2006; revised 31 October 2006; accepted 3 November 2006

Abstract

The Andes of South America contain one of the richest avifaunas in the world, but little is known about how this diversity arises andis maintained. Variation in mitochondrial DNA and morphology within the speckled hummingbird (Adelomyia melanogenys) was used toelucidate the phylogeographic pattern along an Ecuadorian elevational gradient, from the coastal cordillera to the inland Andean mon-tane region. We examined sequence, climatic/remote sensing and morphological data to understand the eVects of topography and ecologyon patterns of variation. Populations on either side of the Andes are genetically divergent and were separated during a period that corre-sponds to the Wnal stages of Andean uplift during the Pliocene. Despite isolation, these two populations were found to be morphologicallysimilar suggesting a strong eVect of stabilizing selection across ecologically similar Andean cloud forests, as assessed using climatic andremote sensing data. In contrast, little genetic divergence was found between coastal and west-Andean individuals, suggesting recentinterruption of gene Xow between these localities. However, coastal populations were found to inhabit diVerent habitats compared toAndean populations as shown by climatic and remote sensing variables. Furthermore, coastal individuals had signiWcantly longer billscompared to their montane relatives, indicative of diVerential directional selection and the inXuence of habitat diVerences in shaping phe-notypic variation. Results highlight the role of both isolation and ecology in diversiWcation in Ecuadorian montane regions, while sug-gesting the two may not always act in concert to produce divergence in adaptive traits.© 2006 Elsevier Inc. All rights reserved.

Keywords: Ecuador; Andes; Morphology; Phylogeography; Speckled hummingbird; Remote sensing; Directional selection

1. Introduction

Neotropical biogeographic and systematic studies havecontributed signiWcantly to our understanding of bioticdiversiWcation (Vuilleumier, 1970; Cracraft and Prum,1988; García-Moreno et al., 2001; Ribas and Miyaki, 2004).This research has yielded important information aboutpossible mechanisms of speciation including the roles ofrefugial isolation, riverine barriers, marine incursion andecological gradients (Bush, 1994; HaVer, 1997; Patton andda Silva, 1998; Moritz et al., 2000; Willmott et al., 2001;

* Corresponding author. Fax: +310 825 5446.E-mail address: [email protected] (J.A. Chaves).

1055-7903/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.ympev.2006.11.006

Please cite this article in press as: Chaves, J.A. et al., The role of geohummingbird (Adelomyia melanogenys) in Ecuador, Mol. Phylogen

Aleixo, 2004; Bates et al., 2004; Hall, 2005a). Several studieshave demonstrated that montane regions are importantareas for speciation through periodic isolation of smallallopatric demes (Patton and Smith, 1992; Fjeldså, 1994;but see Hall, 2005a). In particular, the Andes in SouthAmerica have long been identiWed as an important regionof avian diversiWcation where both dispersal and vicariancemechanisms were inXuenced during periods of active orog-eny and climate changes in the Late Pliocene–Pleistocene(Fjeldså, 1994; Bates and Zink, 1994; Arctander andFjeldså, 1994; Roy et al., 1997; Bleiweiss, 1998a,b; García-Moreno et al., 1999a; García-Moreno et al., 1999b; García-Moreno and Fjeldså, 2000; Chesser, 2000; Burns andNaoki, 2004; Pérez-Emán, 2005; Cheviron et al., 2005a).

graphy and ecology in shaping the phylogeography of the speckledet. Evol. (2007), doi:10.1016/j.ympev.2006.11.006

mailto: [email protected]

mailto: [email protected]

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Nevertheless, few studies have fully answered Vuillemier’s(1980) longstanding questions regarding where, when, andhow Andean avian speciation has occurred.

The inability of many studies to fully address thesequestions is largely due to the reliance on a singleapproach of investigation and lack of integration (seeMoritz et al., 2000). For example, a given study may drawconclusions about the cause of speciation based on geo-graphic variation in molecular genetic data while varia-tion in morphological characters important to Wtness gounsampled, and vice versa. Determining which mecha-nisms promote genetic and phenotypic diVerentiation andhow drift and selection have inXuenced the process arecentral questions (Orr and Smith, 1998; Schneider, 2000).One promising approach to elucidate the relative impor-tance of selection and drift is the use of intraspeciWc,microevolutionary patterns to infer interspeciWc patternsof diversiWcation. IntraspeciWc phylogeography relatescontemporary patterns of genetic diVerentiation amongconspeciWc populations to their geographic distribution(Avise et al., 1987). Inferences regarding the inXuence ofpast events on the process of population diVerentiation(and ultimately speciation) can provide key insights intohistorical patterns of diversiWcation (Vogler and DeSalle,1993; Ellsworth et al., 1994). Unfortunately, such intra-speciWc studies on Neotropical avifauna are few (but seeBrumWeld and Capparella, 1996; Marks et al., 2002; Ale-ixo, 2004; García-Moreno et al., 2004; Cheviron et al.,2005b), and have disregarded the complexity of the evolu-tionary process of diversiWcation.

Integration of molecular approaches with analyses ofphenotypic variation allows for a more thorough examina-tion of the processes involved in divergence and potentiallyallows one to determine the roles of selection and drift indiVerentiation (Moritz et al., 2000; Puorto et al., 2001;Nicholls and Austin, 2005; Smith et al., 2005; Schneider andMoritz, 1999; Clegg et al., 2002). It has been suggested thatgenetic drift in allopatry is unlikely by itself to lead to speci-ation without divergent natural selection (Rice and Host-ert, 1993; Coyne and Orr, 2004). Nevertheless, some studieshave de-emphasized the role of selection in promotingdivergence between allopatric populations (Peterson et al.,1999). An ideal setting for studying the role of divergentselection would be to examine both isolated populationsinhabiting the same habitat and isolated populations indiVerent habitats and ask to what extent Wtness-associatedmorphological characters are also concordant with geneticbreaks. The distributional range of the speckled humming-bird satisWes these requirements. This small, high-elevationhummingbird ranges along both the east and west sides ofthe Andes in the same cloud forest habitat as well as in theisolated lowland coastal mountains, the Chongón Colon-che cordillera in Ecuador.

In this paper, we take an integrated approach, combin-ing mtDNA across the region, remote sensing data andmultivariate analyses of morphological characters toexamine the role of ecology and isolation in leading to


diVerentiation of speckled hummingbird populations. Weexplicitly examine two possibilities of divergence:

1. Phylogeographic breaks in the gene tree correspond togeographically distinct regions (Andes and coastal), withno morphological diVerentiation. This would indicate along history of isolation and no evidence for divergenceon morphology.

2. Phylogeographic breaks in the gene tree that correspondto geographically distinct regions, which are congruentwith morphological diVerentiation. This would indicatenatural selection and/or drift playing a role in morpho-logical divergence:

(a) No pattern of morphological divergence acrosshabitats would suggest the eVect of drift.

(b) A pattern of morphological divergence acrosshabitats would suggest the eVect of directionalselection.

2. Material and methods

2.1. Study species

The speckled hummingbird (Adelomyia melanogenys) isa common and widely distributed hummingbird rangingthroughout much of the Andes Cordillera in South Amer-ica (Fjeldså and Krabbe, 1990). In Ecuador, it ranges acrossboth eastern and western slopes of the Andes and occupiesa broad altitudinal range from subtropical forests (1400 m)to cloud forests (3000 m). In addition, one isolated popula-tion inhabits the Chongón Colonche cordillera in coastalevergreen montane forest (600 m) located ca. 130 km awayfrom the Andes (Fig. 1). Two subspecies are recognized inEcuador: A. melanogenys melanogenys from the easternAndes and A. m. maculata from west of the Andes (Ridgelyand GreenWeld, 2001). Individuals from the melanogenyssubspecies are characterized by a less strongly pale patch atthe base of the outer rectrices (Zimmer, 1951) compared tothe maculata group which presents somewhat wider buVtail-tipping (Ridgely and GreenWeld, 2001). The averagecolor of the upper parts in melanogenys is darker green, lessbronzy than in maculata, and the under parts are morestrongly buVy (Zimmer, 1951).

2.2. Mitochondrial DNA sequences and molecular sexing

We obtained 842 base pairs (bp) sequence data of two par-tially overlapping mitochondrial genes ATPase 6 and 8 (ATP-ase8: 168bp and ATPase6: 684bp) using the polymerasechain reaction (PCR) from 60 speckled hummingbirds repre-senting 18 localities throughout the species range in Ecuador(Fig. 1) (GenBank Accession Nos.: EF028292–EF028323).Whole genomic DNA was extracted from blood samples orfeathers collected from live birds using a commercially avail-able kit (Qiagen™, Valencia, CA, USA). PCR product was ini-tially ampliWed using the standard ATPase primers L8929 andH9947 (Eberhard and Bermingham, 2004), but most of the


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sequences showed double peaks, suggesting nuclear inserts.To eliminate the potential to amplify nuclear inserts, we mod-iWed our protocol to Wrst amplify a larger fragment of »4kb(long range PCR) using ampliWcation primers H9947 andLS5512 (Eberhard and Bermingham, 2004). The PCR prod-uct was then cut from agarose gels and cleaned using a puriW-cation kit (MoBio UltraClean™15). The PCR product wasthen ampliWed using the internal ATPase primers L8929 andH9947 (Eberhard and Bermingham, 2004). This process elim-inated any observation of inserts as validated by absence ofdouble peaks and correct open reading frame (orf) sequence.The PCR product was cleaned and utilized in a dye termina-tor reaction (Beckman Coulter DTCS kit, Fullerton, CA) andsequenced in one direction (L8929 primer) on a BeckmanCoulter CEQ 2000XL Capillary Sequencer. Because theH9947 primer generated poor quality sequences, a primerdesigned for A. melanogenys located in the mid span of theATPase locus (Ame Middle 2F—5�TAATCTTCCTTCTCTCCATCAACC3�) was used to obtain the 3� end of theATPase 6 gene. Sequences were proofed and aligned by eyeusing the program BioEdit (Hall, 2005b). All samples arearchived at UCLA, under speciWc accession numbers. Becausethe species is sexually monomorphic, genetic sexing was per-formed for each sample using primers 2550F (Fridolfsson andEllegren, 1999) and MSZ1R (Sehgal et al., 2005).

2.3. Spatial patterns of genetic variation

We reconstructed intraspeciWc phylogenetic relation-ships among haplotypes using Bayesian inference in theprogram MrBayes 3.1.1 (Huelsenbeck and Ronquist, 2001).For this analysis, a total of 24 substitution models were


evaluated using MrModeltest 2.2 (Nylander, 2004). Thebest model, selected by the Akaike Information Criterion(AIC), was the HKY + I + G (freq. AD0.3100; freq. CD0.3598; freq. GD0.0891; freq. TD0.2411; proportion ofinvariable sitesD0.5348; � distribution shape parameterD0.6398). We ran the analysis for 2 million generationsusing four chains, sampling from the chain every 100thtrees producing 20,000 trees. We discarded the burn-in (Wrst6000 trees) and constructed a 50% majority rule consensustree using PAUP* (SwoVord, 1999). Five independent runsof the same data set with random start trees producednearly identical results. Posterior probabilities (PP) greaterthan 95% are considered signiWcant support for a clade(Ronquist and Huelsenbeck, 2003). We also ran our dataanalysis using the default parameters in MrBayes for thesame number of generations. Phylogenies were also esti-mated using maximum-parsimony (MP) and neighbor-joining (NJ) approaches as implemented in PAUP* (Swo-Vord, 1999) [heuristic searches with random stepwise addi-tion for 1000 replicates]. We determined the robustness ofthe trees found by 1000 bootstrap replications for the MPtrees and we only report clades with support of over 70%(Hillis and Bull, 1993).

To estimate population structure we conducted ananalysis of molecular variance (AMOVA) testing diVer-ent population associations using the program Arlequin2.0 (Schneider et al., 2000). To identify larger-scalegenetic populations, we grouped sampling sites intocoastal, west Andes and east Andes to maximize among-group variance (i.e. �ct -values). Those groupings thatmaximized values of �ct after 1000 random permutationsof the DNA sequences were assumed to reXect the most

Fig. 1. Distribution of the speckled hummingbird in South America (right) and Ecuador (left) showing the location of sampling sites for the phylogeo-graphic analysis. The Andes cordillera is shown where darker colors represent high elevation areas. Sites 1–9 are located in the western slope, sites 10–17 inthe eastern slope, and site 18 in the coastal region.



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probable geographical subdivisions (ExcoYer et al.,1992).

For each population we assessed haplotype diversity(Hd) (Dgene diversity) and nucleotide diversity (�) withinand between groups as well as pairwise FST values betweenregions after 1000 random permutations to test for signiW-cance (Reynolds et al., 1983). We used the violet-tailedsylph (Aglaicocercus coelestis), the closest related taxon(Altshuler et al., 2004) as an outgroup (GenBank AccessionNo.: EF028291). To determine within-clade haplotype rela-tionships we constructed a minimum-spanning networkusing the programs TCS 1.21 (Clement et al., 2000) andArlequin 2.0 (Schneider et al., 2000). Fu’s Fs-test of neutral-ity (Fu, 1997) was used to infer the population history ofthe speckled hummingbird in Ecuador. The Fs value tendsto be negative when there is an excess of recent mutations,and therefore large negative values of Fs are an indicationof deviation caused by population growth and/or selection,and evidence against population stasis (Fu, 1997).

The application of molecular clocks as a temporal com-ponent in biogeographic analyses to time cladogenesisevents have been discussed elsewhere (e.g. García-Moreno,2004; Lovette, 2004; Ho et al., 2005). With these discussionsin mind, we applied the 2% rule for the mtDNA sequencedata for the speckled hummingbird to link the concurrentgenetic breaks to known geographic events for this part ofits Andean range. Corrected and uncorrected pairwise dis-tances were calculated between clades and used as a esti-mate of divergence time. To test for rate constancy amonglineages, we calculated the likelihood scores with and with-out enforcing a molecular clock using PAUP* (SwoVord,1999) and performed a likelihood-ratio test (LRT) to com-pare both likelihoods.

2.4. Climatic and environmental data

For any given point-locality of species presence, spatiallycontinuous layers of climatic conditions as well as topogra-phy and vegetation characteristics can be obtained to inferits current environmental space. To assess these diVerencesbetween coastal and Andean sites occupied by speckledhummingbird populations, we used ground-based measure-ments of temperature and precipitation from the Worldc-lim data base (Hijmans et al., 2004) as well as space-basedobservations of topography (Farr and Kobrick, 2000) andvegetation (Myneni et al., 2002) (Appendix A).

Based on monthly climatologies from station networks,the Worldclim database provides high-resolution climatematrices (1km spatial resolution) that capture seasonal cli-mate variability and extreme or limiting climatic factors.These include matrices such as temperature of the coldestand warmest month, and precipitation of the wettest anddriest quarters (Hijmans et al., 2004).

Topographical data was received from the ShuttleRadar Topography Mission (SRTM; Farr and Kobrick,2000). For consistency with the climate layers, we aggre-gated the original 90 m SRTM gridded data to a resolution


of 1km and extracted the elevation data that correspond toour Weld sites.

In order to obtain information on spatial and temporalvariability in vegetation density at our study sites, we uti-lized satellite leaf area index (LAI) data from the moderateresolution imaging spectroradiometer (MODIS) archive(Myneni et al., 2002). LAI is deWned as one-sided green leafarea per unit ground area in the broadleaf canopies thatexist at our sites. In producing the LAI Welds, the MODISalgorithm uses principles of radiative transfer in vegetationcanopies with multi-spectral surface reXectance input datafrom the MODIS sensor (Myneni et al., 2002). SpeciWcallyfor this study, we used 1km monthly maximum compositesbased on a 4-year period of MODIS LAI data (2001–2004),and computed LAI matrices that correspond to vegetationextremes and variability. LAI maximum compositinggreatly reduces interferences of persistent cloud cover thatis common at our study sites. To gain information on spa-tial diVerences in vegetation structure at our Weld sites, wealso used the percentage tree cover MODIS product at 1kmresolution (Hansen et al., 2002).

We used principal components analysis (PCA) to reducethese variables from 25 georeferencing points from our 18sampling site: 12 in the west Andes, 11 in the east Andesand 3 in the coastal range. We added 8 new georeferencedsites from the coastal region in areas where bird-samplingwas not performed to increase sample size for this analysis.These new sites were selected in areas corresponding to val-ues that predict a high probability for species presence pro-duced by a parallel analysis using maximum entropyapproach (MAXENT) based on climatic and ecologicalvariables (Buermann et al., in prep.). To determine whetherseparation in environmental space was statistically diVerentwe performed a multivariate analysis of variance(MANOVA) with a post-hoc Bonferroni correction formultiple comparisons using “region” as a Wxed factor andPCA axis scores as dependent variables.

2.5. Spatial patterns of morphological variation

We obtained body measurements from mist-netted hum-mingbirds, using only adult individuals in the analysis. Fivemeasurements were taken using dial calipers to an accuracyof 0.1 mm: culmen length (from the anterior end of the nos-tril to the tip of the upper mandible), exposed culmen (fromthe base of the bill to the tip of the upper mandible), billwidth of the upper mandible (at the anterior end of the nos-trils), bill depth (at the anterior end of the nostrils), and taillength (from the base of the uropygial gland to the tip ofthe longest rectrix). Wing length was estimated by measur-ing the unXattened wing chord with a wing ruler to thenearest 0.5 mm. In addition, body mass was recorded usingan Acculab Digital Scale. This last measurement was dis-carded from our analysis because it varied at diVerent timesof day in recaptured individuals. Because diVerent observ-ers took samples from diVerent years, we corrected for mea-surement variability. To do so, each observer measured



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each character on the same individual bird in the Weld(nD 10). We then performed a paired samples t-test to lookfor diVerences of each trait among observers and correctedthe “raw” data by adding or subtracting the diVerence inindividual mean measurements. Morphological data werelog-transformed and tested for normality before statisticalanalyses. Principal component analysis (PCA) was used toexamine size and shape variation. A discriminant functionanalysis (DFA) was performed to predict group member-ship at each region. To control for the eVects of body sizeon morphological traits, we used a general linear model(GLM) to generate adjusted marginal trait means with“region” as the Wxed factor, PC 1 (a “size” factor) as covar-iate to control for shape variation due to body size (i.e. mul-tivariate allometry) (Langerhans et al., 2003), and aBonferroni correction for multiple comparisons. We con-ducted the analyses for males (nD134) and females (nD88) separately using SPSS 11.0 (SPSS, Inc., Chicago IL).

3. Results

3.1. Spatial patterns of genetic variation

We obtained sequences for 60 individuals of A. melanog-enys, ranging from 13 to 30 individuals per population,which contained 74 variables sites of the 842 bp of the ATP-ase 6 and 8 genes analyzed. Thirty-two haplotypes weredeWned for the Ecuadorian populations, haplotype andnucleotide diversity values are summarized in Table 1. Hap-lotype diversity was high at each region but average nucleo-tide diversity was higher in the east Andes population by anorder of magnitude.

Trees produced with MP, NJ and Bayesian analysis hadsimilar topologies and clustered sequences into two well-supported clades corresponding to distinct east and westEcuador with coastal individuals forming a clade with thewestern Andean populations (Fig. 2). We found that impos-ing a molecular clock on the data did not change the taxonrelationships, although the ATPase sequences were foundnot to evolve in clocklike fashion (�2D 51.118, dfD 31,p < 0.01). Average pairwise genetic distances between cladesfrom the likelihood analysis were used only as a heuristicestimate of divergence time between regions. These resultssuggest that the split between east and west Andes (0.063uncorrected and 0.097 corrected distances) may haveoccurred approximately 3.2 and 4.8 myrBP (Mid and EarlyPliocene) using the commonly applied average rate of 2%per million years for mtDNA in birds (Lovette and Ber-mingham, 1999). Furthermore, the split between the coastal


clade and west Andes may have occurred approximately 1.1and 1.4 myrBP during the Pleistocene (0.023 uncorrectedand 0.028 corrected distances). Although the ATPasesequences were found not to evolve in clocklike fashionthese distances are in concordance with the suggestion thatthe split between these populations may have been inXu-enced by the orogenies of the Andes during the Plioceneand climatic Xuctuations in the Quaternary (see Section 4).

The highest �ct value was obtained for the three-regionseparation [C] [W] [E] among several possible groupings per-formed in the analysis (Table 2). Correspondingly, haplo-types were clustered into three main networks divided by 45mutations between east and coastal networks, and 14 muta-tions between coastal haplotypes and the west Andean net-work. The ATPase 6 and 8 haplotypes A and Bpredominated in the west Andes populations, with otherhaplotypes appearing in lower frequencies. Haplotype E wasfound to be more frequent in east Andes with several low fre-quency haplotypes separated by one, two or four base pairs(Fig. 3). All of the haplotypes found in either side of theAndes were found only in these regions. High-frequency hap-lotype S was found in the coastal population accompaniedby several low-frequency ones diVering by few base pairs.

High values of FST were found between the coast and theAndes (0.889; p<0.001 west and 0.8658; p<0.001 east), andbetween west and east Andes (0.904; p<0.001) suggesting aneVect of vicariance in population diVerentiation. Furthermore,Fu’s Fs-test of neutrality, based on 1000 simulated samplings,was signiWcant at the 5% level (FsD¡3.776; pD0.015) for thecoastal population as well as for the west Andean population(FsD¡4.237; pD0.014) an indication of population expan-sion in both scenarios. The Fs value of the east Andean popu-lation was still negative (FsD¡1.486; pD0.224) but was notsigniWcantly diVerent, suggesting a tendency for populationgrowth (Fu, 1997).

3.2. Climatic and environmental data

PCA results revealed that the primary factor (PC1,45.2%) of ecological variation in the sampling sites is domi-nated by temperature and vegetation density variables (i.e.annual mean temperature, mean temperature of warmestand coldest month, dry and wet season vegetation cover).The second most important factor (PC2, 22.9%) isdetermined mainly by precipitation conditions (i.e. annualprecipitation, precipitation in wettest and warmest quarter)and tree cover. The third axis (PC3, 13.6%) is determinedmainly by vegetation density metrics (i.e. annual maxand min, annual mean-NDVI) of the sampling sites

Table 1Summary of mitochondrial DNA diversity for samples of speckled hummingbird in Ecuadorian populations

Values are given § standard deviation.

Population Sample size Haplotypes Polymorphic sites Haplotype diversity (Hb) Nucleotide diversity (�£ 100)

Coast 13 9 11 0.8718§ 0.0913 0.3310§ 0.2097West Andes 30 11 14 0.7885§ 0.0551 0.2358§ 0.1520East Andes 19 12 71 0.9064§ 0.0596 1.0955§ 0.5884



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(Appendix B). The separation of the coastal and Andeansites by MANOVA was highly signiWcant, showing climateand environmental diVerences based on PC scores (PC1:F3,34D235.6; p < 0.001; PC2: F3,34D 3.19; p < 0.5). No signiW-cant diVerences in PC1 were found between Andean sites

Table 2Results from AMOVA for each grouping in diVerent regions after 1000permutations

The three regions are the coastal (C), west (WA) and east Andes (EA)clades as seen in Fig. 2. The three regions were compared individually andthen paired to test for population subdivision. Among group (�ct), withinpopulations (�st), and among populations within groups variance (�sc).

Regions % Varianceexplained

�SC �ST �ct SigniWcance level

[C] [WA] [EA] 88.60 0.09685 0.89706 0.88602 p < 0.001[C + WA] [EA] 78.90 0.60990 0.91768 0.78898 p < 0.001[C + EA] [WA] 55.14 0.76188 0.89318 0.55143 p < 0.001[C] [EA + WA] 12.96 0.85296 0.87203 0.12964 NS


after the post-hoc test with “region” as a Wxed factor. ThePCA (Fig. 6) and MANOVA indicate that coastal andAndean hummingbird populations inhabit clearly distinctecological and climatic regions.

3.3. Spatial pattern of morphological variation

Morphological variation was similar among populationson either side of this barrier but diVered signiWcantlybetween coastal and Andean habitats. The PCA for males(nD134) reduced six morphological measures to three com-ponents that explained 75.8% of the total variance in malemorphology, and two axes were extracted in femalesexplaining 59.3% (nD88). The PC1 for both males andfemales explained approximately one-third of the varianceand was largely a measure of overall bill size (PC1, 32.5% inmales, and 35.7% in females). The second factor (PC2,23.3% in males, and 23.6% in females) is determined by

Fig. 2. Neighbor-joining tree of the ATPase 6 and 8 genes sequenced from 60 speckled hummingbird individuals. Taxon label represent unique haplotypes.Numbers above nodes indicate the support derived from 1000 MP bootstrap replicates, Bayesian posterior probabilities using the default parameters andposterior probabilities from the HKY + I + G model of evolution, only values greater than 70 are presented (¤, <70).

A. coelestis

B3

N

B2

B

M

F

A

L

W

Y

H

S

P

Z

U

R

Q

Q2

T

V

X

C

J

O

E

I

A2

E1

K

D

G

A10.005 substitutions/site

West Andes

East Andes

Coastal

100/100/100

100/100/99

92/100/*

100/100/93



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wing and tail length (Table 3). When used in a univariateANOVA, PC1 scores showed diVerences between coastaland Andean populations but no diVerences where detectedbetween Andean individuals (Table 4).

Two canonical discriminant functions were calculated inthe DFA, where PC1 presented the highest coeYcient score inFunction 1 (0.991) and PC2 in Function 2 (0.966). Further-more, PC1 scores were the best predictors for grouping indi-viduals into coastal (100% of the individuals correctlyassigned in their actual grouping) and Andean morphotypes(73.1% west and 70.7% east correctly assigned) (Table 5 andFig. 4). Size-adjusted bill length measurements were signiW-cantly diVerent in coastal and Andean birds in the GLMamong the estimated marginal means (culmen: F2,101: 34.45,p<0.001; exposed culmen: F2,101: 15.62, p<0.001) (Fig. 5),consistent with the prediction of the eVect of directional selec-tion on these traits at diVerent habitats.

Table 3Factor loadings from the principal component analysis for 134 males and88 female speckled hummingbirds

Males Females

Component

1 2 3 1 2

Wing .166 .794 .202 .446 .606Tail .008 .597 .631 .386 .788Culmen .765 ¡.296 .435 .601 ¡.418Xculmen .818 ¡.341 .260 .485 ¡.443Depth .598 .261 ¡.529 .778 ¡.228Width .558 .380 ¡.479 .771 .089% Variance explained 32.5 23.3 20.0 35.7 23.6


4. Discussion

Data suggest the presence of two reciprocally mono-phyletic mitochondrial lineages within A. melanogenys,corresponding to eastern (melanogenys) and western(maculata) subspecies. The high mtDNA divergencebetween populations on either side of the Andes cordil-

Table 4Univariate ANOVA for males with PC1 scores as dependable variablesand region as Wxed factor

¤¤, p < 0.01; NS: p > 0.05.

Region Mean diVerence (I ¡ J) Std. error p <

CoastWAndes 1.800 .276 .000**

EAndes 1.825 .284 .000**

WAndesCoast ¡1.800 .276 .000**

EAndes .025 .177 1.000 NS

EAndesCoast ¡1.825 .284 .000**

WAndes ¡.025 .177 1.000 NS

Table 5Discriminant function analysis results for male PCA scores per region

Percentage of correctly classiWed cases shown in parenthesis.

Region Predicted group membership Total

Coast WAndes EAndes

Coast 12 (100) 0 (0) 0 (0) 12 (100)WAndes 1 (1.9) 38 (73.1) 13 (25) 52 (100)EAndes 4 (9.8) 8 (19.5) 29 (70.7) 41 (100)

Fig. 3. Minimum spanning network of mtDNA ATPase 6/8 haplotypes from 60 speckled hummingbirds over a diagram of the elevational proWle of themountains of Ecuador. The numbers in each circle correspond to the sampling locations as seen in Fig. 1. Each circle represents a diVerent mtDNA haplo-type and the size of the circles is proportional to the number of individuals sharing that particular haplotype. Haplotypes diVer from each other by one ormore nucleotides represented by hash marks across network branches.



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lera and high FST values suggests diVerentiation as resultof long-term geographic isolation.

The lower sequence divergence between coastal andwestern Andean populations could be the result of either arecent dispersal event from Andean to coastal sites, orrecent cessation of gene Xow between these regions after theAndes separated the high elevation populations (see below).

During the Late Miocene (5.5 MyrBP), the two cordill-eras in Ecuador, which formed a single topographic chainbefore this period, were split apart from north to south bytectonic activity involving two major faults creating theInter-Andean Valley, which was completely open by the

Fig. 4. Discriminant Function Analysis on PC scores (75.5% of the casescorrectly classiWed). Birds from the coastal population (squares) are sepa-rated from Andean individuals (open circles and triangles) by bill mor-phology traits.

Function 1420-2-4-6

Fun

ctio

n 2

3

2

1

0

-1

-2

-3

-4

REGION

Group Centroids

East Andes

West Andes

Coast

3

2

1

Fig. 5. Speckled hummingbird bill-length measurements (a) culmen and(b) exposed culmen, at the three study sites. Means represented with 95%ConWdence Intervals based on size-corrected marginal means after theGLM.

2.5

2.55

2.6

2.65

2.7

2.75

2.8

Coast West Andes East Andes

2.65

2.7

2.75

2.8

2.85

Coast West Andes East Andes

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a


Late Pliocene (2.7 MyrBP) (Spikings and Crowhurst, 2004).Volcanic activity and pyroclastics continued until around3 MyrBP reaching elevations up to 4200 m in Ecuador(Steinmann et al., 1999) and by the end of the Pliocene theCordilleras had uplifted rapidly to the elevations seentoday (Simpson, 1979; Helmens and van der Hammen,1994; BrumWeld and Capparella, 1996; Gregory-Wodzicki,2000). Vicariant events like this one are a recurrent patternin trochilid history (Heindl and Schuchmann, 1998; Blei-weiss, 1998a; García-Moreno et al., 1999a; Schuchmannet al., 2001) as well as in other taxa (Simpson, 1975; Scanlanet al., 1980; Remsen, 1984; BrumWeld and Capparella, 1996;Chesser, 2000) suggesting the profound eVect the uplift ofthe Andes had in creating isolation.

While we acknowledge the diYculties associated with esti-mating divergence times on the basis of a single-locus genetree, our estimate of the age of the main split in A. melanoge-nys in the Ecuadorian Andes (3.2–4.8 MyrBP) is consistentwith the hypothesis that geographic isolation and cladogene-sis in these populations resulted from this uplift. Addition-ally, species of the Andean hummingbird genus Metalluraexhibit diVerentiation between mid-elevation and tree-lineforms which arose during the Pliocene (2–4 MyrBP) (García-Moreno et al., 1999a), suggesting that during this epoch theAndes and its novel environments were important in inXu-encing hummingbird radiations (Bleiweiss, 1998a,b). Thenon-signiWcance of the LRT was surprising given the closerelationship of the intraspeciWc clades. Nevertheless, is notunusual that mitochondrial sequence may not evolve in aclocklife fashion (Overont and Rhoads, 2006). The fact thatimposing a molecular clock on the data did not change thetaxon relationships indicates that the low resolution andshort branches at the tips of the clades may be responsiblefor a dissimilarity between these trees under diVerent hypoth-eses. Under a molecular clock hypothesis it is assumed thatall lineages have had an equal amount of time to evolve, anda violation of this assumption will render the test inapplica-ble. The rejection of a molecular clock in our data set may bean artifact of the within region/clade historical demographiceVects also shown by the dramatic diVerences in polymorphicsites and nucleotide diversity. The relative diVerences in num-ber of polymorphic sites between the east Andes and WestAndes/Coastal populations are probably due to historicalevents. Western populations probably suVered a genetic bot-tleneck and limited gene Xow from long distance populationsdue to landscape fragmentation. High levels of long distancemigration probably explain the high numbers of polymor-phic sites found in the eastern population. Presently this sub-species is found from Venezuela to Peru in a continuousdistribution range in the Andes allowing the potential forgene Xow from distant populations. Future sampling insouthern (Peru, Bolivia and Argentina) and northern(Colombia and Venezuela) localities in the Andes is neces-sary in combination with nuclear markers to assess theimpact of this prominent barrier in the speckled humming-bird genetic diVerentiation and phylogeographic patternalong its entire range.



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There is little mtDNA divergence between samplesfrom the coastal and western Andean sites, characterizedby the absence of shared haplotypes suggesting fully dis-rupted gene Xow between these regions. The coastal popu-lation may have evolved from a range expansion (negativeFs values) from western Andean populations to the west-ern tropical lowlands during the Quaternary with subse-quent isolation and cessation of gene Xow. Almost nopaleobotanical reconstructions exist from this region ofEcuador but current research suggests a reassembling ofthe biota during periodic dry events (Colinvaux et al.,2000; Bush, 2005) characterized by lower temperaturesand reduced rainfall within the past »2 MyrBP (Graham,1997). Geomorphological data conWrm that wet/dry cyclesexisted on the two sides of the Andes during the Pleisto-cene and the climatic processes on one side of the Andeswere the reverse of those found on the other (Vuilleumier,1971). The Chongón Colonche coastal wet forest at pres-ent is surrounded by sclerophylous vegetation and decid-uous forest type (Sierra, 1999) representing a forest/savanna ecotone that could have been very dynamic dur-ing the cycles in the Pleistocene-Holocene (Colinvauxet al., 2000). This may have inXuenced the connectivitybetween Andean and mid elevation coastal forests inEcuador, thus aVecting species distributions. Many otherbird species commonly found in Andean cloud forestinhabit this isolated wet-coastal range (gray-breastedwood wren-Henicorhina leucophrys, streaked Xycatcher-Mionectes straticollis, orange breasted euphonia-Eupho-nia xanthogaster). Future comparison of the phylogeo-graphic patterns and agreement in the age of the splits inthese multiple species will clarify the origin of these low-land populations in the Chongón Colonche range.

The patterns of the mtDNA diversity on either side ofthe Andes are largely consistent with expected patterns ofvariation resulting from fragmentation in response togeographic breaks. However, apparent isolation of a fewmillion years across the Andes had little eVect on pheno-typic divergence. Ecologically important traits such aswing length and bill characteristics are remarkably con-sistent among populations within habitats on either sideof the Andes, suggesting the absence of divergent selec-tion on these traits. In contrast, there is less moleculardiVerentiation between the western and coastal popula-tions but dramatic morphological diVerentiation, sug-gesting a role for ecologically-mediated divergentselection in allopatry (see Graham et al., 2004). The mostdramatic diVerences across these populations are in billmeasurements, which vary with a change in habitat. Sev-eral studies show that hummingbird bill diVerentiation isthe result of selection in response to speciWc Xower mor-phology (Wolf et al., 1976; Feinsenger and Colwell, 1978;Patton and Collins, 1989; Temeles and Roberts, 1993;Temeles et al., 2000; Temeles and Kress, 2003; Traverset al., 2003; Altshuler and Clark, 2003). Therefore, diVer-ences in temperature, precipitation and vegetation den-sity shown here between coastal and Andean regions


(Fig. 6) could be interpreted as indicators that the speck-led hummingbird inhabits substantially diVerent habi-tats. These diVerent environments provide the potentialfor ecological diVerentiation where diVerent selectionregimes might act to shape the relationship between polli-nator and plant. Nevertheless, direct measurements ofXower use by this species in both habitats are needed tofully understand the cause of these diVerentiations.

InterspeciWc comparisons in hummingbirds show thatwings are relatively larger at high elevations (Altshuleret al., 2004), as indicated by a negative relationship betweenwing loading (the ratio of body weight to wing area) andelevation (Feinsinger et al., 1979; Epting, 1980; Altshulerand Dudley, 2002). Interestingly, no statistical diVerences inwing length among individuals across elevations were evi-dent in this study. A possible reason why this importanttrait is probably under stabilizing selection could be thatthe speckled hummingbird can modulate its wing frequencyand stroke amplitude to compensate for the power require-ments during hovering at diVerent elevations (Chaves et al.,in preparation). Exploring the genetic and environmentalbasis of observed morphological patterns represent impor-tant next steps in the investigation of the divergence foundin this species.

The selection of climatic variables such as temperatureand precipitation in our analysis were also identiWed tobe key variables when describing environmental charac-teristics of other Ecuadorian taxa through nichemodeling (Graham et al., 2004; Parra et al., 2004). It hasbeen shown previously that temperature is a very reliableparameter when used in remote sensing compared torainfall, whose interpolation becomes more diYcult inthe complex topography of the Andes (Parra et al., 2004).Hence, our results suggest that the set of Andean sites aresimilar in temperature (PC1) and, to some extent, inprecipitation (PC2). Nevertheless, diVerential atmo-spheric circulation acts upon both sides of the cordilleras

Fig. 6. Analysis of the climatic and environmental space occupied byspeckled hummingbird individuals in Ecuador. Scatter plot of PC1 andPC2 scores for coastal, west Andes and east Andes sites.

2.01.51.0.50.0-.5-1.0-1.5

3

2

1

0

-1

-2

-3

REGION East Andes

West Andes

Coast

PC

A 2

PCA 1



ARTICLE IN PRESS

and the precipitation variation shown in our analysismay be caused by the eVect of tropical Atlantic andPaciWc sea surface temperature anomalies (SSTA’s)reported for the Ecuadorian Andes (Vuille et al., 2000).Despite this problematic interpolation suggested by pre-vious studies in high-elevation sites, the diVerentiationfound here between coast and Andes demonstrates thatthis technique can be a powerful tool to exploreenvironmental characteristics in combination with focalspecies characteristics such as morphology, physiologyand genetics.

In summary, the multidisciplinary approach employedhere endeavors to provide a more comprehensive pictureof the mechanisms behind the diversiWcation process inthe speckled hummingbird. Phylogeographic patternssupport the hypothesis that habitat has led to the mor-phological diVerentiation via divergent natural selectionin this species. The topology in the gene tree supports thehypothesis that breaks in the gene tree correspond togeographically distinct regions and provides additionalevidence that the Andes promote genetic diversiWcationthat may be important in promoting reproductive isola-tion and speciation.

Acknowledgments

We gratefully acknowledge the following institutionsand people for contributing to this research: FulbrightOAS-Ecology Initiative, Society for the Comparative andIntegrative Biology Research Grants-in-Aid and Center forTropical Research. B. Milá and J. McCormack providedcomments on earlier drafts of the manuscript and W. Buer-mann provided information on the environmental layersand MAXENT data for Ecuador. Field work was possibleby the kind collaboration of Jocotoco Foundation, Tellk-amp family, Yanayacu Biological Station, Cabañas San Isi-dro, Verdecocha owner Jorge Enrique Maldonado,Comuna de Loma Alta, HidroPaute, and Richard Parsonsat Bellavista Cloud Forest Reserve. The Wrst author isdeeply grateful to J. Hidalgo, M. Reynolds, B. Milá, C. Din-gle, J.F. Freile, J. Robayo, C. Moncagatta and M.F. Salazarwho provided important Weld assistance. H. Thomassen, K.Ruegg, A. Uy, E. Routman, C. Graham and members of theSmith lab contributed with important feedback in our anal-ysis. We also thank the anonymous reviewer for his helpfulcomments on the manuscript. We are grateful to the nationof Ecuador for approving our collecting and research pro-gram. This research was supported by grants from theNational Science Foundation IRCEB9977072 and NASAto T.B.S.

This research formed part of the graduate work of JaimeChaves at San Francisco State University. Jaime’s primaryinterests focus on the evolution of montane hummingbirdsin South America linking geography and populationgenetic structure. John Pollinger studies conservationgenetics of carnivores and birds, and directs UCLA’s Con-servation Genetics Resource Center. Thomas Smith directs


the Center for Tropical Research at UCLA and has acontinuing research interest in the evaluation of the geneticand morphological variation in natural populations. Gret-chen LeBuhn’s interests focus on the impacts of habitatchange on pollinators as well as the role of environmentalvariation in maintaining genetic and phenotypic variationin populations.

Appendix A

Environmental data used to determine similarities andclimatic characteristics of our sampled sites in coastal habi-tat and inland Andean cloud forest.

Appendix B

Factor loadings for the principal component analysis ontemperature variables, tree cover layer and LAI vegetation

19 Bioclim layers (1km) obtained from http://biogeo. berkeley. edu worldclim /bioclim.htmBIO1DAnnual Mean TemperatureBIO2DMean Diurnal Range (Mean of monthly (max

temp¡min temp))BIO3DIsothermality (P2/P7) (*100)BIO4DTemperature Seasonality (standard deviation *100)BIO5DMax Temperature of Warmest MonthBIO6DMin Temperature of Coldest MonthBIO7DTemperature Annual Range (P5–P6)BIO8DMean Temperature of Wettest QuarterBIO9DMean Temperature of Driest QuarterBIO10DMean Temperature of Warmest QuarterBIO11DMean Temperature of Coldest QuarterBIO12DAnnual PrecipitationBIO13DPrecipitation of Wettest MonthBIO14DPrecipitation of Driest MonthBIO15DPrecipitation Seasonality (CoeYcient of

Variation)BIO16DPrecipitation of Wettest QuarterBIO17DPrecipitation of Driest QuarterBIO18DPrecipitation of Warmest QuarterBIO19DPrecipitation of Coldest Quarter1 Tree Cover layer (1km)7 LAI metrics from a composite of 4-year of MODIS Data

(1km):Metric1: Annual MaxMetric2: Annual MinMetric3*: Max (wet season)–Min (dry season)Metric4*: Max (dry season)–Min (wet season)Metric5*: Mean (dry season)Metric6*: Mean (wet season)Metric7: Annual mean (NDVI)*Dry season: June–October (Coast and West Andes) and

September–January (East Andes)*Wet season: December–April (Coast and West Andes) and

March–July (East Andes).


http://biogeo.berkeley.edu/worldclim/bioclim.htm




ARTICLE IN PRESS

density factors. Detailed information about these variablesfound in Appendix A.

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Variables Component

1 2 3 4 5

BIO1 .865 .407 ¡.172 .230 ¡.004BIO2 ¡.816 ¡.007 .239 .433 .002BIO3 ¡.898 .003 .187 .261 .003BIO4 .867 .006 ¡.214 ¡.196 .266BIO5 .794 .430 ¡.165 .379 ¡.001BIO6 .887 .379 ¡.217 .126 ¡.001BIO7 ¡.720 ¡.115 .239 .467 .000BIO8 .911 .336 ¡.136 .174 ¡.005BIO9 .814 .465 ¡.209 .272 ¡.002BIO10 .886 .379 ¡.183 .189 ¡.001BIO11 .845 .422 ¡.157 .264 ¡.008BIO12 ¡.667 .683 ¡.262 ¡.006 .009BIO13 ¡.551 .691 ¡.330 ¡.005 ¡.005BIO14 ¡.541 .699 ¡.283 ¡.003 .309BIO15 .886 ¡.280 .005 .007 ¡.183BIO16 ¡.567 .677 ¡.306 ¡.007 ¡.008BIO17 ¡.597 .677 ¡.271 ¡.005 .264BIO18 ¡.212 .649 ¡.145 ¡.497 ¡.327BIO19 ¡.628 .498 ¡.327 .279 .322TREECOV ¡.224 .612 ¡.262 ¡.229 ¡.484METRIC1 .223 .489 .800 .001 ¡.006METRIC2 ¡.001 .565 .630 ¡.181 .115METRIC3 .694 .197 .268 ¡.186 .431METRIC4 ¡.667 .309 .157 .376 ¡.218METRIC5 ¡.199 .608 .687 .134 ¡.154METRIC6 .532 .466 .621 ¡.174 .170METRIC7 .188 .528 .811 ¡.005 ¡.002% variance

explained45.2 22.9 13.6 5.9 3.9


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