Four pulses of diversification and differential migration rates across the Andes explain the structuring of genetic diversity in an assemblage of lowland neotropical birds
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(2) Four pulses of diversification and differential migration rates across the Andes explain the structuring of genetic diversity in an assemblage of lowland Neotropical birds. Flórez-Rodríguez Alexander1, Cadena Carlos Daniel1, Burney Curtis Wade2, McCormack John3, Aleixo Alexandre4, Pérez-Eman Jorge5, Brumfield Robb T2,3.. 1. Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia. 2. Deparment of Biological Science and Museum of Natural Science, Lousiana State University, Baton Rouge, LA 70803, United States. 3. Museum of Natural Science, Lousiana State University, Baton Rouge, LA 70803, United States. 4. Coordenação de Zoologia, Museu Paraense Emílio Goeldi, Caixa Postal 399, CEP 66040– 170, Belém, Pará, Brasil. 5. Instituto de Zoología y Ecología Tropical, Universidad Central de Venezuela, Av. Los Ilustres, Los Chaguaramos, Apartado Postal 47058, Caracas 1041-A, Venezuela..
(3) Abstract Spurious conclusions may arise in comparative phylogeographic studies when variation in population parameters across species is not taken into account when making inferences about historical processes or landscape features affecting assemblage-wide patterns of population genetic differentiation. To avoid such potential pitfalls, new coalescent-based analyses allow for the consideration of interspecific variation in population parameters while testing hypotheses about the evolutionary history of assemblages. Here, in order to disentangle the role of the Andes as a barrier influencing patterns of genetic variation between populations of lowland Neotropical birds, we gathered mtDNA sequence data from 24 taxa with populations codistributed across the Andes. We used approximate Bayesian computation to test for simultaneous divergence across the focal species and information theoretical approaches to evaluate the hypothesis that gene flow across the Andes is influenced by foraging preferences (i.e understory versus canopy) focusing on multilocus data for five species. We found that four pulses of cross-Andes diversification dating from the Miocene to Pleistocene are more probable than a single, simultaneous, assemblage-wide divergence across the Andes. The evolutionary history of Neotropical lowland birds with understory foraging preferences has been characterized by no or restricted migration across the Andes, whereas species with canopy foraging preferences have experienced historical migration across the Andes. These results confirm that Andean uplift likely had an important initial influence on the genetic divergence of lowland Neotropical birds, but that species did not all respond to such event in the same way, partly because climatic fluctuations of the Pleistocene could have facilitated episodic crossing of the Andes in some species and not in others..
(4) Introduction The study of the geographic distribution of genealogical lineages has played an important role in unraveling the evolutionary processes responsible for current patterns in the distribution of biological diversity (Avise 2000, Hickerson et al. 2010). Traditionally, to infer the historical processes shaping the contemporary population structure and distribution of organisms, phylogeographers often proposed post hoc historical hypotheses based on observed patterns of genetic variation, an approach in which inferences of historical events that presumably influenced codistributed species were largely qualitative ( Avise 1994, Bermingham and Moritz 1998). Although much has been learned from these approaches, they may lead to misleading conclusions due to issues such as confirmation bias (i.e., when researchers interpret their results based on preconceived ideas) and overinterpretation (Nickerson 1998, Knowles and Maddison 2002, Carstens et al. 2009). On the other hand, because cross-species inferences based on traditional phylogeographic approaches are mainly based on individual gene-tree topologies and on divergence times derived from genealogies estimated for single species independently, they are prone to errors introduced by the influence of variation among species in parameters such as population size, gene flow, and mutation rates (Knowles and Carstens 2007). To account for the above mentioned problems, new phylogeographic approaches using coalescent-based analyses of specific, alternative hypotheses about both the biogeographical and demographic past have been developed (Knowles and Maddison 2002, Carstens et al. 2009, Hickerson et al. 2007, Huang et al. 2011). For example, one can attempt to estimate the temporal congruence in population divergence of multiple codistributed species across putative barriers, evaluating whether genetic variation is consistent with multiple colonization events across barriers, or rather by a single vicariant event affecting species simultaneously (Leache et al. 2007, Huang et al. 2011). Additionally, one can explicitly compare multiple evolutionary hypotheses and thus identify historical processes (e.g. migration, isolation) influencing population divergence. For example, recently developed information-theoretical approaches to phylogeography can be used to compare the fit of data to hypotheses assuming migration across a geographic barrier versus the fit to hypotheses involving no migration across the barrier (Carstens et al. 2009, Carling et al. 2010, Willis 2010). When these approaches are employed to study several codistributed lineages, one can make inferences about the most relevant historical processes influencing the structuring of genetic diversity in assemblages. Geographic barriers play a clear role shaping patterns of variation in faunal and floral assemblages (Avise 2000). Congruence in patterns of phenotypic or genetic variation across specific geographic barriers have been found in several regions such as Australia (e.g. Carpentarian Barrier), the Neotropics (e.g. the Andes and the Amazon River), and North America (e.g. the Mississippi River), among many others (Chapman 1917, Cracraft 1986, Burney et al. 2009, Koopman and Carstens 2010, Lee and Edwards 2008, Brumfield and Capparella 1996, Soltis et al 2006, Burbrink et al 2008). Although the limits of species' distributions often correspond closely with geographic barriers (Graham et al. 2010), analyses of geographic variation in multiple codistributed species frequently reveal ample variation among them in levels of phenotypic and genetic divergence across barriers (Brumfield and Capparella 1996, Hickerson et al. 2006, Leache et al. 2007). This may reflect that populations of different species diverged at different times following the emergence of barriers or that gene flow in some species was more affected by such barriers (Hickerson et al. 2006), but may also be the result of simultaneous vicariance, with the disparity in levels of divergence.
(5) accounted for by cross-species variation in parameters such as ancestral population sizes and mutation rates (Arbogast et al 2002, Edwards and Beerli 2000, Hickerson et al. 2003, Hickerson et al. 2006). This makes it difficult to discern between single vicariance and multiple colonization events as evolutionary forces shaping current patterns of genetic variation across barriers (Bermingham and Mortiz 1998, Hickerson et al. 2010). Moreover, propensity to disperse across a barrier may vary among codistributed species depending on their ecology, which may influence levels of genetic differentiation (Burney and Brumfield 2009). Therefore, studies of the processes influencing population differentiation across barriers should consider potential differences among species in population genetic parameters and in the magnitude of historical gene flow between demes, in addition to accounting for the effect of coalescent stochasticity (Carstens et al. 2009, Hickerson et al. 2007, Hickerson et al. 2010) The remarkable geographic barrier represented by the Andes, which isolates large blocks of lowland environments in South America, is an ideal setting to test hypotheses relative to evolutionary forces shaping current patterns of genetic diversity in the Neotropical avifauna, the most diverse in the World (Haffer 1990; Orme et al. 2005; Brumfield and Edwards 2007). The Andean uplift promoted vicariant events fractioning formerly continuous ranges of species distributed from Amazonia to the Pacific lowlands; such vicariant events undoubtedly were major drivers of the historical diversification of lowland Neotropical birds (Chapman 1917, 1926; Haffer 1967a; Cracraft and Prum 1988). However, phylogeographic studies reveal high variation among species in levels of genetic variation across the Andes (Brumfield and Capparrella 1996; Brumfield and Edwards 2007, Miller et al. 2008, Weir and Price 2011). Beyond the influence of factors such as cross-species variation in effective population sizes, the observed variation in levels of genetic differentiation can partially be attributed to ecological differences between species; species of rainforest understory show stronger genetic structure across the Andes than species of the forest canopy, a likely consequence of the better dispersal abilities of the latter (Burney and Brumfield 2009). However, it remains to be determined whether the variation in genetic differentiation across the Andes existing among species of lowland birds with cross-Andes distributions can be accounted for by a single vicariant event or by multiple independent colonization events across the Andes (i.e. divergence occurring at different times in different species). In addition, no information exists on whether there are differences in historical migration rates across the Andes of lowland Neotropical bird species in relation to ecological traits related with dispersal abilities such as foraging stratum. Most of the Andean mountain system is composed of a single great range extending north-to-south over much of South America; however, in Colombia, the cordillera branches in three sharply defined ranges spanning nearly the entire length of the country and separated by low-lying valleys (Chapman 1917). Because the Cordillera Oriental is the main barrier defining avifaunas from West (Choco-Magdalena) and East (llanos-Amazonia) of the Andes (Chapman 1917, Haffer 1967a, Cracraft 1985), Colombia is a critical region for the study of evolutionary processes in the lowland Neotropical avifauna. However, owing to various factors, sampling in Colombia has been notoriously missing from most previous studies examining the influence of the Andes as a driver of population genetic differentiation in lowland organisms. Using the Andes and adjacent Neotropical lowlands as a geographic setting and lowland birds as a study system, we here address a central question in biogeography, namely whether codistributed lineages share a common history, being affected by similar historical events promoting population divergence (Avise et al 1998, riddle et al. 2000, Hickerson et al. 2006, Hickerson et al. 2010). To attempt to elucidate the effects of shared historical events on the.
(6) history of population differentiation in codistributed species, we examine historical patterns of gene flow and divergence across 24 species of Neotropical lowland birds codistributed in the lowlands of both sides of the Andes using mitochondrial DNA sequences, and test whether a single vicariant event is sufficient to explain the high variance in genetic structuring of codistributed lowland birds across the Andes. For a smaller selection of codistributed species with contrasting ecologies, we examined historical migration processes across the Andes using detailed sampling of mitochondrial and nuclear markers. Specifically, we sought to use a multilocus, coalescent information-theoretic approach to further evaluate the hypothesis that gene flow across the Andes is influenced by foraging preferences (Burney and Brumfield 2009). This hypothesis predicts that in species of the forest understory, phylogeographic hypotheses with no migration across the Andes would adequately account for existing patterns of genetic differentiation. Conversely, the hypothesis predicts that in species of the forest canopy, phylogeographic hypotheses best fit to genetic data would allow for cross-Andes migration.. Methods Sampling and collection of molecular data We obtained molecular data for 24 species of lowland Neotropical birds, 20 of which have populations codistributed on both sides of the Andes; the remaining four correspond to species restricted to one side of the Andes whose corresponding sister lineages occur on the opposite side of the mountain range, for a total of 22 comparisons (Table 1). Geographic sampling was focused on covering species' distribution ranges as thoroughly possible, which was achieved for the most of the 24 focal species; we included material from Colombia for 12 of the 24 species (Figure 2 and Appendix 1). The sampling scheme was divided in two data sets. First, to test for simultaneous divergence across the Andes, analyses were based on DNA sequences of one mitochondrial gene (NADH dehydrogenase subunit 2) for all 24 species; some sequences were generated by us and other were obtained from GenBank. This set consisted of 1051 ND2 sequences with average length of 1038 base pairs for the 24 species. Sample size per species ranged from 2 to 126 individuals (mean= 21, SD= 28) and from 3 to 88 individuals (mean= 27, SD= 22) for populations west and east of the Andes, respectively (Table 1; appendix 1). Second, to evaluate the fit of data to different evolutionary models (i.e. models of isolation vs. models involving migration) we focused on a subset of seven species (four restricted to understory and three to canopy) for which in addition to the mitochondrial region, we obtained sequences for intron 7 of the beta-fibrinogen gene and introns M16214 and M17483 described by Backstöm et al. (2008). Samples from countries different from Colombia (tissues and specimen toe pads) were obtained from the Louisiana State University Museum of Natural Science. The samples from Colombia were obtained from vouchered specimens we collected in the field or obtained from different biological collections as either frozen tissues or toe pads of skin specimens (see appendix 1). All samples used in the study have associated voucher specimens deposited in natural history museums. Total DNA was extracted from all samples using a phenol-chloroform protocol as follows. First, we added 300 µl of pre-heated (60ºC) CTAB and 5µl of Proteinase K to a crushed piece of tissue for cell lysis, incubating for 24 hours or up to 5 days at 65ºC for tissues and toe pad samples, respectively. We then followed the phenol DNA extraction protocol described by.
(7) Sambrook (2001). Subsequently, we amplified three nuclear introns of autosomal DNA (beta fibrinogen intron 7, M16214 and M17483) and the entire mitochondrial gene ND2 using primers published by Backstöm et al. (2008) and Sorenson et al. (1999). PCR amplifications (25 L) consisted of: 0.5 to 2.0 µl of template DNA, 1.2 µl of each primer (10 mM), 1.0 µl of 10 mM dNTPs, 2.5 L 10X reaction buffer with 1.5 µl of MgCl2, 0.125 µl of Taq DNA polymerase, and 16.5 L sterile ddH2O. The thermocycling program ran with an initial 95ºC denaturation phase for 2 min., followed by 37 cycles consisting of a 94ºC denaturation step for 3 sec., a 52 annealing step for 30 sec., and a 72ºC extension step for a min., with a final extension of 10 min. at 72ºC. Amplicons were cleaned using Exosap IT (USB corporation, Cleveland, Ohio) and then sequenced in both directions. Resulting chromatographs were assembled in Geneious 5.1 (Drummond et al. 2010). In cases where double peaks of equal height were detected in the sequence, estimation of the most probable phase of alleles given the entire dataset was conducted using the program PHASE 2.1 (Stephens and Donelly 2003). We used a probability threshold of 0.70 to consider that the haplotype of an individual was correctly inferred, considering that inferred haplotypes with posterior probability greater than 0.60 accurately reflect those haplotypes determined with cloning (Harrigan et al. 2008; Carling et al 2010). When the posterior probability of haplotypes was lower than 0.70, new haplotype estimation was conducted using the previously inferred haplotypes as known sequences. If after that second estimation the posterior probabilty of haplotype was still lower than 0.70 this sequence was discarded for subsequent analyses. Sequences were aligned using the MUSCLE algorithm (Edgar, 2004) implemented in Geneious (Drummond et al. 2010) and edited manually. In order to have visual representation of the overall divergence patterns, we used the mtDNA data set to estimate an unrooted haplotype network for each comparison clade using the haploNet function as implemented in the pegas package (Paradis 2009) in R (R Development Core Team 2009). This package implements the statistical parsimony method for network reconstruction (Templeton et al. 1992). Testing for simultaneous vicariance across the Andes We used the approximate Bayesian computation (ABC) approach implemented in the program MTML-msBayes to test for simutlaneous divergence events across the Andes. MTML-msBayes describes the variation in the parameters across codistributed taxon-pairs using hyper-parameters, which quantify the variability of the population parameters across the number of taxa studied (Hickerson and Meyer 2008). As with all Bayesian analyses, the ABC approach we used is sensitive to the prior probabilities set for parameters. To consider the effect of priors and determine the most suitable strategy for analyses, we conducted 18 different runs using different prior distributions for ancestral population sizes, divergence times (Tau, τ), and number of migrants per generation (Table 2). The posterior probabilities of all models based on different priors were then compared to each other using Bayes factors (Table 2). In final analyses to test the hypothesis of simultaneous vicariance across the Andes, we estimated hyper-priors (the dispersion index characterizing the variability in divergence time) and the number of different times of vicariance for the models with the best Bayes factor in the program MTML-msBayes (Huang et al. 2011). In cases where more than one divergence event was detected, a new analysis constraining the number of divergence times to the value obtained in the first run was performed; this allowed us to estimate the splitting times for each of the estimated divergence events, and to determine the number of species that diverged in each one of them..
(8) Using an observed summary statistic vector from data, 1,000,000 random draws from hyperpriors were conducted to construct the vector of simulated priors. ABC works most efficiently when there is high correlation between the Euclidean distance of the observed summary statistics and hyper-parameters; thus, both vectors (i.e. observed and simulated) were sorted in ascending values of average π between populations, because this summary statistic is highly correlated with divergence time and sorting improves the performance of MTML-msBayes (Takahata and Nei 1985, Hickerson et al 2006, Huang et al. 2011). The 500 vectors simulated under the coalescent with the shortest Euclidean distance to the vector of summary statistics were obtained using the accept/reject script in the MTML-msBayes package, and subsequently were used to construct the posterior distribution of hyper-parameters. The mode of the estimated time of divergence (E(τ)) across species for each event was converted into time in years using the formula E(t)= E(τ)4NAVE, Where 4NAVE correspond to the mean of theta (θ) divided by the mean per generation mutation rate per gene (µ, see below). Information theoretical approach We used the isolation with migration approach for multilocus data implemented in the program IMa (Hey and Nielsen 2007) to estimate divergence time, migration rates and effective population sizes for populations occurring east and west of the Andes of seven species of lowland birds occupying different foraging strata (Table 4). IMa assumes that loci are neutral and no recombination within each locus. To account for these assumptions we tested for neutrality using Tajima’s D in DnaSP (Tajima 1983; Rozas et al. 2003), and obtained the longest non-recombinant block of DNA for each locus using IMcg (Woerner et al. 2007). We used an information-theoretical approach to establish which of a series of nested evolutionary models best accounts for the variation observed in our genetic data set (Kullback 1959; Carstens et al. 2009). Specifically, we compared 16 different models varying in the estimated values for population size: ancestral, east of the Andes, west of the Andes; and migration across the Andes mountains: west to east and east to west. These models are described using a five- letter code, where the position of each letter indicates each of the parameters and letters indicate qualitative values of each parameter. The first three letters are the three parameters related to population size (ancestral, east, west) and the last two the parameters related to migration (west to east, east to west). For example, model AAA00 is one in which three parameters of population size are equal (i.e. ancestral = east = west), and there is no migration in either direction across the Andes. Three separate analyses were conducted in IMa including: (1) all the loci sampled, (2) only the three nuclear genes and (3) only the mitochondrial locus. An initial run with a fixed length of 24 hours with wide priors was carried out to identify the most appropriate priors for each one of the seven species; once the priors were set, we ran the program for 150 hours with 16 coupled Markov chains and a burn-in period of 100,000 steps. To estimate divergence times in units of absolute time, we assumed a neutral mutation rate (µ) of 1.35 x 10-9 substitutions/site/year for the autosomal loci; further assuming that mitochondrial loci have a mutation rate ten times faster than nuclear genes (Ellegren 2007, Graur and Li 2000), 1.35 x 10-8 substitutions/site/year was used for mitochondrial loci..
(9) Results Testing for simultaneous vicariance across the Andes A total of 22841 bp with 2689 variable sites (Table 1) were analyzed. As evidenced by the topologies of haplotype networks, the study species showed a wide range of mitochondrial genetic divergence across the Andes; such variation ranged from reciprocally monophyletic groups on each side (e.g. Pipra erythrocephala) to reticulated topologies (e.g. Dendrocincla fuliginosa, Figure 3). For the 18 msBayes analyses that we conducted to explore sensitivity to different prior values, models with a prior value of 0.5 for ancestral population size always recovered as most probable a single vicariant event affecting all species (Table 2). On the other hand, models with a migration prior value of one always recovered more divergence events than models with migration prior value of zero. For example, for models with prior values of divergence time Tau=1 and population size 0.01, three divergence events were estimated when the prior value of migration was set equal to one, but only one event was estimated when the prior value for migration was set to zero (Table 2). Models with the same prior values (e.g. migration = 0) were summarized and compared pairwise to evaluate the influence of this prior value on the global posterior probability (Table 3). Prior values showing the highest sum of posterior probability (Upper Tau = 1, Population size = 0.1 and Migration = 1) were chosen as the priors for the final analysis to test for a single simultaneous divergence event across the Andes. We further comment on the effect of prior selection in the Discussion. The MTML-msBayes analysis of the 24 Neotropical bird species indicated that the data are most consistent with several pulses of diversification (Fig 1). Specifically, the mode estimate of omega (Ω, a measure of variance in the times of divergence) across the Andes was 0.098; under a scenario of simultaneous divergence the expected value for this hyper-parameter is 0.0 (Hickerson et al. 2007). Furthermore, the estimated mode for Psi (ψ, number of possible divergence times) was 4, suggesting that patterns of genetic variation in the assemblage studied are best accounted for by the occurrence of four divergence events across the Andes. Thus, in a final analysis to estimate divergence times, the number of events was constrained to 4, which led to estimates of divergence time (E(τ)) between populations west and east of the Andes of 0.027, 0.100, 0.226 and 0.356 in global coalescent units (NAVE generations); when converted to million years before present (mya), these values correspond to 0.66, 2.4, 5.4 and 8.6 mya, respectively. The number of taxa that diverged in each of these four estimated events were: 11, 5, 1, and 5, respectively. Information theoretical approach Estimates of Tajima’s D indicate that the loci are evolving in a neutral manner. For nuclear loci in which recombination was detected, the longest block of non-recombinant sequences was used in the analyses; the mean lengths of these blocks were 241, 356 and 475 bp for Bfib7, M16214 and M17483, respectively. The most probable evolutionary model given the data (all loci) for Xenops minutus, Myrmotherula axillaris and Automolus ochrolaemus was ABC00, indicating different estimates for the three population sizes (ancestral, east, west) and no migration across the Andes in either direction (Table 5). For Microcerculus marginatus the most probable model.
(10) was ABB0E, in which the ancestral population size is different from that of current populations from the east and west, and allows for migration only in one direction (east to west of the Andes). All these four species forage in the understory, and analyses suggest that either restricted migration across the Andes or complete isolation have characterized their population history. Remarkably, for X. minutus we found a strong association between the models with different estimated values for all three parameters of population size (ABC--; ancestral ≠ west ≠ east) and Akaike weights; all models ABC, whether involving migration or not, accounted for 93.8% of the variation in the molecular data in this species. This pattern suggests that patterns of population genetic divergence variation in X. minutus have been largely influenced by dissimilar population sizes between ancestral, west and east lineages. Strong relationships between parameters of population size and Akaike weights were not found in any other understory focal species. For Attila spadiceus and Tangara gyrola, the most probable model given the data was AACDD (Table 4), which allows equal rates of migration in both directions across the Andes. Similarly, for Pionus menstruus the best model involved equal rates of migration in both directions, but included distinct parameters for ancestral and derived population sizes. In all these three species of the forest canopy the model with higher Akaike weight included equal rates of migration across the Andes. This suggests that, for these species, cross-Andes migration has been an important process structuring genetic variation. Analyses using data from all loci, only from nuDNA and only from mtDNA did not always result in the selection of the same evolutionary models (Table 5). For example, in X. minutus and M. axillaris analyses with all three data sets showed the same pattern, where evolutionary models with no migration in one or both directions across the Andes fitted the data better (i.e., contributed in a greater way to the total likelihood) than models with migration rates different from zero. For A. spadiceus, A. ochrolaemus, T. gyrola, and M. marginatus the patterns obtained in analyses using mitochondrial and nuclear genes were contrasting. Based on nuclear genes, better-fit evolutionary models for allowed for migration across the Andes, but based on mitochondrial data, higher-ranking models were those with no migration in either one or both directions. In Pionus menstruus models involving migration across the Andes were supported in all analyses, regardless of the type of data used. Discussion The Andes undoubtedly represent a major physical barrier that promoted vicariant events and restricted migration between populations (Chapman 1917, 1926, Haffer 1967a). However, the extent to which the Andes have affected the evolutionary processes shaping the population genetic structure of organisms with distributions on both sides of this barrier remains unclear (Brumfield and Edwards 2007). Coalescent theory predicts that even when multiple species codistributed across a barrier experimented a simultaneous event of genetic differentiation (e.g. the emergence of a barrier curtailing gene flow), the depths of gene trees could vary highly among species (Hudson 1990). Also, gene trees with seemingly congruent topologies in different species do not necessary imply evidence of a common history because similar patterns in genealogies may arise even if some species have experienced independent colonization events across barriers whereas others have suffered a single divergence event. Thus, understanding the role that barriers play shaping current patterns of genetic diversity is often difficult (Avise 2000; Barber and Klicka 2010). Here, using a coalescent-based ABC method we detected that patterns of genetic variation in 24 Neotropical lowland bird species.
(11) are not consistent with a common history of divergence across the Andes; rather, populations of these species are estimated to have been fragmented by four independent vicariant events across this barrier (Fig. 1). The three earlier events are estimated to have taken place in the Neogene: two of them in the Miocene (8.6 and 5.4 Mya with five and one species, respectively) and one in the Pliocene (2.4 Mya, 5 species); the more recent event is estimated to have occurred in the Quaternary (0.66 Mya, 11 species). Previous studies using DNA sequence data focused on single clades of lowland Neotropical birds had suggested the occurrence of more than one divergence event per group across the Andes; three events starting at 3.6 Mya were inferred for Dendrocincla (Weir and Price 2011), five events starting at 3.4 Mya were inferred for Trogon (DaCosta and Klicka 2008), and three events starting 1.9 Mya were inferred for Mionectes (Miller et al 2008). The Eastern Cordillera of Colombia is thought to have reached no more than 40% of its modern elevation from the middle Miocene up until early Pliocene (Van der Hammen 1973 and Wijninga 1996, Gregory-Wodzicki 2000). The eastern Andes then reached their modern elevations following rapid uplift from 5 to 3.5 Mya (Gregory-Wodzicki 2000). Our data suggest that a total of six species of lowland birds experienced splitting events predating this period of rapid uplift, whereas 16 diverged after such period, implying that they likely dispersed over the Andes relatively recently and have since differentiated. This, together with studies focused on single clades mentioned above, indicates that isolation following dispersal across the Andes has occurred more frequently than previously thought. ABC analyses rely on the substitution of the vector of observed summary statistics by a vector of parameter values simulated under the coalescent to approximate the properties of the posterior probabilities of parameters without explicit calculation of likelihoods (Beaumont et al 2002, Hickerson et al 2007, Huang et al 2011). In this process, prior values -especially those of population size and migration- have a high influence on the simulated vectors (Barber and Klicka 2010, Huang et al 2011). Here we found that under a prior value of 0.5 for population size, all models recovered simultaneous divergence (Table 2). Furthermore, we found that models involving migration tend to recover more divergence events than models of complete isolation (Nielsen and Wakeley 2001, Hickerson et al 2007). To avoid bias caused by prior values we evaluated 18 different possible evolutionary models varying in prior values and selected those maximizing posterior probability (i.e., 0.1 for population size, which led to the estimation of four divergence events). To the best of our knowledge, there is no reliable information on ancestral population sizes and migration rates across the Andes that would allow setting informed priors, so the chosen values are the best possible values given our sensitivity approach (Table 3). Thus, we emphasize that our results represent our best estimate of the number of divergence events across the Andes in the study species, but this result is contingent upon prior values we have used in the absence of complete information independent from the data. This potential limitation is common to most other studies employing similar analytical approaches. Other possible sources of error in many ABC analyses, including ours, might relate to sampling scheme. Simulation work shows that increasing the number of loci sampled leads to improved estimates of the dispersion index of the divergence time (Ω) and that this hyperparameter is often overestimated using single-locus datasets (Huang et al 2011). Because our ABC analyses were based on a single locus, this hyper-parameter might not have been accurately estimated. Conversely, accuracy in the estimation of the hyper-parameter describing the time of divergence across species (E(τ)) does not increase linearly with increases in the number of loci; for example, the estimation with a one-locus dataset is more.
(12) accurate than with four loci, but accuracy does increase with more than 16 loci (Huang et al 2011). Future studies should seek to better characterize patterns of genetic variation using relatively large numbers of loci by using next-generation sequencing technologies. Divergence times estimated for the seven lowland Neotropical birds using multilocus data in IMa ranged from 3.9 to 0.8 Mya, a result within the range found for the 24 species in the ABC analysis. According to the hypothesis of forest refugia originally proposed by Haffer (1969), the time of origin of many species of lowland Neotropical birds following vicariance must date to the Pleistocene, particularly around the Last Glacial Maximum (LGM) 0.035 – 0.010 Mya. Thus as corollary, this hypothesis implies that intraspecific divergence in Neotropical lowland birds must be even shallower than that assumed among species. Our results largely seem to disagree with this expectation stemming from the forest refugia hypothesis, because for all the 24 species we studied, within-species divergence across the Andes was estimated to have taken place before the LGM. Remarkably, the estimated divergence dates for 11 out of the 24 focal species were in the early Pleistocene. Climatic fluctuations in this period are hypothesized to have been important drivers of diversification in forest birds in the Neotropics (Haffer 1969, Brumfield and Capparella 1996). Based on the above, we concur with Brumfield and Edwards (2007) in concluding that the major Andean uplift had a large initial influence in the genetic divergence of lowland Neotropical birds, and that more recent climatic fluctuations of the Pleistocene could have facilitated episodic crossing of the Andes followed by population isolation via the cyclic origination and disappearance of putative dispersal corridors (Haffer 1967b, 1969). A previous study revealed that across several barriers in the Neotropical lowlands, there was a strong association between the foraging stratum occupied by species and levels of genetic differentiation, with birds of the understory having greater genetic divergence across barriers than birds from the canopy (Burney and Brumfield 2009). This pattern is probably related to the dispersal abilities of birds, because species from the understory tend to be more dispersallimited than those from the canopy (Moore et al. 2008, Burney and Brumfield 2009). In agreement with such data, here we found that in understory species, the evolutionary models best-fit to the data did not involve migration across the Andes or allowed migration only in one direction (east to west). In contrast, in canopy species, the best-fit model allowed migration (at equal rates) in both directions across the Andes. We are currently exploring whether the different pulses of diversification across the Andes detected by the ABC analysis show an association with this aspect of species' ecology. One could predict that species with reduced dispersal abilities (i.e., those from the understory) would exhibit cross-Andes differentiation earlier than species with better dispersal abilities (i.e., those from the canopy), as the latter would have been better able to cross the cordillera during its uplift. We observed incongruent results in analyses of Isolation with Migration in four species (A. spadiceus, A. ochorlaemus, T. gyrola, and M. marginatus) when nuclear and mitochondrial data were analyzed separately. Models allowing migration were better adjusted to nuclear data, whereas models with no or restricted migration were better fit to mitochondrial data. A similar pattern, where nuclear markers showed much higher levels of introgression than mitochondrial and Z-linked markers was found in a study of the Passerina bunting hybrid zone in North America (Carling et al 2010). When populations show reproductive isolation, this pattern can be the outcome of the acting of Haldane’s rule after secondary contact and hybridization, where the heterogametic sex (females in birds) shows reduced fitness and loci inherited via such line show reduced effective gene flow. However, our study largely focused on species with conspecifc populations on each side of the Andes, so unless there has been.
(13) pervasive cryptic speciation, Haldane’s rule does not appear to be a plausible explanation for the discordance between patterns seen in nuDNA and mtDNA. Alternatively, such discordance could reflect differential rates of migration across the Andes in males and females, such that female gene flow might be reduced, but males may disperse more readily. Because very little is known about difference in rates of dispersal between sexes in Neotropical birds, this hypothesis remains open for future testing. Frequently, researchers assume that the formation of large barriers (e.g. the Amazon river, the Andes) have assemblage-wide effects, interrupting gene flow between populations of codistributed taxa (Chapman 1917, Brumfield and Caparella 1996). This study revealed that the use of new comparative phylogeographic approaches across several taxa is necessary to disentangle the real effect of barriers in assemblages across assumed barriers. Remarkably, the four pulses of diversification for lowland Neotropical birds inferred by our analyses appear to have occurred over a protracted period of time, ranging from the Miocene to the Pleistocene. This result indicates that even for studies of intraspecific genetic variation, considering processes occurring over broad windows of time is important to achieve a comprehensive understanding of the evolutionary history of codistributed lineages. On the other hand, the different patterns seen in canopy and understory species underscore the importance of merging of ecological data with genetic studies to better understand the causes of contrasting histories of diversification in codistributed species. Our data show that, even across one of the most important geographic barriers in the world, responses to barriers in terms of reduction of gene flow can be highly species-specific or variable across groups of species contingent on ecological traits (e.g. foraging preference) and other aspects. References Arbogast, B. S., Edwards, S. V., Wakeley, J., Beerli, P. & Slowinski, J. B. 2002 estimating divergence times from molecular data on phylogenetic and populations genetic timescales. Annu. Rev. Ecol. Syst. 33, 707–740. (doi:10.1146/annurev.ecolsys.33.010802.150500) Avise, J.C. 1994 Molecular Markers, Natural History and Evolution, Chapman and Hall, New York, NY. Avise, J. C., Walker, D. and Johns, G. C. 1998 Speciation durations and Pleistocene effects in vertebrate phylogeography. Proc. R. Soc. Lond. B. 265, 1707–1712. (doi:10. 1098/rspb.1999.0492) Avise, J. C. 2000 Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MA Backström, N., S. Fagerberg, and H. Ellegren. 2008 Genomics of natural bird populations: a genebased set of reference markers evenly spread across the avian genome. Mol. Ecol. 17, 964-980. Barber, B.R., and Klicka, J. 2010 Two pulses of diversification across the Isthmus of Tehuantepec in a montane Mexican bird fauna. Proc. R. Soc. Lond. B. 277, 2675-2681 Bermingham, E. and Mortiz, C. 1998 Comparative phylogeography: concepts and applications. Mol. Ecol. 7, 367–369. (doi:10.1046/j.1365-294x.1998.00424.x).
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(19) Figures and Tables Figure 1. Posterior probability distribution for Hyper-parameter Psi (Ψ, number of possible divergence events) recovered using categorical regression (dark bars). The probability distribution for prior Psi is uniform, with boundary values of zero and number of taxa analyzed (i.e. 0, 22).. Figure 2. Geographic provenance of samples considered for genetic analyses. Maps indicate the sampling scheme for each of the clades involved in the 22 cross-Andes comparisons (R. carbo/dimidiatus is for Ramphocelus dimidiatus and Ramphocelus carbo; C. lanceolata/pareola is for Chiroxiphia lanceolata and Chiroxiphia pareola). Each dot represents one locality from where at least one individual was sampled. Figure 3. Unrooted haplotype network showing relationships between ND2 haplotypes for each of the clades involved in the 22 cross-Andes comparisons (R. carbo/dimidiatus is for Ramphocelus dimidiatus and Ramphocelus carbo; C. lanceolata/pareola is for Chiroxiphia lanceolata and Chiroxiphia pareola). The number of individuals sampled is indicated in parentheses. Black and grey circles represent populations from east and west of the Andes, respectively. Circles are proportional to the number of individuals showing each haplotype. The scale among networks is different..
(20) Automolus ochrolaemus. Attila spadiceus. Coereba flaveola. Dendrocincla fuliginosa. Glyphorynchus spirurus. Myrmotherula axillaris. pipra erythrocephala. Microcerculus marginatus. Tyrannus melancholicus. Schiffornis turdina. Phaeothlypis fulvicauda. Pionus menstruus. Volatinia jacarina. Sclerurus mexicanus. Mionectes oleagineus. Tangara gyrola. R. dimidiatus/carbo. Dixiphia pipra. Myiozetetes cayanensis. Xenops minutus. Poecilotriccus sylvia. C. lanceolata/pareola.
(21) Automolus ochrolaemus (84). Glyphorynchus spirurus (103). Phaeothlypis fulvicauda (22). Volatinia jacarina (18). Attila spadiceus (31). Coereba flaveola (56). Myrmotherula axillaris (49). pipra erythrocephala (23). Pionus menstruus (22). Tyrannus melancholicus (12). Sclerurus mexicanus (11). Mionectes oleagineus (175). Dendrocincla fuliginosa (78). Microcerculus marginatus (29). Schiffornis turdina (125). Tangara gyrola (51). R. dimidiatus/carbo (40). Myiozetetes cayanensis (5). Poecilotriccus sylvia (11). Dixiphia pipra (13). Xenops minutus (77). C. lanceolata/pareola (16).
(22) Tables Table 1. List of focal taxa with number of ND2 (mitochondrial loci) sequences obtained from west and east of the Andes mountains. The number of variable and parsimony informative (Pars. Info.) sites per alignment without outgroups are also indicated. Bold labels indicate species restricted to one side of the Andes whose corresponding sister lineages occur on the opposite side of the mountain range. Totals and averages for each column are given beneath the thick line. Sample west. Sample east. Total sample. SeqLen (bp). Variable sites. Pars. Info.. ochrolaemus. 24. 60. 84. 1041. 112. 84. 0.326,0.322,0.088,0.264. Coereba. flaveola. 33. 23. 56. 1023. 121. 82. 0.281,0.373,0.112,0.234. Dendrocincla. fuliginosa. 17. 61. 78. 1041. 157. 126. 0.327,0.315,0.088,0.27. Glyphorynchus. spirurus. 61. 42. 103. 1041. 227. 200. 0.329,0.331,0.091,0.249. Myiozetetes. cayanensis. 2. 3. 5. 1041. 8. 7. 0.298,0.321,0.1,0.281. Mionectes. oleagineus. 126. 49. 175. 1041. 154. 99. 0.295,0.331,0.112,0.262. Phaeothlypis. fulvicauda. 10. 12. 22. 1040. 119. 101. 0.298,0.356,0.1,0.246. Poecilotriccus. sylvia. 7. 4. 11. 1041. 47. 27. 0.305,0.294,0.103,0.298. Sclerurus. mexicanus. 6. 5. 11. 1041. 224. 166. 0.322,0.334,0.099,0.245. Tangara. gyrola. 29. 22. 51. 1041. 150. 87. 0.295,0.366,0.104,0.235. Volatinia. jacarina. 13. 5. 18. 1040. 33. 17. 0.305,0.351,0.107,0.237. Attila. spadiceus. 8. 23. 31. 1040. 31. 19. 0.306,0.306,0.095,0.293. Chiroxiphia. lanceolata/pareola. 4. 12. 16. 1033. 126. 101. 0.309,0.315,0.096,0.28. Dixiphia. pipra. 2. 11. 13. 1041. 76. 54. 0.315,0.313,0.088,0.284. Myrmotherula. axillaris. 4. 45. 49. 1041. 135. 94. 0.32,0.337,0.091,0.252. Microcerculus. marginatus. 4. 25. 29. 1041. 189. 154. 0.306,0.352,0.112,0.23. Pipra. erythrocephala. 7. 16. 23. 1037. 41. 14. 0.31,0.312,0.094,0.284. Pionus. menstruus. 6. 16. 22. 1041. 90. 82. 0.329,0.362,0.09,0.219. Ramphocelus. dimidiatus/carbo. 14. 26. 40. 1041. 205. 73. 0.288,0.344,0.113,0.255. Schiffornis. turdina. 37. 88. 125. 1020. 214. 196. 0.305,0.322,0.091,0.282. Tyrannus. melancholicus. 6. 6. 12. 1041. 60. 30. 0.292,0.333,0.097,0.278. Xenops. minutus. 47. 30. 77. 1034. 170. 146. 0.33,0.305,0.091,0.274. Total. 467. 584. 1051. 22841. 2689. 1959. Average. 21. 26. 48. 1038. 122. 123. Genus. Species. Automolus. Frequency A,C,G,T.
(23) Table 2. Models with prior values of 0.5 for divergence time (Tau) always recovered as most probable a single simultaneous divergence event (Ψ = 1) across the Andes in the species studied. Models are described as a set of prior values for: divergence time (Tau), population size and migration (each row corresponds to a model). In this analysis models as a whole were compared to each other using Bayes factors (Bayes factors in bold indicate the three models with the highest values). The model indicated in gray was never recovered in the accept/reject step. Model Tau (τ) 1 1 1 1 1 1 2 2 2 2 2 2 0.5 0.5 0.5 0.5 0.5 0.5. Population size 0.5 0.5 0.1 0.1 0.01 0.01 0.5 0.5 0.1 0.1 0.01 0.01 0.5 0.5 0.1 0.1 0.01 0.01. Migration 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0. Psi (ψ) 1 1 4 1 3 1 1 1 4 2 3 2 1 1 10 2 4 3. Posterior probability 0.07906088 0.00018732 0.1331976 0.03937932 0.08041698 0.02126246 0.01086996 1.5303E-07 0.000440239 1.17E-06 0.001478947 0 0.07151748 0.04965128 0.2298715 0.03739322 0.219248 0.02602344. Bayes factor 0.944329228 0.002060901 1.690320293 0.450929854 0.961943413 0.238968122 0.120883566 1.68333E-06 0.004844765 1.28701E-05 0.016292514 0 0.847288243 0.574698632 3.283330857 0.427303703 3.088981082 0.293906308. Table 3. When prior values were compared individually the resulting best-fit model was Tau = 1, population-size = 0.1 and migration 1; Bayes factor associated with such prior values are indicated in bold. Posterior probabilities for models with the same prior values (e.g. Tau = 2) were summed to obtain information about the influence of each value to the global posterior density. Prior Tau. Population size Migration. Value. Sum. Bayes factor. 2. 0.012790469. 0.060187323. 1. 0.35350456. 8.386031502. 0.5. 0.07151748. 0.390491131. 0.5. 0.211287073. 0.535776907. 0.1. 0.440283049. 1.573234789. 0.01. 0.348429827. 1.069508301. 0. 0.173898363. 0.210504816. 1. 0.826101586. 4.750485135.
(24) Table 4. List of taxa used in the Information Theoretical approach with number of ND2 (mitochondrial) and intron (nuclear) sequences occurring west and east of the Andes. Total and average for each column are given beneath the thick line.. Taxon name. Foraging preference. Mitochondrial. Nuclear. ND2. Bfib 7. M16482. M17483. west east length. west east length. west east length. west east length. A. ochrolaemus. Understory. 24. 60. 1041. 34. 34. 132. 28. 28. 120. 40. 40. 267. M. axillaris. Understory. 4. 45. 1041. 10. 14. 207. 10. 14. 465. 8. 18. 577. M. marginatus. Understory. 4. 25. 1041. 7. 16. 310. 8. 16. 445. 8. 16. 573. X. minutus. Understory. 47. 30. 1034. 40. 40. 322. 28. 28. 146. 46. 51. 582. T. gyrola. Canopy. 29. 22. 1041. 6. 14. 212. 6. 14. 465. 6. 17. 570. A. spadiceus. Canopy. 8. 23. 1040. 40. 40. 204. 28. 28. 414. 40. 40. 212. P. menstruus. Canopy. 6. 16. 1041. 4. 10. 298. 4. 10. 437. 4. 9. 542. Total. 122. 221. 7279. 141. 168. 1685. 112. 138. 2492. 152. 191. 3323. Average. 17. 32. 1040. 20. 24. 241. 16. 20. 356. 22. 28. 478. Table 5. Analyses of patterns of genetic variation in species with understory foraging preferences always recovered models involving no migration either in one or both directions across the Andes. Conversely, species with canopy foraging preferences showed highest values of Akaike weights in models involving migration in both directions across Andes. Taxon name A. ochrolaemus M. axillaris M. marginatus X. minutus T. gyrola A. spadiceus P. menstruus. Foraging preference Understory Understory Understory Understory Canopy Canopy Canopy. Best Model ABC00 ABC00 ABB0E ABC00 AACDD AACDD ABCDD. Akaike Weights 0.238279578 0.32986576 0.26376956 0.445903472 0.32897798 0.300382653 0.37890918.
(25) Appendix 1. Geographic and specimen information associated with samples included in analyses. Instituto Alexander von Humboldt (IAvH(, Instituto de Ciencias Naturales (ICN), Banco de Tejidos de la Universidad de los Andes (Andes-BT), Louisiana State University Museum of Natural Science (LSUMZ). Museum Number Genus. Specie. Country. IAvH. 10053. Automolus. ochrolaemus. Colombia. IAvH. 10331. Automolus. ochrolaemus. Colombia. IAvH. 11206. Automolus. ochrolaemus. Colombia. IAvH. 11217. Automolus. ochrolaemus. Colombia. Locality. Detailed Locality. IAvH. 11229. Automolus. ochrolaemus. Colombia. Vichada. IAvH. 12853. Automolus. ochrolaemus. Colombia. Vichada. IAvH. 14393. Automolus. ochrolaemus. Colombia. Vichada. IAvH. 14413. Automolus. ochrolaemus. Colombia. Amazonas. IAvH. 14533. Automolus. ochrolaemus. Colombia. Amazonas. IAvH. 14569. Automolus. ochrolaemus. Colombia. Santander. ICN. 36141. Automolus. ochrolaemus. Colombia. Santander. ICN. 36141. Automolus. ochrolaemus. Colombia. Santander. Cumaribo Cgto.Santa Rita PNN El Tuparro Bosque de Guaipé (tierra firme). Localizado en la margen izquierda (aguas arriba) del Río Tomo a 24 Km de la confluencia de éste con el Orinoco ( 20 5 Km del Centro administrativo del parque) Cumaribo selva de Mataven Río Orinoco bosque de cerros rocosos residuales en granitos del Escudo Guayanés BR Cumaribo selva de Mataven Caño Matavén bosque alto del plano inundable bocas del caño Cajaro BI-b Leticia Reserva Forestal del Río Calderón Estación Biologica El Zafire Bosque Varillal Leticia Reserva Forestal del Río Calderón Estación Biologica El Zafire Bosque Varillal Zapatoca Mata'e Cacao Paz cerro La serranía los Yaguríes Zapatoca Mata'e Cacao Paz cerro La serranía los Yaguríes Zapatoca Mata'e Cacao Paz cerro La serranía los Yaguríes. ICN. 36141. Automolus. ochrolaemus. Colombia. Santander. Simacota Bajo Flores Blancas Pedro Elías montaña. ICN. 36335. Automolus. ochrolaemus. Colombia. Santander. Simacota Bajo Flores Blancas Pedro Elías montaña. ICN. 36335. Automolus. ochrolaemus. Colombia. Santander. Simacota Bajo Flores Blancas Pedro Elías montaña. ICN. 36335. Automolus. ochrolaemus. Colombia. Pasco. Km 41 on Villa Rica - Puerto Bermudez highway. LSUMZ. 2027. Automolus. ochrolaemus. Peru. Pasco. Km 41 on Villa Rica - Puerto Bermudez highway. LSUMZ. 2063. Automolus. ochrolaemus. Peru. Darién. LSUMZ. 2241. Automolus. ochrolaemus. Panama. Loreto. LSUMZ. 4234. Automolus. ochrolaemus. Peru. Loreto. LSUMZ. 4264. Automolus. ochrolaemus. Peru. Loreto. Cana on E slope Cerro Pirré Lower Rio Napo region E. bank Rio Yanayacu ca. 90km N Iquitos Lower Rio Napo region E. bank Rio Yanayacu ca. 90km N Iquitos S Rio Amazonas ca 10km SSW mouth Rio Napo on E bank Quebrada Vainilla. LSUMZ. 5123. Automolus. ochrolaemus. Peru. Beni. LSUMZ. 6785. Automolus. ochrolaemus. Bolivia. Pando. LSUMZ. 8921. Automolus. ochrolaemus. Bolivia. Pando. LSUMZ. 9089. Automolus. ochrolaemus. Bolivia. Pando. Serrania Pilon Nicolás Suarez 12 km by road S of Cobija 8 km W on road to Mucden Nicolás Suarez 12 km by road S of Cobija 8 km W on road to Mucden Nicolás Suarez 12 km by road S of Cobija 8 km W on road to Mucden. LSUMZ. 9124. Automolus. ochrolaemus. Bolivia. Ucayali. W. bank Rio Shesha 65 km ENE Pucallpa. LSUMZ. 10514. Automolus. ochrolaemus. Peru. Ucayali. W. bank Rio Shesha 65 km ENE Pucallpa. LSUMZ. 10655. Automolus. ochrolaemus. Peru. Ucayali. E. bank Rio Shesha 65 km ENE Pucallpa. LSUMZ. 10864. Automolus. ochrolaemus. Peru. Ucayali. SE slope Cerro Tahuaya ca km ENE Pucallpa. LSUMZ. 11048. Automolus. ochrolaemus. Peru. Ucayali. SE slope Cerro Tahuayo ca km ENE Pucallpa. LSUMZ. 11164. Automolus. ochrolaemus. Peru. Ucayali. LSUMZ. 11244. Automolus. ochrolaemus. Peru. SE slope Cerro Tahuayo ca km ENE Pucallpa Velasco 32 km E Aserradero pre Parque Nacional Noel Santa_Cruz Kempff Mercado.. LSUMZ. 12375. Automolus. ochrolaemus. Bolivia. Santa_Cruz Velasco 50 km ESE Florida Arroyo del Encanto. LSUMZ. 12462. Automolus. ochrolaemus. Bolivia. Santa_Cruz Velasco 50 km ESE Florida Arroyo del Encanto. LSUMZ. 12479. Automolus. ochrolaemus. Bolivia. Santa_Cruz Velasco 50 km ESE Florida Arroyo del Encanto. LSUMZ. 12537. Automolus. ochrolaemus. Bolivia. Santa_Cruz Serrania de Huanchaca 45 km E Florida. LSUMZ. 13829. Automolus. ochrolaemus. Bolivia. Santa_Cruz Serrania de Huanchaca 21 km SE Catarata Arco Iris. LSUMZ. 14484. Automolus. ochrolaemus. Bolivia.
(26) LSUMZ. 14488. Automolus. ochrolaemus. Santa_Cruz Department. LSUMZ. 15160. Automolus. ochrolaemus. Bolivia. LSUMZ. 18161. Automolus. ochrolaemus. Bolivia. LSUMZ. 18197. Automolus. ochrolaemus. Bolivia. LSUMZ. 18244. Automolus. ochrolaemus. Bolivia. LSUMZ. 18318. Automolus. ochrolaemus. Bolivia. LSUMZ. 18522. Automolus. ochrolaemus. Bolivia. Velasco Parque Nacional Noel Keonpff Mercado 86 km Santa_Cruz ESE Florida Velasco Parque Nacional Noel Keonpff Mercado 86 km Santa_Cruz ESE Florida Velasco Parque Nacional Noel Keonpff Mercado 86 km Santa_Cruz ESE Florida Velasco Parque Nacional Noel Keonpff Mercado 86 km Santa_Cruz ESE Florida Velasco Parque Nacional Noel Keonpff Mercado 60 km Santa_Cruz ESE of Florida Velasco Parque Nacional Noel Keonpff Mercado 60 km Santa_Cruz ESE of Florida. LSUMZ. 18550. Automolus. ochrolaemus. Bolivia. Amazonas. Manaus km 34 ZF-3 Faz. Esteio ca 80 km N. Manaus. LSUMZ. 20251. Automolus. ochrolaemus. Brazil. Amazonas. LSUMZ. 20424. Automolus. ochrolaemus. Brazil. La_Paz. LSUMZ. 22613. Automolus. ochrolaemus. Bolivia. La_Paz. LSUMZ. 22633. Automolus. ochrolaemus. Bolivia. La_Paz. Manaus km 34 ZF-3 Faz. Esteio ca 80 km N. Manaus Prov. B. Saavedra 83 km by road E charazani Cerro Asunta Pata Prov. B. Saavedra 83 km by road E Charazani Cerro Asunta Pata Prov. B. Saavedra 83 km by road E. Charazani Cerro Asunta Pata. LSUMZ. 22841. Automolus. ochrolaemus. Bolivia. Loreto. Ca. 86 km SE Juanjui on E bank upper Rio Pauya. LSUMZ. 39826. Automolus. ochrolaemus. Peru. Loreto. Ca. 86 km SE Juanjui on E bank upper Rio Pauya. LSUMZ. 39944. Automolus. ochrolaemus. Peru. Loreto. 86 km SE Juanjui on E bank of upper Rio Pauya. LSUMZ. 40504. Automolus. ochrolaemus. Peru. Loreto. 86 km SE Juanjui on E bank of upper Rio Pauya. LSUMZ. 40551. Automolus. ochrolaemus. Peru. San_Martín CA 26 km SSE Tarapoto. LSUMZ. 46009. Automolus. ochrolaemus. Peru. San_Martín Quebrada Upaquihua ca 26 km SSE Tarapoto. LSUMZ. 46133. Automolus. ochrolaemus. Peru. Kopinang. ca 7 km SW Kopinang Village. LSUMZ. 48330. Automolus. ochrolaemus. Guyana. Kopinang. ca 7 km SW Kopinang Village. LSUMZ. 48396. Automolus. ochrolaemus. Guyana. Kopinang. ca 7 km SW Kopinang Village. LSUMZ 48411 ANDESBT 2215 ANDESBT 2216. Automolus. ochrolaemus. Guyana. Attila. spadiceus. Colombia. Caldas. Norcasia Vereda San Roque Reserva Natural Riomanso. Attila. spadiceus. Colombia. Caldas. Norcasia Vereda San Roque Reserva Natural Riomanso. IAvH. 5857. Attila. spadiceus. Colombia. Meta. P.N.N. La Macarena cabaña caño Cafre. IAvH. 7061. Attila. spadiceus. Colombia. IAvH. 12502. Attila. spadiceus. Colombia. Meta Valle del Cauca. P.N.N. La Macarena río Duda de Cauca Mpio La Cumbre Vda. Chicoral Cgto. Bitaco cuenca alta del río Bitaco Bosque 2 de las avispas. IAvH. 13307. Attila. spadiceus. Colombia. Caldas. Norcasia Vereda San Roque Reserva Natural Riomanso. IAvH. 14178. Attila. spadiceus. Colombia. Casanare. Pore Vda. Altamira Finca La Esperanza La Esperanza. IAvH. 14179. Attila. spadiceus. Colombia. Casanare. Pore Vda. Altamira Finca La Esperanza La Esperanza. ICN. 35170. Attila. spadiceus. Colombia. Antioquia. ICN. 35808. Attila. spadiceus. Colombia. Santander. ICN. 36062. Attila. spadiceus. Colombia. Amazonas. Amalfi Salazar Bodega Vieja finca trocha a Aguadeños Río Negro Llano de Palmas La Honda Rinos camino al club margen derecho de quebrada la Honda Leticia La Pedrera San Pablo San Pablo Orilla norte río Caquetá. ICN. 36099. Attila. spadiceus. Colombia. Meta. Villavicencio Mesetas Jardín Botánico Villavicencio. ICN. 36222. Attila. spadiceus. Colombia. Santander. Sabana de Torres Capo Tigre Campo Tigre bosque de. ICN. 36334. Attila. spadiceus. Colombia. Santander. ICN. 36359. Attila. spadiceus. Colombia. Santander. Simacota Bajo Flores Blancas Pedro Elías montaña Simacota Bajo Flores Blancas Pedro Elías montaña El Centro. LSUMZ. 5419. Attila. spadiceus. Peru. San_Martín 20 km by road NE Tarapoto on road to Yurimaguas. LSUMZ. 5429. Attila. spadiceus. Peru. LSUMZ. 9353. Attila. spadiceus. Bolivia. LSUMZ. 9506. Attila. spadiceus. Bolivia. San_Martín 20 km by road NE Tarapoto on road to Yurimaguas Nicolás Suarez 12 km by road S of Cobija 8 km W on road Pando to Mucden Nicolás Suarez 12 km by road S of Cobija 8 km W on road Pando to Mucden. LSUMZ. 10639. Attila. spadiceus. Peru. Ucayali. LSUMZ. 12575. Attila. spadiceus. Bolivia. Santa_Cruz Velasco 50 km ESE Florida Arroyo del Encanto. LSUMZ. 55049. Attila. spadiceus. Honduras. Atlántida. Pico Banito National park Ei Naranjo. LSUMZ. 60798. Attila. spadiceus. Honduras. Lancetilla botanical gardens entrance road. Coereba. flaveola. Colombia. Atlántida Norte de Santander. AndesBT 266. W. bank Rio Shesha 65 km ENE Pucallpa. San Cayetano vereda Tabiro: Predio Corponor.
(27) AndesBT #. Coereba. flaveola. Colombia. Valle Andalucia Neira Vda. El Bohio Hda. Tintina Cuenca del Río Tapias Bosque seco en ladera. IAvH. 11957. Coereba. flaveola. Colombia. Caldas. IAvH. 13278. Coereba. flaveola. Colombia. Caldas. IAvH. 13620. Coereba. flaveola. Colombia. Casanare. IAvH. 13623. Coereba. flaveola. Colombia. Casanare. IAvH. 14120. Coereba. flaveola. Colombia. Huila. Norcasia Vereda San Roque Reserva Natural Riomanso Páz de Ariporo corregimiento La Hermosa Finca Nicaragua Sabana no inundable Páz de Ariporo corregimiento La Hermosa Finca Nicaragua Sabana no inundable Palestina Vda. Jericó Bosque al W del cerro La Mensura. Finca Villa Nora. IAvH. 14627. Coereba. flaveola. Colombia. Arauca. Arauquita Vda. Las Acacias Plataforma el Jiba. IAvH. 14752. Coereba. flaveola. Colombia. Arauca. Arauca Estero La Conquista. IAvH. 15270. Coereba. flaveola. Colombia. Sucre. LSUMZ. 5168. Coereba. flaveola. Peru. Toluviejo Cgto. El Cañito Monte de Los Navas Las Pampas km 885 Pan-American Hwy 11 road km from Lambayeque Olmos. LSUMZ. 10900. Coereba. flaveola. Peru. Ucayali. N. bank Rio Abujao 2 km E Caserio de Abujao. LSUMZ. 10902. Coereba. flaveola. Peru. Ucayali. N. bank Rio Abujao 2 km E Caserio de Abujao. LSUMZ. 10904. Coereba. flaveola. Peru. Ucayali. LSUMZ. 12913. Coereba. flaveola. Bolivia. LSUMZ. 13024. Coereba. flaveola. Bolivia. LSUMZ. 15187. Coereba. flaveola. Bolivia. LSUMZ. 22712. Coereba. flaveola. Bolivia. Santa_Cruz Velasco 10 km SSW Piso Firme Prov. B. Saavedra 83 km by road E. Charzani Cerro La_Paz Asunta Pata.. LSUMZ. 26470. Coereba. flaveola. LSUMZ. 28189. Coereba. flaveola. Panama. Chiriquí. Dist. Gualaca Cordillera Central 4.3 km by road S Lago Fortuna dam. LSUMZ. 32844. Coereba. flaveola. Peru. Cajamarca. Los Juntas junction of Rios Tabaconas and Chinchipe. LSUMZ. 32907. Coereba. flaveola. Peru. Cajamarca. Las Juntas junction of Rios Tabaconas and Chinchipe. LSUMZ. 32934. Coereba. flaveola. Peru. Cajamarca. Las Juntas junction of Rios Tabaconas and Chinchipe. LSUMZ. 32939. Coereba. flaveola. LSUMZ. 32971. Coereba. flaveola. LSUMZ. 46657. Coereba. flaveola. Panama. Veraguas. Isla Coiba Sendero los Monos. LSUMZ. 48542. Coereba. flaveola. Guyana. Region. Ireng River km Karasabai. AndesBT 17. Chiroxiphia. lanceolata. Colombia. AndesBT 1429. Chiroxiphia. lanceolata. Colombia. Cesar Norte de Santander. Valledupar R.N. Los Besotes San Cayetano Norte San Cayetano San Isidro Predio Corponor-San Isidro. LSUMZ. 26896. Chiroxiphia. lanceolata. Panama. Panamá. Old Gamboa Road 5 km NW Paraiso. LSUMZ. 26922. Chiroxiphia. lanceolata. Panama. Panamá. Old Gamboa Road 5 km NW Paraiso. LSUMZ. 28626. Chiroxiphia. lanceolata. Panama. Panamá. Old Gamboa Road 54 meters NW Paraiso. LSUMZ. 46666. Chiroxiphia. lanceolata. Panama. Veraguas. Isla Coiba Sendero los Monos. LSUMZ. 46667. Chiroxiphia. lanceolata. Panama. Veraguas. Isla Coiba Sendero los Monos. LSUMZ. 2595. Chiroxiphia. pareola. Peru. Loreto. 1 km N Rio Napo 157 km by river NNE Iquitos. LSUMZ. 2906. Chiroxiphia. pareola. Peru. Loreto. LSUMZ. 4845. Chiroxiphia. pareola. Peru. Loreto. LSUMZ. 5005. Chiroxiphia. pareola. Peru. Loreto. 1 km N Rio Napo 157 km by river NNE Iquitos S Rio Amazonas ca 10km SSW mouth Rio Napo on E bank Quebrada Vainilla S Rio Amazonas ca 10km SSW mouth Rio Napo on E bank Quebrada Vainilla. LSUMZ. 5470. Chiroxiphia. pareola. Peru. LSUMZ. 8858. Chiroxiphia. pareola. Bolivia. LSUMZ. 8860. Chiroxiphia. pareola. Bolivia. LSUMZ. 9057. Chiroxiphia. pareola. Bolivia. San_Martín 28 km by road NE Tarapoto on road to Yurimaguas Nicolás Suarez 12 km by road S of Cobija 8 km W on road Pando to Mucden Nicolás Suarez 12 km by road S of Cobija 8 km W on road Pando to Mucden Nicolás Suarez 12 km by road S of Cobija 8 km W on road Pando to Mucden. LSUMZ. 10584. Chiroxiphia. pareola. Peru. Ucayali. W. bank Rio Shesha 65 km ENE Pucallpa. LSUMZ. 11041. Chiroxiphia. pareola. Peru. Ucayali. SE slope Cerro Tahuayo ca km ENE Pucallpa. LSUMZ. 11063. Chiroxiphia. pareola. Peru. Ucayali. SE slope Cerro Tahuayo ca km ENE Pucallpa. LSUMZ. 36762. Chiroxiphia. pareola. Brazil. Rondônia. Biologica Rebid Duro Preto ca 70 km E guajara-Miram. N. bank Rio Abujao 2 km E Caserio de Abujao Velasco W Bank Rio Paucerna 4 km upstream from Rio Santa_Cruz Itenez Velasco W Bank Rio Paucerna 4 km upstream from Rio Santa_Cruz Itenez.
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