Tradeoff between robustness and elaboration in carotenoid networks produces cycles of avian color diversification
© Badyaev et al. 2015
Received: 25 May 2015
Accepted: 12 August 2015
Published: 20 August 2015
Resolution of the link between micro- and macroevolution calls for comparing both processes on the same deterministic landscape, such as genomic, metabolic or fitness networks. We apply this perspective to the evolution of carotenoid pigmentation that produces spectacular diversity in avian colors and show that basic structural properties of the underlying carotenoid metabolic network are reflected in global patterns of elaboration and diversification in color displays. Birds color themselves by consuming and metabolizing several dietary carotenoids from the environment. Such fundamental dependency on the most upstream external compounds should intrinsically constrain sustained evolutionary elongation of multi-step metabolic pathways needed for color elaboration unless the metabolic network gains robustness – the ability to synthesize the same carotenoid from an additional dietary starting point.
We found that gains and losses of metabolic robustness were associated with evolutionary cycles of elaboration and stasis in expressed carotenoids in birds. Lack of metabolic robustness constrained lineage’s metabolic explorations to the immediate biochemical vicinity of their ecologically distinct dietary carotenoids, whereas gains of robustness repeatedly resulted in sustained elongation of metabolic pathways on evolutionary time scales and corresponding color elaboration.
The structural link between length and robustness in metabolic pathways may explain periodic convergence of phylogenetically distant and ecologically distinct species in expressed carotenoid pigmentation; account for stasis in carotenoid colors in some ecological lineages; and show how the connectivity of the underlying metabolic network provides a mechanistic link between microevolutionary elaboration and macroevolutionary diversification.
This article was reviewed by Junhyong Kim, Eugene Koonin, and Fyodor Kondrashov. For complete reports, see the Reviewers’ reports section.
KeywordsMetabolic networks Robustness Metabolic distance Diversification
What determines evolutionary pathways of phenotypic diversification? Historically, the emphasis was placed on whether structural consideration of trait development or environment of trait functioning are more influential at predicting and directing evolutionary change . One insightful attempt to integrate these perspectives showed that the patterns of connectivity among elements of the deterministic landscape (e.g., gene networks) that underlie trait variation allows reconciliation of the “structuralist” and “functionalist” perspectives : the patterns of connectivity that are formed by historical association of such elements delineate the pathways available for future evolution [3–6]. In light of recent empirical findings that the age of many genes (and most other components of current adaptations) vastly exceeds the duration of their current genomic and physiological associations (i.e., current functions), such a framework also reconciles historical contingency and contemporary evolutionary dynamics of adaptations [4, 7, 8]. However empirical tests of this framework are rare and require demonstration that historical associations among elements of deterministic network are not themselves modified by recent evolution. A particularly clear example would come from deterministic networks whose origin clearly predates their subsequent use.
A combination of known biochemical reactions among naturally occurring carotenoids (here denoted “global network” of carotenoid biosynthesis) is a connected network (Additional file 1: Appendix S1 and the references therein). The majority of enzymatic reactions of this network appears to have evolved in the context of bacterial evolution [9–16], such that most carotenoid-producing enzymatic networks in other taxa studied to date (e.g., fungi, plants, animals) are subsets of this global network (Additional file 1: Appendix S1). Importantly, carotenoids are commonly used for coloration by taxa that cannot themselves produce carotenoids from non-carotenoids (e.g., most animals) . We therefore hypothesized that the evolutionary diversification in carotenoid-based coloration in these taxa should be a reflection of the structure and patterns of enzymatic connectivity of the underlying “global network” of carotenoid biosynthesis.
Carotenoids play a multitude of functions in birds, and carotenoid-based pigmentation of plumage has evolved multiple times (e.g., [22, 23]). Here we specifically focused on species that express carotenoids in their plumage because these species are most likely to be under selection for metabolic elongation or metabolic efficiency of production of their expressed carotenoids – the amount of carotenoids needed for coloration is typically far larger than required for other carotenoid functions, such as trans-membrane transport or antioxidization. First, we build metabolic networks for plumage carotenoids for 159 species that have been studied to May 2014 (Additional file 1: Appendix S1 and Additional file 3: Appendix S2). Distribution of these species on avian phylogeny gives us insight into 113.5 million years of avian evolution (Additional file 4: Figure S1, Methods). We then compared the metabolic pathway-based phylogeny and the molecular-based supertree for these species and compared evolutionary divergence for upstream and downstream elements of the carotenoid-producing networks. In birds, closely related species often diverge in carotenoid networks, whereas evolutionary distant and ecologically distinct taxa can be highly convergent in their expressed carotenoids [24, 25]. Here we hypothesize that these patterns occur because diversification of avian carotenoid-producing pathways occurs on a limited biochemical space; such that over evolutionary time many species of birds repeatedly occupied shared metabolic space of the global carotenoid network.
Second, we examine whether diversification in carotenoid metabolism in general, and periodic species convergence in particular, are quantitatively predictable from the structure of biochemical network on which birds diversify. We present evidence for strong structural trade-off between robustness – ability to sustain gain or loss of compounds without extinction – and elongation of carotenoid metabolic pathways in species that depend on external (dietary) starting points for their carotenoid production. We test empirical predictions of this trade-off in extant bird species, explore its likely evolutionary trajectories, and use it to calibrate potential for metabolic diversification across avian taxa that utilize diverse dietary carotenoids.
Results and discussion
Metabolic and phylogenetic distances
We thus calculated the extent to which evolutionary divergence correlated with metabolic divergence separately for elements and reactions comprised only of “dietary”, “intermediate” (metabolized, but not expressed), and “expressed” carotenoids and partitioned these correlations by periods of 20 million years (Fig. 1c). We found that diversification in expressed carotenoids was the greatest in recently diverged species (<20 my in lower panel of Fig. 1c) and was produced by metabolic diversification (middle panel, Fig. 1b) and not by shifts in dietary (external) starting points (upper panel, Fig. 1c). Surprisingly, over longer evolutionary time, as species’ diets continued to diverge, intermediate and plumage compounds and reactions became increasingly similar (Fig. 1c), suggesting increasing occupancy of shared metabolic space by longer metabolic pathways originating from different dietary starting points (Fig. 1a). Such recurrent occupancy of shared metabolic space reveals a structural basis for previously unexplained, but often documented evolutionary convergence in plumage carotenoids among distant species and also puzzlingly rapid divergence of closely related species in expressed carotenoids (e.g., [22, 25, 26]) (Fig. 2).
For example, Red-headed quelea (Quelea erythrops) and Cream-backed woodpecker (Campephilus leucopogon) have identical carotenoids in their plumage (lutein, zeaxanthin,α-doradexanthin, astaxanthin, adonirubin, canthaxanthin) despite more than 90 million years since divergence (Additional file 4: Figure S1, Additional file 3: Appendix S2). In contrast, rapid metabolic diversification had led to highly distinct plumage carotenoids between recently diverged and ecologically similar taxa, such as bullfinches Pyrrhula pyrrhula and P. aurantiaca that have diverged less than 5 million years ago or cotingas Rupicola rupicola and R. peruviana that have diverged less than 4 million years ago, with neither sister groups sharing the expressed carotenoids (Additional file 3: Appendix S2 and references therein).
Structure of global carotenoid network predicts avian color diversification
These structural inferences are strongly corroborated by empirical data: only the species with three or more distinct dietary starting points accomplished significant elongation of their carotenoid-producing pathways (Fig. 3b), whereas metabolic pathways of 83 extant species that depended on less than two dietary carotenoids (Additional file 3: Appendix S2) was strictly confined to a single metabolic step around dietary entries. Even when we controlled statistically for variation in dietary (most upstream) starting points (, Additional file 8: Table S1]), network robustness was still associated with pathway elongation across all species (Fig. 3c, “maximum” network: b ST = 0.73, t = 23.53, P < 10−3; “minimum” network: b ST = 0.48, t = 17.53, P < 10−3), likely by lessening the probability of compound’s loss during evolution of long metabolic pathways (Fig. 3a). Comparison of these empirical data with the “maximum possible elaboration” – the longest metabolic pathway structurally possible in the connected avian carotenoid network (Fig. 3b, Additional file 5: Figure S3) – suggests that dietary robustness enables metabolic pathway elongation by extending time available for its evolution.
Compound’s network topology and representation in avian evolution
Metabolic network robustness is thus a central feature of avian carotenoid color elaboration (Fig. 3b and c) and such robustness is accomplished by acquisition of redundant dietary pathways.
Evolution of association between metabolic elongation and dietary robustness
Thus, patterns of connectivity of carotenoid network are reflected in the likelihood of metabolic elongation. It follows, that when enzymatic connectivity in the vicinity of different dietary entry points vary, ecologically-distinct taxa should have different potential for color elaboration and diversification.
Calibrating potential for metabolic pathway elongation across dietary entry points
Gains of dietary robustness (Fig. 9) can sustain ongoing metabolic elongation only when network structure enables the acquisition of a compound connected to a new dietary pathway in the smallest evolutionary step [2, 3, 5] (Fig. 1a). What is the maximum radius (in number of reactions) of the network space an evolving lineage can explore to encounter a beneficial compound whilst maintaining its current level of metabolic elongation? And do dietary carotenoids (and thus ecological groups of birds that utilize them) differ in potential of such gains?
For example, a sequence of chemical reactions starting with dietary lutein, will take ten biochemical generations to reach any compound contained in the combined avian carotenoid network (Fig. 10 and Additional file 2: Figure S2). However the first enzymatic pathway linked to additional external compound (to zeaxanthin in this case) is encountered only by generation seven (Fig. 10a), which means that the linear pathway has to be at least seven reactions long before it acquires dietary robustness (shown in red in Fig. 1a). In the context of avian evolution, such elaboration is highly unlikely given vulnerability rates of long linear pathways (Fig. 3a). Indeed, although 21 bird species in our dataset depended solely on lutein as source of plumage carotenoids (Additional file 3: Appendix S2), none had metabolic pathways longer than two reactions in their carotenoid networks (Fig. 3b). Such lack of enzymatic connectivity in the vicinity of lutein – the most common dietary precursor in birds (Additional file 3: Appendix S2; ) – might explain the overwhelming prevalence of yellow and greenish coloration across avian taxa. For avian species utilizing lutein as a sole dietary precursor (such as many warblers and sparrows, Additional file 3: Appendix S2), metabolic elaborations longer than three enzymatic reactions might be structurally inaccessible (Figs. 3b and 10a).
Among single dietary starting points, consuming zeaxanthin (Fig. 10d) enables the fastest and most sustainable colonization of avian carotenoid network: additional dietary compounds are encountered by 3rd and 4th generation). Only metabolic elongations beyond seven reactions are not accessible in the context of avian evolution. In species consuming two dietary compounds, a combination of lutein and β-carotene (Fig. 10l) enables reaching any compound in avian carotenoid network in only six generations, with encounters of additional dietary compounds by generations 3 and 4, enabling sustained metabolic elongation and diversification.
Applying the metric of biochemical scope across dietary entries and their combinations encompassing possible avian diets (Fig. 10) enabled us to explore in more details the relationship between dietary robustness and carotenoid elaboration (Fig. 9) across dietary entries that vary in enzymatic connectivity (Additional file 2: Figure S2).
We thus calculated the proportion of realized versus potential biochemical scope separately for each dietary carotenoid at each node of the phylogenetic tree (predictions on Fig. 8, partitioned bars at tips on Fig. 9). We did not find evidence for multi-node gains within a biochemical scope of a dietary entry (i.e., scenario 1 in Fig. 8), corroborating the finding that evolutionary diversification along carotenoid metabolic pathways is negligible in birds . Exploration of a single-reaction radius of dietary entry (scenario 2 in Fig. 8) was commonly associated with stasis or network shrinkage, whereas in 32 out of 33 cases sustained elongation was associated with merging with an additional dietary pathway (scenario 3 in Fig. 8; results in Fig. 9). Strikingly, these patterns were observed across all dietary carotenoid domains (different combinations of dietary carotenoids at tip bars on Fig. 9) even though their enzymatic connectivity (e.g., compare Fig. 10a vs d) and likely color diversity (see below) strongly differed. These results, together with nearly identical outcomes of ancestral network reconstructions (Fig. 3a), and metabolic elongation in plumage carotenoids across study species (Fig. 3b), again show that network robustness is universally important in enabling carotenoid-based plumage elaboration starting from any dietary compounds (and thus, across diverse ecological groups of birds).
Interestingly, starting with several dietary entry points (versus sequentially gaining them) does not strongly accelerate metabolic evolution as evidenced by relatively short branches in some seed-eater finch lineages that start with several dietary compounds because of their carotenoid-diverse diet, but do not consistently gain new ones (Fig. 9, Additional file 3: Appendix S2). For example, starting carotenoid metabolism with β-cryptoxanthin, zeaxanthin, and lutein – a common combination in Cardueline finches (Additional file 3: Appendix S2) – enables particularly sustainable evolution of plumage elaboration and diversification (Fig. 10j). Indeed the two species in the dataset that accomplished the greatest elongation of carotenoid pathways (two reactions less than maximum theoretically possible distance from any dietary entry on the avian carotenoid network, Fig. 3b) occurred in this group (two longest branches in Fig. 9). Red Siskin (Carduelis cucullata) and Pallas’s Rosefinch (Carpodacus roseus) combine exceptional metabolic elongation (>6 metabolic steps from a dietary entry) with strong robustness (>4 different dietary pathways sustaining such elongation); reconstructing metabolic networks on internal phylogenetic nodes leading to these taxa revealed that these lineages consistently gained a new dietary compound at each of the last 4 and 5 phylogenetic transitions, correspondingly (Fig. 9).
Carotenoids are ancient compounds, involved in a multitude of biological functions in addition to coloration (e.g., ). Most elements of carotenoid enzymatic pathways have likely evolved in the context of bacterial evolution and were in place millions of years before animals and plants started to use them in coloration and other functions [9, 10, 35]. The central assumption of this study is that, in birds, the evolution of color-producing metabolic pathways involves cooption, duplication, and linkage of compounds already present throughout tissues, their transport, and their integration with feather growth [24, 36], rather than evolution of these specialized pathways de novo in each lineage (but see [24, 37]). There are no data to test this assumption currently, but it is corroborated by similar genomic profiles of carotenoid enzymes in tissues of differently colored sexes and species (e.g., [38, 39]) and by frequent and rapid convergence in complex carotenoid pathways among distantly-related taxa (Fig. 2, Additional file 5: Figure S3).
In this study we did not explore correspondence between carotenoid producing metabolic network and carotenoid-based color space in birds. Three factors can affect such correspondence. First, plumage colors and metabolic elongation can differ across network areas – in some enzymatic sequences a single reaction step produces a different color, whereas in others more steps are required for the same color change (e.g., ). Similarly, some metabolic neighborhoods can underlie greater color diversity than others. Second, metabolic pathways commonly differ in their efficiency (e.g., ); relative concentration of compounds produced by different pathways within species’ network and mixtures of different carotenoids and distribution of flux  further alter correspondence between metabolic and plumage color space. Third, plumage color is affected by integration of carotenoid compounds and feather keratin proteins and species differences in this integration [42, 43] changes correspondence between metabolic and color diversification space across species. However, the structural trade-off between metabolic robustness and elongation in carotenoid pathways identified in this study is central to most of the above-mentioned mechanisms as well. This is because robustness to fluctuations in external inputs enables carotenoid elaboration by extending time available for its evolution (Fig. 3a and b) and this equally applies to the evolution of metabolic pathways (as documented in this study, Fig. 9), their enzymatic efficiency (e.g., ), or the time required for the evolution of species-specific feather protein binding [45, 46]. Indeed, the evolution of carotenoid-protein associations in growing feathers was proportional to the time a lineage was coevolving with a particular carotenoid compound in a subset of species from this dataset (Badyaev, Potticary, Morrison, ms). Nevertheless, explicitly characterizing and contrasting the relative contribution of structural considerations identified in this study with metabolic flux and dynamic properties of avian carotenoid networks (including experimentally confirming assumed directionality of enzymatic reactions in avian networks) is a necessary next step in understanding the tempo and mode of avian carotenoid evolution.
Conclusions and implications
Our results have several implications. First, the structure of underlying metabolic networks mechanistically links microevolutionary elaboration and macroevolutionary diversification in avian carotenoid coloration. Such linkages do not depend on specific evolutionary force: for example, metabolic elaboration within lineages can be driven by sexual selection [47, 48] whist lineage diversification can be due to random sampling of neighboring enzymatic reactions, or, conversely, ecological speciation (and associated diet specialization) can dictate lineage divergence [49–51], but subsequent within-lineage evolution can be driven by the cooption of enzymatic reactions available in the immediate biochemical vicinity of dietary compound (e.g., [29, 52]). Second, the evolutionary potential for carotenoid color elaboration and diversification should differ predictably among clades depending on the density of enzymatic reactions, overlap in biochemical domains of distinct dietary carotenoids, and diversity of dietary carotenoids in species’ metabolic networks (e.g., ). Third, placing ecological and biochemical diversification in the same phylogenetic framework provides an opportunity to directly compare their contribution to the evolution of carotenoid ornamentation in birds and contributes to the conceptual unification of functionalist and structuralists views of evolution.
We retrieved cytochrome b (cytB) sequences for 181 species in the dataset from GenBank  (Additional file 9: Table S3). Twenty five species without cytB sequences were represented by congeners with sequences, inserted at their respective topological positions in the supertree. Sequences of Fregata minor and Zosterops japonicas sequences were trimmed to include only cytB gene before alignment using MUSCLE  with default parameters. To estimate branch lengths on the supertree [56, 57], we fitted these cytB sequences to the supertree topology using maximum-likelihood in RAxML .
The tree (Additional file 4: Figure S1) was built by supertree methods by adding 24 molecular phylogenies containing the focal species (Additional file 10: Table S4) onto a backbone phylogeny . Because no complete molecular phylogeny exists for Phoenicopteriformes, a phylogeny based on DNA-DNA hybridization  was inserted. A morphology based phylogeny  was used for species in the family Pipridae. Given the large number of species in the dataset in the order Passeriformes and the family Picidae, we inserted additional backbone molecular phylogenies of these groups [62, 63] into the overall supertree at the root of their respective clades.
A combination of seven molecular clock calibrations, four fossil calibrations, and one paleogeographic event calibration were used to convert the supertree into an ultrametric tree in PATHd8 . We used as many available calibration points as possible because this is the most accurate method to determine the rate variation across trees that include distantly related taxa . The basal split in Neoaves was fixed at 100 MYA; although this is one of the higher estimates from molecular clock studies , estimates range from about 65 MYA  to 123 MYA  and it has been used elsewhere as a calibration point . Divergence estimates are sensitive to taxon sampling  contributing to uncertainly of divergence time among higher orders [70–72]. Three additional molecular calibration points were from the molecular clock analysis of complete mitochondrial genomes of Neoaves orders . These points were assumed to be fixed in the analysis and include the divergence between the sister suborders of suboscines and oscines at 73.61 MYA, the divergence between Threskiornithidae and Pelecaniformes at 66.0 MYA, and the diversification of the Charadriiformes stem group at 66.57 MYA. The molecular clock estimation of 12.0 MYA was used as the fixed divergence time between the sister genera Campephilus and Picoides in the family Picidae . A fixed point of 20 MYA from the molecular clock analysis  was used as the date of the most recent common ancestor of the genera Fringilla, Emberiza, Thraupis, Cardinalis, and Icterus. Finally, 13 MYA was assigned as the fixed diversification time of the Oriolus stem group, based on a molecular clock analysis . The fossil calibration points consisted of a fixed point at 66 MYA for the age of the Anseriformes stem group , a range of 30-34 MYA for both the ages of the Pici crown group  and the diversification of the order Phoenicopteriformes stem group , and a fixed point at 53 MYA for the age of the Galliformes stem group . In order to have more calibration points within the large Passeriformes clade, we used calibration points of 4.8–5.5 MYA as the range of dates of the Regulidae stem group, based on paleogeographic evidence [79, 80].
After the ultrametric tree was constructed, the congeners with cytB sequences that were inserted in the tree for species without cytB sequence were pared from the tree at the genus level and the focal taxa were inserted in their place so that the branch length of the genus’s root node was retained. Polytomies with branch length values of zero were constructed for genera that consisted of multiple focal taxa without cytB sequences. The final ultrametric supertree included 152 species (Additional file 4: Figure S1).
Comparison with Jetz et al.  avian supertree
To examine the similarity of our molecular supertree with a recently published global avian supertree , we compared the topology of both trees for the subset of species used in this study. A random sample of 1000 trees containing all of our study species was obtained from the Bayesian pseudo-posterior distribution of 10,000 time-calibrated trees from associated website (birdtree.org)  based on the backbone  with all 9,993 species. A 50 % majority rule consensus tree was generated from the sample of the 1000 trees from the pseudo-posterior distribution in R 3.1.0  using the package APE (ver. 3.1-1) . To identify which clades were supported in both  and our supertrees, we generated a strict consensus tree between the majority rule consensus tree of the Jetz et al. supertree and our supertree (Additional file 4: Figure S1) using the package APE (ver. 3.1-1). All higher-level (family or orders) and most within-family and within-genera relationships were retained in the strict consensus tree, signifying topological congruence in these clades in both  and our supertree. The only difference between the trees was a subset of 12 species within the Fringillidae, that collapses into a polytomy in the strict consensus tree.
To quantify the number of clades found in our supertree that are not found in the strict consensus tree based on both our supertree and the  supertree we calculated the Robinson-Foulds distance (RF)  between the strict consensus tree and our supertree with phangorn (ver. 1.00-7) . The RF distance between our supertree (Additional file 4: Figure S1) and this strict consensus tree was 31 out of a maximum possible distance of 298 for 152 species, also indicating concordance of topologies.
Further, both trees are based on the same backbone phylogeny for avian orders  and the same backbone phylogeny for Passeriformes  – the majority of species used in this study. Additionally, both trees share two of the oldest fossil calibration points: at the stem Pici for the split between Ramphastidae, Picidae and Indicataoridae (30 MYA ), and at a basal node at the stem for Antidae (66 MYA ). Further, additional 10 calibration points on our supertree were specific to the clades under this study, thus giving us a more accurate divergence time estimates than would be available from the subsample of the study species in global avian supertree .
Comparison of the Hackett et al.  molecular phylogeny to the Jarvis et al.  genome-scale phylogeny of avian orders
To further assess the validity of the use of the Hackett et al.  phylogeny as a backbone tree in building our supertree, we examined the similarity between the relationships of avian orders on the backbone tree derived from 19 nuclear genes by Hackett et al.  to relationships of these orders on the recently published genome-scale phylogeny of all orders of Neoaves . Analyses by Jarvis et al.  comparing the overall topology of both  tree and their own total evidence nucleotide tree (TENT inferred with ExaML) found that out of all of the previously published relationships in avian phylogenies the  phylogeny was the most congruent with the genomic scale phylogeny. They both recovered the same monophyletic clades of core landbirds and core waterbirds. The core landbirds were also divided into the same two monphyletic clades in both phylogenies. A comparison of the two trees only in relationship among the eight main orders present in our supertree (Anseriformes, Galliformes, Phoenicopteriformes, Pelecaniformes, Charadriiformes, Passeriformes, Piciformes, Accipitriformes) revealed that all of the relative relationships between the orders were identical except for the placement of Charadriiformes. In the Hackett et al.  tree, the order Charadriiformes is more derived than Pelecaniformes while in the Jarvis et al.  tree this relationship is reversed. Of our 152 taxa under our study, only seven (4.6 %) come from these orders. Given this minor difference and for the reasons specified above, we retained the Hackett et al.  phylogeny as the backbone tree for our molecular supertree.
Carotenoid biosynthesis network
Global carotenoid biosynthesis network
Although complete and experimentally verified carotenoid biosynthetic networks are known for only a relatively small number of species (Additional file 1: Appendix S1 and references therein), these represent a subset of a global carotenoid network of known biochemical transitions among the compounds [17, 86]. Thus, as a first step, we constructed a global carotenoid biosynthesis network that included all confirmed enzymatic reactions that occur among naturally occurring carotenoids (Additional file 1: Appendix S1). This global carotenoid biosynthesis network delineated entire biochemical space and pathways available for carotenoid diversification based on biochemical properties of the compounds.
Avian plumage carotenoid biosynthesis network
As a second step, we collected an exhaustive list of all carotenoid compounds and reactions known in birds (n = 248 species, current to May 2014; for dataset updated to July 2015 see appendices in ), including compounds found in plumage, integument (bill, tarsi, skin), plasma, liver, fat, faeces, and seminal fluid, or known to be consumed (Additional file 3: Appendix S2). All these compounds and reactions were used to construct the “avian subset” of the global carotenoid network (Additional file 2: Figure S2, Figure 6a) – combined network of carotenoid reactions found in bird species.
As a third step, for the plumage carotenoid-producing pathway of each species, carotenoid compounds were classified as dietary if they are known to be consumed with diet, intermediate (metabolized) if (i) they are not expressed in the plumage, but found in plasma, liver, gut, fat, or (ii) known to be metabolized on the path from dietary to plumage compounds, and expressed if they are found in plumage (Additional file 1: Appendix S1). Dietary, intermediate, and expressed compounds, and enzymatic reactions among them were linked into a metabolic network for each species and then mapped on the global carotenoid network. When data were available, separate networks were built for male, female and juvenile of the species and the fullest networks for each species were used in subsequent analyses.
In the majority of species, known enzymatic pathways and their directionality inferred from global carotenoid network unambiguously linked identified compounds in carotenoid-producing pathways (Additional file 1: Appendix S1). In several cases (see below), we added an intermediate compound (i.e., a biochemical reaction step) to link observed dietary and expressed compounds when these were located on the same linear pathway or extended a path from an expressed compound to a dietary starting compound if both could be unambiguously linked by a reaction step. When more than one dietary compound was recorded for a species, these were not linked by direct enzymatic pathways (even when one existed on the global carotenoid network – e.g., in bacterial networks) unless such direct linkage was experimentally confirmed for the focal species.
To form a color-producing pathway, birds can either link dietary, intermediate, and expressed compounds present in each species in a linear, step-wise fashion [24, 87, 88], utilize the diversity of redundant pathways that link them [29, 40], or use a mixture of these strategies. Thus, to make an unbiased assessment of the effect of network structure on avian carotenoid diversification, we built two kinds of networks for each species – a “minimum” and “maximum” networks. The minimum network represented the minimum number of reactions required to produce the plumage compounds from dietary carotenoids of each species. To obtain the maximum network, we mapped compounds recorded for the species on the “avian space” of global carotenoid biosynthesis network (Additional file 2: Figure S2) and recorded all biochemically possible reactions that could produce the plumage compounds identified in each species from dietary and intermediate compounds for this species (Additional file 3: Appendix S2). We used both types of networks in the analyses and report when results differed. “Minimum” network was used in descriptive analyses and tests. Considering both minimum and maximum networks, with redundant pathways included, allowed for explicit tests of mode of evolution of avian carotenoid networks on known biochemical network. The use of maximum networks in ancestral reconstructions (see below) provides a particularly conservative test for the effect of network structure on color diversification. Limited genomic data suggest that species possess many more types of carotenoid compounds and enzymes in their tissues than those involved in their color-producing pathways [38, 39], such that evolution of developmental pathways by which feathers are colored likely involves coopting and linking compounds and reactions present in species throughout organismal tissues, a process approximated in this study by the use of the maximum network measure.
Ancestral networks reconstruction
Ancestral network reconstruction in an explicitly phylogenetic context requires evaluations of structural relationships and biochemical rules by which the compounds and enzymes are linked in the networks of closely-related species and reconstruction of ancestral networks by either parsimony [89–91] or maximum likelihood approaches [92–94]. A previous approach to reconstruction of Bayesian networks  infers the most likely evolutionary scenario for each metabolic reaction present in the extant species, but only tracks the gain and loss of reactions and assumes a fixed number of network compounds. To overcome this limitation, we used a modified maximum likelihood approach  to test the fit of models of network evolution in the phylogeny and to track ancestral states of compounds and reactions simultaneously (Additional file 7: Table S2). We further extended this method to reconstructions of the ancestral states of reactions (and thus the phylogenetic changes in the overall topology) [96, 97]. Following the reconstruction of ancestral states, we built ancestral networks at each of the internal nodes on the phylogeny [90, 91, 95] based on the compounds and reactions present at this node, and then calculated their associated network parameters (Additional file 8: Table S1). The presence or absence of compounds and reactions was recorded in a character matrix for 159 species (Additional file 7: Table S2). We used r8s (version 1.8) [96, 97] to obtain maximum likelihood estimates of the rate parameters for compounds and reactions (Additional file 7: Table S2).
Phylogenetic analysis of metabolic networks and pathways
To quantify structural distance between species’ metabolic networks we used a modified version of the metabolic distance  that measures the fraction of reactions and compounds in which any two networks differ. We calculated pairwise distances between each of the species’ metabolic networks and used the neighbor-joining algorithm  in PAUP* 4.0  to build a phylogeny based on metabolic distance between species (Fig. 2). Evolutionary distance between the species was the sum of branch lengths on the shortest phylogenetic path between any the two species (patristic distance, in millions of years) .
Comparison of the metabolic pathway-based tree and molecular-based supertree
To determine the topological similarity between the metabolic pathway-based phylogeny and the molecular-based supertree we used the Robinson-Foulds metric (RF) [84, 102], in R 3.1.0  using the package phangorn (version 1.99-7) . Phylogenies with identical topologies have an RF distance of 0, and the maximum RF distance for a data set of n taxa is 2(n-3) . The likelihood of the RF distance was simulated with 10,000 random stochastic birth-death trees for n = 152 species using the phytools (version 0.3–93) .
Reviewer 1: Junhyong Kim, University of Pennsylvania
In this paper, the authors address a classic question in evolutionary biology: if some distinct trait distribution of organisms is seen, how much of the pattern is due to the distribution of fitness peaks and historical contingency and how much is due to constraints like the set of developmentally reachable phenotypes. We might imagine a spread of people in a landscape of multiple hills and mountains, trying to climb up; but, the landscape also has cliffs and gorges that constrain travel routes. If we take a snapshot of the people, how much of their distribution pattern is due to the location of the hills, their ability to walk, and the location of the forbidden obstacle? Some authors might argue that excluding constraints of physics and chemistry, such questions are really a question of time scale. What is reachable by certain developmental processes can change as the mechanisms of development itself can evolve – that is, the constraints themselves can change over time. Nevertheless, the question, originally made famous by late S.J. Gould, is both conceptually interesting and significant for proper inference of empirical phenotypic distributions.
The current paper has some very strong points in its attempt to address evolutionary questions arising from the above framework: (1) it addresses a deep and important question in macroevolution using approaches that leverage a unique information set; (2) this information set, consisting of biochemical reaction network of carotenoid, is marvelously researched and it is truly fascinating in terms of both its appropriateness and relevance to an phenotype (coloration) of great interest; and (3) the many numerical methods applied throughout the paper are carried out with extremely high degree of technical competency and attention to detail. I believe this paper should be published but with some major revisions.
While the paper has many excellent points of strength, unfortunately, I found the paper very difficult to read and in many parts how the results tie to the models or hypotheses were cryptic. With respect to reading difficulty, I believe some of this is perhaps due to the manuscript addressing a specialized audience that might be already familiar with some terms or concepts but not a more general audience. For example, the paper uses the term “diversification” and “elaboration” – presumably in a specific manner, but to a general reader it is not clear what pattern of data would be considered diversification versus elaboration. I suspect such a reader would stumble at phrases like “…structural consideration of trait construction and development or selective environment of trait functioning over historical time.” It would greatly help the manuscript to revise the text for more general audience. I personally stumble on phrases like “components of current adaptations”, as I do not know whether “components” refers to a specific technical term or whether it simply means things other than genes. The manuscript has a large number of such ambiguous terms: e.g., “structural opportunities”, “elongation of pathways”, “metabolic elaboration”, “biochemical scope”, “biochemical space”, etc. I think each such term needs to be defined for the reader.
Authors’ response: We took this opportunity to edit and clarify the text and eliminate excessive jargon. We made consistent the use of “metabolic elongation” and “plumage elaboration” and now define several terms in the text more explicitly and delete others. Established measures from network biology and biochemistry are listed in Additional file 8 : Table S1.
As mentioned, some of the results are cryptic or it wasn’t entirely clear what hypothesis was being tested by the analyses. For example, on page 5, it states that the hypothesis being tested is whether the biochemical pathway context explains the patterns wherein “closely related species are often strongly diverged…distant and ecologically distinct taxa are often highly convergent.” I am not sure which of the results are directly addressing this problem. Some of this problem may again be due to the writing and my confusion.
Authors’ response: We now rewrote this introductory paragraph to emphasize the general approach of the study: whereas rapid divergence among related species (and frequent convergence among distant species) in plumage carotenoids is commonly documented, here we proposed a novel explanation: recurrent occupancy (i.e., sharing) of a small biochemical space. We specifically address the reason and calibrate the rate for such recurrence for different ecological groups.
For example, referring to Fig. 1c, the text states “We found that diversification in plumage ….by frequent bouts of metabolic (internal) diversification… and not by ….starting points.” I do not understand from what parts of Fig. 1c I can also come to the same conclusion. That is, the figures need more explanation was to why such a figure was made and how the data in the figure leads to the conclusion suggested by the authors. I find conclusions drawn from Figs. 2c, 3a, 4a–d, 5, 6, and 7, similarly cryptic.
Authors’ response: We now expanded both the figure legends and associated text explanation and more explicitly refer to comparisons of panels in Fig. 1 . The conclusion mentioned here refers to comparisons of the upper (dietary subnetwork), middle (metabolized subnetwork) and lower (expressed subnetwork) panel of Fig. 1 c for the same time period (common x-axis for all panels). Figure shows that network divergence of species separated by <20 million years is the largest in metabolized compounds (middle panel), but the smallest in dietary compounds (upper panel). We further clarified presentation of results and made it less abbreviated by simplifying Figs. 3 and 4 (panels are now presented as separate figures) and brining two figures from Additional Files to the main text to streamline the presentation.
There are some concepts or measures that are not explained sufficiently: How is “robustness” defined? What is meant by robustness to fluctuations? How is evolutionary persistence defined?
Authors’ response: Robustness is now defined in both the Background and also in figure legends. Evolutionary persistence was referring to occurrence across distant species and is now rephrased and deleted from figures.
How does Additional file 5: Figure S3 and Fig. 3a result in the conclusion that there were “periodic alternations of expansion and contraction … in frequencies broadly consistent with both estimated gains and losses … and with the role of dietary redundancy …” I think this is an important model and it seems to make sense. However, it is difficult to see from the given results whether this is, in fact, a precise and quantitative statement. For example, what parts of the results suggest this model predicts frequencies consistent with estimated gains and losses?
Authors’ response: We now show former Additional file 5 : Figure S3 and Fig. 3 a as panels to facilitate comparison. New Fig. 6 b shows that most nodes (not only dietary ones) are represented in many species and most nodes are occupied by species under this study. Frequencies of gain and loss in relation to distance from dietary entries in Fig. 3 a shows that long pathways are unlikely to persist unless, as predicted in Fig. 1 a and 3 a (insert), metabolic pathways acquire additional link to dietary compounds. When such evolutionary acquisition is possible, given local enzymatic connectivity, pathway can elongate and more nodes get inflated in Fig. 6 b. When such acquisition is not possible, nodes get smaller. Figure 6 b is necessarily a qualitative illustration of outcome of such expansion-contraction process. Quantitative results for the depicted process are presented in Figs. 8 and 9 .
In sum, this is a paper of unusual depth, technical sophistication, and scholarly breadth. However, the presentation is difficult and at places the models and hypotheses are hard for the reader to derive from the presented results. That is, the conclusions drawn from each figure or results table may be obvious to the authors but a reader is likely to have much harder time. Given the potential impact of the paper it would be a great service to the readership to clarify the presentation. Lastly, given that the results from this paper is supposed to explain, at least in part, the spectacular diversity and distribution of bird coloration, it would really help draw interest to the paper to graphically show to the reader the implications from the models to the macrophenotype of popular interest.
Authors’ response: We greatly appreciate Dr. Kim’s assessment of this work and did our best to clarify the presentation. Some of the results of this work lend themselves for extrapolation into actual color distribution in birds. For example we mention why yellow and green are the colors of stasis in avian pigmentation evolution and also an ancestral state in many lineages. Further, we suggest that prevalence of losses over gains in avian history can be related to a combination of selection for exaggeration of color displays (and associated selection for efficiency of pigment production and their modularity) and birds’ dependence on upstream carotenoids they cannot themselves convert from non-carotenoids. This combination leads to formation of isolated modules that are frequently lost in avian evolution. However, as we discuss in the paper, direct correspondence between enzymatic pathways and expressed plumage color varies across the network.
In my previous review, I noted the significance of this paper and the unique and novel analyses applied to a singularly interesting dataset. The paper addresses a topic of great conceptual depth and overall conclusions seem to be supported by the data. However, I still had great trouble understanding individual pieces of the analyses and how the presented figures relate to the verbal descriptions given in the paper. Here, I list in more detail the particular problematic sections. I hope the authors will address and clarify these sections such that the reader can appreciate the results.
Line 125: “Metabolic divergence”—it would really help the reader here to state in a few words how the authors measured metabolic divergence rather than only in the methods section. Line 126: “Periodic convergence” – Fig. 2 is a large complex tree, side-by-side. As a reader, I cannot pick out where these convergence events is evident on these two trees and why such events would be “periodic.” Figure 1c: Axis labels are ambiguous. Is the Y-axis supposed to be the slope coefficient? That is, the slope of a subset of the points in 1B (different compound categories and within specific intervals of the X-axis of 1B?). Line 139: It is not clear to the reader how a “structural basis” for the phenomenon of “puzzlingly rapid divergence of closely related species in expressed carotenoids” is found in the results presented in Fig. 1c. The last panel seems to show high level of divergence in closely related pairs but it isn’t clear why the figure is supposed to show this “structural basis”. Line 157-158: Where in the figure can the reader see that “combined time required for evolving…four metabolic reactions” is comparable to the age of birds? Figure 5: The figure legend is very sparse and needs more explanation. What are each dot in the figure? Axes should also be explained in the legend. Line 190: Where is the information for “linearity” of the network? Line 202—205: Here, it is suggested that the Fig. 4b is consistent with alternations of expansion and contraction, etc. However, it is very difficult to see this. What is the expected pattern if some other hypotheses were posed? For example, if all species consistently expanded their network? Or, if there were only expansion for some species and contraction for others?
Line 221: The legend for Fig. 8 is difficult to understand and perhaps incomplete. (A typo in line 982 of the legend “biological scope” should be “biochemical scope”.) Line 985 of the legend states “significant elaboration is mostly produced within a biochemical scope.” After reading the description and seeing the inset diagrams in Fig. 8, it is still not clear to me what aspects of the figure is being called “elaboration” and “biochemical scope”. That is, I can see the diagram and understand that there are model differences but the translation of these scenes into phrases like “greater elongation is associated with greater diversity of biochemical scopes” is not clear to me. The concept of “biochemical scope” seems critical to understanding this result as well as the results in Fig. 9. Lastly, this figure contains additional information such as the tree and the bar graphs. The legend does not give information about these additional items.
Line 280: What is “proportion of gained biochemical scope”? Is this the proportion of the metabolic network realized versus reachable in X number of generations?
Authors’ response: We now clarified our references to the figures and made necessary corrections.
Reviewer 2: Eugene Koonin, NCBI, NIH
Badyaev and colleagues explore a truly interesting evolutionary landscape where evolution of a metabolic network is intimately linked to the evolution of a highly visible phenotypic trait, the avian plumage coloration. The analysis is quite elaborate, and the conclusions, in particular, the explanation for convergent evolution of carotenoid pigmentation, seem important.
Yet, I have to confess that, at least to me, the manuscript is difficult to read, and I was unable to follow all the steps that led the authors to these conclusions. I wonder whether it might be possible to explain the connection between metabolic robustness and elaboration in simpler terms and/or in greater detail, perhaps using some diagrams to facilitate understanding. Also, it is not immediately clear to me how and where does this paper address the (dis)connect between microevolution and macroevolution.
Authors’ response: The diagram showing how elongation of metabolic pathways is structurally linked to robustness is shown in Fig. 1 a. We now more explicitly state that it is birds’ dependence on dietary (i.e., external) carotenoids to initiate their metabolism that results in this property. Importantly, the link between metabolic elaboration and dietary robustness does not depend crucially on fluctuations in availability of different dietary carotenoids in environment, but rather refers to the fact that these compounds are external to the organism and thus have different evolutionary dynamic.
The link between micro- and macroevolution in this study comes from bringing together the mechanisms behind changes in metabolic pathways within taxa or lineage (e.g., sexual selection for more elaborate or efficiently produced carotenoid-based ornament) with the patterns of interspecific diversity in these pathways (e.g., frequent convergence across distantly related species, alteration of elongation and stasis in these networks on avian phylogeny). We bring these micro- and macroevolutionary processes together by considering them on the same structural landscape – the enzymatic connectivity in global carotenoid network. Diversity of avian species, their long history, and the relatively small size of carotenoid network on which they diversify also enabled robust ancestral reconstruction of their metabolic networks. This enabled us to consider ecological and biochemical processes within the same phylogenetic framework; for example, comparing contribution of ecological and metabolic components to the evolution of avian carotenoid pigmentation (Fig. 1 c).
Furthermore, would it be possible to explain, in specific terms, how do metabolic networks expand in birds. Given that birds do not appear to be prone to horizontal gene transfer, I suppose that this expansion involves gene duplication followed by neofunctionalization. Is this correct? Would it be possible to present specific cases? I think this would clarify the entire story. The situation with losses is more obvious but again, a specific description at the level of genes and enzymes would be helpful.
In short this is quite a substantial paper on an interesting subject but I think it could gain a lot from a more complete and specific presentation.
Authors’ response: It is very good point and this, indeed, would clarify the entire story. From a companion study involving larger sample size (n = 330 species, ) we know that, structurally, carotenoid-producing networks in birds expand at most connected enzymes – these nodes are the “hotspots” of phenotypic diversification in carotenoid network . But there are currently no empirical data to test the correspondence between genomic and metabolic networks for carotenoid production across birds. Avian gene duplication rate per se seems to insufficient  to account for the fast rate of convergence in carotenoid enzymes across distantly related bird species documented in this study (Fig. 2 and Additional file 5 : Figure S3), although it is very likely that neofunctionalization at genomic or metabolic levels plays a major role. The finding of strongly reductive evolution of carotenoid-producing pathways (Fig. 5 ) corroborates this assumption. The hypothesis advanced in this paper also explains the prevalence of reaction losses during evolution of avian lineages.
Reviewer 3: Fyodor Kondrashov, Center for Genomic Regulation and University of Pompeu Fabra
This reviewer provided no comments on this version for publication.
We thank J. Craft of Craft Technologies, D. Seaman, L. Kennedy, E. Landeen. R. Delaney, F. Hossain, C. Esposito, M. Udovcic, C. Secomb, T. Krecek, and J. Putz for assistance in preparing the samples, carotenoid extraction, HPLC identification for some species (see Additional file 3: Appendix S2) and enzymatic network construction; C. Schmidt-Dannert, R. Duckworth, D. Higginson, S. Gavrilets, J. Kim, E. Koonin and B. Poonen for discussion, comments and insightful suggestions. This work was supported by the grant from the David and Lucile Packard Foundation to AVB. The methods were developed, in part, under support from National Science Foundation grants (DEB-0077804 and IBN-0218313).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Gould SJ. The structure of evolutionary theory. Cambridge, MA: Belknap Press, Harvard University Press; 2002.Google Scholar
- Maynard Smith J. Natural selection and the concept of a protein space. Nature. 1970;225:563–4.Google Scholar
- Gavrilets S. Fitness landscapes and the origin of species. Princeton, NJ: Princeton University Press; 2004.Google Scholar
- Wagner GP. Homology, genes, and evolutionary innovation. Princeton and Oxford: Princeton University Press; 2014.Google Scholar
- Wagner A. The origins of evolutionary innovations: a theory of transformative change in living systems. New York: Oxford University Press; 2011.Google Scholar
- Davidson EH. The regulatory genome: gene regulatory networks in development and evolution. San Diego: Academic; 2006.Google Scholar
- Koonin EV. The logic of chance: the nature and origin of biological evolution. New Jersey, USA: FT Press Science; 2011.Google Scholar
- Koonin EV, Wolf YI. The fundamental units, processes and patterns in evolution, and the tree of life conundrum. Biol Direct. 2009;4:33.PubMed CentralPubMedGoogle Scholar
- Umeno D, Tobias AV, Arnold FH. Diversifying carotenoid biosynthetic pathways by directed evolution. Microbiol Mol Biol Rev. 2005;69:51–78.PubMed CentralPubMedGoogle Scholar
- Klassen JL. Phylogenetic and evolutionary patterns in microbial carotenoid biosynthesis are revealed by comparative genomics. PLoS One. 2010;5:e11257.PubMed CentralPubMedGoogle Scholar
- Britton G, Liaaen-Jensen S, Pfander H. Carotenoid. handbook. Basel, Switzerland: Birkhäuser; 2004.Google Scholar
- Schmidt K, Connor A, Britton G. Analysis of pigments: carotenoids and related polyenes. In: Goodfellow M, O'Donnell AG, editors. Chemical methods in prokaryotic systematics. Chichester, England: John Wiley & Sons; 1994. p. 403–61.Google Scholar
- Schmidt-Dannert C, Umeno D, Arnold FH. Molecular breeding of carotenoid biosynthetic pathways. Nat Biotechnol. 2000;18:750–3.PubMedGoogle Scholar
- Schmidt-Dannert C, Lee PC, Mijts BN. Creating carotenoid diversity in E. coli cells using combinatorial and directed evolution strategies. Phytochem Rev. 2006;5:67–74.Google Scholar
- Nelis HJ, DeLeenheer AP. Microbial sources of carotenoid pigments used in food and feeds. J Appl Bacteriol. 1991;70:181–91.Google Scholar
- Liaaen-Jensen S, Andrewes AG. Microbial carotenoids. Annu Rev Microbiol. 1972;26:225–48.PubMedGoogle Scholar
- Goodwin TW. Biochemistry of carotenoids. Animals, vol. 2. 2nd ed. New York: Chapman & Hall; 1984.Google Scholar
- McGraw KJ. The mechanics of carotenoid coloration in birds. In: Hill GE, McGraw KJ, editors. Bird coloration I mechanisms and measurements. Cambridge, MA: Harvard University Press; 2006. p. 177–242.Google Scholar
- Brush AH. Metabolism of carotenoid pigments in birds. FASEB. 1990;4:2969–77.Google Scholar
- Heijnen JJ, van Gulik WM, Shimizu H, Stephanopoulos G. Metabolic flux control analysis of branch points: an improved approach to obtain flux control coefficients from large perturbation data. Metab Eng. 2004;6(4):391–400.PubMedGoogle Scholar
- LaPorte DC, Walsh K, Koshland DE. The branch point effect. Ultrasensitivity and subsensitivity to metabolic control. J Biol Chem. 1984;259(22):14068–75.PubMedGoogle Scholar
- Friedman NR, McGraw KJ, Omland KE. Evolution of carotenoid pigmentation in caciques and meadowlarks (Icteridae): Repeated gains of red plumage coloration by carotenoid C4-oxygenation. Evolution. 2013;68(3):791–801.PubMedGoogle Scholar
- Omland KE, Lanyon SM. Reconstructing plumage evolution in orioles (Icterus): Repeated convergence and reversal in patterns. Evolution. 2000;54:2119–33.PubMedGoogle Scholar
- Prum RO, LaFountain AM, Berro J, Stoddard MC, Frank HA. Molecular diversity, metabolic transformation, and evolution of carotenoid feather pigments in cotingas (Aves: Cotingidae). J Comp Physiol B. 2012;182:1095–116.PubMedGoogle Scholar
- Prager M, Andersson S. Convergent evolution of red carotenoid coloration in widowbirds and bishops (Euplectes spp.). Evolution. 2010;64:3609–19.PubMedGoogle Scholar
- Prum RO, LaFountain AM, Berg CJ, Tauber MJ, Frank HA. Mechanism of carotenoid coloration in the brightly colored plumages of broadbills (Eurylaimidae). J Comp Physiol B. 2014;184(5):651–72.PubMedGoogle Scholar
- Ebenhöh O, Handorf T, Heinrich R. A cross species comparison of metabolic network functions. Genome Inform. 2005;16(1):203–13.PubMedGoogle Scholar
- Erdös P, Rényi A. On random graphs. Publ Math. 1959;6:290–7.Google Scholar
- Morrison ES, Badyaev AV. Structuring evolution: Biochemical networks and metabolic diversification in birds. In review.Google Scholar
- Gol'dshtein V, Koganov GA, Surdutovich GI. Vulnerability and hierarchy of complex networks. Condens Matter. 2004: arXiv:cond-mat/0409298v1: 1–4.Google Scholar
- Liu WC, Lin WH, Davis A, Jordan F, Yang HT, Hwang MJ. A network perspective on the topological importance of enzymes and their phylogenetic conservation. BMC Bioinformatics. 2007;8:121.PubMed CentralPubMedGoogle Scholar
- Bernhardsson S, Gerlee P, Lizana L. Structural correlations in bacterial metabolic networks. BMC Evol Biol. 2011;11:20.PubMed CentralPubMedGoogle Scholar
- Handorf T, Ebenhöh O, Heinrich R. Expanding metabolic networks: scopes of compounds, robustness, and evolution. J Mol Evol. 2005;61:498–512.PubMedGoogle Scholar
- Vershinin A. Biological functions of carotenoids - diversity and evolution. Biofactors. 1999;10:99–104.PubMedGoogle Scholar
- Sandmann G. Molecular evolution of carotenoid biosynthesis from bacteria to plants. Physiol Planit. 2002;116:431–40.Google Scholar
- Landeen EA, Badyaev AV. Developmental integration of feather growth and pigmentation and its implications for the evolution of diet-derived coloration. J Exp Zool B Mol Dev Evol. 2012;318:59–70.PubMedGoogle Scholar
- Hudon J, Anciaes M, Bertacche V, Stradi R. Plumage carotenoids of the Pin-tailed Manakin (Ilicura militaris): evidence for the endogenous production of rhodoxanthin from a colour variant. Comp Biochem Physiol B Biochem Mol Biol. 2007;147(3):402–11.PubMedGoogle Scholar
- Walsh N, Dale J, McGraw KJ, Pointer MA, Mundy NI. Candidate genes for carotenoid coloration in vertebrates and their expression profiles in the carotenoid-containing plumage and bill of a wild bird. Proc R Soc Lond. 2012;279:58–66.Google Scholar
- Zhang G, Li C, Li Q, Bo L, Larkin DM, Lee C, et al. Comparative genomics reveals insights into avian genome evolution and adaptation. Science. 2014;346:1311–20.PubMed CentralPubMedGoogle Scholar
- Inouye CY, Hill GE, Stradi RD, Montgomerie R, Bosque C. Carotenoid pigments in male house finch plumage in relation to age, subspecies, and ornamental coloration. Auk. 2001;118(4):900–15.Google Scholar
- Kacser H, Burns JA. The control of flux. Symp Soc Exp Biol. 1973;36:35–104.Google Scholar
- Mendes-Pinto MM, LaFountain AM, Stoddard MC, Prum RO, Frank FA, Robert B. Variation in carotenoid-protein interactions in bird feathers produces novel plumage coloration. J R Soc Interface. 2012:doi:10.1098/rsif.2012.0471.
- García de Blas E, Mateo R, Viñuela J, Pérez-Rodríguez L, Alonso-Alvarez C. Free and esterified carotenoids in ornaments of an avian species: the relationship to color expression and sources of variability. Physiol Biochem Zool. 2013;86:483–98.PubMedGoogle Scholar
- Rausher MD. The evolution of genes in branched metabolic pathways. Evolution. 2012;67:34–48.PubMedGoogle Scholar
- Fox DL. Metabolic fractionation, storage and display of carotenoid pigments by flamingoes. Comp Biochem Physiol. 1962;6:1–40.PubMedGoogle Scholar
- Tyczkowski JK, Hamilton PB. Lutein as a model dihydroxycarotenoid for the study of pigmentation in chickens. Poult Sci. 1986;65:1141–5.PubMedGoogle Scholar
- McGraw KJ, Schuetz JG. The evolution of carotenoid coloration in estrildid finches: a biochemical analysis. Comp Biochem Physiol B. 2004;139:45–51.PubMedGoogle Scholar
- Hill GE. Female mate choice for ornamental coloration. In: Hill GE, McGraw KJ, editors. Bird coloration: function and evolution, vol. 2. Cambridge: Harvard; 2006. p. 137–200.Google Scholar
- Ryan PG, Moloney CL, Hudon J. Color variation and hybridization among Nesospiza buntings on Inaccessible Islands, Tristan da Cunha. Auk. 1994;111(2):314–27.Google Scholar
- Mahler B, Araujo LS, Tubaro PL. Dietary and sexual correlates of carotenoid pigment expression in dove plumage. Condor. 2003;105:258–67.Google Scholar
- Hudon J, Brush AH. Probably dietary basis of a color variant of the cedar waxwing. J Field Ornithol. 1989;60:361–8.Google Scholar
- Fox DL, Hopkins TS. Comparative metabolic fractionation of carotenoids in three flamingo species. Comp Biochem Physiol. 1966;17:841–56.PubMedGoogle Scholar
- Tella JL, Figuerola J, Negro JJ, Blanco G, Rodríguez-Estrella R, Forero MG, et al. Ecological, morpholigical and phylogenetic correlates of interspecific variation in plasma carotenoid concentration in birds. J Evol Biol. 2004;17:156–64.PubMedGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2011;39:D32–7.PubMed CentralPubMedGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.PubMed CentralPubMedGoogle Scholar
- Jones KE, Bininda-Emonds ORP, Gittleman JL. Bats, clocks, and rocks: diversification patterns in chiroptera. Evolution. 2005;59(10):2243–55.PubMedGoogle Scholar
- Bininda-Emonds ORP, Cardillo M, Jones KE, MacPhee RDE, Beck RMD, Grenyer R, et al. The delayed rise of present-day mammals. Nature. 2007;446:507–12.PubMedGoogle Scholar
- Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22(21):2688–90.PubMedGoogle Scholar
- Hackett SJ, Kimball RT, Reddy S, Bowie RCK, Braun EL, Braun MJ, et al. A phylogenomic study of birds reveals their evolutionary history. Science. 2008;320(5884):1763–8.PubMedGoogle Scholar
- Sibley CG, Ahlquist JE. Phylogeny and classification of birds. New Haven: Yale University Press; 1990.Google Scholar
- Prum RO. Syringeal morphology, phylogeny, and evolution of the neotropical manakins (Aves: pipridae). Am Museum Nat Hist Novitates. 1992;3043:1–65.Google Scholar
- Barker FK, Cibois A, Schikler P, Feinstein J, Cracraft J, Wake DB. Phylogeny and diversification of the largest avian radiation. Proc Natl Acad Sci U S A. 2004;101(30):11040–5.PubMed CentralPubMedGoogle Scholar
- Fuchs J, Ohlson JI, Ericson PGP, Pasquet E. Synchronous intercontinetal splits between assemblages of woodpeckers suggested by molecular data. Zool Scr. 2007;36(1):11–25.Google Scholar
- Britton T, Anderson CL, Jacquet D, Lundqvist S, Bremer K. Estimating divergence times in large phylogenetic trees. Syst Biol. 2007;56:741–52.PubMedGoogle Scholar
- Smith AB, Pisani D, Mackenzie-Dodds JA, Stockley B, Webster BL, Littlewood DTJ. Testing the molecular clock: molecular and paleontological estimates of divergence times in the echinoidea (echinodermata). Mol Biol Evol. 2006;23(10):1832–51.PubMedGoogle Scholar
- Brown JW, Rest JS, García-Moreno J, Sorenson MD, Mindell DP. Strong mitochondrial DNA support for a cretaceous origin of modern avian lineages. BMC Biol. 2008;6:6.PubMed CentralPubMedGoogle Scholar
- Jarvis ED, Mirarab S, Aberer AJ, Li B, Houde P, Li C, et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science. 2014;346:1320–31.PubMed CentralPubMedGoogle Scholar
- Braun EL, Kimball RT, Han K-L, Iuhasz-Velez NR, Bonilla AJ, Chojnowski JL, et al. Homoplastic microinversions and the avian tree of life. BMC Evol Biol. 2011;11:141.PubMed CentralPubMedGoogle Scholar
- Sanderson MJ, Doyle JA. Sources of error and confidence intervals in estimating the age of angiosperms from rbcL and 18S rDNA data. Am J Bot. 2001;88(8):1499–516.PubMedGoogle Scholar
- Brown JW, Payne RB, Mindell DP. Nuclear DNA does not reconcile ‘rocks’ and ‘clocks’ in Neoaves: a comment on Ericson et al. Biol Lett. 2007;3:257–9.PubMed CentralPubMedGoogle Scholar
- Chojnowski JL, Kimball RT, Braun EL. Introns outperform exons in analyses of basal avian phylogeny using clathrin heavy chain genes. Gene. 2008;410(1):89–96.PubMedGoogle Scholar
- Ericson PGP, Anderson CL, Mayr G. Hangin’ on to our rocks ‘n clocks: a reply to Brown et al. Biol Lett. 2007;3(3):260–1.PubMed CentralGoogle Scholar
- Pacheco MA, Battistuzzi FU, Lentino M, Aguilar RF, Kumar S, Escalante AA. Evolution of modern birds revealed by mitogenomics: timing the radiation and origin of major orders. Mol Biol Evol. 2011;28(6):1927–42.PubMed CentralPubMedGoogle Scholar
- Jønsson KA, Bowie RCK, Moyle RG, Irestedt M, Christidis L, Norman JA, et al. Phylogeny and biogeography of oriolidae (Aves: Passeriformes). Ecography. 2010;33:232–41.Google Scholar
- Clarke JA, Tambussi CP, Noriega JI, Erickson GM, Ketcham RA. Definitive fossil evidence for the extant avian radiation in the Cretaceous. Nature. 2005;433:305–8.PubMedGoogle Scholar
- Mayr G. A tiny barbet-like bird from the lower Oligocene of Germany: the smallest species and earliest substantial fossil record of the pici (woodpeckers and allies). Auk. 2005;122(4):1055–63.Google Scholar
- Mayr G. The Paleogene fossil record of birds in Europe. Biol Rev. 2005;80:515–42.PubMedGoogle Scholar
- Mayr G, Weidig I. The early Eocene bird gallinuloides wyomingensis-a stem group representative of galliformes. Acta Palaeontol Pol. 2004;49(2):211–7.Google Scholar
- Päckert M, Martens J, Severinghaus LL. The Taiwan firecrest (Regulus goodfellowi) belongs to the goldcrest assemblage (Regulus Regulus s. l.): evidence from mitochondrial DNA and the territorial song of the regulidae. J Ornithol. 2009;150:205–20.Google Scholar
- Gladenkov AY, Oleinik AE, Marincovich Jr L, Barinov KB. A refined age for the earliest opening of Bering strait. Palaeogeogr Palaeoclimatol Palaeoecol. 2002;183:321–8.Google Scholar
- Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers AO. The global diversity of birds in space and time. Nature. 2012;491:444–8.PubMedGoogle Scholar
- R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.Google Scholar
- Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20(2):289–90.PubMedGoogle Scholar
- Robinson DF, Foulds LR. Comparison of phylogenetic trees. Math Biosci. 1981;53:131–47.Google Scholar
- Schliep KP. Phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27(4):592–3.PubMed CentralPubMedGoogle Scholar
- Goodwin TW. Biochemistry of carotenoids. Plants, vol. 1. 2nd ed. New York: Chapman & Hall; 1981.Google Scholar
- McGraw KJ, Gregory AJ. Carotenoid pigments in male American goldfinches: what is the optimal strategy for becoming colourful? Biol J Linn Soc. 2004;83:273–80.Google Scholar
- Friedman NR, McGraw KJ, Omland KE. History and mechanisms of carotenoid plumage evolution in the New World orioles (Icterus). Comp Biochem Physiol B. 2014;172–173:1–8.PubMedGoogle Scholar
- Pál C, Papp B, Lercher MJ. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet. 2005;37(12):1372–5.PubMedGoogle Scholar
- Kreimer A, Borenstein E, Gophna U, Ruppin E. The evolution of modularity in bacterial metabolic networks. Proc Natl Acad Sci. 2008;105(19):6976–81.PubMed CentralPubMedGoogle Scholar
- Borenstein E, Kupiec M, Feldman MW, Ruppin E. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc Natl Acad Sci U S A. 2008;105(38):14482–7.PubMed CentralPubMedGoogle Scholar
- Mithani A, Preston GM, Hein J. Rahnuma: hypergraph-based tool for metabolic pathway prediction and network comparison. Bioinformatics. 2009;25(14):1831–2.PubMedGoogle Scholar
- Mithani A, Preston GM, Hein J. A stochastic model for the evolution of metabolic networks with neighbor dependence. Bioinformatics. 2009;25(12):1528–35.PubMedGoogle Scholar
- Mithani A, Preston GM, Hein J. A Bayesian approach to the evolution of metabolic networks on a phylogeny. PLoS Comput Biol. 2010;6(8):e1000868.PubMed CentralPubMedGoogle Scholar
- Ebenhöh O, Handorf T, Kahn D. Evolutionary changes of metabolic networks and their biosynthetic capacities. IEE Proc Syst Biol. 2006;153(5):354–8.Google Scholar
- Marazzi B, Ané C, Simon MF, Delgado-Salinas A, Luckow M, Sanderson MJ. Locating evolutionary precursors on a phylogenetic tree. Evolution. 2012;66(12):3918–30.PubMedGoogle Scholar
- Sanderson MJ. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics. 2003;19(2):301–2.PubMedGoogle Scholar
- Rodrigues JFM, Wagner A. Genotype networks, innovation, and robustness in sulfur metabolism. BMC Syst Biol. 2011;5:39.Google Scholar
- Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4(4):406–25.PubMedGoogle Scholar
- Swofford DL. PAUP*: Phylogenetic analysis using parismony (*and other methods), version 4. Sunderland, MA: Sinauer Associates; 2002.Google Scholar
- Fourment M, Gibbs MJ. PATRISTIC: a program for calculating patristic distances and graphically comparing the components of genetic change. BMC Evol Biol. 2006;6:1.PubMed CentralPubMedGoogle Scholar
- Smythe AB, Sanderson MJ, Nadler SA. Nematode small subunit phylogeny correlates with alignment parameters. Syst Biol. 2006;55(6):972–92.PubMedGoogle Scholar
- Kupczok A, Schmidt HA, von Haeseler A. Accuracy of phylogeny reconstruction methods combining overlapping gene data sets. Algorithms Mol Biol. 2010;5:37.PubMed CentralPubMedGoogle Scholar
- Revell LJ. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;3(2):217–23.Google Scholar