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Biology 2013, 2(1), 419-444; https://doi.org/10.3390/biology2010419
Parsimony and Model-Based Analyses of Indels in Avian Nuclear Genes Reveal Congruent and Incongruent Phylogenetic Signals
Department of Biology, University of Florida, Gainesville, FL 32611, USA
Sam Noble Oklahoma Museum of Natural History, University of Oklahoma, Norman, OK 73072, USA
Pepperwood Way, San Jose, CA 95124, USA
Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, CA 94720, USA
Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, 4210 Silver Hill Road, Suitland, MD 20746, USA
Behavior, Ecology, Evolution and Systematics Program, University of Maryland, College Park, MD 20742, USA
Zoology Department, Field Museum of Natural History, 1400 South Lakeshore Drive, Chicago, IL 60605, USA
Department of Biological Sciences, Wayne State University, 5047 Gullen Mall, Detroit, MI 48202, USA
Biology Department, Loyola University Chicago, Chicago, IL 60660, USA
Museum of Natural Science, 119 Foster Hall, Louisiana State University, Baton Rouge, LA 70803, USA
Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA
Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131, USA
Author to whom correspondence should be addressed.
Received: 28 December 2012; in revised form: 21 February 2013 / Accepted: 22 February 2013 / Published: 13 March 2013
Insertion/deletion (indel) mutations, which are represented by gaps in multiple sequence alignments, have been used to examine phylogenetic hypotheses for some time. However, most analyses combine gap data with the nucleotide sequences in which they are embedded, probably because most phylogenetic datasets include few gap characters. Here, we report analyses of 12,030 gap characters from an alignment of avian nuclear genes using maximum parsimony (MP) and a simple maximum likelihood (ML) framework. Both trees were similar, and they exhibited almost all of the strongly supported relationships in the nucleotide tree, although neither gap tree supported many relationships that have proven difficult to recover in previous studies. Moreover, independent lines of evidence typically corroborated the nucleotide topology instead of the gap topology when they disagreed, although the number of conflicting nodes with high bootstrap support was limited. Filtering to remove short indels did not substantially reduce homoplasy or reduce conflict. Combined analyses of nucleotides and gaps resulted in the nucleotide topology, but with increased support, suggesting that gap data may prove most useful when analyzed in combination with nucleotide substitutions.
Keywords:bird classification; avian phylogeny; nucleotide sequence alignment; total evidence; Columbiformes; Coraciiformes; Galliformes
1. Introduction
In DNA and protein sequence alignments, gaps are used to represent positions where insertion/deletion (indel) events have occurred, reflecting the absence of nucleotides or amino acids in specific sequences. Although indels accumulate in most genomic regions, they are more common in non-coding regions (e.g., introns) than in protein coding regions. Intron sequences have typically been used to examine relatively recent divergences (e.g., [1,2,3,4,5]), but there has been a growing appreciation that non-coding sequences also represent a rich source of phylogenetic information at deeper levels in vertebrate phylogeny. Indeed, non-coding data have been used to estimate phylogeny for a number of vertebrate orders (e.g., [6,7,8]) and classes (e.g., [9,10,11,12,13]).
The process of multiple sequence alignment results in the concurrent inference of gaps that reflect the position of indels [14]. Inferred gap positions are often coded as binary characters that reflect the hypothetical positions where insertions or deletions have occurred (hereafter, called "gap characters," also see [9,15,16,17,18]), although more complex coding schemes are possible [19]. Regardless of the specific gap-coding scheme, including information about indels in phylogenetic analyses can increase the information available in multiple sequence alignments without requiring additional data collection [19,20]. In spite of this, few phylogenetic studies incorporate this information, usually treating gaps as missing data [21,22]. However, phylogenetic analyses that treat gaps as missing data can be statistically inconsistent, even when the model of sequence evolution is simple and the true alignment is available [22]. Moreover, the historical information available from gap characters may be especially valuable, since they appear to exhibit less homoplasy than nucleotide substitutions (e.g., [4,12,20]). Thus, identifying the best methods for coding and analyzing gap characters (or finding other approaches to incorporate indels into phylogenetic analyses) represents an important challenge.
Despite their potential value for phylogenetic analyses, gap characters also have the potential to be sources of error, just like other types of data. First, the multiple sequence alignment used to score the gap characters may be inaccurate. Alignment has a major impact upon phylogenetic estimation (e.g., [23,24,25,26,27]), even when gap characters are not analyzed. In fact, alignment error has been suggested to represent a fundamental problem for the use of non-coding regions to address deep divergences (e.g., [28,29]), although when examined carefully it is clear that phylogenetic analyses of some non-coding data matrices are relatively insensitive to the details of alignment (e.g., [30,31]). Finally, the indels that underlie gap characters may exhibit homoplasy. Some analyses of gap characters have reported misleading signal associated with gaps (e.g., [32,33]), including evidence for long-branch attraction [34]. These issues are expected to introduce error into analyses of gap character matrices, suggesting that empirical studies that establish the relative amounts of historical signal and noise associated with gaps scored for alignments of different types of sequence data.
The congruence of trees based upon gap characters and nucleotide substitutions for the same sequences can be used to assess performance of phylogenetic analyses of gap characters. Because gap characters typically exhibit less homoplasy than nucleotide substitutions (e.g., [20]), it is reasonable to hypothesize that gaps will have stronger phylogenetic signal than nucleotides. However, like other types of low homoplasy characters (e.g., [35]), changes in gap characters accumulate slowly, and this may limit their power to resolve difficult phylogenetic problems [36,37]. Most gap character matrices used in phylogenetic studies have been relatively small and, thus, have been unable to resolve phylogenetic relationships independently of nucleotide data. In fact, a recent study focused on avian phylogeny [38] included only 287 characters; analyses of those gaps alone were unable to resolve the avian tree. A few studies have used large numbers of gap characters [34,39], but those studies analyzed gaps in protein sequence alignments. Similar tests of the utility of gap characters from nucleotide sequence alignments of non-coding regions are desirable.
A rigorous test of the hypothesis that the phylogenetic signal in gap characters is stronger than that in nucleotides also requires a phylogenetic problem that includes at least some difficult to resolve nodes. The Hackett et al. [13] data matrix (hereafter, called the "Early Bird" data matrix) included nearly 4 million base pairs (bp) of avian sequence data, most of which were non-coding (74% intron and 3% UTRs). The number of gaps (12,030 characters) in this data matrix exceeds that in previous studies of non-coding regions by at least an order of magnitude, though Hackett et al. [13] did not consider gaps in their analyses. As avian phylogeny has been a difficult problem to resolve, analyses of a large-scale matrix of gap characters based on the Early Bird [13] data should provide an excellent test of the utility of gaps for phylogenetic analyses.
Here, we address five major questions about the utility of gap characters for phylogenetic analyses in avian non-coding regions. First, is the historical signal in the gap characters from Early Bird [13] stronger than, similar to or weaker than the signal in the nucleotide sequences? Second, do gap characters exhibit more or less homoplasy than nucleotides, and moreover, do gap characters based on the insertion or deletion of a single nucleotide exhibit more homoplasy than those based upon longer indels? Third, are the trees supported by gap and nucleotide characters congruent, and if not, which of the two trees is better corroborated by other lines of evidence? Fourth, does maximum parsimony (MP) or maximum likelihood (ML) represent a better method for analyses of gap characters, or do both methods perform similarly? Finally, are total evidence analyses that combine gap and nucleotide data superior to individual analyses of either data type? We expect the answers to these questions to provide insight into the phylogenetic utility of gap characters that are largely based upon indels in non-coding regions.
2. Methods
2.1. DNA Sequence Data, Alignment and Gap Coding
The Early Bird [13] data matrix comprises ~25 kilobases (kb) of sequence data per species (before alignment) from 19 nuclear loci obtained from 169 bird species (supporting information, file 1). The 19 loci are located on 15 different chromosomes in the chicken genome [40], and they are likely to be unlinked in most or all avian lineages given the general conservation of avian karyotypes [41]. There was clear evidence that one locus (GH1) underwent a gene duplication within birds [42]; a single GH1 paralog was included for the taxa (Passeriformes) with two copies. Other details of the data matrix and alignment methods are provided in Hackett et al. [13] and Braun et al. [35].
The gap character matrix was generated using SeqState [43], which implements the simple indel coding method of Simmons and Ochoterena [19]. This method codes gaps as binary characters with "1" corresponding to presence of a gap (the absence of nucleotides) and "0" corresponding to absence of a gap (the presence of nucleotides). Gaps with different start and/or end positions are coded separately, and any gap that is enclosed within a longer gap is coded as missing ("?") for taxa with the longer gap. Three gap matrices were generated, one based upon all indels, a second with gap characters based on indels longer than 1 bp and a third with gap characters based on indels longer than 2 bp. All data matrices are available from the Early Bird web site [44].
2.2. Phylogenetic Analyses
2.2.1. Parsimony Analyses
We identified MP trees in PAUP* 4.0b10 [45] using the parsimony ratchet [46]. Ratchet searches reweight a random subset of characters and conduct searches using those perturbed matrices, permitting a more thorough exploration of treespace (for a detailed explanation see Nixon [46]). For this study, the ratchet analyses used 100 iterations with 20% of informative characters perturbed and one tree held per iteration. To conduct the ratchet analyses, we used a C++ program (written by E.L.B.) that generates an appropriate PAUP* block. After conducting 100 ratchet iterations, the optimal trees were retained and tree bisection, and reconnection (TBR) branch swapping was conducted to identify the full set of MP trees. When we compared this strategy to a more typical tree search (random additions of taxa followed by TBR branch swapping), we found that the ratchet took a shorter amount of time and identified shorter trees. Ratchet bootstrap analysis used 500 replicates, each of which used 100 ratchet iterations, as described above, with the final swapping limited instead to 1,000 trees per bootstrap replicate.
2.2.2. Likelihood Analyses
Gap characters are binary, so a two-state Markov model (the Cavender-Farris-Neyman [CFN] model [47,48,49]) is appropriate for their analyses, at least in principle. However, all observed gap characters are by definition variable—their occurrence differs among taxa, otherwise they would not be discernible. Thus, gap characters exhibit an "acquisition bias" similar to that found in typical discrete morphological character matrices [50]. The acquisition bias for morphological data reflects the fact that most researchers only score parsimony informative characters; the failure to score uninformative characters is analogous to the inability to recover invariant gap characters (Felsenstein [51] referred to a similar phenomenon for restriction site data as "ascertainment bias"). Because of this issue, we employed a corrected CFN model that accommodates acquisition bias (we call this the CFNv model). The CFNv model is a special case of the more general Mkv model proposed by Lewis [50]; readers are referred to that publication for details. ML analyses using the CFNv model were conducted in PAUP* and GARLI v0.951 [52] after we converted the binary (01) gap characters to RY codes (0→R, 1→Y).
To correct acquisition bias in PAUP* and GARLI, we assumed that the observed variable characters (the gap matrix characters) were drawn from a larger, hypothetical data matrix with an unknown number of invariant characters. Then we approximated this hypothetical matrix by appending invariant characters (i.e., columns that contain only "R" or "Y") to the observed gap matrix. Then, the number of invariant characters necessary to maximize the conditional likelihood [50] of the resulting gap data was estimated by systematically adding invariant characters and calculating the likelihood in PAUP* using the CFN model with Γ-distributed rates (called the CF+Γ model in that program). A Java program written by T.Y. was used to automate the addition of equal numbers of all R and all Y columns. The impact of correcting for acquisition bias was evaluated by analyzing the data without the added sites, but most analyses were conducted using the optimal number of added invariant characters.
GARLI was used to search for the ML tree and to conduct likelihood bootstrap analyses. All analyses of gap data assumed equal state frequencies and a four-category discrete approximation to the Γ distribution (with the shape parameter estimated from the data). This corresponds to the CFNv+Γ model (for analyses with added invariant characters) or the CFN+Γ model (for analyses without added invariant characters). Up to 200 searches were conducted in GARLI to evaluate the ability of that program to identify the ML tree.
2.2.3. Combined Analyses of Nucleotides and Gaps
We analyzed the gap data combined with invariant characters and nucleotide sequence data using PAUP* (for the MP criterion) and GARLI v0.96β (for the ML criterion). GARLI v0.96β is capable of analyzing partitioned data. The gap partition was analyzed using the CFNv+Γ model, as described above, whereas the nucleotide data were analyzed using the general time reversible (GTR) model with Γ-distributed rates and invariant sites (the GTR+I+Γ model). We estimated bootstrap support using 600 replicates.
2.3. Evaluating the Results of Phylogenetic Analyses Using Gap Characters
2.3.1. Evaluating the Gap Phylogeny Using Congruence
The best empirical method to assess the performance of novel phylogenetic methods or sources of phylogenetic information is to examine congruence with a known phylogeny [53] or, if such a phylogeny is unavailable, with topologies generated using independent data [30,54]. Unfortunately, most "known phylogenies" used to assess phylogenetic methods provide relatively weak tests, because they tend to include relatively easy to recover clades (see also Håstad and Björklund [55]). There are a number of strongly supported relationships in the avian tree of life (i.e., those with 100% bootstrap support in Figure 1). These relationships were generally well supported and broadly accepted by avian systematists prior to the Early Bird study [56]; many of these clades correspond to orders in the Clements checklist [57] and the IOC World Bird List [58]. As these strongly supported relationships represent weak tests of phylogenetic methods, we will focus on the difficult to recover supra-ordinal clades present in the Early Bird tree.
Relationships among avian orders have proven to be very difficult to resolve and parts of the Early Bird tree may prove to be inaccurate. However, we note that a subset of the supra-ordinal clades present in the Early Bird tree have been corroborated to varying degrees by independent lines of evidence (Table 1). These independent lines of evidence include the results of analyses using mitochondrial genomes [28,59], transposable element (TE) insertions [60,61,62] and DNA hybridization [63] or phylogenetic analyses of nuclear gene regions not included in the Early Bird study [30,31,64]. Thus, these nodes represent difficult tests for phylogenetic methods, but they can nonetheless be viewed as "known" (with at least some degree of confidence) given their independent corroboration.
To facilitate discussion of the clades supported by the Early Bird tree, we have combined the classification used by Clements checklist [57] with a set of names for supra-ordinal clades (Table 1). The Clements classification was altered in two ways: non-monophyletic orders were split (in most cases, families were elevated to ordinal rank) and a broader circumscription (consistent with Wetmore [65] and the IOC World Bird List [58]) of Piciformes was used. In addition to facilitating the discussion of groups in this manuscript, we believe that the circumscriptions of ordinal and supra-ordinal clades that we present will be useful for two reasons: almost all orders are strongly supported by the bootstrap in the Early Bird tree, and the supra-ordinal clades can be mapped onto the commonly used checklists [57,58] in a straightforward manner.
The supra-ordinal clades listed in Table 1 are defined as the least inclusive clade comprising the relevant species in the Early Bird tree (see supporting information, file 1). Although the International Code of Zoological Nomenclature does not regulate names above the family level we have adhered to priority for several groups as much as possible (see references in Table 1). The name "Australavis", the spelling published by Ericson [66], was modified to have the more appropriate ending "-es". Priority for Strisores [67] is also somewhat problematic, so an alternative name (Cypselomorphae) proposed for a less inclusive clade, but sometimes used as a synonym, is also included in Table 1 (see supporting information, file 2, for additional details regarding the nomenclature of this group), but we retain that terminology. We have also proposed names for as yet unnamed clades; etymology for those names is provided in the supporting information (file 2).
Figure 1. Estimate of avian phylogeny based upon nucleotide sequence data (maximum likelihood [ML] tree using the GTR+I+Γ model) and the higher-level classification described in the text. Nodes with 100% support are indicated with an asterisk. Red asterisks indicate nodes with 100% support that define supra-ordinal clades with extensive independent corroboration (see below). Coloring conventions here will be used in all trees, and named supra-ordinal clades are indicated using letters below branches (see Table 1 for details).
Six strongly supported supra-ordinal clades were omitted from Table 1 (indicated with red asterisks in Figure 1). Four of these clades correspond to the major divisions in the avian tree of life: Palaeognathae (Struthioniformes and Notopalaeognathae), Galloanserae (or Galloanseres [68]; Galliformes and Anseriformes), Neoaves (all other extant birds) and Neognathae (Galloanserae and Neoaves). These clades have received extensive independent corroboration (reviewed by Cracraft et al. [69]). Daedalornithes (Aegotheliformes and Apodiformes [70]) and Mirandornithes (Podicipediformes and Phoenicopteriformes [71]) are also very strongly supported. These groups are typically recovered in phylogenetic trees based upon single genes (examples of individual gene analyses that support some or all of these groups include those based upon RAG1 [72,73,74], EGR1 [73] and up to 18 additional genes [13,75]). Thus, although these groups correspond to supra-ordinal clades, they do not represent difficult tests for phylogenetic methods.
Table 1. Supra-ordinal clades in the Early Bird tree and their corroboration by independent evidence (from mitogenomics [28,59], analyses of nuclear regions [30,31,63,64] that were not used by Hackett et al. [13] and transposable element (TE) insertions [60,61,62]). Strong corroboration (bootstrap support ≥70% or ≥3 TE insertions) was indicated using "++" and moderate corroboration (presence of the clade in with bootstrap support <70% or 1–2 TE insertions) was indicated using "+". Blank cells indicate that the available independent evidence could not address the presence or absence of the clade, whereas "—" indicates evidence contradicting the clade. Citations for the introduction of clade names are included; names without citations were introduced here (supporting information, file 2).
|Support from Independent Evidence
|Other Nuclear
|TE insertions
|Psittacopasserae [60]
|Eufalconimorphae [60]
|Australaves [66] (PPFC clade [30])
|Picocoraciae [68]
|Eucavitaves (CPBT clade [30])
|Telluraves ("Landbirds" [13])
|Aequornithes [68] ("Waterbirds" [13])
|Strisores [67] (Cypselomorphae)
2.3.2. Estimating the Rate at Which Gap Character Changes Accumulate
The rate of gap character change was estimated using ML estimates of branch lengths in the Early Bird [13] tree. Since branch lengths are expressed as substitutions per site (including invariant sites), estimates of branch lengths for gap characters include the added invariant characters. Thus, we multiplied the branch lengths based upon gap characters by the size of the gap character matrix (including the added invariant characters) and then divided by the size of the nucleotide matrix. This allowed the indel rate to be expressed as gap character changes per nucleotide site, making it directly comparable to nucleotide rates.
2.3.3. Evaluating the Information Content of Gap Characters
The phylogenetic information content of gap characters relative to nucleotide data was evaluated using the ML bootstrap support of each node for trees estimated using each data type, but restricted to contain the same number of parsimony informative characters. When datasets differed in size, 100 jackknife pseudomatrices were generated; these reduced the number of parsimony informative sites in the larger dataset to that of the smaller. Each of these 100 jackknifed pseudomatrices was then bootstrapped, and the average bootstrap support values were used. We compared four pairs of data matrices: (1) all gap characters (4,245 informative characters) compared to gap characters based upon indels >1 bp in length (3,160 informative characters); (2) all gap characters compared to gap characters based upon indels >2 bp in length (2,640 informative characters); (3) all gap characters compared to nucleotide substitution characters; and (4) all gap characters compared to RY-coded nucleotide substitution characters (making the nucleotide data binary, like the gap characters).
3. Results and Discussion
3.1. The Power of Gap Characters to Resolve the Avian Tree of Life
Resolving the topology deep in the avian tree of life is a notoriously difficult problem [12,76,77], making it an excellent test case for novel sources of phylogenetic information. The power of specific types of data to resolve phylogenetic relationships depends upon the size of the matrix, rate of evolution, amount of homoplasy and branch lengths in the true tree. The ideal evolutionary rate for phylogenetic characters is rapid enough for a high probability of synapomorphic changes to occur on the shortest branches in the tree, but not so high that homoplastic changes obscure historical signal [36,37]. The nucleotide substitution rate for introns appears to be appropriate for analyses of deep avian phylogeny [12]. In contrast, gap characters accumulate at a much lower rate (the MP treelength given gap data are approximately 10% of the treelength given nucleotide data). The ML estimate of the gap accumulation rate is even lower (Figure 2), although the lower homoplasy of gap characters may prove advantageous if very large gap datasets were analyzed.
Figure 2. Branch lengths estimated from gap data (using the CFNv+Γ model) plotted against branch lengths from all nucleotide data (estimated using the GTR+I+Γ model). Branch length estimates for specific nucleotide partitions (introns, coding exons and 3' untranslated regions [UTRs]) are presented for comparison of relative rates (next page).
To examine the phylogenetic signal in gap characters, we obtained estimates of the avian tree of life based only upon gap characters (Figure 3 and supporting information, files 3 and 4). The gap tree had relatively high bootstrap support for most orders (Figure 3), the structure within orders (supporting information, files 3 and 4) and the small number of strongly supported supra-ordinal clades (i.e., the clades indicated with red asterisks in Figure 1), albeit often with lower bootstrap support than the nucleotide tree. Those supra-ordinal groups recovered in the gap trees (e.g., Novaeratitae, Picocoraciae, Picodynastornithes and Strisores) were much more poorly supported by the bootstrap in the gap character tree than they were in the nucleotide tree. Other independently corroborated supra-ordinal clades were not even present in the gap tree (e.g., Telluraves). However, there was also an interesting exception; McCormack et al. [64] found a strongly supported Eurypygiformes-Phaethontiformes clade. This clade is present in the gap trees. We have refrained from suggesting a name for this clade, since it is absent from the Early Bird tree and lacks independent corroboration, but it could be a case where analyses of gap characters exhibit better agreement with other sources of information than the analyses nucleotides conducted by Hackett et al. [13]. Overall, these analyses demonstrated that a large gap character matrix has sufficient phylogenetic signal to recover many of the most strongly corroborated nodes in the avian tree of life, but few of the most difficult nodes.
Substantial branch length heterogeneity was evident in both the nucleotide and gap trees, and branch lengths appear to be somewhat correlated between the two data types (Figure 4). Several taxa have long branches relative to their close relatives, including Turnix (Charadriiformes), Tinamiformes (Paleognathae) and Phasianidae (represented here by the genera Coturnix, Gallus and Rollulus within the order Galliformes), in both the nucleotide and gap trees (Figure 4). This indicates that rates of nucleotide substitution and the accumulation of gap characters are correlated in birds, as expected based upon analyses of other groups of organisms (e.g., Hardison et al. [78]).
This branch length heterogeneity may influence the estimate of topology, and it is tempting to speculate that the clustering of the long-branched Psittacopasserae and Picocoraciae within Telluraves reflects long branch attraction, especially given the short branches associated with the raptorial taxa (Accipitriformes, Cariamiformes, Falconiformes and Strigiformes) within this supra-ordinal clade. If so, the gap tree would actually provide a less accurate estimate of avian phylogeny than the nucleotide tree given that both Psittacopasserae and Australaves are paraphyletic in the ML gap tree but strongly supported by independent evidence [30,60].
The observed branch length heterogeneity suggests that ML methods might provide better estimates of avian phylogeny than MP, because parsimony equivalent models (i.e., the "no common mechanism" [NCM] model [79]) are unlikely to account effectively for branch length heterogeneity [80,81]. Indeed, it is clear that standard model selection approaches will indicate that the CFNv+Γ model has a better fit to the data than NCM [80], although we do note that there is debate regarding the question of whether MP should be viewed as a model [82]. Despite this prediction, our results are equivocal regarding the relative performance of these methods (e.g., compare Figure 3A to 3B). Indeed, the MP tree supports monophyly of Psittacopasserae and Austrodyptornithes (Figure 3B), unlike the ML tree, albeit with low (<50%) bootstrap support in both cases. The best interpretation of these differences between the MP and ML topologies is unclear, although differences between the trees at the supra-ordinal level provide no clear evidence that ML using the CFNv+Γ model performs substantially better than MP.
Figure 3. Estimates of avian phylogeny obtained using 12,030 gap characters obtained using (a) ML analyses with the CFNv+Γ model and (b) the maximum parsimony (MP) criterion. Orders were collapsed when monophyletic to simplify the trees. Bootstrap support on terminal branches reflects the support of those orders; orders represented by a single taxon are indicated using "(1)". There were a limited number of rearrangements relative to the nucleotide topology within orders, most without bootstrap support. We highlighted the topology for the order Galliformes, because the gap topology included a clade with bootstrap support that conflicts with multiple nuclear gene regions [8,83] and morphology [84].
Figure 4. Branch length heterogeneity evident in the (a) optimal nucleotide tree (based upon the GTR+I+Γ model) and (b) the optimal gap tree (based upon the CFNv+Γ model).
Examining other aspects of model fit, including conducting ML analyses without correcting for acquisition bias (i.e., using the CFN+Γ model), also resulted in similar topologies. These equivocal results are most likely to reflect the limited phylogenetic information in gap data matrices, even ones as large as that analyzed here. This suggests that it will be necessary to examine even larger data matrices to determine whether either analytical approach provides an adequate fit to the underlying process of indel evolution and to establish the impact of these methods upon topology.
3.2. Phylogenetic Signal in Gap Characters Based upon Indels of Different Lengths
Above, we described two reasons why gap trees might have lower bootstrap support than the nucleotide tree. Specifically, the limited bootstrap support we observed could reflect the low rate of accumulation for gap character changes or poor model fit (alternatively, it could reflect a combination of both). Another possibility is that the gap data are sufficiently noisy that neither ML nor MP can recover an accurate estimate of the true tree. Even if noise is not positively misleading, it can have a negative impact upon the phylogenetic analyses [85]. Thus, noise reduction methods might provide a useful complement to model improvement. Short indels, especially 1-bp indels, are more common than long indels in avian non-coding regions [10,86], suggesting that gap characters based upon short indels may contain more noise than those based upon long indels. Thus, the removal of short indels has the potential to enhance phylogeny reconstruction.
To examine the utility of noise reduction based on gap length, we filtered the full gap data matrix (12,030 characters of which 4,245 were parsimony informative) and excluded gap characters based on short (1- and 2-bp) indels. Removing 1-bp gaps reduced the matrix size by almost 25% (to 9,115 characters; 3,160 parsimony informative), whereas excluding both 1- and 2-bp gap characters reduced the matrix size by an additional 11% relative to the original matrix size (to 7,740 characters; 2,640 parsimony informative). Although the rate of longer gap accumulation was lower (the rate after excluding 1-bp gaps is 76% of that for the all gap matrix and the rate after excluding 1- and 2-bp gaps is 64%) all three data matrices exhibit similar levels of homoplasy (Table 2). Estimates of phylogeny obtained after removing short indels did not improve congruence with the nucleotide data tree (supplementary information, file 2). Robinson-Foulds distances [87] between the nucleotide trees and all of the gap trees ranged from 92 to 100, whereas the distance among gap trees ranged from 64 to 70. Removing short gaps may prove beneficial for other data sets, but these results showed that 1- and 2-bp gaps did not contribute substantially to the noise in the gap dataset.
|Data Matrix
|Nucleotide tree
|Gap tree
|>1-bp (excluding 1-bp gaps)
|>2-bp (excluding 1- and 2-bp gaps)
|Coding exons
|3' untranslated regions
Not surprisingly, given the similar level of homoplasy in the full gap-data matrix and the filtered matrix with 1-bp gaps removed, bootstrap support in analyses using identical numbers of informative characters was similar in trees made from both data sets (Figure 5a). In fact, only four nodes exhibited fairly large changes in bootstrap support when 1-bp gaps were removed. In three cases, this was an improvement (from <50% to ≥70%); in the fourth case it was a decrease (from 69% to 12%). The node with reduced support united Picodynastornithes, a clade with independent corroboration (Table 1). In fact, Picodynastornithes was not present in the ML tree for gap data excluding 1-bp gaps; instead, the ML tree included a conflicting clade that comprised Coraciiformes and Bucerotiformes (supporting information, files 3 and 4). Similar results were obtained when both 1-bp and 2-bp gaps were excluded.
Surprisingly, the rearrangement within Picocoraciae observed when long gaps were excluded unites the "traditional" Coraciiformes. Morphological support for traditional Coraciiformes is mixed; traditional Coraciiformes form a clade in the analyses of Livezey and Zusi [89], whereas the analyses of Clarke et al. [90] conflict. However, we found it provocative that the gap trees support a Momotus-Todus clade, a topology that agrees with some morphological analyses [90,91] and conflicts with analyses of nucleotide data (Figure 1). However, none of the analyses of gap data had bootstrap support ≥70% for the Momotus-Todus clade.
Figure 5. (a) Comparison of bootstrap support in trees based on all gap characters and gap characters >1-bp in length. Bipartitions that appeared well supported (≥70% bootstrap) by one analysis and poorly supported (<50% bootstrap) in the other are shaded. Numbers correspond to the following bipartitions: 1. Ardea-Cochlearius-Eudocimus; 2. Alisterus-Psittacula; 3. Chalcopsitta-Platycercus; and 4. Picodynastornites. (b) Comparison of bootstrap support for analyses using all gap characters and RY-coded nucleotide data. The same numbers of informative characters were used in each of these analyses (next page).
In contrast to the modest differences between analyses using different gap data matrices, much larger differences were observed when we compared bootstrap support from the nucleotide and gap trees (Figure 5b). This observation does not reflect differences in state space because the nucleotide data were RY-coded to address the more limited character state space in the binary gap characters. These results suggest the existence of both congruent and incongruent signals in the gap and nucleotide data and indicate that the incongruent signals in the gap data were not disproportionately associated with gaps based upon the shortest indels.
3.3. Combined Analyses of Nucleotide Substitutions and Gap Characters
ML analysis of the combined nucleotide and gap character data (including invariant gap characters) resulted in an estimate of phylogeny (Figure 6) virtually identical to the nucleotide tree (Figure 1). In general, there was a modest increase in the average bootstrap support for groups in the partitioned ML analyses of nucleotide substitutions and gap characters (Figure 6). However, there were also five nodes that exhibited more substantial increases in bootstrap support (>10%); four corresponded to supra-ordinal clades (Figure 6) and the fifth to the Balaeniceps-Scopus clade in Pelecaniformes (which increased to 75%). This general increase in support is consistent with the general assumption that including indel information in phylogenetic analyses would prove useful.
Figure 6. Combined evidence estimate of the avian tree of life. A partitioned ML analysis was conducted using the GTR+I+Γ model for the nucleotide partition and the CFNv+Γ model for the gap partition. Arrows indicate nodes defining supra-ordinal clades where bootstrap support increased or decreased by more than 10% relative to the nucleotide analysis (Figure 1). The combined evidence topology for Columbiformes was congruent with the gap topology instead of the nucleotide topology (inset; bootstrap values are reported for combined analysis [above branches] and for gap characters [below branches]).
There were also four nodes in the combined evidence tree that exhibited fairly large (>10%) decreases in bootstrap support. These decreases were evident for three supra-ordinal groups (Figure 6) and the Dendrocolaptes-Scytolopus clade in Passeriformes (which decreased to 66%). There was another difference between the nucleotide and combined evidence trees within Columbiformes. The combined evidence topology for this order corresponded to that in the gap tree, where the relevant branches had even higher bootstrap support (Figure 6). Although the majority of differences between the nucleotide tree and the gap tree are likely to reflect the more limited power of gap characters to resolve phylogeny, these differences are likely to indicate the existence of conflicting phylogenetic signals in nucleotide substitutions and gap characters. These conflicts are likely to highlight nodes in the Early Bird tree [13] that should receive additional scrutiny.
There were two nodes with high bootstrap support in both the nucleotide (Figure 1) and gap trees (Figure 3) that conflicted; in both cases, the total evidence tree (Figure 6) was consistent with the nucleotide tree. Surprisingly, given the lower homoplasy of gap characters relative to nucleotide data (Table 2), independent evidence suggested that the nucleotide tree was more likely in both cases:
- The nucleotide tree supports the monophyly of Notopalaeognathae in contrast to both the MP and ML gap trees (Figure 3), although only the latter had high bootstrap support. The nucleotide topology is strongly supported by independent evidence, including reanalyses of complete mitochondrial genomes [29], analyses of independent nuclear data matrices [31], TE insertions [62] and analyses of morphological data.
- The nucleotide tree supports a clade comprising New World quail (Colinus) and Phasianidae within Galliformes (Figure 1), whereas the gap tree supports a clade comprising Guineafowl (Numida) and Phasianidae (Figure 3B). The former topology is supported by analyses of multiple nuclear and mitochondrial sequences [8,83], TE insertions [92] and morphology [84].
The combined evidence tree was virtually identical to the nucleotide tree, probably reflecting the ability of rapidly accumulating nucleotide changes to overwhelm the analysis. Nonetheless, the signal in gap characters appears to have an influence, because several supra-ordinal clades exhibited increases or decreases in bootstrap support ≥10% relative to the nucleotide tree. Support for the unnamed clade uniting Novaeratitae and Tinamiformes increased substantially. Although the existence of this clade is supported by analyses of complete mitochondrial genomes [29] analyses of independent nuclear data [31] were equivocal and two TE insertions [62] conflicted with the clade (there were no TE insertions consistent with the combined analysis). Likewise, support for Insolitaves also increased, although there is no independent evidence supporting this clade (Table 1). Finally, support for Accipitriformes, one the few orders with limited bootstrap support, also increased (from 58% to 71%). In contrast, two supra-ordinal clades with independent corroboration (Australaves and Eucavitaves) exhibited decreased support. Neither of those two clades appeared in the gap tree (Figure 3). The decreased support for Australaves and Eucavitaves in the combined evidence topology is consistent with the hypothesis that the nucleotide and gap data exhibit some genuine (albeit limited) conflict.
3.4. Analyses of Gap Characters and Models of Indel Evolution
The conflicts between the gap and nucleotide data may reflect the poor fit of the models we used for analysis. Better models of indel evolution are clearly desirable, because the actual patterns of indel evolution are no doubt more complex than the combination of gap coding and analyses using the CFNv+Γ model or parsimony-equivalent models. Indeed, it is unlikely that any of the models used in phylogenetics have a perfect fit to the underlying processes of sequence evolution. Nonetheless, approximating models have proven very useful for phylogenetic estimation (see Sullivan et al. [93] and Huelsenbeck et al. [81] for additional discussion). Thus, we felt that the simple ML approach we used represented a reasonable starting point that should be tested. However, we did not find this simple ML method performed substantially better than analyses using the MP criterion, suggesting future studies should explore more complex models.
Models of sequence evolution have improved along with our understanding of the processes of sequence evolution [81]. This raises the question of which aspects of indel evolution might prove to be most important for improving models of indel evolution. Although short indels are more common than long indels [10,86], we found that filtering the data matrix to remove short indels did not improve congruence, raising questions about the value of incorporating this correlation into models of indel evolution. The existence of a deletions bias has been established both for birds [17,86,94] and mammals [11], and incorporating this asymmetry might be useful. Indeed, asymmetry should be intrinsic to models of indel evolution; sequence alignments that represent evolutionary history accurately can include homoplastic deletions, but homoplastic insertions should be forbidden (since distinct insertion are, by definition, not homologous; e.g., Alekseyenko et al. [95]). Although more complex and realistic models that combine sequence and indel evolution in this manner have been proposed [95], it is unclear they can be implemented in a way that will prove to computationally tractable for phylogenies of this size. It also remains unclear whether these more complex models capture all of the relevant features of indel evolution, but the observation that analyses excluding indel information can be positively misleading [14,22] suggests that development of improved models of indel evolution remains critical.
Another aspect of model fit that should not be ignored is the assumption that a single tree underlies the observed distribution of gaps. Gene trees can differ from the species tree for several reasons [96]; for avian phylogeny, the most common reason is probably deep coalescence. The short branches at the base of Neoaves (Figure 4) suggest that incomplete lineage sorting due to deep coalescence was common during the radiation of this group [97]. The distribution of TE insertions is consistent with incomplete lineage sorting [60,61]. Discordance among gene trees is known to lead to the incorrect estimation of species trees when concatenated analyses are conducted [98], and we expect concatenated analyses of gaps from multiple loci to inherit all of the properties of similar analyses that use nucleotide data. Although nucleotide and gap data reflect the same genes and, therefore, the same set of gene trees, the number of gap characters and variable nucleotides differs among loci. These differences in the number of characters in each partition effectively result in differential weighting of loci in the gap and nucleotide trees and, therefore, create the potential for analyses of nucleotides and gaps to recover different topologies.
4. Conclusions
Our analyses indicated that a gap data matrix of more than 12,000 characters was unable to resolve the majority of difficult relationships in the avian tree of life (and, thus, were not clearly superior to nucleotide data), although the data did appear to improve bootstrap support when combined with nucleotide data. As expected, gaps accumulated much more slowly than nucleotide substitutions and this low rate likely limited their power for phylogenetic reconstruction. Rates of gap accumulation also differed among taxa in a manner correlated with the rate of nucleotide substitution. The observation that rates of gap accumulation differed among taxa suggested that model-based analyses (i.e., ML with the CFNv+Γ model) might improve phylogenetic analyses of indels. However, we found only modest differences in performance between MP and ML. Additionally, removing short and potentially more homoplasious gaps did not improve tree reconstruction. Since the rate of gap character change is approximately an order of magnitude slower than the nucleotide substitution rate, it seems likely that at least an order of magnitude more data will be necessary to provide sufficient information to resolve the avian tree of life using indels alone. These larger indel datasets are likely to be available from birds very soon, and they should have the potential to contribute to the development of better models of indel evolution, improving future studies that include gap characters in many groups of organisms.
Additional Note
While this manuscript was being reviewed Joel Cracraft generously provided a preprint describing the taxonomy used in the forthcoming Howard and Moore Complete Checklist of the Birds of the World [99]. I have included a brief description of the differences between the clade names used in that taxonomy and those used here in the supporting information (file 2).
This study was funded by the US National Science Foundation Assembling the Tree of Life program (grants DEB-0228682 to R.T.K., E.L.B. and D.W.S.; DEB-0228675 to S.J.H.; DEB-0228688 to F.H.S.; and DEB-0228617 to W.S.M) and facilitated by additional funds from the US National Science Foundation (grant DEB-1118823 to R.T.K. and E.L.B.). Members of the Braun-Kimball lab, Andy Kratter, and two anonymous referees provided helpful comments on earlier versions of this manuscript. We are grateful to Derrick Zwickl for access to the partitioned GARLI and the collectors and museums for access to tissues samples (supporting information, file 1). We thank to the University of Florida High Performance Computing Center, the DePaul Bioinformatics Group (part of the Illinois Bio-grid at DePaul University), and the University of Oklahoma Supercomputing Center for Education and Research for access to their computing facilities.
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Supplementary Files
© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).


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Recovering an Orthopedic Injury Claim
When orthopedic injuries are not properly and promptly diagnosed by medical professionals, these injuries may be allowed to cause permanent damage before they are eventually treated. Bearing that in mind, your physician should have taken special care to ensure that his or her diagnosis was correct before allowing you to begin any particular course of treatment.
If you are coping with the consequences of your doctor's mistakes, you should speak with one of our medical malpractice attorneys in New Hampshire to assess whether you have any legal claim to compensation.
Orthopedic Injury Claims in New Hampshire
While our legal team is prepared to help you pursue any orthopedic injury claim, many of these claims are associated with the following injuries:
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If your orthopedic injury was not correctly diagnosed, speak with one of our New Hampshire orthopedic injury claims attorneys at Crowe & Mulvey, LLP, to begin determining what legal claim to compensation you may be eligible to pursue. To discuss the particulars of your situation with one of our medical malpractice attorneys, please call our New Hampshire offices at (617) 426-4488 today.


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For the fifth time in six years the Championship Series of the College Basketball Invitational presented by Buick will go to a winner-take-all third game after the Santa Clara Broncos (25-12) failed to close out the series in Game 2, falling at George Mason (22-15) 73-66 on April 3. Game 3 will take place on Fri., April 5 at the Patriot Center (10,000) on the GMU campus with tipoff set for 4 p.m. (PDT). AXS TV will be televised live nationally. The game will also be broadcast live on www.SantaClaraBroncos.com at 4 pm. It will be rebroadcast on KDOW 1220 AM at 7 pm with Anthony Passarelli making the call.
The Broncos had their 9-game postseason winning streak halted with the defeat and will match a school record for games played in a season when they take the court on Friday for the 38th time this season. Santa Clara also played 38 games during the 2010-11 season ending that campaign with a CollegeInsider.com Tournament title game win at Iona.
Santa Clara made just three of its first 16 field goals and shot 34.6 percent in the first half (9-of-26). The Patriots took a 22-12 lead on a layup by Bryon Allen with 7:06 to play in the half, it was the first time the Broncos faced a double-digit deficit in their five CBI tournament games. Trailing 39-26 at the break, Santa Clara made its push coming out of halftime. The Broncos went on a 10-2 run to open the second half, and Foster's second 3-pointer of the half cut the deficit to 47-42 with 12:04 remaining. GMU responded with a 9-2 run of its own to push the lead back to 56-44 on two foul shots by Patrick Holloway with 8:43 remaining, the Broncos made a late run to make things exciting at the end, using a 6-0 spurt keyed by two Brandon Clark layups to cut the lead to 70-66 with 16 seconds remaining.
Foster led the Broncos for the fifth time in five CBI games with 16 points, scoring 14 in the second half, but saw his tournament scoring average drop from 29.5 to 26.8. Marc Trasolini scored 13 points to move into No. 6 on the school's career list with 1,679 and added 8 rebounds and Evan Roquemore had 9 points, 8 rebounds and 3 assists, tying the school's single-season assist mark with 190.
Santa Clara has won 25 games this season, the second-highest single-season total in the school's 106-year basketball history and will match a team season record for games played on Friday. The Broncos will play their 38th and final game of the season, equaling the 2010-11 team that also played 38 games and claimed the CollegeInsider.com Tournament championship. Prior to 2010-11 the most games played by a Santa Clara team was 33 in 2008-09. The school record for wins in a season was set by the 1968-69 squad that went 27-2.
The home team has won the last nine games in the Championship Series of the CBI with the last visitor to claim a victory coming in 2010 when VCU won at St. Louis to win the title … Santa Clara looks to become the second team to win a Game 3 on the road joining Oregon State in 2009 who won the title with a win at UTEP … Kevin Foster made two 3-pointers on Wednesday and now holds the CBI tournament record with 19. His 134 points is second-best, trailing the 157 scored by UTEP's Stefon Jackson in 2009 … Santa Clara is the only West Coast Conference team to win a CBI game and just the second to compete in the tournament. San Francisco lost in the first round of the 2008 CBI … The Broncos are 89 points away from tying the 2011 Creighton squad for the CBI points record.
Bronco Post-Season
It is the second time in three years and just the third time in 17 years that Santa Clara has played in the post-season. It is the 17th post-season appearance in school history (22-18: 11-13 NCAA; 2-4 NIT; 5-0 CIT; 4-1 CBI). Santa Clara had its 9-game postseason win streak snapped on Apr. 3 and is now 9-1 in postseason games under Kerry Keating. SCU posted a 5-0 record to win the 2011 CIT with home wins against Northern Colorado and Air Force and wins at San Francisco, SMU and Iona to win the title. In its previous post-season appearance Santa Clara defeated Maryland 91-79 in the first round of the 1996 NCAA Tournament before falling 76-51 to Kansas, who advanced to the Elite Eight before losing to eventual NCAA runner-up Syracuse, in the second round.
Santa Clara won five games in the post-season for the first time in school history in 2011 and can match that feat with a win on Friday. The last time it played at least four post-season games was in 1952 when the Broncos advanced to the NCAA Final Four in Seattle with wins over UCLA (68-59) and Wyoming (64-53). It dropped games to Kansas (75-55) and Illinois (67-64) at that Final Four. Kansas, coached by Phog Allen, won the national title with an 80-63 victory over St. John's, coached by Frank McGuire. The tournament that year was the first to have a true "Final Four" format, with the winners at four regional sites advancing to the final site.
Five players on Santa Clara's roster (Kevin Foster, Marc Trasolini, Evan Roquemore, Raymond Cowels III, Niyi Harrison) played major roles in the team's run to the 2011 CIT title, but none more than Foster. He averaged 25.4 ppg two years ago, collecting two of his eight career 30-point games in the Broncos' five-game title run.
Our Four
There have been 31 players in the history of SCU basketball to score 1,000 career points and four of them are on the current roster. Kevin Foster is the school's all-time points leader and ranks No. 2 in the WCC with 2,399, Marc Trasolini sits at No. 6 with 1,679 and needs 11 to move pass No. 5 Steve Nash. Evan Roquemore moved into No. 19 with 1,297 points and Raymond Cowels III, with 1,081, at No. 26.
SCU was the first Division I school this season with three 1,000 point scorers on its roster when Roquemore reached that mark in a win at Pacific on Dec. 15, 2012 and was the third DI school to have four active 1,000 career point scorers after Raymond Cowels III joined the club in the win over Pepperdine on Feb. 16.
SCU is the first team in WCC history to have four 1,000 point career scorers in the same year. There are now three other teams nationally with a quartet of 1,000 point scorers: N.C. State accomplished the feat on Jan. 22, 2013 vs. Wake Forest, Davidson matched them on Feb. 9, 2013 vs. App. State and No. 1 Indiana accomplished this as well a few nights after Santa Clara on Tues., Feb. 19 vs. Michigan State.
Foster became SCU's all-time scoring leader, surpassing 1980 WCC Player of the Year and former NBA star Kurt Rambis, in a win at St. Louis on Nov. 14 and became the second player in WCC history to score 2,300 career points when he tallied 34 at Purdue on March 25. Foster is the second-highest point scorer in WCC with 2,399 points, trailing only LMU's Hank Gathers, who amassed 2,490 points from 1987-90. Foster has a school-record eight career 30-point games and has reached double-digits 32 times this season and 114 times in his 132 game career.
With 1,679 career points Trasolini is 21 away from becoming the fifth player in school history to score 1,700. With 11 points he will pass No. 5 Steve Nash. He joined Bryant, Dennis Awtrey and Kurt Rambis as the only players in school history to collect 1600+ points and 800+ rebounds in their SCU careers.
Roquemore joined the 1,000-point club early this season and climbed to No. 19 with 1,297 on April 3.
Cowels III has played in all but one game during his four-year career (family funeral) and has started 67 straight times since the beginning of the 2011-12 season. He became the fourth member of the current roster to score 1,000 career points and the 31st in school history when he drained a 3-point basket against Pepperdine on Feb. 16. He is No. 26 with 1,081.
Kevin Foster may well go down as one of the greatest all-around players to ever compete in the West Coast Conference. One of just two players to score over 2,300 career points, Foster leads the WCC this year in steals and is No. 6 in assists. Foster has collected 2,399 points - No. 1 nationally among active players - (second best in WCC history), 460 assists and 231 steals joining LMU's Terrell Lowery as the only players in WCC history to accumulate more than 2,000 points, 400 assists and 200 steals (Lowery: 2,201 points, 689 assists and 231 steals).
With 34 points in a win at Purdue on March 25 and 34 at Wright State on March 27, Foster passed Stanford greats Adam Keefe and Todd Lichti to become the Bay Area career scoring list (includes Cal, Stanford, Santa Clara, Saint Mary's, USF and San Jose State). Foster needs to score 91 points to take over the No. 1 spot from LMU's Hank Gathers, who scored 2,490 points from 1987-90. Foster has reached double figures 32 times in 37 games, has topped the 20-point mark 19 times, and scored at least 30 in a game four times to lead the Broncos in scoring.
Marc Trasolini (108), Raymond Cowels III (87) and Foster (124) have started all 37 games for SCU this year and have combined for 319 starts in their SCU careers. Evan Roquemore has started 34 of 37 games and has 90 career starts. The remaining nine players on the roster have a total of 132 career starts with Niyi Harrison leading the group with 45.
Foster became the all-time leader in games started when he took the court in SCU's win over LMU on Feb. 28. At Saint Mary's on March 2 Foster broke the school record for field goals made, topping Londale Theus who held the record since 1980 (Foster has 763, Theus had 712 from 1976-80). Foster also has the school career record in points scored, steals, field goals attempted, three pointers made, three pointers attempted and minutes played. Foster set the single season record for steals at Wright State March 27 and currently has 89, good for No. 5 in WCC single season history. Trasolini and Cowels III each have played in a school record 136 career games, surpassing Ben Dowdell, who participated in 128 games from 2007-11.
Maybe for the first time in school history, the Broncos have three players with 100 or more assists. Evan Roquemore leads the team and tied John Woolery's 1993-94 single-season record of 190 when he dished out three at GMU on Wednesday. Kevin Foster has found time to dish out 161 assists on the season, which is the seventh-best season mark at SCU and Brandon Clark reached the century mark by handing out a pair on Wednesday. The three have combined for 451 assists and just 263 turnovers for an assist/turnover ratio of 1.71. Michigan leads the NCAA in assist/turnover ratio at 1.55.
Several Broncos have a chance to reach scoring milestones in the final game of the season on Friday. With 2,399 career points Kevin Foster needs just one to reach 2,400. Marc Trasolini needs 21 points to become the fifth player in school history to reach 1,700. Evan Roquemore needs three points for 1,300 in his career and Raymond Cowels III is 19 away from 1,100.
The Broncos were whistled for a season-high 32 personal fouls that led to 43 free throw attempts for George Mason in their 73-66 loss at GMU on Apr. 3. It was just the ninth time in school history the Broncos committed that many fouls in a game and every Bronco that played was called for at least two fouls. The Patriots converted 30 of their 43 attempts, both Santa Clara opponent season-highs, while the Broncos went 13-of-22 from the stripe.
Marc Trasolini, named to the 2013 WCC All-Academic Team on March 1, is one of the 10 finalist's for the Senior CLASS Award nationally. The men's Senior CLASS Award winner will be announced during the 2013 NCAA Final Four. Eligible seniors must have notable achievements in four areas of excellence – community, classroom, character and competition. The finalists were chosen by a media committee from the list of 30 candidates announced in October.
Trasolini became the fourth player in school history to be named to the Capital One CoSIDA Academic All-American list on Feb. 19 when he was named third team nationally. He is the only player in the WCC and Pac 12 to be named to the CoSIDA All-District Academic All-American list three straight competition years (2010, '11, '13). Trasolini is in his fifth quarter of MBA work and graduated cum laude in March 2012 with a degree in Finance (3.65 GPA).
Kevin Foster is the WCC's career leader with 426 after breaking a tie with LMU's Jeff Fryer vs. BYU on Jan. 12. Foster hit his record 364th career three knocking down a shot from the corner with 10:44 left in the first half vs. the Cougars. Foster is No. 5 on the NCAA Division I all-time threes made list, passing Davidson's Stephen Curry who made 414 career threes from 2007-09. He needs five threes made to pass former All-American Chris Lofton of Tennessee (430, 2006-2009)
Foster's 152 made threes in WCC games is No. 3 all-time in league history as he passed Gonzaga's Blake Stepp with 143 threes made in WCC games (2000-04) at Portland on Feb. 23 when he made four. LMU's Jeff Fryer holds the WCC record in league-only games with 166 threes made in 49 games (1987-90) and Portland's Jared Stohl is No. 2 with 160 career WCC threes (2007-11). Foster's three threes at BYU Feb. 2 broke two-time NBA MVP and former Bronco Steve Nash's school record for career WCC threes. Nash made 132 in his 56 league games and is No. 2 all-time for the Broncos with 263 threes, a record Foster eclipsed early his junior year. Foster has attempted 10 or more 3s in a game 16 times this season and 60 times in his 132 game career.
No. Player 3PA From To Last School
1. Keydren Clark 1192 2003 2006 Saint Peter's
2. Kevin Foster 1170 2009 2013 Santa Clara
In SCU's 88-75 win over Air Force on March 18, 2011, Foster broke the school record with nine threes, tying a career-high with 36 points. He also had a career-high 36 in Santa Clara's 85-71 win over Gonzaga on Jan. 20, 2011. Foster shattered the WCC single season threes made record with 140 in 2010-11, leading the country, and grabbed the nation's top spot in threes made per game as well with 3.68. Foster's 768 points in 2010-11 were No. 6 on the WCC's single season points scored list as he finished as the leading scorer in a single season in the Bay Area as well. He broke the school's single season mark for points scored, minutes played, threes made and threes attempted that year. He was named first-team All-WCC and was named the WCC Player of the Month in February and March. He was named the WCC Player of the Year by CollegeInsider.com.
Head coach Kerry Keating needs one win for 100 in his career. His current record in six season at SCU is 99-102 … Santa Clara is the last remaining WCC team still playing after BYU's semifinal NIT loss on April 2 … The Broncos will tie a school record for games when they play their 38th of the season Friday. They will match the 2010-11 team that also played 38 games and ended the season with a win in the CollegeInsider.com championship game at Iona. Prior to the 10-11 season the most games played by a Santa Clara team was 33 in 2008-09 … Prior to Game 1 of the CBI Championship Series on April 1, the Broncos had never played a game in the month of April … The Broncos faced their second-largest halftime deficit of the season when they trailed by 13 (39-26) at GMU on Apr. 3 and have been behind at the break eight times in their last 14 games (two were tied). Over the first 23 games of the season the Broncos trailed at the break six times by a combined 28 points. SCU had its 2-game win streak when trailing at halftime come to an end and is 5-9 this season when playing catch up after 20 minutes … Santa Clara has scored at least 60 points in all but two games this season and its total of 2,747 is the second-most in school history. The Broncos totaled a school record 2,810 points in 2010-11 when they played 38 games … Four of Kevin Foster's school-record eight career 30-point games have come in the Broncos' last nine postseason contests. He tied a career-high with 36 points in a second round CIT win over Air Force on Mar. 18, 2011 and notched 35 in a semifinal CIT win at SMU on Mar. 25, 2011. His 34 points at Purdue on Mar. 25 tied for the third-most in the six-year history of the College Basketball Invitational presented by Buick and he became the third player in school history with back-to-back 30-point games when he scored 33 at Wright State on Mar. 27 … SCU has held opponents to fewer than 20 first half points five times … The Broncos have nine wins by at least 20 points and four wins by at least 30 points ... Brandon Clark is the only non-starter to have more than one double-digit scoring game off the bench. Clark has scored 10 or more points in 11 games this season … Kevin Foster is the only player in SCU history to attempt more than 200 three-point shots in a season and make more than 100. He's attempted more than 200 for a third time this season with 322 and has made over 100 for a second time with 115 … Led by Kevin Foster's 115 and 75 from Raymond Cowels III, the Broncos have set a team season record for 3-point baskets made with 297. With 322 3-point attempts from Foster, 192 from Cowels III and 143 from Evan Roquemore, the Broncos have also established a new team record with 820.
Kevin Foster was nicknamed 'Microwave' by his father Rick Foster during his youth after the Detroit Piston's Vinnie Johnson. Johnson earned the nickname 'the Microwave' from Celtics guard Danny Ainge for his ability to score many points in a short period of time, heating up the offense. Foster has scored 15+ points in 88 of 132 career games and he has scored 19+ in 63 career games. Foster's 19+ games follow:
Opponent/Date Pts. (3s)
1. vs. Arizona/Nov. 25, 2008 21 (5)
2. vs. Belmont/Dec. 29, 2008 24 (3)
3. at Harvard/Jan. 2, 2009 20 (4)
4. at Saint Mary's/Jan. 9, 2009 31 (5)
5. at Portland/Jan. 17, 2009 20 (3)
6. vs. LMU/Jan. 29, 2009 20 (3)
7. vs. Saint Mary's/Feb. 7, 2009 26 (4)
8. at LMU/Feb. 14, 2009 26 (5)
9. at USF/Feb. 21, 2009 20 (2)
10. vs. Gonzaga/Feb. 26, 2009 20 (3)
11. vs. San Diego/March 7, 2009 21 (4)
12. at CSUB/Nov. 13, 2009 21 (3)
13. vs. Menlo/Nov. 19, 2009 19 (4)
14. at Pacific/Nov. 22, 2009 22 (5)
15. vs. Fresno State/Nov. 28, 2009 25 (5)
16. vs. CSU Bakersfield/Nov. 12, 2010 19 (4)
17. at So. California/Nov. 15, 2010 26 (4)
18. vs. Bethune-Cookman/Nov. 21, 2010 21 (6)
19. vs. N. Colorado/Nov. 23, 2010 19 (4)
20. vs. UC Santa Barbara/Dec. 4, 2010 25 (6)
21. at San Jose State/Dec. 10, 2010 26 (3)
22. vs. Washington St./Dec. 19, 2010 29 (4)
23. at CSU Northridge/Dec. 22, 2010 22 (2)
24. vs. Bethany/Jan. 3, 2011 19 (6)
25. vs. USF/Jan. 8, 2011 26 (8)
26. vs. Gonzaga/Jan. 20, 2011 36 (6)
27. vs. Portland/Jan. 22, 2011 19 (3)
28. vs. LMU/Jan. 29, 2011 28 (3)
29. vs. St. Mary's/Feb. 10, 2011 25 (4)
30. vs. San Diego/Feb. 12, 2011 21 (3)
31. at Gonzaga/Feb. 17, 2011 27 (8)
32. at Portland/Feb. 19, 2011 29 (7)
33. vs. Air Force/March 18, 2011 36 (9)
34. at USF/March 22, 2011 27 (3)
35. at SMU (March 25, 2011 35 (5)
36. vs. New Mexico (Nov. 24, 2011) 26 (5)
37. vs. Oklahoma (Nov. 25, 2011) 19 (3)
38. vs. Cal State Northridge (Dec. 3, 2011) 20 (4)
39. vs. Pacific (Dec. 17, 2011) 26 (6)
40. at Houston Baptist (Dec. 21, 2011) 19 (3)
41. vs. Eastern Michigan (Dec. 29, 2011) 25 (6)
42. vs. Wagner (Dec. 30, 2011) 20 (4)
43. at BYU (Jan. 14, 2012) 22 (4)
44. vs. Saint Mary's (Jan. 21, 2012) 22 (5)
45. vs. Simpson (Nov. 10, 2012) 27 (7)
46. at Saint Louis (Nov. 14, 2012) 30 (2)
47. vs. Utah Valley (Nov. 21, 2012) 21 (1)
48. vs. Utah State (Nov. 28, 2012) 27 (5)
49. vs. UC Santa Barbara (Dec. 1, 2012) 28 (4)
50. at Pacific (Dec. 15, 2012) 31 (4)
51. at Duke (Dec. 29, 2012) 29 (5)
52. vs. USF (Jan. 2, 2013) 21 (5)
53. vs. Gonzaga (Jan. 5, 2013) 29 (2)
54. vs. BYU (Jan. 12, 2013) 22 (3)
55. at USF (Jan. 17, 2013) 27 (6)
56. at Pepperdine (Jan. 19, 2013) 29 (6)
57. vs. Pepperdine (Feb. 16, 2013) 22 (5)
58. at Portland (Feb. 23, 2013) 20 (4)
59. vs. LMU (March 8, 2013) 23 (3)
60. vs. Vermont (March 19, 2013) 23 (3)
61. at Purdue (March 25, 2013) 34 (3)
62. at Wright State (March 27, 2013) 33 (6)
63. vs. George Mason (April 1, 2013) 28 (5)


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According to a new survey, the absolute worst city you can go to eat Chinese food is yours truly. In mapping out the food preferences of the 50 largest cities in the U.S., home indexing site Estately found Austin to be the city with the fewest Chinese restaurants, making it the No. 1 worst city to go for your lo mein, cashew chicken, fried rice needs. Personally, the gas-station-turned-Chinese-takeout spot down the street serves me just fine, but maybe that's because I, as the survey suggests, am a resident of a region "confounded by chopsticks."
If you're looking for a few more Chinese restaurant options (but just a few) you can make the eight-hour drive to El Paso, which came in as the second-worst city for Chinese. In fact, El Paso did surprisingly (unsurprisingly?) poorly in many categories. The city was ranked the No. 1 worst for coffee, halal, pizza and Thai, and second-worst for breakfast and Chinese. Similarly San Antonio ranked among the top five worst cities for seven different restaurant categories.
So what categories did Austin shine in? In today's non-news, the city was ranked the No. 2 best for food trucks and No. 4 best for Tex-Mex restaurants. If there's one thing we love in Austin, its eating food made in a vehicle. We don't appreciate the survey's shade suggesting Austin, Portland and Oakland as go-to cities only "If you enjoy standing in line to get food poisoning directly from someone selling food out of the side of truck." Several of Austin's food trucks have gone on to become esteemed brick-and-mortars, and we only got sick that one time and there was no way of being sure why.
Which cities outranked Austin in terms of Tex-Mex? Shockingly Atlanta, Georgia and Washington, D.C. beat out Austin. But in a totally unacceptable oversight that will have us rage-eating an entire bowl of queso later, Dallas ranked No. 2.
Tacos, unfortunately, did not constitute their own category. But we all know where we stand on that.


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|Meir Ettinger, front. Basel Awidat/Flash 90
Meir Ettinger's newborn son was named Netzach Binyamin this past Monday. But he was unable to attend.
This reminds me of how American soldiers coming home from Vietnam used to be persecuted, making their post-traumatic stress disorder worse. Many people felt that the soldiers didn't try hard enough to stay out of the war. But what we didn't know years ago has come back to haunt us in Israel.
Some years ago my husband and I befriended a family who were friends with someone I worked with, and visited them one day. The man of the family had indeed served in 'Nam and seen things that one would wish only on our worst enemies, maybe. Yes, grown men shot down little children...who were holding toys out to them that had bombs embedded in them. This man was obviously in a dilemma as to how to communicate this without being shot at himself. Fortunately a lot of time had gone by and I had grown up, and become able to accept that unbelievable situations like these could occur, even though the very thought of them is nauseating.
What does this have to do with Meir? Because he and his wife (and now, son) are considered Hilltop Youth, they are persecuted for their goal of adding to Israel from the territory that has already been given to us, and note the heavily loaded, exaggerated terms (compared to a Jewish form of ISIS, etc.) used in the Wikipedia definition (of course, the youth themselves cannot be allowed to define themselves!). This is on top of his being from the Kahane family, which condemns him already — and if you think I'm exaggerating, consider that his grandfather, uncle and aunt were assassinated by Arabs in an attempt to severely intimidate the extended family, if not exterminate it, may it never be.
Arutz 7 reports concerning the refusal to let Meir attend his first-born son's brith milah:
Attorney Ariel Atari who represented Ettinger in court said in response: "It is the right of a person that his son not begin his life within the walls of the prison. IPS's argument in court that there is not enough time to prepare logistics to release him today is scandalous, given the fact that we applied for an appeal for eight days already. The reality in which an administrative detainee, with no incriminating evidence against him, cannot leave to attend his son's circumcision, whereas a terrorist convicted of murder would be granted leave - should infuriate every law-abiding citizen."
But the concept of "thought crime" was invented for such as Meir Ettinger, I am convinced. It's only because of what he wrote on the Internet, the expression of his ideas — and not plans — that he is in jail now. This, while books like Mein Kampf and The Protocols of the Elders of Zion are selling like wildfire, and practically setting wild fires, elsewhere without any consequences for the publishers and the fundraisers. It confirms any thoughts that there might be a conspiracy, first to dispirit the Jew and then to destroy him. And, though I hate to say it, some Jews in high places are in on it, thinking they will be spared in the aftermath. They should learn this lesson while there is still time: If you agree that Israel should be dismantled and all Jews wiped out, and you are a Jew, you will be among the first to go. You have to be an example, you know.
The spiritually aware know better. They sense Mashiach in the air, with retribution not far behind. May the Victory of the Son of the Right Hand (and of all knowing Jews, as well as those of the rest of the world who appreciate it) be revealed quickly!
Mazal tov, Meir, Moriyah, Netzach Binyamin and family. May you increase with joy and may nothing harmful touch you.
Release Meir Ettinger! | Zaro Chayo v'Kayomo! (May his child(ren) live and endure!) | Brith of Netzach Binyamin ben Meir David Chai: Exclusive Pictures |