Hack and fish ... for recombination in the SARS group

Following the current flow, we have had a few recent coronavirus posts here on the Genealogical World of Phylogenetic Networks. In this post, I'll show the results of a little experiment coming back to David's original post on the topic. Can we use trees to "fish" for evidence of recombination?

As David pointed out, even when we use a phylogenetic-tree inference method to analyze virus genomes, we don't really end up with a phylogenetic tree. Instead, we have a tree reflecting genetic similarity, which will reflect the phylogeny to some unknown extent. The main problem with virus genomes, however, is that they easily recombine — and thus different parts of a virus genome may have different evolutionary histories. A single tree cannot reflect this.

This does not mean that trees cannot tell is something about virus evolution. However, these trees become part of a fishing exercise, looking for different possible historical pathways, which may reflect recombination events.

The tree

Our SARS harvest matrix includes about a dozen sequence groups, which we have labeled Type 1 (the original SARS-CoV) to 9b. Type 7 is the new SARS-CoV-2. For my experiment here, I picked one place-holder sequence per main type (to speed up calculation time). I added two more types: the newly found direct sister of SARS-CoV-2; and some "unclassified" SARS-like viruses from pangolins, which earlier were proposed as sisters, as shown in this tree from the GISAID web page.

The phylogenetic neighborhood of SARS-CoV-2 (GISAID, screenshot captured 3/6/2020). Note the flatness of the CoV(-1; yellow) and CoV-2 (red) subtrees.

GISAID doesn't give the GenBank accession numbers, so we cannot easily say whether our sample matches theirs. However, the tree we can infer from the complete genomes (high-divergent, non-alignable regions excluded) looks very similar, as shown next, and some of the labels match up.

Fig. 1 Maximum likelihood (ML) tree inferred for our sample using (old, v.8.0.20) RAxML. Roman numbers refer to earlier defined Types 1–9 (Tree and viruses – the SARS group), Arabic numbers give nonparametric bootstrap (BS) support based on 100 BS pseudoreplicates (number of neccessary BS replicates determined by the extended majority rule criterion). Branches without Arabic number are unambiguous (BS = 100).

Most importantly, all but three branches have unambiguous support: the phylogeny of this sample is resolved. Unfortunately, as our recurring readers already know, this nearly resolved tree simplifies a much more complex situation.

The Neighbor-net with recombinations and mutational trends (arrows, connectives; cf. Tree and viruses – the SARS group).

Hack and slash

A simple method to fish for different evolutionary histories in a genome is to cut the virus genomes into sub-sequences, infer a tree for each sub-sequence, and then compare the trees. Most researchers compare trees by showing them and discussing which one makes most sense. Here is an example from Corman et al. (2014), who searched for the root of MERS (Middle East Respiratory Syndrome) virus, an illness closely related to SARS.

Reprint of Corman et al. 2014, fig. 3 with colors added to EriCoV (green) and HKU/BtCoV (olive) groups

Each tree in their Fig. 4A and B (Bayesian majority rule consensus trees) was inferred from a different part of the genome. Corman et al.'s focus was to root the MERS viruses by identifying a better outgroup. However, note that the new sister-group (red, green stars – sister to MERS; orange stars – sister to someone else) moves, and so does the green EriCoV clade and the olive HKU/BtCoV group (clade in some trees and grade in others). Do some of these trees get it wrong? Or is, eg. NeoCoV the product of reticulate evolution (here: ancient recombination)? Some parts of its genome might be derived from a common ancestor with MERS (blues), and others from a common ancestor with KW2E (black) and EriCoV (green).

Our complete matrix has 27,333 characters, providing nearly 6,000 distinct alignment patterns (abbreviated DAP, below), which is a lot — the GISAID link above also provides a graphical representation of site divergence. However, probabilistic tree inference methods (ML, Bayes) can handle moderate to high levels of divergence in the data. On the other hand, they also need a certain amount of data to perform well (see also: Inferring a tree with 12000 [or more] virus genomes). So, for my experiment, I hacked the matrix into nine bits of equal size, ie. each submatrix has a bit more than 3,000 nucleotides, providing between 615 (bit #5) and 1029 (bit #1) DAPs.

Fig. 2 Nine ML trees with BS support annotated along branches, each based on a ~3000 nucleotide long bit of the genomes (ordered left-right, top-bottom). Purple highlights branches conflicting with the complete genome tree.

Our nine trees (shown above) are not badly resolved, as most branches get substantial support. But they are not congruent. If we are dealing with recombination, then we might assume that all of these trees do show an actual aspect of the evolutionary history of the genomes. That is, they are all right and wrong, at the same time.

Moreover, we have high supported clades conflicting with the complete genome tree's (Fig. 1) topology. The signal issues, due to recombination (see Trees and viruses...), did not decrease branch support. That is, 6,000+ DAP is a lot, and recombination only affects a part of the complete genome, possibly quite a small part.

Non-trivial evolution needs more than trivial graphs

To depict the reticulate phylogeny of the virus sample, we need to consider the differences seen in the hacked-and-slashed matrix trees. This can easily be illustrated using a network, instead of a set of trees, as shown here.

Fig. 3 A (strict) consensus network of all nine trees, in which the edge lengths give the sum of the branch lenghts in the tree sample. The gray brackets give the topology of the near-fully resolved complete genome tree.

The graph above is a phylogenetic network: the competing edge bundles represent the different inferred histories of bits of the genomes. The SARS-CoV-2 lineage seems to be the product of (ancient) recombination, and recombination also played a role in forming the members of the original SARS-CoV group.

Fig. 4 Pruned consensus network showing only the CoV(-1) lineage exhibiting various levels of recombination within and between clades as defined by the complete genome tree (tree sample sames as in Fig. 3).

Consensus networks can also be used to summarize the support for alternative splits, as shown next.

Fig. 5 Sum-support consensus network based on the bit-wise BS analyses (111/112 pseudoreplicates generated per bit). Only splits are shown occurring in at least 20% of all BS replicates, i.e. splits supported by at least two bits, trivial splits are collapsed. Colored splits represent according groups/clades in the full-genome tree (Fig. 1). Inlet: 'splits rose' showing competing splits patterns within Types II and III (cf. according subtrees/-trunks in Fig. 2 and Fig. 4).

In contrast to the networks before (Figs. 3, 4), generated using the same algorithm*, the BS consensus network in Fig. 5 is not a phylogenetic network. The boxes don't reflect disparate histories of parts of the genomes but the varying support for competing topological alternatives. By summing up the bit-wise BS analyses instead of bootstrapping the entire data (the BS consensus network for the full data, Fig. 1, shows only two boxes), we get a better idea which aspects of the all-genome tree find robust support across the genome.**

Conclusion

Sub-dividing an alignment is a really quick^† way to fish for evidence of recombination, especially when one then uses a consensus network to summarise the resulting partial trees.

For interpretation, a tree is a very simple, trivial, and hence appealing graph: A is sister to B and so on. Even a child can interpret a tree. Networks are already visually more challenging, but whenever an organism's evolution doesn't follow a tree (as for viruses), we shouldn't use a tree to depict its phylogeny (or reconstruct its evolution).

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Data availability

The dataset used for our experiment is a taxon subset of the original data set, available via figshare (with a permanent, hence, citable DOI):
Grimm GW, Morrison D. 2020. Harvest and phylogenetic network analysis of SARS virus genomes (CoV-1 and CoV-2). figshare Dataset. https://doi.org/10.6084/m9.figshare.12046581 

References

Corman VM, Ithete NL, Richards LR, Schoeman MC, Preiser W, Drosten C, Drexlera JF (2014) Rooting the phylogenetic tree of Middle East respiratory syndrome coronavirus by characterization of a conspecific virus from an African Bat. Journal of Virology 88: 11297–11303.

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* SplitsTree includes five options to determine "edge weights" (= edge-lengths) in case of Consensus networks: "median" and "mean" average the branch-lengths in the tree sample; "count", the setting used to generate Support consensus networks, counts how often a certain taxon bipartition (split) is found in the tree sample – an edge length is proportional to the frequency of a split; "sum", used here to generate the first network, summarizes the branch-lengths; and "none" discards both branch-lengths and split frequency.

** A split supported only by one of the nine bits, even if unambiguous, ie. present in all 111 (112) per bit BS replicates, will not be represented in the sum-Support consenus network using a cut-off of 20%.

† The complete set of ML analyses took 20 min on a stand-alone computer; consensus networks are generated in a blink, and take hardly a minute even when using trees with many leaves.

Fossils and networks 1 – Farris and Felsenstein

Over 60 years ago, Robert Sokal and Peter Sneath changed the way we quantitatively study evolution, by providing the first numerical approach to infer a phylogenetic tree. About the same time, but in German, Willi Hennig established the importance of distinguishing primitive and advanced character states, rather than treating all states as equal. This established a distinction between phenetics and phylogenetics; and the latter is the basis of all modern studies, whether it is explicitly acknowledged or not.

More than two decades later, Steve Farris and the Willi Hennig Society (WHS) established parsimony as the standard approach for evaluating character-state changes for tree inference. In this approach, morphological traits are scored and arranged data matrices, and then the most parsimonious solution is found to explain the data. This tree, usually a collection of most-parsimonious trees (MPT), was considered to be the best approximation of the true tree. Clades in the trees were synonymized with monophyly sensu Hennig (1950, short English version published 1965), and grades with paraphyly: Cladistics was born (see also: Let's distinguish between Hennig and Cladistics).

Why parsimony? Joe Felsenstein, who was not a member of the WHS but brought us, among many other things, the nonparametic bootstrap (Felsenstein 1985), put it like this (Felsenstein 2001):

History: William of Ockham told Popper to tell Hennig to use parsimony

Soon, parsimony and cladistics came under threat by advances in computer technology and Kary Mullis' development of the polymerase-chain-reaction (PCR; Mullis & Faloona 1987) in the early 80s (note: Mullis soon went on with more fun stuff, outside science). While the data analysis took ages (literally) in the early days, more and more speedy heuristics were invented for probabilistic inferences. PCR marked the beginning of the Molecular Revolution, and genetic data became easy to access. Soon, many researchers realized that parsimony trees perform badly for this new kind of data, a notion bitterly rejected by the parsimonists, organized mainly in the WHS: the "Phylogenetic Wars" raged.

The parsimony faction lost. Today, when we analyze (up to) terabytes of molecular data, we use probabilistic methods such as maximum likelihood (ML) and Bayesian inference (BI). However, one parsimony bastion has largely remained unfazed: palaeontology.

In a series of new posts, we will try to change that; and outline what easy-to-compute networks have to offer when analyzing non-molecular data.

It's just similarity, stupid!

One collateral damage of the Phylogenetic Wars was distance-based methods, which, still today, are sometimes classified as "phenetic" in opposite to the "phylogenetic" character-based methods (parsimony, ML, BI). The first numerical phylogenetic trees were not based on character matrices but distance matrices (eg. Michener & Sokal 1957 using a cluster algorithm; Cavalli-Sforza & Edwards 1965 using parsimony; see also Felsenstein 2004, pp.123ff).

But no matter which method, optimality criterion or data-type we use, in principal we do the analysis under the same basic assumptions:

1. the more closely related two taxa are, then the more similar they should be.
2. the more similar two taxa are, then the more recent is their split.

The ingroup (blue clade) and outgroup (red clade) are most distant from each other. Placing the fossils is trivial: Z is closer to O than to C, the member of the ingroup with the fewest advanced character states. Ingroup sister taxa A + C and B + D are most similar to each other. The monophyly of the ingroup and its two subclades is perfectly reflected by the inter-taxon distances.

Assuming that the character distance reflects the phylogenetic distance (ie. the distance along the branches of the true tree), any numerical phylogenetic approach will succeed in finding the true tree. The Neighbor-Joining method (using either the Least Squares or Minimum Evolution criteria) will be the quickest calculation. The signal from such matrices is trivial, we are in the so-called "Farris Zone" (defined below).

We wouldn't even have to infer a tree to get it right (ie. nested monophyly of A + C, B + D, A–D), we could just look at the heat map sorted by inter-taxon distance.

Just from the distance distributions, visualized in the form of a "heat-map", it is obvious that A–D are monophyletic, and fossil Z is part of the outgroup lineage. As expected for the same phylogenetic lineage (because changes accumulate over time), its fossils C and D are still relatively close, having few advanced character states, while the modern-day members A and B are have diverged from each other (based on derived character states). Taxon B is most similar to D, while C is most similar A. So, we can hypothesize that C is either a sister or precursor of A, and D is the same of B. If C and D are stem group taxa (ie. they are paraphyletic), then we would expect that both would show similar distances to A vs. B, and be closer to the outgroup. If representing an extinct sister lineage (ie. CD is monophyletic), they should be more similar to each other than to A or B. In both cases (CD either paraphyletic or monophyletic), A and B would be monophyletic, and so they should be relatively more similar to each other than to the fossils as well.

Having a black hole named after you

The Farris Zone is that part of the tree-space where the signals from the data are trivial, we have no branching artifacts, and any inference (tree or network), gives us the true tree.

It's opposite has been, unsurprisingly perhaps, labeled the "Felsenstein Zone". This is the part of the tree-space where branching artifacts are important — the inferred tree deviates from the true tree. Clades and grades (structural aspects of the tree) are no longer synonymous with monophyly and paraphyly (their evolutionary interpretation).

We can easily shift our example from the Farris into the Felsenstein Zone, by halving the distances between the fossils and the first (FCA) and last (LCA) common ancestors of ingroup and outgroup and adding some (random = convergence; lineage-restricted = homoiology) homoplasy to the long branches leading to the modern-day genera.

The difference between distance, parsimony and probabilistic methods is how we evaluate alternative tree topologies when similarity patterns become ambiguous — ie. when we approach or enter the Felsenstein Zone. Have all inferred mutations the same probability; how clock-like is evolution; are their convergence/ saturation effects; how do we deal with missing data?

For our example, any tree inference method will infer a wrong AB clade, because the fossils lack enough traits shared only with their sisters but not with the other part of the ingroup. Only the roots are supported by exclusively shared (unique) derived traits (Hennigian "synapomorphies"). The long-branch attraction (LBA) between A and B is effectively caused by:

• 'short-branch culling' between C and D: the fossils are too similar to each other; and their modern relatives too modified;
• the character similarity between A and B underestimates the phylogenetic distance between A and B, due to derived traits that evolved in parallel (homoiologies).

While ML and NJ make the same decision, the three maximum-parsimony trees permute options for placing C and D, except for the correct options, and including an impossible one (a hard trichotomy). Standard phylogenetic trees are (by definition) dichotomous being based on the concept of cladogenesis — one lineage splits into two lineages.

MPTs inferred using PAUP*'s branch-and-bound algorithm (no heuristics, this algorithm will find the actual most parsimonious solution); NJ/LS using PAUP* BioNJ implementation and simple (mean) pairwise distances; ML using RAxML, corrected (asc.) and not (unc.*) for ascertainment bias (the character matrix has no invariable sites). All trees are not rooted using a defined outgroup but mid-point rooted. If rooted with the known outgroup O, the fossil Z would be misinterpreted as early member of the ingroup.

We have no ingroup-outgroup LBA, because the three convergent traits shared by O and A or B, respectively, compete with a total of eight lineage-unique and conserved traits (synapomorphies) — six characters are compatible with a O-A or O-B sister-relationship (clade in a rooted tree) but eight are incompatible. We correctly infer an A–D | O + Z split (ie. A–D clade when rooted with O) simply because A and B are still more similar to C and D than to Z and O; not some method- or model-inflicted magic.

The magic of non-parametric bootstrapping

When phylogeneticists perform bootstrapping, they usually do it to try to evaluate branch support values — a clade alone is hardly sufficient to infer an inclusive common origin (Hennig's monophyly), so we add branch support to quantify its quality (Some things you probably don't know about the bootstrap). In palaeontology, however, this is not a general standard (Ockhams Razor applied but not used), for one simple reason: bootstrapping values for critical branches in the trees are usually much lower than the molecular-based (generally accepted) threshold of 70+ for "good support" (All solved a decade ago).

When we bootstrap the Felsenstein Zone matrix that gives us the "wrong" (paraphyletic) AB clade, no matter which tree-inference method we use, we can see why this standard approach undervalues the potential of bootstrapping to explore the signal in our matrices.

Consensus networks based on each 10,000 BS pseudoreplicates, only splits are shown that are at least found in 15% of the replicate trees (trivial splits collapsed). Reddish – false splits (paraphyletic clades), green – true splits (monophyletic clades).

While parsimony and NJ bootstrap pseudoreplicates either fall prey to LBA or don't provide any viable alternative (the bootstrap replicate matrix lacks critical characters), in the example a significant amount of ML pseudoreplicates did escape the A/B long-branch attraction.

Uncorrected, the correct splits A + C vs. rest and B + D vs. rest can be found in 19% of the 10,000 computed pseudoreplicate trees. When correcting for ascertainment bias, their number increases to 41%, while the support for the wrong A + B "clade" collapses to BS_ML = 49. Our BS supports are quite close to what Felsenstein writes in his 2004 book: For the four taxon case, ML has a 50:50 chance to escape LBA (BS_ML = true: 41 vs. false: 49), while MP and distance-methods will get it always wrong (BS_MP = 88, BS_NJ = 86).

The inferred tree may get it wrong but the (ML) bootstrap samples tell us the matrix' signal is far from clear.

Side-note: Bayesian Inference cannot escape such signal-inherent artifacts because its purpose is to find the tree that best matches all signals in the data, which, in our case, is the wrong alternative with the AB clade — supported by five characters, rejected by four each including three that are largely incompatible with the true tree. Posterior Probabilities will quickly converge to 1.0 for all branches, good and bad ones (see also Zander 2004); unless there is very little discriminating signal in the matrix — a CD clade, C sister to ABD, D sister to ABC, ie. the topological alternatives not conflicting with the wrong AB clade, will have PP << 1.0 because these topological alternatives give similar likelihoods.


Long-edge attraction

When it comes to LBA artifacts and the Felsenstein Zone, our preferred basic network-inference method, the Neighbor-Net (NNet), has its limitations, too. The NNet algorithm is in principle a 2-dimensional extension of the NJ algorithm. The latter is prone to LBA, hence, and the NNet can be affected by LEA: long edge attraction. The relative dissimilarity of A and B to C and D, and (relative) similarity of A/B and C/D, respectively, will be expressed in the form of a network neighborhood.

Note, however, the absence of a C/D neighbourhood. If C is a sister of D (as seen in the NJ tree), then there should be at least a small neighbourhood. It's missing because C has a different second-best neighbour than B within the A–D neighbourhood. While the tree forces us into a sequence of dichotomies, the network visualizes the two competing differentiation patterns: general advancement on the one hand (ABO | CDZ split), and on the other potential LBA/LEA, vs. similarity due to a shared common ancestry (ABCD | OZ split; BD neighborhood).

Just from the network, we would conclude that C and D are primitive relatives of A and B, potentially precursors. The same could be inferred from the trees; but if we map the character changes onto the net (Why we may want to map trait evolution on networks), we can notice there may be more to it.

Character splits ('cliques') mapped on the NNet. Green, derived traits shared by all descendants of a common ancestor ('synapomorphies'); blue, lineage-restricted derived traits, ie. shared by some but not all descendants (homoiologies); pink, convergences between in- and outgroup; black, unique traits ('autapomorphies')

Future posts

In each of the upcoming posts in this (irregular) series, we will look at a specific problem with non-molecular data, and test to what end exploratory data analysis can save us from misleading clades; eg. clades in morphology-informed (parts of, in case of total evidence) trees that are not monophyletic.

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* The uncorrected ML tree shows branch lengths that are unrealistic (note the scale), and highly distorted. The reason for this is that the taxon set includes (very) primitive fossils and (highly) derived modern-day genera, but the matrix has no invariable sites telling the ML optimization that changing from 0 ↔ 1 is not that big of a deal. This is where the ascertainment bias correction(s) step(s) in (RAxML-NG, the replacement for classic RAxML 8 used here, has more than one implementation to correct for ascertainment bias. A tip for programmers and coders: effects of corrections have so far not been evaluated for non-molecular data).

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Cited literature
Cavalli-Sforza LL, Edwards AWF. 1965. Analysis of human evolution. In: Geerts SJ, ed. Proceedings of the XI International Congress of Genetics, The Hague, The Netherlands, 1963. Genetics Today, vol. 3. Oxford: Pergamon Press, p. 923–933.
Felsenstein J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791.
Felsenstein J. 2001. The troubled growth of statistical phylogenetics. Systematic Biology 50:465–467.
Felsenstein J. 2004. Inferring Phylogenies. Sunderland, MA, U.S.A.: Sinauer Associates Inc., 664 pp. (in chapter 10, Felsenstein provides an entertaining "degression on history and philosophy" of phylogenetics and systematics).
Hennig W. 1950. Grundzüge einer Theorie der phylogenetischen Systematik. Berlin: Dt. Zentralverlag, 370 pp.
Hennig W. 1965. Phylogenetic systematics. Annual Review of Entomology 10:97–116.
Michener CD, Sokal RR. 1957. A quantitative approach to a problem in classification. Evolution 11:130–162.
Mullis KB, Faloona F. 1987. Specific synthesis of DNA in vitro via a polymerase catalyzed chain reaction. Methods in Enzymology 155:335–350.

Why we may want to map trait evolution on networks, pt. 2 – Topological ambiguity

In last week's Part 1, I gave an introduction to the problem of categorizing the polarity of morphological traits. How can we reconstruct which characters are primitive, or plesiomorphic according to Hennig, and which are derived, or apomorphic? This is something we need to do to reconstruct evolution, because most of the past is only preserved in the form of fossils, usually lacking any DNA. In this second part of the discussion, I'm going to take apart my own tree and show why we inevitably need networks, not trees.

There may be more than one tree

Even with more and more data at hand, some molecular phylogenies refuse to be unambiguous. Even worse, different, well-sampled molecular data sets may tell different stories — ie. there is more than one molecular tree to explain the diversity patterns. The ML tree used for the ML character mapping in Part 1 was pretty well supported, but not telling the entire truth.

For a start, there is no reason to assume that oaks are not monophyletic even though the data fail to resolve them as a clade (evolving something unique like the oaks twice would be a striking trick, even for gambling Mother Nature) — molecular trees may have misleading, sometimes just wrong, branches, even when they are highly supported.

In this case, one complication is that the oligogene dataset combines plastid and nuclear gene regions that not only differ in their information content but also infer different phylogenetic scenarios (and mask a lot of intra-generic and sub-generic incongruencies). This is illustrated in the following tanglegram.

Fig. 3 – A tanglegram, on the left the ML tree inferred from only the plastid gene regions (1406 DAP, alignment 15254 bp long), and on the right the corresponding nuclear data based tree (1691 DAP per only 4983 bp).

Even though the support along the backbone of the plastid tree is low^a (to non-existent), it well reflects the general diversification patterns in Fagaceae plastomes (see also the tree in Manos et al. 2008, Madroño 55:181–190; and Yan et al. 2019, BMC Evol. Biol. 19: 202, for an oak global picture). Plastid signatures show a strong geographic sorting (eg. New World vs. Old World), while the nuclear data provides most of the lineage-differentiating signal expressed in the combined tree (Part 1, Fig. 2).

Mapping along networks

How do we decide what is a real synapomorphy, a homoiology, or a good symplesiomorphy? Mapping the traits along all possible rooted trees is one option. Another option is to just map them along a consensus network of all trees, as shown next.

Fig. 4 – Map of the seven characters on the consensus network of the nuclear and plastid trees shown in Fig. 3. Blue – genus autapomorphies, dark green – synapomorphies/terminal homoiologies, light green – symplesiomorphies, orange – deep homoiologies, red – randomly distributed trait, pink – genus-restricted reversals.

According to the mapping, the newly described South American Castanopsis rothwellii, assigned to the modern (Souteast Asian) genus Castanopsis, is a stem Castanoideae / Fagaceae, while the "extinct" North American genus Castanopsoidea (then the "earliest megafossil evidence of Fagaceae": Crepet & Nixon 1989, Am. J. Bot. 76: 842–855) could be a stem / crown member of the Castanea-Castanopsis lineage. The difference to the ML trait mapping (Fig. 3 in Part 1) on the combined tree is that we get a better picture what is a lineage-specific trait set in Castanea-Castanopsis, because the interference of the monophyletic(!) oak grade is minimized.

Another possibility is to map the characters directly along a distance-based network, and then compare the latter with the molecular-based topological alternatives. This is quite puzzling in this case, because the morphology (Fig. 1 in Part 1) matches neither the nuclear tree nor the plastid tree (Figs. 2–4) — the traits scored for the fossils cover largely morphological Play-Doh of the Fagaceae.

Fig. 5 – Neighbor-nets based on mean morphological distances. Top graph – polymorphisms treated as ambiguities (standard approach), bottom graph – polymorphism treated as additional states (experimental approach). Text coloring as in Fig. 4, light blue – potential autapomorphy of the fossil American castaneoid lineage. Edge colors: green – edge representing a molecular clade/likley monophyletic group; orange – edge representing a paraphyletic group; red – edge rejected by molecular data; blue – edges supporting a distinct fossil American castaneoid lineage.

The likely primitive characters, irrespective of the evolutionary scenario we prefer, are those also found in the Eocene fossils^b. There are no derived traits/character suites pinning the fossils to Castanopsis. The fossils are a bit derived on their own terms (note their position in Fig. 5), and hence we can deduce that the fossils are either: (a) representing a relatively primitive extinct American sister lineage or (b) surviving, somewhat evolved members of the precursors of modern-day core Fagaceae. Note that the derived oaks evolved nearly 60 myrs ago, ie. 8 myrs before the oldest (Patagonian) Castanoideae fossil was deposited. The earliest (known) Fagaceae and castaneoid pollen are from 80+ Ma old Upper Cretaceous sediments in western North America (Grímsson et al. 2016, Acta Palaeobot. 56: 247–305; open access) and Japan (Takahashi et al. 2008, Intl. J. Plant Sci. 169:899–907), giving them plenty of time to migrate into North and then South America during the Paleocene-Eocene green house episode.

Fig. 6 – Earliest fossil record of Fagaceae and Castanoideae mapped on Scotese's Paleoglobes (© Scotese 2013, GoogleEarth layover files are available from here). Note that although there was no continuous land bridge, North and South America were already connected by a chain of large and high islands, providing a corridor for intercontinental dispersal of near- and extra-tropical plant lineages. A potential  crown-group Castanopsis (C. kaulii, cupule with associated seeds and pollen) has been recently recovered from the Baltic Amber (Sadowski et al. 2018 Am. J. Bot 105: 2025–2036).

Both of the mapping procedures described above are crude, in the sense that they ignore the molecular branch lengths, and use Ockham's Razor. But it strikes me as being not a bad start. They are better than just mapping along a single preferred molecular tree (as is done in many neontological papers; see Part 1) or along a morphology-based strict consensus cladogram (as is done in far too many paleontological papers; many palaeobotanical papers do neither the one nor the other: eg. Wilf et al., 2019, Science 364: eaaw5139). It's important to realize that if one taxon or subtree of our modern taxon set is characterized solely by the lack of shared derived traits or unstable expression of derived traits (like Castanopsis here, see position in both graphs in Fig. 5), ie. represents living fossils or little-evolved lineages, any ancient and primitive fossil, stem group, sister group or precursor, will be attracted by them in a total evidence or any other tree-based approach, especially when we rely on change-probability-naive parsimony as inference criterion. As we pointed out repeatedly: forming a clade in tree is neither a necessary nor a sufficient criterion for monophyly.

All gone, what to do when we have no molecular data?

Morphology alone, like genes on their own, will inevitably get some things wrong (compare Fig. 4 with Fig. 5). Without molecular data, one may have little reason to reject the monophyly of the Castaneoideae (when using more than the seven characters scored by Wilf et al. 2019; see eg. the cladogram in Crepet & Nixon 1989, fig. 1 based on an undocumented 25-character matrix). In the process, we would misinterpret overall similarity, due to shared primitive character suites and the lack of shared derived traits as evidence for an inclusive common origin^c.

What can we do if we have no or very few extant taxa, when we only have one set of data prone to circular reasoning? Then using networks is inevitable as well (see Fig. 5; and some examples provided in the reading list below). We need to explore in-depth the signal in our data matrix. Only extremely biased morphological matrices provide clear tree-like signals, comprehensive ones will have internal conflict and allow for inferring many, partly very different but more or less equally optimal trees.

Exploratory data analysis will not eliminate all possible errors — based only on the graph in Fig. 5, we would get the inter-generic phylogenetic relationships in Fagaceae partly wrong. However, this may lead to an informed decision as to which of the many equally probable evolutionary scenarios make more sense than others. It will help to reduce the alternatives, without eliminating those that are equally valid (which every tree does). If the time-coverage is good, exploring morphological differentiation over time can be an asset, too (see eg. Stacking neighbor-nets – a real-world example).

Data

The matrices used, networks etc. can be accessed via figshare.

Selection of related posts on The Genealogical World of Phylogenetic Networks

Clades, Cladograms, Cladistics, and why networks are inevitable — illustrates why paleontologists should also be less tree-naive (see example in footnote c).
Has homoiology be neglected in phylogenetics? — why we should try to assess the phylogenetic quality of our traits.
Let distinguish between Hennig and Cladistics — as said in the title, the post provides reasons why we should distinguish between Hennig's concepts and clades in phylogenetic trees.
Ockham's Razor applied, but not used: can we do DNA-scaffolding with seven characters? — the original post dealing with Wilf et al.'s (2019) "phylogenetic analysis", which obviously was not scrutinized during review.
Please stop use cladograms! — No matter whether you think evolution is tree-like or not, cladograms should be a matter of the past.
Should we try to infer trees on tree-unlikely matrices? —  using well-known (among paleobotanists) examples, I show why networks reveal much more than any tree when we deal with fossils.
More non-treelike data forced into trees: a glimpse into the dinosaurs — the same but for a thunder lizard matrix.
Trivial data, but not so trivial graphs — an inference experiment using very simple artificial binary matrices.

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^a The main reason for the lack of branch support is that individuals of different genera growing in the same area can share plastid haplotypes, while individuals of the same genus / infra-generic lineage, even species, can be quite different. [Note that the standard 4x4 ML nucleotide model treats polymorphisms as such, not as missing data.] Plus, the different lineages show different levels of plastid diversity (highest in Quercus subgenus Cerris, but low in subgenus Quercus, the North American castanoids and Lithocarpus outside Borneo, Castanea-Castanopsis appear to be in-between the extremes), and there is a tendency to preferably mutate sequence patterns within a lineage that otherwise differentiate between lineages (for instance, inversions that distinguish two genera, can be found as intra-lineage variation in the third genus or one of the oak sections).

^b The striking similarity between the newly found South American and long-known slightly older North American fossils is likely the reason for not discussing the latter in the original paper or including them in the "DNA-scaffold" analysis. As is obvious from the graphs, the slightly younger North American fossil could easily be a slightly more derived of the same lineage than the South American fossil (Planchard et al. 2016 Paleont. Electr. 19.3.51A give a revised age of ≥ 49 Ma for the plant-bearing strata), and thus would have been at odds with the narrative of the authors (see also comment by Denk et al. 2019, Science 10.1126/science.aaz2189).

^c As done by Wilf et al. (see also the argumentation in Wilf et al.'s response, Science 10.1126/science.aaz2297, to Denk et al.'s 2019 comment). The combination of circular reasoning, systematic bias, and (parsimony) tree-naivity is well expressed in Wilf et al.'s own words:

Fourth, Denk et al. erroneously contend that Castanopsis rothwellii, a fossil with so many diagnostic characters preserved that it could only be assigned to Castanopsis if “found alive” today (1), has plesiomorphic features and cannot be placed confidently in the extant genus [see Figs. 1–5 in this two-part post]. ... Denk et al.’s phylogenetic conclusions from their emended tree and matrix are misleading, in that any morphological matrix includes characters that are relevant only for the taxa included in the analysis. ... Because the fossils are castaneoid in all features, we did not include all Fagaceae in our original analysis (1) and likewise did not include all characters relevant to non-castaneoid fagaceous taxa. ... By adding just three relevant characters to the Denk et al. scaffold to accommodate the genera they added (Table 1), the fossil Castanopsis rothwellii is placed only with Castanopsis in the single [ie. the strict consensus of two equally parsimonious trees] most parsimonious tree (Fig. 1).

One of the three added traits ("expanded stigma") is exclusively shared by all five Castaneoideae genera, the second ("nut generally rounded in cross section") shared by all but one Castaneoideae and Quercus, and thus are symplesiomorphies of core Fagaceae: shared primitive traits that can be expected in a precursor of several or all modern genera or their less evolved extinct sister lineages. Or positively selected homoiologies, ie. evolved multiple times within the core Fagaceae. The third ("asymmetrical cupule") is an unstable convergence / deep parallelism and a trait of little phylogenetic value, since expressed as intra-generic (intraspecific?) variation in two distantly related genera: the monotypic Formanodendron, a trigonobalanoid, and Castanopsis. These are two genera that share only a very distant (and exclusive fide Hennig) common origin (see Part 1) but inhabit overlapping climate envelopes and ecological niches in modern-day East Asia.

Despite adding three hand-picked characters (from a set of at least 25 at hand, Crepet & Nixon 1989) and accepting a phylogeny closer to the reality, the Castanopsis "clade" in the new "scaffold tree" including the Patagonian fossil remains unsupported by any exclusive or even shared and stable derived trait/set of traits (as in the original study, Wilf et al. refrain from establishing any sort of node or branch support, or test of alternative placements).

Moreover, it is safe to assume that when one adds the extinct genus Castanopsoidea to the scaffold (Wilf et al. deliberately chose not to do so), it would compete with Castanopsis rothwellii for the placement next to the modern-day Castanopsis. According to Crepet & Nixon 1989, fig. 1, one possible placement of Castanopsoidea is a sister to "Castanopsis (1)". This is not necessarily because they share a direct common origin but because these fossils also lack uniquely derived characters or a clearly derived character suite defining all Fagaceae genera except for Castanopsis (which in Crepet & Nixon's morpho-tree, is paraphyletic to Lithocarpus, which, back then, included the potential oak sister genus Notholithocarpus — literally: the 'false Lithocarpus'). Personally, for the same reasons as outlined and applied in Bomfleur et al. 2017, PeerJ 5: e3433 (and like Denk et al. 2019), I would have no problem calling all these fossils Castanopsis by defining the genus as explicitly paraphyletic, which could include the modern-day species of Castanopsis (which are probably monophyletic) and Castanopsis-like fossils that may be more or less related to them and/or other core Fagaceae: the precursors and extinct but similar, underived sister lineages.

Why the emporer has no clothes on – conflict or not?

In the final part of this series dissecting angiosperm gene trees (see: Why the emporer has no clothes on — part 1 and part 2), we will enter muddy ground. Using our example data set, we will try to make a call on whether or not there has been any (detectable) major reticulation in the deep branches of the angiosperm tree.

What triggers conflicting gene histories

Before we look at the data, it may be a good idea to set the scene using simple theoretical examples of what we may look at.

Our two genes, represented by circle and pentagon (could be multigene regions or entire genomes), both follow the same evolutionary history (the gray background tree). In the left lineage, we have a bit of incomplete lineage sorting, because the ancestor was polymorphic for the circles. In the right lineage, we have different fixation rates: the circles evolve faster than the pentagons. With molecular data we usually don't have the ancestors, making any inference straightforward; we only have the tips.

Because of incomplete lineage sorting and different fixation rates in the left and right lineages, the circle gene tree gets the phylogeny pretty wrong. The pentagon gene tree comes closer to the reality – we only infer two sister clades where there is a grade. (With real-world data, the branch support values could give one a clue that three of the inferred blue clades have a higher quality than the fourth supporting a pseudo-monophylum.) The circle and pentagon trees are largely incongruent despite sharing the same history; and we may infer a pseudo-hybrid (the first diverging lineage within the right clade).

Combining these data may allow us to infer a tree that fits the real tree much better. In the left clade the trivial pentagon signal can out-compete the misleading circle signal, and avoid the misplacement of the first diverging lineage of the right clade. In the right clade, the circle signal can help to correct for the pseudo-clade.

Now we can add a late reticulation, and re-infer the gene trees.

Because of the reticulation (the circles are biparentally inherited, the pentagons maternally), the gene trees are more congruent then in the example above (circle and pentagon get it a bit wrong in the left clade), except for the hybrid and its pseudo-hybrid parent. The gene conflict in placing the lineage cross (part of the left clade in the circle-based tree, part of the right clade in the pentagon tree) well reflects its hybrid origin.

Different histories of nuclear genes vs. plastid / mitochondrial genes?

The easiest way to catch reticulation is to compare trees based on plastid / mitochondrial data (maternally inherited) vs. nuclear data (biparentally inherited). If reticulation happened in the past, we can expect that the maternal and biparental genealogy diverge from each other (see part 2).

Strict Consensus network of the plastid (data from 3 protein-coding genes +1 partly coding gene region), mitochondrial (3 protein-coding genes) and nuclear trees (2 nrDNAs). The bold lines represent generally accepted phylogenetic splits (APG IV tree, see also Steven's comprehensive Angiosperm Phylogeny Website).

This network is much more box-like compared to what one would have expected based on the combined tree that can be inferred from the data (Part 1). But are we looking on largely decoupled histories?

This mess is hardly surprising. The combined tree is constrained by the plastid tree, specifically by the signal from the matK gene (Part 1), while the remaining plastid genes (from a different part of the plastome) fall into line. The mitochondrial tree combines genes that on their own inform poorly resolved trees riddled with branching artifacts (Part 2). The nuclear tree, on the other hand, combines the most and least divergent nuclear genes widely known. Because of this, they show topological conflict between each other.

18S-25S rDNA tanglegram. The branch numbers show each gene's bootstrap support (BS) deviating from the combined BS support for the respective branch (indicated by line thickness): green, increased BS support when combining both genes, red, decreased BS support.

However, they are part of the same multi-copy coding unit (the 35S nuclear rDNA) that has very particular evolutionary constraints, such as structural constraints, affected by completeness of concerted evolution and intra-genomic recombination. Polyploid grasses, for example, can have up to three different collections of 35S rDNA, reflecting four different evolutionary origins, being part of the A, B, C or D genomes. You end up with what is called a multi-labelled tree: the A, B, C and D-genome variants of the same taxon pop up (consistently) in different parts of the tree, and you can have recombinants. If we look into the 18S vs. 25S data, however, we find no consistent sequence patterns supporting the topological conflicts between the two trees, or examples for recombination.

As in our theoretical example, each of the trees has certain strengths, and its own set of weaknesses, some of which can be overcome when combining the data (eg. branches with increased combined support in the 18S-25S tanglegram)

Bootstrap (BS) Consensus networks for the combined cp (upper left), mt (upper right), nc (lower left) and full data (lower right). Branches without numbers: BS = 100. Splits conflicting with those present based on the full data highlighted by red font (all with BS < 100).

In contrast to the boxy network appearance and the substantial conflict between the single gene trees (Part 2), most of the relationships (eg. the major clade roots but also many intra-clade relationships) receive high or unambiguous support in all three trees*. Aside from the disparate signals, the data seem to converge on a coalescent. If the genomes had different histories, they wouldn't converge so easily. Also, we would expect to see more consistent conflict between the "genome" trees than between the single-gene trees of the same genome, since the nuclear rDNA is biparentally inherited while the plastid and mitochondrial DNAs are passed on via the mothers only. Many of the angiosperms in our data reproduce sexually.

So far, no conclusive evidence for reticulation

Mere gene-tree incongruence is a poor basis to conclude about decoupled gene histories. We need to dig for sequence-based evidence for reticulation and recombination. For instance, we might find a clearly derived sequence pattern exclusive to the right clade in a member of the left clade.

The importance of rare genomic changes when interpreting conflicting gene trees. The left and right clades obtained a unique and conserved gene or sequence feature before they diversified. The hybrid is the only taxon showing both.

This is where the Walker et al. (2019) and Sullivan et al. (2017) studies seem to fall short — they don't give any example, gene, gene region, or recognizable lineage-diagnostic sequence pattern that could be used as direct evidence for decoupled gene histories and/or reticulation.

For my data set, I cannot pinpoint such evidence either. All high(er)-supported conflict seems to be related to lineage sorting and data/signal issues, the inability of certain gene regions to resolve relationships in parts of the angiosperm tree, or falling prey to (more local than global) long-branch attraction. When looking at the sequences, there's no reason to question, for example, the assumed monophyly of the main lineages and orders, in spite of the topological conflict we face when analyzing these data. If there was reticulation between the ancestors of angiosperm lineages, or later on between the already formed lineages, it left no obvious imprint in the data.

Thus, after having investigated aspects of the seeming conflict by going back to the data (checking highly divergent and conserved sequence patterns, tabulating the partly competing BS support of the single genes, and minus-one gene analyses), I did not hesitate to combine these data and use a Bayesian total-evidence dating procedure. (We never published the results because mid-Cretaceaous angiosperm fossils have much too derived morphologies for total evidence dating; when left unconstrained, MrBayes optimized towards an angiosperm root age of 4.5 Ba, which was the in-built maximum).

A total-evidence Bayes tree based on the full data set. Stars indicate the position of fossil taxa (mid-Cretaceaous). Note their relative long terminal branches, a situation total-evidence dating cannot handle. The matrix can be found at figshare: A basic total evidence matrix for basal angiosperms — combining Soltis et al (2011) with Doyle & Endress (2010).

An example for actual reticulation resulting in gene tree conflict

Working at the coal-face of evolution, I have encountered examples of apparently real reticulation (when analysing biparentally inherited nuclear data). The most compelling was probably the ancient relictual genotypes and pseudogenes that point towards ancient reticulation in the widely known plane trees, Platanus. Platanus subgenus Platanus (which includes all but one species, P. kerrii, a relict of a distant lineage growing in tropical-hot subtropical lowland forests of North Vietnam) falls into two main lineages characterized by unique sets of genotypes, the ANA clade (Atlantic-facing North and Mesoamerica) and the PNA-E clade (NW. Mexico, California and Mediterranean).

Haplo/-genotypic composition of Platanus (Grimm & Denk, Taxon, 2010, ES2 [PDF]). Platanus kerrii represent the sole surviving relative within the Platanaceae (genetically very distinct), an old lineage of angiosperm trees (going back deep into the Cretaceous). Their next kin today are, according to angiosperm molecular trees, the enigmatic Proteaceae, a Gondwanan relict (represented in our angiosperm data by Petrophile). For an even more comprehensive genotypic study that also covers plastid markers check out De Castro et al., Ann. Bot., 2013 [open access])

Individuals in the contact zone between species of the two main lineages (including hybrids) can be heterozygotic / polymorphic for at least one of the sequenced nuclear regions, so that identification of recent hybrids is straightforward. Beyond this, genetically inconspicious members of the ANA clade may show ITS pseudogenes from the PNA-E clade (stippled line in the figures above and below). Furthermore, two of the ANA clade species show (predominately), a PNA-E LEAFY genotype — P. palmeri (pa) and P. rzedowskii (rz), which grow closest to the populations of the PNA-E clade. However, this is not the genotype found in the close-by American PNA-E species (ra, ge), which is one that's sequence is phylogenetically closer to the Mediterranean species, P. orientalis (or), on the other side of the globe.

Overlay of the LEAFY, 5S-IGS and ITS histories in Platanus. This doodle is based on tree- and network-inferences coupled with PCR-RFLP-based genotyping and in-depth analysis of mutation patterns in length-polymorphic sequence regions (Grimm & Denk 2010, ES1). P. x hispanica is the well-known ornamental alley/park tree, the 'London plane'. A cultivated historical hybrid (mid 18th century) of the most hardy North American plane, P. occidentalis, and the frost-vulnerable Mediterranean plane, P. orientalis. In the Mediterranean, due to frequent backcrossing, one can find morphologically mixed individuals showing only the P. orientalis genotypes or homogenous (American or European) type individuals showing occidenatlis and orientalis genotypes (see eg. Pilotti et al., Euphytica, 2009) 

Further reading

An animal example, of seemingly incongruent single-gene trees that may well be the product of a largely shared evolutionary history, is the autosomal intron data compiled for bears by Kutschera et al. (2014. Bears in a forest of gene trees: Phylogenetic inference is complicated by incomplete lineage sorting and gene flow. Mol. Biol. Evol. 31:2004–2017). Rather than a "forest of trees", each gene tree is poorly resolved but, when combined, allows inferring a phylogeny that matches quite well the parental genealogy based on Y-chromosome data, both in strong conflict with the maternal genealogy inferred from mitochondriomes (see Part 2).

In Supplement File S6 [PDF] of Grímsson et al. (2018, Grana 57:16–116), I outline how ambiguous signal from combined gene regions relate to the poor support of critical branches in the Loranthaceae tree; see also the related posts: Using consensus networks to understand poor roots and Trivial but illogical – reconstructing the biogeographic history of the Loranthaceae (again). Some gene-tree conflicts are possibly linked to different histories (nuclear vs. chloroplast data), while others are a mix of insufficient signal and missing data (between chloroplast genes).

In a previous post (All solved a decade ago: the asterisk branch in the Fagales phylogeny), I give another example using an old Fagales matrix, which resulted in a tree that, even today, is the gold standard of Fagales phylogeny. The matrix combines a highly conserved nuclear gene (18S) conflicting with the plastid genes and complemented by an entirely uninformative mitochondrial gene (matR) to provide a "tree based on all three genomes". Also in this case the three-genome tree is essentially the matK tree.

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* That doesn't mean that all highly supported, unconflicted relationships must be true. Note that just by combining a few genes, we obtain a near-unambiguous support for the split between Mesangiosperms and the ANA-grade + gymnosperms, one of the splits defining the root and "basal" part of the angiosperm tree. The outgroup-inferred root is well fixed. Even when using nuclear data, despite the fact that the 18S signal (the one showing the least ingroup-outgroup genetic distance) doesn't support such a root but the 25S does (see part 2), being more divergent and prone to ingroup-outgroup long branch attraction (LBA). That we have LBA issues with the data is obvious from a tiny detail: Ginkgo is supported with BS > 70 as sister of Podocarpus, which is wrong, based on all we know about gymnosperms,(see also Earle's gymnosperm database and literature cited therein). The likely correct split, Ginkgo as sister to Cycas, is present in the nc tree, but represents a much less supported alternative (BS <= 25). It is also obvious when one looks at the alignment(s): Cycas and Ginkgo share some potential genetic 'synapomorphies' in the low-divergent, generally conserved regions (eg. 18S, stem-regions of 25S), but there are essentially none for Gingko + Podocarpus.

Why the emperor has no clothes on – a thicket of trees

A critical question in phylogenetics, and this applies to both the detection and inference of reticulation, is: How much trust do we put in the inferred tree? A phylogenetic tree is just the simplest of all possible phylogenetic networks. Let's assume that there was some phylogenetic reticulation in the past (lineage mixing and crossing), then, in the best-case scenario, our inferred tree shows one of the intertwining pathways but misses the tangles, the crossroads.

An example of simple reticulate evolution: pink is the product of very recent lineage crossing between an early diverged (and otherwise lost) member of the blue lineage and the more recently, hence genetically more coherent, red lineage. Bold lines show the tree we would likely infer in such a situation.

In the worst case, summarizing data with substantially different signals will give us branching artifacts such as:

• terminal branches that are too long,
• too long internal branches with conspicuously low support (ie. BS << 100, PP < 1.0),
• artificial branches representing the least-conflicting solution for the conflicting data,
• low branch support in general.

See eg. the bear data we used as a real-world example for our Intertwining trees and networks paper (Schliep et al. 2017, open access).

Three possible trees for bears, (a), Y-chromosome, paternal, and (c), nuclear-encoded autosomal introns, biparentally inherited, are congruent but disagree with the maternal genealogy (b), based on the mitochondrial genes. When fusing all three data sets, we get a (low) supported sister relationship for Sloth and Sun bears (red clade), not supported by any of the three fused data set – a branching artifact.

Topological incongruence between gene trees and parental genealogies (as above) is commonly taken as evidence for reticulation. If one gene provides high support for taxon A as sister to B, and another gene has high support for B as sister to C, then B is likely the product of reticulation (eg. hybridization)

One simple possibility to put together a phylogenetic network is to summarize all of the trees in the form of a Consensus network, as shown next. (Technically this is a splits graph, it becomes a phylogenetic network as soon as we determine a root, which, here, would be at the edge leading to the Giant Panda.)

A strict Consensus network of the paternal, biparental, and maternal bear genealogies.
The numbers show the non-parametric bootstrap support for each (competing) split.

In this case, low support for a branch in a combined tree (the values on top) can result from strong conflict. For instance, the brownish splits, which are poorly supported using the combined data (BS = 21, 29), receive near unambiguous support from the mitochondrial genes, but are largely or entirely rejected by the Y-chromosome and nc-intron data. In the combined tree, this deep conflict is resolved by introducing the artificial red clade, with similarly low support: the signal in the data is ambiguous and they support splits between equally possible alternatives.

We know lineage crossing took place in bears (the mitochondrial and Y-chromosome tree are very much in conflict). However, does the above mean that earliest bear-ish creatures hybridized, too? Note that the conflict is associated with a short-branched part of the graph, where apparently little evolution happened. Fast ancient radiations usually come with incomplete lineage sorting and diffuse signals. The only data set producing longer roots, but with notably lower support, are the biparentally inherited introns.

We are closing in our own tail and have to ask again: Is this low support in the autosomal intron tree due to internal conflict, (sets of) introns preferring different topologies, supporting an ancient mixing hypothesis, or just reflecting lack of resolution? Check out the original paper by Kutschera et al. (2014, Mol. Biol. Evol. 31: 2004–2017), and make up your own mind.

On to the angiosperms

In my last post, I exemplified what Walker et al. (PeerJ, 2019) found in their angiosperm study: when we look at a plastome tree we are not looking at a summary of all gene trees but instead at a topology forced by very few of the genes in the chloroplast genome, such as the matK. We also have seen that one misplaced sequence (outgroup Podocarpus-matK) doesn't affect at all the combined analysis — it didn't even reduce the ingroup - outgroup split support. Also, I noted that the low-supported part of the combined tree goes hand in hand with lack of decisive signal from the matK.

It's time to take a look at what the other genes in this example data set come up with.

The eight gene trees. Terminal subtrees collapsed. Scales fit to size, scale bar = 0.1 expected substitutions per site. Upper left, matK tree which is very similar to the combined tree using all gene regions (cp = chloroplast, mt = mitochondrial, nc = nuclear genes). Note the low performance of the mt genes.

One thing is obvious: for most genes (except the nuclear-encoded rRNA genes) including the outgroup taxa adds little ingroup information of use — they are just too distant to any of the ingroup taxa. Outgroup rooting is tricky for angiosperms. Outgroup taxa will always be attracted to the ingroup taxon that is the least similar to any other part of the ingroup: Amborella in this case.

Generally, all of the ANA-grade water plants are genetically distinct and topologically isolated; any outgroup-inferred root must be placed in this part of the tree (all other living seed plants are very distant relatives of angiosperms looking back at, at least, ~250 million years of independent evolution, see eg. Age of Angiosperms... and What is an angiosperm pt. 2). The relatively conserved plastid rbcL and mitochondrial matR prefer an Amborella-Nympheales clade as sister to all other angiosperms, while the more divergent atpB, plastid, nad5, mitochondrial, and 25S, nuclear, prefer the Amborella-root — this is a direct indication for ingroup-outgroup long-branch attraction. Any other placement of the outgroup subtree within the ingroup would necessarily decrease the likelihood of the tree (but note the position of the root in the 18S tree, lower-left the tree based on the most-conserved, evolution-constrained gene in our sample; see also All solved a decade ago..., fig. 4A).

We can look at these trees with the strict consensus network, using uninformed edge lengths— that is, the network counterpart to the strict consensus cladograms still common in plant phylogenetic literature.

This is a nice piece of computer-art, but is scientifically quite useless (the boxiness and general graph structure is, however, reminiscent of strict consensus networks of most-parsimonious tree samples inferred for extinct animals, one example, and plants).

We can add some discriminatory information by counting how often each split occurs in the set of gene trees.

Same set of tree, different way of summarizing it. Note how the main clades emerge: one or two genes may have misplaced the one or other OTU but the others get it right.

Alternatively, we can average the actual tree branch lengths to inform the edge length of the consensus network.

The light green, sand-colored, light brown and dark olive (clockwise) splits are likely branching artifacts. The light blue split is the one that supports the ANA-grade when the (combined) tree is rooted with the very distant outgroups.

A pretty little thicket of trees. Some agreement is found towards the leaves, but even here we have conflict among the gene trees. In some trees, there are long branches grouping non-related OTUs, obvious tree inference artifacts. The general rule is that the deeper we go (ie. the farther back in time), the messier it gets. Adding to this is that, irrespective of which gene is used, some OTUs are much closer to the hypothetical common ancestor (of Mesangiosperms, ie. all but ANA grade) than others – in the eudicots, the least-evolved taxa are Platanus (very old tree genus) and Euptelea (the basalmost Ranunculales); in the Magnoliales, the only angiosperm clade that lacks synapomorphies, it's Magnolia and Liriodendron (again, very old and primitive tree genera). Darwin's Abominable Mystery, the sudden appearance and quick dominance of angiosperms, resulted in an abominable chaos of gene trees and signals. How can they possibly converge to a single tree with amply high support along most branches?

The combined tree from the first post.

When compared to the bears, the answer may well be: because there has been very little to no reticulation between these lineages. Our thicket may be not a forest of trees but just a poorly trimmed, wildly overgrown bush. They genes share the same history, but when being analyzed one-by-one, each of their trees get some aspects right, and some others (severely) wrong. Misplacing one OTU (e.g. the light green, dark olive, sand-colored and dark yellow splits in the averaged Consensus network) may have further topological effects; it didn't matter for the matK gene, because we misplaced only one very alien OTU in a data set that otherwise is hardly affected by adding or removing OTUs.

I argue here that, if there had been substantial reticulation messing up the signal of contemporary lineages and reflecting decoupled histories (like in the case of bears), we would expect at least some (artificial) branching patterns with low support in the combined tree, as well. This would also be the case looking at the gene-tree consensus networks, not only in the deepest parts but also closer to the leaves.

We will be explore this alternative hypothesis in the next (and final) post of this mini-series.