"I'm treating the mutation rate as a substitution rate" - Dr. Nathaniel Jeanson

Consilience of independent phylogenies means you take different but similar parts (loci, positions) of the genome from a collection of organisms, say primates, and then you infer a phylogenetic tree for each of those loci. And then you see that the tree you get for each locus is remarkably similar.

Suppose you pick a particular gene shared between all the species you picked, and infer a phylogeny on the basis of the sequence of this gene found in all these species. You get a particular tree for that gene. Then you pick some other locus in the genome, it can also be a gene, it can be an intron, it can be wherever you want. A gene with a wildly different function, or maybe even a pseudogene that is nonfunctional. You again infer a phylogeny on the basis of this new gene and you get a new tree from this.

Now you compare the first tree to the second tree. They’re virtually identical.

So now you ask yourself: Why do you get the same tree from these different loci? The gene tree you get from one locus, which has a particular function unrelated to the function of the other locus, is still extremely similar. Why? Why are they extremely similar?

Predicted what? You’ve got the logic inverted here. A nested hierarchy is what we are demanding an explanation for (and we are explaining it’s existence with the theory of common descent). The nested hierarchy is not itself proposed to explain something else.

The nested hierarchy is an extremely strong pattern in the data we are noticing, and we are demanding an alternative, testable, and superior explanation for it from people who propose to reject the theory of common descent.

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colewd

I tried to find the discussion but maybe it is too old. The discussion was with if the current state of population genetics mathematics assumed the existence of a population. I believe your answer was that this is true today.

The relevance is if current population genetics mathematics could model the origin of the gene patterns in the Howe diagram.
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Ehh, no. It is less reliable than DNA is, but that doesn’t make it unreliable. That’s just such a prototypical example of dichotomous thinking.

Scientists can test phylogenetic methods on known phylogenies (such as the known history of viruses evolving here and now, or our own family histories, and so on). If the methods they use to construct phylogenies from DNA sequences very accurately reproduces the known genealogical histories (and they do), that gives us reason to have confidence in their reliability when we don’t know the real genealogical history.

And scientists can do computer simulations of evolution using many different parameters and assumptions to determine how, when, where, and to what degree these factors result in erroneous phylogenetic trees, and in turn use these to understand phylogenies derived from real biological data to see if these show evidence of having undergone events or mechanisms that produce these errors.

And they can do various statistical tests on trees to see how consistent they are (such as the bootstrap method).

Can be =/= always are.
Can be =/= are to an absolute degree.

Again with dichotomous thinking. Theobald wrote about this all the way back in 1999:

In science, independent measurements of theoretical values are never exact. When inferring any value (such as a physical constant like the charge of the electron, the mass of the proton, or the speed of light) some error always exists in the measurement, and all independent measurements are incongruent to some extent. Of course, the true value of something is never known for certain in science—all we have are measurements that we hope approximate the true value. Scientifically, then, the important relevant questions are “When comparing two measurements, how much of a discrepancy does it take to be a problem?” and “How close must the measurements be in order to give a strong confirmation?” Scientists answer these questions quantitatively with probability and statistics (Box 1978; Fisher 1990; Wadsworth 1997). To be scientifically rigorous we require statistical significance. Some measurements of a given value match with statistical significance (good), and some do not (bad), even though no measurements match exactly (reality).

You can in principle have twenty ultra sensitive thermometers that can measure temperature to one part in a billion, all disagree on the exact value for the temperature in the same room, yet all of them can be within a range of one part in a thousandth of a degree. That would be a remarkable degree of corroboration even if the temperature output by each thermometer is different from every other.

We do find consistency, but you have to understand that it comes in degrees, and in part because anatomical convergence can happen due to both natural selection and certain forms of developmental constraint (which can therefore make trees derived from morphology alone misleading) have biologists largely moved on to molecular phylogenies instead and are testing tree consistency by comparing different molecular trees to each other, rather than by comparing morphology with molecular trees.

In other words the problem of convergence at the level of gross anatomy as a confounding factor in phylogenetics is recognized, not ignored, and it is addressed by other methods of measuring tree consistency using different molecular loci and traits where convergence becomes exceptionally statistically unlikely.

Well it can’t, of course, because in principle at a certain ubiquitous level of convergence the tree disappears entirely leaving you without nesting sets at all. And then there’d be no nested hierarchy, and then it wouldn’t make sense to invoke convergence. You only invoke convergence when something deviates from the main nesting pattern. If convergence was ubiquitous, there would be no main nesting pattern.

But there is still a nested hierarchy, even if convergence does happen in some clades. Despite living in the ocean and sharing a certain degree of gross overall body shape, no phylogenetic method confuses whales for sharks. Or mammals for birds. Or plants for bacteria. Just to pick some more obvious examples.
Phylogenies derived on the basis of morphology can be difficult within certain clades because the number of characters from which to derive the phylogeny(usually a few hundred at best) is relatively small compared to molecular data, that can use many tens of thousands.

You have to understand that consistency of the nested hierarchy goes far beyond a comparison of morphology to DNA too. You can compare different parts of the genome as explained in my previous post. You can compare mitochondrial to nuclear gene trees. Trees from genes on different chromosomes. Tree from coding to non-coding DNA trees. Enzymes to developmental genes. Introns to retrovirus-derived retrotransposons. Pseudogenes to centromeres. And so on ad infinitum.

Have you considered that you actually have no idea of the degree of consistency you get?

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So they don’t agree at all. That there may be two quite different time periods for which both models predict the same population is not agreement at all. And let us point out that Jeanson’s time scale doesn’t match the well calibrated and supported time scale of human history. Any theory that is forced to posit a young earth is dead from the start.

Just not the case. There are problems with morphological character analysis that make it less accurate than molecular analyses, notably the great imbalance in the quantity of data available. But given that, the match is quite good, and discrepancies are rarer than you imagine, especially when there’s enough data available for both data types. Another problem is that scoring morphological characters is inherently more subjective than scoring sequence characters, for which there are only four discrete possible states.

We usually do. But as others have mentioned, consistency in sequence data from different parts of the same genome is an equally valid test.

There is much more than you know. And it’s not just for universal common ancestry. It also works within any subsets of life that you care to name, and creationists need to explain all of it but can explain none of it.

That’s not quite a sentence. You need to rewrite to make it comprehensible. But it’s better evidence (“proof” isn’t a word we use here) than you know.

Literally hundreds. Choose any gene you like and compare it across ape genomes, and you will find the same tree is by far the most frequent. (Some genes will differ because of incomplete lineage sorting, a well understood phenomenon, but even the fraction of different trees is predictable.) “Ape”, of course, includes humans.

There’s nothing in that piece that’s relevant to a nested hierarchy, just the presence of some LINEs. Most species have LINEs.

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Yes, it can. You’ve been told how dozens of times. Why do you insist on pretending you haven’t been given an explanation?

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Huh? Maybe you misunderstand…or I don’t get your point.

We have independent historical population size data. Jeanson says his model matches the population growth data from 1000 BC to the present. That’s in spite of the fact that many of the migrations the evolutionary model says are prehistory actually happen sometime in the last 3000 years in his model.

Sure, I’ll consider it. I’d like to read a paper or two on what you’re describing. Share your favorites…

Results/Discussion

A comprehensive molecular phylogeny based on 34,927 bp (after correction for ambiguous sites from the original dataset of 43,493 bp per operational taxonomic unit, OTU) amplified from 54 nuclear genes in 191 taxa including 186 primates representing 61 genera is presented (Figure 1, Figure 2, Figure S1, Table S1, and Table S2). The phylogeny is highly resolved, with bootstrap values of 90–100% and Bayesian posterior probabilities of 0.9–1.0 at 166 of the 189 nodes (88%)(Table 1, Table 2, Table 3). Further, only 3 of 189 nodes (nodes 28, 38, 158) are polytomies in the bootstrap analyses (Table 1 and Table 3; Figure 2, Figure S1). (Note: nodes listed hereafter refer to Figure 2, Figure S1, Table 1, Table 2, Table 3). Roughly equal amounts of coding (14742 bp) and non-coding (17185 bp) genomic regions were sampled from X chromosome (4870 bp), Y chromosome (2630 bp) and autosomes (27427 bp) (Table 4) using newly developed PCR primers derived from a bioinformatics approach specific to primates in addition to primers from previous large-scale phylogenetic analyses (Materials and Methods, Tables S2, S3, S4).

Please understand that for 191 species approximately 6.21 x 10407 different rooted phylogenetic trees are possible. That means there are an incredible number of ways to make trees, so it is unfathomably unlikely you should get extremely similar ones over and over again when the sequences from different genetic loci obviously don’t constrain each other to produce similar trees(why would they?).
And yet the trees come out virtually identical every time when resampled across 35 thousand basepairs of DNA coming from 54 different genomic loci with wildly different functions. So why do we get this degree of consistency among the trees?

It strains credulity to say it’s just a happy accident, but common descent predicts it (and therefore explains it), so what is the creationist explanation and does it actually make sense?

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Shouldn’t that be “in spite of the fact that many of the migrations the archaeological evidence says are prehistoric happen sometime in the last 3000 years in Jeanson’s model”. That certainly seems to be the case with both haplogroups R1b in Europe and E1b1 in the Middle East.

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If you’re using the first person there, I expect you to describe the data that you have analyzed.

That’s just hearsay from a nonexpert in the field. What does the evidence tell you?

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Watch long enough and you only see repetition. I suspect the median period is somewhat over a year. This example has cycled at least twice.

Where in his writings does Jeanson present this population growth model?

No Valerie. You are misrepresenting this.

The “fact[s]” are that we have a mountain of data (not merely a “model”) that demonstrates that the haplotypes were in Europe millennia before this time period. Jeanson, who has no background whatsoever in genetics, chooses to deny this data – that does not make the data go away (or make his unsubstantiated claims any more credible).

The migrations that Jeanson claims did not “actually happen”: (i) most of them he cannot even point to any ethnic group(s) for, for the single migration (R1b) that he does, the proffered ethnic groups either (ii) definitely did not have R1b (Magyars), or both (iii) fizzled out East of the Carpathians and (iv) have no record of carrying R1b in any concentration.

A more accurate description would be:

In spite of the massive amount of data that demonstrates the fact that many of these migrations occurred in prehistory, a single unqualified YEC says, without presenting any evidence (or even much in the way of a detailed hypothesis), that these migrations occurred in the last 3000 years.

You are entitled to your opinion. But you are not entitled to your own facts.

– Daniel Patrick Moynihan

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Ok, Jeanson says that. Is it true?

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You will just forget any evidence you are shown in the matter of a few days. So why don’t you show us how the process of evolution could not explain this pattern with the mechanisms of gene gain and gene loss.

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I think you overestimate the period.

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Underestimate, perhaps?

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My bad, fixed it!

“Assumed the existence of a population”? So we have, say, a physicist, who is modelling the orbit of a moon about a planet. And you say, ah but there is a limitation of your celestial mechanics equations. They assume the existence of a planet and a moon.

Any physicist will just snort briefly with disbelief and go back to their calculations, ignoring you after that. I think I will do likewise.

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It would not be necessary to ask the physicist this unless someone was claiming that his equations could account for the origin of the planet and the moon.

‘Spherical cows’ may oversimplify things but spherical moons aren’t a bad approximation…

Whoosh!

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