Erika and the Evo-Avengers 005

This may actually be the most obviously retarded section of the Evo-Avenger’s incompetent attempt to refute MITTENS, because they are, quite seriously, arguing that humans and chimpanzees can fixate mutations across their entire populations in the wild faster than bacteria can in the long-term Lenski experiment, otherwise known as the LTEE, due to polymorphism and recombination. This is what happens when you appeal blindly to mechanisms rather than working through the relevant calculations.

(40:47) I don’t know why he wrote a book right because he references the idea that the fixation rate um from bacteria when applied to human populations um uh doesn’t work uh when you just take an average of the number of fixations per so he takes a really simple average um out of the Linsky experiment right and then applies that number as a fixation rate to the human population assuming that all mutations are happening in series. He claims that he’s not assuming that but uh just says nuh-uh like there’s no math to prove that he’s not right. Um and like so like he has one claim and wrote a whole book and never justifies the claim, right? He never proves the methodology which is what’s driving me absolutely bonkers.

So, like this is poorly written even if the idea was sound. Um, and that’s my that’s my hot take here at the top. Before we hear from Zach Sebater, who has a PhD in biology with doctoral and post-doal experience in viral evolution says, “The first thing that caught my attention in Day’s work is that he uses data from bacterial experiments to draw conclusions on human evolution. One major difference, I think, is that E.coli coli reproduced mostly clonally while humans reproduce 100% sexually and KImura’s simple model doesn’t take recombination into account whereas more elaborate models do and in fact recombination does accelerate the rate of beneficial mutations through several means this is reaffirmed by Garrett Ferguson below, who has no science accreditation but noticed it all the same, so I guess if I can jump in the the issue with using even conceptually the Lenski long-term evolution experiment for a fixation rate is that the Lenski long-term experiment begins with a bacterial population that is clonal and has no diversity.

Um, and so because of that, every single mutation that has to occur is novel. Um, but when you think about humans and chimps splitting, they don’t have zero diversity. They’re not building up brand new mutations, right? We have a level of polymorphism that more or less represents mutation drift equilibrium. And at that at that point, we can calculate the rate of fixation, right? Um, and so he’s getting an exorbitantly large number even for bacteria simply because he’s starting with a population that has zero polymorphism, right? And so you have to build up that polymorphism through time. And that’s just not characteristic of how any natural population works.

Allow me to try to simplify a little bit what Zach is saying here. In Lensky long-term evolution experiment, the starting populations of bacteria were clonal, which is to say they were identical to one another. They were clones. Every bacteria is genetically identical to its neighbor, which means their genetic diversity was zero. How many natural populations look like that?
Now, there is a function here because by knowing the starting condition of all of your populations, and by having them all equal, you’re controlling for a lot, and you can more easily track the changes that do occur over time in these lineages. But because there’s no polymorphism, which is just our fancy word here for variation, it takes way longer for novelty to actually generate.

Let’s compare two hypothetical situations. In both situations, we have 12 baggies. And in each one of those baggies, there are 10 randomly selected Scrabble tiles. In situation one, each of those 12 baggies contains the same random 10 letters. Whereas in the other situation, each of the 12 baggies contains a different 10 randomly selected Scrabble tiles. Now, let’s pretend that every week every single one of these baggies gets one new randomly selected Scrabble tile added to it. The word making potential of these two situations is not equal because situation two has far more letters to choose from. And that’s right out the gate and moving forward until all of the baggies have the same general representation of letters in them.

Day is taking situation one there and applying it to the human chimpanzeee divergence which is inappropriate because we aren’t clonal. When the population of measene apes split into the lineages that would become humans and chimpanzees or the hominins and the pannins, it wasn’t clonal. All of those apes weren’t clones of each other. It was diverse, as were the lineages that came from it. So already the use of the Lenski long-term evolution experiment here is just wildly inappropriate, and there’s no fixing that.

Let’s keep in mind that they are seriously arguing, with a straight face and on the basis of their scientific credentials, that humans fixate mutations faster than bacteria in terms of generations per fixation. That is their actual position; don’t forget that.

The polymorphism objection sounds legitimate until you think about what standing variation is. It’s not a warehouse of pre-adapted beneficial alleles waiting for their moment. It’s mostly neutral variation, which is why it’s still segregating. If those variants were beneficial, they’d have already swept to fixation. If they were deleterious, purifying selection would have removed them. What mutation-drift equilibrium gives you is a population full of genetic noise at low frequencies, not a head start on adaptation.

The Scrabble analogy is rather useful for making this point: having twelve bags of random letters doesn’t help you spell words any faster, because the rate-limiting step was never “do you have enough letters?” It’s “how quickly does selection find and fix a combination that works?” That’s governed by s and Ne regardless of how much neutral junk is lying around. Standing variation can reduce the waiting time for a beneficial allele to appear, since one might already be segregating at low frequency. But it cannot reduce the fixation time once selection begins acting on it, which is still (2/s)·ln(2Ne·s) for the beneficial case and 4Ne for the neutral case. And the average s for mutations that actually fix in humans is approximately 10⁻⁴ and is completely unaffected by how much polymorphism the population started with.

There’s a deeper problem the polymorphism argument doesn’t address: even if you grant the one-time head start, it’s one-time. Once those standing beneficial variants fix or are lost, the population is back to waiting for new mutations, and now it’s producing new mutations at a rate roughly 30,000 times slower in real time. The LTEE’s observed fixation rate already includes the waiting time for new mutations to arise in a clonal population, so the critics are correct that it captures a worst-case scenario for mutation supply. But correcting for that makes the human situation worse, not better, because you’ve now isolated the fixation-time component, which scales with Ne and is inversely proportional to s, and both of those parameters are unfavorable in humans compared to E. coli.

So polymorphism and an appeal to ancestral mutations doesn’t help the evolutionists. Now let’s look at recombination, which is the more serious objection since it is, by nature, definitely applicable to humans and chimps and definitely not accounted for in the LTEE. In order to point out how mainstream the basic concept is, consider how Gemini describes the standard model as utilized by population geneticists:

Why Recombination Speeds Up Selection

In populations without recombination (or in regions of low recombination), genomes are inherited as indivisible blocks. This causes two major bottlenecks known as Hill-Robertson interference:

1. Clonal Interference: If two different beneficial mutations happen in different individuals simultaneously, they compete with each other rather than combining. One usually destroys the other, slowing down adaptation.
2. Genetic Hitchhiking & Background Selection: A beneficial mutation might accidentally arise on a chromosome loaded with harmful mutations, dragging it down, or it might get blocked because it’s linked to a competing sweep.

Recombination solves this traffic jam. It acts like a genetic shuffle button, allowing beneficial mutations from different lineages to combine into a single “super-genome” and uncoupling beneficial alleles from deleterious ones. This allows selection to act with maximum efficiency.

Fixation Times for Beneficial Mutations (Enabled by Recombination)

When recombination successfully prevents interference, the average time to fixation for a beneficial mutation with a selection coefficient ($s$) is governed by the deterministic-stochastic hybrid formula:

$$\bar{T}_{\text{fix}} \approx \frac{2}{s} \ln(2N_e s)$$

Because selection scales logarithmically with population size ($N_e$), larger populations actually fix beneficial mutations faster relative to their size than neutral mutations do, provided recombination is active.

Here is what the average generation-to-fixation timeline looks like for a beneficial mutation under two different selection pressures (moderate selection $s = 0.01$ vs. weak selection $s = 0.001$):

Instead of taking hundreds of thousands or millions of generations (as neutral mutations do via pure drift), a beneficial mutation aided by recombination can sweep through an entire population of 1 million people in fewer than 2,000 generations (roughly 50,000 years)—transforming evolution from a sluggish crawl into an efficient mechanism for adaptation.

Problem solved, right? Well, it probably won’t escape your attention that a) even 1,060 generations per fixation isn’t anywhere nearly fast enough or b) this only applies to the small percentage of the human-chimp divergence that is selected. But none of that matters anyhow, because we don’t need to rely upon population genetics theories about selection strength (s) to give us a hypothetical range, we have empirical evidence from the literature to plug into those standard formulas.

Tataru et al. estimated that less than 0.5% of new amino acid mutations are beneficial, with a scaled selection strength of N_e·s ≈ 12. For human N_e ≈ 10,000, that gives s+ ≈ 0.0012 for the beneficial mutations that do arise. The direct McDonald-Kreitman estimate of α — the proportion of amino acid substitutions driven by positive selection — is approximately 12.5% in hominids, with the remainder being neutral or nearly neutral.

About 30% of amino-acid-changing mutations in humans have effects less than about 10⁻⁵ — effectively neutral — and these are the ones that predominantly fix by drift.

The weighted average for a fixed substitution: 87.5% of fixations are neutral with s ≈ 0, and 12.5% are adaptive at s ≈ 0.0012:

0.875 × 0 + 0.125 × 0.0012 = 1.5 × 10⁻⁴

s ≈ 0.00015.

That is a full two orders of magnitude below the “moderate selection” case that produced the theoretical 1,060-generation fixation time that wasn’t fast enough anyhow.

In sum, humans do not fixate faster than bacteria, measured in time or in generations, and recombination cannot sufficiently reduce time to fixation to compensate for the slower process of fixation in humans. And all of this is before taking overlapping generations into account. Incorporating d means the 14,648-generation fixation time at the empirical s for N_e = 10,000 becomes roughly 18,310 generations. The neutral baseline of 40,000 becomes 50,000. And the weighted average across all fixations, where 87.5% are neutral, gets even worse.

Furthermore: “Prokaryotes appear to be much more efficient than eukaryotes at promoting simple to moderately complex molecular adaptations.” – Dr. Michael Lynch, author of The Origins of Genome Architecture

This is Lynch undermining the objection to the LTEE extrapolation objection. They argued I can’t apply bacterial fixation rates to humans because bacteria are clonal and slow. Lynch, who is one of the most respected population geneticists in the field, has stated that bacteria are actually faster and more efficient at molecular adaptation than eukaryotes like humans, precisely because of their population size advantage.

Which is why I utilized the LTEE to demonstrate the applicability of MITTENS in the first place. If it doesn’t work for bacteria, it definitely doesn’t work for chimps or humans.

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