An interesting conversation with ChatGPT 5.2:
Q1. With biological evolution of say mammals there are multiple traits (there must be thousands) that are simultaneously being optimised or conserved. How does does this work with purifying, selection, population genetics, fixation etc?
https://chatgpt.com/s/t_693ffa4b47c881919ebcff6447a49748
A. This question goes to the core of modern evolutionary genetics and is exactly where na|>ve rCLsingle-traitrCY intuitions break down. The short answer is: evolution does not optimise traits one-by-one. Instead,
selection operates on whole organisms whose genomes encode thousands of partially coupled traits, and population genetics provides the machinery
for how this can work without contradiction.
I will structure the answer from first principles, then address the
apparent paradox of rCLthousands of traits at oncerCY.
1. What selection actually rCLseesrCY
Natural selection does not see traits independently. It acts on:
Whole organisms
Whose phenotypes are emergent outcomes of
Genotypes interacting with environments
Formally, selection acts on differences in reproductive success
(fitness). Traits matter only insofar as they contribute (positively, negatively, or neutrally) to that success.
So the unit of selection is not:
rCLeye sharpnessrCY, rCLbone densityrCY, rCLenzyme X efficiencyrCY
but rather:
organism A leaves more descendants than organism B in environment E.
2. Genomes are high-dimensional systems, not collections of knobs
A mammalian genome has:
~20,000 protein-coding genes
Millions of regulatory elements
Extensive pleiotropy (one gene affects many traits)
Extensive epistasis (gene effects depend on other genes)
This creates a high-dimensional fitness landscape, where:
Each genotype is a point in ~millions of dimensions
Fitness is a scalar value assigned to that point
Selection therefore performs gradient ascent in a very high-dimensional space, not independent optimisation of each axis.
Key implication:
Most dimensions are nearly flat most of the time.
3. Purifying selection dominates most of the genome
Empirically, in mammals:
The majority of mutations are neutral or nearly neutral
A substantial fraction are deleterious
Only a tiny fraction are beneficial at any time
This is why purifying (negative) selection is the dominant mode:
Mutations that disrupt conserved functions are removed
Existing functional structures are maintained, not continuously rCLimprovedrCY
Mathematically:
For a deleterious mutation with selection coefficient
EYaa
<
0
s<0,
Its fixation probability is approximately:
EYaa
ree
1
reA
EYaA
2
EYaa
1
reA
EYaA
reA
4
EYaU
EYaA
EYaa
Pree
1reAe
reA4N
e
-a-a-a-arCi
s
1reAe
2s
-a-a-a-arCi
which is vanishingly small for moderate
reu
EYaa
reu
reusreu in large populations.
Thus, thousands of traits are rCLsimultaneously conservedrCY simply because:
Any mutation that degrades them is efficiently removed.
No active optimisation is required.
4. Nearly-neutral theory resolves the rCLtoo many traitsrCY intuition
Motoo KimurarCOs nearly neutral theory is critical here.
Key idea:
Many mutations have effects
reu
EYaa
reu
ree
1
EYaU
EYaA
reusreuree
N
e
-a-a-a-arCi
1
-a-a-a-arCi
Their fate is governed largely by genetic drift, not selection
Consequences:
Small-effect mutations can drift through populations
Large-effect deleterious mutations are purged
Large-effect beneficial mutations are rare
This means:
Traits can wander within tolerance bounds
As long as performance stays within a viable envelope, selection is indifferent
This dramatically reduces the rCLcoordination problemrCY.
5. Fixation does not require global optimisation
A common misunderstanding is thinking fixation requires a trait to be rCLglobally optimalrCY.
In reality:
Fixation only requires local advantage or drift
The population is almost never at a global optimum
Most fixation events involve:
Slight modifiers
Compensatory changes
Context-dependent advantages
Important point:
Fixation is path-dependent and historically contingent.
Evolution rCLlocks inrCY workable solutions, not best ones.
6. Trade-offs and constraint: why perfection is impossible
Many traits are antagonistically coupled:
Bone density vs. weight
Immune sensitivity vs. autoimmunity
Metabolic rate vs. longevity
Formally:
Fitness gradients for different traits often point in different directions
The result is movement toward constraint surfaces, not maxima
This leads to:
Pareto fronts, not optima
Stabilising selection around workable compromises
So when you observe rCLthousands of traits conservedrCY, what you are seeing is:
populations remaining inside a feasible region of phenotype space.
7. Why selection doesnrCOt rCLcollapse under dimensionalityrCY
Putting it together:
Selection avoids a combinatorial explosion because:
Most dimensions are neutral or nearly neutral
Purifying selection removes large disruptions efficiently
Pleiotropy couples traits, reducing effective dimensionality
Selection acts on organisms, not traits
Drift absorbs small-effect noise
Stabilising selection maintains systems near viable equilibria
This is why mammals can persist with:
Highly conserved core systems (development, metabolism, cell biology)
Slowly drifting peripheral traits
Occasional adaptive shifts when environments change
8. A useful analogy (non-hand-wavy)
Think of a modern aircraft:
Thousands of parameters
Most cannot change without catastrophic failure
Some can vary within tolerances
Rare redesigns improve performance under new constraints
Natural selection is not rCLdesigningrCY the aircraft from scratch each generation; it is:
Rejecting planes that crash
Allowing minor tolerated deviations
Occasionally locking in improvements when conditions demand it
9. Where this intersects ID-style intuitions (briefly, since you follow
the literature)
The intuition that rCLtoo many traits must be coordinatedrCY is not wrong, but the resolution is:
Coordination is largely inherited, not newly created
Selection is primarily conservative, not creative
Evolution is maintenance with occasional modification, not constant innovation
This is precisely why:
Evolution is slow
Major innovations are rare
Lineages show long periods of stasis punctuated by change
Summary
Thousands of traits do not need to be optimised simultaneously.
They are:
Mostly conserved by purifying selection
Buffered by developmental and genetic architecture
Allowed to drift within tolerances
Occasionally modified when local fitness gradients align
Population genetics handles this cleanly once you stop thinking in terms
of independent trait optimisation and start thinking in terms of high- dimensional, constrained, organism-level selection.
If you want, next we can:
Walk through a toy population-genetic model with many traits
Discuss genetic load and HaldanerCOs dilemma in this context
Or connect this explicitly to Evo-Devo constraints in mammals
Q2. You say "Major innovations are rare", but these are the driver of macroevolution. But how do these "major innovations" occur via many
small steps?
A. This is the right pressure point. If major innovations are both rare
and essential for macroevolution, then a purely verbal appeal to rCLmany small stepsrCY is insufficient. The question becomes:
What is the mechanism by which qualitatively new biological capacities
arise via quantitatively small genetic changes, without collapsing
fitness along the way?
Below is the modern evolutionary answer, stated mechanistically, not rhetorically...
https://chatgpt.com/s/t_693ffb26a698819184d0d92224b045b0
Q3. Yes [take one concrete case (e.g. mammalian middle ear, placenta, or adaptive immunity) and walk through the actual sequence of small,
selectable steps in detail, so this remains empirical rather than
abstract.]
A. https://chatgpt.com/s/t_693ffc103e388191b2668e65fcca355d
Q4. This describes a possible macro change in the configuration of bones
and associated tissue. But, for this to have functional advantage, simultaneously we need increased nerve connection capacity between the
and brain, and more sophisticated brain auditory processing. What you
have given is an example of irreducible complexity.
A. https://chatgpt.com/s/t_693ffd777ab08191b83d4bdb7283c93c
This is a serious objection, and it is correctly framed. You are not pointing to a gap of imagination (rCLmaybe nerves changed toorCY), but to a coupled-systems problem...
Q5. Examine one of the genuinely hard casesrCofor example: Origin of hair- cell mechanotransduction.
A. https://chatgpt.com/s/t_693ffeaf88808191ae6e17e4d5334efa
Hair-cell mechanotransduction (MET) is rCLhardrCY for exactly the reason you are targeting: it is a multi-component nanomachine (tip links, gating springs, a mechanically gated cation channel, lipid/actin couplings, adaptation motors) that must work at microsecondrComillisecond timescales. We can, however, say substantially more today than we could 15rCo20 years ago, because the core molecular players are now identified and
reconstituted to varying degrees...
Q6. rCLMinimum viable METrCY: what is the smallest set of components that can yield directionally sensitive, tethered mechanotransduction (even if
low fidelity)?
A. https://chatgpt.com/s/t_693fff94b4e881919740963d356fec8a
A rCLminimum viablerCY directionally sensitive, tethered MET system (even low fidelity) requires three functional modules:
a force-bearing extracellular tether with defined polarity,
a mechanically gated cation pore at one end of that tether, and
a coupler/scaffold that converts tether tension into channel gating (and keeps the complex in the right place).
...
On 12/15/2025 6:09 AM, MarkE wrote:
An interesting conversation with ChatGPT 5.2:
Q1. With biological evolution of say mammals there are multiple traits
(there must be thousands) that are simultaneously being optimised or
conserved. How does does this work with purifying, selection,
population genetics, fixation etc?
https://chatgpt.com/s/t_693ffa4b47c881919ebcff6447a49748
A. This question goes to the core of modern evolutionary genetics and
is exactly where na|>ve rCLsingle-traitrCY intuitions break down. The short >> answer is: evolution does not optimise traits one-by-one. Instead,
selection operates on whole organisms whose genomes encode thousands
of partially coupled traits, and population genetics provides the
machinery for how this can work without contradiction.
I will structure the answer from first principles, then address the
apparent paradox of rCLthousands of traits at oncerCY.
1. What selection actually rCLseesrCY
Natural selection does not see traits independently. It acts on:
Whole organisms
Whose phenotypes are emergent outcomes of
Genotypes interacting with environments
Formally, selection acts on differences in reproductive success
(fitness). Traits matter only insofar as they contribute (positively,
negatively, or neutrally) to that success.
So the unit of selection is not:
rCLeye sharpnessrCY, rCLbone densityrCY, rCLenzyme X efficiencyrCY
but rather:
organism A leaves more descendants than organism B in environment E.
2. Genomes are high-dimensional systems, not collections of knobs
A mammalian genome has:
~20,000 protein-coding genes
Millions of regulatory elements
Extensive pleiotropy (one gene affects many traits)
Extensive epistasis (gene effects depend on other genes)
This creates a high-dimensional fitness landscape, where:
Each genotype is a point in ~millions of dimensions
Fitness is a scalar value assigned to that point
Selection therefore performs gradient ascent in a very high-
dimensional space, not independent optimisation of each axis.
Key implication:
Most dimensions are nearly flat most of the time.
3. Purifying selection dominates most of the genome
Empirically, in mammals:
The majority of mutations are neutral or nearly neutral
A substantial fraction are deleterious
Only a tiny fraction are beneficial at any time
This is why purifying (negative) selection is the dominant mode:
Mutations that disrupt conserved functions are removed
Existing functional structures are maintained, not continuously
rCLimprovedrCY
Mathematically:
For a deleterious mutation with selection coefficient
EYaa
<
0
s<0,
Its fixation probability is approximately:
EYaa
ree
1
reA
EYaA
2
EYaa
1
reA
EYaA
reA
4
EYaU
EYaA
EYaa
Pree
1reAe
reA4N
e
-a-a-a-a-arCi
s
1reAe
2s
-a-a-a-a-arCi
which is vanishingly small for moderate
reu
EYaa
reu
reusreu in large populations.
Thus, thousands of traits are rCLsimultaneously conservedrCY simply because: >>
Any mutation that degrades them is efficiently removed.
No active optimisation is required.
4. Nearly-neutral theory resolves the rCLtoo many traitsrCY intuition
Motoo KimurarCOs nearly neutral theory is critical here.
Key idea:
Many mutations have effects
reu
EYaa
reu
ree
1
EYaU
EYaA
reusreuree
N
e
-a-a-a-a-arCi
1
-a-a-a-a-arCi
Their fate is governed largely by genetic drift, not selection
Consequences:
Small-effect mutations can drift through populations
Large-effect deleterious mutations are purged
Large-effect beneficial mutations are rare
This means:
Traits can wander within tolerance bounds
As long as performance stays within a viable envelope, selection is
indifferent
This dramatically reduces the rCLcoordination problemrCY.
5. Fixation does not require global optimisation
A common misunderstanding is thinking fixation requires a trait to be
rCLglobally optimalrCY.
In reality:
Fixation only requires local advantage or drift
The population is almost never at a global optimum
Most fixation events involve:
Slight modifiers
Compensatory changes
Context-dependent advantages
Important point:
Fixation is path-dependent and historically contingent.
Evolution rCLlocks inrCY workable solutions, not best ones.
6. Trade-offs and constraint: why perfection is impossible
Many traits are antagonistically coupled:
Bone density vs. weight
Immune sensitivity vs. autoimmunity
Metabolic rate vs. longevity
Formally:
Fitness gradients for different traits often point in different
directions
The result is movement toward constraint surfaces, not maxima
This leads to:
Pareto fronts, not optima
Stabilising selection around workable compromises
So when you observe rCLthousands of traits conservedrCY, what you are
seeing is:
populations remaining inside a feasible region of phenotype space.
7. Why selection doesnrCOt rCLcollapse under dimensionalityrCY
Putting it together:
Selection avoids a combinatorial explosion because:
Most dimensions are neutral or nearly neutral
Purifying selection removes large disruptions efficiently
Pleiotropy couples traits, reducing effective dimensionality
Selection acts on organisms, not traits
Drift absorbs small-effect noise
Stabilising selection maintains systems near viable equilibria
This is why mammals can persist with:
Highly conserved core systems (development, metabolism, cell biology)
Slowly drifting peripheral traits
Occasional adaptive shifts when environments change
8. A useful analogy (non-hand-wavy)
Think of a modern aircraft:
Thousands of parameters
Most cannot change without catastrophic failure
Some can vary within tolerances
Rare redesigns improve performance under new constraints
Natural selection is not rCLdesigningrCY the aircraft from scratch each
generation; it is:
Rejecting planes that crash
Allowing minor tolerated deviations
Occasionally locking in improvements when conditions demand it
9. Where this intersects ID-style intuitions (briefly, since you
follow the literature)
The intuition that rCLtoo many traits must be coordinatedrCY is not wrong, >> but the resolution is:
Coordination is largely inherited, not newly created
Selection is primarily conservative, not creative
Evolution is maintenance with occasional modification, not constant
innovation
This is precisely why:
Evolution is slow
Major innovations are rare
Lineages show long periods of stasis punctuated by change
Summary
Thousands of traits do not need to be optimised simultaneously.
They are:
Mostly conserved by purifying selection
Buffered by developmental and genetic architecture
Allowed to drift within tolerances
Occasionally modified when local fitness gradients align
Population genetics handles this cleanly once you stop thinking in
terms of independent trait optimisation and start thinking in terms of
high- dimensional, constrained, organism-level selection.
If you want, next we can:
Walk through a toy population-genetic model with many traits
Discuss genetic load and HaldanerCOs dilemma in this context
Or connect this explicitly to Evo-Devo constraints in mammals
What did you learn from this?
ID is still a bait and switch scam because selection was never what you thought that it was.-a How do you think that Behe's three neutral
mutations as evidence for design works?-a He has to put a time limit
because when you deal with reality you find out things like mice and
humans have been evolving as different lineages for around 80 million years.-a Most of their proteins may be around 20% different, so for an average protein of 300 amino acids there are around 60 neutral mutations that have occurred between mice and humans at this time.-a These proteins still do the same things in both mice and humans.-a We can take human proteins and put them into mice and they work.-a If some mutation occurs that changes the function of that gene a lot of those neutral mutations
may be involved in the new function.-a They might not have changed the
gene function enough to be selected for or against, but they could
combine with other mutations to do something different.
Behe needs his time limit because of the way that protein genes evolve.
Your new information is forming all the time, and is how new traits
evolve and are selected for or against.
Everything has to work with what is already working.-a Whales lost a lot
of genes because they didn't need them anymore, or they did things that
were counter productive in the new environment that the whales were
existing in.
Ron Okimoto
On 16/12/2025 3:11 am, RonO wrote:
On 12/15/2025 6:09 AM, MarkE wrote:
An interesting conversation with ChatGPT 5.2:
Q1. With biological evolution of say mammals there are multiple
traits (there must be thousands) that are simultaneously being
optimised or conserved. How does does this work with purifying,
selection, population genetics, fixation etc?
https://chatgpt.com/s/t_693ffa4b47c881919ebcff6447a49748
A. This question goes to the core of modern evolutionary genetics and
is exactly where na|>ve rCLsingle-traitrCY intuitions break down. The
short answer is: evolution does not optimise traits one-by-one.
Instead, selection operates on whole organisms whose genomes encode
thousands of partially coupled traits, and population genetics
provides the machinery for how this can work without contradiction.
I will structure the answer from first principles, then address the
apparent paradox of rCLthousands of traits at oncerCY.
1. What selection actually rCLseesrCY
Natural selection does not see traits independently. It acts on:
Whole organisms
Whose phenotypes are emergent outcomes of
Genotypes interacting with environments
Formally, selection acts on differences in reproductive success
(fitness). Traits matter only insofar as they contribute (positively,
negatively, or neutrally) to that success.
So the unit of selection is not:
rCLeye sharpnessrCY, rCLbone densityrCY, rCLenzyme X efficiencyrCY
but rather:
organism A leaves more descendants than organism B in environment E.
2. Genomes are high-dimensional systems, not collections of knobs
A mammalian genome has:
~20,000 protein-coding genes
Millions of regulatory elements
Extensive pleiotropy (one gene affects many traits)
Extensive epistasis (gene effects depend on other genes)
This creates a high-dimensional fitness landscape, where:
Each genotype is a point in ~millions of dimensions
Fitness is a scalar value assigned to that point
Selection therefore performs gradient ascent in a very high-
dimensional space, not independent optimisation of each axis.
Key implication:
Most dimensions are nearly flat most of the time.
3. Purifying selection dominates most of the genome
Empirically, in mammals:
The majority of mutations are neutral or nearly neutral
A substantial fraction are deleterious
Only a tiny fraction are beneficial at any time
This is why purifying (negative) selection is the dominant mode:
Mutations that disrupt conserved functions are removed
Existing functional structures are maintained, not continuously
rCLimprovedrCY
Mathematically:
For a deleterious mutation with selection coefficient
EYaa
<
0
s<0,
Its fixation probability is approximately:
EYaa
ree
1
reA
EYaA
2
EYaa
1
reA
EYaA
reA
4
EYaU
EYaA
EYaa
Pree
1reAe
reA4N
e
-a-a-a-a-arCi
s
1reAe
2s
-a-a-a-a-arCi
which is vanishingly small for moderate
reu
EYaa
reu
reusreu in large populations.
Thus, thousands of traits are rCLsimultaneously conservedrCY simply because:
Any mutation that degrades them is efficiently removed.
No active optimisation is required.
4. Nearly-neutral theory resolves the rCLtoo many traitsrCY intuition
Motoo KimurarCOs nearly neutral theory is critical here.
Key idea:
Many mutations have effects
reu
EYaa
reu
ree
1
EYaU
EYaA
reusreuree
N
e
-a-a-a-a-arCi
1
-a-a-a-a-arCi
Their fate is governed largely by genetic drift, not selection
Consequences:
Small-effect mutations can drift through populations
Large-effect deleterious mutations are purged
Large-effect beneficial mutations are rare
This means:
Traits can wander within tolerance bounds
As long as performance stays within a viable envelope, selection is
indifferent
This dramatically reduces the rCLcoordination problemrCY.
5. Fixation does not require global optimisation
A common misunderstanding is thinking fixation requires a trait to be
rCLglobally optimalrCY.
In reality:
Fixation only requires local advantage or drift
The population is almost never at a global optimum
Most fixation events involve:
Slight modifiers
Compensatory changes
Context-dependent advantages
Important point:
Fixation is path-dependent and historically contingent.
Evolution rCLlocks inrCY workable solutions, not best ones.
6. Trade-offs and constraint: why perfection is impossible
Many traits are antagonistically coupled:
Bone density vs. weight
Immune sensitivity vs. autoimmunity
Metabolic rate vs. longevity
Formally:
Fitness gradients for different traits often point in different
directions
The result is movement toward constraint surfaces, not maxima
This leads to:
Pareto fronts, not optima
Stabilising selection around workable compromises
So when you observe rCLthousands of traits conservedrCY, what you are
seeing is:
populations remaining inside a feasible region of phenotype space.
7. Why selection doesnrCOt rCLcollapse under dimensionalityrCY
Putting it together:
Selection avoids a combinatorial explosion because:
Most dimensions are neutral or nearly neutral
Purifying selection removes large disruptions efficiently
Pleiotropy couples traits, reducing effective dimensionality
Selection acts on organisms, not traits
Drift absorbs small-effect noise
Stabilising selection maintains systems near viable equilibria
This is why mammals can persist with:
Highly conserved core systems (development, metabolism, cell biology)
Slowly drifting peripheral traits
Occasional adaptive shifts when environments change
8. A useful analogy (non-hand-wavy)
Think of a modern aircraft:
Thousands of parameters
Most cannot change without catastrophic failure
Some can vary within tolerances
Rare redesigns improve performance under new constraints
Natural selection is not rCLdesigningrCY the aircraft from scratch each >>> generation; it is:
Rejecting planes that crash
Allowing minor tolerated deviations
Occasionally locking in improvements when conditions demand it
9. Where this intersects ID-style intuitions (briefly, since you
follow the literature)
The intuition that rCLtoo many traits must be coordinatedrCY is not
wrong, but the resolution is:
Coordination is largely inherited, not newly created
Selection is primarily conservative, not creative
Evolution is maintenance with occasional modification, not constant
innovation
This is precisely why:
Evolution is slow
Major innovations are rare
Lineages show long periods of stasis punctuated by change
Summary
Thousands of traits do not need to be optimised simultaneously.
They are:
Mostly conserved by purifying selection
Buffered by developmental and genetic architecture
Allowed to drift within tolerances
Occasionally modified when local fitness gradients align
Population genetics handles this cleanly once you stop thinking in
terms of independent trait optimisation and start thinking in terms
of high- dimensional, constrained, organism-level selection.
If you want, next we can:
Walk through a toy population-genetic model with many traits
Discuss genetic load and HaldanerCOs dilemma in this context
Or connect this explicitly to Evo-Devo constraints in mammals
What did you learn from this?
ID is still a bait and switch scam because selection was never what
you thought that it was.-a How do you think that Behe's three neutral
mutations as evidence for design works?-a He has to put a time limit
because when you deal with reality you find out things like mice and
humans have been evolving as different lineages for around 80 million
years.-a Most of their proteins may be around 20% different, so for an
average protein of 300 amino acids there are around 60 neutral
mutations that have occurred between mice and humans at this time.
These proteins still do the same things in both mice and humans.-a We
can take human proteins and put them into mice and they work.-a If some
mutation occurs that changes the function of that gene a lot of those
neutral mutations may be involved in the new function.-a They might not
have changed the gene function enough to be selected for or against,
but they could combine with other mutations to do something different.
Behe needs his time limit because of the way that protein genes
evolve. Your new information is forming all the time, and is how new
traits evolve and are selected for or against.
Everything has to work with what is already working.-a Whales lost a
lot of genes because they didn't need them anymore, or they did things
that were counter productive in the new environment that the whales
were existing in.
Ron Okimoto
Calling this a rCLbait and switchrCY presupposes that ID once relied on an incorrect understanding of selection and later changed its claims. That
is historically false.
ID does not deny natural selection, nor has it.
Behe explicitly accepts mutation, selection, genetic drift, and as I've previously noted, common descent. His argument concerns the creative
limits of these mechanisms, not their existence.
BeherCOs position in DarwinrCOs Black Box, as far back as 1996 (which has remained consistent) is that selection explains optimization, not origination of complex molecular systems.
On 12/15/2025 6:12 AM, MarkE wrote:
Q2. You say "Major innovations are rare", but these are the driver of
macroevolution. But how do these "major innovations" occur via many
small steps?
A. This is the right pressure point. If major innovations are both
rare and essential for macroevolution, then a purely verbal appeal to
rCLmany small stepsrCY is insufficient. The question becomes:
What is the mechanism by which qualitatively new biological capacities
arise via quantitatively small genetic changes, without collapsing
fitness along the way?
Below is the modern evolutionary answer, stated mechanistically, not
rhetorically...
https://chatgpt.com/s/t_693ffb26a698819184d0d92224b045b0
What macro "major innovations" needed to occur between chimps and humans?
We have all the same tissues and organs.-a We have the same brain parts, just brain size has changed.-a We have all the same bones, but they have changed their shape and size.-a We are bipedal with a bent and shortened hip, but that deformity may have initally been due to a single gene
defect with later modifications selected over time.-a We are still brachiating apes with shorter arms and longer legs (check out gymnasts).
On 16/12/2025 3:24 am, RonO wrote:
On 12/15/2025 6:12 AM, MarkE wrote:
Q2. You say "Major innovations are rare", but these are the driver of
macroevolution. But how do these "major innovations" occur via many
small steps?
A. This is the right pressure point. If major innovations are both
rare and essential for macroevolution, then a purely verbal appeal to
rCLmany small stepsrCY is insufficient. The question becomes:
What is the mechanism by which qualitatively new biological
capacities arise via quantitatively small genetic changes, without
collapsing fitness along the way?
Below is the modern evolutionary answer, stated mechanistically, not
rhetorically...
https://chatgpt.com/s/t_693ffb26a698819184d0d92224b045b0
What macro "major innovations" needed to occur between chimps and humans?
We have all the same tissues and organs.-a We have the same brain
parts, just brain size has changed.-a We have all the same bones, but
they have changed their shape and size.-a We are bipedal with a bent
and shortened hip, but that deformity may have initally been due to a
single gene defect with later modifications selected over time.-a We
are still brachiating apes with shorter arms and longer legs (check
out gymnasts).
No. That assessment grossly understates the differences.
For details, see my post "Chimp to human evolution - Sandwalk perspective".
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