A curious young elephant got its nose stretched by a crocodile, with
the result that elephants everywhere now carry a trunk. What this
story tells us is that Rudyard Kipling was a thoroughgoing
Lamarckian—a believer in the inheritance of acquired
characteristics. As it happens, Lamarckian ideas were already in
disrepute when Kipling wrote his "Just So" stories. The
German biologist August Weismann, in a remarkably Kiplingesque
experiment, had shown that chopping off a rat's tail did not lead to
the birth of tailless ratlets. Experimental protocols have gotten
more sophisticated since then, but the verdict is the same: There's
no sign of Lamarckian inheritance anywhere in the kingdoms of life.
But why not? A few years ago Colin McGinn wrote (in a review of a
book by Daniel Dennett): "Why have Lamarckian organisms never
evolved? Surely a mutation which made the genes responsive to
changes of phenotype ('learning') would have selectional advantage,
and there seems to be no physical impossibility in such a
set-up. Wouldn't natural selection favour a physiological mechanism
that allowed learned characteristics to be passed genetically to offspring?"
These are good questions. One way of answering them is to note that
the molecular pathways needed for Lamarckian inheritance just don't
exist. Within the context of life-as-we-know-it, there's no way for
the elephant's nose to talk to the elephant's genes—especially
the germ-line genes. The "central dogma" of molecular
biology says that information flows from DNA to RNA to protein, not
the other way around. A Lamarckian feedback loop would seem to
require some mechanism by which the proteins of the phenotype could
alter the DNA of the genotype.
The trouble with such an answer is that it invites a further
annoying question: Why is it that such feedback loops have never
evolved? Given all that has evolved in the way of genetic
detours and shortcuts—plasmids, transposons, retroviruses,
prions—it seems a bit arbitrary to declare this one pathway
out of bounds. The case of retroviruses is particularly provocative,
since they produce an enzyme (reverse transcriptase) that violates
the central dogma, copying information from RNA back into DNA.
Here's another possible reason for the absence of Lamarckian
inheritance in nature: Maybe it's just not worth the bother. Many
authors seem to take for granted that a genetic means of passing on
learned traits would be beneficial if it could exist. They assume
Lamarckism would make for a smoother and quicker kind of evolution
than Darwin's blindfolded selection of random variations. But what
are the true costs and benefits of Lamarckism? Perhaps the reason we
see no Lamarckian organisms is not that nature cannot invent the
necessary apparatus but rather that the result is maladaptive.
Lamarckism could be a trick that nature has tried and discarded.
I have attempted to investigate this issue through some simple
computer simulations. Specifically, I've addressed the following
question: If you were offered a Lamarckian capability, how much
should you be willing to pay for it, when the price is exacted in
the form of some compensating detriment to fitness? My experiments
in free-market genetics are too crude to yield a definitive answer,
but I can report that within the rather narrow bounds of this one
model, I've been unable to find any situation where the benefits of
Lamarckism would justify paying more than a small price.
The Evolution of Evolution
Jean Baptiste Pierre Antoine de Monet, Chevalier de Lamarck, was
treated badly by his contemporaries and worse by history. At the
Muséum d'Histoire Naturelle he held the lowliest
professorship, namely Professor of Insects and Worms, but he turned
this academic insult to good advantage, establishing the division
between vertebrate and invertebrate animals. And he devised a theory
of speciation through gradual evolution 60 years before Darwin
published his Origin. Today, however, Lamarck is remembered
only for his great error—his thesis that evolution works by
the transmission of traits acquired through habits of use or disuse.
The idea must have seemed irresistible. If you play a lot of
basketball, Lamarck says, you'll have taller children. And he
appears to be right: The children of basketball players surely
are taller than average. Likewise, if you want your
children to get into Harvard, go to Harvard yourself; the high rate
of acceptance for children of alumni argues that education too is
heritable. The fallacy in this reasoning is now plain, and no one
would propose a Lamarckian mechanism to explain such correlations.
Nevertheless, the suspicion lingers that if only the world
did work Lamarck's way, it would work a little better.
The Harvard basketball team is not the most convenient context for
a computer model of Lamarckian evolution. In searching for a simpler
system, I have been inspired by the famous case of the melanic moths
in industrial Britain. Dark-pigmented forms of the peppered moth
Biston betularia were first noticed in the 19th
century; they grew in abundance for several decades and then receded
again after the 1950s. The cause of the original color shift was
apparently the darkening of tree trunks by coal soot, which impaired
the camouflage of lighter moths and left them exposed to predators.
The later reversal of the trend coincided with measures to reduce
My model of these events is highly abstract, with all the
naturalistic details stripped away. It is not meant to reveal
anything new about melanic moths but merely uses the idea of
selection based on camouflage to explore some mechanisms of
adaptation. The computer model is written in the programming
language StarLogo, created by Mitchel Resnick of the Massachusetts
Institute of Technology. (I discussed StarLogo in the
January-February, 1999, "Computing Science" column.) For
this project I employed StarLogoT, a variant developed by Uri
Wilensky of Tufts University. The model and additional technical
details are available here.
Costs and Benefits
The rise and fall of melanism in the peppered moth was
unquestionably a Darwinian event, brought about by natural selection
acting on random mutations. Inheritance of acquired characteristics
was impossible simply because there were no acquired characteristics
to inherit. A moth has no way to change its color over the course of
its lifetime, even if it could somehow figure out that making the
change would be advantageous. And if the moth cannot adjust its
color, it obviously cannot transmit any adjustments to its descendants.
But in imagination—or in the computer—we can rerun the
experiment without the constraints of insect physiology. We can
create chameleon moths that sense the color of their environment and
adjust their own color to match. I shall refer to this adjustment
process as learning, although it needn't imply any kind of
cognitive capacity; the term is meant to encompass any adaptation
within the lifetime of an organism.
Would moths that learn have an advantage over those that don't? It
seems like a sure bet—and yet if adjustable camouflage is such
an obvious asset, why don't all prey species have it? A likely
answer is the no-free-lunch theorem. Learning has a cost, which in
some cases may outweigh the benefits. At a minimum there is a
complexity cost: Sensing the state of the environment and responding
to it requires metabolic machinery that a simpler organism could do
without. Building and maintaining that machinery incurs an energy
cost; resources that might have gone into growth and reproduction
have to be diverted into learning. Thus a creature that does a lot
of learning could be expected to have a slower reproductive cycle
than one with more hard-wired traits. (H. sapiens takes 20
or 30 years to accomplish what E. coli can do in 20 or 30
minutes.) Thus adjustable camouflage might reduce mortality, but the
price would be reduced fertility.
The cost-benefit analysis for Lamarckism is similar. In a Darwinian
world, any acquired improvements cannot cross the generation gap. A
smart moth born with white wings might darken gradually to match a
sooty environment, but the moth's offspring would be white again
(barring mutations). The moth's acquired pigment is no more
heritable than a suntan. Lamarckism creates a link between learning
and genetics. A moth that adjusts its color during its lifespan will
give birth to offspring that share at least some of this adjustment.
Is this shortcut advantageous? Again it would seem so. The young
moths are hatched with protective coloration already in place. But,
as with learning, maintaining the Lamarckian mechanism imposes a
metabolic cost, so that lowering the death rate limits the birth
rate. The balance between these two effects determines whether
Lamarckian inheritance pays off. Finding the point of balance is the
aim of the computer simulation.
The main actors in a StarLogo program are mobile, animal-like
objects. For historical reasons they are known as turtles, but they
can just as well represent moths. Each moth has its own internal
state, which includes a genome, a camouflage color and a level of
energy reserves. The moths move over a background of
"patches," which represent the color of the environment.
Melanism is often a polygenic trait, producing a more-or-less
continuous range of hues. For simplicity I encode the camouflage
color in a single gene with a continuous range of alleles; color can
take on any value from 0 (darkest) to 1,000 (lightest). Separate
variables represent the color genotype and the color phenotype; at
birth the two variables have identical values, but in animals that
learn they can later diverge. Lamarckian inheritance is implemented
as feedback from the color phenotype of the parent to the color
genotype of the offspring. In the extreme case of perfect
Lamarckism, the color gene of the offspring is set equal to the
parent's color phenotype; lesser degrees of Lamarckism interpolate
between the original genotype and the acquired phenotype.
The moths have three other genes, which also range in value between
0 and 1,000. Following the custom of geneticists, I give the genes
names: kudzu, harvard and vanderbilt.
Kudzu is a growth gene: It determines the rate at which the
moth absorbs resources, gains weight and grows toward reproductive
maturity. Other things being equal, natural selection would drive
this gene toward its maximum value, and that's what happens in the
purely Darwinian case. For learning and Lamarckian moths, however,
kudzu is linked to the harvard gene, which governs
the rate of learning, and the vanderbilt gene, which
controls the inheritance of acquisitions. The linkage is negative,
so that faster learning or more complete inheritance of acquired
traits entails a more severe penalty in growth rate. The constants
that determine the degree of linkage—the tuition charged for
learning and the inheritance tax imposed on Lamarckism—are the
main parameters under investigation in the model.
A simulation begins with randomly assigned genotypes. Each moth
grows at a rate determined by the value of the kudzu gene.
On reaching a threshold weight (which takes 100 days at the highest
possible growth rate), the moth produces two offspring and
immediately dies. Meanwhile, each day a fraction of the moths are
killed by predators. The probability of being killed increases in
proportion to the difference between the moth's color and the
background color. The overall death rate is adjusted to match the
birth rate, keeping the population constant.
In those moths that learn, the color phenotype is adjusted every
day, bringing it into closer correspondence with the background
color at a rate determined by the value of the harvard
gene. In the same way, Lamarckian inheritance adjusts the color
genotype of the offspring toward the parental phenotype by an amount
proportional to the value of the vanderbilt gene. All of
the genes are also subject to random mutation and natural selection.
(Even in Lamarckian moths, only the camouflage gene evolves by
Lamarckian methods; all the other genes are purely Darwinian.)
The Backdrop to Evolution
When I first started up the model, I got an immediate reminder of a
fundamental principle of evolutionary biology: No organism evolves
in isolation. Evolution only makes sense as an interaction between
the organism and its environment. I should not have needed a
reminder—after all, the driving force in the peppered moth
story was environmental change—but in fact I had given too
little attention to the backdrop against which the moths play out
What matters most about the background is not its specific color but
the rate at which the color changes. In a static environment,
learning is useless; there's nothing to learn. Darwinian mutation
and selection can match an unchanging background just as closely as
learning can, and so organisms unburdened by the overhead of
learning will be favored. Running the model with an unchanging
environment illustrates this effect clearly. Starting with random
values of the harvard gene, the distribution shifts within
a few dozen generations to favor the lowest values—those that
produce the least learning but also incur the least penalty. In a
mixed population of nonlearning Darwinian moths and learners, the
outcome is even more dramatic. The Darwinians take over the
population and drive the learners to extinction. And if learning is
disadvantageous in these circumstances, then Lamarckism must also be
unfavorable, since organisms that don't learn acquire nothing to
bequeath their offspring.
The rate of environmental change does not have to be exactly zero to
favor Darwinians. The rate merely has to be low enough to ensure
that change is insignificant within the lifetime of an individual.
Even a world with large and abrupt environmental transitions can
penalize learners if the upheavals are separated by long interludes
of stasis. Learners are better equipped to deal with the upsets, but
they are wiped out by the faster-breeding Darwinians during the
periods of calm.
Watching the simulations in action gives a new perspective on the
relation between learning and life cycle. The idea that learning
takes so much time and energy that it delays reproductive maturity
is only half the story. The other half is that only a long-lived
organism has any use for learning. Bacteria can rely on Darwinian
evolution to fine-tune their metabolism to seasonal changes in
temperature; as individuals they don't need to learn about hot and
cold. Large mammals, on the other hand, would get no benefit from
winter genes and summer genes, because they must cope with both seasons.
The More Things Change...
If slow change favors Darwinian selection, a rapidly fluctuating
environment is where learning proves its worth. What is a little
less obvious is that evolution becomes irrelevant here.
Even in the absence of learning, natural selection is helpless when
change is faster than a generation time. If bark color can go from
black to white in a week, and moths live several months, then the
genotype can't keep up. Light-colored specimens might be favored one
week, and produce more offspring than dark-colored moths, but the
genes for paleness would be maladaptive by the time these
Turning on the harvard gene does nothing to restore the
efficacy of natural selection; on the contrary, learning further
decouples the genotype from the phenotype. When learning is
rewarded, it becomes so efficient that there is little selection
pressure on the genotype. Learning provides any newborn
moth with excellent camouflage in a day or two, and so survival is
essentially independent of the color gene.
What happens if Lamarckian inheritance is turned on in this
rapid-change regime? Not much. If Lamarckism is assessed the same
penalty as learning, the vanderbilt gene is
disadvantageous, and the distribution of values slides toward the
low end of the scale. This result is not hard to fathom. In the
model the sole benefit of Lamarckian inheritance is being born
pre-adapted to the color of the environment. But if that environment
is changing rapidly, the benefit won't last long. Furthermore, in a
population dominated by fast learners, most of the newborn moths
would come to match their background in a few days anyway, even
without the Lamarckian head start.
To put it another way: Learning is a valuable survival skill every
day of your life, whereas Lamarckism helps only on the first day.
This formulation suggests a way to quantify the worth of the
vanderbilt gene. If your expected lifespan is L,
then you should be willing to pay about 1/L as much for a
Lamarckian legacy as you would pay for learning. In the model,
learners live more than 100 days, so Lamarckism should be worth less
than 1 percent of the price of learning. If the penalties are
adjusted accordingly—making the vanderbilt
inheritance tax less than 1 percent of the harvard
tuition—Lamarckism ought to spread through the population. In
my experiments I could not see this effect clearly. Even at a cost
of zero I couldn't be sure whether the vanderbilt gene was
growing in frequency or merely drifting neutrally, but it may be
that I wasn't patient enough to wait for the trend to become apparent.
Looking for Lamarck in All the Wrong Places
If Lamarckism has no value in a static environment and only the
slimmest of marginal benefits in a fast-changing environment, it's
natural to wonder if there might be some intermediate condition
where the utility of Lamarckian inheritance is maximized. This would
be a condition where change is quick enough to make learning
worthwhile, but not so rapid as to make genetics irrelevant. I have
surveyed a broad range of rates of environmental variation looking
for this point of optimality, without reaching any firm conclusion.
The level of statistical fluctuations in the output of the model
suggests that much longer runs and larger populations would be
needed to settle the question. I do feel confident in saying there
is no level of variation where Lamarckian inheritance is worth as
much as learning is, or even half as much. But there may be a range
of variation rates where a species could benefit from a Lamarckian
mechanism if it cost no more than a tenth of what learning costs.
Even where the model's answers are clear, they are at best
preliminary and provisional. The model is too simple to capture much
detail about the lives of real organisms. On the other hand, it's
not simple enough to explore the entire space of parameter values.
Another reason for caution is that the model sometimes behaves in
ways I don't understand. For example, in certain mixed-population
experiments the Lamarckian gene is driven toward the lowest possible
values, indicating it is unfavorable and "wants" to be
zero, yet at the same time the Lamarckian breed drives its
competitors to extinction. What does that mean?
It would be interesting to test the model on problems other than
moth camouflage, especially problems where the environment is not a
passive background but can react and evolve on its own. One realm
where Lamarckian mechanisms look particularly attractive is the
immune system. Every child must reinvent immunity to measles and
chickenpox and other diseases. It's done through a miniature
Darwinian process of generating many random antibodies and selecting
those that recognize a pathogen. Wouldn't it be better to pass on
the selected antibody genes to later generations, so that babies
would be born pre-immunized? Edward J. Steele of the University of
Wollongong in Australia argues that something like this does go on
in the immune system, through the agency of reverse transcriptase.
But Steele has won few converts. It's hard to be a believer in
genetically transmitted immunity when your parents had chickenpox
and you had chickenpox and your children get chickenpox.
If Steele's mechanism could exist, would it prevent disease? The
obvious drawback is resistance: A generation born with a high titer
of antibody would exert powerful selective pressure on the pathogen,
so that mutants with slightly different surface markers would
proliferate. Thus every generation would have to come up with a new
defense anyway, and Lamarckian inheritance would be rendered
superfluous. But this is speculation; a model might well reveal
In closing, I want to say a word about cultural evolution, which is
often described as a Lamarckian process. Suppose it were
truly Lamarckian: Suppose some neurogenetic innovation
allowed your children to be born already knowing everything you
know. What a boon to humanity! What a head start! No one would ever
again have to spend all those years learning the alphabet and the
multiplication tables and the conjugation of Latin verbs and the law
of cosines and the preamble to Evangeline and the date of
the Battle of Hastings and how to ride a bicycle. But the more items
I add to this list—let's not forget the state capitals or the
nine orders of angels or the 20 amino acids or the recipe for mom's
meatloaf—the more I'm struck by the fundamental problem of
Lamarckism. Which acquired traits do you choose to pass on?
© Brian Hayes