Everything so far has been reconstruction: an argument about the past assembled from rocks, sequences and algebra.
The previous lessons established that the algebra predicts rates, and that a selective advantage too small to notice sweeps a population in under a thousand generations. If that is right, the process should be visible in any population with short generations under a measurable pressure, and the change should match the prediction rather than merely occurring. This lesson checks that on four systems where the starting state was recorded, the selective agent is known and the arithmetic can be done. It is also the part of the subject with a bill.
The breeder's equation
The tool for a quantitative character is one line, and it comes from the animal breeders rather than from the evolutionists. Let , the selection differential, be the difference between the mean of the individuals that actually reproduce and the mean of the population they were drawn from. Let , the response, be the difference between the offspring generation's mean and the parental generation's. Then
where is the narrow-sense heritability, the fraction of the variance in the character that is transmitted additively from parent to offspring. Everything in the equation is measurable in a field season: by weighing the survivors, by regressing offspring on the average of their two parents, and by weighing the next generation. That makes the equation a genuine prediction rather than a description, because is measured after and are.
Daphne Major, 1977
Peter and Rosemary Grant began working on Daphne Major, a small volcanic island in the Galápagos, in 1973, and measured and banded essentially every medium ground finch, Geospiza fortis, on it. Then the weather did the experiment.
The wet season of 1977 brought 24 mm of rain instead of the usual 130 mm or so. Seed production collapsed. The small soft seeds that the finches prefer were eaten first, leaving mainly the large hard mericarps of Tribulus cistoides, which a bird can only crack if its beak is deep enough to generate the force. The population fell from roughly 1,200 birds to about 180, a survival rate of 15 per cent, and the survivors were not a random sample.
Example. Mean beak depth in the population before the drought was about 9.42 mm. Survivors averaged about 10.14 mm. Heritability of beak depth on this island is estimated between 0.74 and 0.82. What shift should appear in the next generation?
The selection differential is mm. With ,
so the offspring generation should average about 9.95 mm, a rise of 5.7 per cent. With the prediction is 6.3 per cent. The observed shift in the birds hatched in 1978 was around 4 to 5 per cent, which sits just below the predicted band and within the uncertainty on the heritability estimate.
Two things are worth extracting. The prediction is quantitative and it is roughly right, which is the standard a theory should be held to. And the magnitude is small: a 5 per cent change in one character in one generation, driven by a mortality event that killed 85 per cent of the population. Selection this violent produces a change a person could easily fail to notice by eye.
Now you. In 1983 an El Niño brought 1,359 mm of rain to the same island. Small soft seeds became abundant and the large-seeded plants were crowded out. What should have happened to beak depth, and what does the answer say about the idea that evolution has a direction?
Answer
It should have reversed, and it did: mean beak size fell over the following years as small-beaked birds, which handle small seeds more efficiently, did better. A further drought in 2003 and 2004 pushed it down again rather than up, because by then the large-beaked Geospiza magnirostris had colonised the island in 1982 and was taking the large seeds, so the best strategy for a fortis under drought had become a small beak rather than a large one. That is character displacement, and it was observed happening.
The point about direction is the one the whole course has been building. Fitness is a relation between a genotype and its current circumstances, and circumstances on Daphne Major reverse on a timescale of years. A lineage under continuous strong selection can end up exactly where it started, and the long-term trend is the sum of a great many oscillations rather than a march. Anything in the fossil record described as stasis may be exactly this: a population tracking a wandering optimum vigorously and going nowhere.
A hundred generations of selection on maize
The finches show selection acting for a few generations. The longest deliberate experiment shows what happens when it acts for a century.
In 1896 Cyril Hopkins at the Illinois Agricultural Experiment Station took 163 ears of an open-pollinated maize variety, measured the oil content of the kernels, and began two lines: one selected each year for the highest oil content, one for the lowest. Protein lines were started alongside. The experiment has run every year since.
Example. The base population averaged 4.7 per cent oil. After 100 generations, reported in 2004, the high line was at about 22 per cent. What per-generation rate of change does that represent, and is the response still going?
The factor is , and the per-generation multiplier is , about 1.6 per cent per generation. That is a large rate by evolutionary standards and an unremarkable one by breeding standards.
The response has not stopped, which is the result that matters. The high line has moved far outside the range of the population it started from: no ear in the original 163 was anywhere near 22 per cent, and the line is many phenotypic standard deviations from its base. That is the transgressive segregation of the fifth lesson operating for a century, and it is the direct refutation of Fleeming Jenkin's claim in the fourth that selection hits a wall which no amount of further effort passes. Jenkin was reasoning from short breeding programmes; given a hundred generations, the wall is not there.
Now you. The low line fell from 4.7 per cent to about 0.5 per cent and then stopped responding. Give two quite different reasons a selection line stops, and say how you would tell them apart.
Answer
The first is exhaustion of additive variance. Selection consumes what it acts on, as the sixth lesson showed, and once every locus contributing to the character is fixed in the favoured direction, falls to zero and the breeder's equation predicts no further response whatever is. The second is a fitness limit: a plant with no oil in its kernels has nothing to fuel germination, so the extreme genotypes stop producing viable seed and natural selection opposes the artificial selection until the two balance. A third, specific to the low line, is simply the floor: oil content cannot go below zero, and the measurement becomes unreliable near it.
You tell them apart by measuring, not by arguing. Estimate in the stalled line: if it is near zero, the variance is gone; if it is still substantial, something is opposing the response. Then relax selection for a few generations. An exhausted line stays where it is, because there is nothing to pull it back. A line held by opposing natural selection retreats towards the middle, which is what the low oil line does. Crossing the stalled line to unrelated material and finding the response resumes confirms the variance diagnosis.
The clinic
The same arithmetic, applied to bacteria, is a public health calculation and the most expensive consequence the theory has.
The seventh lesson established the supply side: in a bacterial population of , cells mutated at essentially every position in the genome are already present before any drug is given, so resistance is not induced by treatment but selected by it. The rest follows from the sixth lesson's algebra: a resistant cell in a treated patient has an enormous selective advantage, often approaching because its competitors are being killed outright, so it sweeps in a handful of generations, which for a bacterium dividing every 30 minutes is a matter of hours.
Example. Resistance to a drug is conferred by a specific point mutation arising at per cell per generation. An infection contains bacteria. Estimate the number of resistant cells present at the start of treatment, and the effect of adding a second, independent drug whose resistance mutation arises at .
Before treatment, the expected number resistant to the first drug is cells, and to the second . Single-drug treatment therefore fails not because resistance evolves but because it has already evolved, in a hundred cells, and the drug clears the field for them.
Cells resistant to both require both mutations, and since the mechanisms are independent the joint rate is , so the expected number in cells is , one chance in a million. That is the entire logic of combination therapy for tuberculosis and HIV, and it is a direct application of mutation supply arithmetic. It also shows why patients must complete a course: stopping early leaves a partially reduced population in which any surviving single-resistant cell can regrow and then acquire the second mutation at leisure.
Now you. In 2016 Michael Baym's group built a 60 by 120 cm agar plate with bands containing 1, 10, 100, 1,000 and 100,000 times the concentration of antibiotic needed to stop growth, inoculated E. coli at the edges, and filmed it. The bacteria crossed the whole plate in about eleven days. Why is this experiment more informative than simply plating bacteria on the highest concentration?
Answer
Because plating on the highest concentration asks for a single leap and gets nothing. The probability that one cell carries all the mutations needed for 100,000-fold resistance simultaneously is essentially zero, which is precisely the objection critics of the theory raise about complex adaptations: the target is too small to hit.
The banded plate asks for the leap to be made in steps, and each step is individually probable. A population expands into the first band, which requires one attainable mutation; while growing there it generates the variation for the next. The experiment makes the cumulative structure of the process visible, and it also makes visible two things the algebra predicts and prose descriptions usually omit: lineages that arrive at a band first are often not the ones that eventually cross it, because an early mutation with a modest benefit can block a better one behind it, and the advancing front is not a single line but many independent excursions, most of which die. It is the sixth, seventh and eighth lessons running simultaneously on a photographable surface.
The long experiment
The most complete real-time record comes from a flask. On 24 February 1988 Richard Lenski started twelve populations of E. coli from a single ancestral clone, and every day since, 1 per cent of each has been transferred into fresh medium. That is about 6.6 generations a day, roughly 2,400 a year, and the populations passed 75,000 generations around 2020. Samples are frozen every 500 generations, so any ancestor can be revived and competed directly against any descendant.
The results bear on several of the earlier lessons at once. Fitness relative to the ancestor rose sharply at first and then decelerated, reaching roughly 70 per cent above the ancestor by 50,000 generations, but it has not plateaued: the trajectory fits a slowly rising power law rather than a ceiling, so the populations are still improving after three decades. The twelve populations, which are genetically identical replicates in identical conditions, have converged on similar fitness by different mutational routes, which is drift and mutation supply acting exactly as the eighth lesson describes. Several populations independently evolved elevated mutation rates.
The most-discussed result is that one population, around generation 31,500 and therefore about 2001, acquired the ability to use citrate as a carbon source in the presence of oxygen, a metabolic capacity so consistently absent from E. coli that it is used as a diagnostic character for the species. Replaying the tape from frozen samples showed that the trait only re-evolved from clones taken after about generation 20,000, meaning earlier mutations had potentiated it: the innovation required a specific history, not merely a lucky mutation. That is a direct experimental demonstration of contingency, and it is the closest thing the field has to running the same evolution twice.
What real-time evidence adds
It does not prove common descent, which the fossil and genomic records establish and which no experiment lasting a century could reach. What it does is close the gap between the mechanism and the history.
The reconstruction argues that adaptation arises from variation, heredity and differential reproduction acting over long spans. The real-time work shows those three ingredients producing measurable adaptation on a schedule the algebra predicts in advance, in wild populations under natural selection, in agricultural populations under artificial selection, and in laboratory populations where every variable is recorded. It converts the theory from an account of the past into an instrument used daily: in resistance management, in breeding programmes, in directed evolution of enzymes, and in the design of drug regimens.
It also fixes the scale honestly. What is observed in real time is change within populations and, in a few cases such as the polyploid goatsbeards of the ninth lesson, the origin of a species. Nobody has watched a major body plan appear, and the argument that such changes are the same process running longer rests on the reasoning of the earlier lessons rather than on direct observation.
Which raises the question the final lesson has to answer. If the process is this well understood, what does it still not explain, what are the standing objections worth taking seriously, and where do competent biologists currently disagree?