Continuing Evolution: Practice Questions (Biology 7.8)

Answer: (A). Mean length rose at every step (by 0.11, 0.09, 0.12 and 0.09 mm), a total of 0.41 mm over 2 million years. Steady, step-by-step change through successive layers is the kind of continuous change in the fossil record that shows a lineage evolving over time.

• (B) describes change within individuals being inherited. Evolution is a change in the population across generations; a trait gained during life is not passed on in DNA.
• (C) is contradicted by the data: the mean kept rising after 3.0 million years ago (3.40 to 3.52 to 3.61 mm).
• (D) may be a reasonable hypothesis, but the table shows only the trend, not its cause. Other evidence from the same layers, such as fossil plants, would be needed to test it.

Answer: (C). Expected mutants = population × rate = 2.0 × 10⁹ × 5 × 10⁻⁹ = 10.

• (A) divides the rate by the population (5 × 10⁻⁹ ÷ 2.0 × 10⁹). The result is far too small to count anything.
• (B) is the misconception that the drug triggers resistance. Mutations arise with or without the drug.
• (D) divides the population by the rate (2.0 × 10⁹ ÷ 5 × 10⁻⁹), giving more resistant cells than there are cells.

Answer: (A). A tumour is a large, varied population of dividing cells. The drug kills susceptible cells, leaving resistant variants to regrow the tumour. This is natural selection inside one patient.

• (B) Mutations are not directed towards what a cell needs. Variation arises at random; selection then acts on it.
• (C) The patient’s normal body cells are not the population under selection, and a body does not evolve during one lifetime.
• (D) Cells do not learn; resistance depends on inherited changes in the cells’ DNA or gene expression.

(a) Year 0: 2% of 500 = 0.02 × 500 = 10 seedlings. Year 8: 71% of 500 = 0.71 × 500 = 355 seedlings.

(b) Survival rises every two years, from 2% to 71%: an increase of 69 percentage points, about 36 times the starting value. The rise is slow at first and fastest between years 6 and 8 (+33 percentage points).

(c) Some weeds were resistant before spraying began (2% survived in year 0). Spraying killed most susceptible plants, so resistant plants produced most of the seeds. Each year the resistance allele became more common in the seed bank, so more seedlings survived the test. The rise speeds up because, as resistant plants become common, more of the next generation comes from them.

(d) Survival in the test would probably fall slowly. Without herbicide, resistant plants are at a disadvantage because they grow more slowly, so susceptible plants leave more seeds and the resistance allele becomes less common. The fall is likely to be gradual, and resistance would rise quickly again if spraying restarted.

(a) Any one: the data show a correlation only; other things changed over ten years (patients arriving already carrying resistant bacteria, hygiene practice, use of other antibiotics, how infections were tested); there was no comparison hospital that did not increase its use of X.

(b) One good design:

1. Start from a single susceptible ancestor, so all populations begin genetically identical. Freeze a sample of the ancestor.
2. Grow, for example, six replicate populations in medium with a low dose of antibiotic X and six in identical medium without it. Transfer a small sample to fresh medium every day for several weeks.
3. Independent variable: presence or absence of antibiotic X. Dependent variable: the proportion of cells able to grow at a standard test dose of X (or the lowest dose that stops growth), measured at regular intervals.
4. Controlled variables (any two): temperature, medium and volume, size of the daily transfer, number of transfers, starting population size.
5. Freeze samples at intervals so later populations can be compared directly with the ancestor.

(c) Resistance rises in the populations exposed to X, consistently across the replicates, but not, or much less, in the unexposed populations.

(a) Virus copying produces many random mutations. In pigs, a few variants by chance had surface-protein changes that let them bind and enter pig (and then human) cells better. These variants infected more hosts and produced more copies, so they became common. Once a variant could also pass efficiently from person to person, selection in humans favoured further changes that improved spread.

(b) Mutations keep changing the surface protein. Antibodies made after vaccination fit this year’s protein shape. Variants with altered shapes bind those antibodies less well, so they infect vaccinated people more often and are favoured. Over time, these variants become common and the vaccine matches less well.

(c) Faster in the large town. More infected people means a larger virus population, so more new mutations arise each generation, and more chances of transmission make selection more effective. In a small village, the outbreak may die out quickly, and chance (drift) has a larger effect.

(a) 0 to 2000: (1.18 − 1.00) ÷ 2000 × 1000 = 0.09 per 1000 generations. 20 000 to 40 000: (1.64 − 1.53) ÷ 20 000 × 1000 = 0.0055 per 1000 generations.

(b) The claim is not supported. Fitness kept rising after generation 10 000, from 1.42 to 1.53 and then to 1.64. The rate of gain slowed, about sixteen times slower at the end than at the start (0.09 ÷ 0.0055 ≈ 16), but it did not stop.

(c) Per genome per generation: 4.6 × 10⁶ × 1 × 10⁻¹⁰ = 4.6 × 10⁻⁴. Whole population: 4.6 × 10⁻⁴ × 5 × 10⁸ = 2.3 × 10⁵ new mutations per generation. Single line of descent over 40 000 generations: 4.6 × 10⁻⁴ × 40 000 = 18.4, about 18 mutations.

(d) Early on, mutations with large benefits were available and spread quickly. Once those had been fixed, the remaining beneficial mutations had smaller effects, so each gain was smaller. New mutations keep arising (part c), so further gains remain possible.