Continuing Evolution: Study Guide (Biology 7.8)

Evolution has not stopped

It is easy to picture evolution as something that happened in the past: dinosaurs, early humans, the first eukaryotes from Topic 7.7. But evolution means a population’s genetic make-up shifting from one generation to the next, and that is happening now, in every species. You can measure it in genomes, see it in fossil sequences, and watch it in hospitals and farms when drugs or pesticides stop working.

Why evolution keeps going

Evolution continues because its causes never switch off.

1. New variation every generation. DNA copying is not perfect, and some errors escape repair. So every generation carries new mutations. In sexually reproducing species, meiosis and fertilization reshuffle alleles too.
2. Environments change. Climate, food supply, predators, parasites and human activity all shift. A trait that was neutral can become favoured or harmful.
3. Other species evolve. A host evolves defences, its parasites evolve ways round them, and the host is under pressure again. Neither side reaches a finish line.
4. Chance. Genetic drift changes allele frequencies at random, especially in small populations (Topic 7.4).

Remember the unit of change. Individuals do not evolve: an individual keeps its genes for life. Populations evolve, as the proportions of alleles change from one generation to the next.

Evidence 1: genomes change over time

How many new mutations appear? Use round model values for a bacterium:

• genome size: 5.0 × 10⁶ base pairs
• mutation rate: 1 × 10⁻¹⁰ per base pair per cell division

Per genome per division: 5.0 × 10⁶ × 1 × 10⁻¹⁰ = 5 × 10⁻⁴. Most divisions copy the genome perfectly.

Per generation in a population of 10⁹ cells: 5 × 10⁻⁴ × 10⁹ = 5 × 10⁵ new mutations.

At one particular base: 10⁹ cells × 1 × 10⁻¹⁰ = 0.1 per generation, so about one cell in every ten generations carries a change at that exact base.

Each cell is unlikely to mutate, but a large population produces a huge supply of new variants every generation. That is why bacteria, viruses and tumours can evolve within weeks.

Genome change has also been followed directly. A long-term laboratory experiment begun in 1988 has kept 12 populations of the bacterium Escherichia coli growing, about six to seven generations a day, and passed 80 000 generations in 2024. Samples are frozen at intervals, so later populations can be compared with their ancestors. The populations have kept accumulating mutations, and after more than 30 000 generations one population evolved the ability to use citrate in its growth medium as food, something the ancestor could not do in the presence of oxygen.

Evidence 2: continuous change in the fossil record

Where rock layers form one on top of another without long gaps, fossils of one lineage can be followed through time. Many such sequences show traits shifting gradually, layer after layer. Figure 1 shows a fictional example.

Reading Figure 1. Mean diameter rose from 2.10 mm to 3.12 mm over 5 million years: a change of 1.02 mm, or about 0.20 mm per million years. Each step between layers is similar (0.16 to 0.23 mm), so the change was continuous rather than one sudden jump. The trend says nothing by itself about why size increased. A change in water temperature, predators or food could each be tested against other data from the same layers.

Evidence 3: resistance to the chemicals we use

Humans apply strong selection when we use chemicals to kill organisms. The same logic works in four settings.

Chemical	Population under selection	What becomes common
Antibiotic	bacteria in a patient, hospital or farm	bacteria that can destroy, pump out or avoid the drug
Pesticide (insecticide)	insects such as mosquitoes or crop pests	insects with altered target proteins or faster breakdown of the poison
Herbicide	weeds in a field	plants that survive spraying
Chemotherapy drug	cancer cells in one patient’s tumour	tumour cells that survive the drug, so the cancer can return

The steps are always the same (Figure 2).

1. Variation exists first. Random mutations mean a few individuals already have some resistance before the chemical is used.
2. Selection. The chemical kills most susceptible individuals. Resistant ones survive.
3. Inheritance and reproduction. Survivors pass on the resistance alleles. (Bacteria can also gain them from other bacteria by horizontal gene transfer.)
4. Change in frequency. After repeated use, most of the population is resistant and the chemical stops working.

Evidence 4: pathogens and emergent diseases

Viruses and bacteria have huge populations and short generations, so they evolve quickly. An emergent disease is one that is new to a host population, or one that is spreading or changing fast. Pathogen evolution drives this in several ways.

• Host jumps. A pathogen that lives in one species gains mutations that let it infect and spread in another, such as humans.
• Escaping immunity. Changes in surface proteins mean antibodies made against an earlier form no longer bind well. This is why influenza vaccines are updated regularly, and why new variants of the virus that causes COVID-19 kept appearing during the pandemic.
• Becoming resistant. A treatable infection becomes hard to treat when resistance spreads.
• Exchanging genes. Bacteria share genes by horizontal transfer, and viruses that infect the same cell can swap genetic material (Topic 6.7), creating new combinations quickly.

Worked example 1: modelling selection by an antibiotic

Question. Model an infection of 1.0 × 10¹⁰ bacteria. Assume resistance arises by mutation at a rate of 1 × 10⁻⁹ per cell, before any drug is given. During treatment, each day 1% of susceptible cells survive and all resistant cells survive. Ignore growth during treatment.

(a) How many resistant cells are expected before treatment? (b) Find the frequency of resistant cells after each of days 1 to 5. (c) Explain why doctors treat some infections with two drugs at once.

(a) 1.0 × 10¹⁰ × 1 × 10⁻⁹ = 10 resistant cells. Their frequency is 10 ÷ 10¹⁰ = 1 × 10⁻⁹.

(b) Multiply the susceptible number by 0.01 each day. Frequency = resistant ÷ (susceptible + resistant).

Day	Susceptible	Resistant	Frequency of resistant
0	about 1.0 × 10¹⁰	10	1 × 10⁻⁹
1	1.0 × 10⁸	10	1 × 10⁻⁷
2	1.0 × 10⁶	10	1 × 10⁻⁵
3	1.0 × 10⁴	10	about 0.001
4	100	10	10 ÷ 110 ≈ 0.091
5	1	10	10 ÷ 11 ≈ 0.91

The number of resistant cells never changed. Only the susceptible cells were removed. In this model the immune system has not been included; in a real patient it usually clears the few surviving cells. If it cannot, the infection that grows back is almost all resistant.

(c) For a cell to survive two drugs with different targets, it needs two separate resistance mutations. With a rate of 1 × 10⁻⁹ for each, the chance for one cell is 1 × 10⁻⁹ × 1 × 10⁻⁹ = 1 × 10⁻¹⁸. Expected double-resistant cells in the infection: 10¹⁰ × 10⁻¹⁸ = 1 × 10⁻⁸, effectively none. So resistance to the combination is very unlikely to be present at the start. This is the reasoning behind combination treatment for infections such as tuberculosis and HIV.

Check. Each resistant frequency is a number between 0 and 1, and it rises every day while the susceptible count falls by a factor of 100. That matches the model.

Worked example 2: evaluating a study and proposing a better one

Question. Health workers test mosquitoes from two villages. Each mosquito is exposed to a standard dose of an insecticide for one hour, and survival is recorded the next day.

Village	History	Mosquitoes tested	Survived
A	sprayed every year for 6 years	100	62
B	never sprayed	100	9

The report concludes: “Spraying has caused the mosquitoes in Village A to evolve resistance.” (a) Calculate the survival percentages. (b) Evaluate the conclusion. (c) Propose a new investigation that would test it more convincingly.

(a) Village A: 62 ÷ 100 × 100 = 62%. Village B: 9 ÷ 100 × 100 = 9%. Survival is about 6.9 times higher in A.

(b) Evaluation.

• Supports: a large difference that fits selection by spraying. Even unsprayed mosquitoes include some survivors (9%), showing that variation in resistance already exists.
• Weaknesses: there is only one sprayed and one unsprayed village, so any other difference between them (climate, other chemicals, mosquito strain) could explain the result. There is no measurement from Village A before spraying, so we cannot see a change over time. Mosquitoes were caught in the wild, so their survival might reflect conditions while they grew, not inherited traits. Only one dose was tested.

(c) A better investigation (one good design).

1. Question: Has resistance in sprayed populations risen over time, and is it inherited?
2. Sampling: collect mosquitoes from several sprayed and several unsprayed villages, matched for climate where possible, every year.
3. Common rearing: raise the offspring of each sample for one generation under identical laboratory conditions, then test them. Differences that remain are likely to be inherited, not caused by the environment.
4. Independent variable: spraying history. Dependent variable: percentage survival at a range of doses. Controls: same age and sex of mosquitoes, same exposure time, temperature and humidity; an untreated group to measure deaths without insecticide.
5. Prediction if spraying selects resistance: survival rises year by year in sprayed villages but not in unsprayed ones, and the difference persists in lab-raised offspring.

Common misconceptions

• “The drug or pesticide causes the resistance mutation.” Mutations are random. The chemical selects variants that already exist.
• “Individuals become resistant or adapt during their lives.” A bacterium cannot change its genes because it needs to. The population changes as resistant individuals leave more offspring.
• “Evolution only happened in the past.” Genomes, fossils and resistance all show change happening continuously, including now.
• “Humans have stopped evolving because of medicine.” Medicine changes which traits are favoured; it does not stop mutation, drift or selection.
• “Evolution always makes organisms better or more complex.” Populations become better suited to their current environment, which can change again. Losing a feature can also be favoured.
• “Evolution takes millions of years, so we can’t observe it.” In large, fast-breeding populations, measurable evolution can happen in days to years.

Where this leads

Ongoing change means that lineages keep splitting and diverging. Topic 7.9 shows how biologists represent those relationships in trees: go on to Phylogeny. Look back at Common Ancestry for the deep past of eukaryotes. Test yourself with the practice questions, then use the revision notes and the checklist to consolidate.