QCE Biology - Unit 3 - Biodiversity and populations
Population Growth and Reproductive Strategies | QCE Biology
Learn exponential and logistic growth, population change calculations, carrying capacity links and r- and K-selected strategies.
Updated 2026-05-18 - 6 min read
QCAA official coverage - Biology 2025 v1.3
Exact syllabus points covered
- Identify and explain different modes of population growth, including exponential growth (J-curve).
- Identify and explain different modes of population growth, including logistic growth (S-curve).
- Compare the reproductive strategies and growth curves of K- and r- strategists.
- Calculate population growth rate and change using birth, death, immigration and emigration data.
Population growth and reproductive strategies is part of the way QCE Biology turns living systems into evidence students can describe, analyse and evaluate. The safest way to study it is to connect each term to a data pattern, a biological mechanism and a limitation.
Original Sylligence diagram for biology population growth.
Core explanation
Exponential growth
Exponential growth occurs when resources are effectively unlimited and the per-capita growth rate stays high. The curve becomes steeper as the population becomes larger.
Logistic growth
Logistic growth slows as limiting factors increase. The curve approaches carrying capacity, where births plus immigration roughly balance deaths plus emigration.
Population change
Population change can be estimated from births, deaths, immigration and emigration. The key is to separate entries into the population from exits.
r- and K-selected strategies
r-selected species tend to produce many offspring with lower parental investment. K-selected species tend to produce fewer offspring with higher survival investment and populations closer to carrying capacity.
Reproductive methods and life-history trade-offs
Sexual reproduction mixes alleles through meiosis and fertilisation. It can increase genetic variation, which gives natural selection more variation to act on when environments change. Its costs include needing a mate, producing fewer offspring in the same time and spending energy on courtship or gamete production.
Asexual reproduction produces offspring from one parent without fusion of gametes. It is fast and efficient in stable environments because every individual can reproduce, but low genetic variation can make the population more vulnerable to disease or environmental change.
| Feature | Sexual reproduction | Asexual reproduction | | --- | --- | --- | | Genetic variation | Usually high because alleles are recombined | Usually low unless mutation occurs | | Speed | Often slower | Often faster | | Mate requirement | Usually requires two compatible gametes | Does not require a mate | | Best suited to | Changing environments | Stable environments where the parent genotype is successful |
r- and K-selection are endpoints on a continuum, not labels that perfectly classify every species. r-selected strategies are favoured where disturbance, mortality and empty habitat are common. K-selected strategies are favoured where competition near carrying capacity is strong.
| Trait | r-selected tendency | K-selected tendency | | --- | --- | --- | | Offspring number | Many | Few | | Offspring size | Small | Larger | | Parental care | Low or absent | Higher | | Life span | Shorter | Longer | | Population pattern | Boom-bust or colonising | More stable near carrying capacity |
Interpreting growth curves
Exponential growth produces a J-shaped curve when the growth rate compounds through time. It can occur after colonisation, after a limiting factor is removed or in laboratory cultures with abundant resources. In natural ecosystems it usually cannot continue indefinitely because food, water, shelter, nesting sites, oxygen, waste build-up, disease or predation become limiting.
Logistic growth produces an S-shaped curve. The early phase can be slow if the population is small and mates are hard to find. The middle phase rises quickly while resources are still available. The late phase slows as density-dependent limiting factors increase. Populations may overshoot carrying capacity, then decline, especially if reproduction responds slowly to resource depletion.
Population growth rate is affected by births, deaths, immigration and emigration, but the biological explanation should go further than the arithmetic. A rise in births could reflect more resources, improved mating success or reduced stress. A rise in deaths could reflect predation, disease, heat stress or food shortage. In exam data, the same curve shape can have different causes, so use the context.
r/K-selection is a useful model, but many species fall between the extremes. For example, some organisms produce many offspring and still show parental care, while others shift strategy depending on environmental conditions.
Sea turtles illustrate why the model is a tendency rather than a rule. They produce many eggs and most hatchlings die before adulthood, which resembles an r-selected strategy, but adults are long-lived and delayed in reproduction, which resembles a K-selected trait. In exam answers, describe the specific traits shown in the data rather than forcing the whole species into one label.
How to use this in data questions
Start by identifying what has been measured. In Biology, a graph or table is rarely just asking for a trend; it is asking whether you can connect the trend to a process. Quote enough data to show the pattern, then use the concept language from the syllabus. If the evidence is limited, name the limitation precisely: sample size, sampling method, uncontrolled variables, measurement precision, population choice or the time scale of the data.
A useful study habit is to turn each heading into a data prompt. Ask what you would expect to happen if the relevant variable increased, decreased or was removed. For ecology topics, think about abundance, distribution, biodiversity, biomass and carrying capacity. For genetics topics, think about genotype, phenotype, gene expression, allele frequency and inheritance pattern. For evolution topics, think about variation, selection pressure, gene flow, isolation and relatedness.
When a question asks you to evaluate, do not just list problems with the experiment. Link the limitation to the confidence of the conclusion. For example, a small sample size matters because a few unusual individuals can distort the pattern. An uncontrolled abiotic factor matters because it gives another possible explanation for the same biological trend. This is the difference between naming a limitation and using it scientifically.
Worked example
Common exam traps
Other traps to watch for:
- using a general word when a syllabus term is available
- ignoring units, sample size or time scale
- treating a model as a perfect copy of the real ecosystem or cell
- writing a memorised paragraph that does not use the given data