IB DP Biology: Populations and communities: sampling, growth, interactions (DP Biology)

This study guide covers IB Diploma Programme Biology, section C4.1 (Populations and communities), as set out in the IB Biology guide, first assessment 2025. It teaches C4.1.1 to C4.1.18 and applies to Standard Level and Higher Level alike, because the guide gives C4.1 no additional higher level content. Use the revision notes for quick recall and the practice set to test yourself. Related: abiotic factors and tolerance and niches and feeding. Hub: IB DP Biology; checklist; free diagnostics.

What C4.1 covers

Section	What you must be able to do	Level
C4.1.1	Define a population; reproductive isolation	SL and HL
C4.1.2	Why sample; why randomly	SL and HL
C4.1.3	Random quadrats; standard deviation	SL and HL
C4.1.4	Capture-mark-release-recapture; Lincoln index; assumptions	SL and HL
C4.1.5 to C4.1.6	Carrying capacity; density-dependent negative feedback	SL and HL
C4.1.7 to C4.1.8	Growth curves; logarithmic test; sigmoid model	SL and HL
C4.1.9	Intraspecific competition and cooperation	SL and HL
C4.1.10 to C4.1.12	Community; six interspecific relationships; mutualism	SL and HL
C4.1.13 to C4.1.15	Invasive species; testing for competition; chi-squared	SL and HL
C4.1.16 to C4.1.18	Predator-prey case study; top-down and bottom-up; allelopathy	SL and HL

C4.1.1 Populations

A population is a group of individuals of one species that live together in an area and normally breed with each other. Genes therefore flow within the group. Two populations of one species are told apart by reproductive isolation: little or no interbreeding between them, for example across a mountain range or open sea.

C4.1.2 Estimating population size

Counting every individual is usually impossible: the area may be large, the organisms small or hidden, and numbers may change during a long count. So you sample and scale up.

Sampling must be random. If you choose sites by eye you tend to pick places that look crowded or convenient, which biases the estimate. Random numbers used as coordinates remove that choice.

A sample never matches the whole population exactly. The gap between the estimate and the true population size is the sampling error. More samples usually reduce it but never remove it.

C4.1.3 Random quadrat sampling (sessile organisms)

Quadrats suit sessile organisms such as plants, barnacles or mussels, where each individual can be counted. Method:

1. Lay two tape measures along the edges of the study area.
2. Use pairs of random numbers as coordinates and place a quadrat of known area at each.
3. Count the individuals in every quadrat, then find the mean per quadrat.

Estimated population = (mean per quadrat ÷ quadrat area) × total area.

The standard deviation of the mean number per quadrat shows how spread out the counts are. A small value means the organisms are spread fairly evenly; a large value means they are clumped or patchy. You do not need to memorise the formula.

Worked example. Ten random 0.25 m² quadrats on a rocky shore strip of 60 m² contain these barnacle counts: 12, 9, 15, 11, 8, 14, 10, 13, 7, 16.

• Mean = 115 ÷ 10 = 11.5 barnacles per quadrat.
• Density = 11.5 ÷ 0.25 = 46 per m².
• Estimated population = 46 × 60 = 2760 barnacles.
• Standard deviation = 3.03, about a quarter of the mean, so the barnacles are fairly evenly spread.

C4.1.4 Capture, mark, release, recapture

Motile animals move, so quadrats fail. Instead:

1. Capture as many as you can; count them (M).
2. Mark each harmlessly and release them.
3. Allow time to mix, then capture a second sample; count the total (N).
4. Count how many in the second sample are marked (R).

Lincoln index: population estimate = M × N ÷ R.

The logic is proportion: the marked fraction of the second sample (R ÷ N) matches the marked fraction of the whole population (M ÷ population).

Worked example. 48 ground beetles are marked and released. The next day 40 are caught and 8 carry marks.

• Estimate = 48 × 40 ÷ 8 = 240 beetles.

Assumptions to state:

• Marked individuals mix fully and evenly with the rest.
• The population is closed: no births, deaths, immigration or emigration between samples.
• Marking does not harm the animal or change its chance of being caught again, and the mark stays on.
• Enough are recaptured: a very small R makes the estimate unreliable. If marks wear off, R is too low and the estimate too high.

C4.1.5 and C4.1.6 Carrying capacity and negative feedback

Carrying capacity is the largest population of a species that an environment can support indefinitely. Limiting resources include food, water, light, nesting sites, space and mineral nutrients.

Density-independent factors act regardless of how crowded the population is, such as drought, fire or severe frost. They make numbers fluctuate. Density-dependent factors act more strongly as density rises:

• Competition for limited resources increases.
• Predators find prey more easily and take more.
• Pathogens and pests pass between crowded hosts more readily.

These factors work as negative feedback. Above carrying capacity, deaths rise or births fall. Below it, competition eases and the population grows. Numbers tend to return towards carrying capacity.

C4.1.7 and C4.1.8 Population growth curves

A population that colonises a new habitat with plenty of resources shows exponential growth first. Each generation produces more offspring because there are more parents, and birth rates exceed death rates.

To test whether growth is exponential, plot the population on a logarithmic vertical axis against time on an ordinary horizontal axis. Exponential growth gives a straight line, because a constant percentage increase per time interval is a constant increase in the logarithm.

As resources and space become scarce, density-dependent factors reduce the growth rate. The curve flattens and levels off near carrying capacity. This gives a sigmoid (S-shaped) curve with three parts: exponential growth, a transition where growth slows, and a plateau where births roughly equal deaths. The guide does not expect a lag phase.

Worked example. Yeast in a flask was counted every 2 hours, in millions of cells per mL: 0.5, 1, 2, 4, 8, 16, 32.

• Each value is double the one before, so the percentage increase per interval is constant at 100%.
• log₁₀ of the counts: −0.30, 0.00, 0.30, 0.60, 0.90, 1.20, 1.51. The values rise by 0.30 each time, so a log graph is a straight line. Growth is exponential in this period.

For practical work use yeast or duckweed: count cells or fronds at regular intervals. The sigmoid curve is an idealised model of a complex system. Case study: about 29 reindeer were released on St Matthew Island, Alaska, in 1944 and grew to roughly 6000 by 1963, then crashed to a few dozen after overgrazing of lichen. Real populations can overshoot, oscillate or crash.

C4.1.9 Intraspecific relationships

Members of one species have identical needs, so they compete for food, mates, territory, light, water and nesting sites. Examples: seedlings competing for light; male red deer competing for mates.

Cooperation helps individuals survive and reproduce. Examples: wolves hunting in packs; meerkats posting sentries; honeybees sharing tasks in a colony.

C4.1.10 and C4.1.11 Communities and interspecific relationships

A community is all the populations of different species (plants, animals, fungi and bacteria) interacting in an ecosystem. The six categories:

Relationship	Description	Example
Herbivory	Animal eats plant tissue	Caterpillars eating leaves
Predation	Predator kills and eats prey	Lions and zebra
Interspecific competition	Different species use the same limited resource; both are harmed	Two seed-eating finch species
Mutualism	Both species benefit	Legumes and nitrogen-fixing bacteria
Parasitism	Parasite lives on or in a host, gaining nutrients; host harmed	Tapeworm in a human gut
Pathogenicity	A microorganism or virus causes disease in its host	Malarial parasite Plasmodium causing malaria

C4.1.12 Mutualism

• Legume root nodules (Fabaceae). Rhizobium bacteria in nodules fix nitrogen gas into ammonia the plant can use. The plant supplies carbohydrates from photosynthesis and a protected, low-oxygen environment.
• Orchid mycorrhizae (Orchidaceae). Orchid seeds are tiny with almost no food store. Fungal hyphae enter the seedling and supply organic carbon and mineral nutrients so it can grow. In return the fungus gains a habitat and, in many species, carbon from the mature plant. The fungal benefit is less clear than in other mycorrhizae.
• Zooxanthellae in hard corals. These photosynthetic dinoflagellates live inside coral tissue. They supply sugars and other organic compounds. The coral supplies shelter, light exposure near the surface, carbon dioxide and nitrogen-containing wastes.

C4.1.13 Endemic and invasive species

An introduced species becomes invasive when it spreads and displaces endemic species. Competitive advantage in acquiring resources is often the basis.

Case study. The grey squirrel, introduced to Britain from North America, has replaced the native red squirrel across much of England. Grey squirrels digest acorns more efficiently, gain weight faster and survive at higher densities, so they use the shared food supply more completely. Grey squirrels also carry squirrelpox virus, which harms red squirrels more.

C4.1.14 and C4.1.15 Testing for interspecific competition

Competition is indicated, not proven, if a species does better when another is absent. Approaches:

• Laboratory experiments: grow two species alone and together under controlled conditions.
• Field observation: random sampling to see whether the species occur together less often than chance.
• Field manipulation: remove one species and monitor the other.

An experiment changes one variable and controls the rest, so it can show cause. An observation records what exists, so it shows association only.

Chi-squared test for association. Record presence or absence of two species at many sites. The null hypothesis is that the species are distributed independently.

Expected value for each cell = row total × column total ÷ grand total. Then χ² = Σ (O − E)² ÷ E. Degrees of freedom = (rows − 1) × (columns − 1), which is 1 for a 2 × 2 table. Compare with the critical value (3.84 at 5% significance for 1 degree of freedom).

Worked example. 80 sites sampled for species A and B.

	B present	B absent	Total
A present	6	26	32
A absent	22	26	48
Total	28	52	80

Expected values: 11.2, 20.8, 16.8, 31.2. χ² = 2.41 + 1.30 + 1.61 + 0.87 = 6.19 (3 significant figures). This exceeds 3.84, so reject the null hypothesis: the distribution is not independent. Both together (6) is below expected (11.2), a negative association consistent with competition. It does not prove competition: the species might simply prefer different soil or moisture.

C4.1.16 Predators and prey

Predator and prey numbers are linked by delayed negative feedback. More prey supports more predators; more predators cut prey numbers; predators then starve and fall, and prey recover.

Case study: snowshoe hare and Canada lynx. Long fur-trading records from northern Canada show both populations rising and falling in cycles of roughly ten years, with lynx peaks following hare peaks by a year or so. Later field studies suggest hare food supply matters too, so predation is not the only driver.

C4.1.17 Top-down and bottom-up control

Top-down control: higher trophic levels limit lower ones, as predators limit herbivores. Bottom-up control: resources limit upward, as nutrient supply limits producers, and so everything that feeds on them. Both occur; one is usually dominant in a given community.

C4.1.18 Allelopathy and antibiotics

Both release a chemical that deters competitors.

• Allelopathy: a plant releases chemicals that inhibit neighbouring plants. Black walnut trees release juglone, which stunts many plants growing nearby.
• Antibiotic secretion: microorganisms secrete substances that kill or inhibit rivals. Penicillium fungi secrete penicillin, which prevents bacteria building cell walls.

Common errors

• Defining carrying capacity as the maximum number ever reached, rather than what the environment can sustain.
• Using Lincoln index with M and R swapped.
• Saying a chi-squared result proves competition.
• Confusing a parasite with a pathogen (causes disease).

Official syllabus

IB Diploma Programme Biology guide, first assessment 2025, published by the International Baccalaureate Organization. Section C4.1, Populations and communities.