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Population ecology examines how populations of organisms change in size over time and the abiotic and biotic factors that drive those changes. At A-Level, the topic operates as a hinge between physiology — the cellular and organismal mechanisms studied earlier in the course — and the system-level questions of ecosystem function, biodiversity and conservation that fill the rest of Section 3.7. A robust grasp of population ecology underpins every applied question on managing fisheries, controlling invasive species, modelling the spread of pathogens, and predicting how communities will respond to climate change.
Spec mapping: This lesson sits in AQA 7402 Section 3.7.4 (populations in ecosystems), with antecedent content in Section 3.7.3 (speciation) and forward links into Sections 3.7.5 (succession and conservation) and the ecosystem-energetics material of Section 3.5.3. Refer to the official AQA specification document for the exact wording of each subsection.
Connects to: Mark-release-recapture statistical analysis using chi-squared (course 8 lesson 0 The Chi-Squared Test, Section 3.7.2); succession and the long-term consequences of intraspecific and interspecific competition (lesson 1 of this course); energy transfer between trophic levels — a direct consequence of how primary-consumer populations are limited by producer biomass (lesson 3 of this course).
Key Definition: A population is a group of organisms of the same species occupying a defined geographical area at a defined time, whose members can interbreed and produce fertile offspring. The closed-population assumption (no immigration or emigration) is an idealisation rarely met in the field; in practice we work with statistical populations sampled within an arbitrary boundary.
The everyday-English use of "population" is loose; in ecology the term is technical. A population is bounded by three constraints: a single biological species, a defined spatial area, and a defined temporal window. The Soay sheep of Hirta in the St Kilda archipelago are a population because the island provides a natural geographical boundary, the species identity is fixed, and the count is taken in a defined year. The "British robin population" is a population in the broader, statistical sense — but the boundary is administrative rather than ecological.
Three quantities are normally tracked:
Population change between two time points is summarised by the balance equation:
ΔN = (B + I) − (D + E)
where B is births, D is deaths, I is immigration and E is emigration. In a closed population (I = E = 0) growth is driven solely by the difference between B and D. When B > D the population grows; when D > B it declines; when B = D it is stationary.
When a small founding population colonises a new habitat with abundant resources, it typically passes through a characteristic sequence of growth phases. The mathematical model of this trajectory is the logistic curve; biologists call its graphical form the sigmoid (S-shaped) curve.
flowchart LR
A["Lag phase<br/>(slow growth)"] --> B["Exponential phase<br/>(B >> D, near-doubling)"]
B --> C["Stationary phase<br/>(B = D, density at K)"]
C --> D["Possible decline<br/>(D > B if conditions deteriorate)"]
1. Lag phase. The population is small. Even if individual reproductive output is high, the absolute number of births is small because there are few reproducing individuals. Acclimatisation to local conditions, mate-finding limitations (the Allee effect) and the slow accumulation of microhabitat modification all slow early growth.
2. Exponential (log) phase. Resources are plentiful, limiting factors are weak, and each individual achieves close to its maximum reproductive output. The per-capita growth rate r is approximately constant, so dN/dt = rN; population size doubles at regular intervals. This phase rarely lasts long in nature but is well documented for bacteria in fresh nutrient broth, for the rabbit population of Australia after introduction, and for invasive species in newly-colonised habitats lacking specialist predators.
3. Stationary (plateau) phase. Density-dependent factors — intraspecific competition for food, water and space; build-up of disease; territoriality; behavioural stress — increase the death rate and reduce the birth rate. When B = D the population stabilises near the carrying capacity (K) of the habitat. Around K, the population fluctuates rather than sitting at a perfectly constant value.
4. Possible decline phase. If conditions deteriorate (food supply crashes, disease sweeps the population, the habitat is degraded, a new predator arrives), D > B and N falls. Whether the population recovers depends on whether the cause is transient or persistent.
The continuous logistic model is:
dN/dt = rN(1 − N/K)
where r is the intrinsic per-capita growth rate and K is carrying capacity. At small N the bracketed factor is close to 1 and growth is near-exponential; as N → K the bracketed factor approaches 0 and growth halts. This is a hypothesis-generating idealisation, not a literal description: real populations overshoot K, oscillate, and exhibit time lags. Beyond-spec: the discrete-time logistic map rN(1 − N/K) generates period-doubling and chaotic dynamics for r above ~3, an early demonstration of deterministic chaos in ecology.
K is the long-run average maximum population size that an environment can sustain given prevailing conditions. Several features of K are routinely tested:
A J-shaped curve corresponds to dN/dt = rN with no upper bound. Sustained J-shaped growth is rarely observed in the field but appears in:
The mismatch between J-shaped potential and S-shaped reality is the gap into which limiting factors fit.
Limiting factors are classified along two orthogonal axes: biotic vs abiotic (the nature of the factor) and density-dependent vs density-independent (how the factor's effect changes with population density). These axes intersect rather than coincide — predation is biotic and usually density-dependent; a frost is abiotic and density-independent; but a drought-driven contraction of waterholes is abiotic and density-dependent in its effect.
This distinction is a common source of mark-loss at A-Level. The test is mechanistic: does the per-capita effect of the factor change as N changes?
| Factor | Type | Mechanism |
|---|---|---|
| Intraspecific competition | Density-dependent | Per-capita resource share falls as N rises |
| Predation (specialist) | Density-dependent | Functional and numerical responses |
| Infectious disease | Density-dependent | Transmission rises with contact rate ∝ N |
| Drought | Mostly density-independent | Affects all individuals regardless of N |
| Frost / fire | Density-independent | Whole-population kill, unrelated to density |
| Habitat destruction | Density-independent | A bulldozer does not check the local density |
Density-dependent factors act as negative-feedback controls that keep the population near K. Density-independent factors generate stochastic crashes that may push the population well above or below K.
A-Level misconception watch. Students often classify factors by "biotic vs abiotic" instead of by mechanism. A drought is abiotic and mostly density-independent — but if it shrinks a waterhole and forces every animal to drink at the same spot, its density-dependent component can dominate. Always argue from mechanism.
Competition occurs when two or more organisms require the same limited resource and the use of that resource by one reduces availability for the other. It is operationally divided into exploitation competition (using up the resource) and interference competition (preventing access — territoriality, allelopathy, aggression).
Members of the same species share identical resource requirements, so competition is intense. As N increases, intraspecific competition tightens, the per-capita growth rate falls, and the population is pulled back toward K. Examples:
When two species have overlapping niches, three outcomes are possible:
The grey-squirrel / red-squirrel system in Britain illustrates competitive exclusion driven by both exploitation competition (greys handle large acorns better than reds) and apparent competition mediated by squirrelpox (greys carry the virus asymptomatically; reds are highly susceptible).
Beyond-spec extension. Lotka–Volterra competition equations introduce competition coefficients α and β quantifying the per-capita effect of one species on the other. The stable-coexistence condition (α and β both < 1) is the simplest mathematical statement of niche differentiation. AQA does not examine the equations, but using the framework verbally adds A* depth.
Predator and prey populations are coupled by feeding interactions and frequently show linked oscillations — out-of-phase cycles in which predator peaks follow prey peaks with a time lag.
flowchart LR
P1["Prey rises"] --> P2["Predator rises (lag)"]
P2 --> P3["Prey falls (heavy predation)"]
P3 --> P4["Predator falls (lag)"]
P4 --> P1
The Hudson's Bay Company fur-trade records of lynx and snowshoe hare across nineteenth-century Canada — paraphrased from the canonical reanalyses — show roughly 10-year cycles with the lynx peak trailing the hare peak. This data set is the most-cited natural example of predator-prey coupling, though modern reinterpretations stress that the hare cycle is driven by interactions among hares, predators and food quality, not by lynx alone.
Beyond spec, but a useful framework:
These coupled differential equations generate neutral oscillations. Real systems differ — predators saturate at high prey density (Type II functional response), and density-dependence in either species damps the cycles — but the coupling and time lag are the essential phenomena students must explain.
Predators rarely drive prey to extinction in stable systems because predation pressure relaxes as prey numbers fall. Specialist predators that cannot switch prey can, however, follow their prey into local extinction. In invaded systems where the predator is naive — for instance, native marsupials in Australia confronted by introduced cats and foxes — the absence of coevolved defences means the prey suffers steep declines without the dampening feedback.
Population size is rarely known exactly; it is estimated. The methods divide by whether the organism is sessile (use quadrats — covered in lesson 4) or mobile (use mark-release-recapture, MRR — covered here and revisited in lesson 4).
MRR is used for mobile animals — woodlice, snails, beetles, fish, small mammals, birds.
Method.
Under the assumption that the ratio of marked to total individuals in the second sample equals the ratio of all marked individuals to the whole population, we have m₂/n₂ = n₁/N, which rearranges to:
N = (n₁ × n₂) / m₂
The intuition is straightforward: if 40 marked woodlice represent the same fraction of the second sample as they do of the whole population, the whole population can be back-calculated.
A researcher captures 40 woodlice, marks them, and releases them. A week later, 50 woodlice are caught of which 8 are marked.
N = (40 × 50) / 8 = 250 woodlice.
The estimate is valid only if:
If marks are lost, m₂ is too low and N is overestimated. If marked individuals are eaten preferentially (paint dots make snails conspicuous to thrushes), m₂ is again too low and N is again overestimated. If marked individuals are trap-shy on the second capture, m₂ is too low and N is overestimated. If the population gains individuals between samples (births / immigration), N is overestimated relative to the original population.
Exam Tip. A 6-mark question asking you to evaluate MRR will reward step-by-step reasoning: identify the assumption violated, trace its effect on m₂, deduce the direction of bias in N. Marks are lost where students assert a direction of bias without explaining the mechanism.
Question (6 marks). Scientists used mark-release-recapture to estimate the population of harvest mice in a meadow. They captured, marked and released 80 mice. Two weeks later, they caught 64 mice of which 16 were marked. (a) Calculate the estimated population size. (b) Suggest two reasons why the estimate might be inaccurate.
AO breakdown. AO2 application (calculation) 2 marks; AO1/AO3 evaluation of method 4 marks.
Grade C response. (a) N = (80 × 64) / 16 = 320 mice (M1 — formula correctly applied; M1 — correct answer with units). (b) Some marked mice may have died. Some mice may have entered or left the meadow.
Examiner commentary: The calculation is correct and earns both M1 marks. The two evaluation points are correct but generic; both relate to violations of the closed-population assumption. A Grade C answer typically stops at naming the issue without linking it to the direction of bias in N. To climb the grade ladder the candidate must say which way each error pushes the estimate and why.
Grade A response.* (a) N = (80 × 64) / 16 = 320 mice (M1 — correct formula; M1 — correct answer). (b) First, the two-week interval allows time for births, deaths, immigration and emigration; if marked individuals have died, m₂ underestimates the true proportion marked, so the calculated N is an overestimate of the original marked-release population (M1). Second, marked mice may be more easily detected by predators (paint marks reduce camouflage), so marked-individual mortality is higher than unmarked, m₂ falls disproportionately, and N is again overestimated (M1). The researchers should reduce the interval between samples and use an inconspicuous marking method (toe-clip, hair-clip, or PIT tag) (additional A* depth — recommends practical mitigation).
Examiner commentary: The answer earns full marks because each evaluative point follows the pattern assumption → mechanism → directional effect on N → practical fix. This sequencing is the A* move — the Grade C answer asserts the failure; the A* answer reasons through it. Naming a specific marking method that addresses the failure (PIT tag) is the kind of applied-biology touch that distinguishes A from A*.
Beyond the lag/log/stationary curve, populations differ systematically in how mortality is distributed across the lifespan. Three classic survivorship curves are recognised:
These patterns map onto the r-selected vs K-selected classification (paraphrased framework — beyond spec but commonly discussed in undergraduate ecology):
| Trait | r-selected | K-selected |
|---|---|---|
| Reproductive output | High | Low |
| Body size | Small | Large |
| Lifespan | Short | Long |
| Parental care | Minimal | Extensive |
| Habitat | Unstable / unpredictable | Stable |
r-selected species do well at the early, exponential stage of colonisation; K-selected species dominate stable, late-successional habitats. The framework links lesson 0 (population dynamics) to lesson 1 (succession).
A team monitored a newly-introduced population of Daphnia magna in an experimental pond over 30 weeks. Counts at 5-week intervals were: week 0 — 20; week 5 — 80; week 10 — 450; week 15 — 1,800; week 20 — 3,200; week 25 — 3,400; week 30 — 3,350.
Step 1 — Identify phase boundaries. Between weeks 0 and 5, growth is modest (×4) — the lag phase. Between weeks 5 and 15, growth is rapid (×~22) — the exponential phase. Between weeks 15 and 25, the rate of increase falls sharply and the population plateaus around 3,300–3,400 — the stationary phase. The carrying capacity K under these conditions is approximately 3,400 individuals.
Step 2 — Compute approximate per-capita growth rate (r). Between weeks 5 and 10 the population rose from 80 to 450, a factor of ~5.6 in 5 weeks. The continuous per-capita rate r ≈ ln(5.6)/5 ≈ 0.34 per week. This is the maximum r realised in the experiment; r is highest when N is small and limiting factors are weakest.
Step 3 — Suggest the limiting factor. Daphnia eat algae; as N approaches K, per-capita algal availability falls — classic intraspecific exploitation competition for food. Build-up of metabolic waste in a closed pond is a plausible additional density-dependent factor.
Step 4 — Predict the response to perturbation. Doubling the algal supply would shift K upward (K depends on the limiting resource); removing 50% of the population would trigger a renewed exponential phase until N approaches K again.
This style of structured reasoning — phase identification, parameter estimation, mechanism, prediction — is the A* template for any field-data interpretation question.
| Quantity | Symbol | Units | Typical magnitude |
|---|---|---|---|
| Population size | N | individuals | Variable |
| Per-capita growth rate | r | week⁻¹ or year⁻¹ | 0.01–10 depending on taxon |
| Carrying capacity | K | individuals per habitat | Species-and-habitat-specific |
| Doubling time during exponential | ln(2)/r | weeks or years | Hours (bacteria) to years (large mammals) |
| Survivorship-curve type | I, II, III | — | Maps to r/K-selected continuum |
| Limiting factor | Density-dependence | Mechanism |
|---|---|---|
| Intraspecific competition | Density-dependent | Per-capita resource share falls with N |
| Predation by specialist | Density-dependent | Functional and numerical responses |
| Disease | Density-dependent | Transmission ∝ contact rate ∝ N |
| Drought | Mostly density-independent | Whole-population effect |
| Fire | Density-independent | Whole-population effect |
| Frost | Density-independent | Whole-population effect |
Spec alignment. This lesson covers AQA 7402 Section 3.7.4 (populations in ecosystems) — see the official AQA specification document for exact wording. Synoptic links to Section 3.7.2 statistical analysis (chi-squared on dispersion data), Section 3.7.5 (succession and conservation), and Section 3.5.3 (energy transfer between trophic levels) are signposted above. Required Practical 11 (distribution of a species and statistical testing) is anchored in lesson 4 of this course.