HSC Exam Practice

Sample response. Carrying capacity (K) is the maximum number of individuals of a species that an environment can sustainably support, given its available food, water, shelter and other resources. Carrying capacity is not fixed because it changes with environmental conditions: a drought reduces food and water availability, lowering K, while a period of high rainfall and plant growth increases food supply, raising K.

Marking criteria. 1 mark — defines K as the maximum sustainable population determined by resource availability. 1 mark — explains that K is variable because environmental conditions (e.g. rainfall, food supply, disease) change over time.

Sample response. Exponential growth occurs when resources are unlimited: the population grows at an ever-increasing rate, producing a J-shaped curve on a population-vs-time graph. The rate of increase is proportional to population size, so the curve becomes steeper over time. Logistic growth occurs when resources are limited: growth rate slows as the population approaches K, producing an S-shaped (sigmoidal) curve. The S-curve shows a lag phase, a rapid growth phase when resources are still adequate, and a deceleration phase as resources become scarce, until the population stabilises near K.

Marking criteria. 1 mark — exponential: J-shaped curve, unlimited resources, rate proportional to current size. 1 mark — logistic: S-shaped curve, limited resources, growth slows as population approaches K. 1 mark — correct identification and description of at least two distinct phases of the S-curve (e.g. lag, rapid growth, deceleration, plateau).

Sample response. The four factors are natality (births), mortality (deaths), immigration (individuals entering) and emigration (individuals leaving). Their relationship is expressed as: Population change = (natality + immigration) − (mortality + emigration). If natality + immigration exceeds mortality + emigration, the population grows; if less, it declines; if equal, it is stable.

Marking criteria. 1 mark — names all four factors correctly (natality, mortality, immigration, emigration). 1 mark — writes the formula correctly with the correct sign convention (+/−).

Sample response. Density-dependent factors — food competition: as kangaroo numbers increase, grass per individual decreases, intensifying competition and reducing reproduction; disease transmission: pathogens spread more rapidly in denser populations. Intensity of both factors increases with population density. Density-independent factors — drought: reduces food and water for kangaroos regardless of their density; wildfire: destroys habitat and kills individuals irrespective of population size. The intensity of these factors does not change with density; they affect sparse and dense populations equally.

Marking criteria. 1 mark — two correctly named density-dependent factors for kangaroos. 1 mark — explanation that their intensity increases with density. 1 mark — two correctly named density-independent factors for kangaroos. 1 mark — explanation that their intensity is unrelated to density.

Sample response (a). 12 hours ÷ 3 hours per doubling = 4 doubling periods. Cells = 200 × 2⁴ = 200 × 16 = 3 200 cells.

Sample response (b). The growth curve would be J-shaped, starting with a nearly flat section (slow growth when numbers are small) and becoming progressively steeper as the population size increases and each generation produces more offspring than the last. This is called exponential growth.

Marking criteria. 1 mark (a) — correct number of doublings (4) and calculation giving 3 200 cells. Subsequent error follows from candidate's doubling count. 1 mark (b) — J-shaped curve correctly described (starts slow, steepens). 1 mark (b) — correctly names the pattern as exponential growth.

Sample response (a). The rabbit population increased continuously and at an accelerating rate from 1930 to 1950, rising from 2 million to 180 million — a 90-fold increase over 20 years. This is exponential growth: the absolute increase in each 5-year interval grew larger over time (e.g. 2 to 15 million = +13 million from 1930–1935, compared with 120 to 180 million = +60 million from 1945–1950), indicating that the rate of increase was proportional to population size.

Sample response (b). Percentage decrease = (180 − 20) ÷ 180 × 100 = 160 ÷ 180 × 100 = 88.9% (accept 88–89%).

Sample response (c). Myxomatosis initially acts as a density-independent factor because it spread as an epidemic across the entire rabbit range regardless of local population density. However, it also has density-dependent characteristics: the virus is transmitted by mosquitoes and rabbit fleas, so disease spread is more rapid at higher rabbit densities. Most examiners accept either classification provided the reasoning is sound; the most defensible answer is that the initial epidemic crash was density-independent (a single, region-wide event affecting all density levels), while the ongoing endemic form acts in a density-dependent manner.

Sample response (d). Before 1952, the carrying capacity was determined by food, water, burrow sites and predation — factors that set K near 180 million. After myxomatosis became endemic, it acted as a permanent additional density-dependent regulator, reducing reproductive success and increasing mortality at any given population level. This lowered the effective carrying capacity. The population now stabilises at a new, lower K (approximately 70–100 million) because even when rabbit numbers recover, myxomatosis re-intensifies and prevents further population growth.

Marking criteria. Part (a) — 1 mark: identifies exponential growth. 1 mark: uses two specific data values (e.g. any two of 2, 15, 60, 120, 180 million with correct years) to justify accelerating rate of increase. Part (b) — 1 mark: correct formula applied. 1 mark: answer of 88–89%. Part (c) — 1 mark: correctly classifies the factor (density-independent accepted; density-dependent with sound reasoning also accepted). 1 mark: clear justification linked to how the factor does or does not change with density. Part (d) — 1 mark: explains that myxomatosis became an endemic (permanent) regulatory factor. 1 mark: links this to a lower equilibrium K, explaining that increased mortality/reduced reproduction prevents the population from returning to 180 million.

Sample response. When Thomas Austin released 24 rabbits in Victoria in 1859, Australia offered conditions ideal for exponential growth: abundant grassland, no rabbit-specific predators or diseases, mild winters allowing year-round breeding, and extensive sandy soils for burrowing. With unlimited resources and no density-dependent constraints, each breeding pair produced up to 4–12 surviving offspring per litter across 6–8 litters per year, and each new generation was larger than the last. The rate of increase was proportional to population size, producing the characteristic J-shaped curve; by the 1920s the population exceeded 600 million.

As the population grew, density-dependent factors intensified. Food competition with livestock (sheep and cattle) and native herbivores (kangaroos) reduced per-capita food availability. Territorial conflict over burrow sites increased. Disease transmission between rabbits became more efficient. These factors progressively slowed the rate of population increase, transitioning growth from the exponential phase into the deceleration phase of the logistic S-curve, with the population oscillating near the environmental carrying capacity set by available food, water and shelter.

Myxomatosis, introduced in 1950, initially crashed the population by approximately 89% (180 million to 20 million). In its endemic form, myxomatosis acts as a density-dependent regulator: the virus spreads via mosquito vectors whose contact rates with rabbits increase at higher rabbit densities. However, its long-term effectiveness has been reduced because intense natural selection in the surviving population favoured rabbits carrying alleles conferring greater resistance. Within decades, the case fatality rate fell from ~99% to ~50%, significantly reducing myxomatosis as a control mechanism. This illustrates a critical limitation of relying on a single biological control agent.

A combination of biological control (myxomatosis, RHDV) and physical control (warren ripping, fencing, shooting) is more sustainable than either method alone for several reasons. Biological agents spread autonomously over large areas at low ongoing cost but lose effectiveness as resistant alleles increase in frequency. Physical control methods directly kill rabbits and destroy habitat (warrens, vegetative cover) but are labour-intensive, expensive to sustain over vast areas, and do not prevent re-invasion. Integrated pest management that combines both approaches attacks the population at multiple levels simultaneously: biological control suppresses numbers broadly while physical control protects high-value areas and reduces the chance of rabbits evolving resistance to any single method. This multi-strategy approach maintains a lower effective carrying capacity over the long term, making population control more durable and ecologically sound than reliance on either method alone.

Marking criteria. 1 mark — explains the specific conditions (unlimited resources, no predators/diseases, year-round breeding) that produced initial exponential growth. 1 mark — explains the transition to logistic growth as density-dependent factors (food competition, predation, disease) intensified as the population approached K. 1 mark — describes myxomatosis as a density-dependent regulatory mechanism (transmission increases with density). 1 mark — evaluates the long-term limitation of myxomatosis: natural selection for resistant alleles reduced its effectiveness, with supporting data (e.g. fatality rate decline, 89% crash vs. later ~50%). 1 mark — names a second biological control agent (RHDV) or additional physical control method with correct explanation. 1 mark — assesses why combined biological and physical control is more sustainable, addressing the limitations of each method alone. 1 mark — constructs a coherent, logically sequenced response that correctly applies the concepts of exponential growth, carrying capacity and density-dependent regulation across the full timeline from 1859 to the present.