C4.1 Populations and communities

A population is an interacting group of organisms of the same species living in a defined area at the same time.

Members normally have opportunities to breed with one another. Reproductive isolation—little or no gene flow through breeding—can distinguish one population of a species from another.

State the species, spatial boundary and time; show interaction or breeding opportunity; use reproductive isolation to separate neighbouring populations when relevant.

Frogs of one species breeding in a connected pond system can form one population, while a geographically isolated group with no interbreeding is treated as a separate population.

A population is not every organism in a habitat—that is closer to a community. Sharing a species name alone is insufficient if groups are reproductively isolated.

Population size is estimated by random sampling when counting every individual is impractical, disruptive or impossible.

Random selection gives locations or individuals an unbiased chance of inclusion, making a sample more representative before its mean or marked fraction is extrapolated to the whole area.

Define target population and area → select positions or individuals randomly → standardize sampling effort → repeat → calculate estimate and uncertainty.

Random quadrat coordinates across a large meadow give an average plant density that can be multiplied by meadow area, avoiding convenient locations near a path.

Sampling error is the difference between the sample-based estimate and the true population size. Randomness reduces placement bias but cannot remove sampling error.

Random quadrat sampling estimates abundance of sessile plants or animals whose individuals can be counted within a known area.

Map the study area, generate random coordinates, place equal-area quadrats at those positions, count individuals using a consistent boundary rule, repeat many times, and scale the mean count per quadrat to the total habitat area.

Use a calculator to obtain the standard deviation of quadrat counts. A small standard deviation suggests counts are similar and distribution is relatively even; a large standard deviation suggests strong spatial variation or clumping.

If 1 m² quadrats average 4 plants and the habitat is 250 m², the estimated population is 4 × 250 = 1,000 plants; the standard deviation shows how variable the counts were around that mean.

Quadrats suit sessile organisms, not freely moving animals. Random placement does not guarantee representation if too few or poorly sized quadrats are used; the standard-deviation formula need not be memorized.

Capture–mark–recapture estimates mobile populations.

A first sample is captured, marked harmlessly and released. After mixing, a second sample is taken; the marked fraction helps estimate the total population.

N≈(first caught × second caught)/marked recaptured; check mixing, mark retention and equal catchability.

If 40 fish are marked, 50 are caught later and 10 are marked, the estimate is about 200 fish.

The calculation fails if marks are lost, animals learn the trap or the population changes between samples.

Carrying capacity is a resource-limited population size.

Carrying capacity is the population size an environment can support over time under specified conditions. Resource limits and feedback make growth slow as the population approaches it.

identify limiting resource; link density to birth/death rate; state that capacity can change.

A drought lowers available food, so the carrying capacity for grazing animals falls even if the species is unchanged.

Carrying capacity is not a fixed species constant; it depends on environment and timescale.

Negative feedback restrains population growth.

As density rises, competition, disease or predation can reduce births or increase deaths. The response opposes the change and tends to return population size toward a range.

disturbance → density-dependent response → birth/death change → correction.

Crowding increases disease transmission, lowering survival and slowing further population growth.

Not every population change is negative feedback; density-independent storms can reduce numbers without crowding.

Population growth is exponential while resources are abundant and limiting factors are weak, but becomes sigmoid as density-dependent limits slow growth near carrying capacity.

During exponential growth, a roughly constant per-capita growth rate produces an increasingly steep rise. In a sigmoid model, competition and other density-dependent factors reduce net growth until births plus immigration balance deaths plus emigration near carrying capacity.

Test exponential growth by plotting population size on a logarithmic vertical axis against time on a linear horizontal axis: exponential data form an approximately straight line. For the IB sigmoid model, a separate lag phase is not expected.

An introduced population with abundant food may initially show a straight line on the semi-log graph; later points fall below that line as resource limitation increases and the population approaches a plateau.

A population curve is an idealized model, not a guaranteed trajectory. A steep slope means rapid change, not the greatest population size; migration, seasons and disturbances can alter the pattern.

A sigmoid growth model can be tested by repeatedly measuring a population grown under controlled conditions, such as yeast or duckweed.

Start replicate cultures with comparable initial populations, keep temperature, nutrients, volume and light conditions controlled, measure population size at regular intervals, calculate means, and plot population size against time.

Look for an initially increasing growth rate, a transition as limiting factors intensify, and a plateau near carrying capacity. Compare replicate data with the ideal S-shaped curve and identify deviations.

Duckweed frond number can be counted daily in replicate containers; nutrient depletion, shading and crowding eventually reduce net growth and produce a plateau.

The sigmoid is a model. A plateau may shift if conditions change, and deviations must not be hidden by forcing a fitted curve through poor data.

Intraspecific relationships occur within one population: individuals compete when they require the same limited resource, but may cooperate when joint action raises survival or reproduction.

As a plant population becomes denser, individuals of the same species shade one another and compete for light; cooperative animal hunting can instead increase each participant's food access.

Competition need not involve aggression, and cooperation can have costs. An interaction between two different plant species is interspecific and does not demonstrate this objective.

A community contains interacting populations.

A community is the populations of different species living and interacting in one area. Its composition and structure depend on abiotic conditions and biotic relationships.

list populations; define boundary; identify interaction and environmental filter.

A pond community includes algae, plants, fish, bacteria and invertebrates interacting under the pond’s chemistry and light.

A community is not the same as an ecosystem: an ecosystem also includes abiotic stores and flows.

Interspecific relationships are interactions between different species that change survival, growth or reproduction within a community.

Classify from the mechanism and effects, not from proximity alone. Commensalism is a valid ecological term but is not one of the six categories required for this objective.

Mutualism is an interspecific relationship in which both species receive a net benefit.

Name the benefit to each organism. Merely living together is not enough to establish mutualism, and the balance of benefit can change with environmental conditions.

An introduced species can become invasive when it acquires limiting resources more effectively than local endemic or native species and spreads at their expense.

In southern China, the introduced vine Mikania micrantha grows rapidly over local vegetation. Its canopy captures light and space, shading slower-growing native plants and reducing their access to photosynthetic resources.

For a local case, identify introduced species → named resource → acquisition advantage → reduced resource availability or performance of local endemic/native species → spread and community impact.

Compare light interception, cover or growth of local plants in plots with and without dense Mikania; greater vine resource capture provides evidence for its competitive advantage.

Non-native does not automatically mean invasive. Spread and harm must be demonstrated, and resource competition must be separated from other mechanisms such as predation or disease.

Interspecific competition is indicated—but not proved—when one species performs better in the absence of another.

In a barnacle removal study, greater occupation of lower shore by one species after its competitor is removed supports competition as the restriction on its realized distribution.

Experiments manipulate a variable; observations do not. Better performance without a second species is evidence for competition, but alternative environmental differences must still be excluded.

A chi-squared test can test whether presence or absence of species A is associated with presence or absence of species B across several sampling sites.

Build a 2 × 2 table: both present, A only, B only, neither. Null hypothesis: the species' distributions are independent. Calculate each expected count as (row total × column total) ÷ grand total.

Compare calculated χ² with the chosen critical value or p-value. A significant result rejects independence and supports an association between distributions.

Association does not prove interspecific competition: both species may respond to the same abiotic factor, or association may reflect another interaction. Sampling sites must be independent and expected counts suitable for the test.

Predator–prey interactions can regulate both animal populations through density-dependent feedback, often producing time-lagged cycles.

More prey support predator survival and reproduction, so predator numbers rise later. Increased predation then lowers prey numbers; food shortage subsequently lowers predator numbers, allowing prey recovery.

Long-term snowshoe hare and Canada lynx records show repeated abundance cycles in which lynx peaks generally follow hare peaks, consistent with the delayed predator response.

Trace hare increase → more food for lynx → delayed lynx increase → higher hare mortality → hare decline → lynx decline from reduced food.

A lagged correlation supports but does not alone prove a predator-driven cycle. Food supply, disease, climate and sampling methods can also influence both populations.

Top-down control begins with consumers at higher trophic levels; bottom-up control begins with resource supply or primary producers at lower levels.

Top-down effects cascade downward when predators alter herbivores and therefore plants. Bottom-up effects propagate upward when nutrients or primary production limit herbivores and predators.

Removing a top predator can release herbivores and reduce plant biomass; low nitrogen can independently limit plant production and therefore all higher levels.

Both pathways can operate in one community, but evidence often shows one is dominant in a particular place and time. Identify the direction and trace effects through trophic levels rather than assuming universal control.

Allelopathy and antibiotic secretion both release chemicals into the environment that deter potential competitors.

The ecological outcome depends on chemical concentration, transport, breakdown and target susceptibility; secretion can improve the producer's access to space or resources.

A laboratory inhibition zone shows chemical activity but does not by itself prove an ecologically important field concentration. Antibiotic secretion here is competition between organisms, not clinical prescribing.