C4.1.8—Modelling sigmoid growth

Modelling sigmoid growth explains how ecological evidence links organisms, resources and interactions to population size, distribution or community structure in a habitat.

The sigmoid model links growth to carrying capacity.

The logistic model predicts fast growth at intermediate size and slowing growth near carrying capacity because the fraction of limiting resources falls.

identify N, r and K; explain why rate changes; compare model with data.

When N is close to K, each additional animal faces more competition, so dN/dt decreases.

A sigmoid is a model, not a guarantee: migration, seasonal resources and disturbances can break its assumptions.

A population is one species in an area; a community is all interacting populations there.
Estimate abundance with unbiased sampling: quadrats for sessile organisms and capture–mark–release–recapture for mobile animals, checking each method’s assumptions.
Density-dependent competition, predation, disease and waste create negative feedback around carrying capacity; exponential growth slows into a sigmoid curve as limits strengthen.
Classify interspecific relationships by costs and benefits: predation, herbivory, competition, mutualism, parasitism and pathogenicity.
Invasive species may escape controls and displace endemic species. Removal experiments can reveal competition and fundamental versus realized niches.
Chi-squared tests assess species association from observed and expected quadrat counts.
Predator peaks usually lag prey peaks; top-down control begins with consumers, while bottom-up control begins with resources or producers.

Concept essentials

Modelling sigmoid growth must be linked to the correct ecological unit, method or species interaction.
The evidence for modelling sigmoid growth depends on measurable abundance, distribution, survival or resource patterns.
Modelling sigmoid growth is clearest when cause, ecological process and population outcome are kept distinct.
Field or graph evidence for modelling sigmoid growth needs biological interpretation, not values alone.

ConceptIB Biology HL