Revise Populations and communities for IB Biology (C4.1)

A population is a group of organisms of the same species living in the same area at the same time. Because they share a place and a species, they can interbreed — all the field mice in one meadow are a population. A community is all the populations of different species living and interacting in that same area: the mice, the grasses they eat, the owls that hunt them, the fungi in the soil and the fleas in their fur. The defining word is interacting — a community is not just a list of species that happen to be present, but the web of relationships between them. Note carefully that a community is entirely biotic (living); the moment you fold in the non-living surroundings such as soil, water and sunlight, you have moved up to describing an ecosystem.

Only four processes can change the number of individuals in a population. Two add individuals: natality (births) and immigration (individuals moving in from elsewhere). Two remove them: mortality (deaths) and emigration (individuals moving out). Every rise and fall you will ever plot is the net result of these four. Natality and mortality are the internal drivers, set by reproduction and survival; immigration and emigration are the movement terms, set by dispersal across the population's boundary. When additions exceed losses the population grows; when losses exceed additions it shrinks; when they balance, it holds steady.

$\Delta N = (\text{natality} + \text{immigration}) - (\text{mortality} + \text{emigration})$

Plot a population that colonises a new, resource-rich habitat and you get a characteristic S-shape — the sigmoid, or logistic, growth curve. It has three phases. In the exponential phase, individuals are few relative to resources: food, space and other requirements are effectively unlimited, competition is low, and the birth rate far exceeds the death rate, so numbers climb ever more steeply. In the transitional phase, the growing crowd begins to strain its resources — food per individual falls, competition sharpens, predators and disease take a larger share — so the birth rate drops and/or the death rate rises, and the growth rate slows. In the plateau phase, the population has reached the carrying capacity (K): births are balanced by deaths, the growth rate is effectively zero, and numbers simply fluctuate around this maximum sustainable level. Carrying capacity is not a fixed constant — a better season raises it, a harsh one lowers it — but it represents the ceiling the current environment can support.

It is worth stating the contrast sharply because examiners test it directly. Exponential growth is what a population would do with genuinely unlimited resources: it produces a J-shaped curve that rises without any ceiling, because nothing checks it. Logistic growth builds in the reality that resources run out: limiting factors slow the population as it grows, producing the S-shaped sigmoid curve that levels off at the carrying capacity. Real populations follow logistic growth; the exponential phase is only the early, uncrowded part of that S-shape, before limiting factors take hold. So 'exponential' describes a phase of the real curve and an idealised unlimited model, while 'logistic' describes the full, ceiling-bounded curve.

The factors that slow and cap population growth fall into two kinds, distinguished by whether their strength depends on crowding. Density-dependent factors act more harshly as the population gets denser: competition for food and space, predation, and the spread of parasites and disease all intensify when individuals are packed together. Because their effect scales with density, these factors regulate a population — they push it back down towards the carrying capacity whenever it overshoots, and ease off when it falls, and they are typically biotic. Density-independent factors act with the same intensity whatever the density: a drought, flood, fire, frost or storm kills roughly the same proportion of a sparse population as a dense one. These are usually abiotic events, and they can knock a population well below its carrying capacity regardless of how crowded it was.

Species in a community affect one another through a set of named interspecific interactions, each with a characteristic pattern of who benefits and who is harmed. In competition, two species vie for the same limited resource, and both are harmed by the other's presence — think of two plant species competing for light. In predation, one species (the predator) kills and eats another (the prey); herbivory is the closely related case of an animal consuming a plant, which may harm rather than kill it. In mutualism, both species benefit — for instance, a pollinator gaining nectar while carrying a flower's pollen. In parasitism, a parasite lives on or in a host, benefiting at the host's expense, and disease is the analogous case of a pathogen harming its host. Reading these interactions off by their (+/−) sign pattern is the reliable way to classify them: competition is (−/−), predation, herbivory and parasitism are (+/−), and mutualism is (+/+).

Where a predator depends heavily on a single prey, their populations often trace linked oscillations. Start with abundant prey: well-fed predators reproduce successfully and their numbers climb. The growing predator population eats prey faster than the prey can replace themselves, so prey numbers fall. With prey now scarce, predators starve and their numbers drop in turn. Relieved of heavy predation, the prey recover, and the cycle begins again. The key feature to describe — and the one examiners look for — is that the predator peak lags behind the prey peak: the predators can only build up after the prey have become plentiful, and they crash after the prey have declined. This is a density-dependent interaction in action, with each population regulating the other.

Counting every individual is almost never practical, so ecologists estimate population size by sampling. For sessile organisms — plants, seaweeds, barnacles, limpets — or very slow movers, a quadrat is used: a frame of known area placed at random positions in the habitat. You count the individuals inside each quadrat, average across many quadrats to get a mean density, then scale that density up to the total area of the habitat. Random placement matters: choosing 'representative-looking' spots biases the estimate, so positions should be set by random coordinates. The more quadrats you sample, the more reliable your mean density.

For mobile animals — insects, small mammals, fish — a quadrat is useless, because the animals move and would be missed or double-counted. Instead you use capture-mark-recapture. Capture a first sample, mark each individual harmlessly, and release them. Allow time for the marked animals to mix back into the population. Then capture a second sample and count how many carry a mark. If the marked individuals have mixed in evenly, the proportion of marked animals in the second sample equals the proportion of marked animals in the whole population, and rearranging that proportion gives the Lincoln index estimate of total population size $N$.

$N = \frac{M \times n}{m}$

where $N$ is the estimated total population, $M$ is the number marked and released in the first sample, $n$ is the total number caught in the second sample, and $m$ is the number of those second-sample animals that were marked. The method rests on three assumptions: that marked individuals mix randomly and evenly with the rest; that the mark neither harms the animal nor changes how likely it is to be caught; and that no significant birth, death, immigration or emigration happens between the two samples. If any assumption fails — a mark that makes prey conspicuous, say, or a long gap in which many animals die — the estimate is thrown off.

C4.1 pairs calculation with explanation, and the explanation marks are awarded analytically — each distinct valid biological point is worth one mark. Method marks (M) credit correct reasoning, answer marks (A) credit a correct conclusion, and error-carried-forward (ECF) means a wrong value early on does not cost you the marks that follow, provided your working is shown. Notice below how each mark is tied to a specific, named idea rather than to loose phrasing.

The ideas in C4.1 are the foundation for the rest of the interaction-and-interdependence unit. Carrying capacity and limiting factors reappear when you study sustainability and the human population; interspecific interactions underpin food webs, energy flow and niche concepts; and the sampling techniques you have practised here are the same ones used in fieldwork to measure biodiversity and monitor conservation. Master the habit of reasoning from births, deaths and movement, and of matching a sampling method to how an organism lives, and you have a template that carries through the whole of ecology.