Relationships Between Organisms

Sorting interactions by who benefits and who is harmed

Every relationship between two organisms can be classified by its effect (+ benefit, − harm, 0 no effect) on each partner.

Predation (+/−): predator eats prey (e.g. dingo and kangaroo). Competition (−/−): both use the same limited resource (e.g. two plants for light). Mutualism (+/+): both benefit (e.g. a pollinating insect and a flower). Commensalism (+/0): one benefits, the other unaffected (e.g. a bird nesting in a tree). Parasitism (+/−): parasite feeds on host without quickly killing it (e.g. a tick on a wallaby). Allelopathy (+/−): a plant releases chemicals that suppress other plants nearby, harming the neighbour while the releasing plant gains from the space, light and water it no longer has to share (e.g. some Eucalyptus species).

To determine a relationship from an example, ask what happens to each organism and assign a +, − or 0 to each. Allelopathy is worth a second look, because its notation is the one students most often get wrong. The neighbouring plant is clearly harmed (−). The releasing plant is scored + because suppressing its neighbours leaves it more light, water and nutrients, which is a benefit, so allelopathy is written (+/−) like predation and parasitism. It is best understood as a chemical form of competition: same outcome, one competitor suppressed, but achieved by releasing a substance rather than by growing faster or rooting deeper.

Pause, copy the highlighted six relationship types, their +/−/0 effects and one example each into your book before moving on.

The classic cycle, and why the predator lags

We just classified the relationships. That raises a question: how does predation actually play out in population numbers over time? This card answers it → as a lagged, repeating cycle you can read off a graph.

Predator and prey numbers rise and fall in a linked cycle, with the predator's peak always coming after the prey's.

The pattern: plenty of prey → predators have lots of food → predator numbers rise → heavy predation drives prey numbers down → predators then starve and decline → with fewer predators, prey recover → and the cycle repeats. The predator peak lags behind the prey peak because predators only increase after there is more food, and only decline after the prey have already dropped.

Predator–prey cycle: prey rise → predators rise (lagging behind) → prey fall from predation → predators fall from starvation → prey recover → repeat. The predator peak always lags the prey peak. Reading the graph: the curve that peaks first is the prey; the lagging curve is the predator.

Pause, copy the cycle sequence and the "predator peak lags" rule into your book.

Now test the idea against real data, because the textbook curve came from somewhere. The best-known record is not a scientific census at all: it is the number of Canada lynx and snowshoe hare pelts the Hudson's Bay Company bought each year from the 1840s onward, compiled and published by MacLulich (1937) and analysed by Elton and Nicholson (Journal of Animal Ecology, 1942). Read as data it shows three things clearly. The two series rise and fall together on a cycle of roughly nine to ten years, the swing is large, roughly ten-fold from trough to peak, and the lynx peak consistently arrives one to two years after the hare peak, never before it. That lag is the pattern the whole card describes, and it is present in a record nobody collected in order to demonstrate it.

Handle that record honestly, though. Fur returns measure how many animals were trapped and sold, not how many were alive, so trapping effort, fur prices and where the company had posts all leave their fingerprints in the numbers. This is a good habit to carry into every data question: state what was actually measured before you state what it shows.

The experiment that settled what was causing the cycle. A lagged correlation cannot tell you whether the hares crash because predators eat them or because they run out of food, since both would produce a similar-looking cycle. Krebs and colleagues tested it directly in the Yukon by fencing hare populations to exclude predators, adding food to others, and doing both together over a full cycle (Science, 1995). Excluding predators roughly doubled hare density. Adding food roughly tripled it. Doing both together raised it about eleven-fold, far more than either effect on its own and far more than the two combined would predict.

Read what that combination means, because it is the point. If predation and food supply acted independently you would expect about a six-fold increase from doing both, two times three; getting eleven-fold means the two interact, hares that are short of food are also much easier for predators to catch. So the predator–prey relationship is real and measurable, but it does not act alone, and a graph showing only two lines will never reveal that on its own.

An Australian version of the same lag. When rabbit haemorrhagic disease spread through arid Australia from 1995, rabbit numbers fell steeply across large areas, and the numbers of foxes and feral cats, which depend heavily on rabbits for food, fell afterwards rather than at the same time. That is the predator following the prey, in a system where the prey crash had an external cause rather than being driven by the predators.

Within and between species, and reading the data

We just analysed predation. That raises a question: what does the other big interaction, competition, do to population numbers? This card answers it → it limits population size, and data shows the weaker competitor declining.

Competition for a limited resource reduces the growth or survival of the competitors, capping population size and sometimes excluding the weaker species.

Competition is intraspecific (within one species, for the same resources, often the strongest control on a population's size) or interspecific (between different species). In data, the signature of competition is that as one population rises, a competitor's population falls, or that both grow more slowly together than either does alone. When two species compete strongly for the same limited resources, the weaker competitor may be pushed out of part of the area altogether, restricting its distribution, as you saw in L21.

To analyse competition or predator–prey data, describe the trend (rising/falling/cyclic), quantify it where possible (e.g. "prey fell by 60% as predators doubled"), identify the lag or inverse relationship, and link it to the interaction. Always support a stated relationship with the specific numbers from the data.

Competition (−/−) limits population size: intraspecific (within a species) is often the main control; interspecific (between species) can push the weaker competitor out of part of its range. In data, competition shows as one population falling as a competitor rises, or both growing more slowly together than either does alone. Analyse by stating the trend, quoting numbers, and linking the lag or inverse pattern to the relationship.

Add the intra/interspecific distinction and the "support with numbers" rule to your notes before the check below.

The classic competition data comes from two species that look almost identical. Georgii Gause grew two ciliates, Paramecium aurelia and Paramecium caudatum, on the same bacterial food supply and published the results in The Struggle for Existence (1934). Grown in separate tubes, each species increased and then levelled off at a steady density, showing that the culture conditions suited both of them perfectly well. Grown together in one tube, P. aurelia reached a similar density to before while P. caudatum declined steadily and was close to gone within about two to three weeks.

Read the comparison, not just the decline. The single-species tubes are what make this evidence rather than an anecdote: they rule out the possibility that P. caudatum simply cannot live in that medium, because on its own it thrives there. The only difference between the two runs is the presence of the other species, so the decline has to be caused by the competition itself. This is the shape of a good competition data set, and it is worth copying in an answer: quote the mixed-culture decline, then quote the single-culture control that shows the species was capable of living there.