Populations and communities

📚The biology

Populations

A population is a group of organisms of the same species living in the same area at the same time, which normally interbreed. Two populations of one species are distinguished by reproductive isolation: if individuals from two areas do not normally breed with each other — separated by a mountain range or a stretch of sea, for example — they are separate populations.

Estimating population size

Counting every individual in a population is usually impossible or impractical: the area is too large, the organisms are too small, too numerous, too mobile or too well hidden, and counting could damage the habitat. Instead, a sample is counted and used to estimate the whole population.

Samples must be taken randomly, so that every individual or location has an equal chance of being sampled and the estimate is not biased by the sampler choosing, for example, the densest or most accessible patches. Even with random sampling, an estimate will differ from the true size: this difference is the sampling error. It is unavoidable, but it can be reduced by taking more samples.

Random quadrat sampling

For sessile organisms — plants, and fixed animals such as barnacles and limpets — where individuals can be counted:

1. Mark out the study area with two measuring tapes at right angles, forming the axes of a grid.
2. Generate random coordinates (with a random number generator) and place a quadrat of known area at each.
3. Count the number of individuals of the species in each quadrat.
4. Calculate the mean number per quadrat, and scale up: population estimate = mean per quadrat × (total area ÷ quadrat area).

The standard deviation of the counts (calculated with a calculator or spreadsheet; you need not memorize the formula) measures the variation between quadrats. A small standard deviation means counts were similar, so the population is evenly spread; a large standard deviation relative to the mean means the population is clumped, and more quadrats are needed for a reliable estimate.

Capture–mark–release–recapture

For motile animals, which move around and cannot be counted in quadrats:

1. Capture a sample, count them (\( M \)), mark each one harmlessly, and release them.
2. Allow time for the marked animals to mix back into the population.
3. Capture a second sample. Count the total (\( N \)) and the number that are marked (\( R \)).

This is the Lincoln index. It assumes that:

• marked individuals mix completely and randomly with the rest of the population;
• marking does not affect survival or the chance of being recaptured (it does not make animals more visible to predators, or trap-shy);
• marks are not lost between samples;
• the population is closed: no significant births, deaths, immigration or emigration between the two samples.

Carrying capacity

The carrying capacity of an environment is the maximum population size of a species that it can support sustainably. It is set by limited resources, which individuals compete for — for example food, water, nesting sites or territory for animals, and light, water, space and mineral nutrients for plants.

Density-dependent control

Population numbers fluctuate. Some factors, such as a severe storm or drought, kill a similar proportion of individuals whatever the population density: these are density-independent factors. Others have a greater effect as density rises: density-dependent factors. They push the population back towards the carrying capacity by negative feedback:

• competition for limited resources is more intense in dense populations;
• there is an increased risk of predation, as predators find dense prey more easily and may increase in number;
• pathogens and pests spread more easily when individuals are crowded together.

When the population rises above the carrying capacity, these factors raise death rates or lower birth rates; when it falls below, they ease, and the population increases.

Population growth curves

When a population colonizes a new, favourable environment, it often grows exponentially at first: resources are plentiful and there is little competition, predation or disease, so birth rate greatly exceeds death rate. Because each new individual can itself reproduce, the population doubles in equal time intervals.

As numbers rise, resources become limiting and density-dependent factors take effect. Growth slows, and the population levels off around the carrying capacity. The overall shape is a sigmoid (S-shaped) curve:

(A lag phase at the start is not expected as part of the syllabus model.) The sigmoid curve is an idealized model: real populations overshoot, fluctuate and crash, and the model is a simplification of a complex system.

Testing for exponential growth: plot population size on a logarithmic vertical axis against time on a normal (non-logarithmic) horizontal axis. Exponential growth produces a straight line on such a graph; where the line starts to bend and flatten, growth is no longer exponential.

Case study — reindeer introduced to St Matthew Island in the Bering Sea in 1944: 29 animals, with abundant lichen and no predators, increased exponentially to about 6000 by 1963. They had overgrazed their food supply, and after a harsh winter the population crashed to fewer than 50.

Modelling in the laboratory: sigmoid growth can be reproduced with organisms that multiply quickly. Yeast in a flask of sugar solution can be sampled daily and counted with a haemocytometer; duckweed in a container of water can be counted as the number of fronds increases until the surface is covered.

Competition and cooperation within a species

Intraspecific competition occurs because members of the same species need exactly the same resources, which are limited. Examples: barnacles competing for space on rocks; trees in a forest competing for light, so that seedlings in the shade of their parents grow slowly; male deer competing for mates.

Members of a species also cooperate, when working together increases survival or reproduction. Examples: wolves hunting in packs to bring down large prey; honeybees dividing labour within a colony; emperor penguins huddling together to reduce heat loss; meerkats taking turns as sentries.

Communities and interspecific relationships

A community is all the populations of different species living and interacting in an area — plants, animals, fungi and bacteria. Relationships between species fall into several categories:

Mutualism

Invasive species

An endemic species is native to an area. An introduced species becomes invasive when it spreads and harms the ecosystem, often because it has a competitive advantage over endemic species in acquiring resources, and has left its natural predators and diseases behind.

A local example: water hyacinth (Eichhornia crassipes) in Indonesian lakes and rivers. Introduced from South America as an ornamental plant, it floats on the water surface and reproduces extremely rapidly. It forms dense mats that shade out native submerged plants and algae, out-competes native floating plants for light and nutrients, and depletes oxygen in the water. (If you study elsewhere, choose an invasive species from your own region.)

Testing for interspecific competition

Competition is indicated, but not proven, if one species is more successful in the absence of another. Approaches include:

• laboratory experiments — growing two species separately and together under controlled conditions and comparing their growth;
• field observations by random sampling — recording whether two species are found together less often than expected by chance;
• field manipulation — removing one species from some plots and comparing the other species’ success with control plots.

Hypotheses can be tested both by experiments, in which a variable is deliberately changed and others are controlled, and by observations, in which natural patterns are recorded without intervention. Experiments give stronger evidence of cause; observations can cover realistic conditions and larger scales.

The chi-squared test for association

Presence or absence of two species in many randomly placed quadrats can be tested for association. If the species are distributed independently, the number of quadrats containing both should match what is expected by chance. A negative association (found together less often than expected) may be evidence of interspecific competition; a positive association may indicate that they share habitat requirements or have a mutualistic relationship.

1. State the null hypothesis: there is no association between the two species (they are distributed independently).
2. Draw a 2 × 2 contingency table of observed frequencies (both present; only A; only B; neither), with row and column totals.
3. Calculate each expected frequency: \( E = \dfrac{\text{row total} \times \text{column total}}{\text{grand total}} \).
4. Calculate \( \chi^{2} = \sum \dfrac{(O - E)^{2}}{E} \).
5. Degrees of freedom = (rows − 1) × (columns − 1) = 1. The critical value at \( p = 0.05 \) is 3.84.
6. If \( \chi^{2} \) is greater than the critical value, reject the null hypothesis: the association is statistically significant.

Predator–prey relationships

Predation is a classic density-dependent control. As prey increase, predators have more food and their numbers rise, after a time lag. More predators eat more prey, so prey numbers fall. With less food, predator numbers then fall, allowing prey to recover, and the cycle repeats.

Case study — snowshoe hares and Canada lynx in the boreal forests of Canada. Fur-trading records of the Hudson’s Bay Company over more than a century show both populations rising and falling in roughly ten-year cycles, with lynx peaks following hare peaks. (Further research showed hare numbers are also driven by the quality of their plant food, so the cycle is not caused by predation alone.)

Top-down and bottom-up control

Both types of control are possible in any community, but one or the other is likely to be dominant.

Allelopathy and antibiotics

In both processes, an organism releases a chemical into its environment that deters potential competitors.