C4.1 Populations and communities

A population is a group of organisms of the same species living in the same area and interacting. Members of a population normally interbreed with each other. A species may consist of one population or many: populations of the same species are separated by reproductive isolation, most often a geographical barrier such as a mountain range or the sea between two islands, so individuals breed mostly within their own population.

Why we estimate rather than count

The population size is the total number of individuals. Counting every one is usually impossible: the population may be spread over a large area, the organisms may be small, hidden or camouflaged, animals move around, and counting can damage the habitat. Instead, we count a sample and scale up to an estimate.

• The sample must be random: every individual (or every part of the habitat) must have an equal chance of being sampled. Choosing “typical-looking” spots introduces bias. Random numbers from a table, calculator or app are used to pick sample positions.
• Any estimate based on a sample differs from the true value. This difference is the sampling error. More samples reduce it, but it can never be eliminated without counting everyone.

Random quadrat sampling

Quadrats are square frames of known area (for example 1 m × 1 m, or 0.5 m × 0.5 m = 0.25 m²). They suit sessile organisms, which stay in one place: plants such as daisies or dandelions, and sessile animals such as barnacles and mussels on rocky shores.

1. Mark out the site and measure its area. Lay two measuring tapes at right angles along two edges as axes.
2. Generate pairs of random coordinates and place a quadrat at each.
3. Count the individuals rooted or attached inside each quadrat.
4. Calculate the mean number per quadrat, then: population estimate = mean per quadrat × total area ÷ area of one quadrat.

Standard deviation of the counts

Use a calculator to find the standard deviation (SD) of the counts per quadrat; you do not need to learn the formula. SD measures the spread of the counts around the mean. To judge whether the spread is large, compare it with the mean (or with other sites):

• If the organisms are scattered at random, the variance (SD²) is roughly equal to the mean, so variance ÷ mean ≈ 1.
• Large SD: variance well above the mean (variance ÷ mean above about 1.5) suggests the organisms are clumped. A large SD relative to other sites with a similar mean shows clumping too. A few quadrats can swing the estimate, so more quadrats are needed.
• Small SD: variance well below the mean (variance ÷ mean below about 0.67) suggests the organisms are evenly spread. A small SD relative to other sites with a similar mean shows even spacing, and the estimate is more reliable.

Motile animals move between samples, so quadrats do not work for them. Instead, capture–mark–release–recapture is used, for example with snails, woodlice, ground beetles or small mammals.

1. Capture as many individuals as possible in the area using nets, traps or careful searching. Count them: this is M.
2. Mark each one harmlessly, for example a dot of non-toxic paint on the underside of a snail shell, where it does not make the snail more visible to birds.
3. Release them where they were caught and leave time for them to mix back into the population.
4. Capture a second sample. Count the total caught (N) and how many of these carry a mark (R).
5. Use the Lincoln index: population estimate = M × N ÷ R.

The logic is that the proportion of marked animals in the second sample (R/N) should equal the proportion of the whole population that was marked (M/population).

Worked example. 60 ground beetles are caught and marked. A week later 45 are caught, of which 9 are marked. Estimate = 60 × 45 ÷ 9 = 300 beetles.

Assumptions

The estimate is valid only if, between the two samples:

• the population is closed: no births, deaths, immigration or emigration;
• marks are not lost and remain visible;
• marking does not affect survival or behaviour (for example, does not make animals easier for predators to spot);
• marked animals mix randomly back into the population, so every individual has the same chance of being caught in the second sample.

If marks wear off or marked animals die, R is too small and the population is overestimated. If marked animals stay near the trap, or become “trap-happy”, R is too large and the population is underestimated.

Every population depends on resources that are in limited supply. If a population grows, there are more individuals sharing the same resources, so they compete. The carrying capacity (K) is the maximum population size that an environment can support. Usually one resource, the one in shortest supply, sets it.

Density-dependent and density-independent factors

Density-independent factors affect a population whatever its density. A flood, a forest fire or a hard frost may kill a large proportion of a population, but roughly the same proportion whether the population is sparse or crowded. They make numbers fluctuate.

Density-dependent factors have stronger effects in denser populations, so they push numbers back towards K:

• Competition for limited resources: in a crowded population each individual gets less food, water or space, so fewer survive or breed.
• Predation: predators find prey more easily, and more predators can be supported, when prey are abundant.
• Pathogens and pests spread more easily between individuals living close together.

This is negative feedback. Above K, birth rate falls and death rate rises, so numbers fall; below K, competition, predation and disease ease, so numbers rise.

When a few individuals colonize a new, favourable habitat, the population usually grows in an S-shaped or sigmoid curve. This curve is an idealized model: a simplification that helps us understand real populations, which rarely fit it exactly.

• Exponential phase. Resources are abundant and there is little competition, predation or disease, so births greatly exceed deaths. Each individual produces offspring at a constant rate, so the more individuals there are, the faster the population grows. The model has no lag phase: growth is exponential from the start.
• Transitional phase. As numbers rise, density-dependent factors take effect. The birth rate falls and/or the death rate rises. The population still grows, but more slowly.
• Plateau phase. The population reaches the carrying capacity. Births equal deaths, and numbers stay roughly constant, fluctuating around K.

Case study: reindeer on St Matthew Island

In 1944, 29 reindeer were released on St Matthew Island in the Bering Sea, Alaska. The island had thick mats of lichen (their winter food) and no predators. Counts rose to about 1350 in 1957 and about 6000 in 1963, then the population crashed to 42 by 1966 after the lichen had been overgrazed and a very severe winter struck. The early growth was exponential because food was plentiful and there was nothing to kill the reindeer; the population then overshot the carrying capacity, so this case did not level off in a plateau.

Testing for exponential growth with a log scale

To test whether growth was exponential, plot population size on a logarithmic vertical axis against time on a normal (linear) axis. Exponential growth multiplies numbers by the same factor in each equal time interval, so on a log scale it gives a straight line. If the points lie close to a straight line, growth was close to exponential; if they curve downwards, growth was slowing.

Modelling the sigmoid curve: duckweed and yeast

Organisms that multiply quickly in the laboratory let you collect your own growth data:

• Duckweed (Lemna) floats on water. Put a few plants in beakers of water containing mineral ions, in bright light and warmth, and count the fronds every two days for several weeks. Space on the water surface or mineral ions eventually limit growth.
• Yeast (Saccharomyces) can be grown in sugar solution and counted under the microscope with a haemocytometer, or estimated from the cloudiness of the culture. Sugar runs out and ethanol builds up, so numbers level off (and later fall).

Use several replicate beakers so the reliability of the results can be judged, and plot mean numbers against time.

Intraspecific competition and cooperation

Members of the same species have the same requirements: the same food, the same nest sites, the same mates. When a resource is limited, they compete (intraspecific competition). Examples:

• Trees and other plants of the same species growing close together compete for light, water and mineral ions; many seedlings die, and the survivors are spaced out (self-thinning).
• European robins defend territories in which they feed and breed; birds without a territory usually do not breed.
• Red deer stags compete, by roaring and fighting, for access to females in the breeding season.

Members of a population also cooperate when working together raises the survival or reproduction of each:

• Honeybee workers share the tasks of foraging, nursing larvae and defending the hive.
• Meerkats take turns as sentinels, watching for predators while others feed.
• Emperor penguins huddle in large groups through the Antarctic winter, reducing heat loss.
• Wolves hunt in packs, so they can kill prey much larger than a single wolf.

Communities

A community is all the interacting populations of the different species in an area: plants, animals, fungi, bacteria and other microorganisms. No population lives in isolation; each depends on others for food, pollination, nutrient cycling or shelter.

Interspecific relationships

Three mutualisms

Root nodules in Fabaceae (legumes). Peas, beans, clover and other legumes form root nodules containing Rhizobium bacteria. The bacteria fix nitrogen: they convert N₂ from the air into ammonium (NH₄⁺), which the plant uses to make amino acids, so legumes thrive in nitrogen-poor soil. The plant supplies the bacteria with carbon compounds from photosynthesis, for respiration, and a protected, low-oxygen environment (oxygen would inactivate the enzyme that fixes nitrogen).

Mycorrhizae in Orchidaceae (orchids). Fungal hyphae grow into the roots of orchids, entering root cells (through the cell wall, but not the plasma membrane) and forming coils. The hyphae also spread far through the soil and act as an extension of the root system, absorbing water and mineral ions (such as phosphate) and passing them to the orchid. Orchid seeds are tiny, with almost no food reserves, so seedlings depend on the fungus for sugars as well. Adult green orchids can pass some photosynthetic sugars to the fungus (shown in some species).

Zooxanthellae in hard corals. Reef-building hard corals have single-celled photosynthetic algae (dinoflagellates called zooxanthellae) living inside their cells. The algae pass most of the sugars and other organic compounds (such as amino acids) they make by photosynthesis, and oxygen, to the coral. The coral provides the algae with CO₂ from its respiration, nitrogen and phosphorus compounds from its wastes, and a protected position in the light. When corals are stressed by high temperatures, they expel the algae and lose their colour (bleaching).

Invasive species and endemic species

Endemic (native) species occur naturally in an area. Alien species have been introduced by humans, deliberately or by accident. An alien species becomes invasive when it spreads rapidly, often because its own predators, parasites and diseases were left behind, and it outcompetes endemic species for resources. Endemic species with an overlapping niche may decline or be excluded.

If one species does better when another species is absent, interspecific competition is indicated, but not proven: the two might simply prefer different conditions. Hypotheses about competition can be tested in several ways:

• Laboratory experiments. Grow the species separately and together under controlled conditions. Gause grew two species of Paramecium on the same food: each thrived alone, but together one species outcompeted and eliminated the other.
• Field manipulation (removal experiments). Remove one species from some areas and compare with control areas. On a Scottish rocky shore, Connell removed the barnacle Balanus (Semibalanus) from rocks, and the barnacle Chthamalus then survived lower on the shore than usual.
• Field observations by random sampling. Record the presence or absence of each species in random quadrats and test whether they occur together more or less often than chance predicts (the chi-squared test below).

Experiments and observations. In an experiment the scientist intervenes, changing one variable and controlling others. In an observational study the scientist records what happens naturally without changing anything. Both can test hypotheses, but observations show only correlation; manipulation gives stronger evidence of cause.

The chi-squared test for association

1. State hypotheses. H₀: the two species are distributed independently. H₁: the species are associated.
2. Record presence/absence in many random quadrats in a 2 × 2 contingency table of observed frequencies (O), with row and column totals.
3. Calculate each expected frequency: E = row total × column total ÷ grand total.
4. Calculate χ² = Σ (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 χ² > 3.84, reject H₀: there is a significant association. Compare O with E for “both present” to find its direction.

Worked example. In 50 quadrats, species A is present in 25 and species B in 23, but they occur together in only 6. Expected (both) = 23 × 25 ÷ 50 = 11.5. The four expected values are 11.5, 11.5, 13.5 and 13.5, and χ² = 9.74 > 3.84. The species are negatively associated (found together less often than expected), which may be due to competition or to different habitat requirements.

Predation is a density-dependent factor, and in many communities predator and prey populations regulate each other. Four linked effects can produce cycles:

1. More prey → more food for predators → predators survive and breed better → predator numbers rise.
2. More predators → more prey eaten → prey numbers fall.
3. Fewer prey → predators starve or fail to breed → predator numbers fall.
4. Fewer predators → fewer prey eaten → prey numbers recover.

Case study: Canada lynx and snowshoe hare

In the boreal forests of Canada, numbers of snowshoe hares rise and fall in cycles lasting about 10 years, and numbers of Canada lynx, which depend heavily on hares for food, follow with a delay of one or two years. The evidence comes from the numbers of pelts traded by trappers to the Hudson’s Bay Company over more than 100 years, and from later field counts. Field experiments in the Yukon showed that hares are limited by both predators and winter food: excluding predators or adding food each raised hare numbers, and doing both raised them far more.

Top-down and bottom-up control

• Top-down control: a population is limited by organisms at the trophic level above it (its predators or herbivores). Example: sea otters eat sea urchins; where otters are present, urchins are kept scarce and kelp forests flourish, but where otters were hunted out, urchins multiplied and grazed the kelp away.
• Bottom-up control: a population is limited by the trophic level below it, ultimately by resources such as mineral nutrients. Example: in many lakes the supply of phosphate limits phytoplankton, which in turn limits the zooplankton and fish that depend on them.

Both kinds of control operate in most communities, but one is usually dominant in a particular community.

Some organisms reduce competition chemically: they release substances into the environment that harm or deter potential competitors. Both allelopathic chemicals and antibiotics are secondary metabolites, made by pathways found only in certain groups of organisms and not needed for basic growth.

Allelopathy

Allelopathy is the release of chemicals by a plant, through its roots, leaves or decaying litter, that inhibit the germination or growth of other plants nearby, so the plant faces less competition for water, light and mineral ions.

Example: black walnut (Juglans nigra). Its roots, leaves and nut husks contain a compound that is converted in the soil into juglone, which inhibits the growth of many other plants, such as tomatoes and apple trees, under and around the tree.

Antibiotic secretion

Antibiotics are chemicals secreted by microorganisms that kill or inhibit the growth of other microorganisms, especially bacteria, competing for the same food.

Example: Streptomyces bacteria in soil secrete many antibiotics, including streptomycin (from Streptomyces griseus), which inhibit other soil bacteria. Similarly, the fungus Penicillium secretes penicillin, which stops bacteria building their cell walls, reducing competition for the nutrients the fungus is digesting.