A population is a group of individuals of the same species living in the same area at the same time. Population ecology asks how many individuals there are, how they are spread out, why numbers rise and fall, and what limits them. These questions are central to conservation, fisheries, pest control, disease and even human demography.
What you will learn
- How ecologists describe populations: size, density, distribution and structure
- The four processes that change population size
- Exponential and logistic growth, and carrying capacity
- Density-dependent and density-independent factors
- Survivorship curves and life-history strategies
Describing a population
- Size (N): the total number of individuals.
- Density: the number per unit area or volume, for example tigers per 100 km².
- Distribution (dispersion): how individuals are spaced. It can be clumped (elephant herds, schools of fish), uniform (nesting seabirds keeping pecking distance, territorial animals) or random (dandelions from wind-blown seeds). Clumped is the most common pattern in nature.
- Age structure: the proportion of individuals at each age. A population with many young individuals is likely to grow.
- Sex ratio: the balance of males and females, which affects how many young can be born.
Counting animals is rarely simple. Ecologists use methods such as quadrats for plants, mark–recapture for mobile animals, distance sampling along transects, camera traps for secretive mammals, aerial surveys for large herbivores and, increasingly, environmental DNA. See Module 15.
What changes population size
Four processes change a population’s size:
Change in N = Births + Immigration − Deaths − Emigration
When births and immigration exceed deaths and emigration, the population grows.
Exponential growth
If resources are unlimited, a population grows by a constant proportion each generation, like compound interest. Its growth rate is described by r, the intrinsic rate of increase:
dN/dt = rN
The result is a J-shaped curve that starts slowly and then shoots upward. Exponential growth happens when a species colonises a new, empty habitat or recovers after a crash. Examples include rabbits introduced to Australia in 1859, which spread across much of the continent within decades, and reindeer introduced to St Matthew Island, Alaska, in 1944. The 29 reindeer grew to about 6,000 by 1963, then crashed to 42 after they exhausted their food.
Logistic growth and carrying capacity
In reality, resources run out. As a population grows, competition for food, space and other resources increases, births fall and deaths rise, and growth slows. The population levels off at the carrying capacity (K), the largest population the environment can support. This is described by the logistic equation:
dN/dt = rN × (1 − N/K)
When N is small, the term (1 − N/K) is close to 1 and growth is almost exponential. When N approaches K, the term approaches 0 and growth stops. The result is an S-shaped (sigmoid) curve. Growth is fastest when the population is at half of K, which is why fisheries managers once aimed to keep fish stocks at around that level to maximise the sustainable catch.
Use the model below to see how r and K change the curve.
Real populations rarely sit neatly at K. They often overshoot and then fall, or fluctuate around K because of time lags, weather and interactions with predators and disease.
Density-dependent and density-independent factors
- Density-dependent factors have a stronger effect as the population becomes more crowded: competition for food, territory or nest sites, disease (which spreads faster in dense populations), predation and parasitism. These factors regulate populations around K.
- Density-independent factors affect populations regardless of their density: severe weather, floods, fires, drought and volcanic eruptions. They can cause sudden crashes but do not regulate populations around a stable level.
Survivorship curves
A survivorship curve plots how many individuals in a group survive to each age. There are three general types:
- Type I: most individuals survive to old age, then die quickly. Examples: humans, elephants, whales.
- Type II: a constant chance of dying at every age. Examples: many songbirds, some lizards.
- Type III: most individuals die very young, and the few survivors live a long time. Examples: sea turtles, oysters, most fish and trees. A green sea turtle lays thousands of eggs in its life, but perhaps only one in a thousand hatchlings survives to adulthood.
Life-history strategies: r and K selection
A species’ life history is its pattern of growth, reproduction and survival. Because energy is limited, organisms face trade-offs: they cannot produce huge numbers of offspring and also invest heavily in each one.
| r-selected species | K-selected species |
|---|---|
| Many small offspring | Few large offspring |
| Little or no parental care | Extensive parental care |
| Mature early | Mature late |
| Short lifespan | Long lifespan |
| Thrive in unstable, disturbed habitats | Thrive in stable, crowded habitats |
| Examples: mice, insects, weeds, many fish | Examples: elephants, great apes, whales, albatrosses |
Most species fall somewhere between these extremes, and modern ecologists study life histories along several axes rather than one. The r/K idea remains useful for understanding why some species recover quickly from losses and others do not. An African elephant has one calf every four to five years; an orangutan mother gives birth about once every six to eight years, one of the slowest rates of any mammal. Such species are very vulnerable to hunting and habitat loss because their numbers recover slowly.
Metapopulations
Many species live as a metapopulation: a network of separate local populations connected by occasional movement of individuals. Local populations may disappear and later be recolonised from neighbouring ones. The Glanville fritillary butterfly on the Åland Islands of Finland, studied for decades by the ecologist Ilkka Hanski, is the classic example. Keeping habitat patches connected is essential for metapopulations to persist; see Module 10.
Case study: the recovery of tigers in Nepal
In 2009 Nepal had an estimated 121 wild tigers. The government committed to doubling that number by 2022 and invested in protected areas, anti-poaching patrols by the army and community groups, prey recovery and corridors linking parks to India. The 2022 national survey, using camera traps and statistical models, estimated 355 tigers. The recovery follows the early, steep part of a logistic curve. As tiger numbers approach the carrying capacity of the protected areas, conflict with people in surrounding villages has increased, showing how population growth interacts with human landscapes.
Try it yourself
Start with 2 rabbits and assume the population doubles every 3 months with no deaths. How many rabbits would there be after 3 years? Now imagine a carrying capacity of 1,000 rabbits. Sketch how the curve would change.
Common misconceptions
- “Populations naturally stay at a fixed size.” Most fluctuate, sometimes widely.
- “Carrying capacity is fixed.” K changes with weather, habitat quality and human activity.
- “Producing more offspring is always better.” Trade-offs mean investing in fewer, better-prepared offspring can be more successful in stable environments.
Key terms
- Population density: individuals per unit area.
- Exponential growth: growth at a constant rate, producing a J-shaped curve.
- Logistic growth: growth that slows as it approaches carrying capacity, producing an S-shaped curve.
- Density-dependent factor: a factor whose effect increases with crowding.
- Survivorship curve: a graph of survival with age.
- Metapopulation: a set of local populations linked by dispersal.
Quick quiz
Test what you have learned. Scoring 60 percent or more marks this module as complete.
Further reading
- Klein, D. R. (1968). “The introduction, increase, and crash of reindeer on St. Matthew Island.” Journal of Wildlife Management.
- Hanski, I. (1999). Metapopulation Ecology.