Populations
| Term | Description |
|---|---|
| Population | A group of organisms of one species, living in the same area, at the same time. (Key: One species only) |
| Community | All of the populations of different species living and interacting in an ecosystem. (Key: Many species) |
| Ecosystem | A unit containing the community of organisms AND their environment (abiotic factors like light, temperature, water), interacting together. (Key: Living + Non-living) |
Key Limiting Factors for Population Growth:
- Food Supply: Lack of food increases competition and starvation.
- Competition: Intraspecific (within species) or interspecific (between species) competition for resources like space, light, or mates.
- Predation: Higher predator numbers increase the death rate of the prey population.
- Disease: High population density facilitates the spread of pathogens, increasing the death rate.
| Phase | Description of Growth | Biological Explanation (Why does this happen?) |
|---|---|---|
| 1. Lag Phase | Slow initial growth; population size remains low. | The population density is low. Individuals are acclimatizing to the new environment and, crucially, taking time to find mates and reproduce. The birth rate is initially low because few individuals are present to breed. |
| 2. Exponential (Log) Phase | Rapid, exponential increase in population size. | Resources (food, space) are abundant and limiting factors are not yet effective. The birth rate is maximized and the death rate is minimized. There is little competition, allowing rapid reproduction. |
| 3. Stationary Phase | Growth slows and stabilizes; population size levels off. | Limiting factors (food shortage, space constraints, waste accumulation) become intense. This leads to increased intraspecific competition. As a result, the birth rate decreases and/or the death rate increases until they equalize. The population has reached the environment's carrying capacity. |
| 4. Death (Decline) Phase | Population size decreases. | Resources are severely depleted or toxins accumulate to harmful levels. The death rate exceeds the birth rate. This may be due to starvation, disease spread, or toxic waste buildup. |
Example 1: Identifying Phases
You are given a graph of bacterial population over time. The curve starts flat, rises steeply, then flattens out.
- Flat start: Lag Phase (low density, finding mates).
- Steep rise: Exponential Phase (abundant resources, high birth rate).
- Flat top: Stationary Phase (carrying capacity reached, birth rate = death rate).
Example 2: Calculating Percentage Change
Cambridge often asks for the percentage increase or decrease in a population between two time points.
Formula:
\text{Percentage Change} = \frac{\text{New Value} - \text{Original Value}}{\text{Original Value}} \times 100
Note: If the result is negative, it is a percentage decrease.
Worked Example:
A red king crab population was 5,000 in 1995 and 21,650 in 1999.
- Identify values: Original = 5,000; New = 21,650.
- Substitute:
\frac{21,650 - 5,000}{5,000} \times 100 - Calculate:
\frac{16,650}{5,000} \times 100 = 3.33 \times 100 = 333% - Answer: The population increased by 333%.
Example 3: Interpreting Data for Sampling
In practical papers (Paper 5/6), you may be asked why a large sample size is used.
- Reason: To obtain a representative sample and avoid bias. A small sample might accidentally include too many or too few individuals of a certain type, leading to unreliable results.
Mistake 1: Confusing Lag and Exponential Phases
- Error: Thinking the lag phase is when growth is fastest.
- Correction: The lag phase is the slowest growth. The exponential phase is the fastest. Remember: Lag = 'Lagging behind' or 'Taking time to start'.
Mistake 2: Defining Population Incorrectly
- Error: Saying 'a group of organisms in an area.'
- Correction: You must specify 'one species' and 'same time'. Without these, you are describing a community or just a group.
Mistake 3: Explaining Stationary Phase as 'No Births'
- Error: Saying 'births stop in the stationary phase.'
- Correction: Births do not stop; they continue but at a rate that equals the death rate. The population size remains constant because \text{Birth Rate} = \text{Death Rate}.
Mistake 4: Ignoring Abiotic Factors in Ecosystem Definitions
- Error: Defining an ecosystem as just 'all living things.'
- Correction: An ecosystem must include the non-living environment (abiotic factors).
Tip 1: Explaining the Stationary Phase
- Context: When asked to explain why population growth slows in the stationary phase.
- Correct Phrasing: Use the phrase 'intraspecific competition' and link it to limiting factors. Example: 'As population density increases, intraspecific competition for limited resources (e.g., food) intensifies. This causes the birth rate to decrease and/or the death rate to increase until they equalize.'
- Why this works: Examiners look for the mechanism of competition driven by limited resources, not just 'lack of food'.
Tip 2: Identifying the Lag Phase Cause
- Context: When asked why growth is slow at the start.
- Correct Phrasing: Focus on low population density and finding mates. Example: 'The initial population size is small, so individuals take time to find mates and reproduce. The birth rate is initially low.'
- Why this works: Cambridge specifically accepts 'time taken to find mates' as the primary biological reason for the lag phase in sexual populations.
Tip 3: Describing Graph Trends
- Context: When asked to describe a change in population size.
- Correct Phrasing: Be specific. Instead of 'it goes up', say 'the population increases rapidly from year X to Y'. If calculating, show your working clearly. For percentage change, always state the unit (%).
- Why this works: Marks are often awarded for identifying the correct phase (e.g., 'exponential') and providing specific data points (e.g., 'increases from 100 to 400').
- A group of organisms of one species (1).
2. Living in the same area at the same time (1).
- Resources (food/space) are depleted or toxins accumulate (1).
2. The death rate exceeds the birth rate (1).
- Use random sampling (avoid bias) (1).
2. Use a large number of quadrats / repeat samples (1).