Living Organisms Vary: The Genetics of Difference (Syllabus 3.1.6)
Hello Biologists! This chapter is all about recognizing and understanding the amazing differences we see in life, from the huge variety of creatures on Earth (lions vs. bees) to the small differences between family members (you vs. your sibling). Understanding this variation is crucial because it forms the very basis of evolution and biodiversity.
Don't worry if statistics or sampling methods seem complicated—we will break them down simply. Let's explore what makes every organism unique!
1. Defining Variation: Comparing Differences
Variation refers to the differences in characteristics (phenotypes) observed among individuals or groups. Biologists split this into two main types:
a) Intraspecific Variation
Intraspecific variation is the variation found within members of a single species.
- Example: The differences in height, hair color, or blood group among humans.
- Mnemonic Trick: Think INTRAnet—a network within one company.
b) Interspecific Variation
Interspecific variation is the variation found between members of different species.
- Example: The difference in structure and biochemistry between a dog and a tree, or even between a dog and a wolf.
- Mnemonic Trick: Think INTERnational—travel between different countries.
Key Takeaway: Intraspecific variation increases the resilience of a species to environmental change, while interspecific variation contributes directly to the overall biodiversity of an ecosystem.
2. Sources of Intraspecific Variation
Why do individuals within the same species look and behave differently? These differences arise from three main sources:
a) Genetic Factors
These are the differences inherited from parents, determined by the unique combination of alleles an individual possesses.
- These factors are passed on through gametes (sperm and egg).
- Examples include eye colour, blood type, and inherited diseases.
- Genetic variation is influenced by processes like crossing over and independent segregation during meiosis, and also by random fertilisation.
b) Environmental Factors
These are the differences caused by the organism’s surroundings, lifestyle, and nutrition throughout its life.
- These characteristics are generally not passed on to the next generation.
- Examples include scars, speaking a certain language, or the strength of muscles developed through exercise.
- Did you know? The colour of hydrangeas (a type of flower) is environmental—they turn blue in acidic soil and pink in alkaline soil!
c) Combined Factors (The Reality)
Most characteristics are the result of an interaction between genes and the environment. This is the most common scenario.
- Example: Height. Your potential maximum height is set by your genes, but whether you reach that potential depends on environmental factors like your diet and access to nutrients during childhood.
Common Mistake to Avoid: Don't assume variation is always "either/or." Often, it’s a sliding scale determined by both genetics and environment.
3. Investigating Variation: Ensuring Reliable Data
When studying variation in a population (intraspecific variation), we often measure a sample of individuals rather than measuring every single organism. This means we must be very careful how we collect our data to ensure it accurately reflects the whole population.
a) The Need for Random Sampling
To avoid bias and ensure your sample truly represents the variation in the population, you must use random sampling.
- Why is it important? It ensures that every individual in the population has an equal chance of being selected. If you only measure the tallest people in a class, your sample mean will be biased (too high).
- How to do it: Use random number generators or a grid system (quadrats) to select sampling locations or individuals without human interference.
b) The Importance of Sample Size
The size of your sample matters hugely. If your sample is too small (e.g., measuring the height of only five people), any unusual individual will skew the data significantly.
- Appropriate Sample Size: A larger sample size (e.g., 50 or 100 individuals) is more likely to include the full range of variation and ensures that the data is representative of the entire population.
- The importance of chance means that differences between small samples are often high, but differences between large, random samples are usually minimal.
Quick Review Box: Sampling
- Randomness: Eliminates bias.
- Large Size: Ensures representativeness and reduces the effect of chance.
4. Analysing Data: Measures of Variation
Data collected on characteristics showing continuous variation (like height or weight) often follow a pattern known as the Normal Distribution.
a) Normal Distribution (The Bell Curve)
The concept of normal distribution describes how most data points cluster around the average value, creating a bell-shaped curve when plotted on a graph.
- The peak of the curve represents the mean (average) value.
- The curve is symmetrical around the mean.
- Fewer individuals exist at the extremes (the tails of the curve).
b) The Mean (Average)
The mean is a measure of the central tendency (what the 'typical' value is) within a sample. It is calculated by adding up all the measured values and dividing by the number of measurements taken.
c) Standard Deviation (SD)
The standard deviation is a measure of the spread or dispersion of the data around the mean.
- It tells you how much the individuals vary from the average.
- Low SD: The data points are tightly clustered around the mean. The individuals in the sample are very similar (low variation).
- High SD: The data points are widely spread out. The individuals in the sample show a wide range of characteristics (high variation).
Important Note for Exams: You are expected to understand what the mean and standard deviation represent as measures of variation, but you will not be required to calculate standard deviation in written papers.
d) Interpreting Variation Data
When analysing data:
- Identify the mean to see the central tendency (e.g., the average wing length of the species).
- Identify the Standard Deviation to determine the level of variation (e.g., a high SD in wing length means some are very small and others very large; a low SD means most wings are about the same size).
- If comparing two different species (interspecific variation), look at whether their means and ranges (often represented by the mean ± SD) overlap. A large difference in means suggests they are distinctly different species.
Key Takeaway: The mean tells us the 'typical' value, and the standard deviation tells us how varied the population is around that typical value. Both are essential for describing variation.