Effect of Different Variables on Species Distribution (AQA A-Level Biology): Revision Notes

Effect of Different Variables on Species Distribution

Purpose and principle

This practical investigates how environmental factors influence where species are found in their habitat. The underlying principle is that species distribution is controlled by both living and non-living factors in the environment, and by measuring these factors alongside species abundance, we can identify relationships that help explain ecological patterns.

The investigation uses random sampling techniques to collect data on species presence and environmental variables, then applies statistical analysis to determine whether correlations exist between the two.

Background theory

• Species distribution refers to where organisms are found within their habitat. This distribution is influenced by two main categories of environmental factors:
• Abiotic factors are non-living environmental variables that affect organisms. Key examples include light intensity, the availability of water and nutrients, and temperature. These physical and chemical conditions directly influence physiological processes like photosynthesis, respiration, and enzyme activity.

• Biotic factors involve living components of the ecosystem. These include competition for resources between organisms, the presence of predators, and the occurrence of disease. These biological interactions affect survival, reproduction, and population dynamics.
• Understanding both factor types is essential because species distribution patterns result from complex interactions between multiple variables rather than single causes.

Apparatus and materials

• Quadrat (for sampling)
• Two tape measures (for creating coordinate system)
• Appropriate equipment to measure chosen variable (e.g. photometer for light intensity, pH probe for soil pH, thermometer for temperature)

Method

1. Select a 5m × 5m study area for sampling. Generate ten sets of random coordinates using a random number generator. This ensures sampling is unbiased and representative of the habitat.
2. Create a coordinate system using two tape measures positioned to form axes at right angles. This allows precise location of sampling points using the generated coordinates.
3. Position the quadrat at each coordinate location, ensuring the bottom left corner aligns with the exact coordinate point every time. Consistent placement prevents sampling bias.

1. Record the percentage cover of the chosen species at each sampling point. Count how many of the quadrat's 100 squares contain the target species, but only include squares where half or more of the area contains the species. This standardises the counting method.
2. Measure the independent variable (chosen environmental factor) at each coordinate. For example, when investigating light intensity effects, use a photometer to record light readings at each sampling location.

Risk assessment

Hazard	Risk	Safety precaution	Emergency response	Risk level
Biohazard	Allergic reactions; exposure to soil bacteria; contamination	Wash hands thoroughly after the practical	Seek medical assistance	Low
Slippery surfaces	Slip hazard during fieldwork	Wear appropriate footwear; avoid running	Seek appropriate medical attention	Low

Both risks are classified as low level, but proper precautions should always be followed during fieldwork to ensure safety. Always inform someone of your fieldwork location and expected return time.

Data collection and processing

Results should be recorded in a table showing coordinates, percentage cover values, and measurements of the chosen environmental variable. Ensure all measurements include appropriate units and are recorded to consistent decimal places.

Create a scatter graph plotting percentage cover (dependent variable) on the y-axis against the chosen independent variable on the x-axis. This visualisation helps identify potential relationships between the environmental factor and species distribution.

Analysis and interpretation

Various statistical tests can be applied to the collected data, including Spearman's Rank correlation, T-test, and Chi Squared test. The choice depends on the data type and what relationship is being tested.

Expected results should show a correlation between the environmental variable and species abundance. For example, light-requiring species might show higher percentage cover in areas with greater light intensity.

However, the strength and direction of correlations will vary depending on the species studied and the environmental factor measured. Some relationships may be positive (abundance increases with the variable), others negative (abundance decreases), or there may be optimal ranges where species abundance peaks.

Links to theory

This practical connects to several ecological concepts including niche theory, habitat preferences, and environmental tolerance ranges. It also links to physiological topics such as photosynthesis (for light-related investigations) or enzyme activity (for temperature studies).

The sampling techniques used here apply to broader ecological survey methods and biodiversity assessment techniques used in conservation biology.

Conclusion

Analysis should reveal correlations between the chosen environmental variable and species distribution patterns. Strong correlations suggest the measured factor influences where the species is found.

However, it is essential to remember that correlation does not necessarily indicate causation. Species distribution results from multiple interacting factors, so the measured variable may not be the direct cause of observed patterns. Other unmeasured environmental factors could be responsible for the correlation, or the measured factor might interact with other variables to produce the observed effect.

Remember!