18.2 Biodiversity

An ecosystem is the interacting system of a community of organisms and the environment they live in. It includes biotic components, abiotic conditions, energy flow and nutrient cycling. A habitat is where a species lives; its niche is the role it plays there.

Ecosystem — a system scale: populations interact with one another and with physical and chemical conditions.
Habitat — a place boundary: where the organism is found.
Niche — a role-and-resource boundary: how the organism obtains energy, uses conditions and interacts with other species and the physical environment.

Niche overlap means two species use some of the same resources or conditions. The more their roles and resource use overlap, the greater the potential for competition; distinct resource use can allow different species to fit into the same ecosystem. This is a relationship-level interpretation, not a biodiversity count or an automatic proof of competition.

Use the terms in this order:

1. Name the ecosystem as the interacting living-and-non-living system.
2. Identify the habitat as the place occupied by a species.
3. Describe the niche through energy use, physical conditions and interactions.
4. Compare niches when explaining overlap, possible competition or how multiple species can occupy an ecosystem; keep measurement of biodiversity for a separate assessment.

A habitat is not a niche: location alone does not describe a species’ role. Ecosystem, habitat and niche are concepts rather than sampling methods; this card does not introduce quadrats, correlation statistics or Simpson’s index.

Biodiversity describes the range and variety of genes, species and habitats within a region. It can be assessed at three linked levels.

• Ecosystem/habitat diversity — the number and range of different ecosystems or habitats in the area.
• Species diversity — the number of different species and how evenly individuals are distributed among them.
• Genetic diversity — the variety of genes and alleles within each species, including differences between populations of the same species.

Species richness is only the number of species. Species diversity also considers evenness: an area can contain many species but still have lower species diversity if most individuals belong to one or two species. Keep these terms separate when interpreting evidence.

Biodiversity can support ecosystem resilience because variation provides more ways for populations and ecosystems to respond to environmental change. This is a general ecological relationship, not a guarantee that every diverse ecosystem resists every disturbance.

Biodiversity is broader than a species count: ecosystem/habitat, species and genetic levels answer different questions. This card defines the assessment levels; random sampling, field methods, correlation tests and Simpson’s index are separate cards.

Random sampling chooses sampling points by chance so the investigator does not deliberately favour particular parts of the study area. It is most suitable when the area is reasonably uniform and has no clear distribution pattern.

1. Define the whole study area and the population or community to be sampled.
2. Generate random coordinates or point locations across that area.
3. Place the same sampling unit, such as a quadrat where appropriate, at each selected point.
4. Use the same sampling rules and record the observations from every point.
5. Repeat across enough independent points, then judge whether the sample represents the whole area.

Because the locations are selected by chance, random sampling reduces selection bias from the person carrying out the investigation. Standardising the sampling unit and recording rule makes comparisons between points fairer; repeated points improve confidence in the estimate.

Systematic sampling places points by a planned pattern chosen by the investigator, so the chosen pattern or starting point can miss parts of the area or introduce bias. Do not treat random sampling as automatically best for a strongly patterned or clearly non-uniform area.

Random sampling is a method for choosing representative sample locations. It does not by itself prove that a sample is large enough or replace later analysis of abundance, distribution, correlation or Simpson’s index.

Choose the field method that matches the evidence needed: a transect shows how distribution changes across a gradient, while quadrats record what is present within a standard area.

1. Define the area, species and physical gradient or comparison being investigated.
2. For distribution along a gradient, lay a line transect across the area and record organisms touching the line at fixed intervals; this gives qualitative presence data.
3. For abundance within areas, place the same-sized quadrat at random or at regular transect intervals, then record each species in every quadrat.
4. Use frequency for how often a species occurs in the quadrats; use density for individuals per unit area, or percentage cover when individuals are difficult to count.
5. Repeat across multiple points, keep quadrat size, interval, observer rule and recording time consistent, and present the data against position or sample area.

A belt transect combines a line across the gradient with quadrats at regular intervals, so it can show how abundance changes quantitatively. A line transect alone records which organisms meet the line and does not provide the same abundance measure.

Randomly choosing quadrat locations reduces selection bias when estimating an area-wide value. A planned interval is useful for a clear environmental gradient, but the starting point and interval should be stated. Use the same area and sampling rules so differences are not caused by inconsistent effort.

Method choice and recording units come before statistical interpretation. Do not treat a single transect or quadrat as the whole ecosystem, and do not move this card into Pearson’s or Spearman’s correlation calculations or Simpson’s index.

Pearson’s linear correlation tests whether two quantitative variables show a linear relationship. The coefficient r ranges from -1 to +1: a value near +1 indicates a strong positive relationship, a value near -1 a strong negative relationship, and a value near 0 little or no linear correlation.

1. Pair each reading of variable x with the reading of variable y from the same sample or quadrat.
2. Plot the paired values on a scatter graph and check that a linear pattern is plausible.
3. State the null hypothesis that there is no linear correlation between the variables.
4. Check the data assumptions: both variables are quantitative, the relationship is approximately linear, and the data show a normal distribution.
5. Calculate the means, products and standard deviations required by the supplied Pearson equation, then substitute the values to obtain r.
6. Compare the result with the appropriate significance criterion or critical value for the sample, then state the strength and direction of the correlation and whether the null hypothesis is rejected.

Interpret the statistic with the scatter graph and the sample size, not from a single point. A statistically significant correlation supports an association in the sampled data under the test assumptions; it does not show that one variable causes the other.

Correlation is not causation: a third factor, sampling pattern or coincidence may explain an association. Pearson’s method is for the stated quantitative, approximately linear and normally distributed case; Spearman’s rank correlation and Simpson’s index are separate cards.

Spearman’s rank correlation tests for association between two variables using their ranks. It is useful when the data are not quantitative, are not normally distributed, or show a non-linear but monotonic relationship.

1. Pair the two observations from each sample, then state the null hypothesis that there is no correlation.
2. Rank each variable separately, using the course convention for tied values when ties occur.
3. For every pair, calculate the difference in rank, d, then calculate d²; add the d² values to obtain Σd².
4. Substitute Σd² and the number of samples n into the supplied Spearman equation to calculate rₛ.
5. Determine the direction from the sign and the strength from how close rₛ is to +1 or -1; values near 0 indicate little rank association.
6. Compare the calculated value with the critical value for n and the stated probability level, then reject or retain the null hypothesis with a conclusion in context.

The test evaluates a monotonic rank relationship: as one variable increases in rank, the other tends to increase or decrease in rank. It does not require a straight-line relationship, but a non-monotonic pattern is not captured reliably by one coefficient.

A significant rank correlation is evidence of association in the sampled data, not proof that one variable causes the other. A third factor, sampling pattern or coincidence may explain the relationship. This is distinct from Pearson’s linear correlation and Simpson’s index.