Biodiversity — Cambridge AS & A Level Biology 9700 Revision Notes & Topical Questions

Section 1 of 24Ecosystems, habitats and niches

Ecosystems, habitats and niches

Ecosystem

• Ecosystem a relatively self-contained community of interacting organisms and the environment they live in.
• It has living (biotic) and non-living (abiotic) components, energy flows through it and nutrients are recycled.

Habitat

• Habitat the place where a species lives within an ecosystem.

Niche

• Niche the role of a species in its ecosystem: where it is, how it gets energy and how it interacts with other species and its physical environment.

Dung beetles roll dung into balls and bury it, which aerates the soil, releases nutrients and helps control flies.

Section 2 of 24Levels of biodiversity

Biodiversity

• Biodiversity all the variation within and between all forms of life: the range of genes, species and habitats in a region.

Ecosystem (habitat) diversity

• Ecosystem (habitat) diversity the range of ecosystems or habitats in an area;
  • coral reefs are high and sandy deserts low.

Species diversity

• Species diversity the number of species and the evenness of their abundance.
  • (species richness)
• Many species with only 3 or 4 individuals each does not give high species diversity.

Genetic diversity

• Genetic diversity the diversity of alleles and genes within a species.
• Measured as the proportion of genes with more than one allele and the number of alleles per gene.
• It helps a population adapt to changes in biotic and abiotic factors.

High species diversity gives more stable, resilient ecosystems, whereas one dominated by one or two trees can collapse if a pathogen targets them.

Section 3 of 24Random and systematic sampling

Random and systematic sampling

Sampling

• Sampling investigating the abundance and distribution of species by examining parts of the area, because counting everything is often impossible.

Random sampling

• Random sampling positions of sampling points are decided by chance, so there is no bias by the person sampling.
• Best when the area is reasonably uniform or has no clear pattern.
• Use a grid of numbered squares and a random number generator.

Systematic sampling

• Systematic sampling positions chosen by the person or at regular intervals.
• Can show bias, e.g. placing quadrats where there are fewest species.
• Suited to areas with changing conditions, such as altitude, soil pH or light intensity.

Section 4 of 24Quadrats and measures of abundance

Distribution

• Distribution how a species is spread through the ecosystem;
  • abundance | the number of individuals of a species.

Quadrats are 25 cm × 25 cm, 50 cm × 50 cm or 1 m × 1 m frames laid at random points.

Species frequency = (quadrats containing the species ÷ total quadrats) × 100.

Species density = number of individuals counted ÷ total area of all quadrats.

Percentage cover

• Percentage cover estimated from the number of squares out of 100 in the quadrat in which the species occurs, used when individuals cannot be counted.
• Cover can total more or less than 100% because of overlap and gaps.

Section 5 of 24Transects

A transect is a line (tape measure) along which samples are taken, used to show how distribution changes with physical conditions.

Line transect

• Line transect record organisms touching the tape at equal distances; gives qualitative data.

Belt transect

• Belt transect place quadrats at regular intervals along the tape and record abundance;
  • gives quantitative data.

Abiotic factors (light, pH, moisture) are measured at each position to explain the pattern.

Section 6 of 24Mark-release-recapture

1

Capture as many individuals as possible in a first large sample, count them and mark them without affecting survival.

2

Release them to mix randomly with the population.

3

After enough time capture a second large sample.

4

Count marked and unmarked individuals in the second sample.

5

Estimate population size with the Lincoln index N = n₁ × n₂ ÷ m₂.

• n₁ = marked individuals released, n₂ = second sample size, m₂ = marked individuals in the second sample.

6

Used for mobile animals, since quadrats and transects suit only stationary organisms.

Section 7 of 24Correlation tests

Correlation

• Correlation an association between two variables; it does not imply causation.

Pearson's linear correlation

• Pearson's linear correlation for quantitative, normally distributed data with a linear relationship.
• r = (Σxy − n x̄ȳ) ÷ ((n − 1) s_xs_y); r near +1 or −1 shows strong correlation, 0 means none.

Spearman's rank correlation

• Spearman's rank correlation for non-quantitative, non-linear or non-normal data.
• r_s = 1 − 6ΣD² ÷ (n³ − n) with D the rank difference.
• If r_s exceeds the critical value at p = 0.05 the null hypothesis is rejected.

Section 8 of 24Simpson's index of diversity

Index of diversity

• Index of diversity a number that takes account of both species richness and evenness.

Simpson's index D = 1 − Σ(n/N)².

• n = total organisms of one species; N = total organisms of all species.

Method: calculate n/N for each species, square each value, add them and subtract the total from 1.

D lies between 0 and 1; values near 1 mean high biodiversity and near 0 low biodiversity.

Section 9 of 24Distribution and abundance

Distribution

• Distribution how a species is spread through the ecosystem.

Abundance

• Abundance the number of individuals of the species.

Species frequency

• × 100.
  • (number of quadrats containing the species ÷ total quadrats)

Species density

• Species density total individuals counted ÷ total area of all quadrats.

Percentage cover

• Percentage cover used for plants that are hard to count.

Section 10 of 24Transects

Transects show how distribution changes with physical conditions.

A transect is a line (measuring tape) along which samples are taken.

Line transect

• Line transect lay a tape across the area and record organisms touching it at equal distances, e.g. every 2 m.

Belt transect

• Belt transect place a quadrat at regular intervals along the tape.

Record abiotic factors (light, pH, soil moisture, salinity) with each sample.

Section 11 of 24Mark-release-recapture

1

Capture a large sample of mobile animals.

2

Mark each harmlessly, e.g. with non-toxic waterproof paint.

3

Release them and allow time to mix.

4

Capture another large sample and count marked and unmarked individuals.

5

Estimate population size from the proportion marked.

Section 12 of 24The Lincoln index

The Lincoln index

N = n₁ × n₂ ÷ m₂

• n₁ = number caught and marked in the first sample.
• n₂ = total caught in the second sample.
• m₂ = marked individuals in the second sample.

Assumptions:

• Marking does not affect survival.
• Marked animals mix completely.
• No births, deaths or migration.
• The mark does not fade.

Section 13 of 24Correlation

Correlation

• Correlation an association or relationship between two variables.

Positive correlation

• Positive correlation both variables increase together.

Negative correlation

• Negative correlation one increases as the other decreases.

No correlation

• No correlation no pattern in the scatter graph.

The correlation coefficient r from Pearson's test shows whether a linear relationship exists and how strong it is.

• Pearson's test suits quantitative data, e.g. counts of individuals.

Section 14 of 24Pearson's linear correlation

Pearson's test

• Pearson's test determines whether there is a linear relationship between two quantitative variables, e.g. the abundance of two plant species.

Conditions:

• Quantitative data.
• Roughly normal distribution with no major outliers.
• A scatter graph that looks roughly linear.

Null hypothesis

• Null hypothesis there is no correlation.

If r is close to +1.0 or −1.0 there is a strong linear correlation and the null hypothesis can be rejected.

Section 15 of 24Steps for Pearson's test

1

State the null hypothesis.

2

Calculate the means x̄ and ȳ.

3

Calculate n x̄ ȳ, where n is the number of quadrat samples.

4

Calculate Σxy.

5

Calculate the standard deviations s_x and s_y.

6

Substitute into r = (Σxy − n x̄ ȳ) ÷ ((n − 1) s_x s_y).

7

Compare r with ±1 or the critical value for n.

Section 16 of 24Spearman's rank correlation

Used when:

• Data are not quantitative, e.g. an abundance scale such as ACFOR rather than counts.
• The relationship appears non-linear.
• The data are not normally distributed.

Null hypothesis

• Null hypothesis there is no correlation between the two variables.

Section 17 of 24Steps for Spearman's rank

1

Rank each set of data, with rank 1 the smallest; ties share the mean rank.

2

Find the difference in rank (D) for each pair.

3

Square each difference (D²) and add to give ΣD².

4

Calculate r_s = 1 − 6ΣD² ÷ (n³ − n).

5

Compare r_s with the critical value for n at p = 0.05.

6

If r_s is greater than the critical value, reject the null hypothesis: there is a significant correlation.

Section 18 of 24Interpreting both tests

Interpreting both tests

The sign of r shows positive or negative correlation.

A significant correlation does not prove one variable causes the other.

Choose Pearson's for linear, normally distributed quantitative data and Spearman's otherwise.

Section 19 of 24Richness, evenness and diversity

Species diversity considers both the number of species (species richness) and the evenness of their abundance.

A community dominated by one species has low evenness and lower diversity than one in which all species are equally common.

An index of diversity

• An index of diversity a number that takes richness and evenness into account.

Simpson's index of diversity (D) is one such index.

Section 20 of 24Calculating Simpson's index

Record the number of individuals of each species (n) and the total (N).

Calculate n ÷ N for each species.

Square each value.

Add all the squared values to give Σ(n ÷ N)².

What the symbols mean:

Subtract the total from 1: D = 1 − Σ(n ÷ N)².

What the symbols mean: D = Simpson’s index of diversity · n = number of individuals of one species · N = total number of individuals of all species

Section 21 of 24Interpreting D

Interpreting D

D ranges from 0 (no diversity) to 1.

The higher the value, the greater the diversity.

A low value suggests one or a few dominant species, possibly under pressure from pollution or disturbance.

Section 22 of 24Using diversity indices

1

Compare sites.

2

Monitor change over time, e.g. after a conservation scheme.

3

Show the effect of human activity such as pollution reducing sensitive species.

4

Limitations: the result depends on how well the sample represents the habitat,

• so use random sampling with enough quadrats and identify each species correctly.

Section 23 of 24Key terms

Term	Meaning
Biodiversity	Variation within and between all forms of life: genes, species and habitats.
Niche	The role of a species in an ecosystem.
Species richness	The number of species in an ecosystem.
Species diversity	Number of species and the evenness of their abundance.
Percentage cover	Percentage of a quadrat covered by a species.
Lincoln index	N = n1 × n2 ÷ m2, the estimate of population size from mark-release-recapture.
Simpson's index	D = 1 − Σ(n/N)2, a measure of diversity.

Term	Meaning
Distribution	How a species is spread through an ecosystem.
Abundance	Number of individuals of a species.
Transect	Line along which samples are taken.
Lincoln index	N = n1 × n2 ÷ m2.
Correlation	An association between two variables.
Percentage cover	Percentage of area covered by a species.

Term	Meaning
Pearson's correlation	Test for a linear relationship between two quantitative variables.
Spearman's rank	Test for correlation using ranks.
Rank	Position in order, 1 for the smallest.
Correlation coefficient	Value of r or rs showing strength and direction of correlation.
Null hypothesis	No correlation between the variables.

Term	Meaning
Species richness	Number of different species.
Species evenness	How evenly individuals are spread among species.
Index of diversity	Number combining richness and evenness.
Simpson's index	D = 1 − Σ(n/N)2.
n	Number of individuals of one species.
N	Total number of individuals of all species.

Section 24 of 24Exam technique

1

Confusing species richness with species diversity — diversity also includes how even the numbers are.

2

Placing quadrats by choice and calling it random — random sampling needs random coordinates.

3

Using quadrats for mobile animals — use mark-release-recapture.

4

Inferring causation from a correlation — a correlation does not show that one variable causes the other.

5

Saying a Simpson's index near 0 means high diversity — high diversity is near 1.

1

Using quadrats for mobile animals — use mark-release-recapture.

2

Calling frequency the number of individuals.

3

Forgetting the assumptions of the Lincoln index.

4

Assuming correlation proves causation.

1

Using Pearson's test on non-normal or ranked data.

2

Using the wrong n for the critical value.

3

Taking correlation as proof of causation.

4

Forgetting to state the null hypothesis.

1

Using richness alone as diversity — evenness also matters.

2

Saying a low D means high diversity.

3

Forgetting to subtract from 1.

4

Using a biased sample.