describe and use suitable methods to assess the distribution and abundance of organisms in an area, limited to frame quadrats, line transects, belt transects and mark-release-recapture using the Lincoln index (the formula for the Lincoln index will b

Biology – Biodiversity: Assessing Distribution and Abundance (Cambridge AS & A Level 9700, Topic 18)

Learning Objective

Describe and apply suitable methods to assess the distribution and abundance of organisms in a defined area, and understand how these data link to the wider study of biodiversity, its measurement, threats and conservation.

1. Biodiversity and Taxonomic Context

1.1 Levels of Biodiversity

• Genetic diversity – variation of genes within a species.
• Species diversity – number of different species (species richness) and their relative abundances (evenness).
• Ecosystem diversity – variety of habitats, biotic communities and ecological processes.

1.2 Classification & Binomial Nomenclature

The Linnaean hierarchy (required for the syllabus) is:

1. Domain
2. Kingdom
3. Phylum
4. Class
5. Order
6. Family
7. Genus
8. Species

A species name is written in binomial form: Genus species (italicised, genus capitalised). Example:

Panthera leo – the African lion (Genus Panthera, species leo).

1.3 IUCN Red‑List Categories (conservation status)

1.4 Major Threats to Biodiversity

• Habitat loss & fragmentation
• Over‑exploitation (hunting, fishing, logging)
• Invasive alien species
• Pollution (chemical, plastic, nutrient)
• Climate change

1.5 Conservation Approaches (brief)

• Protected areas (national parks, nature reserves)
• Ex‑situ conservation (seed banks, captive breeding)
• Legislation & international agreements (CITES, Biodiversity Convention)
• Habitat restoration & ecological corridors
• Community‑based management & sustainable use

2. Quantitative Measures of Biodiversity

2.1 Basic Density

Density = Number of individuals ÷ Area surveyed (units = individuals m⁻²).

2.2 Species Richness (S)

Pure count of different species recorded in the sample.

2.3 Diversity Indices

2.4 Worked Example (Quadrat Data)

1. Four 1 m² quadrats give counts: A = 12, B = 8, C = 4, D = 6.
2. Total individuals, N = 30; species richness, S = 4.
3. Density: 30 individuals ÷ 4 m² = 7.5 ind m⁻².
4. Shannon‑Wiener:
   

   \$pA=0.40,\;pB=0.267,\;pC=0.133,\;pD=0.200\$
   

   \$H' = -(0.40\ln0.40+0.267\ln0.267+0.133\ln0.133+0.200\ln0.200)=1.28\$

5. Simpson’s D: 0.735 (as shown in original notes).
6. Evenness: 0.98 (very even distribution).

3. Designing a Biodiversity Survey (AO3 Guidance)

Use this checklist when planning any of the field methods described later.

3.1 Data Presentation Template

Below is a simple table format that can be adapted for any method.

Typical graphs:

• Bar chart of species abundances (error bars = ± SD of replicates).
• Scatter plot of density versus distance from a disturbance gradient.
• Pie chart for relative abundance (useful for quick visualisation).

4. Field Survey Techniques

4.1 Frame Quadrats

Best for: sessile or slow‑moving organisms (plants, lichens, intertidal invertebrates).

1. Choose a quadrat size (e.g., 0.5 m × 0.5 m = 0.25 m²).
2. Place quadrats at random or at regular intervals (systematic grid is common).
3. Identify and count every individual of each target species inside the frame.
4. Record data and repeat for enough replicates (≥ 20 % of the study area is a useful rule of thumb).

Density calculation (individuals m⁻²):

\$\text{Density} = \frac{\displaystyle\sum{k=1}^{Q} Nk}{Q \times A_q}\$

• Nₖ = individuals in quadrat k
• Q = number of quadrats
• A_q = area of one quadrat (m²)

Assumptions: uniform distribution within each quadrat; sample is representative of the whole habitat.

4.2 Line Transects

Best for: mobile fauna that can be detected from a line (birds, mammals, large insects).

1. Lay a straight measuring tape of known length L (e.g., 100 m).
2. Define a detection width w on each side of the line (total strip width = 2 w). The width is based on the observer’s effective sighting distance.
3. Walk the line at a steady pace, recording every individual that falls within the strip.

Density (individuals m⁻²):

\$\text{Density} = \frac{C}{L \times 2w}\$

• C = total count of individuals observed.

Bias notes: detection probability declines with distance from the line; using a fixed width reduces but does not eliminate this bias. If detection is uncertain, distance‑sampling methods (beyond the syllabus) may be required.

4.3 Belt Transects

Best for: mixed communities where both sessile and moderately mobile organisms are of interest.

1. Define belt dimensions (e.g., 50 m × 5 m = 250 m²).
2. Walk the centre line; at regular intervals (e.g., every 5 m) place a small sub‑quadrat (e.g., 0.25 m²) to record sessile species.
3. Simultaneously note all mobile animals seen within the belt width.

Overall density (individuals m⁻²):

\$\text{Density} = \frac{\displaystyle\sum \text{All individuals recorded}}{\text{Belt area (m²)}}\$

Assumptions: same as quadrats and line transects combined; sub‑quadrats are representative of the belt.

4.4 Mark‑Release‑Recapture (Lincoln Index)

Best for: estimating the size of mobile animal populations where direct counts are impractical (small mammals, amphibians, insects, fish).

Procedure

1. First capture (M): Capture and uniquely mark a sample of M individuals (e.g., coloured, non‑toxic dye or numbered tags).
2. Release: Release all marked individuals and allow sufficient time for mixing with the unmarked population.
3. Second capture (C): Capture a new sample of size C.
4. Recaptures (R): Count how many of the C individuals are already marked; this number is R.
5. Calculate the estimated total population N using the Lincoln index.

Lincoln Index (units = individuals):

\$N = \frac{M \times C}{R}\$

Assumptions & Sources of Bias

• Population is closed during the two sampling events (no births, deaths, immigration or emigration).
• Marked individuals mix completely with the unmarked population before the second capture.
• Marking does not affect survival, behaviour or capture probability.
• All individuals have an equal chance of being captured in both samplings.
• Low recapture numbers (R ≈ 0) give large uncertainties; repeated sampling can improve reliability.

5. Comparison of Methods

6. Links to Other Syllabus Topics

• Selection & Evolution: Population size estimates feed into calculations of genetic drift and effective population size.
• Genetics of Populations: Species‑richness and diversity indices relate to allelic diversity and inbreeding risk.
• Ecosystem Processes: Abundance data are required for modelling energy flow, nutrient cycling and trophic dynamics.
• Energy & Respiration: Density of primary producers influences primary productivity estimates.

7. Suggested Diagrams (to be drawn by the teacher or students)

1. Schematic of a frame quadrat on a rocky shore showing counted organisms.
2. Illustration of a line transect with a fixed detection width on either side.
3. Diagram of a belt transect with sub‑quadrats placed at regular intervals.
4. Flowchart of the Mark‑Release‑Recapture process (M → release → C → R → calculation of N).
5. Example bar‑graph of species abundances with error bars (SD).