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

Biodiversity – Assessing Distribution and Abundance (Cambridge AS & A‑Level Biology 9700)

Understanding where organisms occur and how many individuals are present underpins biodiversity measurement, ecosystem‑health assessment and conservation decision‑making. The notes below cover the full range of syllabus requirements for practical field sampling (Paper 3 & 5) and the theoretical background needed for Paper 1.

1. Theoretical Foundations (Syllabus 18 – Classification, Biodiversity & Conservation)

• Taxonomic hierarchy: kingdom → phylum → class → order → family → genus → species. Binomial nomenclature (genus + specific epithet) is mandatory for all species lists.
• Phylogenetic classification: cladograms illustrate evolutionary relationships; recognise monophyletic (all descendants of a common ancestor) vs. paraphyletic groups.
• IUCN Red‑List categories: Extinct (EX), Extinct in the Wild (EW), Critically Endangered (CR), Endangered (EN), Vulnerable (VU), Near‑Threatened (NT), Least Concern (LC). Criteria are based on population size, rate of decline, geographic range and fragmentation.
• Ecosystem‑level concepts: biomes, keystone species, ecosystem services (provisioning, regulating, cultural, supporting).

These concepts can be linked directly to field methods. For example, a quadrat survey that shows a steady decline in Species X may provide the quantitative evidence required to propose an upgrade from Least Concern to Vulnerable on the IUCN Red List.

2. Core Biodiversity Indices (Mathematical Requirements)

3. Field Sampling Methods

Each method includes (i) planning prompts (AO3), (ii) a step‑by‑step procedure, (iii) data recorded, (iv) basic analysis, and (v) an evaluation checklist (AO2). Where relevant, statistical tests for comparing habitats are suggested.

3.1 Frame Quadrats

When to use: Sessile or slow‑moving organisms on relatively uniform substrates (e.g., plants, lichens, intertidal invertebrates).

• Planning prompts

  • Quadrat size (e.g., 0.5 m × 0.5 m) – must be large enough to contain several individuals but small enough for accurate counting.
  • Random vs. systematic placement – use a random‑number table or a grid.
  • Number of replicates – calculate using the formula for required confidence level (e.g., \(n = \frac{Z^{2}S^{2}}{E^{2}}\)).

• Procedure

  1. Lay out a map of the study area; assign coordinates.
  2. Place the quadrat at the chosen position, ensuring it is flat and undisturbed.
  3. Record every individual of each species inside the frame (count) or estimate % cover for plants.
  4. Repeat for all replicates.

• Data recorded

  • Quadrat ID, coordinates.
  • Species name (binomial).
  • Number of individuals (or % cover).

• Basic analysis

  • Density (indiv m⁻²) = \(\displaystyle\frac{\sum n_i}{\text{total quadrat area}}\).
  • Calculate S, H′, \(1/D\) for each habitat.
  • Mean density ± SD; CV to compare variability between sites.
  • Statistical test: One‑way ANOVA on H′ values when comparing ≥ 3 habitats; state null hypothesis “There is no difference in diversity between habitats”.

• Evaluation checklist (AO2)

  • Randomisation vs. systematic bias.
  • Edge effects – avoid placing quadrats on habitat boundaries.
  • Detection error – cryptic or very small species may be missed.
  • Number of replicates – is statistical power sufficient?
  • Disturbance – repeated placement may alter micro‑habitat.

3.2 Line Transects

When to use: Mobile animals detectable from a distance (birds, mammals, butterflies, insects).

• Planning prompts

  • Transect length (L) and detection strip width (w) – choose w so that all individuals within w are reliably seen.
  • Distance‑sampling? – decide whether perpendicular distances will be recorded.
  • Standardise walking speed (e.g., 1 m s⁻¹) and observer effort.

• Procedure

  1. Stretch a measuring tape or rope along a straight line.
  2. Mark the detection strip (e.g., 5 m each side).
  3. Walk the line at the agreed pace, recording every individual seen within the strip.
  4. If using distance sampling, note the perpendicular distance of each sighting.

• Data recorded

  • Transect ID, length.
  • Species, count.
  • Perpendicular distance (optional).

• Basic analysis

  • Encounter rate = \(\displaystyle\frac{\text{total individuals}}{L\;(\text{km})}\).
  • With distance data, estimate detection probability (p) and density:

    \[

    D = \frac{n}{2wL p}

    \]

  • Calculate diversity indices from the species list per transect.
  • Statistical test: t‑test comparing encounter rates between two habitat types; or ANOVA for >2 types.

• Evaluation checklist (AO2)

  • Assumption of perfect detection within w – rarely true.
  • Observer bias – conspicuous species over‑recorded.
  • Habitat heterogeneity – a single straight line may miss micro‑habitats.
  • Walking speed – faster pace reduces detection probability.
  • Edge effects at start/end of transect.

3.3 Belt Transects

When to use: Patchy environments where a continuous gradient is required (coral reefs, meadow edges, successional gradients).

• Planning prompts

  • Choose belt width (w) and total length (L) to give a representative sample area.
  • Decide quadrat spacing along the belt (e.g., every 1 m).
  • Consider combining quadrat counts with visual counts for mobile taxa.

• Procedure

  1. Mark a straight line of length L.
  2. Place contiguous quadrats of width w at the predetermined intervals, forming a continuous “belt”.
  3. Within each quadrat, record all organisms (count or % cover).
  4. Proceed without skipping sections.

• Data recorded

  • Belt ID, quadrat number (position).
  • Species, count (or % cover).
  • Environmental variables (substrate, canopy cover) if required.

• Basic analysis

  • Total belt area = \(L \times w\).
  • Density = \(\displaystyle\frac{\sum n_i}{L \times w}\) (indiv m⁻²).
  • Plot density or species richness against distance along the belt to reveal gradients.
  • Calculate H′ and \(1/D\) for the whole belt or for sub‑sections.
  • Statistical test: Linear regression of density vs. distance; test slope significance (p < 0.05).

• Evaluation checklist (AO2)

  • Time investment – may be prohibitive in large or difficult terrain.
  • Disturbance – repeated quadrat placement can compact soil or damage vegetation.
  • Overlap – ensure quadrats are edge‑to‑edge, not overlapping.
  • Representativeness – belt must cross all major micro‑habitats.
  • Replication – multiple parallel belts increase statistical robustness.

3.4 Mark‑Release‑Recapture (MRR) – Lincoln–Petersen Index

When to use: Mobile vertebrates or large invertebrates that can be safely captured, marked and released (small mammals, amphibians, butterflies).

• Planning prompts

  • Choose a harmless, durable marking method (e.g., non‑toxic dye, PIT tag).
  • Define the mixing period (usually 24–48 h) to allow random redistribution.
  • Estimate realistic sample sizes for the first (M) and second (C) captures.

• Procedure

  1. Capture a first sample of size M; mark each individual uniquely.
  2. Release all marked individuals at the capture site.
  3. After the mixing period, conduct a second capture effort, obtaining C individuals.
  4. Count the number of previously marked individuals in the second sample (R).
  5. Calculate the population estimate using the Lincoln–Petersen formula:

     \[

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

     \]

• Assumptions (must be stated in exam answers)

  • Closed population (no births, deaths, immigration, emigration) between samplings.
  • All individuals have equal capture probability.
  • Marking does not affect survival or behaviour.
  • Marks are not lost or overlooked.

• Basic analysis

  • Lincoln–Petersen estimate (above).
  • Chapman’s bias‑corrected estimator (95 % CI):

    \[

    N_{c} = \frac{(M+1)(C+1)}{R+1} - 1

    \]

  • Calculate CV of the estimate; larger CV indicates lower reliability.
  • Statistical test: Compare N between habitats using a two‑sample t‑test (log‑transformed if data are skewed).

• Evaluation checklist (AO2)

  • Violation of closure – migrations or births during the study.
  • Trap shyness/happiness – marking may alter capture probability.
  • Low recapture rate (small R) → wide confidence intervals.
  • Ethical considerations – handling stress, tag retention.
  • Sample size – M and C must be sufficiently large for a reliable estimate.

4. Data‑Handling Workflow (From Raw Counts to Biodiversity Indices)

1. Compile a species‑by‑sample table (rows = species, columns = quadrats, transects or capture events).
2. Calculate total individuals (N) and species richness (S) for each sample.
3. Determine the proportion \(pi = ni/N\) for each species.
4. Apply the Shannon‑Wiener and Simpson formulas.
5. Calculate density (or encounter rate) and associated CV.
6. Where required, compute 95 % confidence intervals (e.g., for MRR estimates).
7. Interpret: higher H′ or \(1/D\) → greater diversity; compare across habitats, times or management treatments.

5. Statistical Tools Expected for Cambridge A‑Level (Paper 3 & 5)

6. Conservation Applications – Linking Data to Management

• Habitat monitoring: Long‑term quadrat censuses in tropical rain‑forest plots feed into REDD+ carbon‑sequestration assessments.
• Population assessments for threatened species: MRR of amphibians in fragmented wetlands supplies the quantitative evidence needed for IUCN Red‑List categorisation and for designing protected corridors.
• Marine resource management: Belt‑transect surveys of reef fish are used by fisheries managers to set sustainable catch limits and to monitor Marine Protected Area (MPA) effectiveness.
• Impact assessment: Before‑and‑after line‑transect surveys detect biodiversity changes caused by road construction, logging or invasive‑species control.

In exam answers, always state which method you have chosen, why it is appropriate for the target organisms and habitat, and how the resulting data will inform a specific conservation or management decision.

7. Summary Comparison of Methods

8. Final Checklist for Exam Preparation

• State the relevant syllabus point (e.g., 18.1, 18.2, 18.5) before describing a method.
• Include a clear AO3 planning paragraph: aim, hypothesis, variables, control, replication.
• Present a concise AO2 evaluation: bias, precision, practicality, ethical issues.
• Show all required calculations (density, H′, \(1/D\), N, CV, CI) with units.
• Indicate the appropriate statistical test and null hypothesis for any comparison.
• Link the results to a conservation or management implication (e.g., “A decline in H′ suggests habitat degradation and may warrant designation as a Site of Special Scientific Interest”).