explain the importance of random sampling in determining the biodiversity of an area

Biodiversity

Biodiversity refers to the variety of life at all levels – genes, species, and ecosystems. Measuring biodiversity helps us understand ecosystem health, guide conservation, and assess the impact of human activities.

Why Measure Biodiversity?

• Detect changes over time (e.g., habitat loss, climate change).
• Identify areas of high conservation value.
• Inform management decisions and policy.

Random Sampling in Biodiversity Studies

Because it is rarely possible to count every organism in an area, ecologists use sampling. Random sampling ensures that each individual or unit in the study area has an equal chance of being selected, reducing bias and providing a representative picture of the community.

Key Principles of Random Sampling

1. Equal probability: Every location or organism has the same chance of being chosen.
2. Independence: The selection of one sample does not affect the selection of another.
3. Representativeness: The sample reflects the true composition of the whole area.

Consequences of Non‑Random Sampling

• Over‑ or under‑representation of certain habitats or species.
• Biased estimates of species richness and diversity indices.
• Misleading conclusions that can affect conservation priorities.

Common Random Sampling Methods

• Quadrat sampling: Randomly place square or rectangular frames of known area and record all species within each.
• Transect lines: Randomly choose start points and directions, then record species encountered along a fixed line.
• Pitfall traps: Randomly distribute traps to capture ground‑dwelling invertebrates.
• Random point counts: Use a grid or GPS coordinates generated by a random number table.

Calculating Biodiversity Indices from Random Samples

Once random samples are collected, indices such as species richness, the Shannon–Wiener index, and the Simpson index can be calculated.

Species Richness (S)

Simply the number of different species recorded in the combined samples.

Shannon–Wiener Index (\$H'\$)

\$\$

H' = -\sum{i=1}^{S} pi \ln p_i

\$\$

where \$pi = \frac{ni}{N}\$, \$n_i\$ is the number of individuals of species \$i\$, and \$N\$ is the total number of individuals across all species.

Simpson’s Index (D)

\$\$

D = \sum{i=1}^{S} pi^{2}

\$\$

The complement \$1 - D\$ gives the probability that two randomly selected individuals belong to different species.

Example: Random Quadrat Survey

Ten 1 m² quadrats were placed at random locations in a grassland. The table below shows the number of individuals of three common species recorded in each quadrat.

Combined totals: Species A = 108, Species B = 53, Species C = 39, \$N = 200\$.

Calculating \$H'\$ for the Survey

First compute proportions:

• \$p_A = 108/200 = 0.54\$
• \$p_B = 53/200 = 0.265\$
• \$p_C = 39/200 = 0.195\$

Then:

\$\$

H' = -(0.54\ln0.54 + 0.265\ln0.265 + 0.195\ln0.195) \approx 0.94

\$\$

Calculating Simpson’s \$D\$

\$\$

D = 0.54^{2} + 0.265^{2} + 0.195^{2} \approx 0.43

\$\$

Thus \$1 - D \approx 0.57\$, indicating a moderate probability that two randomly chosen individuals are from different species.

Why Random Sampling Improves These Estimates

• By giving each 1 m² area an equal chance of selection, the sample captures the spatial heterogeneity of the grassland.
• Random placement avoids over‑sampling of patches that are easy to access or appear more diverse.
• Statistical formulas for confidence intervals and variance assume random sampling; violating this assumption invalidates the results.

Key Take‑aways

1. Random sampling provides unbiased, representative data for biodiversity assessment.
2. Accurate indices (species richness, Shannon, Simpson) rely on the randomness of the sample.
3. Proper design (e.g., random quadrats or transects) is essential for reliable conservation decisions.