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Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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Related Experiment Video

Updated: Feb 26, 2026

Sampling Soils in a Heterogeneous Research Plot

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sampling Design and Sample Processing Affect Soil Biodiversity Assessments.

Meirong Chen¹, Olesya Dulya^1,2, Vladimir Mikryukov^1,2

¹Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.

Molecular Ecology Resources

|February 24, 2026

Summary

This summary is machine-generated.

Choosing the right soil sampling design is crucial for biodiversity studies. Different designs significantly impact biodiversity estimates, but pooling samples can reduce costs and variability.

Keywords:

PacBio environmental DNA high‐throughput sequencing molecular artefact sample pooling soil microorganisms

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Last Updated: Feb 26, 2026

Sampling Soils in a Heterogeneous Research Plot

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Area of Science:

Ecology
Genomics
Environmental Science

Background:

Biodiversity surveys rely on effective sampling designs for accurate and comparable results.
Metabarcoding projects utilize various soil sampling strategies for assessing continental and global biodiversity.
Inconsistent sampling designs can lead to significant variations in biodiversity estimates.

Purpose of the Study:

To assess and compare the performance of different soil sampling designs in metabarcoding biodiversity projects.
To evaluate the impact of sampling design factors (e.g., subsample number, area, soil depth) on biodiversity estimates for animals, bacteria, and fungi.
To determine the effectiveness of soil and DNA pooling strategies in mitigating sampling design biases and reducing costs.

Main Methods:

Utilized PacBio and Illumina amplicon sequencing for animals, bacteria, and fungi from various soil sampling designs.
Compared biodiversity estimates across different sampling designs, including variations in subsample numbers and sampling areas.
Investigated the effects of soil pooling (compositing samples) and DNA pooling (combining extracts) on diversity estimates and cost-efficiency.

Main Results:

Sampling designs caused substantial differences in biodiversity estimates (up to 27-fold for animals).
The number of subsamples, not sampling area, primarily influenced taxonomic coverage.
Soil pooling offered significant cost savings (79.5%-98.3%) but did not eliminate sampling design influences; DNA pooling was more effective for animals and fungi than bacteria.

Conclusions:

Sampling design profoundly impacts biodiversity and community composition estimates in metabarcoding studies.
Pooling strategies can reduce variability and costs but do not entirely negate sampling design effects.
Careful consideration of sampling design is essential for robust data collection and future meta-analyses in biodiversity research.