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Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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...

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

Bootstrapping

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...

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Related Experiment Video

Updated: Jun 1, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Linking biodiversity patterns by autocorrelated random sampling.

Brian J McGill¹

¹School of Biology and Ecology, Sustainability Solutions Initiative, Deering 303, University of Maine, Orono, Maine 04469, USA. mail@brianmcgill.org

American Journal of Botany

|May 27, 2011

Summary

Biodiversity macroecology reveals universal patterns in species abundance and distribution. Local communities are autocorrelated samples of regional pools, enabling predictions of biodiversity patterns.

Related Experiment Videos

Last Updated: Jun 1, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Area of Science:

Macroecology
Biodiversity science
Ecological patterns

Background:

Macroecology examines abundance, distribution, occupancy, and range size at local (α) and regional (γ) scales.
Approximately 15 general patterns exist concerning variable distributions, relationships with area/sample size, and inter-variable correlations.

Purpose of the Study:

To demonstrate that local communities can be viewed as samples of regional species pools.
To establish links between regional species abundance distributions and local biodiversity patterns.
To explore the implications of autocorrelated sampling for biodiversity patterns.

Main Methods:

Conceptual framework linking regional and local biodiversity patterns.
Mathematical modeling to predict local species richness and abundance distributions from regional data.
Analysis of spatial autocorrelation in species sampling.

Main Results:

Local communities are autocorrelated samples of regional species pools.
Two mathematical frameworks predict local biodiversity metrics (richness, abundance distributions, β-diversity) from regional species abundance distributions.
Biodiversity patterns are interconnected through autocorrelated sampling.

Conclusions:

Autocorrelated sampling provides a unifying mechanism for biodiversity patterns.
Understanding sampling processes is crucial for predicting and interpreting biodiversity at multiple scales.
This work offers a predictive framework for macroecological patterns.