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Related Concept Videos

Stratified Sampling Method01:16

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...
Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
What are Populations and Communities?00:30

What are Populations and Communities?

Populations are groups of individuals of the same species that inhabit a shared environment. Communities include multiple co-existing, interacting populations of different species. Metapopulations span multiple populations of the same species that occupy different areas. Metapopulations interact through immigration and emigration, providing genetic diversity that lends resilience to harsh environments. Population size and density can be estimated using quadrat and mark and recapture...
Cluster Sampling Method01:20

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...
Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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An incidence-based richness estimator for quadrats sampled without replacement.

Tsung-Jen Shen1, Fangliang He

  • 1Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, 250 Kuo Kuang Road, Tai-Chung, Taiwan. tjshen@amath.nchu.edu.tw

Ecology
|August 19, 2008
PubMed
Summary

This study introduces a new plant species richness estimator suitable for sampling without replacement in fixed areas. The proposed method accurately estimates species diversity and outperforms existing estimators, especially with larger quadrat sizes.

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Area of Science:

  • Ecology
  • Quantitative Biology
  • Botanical Surveys

Background:

  • Current species richness estimators often assume sampling with replacement or infinite populations, which are unsuitable for sessile organisms like plants.
  • Sampling sessile organisms typically involves sampling without replacement within a limited fixed area, such as using quadrats.

Purpose of the Study:

  • To propose a novel incidence-based parametric richness estimator designed for quadrat sampling without replacement in a fixed area.
  • To provide a method that accurately estimates species diversity in ecological studies of sessile organisms.

Main Methods:

  • Developed an estimator based on a zero-truncated binomial distribution for species incidence across quadrats.
  • Utilized a modified beta distribution to model species presence-absence probability within quadrats.
  • Derived explicit formulas for maximum likelihood estimation of richness and its variance.

Main Results:

  • The new estimator demonstrated robustness, being insensitive to sample size.
  • Outperformed nine other incidence-based estimators in root mean squared error tests on tree datasets.
  • Showed particular superiority when employing large quadrat sizes for sampling.

Conclusions:

  • The proposed incidence-based parametric richness estimator is a more accurate and reliable tool for ecological diversity assessments.
  • The findings suggest that using fewer, larger quadrats can be more effective for estimating plant species diversity than using many small quadrats.