Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

48
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
48
Variability: Analysis01:11

Variability: Analysis

234
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
234
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.0K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.0K
One-Way ANOVA01:18

One-Way ANOVA

9.3K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
9.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

110
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
110

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Biogeographical ‍distributions of trickster animals.

Royal Society open science·2024
Same author

AI language tools risk scientific diversity and innovation.

Nature human behaviour·2023
Same author

Macroecological processes drive spiritual ecosystem services obtained from giant trees.

Nature plants·2023
Same author

A curated DNA barcode reference library for parasitoids of northern European cyclically outbreaking geometrid moths.

Ecology and evolution·2022
Same author

Degrees of compositional shift in tree communities vary along a gradient of temperature change rates over one decade: Application of an individual-based temporal beta-diversity concept.

Ecology and evolution·2021
Same author

Geographical co-occurrence of butterfly species: the importance of niche filtering by host plant species.

Oecologia·2018

Related Experiment Video

Updated: Oct 19, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Individual-based multiple-unit dissimilarity: novel indices and null model for assessing temporal variability in

Ryosuke Nakadai1,2,3

  • 1Department of Environmental and Biological Sciences, Faculty of Science and Forestry, University of Eastern Finland, Yliopistokatu 7, 80101, Joensuu, Finland. r.nakadai66@gmail.com.

Oecologia
|September 21, 2021
PubMed
Summary

This study extends individual-based beta-diversity indices to multiple time periods, enabling better analysis of temporal biodiversity changes. New methods help detect individual persistence patterns in ecological communities.

Keywords:
Beta-diversityIndividualIndividual turnoverMultiple-temporal dissimilarityRandomization

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.1K

Related Experiment Videos

Last Updated: Oct 19, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.1K

Area of Science:

  • Ecology
  • Biodiversity research
  • Community ecology

Background:

  • Beta-diversity traditionally measures spatial variation in community composition.
  • Temporal beta-diversity extends this concept to changes over time.
  • Existing individual-based indices are limited to pairwise comparisons.

Purpose of the Study:

  • To extend individual-based beta-diversity indices to multiple temporal units.
  • To introduce novel permutation criteria for detecting individual persistence patterns.
  • To provide tools for analyzing temporal biodiversity dynamics using individual-level data.

Main Methods:

  • Extension of pairwise individual-based beta-diversity indices to multiple-unit cases.
  • Development of a novel random permutation criterion for assessing individual persistence.
  • Application of the new indices to a long-term forest dynamics plot dataset.

Main Results:

  • The study successfully extended individual-based beta-diversity metrics to multiple temporal contexts.
  • Novel indices and permutation criteria were developed and demonstrated.
  • The methods were applied to a real-world dataset, showing their practical utility.

Conclusions:

  • The proposed multiple-unit individual-based beta-diversity indices offer a powerful new approach for ecological research.
  • These methods address a critical gap in analyzing temporal biodiversity dynamics, especially with individual-tracked data.
  • The findings are expected to advance understanding of temporal changes in biodiversity across various ecosystems.