Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

Phylogenetic Trees

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

You might also read

Related Articles

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

Sort by

Same author

A systematic exploration of current limitations of cognate-based phylogenetic inference.

Open research Europe·2026

Same author

Rethinking ambiguity across species.

Neuroscience and biobehavioral reviews·2025

Same author

Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology.

Open research Europe·2024

Same author

Gaussian process models for geographic controls in phylogenetic trees.

Open research Europe·2024

Same author

NorthEuraLex: a wide-coverage lexical database of Northern Eurasia.

Language resources and evaluation·2020

Same author

The evolution of language families is shaped by the environment beyond neutral drift.

Nature human behaviour·2019

Same journal

Effects of integrating a structured design thinking strategy into generative AI-supported design learning on students' design achievement, creative self-efficacy, and problem-solving skills.

Frontiers in psychology·2026

Same journal

Fukushima treated water release and marine sports.

Frontiers in psychology·2026

Same journal

Mindful parenting and preschoolers' screen dependency behavior: the mediating role of parent-child relationship and the moderating role of effortful control.

Frontiers in psychology·2026

Same journal

Dynamic relationships among first-year university students' critical thinking, academic self-concept, and student engagement: a cross-lagged study.

Frontiers in psychology·2026

Same journal

The association between academic major identity and career decision-making difficulty among Chinese college students: a sequential indirect association model of psychological capital and career adaptability.

Frontiers in psychology·2026

Same journal

Job quality and fertility intentions among Chinese migrant workers: the role of traditional fertility beliefs.

Frontiers in psychology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Phylogenetic Typology.

Gerhard Jäger¹, Johannes Wahle¹

¹Department of Linguistics, University of Tübingen, Tübingen, Germany.

Frontiers in Psychology

|August 5, 2021

Summary

This summary is machine-generated.

This study introduces a new statistical method to analyze linguistic variables, accounting for shared ancestry. It offers a more comprehensive approach to understanding language evolution and word-order correlations globally.

Keywords:

Bayesian inference language universals phylogenetics typology word-order

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Related Experiment Videos

Last Updated: Oct 25, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Computational Linguistics
Evolutionary Linguistics
Quantitative Linguistics

Background:

Statistical non-independence due to shared ancestry complicates linguistic variable analysis.
Previous methods often exclude smaller language families or isolates.
A need exists for methods that utilize all available linguistic data and model relatedness continuously.

Purpose of the Study:

To propose a novel statistical method for estimating the frequency distribution of linguistic variables.
To control for statistical non-independence arising from shared ancestry in language data.
To investigate word-order correlations across global languages using the developed method.

Main Methods:

Inferring phylogenetic distributions from lexical data.
Employing phylogenies within a statistical model to estimate transition rates between parameter states.
Computing the long-term equilibrium of the resulting Markov process.

Main Results:

The proposed method effectively utilizes all available linguistic data, including large and small families, and isolates.
It controls for varying degrees of relatedness on a continuous scale.
A case study on word-order correlations demonstrates the method's applicability.

Conclusions:

The novel method provides a robust framework for analyzing linguistic variables while accounting for evolutionary history.
This approach enhances our understanding of language evolution and cross-linguistic patterns.
The technique is broadly applicable to various linguistic research questions, including word-order correlations.