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

Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
Phylogenetic Trees03:21

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.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

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.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Evolutionary Relationships through Genome Comparisons02:54

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...
Phylogeny01:23

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...
Speciation Rates01:07

Speciation Rates

Speciation can proceed at markedly different rates, and evolutionary biologists commonly describe these differences through the models of gradualism and punctuated equilibrium. Both patterns explain how new species arise, but they differ in the tempo and continuity of evolutionary change. In both cases, evolutionary change arises from heritable variation within populations, with natural selection often shaping traits that improve survival and reproduction under specific environmental conditions.

You might also read

Related Articles

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

Sort by
Same author

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
Same author

Principal Components Analysis Fails to Recover Phylogenetic Structure in Hominins.

American journal of biological anthropology·2026
Same author

Genome Scanning Reveals the Genetic Basis of a Color Pattern Morphotype in an Island Population of the European Adder (Vipera berus).

Genome biology and evolution·2026
Same author

Intraoperative C-arm CBCT versus early postoperative MRI for the assessment of fracture reduction and osteosynthesis material positioning in cranio-maxillofacial trauma surgery.

Oral and maxillofacial surgery·2026
Same author

Feasibility of Ultrashort Echo Time MR Imaging for the Detection of Subclinical Achilles Enthesitis in Psoriasis and Psoriatic Arthritis.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2026
Same author

Near-metal MRI Using PETRA With Extended Phase Encoding and Compressed Sensing.

Investigative radiology·2026

Related Experiment Video

Updated: Jul 12, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Stochastic Phylogenetic Models of Shape.

Sofia Stroustrup1, Morten Akhøj Pedersen2, Frank van der Meulen3

  • 1Section for GeoGenetics, University of Copenhagen, Copenhagen, Denmark.

Systematic Biology
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for analyzing evolving shapes in evolutionary biology. The method improves estimates of ancestral shapes and quantifies spatial integration in morphological data.

Keywords:
Ancestral ReconstructionBackward Filtering Forward GuidingBayesian InferenceMCMCStochastic Character MappingStochastic Shape Models

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish
09:03

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish

Published on: February 27, 2026

Related Experiment Videos

Last Updated: Jul 12, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish
09:03

Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish

Published on: February 27, 2026

Area of Science:

  • Evolutionary Biology
  • Geometric Morphometrics
  • Statistical Phylogenetics

Background:

  • Phylogenetic modeling of morphological shape is statistically challenging due to inherent correlations in shape data.
  • Existing methods often fail to accurately represent the biological realities of evolving shapes.
  • Advances in mathematical shape analysis offer new possibilities for modeling shape evolution.

Purpose of the Study:

  • To develop a novel computational framework for the evolutionary analysis of morphological shape.
  • To facilitate stochastic character mapping on landmark shapes using a diffusion process model.
  • To improve the inference of ancestral shapes and model parameters in phylogenetic analyses.

Main Methods:

  • Developed a novel framework based on mathematical shape analysis and diffusion processes.
  • Modeled shape evolution considering evolutionary correlation between nearby landmarks.
  • Employed a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) sampling scheme for inference with a fixed phylogenetic tree.

Main Results:

  • The new framework provides improved estimates of ancestral shapes at the root.
  • It generates well-calibrated credible sets of shapes for internal nodes.
  • The diffusion parameter for spatial autocorrelation aligns with existing metrics of shape integration.

Conclusions:

  • The proposed framework offers a robust method for phylogenetic analysis of morphological shape.
  • It accurately models shape evolution as a correlated diffusion process.
  • The approach enhances our understanding of evolutionary patterns in shape data, as demonstrated with butterfly wing data.