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

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...
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,...
Gene Evolution - Fast or Slow?02:05

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...
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...
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

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

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

The genomic basis of adaptive leaf variation in the Galápagos giant daisies.

Nature communications·2026
Same author

WASTER: Practical de novo Phylogenomics from Low-coverage Short Reads.

Molecular biology and evolution·2026
Same author

Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes.

Systematic biology·2026
Same author

Erratum to: Accuracy of thick and thin intraocular lens power formulas using paraxial vergence calculation.

Journal of cataract and refractive surgery·2026
Same journal

Leafcutter Ant Farmers Prevent Loss of Edible Symbiotic Structures by Maintaining Allelic Diversity in Their Multinucleate Fungal Crop.

Molecular ecology·2026
Same journal

Resolving Emergent Patterns in Community Genetics With Environmental DNA.

Molecular ecology·2026
Same journal

Genomic Offsets Predict Survival With Low Accuracy in a Marine Common Garden.

Molecular ecology·2026
Same journal

Differential Immune Responses Correlate With Chytridiomycosis Severity in Italian Crested Newts.

Molecular ecology·2026
Same journal

Demography and Environment Shapes Genetic Variation: Spatiotemporal Genetic Dynamics in Cyclic Voles at Low Latitudes.

Molecular ecology·2026
Same journal

Gut Microbiome-Metabolome Reconfiguration Associates With Phenotypic Plasticity of Daphnia Under Predation Risk.

Molecular ecology·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Statistical inferences in phylogeography.

Rasmus Nielsen1, Mark A Beaumont

  • 1Departments of Integrative Biology and Statistics, University of California, Berkeley, 94720, USA. rasmus_nielsen@berkeley.edu

Molecular Ecology
|February 12, 2009
PubMed
Summary
This summary is machine-generated.

Phylogeographic studies face challenges interpreting gene trees due to random variations. This review explores statistical methods for robust historical demographic analysis and their limitations.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Related Experiment Videos

Last Updated: Jun 25, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Area of Science:

  • Evolutionary Biology
  • Population Genetics

Background:

  • Conventional phylogeography infers historical demography from gene trees and geographical distributions.
  • Gene tree interpretation is complex, as similar patterns can arise from different demographic histories, complicating inferences.

Purpose of the Study:

  • To review the evolution of statistical methods in phylogeographic analysis.
  • To discuss the inherent challenges and limitations associated with these methods.

Main Methods:

  • Discussion of traditional phylogeographic approaches, including nested clade analysis.
  • Exploration of modern statistical methods grounded in coalescence theory.
  • Highlighting the development and application of computational and statistical techniques.

Main Results:

  • Nested clade analysis faces criticism for subjective interpretation and performance issues in simulations.
  • Coalescence-based methods offer more rigorous statistical inference but can encounter computational hurdles and model selection difficulties.

Conclusions:

  • The development of statistical methods has advanced phylogeographic inference.
  • Ongoing challenges in computational efficiency and model accuracy necessitate further methodological refinement.