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

Speciation Rates

Speciation Rates

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

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...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

You might also read

Related Articles

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

Sort by

Same author

Mink by mink: stitching together signatures of subspecies adaptation through a pangenome of threatened mustelids.

Heredity·2026

Same author

Spatial Patterns of Intraspecific Genetic Diversity Follow no General Rule Across Climatic and Geographic Gradients.

Molecular ecology·2026

Same author

Constitutive Differences in Immune Gene Expression Are Correlated With Wood Frog Populations From Contrasting Winter Environments.

Molecular ecology·2025

Same author

Genome Assembly of a Living Fossil, the Atlantic Horseshoe Crab Limulus polyphemus, Reveals Lineage-Specific Whole-Genome Duplications, Transposable Element-Based Centromeres, and a ZW Sex Chromosome System.

Molecular biology and evolution·2025

Same author

Predicting genetic biodiversity in salamanders using geographic, climatic, and life history traits.

PloS one·2024

Same author

Winter break? The effect of overwintering on immune gene expression in wood frogs.

Comparative biochemistry and physiology. Part D, Genomics & proteomics·2024

Search research articles

Related Experiment Video

Updated: May 18, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Phylogenetic estimation error can decrease the accuracy of species delimitation: a Bayesian implementation of the

Noah M Reid¹, Bryan C Carstens

¹Department of Biological Science, Louisiana State University, Baton Rouge, LA 70803, USA. noah.reid@gmail.com

BMC Evolutionary Biology

|October 4, 2012

Summary

This summary is machine-generated.

This study introduces a Bayesian implementation of the General Mixed Yule-Coalescent (GMYC) model for DNA sequence-based species delimitation. While effective for deep divergences, rapid radiations may lead to inaccurate species boundary assessments.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Related Experiment Videos

Last Updated: May 18, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Ecology
Evolutionary Biology
Genetics

Background:

Accurate species identification is crucial for ecological and evolutionary research.
Limited availability of taxonomic frameworks necessitates DNA sequence-based species delimitation.
The General Mixed Yule-Coalescent (GMYC) model is a proposed method for species boundary estimation.

Purpose of the Study:

To present a Bayesian implementation of the GMYC model.
To integrate model parameters and phylogenetic uncertainty using MCMC simulations.
To generate marginal probabilities for species identities.

Main Methods:

Developed a Bayesian implementation of the GMYC model.
Utilized Markov-Chain Monte Carlo (MCMC) simulation for parameter integration.
Accounted for phylogenetic uncertainty and model parameter uncertainty.

Main Results:

Model performance improves with increased DNA sequence data and intraspecific sampling.
Species evolutionary history is the most critical factor for model success.
Recent, rapid divergences can lead to increased uncertainty and inaccurate species delimitation.

Conclusions:

The GMYC model is valuable for species delimitation, especially with deep divergences or incomplete taxon sampling.
Rapid, recent radiations may produce inaccurate species boundary results.
The new implementation enhances precision by accounting for phylogenetic and parameter uncertainty, with an R package available.