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

Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
Genetic Drift03:33

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
Genetics of Speciation02:16

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.The genetics of speciation involves the different traits or isolating mechanisms preventing gene exchange, leading to reproductive isolation. Reproductive isolation can be due to reproductive barriers that have effects either before or after the formation of a zygote. Pre-zygotic mechanisms prevent fertilization from occurring, and post-zygotic mechanisms...
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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

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

Sort by
Same author

Winter inversions and summer smoke: A season-dependent approach to PM₂.₅ modeling with low-cost sensors in complex terrain.

The Science of the total environment·2026
Same author

Roads, Soil, Snow, and Topography Influence Genetic Connectivity: A Machine Learning Approach for a Peripheral American Badger Population.

Ecology and evolution·2026
Same author

Climate Extremes, Genomic Coverage, and Taxonomy Shape the Detection of Adaptation: A Systematic Review of GEA Studies for the Kingdom Animalia.

Molecular ecology·2026
Same author

Correction: Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling.

Scientific reports·2026
Same author

Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling.

Scientific reports·2025
Same author

Wildfire-season Fine Particulate Matter Exposure and Associations with Influenza and Influenza-like-illness Risk in the Western USA.

Environmental health perspectives·2025

Related Experiment Video

Updated: Jun 11, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Spurious correlations and inference in landscape genetics.

Samuel A Cushman1, Erin L Landguth

  • 1USDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USA. scushman@fs.fed.us

Molecular Ecology
|July 13, 2010
PubMed
Summary

Causal modeling with partial Mantel tests effectively identifies gene flow drivers in landscape genetics, outperforming simple correlational methods. This approach enhances the reliability of interpreting genetic structure and rejecting incorrect hypotheses.

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: Jun 11, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

  • Ecology
  • Genetics
  • Spatial Analysis

Background:

  • Accurate interpretation of landscape genetics relies on robust statistical methods to discern gene flow drivers.
  • The statistical power and inference capabilities of individual-based landscape genetics require further investigation.

Purpose of the Study:

  • To evaluate the power of causal modeling with partial Mantel tests in individual-based landscape genetic analysis.
  • To assess the accuracy of identifying causal processes versus spurious correlations in genetic data.

Main Methods:

  • Utilized a spatially explicit simulation model to generate genetic data under various gene flow scenarios.
  • Applied causal modeling with partial Mantel tests to resistance gradients derived from simulated genetic data.
  • Assessed the ability to detect the correct model and reject incorrect ones over time.

Main Results:

  • Naïve correlational analyses frequently produced spurious correlations, leading to incorrect inferences about gene flow drivers.
  • Causal modeling with partial Mantel tests demonstrated high power in rejecting incorrect models and identifying the true causal process.
  • The effectiveness of causal modeling in detecting the correct process increased over time after the initiation of the genetic process.

Conclusions:

  • Simple correlational approaches in landscape genetics can yield misleading results due to spurious correlations.
  • Causal modeling provides a powerful framework for robustly identifying drivers of spatial genetic structure.
  • A generalized framework for landscape genetics is proposed, focusing on individual genetic relationships and alternative hypotheses of landscape influence.