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

Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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...
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...

You might also read

Related Articles

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

Sort by
Same author

Evaluating vectors for the design of a spillover-disrupting Lassa virus transmissible vaccine.

PLoS computational biology·2026
Same author

Interepidemic Rift Valley fever in East Africa: the recent risk landscape and projected impacts of global change.

Proceedings. Biological sciences·2026
Same author

Integrating Ethnography and Structural Equation Modelling to Assess Brucellosis Knowledge, Attitude, and Practices among Pastoralist Communities in Kenya.

medRxiv : the preprint server for health sciences·2025
Same author

Validation of Body Condition Scoring as a Screening Test for Low Body Condition and Obesity in Common Marmosets (Callithrix jacchus).

American journal of primatology·2025
Same author

Hyperendemicity of Rift Valley Fever in Southwestern Uganda Associated With the Rapidly Evolving Lineage C Viruses.

The Journal of infectious diseases·2025
Same author

Resilience of a stochastic generalized Lotka-Volterra model for microbiome studies.

Mathematical biosciences and engineering : MBE·2025
Same journal

Traffic Reduction during COVID-19 Lockdowns Benefited Species Already Tolerant of Noise Pollution: An Acoustic Analysis.

The American naturalist·2026
Same journal

On Pachycephalosaurs, Trade-Offs, and the Historical Genesis of Sociosexual Display Structures.

The American naturalist·2026
Same journal

Structured Landscapes Promote Persistence by Favoring Prudent Predators.

The American naturalist·2026
Same journal

Can Carbon Economy Explain Leaf Dynamic Seasonality in a Tropical Seasonal Rainforest?

The American naturalist·2026
Same journal

Behavior and Physiology Outpace Form When Linking Traits to Ecological Responses within Populations: A Meta-Analysis.

The American naturalist·2026
Same journal

Seminal Fluid Proteins as Regulation Factors for Optimizing Reproduction: A Modeling Approach.

The American naturalist·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

When is correlation coevolution?

Scott L Nuismer1, Richard Gomulkiewicz, Benjamin J Ridenhour

  • 1Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, USA. snuismer@uidaho.edu

The American Naturalist
|March 24, 2010
PubMed
Summary
This summary is machine-generated.

Trait correlations in interacting species do not reliably indicate coevolution. Mathematical models show non-coevolutionary factors can create significant trait correlations, challenging the geographic mosaic theory. Further mechanistic studies are needed.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Resurrection of Dormant Daphnia magna: Protocol and Applications
07:37

Resurrection of Dormant Daphnia magna: Protocol and Applications

Published on: January 19, 2018

Related Experiment Videos

Last Updated: Jun 14, 2026

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Resurrection of Dormant Daphnia magna: Protocol and Applications
07:37

Resurrection of Dormant Daphnia magna: Protocol and Applications

Published on: January 19, 2018

Area of Science:

  • Ecology
  • Evolutionary Biology
  • Theoretical Biology

Background:

  • Trait correlations between interacting species are commonly used to infer coevolution.
  • These approaches are also applied to test the geographic mosaic theory of coevolution.

Purpose of the Study:

  • To evaluate the utility of trait correlations for identifying coevolution.
  • To assess the support for the geographic mosaic theory using trait correlation analyses.

Main Methods:

  • Mathematical and computational modeling were employed.
  • Simulations predicted trait correlations for various interaction types.

Main Results:

  • Coevolution is not necessary or sufficient for evolving spatially correlated traits.
  • Coevolutionary selection does not consistently yield statistically significant correlations.
  • Non-coevolutionary processes can generate significant trait correlations.

Conclusions:

  • Trait correlations alone are insufficient evidence for or against coevolution or the geographic mosaic theory.
  • Understanding coevolution requires detailed mechanistic studies or advanced statistical methods.