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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Elasticity in Concrete01:20

Elasticity in Concrete

Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear portion of...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka 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...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

You might also read

Related Articles

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

Sort by
Same authorSame journal

Lucky To Be Alive, Luckier to Breed: Lifetime Reproduction in Weddell Seals.

Ecology letters·2026
Same author

Transient dynamics and nonlinear fitness: A matrix approach to pulse and press perturbation.

Ecology·2026
Same author

Plant population responses to environmental variability are primarily driven by survival-reproduction trade-offs and mediated by aridity.

Nature communications·2026
Same author

Environmental predictability drives different routes to adaptation.

Evolution letters·2026
Same author

How and why does aging occur? Updating evolutionary theory to meet a new era of data.

Evolution, medicine, and public health·2026
Same author

Street-dog policy in India is barking up the wrong tree.

Nature·2026
Same journal

Three-Dimensional Correlated Random Walks for Animal Movement and Habitat Selection.

Ecology letters·2026
Same journal

Higher-Order Interactions Can Promote Coexistence by Rewiring Intransitivities Into Competitive Networks.

Ecology letters·2026
Same journal

Plants That Evolved Under High Phylogenetic Diversity Have Higher Invasion Success, Particularly in Undisturbed Communities.

Ecology letters·2026
Same journal

Predictors of Food Web Resistance to Environmental Change.

Ecology letters·2026
Same journal

AI, Comparative Advantage, and the Next Decade of Ecological Research.

Ecology letters·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Estimating stochastic elasticities directly from longitudinal data.

C V Haridas1, Shripad Tuljapurkar, Tim Coulson

  • 1Ecology, Evolution and Environmental Sciences, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA. Haridas.Chirakkal@asu.edu

Ecology Letters
|June 26, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method to directly estimate population growth elasticities from demographic data. This approach accurately calculates selection pressures and informs population management policies, even with limited time-series data.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 22, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Ecology
  • Population Biology
  • Mathematical Biology

Background:

  • Elasticities of population growth rate are crucial for understanding selection on vital rates (births, deaths) and for effective population management.
  • Traditional methods for calculating elasticities in fluctuating environments rely on simulations with assumed environmental probability distributions, which can be imprecise.

Purpose of the Study:

  • To develop a direct statistical method for estimating elasticities of population growth rate from demographic data.
  • To provide a robust framework for analyzing selection and informing management policies in temporally varying environments.

Main Methods:

  • Developed a statistical estimator for elasticity using time-series demographic matrices and age-structure data.
  • Constructed confidence intervals for elasticities derived from temporal data.
  • Utilized data from a natural population to validate the method's accuracy.

Main Results:

  • The developed estimator converges to the correct limiting value with increasing sample length.
  • The method accurately estimates elasticities from relatively short time-series demographic data.
  • Confidence intervals and hypothesis testing tools for selection strength were successfully constructed.

Conclusions:

  • This new method offers a direct and statistically sound approach to elasticity estimation, bypassing simulation assumptions.
  • The findings are applicable to ecological research, population viability analysis, and conservation management.
  • Accurate elasticity estimation is feasible even with limited temporal demographic data.