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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

213
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...
213
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.3K
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...
3.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
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...
8.7K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.5K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.5K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

630
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
630

You might also read

Related Articles

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

Sort by
Same author

Identifying the demographic pathways linking environmental covariates to population dynamics in an avian migrant.

Ecological applications : a publication of the Ecological Society of America·2026
Same author

Use of mercury in mining 125 years ago continues to impact waterfowl populations: Implications for current artisanal gold mining.

The Science of the total environment·2025
Same author

Estimating latent individual demographic heterogeneity using structural equation models.

Ecology·2025
Same author

Shortening migration by 4500 km does not affect nesting phenology or increase nest success for black brant (Branta bernicla nigricans) breeding in Arctic and subarctic Alaska.

Movement ecology·2025
Same author

Estimating Spatially Explicit Survival and Mortality Risk From Telemetry Data With Thinned Point Process Models.

Ecology letters·2025
Same author

Previous reproductive success and environmental variation influence nest-site fidelity of a subarctic-nesting goose.

Ecology and evolution·2024
Same journal

Double Parasitism by Two Cuckoo Gentes in a Daurian Redstart Nest.

Ecology and evolution·2026
Same journal

Size and Ecology of a Giant <i>Pavona clavus</i> Coral Colony in the Kingdom of Tonga.

Ecology and evolution·2026
Same journal

How to Account for Past Selection When Maternal Effects Are Cascading.

Ecology and evolution·2026
Same journal

Light and Pollination Limitation Alter Patterns of Fitness and Phenotypic Selection in <i>Sagittaria trifolia</i> L.: Insights From Sequential Inflorescences.

Ecology and evolution·2026
Same journal

Teaching Macrosystems Ecology Concepts With a Collaborative, Adaptable Education Module.

Ecology and evolution·2026
Same journal

Instance of a Heteroplasmic Mitogenome in Alvinocaridid Shrimp <i>Mirocaris fortunata</i> (Martin & Christiansen 1995) Found at the Moytirra Deep-Sea High-Temperature Hydrothermal Vent Field.

Ecology and evolution·2026
See all related articles

Related Experiment Video

Updated: Jan 1, 2026

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

3.6K

Estimating correlations among demographic parameters in population models.

Thomas V Riecke1,2, Benjamin S Sedinger3, Perry J Williams2

  • 1Program in Ecology, Evolution, and Conservation Biology University of Nevada Reno Nevada.

Ecology and Evolution
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

Estimating demographic parameter correlations is vital for population dynamics and conservation. A new method accurately estimates variances and correlations, outperforming traditional approaches and revealing strong positive correlations in black brent goose survival.

Keywords:
Branta bernicla nigricansblack brentcapture–recapturedemographyfitnesshyperpriorsinverse Wishartmultivariate normal

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Related Experiment Videos

Last Updated: Jan 1, 2026

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

3.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Area of Science:

  • Population Ecology
  • Evolutionary Biology
  • Wildlife Management
  • Statistical Modeling

Background:

  • Estimating correlations among demographic parameters is crucial for understanding population dynamics, life-history evolution, and informing conservation strategies.
  • Common methods use multivariate normal and inverse Wishart distributions, but the inverse Wishart prior can unduly influence posterior distributions.
  • Accurate estimation of variances and correlations is essential for reliable ecological inference.

Purpose of the Study:

  • To develop and evaluate an alternative method for estimating variances and process correlations between demographic parameters.
  • To compare the performance of the new parameterization against the traditional inverse Wishart prior using simulated data.
  • To examine process correlations between adult and juvenile survival in black brent geese.

Main Methods:

  • Individually parameterized the covariance matrix of a multivariate normal distribution to estimate variances and correlations.
  • Evaluated the approach using simulated capture-mark-recapture data.
  • Applied the method to black brent goose capture-mark-recapture data from Alaska (1988-2014).

Main Results:

  • The new parameterization consistently outperformed the inverse Wishart prior with simulated data, showing less bias in posterior means.
  • Adult and juvenile annual apparent survival rates of black brent geese were strongly positively correlated (ρ = 0.563).
  • The method's utility can be limited by small sample sizes or short study durations.

Conclusions:

  • Individual parameterization offers a robust alternative to the inverse Wishart prior for estimating demographic parameter correlations.
  • The strong positive correlation in black brent goose survival suggests shared environmental influences, likely habitat conditions.
  • The developed methods and simulation tools can be adapted for various capture-recapture and recovery frameworks.