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

Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.1K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.1K
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.0K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

65
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
65
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

29
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
29
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

45
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
45
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

39
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
39

You might also read

Related Articles

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

Sort by
Same author

Eco-evolutionary context modifies a destructive plant invader's response to climate.

The New phytologist·2026
Same author

Rejoinder to the discussion on "Continuous-space occupancy models".

Biometrics·2025
Same author

Melding wildlife surveys to improve conservation inference.

Biometrics·2023
Same author

Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems.

Biometrics·2023
Same author

Simple statistical models can be sufficient for testing hypotheses with population time-series data.

Ecology and evolution·2022
Same author

Scale-dependent influence of the sagebrush community on genetic connectivity of the sagebrush obligate Gunnison sage-grouse.

Molecular ecology·2022
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: May 14, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K

Continuous-space occupancy models.

Wilson J Wright1, Mevin B Hooten2

  • 1Department of Statistics, Colorado State University, Fort Collins, CO 80523, United States.

Biometrics
|May 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces novel spatial occupancy models to accurately map species distributions across continuous landscapes, improving upon current methods that struggle with discrete data. The new approach offers more realistic species occurrence inferences at finer resolutions.

Keywords:
Bayesian statisticschange of spatial supporthierarchical modelnearest neighbor Gaussian processspatial statisticsspecies distributions

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K

Related Experiment Videos

Last Updated: May 14, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K

Area of Science:

  • Ecology
  • Spatial Statistics
  • Computational Biology

Background:

  • Occupancy models are crucial for inferring species distributions, but existing methods face limitations in modeling continuous spatial domains with discrete observed data.
  • Current approaches struggle to account for species presence within only a fraction of a surveyed site.

Purpose of the Study:

  • Develop a new class of spatial occupancy models capable of handling a change of spatial support between observed data and the underlying occurrence process.
  • Enable more realistic modeling of species occurrence in continuous space at finer resolutions than observed data.
  • Relate detection probabilities to within-site occurrence proportions.

Main Methods:

  • Introduce a clipped Gaussian process to represent species occurrence in continuous space.
  • Employ Bayesian methods for model fitting, including a computationally efficient Markov chain Monte Carlo (MCMC) algorithm.
  • Utilize a Vecchia approximation for the spatial Gaussian process and a surrogate data approach for joint updates of spatial terms and covariance parameters.

Main Results:

  • The developed model successfully embeds a change of spatial support, allowing for finer-resolution inferences.
  • Demonstrated the model's efficacy using simulated data and comparison with alternative spatial occupancy models.
  • Successfully analyzed ovenbird occurrence data from New Hampshire, USA.

Conclusions:

  • The new spatial occupancy models provide a more realistic and flexible framework for species distribution modeling.
  • The approach enhances the ability to model species occurrence across continuous spatial domains and account for imperfect detection within sites.
  • This method offers significant advancements for ecological research and conservation efforts requiring fine-scale distribution mapping.