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)...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

A long-term ecosystem monitoring dataset from the ICP Integrated Monitoring network: biogeochemical data from 1977-2020 across 14 European countries.

Scientific data·2026
Same author

Contrasting thermophilization among forests, grasslands and alpine summits.

Nature·2026
Same author

Human contributions to global soundscapes are less predictable than the acoustic rhythms of wildlife.

Nature ecology & evolution·2025
Same author

Modeling soil functions of forested ecosystems.

Journal of environmental management·2025
Same author

Unexpected westward range shifts in European forest plants link to nitrogen deposition.

Science (New York, N.Y.)·2024
Same author

Trends in mercury, lead and cadmium concentrations in 27 European streams and rivers: 2000-2020.

Environmental pollution (Barking, Essex : 1987)·2024

Related Experiment Video

Updated: Jun 23, 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

Models for analysing species' presence/absence data at two time points.

Thomas W Yee1, Thomas Dirnböck

  • 1Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand. t.yee@auckland.ac.nz

Journal of Theoretical Biology
|May 21, 2009
PubMed
Summary

This study introduces the bivariate odds-ratio model for analyzing species presence/absence data over time. This ecological modeling approach enhances understanding of niche dynamics and species colonization or extinction probabilities.

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

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 23, 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

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

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
  • Statistical Ecology
  • Environmental Science

Background:

  • Species presence/absence data at two time points are fundamental in ecological studies, including succession, monitoring, and climate change research.
  • Traditional statistical regression methods have limitations for analyzing this common longitudinal ecological data type.

Purpose of the Study:

  • To propose and evaluate the bivariate odds-ratio model for analyzing species presence/absence data.
  • To integrate this model within a constrained ordination framework for ecological niche theory dynamics.

Main Methods:

  • Application of the bivariate odds-ratio model, seldomly used in ecology, within a constrained ordination framework.
  • Exploration of extensions, including complementary log-log links for marginal probabilities with a Poisson abundance model.
  • Development of the model based on the zero-inflated Poisson distribution to account for excess absences.

Main Results:

  • The bivariate odds-ratio model, within a constrained ordination framework, offers a valuable tool for ecological analysis.
  • The proposed framework can describe local extinction and colonization probabilities, contributing to niche theory dynamics.
  • Demonstration of the model's utility using two vegetation datasets.

Conclusions:

  • The constrained ordination-odds ratio framework provides a powerful approach for understanding ecological processes.
  • The zero-inflated Poisson distribution-based model is particularly suitable for ecological data exhibiting excess absences.
  • This statistical methodology advances the analysis of longitudinal species occurrence data.