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

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

217
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...
217
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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...
4.0K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

19
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
19
Introduction to R01:11

Introduction to R

197
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
197
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.3K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.3K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.7K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Anthropogenic Barriers Limit Fish Access to Essential Habitats in the Amazon in the Face of Climate Change.

Global change biology·2026
Same author

Using an ensemble modeling approach to predict the potential distribution for the Kashmir gray langur (Semnopithecus ajax).

Primates; journal of primatology·2025
Same author

Introduced house sparrows (Passer domesticus) have greater variation in DNA methylation than native house sparrows.

The Journal of heredity·2023
Same author

The selection of indicator species of birds and mammals for the monitoring of restoration areas in a highly fragmented forest landscape.

Anais da Academia Brasileira de Ciencias·2023
Same author

Landscape dynamics and diversification of the megadiverse South American freshwater fish fauna.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates.

PloS one·2021

Related Experiment Video

Updated: May 7, 2025

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

3.3K

chooseGCM: A Toolkit to Select General Circulation Models in R.

Luíz Fernando Esser1, Dayani Bailly1, Marcos Robalinho Lima2

  • 1Universidade Estadual de Maringá, Maringa, Brazil.

Global Change Biology
|December 30, 2024
PubMed
Summary

Choosing general circulation models (GCMs) for climate change studies is challenging. Our new R package, chooseGCM, offers a robust framework to evaluate GCM variability, significantly reducing computation time and costs for species distribution modeling.

Keywords:
K‐meansecological niche modelingfuture projectionsmachine learningspecies distribution modeling

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

13.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

1.9K

Related Experiment Videos

Last Updated: May 7, 2025

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

3.3K
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

13.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

1.9K

Area of Science:

  • Climate Science
  • Ecological Modeling

Background:

  • Climate change research relies on projections from General Circulation Models (GCMs).
  • Selecting appropriate GCMs for studies, especially Species Distribution Models (SDMs), lacks standardized consensus.
  • Using all available GCMs is computationally prohibitive.

Purpose of the Study:

  • To introduce a methodological framework for evaluating GCM variability in climate change projections.
  • To provide an accessible R package, chooseGCM, for researchers to analyze GCM data.
  • To enable robust projections while managing computational resources.

Main Methods:

  • Development of an R package implementing a methodological framework for GCM evaluation.
  • Utilizing functions for clusterization, correlation, distance, and exploratory data analysis on GCM projections.
  • Proof-of-concept application using Species Distribution Models (SDMs).

Main Results:

  • The chooseGCM package significantly reduces computation time by over 79% compared to traditional methods.
  • Achieved an output correlation greater than 0.9 with the baseline in SDM applications.
  • The framework supports a wider range of hardware, enabling robust climate projections.

Conclusions:

  • The chooseGCM package offers an efficient and accessible tool for selecting and analyzing GCMs.
  • Facilitates more reliable and computationally feasible climate change projections for diverse research fields.
  • Empowers researchers, regardless of expertise, to perform robust GCM-based analyses.