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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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.
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Related Experiment Video

Updated: Jan 27, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A practical guide for combining data to model species distributions.

Robert J Fletcher¹, Trevor J Hefley², Ellen P Robertson¹

¹Department of Wildlife Ecology and Conservation, University of Florida, P.O. Box 110430, 110 Newins-Ziegler Hall, Gainesville, Florida, 32611-0430, USA.

|March 31, 2019

Summary

This summary is machine-generated.

Integrating diverse data sources improves species distribution models (SDMs). This study highlights methods, including integrated SDMs and weighted joint likelihoods, to enhance ecological predictions by combining varied data types effectively.

Keywords:

Special Feature: Data Integration for Population Models citizen science data fusion ecological niche model habitat suitability model integrated model spatial point process species distribution model

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

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

Published on: October 11, 2016

Area of Science:

Ecology
Evolutionary Biology
Conservation Biology

Background:

Accurate species distribution modeling is crucial for ecological research and conservation efforts.
Multiple data sources (citizen science, museums, surveys) are available but challenging to combine due to varying designs and biases.

Purpose of the Study:

To review, synthesize, and illustrate recent developments in combining multiple data sources for species distribution modeling.
To address challenges in integrating data with different sampling methodologies and biases.

Main Methods:

Identified five common approaches for combining multiple data sources in species distribution models.
Utilized integrated species distribution models (SDMs) to simultaneously combine different data sources.
Applied weighted joint likelihoods to emphasize data sources based on criteria like sample size.

Main Results:

Integrated SDMs effectively quantify environmental relationships and explain species distributions.
Combining planned survey data with eBird data demonstrated benefits like increased precision and predictive accuracy.
Weighted joint likelihoods improved predictions for all species studied, offering a solution for data integration challenges.

Conclusions:

Prudent use of integrated SDMs and techniques like weighted joint likelihoods can overcome challenges in combining diverse data sources.
Data integration enhances the precision, accuracy, and bias-accounting capabilities of species distribution models.
Practical guidance is provided for effectively combining multiple data sources in species distribution modeling.