Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Conservation of Declining Populations

Conservation of Declining Populations

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.

Conservation of Small Populations

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less likely to...

Limits to Natural Selection

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

You might also read

Related Articles

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

Sort by

Same author

Cost-Effectiveness of Semaglutide 2.4 mg for Obesity Disease Management in Japan: A Lifetime Economic Modeling Study Incorporating Retreatment Scenarios.

Journal of health economics and outcomes research·2026

Same author

Geriatric nutritional risk index in antifibrotic therapy can predict tolerability and mortality risk.

Scientific reports·2026

Same author

Simultaneous Resection for Type A Thymoma With Synchronous Solitary Pulmonary Metastasis: A Case Report.

Respirology case reports·2026

Same author

Serum interferon-λ3 as a short-term biomarker of disease control in anti-MDA5-positive dermatomyositis-associated ILD.

Scientific reports·2026

Same author

Biological Aging and Survival Outcomes in Patients With Advanced Non-Small Cell Lung Cancer Receiving Systemic Therapy.

Clinical lung cancer·2025

Same author

Risk stratification of acute exacerbations by autoantibodies in patients with idiopathic interstitial pneumonia.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: May 31, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Optimal invasive species management under multiple uncertainties.

Koji Kotani¹, Makoto Kakinaka, Hiroyuki Matsuda

¹Graduate School of International Relations, International University of Japan, 777 Kokusai-cho, Minami-Uonuma, Niigata 949-7277, Japan. kkotani@iuj.ac.jp

Mathematical Biosciences

|June 28, 2011

Summary

This summary is machine-generated.

Managing invasive species is complex due to uncertainties. Increased growth uncertainty leads to cautious removals, while measurement uncertainty complicates strategies and reduces effectiveness, highlighting the value of accurate stock size data.

More Related Videos

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

Related Experiment Videos

Last Updated: May 31, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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
Environmental Management
Mathematical Modeling

Background:

Invasive species management strategies vary globally.
A consensus on optimal control methods remains elusive.
Uncertainties in management significantly impact strategy effectiveness.

Purpose of the Study:

To develop a dynamic model for invasive species management under uncertainty.
To analyze the effects of growth and measurement uncertainty on optimal strategies.
To provide guidance for effective invasive species control.

Main Methods:

Development of a dynamic management model.
Incorporation of growth uncertainty in species dynamics.
Inclusion of measurement uncertainty in stock size estimation.

Main Results:

Increased growth uncertainty leads to more conservative removal strategies.
Higher growth uncertainty positively impacts the value function.
Increased measurement uncertainty complicates optimal policies and generally decreases value functions.

Conclusions:

Growth uncertainty can be beneficial in invasive species management.
Measurement uncertainty poses challenges, emphasizing the need for precise stock size data.
Understanding and quantifying uncertainties are crucial for effective invasive species control.