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

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.However, realistic environmental conditions limit the number of...

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.

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

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...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

You might also read

Related Articles

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

Sort by

Same author

Multiple Mechanisms Required to Predict Grass Community Composition.

Ecology letters·2026

Same author

Refinement of the classification of DDX41 variants through analysis of aggregated clinical datasets.

Leukemia·2026

Same author

No world-changing discoveries without biodiversity.

Nature·2026

Same author

Quantitatively Testing Predictions From Mechanistic Models: A Case Study for Island Biodiversity.

Ecology letters·2025

Same author

Many-strategy games in groups with relatives and the evolution of coordinated cooperation.

Journal of theoretical biology·2025

Same author

The estimated cost of preventing extinction and progressing recovery for Australia's priority threatened species.

Proceedings of the National Academy of Sciences of the United States of America·2025

Search research articles

Related Experiment Video

Updated: Jul 15, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Incorporating landscape stochasticity into population viability analysis.

Ryan A Chisholm¹, Brendan A Wintle

¹School of Botany, The University of Melbourne, Victoria, Australia. chisholm@princeton.edu

Ecological Applications : a Publication of the Ecological Society of America

|May 11, 2007

Summary

Incorporating landscape stochasticity into population viability analysis (PVA) improves precision. This study provides a method to optimize simulations, making landscape dynamics a crucial factor in conservation planning.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Related Experiment Videos

Last Updated: Jul 15, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Ecology
Conservation Biology
Computational Biology

Background:

Population viability analysis (PVA) traditionally overlooks landscape stochasticity.
Quantifying the impact of landscape stochasticity versus population stochasticity is challenging.
Current PVA software makes generating multiple landscapes for stochasticity analysis inefficient.

Purpose of the Study:

To demonstrate the significant impact of landscape stochasticity on PVA model outputs.
To develop a method for optimally allocating simulation time between landscape and population dynamics.
To automate the generation of multiple landscapes for enhanced PVA.

Main Methods:

Derived a formula to determine the optimal ratio of population to landscape simulations.
Developed a computer program to automate multiple landscape generation within a dynamic landscape metapopulation (DLMP) model.
Applied the method to a bird population case study using RAMAS Landscape software.

Main Results:

Landscape stochasticity was shown to be a major source of variance in model outputs.
Estimates of DLMP model parameters were up to four times more precise compared to single-landscape analyses.
The developed method significantly reduces the user-intensive nature of incorporating landscape stochasticity.

Conclusions:

Landscape stochasticity is a critical, yet often overlooked, component in PVA.
The derived formula and automated program provide a practical solution for incorporating landscape stochasticity.
This approach enhances the precision of conservation predictions and should be adopted by DLMP modelers.