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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

696
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
696
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

323
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...
323
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.8K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.8K
Survival Tree01:19

Survival Tree

496
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
496
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

29.9K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
29.9K

You might also read

Related Articles

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

Sort by
Same author

Indigenous knowledge, forest landscape modeling, and the cumulative effects of environmental changes.

Ecological applications : a publication of the Ecological Society of America·2025
Same author

Climate is stronger than you think: Exploring functional planting and TRIAD zoning for increased forest resilience to extreme disturbances.

PloS one·2025
Same author

Timber harvesting was the most important factor driving changes in vegetation composition, as compared to climate and fire regime shifts, in the mixedwood temperate forests of Temiscamingue since AD 1830.

Landscape ecology·2025
Same author

Human driven climate change increased the likelihood of the 2023 record area burned in Canada.

NPJ climate and atmospheric science·2024
Same author

Wildfires are spreading fast in Canada - we must strengthen forests for the future.

Nature·2024
Same author

Drivers and Impacts of the Record-Breaking 2023 Wildfire Season in Canada.

Nature communications·2024

Related Experiment Video

Updated: Mar 31, 2026

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

3.8K

Model-specification uncertainty in future forest pest outbreak.

Yan Boulanger1, David R Gray2, Barry J Cooke3

  • 1Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC, G1V 4C7, Canada.

Global Change Biology
|October 30, 2015
PubMed
Summary
This summary is machine-generated.

Climate change impacts forest pests like the spruce budworm (SBW). Model uncertainty, not climate scenarios, significantly affects predictions of SBW outbreak duration and shifts, necessitating adaptive forest management.

Keywords:
climate changeconsensus modelinsect outbreakspruce budwormuncertainty

More Related Videos

Author Spotlight: Evaluation of Entomopathogenic Fungi in Wild Monochamus alternatus Populations for Biocontrol Applications in Forest Wood Borers
06:58

Author Spotlight: Evaluation of Entomopathogenic Fungi in Wild Monochamus alternatus Populations for Biocontrol Applications in Forest Wood Borers

Published on: September 29, 2023

1.5K
A Technique to Screen American Beech for Resistance to the Beech Scale Insect Cryptococcus fagisuga Lind.
12:47

A Technique to Screen American Beech for Resistance to the Beech Scale Insect Cryptococcus fagisuga Lind.

Published on: May 27, 2014

10.1K

Related Experiment Videos

Last Updated: Mar 31, 2026

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

3.8K
Author Spotlight: Evaluation of Entomopathogenic Fungi in Wild Monochamus alternatus Populations for Biocontrol Applications in Forest Wood Borers
06:58

Author Spotlight: Evaluation of Entomopathogenic Fungi in Wild Monochamus alternatus Populations for Biocontrol Applications in Forest Wood Borers

Published on: September 29, 2023

1.5K
A Technique to Screen American Beech for Resistance to the Beech Scale Insect Cryptococcus fagisuga Lind.
12:47

A Technique to Screen American Beech for Resistance to the Beech Scale Insect Cryptococcus fagisuga Lind.

Published on: May 27, 2014

10.1K

Area of Science:

  • Ecology
  • Climate Change Biology
  • Forestry

Background:

  • Climate change is altering forest pest dynamics, but projections vary due to model differences.
  • Spruce budworm (SBW) outbreaks are a significant forest disturbance in North America.

Purpose of the Study:

  • To develop a consensus model predicting SBW outbreak duration under various climate scenarios.
  • To quantify model-specification uncertainty in future pest outbreak projections.

Main Methods:

  • A consensus model was built using two datasets and six correlative methods for SBW outbreak duration.
  • Projections were made for 2011-2100 under RCP 2.6, 4.5, and 8.5 scenarios.
  • Model-specification uncertainty was compared against climate forcing uncertainty.

Main Results:

  • The consensus model demonstrated high explanatory power and low bias.
  • Projections indicated a northward shift and decreased outbreak duration under RCP 8.5.
  • Model-specification uncertainty, stemming from data and methods, was a greater driver of projection variability than climate scenarios.

Conclusions:

  • Model-specification uncertainty significantly impacts projections of future forest pest outbreaks.
  • Acknowledging and quantifying this uncertainty is crucial for effective forest management and adaptive strategies.
  • Future forest management plans must incorporate high model-specification uncertainty to mitigate risks associated with pest outbreaks.