Jove
Visualize
Contact Us

Related Concept Videos

Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by
Same author

Conflicts and Collisions With an Endangered Carnivore: Landscape Drivers and Spatial Risk Pattern.

Ecology and evolution·2026
Same author

Disease, Drought, and Warming: A Triple Threat to a Declining High-Elevation Amphibian.

Ecology and evolution·2026
Same author

A hierarchical approach for finding undiscovered populations of an endangered bumble bee.

Scientific reports·2026
Same author

Fragmentation as a population rate-changer: A field experiment.

Ecology·2026
Same author

Unraveling abundance from occurrence: Modeling an endangered rodent population with low capture probability.

Ecological applications : a publication of the Ecological Society of America·2026
Same author

The functional effects of African lions on co-occurring carnivores differ across species pairs and with changes in resource availability and lion abundance.

Oecologia·2026
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 Experiment Video

Updated: Jul 15, 2026

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Sampling design trade-offs in occupancy studies with imperfect detection: examples and software.

Larissa L Bailey1, James E Hines, James D Nichols

  • 1U.S. Geological Survey, Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, Maryland 20708-4017, USA. lbailey@usgs.gov

Ecological Applications : a Publication of the Ecological Society of America
|May 8, 2007
PubMed
Summary

Occupancy modeling is crucial for ecological monitoring. This study introduces GENPRES software to help researchers balance spatial and temporal sampling for accurate detection and occupancy probability estimation.

More Related Videos

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Related Experiment Videos

Last Updated: Jul 15, 2026

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Area of Science:

  • Ecology and Wildlife Biology
  • Conservation Science
  • Statistical Modeling

Background:

  • Occupancy, or probability of occupancy, is a key variable in ecological studies, including habitat modeling.
  • Numerous agencies now favor occupancy-based monitoring programs for wildlife and conservation.
  • Simultaneous estimation of detection and occupancy probabilities is vital for reliable inferences in occupancy studies.

Purpose of the Study:

  • To address design questions in occupancy studies, particularly the trade-off between spatial and temporal replication.
  • To introduce GENPRES, a software tool for exploring these design trade-offs.
  • To facilitate informed decisions in the planning phase of occupancy monitoring programs.

Main Methods:

  • Discussion of common design questions encountered in occupancy studies.
  • Description of the GENPRES software for exploring spatial vs. temporal replication trade-offs.
  • Illustration of GENPRES utility with a case study involving amphibian monitoring in Greater Yellowstone National Park.

Main Results:

  • GENPRES software allows investigators to easily explore design trade-offs based on study system specifics and sampling constraints.
  • The software aids in balancing the need for spatial coverage with the requirement for temporal replication to estimate detection probability.
  • The amphibian example demonstrates the practical application of GENPRES in real-world ecological study design.

Conclusions:

  • Effective occupancy study design requires careful consideration of the balance between spatial and temporal sampling.
  • GENPRES software provides a valuable tool for researchers to optimize their study designs and maximize inferential power.
  • Informed design choices lead to more reliable and cost-effective ecological monitoring programs.