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 Experiment Videos

Generalized site occupancy models allowing for false positive and false negative errors.

J Andrew Royle1, William A Link

  • 1U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. aroyle@usgs.gov

Ecology
|May 9, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Interspecific interactions moderate direct effects of vegetation change resulting from prescribed fires.

Scientific reports·2025
Same author

A flexible framework for N-mixture occupancy models: applications to breeding bird surveys.

Biometrics·2025
Same author

Abundance-mediated species interactions.

Ecology·2024
Same author

Integrated distance sampling models for simple point counts.

Ecology·2024
Same author

Estimating population size: The importance of model and estimator choice.

Biometrics·2023
Same author

Density-habitat relationships of white-tailed deer (<i>Odocoileus virginianus</i>) in Finland.

Ecology and evolution·2023
Same journal

Combining individual and close-kin mark-recapture to design an effective wildlife population survey.

Ecology·2026
Same journal

Cross-stressor resilience of soil microbial growth and carbon metabolism under climate change.

Ecology·2026
Same journal

Oh deer! Videography reveals a range of defensive behaviors against a cervid by a ground-nesting bird.

Ecology·2026
Same journal

Microbial responses to stress do not promote plant tolerance to same or different stressors.

Ecology·2026
Same journal

A 2100-km jaguar journey redefines mobility and large-scale conservation priorities during large carnivore dispersal.

Ecology·2026
Same journal

Linking genome size variation to phenotypic selection on target traits.

Ecology·2026
See all related articles

This study introduces a new site occupancy model accounting for both false negatives and false positives in species detection. This approach corrects biases in occupancy estimates caused by unacknowledged false positive errors.

Area of Science:

  • Ecology
  • Wildlife Biology
  • Conservation Science

Background:

  • Site occupancy models are crucial for wildlife surveys, but traditionally assume no false positive detection errors.
  • Existing models do not account for false positives, which can significantly bias occupancy estimates in real-world surveys.
  • False positive errors, where a species is detected when absent, can occur due to various sampling issues.

Purpose of the Study:

  • To develop and present a novel site occupancy model that incorporates both false negative and false positive error rates.
  • To address the limitations of current models that assume perfect detection accuracy (no false positives).
  • To provide a robust statistical framework for more accurate species distribution and abundance estimations.

Main Methods:

Related Experiment Videos

  • Developed a two-component finite mixture model to simultaneously estimate false negative and false positive error rates.
  • Utilized maximum likelihood estimation for fitting the proposed site occupancy model.
  • Employed a simulation study to evaluate model performance against naive estimators under false positive conditions.

    • Main Results:

    • The proposed model effectively accounts for both false negative and false positive detection errors.
    • Simulation results indicate that naive estimators are highly biased in the presence of false positive errors.
    • The new model provides more accurate site occupancy estimates when false positives are present.

    Conclusions:

    • Accounting for false positive errors is essential for reliable site occupancy estimation in ecological surveys.
    • The developed finite mixture model offers a statistically sound and accessible method for improving survey data analysis.
    • This work has significant implications for conservation planning and wildlife management by enhancing the accuracy of species distribution data.