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

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

Daniel A Griffith1, Pedro R Peres-Neto

  • 1School of Social Sciences, University of Texas-Dallas, P.O. Box 830688, Richardson, Texas 75083-0688, USA.

Ecology
|November 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

Predictions From Evolutionary Theory for Urban Environments.

Evolutionary applications·2026
Same author

BON in a Box: An Open and Collaborative Platform for Biodiversity Monitoring, Indicator Calculation, and Reporting.

Bioscience·2026
Same author

A Multilevel Spatial Survival Analysis of Patients in Texas with End-Stage Renal Disease.

Healthcare (Basel, Switzerland)·2025
Same author

Predicting the way forward for the Global Biodiversity Framework.

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

Emerging Trends and Issues in Geo-Spatial Environmental Health: A Critical Perspective.

International journal of environmental research and public health·2025
Same author

Spatial Disparities in Access to Dialysis Facilities in Texas: An Analysis of End-Stage Renal Data in 1974-2020.

Healthcare (Basel, Switzerland)·2024
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 highlights the utility of eigenfunctions in spatial modeling for ecological research. It compares distance-based (DB) and connectivity-based (CB) eigenvector methods for incorporating spatial predictors into regression models.

Area of Science:

  • Ecology
  • Spatial Statistics
  • Geoinformatics

Background:

  • Spatial autocorrelation is a common challenge in ecological and geographical data analysis.
  • Conventional regression models often struggle to account for spatial dependencies.
  • Eigenfunction-based methods offer a novel approach to explicitly model spatial predictors.

Purpose of the Study:

  • To demonstrate the practical utility of eigenfunctions in spatial modeling for ecological applications.
  • To compare and contrast two primary implementations: distance-based (DB) eigenvector maps and connectivity-based (CB) spatial filtering.
  • To showcase how these methods facilitate the integration of spatial predictors into standard regression frameworks.

Main Methods:

  • Utilized eigenfunctions derived from spatial configuration matrices.

Related Experiment Videos

  • Applied distance-based (DB) eigenvector maps (Legendre et al.).
  • Applied topology-based (CB) spatial filtering using geographic connectivity matrices (Griffith et al.).

    • Main Results:

    • Eigenfunction-based approaches effectively create spatial predictors for regression models.
    • Both DB and CB methods provide valuable tools for handling spatial autocorrelation.
    • The study elucidates the equivalencies and distinctions between the DB and CB methodologies.

    Conclusions:

    • Eigenfunction-based spatial modeling is a powerful and flexible tool for ecological and geographical research.
    • These methods allow the application of general and generalized linear modeling theory in the presence of spatial autocorrelation.
    • The comparison provides guidance for selecting appropriate spatial modeling techniques.