FortinJamesMacKenzieEtAl2012

Référence

Fortin, M.-J., James, P.M.A., MacKenzie, A., Melles, S.J. and Rayfield, B. (2012) Spatial statistics, spatial regression, and graph theory in ecology. Spatial Statistics, 1:100-109. (Scopus )

Résumé

A critical part of ecological studies is to quantify how landscape spatial heterogeneity affects species' distributions. With advancements in remote sensing technology and GIS, we now live in a data-rich era allowing us to investigate species-environment relationships in heterogeneous landscapes at multiple spatial scales. However, the degree and type of spatial heterogeneity changes depending on the spatial scale at which species-environment relationships are analysed. Here we present the current spatial analytic methods used in ecological studies to quantify ecological spatial heterogeneity. To determine the key spatial scales at which underlying ecological processes act upon species, we recommend use of spectral decomposition techniques such as wavelet analysis or Moran's eigenvector maps. Following this, a suite of spatial regression methods can be used to quantify the relative influence of environmental factors on species' distributions. Finally, spatial graph metrics can be employed to quantify the effects of spatial heterogeneity on landscape connectivity across or within species' ranges and can be used as additional predictors in spatial regression models. We emphasize how spatial statistics, spatial regression, and spatial graph theory can be used to provide insights into how landscape spatial complexity influences species distributions and to better understand species response to global change. © 2012 Elsevier Ltd.

Format EndNote

Vous pouvez importer cette référence dans EndNote.

Format BibTeX-CSV

Vous pouvez importer cette référence en format BibTeX-CSV.

Format BibTeX

Vous pouvez copier l'entrée BibTeX de cette référence ci-bas, ou l'importer directement dans un logiciel tel que JabRef .

@ARTICLE { FortinJamesMacKenzieEtAl2012,
    AUTHOR = { Fortin, M.-J. and James, P.M.A. and MacKenzie, A. and Melles, S.J. and Rayfield, B. },
    TITLE = { Spatial statistics, spatial regression, and graph theory in ecology },
    JOURNAL = { Spatial Statistics },
    YEAR = { 2012 },
    VOLUME = { 1 },
    PAGES = { 100-109 },
    ABSTRACT = { A critical part of ecological studies is to quantify how landscape spatial heterogeneity affects species' distributions. With advancements in remote sensing technology and GIS, we now live in a data-rich era allowing us to investigate species-environment relationships in heterogeneous landscapes at multiple spatial scales. However, the degree and type of spatial heterogeneity changes depending on the spatial scale at which species-environment relationships are analysed. Here we present the current spatial analytic methods used in ecological studies to quantify ecological spatial heterogeneity. To determine the key spatial scales at which underlying ecological processes act upon species, we recommend use of spectral decomposition techniques such as wavelet analysis or Moran's eigenvector maps. Following this, a suite of spatial regression methods can be used to quantify the relative influence of environmental factors on species' distributions. Finally, spatial graph metrics can be employed to quantify the effects of spatial heterogeneity on landscape connectivity across or within species' ranges and can be used as additional predictors in spatial regression models. We emphasize how spatial statistics, spatial regression, and spatial graph theory can be used to provide insights into how landscape spatial complexity influences species distributions and to better understand species response to global change. © 2012 Elsevier Ltd. },
    COMMENT = { Cited By (since 1996): 1 Export Date: 23 August 2012 Source: Scopus doi: 10.1016/j.spasta.2012.02.004 },
    ISSN = { 22116753 (ISSN) },
    KEYWORDS = { Connectivity, Fragmentation, Spatial heterogeneity, Spatial scales, Species distribution, Wavelet },
    OWNER = { Luc },
    TIMESTAMP = { 2012.08.23 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84864298306&partnerID=40&md5=77de60d879ae2c21095d2ce4470e82d7 },
}

********************************************************** ***************** Facebook Twitter *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ***************** Pub - Mycorhizes_2019 ****************** **********************************************************

********************************************************** ***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...