Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F12%3A33140015" target="_blank" >RIV/61989592:15310/12:33140015 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/j.1365-2699.2011.02589.x" target="_blank" >http://dx.doi.org/10.1111/j.1365-2699.2011.02589.x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/j.1365-2699.2011.02589.x" target="_blank" >10.1111/j.1365-2699.2011.02589.x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Popis výsledku v původním jazyce

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi-scale framework to account for different origins of SAC and compared non-spatial models with models that accounted for SAC at multiple levels. Location We modelled the spatial distribution of an invasive forest pathogen Phytophthora ramorum in western USA. Methods We applied one conventional statistical method (GLM) and one nonparametric technique(Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of models that included environmental variables that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a

Název v anglickém jazyce

Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Popis výsledku anglicky

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi-scale framework to account for different origins of SAC and compared non-spatial models with models that accounted for SAC at multiple levels. Location We modelled the spatial distribution of an invasive forest pathogen Phytophthora ramorum in western USA. Methods We applied one conventional statistical method (GLM) and one nonparametric technique(Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of models that included environmental variables that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Biogeography

ISSN

0305-0270

e-ISSN

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Svazek periodika

39

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

42-55

Kód UT WoS článku

000298058200005

EID výsledku v databázi Scopus

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