Disentangling spatial and environmental effects: Flexible methods for community ecology and macroecology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A91602" target="_blank" >RIV/60460709:41330/22:91602 - isvavai.cz</a>

Výsledek na webu

<a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4028" target="_blank" >https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4028</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ecs2.4028" target="_blank" >10.1002/ecs2.4028</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Disentangling spatial and environmental effects: Flexible methods for community ecology and macroecology

Popis výsledku v původním jazyce

Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species environment relationships, but their differences to jointly fit and disentangle spatial autocorrelation and structure remain poorly studied. We compared how methods in four broad families of statistical models estimated the contribution of the environment and space to variation in species occurrence and abundance. These methods included redundancy analysis (RDA), generalized linear models (GLMs), generalized additive models (GAMs), and three types of tree based machine learning (ML) methods: boosted regression trees (BRT), random forests, and regression trees. The spatial component of the model consisted of Morans eigenvector maps (MEMs in RDA, GLM, and ML), smooth spatial splines (in GAM), or tree based nonlinear modeling of spatial coordinates (in

Název v anglickém jazyce

Disentangling spatial and environmental effects: Flexible methods for community ecology and macroecology

Popis výsledku anglicky

Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species environment relationships, but their differences to jointly fit and disentangle spatial autocorrelation and structure remain poorly studied. We compared how methods in four broad families of statistical models estimated the contribution of the environment and space to variation in species occurrence and abundance. These methods included redundancy analysis (RDA), generalized linear models (GLMs), generalized additive models (GAMs), and three types of tree based machine learning (ML) methods: boosted regression trees (BRT), random forests, and regression trees. The spatial component of the model consisted of Morans eigenvector maps (MEMs in RDA, GLM, and ML), smooth spatial splines (in GAM), or tree based nonlinear modeling of spatial coordinates (in

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Ecosphere

ISSN

2150-8925

e-ISSN

2150-8925

Svazek periodika

13

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

000787139500001

EID výsledku v databázi Scopus

2-s2.0-85128903784