A spatially-explicit model of alien plant richness in Tenerife (Canary Islands)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F19%3A79753" target="_blank" >RIV/60460709:41330/19:79753 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1476945X18301107?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1476945X18301107?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecocom.2019.03.002" target="_blank" >10.1016/j.ecocom.2019.03.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A spatially-explicit model of alien plant richness in Tenerife (Canary Islands)

Popis výsledku v původním jazyce

Biological invasions are one of the major threats to biodiversity, especially in oceanic islands. In the Canary Islands, the relationships between plant Alien Species Richness (ASR) and their environmental and anthropogenic determinants were thoroughly investigated using ecological models. However, previous predictive models rarely accounted for spatial autocorrelation (SAC) and uncertainty of predictions, thus missing crucial information related to model accuracy and predictions reliability. In this study, we propose a Generalized Linear Spatial Model (GLSM) for ASR under a Bayesian framework on Tenerife Island. Our aim is to test whether the inclusion of SAC into the modelling framework could improve model performance resulting in more reliable predictions. Results demonstrated as accounting for SAC dramatically reduced the models AIC (Delta AIC = 4423) and error magnitudes, showing also better performances in terms of goodness of fit. Calculation of uncertainty related to predicted values pointed

Název v anglickém jazyce

A spatially-explicit model of alien plant richness in Tenerife (Canary Islands)

Popis výsledku anglicky

Biological invasions are one of the major threats to biodiversity, especially in oceanic islands. In the Canary Islands, the relationships between plant Alien Species Richness (ASR) and their environmental and anthropogenic determinants were thoroughly investigated using ecological models. However, previous predictive models rarely accounted for spatial autocorrelation (SAC) and uncertainty of predictions, thus missing crucial information related to model accuracy and predictions reliability. In this study, we propose a Generalized Linear Spatial Model (GLSM) for ASR under a Bayesian framework on Tenerife Island. Our aim is to test whether the inclusion of SAC into the modelling framework could improve model performance resulting in more reliable predictions. Results demonstrated as accounting for SAC dramatically reduced the models AIC (Delta AIC = 4423) and error magnitudes, showing also better performances in terms of goodness of fit. Calculation of uncertainty related to predicted values pointed

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Ecological Complexity

ISSN

1476-945X

e-ISSN

1476-9840

Svazek periodika

2019

Číslo periodika v rámci svazku

38

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

75-82

Kód UT WoS článku

000487000500007

EID výsledku v databázi Scopus

2-s2.0-85064191377