Comparing generalized and customized spread models for nonnative forest pests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F20%3A84325" target="_blank" >RIV/60460709:41320/20:84325 - isvavai.cz</a>

Výsledek na webu

<a href="https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/eap.1988" target="_blank" >https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/eap.1988</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/eap.1988" target="_blank" >10.1002/eap.1988</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing generalized and customized spread models for nonnative forest pests

Popis výsledku v původním jazyce

While generality is often desirable in ecology, customized models for individual species are thought to be more predictive by accounting for context specificity. However, fully customized models require more information for focal species. We focus on pest spread and ask: How much does predictive power differ between generalized and customized models? Further, we examine whether an intermediate semi-generalized model, combining elements of a general model with species-specific modifications, could yield predictive advantages. We compared predictive power of a generalized model applied to all forest pest species (the generalized dispersal kernel or GDK) to customized spread models for three invasive forest pests (beech bark disease Cryptococcus fagisuga, gypsy moth Lymantria dispar, and hemlock woolly adelgid Adelges tsugae), for which time-series data exist. We generated semi-generalized dispersal kernel models (SDK) through GDK correction factors based on additional species-specific information. We f

Název v anglickém jazyce

Comparing generalized and customized spread models for nonnative forest pests

Popis výsledku anglicky

While generality is often desirable in ecology, customized models for individual species are thought to be more predictive by accounting for context specificity. However, fully customized models require more information for focal species. We focus on pest spread and ask: How much does predictive power differ between generalized and customized models? Further, we examine whether an intermediate semi-generalized model, combining elements of a general model with species-specific modifications, could yield predictive advantages. We compared predictive power of a generalized model applied to all forest pest species (the generalized dispersal kernel or GDK) to customized spread models for three invasive forest pests (beech bark disease Cryptococcus fagisuga, gypsy moth Lymantria dispar, and hemlock woolly adelgid Adelges tsugae), for which time-series data exist. We generated semi-generalized dispersal kernel models (SDK) through GDK correction factors based on additional species-specific information. We f

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

2020

Kód důvěrnosti údajů

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

Název periodika

ECOLOGICAL APPLICATIONS

ISSN

1051-0761

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000505549500024

EID výsledku v databázi Scopus

2-s2.0-85075423540