PREDICTING MULTI-SPECIES BARK BEETLE (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE) OCCURRENCE IN ALASKA: OPEN-ACCESS BIG GIS-DATA MINING TO PROVIDE ROBUST INFERENCE

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F21%3A89672" target="_blank" >RIV/60460709:41320/21:89672 - isvavai.cz</a>

Výsledek na webu

<a href="https://journals.ku.edu/jbi/article/view/14758" target="_blank" >https://journals.ku.edu/jbi/article/view/14758</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

PREDICTING MULTI-SPECIES BARK BEETLE (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE) OCCURRENCE IN ALASKA: OPEN-ACCESS BIG GIS-DATA MINING TO PROVIDE ROBUST INFERENCE

Popis výsledku v původním jazyce

Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that ranks among the key disturbances of coniferous forests of western North America. Many landscape-level variables are known to influence beetle outbreaks, such as suitable climatic conditions, spatial arrangement of incipient populations, topography, abundance of mature host trees, and disturbance history that includes former outbreaks and fire. We assembled open-access data for understanding the ecology of bark beetles in Alaska. We used boosted classification and regression trees as a machine-learning data-mining algorithm to predict relationships between 838 occurrence records of 68 bark beetle species and 14 environmental variables, compared to pseudo-absence locations across Alaska. Environmental variables included topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map multi-species bark beetle occurrences across Alaska a

Název v anglickém jazyce

PREDICTING MULTI-SPECIES BARK BEETLE (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE) OCCURRENCE IN ALASKA: OPEN-ACCESS BIG GIS-DATA MINING TO PROVIDE ROBUST INFERENCE

Popis výsledku anglicky

Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that ranks among the key disturbances of coniferous forests of western North America. Many landscape-level variables are known to influence beetle outbreaks, such as suitable climatic conditions, spatial arrangement of incipient populations, topography, abundance of mature host trees, and disturbance history that includes former outbreaks and fire. We assembled open-access data for understanding the ecology of bark beetles in Alaska. We used boosted classification and regression trees as a machine-learning data-mining algorithm to predict relationships between 838 occurrence records of 68 bark beetle species and 14 environmental variables, compared to pseudo-absence locations across Alaska. Environmental variables included topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map multi-species bark beetle occurrences across Alaska a

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10619 - Biodiversity conservation

Projekt

<a href="/cs/project/EF15_003%2F0000433" target="_blank" >EF15_003/0000433: Vybudování excelentního vědeckého týmu na FLD ČZU v Praze a jeho přístrojově-technického zázemí zaměřeného na mitigaci důsledků klimatických změn v lesích (od úrovně genů po úroveň krajiny)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Biodiversity Informatics

ISSN

1546-9735

e-ISSN

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Svazek periodika

16

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000671674500001

EID výsledku v databázi Scopus

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