PREDICTING MULTI-SPECIES BARK BEETLE (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE) OCCURRENCE IN ALASKA: OPEN-ACCESS BIG GIS-DATA MINING TO PROVIDE ROBUST INFERENCE
Result description
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
Keywords
ScolytinaesPest insectsOutbreaksBoosted classification and regression treeForest ecologySpatial modelingMachine-learning algorithm
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
PREDICTING MULTI-SPECIES BARK BEETLE (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE) OCCURRENCE IN ALASKA: OPEN-ACCESS BIG GIS-DATA MINING TO PROVIDE ROBUST INFERENCE
Original language description
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
Czech name
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Czech description
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Classification
Type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10619 - Biodiversity conservation
Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biodiversity Informatics
ISSN
1546-9735
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
1-19
UT code for WoS article
000671674500001
EID of the result in the Scopus database
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Basic information
Result type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
OECD FORD
Biodiversity conservation
Year of implementation
2021