Visiting Scholarshttp://hdl.handle.net/11122/107632024-03-29T06:58:46Z2024-03-29T06:58:46ZPredicting multi-species Bark Beetle (Coleoptera: Curculionidae: Scolytinae) occurrence in Alaska: open-access big GIS-data mining to provide robust inferencehttp://hdl.handle.net/11122/122472021-11-11T11:32:55Z2021-07-03T00:00:00ZPredicting multi-species Bark Beetle (Coleoptera: Curculionidae: Scolytinae) occurrence in Alaska: open-access big GIS-data mining to provide robust inference
Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that rank 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 include former outbreaks and fire. We assembled the first open access data, which can be used in open source GIS platforms, for understanding the ecology of the bark beetle organism in Alaska. We used boosted classification and regression tree as a machine learning data mining algorithm to model-predict the relationship between 14 environmental variables, as model predictors, and 838 occurrence records of 68 bark beetle species compared to pseudo-absence locations across the state of Alaska. The model predictors include topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map the multi-species bark beetle occurrences across the state of Alaska on a 1-km spatial resolution in addition to providing a good quality environmental dataset freely accessible for the public. About 16% of the mixed forest and 59% of evergreen forest are expected to be occupied by the bark beetles based on current climatic conditions and biophysical attributes of the landscape. The open access dataset that we prepared, and the machine learning modeling approach that we used, can provide a foundation for future research not only on scolytines but for other multi-species questions of concern, such as forest defoliators, and small and big game wildlife species worldwide.
2021-07-03T00:00:00ZAlaska Map of Bark Beetle Presence in 2016 and 2017Zabihi, KhodabakhshHuettmann, FalkYoung, Brianhttp://hdl.handle.net/11122/109302021-11-11T11:34:05Z2019-01-01T00:00:00ZAlaska Map of Bark Beetle Presence in 2016 and 2017
Zabihi, Khodabakhsh; Huettmann, Falk; Young, Brian
A shapefile of 68 species locations, surveyed by the USFS from 2016 to 2017, includes 3 bark beetle species. The geographic projection of the map was set at NAD 1983 Alaska Albers.
2019-01-01T00:00:00ZAlaska Map of 1-km Space Grid PointsZabihi, KhodabakhshHuettmann, FalkYoung, Brianhttp://hdl.handle.net/11122/109292021-11-11T11:34:33Z2019-01-01T00:00:00ZAlaska Map of 1-km Space Grid Points
Zabihi, Khodabakhsh; Huettmann, Falk; Young, Brian
A lattice point grid with a 1-km Euclidean distance in a total point number of 1,522,655 in Alaska. The map is prepared as a shapefile and the geographic projection is NAD 1983 Alaska Albers.
2019-01-01T00:00:00ZAlaska Map of Bark Beetle Pseudo-AbsenceZabihi, KhodabakhshHuettmann, FalkYoung, Brianhttp://hdl.handle.net/11122/109282021-11-11T11:35:04Z2019-01-01T00:00:00ZAlaska Map of Bark Beetle Pseudo-Absence
Zabihi, Khodabakhsh; Huettmann, Falk; Young, Brian
We created a shapefile of 5000 pseudo-absence points, with a minimum Euclidean distance from each other of 1-km across Alaska, as a background dataset with which to compare bark beetle presences. The 5000 random point locations were generated in ArcMap 10.4 (ESRI Inc., Redlands, CA), with the geographic projection of NAD 1983 Alaska Albers.
2019-01-01T00:00:00Z