Hazard ratings of pine forests to a pine wilt disease at two spatial scales (individual trees and stands) using self-organizing map and random forest
Autor: | Yeong-Jin Chung, Young-Seuk Park, Yil-Seong Moon |
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Rok vydání: | 2013 |
Předmět: |
Ecology
biology Applied Mathematics Ecological Modeling Forest management Crown (botany) Diameter at breast height Forestry Bursaphelenchus xylophilus biology.organism_classification Hazard Computer Science Applications Random forest Geography Computational Theory and Mathematics Modeling and Simulation PEST analysis Ecology Evolution Behavior and Systematics Wilt disease |
Zdroj: | Ecological Informatics. 13:40-46 |
ISSN: | 1574-9541 |
DOI: | 10.1016/j.ecoinf.2012.10.008 |
Popis: | Pine wilt disease (PWD) caused by the pine wood nematode is the most serious global threat to pine forests. Hazard ratings of trees and forests to pest attacks provide important information to efficiently identify current or future hazardous conditions. However, in spite of the importance of hazard ratings for managing PWD, there are few studies on hazard ratings in this system. In this study, we evaluated the hazard ratings of pine trees and pine stands to PWD by considering environmental factors at the level of the stand and the individual tree. Our results showed that trees with larger diameter at breast height (DBH) showed a higher risk rate than those with smaller DBH, indicating that large trees have an increased probability of exposure to vector beetles because they are tall and have a large crown volume. We also found that reduced tree vigour could be related to susceptibility to PWD. In pine stands, geographical factors showed a high correlation with the occurrence of PWD. PWD occurrence was rare at high altitudes, but was more common on steep and south-facing slopes. These patterns were consistently observed in the results from 2 computational approaches: self-organizing map (SOM) and random forest models. The combination of SOM and random forest was effective to extract ecological information from the dataset. The SOM efficiently characterized relations among variables, and the random forest model was effective at predicting ecological variables, including the hazard rating of trees to disturbances. |
Databáze: | OpenAIRE |
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