Assessing Flight Potential of the Invasive Asian Longhorned Beetle (Coleoptera: Cerambycidae) With Computerized Flight Mills

2017 ◽  
Vol 110 (3) ◽  
pp. 1070-1077 ◽  
Author(s):  
Vanessa M. Lopez ◽  
Mark S. Hoddle ◽  
Joseph A. Francese ◽  
David R. Lance ◽  
Ann M. Ray
Keyword(s):  
Longhorned Beetle ◽  
Asian Longhorned Beetle ◽  
Flight Mills

scholarly journals Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Quan Zhou ◽  
Xudong Zhang ◽  
Linfeng Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Remote Sensing ◽  
Gansu Province ◽  
Tree Level ◽  
Anoplophora Glabripennis ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle ◽  
Remote Sensing Images ◽  
Physiological Factors ◽  
Abnormal Phenotype ◽  
Damage Degree

Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.

Susceptibility of Asian longhorned beetle, Anoplophora glabripennis (Motchulsky) (Coleoptera: Cerambycidae) to entomopathogenic nematodes

2004 ◽  
Vol 30 (2) ◽  
pp. 430-438 ◽  
Author(s):  
Declan J. Fallon ◽  
Leellen F. Solter ◽  
Melody Keena ◽  
Michael McManus ◽  
James R. Cate ◽  
...  
Keyword(s):  
Entomopathogenic Nematodes ◽  
Anoplophora Glabripennis ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle

Tree selection and use by the polyphagous xylophage Anoplophora glabripennis (Coleoptera: Cerambycidae) in Canada

2021 ◽  
Author(s):  
Jean J. Turgeon ◽  
Michael T Smith ◽  
John Pedlar ◽  
Ronald Edward Fournier ◽  
Mary Orr ◽  
...  
Keyword(s):  
High Risk ◽  
Host Selection ◽  
Tree Height ◽  
Anoplophora Glabripennis ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle ◽  
Risk Of Injury ◽  
Breeding Populations ◽  
Over Time

Two breeding populations of the non-native Asian longhorned beetle (Anoplophora glabripennis Motschulsky), a pest of broadleaf trees in its native China, were discovered in Ontario in 2003 and 2013, respectively. Both populations were eradicated by removing all trees injured by the beetle and all uninjured trees deemed at high risk of injury. We used data collected during this removal to study host selection. Signs of A. glabripennis injury were observed on 732 stems from seven (i.e., Acer, Salix, Populus, Betula, Ulmus, Fraxinus and Tilia) of the 45 tree genera available. Complete beetle development was confirmed on only the first four of these seven genera. Most signs of injury were on the genus Acer and on trees with a diameter at 130 cm above ground ranging between 15 cm and 40 cm. On most trees, the lowest sign of injury was within three meters of the ground or within 40% of tree height. Tree height explained 63% of the variance in the location of the lowest sign of injury. Initial attacks were typically near the middle of the tree and expanded both upward and downward with successive attacks over time. We discuss how these findings could improve survey efforts for A. glabripennis.

A Relational Theory of Risk: a Case Study of the Asian Longhorned Beetle Infestation in Worcester, MA

2019 ◽  
Vol 23 (6) ◽  
pp. 781-795 ◽  
Author(s):  
Joey Hersh ◽  
Deborah G. Martin ◽  
Nicholas a. B. Geron ◽  
John Rogan
Keyword(s):  
Relational Theory ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle

scholarly journals The drivers of the Asian longhorned beetle disaster show significant spatial heterogeneity

2020 ◽  
Vol 117 ◽  
pp. 106680
Author(s):  
Jixia Huang ◽  
Borun Qu ◽  
Guofei Fang ◽  
Xiaodong Li ◽  
Shixiang Zong
Keyword(s):  
Spatial Heterogeneity ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle
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