Potential Economic Impacts of the Asian Longhorned Beetle (Coleoptera: Cerambycidae) in Eastern Canada

2019 ◽  
Vol 113 (2) ◽  
pp. 839-850 ◽  
Author(s):  
John H Pedlar ◽  
Daniel W McKenney ◽  
Denys Yemshanov ◽  
Emily S Hope

Abstract The Asian longhorned beetle (Anoplophora glabripennis Motschulsky) continues to pose a significant risk to deciduous forests around the world. We assess Asian longhorned beetle-related risks in eastern Canada by generating current and future climate suitability maps, import-based likelihood of introduction estimates for each urban center in our study area, and potential economic impacts in both urban and natural settings. For the current period, climatic suitability for Asian longhorned beetle was highest in southern Ontario, but was projected to expand significantly northward and eastward by midcentury. High likelihood of Asian longhorned beetle introduction was associated with large urban centers, but also smaller centers with high levels of pest-associated imports. Potential costs for the removal and replacement of Asian longhorned beetle-impacted street trees ranged from CDN$8.6 to $12.2 billion, with the exact amount and city-level ranking depending on the method used to calculate risk. Potential losses of merchantable maple (Acer) timber were estimated at CDN$1.6 billion using provincial stumpage fees and CDN$431 million annually when calculated using a combination of economic and forestry product statistics. The gross value of edible maple products, which could potentially be affected by Asian longhorned beetle, was estimated at CDN$358 million annually. These values can help inform the scale of early detection surveys, potential eradication efforts, and research budgets in the event of future Asian longhorned beetle introductions.

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Quan Zhou ◽  
Xudong Zhang ◽  
Linfeng Yu ◽  
Lili Ren ◽  
Youqing Luo

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.


2004 ◽  
Vol 30 (2) ◽  
pp. 430-438 ◽  
Author(s):  
Declan J. Fallon ◽  
Leellen F. Solter ◽  
Melody Keena ◽  
Michael McManus ◽  
James R. Cate ◽  
...  

Author(s):  
Jean J. Turgeon ◽  
Michael T Smith ◽  
John Pedlar ◽  
Ronald Edward Fournier ◽  
Mary Orr ◽  
...  

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.


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

2020 ◽  
Vol 117 ◽  
pp. 106680
Author(s):  
Jixia Huang ◽  
Borun Qu ◽  
Guofei Fang ◽  
Xiaodong Li ◽  
Shixiang Zong

2004 ◽  
Vol 33 (2) ◽  
pp. 435-442 ◽  
Author(s):  
Michael T. Smith ◽  
Patrick C. Tobin ◽  
Jay Bancroft ◽  
Guohong Li ◽  
Ruitong Gao

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