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

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.

Hybridization Potential of Two Invasive Asian Longhorn Beetles

The Asian longhorned beetle (ALB), Anoplophora glabripennis (Motschulsky) and citrus longhorned beetle (CLB), Anoplophora chinensis (Förster) (both Coleoptera: Cerambycidae: Lamiinae), are high-risk invasive pests that attack various healthy hardwood trees. These two species share some similar host plants and overlapping distributions in large parts of their native ranges in China and the Korean peninsula as well as similar reproductive behaviors. The original Anoplophora malasiaca (Thomson) occurs in Japan and has been synonymized as CLB (hereafter referred to JCLB). In this study, a 30-min behavioral observation of paired adults, followed by a four-week exposure to host bolts, showed that ALB could not successfully cross with CLB. Mating was observed between female CLB and male ALB but not between female ALB and male CLB, no laid eggs hatched. JCLB males successfully crossed with ALB females to produce viable eggs although the overall percentage of hatched eggs was lower than those from conspecific mating pairs. However, ALB males could not successfully cross with JCLB females. CLB and JCLB mated and produced viable hybrid offspring and the hybrid F1 offspring eggs were fertile. These results suggest an asymmetrical hybridization between ALB and JCLB, and that both CLB and JCLB might be considered as two subspecies with different hybridization potential with congeneric ALB. Given their potential impacts on ecosystems and many economically important tree hosts, invasion of these geographically isolated species (ALB and JCLB) or distant subspecies (CLB and JCLB) into the same region may facilitate potential hybridization, which could be a potential concern for the management of these two globally important invasive forest pests. Further studies are needed to determine if fertile hybrid offspring are capable of breeding continually or backcrossing with parental offspring successfully.

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

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.

The eradication of Asian longhorned beetle at Paddock Wood, UK

In March 2012, an outbreak of Anoplophora glabripennis was detected at Paddock Wood, Kent, UK. The epicentre of the outbreak was adjacent to a site that a company had used for storing imported stone in wood packaging. An eradication campaign was initiated involving the agencies responsible for plant health and forestry in England and Wales. The area was initially surveyed by visual inspection of standing trees from the ground and 24 infested trees were detected. This method was more effective for detecting trees with A. glabripennis exit holes than trees at an early stage of infestation. A further 42 infested trees were detected when the infested trees and host trees within 100 m of them were felled and the felled material was inspected. The most important host tree species was Acer pseudoplatanus (43 of the 66 infested trees). Tree climbers inspected the trees between 100 and 300 m of infested trees three times. They found damage caused by native pests that it had not been possible to detect from the ground but no A. glabripennis. Other surveillance techniques used were the regular inspection of favoured host trees over a wide area and the planting and regular inspection of favoured host trees in the core of the outbreak area. Pheromone trapping and the use of detection dog teams were trialled during the outbreak. Public meetings, leaflet drops, press releases, television features and school visits were all used to communicate with local residents and other stakeholders. No A. glabripennis were detected after the initial removal of trees in 2012 and eradication was declared after seven years of surveillance in 2019. The outbreak was likely to have been present for 10 or 11 years, but population development is likely to have been limited by the sub-optimal climatic conditions, especially the UK’s relatively cool summers.

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

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—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.

Optimizing Application Rates of Metarhizium brunneum (Hypocreales: Clavicipitaceae) Microsclerotia for Infecting the Invasive Asian Longhorned Beetle (Coleoptera: Cerambycidae)

The Asian longhorned beetle (Anoplophora glabripennis [Motschulsky]) is an invasive wood-boring beetle that threatens urban trees and forests in North America and Europe. The entomopathogenic fungus Metarhizium brunneum Petch strain F52 can infect and kill A. glabripennis adults. Products containing this fungus were available for commercial use in the United States but not registered for Asian longhorned beetle. This study tested different formulations and application rates of M. brunneum F52 microsclerotial granules for their potential development for management of A. glabripennis adults. Three application rates of M. brunneum microsclerotial granules relative to a 1× formulation from previous experiments (0.03 g/cm2; 2× = 0.06 g/cm2 and 3× = 0.09 g/cm2) were exposed on tree trunks for 4-wk periods during May–September. Increased application rates had better retention (% of initial g applied) than the 1× rate, rather than greater weathering loss. Microsclerotia at the 2× application produced 5.05 × 106 conidia/cm2, which was 18 times more conidia than the 1× application. Since A. glabripennis is under active eradication, bioassays with adult beetles were carried out in a quarantine laboratory, using the formulation samples from field exposures. The 2× application resulted in faster beetle mortality. The 3× and 2× rates were not significantly different in retention of the formulation, conidial production, or mortality, but 2× produced the most conidia per gram applied (3.92 × 109 conidia/g). An augmented formulation containing 70% M. brunneum by weight, rather than 50%, produced significantly more conidia and faster beetle mortality than the 50% formulation.