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.

The Driving Forces of Anoplophora glabripennis Have Spatial Spillover Effects

Anoplophora glabripennis Motschulsky, 1854 (Asian longhorned beetle) does serious damage to forests. It has a long history and wide distribution area in China and is spreading there and elsewhere. Extreme climate events, such as cold surges and droughts, have had a promotive impact on Anoplophora glabripennis occurrence, but the spatial spillover effect of extreme climate events and other environmental factors on the occurrence of this pest has not yet been clarified. Two indices, namely, Standardized Precipitation Evapotranspiration Index (SPEI) and Low Temperature Index (LTI), were used to quantify the effects of drought and low-temperature freezing damage. Based on spatial panel data modeling, this study calculated the spatial spillover effect of environmental factors on the incidence of Anoplophora glabripennis in 666 counties in China’s central plains from 2002 to 2009. The factors examined included LTI, SPEI, average wind speed, hours of sunlight, Gross Domestic Product (GDP) of regional primary industry, population density, Normalized Difference Vegetation Index (NDVI), and pest control rate. Study results indicated that the impacts of environmental factors on the incidence rate of Anoplophora glabripennis are different. Low-temperature freezing damage, drought, wind speed, and pest control rate had a driving impact on pest incidence rates. Overall, the direct effect accounts for about 85% of the total effect, while the indirect effect accounts for about 15% of the total effect.

Successful Eradication of the Asian Longhorn Beetle, Anoplophora glabripennis, from North-Eastern Italy: Protocol, Techniques and Results

The Asian Longhorn Beetle (ALB), Anoplophora glabripennis (Coleoptera: Cerambycidae), is an important and extremely polyphagous wood-boring beetle native to Asia. In the 1990s, ALB was accidentally introduced into North America and Europe. In 2009, a large ALB infestation was found in the Veneto Region (north-eastern Italy), in the municipality of Cornuda (Treviso province). Eradication actions were immediately undertaken, based on delimitation of infested and buffer zones, tree visual inspections, felling and chipping of infested trees, trapping protocols, and citizen alerts. A total of 36,361 trees, belonging to 16 genera, were surveyed twice a year over an area of 7594 hectares. In 2020, after 11 years of eradication measures, the ALB population of Cornuda was declared eradicated. Overall, 2361 trees belonging to 8 genera were felled and destroyed, of which 1157 were found to be infested by ALB. This paper describes all the actions carried out and the procedures applied in order to eradicate ALB from north-eastern Italy, providing a useful example for current and future ALB eradication programs.

Molecular identification of Anoplophora glabripennis (Coleoptera: Cerambycidae) and detection from frass samples based on real-time quantitative PCR

Anoplophora glabripennis (Motschulsky 1853) (Coleoptera: Cerambycidae), the Asian Longhorned Beetle, is native to temperate and subtropical areas of China and the Korean peninsula. Due to its wide range of host plants, it is considered among the most economically important invasive plant pests. The morphological identification of A. glabripennis larvae can be confirmed by DNA barcoding, but obtaining the specimens from infested trees can be a demanding and challenging task. Therefore, non-invasive diagnostic tools based on DNA extracted from frass samples can be of key importance in phytosanitary surveys. In this study, an in silico generated real-time quantitative PCR test was developed for the detection of A. glabripennis DNA from frass material, which is naturally extruded from larval tunnels through cracks in the bark. Specificity was confirmed against a wide range of other wood-boring insect species frequently encountered during phytosanitary surveys and inclusivity was demonstrated for different populations of A. glabripennis from all main European outbreak areas. The test proved sensitive and reliable in detecting A. glabripennis DNA extracted from woody frass material of Acer saccharinum and Aesculus hippocastanum at least up to the 100-fold dilution. Furthermore, the test allowed the molecular identification of any life stage of the insect, including eggs and young larvae, whose morphological identification is impossible or very challenging. This study provides a reliable and sensitive molecular tool to detect A. glabripennis DNA in woody frass material, thus allowing a non-invasive sampling approach.

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.