Arthropods Associated with Invasive Frangula alnus (Rosales: Rhamnaceae): Implications for Invasive Plant and Insect Management.

The invasive shrub glossy buckthorn (Frangula alnus) has been progressively colonizing the Northeastern United States and Southeastern Canada for more than a century. To determine the dominant arthropod orders and species associated with F. alnus, field surveys were conducted for two years across 16 plots within the Allegheny National Forest, Pennsylvania, USA. Statistical analyses were employed to assess the impact of seasonal variation on insect order richness and diversity. The comprehensive arthropod collection yielded 2845 insects and arachnids, with hemipterans comprising the majority (39.8%), followed by dipterans (22.3%) and arachnids (15.5%). Notably, 16.2% of the hemipterans collected were in the immature stages, indicating F. alnus as a host for development. The two dominant insect species of F. alnus were Psylla carpinicola (Hemiptera: Psyllidae) and Drosophila suzukii (Diptera: Drosophilidae); D. suzukii utilized F. alnus fruits for reproduction. Species richness and diversity exhibited significant variations depending on the phenology of F. alnus. The profiles of volatile compounds emitted from the leaves and flowers of F. alnus were analyzed to identify factors that potentially contribute to the attraction of herbivores and pollinators. The results of our study will advance the development of novel F. alnus management strategies leveraging the insects associated with this invasive species.

Spatial Patterns of Frangula alnus (Rosales: Rhamnaceae): Implications for Invasive Plant Management.

Glossy buckthorn (Frangula alnus) (Rosales: Rhamnaceae) is an invasive shrub from Europe that has been invading North America for over a century and threatening native vegetation in open and disturbed habitats. The treatment of F. alnus is currently restricted to the roadside, suggesting any individual F. alnus residing within the forest would be left unmanaged and would continue to spread in the area. This research was conducted to determine the spatial patterns and relationship of F. alnus with forest roads. The presence and density of F. alnus at 1412 sample points were recorded on four sites in the Allegheny National Forest, Pennsylvania, USA. Buffer analyses were conducted along roads to determine the relationship between F. alnus density and proximity to forest roads. Geostatistics and spatial analysis by distance indices (SADIE) were used to characterize the spatial pattern of F. alnus. Results of this study showed that F. alnus was spatially aggregated and resided beyond forest roads. Both the density and presence of F. alnus decreased as the distance from the forest road increased. These results imply the potential for precision management of F. alnus by locating and managing only where F. alnus presents.

Precision Detection and Assessment of Ash Death and Decline Caused by the Emerald Ash Borer Using Drones and Deep Learning.

Emerald ash borer (Agrilus planipennis) is an invasive pest that has killed millions of ash trees (Fraxinus spp.) in the USA since its first detection in 2002. Although the current methods for trapping emerald ash borers (e.g., sticky traps and trap trees) and visual ground and aerial surveys are generally effective, they are inefficient for precisely locating and assessing the declining and dead ash trees in large or hard-to-access areas. This study was conducted to develop and evaluate a new tool for safe, efficient, and precise detection and assessment of ash decline and death caused by emerald ash borer by using aerial surveys with unmanned aerial systems (a.k.a., drones) and a deep learning model. Aerial surveys with drones were conducted to obtain 6174 aerial images including ash decline in the deciduous forests in West Virginia and Pennsylvania, USA. The ash trees in each image were manually annotated for training and validating deep learning models. The models were evaluated using the object recognition metrics: mean average precisions (mAP) and two average precisions (AP50 and AP75). Our comprehensive analyses with instance segmentation models showed that Mask2former was the most effective model for detecting declining and dead ash trees with 0.789, 0.617, and 0.542 for AP50, AP75, and mAP, respectively, on the validation dataset. A follow-up in-situ field study conducted in nine locations with various levels of ash decline and death demonstrated that deep learning along with aerial survey using drones could be an innovative tool for rapid, safe, and efficient detection and assessment of ash decline and death in large or hard-to-access areas.