Spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae), is an invasive insect that was first detected in the United States in 2014 and feeds on a wide variety of plants, with economic impacts on the agricultural, ornamental, and timber industries. Part of what likely contributes to the success of L. delicatula in its invaded range is that it appears to be chemically defended by sequestering toxins from its host plant(s), which may deter predators in the introduced range. To determine the identity and behavior of North American predators that feed on spotted lanternfly, we performed a community science study in which we asked members of the public to contribute reports of animals feeding on spotted lanternfly through a Facebook page. The largest group of reported predators was arthropods followed by birds. Araneae was the arthropod order with the most reports and Phasianidae was the most frequently reported bird family. Using Pearson's χ2 tests, we also identified significant relationships between predator behavior and (1) taxonomic group of the predator, (2) L. delicatula life stage, and (3) host plant L. delicatula was observed on. These results can help to guide future research on predator host shifting to spotted lanternfly and potential for biocontrol as a management tactic.

Invasive plants in the riparian zone can negatively affect the characteristics and quality of a watershed. To support the development of a watershed management plan and foster public appreciation of the value of the riparian zone, Mohonk Preserve established a volunteer monitoring program surveying sites for invasive species. Between 2017 and 2019, citizen scientists repeatedly surveyed 20 sites in the Hudson River Valley in New York for 10 invasive plant species: purple loosestrife (Lythrum salicaria L.), common reed [Phragmites australis (Cav.) Trin. ex Steud], multiflora rose (Rosa multiflora Thunb.), garlic mustard [Alliaria petiolata (M. Bieb.) Cavara & Grande], dame’s rocket (Hesperis matronalis L.), Japanese knotweed (Polygonum cuspidatum Siebold & Zucc.), wineberry (Rubus phoenicolasius Maxim.), barberry (Berberis spp.), Japanese stiltgrass [Microstegium vimineum (Trin.) A. Camus], and Asiatic bittersweet (oriental bittersweet, Celastrus orbiculatus Thunb.). We found that the number of target species detected was higher on sites closer to paved roads and with increasing drainage area size, while lower with higher percentages of forested land in the basin. Our analysis results highlight variation in the presence of target invasive species across the Hudson River Valley region, highlighting sites and areas to monitor for future introductions and take action to prevent species’ invasions. Our results highlight differences in the most relevant abiotic factors for hydrophytes and non-hydrophyte species, underscoring the importance of considering species’ life-history traits before the development of management plans for invasive plant species in the riparian zone. Our case study of community-collected data in the Hudson River Valley region using a relatively simple monitoring protocol can provide a road map for other regions fostering volunteer engagement with invasive plants.

Land managers require tools that improve understanding of suitable habitat for invasive plants and that can be incorporated into survey efforts to improve efficiency. Habitat suitability models (HSMs) contain attributes that can meet these requirements, but it is not known how well they perform, as they are rarely field-tested for accuracy. We developed ensemble HSMs in the state of Wisconsin for 15 species using five algorithms (boosted regression trees, generalized linear models, multivariate regression splines, MaxEnt, and random forests), evaluated performance, determined variables that drive suitability, and tested accuracy. All models had good model performance during the development phase (Area Under the Curve [AUC] > 0.7 and True Skills Statistic [TSS] > 0.4). While variable importance and directionality was species specific, the most important predictor variables across all of the species’ models were mean winter minimum temperatures, total summer precipitation, and tree canopy cover. Post model development, we obtained 5,005 new occurrence records from community science observations for all 15 focal species to test the models’ abilities to accurately predict results. Using a correct classification rate of 80%, just 8 of the 15 species correctly predicted suitable habitat (α ≤ 0.05). Exploratory analyses found the number of reporters of these new data and the total number of new occurrences reported per species contributed to increasing correct classification. Results suggest that while some models perform well on evaluation metrics, relying on these metrics alone is not sufficient and can lead to errors when utilized for surveying. We recommend any model should be tested for accuracy in the field before use to avoid this potential issue.