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Spongy moth (Lymantria dispar dispar) has caused considerable damage to oak trees across eastern deciduous forests. Forest management, post-outbreak, is resource intensive and typically focused on ecosystem restoration or resource loss mitigation. Some local forest managers and government partners are exploring developing technologies such as Unpiloted Aerial Systems (UASs, UAVs, or drones) to enhance their ability to gather reliable fine-scale information. However, with limited resources and the complexity of investing in hardware, software, and technical expertise, the decision to adopt UAS technologies has raised questions on their effectiveness. The objective of this study was to evaluate the abilities of two UAS surveying approaches for classifying the health of individual oak trees following a spongy moth outbreak. Combinations of two UAS multispectral sensors and two Structure from Motion (SfM)-based software are compared. The results indicate that the overall classification accuracy differed by as much as 3.8% between the hardware and software configurations. Additionally, the class-specific accuracy for 'Declining Oaks' differed by 5–10% (producer's and user's accuracies). The processing experience between open-source and commercial SfM software was also documented and demonstrated a 25-to-75-fold increase in processing duration. These results point out major considerations of time and software accessibility when selecting between hardware and software options for fine-scale forest mapping. Based on these findings, future stakeholders can decide between cost, practicality, technical complexity, and effectiveness. [ABSTRACT FROM AUTHOR]

Simple Summary: Spongy moth, formerly known as European gypsy moth, is an invasive forest pest in the United States and is under quarantine in the eastern part of the country. The flighted spongy moth complex, a group of related spongy moths formerly known as Asian gypsy moth, are pests that are not currently present in the United States. As part of our research to characterize spongy moth pathogens, we determined the genome sequences and relative amounts of genomes of an iflavirus, an RNA-based virus, from spongy moths in two locations in the USA and flighted spongy moths in two locations in China. The iflaviruses from the USA and China spongy moth populations were distinguishable from each other on the basis of their genome sequences. Iflavirus genome levels in all moth samples were very high, but these high genome levels were not associated with any obvious disease symptoms. The information in this study raises questions about the effect of RNA viruses such as iflaviruses on their hosts, and will be of interest to scientists who work with iflaviruses, spongy moth, and other forest pests. The spongy moth virus Lymantria dispar iflavirus 1 (LdIV1), originally identified from a Lymantria dispar cell line, was detected in 24 RNA samples from female moths of four populations from the USA and China. Genome-length contigs were assembled for each population and compared with the reference genomes of the first reported LdIV1 genome (Ames strain) and two LdIV1 sequences available in GenBank originating from Novosibirsk, the Russian Federation. A whole-genome phylogeny was generated for these sequences, indicating that LdIV1 viruses observed in North American (flightless) and Asian (flighted) spongy moth lineages indeed partition into clades as would be expected per their host's geographic origin and biotype. A comprehensive listing of synonymous and non-synonymous mutations, as well as indels, among the polyprotein coding sequences of these seven LdIV1 variants was compiled and a codon-level phylogram was computed using polyprotein sequences of these, and 50 additional iflaviruses placed LdIV1 in a large clade consisting mostly of iflaviruses from other species of Lepidoptera. Of special note, LdIV1 RNA was present at very high levels in all samples, with LdIV1 reads accounting for a mean average of 36.41% (ranging from 1.84% to 68.75%, with a standard deviation of 20.91) of the total sequenced volume. [ABSTRACT FROM AUTHOR]

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context. [ABSTRACT FROM AUTHOR]