Assessing effective mechanical and chemical strategies for managing Eucosma giganteana (Lepidoptera: Tortricidae) in the perennial oilseed crop, Silphium integrifolium (Asteraceae: Heliantheae).

Eucosma giganteana (Riley) is a native specialist pest of silflower, Silphium integrifolium Michx., which is currently being domesticated as a perennial oilseeds crop. The larvae of this moth attack silflower capitula and root crowns, causing both seed damage and long-term degradation of plants. To determine methods to manage E. giganteana in silflower crop fields, we conducted a laboratory bioassay and 3 field experiments to assess the effects of a suite of organic, conventional, and mechanical treatments on E. giganteana mortality and colonization of flower heads. Pyrethroids (permethrin, cyfluthrin), chlorantraniliprole, and methoxyfenozide each had significant insecticidal effects on E. giganteana in at least 2 of the experiments conducted. Nematodes marginally increased larva mortality in the laboratory bioassay and could be further investigated as a soil-applied biological control. In 2 separate field experiments, trimming the top 15% of silflower plants to delay flowering did not alone reduce E. giganteana colonization of flower heads throughout the growing season. However, when trimming was paired with a single chlorantraniliprole application, colonization of capitula was reduced by 83% over untreated control plants. Collectively, these experiments provide evidence for several treatments that could be further tested and incorporated into an integrated pest management strategy for E. giganteana.

uafR: An R package that automates mass spectrometry data processing.

Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827-0.999 along concentrations ranging from 1 × 10-5 to 1 × 103 ng/µl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'.