Centinari, M. | Search bioRxiv

bioRxiv2024

Infected grapevines are poor hosts but can serve as source of pathogen transmission for SLF

The potential of the invasive spotted lanternfly (Lycorma delicatula White; SLF) to serve as vector of plant pathogens is especially a concern for grapevine growers, as SLF are known to invade and can heavily infest vineyards, where the insects may encounter grapevines with multiple diseases. In this study, we have found that, when given the choice, SLF preferentially fed on healthy vines and that, when forced, feeding on Pierces disease (PD)-infected vines had negative effects on nymph development. Upon transmission trials, most of the recipient vines showed scorching symptoms typical of PD and one of the recipient vines resulted positive by qPCR. Our study suggests that SLF could be a vector of PD, however further experiments are needed to determine if transmission would occur under field conditions.

plant biology↗

bioRxiv2023

NYUS.2: an Automated Machine Learning Prediction Model for the Large-scale Real-time Simulation of Grapevine Freezing Tolerance in North America

O_LIAccurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data is limited. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. C_LIO_LIWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine. Feature importance was quantified by AutoGluon and SHAP value. The final model was evaluated and compared with previous models for its performance under different climate conditions. C_LIO_LIThe final model achieved an overall 1.36 {degrees}C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model might have adequately extracted some biological mechanisms during training. C_LIO_LIThe final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-2023 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time. C_LI

plant biology↗