Additional Datasets: Low current and future spotted lanternfly suitability in New York wine-growing regions tempers vineyard risk

Additional datasets (for zenodo.org/records/15692201) containing longitude and latitude corrdinates for New York vineyard locations and species occurences (e.g., spotted lanternfly and tree-of-heaven). In anticipation of the risk to New Yorks grape growing industry, the New York State Department of Agriculture and Markets initiated a statewide spotted lanternfly monitoring effort in 2020 that has since collected over 23,000 observations of both spotted lanternfly and tree-of-heaven. Observations of tree-of-heaven from the Global Biodiversity Information Facility (GBIF) provide an important additional dataset of distribution across New York State. ===================================== See R Code Here: SLF Model 2025 This code was implemented using R version 4.4.1 (R Core Team, 2024). We employed a wide range of Comprehensive R Archive Network (CRAN) packages; such as SDMtune (Vignali et al., 2020), flexsdm (Velazco et al., 2022), and blockCV (Valavi et al., 2019). Random Forest classification was used to improve risk assessment (Chen et al., 2021). Default settings in all distribution models were used unless stated otherwise. A K-fold cross-validation approach with spatial blocks was implemented to minimize spatial autocorrelation and ensure an independent dataset for validation (Soley-Guardia et al., 2024). Model performance was evaluated by generating a confusion matrix; particularly looking at accuracy, sensitivity, specificity, positive prediction value, and negative prediction value. To assess model accuracy, we compared the root mean square error between both the training-testing (RMSE) and validation (CV-RMSE) datasets (Nayak et al., 2022).