Data for: "From Pest Traps to Management Maps: Predicting the Abundance and Phenology of Japanese Pine Bast Scale to Guide National Forest Adaptation and Timely Control"

Dataset DescriptionThis dataset contains the processed occurrence data and all 1-km resolution environmental predictor rasters required to reproduce the analyses in the manuscript:"From Pest Traps to Management Maps: Predicting the Abundance and Phenology of Japanese Pine Bast Scale to Guide National Forest Adaptation and Timely Control" (Bang et al., In progress) ContentsThe dataset includes the following files, organized to match the directory structure expected by the accompanying R code: 1. Occurrence Data (data/occurrence/)occurrence2022.csv: Raw biweekly pest capture data from the nationwide pheromone trap network (164 sites) in 2022.occurrence2023.csv: Raw biweekly pest capture data from the nationwide pheromone trap network (65 sites) in 2023. 2. Predictor Rasters (data/predictors/1km/)This folder contains all .tif files used as predictors in the XGBoost models. These include: 2.1. Original Host Abundance Rasters: Ab_AG.tif, Ab_PD.tif, Ab_PT.tifThese host abundance maps were generated from the Korea National Forest Inventory (NFI) data using a random forest imputation approach as described in the manuscript and Kim et al. (2025). 2.2. All other .tif files (e.g., bio1.tif, LST_Day_2022.tif, Elevation.tif).These are derivatives of publicly available data, processed (clipped, aggregated, resampled) to a 1-km grid for the study area (South Korea) to ensure reproducibility. Original data sources include:CHELSA v.2.1 for bioclimatic variables (Karger et al., 2021).MODIS for Land Surface Temperature (Wan et al., 2021) and Vegetation Indices (Didan, 2021).NASA SRTM for elevation (NASA JPL, 2013). UsageThis dataset is intended to be used with the R scripts available in the accompanying GitHub repository: https://github.com/kim-seunguk/pest-spatiotemporal-modeling-JPBS.gitPlease download all files and place them into the 'data/occurrence/' and 'data/predictors/1km/' subdirectories within the cloned repository structure, as detailed in the README.md file. CitationWhen using this dataset, please cite both this Zenodo entry and the associated manuscript.Dataset DOI: 10.5281/zenodo.17403184(Manuscript citation to be added when available)