DataSheet_1_Enhancing pest control interventions by linking species distribution model prediction and population density assessment of pine wilt disease vectors in South Korea.zip

• Main Authors: Inyoo Kim, Youngwoo Nam, Sinyoung Park, Wonhee Cho, Kwanghun Choi, Dongwook W. Ko
• Format: Dataset
• Language: unknown
• Published: 2024
• Subjects: Evolutionary Biology; Ecology; Invasive Species Ecology; Landscape Ecology; Conservation and Biodiversity; Behavioural Ecology; Community Ecology (excl. Invasive Species Ecology); Ecological Physiology; Freshwater Ecology; Marine and Estuarine Ecology (incl. Marine Ichthyology); Population Ecology; Terrestrial Ecology; quantile regression; pest management; pine wilt nematode; biserial correlation; Maxent; Monochamus spp; Sakhalin
• Online Access: https://doi.org/10.3389/fevo.2023.1305573.s001; https://figshare.com/articles/dataset/DataSheet_1_Enhancing_pest_control_interventions_by_linking_species_distribution_model_prediction_and_population_density_assessment_of_pine_wilt_disease_vectors_in_South_Korea_zip/25458598

Pine wilt disease caused by pinewood nematode is one of the most destructive forest diseases, and still spreading in South Korea despite the various control efforts. Japanese pine sawyer (JPS) and Sakhalin pine sawyer (SPS) are the main vectors of the disease. Understanding the distribution and density of the vectors is crucial since the control period is determined by the different emergence periods of the two vectors and the control method by its density and the expected damage severity. In this study, we predicted the distribution of JPS and SPS using Maxent and investigated the relationship between the resulting suitability value and the density. The population densities of JPS and SPS were obtained through a national survey using pheromone traps between 2020-2022. We converted the density data into presence/absence points to externally validate each species distribution model, then we used quantile regression to check the correlation between the suitability and population density, and finally we used three widely used thresholds to convert the model results into binary maps, and tested if they could distinguish the density by comparing the R b value of biserial correlation. The quantile regression revealed a positive relationship between the habitat suitability and population density sampled in the field. Moreover, the binary map with threshold criteria that maximizes the sum of the sensitivity and specificity had the best density discrimination capacity with the highest R b . A quantitative relationship between suitability and vector density measured in the field from our study provides reliability to species distribution model as practical tools for forest pest management.