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 Rb 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 Rb. 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.

中文翻译：

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通过将韩国松材线虫病媒介的物种分布模型预测和种群密度评估联系起来，加强害虫防治干预措施

由松材线虫引起的松材线虫病是最具破坏性的森林病害之一，尽管采取了各种控制措施，但该病仍在韩国蔓延。日本松树(JPS)和库页岛松树(SPS)是该病的主要传播媒介。了解病媒的分布和密度至关重要，因为控制周期由两种病媒的不同出现周期决定，而控制方法则由其密度和预期损害严重程度决定。在本研究中，我们使用 Maxent 预测了 JPS 和 SPS 的分布，并研究了所得适宜性值与密度之间的关系。 JPS和SPS的人口密度是通过2020-2022年期间使用信息素陷阱进行的全国调查获得的。我们将密度数据转换为存在/不存在点，以在外部验证每个物种分布模型，然后使用分位数回归来检查适宜性和种群密度之间的相关性，最后使用三个广泛使用的阈值将模型结果转换为二值图，并测试他们是否可以通过比较来区分密度右乙双列相关的值。分位数回归揭示了栖息地适宜性和实地采样的人口密度之间的正相关关系。此外，具有最大化灵敏度和特异性之和的阈值标准的二元图具有最佳的密度辨别能力，并且具有最高的密度辨别能力。右乙。我们的研究在实地测量的适宜性和媒介密度之间的定量关系为物种分布模型作为森林害虫管理的实用工具提供了可靠性。