Recent outbreaks of the European spruce bark beetle (Ips typographus) in Norway spruce (Picea abies) forests in Central Europe highlight the importance of timely detection and sanitation of infested trees for pest management efficacy. This study provides novel quantitative evidence on the manifestation of infestation symptoms and their visual detectability, to guide accelerated, optimized terrestrial bark beetle monitoring, as well as establishing benchmarks for potential alternative (e.g. sensor-based) monitoring approaches. We employed bi-weekly, individual tree-level assessments on 85 hectares of spruce-dominated unmanaged forest over a 2-year period in south-western Germany (detecting a total of 1,176 infested trees). By applying decision tree-type models, we quantified the predictive power of observed symptoms and their correlation with environmental factors and time. Terrestrial detection accuracy and timeliness were high, suggestive of being sufficient to suppress I. typographus outbreak propagation by subsequent sanitation felling. Among the six studied symptoms, boring dust occurred most frequently (in 82% of correctly detected infestations) and is most suitable for timely detection. Total symptom abundance was best explained by two site parameters (slope, Standardized Precipitation-Evapotranspiration-Index) and I. typographus population density, while it was widely independent of tree parameters and time. Though individual symptoms varied over time and among trees, patterns were clearly identified. For instance, infestations in spring were most critical to be timely detected, while increasing crown discoloration and defoliation facilitated detection in late summer and autumn. Findings further imply that hibernation trees would optimally be detected already in late summer with sanitation felling applied before November.

最近在挪威云杉 ( Picea abies )爆发的欧洲云杉树皮甲虫 ( Ips Typgraphus )) 中欧的森林强调了及时发现和清洁受侵染树木以提高害虫管理效率的重要性。本研究提供了有关侵染症状表现及其视觉可检测性的新定量证据，以指导加速、优化的陆地树皮甲虫监测，并为潜在的替代（例如基于传感器的）监测方法建立基准。我们在德国西南部的 2 年内对 85 公顷以云杉为主的未管理森林进行了每两周一次的个体树木水平评估（共检测到 1,176 棵受侵染的树木）。通过应用决策树型模型，我们量化了观察到的症状的预测能力及其与环境因素和时间的相关性。地面探测精度和时效性高，I. 印刷品暴发通过随后的卫生砍伐传播。在所研究的六种症状中，无聊的灰尘最常发生（在正确检测到的虫害中占 82%），最适合及时检测。总症状丰度最好通过两个站点参数（坡度、标准化降水-蒸散-指数）和I. typographus来解释人口密度，而它与树木参数和时间广泛无关。尽管个体症状会随着时间和树木之间的变化而变化，但可以清楚地识别出模式。例如，春季的虫害是及时发现的最关键，而增加的树冠变色和落叶有助于在夏末和秋季发现。调查结果进一步表明，冬眠树最好在夏末检测到，并在 11 月之前进行卫生采伐。