Weyerhaeuser, Kalispell, MT, United States of America.
Tetra Tech, Missoula, MT, United States of America.
PLoS One. 2020 Oct 22;15(10):e0241052. doi: 10.1371/journal.pone.0241052. eCollection 2020.
Traditional pathogen surveillance methods for white-nose syndrome (WNS), the most serious threat to hibernating North American bats, focus on fungal presence where large congregations of hibernating bats occur. However, in the western USA, WNS-susceptible bat species rarely assemble in large numbers and known winter roosts are uncommon features. WNS increases arousal frequency and activity of infected bats during hibernation. Our objective was to explore the effectiveness of acoustic monitoring as a surveillance tool for WNS. We propose a non-invasive approach to model pre-WNS baseline activity rates for comparison with future acoustic data after WNS is suspected to occur. We investigated relationships among bat activity, ambient temperatures, and season prior to presence of WNS across forested sites of Montana, USA where WNS was not known to occur. We used acoustic monitors to collect bat activity and ambient temperature data year-round on 41 sites, 2011-2019. We detected a diverse bat community across managed (n = 4) and unmanaged (n = 37) forest sites and recorded over 5.37 million passes from bats, including 13 identified species. Bats were active year-round, but positive associations between average of the nightly temperatures by month and bat activity were strongest in spring and fall. From these data, we developed site-specific prediction models for bat activity to account for seasonal and annual temperature variation prior to known occurrence of WNS. These prediction models can be used to monitor changes in bat activity that may signal potential presence of WNS, such as greater than expected activity in winter, or less than expected activity during summer. We propose this model-based method for future monitoring efforts that could be used to trigger targeted sampling of individual bats or hibernacula for WNS, in areas where traditional disease surveillance approaches are logistically difficult to implement or because of human-wildlife transmission concerns from COVID-19.
传统的白鼻综合征（WNS）病原体监测方法主要针对的是北美冬眠蝙蝠群体中大量存在的真菌。然而，在美国西部，WNS 易感蝙蝠物种很少大量聚集，且已知的冬眠栖息地也很少见。WNS 会增加感染蝙蝠在冬眠期间的觉醒频率和活动。我们的目标是探索声学监测作为 WNS 监测工具的有效性。我们提出了一种非侵入性的方法来建立 WNS 发生前的基线活动率模型，以便与未来疑似发生 WNS 后的声学数据进行比较。在 WNS 尚未发生的美国蒙大拿州的森林地区，我们调查了蝙蝠活动、环境温度和季节之间的关系，这些地区之前没有发生过 WNS。我们使用声学监测器在 2011 年至 2019 年期间，在 41 个地点全年收集蝙蝠活动和环境温度数据。我们在管理（n=4）和非管理（n=37）森林地点检测到了一个多样化的蝙蝠群落，并记录了超过 537 万次蝙蝠通过，包括 13 种已识别的物种。蝙蝠全年都很活跃，但每月平均夜间温度与蝙蝠活动之间的正相关关系在春季和秋季最强。根据这些数据，我们为蝙蝠活动开发了特定地点的预测模型，以解释 WNS 已知发生之前的季节性和年度温度变化。这些预测模型可用于监测蝙蝠活动的变化，这些变化可能表明潜在的 WNS 存在，例如冬季活动异常增加，或夏季活动异常减少。我们提出了这种基于模型的方法，用于未来的监测工作，可以用于在传统疾病监测方法在逻辑上难以实施或由于 COVID-19 导致的人与野生动物传播的情况下，触发对个别蝙蝠或冬眠地进行有针对性的采样，以监测 WNS。
