PDF(Pubmed)
Abstract：
UNASSIGNED: From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR is subject to influences from a range of factors, including species and environmental conditions. Identifying the optimal sampling design to obtain representative acoustic data for VAR estimation is crucial for research objectives. PAM commonly uses temporal sampling strategies to decrease the volume of recordings and the resources needed for audio data management. Yet, the comprehensive impact of this sampling approach on VAR estimation remains insufficiently explored.
UNASSIGNED: In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis.
UNASSIGNED: We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes.
UNASSIGNED: While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.
UNASSIGNED: In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis.
UNASSIGNED: We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes.
UNASSIGNED: While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.
摘要：
■来自无源声学监测（PAM）录音，声音活动率(VAR)，每单位时间的发声，可以计算，对于评估鸟类种群丰度至关重要。然而,VAR受到一系列因素的影响，包括物种和环境条件。确定最佳采样设计以获得用于VAR估计的代表性声学数据对于研究目标至关重要。PAM通常使用时间采样策略来减少音频数据管理所需的记录和资源。然而，这种抽样方法对VAR估计的综合影响仍未充分探索。
■在这项研究中，我们使用了从12种鸟类的录音中提取的发声，在四个月的时间里，在亚热带山地森林中的14个PAM站拍摄，在三个不同的尺度上评估时间采样对VAR的影响：短期周期性，diel,和每小时。对于短期定期抽样分析，我们采用层次聚类分析(HCA)和变异系数(CV)。广义加法模型(GAMs)用于diel采样分析，我们确定了每小时采样分析的每分钟VAR值的平均差。
■我们确定了显著的日和物种特异性VAR波动。调查季节分为五个部分；最早的两个显示出很高的变异性，最好避免进行调查。大雨和强风天的数据显示VAR值降低，应排除在分析之外。持续录音至少7天,延长到14天是最小化采样方差的最佳选择。早晨合唱录音有效地捕捉了大多数鸟类的发声，和每小时频繁采样，较短的间隔与连续记录结果密切相关。
■虽然我们的发现是特定于上下文的，他们强调了战略采样在鸟类监测中的重要性，优化资源利用，提高监测工作的广度。
■在这项研究中，我们使用了从12种鸟类的录音中提取的发声，在四个月的时间里，在亚热带山地森林中的14个PAM站拍摄，在三个不同的尺度上评估时间采样对VAR的影响：短期周期性，diel,和每小时。对于短期定期抽样分析，我们采用层次聚类分析(HCA)和变异系数(CV)。广义加法模型(GAMs)用于diel采样分析，我们确定了每小时采样分析的每分钟VAR值的平均差。
■我们确定了显著的日和物种特异性VAR波动。调查季节分为五个部分；最早的两个显示出很高的变异性，最好避免进行调查。大雨和强风天的数据显示VAR值降低，应排除在分析之外。持续录音至少7天,延长到14天是最小化采样方差的最佳选择。早晨合唱录音有效地捕捉了大多数鸟类的发声，和每小时频繁采样，较短的间隔与连续记录结果密切相关。
■虽然我们的发现是特定于上下文的，他们强调了战略采样在鸟类监测中的重要性，优化资源利用，提高监测工作的广度。
