Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems.

Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables.

Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes.

We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs.

生物多样性丧失的空前速度以及人类为纠正生物多样性危机而进行的努力越来越多，这就需要以精细的时间分辨率对标准化的，大规模的，长期的生物多样性进行监测。尽管一些创新技术（例如无源声波监测）非常适合应对此类监测挑战，但仍然存在关于如何在整个生态系统中进行扩展和优化实施的许多问题。

我们的研究问题集中在时间采样机制上-在给定的时间范围内应该多长时间和多长时间收集一次代表生物多样性状况的数据。在无源声学监测的背景下解决这一问题，我们调查了时间音景变化性（环境中的特征性短期声学变化）在整个生态系统和一天中的时间是否一致，并且我们考虑了各种时间子采样方案如何影响声像图的代表性。相对于连续采样的合成声指数值。我们根据时间自相关范围和声学指标值的标准偏差对四大洲八个地点的音景变化进行了量化，并创建了一个启发式模型，基于这两个变量对音景变化的类型进行分类。

利用从三个不同的声学指标得出的值，我们发现声景的特征性时间变异性在地点和白天（黎明，白天，黄昏和夜间）之间有所不同。一些站点在一天时间之间的变异性上几乎没有差异，而其他站点在一天时间之间的站点内差异则比许多站点间差异更大。白天的声景通常倾向于表现出比夜间的声景更大的时间变化性。

我们还通过将子样本误差建模为总分析时间和较大时间块内子样本数量的函数，比较了潜在的子抽样方案，这些子抽样方案在功耗，数据存储和数据分析成本方面可能是有利的。数量更多，分布均匀的细分大大提高了采样方案的代表性，而子采样持续时间的增加却在人们希望代表的全部时间的33％到67％之间产生了相当小的代表性增益。通常，我们的结果表明，对于长期，精细的时间分辨率监视程序，应该以均匀分布的持续时间记录至少至少1分钟，而最多只能记录一个希望代表的时间的三分之一。尽管在许多情况下更连续的监视可能是有利且必要的，