Cooperative Institute for Marine Resources Studies (CIMRS), Oregon State University, Hatfield Marine Science Center, Newport, Oregon 97365, USA.
J Acoust Soc Am. 2011 Jun;129(6):3610-22. doi: 10.1121/1.3583504.
Passive acoustic methods are increasingly being used to estimate animal population density. Most density estimation methods are based on estimates of the probability of detecting calls as functions of distance. Typically these are obtained using receivers capable of localizing calls or from studies of tagged animals. However, both approaches are expensive to implement. The approach described here uses a MonteCarlo model to estimate the probability of detecting calls from single sensors. The passive sonar equation is used to predict signal-to-noise ratios (SNRs) of received clicks, which are then combined with a detector characterization that predicts probability of detection as a function of SNR. Input distributions for source level, beam pattern, and whale depth are obtained from the literature. Acoustic propagation modeling is used to estimate transmission loss. Other inputs for density estimation are call rate, obtained from the literature, and false positive rate, obtained from manual analysis of a data sample. The method is applied to estimate density of Blainville's beaked whales over a 6-day period around a single hydrophone located in the Tongue of the Ocean, Bahamas. Results are consistent with those from previous analyses, which use additional tag data.
被动声学方法越来越多地被用于估计动物种群密度。大多数密度估计方法都是基于对探测到叫声的概率的估计，这些概率是作为距离的函数。这些方法通常是通过能够定位叫声的接收器或对标记动物的研究来获得的。然而，这两种方法的实施成本都很高。本文所描述的方法使用蒙特卡罗模型来估计从单个传感器探测到叫声的概率。被动声纳方程用于预测接收到的点击的信噪比 (SNR)，然后将其与预测 SNR 函数检测概率的检测器特性相结合。源级、波束模式和鲸鱼深度的输入分布是从文献中获得的。声传播建模用于估计传输损耗。密度估计的其他输入是从文献中获得的叫声率和从手动分析数据样本获得的假阳性率。该方法应用于估计在巴哈马的 Tongue of the Ocean 处的单个水听器周围的 6 天期间 Blainville 的喙鲸的密度。结果与之前使用附加标记数据的分析结果一致。
