Palmer K J, Brookes Kate, Rendell Luke
School of Biology, University of St. Andrews, Sir Harold Mitchell Building, St. Andrews, Fife KY16 9TH, United Kingdom.
Marine Laboratory, Marine Scotland Science, PO Box 101, 375 Victoria Road, Aberdeen AB11 9DB, United Kingdom.
J Acoust Soc Am. 2017 Aug;142(2):863. doi: 10.1121/1.4996000.
Passive acoustic monitoring is an efficient way to study acoustically active animals but species identification remains a major challenge. C-PODs are popular logging devices that automatically detect odontocete echolocation clicks. However, the accompanying analysis software does not distinguish between delphinid species. Click train features logged by C-PODs were compared to frequency spectra from adjacently deployed continuous recorders. A generalized additive model was then used to categorize C-POD click trains into three groups: broadband click trains, produced by bottlenose dolphin (Tursiops truncatus) or common dolphin (Delphinus delphis), frequency-banded click trains, produced by Risso's (Grampus griseus) or white beaked dolphins (Lagenorhynchus albirostris), and unknown click trains. Incorrect categorization rates for broadband and frequency banded clicks were 0.02 (SD 0.01), but only 30% of the click trains met the categorization threshold. To increase the proportion of categorized click trains, model predictions were pooled within acoustic encounters and a likelihood ratio threshold was used to categorize encounters. This increased the proportion of the click trains meeting either the broadband or frequency banded categorization threshold to 98%. Predicted species distribution at the 30 study sites matched well to visual sighting records from the region.
被动声学监测是研究发声活跃动物的一种有效方法，但物种识别仍然是一个主要挑战。C-PODs是常用的记录设备，可自动检测齿鲸的回声定位咔哒声。然而，随附的分析软件无法区分海豚科物种。将C-PODs记录的咔哒声序列特征与相邻部署的连续记录器的频谱进行了比较。然后使用广义相加模型将C-POD咔哒声序列分为三组：由宽吻海豚（Tursiops truncatus）或普通海豚（Delphinus delphis）产生的宽带咔哒声序列、由里氏海豚（Grampus griseus）或白喙海豚（Lagenorhynchus albirostris）产生的频带咔哒声序列以及未知咔哒声序列。宽带和频带咔哒声的错误分类率为0.02（标准差0.01），但只有30%的咔哒声序列达到分类阈值。为了提高分类咔哒声序列的比例，在声学相遇中汇总模型预测，并使用似然比阈值对相遇进行分类。这使得符合宽带或频带分类阈值的咔哒声序列比例提高到了98%。在30个研究地点预测的物种分布与该地区的目视观测记录匹配良好。
