Structural Variation in Whistle Patterns of Two Coastal Dolphin Species Co-Occurring in the Southern Israeli Mediterranean Sea

IF 1.7 3区 生物学 Q2 MARINE & FRESHWATER BIOLOGY
Y. E. Zuriel, I. Aharoni, D. Atch, O. Galili, D. Tchernov, A. Scheinin, D. Kerem
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引用次数: 0

Abstract

Two coastal dolphin species, the common bottlenose dolphin (Tursiops truncatus—Tt) and the short-beaked common dolphin (Delphinus delphis—Dd), co-occur along the southern Israeli Mediterranean coast, where the critically endangered Dd population faces ecological pressures, potentially including competitive exclusion by that of the Tt population. In this context, reliable acoustic differentiation between the two species is needed to supplement visual differentiation. This study aimed to assess whether species-specific whistle characteristics could be used to acoustically distinguish between the two taxa using a novel manual contour extraction tool, Sound Annotator, combined with machine learning classification. A total of 940 high-quality whistles (705 Dd, 235 Tt) from field recordings collected between 2018 and 2024 were available for analysis. Sixteen frequency, temporal, and shape parameters were derived from the extracted contours and analyzed with multiple classification models. Principal component analysis revealed high acoustic overlap, yet the XGBoost classifier effectively classified individual whistles, with minimum, median, and beginning frequency emerging as the most informative features. This framework facilitates non-invasive, species-specific monitoring of dolphin populations in challenging visual conditions, particularly at night, providing a foundation for conservation-focused acoustic monitoring in the eastern Mediterranean.

在以色列地中海南部共存的两种海岸海豚鸣笛模式的结构变化
两种海岸海豚,普通宽吻海豚(Tursiops truncatus-Tt)和短喙普通海豚(Delphinus delphis-Dd)共同出现在以色列地中海南部海岸,在那里,极度濒危的Dd种群面临着生态压力,可能包括Tt种群的竞争排斥。在这种情况下,两个物种之间需要可靠的声学区分来补充视觉区分。本研究旨在利用一种新的人工轮廓提取工具Sound Annotator结合机器学习分类,评估物种特异性哨子特征是否可以用于声学区分这两个分类群。从2018年至2024年收集的现场记录中,共有940个高质量的哨声(705个Dd, 235个Tt)可供分析。从提取的轮廓中得到16个频率、时间和形状参数,并使用多种分类模型进行分析。主成分分析显示了较高的声学重叠,但XGBoost分类器有效地对单个哨声进行了分类,最小、中位数和起始频率成为信息量最大的特征。该框架有助于在具有挑战性的视觉条件下,特别是在夜间,对海豚种群进行非侵入性、特定物种的监测,为地中海东部以保护为重点的声学监测奠定基础。
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来源期刊
Marine Mammal Science
Marine Mammal Science 生物-动物学
CiteScore
4.80
自引率
8.70%
发文量
89
审稿时长
6-12 weeks
期刊介绍: Published for the Society for Marine Mammalogy, Marine Mammal Science is a source of significant new findings on marine mammals resulting from original research on their form and function, evolution, systematics, physiology, biochemistry, behavior, population biology, life history, genetics, ecology and conservation. The journal features both original and review articles, notes, opinions and letters. It serves as a vital resource for anyone studying marine mammals.
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