Robust abundance estimates of wild animal populations are needed to inform management policies and are often obtained through mark–recapture (MR) studies. Visual methods are commonly used, which limits data collection to daylight hours and good weather conditions. Passive acoustic monitoring offers an alternative, particularly if acoustic cues are naturally produced and individually distinctive. Here we investigate the potential of using individually distinctive signature whistles in a MR framework and evaluate different components of study design. We analyzed signature whistles of common bottlenose dolphins, Tursiops truncatus, using data collected from static acoustic monitoring devices deployed in Walvis Bay, Namibia. Signature whistle types (SWTs) were identified using a bout analysis approach (SIGnature IDentification [SIGID]—Janik et al. 2013). We investigated spatial variation in capture by comparing 21 synchronized recording days across four sites, and temporal variation from 125 recording days at one high-use site (Aphrodite Beach). Despite dolphin vocalizations (i.e., echolocation clicks) being detected at each site, SWTs were not detected at all sites and there was high variability in capture rates among sites where SWTs were detected (range 0–21 SWTs detected). At Aphrodite Beach, 53 SWTs were captured over 6 months and discovery curves showed an initial increase in newly detected SWTs, approaching asymptote during the fourth month. A Huggins closed capture model constructed from SWT capture histories at Aphrodite Beach estimated a population of 54–68 individuals from acoustic detection, which overlaps with the known population size (54–76 individuals—Elwen et al. 2019). This study demonstrates the potential power of using signature whistles as proxies for individual occurrence and in MR abundance estimation, but also highlights challenges in using this approach.

中文翻译：

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标记——重新捕捉个体独特的叫声——一个关于宽吻海豚（Tursiops truncatus）标志性哨声的案例研究

需要对野生动物种群进行可靠的丰度估计来为管理政策提供信息，并且通常通过标记-重新捕获 (MR) 研究获得。通常使用视觉方法，将数据收集限制在白天和良好的天气条件下。被动声学监测提供了另一种选择，特别是如果声学线索是自然产生的并且具有独特性。在这里，我们研究了在 MR 框架中使用独特的签名哨子的潜力，并评估了研究设计的不同组成部分。我们使用部署在纳米比亚沃尔维斯湾的静态声学监测设备收集的数据分析了常见宽吻海豚 Tursiops truncatus 的标志性哨声。签名哨子类型 (SWT) 是使用回合分析方法（SIGnature IDentification [SIGID]-Janik 等人）识别的。2013）。我们通过比较四个地点的 21 个同步记录日和一个高使用地点（阿芙罗狄蒂海滩）的 125 个记录日的时间变化来研究捕获的空间变化。尽管在每个站点都检测到海豚发声（即回声定位咔嗒声），但并非在所有站点都检测到 SWT，并且在检测到 SWT 的站点之间（检测到的范围为 0-21 SWT）的捕获率存在很大差异。在阿佛洛狄忒海滩，6 个月内捕获了 53 个 SWT，发现曲线显示新检测到的 SWT 最初增加，在第四个月接近渐近线。根据 Aphrodite Beach 的 SWT 捕获历史构建的 Huggins 封闭捕获模型通过声学检测估计了 54-68 个个体，这与已知的种群大小（54-76 个个体 - Elwen 等人，2019 年）重叠。