Estimating marine mammal abundance and distribution from passive acoustic and biotelemetry data

根据被动声学和生物遥测数据估计海洋哺乳动物的丰度和分布

• 批准号: 2872005
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2872005
• 关键词: Estimating; marine; mammal; abundance; distribution

Ecosystem function weakening due to reduction in top predator numbers is a first order global problem. In the oceans anthropogenic activities adversely affect marine mammals, with 25% of species being threatened. Determining their spatiotemporal distribution and abundance is central to understanding ecosystem health. The aim of this studentship is to combine Passive Acoustic Monitoring (PAM) and satellite-linked tracking (biotelemetry) to determine marine mammal abundance and distribution. Determining a reliable distribution of animals from these two contrasting techniques will require careful comparison, data integration and insight, as PAM techniques require identification of individual species from their call types, while in biotelemetry specific animals are tracked. Marine autonomous vehicles are effective in sensing and understanding the oceans and can be equipped with PAM devices that can record a large frequency bandwidth facilitating a high-fidelity and complete record of the marine soundscape. Interrogating the vast datasets that are recorded by fleets of autonomous data is a current challenge. The student will, firstly, apply and further develop machine-learning techniques to identify individual species. Secondly, the student will leverage large marine mammal tracking datasets, as well as abundance and distribution predictions, to compare and integrate tracking, distribution and abundance data with PAM data.

由于顶级捕食者数量减少而导致的生态系统功能减弱是一个一级全球性问题。在海洋中，人为活动对海洋哺乳动物产生不利影响，25%的物种受到威胁。确定它们的时空分布和丰度是了解生态系统健康的关键。本研究的目的是结合被动声监测（PAM）和卫星连接跟踪（生物遥测）来确定海洋哺乳动物的丰度和分布。从这两种截然不同的技术中确定可靠的动物分布将需要仔细的比较、数据整合和洞察力，因为PAM技术需要从它们的呼叫类型中识别单个物种，而在生物遥测技术中则需要跟踪特定的动物。海洋自动驾驶车辆在感知和理解海洋方面是有效的，并且可以配备PAM设备，可以记录大频率带宽，从而促进高保真度和完整的海洋声景记录。询问由自动数据车队记录的大量数据集是当前的一个挑战。首先，学生将应用并进一步发展机器学习技术来识别单个物种。其次，学生将利用大型海洋哺乳动物跟踪数据集，以及丰度和分布预测，将跟踪、分布和丰度数据与PAM数据进行比较和整合。