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Monitoring Marine Mammals from Autonomous Underwater Vehicles

通过自主水下航行器监测海洋哺乳动物

基本信息

批准号：
NE/J020176/1
负责人：
Peter Tyack
金额：
$ 14.16万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FJ020176%2F1
关键词：
Monitoring Marine Mammals Autonomous Underwater

项目摘要

Shelf exchange creates the main supply of nutrients onto the continental shelf and is therefore vital to the health of on-shelf ecosystems. The FASTNEt programme will study shelf exchange mechanisms at a higher spatial resolution than has been previously possible and also throughout all seasons of the year. This will allow the development of high resolution models of shelf edge processes. Much of the FASTNEt data will be collected using Autonomous Underwater Gliders carrying sensors measuring standard oceanographic parameters. Using additional data, our proposed program of work will enable us for the first time to investigate relationships between these low level processes and their effect on animals feeding at higher trophic levels which are ultimately dependent on these exchange processes. Cetaceans are traditionally surveyed using visual line transect methods. These are expensive to implement and can generally only be conducted during the summer months. Passive acoustic monitoring (PAM) offers a real alternative because many species make distinctive calls. Recent advances in PAM technology also mean that it is now possible to produce sensor packages which are ideally suited to glider deployment, being small and able to carry their own power pack for deployments of weeks or even months. Modelling techniques are well-established for estimating cetacean abundance and, to a lesser extent, for investigating habitat use. Covariate data used for this modelling are generally not collected at the same time and place as the whale abundance data. In addition, oceanographic data are typically limited to the upper few cm of the ocean (e.g. sea surface temperature), whereas the animals are feeding at depth. For the species we propose to study here, we can not only identify calls to species, but we can identify sounds associated with foraging. These data identifying locations where animals are actually feeding can be used to fine-tune habitat models. We hypothesise that models of cetacean abundance that incorporate parameters measured at the same depth and the same time as the cetacean data will show a marked improvement over current surface data only models. We will deploy sensor packages for gliders capable of measuring the presence and abundance of cetaceans using PAM. These will be based on low power animal borne sensors developed by two of the project proposers Johnson and Tyack. During the first year we will make use of existing sensors but over the course of the year, we expect the next generation of PAM devices to become available with lower power and enhanced on board processing capability. When combined with other oceanographic and biochemical parameters measured on the Ocean Shelf Exchange programme this will lead to greater understanding of how shelf exchange directly affects biological activity at high trophic levels. By using the vocalisations (calls) of whales and dolphins, instead of visual observations, as measures of their presence and abundance, and by fitting models to fine scale in situ data on their environment, we aim to develop more detailed species-habitat models that will improve our understanding of cetacean ecology. Understanding the processes that influence the distribution and behaviour of whales and dolphins is important both because cetaceans form a major predator group that potentially has a large impact on the structure of the marine environment through top-down forcing, and because their slow life histories make them particularly vulnerable to human activities. Determining the most appropriate conservation measures for these species depends crucially on knowledge of how they utilise the available habitat.

大陆架交换是大陆架营养物的主要供应，因此对大陆架生态系统的健康至关重要。FASTNEt方案将以比以往更高的空间分辨率研究大陆架交换机制，并在一年四季进行研究。这将使开发高分辨率的模型的大陆架边缘过程。FASTNEt的大部分数据将使用携带测量标准海洋学参数的传感器的自主水下滑翔机收集。利用额外的数据，我们提出的工作计划将使我们能够首次调查这些低水平的过程之间的关系，以及它们对最终依赖于这些交换过程的较高营养水平的动物的影响。鲸类的调查传统上使用目视线样方法。这些活动费用昂贵，一般只能在夏季进行。被动声监测（PAM）提供了一个真实的选择，因为许多物种发出独特的声音。PAM技术的最新进展也意味着现在可以生产出非常适合滑翔机部署的传感器包，这些传感器包体积小，能够携带自己的电源组，可用于数周甚至数月的部署。在估计鲸目动物丰度方面，以及在较小程度上在调查生境利用方面，建模技术已得到很好的确立。用于这一建模的协变量数据一般不与鲸鱼丰度数据在同一时间和地点收集。此外，海洋学数据通常仅限于海洋的上部几厘米（例如海面温度），而动物则在深海觅食。对于我们打算在这里研究的物种，我们不仅可以识别出对物种的呼唤，还可以识别出与觅食有关的声音。这些识别动物实际进食位置的数据可用于微调栖息地模型。我们假设，模型的鲸目动物丰度，纳入参数测量在同一深度和同一时间的鲸目动物数据将显示出显着的改善，目前的表面数据只有模型。我们将为滑翔机部署传感器包，能够使用PAM测量鲸目动物的存在和丰度。这些将基于由项目提议人约翰逊和Tyack开发的低功率动物传播传感器。在第一年，我们将利用现有的传感器，但在这一年中，我们预计下一代PAM设备将具有更低的功耗和更强的板载处理能力。如果与海洋架交换方案测量的其他海洋学和生物化学参数相结合，这将有助于更好地了解架交换如何直接影响高营养级的生物活动。通过使用鲸鱼和海豚的发声（呼叫），而不是视觉观察，作为它们的存在和丰度的衡量标准，并通过拟合模型，以精细规模的原位数据对它们的环境，我们的目标是开发更详细的物种栖息地模型，这将提高我们对鲸目动物生态的理解。了解影响鲸鱼和海豚的分布和行为的过程很重要，因为鲸目动物是一个主要的捕食者群体，可能通过自上而下的强迫作用对海洋环境的结构产生重大影响，还因为它们缓慢的生活史使它们特别容易受到人类活动的影响。为这些物种确定最适当的保护措施，关键取决于它们如何利用现有栖息地的知识。