Unlocking the enigma of Scottish harbour porpoise fine-scale habitat use

解开苏格兰港鼠海豚精细栖息地利用的谜团

• 批准号: 2452686
• 金额: --
• 依托单位: University of the Highlands and Islands
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2452686
• 关键词: Unlocking; enigma; Scottish; harbour; porpoise

Harbour porpoises (Phocoena phocoena) are the most abundant and widespread cetacean in Scottish waters but are vulnerable to anthropogenic pressures such as noise from aquaculture and wind farm construction, bycatch in fisheries, and climate change. As a result, various national and international laws and agreements aim to protect porpoises from these pressures. In addition to general protection and inclusion in decision making processes, porpoises were the reason for the designation of one of Europe's largest protected areas, the cSAC along the west coast of Scotland in 2018.Porpoises are found in a range of habitats throughout Scottish waters. However, these habitats are not used equally with some favoured year round and others at particular times. This project will seek to understand the drivers for these differences, making use of existing datasets collected using passive acoustic monitoring (PAM). PAM is a commonly used technique to establish presence and relative abundance of echolocating porpoises in specific locations over extended time periods, using fixed or towed underwater acoustic detectors (hydrophones). Across both the east and west coasts, several projects have been collecting PAM data from fixed stations for up to a decade. While these data have all been collected for slightly different purposes, they have all used similar methods and so this project seeks to consider them at a more holistic level to better understand porpoise ecology and how and why porpoises use different environments. Most Scottish PAM studies to date have focused on large-scale trends in usage, however their use has also revealed intriguing patterns of habitat use that vary at fine-scales (<1km). Some of these differences have been found to occur over subtly different seabed habitats, depths and time scales. Integrating these data with environmental parameters is likely to help reveal and provide insights into which fundamental ecological and/or anthropogenic drivers underpin porpoise distribution and habitat use in Scottish waters. In this project, the student will have the opportunity to combine and explore previously independent datasets collected for a variety of reasons from across Scotland. Doing so will provide opportunities to better understand biases in standard survey methods and reveal wider patterns of habitat use by porpoises. In addition there may be the opportunity to collect new data to better test some of the environmental relationships that emerge. Discoveries associated with this project have the potential to provide information for conservation management efforts particularly in terms of improving monitoring strategies.This project will bring together previously disparate datasets, the student will deal with large datasets and the inherent challenges these bring (particularly extensive manipulation and error-checking). Analysis of these datasets will likely involve sophisticated statistical modelling approaches.

港湾鼠海豚 (Phocoena phocoena) 是苏格兰水域中数量最多、分布最广的鲸目动物，但很容易受到人为压力的影响，例如水产养殖和风电场建设产生的噪音、渔业兼捕以及气候变化。因此，各种国家和国际法律和协议旨在保护海豚免受这些压力。除了一般保护和纳入决策过程之外，2018 年指定欧洲最大保护区之一（苏格兰西海岸的 cSAC）的原因还包括鼠海豚。在整个苏格兰水域的一系列栖息地中都发现了鼠海豚。然而，这些栖息地的使用情况并不相同，有些栖息地全年都受到青睐，而另一些则在特定时间受到青睐。该项目将利用被动声学监测 (PAM) 收集的现有数据集，寻求了解这些差异的驱动因素。 PAM 是一种常用技术，可使用固定或拖曳式水下声学探测器（水听器）在较长时间内确定特定位置中回声定位鼠海豚的存在和相对丰度。在东海岸和西海岸，有几个项目已经从固定站收集 PAM 数据长达十年。虽然收集这些数据的目的略有不同，但它们都使用了类似的方法，因此该项目力求在更全面的层面上考虑它们，以更好地了解江豚生态以及江豚如何以及为何使用不同的环境。迄今为止，大多数苏格兰 PAM 研究都集中在大规模的使用趋势上，但它们的使用也揭示了在精细尺度（<1 公里）上变化的有趣的栖息地使用模式。人们发现，其中一些差异发生在略有不同的海底栖息地、深度和时间尺度上。将这些数据与环境参数相结合可能有助于揭示并深入了解哪些基本生态和/或人为驱动因素支撑着苏格兰水域的鼠海豚分布和栖息地利用。在这个项目中，学生将有机会结合和探索以前因各种原因从苏格兰各地收集的独立数据集。这样做将为更好地了解标准调查方法中的偏差提供机会，并揭示海豚更广泛的栖息地利用模式。此外，可能还有机会收集新数据，以更好地测试出现的一些环境关系。与该项目相关的发现有可能为保护管理工作提供信息，特别是在改进监测策略方面。该项目将汇集以前不同的数据集，学生将处理大型数据集及其带来的固有挑战（特别是广泛的操作和错误检查）。这些数据集的分析可能会涉及复杂的统计建模方法。