Mitigating Commercial Vessel Impacts on Coastal Whales

技术进步带来了新的工具，为我们观察海洋哺乳动物及其栖息地的方式带来了一场监测革命。这些新工具包括使用卫星、自主滑翔机和水下水听器等数据平台。这些数据通常与传统的基于现场的人类观察相结合，这些观察是偶尔收集的。一个主要的挑战是如何将来自不同数据流的所有信息与统计模型联合收割机结合起来。该项目的目标是填补统计方法上的空白，以更好地了解哪些因素影响了南方居民虎鲸（SRKW）濒危种群的运动。 这项建议的核心是制定方法，将在不同时间和空间尺度上收集的数据流中的信息与统计模型充分结合起来。这个问题被称为数据同化，在本提案中，其具体目标是提供SRKW朝向和通过与商船碰撞风险高的区域的运动预报。这将是为萨利希海沿海港口服务的航道上鲸鱼活动预警系统的第一个框架，这些港口包括加拿大的主要港口温哥华、斯夸米什和维多利亚，以及为普吉湾人口中心服务的美国港口。一些预期的结果是减少船舶和鲸鱼之间的碰撞，通过增加船舶和鲸鱼之间的距离降低接收到的声音水平，并激励政策变化，可能需要船舶在SRKW附近改道或减速。 本提案围绕以下短期（5年）目标：1）使用状态空间模型和数据同化方法预测航道内和航道附近的鲸鱼运动2）支持机器学习开发，以改进使用水听器和被动声学对海洋哺乳动物的远程检测和分类3）在复杂几何区域中设计最佳实验以放置监听水听器来探测海洋哺乳动物（自主水听器）虽然，这里提出的方法是集中在一个特定的保护需要，新的方法处理和同化这些新的数据类型到模型的发展将有广泛的应用。随着商业航运和全球贸易的增长，这里面临的鲸鱼保护问题变得无处不在。因此，这项研究的成果将广泛应用于其他沿海地区，以应对全球贸易压力。 我的长期目标是建立我的研究和实验室，通过使用循证科学来影响公共政策，为保护研究做出贡献。这个建议支持我在SFU建立海洋科学中心的长期目标。通过多学科奖学金，可以培训下一代海洋科学家，帮助找到解决真实的世界养护问题的办法。

Technological advances have led to new tools that have created a monitoring revolution in how we observe marine mammals and their habitat. These new tools include using such data platforms as satellites, autonomous gliders and underwater hydrophones. The data are often paired with traditional field-based human observations that are collected episodically. A major challenge is how to combine all the information coming from different data streams with statistical models. The objectives of this projects are to fill statistical methodological gaps to better understand what factors inform movement of an endangered population of Southern Resident killer whales (SRKW). The core of this proposal is to develop methods to fully integrate the information coming from data streams collected at different temporal and spatial scales with statistical models. This problem is known as data assimilation, which in this proposal has the specific goal of providing forecasts of SRKW movement towards and through regions of high collision risk with commercial vessels. This will be the first framework for an advance warning system of whale movement in the shipping lanes that service the coastal ports of the Salish Sea including the major Canadian ports of Vancouver, Squamish and Victoria, and the American ports servicing population centres in Puget Sound. Some of the expected outcomes are fewer collisions between ships and whales, lower received sound levels by increasing distance between ships and whales, and to motivate policy change that could require vessels to reroute or slowdown in the vicinity of SRKW. This proposal centres on the following short-term (5 year) goals: 1) Using state space models and data assimilation methods to forecast whale movement in and adjacent to shipping lanes 2) Supporting machine learning developments to improve remote detection and classification of marine mammals using hydrophones and passive acoustics 3) Designing an optimal experiment in a region of complex geometry to place listening hydrophones to detect marine mammals (autonomous hydrophones) Although, the methods proposed here are focussed on a specific conservation need, the development of new methods for processing and assimilating these new data types into models will have broad application. The whale conservation issues faced here are becoming ubiquitous as trends in commercial shipping and global trade increase. Therefore, the outcomes of this research will have broad application to other coastal regions juggling conservation priorities with global trade pressures. My long term goal is to build my research and my lab to contribute to conservation research by using evidence-based science to influence public policy. This proposal supports my long term goals of creating a centre of marine science at SFU. Through multi-disciplinary scholarship, the next generation of marine scientists could be trained to help find solutions to real world conservation problems.