CAREER: Breaking ground with underwater sound; unraveling elusive predator-prey interactions in marine benthic communities using novel technological approaches

在全球气候变化的影响下，贝类（软体动物、甲壳类等）正面临着前所未有的压力，威胁着这些动物为沿海社区提供的各种生态系统服务。虽然很多研究都致力于了解海洋条件的变化如何影响贝类的发展，但很少有人探索大型、粉碎贝壳的捕食者（即鳐鱼、海龟等）的潜在影响，这些捕食者的活动范围正在向极地扩张。这种知识差距可能是由于研究这些流动物种的挑战，这需要新的技术来跟踪它们的动态分布，从而对贝类群落的觅食影响。该项目将建立易受大型移动捕食者捕食的海洋栖息地的基础知识，以确保贝类物种的可持续未来。此外，这项工作将为昂贵的贝类恢复项目提供指导，否则这些项目在捕食风险方面就会“盲目”。该计划将具有地方、区域和全球的教育层面。首先，该项目将通过支持一名研究生，并为PI开发新的研究生课程提供平台，从而加强FAU的研究生课程，该课程将在奖励期间提供和评估两次。此外，许多本科生暑期实习生和初高中学生将被招募，通过身临其境的实地考察与PI互动。最后，公众和学生对这些有魅力的动物和项目的有形技术组件的着迷将有助于在当地的外展中心开发一个互动式“音频波”展览，该展览将在项目期间进行多次评估，并计划永久展出。由于这些物种难以捉摸的性质所带来的挑战，我们对大型移动硬噬细胞（即碎壳捕食者）的生态作用的科学理解是有限的。这些缺点阻碍了我们对它们在底栖生物群落动态中的作用的科学理解。填补这样的知识空白需要新的方法，可以在现场检测和分类捕食者-猎物的相互作用。使用多种大型捕食动物模型（鳐鱼、海龟、鱼和螃蟹），该项目将：1)捕捉并表征捕食者进食（破碎贝壳）声音和贝壳破碎模式；2)利用模拟了解自然水下噪声背景下捕食信号的原位检测约束；3)通过整合栖息地和个体（动物标签）被动声学，量化百慕大和佛罗里达两个模型海景中捕食者觅食影响的分布。检测和分类（捕食者和猎物）将使用机器学习技术的新应用来完成，机器学习技术将用于从大量数据档案中自动提取捕食事件。记录设备将战略性地分布在整个海景中，以允许对硬噬的多尺度理解和对捕食理论模型的测试（例如，最佳/中心地点觅食）。长期监测还将提供一个机会，评估环境/海洋学变量在推动这些相互作用方面的作用。因此，这项工作将填补海洋食物网动态方面的巨大知识空白。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Shellfish (mollusks, crustaceans, etc.) are facing unprecedented pressures under global climate change, which is threatening the variety of ecosystem services these animals provide to coastal communities. While much research has been dedicated to understanding how changing ocean conditions can influence shellfish development, far less has explored the potential impacts from increasing populations of large, shell-crushing predators (i.e., rays, turtles, etc.) that are experiencing poleward expansions of their ranges. This knowledge gap is likely due to the challenges of working with these mobile species, which require novel technology to track their dynamic distribution and thus foraging effects on shellfish communities. This project will build fundamental knowledge on marine habitats susceptible to predation from large mobile predators in order to ensure a sustainable future for shellfish species. Further, the work will provide guidance to costly shellfish restoration programs that are otherwise “flying blind” with respect to predation risk. The project will have local, regional, and global educational dimensions. Firstly, this project will strengthen FAU’s graduate programs by supporting a graduate student and providing a platform for the PI to develop a new graduate course, which will be offered and evaluated twice throughout the award period. Additionally, numerous undergraduate summer interns and middle-high school students will be recruited to interact with the PI via immersive, hands-on field excursions. Lastly, the fascination of the general public and students with these charismatic animals and the project’s tangible technological components will facilitate developing an interactive “Audio Waves” exhibit at a local outreach center, which will be evaluated several times during the project and slated for permanent display. Our scientific understanding of the ecological role of large mobile durophages (i.e., shell-crushing predators) is limited due to challenges presented by the elusive nature of these species. These shortcomings hinder our scientific understanding of their role in benthic community dynamics. Filling such knowledge gaps requires novel approaches that can detect and classify predator-prey interactions in situ. Using multiple large predator models (rays, sea turtles, fish, and crabs), the project will: 1) capture and characterize predator feeding (shell-crushing) sounds and shell fragmentation patterns, 2) understand in situ detection constraints of the predation signal within the context of natural underwater noise using simulations, and 3) quantify the distribution of predator foraging impacts across two model seascapes in Bermuda and Florida via integration of habitat- and individual-based (animal tags) passive acoustics. Detection and classification (by both predator and prey) will be completed using novel application of machine-learning techniques, which will be used to automate predation event extraction from extensive data archives. Recording equipment will be strategically distributed across seascapes to permit a multi-scale understanding of durophagy and testing of theoretical models of predation (e.g., optimal/central place foraging). Long-term monitoring will also provide an opportunity to assess the role of environmental/oceanographic variables in driving these interactions. Consequently, this work will fill a large knowledge gap in the dynamics of marine food webs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.