Application of acoustical remote sensing techniques for ecosystem monitoring of a seagrass meadow

这项研究的重点是设计和测试一种能够长期实地部署的系统，该系统使用声音来监测海草生物量和生产力，与传统技术相比，时间分辨率有了显着提高。对生态系统生产的相关参数进行长期持续测量对于生态系统健康评估和可持续管理至关重要。利用光学传感器进行的溶解氧测量是估计海草光合作用与水下照度相关的最广泛使用的方法。氧气的释放对维持地下海草根和根茎的有氧条件也是至关重要的，这些根和根茎占植物总生物量的60%-80%。然而，在氧气饱和的条件下，植物会不断地释放自由气泡，这种情况在夏季高光条件下经常发生。在这种过饱和的条件下，光学传感器无法探测到气泡，导致海草光合碳产量被低估。由于在春末到秋初这几个月最普遍的较高温度和光照条件下，氧饱和度更容易发生，因此只有在弱光条件下或在清晨才有可能准确地测定生产率。由于声在水中的传播对气泡的存在非常敏感，声学方法提供了一种高时间分辨率的测量海草草甸真正光合作用产氧量的替代方法。研究人员将参与外展活动，包括德克萨斯大学奥斯汀分校(UT)的新生研究倡议(FRI)，该倡议为一年级学生提供机会，与教职员工和研究生一起开始并参与现实世界的研究体验。该项目还将通过开发基于拟议研究收集的长期数据集的数据块来吸引K-16级人员参与。独立的现场部署系统将使用宽带声学测量来遥感海草生物量和气体沸腾。该系统将由三个主要部分组成：1)一个声源投影仪和一套接收水听器，2)一个仪器压力容器(IPV)，其中装有控制声学数据采集和数据存储的电子设备，以及3)一套环境传感器-记录仪。拟议的测量系统将变得足够紧凑和轻便，可以从一艘能够进入得克萨斯州墨西哥湾海岸浅水湾的小船上手动部署在海草草甸上。目标部署水深为2-3米，这反映了得克萨斯州沿海海草分布的最大深度。将应用地声推断技术来量化海水中气体的空泡率以及海草组织中存在的气体体积。贝叶斯技术将被用来评估参数不确定性并揭示参数相关性。低频声音(3 KHz)对海草组织中携带的气体最敏感，正在开发一种有效的介质模型，用于解释海草组织的弹性和海草叶片的结构，以量化海草叶片、根和根茎中存在的气体体积。中频声音(3.5至35千赫)对海草光合作用产生的水中的自由气泡最为敏感。水中气体的空泡率可以通过考虑氧气气泡向海面的垂直运输而与氧气的产生有关。来自该系统的数据将用于在海草状况指标和环境应激源之间建立新的联系。这项研究将通过考虑影响物理强迫因素的趋势和事件驱动的扰动，如光传输、水循环、温度和盐碱度，来预测海草对气候变化的反应。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research focuses on the design and testing of a system capable of long-term field deployment that uses sound to monitor seagrass biomass and productivity with significantly improved temporal resolution compared to traditional techniques. Long-term continuous measurements of relevant parameters of ecosystem production are of primary importance for ecosystem health assessment and sustainable management. Dissolved oxygen measurements made with optical sensors are the most widely used method for estimating seagrass photosynthesis in relation to underwater irradiance. Oxygen evolution is also critical in maintaining aerobic conditions for below ground seagrass roots and rhizomes that constitute 60-80% of total plant biomass. However, under oxygen saturation conditions, which occurs frequently in summer under high light conditions, free gas bubbles are continuously released by the plants. Optical sensors are unable to detect bubbles under such supersaturated conditions, leading to underestimates in seagrass photosynthetic carbon production. Since oxygen saturation occurs more readily at higher temperatures and light conditions that are most prevalent from the late spring to early fall months, accurate determinations of productivity are only possible under low light conditions or in the early morning hours. Since sound propagation in water is very sensitive to the presence of bubbles, acoustic methods provide an alternative measure of true photosynthetic oxygen production in seagrass meadows with high temporal resolution. The researchers will engage in outreach activities including the Freshman Research Initiative (FRI) at University of Texas at Austin (UT), which gives first-year students the opportunity to initiate and engage in real-world research experience with faculty and graduate students. The project will also engage grades K-16 through the development of a Data Nugget based on the long-term data set collected through the proposed research.The stand-alone field-deployed system will use broadband acoustic measurements to remotely sense both seagrass biomass and gas ebullition. The system will consist of three main components: 1) an acoustic source projector and a set of receiving hydrophones, 2) an instrumentation pressure vessel (IPV) that houses the electronics controlling the acoustic data acquisition and data storage, and 3) a suite of environmental sensor-loggers. The proposed measurement system will be made compact and lightweight enough that it can be hand-deployed in the seagrass meadow from a small watercraft that is capable of accessing the shallow bays of the Texas Gulf of Mexico coast. The target deployment water depth is 2-3 m, which reflects the maximum depths of seagrass distribution in Texas coastal waters. Geoacoustic inference techniques will be applied to quantify the void fraction of gas in the seawater as well as the gas volume present within the seagrass tissue. Bayesian techniques will be used to assess parameter uncertainties and reveal parameter correlations. Low-frequency sound (3 kHz) is most sensitive to gas entrained within the seagrass tissue, and an effective medium model which accounts for the seagrass tissue elasticity and structure of seagrass leaves is being developed to quantify the gas volume present within the seagrass leaves, roots, and rhizomes. Mid-frequency sound (3.5 to 35 kHz) is most sensitive to free bubbles in the water resulting from seagrass photosynthesis. The void fraction of gas in the water can be related to oxygen production by accounting for vertical transport of oxygen bubbles to the sea surface. The data from this system will be used to build new connections between seagrass condition indicators and environmental stressors. This research will lead to predictions of seagrass responses to climate change by accounting for both trend- and event-driven perturbations that affect physical forcing factors such as light transmission, water circulation, temperature, and salinity.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.