MCA Pilot PUI: Leveraging machine learning to better understand biodiversity patterns measured through passive acoustic sampling

新技术正在增加测量和监测生物系统的可用数据量，有助于回答生物学中长期存在的问题，并开辟新的研究方向。例如，在一系列生态系统类型中收集的声学数据可用于解决动物交流中的问题，人类噪音在声景中的作用以及种群动态。然而，这些数据通常需要新的技能来处理和理解。这个中期职业发展项目将使首席研究员与生物声学分析领域的领导者合作，利用新的机器学习工具来解决这些问题。相反，所处理的丰富声学数据也将用于验证和改进机器学习工具。这项研究的更广泛影响将包括通过研究性课程、本科生研究经历和专业会议研讨会，增加接受过使用下一代分析工具（如机器学习）进行生态研究的个人数量。研究成果将通过传统的学术成果和独特的打击乐作品为不同的观众分享。最终，该项目将促进对首席研究员的多个研究和奖学金线的对齐，形成一个越来越有影响力的研究的清晰轨迹。该项目将促进被动声学采样和机器学习应用的进步，以解决基本的生物学问题。这项研究将使用来自不同生态系统的丰富声学数据集，包括森林、农田和城市环境。该项目将通过改进声音和噪音的分析和精确分类，推进三个方面的研究：1)噪音如何影响鸟类的发声和交流，但与过去的研究相比，测量鸟类鸣叫的一致性增加了；2)量化生物多样性的声学指标，特别是如何定义和过滤人为干扰；3)如何通过被动声学采样改进节能应用的占用建模。该项目还将通过课程推进和研讨会培训下一代生物多样性研究的机器学习技术的学生和研究人员。该项目由环境生物学部的人口和社区生态集群以及刺激竞争研究的既定计划（EPSCoR）计划共同资助。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。

New technologies are increasing the amount of data available that measures and monitors biological systems, helping to answer long-standing questions in biology and opening new research directions. For example, acoustic data collected across a range of ecosystem types can be used to address questions in animal communication, the role of human noise in soundscapes, and population dynamics. However, these data often require new skills to process and understand. This mid-career advancement project will partner the principal investigator with a leader in the bioacoustics analysis field to leverage new machine learning tools to address these questions. Conversely, the rich acoustic data being processed will also be used to validate and improve the machine learning tools. Broader impacts of the research will include increasing the number of individuals trained to use next-generation analytical tools like machine learning for ecological research, via research-based coursework, undergraduate research experiences, and workshops at professional conferences. The research will be shared via traditional scholarly outputs as well as a unique percussion production for diverse audiences. Ultimately, this project will facilitate alignment of multiple lines of research and scholarship for the principal investigator, shaping a clear trajectory of increasingly impactful research. This project will contribute to advancements in the application of passive acoustic sampling and machine learning to address fundamental biological questions. This research will use a rich acoustic dataset from diverse ecosystems including forests, farmland, and urban environments. The project will advance, with improved analyses and precise classification of sounds and noise, three lines of inquiry: 1) how noise affects bird vocalizations and communications, but with increased consistency in measuring bird songs as compared to past research; 2) quantification of acoustic indices as proxies for biodiversity, and in particular how to define and filter anthrophony; and 3) how occupancy modeling for conservation applications can be improved by passive acoustic sampling. This project will also train the next generation of students and researchers in machine learning techniques for biodiversity research via curriculum advancement and workshops. This project is jointly funded by the Population and Community Ecology Cluster in the Division of Environmental Biology, and the Established Program to Stimulate Competitive Research (EPSCoR) program.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.