IIBR Multidisciplinary: Locating and counting terrestrial wildlife with an open source, automated acoustic survey platform

匹兹堡大学被授予开发一种自动声学平台，用于定位和计数陆地野生动物。对野生动物种群的准确估计是研究全球变化对生物多样性影响的核心。从历史上看，种群水平的数据既耗时又难以收集，限制了可评估的物种和栖息地的数量。声学记录器有可能以低廉的成本收集有关产生声音的物种的大规模数据，包括鸟类、蝙蝠、两栖动物和昆虫。然而，大多数目前的声学调查只能检测到一个物种的存在或不存在，而不能通过计算单个生物体来估计种群数量。该奖项将支持一个自动化的、开源的声学平台的初步开发，以调查陆地野生动物种群，目标是在更大范围内收集数据，并比人类观察者更准确。它还将支持至少两名研究生的培训，以及一个以生物学和数据科学相结合的本科生STEM教育者专业发展为目标的教育项目。该平台的创建及其在开源许可下的发布将使专业生态学家、公民科学家和大型生物多样性监测项目能够更好地检测物种数量随时间的下降，跟踪由于气候变化而导致的物种分布变化，了解生物多样性变化的交互驱动因素，预测未来的灭绝风险，并制定保护受威胁物种的策略。声学调查平台将使用GPS时间同步记录器在坐标空间中定位声音。然后，这些声音位置将被用来区分和计算物种中的单个有机体。虽然该平台适用于各种各样的物种，但最初将为繁殖美国东部的鸣鸟而设计。该奖项的具体目标，包括硬件和软件开发，包括(1)设计一个开源、廉价的野外记录器，可以收集时间同步的记录；(2)开发和训练一个物体探测卷积神经网络，以识别不同鸟类歌声在时间和频率上的边界；(3)创建一个检测到的到达时间差算法，该算法在坐标空间中定位每一种识别的鸟类，并使用这些数据来计算物种内的个体鸟类；以及(4)完成性能和用户测试，以评估集成的硬件和软件平台。该平台将根据其比人类观察者更准确和准确地估计繁殖鸟类数量的能力进行具体评估。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。

An award is made to the University of Pittsburgh to develop an automated acoustic platform for locating and counting terrestrial wildlife. Accurate estimates of wildlife populations are central to research on the effects of global change on biodiversity. Historically, population-level data has been time-consuming and difficult to collect, limiting the number of species and habitats that can be evaluated. Acoustic recorders have the potential to inexpensively gather large-scale data on sound-producing species, including birds, bats, amphibians, and insects. However, most current acoustic surveys are only able to detect the presence or absence of a species and cannot count individual organisms in order to estimate population sizes. This award will support the initial development of an automated, open source acoustic platform to survey terrestrial wildlife populations, with the goal of gathering data at larger scales and with better accuracy than human observers. It will also support the training of at least two graduate students and an educational project that targets undergraduate STEM educator professional development at the interface of biology and data science. The creation of this platform and its release under open source licenses will enable professional ecologists, citizen scientists, and large biodiversity monitoring programs to better detect declines in species populations over time, track shifts in species distributions due to climate change, understand the interacting drivers of biodiversity changes, predict future extinction risks, and develop conservation strategies to protect threatened species.The acoustic survey platform will use GPS time-synchronized recorders to localize sounds in coordinate space. These sound locations will then be used to distinguish and count individual organisms within species. Although applicable to a wide variety of species, the platform will be initially designed for breeding songbirds of the eastern United States. The specific objectives of the award, involving both hardware and software development, include (1) designing an open source, inexpensive field recorder that can collect time-synchronized recordings, (2) developing and training an object-detecting convolutional neural network to identify the boundaries of distinct bird songs in time and frequency, (3) creating a detection-informed time difference of arrival algorithm that localizes each identified bird song in coordinate space and uses this data to count individual birds within species, and (4) completing performance and user testing to evaluate the integrated hardware and software platform. The platform will be evaluated specifically on its ability to estimate breeding bird populations more accurately and precisely than human observers.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.