EAGER: Censusing via acoustic energy (CVAE) for tracking animal phenology and population size

EAGER：通过声能 (CVAE) 进行普查，用于跟踪动物物候和种群规模

• 批准号: 2226886
• 负责人: Laura Kloepper
• 金额: $ 28.72万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226886&HistoricalAwards=false
• 关键词: EAGER; Censusing; via; acoustic; energy

Estimating animal population sizes is crucial for conservation, especially when predicting how species may respond to changes in their environment. However, traditional methods to estimate populations require significant time, effort and expense, and are often conducted only once per year. This approach is likely to miss within year dynamics. This research, using Adélie penguins and cave roosting gray bats, will develop and test a new method using sound to remotely estimate how the numbers of animals living in groups changes over time. The goal is to develop a new approach to counting animals that reduces hands-on effort, increases the frequency of population counts, and improves data available for species conservation. This project will also support a scientific workshop to discuss data needs and emerging approaches for low-cost continuous population monitoring in remote locations. Lastly, this research will include undergraduate research opportunities for students from under-represented groups. Understanding spatiotemporal population dynamics, especially for mobile animals in patchy habitats, can uncover fundamental processes that regulate populations across taxa and help predict how animals will respond to disturbance events. Models are widely used to study the population dynamics within and among populations. These models require population counts, which are typically conducted in discrete time periods such as yearly counts. Although yearly counts can be adequate for assessing population responses to longer term press events, this approach cannot capture how specific or interconnected populations will respond to short-term perturbations, or pulse disturbances. More frequent monitoring of multiple populations is needed but especially challenging in remote habitats. Using island-nesting seabirds and cave-roosting bats, this research will test the hypothesis that acoustic energy indices are reliable estimates of population sizes for mobile, vocal animals in patchy habitats, and that with the data from this non-invasive approach can be used to model how animals respond to pulse disturbances. If successful, this approach to remote monitoring could benefit demographic and metapopulation modeling, and has the potential to revolutionize how scientists study and understand species that inhabit isolated landscapes. It would also provide critical data needed to protect species experiencing anthropogenic-induced population declines.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.

估计动物种群数量对保护至关重要，特别是在预测物种可能如何应对环境变化时。然而，传统的人口估计方法需要大量的时间、精力和费用，而且通常每年只进行一次。这种方法很可能会错过年内的动态。这项研究使用了Adélie企鹅和栖息在洞穴中的灰蝙蝠，将开发和测试一种新的方法，使用声音来远程估计群居动物的数量随着时间的变化。其目标是开发一种新的动物计数方法，减少实际操作的工作量，增加种群计数的频率，并改善可用于物种保护的数据。该项目还将支持举办一次科学讲习班，讨论偏远地区低成本持续人口监测的数据需求和新出现的办法。最后，这项研究将包括为来自代表性不足群体的学生提供的本科生研究机会。了解时空种群动态，特别是对于零星栖息地中的流动动物，可以揭示调节不同类群种群的基本过程，并有助于预测动物将如何对干扰事件做出反应。模型被广泛用于研究种群内部和种群间的种群动态。这些模型需要人口统计，这通常是在离散的时间段进行的，如每年的统计。尽管年度统计足以评估民众对较长期新闻事件的反应，但这种方法不能捕捉特定或相互关联的人群对短期扰动或脉冲扰动的反应。需要对多个种群进行更频繁的监测，但在偏远栖息地尤其具有挑战性。这项研究将使用筑巢在岛上的海鸟和栖息在洞穴中的蝙蝠来检验这样一种假设，即声能指数是对零星栖息地中移动的发声动物种群数量的可靠估计，并且利用这种非侵入性方法的数据可以用来模拟动物如何对脉搏干扰做出反应。如果成功，这种远程监测的方法可能会有利于人口统计和集合种群建模，并有可能彻底改变科学家研究和理解居住在孤立景观中的物种的方式。这项裁决反映了NSF的法定使命，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。