Understanding the ecology of boreal birds through automated soundscape analysis

通过自动声景分析了解北方鸟类的生态

• 批准号: RGPIN-2020-04370
• 负责人: Bayne, Erin
• 金额: $ 4.74万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739926
• 关键词: Understanding; ecology; boreal; birds; through

Sound is a ubiquitous & important component of the biotic environment. Ecologists use sound to track the state of wildlife by counting vocalizations made by species. However, people cannot be everywhere all the time, so many questions about the timing & spacing of ecological processes, particularly at large spatial scales & over long time-frames are unanswerable using human observers. We will test several key ecological theories  like ideal free vs. despotic selection, abundant centre hypothesis, range-phenology shifts & landscapes of fear using data from the sounds made by boreal forest songbirds. Testing all of these theories requires accurate density, space use & timing information that is far more precise than human observers typically collect. Our technological advance is the use of autonomous recording units (sound recording devices) to do this. We will develop deep learning tools to improve measures of density, seasonality, return rates, occupancy dynamics & breeding state through sound. We are also changing how data is collected, collated & most importantly shared between researchers through innovative online tools. This allows us to provide the information needed by decision makers about pressing large-scale conservation issues. Habitat selection is one of the most studied processes in ecology and is central to applied conservation and theoretical biology. However, current approaches used by most researchers tend to emphasize how local scale patterns of birds vary based on human footprint & vegetation. Our work advances this paradigm by studying variation in habitat selection & the interactions between species that may alter patterns of habitat selection at large scales & over time that are only possible through ARUs. HQP are central to my research program. In the past 6 years I have provided opportunities to over 200 HQP. I foresee similar numbers of students being trained in the next cycle. The success of my program is dependent on developing a strongly-coordinated system of collaborative student projects that individually are unique but when integrated provide greater opportunities to reveal fundamental insights into behavioral, population, community & landscape ecology. As evidenced by my publication record and the employability of past students, this approach creates well-rounded individuals with competencies in both applied and theoretical ecology. The DG program provides critical funds that allow my students to expand applied work to test specific hypotheses about ecological theory and the processes that drive patterns of avian diversity at different spatial scales.

声音是生物环境中无处不在且重要的组成部分。生态学家通过计算物种发出的声音来使用声音来跟踪野生动植物的状态。但是，人们总是无处不在，因此，关于生态过程的时机和间距的许多问题，尤其是在大空间尺度上，并且长期使用人类观察者无法回答。我们将使用北方森林鸣禽的声音中的数据来测试几种关键的生态理论，例如理想的自由与专制选择，丰富的中心假设，范围 - 色谱转移和恐惧的景观。对所有这些理论进行测试需要准确的密度，空间使用和时机信息，这比人类观察者通常收集要精确得多。我们的技术进步是使用自动录制单元（声音录制设备）来执行此操作。我们将开发深度学习工具，以改善通过声音的密度，季节性，回报率，占用动态和育种状态的测量。我们还在改变研究人员通过创新的在线工具之间收集，整理和最重要的数据共享数据的方式。这使我们能够提供决策者所需的信息有关施加大规模保护问题的信息。栖息地的选择是生态学中最研究的过程之一，对于应用保护和理论生物学至关重要。但是，大多数研究人员使用的当前方法倾向于强调鸟类的局部规模模式如何根据人足迹和植被而变化。我们的工作通过研究栖息地选择的变化以及可能改变大尺度和随着时间的流逝的栖息地选择模式的相互作用来推动这一范式。 HQP是我的研究计划的核心。在过去的6年中，我为超过200 hqp提供了机会。我在下一个周期中森林林人数类似的学生接受了培训。我的计划的成功取决于开发一个强烈的协作学生项目系统系统，该系统是单独的独特性，但是当整合提供了更多机会，可以揭示对行为，人口，社区和景观生态学的基本见解。正如我的出版记录和过去学生的雇用性所证明的那样，这种方法创造了全面的人，具有应用和理论生态学能力。 DG计划提供了关键的资金，使我的学生可以扩展应用工作，以测试有关生态理论的特定假设以及在不同空间尺度上推动鸟类多样性模式的过程。