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的重要组成部分。生态学家使用声音通过计算物种的发声来跟踪野生动物的状态。然而，人不可能无时无刻不在任何地方，所以许多关于生态过程的时间和间隔的问题，特别是在大空间尺度上&随着时间的推移，人类观察者是无法回答的。我们将使用来自北方森林鸣鸟发出的声音的数据来测试几个关键的生态学理论，如理想的自由选择与专制选择、丰富的中心假说、范围物候变化和恐惧景观。测试所有这些理论需要准确的密度、空间使用和时间信息，这些信息比人类观察者通常收集的要精确得多。我们的技术进步是使用自主录音装置(录音设备)来做到这一点。我们将开发深度学习工具，通过声音改善密度、季节性、回报率、占用动态和繁殖状态等指标。我们还在改变数据的收集、整理方式，最重要的是，通过创新的在线工具在研究人员之间共享数据。这使我们能够提供决策者关于紧迫的大规模保护问题所需的信息。栖息地选择是生态学中研究最多的过程之一，是应用保护和理论生物学的核心。然而，大多数研究人员目前使用的方法往往倾向于强调鸟类的局部规模模式如何根据人类足迹和植被而变化。我们的工作通过研究栖息地选择的变异来推进这一范式，栖息地选择的变异是指物种之间的相互作用，这些相互作用可能改变大规模的栖息地选择模式&随着时间的推移，只有通过Arus才可能做到这一点。HQP是我研究计划的核心。在过去的6年里，我为200多名总部经理提供了机会。我预计，在下一个周期，将有类似数量的学生接受培训。我的项目的成功取决于发展一个高度协调的合作学生项目系统，这些项目单独是独特的，但当整合时提供更大的机会，以揭示对行为、人口、社区和景观生态的基本见解。正如我的出版记录和过去学生的就业能力所证明的那样，这种方法培养了具有应用生态学和理论生态学能力的全面发展的个人。DG项目提供了关键资金，允许我的学生扩大应用工作，以测试关于生态理论和驱动不同空间尺度上鸟类多样性格局的过程的特定假设。