Collaborative Research: Competition for acoustic space as a driver of species diversity in vibrationally-signaling insects

合作研究：声音空间的竞争作为振动信号昆虫物种多样性的驱动因素

• 批准号: 2313964
• 负责人: Kasey Fowler-Finn
• 金额: $ 20万
• 依托单位: Saint Louis University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313964&HistoricalAwards=false
• 关键词: Collaborative; Research; Competition; acoustic; space

Alarming worldwide insect declines have widespread effects on ecological communities, ranging from bird population reductions to decreased ecological functioning. Understanding how the environment shapes insect diversity is critical to combat these rapidly declining insect populations. In this project, the researchers test whether vibrational communication in insects contributes to these patterns. While many insects use sound to communicate, the vast majority of singing insects (90%) use sound that travels as tiny vibrations through plant stems and leaves. These vibrational signals are important for processes like territorial disputes and mate attraction. However, the signals may be disrupted if other insects living on the same plant produce similar vibrational signals. The researchers test whether communities of vibrationally signaling insects avoid overlap in their signals. If so, then this partitioning of vibrational acoustic space could shape which insects live together. Thus, anything affecting how many different types of vibrational signals can occur on the vegetation would also directly affect the number of insect species living there. The results of the work could open new avenues of research on insect diversity, highlight the untapped potential of vibrational signals to monitor insect communities, and provide insight into how human impacts on vegetation affects insect communities. In addition to testing a novel hypothesis for the environmental variables shaping insect communities, this project will provide multiple training opportunities for a diversity of students.The proposed work tests the acoustic niche hypothesis in shaping prairie insect communities through a novel lens—vibrational signaling space. Competition for the vibrational space on any given plant can restrict the types of vibrational signals that can coexist. Due to the species-specificity of vibrational signals, competition should therefore also restrict insect diversity. The researchers test this novel hypothesis in insect communities living in prairies with varying management histories and current practices. Vibrations and insects will be collected from multiple 1-m radius plots on each of six prairies. Using piezo recorders that pick up tiny insect vibrations from plants, the researchers will record the vibrations in each plot over 24-48 hours. Then, they will immediately collect insects from the plot using a standardized time period of beat netting. Additional intensive recordings from a subset of sites will be conducted to optimize longer-term vibrational sampling strategies. The researchers will process the vibrational data using machine learning algorithms and look for vibrational space partitioning, compare vibrational acoustic diversity indices to insect species diversity indices, and develop novel toolsets for using vibrations to monitor insect communities.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.

全球范围内令人震惊的昆虫数量下降对生态群落产生了广泛的影响，从鸟类数量减少到生态功能下降，了解环境如何影响昆虫多样性对于应对这些昆虫数量的迅速减少至关重要。虽然许多昆虫使用声音进行交流，但绝大多数会唱歌的昆虫（90%）使用通过植物茎和叶传播的微小振动的声音，这些振动信号对于领土争端和交配吸引等过程非常重要。然而，如果生活在同一植物上的其他昆虫产生类似的振动信号，这些信号可能会受到干扰。如果是这样，那么这种振动声学空间的划分可能会影响哪些昆虫生活在一起。因此，任何影响植被上发生多少种不同类型振动信号的因素也会直接影响生活在那里的昆虫物种的数量。这项工作的结果可能会开辟昆虫多样性研究的新途径，凸显振动信号尚未开发的潜力。信号给监测昆虫群落，并深入了解人类对植被的影响如何影响昆虫群落。除了测试影响昆虫群落的环境变量的新假设外，该项目还将为各种学生提供多种培训机会。拟议的工作测试了通过新的视角塑造草原昆虫群落的声学生态位假说——振动信号空间对任何给定植物的振动空间的竞争可以限制可以共存的振动信号的类型，因此，由于振动信号的物种特异性，竞争应该存在。研究人员测试也限制了昆虫的多样性。研究人员将使用压电记录仪从六个草原上的多个 1 米半径的地块中收集振动和昆虫，该记录仪可以从植物中采集微小的昆虫振动。将在 24-48 小时内记录每个地块的振动，然后，他们将立即使用标准化的节拍网时间段从部分地点进行额外的密集记录，以优化长期效果。研究人员将使用机器学习算法处理振动数据并寻找振动空间划分，将振动声学多样性指数与昆虫物种多样性指数进行比较，并开发利用振动监测昆虫群落的新颖工具集。该奖项反映了 NSF 的法定标准。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。