EAGER: A novel investigation of population and community temporal trajectories leveraging experimental disturbances from Konza Prairie Long-Term Ecological Research Network site

EAGER：利用康扎草原长期生态研究网络站点的实验扰动对人口和群落时间轨迹进行新颖的调查

• 批准号: 2227298
• 负责人: Sarah Supp
• 金额: $ 19.45万
• 依托单位: Denison University
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227298&HistoricalAwards=false
• 关键词: EAGER; novel; investigation; population; community

Species do not live in a constant environment. Rather, each species exhibits growth, reproduction, mortality, and movement patterns that are the result of changing conditions. Natural and human-driven disturbances can have substantial short-term or long-term effects on species populations, and in turn, on the overall biodiversity within a system. Short-term disturbances, known as “pulse disturbances”, can have rapid positive or negative impacts on species populations, followed by periods of recovery. Long-term disturbances, known as “press disturbances”, are chronic or ongoing changes to the environment that are predicted to cause directional (increased or decreased) changes in species populations and overall biodiversity. To fully understand the effects of disturbance on biodiversity, it is necessary to distinguish the patterns of species that are entering and exiting the community through time. Such a method will allow identification of species that are becoming locally uprooted, establishing, or exhibiting recurrent or random occurrence through time. This study will use long-term ecological experiments on the effects of fire (pulse) and grazing (press) disturbance treatments from the well-established Konza Prairie Long-Term Ecological Research (LTER) network site across plants, insects, birds, and small mammals. The research will support undergraduate research and education activities at a small liberal arts college and findings will be shared through open access publication. This work investigates a novel classification methodology for sequential species presence and absence (incidence) patterns and uses long-term control-experiment comparisons with 15 or more years of data to test the level of ecological disturbance needed for species to “switch” their incidence. The work will be repeated across two main treatments representing pulse (fire) and press (grazing) disturbance types, and will compare the findings across plant and animal (insect, bird, small mammal) taxonomic groups. The research will generate estimates of changes in population abundance, the proportion of years present, and the incidence classification for each species population and compare among the controls and disturbance treatments. Species-level data will inform understanding of population abundance, persistence, and incidence trends, the co-variation or decoupling of these three measures, and inform how pulse and press disturbance regimes impact populations and ultimately community structure and biodiversity. The incidence classification methodology is not system- or taxa- specific and this research would provide a first test of the new approach in disturbed systems that has the potential to uncover new trends not identified using abundance or persistence methodologies alone.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.

物种并不生活在一个恒定的环境中。相反，每个物种都表现出生长、繁殖、死亡和运动模式，这些都是条件变化的结果。自然和人为干扰可能会对物种种群产生重大的短期或长期影响，进而对系统内的整体生物多样性产生影响。被称为“脉搏干扰”的短期干扰可对物种种群产生迅速的积极或消极影响，随后是恢复期。长期干扰被称为“新闻干扰”，是对环境的长期或持续的变化，预计会导致物种种群和整个生物多样性的方向性(增加或减少)变化。为了充分了解干扰对生物多样性的影响，有必要区分进入和离开群落的物种随时间的模式。这种方法将允许识别正在成为当地连根拔起、建立或表现出随时间重复或随机发生的物种。这项研究将使用长期生态学实验，研究火(脉冲)和放牧(按)干扰处理的影响，这些处理来自久负盛名的Konza Prairie长期生态研究(LTER)网络站点，涉及植物、昆虫、鸟类和小型哺乳动物。这项研究将支持一所小型文理学院的本科生研究和教育活动，并将通过开放获取出版物分享研究结果。这项工作调查了一种新的顺序物种存在和不存在(发生率)模式的分类方法，并使用与15年或更长时间的数据的长期对照实验比较来测试物种“切换”其发生率所需的生态干扰水平。这项工作将在代表脉冲(火灾)和按压(放牧)干扰类型的两种主要处理方法上重复进行，并将在植物和动物(昆虫、鸟类、小型哺乳动物)分类组之间比较研究结果。这项研究将对种群丰度的变化、存在的年份比例和每个物种种群的发病率分类进行估计，并在对照和干扰处理之间进行比较。物种水平的数据将有助于了解种群的丰度、持久性和发病趋势，这三个指标的协变或分离，并了解脉冲和压力干扰制度如何影响种群，最终影响群落结构和生物多样性。事件分类方法不是特定于系统或分类群的，这项研究将在受干扰的系统中首次测试新方法，该方法有可能发现仅使用丰富性或持久性方法无法识别的新趋势。该奖项反映了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。