课题基金 / 基金详情

Collaborative Research: Reconciling the interaction patterns of highly functional and resistant ecological communities

Collaborative Research: Reconciling the interaction patterns of highly functional and resistant ecological communities
合作研究:协调高功能和抗性生态群落的相互作用模式
批准号:
2009075
负责人:
Lauren Ponisio
金额:
$51.05万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-01 至 2024-07-31

项目摘要

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中文摘要
翻译
生态群落由以多种方式相互作用的物种组成。两个物种都受益的相互作用被称为“互惠”相互作用。 例如,在授粉过程中,植物接受花粉以产生种子,而传粉者接受食物。 有些授粉伙伴关系可能是独特和专门的,每个物种都依赖于只能由一个伙伴提供的服务。其他授粉伙伴关系可能是通用的和可互换的,许多物种提供相同的服务。如果所有的伙伴关系都是可互换的,那么如果它们的物种因环境压力而发生变化,群落可能会保持稳定。但是,如果伙伴关系是独特和专门的,那么社区可能对压力非常敏感。因此,了解物种如何以它们的方式相互作用是生态学的中心问题。这项研究将探讨不同类型的相互作用(独特和专门与可互换和一般化)如何影响植物和传粉者的繁殖以及它们的种群在环境变化中能坚持多久。这项研究将集中在马德雷“天空岛”的植物和授粉者,美国西南部被低地沙漠隔开的山顶草甸。这项研究将有助于指导这些不寻常的栖息地的保护和管理,并为对生态实地工作感兴趣的学生提供指导。网络理论预测,高度冗余和广义的相互作用网络最大限度地抵抗干扰,而专门的,互补的网络最大限度地发挥作用。虽然这种权衡被认为是生物网络组织的固有组成部分,但尚不清楚是什么生态和进化过程介导了它们,保护了种群并维持了网络结构。此外,虽然相互作用在生物体(个体到物种)和时间尺度(单次相互作用到多代相互作用)上可能是异质的,但这种可变性驱动网络结构和生态功能的潜力尚未得到系统性的探索。这项研究将通过测试不同生物组织水平的机制如何同时赋予网络内的功能和抵抗力来开拓新的领域。通过量化个体和物种水平的网络,沿着短期和长期的健康结果,研究人员将研究网络结构、功能和抵抗力之间的关系,该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查进行评估,被认为值得支持的搜索.
英文摘要
Ecological communities consist of species that interact with each other in many ways. An interaction in which both species benefit is called a "mutualistic" interaction. For example, in pollination plants receive pollen to produce seeds and the pollinators receive food. Some pollination partnerships may be unique and specialized, with each species depending on services that can be provided only by a single partner. Other pollination partnerships may be general and interchangeable, with many species providing the same services. If all partnerships are interchangeable, then communities may remain stable if their species change due to environmental stress. But if partnerships are unique and specialized, then communities may be very sensitive to stress. Thus, understanding how species interact in the way they do is a central question in ecology. This research will explore how different types of interactions (unique and specialized vs. interchangeable and generalized) affect how well plants and pollinators reproduce and how long their populations can persist in the face of environmental change. The study will focus on the plants and pollinators of the Madrean "sky islands", mountaintop meadows separated by lowland deserts in the southwestern United States. The study will help guide conservation and management of these unusual habitats and provide mentoring of students interested in ecological field work.Network theory predicts that highly redundant and generalized networks of interactions maximize resistant to disturbance, whereas specialized, complementary networks maximize function. While such tradeoffs are assumed to be an inherent part of biological network organization, it is not known what ecological and evolutionary processes mediate them, preserving populations and maintaining network structure. Further, though interactions may be heterogeneous across both organismal (individual to species) and temporal scales (single interactions to multiple interactions across generations), the potential for this variability to drive both network structure and ecological function has not been systematically explored. This research will forge new ground by testing how mechanisms at different levels of biological organization simultaneously confer function and resistance within networks. By quantifying individual- and species-level networks, along with short- and long-term fitness outcomes, the researchers will examine the relationship between network structure, function and resistance, and their impact on plant and animal population dynamics across a biogeographic gradient.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.
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  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
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