Collaborative Research: The Role of Snow Patches on the Spatial Distribution of Soil Microbial Communities and Biogeochemical Cycling in the Antarctic Dry Valleys

合作研究:雪斑对南极干谷土壤微生物群落空间分布和生物地球化学循环的作用

基本信息

  • 批准号:
    0838850
  • 负责人:
  • 金额:
    $ 27.53万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-08-15 至 2013-07-31
  • 项目状态:
    已结题

项目摘要

AbstractThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Two models have been proposed to describe controls over microbial biogeography. One model proposes that microbes are ubiquitously distributed across the global environment, and that environmental conditions select for taxa physiologically adapted to local physical conditions. An alternative model predicts that dispersal is the important limitation to the distribution of microorganisms and that spatial heterogeneity of microbial communities is a result of both dispersal and local environmental limitations. According to both models, spatial heterogeneity of microbial communities may be especially pronounced in extreme ecosystems where the environmental selection for organisms with suitable physiology is most strongly manifest. We propose that Antarctic terrestrial environments are ideal places to examine microbial biogeography for 3 reasons: 1) the pristine nature and remoteness of Antarctica minimizes the prevalence of exotic species dispersed through human vectors; 2) the extreme conditions of Antarctic environments provide a strong environmental filter which limits the establishment of non-indigenous taxa; and 3) extreme heterogeneity in the terrestrial environment provides natural gradients of soil conditions (temperature, water and nutrient availability). In the proposed research we will investigate the influence of snow on the composition and spatial distribution of soil microbial communities and linked biogeochemical cycling in the McMurdo Dry Valleys. We will conduct fieldwork at the landscape scale (repeated remote sensing to characterize snow distribution), at the valley and patch scales (quantify snow patch ablation, microbial communities and biogeochemical cycling in subnivian soils). We hypothesize that snow patches play an important role in structuring the spatial distribution of soil microbial communities and their associated ecosystem functioning because of the physical and hydrological influences that snow patches have on the soil environment. The research will contribute to greater public awareness of the importance of polar research to fundamental questions of biology, ecology and hydrology through direct linkages with International Antarctic Institute public outreach activities, including dissemination of web-based learning units on environmental science and microbiology, targeted as resources for secondary and post-secondary educators. Three graduate students, one postdoctoral scholar and multiple undergraduates will participate in the research activities.
摘要该奖项根据 2009 年美国复苏和再投资法案(公法 111-5)提供资金。已提出两种模型来描述对微生物生物地理学的控制。一种模型提出,微生物普遍分布在全球环境中,环境条件会选择在生理上适应当地物理条件的类群。另一种模型预测,扩散是微生物分布的重要限制,微生物群落的空间异质性是扩散和当地环境限制的结果。根据这两个模型,微生物群落的空间异质性在极端生态系统中可能尤其明显,在极端生态系统中,对具有合适生理学的生物体的环境选择最为明显。我们认为南极陆地环境是研究微生物生物地理学的理想场所,原因有以下三个:1)南极洲的原始性质和地处偏远最大限度地减少了通过人类媒介传播的外来物种的流行; 2)南极环境的极端条件提供了强大的环境过滤器,限制了非本土类群的建立; 3)陆地环境的极端异质性提供了土壤条件(温度、水和养分可用性)的自然梯度。在拟议的研究中,我们将研究雪对麦克默多干谷土壤微生物群落的组成和空间分布以及相关生物地球化学循环的影响。我们将在景观尺度(重复遥感来表征积雪分布)、山谷和斑块尺度(量化雪斑消融、微生物群落和地下土壤中的生物地球化学循环)进行实地考察。我们假设雪斑在构建土壤微生物群落的空间分布及其相关的生态系统功能方面发挥着重要作用,因为雪斑对土壤环境具有物理和水文影响。该研究将通过与国际南极研究所公共宣传活动的直接联系,包括传播基于网络的环境科学和微生物学学习单元,旨在提高公众对极地研究对生物学、生态学和水文学基本问题的重要性的认识。为中等和高等教育工作者提供的资源。三名研究生、一名博士后和多名本科生将参加研究活动。

项目成果

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Michael Gooseff其他文献

Michael Gooseff的其他文献

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{{ truncateString('Michael Gooseff', 18)}}的其他基金

LTER: MCM6 - The Roles of Legacy and Ecological Connectivity in a Polar Desert Ecosystem
LTER:MCM6 - 极地沙漠生态系统中遗产和生态连通性的作用
  • 批准号:
    2224760
  • 财政年份:
    2023
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Continuing Grant
Collaborative Research: Moving Beyond the Margins: Modeling Water Availability and Habitable Terrestrial Ecosystems in the Polar Desert of the McMurdo Dry Valleys
合作研究:超越边缘:麦克默多干谷极地沙漠的水资源可用性和宜居陆地生态系统建模
  • 批准号:
    2045874
  • 财政年份:
    2021
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant
LTER: Ecosystem Response to Amplified Landscape Connectivity in the McMurdo Dry Valleys, Antarctica
LTER:生态系统对南极洲麦克默多干谷景观连通性增强的响应
  • 批准号:
    1637708
  • 财政年份:
    2017
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Continuing Grant
Collaborative Research: How do interactions of transport and stoichiometry maximize stream nutrient retention?
合作研究:运输和化学计量的相互作用如何最大限度地保留河流养分?
  • 批准号:
    1642402
  • 财政年份:
    2017
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Continuing Grant
Collaborative Research: Continuous Metabolism and Nutrient Uptake Across the River Continuum
合作研究:河流连续体的连续代谢和养分吸收
  • 批准号:
    1556937
  • 财政年份:
    2016
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant
Collaborative Research: Arctic Oases - How does the delayed release of winter discharge from aufeis affect the ecosystem structure and function of rivers?
合作研究:北极绿洲 - 冬季排放的延迟释放如何影响河流的生态系统结构和功能?
  • 批准号:
    1504453
  • 财政年份:
    2016
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant
Collaborative Research: THE MCMURDO DRY VALLEYS: A landscape on the Threshold of Change
合作研究:麦克默多干谷:变革门槛上的景观
  • 批准号:
    1601000
  • 财政年份:
    2015
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant
Collaborative Research: THE MCMURDO DRY VALLEYS: A landscape on the Threshold of Change
合作研究:麦克默多干谷:变革门槛上的景观
  • 批准号:
    1246203
  • 财政年份:
    2013
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant
Increased Connectivity in a Polar Desert Resulting from Climate Warming: McMurdo Dry Valley LTER Program
气候变暖导致极地沙漠连通性增强:麦克默多干谷 LTER 计划
  • 批准号:
    1115245
  • 财政年份:
    2011
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Continuing Grant
EAGER: Are the Dry Valleys Getting Wetter? A Preliminary Assessment of Wetness Across the McMurdo Dry Valleys Landscape
EAGER:干燥的山谷正在变得湿润吗?
  • 批准号:
    1045215
  • 财政年份:
    2010
  • 资助金额:
    $ 27.53万
  • 项目类别:
    Standard Grant

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基于可解释机器学习的科学知识角色转变预测研究
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