DISSERTATION RESEARCH: The functional consequences of antagonism in fungal communities

论文研究：真菌群落中拮抗作用的功能后果

• 批准号: 1601036
• 负责人: Mark Bradford
• 金额: $ 2.15万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1601036&HistoricalAwards=false
• 关键词: DISSERTATION; RESEARCH; functional; consequences; antagonism

Understanding how the Earth's climate will change over the next century is of enormous scientific, social, and economic importance. In order to make these projections of future climate, scientists will need to understand how the world's smallest organisms, fungi and bacteria, respond to changing environmental conditions. These microbes play a critical role in the Earth's carbon cycle. They decompose dead plant material, releasing carbon dioxide back into the atmosphere. Current climate models assume that the rate of this release is determined by environmental conditions, such as temperature, moisture, or soil pH. Changes in these conditions are expected to "turn the dial" on microbial activity. If it gets warmer, microbes will release more carbon dioxide; if it gets colder, microbes will release less carbon dioxide. Yet this assumption ignores a key aspect of most microbes: they are highly skilled warriors. Fungi and bacteria release an astonishing number of chemicals and compounds intended to kill their fellow microbes and colonize their territory. This warfare, however, comes at a considerable cost. A microbe must choose between using its energy for combat or using its energy for growth. In the proposed work, the researchers will quantify how microbial combat affects decomposition rates across environmental conditions, helping to understand where and when microbially-driven carbon dioxide releases will respond unpredictably to changing environmental conditions. The investigators will also train a high school student and develop outreach activities with a natural history museum. Building on previous diversity-function studies, the researchers will develop a generalizable model to disentangle the relative importance of antagonistic competition versus environmental conditions as drivers of microbial-mediated decomposition rates. Using baseline trait data collected on 44 wood-decay fungi, 100 unique communities will be selected with varying levels of combative ability, average stress-tolerance ability, and species richness (from 3 to 12 unique species). The communities will be assembled in realistic microcosms and incubated under both optimal conditions (warm and wet) and stressful conditions (cold and dry). Total wood decomposition will be measured after 12 weeks. This study will allow the researchers to address three main questions: (1) Does community-level decomposition differ from what is predicted using the microbes' individual decomposition rates? (2) Do these differences depend on whether the individuals are combative fungi or stress-tolerant fungi? and (3) Does the relative importance of antagonistic interactions differ between stressful and optimal environmental conditions? The answers to these questions will ultimately help restructure existing climate models and better project how global carbon dioxide fluxes will change over time.

了解地球气候在下一世纪将如何变化具有巨大的科学，社会和经济意义。为了对未来气候进行这些预测，科学家将需要了解世界上最小的生物，真菌和细菌如何应对不断变化的环境条件。这些微生物在地球的碳循环中起着至关重要的作用。他们将死植物材料分解，将二氧化碳释放回大气中。当前的气候模型假设该释放的速率取决于环境条件，例如温度，水分或土壤pH。这些条件的变化有望在微生物活动中“转动”。如果变暖，微生物将释放更多二氧化碳。如果变冷，微生物将释放较少的二氧化碳。然而，这个假设忽略了大多数微生物的关键方面：它们是高技能的战士。真菌和细菌释放出令人惊讶的化学物质和化合物，旨在杀死其同伴并定居其领土。然而，这场战争的代价很大。微生物必须在使用其能量进行战斗或使用其能量进行生长之间进行选择。在拟议的工作中，研究人员将量化微生物战斗如何影响跨环境条件的分解率，有助于了解微生物驱动的二氧化碳释放的何时何时会对环境条件的变化做出不可预测的反应。调查人员还将培训一名高中生，并在自然历史博物馆开展外展活动。在以前的多样性功能研究的基础上，研究人员将开发一个可概括的模型，以将拮抗竞争与环境条件与微生物介导的分解速率的驱动因素相对重要。使用在44种木质真菌上收集的基线性状数据，将选择100个独特的社区，具有不同水平的好斗能力，平均压力耐应力和物种丰富度（从3到12个独特的物种）。这些社区将以逼真的缩影组装，并在最佳条件（温暖和湿）和压力条件（冷干燥）下孵育。 12周后将测量总木材分解。这项研究将使研究人员能够解决三个主要问题：（1）社区级分解是否不同于使用微生物的单个分解率预测的？ （2）这些差异是否取决于个体是好斗的真菌还是耐压力的真菌？ （3）压力和最佳环境条件之间的拮抗相互作用的相对重要性是否有所不同？这些问题的答案最终将有助于重组现有的气候模型，并更好地预测全球二氧化碳通量如何随着时间而变化。