DISSERTATION RESEARCH: Refining decomposition models using a model system: Arabidopsis thaliana

论文研究：使用模型系统完善分解模型：拟南芥

基本信息

批准号：
0910054
负责人：
Kathleen Treseder
金额：
$ 1.5万
依托单位：
University of California-Irvine
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：
2009-09-01 至 2011-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0910054&HistoricalAwards=false
关键词：
DISSERTATION RESEARCH Refining decomposition models

项目摘要

This graduate student dissertation study is using genetic mutants of a small plant, Arabidopsis thaliana, to improve predictions of how quickly dead plant material decomposes in ecosystems. Globally, dead plant material stores vast quantities of carbon and other nutrients. As this material decomposes, the carbon is converted to CO2, a greenhouse gas, and released to the atmosphere where it contributes to global warming. Thus, decomposition is a critical process with global consequences.In collaboration with her PhD advisor and other researchers, the student is also helping to refine the ?Guild-based Decomposition Model?. GDM is a theoretical model that predicts decomposition rates based on the chemical composition of dead plant material. In the past, it has been difficult to test the theory underlying this model (and others), because it has been technologically challenging to directly alter specific types of chemical compounds in plant material. However, the student has found a solution by using genetic mutants that differ in the production of lignin and cellulose, the two dominant chemical compounds in plants. She is decomposing dead material from these mutants in an Alaskan boreal forest, and she will compare her results to those predicted by the current version of GDM. She will then refine the model accordingly to improve its predictive power. Her work will advance predictions of climate change.

这项研究生论文研究正在使用小植物的基因突变体，拟南芥，以改善对生态系统中死亡植物材料分解速度的速度的预测。在全球范围内，死植物材料存储了大量的碳和其他营养。当这种材料分解时，碳被转换为二氧化碳，一种温室气体，并释放到大气中，在大气中有助于全球变暖。因此，分解是一个重要的过程，具有全球后果。 GDM是一种理论模型，可根据死植物材料的化学组成来预测分解速率。过去，很难测试该模型（以及其他模型）的理论，因为直接改变植物材料中特定类型的化合物在技术上具有挑战性。但是，该学生通过使用木质素和纤维素产生不同的基因突变体（植物中的两种显性化合物）发现了解决方案。她正在阿拉斯加北方森林中这些突变体中分解死亡材料，并将她的结果与当前版本的GDM预测的结果进行比较。然后，她将相应地完善模型以提高其预测能力。她的工作将推动对气候变化的预测。