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

这个研究生的论文研究是利用一种小型植物拟南芥的遗传突变体来提高对死亡植物物质在生态系统中分解速度的预测。在全球范围内，死亡的植物材料储存了大量的碳和其他营养物质。随着这种材料的分解，碳转化为二氧化碳，一种温室气体，并释放到大气中，导致全球变暖。因此，分解是一个具有全球影响的关键过程。基于行业协会的分解模型？GDM是一种理论模型，它根据死亡植物材料的化学成分预测分解率。在过去，很难测试这个模型（和其他模型）背后的理论，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。然而，这位学生通过使用在木质素和纤维素（植物中的两种主要化合物）的产生方面不同的遗传突变体找到了一种解决方案。她正在阿拉斯加北部森林中分解这些突变体的死亡物质，她将把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。她的工作将推进对气候变化的预测。

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.