Cooperation, Competition and Carbon: A trait-based modelling approach to microbial mediation of natural dissolved organic carbon storage in the ocean.

海洋中溶解有机碳（DOC）的储量是地球表面最大的活性碳库之一，其规模与大气中的碳含量相当。DOC通过微生物呼吸和海洋表面的气体交换与大气中的二氧化碳直接相关，因此在气候变化中具有很高的生物反馈潜力。全球生物地球化学模型是评估未来情景的有力工具，但只有基于机制理解才能获得预测技能。科学家最近警告说，忽视微生物的作用严重降低了对未来气候情景的预测能力。目前的碳模型通常没有考虑微生物与DOC之间复杂的相互作用，这一点令人担忧，因为海洋DOC库规模大、活跃，并已被证明通过与CO2的直接联系在气候系统中发挥作用。目前海洋DOC的建模方法主要采用规定的DOC馏分反应性等级。虽然这种方法优雅地再现了当今的DOC浓度，但它没有反映机械过程，因此缺乏灵活性，无法解释不断变化的环境条件。该项目旨在通过全球海洋生物地球化学模型确定并实施解释当今海洋DOC浓度时空格局所需的微生物特征。在这里，我们选择了一种方法，即模型中的微生物群落适应环境条件，即所谓的自组装方法。该方法将应用于异养（=DOC降解）微生物多样性。通过首次将微生物异养多样性纳入自组装方法的全球模型，我的目标是确定与海洋DOC周转相关的特征和权衡。具体目标包括：1)在案例研究中推导出DOC在浮游植物与异养微生物相互作用中作用的理论约束；2)在全球海洋模型中测试共同特征和权衡的一个集合是否解释了观测到的全球表面DOC浓度；3)识别和量化除颗粒溶解外的地下DOC来源，如之前的几项观测研究所建议的（可选）。为此，我建议在麻省理工学院的Mick Follows教授小组工作12个月，以便将我之前开发的微生物DOC模型与他们的达尔文模型联系起来，并将自组装方法应用于降解DOC的微生物群落。该项目将通过在海洋碳循环模型中改进全球海洋DOC循环的表示，提供对驱动海洋中DOC自然储存机制的更好理解，从而推动该领域的发展。

The oceanic inventory of dissolved organic carbon (DOC) is one of the largest active carbon pools at the Earth’s surface, comparable in size to the carbon content of the atmosphere. DOC is directly linked to atmospheric CO2 via microbial respiration and gas exchange at the ocean’s surface, and thus holds a high potential for biological feedbacks in a changing climate. Global biogeochemical models are powerful tools to assess future scenarios, but only gain predictive skill if they are based on mechanistic understanding. Scientists have recently warned that neglecting the role of microorganisms severely reduces predictive skills for future climate scenarios. Current carbon models usually do not take into account the complex microbial interactions with DOC, which is of concern because the marine DOC pool is large, active and has been shown to play a role in the climate system through its direct link to CO2. Current modelling approaches for marine DOC mainly apply prescribed reactivity classes of DOC fractions. While this approach elegantly reproduces present-day DOC concentration, it does not reflect mechanistic processes and thus lacks the flexibility to account for changing environmental conditions. The proposed project aims to identify and implement the microbial traits required to explain the spatiotemporal pattern of present-day marine DOC concentration in a global biogeochemical ocean model. Here we chose an approach in which the microbial community in the model adapts to environmental conditions, a so called self-assembling approach. This approach will be applied to heterotrophic (=DOC degrading) microbial diversity. By including microbial heterotrophic diversity in a global model with a self-assembling approach for the first time, I aim to identify traits and trade-offs relevant for DOC turnover in the ocean. Specific aims include to 1) derive theoretic constraints on the role of DOC in interactions between phytoplankton and heterotrophic microorganisms in a case study, 2) test whether a subset of common traits and trade-offs explains observed global surface DOC concentrations in a global ocean model, and 3) identify and quantify subsurface DOC sources in addition to particle dissolution, as suggested in several previous observational studies (optional). To do that, I propose to spend 12 months at Prof. Mick Follows group at Massachusetts Institute of Technology, MIT, in order to connect my previously developed microbial DOC model to their Darwin model and apply the self-assembling approach to the microbial community that degrades DOC. The project will advance the field by providing an improved understanding on the mechanisms driving natural DOC storage in the ocean by improving the representation of the global marine DOC cycle in a marine carbon cycle model.