Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403;
Department of Biology, University of Oregon, Eugene, OR 97403.
Proc Natl Acad Sci U S A. 2019 Jun 4;116(23):11329-11338. doi: 10.1073/pnas.1819883116. Epub 2019 May 16.
Microbial metabolism drives biogeochemical fluxes in virtually every ecosystem. Modeling these fluxes is challenged by the incredible diversity of microorganisms, whose kinetic parameters are largely unknown. In poorly mixed systems, such as stagnant water columns or sediments, however, long-term bulk microbial metabolism may become limited by physical transport rates of substrates across space. Here we mathematically show that under these conditions, biogeochemical fluxes are largely predictable based on the system's transport properties, chemical boundary conditions, and the stoichiometry of metabolic pathways, regardless of the precise kinetics of the resident microorganisms. We formalize these considerations into a predictive modeling framework and demonstrate its use for the Cariaco Basin subeuphotic zone, one of the largest anoxic marine basins worldwide. Using chemical concentration data solely from the upper boundary (depth 180 m) and lower boundary (depth 900 m), but without a priori knowledge of metabolite fluxes, chemical depth profiles, kinetic parameters, or microbial species composition, we predict the concentrations and vertical fluxes of biologically important substances, including oxygen, nitrate, hydrogen sulfide, and ammonium, across the entire considered depth range (180-900 m). Our predictions largely agree with concentration measurements over a period of 14 years ([Formula: see text] = 0.78-0.92) and become particularly accurate during a period where the system was near biogeochemical steady state (years 2007-2009, [Formula: see text] = 0.86-0.95). Our work enables geobiological predictions for a large class of ecosystems without knowledge of kinetic parameters or geochemical depth profiles. Conceptually, our work provides a possible explanation for the decoupling between microbial species composition and bulk metabolic function, observed in various ecosystems.
微生物代谢驱动着几乎所有生态系统中的生物地球化学通量。由于微生物的多样性令人难以置信，其动力学参数在很大程度上是未知的，因此对这些通量进行建模具有挑战性。然而，在混合不良的系统中，如停滞的水柱或沉积物中，长期的微生物总体代谢可能会受到基质在空间中物理传输速率的限制。在这里，我们从数学上表明，在这些条件下，生物地球化学通量在很大程度上可以根据系统的传输特性、化学边界条件以及代谢途径的化学计量来预测，而与驻留微生物的精确动力学无关。我们将这些考虑因素形式化为一个预测建模框架，并将其应用于卡里亚科盆地亚透光带进行演示，该盆地是世界上最大的缺氧海洋盆地之一。我们仅使用来自上边界（深度 180 米）和下边界（深度 900 米）的化学浓度数据，而无需事先了解代谢物通量、化学深度分布、动力学参数或微生物物种组成，我们预测了整个考虑深度范围内（180-900 米）生物重要物质的浓度和垂直通量，包括氧气、硝酸盐、硫化氢和铵。我们的预测与 14 年期间的浓度测量结果高度一致（[Formula: see text] = 0.78-0.92），并且在系统接近生物地球化学稳态的一段时间内（2007-2009 年，[Formula: see text] = 0.86-0.95）特别准确。我们的工作使得在不了解动力学参数或地球化学深度分布的情况下，对一大类生态系统进行地球生物学预测成为可能。从概念上讲，我们的工作为在各种生态系统中观察到的微生物物种组成和总体代谢功能之间的解耦提供了一种可能的解释。
