PDF(Pubmed)
Abstract：
Our knowledge of microbial processes-who is responsible for what, the rates at which they occur, and the substrates consumed and products produced-is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are-by themselves-insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.
摘要：
我们对微生物过程的了解-谁负责什么，它们发生的速度，消耗的底物和生产的产品对许多（如果不是大多数）分类群来说是不完美的，但是人们对微型站点过程如何扩展到生态系统乃至全球的了解更少。在自然环境和管理环境中，缩放将基础知识与应用联系起来，还可以对微生物过程的重要性进行全球评估。但很少是直截了当的缩放：更多的时候，原位工艺速率以高度偏斜的方式分布，在多个交互控件的影响下，因此通常很难取样，量化、和预测。迄今为止,许多重要过程的定量模型无法每天捕获，季节性,以及实现有意义的管理成果所需的精度的年度通量。氮循环过程就是一个很好的例子，反硝化就是一个很好的例子.基于机器学习的统计模型可以提高可预测性并确定最佳的环境预测因子，但本身不足以揭示过程级知识差距或预测新环境条件下的结果。混合模型将校准良好的过程模型作为机器学习算法的预测因子，可以在尚未经历的环境条件下提供更好的理解和更可靠的预测。将基于特征的模型纳入此类工作有望进一步改善预测和理解，但是需要更多的发展。
