Predicting Dissolved Organic Matter Lability and Carbon Accumulation in Temperate Freshwater Ecosystems

Dissolved organic matter (DOM) dynamics influence aquatic ecosystem metabolism with ecological and biogeochemical effects. During microbial degradation, certain DOM molecules accumulate in the environments constituting the residual refractory carbon (C) pool that has a key role in the global carbon cycle in lakes and oceans. The present study aims to model the factors driving bacterial C-consumption, thus predicting the potential residual carbon accumulation. We developed mechanistic models to represent bacterial C-consumption, considering the contribution of DOM quality and phosphorus (P) and nitrogen (N) concentrations in the total carbon pool. Based on 59 different environments, we established DOM components and nutrient concentration for deep lakes, shallow lakes, high-altitude lakes, and wetlands from North-Andean Patagonian glacial lake district (around 41S).We applied Bayesianmethods to estimatemodel parameters from laboratory C-lability experiments performed in 26 environments.We tested the statistically predictive accuracy of our models with an external dataset consisting of C-lability experiments with natural lake water enriched with organic matter from different sources.Wefound a model that performed excellently in both fit to training data and prediction to external experiments. The selected model showed that an increase in P concentration stimulates C-consumption, and an increase in the proportion ofDOMprotein-like compounds reduces the amount of residual C. Based on the statistically predictive accuracy,we showedthat ourmodel is very useful to anticipate C-accumulationdue to changes in the inputs to water bodies.

溶解性有机物（DOM）的动态变化对水生生态系统的代谢产生生态学和生物地球化学效应。在微生物降解过程中，某些DOM分子积累于构成残留难降解碳（C）库的环境中，该库在全球碳循环中湖泊和海洋中发挥着关键作用。本研究旨在构建驱动细菌碳消耗的因素模型，从而预测潜在残留碳的积累。我们开发了机制模型以表征细菌碳消耗，考虑了DOM质量以及总碳库中磷（P）和氮（N）浓度的贡献。基于59个不同环境，我们从北安第斯帕塔哥尼亚冰川湖区（约41S）的深湖、浅湖、高海拔湖泊和湿地中建立了DOM成分和营养浓度。我们应用贝叶斯方法，从26个环境中的实验室碳可溶性实验中估计模型参数。我们使用包含来自不同来源有机物富集的天然湖水碳可溶性实验的外部数据集，测试了我们模型的统计预测准确性。我们发现了一个在拟合训练数据和预测外部实验中均表现优异的模型。选定的模型表明，磷浓度的增加刺激了碳消耗，而DOM蛋白质类化合物的比例增加则减少了残留碳的量。基于统计预测准确性，我们证明了我们的模型对于预测水体输入变化导致的碳积累具有极高的预测价值。