Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty
Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty
复制标题
通过识别模型不确定性的预测因素来提高湖泊生态系统代谢估计的精度
DOI：
10.4319/lom.2014.12.303
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Paul C. Hanson
中科院分区：
文献类型：
--
作者：
K. Rose;L. Winslow;J. Read;E. Read;C. Solomon;R. Adrian;Paul C. Hanson
溶解氧的昼夜变化常用于估算水生生态系统的总初级生产量（GPP）和生态系统呼吸（ER）。尽管这种方法被广泛用于理解生态系统代谢，但我们才刚刚开始了解代谢模型参数估计的不确定性程度和潜在原因。在这里，我们提出了一种新的方法，通过识别表明代谢估计高度不确定的物理指标来提高生态系统代谢估计的精度和准确性。利用17个GLEON（全球湖泊生态观测站网络）湖泊的数据集，我们发现了许多与不确定性相关的物理特征，包括PAR（光合有效辐射，400-700 nm）、施密特稳定性的日方差和风速。低PAR是GPP模型参数高方差的一致预测因子，但也与低ER模型参数方差相对应。我们确定了一个阈值（晴空PAR的30%），低于该阈值，几乎所有湖泊的GPP参数方差都迅速增加，与PAR水平高于该阈值的天的方差相比，差异都显著增加。Schmidt稳定性的日方差与GPP模型参数方差的关系取决于营养状态，而Schmidt稳定性的日方差与ER模型参数方差始终呈正相关。风速在~0.8‐3 m s - 1范围内是GPP和ER模型参数高方差的一致预测因子，富营养化湖泊的不确定性更大。我们的发现可以用来减少生态系统代谢模型参数的不确定性，并确定这种不确定性的潜在来源。
Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400–700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8‐3 m s−1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.