Cyanobacterial blooms are increasing worldwide and have negative impacts on aquatic ecosystems and the services they provide to human societies. A lack of long-term environmental monitoring data, however, has prevented the development of a baseline perspective against which drivers of the increasing frequency and severity of cyanobacterial blooms can be identified. In this study, we evaluate application of spectroscopy-based models to infer historical trends in cyanobacterial abundance from lake sediment cores. Using an amendment series (n = 15) of a sediment matrix spiked with increasing amounts of mixed cyanobacterial culture from 0 to 50 parts per thousand (‰), taxonomically diagnostic carotenoids were measured using visible near-infrared reflectance spectroscopy (VNIRS) and conventional but more costly and time-consuming high-performance liquid chromatography (HPLC). A partial least squares regression model was developed to correlate amendment series VNIR spectra to ‰ of added cyanobacteria. Despite challenges in differentiating carotenoid pigments because of overlapping absorption peaks, applications of the resulting 2-component model (r2 = 0.93, RMSEP = 0.23‰) to sediment cores from four Ontario lakes yielded temporal trends that were significantly correlated with downcore HPLC measures of cyanobacterial pigments in three out of four cases. Although our method is simplistic and may be improved in the future with more complex algorithms employing derivative analysis, we present our results as a possible stepping-stone towards spectral reconstruction of cyanobacterial production. Our study provides proof-of-concept that refinement of a method applying VNIRS to detect cyanobacterial carotenoids in lake sediments has the potential to be an important, rapid and non-destructive assessment tool for research and management of cyanobacterial blooms.

中文翻译：

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使用湖泊沉积物的可见近红外反射光谱 (VNIRS) 来估计蓝藻生产的历史变化：潜力和挑战

蓝藻水华在全球范围内不断增加，并对水生生态系统及其为人类社会提供的服务产生负面影响。然而，长期环境监测数据的缺乏阻碍了基线观点的发展，据此可以确定蓝藻水华日益增加的频率和严重程度的驱动因素。在这项研究中，我们评估了基于光谱的模型的应用，以从湖泊沉积物核心推断蓝藻丰度的历史趋势。使用添加了从 0 到 50 ‰ (‰) 增加的混合蓝藻培养物的沉积物基质的修正系列 (n = 15)，使用可见近红外反射光谱 (VNIRS) 和传统但更昂贵和耗时的高效液相色谱 (HPLC) 测量分类诊断类胡萝卜素。开发了偏最小二乘回归模型以将修正系列 VNIR 光谱与添加的蓝藻的 ‰ 相关联。尽管由于吸收峰重叠而在区分类胡萝卜素色素方面存在挑战，但将由此产生的 2 组分模型（r2 = 0.93，RMSEP = 0.23‰）应用于来自四个安大略湖的沉积物核心产生的时间趋势与蓝藻的下游 HPLC 测量显着相关在四分之三的情况下使用颜料。虽然我们的方法很简单，未来可能会通过使用导数分析的更复杂的算法进行改进，我们将我们的结果作为蓝藻生产光谱重建的可能垫脚石。我们的研究提供了概念验证，即改进应用 VNIRS 检测湖泊沉积物中蓝藻类胡萝卜素的方法有可能成为研究和管理蓝藻水华的重要、快速和非破坏性评估工具。