Using visible near-infrared reflectance spectroscopy (VNIRS) of lake sediments to estimate historical changes in cyanobacterial production: potential and challenges

Abstract

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.