A diatom-based predictive model for inferring past conductivity in Chadian Sahara lakes

For decades, diatoms have been recognized as powerful bio-indicators of modern water quality. They have also been utilized in the design of transfer functions, which can be applied to diatom assemblages in lake sediment cores to infer aspects of past lake hydrochemistry and estimate variables that can be incorporated into paleohydrology models. The Ounianga lakes, in the heart of the Chadian Sahara, possess unique and well-preserved sediment records that extend back beyond the middle Holocene. Today, the lakes display a range of hydrochemical conditions, from fresh to hypersaline. Mainly fed by fossil groundwater that originates in the Nubian Sandstone Aquifer System, measured conductivity across these lakes varies from 217 to 352,000 mu S cm(-1), values that are influenced by factors such as hydrology, local geomorphology (e.g., depth and area), and aquatic vegetation. Although these lakes have been on the UNESCO World Heritage List since 2012, they have never been studied in detail because they are located on the fringes of the Chadian Sahara. The distribution of diatom taxa in the lakes today is closely linked to water-column physical and chemical conditions, especially conductivity. Whereas each lake has particular features that influence its diatom flora, diatoms across a conductivity gradient enabled identification of three distinct waterbody types, freshwater lakes, meso-saline to hyper-saline lakes, and freshwater springs. Relationships between diatom species distributions and environmental variables were examined using multivariate analysis, which revealed that conductivity is the variable that explains most of the variance in the diatom flora. We used modern diatom assemblages from the lakes to develop a predictive model (transfer function) for conductivity, using the weighted averaging method. Our conductivity prediction model is strong, with a coefficient of determination (R-2) of 0.89 between estimated and measured values, and a value of 0.78 using jackknife estimates of prediction. This study better constrained conductivity optima and tolerance values for diatom species found in the Ounianga lakes, thereby enabling development of a model that will yield better inferences for past conductivity, using diatoms from lake sediment records in the region.

Automated summary

This study used diatoms as bio-indicators to investigate the water condition in the Ounianga lakes and developed a predictive model for conductivity using these diatoms. The distribution of diatoms in the lakes was closely linked to water-column physical and chemical conditions, especially conductivity. Through multivariate analysis and weighted averaging method, a strong conductivity prediction model was developed, which provided better inferences for past conductivity using diatoms from lake sediment records in the region.