Machine learning reveals that sodium concentration and temperature influence alkenone occurrence in Swiss and worldwide freshwater lakes

Abstract

Lacustrine alkenones are increasingly reported in freshwater lakes worldwide, which makes them a very promising proxy to reconstruct past continental temperatures. However, a more systematic understanding of ecological preferences of freshwater alkenone-producers at global scale is lacking, which limits our understanding of alkenones as a proxy in lakes. Here we investigated 56 Swiss freshwater lakes and report Group 1 alkenones in 33 of them. In twelve of the lakes containing alkenones, a mixed Group 1/Group 2 alkenone signature was detected. We used a random forest (RF) model to investigate the influence of 15 environmental variables on alkenone occurrence in Swiss lakes and found sodium (Na+) concentration and mean annual air temperature (MAAT) to be the most important variables. We also trained a RF model on a database that included Swiss lakes and all freshwater lakes worldwide, which were previously investigated for alkenone presence. Water depth appeared as the most important variable followed by MAAT and Na+, sulfate and potassium concentrations. This is very similar to results found for freshwater and saline lakes, which suggests that Group 1 and Group 2 alkenone occurrence could be controlled by the same variables in freshwater lakes. For each tested variable, we defined the optimal range(s) for the presence of alkenones in freshwater lakes. The similarity of the results for the Swiss and global models suggests that the environmental parameters controlling the occurrence of freshwater alkenone producers could be homogenous worldwide.