Diatom-inference models for surface-water temperature and salinity developed from a 57-lake calibration set from the Sierra Nevada, California, USA

Physical, chemical, and biological data were collected from a suite of 57 lakes that span an elevational gradient of 1360 m ( 2115 to 3475 m a. s. l.) in the eastern Sierra Nevada, California, USA as part of a multiproxy study aimed at developing transfer functions from which to infer past drought events. Multivariate statistical techniques, including canonical correspondence analysis ( CCA), were used to determine the main environmental variables influencing diatom distributions in the study lakes. Lakewater depth, surface- water temperature, salinity, total Kjeldahl nitrogen, and total phosphorus were important variables in explaining variance in the diatom distributions. Weighted- averaging ( WA) and weighted- averaging partial least squares ( WA- PLS) were used to develop diatom- based surface- water temperature and salinity inference models. The two best diatom- inference models for surface- water temperature were developed using simple WA and inverse deshrinking. One model covered a larger surface- water temperature gradient (13.7degreesC) and performed slightly poorer ( r(2) = 0.72, RMSE = 1.4degreesC, RMSEPjack = 2.1degreesC) than a second model, which covered a smaller gradient ( 9.5 degreesC) and performed slightly better ( r(2) = 0.89, RMSE = 0.7degreesC, RMSEPjack = 1.5degreesC). The best diatom- inference model for salinity was developed using WA- PLS with three components ( r(2) = 0.96, RMSE = 4.06 mg L-1, RMSEPjack = 11.13 mg L). These are presently the only diatom- based inference models for surface- water temperature and salinity developed for the southwestern United States. Application of these models to fossil- diatom assemblages preserved in Sierra Nevada lake sediments offers great potential for reconstructing a high- resolution time- series of Holocene and late Pleistocene climate and drought for California.