The importance of model choice on pH inferences from scaled chrysophyte assemblages in North America

The selection of a reliable inference model is a crucial step in developing ecologically sound reconstructions of environmental variables in the past. We compared intra- and inter-regional regression-based models, and an inter-regional Modern Analogue Technique (MAT) model in their ability to infer lakewater pH from scaled chrysophyte assemblages. The performance of each model was assessed by examining cross-validated coefficients of determination and prediction errors, and through reconstructing the pH of 50 modern and fossil samples in south-central Ontario, Canada. Using the intra- and inter-regional data sets, we found little difference in the ability of the regression-based models to infer present-day pH. Partial Least Squares (PLS) regression, Weighted Averaging (WA), and Weighted Averaging Partial Least Squares (WA-PLS) inference models showed similar values for jack-knifed coefficients of determination (r²_jack), root mean squared errors of prediction (RMSEP_jack), and mean and maximum biases. Based on an analogue matching approach, the inferred values from 48 fossil sediment samples suggested that the intra-regional model did not provide reliable reconstructions for approximately half of the fossil samples. However, inferences from the inter-regional MAT and regression-based models were found to have appropriate analogues and thus considered to be more reliable.