Quantifying ecological change through discriminant analysis: a paleoecological example from the Peruvian Amazon

Question: Can discriminant analysis be used to quantify ecological change? Can fossil pollen data be used as a proxy to quantify moisture availability change through discriminant analysis? Location: Lake Sauce, Amazonian piedmont of Peru. Methods: A linear discriminant function was used to classify taxa found through pollen analysis into wet and dry indicators. The data set was filtered to exclude rare taxa from the analysis. Given that after application of the filter there were more variables (samples) than observations (taxa), the model was “de‐saturated” through simulation of samples based on the existing data set. Results: The inclusion of taxa that have a relative abundance of 1% or more in at least 5% of the samples reduces noise in the data set. Application of discriminant analysis to pollen data gave an error of 18% when classifying taxa by affinity with dry or wet conditions. The inferred moisture availability curve shows consistency with independent proxies from the same core and with identified local and sub‐continental moisture patterns. Conclusions: The method provides a reliable means to reduce a complex paleoecological data set to proportional change in a single pre‐defined variable. The output is a relative scale of change of a defined environmental gradient through time, without reliance on an extensive array of modern analogues. The results appear to provide a comparable quality of information to that of isotopic analysis derived from speleothem or sedimentary records.