Non-analogues in paleoecological reconstruction: Model behavior and implications

Simon Goring, J. Sakari Salonen, Misku Luoto & Jack Williams

Fossil pollen is a widespread proxy for past vegetation that is used for paleoclimatic reconstruction, but the limits of its utility are not well known. Newer methods for climate reconstruction (CR) using machine learning techniques may improve the abilities of CR techniques, but little is known about model accuracy under conditions of non-analogue vegetation known to have occurred in the past. Here we generate non-analogue pollen assemblages by excluding close neighbors from calibration datasets, testing the ability of five CR techniques using pollen, including two machine learning techniques, to accurately reconstruct climate under non-analogue conditions.

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