Are pollen-based climate models improved by combining surface samples from soil and lacustrine substrates?

Abstract Differences between pollen assemblages obtained from lacustrine and terrestrial surface sediments may affect the ability to obtain reliable pollen-based climate reconstructions. We test the effect of combining modern pollen samples from multiple depositional environments on various pollen-based climate reconstruction methods using modern pollen samples from British Columbia, Canada and adjacent Washington, Montana, Idaho and Oregon states. This dataset includes samples from a number of depositional environments including soil and lacustrine sediments. Combining lacustrine and terrestrial (soil) samples increases root mean squared error of prediction (RMSEP) for reconstructions of summer growing degree days when weighted-averaging partial-least-squares (WAPLS), weighted-averaging (WA) and the non-metric-multidimensional-scaling/generalized-additive-models (NMDS/GAM) are used but reduces RMSEP for randomForest, the modern analogue technique (MAT) and the Mixed method, although a slight increase occurs for MAT at the highest sample size. Summer precipitation reconstructions using MAT, randomForest and NMDS/GAM suffer from increased RMSEP when both lacustrine and terrestrial samples are used, but WA, WAPLS and the Mixed method show declines in RMSEP. These results indicate that researchers interested in using pollen databases to reconstruct climate variables need to consider the depositional environments of samples within the analytical dataset since pooled datasets can increase model error for some climate variables. However, since the effects of the pooled datasets will vary between climate variables and between pollen-based climate reconstruction methods we do not reject the use of mixed samples altogether. We finish by proposing steps to test whether significant reductions in model error can be obtained by splitting or combining samples from multiple substrates.

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