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Proxy reconstructions of climatic parameters developed using transfer functions are central to the testing of many palaeoclimatic hypotheses on Holocene timescales. However, recent work shows that the mathematical models underpinning many existing transfer functions are susceptible to spatial autocorrelation, clustered training set design and the uneven sampling of environmental gradients. This may result in over-optimistic performance statistics or, in extreme cases, a lack of predictive power. A new testate amoeba-based transfer function is presented that fully incorporates the new recommended statistical tests to address these issues. Leave-one-out cross-validation, the most commonly applied method in recent studies to assess model performance, produced over-optimistic performance statistics for all models tested. However, the preferred model, developed using weighted averaging with tolerance downweighting, retained a predictive capacity equivalent to other published models even when less optimistic performance statistics were chosen. Application of the new statistical tests in the development of transfer functions provides a more thorough assessment of performance and greater confidence in reconstructions based on them. Only when the wider research community have sufficient confidence in transfer function-based proxy reconstructions will they be commonly used in data comparison and palaeoclimate modelling studies of broader scientific relevance. |
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