An improved statistical approach for reconstructing past climates from biotic assemblages: improved palaeoclimate reconstruction

Liu, M., Prentice, I. C., ter Braak, C. J. F. and Harrison, S. P. ORCID: https://orcid.org/0000-0001-5687-1903 (2020) An improved statistical approach for reconstructing past climates from biotic assemblages: improved palaeoclimate reconstruction. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476. 2243. ISSN 1471-2946 doi: 10.1098/rspa.2020.0346

Abstract/Summary

Quantitative reconstructions of past climates are an important resource for evaluating how well climate models reproduce climate changes. One widely used statistical approach for making such reconstructions from fossil biotic assemblages is weighted averaging partial least-squares regression (WA-PLS). There is however a known tendency for WA-PLS to yield reconstructions compressed towards the centre of the climate range used for calibration, potentially biasing the reconstructed past climates. We present an improvement of WA-PLS by assuming that: (i) the theoretical abundance of each taxon is unimodal with respect to the climate variable considered; (ii) observed taxon abundances follow a multinomial distribution in which the total abundance of a sample is climatically uninformative; and (iii) the estimate of the climate value at a given site and time makes the observation most probable, i.e. it maximizes the log-likelihood function. This climate estimate is approximated by weighting taxon abundances in WA-PLS by the inverse square of their climate tolerances. We further improve the approach by considering the frequency ( fx) of the climate variable in the training dataset. Tolerance-weighted WA-PLS with fx correction greatly reduces the compression bias, compared with WA-PLS, and improves model performance in reconstructions based on an extensive modern pollen dataset.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/95534
Item Type	Article
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Publisher	Royal Society Publishing
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