An improved statistical approach for reconstructing past climates from biotic assemblages

Mengmeng Liu*, Iain Colin Prentice, Cajo J. F. ter Braak, Sandy P. Harrison

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number20200346
    Pages (from-to)1-21
    Number of pages21
    JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
    Volume476
    Issue number2243
    DOIs
    Publication statusPublished - Nov 2020

    Keywords

    • bias reduction
    • climate reconstruction
    • model calibration
    • palaeoclimate
    • pollen data
    • WA-PLS

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