An improved statistical approach for reconstructing past climates from biotic assemblages
File(s)
Supporting information
Supporting information
Author(s)
Prentice, Iain Colin
Liu, Mengmeng
ter Braak, Cajo JF
Harrison, Sandy P
Type
Journal Article
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.
Date Issued
2020-11-25
Date Acceptance
2020-11-02
Citation
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020, 476 (2243), pp.1-21
ISSN
1364-5021
Publisher
The Royal Society
Start Page
1
End Page
21
Journal / Book Title
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume
476
Issue
2243
Copyright Statement
© 2020 The Author(s)
Published by the Royal Society. All rights reserved.
Published by the Royal Society. All rights reserved.
Sponsor
European Research Council
Identifier
https://royalsocietypublishing.org/doi/full/10.1098/rspa.2020.0346
Grant Number
787203
Subjects
01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status
Published
Date Publish Online
2020-11-25