Quantitative reconstruction of paleoclimate—A new understanding of the normal distribution method for coexistence likelihood estimation
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Abstract:
Using plants as a proxy to estimate climate is one of the main approaches to quantitatively reconstructing past climate parameters. The parametric function of Climate Reconstruction Analysis using Coexistence Likelihood Estimation (P-CRACLE) is a quantitative climate reconstruction method based on biological taxonomy. P-CRACLE uses worldwide plant distribution data to obtain corresponding climate parameters. This method assumes that the occurrence of a species is normally distributed along the variables of the climate to calculate the climate intervals where plants coexist. Based on 165 modern floras of the world, the reliability of this method is well verified, but there are still controversies over the acquisition and processing of plant distribution and climate data. To further verify the reliability of this method, we selected 26 research sites of modern floras, obtained plant distribution data from the Global Biodiversity Information Facility (GBIF) database and the climate parameters by using the meteorological grid data (WorldClim 2.1) with two different resolutions (0.5 arcminute and 2.5 arcminute). We apply the traditional mutual climate range (MCR) and P-CRACLE to jointly calculate the climate parameters for all research sites. The results show no apparent difference when using different resolutions of climate grid data. Annual average temperatures obtained using MCR and P-CRACLE are 8.3 ℃ and 1.7 ℃, respectively, and the average resolutions of annual average precipitation are 1120 mm and 280 mm, respectively. Therefore, the resolution of climate intervals obtained by P-CRACLE is far more accurate than that by MCR. Then, based on the percentile of each plant taxon’s given climate parameter to the observed value of the research site, the plant taxon with a percentile higher than 10% and lower than 90% was selected to re-analyze the coexistence of P-CRACLE. Comparison with the coexistence results of previous studies shows that the accuracy of the reconstruction is significantly improved, providing a direction for increased accuracy of quantitative paleoclimate reconstruction in the future. This study suggests that P-CRACLE can be applied to the quantitative reconstruction of paleoclimate since the Cenozoic with very high resolution and reliability.
