BayClump: Bayesian Calibration and Temperature Reconstructions for
Clumped Isotope Thermometry
Abstract
Carbonate clumped isotope thermometry (Δ_47) is a temperature proxy
that is becoming more widely used in the geosciences. Most calibration
studies have used ordinary least squares linear regressions or York
models to describe the relationship between Δ_47 and temperature.
However, Bayesian models have not yet been explored for clumped
isotopes. There also has not yet been a comprehensive study assessing
the performance of commonly used regression models in the field. Here,
we use simulated datasets to compare the performance of seven regression
models, three of which are new and fit using a Bayesian framework. While
Bayesian and non-Bayesian ordinary least squares linear regression
models show the best overall accuracy for calibrations, Bayesian models
outperform other models in terms of precision, especially if datasets
are sufficiently large (>50 data points). For temperature
reconstructions where a given regression model is applied to predict
temperature from Δ_47), Bayesian and non-Bayesian models show variable
performance advantages depending on the the structure of errors in the
calibration dataset. Overall, our analyses suggest that the advantages
of using Bayesian models for calibrating and reconstructing temperatures
using clumped isotope paleothermometry are realized through the use of
large calibration datasets (>50 data points). When used
with large datasets, Bayesian regressions are expected to substantially
improve the accuracy and precision of (i) calibration parameter
estimates and (ii) temperature reconstructions (e.g., typically
improving precision by at least a factor of two). We implement our
comparative framework into a new web-based interface, BayClump. This
data tool should increase reproducibility by enabling access to the
different Bayesian and non-Bayesian regression models. Finally, we
utilize BayClump with three published datasets to examine precision and
accuracy in regression parameters and reconstructed temperatures. We
show that BayClump yields similarly accurate results to published
studies. However, the use of BayClump generally produces temperature
reconstructions with meaningful reductions in temperature uncertainty,
as demonstrated through reanalysis of data from a Late Miocene hominoids
site in Yunnan, China.