ClamR: A statistical evaluation of isotopic and temperature records in sclerochronologic studies
Abstract Isotope sclerochronology has been widely used to reconstruct paleoclimate from the hard part remains of a broad range of taxa from many different environmental settings and geological and cultural contexts. Oxygen isotope (δ18O) time series derived from these ultra high-resolution (daily, seasonal, annual) records have been used to estimate annual averages, seasonal ranges, and seasonal extremes. These time series, however, inherently include uncertainties such as unconstrained environmental noise, changes in seasonal growth rates resulting in weighting annual temperature estimates toward seasons of maximum growth, growth cessation, and reliance on the most extreme δ18O values in the time series to estimate seasonal temperature range, etc. To better quantify uncertainties in paleoclimate reconstructions using sclerochronologic data, we developed an open-source R package, ClamR. The ClamR evaluation tool improves upon the previous best-fit sinusoid approach by introducing additional statistical computation and analysis, including Discrete Fourier Transform to initialize parameters for the sinusoidal function, Nelder–Mead method to optimize parameters, jackknife error estimation, and window size calculation for sinusoidal smoothing. It can automatically calculate and plot statistical errors for oxygen isotope or temperature data series and provide statistical estimation of temperature and isotopic variables such as annual averages, seasonal ranges, and seasonal extremes. Moreover, it allows evaluation of the influence of environmental and/or physiological noise added to the climate signal. Therefore, ClamR enables more comprehensive and justified comparison between paleoclimate reconstructions and modern instrument records and ultimately advances our interpretation of seasonal isotope records from biogenic hard parts.