ABSTRACT
PREMISE: The Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE) method utilizes a robust set of modeling tools for estimating climate and paleoclimate from vegetation using large repositories of biodiversity data and open access R software. METHODS: Here, we implement a new R package for the estimation of climate from extant and fossil vegetation. The 'cRacle' package implements functions for data access, aggregation, and modeling to estimate climate from plant community compositions. 'cRacle' is modular and includes many best-practice features. RESULTS: Performance tests using modern vegetation survey data from North and South America shows that CRACLE outperforms alternative methods. CRACLE estimates of mean annual temperature are usually within 1°C of the actual values when optimal model parameters are used. Generalized boosted regression (GBR) model correction improves CRACLE estimates by reducing bias. DISCUSSION: CRACLE provides accurate estimates of climate based on the composition of modern plant communities. Non-parametric CRACLE modeling coupled with GBR model correction produces the most accurate results to date. The 'cRacle' R package streamlines the estimation of climate from plant community data, which will make this modeling more accessible to a wider range of users.
