Harbert Robert S, Baryiames Alex A
Department of Biology Stonehill College 320 Washington Street North Easton Massachusetts 02357 USA.
American Museum of Natural History 79th Street and Central Park West New York New York 10024 USA.
Appl Plant Sci. 2020 Feb 13;8(2):e11322. doi: 10.1002/aps3.11322. eCollection 2020 Feb.
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.
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.
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.
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.
使用共存似然估计（CRACLE）方法进行气候重建分析，利用一套强大的建模工具，通过生物多样性数据的大型存储库和开源R软件，从植被中估计气候和古气候。
在此，我们实现了一个用于从现存和化石植被估计气候的新R包。“cRacle”包实现了数据访问、汇总和建模功能，以从植物群落组成估计气候。“cRacle”是模块化的，包含许多最佳实践特性。
使用来自北美洲和南美洲的现代植被调查数据进行的性能测试表明，CRACLE优于其他方法。当使用最佳模型参数时，CRACLE对年平均温度的估计通常在实际值的1°C范围内。广义增强回归（GBR）模型校正通过减少偏差改进了CRACLE估计。
CRACLE基于现代植物群落组成提供准确的气候估计。非参数CRACLE建模与GBR模型校正相结合产生了迄今为止最准确的结果。“cRacle”R包简化了从植物群落数据估计气候的过程，这将使更多用户能够使用这种建模方法。
