cRacle

cRacle estimates climate and paleoclimate parameters from plant community composition using the CRACLE (Climate Reconstruction Analysis using Coexistence Likelihood Estimation) coexistence likelihood approach.

Key Features:

• Modular Design: Modular functions to access, aggregate, and analyze plant community composition data.
• Data Integration: Integrates large repositories of biodiversity datasets to inform climate estimation.
• Modeling Functions: Includes functions to estimate climate parameters from vegetation data, with an emphasis on mean annual temperature (MAT).
• Performance and Accuracy: Performance tests using modern vegetation survey data from North and South America indicate CRACLE outperforms alternative methods and, with optimal parameters, MAT estimates are typically within 1°C of observed values.
• Bias Reduction: Incorporates Generalized Boosted Regression (GBR) model correction to reduce bias in climate estimates.

Scientific Applications:

• Climate Reconstruction: Reconstruction of past climates using current and historical plant community data.
• Paleoclimate Studies: Estimation of paleoclimates from vegetation records to infer historical climate patterns and impacts on biodiversity.
• Ecological Research: Analysis of relationships between plant communities and climatic conditions for ecological modeling and conservation studies.

Methodology:

The method uses a non-parametric CRACLE coexistence likelihood estimation coupled with Generalized Boosted Regression (GBR) model correction.