Abstract

Environmental reconstructions based on fossil pollen rely on the understanding of modern pollen distribution along climatic and biogeographic gradients. This study analyses the modern pollen spectra of Central America using three basic approaches: (1) the evaluation of using modern pollen spectra to differentiate the main vegetation types of the region, (2) the usage of non-linear regression to predict individual pollen abundances as a function of climate, and (3) the construction of pollen—climate transfer functions. Standard pollen analysis was carried out on mud—water interface samples from 81 lakes in the Yucatan Peninsula and adjacent mountains of Guatemala and Mexico. Detrended correspondence and cluster analyses were used to evaluate the biogeographic patterns revealed by this modern pollen data set. Non-parametric locally weighted scatterplot smoothing (LOESS) regression was used to construct pollen—climate functional relationships. Five modern vegetation types were clearly identifiable through their associated pollen spectra: Pinus forest, Quercus forest, mountain mesophyllous forest, tropical rainforest, and tropical seasonal forest. The last group includes three subcategories (evergreen seasonal, tropical semi-deciduous, and tropical deciduous forests), which were not separable via this analysis. Precipitation and temperature trends were consistent and robust for at least 28 and 30 taxa, respectively, in the LOESS regression. While floristic patterns driven by temperature were clearly reflected by the pollen spectra, those driven by precipitation were less sharply defined. Nevertheless, pollen data from the study area offered good resolution to identify broad biogeographic patterns. Furthermore, individual taxa showed high predictability along precipitation and temperature gradients, allowing the theoretical construction of pollen—climate transfer functions. This study provided valuable tools for the interpretation of fossil pollen sequences from Central America.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.