LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 kyr

Abstract

With rapid anthropogenic climate change, future vegetation trajectories are uncertain. Climate-vegetation models can be useful for predictions, but they require extensive data on past vegetation for validation and improving systemic understanding. Even though pollen data provide a great source of this information, they are compositionally biased due to differences in taxon-specific relative pollen productivity (RPP) and dispersal. Here, we present a Northern Hemisphere reconstruction of quantitative regional vegetation cover from a sedimentary pollen dataset for the last 14 kyr using the REVEALS model to correct for taxon- and basin-specific biases. For the reconstruction, we expanded on a previously published synthesis of continental RPP values. The datasets include the taxonomic composition and reconstructed tree cover for each original pollen sample. Additional metadata include modeled ages, age model sources, basin locations, types, and sizes. The improvements in tree cover reconstructions with the REVEALS reconstruction using continental RPP values range from 22 % (Asia) to 67 % (Europe) relative to the mean absolute error (MAE) of the pollen-based tree cover. The dataset can be used as a grid with binned and aggregated samples (adjustable script provided on Zenodo at 10.5281/zenodo.13902976, ) or as individual time series if the record's basin size exceeds 50 ha. This alternative quantitative reconstruction of vegetation cover is beneficial for the investigation of past vegetation dynamics and modern model validation when varying spatial and temporal resolutions may be required. By collecting more RPP estimates, especially in North America, and adding more records to existing pollen data syntheses, reconstructions may be improved even further. The new REVEALS dataset is freely available on PANGAEA (see the Code and data availability section; https://doi.org/10.1594/PANGAEA.974798, ).