Abstract

Realistic and accurate reconstructions of past vegetation cover are necessary to study past environmental changes. This is important since the effects of human land-use changes (e.g. agriculture, deforestation and afforestation/reforestation) on biodiversity and climate are still under debate. Over the last decade, development, validation, and application of pollen-vegetation relationship models have made it possible to estimate plant abundance from fossil pollen data at both local and regional scales. In particular, the REVEALS model has been applied to produce datasets of past regional plant cover at 1° spatial resolution at large subcontinental scales (North America, Europe, and China). However, such reconstructions are spatially discontinuous due to the discrete and irregular geographical distribution of sites (lakes and peat bogs) from which fossil pollen records have been produced. Therefore, spatial statistical models have been developed to create continuous maps of past plant cover using the REVEALS-based land cover estimates. In this paper, we present the first continuous time series of spatially complete maps of past plant cover across Europe during the Holocene (25 time windows covering the period from 11.7 k BP to present). We use a spatial-statistical model for compositional data to interpolate REVEALS-based estimates of three major land-cover types (LCTs), i.e., evergreen trees, summer-green trees and open land (grasses, herbs and low shrubs); producing spatially complete maps of the past coverage of these three LCTs. The spatial model uses four auxiliary data sets—latitude, longitude, elevation, and independent scenarios of past anthropogenic land-cover change based on per-capita land-use estimates (“standard” KK10 scenarios)—to improve model performance for areas with complex topography or few observations. We evaluate the resulting reconstructions for selected time windows using present day maps from the European Forest Institute, cross validate, and compare the results with earlier pollen-based spatially-continuous estimates for five selected time windows, i.e., 100 BP-present, 350–100 BP, 700–350 BP, 3.2–2.7 k BP, and 6.2–5.7 k BP. The evaluations suggest that the statistical model provides robust spatial reconstructions. From the maps we observe the broad change in the land-cover of Europe from dominance of naturally open land and persisting remnants of continental ice in the Early Holocene to a high fraction of forest cover in the Mid Holocene, and anthropogenic deforestation in the Late Holocene. The temporal and spatial continuity is relevant for land-use, land-cover, and climate research.

Highlights

  • Anthropogenically-induced climate change has been at the center of global interest for several decades

  • (sections 4, 5), the terms “reconstruction” and “reconstructed” refer to the spatial-statistical model-based land cover reconstructions unless it is specified that we refer to pollen-based REVEALS land-cover reconstructions

  • The values of the area of confidence region (CR-smaller values indicate higher confidence, see Figure 2 for an example.) show that the reconstructions of the remote areas in north-eastern and south-eastern Europe where we have no REVEALS data are of considerably lower quality than the rest of Europe

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Summary

Introduction

Anthropogenically-induced climate change has been at the center of global interest for several decades. Many mitigation strategies involve considerable changes in global land use, such as largescale afforestation of former agrarian lands as well as natural and semi-natural grasslands in order to increase the carbon uptake and storage capacity of terrestrial land cover (Law et al, 2018; Forster et al, 2021). The quantitative reconstructions of past land cover are an important information source for disentangling natural and anthropogenic components of land cover change, which in turn help determine the effects of long-term land use on Earth’s climate and predict the possible implications of the intended large-scale afforestation effort (Roberts et al, 2018)

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