Reconstructing past biomes states using machine learning and modern pollen assemblages: A case study from Southern Africa

Abstract

Fossil pollen assemblages can assist in understanding biome responses to global climate change if there is reasonable probability that they represent specific biomes or bioregions. In this paper, we introduce a novel probabilistic presentation of pollen data and biome assignment. We apply a recently developed pollen-based vegetation classification method utilizing supervised machine learning to Southern Africa modern pollen assemblages. We present an updated modern pollen dataset from Southern Africa, linking the sites to previously defined vegetation units and, ultimately, we generate probabilistic classification for fossil assemblages to reconstruct past vegetation.The modern pollen dataset (N = 211 sites) represents a long vegetation gradient, from desert to forest biomes, capturing broad climate gradients ranging from arid to subtropical. We validate two models using Random Forest algorithm to classify modern vegetation at different spatial resolutions: subcontinental (biomes) and regional (bioregions). When the modern pollen assemblages (N = 164 sites) are used to predict the vegetation types, the classification models are correct in a number of cases. In our dataset of 164 sites, the classification model correctly classifies pollen assemblages from savanna (91% correct), grassland (87%), and coastal forest (82%) vegetation types, while the best results for classification of regional vegetation are achieved for sub-humid savanna (95%), dry savanna (95%), coastal forest (91%), and wet grassland (90%).We apply the models to a fossil pollen sequence at Wonderkrater in the South African savanna, to reconstruct subcontinental and regional changes in past vegetation states over the last 60 000 years. The most probable vegetation state dominating the region since the Late Pleistocene is sub-humid savanna yet grassland occurred at times associated with high vegetation variability. Within the record, the most frequent and amplified variability in the inferred vegetation states occurred during the transitional phase between the Late Pleistocene and the Holocene. The machine learning approach for reconstructing past vegetation, offers a more complex and nuanced view of past vegetation dynamics and has the potential to support quantitative proxy-based techniques for palaeoclimatic reconstructions.