Quantified Reconstructions of late Quaternary southern African Climate Change

Abstract

Southern African drylands are not suited for the application of climate reconstruction methods based on surface samples. Methods based on the co-existence approach, while still in their early days, are really promising, particularly those using probability density functions (pdfs) that have proven particularly valuable in certain cases as they can be applied to a wide range of plants assemblages. Most commonly applied to fossil pollen data, their performance can be limited by the taxonomic resolution of the pollen data, as many species may belong to a given pollen-type. Consequently, climate information associated with a taxon cannot always be precisely identified, resulting in less accurate reconstructions. This can become particularly problematic in regions of high biodiversity, such as southern African botanical hotspots. The first part of this PhD thesis presents the development of a novel pdf-based climate reconstruction method adapted to the southern African context. The method, which comes along with a dedicated software pack- age entitled CREST, sorts out this diversity issue by taking into account the different climatic requirements of each species constituting the broader pollen-type: pdfs are fitted in two successive steps, with parametric univariate pdfs fitted first for each species (pdfsp) followed by a combination of those individual species pdfs into a broader single pdf to represent the pollen-type as a unit (pdfpol). The curve resulting from the multiplication of the pdfpol describes the likelihood of different climate parameters based on the co-existence of a given set of taxa, each being weighted according to its normalized pollen percentage. Three majors properties were derived from this continental-scale statistical analysis: 1) the method saturates when the number of species composing a pollen type becomes larger than 30-40 species, 2) the per- formance decreases with distance to the core of the climatic space and 3) climate vari