Testing quantitative pollen dispersal models in animal-pollinated vegetation mosaics: An example from temperate Tasmania, Australia. (15th December 2016)

• Record Type: Journal Article
• Authors: Mariani, M.; Connor, S.E.; Theuerkauf, M.; Kuneš, P.; Fletcher, M.-S.
• Is Part Of: Quaternary science reviews. Volume 154(2016)
• Page Start: 214
• Page End: 225
• Publication Date: 2016-12-15
• Subjects: Palynology -- Pollen dispersal -- Gaussian plume model -- Lagrangian stochastic model -- Tasmania -- Australia -- Animal-pollinated -- Zoophilous -- Anemophilous; Geology, Stratigraphic -- Quaternary -- Periodicals; Stratigraphie -- Quaternaire -- Périodiques; 551.79
• DOI: 10.1016/j.quascirev.2016.10.020
• Languages: English
• ISSNs: 0277-3791

Abstract

Abstract: Reconstructing past vegetation abundance and land-cover changes through time has important implications in land management and climate modelling. To date palaeovegetation reconstructions in Australia have been limited to qualitative or semi-quantitative inferences from pollen data. Testing pollen dispersal models constitutes a crucial step in developing quantitative past vegetation and land cover reconstructions. Thus far, the application of quantitative pollen dispersal models has been restricted to regions dominated by wind-pollinated plants (e.g. Europe) and their performance in a landscape dominated by animal-pollinated plant taxa is still unexplored. Here we test, for the first time in Australia, two well-known pollen dispersal models to assess their performance in the wind- and animal-pollinated vegetation mosaics of western Tasmania. We focus on a mix of wind- (6 taxa) and animal- (7 taxa) pollinated species that comprise the most common pollen types and key representatives of the dominant vegetation formations. Pollen Productivity Estimates and Relevant Source Area of Pollen obtained using Lagrangian Stochastic turbulent simulations appear to be more realistic when compared to the results from the widely used Gaussian Plume Model. Highlights: First-ever quantification of pollen productivity in Australia. Choice of dispersal model has strong bearing on estimated pollen productivity. Wind dispersal models shown to be applicable to animal-pollinated taxa. Better performance of the Lagrangian Stochastic Model in our case-study. Results extend land-cover reconstruction methods to the Southern Hemisphere.