A statistical sub-sampling tool for extracting vegetation community and diversity information from pollen assemblage data

Results demonstrate that Model 1 predicts count sizes relating to palynological richness and evenness consistently. To characterise major vegetation community components model 1 indicates that, for samples with a lower richness and higher evenness lower count sizes than are considered standard can be used (< 300, e.g. 122); however, for samples of high richness and low evenness, higher count sizes are required (> 300, e.g. 870). Model 2 calculates the additional number of pollen grains needed to be counted to detect the next not yet seen pollen taxa, outputs were strongly related to input data count size as well as richness and evenness characteristics. We conclude that, given the temporal and spatial variations in vegetation communities and also pollen assemblages, pollen count sizes should be determined for each individual sample to ensure that effective and efficient data are generated and that detection of rare taxa is checked iteratively throughout the counting process.