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

Keen, Hayley F.; Gosling, William D.; Hanke, Felix; Miller, Charlotte S.; Montoya Romo, Encarni; Valencia, Bryan G. and Williams, Joseph J. (2014). A statistical sub-sampling tool for extracting vegetation community and diversity information from pollen assemblage data. Palaeogeography, Palaeoclimatology, Palaeoecology, 408 pp. 48–59.

DOI: https://doi.org/10.1016/j.palaeo.2014.05.001


Pollen assemblages are used extensively across the globe, providing information on various characteristics of the vegetation communities that originally produced them, and how these vary temporally and spatially. However, anticipating a statistically based robust pollen count size, sufficient to characterise a pollen assemblage is difficult; particularly with regard to highly diverse pollen assemblages. To facilitate extraction of ecologically meaningful information from pollen assemblage data, a two part statistical sub-sampling tool has been developed (Model 1 and 2), which determines the pollen count size required to capture major vegetation communities of varying palynological richness and evenness, and the count size required to find the next not yet seen pollen grain. The sub-sampling tools presented here facilitate the rapid assessment of individual pollen samples (initial information input of 100 pollen grains) and can, therefore, on a sample by sample basis achieve maximum effectiveness and efficiency. The sub-sampling tools are tested on fossil pollen data from five tropical sites.

Results demonstrate that Model 1 predicts count sizes relating to palynological richness and evenness consistently. Model 2 relates more closely to input count size, however, still considering the individual richness and evenness of a sample. Model 1 indicates that, for samples with a lower richness and higher evenness, to characterise major vegetation community components, 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). To detect additional taxa within the samples count sizes of between 1518 and 8553 were predicted, much higher than the ‘standard’ count size. We conclude that, given the temporal and spatial variation 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.

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