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Coccolith mass is an important parameter for estimating coccolithophore contribution to carbonate sedimentation, organic carbon ballasting and coccolithophore calcification. Single coccolith mass is often estimated based on the ks model, which assumes that length and thickness increase proportionally. To evaluate this assumption, this study compared coccolith length, thickness, and mass of seven Emiliania huxleyi strains and one Gephyrocapsa oceanica strain grown in 25, 34, and 44 salinity artificial seawater. While coccolith length increased with salinity in four E. huxleyi strains, thickness did not increase significantly with salinity in three of these strains. Only G. oceanica showed a consistent increase in length with salinity that was accompanied by an increase in thickness. Coccolith length and thickness was also not correlated in 14 of 24 individual experiments, and in the experiments in which there was a positive relationship r2 was low (<0.4). Because thickness did not increase with length in E. huxleyi, the increase in mass was less than expected from the ks model, and thus, mass can not be accurately estimated from coccolith length alone.
Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features.