The palaeobiological significance of clustering in acritarchs: a case study from the early Cambrian of North Greenland

Aggregated clusters of acritarchs are relatively common among assemblages of organic-walled microfossils, yet such associations have received relatively little attention. Here we report a new diversity of acritarch clusters from the early Cambrian Buen Formation of North Greenland. The aggregation patterns of four genera (Skiagia, Comasphaeridium, Asteridium, and Synsphaeridium) are described together with their background population characteristics (presence of openings, inner bodies, and overall disparity) in order to better understand the palaeobiology of these acritarchs. The majority of Skiagia clusters were found to be monospecific despite significant intraspecific variability, a pattern that is suggestive of a strong environmental influence on Skiagia morphologies and aggregation habits. Abundant small (< 20 µm) Comasphaeridium vesicles were recovered in a broad range of chiefly monospecific clusters that have likely been formed under bloom conditions. A colonial habit is tentatively inferred from the tightly packed appearance of monogeneric Asteridium aggregates, and clearly evidenced by the highly conserved cellular structure of recovered Synsphaeridium clusters. A lack of excystment structures in Comasphaeridium and Asteridium vesicles suggests these taxa represent actively growing cells rather than resting cysts. Altogether, these findings shed new light on the diversity of cellular structures and lifestyles represented among Cambrian acritarchs, and illustrate a range of reproduction and defence strategies adopted by plankton in the face of novel environmental pressures.

Methods
Acritarchs were measured and counted based on palynological slides prepared by Vidal and Peel (1993). The material was collected by John S. Peel on 16th June 1974 and processed using a standard acid maceration procedure (Vidal 1988). Measurements were performed using the software ImageJ.

The statistical significance of correlations was calculated using Spearman’s rank-based correlation coefficient rs (Press et al. 1992), a non-parametric alternative to Pearson’s r. Rs and p values were obtained using the software package PAST (Hammer and Harper 2006).

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