Data from: Morphometric analysis of Skiagia-plexus acritarchs from the early Cambrian of North Greenland
Wallet, Elise; Willman, Sebastian; Slater, Ben J. (2022), Data from: Morphometric analysis of Skiagia-plexus acritarchs from the early Cambrian of North Greenland, Dryad, Dataset, https://doi.org/10.5061/dryad.zs7h44jbv
The Cambrian evolutionary radiations are marked by spectacular biotic turnovers and the establishment of increasingly tiered food chains. At their base are primary producers, which in the Cambrian fossil record are chiefly represented among organic-walled microfossils. The majority of these microfossil remains have traditionally been attributed to an informal category of incertae sedis called “acritarchs”, based entirely on form taxonomy. Acritarch form-taxa have been intensely used for biostratigraphy, and in large-scale studies of phytoplankton diversity. However, both prospects have been challenged by cases of taxonomic inconsistencies and over-splitting arising from the large phenotypic plasticity seen among these microfossils. The acritarch form-genus Skiagia stands as an ideal case-study to explore these taxonomic challenges, since it encompasses a number of form-species widely used in lower Cambrian biostratigraphy. Moreover, subtle morphological differences among Skiagia species were suggested to underlie key evolutionary innovations towards complex reproduction strategies. Here we apply a multivariate morphometric approach to investigate the morphological variation of Skiagia-plexus acritarchs using an assemblage sourced from the Buen Formation (Cambrian Series 2, Stage 3–4) of North Greenland. Our analysis showed that the specific-level classification of Skiagia discretizes a continuous spectrum of morphologies. While these findings bring important taxonomic and biostratigraphic hurdles to light, the unequal frequency distribution of life cycle stages among Skiagia species suggests that certain elements of phytoplankton paleobiology are nonetheless captured by Skiagia form-taxonomy. These results demonstrate the value of using morphometric tools to explore acritarch phenotypic plasticity and its potential ontogenetic and paleoecological drivers in Cambrian ecosystems.
Three fine-grained samples (labelled 184002, 184003, 184004) from the early Cambrian of North Greenland (Brillesø locality, Skiagia ciliosa‒Heliosphaeridium dissimilare Zone) have been collected by Grønlands Geologiske Undersøgelse in 1974. Nine palynological preparations were produced by Vidal and Peel (1993) using a standard HF acid maceration procedure (Vidal 1988).
All 9 palynological preparations yielded acritarchs attributable to five species (S. scottica, S. ciliosa, S. orbiculare, S. compressa, S. ornata) that were counted and measured from photomicrographs by E. Wallet. A total of 10 continuous parameters capturing the shape of processes and size of the central body were measured for each Skiagia species. This dataset was used as a basis for principal component and discriminant analyses. A seprate dataset was compiled to include counts of discrete features related to various stages in the life cycle of Skiagia.
Measurements were performed using the free software Image J; and multivariate analyses and plots were computed using the free software package PAST.
More information on methods and measured parameters can be found in Wallet et al. 2022.
Vidal, G. 1988: A palynological preparation method. Palynology 12:215–220.
Vidal, G., and J. S. Peel. 1993: Acritarchs from the Lower Cambrian Buen Formation in North Greenland. Grønlands Geologiske Undersøgelse Bulletin 164:1–35.
Wallet, E., Willman, S. and Slater, B. J. 2022: Morphometric analysis of Skiagia-plexus acritarchs from the early Cambrian of North Greenland: towards a meaningful evaluation of phenotypic plasticity. Paleobiology.
The dataset is divided into two folders:
- Supplementary files cited in Wallet et al. 2022:
- Captions for these figures and tables can be found in the file [Wallet_et_al._2022_Supplementary_Figures_and_tables_captions.docx].
- Raw data:
Information about raw data, collection/methods data and overall content of the dataset can be found in [README_data_from_Wallet_et_al._2022.txt].