Data from: Heterogeneous palaeo-ecogeography of brachiopods during the Late Ordovician mass extinction in South China
Data files
Oct 14, 2024 version files 5.55 KB
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Abundance_data_for_models.csv
1.19 KB
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Occurrences_data_for_NMDS_and_NA.csv
2.50 KB
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README.md
1.86 KB
Abstract
Following the first phase of the Late Ordovician Mass Extinction (LOME), the globally distributed Hirnantia brachiopod fauna exhibited homogeneity both latitudinally and longitudinally. The uniform global paleobiogeography of the Hirnantia Fauna, solely based on occurrence data, might obscure its heterogeneous nature during glaciation. In this study, leveraging diversity and abundance data from well-sampled collections in South China, along with NMDS using the Raup-Crick measure, Network Analysis (NA), and abundance models, we unveil the paleo-ecogeography of the Hirnantia Fauna for the first time. The distribution of the Hirnantia Fauna in South China is categorized into three areas: the deep water area, the shallower water area, and the very shallow water area based on NMDS and NA. Drastically different benthic assemblages among these areas reveal diverse environmental settings, with a rough westward shallower trend and a deeper trend extending both northward and southeastward. Analysis of species-abundance models for eight well-sampled collections demonstrates different best-fitted models, suggesting competitive ecology operates in varied contexts despite common BA 3 environmental settings and close geographic proximity. While a substantial global sea level falling and climate cooling around the Katian-Hirnantian boundary plays important roles in the LOME, the paleo-ecogeography of the Hirnantia Fauna in South China is predominantly influenced by the expansion of the Cathaysian oldland and the Qianzhong uplift. The interval between the two LOME phases, marked by kinetic conditions, witnessed heterogeneous thriving of the Hirnantia Fauna.
https://doi.org/10.5061/dryad.1vhhmgr33
There are two datasets files: “Occurrences_data_for_NMDS_and_NA.csv” based on occurrence information and “Abundance_data_for_models.csv” on abundance data. For the occurrence dataset of Hirnantian brachiopod fauna from South China, we aggregated adjacent localities into a unified palaeogeographical analysis unit to balance the distribution of the occurrence data. Consequently, our analysis involved a total of 22 distinct units. The abundance data is derived from 9070 specimens collected from 8 representative sections of the Hirnantia Fauna.
Description of the data and file structure
Occurrences_data_for_NMDS_and_NA.csv
includes occurrence information in binary data, with palaeogeographic units as rows and brachiopod genera as columns. This dataset involves 41 genera and 22 units. Please note that this is 0-1 data, and when using it, especially for calculating similarity matrices, it is important to choose algorithms appropriate for binary data.
Abundance_data_for_models.csv
contains specimen count information of brachiopod species from 8 selected representative sections, involving a total of 9070 specimens. Relevant abundance model analyses should be performed based on an assessment of sampling sufficiency. It is recommended to use Chao-1 diversity estimation.
Sharing/Access information
n/a
Code/Software
We also built species-abundance models using R programming with the Vegan package to test the abundance models for these sections. The R script uploaded is designed to analyze species abundance data by fitting species abundance models to different collections of data and then plotting the results.
There are two datasets: one based on occurrence information and the other on abundance data.For the occurrence dataset of Hirnantian brachiopod fauna from South China, we aggregated adjacent localities into a unified palaeogeographical analysis unit to balance the distribution of the occurrence data. Consequently, our analysis involved a total of 22 distinct units.The abundance data is derived from 9070 specimens collected from 8 representative sections of the Hirnantia Fauna.
We conducted the palaeobiogeographic study using NMDS with PAST (version 4.01) and network analysis with Gephi (version 0.10) for the 22 occurrence data units. Additionally, we used rarefaction analysis with PAST (version 4.01) to test the sampling sufficiency of the 8 representative sections. We also built species-abundance models using R programming with the Vegan package to test the abundance models for these sections.