the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Apr 2025
Early Permian longitudinal position of the South China Block from brachiopod paleobiogeography
Abstract. Knowledge of the past location of tectonic plates is essential to understanding the evolution of climate, ocean systems, and mantle flow. Tectonic reconstructions become increasingly uncertain back in geological time. Paleomagnetic data constrain the past latitude of continental blocks, however, their past longitude is unconstrained. For example, the longitude of the South China Block during the Early Permian is unknown. Paleobiogeographic data, which have long been used in tectonic reconstructions, make it possible to evaluate the faunal similarity between continental blocks. In this study, we use the Early Permian global brachiopod distribution from the Paleobiology Database to evaluate the correlation between faunal similarity and physical distance of continental blocks for three distinct tectonic reconstruction models. We use this approach to assess which of the three tectonic scenarios places the South China Block in a location that best accounts for the Early Permian brachiopod distribution data. Based on this analysis, the preferred tectonic reconstruction places the South China Block in a central position within the Paleo-Tethys Ocean instead of on its outskirts. The framework developed in this study is openly available and our approach could be applied to other tectonic blocks, time periods, and faunal data.
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RC1: 'Comment on egusphere-2025-1018', Anonymous Referee #1, 28 Apr 2025
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This study presents a novel and ambitious approach to constrain the Early Permian longitudinal position of the South China Block (SCB) using brachiopod paleobiogeography, offering a creative solution to the longstanding challenge of reconstructing paleolongitude in deep-time tectonic models. By integrating quantitative faunal similarity indices (Jaccard, Simpson, and cME) with global plate reconstructions, the authors provide a framework that bridges paleobiology and geodynamics, marking a significant methodological advance. The conclusion that the SCB occupied a central position within the Paleo-Tethys Ocean (as per Young et al., 2019) challenges previous marginal placements and has implications for paleoceanographic and climatic interpretations. The open accessibility of the analytical framework further enhances its utility for future studies.
However, several uncertainties and limitations warrant caution. First, the reliance on brachiopod distribution assumes that faunal similarity inversely correlates with physical distance, yet environmental heterogeneity, larval dispersal barriers (e.g., landmasses, currents), and sampling biases (e.g., uneven fossil preservation/collection) could decouple this relationship. While the authors acknowledge these issues, the extent to which they influence the indices—particularly given the SCB’s disproportionately large dataset—remains unclear. For instance, the Jaccard index’s poor performance highlights the vulnerability of binary presence-absence metrics to sampling disparities, suggesting that results may overemphasize reconstruction Y19’s plausibility.
Second, the tectonic models themselves inherit uncertainties. The assumption of fixed LLSVPs in Matthews et al. (2016) versus their potential mobility in Young et al. (2019) reflects debated geodynamic hypotheses, yet the study does not fully disentangle how these contrasting assumptions propagate into the faunal-distance correlations. Additionally, the choice of 277 Ma as a representative time slice overlooks temporal dynamics within the ~27 Myr Early Permian, during which climatic shifts (e.g., deglaciation) and biotic turnover could skew biogeographic patterns.
A critical but unaddressed issue lies in the taxonomic accuracy of brachiopod genera extracted from the Paleobiology Database. Fossil identifications in large-scale databases are prone to errors due to misclassification, synonymies, or outdated taxonomy. For example, brachiopod genera with overlapping morphological features or poorly preserved specimens may be mis-assigned, directly distorting faunal similarity calculations. Such inaccuracies could artificially inflate or diminish correlations between biogeographic indices and physical distance. To strengthen the robustness of the analysis, future iterations of this framework should involve systematic re-evaluation of the brachiopod taxonomic data by domain experts to resolve ambiguities and validate species assignments. I believe some of the authors are brachiopod experts, not sure if they reviewed the taxonomy of the genera extracted from PBDB.
Lastly, the statistical approach—while rigorous—simplifies complex biogeographic processes into linear relationships. Nonlinear effects (e.g., threshold distances for provinciality) or geographic barriers (e.g., continental shelves) may distort correlations, particularly for marine taxa like brachiopods. The framework’s scalability to other taxa/periods, though promising, requires validation against independent datasets (e.g., paleomagnetic or stratigraphic constraints).
In summary, this work innovatively leverages paleobiogeography to address a critical gap in plate reconstructions, but the conclusions should be tempered by the inherent uncertainties in fossil data completeness, model assumptions, and temporal/spatial resolution. Future studies could strengthen the approach by incorporating multivariate biogeographic methods, higher-resolution time slices, cross-validation with geodynamic models that explicitly test LLSVP mobility, and rigorous taxonomic vetting of fossil datasets.
Citation: https://doi.org/10.5194/egusphere-2025-1018-RC1
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