A new model for Early Paleozoic ichnostratigraphy based on trace fossil assemblages from Brazil
DOI | 10.1016/j.eve.2023.100026 |
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Aasta | 2023 |
Ajakiri | Evolving Earth |
Köide | 1 |
Leheküljed | 100026 |
Tüüp | artikkel ajakirjas |
Keel | inglise |
Id | 48414 |
Abstrakt
Trace fossils are not generally utilized as biostratigraphic indicators due to their long stratigraphic ranges. Despite the use of intricate behavioral traces in the absence of other indicators, existing models like the Precambrian–Cambrian boundary and Cruziana stratigraphy encounter limitations due to crucial data gaps and regional constraints. To surmount these challenges, in this paper, we critically assess established models and present a new framework for Early Paleozoic strata, drawing on trace fossils from the intracratonic basins of Brazil. Our ichnostratigraphic model is calibrated using ichnological data from the Parnaíba, Paraná, and Amazonas basins, including new data. The analysis focuses on trace fossils in strata that are independently dated using chitinozoan, miospore, and acritarch biozonation. Key ichnotaxa, such as Arthrophycus and Cruziana, are identified as prominent indicators of the Llandovery Stage in Brazil. Occurrences of Heimdallia and Musculopodus in the Tianguá Formation also may be used to suggest a Llandovery interval. Notably, Bifungites, found widely across Brazilian basins, emerges as a potential ichnomarker for the Early to mid-Paleozoic interval, with a global presence throughout Cambrian to Mississippian deposits. While current ichnostratigraphic models lack robust calibration with chronostratigraphic or biostratigraphic data, our new proposed model integrates key ichnotaxa, including Bifungites, Climactichnites, Heimdallia, Oldhamia, and Musculopodus, surpassing those pre-existing zonations based on Cruziana and arthrophycids. These ichnotaxa exhibit unique features and narrow temporal ranges, meeting essential biostratigraphic criteria. Although their spatial distribution is somewhat limited, our new model, which is continually evolving with new data, holds promise for enhancing global stratigraphic correlations, particularly where independent age information is available