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一种新的化石组合共现网络分析方法。

A new method for examining the co-occurrence network of fossil assemblages.

机构信息

College of Life Sciences, Capital Normal University, Beijing, 100048, PR China.

Department of Bioinformatics, Freshwind Biotechnology (Tianjin) Limited Company, Tianjin, 300301, PR China.

出版信息

Commun Biol. 2023 Oct 31;6(1):1102. doi: 10.1038/s42003-023-05417-6.

DOI:10.1038/s42003-023-05417-6
PMID:37907587
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10618518/
Abstract

Currently, studies of ancient faunal community networks have been based mostly on uniformitarian and functional morphological evidence. As an important source of data, taphonomic evidence offers the opportunity to provide a broader scope for understanding palaeoecology. However, palaeoecological research methods based on taphonomic evidence are relatively rare, especially for body fossils in lacustrine sediments. Such fossil communities are not only affected by complex transportation and selective destruction in the sedimentation process, they also are strongly affected by time averaging. Historically, it has been believed that it is difficult to study lacustrine entombed fauna by a small-scale quadrat survey. Herein, we developed a software, the TaphonomeAnalyst, to study the associational network of lacustrine entombed fauna, or taphocoenosis. TaphonomeAnalyst allows researchers to easily perform exploratory analyses on common abundance profiles from taphocoenosis data. The dataset for these investigations resulted from fieldwork of the latest Middle Jurassic Jiulongshan Formation near Daohugou Village, in Ningcheng County of Inner Mongolia, China, spotlighting the core assemblage of the Yanliao Fauna. Our data included 27,000 fossil specimens of animals from this deposit, the Yanliao Fauna, whose analyses reveal sedimentary environments, taphonomic conditions, and co-occurrence networks of this highly studied assemblage, providing empirically robust and statistically significant evidence for multiple Yanliao habitats.

摘要

目前,对古代动物群网络的研究主要基于均变论和功能形态学证据。作为数据的重要来源,埋藏学证据为理解古生态学提供了更广泛的机会。然而,基于埋藏学证据的古生态学研究方法相对较少,尤其是对于湖泊沉积物中的体化石。这些化石群落不仅受到沉积过程中复杂的搬运和选择性破坏的影响,还受到时间平均化的强烈影响。历史上,人们认为通过小规模的样方调查来研究湖泊埋藏动物群是困难的。在这里,我们开发了一个软件,即 TaphonomeAnalyst,用于研究湖泊埋藏动物群或埋藏组合的关联网络。TaphonomeAnalyst 允许研究人员轻松地对埋藏组合数据中的常见丰度分布进行探索性分析。这些研究的数据集来自中国内蒙古宁城道虎沟村附近最新的中侏罗世九龙山组的野外工作,重点是燕辽动物群的核心组合。我们的数据包括来自这个沉积物的 27000 个动物化石标本,即燕辽动物群,其分析揭示了这个高度研究的组合的沉积环境、埋藏条件和共存网络,为多个燕辽栖息地提供了经验上可靠和统计上显著的证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/2ff6a75fe23a/42003_2023_5417_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/b8a13748f533/42003_2023_5417_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/f6933b9e9569/42003_2023_5417_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/648908e6df9e/42003_2023_5417_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/4fb10681f6b2/42003_2023_5417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/625f94389e74/42003_2023_5417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/289233a120e9/42003_2023_5417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/9b5558aa1557/42003_2023_5417_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/2ff6a75fe23a/42003_2023_5417_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/b8a13748f533/42003_2023_5417_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/f6933b9e9569/42003_2023_5417_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/648908e6df9e/42003_2023_5417_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/4fb10681f6b2/42003_2023_5417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/625f94389e74/42003_2023_5417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/289233a120e9/42003_2023_5417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/9b5558aa1557/42003_2023_5417_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f0c/10618518/2ff6a75fe23a/42003_2023_5417_Fig8_HTML.jpg

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