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全球海洋沉积物生物扰动强度、通风率和混合深度的测量。

Worldwide measurements of bioturbation intensity, ventilation rate, and the mixing depth of marine sediments.

机构信息

Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Waterfront Campus, European Way, Southampton, SO14 3ZH, UK.

Department of Ocean Sciences, Memorial University of Newfoundland, St. John's, Newfoundland, NL, Canada.

出版信息

Sci Data. 2019 May 13;6(1):58. doi: 10.1038/s41597-019-0069-7.

DOI:10.1038/s41597-019-0069-7
PMID:31086191
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6513814/
Abstract

The activities of a diverse array of sediment-dwelling fauna are known to mediate carbon remineralisation, biogeochemical cycling and other important properties of marine ecosystems, but the contributions that different seabed communities make to the global inventory have not been established. Here we provide a comprehensive georeferenced database of measured values of bioturbation intensity (Db, n = 1281), burrow ventilation rate (q, n = 765, 47 species) and the mixing depth (L, n = 1780) of marine soft sediments compiled from the scientific literature (1864-2018). These data provide reference information that can be used to inform and parameterise global, habitat specific and/or species level biogeochemical models that will be of value within the fields of geochemistry, ecology, climate, and palaeobiology. We include metadata relating to the source, timing and location of each study, the methodology used, and environmental and experimental information. The dataset presents opportunity to interrogate current ecological theory, refine functional typologies, quantify uncertainty and/or test the relevance and robustness of models used to project ecosystem responses to change.

摘要

多种底栖动物的活动被认为可以调节碳的再矿化、生物地球化学循环和海洋生态系统的其他重要特性,但不同海底群落对全球碳储量的贡献尚未确定。在这里,我们提供了一个综合的、具有地理位置参考的海洋软沉积物生物搅动强度(Db,n=1281)、洞穴通风率(q,n=765,47 种)和混合深度(L,n=1780)测量值的综合数据库,这些数据是从科学文献中汇编而来的(1864-2018 年)。这些数据提供了参考信息,可以用来为全球、特定生境和/或物种水平的生物地球化学模型提供信息和参数,这些模型在地球化学、生态学、气候学和古生物学领域将具有重要价值。我们还包括了与每个研究的来源、时间和地点、使用的方法以及环境和实验信息有关的元数据。该数据集提供了一个机会,可以对当前的生态理论进行检验,完善功能类型学,量化不确定性,或测试用于预测生态系统对变化反应的模型的相关性和稳健性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/6513814/f82253a5ac4f/41597_2019_69_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/6513814/f82253a5ac4f/41597_2019_69_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/6513814/f82253a5ac4f/41597_2019_69_Fig1_HTML.jpg

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