Size normalizing planktonic Foraminifera abundance in the water column

IF 2.1 3区 地球科学 Q2 LIMNOLOGY
Sonia Chaabane, Thibault de Garidel-Thoron, Xavier Giraud, Julie Meilland, Geert-Jan A. Brummer, Lukas Jonkers, P. Graham Mortyn, Mattia Greco, Nicolas Casajus, Michal Kucera, Olivier Sulpis, Azumi Kuroyanagi, Hélène Howa, Gregory Beaugrand, Ralf Schiebel
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Abstract

Planktonic Foraminifera have been collected from the water column with different plankton sampling devices equipped with nets of various mesh sizes, which impedes direct comparison of observed quantifications. Here, we use data on the community size structure of planktonic Foraminifera to assess the impact of mesh size on the measured abundance (ind m−3) of planktonic Foraminifera. We use data from the FORCIS database (Chaabane et al., 2023, Scientific Data 10: 354) on the global ocean at different sampling depths over the past century. We find a global cumulative increase in abundance with size, which is best described using a Michaelis–Menten function. This function yields multiplication factors by which one size fraction can be normalized to any other size fraction equal to or larger than 100 μm. The resulting size normalization model is calibrated over a range of different depth intervals, and validated with an independent dataset from various depth ranges. The comparison to Berger's (1969, Deep. Res. Oceanogr. Abstr. 16: 1–24) equivalent catch approach shows a significant increase in the predictive skill of the model. The new size normalization scheme enables comparison of Foraminifera abundance data sampled with plankton nets of different mesh sizes, such as compiled in the FORCIS database. The correction methodology may be effectively employed for various other plankton groups such as diatoms and dinoflagellates.

Abstract Image

水体中浮游有孔虫丰度的大小正常化
浮游有孔虫是通过不同的浮游生物采样装置从水体中采集的,这些采样装置配备了不同网目尺寸的网,这妨碍了对观测到的数量进行直接比较。在此,我们利用浮游有孔虫群落大小结构的数据来评估网目大小对浮游有孔虫测量丰度(ind m-3)的影响。我们使用了 FORCIS 数据库(Chaabane 等,2023 年,科学数据 10:354)中上个世纪全球海洋不同采样深度的数据。我们发现,全球丰度随大小的累积增加,这可以用迈克尔-门顿函数(Michaelis-Menten function)得到最好的描述。该函数可产生乘法因子,通过这些乘法因子,可将一个尺寸分数归一化为等于或大于 100 μm 的任何其他尺寸分数。由此产生的粒度归一化模型在一系列不同的深度区间内进行了校准,并用来自不同深度区间的独立数据集进行了验证。与 Berger(1969 年,Deep. Res. Oceanogr. Abstr.新的尺寸归一化方案可以比较用不同网目尺寸的浮游生物网采样的有孔虫丰度数据,如 FORCIS 数据库中的数据。这种校正方法也可有效地用于硅藻和甲藻等其他浮游生物类群。
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来源期刊
CiteScore
4.80
自引率
3.70%
发文量
56
审稿时长
3 months
期刊介绍: Limnology and Oceanography: Methods (ISSN 1541-5856) is a companion to ASLO''s top-rated journal Limnology and Oceanography, and articles are held to the same high standards. In order to provide the most rapid publication consistent with high standards, Limnology and Oceanography: Methods appears in electronic format only, and the entire submission and review system is online. Articles are posted as soon as they are accepted and formatted for publication. Limnology and Oceanography: Methods will consider manuscripts whose primary focus is methodological, and that deal with problems in the aquatic sciences. Manuscripts may present new measurement equipment, techniques for analyzing observations or samples, methods for understanding and interpreting information, analyses of metadata to examine the effectiveness of approaches, invited and contributed reviews and syntheses, and techniques for communicating and teaching in the aquatic sciences.
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