Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale

被引:2
|
作者
Smyth, Timothy [1 ]
Moffat, David [1 ]
Tarran, Glen [1 ]
Sathyendranath, Shubha [1 ]
Ribalet, Francois [2 ]
Casey, John [3 ,4 ]
机构
[1] Plymouth Marine Lab, Prospect Pl, Plymouth, England
[2] Univ Washington, Ctr Environm Genom, Sch Oceanog, Seattle, WA USA
[3] Univ Hawaii, Daniel K Inouye Ctr Microbial Oceanog, Sch Ocean & Earth Sci & Technol, Honolulu, HI USA
[4] Lawrence Livermore Natl Lab, Biosci & Biotechnol Div, Livermore, CA USA
基金
英国自然环境研究理事会;
关键词
flow cytometry; carbon; chlorophyll; machine learning; random forests; Atlantic Ocean; phytoplankton; physiology; REFRACTIVE-INDEX; PROCHLOROCOCCUS ECOTYPES; DIEL VARIATIONS; ARABIAN SEA; GROWTH-RATE; OCEAN; SIZE; LIGHT; SYNECHOCOCCUS; PICOPLANKTON;
D O I
10.3389/fmars.2023.1191216
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We demonstrate the ability of flow cytometry to determine species specific cellular carbon and chlorophyll content in vivo by using laboratory cultures of phytoplankton encompassing a wide range of cell sizes. When applied to the large Atlantic Meridional Transect flow cytometry dataset, we reveal patterns in the species-specific phytoplankton carbon (C), chlorophyll (Chl) and C:Chl ratio. For Prochlorococcus the range of C:Chl is between 2 - 604; for Synechococcus 0.5 - 558. Using a Random Forest machine learning approach, we show that predictability of phytoplankton C:Chl, dominated by the prevalence of Prochlorococcus, is largely driven by silicate and nitrite concentration in the Atlantic Ocean.
引用
收藏
页数:14
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