Satellite ocean colour algorithm for Prochlorococcus, Synechococcus, and picoeukaryotes concentration retrieval in the South China Sea

Morozov, Evgeny; Tang, Danling

doi:10.1016/j.asr.2018.07.005

Cited by 6 publications

(13 citation statements)

References 45 publications

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“…HL ecotypes were able to express chlorophyll at similar levels as LL ecotypes in order to photoacclimate to low light intensities, demonstrating a physiological plasticity that was unexpected based on previously published laboratory experiments. These results emphasize the importance of incorporating the process of photoacclimation into prediction of phytoplankton biomass from chlorophyll concentrations (Behrenfeld et al, 2016;Lange et al, 2018;Morozov and Tang, 2018), as we show that chlorophyll properties of the subtropical ocean's dominant phototroph are highly dynamic in space and time.…”

Section: Resultssupporting

confidence: 59%

“…The dim and bright populations are reminiscent of the HL/LL ecotype dichotomy but their genetic identity has not been directly linked to their in situ chlorophyll physiology, despite the many studies of Prochlorococcus ecotype distribution mentioned above. The relationships between Prochlorococcus chlorophyll concentration in situ, ecotype community structure, and productivity remain unknown but is critical for measuring and modeling Prochlorococcus contributions to global cycles under different oceanic scenarios (Follows et al, 2007;Lange et al, 2018;Morozov and Tang, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of Prochlorococcus Diversity and Photoacclimation During Short-Term Shifts in Water Column Stratification at Station ALOHA

Thompson

Engh²,

Ahlgren

et al. 2018

Front. Mar. Sci.

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The cyanobacterium Prochlorococcus is the dominant phototroph in surface waters of the vast oligotrophic oceans, the foundation of marine food webs, and an important component of global biogeochemical cycles. The prominence of Prochlorococcus across the environmental gradients of the open ocean is attributed to its extensive genetic diversity and flexible chlorophyll physiology, enabling light capture over a wide range of intensities. What remains unknown is the balance between temporal dynamics of genetic diversity and chlorophyll physiology in the ability of Prochlorococcus to respond to a variety of short (approximately 1 day) and longer (months to year) changes in the environment. Previous field research established depth-dependent Prochlorococcus single cell chlorophyll distributions in the North Pacific Subtropical Gyre. Here, we examined whether the shifts in chlorophyll distributions correspond to changes in Prochlorococcus genetic diversity (i.e., ecotype-level community structure) or photoacclimation of stable communities over short time intervals. We report that community structure was relatively stable despite abrupt shifts in Prochlorococcus chlorophyll physiology, due to unexpected physiological plasticity of high-light adapted Prochlorococcus ecotypes. Through comparison with seasonalscale changes, our data suggest that variability on daily scales triggers shifts in Prochlorococcus photoacclimation, while seasonal-scale dynamics trigger shifts in community structure. Together, these data highlight the importance of incorporating the process of photoacclimation and chlorophyll dynamics into interpretations of phytoplankton population dynamics from chlorophyll data as well as the importance of daily-scale variability to Prochlorococcus ecology.

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Section: Resultssupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Dynamics of Prochlorococcus Diversity and Photoacclimation During Short-Term Shifts in Water Column Stratification at Station ALOHA

Thompson

Engh²,

Ahlgren

et al. 2018

Front. Mar. Sci.

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show abstract

“…The algorithm outputs are the four biological response variables (Chl a and abundances of Prochlorococcus, Synechococcus, and picoeukaryotes), which were log-transformed before analysis to achieve normal distributions (Fig. S1; Morozov and Tang 2019;Mattei and Scardi 2020). The algorithm inputs include 7 predictors: latitude (Lat) and longitude (Lon), a cosine transformation of the Date of the Year (DOY; 𝑡 = cos 2𝜋 𝐷𝑂𝑌 365…”

Section: General Algorithm Structurementioning

confidence: 99%

“…were calculated for the pairwise log-transformed observed values and model predictions of the test dataset (Chen et al 2019b;Morozov and Tang 2019;Mattei and Scardi 2020). This random process was repeated for ten times to obtain the mean and standard error of RMSE, R 2 , and MB.…”

Section: Comparison Of Four Algorithmsmentioning

confidence: 99%

“…Previous studies are confined to either local areas (Liu et al 2007;Qiu et al 2010) or short snapshots (Ning et al 2005;Pan et al 2006;Chen et al 2011). Pan et al (2013) and Morozov and Tang (2019) have pioneered using satellite observations to estimate surface picophytoplankton abundances in the SCS. While their algorithms have the potential to estimate large-scale picophytoplankton abundances by using satellite data, their approach can only estimate surface abundances and cannot predict the future changes of picophytoplankton abundances.…”

Section: Introductionmentioning

confidence: 99%

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A machine-learning approach to modeling picophytoplankton abundances in the South China Sea

Chen

Liu

Xiao

et al. 2020

Progress in Oceanography

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Picophytoplankton, the smallest phytoplankton (<3 micron), contribute significantly to primary production in the oligotrophic South China Sea. To improve our ability to predict picophytoplankton abundances in the South China Sea and infer the underlying mechanisms, we compared four machine learning algorithms to estimate the horizontal and vertical distributions of picophytoplankton abundances. The inputs of the algorithms include spatiotemporal (longitude, latitude, sampling depth and date) and environmental variables (sea surface temperature, chlorophyll, and light). The algorithms were fit to a dataset of 2442 samples collected from 2006 to 2012. We find that the Boosted Regression Trees (BRT) gives the best prediction performance with R 2 ranging from 77% to 85% for Chl a concentration and abundances of three picophytoplankton groups. The model outputs confirm that temperature and light play important roles in affecting picophytoplankton distribution.Prochlorococcus, Synechococcus, and picoeukaryotes show decreasing preference to oligotrophy. These insights are reflected in the vertical patterns of Chl a and picoeukaryotes that form subsurface maximal layers in summer and spring, contrasting with those of Prochlorococcus and Synechococcus that are most abundant at surface. Our forecasts suggest that, under the "business-as-usual" scenario, total Chl a will decrease but Prochlorococcus abundances will increase significantly to the end of this century. Synechococcus abundances will also increase, but the trend is only significant in coastal waters. Our study has advanced the ability of predicting picophytoplankton abundances in the South China Sea and suggests that BRT is a useful machine learning technique for modelling plankton distribution.

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Estimation of cell abundances of picophytoplankton based on the absorption coefficient of phytoplankton in the South China sea

Zheng

Zhou

Cao

et al. 2021

Continental Shelf Research

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Satellite ocean colour algorithm for Prochlorococcus, Synechococcus, and picoeukaryotes concentration retrieval in the South China Sea

Cited by 6 publications

References 45 publications

Dynamics of Prochlorococcus Diversity and Photoacclimation During Short-Term Shifts in Water Column Stratification at Station ALOHA

Dynamics of Prochlorococcus Diversity and Photoacclimation During Short-Term Shifts in Water Column Stratification at Station ALOHA

A machine-learning approach to modeling picophytoplankton abundances in the South China Sea

Estimation of cell abundances of picophytoplankton based on the absorption coefficient of phytoplankton in the South China sea

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