Sinking rates of phytoplankton in the Changjiang (Yangtze River) estuary: A comparative study between Prorocentrum dentatum and Skeletonema dorhnii bloom

Guo, Shengli; Sun, Jun; Zhao, Qibiao; Feng, Yuanyuan; Huang, Daji; Liu, Sumei

doi:10.1016/j.jmarsys.2015.07.003

Cited by 37 publications

(19 citation statements)

References 65 publications

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“…The higher turbidity, lower COD Mn , and TP contents shaped the physicochemical habitat of the Wangyu River in ratios similar to those of the Yangtze River. In the Yangtze River, the Bacillariophyta dominated the phytoplankton community in winter [29][30][31], which is consistent with the phytoplankton communities in the Wangyu River during the water diversion periods in this study ( Figure 5). However, these three physicochemical parameters only contributed a small proportion of phytoplankton community variations, most of which might be related to the inputs of the phytoplankton species from the Wangyu River and tributaries along with the Wangyu River.…”

Section: Correlations Between Sensitive Physicochemical Parameters Ansupporting

confidence: 83%

“…During the non-diversion period of 2016, Cyanophyta dominated at the sites in the middle and lower reaches of the Wangyu River. Since Bacillariophyta is the dominant phytoplankton phyla in the Yangtze River especially in winter [29][30][31], the dominance of Bacillariophyta in the Wangyu River might be mainly ascribed to the allochthonous species input. In this study, the Cyclotella sp.…”

Section: Variations In Phytoplankton Diversity and Community Compositionmentioning

confidence: 99%

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Effects of Water Diversion from Yangtze River to Lake Taihu on the Phytoplankton Habitat of the Wangyu River Channel

Dai

et al. 2018

Water

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Abstract:To reveal the effects of water diversion from the Yangtze River to Lake Taihu on the phytoplankton habitat of the main water transfer channel of the Wangyu River, we investigated the water's physicochemical parameters and phytoplankton communities during the water diversion and non-diversion periods over the winters between 2014-2016, respectively. During the water diversion periods in the winter of 2014 and 2015, the nutrients and organic pollutant contents of the Wangyu River channel were significantly lower than those during the non-diversion period in 2016. Moreover, the phytoplankton diversities and relative proportions of Bacillariophyta during the diversion periods evidently increased during the water diversion periods in winter. The increase in the water turbidity content, the decrease in the contents of the permanganate index, and the total phosphorus explained only 21.4% of the variations in the phytoplankton communities between the diversion and non-diversion periods in winter, which revealed significant contributions of the allochthonous species from the Yangtze River and tributaries of the Wangyu River to phytoplankton communities in the Wangyu River. The increasing gradient in the contents of nutrients and organic pollutants from the Yangtze River to Lake Taihu indicated the potential allochthonous pollutant inputs along with the Wangyu River. Further controlling the pollutants from the tributaries of the Wangyu River is critical in order to improve the phytoplankton habitats in river channels and Lake Taihu.

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Section: Correlations Between Sensitive Physicochemical Parameters Ansupporting

confidence: 83%

Section: Variations In Phytoplankton Diversity and Community Compositionmentioning

confidence: 99%

Effects of Water Diversion from Yangtze River to Lake Taihu on the Phytoplankton Habitat of the Wangyu River Channel

Dai

et al. 2018

Water

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“…6). However, both in the Yangtze River estuary and in the Rhine ROFI, some observations indicate higher sinking rates at the deeper layers during spring (Peperzak et al, 2003;Guo et al, 2016). This is explained by the species-specific differences in sinking rates (e.g., with sinking rates of diatoms being higher than those of dinoflagellates and Phaeocystis); therefore, the differences in community composition at the surface (dominated by dinoflagellates or Phaeocystis) and bottom layers (dominated by di- Figure B2.…”

Section: B2 Phytoplankton Sinking Rates and Vertical Chlorophyll Profmentioning

confidence: 99%

The acclimative biogeochemical model of the southern North Sea

Kerimoglu¹,

Hofmeister²,

Maerz³

et al. 2017

Biogeosciences

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Abstract. Ecosystem models often rely on heuristic descriptions of autotrophic growth that fail to reproduce various stationary and dynamic states of phytoplankton cellular composition observed in laboratory experiments. Here, we present the integration of an advanced phytoplankton growth model within a coupled three-dimensional physicalbiogeochemical model and the application of the model system to the southern North Sea (SNS) defined on a relatively high resolution ( ∼ 1.5-4.5 km) curvilinear grid. The autotrophic growth model, recently introduced by Wirtz and Kerimoglu (2016), is based on a set of novel concepts for the allocation of internal resources and operation of cellular metabolism. The coupled model system consists of the General Estuarine Transport Model (GETM) as the hydrodynamical driver, a lower-trophic-level model and a simple sediment diagenesis model. We force the model system with realistic atmospheric and riverine fluxes, background turbidity caused by suspended particulate matter (SPM) and open ocean boundary conditions. For a simulation for the period 2000-2010, we show that the model system satisfactorily reproduces the physical and biogeochemical states of the system within the German Bight characterized by steep salinity; nutrient and chlorophyll (Chl) gradients, as inferred from comparisons against observation data from long-term monitoring stations; sparse in situ measurements; continuous transects; and satellites. The model also displays skill in capturing the formation of thin chlorophyll layers at the pycnocline, which is frequently observed within the stratified regions during summer. A sensitivity analysis reveals that the vertical distributions of phytoplankton concentrations estimated by the model can be qualitatively sensitive to the description of the light climate and dependence of sinking rates on the internal nutrient reserves. A non-acclimative (fixed-physiology) version of the model predicted entirely different vertical profiles, suggesting that accounting for physiological flexibility might be relevant for a consistent representation of the vertical distribution of phytoplankton biomass. Our results point to significant variability in the cellular chlorophyll-to-carbon ratio (Chl : C) across seasons and the coastal to offshore transition. Up to 3-fold-higher Chl : C at the coastal areas in comparison to those at the offshore areas contribute to the steepness of the chlorophyll gradient. The model also predicts much higher phytoplankton concentrations at the coastal areas in comparison to its non-acclimative equivalent. Hence, findings of this study provide evidence for the relevance of physiological flexibility, here reflected by spatial and seasonal variations in Chl : C, for a realistic description of biogeochemical fluxes, particularly in the environments displaying strong resource gradients.

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“…Samples were fixed with formaldehyde (5%) immediately after collection and transferred back for the laboratory analyses. In the laboratory, phytoplankton cells were first concentrated with 100 mL settlement columns for 24 to 48 h, then identified and counted using an inverted microscope (Olympus corporation, Tokyo, Japan) [50,51]; for the method, we referred mainly to Utermöhl [52]. A total of 242 phytoplankton species were identified, including 129 diatoms, 97 dinoflagellates, 5 chlorophytas, 5 chrysophytas, 4 cyanophytas, 1 xanthophyta, and 1 euglenophyta (the classification was mainly based on http://www.algaebase.org/); after species identification and cell counting, the composition of each species in each station was calculated.…”

Section: Taxonomic Species Identificationmentioning

confidence: 99%

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

et al. 2019

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Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R 2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.focused on the identification of specific phytoplankton species or groups under blooming conditions using ocean color satellites with a moderate spectral resolution [14][15][16]. Furthermore, discrimination of phytoplankton phyla in blooming conditions has been achieved based on differences in optical signals among different phytoplankton populations [17]. However, these endeavors are constrained to a limited type of phytoplankton species or groups because of the limitations in band settings for multi-spectral remote sensing. Moreover, some studies have been focused on Case 1 water with relatively simple optical properties where optically active constituents co-vary with chlorophyll concentration [18,19]; however, it is more challenging to invert phytoplankton species composition from current ocean color remote sensing data with moderate spectral resolution in optically complex waters. Comparatively, hyperspectral remote sensing presents a promising tool to resolve the spectral variability of phytoplankton species [20].Several ocean color algorithms have also been proposed for phytoplankton information inversion in natural waters [21][22][23][24]. Among these efforts, a derivative spectroscopy/similarity index (SI) approach is the most common method for identifying dominant phytoplankton species or groups [25][26][27]. However, because SI-based approaches assign unknown spectra to the reference spectra that have the largest SI, only dominant species or groups can be identified, so it is difficult to determine phytoplankton species compo...

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Sinking rates of phytoplankton in the Changjiang (Yangtze River) estuary: A comparative study between Prorocentrum dentatum and Skeletonema dorhnii bloom

Cited by 37 publications

References 65 publications

Effects of Water Diversion from Yangtze River to Lake Taihu on the Phytoplankton Habitat of the Wangyu River Channel

Effects of Water Diversion from Yangtze River to Lake Taihu on the Phytoplankton Habitat of the Wangyu River Channel

The acclimative biogeochemical model of the southern North Sea

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

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