Spatiotemporal patterns of phytoplankton composition and abundance in the Maryland Coastal Bays: The influence of freshwater discharge and anthropogenic activities

Oseji, Ozuem F.; Chigbu, Paulinus; Oghenekaro, Efeturi U.; Waguespack, Yan; Chen, Nianhong

doi:10.1016/j.ecss.2018.04.004

Cited by 21 publications

(11 citation statements)

References 47 publications

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“…This study addressed the seasonal and spatial variations in the composition of phytoplankton in the eutrophic northern and southern MCBs using a combination of photosynthetic pigments and microscopic counts. The results presented here give a more robust description of the phytoplankton community compared to results of previous phytoplankton studies in the Bays (e.g., [18,19,23,24,27]).…”

Section: Discussionsupporting

confidence: 49%

Composition and Dynamics of Phytoplankton in the Coastal Bays of Maryland, USA, Revealed by Microscopic Counts and Diagnostic Pigments Analyses

Oseji

Fan

Chigbu

2019

Water

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Maryland Coastal Bays (MCBs) have undergone changes in water quality in the past two decades due to nutrient enrichment but the composition and dynamics of the phytoplankton community have not been adequately described. Microscopic counts and photosynthetic pigments of samples collected monthly in 2012 at selected sites in MCBs that differed with regard to the degree of anthropogenic impacts were examined. Sixty-three (63) phytoplankton genera were recorded, of which 40 species are being reported for the first time in the Bays. Among the dominant species were Dactyliosolen fragilissimus (Bacillariophyta), Paulinella ovalis (Cercozoa) and Cryptomonas sp. (Cryptophyta). Bloom densities of Heterocapsa rotundata (Miozoa), which previously had not been reported in the Bays, were observed bay-wide in December, particularly at the mouth of St. Martin River. Diatoms dominated (>40%) the phytoplankton community in winter and decreased in spring (<40%), while Cercozoa and microphytoflagellates (MPF) co-dominated in summer (July). From August to October, diatoms dominated with maximum contributions from an unidentified small (<10 µM) centric species and co-dominated the assemblage with cryptophytes in late fall (November). Canonical correspondence analysis indicated that diatoms were favored by high salinity and total dissolved phosphorus (TDP), cercozoans and chlorophytes by total dissolved nitrogen (TDN) and cryptophytes by dissolved organic carbon. The spatial and seasonal differences in the composition of phytoplankton species, coupled with the occurrence of potentially toxic species and bloom densities of H. rotundata suggest that important changes have occurred in the phytoplankton assemblage that likely have affected the food web of these eutrophic bays.

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Section: Discussionsupporting

confidence: 49%

Composition and Dynamics of Phytoplankton in the Coastal Bays of Maryland, USA, Revealed by Microscopic Counts and Diagnostic Pigments Analyses

Oseji

Fan

Chigbu

2019

Water

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“…Besides, the Bohai Sea and the Yellow Sea were also an important farm for marine aquaculture with abundant crustaceans and fishes, which can release dissolved nutrients and sediments [38]. The input of artificial feeds also aggravated the degree of eutrophication [14]. The stronger stratification and less mixing not only provide a higher percentage of photosynthetically available radiation (PAR) that is available for photosynthesis, but also lead to high water clarity and thereby deepen the euphotic layer depths.…”

Section: ) Impact Of Environmental Factors On the Variation Of Chl-amentioning

confidence: 99%

“…Joo et al [13] used algal communities to identify patterns in the eutrophication process of a river, revealing the seasonal occurrence of an algal genus that resulted in the occurrence of a seasonal cyanobacteria bloom. The variation in phytoplankton community structure may occur in response to the environmental variables [14]. Nishijima et al [15] explored the Chl-a changes at different Secchi depths in the different time and space scales and found that the lack of nutrients, especially phosphorus, limited the increase in phytoplankton biomass.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

Zhao

Zhang

et al. 2019

IEEE Access

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Better understanding of the temporal-spatial distribution of chlorophyll-a concentration (Chl-a) is crucial in controlling harmful water blooms. In this study, the dynamical change of Chl-a over the Bohai Sea and Yellow Sea from 2003-2017 were analyzed by using the MODIS/Aqua satellite data, and the effects of sea surface temperature (SST), wind and wave were investigated. The typical distribution modes of long-term surface Chl-a were extracted by using the Self-organizing Mapping (SOM), neural network model. The results showed distinct seasonal variations of the Chl-a along with a gradual increase in the study period. The total Chl-a of the whole area reached the lowest value of 2.41mg/m 3 in July, and the highest value 3.43mg/m 3 in April; though in Laizhou Bay, the Chl-a concentration was significantly higher than other regions and the value reached at the peak in September. The spatial distribution showed that Chl-a decreased from inshore to offshore. Meanwhile, from clear mode to low, medium, and high concentration modes, the Chl-a gradually increased in coverage and concentration, and modes extracted by the SOM neural network have effectively elucidated the trend of Chl-a in spatial, seasonal, and interannual variability. The Generalized Additive Model (GAM) was used to evaluate the effect of SST, wind, and wave on the changing patterns of Chl-a. It was found that there is a significant nonlinear correlation between Chl-a and SST, wind speed, mean wave direction and significant height of the wave. These influencing factors accounted for 47.9% of the change of Chl-a, which had significant effects on Chl-a change. Compared with wind speed, mean wave direction and significant height of wave, SST can better explain the change of Chl-a. Besides, wind direction and increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in the Bohai Sea and Yellow Sea. INDEX TERMS Chl-a, self-organizing mapping neural network, generalized additive model, environmental factors, the Bohai sea and Yellow sea.

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“…This flooding likely carried inorganic fertilizer, soil and organic matter from farmlands into the MCBs and/or overwhelmed the sewerage system resulting in the transport of untreated sewage into the bays, hence the dramatic decrease in δ 15 SPOM C:N ratios in this study varied temporally. The lower average value of 3.4 ± 0.2 SE mol/mol recorded for SPOM C/N ratio in July 2017, when phytoplankton biomass was at its peak [42], may reflect the importance of heterotrophic organisms in the samples [13,71], and higher bacterial activity [33,47]. Bacterial C:N is lower than that of phytoplankton [8,24].…”

Section: General Pattern Of Spom δ 13 C δ 15 N and C/n Ratiosmentioning

confidence: 88%

Spatial and Temporal Patterns of δ13C and δ15N of Suspended Particulate Organic Matter in Maryland Coastal Bays, USA

Edje

Ishaque

Chigbu

2020

Water

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The suspended particulate organic matter (SPOM) in transitional waters such as the Maryland Coastal Bays (MCBs) is derived from allochthonous and autochthonous sources. Little is known, however, about the contribution of terrestrially derived organic matter to SPOM in the MCBs. The sources of SPOM in the MCBs were evaluated using stable isotope ratios of nitrogen (δ15N) and carbon (δ13C), and C/N molar ratios. The values of SPOM δ15N, δ13C and C/N ratios from samples collected seasonally (July 2014 to October 2017) at 13 sites ranged from −0.58 to 10.51‰, −26.85 to −20.33‰, and 1.67 to 11.36, respectively, indicating a mixture of terrestrial SPOM transported by tributaries, marine organic matter from phytoplankton, and sewage. SPOM δ13C levels less than −24‰, suggesting the dominance of terrestrially derived carbon, occurred mainly at sites close to the mouths of tributaries, and were less depleted at sites near the ocean. The mean value of SPOM δ13C was higher in October 2014 (−22.76‰) than in October 2015 (−24.65‰) and 2016 (−24.57‰) likely due to differences in river discharge. Much lower values (<4‰) of δ15N observed in February 2016 coincided with a high freshwater inflow that accompanied a major storm, indicating a strong influence of untreated sewage. Results from a two end-member mixing model suggest that on average, the SPOM in the MCBs is composed of 44% terrestrial materials with the highest percent contributions in October 2015 and 2016 (61%), and lowest (28%) in July 2015. The contribution of terrestrial materials to the SPOM was highest (58%) near the mouth of St. Martin River and lowest (25%) near the Ocean City inlet. SPOM composition and distribution in MCBs are, therefore, a function of land use, freshwater inflow, and water circulation that influence in situ phytoplankton production, and the transport and distribution of terrestrially derived materials.

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Spatiotemporal patterns of phytoplankton composition and abundance in the Maryland Coastal Bays: The influence of freshwater discharge and anthropogenic activities

Cited by 21 publications

References 47 publications

Composition and Dynamics of Phytoplankton in the Coastal Bays of Maryland, USA, Revealed by Microscopic Counts and Diagnostic Pigments Analyses

Composition and Dynamics of Phytoplankton in the Coastal Bays of Maryland, USA, Revealed by Microscopic Counts and Diagnostic Pigments Analyses

Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

Spatial and Temporal Patterns of δ13C and δ15N of Suspended Particulate Organic Matter in Maryland Coastal Bays, USA

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