scholarly journals Effect of suspended sediment on the nutritional mode of scleractinian corals and their adaptability: state of knowledge and research

2021 ◽  
Vol 41 (21) ◽  
Author(s):  
罗勇，俞晓磊，黄晖 LUO Yong
Keyword(s):  
Suspended Sediment ◽  
Scleractinian Corals ◽  
Nutritional Mode

The relationship of the scleractinian corals to the rugose corals

Paleobiology ◽  
1980 ◽  
Vol 6 (02) ◽  
pp. 146-160 ◽  
Author(s):  
William A. Oliver
Keyword(s):  
Critical Points ◽  
Scleractinian Corals ◽  
Convincing Evidence ◽  
Early Triassic ◽  
Rugose Corals ◽  
History Of ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship ◽  
Biologic Factors

The Mesozoic-Cenozoic coral Order Scleractinia has been suggested to have originated or evolved (1) by direct descent from the Paleozoic Order Rugosa or (2) by the development of a skeleton in members of one of the anemone groups that probably have existed throughout Phanerozoic time. In spite of much work on the subject, advocates of the direct descent hypothesis have failed to find convincing evidence of this relationship. Critical points are:(1) Rugosan septal insertion is serial; Scleractinian insertion is cyclic; no intermediate stages have been demonstrated. Apparent intermediates are Scleractinia having bilateral cyclic insertion or teratological Rugosa.(2) There is convincing evidence that the skeletons of many Rugosa were calcitic and none are known to be or to have been aragonitic. In contrast, the skeletons of all living Scleractinia are aragonitic and there is evidence that fossil Scleractinia were aragonitic also. The mineralogic difference is almost certainly due to intrinsic biologic factors.(3) No early Triassic corals of either group are known. This fact is not compelling (by itself) but is important in connection with points 1 and 2, because, given direct descent, both changes took place during this only stage in the history of the two groups in which there are no known corals.

Observed Suspended Sediment Profiles under the Effect of Single and Double Peaked Wave Spectra

2006 ◽  
Author(s):  
M. R. Delgado Blanco ◽  
M. Olabarrieta Lizaso ◽  
A. Giardino ◽  
R. Banasiak ◽  
R. Verhoeven ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Wave Spectra ◽  
Sediment Profiles

Skeletal growth of four scleractinian corals is not enhanced by in situ mesozooplankton enrichment

2013 ◽  
Vol 489 ◽  
pp. 143-153 ◽  
Author(s):  
AL Alldredge ◽  
SJ Holbrook ◽  
RJ Schmitt ◽  
AJ Brooks ◽  
H Stewart
Keyword(s):  
Scleractinian Corals ◽  
Skeletal Growth

THE INITIAL RESULTS OF THE SUSPENDED SEDIMENT DYNAMICS DURING THE DRY SEASON IN THE HAU RIVER MOUTH AREA

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Nguyen Ngoc Tien ◽  
Dinh Van Uu ◽  
Nguyen Tho Sao ◽  
Do Huy Cuong ◽  
Nguyen Trung Thanh ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
River Mouth ◽  
Dry Season ◽  
Sediment Dynamics ◽  
Mouth Area ◽  
River Mouth Area ◽  
Initial Results

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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