The roles of anoxia, H2S, and storm events in fish kills of dead-end canals of Delaware inland bays

Estuaries ◽  
2004 ◽  
Vol 27 (3) ◽  
pp. 551-560 ◽  
Author(s):  
George W. Luther ◽  
Shufen Ma ◽  
Robert Trouwborst ◽  
Brian Glazer ◽  
Michelle Blickley ◽  
...  
Keyword(s):  
Storm Events ◽  
Fish Kills ◽  
Inland Bays ◽  
Dead End ◽  
Delaware Inland Bays

scholarly journals Monitoring Heavy Metals near Wastewater Facility in Delaware Inland Bays Tidal Canal

2017 ◽  
Vol 09 (08) ◽  
pp. 985-998
Author(s):  
Amy C. Cannon ◽  
Lathadevi K. Chintapenta ◽  
Gulnihal Ozbay
Keyword(s):  
Heavy Metals ◽  
Inland Bays ◽  
Delaware Inland Bays

Using quantitative real-time PCR to study competition and community dynamics among Delaware Inland Bays harmful algae in field and laboratory studies

Harmful Algae ◽  
2008 ◽  
Vol 7 (5) ◽  
pp. 599-613 ◽  
Author(s):  
Sara M. Handy ◽  
Elif Demir ◽  
David A. Hutchins ◽  
Kevin J. Portune ◽  
Edward B. Whereat ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Community Dynamics ◽  
Harmful Algae ◽  
Laboratory Studies ◽  
Quantitative Real Time Pcr ◽  
Inland Bays ◽  
Delaware Inland Bays

Atmospheric nitrogen inputs to the Delaware Inland Bays: the role of ammonia

2005 ◽  
Vol 135 (3) ◽  
pp. 433-443 ◽  
Author(s):  
Joseph R. Scudlark ◽  
Jennifer A. Jennings ◽  
Megan J. Roadman ◽  
Karen B. Savidge ◽  
William J. Ullman
Keyword(s):  
Atmospheric Nitrogen ◽  
Inland Bays ◽  
Nitrogen Inputs ◽  
Delaware Inland Bays

scholarly journals Using machine learning to understand the implications of meteorological conditions for fish kills

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
You-Jia Chen ◽  
Emily Nicholson ◽  
Su-Ting Cheng
Keyword(s):  
Machine Learning ◽  
Fishery Management ◽  
Meteorological Data ◽  
Water Quality Monitoring ◽  
Meteorological Conditions ◽  
Air Pressure ◽  
Self Organizing Map ◽  
Storm Events ◽  
Fish Kills ◽  
Low Levels

Abstract Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2–3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.

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