Current Search: ("jiangm@fau.edu") (x) » White, Jason (x)
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Title
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Cuba's Mesophotic Coral Reefs and Associated Fish Communities.
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Creator
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Reed, John K., Gonzalez-Diaz, Patricia, Busutil, Linnet, Farrington, Stephanie, Martínez-Daranas, Beatriz, Cobián Rojas, Dorka, Voss, Joshua, Diaz, Maria Cristina, David, Andrew, Hanisak, M. Dennis, Mendez, Juliett Gonzalez, Garcia Rodriguez, Alain, González-Sanchez, Patricia M., Viamontes Fernandez, Jorge, Estrada Perez, Daniel, Studivan, Michael, Drummond, Felicia, Jiang, Mingshun, Pomponi, Shirley A.
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Date Issued
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2018
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000351
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Format
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Citation
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Title
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Variability of Winter-Spring Bloom Phaeocystis pouchetii Abundance in Massachusetts Bay.
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Creator
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David G. Borkman, P. Scott Libby, Michael J. Mickelson, Jefferson T. Turner, Mingshun Jiang
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Abstract/Description
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Abundance of the prymnesiophyte Phaeocystis pouchetii was quantified via light microscopy at 2-week to monthly intervals in Massachusetts Bay (southern Gulf of Maine, NW Atlantic) during 1992–2012. Variability in the abundance and seasonal cycle of Phaeocystis are described and synoptic hydrographic, nutrient, and meteorological data were analyzed to identify factors that may influence Phaeocystis abundance. The maximum Phaeocystis abundance was 14 × 106 cells L−1 (10 Apr 2008). It was...
Show moreAbundance of the prymnesiophyte Phaeocystis pouchetii was quantified via light microscopy at 2-week to monthly intervals in Massachusetts Bay (southern Gulf of Maine, NW Atlantic) during 1992–2012. Variability in the abundance and seasonal cycle of Phaeocystis are described and synoptic hydrographic, nutrient, and meteorological data were analyzed to identify factors that may influence Phaeocystis abundance. The maximum Phaeocystis abundance was 14 × 106 cells L−1 (10 Apr 2008). It was frequently (5 of 8 years) absent prior to year 2000, but not thereafter. Seasonally, it first appeared in February to early March, reached peak abundance in mid-April, and persisted until May or early June for a duration of 0–112 days (mean 34 days). A long-term alternation between Phaeocystis and centric diatom abundance was apparent, suggesting winter-spring selection of either Phaeocystis or centric diatoms. Phytoplankton community analysis suggested that blooms affected the rest of the phytoplankton community. Phaeocystis blooms were manifest as a substantial increase in particulate nutrients above normal levels. Phaeocystis blooms were preceded in February by a slightly elevated concentration of NO3 (9.3 vs. 6.5 μM when absent) and PO4 (0.99 vs. 0.79 μM when absent). Blooms were also preceded by elevated ratios of NO3/PO4, NO3/Si, and PO4/Si, and warmer, saltier waters reflecting reduced river discharge. The correlation with salinity and river discharge suggests that Phaeocystis bloom variability is partially determined by annually varying circulation processes that determine the degree of low nutrient, low salinity coastal water intrusion into Massachusetts Bay.
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Date Issued
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2016
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000519
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Format
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Document (PDF)
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Title
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Adaptive Decentralized Control of Mobile Underwater Sensor Networks and Robots for Modeling Underwater Phenomena.
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Creator
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Carrick Detweiler, Sreeja Banerjee, Marek Doniec, Mingshun Jiang, Francesco Peri, Robert F. Chen, Daniela Rus
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Abstract/Description
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Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum and show how the controller can be extended to work with hybrid robot and...
Show moreUnderstanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum and show how the controller can be extended to work with hybrid robot and sensor network systems. We implement the controller on an underwater sensor network with depth adjustment capabilities. Through simulations and in-situ experiments, we verify the functionality and performance of the system and algorithm.
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Date Issued
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2014
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000520
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Format
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Document (PDF)
