Mingshun Jiang

Odontocetes obtain nutrients including essential elements through their diet and are exposed to
heavy metal contaminants via ingestion of contaminated prey. We evaluated the prevalence,
concentration, and tissue distribution of essential and non-essential trace elements, including
heavy metal toxicants, in tissue (blubber, kidney, liver, skeletal muscle, skin) and fecal samples
collected from 90 odontocetes, representing nine species, that stranded in Georgia and Florida,
USA during 2007–2021. Samples were analyzed for concentrations of seven essential (cobalt,
copper, iron, manganese, molybdenum, selenium, zinc) and five non-essential (arsenic, cadmium,
lead, mercury, thallium) elemental analytes using inductively-coupled plasma mass spectrometry.
Risso’s dolphins (Grampus griseus) and short-finned pilot whales (Globicephala macrorhynchus) had
the highest median concentrations of mercury, cadmium, and lead, while dwarf sperm whales
(Kogia sima) had the lowest. Adult pygmy and dwarf sperm whales that stranded in 2019–2021
had higher concentrations of arsenic, copper, iron, lead, manganese, selenium, thallium, and zinc
compared to those that stranded in 2010–2018, suggesting an increasing risk of exposure over
time. The highest concentrations of many elements (e.g., cadmium, cobalt, copper, manganese,
molybdenum, thallium, zinc) were in fecal samples, illustrating the usefulness of this noninvasively
collected sample. Aside from fecal samples, hepatic tissues had the highest concentrations
of iron, manganese, mercury, molybdenum, and selenium in most species; renal tissues
had the highest concentrations of cadmium; skin had the highest concentrations of zinc; and
copper, arsenic, and lead concentrations were primarily distributed among the liver and kidneys.
Phylogenetic differences in patterns of trace element concentrations likely reflect species-specific
differences in diet, trophic level, and feeding strategies, while heterogeneous distributions of
elemental analytes among different organ types reflect differences in elemental biotransformation,
elimination, and storage. This study illustrates the importance of monitoring toxic contaminants in stranded odontocetes, which serve as important sentinels of environmental
contamination, and whose health may be linked to human health.

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 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.

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 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.