Journal of Fish Biology,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Abstract
The
roughskin
dogfish
Centroscymnus
owstonii
,
a
deep‐sea
shark,
has
patchy
global
distribution,
with
most
knowledge
stemming
from
incidentally
captured
specimens.
Using
remote
lander
video
system,
we
observed
multiple
C.
individuals
alive
on
the
footage
at
1054
m
off
Little
Cayman,
Cayman
Islands,
Western
Atlantic
Ocean,
marking,
to
our
knowledge,
first
record
of
species
in
Greater
Antilles,
central
Caribbean
Sea,
while
also
adding
new
locality
for
Islands.
This
study
expands
distribution
region,
and
highlights
utility
systems
enhancing
expanding
understanding
biology
diversity
sharks.
Frontiers in Marine Science,
Год журнала:
2023,
Номер
10
Опубликована: Авг. 9, 2023
The
2022
Global
Deep-Sea
Capacity
Assessment
is
a
baseline
assessment
of
the
technical
and
human
capacity
for
deep-sea
exploration
research
in
every
coastal
area
with
deep
ocean
worldwide.
From
200
to
nearly
11,000
meters
below
sea
level,
encompasses
single
largest—and
arguably
most
critical—biosphere
on
Earth.
Globally,
two-thirds
all
exclusive
economic
zones
combined
have
water
depths
between
2,000
6,000
meters,
making
this
particularly
critical
depth
range
access.
This
study
includes
information
186
countries
territories,
analyzed
by
subregional,
regional,
income
groups.
data
were
collected
through
both
an
online
survey
manual
research.
We
found
that
globally,
52%
respondents
agreed
considered
important
their
community.
A
third
they
had
in-country
technology
conduct
research,
half
expertise.
Survey
results
revealed
challenges
worldwide
are
funding,
access
vessels,
capacity.
top
three
global
opportunities
training
opportunities,
less
expensive
collection
technology,
better
analysis
tools.
provides
necessary
strategically
develop,
equitably
implement,
quantitatively
measure
impact
development
over
coming
years.
It
now
possible
evolution
next
decade,
indicator
progress
during
UN
Decade
Ocean
Science
Sustainable
Development.
Life,
Год журнала:
2024,
Номер
14(4), С. 432 - 432
Опубликована: Март 24, 2024
Bioluminescence
is
the
production
of
visible
light
by
an
organism.
This
phenomenon
particularly
widespread
in
marine
animals,
especially
deep
sea.
While
luminescent
status
numerous
animals
has
been
recently
clarified
thanks
to
advancements
deep-sea
exploration
technologies
and
phylogenetics,
that
others
become
more
obscure
due
dramatic
changes
systematics
(themselves
triggered
molecular
phylogenies).
Here,
we
combined
a
comprehensive
literature
review
with
unpublished
data
establish
catalogue
animals.
Inventoried
were
identified
species
level
over
97%
cases
associated
score
reflecting
robustness
their
luminescence
record.
capability
established
695
genera
reports
from
99
additional
need
further
confirmation.
Altogether,
these
potentially
encompass
9405
species,
which
2781
are
luminescent,
136
(e.g.,
suggested
those
needs
confirmation),
non-luminescent,
6389
have
unknown
status.
Comparative
analyses
reveal
new
insights
into
occurrence
among
animal
groups
highlight
promising
research
areas.
work
will
provide
solid
foundation
for
future
studies
related
field
bioluminescence.
npj Ocean Sustainability,
Год журнала:
2023,
Номер
2(1)
Опубликована: Дек. 13, 2023
Abstract
The
global
scientific
community
is
currently
going
through
a
self-reckoning
in
which
it
questioning
and
re-examining
its
existing
practices,
many
of
are
based
on
colonial
neo-colonial
perceptions.
This
particularly
acute
for
the
ocean
research
community,
where
unequal
unbalanced
international
collaborations
have
been
rife.
Consequently,
numerous
discussions
calls
made
to
change
current
status
quo
by
developing
guidelines
frameworks
addressing
key
issues
plaguing
our
community.
Here,
we
provide
an
overview
topics
that
has
debated
over
last
three
four
years,
with
emphasis
research,
coupled
actions
per
stakeholder
groups
(research
institutions,
funding
agencies,
publishers).
We
also
outline
some
missing
suggest
path
forward
tackle
these
gaps.
hope
this
contribution
will
further
accelerate
efforts
bring
more
equity
justice
into
sciences.
Technologies,
Год журнала:
2025,
Номер
13(1), С. 41 - 41
Опубликована: Янв. 20, 2025
Marine
life
exploration
is
constrained
by
factors
such
as
limited
scuba
diving
time,
depth
restrictions
for
divers,
costly
expeditions,
safety
risks
to
divers’
health,
and
minimizing
harm
marine
ecosystems,
where
traditional
often
disturbing
life.
This
paper
introduces
Nu
(named
after
an
ancient
Egyptian
deity),
a
3D-printed
Remotely
Operated
Underwater
Vehicle
(ROUV)
designed
in
attempt
address
these
challenges.
employs
Long
Range
(LoRa),
low-power
long-range
communication
technology,
enabling
wireless
operation
via
manual
controller.
The
vehicle
features
onboard
live-feed
camera
with
separate
system
that
transmits
video
external
real-time
machine
learning
(ML)
pipeline
fish
species
classification,
reducing
human
error
taxonomists.
It
uses
Brushless
Direct
Current
(BLDC)
motors
long-distance
movement
water
pump
precise
navigation,
disturbance,
damage
surrounding
species.
Nu’s
functionality
was
evaluated
controlled
2.5-m-deep
body
of
water,
focusing
on
connectivity,
maneuverability,
identification
accuracy.
detection
algorithm
achieved
average
precision
60%
identifying
presence,
while
the
classification
model
97%
assigning
labels,
unknown
flagged
correctly.
testing
environment
has
met
design
expectations.
Understanding
and
preserving
the
deep
sea
ecosystems
is
paramount
for
marine
conservation
efforts.
Automated
object
(deep-sea
biota)
classification
can
enable
creation
of
detailed
habitat
maps
that
not
only
aid
in
biodiversity
assessments
but
also
provide
essential
data
to
evaluate
ecosystem
health
resilience.
Having
a
significant
source
labelled
helps
prevent
overfitting
enables
training
learning
models
with
numerous
parameters.
In
this
paper,
we
contribute
establishment
deep-sea
remotely
operated
vehicle
(ROV)
image
dataset
3994
images
featuring
biota
belonging
33
classes.
We
manually
label
through
rigorous
quality
control
human-in-the-loop
labelling.
Leveraging
from
ROV
equipped
advanced
imaging
systems,
our
study
provides
results
using
novel
deep-learning
classification.
use
including
ResNet,
DenseNet,
Inception,
Inception-ResNet
benchmark
features
class
imbalance
many
Our
show
model
mean
accuracy
65%,
AUC
scores
exceeding
0.8
each
class.
