Ecological Processes,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: Feb. 18, 2024
Abstract
Background
Although
phytoplankton
are
important
primary
producers
in
food
webs,
they
relatively
less
studied
large
rivers
compared
to
other
types
of
systems.
To
fill
this
research
gap,
we
taxonomic
and
functional
composition
their
relationships
with
water
quality,
habitat,
climate,
land
use
across
30
river
sections
the
middle
lower
reaches
Yangtze
River
during
2017–2018.
Results
Major
observed
groups
were
cyanobacteria,
bacillariophyta,
chlorophyta.
Phytoplankton
total
abundance,
biomass,
species
richness
significantly
decreased
dry
season
wet
season,
differing
between
seasons.
differences
seasons
mainly
contributed
by
Oscillatoria
sp.,
Pseudanabaena
Melosira
granulata
.
The
dfferences
P
(including
Closterium
sp.),
Lo
Merismopedia
Peridinium
Ceratium
Gymnodinium
J
Pediastrum
Tetraedron
Crucigenia
Scenedesmus
Coelastrum
sp.).
variance
partitioning
showed
that
quality
(NO
3
-N,
suspended
solids,
turbidity)
habitat
(water
flow,
bank
channel
conditions)
critical
factors
shaping
patterns,
followed
climate
use.
Conclusions
indicated
there
was
significant
seasonal
variation
River,
primarily
driving
patterns.
Our
study
contributes
understanding
natural
anthropogenic
drive
successional
processes
River.
These
findings
have
implications
for
environmental
management
as
well
towards
ecological
restoration
rivers.
Water,
Journal Year:
2025,
Volume and Issue:
17(5), P. 725 - 725
Published: March 1, 2025
Algal
blooms
are
a
major
risk
to
aquatic
ecosystem
health
and
potable
water
safety.
Traditional
statistical
models
often
fail
accurately
predict
algal
bloom
dynamics
due
their
complexity.
Machine
learning,
adept
at
managing
high-dimensional
non-linear
data,
provides
superior
predictive
approach
this
challenge.
In
study,
we
employed
support
vector
machine
(SVM),
random
forest
(RF),
backpropagation
neural
network
(BPNN)
the
severity
of
in
Anzhaoxin
River
Basin
based
on
an
density-based
grading
standard.
The
SVM
model
demonstrated
highest
accuracy
with
training
test
set
accuracies
0.96
0.92,
highlighting
its
superiority
small-sample
learning.
Shapley
Additive
Explanations
(SHAP)
technique
was
utilized
evaluate
contribution
environmental
variables
various
models.
results
show
that
TP
is
most
significant
factor
affecting
outbreak
River,
phosphorus
management
strategy
more
suitable
for
artificial
body
northeast
China.
This
study
contributes
exploring
potential
application
learning
diagnosing
predicting
riverine
ecological
issues,
providing
valuable
insights
protection
ecosystems
Basin.
Limnology and Oceanography,
Journal Year:
2022,
Volume and Issue:
67(9), P. 1943 - 1958
Published: July 8, 2022
Abstract
Longitudinal
environmental
heterogeneity
and
directionality
of
the
water
movement
are
key
features
that
may
exert
contrasting
forces
on
riverine
plankton
assembly.
Directionality
strengthens
dispersal‐driven
assembly,
but
this
can
be
masked
by
urbanization‐induced
along
river
continuum.
In
light
contrast,
we
aimed
at
delineating
relative
importance
assembly
processes
generating
distribution
patterns
bacterioplankton
phytoplankton
communities
in
a
draining
an
urbanizing
watershed
Southeast
China.
We
applied
variation
partitioning
analysis,
neutral
community
model,
quantitative
process
estimate
molecular
morphological
data
obtained
over
years
2012–2016.
Despite
relatively
short
distance
between
sampling
sites
(<
20
km),
similarity
decreased
with
increasing
from
upstream
pristine
site
toward
downstream
urban
area,
formed
clusters
roughly
corresponded
to
five
habitat
patches,
predefined
based
hydrology
longitudinal
landscape
change.
These
were
predominantly
driven
deterministic
stochastic
for
bacterioplankton,
respectively,
indicating
balance
dispersal
due
fluvial
connectivity
local
selective
pressures.
Considering
global
loss
fragmentation
flow
regulation,
our
findings
imply
plankton‐based
ecological
approaches
could
useful
hedge
against
uncertain
future
rivers
watersheds
ecologically
sustainable
way.
