Frontiers in Microbiology,
Journal Year:
2024,
Volume and Issue:
15
Published: Aug. 26, 2024
Bacteria
play
a
crucial
role
in
pollutant
degradation,
biogeochemical
cycling,
and
energy
flow
within
river
ecosystems.
However,
the
underlying
mechanisms
governing
bacterial
community
assembly
their
response
to
environmental
factors
at
seasonal
scales
subtropical
rivers
remain
poorly
understood.
In
this
study,
we
conducted
16S
rRNA
gene
amplicon
sequencing
on
water
samples
from
Liuxi
River
investigate
composition,
processes,
co-occurrence
relationships
of
communities
during
wet
season
dry
season.
The
results
demonstrated
that
differences
hydrochemistry
significantly
influenced
composition
communities.
A
more
heterogeneous
structure
increased
alpha
diversity
were
observed
Water
temperature
emerged
as
primary
driver
for
changes
Dispersal
limitation
predominantly
governed
assembly,
however,
season,
its
contribution
due
decreased
immigration
rates.
Co-occurrence
network
analysis
reveals
mutualism
played
prevailing
shaping
structure.
Compared
exhibited
higher
modularity,
competition,
keystone
species
resulting
stable
Although
displayed
distinct
variations,
Proteobacteria
Actinobacteria
consistently
abundant
maintaining
both
seasons.
Our
findings
provide
insights
into
how
respond
changes,
uncovering
rivers,
which
are
effective
management
conservation
riverine
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: June 26, 2024
Soil
microorganisms
play
pivotal
roles
in
driving
essential
biogeochemical
processes
terrestrial
ecosystems,
and
they
are
sensitive
to
heavy
metal
pollution.
However,
our
understanding
of
multiple
environmental
factors
interaction
polluted
paddy
fields
shape
microbial
community
assembly
remain
limited.
In
the
current
study,
we
used
16S
rRNA
amplicon
sequencing
characterize
composition
soils
collected
from
a
typical
industry
town
Taihu
region,
eastern
China.
The
results
revealed
that
Cd
Pb
were
major
pollutant,
Proteobacteria,
Acidobacteria
Chloroflexi
dominate
indigenous
bacterial
phyla.
Linear
regression
random
forest
analysis
demonstrated
soil
pH
was
most
important
predictor
diversity.
Mantel
showed
structure
mainly
driven
by
pH,
CEC,
silt,
sand,
AK,
total
DTPA-Cd.
constructed
co-occurrence
network,
utilizing
matrix
theory-based
approach,
exhibited
non-random
with
scale-free
modularity
features.
modules
within
networks
also
significant
correlations
pH.
Overall,
study
indicated
physiochemical
properties
made
predominant
contribution
diversity,
their
association
Cd/Pb
fields.
These
findings
expand
knowledge
key
drivers
patterns
