PLoS ONE,
Journal Year:
2021,
Volume and Issue:
16(9), P. e0257510 - e0257510
Published: Sept. 21, 2021
Anthropogenic
activities
are
changing
the
state
of
ecosystems
worldwide,
affecting
community
composition
and
often
resulting
in
loss
biodiversity.
Rivers
among
most
impacted
ecosystems.
Recording
their
current
with
regular
biomonitoring
is
important
to
assess
future
trajectory
Traditional
monitoring
methods
for
ecological
assessments
costly
time-intensive.
Here,
we
compared
macroinvertebrates
based
on
environmental
DNA
(eDNA)
sampling
traditional
kick-net
biodiversity
patterns
at
92
river
sites
covering
all
major
Swiss
catchments.
From
data,
a
biotic
index
(IBCH)
145
indicator
taxa
had
been
established.
The
was
matched
by
taxonomically
annotated
eDNA
data
using
machine
learning
approach.
Our
comparison
diversity
only
uses
zero-radius
Operational
Taxonomic
Units
assigned
taxa.
Overall,
found
strong
congruence
between
both
assessment
total
(gamma
diversity).
However,
when
assessing
site
level
(alpha
diversity),
were
less
consistent
gave
complementary
composition.
Specifically,
retrieved
significantly
fewer
per
than
Importantly,
however,
subsequent
classification
rivers
detected
indicators
resulted
similar
scores
that
classified
random
forest
majority
predictions
(72%)
from
same
status
categories
as
Thus,
validly
communities
and,
combined
learning,
provided
reliable
classifications
rivers.
while
gives
macroinvertebrate
approach,
subsequently
calculated
indices
nevertheless
directly
comparable
consistent.
Molecular Ecology,
Journal Year:
2020,
Volume and Issue:
30(13), P. 3189 - 3202
Published: Sept. 27, 2020
Abstract
Metabarcoding
of
bulk
or
environmental
DNA
has
great
potential
for
biomonitoring
freshwater
environments.
However,
successful
application
metabarcoding
to
biodiversity
monitoring
requires
universal
primers
with
high
taxonomic
coverage
that
amplify
highly
variable,
short
metabarcodes
resolution.
Moreover,
reliable
and
extensive
reference
databases
are
essential
match
the
outcome
analyses
available
taxonomy
indices.
Benthic
invertebrates,
particularly
insects,
key
taxa
bioassessment.
Nevertheless,
few
studies
have
so
far
assessed
markers
macrobenthos.
Here
we
combined
in
silico
laboratory
test
performance
different
amplifying
regions
18S
rDNA
(Euka02),
16S
(Inse01)
COI
(BF1_BR2‐COI)
genes,
developed
an
database
benthic
macroinvertebrates
France
Europe,
a
particular
focus
on
insect
orders
(Ephemeroptera,
Plecoptera
Trichoptera).
Analyses
1,514
individuals
representing
showed
very
amplification
success
across
primer
combinations.
The
Euka02
marker
highest
universality,
while
Inse01
excellent
insects.
BF1_BR2‐COI
resolution,
resolution
was
often
limited.
By
combining
our
data
GenBank
information,
curated
including
sequences
822
genera.
heterogeneous
highlights
complexity
identifying
best
markers,
advocates
integration
multiple
more
comprehensive
accurate
understanding
ecological
impacts
biodiversity.
PLoS ONE,
Journal Year:
2021,
Volume and Issue:
16(9), P. e0257510 - e0257510
Published: Sept. 21, 2021
Anthropogenic
activities
are
changing
the
state
of
ecosystems
worldwide,
affecting
community
composition
and
often
resulting
in
loss
biodiversity.
Rivers
among
most
impacted
ecosystems.
Recording
their
current
with
regular
biomonitoring
is
important
to
assess
future
trajectory
Traditional
monitoring
methods
for
ecological
assessments
costly
time-intensive.
Here,
we
compared
macroinvertebrates
based
on
environmental
DNA
(eDNA)
sampling
traditional
kick-net
biodiversity
patterns
at
92
river
sites
covering
all
major
Swiss
catchments.
From
data,
a
biotic
index
(IBCH)
145
indicator
taxa
had
been
established.
The
was
matched
by
taxonomically
annotated
eDNA
data
using
machine
learning
approach.
Our
comparison
diversity
only
uses
zero-radius
Operational
Taxonomic
Units
assigned
taxa.
Overall,
found
strong
congruence
between
both
assessment
total
(gamma
diversity).
However,
when
assessing
site
level
(alpha
diversity),
were
less
consistent
gave
complementary
composition.
Specifically,
retrieved
significantly
fewer
per
than
Importantly,
however,
subsequent
classification
rivers
detected
indicators
resulted
similar
scores
that
classified
random
forest
majority
predictions
(72%)
from
same
status
categories
as
Thus,
validly
communities
and,
combined
learning,
provided
reliable
classifications
rivers.
while
gives
macroinvertebrate
approach,
subsequently
calculated
indices
nevertheless
directly
comparable
consistent.