Remote Sensing,
Год журнала:
2024,
Номер
16(19), С. 3723 - 3723
Опубликована: Окт. 7, 2024
Gross
primary
productivity
(GPP)
is
vital
for
ecosystems
and
the
global
carbon
cycle,
serving
as
a
sensitive
indicator
of
ecosystems’
responses
to
climate
change.
However,
impact
future
changes
on
GPP
in
Tibetan
Plateau,
an
ecologically
important
climatically
region,
remains
underexplored.
This
study
aimed
develop
data-driven
approach
predict
seasonal
annual
variations
Plateau
up
year
2100
under
changing
climatic
conditions.
A
convolutional
neural
network
(CNN)
was
employed
investigate
relationships
between
various
environmental
factors,
including
variables,
CO2
concentrations,
terrain
attributes.
analyzed
projected
from
Coupled
Model
Intercomparison
Project
Phase
6
(CMIP6)
four
scenarios:
SSP1–2.6,
SSP2–4.5,
SSP3–7.0,
SSP5–8.5.
The
results
suggest
that
expected
significantly
increase
throughout
21st
century
all
scenarios.
By
2100,
reach
1011.98
Tg
C,
1032.67
1044.35
1055.50
C
scenarios,
representing
0.36%,
4.02%,
5.55%,
5.67%
relative
2021.
analysis
indicates
spring
autumn
shows
more
pronounced
growth
SSP3–7.0
SSP5–8.5
scenarios
due
extended
growing
season.
Furthermore,
identified
elevation
band
3000
4500
m
particularly
change
terms
response.
Significant
increases
would
occur
east
Qilian
Mountains
upper
reaches
Yellow
Yangtze
Rivers.
These
findings
highlight
pivotal
role
driving
dynamics
this
region.
insights
not
only
bridge
existing
knowledge
gaps
regarding
over
coming
decades
but
also
provide
valuable
guidance
formulation
adaptation
strategies
at
ecological
conservation
management.
Science,
Год журнала:
2023,
Номер
380(6647), С. 835 - 840
Опубликована: Май 25, 2023
Climate
change
is
pushing
species
outside
of
their
evolved
tolerances.
Plant
populations
must
acclimate,
adapt,
or
migrate
to
avoid
extinction.
However,
because
plants
associate
with
diverse
microbial
communities
that
shape
phenotypes,
shifts
in
associations
may
provide
an
alternative
source
climate
tolerance.
Here,
we
show
tree
seedlings
inoculated
sourced
from
drier,
warmer,
colder
sites
displayed
higher
survival
when
faced
drought,
heat,
cold
stress,
respectively.
Microbially
mediated
drought
tolerance
was
associated
increased
diversity
arbuscular
mycorrhizal
fungi,
whereas
lower
fungal
richness,
likely
reflecting
a
reduced
burden
nonadapted
taxa.
Understanding
microbially
enhance
our
ability
predict
and
manage
the
adaptability
forest
ecosystems
changing
climates.
Journal of Biogeography,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 14, 2025
ABSTRACT
Aim
Pollen
assemblages
are
widely
used
to
infer
paleoenvironment
features,
aiming
at
reconstructing
both
past
climates
and
biomes.
However,
the
functional
link
between
environmental
conditions
pollen
is
not
straightforward
requires
thorough
testing
be
confidently.
Here,
we
use
a
trait‐based
approach
assess
consistency
of
signatures
plant
assemblages.
Location
Arid
Central
Asia
(ACA).
Taxon
Spermatophytes
(pollen‐producing
plants).
Methods
We
whether
trait
values
distributions
consistent
for
surface
samples
extant
vegetation
in
biogeographic
region.
A
working
checklist
was
compiled
ACA
order
assign
types
taxa.
This
done
two
methods
aggregation
schemes
(coarse
fine
type
depend
on
level
identification).
The
were
compared
taxon
community
levels,
using
large‐scale
databases,
six
traits
global
spectrum
form
function
(i.e.,
height,
seed
mass,
leaf
area,
specific
nitrogen
content
per
stem‐specific
density).
Results
Trait
bivariate
relationships
broadly
similar
taxa,
which
also
case
multivariate
spaces
function.
At
scale,
weighted
by
abundance
significantly
differed
among
biomes,
these
differences
extant.
Main
Conclusions
scheme
does
impact
organisation
space
function,
compares
well
with
that
based
species
actually
present
plots.
true
scale.
These
findings
very
promising
improving
climate
biome
reconstructions
from
pave
way
“pollen
biogeography”.
The Lancet Planetary Health,
Год журнала:
2024,
Номер
8(3), С. e163 - e171
Опубликована: Март 1, 2024
BackgroundClimate
change
is
expected
to
have
profound
effects
on
the
distribution
of
venomous
snake
species,
including
reductions
in
biodiversity
and
changes
patterns
envenomation
humans
domestic
animals.
We
estimated
effect
future
climate
species
potential
knock-on
public
health.MethodsWe
built
models
based
geographical
209
medically
relevant
(WHO
categories
1
2)
present
climatic
variables,
used
these
project
2070.
incorporated
different
scenarios
into
model,
which
we
estimate
loss
gain
areas
potentially
suitable
for
each
species.
also
assessed
countries
were
likely
new
as
a
result
crossing
national
borders.
integrated
with
socioeconomic
would
become
more
vulnerable
snakebites
2070.FindingsOur
results
suggest
that
substantial
losses
survival
most
will
occur
by
However,
some
high
risk
health
could
climatically
habitation.
Countries
such
Niger,
Namibia,
China,
Nepal,
Myanmar
several
from
neighbouring
countries.
Furthermore,
combination
an
increase
factors
(including
low-income
rural
populations)
means
southeast
Asia
Africa
(and
Uganda,
Kenya,
Bangladesh,
India,
Thailand
particular)
increased
vulnerability
future,
human
veterinary
health.InterpretationLoss
affect
ecosystem
functioning
valuable
genetic
resources.
Additionally,
create
challenges
countries,
particularly
Africa.
The
international
community
needs
its
efforts
counter
coming
decades.FundingGerman
Research
Foundation,
Conselho
Nacional
de
Desenvolvimento
Científico
e
Tecnológico,
Coordenação
Aperfeiçoamento
Pessoal
Nível
Superior,
German
Centre
Integrative
Biodiversity
Research,
Ministerio
Ciencia
Innovación
España,
European
Regional
Development
Fund.
Remote Sensing of Environment,
Год журнала:
2024,
Номер
311, С. 114276 - 114276
Опубликована: Июнь 27, 2024
Foliar
traits
such
as
specific
leaf
area
(SLA),
nitrogen
(N),
and
phosphorus
(P)
concentrations
play
important
roles
in
plant
economic
strategies
ecosystem
functioning.Various
global
maps
of
these
foliar
have
been
generated
using
statistical
upscaling
approaches
based
on
in-situ
trait
observations.Here,
we
intercompare
upscaled
at
0.5
•
spatial
resolution
(six
for
SLA,
five
N,
three
P),
categorize
the
used
to
generate
them,
evaluate
with
estimates
from
a
database
vegetation
plots
(sPlotOpen).We
disentangled
contributions
different
functional
types
(PFTs)
quantified
impacts
plot-level
metrics
evaluation
sPlotOpen:
community
weighted
mean
(CWM)
top-of-canopy
(TWM).We
found
that
SLA
N
differ
drastically
fall
into
two
groups
are
almost
uncorrelated
(for
P
only
one
group
were
available).The
primary
factor
explaining
differences
between
is
use
PFT
information
combined
remote
sensing-derived
land
cover
products
while
other
mostly
relied
environmental
predictors
alone.The
corresponding
exhibit
considerable
similarities
patterns
strongly
driven
by
cover.The
not
PFTs
show
lower
level
similarity
tend
be
individual
variables.Upscaled
both
moderately
correlated
sPlotOpen
data
aggregated
grid-cell
(R
=
0.2-0.6)when
processing
way
consistent
respective
approaches,
including
metric
(CWM
or
TWM)
scaling
grid
cells
without
accounting
fractional
impact
TWM
CWM
was
relevant,
but
considerably
smaller
than
information.The
better
reproduce
between-PFT
data,
performed
similarly
capturing
within-PFT
variation.Our
findings
highlight
importance
explicitly
within-grid-cell
variation,
which
has
implications
applications
existing
future
efforts.Remote
sensing
great
potential
reduce
uncertainties
related
observations
regression-based
mapping
steps
involved
upscaling.
Global Ecology and Biogeography,
Год журнала:
2023,
Номер
32(7), С. 1152 - 1162
Опубликована: Апрель 14, 2023
Abstract
Aim
Leaf
traits
are
central
to
plant
function,
and
key
variables
in
ecosystem
models.
However
recently
published
global
trait
maps,
made
by
applying
statistical
or
machine‐learning
techniques
large
compilations
of
environmental
data,
differ
substantially
from
one
another.
This
paper
aims
demonstrate
the
potential
an
alternative
approach,
based
on
eco‐evolutionary
optimality
theory,
yield
predictions
spatio‐temporal
patterns
leaf
that
can
be
independently
evaluated.
Innovation
Global
community‐mean
specific
area
(SLA)
photosynthetic
capacity
(
V
cmax
)
predicted
climate
via
existing
Then
nitrogen
per
unit
N
mass
inferred
using
their
(previously
derived)
empirical
relationships
SLA
.
Trait
data
thus
reserved
for
testing
model
across
sites.
Temporal
trends
also
predicted,
as
consequences
change,
compared
those
leaf‐level
measurements
and/or
remote‐sensing
methods,
which
increasingly
important
source
information
variation
traits.
Main
conclusions
Model
evaluated
against
site‐mean
>
2,000
sites
Plant
database
yielded
R
2
=
73%
SLA,
38%
28%
Declining
species‐level
,
increasing
community‐level
have
both
been
reported
were
correctly
predicted.
Leaf‐trait
mapping
theory
holds
promise
macroecological
applications,
including
improved
understanding
community
leaf‐trait
responses
change.
Global Ecology and Biogeography,
Год журнала:
2023,
Номер
33(2), С. 259 - 271
Опубликована: Ноя. 21, 2023
Abstract
Aim
Biome
classification
schemes
are
widely
used
to
map
biogeographic
patterns
of
vegetation
formations
on
large
spatial
scales.
Future
climate
change
will
influence
biome
patterns,
and
models
can
be
assess
the
susceptibility
biomes
experience
transitions.
However,
is
not
unique,
various
maps
exist.
Here,
we
aimed
how
choice
influences
current
projected
future
patterns.
Location
Africa,
Australia,
Tropical
Asia.
Time
period
2000–2099.
Major
taxa
studied
vegetation.
Methods
We
adaptive
dynamic
global
model
version
2
(aDGVM2)
simulate
in
study
region.
classified
into
using
(1)
a
scheme
based
cover
functional
types,
(2)
cluster
analysis
types
(3)
trait
simulated
by
aDGVM2.
compared
resulting
multiple
observation‐based
quantified
differences
changes
under
RCP8.5
scenario
for
different
schemes.
Results
As
expected,
were
strongly
related
classification.
The
highest
data‐model
agreement
was
derived
21
traits.
Traits
size
most
important
Considering
all
schemes,
area
undergo
transitions
varied
between
16.5%
32.1%.
Despite
this
variability,
consistently
showed
that
grassland
savanna
areas
susceptible
change,
whereas
tropical
forests
deserts
stable.
Our
results
demonstrate
traits
aDGVM2
appropriate
delimit
biomes.
Main
conclusions
Studies
projecting
should
consider
applying
avoid
biases
such
projections
caused