ABSTRACT
The
Risk
Assessment
Mapping
Program
(RAMP)
is
a
user-friendly
tool
that
uses
climate
data
and
known
occurrences
of
nonnative
species
to
predict
where
the
may
be
able
survive.
We
compared
performance
RAMP
two
machine
learning
methods,
boosted
regression
trees
maximum
entropy,
at
estimating
distributions
30
aquatic
are
Laurentian
Great
Lakes
Basin.
For
each
method,
we
created
models
tested
them
against
subsets
calculate
true
skill
statistics
(TSS).
This
measure
ranges
between
–1
(no
better
than
random)
1
(perfect
assessment).
Average
TSS
values
were
0.81
±
0.09
(boosted
tree),
0.76
0.12
(maximum
entropy),
0.06
(RAMP).
Despite
having
high
values,
our
generally
underestimate
potential
across
forecasts
much
greater
areas
basin
climatically
appropriate
for
therefore
more
suitable
conservative
management
decisions.
Ecology and Evolution,
Год журнала:
2023,
Номер
13(2)
Опубликована: Фев. 1, 2023
Abstract
Species
distribution
models
(SDMs)
are
practical
tools
to
assess
the
habitat
suitability
of
species
with
numerous
applications
in
environmental
management
and
conservation
planning.
The
manipulation
input
data
deal
their
spatial
bias
is
one
advantageous
methods
enhance
performance
SDMs.
However,
development
a
model
parameterization
approach
covering
different
SDMs
achieve
well‐performing
has
rarely
been
implemented.
We
integrated
tuning
for
four
commonly‐used
SDMs:
generalized
linear
(GLM),
gradient
boosted
(GBM),
random
forest
(RF),
maximum
entropy
(MaxEnt),
compared
predictive
geographically
imbalanced‐biased
rare
complex
mountain
vipers.
Models
were
tuned
up
based
on
range
model‐specific
parameters
considering
two
background
selection
methods:
weighting
schemes.
fine‐tuned
was
assessed
recently
identified
localities
species.
results
indicated
that
although
version
all
shows
great
predicting
training
(AUC
>
0.9
TSS
0.5),
they
produce
classifying
out‐of‐bag
data.
GBM
RF
higher
sensitivity
showed
more
performances.
GLM,
despite
having
high
test
data,
lower
specificity.
It
only
MaxEnt
comparable
identifying
both
procedures.
Our
highlight
while
prone
overfitting
GLM
over‐predict
nonsampled
areas
capable
producing
predictable
(extrapolative)
(interpolative).
discuss
assumptions
each
conclude
could
be
considered
as
method
cope
modeling
approaches.
Ecology Letters,
Год журнала:
2023,
Номер
26(12), С. 2043 - 2055
Опубликована: Окт. 3, 2023
Species
distributions
are
conventionally
modelled
using
coarse-grained
macroclimate
data
measured
in
open
areas,
potentially
leading
to
biased
predictions
since
most
terrestrial
species
reside
the
shade
of
trees.
For
forest
plant
across
Europe,
we
compared
conventional
macroclimate-based
distribution
models
(SDMs)
with
corrected
for
microclimate
buffering.
We
show
that
microclimate-based
SDMs
at
high
spatial
resolution
outperformed
and
coarser
resolution.
Additionally,
introduced
a
systematic
bias
response
curves,
which
could
result
erroneous
range
shift
predictions.
Critically
important
conservation
science,
these
were
unable
identify
warm
cold
refugia
edges
distributions.
Our
study
emphasizes
crucial
role
when
used
gain
insights
into
biodiversity
face
climate
change,
particularly
given
growing
policy
management
focus
on
worldwide.
Progress in Physical Geography Earth and Environment,
Год журнала:
2023,
Номер
47(3), С. 467 - 482
Опубликована: Фев. 21, 2023
There
is
a
lack
of
guidance
on
the
choice
spatial
grain
predictor
and
response
variables
in
species
distribution
models
(SDM).
This
review
summarizes
current
state
art
with
regard
to
following
points:
(i)
effects
changing
resolution
model
performance;
(ii)
effect
conducting
multi-grain
versus
single-grain
analysis
(iii)
role
land
cover
type
autocorrelation
selecting
appropriate
size.
In
reviewed
literature,
we
found
that
coarsening
variable
typically
leads
declining
performance.
Therefore,
recommend
aiming
for
finer
resolutions
unless
there
reason
do
otherwise
(e.g.
expert
knowledge
ecological
scale).
We
also
so
far,
improvements
performance
reported
have
been
relatively
low
useful
predictions
can
be
generated
even
from
single-scale
models.
addition,
use
high-resolution
predictors
improves
however,
only
limited
evidence
whether
this
applies
coarser-resolution
100
km
2
coarser).
Low-resolution
are
usually
sufficient
associated
fairly
common
environmental
conditions
but
not
less
ones
vs
rare
category).
because
reduces
variability
within
heterogeneous
underrepresentation
environments,
which
lead
decrease
Thus,
assessing
at
multiple
grains
provide
insights
into
impacts
their
Overall,
observed
studies
examining
simultaneous
manipulation
variables.
stress
need
explicitly
report
all
Diversity and Distributions,
Год журнала:
2023,
Номер
29(10), С. 1245 - 1262
Опубликована: Июль 27, 2023
Abstract
Aim
Understanding
how
grain
size
affects
our
ability
to
characterize
species
responses
ongoing
climate
change
is
of
crucial
importance
in
the
context
an
increasing
awareness
for
substantial
difference
that
exists
between
coarse
spatial
resolution
macroclimatic
data
sets
and
microclimate
actually
experienced
by
organisms.
Climate
impacts
on
biodiversity
are
expected
peak
mountain
areas,
wherein
differences
macro
microclimates
precisely
largest.
Based
a
newly
generated
fine‐scale
environmental
Canary
Islands,
we
assessed
whether
at
100
m
able
provide
more
accurate
predictions
than
available
1
km
resolution.
We
also
analysed
future
suitability
island
endemic
bryophytes
differ
depending
grids.
Location
Islands.
Time
period
Present
(1979–2013)
late‐century
(2071–2100).
Taxa
Bryophytes.
Methods
compared
accuracy
using
ensemble
small
models
14
Macaronesian
bryophyte
species.
used
two
sets:
CHELSA
v1.2
(~1
km)
CanaryClim
v1.0
(100
m),
downscaled
version
latter
utilizing
from
local
weather
stations.
encompasses
five
individual
model
intercomparison
projects
three
warming
shared
socio‐economic
pathways.
Results
Species
distribution
exhibited
similar
accuracy,
but
predicted
buffered
trends
mid‐elevation
ridges.
consistently
returned
higher
proportions
suitable
pixels
(8%–28%)
(0%–3%).
Consequently,
proportion
occupy
uncertain
was
with
(3–8
species)
(0–2
species).
Main
conclusions
The
impacted
rather
performance
models.
Our
results
highlight
role
fine‐resolution
can
play
predicting
potential
both
microrefugia
new
range
under
climate.
Nature Ecology & Evolution,
Год журнала:
2024,
Номер
8(3), С. 454 - 466
Опубликована: Янв. 22, 2024
Abstract
To
meet
the
COP15
biodiversity
framework
in
European
Union
(EU),
one
target
is
to
protect
30%
of
its
land
by
2030
through
a
resilient
transnational
conservation
network.
The
Alps
are
key
hub
this
network
hosting
some
most
extensive
natural
areas
and
hotspots
Europe.
Here
we
assess
robustness
current
reserve
safeguard
Alps’
flora
2080
using
semi-mechanistic
simulations.
We
first
highlight
that
needs
strong
readjustments
as
it
does
not
capture
patterns
well
our
Overall,
predict
shift
need
time
along
latitudes,
from
lower
higher
elevations
plants
migrate
upslope
shrink
their
distribution.
While
increasing
species,
trait
evolutionary
diversity,
migration
could
also
threaten
70%
resident
flora.
In
face
global
changes,
future
will
ensure
elevation
latitudinal
connections
complementarily
multifaceted
beyond
national
borders.
Land,
Год журнала:
2023,
Номер
12(7), С. 1433 - 1433
Опубликована: Июль 18, 2023
The
conservation
of
threatened
species
and
the
restoration
ecosystems
have
emerged
as
crucial
ecological
prerequisites
in
context
a
changing
global
environment.
One
such
significant
commercial
value
is
Bael
tree,
scientifically
known
Aegle
marmelos,
which
native
to
semi-arid
regions
Pakistan.
However,
faces
threats
Pakistan
due
overexploitation
land
use.
To
support
sustainable
production
practices
agricultural
planning,
it
important
investigate
how
climate
change
has
affected
geographic
distribution
marmelos.
Additionally,
impact
on
its
frequency
remains
uncertain.
address
these
concerns,
we
employed
modeling
techniques
using
MaxEnt
GIS
predict
present
future
favorable
habitats
for
Based
our
findings,
several
key
bioclimatic
variables
were
identified
influencers
marmelos
distribution.
These
include
soil
bulk
density
(bdod),
isothermality
(bio03),
precipitation
during
warmest
quarter
(bio18),
mean
temperature
wettest
(bio08).
Currently,
potential
suitable
habitat
spans
an
area
approximately
396,869
square
kilometers,
primarily
concentrated
Punjab,
Khyber
Pakhtunkhwa,
Balochistan
deemed
highly
are
predominantly
found
upper
central
Punjab.
if
persists,
likely
become
more
fragmented,
resulting
shift
overall
area.
Moreover,
center
expected
relocate
towards
southeast,
leading
increased
spatial
separation
over
time.
results
this
research
significantly
contribute
understanding
geo-ecological
aspects
related
Furthermore,
they
provide
valuable
recommendations
protection,
management,
monitoring,
species.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 29, 2024
Abstract
Recently,
the
tiger-cat
species
complex
was
split
into
Leopardus
tigrinus
and
guttulus
,
along
with
other
proposed
schemes.
We
performed
a
detailed
analysis
integrating
ecological
modeling,
biogeography,
phenotype
of
four
originally
recognized
subspecies—
oncilla
pardinoides
—and
presented
new
multidimensional
niche
depiction
species.
Species
distribution
models
used
>
1400
records
from
museums
photographs,
all
checked
for
accuracy.
Morphological
data
were
obtained
institutional/personal
archives.
Spotting
patterns
established
by
museum
photographic/camera-trap
records.
Principal
component
showed
three
clearly
distinct
groups,
Central
American
specimens
(
)
clustering
entirely
within
those
Andes,
namely
group
cloud
forests
southern
Central-American
Andean
mountain
chains
(clouded
tiger-cat);
savannas
Guiana
Shield
central/northeastern
Brazil
(savanna
in
lowland
Atlantic
Forest
domain
(Atlantic
tiger-cat).
This
scheme
is
supported
recent
genetic
analyses.
All
displayed
different
spotting
patterns,
some
significant
differences
body
measurements/proportions.
The
alarming
reductions
historic
range
−
50.4%
to
68.2%.
approach
revealed
elusive
threatened
complex.
Journal of Applied Ecology,
Год журнала:
2024,
Номер
61(4), С. 713 - 732
Опубликована: Март 1, 2024
Abstract
Conservation
translocations
are
an
important
tool
for
combating
species
declines
and
population
losses.
Species
distribution
models
(SDMs)
can
facilitate
the
selection
of
suitable
release
sites
translocation
programs.
However,
these
be
sensitive
to
several
modelling
decisions.
In
this
study,
we
explore
impacts
three
key
decisions
on
Maxent
developed
inform
reintroductions
long‐toed
salamander
(
Ambystoma
macrodactylum
)
in
southwestern
Alberta.
We
specifically
test
sensitivity
model
predictions
(1)
type
environmental
variables
used
generate
models,
(2)
whether
background
points
calibrate
reflects
potential
bias
input
locality
records
(3)
choice
geographic
study
extent.
use
independent
presence‐absence
data
from
extensive
field
survey
accuracy
based
different
Both
performance
were
Models
using
local
extents
more
accurate
than
those
range‐wide
extents.
extent
impacted
set
included
species.
further
demonstrate
ranking
present
a
final
recommendations
that
accounts
uncertainty
under
both
current
future
climatic
conditions.
identify
expected
time
periods
as
Synthesis
applications
:
Our
adds
our
understanding
how
impact
SDMs
downstream
conclusions
while
simultaneously
demonstrating
rigorous
approach
conservation
planning.