Global Ecology and Conservation,
Год журнала:
2021,
Номер
30, С. e01762 - e01762
Опубликована: Авг. 20, 2021
Identifying
conservation
priorities
for
an
understudied
species
can
be
challenging,
as
the
amount
and
type
of
data
available
to
work
with
are
often
limited.
Here,
we
demonstrate
a
flexible
workflow
identifying
such
data-limited
species,
focusing
on
little-studied
Asian
golden
cat
(Catopuma
temminckii)
in
mainland
Tropical
Asia.
Using
recent
occurrence
records,
modeled
cat's
expected
area
identified
remaining
habitat
strongholds
(i.e.,
large
intact
areas
moderate-to-high
occurrence).
We
then
classified
these
by
camera-trap
survey
status
(from
literature
review)
near-future
threat
(based
publicly
forest
loss
projections
Bayesian
Belief
Network
derived
estimates
hunting-induced
extirpation
risk)
identify
priorities.
Finally,
projected
species'
year
2000,
approximately
three
generations
prior
today,
define
past
declines
better
evaluate
current
status.
Lower
levels
risk
higher
closed-canopy
cover
were
strongest
predictors
records.
Our
suggest
68%
decline
between
2000
2020,
further
18%
predicted
over
next
20
years.
Past
primarily
driven
cumulatively
increasing
risk,
suggesting
assessments
based
solely
may
underestimate
actual
population
declines.
Of
40
strongholds,
77.5%
seriously
threatened
hunting.
Only
52%
had
at
least
one
site
surveyed,
compared
100%
low-to-moderate
thus
highlighting
important
knowledge
gap
concerning
distribution
results
has
experienced,
will
likely
continue
experience,
considerable
should
considered
up-listing
category
VU/EN)
under
criteria
A2c
IUCN
Red
List.
Ecological Indicators,
Год журнала:
2023,
Номер
148, С. 110121 - 110121
Опубликована: Март 14, 2023
Protected
areas
(PAs)
play
a
key
role
in
mitigating
ecological
crises.
Currently,
priority
protected
(PPAs)
focus
on
biological
conservation,
and
few
studies
have
considered
the
connectivity
between
patches.
Few
formulated
future
conservation
measures
from
two
dimensions
of
security
pattern
(ESP)
reserve
effectiveness.
To
fill
this
gap,
study
use
ESP
to
identify
that
meet
objectives.
We
take
Wuhan
metropolitan
area
as
research
area.
constructed
framework
for
formulating
development
plans
based
areas.
The
complete
method
system,
we
focused
construction
evaluation
index
system
landscape
connectivity.
Then,
effectiveness
PAs
could
be
evaluated,
PPAs
identified.
results
showed
there
were
five
isolated
among
existing
PAs.
Moreover,
total
was
9328.91
km2,
they
had
high
value.
Due
low
protection
rate
PPAs,
are
not
main
target
PAs;
thus,
new
According
our
plan,
with
different
classes
will
achieve
functions
work.
Our
focuses
achieving
sustainable
formulates
environmental
land
planning
balance
urban
development.
It
can
provide
information
support
realization
2030
vision
Communications Biology,
Год журнала:
2022,
Номер
5(1)
Опубликована: Ноя. 4, 2022
Abstract
Identifying
hotspots
of
biological
diversity
is
a
key
step
in
conservation
prioritisation.
Melanesia—centred
on
the
vast
island
New
Guinea—is
increasingly
recognised
for
its
exceptionally
species-rich
and
endemic
biota.
Here
we
show
that
Melanesia
has
world’s
most
diverse
insular
amphibian
fauna,
with
over
7%
global
frog
species
less
than
0.7%
land
area,
97%
endemic.
We
further
estimate
nearly
200
additional
candidate
have
been
discovered
but
remain
unnamed,
pointing
to
total
fauna
excess
700
species.
Nearly
60%
Melanesian
lineage
direct-developing
microhylids
characterised
by
smaller
distributions
co-occurring
families,
suggesting
lineage-specific
high
beta
driver
anuran
megadiversity.
A
comprehensive
status
assessment
highlights
geographic
concentrations
recently
described
range-restricted
threatened
taxa
warrant
urgent
actions.
Nonetheless,
world
standards,
relatively
intact,
6%
assessed
listed
as
no
documented
extinctions;
thus
it
provides
an
unparalleled
opportunity
understand
conserve
megadiverse
intact
Ecological Informatics,
Год журнала:
2023,
Номер
75, С. 102026 - 102026
Опубликована: Фев. 18, 2023
Species
Distribution
Models
(SDMs)
are
a
powerful
tool
to
derive
habitat
suitability
predictions
relating
species
occurrence
data
with
features.
Two
of
the
most
frequently
applied
algorithms
model
species-habitat
relationships
Generalised
Linear
(GLM)
and
Random
Forest
(RF).
The
former
is
parametric
regression
providing
functional
models
direct
interpretability.
latter
machine
learning
non-parametric
algorithm,
more
tolerant
than
other
approaches
in
its
assumptions,
which
has
often
been
shown
outperform
algorithms.
Other
have
developed
produce
robust
SDMs,
like
training
bootstrapping
spatial
scale
optimisation.
Using
felid
presence-absence
from
three
study
regions
Southeast
Asia
(mainland,
Borneo
Sumatra),
we
tested
performances
SDMs
by
implementing
four
modelling
frameworks:
GLM
RF
bootstrapped
non-bootstrapped
data.
With
Mantel
ANOVA
tests
explored
how
combinations
influenced
their
predictive
performances.
Additionally,
scale-optimisation
responded
species'
size,
taxonomic
associations
(species
genus),
area
algorithm.
We
found
that
choice
algorithm
had
strong
effect
determining
differences
between
SDMs'
predictions,
while
no
effect.
followed
species,
were
main
factors
driving
scales
identified.
trained
showed
higher
performance,
however,
revealed
significant
only
explaining
variance
observed
sensitivity
specificity
and,
when
interacting
bootstrapping,
Percent
Correctly
Classified
(PCC).
Bootstrapping
significantly
explained
specificity,
PCC
True
Skills
Statistics
(TSS).
Our
results
suggest
there
systematic
identified
produced
vs.
RF,
but
neither
approach
was
consistently
better
other.
divergent
inconsistent
abilities
analysts
should
not
assume
inherently
superior
test
multiple
methods.
implications
for
SDM
development,
revealing
inconsistencies
introduced
on
optimisation,
selecting
broader
RF.
Animal Conservation,
Год журнала:
2022,
Номер
25(5), С. 660 - 679
Опубликована: Фев. 27, 2022
Abstract
Species
occur
in
sympatric
assemblages,
bound
together
by
ecological
relationships
and
interspecific
interactions.
Borneo
Sumatra
host
some
of
the
richest
assemblages
biota
worldwide.
The
region,
however,
faces
highest
global
deforestation
rates,
which
seriously
threaten
its
unique
biodiversity.
We
used
a
large
camera
trap
dataset
that
recorded
data
for
70
terrestrial
species
mammals
birds,
to
explore
drivers
regional
richness
patterns.
Using
multi‐scale,
multivariate
modelling
framework
quantified
main
environmental
factors
associated
with
patterns
biodiversity,
while
simultaneously
assessing
individual
each
species,
we
determined
sampled
their
contributions
community
assemblages.
then
mapped
predicted
richness,
evaluated
effectiveness
protected
areas
securing
biodiversity
hotspots,
performed
gap
analysis
highlight
biodiverse
lacking
protection
compared
our
predictions
maps
produced
using
IUCN
range
layers.
Finally,
investigated
performance
as
an
indicator
demonstrate
is
primarily
affected
gradients
anthropogenic
factors,
only
marginally
topographic
spatial
factors.
In
both
islands,
are
elevational
vegetation
climate,
leading
altitudinal
zonation
niche
separation
major
factor
characterizing
islands'
was
north‐eastern
western
Sumatra.
found
most
hotspots
not
formally
either
island;
9.2
18.2%
modelled
occurred
within
Sumatra,
respectively.
highlighted
prediction
better
than,
differed
drastically
from,
layer,
layer
one
were
similar,
showed
low
predictive
power.
Our
suggests
common
generalist
carnivores
effective
indicators
have
high
potential
focal,
umbrella
or
assist
multi‐species
vertebrate
conservation
planning.
Understanding
existing
critical
support
development
strategies
this
rapidly
changing
region.
Insects,
Год журнала:
2025,
Номер
16(1), С. 79 - 79
Опубликована: Янв. 14, 2025
Butterflies
are
highly
sensitive
to
climate
change,
and
Troides
helena,
as
an
endangered
butterfly
species,
is
also
affected
by
these
changes.
To
enhance
the
conservation
of
T.
helena
effectively
plan
its
protected
areas,
it
crucial
understand
potential
impacts
change
on
distribution.
This
study
utilized
a
MaxEnt
model
in
combination
with
ArcGIS
technology
predict
global
suitable
habitats
under
current
future
conditions,
using
species’
distribution
data
relevant
environmental
variables.
The
results
indicated
that
provided
good
prediction
accuracy
for
helena.
Under
scenario,
species
primarily
distributed
tropical
regions,
high
suitability
areas
concentrated
rainforest
climates.
In
scenarios,
habitat
medium
categories
generally
show
expansion
trend,
which
increases
over
time.
Especially
SSP5-8.5
2090s,
area
projected
increase
42.85%.
analysis
key
factors
revealed
precipitation
wettest
quarter
(Bio16)
was
most
significant
factor
affecting
has
demands
temperature
can
adapt
warming.
valuable
identifying
optimal
provides
reference
efforts.
