Biosystems Diversity,
Год журнала:
2025,
Номер
33(1), С. e2507 - e2507
Опубликована: Фев. 21, 2025
The
present
investigation
aims
to
analyse
the
influence
of
bioclimatic
predictors
on
geographical
distribution
species
Opatrum
sabulosum
(Linnaeus,
1761)
and
predict
changes
in
its
range
context
global
warming.
sabulosum,
a
belonging
Tenebrionidae
family,
exhibits
high
degree
plasticity
environmental
requirements,
yet
remains
susceptible
impacts
climate
change.
maximum
entropy
algorithm
(MaxEnt)
was
employed
model
ecological
niche,
with
data
from
GBIF
database
key
variables
such
as
temperature,
precipitation,
their
seasonality
being
utilised.
Forecasts
were
made
for
up
2080
under
four
change
scenarios:
SSP1-2.6,
SSP2-4.5,
SSP3-7.0
SSP5-8.5.
results
indicate
that
factors
affecting
O.
are
minimum
temperature
coldest
month,
average
quarter,
amount
precipitation
warmest
wettest
quarters.
analysis
indicates
that,
current
conditions,
most
favourable
habitats
located
Western
Europe,
southern
Britain,
Scandinavia
northern
Black
Sea
region.
In
moderate
warming
scenario
(SSP1-2.6),
species'
is
projected
expand
an
eastward
northeasterly
direction,
driven
by
rising
temperatures
enhanced
water
balance.
Conversely,
extreme
scenarios
(SSP3-7.0,
SSP5-8.5),
decline
habitat
quality
southeastern
part
due
elevated
temperatures,
reduced
humidity,
instability
climatic
parameters.
practical
significance
these
lies
possibility
using
develop
adaptation
strategies
biodiversity
conservation
effective
management
natural
resources.
can
serve
basis
assessing
risks
ecosystem
creating
new
protected
areas.
Information
regarding
sensitivity
also
important
sustainable
development
agroecosystems,
which
this
plays
role
maintaining
soil
fertility.
findings
study
directly
pertinent
attainment
Sustainable
Development
Goals
(SDGs)
established
United
Nations
2015.
Specifically,
contributes
implementation
SDG
13
'Combat
change'
providing
more
nuanced
understanding
effects
ecosystems
conditions.
15,
'Conserve
terrestrial
ecosystems',
predicting
helps
conserve
restore
degraded
ecosystems.
integration
into
practices
expected
contribute
ensuring
sustainability,
efficient
use
resources,
creation
harmonious
environment
future
generations.
Prospects
further
research
include
long-term
monitoring
populations,
genetic
assess
adaptive
potential,
expanding
anthropogenic
land
change,
urbanisation
agricultural
activities.
This
will
allow
accurate
forecasting
future.
Environmental Geochemistry and Health,
Год журнала:
2024,
Номер
46(8)
Опубликована: Июль 5, 2024
Abstract
Water
scarcity
is
a
growing
concern
due
to
rapid
urbanization
and
population
growth.
This
study
assesses
spring
water
quality
at
20
stations
in
Giresun
province,
Türkiye,
focusing
on
potentially
toxic
elements
physicochemical
parameters.
The
Quality
Index
rated
most
samples
as
"excellent"
during
the
rainy
season
"good"
dry
season,
except
4
(40°
35′
12″
North/38°
26′
34″
East)
19
44′
28″
06′
53″
West),
indicating
"poor"
quality.
Mean
macro-element
concentrations
(mg/L)
were:
Ca
(34.27),
Na
(10.36),
Mg
(8.26),
K
(1.48).
trace
element
values
(μg/L)
Al
(1093),
Zn
(110.54),
Fe
(67.45),
Mn
(23.03),
Cu
(9.79),
As
(3.75),
Ni
(3.00),
Cr
(2.84),
Pb
(2.70),
Co
(1.93),
Cd
(0.76).
Health
risk
assessments
showed
minimal
non-carcinogenic
risks,
while
carcinogenic
from
arsenic
slightly
exceeded
safe
limits
(CR
=
1.75E−04).
Higher
were
increased
recharge,
arsenic-laden
surface
runoff,
human
activities.
Statistical
analyses
(PCA,
PCC,
HCA)
suggested
that
metals
physico-chemical
parameters
originated
lithogenic,
anthropogenic,
or
mixed
sources.
Regular
monitoring
of
recommended
mitigate
potential
public
health
risks
waterborne
contaminants.
Water,
Год журнала:
2024,
Номер
16(19), С. 2748 - 2748
Опубликована: Сен. 27, 2024
Groundwater
salinization
poses
a
critical
threat
to
sustainable
development
in
arid
and
semi-arid
rurbanizing
regions,
exemplified
by
Kerman
Province,
Iran.
This
region
experiences
groundwater
ecosystem
degradation
as
result
of
the
rapid
conversion
rural
agricultural
land
urban
areas
under
chronic
drought
conditions.
study
aims
enhance
Pollution
Risk
(GwPR)
mapping
integrating
DRASTIC
index
with
machine
learning
(ML)
models,
including
Random
Forest
(RF),
Boosted
Regression
Trees
(BRT),
Generalized
Linear
Model
(GLM),
Support
Vector
Machine
(SVM),
Multivariate
Adaptive
Splines
(MARS),
alongside
hydrogeochemical
investigations,
promote
water
management
Province.
The
RF
model
achieved
highest
accuracy
an
Area
Under
Curve
(AUC)
0.995
predicting
GwPR,
outperforming
BRT
(0.988),
SVM
(0.977),
MARS
(0.951),
GLM
(0.887).
RF-based
map
identified
new
high-vulnerability
zones
northeast
northwest
showed
expanded
moderate
vulnerability
zone,
covering
48.46%
area.
Analysis
revealed
exceedances
WHO
standards
for
total
hardness
(TH),
sodium,
sulfates,
chlorides,
electrical
conductivity
(EC)
these
areas,
indicating
contamination
from
mineralized
aquifers
unsustainable
practices.
findings
underscore
model’s
effectiveness
prediction
highlight
need
stricter
monitoring
management,
regulating
extraction
improving
use
efficiency
riverine
aquifers.
Environmental Sciences Europe,
Год журнала:
2024,
Номер
36(1)
Опубликована: Сен. 2, 2024
Groundwater
is
a
primary
source
of
drinking
water
for
billions
worldwide.
It
plays
crucial
role
in
irrigation,
domestic,
and
industrial
uses,
significantly
contributes
to
drought
resilience
various
regions.
However,
excessive
groundwater
discharge
has
left
many
areas
vulnerable
potable
shortages.
Therefore,
assessing
potential
zones
(GWPZ)
essential
implementing
sustainable
management
practices
ensure
the
availability
present
future
generations.
This
study
aims
delineate
with
high
Bankura
district
West
Bengal
using
four
machine
learning
methods:
Random
Forest
(RF),
Adaptive
Boosting
(AdaBoost),
Extreme
Gradient
(XGBoost),
Voting
Ensemble
(VE).
The
models
used
161
data
points,
comprising
70%
training
dataset,
identify
significant
correlations
between
presence
absence
region.
Among
methods,
(RF)
(XGBoost)
proved
be
most
effective
mapping
potential,
suggesting
their
applicability
other
regions
similar
hydrogeological
conditions.
performance
metrics
RF
are
very
good
precision
0.919,
recall
0.971,
F1-score
0.944,
accuracy
0.943.
indicates
strong
capability
accurately
predict
minimal
false
positives
negatives.
(AdaBoost)
demonstrated
comparable
across
all
(precision:
recall:
F1-score:
accuracy:
0.943),
highlighting
its
effectiveness
predicting
accurately;
whereas,
outperformed
slightly,
higher
values
metrics:
(0.944),
(0.971),
(0.958),
(0.957),
more
refined
model
performance.
(VE)
approach
also
showed
enhanced
performance,
mirroring
XGBoost's
0.958,
0.957).
that
combining
strengths
individual
leads
better
predictions.
potentiality
zoning
varied
significantly,
low
accounting
41.81%
at
24.35%.
uncertainty
predictions
ranged
from
0.0
0.75
area,
reflecting
variability
need
targeted
strategies.
In
summary,
this
highlights
critical
managing
resources
effectively
advanced
techniques.
findings
provide
foundation
practices,
ensuring
use
conservation
beyond.
