Unrecorded Butterfly Species and Potential Local Extinctions: The Role of Citizen Science and Sampling
Ecology and Evolution,
Год журнала:
2025,
Номер
15(2)
Опубликована: Фев. 1, 2025
ABSTRACT
Estimating
species
extinction
risk
is
crucial
to
reverse
biodiversity
loss
and
adopt
proper
conservation
measures.
Different
sources
may
play
a
pivotal
role
in
prioritising
conservation.
Recently,
citizen
science
demonstrated
substantial
role,
especially
when
it
comes
butterflies.
This
study
examines
records
richness
Aosta
Valley,
which
represents
one
of
the
highest
mountain
areas
Europe.
Through
30,351
data
points
from
1825
2022,
impact
efficiency
three
groups
were
investigated:
literature
(i.e.,
publications
collections),
sampling
(butterfly
experts'
recording),
(open‐source
databases).
The
also
aims
assess
potential
butterflies
relation
functional
traits.
results
showed
that
even
if
there
significant
differences
number
between
sources,
no
for
recorded.
Moreover,
2.9%
butterfly
community
risks
extinction,
related
some
response
Indeed,
increase
altitudinal
range
decreases
multivoltines.
In
conclusion,
has
strong
on
amount
could
be
exploited
fill
gaps
at
low/medium
altitudes.
However,
professional
needed
focus
longer
reported,
particular
are
difficult
identify,
have
specific
distributions
or
traits
(e.g.,
limited
range).
Using
different
estimation,
trait
analysis,
possible
prioritise
studies
using
efforts
(sampling
and/or
sciences).
Язык: Английский
Evidence of novelty and specialization behavior in participatory science reporting
Oikos,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 4, 2025
Participatory
science
(or
‘citizen
science')
records
are
becoming
increasingly
useful
for
wildlife
monitoring
due
to
their
volume
and
spatiotemporal
coverage.
Statistical
analysis
of
these
data
can
be
challenging
the
many
sources
sampling
heterogeneity
that
need
accounted
for.
Many
previous
studies
characterize
variability
across
entire
participatory
datasets,
such
as
spatial
in
effort
or
species
preferences.
User‐level
behavior
is
less
well
studied,
but
it
may
just
important
dataset‐level
contributing
downstream
analyses.
Here,
we
investigate
user‐level
novelty
specialization
behavior.
Novelty‐seeking
occurs
when
an
individual
observer
preferentially
reports
they
have
not
seen
before,
while
specialization,
novelty‐avoidant
behavior,
previously
observed
(i.e.
specialize
particular
taxa).
We
provide
first
test
by
analyzing
histories
more
than
2000
observers
on
popular
platform
iNaturalist
Pennsylvania,
USA.
find
evidence
overall
70%
considered.
identified
six
times
indicating
a
large
proportion
reported
had
at
higher
rate
expected.
Within
taxonomic
groups,
61%
deviated
from
neutral
Novelty
were
both
common
within
taxa
well.
These
findings
suggest
often
favorite
species,
some
users
simultaneously
seek
out
unobserved
species.
Язык: Английский
Can gamification save the planet? Revolutionizing citizen science for biodiversity conservation
Biological Conservation,
Год журнала:
2025,
Номер
302, С. 111001 - 111001
Опубликована: Янв. 31, 2025
Язык: Английский
A nature tourism and citizen science alliance
BioScience,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 6, 2025
Язык: Английский
Evidence of novelty bias and specialization in participatory science sampling behavior
Authorea (Authorea),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 31, 2024
Participatory
science
(or
"citizen
science")
records
are
becoming
increasingly
useful
for
wildlife
monitoring
due
to
their
volume
and
spatiotemporal
coverage.
However,
statistical
analysis
using
these
data
can
be
challenging
the
many
sources
of
bias
that
need
corrected.
Many
previous
studies
characterize
sampling
biases
across
entire
participatory
datasets,
such
as
spatial
heterogeneity
in
effort
or
species
preferences.
User-level
behavior
is
less
well
studied,
but
it
may
just
important
dataset-level
contributing
error
downstream
analyses.
Here,
we
investigate
user-level
novelty
specialization
bias.
Novelty
occurs
when
an
individual
observer
preferentially
reports
have
not
seen
before,
while
they
previously
observed
(i.e.,
specialize
particular
species).
We
provide
first
test
this
kind
by
analyzing
histories
more
than
540
observers
on
popular
platform
iNaturalist
Pennsylvania,
USA.
find
evidence
overall
66%
considered.
Specialization
was
5
times
common
bias,
indicating
reported
had
at
a
higher
rate
expected.
Looking
within
taxonomic
groups,
41%
deviated
from
unbiased
sampling.
were
both
taxa.
These
findings
suggest
often
favorite
taxa
species,
some
users
simultaneously
seek
out
unobserved
species.
Язык: Английский
A Comparison of Butterfly Diversity Results between iNaturalist and Expert Surveys in Eastern Oklahoma
Diversity,
Год журнала:
2024,
Номер
16(9), С. 515 - 515
Опубликована: Авг. 27, 2024
Ongoing
worldwide
biodiversity
declines
and
range
shifts
associated
with
climate
change
increase
the
importance
of
documenting
current
distributions
species
to
establish
baseline
data.
However,
financial
logistical
constraints
make
it
impossible
for
taxonomic
experts
conduct
thorough
surveys
in
most
locations.
One
popular
approach
offset
lack
expert
sampling
is
using
community
science
data
collected
by
public,
curated,
made
available
research.
These
datasets,
however,
contain
different
biases
than
those
typically
present
through
conventional
survey
practices,
often
leading
results.
Recent
studies
have
used
massive
datasets
generated
over
large
areas;
less
known
about
results
obtained
at
smaller
scales
or
more
limited
intervals.
We
compared
butterfly
observations
eastern
Oklahoma
a
dataset
from
website
iNaturalist
one
during
targeted
glade
habitats
conducted
experts.
At
county-level
scale,
relative
abundances
correlated
well
between
observations,
there
was
no
difference
abundance
families
two
methods.
as
anticipated,
outperformed
measuring
geographic
scale.
Язык: Английский