The
movement
behavior
of
an
animal
is
determined
by
extrinsic
and
intrinsic
factors
that
operate
at
multiple
spatio-temporal
scales,
yet
much
our
knowledge
comes
from
studies
examine
only
one
or
two
scales
concurrently.
Understanding
the
drivers
across
crucial
for
understanding
fundamentals
ecology,
predicting
changes
in
distribution,
describing
disease
dynamics,
identifying
efficient
methods
wildlife
conservation
management.We
obtained
over
400,000
GPS
locations
wild
pigs
13
different
spanning
six
states
southern
U.S.A.,
quantified
rates
home
range
size
within
a
single
analytical
framework.
We
used
generalized
additive
mixed
model
framework
to
quantify
effects
five
broad
predictor
categories
on
movement:
individual-level
attributes,
geographic
factors,
landscape
meteorological
conditions,
temporal
variables.
examined
predictors
three
scales:
daily,
monthly,
using
all
data
during
study
period.
considered
both
local
environmental
such
as
daily
weather
distance
various
resources
landscape,
well
acting
broader
spatial
scale
ecoregion
season.We
found
variables
(temperature
pressure),
features
(distance
water
sources),
broad-scale
factor
(ecoregion),
characteristics
(sex-age
class),
drove
pig
but
magnitude
shape
covariate
relationships
differed
scales.The
we
present
can
be
assess
patterns
arising
sources
species
while
accounting
correlations.
Our
analyses
show
which
reaction
norms
change
based
response
data,
illustrating
importance
appropriately
defining
covariates
depending
intended
implications
research
(e.g.,
due
climate
versus
planning
local-scale
management).
argue
consideration
same
(rather
than
comparing
separate
post-hoc)
gives
more
accurate
quantification
cross-scale
error
correlation.
Vegetation
phenology
controls
the
seasonality
of
many
ecosystem
processes,
as
well
numerous
biosphere-atmosphere
feedbacks.
Phenology
is
also
highly
sensitive
to
climate
change
and
variability.
Here
we
present
a
series
datasets,
together
consisting
almost
750
years
observations,
characterizing
vegetation
in
diverse
ecosystems
across
North
America.
Our
data
are
derived
from
conventional,
visible-wavelength,
automated
digital
camera
imagery
collected
through
PhenoCam
network.
For
each
archived
image,
extracted
RGB
(red,
green,
blue)
colour
channel
information,
with
means
other
statistics
calculated
region-of-interest
(ROI)
delineating
specific
type.
From
high-frequency
(typically,
30
min)
imagery,
time
colour,
including
"canopy
greenness",
processed
1-
3-day
intervals.
one
or
more
annual
cycles
activity,
provide
estimates,
uncertainties,
for
start
"greenness
rising"
end
falling"
stages.
The
database
can
be
used
phenological
model
validation
development,
evaluation
satellite
remote
sensing
products,
benchmarking
earth
system
models,
studies
impacts
on
terrestrial
ecosystems.
Abstract
Monitoring
vegetation
phenology
is
critical
for
quantifying
climate
change
impacts
on
ecosystems.
We
present
an
extensive
dataset
of
1783
site-years
phenological
data
derived
from
PhenoCam
network
imagery
393
digital
cameras,
situated
tropics
to
tundra
across
a
wide
range
plant
functional
types,
biomes,
and
climates.
Most
cameras
are
located
in
North
America.
Every
half
hour,
upload
images
the
server.
Images
displayed
near-real
time
provisional
products,
including
timeseries
Green
Chromatic
Coordinate
(Gcc),
made
publicly
available
through
project
web
page
(
https://phenocam.sr.unh.edu/webcam/gallery/
).
Processing
conducted
separately
each
type
camera
field
view.
The
Dataset
v2.0,
described
here,
has
been
fully
processed
curated,
outlier
detection
expert
inspection,
ensure
high
quality
data.
This
can
be
used
validate
satellite
evaluate
predictions
land
surface
models,
interpret
seasonality
ecosystem-scale
CO
2
H
O
flux
data,
study
terrestrial
biosphere.
Global Change Biology,
Год журнала:
2021,
Номер
28(4), С. 1433 - 1445
Опубликована: Окт. 20, 2021
Carbon
offsets
are
widely
used
by
individuals,
corporations,
and
governments
to
mitigate
their
greenhouse
gas
emissions
on
the
assumption
that
reflect
equivalent
climate
benefits
achieved
elsewhere.
These
climate-equivalence
claims
depend
providing
real
additional
beyond
what
would
have
happened,
counterfactually,
without
project.
Here,
we
evaluate
design
of
California's
prominent
forest
carbon
program
demonstrate
its
fall
far
short
basis
directly
observable
evidence.
By
design,
awards
large
volumes
offset
credits
projects
with
stocks
exceed
regional
averages.
This
paradigm
allows
for
adverse
selection,
which
could
occur
if
project
developers
preferentially
select
forests
ecologically
distinct
from
unrepresentative
digitizing
analyzing
comprehensive
records
alongside
detailed
inventory
data,
provide
direct
evidence
comparing
against
coarse
averages
has
led
systematic
over-crediting
30.0
million
tCO2
e
(90%
CI:
20.5-38.6
e)
or
29.4%
analyzed
20.1%-37.8%).
excess
worth
an
estimated
$410
$280-$528
million)
at
recent
market
prices.
Rather
than
improve
management
store
carbon,
creates
incentives
generate
do
not
benefits.
Water Resources Research,
Год журнала:
2022,
Номер
58(4)
Опубликована: Март 17, 2022
Abstract
When
fitting
statistical
models
to
variables
in
geoscientific
disciplines
such
as
hydrology,
it
is
a
customary
practice
stratify
large
domain
into
multiple
regions
(or
regimes)
and
study
each
region
separately.
Traditional
wisdom
suggests
that
built
for
separately
will
have
higher
performance
because
of
homogeneity
within
region.
However,
stratified
model
has
access
fewer
less
diverse
data
points.
Here,
through
two
hydrologic
examples
(soil
moisture
streamflow),
we
show
conventional
may
no
longer
hold
the
era
big
deep
learning
(DL).
We
systematically
examined
an
effect
call
synergy
,
where
results
DL
improved
when
were
pooled
together
from
characteristically
different
regions.
The
benefited
modest
diversity
training
compared
homogeneous
set,
even
with
similar
quantity.
Moreover,
allowing
heterogeneous
makes
eligible
much
larger
datasets,
which
inherent
advantage
DL.
A
large,
set
advantageous
terms
representing
extreme
events
future
scenarios,
strong
implications
climate
change
impact
assessment.
here
suggest
research
community
should
place
greater
emphasis
on
sharing.
