SSN2: The next generation of spatial stream network modeling in R
The Journal of Open Source Software,
Journal Year:
2024,
Volume and Issue:
9(99), P. 6389 - 6389
Published: July 26, 2024
The
SSN2
R
package
provides
tools
for
spatial
statistical
modeling,
parameter
estimation,
and
prediction
on
stream
(river)
networks.SSN2
is
the
successor
to
SSN
(Ver
Hoef,
Peterson,
Clifford,
&
Shah,
2014),
which
was
archived
alongside
broader
changes
in
Language: Английский
Impacts of different types of data integration on the predictions of spatio-temporal models: A fishery application and simulation experiment
Fisheries Research,
Journal Year:
2025,
Volume and Issue:
284, P. 107321 - 107321
Published: March 11, 2025
Language: Английский
tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations
Published: March 13, 2025
Language: Английский
tinyVAST: R Package With an Expressive Interface to Specify Lagged and Simultaneous Effects in Multivariate Spatio‐Temporal Models
Global Ecology and Biogeography,
Journal Year:
2025,
Volume and Issue:
34(4)
Published: April 1, 2025
ABSTRACT
Aim
Multivariate
spatio‐temporal
models
are
widely
applicable,
but
specifying
their
structure
is
complicated
and
may
inhibit
wider
use.
We
introduce
the
R
package
tinyVAST
from
two
viewpoints:
software
user
statistician.
Innovation
From
viewpoint,
adapts
a
used
formula
interface
to
specify
generalised
additive
combines
this
with
arguments
spatial
interactions
among
variables.
These
specified
using
arrow
notation
(from
structural
equation
models)
or
an
extended
arrow‐and‐lag
that
allows
simultaneous,
lagged
recursive
dependencies
variables
over
time.
The
also
specifies
domain
for
areal
(gridded),
continuous
(point‐count)
stream‐network
data.
statistician
constructs
sparse
precision
matrices
representing
multivariate
variation,
parameters
estimated
by
linear
mixed
model
(GLMM).
This
expressive
encompasses
vector
autoregressive,
empirical
orthogonal
functions,
factor
analysis
ARIMA
models.
Main
Conclusion
To
demonstrate,
we
fit
data
survey
platforms
sampling
corals,
sponges,
rockfishes
flatfishes
in
Gulf
of
Alaska
Aleutian
Islands.
then
compare
eight
alternative
structures
different
assumptions
about
habitat
drivers
detectability.
Model
selection
suggests
towed‐camera
bottom
trawl
gears
have
variation
detectability
sample
same
underlying
density
positively
associated
sponges
while
negatively
corals.
conclude
can
be
test
hypotheses
research
real‐world
policy
evaluation.
Language: Английский
Spatially varying catchability for integrating research survey data with other data sources: case studies involving observer samples, industry-cooperative surveys, and predators as samplers
Canadian Journal of Fisheries and Aquatic Sciences,
Journal Year:
2023,
Volume and Issue:
unknown
Published: June 15, 2023
Spatio-temporal
models
are
widely
applied
to
standardise
research
survey
data
and
increasingly
used
generate
density
maps
indices
from
other
sources.
We
developed
a
spatio-temporal
modelling
framework
that
integrates
(treated
as
“reference
dataset”)
sources
(“non-reference
datasets”)
while
estimating
spatially
varying
catchability
for
the
non-reference
datasets.
demonstrated
it
using
two
case
studies.
The
first
involved
bottom
trawl
observer
spiny
dogfish
(
Squalus
acanthias)
on
Chatham
Rise,
New
Zealand.
second
cod
predators
samplers
of
juvenile
snow
crab
Chionoecetes
opilio)
abundance,
integrated
with
industry-cooperative
surveys
in
eastern
Bering
Sea.
Our
leveraged
strengths
individual
(the
quality
reference
dataset
quantity
data),
downweighting
influence
datasets
via
estimated
catchabilities.
They
allowed
generation
annual
longer
time-period
provision
one
single
index
rather
than
multiple
each
covering
shorter
time-period.
Language: Английский
Integrating survey and observer data improves the predictions of New Zealand spatio-temporal models
ICES Journal of Marine Science,
Journal Year:
2023,
Volume and Issue:
80(7), P. 1991 - 2007
Published: Aug. 22, 2023
Abstract
In
many
situations,
species
distribution
models
need
to
make
use
of
multiple
data
sources
address
their
objectives.
We
developed
a
spatio-temporal
modelling
framework
that
integrates
research
survey
and
collected
by
observers
onboard
fishing
vessels
while
accounting
for
physical
barriers
(islands,
convoluted
coastlines).
demonstrated
our
two
bycatch
in
New
Zealand
deepwater
fisheries:
spiny
dogfish
(Squalus
acanthias)
javelinfish
(Lepidorhynchus
denticulatus).
Results
indicated
employing
observer-only
or
integrated
is
necessary
map
fish
biomass
at
the
scale
exclusive
economic
zone,
interpolate
local
indices
(e.g.,
east
coast
South
Island)
years
with
no
but
available
observer
data.
also
showed
that,
if
enough
are
available,
fisheries
analysts
should:
(1)
develop
both
an
model
relying
on
survey-only
data;
(2)
given
geographic
area,
ultimately
choose
index
produced
based
reliability
interannual
variability
index.
conducted
simulation
experiment,
which
predictions
virtually
insensitive
consideration
barriers.
Language: Английский
Understanding the dynamics of fish spawning phenology and habitat in a changing ecosystem using a long-term ichthyoplankton monitoring dataset
Canadian Journal of Fisheries and Aquatic Sciences,
Journal Year:
2024,
Volume and Issue:
81(12), P. 1728 - 1739
Published: Aug. 9, 2024
As
ecosystems
change,
understanding
the
consequences
for
fish
population
dynamics
and
habitat
use
is
essential
resource
management.
Using
white
perch
(
Morone
americana)
survey
data
on
early
life
stages
collected
during
a
long-term
ichthyoplankton
monitoring
program
in
Hudson
River
(New
York,
USA),
an
ecosystem
under
immense
pressure
from
climate
ecological
shifts,
anthropogenic
activities,
we
evaluated
drivers
of
changes
egg
abundance
spawning
between
1980
2017.
Results
indicated
that
associated
nonlinearly
with
temperature,
conductivity,
discharge,
depth,
location,
week
year.
We
also
found
has
declined
within
river
over
time.
Additionally,
shifts
hotspots
activity
were
identified,
including
evidence
lower
extent
moved
upriver
since
1980.
This
study
indicates
histories
are
changing.
It
highlights
utility
broadening
our
ecology
age
big
changing
ecosystems.
Language: Английский
Connecting population functionality with distribution model predictions to support freshwater and marine management of diadromous fish species
Biological Conservation,
Journal Year:
2023,
Volume and Issue:
287, P. 110324 - 110324
Published: Oct. 9, 2023
Diadromous
fish
species
have
a
complex
life
cycle
during
which
they
migrate
between
marine
and
freshwater
habitats.
They
experience
multiple
human-induced
pressures
in
both
environments,
likely
exacerbated
by
climate
change,
leading
to
dramatic
population
declines
across
their
distribution
ranges.
Currently
Species
Distribution
Models
(SDMs)
been
applied
separately
continental
habitats
improve
our
understanding
of
lifecycles
help
with
management.
Integrating
the
freshwater-sea
continuum
into
decisions
would
now
be
step
further
improving
With
this
objective,
we
developed
decision
tree
that
links
SDM
outputs
current
observations
functionality
suggested
management
guidance
options
for
viability
these
species.
Potential
effects
change
were
included
through
future
projections
guide
integrative
long-term
Several
criteria
proposed
assess
validity
considering
main
sources
uncertainties
local
expert
knowledge
on
habitat
status.
The
framework
was
approximately
one
hundred
catchments
from
southern
Portugal
Scandinavia
four
diadromous
At
European
level,
differed
two
anadromous
catadromous
Platichthys
flesus
Chelon
ramada
populations
seemed
better
state
than
those
Alosa
alosa
A.
fallax.
Finally,
national
experts,
focused
distributed
along
latitudinal
gradient
test
methodology
demonstrate
challenges
terms
continuity.
Language: Английский
Comparing the performance of three common species distribution modelling frameworks for freshwater environments through application to eel species in New Zealand
Canadian Journal of Fisheries and Aquatic Sciences,
Journal Year:
2022,
Volume and Issue:
80(3), P. 533 - 548
Published: Nov. 22, 2022
Globally,
many
freshwater
species
are
depleting
and
require
population-level
assessments.
Many
distribution
modelling
frameworks
available
for
such
assessments,
but
comparisons
needed
to
understand
their
predictive
performance
under
different
settings.
K-fold
cross-validation
techniques
were
employed
compare
the
of
three
commonly
used
frameworks:
machine
learning,
spatiotemporal
modelling,
Gaussian
process
(GP)
modelling.
Through
application
New
Zealand
populations
longfin
eel
(
Anguilla
dieffenbachii)
shortfin
australis),
area
receiver
operating
characteristic
curve
(AUC)
true
skill
statistic
(TSS)
model
metrics
estimated.
All
produced
approximately
consistent
maps
differed
in
performance.
AUC
TSS
results
indicated
that
predictions
from
framework
most
accurate,
followed
by
GP
However,
all
performed
similarly
when
training
test
data
spatially
independent.
In
addition
having
best
performance,
showed
greatest
promise
advancement
assessment
is
therefore
recommended.
The
useful
ecologists
resource
managers
make
informed
decisions
on
appropriateness
a
research
objective.
Language: Английский