Last updated on 2024-08-31 23:49:32 CEST.
| Package | ERROR | OK |
|---|---|---|
| geohabnet | 7 | 6 |
Current CRAN status: ERROR: 7, OK: 6
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘LinkWeightsAnalysis.Rmd’ using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building ‘LinkWeightsAnalysis.Rmd’
--- re-building ‘analysis.Rmd’ using rmarkdown
geohabnet-package package:geohabnet R Documentation
_<08>g_<08>e_<08>o_<08>h_<08>a_<08>b_<08>n_<08>e_<08>t: _<08>G_<08>e_<08>o_<08>g_<08>r_<08>a_<08>p_<08>h_<08>i_<08>c_<08>a_<08>l _<08>R_<08>i_<08>s_<08>k _<08>A_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>B_<08>a_<08>s_<08>e_<08>d _<08>o_<08>n _<08>H_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>C_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The geohabnet package is designed to perform a geographically or
spatially explicit risk analysis of habitat connectivity. Xing et
al (2021) doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067> proposed the concept of
cropland connectivity as a risk factor for plant pathogen or pest
invasions. As the functions in geohabnet were initially developed
thinking on cropland connectivity, users are recommended to first
be familiar with the concept by looking at the Xing et al paper.
In a nutshell, a habitat connectivity analysis combines
information from maps of host density, estimates the relative
likelihood of pathogen movement between habitat locations in the
area of interest, and applies network analysis to calculate the
connectivity of habitat locations. The functions of geohabnet are
built to conduct a habitat connectivity analysis relying on
geographic parameters (spatial resolution and spatial extent),
dispersal parameters (in two commonly used dispersal kernels:
inverse power law and negative exponential models), and network
parameters (link weight thresholds and network metrics). The
functionality and main extensions provided by the functions in
geohabnet to habitat connectivity analysis are a) Capability to
easily calculate the connectivity of locations in a landscape
using a single function, such as sensitivity_analysis() or
msean(). b) As backbone datasets, the geohabnet package supports
the use of two publicly available global datasets to calculate
cropland density. The backbone datasets in the geohabnet package
include crop distribution maps from Monfreda, C., N. Ramankutty,
and J. A. Foley (2008) doi:10.1029/2007gb002947
<https://doi.org/10.1029/2007gb002947> "Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological
types, and net primary production in the year 2000, Global
Biogeochem. Cycles, 22, GB1022" and International Food Policy
Research Institute (2019) doi:10.7910/DVN/PRFF8V
<https://doi.org/10.7910/DVN/PRFF8V> "Global
Spatially-Disaggregated Crop Production Statistics Data for 2010
Version 2.0, Harvard Dataverse, V4". Users can also provide any
other geographic dataset that represents host density. c) Because
the geohabnet package allows R users to provide maps of host
density (as originally in Xing et al (2021)), host landscape
density (representing the geographic distribution of either crops
or wild species), or habitat distribution (such as host landscape
density adjusted by climate suitability) as inputs, we propose the
term habitat connectivity. d) The geohabnet package allows R users
to customize parameter values in the habitat connectivity
analysis, facilitating context-specific (pathogen- or
pest-specific) analyses. e) The geohabnet package allows users to
automatically visualize maps of the habitat connectivity of
locations resulting from a sensitivity analysis across all
customized parameter combinations. The primary function is sean()
and sensitivity analysis(). Most functions in geohabnet provide as
three main outcomes: i) A map of mean habitat connectivity across
parameters selected by the user, ii) a map of variance of habitat
connectivity across the selected parameters, and iii) a map of the
difference between the ranks of habitat connectivity and habitat
density. Each function can be used to generate these maps as
'final' outcomes. Each function can also provide intermediate
outcomes, such as the adjacency matrices built to perform the
analysis, which can be used in other network analysis. Refer to
article at
<https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html>
to see examples of each function and how to access each of these
outcome types. To change parameter values, the file called
parameters.yaml stores the parameters and their values, can be
accessed using get_parameters() and set new parameter values with
set_parameters(). Users can modify up to ten parameters.
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
*Maintainer*: Krishna Keshav <mailto:kkeshav@ufl.edu>
Authors:
• Aaron Plex <mailto:plexaaron@ufl.edu> (ORCID)
• Karen Garrett <mailto:karengarrett@ufl.edu> (ORCID)
Other contributors:
• Garrett Lab <mailto:karengarrett@ufl.edu>
(https://garrettlab.com) [contributor]
• University of Florida (https://www.ufl.edu) [copyright
holder, funder]
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://garrettlab.github.io/HabitatConnectivity/>
• <https://CRAN.R-project.org/package=geohabnet/>
•
<https://github.com/GarrettLab/HabitatConnectivity/tree/main/geohabnet/>
• <https://www.garrettlab.com/>
• Report bugs at
<https://github.com/GarrettLab/HabitatConnectivity/issues>
sean package:geohabnet R Documentation
_<08>S_<08>e_<08>n_<08>s_<08>i_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y _<08>a_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>a_<08>c_<08>r_<08>o_<08>s_<08>s _<08>m_<08>a_<08>p_<08>s _<08>o_<08>f _<08>h_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>c_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
This function performs a sensitivity analysis across different
values of habitat connectivity for each location in a map. For
each combination of selected parameters, an index of habitat
connectivity is calculated. 'sensitivity_analysis()' is a wrapper
around 'sean()' function.
• 'msean()' is a wrapper around 'sean()' function. It has
additional argument to specify maps which are calculated
using 'connectivity()' function. The maps are essentially the
risk network.
_<08>U_<08>s_<08>a_<08>g_<08>e:
sean(
rast,
global = TRUE,
geoscale = NULL,
agg_methods = c("sum", "mean"),
dist_method = "geodesic",
link_threshold = 0,
hd_threshold = 0,
res = reso(),
inv_pl = inv_powerlaw(NULL, betas = c(0.5, 1, 1.5), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1),
neg_exp = neg_expo(NULL, gammas = c(0.05, 1, 0.2, 0.3), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1)
)
msean(
rast,
global = TRUE,
geoscale = NULL,
res = reso(),
...,
outdir = tempdir()
)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rast: Raster object which will be used in analysis.
global: Logical. 'TRUE' if global analysis, 'FALSE' otherwise.
Default is 'TRUE'
geoscale: Numeric vector. Geographical coordinates in the form of
c(Xmin, Xmax, Ymin, Ymax) which EPSG:4326 in coordinate
reference system. If 'geoscale' is NuLL, the extent is
extracted from 'rast'(SpatRaster) using 'terra::ext()'.
agg_methods: Character. One or both the values - SUM, MEAN. Aggregation
strategy for scaling the input raster to the desired
resolution.
dist_method: Character. The method to calculate the distance matrix.
link_threshold: Numeric. A threshold value for link weight. All link
weights that are below this threshold will be replaced with
zero for the connectivity analysis. Link weights represent
the relative likelihood of pathogen, pest, or invasive
species movement between a pair of host locations, which is
calculated using gravity models based on host density (or
availability) and dispersal kernels.
hd_threshold: Numeric. A threshold value for host density. All
locations with a host density below the selected threshold
will be excluded from the connectivity analysis, which
focuses the analysis on the most important locations. The
values for the host density threshold can range between 0 and
1; if value is 1, all locations will be excluded from the
analysis and 0 will include all locations in the analysis.
Selecting a threshold for host density requires at least
knowing what is the maximum value in the host density map to
avoid excluding all locations in the analysis. if value is 1,
all locations will be excluded from the analysis and 0 will
include all locations in the analysis. Selecting a threshold
for host density requires at least knowing what is the
maximum value in the host density map to avoid excluding all
locations in the analysis.
res: Numeric. Resolution of the raster. Default is 'reso()'.
inv_pl: List. A named list of parameters for inverse power law. See
details.
neg_exp: List. A named list of parameters for inverse negative
exponential. See details. All locations with a host density
below the selected threshold will be excluded from the
connectivity analysis, which focuses the analysis on the most
important locations. The values for the host density
threshold can range between 0 and 1;
...: arguments passed to 'sean()'
outdir: Character. Output directory for saving raster in TIFF format.
Default is 'tempdir()'.
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
When 'global = TRUE', 'geoscale' is ignored and 'global_scales()'
is used by default.
The functions 'sean()' and 'msean()' perform the same sensitivity
analysis, but they differ in their return value. The return value
of 'msean()' is 'GeoNetwork', which contains the result from
applying the 'connectivity()' function on the habitat connectivity
indexes. Essentially, the risk maps.
If neither the inverse power law nor the negative exponential
dispersal kernel is specified, the function will return an error.
In 'msean()', three spatRasters are produced with the following
values. For each location in the area of interest, the mean in
habitat connectivity across selected parameters is calculated. For
each location in the area of interest, the variance in habitat
connectivity across selected parameters is calculated. For each
location in the area of interest, the difference between the rank
of habitat connectivity and the rank of host density is
calculated. By default, each of these spatRasters is plotted for
visualization.
_<08>V_<08>a_<08>l_<08>u_<08>e:
GeoRasters.
GeoNetwork.
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Yanru Xing, John F Hernandez Nopsa, Kelsey F Andersen, Jorge L
Andrade-Piedra, Fenton D Beed, Guy Blomme, Mónica Carvajal-Yepes,
Danny L Coyne, Wilmer J Cuellar, Gregory A Forbes, Jan F Kreuze,
Jürgen Kroschel, P Lava Kumar, James P Legg, Monica Parker, Elmar
Schulte-Geldermann, Kalpana Sharma, Karen A Garrett, _Global
Cropland connectivity: A Risk Factor for Invasion and Saturation
by Emerging Pathogens and Pests_, BioScience, Volume 70, Issue 9,
September 2020, Pages 744–758, doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067>
Hijmans R (2023). _terra: Spatial Data Analysis_. R package
version 1.7-46, <https://CRAN.R-project.org/package=terra>
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Uses 'connectivity()'
Uses 'msean()' 'inv_powerlaw()' 'neg_expo()'
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
avocado <- cropharvest_rast("avocado", "monfreda")
# global
ri <- sean(avocado) # returns a list of GeoRasters
mri <- msean(rast = avocado) # returns GeoNetwork object
# non-global
# geoscale is a vector of xmin, xmax, ymin, ymax
# returns GeoRasters object
ri <- sean(avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
ri
# returns GeoNetwork object
mri <- msean(rast = avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
mri
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
** Processing: /home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/geohabnet.Rcheck/vign_test/geohabnet/vignettes/analysis_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 6415 bytes
Input file size = 6493 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
Output IDAT size = 5219 bytes (1196 bytes decrease)
Output file size = 5297 bytes (1196 bytes = 18.42% decrease)
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature 'x = "NULL"'
--- failed re-building ‘analysis.Rmd’
SUMMARY: processing the following file failed:
‘analysis.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘LinkWeightsAnalysis.Rmd’ using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building ‘LinkWeightsAnalysis.Rmd’
--- re-building ‘analysis.Rmd’ using rmarkdown
geohabnet-package package:geohabnet R Documentation
_<08>g_<08>e_<08>o_<08>h_<08>a_<08>b_<08>n_<08>e_<08>t: _<08>G_<08>e_<08>o_<08>g_<08>r_<08>a_<08>p_<08>h_<08>i_<08>c_<08>a_<08>l _<08>R_<08>i_<08>s_<08>k _<08>A_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>B_<08>a_<08>s_<08>e_<08>d _<08>o_<08>n _<08>H_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>C_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The geohabnet package is designed to perform a geographically or
spatially explicit risk analysis of habitat connectivity. Xing et
al (2021) doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067> proposed the concept of
cropland connectivity as a risk factor for plant pathogen or pest
invasions. As the functions in geohabnet were initially developed
thinking on cropland connectivity, users are recommended to first
be familiar with the concept by looking at the Xing et al paper.
In a nutshell, a habitat connectivity analysis combines
information from maps of host density, estimates the relative
likelihood of pathogen movement between habitat locations in the
area of interest, and applies network analysis to calculate the
connectivity of habitat locations. The functions of geohabnet are
built to conduct a habitat connectivity analysis relying on
geographic parameters (spatial resolution and spatial extent),
dispersal parameters (in two commonly used dispersal kernels:
inverse power law and negative exponential models), and network
parameters (link weight thresholds and network metrics). The
functionality and main extensions provided by the functions in
geohabnet to habitat connectivity analysis are a) Capability to
easily calculate the connectivity of locations in a landscape
using a single function, such as sensitivity_analysis() or
msean(). b) As backbone datasets, the geohabnet package supports
the use of two publicly available global datasets to calculate
cropland density. The backbone datasets in the geohabnet package
include crop distribution maps from Monfreda, C., N. Ramankutty,
and J. A. Foley (2008) doi:10.1029/2007gb002947
<https://doi.org/10.1029/2007gb002947> "Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological
types, and net primary production in the year 2000, Global
Biogeochem. Cycles, 22, GB1022" and International Food Policy
Research Institute (2019) doi:10.7910/DVN/PRFF8V
<https://doi.org/10.7910/DVN/PRFF8V> "Global
Spatially-Disaggregated Crop Production Statistics Data for 2010
Version 2.0, Harvard Dataverse, V4". Users can also provide any
other geographic dataset that represents host density. c) Because
the geohabnet package allows R users to provide maps of host
density (as originally in Xing et al (2021)), host landscape
density (representing the geographic distribution of either crops
or wild species), or habitat distribution (such as host landscape
density adjusted by climate suitability) as inputs, we propose the
term habitat connectivity. d) The geohabnet package allows R users
to customize parameter values in the habitat connectivity
analysis, facilitating context-specific (pathogen- or
pest-specific) analyses. e) The geohabnet package allows users to
automatically visualize maps of the habitat connectivity of
locations resulting from a sensitivity analysis across all
customized parameter combinations. The primary function is sean()
and sensitivity analysis(). Most functions in geohabnet provide as
three main outcomes: i) A map of mean habitat connectivity across
parameters selected by the user, ii) a map of variance of habitat
connectivity across the selected parameters, and iii) a map of the
difference between the ranks of habitat connectivity and habitat
density. Each function can be used to generate these maps as
'final' outcomes. Each function can also provide intermediate
outcomes, such as the adjacency matrices built to perform the
analysis, which can be used in other network analysis. Refer to
article at
<https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html>
to see examples of each function and how to access each of these
outcome types. To change parameter values, the file called
parameters.yaml stores the parameters and their values, can be
accessed using get_parameters() and set new parameter values with
set_parameters(). Users can modify up to ten parameters.
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
*Maintainer*: Krishna Keshav <mailto:kkeshav@ufl.edu>
Authors:
• Aaron Plex <mailto:plexaaron@ufl.edu> (ORCID)
• Karen Garrett <mailto:karengarrett@ufl.edu> (ORCID)
Other contributors:
• Garrett Lab <mailto:karengarrett@ufl.edu>
(https://garrettlab.com) [contributor]
• University of Florida (https://www.ufl.edu) [copyright
holder, funder]
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://garrettlab.github.io/HabitatConnectivity/>
• <https://CRAN.R-project.org/package=geohabnet/>
•
<https://github.com/GarrettLab/HabitatConnectivity/tree/main/geohabnet/>
• <https://www.garrettlab.com/>
• Report bugs at
<https://github.com/GarrettLab/HabitatConnectivity/issues>
sean package:geohabnet R Documentation
_<08>S_<08>e_<08>n_<08>s_<08>i_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y _<08>a_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>a_<08>c_<08>r_<08>o_<08>s_<08>s _<08>m_<08>a_<08>p_<08>s _<08>o_<08>f _<08>h_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>c_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
This function performs a sensitivity analysis across different
values of habitat connectivity for each location in a map. For
each combination of selected parameters, an index of habitat
connectivity is calculated. 'sensitivity_analysis()' is a wrapper
around 'sean()' function.
• 'msean()' is a wrapper around 'sean()' function. It has
additional argument to specify maps which are calculated
using 'connectivity()' function. The maps are essentially the
risk network.
_<08>U_<08>s_<08>a_<08>g_<08>e:
sean(
rast,
global = TRUE,
geoscale = NULL,
agg_methods = c("sum", "mean"),
dist_method = "geodesic",
link_threshold = 0,
hd_threshold = 0,
res = reso(),
inv_pl = inv_powerlaw(NULL, betas = c(0.5, 1, 1.5), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1),
neg_exp = neg_expo(NULL, gammas = c(0.05, 1, 0.2, 0.3), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1)
)
msean(
rast,
global = TRUE,
geoscale = NULL,
res = reso(),
...,
outdir = tempdir()
)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rast: Raster object which will be used in analysis.
global: Logical. 'TRUE' if global analysis, 'FALSE' otherwise.
Default is 'TRUE'
geoscale: Numeric vector. Geographical coordinates in the form of
c(Xmin, Xmax, Ymin, Ymax) which EPSG:4326 in coordinate
reference system. If 'geoscale' is NuLL, the extent is
extracted from 'rast'(SpatRaster) using 'terra::ext()'.
agg_methods: Character. One or both the values - SUM, MEAN. Aggregation
strategy for scaling the input raster to the desired
resolution.
dist_method: Character. The method to calculate the distance matrix.
link_threshold: Numeric. A threshold value for link weight. All link
weights that are below this threshold will be replaced with
zero for the connectivity analysis. Link weights represent
the relative likelihood of pathogen, pest, or invasive
species movement between a pair of host locations, which is
calculated using gravity models based on host density (or
availability) and dispersal kernels.
hd_threshold: Numeric. A threshold value for host density. All
locations with a host density below the selected threshold
will be excluded from the connectivity analysis, which
focuses the analysis on the most important locations. The
values for the host density threshold can range between 0 and
1; if value is 1, all locations will be excluded from the
analysis and 0 will include all locations in the analysis.
Selecting a threshold for host density requires at least
knowing what is the maximum value in the host density map to
avoid excluding all locations in the analysis. if value is 1,
all locations will be excluded from the analysis and 0 will
include all locations in the analysis. Selecting a threshold
for host density requires at least knowing what is the
maximum value in the host density map to avoid excluding all
locations in the analysis.
res: Numeric. Resolution of the raster. Default is 'reso()'.
inv_pl: List. A named list of parameters for inverse power law. See
details.
neg_exp: List. A named list of parameters for inverse negative
exponential. See details. All locations with a host density
below the selected threshold will be excluded from the
connectivity analysis, which focuses the analysis on the most
important locations. The values for the host density
threshold can range between 0 and 1;
...: arguments passed to 'sean()'
outdir: Character. Output directory for saving raster in TIFF format.
Default is 'tempdir()'.
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
When 'global = TRUE', 'geoscale' is ignored and 'global_scales()'
is used by default.
The functions 'sean()' and 'msean()' perform the same sensitivity
analysis, but they differ in their return value. The return value
of 'msean()' is 'GeoNetwork', which contains the result from
applying the 'connectivity()' function on the habitat connectivity
indexes. Essentially, the risk maps.
If neither the inverse power law nor the negative exponential
dispersal kernel is specified, the function will return an error.
In 'msean()', three spatRasters are produced with the following
values. For each location in the area of interest, the mean in
habitat connectivity across selected parameters is calculated. For
each location in the area of interest, the variance in habitat
connectivity across selected parameters is calculated. For each
location in the area of interest, the difference between the rank
of habitat connectivity and the rank of host density is
calculated. By default, each of these spatRasters is plotted for
visualization.
_<08>V_<08>a_<08>l_<08>u_<08>e:
GeoRasters.
GeoNetwork.
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Yanru Xing, John F Hernandez Nopsa, Kelsey F Andersen, Jorge L
Andrade-Piedra, Fenton D Beed, Guy Blomme, Mónica Carvajal-Yepes,
Danny L Coyne, Wilmer J Cuellar, Gregory A Forbes, Jan F Kreuze,
Jürgen Kroschel, P Lava Kumar, James P Legg, Monica Parker, Elmar
Schulte-Geldermann, Kalpana Sharma, Karen A Garrett, _Global
Cropland connectivity: A Risk Factor for Invasion and Saturation
by Emerging Pathogens and Pests_, BioScience, Volume 70, Issue 9,
September 2020, Pages 744–758, doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067>
Hijmans R (2023). _terra: Spatial Data Analysis_. R package
version 1.7-46, <https://CRAN.R-project.org/package=terra>
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Uses 'connectivity()'
Uses 'msean()' 'inv_powerlaw()' 'neg_expo()'
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
avocado <- cropharvest_rast("avocado", "monfreda")
# global
ri <- sean(avocado) # returns a list of GeoRasters
mri <- msean(rast = avocado) # returns GeoNetwork object
# non-global
# geoscale is a vector of xmin, xmax, ymin, ymax
# returns GeoRasters object
ri <- sean(avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
ri
# returns GeoNetwork object
mri <- msean(rast = avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
mri
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM sysdefault
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM sysdefault
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.front
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround21
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround21
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround40
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround41
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround50
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround51
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround71
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM hdmi
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM hdmi
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM default
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM default
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_card_id returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1342:(snd_func_refer) error evaluating name
ALSA lib conf.c:5204:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5727:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2722:(snd_pcm_open_noupdate) Unknown PCM dmix
Cannot connect to server socket err = No such file or directory
Cannot connect to server request channel
jack server is not running or cannot be started
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
** Processing: /home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/geohabnet.Rcheck/vign_test/geohabnet/vignettes/analysis_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 6415 bytes
Input file size = 6493 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
Output IDAT size = 5219 bytes (1196 bytes decrease)
Output file size = 5297 bytes (1196 bytes = 18.42% decrease)
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature 'x = "NULL"'
--- failed re-building ‘analysis.Rmd’
SUMMARY: processing the following file failed:
‘analysis.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building 'LinkWeightsAnalysis.Rmd' using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building 'LinkWeightsAnalysis.Rmd'
--- re-building 'analysis.Rmd' using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature 'x = "NULL"'
--- failed re-building 'analysis.Rmd'
SUMMARY: processing the following file failed:
'analysis.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-windows-x86_64, r-release-windows-x86_64
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘LinkWeightsAnalysis.Rmd’ using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building ‘LinkWeightsAnalysis.Rmd’
--- re-building ‘analysis.Rmd’ using rmarkdown
geohabnet-package package:geohabnet R Documentation
_<08>g_<08>e_<08>o_<08>h_<08>a_<08>b_<08>n_<08>e_<08>t: _<08>G_<08>e_<08>o_<08>g_<08>r_<08>a_<08>p_<08>h_<08>i_<08>c_<08>a_<08>l _<08>R_<08>i_<08>s_<08>k _<08>A_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>B_<08>a_<08>s_<08>e_<08>d _<08>o_<08>n _<08>H_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>C_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The geohabnet package is designed to perform a geographically or
spatially explicit risk analysis of habitat connectivity. Xing et
al (2021) doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067> proposed the concept of
cropland connectivity as a risk factor for plant pathogen or pest
invasions. As the functions in geohabnet were initially developed
thinking on cropland connectivity, users are recommended to first
be familiar with the concept by looking at the Xing et al paper.
In a nutshell, a habitat connectivity analysis combines
information from maps of host density, estimates the relative
likelihood of pathogen movement between habitat locations in the
area of interest, and applies network analysis to calculate the
connectivity of habitat locations. The functions of geohabnet are
built to conduct a habitat connectivity analysis relying on
geographic parameters (spatial resolution and spatial extent),
dispersal parameters (in two commonly used dispersal kernels:
inverse power law and negative exponential models), and network
parameters (link weight thresholds and network metrics). The
functionality and main extensions provided by the functions in
geohabnet to habitat connectivity analysis are a) Capability to
easily calculate the connectivity of locations in a landscape
using a single function, such as sensitivity_analysis() or
msean(). b) As backbone datasets, the geohabnet package supports
the use of two publicly available global datasets to calculate
cropland density. The backbone datasets in the geohabnet package
include crop distribution maps from Monfreda, C., N. Ramankutty,
and J. A. Foley (2008) doi:10.1029/2007gb002947
<https://doi.org/10.1029/2007gb002947> "Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological
types, and net primary production in the year 2000, Global
Biogeochem. Cycles, 22, GB1022" and International Food Policy
Research Institute (2019) doi:10.7910/DVN/PRFF8V
<https://doi.org/10.7910/DVN/PRFF8V> "Global
Spatially-Disaggregated Crop Production Statistics Data for 2010
Version 2.0, Harvard Dataverse, V4". Users can also provide any
other geographic dataset that represents host density. c) Because
the geohabnet package allows R users to provide maps of host
density (as originally in Xing et al (2021)), host landscape
density (representing the geographic distribution of either crops
or wild species), or habitat distribution (such as host landscape
density adjusted by climate suitability) as inputs, we propose the
term habitat connectivity. d) The geohabnet package allows R users
to customize parameter values in the habitat connectivity
analysis, facilitating context-specific (pathogen- or
pest-specific) analyses. e) The geohabnet package allows users to
automatically visualize maps of the habitat connectivity of
locations resulting from a sensitivity analysis across all
customized parameter combinations. The primary function is sean()
and sensitivity analysis(). Most functions in geohabnet provide as
three main outcomes: i) A map of mean habitat connectivity across
parameters selected by the user, ii) a map of variance of habitat
connectivity across the selected parameters, and iii) a map of the
difference between the ranks of habitat connectivity and habitat
density. Each function can be used to generate these maps as
'final' outcomes. Each function can also provide intermediate
outcomes, such as the adjacency matrices built to perform the
analysis, which can be used in other network analysis. Refer to
article at
<https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html>
to see examples of each function and how to access each of these
outcome types. To change parameter values, the file called
parameters.yaml stores the parameters and their values, can be
accessed using get_parameters() and set new parameter values with
set_parameters(). Users can modify up to ten parameters.
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
*Maintainer*: Krishna Keshav <mailto:kkeshav@ufl.edu>
Authors:
• Aaron Plex <mailto:plexaaron@ufl.edu> (ORCID)
• Karen Garrett <mailto:karengarrett@ufl.edu> (ORCID)
Other contributors:
• Garrett Lab <mailto:karengarrett@ufl.edu>
(https://garrettlab.com) [contributor]
• University of Florida (https://www.ufl.edu) [copyright
holder, funder]
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://garrettlab.github.io/HabitatConnectivity/>
• <https://CRAN.R-project.org/package=geohabnet/>
•
<https://github.com/GarrettLab/HabitatConnectivity/tree/main/geohabnet/>
• <https://www.garrettlab.com/>
• Report bugs at
<https://github.com/GarrettLab/HabitatConnectivity/issues>
sean package:geohabnet R Documentation
_<08>S_<08>e_<08>n_<08>s_<08>i_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y _<08>a_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>a_<08>c_<08>r_<08>o_<08>s_<08>s _<08>m_<08>a_<08>p_<08>s _<08>o_<08>f _<08>h_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>c_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
This function performs a sensitivity analysis across different
values of habitat connectivity for each location in a map. For
each combination of selected parameters, an index of habitat
connectivity is calculated. 'sensitivity_analysis()' is a wrapper
around 'sean()' function.
• 'msean()' is a wrapper around 'sean()' function. It has
additional argument to specify maps which are calculated
using 'connectivity()' function. The maps are essentially the
risk network.
_<08>U_<08>s_<08>a_<08>g_<08>e:
sean(
rast,
global = TRUE,
geoscale = NULL,
agg_methods = c("sum", "mean"),
dist_method = "geodesic",
link_threshold = 0,
hd_threshold = 0,
res = reso(),
inv_pl = inv_powerlaw(NULL, betas = c(0.5, 1, 1.5), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1),
neg_exp = neg_expo(NULL, gammas = c(0.05, 1, 0.2, 0.3), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1)
)
msean(
rast,
global = TRUE,
geoscale = NULL,
res = reso(),
...,
outdir = tempdir()
)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rast: Raster object which will be used in analysis.
global: Logical. 'TRUE' if global analysis, 'FALSE' otherwise.
Default is 'TRUE'
geoscale: Numeric vector. Geographical coordinates in the form of
c(Xmin, Xmax, Ymin, Ymax) which EPSG:4326 in coordinate
reference system. If 'geoscale' is NuLL, the extent is
extracted from 'rast'(SpatRaster) using 'terra::ext()'.
agg_methods: Character. One or both the values - SUM, MEAN. Aggregation
strategy for scaling the input raster to the desired
resolution.
dist_method: Character. The method to calculate the distance matrix.
link_threshold: Numeric. A threshold value for link weight. All link
weights that are below this threshold will be replaced with
zero for the connectivity analysis. Link weights represent
the relative likelihood of pathogen, pest, or invasive
species movement between a pair of host locations, which is
calculated using gravity models based on host density (or
availability) and dispersal kernels.
hd_threshold: Numeric. A threshold value for host density. All
locations with a host density below the selected threshold
will be excluded from the connectivity analysis, which
focuses the analysis on the most important locations. The
values for the host density threshold can range between 0 and
1; if value is 1, all locations will be excluded from the
analysis and 0 will include all locations in the analysis.
Selecting a threshold for host density requires at least
knowing what is the maximum value in the host density map to
avoid excluding all locations in the analysis. if value is 1,
all locations will be excluded from the analysis and 0 will
include all locations in the analysis. Selecting a threshold
for host density requires at least knowing what is the
maximum value in the host density map to avoid excluding all
locations in the analysis.
res: Numeric. Resolution of the raster. Default is 'reso()'.
inv_pl: List. A named list of parameters for inverse power law. See
details.
neg_exp: List. A named list of parameters for inverse negative
exponential. See details. All locations with a host density
below the selected threshold will be excluded from the
connectivity analysis, which focuses the analysis on the most
important locations. The values for the host density
threshold can range between 0 and 1;
...: arguments passed to 'sean()'
outdir: Character. Output directory for saving raster in TIFF format.
Default is 'tempdir()'.
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
When 'global = TRUE', 'geoscale' is ignored and 'global_scales()'
is used by default.
The functions 'sean()' and 'msean()' perform the same sensitivity
analysis, but they differ in their return value. The return value
of 'msean()' is 'GeoNetwork', which contains the result from
applying the 'connectivity()' function on the habitat connectivity
indexes. Essentially, the risk maps.
If neither the inverse power law nor the negative exponential
dispersal kernel is specified, the function will return an error.
In 'msean()', three spatRasters are produced with the following
values. For each location in the area of interest, the mean in
habitat connectivity across selected parameters is calculated. For
each location in the area of interest, the variance in habitat
connectivity across selected parameters is calculated. For each
location in the area of interest, the difference between the rank
of habitat connectivity and the rank of host density is
calculated. By default, each of these spatRasters is plotted for
visualization.
_<08>V_<08>a_<08>l_<08>u_<08>e:
GeoRasters.
GeoNetwork.
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Yanru Xing, John F Hernandez Nopsa, Kelsey F Andersen, Jorge L
Andrade-Piedra, Fenton D Beed, Guy Blomme, Mónica Carvajal-Yepes,
Danny L Coyne, Wilmer J Cuellar, Gregory A Forbes, Jan F Kreuze,
Jürgen Kroschel, P Lava Kumar, James P Legg, Monica Parker, Elmar
Schulte-Geldermann, Kalpana Sharma, Karen A Garrett, _Global
Cropland connectivity: A Risk Factor for Invasion and Saturation
by Emerging Pathogens and Pests_, BioScience, Volume 70, Issue 9,
September 2020, Pages 744–758, doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067>
Hijmans R (2023). _terra: Spatial Data Analysis_. R package
version 1.7-46, <https://CRAN.R-project.org/package=terra>
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Uses 'connectivity()'
Uses 'msean()' 'inv_powerlaw()' 'neg_expo()'
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
avocado <- cropharvest_rast("avocado", "monfreda")
# global
ri <- sean(avocado) # returns a list of GeoRasters
mri <- msean(rast = avocado) # returns GeoNetwork object
# non-global
# geoscale is a vector of xmin, xmax, ymin, ymax
# returns GeoRasters object
ri <- sean(avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
ri
# returns GeoNetwork object
mri <- msean(rast = avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
mri
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/geohabnet.Rcheck/vign_test/geohabnet/vignettes/analysis_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 6415 bytes
Input file size = 6493 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
Output IDAT size = 5219 bytes (1196 bytes decrease)
Output file size = 5297 bytes (1196 bytes = 18.42% decrease)
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature 'x = "NULL"'
--- failed re-building ‘analysis.Rmd’
SUMMARY: processing the following file failed:
‘analysis.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘LinkWeightsAnalysis.Rmd’ using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building ‘LinkWeightsAnalysis.Rmd’
--- re-building ‘analysis.Rmd’ using rmarkdown
geohabnet-package package:geohabnet R Documentation
_<08>g_<08>e_<08>o_<08>h_<08>a_<08>b_<08>n_<08>e_<08>t: _<08>G_<08>e_<08>o_<08>g_<08>r_<08>a_<08>p_<08>h_<08>i_<08>c_<08>a_<08>l _<08>R_<08>i_<08>s_<08>k _<08>A_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>B_<08>a_<08>s_<08>e_<08>d _<08>o_<08>n _<08>H_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>C_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The geohabnet package is designed to perform a geographically or
spatially explicit risk analysis of habitat connectivity. Xing et
al (2021) doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067> proposed the concept of
cropland connectivity as a risk factor for plant pathogen or pest
invasions. As the functions in geohabnet were initially developed
thinking on cropland connectivity, users are recommended to first
be familiar with the concept by looking at the Xing et al paper.
In a nutshell, a habitat connectivity analysis combines
information from maps of host density, estimates the relative
likelihood of pathogen movement between habitat locations in the
area of interest, and applies network analysis to calculate the
connectivity of habitat locations. The functions of geohabnet are
built to conduct a habitat connectivity analysis relying on
geographic parameters (spatial resolution and spatial extent),
dispersal parameters (in two commonly used dispersal kernels:
inverse power law and negative exponential models), and network
parameters (link weight thresholds and network metrics). The
functionality and main extensions provided by the functions in
geohabnet to habitat connectivity analysis are a) Capability to
easily calculate the connectivity of locations in a landscape
using a single function, such as sensitivity_analysis() or
msean(). b) As backbone datasets, the geohabnet package supports
the use of two publicly available global datasets to calculate
cropland density. The backbone datasets in the geohabnet package
include crop distribution maps from Monfreda, C., N. Ramankutty,
and J. A. Foley (2008) doi:10.1029/2007gb002947
<https://doi.org/10.1029/2007gb002947> "Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological
types, and net primary production in the year 2000, Global
Biogeochem. Cycles, 22, GB1022" and International Food Policy
Research Institute (2019) doi:10.7910/DVN/PRFF8V
<https://doi.org/10.7910/DVN/PRFF8V> "Global
Spatially-Disaggregated Crop Production Statistics Data for 2010
Version 2.0, Harvard Dataverse, V4". Users can also provide any
other geographic dataset that represents host density. c) Because
the geohabnet package allows R users to provide maps of host
density (as originally in Xing et al (2021)), host landscape
density (representing the geographic distribution of either crops
or wild species), or habitat distribution (such as host landscape
density adjusted by climate suitability) as inputs, we propose the
term habitat connectivity. d) The geohabnet package allows R users
to customize parameter values in the habitat connectivity
analysis, facilitating context-specific (pathogen- or
pest-specific) analyses. e) The geohabnet package allows users to
automatically visualize maps of the habitat connectivity of
locations resulting from a sensitivity analysis across all
customized parameter combinations. The primary function is sean()
and sensitivity analysis(). Most functions in geohabnet provide as
three main outcomes: i) A map of mean habitat connectivity across
parameters selected by the user, ii) a map of variance of habitat
connectivity across the selected parameters, and iii) a map of the
difference between the ranks of habitat connectivity and habitat
density. Each function can be used to generate these maps as
'final' outcomes. Each function can also provide intermediate
outcomes, such as the adjacency matrices built to perform the
analysis, which can be used in other network analysis. Refer to
article at
<https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html>
to see examples of each function and how to access each of these
outcome types. To change parameter values, the file called
parameters.yaml stores the parameters and their values, can be
accessed using get_parameters() and set new parameter values with
set_parameters(). Users can modify up to ten parameters.
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
*Maintainer*: Krishna Keshav <mailto:kkeshav@ufl.edu>
Authors:
• Aaron Plex <mailto:plexaaron@ufl.edu> (ORCID)
• Karen Garrett <mailto:karengarrett@ufl.edu> (ORCID)
Other contributors:
• Garrett Lab <mailto:karengarrett@ufl.edu>
(https://garrettlab.com) [contributor]
• University of Florida (https://www.ufl.edu) [copyright
holder, funder]
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://garrettlab.github.io/HabitatConnectivity/>
• <https://CRAN.R-project.org/package=geohabnet/>
•
<https://github.com/GarrettLab/HabitatConnectivity/tree/main/geohabnet/>
• <https://www.garrettlab.com/>
• Report bugs at
<https://github.com/GarrettLab/HabitatConnectivity/issues>
sean package:geohabnet R Documentation
_<08>S_<08>e_<08>n_<08>s_<08>i_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y _<08>a_<08>n_<08>a_<08>l_<08>y_<08>s_<08>i_<08>s _<08>a_<08>c_<08>r_<08>o_<08>s_<08>s _<08>m_<08>a_<08>p_<08>s _<08>o_<08>f _<08>h_<08>a_<08>b_<08>i_<08>t_<08>a_<08>t _<08>c_<08>o_<08>n_<08>n_<08>e_<08>c_<08>t_<08>i_<08>v_<08>i_<08>t_<08>y
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
This function performs a sensitivity analysis across different
values of habitat connectivity for each location in a map. For
each combination of selected parameters, an index of habitat
connectivity is calculated. 'sensitivity_analysis()' is a wrapper
around 'sean()' function.
• 'msean()' is a wrapper around 'sean()' function. It has
additional argument to specify maps which are calculated
using 'connectivity()' function. The maps are essentially the
risk network.
_<08>U_<08>s_<08>a_<08>g_<08>e:
sean(
rast,
global = TRUE,
geoscale = NULL,
agg_methods = c("sum", "mean"),
dist_method = "geodesic",
link_threshold = 0,
hd_threshold = 0,
res = reso(),
inv_pl = inv_powerlaw(NULL, betas = c(0.5, 1, 1.5), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1),
neg_exp = neg_expo(NULL, gammas = c(0.05, 1, 0.2, 0.3), mets = c("betweeness",
"NODE_STRENGTH", "Sum_of_nearest_neighbors", "eigenVector_centrAlitY"), we = c(50,
15, 15, 20), linkcutoff = -1)
)
msean(
rast,
global = TRUE,
geoscale = NULL,
res = reso(),
...,
outdir = tempdir()
)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rast: Raster object which will be used in analysis.
global: Logical. 'TRUE' if global analysis, 'FALSE' otherwise.
Default is 'TRUE'
geoscale: Numeric vector. Geographical coordinates in the form of
c(Xmin, Xmax, Ymin, Ymax) which EPSG:4326 in coordinate
reference system. If 'geoscale' is NuLL, the extent is
extracted from 'rast'(SpatRaster) using 'terra::ext()'.
agg_methods: Character. One or both the values - SUM, MEAN. Aggregation
strategy for scaling the input raster to the desired
resolution.
dist_method: Character. The method to calculate the distance matrix.
link_threshold: Numeric. A threshold value for link weight. All link
weights that are below this threshold will be replaced with
zero for the connectivity analysis. Link weights represent
the relative likelihood of pathogen, pest, or invasive
species movement between a pair of host locations, which is
calculated using gravity models based on host density (or
availability) and dispersal kernels.
hd_threshold: Numeric. A threshold value for host density. All
locations with a host density below the selected threshold
will be excluded from the connectivity analysis, which
focuses the analysis on the most important locations. The
values for the host density threshold can range between 0 and
1; if value is 1, all locations will be excluded from the
analysis and 0 will include all locations in the analysis.
Selecting a threshold for host density requires at least
knowing what is the maximum value in the host density map to
avoid excluding all locations in the analysis. if value is 1,
all locations will be excluded from the analysis and 0 will
include all locations in the analysis. Selecting a threshold
for host density requires at least knowing what is the
maximum value in the host density map to avoid excluding all
locations in the analysis.
res: Numeric. Resolution of the raster. Default is 'reso()'.
inv_pl: List. A named list of parameters for inverse power law. See
details.
neg_exp: List. A named list of parameters for inverse negative
exponential. See details. All locations with a host density
below the selected threshold will be excluded from the
connectivity analysis, which focuses the analysis on the most
important locations. The values for the host density
threshold can range between 0 and 1;
...: arguments passed to 'sean()'
outdir: Character. Output directory for saving raster in TIFF format.
Default is 'tempdir()'.
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
When 'global = TRUE', 'geoscale' is ignored and 'global_scales()'
is used by default.
The functions 'sean()' and 'msean()' perform the same sensitivity
analysis, but they differ in their return value. The return value
of 'msean()' is 'GeoNetwork', which contains the result from
applying the 'connectivity()' function on the habitat connectivity
indexes. Essentially, the risk maps.
If neither the inverse power law nor the negative exponential
dispersal kernel is specified, the function will return an error.
In 'msean()', three spatRasters are produced with the following
values. For each location in the area of interest, the mean in
habitat connectivity across selected parameters is calculated. For
each location in the area of interest, the variance in habitat
connectivity across selected parameters is calculated. For each
location in the area of interest, the difference between the rank
of habitat connectivity and the rank of host density is
calculated. By default, each of these spatRasters is plotted for
visualization.
_<08>V_<08>a_<08>l_<08>u_<08>e:
GeoRasters.
GeoNetwork.
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Yanru Xing, John F Hernandez Nopsa, Kelsey F Andersen, Jorge L
Andrade-Piedra, Fenton D Beed, Guy Blomme, Mónica Carvajal-Yepes,
Danny L Coyne, Wilmer J Cuellar, Gregory A Forbes, Jan F Kreuze,
Jürgen Kroschel, P Lava Kumar, James P Legg, Monica Parker, Elmar
Schulte-Geldermann, Kalpana Sharma, Karen A Garrett, _Global
Cropland connectivity: A Risk Factor for Invasion and Saturation
by Emerging Pathogens and Pests_, BioScience, Volume 70, Issue 9,
September 2020, Pages 744–758, doi:10.1093/biosci/biaa067
<https://doi.org/10.1093/biosci/biaa067>
Hijmans R (2023). _terra: Spatial Data Analysis_. R package
version 1.7-46, <https://CRAN.R-project.org/package=terra>
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Uses 'connectivity()'
Uses 'msean()' 'inv_powerlaw()' 'neg_expo()'
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
avocado <- cropharvest_rast("avocado", "monfreda")
# global
ri <- sean(avocado) # returns a list of GeoRasters
mri <- msean(rast = avocado) # returns GeoNetwork object
# non-global
# geoscale is a vector of xmin, xmax, ymin, ymax
# returns GeoRasters object
ri <- sean(avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
ri
# returns GeoNetwork object
mri <- msean(rast = avocado, global = FALSE, geoscale = c(-115, -75, 5, 32))
mri
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
** Processing: /home/hornik/tmp/R.check/r-release-gcc/Work/PKGS/geohabnet.Rcheck/vign_test/geohabnet/vignettes/analysis_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 6415 bytes
Input file size = 6493 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 5219
Output IDAT size = 5219 bytes (1196 bytes decrease)
Output file size = 5297 bytes (1196 bytes = 18.42% decrease)
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature 'x = "NULL"'
--- failed re-building ‘analysis.Rmd’
SUMMARY: processing the following file failed:
‘analysis.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-release-linux-x86_64
Version: 2.1.3
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building 'LinkWeightsAnalysis.Rmd' using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/potato_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 10812124 bytes (10.3 MB)
==================================================
downloaded 10.3 MB
--- finished re-building 'LinkWeightsAnalysis.Rmd'
--- re-building 'analysis.Rmd' using rmarkdown
trying URL 'https://s3.us-east-2.amazonaws.com/earthstatdata/HarvestedAreaYield175Crops_Indvidual_Geotiff/avocado_HarvAreaYield_Geotiff.zip'
Content type 'application/zip' length 6271196 bytes (6.0 MB)
==================================================
downloaded 6.0 MB
trying URL 'https://dataverse.harvard.edu/api/access/datafile/3985008?format=original'
Quitting from lines 217-223 [fetch_sp_ba] (analysis.Rmd)
Error: processing vignette 'analysis.Rmd' failed with diagnostics:
unable to find an inherited method for function 'sources' for signature '"NULL"'
--- failed re-building 'analysis.Rmd'
SUMMARY: processing the following file failed:
'analysis.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-windows-x86_64