CRAN Package Check Results for Package geohabnet

Last updated on 2024-09-28 10:49:59 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.1.3 5.28 264.64 269.92 ERROR
r-devel-linux-x86_64-debian-gcc 2.1.3 3.76 193.97 197.73 ERROR
r-devel-linux-x86_64-fedora-clang 2.1.3 633.69 OK
r-devel-linux-x86_64-fedora-gcc 2.1.3 679.75 OK
r-devel-windows-x86_64 2.1.3 8.00 354.00 362.00 ERROR
r-patched-linux-x86_64 2.1.3 ERROR
r-release-linux-x86_64 2.1.3 3.71 276.17 279.88 ERROR
r-release-macos-arm64 2.1.3 253.00 OK
r-release-macos-x86_64 2.1.3 528.00 OK
r-release-windows-x86_64 2.1.3 9.00 349.00 358.00 ERROR
r-oldrel-macos-arm64 2.1.3 454.00 OK
r-oldrel-macos-x86_64 2.1.3 747.00 OK
r-oldrel-windows-x86_64 2.1.3 10.00 393.00 403.00 ERROR

Check Details

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