Sensitivity analysis for habitat connectivity

This function runs a sensitivity analysis on habitat connectivity calculated based on every combination of selected parameters. Parameter values in sensitivity_analysis() should be provided using the function set_parameters(). If no parameters are provided, then the sensitivity_analysis() function will run the sensitivity analysis using a default set of parameter values, which is accessible through the function get_parameters(). To customize parameter values, open the parameters.yaml that was automatically downloaded when geohabnet was installed, change, remove, or add parameter values directly in the parameters.yaml and save it. Once the values have been changed manually, run set_parameters() to set the new parameter values, which will return TRUE if the parameters were set successfully.

Usage

sensitivity_analysis(maps = TRUE, alert = TRUE)

Arguments

maps: logical. TRUE if maps of outcomes are to be plotted, FALSE otherwise. If TRUE, three maps are possible: a map of mean habitat connectivity, a map of variance of habitat connectivity, and a map of the difference between the ranks in habitat connectivity and habitat density.
alert: logical. TRUE if a beep sound is to be played once the analysis is completed, FALSE otherwise

Value

GeoNetwork. Errors are not handled.

Details

For each location in a region, sensitivity_analysis() calculates the habitat connectivity risk index (CCRI) proposed by Xing et al. (2021). By default, sensitivity_analysis() runs a sensitivity analysis on a global extent, see global_scales() for details. This function also plots maps of the outcomes automatically, but it will suppress maps for outcomes if maps = FALSE or interactive() is FALSE. The returned object is of class GeoNetwork, which contains two types of outcomes. One outcome type corresponds to spatRasters representing the maps of habitat connectivity. The second type corresponds to adjacency matrices used to calculate the habitat connectivity, where columns and rows represent locations in the maps and entries are the relative likelihood of pathogen or pest movement between each pair of nodes.

References

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

Hijmans R (2023). terra: Spatial Data Analysis. R package version 1.7-46, https://CRAN.R-project.org/package=terra

Examples

# \donttest{
# Run analysis on specified parameters.yaml
ss1 <- sensitivity_analysis()
ss2 <- sensitivity_analysis(FALSE, FALSE)
ss3 <- sensitivity_analysis(TRUE, FALSE)
# }