Use the function to compute the Protected Connected (ProtConn), EC, PC or IIC indexes in a regular grid.
MK_dECA_grid( nodes, area_unit = "m2", region = NULL, grid_param = list(grid_pol = NULL, grid_id = NULL, hexagonal = TRUE, cellsize = NULL, grid_boundary = FALSE, clip = FALSE, tolerance = NULL), distance = list(type = "centroid"), metric = "IIC", distance_threshold = NULL, probability = NULL, intern = TRUE, parallel = NULL )
| nodes | list of objects class sf, SpatialPolygonsDataFrame. Nodes of each time to analyze. The shapefiles must be in a projected coordinate system. |
|---|---|
| area_unit | character. Attribute area units. You can set an area unit, "Makurhini::unit_covert()" compatible unit ("m2", "Dam2, "km2", "ha", "inch2", "foot2", "yard2", "mile2"). Default equal to hectares "ha". |
| region | object of class sf, sfc, sfg or SpatialPolygons. Region shapefile, the shapefile must be in a projected coordinate system. |
| grid_param | list. Parameters of the grid shapefile, see get_grid.Just omit the parameter 'region'. Example, list(grid_pol = NULL, hexagonal = TRUE, grid_id = NULL, cellsize = unit_convert(1000, "km2", "m2"), grid_boundary = FALSE, clip = FALSE, tolerance = NULL). |
| distance | list. See distancefile. Example, list(type= "centroid", resistance = NULL). |
| metric | character. Choose a connectivity metric: "IIC" considering topologycal distances or "PC" considering maximum product probabilities. |
| distance_threshold | numeric. Distance threshold to establish connections (crs units, usually meters). |
| probability | numeric. Probability of direct dispersal between nodes, Default, 0.5, that is 50 percentage of probability connection. If probability = NULL, then it will be the inverse of the mean dispersal distance for the species (1/α; Hanski and Ovaskainen 2000). |
| intern | logical. Show the progress of the process, default = TRUE. Sometimes the advance process does not reach 100 percent when operations are carried out very quickly. |
| parallel | numeric. Specify the number of cores to use for parallel processing, default = NULL. Parallelize the function using furrr package and multiprocess plan when there are more than ONE transboundary. |
Matt Strimas-Mackey. http://strimas.com/spatial/hexagonal-grids/.
Saura, S., Bastin, L., Battistella, L., Mandrici, A., & Dubois, G. (2017). Protected areas in the
world's ecoregions: How well connected are they? Ecological Indicators, 76, 144–158.
Saura, S. & Torne, J. (2012). Conefor 2.6. Universidad Politecnica de Madrid. Available
at www.conefor.org.
Pascual-Hortal, L. & Saura, S. (2006). Comparison and development of new graph-based landscape
connectivity indices: towards the priorization of habitat patches and corridors for conservation.
Landscape Ecology, 21(7): 959-967.
Saura, S. & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity
in landscape conservation planning: comparison with existing indices and application to a case study.
Landscape and Urban Planning, 83(2-3): 91-103.
if (FALSE) { library(Makurhini) library(sf) # Four times (T1.2, T2.3, T3.4) data("list_forest_patches", package = "Makurhini") data("study_area", package = "Makurhini") class(list_forest_patches) hexagons_dECA <- MK_dECA_grid(nodes = list_forest_patches, region = study_area, area_unit = "ha", metric = "IIC", grid_param = list(hexagonal = TRUE, cellsize = unit_convert(100, "km2", "m2")), distance_threshold = 3000, probability = 0.5, distance = list(type = "centroid"), intern = TRUE, parallel = NULL) hexagons_dECA plot(hexagons_dECA["T3.4.dECA"], breaks = "quantile") plot(hexagons_dECA["T3.4.Type.Change"], key.pos = 1) }