The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
BoundaryStats was designed to test for the presence of geographic boundaries in ecological variables and overlap between such boundaries. Users can calculate boundary and boundary overlap statistics with raster data. BoundaryStats can create null distributions for the statistics based on various neutral landscape models that are parameterized on the empirical data. The primary functions are statistical tests for the presence of spatial boundaries of a variable and significant overlap between the spatial boundaries of two variables.
You can install BoundaryStats with either:
install.packages('BoundaryStats')
remotes::install_github("aluo734/BoundaryStats")| Function | Category | Description |
|---|---|---|
| n_subgraph | Boundary | The number of subgraphs, or sets of contiguous boundary elements, in the data. |
| max_subgraph | Boundary | The length of the longest subgraph. |
| Odirect | Boundary Overlap | The number of directly overlapping boundary elements, or raster cells labelled as part of a boundary, of two traits. |
| Ox | Boundary Overlap | The average minimum distance between each boundary element in raster x and the nearest boundary element in raster y. Uses Euclidean distance. The boundaries of trait x depend on the boundaries of trait y. |
| Oxy | Boundary Overlap | The average minimum distance between boundary elements in two raster layers. Uses Euclidean distance. Boundaries for each trait affect one another reciprocally (x affects y and y affects x). |
library(BoundaryStats)
library(tidyverse)
data(T.cristatus)
T.cristatus <- terra::rast(T.cristatus_matrix, crs = T.cristatus_crs)
ext(T.cristatus) <- T.cristatus_ext
data(grassland)
grassland <- terra::rast(grassland_matrix, crs = grassland_crs)
ext(grassland) <- grassland_ext
Tcrist_boundaries <- categorical_boundary(T.cristatus)
grassland_boundaries <- define_boundary(grassland, threshold = 0.1)
plot_boundary(Tcrist_boundaries, grassland_boundaries)
Tcrist_ovlp_null <- overlap_null_distrib(T.cristatus, grassland, rand_both = FALSE, x_cat = T, n_iterations = 100, x_model = 'random_cluster')
Odirect(Tcrist_boundaries, grassland_boundaries, Tcrist_ovlp_null)
Ox(Tcrist_boundaries, grassland_boundaries, Tcrist_ovlp_null)
Oxy(Tcrist_boundaries, grassland_boundaries, Tcrist_ovlp_null)Data source: Cox, Karen; Schepers, Robbert; Van Breusegem, An; Speybroeck, Jeroen (2023), The common ground in landscape effects on gene flow in two newt species in an agroecosystem, Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kdhz.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.