This function explores different numbers of environmental partitions (clusters) based on the K-means clustering algorithm and returns the number of partitions best suited for a given presence, presence-absences, or presence-pseudo-absences database. Selection of the best number of partitions is performed automatically considering spatial autocorrelation, environmental similarity, and the number of presence and/or absence records in each partition.
Usage
part_senv(
env_layer,
data,
x,
y,
pr_ab,
min_n_groups = 2,
max_n_groups = 10,
min_occ = 10,
prop = 0.5
)Arguments
- env_layer
SpatRaster. Raster with environmental variable. This will be used to evaluate spatial autocorrelation and environmental similarity between training and testing partitions. Because this function calculate dissimilarity based on Euclidean distances, it can only be used with continuous variables
- data
data.frame. Data.frame or tibble object with presence (or presence-absence, or presences-pseudo-absence) records, and coordinates
- x
character. Column name with spatial x coordinates
- y
character. Column name with spatial y coordinates
- pr_ab
character. Column with presences, presence-absence, or pseudo-absence. Presences must be represented by 1 and absences by 0
- min_n_groups
integer. Minimum number of groups to be tested. Default 2.
- max_n_groups
integer. Maximum number of groups to be tested. Default 10.
- min_occ
numeric. Minimum number of presences or absences in a partition fold. The min_occ value should be base on the amount of predictors in order to avoid over-fitting or error when fitting models for a given fold. Default 10.
- prop
numeric. Proportion of point used for testing autocorrelation between groups (values > 0 and <=1). The smaller this number is, the faster the function will work. Default 0.5
Value
A list with:
part: A tibble object with information used in 'data' arguments and a additional column .part with partition group.
best_part_info: A tibble with information about the best partition. It contains the number of partition (n_groups), standard deviation of presences (sd_p), standard deviation of absences (sd_a), Moran's I spatial autocorrelation (spa_auto) and environmental similarity based on Euclidean distance (env_sim)
Details
The part_sblock allows test with different numbers of partitions defined in the envirnomental clusters delimited the K-mean cluster algorithm. This function explores a range of environmental clusters and automatically selects best number of cluster for a given given presence, presence-absences, or presence-pseudo-absences dataset. Such selection of number of clusters is based on an optimization procedure that explores partition size in three dimensions determined by spatial autocorrelation (measured by Moran's I), environmental similarity (Euclidean distance), and difference in the amount of data among clusters (Standard Deviation - SD; Velazco et al., 2019). This procedure will cyclically select those partitions with autocorrelation values less than the lowest quartile of Morans I, then those with environmental similarity values greater than the third quartile of the Euclidean distances than those with a difference in the amount of data less than the lowest quartile of SD. This selection is repeated until only one partition is retained (Velazco et al., 2019). The main benefit of this partition selection are i) this is not subjective, ii) balances the environmental similarity and special autocorrelation between partitions, and iii) controls the partition selection with few data that may be problematic for model fitting ("min_occ" argument)..
Partitions geographically structured tend to evaluate model transferability more directly than
conventional ones (e.g., those performed by part_random) (Roberts et al., 2017;
Santini et al., 2021), being relevant for models that want to be used for projections in other
regions outside the calibration area or for other periods.
This function can interact with get_block, sample_background,
and sample_pseudoabs for sampling background points or pseudo-absences within
spatial partition broups
References
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schroder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40, 913-929. https://doi.org/10.1111/ecog.02881
Santini, L., Benitez-Lopez, A., Maiorano, L., Cengic, M., & Huijbregts, M. A. J. (2021). Assessing the reliability of species distribution projections in climate change research. Diversity and Distributions, ddi.13252. https://doi.org/10.1111/ddi.13252
Velazco, S. J. E., Villalobos, F., Galvao, F., & De Marco Junior, P. (2019). A dark scenario for Cerrado plant species: Effects of future climate, land use and protected areas ineffectiveness. Diversity and Distributions, 25(4), 660-673. https://doi.org/10.1111/ddi.12886
See also
part_random, part_sblock, and part_sband
Examples
if (FALSE) { # \dontrun{
require(terra)
require(ggplot2)
f <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(f)
# Select a species
spp1 <- spp %>% dplyr::filter(species == "sp1")
part1 <- part_senv(
env_layer = somevar,
data = spp1,
x = "x",
y = "y",
pr_ab = "pr_ab",
min_n_groups = 2,
max_n_groups = 10,
min_occ = 10,
prop = 0.2
)
part1
ggplot(part1$part, aes(x, y, col = factor(.part))) +
geom_point(aes(shape = factor(pr_ab)))
ggplot(part1$part, aes(x, y, col = factor(.part))) +
geom_point(aes(shape = factor(pr_ab))) +
facet_wrap(. ~ .part)
ggplot(part1$part, aes(x, y, col = factor(.part))) +
geom_point(aes(shape = factor(pr_ab))) +
facet_wrap(. ~ pr_ab)
} # }