part_senv: Environmental and spatial cross-validation

In sjevelazco/flexsdm: Tools for Data Preparation, Fitting, Prediction, Evaluation, and Post-Processing of Species Distribution Models

Environmental and spatial cross-validation

Description

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

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

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)

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

## Not run: 
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)

## End(Not run)

sjevelazco/flexsdm documentation built on Feb. 28, 2025, 9:07 a.m.