single_mop: Analysis of extrapolation risks using the MOP metric (for...

In kuenm2: Detailed Development of Ecological Niche Models

Analysis of extrapolation risks using the MOP metric (for single scenario)

Description

Calculates the mobility-oriented parity metric and other sub-products to represent dissimilarities and non-analogous conditions when comparing a set of reference conditions (M) against another set of scenario conditions (G).

Usage

single_mop(data, new_variables, subset_variables = FALSE,
           mask = NULL, type = "basic", na_in_range = TRUE,
           calculate_distance = FALSE, where_distance = "in_range",
           distance = "euclidean", scale = FALSE, center = FALSE,
           fix_NA = TRUE, percentage = 1, comp_each = 2000, tol = NULL,
           rescale_distance = FALSE, parallel = FALSE, ncores = NULL,
           progress_bar = TRUE, write_files = FALSE, out_dir = NULL,
           overwrite = FALSE)

Arguments

data	an object of class fitted_models returned by the fit_selected() function, an object of class prepared_data returned by the prepare_data() function, or an object of class calibration_results returned by the calibration() function.
new_variables	a SpatRaster or data.frame of predictor variables. The names of these variables must match those used to prepare the date or calibrate the models provided in data.
subset_variables	(logical) whether to include in the analysis only the variables present in the selected models. Only applicable if data is a fitted_models object. Default is FALSE
mask	(SpatRaster, SpatVector, or SpatExtent) spatial object used to mask the variables (optional). Default is NULL.
type	(character) type of MOP analysis to be performed. Options available are "basic", "simple" and "detailed". See Details for further information.
na_in_range	(logical) whether to assign NA to regions within the projected area (G) where environmental conditions fall within the range of the calibration data (M). If TRUE (default), these regions are assigned NA. If FALSE, they are assigned 0 in the simple and basic MOP outputs, and "within ranges" in the detailed MOP output.
calculate_distance	(logical) whether to calculate distances (dissimilarities) between m and g. The default, FALSE, runs rapidly and does not assess dissimilarity levels.
where_distance	(character) where to calculate distances, considering how conditions in g are positioned in comparison to the range of conditions in m. Options available are "in_range", "out_range" and "all". Default is "in_range".
distance	(character) which distances are calculated, euclidean or mahalanobis. Only applicable if calculate_distance = TRUE.
scale	(logical or numeric) whether to scale as in scale. Default is FALSE.
center	(logical or numeric) whether to center as in scale. Default is FALSE.
fix_NA	(logical) whether to fix layers so cells with NA values are the same in all layers. Setting to FALSE may save time if the rasters are big and have no NA matching problems. Default is TRUE.
percentage	(numeric) percentage of m closest conditions used to derive mean environmental distances to each combination of conditions in g.
comp_each	(numeric) number of combinations in g to be used for distance calculations at a time. Increasing this number requires more RAM
tol	(numeric) tolerance to detect linear dependencies when calculating Mahalanobis distances. The default, NULL, uses .Machine$double.eps.
rescale_distance	(logical) whether to re-scale distances 0-1. Re-scaling prevents comparisons of dissimilarity values obtained from runs with different values of percentage.
parallel	(logical) whether to compute MOP in parallel. Default is FALSE.
ncores	(numeric) number of cores to use for parallel processing. Default is NULL and uses available cores - 1. This is only applicable if parallel = TRUE.
progress_bar	(logical) whether to display a progress bar during distance calculation. Only applicable if calculate_distance is TRUE. Default is TRUE.
write_files	(logical) whether to save the MOP results (SpatRasters and data.frames) to disk. Default is FALSE.
out_dir	(character) directory path where results will be saved. Only relevant if write_files = TRUE.
overwrite	(logical) whether to overwrite SpatRasters if they already exist. Only applicable if write_files = TRUE. Default is FALSE.

Details

type options return results that differ in the detail of how non-analogous conditions are identified.

• basic - makes calculation as proposed by Owens et al. (2013) doi:10.1016/j.ecolmodel.2013.04.011.

• simple - calculates how many variables in the set of interest are non-analogous to those in the reference set.

• detailed - calculates five additional extrapolation metrics. See mop_detailed under Value below for full details.

where_distance options determine what values should be used to calculate dissimilarity

• in_range - only conditions inside m ranges

• out_range - only conditions outside m ranges

• all - all conditions

When the variables used to represent conditions have different units, scaling and centering are recommended. This step is only valid when Euclidean distances are used.

Value

An object of class mop_results containing:

• summary - a data.frame with details of the data used in the analysis:

  • variables - names of variables considered.

  • type - type of MOP analysis performed.

  • scale - value according to the argument scale.

  • center - value according to the argument center.

  • calculate_distance - value according to the argument calculate_distance.

  • distance - option regarding distance used.

  • percentage - percentage of m used as reference for distance calculation.

  • rescale_distance - value according to the argument rescale_distance.

  • fix_NA - value according to the argument fix_NA.

  • N_m - total number of elements (cells with values or valid rows) in m.

  • N_g - total number of elements (cells with values or valid rows) in g.

  • m_ranges - the range (minimum and maximum values) of the variable in reference conditions (m)

• mop_distances - if calculate_distance = TRUE, a SpatRaster with distance values for the set of interest (g). Higher values represent greater dissimilarity compared to the set of reference (m).

• mop_basic - a SpatRaster for the set of interest representing conditions in which at least one of the variables is non-analogous to the set of reference. Values should be: 1 for non-analogous conditions, and NA for conditions inside the ranges of the reference set.

• mop_simple - a SpatRaster, for the set of interest, representing how many variables in the set of interest are non-analogous to those in the reference set. NA is used for conditions inside the ranges of the reference set.

• mop_detailed - a list containing:

  • interpretation_combined - a data.frame to help identify combinations of variables in towards_low_combined and towards_high_combined that are non-analogous to m.

  • towards_low_end - a SpatRaster for all variables representing where non-analogous conditions were found towards low values of each variable.

  • towards_high_end - a SpatRaster for all variables representing where non-analogous conditions were found towards high values of each variable.

  • towards_low_combined - a SpatRaster with values representing the identity of the variables found to have non-analogous conditions towards low values.

  • towards_high_combined - a SpatRaster with values representing the identity of the variables found to have non-analogous conditions towards high values.

See Also

projection_mop()

Examples

# Import an example of fitted models (output of fit_selected())
data("fitted_model_maxnet", package = "kuenm2")

# Import variables under a new set of conditions
# Here, future climate data
future_scenario <- terra::rast(system.file("extdata",
                                          "wc2.1_10m_bioc_ACCESS-CM2_ssp585_2081-2100.tif",
                                          package = "kuenm2"))



# Rename variables to match the variable names in the fitted models
names(future_scenario) <- sub("bio0", "bio", names(future_scenario))
names(future_scenario) <- sub("bio", "bio_", names(future_scenario))

# Run MOP
sm <- single_mop(data = fitted_model_maxnet, new_variables = future_scenario,
                 type = "detailed")

kuenm2 documentation built on April 21, 2026, 1:07 a.m.