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Calculate environmental similarity of reference datasets to predictors.

Source: R/similarity.R
similarity.Rd

Calculate the environmental similarity of the provided covariates with respect to a reference dataset. Currently supported is Multivariate Environmental Similarity index and the multivariate combination novelty index (NT2) based on the Mahalanobis divergence (see references).

Usage

similarity(
  obj,
  ref,
  ref_type = "poipo",
  method = "mess",
  predictor_names = NULL,
  full = FALSE,
  plot = TRUE,
  ...
)

# S4 method for BiodiversityDistribution
similarity(
  obj,
  ref,
  ref_type = "poipo",
  method = "mess",
  predictor_names = NULL,
  full = FALSE,
  plot = TRUE,
  ...
)

# S4 method for SpatRaster
similarity(
  obj,
  ref,
  ref_type = "poipo",
  method = "mess",
  predictor_names = NULL,
  full = FALSE,
  plot = TRUE,
  ...
)

Arguments

obj

A BiodiversityDistribution, DistributionModel or alternatively a SpatRaster object.

ref

A BiodiversityDistribution, DistributionModel or alternatively a data.frame with extracted values (corresponding to those given in obj).

ref_type

A character specifying the type of biodiversity to use when obj is a BiodiversityDistribution.

method

A specifc method for similarity calculation. Currently supported: 'mess', 'nt'.

predictor_names

An optional character specifying the covariates to be used (Default: NULL).

full

should similarity values be returned for all variables (Default:FALSE)?

plot

Should the result be plotted? Otherwise return the output list (Default: TRUE).

...

other options (Non specified).

Value

This function returns a list containing:

  • similarity: A SpatRaster object with multiple layers giving the environmental similarities for each variable in x (only included when "full=TRUE");

  • mis: a SpatRaster layer giving the minimum similarity value across all variables for each location (i.e. the MESS);

  • exip: a SpatRaster layer indicating whether any model would interpolate or extrapolate to this location based on environmental surface;

  • mod: a factor SpatRaster layer indicating which variable was most dissimilar to its reference range (i.e. the MoD map, Elith et al. 2010); and

  • mos: a factor SpatRaster layer indicating which variable was most similar to its reference range.

Details

similarity implements the MESS algorithm described in Appendix S3 of Elith et al. (2010) as well as the Mahalanobis dissimilarity described in Mesgaran et al. (2014).

References

  • Elith, J., Kearney, M., and Phillips, S. (2010) "The art of modelling range-shifting species". Methods in Ecology and Evolution, 1: 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x

  • Mesgaran, M.B., Cousens, R.D. and Webber, B.L. (2014) "Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models". Diversity and Distributions, 20: 1147-1159. https://doi.org/10.1111/ddi.12209

See also

dismo R-package.

Examples

 if (FALSE) {
plot(
  similarity(x) # Where x is a distribution or Raster object
)
 }