modEvA (version 2.0)

MESS: Multivariate Environmental Similarity Surfaces based on a data frame

Description

This function performs the MESS analysis of Elith et al. (2010) to determine the extent of the environmental differences between model training and model projection (extrapolation) data. It is applicable to variables in a matrix or data frame.

Usage

MESS(V, P, id.col = NULL)

Arguments

V

a matrix or data frame containing the variables (one in each column) in the training dataset.

P

a matrix or data frame containing the same variables in the area to which the model(s) will be projected. Variables (columns) must be in the same order as in V, and colnames(P) must exist.

id.col

optionally, the index number of a column containing the row identifiers in P. If provided, this column will be excluded from MESS calculations but included in the output.

Value

The function returns a data frame with the same column names as P, plus a column named TOTAL, quantifying the similarity between each point in the projection dataset and those in the reference dataset. Negative values indicate localities that are environmentally dissimilar from the reference region. The last column, MoD, indicates which of the column names of P corresponds to the most dissimilar variable, i.e., the limiting factor or the variable that drives the MESS in that locality (Elith et al. 2010).

Details

When model predictions are projected into regions, times or spatial resolutions not analysed in the training data, it may be important to measure the similarity between the new environments and those in the training sample (Elith et al. 2010), as models are not so reliable when predicting outside their domain (Barbosa et al. 2009). The Multivariate Environmental Similarity Surfaces (MESS) analysis measures the similarity in the analysed variables between any given locality in the projection dataset and the localities in the reference (training) dataset (Elith et al. 2010).

MESS analysis is implemented in the MAXENT software (Phillips et al. 2006) and in the dismo R package, but there it requires input variables in raster format. This implies not only the use of complex spatial data structures, but also that the units of analysis are rectangular pixels, whereas we often need to model distribution data recorded on irregular units (e.g. provinces, river basins), or on equal-area units (e.g. UTM cells, equal-area hexagons), whereas pixels vary in area between the equator and the poles. The MESS function computes this analysis for variables in a data frame, where localities (in rows) may be of any size or shape.

References

Barbosa A.M., Real R. & Vargas J.M. (2009) Transferability of environmental favourability models in geographic space: the case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain. Ecological Modelling 220: 747-754

Elith J., Kearney M. & Phillips S. (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution 1: 330-342

Phillips S.J., Anderson R.P. & Schapire R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259

See Also

mess in package dismo; OA

Examples

# NOT RUN {
# load package 'fuzzySim' and its sample data:
require(fuzzySim)
data(rotif.env)


# add a column specifying the hemisphere:

unique(rotif.env$CONTINENT)

rotif.env$HEMISPHERE <- "Eastern"

rotif.env$HEMISPHERE[rotif.env$CONTINENT %in%
c("NORTHERN_AMERICA", "SOUTHERN_AMERICA")] <- "Western"

head(rotif.env)


# perform a MESS analysis
# suppose you'll extrapolate models from the Western hemisphere (Americas)
# to the Eastern hemisphere (rest of the world):

names(rotif.env)  # variables are in columns 5:17

west <- subset(rotif.env, HEMISPHERE == "Western", select = 5:17)
east <- subset(rotif.env, HEMISPHERE == "Eastern", select = 5:17)
east.with.ID <- subset(rotif.env, HEMISPHERE == "Eastern", 
select = c(1, 5:17))

head(east)
head(east.with.ID)  # ID is in column 1

mess <- MESS(V = west, P = east)
mess.with.ID <- MESS(V = west, P = east.with.ID, id.col = 1)

head(mess)
head(mess.with.ID)

range(mess[ , "TOTAL"])
# }