Multivariate Environmental Similarity Surfaces based on a data frame
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.
MESS(V, P, id.col = NULL)
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 |
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. |
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.
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).
A new, apparently more complete method for analysing environmental dissimilarities has recently become available (see https://www.climond.org/ExDet.aspx). We are planning on implementing a dataframe version of that too.
Alberto Jimenez-Valverde, A. Marcia Barbosa
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
mess in package dismo; OA
## 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"])
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.