mapDiversity {SSDM}

Map Diversity

Description

Methods for Stacked.SDM or SSDM to map diversity and communities composition.

Usage

mapDiversity(obj, ...)

## S4 method for signature 'Stacked.SDM'
mapDiversity(obj, method, rep.B = 1000, verbose = TRUE, Env = NULL, ...)

Arguments

obj

Stacked.SDM. SSDM to map diversity with.

...

other arguments pass to the method.

method

character. Define the method used to create the local species richness map (see details below).

rep.B

integer. If the method used to create the local species richness is the random Bernoulli (Bernoulli), rep.B parameter defines the number of repetitions used to create binary maps for each species.

verbose

logical. If set to true, allows the function to print text in the console.

Env

raster object. Stacked raster object of environmental variables (can be processed first by load_var). Needed only for stacking method using probability ranking from richness (PRR).

Details

Methods: Choice of the method used to compute the local species richness map (see Calabrese et al. (2014) and D'Amen et al (2015) for more informations, see reference below):

pSSDM

sum probabilities of habitat suitability maps

Bernoulli

draw repeatedly from a Bernoulli distribution

bSSDM

sum the binary map obtained with the thresholding (depending on the metric of the ESDM).

MaximumLikelihood

adjust species richness of the model by linear regression

PRR.MEM

model richness with a macroecological model (MEM) and adjust each ESDM binary map by ranking habitat suitability and keeping as much as predicted richness of the MEM

PRR.pSSDM

model richness with a pSSDM and adjust each ESDM binary map by ranking habitat suitability and keeping as much as predicted richness of the pSSDM

Value

a list with a diversity map and eventually ESDMs for stacking method using probability ranking from richness (PPR).

References

M. D'Amen, A. Dubuis, R. F. Fernandes, J. Pottier, L. Pelissier, & A Guisan (2015) "Using species richness and functional traits prediction to constrain assemblage predicitions from stacked species distribution models" Journal of Biogeography 42(7):1255-1266 http://doc.rero.ch/record/235561/files/pel_usr.pdf

J.M. Calabrese, G. Certain, C. Kraan, & C.F. Dormann (2014) "Stacking species distribution models and adjusting bias by linking them to macroecological models." Global Ecology and Biogeography 23:99-112 https://onlinelibrary.wiley.com/doi/full/10.1111/geb.12102

See Also

stacking to build SSDMs.

Examples

## Not run: 
# Loading data
data(Env)
data(Occurrences)
# SSDM building
SSDM <- stack_modelling(c('CTA', 'SVM'), Occurrences, Env, rep = 1,
                       Xcol = 'LONGITUDE', Ycol = 'LATITUDE',
                       Spcol = 'SPECIES')

# Diversity mapping
mapDiversity(SSDM, mathod = 'pSSDM')


## End(Not run)