SSDM (version 0.2.9)

stacking: Stack different ensemble SDMs in an SSDM

Description

This is a function to stack several ensemble SDMs in an SSDM. The function takes as inputs several S4 Ensemble.SDM class objects produced with ensemble_modelling or ensemble functions. The function returns an S4 Stacked.SDM class object containing the local species richness map, the between-algorithm variance map, and all evaluation tables coming with (model evaluation, algorithm evaluation, algorithm correlation matrix and variable importance), and a list of ensemble SDMs for each species (see ensemble_modelling).

Usage

stacking(
  esdm,
  ...,
  name = NULL,
  method = "pSSDM",
  rep.B = 1000,
  Env = NULL,
  range = NULL,
  endemism = c("WEI", "Binary"),
  eval = TRUE,
  uncertainty = TRUE,
  verbose = TRUE,
  GUI = FALSE
)
# S4 method for Ensemble.SDM
stacking(
  esdm,
  ...,
  name = NULL,
  method = "pSSDM",
  rep.B = 1000,
  Env = NULL,
  range = NULL,
  endemism = c("WEI", "Binary"),
  eval = TRUE,
  uncertainty = TRUE,
  verbose = TRUE,
  GUI = FALSE
)

Value

an S4 Stacked.SDM class object viewable with the

plot.model function.

Arguments

esdm, ...

character. Ensemble SDMs to be stacked.

name

character. Optional name given to the final SSDM produced (by default 'Species.SDM').

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.

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).

range

integer. Set a value of range restriction (in pixels) around presences occurrences on habitat suitability maps (all further points will have a null probability, see Crisp et al (2011) in references). If NULL, no range restriction will be applied.

endemism

character. Define the method used to create an endemism map (see details below).

eval

logical. If set to FALSE, disable stack evaluation.

uncertainty

logical. If set to TRUE, generates an uncertainty map and an algorithm correlation matrix.

verbose

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

GUI

logical. Don't take that argument into account (parameter for the user interface).

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):

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

Endemism: Choice of the method used to compute the endemism map (see Crisp et al. (2001) for more information, see reference below):

WEI

(Weighted Endemism Index) Endemism map built by counting all species in each cell and weighting each by the inverse of its range

CWEI

(Corrected Weighted Endemism Index) Endemism map built by dividing the weighted endemism index by the total count of species in the cell.

First string of the character is the method either WEI or CWEI, and in those cases second string of the vector is used to precise range calculation, whether the total number of occurrences 'NbOcc' whether the surface of the binary map species distribution 'Binary'.

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

M. D. Crisp, S. Laffan, H. P. Linder & A. Monro (2001) "Endemism in the Australian flora" Journal of Biogeography 28:183-198 http://biology-assets.anu.edu.au/hosted_sites/Crisp/pdfs/Crisp2001_endemism.pdf

C. Liu, P. M. Berry, T. P. Dawson, R. & G. Pearson (2005) "Selecting thresholds of occurrence in the prediction of species distributions." Ecography 28:85-393 http://www.researchgate.net/publication/230246974_Selecting_Thresholds_of_Occurrence_in_the_Prediction_of_Species_Distributions

See Also

stack_modelling to build SSDMs.

Examples

if (FALSE) {
# Loading data
data(Env)
data(Occurrences)
Occ1 <- subset(Occurrences, Occurrences$SPECIES == 'elliptica')
Occ2 <- subset(Occurrences, Occurrences$SPECIES == 'gracilis')

# SSDM building
ESDM1 <- ensemble_modelling(c('CTA', 'SVM'), Occ1, Env, rep = 1,
                           Xcol = 'LONGITUDE', Ycol = 'LATITUDE',
                           name = 'elliptica', ensemble.thresh = c(0.6))
ESDM2 <- ensemble_modelling(c('CTA', 'SVM'), Occ2, Env, rep = 1,
                           Xcol = 'LONGITUDE', Ycol = 'LATITUDE',
                           name = 'gracilis', ensemble.thresh = c(0.6))
SSDM <- stacking(ESDM1, ESDM2)

# Results plotting
plot(SSDM)
}