stacking: Stack different ensemble SDMs in an SSDM
In sylvainschmitt/SSDM: Stacked Species Distribution Modelling

stacking

R Documentation

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 signature '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
)

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):

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

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

NULL: No endemism map
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'.

Value

an S4 Stacked.SDM class object viewable with the plot.model function.

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

Examples

## Not run: 
# 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)

## End(Not run)

sylvainschmitt/SSDM documentation built on Oct. 25, 2023, 11:19 p.m.