stacking {SSDM} | 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 |
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
See Also
stack_modelling
to build SSDMs.
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)