Running 'tests.R' [311s]
Running the tests in 'tests/tests.R' failed.
Complete output:
  > library(virtualspecies)
  Loading required package: terra
  terra 1.7.71
  > a <- matrix(rep(dnorm(1:100, 50, sd = 25)),
  +             nrow = 100, ncol = 100, byrow = TRUE)
  > env <- c(rast(a * dnorm(1:100, 50, sd = 25)),
  +          rast(a * 1:100))
  > names(env) <- c("variable1", "variable2")
  > plot(env) # Illustration of the variables
  > 
  > # Easy creation of the parameter list:
  > # see in real time the shape of the response functions
  > parameters <- formatFunctions(variable1 = c(fun = 'dnorm', mean = 1e-04,
  +                                              sd = 1e-04),
  +                               variable2 = c(fun = 'linearFun', a = 1, b = 0))
  > 
  > plotResponse(x = env, parameters = parameters, 
  +              approach = "response")
  No default value was defined for rescale.each.response, setting rescale.each.response = TRUE
  No default value was defined for rescale.each.response, setting rescale.each.response = TRUE
  > 
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
  > 
  > # If you provide env, then you can see the shape of response functions:
  > parameters <- formatFunctions(x = env,
  +                               variable1 = c(fun = 'dnorm', mean = 1e-04,
  +                                              sd = 1e-04),
  +                               variable2 = c(fun = 'linearFun', a = 1, b = 0))
  No default value was defined for rescale.each.response, setting rescale.each.response = TRUE
  No default value was defined for rescale.each.response, setting rescale.each.response = TRUE
  > 
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = NULL,
  +                          species.type = "additive")
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = NULL,
  +                          species.type = "multiplicative")
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = NULL,
  +                          species.type = "multiplicative",
  +                          rescale = FALSE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = NULL,
  +                          species.type = "multiplicative",
  +                          rescale = FALSE,
  +                          rescale.each.response = FALSE)
  Generating virtual species environmental suitability...
  
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = "variable1 + variable2",
  +                          species.type = "multiplicative",
  +                          rescale = FALSE,
  +                          rescale.each.response = TRUE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
  [1] "variable1 + variable2"
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3",
  +                          species.type = "multiplicative",
  +                          rescale = FALSE,
  +                          rescale.each.response = TRUE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
  [1] "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3"
  > sp1 <- generateSpFromFun(env, parameters, plot = TRUE,
  +                          formula = "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3",
  +                          species.type = "multiplicative",
  +                          rescale = TRUE,
  +                          rescale.each.response = TRUE)
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
  [1] "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3"
  > 
  > 
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold")
     --- Generating a random value of beta for the threshold conversion
  
  
     Threshold conversion finished:
                
  - cutoff = 0.748748748748749
  - species prevalence =0.0329
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    beta = 0.5)
     Threshold conversion finished:
                
  - cutoff = 0.5
  - species prevalence =0.1023
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    beta = 0)
     Threshold conversion finished:
                
  - cutoff = 0
  - species prevalence =0.9999
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    beta = 1)
     Threshold conversion finished:
                
  - cutoff = 1
  - species prevalence =0
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    beta = "random")
     --- Generating a random value of beta for the threshold conversion
  
  
     Threshold conversion finished:
                
  - cutoff = 0.846846846846847
  - species prevalence =0.0149
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    species.prevalence = .2)
     Threshold conversion finished:
                
  - cutoff = 0.298037629032326
  - species prevalence =0.2
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    species.prevalence = .02)
     Threshold conversion finished:
                
  - cutoff = 0.8172017320348
  - species prevalence =0.0199
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    species.prevalence = .9)
     Threshold conversion finished:
                
  - cutoff = 0.0470198966430044
  - species prevalence =0.9
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "threshold",
  +                    species.prevalence = .99)
     Threshold conversion finished:
                
  - cutoff = 0.00959072698507723
  - species prevalence =0.9899
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = "random",
  +                    a = NULL,
  +                    b = NULL,
  +                    species.prevalence = .99)
     --- Determing beta automatically according to alpha and species.prevalence
  
  
     Logistic conversion finished:
                
  - beta = -0.1328125
  - alpha = -0.05
  - species prevalence =0.9913
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = "random",
  +                    a = NULL,
  +                    b = NULL,
  +                    species.prevalence = .5)
     --- Determing beta automatically according to alpha and species.prevalence
  
  
     Logistic conversion finished:
                
  - beta = 0.1474609375
  - alpha = -0.05
  - species prevalence =0.5017
  
  
  > sp2 <- convertToPA(sp1,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = .5,
  +                    a = NULL,
  +                    b = NULL)
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.5
  - alpha = -0.05
  - species prevalence =0.1057
  
  
  > 
  > 
  > sp3 <- generateRandomSp(env)
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.465465465465465
  - alpha = -0.1
  - species prevalence =0.3986
  
  
  > 
  > a <- matrix(rep(dnorm(1:100, 50, sd = 25)),
  +             nrow = 100, ncol = 100, byrow = TRUE)
  > 
  > env1 <- c(rast(a * dnorm(1:100, 50, sd = 25)),
  +           rast(a * 1:100),
  +           rast(a),
  +           rast(t(a)))
  > names(env1) <- c("var1", "var2", "var3", "var4")
  > b <- matrix(rep(dnorm(1:100, 25, sd = 50)),
  +             nrow = 100, ncol = 100, byrow = TRUE)
  > 
  > env2 <- c(rast(b * dnorm(1:100, 50, sd = 25)),
  +           rast(b * 1:100),
  +           rast(b),
  +           rast(t(b)))
  > 
  > names(env2) <- c("var1", "var2", "var3", "var4")
  > 
  > # Generating a species with the BCA
  > sp4 <- generateSpFromBCA(raster.stack.current = env1, raster.stack.future = env2)
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the between component analysis
  
   - Defining the response of the species along the axis
  
   - Calculating current suitability values
  
   - Calculating future suitability values
  
     The final environmental suitability was rescaled between 0 and1. To disable, set argument rescale = FALSE
   - Ploting response and suitability
  
  > plotResponse(sp4)
  > 
  > sp4 <- convertToPA(sp4)
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.641641641641642
  - alpha = -0.05
  - species prevalence =0.2468
  
  
  > plotSuitabilityToProba(sp4)
  > 
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold")
     --- Generating a random value of beta for the threshold conversion
  
  
     Threshold conversion finished:
                
  - cutoff = 0.601601601601602
  - species prevalence =0.2705
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    beta = 0.5)
     Threshold conversion finished:
                
  - cutoff = 0.5
  - species prevalence =0.3348
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    beta = 0)
     Threshold conversion finished:
                
  - cutoff = 0
  - species prevalence =0.9999
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    beta = 1)
     Threshold conversion finished:
                
  - cutoff = 1
  - species prevalence =0
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    beta = "random")
     --- Generating a random value of beta for the threshold conversion
  
  
     Threshold conversion finished:
                
  - cutoff = 0.693693693693694
  - species prevalence =0.2103
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    species.prevalence = .2)
     Threshold conversion finished:
                
  - cutoff = 0.711587831631899
  - species prevalence =0.2
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    species.prevalence = .02)
     Threshold conversion finished:
                
  - cutoff = 0.987413489156593
  - species prevalence =0.02
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    species.prevalence = .9)
     Threshold conversion finished:
                
  - cutoff = 0.0232795315895938
  - species prevalence =0.9
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "threshold",
  +                    species.prevalence = .99)
     Threshold conversion finished:
                
  - cutoff = 5.14461849387511e-05
  - species prevalence =0.9899
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = "random",
  +                    a = NULL,
  +                    b = NULL,
  +                    species.prevalence = .99)
     --- Determing beta automatically according to alpha and species.prevalence
  
  
     Logistic conversion finished:
                
  - beta = -0.125
  - alpha = -0.05
  - species prevalence =0.9882
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = "random",
  +                    a = NULL,
  +                    b = NULL,
  +                    species.prevalence = .5)
     --- Determing beta automatically according to alpha and species.prevalence
  
  
     Logistic conversion finished:
                
  - beta = 0.29296875
  - alpha = -0.05
  - species prevalence =0.4981
  
  
  > plotSuitabilityToProba(sp4)
  > sp4 <- convertToPA(sp4,
  +                    PA.method = "probability",
  +                    prob.method = "logistic",
  +                    beta = .5,
  +                    a = NULL,
  +                    b = NULL)
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.5
  - alpha = -0.05
  - species prevalence =0.3343
  
  
  > plotSuitabilityToProba(sp4)
  > 
  > 
  > for(i in 1:100){
  +   sp5 <- generateRandomSp(env)
  + }
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0880880880880881
  - alpha = -0.1
  - species prevalence =0.7215
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.697697697697698
  - alpha = -0.1
  - species prevalence =0.0963
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.534534534534534
  - alpha = -0.1
  - species prevalence =0.5879
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.362362362362362
  - alpha = -0.1
  - species prevalence =0.4608
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.389389389389389
  - alpha = -0.1
  - species prevalence =0.4438
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.144144144144144
  - alpha = -0.1
  - species prevalence =0.4815
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.437437437437437
  - alpha = -0.1
  - species prevalence =0.4968
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.5995995995996
  - alpha = -0.1
  - species prevalence =0.0802
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.586586586586587
  - alpha = -0.1
  - species prevalence =0.3147
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.474474474474474
  - alpha = -0.1
  - species prevalence =0.0292
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.013013013013013
  - alpha = -0.1
  - species prevalence =0.5448
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.673673673673674
  - alpha = -0.1
  - species prevalence =0.2705
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.62962962962963
  - alpha = -0.1
  - species prevalence =0.3549
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.976976976976977
  - alpha = -0.1
  - species prevalence =0.0909
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.602602602602603
  - alpha = -0.1
  - species prevalence =0.3137
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0880880880880881
  - alpha = -0.1
  - species prevalence =0.7412
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.801801801801802
  - alpha = -0.1
  - species prevalence =0.0675
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.63963963963964
  - alpha = -0.1
  - species prevalence =0.1321
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.147147147147147
  - alpha = -0.1
  - species prevalence =0.4601
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.221221221221221
  - alpha = -0.1
  - species prevalence =0.2465
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.33033033033033
  - alpha = -0.1
  - species prevalence =0.338
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.72972972972973
  - alpha = -0.1
  - species prevalence =0.2952
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.616616616616617
  - alpha = -0.1
  - species prevalence =0.3591
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.027027027027027
  - alpha = -0.1
  - species prevalence =0.9114
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.873873873873874
  - alpha = -0.1
  - species prevalence =0.0622
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.234234234234234
  - alpha = -0.1
  - species prevalence =0.7345
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.66966966966967
  - alpha = -0.1
  - species prevalence =0.1153
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.584584584584585
  - alpha = -0.1
  - species prevalence =0.219
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.184184184184184
  - alpha = -0.1
  - species prevalence =0.6673
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.708708708708709
  - alpha = -0.1
  - species prevalence =0.4951
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.566566566566567
  - alpha = -0.1
  - species prevalence =0.6207
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.017017017017017
  - alpha = -0.1
  - species prevalence =0.8498
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.866866866866867
  - alpha = -0.1
  - species prevalence =0.1102
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.186186186186186
  - alpha = -0.1
  - species prevalence =0.6362
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.149149149149149
  - alpha = -0.1
  - species prevalence =0.7019
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.923923923923924
  - alpha = -0.1
  - species prevalence =0.0837
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.224224224224224
  - alpha = -0.1
  - species prevalence =0.9146
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.249249249249249
  - alpha = -0.1
  - species prevalence =0.3461
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.307307307307307
  - alpha = -0.1
  - species prevalence =0.0844
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.670670670670671
  - alpha = -0.1
  - species prevalence =0.1331
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.688688688688689
  - alpha = -0.1
  - species prevalence =0.2201
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.296296296296296
  - alpha = -0.1
  - species prevalence =0.3754
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0590590590590591
  - alpha = -0.1
  - species prevalence =0.711
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.153153153153153
  - alpha = -0.1
  - species prevalence =0.5667
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.379379379379379
  - alpha = -0.1
  - species prevalence =0.5709
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.262262262262262
  - alpha = -0.1
  - species prevalence =0.493
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.77977977977978
  - alpha = -0.1
  - species prevalence =0.1349
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.291291291291291
  - alpha = -0.1
  - species prevalence =0.0631
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.833833833833834
  - alpha = -0.1
  - species prevalence =0.0257
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.804804804804805
  - alpha = -0.1
  - species prevalence =0.0784
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.408408408408408
  - alpha = -0.1
  - species prevalence =0.814
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.616616616616617
  - alpha = -0.1
  - species prevalence =0.0316
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.172172172172172
  - alpha = -0.1
  - species prevalence =0.4919
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.895895895895896
  - alpha = -0.1
  - species prevalence =0.1325
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.207207207207207
  - alpha = -0.1
  - species prevalence =0.8405
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.836836836836837
  - alpha = -0.1
  - species prevalence =0.0265
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.816816816816817
  - alpha = -0.1
  - species prevalence =0.0614
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.894894894894895
  - alpha = -0.1
  - species prevalence =0.0715
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.804804804804805
  - alpha = -0.1
  - species prevalence =0.047
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.175175175175175
  - alpha = -0.1
  - species prevalence =0.8679
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.561561561561562
  - alpha = -0.1
  - species prevalence =0.1713
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.642642642642643
  - alpha = -0.1
  - species prevalence =0.2664
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.317317317317317
  - alpha = -0.1
  - species prevalence =0.6723
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.141141141141141
  - alpha = -0.1
  - species prevalence =0.8184
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.564564564564565
  - alpha = -0.1
  - species prevalence =0.1018
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.538538538538539
  - alpha = -0.1
  - species prevalence =0.2149
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.775775775775776
  - alpha = -0.1
  - species prevalence =0.025
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.442442442442442
  - alpha = -0.1
  - species prevalence =0.5911
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.746746746746747
  - alpha = -0.1
  - species prevalence =0.1671
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.987987987987988
  - alpha = -0.1
  - species prevalence =0.0099
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.72972972972973
  - alpha = -0.1
  - species prevalence =0.1878
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.498498498498498
  - alpha = -0.1
  - species prevalence =0.2977
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.175175175175175
  - alpha = -0.1
  - species prevalence =0.9325
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.975975975975976
  - alpha = -0.1
  - species prevalence =0.0364
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.147147147147147
  - alpha = -0.1
  - species prevalence =0.3608
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.944944944944945
  - alpha = -0.1
  - species prevalence =0.0254
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.371371371371371
  - alpha = -0.1
  - species prevalence =0.0739
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.232232232232232
  - alpha = -0.1
  - species prevalence =0.4192
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.845845845845846
  - alpha = -0.1
  - species prevalence =0.0751
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.725725725725726
  - alpha = -0.1
  - species prevalence =0.0337
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.033033033033033
  - alpha = -0.1
  - species prevalence =0.5121
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.427427427427427
  - alpha = -0.1
  - species prevalence =0.2847
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.32032032032032
  - alpha = -0.1
  - species prevalence =0.6054
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.946946946946947
  - alpha = -0.1
  - species prevalence =0.0756
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.343343343343343
  - alpha = -0.1
  - species prevalence =0.1242
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.352352352352352
  - alpha = -0.1
  - species prevalence =0.6145
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.874874874874875
  - alpha = -0.1
  - species prevalence =0.0097
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.785785785785786
  - alpha = -0.1
  - species prevalence =0.2784
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.815815815815816
  - alpha = -0.1
  - species prevalence =0.0674
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.797797797797798
  - alpha = -0.1
  - species prevalence =0.0582
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.815815815815816
  - alpha = -0.1
  - species prevalence =0.0617
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0770770770770771
  - alpha = -0.1
  - species prevalence =0.6401
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.662662662662663
  - alpha = -0.1
  - species prevalence =0.2633
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.2002002002002
  - alpha = -0.1
  - species prevalence =0.6404
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.212212212212212
  - alpha = -0.1
  - species prevalence =0.3195
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.801801801801802
  - alpha = -0.1
  - species prevalence =0.1462
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.011011011011011
  - alpha = -0.1
  - species prevalence =0.9393
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.012012012012012
  - alpha = -0.1
  - species prevalence =0.7859
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.303303303303303
  - alpha = -0.1
  - species prevalence =0.0553
  
  
   - Determining species' response to predictor variables
  
   - Calculating species suitability
  
  Generating virtual species environmental suitability...
  
   - The response to each variable was rescaled between 0 and 1. To
              disable, set argument rescale.each.response = FALSE
  
   - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE
  
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.903903903903904
  - alpha = -0.1
  - species prevalence =0.0328
  
  
  > 
  > env <- c(rast(a * dnorm(1:100, 50, sd = 25)),
  +          rast(a * 1:100),
  +          rast(a * logisticFun(1:100, alpha = 10, beta = 70)),
  +          rast(t(a)),
  +          rast(exp(a)),
  +          rast(log(a)))
  > names(env) <- paste("Var", 1:6, sep = "")
  > 
  > for(i in 1:100){
  +   sp5 <- generateRandomSp(env)
  + }
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.388388388388388
  - alpha = -0.1
  - species prevalence =0.6624
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.183183183183183
  - alpha = -0.1
  - species prevalence =0.2887
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.222222222222222
  - alpha = -0.1
  - species prevalence =0.1766
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.945945945945946
  - alpha = -0.1
  - species prevalence =0.0126
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.011011011011011
  - alpha = -0.1
  - species prevalence =0.4924
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.365365365365365
  - alpha = -0.1
  - species prevalence =0.0662
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.958958958958959
  - alpha = -0.1
  - species prevalence =0.0119
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.748748748748749
  - alpha = -0.1
  - species prevalence =0.2563
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.627627627627628
  - alpha = -0.1
  - species prevalence =0.1149
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.720720720720721
  - alpha = -0.1
  - species prevalence =0.1139
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.588588588588589
  - alpha = -0.1
  - species prevalence =0.4438
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.314314314314314
  - alpha = -0.1
  - species prevalence =0.5351
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.818818818818819
  - alpha = -0.1
  - species prevalence =0.2895
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.830830830830831
  - alpha = -0.1
  - species prevalence =0.0092
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.161161161161161
  - alpha = -0.1
  - species prevalence =0.3287
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.392392392392392
  - alpha = -0.1
  - species prevalence =0.7429
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.566566566566567
  - alpha = -0.1
  - species prevalence =0.036
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0820820820820821
  - alpha = -0.1
  - species prevalence =0.3611
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.775775775775776
  - alpha = -0.1
  - species prevalence =0.0749
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.923923923923924
  - alpha = -0.1
  - species prevalence =0.0312
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.750750750750751
  - alpha = -0.1
  - species prevalence =0.236
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.016016016016016
  - alpha = -0.1
  - species prevalence =0.8864
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.409409409409409
  - alpha = -0.1
  - species prevalence =0.7112
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.671671671671672
  - alpha = -0.1
  - species prevalence =0.0168
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.572572572572573
  - alpha = -0.1
  - species prevalence =0.2476
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.495495495495495
  - alpha = -0.1
  - species prevalence =0.3944
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.173173173173173
  - alpha = -0.1
  - species prevalence =0.3931
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.674674674674675
  - alpha = -0.1
  - species prevalence =0.059
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0530530530530531
  - alpha = -0.1
  - species prevalence =0.6459
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.802802802802803
  - alpha = -0.1
  - species prevalence =0.0095
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.353353353353353
  - alpha = -0.1
  - species prevalence =0.1446
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.167167167167167
  - alpha = -0.1
  - species prevalence =0.8971
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.447447447447447
  - alpha = -0.1
  - species prevalence =0.3074
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.63963963963964
  - alpha = -0.1
  - species prevalence =0.1244
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.288288288288288
  - alpha = -0.1
  - species prevalence =0.3106
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.556556556556557
  - alpha = -0.1
  - species prevalence =0.1523
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.429429429429429
  - alpha = -0.1
  - species prevalence =0.2165
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.728728728728729
  - alpha = -0.1
  - species prevalence =0.066
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.233233233233233
  - alpha = -0.1
  - species prevalence =0.1485
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.148148148148148
  - alpha = -0.1
  - species prevalence =0.6383
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.84984984984985
  - alpha = -0.1
  - species prevalence =0.1371
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.675675675675676
  - alpha = -0.1
  - species prevalence =0.0553
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.417417417417417
  - alpha = -0.1
  - species prevalence =0.5084
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.74974974974975
  - alpha = -0.1
  - species prevalence =0.2048
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.274274274274274
  - alpha = -0.1
  - species prevalence =0.0894
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.533533533533533
  - alpha = -0.1
  - species prevalence =0.5287
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.476476476476476
  - alpha = -0.1
  - species prevalence =0.2765
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0590590590590591
  - alpha = -0.1
  - species prevalence =0.6472
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.406406406406406
  - alpha = -0.1
  - species prevalence =0.0952
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.527527527527528
  - alpha = -0.1
  - species prevalence =0.2542
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.828828828828829
  - alpha = -0.1
  - species prevalence =0.1146
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.807807807807808
  - alpha = -0.1
  - species prevalence =0.0603
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0630630630630631
  - alpha = -0.1
  - species prevalence =0.9482
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.992992992992993
  - alpha = -0.1
  - species prevalence =0.0263
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.364364364364364
  - alpha = -0.1
  - species prevalence =0.4024
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.887887887887888
  - alpha = -0.1
  - species prevalence =0.1776
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.98998998998999
  - alpha = -0.1
  - species prevalence =9e-04
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.144144144144144
  - alpha = -0.1
  - species prevalence =0.5072
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.161161161161161
  - alpha = -0.1
  - species prevalence =0.2639
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.564564564564565
  - alpha = -0.1
  - species prevalence =0.0237
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.735735735735736
  - alpha = -0.1
  - species prevalence =0.183
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.852852852852853
  - alpha = -0.1
  - species prevalence =0.2315
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.322322322322322
  - alpha = -0.1
  - species prevalence =0.3459
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.142142142142142
  - alpha = -0.1
  - species prevalence =0.432
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.681681681681682
  - alpha = -0.1
  - species prevalence =0.116
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.607607607607608
  - alpha = -0.1
  - species prevalence =0.3778
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.272272272272272
  - alpha = -0.1
  - species prevalence =0.6852
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.655655655655656
  - alpha = -0.1
  - species prevalence =0.2568
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.808808808808809
  - alpha = -0.1
  - species prevalence =0.0418
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.384384384384384
  - alpha = -0.1
  - species prevalence =0.2691
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.674674674674675
  - alpha = -0.1
  - species prevalence =0.3115
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.105105105105105
  - alpha = -0.1
  - species prevalence =0.5766
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.684684684684685
  - alpha = -0.1
  - species prevalence =0.0927
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.842842842842843
  - alpha = -0.1
  - species prevalence =0.2381
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.388388388388388
  - alpha = -0.1
  - species prevalence =0.3561
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.675675675675676
  - alpha = -0.1
  - species prevalence =0.1073
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.752752752752753
  - alpha = -0.1
  - species prevalence =0.2495
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.269269269269269
  - alpha = -0.1
  - species prevalence =0.4522
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.26026026026026
  - alpha = -0.1
  - species prevalence =0.3133
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.836836836836837
  - alpha = -0.1
  - species prevalence =0.0571
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.981981981981982
  - alpha = -0.1
  - species prevalence =0.0567
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.0600600600600601
  - alpha = -0.1
  - species prevalence =0.6061
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.483483483483483
  - alpha = -0.1
  - species prevalence =0.2053
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.616616616616617
  - alpha = -0.1
  - species prevalence =0.4004
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.259259259259259
  - alpha = -0.1
  - species prevalence =0.7512
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.687687687687688
  - alpha = -0.1
  - species prevalence =0.163
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.344344344344344
  - alpha = -0.1
  - species prevalence =0.4603
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.748748748748749
  - alpha = -0.1
  - species prevalence =0.0459
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.751751751751752
  - alpha = -0.1
  - species prevalence =0.1528
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.924924924924925
  - alpha = -0.1
  - species prevalence =0.0197
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.203203203203203
  - alpha = -0.1
  - species prevalence =0.7841
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.531531531531532
  - alpha = -0.1
  - species prevalence =0.0347
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.368368368368368
  - alpha = -0.1
  - species prevalence =0.4061
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.48048048048048
  - alpha = -0.1
  - species prevalence =0.6775
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.253253253253253
  - alpha = -0.1
  - species prevalence =0.1187
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.359359359359359
  - alpha = -0.1
  - species prevalence =0.6649
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.11011011011011
  - alpha = -0.1
  - species prevalence =0.4967
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.627627627627628
  - alpha = -0.1
  - species prevalence =0.4934
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.196196196196196
  - alpha = -0.1
  - species prevalence =0.4186
  
  
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.690690690690691
  - alpha = -0.1
  - species prevalence =0.2524
  
  
  > 
  > env <- c(rast(a * dnorm(1:100, 50, sd = 25)),
  +          rast(a * 1:100),
  +          rast(a * logisticFun(1:100, alpha = 10, beta = 70)),
  +          rast(t(a)),
  +          rast(exp(a)),
  +          rast(log(a)))
  > names(env) <- paste("Var", 1:6, sep = "")
  > 
  > sp5 <- generateRandomSp(env)
  Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'.
   - Perfoming the pca
  
   - Defining the response of the species along PCA axes
  
   - Calculating suitability values
  
     The final environmental suitability was rescaled between 0 and 1.
                    To disable, set argument rescale = FALSE
   - Converting into Presence - Absence
  
     --- Generating a random value of beta for the logistic conversion
  
  
     --- Determing species.prevalence automatically according to alpha and beta
  
  
     Logistic conversion finished:
                
  - beta = 0.393393393393393
  - alpha = -0.1
  - species prevalence =0.5267
  
  
  > 
  > samp1 <- sampleOccurrences(sp5,
  +                            n = 50)
  > samp1 <- sampleOccurrences(sp5,
  +                            n = 50,
  +                            type = "presence-absence")
  > samp1 <- sampleOccurrences(sp5,
  +                            n = 50,
  +                            type = "presence-absence",
  +                            extract.probability = TRUE)
  > samp1 <- sampleOccurrences(sp5,
  +                            n = 50,
  +                            type = "presence-absence",
  +                            sample.prevalence = .9,
  +                            extract.probability = TRUE)
  > samp1 <- sampleOccurrences(sp5,
  +                            n = 50,
  +                            type = "presence-absence",
  +                            sample.prevalence = .1,
  +                            extract.probability = TRUE)
  > 
  > # 
  > # 
  > # worldclim <- geodata::worldclim_global(var = "bio", res = 10, path = tempdir())
  > # names(worldclim) <- paste0("bio", 1:19)
  > # 
  > # my.stack <- worldclim[[c("bio2", "bio5", "bio6", "bio12", "bio13", "bio14")]]
  > # random.sp <- generateSpFromPCA(my.stack,
  > #                                axes = 1:3,
  > #                                niche.breadth = "narrow")
  > # 
  > # random.sp <- convertToPA(random.sp)
  > # 
  > # worldmap <- rnaturalearth::ne_countries(returnclass = "sf")
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50)
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = "Morocco",
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = worldmap[worldmap$sovereignt == 
  > #                                                       "France", ],
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "extent",
  > #                            bias.strength = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "country",
  > #                            bias.area = "Egypt",
  > #                            bias.strength = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "region",
  > #                            bias.area = "Africa",
  > #                            bias.strength = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "continent",
  > #                            bias.area = "Africa",
  > #                            bias.strength = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "polygon",
  > #                            bias.area = worldmap[worldmap$sovereignt == 
  > #                                                   "Egypt", ],
  > #                            bias.strength = 200,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "polygon",
  > #                            bias.area = NULL,
  > #                            bias.strength = 200,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "extent",
  > #                            bias.area = NULL,
  > #                            bias.strength = 200,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            type = "presence-absence",
  > #                            n = 50,
  > #                            sampling.area = ext(0, 180, 0, 90),
  > #                            bias = "extent",
  > #                            bias.area = NULL,
  > #                            bias.strength = 200,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9)
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            type = "presence-absence",
  > #                            n = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9,
  > #                            correct.by.suitability = TRUE)
  > # 
  > # 
  > # samp1 <- sampleOccurrences(random.sp,
  > #                            type = "presence-absence",
  > #                            n = 50,
  > #                            error.probability = 0.1,
  > #                            detection.probability = .9,
  > #                            correct.by.suitability = TRUE,
  > #                            bias = "manual",
  > #                            weights = exp(random.sp$suitab.raster))
  > 
  > proc.time()
     user  system elapsed 
   156.92    4.18  161.23