map.easy: Species distributions made easy (multiple species).

Description Usage Arguments Value References

View source: R/red.R


Single step for prediction of multiple species distributions. Output of maps (in pdf format), klms (for Google Earth) and relevant data (in csv format).


map.easy(longlat, layers = NULL, error = NULL, dem = NULL, pca = 0,
  file = NULL, minmodel = 0, testpercentage = 0, mintest = 20,
  runs = 0, subset = 0)



data.frame of taxon names, longitude and latitude or eastness and northness (three columns in this order) of each occurrence record.


If NULL analyses are done with environmental layers read from data files of red.setup(). If a Raster* object as defined by package raster, analyses use these.


Vector of spatial error in longlat (one element per row of longlat). Used to move any point randomly within the error radius.


RasterLayer object. It should be a digital elevation model for calculation of elevation limits of the species. If NULL, dem from red.setup() is used if possible, otherwise it will be 0.


Number of pca axes for environmental data reduction. If 0 (default) no pca is made.


Name of output csv file with all results. If NULL it is named "Results_All.csv".


Minimum number of occurrence records to perform a maxent model. If 0 (default), the function will perform analyses without modelling any species.


Percentage of records used for testing only. If 0 all records will be used for both training and testing.


Minimim number of total occurrence records of any species to set aside a test set. Only used if testpercentage > 0.


If <= 0 no ensemble modelling is performed. If > 0, ensemble modelling with n runs is made. For each run, a new random sample of occurrence records (if testpercentage > 0), background points and predictive variables (if subset > 0) are chosen. In the ensemble model, each run is weighted as max(0, (runAUC - 0.5)) ^ 2.


Number of predictive variables to be randomly selected from layers for each run if runs > 0. If <= 0 all layers are used on all runs. Using a small number of layers is usually better than using many variables for rare species, with few occurrence records (Lomba et al. 2010, Breiner et al. 2015).


Outputs maps in asc, pdf and kml format, plus a file with EOO, AOO and a list of countries where the species is predicted to be present if possible to extract.


Breiner, F.T., Guisan, A., Bergamini, A., Nobis, M.P. (2015) Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6: 1210-1218.

Lomba, A., Pellissier, L., Randin, C.F., Vicente, J., Moreira, F., Honrado, J., Guisan, A. (2010) Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143: 2647-2657.

red documentation built on June 23, 2017, 4:42 a.m.

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