A Surface Range Envelope is an envelope-style method similar to Bioclim. It is a presence-only model that uses the environmental conditions of locations of occurrence data to profile the environments where a species can be found. The envelope is defined by the minimum and maximum values of the environmental variables for all occurrences. Any location with environmental conditions that falls within this envelope is included in the potential range for a species. To avoid the over-predictive effect of outliers, the envelope can be reduced at specified percentiles or standard deviations.


  • Simple and intuitive

  • Presence only model, no absence data needed

  • Provides ranking of environmental predictor variables

  • Useful in teaching species distribution modelling


  • Susceptible to over-prediction

  • Does not account for interactions between predictors

  • Cannot use categorical variables

  • Does not make quantitative predictions or provide confidence levels


The model assumes a normal distribution of the predictor variables.

Requires absence data


Configuration options

EcoCommons allows the user to set model arguments as specified below. 


Seed used for generating random values. Using the same seed value, i.e. 123, ensures that running the same model, with the same data and settings generates the same result, despite stochastic processes such as machine learning or cross-validation. 

Number of repetitions (nb_run_eval) 

Integer value, corresponding to the number of repetitions to be done for calibration/validation splitting (default = 10) 

Data split percentage (data_split) 

Numeric value between 0 and 100, corresponding to the percentage of data used to calibrate the models (calibration/validation splitting) (default = 100) 


Allows to give more or less weight to particular observations; default = NULL: each observation (presence or absence) has the same weight; if value < 0.5: absences are given more weight; if value > 0.5: presences are given more weight. 

Variable importance (var_import) 

Integer value, corresponding to the number of permutations to be done for each variable to estimate variable importance (default = 0) 

Scale models (rescale_all_models) 

A logical value defining whether all models predictions should be scaled with a binomial GLM or not (default = FALSE) 

Evaluate all models (do_full_models) 

A logical value defining whether models calibrated and evaluated over the whole dataset should be computed or not (default = TRUE) 

Quantile (quant)  

The value defines the most extreme values for each variable not to be taken into account for determining the tolerance boundaries for the considered species. The default value is 0.025, which corresponds to a 95% confidence interval in normal distributions (default = 0.025) 


  • Araujo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93(7): 1527-1539.

  • Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions, 20(1): 1-9.

  • Thuiller W, Lafourcade B, Araujo M (2012) Presentation manual for BIOMOD. Laboratoire d'Ecologie Alpine, Universite Joseph Fourier, Grenoble, France.

Additional Reading 

  • Phillips, N. D., Reid, N., Thys, T., Harrod, C., Payne, N. L., Morgan, C. A., White, H. J., Porter, S., & Houghton, J. D. R. (2017). Applying species distribution modelling to a data poor, pelagic fish complex: The ocean sunfishes. Journal of Biogeography, 44(10), 2176–2187.  

  • Quillfeldt, P., Engler, J. O., Silk, J. R. D., & Phillips, R. A. (2017). Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: A case study using black-browed albatrosses. Journal of Avian Biology, 48(12), 1549–1555.  

  • Zhang, Z., Xu, S., Capinha, C., Weterings, R., & Gao, T. (2019). Using species distribution model to predict the impact of climate change on the potential distribution of Japanese whiting Sillago japonica. Ecological Indicators, 104, 333–340.