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For most engines, background or pseudo-absence points are necessary. The distinction lies in how the absence data are handled. For poisson distributed responses, absence points are considered background points over which the intensity of sampling (lambda) is integrated (in a classical Poisson point-process model).

In contrast in binomial distributed responses, the absence information is assumed to be an adequate representation of the true absences and treated by the model as such... Here it is advised to specify absence points in a way that they represent potential true absence, such as for example through targeted background sampling or by sampling them within/outside a given range.


  field_occurrence = "observed",
  template = NULL,
  settings = getOption("ibis.pseudoabsence")



A sf, data.frame or tibble object containing point data.


A character name of the column containing the presence information (Default: observed).


A SpatRaster object that is aligned with the predictors (Default: NULL). If set to NULL, then background in the pseudoabs_settings() has to be a SpatRaster object.


A pseudoabs_settings() objects. Absence settings are taken from ibis_options otherwise (Default).


A data.frame containing the newly created pseudo absence points.


A pseudoabs_settings() object can be added to setup how absence points should be sampled. A bias parameter can be set to specify a bias layer to sample from, for instance a layer of accessibility. Note that when modelling several datasets, it might make sense to check across all datasets whether certain areas are truly absent. By default, the pseudo-absence points are not sampled in areas in which there are already presence points.


This method removes all columns from the input df object other than the field_occurrence column and the coordinate columns (which will be created if not already present).


  • Stolar, J., & Nielsen, S. E. (2015). Accounting for spatially biased sampling effort in presence‐only species distribution modelling. Diversity and Distributions, 21(5), 595-608.

  • Bird, T.J., Bates, A.E., Lefcheck, J.S., Hill, N.A., Thomson, R.J., Edgar, G.J., Stuart-Smith, R.D., Wotherspoon, S., Krkosek, M., Stuart-Smith, J.F. and Pecl, G.T., 2014. Statistical solutions for error and bias in global citizen science datasets. Biological Conservation, 173, pp.144-154.