A framework for species distribution modelling with improved pseudo-absence generation | Santander Meteorology Group
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A framework for species distribution modelling with improved pseudo-absence generation

Journal: Ecological Modelling
Year: 2015   Volume: 31424
Initial page: 166   Last page: 174
Status: Published
In this status since: 10 Jun 2015
PDF file: 2015_Iturbide_ECOMOD.pdf
DOI: 10.1016/j.ecolmodel.2015.05.018
Authors:
, , , del Hierro, O., Pinto, M.,

Species distribution models (SDMs) are an important tool in biogeography and phylogeography studies, that most often require explicit absence information to adequately model the environmental space on which species can potentially inhabit. In the so called background pseudoabsences approach, absence locations are simulated in order to obtain a complete sample of the environment. Whilst the commonest approach is random sampling of the entire study region, in its multiple variants, its performance may not be optimal, and the method of generation of pseudoabsences is known to have a significant influence on the results obtained. Here, we compare a suite of classic (random sampling) and novel methods for pseudo-absence data generation and propose a generalizable three-step method combining environmental profiling with a new technique for background extent restriction. To this aim, we consider 11 phylogenetic groups of Oak (Quercus sp.) described in Europe. We evaluate the influence of different pseudo-absence types on model performance (area under the ROC curve), calibration (reliability diagrams) and the resulting suitability maps, using a cross-validation approach. Regardless of the modelling algorithm used, randomsampling models were outperformed by the methods that incorporate environmental profiling of the background, stressing the importance of the pseudo-absence generation techniques for the development of accurate and reliable SDMs. We also provide an integrated modelling framework implementing the methods tested in a software package for the open source R environment.

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