Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models

@article{Sta2018SamplingBI,
  title={Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models},
  author={Bente St{\o}a and Rune Halvorsen and Sabrina Mazzoni and Vladimir I. Gusarov},
  journal={Sommerfeltia},
  year={2018},
  volume={38},
  pages={1 - 53}
}
Abstract This paper provides a theoretical understanding of sampling bias in presence-only data in the context of species distribution modelling. This understanding forms the basis for two integrated frameworks, one for detecting sampling bias of different kinds in presence-only data (the bias assessment framework) and one for assessing potential effects of sampling bias on species distribution models (the bias effects framework). We exemplify the use of these frameworks to museum data for nine… 

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