Data quantity is more important than its spatial bias for predictive species distribution modelling

Willson Gaul*, Dinara Sadykova, Hannah J. White, Lupe Leon-Sanchez, Paul Caplat, Mark C. Emmerson, Jon M. Yearsley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

Original languageEnglish
Article numbere10411
Publication statusPublished - 27 Nov 2020


  • Biological records
  • Sample selection bias
  • Simulation
  • Spatial bias
  • Species distribution model
  • Virtual ecology

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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