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

• Published in: PeerJ (San Francisco, CA) Vol. 8; p. e10411
• Main Authors: Gaul, Willson, Sadykova, Dinara, White, Hannah J, Leon-Sanchez, Lupe, Caplat, Paul, Emmerson, Mark C, Yearsley, Jon M
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 27.11.2020; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Biogeography; Datasets; Ecologists; Ecology; Econometrics; Geographical distribution; Geospatial data; Investigations; Predictions; Sample size; Sampling; Simulation; Species; Taxonomy; Variables; Zoology; Species distribution model; Sample selection bias; Biological records; Simulation; Spatial bias; Virtual ecology
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.10411

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.