The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models
Microsoft Research (United Kingdom) · University of Cambridge
Abstract
Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small,…
Citation impact
- FWCI
- 50.32
- Percentile
- 100%
- References
- 57
Authors
3Topics & keywords
- Sampling (signal processing)
- Sampling bias
- Herbarium
- Statistics
- Data set
- Goodness of fit
- Sampling design
- Computer science
- Life in Land