Presence‐only modelling using MAXENT : when can we trust the inferences?
United States Geological Survey · Princeton University
Abstract
Summary Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence‐only data and the deployment of these methods in user‐friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. Based on our interactions with researchers using these tools, we hypothesized that many presence‐only studies were ignoring important assumptions of presence‐only…
Citation impact
- FWCI
- 45.69
- Percentile
- 100%
- References
- 31
Authors
7Topics & keywords
- Computer science
- Inference
- Selection bias
- Selection (genetic algorithm)
- Sampling bias
- Sample size determination
- Sampling (signal processing)
- Covariate
- Life in Land