Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
AT&T (United States) · Princeton University · +1 more institution
Abstract
Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time‐consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional…
Citation impact
- FWCI
- 224.20
- Percentile
- 100%
- References
- 47
Authors
2Topics & keywords
- Computer science
- Data mining
- Sample size determination
- Machine learning
- Selection (genetic algorithm)
- Sample (material)
- Quality (philosophy)
- Sampling (signal processing)
- Life in Land