On the selection of thresholds for predicting species occurrence with presence‐only data
Arthur Rylah Institute for Environmental Research
Abstract
Presence-only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP…
Citation impact
- FWCI
- 24.53
- Percentile
- 100%
- References
- 45
Authors
3Topics & keywords
- Statistics
- Random forest
- Selection (genetic algorithm)
- Contrast (vision)
- Mathematics
- Statistic
- Model selection
- Random effects model
- Life in Land