Also available here for better readability / zooming functionalities.
Figure X. Comprehensive overview of the experimental workflow for simulating and evaluating species distribution models using virtual species.
Also available here for better readability / zooming functionalities.
Feature scaling (Min-Max-Normalisierung):
- x is an original value
- x' is the normalized value
- min(x) lower bound of target range (for AUC 0.5)
- max(x) upper bound of target range (for AUC 1)
| Metric | Baseline | Min | Max | Higher Better? |
|---|---|---|---|---|
| AUC | 0.5 | 0 | 1 | Yes |
| COR | - | -1 | 1 | Yes |
| Spec | - | 0 | 1 | Yes |
| Sens | - | 0 | 1 | Yes |
| Kappa | - | -1 | 1 | Yes |
| PCC | - | 0 | 1 | Yes |
| TSS | 0 | -1 | 1 | Yes |
| PRG | 0.5 | 0 | 1 | Yes |
| MAE | - | 0 | 1 | No |
| BIAS | - | -1 | 1 | No |
We plot the results of the evaluation metrics against the Pearson correlation between the suitability raster and the prediction map of virtual species.
If the model (points) are plotted on the diagonal, then the metric is performing well.
