To elaborate the model of this species we removed two records in ProAves and DatAves datasets from Santander and Nariño. The record from Santander is clearly corrupted, while the record from Nariño was assigned wrong coordinates (El Pangan); since there were other records from ProAves correctly located we deleted this one from DatAves and did not reassign coordinates to it.
The habitat suitability model generated in Maxent predicted extensive areas in the three Andean cordilleras, west of the western slope of the Eastern Andes and the Pacific and the Uraba regions. Additionally, we trimmed areas below 700 m and above 2,100 m of elevation.
Assuming that the distribution of the species may have filled the areas predicted as suitable (i.e. marginally suitable, suitable and highly suitable), its potential distribution today in remnants of forest is about 1,785 km2, which corresponds to a loss of ≈ 38 % of its potential original distribution due to deforestation.
Regularized training gain is 2.179, training AUC is 0.975, unregularized training gain is 2.699.
Algorithm converged after 100 iterations (0 seconds).
The follow settings were used during the run:
7 presence records used for training.
10007 points used to determine the Maxent distribution (background points and presence points).
Environmental layers used (all continuous): bio10co bio11co bio12co bio13co bio14co bio15co bio16co bio17co bio18co bio19co bio1co bio2co bio3co bio4co bio5co bio6co bio7co bio8co bio9co
Regularization values: linear/quadratic/product: 1.000, categorical: 0.545, threshold: 1.930, hinge: 0.500
Feature types used: linear
'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:
0.086-0.113-Fractional predicted area
0.143-0-Training omission rate