Glaucis aeneus


To elaborate our model we removed uncertain localities such as 'Bogota skins' and all records with coordinates laying down in sites with estimated elevations above 1,500 m.

The habitat suitability model generated in Maxent showed a few areas that are suitable in climatic terms for this species east of the West Andes. These areas are not known to be occupied by the species and were excluded from the potential distribution map.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 43,948 km2, which corresponds to a loss of 45 % of its potential original distribution due to deforestation.


Regularized training gain is 2.444, training AUC is 0.977, unregularized training gain is 2.894.

Algorithm converged after 440 iterations (12 seconds).

The follow settings were used during the run:

21 presence records used for training.

10020 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: 0.423, categorical: 0.250, threshold: 1.790, hinge: 0.500

Feature types used: hinge linear quadratic

responsecurves: true

jackknife: true

maximumiterations: 2000

'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:


11.565-12.981-Cumulative threshold

0.131-0.147-Logistic threshold

0.094-0.087-Fractional predicted area

0.095-0.095-Training omission rate