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 1,801 m. A record from Villavicencio (Meta) that we know represents corrupted information was left by mistake during the modelling. Nevertheless, Maxent is robust enough not to let this unique error affect results.
The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species in a continuous belt along the eastern slope of the Eastern Anden from Arauca to the south, and a very few areas in the far east of Colombia and in the Alta Guajira. 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 115,698 km2, which corresponds to a loss of 57 % of its potential original distribution due to deforestation in Colombia. However, it is important to note that this species favours borders, secondary growth and semi-open areas and in this sense it might have been favoured by deforestation to certain extent.
Regularized training gain is 1.492, training AUC is 0.950, unregularized training gain is 1.948.
Algorithm terminated after 2000 iterations (66 seconds).
The follow settings were used during the run:
102 presence records used for training.
10100 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: 0.050, categorical: 0.250, threshold: 1.000, hinge: 0.500
Feature types used: hinge product linear threshold quadratic
'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:
0.129-0.225-Fractional predicted area
0.127-0.020-Training omission rate