Heliangelus exortis


To elaborate our model we removed uncertain localities such as those with no coordinates and all records with coordinates laying down in sites with estimated elevations below 2,000 m and above 3,509 m. Also a record in BioMap from Amazonas Department (at low elevations) was deleted previous to modelling.

The habitat suitability model generated in Maxent did not showed areas suitable in climatic terms for this species out of the known geographical range and in this sense the model was very accurate.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 24,340 km2, which corresponds to a loss of 62 % of its potential original distribution due to deforestation. This species favours edges and secondary vegetation and is fairly common along its distribution in the Andes. Thus, possibly deforestation has not affected greatly its populations.


Regularized training gain is 2.783, training AUC is 0.985, unregularized training gain is 3.094.

Algorithm converged after 920 iterations (36 seconds).

The follow settings were used during the run:

85 presence records used for training.

10084 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: 0.157, categorical: 0.250, threshold: 1.150, hinge: 0.500

Feature types used: hinge product linear threshold quadratic

responsecurves: true

jackknife: true

maximumiterations: 2000

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


13.14-6.172-Cumulative threshold

0.285-0.155-Logistic threshold

0.047-0.062-Fractional predicted area

0.047-0.024-Training omission rate