Amazilia amabilis


To elaborate our model we removed uncertain localities such as 'Bogota skins', those localities with no coordinates and all records with coordinates laying down in sites with estimated elevations above 2,200 m. Also five records in BioMap, nine in DatAves and one from EBIRD, which represents corrupted or suspicious information in the database were deleted previous to modelling.

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species in the mid-high Cauca and high Maddalena valleys, the eastern slope of the Andes and in the far eastern Amazonia. These areas are not known to be occupied by the species and were excluded from the potential distribution map of this hummingbird.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 111,393 km2, which corresponds to a loss of 57 % of its potential original distribution due to deforestation.


Regularized training gain is 1.692, training AUC is 0.951, unregularized training gain is 2.045.

Algorithm terminated after 2000 iterations (74 seconds).

The follow settings were used during the run:

174 presence records used for training.

10172 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

responsecurves: true

jackknife: true

maximumiterations: 2000

'Fixed Cumulative Value 5%', 'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:


5.000-21.746-10.702-Cumulative threshold

0.082-0.300-0.153-Logistic threshold

0.266-0.111-0.185-Fractional predicted area

0.023-0.109-0.052-Training omission rate