Lophornis chalybeus


To elaborate our models 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 600 m.

The habitat suitability model generated in Maxent showed a very few areas suitable in climatic terms for this hummingbird west of the Andes in the northern end of the Central Andes, also east of the Andes in central northeast Colombia in Norte de Santander. These areas are not known to be occupied by this hummingbird and were deleted from our final potential distribution map. A good portion of areas likely to be occupied by this species in the southern Amazonia were not predicted by our model as presence, they were identified to reflect this in our maps.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 427,187 km2, which corresponds to a loss of 10 % of its potential original distribution due to deforestation.


Regularized training gain is 1.405, training AUC is 0.958, unregularized training gain is 2.416.

Algorithm converged after 440 iterations (13 seconds).

The follow settings were used during the run:

15 presence records used for training.

10015 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: 0.586, categorical: 0.321, threshold: 1.850, hinge: 0.500

Feature types used: hinge linear quadratic

responsecurves: true

jackknife: true

askoverwrite: false

maximumiterations: 2000

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


30.814-12.281-Cumulative threshold

0.356-0.204-Logistic threshold

0.133-0.245-Fractional predicted area

0.133-0.067-Training omission rate