Hylocharis humboldtii


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 572 m.

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species in a very few small pockets in the Eastern Andes. 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 27,165 km2, which corresponds to a loss of 36 % of its potential original distribution due to deforestation.

According to McMullan & Donegan (2014) it wanders seasonally to forest clearings in the lowlands. However, these movements are not very well known. BioMap Data shows a record relatively far from the coast line, which may indicate possibly it wanders farther inside the lowlands than what has been previously though. This needs further research. A better definition of its ecology may give better clues to categorise this species. In case it is more restricted to mangrove forests its extension of occurrence may be much less that what has been estimated here to be occupied by forested areas and the species may be categorised as Near Threatened (NT) or even within a threatened category.

Recent studies suggest that this species may be better placed within genus Amazilia (HBW Alive, 2017).


Regularized training gain is 3.351, training AUC is 0.996, unregularized training gain is 4.315.

Algorithm converged after 740 iterations (24 seconds).

The follow settings were used during the run:

20 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: 0.442, categorical: 0.250, threshold: 1.800, 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:


13.907-12.791-Cumulative threshold

0.151-0.131-Logistic threshold

0.033-0.035-Fractional predicted area

0.050-0.000-Training omission rate