Amazilia viridigaster


To elaborate our model we removed uncertain localities such as those localities with no coordinates and all records with coordinates laying down in sites with estimated elevations below 172 m and above 2,389 m. Also seven records in BioMap from Nariño, Valle del Cauca, Caqueta and Santander and one in DatAves from Antioquia, which represent corrupted information in the database were deleted previous to modelling (except the Santander point that was left by mistake).

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species in the northern end of the Central and Western Andes and in the southern Andes, also some few areas in the Amazon region. 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 17,528 km2, which corresponds to a loss of 62 % of its potential original distribution due to deforestation. Despite this species occupies edges, secondary vegetation and plantations it is not clear if deforestation has or not negatively affected its populations. The species is considered uncommon to locally fairly common (McMullan & Donegan, 2014; HBW Alive, 2017). Although the species has a relatively large extent of occurrence, estimated in our maps as 46,653 km2, given the massive destruction of habitat within its potencial distribution, which still continues we believe this species must be uplisted as Near Threatened (NT). This species needs urgently more research of its ecology, particularly to define its dependence from forest fragments.


Regularized training gain is 2.327, training AUC is 0.975, unregularized training gain is 2.870.

Algorithm converged after 1640 iterations (122 seconds).

The follow settings were used during the run:

81 presence records used for training.

10081 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: 0.186, categorical: 0.250, threshold: 1.190, 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:


14.066-12.829-Cumulative threshold

0.130-0.110-Logistic threshold

0.089-0.098-Fractional predicted area

0.086-0.049-Training omission rate