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 232 m and above 3,436 m.
The habitat suitability model generated in Maxent showed a areas suitable in climatic terms for this species in the southern half of the Western and Central Andes and serrania de San Lucas. With some exceptions, 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 54,245 km2, which corresponds to a loss of 64 % of its potential original distribution due to deforestation. Although it is a rare species along its distribution, apparently this species uses secondary vegetation and plantations and therefore possibly deforestation has not negatively affected greatly its populations. Nevertheless, it is in great need of further research of its ecology to define better the threats to its populations.
Regularized training gain is 2.018, training AUC is 0.961, unregularized training gain is 2.363.
Algorithm terminated after 2000 iterations (68 seconds).
The follow settings were used during the run:
113 presence records used for training.
10113 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
'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:
0.101-0.134-Fractional predicted area
0.097-0.071-Training omission rate