To elaborate our model we removed uncertain localities such as 'Bogota skins', those with no coordinates and all records with coordinates laying down in sites with estimated elevations below 1,785 m and above 3,200 m.
The habitat suitability model generated in Maxent showed extensively areas suitable in climatic terms for this species in the Eastern and Central Andes, also in the southwestern Caribbean. Except areas south of Popayan in the Central Andes, most of 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 1,858 km2, which corresponds to a loss of 75 % of its potential original distribution due to deforestation. Although this species uses edges and secondary vegetation it needs forest to certain extent and therefore possibly deforestation has affected its populations. However, there is no clarity on the status of its populations. BirdLife International (2016) recognises the species has a restricted range (Ecuador and Colombia) but does not believe it approaches to the thresholds to consider it a threatened species. In the specific case of Colombia it is possible that this species approaches the thresholds to be considered Vulnerable given the small range and fragmented habitat, and possibly its status needs re-evaluation.
Regularized training gain is 2.239, training AUC is 0.986, unregularized training gain is 2.915.
Algorithm converged after 100 iterations (0 seconds).
The follow settings were used during the run:
4 presence records used for training.
10004 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: 1.000, categorical: 0.590, threshold: 1.960, hinge: 0.500
Feature types used: linear
'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:
0.026-0.107-Fractional predicted area
0.000-0.000-Training omission rate