Coeligena bonapartei


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 1,681 m and above 3,384 m. Also one record in DatAves from Park Ucumari (Risaralda), which represent corrupted information in the database was deleted.

The habitat suitability model generated in Maxent showed a very few pixels suitable in climatic terms for this species in the Central Andes. Those 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 5,184 km2, which corresponds to a loss of 79 % of its potential original distribution due to deforestation.

This endemic species has been catalogued by BirdLife International (2016) as of Low Concern (LC) because it is believed it does not approach to the thresholds of Vulnerable (VU) according to the range size, population trend or population size criterion. Its extent of occurrence has been estimated in 16,100 km2 (BirdLife International 2016), which is below the threshold of 20,000 km2 to Vulnerable (VU), Nonetheless, its populations are not considered highly fragmented or very few, so does not qualify for Vulnerable. Our maps suggest its extent of occurrence is about 24,510 km2. However, forested areas have been severely degraded in is potential original geographical distribution as our analyses suggest. It is believed to be local and uncommon, although its population has not been quantified yet (BirdLife International 2016). Giving that the species uses border and secondary habitats possibly its populations have not been greatly affected. Nonetheless, forest degradation and fragmentation continues along its potential range and therefore we believe it must be considered as Near Threatened (NT). This species is not very well known and needs further research of its general ecology.


Regularized training gain is 3.607, training AUC is 0.993, unregularized training gain is 3.887.

Algorithm converged after 880 iterations (26 seconds).

The follow settings were used during the run:

65 presence records used for training.

10065 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: 0.150, categorical: 0.250, threshold: 1.350, 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:

8.457-8.095-Cumulative threshold

0.113-0.108-Logistic threshold

0.026-0.027-Fractional predicted area

0.031-0.015-Training omission rate