Sternoclyta cyanopectus


To elaborate our model we produced three pseudoreplicas of a unique locality from Cerro San Agustin in PNN Tama. The new points were located at approximate 1.5-2 km west, south and southwest from the original locality, at elevations in the range 1,203-1,543 m.

The habitat suitability model generated in Maxent showed a few sparse areas suitable in climatic terms for this species in Sierra Nevada de Santa Marta, serrania de San Lucas, the southern Western and Central Andes and the mid Eastern Andes. Those areas are not known to be occupied by this 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 371 km2, which corresponds to a loss of 40 % of its potential original distribution due to deforestation.

This near-endemic species is mostly distributed in Venezuela and 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. It is considered rare in Colombia (McMullan & Donegan 2014), which is in the outskirts of its range and its extent of occurrence has been estimated in 117,000 km2 (BirdLife International 2016). Our maps suggest its extent of occurrence just in Colombia is about 626 km2. Forested areas have been fairly degraded in its potential original geographical distribution as our analyses suggest. Forest degradation and fragmentation continues along its potential range and therefore we believe that survival of this species is intended in Colombia it must be considered of some importance for conservation given its very small range in the country.


Regularized training gain is 5.151, training AUC is 1.000, unregularized training gain is 6.677.

Algorithm converged after 260 iterations (1 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

responsecurves: true

jackknife: true

maximumiterations: 2000

'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:

59.485-17.612-Cumulative threshold

0.725-0.115-Logistic threshold

0.000-0.006-Fractional predicted area

0.000-0.000-Training omission rate