Coeligena orina


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 2,500 m and above 3,588 m.

The habitat suitability model generated in Maxent showed extensive areas suitable in climatic terms for this species in the southern Western Andes and along the Central Andes. Those areas are not known to be occupied by this species and were deleted from our final potential distribution map.

Interestingly, a record from Alto Ventanas (Antioquia) in the northern portion of the Central Andes was deleted previous to modelling because the record was at a slightly low elevation. Nonetheless, our model predicted as suitable the adjacent areas to the south from this locality. Those areas and those predicted on the far end of the Western Andes require further confirmation of the presence of the species.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 1,701 km2, which corresponds to a loss of 33 % of its potential original distribution due to deforestation. However, it is important to note that possibly this figure is slightly overestimated since its distribution may include some areas of open paramo.

This species has been catalogued by BirdLife International (2016) as Critically Endangered (CR). The species has a extent of occurrence estimated in 25 km2 (BirdLife International 2016), which probably is much smaller than its actual size; our analysis suggest an extent of occurrence of 2,557 km2. Equally, suggest the species has not lost much of what potentially was its original distribution due to deforestation. Giving the extent of occurrence and the threats to its habitat such as deforestation and fragmentation, which still continue, this species possibly can be correctly down-listed as Endangered (EN).


Regularized training gain is 4.526, training AUC is 0.999, unregularized training gain is 5.331.

Algorithm converged after 780 iterations (3 seconds).

The follow settings were used during the run:

13 presence records used for training.

10013 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: 0.671, categorical: 0.393, threshold: 1.870, hinge: 0.500

Feature types used: 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:

35.731-11.751-Cumulative threshold

0.439-0.134-Logistic threshold

0.003-0.011-Fractional predicted area

0.000-0.000-Training omission rate