Coeligena consita


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 1,676 m and above 2,952 m. Also a record in DatAves from San Lorenzo (Magdalena), which represents corrupted information in the database was deleted.

The habitat suitability model generated in Maxent showed extensive areas suitable in climatic terms for this species in the Sierra Nevada de Santa Marta and a few areas in the northern section of the Eastern Andes. Those areas are not known to be occupied by this species and were deleted from our final potential distribution map.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 410 km2, which corresponds to a loss of 66 % of its potential original distribution due to deforestation.

This species has been usually considered a subspecies of the complex C. bonapartei-C. eos.

This near-endemic species has been catalogued by BirdLife International (2016) as Vulnerable (VU) because it is believed it has a very small range very fragmented which is highly threatened by ilegal crops cultivation, cattle ranching and agriculture (BirdLife International 2016). The species has a extent of occurrence estimated in 4,500 km2 (BirdLife International 2016); our analysis suggest an extent of occurrence in Colombia of 1,221 km2. Equally, suggest the species has lost a major portion 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, we believe this species can be correctly up-listed as Endangered (EN) at country level. There is urgent need to research and know better the general ecology of this species.


Regularized training gain is 4.596, training AUC is 0.999, unregularized training gain is 5.299.

Algorithm converged after 380 iterations (1 seconds).

The follow settings were used during the run:

7 presence records used for training.

10007 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: 0.545, threshold: 1.930, 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:

38.328-8.906-Cumulative threshold

0.512-0.132-Logistic threshold

0.003-0.010-Fractional predicted area

0.000-0.000-Training omission rate