Eriocnemis vestita


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 1,615 m and above 3,810 m. Also one record in BioMap from Atlantico and three in DatAves from Amazonas, which represent corrupted information in the database were deleted.

The habitat suitability model generated in Maxent showed some areas suitable in climatic terms for this species in the most southwestern portion of the Andes in Cauca/Nariño. Those areas are not known to be occupied by this species and were deleted from our final potential distribution map.

Distribution of specimens according to BioMap suggest a possible zone of intergradation (5,654 km2) between subspecies smaragdinipecta and the nominate subspecies in the border between the departments of Tolima, Huila and Cauca.

Our model suggest there are extensively areas suitable in climatic terms for this species in the southern half of the Central and Eastern Andes. These areas possibly are occupied by nominate subspecies. However, in the Eastern Andes portion there are no known records and in the Central Andes in the south there is a possible area of intergradation as mentioned previously with subspecies smaragdinipecta. Additionally, towards the north most records are sightings and there are no collections confirming the presence of nominate subspecies.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 27,771 km2, which corresponds to a loss of 69 % of its potential original distribution due to deforestation. Nonetheless, this species favours edges and secondary vegetation and therefore possibly deforestation has not negatively affected greatly its populations.


Regularized training gain is 2.371, training AUC is 0.971, unregularized training gain is 2.538.

Algorithm converged after 1340 iterations (43 seconds).

The follow settings were used during the run:

283 presence records used for training.

10280 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: 0.050, categorical: 0.250, threshold: 1.000, hinge: 0.500

Feature types used: hinge product linear threshold quadratic

responsecurves: true

jackknife: true

maximumiterations: 2000

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

13.308-7.917-Cumulative threshold

0.245-0.177-Logistic threshold

0.074-0.093-Fractional predicted area

0.074-0.042-Training omission rate