Phaethornis griseogularis


To elaborate our model we removed uncertain localities such as 'Bogota skins' and all records with coordinates laying down in sites with estimated elevations off the range 240-1,600 m. Also three records in BioMap from Norte de Santander and one record in DatAves from Amazonas that may represent range extensions but need confirmation (particularly that from DatAves) were removed before modelling. Also one record in ProAves from Santander and another in DatAves from Magdalena that may represent corrupted data (particularly that from DatAves), were removed previously to modelling. A further record in BioMap from Carimagua (Meta) in the Delaware Museum of Natural History was apparently assigned by mistake to this species, the data is corrupted and was removed before modelling.

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species in several areas west of the Andes in the Sierra Nevada de Santa Marta, Serrania del Perija and to the west of them; particularly in the northern Central Andes. These areas are not known to be occupied by the species and were excluded from the potential distribution map. Interestingly, areas in Norte de Santander not included to train the model were predicted as potential areas of occurrence of this species, they were included in our final distribution map 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 28,731 km2, which corresponds to a loss of 55 % of its potential original distribution due to deforestation in Colombia.


Regularized training gain is 2.079, training AUC is 0.977, unregularized training gain is 2.937.

Algorithm converged after 860 iterations (22 seconds).

The follow settings were used during the run:

18 presence records used for training.

10018 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: 0.481, categorical: 0.250, threshold: 1.820, 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:


24.681-22.253-Cumulative threshold

0.152-0.135-Logistic threshold

0.107-0.125-Fractional predicted area

0.111-0.000-Training omission rate