To elaborate our model we created four pseudolocalities from the only known locality record known for the country, which comes from an specimen collected in Caqueta in 2000 and placed in the avian collection of the IAvH (Cordoba and Echeverry 2006). The first pseudolocality was created 4.2 km ESE of the original coordinates found in the paper of Cordoba and Echeverry (2006), in the same locality in Google Earth. From there we created three more pseudolocalities north, south and east at 1.8 km. All localities were placed at elevations between 814-1,240 m.
The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species west of the Andes particularly in the Pacific, also in the Amazon region in the far east (Rio Negro). 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 plus the areas not predicted but likely suitable, its distribution today in remnants of forest is about 6,619 km2, which corresponds to a loss of 23 % of its potential original distribution due to deforestation.
This species has been catalogued by BirdLife International (2017) as of Low Concern (LC) because it is believed it has a large range and do not approach to the thresholds to be considered Vulnerable (VU) under the range size, population trend and/or the population size criterion. It is considered to be uncommon (McMullan & Donegan 2014) and its extent of occurrence has been estimated in 1,120,000 km2 (BirdLife International 2017). Our maps suggest its extent of occurrence just in Colombia is about 8,549 km2. Forested areas have not been much degraded in is potential original geographical distribution as our analyses suggest. Nonetheless, given its small extent of occurrence in Colombia and the fact that forest areas in the eastern slope of the Andes continue being threatened we believe it is important to consider this species of interest for conservation if it is intended to conserve it in the country. It is poorly known in the country and needs research of its ecology.
Regularized training gain is 1.582, training AUC is 0.978, unregularized training gain is 2.505.
Algorithm converged after 440 iterations (2 seconds).
The follow settings were used during the run:
5 presence records used for training.
10005 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: 0.575, threshold: 1.950, hinge: 0.500
Feature types used: linear
'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:
0.061-0.206-Fractional predicted area
0.000-0.000-Training omission rate