Thalurania furcata


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 above 1,939 m. Also four records in BioMap, 13 in DatAves, one in ProAves and two in EBIRD are placed west of the Andes, which represent corrupted information in the database and were deleted previous to modelling.

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this species west of the Andes. These areas are not known to be occupied by the species and were excluded from the potential distribution map of this hummingbird. Also is important to note that some areas in the southern Orinoco and central Amazon regions were not predicted as suitable by our model, these areas were noted as such in our maps.

Assuming that the distribution of the species may have filled the complete climatic model generated in the Amazon region, its distribution today in remnants of forest is about 476,456 km2, which corresponds to a loss of 9 % of its potential original distribution due to deforestation in that region.

Distribution of specimens according to BioMap shows many individuals identified as subspecies nigrofasciata along the Eastern Andes slopes in what is the range of subspecies viridipectus. Identification errors are unlikely given that both subspecies were described in the mid 19th century and skins were collected post 1900's. This raises questions about how diagnosable are both subspecies and/or if this mean a wide zone of intergradation along the Andes between both subspecies. This need further revision and research.


Regularized training gain is 1.544, training AUC is 0.937, unregularized training gain is 2.215.

Algorithm terminated after 2000 iterations (66 seconds).

The follow settings were used during the run:

135 presence records used for training.

10133 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

'Fixed Cumulative Value of 1%', 'Equal Training Sensitivity and Specificity' and 'Equate Entropy of Thresholded and Original Distributions' thresholds and omission rates:


1.000-26.447-18.799-Cumulative threshold

0.020-0.210-0.159-Logistic threshold

0.668-0.141-0.215-Fractional predicted area

0.000-0.141-0.089-Training omission rate