Penelope jacquacu


There are two records in BioMap and three in DatAves located west of the Andes that are erroneous. To elaborate our model, we removed those records and all records with coordinates laying down in sites with estimated elevations above 600 m. In spite of this, the habitat suitability model generated in Maxent showed a few areas that are suitable in climatic terms for this species west of the Andes in Sierra Nevada de Santa Marta, the northern Andean Region, the mid-high Magdalena valley and the northern and southern Pacific region. These areas are not known to be occupied by the species and were excluded from the potential distribution map of this guan. Otherwise, there were several areas in the central Orinoco and the southern Amazon regions that the model failed to predict. We believe that this was possibly because of the lack of records from those particular areas. These areas were added in the final potential distribution map proposed.

Distribution of specimens, according to BioMap, suggests a possible area (uncertain size) of intergradation between subspecies orienticola and jacquacu in Vaupes. Most specimens in this area have been collected after the description of both subspecies involved and we believe it is unlikely they are erroneously identified. Its is not clear how far goes the distribution of the subspecies orienticola into Colombia and proposed limits are tentative. It is interesting to note that these seven specimens of orienticola deposited in the 'Instituto de Ciencias Naturales' are the first reported for this subspecies in the country.

This species is found in secondary forest, borders, semi-open areas and riparian forests (Hilty & Brown, 1986), and therefore we did not overlapped its potential distribution with forested areas since it might appear almost anywhere east of the Andes in its potential area of distribution.


Regularized training gain is 0.815, training AUC is 0.895, unregularized training gain is 1.359.

Algorithm converged after 860 iterations (24 seconds).

The follow settings were used during the run:

35 presence records used for training.

10034 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: 0.236, categorical: 0.250, threshold: 1.650, hinge: 0.500

Feature types used: linear quadratic hinge

responsecurves: true

jackknife: true

maximumiterations: 2000

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


37.637-9.479-Cumulative threshold

0.44-0.201-Logistic threshold

0.171-0.443-Fractional predicted area

0.171-0.057-Training omission rate