Crypturellus brevirostris


Due to the lack of records for this tinamou in natural history museums (BioMap) and in the literature, to model its distribution in Colombia we selected five points at random in the nearby area around serrania de Naquen, from where are known the only 'sightings' for the species in the country (MacMullan & Donegan, 2014; BirdLife International). Therefore, this map must be used with caution, although we believe may give a good first approximation of the suitability of the areas where the species might be present.

The habitat suitability model generated in Maxent showed a few areas that are suitable in climatic terms for this species in the eastern slope of the Eastern Andes as well as west of the Andes. It is well known these areas are not occupied by this species and they were excluded from its potential distribution map. The model also predicted a few more areas in the southern Amazon region that may be occupied by this tinamou. Nevertheless, with the knowledge that we have a t present time these seem unlikely and were trimmed from the final potential distribution map.

Assuming the distribution of the species may have filled the complete climatic model generated in the Amazon region of Colombia, its distribution today in remnants of forest is potentially about 39,082 km2, which corresponds to a loss of ≈ 0.6 % of its potential original distribution in the region due to deforestation.


Regularized training gain is 2.930, training AUC is 0.994, unregularized training gain is 3.645.

Algorithm converged after 480 iterations (1 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

responsecurves: true

jackknife: true

maximumiterations: 2000

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


40.592-15.083-Cumulative threshold

0.517-0.106-Logistic threshold

0.011-0.053-Fractional predicted area

0-0-Training omission rate