Mitu tuberosum


The habitat suitability model generated in Maxent showed a few areas that are suitable in climatic terms for this species east of the Andes in the Catatumbo and west of the Andes in the low Magdalena valley, the inter-Andean valleys and the Patia valley. Those areas are not known to be occupied by this curassow and were excluded from its potential distribution map. It is important to bear in mind that our model was elaborated with just three locality records and therefore in that sense it is relatively limited. Nevertheless, it gives a first good approximation of the potential distribution of this species in Colombia.

Assuming that the distribution of the species may have filled the areas predicted as suitable (i.e. marginally suitable, suitable and highly suitable) and not predicted as suitable in its most likely area of distribution south of the Caqueta river, its potential distribution today in remnants of forest is about 68,956 km2, which corresponds to a loss of 2 % of its potential original distribution due to deforestation.


Regularized training gain is 2.925, training AUC is 0.997, unregularized training gain is 4.132.

Algorithm converged after 380 iterations (1 seconds).

The follow settings were used during the run:

3 presence records used for training.

10003 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: 0.605, threshold: 1.970, 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:


70.168-14.022-Cumulative threshold

0.743-0.161-Logistic threshold

0.003-0.054-Fractional predicted area

0-0-Training omission rate