Crypturellus berlepschi


There are six specimens in BioMap that allegedly were collected east of the Andes in Meta, Caqueta and Amazonas, which clearly are erroneous records in the database. Apparently, C. berlepschi has been previously considered a subspecies of C. cinereus (Hilty & Brown, 1986). Additionally, five of those records have notes suggesting the taxonomy was incomplete. Therefore, it is possible those records inherited an erroneous taxonomy that was not corrected or that became corrupted during the compilation of the data if the taxonomy was incomplete. Independently of the cause, these records were excluded when modelling the distribution of this species. In spite of that, the habitat suitability model generated in Maxent showed a few zones in the foothills of the Eastern Andes where exist suitable areas in climatic terms for the species. Since it is well established this zones are not occupied by the species, those areas were removed from the potential distribution map. Additionally, a few more areas predicted as 'marginally suitable' in the serrania de San Lucas also were removed. Interestingly, there is a belt predicted as suitable (marginally to highly) in the northeastern end of the Central Andes, which we think is the result of a recent sighting of the species in Antioquia (DatAves). That particular record extends the known range of the species some 100 km to the east and the presence of the species in those areas deserves further investigation. On the other hand, the model failed to predict a very small area in the extreme southwest, possibly due to the lack of records coming from that particular area in Colombia. Nevertheless, it is suspected the species definitely can be there since there are several sightings in Ecuador near the border (EBIRD), and therefore we decided to include that small area in the potential distribution for the species in the country.

Assuming that the distribution of the species may have filled the complete climatic model generated and predicted as suitable plus the small area included in the southwest, its distribution today in remnants of forest is about 59,062 km2, which corresponds to a loss of 44 % of its potential original distribution due to deforestation.


Regularized training gain is 2.172, training AUC is 0.970, unregularized training gain is 2.716.

Algorithm converged after 500 iterations (15 seconds).

The follow settings were used during the run:

16 presence records used for training.

10016 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: 0.543, categorical: 0.286, threshold: 1.840, 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:


19.044-12.261-Cumulative threshold

0.228-0.121-Logistic threshold

0.074-0.114-Fractional predicted area

0.062-0.062-Training omission rate