Boissonneaua flavescens


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 below 850 m and above 3,857 m.

The habitat suitability model generated in Maxent showed very good fit not showing suitable areas out of the main Andean Ranges. a Few pixels were predicted in the border with Panama and in the southern portion of serrania de San Lucas, those were left in the final potential map to inform, although must be considered areas where the species most likely does not occur.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 61,090 km2, which corresponds to a loss of 64 % of its potential original distribution due to deforestation.

This species has a large extent of occurrence, estimated in 384,000 km2 and therefore it is considered of Low Concern given it does not approach to the thresholds for Vulnerable under the range size, population trend or population size criterion (BirdLife International 2016). Nevertheless, interestingly, considering the major proportion of its habitat that has been degraded and looking at it at subspecies level we can see that, in Colombia, subspecies tinochlora has a potential extent of occurrence of 10,274 km2 and of this it may occur in some 2,657 km2 remnants of forest, which corresponds to a loss of 64 % of its potential original distribution. Giving the fact that the extent of forested areas are well below the threshold of Vulnerable (VU) that is 20,000 km2 and that the habitat of this species still continues to be threatened by deforestation and fragmentation in Colombia, possibly subspecies tinochlora may be considered of certain importance for conservation in Colombia.


Regularized training gain is 1.862, training AUC is 0.953, unregularized training gain is 2.040.

Algorithm terminated after 2000 iterations (66 seconds).

The follow settings were used during the run:

389 presence records used for training.

10388 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

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

16.348-5.874-Cumulative threshold

0.333-0.206-Logistic threshold

0.112-0.155-Fractional predicted area

0.111-0.036-Training omission rate