Discosura langsdorffi


To elaborate our models we removed uncertain localities such as those with no coordinates and all records with coordinates laying down in sites with estimated elevations above 540 m.

The habitat suitability model generated in Maxent showed areas suitable in climatic terms for this hummingbird in most of the lowlands of the country suggesting a very low specificity. Sensu lato the species must occupy most of the Amazonia and therefore its final potential range was circumscribed to that region.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 478,393 km2, which corresponds to a loss of 9 % of its potential original distribution due to deforestation.


Regularized training gain is 0.079, training AUC is 0.611, unregularized training gain is 0.161.

Algorithm converged after 60 iterations (0 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:


45.909-1.265-Cumulative threshold

0.510-0.204-Logistic threshold

0.432-0.924-Fractional predicted area

0.400-0.000-Training omission rate