Glaucis hirsutus

NOTES

To elaborate our model we removed uncertain localities such as 'Bogota skins' and all records with coordinates laying down in sites with estimated elevations above 1,500 m.

The habitat suitability model generated in Maxent showed a very few areas that are suitable in climatic terms for this species in the Alta Guajira. These areas are not known to be occupied by the species and were excluded from the potential distribution map.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 92,619 km2, which corresponds to a loss of 70 % of its potential original distribution due to deforestation in the region west of the Andes and about 472,918 km2, which corresponds to a loss of 10 % in the Amazonia. However, it is important to note that this species favours borders, secondary growth and semi-open areas and in this sense it might have been favoured by deforestation to certain extent.

MODEL METADATA

West model

Regularized training gain is 1.367, training AUC is 0.936, unregularized training gain is 1.753.

Algorithm converged after 1940 iterations (65 seconds).

The follow settings were used during the run:

164 presence records used for training.

10162 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:

ETSS-EETOD-Description

24.235-7.523-Cumulative threshold

0.366-0.156-Logistic threshold

0.140-0.255-Fractional predicted area

0.140-0.018-Training omission rate

East model

Regularized training gain is 0.846, training AUC is 0.858, unregularized training gain is 1.171.

Algorithm converged after 980 iterations (28 seconds).

The follow settings were used during the run:

65 presence records used for training.

10062 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: 0.150, categorical: 0.250, threshold: 1.350, hinge: 0.500

Feature types used: hinge linear quadratic

responsecurves: true

jackknife: true

maximumiterations: 2000

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

ETSS-EETOD-Description

28.824 12.831 Cumulative threshold

0.334 0.215 Logistic threshold

0.246 0.429 Fractional predicted area

0.246 0.062 Training omission rate