Chaetocercus heliodor

NOTES

To elaborate our model we removed uncertain localities such as those with no coordinates and all records with coordinates laying down in sites with estimated elevations below 500 m and above 3,247 m.

The habitat suitability model generated in Maxent showed a very few areas suitable in climatic terms for this species in the border with Panama, serrania de San Lucas, the eastern slope of the Sierra Nevada de Santa Marta and serrania del Perija. These areas are not known to be occupied by the species and were excluded from the potential distribution map of this hummingbird.

Assuming that the distribution of the species may have filled the complete climatic model generated, its distribution today in remnants of forest is about 58,515 km², which corresponds to a loss of 66 % of its potential original distribution due to deforestation. Nonetheless, this near-endemic species favours edges, secondary vegetation and plantations and therefore possibly deforestation has not negatively affected greatly its populations.

MODEL METADATA

Regularized training gain is 1.917, training AUC is 0.965, unregularized training gain is 2.338.

Algorithm terminated after 2000 iterations (83 seconds).

The follow settings were used during the run:

118 presence records used for training.

10116 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

17.975-7.297-Cumulative threshold

0.318-0.171-Logistic threshold

0.100-0.148-Fractional predicted area

0.102-0.042-Training omission rate