Chrysolampis mosquitus

NOTES

To elaborate our models we removed uncertain localities such as 'Bogota skins' and all records with coordinates laying down in sites with estimated elevations above 1,800 m.

A first habitat suitability model generated in Maxent using all data showed very poor predictions in the Orinoco region. Therefore, we decided to model separately regions west and east of the Andes. The new habitat suitability model generated in Maxent west of the Andes showed very good predictions although probably with some omission error in the Pacific and Amazonian slopes. Additionally, the habitat suitability model generated in Maxent east of the Andes showed a few areas suitable in climatic terms for this species west of the Andes. These areas were removed previously to add up both models to have complete predictions from each one in the specified regions.

MODEL METADATA

West model

Regularized training gain is 1.553, training AUC is 0.955, unregularized training gain is 2.067.

Algorithm converged after 1680 iterations (57 seconds).

The follow settings were used during the run:

83 presence records used for training.

10083 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: 0.171, categorical: 0.250, threshold: 1.170, hinge: 0.500

Feature types used: hinge product linear threshold quadratic

responsecurves: true

jackknife: true

maximumiterations: 2000

East Model

Regularized training gain is 0.524, training AUC is 0.885, unregularized training gain is 0.924.

Algorithm converged after 100 iterations (0 seconds).

The follow settings were used during the run:

4 presence records used for training.

10004 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: 0.590, threshold: 1.960, 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:

West model

ETSS-EETOD-Description

24.262-8.195-Cumulative threshold

0.355-0.169-Logistic threshold

0.120-0.212-Fractional predicted area

0.120-0.036-Training omission rate

East model

ETSS-EETOD-Description

40.831-6.458-Cumulative threshold

0.512-0.190-Logistic threshold

0.213-0.592-Fractional predicted area

0.250-0.000-Training omission rate