Evaluating performance of four species distribution models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata) as model organism from the Gangetic riparian zone | Journal of Threatened Taxa

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Published: Oct 26, 2020

DOI: https://doi.org/10.11609/jott.6106.12.14.16962-16970

Keywords:

Generalized linear model, Kappa statistic, maximum entropy model, omission and commission error, random forest model, receiver operating characteristic curve, sensitivity, support vector machines model, true skill statistic

Kritish De

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

https://orcid.org/0000-0003-1410-7733

S. Zeeshan Ali

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

https://orcid.org/0000-0002-7580-560X

Niladri Dasgpta

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

https://orcid.org/0000-0001-9208-822X

Virendra Prasad Uniyal

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

Jeyaraj Antony Johnson

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

https://orcid.org/0000-0002-6089-6182

Syed Ainul Hussain

Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.

https://orcid.org/0000-0003-3229-806X

Abstract

In this paper we evaluated the performance of four species distribution models: generalized linear (GLM), maximum entropy (MAXENT), random forest (RF) and support vector machines (SVM) model, using the distribution of the dragonfly Blue-tailed Green Darner Anax guttatus in the Gangetic riparian zone between Bijnor and Kanpur barrage, Uttar Pradesh, India. We used forest cover type, land use, land cover and five bioclimatic variable layers: annual mean temperature, isothermality, temperature seasonality, mean temperature of driest quarter, and precipitation seasonality to build the models. We found that the GLM generated the highest values for AUC, Kappa statistic, TSS, specificity and sensitivity, and the lowest values for omission error and commission error, while the MAXENT model generated the lowest variance in variable importance. We suggest that researchers should not rely on any single algorithm, instead, they should test performance of all available models for their species and area of interest, and choose the best one to build a species distribution model.

Issue

Vol. 12 No. 14 (2020)

Section

Communications

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