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

Main Article Content

Kritish De
S. Zeeshan Ali
Niladri Dasgpta
Virendra Prasad Uniyal
Jeyaraj Antony Johnson
Syed Ainul Hussain


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.


Article Details



Allouche, O., A. Tsoar & R. Kadmon (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43(6): 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x DOI: https://doi.org/10.1111/j.1365-2664.2006.01214.x

Baldwin R. (2009). Use of Maximum Entropy Modeling in Wildlife Research. Entropy 11(4): 854–866. https://doi.org/10.3390/e11040854 DOI: https://doi.org/10.3390/e11040854

Bried, J.T. & M.J. Samways (2015). A review of odonatology in freshwater applied ecology and conservation science. Freshwater Science, 34(3): 1023–1031. https://doi.org/10.1086/682174 DOI: https://doi.org/10.1086/682174

Buckley, L.B., M.C. Urban, M.J. Angilletta, L.G. Crozier, L.J. Rissler & M.W. Sears (2010). Can mechanism inform species’ distribution models? Ecology Letters 13(8): 1041–1054. https://doi.org/10.1111/j.1461-0248.2010.01479.x DOI: https://doi.org/10.1111/j.1461-0248.2010.01479.x

Chefaoui, R.M. & J.M. Lobo (2008). Assessing the effects of pseudo-absences on predictive distribution model performance.Ecological Modelling 210(4): 478–486. https://doi.org/10.1016/j.ecolmodel.2007.08.010 DOI: https://doi.org/10.1016/j.ecolmodel.2007.08.010

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37–46. https://doi.org/10.1177/001316446002000104 DOI: https://doi.org/10.1177/001316446002000104

Collins, S.D. & N.E. McIntyre (2015). Modeling the distribution of odonates: a review. Freshwater Science 34(3): 1144–1158. https://doi.org/10.1086/682688 DOI: https://doi.org/10.1086/682688

Cutler, D.R., T.C. Edwards Jr, K.H. Beard, A. Cutler, K.T. Hess, J. Gibson & J.J. Lawler (2007). Random forests for classification in ecology. Ecology 88(11): 2783–2792. https://doi.org/10.1890/07-0539.1 DOI: https://doi.org/10.1890/07-0539.1

Décamps, H., R.J. Naiman & M.E. McClain (2009). Riparian Zones. In: Encyclopedia of Inland Waters (pp. 396–403). https://doi.org/10.1016/b978-012370626-3.00053-3 DOI: https://doi.org/10.1016/B978-012370626-3.00053-3

Dijkstra, K.D.B., G. Bechly, S.M. Bybee, R.A. Dow, H.J. Dumont, G. Fleck, R.W. Garrison, M. Hämäläinen, V.J. Kalkman, H. Karube, M.L. May, A.G. Orr, D.R. Paulson, A.C. Rehn, G. Theischinger, J.W.H. Trueman, J.V. Tol, N.V. Ellenrieder & J. Ware (2013). The classification and diversity of dragonflies and damselflies (Odonata). Zootaxa 3703(1): 036–045. https://doi.org/10.11646/zootaxa.3703.1.9 DOI: https://doi.org/10.11646/zootaxa.3703.1.9

Duan, R.Y., X.Q. Kong, M.Y. Huang, W.Y. Fan & Z.G. Wang (2014). The predictive performance and stability of six species distribution models. PLoS ONE 9(11): e112764. https://doi.org/10.1371/journal.pone.0112764 DOI: https://doi.org/10.1371/journal.pone.0112764

Elith, J. & J.R. Leathwick (2009). Species distribution models: ecological explanation and prediction across space and time. Annual review of Ecology, Evolution and Systematics 40: 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159 DOI: https://doi.org/10.1146/annurev.ecolsys.110308.120159

Elith, J., C.H. Graham, R.P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J.McC.M. Overton, A.T. Peterson, Steven J. Phillips, K. Richardson, R. Scachetti‐Pereira, R.E. Schapire, J. Soberón, S. Williams, M.S. Wisz & N.E. Zimmermann (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2): 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x DOI: https://doi.org/10.1111/j.2006.0906-7590.04596.x

F.S.I. (2009). India State of Forest Report – 2009. Forest Survey of India (Ministry of Environment Forests and Climate Change, Government of India), Dehradun.

Fick, S.E. & R.J. Hijmans (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12): 4302–4315. https://doi.org/10.1002/joc.5086 DOI: https://doi.org/10.1002/joc.5086

Fielding, A.H. & J.F. Bell (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(1): 38–49. https://doi.org/10.1017/S0376892997000088 DOI: https://doi.org/10.1017/S0376892997000088

He, F., C. Zarfl, V. Bremerich, J.N.W. David, Z. Hogan, G. Kalinkat, K. Tockner & S.C. Jähnig (2019). The global decline of freshwater megafauna.Global Change Biology 25(11): 3883–3892. https://doi.org/10.1111/gcb.14753 DOI: https://doi.org/10.1111/gcb.14753

Hijmans, R.J. (2019). raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://CRAN.R-project.org/package=raster

Hill, L., A. Hector, G. Hemery, S. Smart, M. Tanadini & N. Brown (2017). Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest. Ecology and Evolution 7(4): 1043–1056. https://doi.org/10.1002/ece3.2661 DOI: https://doi.org/10.1002/ece3.2661

Hirzel, A.H., J. Hausser, D. Chessel & N. Perrin (2002). Ecological‐niche factor analysis: how to compute habitat‐suitability maps without absence data?. Ecology 83(7): 2027–2036. https://doi.org/10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2

Howard, C., P.A. Stephens, J.W. Pearce‐Higgins, R.D. Gregory & S.G. Willis (2014). Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution 5(6): 506–513. https://doi.org/10.1111/2041-210X.12184 DOI: https://doi.org/10.1111/2041-210X.12184

Howley, T. & M.G. Madden (2005). The genetic kernel support vector machine: Description and evaluation. Artificial Intelligence Review 24(3–4): 379–395. https://doi.org/10.1007/s10462-005-9009-3 DOI: https://doi.org/10.1007/s10462-005-9009-3

Huang, C.L. & C.J. Wang (2006). A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31(2): 231–240. https://doi.org/10.1016/j.eswa.2005.09.024 DOI: https://doi.org/10.1016/j.eswa.2005.09.024

Huerta, M.A.O. & A.T. Peterson (2008). Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods. Revista Mexicana de Biodiversidad 1(1): 205–216.

Marcot, B.G. (2012). Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling 230: 50–62. https://doi.org/10.1016/j.ecolmodel.2012.01.013 DOI: https://doi.org/10.1016/j.ecolmodel.2012.01.013

McCullagh, P. & J.A. Nelder (1989). Generalized Linear Models. Chapman and Hall, London, 511pp DOI: https://doi.org/10.1007/978-1-4899-3242-6

Meragiaw, M., Z. Woldu, V. Martinsen & B.R. Singh (2018). Woody species composition and diversity of riparian vegetation along the Walga River, Southwestern Ethiopia. PLoS ONE 13(10): e0204733. https://doi.org/10.1371/journal.pone.0204733 DOI: https://doi.org/10.1371/journal.pone.0204733

Mi, C., F. Huettmann, Y. Guo​, X. Han & L. Wen (2017). Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ 5: p.e2849. https://doi.org/10.7717/peerj.2849 DOI: https://doi.org/10.7717/peerj.2849

Monserud, R.A. & R. Leemans (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling 62(4): 275–293. https://doi.org/10.1016/0304-3800(92)90003-W DOI: https://doi.org/10.1016/0304-3800(92)90003-W

N.R.S.C. (2016). National Land Use and Land Cover (LULC) mapping using multi-temporal AWiFS Data (2015–16). National Remote Sensing Centre, Hyderabad, India.

Phillips, S.J., R.P. Anderson & R.E. Schapire (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190(3–4): 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 DOI: https://doi.org/10.1016/j.ecolmodel.2005.03.026

Pozzobom, U.M., J. Heino, M.T. da S. Brito & V.L. Landeiro (2020). Untangling the determinants of macrophyte beta diversity in tropical floodplain lakes: insights from ecological uniqueness and species contributions. Aquatic Sciences 82(3): 56. https://doi.org/10.1007/s00027-020-00730-2 DOI: https://doi.org/10.1007/s00027-020-00730-2

Prasad, A.M., L.R. Iverson & A. Liaw (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2): 181–199. https://doi.org/10.1007/s10021-005-0054-1 DOI: https://doi.org/10.1007/s10021-005-0054-1

R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Ruete, A. & G.C. Leynaud (2015). Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps. PeerJ PrePrints 3: e1208v1. https://doi.org/10.7287/peerj.preprints.1208v1 DOI: https://doi.org/10.7287/peerj.preprints.1208v1

Schmitt, S., R. Pouteau, D. Justeau, F. de Boissieu & P. Birnbaum (2017). ssdm: An r package to predict distribution of species richness and composition based on stacked species distribution models. Methods in Ecology and Evolution 8(12): 1795–1803. https://doi.org/10.1111/2041-210X.12841 DOI: https://doi.org/10.1111/2041-210X.12841

Segal, M.R. (2004). Machine Learning Benchmarks and Random Forest Regression. UCSF: Center for Bioinformatics and Molecular Biostatistics. Retrieved from https://escholarship.org/uc/item/35x3v9t4

Shabani, F., L. Kumar & M. Ahmadi (2016). A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecology and Evolution 6(16): 5973–5986. https://doi.org/10.1002/ece3.2332 DOI: https://doi.org/10.1002/ece3.2332

Sofaer, H.R., C.S. Jarnevich, I.S. Pearse, R.L. Smyth, S. Auer, G.L. Cook, T.C. Edwards Jr, G.F. Guala, T.G. Howard, J.T. Morisette & H. Hamilton (2019). Development and delivery of species distribution models to inform decision-making. BioScience 69(7): 544–557. https://doi.org/10.1093/biosci/biz045 DOI: https://doi.org/10.1093/biosci/biz045

Srinivasulu, A. & C. Srinivasulu (2016). All that glitters is not gold: A projected distribution of the endemic Indian Golden Gecko Calodactylodes aureus (Reptilia: Squamata: Gekkonidae) indicates a major range shrinkage due to future climate change. Journal of Threatened Taxa 8(6): 8883–8892. https://doi.org/10.11609/jott.2723.8.6.8883-8892 DOI: https://doi.org/10.11609/jott.2723.8.6.8883-8892

Stanton, J.C., R.G. Pearson, N. Horning, P. Ersts & H. ReşitAkçakaya (2012). Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution 3(2): 349–357. https://doi.org/10.1111/j.2041-210X.2011.00157.x DOI: https://doi.org/10.1111/j.2041-210X.2011.00157.x

Subramanian, K.A. (2005). Dragonflies and Damselflies of Peninsular India-A Field Guide. E-Book of Project Lifescape. Centre for Ecological Sciences, Indian Institute of Science and Indian Academy of Sciences, Bangalore, 118pp.

Swets, J.A. (1988). Measuring the accuracy of diagnostic systems. Science 240(4857): 1285–1293. https://doi.org/10.1126/science.3287615 DOI: https://doi.org/10.1126/science.3287615

Tickner, D., J.J. Opperman, R. Abell, M. Acreman, A.H. Arthington, S.E. Bunn, S.J. Cooke, J. Dalton, W. Darwall, G. Edwards, I. Harrison, K. Hughes, T. Jones, D. Leclère, A.J. Lynch, P. Leonard, M.E. McClain, D. Muruven, J.D. Olden, S.J. Ormerod, J. Robinson, R.E. Tharme, M. Thieme, K. Tockner, M. Wright & L. Young (2020). Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. BioScience 70(4): 330–342. https://doi.org/10.1093/biosci/biaa002 DOI: https://doi.org/10.1093/biosci/biaa002

Václavík, T. & R.K. Meentemeyer (2009). Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions?. Ecological Modelling 220(23): 3248–3258. https://doi.org/10.1016/j.ecolmodel.2009.08.013 DOI: https://doi.org/10.1016/j.ecolmodel.2009.08.013

Ward, D.F. (2007). Modelling the potential geographic distribution of invasive ant species in New Zealand. Biological Invasions 9(6): 723–735. https://doi.org/10.1007/s10530-006-9072-y DOI: https://doi.org/10.1007/s10530-006-9072-y

Wilson, M.D. (2008). Support Vector Machines. pp. 3431–3437. In: Jørgensen, S.E. & B.D. Fath (eds.). Encyclopedia of Ecology. Elsevier, 3120pp. https://doi.org/10.1016/B978-008045405-4.00168-3 DOI: https://doi.org/10.1016/B978-008045405-4.00168-3

Most read articles by the same author(s)

1 2 > >>