Comparing Different Modeling Techniques for Predicting Presence-absence of Some Dominant Plant Species in Mountain Rangelands, Mazandaran Province

Document Type: Research and Full Length Article


1 Ph.D. Rangeland sciences, Watershed and Natural Resources Administration of Alborz Province, Karaj, Iran

2 Department of Range and Watershed Management, Malayer University, Iran

3 Department of Medicinal Plant, Research and Technology Institute of Plant Production (RTIPP), Shahid Bahonar University of Kerman, Kerman, Iran


In applied studies, the investigation of the relationship between a plant species and environmental variables is essential to manage ecological problems and rangeland ecosystems. This research was conducted in summer 2016. The aim of this study was to compare the predictive power of a number of Species Distribution Models (SDMs) and to evaluate the importance of a range of environmental variables as predictors in the context of rangeland vegetation. In this study, Aflah rangelands with 5721 ha were selected. In this research, predictor variables included climatic, topographic and edaphic parameters. The sampling method was equal random-classification for vegetation and soil. Topographic factors including slope, elevation and aspect were determined in Arc GIS software. In each sample unit, 10 plots were established (total 350 plots) and the lists of the species, their number, their presence or absence were recorded. The efficacy of five different modelling techniques to predict the distribution of five dominant rangeland plant species (Agropyron repens, Festuca ovina, Leucopoa sclerophylla, Stachys lavandulifolia and Tragopogon graminifolius) was evaluated. The models were generalized linear regression (GLM), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and random forest (RF). Data analysis was done using the R software, version 3.1.1. The results showed that GAM model demonstrated most consistently high predictive power over the species in the rangeland context investigated here. GAM model exhibited the most predictive power. The importance analysis of the environmental variables showed that N, pH and aspect were the most important variables in the GAM model. Overall, N, P and C/N soil (0.452, 0.437 and 0.389) were the most important environmental variables.


Main Subjects

Asadian, GH., Javadi, S.A., Jafari, M., Arzani, H., Akbarzadeh, M., 2017.Relationships between Environmental Factors and Plant Communities in Enclosure Rangelands (Case study: Gonbad, Hamadan) J. of Range. Sci., 7(1): 20-34.

Aertsena, W., Kinta, V., Orshovena, J., Özkanb, K., and Muysa, B., 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecolo Modell.,9 (221): 1119–1130.

Barry, S., and Elith, J., 2006. Error and uncertainty in habitat models. J. Appli Ecol., 10 (43): 413–423.

Beretta, A. N., Silbermann,A.V., Paladino, L., Torres,D., Bassahun,D., Musselli, R., and García-Lamohte, A., 2014. Soil texture analyses using a hydrometer: modification of the Bouyoucos method, Cien. Inv. Agr., 41(2):263-271.

Bergamini, A., Stofer, S., Bolliger J., and Scheidegger C., 2007. Evaluating macrolichens and environmental variables as predictors of the diversity of epiphytic microlichens. Lichenol, 4 (39): 475–489.

Boitani, L., Sinibaldi, I., Corsi, F., Biase, A., Carranza, I, d’Inzillo, Ravagli, M., Reggiani, G., Rondinini, C., and Trapanese, P., 2007. Distribution of medium- to large-sized African mammals based on habitat suitability models. Biodi and Conse., 3 (17): 605–621.

Breiman, L, Friedman, J.H, Olshen, R.A., Stone, C.J., 1984. Classification and Regression, Amazon publication, London, 354 pp.

Breiman, L., 2001. Random forests. Machine Learning, 45:15–32.

Cao, B., Chengke, B., Linlin, Z., Guishuang, L., Mingce, M., 2016. Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China. J. Plant Ecol., 9 (6): 742-51.

Cutler, A., and Stevens, J. R., 2006. Random forests for microarrays. Methods in Enzy., 411:422–432.

Dirnböck, T., Dullinger, S., Grabherr, G., and Dirnb, T., 2003. A regional impact assessment of climate and land-use change on alpine vegetation. J. of Biogeo., 5 (101): 401–417.

Dubuis, A., 2013. Predicting spatial patterns of plant biodiversity: from species to communities. Thesis Ph.D. University of Lausanne, 295 pp.

Elith, J., Leathwick, J. R., Hastie, T., 2008. A working guide to boosted regression trees. J Anim Ecol., 2 (77): 802–813.

Engler, R., Randin, C.F., Thuiller,W., Dullinger, S., Zimmermann, N.E., Araujo, M.B., Pearman, P.B., Le Lay, G., Piedallu, C., Albert, C.H., Choler P., Coldea, G., De Lamo, X., Dirnbock T., Gegout, J.C., Gomez-GarcianD., Grytnes, J.-A.A., Heegaard E., Hoistad, F., Nogues-Bravo, D., Normand S., Puscas, M., Sebastia, M.T., Stanisci, A., Theurillat J.-P.P., Trivedi, M.R., Vittoz, P., Guisan, A., Araújo M.B., Dirnböck, T., Gégout, J.C., Gómez-García, D., Høistad, F., Nogués-Bravo, D., Puşcaş M., and Sebastià, M.T., 2011. 21st century climate change threatens mountain flora unequally across Europe. Glob Change Biol., 7 (17): 2330–2341.

Fielding, A.H., and Bell J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/ absence models. Environ Conserve., 2 (24): 38–49.

Garzon, M.B., Blazek, R., Neteler, M., de Diosa, R., Ollero, H., and Furlanello, C., 2006. Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula, Ecolog mode., 197:383–393.

Guisan, A., Edwards, T.C., and Hastie, T., 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecolog Modell., 8 (157): 89–100.

Guisan, A., Theurillat, J.P., and Kienast, F., 1998. Predicting the potential distribution of plant species in an Alpine environment. J. Veget Scie., 3 (9): 65–74.

Guisan, A., and Zimmermann, N.E., 2000. Predictive Habitat Distribution Models in Ecology. Ecol Model., 135:147-186.

Hallstan, S., Johnson, R. K., Willén, E., and Grandin, U., 2012. Comparison of classification-then modelling and species-by-species modelling for predicting Lake Phytoplankton assemblages. Ecol Model., 2 (231):11–19.

Hastie, T., and Tibshirani, R., 1990. Non-parametric logistic and proportional odds regression, Appli statis., 260-276.

Hengl, T., Sierdsema, H., Radovi, A., and Dilo, A., 2009. Spatial prediction of species distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecolo Model., 6 (220): 3133-3222.

Hirzel, A., Guisan, A., 2002. Which is the optimal sampling strategy for habitat suitability modeling? EcoloModell., 157: 331–341.

Jafarian, Z., Arzani, H., Zahedi, GH. And Azarnivand, H., 2009. Application of discriminate analysis for determination relationship between distribution of plant species with environmental factors and satellite data at Rineh rangeland in province of Mazandaran.watershed researches., 3 (88):64-71.

Jafarian, Z., and Kargar, M., 2012. Environmental factors affecting the ecological species groups using logistic regression at Polour rangeland, Mazndaran. J. Environ Scie., 10 (2): 107-118.

Jafarian, Z., and Kargar, M., 2016. Modelling plant protected and valued species distributions using GLM and GAM in Polour rangeland. Geo and develop., 15(46):117-132.

Jafari haghighi, M.,2003. Sampling method analysis methods, Neda Zoha publication, 272 pp.

Jones M.C, Dye S.R, Pinnegar J.K, Warren R, and Cheung W.W.L., 2012. Modelling commercial fish distributions: Prediction and assessment using different approaches. Ecolo Model., 4 (225):133–145.

Khalasi Ahvazi, L., Zare Chahouki, M.A., Ghorbannezhad, F., 2012. Comparing Discriminant Analysis, Ecological Niche Factor Analysis and Logistic Regression Methods for Geographic Distribution Modelling of Eurotia ceratoides (L.) C. A. Mey. J. Range Sci., 3(1): 45-57.

Leathwick, J.R., Elith, J., Francis, M.P., Hastie, T., and Taylor, P., 2006. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar. Ecolog –Progr., 12 (321): 267–281.

Lütolf, M., Kienast, F., Guisan, A., 2006. The ghost of past species occurrence: improving species Distribution models for presence-only data. J. Appli Ecol., 43: 802–815.

Machado, E.A., 2012. Empirical mapping of suitability to dengue fever in Mexico using species distribution modeling. Appl Geogr., 11 (33): 82–93.

Maggini, R., Guisan, A., and Cherix, D., 2002. A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park. Biodiv and Conser., 11: 2117–2141.

Maggini, R., Lehmann, A., Zimmermann, N.E., and Guisan, A., 2006. Improving generalized regression analysis for the spatial prediction of forest communities. J. Biogeo., 33: 1729–1749.

Marmion, M., Luoto, M., Heikkinen, R.K., and Thuiller, W., 2009. The performance of state-of-the-art modeling techniques depends on geographical distribution of species. Ecolo Modell., 220: 3512–3520.

Mckenney, D.W., Pedlar, J.H., 2003. Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecolo Manage., 3 (175): 497–507.

Meier, E.S., Kienast, F., Pearman, P.B, Svenning, J-C., Thuiller, W., Araújo, M.B., Guisan, A., and Zimmermann, N.E., 2010. Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecog, 12(33): 1038–1048.

Moghari, M., Arzani, H., Tavili, A., Azarnivand, H., Saravi, M., and Farahpoor, H. 2014. Identifying and Determining the Competency of Medicinal Herbs in the Pashang Haraz Basin Rangelands, Amol County, Mazandaran Province. Two Iranian. J. Medic. Arom. Plants., 30 (6): 898-914.

Moisen, G.G., Freeman, E.A, Blackard, J.A, Frescino, T.S, Zimmermann, N.E, and Edwards,T.C., 2006. Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecol. Model., 8 (199): 176–187.

Moisen, G.G., and Frescino, T.S., 2002. Comparing five modelling techniques for predicting. Forest characteristics. Ecol Model, 4 (157): 209–225.

Pellissier, L., Bråthen, K.A., Pottier, J., Randin, C.F., Vittoz, P., Dubuis, A., Yoccoz, N.G., Alm, T., Zimmermann, N.E., Guisan, A., and Brathen, K.A., 2010. Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants. Ecogr, 8 (33): 1004–1014.

Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C, and Guisan, A., 2012. Climatic niche shifts are rare among terrestrial plant invaders. Scie., 8 (335): 1344–8.

Piri Sahragard, H., and Zare Chahoki, M. A., 2015. An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province. Ecol Model., 2 (309): 64-71.

Piri Sahragard, H., and Zare Chahouki, M.A., 2016. Modeling of Artemisia sieberi Besser Habitat Distribution Using Maximum Entropy Method in Desert Rangelands. Journal of Rangeland Science. 6(2): 93-101.

Pottier, J., Dubuis, A., and Pellissier, L., 2012. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients. Glob Ecolog Biogeo., 6 (22): 52–63.

Rondinini, C., Di Marco, M., Chiozza, F., Santulli, G., Baisero, D., Visconti, P., Hoffmann, M., Schipper, J., Stuart, S.N., Tognelli M.F., Amori, G., Falcucci, A., Maiorano, L, and Boitani, L., 2011. Global habitat suitability models of terrestrial mammals. Philosophical transactions of the Royal Society of London. Series B, Bio scie., 366: 2633–41.

Schorr, G., Holstein, N., Pearman, P.B., Guisan, A., and Kadereit, J.W., 2012. Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula. Ecol. Evol.,10 (2): 1260–77.

Sor, R., Park, Y.S., Boets, P., Goethals, L. M., Sovan, L., 2017. Effects of species prevalence on the performance of predictive models. Ecolog Mode, 23 (354): 11-19.

Sundblad, G., Härmä, M., Lappalainen, A., Urho, L., and, U., 2009. Transferability of predictive fish distribution models in two coastal systems. Estuarine, Coas. Shelf Science., 6 (83): 90–96.

Sweets, J. A., 1988. Measuring the accuracy of Diagnostic system. Science, 27 (240): 1285-1293.

Tarkesh, M., 2012. Comparison of six correlative models in predictive vegetation mapping on a local scale. Thesis Ph.D. University of Jena, 106 p.

Vega, R., Flojgaard, C., Lira-Noriega, A., Nakazawa, Y., Svenning, J.C., and Searle, J.B., 2010. Northern glacial refugia for the pygmy shrew Sorex minutus in Europe revealed by phylogeographic analyses and species distribution modelling. Ecogr, 10 (33): 260–271.

Vicente, J., Randin, C.F., Goncalves, J., Metzger, M.J., Lomba, A., Honrado, J., and Guisan, A., 2011. Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach. Biolo Invas., 9 (13):1209–1227.

Wisz, M.S., Walther, B., and Rahbek, C., 2007. Using potential distributions to explore determinants of Western Palaearctic migratory songbird species richness in sub-Saharan Africa. J. Biogeogr., 12 (34): 828–841.