Document Type : Research Paper

Authors

1 Ph.D. Student in Rangeland Sciences, Sari Agricultural Science and Natural Resources University, Iran

2 Associate Professor, Sari Agriculture Science and Natural Resources University, Iran

3 Assistant Professor, Faculty of Natural Resources, Sari Agriculture Science and Natural Resources University, Iran

4 Assistant Professor, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran

Abstract

    Ecologists and environmental managers emphasize the use of predictive models to examine the species distribution patterns. The purpose of the present study was to investigate the efficiency of the generalized linear model (GLM) and generalized additive model (GAM) in determining the relationship between vegetation and environmental factors in Khetteh Riz Rangelands. Environmental factors studied included soil characteristics, topographic factors and climatic factors. A classified-random sampling was performed and three dominant species, Bromus tomentollus, Ferula ovina, and Agropyron repens, were identified. The results showed that in the GLM model for Ferula ovina species, the variables of phosphorus content and slope were effective. For species Bromus tomentollus and Agropyron repens, the variables of annual moisture, rainfall, silt, and slope were effective. In the GAM model, the available moisture, silt and organic matter were the factors affecting the distribution of Ferula ovina. The silt, potassium, pH, and annual moisture content were the factors affecting the distribution of Agropyron repens. In addition, slope and silt were the variables affecting the distribution of Bromus tomentollus in the GAM model. The values of AUC, calculated for the GLM (0.63) and GAM (0.70), indicate the accuracy of the model to be acceptable.

Keywords

-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. Ecological Modeling, 221:1119–1130.
-Abbasi,M. and Zarechahoki, M. A., 2014. Modeling the spatial distribution of Stipa barbata and Agropyron intermedium using artificial neural network in the middle of Taleghan rangelands. Renewable Natural Resources Research ,5 (2):47-57.
-Abd El-Ghani, A. and Wafa M. A., 2003. Soil – vegetation relationships in coastal desert plain of southern Sinai, Egypt. Journal of Arid Environment, 55: 607-628.
-Brown, G.,1994. Predicting Vegetation types at tree line using topography and biophysical disturbance variables. Journal of Vegetation Science, 5: 641-656.
-Coudun C., Gegout, J. C., Piedallu, C. and Rameau, J. C., 2006. Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. Journal of Biogeography, 33: 1750–1763.
-Dubuis, A., 2013. Predicting spatial patterns of plant biodiversity: from species to communities. Thesis of Ph.D. 295p.
-Dubuis A., Giovanettina S., Pellisier L., Pottier, J., Vittoz, P. and Gusian, A., 2013. A Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variable. Journal of Vegetation Science, 24: 593-606.
-Elith, J., Graham, C. H. and Anderson, R. P., 2006. Novel methods improve prediction of species‟ distributions from occurrence data. Ecography, 135:213-222.
- Freeman, E. A. and Moisen, G., 2008. Presence absence: an R package for presence absence analysis. Journal of Statistical Software, 23 (11): 1-31.
-Ghazi Moradi, M., Tarkesh. M., Bashari, H. and Vahabbi, M. R., 2016. Modeling potential habitat of coma using two models Baverbeizin network (BBN) and generalized additive models (GAM) in Isfahan, Iran Fereydoon. Range Management and eatershed, 69(3): 677-689.
-Guisan, A., Edwards, T. C. and Hastie, T. J., 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modeling, 157(2): 89-100.
- Hastie, T. and Tibshirani, R., 1990. Non-parametric logistic and proportional odds regression. Applied statistics, 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. Ecological Modeling,220: 3133-3222.
-Herreraa B. J., J. Camposa, B, Finegana and Alvarado, A., 2004. Factors affecting site productivity of a Costa Rican secondary rain forest in relation to Vochysia ferruginea, a commercially valuable canopy tree species. Forest Ecology and Management, 118 (1): 73-81.
-Hirzel A. & Guisan, A., 2002. Which is the optimal sampling strategy for habitat suitability modeling? Ecological Modeling, 157: 331-341.
-Jafarian, Z., Arzani, H., Jafari, M., Zahedi, Gh. and Zahedi, H., 2009. Analysis of the relationship between the distribution of plant communities and climatic factors and using methods developed for classification and ordination. Journal of Range Journal of Range,2(2):125-140.
-Jafarian, Z., Arzani, H. Jafari, M., Zahedi, Gh. and Azarnivand, H., 2012.Spatial maps of plant species in pastures Rineh using logistic regression. Natural geographic research, 79:1-18.
-Jafarian,z. and Kargar, M., 2012. Environmental factors affecting the ecological species groups using logistic regression pastures pleural. Environmental Science, 10(2):107-118.
-Jensen, M., P. Jeff, A., Barber, J. and Patric, S., 2001. Spatial Modeling of Rangeland Potential Vegetation Environments. Journal of Range Management, 54(5): 528-536.
-Lehmann, A., Overton, J. M. C. and Leathwick, J. R., 2002. GRASP: generalized regression analysis and spatial prediction. Ecological modeling, 157(2): 189-207.
-Mahdavi, M., 2005. Applied hydrology, Tehran University Publication, Iran, 346p.
-Mostafivi, M.,Alizadeh,A. and Kaboli, M., 2010. Pazanan species habitat suitability map for the spring and summer in Lar National Park. Journal of Natural Resources, Science and Technology, 2:111-121.
- Pinke G., Pal, R. and Botta–Dukat, Z., 2010. Effect of environmental factors on weed species composition of cereal and stubble fields in western Hungary. Journal of Biology, 5(2): 283-292.
-Safai, M.,Tarkesh, M., and Bashari, H., 2012. Modeling the impact of climate on habitat use in determining the geographical distribution of plant species. Third Conference on Climate Change and dendrochronology. University of Sari, 23-25 Augest, 31-39.
-Sahragard, H. P. and Zare chahoki, M. A., 2015. An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province. Ecological Modelling. 309, 64-71.
-Saki, M., Tarkesh, M., Basiri, M. and Vahabi, M., 2012. Determine potential habitat for (Astragalous verus) using logistic regression tree (LRT). Journal of Applied Ecology, 1(2): 27-37.
-Sor, R., Park, Y. S., Boets, P., Goethals, L. M. and Sovan, L., 2017. Effects of species prevalence on the performance of predictive models. Ecological Modelling,354, 11-19.
-Tamura N., Miamoto A., Sugimura, K. and Yamada, F., 2004. Predicting habitat distribution of the alien formosan squirrel using logistic regression model. Global Environmental Research, 8: 13-21.
-Tarkesh, M. and Gottfried, J., 2012. Comparison of six correlative models in predictive vegetation mapping on a local scale. Environmental Ecolology Statistics, 437-457.