Predicting the Distribution of Leucanthemum Vulgare Lam. Using Logistic Regression in Fandoghlou Rangelands of Ardabil Province, Iran

محورهای موضوعی : Modeling

Ardavan Ghorbani ¹ , Sahar Samadi Khangah ² , Mehdi Moameri ³ , Javad Esfanjani ⁴

1 - Associate Professor, University of Mohaghegh Ardabili, Ardabil, Iran
2 - Ph.D. Student of Rangeland Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
3 - Assistant Professor, University of Mohaghegh Ardabili, Ardabil, Iran
4 - Ph.D. Candidate of Range Management, Department of Range & Watershed Management, University of Mohaghegh Ardabili, Ardabil, Iran

تاریخ دریافت : 1398/02/01 تاریخ پذیرش : 1398/05/15 تاریخ انتشار : 1398/10/11

کلید واژه: Environmental factors, Species distribution modelling, Invasive species, Namin County,

چکیده مقاله :

Species Distribution Modelling (SDM) is an important tool for conservation planning and resource management. Invasive species represent a good opportunity to evaluate SDMs predictive accuracy with independent data as their invasive range can expand quickly. Thus, the aim of this study was to investigate the relationships between presence of Leucanthemum vulgare Lam. and environmental variables in Fandoghlou rangeland, Ardabil, Iran using logistic regression model. Sampling was conducted in six sites as presence/absence of L. vulgare by a systematic random method in 2016. Physiographic, climatic, surface coverage and density of L. vulgare were measured in sampling sites. In the beginning, middle and end of each transect, soil samples were taken from the depth of rootstock of range plants including L. vulgare. Soil attributes were measured in the laboratory. The maps of physiographic and climate were derived from digital elevation model, and selected soil attributes were derived using Kriging interpolation method. Derived regression equation from the presence of L. vulgare was applied to map the effective environmental variables, and a prediction map was produced for the study area. The comparison between the predicted and actual maps was assessed using the Kappa coefficient. Results showed that the presence of L. vulgare had a positive relationship with temperature and volumetric soil water content factors and had a negative relationship with electrical conductivity, sodium, diffusible clay factors. Therefore, L. vulgare type is significantlyaffected by the presence of these factors (p<0.01). The Kappa coefficient was 0.55 for derived predicted map. The evaluation of the model indicated that logistic regression was able to predict the distribution of L. vulgare habitats. The results of this study gave more insight and understanding from the habitats and effective environmental factors in L. vulgare distribution.

منابع و مأخذ:

Aghajanlou, F. Ghorbani, A. Zare Chahouki, M.A. Hashemimajd, K. 2018. The impact of environmental factors on distribution of Ferula ovina (Boiss.) Boiss. in northwest Iran. Applied Ecology and Environmental Research, 16(2): 977-992. http://dx.doi.org/10.15666/aeer/1602_977992

Araújo, M.B. Guisan, A. 2006. Five (or so) challenges for species distribution modelling. Biogeography, 33: 1677–1688. https://doi.org/10.1111/j.1365-2699.2006.01584.x

Arzani, H. 1997. Manual of rangeland assessment plan in rangelands of Iran with various climate conditions. Iranian Research Institute of forests and rangelands press, 65 p.

Aslami, F. Ghorbani, A. 2018. Object-based land use/land cover change detection using Landsat imagery (A case study: Ardabil, Namin, and Nir counties in northwest Iran), Environmental Monitoring and Assessment, 190(7): 376. https://doi.org/10.1007/s10661-018-6751-y

Assadi, M. 2006. Distribution patterns of the genus Acantholimon (Plumbaginaceae) in Iran. Iranian Journal of Botany, 12(2): 120-140. (In Persian)

Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157(2-3): 101–118.

Austin, M.P. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200(1-2): 1–19. https://doi.org/10.1016/j.ecolmodel.2006.07.005

Azimi Motem, F. Talai, R. Asiabizadeh, F. Houshyar, M. 2011. A survey on flora, life forms and geographical distribution of plant species in the protected forests of Fandoghlu (Ardabil province). Journal of Taxonomy and Biosystematics, 9(3): 75-88. (In Persian)

Bagheri, H. Ghorbani, A. Zare Chahouki, M.A. Jafari, A.A. Sefidy, K. 2017. Halophyte species distribution modeling with maxent model in the surrounding rangelands of Meighan playa, Iran. Applied Ecology and Environmental Research, 15(3): 1472- 1484.

http://dx.doi.org/10.15666/aeer/1503_14731484

Barry, S. Elith, J. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology, 43(3): 413- 423. https://doi.org/10.1111/j.1365-2664.2006.01136.x

Bellard, C. Cassey, P. Blackburn, T.M. 2016. Alien species as a driver of recent extinctions. Biology Letters, 12(2): 1-5. https://doi.org/10.1098/rsbl.2015.0623

Borna, F. Tamrtash, R. Tatian, M.R. Gholami, V. 2017. Habitat potential modeling of Astragalus gossypinus using ecological niche factor analysis and logistic regression (Case study: summer rangelands of Baladeh, Nour). Journal of RS and GIS for Natural Resources, 7(4): 45-61. (In Persian)

Bradshaw, C.J.A. Leroy, B. Bellard, C. Roiz, D. Albert, C. Fournier, A. Massin, M.B. Salles, J.M.  Simard, F. Courchamp, F. 2016. Massive yet grossly underestimated global costs of invasive insects. Nature Communications, 7: 1-8. https://doi.org/10.1038/ncomms12986

Breiman, L. Friedman, J.H. Olshen, R.A. Stone, C.J. 1984. Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks/Cole Advanced Books and Software, 358p.

Calero, N. Barron, V. Torrent, J. 2008. Water dispersible clay in calcareous soils of southwestern Spain. Catena, 74(1): 22-30.

Elith, J. 2016. Predicting distributions of invasive species. arXiv, 28p.

Esfanjani, J. Ghorbani, A. Zare Chahouki, M.A. 2017. Modeling habitat distribution of Festuca ovina-Astragalus gossypinus by using maximum entropy method in the Chaharbagh rangelands of Iran. Range Management and Agroforestry, 38(2): 171-175.

Esfanjani, J. Ghorbani, A. Zare Chahouki, M.A. Esfanjani, E. 2018. Evaluation of maxent Method for habitats distribution modeling of Bromus tomentellus in the southern rangeland of Golestan province (Iran). Ecology, Environment and Conservation, 24(1): 212-217.

Falk, W. Mellert, K.H. 2011. Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria. Vegetation Science, 22(4): 621–634.  https://doi.org/10.1111/j.1654-1103.2011.01294.x

Fourie, M. 2017. What can electrical conductivity tell us about our soil? Trace and save.

Guisan, A. Zimmermann, N.E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3): 147-186.  https://doi.org/10.1016/S0304-3800(00)00354-9

Guisan, A. Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8: 993–1009.  https://doi.org/10.1111/j.1461-0248.2005.00792.x

Hasanuzzaman, M. Hossain, M.A. da Silva, JAT. Fujita, M. 2012. Plant responses and tolerance to abiotic oxidative stress: Antioxidant defenses is a key factor. In: Bandi, V. Shanker, A.K, Shanker, C. Mandapaka, M. Eds. Crop stress and its management: Perspectives and strategies. Springer; Berlin, Germany, 261-316.

Hassanzade, E. Ghorbani, A. Moameri, M. Hashemi Majd, K. 2018. Net primary production variations under the effect of topographic factors in mountain rangelands of Namin county. Journal of Rangeland and Watershed Management, 70(4): 851-867. https://doi.org/10.22059/jrwm.2018.244026.1176 (In Persian)  

Hastie, T. Tibshirani, R. Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

Heathwaite, A.L. Sharpley, A. Bechmann, M. Rekolaine, S. 2005. Assessing the risk and magnitude of agricultural nonpoint source phosphorus pollution. In: Phosphorus: Agriculture and the Environment, Agronomy (Eds J.T. Sims, A.N. Sharpley). ASA Press, Madison, WI, USA.

Hosmer D.W. Lemeshow, S. 1980. A comparison of goodness-of-fit tests for the multiple logistic regression models. Communications in statistics-Theory and Methods, 9: 1043-1069.

Hulme, P.E. 2009. Trade, transport and trouble: managing invasive species pathways in an era of globalization. Applied Ecology, 46(1): 10–18. https://doi.org/10.1111/j.1365-2664.2008.01600.x

Jacobs, J. 2008. Ecology and Management of Oxeye Daisy (Leucanthemum vulgare Lam.). Invasive Species Technical, 19: 10-15.

Kjaergaard, C. de Jonge, L.W. Moldrup, P. Schjønning, P. 2004. Water-dispersible colloids: effects of measurement method, clay content, initial soil matric potential, and wetting rate. Vadose Zone Journal, 3(2): 403-412. https://doi.org/10.2136/vzj2004.0403

Khuroo, A.A. Maliki, A.H. Reshi, Z.A. Dar, G.H. 2010. From ornamental to detrimental: plant invasion of Leucanthemum vulgare Lam. (Ox-eye Daisy) in Kashmir valley, India. Current Science, 98(5): 10p.

Madsen, A.B. Prang, A. 2001. Habitat factors and the presence or absence of otters Lutra lutra in Denmark. Acta Theriologica, 46(2):171–179.

Magharri, E. Razavi, S.M. Ghorbani, A. Nahar, L. Sarker, S.D. 2015. Chemical composition, some allelopathic aspects, free-radical-scavenging property and antifungal activity of the volatile oil of the flowering tops of Leucanthemum vulgare Lam. Journal of NaturalProduct Research, 9(4): 538-545.

Mainali, K.P. Warren, D.L. Dhileepan, K. McConnachie, A. Strathie, L. Hassan, G. Karki, D. Shrestha, B.B. Parmesan, C. 2015. Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Global Change Biology, 21: 4464-4480. https://doi.org/10.1111/gcb.13038    

Mangold, J. Sheley, R. Brown, M. 2009. Oxeye Daisy: Identification, Biology and Integrated Management. Montana State University, 4p.

Mamedov, A.I. Wagner, A.E. Huang, C. Norton, L.D. Levy, G.J. 2010. Polyacrylamide effects on aggregate and structure stability of soils with different clay mineralogy. Soil Science Society of America Journal, 74: 1720–1732. https://doi.org/10.2136/sssaj2009.0279

Merow, C. Smith, M.J. Edwards, T.C. Guisan, A. McMahon, S.M. Normand, S. Thuiller, W. Wüest, R.O. Zimmermann, N.E. Elith, J. 2014. What do we gain from simplicity versus complexity in species distribution models? Ecography, 37: 1267-1281. https://doi.org/10.1111/ecog.00845

Milanesio, D. Saccani, M. Maggiora, R. Laurino, D. Porporato, M. 2017. Recent upgrades of the harmonic radar for the tracking of the Asian yellow-legged hornet. Ecology and Evolution, 7(13): 4599–4606. https://doi.org/10.1002/ece3.3053

Miller, J. Franklin, J. 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157 (2-3): 227-247.

Mirzaei Mossivand, A., Ghorbani, A. Keivan Behjou, F. (2017) Effects of some ecological factors on distribution of Prangos uloptera and Prangos pabularia in rangelands of Ardabil province, Iran, Applied Ecology and Environmental Research, 15(4): 957-968. http://dx.doi.org/10.15666/aeer/1504_957968 (In Persian)  

Molaei Sham Asbi, M. Ghorbani, A. Sefidi, K. Bahrami, B. Hashemi Majd, K. 2017. Effects of ecological factors on distribution of Artemisia aucheri Boiss.in southeast faced slopes of Sabalan. Iranian Journal of Rangeland, 11(2): 139-151. (In Persian)  

Monserud, R.A. Leemans, R. 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

Mouquet, N. Lagadeuc, Y. Devictor, V. Doyen, L. Duputie, A. Eveillard, D. Faure, D. Garnier, E. Gimenez, O. Huneman, P. Jabot, F. Jarne, Ph. Joly, D. Julliard, R. Kéfi, S. Kergoat, G.J. Lavorel, S. Le Gall, L. Meslin, L. Morand, S. Morin, X. Morlon, H. Pinay, G. Pradel, R. Schurr, F.M. Thuiller, M. Loreau, M. 2015. Review: Predictive ecology in a changing world. Applied Ecology, 52: 1293–1310. https://doi.org/10.1111/1365-2664.12482

Murray, K. Conner, M.M. 2009. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology, 90: 348–355. https://doi.org/10.1890/07-1929.1

Nemali, K.S. Van Iersel, M.W.  2004. Light intensity and fertilizer concentration: I. Estimating optimal fertilizer concentration from water-use efficiency of wax begonia. Hort Science, 39(6): 1287–1292. https://doi.org/10.21273/HORTSCI.39.6.1287

Oksanen, J. Minchin, P.R. 2002. Continuum theory revisited: what shape are species responses along ecological gradients? Ecological Modelling, 157: 119–129. https://doi.org/10.1016/S0304-3800(02)00190-4

Parker, V.T. Schile, L.M. Vasey, M.C. Callaway, J.C. 2011. Efficiency in assessment and monitoring methods: scaling down gradient-directed transects. Journal of Ecosphere, 9(2):1-11. https://doi.org/10.1890/ES11-00151.1

Piri Sahragard, H. Zare Chahouki, M.A. Azarnivand, H. 2014. Modelling of plant species distribution nin the Hoze sultan west rangelands of by logistic regression analysis. Journal of range management of Gorgan University, 1(1): 15-25. (In Persian)  

Piri Sahragard, H. Ajorlo, M. 2018. Comparison of logistic regression and maximum entropy for distribution modeling of range plant species (a case study in rangelands of western Taftan, southeastern Iran). Turkish Journal of Botany, 42: 28-37. https://doi.org/10.3906/bot-1612-5

Piri Sahragard, H. Zare Chahouki, M.A. 2016. Comparison of logistic regression and machine learning techniques in prediction of habitat distribution of plant species. Range Management and Agroforestry, 37(1): 21-26.

Platts, P.J. McClean, C.J. Lovett, J.C. Marchant, R. 2008. Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty. Ecological Modelling, 218: 121–134. https://doi.org/10.1016/j.ecolmodel.2008.06.028

Rezai Poorbaghedar, A. Sadeghinia, A. Nohegar, A. Hakimi, M.H. 2014. Determination of some soil properties on distribution of vegetation types and Dorema ammoniacum and Rheum ribes in ranges of Baghedar region in Bafgh city. Desert Ecosystem Engineering Journal. 3(4): 69-78. (In Persian)  

Rodrigues, J.F.M. Coelho, M.T.P. Varela, S. Diniz-Filho, J.A.F. 2016. Invasion risk of the pond slider turtle is underestimated when niche expansion occurs. Freshwater Biology, 61: 1119-1127. https://doi.org/10.1111/fwb.12772

Samadi, S. 2017. Investigation of effective ecological factors on distribution of Leucanthemum vulgare Lam. in Namin County. Thesis is approved for the degree of M.Sc. In Natural Resources Engineering Range Management. University of Mohaghegh Ardabili, Ardabil, Iran, 120p. (In Persian)  

Seebens, H. Blackburn, T.M. Dyer, E.E. Genovesi, P. Hulme, P.E. Jeschke, J.M. Pagad, Sh. Pyšek, P. Winter, M. Arianoutsou, M. Bacher, S. Blasius, B. Brundu, G. Capinha, C. Celesti-Grapow, L. Dawson, W. Dullinger, S. Fuentes, N. Jäger, H. Kartesz, J. Kenis, M. Kreft, H. Kühn, I. Lenzner, B. Liebhold, A. Mosena, A. Moser, D. Nishino, M. Pearman, D. Pergl, J. Rabitsch, W. Rojas-Sandoval, J. Roques, A. Rorke, S. Rossinelli, S. Roy, H.E. Scalera, R. Schindler, S. Štajerová, K. Tokarska-Guzik, B. van Kleunen, M. Walker, K. Weigelt, P. Yamanaka, T. Essl, F. 2017. No saturation in the accumulation of alien species worldwide. Nature Communications, 15(8):1-9.  https://doi.org/10.1038/ncomms14435

Shojaee, M. Kiani, B. Setoodeh, A. Azimzadeh, H.R. 2017. Investigating the role of topographic factors on spatial distribution of plant species using logistic regression (Case study: Baghe-Shadi forest, Harat, Yazd). Arid Biome Scientific and Research Journal, 7(1): 1.-11.

Signore, A. Serio, F. Santamaria, P. 2016. A targeted management of the nutrient solution in a soilless tomato crop according to plant needs. Frontiers in Plant Science, 7: 1-15. https://doi.org/10.3389/fpls.2016.00391

Simberloff, D. Martin, J.L. Genovesi, P. Maris, V. Wardle, D.A. Aronson, J. Courchamp, F. Galil, B. García-Berthou, E. Pascal, M. Pyšek, P. Sousa, R. Tabacchi, E. Vilà, M. 2013. Impacts of biological invasions: what’s what and the way forward. Trends Ecology Evolution, 28(1): 58–66. https://doi.org/10.1016/j.tree.2012.07.013

Sonneveld, C. Voogt, W. 2009. Plant nutrition of greenhouse crops, Springer, New York, U.S.A.

Tavakoli Neko, H. Pourmeydani, A. Adnani, S.M. Sagheb-Talebi, Kh. 2012. Impact of some important ecological factors on presence of mountain Almond (Amygdalus scoparia Spach.) in Qom province, Iran. Iranian Journal of Forest and Poplar Research, 19(4): 523-542. (In Persian)   

Teimoorzadeh, A. Ghorbani, A. Kavianpoor, A.H. 2015. Study on the flora, life forms and chorology of the south eastern of Namin forests (Asi-Gheran, Fandoghloo, Hasani and Bobini), Ardabil province. Plant Researches, 28(2): 265-275. (In Persian)  

Tingley, R, García-Díaz, P. Arantes, CRR. Cassey, P. 2017. Integrating transport pressure data and species distribution models to estimate invasion risk for alien stowaways. Ecography, 41(4): 635-646. https://doi.org/10.1111/ecog.02841

Tulloch, A.I.T. Tulloch, V.J.D. Evans, M.C. Mills, M. 2014. The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake. Conservation Biology, 28(6): 1462–1473.  https://doi.org/10.1111/cobi.12403

Vaz, S. Martin, C.S. Eastwood, P.D. Ernande, B. Carpentier, A. Meaden, G.J. Coppin, F. 2008. Modelling species distributions using regression quantiles. Applied Ecology, 45(1): 204–217.  https://doi.org/10.1111/j.1365-2664.2007.01392.x 

West, A.M. Kumar, S. Brown, C.S. Stohlgren, T.J. Bromberg, J. 2016. Field validation of an invasive species Maxent model. Ecological Informatics, 36: 126–134.  https://doi.org/10.1016/j.ecoinf.2016.11.001 

Yilmaz, H. Yilmaz, O.Y. Akyuz, Y.F. 2017. Determining the factors affecting the distribution of Muscari latifolium, an endemic plant of Turkey, and a mapping species distribution model. Ecology and Evolution, 7(4): 1112–1124. https://doi.org/10.1002/ece3.2766

Zare Chahouki, M.A. Jafari, M. Azarnivand, H. Moghaddam, M.R. Farahpour, M. Shafizadeh NasrAbadi, M. 2008. Application of logistic regression to study the relationship between presence of plant species and environmental factors. Journal of Watershed Management, 76: 136-143.

Zare Chahoukia, M.A. Zare Chahouki, A. 2010. Predicting the distribution of plant species using logistic regression (Case study: Garizat rangelands of Yazd province). Desert, 15: 151-158.