Investigating the role of topographic factors on spatial distribution of plant species using logistic regression (Case study: Baghe-Shadi forest, Harat, Yazd)

Document Type : Research Paper

Authors

1 MSc Graduated of Forestry, Yazd University

2 Assistant Professor, Department of Forestry, Yazd University

3 Assistant Professor, Department of Environmental Sciences, Yazd University

4 Associate Professor, Department of Environmental Sciences, Yazd University

10.29252/aridbiom.7.1.1

Abstract

To investigate relation of slope, aspect and elevation in predicting spatial distribution of tree and shrub species in arid forests of south Yazd, 125 sampling plots were selected in randomized block pattern in five sub-district of study area. In addition to topographic factors, presence of plant species and their frequency were measured and recorded. Logistic regression was conducted and in the case of significance, suitable model was provided. Distribution map of species was drawn according to probabilities derived from logistic regression analysis. To determine accuracy of maps we used 20 percent of primary data. According to these data the rate of accuracy was in range of 90-75 percent. Finally relations between topographic factors and presence of species were interpreted. Results found that elevation is the most important factor for predicting spatial distribution of plant species in study area and predicts from 16 to 46 percents of variations in presence. But slope and aspect were not suitable to be included in the models. According to thresholds determined in the results it can be said that Acer cinerascens and Amygdalus eleagnifolia can be regarded for planting in higher elevations but Amygdalus lycioides is suitable for planting in lower elevations.

Keywords

References

[1]. Agresti A. (2007). An introduction to categorical data analysis (Second Edition). John Wiley & Sons, 400 p.
[2]. Alvani Nezhad, S. (1999). Investigation of effective factors for distribution of Amygdalus scoparia in two regions of Fars province, Master of Science Dissertation, Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Tehran, 134p (in Farsi).
[3]. Austin, M.P., Nicholls, A.O., and Margules, C.R., (1990). Measurement of the realized qualitative niche: Environmental niches of five eucalyptus species. Journal of Ecological Monographs, 60 (2): 161-177.
[4]. Bagheri, R., Shataee, Sh. (2010). Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province). Iranian Journal of Forest, 3(2): 243-252, (in Farsi).
[5]. Bio, A.M.F., Becker, P.D., Bie, E.D., Huybrechts, W., & M. Wassen. (2002). Prediction of plant species distribution in lowland river valleys in Belgium: Modeling species response of site conditions. Journal of Biodiversity and Conservation, 11: 2189-2216.
 
[6]. Barry, S., & Elith, J., (2006). Error and uncertainty in habitat models. Journal of Applied Ecology, 43 (3): 413–423.
[7]. Carter G.M., Stolen, E.D., & Breininger, D.R.  (2006). A rapid approach to modeling species–habitat relationships. Journal of Biological Conservation, 127: 237 –244.
[8]. Davarpanah, G., Fattahi, M., Golmohammadi, M., Aghajanlu, F., Hajighaderi T., Tarasi, J. (2009). Investigation on effective factors influencing distribution of wild pistachio species at Zanjan province. Iranian Journal of Forest and Poplar Research, 1(17): 33-50, (in Farsi).
[9]. Ghanbari, F., Shataee Joybari Sh., Azim Mohseni, M., Habashi, H. (2011). Application of topography and logistic regression in forest type spatial prediction. Application of Topography and Logistic Regression in Forest Type Spatial Prediction, 1(19): 27-41, (in Farsi).
[10].Goodarzi, Gh.R., Sagheb-Talebi, Kh. and Ahmadloo, F. (2012). The study of effective factors on Almond (Amygdalus scoparia Spach.) distribation in Markazi province. Iranian Journal of Forest, 4(3): 209-220 (in Farsi).
[11]. Guisan, A., & Zimmermann, N.E. (2000). Predictive Habitat Distribution Models in Ecology. Ecological Modeling, 135: 147-186.
[12]. Guisan A., Weiss, S.B., & Weiss, A.D. (1999). GLM versus CCA spatial modeling of plant species distribution (abstract-GEOBASE). Plant Ecology, 143(1): 107-122.
[13]. Jafarian, Z., Arzani, H., Jafari, M., Zahedi, G.H., and Azarnivand, H. (2012). Etermination of relationships between dominant plant species with environmental factors and satellite data using logistic regression (case study: Rineh Rangeland, Mazandaran province). Iranian journal of Range and Desert Reseach, 3(19): 371-383, (in Farsi).
[14]. Jafarian, Z., Arzani H., Jafari M., Zahedi GH., Azarnivand H. (2012). Mapping Spatial Prediction of Plant Species Using Logistic Regression (Case Study: in Rineh Rangeland; Damavand Mountain), Natural Geography Researches, 79: 1-18, (in Farsi).
[15]. Jongman, R.H.G.; Ter. Break, C.J.F., & Van Tongeren, O.F.R. (1987). Data analysis in community and landscape ecology. Cambridge University Press, Wageningen, 86-212.
[16]. Lassueur T., Joost, S.,Randin, C.F. (2006). Very high resolution digital elevation models: Do they improve models of plant species distribution. Journal of Ecological Modeling, 198: 139-153.
 [17]. Latimer A.M., Shanshan, Wu., Gelfand, A.E., & Silander, J.A. (2005). Building statistical models to analyze species distributions. Department of Ecology and Evolutionary Biology, University of Connecticut, 16:33-50.
[18]. McCune B. (2004). Nonparametric multiplicative for habitat modeling. Oregan state university, USA, 17:819-830.
[19]. Miller, J., & Franklin, J. (2002). Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Journal of Ecological Modelling, 157(2-3): 227-247.
[20]. Miller, J., (2005). Incorporating spatial dependence in predictive vegetation models: Residual Interpolation Methods. The Professional Geographer, 57(2): 169 184.
[21]. Miller, J., & Franklin, J. (2006). Explicitly incorporating spatial dependence in predictive vegetation models in the form of explanatory variables: a Mojave Desert case study. Journal of Geographical Systems, 8 (4): 411–435.
[22]. Rahmani, Sh., Ebrahimi, A., Davoodian Dehkordi, A. (2013). Producing the map for predicting vegetattion cover in mountain areas of Sabz-Kooh, using digital elevation model. Journal of Rangeland and Veterinary, 1(66): 89-109, (in Farsi).
[23]. Safaee M., Tarkesh M., Basiri, M., Bashari, H. (2013). Determining the potential habitat of Astragalus verus Olivier using the geostatistical and logistic regression methods. Arid Biome, 1(3): 42-55, (in Farsi).
[24]. Saki M., Tarkesh M., Vahhabi, M.R. (2011). Aplication of tree logistic regression model in determining proper site for Astragalus verus. Applied Ecology, 1(2): 27-37, (in Farsi).
[25]. Schwab, A.J. (2007). Solving standard binary logistic regression problems, The University of Texas at Austin, School of Social Work, 25 p.
[26]. Wu, H., & Huffer, F.W. (1997). Modeling the distribution of plant species using the autologistic regression model. Journal of Ecological Statistics, 4:49-64
[27]. Zare Chahouki, M.A., Jafari, M., Azarnivand, H., Shafizadeh, M. (2007). Comparison of modelling techniques for predicting the probability of species presence in arid and semi-arid rangelands (Case study: Poshtkouh region of Yazd province). Journal of Rangelands, 1(4): 342-356, (in Farsi).
[28]. Zare Chahouki, M.A., Jafari, M., Azarnivand H., Moghadam M., Farahpoor M., Shafizadeh, M. (2007). Application of logistic regression to study the relationship between presence of plant species and environmental factors. Pajouhesh & Sazandegi, 76: 136-143, (in Farsi).
Journal of Arid Biome
Volume 7, Issue 1
November 2017
Pages 1-11

Files

Share

How to cite

Statistics