An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province

Sahragard, Hossein Piri; Chahouki, Mohammad Ali Zare

doi:10.1016/j.ecolmodel.2015.04.005

Cited by 31 publications

(22 citation statements)

References 44 publications

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“…In fact, AUC can be interpreted as the probability that a model will correctly distinguish between a presence record and an absence record if each record is selected randomly from the set of presence and absence (Fielding and Bell, 1997;Pearce and Ferrier, 2000). Previous studies such as Piry Sahragard and Zare Chahouki (2015) reported that MaxEnt model, compared to the Logistic Regression and Artificial Neural Network models, is more appropriate for species distribution modeling, thus supporting our results.…”

Section: Discussionsupporting

confidence: 79%

“…Even if the data is available, the values are associated with doubts Zimmermann et al, 2010). Therefore, modeling methods using the presence-only data are the proper tool to overcome this problem (Graham et al, 2004; Piry Sahragard and Zare Chahouki, 2015). The MaxEnt method does not require absence data for the species being modeled; instead, it uses background environmental data for the entire study area .…”

Section: Introductionmentioning

confidence: 99%

“…MaxEnt applies the maximum entropy principle to describe the relative likelihood of each environmental predictor independently in a bid to define the features of sites in which the species occur compared to features of the environment as a whole Pirathiban et al, 2015), and treats the problem of species distribution modeling as conditional probability estimation (Elith et al, 2011). MaxEnt has been shown to perform well in comparison to alternative methods (Elith et al, 2006;Phillips et al, 2006;Piry Sahragard and Zare Chahouki, 2015). Moreover, when we do not have enough data for model training, one of the proposed solutions is MaxEnt method .…”

Section: Introductionmentioning

confidence: 99%

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Halophyte species distribution modeling with MaxEnt model in the surrounding rangelands of Meighan playa, Iran

Baghieri¹

2017

Appl Ecol Env Res

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Abstract. The aim of this study is to examine halophyte species (six species) distribution modeling using the maximum entropy (MaxEnt) method in the surrounding rangelands of Meighan playa, central Iran. Sampling method was random-systematic. A total of 150 plots (30 plots of 2 to 25 m 2 in each type) were collected. In each plot, species density and canopy cover were recorded. Soil samples were collected at the start and end of each transect from a depth of 0-30 cm and 30-60 cm. Soil characteristics, including texture, available water, lime, organic matter, acidity (pH), electrical conductivity (EC) and soluble solute (Na+, Ca2+, Mg2+, K+, Co3−2 and Hco3−) were measured in the lab. Maps of environmental variables, including elevation, slope and aspect were prepared using geographic information system (GIS). Soil layer maps were prepared using geostatistical method, including semivariogram analysis and Kriging interpolation. After preparing maps of environmental variables, plant distribution and modeling was conducted using MaxEnt model. Before running the model, the area under the curve (AUC), receiver operating characteristics (ROC) function, equal sensitivity and specificity method, Jackknife test and kappa coefficient were done. Models were built for each species using 70% of the data; the remaining 30% was used to test the models. According to the results, species habitats prefer variables such as soil texture, potassium, lime and organic matter. Kappa coefficient was considered excellent for species habitats of Halocnemum strobilaceum M. Bieb., 1819.) (0.91) and Aeluropus littoralis (Gouan) Parl., 1850) (0.89), for Salsola incanescens C.A. Mey., 1833) (0.84) and Halimione verrucifera (M. Bieb) Aellen, 1938) (0.83) as very good, and good for the habitats of Suaeda aegyptiaca (Hasselq.) Zohary, 1957) and Limonium iranicum (Bornm.) Lincz., 1952) species (kappa for both=0.7). The results of this study could be used in convenient locations that have resuscitation potential habitat for this species.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Halophyte species distribution modeling with MaxEnt model in the surrounding rangelands of Meighan playa, Iran

Baghieri¹

2017

Appl Ecol Env Res

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“…Despite the limited adoption of statistical methods for aquifer vulnerability studies, other branches of the earth sciences have extensively used these approaches in a wide range of applications (e.g., Conoscenti et al 2013;Lombardo et al 2014;Sahragard and Chahouki 2015). The procedure involves the identification of a dependent variable that a given algorithm, among the several available in literature, multivariately explains through a set of covariates.…”

Section: Methodsmentioning

confidence: 99%

Estimation of intrinsic aquifer vulnerability with index-overlay and statistical methods: the case of eastern Kopaida, central Greece

Tziritis¹,

Lombardo

2016

Appl Water Sci

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The intrinsic vulnerability of a karstic aquifer system in central Greece was jointly assessed with the use of a statistical approach and PI method, as a function of topography, protective cover effectiveness and the degree to which this cover is bypassed due to flow conditions. The input data for the index-overlay PI method were derived from field works and 71 boreholes of the area; the information was obtained, subsequently its critical factors were compiled which included lithology, fissuring and karstification of bedrock, soil characteristics, hydrology, hydrogeology, topography and vegetation. The aforementioned parameters were processed jointly with the aid of a GIS and yielded the final estimation of intrinsic aquifer vulnerability to contamination. Results were compared with an equivalent spatially distributed probability map obtained through a stochastic approach. The calibration and test phase of the latter relied on morphometric conditions derived by terrain analyses of a digital elevation model as well as on geology and land use from thematic maps. This procedure allowed taking into account the topographic influences with respect to a deep system such as the local karstic aquifer of eastern Kopaida basin. Finally, results were validated with ground truth nitrate values obtained from 41 groundwater samples, highlighted the spatial delineation of susceptible areas to contamination in both cases and provided a robust tool for regional planning actions and water resources management schemes.

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“…Though relationships between plant and both soil properties and other environmental factors have been well developed for some plants, comparable understanding of how a variety of plant species in native rangelands responds to soil properties and other environmental factors is poorly developed (Chuangye et al, 2015;Ghorbani et al, 2015;Sahragard and Zare Chahouki, 2015). Xu et al (2008a) have studied the relationships between vegetation, soil and topography in the dry valley of China.…”

Section: Introductionmentioning

confidence: 99%

Effects of Some Ecological Factors on Distribution of Prangos Uloptera and Prangos Pabularia in Rangelands of Ardabil Province, Iran

Mossivand¹

2017

Appl. Ecol. Env. Res.

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Mossivand et al.: Effects of some ecological factors on distribution of Prangos uloptera and Prangos pabularia in rangelands ofArdabil province, Abstract. The aim of this study is to assess the effects of some environmental factors on the distribution of Prangos uloptera DC. and Prangos pabularia Lindl. In the rangelands of Ardabil province, in the north-west of Iran. Six sites with the distribution of the genus Prangos (presence) and six sites without Prangos (absence) were identified. Three 100m transects were established. On each transect, ten 4m 2 plots were located, and the total canopy cover and density of plants were recorded. Soil samples were collected from a depth of 0-30cm on each transect. Elevation, slope and aspect were extracted from the Digital Elevation Model (DEM), and rainfall, temperature were also extracted from the derived gradient equation and DEM for each plot. Soil parameters such as organic matter (OM), nitrogen (N), phosphate (P), potassium (K), pH, electric conductivity (EC) and texture were determined at the Soil Lab. Normality of data was tested. To compare the related parameters for the sites with the presence and absence of species, the one-way ANOVA was conducted. Cluster analysis was used to classify the collected samples.To determine the variables that were significant in the distinction of each groups and mean comparison, ANOVA and Tukey tests were conducted. To determine the importance of ecological factors for the presence and absence of the selected species, discriminant analysis was conducted. The results of the analysis of variance between presence and absence of P. uloptera and P. pabularia in habitats demonstrated significant differences for the parameters of elevation, slope, aspect, temperature, EC, pH, OM, nitrogen, phosphorus, clay, silt and sand and total canopy cover, P<0.01. The result of grouping sampling plots using cluster analysis showed that plots in three groups were separated by significant differences (P<0.05) in the multivariate analysis of variance. The results showed that P. uloptera was adapted in habitats with OM of 1.402%, N 0.126%, P 2.627 ppm , K 258 ppm , 1992m a.s.l., with average slope of 83.40%, clay 10.90%, silt 37.90%, sand 51.20%, and average precipitation of 353mm. However, P. pabularia was adapted in habitats with OM of 0.915%, N 0.088%, P 2.339 ppm , K 236 ppm , 1672m a.s.l., and with average slope of 84.80%, clay 13.60%, silt 29.70%, sand 56.70% and average precipitation of 370mm. With regard to the results from discriminant analysis, the percentages of slope, clay, sand, silt, OM, P and pH, aspect, precipitation and temperature play an effective role in the separation and distribution of the species studied. These results can be used to suggest suitable species in management and restoration of rangelands with the same ecological characteristics.

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An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province

Cited by 31 publications

References 44 publications

Halophyte species distribution modeling with MaxEnt model in the surrounding rangelands of Meighan playa, Iran

Halophyte species distribution modeling with MaxEnt model in the surrounding rangelands of Meighan playa, Iran

Estimation of intrinsic aquifer vulnerability with index-overlay and statistical methods: the case of eastern Kopaida, central Greece

Effects of Some Ecological Factors on Distribution of Prangos Uloptera and Prangos Pabularia in Rangelands of Ardabil Province, Iran

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