جستجوی مقالات مرتبط با کلیدواژه « maxent » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

This research aimed to determine the potential habitats of Vaccinium arctostaphylos as a rare species using the MaxEnt method. For this purpose, sampling was performed from the areas of V. arctostaphylos at eight habitats in Namin rangelands, Ardabil, Iran in 2020. Also, latitude and longitude as well as physiographic factors were recorded using GPS. Soil samples were collected from depth of 0-30 cm. Then, some physicochemical properties of soil such as pH, electrical conductivity, soil texture, lime, soluble potassium, magnesium, soluble sodium, particulate organic matter, absorbable phosphorus, soluble bicarbonate and clay, silt and sand percentage were measured in the laboratory. The MaxEnt model was used to determine the relationship between environmental factors and the distribution of V. arctostaphylos. The map of environmental factors affecting the distribution of the species was prepared using IDW geostatistical method in Arc-GISver.10.8 software. Kappa index was used to evaluate the conformity of the forecast map with the actual map. Results of the Jackknife efficiency index showed the variables of elevation, rainfall and temperature were the most important variables in the distribution of V. arctostaphylos, respectively. Results of response curves showed this species is likely to be present in the altitude range of 1500-1900 m. Also, the precipitation range in the presence habitats of species is 350-450 mm and the average annual temperature range is about 7-10 °C. Results showed that the accuracy of maximum entropy modeling with a Kappa index of 0.64 is acceptable and this model has good accuracy in predicting the presence of species with main habitat. Generally, results showed MaxEnt model could be used as a prediction tool to determine the distribution area of V. arctostaphylos. Moreover, the prepared forecast map can be used for better conservation of species and rangelands management and improvement of the regions and similar areas.

Investigation of the relationship between plant species and environmental factors plays an important role in plant ecology. The present study aimed to develop the best predicting model for distribution of Prangos uloptera DC. using logistic regression (LR) and Maximum Entropy Methods (MaxEnt) in its habitat in the southern rangeland of Ardabil province, Iran. Vegetation data (presence and absence of P. uloptera) and environmental factors (including soil, topography and climatic variables) were collected. The original vegetation type map was prepared using slope and elevation maps (1: 25000 scale) and satellite imagery (Landsat). Vegetation samples were collected in 2016. In each site, three transects of 100 m length were deployed (two transects in the direction of a gradient and one transect perpendicular to the slope direction). On each transect, ten 4m2 plots were placed along each transect, and the total canopy cover and plant density were recorded. Overall, 180 plots were sampled in six sites. Soil samples were collected at a depth of 0-30 cm at the beginning and end of each transect. The LR method was performed in the SPSS Ver. 19 software, and the Maximum Entropy method was carried out in the MaxEnt Ver. 3.1 software. The LR model showed that rainfall had the highest effect on the distribution of the P. uloptera habitat. The accuracy of the LR method for the prediction map was good (Kappa index= 0.65). The MaxEnt method showed that variables, including sand, nitrogen (N), silt, and potassium (K) had the highest effect on distribution of P. uloptera habitat. However, the accuracy of the MaxEnt method was low (Kappa index=0.35). It was concluded that modeling methods could be used as a prediction tool to determine the location of plant species. This may lead to better rangelands management and improvement in areas with similar conditions.

This study aimed to identify the most important physical variables affecting the distribution of four range plants species (Tamarix aphylla, Calligonum comosum, Prosopis spicigera and Salsola rigida) habitats and to prepare potential habitat map of the species using Maximum Entropy (MaxEnt) method in rangelands of Jiroft city, Kerman province, located in SE Iran. To this end, sampling of vegetation including species type and percent cover was conducted with randomized-systematic method in 2015. Sample size was determined as 60 plots with a quadrat size of 25-100 m2. For soil sampling, eight profiles were dug in each habitat and samples were taken at two depths, i.e., 0–30 and 30–60 cm. Results indicated that the classification accuracy of the model was acceptable and soil variables including EC, percentage of lime, organic matter, moisture content and texture had the greatest effect on the distribution of the studied plant species habitats. Correlations between the actual and predicted maps for Tamarix aphylla and Calligonum comosum habitats were at a very good level, Kappa = 0.81 and 0.79, respectively, for Prosopis spicigera habitat was at a good level, Kappa = 0.75, and finally for Salsola rigida was at a moderate level, Kappa = 0.53. These results indicate that the MaxEnt method can provide valuable information about the physical conditions of plant habitats in arid rangeland. Knowledge on physical characteristics of plant habitats can be useful in determination of potential habitats and rangeland improvement projects.

Climate change has a great impact on the species distribution range and many endangered plant species. Fritillaria imperialis as a species that is native to Central Zagros, Iran is a medicinal plant with great ecological and commercial profits. Its population has decreased considerably and the species would be endangered in later decades. Understanding the habitat needs of this species, evaluating habitat conditions, and forecasting its potential habitat are important for protecting F. imperialis. The presence of F. imperialis points recorded from our field surveys in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran in spring 2017. In order to model its distribution based on correlation analysis, two topographic variables and eight bioclimatic ones as the input of Maximum Entropy model (MaxEnt) were used. The results showed that temperature seasonality (55.1%) and precipitation of driest quarter (22.9%) were important factor drivers of F. imperialis suitable habitat. The accuracy of the maximum entropy model in predicting the distribution of the studied species was high (AUC=0.91) as 2.33% (37986 ha) in Chaharmahal-va-Bakhtiari Province for the F. imperialis, which has had suitability. About 18% and 16.5% of F. imperialis habitats in the area may be lost due to climate change by 2070 under two climate warming scenarios (RCP4.5 and RCP8.5, given by the IPCC). As shown by the model, under the current climatic conditions, the suitable habitat would be rendered to an unsuitable one in the future resulting in local extinction. The results of this study can be used to identify sites with high extinction probability of F. imperialis and protect susceptible habitats against the effects of climate change.

Predictive modeling of habitat distribution of range plant species and identification of their potential habitats play important roles in the restoration of disturbed rangelands. This study aimed to predict the geographical distribution of Artemisia sieberi and find the influential variables in the distribution of A. sieberi in the desert rangelands of central Iran. Maps of environmental variables were generated by GIS software (version 9.3). Predictive distribution maps of A. sieberi were produced with Maximum Entropy Method (MaxEnt) and the existing data regarding this species. The agreement of predictive map with the actual map was checked by calculating Kappa coefficient value. Accuracy of predictive models was evaluated using the Area Under the Curve (AUC). Results showed that soil pH and lime content in the surface layer (0-30 cm) and silt percent in both surface and sub-surface soil depths (0-30 and 30-60) had the greatest impacts on the distribution of A. sieberi in the study area. Correspondence of actual map with the predictive one was assessed at a satisfactory level (Kappa coefficient = 0.70). MaxEnt is widely used as compared with the other standard methods since it only requires the presence data of a specific plant species to draw the distribution map of its habitat. Additionally, MaxEnt is a generative method and its output can be easily understood by the field practitioners.