binary logistic regression

The Survey of the ecological characteristics of the species and determining their habitat suitability is one of the main principles of management and conservation of wildlife species. Hence, we need methods that can help assess habitats and can estimate, over time, reduce of their quality. To understand the effects of human activities and the changes of a habitat, it is necessary to make quantitative assessment of the habitat. In this study, the habitat suitability of the red-winged Pratincole (Glareola partincola) in the Shadegan international wetland was investigated using binary logistic regression methods based on presence and absence data, as well as Maxent methods (maximum entropy) based on presence-absence data in 2018. For this purpose, in all accessible sites for randomly, sampling of habitat variables, including vegetation type, water depth, water temperature, water soluble oxygen (DO), PH, water conductivity (EC), vegetation density index (NDVI), distance from the village with the presence and absence data of the red-winged Pratincole was done. Totally, 62 sampling stations were registered in Shadegan international wetlands. The results of the binary logistic regression model showed that the most important environmental variables affecting the presence of the species included water temperature, vegetation type and electrical conductivity of water with the inverse relationship and water depth with direct relation. The results of Maxent analysis showed that the electrical conductivity, PH and water temperature were the most effective factors in the presence of the red-winged Pratincole. In general, factors related to the water characteristics of wetland had the most effect on the presence of the red-winged Pratincole. Therefore, attention to factors affecting water quality and preventing the entry of polluted wastewater into the wetland to maintain the optimal habitat for the red-winged Pratincole can be of great importance.

Fires in natural areas are one of the factors decreasing forested area of northern Iran. In this study, forest and rangelands susceptibility to fire were analyzed using data-driven methods over Mazandaran Province. Fourteen important environmental and anthropogenic parameters influencing forest and rangelands susceptibility to fire were used to model probability of fire susceptibility. Binary logistic regression and artificial neural network, as two well-known data driven methods was then used to evaluate environmental and anthropogenic performance on landfire and map of forest fire susceptibility estimates were prepared in GIS environment. The area under the successive rate curve (AUSC) showed that ANN method modeled forest fire susceptibility with an accuracy of around 88% and BLR with 85%. 21.6% of the total area of Mazandaran province is located in areas with high and very high susceptibility levels of forest and rangeland fire. Overall, ANN method showed promising results to estimate landfire susceptibility. The forestry and rangelands fire susceptibility map presented in this study can be used as a basic map of the strategic planing in Mazandaran province to reduce probability fire damages.

Indian Gerbil (Tatera indica Hardwicke، 1807) is from Muridae family and Gerbillinae subfamily. Habitat range of this species involves arid and semi -arid areas. The species has been listed in IUCN (2013) at the lowest level of concern (Lc). This study was conducted using logistic regression approach and all effective factors on habitat suitability of T. indica were identified in Sistan region. Field studies were conducted at five different sampling stations in Zabol during spring 2013. Seven environmental variables including the percentage of vegetation cover، soil، gravel cover (<0/2 mm diameter)، percentage of cover of Tamarix species (Haloxylon sp)، percentage of coverage of Desmatchia-Bipinata، cover of Salsola aucheri and distance from water resources in 60 plots of presence/absence were performed and analyzed using binary logistic regression method. Results showed that variables of soil cover، cover gravel and coverage percentage of Desmatchia-Bipinata and distance from water resources have important role (P< 0. 05) in relation to the distribution of T. indica in Sistan.

Digital soil mapping includes soils, spatial prediction and their properties based on the relationship with covariates. This study was designed for digital soil mapping using binary logistic regression and boosted regression tree in Zarand region of Kerman. A stratified sampling scheme was adopted for the 90,000 ha area based on which, 123 soil profiles were described. In both approaches, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. Covariates included a geomorphology map, terrain attributes and remote sensing indices. Among the predictors, geomorphology map was identified as an important tool for digital soil mapping approaches as it helped increase the prediction accuracy. After geomorphic surfaces, the terrain attributes were identified as the most effective auxiliary parameters in predicting the diagnostic horizons. The methods predicted high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. Both models predicted Calcigypsids with very low reliability and accuracy, while prediction of Haplosalids and Haplogypsids was carried out with high accuracy.