Abstract
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.
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Claims 5
Claim · model-written summary
Rainfall had the highest effect on the distribution of the P. uloptera habitat in the logistic regression model.
- Taxon
- Prangos UlopteraPrangos ulopterat0
- Treatment
- rainfalle4
- Outcome
- distribution of the P. uloptera habitate4
- Direction
- higheste4
Verbatim
t0“Prediction of Distribution of Prangos Uloptera DC.”
e4“The LR model showed that rainfall had the highest effect on the distribution of the P. uloptera habitat”
Claim · model-written summary
The logistic regression prediction map had good accuracy (Kappa index 0.65).
- Taxon
- Prangos UlopteraPrangos ulopterat0
- Outcome
- accuracy of the LR method for the prediction mape5
- Value
- 0.65e5
Verbatim
t0“Prediction of Distribution of Prangos Uloptera DC.”
e5“The accuracy of the LR method for the prediction map was good (Kappa index= 0.65)”
Claim · model-written summary
In the MaxEnt model, sand, nitrogen, silt and potassium had the highest effect on the distribution of the P. uloptera habitat.
- Taxon
- Prangos UlopteraPrangos ulopterat0
- Treatment
- sand, nitrogen (N), silt, and potassium (K)e6
- Outcome
- distribution of P. uloptera habitate6
- Direction
- higheste6
Verbatim
t0“Prediction of Distribution of Prangos Uloptera DC.”
e6“The MaxEnt method showed that variables, including sand, nitrogen (N), silt, and potassium (K) had the highest effect on distribution of P. uloptera habitat”
Claim · model-written summary
The MaxEnt prediction had low accuracy (Kappa index 0.35).
- Taxon
- Prangos UlopteraPrangos ulopterat0
- Outcome
- accuracy of the MaxEnt methode7
- Value
- 0.35e7
Verbatim
t0“Prediction of Distribution of Prangos Uloptera DC.”
e7“However, the accuracy of the MaxEnt method was low (Kappa index=0.35)”
Claim · model-written summary
Modelling methods could be used as a prediction tool for the location of plant species (author conclusion).
- Taxon
- Prangos UlopteraPrangos ulopterat0
- Outcome
- modeling methods could be used as a prediction tool to determine the location of plant speciese7
Verbatim
t0“Prediction of Distribution of Prangos Uloptera DC.”
e7“It was concluded that modeling methods could be used as a prediction tool to determine the location of plant species”